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How to Efficiently Reduce the Carbon Intensity of the Heavy Industry in China? Using Quantile Regression Approach

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  • Bin Xu

    (School of Management, China Institute for Studies in Energy Policy, Collaborative Innovation Center for Energy Economics and Energy Policy, Xiamen University, Xiamen 361005, China)

Abstract

This decoupling between carbon dioxide emissions and the heavy industry is one of the main topics of government managers. This paper uses the quantile regression approach to investigate the carbon intensity of China’s heavy industry, based on 2005–2019 panel data. The main findings are as follows: (1) incentive-based environmental regulations have the greater impact on the carbon intensity in Jiangsu, Shandong, Zhejiang, Henan, Liaoning, and Shaanxi, because these provinces invest more in environmental governance and levy higher resource taxes; (2) the impact of mandatory environmental regulations on carbon intensity in Beijing, Tianjin, and Guangdong provinces is smaller, since these three provinces have the fewest enacted environmental laws and rely mainly on market incentives; (3) conversely, foreign direct investment has contributed most to carbon intensity reduction in Tianjin, Beijing, and Guangdong provinces, because these three have attracted more technologically advanced foreign-funded enterprises; (4) technological progress contributes more to the carbon intensity in the low quantile provinces, because these provinces have more patented technologies; (5) the carbon intensity of Shaanxi, Shanxi, and Inner Mongolia provinces is most affected by energy consumption structures because of their over-reliance on highly polluting coal.

Suggested Citation

  • Bin Xu, 2022. "How to Efficiently Reduce the Carbon Intensity of the Heavy Industry in China? Using Quantile Regression Approach," IJERPH, MDPI, vol. 19(19), pages 1-24, October.
    • Handle: RePEc:gam:jijerp:v:19:y:2022:i:19:p:12865-:d:935981
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    as
    1. Berner, Anne & Bruns, Stephan & Moneta, Alessio & Stern, David I., 2022. "Do energy efficiency improvements reduce energy use? Empirical evidence on the economy-wide rebound effect in Europe and the United States," Energy Economics, Elsevier, vol. 110(C).
    2. Andrea Amerio & Andrea Brambilla & Alessandro Morganti & Andrea Aguglia & Davide Bianchi & Francesca Santi & Luigi Costantini & Anna Odone & Alessandra Costanza & Carlo Signorelli & Gianluca Serafini , 2020. "COVID-19 Lockdown: Housing Built Environment’s Effects on Mental Health," IJERPH, MDPI, vol. 17(16), pages 1-10, August.
    3. Jia, Zhijie & Wen, Shiyan & Lin, Boqiang, 2021. "The effects and reacts of COVID-19 pandemic and international oil price on energy, economy, and environment in China," Applied Energy, Elsevier, vol. 302(C).
    4. Faustin Habyarimana & Temesgen Zewotir & Shaun Ramroop, 2017. "Structured Additive Quantile Regression for Assessing the Determinants of Childhood Anemia in Rwanda," IJERPH, MDPI, vol. 14(6), pages 1-15, June.
    5. Zhang, Ruchuan & Wei, Qian & Li, Aijun & Ren, LiYing, 2022. "Measuring efficiency and technology inequality of China's electricity generation and transmission system: A new approach of network Data Envelopment Analysis prospect cross-efficiency models," Energy, Elsevier, vol. 246(C).
    6. Liu, Da & Liu, Yumeng & Sun, Kun, 2021. "Policy impact of cancellation of wind and photovoltaic subsidy on power generation companies in China," Renewable Energy, Elsevier, vol. 177(C), pages 134-147.
    7. Tan, Xiujie & Choi, Yongrok & Wang, Banban & Huang, Xiaoqi, 2020. "Does China's carbon regulatory policy improve total factor carbon efficiency? A fixed-effect panel stochastic frontier analysis," Technological Forecasting and Social Change, Elsevier, vol. 160(C).
    8. Yu, Shiwei & Zheng, Shuhong & Li, Xia, 2018. "The achievement of the carbon emissions peak in China: The role of energy consumption structure optimization," Energy Economics, Elsevier, vol. 74(C), pages 693-707.
    9. Csereklyei, Zsuzsanna & Stern, David I., 2020. "Flying More Efficiently: Joint Impacts of Fuel Prices, Capital Costs and Fleet Size on Airline Fleet Fuel Economy," Ecological Economics, Elsevier, vol. 175(C).
    10. Engle, Robert & Granger, Clive, 2015. "Co-integration and error correction: Representation, estimation, and testing," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 39(3), pages 106-135.
    11. Xu, Bin & Luo, Yuemei & Xu, Renjing & Chen, Jianbao, 2021. "Exploring the driving forces of distributed energy resources in China: Using a semiparametric regression model," Energy, Elsevier, vol. 236(C).
    12. Abhinav Bhaskar & Mohsen Assadi & Homam Nikpey Somehsaraei, 2020. "Decarbonization of the Iron and Steel Industry with Direct Reduction of Iron Ore with Green Hydrogen," Energies, MDPI, vol. 13(3), pages 1-23, February.
    13. Pei Li & Jinyuan Xin & Xiaoping Bai & Yuesi Wang & Shigong Wang & Shixi Liu & Xiaoxin Feng, 2013. "Observational Studies and a Statistical Early Warning of Surface Ozone Pollution in Tangshan, the Largest Heavy Industry City of North China," IJERPH, MDPI, vol. 10(3), pages 1-14, March.
    14. David I. Stern, 2017. "The environmental Kuznets curve after 25 years," Journal of Bioeconomics, Springer, vol. 19(1), pages 7-28, April.
    15. Xie, Kechang & Li, Wenying & Zhao, Wei, 2010. "Coal chemical industry and its sustainable development in China," Energy, Elsevier, vol. 35(11), pages 4349-4355.
    16. Seema Jayachandran, 2021. "Social Norms as a Barrier to Women’s Employment in Developing Countries," IMF Economic Review, Palgrave Macmillan;International Monetary Fund, vol. 69(3), pages 576-595, September.
    17. Yue, Hui & Worrell, Ernst & Crijns-Graus, Wina, 2021. "Impacts of regional industrial electricity savings on the development of future coal capacity per electricity grid and related air pollution emissions – A case study for China," Applied Energy, Elsevier, vol. 282(PB).
    18. Xu, Bin & Lin, Boqiang, 2021. "Investigating spatial variability of CO2 emissions in heavy industry: Evidence from a geographically weighted regression model," Energy Policy, Elsevier, vol. 149(C).
    19. Festus Victor Bekun, 2022. "Mitigating Emissions in India: Accounting for the Role of Real Income, Renewable Energy Consumption and Investment in Energy," International Journal of Energy Economics and Policy, Econjournals, vol. 12(1), pages 188-192.
    20. Jia, Zhijie & Wen, Shiyan & Sun, Zao, 2022. "Current relationship between coal consumption and the economic development and China's future carbon mitigation policies," Energy Policy, Elsevier, vol. 162(C).
    21. Ouyang, Xiaoling & Fang, Xingming & Cao, Yan & Sun, Chuanwang, 2020. "Factors behind CO2 emission reduction in Chinese heavy industries: Do environmental regulations matter?," Energy Policy, Elsevier, vol. 145(C).
    22. Vit Ulmann & Anna Kracalikova & Radka Dziedzinska, 2015. "Mycobacteria in Water Used for Personal Hygiene in Heavy Industry and Collieries: A Potential Risk for Employees," IJERPH, MDPI, vol. 12(3), pages 1-8, March.
    23. Andrée, Bo Pieter Johannes & Chamorro, Andres & Spencer, Phoebe & Koomen, Eric & Dogo, Harun, 2019. "Revisiting the relation between economic growth and the environment; a global assessment of deforestation, pollution and carbon emission," Renewable and Sustainable Energy Reviews, Elsevier, vol. 114(C), pages 1-1.
    24. Jia, Zhijie & Wen, Shiyan & Liu, Yu, 2022. "China's urban-rural inequality caused by carbon neutrality: A perspective from carbon footprint and decomposed social welfare," Energy Economics, Elsevier, vol. 113(C).
    25. Cheng, Yuanyuan & Yao, Xin, 2021. "Carbon intensity reduction assessment of renewable energy technology innovation in China: A panel data model with cross-section dependence and slope heterogeneity," Renewable and Sustainable Energy Reviews, Elsevier, vol. 135(C).
    26. Chao Sun & Boya Zhu & Sirong Zhu & Longjiang Zhang & Xiaoan Du & Xiaodong Tan, 2021. "Risk Factors Analysis of Bone Mineral Density Based on Lasso and Quantile Regression in America during 2015–2018," IJERPH, MDPI, vol. 19(1), pages 1-11, December.
    27. Stephan B. Bruns & Alessio Moneta & David I. Stern, 2019. "Macroeconomic time-series evidence that energy efficiency improvements do not save energy," CAMA Working Papers 2019-21, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University.
    28. Alshammari, Yousef M., 2021. "Scenario analysis for energy transition in the chemical industry: An industrial case study in Saudi Arabia," Energy Policy, Elsevier, vol. 150(C).
    29. Zifeng Liang & Manli Zhang & Qingduo Mao & Bingxin Yu & Ben Ma, 2018. "Improvement of Eco-Efficiency in China: A Comparison of Mandatory and Hybrid Environmental Policy Instruments," IJERPH, MDPI, vol. 15(7), pages 1-20, July.
    30. Hu, Yucai & Ren, Shenggang & Wang, Yangjie & Chen, Xiaohong, 2020. "Can carbon emission trading scheme achieve energy conservation and emission reduction? Evidence from the industrial sector in China," Energy Economics, Elsevier, vol. 85(C).
    31. Xu, Renjing & Xu, Bin, 2022. "Exploring the effective way of reducing carbon intensity in the heavy industry using a semiparametric econometric approach," Energy, Elsevier, vol. 243(C).
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