IDEAS home Printed from https://ideas.repec.org/a/eee/enepol/v98y2016icp328-342.html
   My bibliography  Save this article

A quantile regression analysis of China's provincial CO2 emissions: Where does the difference lie?

Author

Listed:
  • Xu, Bin
  • Lin, Boqiang

Abstract

China is already the largest carbon dioxide emitter in the world. This paper adopts provincial panel data from 1990 to 2014 and employs quantile regression model to investigate the influencing factors of China's CO2 emissions. The results show that economic growth plays a dominant role in the growth of CO2 emissions due to massive fixed–asset investment and export trade. The influences of energy intensity on the lower 10th and upper 90th quantile provinces are stronger than those in the 25th–50th quantile provinces because of big differences in R&D expenditure and human resources distribution. The impact of urbanization increases continuously from the lower 10th quantile provinces to the 10th–25th, 25th–50th, 50th–75th, 75th–90th and upper 90th quantile provinces, owing to the differences in R&D personnel, real estate development and motor–vehicle ownership. The effect of industrialization on the upper 90th quantile provinces is greater than those on other quantile provinces on account of the differences in the industrial scale and the development of the building industry. Thus, the heterogeneity effects of influencing factors on different quantile provinces should be taken into consideration when discussing the mitigation of CO2 emissions in China.

Suggested Citation

  • Xu, Bin & Lin, Boqiang, 2016. "A quantile regression analysis of China's provincial CO2 emissions: Where does the difference lie?," Energy Policy, Elsevier, vol. 98(C), pages 328-342.
    • Handle: RePEc:eee:enepol:v:98:y:2016:i:c:p:328-342
    DOI: 10.1016/j.enpol.2016.09.003
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0301421516304694
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.enpol.2016.09.003?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Marimoutou, Vêlayoudom & Soury, Manel, 2015. "Energy markets and CO2 emissions: Analysis by stochastic copula autoregressive model," Energy, Elsevier, vol. 88(C), pages 417-429.
    2. Vêlayoudom Marimoutou & Manel Soury, 2015. "Energy markets and CO2 emissions: Analysis by stochastic copula autoregressive model," Post-Print hal-01456125, HAL.
    3. Ning Chang & Michael L. Lahr, 2016. "Changes in China’s production-source CO 2 emissions: insights from structural decomposition analysis and linkage analysis," Economic Systems Research, Taylor & Francis Journals, vol. 28(2), pages 224-242, June.
    4. Baek, Jungho, 2015. "Environmental Kuznets curve for CO2 emissions: The case of Arctic countries," Energy Economics, Elsevier, vol. 50(C), pages 13-17.
    5. Chen, Zhan-Ming & Liu, Yu & Qin, Ping & Zhang, Bo & Lester, Leo & Chen, Guanghua & Guo, Yumei & Zheng, Xinye, 2015. "Environmental externality of coal use in China: Welfare effect and tax regulation," Applied Energy, Elsevier, vol. 156(C), pages 16-31.
    6. Zhiyong Yang & Wenjie Dong & Jinfeng Xiu & Rufeng Dai & Jieming Chou, 2015. "Structural Path Analysis of Fossil Fuel Based CO2 Emissions: A Case Study for China," PLOS ONE, Public Library of Science, vol. 10(9), pages 1-25, September.
    7. Shao, Shuai & Liu, Jianghua & Geng, Yong & Miao, Zhuang & Yang, Yingchun, 2016. "Uncovering driving factors of carbon emissions from China’s mining sector," Applied Energy, Elsevier, vol. 166(C), pages 220-238.
    8. Li, Ke & Lin, Boqiang, 2016. "Impact of energy technology patents in China: Evidence from a panel cointegration and error correction model," Energy Policy, Elsevier, vol. 89(C), pages 214-223.
    9. Alexander Aue & Rex C. Y. Cheung & Thomas C. M. Lee & Ming Zhong, 2014. "Segmented Model Selection in Quantile Regression Using the Minimum Description Length Principle," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 109(507), pages 1241-1256, September.
    10. Tang, Bo, 2015. "Real exchange rate and economic growth in China: A cointegrated VAR approach," China Economic Review, Elsevier, vol. 34(C), pages 293-310.
    11. Lin, Boqiang & Du, Kerui, 2015. "Energy and CO2 emissions performance in China's regional economies: Do market-oriented reforms matter?," Energy Policy, Elsevier, vol. 78(C), pages 113-124.
    12. Moshe Buchinsky, 1998. "Recent Advances in Quantile Regression Models: A Practical Guideline for Empirical Research," Journal of Human Resources, University of Wisconsin Press, vol. 33(1), pages 88-126.
    13. Habib, Mohammad Ahsan & Hasanuzzaman, M. & Hosenuzzaman, M. & Salman, Asif & Mehadi, Md Riyad, 2016. "Energy consumption, energy saving and emission reduction of a garment industrial building in Bangladesh," Energy, Elsevier, vol. 112(C), pages 91-100.
    14. Bera Anil K. & Galvao Antonio F. & Montes-Rojas Gabriel V. & Park Sung Y., 2016. "Asymmetric Laplace Regression: Maximum Likelihood, Maximum Entropy and Quantile Regression," Journal of Econometric Methods, De Gruyter, vol. 5(1), pages 79-101, January.
    15. Makridou, Georgia & Andriosopoulos, Kostas & Doumpos, Michael & Zopounidis, Constantin, 2016. "Measuring the efficiency of energy-intensive industries across European countries," Energy Policy, Elsevier, vol. 88(C), pages 573-583.
    16. Kang, Lixia & Liu, Yongzhong, 2015. "Multi-objective optimization on a heat exchanger network retrofit with a heat pump and analysis of CO2 emissions control," Applied Energy, Elsevier, vol. 154(C), pages 696-708.
    17. Ando, Michihito, 2015. "Dreams of urbanization: Quantitative case studies on the local impacts of nuclear power facilities using the synthetic control method," Journal of Urban Economics, Elsevier, vol. 85(C), pages 68-85.
    18. Unknown, 2016. "Energy for Sustainable Development," Conference Proceedings 253270, Guru Arjan Dev Institute of Development Studies (IDSAsr).
    19. 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.
    20. 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.
    21. Zhu, Huiming & Duan, Lijun & Guo, Yawei & Yu, Keming, 2016. "The effects of FDI, economic growth and energy consumption on carbon emissions in ASEAN-5: Evidence from panel quantile regression," Economic Modelling, Elsevier, vol. 58(C), pages 237-248.
    22. Elzen, Michel den & Fekete, Hanna & Höhne, Niklas & Admiraal, Annemiek & Forsell, Nicklas & Hof, Andries F. & Olivier, Jos G.J. & Roelfsema, Mark & van Soest, Heleen, 2016. "Greenhouse gas emissions from current and enhanced policies of China until 2030: Can emissions peak before 2030?," Energy Policy, Elsevier, vol. 89(C), pages 224-236.
    23. Jochem, Patrick & Babrowski, Sonja & Fichtner, Wolf, 2015. "Assessing CO2 emissions of electric vehicles in Germany in 2030," Transportation Research Part A: Policy and Practice, Elsevier, vol. 78(C), pages 68-83.
    24. Koenker,Roger, 2005. "Quantile Regression," Cambridge Books, Cambridge University Press, number 9780521845731.
    25. Granger, C. W. J., 1988. "Some recent development in a concept of causality," Journal of Econometrics, Elsevier, vol. 39(1-2), pages 199-211.
    26. Zhao, Xiaoli & Yin, Haitao & Zhao, Yue, 2015. "Impact of environmental regulations on the efficiency and CO2 emissions of power plants in China," Applied Energy, Elsevier, vol. 149(C), pages 238-247.
    27. Xu, Bin & Lin, Boqiang, 2016. "Regional differences in the CO2 emissions of China's iron and steel industry: Regional heterogeneity," Energy Policy, Elsevier, vol. 88(C), pages 422-434.
    28. Chen, Jiandong & Cheng, Shulei & Song, Malin & Wang, Jia, 2016. "Interregional differences of coal carbon dioxide emissions in China," Energy Policy, Elsevier, vol. 96(C), pages 1-13.
    29. Kazi Sohag & Rawshan Begum & Sharifah Abdullah, 2015. "Dynamic impact of household consumption on its CO 2 emissions in Malaysia," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 17(5), pages 1031-1043, October.
    30. 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.
    31. Valeria Di Cosmo & Marie Hyland, 2015. "Decomposing patterns of emission intensity in the EU and China: how much does trade matter?," Journal of Environmental Planning and Management, Taylor & Francis Journals, vol. 58(12), pages 2176-2192, December.
    32. Alper, Aslan & Oguz, Ocal, 2016. "The role of renewable energy consumption in economic growth: Evidence from asymmetric causality," Renewable and Sustainable Energy Reviews, Elsevier, vol. 60(C), pages 953-959.
    33. Lin, Boqiang & Wang, Xiaolei, 2015. "Carbon emissions from energy intensive industry in China: Evidence from the iron & steel industry," Renewable and Sustainable Energy Reviews, Elsevier, vol. 47(C), pages 746-754.
    34. Pablo-Romero, María del P. & Pozo-Barajas, Rafael & Sánchez-Braza, Antonio, 2015. "Understanding local CO2 emissions reduction targets," Renewable and Sustainable Energy Reviews, Elsevier, vol. 48(C), pages 347-355.
    35. Shaojian Wang & Chuanglin Fang & Guangdong Li, 2015. "Spatiotemporal Characteristics, Determinants and Scenario Analysis of CO2 Emissions in China Using Provincial Panel Data," PLOS ONE, Public Library of Science, vol. 10(9), pages 1-23, September.
    36. Lin, Boqiang & Liu, Chang, 2016. "Why is electricity consumption inconsistent with economic growth in China?," Energy Policy, Elsevier, vol. 88(C), pages 310-316.
    37. Yuan, Baolong & Ren, Shenggang & Chen, Xiaohong, 2015. "The effects of urbanization, consumption ratio and consumption structure on residential indirect CO2 emissions in China: A regional comparative analysis," Applied Energy, Elsevier, vol. 140(C), pages 94-106.
    38. Koenker, Roger W & Bassett, Gilbert, Jr, 1978. "Regression Quantiles," Econometrica, Econometric Society, vol. 46(1), pages 33-50, January.
    39. Cansino, José M. & Sánchez-Braza, Antonio & Rodríguez-Arévalo, María L., 2015. "Driving forces of Spain׳s CO2 emissions: A LMDI decomposition approach," Renewable and Sustainable Energy Reviews, Elsevier, vol. 48(C), pages 749-759.
    40. F. Wu & P. Zhou & D. Zhou, 2015. "Measuring Energy Congestion in Chinese Industrial Sectors: A Slacks-Based DEA Approach," Computational Economics, Springer;Society for Computational Economics, vol. 46(3), pages 479-494, October.
    41. Maciejowska, Katarzyna & Nowotarski, Jakub & Weron, Rafał, 2016. "Probabilistic forecasting of electricity spot prices using Factor Quantile Regression Averaging," International Journal of Forecasting, Elsevier, vol. 32(3), pages 957-965.
    42. Vêlayoudom Marimoutou & Manel Soury, 2015. "Energy Markets and CO2 Emissions: Analysis by Stochastic Copula Autoregressive Model," Working Papers halshs-01148746, HAL.
    43. Zhu, Huiming & Guo, Yawei & You, Wanhai & Xu, Yaqin, 2016. "The heterogeneity dependence between crude oil price changes and industry stock market returns in China: Evidence from a quantile regression approach," Energy Economics, Elsevier, vol. 55(C), pages 30-41.
    44. Xu, Bin & Lin, Boqiang, 2015. "How industrialization and urbanization process impacts on CO2 emissions in China: Evidence from nonparametric additive regression models," Energy Economics, Elsevier, vol. 48(C), pages 188-202.
    45. He, Qianchuan & Kong, Linglong & Wang, Yanhua & Wang, Sijian & Chan, Timothy A. & Holland, Eric, 2016. "Regularized quantile regression under heterogeneous sparsity with application to quantitative genetic traits," Computational Statistics & Data Analysis, Elsevier, vol. 95(C), pages 222-239.
    46. Wang, Jianjun & Li, Li, 2016. "Sustainable energy development scenario forecasting and energy saving policy analysis of China," Renewable and Sustainable Energy Reviews, Elsevier, vol. 58(C), pages 718-724.
    47. De Silva, P.N.K. & Simons, S.J.R. & Stevens, P., 2016. "Economic impact analysis of natural gas development and the policy implications," Energy Policy, Elsevier, vol. 88(C), pages 639-651.
    48. Xu, Bin & Lin, Boqiang, 2016. "Differences in regional emissions in China's transport sector: Determinants and reduction strategies," Energy, Elsevier, vol. 95(C), pages 459-470.
    49. Buchinsky, Moshe, 1995. "Estimating the asymptotic covariance matrix for quantile regression models a Monte Carlo study," Journal of Econometrics, Elsevier, vol. 68(2), pages 303-338, August.
    50. Jinpei Ou & Xiaoping Liu & Xia Li & Meifang Li & Wenkai Li, 2015. "Evaluation of NPP-VIIRS Nighttime Light Data for Mapping Global Fossil Fuel Combustion CO2 Emissions: A Comparison with DMSP-OLS Nighttime Light Data," PLOS ONE, Public Library of Science, vol. 10(9), pages 1-20, September.
    51. Roger Koenker & Kevin F. Hallock, 2001. "Quantile Regression," Journal of Economic Perspectives, American Economic Association, vol. 15(4), pages 143-156, Fall.
    52. Zhao Liu & Ling Li & Yue-Jun Zhang, 2015. "Investigating the CO 2 emission differences among China’s transport sectors and their influencing factors," 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. 77(2), pages 1323-1343, June.
    53. Vêlayoudom Marimoutou & Manel Soury, 2015. "Energy Markets and CO2 Emissions: Analysis by Stochastic Copula Autoregressive Model," AMSE Working Papers 1520, Aix-Marseille School of Economics, France.
    54. Sun, Chuanwang & Yuan, Xiang & Yao, Xin, 2016. "Social acceptance towards the air pollution in China: Evidence from public's willingness to pay for smog mitigation," Energy Policy, Elsevier, vol. 92(C), pages 313-324.
    55. Subash Dhar & Charles Marpaung, 2015. "Technology priorities for transport in Asia: assessment of economy-wide CO 2 emissions reduction for Lebanon," Climatic Change, Springer, vol. 131(3), pages 451-464, August.
    56. Wang, Xiaolei & Lin, Boqiang, 2016. "How to reduce CO2 emissions in China׳s iron and steel industry," Renewable and Sustainable Energy Reviews, Elsevier, vol. 57(C), pages 1496-1505.
    57. Moutinho, Victor & Moreira, António Carrizo & Silva, Pedro Miguel, 2015. "The driving forces of change in energy-related CO2 emissions in Eastern, Western, Northern and Southern Europe: The LMDI approach to decomposition analysis," Renewable and Sustainable Energy Reviews, Elsevier, vol. 50(C), pages 1485-1499.
    58. Shahiduzzaman, Md. & Layton, Allan, 2015. "Changes in CO2 emissions over business cycle recessions and expansions in the United States: A decomposition analysis," Applied Energy, Elsevier, vol. 150(C), pages 25-35.
    59. Thomakos, Dimitrios D. & Alexopoulos, Thomas A., 2016. "Carbon intensity as a proxy for environmental performance and the informational content of the EPI," Energy Policy, Elsevier, vol. 94(C), pages 179-190.
    60. Cansino, José M. & Román, Rocío & Ordóñez, Manuel, 2016. "Main drivers of changes in CO2 emissions in the Spanish economy: A structural decomposition analysis," Energy Policy, Elsevier, vol. 89(C), pages 150-159.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Xu, Bin & Lin, Boqiang, 2016. "Differences in regional emissions in China's transport sector: Determinants and reduction strategies," Energy, Elsevier, vol. 95(C), pages 459-470.
    2. Khan, Ali Nawaz & En, Xie & Raza, Muhammad Yousaf & Khan, Naseer Abbas & Ali, Ahsan, 2020. "Sectorial study of technological progress and CO2 emission: Insights from a developing economy," Technological Forecasting and Social Change, Elsevier, vol. 151(C).
    3. Chao-Qun Ma & Jiang-Long Liu & Yi-Shuai Ren & Yong Jiang, 2019. "The Impact of Economic Growth, FDI and Energy Intensity on China’s Manufacturing Industry’s CO 2 Emissions: An Empirical Study Based on the Fixed-Effect Panel Quantile Regression Model," Energies, MDPI, vol. 12(24), pages 1-16, December.
    4. Chen, Jinyu & Liang, Zhipeng & Ding, Qian & Liu, Zhenhua, 2022. "Quantile connectedness between energy, metal, and carbon markets," International Review of Financial Analysis, Elsevier, vol. 83(C).
    5. Xu, Bin & Lin, Boqiang, 2017. "Assessing CO2 emissions in China's iron and steel industry: A nonparametric additive regression approach," Renewable and Sustainable Energy Reviews, Elsevier, vol. 72(C), pages 325-337.
    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. Marcelo Fernandes & Emmanuel Guerre & Eduardo Horta, 2021. "Smoothing Quantile Regressions," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 39(1), pages 338-357, January.
    8. Xu, Bin & Lin, Boqiang, 2017. "Factors affecting CO2 emissions in China’s agriculture sector: Evidence from geographically weighted regression model," Energy Policy, Elsevier, vol. 104(C), pages 404-414.
    9. LI, Tao & SUN, Laixiang & ZOU, Liang, 2009. "State ownership and corporate performance: A quantile regression analysis of Chinese listed companies," China Economic Review, Elsevier, vol. 20(4), pages 703-716, December.
    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. Yi Yao & Lixin Tian & Guangxi Cao, 2022. "The Information Spillover among the Carbon Market, Energy Market, and Stock Market: A Case Study of China’s Pilot Carbon Markets," Sustainability, MDPI, vol. 14(8), pages 1-18, April.
    12. Bassetti, Federico & De Giuli, Maria Elena & Nicolino, Enrica & Tarantola, Claudia, 2018. "Multivariate dependence analysis via tree copula models: An application to one-year forward energy contracts," European Journal of Operational Research, Elsevier, vol. 269(3), pages 1107-1121.
    13. Fattouh, Bassam & Scaramozzino, Pasquale & Harris, Laurence, 2005. "Capital structure in South Korea: a quantile regression approach," Journal of Development Economics, Elsevier, vol. 76(1), pages 231-250, February.
    14. Wiji Arulampalam & Alison Booth & Mark Bryan, 2010. "Are there asymmetries in the effects of training on the conditional male wage distribution?," Journal of Population Economics, Springer;European Society for Population Economics, vol. 23(1), pages 251-272, January.
    15. Shahbaz, Muhammad & Haouas, Ilham & Hoang, Thi Hong Van, 2019. "Economic growth and environmental degradation in Vietnam: Is the environmental Kuznets curve a complete picture?," Emerging Markets Review, Elsevier, vol. 38(C), pages 197-218.
    16. Micheline Goedhuys & Leo Sleuwaegen, 2010. "High-growth entrepreneurial firms in Africa: a quantile regression approach," Small Business Economics, Springer, vol. 34(1), pages 31-51, January.
    17. Halkos, George E., 2011. "Nonparametric modelling of biodiversity: Determinants of threatened species," Journal of Policy Modeling, Elsevier, vol. 33(4), pages 618-635, July.
    18. Guilherme Resende Oliveira & Benjamin Miranda Tabak & José Guilherme de Lara Resende & Daniel Oliveira Cajueiro, 2012. "Determinantes da Estrutura de Capital das Empresas Brasileiras: uma abordagem em regressão quantílica," Working Papers Series 272, Central Bank of Brazil, Research Department.
    19. Jesus regstdpo-Cuaresma & Neil Foster & Robert Stehrer, 2011. "Determinants of Regional Economic Growth by Quantile," Regional Studies, Taylor & Francis Journals, vol. 45(6), pages 809-826.
    20. Gilles Dufrenot & Valerie Mignon & Charalambos Tsangarides, 2010. "The trade-growth nexus in the developing countries: a quantile regression approach," Review of World Economics (Weltwirtschaftliches Archiv), Springer;Institut für Weltwirtschaft (Kiel Institute for the World Economy), vol. 146(4), pages 731-761, December.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:enepol:v:98:y:2016:i:c:p:328-342. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/enpol .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.