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Analyzing Temporal and Spatial Characteristics and Determinant Factors of Energy-Related CO 2 Emissions of Shanghai in China Using High-Resolution Gridded Data

Author

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  • Hanxiong Zhu

    (School of Social Development and Public Policy, Big Data Institute for Carbon Emission and Environmental Pollution, Fudan University, Shanghai 200433, China)

  • Kexi Pan

    (School of Social Development and Public Policy, Big Data Institute for Carbon Emission and Environmental Pollution, Fudan University, Shanghai 200433, China)

  • Yong Liu

    (Shanghai Academy of Fine Arts, Shanghai University, Shanghai 200444, China)

  • Zheng Chang

    (Shanghai Advanced Research Institute, Chinese Academy of Science, Shanghai 201210, China)

  • Ping Jiang

    (Department of Environmental Science & Engineering, Fudan Tyndall Center, Fudan University, Shanghai 200433, China)

  • Yongfu Li

    (Shanghai Academy of Fine Arts, Shanghai University, Shanghai 200444, China)

Abstract

In this study, we create a high-resolution (1 km x 1 km) carbon emission spatially gridded dataset in Shanghai for 2010 to 2015 to help researchers understand the spatial pattern of urban CO 2 emissions and facilitate exploration of their driving forces. First, we conclude that high spatial agglomeration, CO 2 emissions centralized along the river and coastline, and a structure with three circular layers are the three notable temporal–spatial characteristics of Shanghai fossil fuel CO 2 emissions. Second, we find that large point sources are the leading factors that shaped the temporal–spatial characteristics of Shanghai CO 2 emission distributions. The changes of CO 2 emissions in each grid during 2010–2015 indicate that the energy-controlling policies of large point emission sources have had positive effects on CO 2 reduction since 2012. The changes suggest that targeted policies can have a disproportionate impact on urban emissions. Third, area sources bring more uncertainties to the forecasting of carbon emissions. We use the Geographical Detector method to identify these leading factors that influence CO 2 emissions emitted from area sources. We find that Shanghai’s circular layer structure, population density, and population activity intensity are the leading factors. This result implied that urban planning has a large impact on the distribution of urban CO 2 emissions. At last, we find that unbalanced development within the city will lead to different leading impact factors for each circular layer. Factors such as urban development intensity, traffic land, and industrial land have stronger power to determine CO 2 emissions in the areas outside the Outer Ring, while factors such as population density and population activity intensity have stronger impacts in the other two inner areas. This research demonstrates the potential utility of high-resolution carbon emission data to advance the integration of urban planning for the reduction of urban CO 2 emissions and provide information for policymakers to make targeted policies across different areas within the city.

Suggested Citation

  • Hanxiong Zhu & Kexi Pan & Yong Liu & Zheng Chang & Ping Jiang & Yongfu Li, 2019. "Analyzing Temporal and Spatial Characteristics and Determinant Factors of Energy-Related CO 2 Emissions of Shanghai in China Using High-Resolution Gridded Data," Sustainability, MDPI, vol. 11(17), pages 1-21, August.
  • Handle: RePEc:gam:jsusta:v:11:y:2019:i:17:p:4766-:d:262797
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    References listed on IDEAS

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    1. Parshall, Lily & Gurney, Kevin & Hammer, Stephen A. & Mendoza, Daniel & Zhou, Yuyu & Geethakumar, Sarath, 2010. "Modeling energy consumption and CO2 emissions at the urban scale: Methodological challenges and insights from the United States," Energy Policy, Elsevier, vol. 38(9), pages 4765-4782, September.
    2. Peter Marcotullio & Andrea Sarzynski & Jochen Albrecht & Niels Schulz & Jake Garcia, 2013. "The geography of global urban greenhouse gas emissions: an exploratory analysis," Climatic Change, Springer, vol. 121(4), pages 621-634, December.
    3. Kexi Pan & Yongfu Li & Hanxiong Zhu & Anrong Dang, 2017. "Spatial Configuration of Energy Consumption and Carbon Emissions of Shanghai, and Our Policy Suggestions," Sustainability, MDPI, vol. 9(1), pages 1-15, January.
    4. Kevin Robert Gurney & Paty Romero-Lankao & Karen C. Seto & Lucy R. Hutyra & Riley Duren & Christopher Kennedy & Nancy B. Grimm & James R. Ehleringer & Peter Marcotullio & Sara Hughes & Stephanie Pince, 2015. "Climate change: Track urban emissions on a human scale," Nature, Nature, vol. 525(7568), pages 179-181, September.
    5. Riley M. Duren & Charles E. Miller, 2012. "Measuring the carbon emissions of megacities," Nature Climate Change, Nature, vol. 2(8), pages 560-562, August.
    6. Wang, Shaojian & Liu, Xiaoping & Zhou, Chunshan & Hu, Jincan & Ou, Jinpei, 2017. "Examining the impacts of socioeconomic factors, urban form, and transportation networks on CO2 emissions in China’s megacities," Applied Energy, Elsevier, vol. 185(P1), pages 189-200.
    7. Rina Wu & Jiquan Zhang & Yuhai Bao & Feng Zhang, 2016. "Geographical Detector Model for Influencing Factors of Industrial Sector Carbon Dioxide Emissions in Inner Mongolia, China," Sustainability, MDPI, vol. 8(2), pages 1-12, February.
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    3. Guohui Yao & Haidong Li & Nan Wang & Lijun Zhao & Hanbei Du & Longjiang Zhang & Shouguang Yan, 2022. "Spatiotemporal Variations and Driving Factors of Ecological Land during Urbanization—A Case Study in the Yangtze River’s Lower Reaches," Sustainability, MDPI, vol. 14(7), pages 1-15, April.
    4. Lin Chu & Tiancheng Sun & Tianwei Wang & Zhaoxia Li & Chongfa Cai, 2020. "Temporal and Spatial Heterogeneity of Soil Erosion and a Quantitative Analysis of its Determinants in the Three Gorges Reservoir Area, China," IJERPH, MDPI, vol. 17(22), pages 1-20, November.
    5. Hui Liu & Jiwei Liu & Qun Li, 2022. "Asymmetric Effects of Economic Development, Agroforestry Development, Energy Consumption, and Population Size on CO 2 Emissions in China," Sustainability, MDPI, vol. 14(12), pages 1-34, June.

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