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Strategies for addressing climate change on the industrial level: affecting factors to CO 2 emissions of energy-intensive industries in China

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  • Zhaohua Wang
  • Chen Wang
  • Jianhua Yin

Abstract

This paper explores China’s strategies for addressing climate change on the industrial level. Focusing on six energy-intensive industries, this paper applies gray relational analysis theory to the affecting factors to CO 2 emissions of each industry after calculating each industry’s CO 2 emissions during 2001–2010. Further research based on GM(1, 1) model is conducted to forecast the trend of the factors, the energy consumption and each industry’s CO 2 emissions during the 12th Five-Year Plan period. As a breakthrough in previous conclusions, energy consumption structure was divided into the respective proportion of coal, oil, natural gas and electricity in the primary energy consumption, with which industrial output and energy intensity are combined to analyze each of their impacts on the energy-intensive industries. It turns out that all the factors’ impacts on emissions of the six major energy-intensive industries are significant, despite their differentiated extents. It is worth noting that, contrary to previous findings, industrial output is not the leading affecting factor to CO 2 emissions of the energy-intensive industries compared with the proportion of coal and electricity in the primary energy consumption. The GM(1, 1) forecast results of energy consumption and CO 2 emissions by the end of 2015 show that coal and electricity will remain a large proportion in primary energy consumption. This research may shed some light on China’s adjustment of energy structure under the pressure of addressing climate change and hence provide decision support for the acceleration of renewable energy utilization in the industrial departments. Copyright Springer Science+Business Media Dordrecht 2015

Suggested Citation

  • Zhaohua Wang & Chen Wang & Jianhua Yin, 2015. "Strategies for addressing climate change on the industrial level: affecting factors to CO 2 emissions of energy-intensive industries in China," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 75(2), pages 303-317, February.
    • Handle: RePEc:spr:nathaz:v:75:y:2015:i:2:p:303-317
    DOI: 10.1007/s11069-014-1115-6
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    References listed on IDEAS

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    1. Rout, Ullash K. & Voβ, Alfred & Singh, Anoop & Fahl, Ulrich & Blesl, Markus & Ó Gallachóir, Brian P., 2011. "Energy and emissions forecast of China over a long-time horizon," Energy, Elsevier, vol. 36(1), pages 1-11.
    2. Wang, Zhaohua & Yang, Zhongmin & Zhang, Yixiang & Yin, Jianhua, 2012. "Energy technology patents–CO2 emissions nexus: An empirical analysis from China," Energy Policy, Elsevier, vol. 42(C), pages 248-260.
    3. Feng, Zhen-Hua & Zou, Le-Le & Wei, Yi-Ming, 2011. "The impact of household consumption on energy use and CO2 emissions in China," Energy, Elsevier, vol. 36(1), pages 656-670.
    4. Zhang, Yan & Zhang, Jinyun & Yang, Zhifeng & Li, Shengsheng, 2011. "Regional differences in the factors that influence China’s energy-related carbon emissions, and potential mitigation strategies," Energy Policy, Elsevier, vol. 39(12), pages 7712-7718.
    5. Zhaohua Wang & Bin Zhang & Jianhua Yin, 2012. "Determinants of the increased CO 2 emission and adaption strategy in Chinese energy-intensive industry," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 62(1), pages 17-30, May.
    6. Li, Der-Chiang & Chang, Che-Jung & Chen, Chien-Chih & Chen, Wen-Chih, 2012. "Forecasting short-term electricity consumption using the adaptive grey-based approach—An Asian case," Omega, Elsevier, vol. 40(6), pages 767-773.
    7. Lin, Boqiang & Wu, Ya & Zhang, Li, 2012. "Electricity saving potential of the power generation industry in China," Energy, Elsevier, vol. 40(1), pages 307-316.
    8. Pao, Hsiao-Tien & Fu, Hsin-Chia & Tseng, Cheng-Lung, 2012. "Forecasting of CO2 emissions, energy consumption and economic growth in China using an improved grey model," Energy, Elsevier, vol. 40(1), pages 400-409.
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    Cited by:

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    2. Yuanfang Wang & Qijin Geng & Xiaohui Si & Liping Kan, 2021. "Coupling and coordination analysis of urbanization, economy and environment of Shandong Province, China," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 23(7), pages 10397-10415, July.
    3. Antonakakis, Nikolaos & Chatziantoniou, Ioannis & Filis, George, 2017. "Energy consumption, CO2 emissions, and economic growth: An ethical dilemma," Renewable and Sustainable Energy Reviews, Elsevier, vol. 68(P1), pages 808-824.
    4. Tang, Pengcheng & Yang, Shuwang & Shen, Jun & Fu, Shuke, 2018. "Does China's low-carbon pilot programme really take off? Evidence from land transfer of energy-intensive industry," Energy Policy, Elsevier, vol. 114(C), pages 482-491.
    5. Apergis, Nicholas & Chang, Tsangyao & Gupta, Rangan & Ziramba, Emmanuel, 2016. "Hydroelectricity consumption and economic growth nexus: Evidence from a panel of ten largest hydroelectricity consumers," Renewable and Sustainable Energy Reviews, Elsevier, vol. 62(C), pages 318-325.
    6. Kong-Qing Li & Ran Lu & Rui-Wen Chu & Dou-Dou Ma & Li-Qun Zhu, 2018. "Trends and Driving Forces of Carbon Emissions from Energy Consumption: A Case Study of Nanjing, China," Sustainability, MDPI, vol. 10(12), pages 1-13, November.
    7. Wei-Feng Gong & Zhen-Yue Fan & Chuan-Hui Wang & Li-Ping Wang & Wen-Wen Li, 2022. "Spatial Spillover Effect of Carbon Emissions and Its Influencing Factors in the Yellow River Basin," Sustainability, MDPI, vol. 14(6), pages 1-17, March.
    8. Cui, Qiang & Li, Ye, 2015. "An empirical study on the influencing factors of transportation carbon efficiency: Evidences from fifteen countries," Applied Energy, Elsevier, vol. 141(C), pages 209-217.
    9. Qi Li & Ya-Ni Wei & Yanfang Dong, 2016. "Coupling analysis of China’s urbanization and carbon emissions: example from Hubei Province," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 81(2), pages 1333-1348, March.

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