IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v11y2019i17p4650-d261165.html
   My bibliography  Save this article

What Factors Drive Air Pollutants in China? An Analysis from the Perspective of Regional Difference Using a Combined Method of Production Decomposition Analysis and Logarithmic Mean Divisia Index

Author

Listed:
  • Shichun Xu

    (Management School, China University of Mining and Technology, Xuzhou 221116, China)

  • Yongmei Miao

    (Management School, China University of Mining and Technology, Xuzhou 221116, China)

  • Yiwen Li

    (Management School, China University of Mining and Technology, Xuzhou 221116, China)

  • Yifeng Zhou

    (Management School, China University of Mining and Technology, Xuzhou 221116, China)

  • Xiaoxue Ma

    (Management School, China University of Mining and Technology, Xuzhou 221116, China)

  • Zhengxia He

    (Business School, Jiangsu Normal University, Xuzhou 221116, China)

  • Bin Zhao

    (Pacific Northwest National Laboratory, Richland, WA 99352, USA)

  • Shuxiao Wang

    (State Key Joint Laboratory of Environmental Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing 100084, China)

Abstract

Air pollution in China attracts the world’s attention, so it is important to study its driving factors for air pollutants. The combined Production Decomposition Analysis and Logarithmic Mean Divisia Index (PDA–LMDI) model is applied to construct a regional contribution index in this study to explore the regional differences in factors affecting sulfur dioxide (SO 2 ), nitrogen oxides (NO x ), and particulate matter with diameter not greater than 2.5 µm (PM 2.5 ) from 2005 to 2015 in China. The regional emission coefficient had a great inhibitory effect, which reduced SO 2 , NO x , and PM 2.5 by 25,364.9, 10,449.3, and 11,295.3 kilotons (kt) from 2005 to 2015, respectively. For this inhibitory effect, the degree to emission reduction was great for North and East China, followed by South and Central China, and small for Southwest. Northwest. and Northeast China. The regional technical efficiency, technology improvement, capital-energy substitution and labor-energy substitution effects each reduced SO 2 , NO x , and PM 2.5 by about 3500, 3100, and 1500 kt from 2005 to 2015, respectively. For the regional technical efficiency and technology improvement effects, the degree to emission reduction was great in East and Central China, and small in South Northwest and Northeast China. For the regional capital- and labor-energy substitution effects, the degree of emission reduction was great for North East and Central China, and small for Northwest and South China. The regional output proportion effect increased SO 2 , NO x , and PM 2.5 by 1211.2, 320.1, and 277.8 kt from 2005 to 2015, respectively. The national economic growth had a relatively great promoting effect and increased SO 2 , NO x , and PM 2.5 by 26,445.5, 23,827.5, and 11,925.5 kt from 2005 to 2015, respectively. Each region should formulate relevant policies and measures for emission reduction according to local conditions.

Suggested Citation

  • Shichun Xu & Yongmei Miao & Yiwen Li & Yifeng Zhou & Xiaoxue Ma & Zhengxia He & Bin Zhao & Shuxiao Wang, 2019. "What Factors Drive Air Pollutants in China? An Analysis from the Perspective of Regional Difference Using a Combined Method of Production Decomposition Analysis and Logarithmic Mean Divisia Index," Sustainability, MDPI, vol. 11(17), pages 1-19, August.
    • Handle: RePEc:gam:jsusta:v:11:y:2019:i:17:p:4650-:d:261165
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/11/17/4650/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/11/17/4650/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Wang, Zhaohua & Lu, Milin & Wang, Jian-Cai, 2014. "Direct rebound effect on urban residential electricity use: An empirical study in China," Renewable and Sustainable Energy Reviews, Elsevier, vol. 30(C), pages 124-132.
    2. Du, Kerui & Lin, Boqiang, 2015. "Understanding the rapid growth of China's energy consumption: A comprehensive decomposition framework," Energy, Elsevier, vol. 90(P1), pages 570-577.
    3. Wang, Chunhua, 2007. "Decomposing energy productivity change: A distance function approach," Energy, Elsevier, vol. 32(8), pages 1326-1333.
    4. Yang, Xue & Wang, Shaojian & Zhang, Wenzhong & Li, Jiaming & Zou, Yafeng, 2016. "Impacts of energy consumption, energy structure, and treatment technology on SO2 emissions: A multi-scale LMDI decomposition analysis in China," Applied Energy, Elsevier, vol. 184(C), pages 714-726.
    5. Liyun Chen & Qi Duan, 2016. "Decomposition analysis of factors driving CO2 emissions in Chinese provinces based on production-theoretical decomposition analysis," 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. 84(1), pages 267-277, November.
    6. Chen, Jing & Zhou, Chunshan & Wang, Shaojian & Li, Shijie, 2018. "Impacts of energy consumption structure, energy intensity, economic growth, urbanization on PM2.5 concentrations in countries globally," Applied Energy, Elsevier, vol. 230(C), pages 94-105.
    7. Cosimo Magazzino, 2017. "The relationship among economic growth, CO2 emissions, and energy use in the APEC countries: a panel VAR approach," Environment Systems and Decisions, Springer, vol. 37(3), pages 353-366, September.
    8. Liu, Chang & Hong, Tao & Li, Huaifeng & Wang, Lili, 2018. "From club convergence of per capita industrial pollutant emissions to industrial transfer effects: An empirical study across 285 cities in China," Energy Policy, Elsevier, vol. 121(C), pages 300-313.
    9. Gao, Yuning & Zhang, Meichen, 2019. "The measure of technical efficiency of China’s provinces with carbon emission factor and the analysis of the influence of structural variables," Structural Change and Economic Dynamics, Elsevier, vol. 49(C), pages 120-129.
    10. Moutinho, Victor & Madaleno, Mara & Inglesi-Lotz, Roula & Dogan, Eyup, 2018. "Factors affecting CO2 emissions in top countries on renewable energies: A LMDI decomposition application," Renewable and Sustainable Energy Reviews, Elsevier, vol. 90(C), pages 605-622.
    11. Zhang, Xing-Ping & Tan, Ya-Kun & Tan, Qin-Liang & Yuan, Jia-Hai, 2012. "Decomposition of aggregate CO2 emissions within a joint production framework," Energy Economics, Elsevier, vol. 34(4), pages 1088-1097.
    12. Zhou, P. & Ang, B.W., 2008. "Decomposition of aggregate CO2 emissions: A production-theoretical approach," Energy Economics, Elsevier, vol. 30(3), pages 1054-1067, May.
    13. Shichun Xu & Wenwen Zhang & Qinbin Li & Bin Zhao & Shuxiao Wang & Ruyin Long, 2017. "Decomposition Analysis of the Factors that Influence Energy Related Air Pollutant Emission Changes in China Using the SDA Method," Sustainability, MDPI, vol. 9(10), pages 1-18, September.
    14. Song, Malin & Li, Hui, 2019. "Estimating the efficiency of a sustainable Chinese tourism industry using bootstrap technology rectification," Technological Forecasting and Social Change, Elsevier, vol. 143(C), pages 45-54.
    15. Rashid Gill, Abid & Viswanathan, Kuperan K. & Hassan, Sallahuddin, 2018. "The Environmental Kuznets Curve (EKC) and the environmental problem of the day," Renewable and Sustainable Energy Reviews, Elsevier, vol. 81(P2), pages 1636-1642.
    16. Lyu, Wanning & Yuan Li & Dabo Guan & Hongyan Zhao & Qiang Zhang & Zhu Liu, "undated". "Driving forces of Chinese primary air pollution emissions: an index decomposition analysis," Working Paper 428386, Harvard University OpenScholar.
    17. Tan, Ruipeng & Lin, Boqiang, 2018. "What factors lead to the decline of energy intensity in China's energy intensive industries?," Energy Economics, Elsevier, vol. 71(C), pages 213-221.
    18. Song, Malin & Peng, Jun & Wang, Jianlin & Zhao, Jiajia, 2018. "Environmental efficiency and economic growth of China: A Ray slack-based model analysis," European Journal of Operational Research, Elsevier, vol. 269(1), pages 51-63.
    19. Lin, Boqiang & Du, Kerui, 2014. "Decomposing energy intensity change: A combination of index decomposition analysis and production-theoretical decomposition analysis," Applied Energy, Elsevier, vol. 129(C), pages 158-165.
    20. Zhang, Wei & Li, Ke & Zhou, Dequn & Zhang, Wenrui & Gao, Hui, 2016. "Decomposition of intensity of energy-related CO2 emission in Chinese provinces using the LMDI method," Energy Policy, Elsevier, vol. 92(C), pages 369-381.
    21. Xu, Shi-Chun & He, Zheng-Xia & Long, Ru-Yin, 2014. "Factors that influence carbon emissions due to energy consumption in China: Decomposition analysis using LMDI," Applied Energy, Elsevier, vol. 127(C), pages 182-193.
    22. Chen, Han & Huang, Ye & Shen, Huizhong & Chen, Yilin & Ru, Muye & Chen, Yuanchen & Lin, Nan & Su, Shu & Zhuo, Shaojie & Zhong, Qirui & Wang, Xilong & Liu, Junfeng & Li, Bengang & Tao, Shu, 2016. "Modeling temporal variations in global residential energy consumption and pollutant emissions," Applied Energy, Elsevier, vol. 184(C), pages 820-829.
    23. Ji, Xi & Yao, Yixin & Long, Xianling, 2018. "What causes PM2.5 pollution? Cross-economy empirical analysis from socioeconomic perspective," Energy Policy, Elsevier, vol. 119(C), pages 458-472.
    24. Wang, Qunwei & Chiu, Yung-Ho & Chiu, Ching-Ren, 2015. "Driving factors behind carbon dioxide emissions in China: A modified production-theoretical decomposition analysis," Energy Economics, Elsevier, vol. 51(C), pages 252-260.
    25. Seifert, Stefan & Cullmann, Astrid & von Hirschhausen, Christian, 2016. "Technical efficiency and CO2 reduction potentials — An analysis of the German electricity and heat generating sector," Energy Economics, Elsevier, vol. 56(C), pages 9-19.
    26. Peter Rafaj & Markus Amann, 2018. "Decomposing Air Pollutant Emissions in Asia: Determinants and Projections," Energies, MDPI, vol. 11(5), pages 1-14, May.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Xiaoyu Yang & Jianqiang Dong & Xiaopeng Guo, 2023. "Spatial Dependence of SO 2 Emissions and Energy Consumption Structure in Northern China," Sustainability, MDPI, vol. 15(3), pages 1-14, January.

    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. Jun Yang & Yongmei Miao & Yunfan Li & Yiwen Li & Xiaoxue Ma & Shichun Xu & Shuxiao Wang, 2019. "Decomposition Analysis of Factors that Drive the Changes of Major Air Pollutant Emissions in China at a Multi-Regional Level," Sustainability, MDPI, vol. 11(24), pages 1-22, December.
    2. Tan, Ruipeng & Lin, Boqiang, 2018. "What factors lead to the decline of energy intensity in China's energy intensive industries?," Energy Economics, Elsevier, vol. 71(C), pages 213-221.
    3. Zha, Donglan & Yang, Guanglei & Wang, Qunwei, 2019. "Investigating the driving factors of regional CO2 emissions in China using the IDA-PDA-MMI method," Energy Economics, Elsevier, vol. 84(C).
    4. Zhao, Zhibo & Shi, Xunpeng & Zhao, Lingdi & Zhang, Jinggu, 2020. "Extending production-theoretical decomposition analysis to environmentally sensitive growth: Case study of Belt and Road Initiative countries," Technological Forecasting and Social Change, Elsevier, vol. 161(C).
    5. Wang, Qunwei & Hang, Ye & Su, Bin & Zhou, Peng, 2018. "Contributions to sector-level carbon intensity change: An integrated decomposition analysis," Energy Economics, Elsevier, vol. 70(C), pages 12-25.
    6. Wang, Miao & Feng, Chao, 2017. "Analysis of energy-related CO2 emissions in China’s mining industry: Evidence and policy implications," Resources Policy, Elsevier, vol. 53(C), pages 77-87.
    7. Zhou, P. & Zhang, H. & Zhang, L.P., 2022. "The drivers of energy intensity changes in Chinese cities: A production-theoretical decomposition analysis," Applied Energy, Elsevier, vol. 307(C).
    8. Wang, Hui & Li, Rupeng & Zhang, Ning & Zhou, Peng & Wang, Qiang, 2020. "Assessing the role of technology in global manufacturing energy intensity change: A production-theoretical decomposition analysis," Technological Forecasting and Social Change, Elsevier, vol. 160(C).
    9. Sueyoshi, Toshiyuki & Li, Aijun & Liu, Xiaohong, 2019. "Exploring sources of China's CO2 emission: Decomposition analysis under different technology changes," European Journal of Operational Research, Elsevier, vol. 279(3), pages 984-995.
    10. Xie, Xuan & Lin, Boqiang, 2019. "Understanding the energy intensity change in China's food industry: A comprehensive decomposition method," Energy Policy, Elsevier, vol. 129(C), pages 53-68.
    11. Lin, Boqiang & Wang, Miao, 2021. "What drives energy intensity fall in China? Evidence from a meta-frontier approach," Applied Energy, Elsevier, vol. 281(C).
    12. Lin, Boqiang & Xu, Mengmeng, 2019. "Quantitative assessment of factors affecting energy intensity from sector, region and time perspectives using decomposition method: A case of China’s metallurgical industry," Energy, Elsevier, vol. 189(C).
    13. 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.
    14. Fan, Wei & Huang, Shasha & Xu, Yiyin & Zhu, Chunxia & Chen, Jiandong, 2023. "Drivers of global energy export dependency: A decomposition analysis," Renewable and Sustainable Energy Reviews, Elsevier, vol. 187(C).
    15. Kerui Du & Boqiang Lin & Chunping Xie, 2017. "Exploring Change in China’s Carbon Intensity: A Decomposition Approach," Sustainability, MDPI, vol. 9(2), pages 1-14, February.
    16. Liu, Xiao & Zhou, Dequn & Zhou, Peng & Wang, Qunwei, 2017. "What drives CO2 emissions from China’s civil aviation? An exploration using a new generalized PDA method," Transportation Research Part A: Policy and Practice, Elsevier, vol. 99(C), pages 30-45.
    17. Dequn Zhou & Xiao Liu & Peng Zhou & Qunwei Wang, 2017. "Decomposition Analysis of Aggregate Energy Consumption in China: An Exploration Using a New Generalized PDA Method," Sustainability, MDPI, vol. 9(5), pages 1-13, April.
    18. Wang, Miao & Feng, Chao, 2018. "Decomposing the change in energy consumption in China's nonferrous metal industry: An empirical analysis based on the LMDI method," Renewable and Sustainable Energy Reviews, Elsevier, vol. 82(P3), pages 2652-2663.
    19. Huang, Fei & Zhou, Dequn & Wang, Qunwei & Hang, Ye, 2019. "Decomposition and attribution analysis of the transport sector’s carbon dioxide intensity change in China," Transportation Research Part A: Policy and Practice, Elsevier, vol. 119(C), pages 343-358.
    20. Du, Kerui & Xie, Chunping & Ouyang, Xiaoling, 2017. "A comparison of carbon dioxide (CO2) emission trends among provinces in China," Renewable and Sustainable Energy Reviews, Elsevier, vol. 73(C), pages 19-25.

    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:gam:jsusta:v:11:y:2019:i:17:p:4650-:d:261165. 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: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    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.