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

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 me...

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Main Authors: Neng Shen, Yifan Wang, Hui Peng, Zhiping Hou
Format: Article
Language:English
Published: MDPI AG 2020-08-01
Series:Sustainability
Subjects:
Online Access:https://www.mdpi.com/2071-1050/12/16/6397
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spelling doaj-8f065199959845f1a6aa6bf6b932b9bf2020-11-25T03:09:30ZengMDPI AGSustainability2071-10502020-08-01126397639710.3390/su12166397Renewable Energy Green Innovation, Fossil Energy Consumption, and Air Pollution—Spatial Empirical Analysis Based on ChinaNeng Shen0Yifan Wang1Hui Peng2Zhiping Hou3College of Public Administration, Huazhong University of Science and Technology, Wuhan 430074, ChinaCollege of Public Administration, Huazhong University of Science and Technology, Wuhan 430074, ChinaSchool of Management, Huazhong University of Science and Technology, Wuhan 430074, ChinaBusiness College, Guilin University of Technology, Guilin 532100, ChinaExcessive 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<sub>2</sub>) 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.https://www.mdpi.com/2071-1050/12/16/6397renewable energyenergy consumptionair pollutionspatial Durbin modelspatial analysis
collection DOAJ
language English
format Article
sources DOAJ
author Neng Shen
Yifan Wang
Hui Peng
Zhiping Hou
spellingShingle Neng Shen
Yifan Wang
Hui Peng
Zhiping Hou
Renewable Energy Green Innovation, Fossil Energy Consumption, and Air Pollution—Spatial Empirical Analysis Based on China
Sustainability
renewable energy
energy consumption
air pollution
spatial Durbin model
spatial analysis
author_facet Neng Shen
Yifan Wang
Hui Peng
Zhiping Hou
author_sort Neng Shen
title Renewable Energy Green Innovation, Fossil Energy Consumption, and Air Pollution—Spatial Empirical Analysis Based on China
title_short Renewable Energy Green Innovation, Fossil Energy Consumption, and Air Pollution—Spatial Empirical Analysis Based on China
title_full Renewable Energy Green Innovation, Fossil Energy Consumption, and Air Pollution—Spatial Empirical Analysis Based on China
title_fullStr Renewable Energy Green Innovation, Fossil Energy Consumption, and Air Pollution—Spatial Empirical Analysis Based on China
title_full_unstemmed Renewable Energy Green Innovation, Fossil Energy Consumption, and Air Pollution—Spatial Empirical Analysis Based on China
title_sort renewable energy green innovation, fossil energy consumption, and air pollution—spatial empirical analysis based on china
publisher MDPI AG
series Sustainability
issn 2071-1050
publishDate 2020-08-01
description 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<sub>2</sub>) 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.
topic renewable energy
energy consumption
air pollution
spatial Durbin model
spatial analysis
url https://www.mdpi.com/2071-1050/12/16/6397
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