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Summary | ||
Name of source | 2010 Guidelines to Defra / DECC's GHG Conversion Factors for Company Reporting | |
Provider | Produced by AEA for DECC and Defra | |
Summary text | Conversion factors allowing organizations and individuals to calculate greenhouse gas (GHG) emissions from a range of activities, including energy use, water consumption, waste disposal, recycling and transport activities. | |
Contact | ||
Licensing | Free | |
Language(s) | English | |
Website |
Access – data formats and accessibility | ||
File type | HTML (web) access to .xls or .pdf file | |
Software needs | Microsoft Office, Adobe Reader |
Contents – breadth and depth of datasets | ||
Age | 1990-2010 | |
Geography | UK, Global | |
Original Data Source(s) | Original research, Industry statistics, Government publications, Other LCA databases | |
Other Databases Included | ; | |
Life cycle stages | Cradle-to-Grave | |
Modeling approach | Various | |
Emissions results | Total CO2e, Separate GHGs, Separate scopes, Direct and Indirect emissions | |
Number of datasets | +300 | |
Main topics | Electricity; Crude oil based fuels; Natural gas based fuels; Road; Rail; Air | |
Other topics | End-of-life treatment; Water; Materials production; Other Services |
Data transparency – what metadata is provided for each dataset? | ||
System boundaries | Yes | |
Data Types | Process, Input-Output, Other | |
Allocation Methods | n/a | |
Technology | Yes | |
Data year | Yes | |
Original source | Yes | |
Uncertainty | No |
Quality – is information provided on data quality? | ||
Data quality score | No | |
Quality assurance | Yes | |
Standards compliant | Defra/DECC; GHG Protocol; Possible to use in product footprints |
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Research models and methodologies on the smart city: a systematic literature review.
1. Introduction
2. theoretical background and previous studies, 2.1. definition of smart city, 2.2. previous studies, 3. research method, 3.1. research subject, 3.2. systematic review, 4. results and discussion, 4.1. results of research method analysis, 4.2. results of research content analysis, 4.2.1. infrastructure/monitoring, 4.2.2. citizen/sustainability, 4.2.3. big data/algorithm, 4.2.4. smart grid, 4.2.5. the internet of things/cloud, 4.2.6. governance, 4.2.7. transportation, 5. conclusions and discussion, author contributions, institutional review board statement, informed consent statement, data availability statement, conflicts of interest.
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Items | Contents |
---|---|
Keyword | ‘smart city’, ‘smart cities’ |
Language | English |
Document type | Journal articles |
Source | Web of Science |
Time interval | 2011–2020 |
Journal | Count | Rate (%) | IF | |
---|---|---|---|---|
SCI | IEEE Access | 136 | 35.5 | 3.745 |
Sensors | 106 | 27.7 | 3.275 | |
SSCI | Sustainability | 85 | 22.2 | 3.251 |
Sustainable Cities and Society | 56 | 14.6 | 7.587 | |
Total | 383 | 100 |
Year | Total |
---|---|
2011 | 0 |
2012 | 1 |
2013 | 1 |
2014 | 4 |
2015 | 14 |
2016 | 28 |
2017 | 35 |
2018 | 63 |
2019 | 92 |
2020 | 145 |
Total | 383 |
Quantitative | Qualitative | Mixed | Total | |
---|---|---|---|---|
2011 | 0 | 0 | 0 | 0 |
2012 | 1 | 0 | 0 | 1 |
2013 | 1 | 0 | 0 | 1 |
2014 | 4 | 0 | 0 | 4 |
2015 | 9 | 5 | 0 | 14 |
2016 | 16 | 10 | 2 | 28 |
2017 | 25 | 10 | 0 | 35 |
2018 | 46 | 17 | 0 | 63 |
2019 | 51 | 31 | 10 | 92 |
2020 | 96 | 41 | 8 | 145 |
Total | 249 | 114 | 20 | 383 |
Interview | Case Study | Survey | Experiment | Literature Study | Total | |
---|---|---|---|---|---|---|
2011 | 0 | 0 | 0 | 0 | 0 | 0 |
2012 | 0 | 0 | 0 | 1 | 0 | 1 |
2013 | 0 | 0 | 0 | 1 | 0 | 1 |
2014 | 0 | 0 | 0 | 4 | 0 | 4 |
2015 | 0 | 1 | 0 | 8 | 5 | 14 |
2016 | 0 | 5 | 0 | 17 | 6 | 28 |
2017 | 0 | 5 | 0 | 24 | 6 | 35 |
2018 | 1 | 5 | 3 | 44 | 10 | 63 |
2019 | 1 | 13 | 1 | 55 | 22 | 92 |
2020 | 1 | 23 | 7 | 87 | 27 | 145 |
Total | 3 | 52 | 11 | 241 | 76 | 383 |
Exploratory | Descriptive | Explanatory | Total | |
---|---|---|---|---|
2011 | 0 | 0 | 0 | 0 |
2012 | 1 | 0 | 0 | 1 |
2013 | 1 | 0 | 0 | 1 |
2014 | 4 | 0 | 0 | 4 |
2015 | 11 | 2 | 1 | 14 |
2016 | 21 | 4 | 3 | 28 |
2017 | 31 | 2 | 2 | 35 |
2018 | 56 | 4 | 3 | 63 |
2019 | 80 | 9 | 3 | 92 |
2020 | 103 | 29 | 13 | 145 |
Total | 308 | 50 | 25 | 383 |
Primary Data | Secondary Data | Total | |
---|---|---|---|
2011 | 0 | 0 | 0 |
2012 | 1 | 0 | 1 |
2013 | 1 | 0 | 1 |
2014 | 4 | 0 | 4 |
2015 | 8 | 6 | 14 |
2016 | 14 | 14 | 28 |
2017 | 25 | 10 | 35 |
2018 | 46 | 17 | 63 |
2019 | 59 | 33 | 92 |
2020 | 87 | 58 | 145 |
Total | 245 | 138 | 383 |
Basic Research | Applied Research | Evaluated Research | Total | |
---|---|---|---|---|
2011 | 0 | 0 | 0 | 0 |
2012 | 1 | 0 | 0 | 1 |
2013 | 0 | 1 | 0 | 1 |
2014 | 2 | 0 | 2 | 4 |
2015 | 8 | 4 | 2 | 14 |
2016 | 18 | 5 | 5 | 28 |
2017 | 25 | 7 | 3 | 35 |
2018 | 35 | 25 | 3 | 63 |
2019 | 51 | 34 | 7 | 92 |
2020 | 60 | 61 | 24 | 145 |
Total | 200 | 137 | 46 | 383 |
Year | Infrastructure /Monitoring | Citizens/ Sustainability | Big Data/Algorithm | Smart Grid | Internet of Things/ Cloud | Governance | Transportation | Total |
---|---|---|---|---|---|---|---|---|
2011 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
2012 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 |
2013 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 |
2014 | 1 | 0 | 1 | 1 | 0 | 0 | 1 | 4 |
2015 | 1 | 2 | 2 | 1 | 2 | 3 | 3 | 14 |
2016 | 4 | 3 | 3 | 0 | 10 | 5 | 3 | 28 |
2017 | 4 | 3 | 7 | 8 | 9 | 3 | 1 | 35 |
2018 | 5 | 13 | 13 | 5 | 14 | 9 | 4 | 63 |
2019 | 13 | 15 | 13 | 8 | 28 | 7 | 8 | 92 |
2020 | 18 | 30 | 20 | 18 | 34 | 3 | 22 | 145 |
Total | 46 | 67 | 60 | 41 | 97 | 30 | 42 | 383 |
Country | Local Government | Private Sector | Technology | Etc. | Total | |
---|---|---|---|---|---|---|
2011 | 0 | 0 | 0 | 0 | 0 | 0 |
2012 | 0 | 0 | 0 | 1 | 0 | 1 |
2013 | 0 | 1 | 0 | 0 | 0 | 1 |
2014 | 0 | 1 | 0 | 3 | 0 | 4 |
2015 | 1 | 1 | 0 | 11 | 1 | 14 |
2016 | 1 | 9 | 0 | 16 | 2 | 28 |
2017 | 3 | 8 | 2 | 21 | 1 | 35 |
2018 | 2 | 8 | 2 | 44 | 7 | 63 |
2019 | 7 | 17 | 4 | 51 | 12 | 92 |
2020 | 25 | 52 | 8 | 49 | 12 | 145 |
Total | 39 | 97 | 16 | 196 | 35 | 383 |
Technology | Legal Systems | Human Beings | Total | |
---|---|---|---|---|
2011 | 0 | 0 | 0 | 0 |
2012 | 1 | 0 | 0 | 1 |
2013 | 1 | 0 | 0 | 1 |
2014 | 4 | 0 | 0 | 4 |
2015 | 12 | 2 | 0 | 14 |
2016 | 22 | 3 | 3 | 28 |
2017 | 27 | 4 | 4 | 35 |
2018 | 52 | 7 | 4 | 63 |
2019 | 73 | 12 | 7 | 92 |
2020 | 106 | 26 | 13 | 145 |
Total | 298 | 54 | 31 | 383 |
Sort | Technology | Legal Systems | Human Beings |
---|---|---|---|
Main Source | Technology integration | Governance | Creativity |
Details | Infrastructure, network facility, information and communication technology, and platform system | Department teamwork, policy, transparency, civic participation, and public partnership | Creative education, innovative job, open mind, public participation, and collective intelligence |
Cybersecurity | Privacy | Total | |
---|---|---|---|
2011 | 0 | 0 | 0 |
2012 | 1 | 0 | 1 |
2013 | 0 | 0 | 0 |
2014 | 0 | 0 | 0 |
2015 | 0 | 0 | 0 |
2016 | 2 | 0 | 2 |
2017 | 3 | 0 | 3 |
2018 | 9 | 5 | 14 |
2019 | 15 | 8 | 23 |
2020 | 26 | 8 | 34 |
Total | 56 | 21 | 77 |
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Myeong, S.; Park, J.; Lee, M. Research Models and Methodologies on the Smart City: A Systematic Literature Review. Sustainability 2022 , 14 , 1687. https://doi.org/10.3390/su14031687
Myeong S, Park J, Lee M. Research Models and Methodologies on the Smart City: A Systematic Literature Review. Sustainability . 2022; 14(3):1687. https://doi.org/10.3390/su14031687
Myeong, Seunghwan, Jaehyun Park, and Minhyung Lee. 2022. "Research Models and Methodologies on the Smart City: A Systematic Literature Review" Sustainability 14, no. 3: 1687. https://doi.org/10.3390/su14031687
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2012 guidelines to Defra/DECC’s GHG conversion factors for company reporting: Methodology paper for emission factors
This paper outlines the methodology used for the 2012 GHG Conversion Factors. These have been superseded by the 2013 factors, integrated into a web based tool.
2012 Guidelines to Defra / DECC’s GHG Conversion Factors for Company Reporting: Methodology Paper for Emission Factors
Ref: PB13792
PDF , 1.35 MB , 85 pages
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The 2012 Guidelines to Defra and DECC’s Greenhouse Gas (GHG) Conversion Factors for Company Reporting have been superseded by the 2013 factors which are integrated into a new web based tool .
A new methodology paper for the 2013 factors will be available in July 2013.
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Deep learning applied to seismic facies classification: A methodology for training
In this work, we discuss how to train convolutional neural networks to classify seismic images. We present a methodology to process and organize post-stack data into appropriate data sets for training and testing the model. We generated a few data sets by varying several parameters and analyzed the effects of those modifications on the performance of the model. In our experiments, we simulated the workflow using real data where the expert feeds the system with some interpreted lines from a cube, and a CNN classifies the remaining lines. We used two public seismic data sets: the Netherlands Offshore in F3 block and Penobscot. Finally, we obtained up to 99\% of accuracy using less than 5\% of the available data for training. It is important to highlight that the model had a good performance in identifying the main portions of the seismic images and distinguishing the layer related to salt deposit in Netherlands.
Publication
- Daniel Civitarese
- Daniela Szwarcman
- R.M. Gamae Silva
- Emilio Vital Brazil
Ore content estimation based on spatial geological data through 3D convolutional neural networks
A benchmark dataset for semi-automatic seismic interpretation based on a new zealand's seismic survey, quantum-inspired evolutionary algorithm applied to neural architecture search.
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Deep Learning Applied to Seismic Facies Classification: a Methodology for Training
- Authors D.S. Chevitarese 1 , D. Szwarcman 1 , R.M. Gama e Silva 1 and E. Vital Brazil 1
- View Affiliations Hide Affiliations Affiliations: 1 IBM
- Publisher: European Association of Geoscientists & Engineers
- Source: Conference Proceedings , Saint Petersburg 2018 , Apr 2018, Volume 2018, p.1 - 5
- DOI: https://doi.org/10.3997/2214-4609.201800237
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In this work, we discuss how to train convolutional neural networks to classify seismic images. We present a methodology to process and organize post-stack data into appropriate data sets for training and testing the model. We generated a few data sets by varying several parameters and analyzed the effects of those modifications on the performance of the model. In our experiments, we simulated the workflow using real data where the expert feeds the system with some interpreted lines from a cube, and a CNN classifies the remaining lines. We used two public seismic data sets: the Netherlands Offshore in F3 block and Penobscot.
Finally, we obtained up to 99\% of accuracy using less than 5\% of the available data for training. It is important to highlight that the model had a good performance in identifying the main portions of the seismic images and distinguishing the layer related to salt deposit in Netherlands.
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- I.Goodfellow, Y.Bengio, and A.Courville . Deep Learning . MIT Press, 2016 . [Google Scholar]
- L.Huang, X.Dong, and T. E.Clee . A scalable deep learning platform for identifying geologic features from seismic attributes. The Leading Edge , 36(3):249–256, 2017 . [Google Scholar]
- A.Krizhevsky, I.Sutskever, and G. E.Hinton . Imagenet classification with deep convolutional neural networks. In Advances in Neural Information Processing Systems 25 , pages 1097–1105. Curran Associates, Inc., 2012 . [Google Scholar]
- Y.Liu . Application of deep learning for seismic image interpretation . In GeoConvention, Calgary, 2017 . Extended abstrac. [Google Scholar]
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Most cited this month most cited rss feed, the natural combination of full and image‐based waveform inversion, poststack diffraction imaging using reverse‐time migration, characterizing the effect of elastic interactions on the effective elastic properties of porous, cracked rocks, fracture detection by gaussian beam imaging of seismic data and image spectrum analysis, laboratory measurements of guided‐wave propagation within a fluid‐saturated fracture.
Publication Date: 09 Apr 2018
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We present a methodology to process and organize post-stack data into appropriate data sets for training and testing the model. We generated a few data sets by varying several parameters and analyzed the effects of those modifications on the performance of the model.
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