scholarly journals Double Effects of Environmental Regulation on Carbon Emissions in China: Empirical Research Based on Spatial Econometric Model

2020 ◽  
Vol 2020 ◽  
pp. 1-12
Author(s):  
Yuanhua Yang ◽  
Dengli Tang ◽  
Peng Zhang
Keyword(s):  
Energy Consumption ◽  
Environmental Regulation ◽  
Technological Innovation ◽  
Carbon Emissions ◽  
Industrial Structure ◽  
Significance Test ◽  
Direct Impact ◽  
Spatial Lag Model ◽  
Lag Model ◽  
The Impact

This paper theoretically analyzes the direct impact of environmental regulation on carbon emissions and its indirect effects on carbon emissions through foreign direct investment (FDI), energy consumption, industrial structure, and technological innovation. Then, this paper constructs a spatial lag model to empirically test the dual effects of environmental regulation on carbon emissions based on the provincial panel data of 2003–2017 in China. The results show that the average Moran’s I value of carbon emissions during 2003–2017 is 0.2506, passing the significance test at 1% level, and carbon emissions have spatial correlation characteristics. The direct impact of environmental regulation on carbon emissions is significant and positive. Environmental regulation could indirectly influence carbon emissions by influencing FDI, energy consumption, and technological innovation, and meanwhile, FDI, energy consumption, and technological innovation help to reduce carbon emissions under the constraint of environmental regulation, specifically. However, the impact of environmental regulation on carbon emissions through industrial structure is not significant.

scholarly journals Carbon Productivity and Mitigation: Evidence from Industrial Development and Urbanization in the Central and Western Regions of China

2021 ◽  
Vol 13 (16) ◽  
pp. 9014
Author(s):  
Yongjiao Wu ◽  
Huazhu Zheng ◽  
Yu Li ◽  
Claudio O. Delang ◽  
Jiao Qian
Keyword(s):  
Carbon Emissions ◽  
Industrial Development ◽  
Industrial Structure ◽  
Spillover Effects ◽  
Spatial Lag Model ◽  
Workforce Allocation ◽  
Lag Model ◽  
Using Data ◽  
Shape Curve ◽  
Carbon Productivity

This paper investigates carbon productivity (CP) from the perspectives of industrial development and urbanization to mitigate carbon emissions. We propose a hybrid model that includes a spatial lag model (SLM) and a fixed regional panel model using data from the 17 provinces in the central and western regions of China from 2000 to 2018. The results show that the slowly increasing CP has significant spatial spillover effects, with High–High (H–H) and Low–Low (L–L) spatial distributions in the central and western regions of China. In addition, industrial development and urbanization in the study area play different roles in CP, while economic urbanization and industrial fixed investment negatively affect CP, and population urbanization affects CP along a U-shape curve. Importantly, the results show that the patterns of industrial development and urbanization that influence CP are homogenous and mutually imitated in the 17 studied provinces. Furthermore, disparities in CP between regions are due to industrial workforce allocation (TL), but TL has been inefficient; industrial structure upgrades are slowly improving conditions. Therefore, the findings suggest that, in the short term, policymakers in China should implement industrial development policies that reduce carbon emissions in the western and central regions by focusing on improving industrial workforce allocation.

scholarly journals Does Low-Carbon City Pilot Policy Alleviate Urban Haze Pollution? Empirical Evidence from a Quasi-Natural Experiment in China

2021 ◽  
Vol 18 (21) ◽  
pp. 11287
Author(s):  
Jinling Yan ◽  
Junfeng Zhao ◽  
Xiaodong Yang ◽  
Xufeng Su ◽  
Hailing Wang ◽  
...  
Keyword(s):  
Energy Consumption ◽  
Technological Innovation ◽  
Industrial Structure ◽  
Pollution Prevention ◽  
Empirical Support ◽  
Low Carbon ◽  
Haze Pollution ◽  
Starting Point ◽  
Low Carbon City ◽  
The Impact

As a comprehensive environmental regulation, the low-carbon city pilot policy (LCCP) may have an impact on haze pollution. The evaluation of the effectiveness of LCCP on haze pollution is greatly significant for air pollution prevention and control. Taking LCCP as the starting point, in this study we constructed DID, PSM-DID, and intermediary effect models to empirically test the impact and mechanism of LCCP on haze pollution, based on the panel data of 271 cities in China from 2005 to 2018. The findings show that (1) LCCP has significantly reduced the urban haze pollution, and the average annual concentration of PM2.5 in pilot cities decreased by 14.29%. (2) LCCP can inhibit haze pollution by promoting technological innovation, upgrading the industrial structure, and reducing energy consumption. Among these impacts, the effect of technological innovation is the strongest, followed by industrial structure, and energy consumption. (3) LCCP has significantly curbed the haze pollution of non-resource dependent cities, Eastern cities, and large cities, but exerted little impact on resource-dependent cities, Central and Western regions, and small and medium-sized cities. (4) LCCP has a spatial spillover effect. It can inhibit the haze pollution of adjacent cities through demonstration and warning effects. This study enriches the relevant research on LCCP and provides empirical support and policy enlightenment for pollution reduction.

scholarly journals Research on the spatial effect and threshold effect of industrial structure upgrading on carbon emissions in China

2021 ◽  
Author(s):  
Zhenqiang Li ◽  
Qiuyang Zhou
Keyword(s):  
Energy Consumption ◽  
Population Size ◽  
Carbon Emissions ◽  
Carbon Emission ◽  
Industrial Structure ◽  
Threshold Effect ◽  
Local Areas ◽  
The Impact ◽  
Consumption Intensity ◽  
Industrial Structure Upgrading

Abstract Based on panel data from 2000 to 2017 in 29 Chinese provinces, this paper analyzes the impact of industrial structure upgrading on carbon emissions by constructing a spatial panel model and a panel threshold model. The results show that (1) there is a significant spatial correlation between carbon emissions in Chinese provinces, and the carbon emissions of a province are affected by the carbon emissions of surrounding provinces; (2) in China, carbon emissions have a significant time lag feature, and current carbon emissions are largely affected by previous carbon emissions; (3) industrial structure upgrading can effectively promote carbon emission reductions in local areas, and the impact of industrial structure upgrading on carbon emissions has a significant threshold effect. With continued economic development, the promotion effect of industrial structure upgrading on carbon emission reductions will decrease slightly, but this carbon emission reduction effect is still significant. (4) In addition, there is a clear difference between the impact of energy consumption intensity and population size on carbon emissions in short and long terms. In the short term, the increase in energy consumption intensity and the expansion of population size not only increase the carbon emissions of a local area but also increase the carbon emissions of neighboring areas. In the long term, the impact of energy consumption intensity and population size on carbon emissions of neighboring areas will be weakened, but the promotion impact on carbon emissions in local areas will be strengthened.

scholarly journals Threshold Effects of Environmental Regulation on Total Factor Energy Efficiency in China

2015 ◽  
Vol 8 (1) ◽  
pp. 33-37
Author(s):  
Ling Yun Huang ◽  
Hui Qiang Xie
Keyword(s):  
Energy Efficiency ◽  
Environmental Regulation ◽  
Technological Innovation ◽  
Industrial Structure ◽  
Threshold Value ◽  
Threshold Effects ◽  
Threshold Values ◽  
Positive Effects ◽  
Negative Effect ◽  
The Impact

This paper examines the threshold effects of environmental regulation on China’s total factor energy efficiency (TFEE) using technological innovation (as measured by patents) as a threshold variable. Using the Slacksbased measure-undesirable (SBM-undesirable) output model, we first estimate TFEEs in 30 Chinese provinces from 2000 to 2011 under the constraints of energy conservation and emissions reduction. We then analyze the impact of environmental regulation on TFEE based on the panel threshold regression model. The results show that the average TFEE in China from 2000 to 2011 is 0.503, indicating that this measure can be significantly improved. However, environmental regulation has threshold effects on TFEE. Stringent environmental regulation can only improve TFEEs in provinces with technological innovation levels between the first and second threshold values. When technological innovation levels are below the first or above the second threshold value, tighter environmental regulation would lower TFEE. The results suggest that environmental regulation does not always enhance TFEE and that the positive effect of environmental regulation on TFEE must fall within a range of threshold values. In addition, improving the technological innovation level and adjusting the industrial structure have positive effects on TFEE, while the irrational energy consumption structure has a negative effect on TFEE.

scholarly journals Analysis of the Upgrading Effect of the Industrial Structure of Environmental Regulation: Evidence From 113 Cities in China

2021 ◽  
Vol 9 ◽  
Author(s):  
Wei Shao ◽  
Yufei Yin ◽  
Xiao Bai ◽  
Farhad Taghizadeh-Hesary
Keyword(s):  
Environmental Regulation ◽  
Technological Innovation ◽  
Industrial Structure ◽  
Threshold Effect ◽  
Sustainable Growth ◽  
Chinese Government ◽  
Eastern Region ◽  
Substantial Impact ◽  
The Impact ◽  
Industrial Structure Upgrading

At present, China’s economy is in a crucial period of economic structural transformation. To alleviate the downward pressure on the economy and explore sustainable growth paths, the Chinese government has issued several environmental regulations. However, the impact of environmental regulation on industrial structure upgrading has not been carefully examined yet. This study utilizes the Pollution Information Transparency Index (PITI) to measure environmental regulation (ER) and examines the impact of ER on industrial structure upgrading (ISU). The sample cities are divided into 36 resource-based cities (RBCs) and 77 non–resource-based cities (NRBCs). The panel data containing 113 cities during 2008–2017 are used in this study. The empirical results show that ER has a significant impact on ISU of RBCs and NRBCs, and robust tests proved the reliability of this result. Analysis of heterogeneity shows ER has a more substantial role in promoting ISU in RBCs and the eastern region. Meanwhile, inside RBCs, ER has a more substantial impact on ISU in growth-RBCs than on that in other RBCs. The mechanism test shows that the mediation effect of technological innovation in RBCs and NRBCs is significant. At last, the impact of ER on ISU has a double-threshold effect in RBCs and a single-threshold effect in NRBCs. With the technological innovation progress, ER produces an increasing effect on ISU of RBCs and NRBCs.

scholarly journals Study on Influencing Factors of Carbon Emissions from Energy Consumption of Shandong Province of China from 1995 to 2012

2014 ◽  
Vol 2014 ◽  
pp. 1-12 ◽  
Author(s):  
Jiekun Song ◽  
Qing Song ◽  
Dong Zhang ◽  
Youyou Lu ◽  
Long Luan
Keyword(s):  
Energy Consumption ◽  
Carbon Emissions ◽  
Industrial Structure ◽  
Shandong Province ◽  
Stirpat Model ◽  
Per Capita Gdp ◽  
Consumption Structure ◽  
Per Capita ◽  
The Impact ◽  
Consumption Intensity

Carbon emissions from energy consumption of Shandong province from 1995 to 2012 are calculated. Three zero-residual decomposition models (LMDI, MRCI and Shapley value models) are introduced for decomposing carbon emissions. Based on the results, Kendall coordination coefficient method is employed for testing their compatibility, and an optimal weighted combination decomposition model is constructed for improving the objectivity of decomposition. STIRPAT model is applied to evaluate the impact of each factor on carbon emissions. The results show that, using 1995 as the base year, the cumulative effects of population, per capita GDP, energy consumption intensity, and energy consumption structure of Shandong province in 2012 are positive, while the cumulative effect of industrial structure is negative. Per capita GDP is the largest driver of the increasing carbon emissions and has a great impact on carbon emissions; energy consumption intensity is a weak driver and has certain impact on carbon emissions; population plays a weak driving role, but it has the most significant impact on carbon emissions; energy consumption structure is a weak driver of the increasing carbon emissions and has a weak impact on carbon emissions; industrial structure has played a weak inhibitory role, and its impact on carbon emissions is great.

scholarly journals Economic Development, CO2 Emissions and Energy Use Nexus-Evidence from the Danube Region Countries

Energies ◽  
2021 ◽  
Vol 14 (11) ◽  
pp. 3165
Author(s):  
Eva Litavcová ◽  
Jana Chovancová
Keyword(s):  
Economic Growth ◽  
Energy Consumption ◽  
Co2 Emissions ◽  
Carbon Emissions ◽  
Bounds Testing ◽  
Mutual Cooperation ◽  
Long Run ◽  
Short Run ◽  
Danube Region ◽  
The Impact

The aim of this study is to examine the empirical cointegration, long-run and short-run dynamics and causal relationships between carbon emissions, energy consumption and economic growth in 14 Danube region countries over the period of 1990–2019. The autoregressive distributed lag (ARDL) bounds testing methodology was applied for each of the examined variables as a dependent variable. Limited by the length of the time series, we excluded two countries from the analysis and obtained valid results for the others for 26 of 36 ARDL models. The ARDL bounds reliably confirmed long-run cointegration between carbon emissions, energy consumption and economic growth in Austria, Czechia, Slovakia, and Slovenia. Economic growth and energy consumption have a significant impact on carbon emissions in the long-run in all of these four countries; in the short-run, the impact of economic growth is significant in Austria. Likewise, when examining cointegration between energy consumption, carbon emissions, and economic growth in the short-run, a significant contribution of CO2 emissions on energy consumptions for seven countries was found as a result of nine valid models. The results contribute to the information base essential for making responsible and informed decisions by policymakers and other stakeholders in individual countries. Moreover, they can serve as a platform for mutual cooperation and cohesion among countries in this region.

scholarly journals How Does Environmental Regulation Affect the Relationship between FDI and Technological Innovation: From the Perspective of Technology Transactions

Processes ◽  
2021 ◽  
Vol 9 (8) ◽  
pp. 1264
Author(s):  
Meng Zeng ◽  
Lihang Liu ◽  
Fangyi Zhou ◽  
Yigui Xiao
Keyword(s):  
Environmental Regulation ◽  
Technological Innovation ◽  
Pollutant Emissions ◽  
Regulatory Effect ◽  
Host Countries ◽  
Regional Heterogeneity ◽  
Industrial Sectors ◽  
Reduction Effect ◽  
The Impact ◽  
The Relationship

Many studies have found that FDI can reduce the pollutant emissions of host countries. At the same time, the intensity of environmental regulation would affect the emission reduction effect of FDI in the host country. This study aims to reveal the internal mechanisms of this effect. Specifically, this paper studies the impact of FDI on technological innovation in China’s industrial sectors from the perspective of technology transactions from 2001 to 2019, and then analyzes whether the intensity of environmental regulation can promote the relationship. Results indicate that FDI promotes technological innovation through technology transactions. In addition, it finds that the intensity of environmental regulation significantly positively moderates the relationship between FDI and technological innovation, which is achieved by positively moderating the FDI–technology transaction relationship. Regional heterogeneity analysis is further conducted, and results show that in the eastern and western regions of China, FDI can stimulate technological innovation within regional industrial sectors through technology trading. Moreover, environmental regulation has a significant positive regulatory effect on the above relationship, but these effects are not supported by evidence in the central region of China.

scholarly journals An Examination of Green Credit Promoting Carbon Dioxide Emissions Reduction: A Provincial Panel Analysis of China

2021 ◽  
Vol 13 (13) ◽  
pp. 7148
Author(s):  
Wenjie Zhang ◽  
Mingyong Hong ◽  
Juan Li ◽  
Fuhong Li
Keyword(s):  
Carbon Dioxide ◽  
Carbon Emissions ◽  
Carbon Dioxide Emissions ◽  
Industrial Structure ◽  
Emissions Reduction ◽  
Green Finance ◽  
Carbon Emissions Reduction ◽  
Negative Effect ◽  
Emissions Intensity ◽  
The Impact

The implementation of green finance is a powerful measure to promote global carbon emissions reduction that has been highly valued by academic circles in recent years. However, the role of green credit in carbon emissions reduction in China is still lacking testing. Using a set of panel data including 30 provinces and cities, this study focused on the impact of green credit on carbon dioxide emissions in China from 2006 to 2016. The empirical results indicated that green credit has a significantly negative effect on carbon dioxide emissions intensity. Furthermore, after the mechanism examination, we found that the promotion impacts of green credit on industrial structure upgrading and technological innovation are two effective channels to help reduce carbon dioxide emissions. Heterogeneity analysis found that there are regional differences in the effect of green credit. In the western and northeastern regions, the effect of green credit is invalid. Quantile regression results implied that the greater the carbon emissions intensity, the better the effect of green credit. Finally, a further discussion revealed there exists a nonlinear correlation between green credit and carbon dioxide emissions intensity. These findings suggest that the core measures to promote carbon emission reduction in China are to continue to expand the scale of green credit, increase the technology R&D investment of enterprises, and to vigorously develop the tertiary industry.

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