a critical analysis of research in environmental education

Consortium for Social Movements and Education Research and Practice

A critical analysis of research in environmental education, social movements.

Collaborate

Search form

A Critical Analysis of Research in Environmental Education

Title	A Critical Analysis of Research in Environmental Education
Publication Type	Journal Article
Authors	, and
Journal	Studies in Science Education
Volume	34
Issue	1
Pagination	1-69
ISSN	0305-7267
URL
DOI

Log in or register to post comments
Google Scholar

A Critical Analysis of Research in Environmental Education

Nolan, Kathleen

DOI: 10.1080/13504620120065230
Corpus ID: 144167880

Learners and Learning in Environmental Education: A critical review of the evidence

Mark Rickinson
Published 1 August 2001
Environmental Science, Education
Environmental Education Research

749 Citations

Reviewing research evidence in environmental education: some methodological reflections and challenges.

Highly Influenced

Commentary on Rickinson's 'Learners and Learning in Environmental Education: A critical review of the evidence' (EER 7(3))

16 Excerpts

Exploring students' learning challenges in environmental education

Environmental education and curriculum theory, evaluation of environmental education, researching and understanding environmental learning: hopes for the next 10 years, exploring the impact of integrated fieldwork, reflective and metacognitive experiences on student environmental learning outcomes, implementing curriculum guidance on environmental education: the importance of teachers' beliefs, environmental education through listening to children, the value of teachers' knowledge: environmental education as a case study., 150 references, making sense of environmental education research as an evidence base., problematizing enquiry in environmental education: issues of method in a study of teacher thinking and practice, people-environment issues in the geography classroom: towards an understanding of students' experiences, outcome research in environmental education: a critical review, environmental consciousness among students in senior secondary schools: the case of hong kong, research matters: a call for the application of empirical evidence to the task of improving the quality and impact of environmental education..

Highly Influential

Science Education and Sustainability: a case‐study in discussion‐based learning

Discussing the greenhouse effect: children's collaborative discourse reasoning and conceptual change., education for environmental action in the community: new roles and relationships, environmental education and primary children's attitudes towards nature and the environment.

13 Excerpts

Toward critical environmental education: a standpoint analysis of race in the American environmental context

2019, Environmental Education Research

Related Papers

Jada R Koushik

Hannah K Miller

Environmental education (EE) has a history of support for critical place- based pedagogy as a means of learning through engagement in space, both cultural and biophysical. In this paper I tell the story of how Franco – a non-white, non-American undergraduate – engaged with local discourses in a watershed-focused EE program in the rural Midwestern US. I examine how the five tenets of critical race theory (CRT) can be used to interpret Franco’s experience, where he encountered multiple instances of racism and xenophobia. I argue that without a critical analysis of race in place-based EE programs, instructors may (a) privilege their own ways of knowing in local settings, (b) rely on ‘grit’ narratives as mechanisms for mediation of racism, and (c) send non-white students home having learned that they cannot effect meaningful change for sustainability. I conclude with recommendations for faculty in predominantly white institutions on how CRT might foster the development of critical consciousness of race in place-based EE programs.

SN Social Sciences

Robert Chiles

Journal of Outdoor and Environmental Education

Jedda Foreman

In the United States of America, societal structures of oppression frame and underpin nearly every field and industry, including environmental education. Despite growing attention on efforts to diversify the environmental education workforce in the United States, environmental fields have had minimal success attracting and retaining professionals of color. This study sought to explore how Environmental Educators of Color experience and are impacted by workplace culture, practices, and policies. Through focus groups, Environmental Educators of Color (n = 26) shared their perspectives related to equity and inclusion in environmental education work environments. This study draws on critical race theory (Delgado & Stefancic, 2017; Tate, 1997) as an analytical framework to center racial equity in our understanding of the experiences and perspectives of Environmental Educators of Color. Analysis of data included thematic emergent analysis (Merriam & Tisdell, 2016) to identify themes in fo...

Queer Ecopedagogies: Explorations in Nature, Sexuality, and Education

Annette Gough

In the past, women, queer, Indigenous and disabled people, as well as race, class and body size issues, have been overlooked in most environmental education programs through being subsumed into the notion of “universalised people,” the “norm.” However, people in each of these marginalised groups has a distinctive contribution to make to environmental education, as a form of anti-oppressive resistance, which needs to be foregrounded. In this chapter I problematize the relative silencing of queer theory and theorizing in environmental education and discuss the contributions that queer(y)ing perspectives bring to environmental education. In particular, I discuss some possibilities for new directions for environmental education where queer ecopedagogies and research, intersectionality and assemblages are used to listen to, and work with, “queer” voices.

UNITED STATES DEPARTMENT OF AGRICULTURE …

Dorceta Taylor

Journal of Environmental Studies and Sciences

Eve Bratman

Many academic disciplines are presently striving to reveal and dismantle structures of domination by working to reform and reimagine their curricula, and the ethics and values that underpin classroom settings. This trend is impelled by momentum from the Black Lives Matter movement in tandem with a worldwide call from Indigenous scholars and their allies for more equality in research and epistemological plurality. We contribute to such efforts through applying perspective and analysis concerning anti-racist and decolonized approaches to teaching environmental studies and sciences (ESS). This article discusses the opportunities and challenges of embracing a decolonized and anti-racist approach with an emphasis on courses in higher education in North America. We conclude with guidance for educators about strategies for incorporating such approaches.

The Southern African Journal of Environmental Education

Roger Firth

The increased calls for transformation in response to the socio-ecological 'crisis' and the movement towards more sustainable societies; the dramatic political changes in South Africa, Eastern Europe and elsewhere; the epistemological shifts in the conceptualisation of science, education and research, and the paradoxes of the world of the late twentieth century provide the context for this paper. The need for educational organisations and educators to make an effective curriculum response to environmental and development concerns legitimises socially critical approaches to environmental education and a concern with processes of social change. We are faced, therefore, with dilemmas of personal, professional and political change: the need for transformation of both actors and structures. The paper highlights the unquestioned assumptions which underlie growing calls for social transformation and considers the significance of a socially critical orientation to environmental educ...

Perlita Dicochea

Kimberly Compton

Environmentalist narratives have left out the labor of Black environmentalists, mischaracterizing Black communities as disinterested in environmental problems or solutions. The purpose of this critical transformative exploratory qualitative study, guided by critical race theory, was to explore the meaning of pro-environmental behaviors among Black environmental advocates. Ten adult Black environmental advocates and organizers were interviewed using semi-structured interviews which were transcribed and analyzed using directed content analysis and thematic analysis. The findings demonstrated divergences between definitions of the environment and how that impacts what is considered pro-environmental behavior; the breadth of the pro-environmental behaviors in which Black environmentalists are engaged; racism’s impact on Black visibility in environmental spaces in the past, present and future; and the need to address Black visibility in environmental spaces as a critical pro-environmenta...

Loading Preview

Sorry, preview is currently unavailable. You can download the paper by clicking the button above.

RELATED PAPERS

Proceedings of the Proceedings of the 1st International Conference on Environment and Sustainability Issues, ICESI 2019, 18-19 July 2019, Semarang, Central Java, Indonesia

Heribertus jaka triyana

Amiko Mayeno

Donny J Donnique

David Pellow

Canadian Journal of Environmental Education

Mark Fettes

Berkeley Workshop on …

Robin Turner

Gwen D'Arcangelis

Lauren Richter

Cognition and Instruction

Natalie R . Davis

William Mary Environmental Law and Policy Review

Browne Lewis

Sociology Compass

Jonathan London

Environmental Education Research

Karen Malone

Brissa Quiroz

Environmental Sociology

Michael Mascarenhas

Du Bois Review: Social Science Research on Race

Proceedings of the 1st National Environmental Humanities Conference: Perspectives from Morocco, May 2022

Afaf Charef

Jennifer Sandoval (she/her/ella) , Tema Milstein , Yea-Wen Chen , Claudia ( A N G U I A N O ) Evans-Zepeda

Pesquisa em Educação Ambiental

Isabel Martins

Margaret McKeon

Stacia Ryder

Race Ethnicity and Education

Fikile Nxumalo

Online Submission

Nicholas D Hartlep

Nature Sustainability

Brittany Rivera

Journal of Environmental Education

Aims and scope

The Journal of Environmental Education (JEE) is a research-oriented, refereed periodical intended to provide a forum for critical and constructive debate on all aspects of research, theory and practice in environmental and sustainability education (EE & SE). Publication of diverse theoretical and methodological approaches and perspectives for international audiences is aimed at improving the quality of research and practice in the fields of EE & SE. Articles are encouraged that focus on methodological issues, challenges to existing theoretical discourses, conceptual work that links theory and practice and that crosses disciplinary boundaries. Readers will be encouraged to respond to these papers, thus engaging with ideas that advance the theory and practice of EE/SE research. Research Articles: The JEE invites submissions of unpublished articles on empirical and theoretical research, including critical essays, conceptual or policy analyses, literature reviews and program evaluations, on environment-or sustainability-related education. Articles may be interdisciplinary or draw on any appropriate discipline and address any level from early childhood to higher education and any educational sector from formal to informal to non-formal. To be published submissions must advance the contemporary theory and/or practice of environmental or sustainability education. Papers are judged on their merit as demonstrations of sound scholarship across diverse methodological and representational approaches for broad audiences of scholars, policymakers and practitioners.

All research articles must include a clear statement of the problem being addressed, be grounded in relevant international literature and be accessible to an international audience. Empirical articles should also include a description of research methodology and methods, findings, and a critical analysis/discussion. Recommendations or implications for policy and/or practice are encouraged. Critical essays and analyses related to environment or sustainability-related education policies, philosophies, theories, or historical perspectives should follow a representational approach appropriate to the particular kind of analysis. Program evaluations must demonstrate innovative advances in the field and should state goals and/or objectives; document the context(s), processes and outcomes; and be transferable to other educational and cultural contexts. All articles are judged on the coherence and quality of the arguments presented and should avoid making unsubstantiated claims. A 100-word abstract and 3 to 6 short key words (but excluding “environmental education”) must be included.

Reviews: Reviews cover numerous formats, including educational materials (books, films, videos, software, course designs, curricula) or reviews of research project results. The book review editor selects and approves all reviews.

Editorial board

EDITOR IN CHIEF

Robert B. Stevenson | James Cook University, Cairns, Australia

EXECUTIVE EDITORS

Paul Hart | University of Regina, Saskatchewan, Canada

John Shultis | University of Northern British Columbia, Prince George, British Columbia, Canada

Hilary Whitehouse | James Cook University, Cairns, Australia

MANAGING EDITOR

Jen Nicholls | James Cook University

ASSOCIATE EDITORS

Maxine Newlands (Book Review) | James Cook University, Cairns, Australia

Neus (Snowy) Evans | James Cook University, Cairns, Australia

Phillip G. Payne (Special Issues) | Monash University, Melbourne, Australia

Go to journal website

Research Reports
Newsletters
Annual Reports
Research Publications

A Critical Analysis of Research in Environmental Education

Chat with Paper

Learners and Learning in Environmental Education: A Critical Review of the Evidence.

Researching education and the environment: retrospect and prospect, on the construction, deconstruction and reconstruction of experience in 'critical' outdoor education, environmental education research: 30 years on from tbilisi, taking a stance: child agency across the dimensions of early adolescents' environmental involvement., the landscape of qualitative research : theories and issues, young people's images of science, environmental values in american culture, the action competence approach in environmental education, transformative learning: educational vision for the 21st century, related papers (5), environmental education in the 21st century, sustainable development and learning: framing the issues, the concept of environmental education.

Back to Menu myHilltop
Mail and Calendar
Collegiate Link
Academic Calendar
Back to Menu Current Students
Future Students
Parents and Families
Athletes and Fans
Faculty and Staff
Request Info

Undergraduate

Environmental Biology and Climate Change

Use Science to Tackle the World’s Most Pressing Environmental Problems

Draw on your foundational studies in biology, chemistry, mathematics and analysis to inform and solve critical environmental issues related to climate change. Conduct one-on-one research and apply geographic information systems as you develop solutions to this existential threat.

Why earn your Environmental Biology and Climate Change degree at St. Edward’s?

Whether you want to help minimize the impact of climate change through public policy, natural resource management, corporate sustainability practices, teaching or other field, one thing is certain: The advantages of your St. Edward’s education will prepare you to succeed. You’ll find opportunities in and outside the classroom to learn, give back and achieve your goals. And your mentors will support you every step of the way.

Build relationships with your professors

You’ll learn in small classes taught by award-winning professors who make a point of getting to know you and becoming your trusted advisors. They’ll help you identify and focus on your goals, and provide guidance and insight during and after your college years.

Conduct graduate-level research

You’ll have the opportunity to engage in faculty-mentored research in the university’s state-of-the-art labs — or Wild Basin Creative Research Center in Austin, a nature preserve managed by St. Edward’s — and present your work at academic conferences and for publication.

Tap into special funding for STEM students

As a student in the biological sciences, you’ll gain access to funding programs, including paid internships and tuition awards, and other benefits offered exclusively to STEM students at St. Edward’s through our NSF-funded Institute for Interdisciplinary Science (i4) .

Get involved in the professional science community

The St. Edward’s chapters of the Texas Academy of Science, TriBeta National Biological Honor Society and other academic organizations open doors to research funding; presentations and awards; and connecting with students and professionals who are passionate about science.

Reap the Rewards of Austin

Austin is a leading eco-friendly city in the U.S., with close access to major ecosystems in Texas that you can study on day trips to field sites and reserves. Explore career paths and practical application of your studies through internships and interactions within the greater Austin community.

What do our graduates do?

Students who earn this degree will be prepared for a variety of careers, as well as graduate school. Potential careers include:

Natural resource management with federal, state and public agencies
Sustainability specialists with private corporations
Public policy advisory positions
Excellent preparation for a variety of graduate programs

Explore Details About a Degree in Environmental Biology and Climate Change

Degree requirements.

Major Requirements: The BS in Environmental Biology and Climate Change requires 71 hours of major courses, including courses in subjects such as Bioinformatics, Chemistry, Environmental Science, Mathematics and Philosophy.

General Education Requirements: In addition to the major program requirements, all students must satisfy the general education requirements . Talk with your success coach and faculty advisor about which courses are right for you.

Examples of major courses:

General Biology I and II
Ecology and Global Change
Terrestrial and Plant Ecology
Expeditionary Ecosystem Studies
General Chemistry I and II
Climate Change Science, Impacts & Responses
Chemistry in the Environment

Examples of electives:

Ecophysiology
Vertebrate Biology
Environmental Controversies in Austin
Environmental Politics and Policy

View and download the full degree plan for the BS in Environmental Biology and Climate Change degree plan (PDF)

Learning Outcomes

What you will learn.

Majoring in Environmental Biology and Climate Change provides a broad understanding of the world around you. You’ll study the fundamental principles of biology at work in ecosystems and their role in climate change. Here’s a sample of what you’ll learn and experience:

Use foundational studies in biology, chemistry, mathematics and analysis to inform and solve critical environmental issues related to climate change.
Conduct one-on-one research and apply geographic information systems as you develop solutions to this existential threat.
Study the effect of climate change on ecosystems in Texas and around the world, with extensive field experience locally and beyond.
Identify critical components of climate change issues, analyze them and offer sustainable solutions.

Skills You Will Gain

Your classroom studies and experiential learning activities prepare you with skills that are valuable across many workplaces and employment scenarios. You’ll learn to …

Collect data in the field
Use cutting-edge technology to study climate change
Write scientific grant proposals
Read scientific literature critically
Solve problems in teams
Practice communicating science to a broad audience

Experiential Learning

Hands-on learning is a strong component of the Environmental Biology and Climate Change major, which is seen in courses like these:

In Ecology and Global Change , you’ll take two or more field trips to different ecosystems such as the Wichita Mountains of southwestern Oklahoma, the Four Corners of the U.S. Southwest or the Texas coast.
Entomology , which focuses on the evolutionary adaptations and biodiversity found among insect groups, includes a field-based collection component. You’ll travel to different sites and learn how to collect, preserve and identify insects.
A hallmark of the Environmental Biology and Climate Change major is the course in Expeditionary Ecosystem Studies , in which you’ll visit local examples of the course’s focal ecosystem.

All Environmental Biology and Climate Change students will complete at least a year of research individually mentored by a professor. You’ll have the opportunity to complete fieldwork at Blunn Creek, right next to campus; at Wild Basin, an urban preserve in West Austin; or at the St. Edward’s ecolab in Spicewood, in the Hill Country. Along with fellow students, you’ll attend professional conferences to network with professionals in the field, learn about others’ research and present your own work.

Outstanding students complete Research Experiences for Undergraduates, in which they join the lab of a professor at a major research university over a summer. These competitive experiences, funded by the National Science Foundation, introduce you to research with different mentors and help you evaluate whether a research career is right for you.

Internships and Student Organizations

Internships.

As a student in the Environmental Biology and Climate Change program, you have access to the funding programs, including paid internships, offered exclusively to STEM students at St. Edward’s University by the Institute for Interdisciplinary Science (i4). For more information on these programs please visit the Institute for Interdisciplinary Science (i4) .

Student Organizations

Students for Sustainability educates the St. Edward’s community about eco-friendly practices and works with the St. Edward’s administration to implement environmental initiatives. Members help maintain the campus garden, learn about biking and public transportation, encourage fellow students to reduce their use of plastic water bottles, and have clothing swaps and upcycled craft nights.

The St. Edward’s chapter of the Texas Academy of Science supports student research, including presentation and publication opportunities. Members also tutor in the sciences and complete other service projects.

Students with strong academic achievement in the sciences are eligible to join the St. Edward’s chapter of Beta Beta Beta, the National Biological Honor Society .

You can contribute to the launch of new organizations like the St. Edward’s student chapters of the Ecological Society of America and The Wildlife Society .

Minor in Environmental Biology and Climate Change

Through the Environmental Biology and Climate Change minor, you’ll learn about the impacts of global change on ecosystems in Texas and around the world. You’ll gain the tools needed to identify, understand and analyze climate change issues with the goal of developing solutions to the most pressing environmental challenges of our time.

In addition to fundamental topics in environmental biology and climate change, this program combines courses that will broaden your knowledge and participation in the sub-disciplines of ecology, conservation and evolution.

This minor is designed to dramatically enhance your connection to the Austin community through an experiential learning-based curriculum in which you will help implement meaningful environmental projects at the local, regional and global scales. For example, students in Environmental Conservation and Climate Change are helping local researchers understand the impacts of urbanization on vegetation at different locations within Austin and the nearby Texas Hill Country. Our faculty also support student opportunities to investigate environmental problems internationally — e.g., to investigate the impacts of climate change on South African savannas.

Based in the Department of Biological Sciences, the minor in Environmental Biology and Climate Change is available to all St. Edward’s undergraduates.

This type of training supports professionals in field research, GIS (geographic information system) analysis and biostatistics. However, the experience and skills you gain can be leveraged in any major, whether it’s business, communication or political science.

Climate change is a pervasive problem that has dramatic impacts on many aspects of human life — e.g., infectious diseases, social justice, food security and the global economy. Furthermore, fields such as technology, public policy and business enterprises will be central to helping mitigate these impacts. This minor is relevant to those who are interested in a wide range of fields such as medicine, public health, biotechnology, environmental law, sustainable agriculture, sustainable business practices and more.

Minor Requirements

Minor Core Courses (16 credit hours)

General Biology II, Lab (4 credit hours)
Ecology and Global Change / Environmental Conservation / Climate Change: (4 credit hours)
Terrestrial and Plant Ecology (4 credit hours)
Population Biology and Ecology, Lab (4 credit hours)

Minor Electives

Choose a minimum of 5 credit hours from the following courses:

Biostatistics (3 credit hours)
Biological Programming (3 credit hours)
Geographic Information Systems (3 credit hours)
Ecophysiology (3 credit hours)
Expeditionary Ecosystem Studies (4 credit hours)
Research in Biology (1–3 credit hours)
Evolution (3 credit hours)
Natural Resources Conservation & Management (3 credit hours)
Environmental and Ecological Field Methods (3 credit hours)
Environmental Controversies in Austin (3 credit hours)

Evolutionary Foundation for Curricula

At St. Edward’s University, all curricula in the Biological Sciences are founded upon evolutionary theory. As a subject of scientific inquiry, the theory of evolution provides opportunities for testing of hypotheses that strengthen our understanding of the processes that account for the diversity of life on earth, and existing data overwhelmingly support the theory as scientifically sound. We regard any non-scientific or teleological attempts that distract from the scientific processes that underlie science as, at best, a diversion to our mission to provide exceptional education to our students in the Biological Sciences. We stand with the numerous scientific societies that have issued statements on the subject of evolution and intelligent design, confirming the demonstrated success of the former and rejecting the scientific viability of the latter.

Meet our Faculty

Open access
Published: 07 August 2024

Factors critical for the successful delivery of telehealth to rural populations: a descriptive qualitative study

Rebecca Barry ORCID: orcid.org/0000-0003-2272-4694 1 ,
Elyce Green ORCID: orcid.org/0000-0002-7291-6419 1 ,
Kristy Robson ORCID: orcid.org/0000-0002-8046-7940 1 &
Melissa Nott ORCID: orcid.org/0000-0001-7088-5826 1

BMC Health Services Research volume 24 , Article number: 908 ( 2024 ) Cite this article

97 Accesses

Metrics details

The use of telehealth has proliferated to the point of being a common and accepted method of healthcare service delivery. Due to the rapidity of telehealth implementation, the evidence underpinning this approach to healthcare delivery is lagging, particularly when considering the uniqueness of some service users, such as those in rural areas. This research aimed to address the current gap in knowledge related to the factors critical for the successful delivery of telehealth to rural populations.

This research used a qualitative descriptive design to explore telehealth service provision in rural areas from the perspective of clinicians and describe factors critical to the effective delivery of telehealth in rural contexts. Semi-structured interviews were conducted with clinicians from allied health and nursing backgrounds working in child and family nursing, allied health services, and mental health services. A manifest content analysis was undertaken using the Framework approach.

Sixteen health professionals from nursing, clinical psychology, and social work were interviewed. Participants mostly identified as female (88%) and ranged in age from 26 to 65 years with a mean age of 47 years. Three overarching themes were identified: (1) Navigating the role of telehealth to support rural healthcare; (2) Preparing clinicians to engage in telehealth service delivery; and (3) Appreciating the complexities of telehealth implementation across services and environments.

Conclusions

This research suggests that successful delivery of telehealth to rural populations requires consideration of the context in which telehealth services are being delivered, particularly in rural and remote communities where there are challenges with resourcing and training to support health professionals. Rural populations, like all communities, need choice in healthcare service delivery and models to increase accessibility. Preparation and specific, intentional training for health professionals on how to transition to and maintain telehealth services is a critical factor for delivery of telehealth to rural populations. Future research should further investigate the training and supports required for telehealth service provision, including who, when and what training will equip health professionals with the appropriate skill set to deliver rural telehealth services.

Peer Review reports

Introduction

Telehealth is a commonly utilised application in rural health settings due to its ability to augment service delivery across wide geographical areas. During the COVID-19 pandemic, the use of telehealth became prolific as it was rapidly adopted across many new fields of practice to allow for healthcare to continue despite requirements for physical distancing. In Australia, the Medicare Benefits Scheme (MBS) lists health services that are subsidised by the federal government. Telehealth items were extensively added to these services as part of the response to COVID-19 [ 1 ]. Although there are no longer requirements for physical distancing in Australia, many health providers have continued to offer services via telehealth, particularly in rural areas [ 2 , 3 ]. For the purpose of this research, telehealth was defined as a consultation with a healthcare provider by phone or video call [ 4 ]. Telehealth service provision in rural areas requires consideration of contextual factors such as access to reliable internet, community members’ means to finance this access [ 5 ], and the requirement for health professionals to function across a broad range of specialty skills. These factors present a case for considering the delivery of telehealth in rural areas as a unique approach, rather than one portion of the broader use of telehealth.

Research focused on rural telehealth has proliferated alongside the rapid implementation of this service mode. To date, there has been a focus on the impact of telehealth on areas such as client access and outcomes [ 2 ], client and health professional satisfaction with services and technology [ 6 ], direct and indirect costs to the patient (travel cost and time), healthcare service provider staffing, lower onsite healthcare resource utilisation, improved physician recruitment and retention, and improved client access to care and education [ 7 , 8 ]. In terms of service implementation, these elements are important but do not outline the broader implementation factors critical to the success of telehealth delivery in rural areas. One study by Sutarsa et al. explored the implications of telehealth as a replacement for face-to-face services from the perspectives of general practitioners and clients [ 9 ] and articulated that telehealth services are not a like-for-like service compared to face-to-face modes. Research has also highlighted the importance of understanding the experience of telehealth in rural Australia across different population groups, including Aboriginal and Torres Strait Islander peoples, and the need to consider culturally appropriate services [ 10 , 11 , 12 , 13 ].

Research is now required to determine what the critical implementation factors are for telehealth delivery in rural areas. This type of research would move towards answering calls for interdisciplinary, qualitative, place-based research [ 12 ] that explores factors required for the sustainability and usability of telehealth in rural areas. It would also contribute to the currently limited understanding of implementation factors required for telehealth delivery to rural populations [ 14 ]. There is a reasonable expectation that there is consistency in the way health services are delivered, particularly across geographical locations. Due to the rapid implementation of telehealth services, there was limited opportunity to proactively identify factors critical for successful telehealth delivery in rural areas and this has created a lag in policy, process, and training. This research aimed to address this gap in the literature by exploring and describing rural health professionals’ experiences providing telehealth services. For the purpose of this research, rural is inclusive of locations classified as rural or remote (MM3-6) using the Modified Monash Model which considers remoteness and population size in its categorisation [ 15 ].

This research study adopted a qualitative descriptive design as described by Sandelowski [ 16 ]. The purpose of a descriptive study is to document and describe a phenomenon of interest [ 17 ] and this method is useful when researchers seek to understand who was involved, what occurred, and the location of the phenomena of interest [ 18 ]. The phenomenon of interest for this research was the provision of telehealth services to rural communities by health professionals. In line with this, a purposive sampling technique was used to identify participants who have experience of this phenomenon [ 19 ]. This research is reported in line with the consolidated criteria for reporting qualitative research [ 20 ] to enhance transparency and trustworthiness of the research process and results [ 21 ].

Research aims

This research aimed to:

Explore telehealth service provision in rural areas from the perspective of clinicians.

Describe factors critical to the successful delivery of telehealth in rural contexts.

Participant recruitment and data collection

People eligible to participate in the research were allied health (using the definition provided by Allied Health Professions Australia [ 22 ]) or nursing staff who delivered telehealth services to people living in the geographical area covered by two rural local health districts in New South Wales, Australia (encompassing rural areas MM3-6). Health organisations providing telehealth service delivery in the southwestern and central western regions of New South Wales were identified through the research teams’ networks and invited to be part of the research.

Telehealth adoption in these organisations was intentionally variable to capture different experiences and ranged from newly established (prompted by COVID-19) to well established (> 10 years of telehealth use). Organisations included government, non-government, and not-for-profit health service providers offering child and family nursing, allied health services, and mental health services. Child and family nursing services were delivered by a government health service and a not-for-profit specialist service, providing health professional advice, education, and guidance to families with a baby or toddler. Child and family nurses were in the same geographical region as the families receiving telehealth. Transition to telehealth services was prompted by the COVID-19 pandemic. The participating allied health service was a large, non-government provider of allied health services to regional New South Wales. Allied health professionals were in the same region as the client receiving telehealth services. Use of telehealth in this organisation had commenced prior to the COVID-19 pandemic. Telehealth mental health services were delivered by an emergency mental health team, located at a large regional hospital to clients in another healthcare facility or location to which the health professional could not be physically present (typically a lower acuity health service in a rural location).

Once organisations agreed to disseminate the research invitation, a key contact person employed at each health organisation invited staff to participate via email. Staff were provided with contact details of the research team in the email invitation. All recruitment and consent processes were managed by the research team to minimise risk of real or perceived coercion between staff and the key contact person, who was often in a supervisory or managerial position within the organisation. Data were collected using semi-structured interviews using an online platform with only the interviewer and participant present. Interviews were conducted by a research team member with training in qualitative data collection during November and December 2021 and were transcribed verbatim by a professional transcribing service. All participants were offered the opportunity to review their transcript and provide feedback, however none opted to do so. Data saturation was not used as guidance for participant numbers, taking the view of Braun and Clarke [ 23 ] that meaning is generated through the analysis rather than reaching a point of saturation.

Data analysis

Researchers undertook a manifest content analysis of the data using the Framework approach developed by Ritchie and Spencer [ 24 ]. All four co-authors were involved in the data analysis process. Framework uses five stages for analysis including (1) familiarisation (2) identifying a thematic framework based on emergent overarching themes, (3) application of the coding framework to the interview transcripts [indexing], (4) reviewing and charting of themes and subthemes, and (5) mapping and interpretation [ 24 , p. 178]. The research team analysed a common interview initially, identified codes and themes, then independently applied these to the remaining interviews. Themes were centrally recorded, reviewed, and discussed by the research team prior to inclusion into the thematic framework. Final themes were confirmed via collaborative discussion and consensus. The iterative process used to review and code data was recorded into an Excel spreadsheet to ensure auditability and credibility, and to enhance the trustworthiness of the analysis process.

This study was approved by the Greater Western NSW Human Research Ethics Committee and Charles Sturt University Human Research Ethics Committee (approval numbers: 2021/ETH00088 and H21215). All participants provided written consent.

Eighteen health professionals consented to be interviewed. Two were lost to follow-up, therefore semi-structured interviews were conducted with 16 of these health professionals, the majority of which were from the discipline of nursing ( n = 13, 81.3%). Participant demographics and their pseudonyms are shown in Table 1 .

Participants mostly identified as female ( n = 14, 88%) and ranged in age from 26 to 65 years with a mean age of 47 years. Participants all delivered services to rural communities in the identified local health districts and resided within the geographical area they serviced. The participants resided in areas classified as MM3-6 but were most likely to reside in an area classified MM3 (81%). Average interview time was 38 min, and all interviews were conducted online via Zoom.

Three overarching themes were identified through the analysis of interview transcripts with health professionals. These themes were: (1) Navigating the role of telehealth to support rural healthcare; (2) Preparing clinicians to engage in telehealth service delivery; and (3) Appreciating the complexities of telehealth implementation across services and environments.

Theme 1: navigating the role of telehealth to support rural healthcare

The first theme described clinicians’ experiences of using telehealth to deliver healthcare to rural communities, including perceived benefits and challenges to acceptance, choice, and access. Interview participants identified several factors that impacted on or influenced the way they could deliver telehealth, and these were common across the different organisational structures. Clinicians highlighted the need to consider how to effectively navigate the role of telehealth in supporting their practice, including when it would enhance their practice, and when it might create barriers. The ability to improve rural service provision through greater access was commonly discussed by participants. In terms of factors important for telehealth delivery in rural contexts, the participants demonstrated that knowledge of why and how telehealth was used were important, including the broadened opportunity for healthcare access and an understanding of the benefits and challenges of providing these services.

Access to timely and specialist healthcare for rural communities

Participants described a range of benefits using telehealth to contact small, rural locations and facilitate greater access to services closer to home. This was particularly evident when there was lack of specialist support in these areas. These opportunities meant that rural people could receive timely care that they required, without the burden of travelling significant distances to access health services.

The obvious thing in an area like this, is that years ago, people were being transported three hours just to see us face to face. It’s obviously giving better, more timely access to services. (Patrick)

Staff access to specialist support was seen as an important aspect for rural healthcare by participants, because of the challenges associated with lack of staffing and resources within these areas which potentially increased the risks for staff in these locations, particularly when managing clients with acute mental illnesses.

Within the metro areas they’ve got so many staff and so many hospitals and they can manage mental health patients quite well within those facilities, but with us some of these hospitals will have one RN on overnight and it’s just crappy for them, and so having us able to do video link, it kind of takes the pressure off and we’re happy to make the decisions and the risky decisions for what that person needs. (Tracey)

Participants described how the option to use telehealth to provide specialised knowledge and expertise to support local health staff in rural hospitals likely led to more appropriate outcomes for clients wanting to be able to remain in their community. Conversely, Amber described the implications if telehealth was not available.

If there was some reason why the telehealth wasn’t available… quite often, I suppose the general process be down to putting the pressure on the nursing and the medical staff there to make a decision around that person, which is not a fair or appropriate thing for them to do. (Amber)

Benefits and challenges to providing telehealth in rural communities

Complementing the advantage of reduced travel time to access services, was the ability for clients to access additional support via telehealth, which was perceived as a benefit. For example, one participant described how telehealth was useful for troubleshooting client’s problems rather than waiting for their next scheduled appointment.

If a mum rings you with an issue, you can always say to them “are you happy to jump onto My Virtual Care with me now?” We can do that, do a consult over My Virtual Care. Then I can actually gauge how mum is. (Jade)

While accessibility was a benefit, participants highlighted that rural communities need to be provided with choice, rather than the assumption that telehealth be the preferred option for everyone, as many rural clients want face-to-face services.

They’d all prefer, I think, to be able to see someone in person. I think that’s generally what NSW rural [want] —’cause I’m from country towns as well—there’s no substitute, like I said, for face-to-face assessment. (Adam)

Other, more practical limitations of broad adoption of telehealth raised by the participants included issues with managing technology and variability in internet connectivity.

For many people in the rural areas, it’s still an issue having that regular [internet] connection that works all the time. I think it’s a great option but I still think it’s something that some rural people will always have some challenges with because it’s not—there’s so many black spots and so many issues still with the internet connection in rural areas. Even in town, there’s certain areas that are still having lots of problems. (Chloe)

Participants also identified barriers related to assumptions that all clients will have access to technology and have the necessary data to undertake a telehealth consultation, which wasn’t always the case, particularly with individuals experiencing socioeconomic disadvantage.

A lot of [Aboriginal] families don’t actually have access to telehealth services. Unless they use their phone. If they have the technology on their phones. I found that was a little bit of an issue to try and help those particular clients to get access to the internet, to have enough data on their phone to make that call. There was a lot of issues and a lot of things that we were putting in complaints about as they were going “we’re using up a lot of these peoples’ data and they don’t have internet in their home.” (Evelyn).

Other challenges identified by the participants were related to use of telehealth for clients that required additional support. Many participants talked about the complexities of using an interpreter during a telehealth consultation for culturally and linguistically diverse clients.

Having interpreters, that’s another element that’s really, really difficult because you’re doing video link, but then you’ve also got the phone on speaker and you’re having this three-way conversation. Even that, in itself, that added element on video link is really, really tough. It’s a really long process. (Tracey)

In summary, this theme described some of the benefits and constraints when using telehealth for the delivery of rural health services. The participants demonstrated the importance of understanding the needs and contexts of individual clients, and accounting for this when making decisions to incorporate telehealth into their service provision. Understanding how and why telehealth can be implemented in rural contexts was an important foundation for the delivery of these services.

Theme 2: preparing clinicians to engage in telehealth service delivery

The preparation required for clinicians to engage with telehealth service delivery was highlighted and the participants described the unique set of skills required to effectively build rapport, engage, and carry out assessments with clients. For many participants who had not routinely used telehealth prior to the COVID-19 pandemic, the transition to using telehealth had been rapid. The participants reflected on the implications of rapidly adopting these new practices and the skills they required to effectively deliver care using telehealth. These skills were critical for effective delivery of telehealth to rural communities.

Rapid adoption of new skills and ways of working

The rapid and often unsupported implementation of telehealth in response to the COVID-19 pandemic resulted in clinicians needing to learn and adapt to telehealth, often without being taught or with minimal instruction.

We had to do virtual, virtually overnight we were changed to, “Here you go. Do it this way,” without any real education. It was learned as we went because everybody was in the same boat. Everyone was scrabbling to try and work out how to do it. (Chloe)

In addition to telehealth services starting quickly, telehealth provision requires clinicians to use a unique set of skills. Therapeutic interventions and approaches were identified as being more challenging when seeing a client through a screen, compared to being physically present together in a room.

The body language is hidden a little bit when you’re on teleconference, whereas when you’re standing up face to face with someone, or standing side by side, the person can see the whole picture. When you’re on the video link, the patient actually can’t—you both can’t see each other wholly. That’s one big barrier. (Adam)

There was an emphasis on communication skills such as active listening and body language that were required when engaging with telehealth. These skills were seen as integral to building rapport and connection. The importance of language in an environment with limited visualisation of body language, is further demonstrated by one participant describing how they tuned into the timing and flow of the conversation to avoid interrupting and how these skills were pertinent for using telehealth.

In the beginning especially, we might do this thing where I think they’ve finished or there’s a bit of silence, so I go to speak and then they go to speak at the same time, and that’s different because normally in person you can really gauge that quite well if they’ve got more to say. I think those little things mean that you’ve got to work a bit harder and you’ve got to bring those things to the attention of the client often. (Robyn)

Preparing clinicians to engage in telehealth also required skills in sharing clear and consistent information with clients about the process of interacting via telehealth. This included information to reassure the client that the telehealth appointment was private as well as prepare them for potential interruptions due to connection issues.

I think being really explicitly clear about the fact that with our setups we have here, no one can dial in, no one else is in my room even watching you. We’re not recording, and there’s a lot of extra information, I think around that we could be doing better in terms of delivering to the person. (Amber)

Becoming accustomed to working through the ‘window’

Telehealth was often described as a window and not a view of the whole person which presented limitations for clinicians, such as seeing nuance of expression. Participants described the difficulties of assessing a client using telehealth when you cannot see the whole picture such as facial expressions, movement, behaviour, interactions with others, dress, and hygiene.

I found it was quite difficult because you couldn’t always see the actual child or the baby, especially if they just had their phone. You couldn’t pick up the body language. You couldn’t always see the facial expressions. You couldn’t see the child and how the child was responding. It did inhibit a lot of that side of our assessing. Quite often you’d have to just write, “Unable to view child.” You might be able to hear them but you couldn’t see them. (Chloe)

Due to the window view, the participants described how they needed to pay even greater attention to eye contact and tone of voice when engaging with clients via telehealth.

I think the eye contact is still a really important thing. Getting the flow of what they’re comfortable with a little bit too. It’s being really careful around the tone of voice as well too, because—again, that’s the same for face-to-face, but be particularly careful of it over telehealth. (Amber)

This theme demonstrates that there are unique and nuanced skills required by clinicians to effectively engage in provision of rural healthcare services via telehealth. Many clinicians described how the rapid uptake of telehealth required them to quickly adapt to providing telehealth services, and they had to modify their approach rather than replicate what they would do in face-to-face contexts. Appreciating the different skills sets required for telehealth practice was perceived as an important element in supporting clinicians to deliver quality healthcare.

Theme 3: appreciating the complexities of telehealth implementation across services and environments

It was commonly acknowledged that there needed to be an appreciation by clinicians of the multiple different environments that telehealth was being delivered in, as well as the types of consultations being undertaken. This was particularly important when well-resourced large regional settings were engaging with small rural services or when clinicians were undertaking consultations within a client’s home.

Working from a different location and context

One of the factors identified as important for the successful delivery of services via telehealth was an understanding of the location and context that was being linked into. Participants regularly talked about the challenges when undertaking a telehealth consultation with clients at home, which impacted the quality of the consultation as it was easy to “ lose focus” (Kelsey) and become distracted.

Instead of just coming in with one child, they had all the kids, all wanting their attention. I also found that babies and kids kept pressing the screen and would actually disconnect us regularly. (Chloe)

For participants located in larger regional locations delivering telehealth services to smaller rural hospitals, it was acknowledged that not all services had equivalent resources, skills, and experience with this type of healthcare approach.

They shouldn’t have to do—they’ve gotta double-click here, login there. They’re relying on speakers that don’t work. Sometimes they can’t get the cameras working. I think telehealth works as long as it’s really user friendly. I think nurses—as a nurse, we’re not supposed to be—I know IT’s in our job criteria, but not to the level where you’ve got to have a degree in technology to use it. (Adam)

Participants also recognised that supporting a client through a telehealth consultation adds workload stress as rural clinicians are often having pressures with caseloads and are juggling multiple other tasks while trying to trouble shoot technology issues associated with a telehealth consultation.

Most people are like me, not great with computers. Sometimes the nurse has got other things in the Emergency Department she’s trying to juggle. (Eleanor)

Considerations for safety, privacy, and confidentiality

Participants talked about the challenges that arose due to inconsistencies in where and how the telehealth consultation would be conducted. Concerns about online safety and information privacy were identified by participants.

There’s the privacy issue, particularly when we might see someone and they might be in a bed and they’ve got a laptop there, and they’re not given headphones, and we’re blaring through the speaker at them, and someone’s three meters away in another bed. That’s not good. That’s a bit of a problem. (Patrick)

When telehealth was offered as an option to clients at a remote healthcare site, clinicians noted that some clients were not provided with adequate support and were left to undertake the consultation by themselves which could cause safety risks for the client and an inability for the telehealth clinician to control the situation.

There were some issues with patients’ safety though. Where the telehealth was located was just in a standard consult room and there was actually a situation where somebody self-harmed with a needle that was in a used syringe box in that room. Then it was like, you just can’t see high risk—environment. (Eleanor)

Additionally, participants noted that they were often using their own office space to conduct telehealth consultations rather than a clinical room which meant there were other considerations to think about.

Now I always lock my room so nobody can enter. That’s a nice little lesson learnt. I had a consult with a mum and some other clinicians came into my room and I thought “oh my goodness. I forgot to lock.” I’m very mindful now that I lock. (Jade)

This theme highlights the complexities that exist when implementing telehealth across a range of rural healthcare settings and environments. It was noted by participants that there were variable skills and experience in using telehealth across staff located in smaller rural areas, which could impact on how effective the consultation was. Participants identified the importance of purposely considering the environment in which the telehealth consultation was being held, ensuring that privacy, safety, and distractibility concerns have been adequately addressed before the consultation begins. These factors were considered important for the successful implementation of telehealth in rural areas.

This study explored telehealth service delivery in various rural health contexts, with 16 allied health and nursing clinicians who had provided telehealth services to people living in rural communities prior to, and during the COVID-19 pandemic. Reflections gained from clinicians were analysed and reported thematically. Major themes identified were clinicians navigating the role of telehealth to support rural healthcare, the need to prepare clinicians to engage in telehealth service delivery and appreciating the complexities of telehealth implementation across services and environments.

The utilisation of telehealth for health service delivery has been promoted as a solution to resolve access and equity issues, particularly for rural communities who are often impacted by limited health services due to distance and isolation [ 6 ]. This study identified a range of perceived benefits for both clients and clinicians, such as improved access to services across large geographic distances, including specialist care, and reduced travel time to engage with a range of health services. These findings are largely supported by the broader literature, such as the systematic review undertaken by Tsou et al. [ 25 ] which found that telehealth can improve clinical outcomes and increase the timeliness to access services, including specialist knowledge. Clinicians in our study also noted the benefits of using telehealth for ad hoc clinical support outside of regular appointment times, which to date has not been commonly reported in the literature as a benefit. Further investigation into this aspect may be warranted.

The findings from this study identify a range of challenges that exist when delivering health services within a virtual context. It was common for participants to highlight that personal preference for face-to-face sessions could not always be accommodated when implementing telehealth services in rural areas. The perceived technological possibilities to improve access can have unintended consequences for community members which may contribute to lack of responsiveness to community needs [ 12 ]. It is therefore important to understand the client and their preferences for using telehealth rather than making assumptions on the appropriateness of this type of health service delivery [ 26 ]. As such, telehealth is likely to function best when there is a pre-established relationship between the client and clinician, with clients who have a good knowledge of their personal health and have access to and familiarity with digital technology [ 13 ]. Alternatively, it is appropriate to consider how telehealth can be a supplementary tool rather than a stand-alone service model replacing face-to-face interactions [ 13 ].

As identified in this study, managing technology and internet connectivity are commonly reported issues for rural communities engaging in telehealth services [ 27 , 28 ]. Additionally, it was highlighted that within some rural communities with higher socioeconomic disadvantage, limited access to an appropriate level of technology and the required data to undertake a telehealth consult was a deterrent to engage in these types of services. Mathew et al. [ 13 ] found in their study that bandwidth impacted video consultations, which was further compromised by weather conditions, and clients without smartphones had difficulty accessing relevant virtual consultation software.

The findings presented here indicate that while telehealth can be a useful model, it may not be suitable for all clients or client groups. For example, the use of interpreters in telehealth to support clients was a key challenge identified in this study. This is supported by Mathew et al. [ 13 ] who identified that language barriers affected the quality of telehealth consultations and accessing appropriate interpreters was often difficult. Consideration of health and digital literacy, access and availability of technology and internet, appropriate client selection, and facilitating client choice are all important drivers to enhance telehealth experiences [ 29 ]. Nelson et al. [ 6 ] acknowledged the barriers that exist with telehealth, suggesting that ‘it is not the groups that have difficulty engaging, it is that telehealth and digital services are hard to engage with’ (p. 8). There is a need for telehealth services to be delivered in a way that is inclusive of different groups, and this becomes more pertinent in rural areas where resources are not the same as metropolitan areas.

The findings of this research highlight the unique set of skills required for health professionals to translate their practice across a virtual medium. The participants described these modifications in relation to communication skills, the ability to build rapport, conduct healthcare assessments, and provide treatment while looking at a ‘window view’ of a person. Several other studies have reported similar skillsets that are required to effectively use telehealth. Uscher-Pines et al. [ 30 ] conducted research on the experiences of psychiatrists moving to telemedicine during the COVID-19 pandemic and noted challenges affecting the quality of provider-patient interactions and difficulty conducting assessment through the window of a screen. Henry et al. [ 31 ] documented a list of interpersonal skills considered essential for the use of telehealth encompassing attributes related to set-up, verbal and non-verbal communication, relationship building, and environmental considerations.

Despite the literature uniformly agreeing that telehealth requires a unique skill set there is no agreement on how, when and for whom education related to these skills should be provided. The skills required for health professionals to use telehealth have been treated as an add-on to health practice rather than as a specialty skill set requiring learning and assessment. This is reflected in research such as that by Nelson et al. [ 6 ] who found that 58% of mental health professionals using telehealth in rural areas were not trained to use it. This gap between training and practice is likely to have arisen from the rapid and widespread implementation of telehealth during the COVID-19 pandemic (i.e. the change in MBS item numbers [ 1 ]) but has not been addressed in subsequent years. For practice to remain in step with policy and funding changes, the factors required for successful implementation of telehealth in rural practice must be addressed.

The lack of clarity around who must undertake training in telehealth and how regularly, presents a challenge for rural health professionals whose skill set has been described as a specialist-generalist that covers a significant breadth of knowledge [ 32 ]. Maintaining knowledge currency across this breadth is integral and requires significant resources (time, travel, money) in an environment where access to education can be limited [ 33 ]. There is risk associated with continually adding skills on to the workload of rural health professionals without adequate guidance and provision for time to develop and maintain these skills.

While the education required to equip rural health professionals with the skills needed to effectively use telehealth in their practice is developing, until education requirements are uniformly understood and made accessible this is likely to continue to pose risk for rural health professionals and the community members accessing their services. Major investment in the education of all health professionals in telehealth service delivery, no matter the context, has been identified as critical [ 6 ].

This research highlights that the experience of using telehealth in rural communities is unique and thus a ‘one size fits all’ approach is not helpful and can overlook the individual needs of a community. Participants described experiences of using telehealth that were different between rural communities, particularly for smaller, more remote rural locations where resources and staff support and experience using telehealth were not always equivalent to larger rural locations. Research has indicated the need to invest in resourcing and education to support expansion of telehealth, noting this is particularly important in rural, regional, and remote areas [ 34 ]. Our study recognises that this is an ongoing need as rural communities continue to have diverse experiences of using telehealth services. Careful consideration of the context of individual rural health services, including the community needs, location, and resource availability on both ends of the consultation is required. Use of telehealth cannot have the same outcomes in every area. It is imperative that service providers and clinicians delivering telehealth from metropolitan areas to rural communities appreciate and understand the uniqueness of every community, so their approach is tailored and is helpful rather than hindering the experience for people in rural communities.

Limitations

There are a number of limitations inherent to the design of this study. Participants were recruited via their workplace and thus although steps were taken to ensure they understood the research would not affect their employment, it is possible some employees perceived an association between the research and their employment. Health professionals who had either very positive or very negative experiences with telehealth may have been more likely to participate, as they may be more likely to want to discuss their experiences. In addition to this, only health services that were already connected with the researchers’ networks were invited to participate. Other limitations include purposive sampling, noting that the opinions of the participants are not generalisable. The participant group also represented mostly nursing professionals whose experiences with telehealth may differ from other health disciplines. Finally, it is important to acknowledge that the opinions of the health professionals who participated in the study, may not represent, or align with the experience and opinions of service users.

This study illustrates that while telehealth has provided increased access to services for many rural communities, others have experienced barriers related to variability in connectivity and managing technology. The results demonstrated that telehealth may not be the preferred or appropriate option for some individuals in rural communities and it is important to provide choice. Consideration of the context in which telehealth services are being delivered, particularly in rural and remote communities where there are challenges with resourcing and training to support health professionals, is critical to the success of telehealth service provision. Another critical factor is preparation and specific, intentional training for health professionals on how to transition to manage and maintain telehealth services effectively. Telehealth interventions require a unique skill set and guidance pertaining to who, when and what training will equip health professionals with the appropriate skill set to deliver telehealth services is still to be determined.

Data availability

The qualitative data collected for this study was de-identified before analysis. Consent was not obtained to use or publish individual level identified data from the participants and hence cannot be shared publicly. The de-identified data can be obtained from the corresponding author on reasonable request.

Commonwealth of Australia. COVID-19 Temporary MBS Telehealth Services: Department of Health and Aged Care, Australian Government; 2022 [ https://www.mbsonline.gov.au/internet/mbsonline/publishing.nsf/Content/Factsheet-TempBB .

Caffery LA, Muurlink OT, Taylor-Robinson AW. Survival of rural telehealth services post‐pandemic in Australia: a call to retain the gains in the ‘new normal’. Aust J Rural Health. 2022;30(4):544–9.

Article PubMed PubMed Central Google Scholar

Shaver J. The state of telehealth before and after the COVID-19 pandemic. Prim Care: Clin Office Pract. 2022;49(4):517–30.

Article Google Scholar

Australian Digital Health Agency. What is telehealth? 2024 [ https://www.digitalhealth.gov.au/initiatives-and-programs/telehealth .

Hirko KA, Kerver JM, Ford S, Szafranski C, Beckett J, Kitchen C, et al. Telehealth in response to the COVID-19 pandemic: implications for rural health disparities. J Am Med Inform Assoc. 2020;27(11):1816–8.

Nelson D, Inghels M, Kenny A, Skinner S, McCranor T, Wyatt S, et al. Mental health professionals and telehealth in a rural setting: a cross sectional survey. BMC Health Serv Res. 2023;23(1):200.

Butzner M, Cuffee Y. Telehealth interventions and Outcomes Across Rural Communities in the United States: Narrative Review. J Med Internet Res. 2021;23(8):NPAG–NPAG.

Calleja Z, Job J, Jackson C. Offsite primary care providers using telehealth to support a sustainable workforce in rural and remote general practice: a rapid review of the literature. Aust J Rural Health. 2023;31(1):5–18.

Article PubMed Google Scholar

Sutarsa IN, Kasim R, Steward B, Bain-Donohue S, Slimings C, Hall Dykgraaf S, et al. Implications of telehealth services for healthcare delivery and access in rural and remote communities: perceptions of patients and general practitioners. Aust J Prim Health. 2022;28(6):522–8.

Bradford NK, Caffery LJ, Smith AC. Telehealth services in rural and remote Australia: a systematic review of models of care and factors influencing success and sustainability. Rural Remote Health. 2016;16(4):1–23.

Google Scholar

Caffery LJ, Bradford NK, Wickramasinghe SI, Hayman N, Smith AC. Outcomes of using telehealth for the provision of healthcare to Aboriginal and Torres Strait Islander people: a systematic review. Aust N Z J Public Health. 2017;41(1):48–53.

Warr D, Luscombe G, Couch D. Hype, evidence gaps and digital divides: Telehealth blind spots in rural Australia. Health. 2023;27(4):588–606.

Mathew S, Fitts MS, Liddle Z, Bourke L, Campbell N, Murakami-Gold L, et al. Telehealth in remote Australia: a supplementary tool or an alternative model of care replacing face-to-face consultations? BMC Health Serv Res. 2023;23(1):1–10.

Campbell J, Theodoros D, Hartley N, Russell T, Gillespie N. Implementation factors are neglected in research investigating telehealth delivery of allied health services to rural children: a scoping review. J Telemedicine Telecare. 2020;26(10):590–606.

Commonwealth of Australia. Modified Monash Model: Department of Health and Aged Care Commonwealth of Australia; 2021 [updated 14 December 2021. https://www.health.gov.au/topics/rural-health-workforce/classifications/mmm .

Sandelowski M. Whatever happened to qualitative description? Research in nursing & health. 2000;23(4):334 – 40.

Marshall C, Rossman GB. Designing qualitative research: Sage; 2014.

Caelli K, Ray L, Mill J. Clear as mud’: toward greater clarity in generic qualitative research. Int J Qualitative Methods. 2003;2(2):1–13.

Tolley EE. Qualitative methods in public health: a field guide for applied research. Second edition. ed. San Francisco, CA: Jossey-Bass & Pfeiffer Imprints, Wiley; 2016.

Tong A, Sainsbury P, Craig J. Consolidated criteria for reporting qualitative research (COREQ): a 32-item checklist for interviews and focus groups. Int J Qual Health Care. 2007;19(6):349–57.

Levitt HM, Motulsky SL, Wertz FJ, Morrow SL, Ponterotto JG. Recommendations for designing and reviewing qualitative research in psychology: promoting methodological integrity. Qualitative Psychol. 2017;4(1):2.

Allied Health Professions Australia. Allied health professions 2024 [ https://ahpa.com.au/allied-health-professions/ .

Braun V, Clarke V. To saturate or not to saturate? Questioning data saturation as a useful concept for thematic analysis and sample-size rationales. Qualitative Res Sport Exerc Health. 2021;13(2):201–16.

Ritchie J, Spencer L. In: Alan B, Burgess RG, editors. Qualitative data analysis for applied policy research. Analyzing qualitative data: Routledge; 1994. pp. 173–94.

Tsou C, Robinson S, Boyd J, Jamieson A, Blakeman R, Yeung J, et al. Effectiveness of telehealth in rural and remote emergency departments: systematic review. J Med Internet Res. 2021;23(11):e30632.

Pullyblank K. A scoping literature review of rural beliefs and attitudes toward telehealth utilization. West J Nurs Res. 2023;45(4):375–84.

Jonnagaddala J, Godinho MA, Liaw S-T. From telehealth to virtual primary care in Australia? A Rapid scoping review. Int J Med Informatics. 2021;151:104470.

Jonasdottir SK, Thordardottir I, Jonsdottir T. Health professionals’ perspective towards challenges and opportunities of telehealth service provision: a scoping review. Int J Med Informatics. 2022;167:104862.

Clay-Williams R, Hibbert P, Carrigan A, Roberts N, Austin E, Fajardo Pulido D, et al. The diversity of providers’ and consumers’ views of virtual versus inpatient care provision: a qualitative study. BMC Health Serv Res. 2023;23(1):724.

Uscher-Pines L, Sousa J, Raja P, Mehrotra A, Barnett ML, Huskamp HA. Suddenly becoming a virtual doctor: experiences of psychiatrists transitioning to telemedicine during the COVID-19 pandemic. Psychiatric Serv. 2020;71(11):1143–50.

Henry BW, Ames LJ, Block DE, Vozenilek JA. Experienced practitioners’ views on interpersonal skills in telehealth delivery. Internet J Allied Health Sci Pract. 2018;16(2):2.

McCullough K, Bayes S, Whitehead L, Williams A, Cope V. Nursing in a different world: remote area nursing as a specialist–generalist practice area. Aust J Rural Health. 2022;30(5):570–81.

Reeve C, Johnston K, Young L. Health profession education in remote or geographically isolated settings: a scoping review. J Med Educ Curric Dev. 2020;7:2382120520943595.

PubMed PubMed Central Google Scholar

Cummings E, Merolli M, Schaper L, editors. Barriers to telehealth uptake in rural, regional, remote Australia: what can be done to expand telehealth access in remote areas. Digital Health: Changing the Way Healthcare is Conceptualised and Delivered: Selected Papers from the 27th Australian National Health Informatics Conference (HIC 2019); 2019: IOS Press.

Download references

Acknowledgements

The authors would like to acknowledge Georgina Luscombe, Julian Grant, Claire Seaman, Jennifer Cox, Sarah Redshaw and Jennifer Schwarz who contributed to various elements of the project.

The study authors are employed by Three Rivers Department of Rural Health. Three Rivers Department of Rural Health is funded by the Australian Government under the Rural Health Multidisciplinary Training (RHMT) Program.

Author information

Authors and affiliations.

Three Rivers Department of Rural Health, Charles Sturt University, Locked Bag 588, Tooma Way, Wagga Wagga, NSW, 2678, Australia

Rebecca Barry, Elyce Green, Kristy Robson & Melissa Nott

You can also search for this author in PubMed Google Scholar

Contributions

RB & EG contributed to the conceptualisation of the study and methodological design. RB & MN collected the research data. RB, EG, MN, KR contributed to analysis and interpretation of the research data. RB, EG, MN, KR drafted the manuscript. All authors provided feedback on the manuscript and approved the final submitted manuscript.

Corresponding author

Correspondence to Rebecca Barry .

Ethics declarations

Ethics approval and consent to participate.

Ethics approvals were obtained from the Greater Western NSW Human Research Ethics Committee and Charles Sturt University Human Research Ethics Committee (approval numbers: 2021/ETH00088 and H21215). Informed written consent was obtained from all participants. All methods were carried out in accordance with the relevant guidelines and regulations.

Consent for publication

Not applicable.

Competing interests

The authors declare no competing interests.

Additional information

Publisher’s note.

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/ .

Reprints and permissions

About this article

Cite this article.

Barry, R., Green, E., Robson, K. et al. Factors critical for the successful delivery of telehealth to rural populations: a descriptive qualitative study. BMC Health Serv Res 24 , 908 (2024). https://doi.org/10.1186/s12913-024-11233-3

Download citation

Received : 19 March 2024

Accepted : 23 June 2024

Published : 07 August 2024

DOI : https://doi.org/10.1186/s12913-024-11233-3

Share this article

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

Service provision
Rural health
Allied health
Rural workforce

BMC Health Services Research

ISSN: 1472-6963

General enquiries: [email protected]

a critical analysis of research in environmental education

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

View all journals
Explore content
About the journal
Publish with us
Sign up for alerts
Open access
Published: 21 July 2023

Critical assessment of emissions, costs, and time for last-mile goods delivery by drones versus trucks

Aishwarya Raghunatha 1 , 2 ,
Emma Lindkvist 3 ,
Patrik Thollander 1 , 3 ,
Erika Hansson 3 &
Greta Jonsson 3

Scientific Reports volume 13 , Article number: 11814 ( 2023 ) Cite this article

6989 Accesses

5 Citations

3 Altmetric

Metrics details

Energy efficiency
Environmental impact
Sustainability

An Author Correction to this article was published on 02 September 2023

This article has been updated

Electric drones as an autonomous mode of transport are scaling up to transform last-mile goods delivery, raising an urgent need for assessing impacts of drone transport from a systems perspective. In this paper, we conduct systems analyses to assess the environmental, economic, and delivery time impact of large drones for delivery scenarios to pick-up centers between mid-size cities predominantly in rural areas, and deliveries within city limits compared with electric and diesel trucks. Results show that large drones have lower emissions than diesel trucks for deliveries in rural areas and that drones don’t compete with electric trucks, mainly due to the high energy demand required for take-off and landing for each delivery. Furthermore, we show that electric drones are an economically more cost-effective option than road-bound transport modes such as diesel and electric trucks due to the high degree of automation, and also provide the fastest delivery times. Our analysis provides unique insights that drones can address rapid electrification and emergency applications due to low costs, high flexibility, and fast operations. However, for regulators and practitioners to realize it as an emission-friendly option it is necessary to determine the optimal size of drones, particularly for use cases in urban areas, avoid very low landings for deliveries, and have home deliveries instead of pick-up points.

Drone routing problem model for last-mile delivery using the public transportation capacity as moving charging stations

Challenges and opportunities in truck electrification revealed by big operational data

Decarbonising road freight transport: The role of zero-emission trucks and intangible costs

Introduction.

The transportation sector globally gave rise to over 7 Gt CO 2 in 2020 which is estimated in the IEA (2021) Net Zero Emission scenario to be reduced to 5.5 Gt CO 2 by 2030 and 0.7 Gt CO 2 by 2050 1 . This vast reduction in global CO 2 emissions from the transportation sector is estimated even though freight transports are estimated to increase by a factor of 2.5 1 . Currently, 96% of all medium and heavy trucks in the European Union are diesel-driven 2 . Along with this growth in demand for transportation, demand for fast deliveries has increased significantly due to the growth in e-commerce, which brings about an amplified impact on the environment, particularly in the last part of the supply chain where the load reaches the destination 3 . The environmental impact is mainly due to the last-mile delivery trucks being partially loaded, an increase in delivery frequency, and suboptimal delivery logistics 4 . Such low efficiency and poor environmental performance call for optimizing delivery efficiency via development of new freight systems and usage of vehicles with low-emission fuels 3 , 5 . This in turn calls for exploring disruptive new technology solutions to phase out use of fossil fuels and reach this massive reduction in CO 2 emissions in transportation. Though both e-mobility and change in transport modes are key means in the built environment to reach this change according to the International Energy Agency’s Net Xero roadmap, the 2030 and 2050 scenarios for transportation remain conventional and land-based 1 .

One novel solution that is deemed to hold such potential is electric drones, or electric vertical take-off and landing vehicles (eVTOLs) as a mode of transport. While some studies claim no clear environmental benefits 6 , other studies show a vast (94%) reduced energy demand for small drones 7 . The concept, entitled Urban Air Mobility (UAM) begun in recent years to address transportation issues in dense cities 8 . It was estimated that the introduction of drones could bring new conditions to transportation systems in and around urban areas, where some of the possible benefits could be traffic reduction, time savings, environmental relief, and improved accessibility 9 . Quickly, the potential for use of such drones in a wide range of transportation use cases in urban, suburban, and rural regions has been recognized under an umbrella term called Advanced Air Mobility (AAM) 10 .

Studies show that environmental benefits of AAM would be greater in rural areas 11 , better than fossil-fueled trucks 12 and electric trucks 13 , and the estimated effectiveness of drones deliveries to be within shorter distances 14 . Small drones delivering in urban areas are seen to have higher energy consumption due to high customer density and low delivery distances 15 . However, optimally routing drone logistics is suggested to improve energy efficiency and carbon emissions 16 . While drone delivery is shown to be cost efficient for the provider 16 , 17 , there is conjecture that they may be very expensive and unaffordable for people 18 and possibly lead to increase in emissions in urban use cases and possibly give more incentive for urban sprawl 19 in growing mid-sized cities. One main advantage of drone deliveries is stated to be speed and reduction of delivery times 20 . Impacts of drone delivery is context-dependent and vary upon the system in which they are employed 14 and emissions are greatly dependent on the drones’ energy requirements, distance travelled, and number of deliveries 21 with energy being a critical constraint in maximizing the benefits of using drones 22 . Despite such uncertainty there is research suggesting the potential benefits of promoting green image of drone deliveries to cater to certain consumer demographics and increase profitability 23 , 24 . As this field gains increased attention, there is an urgent need for systems analysis of drone impacts for different delivery scenarios for environmental, economic, and delivery time impact in order to make the relationship between the impacts of drone delivery and the context of drone use clear 9 , 25 .

Therefore, the aim of this paper is to address this gap by evaluating the systems performance of an electric drone for parcel deliveries in mid-sized cities in urban and rural environments. We perform a systems analysis with focus on environmental and economic impacts as well as delivery time. We calculate CO 2 emissions to assess the global warming potential and conduct life cycle cost (LCC) assessment to uncover the economic impacts. The functional unit (FU) is an important basis for environmental assessment and enables alternative services or goods to be compared and analysed 26 . In this study the FU was chosen as one standard delivery of 250 packages, weighing 2 kg each, divided into three stops, with maximum payload of 500 kg. The calculations are made for drones as well as road-bound delivery vehicles that run on (1) electricity (e-truck), and (2) diesel (diesel truck) in urban and rural scenarios. The system included last-mile deliveries from a logistics center to a pick-up point for both the drone and the alternative road-bound vehicles. Accordingly, four scenarios are evaluated: (1) long route between cities (LBC); (2) short route between cities (SBC); (3) long route within city (LWC); and (4) short route within city (SWC). These estimations provide critical insights regarding the impact drones may have in serving as a disruptive new technology solution to bring down CO 2 emissions in transportation. Furthermore, sensitivity analysis was conducted particularly for varying energy use and reduced distance in connection with the environmental impact, the number of deliveries in connection with economic impact, and the increase in velocity for delivery time. It should be noted that drones are in an early phase of development, and as for renewable technologies such as photovoltaics that experience rapid advancement until they reach a certain level of maturity 27 the same can be expected for drones. Thereby we take an ex-ante approach to predict the possible transitions in transport technologies in upcoming decades to determine the factors related to the necessary advancement of technology and the role it will play in society, as urgently needed during early adoption.

This section presents the findings of the environmental impact assessment for the use phase, economic impact assessment for the life cycle, and delivery time for the chosen electric drone, e-truck, and diesel truck. It was found that overall, in the use phase, the electric drone competes with diesel trucks, but the e-truck is the option with the least environmental impact. In the life cycle of vehicles, the electric drone is the most economically feasible and the fastest alternative. The three assessments and sensitivity analysis of critical parameters are presented separately.

Environmental impact assessment

In Fig. 1 , the results of the environmental assessment are presented. We set the result for the drone to 100% for all routes and present the results for the trucks in relation to this. In all cases, the e-truck has significantly lower GHG emissions per FU in all cases. This can be explained by the low energy demand per km compared with the drone and the diesel truck. We find that the environmental performance of the drone is more comparable to the diesel truck where the drone has lower GHG emissions than the diesel truck for LBC by 36% and SBC by 8% but higher GHG emissions for LWC by 6% and SWC by 59%. However, e-truck outperforms environmentally by a minimum of 75% to a maximum of 92%.

Results from the environmental assessment, where the greenhouse gas emissions per functional unit are presented relative to the greenhouse gas emissions caused by the drone per functional unit.

The results indicate that the environmental performance of the drone improves when the distance increases. This is mainly due to the reduced energy demand per km for the drone for the longer cases, where the efficient cruise mode is utilized to a higher extent. Since the two trucks follow the same roads in all routes, the only difference between the e-truck and the diesel truck is the fuel consumption and the emission factor for the fuel. Both aspects are larger for the diesel truck compared with the e-truck, which is reflected in the difference in their estimated emissions.

Economic impact assessment

The results for the economic assessment are presented below in Fig. 2 , where costs over the life cycle per FU are presented, and we set the outcome for the drone to 100% and the outcomes for the other vehicles are in relation to the drone. We observe that the drone significantly has the lowest LCC per FU for all routes, while the LCC for the trucks are in the same magnitude. Drones were cheaper than electric trucks by a minimum of 117% and cheaper than diesel trucks by a minimum of 131%.

Results from the economic assessment, where the lifecycle costs per functional unit are presented relative to the lifecycle costs of the drone per functional unit.

The results indicate that drone overall has less than half of the cost per FU in all cases compared with the other vehicles. The largest difference is however seen in the LWC scenario, where the drone has 2.5 times lower LCC per FU. We visualize the reasons for the differences in the LCC for each vehicle by presenting the average distribution of LCC of each vehicle in Fig. 3 below. The largest cost during the lifetime of the drone is from fuel consumption (40%), followed by purchase cost (31%). The largest cost for both trucks is however from staff (76% for the e-truck and 70% for the diesel truck). We therefore conclude that the biggest reason for the lower LCC for the drone is due to cost savings for staff.

Lifecycle costs split for each vehicle during their lifetime.

Since fuel costs are high for the drone during its life cycle, we find that parameters linked to this are of key importance. The drone is most favorable compared with the other vehicles when the distance saved is longer. This can explain why the LBC scenario is more beneficial for the drone than the SBC scenario, where the reduced distance is 23% compared with 14% in the shorter route. The same reasoning can be used for the routes within cities, where the reduced distance for LWC scenario is 39% versus 30% for SWC scenario, which is an explanation for the lower costs compared with the other vehicles in this route. As stated earlier, the energy usage per km also differs between the routes depending on the distance between the stops, which affects the cost for fuel on the routes. Other variable parameters affecting the results for the economic assessment are factors like cost for service and cost for new parts which vary depending on distance.

Delivery time assessment

The results of the assessment regarding delivery time are presented in Fig. 4 , where the results per FU are presented in relation to the drone for all routes. The delivery time for the drone is seen to be evidently less than the delivery time of both trucks in all cases by a minimum of 128%.

Results from the assessment of delivery time, where the delivery time for trucks per functional unit is presented relative to the delivery time of the drone per functional unit.

Indeed, more time is saved by the drone since the reduced distance is larger for free flying without pre-determined flightpaths. The most time effective route, LWC scenario, is the route in which the most distance is reduced (39%) and the least time effective route, SBC scenario, is the route with the least distance reduced (14%). Furthermore, the reduction in delivery time per FU is affected by the velocity of the vehicles. The speed limits are in general lower in urban areas which leads to a bigger difference between the drone and the trucks regarding speed in these cases. However, we find that the velocity does not seem to have as big an impact as the results for reduced distance.

Sensitivity analysis

The results from the three assessments have been assessed for varying emission factors and energy usage for all vehicles, the distance taken, number of deliveries made, and velocity assumed for the drone.

Sensitivity analysis for emission factor

The results from the sensitivity analysis for different emission factors for the vehicles for each scenario are presented in Fig. 5 below. We varied the emission factors by assuming different primary fuel from each vehicle. For electric drone and truck primary fuel, the base case emission was calculated assuming primary fuel to be electricity mix from Sweden. Therefore, low estimation was made using electricity from nuclear power and high estimation using European electricity mix. For diesel trucks however lower emission factors were considered by assuming hydrogenated vegetable oil (HVO) from Sweden for medium estimation, and HVO from pine oil for low estimation, since existing combustion engine trucks are expected to transition to biofuel use in the next decade.

Sensitivity analysis using the low, base case, and high estimation of emissions factors for the fuels used by vehicles under consideration.

The origin of electricity indeed has a big impact on the results. The GHG emissions for the drone increase by approximately 5 times when using the higher estimation compared with the base case and decrease by approximately 12 times when using the low estimation. Compared with the truck fueled by diesel, the drone charged with a Swedish electricity mix is better in all cases. Drone charged with Swedish electricity mix has slightly lower emissions than truck fueled with Swedish HVO for BC scenarios, however the opposite is true for WC scenarios. E-trucks, however, remain less affected since the energy demand per km is low. Although electricity from nuclear and HVO pine seem to have the lowest emissions for the use case of all vehicles, they raise concerns for other sustainability-related issues over their life cycle.

Sensitivity analysis for energy use and distance

Variations for the environmental impact are dependent on the energy demand per km of the drone as visualized in absolute values in Fig. 6 below. The black line for every bar of the drone represents the environmental impact if the energy usage per km is either increased or decreased by 50%. When we assume the energy usage of the drone to be 50% higher, the environmental impact of the drone is greater than both trucks in all cases. If the energy usage of the drone instead is decreased by 50%, the drone has lower emissions than the diesel truck for all routes, except the SWC scenario, but still higher than the e-truck for all cases.

Greenhouse gas emissions from varied energy usage for the drone. The black lines represent ± 50% of the energy usage calculated in the main results.

Since both the drone and the e-truck use electricity as fuel, the emission factor is assumed to be the same (Swedish electricity mix). Therefore, the only factor that differentiates them regarding environmental impact is the energy usage per km and the distance. The environmental impact of the drone is seen to be lower than the impact from the trucks, the distance reduced either needs to be sufficiently large and/or the energy usage of the drone needs to be sufficiently low. To get a better understanding of the relation between reduced distance and energy usage, these two parameters were combined in a break-even analysis. See Fig. 7 a, b where the drone is compared with an e-truck and diesel truck. We varied the energy usage per km between 0.5 and 4.5 kWh/km, and the distance reduction between 0 and 60% to include all the values obtained in the results. A green box indicates that the drone has lower GHG emissions than the alternative vehicle, while a red box indicates an increase.

Illustration of the increased (red) and reduced (green) emissions for the drone compared with ( a ) e-truck and ( b ) diesel truck, where the energy demand and distance for the drone is varied, darker colour indicates larger increase/reduction.

It is seen that the drone generally causes larger GHG emissions than the e-truck and generally lower GHG emissions than the maximum emission value of diesel truck for a variation of value. The drone starts to perform better than e-truck when energy usage is reduced to and the distance reduced by the following: 3 kWh/km, and the distance reduced by 60%, 2.5 kWh/km and 50%, 2 kWh/km and 40%, 1.5 kWh/km and 15%. Further reduction in energy usage and distance makes drones better perform. We note from this that drones must then have energy usage lower than 1.5 or 1 kWh/km, which would be the energy usage of smaller drones than the one in consideration. Since the energy usage per km for the e-truck is only 0.23 kWh/km, and it is assumed that the energy demand per km for a drone of this size will be 0.5 kWh/km at the lowest, the reduced distance for the drone needs to be at least 55% shorter than for the e-truck for the drone to be the better option. In that case, less than 0.1 kg CO2-eq/FU can be saved. For the worst-case scenario, when it is assumed that the reduced distance is 0% and the energy usage per km is 4.5 kWh/km, the drone accounts for more than 2 kg CO2-eq/FU compared with the e-truck.

When comparing the drone to the diesel truck, the drone accounts for less GHG emissions in all cases compared. Even when the reduced distance is 0%, and the energy usage is 4.5 kWh/km, the drone decreases the GHG emissions by less than 0.1 kg CO2-eq/FU compared with the diesel truck. For the best-case scenario of the drone, when the reduced distance is 60% and the energy usage is 0.5 kWh/km, the drone causes more than 2 kg CO2-eq/FU less than the diesel truck.

Sensitivity analysis for varied number of deliveries

In the base case, we assume all vehicles perform delivery missions 8 h a day, where the number of deliveries depends on the delivery time which means that the drone can perform more deliveries per year than the truck, making the solid costs lower per FU. In the sensitivity analysis, we set the lower limit to the same number of deliveries per year as the trucks, assuming the demand of parcel deliveries to be limiting. We set the higher limit of deliveries to 24 h a day instead of 8 h a day, neglecting the time needed for charging. The result from the analysis is presented in Fig. 8 where the lower limit of deliveries is represented by the higher LCC per FU and the increased number of deliveries gives a reduced LCC.

Results for lifecycle costs per functional unit depending on the number of deliveries made by the drone.

The results of the sensitivity analysis show that when drones deliver all day, costs are reduced by almost 100%. With a decrease in number of deliveries to the same number as trucks, costs increase by close to 100%. Even with this 100% increase in costs, the drone still has lower costs than the trucks in all cases. This indicates that the economic result for the drone can be changed significantly without the cost exceeding the trucks.

Sensitivity analysis for varied velocity

We varied the velocity of the drone from 50 to 150% and observed its effect on the delivery time but did not vary the velocity of the trucks since they are bound by speed limits. This relative decreased time of drones compared with trucks is presented in Fig. 9 below. The results are shown for base case, low and high estimation (50% and 150% respectively) for all scenarios. Even when the velocity is lowered by 50% from the base case, the delivery time of the drone still outperforms that of the trucks.

Results for relative time saved per functional unit when using a drone instead of trucks. The low and high estimations represent ± 50% of the velocity in the base case.

The relative time saved by using drone for low estimation is the lowest for LBC scenario, and highest for SWC. High estimation shows drone to have highest time saved for LBC scenario and lowest for SWC. However, for all scenarios, even the low estimates provide significant time savings. The decreased time per FU is always positive, hence, we observe that delivery with drone is faster than delivery with trucks even if the velocity is reduced by half. This further reinforces that the most important parameter for the decreased delivery time is the shorter distance that the drone uses for deliveries and not the velocity of the drone. It is also natural that the decrease in delivery time is larger in the longer routes, i.e., for BC scenarios, than for the shorter routes, i.e., WC scenarios, because the absolute numbers are generally larger for these routes and additionally reduced distance is longer. It can further be seen that more time per FU is saved when going from the low estimation to base case, than from base case to the high estimation. The BC scenarios are more affected by the change of velocity compared with the shorter WC scenarios, which is a consequence of the longer distance travelled in these cases.

The primary objective of this study was to fill a gap in scientific systems analysis of drone deployment for delivery by undertaking a comparative analysis of drones with electric trucks and diesel trucks for a case study conducted in mid-sized cities. The results of this paper show the emissions, life-cycle costs, and delivery time delimited to the use phase of the vehicles compared for two mid-size cities. Typically for transportation and city planners, the paper shows the importance of understanding various factors that come into play to determine the most suitable options for future transportation design. Implicit decision-making for these actors occurs within the frames of environmental mobility goals, citizens’ needs, and business interests. Results show that it is crucial to understand that the impacts of drones are highly use-case dependent, primarily based on factors explored in this study.

The key findings of this study show that out of the three vehicles, e-trucks consistently emerge as the option with significantly lowest emissions. In contrast, the drone has the highest emissions for the scenarios within city limits and the diesel-fueled truck has the highest emissions in the scenario between two cities. This study indicates that drones compete directly with diesel trucks when it comes to environmental impact. This has to do with the energy efficiency of the vehicles. Notably, the study is carried out without including the infrastructural requirements for the different transport modes, i.e., construction and maintenance of roads needed for the trucks and vertiports for drones.

Irrespective of the scenarios, drones surprisingly turn out to be the most economically efficient option. The major cause for this is due to the lack of need for staff to operate the drones. Lastly, drones are also the fastest option in all cases due to their flexibility as an air mode of transport. The findings from this paper in part contradict the results from Rodrigues et al. (2022) 7 , which shows drones to be able to reduce emissions by up to 94%. Such difference in results could be dependent on the choice of the drones, putting its application in a real-systems context in varying topographies, and getting a fair comparison based on the future technological capacities of the vehicles in comparison. This contradiction also shows that the results of this paper can be generalizable only for drones carrying up to 500 kg in and around mid-sized cities.

In this study, the nature of delivery points in the delivery scenarios between cities, where drones can be considered instead of diesel-fueled trucks, is within rural areas, as supported by previous studies 11 . Rural areas, which struggle with infrastructure issues and lower loads as well as the lower frequency of package deliveries, can be an effective place to consider the use of drones. However, the use of drones is indicated in the industry to begin within urban areas so that a bigger market can be reached, and the business segment can obtain a higher chance of developing the technology, infrastructure, and services. The issue with such a development scenario, in light of drones having the highest emissions in delivery scenarios within urban environments while being the most cost-effective and fastest option, is that this can create increased competition in business, thus increasing interest of business and end users, creating lock-ins that are difficult to change over time, particularly within complex urban infrastructures. This can lead to increasing and even worsening emissions due to the convenience of enhancing the use of the delivery system.

Drones could create a possibility to achieve more optimized load factors than trucks since they are not dependent on the ability to carry a pilot. The drone could therefore potentially be improved by adjusting the size of the drone according to the size and weight of the parcel, giving a higher load factor, which is not as applicable for trucks. In that case, a smaller drone with less energy demand could be utilized when delivery size does not require more, and a larger one applied when needed. Furthermore, trucks and electric motors are innovations that have been built for a very long time, leading to improved efficiency. However, this is not the case for drones which is an emerging technology, wherefore results from this paper should be seen in this perspective that as drone operations may become more efficient attributable to steep learning curves in its production, results may very well fall in the favor of drones, e.g. as has been the case for PV 27 . Until then it is practical to limit the size of drone based on their application, as suggested by Stolaroff et al., (2018) 14 .

Results show that the emissions from drones reduce with smaller distances, and the use of drones is not yet valid in the same way as conventional logistics systems, i.e., deliveries from the logistics center to pick-up points. Instead, final delivery to customers directly from pick-up points to end users would be a rather valid use for drones. Therefore, the logistics system can be multimodal, drones can complement the use of trucks, and can be flexible and localized to improve efficiency.

Drones are mostly acclaimed for their ability to provide delivery services to end users’ doorsteps. Although this aspect is delimited by this study, the findings of the study nonetheless show that the most energy-using operations are during take-off and landing, while cruise and hover are energy-optimized. This significantly implies that for deliveries at multiple stops using drones, the drones may be used to make rooftop deliveries and not doorstep deliveries, thus reducing take-offs and landings and improving their energy efficiency during the deliveries.

There is a possibility that if the technology readiness levels increase with payload increase, the energy efficiency may not necessarily improve due to higher operational energy requirement 28 . This implies that technology developers must have energy efficiency as the target parameter and not very high payloads. From the findings, it is also evident that from an emission standpoint, tactical drones are comparable with diesel trucks in the evaluated scenarios and not with e-trucks since e-trucks have the highest energy efficiency. This further indicates that while designing logistics models, one might not have to consider drones as a competition to trucks, but rather as complements in specific use cases that are highly time-critical, and/or energy efficient in case of unavailability of e-trucks.

Another context to be considered is the actual transition of delivery trucks to electric. A major share of delivery trucks currently use diesel as the primary fuel 2 . Transition pathways for e-trucks today face challenges such as heavy weights of the trucks, reduced payloads, and reduced distances per charge. Through such operational and human-based challenges, the findings from this paper imply that drones may hold the potential to accelerate the transition from fossil-driven vehicles in intra-city last-mile delivery logistics. The adoption of electric drones can be higher due to cost savings and fast delivery times. This could further enable possibilities for accelerating renewable energy production and charging infrastructure.

The significantly high cost-efficiency for 24-h services and low delivery times implies that drones’ deliveries can be particularly beneficial for cases that involve medical services and deliveries to hospitals and rehabilitation centers. This would help significantly cut costs for healthcare.

The deliveries in the study are limited to deliveries from warehouses to pick-up points, excluding the first mile of the delivery mission, the last delivery to the customer, and the parcels reaching the logistics center from several warehouses. A strong recommendation for future studies is to develop scenarios for the last part of the supply chain, from pick-up point to customer, where the supply chain varies depending on the customers (e.g., distance travelled, vehicle type, and allocation for purpose of the trip).

The values and assumptions considered in this study are based on highly reliable data e.g., peer reviewed publications, vehicle manufacturers’ data, in-depth interviews, and inputs from relevant actors in the field. In-depth explanation and sources for data used for calculations are presented in the “ Methods ” section and Supplementary material. Due to the scope, this study is delimited to consider combined truck and drone delivery scenarios, which has been previously modeled in transportation research using small drones carried by trucks 29 , 30 , 31 , 32 , 33 , 34 . Given the substantial variation in impacts and reliance on specific scenarios, integrating them with trucks is a possible concept for more reliable and better energy efficient transportation. However, it is important to note that the feasibility of this combined truck-drone approach becomes questionable when considering larger drones with payloads comparable to trucks as done in this study due to size and efficiency. The results of this paper are therefore limited to holding significance while weighing the choice between a drone and a truck of similar payloads for the last-mile delivery. In addition, pricing for drone transportation could be significantly impacted by congestion prices for lower altitude airspace management systems 17 , 35 . However, the consideration for such pricing when drones are yet to be deployed poses huge uncertainty and depends majorly on the demand for drones which this study is delimited to do. Therefore, we strongly recommend future research to consider multimodal scenarios and drone congestion pricing.

Finally, during the study, it was identified that there is a lack of regulation regarding the velocity of drone flights. This lack of regulatory standards for what the velocity should be during take-off, landing, and cruise needs to be addressed in future studies, to determine the right velocity for safety within flight paths, built infrastructure, nature, impact on package items, and such. This study provides significant implications particularly for transportation and city planners, with respect to impacts that need to be considered while contemplating implementation of drone transportation systems. Indications are provided regarding the consequences of future scenarios, along with insights into considerations regarding what type of applications can be beneficial while designing such logistics. For technology developers, this study emphasizes the importance of focusing on energy efficiency primarily instead of load capacity for improving the impacts of use of drones for delivery. Additionally for regulators a critical policy gap regarding the lack of regulations for the drone during take-off and landing as well as cruising of the drone is identified.

In this paper, a comparison between electric drone, e-truck, and diesel truck was conducted through environmental impact assessment, life cycle cost (LCC) assessment, and delivery time assessments. The functional unit was chosen as one standard delivery of 250 packages, weighing 2 kg each, divided into three stops, with maximum payload of 500 kg. Parcel deliveries using these vehicles from a logistics center to several pick-up points (often located at grocery stores in Sweden) was studied, see Fig. 10 . An ex-ante approach was used by predicting the technological advancements for the years 2027–2032, when the usage of drones is more likely to be in a commercial stage, and assuming parameters and scenarios accordingly. The system boundaries were defined individually according to the appropriate parameters for each assessment. The ex-ante approach of this study creates a level of uncertainty for the results, especially for the drone that has not reached a commercial stage in Sweden today. Sensitivity analyses were therefore performed for the results of the assessments to evaluate the robustness of the estimated parameters, and to further analyse the impact of different parameters.

The different parts of a delivery chain. The orange border shows the parts included in this study.

Scenario description

The established functional unit gives a delivery of approximately 83 packages per pick-up point. To make a representative comparison of the drone and the trucks in different topographical contexts, four scenarios have been formulated in mid-sized Swedish cities Norrköping and Linköping, as follows:

Long route between cities (LBC)

Short route between cities (SBC)

Long route within city (LWC)

Short route within city (SWC)

Intercity deliveries or route between cities entails deliveries to pick-up points in rural areas between Linköping and Norrköping, where the starting point is Linköping’s logistics center in the north of Linköping and the end point is the logistics center located in the north of Norrköping. Intracity deliveries or routes within cities refer to deliveries in urban and suburban areas in Norrköping and the end point is the same as the starting point.

Routes between cities—BC scenarios

In intercity delivery cases packages are delivered from Linköping to Norrköping with stops in smaller villages in between the cities. Two possible routes of intercity deliveries are investigated, where one was longer and included villages further away from the highway connecting the two cities and the other was shorter with stops closer to the highway. Even though the longer route gives a detour for the truck and therefore could be seen as unlikely for real-life delivery-missions, it was seen as interesting variations to include different contexts in the study. The longer route has stops in Ljungsbro, Kimstad and Söderköping before arriving in Norrköping, which gives a total distance of 95.1 km for the road-bound vehicles and 72.8 for the drone. The shorter route has stops in Tallboda, Linghem and Norsholm which gives a total distance of 47.5 km for the trucks and 41.0 km for the drone. The two routes LBC and SBC are shown in Figs. 11 and 12 respectively.

The longer truck route between cities (left) with a total distance of 95.1 km and the drone path (right) with a total distance of 72.8 km (Retrieved from Google Maps 36 , https://goo.gl/maps/ngLZEXyrxQwdQxpk7 ). The starting and end points are indicated by orange circles and the three stops are indicated by yellow circles.

The shorter truck route between cities (left) with a total distance of 47.5 km and the drone path (right) with a total distance of 41.0 km (Retrieved from Google Maps 36 , https://goo.gl/maps/JrSxpXLfeJhwE3XaA ). The starting and end points are indicated by orange circles and the three stops are indicated by yellow circles.

Routes within cities—WC scenarios

Two different routes for the second case, deliveries within Norrköping, were computed where one included transport to the more suburban areas, and one was focused on the city center. Both routes depart and arrive at the logistics center in Norrköping. The different routes for the two cases and different vehicles are shown in Figs. 13 and 14 below. The longer route (LWC gives a total distance of 23.1 km for the trucks and 14.1 km for the drone, and the SWC gives a total distance of 7.4 km for the trucks and 5.2 km for the drone). The stops were combined into routes suitable for the urban and suburban scenario based on where the pick-up places in Norrköping are located today.

The longer truck route within the city (left) with a total distance of 23.1 km and the drone path (right) with a total distance of 14.1 km (Retrieved from Google Maps 36 , https://goo.gl/maps/XHLHCTVaGgDmxsgM9 ). The starting and end point are indicated by an orange circle and the three stops are indicated by yellow circles.

The shorter truck route within the city (left) with a total distance of 8.4 km and the drone path (right) with a total distance of 5.2 km (Retrieved from Google Maps 36 , https://goo.gl/maps/jACU616etC9YkbeY7 ). The starting and end point are indicated by an orange circle and the three stops are indicated by yellow circles.

Electric drone under consideration

The parameters for the drone chosen in this study are mainly based on the conceptual electrical “side-by-side” (see Fig. 12 ) designed by Johnson et al. (2018) 37 . This eVTOL is based on a series of studies performed by NASA to analyse the drone’s requirements for future AAM system and the type of use cases expected 37 , 38 , 39 . The electrical side-by-side is a duo-copter with a range of 137 km (and a reserve of 20-min cruise mode). It is a tactical drone with the ability to carry a payload of 544.3 kg. The drone is designed to carry people, but since it has the desired capacity for this study it is assumed to be representative for deliveries of parcels without a pilot, complete specifications for which are provided in supplementary material. eVTOL manufacturers estimate the price of the drone to be between 0.6 and 1.5 MSEK while the lifetime of the drone is estimated to be 10,000 flight hours. Based on 2024 yearly flight hours (8 h a day for 253 days) this would equal a lifetime of five years with no residual value at the end. See Table 1 for more technical information about the electric drone.

Delivery trucks under consideration

Most delivery trucks today run on diesel. However, the ex-ante approach of this study considers the competitive vehicles for technologically advanced drones in the period 2027–2032 for a fair comparison. While road vehicles are largely expected to be electric by this period, diesel trucks are included as well, since currently 96.3% of all medium and heavy trucks in the European Union are diesel-driven 2 . Thus, the two vehicles for comparison with drones are e-truck and diesel truck, with payloads of 975 kg and 846 kg, prices of 0.605 MSEK and 0.355 MSEK, and residual values of 0.301 MSEK and 0.177 MSEK respectively as shown in Table 1 . The lifetimes of the trucks were assumed to be 5 years, to facilitate the calculations. Complete specifications of the trucks are provided in supplementary material.

Energy and emissions calculations

In Fig. 15 , an overview of operations for the mission of drone flight is presented. There are five stages of drone flight operations: hover, climb, cruise, descend and hover 40 . The flight operations are performed one time for each stop plus one extra to reach the destination. The flight between cities is assumed to be performed at an altitude of 500 feet (152.4 meters). Flights within cities are assumed to be performed at an altitude of 100 meters since the shorter distance between the stops limits the time of climb. The vertical and horizontal speeds in the different stages are assumed from Silva et al. (2018) 39 where the cruise speed is chosen as the best range speed.

Drone speed vertically and horizontally, and flight altitude during different stages of flight.

The different stages of the flight require different amounts of energy where hovering and climbing are the most demanding stages. Given that the studied scenarios have different length and duration in the different stages, the energy requirements per km for the drone will differ between the scenarios. Therefore, the total energy needed for drone during one delivery will be the sum of the energy required for each flight operation 40 , 41 .

Following Kasliwal et al. (2019) 41 , power required for the different stages is calculated using Eqs. ( 1 , 2 , 3 , 4 ) which are modelled upon parameters such as sea level air density ( $\rho$ ), disk load of the drone ( $\delta$ ), hover system efficiency ( ${\eta }_{h}$ ), climb system efficiency ( ${\eta }_{cl}$ ), cruise system efficiency ( ${\eta }_{cl}$ ), potential energy conversion from altitude into distance ( $L/D$ ) for cruise, climb and descent, rate of climb ( $ROC$ ), rate of descent ( $ROD$ , which is − $ROC$ ), speeds in different phases ( $V$ ), mass of the drone $m$ , and gravitational acceleration ( $g$ =9.82 m/s 2 ) 41 . These parameters for the drone in consideration in this paper are provided in supplementary material.

The energy requirements for drones thus calculated are presented in Table 2 . Out of the four scenarios the required energy per km for LBC scenario, which is the longest of the four routes, is the lowest and the required energy per km for SWC scenario, which is the shortest route, is the highest.

The energy requirements per km for the trucks are collected from the manufacturers’ websites and are based on the Worldwide Harmonised Light Vehicles Test Procedure. The value is then adjusted based on the changed load factor during the route. The energy requirement is increased by 5.6 Wh/km per 100 kg additional weight according to the assumption made by Ellingsen et al. (2016) 42 . The load factor has low impact on the required energy for the trucks as shown in Table 2 .

The objective of the environmental assessment was to evaluate the impact for global warming potential (GWP) of the scenarios. Each package is assumed to weigh 2 kg, since 75–90% of large companies’ last-mile deliveries weigh below 2.3 kg 13 . The study took a tank-to-wheel approach, assessing the potential environmental impact from the vehicles’ use phase. The system boundary for fuel and electricity was however wider, applying cradle-to-grave, including the environmental impact from production to combustion. To calculate the environmental impact from the different cases and vehicles, emission factors for electricity and diesel were found through the Swedish Environmental Institute 43 , see Table 3 . The emission factor for electricity is a complex parameter since it is dynamic depending on the momentary production of electricity and on geographic location. In this study Swedish electricity mix including consideration of import and export was chosen. For gaining the environmental impact from the vehicles’ use phase, the emission factors were multiplied by the vehicle’s fuel consumption and the distance of the route.

Life-cycle costs calculation

In the economic assessment, the aim was to get an overview of the costs for each of the vehicles. Therefore, a life cycle cost analysis (LCCA) was performed using the equation for net present value (NPV). The NPV represents the present value of all costs and benefits linked to an investment where the future costs are discounted to present value 44 . The net present value benefits from the vehicles’ operations are however excluded due to the study being conducted for a future scenario. Therefore, the most economically beneficial alternative in the study is represented by the alternative with the lowest costs during their life cycle. The equation for calculating the NPV 44 , 45 is presented below.

To discount future values, the discount rate was set to 12% based on a recommended required rate of return of 10% and 2% inflation 43 . Furthermore, most costs are assumed to be nominally unchanged and are based on today’s values (excluding value-added tax). The values assumed with today’s values are then recalculated with the rate of inflation to each year of the study period, except fuel costs which are assumed to increase above inflation, and battery cost which is expected to decrease from current value. Costs included in the study are initial costs, service and maintenance cost, operating costs, disposal costs, and vehicle insurance costs. These costs differ for each alternative vehicle and hence are deemed important; other costs that are uncertain in terms of predictability such as taxes, fees, and insurance of goods are excluded. Thus, the system boundaries for LCC conducted is shown in Fig. 16 . The summary of all cost parameters used is shown in Table 4 . For more details regarding procurement of parameters see supplementary material.

The system boundaries for life cycle cost assessment of the vehicles, where the orange border shows the parts included in this study.

Delivery time calculations

The delivery time was defined as the time from departure at one logistic center, via deliveries to three pick-up points, to arrival at the other logistic center for each route and vehicle. The assessment includes both time for driving and delivery. We assume that both electric and diesel trucks drive at the same speed limits and on the same route in all cases. Hence, the delivery times for drones are identical. To estimate the time of driving for the trucks, the fastest route was used during non-congested times on Google Maps. The drone is assumed to fly the shortest possible path between the nodes at a constant speed (see scenario description). Drop-off with a truck is assumed to take an average time of 3 min 13 . It is furthermore assumed that the drone will land on the ground when delivering, requiring extra time for take-off and landing. The time required for the drone to lift and land is set to 1 min. The procedure consists of 30 s hovering down from an altitude of 30 meters and then 30 s hovering up to an altitude of 30 meters. The extra minute is then added to the delivery time for the drone, making the total delivery time 4 min for the aerial vehicle.

Sensitivity analysis calculations

Parameters that were tested in sensitivity analysis were:

Emission factor—for all vehicles

Energy usage—for the drone

Reduced distance by the drone

Numbers of deliveries—for the drone

Velocity—for the drone

Emission factor, energy usage and reduced distance when using a drone instead of road-bound vehicles, are important categories for the study in general, and especially for the environmental assessment since these parameters affect the consequences of the use phase. The emission factors are a critical parameter for all vehicles since it is dependent on the future market scenarios and production of fuel. To see how the results were affected by different scenarios, the emission factor for electricity was changed to both higher and lower values, and the emission factor for diesel was changed to two different lower values. The emission factor for electricity was adjusted to a higher value corresponding to the electricity mix in the EU in 2019, and a lower value corresponding to electricity from nuclear power. The diesel truck can also be fueled with HVO without conversion and therefore, the emissions corresponding to HVO mix in Sweden 2020 are used as a medium estimate. The lower case for the diesel truck is HVO made from pine oil. The values used for the different fuels can be found in Table 5 .

Energy usage per km and reduced distance when using an electric drone are vital parameters for the environmental assessment. To see the robustness of the results and to find break-even points, sensitivity analyses were performed where different energy and reduced distance were varied to see when the electric drone is beneficial. Break-even analyses were also performed for an e-truck and a diesel truck, as well as a truck fueled with HVO, to get a broader picture of the variations on the environmental impact of the electric drone when comparing to different vehicles.

To test how much the number of deliveries affects the results from the economic assessment, the number of deliveries made by the drone were set to a higher and a lower value. The lower limit is set to the same number of deliveries per year as the trucks, assuming the demand for parcel deliveries to be limiting. The higher limit of deliveries is set to 24 h a day instead of 8 h a day, which was assumed to be possible since the drone is unmanned. The number of deliveries made during a lifetime impacts the results for economic assessment where the impact is total cost per standard delivery based on the functional unit for the lifetime of the vehicles.

For delivery time, the velocity of the drone during cruising and climbing is an uncertain parameter due to current lack of legal standards. Sensitivity analysis for higher (increased by 50%) and lower (decreased by 50%) velocities of the drone is conducted to understand the impact on delivery time. It is assumed that the drone flies at constant speed in all cases while the speed of the truck remains constant since it is dependent upon established speed limits.

Data availability

All data generated or analysed during this study are included in this published article [and its supplementary information files].

Change history

02 september 2023.

A Correction to this paper has been published: https://doi.org/10.1038/s41598-023-41725-x

IEA. Net Zero by 2050 (2021).

ACE., Vehicles in use Europe 2022 (2022).

Jaller, M. et al. Empirical analysis of the role of incentives in zero-emission last-mile deliveries in California. J. Clean. Prod. 317 , 128353 (2021).

Article Google Scholar

Muñoz-Villamizar, A. et al. The environmental impact of fast shipping ecommerce in inbound logistics operations: A case study in Mexico. J. Clean. Prod. 283 , 125400 (2021).

Savchenko, L. et al. Combination of different means of parcel deliveries in urban logistics in adverse weather conditions. J. Sustain. Dev. Transp. Logist. 6 (1), 6–17 (2021).

Article MathSciNet Google Scholar

Koiwanit, J. Analysis of environmental impacts of drone delivery on an online shopping system. Adv. Clim. Chang. Res. 9 (3), 201–207 (2018).

Rodrigues, T. A. et al . Drone flight data reveal energy and greenhouse gas emissions savings for very small package delivery. Patterns . 3 (8) (2022).

Hasan, S. Urban Air Mobility (UAM) Market Study (2019).

Kellermann, R., Biehle, T. & Fischer, L. Drones for parcel and passenger transportation: A literature review. Transp. Res. Interdiscip. Perspect. 4 , 100088 (2020).

Google Scholar

Cohen, A. P., Shaheen, S. A. & Farrar, E. M. Urban air mobility: History, ecosystem, market potential, and challenges. IEEE Trans. Intell. Transp. Syst. 22 (9), 6074–6087 (2021).

Park, J., Kim, S. & Suh, K. A comparative analysis of the environmental benefits of drone-based delivery services in urban and rural areas. Sustainability 10 (3), 888 (2018).

Bulusu, V. et al. A traffic demand analysis method for urban air mobility. IEEE Trans. Intell. Transp. Syst. 22 (9), 6039–6047 (2021).

Figliozzi, M. A. Lifecycle modeling and assessment of unmanned aerial vehicles (Drones) CO 2 e emissions. Transp. Res. Part D Transp. Environ. 57 , 251–261 (2017).

Stolaroff, J. K. et al. Energy use and life cycle greenhouse gas emissions of drones for commercial package delivery. Nat. Commun. 9 (1), 409 (2018).

Article ADS PubMed PubMed Central Google Scholar

Kirschstein, T. Comparison of energy demands of drone-based and ground-based parcel delivery services. Transp. Res. Part D Transp. Environ. 78 , 102209 (2020).

Chiang, W.-C. et al. Impact of drone delivery on sustainability and cost: Realizing the UAV potential through vehicle routing optimization. Appl. Energy 242 , 1164–1175 (2019).

Borghetti, F. et al. The use of drones for last-mile delivery: A numerical case study in Milan, Italy. Sustainability 14 (3), 1766 (2022).

Fleischer, T. et al. Personal aerial transportation systems (PATS)—A potential solution for the urban mobility challenges? Futures 109 , 50–62 (2019).

Straubinger, A. et al. An overview of current research and developments in urban air mobility—Setting the scene for UAM introduction. J. Air Transp. Manag. 87 , 101852 (2020).

Fu, M. et al. Modelling mode choice including urban air mobility—A case-study of Munich. Transp. Res. Procedia 41 , 45–47 (2019).

Goodchild, A. & Toy, J. Delivery by drone: An evaluation of unmanned aerial vehicle technology in reducing CO 2 emissions in the delivery service industry. Transp. Res. Part D Transp. Environ. 61 , 58–67 (2017).

Zhang, J. et al. Energy consumption models for delivery drones: A comparison and assessment. Transp. Res. Part D-Transp. Environ. 90 , 102668–102668 (2021).

Hwang, J., Kim, H. & Kim, W. Investigating motivated consumer innovativeness in the context of drone food delivery services. J. Hosp. Tour. Manag. 38 , 102–110 (2019).

Hwang, J., Lee, J.-S. & Kim, H. Perceived innovativeness of drone food delivery services and its impacts on attitude and behavioral intentions: The moderating role of gender and age. Int. J. Hosp. Manag. 81 , 94–103 (2019).

Raghunatha, A., Thollander, P. & Barthel, S. Addressing the emergence of drones—A policy development framework for regional drone transportation systems. Transp. Res. Interdiscip. Perspect. 18 , 100795 (2023).

Rebitzer, G. et al. Life cycle assessment: Part 1: Framework, goal and scope definition, inventory analysis, and applications. Environ. Int. 30 (5), 701–720 (2004).

Article CAS PubMed Google Scholar

Elshurafa, A. M. et al. Estimating the learning curve of solar PV balance–of–system for over 20 countries: Implications and policy recommendations. J. Clean. Prod. 196 , 122–134 (2018).

Thibbotuwawa, A., et al. Energy consumption in unmanned aerial vehicles: A review of energy consumption models and their relation to the UAV routing, in Information Systems Architecture and Technology: Proceedings of 39th International Conference on Information Systems Architecture and Technology—ISAT 2018 (Springer International Publishing, 2019).

Aurambout, J., Gkoumas, K. & Ciuffo, B. Last mile delivery by drones: An estimation of viable market potential and access to citizens across European cities. Eur. Transp. Res. Rev. 11 , 1–21 (2019).

Dukkanci, O., Kara, B. & Bektaş, T. Minimizing energy and cost in range-limited drone deliveries with speed optimization. Transp. Res. Part C Emerg. Technol. 125 , 102985 (2020).

Gonzalez-R, P. L. et al. Truck-drone team logistics: A heuristic approach to multi-drop route planning. Transp. Res. Part C-Emerg. Technol. 114 , 657–680 (2020).

Moshref-Javadi, M., Lee, S. & Winkenbach, M. Design and evaluation of a multi-trip delivery model with truck and drones. Transp. Res. Part E Logist. Transp. Rev. 136 , 101887 (2020).

Murray, C. & Raj, R. The multiple flying sidekicks traveling salesman problem: Parcel delivery with multiple drones. Transp. Res. Part C Emerg. Technol. 110 , 368–398 (2020).

Wang, D. et al. Routing and scheduling for hybrid truck-drone collaborative parcel delivery with independent and truck-carried drones. IEEE Internet Things J. 6 (6), 10483–10495 (2019).

Merkert, R. & Bushell, J. Managing the drone revolution: A systematic literature review into the current use of airborne drones and future strategic directions for their effective control. J. Air Transp. Manag. 89 , 101929 (2020).

Article PubMed PubMed Central Google Scholar

Author created routes for delivery scenarios using roads and direct flight paths . Google Maps (2022).

Johnson, W., Silva, C., & Solis, E. Concept vehicles for VTOL air taxi operations, in AHS Specialists''Conference on Aeromechanics Design for Transformative Vertical Flight (2018).

Patterson, M. D., Antcliff, K. R., & Kohlman, L. W. A proposed approach to studying urban air mobility missions including an initial exploration of mission requirements, in Annual Forum and Technology Display (2018).

Silva, C. et al . VTOL urban air mobility concept vehicles for technology development, in 2018 Aviation Technology, Integration, and Operations Conference .

Mudumba, S. V. et al. Modeling CO 2 emissions from trips using urban air mobility and emerging automobile technologies. Transp. Res. Rec. 2675 (9), 1224–1237 (2021).

Kasliwal, A. et al. Role of flying cars in sustainable mobility. Nat. Commun. 10 (1), 1555 (2019).

Ellingsen, L.A.-W., Singh, B. & Strømman, A. H. The size and range effect: Lifecycle greenhouse gas emissions of electric vehicles. Environ. Res. Lett. 11 (5), 054010 (2016).

Article ADS Google Scholar

Administration, S. T. Analysis Methodology and Socio-Economic Costings for the Transport Sector: ASEK 7.0. (2020).

Žižlavský, O. Net present value approach: Method for economic assessment of innovation projects. Procedia Soc. Behav. Sci. 156 , 506–512 (2014).

Analysis, T. Development of the Road Vehicle Fleet by 2030 (2020).

BNEF. Battery Pack Prices Fall As Market Ramps Up With Market Average At $156/kWh In 2019 (2019).

Preem. List Prices Business Cards . (2020).

Verksamt. Calculate the cost of an employee . 2020 [cited 27-04-2022; Available from: https://www.verksamt.se/alla-e-tjanster/rakna-ut/rakna-ut-vad-en-anstalld-kostar?p_auth=hrzMSxBR&p_p_id=tvv_webcalc_WAR_tvv_webcalc&p_p_lifecycle=1&p_p_state =.

Sweden, S. Salary Statistics—How Much do you Earn...? 2020 27-04-22]; Available from: https://www.scb.se/hitta-statistik/sverige-i-siffror/lonesok/ .

Agency, S. E. Fuels 2020: Reporting of Reported Data Under the Fuels Act, the Sustainability Act and the Reduction Obligation (2021).

Fransson, N., Lundblad, M., & Lätt, A. Emission factors for fuels for electricity and heat production. IVL Svenska Miljöinstitutet (2020).

Gode, J. et al . Estimated emission factors for fuels, electricity, heat and transport (2011).

Download references

Acknowledgements

We sincerely thank the drone manufacturers, logistics companies, and County Insurance in Sweden for their inputs on quantitative values assumed in this paper.

Open access funding provided by University of Gävle. This work has been carried out under the auspices of the industrial post-graduate school Future Proof Cities (Grant Number 2019-0129), which is financed by the Knowledge Foundation (KK-Stiftelsen). We kindly thank the funding bodies for their financial support.

Author information

Authors and affiliations.

Department of Building Engineering, Energy Systems and Sustainability Science, University of Gävle, 801 76, Gävle, Sweden

Aishwarya Raghunatha & Patrik Thollander

Independent Business Group Sweden AB, 602 21, Norrköping, Sweden

Aishwarya Raghunatha

Division of Energy Systems, Department of Management and Engineering, Linköping University, 581 83, Linköping, Sweden

Emma Lindkvist, Patrik Thollander, Erika Hansson & Greta Jonsson

You can also search for this author in PubMed Google Scholar

Contributions

A.R. Conceptualization, Supervision, Analysis, Writing- Original draft preparation. E.L. Methodology, Supervision, Writing-Original draft preparation. P.T. Conceptualization, Supervision, Writing-Reviewing and Editing. E.H. and G.J. Methodology, Calculations, Results, Writing-Thesis report. All authors have contributed to and reviewed the manuscript.

Corresponding author

Correspondence to Aishwarya Raghunatha .

Ethics declarations

Competing interests.

The authors declare no competing interests.

Additional information

Publisher's note.

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

The original online version of this Article was revised: The original version of this Article contained an error in Figure 15, where the values for vertical and horizontal speed in the cruise phase and the vertical speed in the hover phase for landing were incorrect.

Supplementary Information

Supplementary information., rights and permissions.

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ .

Reprints and permissions

About this article

Cite this article.

Raghunatha, A., Lindkvist, E., Thollander, P. et al. Critical assessment of emissions, costs, and time for last-mile goods delivery by drones versus trucks. Sci Rep 13 , 11814 (2023). https://doi.org/10.1038/s41598-023-38922-z

Download citation

Received : 06 April 2023

Accepted : 17 July 2023

Published : 21 July 2023

DOI : https://doi.org/10.1038/s41598-023-38922-z

Share this article

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

By submitting a comment you agree to abide by our Terms and Community Guidelines . If you find something abusive or that does not comply with our terms or guidelines please flag it as inappropriate.

Quick links

Explore articles by subject
Guide to authors
Editorial policies

Sign up for the Nature Briefing: Anthropocene newsletter — what matters in anthropocene research, free to your inbox weekly.

Environmental Analysis: Definition, Steps, Tools, Examples

Appinio Research · 08.08.2024 · 30min read

Have you ever wondered how we can make better decisions to protect our environment and ensure a sustainable future? Environmental analysis is the answer, providing a systematic way to understand the complex interactions between natural and human systems. This guide delves into the methods, tools, and frameworks used in environmental analysis to assess the physical, biological, and socioeconomic components of our world. By examining these factors, we can identify potential impacts, anticipate risks, and develop strategies that promote sustainability and resilience. Whether you're in business, urban planning, natural resource management, or another sector, understanding environmental analysis can help you make informed decisions that benefit both people and the planet.

What is Environmental Analysis?

Environmental analysis is a systematic process of examining the external factors that can influence an organization, project, or ecosystem. The primary purpose of environmental analysis is to understand the current and future conditions of the environment and how they can impact various aspects of human activity and natural systems. This comprehensive examination involves collecting, analyzing, and interpreting data related to the environment's physical, biological, and socioeconomic components.

Environmental analysis serves multiple purposes, including:

Identifying Environmental Impacts : Assessing the potential effects of projects, policies, or activities on the environment.
Supporting Decision-Making : Providing essential data and insights to inform strategic planning and policy development.
Ensuring Compliance : Helping organizations adhere to environmental regulations and standards.
Promoting Sustainability : Identifying opportunities to reduce negative environmental impacts and enhance positive outcomes.
Risk Management : Anticipating and mitigating potential environmental risks to avoid adverse effects on human health and ecosystems.

Importance in Various Sectors

Environmental analysis is vital across numerous sectors, each benefiting uniquely from its insights and findings. Here are some of the key sectors where environmental analysis plays a critical role:

Business : Companies use environmental analysis to manage risks, comply with regulations, and identify opportunities for sustainable practices. This can enhance brand reputation, reduce costs, and improve long-term profitability.
Urban Planning : Planners use environmental analysis to design sustainable cities that minimize environmental impact, improve quality of life, and ensure resilient infrastructure against climate change.
Natural Resource Management : This sector relies on environmental analysis to ensure the sustainable use of resources such as water, minerals, forests, and fisheries, balancing economic needs with conservation.
Public Policy : Policymakers use environmental analysis to develop regulations and policies that protect the environment and public health. This includes laws on pollution control, land use, and wildlife protection.
Healthcare : The healthcare sector uses environmental analysis to understand the links between environmental factors and public health, aiding in the prevention and management of diseases related to environmental conditions.
Agriculture : Farmers and agricultural planners use environmental analysis to optimize crop production, manage water resources, and reduce the environmental impacts of farming practices.
Energy : The energy sector employs environmental analysis to assess the effects of energy production and consumption, promoting the use of renewable energy sources and improving energy efficiency.
Tourism : Sustainable tourism planning relies on environmental analysis to preserve natural and cultural resources while promoting economic development.

Components of Environmental Analysis

Understanding the key components of environmental analysis is crucial for comprehensively assessing the various factors that influence our environment. These components are categorized into three primary areas: the physical environment, the biological environment, and the socioeconomic environment. Each of these areas plays a vital role in shaping the interactions and impacts within an ecosystem.

Physical Environment

The physical environment includes the natural features and conditions that occur in the world around us. It forms the foundation upon which all biological and human activities are built.

Climate and Weather Patterns

Climate and weather patterns are fundamental aspects of the physical environment. Climate refers to the long-term patterns of temperature, humidity, wind, and precipitation in a region, while weather pertains to short-term atmospheric conditions. Understanding these patterns is essential for predicting natural events and preparing for their impacts.

Climate analysis involves studying historical data and current trends to predict future climatic conditions. This can help in planning agricultural activities, managing water resources, and preparing for extreme weather events such as hurricanes, droughts, and floods. For instance, regions prone to drought can implement water-saving technologies and diversify their water sources to ensure a steady supply.

Natural Resources

Natural resources are materials and substances that occur naturally and can be used for economic gain. These include water, minerals, forests, and fossil fuels. Analyzing their availability, distribution, and sustainable use is critical for long-term environmental planning.

Sustainable management of natural resources involves balancing the extraction and use of these resources with the need to preserve the environment for future generations. For example, sustainable forestry practices might include selective logging and replanting to maintain forest health and biodiversity.

Topography is the study of the Earth's surface features, including mountains, valleys, plains, and bodies of water. It influences various environmental factors such as weather patterns, water flow, and human settlement patterns.

Topographic analysis is crucial for land use planning, infrastructure development, and disaster management. For instance, understanding the topography of an area can help in designing effective drainage systems to prevent flooding or in selecting suitable locations for building infrastructure.

Biological Environment

The biological environment encompasses the living components of the environment and their interactions with each other and their physical surroundings. It includes ecosystems, biodiversity, flora, and fauna.

Ecosystems and Biodiversity

Ecosystems are communities of living organisms interacting with their physical environment. They provide essential services such as pollination, water purification, and climate regulation. Biodiversity refers to the variety of life within an ecosystem, encompassing the different species of plants, animals, and microorganisms.

High biodiversity is often an indicator of a healthy, resilient ecosystem. Conservation efforts focus on preserving biodiversity to maintain ecosystem stability and functionality. For example, protecting diverse habitats like wetlands, forests, and coral reefs helps ensure the survival of various species and the services they provide.

Flora and Fauna

Flora and fauna are terms used to describe plant and animal life, respectively. Studying flora and fauna involves understanding species distribution, population dyn amics, and their roles within ecosystems.

Knowledge of local flora and fauna is crucial for conservation and management efforts. For example, protecting endangered species requires understanding their habitat needs, breeding behaviors, and threats. This information can then be used to implement conservation strategies, such as creating protected areas or restoring habitats.

Socioeconomic Environment

The socioeconomic environment examines the social and economic factors that influence and are influenced by the environment. This includes demographics, economic activities, and cultural aspects.

Demographics

Demographic analysis involves studying population characteristics such as age, gender, income, and education levels. These factors significantly impact resource consumption, waste generation, and overall environmental impact.

For instance, a growing population increases the demand for water, food, and energy resources, leading to higher waste production and environmental degradation. Understanding these demographic trends helps in planning sustainable development strategies that balance population growth with environmental conservation.

Economic Activities

Economic activities, including agriculture, industry, and services, directly affect and are affected by the environment. Analyzing these activities helps in understanding their environmental footprint and developing strategies for sustainable economic development.

For example, industrial activities may lead to pollution and resource depletion, while sustainable practices such as green manufacturing and renewable energy can reduce environmental impacts. Assessing the environmental impacts of economic activities is essential for creating policies and practices that promote sustainable development.

Cultural Aspects

Cultural aspects include the beliefs, practices, and values of different communities. They influence how people interact with the environment and can affect environmental policies and practices.

Cultural attitudes towards the environment can vary significantly. For instance, some cultures may have traditional practices promoting conservation and sustainable resource use, while others may prioritize economic development over environmental protection. Understanding these cultural aspects is crucial for developing effective environmental policies that are culturally sensitive and widely accepted.

Environmental Analysis Methodologies

Environmental analysis involves a variety of methodologies, each suited to different aspects of study and analysis. These methodologies can be broadly categorized into qualitative and quantitative methods . Both approaches are essential for a comprehensive understanding of environmental factors and their impacts.

Qualitative Methods

Qualitative methods focus on understanding the deeper, often subjective aspects of environmental issues. These methods provide rich, detailed data that help uncover insights into human behaviors, experiences, and interactions with the environment.

Interviews and Surveys

Interviews and surveys are powerful tools for gathering information directly from individuals or groups. They help capture personal experiences, opinions, and attitudes toward environmental issues.

Interviews involve direct, one-on-one, or small group conversations with stakeholders such as community members, experts, or policymakers. Interviews can be structured, with a set list of questions, or unstructured, allowing for more open-ended discussions. For instance, interviewing local farmers about their experiences with changing weather patterns can reveal insights into climate impacts on agriculture.

Surveys are typically distributed to a larger audience and can be conducted online, via phone, or in person. Surveys use standardized questions to collect data on various topics, such as public awareness of environmental policies or community concerns about pollution. The collected data can then be analyzed to identify common themes and patterns.

Harnessing the power of cutting-edge tools is crucial for conducting effective environmental analysis. With platforms like Appinio , gathering real-time insights becomes incredibly efficient and straightforward. This approach not only accelerates the data collection process but also enhances the quality and relevance of the insights obtained. By leveraging advanced technology, organizations can seamlessly integrate comprehensive data into their environmental assessments, making informed decisions faster than ever.

Ready to elevate your environmental analysis with state-of-the-art data collection?

Book a Demo

Case Studies

Case studies provide an in-depth analysis of specific instances, events, or projects. They offer detailed insights into complex environmental issues by examining real-world examples. Case studies often involve multiple data collection methods , including observations, interviews, and document analysis.

For example, a case study of a successful reforestation project might explore the strategies used, the challenges faced, and the outcomes achieved. This approach helps in understanding how similar initiatives can be replicated or improved.

Quantitative Methods

Quantitative methods use numerical data and statistical techniques to analyze environmental factors. These methods provide objective, measurable evidence that can be used to identify trends, make predictions, and inform decision-making.

Statistical Analysis

Statistical analysis involves collecting and analyzing numerical data to identify patterns, trends, and relationships. This method is widely used in environmental research to quantify impacts and predict future scenarios.

Standard statistical techniques include regression analysis , correlation analysis , and hypothesis testing. For example, regression analysis can be used to examine the relationship between air pollution levels and respiratory health outcomes. By analyzing data from various sources, researchers can determine whether higher pollution levels are associated with increased rates of respiratory illnesses.

Geographic Information Systems (GIS)

Geographic Information Systems (GIS) are powerful tools for capturing, storing, analyzing, and visualizing spatial data. GIS integrates various data types, such as satellite imagery, topographic maps, and demographic information, to provide a comprehensive view of environmental conditions.

GIS applications include:

Mapping and Spatial Analysis : Creating detailed maps that visualize environmental data, such as land use patterns, water resources, and pollution sources.
Site Selection : Identifying suitable locations for new developments, conservation areas, or infrastructure projects based on environmental criteria.
Risk Assessment : Analyzing spatial data to assess the risks of natural hazards, such as floods, earthquakes, or wildfires.

For instance, GIS can be used to map areas at risk of flooding by overlaying rainfall data with topographic information. This helps in planning flood prevention measures and emergency response strategies.

Remote Sensing

Remote sensing involves collecting data from a distance, typically using satellites, aircraft, or drones. This method provides valuable information on large-scale environmental changes and conditions that might be difficult or impossible to measure directly.

Remote sensing applications include:

Land Use and Land Cover Monitoring : Tracking changes in land use, such as deforestation, urbanization, and agricultural expansion, over time.
Environmental Monitoring : Observing and measuring environmental parameters, such as vegetation health, soil moisture, and water quality, on a regional or global scale.
Disaster Management : Providing real-time data on natural disasters, such as hurricanes, wildfires, and earthquakes, to support emergency response efforts.

For example, satellite imagery can be used to monitor the extent and health of coral reefs, providing crucial data for conservation efforts. By analyzing changes in reef color and structure, researchers can assess the impacts of factors like water temperature and pollution on reef health.

Both qualitative and quantitative methods are essential for a comprehensive environmental analysis. While qualitative methods provide detailed insights into human experiences and behaviors, quantitative methods offer objective, measurable evidence. Together, these approaches enable a thorough understanding of environmental issues and support informed decision-making for sustainable development.

Environmental Analysis Frameworks and Models

Frameworks and models provide structured approaches to environmental analysis, helping to organize and interpret complex information. Three widely used frameworks are PESTLE analysis, SWOT analysis, and Porter's Five Forces. Each offers a unique perspective and set of tools for understanding different aspects of the environment and their impacts on organizations and projects.

PESTLE Analysis

PESTLE analysis examines the macro-environmental factors that can affect an organization or project. This framework considers six key dimensions:

Political Factors : These include government policies, political stability, tax policies, trade tariffs, and other regulatory measures. For example, changes in environmental regulations can impact business operations, requiring companies to adapt their processes to comply with new standards.
Economic Factors : Economic conditions such as inflation rates, interest rates, economic growth, and exchange rates influence organizational performance. For instance, an economic downturn can reduce consumer spending, affecting industries reliant on discretionary spending.
Social Factors : Social trends, demographic changes, cultural attitudes, and lifestyle changes shape consumer behavior and societal expectations. An aging population, for example, might increase demand for healthcare services and products.
Technological Factors : Technological advancements and innovation can create new opportunities and threats. Companies must keep pace with technological developments to maintain competitiveness. The rise of renewable energy technologies is reshaping the energy sector and reducing reliance on fossil fuels.
Legal Factors : Legal considerations include laws and regulations related to labor, health and safety, consumer protection, and environmental protection. Compliance with these laws is mandatory and can impact operational costs and practices.
Environmental Factors : Environmental concerns such as climate change, resource scarcity, and ecological sustainability are increasingly important. Companies need to address these issues to meet regulatory requirements and consumer expectations. For example, sustainable sourcing practices are becoming critical in industries like fashion and food.

SWOT Analysis

Strengths : Internal attributes that provide a competitive advantage. Examples include strong brand reputation, proprietary technology, or a skilled workforce. Identifying strengths helps leverage them to achieve strategic goals.
Weaknesses : Internal limitations that hinder performance. These could be outdated technology, limited resources, or poor customer service. Recognizing weaknesses is crucial for addressing and mitigating them.
Opportunities : External factors that can be exploited for growth and success. These might include emerging markets, technological advancements, or favorable regulatory changes. Seizing opportunities involves proactive planning and investment.
Threats : External challenges that could impact success. These include competitive pressures, regulatory changes, and economic downturns. Identifying threats allows for the development of contingency plans and risk management strategies.

Porter's Five Forces

Competitive Rivalry : The degree of competition among existing firms. Intense rivalry can drive down prices and profits. Factors influencing rivalry include the number of competitors, rate of industry growth, and product differentiation.
Threat of New Entrants : The ease with which new competitors can enter the market. High entry barriers, such as capital requirements, brand loyalty, and regulatory hurdles, protect existing firms from new entrants.
Bargaining Power of Suppliers : The power of suppliers to influence prices and terms. When suppliers are few or offer unique products, their bargaining power increases, potentially raising costs for firms.
Bargaining Power of Buyers : The power of customers to demand lower prices or higher quality. Buyers have more power when they purchase in large volumes or when products are undifferentiated.
Threat of Substitutes : The availability of alternative products or services that can perform the same function. High threat of substitutes can limit industry profitability by capping prices. For example, the rise of digital media has significantly impacted the print media industry.

These frameworks and models are essential tools in environmental analysis, providing structured methods for assessing various factors that influence the environment and organizational strategies. By systematically applying these frameworks, organizations can better understand their operating environment, identify key challenges and opportunities, and develop effective strategies for sustainable growth.

How to Conduct Environmental Analysis?

Conducting environmental analysis is a systematic process that involves several critical steps. Each step is crucial to ensure a comprehensive understanding of environmental conditions and to inform decision-making processes.

1. Planning and Scoping

Effective environmental analysis begins with careful planning and scoping. This phase involves defining the objectives, identifying stakeholders, and outlining the scope of the study.

Defining Objectives : Clearly defined objectives provide direction and focus for the analysis. Objectives should be specific, measurable, achievable, relevant, and time-bound (SMART). For example, an objective might be to assess the impact of industrial pollution on local water quality over the next five years.
Stakeholder Engagement : Engaging with stakeholders, including government agencies, local communities, businesses, and non-governmental organizations, is essential. Stakeholders can provide valuable insights, data, and perspectives that enrich the analysis. Effective stakeholder engagement also helps address concerns and build support for the project.
Scoping the Analysis : Scoping involves determining the boundaries of the analysis, including the geographic area, time frame, and specific environmental factors to be studied. It also includes identifying the key questions to be answered and the methods to be used.

2. Data Collection and Management

Accurate data collection and efficient data management are the backbone of any environmental analysis. This phase involves gathering relevant data from various sources and ensuring its quality and reliability.

Sources of Data : Data can be collected from primary sources, such as field measurements and surveys, and secondary sources, such as scientific literature, government reports, and databases. Using multiple sources helps in cross-validation and enhances the robustness of the analysis.
Data Quality and Validation : Ensuring data quality involves checking for accuracy, completeness, and consistency. Validation techniques, such as cross-referencing data from different sources and using control samples, help in verifying the reliability of the data. High-quality data is essential for producing credible and actionable insights.
Data Management : Effective data management involves organizing and storing data in a way that facilitates easy access and analysis. Using database management systems (DBMS) and data management software can help handle large datasets efficiently. Proper documentation of data sources and methods is also crucial for transparency and reproducibility.

3. Data Analysis and Interpretation

The final environmental analysis phase involves analyzing the collected data to identify trends, patterns, and relationships, and interpreting the results to draw meaningful conclusions.

Identifying Trends : Data analysis involves using statistical and computational techniques to identify trends and patterns. For example, time-series analysis can reveal changes in air quality over time, while spatial analysis can identify pollution hotspots.
Predictive Analytics : Predictive analytics uses historical data and statistical models to forecast future environmental conditions. Techniques such as regression analysis, machine learning, and simulation modeling are commonly used. For instance, predictive models can estimate future water demand based on population growth and climate change scenarios.
Interpreting Results : Interpretation involves translating analytical results into meaningful insights that can inform decision-making. This includes assessing the implications of the findings, identifying potential risks and opportunities, and making recommendations for action. Clear and effective communication of the results through reports, presentations, and visualizations is also crucial.

Examples of Environmental Analysis

Environmental analysis is applied in various real-world contexts to address a wide range of issues. These examples illustrate how the methodologies and tools discussed in this guide are used to assess environmental impacts, inform policy, and promote sustainable practices.

Environmental Impact Assessment (EIA) for a New Highway

When planning the construction of a new highway, an Environmental Impact Assessment (EIA) is essential to evaluate potential environmental effects and ensure sustainable development.

Data Collection : The EIA team collects data on local ecosystems, water sources, air quality, and noise levels. This involves using GIS to map sensitive areas, conducting field surveys to document wildlife, and installing air and noise sensors.
Analysis : Using statistical analysis and predictive modeling, the team assesses how the highway might affect local flora and fauna, water runoff patterns, and air quality. They examine scenarios such as increased traffic and potential pollution.
Mitigation Measures : Based on the findings, the EIA proposes mitigation measures such as wildlife corridors, noise barriers, and water management systems. These measures aim to minimize negative impacts and enhance the highway's environmental compatibility.

Urban Planning for Green Spaces

Urban planners use environmental analysis to incorporate green spaces into city designs, promoting ecological balance and improving residents' quality of life.

Data Collection : Planners gather data on current land use, population density, and local climate conditions. They use remote sensing to identify potential areas for green space and assess existing vegetation cover.
Analysis : GIS and spatial analysis help planners understand how green spaces can be optimally distributed to provide maximum environmental and social benefits. This includes analyzing heat maps to identify urban heat islands and assessing the accessibility of green spaces for residents.
Implementation : The analysis leads to the creation of parks, green rooftops, and urban forests. These green spaces help reduce heat, improve air quality, and provide recreational areas for the community.

Sustainable Agriculture Practices

Farmers and agricultural planners utilize environmental analysis to implement sustainable practices that enhance productivity while preserving the environment.

Data Collection : Farmers collect soil samples and use soil sensors to monitor moisture and nutrient levels. Climate data is gathered to understand local weather patterns and predict future conditions.
Analysis : Statistical and predictive models analyze the data to determine the best crop rotation practices, irrigation schedules, and fertilizer application rates. This helps optimize resource use and minimize environmental impacts.
Sustainable Practices : Based on the analysis, farmers adopt practices such as no-till farming, cover cropping, and precision agriculture. These methods improve soil health, reduce water use, and lower greenhouse gas emissions.

Assessing Climate Change Impacts on Coastal Areas

Coastal regions are particularly vulnerable to climate change impacts such as sea-level rise and increased storm intensity. Environmental analysis helps assess these risks and develop adaptation strategies.

Data Collection : Researchers collect data on sea levels, coastal erosion rates, and weather patterns. They use satellite imagery and remote sensing to monitor changes in coastal landscapes.
Analysis : GIS and simulation models predict future sea-level rise and storm surge scenarios. The analysis identifies areas at high risk of flooding and erosion, helping to prioritize adaptation efforts.
Adaptation Strategies : Based on the analysis, coastal communities implement strategies such as building seawalls, restoring mangroves, and updating zoning laws to prevent development in high-risk areas. These measures help protect infrastructure and natural habitats from the impacts of climate change.

Evaluating Renewable Energy Projects

The transition to renewable energy sources, such as wind and solar power, involves environmental analysis to ensure these projects are sustainable and beneficial.

Data Collection : Project developers collect data on wind speeds, solar radiation levels, and local wildlife populations. This data is gathered through field measurements, remote sensing, and wildlife surveys.
Analysis : Using GIS and statistical models, developers assess the suitability of potential sites for wind farms or solar panels. They evaluate factors such as energy potential, land use compatibility, and possible impacts on local wildlife.
Project Implementation : The analysis informs the design and placement of renewable energy infrastructure. For instance, wind turbines are sited away from migratory bird routes, and solar panels are installed on previously disturbed lands to minimize ecological impacts.

Environmental Analysis Challenges and Limitations

Conducting environmental analysis is not without its challenges and limitations. These factors can impact the accuracy, reliability, and effectiveness of the analysis:

Data Limitations and Uncertainties : Incomplete, inaccurate, or inconsistent data can lead to unreliable results. Uncertainties in data collection and measurement can also affect the analysis.
Technological Constraints : Limited access to advanced tools and technologies, as well as technical expertise, can hinder the effectiveness of the analysis.
Regulatory and Policy Barriers : Complex and changing regulatory environments can pose challenges to conducting a comprehensive environmental analysis. Regulatory compliance can also add to the costs and time required.
Resource Constraints : Limited financial, human, and technical resources can restrict the scope and depth of the analysis. This includes funding, skilled personnel, and technological infrastructure.
Ethical Considerations : Ensuring ethical practices in data collection, stakeholder engagement, and reporting is crucial. Ethical considerations include obtaining informed consent, protecting privacy, and avoiding conflicts of interest.
Time Constraints : Environmental analysis often needs to be completed within specific timeframes, which can pressure researchers and potentially compromise the quality of the analysis.
Complex Interdependencies : The interconnectedness of environmental factors can complicate the analysis. Understanding and modeling these interdependencies require sophisticated tools and approaches.
Geopolitical Factors : Political instability and geopolitical tensions can affect access to data and the ability to conduct fieldwork in certain regions.
Climate Variability : The inherent variability and unpredictability of climate conditions can introduce uncertainties in predictive models and assessments.
Communication Challenges : Effectively communicating complex technical findings to non-expert stakeholders and decision-makers is crucial but challenging. Clear, accessible, and impactful communication is essential for informed decision-making.

Conclusion for Environmental Analysis

Environmental analysis is essential for understanding the myriad factors affecting our natural and human environments. By employing various methodologies, such as qualitative and quantitative techniques, and utilizing advanced tools and technologies, we can gather and interpret critical data. This information helps us make informed decisions that minimize negative impacts and enhance positive outcomes. From businesses striving for sustainability to urban planners designing resilient cities, the insights gained from environmental analysis are invaluable. They not only help in compliance with regulations but also foster innovative solutions that contribute to long-term environmental health and economic stability. Moreover, the frameworks and models discussed in this guide, like PESTLE, SWOT, and Porter's Five Forces, provide structured approaches to dissecting complex environmental factors. While the process is not without its challenges—such as data limitations, technological constraints, and regulatory barriers—the benefits far outweigh the difficulties. By understanding these challenges and working to overcome them, we can improve the accuracy and effectiveness of our analyses. Ultimately, environmental analysis equips us with the knowledge needed to address today's pressing environmental issues and build a sustainable future. Whether you're a policymaker, business leader, or concerned citizen, mastering the principles of environmental analysis can empower you to contribute positively to our planet's health and resilience.

How to Streamline Your Environmental Analysis?

Appinio is revolutionizing the way companies conduct environmental analysis by providing a real-time market research platform that delivers rapid, reliable consumer insights. By leveraging Appinio's intuitive technology, organizations can effortlessly gather and analyze data crucial for environmental studies, making fast, data-driven decisions that enhance their projects' sustainability and effectiveness. Appinio's unique approach simplifies the research process, eliminating the traditionally tedious and expensive aspects of market research, and transforming it into an exciting and accessible task.

Rapid Insights : From formulating questions to obtaining insights in minutes, Appinio allows companies to quickly gather the data needed for environmental analysis, enabling swift and informed decision-making.
User-Friendly Interface : With a platform so intuitive that anyone can use it, Appinio makes conducting environmental research easy without requiring extensive expertise.
Comprehensive Reach : Define the right target group from over 1,200 characteristics and survey participants in more than 90 countries, ensuring diverse and relevant data for robust environmental analysis.

Get free access to the platform!

Join the loop 💌

Be the first to hear about new updates, product news, and data insights. We'll send it all straight to your inbox.

Get the latest market research news straight to your inbox! 💌

Wait, there's more

13.08.2024 | 30min read

What is ANOVA Test? Definition, Types, Examples

08.08.2024 | 30min read

Marketing Mix The 4 Ps of Marketing and How to Use Them

06.08.2024 | 32min read

Marketing Mix: The 4 Ps of Marketing & How to Use Them?

Information

Author Services

Initiatives

You are accessing a machine-readable page. In order to be human-readable, please install an RSS reader.

All articles published by MDPI are made immediately available worldwide under an open access license. No special permission is required to reuse all or part of the article published by MDPI, including figures and tables. For articles published under an open access Creative Common CC BY license, any part of the article may be reused without permission provided that the original article is clearly cited. For more information, please refer to https://www.mdpi.com/openaccess .

Feature papers represent the most advanced research with significant potential for high impact in the field. A Feature Paper should be a substantial original Article that involves several techniques or approaches, provides an outlook for future research directions and describes possible research applications.

Feature papers are submitted upon individual invitation or recommendation by the scientific editors and must receive positive feedback from the reviewers.

Editor’s Choice articles are based on recommendations by the scientific editors of MDPI journals from around the world. Editors select a small number of articles recently published in the journal that they believe will be particularly interesting to readers, or important in the respective research area. The aim is to provide a snapshot of some of the most exciting work published in the various research areas of the journal.

Original Submission Date Received: .

Active Journals
Find a Journal
Proceedings Series
For Authors
For Reviewers
For Editors
For Librarians
For Publishers
For Societies
For Conference Organizers
Open Access Policy
Institutional Open Access Program
Special Issues Guidelines
Editorial Process
Research and Publication Ethics
Article Processing Charges
Testimonials
Preprints.org
SciProfiles
Encyclopedia

Article Menu

Subscribe SciFeed
Recommended Articles
Author Biographies
Google Scholar
on Google Scholar
Table of Contents

Find support for a specific problem in the support section of our website.

Please let us know what you think of our products and services.

Visit our dedicated information section to learn more about MDPI.

JSmol Viewer

Impacts of generative artificial intelligence in higher education: research trends and students’ perceptions.

1. Introduction

2. materials and methods.

“Generative Artificial Intelligence” or “Generative AI” or “Gen AI”, AND;
“Higher Education” or “University” or “College” or “Post-secondary”, AND;
“Impact” or “Effect” or “Influence”.
Q1— Does GenAI have more positive or negative effects on higher education? Options (to choose one): 1. It has more negative effects than positives; 2. It has more positive effects than negative; 3. There is a balance between positive and negative effects; 4. Don’t know.
Q2— Identify the main positive effect of Gen AI in an academic context . Open-ended question.
Q3— Identify the main negative effect of Gen AI in an academic context . Open-ended question.

3.1. Impacts of Gen AI in HE: Research Trends

3.1.1. he with gen ai, the key role that pedagogy must play, new ways to enhance the design and implementation of teaching and learning activities.

Firstly, prompting in teaching should be prioritized as it plays a crucial role in developing students’ abilities. By providing appropriate prompts, educators can effectively guide students toward achieving their learning objectives.
Secondly, configuring reverse prompting within the capabilities of Gen AI chatbots can greatly assist students in monitoring their learning progress. This feature empowers students to take ownership of their education and fosters a sense of responsibility.
Furthermore, it is essential to embed digital literacy in all teaching and learning activities that aim to leverage the potential of the new Gen AI assistants. By equipping students with the necessary skills to navigate and critically evaluate digital resources, educators can ensure that they are prepared for the digital age.

The Student’s Role in the Learning Experience

The key teacher’s role in the teaching and learning experience, 3.1.2. assessment in gen ai/chatgpt times, the need for new assessment procedures, 3.1.3. new challenges to academic integrity policies, new meanings and frontiers of misconduct, personal data usurpation and cheating, 3.2. students’ perceptions about the impacts of gen ai in he.

“It harms the learning process”: ▪ “What is generated by Gen AI has errors”; ▪ “Generates dependence and encourages laziness”; ▪ “Decreases active effort and involvement in the learning/critical thinking process”.

4. Discussion

Training: providing training for both students and teachers on effectively using and integrating Gen AI technologies into teaching and learning practices.
Ethical use and risk management: developing policies and guidelines for ethical use and risk management associated with Gen AI technologies.
Incorporating AI without replacing humans: incorporating AI technologies as supplementary tools to assist teachers and students rather than replacements for human interaction.
Continuously enhancing holistic competencies: encouraging the use of AI technologies to enhance specific skills, such as digital competence and time management, while ensuring that students continue to develop vital transferable skills.
Fostering a transparent AI environment: promoting an environment in which students and teachers can openly discuss the benefits and concerns associated with using AI technologies.
Data privacy and security: ensuring data privacy and security using AI technologies.
The dynamics of technological support to align with the most suitable Gen AI resources;
The training policy to ensure that teachers, students, and academic staff are properly trained to utilize the potential of Gen AI and its tools;
Security and data protection policies;
Quality and ethical action policies.

5. Conclusions

Database constraints: the analysis is based on existing publications in SCOPUS and the Web of Science, potentially omitting relevant research from other sources.
Inclusion criteria: due to the early stage of scientific production on this topic, all publications were included in the analysis, rather than focusing solely on articles from highly indexed journals and/or with a high number of citations as recommended by bibliometric and systematic review best practices.
Dynamic landscape: the rate of publications on Gen AI has been rapidly increasing and diversifying in 2024, highlighting the need for ongoing analysis to track trends and changes in scientific thinking.

Author Contributions

Institutional review board statement, informed consent statement, data availability statement, conflicts of interest.

Akakpo, Martin Gameli. 2023. Skilled for the Future: Information Literacy for AI Use by University Students in Africa and the Role of Librarians. Internet Reference Services Quarterly 28: 19–26. [ Google Scholar ] [ CrossRef ]
AlAfnan, Mohammad Awad, Samira Dishari, Marina Jovic, and Koba Lomidze. 2023. ChatGPT as an Educational Tool: Opportunities, Challenges, and Recommendations for Communication, Business Writing, and Composition Courses. Journal of Artificial Intelligence and Technology 3: 60–68. [ Google Scholar ] [ CrossRef ]
Almaraz-López, Cristina, Fernando Almaraz-Menéndez, and Carmen López-Esteban. 2023. Comparative Study of the Attitudes and Perceptions of University Students in Business Administration and Management and in Education toward Artificial Intelligence. Education Sciences 13: 609. [ Google Scholar ] [ CrossRef ]
Al-Zahrani, Abdulrahman. 2023. The impact of generative AI tools on researchers and research: Implications for academia in higher education. Innovations in Education and Teaching International , 1–15. [ Google Scholar ] [ CrossRef ]
Athilingam, Ponrathi, and Hong-Gu He. 2023. ChatGPT in nursing education: Opportunities and challenges. Teaching and Learning in Nursing 19: 97–101. [ Google Scholar ] [ CrossRef ]
Álvarez-Álvarez, Carmen, and Samuel Falcon. 2023. Students’ preferences with university teaching practices: Analysis of testimonials with artificial intelligence. Educational Technology Research and Development 71: 1709–24. [ Google Scholar ] [ CrossRef ]
Bannister, Peter, Elena Alcalde Peñalver, and Alexandra Santamaría Urbieta. 2023. Transnational higher education cultures and generative AI: A nominal group study for policy development in English medium instruction. Journal for Multicultural Education . ahead-of-print . [ Google Scholar ] [ CrossRef ]
Bearman, Margaret, and Rola Ajjawi. 2023. Learning to work with the black box: Pedagogy for a world with artificial intelligence. British Journal of Educational Technology 54: 1160–73. [ Google Scholar ] [ CrossRef ]
Boháček, Matyas. 2023. The Unseen A+ Student: Evaluating the Performance and Detectability of Large Language Models in the Classroom. CEUR Workshop Proceedings 3487: 89–100. Available online: https://openreview.net/pdf?id=9ZKJLYg5EQ (accessed on 7 January 2024).
Chan, Cecilia Ka Yuk. 2023. A comprehensive AI policy education framework for university teaching and learning. International Journal of Educational Technology in Higher Education 20: 38. [ Google Scholar ] [ CrossRef ]
Chan, Cecilia Ka Yuk, and Wenjie Hu. 2023. Students’ voices on generative AI: Perceptions, benefits, and challenges in higher education. International Journal of Educational Technology in Higher Education 20: 43. [ Google Scholar ] [ CrossRef ]
Chan, Cecilia Ka Yuk, and Wenxin Zhou. 2023. An expectancy value theory (EVT) based instrument for measuring student perceptions of generative AI. Smart Learning Environments 10: 64. [ Google Scholar ] [ CrossRef ]
Chang, Daniel H., Michael Pin-Chuan Lin, Shiva Hajian, and Quincy Q. Wang. 2023. Educational Design Principles of Using AI Chatbot That Supports Self-Regulated Learning in Education: Goal Setting, Feedback, and Personalization. Sustainability 15: 12921. [ Google Scholar ] [ CrossRef ]
Chiu, Thomas. 2023. The impact of Generative AI (GenAI) on practices, policies and research direction in education: A case of ChatGPT and Midjourney. Interactive Learning Environments , 1–17. [ Google Scholar ] [ CrossRef ]
Chun, John, and Katherine Elkins. 2023. The Crisis of Artificial Intelligence: A New Digital Humanities Curriculum for Human-Centred AI. International Journal of Humanities and Arts Computing 17: 147–67. [ Google Scholar ] [ CrossRef ]
Cowling, Michael, Joseph Crawford, Kelly-Ann Allen, and Michael Wehmeyer. 2023. Using leadership to leverage ChatGPT and artificial intelligence for undergraduate and postgraduate research supervision. Australasian Journal of Educational Technology 39: 89–103. [ Google Scholar ] [ CrossRef ]
Crawford, Joseph, Carmen Vallis, Jianhua Yang, Rachel Fitzgerald, Christine O’Dea, and Michael Cowling. 2023a. Editorial: Artificial Intelligence is Awesome, but Good Teaching Should Always Come First. Journal of University Teaching & Learning Practice 20: 01. [ Google Scholar ] [ CrossRef ]
Crawford, Joseph, Michael Cowling, and Kelly-Ann Allen. 2023b. Leadership is needed for ethical ChatGPT: Character, assessment, and learning using artificial intelligence (AI). Journal of University Teaching & Learning Practice 20: 02. [ Google Scholar ] [ CrossRef ]
Currie, Geoffrey. 2023a. A Conversation with ChatGPT. Journal of Nuclear Medicine Technology 51: 255–60. [ Google Scholar ] [ CrossRef ] [ PubMed ]
Currie, Geoffrey. 2023b. GPT-4 in Nuclear Medicine Education: Does It Outperform GPT-3.5? Journal of Nuclear Medicine Technology 51: 314–17. [ Google Scholar ] [ CrossRef ] [ PubMed ]
Currie, Geoffrey, and Kym Barry. 2023. ChatGPT in Nuclear Medicine Education. Journal of Nuclear Medicine Technology 51: 247–54. [ Google Scholar ] [ CrossRef ]
Currie, Geoffrey, Clare Singh, Tarni Nelson, Caroline Nabasenja, Yazan Al-Hayek, and Kelly Spuur. 2023. ChatGPT in medical imaging higher education. Radiography 29: 792–99. [ Google Scholar ] [ CrossRef ]
Dai, Yun, Ang Liu, and Cher P. Lim. 2023. Reconceptualizing Chatgpt and Generative AI as a Student-driven Innovation in Higher Education. Procedia CIRP Volume 119: 84–90. [ Google Scholar ] [ CrossRef ]
Dogru, Tarik, Nathana Line, Lydia Hanks, Fulya Acikgoz, Je’Anna Abbott, Selim Bakir, Adiyukh Berbekova, Anil Bilgihan, Ali Iskender, Murat Kizildag, and et al. 2023. The implications of generative artificial intelligence in academic research and higher education in tourism and hospitality. Tourism Economics 30: 1083–94. [ Google Scholar ] [ CrossRef ]
Duong, Cong Doanh, Trong Nghia Vu, and Thi Viet Nga Ngo. 2023. Applying a modified technology acceptance model to explain higher education students’ usage of ChatGPT: A serial multiple mediation model with knowledge sharing as a moderator. The International Journal of Management Education 21: 100883. [ Google Scholar ] [ CrossRef ]
Eager, Bronwyn, and Ryan Brunton. 2023. Prompting Higher Education Towards AI-Augmented Teaching and Learning Practice. Journal of University Teaching & Learning Practice 20: 5. [ Google Scholar ] [ CrossRef ]
Elkhodr, Mahmoud, Ergun Gide, Robert Wu, and Omar Darwish. 2023. ICT students’ perceptions towards ChatGPT: An experimental reflective lab analysis. STEM Education 3: 70–88. [ Google Scholar ] [ CrossRef ]
Farrelly, Tom, and Nick Baker. 2023. Generative Artificial Intelligence: Implications and Considerations for Higher Education Practice. Education Sciences 13: 1109. [ Google Scholar ] [ CrossRef ]
Farrokhnia, Mohammadreza, Seyyed Banihashem, Seyyed Kazem Banihashem, Omid Noroozi, and Arjen Wals. 2023. A SWOT analysis of ChatGPT: Implications for educational practice and research. Innovations in Education and Teaching International 61: 460–74. [ Google Scholar ] [ CrossRef ]
Gong, Furong. 2023. The Impact of Generative AI like ChatGPT on Digital Literacy Education in University Libraries. Documentation, Information & Knowledge 40: 97–106, 156. [ Google Scholar ] [ CrossRef ]
Han, Bingyi, Sadia Nawaz, George Buchanan, and Dana McKay. 2023. Ethical and Pedagogical Impacts of AI in Education. In Artificial Intelligence in Education . Edited by Ning Wang, Genaro Rebolledo-Mendez, Noboru Matsuda, Olga Santos and Vania Dimitrova. Lecture Notes in Computer Science. Cham: Springer, pp. 667–73. [ Google Scholar ] [ CrossRef ]
Hassoulas, Athanasios, Ned Powell, Lindsay Roberts, Katja Umla-Runge, Laurence Gray, and Marcus J. Coffey. 2023. Investigating marker accuracy in differentiating between university scripts written by students and those produced using ChatGPT. Journal of Applied Learning and Teaching 6: 71–77. [ Google Scholar ] [ CrossRef ]
Hernández-Leo, Davinia. 2023. ChatGPT and Generative AI in Higher Education: User-Centered Perspectives and Implications for Learning Analytics. CEUR Workshop Proceedings , 1–6. Available online: https://ceur-ws.org/Vol-3542/paper2.pdf (accessed on 7 January 2024).
Hidayat-ur-Rehman, Imdadullah, and Yasser Ibrahim. 2023. Exploring factors influencing educators’ adoption of ChatGPT: A mixed method approach. Interactive Technology and Smart Education . ahead-of-print . [ Google Scholar ] [ CrossRef ]
Ilieva, Galina, Tania Yankova, Stanislava Klisarova-Belcheva, Angel Dimitrov, Marin Bratkov, and Delian Angelov. 2023. Effects of Generative Chatbots in Higher Education. Information 14: 492. [ Google Scholar ] [ CrossRef ]
Javaid, Mohd, Abid Haleem, Ravi Pratap Singh, Shahbaz Khan, and Haleem Ibrahim. 2023. Unlocking the opportunities through ChatGPT Tool towards ameliorating the education system. Bench Council Transactions on Benchmarks, Standards and Evaluations 3: 100115. [ Google Scholar ] [ CrossRef ]
Kaplan-Rakowski, Regina, Kimberly Grotewold, Peggy Hartwick, and Kevin Papin. 2023. Generative AI and Teachers’ Perspectives on Its Implementation in Education. Journal of Interactive Learning Research 34: 313–38. Available online: https://www.learntechlib.org/primary/p/222363/ (accessed on 7 January 2024).
Karunaratne, Thashmee, and Adenike Adesina. 2023. Is it the new Google: Impact of ChatGPT on Students’ Information Search Habits. Paper presented at the European Conference on e-Learning (ECEL 2023), Pretoria, South Africa, October 26–27; pp. 147–55. [ Google Scholar ] [ CrossRef ]
Kelly, Andrew, Miriam Sullivan, and Katrina Strampel. 2023. Generative artificial intelligence: University student awareness, experience, and confidence in use across disciplines. Journal of University Teaching & Learning Practice 20: 12. [ Google Scholar ] [ CrossRef ]
Kohnke, Lucas, Benjamin Luke Moorhouse, and Di Zou. 2023. Exploring generative artificial intelligence preparedness among university language instructors: A case study. Computers and Education: Artificial Intelligence 5: 100156. [ Google Scholar ] [ CrossRef ]
Laker, Lauren, and Mark Sena. 2023. Accuracy and detection of student use of ChatGPT in business analytics courses. Issues in Information Systems 24: 153–63. [ Google Scholar ] [ CrossRef ]
Lemke, Claudia, Kathrin Kirchner, Liadan Anandarajah, and Florian Herfurth. 2023. Exploring the Student Perspective: Assessing Technology Readiness and Acceptance for Adopting Large Language Models in Higher Education. Paper presented at the European Conference on e-Learning, (ECEL 2023), Pretoria, South Africa, October 26–27; pp. 156–64. [ Google Scholar ] [ CrossRef ]
Limna, Pongsakorn, Tanpat Kraiwanit, Kris Jangjarat, and Prapasiri Klayklung. 2023a. The use of ChatGPT in the digital era: Perspectives on chatbot implementation. Journal of Applied Learning and Teaching 6: 64–74. [ Google Scholar ] [ CrossRef ]
Limna, Pongsakorn, Tanpat Kraiwanit, Kris Jangjarat, and Yarnaphat Shaengchart. 2023b. Applying ChatGPT as a new business strategy: A great power comes with great responsibility [Special issue]. Corporate & Business Strategy Review 4: 218–26. [ Google Scholar ] [ CrossRef ]
Lopezosa, Carlos, Carles Lluís Codina, Carles Pont-Sorribes, and Mari Vállez. 2023. Use of Generative Artificial Intelligence in the Training of Journalists: Challenges, Uses and Training Proposal. Profesional De La información Information Professional 32: 1–12. [ Google Scholar ] [ CrossRef ]
Martineau, Kim. 2023. What Is Generative AI? IBM Research Blog . April 20. Available online: https://research.ibm.com/blog/what-is-generative-AI (accessed on 7 January 2024).
Mondal, Himel, Shaikat Mondal, and Indrashis Podder. 2023. Using ChatGPT for Writing Articles for Patients’ Education for Dermatological Diseases: A Pilot Study. Indian Dermatology Online Journal 14: 482–86. [ Google Scholar ] [ CrossRef ]
Moorhouse, Benjamin, Marie Alina Wan, and Yuwei Wan. 2023. Generative AI tools and assessment: Guidelines of the world’s top-ranking universities. Computers and Education Open 5: 100151. [ Google Scholar ] [ CrossRef ]
Overono, Acacia L., and Annie Ditta. 2023. The Rise of Artificial Intelligence: A Clarion Call for Higher Education to Redefine Learning and Reimagine Assessment. College Teaching , 1–4. [ Google Scholar ] [ CrossRef ]
Page, Matthew J., Joanne E. McKenzie, Patrick M. Bossuyt, Isabelle Boutron, Tammy C. Hoffmann, Cynthia D. Mulrow, Larissa Shamseer, Jennifer M. Tetzlaff, Elie A. Akl, Sue E. Brennan, and et al. 2021. The PRISMA 2020 statement: An updated guideline for reporting systematic reviews. BMJ 372: n71. [ Google Scholar ] [ CrossRef ]
Pechenkina, Ekaterina. 2023. Artificial intelligence for good? Challenges and possibilities of AI in higher education from a data justice perspective. In Higher Education for Good: Teaching and Learning Futures . Edited by Laura Czerniewicz and Catherine Cronin. Cambridge, UK: Open Book Publishers, pp. 239–66. [ Google Scholar ] [ CrossRef ]
Perkins, Mike, Jasper Roe, Darius Postma, James McGaughran, and Don Hickerson. 2023. Detection of GPT-4 Generated Text in Higher Education: Combining Academic Judgement and Software to Identify Generative AI Tool Misuse. Journal of Academic Ethics 22: 89–113. [ Google Scholar ] [ CrossRef ]
Pitso, Teboho. 2023. Post-COVID-19 Higher Learning: Towards Telagogy, A Web-Based Learning Experience. IAFOR Journal of Education 11: 39–59. [ Google Scholar ] [ CrossRef ]
Plata, Sterling, Maria Ana De Guzman, and Arthea Quesada. 2023. Emerging Research and Policy Themes on Academic Integrity in the Age of Chat GPT and Generative AI. Asian Journal of University Education 19: 743–58. [ Google Scholar ] [ CrossRef ]
Rudolph, Jürgen, Samson Tan, and Shannon Tan. 2023a. War of the chatbots: Bard, Bing Chat, ChatGPT, Ernie and beyond. The new AI gold rush and its impact on higher education. Journal of Applied Learning and Teaching 6: 364–89. [ Google Scholar ] [ CrossRef ]
Rudolph, Jürgen, Samson Tan, and Shannon Tan. 2023b. ChatGPT: Bullshit spewer or the end of traditional assessments in higher education? Journal of Applied Learning and Teaching 6: 342–63. [ Google Scholar ] [ CrossRef ]
Ryall, Adelle, and Stephen Abblitt. 2023. “A Co-Pilot for Learning Design?”: Perspectives from Learning Designers on the Uses, Challenges, and Risks of Generative Artificial Intelligence in Higher Education. In People, Partnerships and Pedagogies. Proceedings ASCILITE 2023 . Edited by Thomas Cochrane, Vickel Narayan, Cheryl Brown, MacCallum Kathryn, Elisa Bone, Christopher Deneen, Robert Vanderburg and Brad Hurren. Christchurch: Te Pae Conference Center, pp. 525–30. [ Google Scholar ] [ CrossRef ]
Santiago, Cereneo S., Steve I. Embang, Ricky B. Acanto, Kem Warren P. Ambojia, Maico Demi B. Aperocho, Benedicto B. Balilo, Erwin L. Cahapin, Marjohn Thomas N. Conlu, Samson M. Lausa, Ester Y. Laput, and et al. 2023. Utilization of Writing Assistance Tools in Research in Selected Higher Learning Institutions in the Philippines: A Text Mining Analysis. International Journal of Learning, Teaching and Educational Research 22: 259–84. [ Google Scholar ] [ CrossRef ]
Solopova, Veronika, Eiad Rostom, Fritz Cremer, Adrian Gruszczynski, Sascha Witte, Chengming Zhang, Fernando Ramos López, Lea Plößl, Florian Hofmann, Ralf Romeike, and et al. 2023. PapagAI: Automated Feedback for Reflective Essays. In KI 2023: Advances in Artificial Intelligence. KI 2023 . Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Cham: Springer, vol. 14236, pp. 198–206. [ Google Scholar ] [ CrossRef ]
Sridhar, Pragnya, Aidan Doyle, Arav Agarwal, Christopher Bogart, Jaromir Savelka, and Majd Sakr. 2023. Harnessing LLMs in Curricular Design: Using GPT-4 to Support Authoring of Learning Objectives. CEUR Workshop Proceedings 3487: 139–50. [ Google Scholar ]
Sullivan, Miriam, Andrew Kelly, and Paul McLaughlan. 2023. ChatGPT in higher education: Considerations for academic integrity and student learning. Journal of Applied Learning and Teaching 6: 31–40. [ Google Scholar ] [ CrossRef ]
Tominc, Polona, and Maja Rožman. 2023. Artificial Intelligence and Business Studies: Study Cycle Differences Regarding the Perceptions of the Key Future Competences. Education Sciences 13: 580. [ Google Scholar ] [ CrossRef ]
van den Berg, Geesje, and Elize du Plessis. 2023. ChatGPT and Generative AI: Possibilities for Its Contribution to Lesson Planning, Critical Thinking and Openness in Teacher Education. Education Sciences 13: 998. [ Google Scholar ] [ CrossRef ]
Walczak, Krzysztof, and Wojciech Cellary. 2023. Challenges for higher education in the era of widespread access to Generative AI. Economics and Business Review 9: 71–100. [ Google Scholar ] [ CrossRef ]
Wang, Ting, Brady D. Lund, Agostino Marengo, Alessandro Pagano, Nishith Reddy Mannuru, Zoë A. Teel, and Jenny Pange. 2023. Exploring the Potential Impact of Artificial Intelligence (AI) on International Students in Higher Education: Generative AI, Chatbots, Analytics, and International Student Success. Applied Sciences 13: 6716. [ Google Scholar ] [ CrossRef ]
Watermeyer, Richard, Lawrie Phipps, Donna Lanclos, and Cathryn Knight. 2023. Generative AI and the Automating of Academia. Postdigital Science and Education 6: 446–66. [ Google Scholar ] [ CrossRef ]
Wolf, Leigh, Tom Farrelly, Orna Farrell, and Fiona Concannon. 2023. Reflections on a Collective Creative Experiment with GenAI: Exploring the Boundaries of What is Possible. Irish Journal of Technology Enhanced Learning 7: 1–7. [ Google Scholar ] [ CrossRef ]
Yilmaz, Ramazan, and Fatma Gizem Karaoglan Yilmaz. 2023. The effect of generative artificial intelligence (AI)-based tool use on students’ computational thinking skills, programming self-efficacy and motivation. Computers and Education: Artificial Intelligence 4: 100147. [ Google Scholar ] [ CrossRef ]
Zawiah, Mohammed, Fahmi Y. Al-Ashwal, Lobna Gharaibeh, Rana Abu Farha, Karem H. Alzoubi, Khawla Abu Hammour, Qutaiba A. Qasim, and Fahd Abrah. 2023. ChatGPT and Clinical Training: Perception, Concerns, and Practice of Pharm-D Students. Journal of Multidisciplinary Healthcare 16: 4099–110. [ Google Scholar ] [ CrossRef ]

Click here to enlarge figure

	Selected Group of Students		Students Who Answered the Questionnaire
	M	F	M	F

1st year	59	5	34	2
2nd year	36	5	29	4

1st year	39	3	24	2
2nd year	21	2	15	2

Country	N.	Country	N.	Country	N.	Country	N.
Australia	16	Italy	2	Egypt	1	South Korea	1
United States	7	Saudi Arabia	2	Ghana	1	Sweden	1
Singapore	5	South Africa	2	Greece	1	Turkey	1
Hong Kong	4	Thailand	2	India	1	United Arab Emirates	1
Spain	4	Viet Nam	2	Iraq	1	Yemen	1
United Kingdom	4	Bulgaria	1	Jordan	1
Canada	3	Chile	1	Malaysia	1
Philippines	3	China	1	Mexico	1
Germany	2	Czech Republic	1	New Zealand	1
Ireland	2	Denmark	1	Poland	1

Country	N.	Country	N.	Country	N.	Country	N.
Singapore	271	United States	15	India	2	Iraq	0
Australia	187	Italy	11	Turkey	2	Jordan	0
Hong Kong	37	United Kingdom	6	Denmark	1	Poland	0
Thailand	33	Canada	6	Greece	1	United Arab Emirates	0
Philippines	31	Ireland	6	Sweden	1	Yemen	0
Viet Nam	29	Spain	6	Saudi Arabia	1
Malaysia	29	South Africa	6	Bulgaria	1
South Korea	29	Mexico	3	Czech Republic	0
China	17	Chile	3	Egypt	0
New Zealand	17	Germany	2	Ghana	0

Categories	Nr. of Documents	References
HE with Gen AI	15	( ); ( ); ( ); ( ); ( ); ( ); ( ); ( ); ( ); ( ); ( ); ( ); ( ); ( ); ( ); ( ); ( ); ( ).
	15	( ); ( ); ( ); ( ); ( ); ( ); ( ); ( ); ( ); ( ); ( ); ( ); ( ); ( ); ( ); ( ).
	14	( ); ( ); ( ); ( ); ( ); ( ); ( ); ( ); ( ); ( ); ( ); ( ); ( ); ( ).
	8	( ); ( ); ( ); ( ); ( ); ( ); ( ); ( ).
Assessment in Gen AI/ChatGPT times	8	( ); ( ); ( ); ( ); ( ); ( ); ( ); ( ).
New challenges to academic integrity policies	4	( ); ( ); ( ); ( ).

Have You Tried Using a Gen AI Tool?	Nr.	%
Yes	52	46.4%
No	60	53.6%

Categories and Subcategories	%	Unit of Analysis (Some Examples)
1. Learning support:
1.1. Helpful to solve doubts, to correct errors	34.6%
1.2. Helpful for more autonomous and self-regulated learning	19.2%
2. Helpful to carry out the academic assignments/individual or group activities	17.3%
3. Facilitates research/search processes
3.1. Reduces the time spent with research	13.5%
3.2. Makes access to information easier	9.6%
4. Reduction in teachers’ workload	3.9%
5. Enables new teaching methods	1.9%

Categories and Subcategories	%	Unit of Analysis (Some Examples)
1. Harms the learning process:
1.1. What is generated by Gen AI has errors	13.5%
1.2. Generates dependence and encourages laziness	15.4%
1.3. Decreases active effort and involvement in the learning/critical thinking process	28.8%
2. Encourages plagiarism and incorrect assessment procedures	17.3%
3. Reduces relationships with teachers and interpersonal relationships	9.6%
4. No negative effect—as it will be necessary to have knowledge for its correct use	7.7%
5. Don’t know	7.7%

The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Share and Cite

Saúde, S.; Barros, J.P.; Almeida, I. Impacts of Generative Artificial Intelligence in Higher Education: Research Trends and Students’ Perceptions. Soc. Sci. 2024 , 13 , 410. https://doi.org/10.3390/socsci13080410

Saúde S, Barros JP, Almeida I. Impacts of Generative Artificial Intelligence in Higher Education: Research Trends and Students’ Perceptions. Social Sciences . 2024; 13(8):410. https://doi.org/10.3390/socsci13080410

Saúde, Sandra, João Paulo Barros, and Inês Almeida. 2024. "Impacts of Generative Artificial Intelligence in Higher Education: Research Trends and Students’ Perceptions" Social Sciences 13, no. 8: 410. https://doi.org/10.3390/socsci13080410

Article Metrics

Article access statistics, further information, mdpi initiatives, follow mdpi.

Subscribe to receive issue release notifications and newsletters from MDPI journals

Assessment of Self-reported Food Waste from Households via Two Routes in Pakistan

Published: 12 August 2024

Cite this article

Sania Zafar 1 , 2 ,
Ehsan Ullah 3 ,
Syed Asif Ali Naqvi 1 ,
Sofia Anwar 1 &
Bilal Hussain 1 , 4

Food loss and waste are consistent threats to food security which, if left unrelieved, may have serious social, economic, and environmental aftermaths. Food waste from consumers is a critical issue which influences the economy and the environment. The resolution of this study is to understand how food waste is influenced by psychological and household routine-related factors and what further research is needed. This study developed a questionnaire and collected data from the consumers in Faisalabad. We conducted data analyses in two stages. At first, the convergent validity and reliability of the measurement scales are tested by executing a preliminary confirmatory factor analysis. Secondly, two proposed models are tested by applying the Structural Equation Model. The outcomes of this study revealed that the pooled model including both psychological and routinized factors proved to be more explanatory as compared to the restricted model. The significant contribution of this study is considering the behavior of consumers which may help to ensure the implementation of food waste reduction campaigns.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Subscribe and save.

Get 10 units per month
Download Article/Chapter or eBook
1 Unit = 1 Article or 1 Chapter
Cancel anytime

Price includes VAT (Russian Federation)

Instant access to the full article PDF.

Rent this article via DeepDyve

Institutional subscriptions

(Source: www.fao.org/save-food/resources/infographic/en/ )

Data Availability

Study data are available with the corresponding author and will be provided on serious request.

https://faisalabad.punjab.gov.pk/district_profile

Aamir, M., Ahmad, H., Javaid, Q., & Hasan, S. M. (2018). Waste not, want not: A case study on food waste in restaurants of Lahore. Pakistan. Journal of Food Products Marketing, 24 (5), 591–610.

Article Google Scholar

Ajzen, I. (1991). The theory of planned behavior. Organizational Behavior and Human Decision Processes, 50 (2), 179–211.

Akhtar, M. A., Khan, I., & Ajaz Khan, and K. (2023). Linking financial capability, advice, anxiety, social security, and residential status with outward remittances: Evidence from the UAE. contemporary review of the Middle East, 10 (4), 386–407. https://doi.org/10.1177/23477989231198327

Aktas, E., Sahin, H., Topaloglu, Z., Oledinma, A., Huda, A. K. S., Irani, Z., & Kamrava, M. (2018). A consumer behavioural approach to food waste. Journal of Enterprise Information Management, 31 (5), 658–673.

Al-Mahaseneh, S., & Harb, Y. (2023). The assimilation of ICT knowledge management practices in organizations: An empirical study. Journal of the Knowledge Economy, 14 (2), 752–779.

Anggraini, F. R. R., & Siswanto, F. A. J. (2016). The role of perceived behavioural control and subjective norms to internal auditors’ intention in conveying unethical behaviour: A case study in Indonesia. Review of Integrative Business and Economics Research, 5 (2), 141.

Google Scholar

Armitage, C. J., & Conner, M. (2001). Efficacy of the theory of planned behaviour: A meta-analytic review. British Journal of Social Psychology, 40 (4), 471–499.

Aryal, N. P., Hamal, J. B., & Bhatta, N. (2022). Performance appraisal, job satisfaction and organizational commitment in Nepalese Commercial Banks. Marsyangdi Journal, 3 (1), 38–50.

Aschemann-Witzel, J., De Hooge, I., Amani, P., Bech-Larsen, T., & Oostindjer, M. (2015). Consumer-related food waste: Causes and potential for action. Sustainability, 7 (6), 6457–6477.

Bagozzi, R. P. (1992). The self-regulation of attitudes, intentions, and behavior. Social Psychology Quarterly, 55 , 178–204.

Baig, M. B., Gorski, I., & Neff, R. A. (2018). Understanding and addressing waste of food in the Kingdom of Saudi Arabia. Saudi Journal of Biological Sciences . https://doi.org/10.1016/j.sjbs.2018.08.030

Bell, D. R., Corsten, D., & Knox, G. (2011). From point of purchase to path to purchase: How preshopping factors drive unplanned buying. Journal of Marketing, 75 (1), 31–45.

Bhatti, S. H., Saleem, F., Zakariya, R., & Ahmad, A. (2019). The determinants of food waste behavior in young consumers in a developing country. British Food Journal, 125 (6), 1953–67.

Britt, R. K., Hatten, K. N., & Chappuis, S. O. (2014). Perceived behavioural control, intention to get vaccinated, and usage of online information about the human papillomavirus vaccine. Health Psychology and Behavioural Medicine: An Open Access Journal, 2 (1), 52–65.

Chalak, A., Abiad, M. G., Diab, M., & Nasreddine, L. (2019). The determinants of household food waste generation and its associated caloric and nutrient losses: The case of Lebanon. PLoS ONE, 14 (12), e0225789.

Charbel, L., Capone, R., Grizi, L., Debs, P., Khalife, D., El Bilali, H., & Bottalico, F. (2016). Preliminary insights on household food wastage in Lebanon. Journal of Food Security, 4 (6), 131–137. https://doi.org/10.12691/jfs-4-6-2

Cochran, W. G. (1977). Sampling techniques (3rd ed.). John Wiley & Sons.Return to ref 1977 in article

Collins, S. E., & Carey, K. B. (2007). The theory of planned behavior as a model of heavy episodic drinking among college students. Psychology of Addictive Behaviors, 21 (4), 498–507.

Elimelech, E., Mintz, K. K., Katz-Gerro, T., Segal-Klein, H., Hussein, L., & Ayalon, O. (2023). Between perceptions and practices: The religious and cultural aspects of food wastage in households. Appetite, 180 , 106374.

Elkaseh, A. M., Wong, K. W., & Fung, C. C. (2016). Perceived ease of use and perceived usefulness of social media for e-learning in Libyan higher education: A structural equation modeling analysis. International Journal of Information and Education Technology, 6 (3), 192.

EPA United States Environmental Protection Agency (2018). Available online: https://www.epa.gov/sustainable-managementfood/sustainable-management-food-basics (accessed on 15 October 2020).

Fami, H. S., Aramyan, L. H., Sijtsema, S. J., & Alambaigi, A. (2019). Determinants of household food waste behavior in Tehran city: A structural model. Resources, Conservation and Recycling, 143 , 154–166. https://doi.org/10.1016/j.resconrec.2018.12.033

FAO Food and Agriculture Organization of the United Nations. Food wastage footprint: Impacts on natural resources; summary report; natural resources management and environment department: Rome, Italy, 2013; Available online: http://www.fao.org/ 3/i3347e/i3347e.pdf (accessed on 24 December 2020)

Ferraz, S. B., Buhamra, C., Laroche, M., & Veloso, A. R. (2017). Green products: A cross-cultural study of attitude, intention and purchase behaviour. Revista de Administraç~ao Mackenzie, 18 (5), 12–38.

Fornell, C., & Larcker, D. F. (1981). Evaluating structural equation models with unobservable variables and 617 measurement error. Journal of Marketing Research, 18 (1), 39–50.

Gilli, M., Nicolli, F., & Farinelli, P. (2018). Behavioural attitudes towards waste prevention and recycling. Ecological Economics, 154 , 294–305.

Graham-Rowe, E., Jessop, D. C., & Sparks, P. (2015). Predicting household food waste reduction using an extended theory of planned behaviour. Resources, Conservation and Recycling, 101 , 194–202.

Gustavsson J, Cederberg C, Sonesson U, Otterdijk R, Meybeck A (2011) Global food losses and food waste: Extent causes and prevention. Food and Agriculture Organization (FAO) of the United Nations, Rome

Hair, J. F., Black, W. C., Babin, B. J., Anderson, R. E., & Tatham, R. L. (2006). Multivariate data analysis (Vol. 6). Online available at: https://www.drnishikantjha.com/papersCollection/Multivariate%20Data%20Analysis.pdf

Hair, J. F., Black, W. C., Babin, B. J., & Anderson, R. E. (2009). Multivariate data analysis . (7th ed., p. 761). Prentice Hall, Upper Saddle River.

Hair, J.F. Jr, Hult, G.T.M., Ringle, C.M. and Sarstedt, M. (2017), “A primer on partial least squares structural equation modeling (PLS-SEM)”, in Henseler, J., Dijkstra, T.K., Sarstedt, M., Ringle, C.M., Diamantopoulos, A. and Straub, D.W. (Eds), 2nd ed., Sage Publications.

Haque, A., Karunasena, G. G., & Pearson, D. (2022). Household food waste and pathways to responsible consumer behaviour: Evidence from Australia. British Food Journal, 124 (11), 3783–3802.

Hartmann, C., Dohle, S., & Siegrist, M. (2013). Importance of cooking skills for balanced food choices. Appetite, 65 , 125–131. https://doi.org/10.1016/j.appet.2013.01.016

Hoyle, R. (1995). The structural equation modeling approach: Basic concepts and fundamental issues. In R. H. Hoyle (Ed.), Structural Equation Modeling: Concepts, Issues, and Applications (pp. 1–15). Sage Publications, Thousand Oaks.

Huang, C. H., Liu, S. M., & Hsu, N. Y. (2020). Understanding global food surplus and food waste to tackle economic and environmental sustainability. Sustainability, 12 (7), 2892.

Hulin, C., Netemeyer, R., & Cudeck, R. (2001). Can a reliability coefficient be too high? Journal of Consumer Psychology, 10 (1/2), 55–58.

Hussain, B., Naqvi, S. A. A., Anwar, S., Shah, S. A. R., Hassan, R. H. U., & Shah, A. A. (2021). Zig-zag technology adoption behavior among brick kiln owners in Pakistan. Environmental Science and Pollution Research, 28 , 45168–45182.

Hussain, B., Naqvi, S. A. A., & Anwar, S. (2023). Analyzing the impact of critical barriers on the stakeholder’s adoption behavior toward green building technologies in Pakistan. International Journal of Management Research and Emerging Sciences , 13 (1) https://doi.org/10.56536/ijmres.v13i1.371

Jöreskog, K. G. (1971). Statistical analysis of sets of congeneric tests. Psychometrika, 36 (2), 109–133. https://doi.org/10.1007/BF02291393

Kaiser, F. G., & Scheuthle, H. (2003). Two challenges to a moral extension of the theory of planned behavior: Moral norms and just world beliefs in conservationism. Personality and Individual Differences, 35 (5), 1033–1048.

Kandemir, C., Reynolds, C., Tom, Q., Fisher, K., Devine, R., Herszenhorn, E., ... & Evans, D. (2022). Using discrete event simulation to explore food wasted in the home. Journal of Simulation , 16 (4), 415–435.

Karim, G. W. A., Rusli, I. F., Biak, D. R. A., & Idris, A. (2013). An application of the theory of planned behaviour to study the influencing factors of participation in source separation of food waste. Waste Management, 33 (5), 1276–1281.

Khalid, S., Naseer, A., Shahid, M., Shah, G. M., Ullah, M. I., Waqar, A., Abbas, T., Imran, M., & Rehman, F. (2019). Assessment of nutritional loss with food waste and factors governing this waste at household level in Pakistan. Journal of Cleaner Production, 206 , 1015–1024. https://doi.org/10.1016/j.jclepro.2018.09.138

Khan, I. and Gunwant, D.F. (2023), "An impact analysis of macroeconomic factors on South Asia’s renewable energy output", International Journal of Energy Sector Management, Vol. ahead-of-print No. ahead-of-print. https://doi.org/10.1108/IJESM-01-2023-0013

Koivupuro, H. K., Hartikainen, H., Silvennoinen, K., Katajajuuri, J.-M., Heikintalo, N., Reinikainen, A., & Jalkanen, L. (2012). Influence of socio-demographical, behavioural and attitudinal factors on the amount of avoidable food waste generated in Finnish households. International Journal of Consumer Studies, 36 (2), 183–191. https://doi.org/10.1111/j.1470-6431.2011.01080.x

Liang, Y., Song, Q., Liu, G., & Li, J. (2021). Uncovering residents and restaurants’ attitude and willingness toward effective food waste management: A case study of Macau. Waste Management, 130 , 107–116.

Liu, J., Ding, W., Wang, R., Yang, H., Wang, X., & Li, A. (2018). Correlation analysis of element contents and mechanical characteristics of shale reservoirs: A case study in the Cen’gong block, South China. Marine and Petroleum Geology, 91 , 19–28.

Mughal FH (2018) Food waste and its consequences. The Dawn (March 12, 2018). Available online. https://www.dawn.com/news/1394618 . (accessed 25th Dec 2019)

Nair, G. (2021). The food we waste: Antecedents of food wastage management behaviour. International Journal of Social Economics, 48 (6), 826–842.

Olsen, N. V., Sijtsema, S. J., & Hall, G. (2010). Predicting consumers’ intention to consume ready-to-eat meals The Role of Moral Attitude. Appetite, 55 (3), 534–539. https://doi.org/10.1016/j.appet.2010.08.016

Qi, D., & Roe, B. E. (2018). Creating value for customers, clients, partners and society: An introduction to the special issue on food waste. Journal of Food Products Marketing, 24 (5), 495–499.

Ramzi, T., Rahim, K., & Skhiri, M. (2023). Scientific knowledge valorization in the public R&D sector: A survey and a PLS-SEM approach. Journal of the Knowledge Economy, 14 (1), 226–54.

Raza, A., Naqvi, S. A. A., Mehdi, M. A., Usman, M., Ali, S., Shah, A. A., & Hussain, B. (2022). An analysis of the circular economy practices of pesticide container waste in Pakistan. Recycling, 8 (1), 4.

Russell, S. V., Young, C. W., Unsworth, K. L., & Robinson, C. (2017). Bringing habits and emotions into food waste behaviour. Resources, Conservation and Recycling, 125 , 107–114. https://doi.org/10.1016/J.RESCONREC.2017.06.007

Schanes, K., Dobernig, K., & G€ozet, B. (2018). Food waste matters-A systematic review of household food waste practices and their policy implications. Journal of Cleaner Production, 182 , 978–991.

Secondi, L., Principato, L., & Laureti, T. (2015). Household food waste behaviour in EU-27 countries: A multilevel analysis. Food Policy, 56 , 25–40.

Stancu, V., Haugaard, P., & Lähteenmäki, L. (2016). Determinants of consumer food waste behaviour: Two routes to food waste. Appetite, 96 , 7–17. https://doi.org/10.1016/J.APPET.2015.08.025

Stefan, V., van Herpen, E., Tudoran, A. A., & Lähteenmäki, L. (2013). Avoiding food waste by Romanian consumers: The importance of planning and shopping routines. Food Quality and Preference, 28 (1), 375–381. https://doi.org/10.1016/j.foodqual.2012.11.001

Stuart, T. (2009). Waste: Uncovering the global food scandal. New York, NY: WW Norton & Company.

The Agha Khan University. (2011). Pakistan national nutrition survey, 2011 . Agha Khan University, Pakistan Medical Research Council and Nutrition Wing, Cabinet Division. Government of Pakistan, Islamabad, Pakistan. Online available at: https://pndajk.gov.pk/uploadfiles/downloads/NNS%20Survey.pdf

Turhan, G., & Ozbek, A. (2013). € “Factors affecting consumers’ behavioural intention towards apparel stores: A test of the mediating role of brand satisfaction”. Fibres and Textiles in Eastern Europe, 1 (97), 7–13.

United Nations Environment Programme. (2021). Food waste index report 2021 . Nairobi. Online available at: https://www.unep.org/resources/report/unep-food-waste-index-report-2021

Van Geffen, E. J., van Herpen, H. W. I., & van Trijp, J. C. M. (2016). Causes & determinants of consumers food waste.: project report, EU Horizon 2020 REFRESH. Wageningen University and Research.

Visschers, V. H. M., Wickli, N., & Siegrist, M. (2016). Sorting out food waste behaviour: A survey on the motivators and barriers of self-reported amounts of food waste in households. Journal of Environmental Psychology, 45 , 66–78. https://doi.org/10.1016/J.JENVP.2015.11

Wong, K. K. K. (2019). Mastering partial least squares structural equation modeling (PLS-Sem) with Smartpls in 38 Hours . IUniverse. Online available at: https://www.researchgate.net/publication/332031150_Mastering_Partial_Least_Squares_Structural_Equation_Modeling_PLS-SEM_with_SmartPLS_in_38_Hours#full-text

Xu, Z., Zhang, Z., Liu, H., Zhong, F., Bai, J., & Cheng, S. (2020). Food-away-from-home plate waste in China: Preference for variety and quantity. Food Policy, 97 , 101918.

Download references

Acknowledgements

The authors are very grateful to all those who provided help during this research.

Author information

Authors and affiliations.

Department of Economics, Government College University Faisalabad, Faisalabad, 38000, Pakistan

Sania Zafar, Syed Asif Ali Naqvi, Sofia Anwar & Bilal Hussain

Department of Economics, University of Jhang, Jhang, 35200, Pakistan

Sania Zafar

Graduate School of Economics and Management, Ural Federal University, Yekaterinburg, 620002, Russia

Ehsan Ullah

MEU Research Unit, Middle East University, Amman, Jordan

Bilal Hussain

You can also search for this author in PubMed Google Scholar

Contributions

Conceptualization, methodology, and formal analysis: S.Z. and S.A.; writing—original draft preparation: S.A.A.N.; writing—review and editing: B.H., E.U., and S.A.A.N.

Corresponding author

Correspondence to Sofia Anwar .

Ethics declarations

Ethical approval.

Not applicable.

Consent to Participate

Informed consent was obtained from all respondents belonging to this study.

Consent for Publication

All authors approved the manuscript and this submission.

Competing Interests

The authors declare no competing interests.

Additional information

Publisher's note.

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Zafar, S., Ullah, E., Naqvi, S.A.A. et al. Assessment of Self-reported Food Waste from Households via Two Routes in Pakistan. J Knowl Econ (2024). https://doi.org/10.1007/s13132-024-02244-w

Download citation

Received : 16 November 2023

Accepted : 23 June 2024

Published : 12 August 2024

DOI : https://doi.org/10.1007/s13132-024-02244-w

Share this article

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

Consumer behavior
Structural equation model
Environmental impact
Find a journal
Publish with us
Track your research

COMMENTS

A Critical Analysis of Research in Environmental Education
(1999). A Critical Analysis of Research in Environmental Education. Studies in Science Education: Vol. 34, No. 1, pp. 1-69.
A Critical Analysis of Research in Environmental Education
Abstract Describes the nature of environmental education research in the 1990s, and comments critically on the focus and quality of inquiry in this field with a view toward improvements in the new millennium. Organizes the literature around the themes of quantitative reports, qualitative research, descriptive reports, focused inquiries, metamethodological discussions, and research-based ...
A Critical Analysis of Research in Environmental Education
The Distinctive Characteristics of Environmental Education Research in Australia: An Historical and Comparative Analysis. Abstract This paper addresses the question of how Australian environmental education (EE) research was conceptualised and contextualised in the decade of the 1990s.
Environmental education outcomes for conservation: A systematic review
Given the variation in research designs and data, we used a mixed-methods approach to the review; analysis of the 105 resulting studies documented strongly positive environmental education outcomes overall and highlighted productive research-implementation spaces.
A Critical Analysis of Research in Environmental Education
A Critical Analysis of Research in Environmental Education. Submitted by Grant Jun Otsuki on July 7, 2021 - 3:23am . Title: A Critical Analysis of Research in Environmental Education: Publication Type: Journal Article: Authors: Hart, Paul, and Kathleen Nolan: Journal: Studies in Science Education:
Environmental education and K-12 student outcomes: A review and
Focusing on the content, structure, and outcomes of interest in 119 peer-reviewed articles related to environmental education with K-12 students and published over a 20-year span, our analysis indicates that the field of EE research is expanding across various dimensions, particularly the diversity of outcomes under study.
Learners and Learning in Environmental Education: A critical review of
Abstract Recent analyses of the field of environmental education research have highlighted its rapidly expanding size and increasingly diverse nature (e.g. Hart & Nolan, 1999). This article reports on a review of a particular part of this field - namely, recent empirical studies of learners and learning in primary or secondary school environmental education. The review focuses specifically on ...
A Critical Analysis of Research in Environmental Education
A Critical Analysis of Research in Environmental Education Hart, Paul ; Nolan, Kathleen Publication: Studies in Science Education Pub Date: 1999 DOI: 10.1080/03057269908560148 Bibcode: 1999SScEd..34....1H
Sensitivity analysis of trends in environmental education in schools
The analysis methodology follows sequential phases: (i) analysis of the bibliographic records of the research field; (ii) performance analysis and mapping of scientific production; and (iii) comparison between the different policy frameworks on Environmental Education and Education for Sustainable Development.
(PDF) Environmental education in transition: A critical review of
This review of recent environmental education (EE) research identifies gaps and opportunities for promoting environmental action in this new context.
Enhancing Educational and Environmental Awareness Outcomes Through
Abstract In the United States, there are substantial barriers to youth nature access and environmental education (EE). These barriers may lead to racial, geographic, and socioeconomic disparities in both nature contact and environmental awareness. This study investigated the impacts of a Photovoice EE intervention on the environmental perceptions, STEM-capacity, and environmental awareness of ...
PDF International Handbook of Research on Environmental Education
ENVIRONMENTAL EDUCATION The environment and contested notions of sustainability are increasingly topics of public interest, political debate, and legislation across the world. Environmental education journals now publish research from a wide variety of methodological traditions that show linkages between the environment, health, development, and education. The growth in scholarship makes this ...
[PDF] Learners and Learning in Environmental Education: A critical
Recent analyses of the field of environmental education research have highlighted its rapidly expanding size and increasingly diverse nature (e.g. Hart & Nolan, 1999). This article reports on a review of a particular part of this field - namely, recent empirical studies of learners and learning in primary or secondary school environmental education. The review focuses specifically on the ...
(PDF) Toward critical environmental education: a standpoint analysis of
110) Toward critical environmental education What does a standpoint theoretical analysis of race and the environment within the US mean for EE researchers and practitioners?
A Critical Analysis of Research in Environmental Education
Andrew J., Community-based environmental education: government and community; economics and environment Andrew J., Contemporary approaches to research in mathematics, science, health, and environmental education
Journal of Environmental Education
The Journal of Environmental Education (JEE) is a research-oriented, refereed periodical intended to provide a forum for critical and constructive debate on all aspects of research, theory and practice in environmental and sustainability education (EE & SE). Publication of diverse theoretical and methodological approaches and perspectives for ...
Outcome research in environmental education: A critical review
Reviewed 34 environmental education studies published since 1974 that attempted to demonstrate changes in environmentally relevant knowledge, attitudes, or behaviors. The studies are divided into 2 major categories: 17 evaluating classroom interventions, and 17 evaluating out-of-class interventions. Ignoring methodological problems concerning unit of analysis and demand or experimenter ...
The Journal of Environmental Education
The Journal of Environmental Education ( JEE) is a bimonthly, international forum for critical and constructive debate on all aspects of research, theory and praxis in environmental and sustainability education and related areas. The journal publishes refereed research papers that include quantitative, qualitative, mixed, and post-qualitative ...
Toward critical environmental education: a standpoint analysis of race
In this article, I advocate for critical environmental education that is responsive to power inequities and use standpoint theory to help explain why environmental education has been slow to ...
A Critical Analysis of Research in Environmental Education
(DOI: 10.1080/03057269908560148) (1999). A Critical Analysis of Research in Environmental Education. Studies in Science Education: Vol. 34, No. 1, pp. 1-69.
Interventions in education
Environmental education is a process that allows individuals to explore environmental issues, engage in problem solving and take action to improve the environment, which is a crucial path to ...
Water Insecurity and Rights Erosion: A Comprehensive Analysis of ...
This study presents a comprehensive analysis of the critical issue of water insecurity and its multifaceted impact on and relationship to the human rights and environmental justice conditions within Rohingya refugee camps located in New Delhi, India. A comprehensive mixed-methods approach was employed to investigate water-related challenges within a refugee camp in Madanpur Khadar, region in ...
School Funding and Equity in Australia: Critical Moments in the Context
The policy cycle approach to policy analysis has a long tradition in critical education policy studies, ... A critical analysis of the Rudd government's national education agenda ... His research focuses on education policy, school funding, globalization, and social justice. ...
Environmental Biology and Climate Change
Draw on your foundational studies in biology, chemistry, mathematics and analysis to inform and solve critical environmental issues related to climate change. Conduct one-on-one research and apply geographic information systems as you develop solutions to this existential threat.
Factors critical for the successful delivery of telehealth to rural
This research used a qualitative descriptive design to explore telehealth service provision in rural areas from the perspective of clinicians and describe factors critical to the effective delivery of telehealth in rural contexts.
Critical assessment of emissions, costs, and time for last-mile goods
The three assessments and sensitivity analysis of critical parameters are presented separately. Environmental impact assessment. In Fig. 1, the results of the environmental assessment are ...
Environmental Analysis: Definition, Tools, Examples
Environmental analysis is a systematic process of examining the external factors that can influence an organization, project, or ecosystem. The primary purpose of environmental analysis is to understand the current and future conditions of the environment and how they can impact various aspects of human activity and natural systems.
Toward critical environmental education: a standpoint analysis of race
Sarah Riggs Stapleton is an Assistant Professor in Education Studies at the University of Oregon. She earned her PhD in Curriculum, Instruction, and Teacher Education at Michigan State University. Sarah uses critical and participatory methodologies to investigate social and environmental inequities, with a particular interest in food and schools.
Impacts of Generative Artificial Intelligence in Higher Education
In this paper, the effects of the rapid advancement of generative artificial intelligence (Gen AI) in higher education (HE) are discussed. A mixed exploratory research approach was employed to understand these impacts, combining analysis of current research trends and students' perceptions of the effects of Gen AI tools in academia. Through bibliometric analysis and systematic literature ...
Assessment of Self-reported Food Waste from Households via ...
Food waste from consumers is a critical issue which influences the economy and the environment. The resolution of this study is to understand how food waste is influenced by psychological and household routine-related factors and what further research is needed. This study developed a questionnaire and collected data from the consumers in ...

Consortium for Social Movements and Education Research and Practice

Search form

A Critical Analysis of Research in Environmental Education

A Critical Analysis of Research in Environmental Education

Learners and Learning in Environmental Education: A critical review of the evidence

749 Citations

Commentary on Rickinson's 'Learners and Learning in Environmental Education: A critical review of the evidence' (EER 7(3))

Exploring students' learning challenges in environmental education

Science Education and Sustainability: a case‐study in discussion‐based learning

Related Papers

Toward critical environmental education: a standpoint analysis of race in the American environmental context

Related Papers

RELATED PAPERS

RELATED TOPICS

Journal of Environmental Education

Aims and scope

Editorial board

A Critical Analysis of Research in Environmental Education

Learners and Learning in Environmental Education: A Critical Review of the Evidence.

Environmental Biology and Climate Change

Use Science to Tackle the World’s Most Pressing Environmental Problems

Why earn your Environmental Biology and Climate Change degree at St. Edward’s?

Build relationships with your professors

Conduct graduate-level research

Tap into special funding for STEM students

Get involved in the professional science community

Reap the Rewards of Austin

What do our graduates do?

Explore Details About a Degree in Environmental Biology and Climate Change

Learning Outcomes

Skills You Will Gain

Experiential Learning

Internships and Student Organizations

Student Organizations

Minor in Environmental Biology and Climate Change

Evolutionary Foundation for Curricula

Meet our Faculty

Factors critical for the successful delivery of telehealth to rural populations: a descriptive qualitative study

Conclusions

Introduction

Research aims

Participant recruitment and data collection

Data analysis

Theme 1: navigating the role of telehealth to support rural healthcare

Access to timely and specialist healthcare for rural communities

Benefits and challenges to providing telehealth in rural communities

Theme 2: preparing clinicians to engage in telehealth service delivery

Rapid adoption of new skills and ways of working

Becoming accustomed to working through the ‘window’

Theme 3: appreciating the complexities of telehealth implementation across services and environments

Working from a different location and context

Considerations for safety, privacy, and confidentiality

Limitations

Data availability

Acknowledgements

Author information

Contributions

Corresponding author

Ethics declarations

Consent for publication

Competing interests

Additional information

Rights and permissions

About this article

Share this article

BMC Health Services Research

Critical assessment of emissions, costs, and time for last-mile goods delivery by drones versus trucks

Similar content being viewed by others

Drone routing problem model for last-mile delivery using the public transportation capacity as moving charging stations

Challenges and opportunities in truck electrification revealed by big operational data

Decarbonising road freight transport: The role of zero-emission trucks and intangible costs

Environmental impact assessment

Economic impact assessment

Delivery time assessment

Sensitivity analysis

Sensitivity analysis for emission factor

Sensitivity analysis for energy use and distance

Sensitivity analysis for varied number of deliveries

Sensitivity analysis for varied velocity

Scenario description