IDEAS home Printed from https://ideas.repec.org/a/bla/inecol/v28y2024i4p636-647.html
   My bibliography  Save this article

Machine learning for gap‐filling in greenhouse gas emissions databases

Author

Listed:
  • Luke Cullen
  • Andrea Marinoni
  • Jonathan Cullen

Abstract

Greenhouse gas (GHG) emissions datasets are often incomplete due to inconsistent reporting and poor transparency. Filling the gaps in these datasets allows for more accurate targeting of strategies aiming to accelerate the reduction of GHG emissions. This study evaluates the potential of machine learning methods to automate the completion of GHG datasets. We use three datasets of increasing complexity with 18 different gap‐filling methods and provide a guide to which methods are useful in which circumstances. If few dataset features are available, or the gap consists only of a missing time step in a record, then simple interpolation is often the most accurate method and complex models should be avoided. However, if more features are available and the gap involves non‐reporting emitters, then machine learning methods can be more accurate than simple extrapolation. Furthermore, the secondary output of feature importance from complex models allows for data collection prioritization to accelerate the improvement of datasets. Graph‐based methods are particularly scalable due to the ease of updating predictions given new data and incorporating multimodal data sources. This study can serve as a guide to the community upon which to base ever more integrated frameworks for automated detailed GHG emissions estimations, and implementation guidance is available at https://hackmd.io/@luke‐scot/ML‐for‐GHG‐database‐completion and https://doi.org/10.5281/zenodo.10463104. This article met the requirements for a gold‐gold JIE data openness badge described at http://jie.click/badges.

Suggested Citation

  • Luke Cullen & Andrea Marinoni & Jonathan Cullen, 2024. "Machine learning for gap‐filling in greenhouse gas emissions databases," Journal of Industrial Ecology, Yale University, vol. 28(4), pages 636-647, August.
  • Handle: RePEc:bla:inecol:v:28:y:2024:i:4:p:636-647
    DOI: 10.1111/jiec.13507
    as

    Download full text from publisher

    File URL: https://doi.org/10.1111/jiec.13507
    Download Restriction: no

    File URL: https://libkey.io/10.1111/jiec.13507?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Hadi Arbabi & Maud Lanau & Xinyi Li & Gregory Meyers & Menglin Dai & Martin Mayfield & Danielle Densley Tingley, 2022. "A scalable data collection, characterization, and accounting framework for urban material stocks," Journal of Industrial Ecology, Yale University, vol. 26(1), pages 58-71, February.
    2. Jiayuan Dong & Jiankan Liao & Xun Huan & Daniel Cooper, 2023. "Expert elicitation and data noise learning for material flow analysis using Bayesian inference," Journal of Industrial Ecology, Yale University, vol. 27(4), pages 1105-1122, August.
    3. Tajda Potrč Obrecht & Martin Röck & Endrit Hoxha & Alexander Passer, 2020. "BIM and LCA Integration: A Systematic Literature Review," Sustainability, MDPI, vol. 12(14), pages 1-19, July.
    4. Richard C. Lupton & Julian M. Allwood, 2018. "Incremental Material Flow Analysis with Bayesian Inference," Journal of Industrial Ecology, Yale University, vol. 22(6), pages 1352-1364, December.
    5. Stefan Pauliuk & Niko Heeren & Mohammad Mahadi Hasan & Daniel B. Müller, 2019. "A general data model for socioeconomic metabolism and its implementation in an industrial ecology data commons prototype," Journal of Industrial Ecology, Yale University, vol. 23(5), pages 1016-1027, October.
    6. Xaysackda Vilaysouk & Savath Saypadith & Seiji Hashimoto, 2022. "Semisupervised machine learning classification framework for material intensity parameters of residential buildings," Journal of Industrial Ecology, Yale University, vol. 26(1), pages 72-87, February.
    7. Shiva Zargar & Yuan Yao & Qingshi Tu, 2022. "A review of inventory modeling methods for missing data in life cycle assessment," Journal of Industrial Ecology, Yale University, vol. 26(5), pages 1676-1689, October.
    8. Joeri Rogelj & Michel den Elzen & Niklas Höhne & Taryn Fransen & Hanna Fekete & Harald Winkler & Roberto Schaeffer & Fu Sha & Keywan Riahi & Malte Meinshausen, 2016. "Paris Agreement climate proposals need a boost to keep warming well below 2 °C," Nature, Nature, vol. 534(7609), pages 631-639, June.
    9. Rong He & Le Luo & Abul Shamsuddin & Qingliang Tang, 2022. "Corporate carbon accounting: a literature review of carbon accounting research from the Kyoto Protocol to the Paris Agreement," Accounting and Finance, Accounting and Finance Association of Australia and New Zealand, vol. 62(1), pages 261-298, March.
    10. Franco Donati & Sébastien M. R. Dente & Chen Li & Xaysackda Vilaysouk & Andreas Froemelt & Rohit Nishant & Gang Liu & Arnold Tukker & Seiji Hashimoto, 2022. "The future of artificial intelligence in the context of industrial ecology," Journal of Industrial Ecology, Yale University, vol. 26(4), pages 1175-1181, August.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Stefan Pauliuk & Niko Heeren, 2020. "ODYM—An open software framework for studying dynamic material systems: Principles, implementation, and data structures," Journal of Industrial Ecology, Yale University, vol. 24(3), pages 446-458, June.
    2. John Ryter & Karan Bhuwalka & Michelena O'Rourke & Luca Montanelli & David Cohen‐Tanugi & Richard Roth & Elsa Olivetti, 2024. "Understanding key mineral supply chain dynamics using economics‐informed material flow analysis and Bayesian optimization," Journal of Industrial Ecology, Yale University, vol. 28(4), pages 709-726, August.
    3. Wang, Bingzheng & Lu, Xiaofei & Zhang, Cancan & Wang, Hongsheng, 2022. "Cascade and hybrid processes for co-generating solar-based fuels and electricity via combining spectral splitting technology and membrane reactor," Renewable Energy, Elsevier, vol. 196(C), pages 782-799.
    4. Sapkota, Krishna & Gemechu, Eskinder & Oni, Abayomi Olufemi & Ma, Linwei & Kumar, Amit, 2022. "Greenhouse gas emissions from Canadian oil sands supply chains to China," Energy, Elsevier, vol. 251(C).
    5. Piris-Cabezas, Pedro & Lubowski, Ruben N. & Leslie, Gabriela, 2023. "Estimating the potential of international carbon markets to increase global climate ambition," World Development, Elsevier, vol. 167(C).
    6. Alt, Marius & Gallier, Carlo & Kesternich, Martin & Sturm, Bodo, 2023. "Collective minimum contributions to counteract the ratchet effect in the voluntary provision of public goods," Journal of Environmental Economics and Management, Elsevier, vol. 122(C).
    7. Rong Li & Brent Sohngen & Xiaohui Tian, 2022. "Efficiency of forest carbon policies at intensive and extensive margins," American Journal of Agricultural Economics, John Wiley & Sons, vol. 104(4), pages 1243-1267, August.
    8. Riza Radmehr & Samira Shayanmehr & Ernest Baba Ali & Elvis Kwame Ofori & Elżbieta Jasińska & Michał Jasiński, 2022. "Exploring the Nexus of Renewable Energy, Ecological Footprint, and Economic Growth through Globalization and Human Capital in G7 Economics," Sustainability, MDPI, vol. 14(19), pages 1-19, September.
    9. Agnieszka Leśniak & Monika Górka & Izabela Skrzypczak, 2021. "Barriers to BIM Implementation in Architecture, Construction, and Engineering Projects—The Polish Study," Energies, MDPI, vol. 14(8), pages 1-20, April.
    10. Marie Nehasilová & Antonín Lupíšek & Petra Lupíšková Coufalová & Tomáš Kupsa & Jakub Veselka & Barbora Vlasatá & Julie Železná & Pavla Kunová & Martin Volf, 2022. "Rapid Environmental Assessment of Buildings: Linking Environmental and Cost Estimating Databases," Sustainability, MDPI, vol. 14(17), pages 1-20, September.
    11. Róbert Csalódi & Tímea Czvetkó & Viktor Sebestyén & János Abonyi, 2022. "Sectoral Analysis of Energy Transition Paths and Greenhouse Gas Emissions," Energies, MDPI, vol. 15(21), pages 1-26, October.
    12. Sanzana Tabassum & Tanvin Rahman & Ashraf Ul Islam & Sumayya Rahman & Debopriya Roy Dipta & Shidhartho Roy & Naeem Mohammad & Nafiu Nawar & Eklas Hossain, 2021. "Solar Energy in the United States: Development, Challenges and Future Prospects," Energies, MDPI, vol. 14(23), pages 1-65, December.
    13. Heleen L. Soest & Lara Aleluia Reis & Luiz Bernardo Baptista & Christoph Bertram & Jacques Després & Laurent Drouet & Michel Elzen & Panagiotis Fragkos & Oliver Fricko & Shinichiro Fujimori & Neil Gra, 2021. "Global roll-out of comprehensive policy measures may aid in bridging emissions gap," Nature Communications, Nature, vol. 12(1), pages 1-10, December.
    14. Thananya Janhuaton & Vatanavongs Ratanavaraha & Sajjakaj Jomnonkwao, 2024. "Forecasting Thailand’s Transportation CO 2 Emissions: A Comparison among Artificial Intelligent Models," Forecasting, MDPI, vol. 6(2), pages 1-23, June.
    15. Eryu Zhang & Xiaoyu He & Peng Xiao, 2022. "Does Smart City Construction Decrease Urban Carbon Emission Intensity? Evidence from a Difference-in-Difference Estimation in China," Sustainability, MDPI, vol. 14(23), pages 1-16, December.
    16. Wang, Mengmeng & Liu, Kang & Dutta, Shanta & Alessi, Daniel S. & Rinklebe, Jörg & Ok, Yong Sik & Tsang, Daniel C.W., 2022. "Recycling of lithium iron phosphate batteries: Status, technologies, challenges, and prospects," Renewable and Sustainable Energy Reviews, Elsevier, vol. 163(C).
    17. Joseph L.-H. Tsui & Rosario Evans Pena & Monika Moir & Rhys P. D. Inward & Eduan Wilkinson & James Emmanuel San & Jenicca Poongavanan & Sumali Bajaj & Bernardo Gutierrez & Abhishek Dasgupta & Tulio Ol, 2024. "Impacts of climate change-related human migration on infectious diseases," Nature Climate Change, Nature, vol. 14(8), pages 793-802, August.
    18. Ramzi Benkraiem & Maria Qureshi & Asif Saeed & Constantin Zopounidis, 2024. "Corporate social responsibility, carbon footprints and stock market valuation," Financial Markets, Institutions & Instruments, John Wiley & Sons, vol. 33(3), pages 213-237, August.
    19. Qi, Ye & Lu, Jiaqi & Liu, Tianle, 2024. "Measuring energy transition away from fossil fuels: A new index," Renewable and Sustainable Energy Reviews, Elsevier, vol. 200(C).
    20. Yang, Shenyao & Hu, Shilai & Qi, Zhilin & Qi, Huiqing & Zhao, Guanqun & Li, Jiqiang & Yan, Wende & Huang, Xiaoliang, 2024. "Experiment and prediction for dynamic storage capacity of underground gas storage rebuilt from hydrocarbon reservoir," Renewable Energy, Elsevier, vol. 222(C).

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:bla:inecol:v:28:y:2024:i:4:p:636-647. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Wiley Content Delivery (email available below). General contact details of provider: http://www.blackwellpublishing.com/journal.asp?ref=1088-1980 .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.