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

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  • 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
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    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.
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    2. 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.
    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.
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    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.

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