IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v10y2018i3p644-d133973.html
   My bibliography  Save this article

Can Energy Structure Optimization, Industrial Structure Changes, Technological Improvements, and Central and Local Governance Effectively Reduce Atmospheric Pollution in the Beijing–Tianjin–Hebei Area in China?

Author

Listed:
  • Xinxuan Cheng

    (School of Management, Hebei University, Baoding 071002, China)

  • Longfei Fan

    (School of Economics, Hebei University, Baoding 071002, China)

  • Jiachen Wang

    (School of Management, Macquarie University, Sydney 2113, Australia)

Abstract

Economic growth in the Beijing–Tianjin–Hebei region has been achieved by consuming large amounts of fossil fuels. This produces a large number of pollutants, which damage the physical and mental health of residents, and prevent sustainable economic development. The most urgent task at present is improving the quality of the environment. This paper takes carbon emission as a pollution index, and adopts an extended stochastic impacts by regression on population, affluence, and technology (STIRPAT) model in order to study the impact of the optimization of industry structure (in particular the reduction of the proportion of energy-intensive secondary industry), the optimization of the energy structure, and technological improvements on the atmospheric environmental quality. We obtain some important and enlightening discoveries. First of all, the rapid economic growth that has been based on magnanimous fossil fuel consumption is still the main reason for the deterioration of the atmospheric environment. This means that the main driving force of economic growth still comes from high pollution industries, despite a strategy for the transformation of the pattern of economic growth having been proposed for many years. Second, the optimization of the industrial structure has not played a significant role in promoting the reduction of carbon emissions. Through further research, we believe that this may be due to the low-quality development of the third industry. In other words, the traditional service industry related to high energy consumption accounts for a large proportion in regional total output, while the high-end service industry related to small pollution accounts for a relatively small proportion. Third, reducing the consumption of coal and improving the technological level can effectively curb the deterioration of the environmental quality. In addition, we find that transboundary pollution is an important factor affecting the environment in this region, and the earnings of any unilateral treatment action is small. As a result, joint pollution control under the supervision of the central government can produce greater benefits. Therefore, we believe that the transition of the economic growth pattern, and the optimization of the energy and industry structures (especially developing the high-end service industry) are effective ways to improve the environmental quality.

Suggested Citation

  • Xinxuan Cheng & Longfei Fan & Jiachen Wang, 2018. "Can Energy Structure Optimization, Industrial Structure Changes, Technological Improvements, and Central and Local Governance Effectively Reduce Atmospheric Pollution in the Beijing–Tianjin–Hebei Area," Sustainability, MDPI, vol. 10(3), pages 1-16, February.
    • Handle: RePEc:gam:jsusta:v:10:y:2018:i:3:p:644-:d:133973
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/10/3/644/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/10/3/644/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Shahbaz, Muhammad & Loganathan, Nanthakumar & Muzaffar, Ahmed Taneem & Ahmed, Khalid & Ali Jabran, Muhammad, 2016. "How urbanization affects CO2 emissions in Malaysia? The application of STIRPAT model," Renewable and Sustainable Energy Reviews, Elsevier, vol. 57(C), pages 83-93.
    2. Haitao Zheng & Jie Hu & Rong Guan & Shanshan Wang, 2016. "Examining Determinants of CO 2 Emissions in 73 Cities in China," Sustainability, MDPI, vol. 8(12), pages 1-17, December.
    3. James Cramer, 1998. "Population growth and air quality in California," Demography, Springer;Population Association of America (PAA), vol. 35(1), pages 45-56, February.
    4. Raskin, Paul D., 1995. "Methods for estimating the population contribution to environmental change," Ecological Economics, Elsevier, vol. 15(3), pages 225-233, December.
    5. Abdallh, Atif Awad & Abugamos, Hoda, 2017. "A semi-parametric panel data analysis on the urbanisation-carbon emissions nexus for the MENA countries," Renewable and Sustainable Energy Reviews, Elsevier, vol. 78(C), pages 1350-1356.
    6. Yu Liu & Hongwei Xiao & Precious Zikhali & Yingkang Lv, 2014. "Carbon Emissions in China: A Spatial Econometric Analysis at the Regional Level," Sustainability, MDPI, vol. 6(9), pages 1-19, September.
    7. Shi, Anqing, 2003. "The impact of population pressure on global carbon dioxide emissions, 1975-1996: evidence from pooled cross-country data," Ecological Economics, Elsevier, vol. 44(1), pages 29-42, February.
    8. Wang, Ping & Wu, Wanshui & Zhu, Bangzhu & Wei, Yiming, 2013. "Examining the impact factors of energy-related CO2 emissions using the STIRPAT model in Guangdong Province, China," Applied Energy, Elsevier, vol. 106(C), pages 65-71.
    9. Liddle, Brantley, 2015. "What Are the Carbon Emissions Elasticities for Income and Population? Bridging STIRPAT and EKC via robust heterogeneous panel estimates," MPRA Paper 61304, University Library of Munich, Germany.
    10. Richard York & Eugene A. Rosa & Thomas Dietz, 2002. "Bridging Environmental Science with Environmental Policy: Plasticity of Population, Affluence, and Technology," Social Science Quarterly, Southwestern Social Science Association, vol. 83(1), pages 18-34, March.
    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. Luan, Bingjiang & Zou, Hong & Chen, Shuxing & Huang, Junbing, 2021. "The effect of industrial structure adjustment on China’s energy intensity: Evidence from linear and nonlinear analysis," Energy, Elsevier, vol. 218(C).
    2. Zhengping Liu & Wang Zhang & Hongxian Liu & Guohe Huang & Jiliang Zhen & Xin Qi, 2019. "Characterization of Renewable Energy Utilization Mode for Air-Environmental Quality Improvement through an Inexact Factorial Optimization Approach," Sustainability, MDPI, vol. 11(8), pages 1-19, April.
    3. Jianguo Zhou & Baoling Jin & Shijuan Du & Ping Zhang, 2018. "Scenario Analysis of Carbon Emissions of Beijing-Tianjin-Hebei," Energies, MDPI, vol. 11(6), pages 1-17, June.
    4. Weijie Jiang & Kairui Cao & Laiqun Jin & Yongyi Cheng & Qunfang Xu, 2022. "How Do China’s Development Zones Affect Environmental Pollution under Government Domination," Sustainability, MDPI, vol. 14(7), pages 1-18, March.

    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. Johan-Andrés Vélez-Henao, 2020. "Does urbanization boost environmental impacts in Colombia? An extended STIRPAT–LCA approach," Quality & Quantity: International Journal of Methodology, Springer, vol. 54(3), pages 851-866, June.
    2. York, Richard & Rosa, Eugene A. & Dietz, Thomas, 2003. "STIRPAT, IPAT and ImPACT: analytic tools for unpacking the driving forces of environmental impacts," Ecological Economics, Elsevier, vol. 46(3), pages 351-365, October.
    3. Yu Liu & Hongwei Xiao & Ning Zhang, 2016. "Industrial Carbon Emissions of China’s Regions: A Spatial Econometric Analysis," Sustainability, MDPI, vol. 8(3), pages 1-14, February.
    4. Vélez-Henao, Johan-Andrés & Font Vivanco, David & Hernández-Riveros, Jesús-Antonio, 2019. "Technological change and the rebound effect in the STIRPAT model: A critical view," Energy Policy, Elsevier, vol. 129(C), pages 1372-1381.
    5. Chang, Chun-Ping & Dong, Minyi & Sui, Bo & Chu, Yin, 2019. "Driving forces of global carbon emissions: From time- and spatial-dynamic perspectives," Economic Modelling, Elsevier, vol. 77(C), pages 70-80.
    6. Wang, Shaojian & Fang, Chuanglin & Wang, Yang, 2016. "Spatiotemporal variations of energy-related CO2 emissions in China and its influencing factors: An empirical analysis based on provincial panel data," Renewable and Sustainable Energy Reviews, Elsevier, vol. 55(C), pages 505-515.
    7. Puliafito, Salvador Enrique & Puliafito, José Luis & Grand, Mariana Conte, 2008. "Modeling population dynamics and economic growth as competing species: An application to CO2 global emissions," Ecological Economics, Elsevier, vol. 65(3), pages 602-615, April.
    8. Shafiei, Sahar & Salim, Ruhul A., 2014. "Non-renewable and renewable energy consumption and CO2 emissions in OECD countries: A comparative analysis," Energy Policy, Elsevier, vol. 66(C), pages 547-556.
    9. Wang, Mingwei & Che, Yue & Yang, Kai & Wang, Min & Xiong, Lijun & Huang, Yuchi, 2011. "A local-scale low-carbon plan based on the STIRPAT model and the scenario method: The case of Minhang District, Shanghai, China," Energy Policy, Elsevier, vol. 39(11), pages 6981-6990.
    10. Carmelo J. Le�n & Jorge E. Arana & Anastasia Hern�ndez Alem�n, 2014. "CO 2 Emissions and tourism in developed and less developed countries," Applied Economics Letters, Taylor & Francis Journals, vol. 21(16), pages 1169-1173, November.
    11. Shoufu Lin & Ji Sun & Dora Marinova & Dingtao Zhao, 2017. "Effects of Population and Land Urbanization on China’s Environmental Impact: Empirical Analysis Based on the Extended STIRPAT Model," Sustainability, MDPI, vol. 9(5), pages 1-21, May.
    12. Zhang, Chuanguo & Tan, Zheng, 2016. "The relationships between population factors and China's carbon emissions: Does population aging matter?," Renewable and Sustainable Energy Reviews, Elsevier, vol. 65(C), pages 1018-1025.
    13. Haibin Xia & Hui Wang & Guangxing Ji, 2019. "Regional Inequality and Influencing Factors of Primary PM Emissions in the Yangtze River Delta, China," Sustainability, MDPI, vol. 11(8), pages 1-14, April.
    14. Yu, Xiang & Chen, Hongbo & Wang, Bo & Wang, Ran & Shan, Yuli, 2018. "Driving forces of CO2 emissions and mitigation strategies of China’s National low carbon pilot industrial parks," Applied Energy, Elsevier, vol. 212(C), pages 1553-1562.
    15. Zhou, Yang & Liu, Yansui, 2016. "Does population have a larger impact on carbon dioxide emissions than income? Evidence from a cross-regional panel analysis in China," Applied Energy, Elsevier, vol. 180(C), pages 800-809.
    16. Hanen Ragoubi & Zouheir Mighri, 2021. "Spillover effects of trade openness on CO2 emissions in middle‐income countries: A spatial panel data approach," Regional Science Policy & Practice, Wiley Blackwell, vol. 13(3), pages 835-877, June.
    17. Thai-Ha Le, 2021. "Drivers of greenhouse gas emissions in ASEAN + 6 countries: a new look," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 23(12), pages 18096-18115, December.
    18. Yu Liu & Hongwei Xiao & Precious Zikhali & Yingkang Lv, 2014. "Carbon Emissions in China: A Spatial Econometric Analysis at the Regional Level," Sustainability, MDPI, vol. 6(9), pages 1-19, September.
    19. Du, W.C. & Xia, X.H., 2018. "How does urbanization affect GHG emissions? A cross-country panel threshold data analysis," Applied Energy, Elsevier, vol. 229(C), pages 872-883.
    20. Zhang, Ning & Yu, Keren & Chen, Zhongfei, 2017. "How does urbanization affect carbon dioxide emissions? A cross-country panel data analysis," Energy Policy, Elsevier, vol. 107(C), pages 678-687.

    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:10:y:2018:i:3:p:644-:d:133973. 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.