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Sci Adv. 2018 Jun 27;4(6):eaaq0390. doi: 10.1126/sciadv.aaq0390. eCollection 2018 Jun.

City-level climate change mitigation in China.

Shan Y1,2, Guan D2,3, Hubacek K4,5,6, Zheng B3,7, Davis SJ3,8,9, Jia L10, Liu J11, Liu Z2,3, Fromer N12, Mi Z13, Meng J14, Deng X15,16, Li Y2,17, Lin J18, Schroeder H2, Weisz H19,20, Schellnhuber HJ19,21.

Author information

1
School of Environment, Tsinghua University, Beijing 100084, China.
2
Water Security Research Centre, Tyndall Centre for Climate Change Research, School of International Development, University of East Anglia, Norwich NR4 7TJ, UK.
3
Department of Earth System Science, Tsinghua University, Beijing 100080, China.
4
Department of Geographical Sciences, University of Maryland, College Park, MD 20742, USA.
5
Department of Environmental Studies, Masryk University, Joštova 10, 602 00 Brno, Czech Republic.
6
International Institute for Applied Systems Analysis (IIASA), Schlossplatz 1, A-2361 Laxenburg, Austria.
7
Laboratoire des Sciences du Climat et de l'Environnement, CEA-CNRS-UVSQ, UMR8212, Gif-sur-Yvette, Paris, France.
8
Department of Earth System Science, University of California, Irvine, Irvine, CA 92697, USA.
9
Department of Civil and Environmental Engineering, University of California, Irvine, Irvine, CA 92697, USA.
10
School of Materials Science and Engineering, State Key Lab of Material Processing and Die and Mould Technology, Huazhong University of Science and Technology, Wuhan, Hubei 430074, China.
11
Institute of Finance and Economics Research, School of Urban and Regional Science, Shanghai University of Finance and Economics, Shanghai 200433, China.
12
Resnick Sustainability Institute, California Institute of Technology, Pasadena, CA 911125, USA.
13
Bartlett School of Construction and Project Management, University College London, London WC1E 7HB, UK.
14
Department of Politics and International Studies, University of Cambridge, Cambridge CB3 9DT, UK.
15
Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China.
16
University of Chinese Academy of Sciences, Beijing 100049, China.
17
College of Economics, Jinan University, Guangzhou 510632, China.
18
Laboratory for Climate and Ocean-Atmosphere Studies, Department of Atmospheric and Oceanic Sciences, School of Physics, Peking University, Beijing 100871, China.
19
Potsdam Institute for Climate Impact Research, 14473 Potsdam, Germany.
20
Department of Cultural History and Theory and Department of Social Sciences, Humboldt University of Berlin, Unter den Linden 6, 10117 Berlin, Germany.
21
University of Potsdam Stockholm Resilience Centre, Stockholm, Sweden.

Abstract

As national efforts to reduce CO2 emissions intensify, policy-makers need increasingly specific, subnational information about the sources of CO2 and the potential reductions and economic implications of different possible policies. This is particularly true in China, a large and economically diverse country that has rapidly industrialized and urbanized and that has pledged under the Paris Agreement that its emissions will peak by 2030. We present new, city-level estimates of CO2 emissions for 182 Chinese cities, decomposed into 17 different fossil fuels, 46 socioeconomic sectors, and 7 industrial processes. We find that more affluent cities have systematically lower emissions per unit of gross domestic product (GDP), supported by imports from less affluent, industrial cities located nearby. In turn, clusters of industrial cities are supported by nearby centers of coal or oil extraction. Whereas policies directly targeting manufacturing and electric power infrastructure would drastically undermine the GDP of industrial cities, consumption-based policies might allow emission reductions to be subsidized by those with greater ability to pay. In particular, sector-based analysis of each city suggests that technological improvements could be a practical and effective means of reducing emissions while maintaining growth and the current economic structure and energy system. We explore city-level emission reductions under three scenarios of technological progress to show that substantial reductions (up to 31%) are possible by updating a disproportionately small fraction of existing infrastructure.

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