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How to Write the Results/Findings Section in Research

What is the research paper Results section and what does it do?

The Results section of a scientific research paper represents the core findings of a study derived from the methods applied to gather and analyze information. It presents these findings in a logical sequence without bias or interpretation from the author, setting up the reader for later interpretation and evaluation in the Discussion section. A major purpose of the Results section is to break down the data into sentences that show its significance to the research question(s).

The Results section appears third in the section sequence in most scientific papers. It follows the presentation of the Methods and Materials and is presented before the Discussion section —although the Results and Discussion are presented together in many journals. This section answers the basic question “What did you find in your research?”

What is included in the Results section?

The Results section should include the findings of your study and ONLY the findings of your study. The findings include:

• Data presented in tables, charts, graphs, and other figures (may be placed into the text or on separate pages at the end of the manuscript)
• A contextual analysis of this data explaining its meaning in sentence form
• All data that corresponds to the central research question(s)
• All secondary findings (secondary outcomes, subgroup analyses, etc.)

If the scope of the study is broad, or if you studied a variety of variables, or if the methodology used yields a wide range of different results, the author should present only those results that are most relevant to the research question stated in the Introduction section .

As a general rule, any information that does not present the direct findings or outcome of the study should be left out of this section. Unless the journal requests that authors combine the Results and Discussion sections, explanations and interpretations should be omitted from the Results.

How are the results organized?

The best way to organize your Results section is “logically.” One logical and clear method of organizing research results is to provide them alongside the research questions—within each research question, present the type of data that addresses that research question.

Let’s look at an example. Your research question is based on a survey among patients who were treated at a hospital and received postoperative care. Let’s say your first research question is:

“What do hospital patients over age 55 think about postoperative care?”

This can actually be represented as a heading within your Results section, though it might be presented as a statement rather than a question:

Attitudes towards postoperative care in patients over the age of 55

Now present the results that address this specific research question first. In this case, perhaps a table illustrating data from a survey. Likert items can be included in this example. Tables can also present standard deviations, probabilities, correlation matrices, etc.

Following this, present a content analysis, in words, of one end of the spectrum of the survey or data table. In our example case, start with the POSITIVE survey responses regarding postoperative care, using descriptive phrases. For example:

“Sixty-five percent of patients over 55 responded positively to the question “ Are you satisfied with your hospital’s postoperative care ?” (Fig. 2)

Include other results such as subcategory analyses. The amount of textual description used will depend on how much interpretation of tables and figures is necessary and how many examples the reader needs in order to understand the significance of your research findings.

Next, present a content analysis of another part of the spectrum of the same research question, perhaps the NEGATIVE or NEUTRAL responses to the survey. For instance:

  “As Figure 1 shows, 15 out of 60 patients in Group A responded negatively to Question 2.”

After you have assessed the data in one figure and explained it sufficiently, move on to your next research question. For example:

  “How does patient satisfaction correspond to in-hospital improvements made to postoperative care?”

This kind of data may be presented through a figure or set of figures (for instance, a paired T-test table).

Explain the data you present, here in a table, with a concise content analysis:

“The p-value for the comparison between the before and after groups of patients was .03% (Fig. 2), indicating that the greater the dissatisfaction among patients, the more frequent the improvements that were made to postoperative care.”

Let’s examine another example of a Results section from a study on plant tolerance to heavy metal stress . In the Introduction section, the aims of the study are presented as “determining the physiological and morphological responses of Allium cepa L. towards increased cadmium toxicity” and “evaluating its potential to accumulate the metal and its associated environmental consequences.” The Results section presents data showing how these aims are achieved in tables alongside a content analysis, beginning with an overview of the findings:

“Cadmium caused inhibition of root and leave elongation, with increasing effects at higher exposure doses (Fig. 1a-c).”

The figure containing this data is cited in parentheses. Note that this author has combined three graphs into one single figure. Separating the data into separate graphs focusing on specific aspects makes it easier for the reader to assess the findings, and consolidating this information into one figure saves space and makes it easy to locate the most relevant results.

Following this overall summary, the relevant data in the tables is broken down into greater detail in text form in the Results section.

• “Results on the bio-accumulation of cadmium were found to be the highest (17.5 mg kgG1) in the bulb, when the concentration of cadmium in the solution was 1×10G2 M and lowest (0.11 mg kgG1) in the leaves when the concentration was 1×10G3 M.”

Captioning and Referencing Tables and Figures

Tables and figures are central components of your Results section and you need to carefully think about the most effective way to use graphs and tables to present your findings . Therefore, it is crucial to know how to write strong figure captions and to refer to them within the text of the Results section.

The most important advice one can give here as well as throughout the paper is to check the requirements and standards of the journal to which you are submitting your work. Every journal has its own design and layout standards, which you can find in the author instructions on the target journal’s website. Perusing a journal’s published articles will also give you an idea of the proper number, size, and complexity of your figures.

Regardless of which format you use, the figures should be placed in the order they are referenced in the Results section and be as clear and easy to understand as possible. If there are multiple variables being considered (within one or more research questions), it can be a good idea to split these up into separate figures. Subsequently, these can be referenced and analyzed under separate headings and paragraphs in the text.

To create a caption, consider the research question being asked and change it into a phrase. For instance, if one question is “Which color did participants choose?”, the caption might be “Color choice by participant group.” Or in our last research paper example, where the question was “What is the concentration of cadmium in different parts of the onion after 14 days?” the caption reads:

 “Fig. 1(a-c): Mean concentration of Cd determined in (a) bulbs, (b) leaves, and (c) roots of onions after a 14-day period.”

Steps for Composing the Results Section

Because each study is unique, there is no one-size-fits-all approach when it comes to designing a strategy for structuring and writing the section of a research paper where findings are presented. The content and layout of this section will be determined by the specific area of research, the design of the study and its particular methodologies, and the guidelines of the target journal and its editors. However, the following steps can be used to compose the results of most scientific research studies and are essential for researchers who are new to preparing a manuscript for publication or who need a reminder of how to construct the Results section.

Step 1 : Consult the guidelines or instructions that the target journal or publisher provides authors and read research papers it has published, especially those with similar topics, methods, or results to your study.

• The guidelines will generally outline specific requirements for the results or findings section, and the published articles will provide sound examples of successful approaches.
• Note length limitations on restrictions on content. For instance, while many journals require the Results and Discussion sections to be separate, others do not—qualitative research papers often include results and interpretations in the same section (“Results and Discussion”).
• Reading the aims and scope in the journal’s “ guide for authors ” section and understanding the interests of its readers will be invaluable in preparing to write the Results section.

Step 2 : Consider your research results in relation to the journal’s requirements and catalogue your results.

• Focus on experimental results and other findings that are especially relevant to your research questions and objectives and include them even if they are unexpected or do not support your ideas and hypotheses.
• Catalogue your findings—use subheadings to streamline and clarify your report. This will help you avoid excessive and peripheral details as you write and also help your reader understand and remember your findings. Create appendices that might interest specialists but prove too long or distracting for other readers.
• Decide how you will structure of your results. You might match the order of the research questions and hypotheses to your results, or you could arrange them according to the order presented in the Methods section. A chronological order or even a hierarchy of importance or meaningful grouping of main themes or categories might prove effective. Consider your audience, evidence, and most importantly, the objectives of your research when choosing a structure for presenting your findings.

Step 3 : Design figures and tables to present and illustrate your data.

• Tables and figures should be numbered according to the order in which they are mentioned in the main text of the paper.
• Information in figures should be relatively self-explanatory (with the aid of captions), and their design should include all definitions and other information necessary for readers to understand the findings without reading all of the text.
• Use tables and figures as a focal point to tell a clear and informative story about your research and avoid repeating information. But remember that while figures clarify and enhance the text, they cannot replace it.

Step 4 : Draft your Results section using the findings and figures you have organized.

• The goal is to communicate this complex information as clearly and precisely as possible; precise and compact phrases and sentences are most effective.
• In the opening paragraph of this section, restate your research questions or aims to focus the reader’s attention to what the results are trying to show. It is also a good idea to summarize key findings at the end of this section to create a logical transition to the interpretation and discussion that follows.
• Try to write in the past tense and the active voice to relay the findings since the research has already been done and the agent is usually clear. This will ensure that your explanations are also clear and logical.
• Make sure that any specialized terminology or abbreviation you have used here has been defined and clarified in the  Introduction section .

Step 5 : Review your draft; edit and revise until it reports results exactly as you would like to have them reported to your readers.

• Double-check the accuracy and consistency of all the data, as well as all of the visual elements included.
• Read your draft aloud to catch language errors (grammar, spelling, and mechanics), awkward phrases, and missing transitions.
• Ensure that your results are presented in the best order to focus on objectives and prepare readers for interpretations, valuations, and recommendations in the Discussion section . Look back over the paper’s Introduction and background while anticipating the Discussion and Conclusion sections to ensure that the presentation of your results is consistent and effective.
• Consider seeking additional guidance on your paper. Find additional readers to look over your Results section and see if it can be improved in any way. Peers, professors, or qualified experts can provide valuable insights.

One excellent option is to use a professional English proofreading and editing service  such as Wordvice, including our paper editing service . With hundreds of qualified editors from dozens of scientific fields, Wordvice has helped thousands of authors revise their manuscripts and get accepted into their target journals. Read more about the  proofreading and editing process  before proceeding with getting academic editing services and manuscript editing services for your manuscript.

As the representation of your study’s data output, the Results section presents the core information in your research paper. By writing with clarity and conciseness and by highlighting and explaining the crucial findings of their study, authors increase the impact and effectiveness of their research manuscripts.

For more articles and videos on writing your research manuscript, visit Wordvice’s Resources page.

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The results section is where you report the findings of your study based upon the methodology [or methodologies] you applied to gather information. The results section should state the findings of the research arranged in a logical sequence without bias or interpretation. A section describing results should be particularly detailed if your paper includes data generated from your own research.

Annesley, Thomas M. "Show Your Cards: The Results Section and the Poker Game." Clinical Chemistry 56 (July 2010): 1066-1070.

Importance of a Good Results Section

When formulating the results section, it's important to remember that the results of a study do not prove anything . Findings can only confirm or reject the hypothesis underpinning your study. However, the act of articulating the results helps you to understand the problem from within, to break it into pieces, and to view the research problem from various perspectives.

The page length of this section is set by the amount and types of data to be reported . Be concise. Use non-textual elements appropriately, such as figures and tables, to present findings more effectively. In deciding what data to describe in your results section, you must clearly distinguish information that would normally be included in a research paper from any raw data or other content that could be included as an appendix. In general, raw data that has not been summarized should not be included in the main text of your paper unless requested to do so by your professor.

Avoid providing data that is not critical to answering the research question . The background information you described in the introduction section should provide the reader with any additional context or explanation needed to understand the results. A good strategy is to always re-read the background section of your paper after you have written up your results to ensure that the reader has enough context to understand the results [and, later, how you interpreted the results in the discussion section of your paper that follows].

Bavdekar, Sandeep B. and Sneha Chandak. "Results: Unraveling the Findings." Journal of the Association of Physicians of India 63 (September 2015): 44-46; Brett, Paul. "A Genre Analysis of the Results Section of Sociology Articles." English for Specific Speakers 13 (1994): 47-59; Go to English for Specific Purposes on ScienceDirect;Burton, Neil et al. Doing Your Education Research Project . Los Angeles, CA: SAGE, 2008; Results. The Structure, Format, Content, and Style of a Journal-Style Scientific Paper. Department of Biology. Bates College; Kretchmer, Paul. Twelve Steps to Writing an Effective Results Section. San Francisco Edit; "Reporting Findings." In Making Sense of Social Research Malcolm Williams, editor. (London;: SAGE Publications, 2003) pp. 188-207.

Structure and Writing Style

I.  Organization and Approach

For most research papers in the social and behavioral sciences, there are two possible ways of organizing the results . Both approaches are appropriate in how you report your findings, but use only one approach.

• Present a synopsis of the results followed by an explanation of key findings . This approach can be used to highlight important findings. For example, you may have noticed an unusual correlation between two variables during the analysis of your findings. It is appropriate to highlight this finding in the results section. However, speculating as to why this correlation exists and offering a hypothesis about what may be happening belongs in the discussion section of your paper.
• Present a result and then explain it, before presenting the next result then explaining it, and so on, then end with an overall synopsis . This is the preferred approach if you have multiple results of equal significance. It is more common in longer papers because it helps the reader to better understand each finding. In this model, it is helpful to provide a brief conclusion that ties each of the findings together and provides a narrative bridge to the discussion section of the your paper.

NOTE :   Just as the literature review should be arranged under conceptual categories rather than systematically describing each source, you should also organize your findings under key themes related to addressing the research problem. This can be done under either format noted above [i.e., a thorough explanation of the key results or a sequential, thematic description and explanation of each finding].

II.  Content

In general, the content of your results section should include the following:

• Introductory context for understanding the results by restating the research problem underpinning your study . This is useful in re-orientating the reader's focus back to the research problem after having read a review of the literature and your explanation of the methods used for gathering and analyzing information.
• Inclusion of non-textual elements, such as, figures, charts, photos, maps, tables, etc. to further illustrate key findings, if appropriate . Rather than relying entirely on descriptive text, consider how your findings can be presented visually. This is a helpful way of condensing a lot of data into one place that can then be referred to in the text. Consider referring to appendices if there is a lot of non-textual elements.
• A systematic description of your results, highlighting for the reader observations that are most relevant to the topic under investigation . Not all results that emerge from the methodology used to gather information may be related to answering the " So What? " question. Do not confuse observations with interpretations; observations in this context refers to highlighting important findings you discovered through a process of reviewing prior literature and gathering data.
• The page length of your results section is guided by the amount and types of data to be reported . However, focus on findings that are important and related to addressing the research problem. It is not uncommon to have unanticipated results that are not relevant to answering the research question. This is not to say that you don't acknowledge tangential findings and, in fact, can be referred to as areas for further research in the conclusion of your paper. However, spending time in the results section describing tangential findings clutters your overall results section and distracts the reader.
• A short paragraph that concludes the results section by synthesizing the key findings of the study . Highlight the most important findings you want readers to remember as they transition into the discussion section. This is particularly important if, for example, there are many results to report, the findings are complicated or unanticipated, or they are impactful or actionable in some way [i.e., able to be pursued in a feasible way applied to practice].

NOTE:   Always use the past tense when referring to your study's findings. Reference to findings should always be described as having already happened because the method used to gather the information has been completed.

III.  Problems to Avoid

When writing the results section, avoid doing the following :

• Discussing or interpreting your results . Save this for the discussion section of your paper, although where appropriate, you should compare or contrast specific results to those found in other studies [e.g., "Similar to the work of Smith [1990], one of the findings of this study is the strong correlation between motivation and academic achievement...."].
• Reporting background information or attempting to explain your findings. This should have been done in your introduction section, but don't panic! Often the results of a study point to the need for additional background information or to explain the topic further, so don't think you did something wrong. Writing up research is rarely a linear process. Always revise your introduction as needed.
• Ignoring negative results . A negative result generally refers to a finding that does not support the underlying assumptions of your study. Do not ignore them. Document these findings and then state in your discussion section why you believe a negative result emerged from your study. Note that negative results, and how you handle them, can give you an opportunity to write a more engaging discussion section, therefore, don't be hesitant to highlight them.
• Including raw data or intermediate calculations . Ask your professor if you need to include any raw data generated by your study, such as transcripts from interviews or data files. If raw data is to be included, place it in an appendix or set of appendices that are referred to in the text.
• Be as factual and concise as possible in reporting your findings . Do not use phrases that are vague or non-specific, such as, "appeared to be greater than other variables..." or "demonstrates promising trends that...." Subjective modifiers should be explained in the discussion section of the paper [i.e., why did one variable appear greater? Or, how does the finding demonstrate a promising trend?].
• Presenting the same data or repeating the same information more than once . If you want to highlight a particular finding, it is appropriate to do so in the results section. However, you should emphasize its significance in relation to addressing the research problem in the discussion section. Do not repeat it in your results section because you can do that in the conclusion of your paper.
• Confusing figures with tables . Be sure to properly label any non-textual elements in your paper. Don't call a chart an illustration or a figure a table. If you are not sure, go here .

Annesley, Thomas M. "Show Your Cards: The Results Section and the Poker Game." Clinical Chemistry 56 (July 2010): 1066-1070; Bavdekar, Sandeep B. and Sneha Chandak. "Results: Unraveling the Findings." Journal of the Association of Physicians of India 63 (September 2015): 44-46; Burton, Neil et al. Doing Your Education Research Project . Los Angeles, CA: SAGE, 2008;  Caprette, David R. Writing Research Papers. Experimental Biosciences Resources. Rice University; Hancock, Dawson R. and Bob Algozzine. Doing Case Study Research: A Practical Guide for Beginning Researchers . 2nd ed. New York: Teachers College Press, 2011; Introduction to Nursing Research: Reporting Research Findings. Nursing Research: Open Access Nursing Research and Review Articles. (January 4, 2012); Kretchmer, Paul. Twelve Steps to Writing an Effective Results Section. San Francisco Edit ; Ng, K. H. and W. C. Peh. "Writing the Results." Singapore Medical Journal 49 (2008): 967-968; Reporting Research Findings. Wilder Research, in partnership with the Minnesota Department of Human Services. (February 2009); Results. The Structure, Format, Content, and Style of a Journal-Style Scientific Paper. Department of Biology. Bates College; Schafer, Mickey S. Writing the Results. Thesis Writing in the Sciences. Course Syllabus. University of Florida.

Writing Tip

Why Don't I Just Combine the Results Section with the Discussion Section?

It's not unusual to find articles in scholarly social science journals where the author(s) have combined a description of the findings with a discussion about their significance and implications. You could do this. However, if you are inexperienced writing research papers, consider creating two distinct sections for each section in your paper as a way to better organize your thoughts and, by extension, your paper. Think of the results section as the place where you report what your study found; think of the discussion section as the place where you interpret the information and answer the "So What?" question. As you become more skilled writing research papers, you can consider melding the results of your study with a discussion of its implications.

Driscoll, Dana Lynn and Aleksandra Kasztalska. Writing the Experimental Report: Methods, Results, and Discussion. The Writing Lab and The OWL. Purdue University.

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• How to Write a Results Section | Tips & Examples

How to Write a Results Section | Tips & Examples

Published on 27 October 2016 by Bas Swaen . Revised on 25 October 2022 by Tegan George.

A results section is where you report the main findings of the data collection and analysis you conducted for your thesis or dissertation . You should report all relevant results concisely and objectively, in a logical order. Don’t include subjective interpretations of why you found these results or what they mean – any evaluation should be saved for the discussion section .

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Table of contents

How to write a results section, reporting quantitative research results, reporting qualitative research results, results vs discussion vs conclusion, checklist: research results, frequently asked questions about results sections.

When conducting research, it’s important to report the results of your study prior to discussing your interpretations of it. This gives your reader a clear idea of exactly what you found and keeps the data itself separate from your subjective analysis.

Here are a few best practices:

• Your results should always be written in the past tense.
• While the length of this section depends on how much data you collected and analysed, it should be written as concisely as possible.
• Only include results that are directly relevant to answering your research questions . Avoid speculative or interpretative words like ‘appears’ or ‘implies’.
• If you have other results you’d like to include, consider adding them to an appendix or footnotes.
• Always start out with your broadest results first, and then flow into your more granular (but still relevant) ones. Think of it like a shoe shop: first discuss the shoes as a whole, then the trainers, boots, sandals, etc.

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If you conducted quantitative research , you’ll likely be working with the results of some sort of statistical analysis .

Your results section should report the results of any statistical tests you used to compare groups or assess relationships between variables . It should also state whether or not each hypothesis was supported.

The most logical way to structure quantitative results is to frame them around your research questions or hypotheses. For each question or hypothesis, share:

• A reminder of the type of analysis you used (e.g., a two-sample t test or simple linear regression ). A more detailed description of your analysis should go in your methodology section.
• A concise summary of each relevant result, both positive and negative. This can include any relevant descriptive statistics (e.g., means and standard deviations ) as well as inferential statistics (e.g., t scores, degrees of freedom , and p values ). Remember, these numbers are often placed in parentheses.
• A brief statement of how each result relates to the question, or whether the hypothesis was supported. You can briefly mention any results that didn’t fit with your expectations and assumptions, but save any speculation on their meaning or consequences for your discussion  and conclusion.

A note on tables and figures

In quantitative research, it’s often helpful to include visual elements such as graphs, charts, and tables , but only if they are directly relevant to your results. Give these elements clear, descriptive titles and labels so that your reader can easily understand what is being shown. If you want to include any other visual elements that are more tangential in nature, consider adding a figure and table list .

As a rule of thumb:

• Tables are used to communicate exact values, giving a concise overview of various results
• Graphs and charts are used to visualise trends and relationships, giving an at-a-glance illustration of key findings

Don’t forget to also mention any tables and figures you used within the text of your results section. Summarise or elaborate on specific aspects you think your reader should know about rather than merely restating the same numbers already shown.

Figure 1: Intention to donate to environmental organisations based on social distance from impact of environmental damage.

In qualitative research , your results might not all be directly related to specific hypotheses. In this case, you can structure your results section around key themes or topics that emerged from your analysis of the data.

For each theme, start with general observations about what the data showed. You can mention:

• Recurring points of agreement or disagreement
• Patterns and trends
• Particularly significant snippets from individual responses

Next, clarify and support these points with direct quotations. Be sure to report any relevant demographic information about participants. Further information (such as full transcripts , if appropriate) can be included in an appendix .

‘I think that in role-playing games, there’s more attention to character design, to world design, because the whole story is important and more attention is paid to certain game elements […] so that perhaps you do need bigger teams of creative experts than in an average shooter or something.’

Responses suggest that video game consumers consider some types of games to have more artistic potential than others.

Your results section should objectively report your findings, presenting only brief observations in relation to each question, hypothesis, or theme.

It should not  speculate about the meaning of the results or attempt to answer your main research question . Detailed interpretation of your results is more suitable for your discussion section , while synthesis of your results into an overall answer to your main research question is best left for your conclusion .

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I have completed my data collection and analyzed the results.

I have included all results that are relevant to my research questions.

I have concisely and objectively reported each result, including relevant descriptive statistics and inferential statistics .

I have stated whether each hypothesis was supported or refuted.

I have used tables and figures to illustrate my results where appropriate.

All tables and figures are correctly labelled and referred to in the text.

There is no subjective interpretation or speculation on the meaning of the results.

You've finished writing up your results! Use the other checklists to further improve your thesis.

The results chapter of a thesis or dissertation presents your research results concisely and objectively.

In quantitative research , for each question or hypothesis , state:

• The type of analysis used
• Relevant results in the form of descriptive and inferential statistics
• Whether or not the alternative hypothesis was supported

In qualitative research , for each question or theme, describe:

• Recurring patterns
• Significant or representative individual responses
• Relevant quotations from the data

Don’t interpret or speculate in the results chapter.

Results are usually written in the past tense , because they are describing the outcome of completed actions.

The results chapter or section simply and objectively reports what you found, without speculating on why you found these results. The discussion interprets the meaning of the results, puts them in context, and explains why they matter.

In qualitative research , results and discussion are sometimes combined. But in quantitative research , it’s considered important to separate the objective results from your interpretation of them.

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• How to Write Discussions and Conclusions

The discussion section contains the results and outcomes of a study. An effective discussion informs readers what can be learned from your experiment and provides context for the results.

What makes an effective discussion?

When you’re ready to write your discussion, you’ve already introduced the purpose of your study and provided an in-depth description of the methodology. The discussion informs readers about the larger implications of your study based on the results. Highlighting these implications while not overstating the findings can be challenging, especially when you’re submitting to a journal that selects articles based on novelty or potential impact. Regardless of what journal you are submitting to, the discussion section always serves the same purpose: concluding what your study results actually mean.

A successful discussion section puts your findings in context. It should include:

• the results of your research,
• a discussion of related research, and
• a comparison between your results and initial hypothesis.

Tip: Not all journals share the same naming conventions.

You can apply the advice in this article to the conclusion, results or discussion sections of your manuscript.

Our Early Career Researcher community tells us that the conclusion is often considered the most difficult aspect of a manuscript to write. To help, this guide provides questions to ask yourself, a basic structure to model your discussion off of and examples from published manuscripts. 

Questions to ask yourself:

• Was my hypothesis correct?
• If my hypothesis is partially correct or entirely different, what can be learned from the results? 
• How do the conclusions reshape or add onto the existing knowledge in the field? What does previous research say about the topic? 
• Why are the results important or relevant to your audience? Do they add further evidence to a scientific consensus or disprove prior studies? 
• How can future research build on these observations? What are the key experiments that must be done? 
• What is the “take-home” message you want your reader to leave with?

How to structure a discussion

Trying to fit a complete discussion into a single paragraph can add unnecessary stress to the writing process. If possible, you’ll want to give yourself two or three paragraphs to give the reader a comprehensive understanding of your study as a whole. Here’s one way to structure an effective discussion:

Writing Tips

While the above sections can help you brainstorm and structure your discussion, there are many common mistakes that writers revert to when having difficulties with their paper. Writing a discussion can be a delicate balance between summarizing your results, providing proper context for your research and avoiding introducing new information. Remember that your paper should be both confident and honest about the results! 

• Read the journal’s guidelines on the discussion and conclusion sections. If possible, learn about the guidelines before writing the discussion to ensure you’re writing to meet their expectations. 
• Begin with a clear statement of the principal findings. This will reinforce the main take-away for the reader and set up the rest of the discussion. 
• Explain why the outcomes of your study are important to the reader. Discuss the implications of your findings realistically based on previous literature, highlighting both the strengths and limitations of the research. 
• State whether the results prove or disprove your hypothesis. If your hypothesis was disproved, what might be the reasons? 
• Introduce new or expanded ways to think about the research question. Indicate what next steps can be taken to further pursue any unresolved questions. 
• If dealing with a contemporary or ongoing problem, such as climate change, discuss possible consequences if the problem is avoided. 
• Be concise. Adding unnecessary detail can distract from the main findings. 

Don’t

• Rewrite your abstract. Statements with “we investigated” or “we studied” generally do not belong in the discussion. 
• Include new arguments or evidence not previously discussed. Necessary information and evidence should be introduced in the main body of the paper. 
• Apologize. Even if your research contains significant limitations, don’t undermine your authority by including statements that doubt your methodology or execution. 
• Shy away from speaking on limitations or negative results. Including limitations and negative results will give readers a complete understanding of the presented research. Potential limitations include sources of potential bias, threats to internal or external validity, barriers to implementing an intervention and other issues inherent to the study design. 
• Overstate the importance of your findings. Making grand statements about how a study will fully resolve large questions can lead readers to doubt the success of the research. 

Snippets of Effective Discussions:

Consumer-based actions to reduce plastic pollution in rivers: A multi-criteria decision analysis approach

Identifying reliable indicators of fitness in polar bears

• How to Write a Great Title
• How to Write an Abstract
• How to Write Your Methods
• How to Report Statistics
• How to Edit Your Work

The contents of the Peer Review Center are also available as a live, interactive training session, complete with slides, talking points, and activities. …

The contents of the Writing Center are also available as a live, interactive training session, complete with slides, talking points, and activities. …

There’s a lot to consider when deciding where to submit your work. Learn how to choose a journal that will help your study reach its audience, while reflecting your values as a researcher…

• Manuscript Preparation

How to write the results section of a research paper

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Table of Contents

At its core, a research paper aims to fill a gap in the research on a given topic. As a result, the results section of the paper, which describes the key findings of the study, is often considered the core of the paper. This is the section that gets the most attention from reviewers, peers, students, and any news organization reporting on your findings. Writing a clear, concise, and logical results section is, therefore, one of the most important parts of preparing your manuscript.

Difference between results and discussion

Before delving into how to write the results section, it is important to first understand the difference between the results and discussion sections. The results section needs to detail the findings of the study. The aim of this section is not to draw connections between the different findings or to compare it to previous findings in literature—that is the purview of the discussion section. Unlike the discussion section, which can touch upon the hypothetical, the results section needs to focus on the purely factual. In some cases, it may even be preferable to club these two sections together into a single section. For example, while writing  a review article, it can be worthwhile to club these two sections together, as the main results in this case are the conclusions that can be drawn from the literature.

Structure of the results section

Although the main purpose of the results section in a research paper is to report the findings, it is necessary to present an introduction and repeat the research question. This establishes a connection to the previous section of the paper and creates a smooth flow of information.

Next, the results section needs to communicate the findings of your research in a systematic manner. The section needs to be organized such that the primary research question is addressed first, then the secondary research questions. If the research addresses multiple questions, the results section must individually connect with each of the questions. This ensures clarity and minimizes confusion while reading.

Consider representing your results visually. For example, graphs, tables, and other figures can help illustrate the findings of your paper, especially if there is a large amount of data in the results.

Remember, an appealing results section can help peer reviewers better understand the merits of your research, thereby increasing your chances of publication.

Practical guidance for writing an effective results section for a research paper

• Always use simple and clear language. Avoid the use of uncertain or out-of-focus expressions.
• The findings of the study must be expressed in an objective and unbiased manner. While it is acceptable to correlate certain findings in the discussion section, it is best to avoid overinterpreting the results.
• If the research addresses more than one hypothesis, use sub-sections to describe the results. This prevents confusion and promotes understanding.
• Ensure that negative results are included in this section, even if they do not support the research hypothesis.
• Wherever possible, use illustrations like tables, figures, charts, or other visual representations to showcase the results of your research paper. Mention these illustrations in the text, but do not repeat the information that they convey.
• For statistical data, it is adequate to highlight the tests and explain their results. The initial or raw data should not be mentioned in the results section of a research paper.

The results section of a research paper is usually the most impactful section because it draws the greatest attention. Regardless of the subject of your research paper, a well-written results section is capable of generating interest in your research.

For detailed information and assistance on writing the results of a research paper, refer to Elsevier Author Services.

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From Data to Discovery: The Findings Section of a Research Paper

Discover the role of the findings section of a research paper here. Explore strategies and techniques to maximize your understanding.

Are you curious about the Findings section of a research paper? Did you know that this is a part where all the juicy results and discoveries are laid out for the world to see? Undoubtedly, the findings section of a research paper plays a critical role in presenting and interpreting the collected data. It serves as a comprehensive account of the study’s results and their implications.

Well, look no further because we’ve got you covered! In this article, we’re diving into the ins and outs of presenting and interpreting data in the findings section. We’ll be sharing tips and tricks on how to effectively present your findings, whether it’s through tables, graphs, or good old descriptive statistics.

Overview of the Findings Section of a Research Paper

The findings section of a research paper presents the results and outcomes of the study or investigation. It is a crucial part of the research paper where researchers interpret and analyze the data collected and draw conclusions based on their findings. This section aims to answer the research questions or hypotheses formulated earlier in the paper and provide evidence to support or refute them.

In the findings section, researchers typically present the data clearly and organized. They may use tables, graphs, charts, or other visual aids to illustrate the patterns, trends, or relationships observed in the data. The findings should be presented objectively, without any bias or personal opinions, and should be accompanied by appropriate statistical analyses or methods to ensure the validity and reliability of the results.

Organizing the Findings Section

The findings section of the research paper organizes and presents the results obtained from the study in a clear and logical manner. Here is a suggested structure for organizing the Findings section:

Introduction to the Findings

Start the section by providing a brief overview of the research objectives and the methodology employed. Recapitulate the research questions or hypotheses addressed in the study.

To learn more about methodology, read this article .

Descriptive Statistics and Data Presentation

Present the collected data using appropriate descriptive statistics. This may involve using tables, graphs, charts, or other visual representations to convey the information effectively. Remember: we can easily help you with that.

Data Analysis and Interpretation

Perform a thorough analysis of the data collected and describe the key findings. Present the results of statistical analyses or any other relevant methods used to analyze the data. 

Discussion of Findings

Analyze and interpret the findings in the context of existing literature or theoretical frameworks . Discuss any patterns, trends, or relationships observed in the data. Compare and contrast the results with prior studies, highlighting similarities and differences. 

Limitations and Constraints

Acknowledge and discuss any limitations or constraints that may have influenced the findings. This could include issues such as sample size, data collection methods, or potential biases. 

Summarize the main findings of the study and emphasize their significance. Revisit the research questions or hypotheses and discuss whether they have been supported or refuted by the findings.

Presenting Data in the Findings Section

There are several ways to present data in the findings section of a research paper. Here are some common methods:

• Tables : Tables are commonly used to present organized and structured data. They are particularly useful when presenting numerical data with multiple variables or categories. Tables allow readers to easily compare and interpret the information presented. Learn how to cite tables in research papers here .
• Graphs and Charts: Graphs and charts are effective visual tools for presenting data, especially when illustrating trends, patterns, or relationships. Common types include bar graphs, line graphs, scatter plots, pie charts, and histograms. Graphs and charts provide a visual representation of the data, making it easier for readers to comprehend and interpret.
• Figures and Images: Figures and images can be used to present data that requires visual representation, such as maps, diagrams, or experimental setups. They can enhance the understanding of complex data or provide visual evidence to support the research findings.
• Descriptive Statistics: Descriptive statistics provide summary measures of central tendency (e.g., mean, median, mode) and dispersion (e.g., standard deviation, range) for numerical data. These statistics can be included in the text or presented in tables or graphs to provide a concise summary of the data distribution.

How to Effectively Interpret Results

Interpreting the results is a crucial aspect of the findings section in a research paper. It involves analyzing the data collected and drawing meaningful conclusions based on the findings. Following are the guidelines on how to effectively interpret the results.

Step 1 – Begin with a Recap

Start by restating the research questions or hypotheses to provide context for the interpretation. Remind readers of the specific objectives of the study to help them understand the relevance of the findings.

Step 2 – Relate Findings to Research Questions

Clearly articulate how the results address the research questions or hypotheses. Discuss each finding in relation to the original objectives and explain how it contributes to answering the research questions or supporting/refuting the hypotheses.

Step 3 – Compare with Existing Literature

Compare and contrast the findings with previous studies or existing literature. Highlight similarities, differences, or discrepancies between your results and those of other researchers. Discuss any consistencies or contradictions and provide possible explanations for the observed variations.

Step 4 – Consider Limitations and Alternative Explanations

Acknowledge the limitations of the study and discuss how they may have influenced the results. Explore alternative explanations or factors that could potentially account for the findings. Evaluate the robustness of the results in light of the limitations and alternative interpretations.

Step 5 – Discuss Implications and Significance

Highlight any potential applications or areas where further research is needed based on the outcomes of the study.

Step 6 – Address Inconsistencies and Contradictions

If there are any inconsistencies or contradictions in the findings, address them directly. Discuss possible reasons for the discrepancies and consider their implications for the overall interpretation. Be transparent about any uncertainties or unresolved issues.

Step 7 – Be Objective and Data-Driven

Present the interpretation objectively, based on the evidence and data collected. Avoid personal biases or subjective opinions. Use logical reasoning and sound arguments to support your interpretations.

Reporting Statistical Significance

When reporting statistical significance in the findings section of a research paper, it is important to accurately convey the results of statistical analyses and their implications. Here are some guidelines on how to report statistical significance effectively:

• Clearly State the Statistical Test: Begin by clearly stating the specific statistical test or analysis used to determine statistical significance. For example, you might mention that a t-test, chi-square test, ANOVA, correlation analysis, or regression analysis was employed.
• Report the Test Statistic: Provide the value of the test statistic obtained from the analysis. This could be the t-value, F-value, chi-square value, correlation coefficient, or any other relevant statistic depending on the test used.
• State the Degrees of Freedom: Indicate the degrees of freedom associated with the statistical test. Degrees of freedom represent the number of independent pieces of information available for estimating a statistic. For example, in a t-test, degrees of freedom would be mentioned as (df = n1 + n2 – 2) for an independent samples test or (df = N – 2) for a paired samples test.
• Report the p-value: The p-value indicates the probability of obtaining results as extreme or more extreme than the observed results, assuming the null hypothesis is true. Report the p-value associated with the statistical test. For example, p < 0.05 denotes statistical significance at the conventional level of α = 0.05.
• Provide the Conclusion: Based on the p-value obtained, state whether the results are statistically significant or not. If the p-value is less than the predetermined threshold (e.g., p < 0.05), state that the results are statistically significant. If the p-value is greater than the threshold, state that the results are not statistically significant.
• Discuss the Interpretation: After reporting statistical significance, discuss the practical or theoretical implications of the finding. Explain what the significant result means in the context of your research questions or hypotheses. Address the effect size and practical significance of the findings, if applicable.
• Consider Effect Size Measures: Along with statistical significance, it is often important to report effect size measures. Effect size quantifies the magnitude of the relationship or difference observed in the data. Common effect size measures include Cohen’s d, eta-squared, or Pearson’s r. Reporting effect size provides additional meaningful information about the strength of the observed effects.
• Be Accurate and Transparent: Ensure that the reported statistical significance and associated values are accurate. Avoid misinterpreting or misrepresenting the results. Be transparent about the statistical tests conducted, any assumptions made, and potential limitations or caveats that may impact the interpretation of the significant results.

Conclusion of the Findings Section

The conclusion of the findings section in a research paper serves as a summary and synthesis of the key findings and their implications. It is an opportunity to tie together the results, discuss their significance, and address the research objectives. Here are some guidelines on how to write the conclusion of the Findings section:

Summarize the Key Findings

Begin by summarizing the main findings of the study. Provide a concise overview of the significant results, patterns, or relationships that emerged from the data analysis. Highlight the most important findings that directly address the research questions or hypotheses.

Revisit the Research Objectives

Remind the reader of the research objectives stated at the beginning of the paper. Discuss how the findings contribute to achieving those objectives and whether they support or challenge the initial research questions or hypotheses.

Suggest Future Directions

Identify areas for further research or future directions based on the findings. Discuss any unanswered questions, unresolved issues, or new avenues of inquiry that emerged during the study. Propose potential research opportunities that can build upon the current findings.

The Best Scientific Figures to Represent Your Findings 

Have you heard of any tool that helps you represent your findings through visuals like graphs, pie charts, and infographics? Well, if you haven’t, then here’s the tool you need to explore – Mind the Graph . It’s the tool that has the best scientific figures to represent your findings. Go, try it now, and make your research findings stand out!

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How To Write The Results/Findings Chapter

For qualitative studies (dissertations & theses).

By: Jenna Crossley (PhD). Expert Reviewed By: Dr. Eunice Rautenbach | August 2021

So, you’ve collected and analysed your qualitative data, and it’s time to write up your results chapter. But where do you start? In this post, we’ll guide you through the qualitative results chapter (also called the findings chapter), step by step. 

Overview: Qualitative Results Chapter

• What (exactly) the qualitative results chapter is
• What to include in your results chapter
• How to write up your results chapter
• A few tips and tricks to help you along the way
• Free results chapter template

What exactly is the results chapter?

The results chapter in a dissertation or thesis (or any formal academic research piece) is where you objectively and neutrally present the findings of your qualitative analysis (or analyses if you used multiple qualitative analysis methods ). This chapter can sometimes be combined with the discussion chapter (where you interpret the data and discuss its meaning), depending on your university’s preference.  We’ll treat the two chapters as separate, as that’s the most common approach.

In contrast to a quantitative results chapter that presents numbers and statistics, a qualitative results chapter presents data primarily in the form of words . But this doesn’t mean that a qualitative study can’t have quantitative elements – you could, for example, present the number of times a theme or topic pops up in your data, depending on the analysis method(s) you adopt.

Adding a quantitative element to your study can add some rigour, which strengthens your results by providing more evidence for your claims. This is particularly common when using qualitative content analysis. Keep in mind though that qualitative research aims to achieve depth, richness and identify nuances , so don’t get tunnel vision by focusing on the numbers. They’re just cream on top in a qualitative analysis.

So, to recap, the results chapter is where you objectively present the findings of your analysis, without interpreting them (you’ll save that for the discussion chapter). With that out the way, let’s take a look at what you should include in your results chapter.

What should you include in the results chapter?

As we’ve mentioned, your qualitative results chapter should purely present and describe your results , not interpret them in relation to the existing literature or your research questions . Any speculations or discussion about the implications of your findings should be reserved for your discussion chapter.

In your results chapter, you’ll want to talk about your analysis findings and whether or not they support your hypotheses (if you have any). Naturally, the exact contents of your results chapter will depend on which qualitative analysis method (or methods) you use. For example, if you were to use thematic analysis, you’d detail the themes identified in your analysis, using extracts from the transcripts or text to support your claims.

While you do need to present your analysis findings in some detail, you should avoid dumping large amounts of raw data in this chapter. Instead, focus on presenting the key findings and using a handful of select quotes or text extracts to support each finding . The reams of data and analysis can be relegated to your appendices.

While it’s tempting to include every last detail you found in your qualitative analysis, it is important to make sure that you report only that which is relevant to your research aims, objectives and research questions .  Always keep these three components, as well as your hypotheses (if you have any) front of mind when writing the chapter and use them as a filter to decide what’s relevant and what’s not.

Need a helping hand?

How do I write the results chapter?

Now that we’ve covered the basics, it’s time to look at how to structure your chapter. Broadly speaking, the results chapter needs to contain three core components – the introduction, the body and the concluding summary. Let’s take a look at each of these.

Section 1: Introduction

The first step is to craft a brief introduction to the chapter. This intro is vital as it provides some context for your findings. In your introduction, you should begin by reiterating your problem statement and research questions and highlight the purpose of your research . Make sure that you spell this out for the reader so that the rest of your chapter is well contextualised.

The next step is to briefly outline the structure of your results chapter. In other words, explain what’s included in the chapter and what the reader can expect. In the results chapter, you want to tell a story that is coherent, flows logically, and is easy to follow , so make sure that you plan your structure out well and convey that structure (at a high level), so that your reader is well oriented.

The introduction section shouldn’t be lengthy. Two or three short paragraphs should be more than adequate. It is merely an introduction and overview, not a summary of the chapter.

Pro Tip – To help you structure your chapter, it can be useful to set up an initial draft with (sub)section headings so that you’re able to easily (re)arrange parts of your chapter. This will also help your reader to follow your results and give your chapter some coherence.  Be sure to use level-based heading styles (e.g. Heading 1, 2, 3 styles) to help the reader differentiate between levels visually. You can find these options in Word (example below).

Section 2: Body

Before we get started on what to include in the body of your chapter, it’s vital to remember that a results section should be completely objective and descriptive, not interpretive . So, be careful not to use words such as, “suggests” or “implies”, as these usually accompany some form of interpretation – that’s reserved for your discussion chapter.

The structure of your body section is very important , so make sure that you plan it out well. When planning out your qualitative results chapter, create sections and subsections so that you can maintain the flow of the story you’re trying to tell. Be sure to systematically and consistently describe each portion of results. Try to adopt a standardised structure for each portion so that you achieve a high level of consistency throughout the chapter.

For qualitative studies, results chapters tend to be structured according to themes , which makes it easier for readers to follow. However, keep in mind that not all results chapters have to be structured in this manner. For example, if you’re conducting a longitudinal study, you may want to structure your chapter chronologically. Similarly, you might structure this chapter based on your theoretical framework . The exact structure of your chapter will depend on the nature of your study , especially your research questions.

As you work through the body of your chapter, make sure that you use quotes to substantiate every one of your claims . You can present these quotes in italics to differentiate them from your own words. A general rule of thumb is to use at least two pieces of evidence per claim, and these should be linked directly to your data. Also, remember that you need to include all relevant results , not just the ones that support your assumptions or initial leanings.

In addition to including quotes, you can also link your claims to the data by using appendices , which you should reference throughout your text. When you reference, make sure that you include both the name/number of the appendix , as well as the line(s) from which you drew your data.

As referencing styles can vary greatly, be sure to look up the appendix referencing conventions of your university’s prescribed style (e.g. APA , Harvard, etc) and keep this consistent throughout your chapter.

Section 3: Concluding summary

The concluding summary is very important because it summarises your key findings and lays the foundation for the discussion chapter . Keep in mind that some readers may skip directly to this section (from the introduction section), so make sure that it can be read and understood well in isolation.

In this section, you need to remind the reader of the key findings. That is, the results that directly relate to your research questions and that you will build upon in your discussion chapter. Remember, your reader has digested a lot of information in this chapter, so you need to use this section to remind them of the most important takeaways.

Importantly, the concluding summary should not present any new information and should only describe what you’ve already presented in your chapter. Keep it concise – you’re not summarising the whole chapter, just the essentials.

Tips for writing an A-grade results chapter

Now that you’ve got a clear picture of what the qualitative results chapter is all about, here are some quick tips and reminders to help you craft a high-quality chapter:

• Your results chapter should be written in the past tense . You’ve done the work already, so you want to tell the reader what you found , not what you are currently finding .
• Make sure that you review your work multiple times and check that every claim is adequately backed up by evidence . Aim for at least two examples per claim, and make use of an appendix to reference these.
• When writing up your results, make sure that you stick to only what is relevant . Don’t waste time on data that are not relevant to your research objectives and research questions.
• Use headings and subheadings to create an intuitive, easy to follow piece of writing. Make use of Microsoft Word’s “heading styles” and be sure to use them consistently.
• When referring to numerical data, tables and figures can provide a useful visual aid. When using these, make sure that they can be read and understood independent of your body text (i.e. that they can stand-alone). To this end, use clear, concise labels for each of your tables or figures and make use of colours to code indicate differences or hierarchy.
• Similarly, when you’re writing up your chapter, it can be useful to highlight topics and themes in different colours . This can help you to differentiate between your data if you get a bit overwhelmed and will also help you to ensure that your results flow logically and coherently.

If you have any questions, leave a comment below and we’ll do our best to help. If you’d like 1-on-1 help with your results chapter (or any chapter of your dissertation or thesis), check out our private dissertation coaching service here or book a free initial consultation to discuss how we can help you.

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20 Comments

This was extremely helpful. Thanks a lot guys

Hi, thanks for the great research support platform created by the gradcoach team!

I wanted to ask- While “suggests” or “implies” are interpretive terms, what terms could we use for the results chapter? Could you share some examples of descriptive terms?

I think that instead of saying, ‘The data suggested, or The data implied,’ you can say, ‘The Data showed or revealed, or illustrated or outlined’…If interview data, you may say Jane Doe illuminated or elaborated, or Jane Doe described… or Jane Doe expressed or stated.

I found this article very useful. Thank you very much for the outstanding work you are doing.

What if i have 3 different interviewees answering the same interview questions? Should i then present the results in form of the table with the division on the 3 perspectives or rather give a results in form of the text and highlight who said what?

I think this tabular representation of results is a great idea. I am doing it too along with the text. Thanks

That was helpful was struggling to separate the discussion from the findings

this was very useful, Thank you.

Very helpful, I am confident to write my results chapter now.

It is so helpful! It is a good job. Thank you very much!

Very useful, well explained. Many thanks.

Hello, I appreciate the way you provided a supportive comments about qualitative results presenting tips

I loved this! It explains everything needed, and it has helped me better organize my thoughts. What words should I not use while writing my results section, other than subjective ones.

Thanks a lot, it is really helpful

Thank you so much dear, i really appropriate your nice explanations about this.

Thank you so much for this! I was wondering if anyone could help with how to prproperly integrate quotations (Excerpts) from interviews in the finding chapter in a qualitative research. Please GradCoach, address this issue and provide examples.

what if I’m not doing any interviews myself and all the information is coming from case studies that have already done the research.

Very helpful thank you.

This was very helpful as I was wondering how to structure this part of my dissertation, to include the quotes… Thanks for this explanation

This is very helpful, thanks! I am required to write up my results chapters with the discussion in each of them – any tips and tricks for this strategy?

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Home » Research Report – Example, Writing Guide and Types

Research Report – Example, Writing Guide and Types

Table of Contents

Research Report

Definition:

Research Report is a written document that presents the results of a research project or study, including the research question, methodology, results, and conclusions, in a clear and objective manner.

The purpose of a research report is to communicate the findings of the research to the intended audience, which could be other researchers, stakeholders, or the general public.

Components of Research Report

Components of Research Report are as follows:

Introduction

The introduction sets the stage for the research report and provides a brief overview of the research question or problem being investigated. It should include a clear statement of the purpose of the study and its significance or relevance to the field of research. It may also provide background information or a literature review to help contextualize the research.

Literature Review

The literature review provides a critical analysis and synthesis of the existing research and scholarship relevant to the research question or problem. It should identify the gaps, inconsistencies, and contradictions in the literature and show how the current study addresses these issues. The literature review also establishes the theoretical framework or conceptual model that guides the research.

Methodology

The methodology section describes the research design, methods, and procedures used to collect and analyze data. It should include information on the sample or participants, data collection instruments, data collection procedures, and data analysis techniques. The methodology should be clear and detailed enough to allow other researchers to replicate the study.

The results section presents the findings of the study in a clear and objective manner. It should provide a detailed description of the data and statistics used to answer the research question or test the hypothesis. Tables, graphs, and figures may be included to help visualize the data and illustrate the key findings.

The discussion section interprets the results of the study and explains their significance or relevance to the research question or problem. It should also compare the current findings with those of previous studies and identify the implications for future research or practice. The discussion should be based on the results presented in the previous section and should avoid speculation or unfounded conclusions.

The conclusion summarizes the key findings of the study and restates the main argument or thesis presented in the introduction. It should also provide a brief overview of the contributions of the study to the field of research and the implications for practice or policy.

The references section lists all the sources cited in the research report, following a specific citation style, such as APA or MLA.

The appendices section includes any additional material, such as data tables, figures, or instruments used in the study, that could not be included in the main text due to space limitations.

Types of Research Report

Types of Research Report are as follows:

Thesis is a type of research report. A thesis is a long-form research document that presents the findings and conclusions of an original research study conducted by a student as part of a graduate or postgraduate program. It is typically written by a student pursuing a higher degree, such as a Master’s or Doctoral degree, although it can also be written by researchers or scholars in other fields.

Research Paper

Research paper is a type of research report. A research paper is a document that presents the results of a research study or investigation. Research papers can be written in a variety of fields, including science, social science, humanities, and business. They typically follow a standard format that includes an introduction, literature review, methodology, results, discussion, and conclusion sections.

Technical Report

A technical report is a detailed report that provides information about a specific technical or scientific problem or project. Technical reports are often used in engineering, science, and other technical fields to document research and development work.

Progress Report

A progress report provides an update on the progress of a research project or program over a specific period of time. Progress reports are typically used to communicate the status of a project to stakeholders, funders, or project managers.

Feasibility Report

A feasibility report assesses the feasibility of a proposed project or plan, providing an analysis of the potential risks, benefits, and costs associated with the project. Feasibility reports are often used in business, engineering, and other fields to determine the viability of a project before it is undertaken.

Field Report

A field report documents observations and findings from fieldwork, which is research conducted in the natural environment or setting. Field reports are often used in anthropology, ecology, and other social and natural sciences.

Experimental Report

An experimental report documents the results of a scientific experiment, including the hypothesis, methods, results, and conclusions. Experimental reports are often used in biology, chemistry, and other sciences to communicate the results of laboratory experiments.

Case Study Report

A case study report provides an in-depth analysis of a specific case or situation, often used in psychology, social work, and other fields to document and understand complex cases or phenomena.

Literature Review Report

A literature review report synthesizes and summarizes existing research on a specific topic, providing an overview of the current state of knowledge on the subject. Literature review reports are often used in social sciences, education, and other fields to identify gaps in the literature and guide future research.

Research Report Example

Following is a Research Report Example sample for Students:

Title: The Impact of Social Media on Academic Performance among High School Students

This study aims to investigate the relationship between social media use and academic performance among high school students. The study utilized a quantitative research design, which involved a survey questionnaire administered to a sample of 200 high school students. The findings indicate that there is a negative correlation between social media use and academic performance, suggesting that excessive social media use can lead to poor academic performance among high school students. The results of this study have important implications for educators, parents, and policymakers, as they highlight the need for strategies that can help students balance their social media use and academic responsibilities.

Introduction:

Social media has become an integral part of the lives of high school students. With the widespread use of social media platforms such as Facebook, Twitter, Instagram, and Snapchat, students can connect with friends, share photos and videos, and engage in discussions on a range of topics. While social media offers many benefits, concerns have been raised about its impact on academic performance. Many studies have found a negative correlation between social media use and academic performance among high school students (Kirschner & Karpinski, 2010; Paul, Baker, & Cochran, 2012).

Given the growing importance of social media in the lives of high school students, it is important to investigate its impact on academic performance. This study aims to address this gap by examining the relationship between social media use and academic performance among high school students.

Methodology:

The study utilized a quantitative research design, which involved a survey questionnaire administered to a sample of 200 high school students. The questionnaire was developed based on previous studies and was designed to measure the frequency and duration of social media use, as well as academic performance.

The participants were selected using a convenience sampling technique, and the survey questionnaire was distributed in the classroom during regular school hours. The data collected were analyzed using descriptive statistics and correlation analysis.

The findings indicate that the majority of high school students use social media platforms on a daily basis, with Facebook being the most popular platform. The results also show a negative correlation between social media use and academic performance, suggesting that excessive social media use can lead to poor academic performance among high school students.

Discussion:

The results of this study have important implications for educators, parents, and policymakers. The negative correlation between social media use and academic performance suggests that strategies should be put in place to help students balance their social media use and academic responsibilities. For example, educators could incorporate social media into their teaching strategies to engage students and enhance learning. Parents could limit their children’s social media use and encourage them to prioritize their academic responsibilities. Policymakers could develop guidelines and policies to regulate social media use among high school students.

Conclusion:

In conclusion, this study provides evidence of the negative impact of social media on academic performance among high school students. The findings highlight the need for strategies that can help students balance their social media use and academic responsibilities. Further research is needed to explore the specific mechanisms by which social media use affects academic performance and to develop effective strategies for addressing this issue.

Limitations:

One limitation of this study is the use of convenience sampling, which limits the generalizability of the findings to other populations. Future studies should use random sampling techniques to increase the representativeness of the sample. Another limitation is the use of self-reported measures, which may be subject to social desirability bias. Future studies could use objective measures of social media use and academic performance, such as tracking software and school records.

Implications:

The findings of this study have important implications for educators, parents, and policymakers. Educators could incorporate social media into their teaching strategies to engage students and enhance learning. For example, teachers could use social media platforms to share relevant educational resources and facilitate online discussions. Parents could limit their children’s social media use and encourage them to prioritize their academic responsibilities. They could also engage in open communication with their children to understand their social media use and its impact on their academic performance. Policymakers could develop guidelines and policies to regulate social media use among high school students. For example, schools could implement social media policies that restrict access during class time and encourage responsible use.

References:

• Kirschner, P. A., & Karpinski, A. C. (2010). Facebook® and academic performance. Computers in Human Behavior, 26(6), 1237-1245.
• Paul, J. A., Baker, H. M., & Cochran, J. D. (2012). Effect of online social networking on student academic performance. Journal of the Research Center for Educational Technology, 8(1), 1-19.
• Pantic, I. (2014). Online social networking and mental health. Cyberpsychology, Behavior, and Social Networking, 17(10), 652-657.
• Rosen, L. D., Carrier, L. M., & Cheever, N. A. (2013). Facebook and texting made me do it: Media-induced task-switching while studying. Computers in Human Behavior, 29(3), 948-958.

Note*: Above mention, Example is just a sample for the students’ guide. Do not directly copy and paste as your College or University assignment. Kindly do some research and Write your own.

Applications of Research Report

Research reports have many applications, including:

• Communicating research findings: The primary application of a research report is to communicate the results of a study to other researchers, stakeholders, or the general public. The report serves as a way to share new knowledge, insights, and discoveries with others in the field.
• Informing policy and practice : Research reports can inform policy and practice by providing evidence-based recommendations for decision-makers. For example, a research report on the effectiveness of a new drug could inform regulatory agencies in their decision-making process.
• Supporting further research: Research reports can provide a foundation for further research in a particular area. Other researchers may use the findings and methodology of a report to develop new research questions or to build on existing research.
• Evaluating programs and interventions : Research reports can be used to evaluate the effectiveness of programs and interventions in achieving their intended outcomes. For example, a research report on a new educational program could provide evidence of its impact on student performance.
• Demonstrating impact : Research reports can be used to demonstrate the impact of research funding or to evaluate the success of research projects. By presenting the findings and outcomes of a study, research reports can show the value of research to funders and stakeholders.
• Enhancing professional development : Research reports can be used to enhance professional development by providing a source of information and learning for researchers and practitioners in a particular field. For example, a research report on a new teaching methodology could provide insights and ideas for educators to incorporate into their own practice.

How to write Research Report

Here are some steps you can follow to write a research report:

• Identify the research question: The first step in writing a research report is to identify your research question. This will help you focus your research and organize your findings.
• Conduct research : Once you have identified your research question, you will need to conduct research to gather relevant data and information. This can involve conducting experiments, reviewing literature, or analyzing data.
• Organize your findings: Once you have gathered all of your data, you will need to organize your findings in a way that is clear and understandable. This can involve creating tables, graphs, or charts to illustrate your results.
• Write the report: Once you have organized your findings, you can begin writing the report. Start with an introduction that provides background information and explains the purpose of your research. Next, provide a detailed description of your research methods and findings. Finally, summarize your results and draw conclusions based on your findings.
• Proofread and edit: After you have written your report, be sure to proofread and edit it carefully. Check for grammar and spelling errors, and make sure that your report is well-organized and easy to read.
• Include a reference list: Be sure to include a list of references that you used in your research. This will give credit to your sources and allow readers to further explore the topic if they choose.
• Format your report: Finally, format your report according to the guidelines provided by your instructor or organization. This may include formatting requirements for headings, margins, fonts, and spacing.

Purpose of Research Report

The purpose of a research report is to communicate the results of a research study to a specific audience, such as peers in the same field, stakeholders, or the general public. The report provides a detailed description of the research methods, findings, and conclusions.

Some common purposes of a research report include:

• Sharing knowledge: A research report allows researchers to share their findings and knowledge with others in their field. This helps to advance the field and improve the understanding of a particular topic.
• Identifying trends: A research report can identify trends and patterns in data, which can help guide future research and inform decision-making.
• Addressing problems: A research report can provide insights into problems or issues and suggest solutions or recommendations for addressing them.
• Evaluating programs or interventions : A research report can evaluate the effectiveness of programs or interventions, which can inform decision-making about whether to continue, modify, or discontinue them.
• Meeting regulatory requirements: In some fields, research reports are required to meet regulatory requirements, such as in the case of drug trials or environmental impact studies.

When to Write Research Report

A research report should be written after completing the research study. This includes collecting data, analyzing the results, and drawing conclusions based on the findings. Once the research is complete, the report should be written in a timely manner while the information is still fresh in the researcher’s mind.

In academic settings, research reports are often required as part of coursework or as part of a thesis or dissertation. In this case, the report should be written according to the guidelines provided by the instructor or institution.

In other settings, such as in industry or government, research reports may be required to inform decision-making or to comply with regulatory requirements. In these cases, the report should be written as soon as possible after the research is completed in order to inform decision-making in a timely manner.

Overall, the timing of when to write a research report depends on the purpose of the research, the expectations of the audience, and any regulatory requirements that need to be met. However, it is important to complete the report in a timely manner while the information is still fresh in the researcher’s mind.

Characteristics of Research Report

There are several characteristics of a research report that distinguish it from other types of writing. These characteristics include:

• Objective: A research report should be written in an objective and unbiased manner. It should present the facts and findings of the research study without any personal opinions or biases.
• Systematic: A research report should be written in a systematic manner. It should follow a clear and logical structure, and the information should be presented in a way that is easy to understand and follow.
• Detailed: A research report should be detailed and comprehensive. It should provide a thorough description of the research methods, results, and conclusions.
• Accurate : A research report should be accurate and based on sound research methods. The findings and conclusions should be supported by data and evidence.
• Organized: A research report should be well-organized. It should include headings and subheadings to help the reader navigate the report and understand the main points.
• Clear and concise: A research report should be written in clear and concise language. The information should be presented in a way that is easy to understand, and unnecessary jargon should be avoided.
• Citations and references: A research report should include citations and references to support the findings and conclusions. This helps to give credit to other researchers and to provide readers with the opportunity to further explore the topic.

Advantages of Research Report

Research reports have several advantages, including:

• Communicating research findings: Research reports allow researchers to communicate their findings to a wider audience, including other researchers, stakeholders, and the general public. This helps to disseminate knowledge and advance the understanding of a particular topic.
• Providing evidence for decision-making : Research reports can provide evidence to inform decision-making, such as in the case of policy-making, program planning, or product development. The findings and conclusions can help guide decisions and improve outcomes.
• Supporting further research: Research reports can provide a foundation for further research on a particular topic. Other researchers can build on the findings and conclusions of the report, which can lead to further discoveries and advancements in the field.
• Demonstrating expertise: Research reports can demonstrate the expertise of the researchers and their ability to conduct rigorous and high-quality research. This can be important for securing funding, promotions, and other professional opportunities.
• Meeting regulatory requirements: In some fields, research reports are required to meet regulatory requirements, such as in the case of drug trials or environmental impact studies. Producing a high-quality research report can help ensure compliance with these requirements.

Limitations of Research Report

Despite their advantages, research reports also have some limitations, including:

• Time-consuming: Conducting research and writing a report can be a time-consuming process, particularly for large-scale studies. This can limit the frequency and speed of producing research reports.
• Expensive: Conducting research and producing a report can be expensive, particularly for studies that require specialized equipment, personnel, or data. This can limit the scope and feasibility of some research studies.
• Limited generalizability: Research studies often focus on a specific population or context, which can limit the generalizability of the findings to other populations or contexts.
• Potential bias : Researchers may have biases or conflicts of interest that can influence the findings and conclusions of the research study. Additionally, participants may also have biases or may not be representative of the larger population, which can limit the validity and reliability of the findings.
• Accessibility: Research reports may be written in technical or academic language, which can limit their accessibility to a wider audience. Additionally, some research may be behind paywalls or require specialized access, which can limit the ability of others to read and use the findings.

About the author

Muhammad Hassan

Researcher, Academic Writer, Web developer

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Interpretation and display of research results

Dilip kumar kulkarni.

Department of Anaesthesiology and Intensive Care, Nizam's Institute of Medical Sciences, Hyderabad, Telangana, India

It important to properly collect, code, clean and edit the data before interpreting and displaying the research results. Computers play a major role in different phases of research starting from conceptual, design and planning, data collection, data analysis and research publication phases. The main objective of data display is to summarize the characteristics of a data and to make the data more comprehensible and meaningful. Usually data is presented depending upon the type of data in different tables and graphs. This will enable not only to understand the data behaviour, but also useful in choosing the different statistical tests to be applied.

INTRODUCTION

Collection of data and display of results is very important in any study. The data of an experimental study, observational study or a survey are required to be collected in properly designed format for documentation, taking into consideration the design of study and different end points of the study. Usually data are collected in the proforma of the study. The data recorded and documented should be stored carefully in documents and in electronic form for example, excel sheets or data bases.

The data are usually classified into qualitative and quantitative [ Table 1 ]. Qualitative data is further divided into two categories, unordered qualitative data, such as blood groups (A, B, O, AB); and ordered qualitative data, such as severity of pain (mild, moderate, severe). Quantitative data are numerical and fall into two categories: discrete quantitative data, such as the internal diameter of endotracheal tube; and continuous quantitative data, such as blood pressure.[ 1 ]

Examples of types of data and display of data

Data Coding is needed to allow the data recorded in categories to be used easily in statistical analysis with a computer. Coding assigns a unique number to each possible response. A few statistical packages analyse categorical data directly. If a number is assigned to categorical data, it becomes easier to analyse. This means that when the data are analysed and reported, the appropriate label needs to be assigned back to the numerical value to make it meaningful. The codes such as 1/0 for yes/no has the added advantage that the variable's 1/0 values can be easily analysed. The record of the codes modified is to be stored for later reference. Such coding can also be done for categorical ordinal data to convert in to numerical ordinal data, for example the severity of pain mild, moderate and severe into 1, 2 and 3 respectively.

PROCESS OF DATA CHECKING, CLEANING AND EDITING

In clinical research, errors occur despite designing the study properly, entering data carefully and preventing errors. Data cleaning and editing are carried out to identify and correct these errors, so that the study results will be accurate.[ 2 ]

Data entry errors in case of sex, dates, double entries and unexpected results are to be corrected unquestionably. Data editing can be done in three phases namely screening, diagnosing and editing [ Figure 1 ].

Process of data checking, cleaning and editing in three phases

Screening phase

During screening of data, it is possible to distinguish the odd data, excess of data, double entries, outliers, and unexpected results. Screening methods are checking of questionnaires, data validation, browsing the excel sheets, data tables and graphical methods to observe data distribution.

Diagnostic phase

The nature of the data can be assessed in this phase. The data entries can be true normal, true errors, outliers, unexpected results.

Treatment phase

Once the data nature is identified the editing can be done by correcting, deleting or leaving the data sets unchanged.

The abnormal data points usually have to be corrected or to be deleted.[ 2 ] However some authors advocate these data points to be included in analysis.[ 3 ] If these extreme data points are deleted, they should be reported as “excluded from analysis”.[ 4 ]

ROLE OF COMPUTERS IN RESEARCH

The role of computers in scientific research is very high; the computers have the ability to perform the analytic tasks with high speed, accuracy and consistency. The Computers role in research process can be explained in different phases.[ 5 ]

Role of computer in conceptual phase

The conceptual phase consists of formulation of research problem, literature survey, theoretical frame work and developing the hypothesis. Computers are useful in searching the literatures. The references can be stored in the electronic database.

Role of computers in design and planning phase

This phase consists of research design preparation and determining sample design, population size, research variables, sampling plan, reviewing research plan and pilot study. The role of computers in these process is almost indispensable.

Role of computers in data collection phase

The data obtained from the subjects stored in computers are word files or excel spread sheets or statistical software data files or from data centers of hospital information management systems (data warehouse). If the data are stored in electronic format checking the data becomes easier. Thus, computers help in data entry, data editing, and data management including follow up actions. Examples of editors are Word Pad, SPSS data editor, word processors.

Role of computers in data analysis

This phase mainly consist of statistical analysis of the data and interpretation of results. Software like Minitab (Minitab Inc. USA.), SPSS (IBM Crop. New York), NCSS (LLC. Kaysville, Utah, USA) and spreadsheets are widely used.

Role of computer in research publication

Research article, research paper, research thesis or research dissertation is typed in word processing software in computers and stored. Which can be easily published in different electronic formats.[ 5 ]

DATA DISPLAY AND DESCRIPTION OF RESEARCH DATA

Data display and description is an important part of any research project which helps in knowing the distribution of data, detecting errors, missing values and outliers. Ultimately the data should be more comprehensible and meaningful.

Tables are commonly used for describing both qualitative and quantitative data. The graphs are useful for visualising the data and understanding the variations and trends of the data. Qualitative data are usually described by using bar or pie charts. Histograms, polygons or box plots are used to represent quantitative data.[ 1 ]

Qualitative data

Tabulation of qualitative data.

The qualitative observations are categorised in to different categories. The category frequency is nothing but the number of observations with in that category. The category relative frequency can be calculated by dividing the number of observations in the category by total number of observations. The Percentage for a category is more commonly used to describe qualitative data. It can be computed by multiplying relative frequency with hundred.[ 6 , 7 ]

The classification of 30 Patients of a group by severity of postoperative pain presented in Table 2 . The frequency table for this data computed by using the software NCSS[ 8 ] is shown in Table 3 .

The classification of post-operative pain in patients

The frequency table for the variable pain

Graphical display of qualitative data

The qualitative data are commonly displayed by bar graphs and pie charts.[ 9 ]

Bar graphs displays information of the frequency, relative frequency or percentage of each category on vertical axis or horizontal axis of the graph. [ Figure 2 ] Pie charts depicts the same information in divided slices in a complete circle. The area for the circle is equal to the frequency, relative frequency or percentage of that category [ Figure 3 ].

The bar graph generated by computer using NCSS software for the variable pain

The Pie graph generated by computer using NCSS software for the variable pain

Quantitative data

Tabulation of quantitative data.

The quantitative data are usually presented as frequency distribution or relative frequency rather than percentage. The data are divided into different classes. The upper and lower limits or the width of classes will depend up on the size of the data and can easily be adjusted.

The frequency distribution and relative frequency distribution table can be constructed in the following manner:

• The quantitative data are divided into number of classes. The lower limit and upper limit of the classes have to be defined.
• The range or width of the class intervals can be calculated by dividing the difference in the upper limit and lower limit by total number of classes.
• The class frequency is the number of observations that fall in that class.
• The relative class frequency can be calculated by dividing class frequency by total number of observations.

Example of frequency table for the data of Systolic blood pressure of 60 patients undergoing craniotomy is shown in Table 4 . The number of classes were 20, the lower limit and the upper limit were 86 mm of Hg and 186 mm of Hg respectively.

Frequency tabulation of systolic blood pressure in sixty patients (unit is mm Hg)

Graphical description of quantitative data

The frequency distribution is usually depicted in histograms. The count or frequency is plotted along the vertical axis and the horizontal axis represents data values. The normality of distribution can be assessed visually by histograms. A frequency histogram is constructed for the dataset of systolic blood pressure, from the frequency Table 4 [ Figure 4 ].

The frequency histogram for the data set of systolic blood pressure (BP), for which the frequency table is constructed in Table 4

Box plot gives the information of spread of observations in a single group around a centre value. The distribution pattern and extreme values can be easily viewed by box plot. A boxplot is constructed for the dataset of systolic blood pressure, from the frequency Table 4 [ Figure 5 ].

Box plot is constructed from data of Table 4

Polygon construction is similar to histogram. However it is a line graph connecting the data points at mid points of class intervals. The polygon is simpler and outline the data pattern clearly[ 8 ] [ Figure 6 ].

A frequency polygon constructed from data of Table 4 in NCSS software

It is often necessary to further summarise quantitative data, for example, for hypothesis testing. The most important elements of a data are its location, which is measured by mean, median and mode. The other parameters are variability (range, interquartile range, standard deviation and variance) and shape of the distribution (normal, skewness, and kurtosis). The details of which will be discussed in the next chapter.

The proper designing of research methodology is an important step from the conceptual phase to the conclusion phase and the computers play an invaluable role from the beginning to the end of a study. The data collection, data storage and data management are vital for any study. The data display and interpretation will help in understating the behaviour of the data and also to know the assumptions for statistical analysis.

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A New Model for Studying Social Isolation and Health in People with Serious Mental Illnesses

Researchers have developed a promising new framework for studying the link between social disconnection and poor physical health in people living with serious mental illnesses (SMI). Drawing on published research from animal models and data from the general population, this framework builds on existing social isolation and loneliness models by integrating insights from evolutionary and cognitive theories. This research was supported by the Office of Behavioral and Social Sciences Research and the National Institute of Mental Health.

What were the researchers studying and why?

One of the most challenging aspects of living with SMI is difficulties with social perception, motivation, and social behaviors. These difficulties can lead to social withdrawal and loneliness, outcomes that can contribute to poor heart health and early death. However, researchers have an incomplete understanding of how differences in the brain functions in people living with SMIs impact the connection between their social perception and self-reported, lived experience of social withdrawal, isolation, or loneliness.

How did the researchers conduct the study?

Researchers from Boston University and Harvard Medical School conducted a selective narrative review of studies addressing social withdrawal, isolation, loneliness, and health in SMI.

Their review highlighted evidence indicating differences in brain activity between people experiencing loneliness and those who are not, particularly in regions associated with social cognitive processes. Additionally, neuroimaging studies have shown increased activation in brain areas responsible for risk assessment among lonely individuals.

Furthermore, the researchers discussed findings suggesting that individuals experiencing loneliness, who perceive others negatively and exhibit signs of psychopathology, may misinterpret social cues, leading to social disconnection. Over time, this social disconnection can prompt a defensive response to social situations, further reducing motivation for social interaction.

What did the study results show?

Based on a synthesis of recent findings that indicate a causal relationship between loneliness and nervous system responses in the human body that cause inflammation and reduce immunity, the authors developed a testable model of the psychological and neural mechanisms of social disconnection in SMI. They hypothesize that people living with SMI are more likely to experience high levels of chronic psychological stress and therefore, more likely to experience persistently high levels of physiological inflammation. Stress and inflammation biomarkers can serve as indicators of an unmet need for social connection. Health providers and caregivers could use these indicators to provide social support and connection to those experiencing this need.

What is the potential impact of these findings?

The authors suggest that once their hypothesis has been rigorously tested and verified, new methods to improve health outcomes for people living with SMI may be developed, including potential “just-in-time” digital interventions through mobile devices. The authors also suggest that people living with SMI and experiencing loneliness can receive interventions that address any potential negative beliefs they hold about rejection, thus interrupting the cycle of social isolation.

Citation: Fulford D, Holt DJ. Social Withdrawal, Loneliness, and Health in Schizophrenia: Psychological and Neural Mechanisms . Schizophr Bull. 2023 Sep 7;49(5):1138-1149. doi: 10.1093/schbul/sbad099. PMID: 37419082; PMCID: PMC10483452.

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• CAREER FEATURE
• 08 May 2024

Illuminating ‘the ugly side of science’: fresh incentives for reporting negative results

• Rachel Brazil 0

Rachel Brazil is a freelance journalist in London, UK.

You can also search for this author in PubMed   Google Scholar

The editor-in-chief of the Journal of Trial & Error , Sarahanne Field wants to publish the messy, null and negative results sitting in researchers’ file drawers. Credit: Sander Martens

Editor-in-chief Sarahanne Field describes herself and her team at the Journal of Trial & Error as wanting to highlight the “ugly side of science — the parts of the process that have gone wrong”.

She clarifies that the editorial board of the journal, which launched in 2020 , isn’t interested in papers in which “you did a shitty study and you found nothing. We’re interested in stuff that was done methodologically soundly, but still yielded a result that was unexpected.” These types of result — which do not prove a hypothesis or could yield unexplained outcomes — often simply go unpublished, explains Field, who is also an open-science researcher at the University of Groningen in the Netherlands. Along with Stefan Gaillard, one of the journal’s founders, she hopes to change that.

Calls for researchers to publish failed studies are not new. The ‘file-drawer problem’ — the stacks of unpublished, negative results that most researchers accumulate — was first described in 1979 by psychologist Robert Rosenthal . He argued that this leads to publication bias in the scientific record: the gap of missing unsuccessful results leads to overemphasis on the positive results that do get published.

Careers Collection: Publishing

Over the past 30 years, the proportion of negative results being published has decreased further. A 2012 study showed that, from 1990 to 2007, there was a 22% increase in positive conclusions in papers; by 2007, 85% of papers published had positive results 1 . “People fail to report [negative] results, because they know they won’t get published — and when people do attempt to publish them, they get rejected,” says Field. A 2022 survey of researchers in France in chemistry, physics, engineering and environmental sciences showed that, although 81% had produced relevant negative results and 75% were willing to publish them, only 12.5% had the opportunity to do so 2 .

One factor that is leading some researchers to revisit the problem is the growing use of predictive modelling using machine-learning tools in many fields. These tools are trained on large data sets that are often derived from published work, and scientists have found that the absence of negative data in the literature is hampering the process. Without a concerted effort to publish more negative results that artificial intelligence (AI) can be trained on, the promise of the technology could be stifled.

“Machine learning is changing how we think about data,” says chemist Keisuke Takahashi at Hokkaido University in Japan, who has brought the issue to the attention of the catalysis-research community . Scientists in the field have typically relied on a mixture of trial and error and serendipity in their experiments, but there is hope that AI could provide a new route for catalyst discovery. Takahashi and his colleagues mined data from 1,866 previous studies and patents to train a machine-learning model to predict the best catalyst for the reaction between methane and oxygen to form ethane and ethylene, both of which are important chemicals used in industry 3 . But, he says, “over the years, people have only collected the good data — if they fail, they don’t report it”. This led to a skewed model that, in some cases, enhanced the predicted performance of a material, rather than realistically assessing its properties.

Synthetic organic chemist Felix Strieth-Kalthoff found that published data were too heavily biased toward positive results to effectively train an AI model to optimize chemical reaction yields. Credit: Cindy Huang

Alongside the flawed training of AI models, the huge gap of negative results in the scientific record continues to be a problem across all disciplines. In areas such as psychology and medicine, publication bias is one factor exacerbating the ongoing reproducibility crisis — in which many published studies are impossible to replicate. Without sharing negative studies and data, researchers could be doomed to repeat work that led nowhere. Many scientists are calling for changes in academic culture and practice — be it the creation of repositories that include positive and negative data, new publication formats or conferences aimed at discussing failure. The solutions are varied, but the message is the same: “To convey an accurate picture of the scientific process, then at least one of the components should be communicating all the results, [including] some negative results,” says Gaillard, “and even where you don’t end up with results, where it just goes wrong.”

Science’s messy side

Synthetic organic chemist Felix Strieth-Kalthoff, who is now setting up his own laboratory at the University of Wuppertal, Germany, has encountered positive-result bias when using data-driven approaches to optimize the yields of certain medicinal-chemistry reactions. His PhD work with chemist Frank Glorius at the University of Münster, Germany, involved creating models that could predict which reactants and conditions would maximize yields. Initially, he relied on data sets that he had generated from high-throughput experiments in the lab, which included results from both high- and low-yield reactions, to train his AI model. “Our next logical step was to do that based on the literature,” says Strieth-Kalthoff. This would allow him to curate a much larger data set to be used for training.

But when he incorporated real data from the reactions database Reaxys into the training process, he says, “[it] turned out they don’t really work at all”. Strieth-Kalthoff concluded the errors were due the lack of low-yield reactions 4 ; “All of the data that we see in the literature have average yields of 60–80%.” Without learning from the messy ‘failed’ experiments with low yields that were present in the initial real-life data, the AI could not model realistic reaction outcomes.

Although AI has the potential to spot relationships in complex data that a researcher might not see, encountering negative results can give experimentalists a gut feeling, says molecular modeller Berend Smit at the Swiss Federal Institute of Technology Lausanne. The usual failures that every chemist experiences at the bench give them a ‘chemical intuition’ that AI models trained only on successful data lack.

Smit and his team attempted to embed something similar to this human intuition into a model tasked with designing a metal-organic framework (MOF) with the largest known surface area for this type of material. A large surface area allows these porous materials to be used as reaction supports or molecular storage reservoirs. “If the binding [between components] is too strong, it becomes amorphous; if the binding is too weak, it becomes unstable, so you need to find the sweet spot,” Smit says. He showed that training the machine-learning model on both successful and unsuccessful reaction conditions created better predictions and ultimately led to one that successfully optimized the MOF 5 . “When we saw the results, we thought, ‘Wow, this is the chemical intuition we’re talking about!’” he says.

According to Strieth-Kalthoff, AI models are currently limited because “the data that are out there just do not reflect all of our knowledge”. Some researchers have sought statistical solutions to fill the negative-data gap. Techniques include oversampling, which means supplementing data with several copies of existing negative data or creating artificial data points, for example by including reactions with a yield of zero. But, he says, these types of approach can introduce their own biases.

Computer scientist Ella Peltonen helped to organize the first International Workshop on Negative Results in Pervasive Computing in 2022 to give researchers an opportunity to discuss failed experiments. Credit: University of Oulu

Capturing more negative data is now a priority for Takahashi. “We definitely need some sort of infrastructure to share the data freely.” His group has created a website for sharing large amounts of experimental data for catalysis reactions . Other organizations are trying to collect and publish negative data — but Takahashi says that, so far, they lack coordination, so data formats aren’t standardized. In his field, Strieth-Kalthoff says, there are initiatives such as the Open Reaction Database , launched in 2021 to share organic-reaction data and enable training of machine-learning applications. But, he says, “right now, nobody’s using it, [because] there’s no incentive”.

Smit has argued for a modular open-science platform that would directly link to electronic lab notebooks to help to make different data types extractable and reusable . Through this process, publication of negative data in peer-reviewed journals could be skipped, but the information would still be available for researchers to use in AI training. Strieth-Kalthoff agrees with this strategy in theory, but thinks it’s a long way off in practice, because it would require analytical instruments to be coupled to a third-party source to automatically collect data — which instrument manufacturers might not agree to, he says.

Publishing the non-positive

In other disciplines, the emphasis is still on peer-reviewed journals that will publish negative results. Gaillard, a science-studies PhD student at Radboud University in Nijmegen, the Netherlands, co-founded the Journal of Trial & Error after attending talks on how science can be made more open. Gaillard says that, although everyone whom they approached liked the idea of the journal, nobody wanted to submit articles at first. He and the founding editorial team embarked on a campaign involving cold calls and publicity at open-science conferences. “Slowly, we started getting our first submissions, and now we just get people sending things in [unsolicited],” he says. Most years the journal publishes one issue of about 8–14 articles, and it is starting to publish more special issues. It focuses mainly on the life sciences and data-based social sciences.

In 2008, David Alcantara, then a chemistry PhD student at the University of Seville in Spain who was frustrated by the lack of platforms for sharing negative results, set up The All Results journals, which were aimed at disseminating results regardless of the outcome . Of the four disciplines included at launch, only the biology journal is still being published. “Attracting submissions has always posed a challenge,” says Alcantara, now president at the consultancy and training organization the Society for the Improvement of Science in Seville.

But Alcantara thinks there has been a shift in attitudes: “More established journals [are] becoming increasingly open to considering negative results for publication.” Gaillard agrees: “I’ve seen more and more journals, like PLoS ONE , for example, that explicitly mentioned that they also publish negative results.” ( Nature welcomes submissions of replication studies and those that include null results, as described in this 2020 editorial .)

Journals might be changing their publication preferences, but there are still significant disincentives that stop researchers from publishing their file-drawer studies. “The current academic system often prioritizes high-impact publications and ground-breaking discoveries for career advancement, grants and tenure,” says Alcantara, noting that negative results are perceived as contributing little to nothing to these endeavours. Plus, there is still a stigma associated with any kind of failure . “People are afraid that this will look negative on their CV,” says Gaillard. Smit describes reporting failed experiments as a no-win situation: “It’s more work for [researchers], and they don’t get anything in return in the short term.” And, jokes Smit, what’s worse is that they could be providing data for an AI tool to take over their role.

Ultimately, most researchers conclude that publishing their failed studies and negative data is just not worth the time and effort — and there’s evidence that they judge others’ negative research more harshly than positive outcomes. In a study published in August, 500 researchers from top economics departments around the world were randomized to two groups and asked to judge a hypothetical research paper. Half of the participants were told that the study had a null conclusion, and the other half were told the results were sizeably significant. The null results were perceived to be 25% less likely to be published, of lower quality and less important than were the statistically significant findings 6 .

Some researchers have had positive experiences sharing their unsuccessful findings. For example, in 2021, psychologist Wendy Ross at the London Metropolitan University published her negative results from testing a hypothesis about human problem-solving in the Journal of Trial & Error 7 , and says the paper was “the best one I have published to date”. She adds, “Understanding the reasons for null results can really test and expand our theoretical understanding.”

Fields forging solutions

The field of psychology has introduced one innovation that could change publication biases — registered reports (RRs). These peer-reviewed reports , first published in 2014, came about largely as a response to psychology’s replication crisis, which began in around 2011. RRs set out the methodology of a study before the results are known, to try to prevent selective reporting of positive results. Daniël Lakens, who studies science-reward structures at Eindhoven University of Technology in the Netherlands, says there is evidence that RRs increase the proportion of negative results in the psychology literature.

In a 2021 study, Lakens analysed the proportion of published RRs whose results eventually support the primary hypothesis. In a random sample of hypothesis-testing studies from the standard psychology literature, 96% of the results were positive. In RRs, this fell to only 44% 8 . Lakens says the study shows “that if you offer this as an option, many more null results enter the scientific literature, and that is a desirable thing”. At least 300 journals, including Nature , are now accepting RRs, and the format is spreading to journals in biology, medicine and some social-science fields.

Yet another approach has emerged from the field of pervasive computing, the study of how computer systems are integrated into physical surroundings and everyday life. About four years ago, members of the community started discussing reproducibility, says computer scientist Ella Peltonen at the University of Oulu in Finland. Peltonen says that researchers realized that, to avoid the repetition of mistakes, there was a need to discuss the practical problems with studies and failed results that don’t get published. So in 2022, Peltonen and her colleagues held the first virtual International Workshop on Negative Results in Pervasive Computing (PerFail) , in conjunction with the field’s annual conference, the International Conference on Pervasive Computing and Communications.

Peltonen explains that PerFail speakers first present their negative results and then have the same amount of time for discussion afterwards, during which participants tease out how failed studies can inform future work. “It also encourages the community to showcase that things require effort and trial and error, and there is value in that,” she adds. Now an annual event, the organizers invite students to attend so they can see that failure is a part of research and that “you are not a bad researcher because you fail”, says Peltonen.

In the long run, Alcantara thinks a continued effort to persuade scientists to share all their results needs to be coupled with policies at funding agencies and journals that reward full transparency. “Criteria for grants, promotions and tenure should recognize the value of comprehensive research dissemination, including failures and negative outcomes,” he says. Lakens thinks funders could be key to boosting the RR format, as well. Funders, he adds, should say, “We want the research that we’re funding to appear in the scientific literature, regardless of the significance of the finding.”

There are some positive signs of change about sharing negative data: “Early-career researchers and the next generation of scientists are particularly receptive to the idea,” says Alcantara. Gaillard is also optimistic, given the increased interest in his journal, including submissions for an upcoming special issue on mistakes in the medical domain. “It is slow, of course, but science is a bit slow.”

doi: https://doi.org/10.1038/d41586-024-01389-7

Fanelli, D. Scientometrics 90 , 891–904 (2012).

Article   Google Scholar  

Herbet, M.-E., Leonard, J., Santangelo, M. G. & Albaret, L. Learned Publishing 35 , 16–29 (2022).

Fujima, J., Tanaka, Y., Miyazato, I., Takahashi, L. & Takahashi, K. Reaction Chem. Eng. 5 , 903–911 (2020).

Strieth-Kalthoff, F. et al. Angew. Chem. Int. Edn 61 , e202204647 (2022).

Moosavi, S. M. et al. Nature Commun. 10 , 539 (2019).

Article   PubMed   Google Scholar  

Chopra, F., Haaland, I., Roth, C. & Stegmann, A. Econ. J. 134 , 193–219 (2024).

Ross, W. & Vallée-Tourangeau, F. J. Trial Error https://doi.org/10.36850/e4 (2021).

Scheel, A. M., Schijen, M. R. M. J. & Lakens, D. Adv. Methods Pract. Psychol. Sci . https://doi.org/10.1177/25152459211007467 (2021).

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Exploring the Relationship Between Early Life Exposures and the Comorbidity of Obesity and Hypertension: Findings from the 1970 The British Cohort Study (BCS70)

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Abstract Background: Epidemiological research commonly investigates single exposure-outcome relationships, while childrens experiences across a variety of early lifecourse domains are intersecting. To design realistic interventions, epidemiological research should incorporate information from multiple risk exposure domains to assess effect on health outcomes. In this paper we identify exposures across five pre-hypothesised childhood domains and explored their association to the odds of combined obesity and hypertension in adulthood. Methods: We used data from 17,196 participants in the 1970 British Cohort Study. The outcome was obesity (BMI of over 30) and hypertension (blood pressure>140/90mm Hg or self-reported doctors diagnosis) comorbidity at age 46. Early life domains included: prenatal, antenatal, neonatal and birth, developmental attributes and behaviour, child education and academic ability, socioeconomic factors and parental and family environment. Stepwise backward elimination selected variables for inclusion for each domain. Predicted risk scores of combined obesity and hypertension for each cohort member within each domain were calculated. Logistic regression investigated the association between domain-specific risk scores and odds of obesity-hypertension, controlling for demographic factors and other domains. Results: Adjusting for demographic confounders, all domains were associated with odds of obesity-hypertension. Including all domains in the same model, higher predicted risk values across the five domains remained associated with increased odds of obesity-hypertension comorbidity, with the strongest associations to the parental and family environment domain (OR1.11 95%CI 1.05-1.18) and the socioeconomic factors domain (OR1.11 95%CI 1.05-1.17). Conclusions: Targeted prevention interventions aimed at population groups with shared early-life characteristics could have an impact on obesity-hypertension prevalence which are known risk factors for further morbidity including cardiovascular disease.

Competing Interest Statement

R.O. is a member of the National Institute for Health and Care Excellence (NICE) Technology Appraisal Committee, member of the NICE Decision Support Unit (DSU), and associate member of the NICE Technical Support Unit (TSU). She has served as a paid consultant to the pharmaceutical industry and international reimbursement agencies, providing unrelated methodological advice. She reports teaching fees from the Association of British Pharmaceutical Industry (ABPI). R.H. is a member of the Scientific Board of the Smith Institute for Industrial Mathematics and System Engineering.

Funding Statement

This work is part of the multidisciplinary ecosystem to study lifecourse determinants and prevention of early-onset burdensome multimorbidity (MELD-B) project which is supported by the National Institute for Health Research (NIHR203988). The views expressed are those of the authors and not necessarily those of the NIHR or the Department of Health and Social Care.

Author Declarations

I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained.

The details of the IRB/oversight body that provided approval or exemption for the research described are given below:

Ethics approval for this work has been obtained from the University of Southampton Faculty of Medicine Ethics committee (ERGO II Reference 66810).

I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals.

I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance).

I have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable.

Data Availability

The BCS70 datasets generated and analysed in the current study are available from the UK Data Archive repository (available here: http://www.cls.ioe.ac.uk/page.aspx?&sitesectionid=795).

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The role of environmental tax on the environmental quality in EU counties: evidence from panel vector autoregression approach

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• Buket Savranlar 1 ,
• Seyyid Ali Ertas 2 &
• Alper Aslan   ORCID: orcid.org/0000-0003-1408-0921 3  

This study intends to analyze the influence of environmental taxes on pollution in EU-27 nations. Furthermore, energy from renewable sources consumption and urbanization are employed to clarify CO 2 emissions in this study that tests the EKC hypothesis. According to the findings, an increase in environmental taxes reduces CO 2 emissions by 0.14%. Also, the data supported the validity of the EKC concept. The findings of the causality test demonstrated that there is a bidirectional causal link between CO 2 emissions and environmental taxes. These results reflect that environmental tax revenues contribute to sustainability as an effective policy tool in EU countries. Policies regarding environmental tax enforcement come to the fore in terms of both keeping the balance in economic activities and serving sustainability.

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Introduction

Environmental issues such as global warming with air pollution, global climate change, and biodiversity reduction are within the main scope and impact of the economy. The quality of the air and the protection of the environment, which are environmental elements, are an important public commodity (Theeuwes 1991 :67–68; He et al. 2018 :7456). Reducing air pollution and therefore improving the quality of air can be treated as a national public commodity with no competition in its consumption and inability to exclude it. Because of this dimension of publicity, countries have various responsibilities in solving environmental problems and improving environmental quality. Accordingly, countries develop policies to ensure and sustain the environment, while also having a regulatory function through regulations, especially in the elimination of environmental negative externalities. All around the world, air pollution has been linked to health issues for people (Lelieveld et al. 2015 ; Cohen et al. 2017 ; Heft-Neal et al. 2018 ). PM 2:5 , which might permeate profoundly entering the bloodstream and respiratory system, leading to illnesses (Lelieveld c., 2015 ; Li et al. 2019 ), is the principal source of air pollution. Climate change is the most serious environmental issue that humanity has ever faced Change I P O C ( 2001 ). The earth’s surface temperature has been rising over the past 30 years. The dangers of major detrimental consequences on human life, property, the economy, and the environment have considerably grown as global warming rates and amplitudes continue to rise.

The major component of greenhouse gas emissions is CO2. CO2 emissions from economic activities, particularly conventional patterns of energy use based mostly on non-renewable, have become the principal human driver driving global warming from a global viewpoint (Meinshausen et al. 2009 ; Sari and Soytas 2009 ). When compared to 2005, worldwide CO2 emissions increased by around 5 109 tons in 2015. CO2 emissions in wealthy nations decreased by 1:1 109 tons, whereas those in developing nations climbed by 6:1 109 tons, essentially canceling out the developed countries’ emission reduction efforts (BP 2016 ). Developing nations emit enormous amounts of CO2 because of their aggressive promotion of industrialization and urbanization. Developing countries would endure more severe environmental damage and climate change repercussions than developed countries. More than 170 nations agreed to the Paris Agreement in 2016, pledging to work toward a long-term objective of keeping global warming to 2 degrees Celsius or less. It is critical to examine variations in global Carbon emissions to achieve this aim. Environmental Two factors determine the quality of the air: first, the direct output of human production activities, which are the primary causes of pollution and include economic expansion (Li et al. 2019 ), industrialization development (Gan et al. 2018 ), and energy use (Khan et al. 2019 ). In addition to this, excessive emissions and inadequate environmental spending will result in a decline in air quality.

At this point, the effects of regulating environmental stewardship are investigated using a variety of methodologies (Laplante and Ristone 1996 ) emphasizing three distinct perspectives: (1) Blackman and Kildegaard ( 2010 ) investigate environmental agency safety checks in Mexico and obtain that regulatory stress is unrelated to reducing pollution; According to (2) Lanoie et al. ( 2011 ), regulations will make it more expensive for companies to minimize pollution and release, drive away valuable resources, and lower efficiency and market competitiveness, making environmental issues unmanageable. By global cooperation, agreements for the solution of environmental problems have been raised, the negative effects and negative effects of environmental pollution have become a concern in the social sphere, the causes and consequences of pollution have been the focus of research in academic circles, and ways to improve the quality of the air have been discussed in the prevention and reduction of pollution.

Only a few academics have highlighted concerns regarding environmental taxes. Using the variance (DID) approach, Lin and Li ( 2011 ) studied the effects of carbon pricing on governance systems in five Northern countries. Many studies investigated the various aspects of a carbon tax. Lin and Li ( 2011 ) discovered that while the carbon price lowered CO 2 emissions substantially in Finland, the effects were significantly negative in Denmark and Sweden. The majority of academics still think that environmental levies improve environmental governance. González and Hosoda ( 2016 ) used a Bayesian structure time series model to assess the impact of an aviation oil fuel tax on national transportation consumption growth in Japan, and they discovered that the tax decreased Emissions of CO 2 by planes. In this context, agreements for solving environmental problems have been raised by global cooperation, the negative effects and negatives created by environmental pollution in the social sphere have become a concern, and in academic circles, the causes and consequences of pollution have been the subject of research and ways to prevent and reduce pollution have been discussed.

In keeping with the emissions and sustainable development targets, there has been a discernible growth in environmental pollution taxes inside the European Union (EU). The goal of taxation is to reduce carbon emissions to a manageable 5%. Energy, environment, and transportation taxes are among the taxes imposed for this purpose, particularly in Slovenia, Poland, France, Portugal, Finland, Latvia, Ireland, and Denmark. The EU is taking what are arguably the most obvious actions in the world in this regard. To lessen the detrimental externalities that third parties produce in production and consumption, the EU has imposed emission and environmental fees. Pollution, land degradation, and the greenhouse effect which raises living standards, lowers product quality, lowers income, and consumes more energy are examples of negative externalities.

More work is needed to regulate the release of toxic compounds into the atmosphere to safeguard the sustainability of ecosystem services, and the well-being of European populations, and prevent hazardous disruption of the global climate system. Numerous strategies are theoretically possible to further lower pollution in the future. For instance, one of its main objectives might be to lower environmental pollution and raise air quality through the usage of environmental levies.

Ecological taxation aims to transfer the tax burden from economically desirable social goods, such as jobs, income, and investments, to economically undesirable social goods, such as waste and environmental damage (Bosquet 2000 ). In addition, environmental taxes have nearly also been set at a lower level than what is warranted by environmental damages in Europe and have instead been utilized to raise income. Is it reasonable to assess the environmental impact and economic efficiency of a tool whose primary objective is to generate revenue? Secondly, environmental taxes are not self-contained. In Europe, they are frequently used to supplement existing rules with standards and guidelines, and they are frequently utilized to accelerate the adoption of new technology. A fundamental challenge is separating the effects of environmental taxes from other forms of regulation that are in force at the same time.

Unlike previous research, this study has looked at how environmental taxes affect EU carbon emissions. The impact of urbanization and the use of renewable energy on emissions of carbon are investigated for this reason. To the best of our knowledge, this study used the panel VAR technique to investigate, for the first time, the impact of energy use, urbanization, and environmental levies on the quality of the environment for EU nations. Our analysis also contributes to the econometric structure. We must enforce the requirement that the fundamental framework be the same for every cross-section unit when using the VAR process on panel data. One method to get around the parameter limitation is to introduce the fixed effects that are depicted in the model to make room for “individual variability” in the variable values, as this restriction is likely to be broken in practice. The delays of the variables that are dependent link fixed effects to regressors; therefore, biased coefficients will result from the standard averaging approach used to remove fixed effects. We employ a forward mean difference, also known as the “Helmert procedure,” to get around this issue. Only the forward average—that is, the average of all future data that are accessible for each nation year—is eliminated by this process. We may employ lagged regression coefficients as tools and estimate the coefficients using the system GMM since this transformation maintains the orthogonality between the modified variables and the regressors (Love and Zicchino 2006 ).

The remaining research is organized as follows: The relevant empirical literature is presented in the “ Literature review ” section. The “Model specification, data and methodology” section explains the technique, model, and data. Empirical results are included in the “ Empirical findings ” section. Concluding thoughts and policy implications are presented in the “ Conclusion ” section.

Literature review

Scholars, policymakers, and economists have been debating the connections between energy usage, environmental quality, and taxes connected to the environment for the past thirty years. The single-country and multi-country data analysis situations covered in this literature review have received the majority of attention in the research that is currently accessible. After reviewing the literature, we can organize it into three broad categories since research has been done on topics like environmental taxes, consumption of energy and the economy, and the relationship between energy use and the environment.

Very recently, Bekun ( 2024 ) has examined the relationship between conventional energy use, agricultural practices, economic growth, and environmental sustainability in South Africa by using Pesaran’s Autoregressive distributed lag (ARDL) method, as well as the dynamic ARDL simulations method. To meet the study’s objectives, a carbon income function is fitted to annual frequency data from 1975 to 2020. Bekun observed that economic expansion, fossil fuel energy use, and agricultural activities all have a negative impact on environmental sustainability in South Africa, implying a trade-off between economic growth and environmental quality. Bekun ( 2022 ) studied how renewable and non-renewable energy, economic growth, and energy sector investment affect CO2 emissions in India. The long-run elasticity of the variables was determined using canonical cointegration regression (CCR), completely modified least squares (FMOLS), and dynamic least squares (DOLS). Granger causality analysis was employed to determine the direction of the causal relationship between the variables that were highlighted. The results of empirical regression indicate a negative correlation between renewable energy and CO 2 emissions. The long-run elasticity of the variables was determined using canonical cointegration regression (CCR), completely modified least squares (FMOLS), and dynamic least squares (DOLS), and the direction of the causal relationship between the highlighted variables was determined using Granger causality analysis. The results of empirical regression indicate a negative correlation between renewable energy and CO 2 emissions. Nadiri et al. ( 2024 ) identified carbon dioxide (CO2) emissions as the main cause of the urgent problem of environmental deterioration, endangering the sustainability of the environment worldwide, particularly the member states of the European Union (EU). The findings demonstrate that while globalization, eco-innovation, carbon taxes, and renewable energy all help to slow down environmental degradation, economic development also helps to lessen the problems associated with environmental sustainability in EU member states. Xu et al. ( 2023 ) gathered information from 287 cities between 2010 and 2019 to examine the mechanism behind the fee and determine its effect on lowering pollution. The findings indicate that the environmental tax had a major knock-on effect on sewage, waste gas, and solid waste emissions. This suggests that intergovernmental cooperation and regional collaboration can improve the implementation of environmental tax policies and their emission reduction effects. Dmytrenko et al. ( 2024 ) investigated the effects of environmental levies and stricter environmental regulations on greenhouse gas emissions in a sample of eight European nations from Central and Western Europe. Our findings indicate that only in Western Europe does the strictness of environmental regulations have a major impact. It is interesting to note that R&D spending ended up having the biggest impact on both groups. Guo and Wang ( 2018 ) used annual time series data from 1985 to 2014 to investigate the connection between Beijing, China’s carbon emissions and environmental regulations. The authors experimentally investigated the consequences of environmental legislation in Beijing using the Johansen Cointegration, VAR model, and impulsive reaction function methodologies. Environmental restrictions, according to the research, could help to foster technical development while also reducing the effects of carbon emissions. with relation to energy, the environment, the economy, and economic competitiveness. In a similar vein, Wolde-Rufael and Mulat-Weldemeskel ( 2022 ) discovered that environmental taxes and CO2 emissions in Latin American and Caribbean nations were negatively correlated. A similar conclusion was obtained about environmental taxes by Safi et al. ( 2021 ) after analyzing the impact of R&D and environmental taxes on carbon emissions in G7 nations. The impact of environmental tax measures on developing, developed, and growing nations was studied by Cottrell et al. ( 2015 ). Environmental fiscal policies that stray from the ideal tax design, according to the research, help to avert negative implications for international competition. The research argues that environment tax reform is a viable and affordable policy instrument for both long-term environmental conservation and economic prosperity. Morley ( 2012 ) examined the long-term impact of ecological tariffs on energy usage and pollutant emissions using data from 25 European nations between 1995 and 2005. The two-step GMM technique was used in this study to account for variability and unobserved heterogeneity difficulties in econometric assumptions. Environmental levies do not have a major impact on energy usage in the nations analyzed, according to the findings of the empirical research. Wang et al. ( 2015 ) used data from 36 Chinese industries to investigate the effects of carbon prices on industrial competitiveness. The authors conducted a thorough examination of the impact of carbon-related levies on various economic sectors. Fremstad and Paul ( 2019 ) looked at how carbon prices affect income disparity and general economic development in the United States. The authors came to the conclusion that the labor tax cuts associated with the carbon tax are being reduced, which is not enough to preserve Americans’ purchasing power. Peng et al. ( 2019 ) looked at how energy taxes might affect Jiangsu’s potential for prosperity and economy. Empirical research indicates that while energy taxes are advantageous for conserving resources and lowering energy usage, they also compromise economic and welfare goals. Rogan et al. ( 2011 ) investigated the impact of targeted policies on vehicle purchasing trends in the direction of lowering carbon-emitting automobiles. Drawing on statistics from Ireland, the authors concluded that at the start of a new taxing scheme, new-vehicle carbon emissions might be decreased by up to 13%. Ciaschini et al. ( 2012 ) assessed the relationship between taxes on the environment and carbon emissions from Italy using yearly time series data spanning from 2004 to 2007. The authors used an empirical general equilibrium technique (CGE) to hypothesize that tax policies might have an impact on regional prices, employment rates, economic growth, and carbon dioxide emissions. Miller and Vela ( 2013 ) investigated how environmental levies affected the reduction of pollutant emissions in fifty distinct nations. To test the key hypothesis, the researchers gathered annual data from 1995 to 2010 and used pass dynamics regression analysis. Stern et al. ( 1993 ) established a popular theoretical framework to explain pro-environmental behavior. They propose a social–psychology paradigm in which egoistic, social–altruistic, and biocentric value perspectives can all motivate impact on the environment. They next put the model to the test utilizing survey results. While they find widespread agreement for their approach, they also discover that only self-interested reasons are a true indicator of willingness to pay through taxes. The implicit pollution tax in the US quadrupled between 1990 and 2008, resulting in a 60% decrease in air pollutants from manufacturing industries despite a notable increase in industrial production (Shapiro and Walker 2018 ). However, other research has demonstrated that a carbon price has little effect on lowering carbon emissions (Klenert and Mattauch 2016 ; King et al. 2019 ).

Model specification, data, and methodology

This paper aims to analyze the effect of environmental taxation, economic growth, renewable energy, and urbanization on CO 2 emissions. The functional representation of this relationship is as follows:

This nexus is expressed as a panel data model as follows:

where i implies each unit of the panel (EU-27 countries Footnote 1 ), t denotes the data period (1995–2018). CO 2 is the dependent variable and implies CO 2 emissions in metric tons per capita. Independent variables are GDP per capita (constant 2015 US$), squared of GDP, environmental tax revenues (million dollars), the percentage of urban to total population, and the fraction of energy from renewable sources utilized for total final consumption of energy, respectively. The World Bank’s World Development Indicators provides other statistics, while the Eurostat database is the source of data for the environmental tax indicator. For every variable, the logarithmic transformation is utilized. Table 1 displays descriptive data for the series.

In this study, the panel vector autoregression (PVAR) method is adopted to estimate Eq. ( 2 ). Before estimating the equation, whether the variables contain a unit root is checked by both IPS test developed by Im et al. ( 2003 ) and CIPS panel unit root test developed by Pesaran ( 2007 ). The reason why these tests are preferred is that the IPS considers the heterogeneity of the panel and the CIPS test handled also the cross-section dependence. After the unit root testing, the procedure for the PVAR approach is followed.

The PVAR approach is developed by Abrigo and Love ( 2016 ). The estimation procedure of this method is based on the Generalized Method of Moments (GMM) approach. This method provides a detailed empirical evidence framework by enabling long-run coefficient estimation, causality analysis, variance decomposition analysis, and obtaining impulse-response graphs for the relationship under consideration. The main PVAR model is constructed as follows:

where \({X}_{pt}=\left[{CO2}_{pt},{GDP}_{pt}{,GDP2}_{pt},{TAX}_{pt},{REN}_{pt},{URB}_{pt}\right]\) implies a vector of the exogenous variables. \({Y}_{pt}\) is \(\left(kxk\right)\) vector of independent variables. \({\cup }_{c}\) is a vector of country-fixed effects and \({\mu }_{ct}\) is idiosyncratic error.

The PVAR approach considers unobserved heterogeneity and eliminates estimation errors caused by cross-sectional dependence panel VAR models add the cross-section to regular VAR models, but they are otherwise identical to standard VAR models in that each of the variables is endogenous and interdependent. The panel VAR method, however, has a few unique characteristics.

Due to the consideration of all endogenous variable delays, there is a dynamic interdependency between the variables. Additionally, error terms are typically associated across units; this characteristic is known as static interdependency.

Lastly, the shocks’ intercept, slope, and variance could vary depending on the unit. According to Canova and Ciccarelli ( 2013 ), this suggests that cross-sectional heterogeneity is available.

Empirical findings

In the first stage of the analysis, it is tested whether the series are stationary or not. Regarding this, both IPS and CIPS test results are presented in Table  2 . According to the IPS test results, it is understood that all variables are stationary at the first difference. When the results of the CIPS unit root test, which is another unit root testing approach adopted in this study, are examined, it is seen that the GDP , GDP 2 , and TAX variables are stationary at level, but others are stationary at first difference.

After the unit root tests, the panel VAR procedure is followed. First, the optimal lag length is determined, and the results are presented in Table  3 . According to Table  3 , it is concluded that the optimal lag length is 1 since the MBIC, MAIC, and MQIC have the lowest values at the lag(1).

The long-run coefficient estimates determined using lag(1) are shown in Table  4 . Because GDP has a positive influence on CO2 and GDP 2 has a negative impact on it, initial data show the presence of an inverted U-shaped link between economic growth and air pollution, supporting the validity of the EKC hypothesis in these nations. Another finding indicates that environmental fees improve the quality of the air in these nations. An increase in environmental taxes reduces CO 2 emissions by 0.14% in the European Union. This result is in line with Miller and Vela ( 2013 ), Guo and Wang ( 2018 ), Wolde-Rufael and Mulat-Weldemeskel ( 2022 ), Safi et al. ( 2021 ), Xu et al. ( 2023 ), and Nadiri et al. ( 2024 ). The findings regarding the relationship between environmental taxes and emissions may be evaluated in line with the widespread literature. This result confirms that the positive effects of environmental practices supported by strict policies are inevitable in the long run. On the other hand, the findings of Dmytrenko et al. ( 2024 ). Their results are important because they involve the comparison of two samples, central and western Europe. Since this study covers mostly Western European countries, it is compatible with their results for Western Europe. In fact, when the relevant literature and the results of this study are evaluated together, the success in the implementation of environmental taxes may be evaluated in connection with the institutional structure of the county or region and its success in public policies. Also, renewable energy consumption and urbanization have a negative impact on emissions, but this effect of renewable energy is statistically insignificant.

Empirical results show that environmental taxes are an effective policy tool in tackling the problem of negative environmental externalities in European countries. The main purpose of environmental taxes is to reduce pollution by preventing activities that are harmful to the environment. Therefore, the results reflect a taxation system that serves this purpose. Beyond this direct effect, another measure of the effectiveness of environmental taxes is that these taxes encourage companies to develop environmentally friendly technologies and consume renewable energy. In these countries, the positive effect of environmental tax on renewable energy consumption is another important finding, and the effectiveness of environmental tax becomes clearer at this point. Accordingly, an increase in environmental tax revenues increases renewable energy consumption by about 0.09% in the long run. Therefore, this result means that tax revenues are used for sustainable purposes. Unlike the positive contribution of environmental tax to air quality and renewable energy consumption, it is observed that it reduces economic growth, albeit slightly. Its negative effect on economic growth is that taxes are an element that increases the cost of companies and therefore reduces international competition.

According to other empirical findings, both renewable energy consumption and urbanization cause an increase in environmental tax revenues. An increase in renewable energy consumption increases environmental tax revenues by about 0.6%, while an increase in urbanization increases it by about 8%. It is an expected conclusion that environmental tax revenues increase with urbanization. It can be said that urbanization accelerates industrialization and accordingly production and consumption process and causes environmental sanctions such as taxes to become widespread in case they pose a threat to environmental quality. At this point, it is possible to explain the reducing effect of urbanization on emissions. Therefore, environmental tax practices caused by urbanization mean that tax revenues increase and these revenues are qualified as a source for environmentally friendly incentive practices. The fact that renewable energy consumption causes an increase in environmental tax revenues reflects the deviations from the environmentally friendly approach to renewable energy consumption.

Investigating the stability of the model discussed in the study is another stage of panel VAR analysis. The results of the stability test are presented both in Table  5 and graphically in Fig.  1 . The fact that all the results in Table  5 are less than 1 and therefore all the points in Fig.  1 are within the unit circle indicates that the model is stable. This result allows the analysis to be considered in more detail and the causality test to be performed in the next step. Causality test results are reported in Table  6 .

Stability of the PVAR model

The results of the causality test performed after the coefficient estimation point to some important findings. Accordingly, GDP and energy from renewable sources use have a one-way causal connection, as do emissions of carbon dioxide and renewable consumption of energy. Furthermore, bidirectional causation is shown between environmental tax and renewable energy use and urbanization, GDP and environmental tax and CO2 emissions, and so on. The causality test results support the interrelationships in the long-run coefficient estimation findings. Therefore, the strong links between emissions, environmental taxes, growth, clean energy consumption, and urbanization are emphasized once again. With this determination, in the next step, the variance decomposition between emissions and environmental tax is examined in the context of the main focus of the subject. The results obtained provide a significant inference.

Findings related to variance decomposition analysis are reported in Table  7 within the scope of emissions and environmental tax nexus. The first part of the table describes the change in emissions over 10 periods ahead through shocks in emissions and environmental tax. Accordingly, about 87% of changes in emissions are due to shocks in itself, while about 10% is caused by shocks in environmental taxes. In the second part of the table, changes in environmental taxes are explained by shocks in emissions and environmental tax. This part differs from the result in the first part of the table. Accordingly, the changes in environmental taxes over 10 periods ahead are explained by shocks in emissions of about 38%, while about 34% are explained by shocks in itself. Therefore, emissions have a critical role in the future of EU member countries as an important component that brings up the regulations regarding environmental tax.

The shocks and their effects in the variance decomposition analysis can be represented more comprehensively and clearly in the impulse-response functions. These functions with 95% confidence intervals are presented graphically in Fig.  2 . According to these results, the response of CO 2 emissions to a standard deviation shock in GDP is firstly positive and then negative. However, the response of emissions to a standard deviation shock in urbanization, renewable energy consumption, and environmental tax is firstly negative, and then positive.

Impulse-response graphs

This study examined the impact of economic development, urbanization, renewable energy usage, and environmental taxes on CO2 emissions in the EU-27. The PVAR technique was applied for this purpose between 1995 and 2018. The primary findings showed that urbanization, renewable energy use, and environmental taxes all had long-term detrimental effects on emissions. Additionally, the findings supported the EKC hypothesis’s validity in these nations.

In the light of the main findings, it is possible to make some policy implications for these countries. The results revealed that environmental taxes have a positive contribution to air quality more than renewable energy in these countries. However, considering that the growth-reducing effect of environmental taxes is greater than that of renewable energy consumption, the possible costs of environmental taxes should be reconsidered. At this point, it is necessary to complete the taxes applied for polluting sectors with a system that encourages the use of environmentally friendly technology to balance the costs. This means the development of a new reward-punishment approach in environmental regulations. One of the most critical points about taxes is the determination of tax rates. Accordingly, another important criterion is to determine the rates in polluting sectors by considering the sector-specific cost structures. With such an approach, a tax burden will be created that allows companies operating in related sectors to invest in environmentally friendly technologies. In these economies that are on the edge of economic, social, and democratic development, there is a suitable basis for more effective implementation of environmental taxes. Unless inactive choices (such as poverty) are made in the use of resources, developments such as environmental regulations in these countries produce positive results in favor of improving air quality.

In the following studies, factors such as economic growth, foreign trade, investment, poverty, democracy, corruption, and population can be considered the impact of environmental taxes on the effectiveness of environmental taxes, and the indirect effects of related factors on air quality, and how environmental taxes form the basis for the effectiveness of environmental taxes in developed and developing economies.

Data availability

The data used to support the findings of this study are included within the article.

Austria, Belgium, Bulgaria, Croatia, Cyprus, Czech Republic, Denmark, Estonia, Finland, France, Germany, Greece, Hungary, Ireland, Italy, Latvia, Lithuania, Luxembourg, Malta, Netherlands, Poland, Portugal, Romania, Slovak Republic, Slovenia, Spain, Sweden.

Abrigo MR, Love I (2016) Estimation of panel vector autoregression in Stata. Stata J 16(3):778–804

Azam M, Khan AQ (2016) Urbanization and environmental degradation: evidence from four SAARC countries Bangladesh, India, Pakistan, and Sri Lanka. Environ Prog Sustain Energy 35(3):823–832

Article   CAS   Google Scholar  

Bekun FV (2022) Mitigating emissions in India: accounting for the role of real income, renewable energy consumption and investment in energy. Int J Energy Econ Policy 12(1):188–192

Article   Google Scholar  

Bekun FV (2024) Race to carbon neutrality in South Africa: what role does environmental technological innovation play? Appl Energy 354:122212

Blackman A, Kildegaard A (2010) Clean technological change in developing country industrial clusters: Mexican leather tanning. Environ Econ Policy Stud 12:115–132

Bosquet B (2000) Environmental tax reform: does it work? A survey of the empirical evidence. Ecol Econ 34(1):19–32

British Petroleum Company (2016) BP statistical review of world energy. https://www.bp.com/content/dam/bp/pdf/energy-economics/statistical-review-2016/bp-statistical-review-of-world-energy-2016-full . Accessed 31 Mar 2024

Canova F, Ciccarelli M (2013) Panel vector autoregressive models: a survey. ECB Working Paper, No. 1507, European Central Bank (ECB), Frankfurt a. Available at SSRN: https://www.ssrn.com/abstract=2201610

Change I P O C (2001) Climate change 2007: impacts, adaptation and vulnerability. IPCC Sixth Assessment Report, Genebra, Suíça

Ciaschini M, Pretaroli R, Severini F, Socci C (2012) Regional double dividend from environmental tax reform: an application for the Italian economy. Res Econ 66(3):273–283

Cohen AJ, Brauer M, Burnett R, Anderson HR, Frostad J, Estep K, Balakrishnan K, Brunekreef B, Dandona L, Dandona R (2017) Estimates and 25-year trends of the global burden of disease attributable to ambient air pollution: an analysis of data from the Global Burden of Diseases Study 2015. Lancet 389:1907–1918

Cottrell J (2015) Reclaiming the T Word: ways of improving communication and public acceptance of environmental fiscal reform in Europe. In: Kreiser L, Andersen MS, Olsen BE, Speck S, Milne JE, Ashiabor H (eds) Environmental Pricing: Studies in Policy Choices and Interactions. Edward Elgar, Cheltenham, United Kingdom, pp 195–212

Google Scholar  

Dmytrenko D, Prokop V, Zapletal D (2024) The impact of environmental policy stringency and environmental taxes on GHG emissions in Western and Central European countries. Energy Syst.  https://doi.org/10.1007/s12667-023-00651-7

Fremstad A, Paul M (2019) The impact of a carbon tax on inequality. Ecol Econ 163:88–97

Gan D, Wang Y, Yang S, Kang C (2018) Embedding based quantile regression neural network for probabilistic load forecasting. J Mod Power Syst Clean Energy 6(2):244–254

González R, Hosoda EB (2016) Environmental impact of aircraft emissions and aviation fuel tax in Japan. J Air Transp Manag 57:231–240

Guo L, Wang Y (2018) How does government environmental regulation “unlock” carbon emission effect? Evidence from China. Chin J Popul Resour Environ 16(3):232–241

He L, Wu M, Wang D, Zhong Z (2018) A study of the influence of regional environmental expenditure on air quality in China: the effectiveness of environmental policy. Environ Sci Pollut Res 25(8):7454–7468

Heft-Neal S, Burney J, Bendavid E, Burke M (2018) Robust relationship between air quality and infant mortality in Africa. Nature 559:254–258

Im KS, Pesaran MH, Shin Y (2003) Testing for unit roots in heterogeneous panels. J Econ 115(1):53–74

Khan Z, Shahbaz M, Ahmad M, Rabbi F, Siqun Y (2019) Total retail goods consumption, industry structure, urban population growth and pollution intensity: an application of panel data analysis for China. Environ Sci Pollut Res 26(31):32224–32242

King M, Tarbush B, Teytelboym A (2019) Targeted carbon tax reforms. Eur Econ Rev 119:526–547

Klenert D, Mattauch L (2016) How to make a carbon tax reform progressive: the role of subsistence consumption. Econ Lett 138:100–103

Lanoie P, Laurent-Lucchetti J, Johnstone N, Ambec S (2011) Environmental policy, innovation and performance: new insights on the Porter hypothesis. J Econ Manag Strateg 20(3):803–842

Laplante B, Ristone P (1996) Environmental inspections and emissions of the pulp and paper industry in Quebec. J Environ Econ Manag 31:19–36

Lelieveld J, Evans JS, Fnais M, Giannadaki D, Pozzer A (2015) The contribution of outdoor air pollution sources to premature mortality on a global scale. Nature 525:367–371

Li X, Jin L, Kan H (2019) Air pollution: a global problem needs local fixes. Nature 570:437–439

Lin B, Li X (2011) The effect of carbon tax on per capita CO2 emissions. Energy Policy 39:5137–46

Love I, Zicchino L (2006) Financial development and dynamic investment behavior: evidence from panel VAR. Q Rev Econ Finance 46(2):190–210 (ISSN 1062-9769)

Meinshausen M, Meinshausen N, Hare W, Raper SC, Frieler K, Knutti R, Frame DJ, Allen MR (2009) Greenhouse-gas emission targets for limiting global warming to 2 degrees Celsius. Nature 458:1158e1162

Miller S, Vela MA (2013) Are environmentally related taxes effective? Inter-American Dev Bank IDB Workin Paper No: 467. https://doi.org/10.2139/ssrn.2367708

Morley B (2012) Empirical evidence on the effectiveness of environmental taxes. Appl Econ Lett 19:1817–1820

Nadiri A, Gündüz V, Adebayo TS (2024) The role of financial and trade globalization in enhancing environmental sustainability: evaluating the effectiveness of carbon taxation and renewable energy in EU member countries. Borsa Istanbul Rev 24(2):235–247

Peng JT, Wang Y, Zhang X, He Y, Taketani M, Shi R, Zhu XD (2019) Economic and welfare influences of an energy excise tax in Jiangsu province of China: a computable general equilibrium approach. J Clean Prod 211:1403–1411

Pesaran MH (2007) A simple panel unit root test in the presence of cross-section dependence. J Appl Economet 22:265–312

Rogan F et al (2011) Impacts of an emission based private car taxation policy-first year ex-post analysis. Transp Res A: Policy Pract 45:583

Safi A, Chen Y, Wahab S, Zheng L, Rjoub H (2021) Does environmental taxes achieve the carbon neutrality target of G7 economies? Evaluating the importance of environmental R&D. J Environ Manage 293:112908

Sari R, Soytas U (2009) Are global warming and economic growth compatible? Evidence from five OPEC countries. Appl. Energy 86:1887e1893

Shapiro JS, Walker R (2018) Why is pollution from US manufacturing declining? The roles of environmental regulation, productivity, and trade Am. Econ Rev 108:3814–3854

Stern PC, Dietz T, Kalof L (1993) Value orientations, gender, and environmental concern. Environ Behav 25:322–348

Theeuwes J (1991) Regulation or taxation. In: Kraan DJ, In’t Veld RJ (ed) Environmental protection: public or private choice (51–69), Netherlands, Kluwer Academic Publish

Wang Q, Su B, Sun J, Zhou P, Zhou D (2015) Measurement and decomposition of energy-saving and emissions reduction performance in Chinese cities. Appl Energy 151:85–92. https://doi.org/10.1016/j.apenergy.2015.04.034

Wolde-Rufael Y, Mulat-Weldemeskel E (2022) The moderating role of environmental tax and renewable energy in CO 2 emissions in Latin America and Caribbean countries: evidence from method of moments quantile regression. Environ Chall 6:100412

Xu Y, Wen S, Tao CQ (2023) Impact of environmental tax on pollution control: a sustainable development perspective. Econ Anal Policy 79:89–106

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Savranlar, B., Ertas, S.A. & Aslan, A. The role of environmental tax on the environmental quality in EU counties: evidence from panel vector autoregression approach. Environ Sci Pollut Res (2024). https://doi.org/10.1007/s11356-024-33632-z

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