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A dual strategy for controlling energy consumption and air pollution in China's metropolis of Beijing

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  • Zhang, Yanxia
  • Wang, Haikun
  • Liang, Sai
  • Xu, Ming
  • Zhang, Qiang
  • Zhao, Hongyan
  • Bi, Jun

Abstract

It is critical to alleviate problems of energy and air pollutants emissions in the metropolis because these areas serve as economic engines and have large and dense populations. Drivers of fossil fuel use and air pollutants emissions were analyzed in metropolis of Beijing during 1997–2010. The analyses were conducted from both a bottom-up and a top-down perspective based on the sectoral inventories and structural decomposition analysis (SDA). From a bottom-up perspective, the key energy-intensive industrial sectors directly caused the variations in Beijing's air pollution by means of a series of energy and economic policies. From a top-down perspective, variations in production structures caused increases in most materials during 2000–2010, but there were decreases in PM10 and PM2.5 emissions during 2005–2010. Population growth was found to be the largest driver of energy consumption and air pollutants emissions during 1997–2010. This finding suggests that avoiding rapid population growth in Beijing could simultaneously control energy consumptions and air pollutants emissions. Mitigation policies should consider not only the key industrial sectors but also socioeconomic drivers to co-reduce energy consumption and air pollutions in China's metropolis.

Suggested Citation

  • Zhang, Yanxia & Wang, Haikun & Liang, Sai & Xu, Ming & Zhang, Qiang & Zhao, Hongyan & Bi, Jun, 2015. "A dual strategy for controlling energy consumption and air pollution in China's metropolis of Beijing," Energy, Elsevier, vol. 81(C), pages 294-303.
  • Handle: RePEc:eee:energy:v:81:y:2015:i:c:p:294-303
    DOI: 10.1016/j.energy.2014.12.041
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    2. Feng, Xuning & Lu, Languang & Ouyang, Minggao & Li, Jiangqiu & He, Xiangming, 2016. "A 3D thermal runaway propagation model for a large format lithium ion battery module," Energy, Elsevier, vol. 115(P1), pages 194-208.
    3. Gonçalves, Rui & Ribeiro, Vitor Miguel, 2024. "Convolutional attention with roll padding: Classifying PM2.5 concentration levels in the city of Beijing," Energy, Elsevier, vol. 289(C).
    4. Alicja Kolasa-Więcek & Dariusz Suszanowicz & Agnieszka A. Pilarska & Krzysztof Pilarski, 2021. "Modelling the Interaction between Air Pollutant Emissions and Their Key Sources in Poland," Energies, MDPI, vol. 14(21), pages 1-14, October.
    5. Han Sun & Chao Huang & Shan Ni, 2022. "Driving factors of consumption-based PM2.5 emissions in China: an application of the generalized Divisia index," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 24(8), pages 10209-10231, August.
    6. Huang, He & Hong, Jingke & Wang, Xianzhu & Chang-Richards, Alice & Zhang, Jingxiao & Qiao, Bei, 2022. "A spatiotemporal analysis of the driving forces behind the energy interactions of the Chinese economy: Evidence from static and dynamic perspectives," Energy, Elsevier, vol. 239(PB).

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