Search

In this study, we examine when creativity is positively or negatively related to firm performance. Building on the creation-implementation tension theorized in the literature and the attention capacity perspective, we argue that the relationship between creativity and firm performance is contingent on riskiness orientation, firm size, and realized absorptive capacity. Data were collected from 761 core knowledge employees, 148 CEOs, and 148 HR executives from 148 high-technology firms. The results indicated that core knowledge employee creativity was negatively related to firm performance when riskiness orientation was high. The relationship was positive when realized absorptive capacity was high. Finally, the relationship was more positive in small firms than in large firms. We discuss the implications of our findings for creativity research and managerial practices. (Contains 2 tables, 3 figures and 4 footnotes.)

This article attempts to analyze the changing circumstances of the regional disparities in the allocation of China's higher educational resources before and after the increase in college enrollments, as well as the mechanisms that have affected these circumstances. The conclusions are that regional disparities in the allocation of China's funding for higher education have tended to increase after the college enrollments have multiplied; the level of per-student expenditure for higher education institutes in various localities is, to a certain extent, tied to local financial capabilities; and fiscal input for higher education is closely correlated with the levels of local economic development, financial capability, and industrial structure. (Contains 3 tables and 2 figures.)

The entry of China in the World Trade Organization (WTO) in 2001 holds a number of promises for educational services. The purpose of this article is to explore both the direct and the indirect influence of China's entry into WTO on its education system. The direct influence mainly refers to the increase in the demand for education overseas and in the number of foreign students studying in China, the cooperation between China and educational institutions overseas, and China's administrative and legal frameworks for education. As to the indirect influence, the article focuses on the impact of economic growth on public and private expenditure on education, of income growth on demand for education and of changes in industrial structure on educational output. On this basis, the article examines the challenges that China's education system will face following its entry into the WTO.

Describes rise of rural industries in People's Republic of China following 1978 reforms. Discusses government's emphasis on urban industrialization, role of rural agricultural production, rural incomes, and policy changes. Describes new industrialization, combining peasant and modern economies, and resulting increases in rural employment and development. (TES)

The development of high-speed rail (HSR) contributes to time-space compression and accelerates economic transformation as well as industrial structure optimization. Using a time-varying difference-in-difference estimator, this study first explores the HSR's impact on green total factor productivity (GTFP) in China's 285 cities from 2003 to 2019. We then demonstrate how industrial structure moderates HSR's effect on GTFP from two perspectives: advanced industrial structure (IS) and rationalized industrial structure (IR). Our results show that the magnitude of HSR's effect exhibits considerable region-based heterogeneity. We notice that HSR stimulates GTFP across 285 cities for a full sample. The development of HSR indirectly stimulates GTFP through the development of IS and decelerates through IR for a whole sample and large cities. In non-provincial capital cities, HSR directly promotes GTFP and indirectly impedes it through IR. In the provincial capital and eastern cities, HSR indirectly improves GTFP through IS. In central-western cities, HSR deteriorates GTFP by promoting IR. In small and medium-sized cities, HSR directly inhibits GTFP, while industrial structure plays an insignificant role in an indirect way. Policy implications are documented to enhance the HSR's economic and environmental aspects. [ABSTRACT FROM AUTHOR]

This study is intended to expose the connectedness of green financing and other green policies like renewable energy and technological progress towards identifying the best practice to achieve climate goal cum sustainable environmental development for China within the period 1985–2021. Amid the increased utilization of fossil fuels in China's economic activities and its position as the highest carbon emitting country in the world, China is considered a financial stable economy with access to renewable energy source such as hydroelectric power. For effective research into the objective of this study, the study builds on mechanism of the green finance—renewable—technology and emission nexus with the help of linear dynamics of ARDL bound and VECM granger causality methods. Empirical results from both approaches have given insight to the selected objectives of this study. According to ARDL dynamics: (i) the coefficients of linear (1.802) and non-linear(− 1.860) (i.e. square term) of financial development are positively and negatively connected to the carbon emissions. This confirms the presence of inverted U-shape relationship between the financial development and carbon emissions. (ii) the relationship between hydroelectric power representing renewable energy and CO2 is negative—(− 0.19) and significant at 1% level. This results depicts the reduction power of hydroelectric power on CO2, (iii) also, a negative (− 0.000982) linkage between number of patent application representing technological innovation and environmental development through carbon emissions responses is established. Also, findings from VECM granger causality approach back the findings from ARDL through a nexus between the variables of interest (finance, hydroelectric power, and technology and carbon emissions). Findings point to the ability of green financing to reduce carbon emissions if green financing policies are utilized effectively in regulating carbon emissions. [ABSTRACT FROM AUTHOR]

[Objective] Digital finance based on information technology provides a new opportunity for the construction of a water-saving society. Effectively release the dividend of water saving and emission reduction brought by the development of digital finance is an important issue for promoting the development of the Chinese-style modernization. [Methods] Based on panel data from 30 provinces in China from 2011 to 2020, this study empirically tested the relationship between digital finance development and water resource utilization using methods such as fixed effect and mediation effect models. [Results] (1) The development of digital finance has an inhibitory effect on water use intensity, which is mainly achieved by improving the coverage and depth of use of digital finance, and the result is still valid after robustness tests such as substitution of variables, instrumental variable method, and limited information maximum likelihood method. (2) From the perspective of transmission pathways, technological innovation and industrial structure adjustment have played a significant mediating role between digital finance and water use intensity, but the mediating role of industrial structure upgrading is not significant. (3) From the perspective of heterogeneity, digital finance has a positive impact on water use efficiency in the eastern region; Meanwhile, digital finance has a significant inhibitory effect on the water use intensity of the primary and secondary industries, but the impact on the water use intensity of the tertiary industry is not significant; Regions with favorable water endowments are more likely to reap the benefits of digital finance. [Conclusion] Therefore, it is necessary to strengthen the construction of digital finance, actively innovate financial services, and give full play to the enabling role of digital finance in resource utilization according to local conditions and production policies.

Improving energy efficiency is vital for addressing climate change and reducing carbon emissions in emerging economies. Financial development (FD) is crucial for economic growth, and its environmental impact and the adjustment of the industrial structure (IND) is a crucial lever in China's economic transition period. This study explored the relationship between FD, IND, and China's green total factor productivity (GTFEE) from 2000 to 2020 using the super-efficiency SBM-undesirable model, which estimates China's GTFEE. The ARDL results suggest that FD and IND enhance GTFEE in the long term, with FD promoting GTFEE by facilitating industrial structure adjustments. The Dumitrescu–Hurlin panel causality tests supported this finding. The QRPD panel quantile regression and heterogeneity analysis revealed significant heterogeneity in the effects. With increasing GTFEE, FD exerts a restraining effect, gradually weakening and transitioning into a promoting effect, while the IND consistently plays a promoting role. [ABSTRACT FROM AUTHOR]

In this paper, panel data from Chinese cities from 2000 to 2020 were used, and the intellectual property pilot was used as a quasi-natural experiment. The double difference model mainly tests the underlying mechanism of the impact of intellectual property rights pilots on urban carbon emissions. This study revealed that IPR pilots play an important role in reducing urban carbon emissions. Heterogeneity experiments reveal that IPR pilots are conducive to reducing carbon emissions in coastal as well as high greening cities and that the underlying mechanism through which IPR pilots affect urban carbon emissions mainly occurs at the level of innovation and the optimization of the industrial structure. In addition, this paper also validates the smoothness of the model by replacing the explanatory variables and performing a placebo test, which further strengthens the reliability of the paper’s conclusions.

The present study has investigated the impact of enterprises, collaborative innovation, industrial structure, inflation, and entrepreneurship in 27 provinces of China. The study used annual time series data from 2003 to 2019. This study adopts the CC-EMG and AMG tests to estimate the long-term association between the variables. The study applied one-step system GMM, two-step system GMM, Cup-FM, and Cup-BC regression estimations to calculate robust and reliable outcomes. The findings show that collaborative innovation, industrial structure, and entrepreneurship positively impact economic growth, whereas enterprises and inflation negatively impact economic growth. The estimated results also provide important policy implications for the selected and the other emerging economies in designing an appropriate way forward to economic development. [ABSTRACT FROM AUTHOR]

With the increasing complexity and uncertainty of regional economies, economic resilience has become a hot research topic in economic geography in recent years. Industrial structure is considered the most important factor in explaining differences in regional economic resilience. However, they tend to focus on the industrial sector without paying adequate attention to producer services, which have played an increasingly important role in the economy. Theoretically, producer services can deepen the division of labor and provide specialized services, reduce transaction costs and improve efficiency, promote knowledge and technology spillover effects to resist crises, create new paths through related diversification, and spread the risk of external shocks through unrelated diversification. This study attempted to probe into the relationship between producer services and economic resilience. According to China's GDP growth, the period from 2007 to 2012 was the resistance period and the period from 2012 to 2017 was the adjustment period. In a period of resistance, economic resilience refers to economic resistance, and in a period of adjustment, resilience refers to economic recoverability. Based on an analysis of 285 cities at and above the prefectural level in China in the "post-crisis" era using a multiple mediation effect model, three main findings can be drawn. First, producer services have not only directly enhanced the economic resilience of cities by enlarging the unrelated variety of the industrial structure but also indirectly enhanced urban economic resistance and recoverability by increasing the related variety of urban industrial structures to induce technological innovation. Second, the degree of separation between producer services and the manufacturing industry in China remains low, and the mediating effect of producer services on economic resilience through deepening specialization and reducing transaction costs is not significant at present. Third, the impact of producer services on urban economic resistance and recoverability differs among eastern, central, and western cities owing to differences in the development level of producer services and economic structure. The direct and indirect impacts of producer services in the eastern cities are more prominent. The impact in central cities is likely to be indirect, mainly through the mediation effect of scientific and technological innovation. The effect of producer services on economic resilience in western cities is not significant. Fourth, among the four subgroups of producer services, the direct and indirect impacts of information transmission, software, and information technology services are the most significant. Owing to their relatively close relationship with low-tech industries, the impact of business services on economic resilience is weaker than that of other producer service sectors. This study is a useful supplement to the existing studies on economic resilience which tends to be "industry-oriented".

Improving energy efficiency is vital for addressing climate change and reducing carbon emissions in emerging economies. Financial development (FD) is crucial for economic growth, and its environmental impact and the adjustment of the industrial structure (IND) is a crucial lever in China’s economic transition period. This study explored the relationship between FD, IND, and China’s green total factor productivity (GTFEE) from 2000 to 2020 using the super-efficiency SBM-undesirable model, which estimates China’s GTFEE. The ARDL results suggest that FD and IND enhance GTFEE in the long term, with FD promoting GTFEE by facilitating industrial structure adjustments. The Dumitrescu–Hurlin panel causality tests supported this finding. The QRPD panel quantile regression and heterogeneity analysis revealed significant heterogeneity in the effects. With increasing GTFEE, FD exerts a restraining effect, gradually weakening and transitioning into a promoting effect, while the IND consistently plays a promoting role.

Significant challenges confronting China include reducing carbon emissions, dealing with the resulting problems, and meeting various requirements for long-term economic growth. As a result, the shift in industrial structure best reflects how human society utilizes resources and impacts the environment. To meet China's 2050 net-zero emissions target, we look at how technological innovations, financial development, renewable energy investment, population age, and the economic complexity index all play a role in environmental sustainability in China. Analyzing short- and long-term relationships using auto-regressive distributive lag (ARDL) bounds testing, we used historical data spanning 1990 to 2018. According to the study's findings, the cointegration between CO2 emissions and their underlying factors was found. The deterioration of the environment directly results from the financial development, increasing economic complexity, and population aging. Technical advancements, investments in renewable energy sources, and changes to the industrial structure all contribute to lower CO2 emissions. Granger causality results were also reliably obtained in this study. According to our findings in the fight against environmental problems, a key tool for meeting long-term sustainability goals is policy prescriptions that use technological innovations, renewable energy investment, and industrial structure. [ABSTRACT FROM AUTHOR]

THe central challenge facing China's sustainable development is how to strike a balance between economic growth and environmental conservation. In China's ongoing economic revolution, green finance is more important than ever. The study empirically examined how green finance and innovation affect carbon emissions using panel data from 30 Chinese provinces gathered between 2010 and 2020. The empirical analysis is undertaken to utilize a series of methods to investigate the impact of green finance on carbon emissions. The findings show that increased green finance, innovation, and industrial structure reduce carbon output. Moreover, carbon emissions increase with increasing trade openness and economic growth. In order to achieve sustainable development goals through economic and environmental sustainability, it has been discovered that green finance can foster green technology innovation and green business. [ABSTRACT FROM AUTHOR]

This paper uses Chinese provincial data from 2006 to 2021 as a sample period to study the relationship between higher education development, industrial structure adjustment, and environmental pollution. Conclusions were as follows: (1) the industry structure adjustment can reduce environmental pollution in Chinese regions except eastern, and the increase in the proportion of the tertiary industry will increase pollution emissions in the eastern region. (2) Although there is a negative correlation between higher education and environmental pollution in China, it is not significant. From different regions, the coefficients in the eastern are positive which means aggravated environmental pollution, and the coefficients in the central region are not significant, but higher education in the western region improves environmental pollution. (3) Urbanization has a significant moderating effect on the national and regional environmental pollution, but in the central and western regions, it is smaller than the eastern region; although environmental regulation has a certain inhibitory effect on environmental pollution, the coefficient in the eastern region is significantly positive, and there is a situation of "more pollution, more control." Further, the increase of foreign direct investment will aggravate environmental pollution; although the elasticity coefficient in the eastern region is negative, there is a trend of improving environmental pollution, but it is not significant. The study holds promising implications for the development of policies related to education, industry, and the environment. Through the research on the relationship between the three, exploring and improving the regional environmental pollution level from the perspective of higher education and industrial structure have important practical significance for the regional green development. [ABSTRACT FROM AUTHOR]

This study aims to understand the relationship between China’s industrial structure and human resources employment structure. This paper reviewed the relevant data from the China Statistical Yearbook, China Population and Employment Statistical Yearbook between 2010 and 2019 and quantitatively analyzed the interrelationship between the two using the CORREL function, employment elasticity coefficient, and employment structure deviation coefficient. Results indicate that the two have a strong correlation, but the coordination is poor; from the perspective of stimulating economic effect on employment, the total employment elasticity coefficient of the three industries is primarily positive, but the absolute value is close to 0. The synchronous inspiring effect of economic growth of various industries on employment is not apparent. In terms of equilibrium, the degree of structural deviation of the primary industry fluctuated between –0.70 and –0.75 each year, and the degree of structural deviation of the secondary industry has always hovered around 0.4. Although the degree of structural deviation of the tertiary industry was stable overall, mainly in some years, the deviation coefficient was still higher than 0.2, and the degree of imbalance between the economic development of various industries and the employment structure is more severe.

With the increasing complexity and uncertainty of regional economies, economic resilience has become a hot research topic in economic geography in recent years. Industrial structure is considered the most important factor in explaining differences in regional economic resilience. However, they tend to focus on the industrial sector without paying adequate attention to producer services, which have played an increasingly important role in the economy. Theoretically, producer services can deepen the division of labor and provide specialized services, reduce transaction costs and improve efficiency, promote knowledge and technology spillover effects to resist crises, create new paths through related diversification, and spread the risk of external shocks through unrelated diversification. This study attempted to probe into the relationship between producer services and economic resilience. According to China's GDP growth, the period from 2007 to 2012 was the resistance period and the period from 2012 to 2017 was the adjustment period. In a period of resistance, economic resilience refers to economic resistance, and in a period of adjustment, resilience refers to economic recoverability. Based on an analysis of 285 cities at and above the prefectural level in China in the "post-crisis" era using a multiple mediation effect model, three main findings can be drawn. First, producer services have not only directly enhanced the economic resilience of cities by enlarging the unrelated variety of the industrial structure but also indirectly enhanced urban economic resistance and recoverability by increasing the related variety of urban industrial structures to induce technological innovation. Second, the degree of separation between producer services and the manufacturing industry in China remains low, and the mediating effect of producer services on economic resilience through deepening specialization and reducing transaction costs is not significant at present. Third, the impact of producer services on urban economic resistance and recoverability differs among eastern, central, and western cities owing to differences in the development level of producer services and economic structure. The direct and indirect impacts of producer services in the eastern cities are more prominent. The impact in central cities is likely to be indirect, mainly through the mediation effect of scientific and technological innovation. The effect of producer services on economic resilience in western cities is not significant. Fourth, among the four subgroups of producer services, the direct and indirect impacts of information transmission, software, and information technology services are the most significant. Owing to their relatively close relationship with low-tech industries, the impact of business services on economic resilience is weaker than that of other producer service sectors. This study is a useful supplement to the existing studies on economic resilience which tends to be "industry-oriented". [ABSTRACT FROM AUTHOR]

Accurately measuring carbon dioxide (CO2) emissions is critical for effectively implementing carbon reduction policies, and China's increased investment in reducing CO2 emissions is expected to significantly impact the world. In this study, the potential of shallow learning for predicting CO2 emissions was explored. Data included CO2 emissions, renewable energy consumption, and the share of primary, secondary, and tertiary industries in China from 1965 to 2021. These time-series data were converted into labeled sample data using the sliding window method to facilitate a supervised learning model for CO2 emission prediction. Then, different shallow learning models with k-fold cross-validation were used to predict China's short-term CO2 emissions. Finally, optimal models were presented, and the important features were identified. The key findings were as follows. (1) The combined model of RF and LASSO performed best at predicting China's short-term CO2 emissions, followed by LASSO and SVR. The prediction performance of RF was very fragile to the window width. (2) The sliding window method is used to convert time series predictions into supervision learning problems, and historical data can be used to predict future carbon dioxide emissions. To ensure that the feature data are real, the model can predict CO2 emissions for up to six years ahead. (3) Cross-validation and grid search were critical for optimizing China's CO2 emissions prediction with small datasets. (4) By 2027, carbon dioxide emissions will continue to grow and reach 10.3 billion tons. It can be seen that the task of China to achieve its carbon peak on schedule is very heavy. The results indicate that an increase in renewable energy consumption and adjustments in industrial structure will continue to play an important role in curbing China's CO2 emissions. [ABSTRACT FROM AUTHOR]

In order to lessen China’s carbon footprint, the government has turned to environmentally friendly financing. A reduction in CO2 has been reported in some Chinese provinces where green finance has been developed. Numerous regions in China from 2010 to 2020 are selected for this study. Based on a Dynamic Seemingly Uncorrelated, fully modified ordinary least squares and dynamic ordinary least squares regressions model, empirical research is performed with per capita growth in the economy, public spending, and the relationship between economic growth, human resources, and industrial arrangement as core variables to test the influence of green financing on CO2 emission in Chinese provinces. According to the findings, green financing speeds up the reduction of carbon emissions. Moreover, the outcomes present that industrial structure, economic growth per capita, and trade openness increase carbon emissions. Likewise, public expenditures and human capital are significantly contributing to emissions reduction. The findings show that sustainable green environment can only be achieved by boosting the performance of green finance and increasing the level of green finance supported by the Chinese economy. Last but not least, policymakers should promote public health and education spending to fully engage in the protection of the environmental efforts to encourage green consumption while minimizing the structural problems resulting from economic activity.

Industrial upgrading is the key to promoting green economic efficiency. Coordination between environmental regulations and industrial structure can lead to sustainable economic growth. However, insufficient attention has been paid to the mechanism by which environmental regulation (ER) promote green economic efficiency (GEE) under the mediating role of industrial structure optimization. Using robust and comprehensive measures of green economic efficiency, we assess how various environmental regulations affect green economic efficiency as well as the intermediate effect of industrial structure of a certain province with provincial panel data during the period 2003–2017. The results of dynamic panel two-step system generalized method of moments (GMM) confirm the heterogeneous effects of the three types of ER, namely control-and-command regulation, market-based regulation, and voluntary regulation on GEE in China. The spatial analysis demonstrates that control-and-command and voluntary regulations significantly accelerate GEE in inland provinces, while they have insignificant effect in coastal provinces. Based on the mediating analysis, we find that market-based regulation is conducive to GEE through both advanced and rationalized industrial structure, whereas control-and-command regulation improves GEE through advanced industrial structure only. The voluntary-based regulation on one hand stimulates GEE through advanced industrial structure, but on other hand impedes it through rationalized industrial structure. The results could provide critical insights and a theoretical basis for policy makers in reasonable optimization of industrial structure and guaranteeing green economic efficiency. [ABSTRACT FROM AUTHOR]

Significant challenges confronting China include reducing carbon emissions, dealing with the resulting problems, and meeting various requirements for long-term economic growth. As a result, the shift in industrial structure best reflects how human society utilizes resources and impacts the environment. To meet China’s 2050 net-zero emissions target, we look at how technological innovations, financial development, renewable energy investment, population age, and the economic complexity index all play a role in environmental sustainability in China. Analyzing short- and long-term relationships using ARDL bounds testing, we used historical data spanning 1990–2018. According to the study’s findings, the cointegration between CO2 emissions and their underlying factors was found. The deterioration of the environment directly results from financial development, increasing economic complexity, and population aging. Technical advancements, investments in renewable energy sources, and changes to the industrial structure all contribute to lower CO2 emissions. Granger causality results were also reliably obtained in this study. According to our findings in the fight against environmental problems, a key tool for meeting long-term sustainability goals is policy prescriptions that use technological innovations, renewable energy investment, and industrial structure. [ABSTRACT FROM AUTHOR]

Since China’s reform and opening up, the urban economy has substantially developed, and the nation’s urbanization process has advanced to gradually enter a “new normal” phase. In this process, China’s population and industries continue to inundate its cities, and environmental pollution has become a critical problem. This study uses panel data spanning 1998 to 2015 from 29 Chinese provinces and a comprehensive framework of well-designed generalized method of moments (GMM) models to accurately examine the relationships between urbanization and per capita sulfur dioxide (PSO2), per capita industrial soot (PSOOT), and per capita industrial waste gas (PIWG). A newly developed dynamic threshold panel model that incorporates GMM characteristics is utilized to explore how urbanization affects environmental pollution under different industrial structures. This study finds evidence of a comprehensive relationship among urbanization development, the industrial structure, and environmental pollution. Urbanization has contributed to increased environmental pollution. A nonlinear relationship exists between urbanization and environmental pollution in China. Further, urbanization increasingly promotes environmental pollution with an increase in the proportion of secondary industries; however, such promotion decreases with an increase in the proportion of tertiary industries.

Accurately measuring carbon dioxide (CO2) emissions is critical for effectively implementing carbon reduction policies, and China’s increased investment in reducing CO2 emissions is expected to significantly impact the world. In this study, the potential of shallow learning for predicting CO2 emissions was explored. Data included CO2 emissions, renewable energy consumption, and the share of primary, secondary, and tertiary industries in China from 1965 to 2021. These time-series data were converted into labeled sample data using the sliding window method to facilitate a supervised learning model for CO2 emission prediction. Then, different shallow learning models with k-fold cross-validation were used to predict China’s short-term CO2 emissions. Finally, optimal models were presented, and the important features were identified. The key findings were as follows. (1) The combined model of RF and LASSO performed best at predicting China’s short-term CO2 emissions, followed by LASSO and SVR. The prediction performance of RF was very fragile to the window width. (2) The sliding window method is used to convert time series predictions into supervision learning problems, and historical data can be used to predict future carbon dioxide emissions. To ensure that the feature data are real, the model can predict CO2 emissions for up to six years ahead. (3) Cross-validation and grid search were critical for optimizing China’s CO2 emissions prediction with small datasets. (4) By 2027, carbon dioxide emissions will continue to grow and reach 10.3 billion tons. It can be seen that the task of China to achieve its carbon peak on schedule is very heavy. The results indicate that an increase in renewable energy consumption and adjustments in industrial structure will continue to play an important role in curbing China’s CO2 emissions.

Abstract Improving energy efficiency of industrial sectors is essential to achieve energy conservation in the Pearl River Delta urban agglomeration (PRDUA) in China. This study adopted stochastic frontier analysis (SFA) to evaluate the industrial total factor energy efficiency (TFEE) of nine cities in PRDUA during 2004–2016, and then to decompose the growth of TFEE into technical change (TC), technical efficiency change (TEC), and economies of scale (SC) effects. Results show that the TFEE of industrial sectors of PRDUA had an overall downward trend during the study period, and that technical change was the major driving force behind the growth of TFEE. The Tobit model was then adopted to further investigate the determinants of energy efficiency improvement and test the robustness of empirical results. The main contributions of this study to the existing literature lie in showing that (1) the degree of openness, local government spending, foreign direct investment, factor input structure, environmental regulation strength, and GDP per capita have positive impacts on the industrial TFEE; (2) enterprise scale and energy consumption structure have a negative impact on industrial energy efficiency. Finally, we address policy implications for energy management regarding their economic and environmental aspects in industrial sectors of the PRDUA. [ABSTRACT FROM AUTHOR]

Under the goal of "carbon peaking" and "carbon neutrality" in China, it is significant to explore whether green finance contributes to carbon emission reduction. Therefore, based on the panel data of 30 provinces in China from 2005 to 2020, this paper empirically examines the impact of green finance on carbon emissions and its mechanism using the FE model, GMM model, DID, and the mediating effect model. Results find that green finance can significantly promote carbon emissions reduction, and this promotion remains still after robustness tests. The heterogeneity analysis shows that green finance's carbon emission reduction effect is more significant in developed and western regions. Moreover, the results of policy intervention effects confirm that green finance reform and innovation pilot zones (GFRIs) for carbon emission reduction are more pronounced than in other regions. Finally, the mediating effects reveal that industrial structure and green innovation are the core transmission channels through which green finance influences carbon emission reduction. These findings offer meaningful implications for formulating reasonable carbon emission reduction plans and determining the future development direction of green finance. [ABSTRACT FROM AUTHOR]

China has made remarkable achievements in its industrialization process, and its gross domestic product (GDP) has jumped up to the second place in the world. However, total energy consumption, total carbon emissions and annual emission growth have leaped to the top in the world. Consequently, the coal-dominated energy consumption structure has resulted in many serious environmental problems. The evolution of global energy consumption shows that the goal of clean and low-carbon energy is not only the general trend of energy development, but the only way to reduce the risks of climate change and ecological damage. This paper systematically analyzes the current status of energy consumption in China and the necessity for achieving the goal of clean and low-carbon energy, and proposes the corresponding practical measures. It also discusses the roles of different types of energy like coal, oil, gas and non-fossil fuels in the medium- and short-term planning of clean and low-carbon energy. It is concluded that China needs to make joint efforts in such aspects to achieve the goal of clean and low-carbon energy as optimizing the industrial structure, controlling the amount of total energy consumption, and improving energy consumption structure. On the other hand, in view of the resource endowments and consumption status in China, the clean-and-low-carbon utilization of coal is a realistic choice; to develop non-fossil energy sources vigorously is a strategic choice; as a dominated source of power fuels, oil will not be wavered in its role in the medium and short term, and natural gas will play an important role as a bridge in the process of achieving the goal of clean and low-carbon energy.

Industrial structure adjustment is one solution for responding economic slowdown and environmental problems occurred during China’s rapid industrialization. Such an adjustment may also lead to carbon emission reduction since it encourages industrial innovation and resource efficiency. However, few studies have been conducted on examining the effect of industrial structure adjustment on carbon emission reduction. Under such a circumstance, this study develops an integrated evaluation model based on Input-Output Analysis and Social Network Analysis to quantify the evolutionary trends of industrial structure, demonstrate the inner-relationship between different sectors and investigate the industrial structure-related carbon emissions. China and Japan were selected as case study countries. Results show that industrial structure was gradually improved in China and various connections were established between different sectors. For Japan, the industrial network densities were lower than for China and exhibited a downward trend that reflected the weakened relationship between different industries. Service sectors dominated the Japanese economy, as shown by the relatively higher degree centrality and betweenness centrality of service sectors. The electricity and heat production sector was further investigated to illustrate the industrial structure-related carbon emissions. Finally, this study concluded that compared to the industrial structure features in a developed country, such as Japan, China’s industrialization is still in its infancy. Thus, it is crucial to prepare industrial structure adjust policies so that the overall social-economic performance can be improved. [ABSTRACT FROM AUTHOR]

Using data for 30 provincial panels in China from the period 1997-2014, this study analyzes the impact of multi-dimensional industrial structures and technological progress on carbon emissions in the STIRPAT framework. A spatial autocorrelation test demonstrated that there were significant positive global spatial correlations and local spatial agglomerations among the regions that were assessed. The dynamic spatial regression results show that industrial structure rationalization, industrial structural transformation and industrial structural upgrading significantly reduced carbon emissions. Industrial structural transformation provided the greatest contribution to carbon emissions. Technological progress was also conducive to reducing carbon emissions. Furthermore, efficiency improvements and technological innovation reduced carbon emissions, and efficiency improvements played a relatively greater role. There was an inverted U-shaped relationship between regional affluence and carbon emissions. The energy consumption structure, population and urbanization had significantly positive effects on carbon dioxide emissions, but the impact of foreign direct investments on carbon reduction was insignificant. Finally, some policy recommendations are given. [ABSTRACT FROM AUTHOR]

• We explore how urbanization affects industrial structure adjustment and land use • The effect of land use efficiency on industrial structure is divergent in China • Tailored policy recommendation to different regions for urban development sustainability It remains unclear on how industrial structure adjustment interacts with urban land use efficiency and how urbanization affects their interaction, despite its significance for urban development sustainability. We explore such interactions in China based on a panel dataset of 31 provincial administrative units from 1978 to 2018. The results show that as urbanization proceeds, the industrial structure optimization exhibits a positive linear correlation with urban land use efficiency, while the relationship between industrial structure upgrade and urban land use efficiency depends on level of urbanization, which follows an inverted U-shape. The effect of urban land use efficiency on the optimization of industrial structure is divergent across provinces. We also find that a higher level of urbanization facilitates the positive interactions between industrial structure adjustment and urban land use efficiency in the central and western regions of China, but the effect is attenuated in eastern China. We propose policy recommendations tailored to different regions with distinct levels of economic development status and industrial structure for urban development sustainability. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Physical Education / Tiyu Xuekan is the property of Journal of Physical Education Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Based on the computable general equilibrium (CGE) modelling method, this research analyzes the relationship between industrial structure and land use structure in China. The results show that our model is feasible, and the simulation results are of a certain stability. Under the scenario analysis and projection of the relationship between the industrial structure and land use structure of the thirty-one provinces in China from 2010 to 2020, the proportions of secondary and tertiary industry in each province have been increasing; correspondingly, the proportion of agriculture has been decreasing. This means that the industrial structure of China is changing. As for land use, in general, the trend is similar to the industrial structure changes. The transformation of the structure of industrial development and land use has driven economic structure changes in China. The economic structure has an inclination to transform from agriculture to both secondary and tertiary industry. Along with industrial transformation, the cultivated land in China shows a trend of continuous decline. Empirical analysis results indicate that a decrease of cultivated land is acceptable under the scenario of economic growth in the next ten years. This shows a possibility that the economic efficiency of land use for cultivation and business services will decline, and more attention ought to be paid to increasing the economic efficiency of land use. [ABSTRACT FROM AUTHOR]

This paper uses panel data between 2000 and 2010 to explore how firm ownership and regional industrial structures contribute to regional innovation performance in Chinese provinces. Specifically, we explore how the extent of specialisation and diversification in regional industrial structures at the province level fosters both Marshall–Arrow–Romer and Jacobs spillovers, as well as how foreign and state ownership influence regional innovation. We find: (i) China's regional innovation systems benefit from Jacobs but not MAR externalities, with the former spurring higher quality innovation in the form of increased invention patenting; (ii) state-owned enterprises and foreign-invested enterprises advance local innovation, with the latter again fostering higher quality innovation; and (iii) a convergence towards a combination of low specialisation and high diversity in provincial industrial structure is taking place between China's more developed inland coastal provinces and less developed inland provinces. Implications and suggestions for policy-making and future research are discussed. [ABSTRACT FROM AUTHOR]