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Investigating drivers of CO2 emission in China’s heavy industry: A quantile regression analysis

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  • Xu, Bin
  • Lin, Boqiang

Abstract

High energy-consuming heavy industry is one of the main sources of China’s carbon dioxide (CO2) emissions. Based on 2005–2017 panel data of China’s 30 provinces, this paper uses a quantile regression model to investigate CO2 emissions in the heavy industry. The empirical results show that economic growth exerts a stronger influence on the heavy industry’s CO2 emissions in the 25th-50th quantile provinces, due to the difference in the fixed asset investment and heavy industrial output. The impact of urbanization on CO2 emissions in the 10th-25th quantile provinces is lower than that in other quantile provinces because these provinces have the least number of college graduates. Energy efficiency has a smaller impact on CO2 emissions in the upper 90th quantile province, owing to the difference in R&D personnel investment and the number of patents granted. Similarly, environmental regulations have minimal impact on CO2 emissions in the upper 90th quantile province, since the growth rate of industrial pollution treatment investment in these provinces is the lowest. However, the impact of energy consumption structure on CO2 emissions in the 10th-25th and 25th-50th quantile provinces is the highest, because of the provincial differences in coal consumption.

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  • Xu, Bin & Lin, Boqiang, 2020. "Investigating drivers of CO2 emission in China’s heavy industry: A quantile regression analysis," Energy, Elsevier, vol. 206(C).
    • Handle: RePEc:eee:energy:v:206:y:2020:i:c:s0360544220312664
    DOI: 10.1016/j.energy.2020.118159
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    1. Kaab, Ali & Sharifi, Mohammad & Mobli, Hossein & Nabavi-Pelesaraei, Ashkan & Chau, Kwok-wing, 2019. "Use of optimization techniques for energy use efficiency and environmental life cycle assessment modification in sugarcane production," Energy, Elsevier, vol. 181(C), pages 1298-1320.
    2. Sun, Huaping & Geng, Yong & Hu, Lingxiang & Shi, Longyu & Xu, Tong, 2018. "Measuring China's new energy vehicle patents: A social network analysis approach," Energy, Elsevier, vol. 153(C), pages 685-693.
    3. Pedroni, Peter, 2004. "Panel Cointegration: Asymptotic And Finite Sample Properties Of Pooled Time Series Tests With An Application To The Ppp Hypothesis," Econometric Theory, Cambridge University Press, vol. 20(3), pages 597-625, June.
    4. Georges Bresson & Badi H. Baltagi & Alain Pirotte, 2007. "Panel unit root tests and spatial dependence," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 22(2), pages 339-360.
    5. Liu, Kui & Bai, Hongkun & Yin, Shuo & Lin, Boqiang, 2018. "Factor substitution and decomposition of carbon intensity in China's heavy industry," Energy, Elsevier, vol. 145(C), pages 582-591.
    6. Lin, Boqiang & Xu, Bin, 2018. "Factors affecting CO2 emissions in China's agriculture sector: A quantile regression," Renewable and Sustainable Energy Reviews, Elsevier, vol. 94(C), pages 15-27.
    7. Pinto, Raphael Guimarães D. & Szklo, Alexandre S. & Rathmann, Regis, 2018. "CO2 emissions mitigation strategy in the Brazilian iron and steel sector–From structural to intensity effects," Energy Policy, Elsevier, vol. 114(C), pages 380-393.
    8. Yang, Mian & Yang, Fuxia & Sun, Chuanwang, 2018. "Factor market distortion correction, resource reallocation and potential productivity gains: An empirical study on China's heavy industry sector," Energy Economics, Elsevier, vol. 69(C), pages 270-279.
    9. Cheng, Fenfen & Yang, Shanlin & Zhou, Kaile, 2020. "Quantile partial adjustment model with application to predicting energy demand in China," Energy, Elsevier, vol. 191(C).
    10. Xu, Bin & Lin, Boqiang, 2016. "Assessing CO2 emissions in China’s iron and steel industry: A dynamic vector autoregression model," Applied Energy, Elsevier, vol. 161(C), pages 375-386.
    11. 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.
    12. Gale A. Boyd and Jonathan M. Lee, 2020. "Relative Effectiveness of Energy Efficiency Programs versus Market Based Climate Policies in the Chemical Industry," The Energy Journal, International Association for Energy Economics, vol. 0(Number 3), pages 39-62.
    13. Wu, Jianxin & Ma, Chunbo & Tang, Kai, 2019. "The static and dynamic heterogeneity and determinants of marginal abatement cost of CO2 emissions in Chinese cities," Energy, Elsevier, vol. 178(C), pages 685-694.
    14. Lu, Hongfang & Ma, Xin & Azimi, Mohammadamin, 2020. "US natural gas consumption prediction using an improved kernel-based nonlinear extension of the Arps decline model," Energy, Elsevier, vol. 194(C).
    15. Tan, Ruipeng & Liu, Kui & Lin, Boqiang, 2018. "Transportation infrastructure development and China’s energy intensive industries - A road development perspective," Energy, Elsevier, vol. 149(C), pages 587-596.
    16. Wu, Yunna & Wang, Jing & Ji, Shaoyu & Song, Zixin, 2020. "Renewable energy investment risk assessment for nations along China’s Belt & Road Initiative: An ANP-cloud model method," Energy, Elsevier, vol. 190(C).
    17. Nabavi-Pelesaraei, Ashkan & Rafiee, Shahin & Mohtasebi, Seyed Saeid & Hosseinzadeh-Bandbafha, Homa & Chau, Kwok-wing, 2019. "Assessment of optimized pattern in milling factories of rice production based on energy, environmental and economic objectives," Energy, Elsevier, vol. 169(C), pages 1259-1273.
    18. Joakim Westerlund, 2009. "A note on the use of the LLC panel unit root test," Empirical Economics, Springer, vol. 37(3), pages 517-531, December.
    19. Xu, Bin & Lin, Boqiang, 2019. "Can expanding natural gas consumption reduce China's CO2 emissions?," Energy Economics, Elsevier, vol. 81(C), pages 393-407.
    20. Xu, Guangyue & Wang, Weimin, 2020. "China’s energy consumption in construction and building sectors: An outlook to 2100," Energy, Elsevier, vol. 195(C).
    21. Salisu, Afees A. & Adediran, Idris A., 2019. "Assessing the inflation hedging potential of coal and iron ore in Australia," Resources Policy, Elsevier, vol. 63(C), pages 1-1.
    22. Lin, Boqiang & Xu, Bin, 2018. "Growth of industrial CO2 emissions in Shanghai city: Evidence from a dynamic vector autoregression analysis," Energy, Elsevier, vol. 151(C), pages 167-177.
    23. Albulescu, Claudiu Tiberiu & Tiwari, Aviral Kumar & Yoon, Seong-Min & Kang, Sang Hoon, 2019. "FDI, income, and environmental pollution in Latin America: Replication and extension using panel quantiles regression analysis," Energy Economics, Elsevier, vol. 84(C).
    24. Cai, Wenjia & Wang, Can & Wang, Ke & Zhang, Ying & Chen, Jining, 2007. "Scenario analysis on CO2 emissions reduction potential in China's electricity sector," Energy Policy, Elsevier, vol. 35(12), pages 6445-6456, December.
    25. Song, Yi & Huang, Jian-Bai & Feng, Chao, 2018. "Decomposition of energy-related CO2 emissions in China's iron and steel industry: A comprehensive decomposition framework," Resources Policy, Elsevier, vol. 59(C), pages 103-116.
    26. Gong, Bing & Zheng, Xiaochen & Guo, Qing & Ordieres-Meré, Joaquín, 2019. "Discovering the patterns of energy consumption, GDP, and CO2 emissions in China using the cluster method," Energy, Elsevier, vol. 166(C), pages 1149-1167.
    27. Brandão, Lucas G.L. & Ehrl, Philipp, 2019. "International R&D spillovers to the electric power industries," Energy, Elsevier, vol. 182(C), pages 424-432.
    28. Koenker, Roger W & Bassett, Gilbert, Jr, 1978. "Regression Quantiles," Econometrica, Econometric Society, vol. 46(1), pages 33-50, January.
    29. Tabatabaie, Seyed Mohammad Hossein & Rafiee, Shahin & Keyhani, Alireza & Heidari, Mohammad Davoud, 2013. "Energy use pattern and sensitivity analysis of energy inputs and input costs for pear production in Iran," Renewable Energy, Elsevier, vol. 51(C), pages 7-12.
    30. Xia, Fang & Lu, Xi & Song, Feng, 2020. "The role of feed-in tariff in the curtailment of wind power in China," Energy Economics, Elsevier, vol. 86(C).
    31. Roger Koenker & Zhijie Xiao, 2002. "Inference on the Quantile Regression Process," Econometrica, Econometric Society, vol. 70(4), pages 1583-1612, July.
    32. Karen Fisher-Vanden, 2003. "The Effect of Market Reforms on Structural Change: Implications for Energy Use and Carbon Emissions in China," The Energy Journal, International Association for Energy Economics, vol. 0(Number 3), pages 27-62.
    33. Wang, Ke & Wang, Can & Lu, Xuedu & Chen, Jining, 2007. "Scenario analysis on CO2 emissions reduction potential in China's iron and steel industry," Energy Policy, Elsevier, vol. 35(4), pages 2320-2335, April.
    34. Levin, Andrew & Lin, Chien-Fu & James Chu, Chia-Shang, 2002. "Unit root tests in panel data: asymptotic and finite-sample properties," Journal of Econometrics, Elsevier, vol. 108(1), pages 1-24, May.
    35. Ghasemi-Mobtaker, Hassan & Kaab, Ali & Rafiee, Shahin, 2020. "Application of life cycle analysis to assess environmental sustainability of wheat cultivation in the west of Iran," Energy, Elsevier, vol. 193(C).
    Full references (including those not matched with items on IDEAS)

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