Impact of Industrial Structure Upgrading on Green Total Factor Productivity in the Yangtze River Economic Belt

This paper measures the transformation and upgrading of industrial structure from two aspects of rationalization and upgrading of industrial structure, and empirically analyzes the impact of environmental regulation on industrial structure transformation and upgrading by using data of 29 provinces in China from 2004 to 2015. It was found that there is a significant nonlinear effect between environmental regulation and the transformation and upgrading of industrial structure. Specifically, environmental regulation is not conducive to the rational development of industrial structure, but with the continuous improvement of economic development level and human capital level, the inhibitory effect of environmental regulation on the rationalization of industrial structure is gradually weakened. The influence coefficient of environmental regulation on the rationalization of industrial structure is 0.0619~0.2648. Moreover, environmental regulation effectively drives the upgrading of industrial structure, and when the level of economic development and human capital are higher than the threshold, the role of environmental regulation in promoting the high development of industrial structure is gradually enhanced. The influence coefficient of environmental regulation on the upgrading of industrial structure is 0.0540~0.5626. Therefore, it is of great significance to formulate appropriate environmental regulation policies according to local conditions in the transformation and upgrading of industrial structure.

Section: Introductionmentioning

confidence: 85%

The Nonlinear Influence of Environmental Regulation on the Transformation and Upgrading of Industrial Structure

Guan

Liu

et al. 2022

“…The entropy weight TOPSIS method is a combination of the entropy weight and TOPSIS. The entropy weight was used to assign the weights of evaluation indicators, which was based on the degree of variability of the evaluation indicators, thus avoiding the influence of bias caused by human factors [ 5 ]. The order preference by similarity to ideal solution (TOPSIS) method, also known as the distance method of superiority and inferiority solution, is a multi-attribute decision-making method.…”

Section: Methodsmentioning

confidence: 99%

“…Therefore, how to decouple regional economic development from environmental pollution and promote the synergistic development of the economy, society, and eco-environment are still important issues [ 4 ]. As an important strategic region in China [ 5 , 6 ], the Yangtze River Economic Belt has become one of the leading demonstration areas for the green transformation of China’s economic development due to its resource endowment and high industrial agglomeration. In the context of China’s comprehensive promotion of green development, taking the Yangtze River Economic Belt as the research area to explore the temporal and spatial evolution characteristics and influencing factors of its green development performance is of great practical significance for China to further promote green development.…”

Section: Introductionmentioning

confidence: 99%

Green Development Performance Evaluation Based on Dual Perspectives of Level and Efficiency: A Case Study of the Yangtze River Economic Belt, China

Zhang

Ren

2022

In the context of continuing to promote the construction of an ecological civilization, it is of great significance to explore green development performance. However, most of the literature is based on a single perspective of level or efficiency, lacking a comprehensive examination of both. It is not scientific to explore how to promote green development only from a single perspective, which may be a new advancement by breaking the conventional thinking focusing only on level or efficiency. On this basis, we first established evaluation index systems of green development performance based on a theoretical framework. Furthermore, green development performance was measured with the entropy weight technique for order preference by similarity to ideal solution (TOPSIS) and super-EBM models, and finally, we analyzed the spatial and temporal evolution patterns of green development performance using the ESDA method and examined its influencing factors with a geographic detector (GD) and econometric models. The main results were as follows: (1) The trend of the green development level in the Yangtze River Economic Belt from 2004 to 2017 had an inverted “N” shape, while the overall average green development efficiency continuously increased. (2) In terms of spatial and temporal patterns, both the green development level and green development efficiency showed “high in the east and low in the west” spatial divergence characteristics. In terms of the spatial and temporal evolution pattern of the green development level, the L-L clusters were mainly distributed in the western region. However, for green development efficiency, the L-L clusters were mostly distributed around the H-H clusters. (3) The results of the influencing factor analysis indicated that industrial structure and people’s welfare are still important factors of the green development level. The improvement of green development efficiency was mainly driven by economic development, and the inhibiting effect of energy consumption is significant. In addition, the effect of opening up has not yet changed from a “pollution paradise” to a “pollution halo”.

“…In addition, the “structural dividend” released by industrial-structure upgrading can promote the emergence of clean industries and provide effective support to improve GTFP. Wang et al (2018), Zhu et al (2019), and Sun et al (2022) have further maintained that industrial-structure upgrading can significantly improve urban GTFP in the context of environmental regulation, energy control, and low-carbon city construction [ 48 , 49 , 50 ]. Accordingly, we propose the following research hypothesis:…”

Section: Theoretical Analysis and Research Hypothesesmentioning

confidence: 99%

“…According to the above analysis, it is clear that both specialized and diversified agglomeration of productive services can directly promote GTFP through agglomeration externalities. Meanwhile, some theoretical and empirical studies have shown that industrial-structure upgrading is an important inducement to promote regional GTFP [ 46 , 48 , 50 ]. Considering the influence of economic-development level and industrial type, specialized agglomeration can improve GTFP through industrial-structure upgrading, while this transmission path has not been confirmed in diversified agglomeration.…”

Section: Theoretical Analysis and Research Hypothesesmentioning

confidence: 99%

Agglomeration of Productive Services, Industrial Structure Upgrading and Green Total Factor Productivity: An Empirical Analysis Based on 68 Prefectural-Level-and-Above Cities in the Yellow River Basin of China

Dong

Chen

Zhuang

et al. 2022

Improving green total factor productivity (GTFP) is the inherent requirement for practicing the philosophy of green development and achieving regional high-quality development. Based on panel data for 68 prefectural-level-and-above cities in the Yellow River Basin of China from 2006 to 2019, we measured their GTFPs and degrees of productive-services agglomeration using the non-radial directional distance function and industrial agglomeration index formulas, respectively. Furthermore, we empirically investigated the interactive relationship between agglomeration of productive services, industrial-structure upgrading, and GTFP using the dual fixed-effects model, the mediating-effect model, and the moderating-effect model. The findings were as follows. (1) Both specialized and diversified agglomeration of productive services significantly improved the GTFPs of cities in the Yellow River Basin, and the promoting effect of specialized agglomeration was stronger than that of diversified agglomeration. (2) The diversified agglomeration of productive services (hereinafter referred to as diversified agglomeration) made a significant contribution to GTFP in all sample cities of the Yellow River Basin, while the specialized agglomeration of productive services (hereinafter referred to as specialized agglomeration) only significantly improved GTFP in the upstream cities and had no significant effect on the midstream and downstream cities. (3) When examined according to city size, specialized agglomeration was found to have a positive impact on the GTFPs of small and medium-sized cities in the Yellow River Basin but a non-significant negative impact on large cities, while the effect of diversified agglomeration on GTFP was found not to be significant. (4) Industrial-structure upgrading played partially mediating and negative moderating roles in the process of specialized agglomeration affecting the GTFPs of cities in the Yellow River Basin, but it did not become a mediating channel and moderating factor that influenced diversified agglomeration in relation to GTFP.