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Over the last twenty years, there has been swift growth in industrialization and technological advancements, driving economic progress. Nevertheless, it is inevitable that these sectors will bring about environmental shifts. Thus far, endeavors have been undertaken to assess the influence of industrialization and technological advancements on environmental deterioration. Additionally, the extensive discussion surrounding the impact of financial development, trade openness, and technological innovation on the environment has not yielded conclusive empirical findings. Studies often operate under the assumption of symmetric relationships, potentially leading to biased results. Adding to the discussion on the drivers of carbon neutrality, the time-dependent effects of critical aspects such as financial development and technological innovation should inform meaningful policies for environmental management. This article explores the time-varying causal association between trade openness, industrialization, financial development, technological innovation, and CO2 emissions in Thailand using novel time-varying Granger causality tests. The time-varying causality outcomes demonstrate that the associations change significantly over time, in contrast to the results of Toda-Yamamoto causality. Overall, there exists a bidirectional relationship between industrialization, financial development, trade openness, technological innovation, and CO2 emissions over different time sequences. These outcomes have implications for both policy and research. [ABSTRACT FROM AUTHOR]

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

With the development of the digital economy, industrial structure upgrading plays an important role in realizing high-quality development. Exploiting the quasi-natural experimental setting provided by the Big Data Comprehensive Pilot Zone (BDCPZ) policy in China in 2016, this study evaluates the impacts of the BDCPZ policies on regional industrial structure upgrading using a combination of propensity score matching and difference-in-differences (PSM-DID) with panel data of 30 regions for the period 2008–2021. The results are as follows: (1) BDCPZ policies significantly promote regional industrial structure upgrading. This finding holds after conducting the placebo test and replacing explained variables. (2) BDCPZ policies enhance upgrading through technological innovation and financial deepening. (3) Heterogeneity analysis shows that the promotional effect of BDCPZ policies on industrial structure upgrading is more obvious in economically developed regions, megacities, and east-central regions; overall, regions with high industrialization benefit more. These findings have important implications: First, they provide new empirical evidence from the perspective of policy evaluation on how the digital economy affects industrial structure upgrading. Second, this study sheds light on the mechanism underlying this relationship, helping us understand how the digital economy can further affect the development of the industrial structure. Third, the policy effect is heterogenous, providing a scientific basis for the government to formulate differentiated implementation policies for different regions. This can help local industrial transformation and upgrading, and economic development in these regions through the implementation of big data and digital technologies. [ABSTRACT FROM AUTHOR]

The logistics industry and the manufacturing industry are mutually productive factors and service objects, which determines that the two must develop cooperatively. In the increasingly fierce market competition, open collaborative innovation is more conducive to improving the level of linkage between the logistics industry and the manufacturing industry, and promoting industrial development. Based on the patent data of 284 prefecture-level cities in China from 2006 to 2020, this paper uses Gis spatial analysis, spatial Dubin model and other methods to analyze the collaborative innovation between the logistics industry and manufacturing industry. The results lead to several conclusions. (i) The overall collaborative innovation level is not high, and in terms of the evolution cycle, collaborative innovation has experienced three stages: embryo, rapid development and stable development. (ii) The spatial agglomeration characteristics of the collaborative innovation between the two industries are more and more obvious, and the Yangtze River Delta urban agglomeration and the middle reaches of the Yangtze River urban agglomeration play an important role in the collaborative innovation between the two industries. (iii) The hot spots of collaborative innovation between the two industries are concentrated in the eastern and northern coastal areas, while the cold spots are concentrated in the south of the northwest and southwest areas in the late stage of the study. (iiii) The positive influencing factors of local collaborative innovation between the two industries include economic development, scientific and technological level, government behavior, and employment, while the negative influencing factors include information technology level, and logistics infrastructure. Economic development has a negative spatial spillover effect on surrounding areas, while the spatial spillover effect of scientific and technological level is significantly positive. This article aims to explore the current situation and influencing factors of collaborative innovation between the two industries, with a view to proposing countermeasures and suggestions for improving the level of collaborative innovation between the two industries, and also providing new ideas for research on cross industry collaborative innovation. [ABSTRACT FROM AUTHOR]

Innovation efficiency is an important manifestation of regional innovation capacity, and how to improve regional innovation efficiency is crucial to regional development. This study empirically explores the impact of industrial intelligence on regional innovation efficiency and the possible influence of approaches and mechanisms. The empirical results revealed the following. First, the development level of industrial intelligence can positively affect regional innovation efficiency, while beyond a certain level, its role in promoting regional innovation efficiency will weaken, exhibiting an inverted U-shaped effect. Second, compared with the application research by enterprises, industrial intelligence plays a stronger role in promoting the innovation efficiency of basic research by scientific research institutes. Third, human capital situation, financial development level, and industrial structure upgrading are three significant channels for industrial intelligence to promote regional innovation efficiency. Therefore, accelerating the development of industrial intelligence, formulating individualized policies for different innovative entities, and rationally allocating resources related to the development of industrial intelligence are needed to be taken to improve regional innovation. [ABSTRACT FROM AUTHOR]

Infrastructure construction related to the new generation of information technology and 5G technology is an important measure taken by the Chinese government to promote regional economic development. Large-scale infrastructure investment is being carried out simultaneously in China's core and peripheral regions. The COVID-19 pandemic has dealt a severe blow to China's tourism industry, and the application of new technologies seems to blur the spatial boundaries of the tourism industry. Therefore, it is debatable whether the zealous development of large investment projects can really improve the competitiveness of the regional tourism industry. This paper discusses this topical issue by empirically analyzing data from 31 Chinese provinces and cities from 2008–2019 and draws the following conclusions (1) The continuous expansion of new infrastructure investment in China indeed has a positive effect on improving China's overall tourism competitiveness. However, the inverted U-shaped relationship between the two shows that China should not blindly expand the scale of infrastructure construction and make appropriate investment according to the regional industrial development level. (2) Although convergent infrastructure plays an important role in regional industrial competitiveness, the marginal effect has begun to weaken, so the problem of scale inefficiency needs to be addressed. In contrast, the input of innovation infrastructure is insufficient to enhance industrial competitiveness and can be moderately increased to achieve better results. (3) China's core economic areas have a good driving effect on new infrastructure investment, but the original technological innovation and transformation-type facilities are still the key to limiting the improvement of industrial competitiveness. Peripheral areas are more passive recipients with strong demand. Therefore, investment in various types of infrastructure can drive regional development. [ABSTRACT FROM AUTHOR]

Policy synergy is necessary to promote technological innovation and sustainable industrial development. A radial basis function (RBF) neural network model with an automatic coding machine and fractional momentum was proposed for the prediction of technological innovation. Policy keywords for China's new energy vehicle policies issued over the years were quantified by the use of an Latent Dirichlet Allocation (LDA) model. The training of the neural network model was completed by using policy keywords, synergy was measured as the input layer, and the number of synchronous patent applications was measured as the output layer. The predictive efficacies of the traditional neural network model and the improved neural network model were compared again to verify the applicability and accuracy of the improved neural network. Finally, the influence of the degree of synergy on technological innovation was revealed by changing the intensity of policy measures. This study provides a basis for the relevant departments to formulate industrial policies and improve innovation performance by enterprises. [ABSTRACT FROM AUTHOR]

The aims are to explore the construction of the knowledge management model for engineering cost consulting enterprises, and to expand the application of data mining techniques and machine learning methods in constructing knowledge management model. Through a questionnaire survey, the construction of the knowledge management model of construction-related enterprises and engineering cost consulting enterprises is discussed. First, through the analysis and discussion of ontology-based data mining (OBDM) algorithm and association analysis (Apriori) algorithm, a data mining algorithm (ML-AR algorithm) on account of ontology-based multilayer association and machine learning is proposed. The performance of the various algorithms is compared and analyzed. Second, based on the knowledge management level, analysis and statistics are conducted on the levels of knowledge acquisition, sharing, storage, and innovation. Finally, according to the foregoing, the knowledge management model based on engineering cost consulting enterprises is built and analyzed. The results show that the reliability coefficient of this questionnaire is above 0.8, and the average extracted value is above 0.7, verifying excellent reliability and validity. The efficiency of the ML-AR algorithm at both the number of transactions and the support level is better than the other two algorithms, which is expected to be applied to the enterprise knowledge management model. There is a positive correlation between each level of knowledge management; among them, the positive correlation between knowledge acquisition and knowledge sharing is the strongest. The enterprise knowledge management model has a positive impact on promoting organizational innovation capability and industrial development. The research work provides a direction for the development of enterprise knowledge management and the improvement of innovation ability. [ABSTRACT FROM AUTHOR]

Anecdotes abound regarding the decline of basic research in industrial and governmental settings, but very little empirical evidence exists about the phenomenon. This article provides a systematic and historical analysis of the contribution of various institutional sectors to knowledge production at the world and country levels across the past four decades. It highlights a dramatic decline in the diffusion of basic research by industrial and governmental sectors across all countries—with a corresponding increase in the share from universities—as well as an increase of partnerships between universities and other sectors. Results also shows an increase in the relative share of industries in applied research, as measured through patents. Such divergence in university and industry research activities may hinder industries’ ability to translate basic knowledge into technological innovation, and could lead to a growing misalignment between doctoral training and future job expectations. Industries and universities must rethink strategies for partnerships and publishing to maximize scientific progress and to ensure the greatest gains for society. [ABSTRACT FROM AUTHOR]