IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v12y2020i16p6397-d396375.html
   My bibliography  Save this article

Renewable Energy Green Innovation, Fossil Energy Consumption, and Air Pollution—Spatial Empirical Analysis Based on China

Author

Listed:
  • Neng Shen

    (College of Public Administration, Huazhong University of Science and Technology, Wuhan 430074, China
    School of Economics and Management, Fuzhou University, Fuzhou 350108, China)

  • Yifan Wang

    (College of Public Administration, Huazhong University of Science and Technology, Wuhan 430074, China)

  • Hui Peng

    (School of Management, Huazhong University of Science and Technology, Wuhan 430074, China)

  • Zhiping Hou

    (Business College, Guilin University of Technology, Guilin 532100, China)

Abstract

Excessive consumption of traditional fossil energy has led to more serious global air pollution. This article incorporates renewable energy green innovation (REGI), fossil energy consumption (FEC), and air pollution into a unified analysis framework. Using China’s provincial panel data, a spatial measurement model was used to investigate the spatial effects of renewable energy green innovation and fossil energy consumption on air pollution in China from 2011 to 2017. The global Moran index shows that over time, the spatial correlation of air pollution has gradually weakened, while the global correlation of renewable energy green innovation and fossil energy consumption is increasing year by year. ArcGIS visualization and partial Moran index show that air pollution, renewable energy green innovation, and fossil energy consumption are extremely uneven in geographic space. The spatial distribution of air pollution, renewable energy green innovations, and fossil energy consumption are all characterized by high in the east and low in the west and they all show a strong spatial aggregation. Applying the spatial adjacency matrix to the spatial Durbin model gave the results that China’s air pollution has a significant spatial spillover effect. Replacing fossil fuels with clean renewable energy will reduce air pollutant emissions. The Environment Kuznets Curve (EKC) hypothesis has not been supported and verified in China. The partial differential method test found that the spatial spillover benefits can be decomposed into direct effects and indirect effects. The direct and indirect effects of renewable energy green innovation on air pollution are both significantly negative, indicating that green innovation of renewable energy not only inhibits local air pollution, but also inhibits air pollution in nearby areas. The consumption of fossil energy will significantly increase the local air pollution, while the impact of sulfur dioxide (SO 2 ) and soot (DS) pollution in nearby areas is not obvious. It is recommended to increase investment in renewable energy green innovation, reduce the proportion of traditional fossil energy consumption, and pay attention to the spatial connection and overflow of renewable energy green innovation and air pollution.

Suggested Citation

  • Neng Shen & Yifan Wang & Hui Peng & Zhiping Hou, 2020. "Renewable Energy Green Innovation, Fossil Energy Consumption, and Air Pollution—Spatial Empirical Analysis Based on China," Sustainability, MDPI, vol. 12(16), pages 1-23, August.
    • Handle: RePEc:gam:jsusta:v:12:y:2020:i:16:p:6397-:d:396375
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/12/16/6397/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/12/16/6397/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Zhao, Xueting & Burnett, J. Wesley & Fletcher, Jerald J., 2013. "Spatial Analysis of China Provincial-Level CO2 Emission Intensity," 2013 Annual Meeting, August 4-6, 2013, Washington, D.C. 149006, Agricultural and Applied Economics Association.
    2. Salim, Ruhul A. & Shafiei, Sahar, 2014. "Urbanization and renewable and non-renewable energy consumption in OECD countries: An empirical analysis," Economic Modelling, Elsevier, vol. 38(C), pages 581-591.
    3. 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.
    4. Li, Li & Hong, Xuefei & Wang, Jun, 2020. "Evaluating the impact of clean energy consumption and factor allocation on China’s air pollution: A spatial econometric approach," Energy, Elsevier, vol. 195(C).
    5. R. Pace & James LeSage, 2009. "A sampling approach to estimate the log determinant used in spatial likelihood problems," Journal of Geographical Systems, Springer, vol. 11(3), pages 209-225, September.
    6. Zeng, Jingjing & Liu, Ting & Feiock, Richard & Li, Fei, 2019. "The impacts of China's provincial energy policies on major air pollutants: A spatial econometric analysis," Energy Policy, Elsevier, vol. 132(C), pages 392-403.
    7. Kaneko, Shinji & Fujii, Hidemichi & Sawazu, Naoya & Fujikura, Ryo, 2010. "Financial allocation strategy for the regional pollution abatement cost of reducing sulfur dioxide emissions in the thermal power sector in China," Energy Policy, Elsevier, vol. 38(5), pages 2131-2141, May.
    8. Alvarez-Herranz, Agustin & Balsalobre-Lorente, Daniel & Shahbaz, Muhammad & Cantos, José María, 2017. "Energy innovation and renewable energy consumption in the correction of air pollution levels," Energy Policy, Elsevier, vol. 105(C), pages 386-397.
    9. Wang, Xue-Chao & Klemeš, Jiří Jaromír & Dong, Xiaobin & Fan, Weiguo & Xu, Zihan & Wang, Yutao & Varbanov, Petar Sabev, 2019. "Air pollution terrain nexus: A review considering energy generation and consumption," Renewable and Sustainable Energy Reviews, Elsevier, vol. 105(C), pages 71-85.
    10. Zhang, Shaohui & Worrell, Ernst & Crijns-Graus, Wina & Krol, Maarten & de Bruine, Marco & Geng, Guangpo & Wagner, Fabian & Cofala, Janusz, 2016. "Modeling energy efficiency to improve air quality and health effects of China’s cement industry," Applied Energy, Elsevier, vol. 184(C), pages 574-593.
    11. Katsuya Ito, 2017. "CO2 emissions, renewable and non-renewable energy consumption, and economic growth: Evidence from panel data for developing countries," International Economics, CEPII research center, issue 151, pages 1-6.
    12. Li, Kunming & Fang, Liting & He, Lerong, 2019. "How population and energy price affect China's environmental pollution?," Energy Policy, Elsevier, vol. 129(C), pages 386-396.
    13. 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.
    14. Boudri, J. C. & Hordijk, L. & Kroeze, C. & Amann, M. & Cofala, J. & Bertok, I. & Junfeng, Li & Lin, Dai & Shuang, Zhen & Runquing, Hu & Panwar, T. S. & Gupta, S. & Singh, D. & Kumar, A. & Vipradas, M., 2002. "The potential contribution of renewable energy in air pollution abatement in China and India," Energy Policy, Elsevier, vol. 30(5), pages 409-424, April.
    15. Ito, Katsuya, 2017. "CO2 emissions, renewable and non-renewable energy consumption, and economic growth: Evidence from panel data for developing countries," International Economics, Elsevier, vol. 151(C), pages 1-6.
    16. Bilgili, Faik & Koçak, Emrah & Bulut, Ümit, 2016. "The dynamic impact of renewable energy consumption on CO2 emissions: A revisited Environmental Kuznets Curve approach," Renewable and Sustainable Energy Reviews, Elsevier, vol. 54(C), pages 838-845.
    17. J. Paul Elhorst, 2014. "Spatial Panel Data Models," SpringerBriefs in Regional Science, in: Spatial Econometrics, edition 127, chapter 0, pages 37-93, Springer.
    18. Pao, Hsiao-Tien & Fu, Hsin-Chia, 2013. "Renewable energy, non-renewable energy and economic growth in Brazil," Renewable and Sustainable Energy Reviews, Elsevier, vol. 25(C), pages 381-392.
    19. Hao, Yu & Liu, Yiming & Weng, Jia-Hsi & Gao, Yixuan, 2016. "Does the Environmental Kuznets Curve for coal consumption in China exist? New evidence from spatial econometric analysis," Energy, Elsevier, vol. 114(C), pages 1214-1223.
    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. Patrycjusz Zarębski & Vitaliy Krupin & Dominika Zwęglińska-Gałecka, 2021. "Renewable Energy Generation Gaps in Poland: The Role of Regional Innovation Systems and Knowledge Transfer," Energies, MDPI, vol. 14(10), pages 1-26, May.
    2. Yu, Lu & Liu, Yinwei & Niu, Yiran & Xiao, Zumian, 2023. "Greener together: The impact of China's mixed-ownership reform on firm carbon emissions," Energy Policy, Elsevier, vol. 180(C).
    3. Huiping Wang & Yi Wang, 2022. "Estimating per Capita Primary Energy Consumption Using a Novel Fractional Gray Bernoulli Model," Sustainability, MDPI, vol. 14(4), pages 1-22, February.
    4. Grzegorz Pełka & Marta Jach-Nocoń & Marcin Paprocki & Artur Jachimowski & Wojciech Luboń & Adam Nocoń & Mateusz Wygoda & Paweł Wyczesany & Przemysław Pachytel & Tomasz Mirowski, 2023. "Comparison of Emissions and Efficiency of Two Types of Burners When Burning Wood Pellets from Different Suppliers," Energies, MDPI, vol. 16(4), pages 1-18, February.
    5. Li, Feng & Liu, Hao & Ma, Yinhan & Xie, Xiaohua & Wang, Yunshu & Yang, Yejun, 2022. "Low-carbon spatial differences of renewable energy technologies: Empirical evidence from the Yangtze River Economic Belt," Technological Forecasting and Social Change, Elsevier, vol. 183(C).
    6. Shizhong Peng & Haoran Peng & Shirong Pan & Jun Wu, 2023. "Digital Transformation, Green Innovation, and Pollution Abatement: Evidence from China," Sustainability, MDPI, vol. 15(8), pages 1-18, April.

    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. Matheus Koengkan, 2018. "The decline of environmental degradation by renewable energy consumption in the MERCOSUR countries: an approach with ARDL modeling," Environment Systems and Decisions, Springer, vol. 38(3), pages 415-425, September.
    2. Suyi Kim, 2022. "The Effects of Information and Communication Technology, Economic Growth, Trade Openness, and Renewable Energy on CO 2 Emissions in OECD Countries," Energies, MDPI, vol. 15(7), pages 1-15, March.
    3. Shahbaz, Muhammad & Abbas Rizvi, Syed Kumail & Dong, Kangyin & Vo, Xuan Vinh, 2022. "Fiscal decentralization as new determinant of renewable energy demand in China: The role of income inequality and urbanization," Renewable Energy, Elsevier, vol. 187(C), pages 68-80.
    4. Suyi Kim, 2020. "The Effects of Foreign Direct Investment, Economic Growth, Industrial Structure, Renewable and Nuclear Energy, and Urbanization on Korean Greenhouse Gas Emissions," Sustainability, MDPI, vol. 12(4), pages 1-14, February.
    5. Magazzino, Cosimo & Mele, Marco & Schneider, Nicolas, 2021. "A machine learning approach on the relationship among solar and wind energy production, coal consumption, GDP, and CO2 emissions," Renewable Energy, Elsevier, vol. 167(C), pages 99-115.
    6. Dong, Kangyin & Hochman, Gal & Zhang, Yaqing & Sun, Renjin & Li, Hui & Liao, Hua, 2018. "CO2 emissions, economic and population growth, and renewable energy: Empirical evidence across regions," Energy Economics, Elsevier, vol. 75(C), pages 180-192.
    7. Sun, Yunpeng & Bao, Qun & Siao-Yun, Wei & Islam, Misbah ul & Razzaq, Asif, 2022. "Renewable energy transition and environmental sustainability through economic complexity in BRICS countries: Fresh insights from novel Method of Moments Quantile regression," Renewable Energy, Elsevier, vol. 184(C), pages 1165-1176.
    8. Shahzad, Umer & Schneider, Nicolas & Ben Jebli, Mehdi, 2021. "How coal and geothermal energies interact with industrial development and carbon emissions? An autoregressive distributed lags approach to the Philippines," Resources Policy, Elsevier, vol. 74(C).
    9. Anwar, Ahsan & Siddique, Muhammad & Eyup Dogan, & Sharif, Arshian, 2021. "The moderating role of renewable and non-renewable energy in environment-income nexus for ASEAN countries: Evidence from Method of Moments Quantile Regression," Renewable Energy, Elsevier, vol. 164(C), pages 956-967.
    10. Asif Raihan & Rawshan Ara Begum & Mohd Nizam Mohd Said & Joy Jacqueline Pereira, 2022. "Relationship between economic growth, renewable energy use, technological innovation, and carbon emission toward achieving Malaysia’s Paris agreement," Environment Systems and Decisions, Springer, vol. 42(4), pages 586-607, December.
    11. Shazia Kousar & Farhan Ahmed & María de las Nieves López García & Nimra Ashraf, 2020. "Renewable Energy Consumption, Water Crises, and Environmental Degradation with Moderating Role of Governance: Dynamic Panel Analysis under Cross-Sectional Dependence," Sustainability, MDPI, vol. 12(24), pages 1-16, December.
    12. Fangming Xie & Chuanzhe Liu & Huiying Chen & Ning Wang, 2018. "Threshold Effects of New Energy Consumption Transformation on Economic Growth," Sustainability, MDPI, vol. 10(11), pages 1-14, November.
    13. Kahia, Montassar & Moulahi, Tarek & Mahfoudhi, Sami & Boubaker, Sabri & Omri, Anis, 2022. "A machine learning process for examining the linkage among disaggregated energy consumption, economic growth, and environmental degradation," Resources Policy, Elsevier, vol. 79(C).
    14. Liobikienė, Genovaitė & Butkus, Mindaugas, 2019. "Scale, composition, and technique effects through which the economic growth, foreign direct investment, urbanization, and trade affect greenhouse gas emissions," Renewable Energy, Elsevier, vol. 132(C), pages 1310-1322.
    15. Abbruzzese, Matteo & Infante, Davide & Smirnova, Janna, 2020. "European Countries on a green path. Connections between environmental quality, renewable energy and economic growth," MPRA Paper 106247, University Library of Munich, Germany.
    16. Adewuyi, Adeolu O. & Awodumi, Olabanji B., 2017. "Renewable and non-renewable energy-growth-emissions linkages: Review of emerging trends with policy implications," Renewable and Sustainable Energy Reviews, Elsevier, vol. 69(C), pages 275-291.
    17. Zheng, Jiajia & Wang, Xingwu, 2021. "Can mobile information communication technologies (ICTs) promote the development of renewables?-evidence from seven countries," Energy Policy, Elsevier, vol. 149(C).
    18. Radmehr, Riza & Henneberry, Shida Rastegari & Shayanmehr, Samira, 2021. "Renewable Energy Consumption, CO2 Emissions, and Economic Growth Nexus: A Simultaneity Spatial Modeling Analysis of EU Countries," Structural Change and Economic Dynamics, Elsevier, vol. 57(C), pages 13-27.
    19. Pata, Ugur Korkut, 2018. "The influence of coal and noncarbohydrate energy consumption on CO2 emissions: Revisiting the environmental Kuznets curve hypothesis for Turkey," Energy, Elsevier, vol. 160(C), pages 1115-1123.
    20. Abdulsalam Altarhouni & Danbala Danju & Ahmed Samour, 2021. "Insurance Market Development, Energy Consumption, and Turkey’s CO 2 Emissions. New Perspectives from a Bootstrap ARDL Test," Energies, MDPI, vol. 14(23), pages 1-13, November.

    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:16:p:6397-:d:396375. 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.