Industrial Structure Advancement Research Articles

Industrial structure optimization and green growth in China based on a population heterogeneity perspective

AbstractPopulation and industrial structure, as foundational characteristics of economic and social systems, exhibit significant spatial heterogeneity and dynamic evolutionary trends in their impact on sustainable economic and social development. However, existing research often employs subjective spatial categorization of samples and overlooks the dynamic transitions of influencing patterns, potentially leading to biases in empirical results. To address this, the current study, based on the calculation of green total factor productivity (GTFP) for 30 provinces in China from 2000 to 2018, incorporates a finite mixture model. This model examines the objective heterogeneity and dynamic transition patterns of industrial structure's impact on GTFP, both from the perspectives of industrial structure advancement (ISA) and rationalization (ISR), and reveals the mechanisms of heterogeneity and dynamic changes from a population standpoint. The findings indicate that there are three patterns in the impact of industrial structure on GTFP, with nearly half of the provinces undergoing pattern transitions during the observation period. The key factors for these transitions are identified as the improvement in human capital levels and urbanization rates. In provinces like Beijing, Guangdong, and Shanghai, ISA and ISR significantly promote GTFP, with their effects further enhanced by increased urbanization and human capital levels. Conversely, in regions such as Shanxi and Hebei, ISA does not favor GTFP improvement, and while ISR can enhance GTFP, this positive effect diminishes with increasing urbanization and labor force numbers. This research not only enriches the literature on the positive interaction patterns between industrial and population structures but also provides a comprehensive analytical framework for governments to adopt differentiated policy measures for sustainable economic development.

How do industrial land transfer modes impact carbon emissions? An intermediation perspective based on industrial structure.

Industrial land is currently the main carrier and important source of global carbon emissions, and as the world's largest developing country, China's large-scale and diversified industrial land supply has made it the world's largest carbon emitter. Therefore, researching the impact of different supply methods of industrial land on carbon emissions and its impact paths in China can help provide a reference for other countries to reduce carbon emissions from the perspective of urban industrial land management, which is of great significance for effectively promoting global carbon reduction. Based on this, this paper analyses the impact of different supply methods of industrial land on carbon emissions and its urban heterogeneity using the SYS-GMM and chain-mediated effects models for 285 cities in China from 2008 to 2020. The study found that, in general, the impact of different industrial land transfer modes on carbon emission has hysteresis and persistence. Agreement and listing transfer with government intervention can significantly exacerbate carbon emissions, while more market-based bidding and auction transfer can dampen carbon emissions. In terms of intermediary effects, the transfer of industrial land by agreement and listing will inhibit the rationalization and advancement of industrial structure, thus aggravating carbon emissions. The transfer of industrial land by bidding and auction will create barriers to entry and a crowding-out effect, promote the rationalization of industrial structure and the transformation and upgrading of industrial structure and moderate carbon emissions. In terms of city heterogeneity, there is urban heterogeneity in the impact of industrial land transfer on carbon emissions in cities with different economic types. Bidding and auction transfer for industrial land in both economically developed and less developed cities can promote carbon pollution. While the more developed urban economy makes the intermediary effect of industrial structure not significant. In the future, it is necessary to strictly control the scale of industrial land supply; the whole process supervision mechanism of industrial land allocation and differentiated industrial land supply strategies will provide useful experience for many developing countries in allocating industrial land to mitigate carbon emissions, generating effective contributions to global carbon emission reduction.

Research On Effectiveness of Low-Carbon Policies on Labor Mobility

Abstract The internal and external environment of China's development has undergone profound changes, and the promotion of “dual-carbon” has become an urgent need for cracking the outstanding problems of resource and environmental constraints and realizing sustainable development, while labor mobility can bring “demographic dividend” to the region, which is an important part of realizing the dual-carbon goal. This paper focuses on the low-carbon pilot policies, adopts the panel data of 281 prefecture-level cities from 2005 to 2019, and constructs a multi-period double difference model to investigate the effects of low-carbon policies on labor mobility. The study finds that: low-carbon pilot can significantly promote inter-regional labor inflow, and the above conclusion still holds after a series of robustness tests; mechanism analysis shows that the advancement of industrial structure plays a significant positive moderating role, while government expenditures on finance, science and technology play a mediating role. The research in this paper not only provides a new perspective for the evaluation of the effects of low-carbon policies, but also provides a new basis for city branding to attract labor inflow, and the heterogeneity analysis also provides a theoretical reference for the government to formulate talent policies. JEL classification numbers: J61. Keywords: Low-carbon policies, Labor mobility, Multi-period double difference model, Advancement of industrial structure.

The energy saving effects of digital infrastructure construction: Empirical evidence from Chinese industry

Digital infrastructure serves as a crucial foundation for development in the era of the digital economy. In light of this, this paper takes digital infrastructure construction as its focal point. Utilizing Python technology and the Meta-frontier non-radial directional distance function to calculate the level of digital infrastructure construction and industrial energy efficiency. It then employs a dynamic panel data model to analyze the impact effects and mechanisms of digital infrastructure construction on industrial energy efficiency. Additionally, threshold models and modified gravity models are employed to explore the nonlinear effects and spatial impacts of digital infrastructure construction on industrial energy efficiency. The research findings reveal that the enhancement of digital infrastructure significantly improves industrial energy efficiency. Moreover, indicate a shift in the impact of digital infrastructure construction on industrial energy efficiency from inhibitory to promotional as the level of digital infrastructure construction increases. The mechanism of this effect suggests that digital infrastructure construction can promote the improvement of industrial energy efficiency through green technological innovation and the advancement of industrial structure. Further analysis shows that the spatial network characteristics of digital infrastructure construction notably elevate the level of industrial energy utilization efficiency. This study provides practical policy implications for sustainable development.

How Does the Digital Economy Drive the Optimization and Upgrading of Industrial Structure? The Mediating Effect of Innovation and the Role of Economic Resilience

The digital economy (DE) has become a major driving force behind the optimization and upgrading of industrial structures (ISOU), but research on its driving mechanisms has yet to be verified. To this end, the driving influence of the DE on ISOU is targeted and the role played by economic resilience (ER) and innovation is explored. Based on the panel data of 31 provinces in China from 2011 to 2020, the driving influence of the DE on ISOU is analyzed using a two-way fixed-effects model. This empirical study finds that the DE has a positive driving effect on both industrial structure advancement and rationalization and that the driving effect is still significant after the addition of control variables and the robust-type test with one period of lag. The DE presents regional differences in ISOU, with the driving effect of the DE on the industrial structure advancement in the eastern region having a more significant performance than that in the central and western regions and the driving influence on the rationalization of the industrial structure in the eastern and western regions exceeds that in the central region. The impact of the DE on ISOU has a mediating role through innovation. ER has a moderating role in the process and innovation, as a partial mediator, also has a threshold effect. Finally, based on the research and discussion, conclusions and countermeasure recommendations are presented.

Can a new national system policy for science and technology promote industrial restructuring in China’s national innovation center cities?

AbstractWe establish two types of difference-in-difference (DID) models to study the effects of the policy shocks of the new national system of science and technology on the rationalization and advancedization of industrial structure of national innovation center (NIC) cities. Meanwhile, considering the differences in resource endowment between NIC cities and general cities, we use propensity matching analysis (PSM) to establish matching data on the basis of benchmark data to empirically demonstrate the effect of this policy shock on the industrial structure of NIC cities. The study shows that the policy shock has a promoting effect on the level of industrial structure rationalization in the NIC cities, but not significant effect on the industrial structure advancedization, the improvement of the financing environment can promote the development of industrial structure advancement, the growth of personal wealth has a promoting effect on the industrial structure rationalization, and the increase of the government investment is not conducive to the advancement of industrial structure, although it can promote the rationalization of the industrial structure in China’s NIC cities. Compared with the eastern NIC cities, this policy shock has a greater effect on the rationalization of industrial structure in the Central-western NIC cities of China. In addition, the financing environment and per capita income of NIC cities have a moderating effect on the rationalization of industrial structure under the policy shock. Therefore, NIC cities should actively promote the promotion effect of this policy on the rationalization of industrial structure.

Impact of green finance on industrial structure upgrading: implications for environmental sustainability in Chinese regions.

Marking green finance to revolutionize industrial structure and achieve environmental sustainability is increasingly critical. We explore the impact of green finance (GF) on industrial structure advancement and industrial structure rationalization employing the panel data of 30 Chinese provinces from 2011 to 2020 in terms of technology inputs, environmental regulation, and green technology innovation. Results indicate that GF significantly stimulates China's industrial structure in both advanced and rationalization. The driving effect of GF on the industrial structure is mainly reflected in the advanced dimension of industrial structure in the eastern and economically developed area but mainly in the rationalization dimension of industrial structure in the economically less developed area. GF's role in facilitating industrial structure upgrading largely depends on technology inputs, environmental regulation, and green technology innovation.

Research on industrial structure adjustment and spillover effect in resource-based regions in the post-pandemic era.

Resource-based regions support national economic development and are essential sources of basic energy and raw materials. In the post-pandemic era, however, there are practical situations to deal with, such as a fractured industrial chain, a weaker industrial structure, and a sharp reduction in economic benefits. Based on data collected from 68 cities in China, from 2010 to 2021, with 816 observations, this paper explores the industrial development process of resource-based regions in China and the change in the toughness of the industrial structure under the impact of COVID-19. The paper studies and analyzes industrial development trends, industrial structure toughness, and spatial spillover effects. The methods used are the Markov chain model and the Industrial Structure Advancement Index. By building the spatial Dubin model, the paper analyzes the spatial spillover effect of regional industrial development. It decomposes the spillover effect using the partial differential model based on regression. The results show that, during the study period, the comprehensive development level of industries in resource-based regions in China was slowly improving and tended to stabilize after entering the post-pandemic era. The evolution of an advanced industrial structure is significantly heterogeneous among regions, and each region has different toughness. The impact of COVID-19 has reduced the toughness of China's resource-based regions' industrial structure. The spatial spillover effect of regional industrial development is significant. Labor force, technology input, and industrial-structure optimization have different impacts on the industrial development of neighboring regions. In the post-pandemic era, China has used new management methods for more innovation. In order to achieve low-carbon, environmental protection, and sustainable development of resources, realize the rapid recovery of the toughness of industrial structure in China's resource-based cities, and reduce the impact of the COVID-19 pandemic, China proposes to expand the supply of resources, improve the allocation of resources, optimize the direction, promote the rational flow and efficient aggregation of various factors, and enhance the impetus for innovation and development.

The impact of the digital economy on urban total factor productivity: mechanisms and spatial spillover effects

Digital economy is the indispensable pathway for driving industrial structural upgrading and fostering innovation and entrepreneurship in cities, ultimately facilitating China's economic transformation. Simultaneously, the enhancement of urban total factor productivity (TFP) serves as a crucial means to achieve high-quality economic development in cities. This study examines the specific impact of the digital economy on urban TFP using a panel data model with a sample of 285 Chinese prefecture-level cities from 2011 to 2019. Additionally, it employs a mediation effect model to test the mechanisms through which the digital economy influences urban TFP and utilizes a spatial Durbin model to analyze the spatial spillover effects of the digital economy on urban TFP. The research findings reveal the following key points: (1)The digital economy has an overall significantly positive impact on urban TFP. (2)The digital economy indirectly promotes urban TFP by encouraging the advancement of industrial structure and fostering innovation and entrepreneurship in cities. (3)The influence of the digital economy on urban TFP exhibits spatial spillover effects, where the digital economy in neighboring cities significantly enhances the TFP growth of local cities.The results of this study contribute to elucidating the mechanistic pathways through which the digital economy affects urban TFP, holding significant practical implications for achieving high-quality economic development in urban areas.

Analysis of the Relationship between Regional Carbon Financial Market Development and Industrial Structure Upgrading: A Grey Relational Model Approach

Carbon finance, recognized globally as one of the effective solutions to combat global warming, is poised to inject fresh vitality into economic and societal production as it develops. Based on data from 13 cities in the Beijing-Tianjin-Hebei region, this study explores how the development of the carbon finance market affects industrial structure upgrading from the perspectives of carbon finance market development level and industrial structure upgrading. Firstly, by constructing an evaluation indicator system, a comprehensive comparative analysis of the carbon finance market development and industrial structure upgrading of these cities is conducted. Secondly, the study uses the grey relational model to delve into the degree of correlation between the development level of the carbon finance market and the upgrading of the industrial structure. The result shows significant regional disparities among these cities in terms of the development of the carbon finance market and the upgrading of industrial structure. Beijing and Tianjin lead in terms of carbon finance market development. Through the analysis using the grey relational model, it is found that there is a moderate to strong correlation between the level of development of the carbon finance market and industrial structure optimization, indicating the importance of all factors in influencing industrial structure upgrading. Empirical results demonstrate that, on a holistic scale, the development of carbon finance has a significantly positive impact on industrial structure advancement and rationalization. However, disparities exist among different cities, primarily attributed to varying levels of economic development, policy orientation, and developmental stages.

How Financial Development Mitigates Carbon Intensity: Insight from China’s 30 Provinces

Given the importance of financial development in promoting socioeconomic green transition, this study used a balanced panel data set spanning China’s 30 provinces from 1995 to 2018 to investigate how financial development has reduced carbon emission intensity from linear and nonlinear perspectives. First, the quantile regression results indicated that financial development (FD) significantly eradicated carbon emission intensity (CEI) across all quantiles with minor fluctuations in an influential degree. Second, FD significantly reduced CEI in nearby and local areas after implementing spatial econometric models. Third, using a spatial mediating effect model, FD's promoting effects on technological innovation and industrial structure advancement were two channels to help reduce CEI. Third, using a spatial mediating effect model, FD's promoting effects on technological innovation and industrial structure advancement were two channels to help reduce CEI. Finally, the nonlinear relationship between FD and the CEI was identified at the national level using a panel threshold model with spatial elements to recognize the mediating effects of technological innovation and industrial structure advancement. These findings emphasized the importance of continuing to refine and develop the financial mechanism and financial market, encouraging firm R&D investment, and vigorously upgrading and optimizing the industrial structure to reduce China’s carbon emissions reduction intensity.

The Relationship between Structural Upgrading and Economic Growth of Sports Industry Based on AD-AS Modeling

Abstract This paper determines the structural upgrading measurement indexes of the sports industry by analyzing the metrics from three perspectives: industrial structure advancement, rationalization, and coordination. With reference to the AD-AS model in economics, it introduces the positive exchange factor to improve the comprehensive supply model, utilizes the pressure factor and the negative exchange factor to improve the comprehensive supply-demand model, and obtains the improved SAD-SAS model. Based on the equilibrium relationship between aggregate supply and aggregate demand, five hypotheses regarding influence are proposed. The results show that the output growth rate fluctuation coefficient of the sports industry is 0.25-0.45, and the time, robust, and spatial elasticity economic growth volume shows a stable incremental curve with the incremental increase of time, which illustrates that the structural upgrading of the sports industry and the economic growth is a positive effect significant relationship.

How does urban land use efficiency improve resource and environment carrying capacity?

Considering the constraints on urban land expansion, improving the efficiency of existing urban land becomes a key factor in promoting resource sustainability and environmental protection. This paper examines the impact mechanism and threshold effect of urban land use efficiency (ULUE) on resources and environment carrying capacity (RECC) using panel data covering 282 Chinese cities from 2007 to 2018. The empirical results show that: (1) ULUE enhances RECC in China, and the rationalization and advancement of industrial structure are important transmission mechanisms; (2) The impact of ULUE on RECC is heterogeneous, showing significant differences in different regions, periods, administrative levels, and resource endowments; (3) With industrial rationalization and industrial advancement as threshold variables, ULUE exhibits a “gradient-style” enhancement with significant threshold characteristics in improving RECC. These findings can provide practical guidance for various land policies.

Has the ecological civilization pilot promoted the transformation of industrial structure in China?

Industrial structure transformation is important for realizing Chinese modernization, and ecological civilization is necessary for building a beautiful China. How to use the construction of ecological civilization to realize industrial structure transformation is a major issue for the economic development of China and other developing countries. The difference-in-differences method was used to evaluate the impact of the ecological civilization pilot policy (ECPP) on industrial structure transformation. The three-stage mediating effect method is used to explore the mechanism channel. Through heterogeneity discussion and spatial spillover analysis method, the effects of ECPP were further understood. The study reveals the following: Overall, the ECPP has promoted the transformation of the industrial structure. Specifically, the ECPP has a significant positive impact on industrial structure rationalization but a negative impact on industrial structure advancement. Several robustness checks support this finding. Second, the mechanism channels are primarily achieved through environmental investment, green finance infusion, and congestion alleviation. Third, good central-local relationships can enhance the trend toward industrial structure rationalization, while unfavorable relationships may pose hindrances to industrial structure advancement. In addition, the effect of the ECPP varies depending on the population density and environmental pollution. Lastly, the ECPP has a spatial positive spillover effect promoting industrial structure rationalization. The results above provide additional information about the effects of ECPPs on industrial structure transformation, both positive and negative, and help the ecological civilization strategy system be adjusted promptly.

Urban economic resilience and human capital: An exploration of heterogeneity and mechanism in the context of spatial population mobility

Human capital is a critical factor that affects urban economic resilience (UER). We utilize Baidu migration big data to construct a population mobility matrix and provide an analysis of the mechanism of UER from four different perspectives: innovation, industrial structure advancement, industrial structure rationalization, and economic development. Our findings suggest that human capital contributes to approximately 7 % improvement in UER through innovation and economic development, while the role of industrial structure advancement and rationalization is not significant. To address potential endogeneity, we employ an instrumental variable (IV) using spatial two-stage least squares. Additionally, we conduct a heterogeneity analysis based on the city tier, human capital level, and UER size. We replenish the study on UER and offer feasible solutions from the perspective of human capital to enhance UER.

Understanding the efficiency and evolution of China's Green Economy: A province-level analysis

The efficiency level, evolution characteristics, and factors driving the green economy in all provinces and regions should be clarified to achieve high-quality economic development and meet China's “double carbon” target. This study conducted the Super-Effective Slack-Based Model considering unexpected outputs to evaluate province-level Green Economic Efficiency (GEE) analysis (including 30 provinces, autonomous regions, and municipalities directly under the Central Government) in China from 2005 to 2020. Moreover, the distribution and dynamic evolution trend of GEE development was estimated through Kernel density estimation. Besides, GEE and its factors (i.e., industrial structure rationalization [ISR], industrial structure advancement [ISA], and urbanization level [UL]) were examined using a Panel vector autoregressive model that was built in this study. As indicated by the result of this study, China's GEE level generally displayed a “U-shaped” development trend of declining, stabilizing, and then rising, whereas the overall efficiency level is low, where the national GEE average reached 0.6934. The regional GEE level exhibited a significant “ladder” distribution, with the highest level, the second level, and the lowest level in the east, the middle, and the west, respectively. The GEE level varied significantly with the province, and most of the levels were at a medium efficiency level. Notably, 60% of regions had medium efficiency in 2020. The levels of ISR, ISA, and UL play significant roles in boosting green economic growth. This study provides valuable insights into the drivers of green economic growth in China guiding policy decisions on achieving a sustainable and low-carbon economy. As China strives to fulfill its ambitious carbon reduction goals, the findings of this study highlight the significance of continuing to prioritize green economic development at the provincial level.

How do land supply admittance regulations affect urban carbon emissions?

The rationalization of land resource utilization, affected by increasing stringent guidance of urban land supply regulations, is a vital path for countries to achieve their sustainable development goals. However, evidence on environmental effects of land supply regulations is scarce. Thus, adopting China's land supply policy, we investigate the impact of land supply admittance regulations (LSARs) on urban carbon emissions (UCEs) by using the land market transaction data and carbon emission data of China's 285 cities from 2007 to 2019. The result shows that LSARs notably curb UCEs, with UCEs decreasing by 0.051 standard units (approximately 1.052 g CO2 per RMB) for each 1 standard unit increase in LSARs. After introducing the instrumental variable to deal with endogenous issues, this conclusion remains robust. Mechanism analysis indicates that the carbon abatement effect of LSARs is through structural and efficiency two main channels: the industrial structure advancement from quantity and quality; the green production efficiency from scale and technology. Furthermore, heterogeneity results demonstrate that the reduction effect varies in admittance regulations setting, government intervention, land supply marketization, and environmental regulations. Our findings provide valuable insights for other economies seeking to adopt land-based policy instruments for carbon governance and urban sustainability.

How does science and technology innovation improve carbon productivity?-Evidence at China's province level.

Science and technological innovation play a crucial role in achieving the goals of carbon peaking and carbon neutrality. They are also vital for improving the scale, structure, and allocation of input and output factors, as well as enhancing the marginal efficiency of production factors. This study investigates the impact of science and technological innovation on carbon productivity using data from 30 provinces (cities, and autonomous regions) in China spanning the period from 2004 to 2020. The findings are as follows: First, the spatial characteristics of carbon productivity in China have gradually evolved into "low in the northern regions and high in the southern regions." The spatial distribution of science and technological innovation in China shows a gradual decrease from the southeast to the northwest. Second, employing the fixed effect model, we find that science and technological innovation has a significant positive effect on carbon productivity, with regional variations in the magnitude of the effect. Furthermore, we identify industrial structure rationalization, industrial structure advancement, and industrial structure ecology as important mechanisms through which science and technological innovation influence carbon productivity. The mediation effects are measured at 3.37%, 8%, and 73.94%, respectively. These research findings provide valuable insights for enhancing technological innovation capabilities, realizing low-carbon development, and addressing the challenges posed by climate change.

Spatio-temporal evolution of resources and environmental carrying capacity and its influencing factors: A case study of Shandong Peninsula urban agglomeration

Promoting ecological conservation and high-quality development in the Yellow River basin is an important objective in China's 14th Five-Year Plan. Understanding the spatio-temporal evolution of and factors affecting the resources and environmental carrying capacity (RECC) of the urban agglomerations is critical for boosting high-quality green-oriented development. We first combined the Driver-Pressure-State-Impact-Response (DPSIR) framework and the improved Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) model to evaluate the RECC of Shandong Peninsula urban agglomeration in 2000, 2010 and 2020; we then used trend analysis and spatial autocorrelation analysis to understand the spatio-temporal evolution and distribution pattern of RECC. Furthermore, we employed Geodetector to detect the influencing factors and classified the urban agglomeration into six zones based on the weighted Voronoi diagram of RECC as well as specific conditions of the study area. The results show that the RECC of Shandong Peninsula urban agglomeration increased consistently over time, from 0.3887 in 2000 to 0.4952 in 2010 and 0.6097 in 2020, respectively. Geographically, RECC decreased gradually from the northeast coast to the southwest inland. Globally, only in 2010 the RECC presented a significant spatial positive correlation, and that in the other years were not significant. The high-high cluster was mainly located in Weifang, while the low-low cluster in Jining. Furthermore, our study reveals three key factors—advancement of industrial structure, resident consumption level, and water consumption per ten thousand yuan of industrial added value—that affected the distribution of RECC. Other factors, including the interactions between residents’ consumption level and environmental regulation, residents’ consumption level and advancement of industrial structure, as well as between the proportion of R&D expenditure in GDP and resident consumption level also played important roles resulting in the variation of RECC among different cities within the urban agglomeration. Accordingly, we proposed suggestions for achieving high-quality development for different zones.

How Does New Energy Demonstration City Policy Promote Urban Land Use Efficiency in China? The Mediating Effect of Industrial Structure

As an effective measure to solve the dilemma of urban energy consumption and economic development, the new energy demonstration city (NEDC) policy in China could greatly promote the development of the new energy industry and urban economy. This study aims to explore how the NEDC policy effectively promotes the growth of urban land use efficiency (ULUE), an essential indicator of economic development, through the urban industrial structure. Based on the panel data of 285 Chinese cities during 2003–2019, this study took the NEDC policy as a quasi-natural experiment and employed the PSM-DID method and the mediating effect model to objectively evaluate its policy effects. We found that the NEDC policy could significantly promote the growth of the ULUE. Specifically, the ULUE has been significantly improved by 17.0%. The NEDC policy could also promote the ULUE indirectly through the mediating effect of industrial structure advancement (ISA), but the mediating effect of industrial structure rationalization (ISR) was not significant. Furthermore, the promotional effect of the NEDC policy on the ULUE has regional heterogeneity. Compared with eastern cities and high-innovation cities, central and western cities and medium-innovation and low-innovation cities can obtain much higher promotion effects. This study may provide some policy inspiration for policymakers to support low-corban and sustainable economic development and urban land use.