Changes In Industrial Structure Research Articles

Research on Talent Cultivation Mode of Vocational Education in the Context of Information Technology

Abstract This paper mainly analyzes the educational level of vocational education in the context of information technology from the perspective of matching degrees of vocational education specialty settings and talent demand. Combined with the framework of vocational education service supply under information technology, the SARIMA-BP model is utilized to predict the demand for talent training, and then the match between the professional setting and the education level is explored in a quantitative description. The PSO algorithm is used to optimize the linear combination prediction model of the SARIMA-BP neural network, and the prediction accuracy is compared. The degree of vocational education development and talent cultivation mode are analyzed from four aspects. According to the division of secondary vocational school graduates according to the three industries, the proportion of graduates from Guangdong’s primary industry decreased by 0.1053, and the proportion of graduates from the secondary industry decreased by 0.0123. At the same time, the proportion of graduates from the tertiary industry increased by 0.0243. It can be seen that, to cope with the change of industrial structure in the context of information technology, secondary vocational education should adjust the relevant specialties offered to upgrade the industry’s development and the market’s need for the Talent cultivation mode.

Assessment of the Level, Direction and Factors of Structural Changes in the Irkutsk Region Industry

It is known that the structure of industrial production in Russia was formed under the influence of many factors that caused its raw materials specialization and accelerated development of the military-industrial complex. Since the realization of the need for structural transformations (the beginning of the 90s), authorities, associations of industrialists and the scientific community have offered various options for actions aimed at mitigating (eliminating) accumulated and continuing to intensify structural deformations, some measures (programs) have been implemented in whole or in part. In addition, the modern actualization of import substitution processes should also have a significant impact on the structure of the economy and industry, in particular. As a result, there is a need to assess the direction and intensity of structural changes in industrial production over the period 1991–2023, as a result of which it is possible to judge the degree of impact and effectiveness of implemented measures, including in the field of import substitution, on the structure of industry. In the article, this assessment is carried out on the example of the Irkutsk region. According to calculations, the conclusion is made about the preservation and deepening of the raw materials specialization of the industry of the Irkutsk region, despite the measures taken. The key factor was the implementation of major investment projects in the field of oil and gas production, as well as their transportation, which led to a significant increase in the share of extractive industries. At the same time, there is a point-by-point growth in some manufacturing industries, including high-tech ones, which does not affect the structural dynamics as a whole, but proves the effectiveness of the measures taken in the field of import substitution and the development of manufacturing industries.

Decoupling livestock and poultry pollution emissions from industrial development: A step towards reducing environmental emissions

China has implemented policies like Leading areas for Agricultural Green Development (LAGD) to mitigate livestock and poultry farming pollution while promoting industry growth. However, it remains uncertain whether LAGDs have successfully balanced emission reduction with stable development. This study examines 165 LAGDs to analyze changes in emissions, assess the decoupling of emission reduction from output value, and identify influencing factors. Findings reveal that emissions from livestock and poultry in LAGDs initially increased and then decreased between 2010 and 2019. Cattle were responsible for over 40% of fecal emissions, and pigs for more than 20%. Additionally, pigs contributed to over 61% of urine emissions. From 2010 to 2014, increases in chemical oxygen demand were mainly due to pigs and cattle. Total nitrogen levels were significantly impacted by cattle, while pigs were affected by total phosphorus. From 2014 to 2019, reductions in emissions were largely attributed to a decrease in pig-related pollutants. The decoupling status shifted from strong to weak and then back to strong between 2014 and 2019. Production efficiency played a crucial role in reducing emissions, while changes in industrial structure moved from supporting to hindering this reduction. Economic development was a primary factor in driving these changes. Standard emissions in Chinese regions showed a rising and then declining trend from 2010 to 2019. The Northeast and Northwest regions of China demonstrated emission trends that were in sync with the growth in rural income. This study offers insights into the successes and challenges of LAGDs in achieving a balance between reduced emissions and development, using quantitative analysis. The findings are instrumental in informing policies for a sustainable livestock and poultry industry. Recommendations include evaluating coordinated approaches to pollution reduction and industrial growth, setting decoupling goals, designing policies based on influential factors, conducting regional assessments of livestock and poultry demand, and implementing region-specific strategies.

A holistic analysis of China's consumption-based water footprint (2012–2017) from a multilevel perspective

As the world's second-largest economy, rapid socioeconomic development in China has exerted unprecedented pressure on water resources. However, there is still uncertainty regarding consumption-based water footprint (CWF) assessment across different spatiotemporal scales, especially those differentiated by green, blue, and grey water footprints. To address this knowledge gap, this study employed a multi-regional input–output (MRIO) model and structural decomposition analysis (SDA) to quantify the spatiotemporal dynamics and key drivers of consumption-based green (GCWF), blue (BCWF), and grey (HCWF) water footprints in mainland China during 2012–2017. The main findings of the study are as follows. (1) The HCWF is approximately 4.4 times greater than the sum of the GCWF and BCWF, with water consumption caused by pollution significantly exceeding direct consumption. However, there was a significant decrease in all three CWFs after 2015, particularly HCWF. (2) The spatial distribution pattern of the CWF shows high values in the east and south and low values in the west and north, while virtual water (VW) transfer through commodity trade occurs mainly from inland to coastal regions. (3) The three highest water-consuming sectors were agriculture, food and tobacco processing, and construction, accounting for over 70% of the total water consumption. (4) Water efficiency and consumption patterns are crucial factors in reducing CWF, while affluence levels have the greatest impact on increasing CWF, followed by changes in industrial structure after 2015. Overall, the results of this study contribute to a comprehensive understanding of China's regional and sectoral water consumption throughout the supply chain. In addition, our findings identified regions and sectors with high water consumption and their drivers, which can inform policy decisions and contribute to synergistic cross-regional regulation and sustainable water resources management.

Analysis of influencing factors of industrial green and low-carbon transformation under the background of “double carbon”: evidence from Sichuan province, China

Introduction: Industrial green and low-carbon transformation is the key to improve economic development and necessary process to achieve the goal of the carbon peaking and carbon neutrality. Few studies have been done on the decomposition of carbon emission factors in industries and sub-industries and the impact of green and low-carbon transformation about carbon emission in each industry quantitatively. However, the study of industries and sub-industries can comprehensively analyze the development path of green and low-carbon transformation from a more detailed perspective, and provide scientific reasons for the optimization of industrial structure and energy structure.Methods: The extended Kaya identity for industrial carbon emission is constructed to obtain four factors influencing industrial carbon emission: economic output effect, industrial structure effect, energy intensity effect, carbon consumption intensity in this paper. Then, the LMDI decomposition method is combined with the above identity to innovatively obtain the contribution value of carbon emissions from the perspective of overall, industrial sector and tertiary industry. Then, based on the results of factor decomposition, a multi-index scenario prediction model is constructed. On this basis, the extreme learning machine model optimized by particle swarm optimization (PSO-ELM) was used to predict the influence of the changes in the driving factors on the reduction of industrial carbon emissions. By setting the baseline and industrial green and low-carbon transformation scenarios, it is predicted that industrial carbon emission in Sichuan Province.Results and discussion: (1) Economic output effect always promotes the growth of industrial carbon emissions, and with the adjustment of industrial structure and energy structure, the other three factors begin to restrain the growth of carbon emissions. (2) Scenario prediction shows that without considering the economic costs of transformation, improving carbon emission reduction efficiency can be obtained through accelerating the rate of change of industrial structure of the secondary and tertiary industries, increasing the proportion of energy intensity reduction, and strengthening the proportion of non-fossil energy use.

The digital economy and environmental pollution: New evidence based on the support of logistics development

As the world's largest manufacturing and export country, environmental preservation in China has received widespread public attention. Although existing literature has focused on the relationship between the digital economy (DE) and environmental degradation from the supply side, it fails to incorporate the role of logistics and demand side. This study employs two-way fixed-effects model and two-stage least square method (2SLS) to empirically evaluate the emission abatement effect mechanisms of the digital economy under local logistics development for China's 31 provinces from 2015 to 2019, constructing an integrated framework and narrowing the gaps of existing studies. The empirical results suggest that digital economy growth can have an inhibitory effect on pollution emissions, while logistics development has a negative moderating effect on this impact. These conclusions still hold after a series of robustness tests to eliminate the influence of outliers and regionally differentiated policy interference. Mechanism analysis indicates that technological innovation, changes in industrial structure and consumption structure are significant mediating mechanisms, while logistics development provides indispensable support for the above mediation effect. Hence, this study overall implies that to further unleash the potential of emission abatement effect, adequate attention should be given to exerting the advantages of the digital economy and weighing the green transformation of logistics, enhancing the public's sense of environmental happiness worldwide.

Low-carbon strategy, entrepreneurial activity, and industrial structure change: Evidence from a quasi-natural experiment

As the top carbon emitter in the world, a key step taken by China to encourage energy conservation, pollution reduction, and environmentally friendly development is the low-carbon city pilot (LCCP) policy, which has made important contributions to reducing global emissions. The LCCP policy aims to conserve energy and reduce emissions, inexorably influence many facets of the economic system because it is a well-rounded environmental management strategy. So, has this policy impacted the entrepreneurial activity of the region while achieving green development? Using panel data from China's 279 cities from 2002 to 2019, this article employs the difference-in-differences (DID) model to assess the influence of the LCCP policy on urban entrepreneurial activity. Through the results, the LCCP policy generally inhibits entrepreneurial activity. But the level of green innovation can alleviate this inhibitory effect. Based on the heterogeneity analysis, the LCCP policy exerts a more pronounced inhibitory effect on entrepreneurial activity in central and western regions, resource-based cities, and non-central cities. Further, the LCCP policy inhibits entrepreneurial activity in high-carbon industries while encouraging entrepreneurial activity in emerging industries, resulting in industrial structure change and upgrading. This paper supplies new empirical evidence for the effects of the LCCP policy on entrepreneurial activity and industrial structure change.

Impact of Energy Productivity and Industrial Structural Change on Energy Intensity in China: Analysis Based on Provincial Panel Data

Since 2000, China’s energy intensity has shown a fluctuating downward trend. Most existing studies have attributed the decline to technological progress, and only a few studies have recognized the role of industrial structure change. In this paper, a multi-region and multi-sector CGE (computable general equilibrium) model and a numerical simulation method are used to study how technological progress and structural transformation affected the energy intensity of 30 provincial-level regions in China from 2000 to 2019. The results show the following points. (1) The contribution of technological progress to the decline in energy intensity was the highest in the central region, followed by the western region, and was the lowest in the eastern region. (2) The progress of energy technology in the agriculture and industry sectors promoted the transition of energy consumption to the service sector, thereby reducing the overall energy intensity. This effect was the strongest in the eastern region, followed by the central region, and was the weakest in the western region. (3) The progress of energy technology in the service industry promoted the transition of energy consumption to industry and agriculture, thereby enhancing the energy intensity, and this effect was the strongest in the eastern region, followed by the western region, and the weakest in the central region. The conclusion of this paper provides a theoretical basis for realizing regional carbon peaking in sequence in China.

Exploring the Spatiotemporal Dynamics of CO2 Emissions through a Combination of Nighttime Light and MODIS NDVI Data

Climate change caused by CO2 emissions is posing a huge challenge to human survival, and it is crucial to precisely understand the spatial and temporal patterns and driving forces of CO2 emissions in real time. However, the available CO2 emission data are usually converted from fossil fuel combustion, which cannot capture spatial differences. Nighttime light (NTL) data can reveal human activities in detail and constitute the shortage of statistical data. Although NTL can be used as an indirect representation of CO2 emissions, NTL data have limited utility. Therefore, it is necessary to develop a model that can capture spatiotemporal variations in CO2 emissions at a fine scale. In this paper, we used the nighttime light and the Moderate Resolution Imaging Spectroradiometer (MODIS) normalized difference vegetation index (NDVI), and proposed a normalized urban index based on combination variables (NUI-CV) to improve estimated CO2 emissions. Based on this index, we used the Theil–Sen and Mann–Kendall trend analysis, standard deviational ellipse, and a spatial economics model to explore the spatial and temporal dynamics and influencing factors of CO2 emissions over the period of 2000–2020. The experimental results indicate the following: (1) NUI-CV is more suitable than NTL for estimating the CO2 emissions with a 6% increase in average R2. (2) The center of China’s CO2 emissions lies in the eastern regions and is gradually moving west. (3) Changes in industrial structure can strongly influence changes in CO2 emissions, the tertiary sector playing an important role in carbon reduction.

OTT 기업결합의 경쟁법상 규제에 관한 고찰

The Korean Fair Trade Commission's 2019 decision on the merger of Oksusu and POOQ is important in that it dealt with the first merger between major OTTs, but it is also meaningful in that it dealt with merger in areas where rapid industrial structure changes are taking place. The Korean Fair Trade Commission distinguished the significance of the merger into horizontal and vertical aspects, and recognized competition restraints based on the fact that increased concentration by merger parties in the broadcasting content provision market can negatively affect competition between OTT operators. At this point, it is difficult to avoid the perception that the judgment at the time was inappropriate, considering the rapid growth of Netflix and the continuous decline in the integrated OTT share after the merger. However, it is not possible to make a negative assessment of the innocence just by failing to predict. But the lack of a detailed analysis of the transaction of video content needed to be supported in order to recognize competition restrictions in the vertical sector, as the US courts did in the AT&T and Time-Warner business combination case, sufficient understanding of the competition pattern between OTTs centered on original content and consideration of the industrial situation in which innovative changes are occurring may be a problem. These problems will need to be corrected in the regulation of merger that may occur in the field in the future.

The Impact of Digital Economy and Industrial Agglomeration on the Changes of Industrial Structure in the Yangtze River Delta

The Impact of Digital Economy and Industrial Agglomeration on the Changes of Industrial Structure in the Yangtze River Delta

Impact of labor and energy allocation imbalance on carbon emission efficiency in China's industrial sectors

Greenhouse gas emission is the focus of global climate change concerns. The change in industrial structure can impact carbon emission efficiency (CEE) by affecting labor and energy input. However, there is an obvious imbalance of labor and energy allocation within China's industrial sectors. Here, we use the super-slacks-based model data envelopment analysis (Super-SBM-DEA) to calculate the CEE of 32 industrial sectors and adopt the Tobit model to analyze the impact of industrial allocation imbalance on CEE. The results show that the overall industry and manufacturing CEE is still at a low level, with an average CEE of 0.53. The industrial sectors with higher CEE are these sectors with advanced innovative technology and low energy consumption. The results of the Tobit model show that the imbalance of labor and energy allocation is the key factor limiting carbon emission efficiency improvement. Furthermore, the imbalance of labor allocation hurts the CEE of labor-intensive sectors. The coefficient of labor allocation imbalance (distL) is −2.483, and the inflow of labor can improve the CEE of non-labor-intensive sectors. The CEE of energy-intensive sectors is sensitive to the imbalance of energy allocation, the marginal impact of energy allocation imbalance (distE) is −2.296. Improving energy efficiency is a key task to reduce carbon emissions in sectors relying on energy input. But for non-energy-intensive sectors, optimizing energy allocation has a limited effect on reducing carbon emissions. This research can provide insights for emerging economies to coordinate carbon reduction and industrial transformation.

An Empirical Study on the Impact of Industrial Structure Change on Environmental Level in China

An Empirical Study on the Impact of Industrial Structure Change on Environmental Level in China

아산-천안지역 개발사업 토지이용계획이 인구 및 산업구조 변화에 미치는 영향에 관한 연구

This study examines the impact of various development projects, such as housing complex development projects and industrial complex development projects, on changes in urban characteristics through time series analysis and statistical analysis for each item of land use plan that determines the urban spatial structure in developing a city. By selecting two geographically adjacent cities as the research subjects, a comparative analysis of the industrial structure change in the process of urban development was possible. Through a comparative analysis of the two cities, the effect of land use planning items that determine the urban space structure on population growth and the increase in the number of industrial workers differs depending on the urban characteristics, and the effect of the gravity model theory of population movement also influences the increase and development of industrial workers. It was derived as a result of the study that it appears. It was confirmed that two cities with different characteristics as a production city and a consumption city within the same living area grow in a mutually complementary relationship as their competitive areas in the industrial field develop more specialized.

Estimating Inter-Regional Freight Demand in China Based on the Input–Output Model

The inter-regional freight volume is a crucial factor for transportation infrastructure planning and investment decision-making. However, existing studies on freight volume estimation have mainly focused on the total freight volume within a specific region, without taking freight flow into consideration. In this research, a gravity model was employed to estimate the inter-regional trade coefficient matrix based on the input–output tables of the 31 provinces in China in 2017. The inter-regional freight volume was then determined by converting the value flow into freight flow. To determine the model parameters, we used information from 2017 and subsequently validated the results using dates from 2012 to 2020. We also studied the impact of industrial structure change on freight volume by simulating dates from the aforementioned model in 2017. The results indicated that the model can effectively simulate inter-regional freight volume while taking into account the influence of industrial restructuring.

Does industrial structure changes matter in renewable energy development? Mediating role of green finance development

Green financing development mediated the interaction between industrial structure changes and renewable energy growth in China from 2000 to 2021. The research examines whether green financing may boost renewable energy development via industrial structural modifications. The research uses an econometric models to explore how green funding affects industrial structural changes and renewable energy development. Industrial structural changes boost renewable energy growth in China. Green finance development mediates the relationship between industrial structure changes and renewable energy development, suggesting that green finance can enhance the positive effect of industrial structure changes on renewable energy development. The research also reveals that green funding has a more significant long-term impact on renewable energy development. Industrial structural changes have a more significant impact on renewable energy development in economically developed areas. The results are significant for developing country policymakers promoting sustainable energy development via trade and fiscal decentralization.

Main Pathways of Carbon Reduction in Cities under the Target of Carbon Peaking: A Case Study of Nanjing, China

As a designated national low-carbon pilot city, Nanjing faces the challenge of reducing energy consumption and carbon emissions while experiencing rapid economic growth. This study developed a localized Long-range Energy Alternatives Planning System (LEAP) model specifically for Nanjing and constructed four different development scenarios. By utilizing the Log Mean Divisia Index (LMDI) decomposition, the Tapio decoupling elasticity coefficient, and comparing the emission reduction effects of individual measures and their cross-elasticity of carbon reduction, this study investigated the key factors and their carbon reduction path characteristics in Nanjing toward its carbon peak target by 2030. The results indicate that: (i) Nanjing could reach its peak carbon target of about 3.48 million tons by 2025 if carbon reduction measures are strengthened; (ii) The main elements influencing Nanjing’s carbon peak include controlling industrial energy consumption, restructuring the industry, promoting the construction of a new power system, and developing green transportation; (iii) Controlling industrial energy consumption and changing industrial structure have a greater impact on reducing carbon emissions than other measures, and both have a synergistic effect. Therefore, Nanjing should prioritize these two strategies as the most effective methods to reduce carbon emissions. Additionally, to slow down the growth of urban carbon emissions, policies aimed at reducing the energy intensity and carbon intensity of energy consumption should be formulated. For instance, the integration and innovation of green industries within the city region, such as new energy vehicles, new energy materials, and big data, should be accelerated, and the proportion of clean energy consumption in urban areas should be increased. The LEAP (Nanjing) model has successfully explored Nanjing’s low-carbon pathway and provided policy guidance for the optimal transformation of industrial cities and early carbon peaking.

The Impact of Coordinated Development Policy on the Spatiotemporal Changes of Industrial Structure of Beijing–Tianjin–Hebei Urban Agglomeration

The coordinated development of Beijing–Tianjin–Hebei (BTH) is a major regional strategy in China that aims to alleviate Beijing’s non-capital functions and address the “big city disease”. Understanding the spatial distribution and changing trends of industrial development in BTH is critical for achieving BTH’s coordinated development goals. In particular, it is important to assess the effectiveness of Beijing’s non-capital functions at the industrial level. This study utilized the 2013 and 2018 economic census data and statistical methods such as spatial Gini coefficient, trend analysis, specialization index, and industry similarity. We first characterized the industrial development pattern by analyzing the spatiotemporal changes of the operating income and the number of legal entities in BTH urban agglomeration. Then, we identified the changes in the leading industries and industrial structure of different cities in BTH urban agglomeration from 2013 to 2018. The results indicate that the coordinated development policy has influenced the industrial structure of the BTH urban agglomeration, with an 85.53% increase in the number of legal entities and a 14.61% increase in operating income. Beijing’s non-capital functions have achieved initial results, mainly involving technology-intensive and knowledge-intensive tertiary industries such as information technology, finance, and scientific research. The division of industry and the development positioning of the three regions are gradually becoming clear. Our results show how the economic census data and spatial analysis can support significant advances in evaluating industrial and economic development patterns, and they can be used worldwide in the future.

The Road of Post-Industrialization Transformation in Developing Countries Based on Weighted Markov and Grey Correlation Theory, Taking the Change of Industrial Structure in Heilongjiang Province of China as a Case Study

In the context of economic globalization, the comparative advantages of developing countries and developed countries tend to be more complex. After the economic crisis in 2008, the challenges of developing countries are intensified, and the game is more cyclical; Especially in the critical period when the “Fourth Industrial Revolution” is pending, many countries have increased their efforts in policy formulation and efficiency improvement, focusing on industrial transformation and upgrading, and closely combining structural reform with the industrialization process. Therefore, it is particularly important to analyze, change and forecast the industrial structure of post-industrial regions in developing countries based on data science algorithms, and to reshape the understanding of the adjustment of the world economic order and the evolution of the international trade division system. Based on the proportion of gross regional product and output value of three industries in Heilongjiang Province of China from 2001 to 2020, the minimum deviation model of industrial structure in Heilongjiang Province is constructed through Markov theory. The Lingo software is used to obtain the transition probability matrix of the industrial structure state, and combined with the autocorrelation coefficient of each order and the transfer weight, the change trend of the proportion of the output value of the three industries and the change of the contribution rate of the three industries (to GDP) in Heilongjiang Province in the next 10 years are obtained; At the same time, through the grey correlation index and spss software, this paper analyzes the correlation changes between the three industrial adjustments and economic development in Heilongjiang Province in the past 20 years, discusses the new growth points of economic development in Heilongjiang Province and puts forward corresponding suggestions for the adjustment of different industrial structures in Heilongjiang Province, and finally extends the general rules of the development of post-industrialization in the world. This article believes that it is necessary to adjust the structure of the first, second, and third industries reasonably based on different historical and natural endowments, contemporary backgrounds, and other practical factors, in accordance with local conditions, circumstances, and each with its own emphasis. At the same time, it also requires the support and inclination of government policies; Adapting industrial structure to local economic development while actively leading productivity and local economic development.

Digital transformation, industrial structure change, and economic growth motivation: An empirical analysis based on manufacturing industry in Yangtze River Delta.

China is in a critical stage of economic growth mode transformation. The digital transformation of the manufacturing industry may create new impetus and new models for economic growth. Taking the manufacturing industry of 25 prefecture-level cities in the Yangtze River Delta region as the research object, we explore the digital transformation process of the manufacturing industry and verifies its theoretical mechanism of promoting economic growth through the industrial structure. A panel model based on the improved Feder two-sector model and a multiple mediating effect model are established to explore the dynamic mechanism of manufacturing digital transformation to promote economic growth through industrial restructuring. The results show that the digital transformation of the manufacturing industry in the Yangtze River Delta region of China is relatively high, and the speed of digital transformation has been accelerating in recent years. The digital transformation of the manufacturing industry can promote the change in industrial structure and form a new driving force for economic growth. The key is to improve the level of industrial structure and extend the length of the industrial chain. Based on these, we propose measures to promote the transformation and upgrading of industrial structure for the sustainable development of China's economy.