IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v12y2020i4p1389-d320243.html
   My bibliography  Save this article

Characteristics of the Spatio-Temporal Trends and Driving Factors of Industrial Development and Industrial SO 2 Emissions Based on Niche Theory: Taking Henan Province as an Example

Author

Listed:
  • Pengyan Zhang

    (Key Laboratory of Geospatial Technology for Middle and Lower Yellow River Region, Research Center of Regional Development and Planning, Institute of Agriculture and Rural Sustainable Development, Henan Overseas Expertise Introduction Center for Discipline Innovation (Ecological Protection and Rural Revitalization along the Yellow River), Henan University, Kaifeng 475004, China
    Collaborative Innovation Center on Yellow River Civilization of Henan Province, Henan University, Kaifeng 475001, China
    College of Environment and Planning, Henan University, Kaifeng 475001, China)

  • Yu Zhang

    (Key Laboratory of Geospatial Technology for Middle and Lower Yellow River Region, Research Center of Regional Development and Planning, Institute of Agriculture and Rural Sustainable Development, Henan Overseas Expertise Introduction Center for Discipline Innovation (Ecological Protection and Rural Revitalization along the Yellow River), Henan University, Kaifeng 475004, China
    College of Environment and Planning, Henan University, Kaifeng 475001, China)

  • Jay Lee

    (College of Environment and Planning, Henan University, Kaifeng 475001, China
    Department of Geography, Kent State University, Kent, OH 44242-0002, USA)

  • Yanyan Li

    (Key Laboratory of Geospatial Technology for Middle and Lower Yellow River Region, Research Center of Regional Development and Planning, Institute of Agriculture and Rural Sustainable Development, Henan Overseas Expertise Introduction Center for Discipline Innovation (Ecological Protection and Rural Revitalization along the Yellow River), Henan University, Kaifeng 475004, China
    College of Environment and Planning, Henan University, Kaifeng 475001, China)

  • Jiaxin Yang

    (Key Laboratory of Geospatial Technology for Middle and Lower Yellow River Region, Research Center of Regional Development and Planning, Institute of Agriculture and Rural Sustainable Development, Henan Overseas Expertise Introduction Center for Discipline Innovation (Ecological Protection and Rural Revitalization along the Yellow River), Henan University, Kaifeng 475004, China
    College of Environment and Planning, Henan University, Kaifeng 475001, China)

  • Wenliang Geng

    (Key Laboratory of Geospatial Technology for Middle and Lower Yellow River Region, Research Center of Regional Development and Planning, Institute of Agriculture and Rural Sustainable Development, Henan Overseas Expertise Introduction Center for Discipline Innovation (Ecological Protection and Rural Revitalization along the Yellow River), Henan University, Kaifeng 475004, China
    College of Environment and Planning, Henan University, Kaifeng 475001, China)

  • Ying Liu

    (Key Laboratory of Geospatial Technology for Middle and Lower Yellow River Region, Research Center of Regional Development and Planning, Institute of Agriculture and Rural Sustainable Development, Henan Overseas Expertise Introduction Center for Discipline Innovation (Ecological Protection and Rural Revitalization along the Yellow River), Henan University, Kaifeng 475004, China
    College of Environment and Planning, Henan University, Kaifeng 475001, China)

  • Tianqi Rong

    (Key Laboratory of Geospatial Technology for Middle and Lower Yellow River Region, Research Center of Regional Development and Planning, Institute of Agriculture and Rural Sustainable Development, Henan Overseas Expertise Introduction Center for Discipline Innovation (Ecological Protection and Rural Revitalization along the Yellow River), Henan University, Kaifeng 475004, China
    College of Environment and Planning, Henan University, Kaifeng 475001, China)

  • Jingwen Shao

    (Key Laboratory of Geospatial Technology for Middle and Lower Yellow River Region, Research Center of Regional Development and Planning, Institute of Agriculture and Rural Sustainable Development, Henan Overseas Expertise Introduction Center for Discipline Innovation (Ecological Protection and Rural Revitalization along the Yellow River), Henan University, Kaifeng 475004, China
    College of Environment and Planning, Henan University, Kaifeng 475001, China)

  • Bin Li

    (Key Laboratory of Geospatial Technology for Middle and Lower Yellow River Region, Research Center of Regional Development and Planning, Institute of Agriculture and Rural Sustainable Development, Henan Overseas Expertise Introduction Center for Discipline Innovation (Ecological Protection and Rural Revitalization along the Yellow River), Henan University, Kaifeng 475004, China
    College of Environment and Planning, Henan University, Kaifeng 475001, China)

Abstract

Industrial development is critical in improving a nation’s economy and in how it consumes energy resources. However, such development often causes environmental problems. Among others, the haze caused by industrial SO 2 emissions is particularly prominent. Based on Niche theory and combined with Exploratory Spatial Data Analysis (ESDA, a decoupling index model, and a Logarithmic Mean Divisia Index (LMDI) factor decomposition model, this paper reports a study on the spatio-temporal distribution and the driving factors of industrial development and industrial SO 2 emissions of cities in Henan, China between 2005 and 2014. The results showed that over the studied period in Henan: (1) SO 2 emissions reduced by 4.56 × 10 5 tons and showed a slowly decreasing trend, which gradually transitioned to a “green health” industrial structure in Henan cities; (2) studied cities with high and low industrial niche values (0.038–0.139) showed an absolute decoupling relationship between industrial development and industrial SO 2 emissions; (3) except for Zhengzhou city and Hebi city, other studied cities showed a trend of gradually increasing industrial output; (4) along with increases in the values of industrial output, studied cities showed increased levels of SO 2 emissions but with energy intensity and energy structure showing a fluctuating trend of increases and decreases in their contributions to SO 2 emissions; and (5) the energy consumption intensity and environmental technology were critical factors that were conducive to industrial SO 2 emissions and the evolving industrial structure. These findings are important for the control of industrial SO 2 emissions, though the levels of their influences are different in different cities. The scale of industrial production and the composition of energy structure in a region could lead to further deterioration of industrial SO 2 emissions in the future.

Suggested Citation

  • Pengyan Zhang & Yu Zhang & Jay Lee & Yanyan Li & Jiaxin Yang & Wenliang Geng & Ying Liu & Tianqi Rong & Jingwen Shao & Bin Li, 2020. "Characteristics of the Spatio-Temporal Trends and Driving Factors of Industrial Development and Industrial SO 2 Emissions Based on Niche Theory: Taking Henan Province as an Example," Sustainability, MDPI, vol. 12(4), pages 1-21, February.
    • Handle: RePEc:gam:jsusta:v:12:y:2020:i:4:p:1389-:d:320243
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/12/4/1389/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/12/4/1389/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Zhang, Yue-Jun & Da, Ya-Bin, 2015. "The decomposition of energy-related carbon emission and its decoupling with economic growth in China," Renewable and Sustainable Energy Reviews, Elsevier, vol. 41(C), pages 1255-1266.
    2. Werner Antweiler & Brian R. Copeland & M. Scott Taylor, 2001. "Is Free Trade Good for the Environment?," American Economic Review, American Economic Association, vol. 91(4), pages 877-908, September.
    3. Climent, Francisco & Pardo, Angel, 2007. "Decoupling factors on the energy-output linkage: The Spanish case," Energy Policy, Elsevier, vol. 35(1), pages 522-528, January.
    4. Selden Thomas M. & Song Daqing, 1994. "Environmental Quality and Development: Is There a Kuznets Curve for Air Pollution Emissions?," Journal of Environmental Economics and Management, Elsevier, vol. 27(2), pages 147-162, September.
    5. De Bruyn, Sander M., 1997. "Explaining the environmental Kuznets curve: structural change and international agreements in reducing sulphur emissions," Environment and Development Economics, Cambridge University Press, vol. 2(4), pages 485-503, November.
    6. Pengyan Zhang & Jianjian He & Xin Hong & Wei Zhang & Chengzhe Qin & Bo Pang & Yanyan Li & Yu Liu, 2017. "Regional-Level Carbon Emissions Modelling and Scenario Analysis: A STIRPAT Case Study in Henan Province, China," Sustainability, MDPI, vol. 9(12), pages 1-15, December.
    7. de Freitas, Luciano Charlita & Kaneko, Shinji, 2011. "Decomposing the decoupling of CO2 emissions and economic growth in Brazil," Ecological Economics, Elsevier, vol. 70(8), pages 1459-1469, June.
    8. 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.
    9. Shuhua Ma & Zongguo Wen & Jining Chen, 2012. "Scenario Analysis of Sulfur Dioxide Emissions Reduction Potential in China's Iron and Steel Industry," Journal of Industrial Ecology, Yale University, vol. 16(4), pages 506-517, August.
    10. Shi, Xunpeng, 2011. "China's attempts to minimize non-CO2 emissions from coal: evidence of declining emission intensity," Environment and Development Economics, Cambridge University Press, vol. 16(5), pages 573-590, October.
    11. Tetsuya Tsurumi & Shunsuke Managi, 2010. "Decomposition of the environmental Kuznets curve: scale, technique, and composition effects," Environmental Economics and Policy Studies, Springer;Society for Environmental Economics and Policy Studies - SEEPS, vol. 11(1), pages 19-36, February.
    12. Luken, Ralph A. & Piras, Stefano, 2011. "A critical overview of industrial energy decoupling programs in six developing countries in Asia," Energy Policy, Elsevier, vol. 39(6), pages 3869-3872, June.
    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. Sabrina Auci & Giovanni Trovato, 2018. "The environmental Kuznets curve within European countries and sectors: greenhouse emission, production function and technology," Economia Politica: Journal of Analytical and Institutional Economics, Springer;Fondazione Edison, vol. 35(3), pages 895-915, December.
    2. Jha, Raghbendra & Murthy, K. V. Bhanu, 2003. "An inverse global environmental Kuznets curve," Journal of Comparative Economics, Elsevier, vol. 31(2), pages 352-368, June.
    3. Wang, Wenwen & Li, Man & Zhang, Ming, 2017. "Study on the changes of the decoupling indicator between energy-related CO2 emission and GDP in China," Energy, Elsevier, vol. 128(C), pages 11-18.
    4. Jie HE, 2005. "Economic Determinants for China’s Industrial SO2 Emission: Reduced vs. Structural form and the role of international trade," Working Papers 200505, CERDI.
    5. Liang, Wei & Gan, Ting & Zhang, Wei, 2019. "Dynamic evolution of characteristics and decomposition of factors influencing industrial carbon dioxide emissions in China: 1991–2015," Structural Change and Economic Dynamics, Elsevier, vol. 49(C), pages 93-106.
    6. Novice patrick Bakehe, 2018. "Decomposition of the Environmental Kuznets curve for Deforestation in the Congo Basin," Economics Bulletin, AccessEcon, vol. 38(2), pages 1058-1068.
    7. Hauer, Grant & Runge, C. Ford, 2000. "Transboundary Pollution And The Kuznet'S Curve In The Global Commons," Working Papers 14423, University of Minnesota, Center for International Food and Agricultural Policy.
    8. Jiang, Lei & Folmer, Henk & Ji, Minhe, 2014. "The drivers of energy intensity in China: A spatial panel data approach," China Economic Review, Elsevier, vol. 31(C), pages 351-360.
    9. Dinda, Soumyananda, 2004. "Environmental Kuznets Curve Hypothesis: A Survey," Ecological Economics, Elsevier, vol. 49(4), pages 431-455, August.
    10. Leal, Patrícia Alexandra & Marques, António Cardoso & Fuinhas, José Alberto, 2019. "Decoupling economic growth from GHG emissions: Decomposition analysis by sectoral factors for Australia," Economic Analysis and Policy, Elsevier, vol. 62(C), pages 12-26.
    11. Onafowora, Olugbenga A. & Owoye, Oluwole, 2014. "Bounds testing approach to analysis of the environment Kuznets curve hypothesis," Energy Economics, Elsevier, vol. 44(C), pages 47-62.
    12. Yang, Lin & Yang, Yuantao & Zhang, Xian & Tang, Kai, 2018. "Whether China's industrial sectors make efforts to reduce CO2 emissions from production? - A decomposed decoupling analysis," Energy, Elsevier, vol. 160(C), pages 796-809.
    13. Barbier,Edward B., 2007. "Natural Resources and Economic Development," Cambridge Books, Cambridge University Press, number 9780521706513.
    14. Tanger, Shaun M. & Zeng, Peng & Morse, Wayde & Laband, David N., 2011. "Macroeconomic conditions in the U.S. and congressional voting on environmental policy: 1970-2008," Ecological Economics, Elsevier, vol. 70(6), pages 1109-1120, April.
    15. Robert T. Deacon & Catherine S. Norman, 2006. "Does the Environmental Kuznets Curve Describe How Individual Countries Behave?," Land Economics, University of Wisconsin Press, vol. 82(2), pages 291-315.
    16. Michael L. Polemis & Panagiotis Fotis & Panayiotis G. Tzeremes & Nickolaos G. Tzeremes, 2022. "On the examination of the decoupling effect of air pollutants from economic growth: a convergence analysis for the US," Letters in Spatial and Resource Sciences, Springer, vol. 15(3), pages 691-707, December.
    17. David Maddison, 2007. "Modelling sulphur emissions in Europe: a spatial econometric approach," Oxford Economic Papers, Oxford University Press, vol. 59(4), pages 726-743, October.
    18. Pradyot Ranjan Jena, 2018. "Does trade liberalization create more pollution? Evidence from a panel regression analysis across the states of India," Environmental Economics and Policy Studies, Springer;Society for Environmental Economics and Policy Studies - SEEPS, vol. 20(4), pages 861-877, October.
    19. Wang, Qunwei & Hang, Ye & Zhou, P. & Wang, Yizhong, 2016. "Decoupling and attribution analysis of industrial carbon emissions in Taiwan," Energy, Elsevier, vol. 113(C), pages 728-738.
    20. Pradyot Ranjan Jena & Babita Majhi & Ritanjali Majhi, 2022. "Estimating Long-Run Relationship between Renewable Energy Use and CO 2 Emissions: A Radial Basis Function Neural Network (RBFNN) Approach," Sustainability, MDPI, vol. 14(9), pages 1-17, April.

    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:12:y:2020:i:4:p:1389-:d:320243. 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.