Level Of Industrial Development Research Articles

What were the spatiotemporal evolution characteristics and influencing factors of global land use carbon emission efficiency? A case study of the 136 countries

Nations worldwide must collaborate to tackle the challenge of improving carbon emission efficiency and optimizing land use effectively. This study extends the study area from provinces and cities, regional city clusters, and countries to 136 major countries in the world, and shows the differences between countries. The paper analyzed the spatiotemporal evolution characteristics and influencing factors of land use carbon emission efficiency (LCEE) in these 136 countries from 2000 to 2019 using the super-efficiency slacks-based measure data envelopment analysis (Super-SBM DEA) model, global malmquist index, spatial autocorrelation measurement tools and Spatial Durbin model. It can help to promote low-carbon and green development while simultaneously improving the global LCEE. The findings indicate that: (1) Over the study period, there is a trend toward a decline in the LCEE worldwide. (2) The primary cause of the worldwide LCEE’s declining trend during the study period is the reduction in technical efficiency. (3) The low-low cluster type accounts for more, while the high-high agglomeration type accounts for less and exhibits a sluggish growth pattern. On the whole, there is a phenomenon of non-aggregation and non-uniformity. It is necessary to increase the driving effect of high-high cluster radiation. (4) Economic development level, investment level, population size, public service level and environmental sustainability level all have a significant impact on LCEE, while industrial development level has no significant impact on LCEE. From a global perspective, the radiation driving role of countries with high LCEE should be increased. Improve the efficiency and technological level of land use emission in light of local conditions, and promote independent innovation in green and low-carbon technologies. This is conducive to the establishment of long-term low-carbon growth.

Репрезентативность показателей и объективность оценок в компаративистике: экономика Российской империи второй половины XIX столетия в современных зарубежных исследованиях

The relevance of comparative studies of the level of industrial and economic development of the Russian Empire in the second half of the XIX century is determined by the fact that their results in some cases are used to substantiate theoretical ideas about the national characteristics of the Russian economy and society in comparison with the leading countries of Industrial Revolution. In economic literature, assertions about the “catastrophic” backwardness of Russia during this period are often encountered. Modern foreign historical-economic research allows for a more objective assessment of the gap between the level of industrial and economic development of the Russian Empire and the “advanced” states. However, the authors of these studies do not always succeed in consistently applying the principle of uniform criteria for comparing different economies. In particular, it is hardly justified: in some cases, to take into account, and in others, not to take into account the population and economic potential of territorial isolated peripheral regions of the respective economies. For example, the value of historical and economic comparisons is reduced by the confusion in research between the actual territory of the Russian Empire in the middle and second half of the XIX century, its territory within the conditional “borders of 1913” and the territory within “former USSR” borders. In this regard the article notes that conclusions drawn on the basis of fragmentary and arbitrarily interpreted information can’t be considered as fully correct. According to the author, in a comparative analysis of the economies-leaders of the Industrial Revolution, it is necessary to use assessment methods that take into account the population and indicators of economic development of peripheral territories were included in trade and financial exchanges with these economies, because this inclusion strongly spired the development of economies-leaders.

Application of KNN algorithm incorporating Gaussian functions in green and high-quality development of cities empowered by circular economy

A growing number of industries have started to adapt to the circular economy since the concept's introduction. Therefore, in order to accurately evaluate the development level of circular economy, the circular economy prediction model based on support vector machine-Gaussian K-nearest neighbor is proposed. This model first uses the improved K-nearest neighbor algorithm based on Gaussian function to classify the index data of various levels, and then uses Support Vector Machine to make predictions based on relevant data. According to the experimental findings, the model's average prediction accuracy for each level of indicator was approximately 98.1%, 98.8%, 94.9%, and 95.9% for the levels of industrial development, resource consumption, ecological protection, and resource recycling and reuse, respectively. This prediction accuracy was higher than that of the multi-vector autoregressive model and the grey prediction model. The average prediction accuracy of the multi-vector autoregressive model, the grey prediction model, and the support vector machine-Gaussian K-nearest neighbor-based model in predicting the overall development level of the circular economy were about 94.3%, 96.2%, and 99.3%, respectively, with average recalls of about 86.6%, 87.7%, and 89.1%, and the average F1-measure was about 0.88, 0.89, and 0.92. Moreover, the average relative error based on the support vector machine-Gaussian K-nearest neighbour model was only approximately 0.6%, which was lower than the 3.7% and 2.8% for the multi-vector autoregressive model and the grey prediction model, respectively. Meanwhile, compared with the existing time series analysis techniques, the proposed SVM-Gaussian K nearest neighbor model fitted up to 0.95, which achieved good prediction performance. According to the above data, the support vector machine-Gaussian K-nearest neighbour model has the highest accuracy in predicting the amount of development of the circular economy.

Evaluation of the Coupling and Coordination Degree of the Wellness Tourism Industry in Panxi Area

The party's 20th report points out that green and healthy development is an extremely important part of the China's modernization drive, an important guarantee for the sustainable development of the country, and a basic means to realize that "clear waters and green mountains are gold and silver mountains". Sichuan province actively promotes the construction of a strong wellness industry, and strives to build Panxi region into an international sunshine wellness tourism destination. In recent years, the mass tourism market demand escalating, promote the tourism market emerging industry development, wellness industry, in response to the mass tourism market development status, this paper built covers two evaluation dimensions eight elements 16 evaluation index wellness brigade industry coupling coordinated development index system, using the entropy method and coupling coordination model in panxi area wellness industry and tourism industry coupling coordinated development level, based on the evaluation results. The study results show that: (1) with good coupling development degree and strong interaction; (2) the coordinated development level of wellness tourism industry in Panzhihua is low, Panzhihua is in moderate imbalance, Liangshan Prefecture is on the verge of imbalance, with large development space; (3) from 2017 to 2021 is wellness lag type, Liangshan Prefecture is tourism lag type in 2020 alone. Through the research and analysis of the coordinated development of wellness tourism in Panxi region, the comprehensive level of coordinated development of wellness tourism in Panzhihua is revealed, and then three suggestions are put forward: (1) improve the construction of wellness facilities and enhance the coordinated development level of wellness tourism; (2) build the integrated development mechanism of wellness tourism and tourism and promote the coordinated development of wellness tourism industry; (3) optimize the wellness tourism environment and promote the sustainable development of wellness tourism.

Digital Inclusive Finance Helps Green Industry in Fujian Province Development

Aims: Digital inclusive finance, as a combination of modern technology and financial services, has developed rapidly in recent years. This new financial model has not only changed the traditional way of providing financial services, but also profoundly affected all aspects of economic and social development. The report of the 18th CPC National Congress made the important decision of "vigorously promoting the construction of ecological civilization " from the strategic height of the sustainable development of the Chinese nation. China's green industry has ushered in a new phase of rapid development. As an important province on the southeast coast of China, Fujian Province, with rich natural resources and vast rural areas, is an important region to promote the development of green industry. Methodology: In this paper, the green industry index of nine prefecture-level cities in Fujian Province from 2011 to 2021 is selected as the explanatory variable, and the development level of digital inclusive finance is taken as the core explanatory variable to explore the impact of digital inclusive finance on the development of green industry in Fujian Province. Results: According to the multiple linear regression, it is found that the level of green industry development is positively correlated with the level of digital inclusive finance development at the 1% significant level, i.e., digital inclusive finance helps to promote the development of green industry in Fujian Province. Conclusion: Based on the findings of the study, the paper puts forward the following recommendations: through the provision of low-cost financing, innovative financial products, digitalized payments and settlements, popularisation of financial education, and the promotion of cooperation, digital inclusive finance can effectively support the development of the green industry. This not only helps to achieve the goal of environmentally sustainable development, but also promotes economic growth, creates employment opportunities and enhances social well-being.

A novel data-characteristic-driven modeling approach for imputing missing value in industrial statistics: A case study of China electricity statistics

As a direct reference tool to reflect the operational status and development level of national industry, industrial statistics hold significant value for numerous systematic studies. Nevertheless, it is crucial to recognize that the quality of these statistics can be compromised by the common occurrence of missing value. This issue poses a substantial challenge for analyzing and utilizing industrial statistics, impeding progress in tasks reliant upon them. Given the severity of the missing value problem in industrial statistical databases and the limitations of existing literatures on missing value imputation in terms of research objects and modeling approaches, this paper proposes a novel missing value imputation modeling approach for single-indicator panels of industrial statistics based on the idea of data-characteristic-driven (DCD). Accordingly, taking the inter-provincial “monthly power generation” data from China as an example, the imputation model was constructed and its validity was tested under different imputed objects (Jiangsu and Jilin), different missing types (continuous and discrete), and different missing rates (5%, 10% and 20%) respectively. The results indicate that the proposed DCD modeling approach in this paper exhibits excellent efficacy. The imputation model, constructed based on the data characteristic of the imputed object, demonstrates clear advantages in handling missing value with different missing types and rates. This is evident in its superior consideration of numerical accuracy, directional accuracy, and imputation stability, resulting in an outstanding comprehensive imputation effect.

Research on the Coupled and Coordinated Development of Tourism and Transport in Chengdu City

Based on a total of 16 indicators selected from the tourism and transport industries, an evaluation index system of the coupling and coordination development level of tourism and transport is constructed. The entropy value method and the coupling coordination degree model are used to conduct an empirical study on the development level and coupling coordination level of the transport and tourism industries in Chengdu City from 2011 to 2020. The results show that, on the whole, the coupling coordination degree of transport and tourism in Chengdu is poor and has been in a state of mild to moderate dysfunction. The development level of tourism lagged behind the development of transport from 2011 to 2012, and the two were in a state of mild dysfunction. However, from 2013 onwards, the development level of tourism was prioritized over the development level of transport. This shift caused the coupling coordination degree of the two industries to decline sharply to 0.23305 in 2013. The development level of the tourism industry increased again, reaching 0.34206 in 2019, which marked an improvement. Consequently, the coordination degree of the transport and tourism industries evolved from moderate dislocation to mild dislocation. Finally, the results of the empirical research are analyzed, and corresponding suggestions are put forward to promote the sustainable growth of the transport and tourism industries in Chengdu City. These suggestions aim to improve the coupled and coordinated development level of the two industries.

Research on Landscape Complexes in the Context of Rural Revitalization

Agriculture is the foundation of a country and the foundation of a strong country. In 2024, the No. 1 document of the Central Committee proposes that "to promote the comprehensive revitalization of the countryside, it is necessary to ensure national food security, ensure that there is no large-scale return to poverty, improve the level of rural industry development, improve the level of rural construction, improve the level of rural governance, and strengthen the party's overall leadership over the "three rural" work". Based on the strategy of rural revitalization, the construction of landscape complex is of positive significance for creating a unique rural landscape and promoting the rapid development of rural areas in the new era. Based on the case study of "Pastoral Lujia" in Anji County, Zhejiang Province, this paper deeply explores the landscape complex from the perspective of rural revitalization, and summarizes the design strategies of the landscape complex. This provides theoretical support and specific case reference for the future construction of rural areas, and has far-reaching practical significance for rural revitalization.

Difference in regional logistics development level in China based on entropy weight TOPSIS and Dagum models

The high-quality development of the logistics industry, which is an essential and strategic industry supporting the national economic operation and a fundamental component of modern industrial system construction, is not only a key component of the high-quality development of the national economy but also the main driving force for the high-quality growth of the national economy. As the supporting industry of the national economy, the logistics industry will also face spatial disequilibrium during development. Therefore, to achieve the coordinated development of the logistics industry, the high-quality development and the spatial-temporal unbalanced development status of the logistics industry in each province must be figured out first. This research established a comprehensive evaluation system for the logistics industry development, which included 14 basic indexes based on the provincial-level panel data of 30 provinces in China during 2009–2020. Then, the regional logistics development level score in China was measured using the entropy weight TOPSIS method, and the differences in the regional logistics development level in China and the dynamic evolution law of their distribution were deeply explored through the Dagum Gini coefficient model. The research results revealed that the evaluation index system (14 basic indexes) for the regional logistics industry development level in China was relatively scientific and reasonable; the regional logistics industry development level in China was increasing year by year, showing a steady upward trend, and the imbalance in the eastern, central, and western regions regarding the regional logistics development was shrinking year by year; the average intergroup contribution rate was 36.33%, the intragroup contribution rate was 31.49%, and the contribution rate of intensity of trans variation was 32.19%, proving that the regional differences exerted a most extraordinary influence on the spatial differences in the regional logistics industry development level in China. The research results have important reference value for summarizing the meaning of high-quality logistics industry development, constructing the evaluation index system for logistics industry development, and exploring the reasons for the temporal and spatial differences in logistics industry development in China.

Research of China's New Energy Vehicle Market Based on TOPSIS and LSTM Models

This research paper provides an in-depth analysis of the development trajectory of China's New Energy Vehicle (NEV) market, utilizing both the TOPSIS and LSTM models. The TOPSIS model is ingeniously applied to assess the development level of the NEV industry, identifying key factors such as government policies, charging infrastructure, public acceptance, technological progress, and economic indicators. Subsequently, the paper delves into the LSTM model, a specialized form of Recurrent Neural Network (RNN) designed for handling time-series data with long-term dependencies. The LSTM architecture, equipped with forget, input, and output gates, is trained to minimize Mean Square Error (MSE) and Root Mean Square Error (RMSE), thereby optimizing model performance and preventing overfitting. The predictive capabilities of the LSTM model are demonstrated through a forecast of NEV sales in China for the next decade, showcasing the model's effectiveness in capturing market dynamics and growth trends. The integration of TOPSIS for evaluative analysis and LSTM for predictive forecasting offers a robust framework for understanding and anticipating the complexities of the NEV market.

Analysis of dynamic relationship between agricultural products circulation service industry and circulation efficiency in less developed regions: based on data from Western China

The market circulation system for agricultural products in less-developed regions lags behind; it fails to meet the requirements for efficient circulation. Therefore, it is crucial to explore the dynamic relationship between the Agricultural Products Circulation (APC) service industry and circulation efficiency. Using panel data from 29 regions in China, the entropy method measured the Development Level (DL) of the APC service industry, while the non-oriented super-efficiency Slacks-Based Measure (SBM) model was employed to assess the APC efficiency. A Panel Vector Autoregressive (PVAR) model was built for estimation. The impulse response function and variance decomposition were utilized to analyze the differences in the DL of the APC service industry, circulation efficiency, and their interaction mechanism across the Western, Central, and Eastern regions of China. The findings indicate that the DL of the APC service industry and APC efficiency in the less-developed regions of the West have a positive driving effect on each other. However, the magnitude of this effect was asymmetric and transient. The APC service industry’s DL and APC efficiency are more dependent on their inertia, and no efficient two-way feedback mechanism has been formed. This study provides a reference for the construction and sustainable development of modernized APC systems in less-developed regions.

Exploring the Impact of Influencing Factors on High-Quality Development in the Manufacturing Industry: A Comprehensive Analysis using Citespace Visualization and WSR-DEMATEL

High-quality development of the manufacturing industry is a key path to meet the growing needs of the people for a better life and boost China's economy under the double-cycle development pattern. The study focuses on the influencing factors of the high-quality development level of the manufacturing industry. Firstly, the study utilizes the Citespace visualization analysis tool to outline the research hotspots and development trends in this field, comprehensively understanding the interrelationships among influencing factors and their evolving tendencies. This provides valuable information resources for constructing the final indicator system of influencing factors; Secondly, the study constructed an index system of influencing factors based on the WSR (wuli-shili-renli) system methodology, and at the same time, it used the decision-making experiment and experimentation method (DEMATEL) to explore the interrelationships among the 11 influences and to calculate their centrality and causality; Finally, this study portrayed and comparatively analyzed the degree of importance of each influencing factor, and identifies the key influencing factors. The result shows that: Executive team heterogeneity, human capital structure, government subsidy, digital economy, business environment, and green technology innovation are the key influencing factors for the high-quality development of the manufacturing industry. The study can provide theoretical significance and practical enlightenment for the manufacturing industry to enhance its development level to realize high-quality development.

Efficiency measurement and inefficiency analysis of low-carbon logistics in the Beijing–Tianjin–Hebei region, China

Under the dual-carbon goals, enhancing the green development level of logistics industry and realizing its low-carbon transformation are important issues that need to be solved urgently. Amidst the continuous escalation in the total energy consumption of the national logistics industry, the Beijing-Tianjin-Hebei (BTH) region has exhibited a favorable descending trajectory in this respect. It is necessary to investigate the underlying reasons. Based on the panel data from 2012 to 2021, the DEA and Malmquist index are employed to analyze the low-carbon logistics efficiency of the BTH region from both static and dynamic perspectives. Furthermore, the inefficiency analysis is conducted to identify the deficiencies of low-carbon logistics industry in this region. Results show that (1) from the static perspective, the development of low-carbon logistics industry in the BTH region is relatively unbalanced. Compared to Tianjin and Hebei, Beijing's low-carbon logistics efficiency is significantly lower, becoming the focal area for attention; (2) from the dynamic perspective, technological progress is the main reason for the fluctuation of total factor productivity in the BTH region and a constraining factor for further improvements; (3) from the results of inefficiency analysis, the forthcoming emphasis on low-carbon logistics in Beijing should be on optimizing the number of logistics practitioners, transportation efficiency, and energy efficiency. Economic output and energy efficiency are relatively vulnerable aspects in Tianjin and Hebei, respectively, warranting due consideration. The research results of this paper have important practical implications for better developing low-carbon logistics in the BTH region and leveraging its leading role nationwide.

Analysis of the spatiotemporal evolution and influencing factors of green development level in the manufacturing industry

The manufacturing sector is the main battlefield of energy saving and carbon reduction in China, and vigorously promoting energy saving and carbon reduction in manufacturing and enhancing the green development level are the key links to support China's realization of the dual-carbon goal. The article adopts the SBM-GML model to measure the level of green development of the manufacturing industry in China. Based on this, it analyzes the spatio-temporal characteristics and the evolution law of the level of green development of the manufacturing industry by using the Dagum Gini Coefficient and Kernel Density Estimation. Using a spatial econometric model to explore the influencing factors of the level of green development of the manufacturing industry. The study finds that the green development level of the manufacturing industry has achieved remarkable results in recent years, but there are differences in the development level of each region. The regional differences in the level of green development of the manufacturing industry are significant. The optimization of manufacturing structure is a key factor influencing the level of green development of the manufacturing industry, and there is a positive spatial spillover effect of manufacturing structure optimization. However, The green development of the manufacturing industry shows a negative spatial spillover effect. The article proposes optimization paths based on the requirements of dual-carbon targets and regional characteristics, which is an important inspiration and reference for the green development level of the manufacturing industry in the world.

The influence of AI on the economic growth of different regions in China

High-quality development plays a crucial role in China’s economic progress in the new era. It represents a new concept of advancement and mirrors the increasing aspirations of the populace for an improved standard of living. In this context, the role of artificial intelligence (AI) in promoting sustainable development cannot be overemphasized. This paper explores how AI technologies can drive the transition to a green, low-carbon and circular economy. We have established an index system to measure the development level of the artificial intelligence industry and the high-quality development of the economy, which is relevant to the current state of the artificial intelligence industry and the advancement of the economy. Panel data from 2008 to 2017 has been utilized for this purpose. Global principal component analysis method and entropy value method are employed in the evaluation. Through in-depth analysis of the application of artificial intelligence and environmental protection in various provinces and cities, we clarify that artificial intelligence promotes innovation, saves resources, and is conducive to the development of green economy in the new era.

Research on the factors and path of sports industry development in China: A fsQCA study of 31 provinces.

The advancement of the sports industry's development constitutes a critical concern shared by regional authorities and the scholarly community, reflecting its significant role in economic and social development. This study employs a Fuzzy-set Qualitative Comparative Analysis (fsQCA) methodology to examine the 31 provincial-level administrative units in China. The objective is to elucidate the influence of technological, organizational, and environmental factors on the industry's development level, considering both a holistic national framework and dissected regional approaches (Eastern, Central, and Western China). This paper's contribution to the literature is structured around the following core findings: (1) The study establishes that a singular condition does not suffice as an essential prerequisite for achieving a heightened development state within the sports industry. (2) At the national level, there are three pathways to enhance the development level of the sports industry, specifically identified as "network-human resources dominant pathway," "technological innovation-human resources dominant pathway," and "comprehensive synergistic pathway."(3) From a regional perspective, the Eastern region has two pathways for sports industry enhancement: "network-economic pathway" and "comprehensive synergistic pathway." The Central region follows a "technology pathway," while the Western region has three pathways: "organization-environment pathway," "network-organization-environment pathway," and "organization pathway."(4) The synthesis of these findings underscores the multifactorial nature of sports industry development, suggesting a paradigm where diverse routes can lead to equivalent outcomes. This heterogeneity indicates that provinces or regions can tailor their development strategies to their unique situational contexts.

Classification of old industrial regions in order to implement differentiated regional policy

Subject. The article addresses problems of production for the domestic market, compliance of the level of industry development with the requirements of the new technological paradigm. Objectives. The article aims at systematization of economic and geographical characteristics of old industrial regions of Russia. Methods. I employ methods of statistical analysis. Results. Two factors enable to fully reveal the specifics of the development of old industrial regions, i.e. historical and sectoral. I developed an algorithm to classify old industrial regions. It helps specify their composition and identify their main subtypes based on the analysis of the ‘sectoral kernel’. Conclusions. The research results can be used in the development of policies focused on creating conditions for the transformation of old industrial regions into ‘growth centers’ of the Russian economy.

Analysis of the Influence of High - Tech industry Agglomeration on Sulfur Dioxide Emissions in Yangtze River Delta

This article mainly studies the impact of high-tech industry agglomeration on sulfur dioxide emissions in the Yangtze River Delta region. By establishing an index system, it is found that the development level of high-tech industry in different regions of the Yangtze River Delta is obviously different, among which Anhui province has the lowest level , and Shanghai and Jiangsu province have the highest level . In further empirical analysis, this paper uses panel data regression analysis, found that the high-tech industry agglomeration level of sulfur dioxide emissions have influences on inverted "U" type, namely with the improvement of agglomeration level, sulfur dioxide emission levels fall after rise, and in recent years, the population is not the key factors influencing the sulfur dioxide emission levels in Yangtze river delta, The secondary industry is still an important factor affecting sulfur dioxide emissions.

Retraction Note: Analysis of modern circulation industry development level using industrial structure mechanism

Retraction Note: Analysis of modern circulation industry development level using industrial structure mechanism

Empirical Analysis of the Factors Affecting the High-quality Development of the Logistics Industry

This paper selects China from 2006-2023 panel data, with cargo quantity as explained variable to measure the logistics industry development level, and the social logistics total cost as a proportion of GDP, the first industry output value, the second industry output, the tertiary industry, total retail sales of social consumer goods and total import and export as the main explanatory factors, establish multiple linear regression model, the development of China's logistics industry high quality impact factors empirical analysis, and combined with the present situation of China's logistics industry development and influence factors concluded and put forward Suggestions.