Impact Of Industrial Structure Research Articles

Environmental regulation, industrial structure and energy efficiency: Evidence from 30 provinces in China.

The government's environmental protection policy can significantly contribute to alleviating resource shortages and curbing environmental pollution, but the impact of various policy instruments implemented by the government on energy efficiency is unclear. Based on the panel data of 30 provinces in China from 2005 to 2021, this paper analyses the impact of environmental regulation and the industrial structure on energy efficiency from the perspective of resource taxes. The U-shaped relationship between environmental regulation and energy efficiency and between the optimization of industrial structure can significantly improve energy efficiency, and the optimization of industrial structure is conducive to weakening the initial inhibitory effect of environmental regulation. In addition, the analysis of regional heterogeneity showed that the impact of environmental regulation was stronger in the central and western regions, while the impact of industrial structure was stronger in the eastern and western regions. The conclusions of this study can help to expand the understanding of the relationship between environmental regulation and industrial structure on energy efficiency, provide policy enlightenment for the realization of green development and high-quality development, and provide Chinese examples and experiences for developing countries to improve energy efficiency.

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.

Spatiotemporal evolution and determinants of urban land use efficiency under green development orientation: Insights from 284 cities and eight economic zones in China, 2005–2019

Green development is essential for improving urban land use efficiency (ULUE) as it seeks to optimize resource utilization and minimize waste and pollution. However, a long-term evolution of ULUE and its determinants under the context of green development are less discussed in existing studies. Drawing on remote sensing and statistical data and utilizing the super efficiency slack-based model (SBM) and the geographically and temporally weighted regression (GTWR) model, this study evaluates green development-oriented ULUE and its spatiotemporal associations with determinants across 284 cities and eight economic zones in China from 2005 to 2019. We found that the green development-oriented ULUE in China generally increased in 15 years (from 0.404 to 0.55), with higher levels in coastal and northwestern regions than in central regions. Besides, per capita GDP, investment in technology and science, and degree of openness generally contributed to ULUE (over 75% observations showed positive coefficients), while investment in real estate had a negative impact on ULUE. The impact of industrial structure on ULUE experienced a transition from positive to negative in northern and eastern cities, with the highest coefficient decreasing from 0.194 in 2005 to −0.032 in 2019. Population density contributed to ULUE in southern and northwestern cities during 2005–2010 (coefficients ranging from 0.008 to 0.198), while it negatively influenced ULUE in most cities since 2015 (coefficients ranging from −0.009 to −0.283). The correlation between nighttime light, per capita road, and ULUE showed noticeable south-north differentiation. Our study provides valuable guidelines for Tailor-made strategies of efficient urban management towards sustainable urbanization.

Which is the more important factor of carbon emission, coal consumption or industrial structure?

Since China is the world's largest carbon emitter with a high dependence on coal and a constantly upgrading industrial structure, therefore, we study which of these two factors can affect carbon emissions more significantly. In this study, the Cross-sectional augmented error correction method (CS-ECM) is used to investigate the impact of coal consumption and industrial structure on carbon emissions in 30 provinces of China from 2000 to 2019. For robustness check, the common dynamic process augmented mean group (AMG) is also adopted. The study suggests that in the short term, both coal consumption and industrial structure have no significant impact on carbon emissions. In the long term, coal consumption plays a decisive role in reducing carbon emissions, while the impact of industrial structure is still not. According to the CS-ECM approach, in the long run, a 1% rise in coal consumption increases carbon emissions by 1.057%; it indicates that coal consumption is the more important factor for the increase of long-term carbon emission. Government should adopt necessitating coal consumption-control measures to avoid further deterioration of carbon emissions.

Evaluation of Energy Utilization Efficiency in the Yangtze River Economic Belt

The reasonable optimization of energy structures and improvement of energy utilization efficiency are the inevitable way to achieve new progress in ecological civilization construction. The Yangtze River Economic Belt, as the leading demonstration area of China’s ecological civilization construction, is of great significance to take the lead in clarifying its energy efficiency situation under the dual-carbon goal. For this purpose, this paper uses the super-efficiency SBM model, ML index and Tobit model considering undesired output to explore the energy efficiency and the main factors affecting it of nine provincial capitals and two municipalities in the Yangtze River Economic Belt from 2003 to 2019. The results show that: (1) During the investigation period, the energy efficiency values of 11 cities are above 1, with a recent trend of decline, and the energy efficiency difference between regions is still expanding. (2) From 2003 to 2019, the overall energy efficiency of the Yangtze River Economic Belt has gradually improved with an average annual growth rate of 4.7%. Technological progress is the main force behind efficiency improvement, technical efficiency plays a small role, and the growth rate of the scale efficiency change index is only 1.3%, which means that the existing scale system of the Yangtze River Economic Belt still needs to be further improved. (3) The impact of industrial structure and government dominance on energy efficiency is significantly positive, while the degree of opening to the outside world is significantly negative. The relationship between economic development level and energy efficiency is an inverted “U” shape.

Spatiotemporal Characteristics and Influencing Factors of Environment Emergency Incident in China from 1991 to 2018

Research on the spatiotemporal characteristics and influencing factors of environmental emergency incidents in China in recent decades can improve the effectiveness and accuracy of risk supervision of environmental emergency incidents. Based on the data of environmental emergency incidents in 31 provincial regions in China from 1991 to 2018, this study used spatial autocorrelation analysis and a geographically and temporally weighted regression model to analyze the spatial dependence of environmental emergency incidents and the temporal and spatial heterogeneity of influencing factors. The results showed that: ① there was a significant positive spatial correlation between environmental emergency incidents during 1991-1994 and 2001-2014, and the spatial agglomeration was gradually increasing, that is, environmental emergency incidents existed in the provinces of China; clearly, the space depended on the characteristics and was not completely random. ② There was an unbalanced development pattern of environmental emergency incidents in China. The provinces with "L-L" agglomeration were concentrated in the western and northeastern regions, and the number increased and then decreased; by contrast, the ones with "H-H" agglomeration shifted from the east and south to the central and western regions, and the number increased following the decrease. The role of environmental emergency incident in different provincial regions in the spatial agglomeration was different and constantly changing. ③ The effects of various influencing factors on environmental emergency incidentshad obvious temporal and spatial heterogeneity in different periods and different provinces. The impact of the level of economic development on environmental emergency incidents was shown as a "negative-positive-negative" pattern. The impact of industrial structure on environmental emergency incidents was shown as a "negative-positive" pattern. The overall impact of pollution emissions on environmental emergency incident presented a "positive-negative-positive" pattern. Environmental letters and visits had a positive impact on the occurrence of environmental emergency incidents. The negative impact of the legal environment on environmental emergency incidents was gradually weakening. The negative impact of pollution control on environmental emergency incidents at the provincial level has gradually become apparent.

The Impact of Industrial Structure on Household Expenditure—Based on a Personality Analysis Perspective

The Impact of Industrial Structure on Household Expenditure—Based on a Personality Analysis Perspective

Financial development, industrial structure and environmental sustainability: new evidence from Japan

Considering the significant importance of environmental sustainability, it is vital to understand the determinants of environmental sustainability and to draw the policy implications for a sustainable environment. This paper examines the impacts of financial development and industrial structure on environmental sustainability in China from 1995 to 2020 using the A.R.D.L. approach. The result indicates that financial development negatively impacts CO2 emissions and greenhouse gas emissions in the long-run. The impact of industrial structure is also enormous. The results also suggest that industrial structure negatively impacts environmental sustainability by decreasing CO2 emissions and greenhouse gas emissions in the long-run. While Internet development accelerates environmental sustainability in Japan in the long run, economic development and energy negatively impact environmental sustainability in the long run. The findings of this study add to the current literature and provide some significant policy implications for improving environmental sustainability.

The impact of industrial structure on the environment pollution and its spatial-temporal differences in Henan, China

The industrial economy in Henan has developed rapidly, and Henan has transformed from an agricultural province into an industrial one. However, the previous rough development model led to a certain degree of imbalance in the industrial structure (IS), which had impact on environment pollution (EP). Based on the existing problems of industrial pollution in Henan, it is still necessary to explore whether the evolution of IS had impact on EP or not. Thus, firstly, the paper used the latest available data of the whole Henan, the prefecture-level cities and the different types of industries from 2008 to 2019 to measure the temporal and spatial evolution of IS using the IS rationalization index, the IS advanced index and the IS change index. Secondly, based on the three indicators of the IS evolution and the comprehensive index of EP, multiple linear regression models and mediation effect models were constructed to assess the impact of the IS evolution on EP. The results showed that the economic growth of industrial sectors presented the characteristics of the structure optimization. The improvement of the rationalization index and the advanced index of IS could reduce the pollution level significantly.

Prediction and Industrial Structure Analysis of Local GDP Economy Based on Machine Learning

The process of regional economic growth is a long-term evolutionary law. During this long evolutionary process, some regions may continue to grow, while others may fall into decline. It takes a long time. For example, from the perspective of our country’s regional economic growth since the turn of the century, the east coast has been in a relatively developed state, while the economy of some western regions is relatively backward. Therefore, how to promote the long-term growth of developed regions and revitalize the troubled regional economy by studying the long-term growth mechanism of the regional economy is an important topic of regional economic research. In this context, we can draw the following conclusions. (1) The employment structure of major industries has been declining year by year since 2000, and this trend is relatively obvious and the decline is relatively large. Despite some changes in industrial growth, the overall trend is upward. The employment structure of the service industry has increased year by year, and its proportion in total employment usually exceeds that of major industries, and it is the industry with the largest number of employees. (2) The accuracy under the machine learning model is 79.46%, the reliability is 89.27%, and it is feasible; the accuracy under the data mining model is 68.45%, the reliability is 75.43%, and the feasibility is 86.18%; the accuracy rate under the traditional statistical model is 60.14%, the feasibility is 68.24%, and the reliability is 75.12%. GDP not only is the core indicator of national economic accounting but also can be used to measure the economic status and development level of a country or region. The impact of industrial structure on GDP is huge, and a suitable industrial structure can promote a healthier growth of GDP. In order to analyze the relationship between our country’s GDP and industrial structure, the quantitative analysis method of grey correlation analysis is used to study it, and then according to the calculation results, suggestions for adjusting and optimizing the industrial structure will be put forward to the relevant ministries.

Influence of Green Finance on Ecological Environment Quality in Yangtze River Delta.

Along with the development of society and the deepening contradiction between economic growth and natural resources, green finance has attracted more and more attention. As an area of great strategic significance in China’s modernization, the development of green finance can improve the quality of its ecological environment and find new economic growth points for it. Based on the index system of pressure state response and while considering the scientific nature and desirability of the indicators, this paper selects 12 indicators to construct an index system of eco-environmental quality. It uses the entropy method to calculate the level of eco-environmental quality. Then, three control variables are selected, and the difference-in-difference model is used for empirical analysis. It is found that green finance positively affects the ecological environment quality of the Yangtze River Delta. In addition, the level of opening to the outside world and the level of economic development also have a positive effect on the quality of the ecological environment to a certain extent. Still, the impact of industrial structure on it is negative. Therefore, this paper puts forward some suggestions for strengthening the disclosure of green financial information, paying attention to the concept of green development and strengthening regional cooperation and exchange to promote the development of green finance further and promote the coordination of economic development and ecological protection in the Yangtze River Delta.

Research and Analysis on the Influencing Factors of China’s Carbon Emissions Based on a Panel Quantile Model

Since the beginning of the new century, China’s carbon emissions have increased significantly, and the country has become the world’s largest carbon emitter. Therefore, determining the influencing factors of carbon emissions is an important issue for policymakers. Based on the panel data of 30 provinces and cities across the country from 2000 to 2018, this study empirically tested how per capita disposable income, industrial structure, urbanization level, average family size, and technological innovation level impacts carbon emissions at different quantile levels by using the panel quantile STIRPAT model. The results showed that per capita disposable income and industrial structure had significant promoting effects on carbon emissions, while urbanization level, average family size, and technological innovation level had significant inhibitory effects on carbon emissions. The main thing is that the emission distributions of the 10th and 90th quantiles of the independent variables were quite different, which shows that the influence of each factor on carbon emissions has obvious heterogeneity at different levels. Specifically, the impact of per capita disposable income and technological innovation level on carbon emissions in low carbon emission areas were higher than that in high carbon emission areas, and the impact of industrial structure, urbanization level, and average household size on carbon emissions in high carbon emission areas was higher. Finally, specific policy implications are provided based on these results.

Spatio-temporal evolution relationships between provincial CO2 emissions and driving factors using geographically and temporally weighted regression model

• There was a significant positive spatial correlation between provincial CO 2 emissions in China. • Spatio-temporal heterogeneity analysis of driving factors of CO 2 emissions was carried out using GTWR model. • Inter-provincial differences in the impact of industrial structure on CO 2 emissions were minimal. • Energy intensity is a key factor in achieving dual reduction in provincial CO 2 emissions, followed by trade openness. To achieve the 2060 carbon neutrality target, each province in China needs to ensure rapid reduction in carbon dioxide (CO 2 ) emission according to its own developmental characteristics. Meanwhile, to achieve sustainable emission reduction, it is important to explore the development path of dual reduction of total CO 2 emissions and CO 2 emission intensity in each province. Based on the data of 30 provinces in China for the period 2005–2019, in this study, we analyzed the spatial and temporal evolution trends of CO 2 emissions in each province and determined the spatial autocorrelation of provincial CO 2 emissions. We used the geographically and temporally weighted regression (GTWR) model to analyze the spatio-temporal evolution of the driving factors of provincial CO 2 emissions. The results showed that CO 2 emission intensity of each province gradually decreased, and the CO 2 emissions between provinces were spatially autocorrelated. Energy intensity had the highest influence on total CO 2 emissions, and the influence of trade openness on CO 2 emission intensity had the largest inter-provincial differences. At present, reducing energy intensity and the proportion of secondary industries, improving trade openness, and using electricity alternatives are the key for some provinces to achieve dual reduction of total CO 2 emissions and CO 2 emission intensity.

A Heterogeneity Study of Carbon Emissions Driving Factors in Beijing-Tianjin-Hebei Region, China, Based on PGTWR Model.

The Beijing–Tianjin–Hebei region is an important economic growth pole in China and achieving carbon emission reduction in the region is of great practical significance. Studying the heterogeneity of the influencing factors of carbon emission in this region contributes to formulating targeted regional carbon emission reduction policies. Therefore, this paper adopted thirteen cities as individuals of cross-section and conducted spatial and temporal heterogeneity analysis of the influencing factors of converted carbon emissions in the region with panel data from 2013 to 2018 based on the PGTWR model. From a space-time perspective, the regression coefficient of each influencing factor in this region has obvious heterogeneity, which is mainly reflected in the time dimension. In the study period, the impact of industrial structure, the level of urbanization, energy intensity, and the level of economic growth on carbon emission showed a decline curve, while the impact of the level of opening up and the size of population was on the rise, indicating that more attention should be paid to the latter two factors for the time to come. In terms of space, the differences in the influence of industrial structure and energy intensity on carbon emission vary significantly.

Industry Structure as an Antecedent of Organizational Performance: A Review of Literature

Since an organization's responsiveness to changes in the competitive environment is highly dependent on how well they scan the business environment, organizations are constantly looking for ways to harness their characteristics to improve their performance and gain a competitive advantage in a hypercompetitive and dynamic marketplace. Organizations around the world have been forced to re-examine themselves internally to improve their performance as the growth and diversity of international business has brought new challenges and heightened their competitiveness. To stay relevant, organizations have been forced to re-examine themselves internally to improve their performance. Understanding the underlying structure of a company's industry today and in the future is a basic discipline in strategy creation, as evidenced by a large body of empirical literature indicating that industry structure has consistently been proven to impact organizational success. Given that empirical research have provided conflicting results demonstrating both positive and negative links, the argument over the impact of industry structure on performance is inconclusive. It could be argued that the lack of conclusive studies is due to previous studies' failure to include moderating and mediating variables. As a result, this study has systematically reviewed extant literature in the field of industry structure to develop a comprehensive definition of the construct, identify its perspectives and dimensions, and, ultimately, the review of literature has led to the development of a theoretical model that can be used in future empirical studies. As a result of industrial structure in organizations, the research also highlighted critical markers of organizational success. Finally, a set of linkages was modeled for the viewpoints of industry structure and organizational performance as a reference for future strategic management study. The goal of this study was to examine the current conceptual, theoretical, and empirical literature on industrial structure and organizational performance in order to identify knowledge gaps that may be used to guide future research.

Study on the Impact of Industrial Structure on Carbon Emissions in Sichuan Province -- Empirical Analysis Based on VAR Model

This paper analyses the dynamic relationship between industrial structure and carbon emissions in Sichuan Province using VAR models, using data on industrial structure and 8 carbon emissions of major energy consumption in Sichuan Province from 2001-2018. The results of the study show that in the short term, the advanced industrial structure will suppress the growth of carbon emissions, and the change in industrial structure, i.e., the increase in the share of secondary industry, will lead to an increase in carbon emissions; in the long term, the optimization of industrial structure has a significant impact on carbon emissions and specifically shows a suppressive effect. Therefore, in the short term, upgrading the industrial structure will not immediately reduce carbon emissions, but in the long term, adjusting the industrial structure is an important measure to reduce carbon emissions. In order to develop a low-carbon economy and accelerate the construction of a "clean energy demonstration province", Sichuan Province needs to optimise and upgrade its industrial structure, and specific measures are suggested, including: strengthening the role of regional radiation and promoting the balanced development of industries between regions; making full use of the province's human resources, increasing investment in research and development, and creating an important industrial cluster in China; strengthening the cooperation between Chengdu and Chongqing to promote the transformation and upgrading of the industrial structure.

Decomposition Analysis of CO2 Emissions in Northeast China: Insights From Investment Factors

Global warming caused by carbon emissions is a central concern across the world. Given the immense contribution of Northeast China to total carbon dioxide emissions, this study explores the driving forces of carbon emissions in that region from the perspective of investment-related factors, applying a logarithmic mean Divisia index (LMDI) model. We assess the driving forces on carbon emissions using a temporal LMDI model and explore interregional differences using a spatial LMDI model. The main findings are as follows. First, the trajectory of investment evolution is consistent with the mitigation of carbon emissions, which indicates that investment-related factors have a significant impact on carbon emissions. Second, the region’s carbon emissions are mainly generated by industry in Inner Mongolia and Liaoning, which suggests that the industry structure in Inner Mongolia should be adjusted to improve its coal-based energy consumption. In Liaoning, in contrast, the investment efficiency effect is a major driving force, which suggests that the regional investment structure should be adjusted to generate a more significant and orderly momentum. Lastly, although the impact of industry structure on carbon emission mitigation has significantly increased over time in Northeast China, local policy needs to be updated regularly. In line with these findings, several potential suggestions are offered for the formulation of practical and appropriate guidelines by the regional government.

Eco-Province Construction Performance and Its Influencing Factors of Shandong Province in China: From Regional Eco-Efficiency Perspective

Eco-province construction (EPC) innovated by China is an important approach for regional sustainable development. The core of this paper relates to how to quantitatively evaluate EPC performance and explore its influencing factors. We propose regional total-factor eco-efficiency as a proxy of EPC performance and apply the methods of the SBM directional distance function, the Theil index, hot-spot analysis, and the Tobit model to investigate spatial-temporal evolutionary characteristics and influencing factors of Shandong’s EPC performance from 2003 to 2017. The results showed that: (1) The EPC performance of Shandong presented a fluctuating rising trend as a whole and was unbalanced among regions with gradient-descending characteristics from the eastern, central to western regions. (2) The gravity center of Shandong’s EPC performance shifted northwards, eastwards, southwards, and westwards successively; the spatial pattern of regional EPC performance evolved from characteristics of high- and low-dispersion distribution in the initial stage to a significant H-shaped spatial conglomeration with hot spots distributed in Binzhou and Yantai and cold spots in the southern region. (3) A U-shaped environmental Kuznets curve existed between economic development level and Shandong’s EPC performance. Shandong’s EPC performance was positively related to R&D intensity while negatively related to urbanization. The impact of industrial structure, openness, and environmental regulation on Shandong’s EPC performance presented spatial heterogeneity. Shandong should establish a cross-regional collaborative governance mechanism of ecological construction and implement an ecological-innovation-driven development strategy to promote EPC performance.

高质量发展视角下产业结构升级对我国碳减排的影响

高质量发展视角下产业结构升级对我国碳减排的影响

The heterogeneous effects of socioeconomic determinants on PM2.5 concentrations using a two-step panel quantile regression

The haze pollution caused by high PM2.5 concentrations has adverse health effects worldwide, especially in rapidly developing China. As meteorological conditions are uncontrollable, this study aims to investigate how anthropogenic factors affect the PM2.5 concentration under high, medium and low emission levels. The distribution of socioeconomic variables is often non-normal, with important information hidden in the tail. By using balanced panel data of 273 Chinese cities from 2010 to 2016, two-step panel quantile regression is adopted to examine the cross-quantile heterogeneity of seven socioeconomic variables: economic growth, industrial structure, urbanization, foreign direct investment (FDI), population density, public transportation and energy consumption. The empirical results show that the relationships of PM2.5 concentration with economic growth, urbanization, industrialization and FDI are heterogeneous. Compared with other variables, population density exerts the greatest positive effect on PM2.5 pollution across all quantile cities. Moreover, the impact of GDP per capita on PM2.5 concentration in the lower 25th quantile cities is stronger than those in the 25th-50th, 50th-75th and upper 75th quantile cities. The effects of FDI in the upper 75th and lower 25th quantile cities are higher than those in the 25th-50th and 50th-75th quantile cities, which supports the “pollution haven” hypothesis. The impact of industrial structure on PM2.5 concentration in the upper 75th quantile cities is larger than those in the 0-25th, 25th-50th, and 50th-75th quantile cities. The heterogeneous effects of these socioeconomic determinants could assist policymakers in implementing differentiated policies that fit cities with different levels of air pollution.