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

Hanxiong Zhu, Kexi Pan, Yong Liu, Zheng Chang, Ping Jiang and Yongfu Li
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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

Sustainability, 2019, vol. 11, issue 17, 1-21

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.

Keywords: urban CO 2 emissions; urban planning; high-resolution grid; temporal and spatial process; impact mechanism; Shanghai (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2019
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (5)

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