Upgrading Of Industrial Structure Research Articles

Towards a low-carbon economy: how does green credit affect carbon productivity?

As a financial instrument and a crucial component of the environmental governance system, the comprehensive impacts of green credit on both the economy and the environment remain unclear. This study examines Chinese provincial data from 2001 to 2018 and employs multiple models to assess the dynamic effects of green credit on carbon productivity. The findings indicate a significant positive influence of green credit on carbon productivity within local and surrounding areas. Furthermore, the promotion effect of green credit on carbon productivity is mainly through energy structure transformation, upgrading of industrial structure and green technology innovation. Interestingly, when considering the spatiotemporal aspect, we observe an initial marginal negative impact of green credit on carbon productivity, which subsequently transitions to a positive effect. The regions demonstrating the highest positive impacts of green credit are primarily concentrated in the southwestern region rich in renewable energy and the economically developed eastern coastal region.

Opening to the outside world, Industrial upgrading and Labor market integration: An empirical study of Chinese data from the perspective of enterprise ownership structure

Based on the panel data of 31 provinces in China from 2011 to 2020, this paper studies the impact of opening to the outside world on labor market integration under the mechanism of industrial structure upgrading from the perspective of enterprise ownership structure. The findings are as follows: (1) the increase in opening to the outside world significantly reduces the average wage gap and employment gap between state-owned and non-state-owned sectors, which contributes to the integration of the Chinese labor market; (2) The level of opening to the outside world has a significant effect on the upgrading of industrial structure, industrial upgrading is a mechanism of opening to the outside world affecting our labor market integration; (3) The effect of the level of opening to the outside world on labor market integration has obvious regional characteristics. The findings of this paper have important implications for understanding the relationship between opening to the outside world and labor market integration from the perspective of different enterprise ownership structures and the mechanism of industrial structure upgrading.

How does the global metal industry value network affect industrial structure upgrading ? The perspective of industry chain

The global metal industry value network (GMIVN) can reveal the status of an economy in the global metal industry chain and value chain, while the traditional metal trade networks cannot deeply reflect the global metal industry pattern and the competitive capacity of an economy's metal industry. Moreover, the GMIVN also performs an irreplaceable role in the process of the industrial structure upgrading. This paper uses network analysis and panel regression to investigate the characteristics of the GMIVN and its influence on the upgrading of industrial structure in the ways of the industry chain. The main findings are as follows: (1) The GMIVN has always been compacting shows “small world” characteristics. The network structure features of the value network of the middle and lower industry are highly consistent with those of GMIVN. (2) The distribution pattern of the core economies in the value network of the global metal industry is stable and concentrated. The U.S., Germany, China, and the U.K. exert significant impact in all links of the global metal industry chain and value chain. However, major metal resource exporters are at a lower position in the global value network of the metal industry and exert limited influence. (3) The higher the central and independent status of an economy in global metal trade or the more high-influence trading partners it has, the more advanced its industrial structure will be. Furthermore, the centrality of the value network of the middle and lower industry exerts a larger influence on the upgrading of industrial structure than that of the value network of the upper industry.

Exploring the role of technical and financial support in upgrading the marine industrial structure in the Bohai Rim region: Evidence from coastal cities

This study examines the role of technical and financial support in promoting the upgrading of the marine industrial structure in the Bohai Rim region. Using panel data from coastal cities around the Bohai Sea from 2006 to 2020, the study applies financial constraint theory and resource endowment theory to analyze the spatial spillover of technical and financial support on the marine industry. The results indicate that technical and financial support positively affects the upgrading of the marine industrial structure. Still, technical support negatively affects coastal cities around the Bohai Sea. The study also reveals that financial support has a threshold effect, and once this threshold is crossed, the government can promote the upgrading of the marine industrial structure through financial intervention, environmental regulation, and improving market opening. The findings also suggest that upgrading the marine industry will promote local economic growth but may have a siphon effect and inhibit economic growth in surrounding cities. However, it will optimize the local environment and show a significant positive spillover effect on the environmental optimization of surrounding cities. The study's conclusions can inform the construction of cross-regional industry-university research platforms and facilitate the high-quality development of marine-related industries in the Bohai Rim region.

Trend Analysis of the Impact of Ecological Governance on Industrial Structural Upgrading under the Dual Carbon Target

The Chinese government faces significant challenges in achieving the goals of carbon peaking and carbon neutrality (dual carbon targets), particularly in the realms of implementing a low-carbon economy and achieving ecological balance. In order to assist the Chinese government in formulating more effective ecological governance policies, this paper focuses on 288 cities in China and proposes a predictive model combining gray forecasting, Backpropagation Neural Network, and threshold effect testing to forecast yearly ecological governance intensity. Under the premise of examining the predictive effect, fixed effects testing and threshold regression analysis were conducted to assess the future intensity of ecological governance. The empirical research results reveal that the increasing intensity of future ecological governance has a promoting effect on China’s upgrading of industrial structure, but this effect gradually diminishes. On the contrary, there is significant potential for optimizing industry’s internal structure. Efforts should be directed towards intensified governance, emphasizing energy-saving and emission reduction in high-carbon industries, and promoting environmentally and economically beneficial models. Our research provides a widely applicable method for studying the trend of research as it pertains to government decision-making effectiveness and valuable insights for governments to make more informed decisions in the pursuit of sustainable development.

Research on the implementation path of industrial structure optimization in Fujian Province under the goal of “double carbon”

Abstract Facing the dual pressure of domestic environmental pollution and international climate change mitigation, China, the world’s largest emitter of carbon emissions, has proposed a “double carbon” target of 3060. Fujian Province is a clean energy province and a major economic province in southeastern China, with inherent energy conservation and emission reduction advantages. However, as industrialization is still in progress and urbanization is accelerating, the problems of low industrial level and sloppy development are still prominent, and the future low-carbon transformation will be difficult and long. Based on the theory of industrial structure optimization and the theory of low carbon economy, this paper analyzes the influence mechanism of industrial structure optimization on carbon emission from three perspectives: efficiency improvement, technological progress, and structural adjustment. Secondly, an econometric model is constructed by combining the environmental Kuznets curve, and the time series data of Fujian Province from 2000-2021 are analyzed empirically to derive the optimization path of industrial structure on carbon emissions in Fujian Province through co-integration analysis, multiple regression, mediating effect analysis, and robustness testing methods. The results of this paper show that from the structure of energy consumption in Fujian Province, coal and oil consumption account for 72% of the total consumption, which is the main cause of carbon emissions in Fujian Province. The mediating effect and stability analysis of the optimization path through the benchmark regression model shows that the p-value variance is 0.0862, and there is no significant difference between them. The optimization path proposed in this paper has significant utility for carbon emission reduction, improves the efficiency of energy use, strengthens the introduction of FDI and industrial structure optimization in various regions of Fujian Province in a targeted manner, and provides a reference for decision-making for global industrial structure optimization and upgrading.

The Impact of Rural Industrial Development on Farmers’ Livelihoods—Taking Fruit-Producing Area as an Example

Analyzing the impact of industry development on farmers’ livelihoods is of great significance for promoting rural revitalization and the sustainable development of farmers’ livelihoods in China. In this paper, taking Dali County of the Guanzhong Plain as an example, in accordance with the sustainable livelihoods approach (SLA), we adopted the comprehensive index method, statistical analysis method and relevant analysis method to explore the impact of rural industry development on farmers’ livelihoods by analyzing the evolution of farmers’ livelihood strategies in different times (2000, 2010 and 2019) and the differentiation of forms of livelihood capital and livelihood outcomes among different types of farmers in 2019 under the background of industrial development. The main conclusions were as follows: The degree of commercialization of the agricultural industry has improved, and the overall development of the rural industry presents an obvious trend away from agriculture. With the upgrading of the rural industrial structure, the leading livelihood strategies of farmers have gradually changed from the crop-planting type to the work-oriented type, and forms of livelihood capital and livelihood outcomes differ significantly among farmer households. Compared to traditional agriculture, the development of the fruit industry and service industry and the degree of participation in these industries are conducive to the accumulation of farmers’ livelihood capital, while migrant work alone has a negative impact on the improvement in farmers’ livelihood capital. The development of the fruit industry and nonagricultural industries and the degree of participation in these industries are more conducive to the improvement in farmers’ livelihood outcomes than is participation in traditional agriculture.

Study on the impact of industrial structure upgrading on soil conservation in the Yellow River basin counties

The problem of soil and water loss in the Yellow River Basin has been impacting the local high-quality development. The upgrading of industrial structure is a method to alleviate the issue of soil and water loss. In order to address the soil and water loss problem in the Yellow River Basin, this study investigates the internal mechanisms of how the upgrading of industrial structure impacts soil conservation, promoting coordinated development in the region. This research utilizes the entropy weight method, the InVEST model, and the fixed spatial Durbin model to explore how the greening, upgrading, and rationalization of industries affect the average soil conservation. The main conclusions of this study are: (1) For every 0.1 increase in the greening index, it effectively increases the local average soil conservation by 17.25 (t/hm2) and 49.61 (t/hm2). It is necessary to reduce the emission of pollutants in the upstream region in order to effectively improve the average soil conservation; (2) For every 0.1 decrease in the industry upgrading index, it will lead to a decrease in the average soil conservation in surrounding areas by 2.82 (t/hm2). Only by continuously increasing the financial investment in the upgrading of industries in the downstream region can the average soil conservation be increased. (3) An increase in the rationalization of industries in the upstream and midstream regions will lead to a decrease in the average soil conservation in surrounding areas, while in the downstream region, it will increase the local average soil conservation.

The impact of artificial intelligence on pollution emission intensity-evidence from China.

Artificial intelligence (AI) is a crucial component of sustainable economic development and an indicator of the next wave of technological progress. This study examines the effects and mechanisms of AI on the intensity of pollution emissions, using China as an example. Theoretical analysis demonstrates that the scale expansion effect and the technological innovation effect of AI can reduce the intensity of pollution emissions. In the meantime, AI can have a positive structural influence on reducing the intensity of pollution emissions through the upgrading of industrial structures. Therefore, we use panel data for 30 Chinese provinces from 2006 to 2019 to test the effect of AI on pollution emission intensity using a fixed effects model, employ explanatory variable substitution, endogenous analysis, regression after tailing, and eliminate related policy interference for robustness analysis. The results indicate that AI can significantly decrease the intensity of pollution emissions, with a 6.63% reduction for every 10% increase in AI utilization. We use the mediating effect model to conclude that AI can reduce the intensity of pollution emissions via the rationalization of industrial structure and advanced industrial structure, with the rationalization of industrial structure being the main mechanism. The examination of heterogeneity revealed that the implementation of AI in technology-intensive industries is an effective method for reducing the intensity of pollution emissions and that the positive impact of AI on the intensity of pollution emissions is more pronounced in the western region.

Impact of Digital Economy Development on Industrial Structure Upgrading--Based on Two-way Fixed Effects Model Analysis

Digital economy is an important engine for building a modernized economic system. Through new generation information technology, it improves the operation efficiency of industrial chain and expands the innovation space of industrial chain, thus helping to upgrade the industrial structure. Based on the balanced panel data of 30 provincial administrative regions in China from 2012 to 2020, a two-way fixed effects regression model is constructed to empirically test and explore the impact of digital economy development on industrial structure upgrading. It is found that: Digital economy development can significantly promote industrial structure upgrading, and the conclusion still holds after a series of robustness tests. The promotion effect of digital economy development on industrial structure upgrading is regionally heterogeneous and social consumption level heterogeneous, which can significantly promote industrial structure upgrading in western and low social consumption level regions, but not in eastern, central and high social consumption level regions. Accordingly, policy suggestions are made in three aspects: vigorously developing digital economy, formulating regional differentiated digital economy development policies, and coordinating the relationship between consumption upgrading and supply side

Research on environmental monitoring and governance of air contamination in the Beijing‐Tianjin‐Hebei region stemmed from spatiotemporal data collection

AbstractFrom 2013 to now, Beijing‐Tianjin‐Hebei (Hereinafter referred to as “the region”) has carried out comprehensive air pollution governance, which has promoted the sustained and rapid development of the regional economy while significantly improving regional air quality. However, the spatial and seasonal differences in atmospheric quality are obvious, and the regional and structural problems are still prominent. There is a long way to go for new challenges of cooperate governance of PM2.5 and O3. Therefore, first, we should further deepen the joint prevention and control mechanism based on regional collaborative governance. Second, we should rely on technological innovation to impetus the upgrading of energy and industrial structure. Third, we should adjust the transportation structure and create a new pattern of transportation network. Fourth, we should improve the ecological compensation mechanism and give full play to the functions of ecological conservation areas. Fifth, we should think highly of the self‐purification capacity of the ecosystem and build urban forest parks. At last, we should Strengthen publicity and mobilization, and participate in joint governance through nationwide action.

Can China’s transfer payment in key ecological function areas reduce the carbon intensity? – Quasi – Natural experimental evidence from Jiangxi, China

Reducing carbon intensity while sustaining economic growth is a major challenge for China's ecological civilization construction in the context of global warming. China’s transfer payment policy in National Key Ecological Function areas plays a crucial role in promoting ecological improvement and reducing local carbon intensity. In this study, we examine the impact of transfer payment policies on carbon intensity in Jiangxi Province, a demonstration area for national ecological civilization, and we further investigate the carbon reduction mechanism and the spatial spillover effect of the policies. Results show the following: (1) the transfer payment policy in key ecological function areas has significantly reduced the carbon intensity, and the research conclusion is still valid after the robustness test; (2) the transfer payment policy in key ecological function areas reduces the carbon intensity by promoting the upgrading of industrial structure; (3) the transfer payment policy in key ecological function areas has a significant role in promoting the reduction of carbon intensity in this region and the adjacent regions. The findings highlight the significant promoting role of transfer payment policies in key ecological function zones on carbon emission reduction in both local and neighboring areas. It also underscores the importance of expanding policy coverage, establishing horizontal ecological compensation mechanisms, exploring multidimensional approaches to carbon reduction, and fostering cross-regional exchanges and cooperation. These insights provide valuable guidance for policymakers working towards effective and sustainable carbon emission reduction.

Spatial Impact of Industrial Structure Upgrading and Corporate Social Responsibility on Carbon Emissions: Evidence from China

Identifying the spatial attributes of economic, social and environmental development is a prerequisite for China to raise the quality of development. Based on the parallel data of 30 Chinese provinces from 2010 to 2019, this study uses a spatial econometric model to explore the spatial impacts of corporate social responsibility (CSR) and industrial structure upgrading on carbon emissions. The regulating effect of CSR during industrial structure adjustment for carbon emission reduction was also analyzed. It was found that regional carbon emissions were reduced due to CSR and industrial structure adjustment, and the former was beneficial during industrial structure adjustment for carbon emission reduction. The carbon emissions in neighboring areas have also been suppressed to some extent as a result of industrial structure adjustment. However, CSR can encourage some “free riding” behaviors, due to the economic externalities, which emit more carbon into the surroundings. Additionally, carbon emissions show different responses to CSR in various regions. Therefore, strategies must enhance overall social responsibility and formulate different policies in various regions to promote CSR as an influential factor in curbing carbon emissions. Moreover, spatial governance should consider the comparative advantages of different regions, form complementary advantages and fully enhance the cooperation between CSR and industrial structure upgrading on carbon emission reduction.

Can the Digital Economy Enable Carbon Emission Reduction: Analysis of Mechanisms and China’s Experience

With the continuous advancement of global scientific and technological capabilities, the issue of global warming caused by greenhouse gas emissions has received widespread attention from countries worldwide. Promoting carbon reduction and curbing the trend of global warming have become urgent and significant challenges for China and the world. Therefore, it is of great practical significance to explore the impact and mechanism of the digital economy on carbon reduction. This paper empirically analyzes the impact and means of the digital economy on carbon emissions using panel regression models and mediation effect models. The research indicates that the digital economy significantly impacts carbon emissions, and the following main conclusions are drawn: (1) The influence of the digital economy on carbon intensity exhibits an inverted U-shaped curve, starting with promotion and then inhibition. (2) The digital economy can affect carbon emissions through industrial structural upgrading and technological innovation. (3) Regions with a relatively high level of digital economy development also tend to have higher energy utilization efficiency, leading to a more pronounced impact of the digital economy on carbon emissions levels.

Retracted: Research on the Spatial-Temporal Coupling Effect of Industrial Structure Upgrading and Human Resources in Western China

Retracted: Research on the Spatial-Temporal Coupling Effect of Industrial Structure Upgrading and Human Resources in Western China

Industrial restructuring and optimization for sustainable development of resource cities based on dynamic simulation perspective

Resource cities are highly dependent on resource industries and have a single industrial structure, so how to adjust and optimize the industrial structure is of practical significance to the sustainable development of resource cities. This paper combines the principle of data envelopment analysis (DEA) and the principle of industrial layout optimization to evaluate and position the current situation of industrial structure in resource cities. Based on this study, the simulation dynamic simulation of the path optimization of sustainable industrial development is conducted through the study of the dynamics principle, and the realism and validity of the model are tested by combining it with the actual data to realize the prediction of sustainable industrial path development. The results show that: In the simulation dynamic simulation prediction, the scale of the resource industry in resource cities decreases by about 6%-8% in 2020, and its simulation prediction data and real data have high consistency, which verifies the effectiveness of industrial structure adjustment and optimization strategy. This paper studies the optimization and upgrading of industrial structure to promote the sustainable development of a resource-based city economy, narrow the gap between the economic development of east and west regions, promote urbanization, and improve inter- and intra-regional development imbalance.

Fostering inclusive green growth in China: what is the role of industrial intellectualisation?

ABSTRACT Inclusive green growth is a style of long-term development that prioritises economic growth, social fairness, human well-being, and energy conservation. This research explores the impact of industrial intellectualisation on regional inclusive green growth using panel data from 30 provinces from 2006 to 2018. The research begins by calculating the level of industrial intellectualisation and inclusive green growth employing the entropy technique in China. Second, the benchmark regression results show that industrial intellectualisation boosts regional inclusive green growth significantly. This is true for a variety of robustness tests, including IV regression. Furthermore, spatial autocorrelation exists in regional inclusive green growth, and industrial intellectualisation has a favourable stimulating influence as well as a considerable spatial repercussions on regional inclusive green growth. Furthermore, the mediating effect model is employed to explore the mechanism of industrial intellectualisation's effects on inclusive green growth. The findings demonstrate that industrial intellectualisation has an effect on regional inclusive green growth via industrial structure upgrades, urbanisation, education, and financial development.

Strategic Policy Innovation of Regional Development Strategy Transformation in the New Era Based on Progressive Differential Model

Abstract In the new era, the national high-tech industrial development Zone is a powerful platform for implementing the innovation-driven development strategy and promoting China’s high-level development. An empirical study of panel data from 285 county-level cities in China during 2001-2022 was conducted using the binary difference method. The results show that high-tech enterprises have a noticeable promoting effect on improving the quantity of industrial structure. The transformation and upgrading of our industrial design by high-tech enterprises have significant cyclical heterogeneity. It has significantly enhanced the industrial system compared with the “growth type” and “mature type” national high-tech development zones. There is no apparent regional difference in the quality effect of high-tech enterprises on industrial structure upgrading. Still, there is an evident regional difference in the quantity and rationality of industrial structure height.

Can green finance development abate carbon emissions: Evidence from China

Green finance has been treated as an important tool in the transformation process to low-carbon economy; however, it still remains a question whether the development of green finance could offer carbon savings, or inversely lead to more carbon debts. Based on panel data of 30 provincial administrative regions during 2008–2019, we use the fixed-effect model to examine the carbon emissions reduction effect of green finance. The empirical results suggest that green finance development exerts significantly negative influence on carbon emissions by energy consumption structure optimization and industrial structure upgrading. Besides, we also find that compared with green bond, green credit has a more pronounced carbon emissions reduction effect at present. Furthermore, the heterogeneity of green credit types and regional heterogeneity are both discussed in this study. It is indicated that the green credit oriented for energy conservation and environmental protection could restrain the carbon emissions more significantly while the green credit oriented for clean energy cannot contribute to cutting down carbon emissions as remarkably as other types of green credit. As for regional heterogeneity, results suggest that green finance could curtail carbon emissions in the central and western regions, while this effect is not significant for provinces in the eastern part of China. Thus, the green finance policies should be implemented based on regional characteristics in order to abate carbon emissions more effectively.

Economic growth and environmental pollution in China: New evidence from government work reports

In China, economic growth targets significantly affect the economic behavior of local governments. This paper analyzes a dataset of the economic growth targets from 230 city government work reports from 2004 to 2019 to reveal how economic growth targets affect local environmental pollution. The empirical results show that the economic growth targets are “overweight” from the higher-level governments to the lower-level governments, which significantly aggravates regional environmental pollution because of environmental regulation relaxation, blocked industrial structure upgrades and technological innovation inhibition. Furthermore, to exceed the economic growth targets, local governments usually adopt the “riding a seesaw” strategy for the treatment of different pollutants. If the economic growth target statements are ambiguous or attainable, then environmental pollution is reduced. Interestingly, the “preemptive” city government, which sets economic growth goals before its province, is more inclined to maintain growth at the expense of the environment. This study provides important evidence for the literature on the impacts of government behavior on environmental pollution.