Spatial Configuration of Energy Consumption and Carbon Emissions of Shanghai, and Our Policy Suggestions

Kexi Pan, Yongfu Li, Hanxiong Zhu and Anrong Dang
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Kexi Pan: Fudan University Energy Research Center, School of Social Development and Public Policy, Fudan University, Shanghai 200433, China
Yongfu Li: Shanghai Academy of Fine Arts, Shanghai University, Shanghai 200444, China
Hanxiong Zhu: Fudan University Energy Research Center, School of Social Development and Public Policy, Fudan University, Shanghai 200433, China
Anrong Dang: School of Architecture, Tsinghua University, Beijing 100084, China

Sustainability, 2017, vol. 9, issue 1, 1-15

Abstract: This research constructs a 1 km × 1 km Shanghai energy consumption and carbon emission spatial grid through a bottom-up approach. First, we locate all energy consumption locations in Shanghai via GIS. Second, we calculate energy consumption and associated CO 2 emissions by energy type, by usage type, and by facilities. Finally, we use a spatial grid to represent the energy consumption and CO 2 emissions. The grid shows CO 2 emissions in Shanghai are highly spatially correlated with energy types and volumes of consumption. This research also finds out that high energy consumption and carbon emission locations in Shanghai display significant spatial aggregation. In 7209 spatial energy consumption cells, the top 10 grids of emissions account for 52.8% of total CO 2 emissions in Shanghai; the top 20 grids account for 64.5% and the top 50 grids account for 76.5%. The most critical point emission sources are coal-fired power plants and iron and steel plants. The most important line emission sources are the Yan’an Road and Inner Ring viaducts. The area emission sources that account for the most future-projected growth are commercial and residential natural gas. After this spatial analysis, this paper makes policy suggestions and solutions to conserve energy consumption and mitigate carbon emissions in Shanghai.

Keywords: energy; carbon emissions; spatial grid; Shanghai; policy decision support systems (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2017
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (4)

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