IDEAS home Printed from https://ideas.repec.org/a/gam/jijerp/v19y2022i10p5986-d815831.html
   My bibliography  Save this article

Mapping Building-Based Spatiotemporal Distributions of Carbon Dioxide Emission: A Case Study in England

Author

Listed:
  • Yue Zheng

    (Guangdong Key Laboratory for Urbanization and Geo-Simulation, School of Geography and Planning, Sun Yat-sen University, Guangzhou 510275, China)

  • Jinpei Ou

    (Guangdong Key Laboratory for Urbanization and Geo-Simulation, School of Geography and Planning, Sun Yat-sen University, Guangzhou 510275, China)

  • Guangzhao Chen

    (Faculty of Architecture, The University of Hong Kong, Pokfulam, Hong Kong SAR, China)

  • Xinxin Wu

    (Guangdong Key Laboratory for Urbanization and Geo-Simulation, School of Geography and Planning, Sun Yat-sen University, Guangzhou 510275, China)

  • Xiaoping Liu

    (Guangdong Key Laboratory for Urbanization and Geo-Simulation, School of Geography and Planning, Sun Yat-sen University, Guangzhou 510275, China
    Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai), Zhuhai 519000, China)

Abstract

The spatiotemporal inventory of carbon dioxide (CO 2 ) emissions from the building sector is significant for formulating regional and global warming mitigation policies. Previous studies have attempted to use energy consumption models associated with field investigations to estimate CO 2 emissions from buildings at local scales, or they used spatial proxies to downscale emission sources from large geographic units to grid cells for larger scales. However, mapping the spatiotemporal distributions of CO 2 emissions on a large scale based on buildings remains challenging. Hence, we conducted a case study in England in 2015, wherein we developed linear regression models to analyze monthly CO 2 emissions at the building scale by integrating the Emissions Database for Global Atmospheric Research, building data, and Visible Infrared Imaging Radiometer Suite night-time lights images. The results showed that the proposed model that considered building data and night-time light imagery achieved the best fit. Fine-scale spatial heterogeneity was observed in the distributions of building-based CO 2 emissions compared to grid-based emission maps. In addition, we observed seasonal differences in CO 2 emissions. Specifically, buildings emitted significantly more CO 2 in winter than in summer in England. We believe our results have great potential for use in carbon neutrality policy making and climate monitoring.

Suggested Citation

  • Yue Zheng & Jinpei Ou & Guangzhao Chen & Xinxin Wu & Xiaoping Liu, 2022. "Mapping Building-Based Spatiotemporal Distributions of Carbon Dioxide Emission: A Case Study in England," IJERPH, MDPI, vol. 19(10), pages 1-22, May.
    • Handle: RePEc:gam:jijerp:v:19:y:2022:i:10:p:5986-:d:815831
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1660-4601/19/10/5986/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1660-4601/19/10/5986/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Wang, Shaojian & Liu, Xiaoping, 2017. "China’s city-level energy-related CO2 emissions: Spatiotemporal patterns and driving forces," Applied Energy, Elsevier, vol. 200(C), pages 204-214.
    2. Xuan Luo & Tianzhen Hong & Yu-Hang Tang, 2020. "Modeling Thermal Interactions between Buildings in an Urban Context," Energies, MDPI, vol. 13(9), pages 1-17, May.
    3. Vittorini, Diego & Cipollone, Roberto, 2016. "Energy saving potential in existing industrial compressors," Energy, Elsevier, vol. 102(C), pages 502-515.
    4. Jinpei Ou & Xiaoping Liu & Xia Li & Meifang Li & Wenkai Li, 2015. "Evaluation of NPP-VIIRS Nighttime Light Data for Mapping Global Fossil Fuel Combustion CO2 Emissions: A Comparison with DMSP-OLS Nighttime Light Data," PLOS ONE, Public Library of Science, vol. 10(9), pages 1-20, September.
    5. Milanovic, Branko, 1997. "A simple way to calculate the Gini coefficient, and some implications," Economics Letters, Elsevier, vol. 56(1), pages 45-49, September.
    6. Cui, Yuanzheng & Zhang, Weishi & Wang, Can & Streets, David G. & Xu, Ying & Du, Mingxi & Lin, Jintai, 2019. "Spatiotemporal dynamics of CO2 emissions from central heating supply in the North China Plain over 2012–2016 due to natural gas usage," Applied Energy, Elsevier, vol. 241(C), pages 245-256.
    7. Lowes, Richard & Woodman, Bridget & Fitch-Roy, Oscar, 2019. "Policy change, power and the development of Great Britain's Renewable Heat Incentive," Energy Policy, Elsevier, vol. 131(C), pages 410-421.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Yan Wang & Xi Wu, 2022. "Research on High-Quality Development Evaluation, Space–Time Characteristics and Driving Factors of China’s Construction Industry under Carbon Emission Constraints," Sustainability, MDPI, vol. 14(17), pages 1-19, August.

    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. Shi, Kaifang & Chen, Yun & Li, Linyi & Huang, Chang, 2018. "Spatiotemporal variations of urban CO2 emissions in China: A multiscale perspective," Applied Energy, Elsevier, vol. 211(C), pages 218-229.
    2. Chen, Huadun & Du, Qianxi & Huo, Tengfei & Liu, Peiran & Cai, Weiguang & Liu, Bingsheng, 2023. "Spatiotemporal patterns and driving mechanism of carbon emissions in China's urban residential building sector," Energy, Elsevier, vol. 263(PE).
    3. Ogwang, Tomson, 2007. "Additional properties of a linear pen's parade for individual data using the stochastic approach to the Gini index," Economics Letters, Elsevier, vol. 96(3), pages 369-374, September.
    4. Massimo Borg & Paul Refalo & Emmanuel Francalanza, 2023. "Failure Detection Techniques on the Demand Side of Smart and Sustainable Compressed Air Systems: A Systematic Review," Energies, MDPI, vol. 16(7), pages 1-36, March.
    5. Wanbei Jiang & Weidong Liu, 2020. "Provincial-Level CO 2 Emissions Intensity Inequality in China: Regional Source and Explanatory Factors of Interregional and Intraregional Inequalities," Sustainability, MDPI, vol. 12(6), pages 1-16, March.
    6. repec:cuf:journl:y:2014:v:15:i:2:calderon:serven is not listed on IDEAS
    7. Stéphane Mussard & J. Sadefo Kamdem & Françoise Seyte & Michel Terraza, 2011. "Quadratic Pen'S Parade And The Computation Of The Gini Index," Review of Income and Wealth, International Association for Research in Income and Wealth, vol. 57(3), pages 583-587, September.
    8. Esmaiel Abounoori & Patrick McCloughan, 2003. "A simple way to calculate the Gini Coefficient for grouped as well as ungrouped data," Applied Economics Letters, Taylor & Francis Journals, vol. 10(8), pages 505-509.
    9. Xavier Faure & Tim Johansson & Oleksii Pasichnyi, 2022. "The Impact of Detail, Shadowing and Thermal Zoning Levels on Urban Building Energy Modelling (UBEM) on a District Scale," Energies, MDPI, vol. 15(4), pages 1-18, February.
    10. Lowes, Richard & Woodman, Bridget, 2020. "Disruptive and uncertain: Policy makers’ perceptions on UK heat decarbonisation," Energy Policy, Elsevier, vol. 142(C).
    11. Osvaldo Nina, 2006. "El Impacto Distributivo de la Política Fiscal en Bolivia," Development Research Working Paper Series 16/2006, Institute for Advanced Development Studies.
    12. Adriana Marina, 2000. "Economic convergence of the first and second moment in the provinces of Argentina," Estudios de Economia, University of Chile, Department of Economics, vol. 27(2 Year 20), pages 259-277, December.
    13. Li, Jia Shuo & Zhou, H.W. & Meng, Jing & Yang, Q. & Chen, B. & Zhang, Y.Y., 2018. "Carbon emissions and their drivers for a typical urban economy from multiple perspectives: A case analysis for Beijing city," Applied Energy, Elsevier, vol. 226(C), pages 1076-1086.
    14. Chunshan Zhou & Jing Chen & Shaojian Wang, 2018. "Does Migrant Status and Household Registration Matter? Examining the Effects of City Size on Self-Rated Health," Sustainability, MDPI, vol. 10(7), pages 1-15, June.
    15. Kevin Sylwester, 2003. "Changes in income inequality and the black market premium," Applied Economics, Taylor & Francis Journals, vol. 35(4), pages 403-413.
    16. Banglong Pan & Doudou Dong & Zhuo Diao & Qi Wang & Jiayi Li & Shaoru Feng & Juan Du & Jiulin Li & Gen Wu, 2024. "The Relationship Between Three-Dimensional Spatial Structure and CO 2 Emission of Urban Agglomerations Based on CNN-RF Modeling: A Case Study in East China," Sustainability, MDPI, vol. 16(17), pages 1-16, September.
    17. Channing Arndt & Sam Jones & Vincenzo Salvucci, 2015. "When do relative prices matter for measuring income inequality? The case of food prices in Mozambique," The Journal of Economic Inequality, Springer;Society for the Study of Economic Inequality, vol. 13(3), pages 449-464, September.
    18. Luyao Wang & Hong Fan & Yankun Wang, 2018. "Estimation of consumption potentiality using VIIRS night-time light data," PLOS ONE, Public Library of Science, vol. 13(10), pages 1-19, October.
    19. Guglielmina Mutani & Valeria Todeschi & Simone Beltramino, 2020. "Energy Consumption Models at Urban Scale to Measure Energy Resilience," Sustainability, MDPI, vol. 12(14), pages 1-31, July.
    20. Simone Pellegrino, 2020. "The Gini Coefficient: Its Origins," Working papers 070, Department of Economics, Social Studies, Applied Mathematics and Statistics (Dipartimento di Scienze Economico-Sociali e Matematico-Statistiche), University of Torino.
      • Simone Pellegrino, 2024. "The Gini Coefficient: Its Origins," Working papers 086, Department of Economics, Social Studies, Applied Mathematics and Statistics (Dipartimento di Scienze Economico-Sociali e Matematico-Statistiche), University of Torino.
    21. Tehmina Zahid & Noman Arshed & Mubbasher Munir & Kamran Hameed, 2021. "Role of energy consumption preferences on human development: a study of SAARC region," Economic Change and Restructuring, Springer, vol. 54(1), pages 121-144, February.

    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:gam:jijerp:v:19:y:2022:i:10:p:5986-:d:815831. 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: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    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.