IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v11y2019i7p2093-d220985.html
   My bibliography  Save this article

Disaggregation Method of Carbon Emission: A Case Study in Wuhan, China

Author

Listed:
  • Minghai Luo

    (Wuhan Geomatics Institute, Wuhan 430022, China)

  • Sixian Qin

    (Wuhan Geomatics Institute, Wuhan 430022, China)

  • Haoxue Chang

    (Wuhan University, Wuhan 430079, China)

  • Anqi Zhang

    (Wuhan University, Wuhan 430079, China)

Abstract

Urban areas contribute significant carbon emissions. Evaluating and analysing the spatial distribution of carbon emissions are the foundations of low-carbon city development and carbon emissions reduction. In this study, carbon emission inventory was first constructed and carbon emissions in Wuhan were estimated on the basis of energy consumption. Second, the spatial distribution models of carbon emissions in different sectors were developed on the basis of the census of the Wuhan geographical conditions data and other thematic data. Third, the carbon emission distribution in Wuhan was analyzed at the central urban, functional, new urban, built-up, and metropolitan development area scale. Results show that the industry sector emits most of the carbon emissions in Wuhan, followed by the residential population. Carbon emissions in the metropolitan development area can stand for the true carbon emissions in Wuhan. Thus, a geographically weighted (GW) model was adopted to analyze the correlation coefficients between economical-social factors (gross domestic product, population density, road density, industrial land and residential land) and carbon emissions in the metropolitan development area. Comparisons with other studies show that the disaggregation method we proposed in this work, especially the adoption of geographical condition census data, can reflect the spatial distribution of carbon emissions of different sectors at the city scale.

Suggested Citation

  • Minghai Luo & Sixian Qin & Haoxue Chang & Anqi Zhang, 2019. "Disaggregation Method of Carbon Emission: A Case Study in Wuhan, China," Sustainability, MDPI, vol. 11(7), pages 1-17, April.
    • Handle: RePEc:gam:jsusta:v:11:y:2019:i:7:p:2093-:d:220985
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/11/7/2093/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/11/7/2093/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Reid Ewing & Fang Rong, 2008. "The impact of urban form on U.S. residential energy use," Housing Policy Debate, Taylor & Francis Journals, vol. 19(1), pages 1-30, January.
    2. Clinton Andrews, 2008. "Greenhouse gas emissions along the rural-urban gradient," Journal of Environmental Planning and Management, Taylor & Francis Journals, vol. 51(6), pages 847-870.
    3. Gollini, Isabella & Lu, Binbin & Charlton, Martin & Brunsdon, Christopher & Harris, Paul, 2015. "GWmodel: An R Package for Exploring Spatial Heterogeneity Using Geographically Weighted Models," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 63(i17).
    4. Meng, Lina & Graus, Wina & Worrell, Ernst & Huang, Bo, 2014. "Estimating CO2 (carbon dioxide) emissions at urban scales by DMSP/OLS (Defense Meteorological Satellite Program's Operational Linescan System) nighttime light imagery: Methodological challenges and a ," Energy, Elsevier, vol. 71(C), pages 468-478.
    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. Fangjie Cao & Yun Qiu & Qianxin Wang & Yan Zou, 2022. "Urban Form and Function Optimization for Reducing Carbon Emissions Based on Crowd-Sourced Spatio-Temporal Data," IJERPH, MDPI, vol. 19(17), pages 1-17, 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. Sovacool, Benjamin K. & Brown, Marilyn A., 2010. "Twelve metropolitan carbon footprints: A preliminary comparative global assessment," Energy Policy, Elsevier, vol. 38(9), pages 4856-4869, September.
    2. Ivan Muñiz & Andrés Dominguez, 2020. "The Impact of Urban Form and Spatial Structure on per Capita Carbon Footprint in U.S. Larger Metropolitan Areas," Sustainability, MDPI, vol. 12(1), pages 1-19, January.
    3. Cai, Bofeng & Zhang, Lixiao, 2014. "Urban CO2 emissions in China: Spatial boundary and performance comparison," Energy Policy, Elsevier, vol. 66(C), pages 557-567.
    4. Kaza, Nikhil, 2010. "Understanding the spectrum of residential energy consumption: A quantile regression approach," Energy Policy, Elsevier, vol. 38(11), pages 6574-6585, November.
    5. Yen-Jong Chen & Rodney H Matsuoka & Tzu-Min Liang, 2018. "Urban form, building characteristics, and residential electricity consumption: A case study in Tainan City," Environment and Planning B, , vol. 45(5), pages 933-952, September.
    6. Zhonghua Cheng & Xiaowen Hu, 2023. "The effects of urbanization and urban sprawl on CO2 emissions in China," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 25(2), pages 1792-1808, February.
    7. Liu, Xingjian & Wang, Mingshu & Qiang, Wei & Wu, Kang & Wang, Xiaomi, 2020. "Urban form, shrinking cities, and residential carbon emissions: Evidence from Chinese city-regions," Applied Energy, Elsevier, vol. 261(C).
    8. Bakirtas, Tahsin & Akpolat, Ahmet Gokce, 2018. "The relationship between energy consumption, urbanization, and economic growth in new emerging-market countries," Energy, Elsevier, vol. 147(C), pages 110-121.
    9. Xuemei Wang & Mingguo Ma, 2017. "The luminous intensity of regional ‘night-light’ output can predict the growing volume of published scientific research by ‘luminaries’ in developing countries," Scientometrics, Springer;Akadémiai Kiadó, vol. 110(2), pages 1005-1010, February.
    10. Estiri, Hossein, 2014. "Building and household X-factors and energy consumption at the residential sector," Energy Economics, Elsevier, vol. 43(C), pages 178-184.
    11. Gainbi Park & Zengwang Xu, 2022. "The constituent components and local indicator variables of social vulnerability index," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 110(1), pages 95-120, January.
    12. Zaijun Li & Jianquan Cheng & Qiyan Wu, 2016. "Analyzing regional economic development patterns in a fast developing province of China through geographically weighted principal component analysis," Letters in Spatial and Resource Sciences, Springer, vol. 9(3), pages 233-245, October.
    13. Andréia S. Santos & Lucas Teles Faria & Mara Lúcia M. Lopes & Carlos R. Minussi, 2023. "Power Distribution Systems’ Vulnerability by Regions Caused by Electrical Discharges," Energies, MDPI, vol. 16(23), pages 1-19, November.
    14. Kirtonia, Sajeeb & Sun, Yanshuo, 2022. "Evaluating rail transit's comparative advantages in travel cost and time over taxi with open data in two U.S. cities," Transport Policy, Elsevier, vol. 115(C), pages 75-87.
    15. Gang Xu & Tianyi Zeng & Hong Jin & Cong Xu & Ziqi Zhang, 2023. "Spatio-Temporal Variations and Influencing Factors of Country-Level Carbon Emissions for Northeast China Based on VIIRS Nighttime Lighting Data," IJERPH, MDPI, vol. 20(1), pages 1-17, January.
    16. Zhang, Junyi & Teng, Fei & Zhou, Shaojie, 2020. "The structural changes and determinants of household energy choices and energy consumption in urban China: Addressing the role of building type," Energy Policy, Elsevier, vol. 139(C).
    17. Alexis Comber & Paul Harris, 2018. "Geographically weighted elastic net logistic regression," Journal of Geographical Systems, Springer, vol. 20(4), pages 317-341, October.
    18. Javier Bueno & Desiderio Romero-Jordán & Pablo del Río, 2020. "Analysing the Drivers of Electricity Demand in Spain after the Economic Crisis," Energies, MDPI, vol. 13(20), pages 1-18, October.
    19. Bereitschaft, Bradley, 2020. "Gentrification and the evolution of commuting behavior within America's urban cores, 2000–2015," Journal of Transport Geography, Elsevier, vol. 82(C).
    20. Lee, Sungwon & Lee, Bumsoo, 2014. "The influence of urban form on GHG emissions in the U.S. household sector," Energy Policy, Elsevier, vol. 68(C), pages 534-549.

    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:jsusta:v:11:y:2019:i:7:p:2093-:d:220985. 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.