Rational allocation of clean energy is a critical strategy for ensuring national energy security. As the central mechanism for realizing the economic value of data in the digital economy era, the construction and enhancement of data infrastructure (DIC) are of significant importance for mitigating clean energy misallocation (CEM). This study utilizes panel data from 30 provincial-level administrative regions in China from 2015 to 2023. It measures CEM from the perspective of production-consumption coordination, develops the technology-industry-region-system TIRS theoretical framework based on the four core functions of data infrastructure, and applies dual machine learning models to examine the impact and mechanisms of DIC on CEM. The results demonstrate that the following: (1) DIC significantly reduces CEM, with more substantial effects observed in eastern regions, economically developed areas, and resource-abundant provinces; (2) mechanism analysis reveals that DIC alleviates CEM through technological enabling (T), industrial upgrading (I), regional interconnecting (R), and system safeguards (S); and (3) further analysis identifies a negative spatial spillover effect of DIC on CEM. These findings offer important policy implications for promoting the coordinated development of green energy and computational resources, thereby supporting the timely realization of the "dual carbon" objectives.

High-standard farmland construction (HSFC) is a key measure to ensure national food security. The development of digital technology has brought new opportunities for HSFC. Based on panel data from 30 provinces (municipalities and autonomous regions), this paper employs a two-way fixed effects model, a panel threshold model, and a spatial Durbin model (SDM) to empirically examine the impact of digital technology on HSFC efficiency, as well as its threshold and spatial spillover effects. The study yielded the following key findings: First, digital technology has significantly improved the efficiency of HSFC. Simultaneously, its positive impact is more pronounced in areas with smaller terrain undulations, areas with higher agricultural land transfer, and eastern regions. Second, the impact of digital technology on the efficiency of HSFC exhibited a double threshold effect. When the level of digital technology is below the second threshold, it will significantly affect the efficiency of HSFC, and when the level of digital technology is between the first threshold and the second threshold, it will significantly affect the efficiency of HSFC. The effect is particularly strong when digital technology falls between the first and second thresholds. Third, digital technology has generated significant negative spatial spillover effects on the efficiency of HSFC. Based on this, we should strengthen the construction of rural digital infrastructure, build a digital management system for HSFC, and at the same time promote regional coordinated development of HSFC to achieve efficiency improvement in HSFC.

To explore how the diffusion of digital technologies shapes corporate green innovation, this study uses panel data from A-share listed companies between 2007 and 2021. The empirical findings reveal that digital diffusion significantly enhances the quality, quantity, and efficiency of green innovation. The effects are heterogeneous across firms: in high-tech enterprises, digital diffusion primarily improves innovation quality, while in non-high-tech enterprises, it mainly boosts innovation quantity. Moreover, the positive effects are stronger in heavily polluting industries than in cleaner ones. Mechanism analysis suggests that digital diffusion advances green innovation by strengthening internal corporate capabilities—particularly in production, automation, R&D, and management. These enhanced capabilities lead to more efficient and higher-quality green innovation outcomes. Interestingly, the study uncovers an inverted U-shaped relationship between digital diffusion and the quantity of green innovation, implying that while early-stage diffusion stimulates innovation, its marginal benefits may decline after a certain threshold. This finding offers valuable insights into the stages of technological adoption and their varying impacts. The research provides strategic implications for both policymakers and corporate leaders. For governments, it underscores the need to balance support for digital infrastructure with regulation to avoid diminishing returns. For firms, especially those in high-pollution or low-tech sectors, the study highlights the importance of timing and scale in digital transformation strategies.

In light of rising global climate change and severe weather, a low-carbon emission reduction strategy that includes energy system resilience is crucial. Game theory is used to study resilience and low-carbon options. This paper analyzes panel data from 30 provinces from 2013 to 2022 using time-weighted rough set theory, game-theoretic combinatorial empowerment, and uncertainty models to evaluate China’s energy transition. To investigate technological, organizational, and environmental factors causing regional differences, kernel density estimation and the Dagum Gini coefficient were used, followed by a regionally and chronologically weighted regression model. Key findings: China’s energy transformation is “increase-decrease-increase again” with “high levels in the East and low levels in the West” for low-carbon efforts and “greater resilience in the South compared to the North”. Absolute levels rise, but social restrictions limit low-carbon features and resilience increases systemic vulnerabilities. Green technology dominates the technological, organizational, and environmental framework, whereas industrial structure negatively impacts regional heterogeneity and nonlinear temporal dynamics. Beyond single evaluations, this study addresses multi-objective energy transition concerns and informs regional multi-energy systems and institutional innovation.

China attaches great importance to land use and ecological civilization; hence, clarifying the relationship of land use on ecological resilience is crucial for urban development. The aim of this paper is to study the impact of land-use carbon efficiency on ecological resilience and the moderating role played by different environmental regulatory policies between the two, with the aim of providing a research basis and decision-making reference for the country’s ecological high-quality development by proposing suggestions for different subjects based on the results of this study. Taking 30 provinces and cities in mainland China from 2009 to 2022 as samples, the authors constructed an indicator system to measure their ecological resilience using the entropy method, measured their land-use carbon efficiency using the super SBM, and verified the mechanism of land-use carbon efficiency on ecological resilience by using the bidirectional fixed-effects model. Robustness and endogeneity tests confirmed the validity of the regression results. The following is a summary of this study’s findings: (1) Land-use carbon efficiency can enhance ecological resilience through various mechanisms such as scale promotion, structural upgrading, and technological progress. (2) Regional research shows that different regions have distinct effects of land-use carbon efficiency on ecological resilience. The northeastern region shows a non-significant inhibitory effect, whereas the eastern, middle, and western regions show varying degrees of promotion effects. Land-use carbon efficiency contributes to increased ecological resilience in resource-based and non-resource-based provinces, with resource-based provinces witnessing a greater increase in ecological resilience. The effects of land-use carbon efficiency on different aspects of ecological resilience are diverse, with ecosystem resistance and recovery being empowered. However, the precise mechanism through which ecosystem adaptability influences ecological resilience remains unclear. (3) Moreover, there is variation in the moderating impact of environmental legislation. Command-and-control environmental regulation impedes the positive impact of land-use carbon efficiency, and market-incentive environmental regulation strengthens their relationship, while spontaneous-participation environmental regulation does not significantly enhance their connection. It provides a new theoretical perspective for the study of ecological resilience, deepens the understanding of ecological resilience, and provides theoretical support for enhancing the resilience of ecosystems.

Enhancing green resilience is an important element in realizing environmental protection and green development, and with the continuous development of digital technology, digital transformation has become a new driving force for enhancing green resilience. Based on the panel data of 31 provinces in China from 2013 to 2021, this study examines the impact of digital transformation on green resilience. The results show that digital transformation can significantly enhance green resilience, and this conclusion is still valid after considering a series of robustness tests and endogenous problems; the heterogeneity analysis shows that in the eastern and central regions, the enhancement of green resilience by digital transformation is still significant, while in the western region, digital transformation has curbed green resilience but is not significant; both resource-based and non-resource-based provinces’ digital transformation has enhanced green resilience, and the enhancement is more significant in resource-based provinces; in different levels of green resilience, digital transformation has a stronger impact on provinces with high levels of green resilience, showing the “Matthew effect”; the mediation effect results show that digital transformation can enhance green resilience by attracting government investment, fostering industrial integration and increasing public environmental concern; the threshold results show that digital transformation contributes to green resilience across the sample. However, with the development of digital platforms, the growth rate of digital transformation on the promotion of green resilience will first increase and then gradually decrease and eventually rebound, showing an “N-shaped” relationship.

As China's economic and social development enters a new growth stage, urban green transformation has gradually become an important means to promote high-quality development. In order to explore the effective methods of the impact of China's digital economy development level on the efficiency of urban green low-carbon transition, this paper, based on the statistical data of 263 prefectural-level cities in China during the period of 20122021, adopts the "China Digital Economy Development Index Report (2023)" jointly developed by the Ministry of Industry and Information Technology of China and the Zero One Think Tank, as well as the five selected control variables , measures the efficiency of urban green low- carbon transition by using the SBM model and examines the impact of driving factors on urban green low-carbon transition by using the spatial Durbin lagged panel fixed- effects model. the influence of driving factors on the efficiency of green low-carbon transition in Chinese cities. It was found that: the digital economy index showed a positive correlation with urban green transition efficiency, and the corresponding positive influence coefficient was 0.523, and the influence of control variables on urban green transition efficiency showed differentiation characteristics, among which: the investment rate in environmental pollution control, the contribution rate of GDP in the service industry, and the professional investment rate in green transition showed a positive correlation with urban green transition efficiency, and the corresponding influence coefficients were 0.437, 0.304 and 0.348; the contribution rate of urban industrial GDP and urbanization level show inverse correlation with the efficiency of urban green transformation, and the corresponding inverse impact coefficients are-0.412 and- 0.276, respectively. The conclusions of this paper are of great practical value for the formulation of policies to improve the efficiency of urban green transformation.

The advancement of emerging technology has become an important engine to promote economic progress in our time. This paper aims to explore how modern technology shapes and influences the new pattern of economic development. Through an in-depth analysis of cross-border trade cooperation, changing the employment structure, and changing the issuance and circulation of money, this paper reveals the positive role of these technologies in improving production efficiency, optimizing resource allocation, and promoting innovation-driven development. At the same time, it also pays attention to the challenges brought about by the development of science and technology, such as unemployment caused by the changing employment structure and the widening of the digital divide, and puts forward corresponding suggestions. The results show that the rational use of modern technology can not only accelerate economic growth but also promote the overall progress of society. Therefore, modern science and technology should be actively developed to achieve sustainable development.

As a pivotal driver of high-quality development, the impact of digitalization on industrial green transformation remains somewhat opaque. This study utilizes panel data from 35 industrial sectors in China, covering the years 2005-2021, to construct a fixed effects model that explores the relationship between digitalization and industrial green transformation, as well as its potential mechanisms. The results show that digitalization has a significant role in promoting industrial green transformation, a conclusion validated even after robustness tests. Furthermore, heterogeneity analysis reveals that industries characterized by low energy consumption, low pollution, and large scale experience a more pronounced positive effect from digitalization on industrial green transformation. The mechanism analysis reveals that digitalization propels industrial green transformation by enhancing the capability for green technological innovation. Moreover, digitalization expedites technological transformation, thereby fostering capital renewal and bolstering end-of-pipe treatment capabilities, all of which collectively propel the industrial green transformation. This study contributes to the novel insights from the dual perspectives of green technological innovation and technological transformation, which provides policy implications for developing countries worldwide to enhance industrial green transition through digitalization.

The carbon neutralization process continues to be a significant concern in China, particularly in the Yangtze River Economic Belt. This study defines carbon neutrality and presents a regional capacity measurement index system that includes 12 indicators across four dimensions: production reduction, lifestyle reduction, ecological removal, and technological removal. To assess the relative and absolute levels of carbon neutrality capacity in the Yangtze River Economic Belt, the study uses the time-weighted rough set theory and the uncertain measurement model. Additionally, the study also uses Kernel density estimation, Dagum Gini coefficient decomposition, and convergence analysis to examine the development differences in the relative capacity of carbon neutrality in the region. The findings indicate that from 2013 to 2021, the relative capacity of carbon neutrality in the Yangtze River Economic Belt exhibited an upward trend with fluctuations, and there were significant disparities in development between the upper, middle, and lower reaches of the region. However, these differences showed signs of convergence and a general convergence trend. Moreover, the production reduction and lifestyle reduction aspects in the Yangtze River Economic Belt region are currently at a moderate level, while there is still considerable room for improvement in terms of ecological reduction and technological reduction. Furthermore, there are obvious regional differences in the absolute carbon neutral capacity of the Yangtze River Economic Belt region: the upstream region of the Yangtze River Economic Belt is primarily driven by carbon emissions reduction, the middle reaches are driven by production reduction and the downstream region is driven by carbon removal. Consequently, it is crucial to consider the regional development characteristics of the Yangtze River Economic Belt and implement targeted strategies tailored to local conditions.

Guangdong Supply and Marketing Cooperative serves as an important institution for the “Three Rural Areas” in the Guangdong-Hong Kong-Macao Greater Bay Area. Its digital transformation and optimization serve as powerful supports for rural revitalization. This paper systematically reviews the main practices and effectiveness of the digital transformation within the Guangdong Supply and Marketing Cooperative. It also analyzes the current development dilemmas faced in the construction of digital supply and marketing. Additionally, it proposes targeted solutions, including building a big data resource base, optimizing the digital supply and marketing cloud platform, developing digital public-type agricultural social service applications, establishing a new model of rural e-commerce, enhancing the traceability management system for agricultural products, and strengthening the construction of the digital human resources system. These proposals aim to further promote the strategy for revitalizing the countryside.

With the rapid development of information technology and the deepening of globalization, big data technology and digital economy, as the most important form of technological and economic change, are having a far-reaching impact on China's economic and social development. In view of the problems faced by the development of digital economy in China under the current background of big data, such as lagging institutional construction, lack of human resources, inadequate core technology and unbalanced development of related industries caused by the digital divide, this paper puts forward measures to strengthen the relevant institutional construction, attach importance to the training and introduction of talents, and promote technological innovation. In the past, it provided strategic suggestions for the healthy and stable development of China's digital economy, and also provided some reference for the development of China's digital economy.

Digital infrastructure inputs (DIIs) are vital in strengthening the framework for developing the digital economy and encouraging economic growth. Nonetheless, the risks of environmental contamination are pervasively caused by the rapid expansion and utilization of digital infrastructure. Assessing the carbon emission intensity (CEI) and level of the DIIs of 18 manufacturing in China as the research subject, this study discusses the heterogeneous behavior of various input sources and industries. Furthermore, a two-way fixed effects model, threshold effects model, mediating effects model and moderated mediation effects model have been adopted to examine the nexus between DIIs and CEI of manufacturing. The results show that (1) DIIs raise China's manufacturing CEI and exert a non-linear threshold effect. (2) From the perspective of national attributes, the foreign DIIs will put more pressure on reducing the CEI in China. From the perspective of industry characteristics, DIIs are the most unfavorable for low-carbon development in capital-intensive industries. (3) Due to the mediating effect of total factor productivity (TFP), the positive influence of DIIs on CEI has dramatically diminished. (4) Participation in the global value chain (PAR) and foreign direct investment (FDI) exert moderating effects in the process of the direct effect and mediating effects. In light of the aforementioned conclusions, specific recommendations for developing digital infrastructure and reducing carbon emissions are proposed.

Impact of Science Big Breakthroughs and Their Cost

The digital economy now drives China's new economic growth. However, with various industries' digital transformation (DT), the consequent environmental pollution problems must be addressed. To achieve a better balance between economic growth and environmental protection, digitalization and carbon dioxide (CO2) emissions reduction targets should be achieved. In promoting the DT of manufacturing, it is essential to consider the industry's CO2 emissions and the CO2 emission spillover effects (CO2SE). This paper first constructs a model of CO2SE generated and suffered in the DT of industries using input–output techniques, i.e., the perpetrator and victim effects, and measures these effects. Then, the cross-sectional inter-industry differences and vertical time-series characteristics are analyzed based on the measurement results. Finally, the convergence test of CO2 emission spillover effects in the digital transformation (CO2SE-DT) of China's manufacturing industry is conducted, and the test results are analyzed. The research results show that (1) digital infrastructure-dependent and technology-intensive manufacturing have the largest CO2SE-DT, followed by capital-intensive and labor-intensive manufacturing in that order. (2) The whole victim effect in the DT of China's manufacturing industry shows an "N" trend. In addition, according to the time-series changes of the perpetrator effect in the DT of China's manufacturing industry between 2000 and 2014, China's manufacturing industry can be classified into three types: stable, fluctuating downward, and fluctuating upward, among which the fluctuating upward type is the key industry of concern for CO2 emissions. (3) There is no gradual decrease in the industry differences in CO2SE-DT of China's manufacturing industry, but manufacturing industries with low CO2SE-DT levels have faster growth rates compared with high-level manufacturing industries and eventually achieve convergence of CO2SE-DT rate of change. However, there is a significant difference in the results of the industry difference analysis for different types of manufacturing industries.

At present, the life and production of our society are still accompanied by high pollution emissions. The proposal of the “double carbon” goal puts forward clearer requirements for China’s energy conservation and emission reduction process. To cope with stricter green development requirements, green finance came into being, contributing a new path to the realization of the “double carbon” goal. This paper is based on the panel data of 30 provinces and cities in China (except Tibet, Hong Kong, Macao, and Taiwan) from 2011 to 2020, using the fixed effect model, threshold model, mediator model, and SDM model to study the impact of green finance on carbon neutralization capacity and its impact path based on reasonable measurement of regional green finance development level and carbon neutralization capacity. The study found that due to the existence of the “green paradox” and “forced emission reduction”, China’s green finance has a significant positive U-shaped impact path on carbon neutralization capacity; at the same time, informal regulation has a significant single-threshold effect in this path. When informal regulation crosses 0.47, the impact of green finance on carbon neutralization capacity will change from negative to positive, overcoming the impact of the “green paradox”; there is a nonlinear mediating effect of marketization level and technological innovation in the path of green finance affecting carbon neutralization capacity. In addition, green finance not only affects local carbon neutralization capacity but also has a positive spillover effect on neighboring regions.

通过风能科学与工程课程传统教学和PBL教学模式进行对比,讨论了PBL教学模式及主动学习在课程教学中的效果.采用线下线上结合形式进行课程教学互动和考核.PBL及主动学习模式促进了学生自主学习意识,培养了社会性能力,锻炼了高层次思维.

Green technology progress is an important link to achieve green development.Through a dual mechanism of incentive and restraint, green credit can influence the Chinese industry’s level of green technology innovation efficiency.Based on panel data from China’s 30 provinces（autonomous regions and municipalities that fall under the direct control of a Central Government） between 2011 and 2019.Firstly, the industrial pollution index is calculated using the enhanced panel entropy approach.Secondly, the basic formula for CO 2 emissions in the IPCC recommendations for national greenhouse gas inventories is cited, and the sum of industrial carbon emissions is determined by using the pertinent carbon emission factors in the recommendations and the consumption of pertinent energy sources in the statistical yearbook.Finally, the SBM-DEA model is employed to gauge the adoption of green technology in business.The two-way fixed effect model is used to examine the influence of green credit on the effectiveness of green technology innovation in industry, based on the findings of the Hausman test.Utilize the level of financial development to further investigate regional heterogeneity while taking into account the impact of regional financial development level on green credit.Finally, use the stepwise regression approach to investigate the indirect influence path of credit scale and credit cost in order to investigate the mechanism of the impact of green credit on the effectiveness of industrial green technology innovation.The findings indicate that all regions of China exhibit an “inverted N” trend in terms of the level of industrial green technology innovation efficiency.Of these, Hainan Province has been at the highest level during the study period, while Hebei Province has been at the lowest level nationwide.Baseline regression findings demonstrate that green credit is crucial in fostering the effectiveness of regional industrial green technology innovation.According to the double test of the system GMM model and the 2SLS model, the endogeneity test demonstrates that the promotion effect of green credit on the effectiveness of green technology innovation in regional industries is still considerable.Traditional regional heterogeneity analysis finds that the promotion effect of green credit on the effectiveness of industrial green technology innovation is more pronounced in the western region, with regional heterogeneity analysis based on the level of financial development revealing the most significant contribution of green credit to the effectiveness of industrial green technology innovation in regions in the second time period.According to the examination of the impact mechanism, green credit also has an indirect impact on the effectiveness of industrial green technology innovation through the use of credit scale and credit cost.

The greening of the industry is the core of the green development of Beijing, Tianjin, and Hebei, and also the main carrier to promote the construction of ecological civilization, of which the green development of the industry is the top priority. To recognize the current situation of industrial green development in Beijing, Tianjin, and Hebei, and identify the obstacle factors, it is necessary to make a scientific and reasonable measurement of industrial green development in Beijing, Tianjin, and Hebei, which can promote the systematization and standardization of industrial green development measurement, and also provide a theoretical basis for different regions to tailor their policies and measures for industrial green development. However, the concept of “green development” has not been explicitly introduced in the international academic community, and since Pearce first proposed the “green economy” in 1989, the focus has been on ecosystems, economic-ecological systems, and economic-ecological-social systems. The measurement of green development has also emerged in three ways: First, using a single indicator to characterize. Second, incorporating resources and environment into the production function, and calculating green total factor productivity by treating pollution emissions as non-desired output. Although the research has made great progress, it still holds the view of weak sustainability. Third, as the research progresses, the construction of a comprehensive index system for green development becomes an accepted approach. On this basis, current research has examined the relationship among green development and environmental regulation, technological innovation, capital, labor, energy, industrial structure, FDI, urbanization level, and investment in education, mainly using econometric methods, with more and more attention being paid to the industry and regional differences of the influencing factors. Although more authoritative green development index systems have been constructed in the literature, they are mostly evaluated at relative levels and lack absolute levels. And there are few targeted studies on Beijing-Tianjin-Hebei, a major national strategic development region, and industry, an important sector, in terms of research objects. Although the barrier factors can be extracted by analyzing the temporal trend of green development level or regional variability, and the barrier factors can also be summarized by identifying the influence direction, influence effect and significance of the influencing factors, the existing studies have less analysis of the contribution degree of the barrier factors, ignoring the dynamic and spatial variability of the barrier factors and their barrier degree.Therefore, this paper refers to the authoritative evaluation index system and constructs the industrial green development index system based on the connotation of green development of resource, environment and economic coordination. Taking into account both subjective and objective methods, the weights are calculated using AHP and the improved entropy weighting method that introduces time variables. On this basis, grading criteria are set, and the relative level and absolute level of industrial green development in Beijing, Tianjin, and Hebei from 2012 to 2018, as well as the barrier degree of barrier factors, are measured and temporal trends are summarized by combining the unconfirmed model and the improved barrier degree model, and a time-weighted vector is further introduced to describe the spatial pattern. The results show that: First, the level of industrial green development in Beijing, Tianjin and Hebei is rising year by year and has reached C1 level; most of the top-ranked cities are located in the “atrium” area near Beijing and Tianjin, while the bottom-ranked ones are mainly located in the “bottom of the heart” area of the aorta; the cities in higher grades are located in the center of the “heart” near Beijing and Tianjin, while the lower-ranked cities are located in the “tip” and “bottom” of the heart. Second, research input intensity, electricity consumption per unit of GDP, energy consumption per unit of industrial value-added, and outward orientation are the key obstacle factors for the overall industrial green development of 13 cities in Beijing, Tianjin, and Hebei; and their obstacle degrees are all on an increasing trend.