scholarly journals Environmental Regulation, Resource Misallocation and Industrial Total Factor Productivity: A Spatial Empirical Study Based on China’s Provincial Panel Data

2021 ◽  
Vol 13 (4) ◽  
pp. 2390
Author(s):  
Xu Dong ◽  
Yali Yang ◽  
Xiaomeng Zhao ◽  
Yingjie Feng ◽  
Chenguang Liu
Keyword(s):  
Panel Data ◽  
Environmental Regulation ◽  
Total Factor Productivity ◽  
Spillover Effect ◽  
Factor Productivity ◽  
Resource Reallocation ◽  
Spatial Spillover ◽  
Resource Misallocation ◽  
Capital Misallocation ◽  
Spatial Spillover Effect

A vast theoretical and empirical literature has been devoted to exploring the relationship between environmental regulation and total factor productivity (TFP), but no consensus has been reached and the reason may be attributed to the fact that the resource reallocation effect of environmental regulation is ignored. In this paper, we introduce resource misallocation in the process of discussing the impact of environmental regulation on TFP, taking China’s provincial industrial panel data from 1997 to 2017 as a sample, and the spatial econometric method is employed to investigate whether environmental regulation has a resource reallocation effect and affects TFP. The results indicate that there is a U-shaped relationship between environmental regulation and industrial TFP and a negative spatial spillover effect of environmental regulation on industrial TFP at the provincial level in China. Both capital misallocation and labor misallocation will lead to the loss of industrial TFP. Capital misallocation has a negative spatial spillover effect on industrial TFP, while labor misallocation is just the opposite. Environmental regulation can produce a positive resource reallocation effect, which in turn promotes the industrial TFP in the range of 28% to 33%, while capital misallocation and labor misallocation are only partial mediator.

scholarly journals Impact of Financial Development and Its Spatial Spillover Effect on Green Total Factor Productivity: Evidence from 30 Provinces in China

2020 ◽  
Vol 2020 ◽  
pp. 1-11
Author(s):  
Junhao Zhong ◽  
Tinghui Li
Keyword(s):  
Total Factor Productivity ◽  
Financial Development ◽  
Spillover Effect ◽  
Policy Implications ◽  
Pollutant Emissions ◽  
Factor Productivity ◽  
Spatial Spillover ◽  
Surrounding Areas ◽  
The Relationship ◽  
Spatial Spillover Effect

The relationship between financial development and green economic growth has received much attention in recent years. Research on the relationship between financial development and green total factor productivity (GTFP) is of great importance to China and other countries. This study has attempted to reveal the spatial distribution of China’s provincial GTFP and impact of financial development on GTFP by using the method of GML index based on SBM-DDF and the spatial Durbin model (SDM) during the period 1996–2015. Innovation is added to the SDM to reflect the influencing mechanism of financial development on GTFP. The empirical results show the following: (1) The mean of China’s provincial GTFP showed a U-shaped curve in 1996–2015. (2) China’s provincial financial development promotes the growth of GTFP through innovation channel. The reason is that financial development boosts eco-friendly innovation and the introduction of energy saving technology, leading to a decrease in energy consumption and pollutant emissions. (3) Increasing the level of financial development in the surrounding areas will restrain local GTFP. Our results provide new evidence that China’s regional financial development has a spatial spillover effect. (4) China’s provincial GTFP has a significant spatial positive correlation. Finally, several policy implications can be summarized to China’s 30 provinces.

scholarly journals The effect of total factor productivity of forestry industry on CO2 emissions: a spatial econometric analysis of China

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Shen Zhong ◽  
Hongli Wang
Keyword(s):  
Total Factor Productivity ◽  
Industrial Structure ◽  
Spillover Effect ◽  
Factor Productivity ◽  
Spatial Econometric ◽  
Spatial Spillover ◽  
Negative Case ◽  
Forestry Industry ◽  
The Impact ◽  
Spatial Spillover Effect

AbstractForestry plays an essential role in reducing CO2 emissions and promoting green and sustainable development. This paper estimates the CO2 emissions of 30 provinces in China from 2008 to 2017, and uses Global DEA-Malmquist to measure the total factor productivity of the forestry industry and its decomposition index. On this basis, by constructing a spatial econometric model, this paper aims to empirically study the impact of forestry industry's total factor productivity and its decomposition index on CO2 emissions, and further analyze its direct, indirect and total effects. The study finds that the impact of forestry industry's total factor productivity on CO2 emissions shows an "inverted U-shaped" curve and the inflection point is 0.9395. The spatial spillover effect of CO2 emissions is significantly negative. The increase of CO2 emissions in adjacent areas will provide a "negative case" for the region, so that the region can better address its own energy conservation and emission reduction goals. TFP of forestry industry also has positive spatial spillover effect. However, considering the particularity of forestry industry, this effect is not very significant. For other factors, such as foreign direct investment, urbanization level, industrial structure and technology market turnover will also significantly affect regional CO2 emissions.

scholarly journals Empirical Study of the Spatial Spillover Effect of Transportation Infrastructure on Green Total Factor Productivity

2020 ◽  
Vol 13 (1) ◽  
pp. 326
Author(s):  
Xi Liang ◽  
Pingan Li
Keyword(s):  
Total Factor Productivity ◽  
Spillover Effect ◽  
Spillover Effects ◽  
Transportation Infrastructure ◽  
Factor Productivity ◽  
Spatial Durbin Model ◽  
Spatial Spillover ◽  
Spatial Spillover Effects ◽  
Dynamic Spatial Durbin Model ◽  
Spatial Spillover Effect

Transportation infrastructure promotes the regional flow of production. The construction and use of transportation infrastructure have a crucial effect on climate change, the sustainable development of the economy, and Green Total Factor Productivity (GTFP). Based on the panel data of 30 provinces in China from 2005 to 2017, this study empirically analyses the spatial spillover effect of transportation infrastructure on the GTFP using the Malmquist–Luenberger (ML) index and the dynamic spatial Durbin model. We found that transportation infrastructure has direct and spatial spillover effects on the growth of GTFP; highway density and railway density have significant positive spatial spillover effects, and especially-obvious immediate and lagging spatial spillover effects in the short-term. We also note that the passenger density and freight density of transportation infrastructure account for a relatively small contribution to the regional GTFP. Considering environmental pollution, energy consumption, and the enriching of the traffic infrastructure index system, we used the dynamic spatial Durbin model to study the spatial spillover effects of transportation infrastructure on GTFP.

scholarly journals The Relationship between Environmental Regulation and Green Total Factor Productivity in China: An Empirical Study Based on the Panel Data of 177 Cities

2020 ◽  
Vol 17 (15) ◽  
pp. 5287 ◽  
Author(s):  
Mingliang Zhao ◽  
Fangyi Liu ◽  
Wei Sun ◽  
Xin Tao
Keyword(s):  
Economic Development ◽  
Panel Data ◽  
Environmental Regulation ◽  
Total Factor Productivity ◽  
Inflection Point ◽  
Factor Productivity ◽  
Positive Effects ◽  
Development Level ◽  
The Mean ◽  
The Relationship

Promoting the coordinated development of industrialization and the environment is a goal pursued by all of the countries of the world. Strengthening environmental regulation (ER) and improving green total factor productivity (GTFP) are important means to achieving this goal. However, the relationship between ER and GTFP has been debated in the academic circles, which reflects the complexity of this issue. This paper empirically tested the relationship between ER and GTFP in China by using panel data and a systematic Gaussian Mixed Model (GMM) of 177 cities at the prefecture level. The research shows that the relationship between ER and GTFP is complex, which is reflected in the differences and nonlinearity between cities with different monitoring levels and different economic development levels. (1) The relationship between ER and GTFP is linear and non-linear in different urban groups. A positive linear relationship was found in the urban group with high economic development level, while a U-shaped nonlinear relationship was found in other urban groups. (2) There are differences in the inflection point value and the variable mean of ER in different urban groups, which have different promoting effects on GTFP. In key monitoring cities and low economic development level cities, the mean value of ER had not passed the inflection point, and ER was negatively correlated with GTFP. The mean values of ER variables in the whole sample, the non-key monitoring and the middle economic development level cities had all passed the inflection point, which gradually promoted the improvement of GTFP. (3) Among the control variables of the different city groups, science and technology input and the financial development level mainly had positive effects on GTFP, while foreign direct investment (FDI) and fixed asset investment variables mainly had negative effects.

scholarly journals Spatial Spillover Effect of Heterogeneous Environmental Regulation On Ecological Welfare Performance in China

2021 ◽  
Author(s):  
guo bing nan ◽  
tang li ◽  
jia ru ◽  
lin ji
Keyword(s):  
Spatial Distribution ◽  
Environmental Regulation ◽  
Regional Differences ◽  
Environmental Regulations ◽  
Spillover Effect ◽  
Voluntary Participation ◽  
Spatial Spillover ◽  
Different Types ◽  
The Impact ◽  
Spatial Spillover Effect

Abstract This paper constructs a theoretical model to deduce the mechanism of environmental regulation on ecological welfare performance, selects the panel data of 30 provinces in China from 2005 ~ 2019, uses the Super-SBM model to measure the ecological welfare performance of China, and the influence of heterogeneous environmental regulation on ecological welfare performance in China is empirically tested by spatial Durbin model. The results show: (1) there are regional differences in the ecological welfare performance of different provinces in China, which illustrates an unbalanced spatial distribution; (2) there is significant positive spatial correlation between market incentive, command -control and voluntary participation environmental regulation and ecological welfare performance; (3) The impact of different types of environmental regulations on the performance of ecological welfare in China is heterogeneous. Command-control and market incentive environmental regulations can improve the performance of ecological welfare, while voluntary participation environmental regulations have no significant impact on the performance of ecological welfare; (4) From the perspective of spatial spillover effect, command-control environmental regulation is not conducive to the ecological welfare performance of neighboring regions, while market incentive environmental regulation is conducive to the improvement of ecological welfare performance of adjacent areas. The spatial spillover effect of voluntary participation environmental regulation on ecological welfare performance in adjacent areas is not significant.

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