scholarly journals The Impact of Environmental Protection Tax on the Upgrading of Industrial Structure-Based on Spatial Econometric Analysis

2019 ◽  
Vol 131 ◽  
pp. 01070
Author(s):  
Aiping Guan ◽  
Jing Xie ◽  
Yu Meng
Keyword(s):  
Environmental Protection ◽  
Industrial Structure ◽  
Econometric Model ◽  
Positive Impact ◽  
Spillover Effects ◽  
Spatial Correlations ◽  
Spatial Econometric ◽  
Spatial Weight ◽  
Spatial Econometric Model ◽  
The Impact

Based on the provincial panel data of 30 provinces in China from 2000 to 2017, this paper uses the spatial econometric model to analyze the impact of environmental protection tax on the upgrading of industrial structure under three spatial weight matrices. The results show that there are significant positive spatial correlations in the upgrading of provincial industrial structure in China, and environmental protection tax has a significant positive spatial spillover effects on the upgrading of industrial structure. The implementation of environmental protection tax in provinces and regions will promote the upgrading of local industrial structure, which will have a positive impact on the upgrading of industrial structure in neighboring provinces and regions.

scholarly journals Spatio-Temporal Variation of Health Production Efficiency Considering Environmental Pollution in China Based on Modified EBM and Spatial Econometric Model

2021 ◽  
Vol 9 ◽  
Author(s):  
Fan Liu ◽  
Gen Li ◽  
Ying Zhou ◽  
Yinghui Ma ◽  
Tao Wang
Keyword(s):  
Temporal Variation ◽  
Production Efficiency ◽  
Econometric Model ◽  
Spillover Effects ◽  
Eastern Region ◽  
Health Production ◽  
Spatial Econometric ◽  
Average Efficiency ◽  
Spatial Econometric Model ◽  
Spatio Temporal

In order to strengthen the construction of China's health industry and improve the health of the people, based on the data of 31 provinces and cities in China from 2009 to 2019, the improved EBM model is used to measure the health production efficiency of each region, and Moran index is used to study the Spatio-temporal variation of health production efficiency of each province. Finally, the spatial econometric model is applied to study the influencing factors of the Spatio-temporal variation of health production efficiency. The results show that generally speaking, the average efficiency of 31 provinces and cities is above 0.7, and the average efficiency of some regions is above 1. From the perspective of time variation, the average efficiency value in the eastern region and the middle region increases from 0.816 to 0.882 and from 0.851 to 0.861, respectively. However, the average efficiency value in the western region and northeast region decreases from 0.861 to 0.83 and from 0.864 to 0.805, respectively. From the perspective of spatial distribution, HH agglomeration and LL agglomeration exist in most regions. By comparing Moran scatter plots in 2009 and 2019, it is found that the quadrants of most regions remain unchanged, and LL agglomeration is the main agglomeration type in local space. There is a significant spatial dependence among different regions. From the perspective of spatial empirical results, Pgdp, Med, and Pd have a positive effect on health production efficiency. The direct effect and indirect effect of Pgdp, Med, and Gov all pass the significance test of 1%, indicating that there are spatial spillover effects of the three indicators. Each region should reasonably deal with the spillover effect of surrounding regions, vigorously develop economic activities, carry out cooperation with surrounding regions and apply demonstration effect to accelerate the development of overall health production.

scholarly journals Nonlinear Influence of Chinese Real Estate Development on Environmental Pollution: New Evidence from Spatial Econometric Model

2022 ◽  
Vol 19 (1) ◽  
pp. 588
Author(s):  
Wenqin Gong ◽  
Yu Kong
Keyword(s):  
Real Estate ◽  
Environmental Pollution ◽  
Econometric Model ◽  
Real Estate Development ◽  
Real Estate Industry ◽  
Spatial Econometric ◽  
The Real ◽  
The Real Estate ◽  
Spatial Econometric Model ◽  
The Impact

Environmental pollution is a problem of universal concern throughout the globe. The development of real estate industry not only consumes huge resources, but also has close ties with high-consumption industries such as the construction industry. However, previous studies have rarely explored the impact of real estate development on environmental pollution. Therefore, this paper employs the entropy method to construct a comprehensive index of environmental pollution based on panel data of 31 provinces in China from 2000 to 2017, and empirically examines the impact of real estate development on environmental pollution. This article uses real estate investment to measure the development of the real estate industry. In view of the high spatial autocorrelation of environmental pollution, this paper selects a spatial econometric model. The empirical study found that: (1) By using the Spatial Durbin Model, real estate development has an inverted U-shaped impact on environmental pollution. Meanwhile, most cities have not yet reached the turning point; that is, with the continuous development of the real estate industry, environmental pollution will continue to increase. (2) Further regional heterogeneity found that the inverted U-shaped relationship still exists in coastal and inland areas. (3) Finally, this article used the Spatial Mediation Model to explain the nonlinear impact of real estate development on environmental pollution, with two important mediating variables: population density and industrial structure. Through the above analysis, it can be observed that real estate development has a significant impact on environmental pollution. Thus, the country and the government can reduce environmental pollution by improving the investment structure, using environmentally friendly building materials, guiding population flow and promoting industrial upgrading.

scholarly journals How Does Energy Misallocation Affect Carbon Emission Efficiency in China? An Empirical Study Based on the Spatial Econometric Model

2019 ◽  
Vol 11 (7) ◽  
pp. 2115 ◽  
Author(s):  
Chu ◽  
Geng ◽  
Guo
Keyword(s):  
Carbon Emission ◽  
Econometric Model ◽  
Survey Period ◽  
Spatial Econometric ◽  
Emission Efficiency ◽  
East Region ◽  
Low Degree ◽  
Policy Suggestions ◽  
Spatial Econometric Model ◽  
The Impact

As an essential factor of production, energy is receiving increased attention. Yet, other than some fundamental policy suggestions towards China’s energy issues, there have been very few investigations into energy misallocation so far. The measurements of energy misallocation index and carbon emission efficiency were made based on the panel data from 30 provinces in China. To empirically study the impact of energy misallocation on carbon emission efficiency, a spatial econometric model was built. It is found that during the survey period, there was a certain degree of energy misallocation in all regions of China, and the differences between the regions were obvious. There is an inverted U-shaped relationship between the impact of energy misallocation and carbon emission efficiency in which intensified allocation distortion accelerates the arrival of the critical point that is not conducive to energy conservation and emission reduction. The results viewed by regions show that due to a low degree o*f misallocation, the impact of carbon emission efficiency in the east region is positive, while that of the central and west regions are mostly negative. Accordingly, it is necessary to accelerate the marketization process of the energy market and improve the ecological quality.

Research on Innovation Knowledge Spillover Effect of China’s High-Tech Industry R&D-Base on Multidimensional Spatial Weight Matrices

2018 ◽  
Vol 22 (4) ◽  
pp. 437-447
Author(s):  
Xiuwu Zhang ◽  
Chengkun Liu ◽  
◽  
Keyword(s):  
Econometric Model ◽  
Spillover Effect ◽  
Knowledge Spillover ◽  
High Tech ◽  
Spatial Econometric ◽  
High Tech Industry ◽  
Panel Model ◽  
Spatial Weight ◽  
Innovation Output ◽  
Spatial Econometric Model

Based on the panel data of R&D activities of the provincial high-tech industry in China from 1998 to 2014, this paper adopts the spatial weight matrix of different dimensions including geographical distance, technical distance, economic distance, proximity distance, and human capital distance, to construct a spatial econometric model to analyze the knowledge spillover effects of R&D activities through both local and transnational routes. The results show that in the case of spatial auto-correlation of the dependent variables, the results of the spatial panel model are more accurate and reliable than those obtained by the conventional panel model. The spatial coefficients of the spatial econometric model based on five different spatial weight matrices are all very significant, and there is a clear spatial correlation between the R&D activities of high-tech industries in different regions. Labor input and exports have a positive impact on innovation output, but the introduction of technology will hinder independent innovation in China’s high-tech industry, and the impact of capital investment to innovation output is uncertain, as it closely relates to the set of models. In addition, the space knowledge spillover effect through the local approach is larger than that produced by the transnational route.

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