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Spatiotemporal dynamics of CO2 emissions from central heating supply in the North China Plain over 2012–2016 due to natural gas usage

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  • Cui, Yuanzheng
  • Zhang, Weishi
  • Wang, Can
  • Streets, David G.
  • Xu, Ying
  • Du, Mingxi
  • Lin, Jintai

Abstract

Energy consumption from central heating has rapidly increased in the cities of the North China Plain (NCP). The increasing use of natural gas in the central heating supply system may have altered the spatial and temporal patterns of CO2 emissions from central heating, yet the quantitative impacts are poorly understood. Here we detect the spatio-temporal dynamics of CO2 emissions of central heating from 2012 to 2016 at the prefectural-city level in the NCP region, by using the satellite NPP-VIIRS nighttime light data and a panel regression model to estimate CO2 emissions on a 5 × 5 km2 grid. We find that despite a slight decrease (2%) in 2014 under the “Natural Gas Utilization Policy”, CO2 emissions continued to grow. Between 2012 and 2016, CO2 emissions from central heating in the NCP increased from 106 to 121 Tg, although CO2 emissions declined by 12% in Beijing due to the increasing contribution of natural gas boilers. The gridded CO2 emissions map shows that over 2012–2016 coal burning is the main driving force of CO2 emissions in both urban and non-urban regions, despite the increasing fraction of gas-based heating. Our results contribute to city-level policymaking on carbon reduction and climate change mitigation. The high-resolution gridded CO2 emissions can also be applied in physical models to facilitate carbon cycle studies.

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  • Cui, Yuanzheng & Zhang, Weishi & Wang, Can & Streets, David G. & Xu, Ying & Du, Mingxi & Lin, Jintai, 2019. "Spatiotemporal dynamics of CO2 emissions from central heating supply in the North China Plain over 2012–2016 due to natural gas usage," Applied Energy, Elsevier, vol. 241(C), pages 245-256.
    • Handle: RePEc:eee:appene:v:241:y:2019:i:c:p:245-256
    DOI: 10.1016/j.apenergy.2019.03.060
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    as
    1. Yang, Liu & Lam, Joseph C. & Tsang, C.L., 2008. "Energy performance of building envelopes in different climate zones in China," Applied Energy, Elsevier, vol. 85(9), pages 800-817, September.
    2. Shi, Kaifang & Chen, Yun & Yu, Bailang & Xu, Tingbao & Yang, Chengshu & Li, Linyi & Huang, Chang & Chen, Zuoqi & Liu, Rui & Wu, Jianping, 2016. "Detecting spatiotemporal dynamics of global electric power consumption using DMSP-OLS nighttime stable light data," Applied Energy, Elsevier, vol. 184(C), pages 450-463.
    3. Tilottama Ghosh & Christopher D. Elvidge & Paul C. Sutton & Kimberly E. Baugh & Daniel Ziskin & Benjamin T. Tuttle, 2010. "Creating a Global Grid of Distributed Fossil Fuel CO 2 Emissions from Nighttime Satellite Imagery," Energies, MDPI, vol. 3(12), pages 1-19, December.
    4. Weishi Zhang & David I. Stern & Xianbing Liu & Wenjia Cai & Can Wang, 2017. "An analysis of the costs of energy saving and CO2 mitigation in rural households in China," CCEP Working Papers 1704, Centre for Climate & Energy Policy, Crawford School of Public Policy, The Australian National University.
    5. Wang, Can & Zhang, Weishi & Cai, Wenjia & Xie, Xi, 2013. "Employment impacts of CDM projects in China's power sector," Energy Policy, Elsevier, vol. 59(C), pages 481-491.
    6. Zhu Liu & Dabo Guan & Wei Wei & Steven J. Davis & Philippe Ciais & Jin Bai & Shushi Peng & Qiang Zhang & Klaus Hubacek & Gregg Marland & Robert J. Andres & Douglas Crawford-Brown & Jintai Lin & Hongya, 2015. "Reduced carbon emission estimates from fossil fuel combustion and cement production in China," Nature, Nature, vol. 524(7565), pages 335-338, August.
    7. Zhang, Weishi & Wang, Can & Zhang, Long & Xu, Ying & Cui, Yuanzheng & Lu, Zifeng & Streets, David G., 2018. "Evaluation of the performance of distributed and centralized biomass technologies in rural China," Renewable Energy, Elsevier, vol. 125(C), pages 445-455.
    8. Mazhar, Abdur Rehman & Liu, Shuli & Shukla, Ashish, 2018. "A state of art review on the district heating systems," Renewable and Sustainable Energy Reviews, Elsevier, vol. 96(C), pages 420-439.
    9. Doll, Christopher N.H. & Pachauri, Shonali, 2010. "Estimating rural populations without access to electricity in developing countries through night-time light satellite imagery," Energy Policy, Elsevier, vol. 38(10), pages 5661-5670, October.
    10. Dominik Wiedenhofer & Dabo Guan & Zhu Liu & Jing Meng & Ning Zhang & Yi-Ming Wei, 2017. "Unequal household carbon footprints in China," Nature Climate Change, Nature, vol. 7(1), pages 75-80, January.
    11. Xiong, Weiming & Wang, Yu & Mathiesen, Brian Vad & Lund, Henrik & Zhang, Xiliang, 2015. "Heat roadmap China: New heat strategy to reduce energy consumption towards 2030," Energy, Elsevier, vol. 81(C), pages 274-285.
    12. Du, Mingxi & Wang, Xiaoge & Peng, Changhui & Shan, Yuli & Chen, Huai & Wang, Meng & Zhu, Qiuan, 2018. "Quantification and scenario analysis of CO2 emissions from the central heating supply system in China from 2006 to 2025," Applied Energy, Elsevier, vol. 225(C), pages 869-875.
    13. 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.
    14. Zheng, Bo & Zhang, Qiang & Borken-Kleefeld, Jens & Huo, Hong & Guan, Dabo & Klimont, Zbigniew & Peters, Glen P. & He, Kebin, 2015. "How will greenhouse gas emissions from motor vehicles be constrained in China around 2030?," Applied Energy, Elsevier, vol. 156(C), pages 230-240.
    15. Raupach, M.R. & Rayner, P.J. & Paget, M., 2010. "Regional variations in spatial structure of nightlights, population density and fossil-fuel CO2 emissions," Energy Policy, Elsevier, vol. 38(9), pages 4756-4764, September.
    16. Su, Yongxian & Chen, Xiuzhi & Li, Yong & Liao, Jishan & Ye, Yuyao & Zhang, Hongou & Huang, Ningsheng & Kuang, Yaoqiu, 2014. "China׳s 19-year city-level carbon emissions of energy consumptions, driving forces and regionalized mitigation guidelines," Renewable and Sustainable Energy Reviews, Elsevier, vol. 35(C), pages 231-243.
    17. Shi, Kaifang & Chen, Yun & Yu, Bailang & Xu, Tingbao & Chen, Zuoqi & Liu, Rui & Li, Linyi & Wu, Jianping, 2016. "Modeling spatiotemporal CO2 (carbon dioxide) emission dynamics in China from DMSP-OLS nighttime stable light data using panel data analysis," Applied Energy, Elsevier, vol. 168(C), pages 523-533.
    18. Dhakal, Shobhakar, 2009. "Urban energy use and carbon emissions from cities in China and policy implications," Energy Policy, Elsevier, vol. 37(11), pages 4208-4219, November.
    19. Liu, Zhu & Liang, Sai & Geng, Yong & Xue, Bing & Xi, Fengming & Pan, Ying & Zhang, Tianzhu & Fujita, Tsuyoshi, 2012. "Features, trajectories and driving forces for energy-related GHG emissions from Chinese mega cites: The case of Beijing, Tianjin, Shanghai and Chongqing," Energy, Elsevier, vol. 37(1), pages 245-254.
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    4. Gao, Kang & Yuan, Yijun, 2022. "Spatiotemporal pattern assessment of China’s industrial green productivity and its spatial drivers: Evidence from city-level data over 2000–2017," Applied Energy, Elsevier, vol. 307(C).

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