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The purpose of this paper is to examine whether the development of carbon finance can promote sustainable economic growth in China through spatial spillover effects. On the basis of a theoretical analysis of this mechanism, this paper takes 30 provincial-level administrative regions in China as the research object and uses a variety of spatial econometric analysis techniques to empirically test them. It assesses sustainable economic development measured by green total factor productivity (GTFP). This study documents a significant and positive impact of province-level carbon finance improvement on sustainable economic growth through improving technological progress and increasing technological market turnover. The economic influence of carbon finance on sustainable economic growth is still positive after considering possible endogeneity concerns. Results reveal heterogeneity and spatial spillover effects of carbon finance on GTFP, notably stronger in eastern regions compared to central and western ones. We find that the technological progress and technological market turnover have significant and positive promotional effects on GTFP with increasing levels of carbon finance. This paper provides policy implications for improving sustainable economic development. [ABSTRACT FROM AUTHOR]

This paper examines big data analytics implications on the central banking financial system's technological progress. A digital technological progress framework and model is established to analyze the economy's aggregate supply via covering the monetary policy, big data analytics, pollutants emissions as independent variables and the economy's aggregate demand as a moderating variable in a modified extensive growth theory framework and model to compute the productivity indicators and the total factor productivity (TFP) as the central banking technological progress that combined the mentioned variables qualities contribution. Besides, data analytics positive and negative externalities that include data analytics shortcomings as unpriced undesirable output in the form of cybersecurity and pollutants' emissions among other proxies are internalized in the framework and the model to integrate the digital technology innovation with digital technology shortcomings and climate change. This revised extensive theory framework and model is a remarkable technique comprehensive of the technological progress matters and sustainable economic development and is considered one of the most important sustainable development and long-run economic growth proportions in the central banking financial system functions to manage the economy's aggregate supply and demand that unnoticed by previous studies. [ABSTRACT FROM AUTHOR]

Enhancing efficiency and productivity in countries' healthcare systems is a global challenge. The Chinese government invested huge resources to improve the efficiency and productivity of the healthcare system across the country. To assess the success of the mission above, this research utilized DEA-SBM Meta frontier analysis alongside the Malmquist Productivity Index. These methodologies were employed to gauge Efficiency, production technology heterogeneity, and productivity of healthcare systems change across 31 mainland Chinese provinces and four distinct geographical regions throughout the study period spanning from 1997 to 2022. Results revealed that the mean efficiency score of China's healthcare system is 0.7672. It indicates a growth potential of 23.28 percent in the operational efficiency of healthcare systems. The eastern region's efficiency level (0.86917) is higher among all four regions. Zhejiang, Shandong, and Guangdong are the top three healthcare-efficiency performers. The technology gap ratio indicates that eastern regions witnessed a high TGR (0.9909), showing the country's attainment of superior healthcare technologies. Beijing, Guangdong, Shanghai, Tianjin, and Zhejiang witnessed higher TGR values among all 31 mainland Chinese provinces. The total factor productivity index of the healthcare system witnessed a slight growth of 0.33%, with an average MI score of 1.0033. Efficiency change (EC) was found to be the main determinant of TFPC as technology change TC is less than EC. Moreover, the MI score of the Western region (1.033) is higher than the corresponding Eastern, northeastern, and central regions. Guizhou, Anhui, and Yunnan were found to be the top three performers in TFPC growth. Finally, the Kruskal–Wallis test confirmed the statistically significant difference among 4 Chinese regions for the healthcare system's efficiency, TFPC, and TGR. [ABSTRACT FROM AUTHOR]

The power industry's low carbon transition is pivotal for achieving carbon reduction and sustainable development. This study uses the super epsilon-based measurement (Super-EBM) model and the Malmquist index to evaluate the power industry's low carbon transition efficiency using data from 30 provinces in China from 2010 to 2020, and utilizes the Tobit model to comprehensively analyze the factors affecting the low carbon transition of power industry. In addition, this paper examines the spatial differences in the power industry's low carbon transition efficiency as well as its distributional characteristics and dynamic evolutionary patterns. Conclusion is drawn as follows this paper analyzes the regional differences, spatial distribution characteristics and dynamic evolutionary trends of the power industry's low carbon transition. The main conclusions are as follows: (1) The power industry's low carbon transition efficiency in China shows an uptrend, with the western China region having the highest overall level of efficiency, greater fluctuations in the central China region, and more stability in the eastern China region, technological progress is a central factor in increasing total factor productivity, the efficiency of the power industry's low carbon transition is positively influenced by the electricity prices, and negatively influenced by the energy structure, environmental regulations and economic structure; (2) the Intraregional differences and hypervariable density are the main reasons sources of the overall differences in the efficiency of the power industry's low carbon transition; Intraregional differences in the eastern, central, and western China regions are decreasing year by year, but the efficiency of the power industry's low carbon transition in the western China region is still distributed in a multipolar way; (3) The dynamic evolutionary trends of the efficiency distribution of the low carbon transition in power industry is influenced by the type of spatial lag in the neighboring area. Where areas with low efficiency makes it difficult to achieve short-term leapfrog development, and areas with a cluster of high-efficiency provinces are prone to "Siphon Effect". The findings provide a theoretical basis for promoting the efficiency of the power industry's low carbon transition and coordinating the strategic adjustment of economic and environmental green development. [ABSTRACT FROM AUTHOR]

The key to high-quality development in the textile and apparel industry lies in enhancing technological innovation and optimizing the efficiency of technological innovation. Based on data from 60 A-share listed companies in the textile and apparel sector in China from 2013 to 2022, this study employs a three-stage DEA model and the Malmquist index model to measure changes in technological innovation efficiency from static and dynamic perspectives. Additionally, it uses a Tobit model to analyze the impact and mechanisms of management and financial factors on technological innovation efficiency. The results indicate that: (1) Compared to the manufacturing industry and its sub-sectors, the overall technological innovation efficiency of listed textile and apparel companies was relatively low and showed a declining trend between 2013 and 2022; (2) Over the decade, the average total factor productivity of these listed companies increased by 1.7%, exhibiting a "W" shaped fluctuation, with technological progress, pure technical efficiency, and scale efficiency all showing weak improvement; (3) Management and financial factors significantly influence technological innovation efficiency. Specifically, employee quality, profitability, and operational capability are positively correlated with technological innovation efficiency and have long-term effectiveness, while firm age, management costs, equity concentration, development ability, and debt repayment capacity are negatively correlated with technological innovation efficiency; (4) Different types of enterprises show differences in the significance of management factors, while whether the same person holds both managerial positions significantly affects financial factors. [ABSTRACT FROM AUTHOR]

In the context of the ongoing process of high-quality development in the new era, which is focused on improving total factor productivity, it is of great importance to explore the spatial and temporal variations of total factor productivity growth and its driving factors in China's county regions' grain cultivation industry. This paper employs a three-stage DEA-Malmquist productivity method, the Gini coefficient method, and a panel fixed-effects model to analyze data from Chinese counties between 2009 and 2019. The analysis indicates that the growth of county food total factor productivity (FTFP) exhibits a fluctuating upward trend during the examination period, with an average annual growth rate of 2.43%. This is primarily driven by technological progress, yet the core driving role of technological efficiency is not effectively played. The average annual growth rate of county FTFP varies across different regions. The highest average annual growth rate of county FTFP in the eastern region and the primary grain-producing area is 2.75 and 3.04%, respectively. The lowest growth rates were observed in the western region and the main grain marketing area, at 1.44 and 1.23%, respectively. Secondly, the Gini coefficient of county FTFP continues to demonstrate a persistent upward trend during the examination period, with an average annual growth rate of 14.729%. The primary factor contributing to the observed variation in total factor productivity growth of the food sector at the regional level is the existence of disparate technological progress. Thirdly, there is a notable positive correlation between county financial deepening and financial self-sufficiency rates and county FTFP growth, with impact coefficients of 0.0503 and 0.0924, respectively. Conversely, county population density, degree of economic development, farmers' income level, and industrial structure exert a significant negative influence on county FTFP growth and technological progress. [ABSTRACT FROM AUTHOR]

The impact of climate change on agricultural quality development under the constraint of China's "Double Carbon" target has been widely discussed by policy practitioners and academic theorists. This paper attempts to deconstruct the logic of how climate change affects agricultural total factor productivity (TFP) in three dimensions—the structure of agricultural input factors, the change in the cropping system, and the stability of crop supply. This paper also reveals the mechanism through which biased technological progress increases agricultural TFP by weakening the magnitude of climate change and empirically tests it by using China's provincial-level data from 2000 to 2021. This study showed that average annual temperature and annual precipitation had significant negative effects on agricultural TFP, that the number of sunshine hours had a significant positive effect on agricultural TFP, and that obvious regional differences existed in the effect of climate change on agricultural TFP. Further mechanism tests revealed that biased technological progress positively moderated the effect of climate change on agricultural TFP. Based on these findings, the appropriate countermeasures for improving climate early warning mechanisms, promoting the progress of appropriate technology, and fostering new agricultural management bodies. [ABSTRACT FROM AUTHOR]

India is often referred to as the next development superpower, and generally, becoming a large-scale industrialization center is seen as an achievable goal for the country. This article investigates the output elasticity, substitution elasticity, and technological advancement between the various factors (i.e., labor, capital, and energy use) in the industrial sector of India. To investigate the factor's productivity, a trans-log production function was applied; however, ridge regression was used to analyze the various parameters to check the multicollinearity issue. The results show that (1) the analyzed inputs are optimistic and return-to-scale averages of 1.18, 1.41, and 1.24 between labor, capital, and energy, respectively, are increasing; (2) the pairs substitution between labor--industrial energy utilization and capital--industrial energy consumption is found to be 0.96 and 0.98, respectively, on average, indicating that capital, labor, and energy are good substitutes that need more attention in the production process; and (3) the technological progress between factors ranges from -0.4 to 0.02, in which labor--energy and capital--energy utilizations provide quicker outcomes than a capital--labor utilization. Finally, the industrial sector can attain maximum productivity if capital and skilled labor are improved under the sustainable development goals, as energy and capital are optimized for maximum efficiency. Finally, energy substitution and low-carbon technological efforts can be better suited for attaining dual-carbon goals in the industrial sector. [ABSTRACT FROM AUTHOR]

As a new engine driving economic development, new digital infrastructure plays a significant role in enhancing green total factor productivity. Based on 2011-2020 panel data covering 30 Chinese provinces, this study empirically investigates the effects and mechanisms of new digital infrastructure on green total factor productivity. The results show that new digital infrastructure can significantly improve regional green total factor productivity, and this conclusion remains valid after a series of robustness tests and regressions of instrumental variables. Further mechanism research shows that new digital infrastructure indirectly promotes the growth of green total factor productivity by improving capital misallocation and driving technological innovation, while there is no mediating mechanism of labor misallocation. In addition, there is significant heterogeneity in the impact of new digital infrastructure on green total factor productivity. Especially during periods of high government attention, in the eastern regions, and in areas with higher levels of human capital, the positive incentive effect of new digital infrastructure is more significant. This study provides empirical evidence and policy references for promoting and amplifying the green growth effects of new digital infrastructure. [ABSTRACT FROM AUTHOR]

In China, the number of senior citizens has grown, along with the burden of old age, and aging has hampered economic growth. The advent of the digital age has led to the emergence of the digital economy as a new engine for economic growth. This paper uses DEA-Malmquist index model to measure the total factor productivity growth rate of 31 provinces in China from 2011 to 2021, and uses the moderating effects model to empirically investigate the relationship between the digital economy, aging and total factor productivity, and to verify whether the development of the digital economy can mitigate the negative impact of aging on total factor productivity. The results show that aging inhibits total factor productivity growth, and the digital economy can promote total factor productivity growth. Digital economy can alleviate the negative impact of aging on total factor productivity growth, and has a moderating effect. Digital economy plays a moderating role by improving the level of human capital and facilitating technological progress. The regional heterogeneity analysis shows that the moderating effect of the digital economy exists in the eastern and western regions and the southern region, but not in the central region and the northern region. Furthermore, the digital economy has a moderating effect on both the high and low aging groups. The research in this paper not only helps to evaluate the productivity effects of the digital economy, but also has important implications for finding ways to mitigate the negative effects of aging. [ABSTRACT FROM AUTHOR]

The International Journal of Information Technology & Decision Making has published its fourth issue, featuring 10 original papers from distinguished authors in eight countries. The papers cover a range of topics, including deep learning for malaria detection, social banking, multi-attribute decision-making, video event detection, ERP system evaluation, COVID-19 vaccine analysis, order picking systems, big data analytics in central banking, and estimation of missing entries in preference matrices. The articles provide valuable insights and innovative solutions in the field of information technology and decision making. [Extracted from the article]

Under the ongoing implementation of the "dual carbon" goal, research has focused on the impact of Environmental, Social, and Governance (ESG) initiatives on green innovation. However, few studies have analyzed in depth the mechanisms of ESG impact on green total factor productivity (green TFP). Here, we explored the impact of ESG performance on green TFP, green technical efficiency, and green technological progress using A-share listed companies in China's Shanghai and Shenzhen stock markets from 2011 to 2021. The results show that good ESG performance can significantly improve the green TFP of enterprises, and that this effect is more prominent in industries with lower environmental risks and enterprises in the growth and maturity stages. We identified the importance of the psychological account path and propose that the promotional effect of ESG performance on green TFP mainly derives from improving green technical efficiency, rather than from expanding green technological boundaries. These findings have practical implications for guiding companies to implement ESG concepts, strengthening the synergistic role of government regulation and professional supervision, and promoting micro-level implementation of innovation-driven and sustainable development strategies, thereby promoting high-quality development. [ABSTRACT FROM AUTHOR]

Promoting the high-quality development of the marine economy is an inevitable choice for building a maritime power, and technological innovation can provide strong impetus for the high-quality development of the marine economy. Therefore, it is urgent to clarify the impact mechanism of technological innovation on the high-quality development of the marine economy, and promote the high-quality development of the marine economy. This study employs panel data from 11 coastal provinces and municipalities (autonomous regions) in China, spanning the years 2006 to 2020. The objective is to empirically evaluate the mechanism through which scientific and technical innovation impacts the high-quality development of the marine economy. This is achieved by utilizing the PVAR model and the mediation effect model. The research findings indicate that there is a noteworthy impact of enhancing scientific and technological innovation on the marine economy of China. Moreover, there exists a significant reciprocal relationship between scientific and technological innovation and the pursuit of high-quality development in the marine economy. It is observed that scientific and technological innovation not only has a positive influence on the high-quality development of the marine economy by enhancing green total factor productivity and optimizing the industrial structure, but it also facilitates the advancement of the marine economy through the chain mediation path of "improving green total factor productivity and optimizing industrial structure". [ABSTRACT FROM AUTHOR]

Continuous improvement of technological innovation ability, adjustment of the development strategy, and enhancement of operational performance are of great theoretical and practical significance for logistics enterprises. This paper aims to analyze and evaluate the innovation efficiency of the logistics industry. The study utilizes the static three‐stage DEA model and the dynamic Malmquist index model, considering a total of 12 indicators related to innovation input, output, and environmental variables. A dataset of 2940 entries from 49 listed logistics enterprises from 2017 to 2021 was calculated. The analysis provides insights into the innovation efficiency of logistics enterprises from a static perspective and the innovation total factor productivity from a dynamic perspective and decomposition terms. Based on the analysis of environmental variables by the SFA model, it was found that DEA inefficiency is the combined result of environmental factors and management inefficiency. Environmental variables have both positive and negative effects on innovation. The improvement of the economic development level will lead to excess R&D investment. Increased government simple fund subsidies are not conducive to the efficient allocation of innovation resources within enterprises. The expansion of enterprise scale will increase R&D personnel and investment in fixed assets. A thriving technology market can encourage enterprises to improve their own conversion rate of scientific and technological output and give full play to their innovation ability. The dynamic Malmquist model analysis reveals a recution in the overall innovation efficiency of listed logistics enterprises over 5 years. The changes in total factor productivity and technological progress efficiency of all listed logistics enterprises are synchronized, with most enterprises exhibiting higher technological progress efficiency compared to comprehensive technical efficiency. The total factor productivity of logistics enterprise innovation is mainly affected by comprehensive technical efficiency. [ABSTRACT FROM AUTHOR]

In search of an alternative representation of the economic system, Pasinetti ended up with a notion of vertical integration (VI) that takes interdependencies as its starting point but does not explicitly consider their role in the unfolding structural economic dynamics (SED). In this paper, we show that SED may be more inclusive of such interrelations to appropriately consider VI as a tool for analysing economic systems under the conditions of technical change and structural transformation. Our strategy is to focus on Pasinetti's shift from his original development building on Sraffa's sub-systems to his formulation of VI in terms of a pure labour technology in which all goods are produced by labour only. To carry out the analysis, we resume his iterative algorithm of VI, showing that we can extend it to the case of an expanding economy as a bridge between the horizontal and vertical production representations. Following this route, we can effectively address the features of structural transformation associated with changing patterns of interdependence. Besides, we can go from an assessment of productivity growth at an industrial level to determining the pace of technical change in a vertically integrated sector (VIS), which allows focussing on a concept of productivity derived from the VI, labelled as the total labour productivity (TLP). The TLP is robust to outsourcing of production, whose effects are adequately dealt with insofar as it considers not only the direct but also the indirect labour required to produce the final output. [ABSTRACT FROM AUTHOR]

The free flow of factors resulting from regional integration affects not only economic and social activities between cities, but also climate and environmental changes. Under the goal of sustainable development, regional integration as a policy pathway to lower carbon intensity has rarely been clarified. In light of this, using panel data of 279 prefecture-level cities in China from 2006 to 2019, this paper treats city cluster planning as a quasi-natural experiment of regional integration and aims to discuss the impact and mechanism of regional integration on carbon intensity through difference-in-differences (DID) method, mediating effect model and moderating effect model. The following results are reached: (1) The implementation of regional integration policy effectively reduces carbon intensity, and the conclusion still holds after a series of robustness tests. (2) Total factor productivity (TFP) is the mediator of regional integration to reduce carbon intensity, and population density, technological innovation and industrial upgrading are the moderators. (3) The effect of regional integration in reducing carbon intensity varies from city location and city features. The proposed theoretical framework of regional integration affecting carbon intensity, containing mediator and moderators, not only enriches the theoretical research in the field of environment and region, but also provides a new policy path for exploring sustainable development. Therefore, it is necessary to build urban agglomerations integrating the concepts of green, low carbon, intensive and efficient. [ABSTRACT FROM AUTHOR]

Educational resource utilization efficiency (ERUE) and productivity growth are considered current global challenges that the modern world faces. This study evaluates the educational resource utilization efficiency, dynamic productivity change, and regional discrepancies in technologies involved in educational resource utilization in 35 European countries and four regions. DEA Super SBM, Meta frontier analysis, and Malmquist productivity index are employed to gauge the ERUE, technology gap ratio (TGR), and total factor education resources productivity change. A set of inputs and outputs is used from 35 European countries for the study period of 1998–2021. Results revealed that the average ERUE in European countries is 0.6312, Which indicates a 36.88% improvement potential in educational resource utilization. Southern Europe continuously exhibits superior average ERUE scores (0.6871) compared to other regions, indicating a higher efficiency in using educational resources. Luxembourg (1.0813), Czechia (0.9356), and Slovenia (0.8984) are found to be the top three performers in terms of ERUE level. The technology gap ratio value is highest in Southern Europe. It demonstrates that southern European countries used the most advanced technology in education resource utilization. The average Malmquist Index (MI) in European countries is 1.0349. It Indicates a 3.49% growth in educational resource utilization. Technology is the primary determinant of productivity growth, as Technological change is higher than efficiency change. Southern European countries showed the highest MI of 1.0542. Italy, Lithuania, and Serbia were found to have higher average MI scores over the study period (1998–2021). Finally, the Kruskal Wallis test proved that ERUE and TGR in 4 different regions of Europe are heterogeneous. In contrast, the MI in European regions isn't found to be significantly different. [ABSTRACT FROM AUTHOR]

The efficient and sustainable management of forestry resources is crucial in ensuring economic and societal sustainability. The Chinese government has invested significantly in regulations, afforestation, and technology to enhance the forest resource efficiency, reduce technological disparities, and boost productivity growth. However, the success level of this undertaking is unclear and worth exploring. To this end, this study applied DEA-SBM, meta-frontier analysis, and the Malmquist productivity index to gauge the forest resource efficiency (FRE), regional technology heterogeneity (TGR), and total factor productivity growth (MI) in 31 Chinese provinces for a study period of 2001–2020. Results revealed that the average FRE was 0.5430, with potential growth of 45.70%, to enhance the efficiency level in forestry resource utilization. Anhui, Tibet, Fujian, Shanghai, and Hainan were found to be the top performers in forestry utilization during the study period. The southern forest region was ranked highest, with the highest TGR of 0.915, indicating advanced production technologies. The average MI score was 0.9644, signifying a 3.56% decline in forestry resource productivity. This deterioration is primarily attributed to technological change (TC), which decreased by 5.2%, while efficiency change (EC) witnessed 1.74% growth over the study period. The Southern Chinese forest region, indicating an average 3.06% increase in total factor productivity, ranked highest in all four regions. Guangxi, Tianjin, Shandong, Chongqing, and Jiangxi were the top performers, with prominent growth in MI. Finally, the Kruskal–Wallis test found a significant statistical difference among all four regions for FRE and TGR. [ABSTRACT FROM AUTHOR]

There exist many examples of countries that have seriously reduced, over the past 150 years, the development gap vis-à-vis economic leaders. The decisive factor was the rapid increase of the total factor productivity (TFP), possible due to the technological progress. In the article, the author verified 6 important hypotheses underlying the Productivity Strategy 2030 published by the Polish government, on the basis of the experience of 96 countries around the world over the last 60 years. Most of these hypotheses were positively verified. However, when implementing the Strategy, one should remember that the increase in the aggregate productivity observed throughout the economy is only the combined effect of the increase in productivity occurring at the enterprise level. Therefore, the pursuit of more developed countries requires, first of all, the creation of appropriate conditions for the development of enterprises. [ABSTRACT FROM AUTHOR]

Green innovation (GI) can increasingly contribute to green total factor productivity (GTFP) in the process of high-quality development transformation. However, the heterogeneous, spatial, and non-linear characteristics of GI are usually ignored when investigating its impact on GTFP. Using panel data on 275 Chinese cities from 2004 to 2022, two-way fixed, spatial Durbin, threshold, and mediation effect models are adopted to comprehensively examine how GI affects GTFP. The benchmark regression shows that GI can promote GTFP, and this result is maintained after robustness tests. The invention innovation is more likely to increase GTFP than green utility and green design innovations; furthermore, the enhanced impact driven by GI is primarily manifested via technological progress. GI can promote GTFP in neighboring regions through spillover effects under various spatial weight matrixes. Using the technological gap as the threshold variable, GI can promote GTFP through a gradually increasing feature. Furthermore, industrial upgrading, pollution reduction, and factor agglomeration are the three main mechanisms through which GI promotes GTFP. Based on the perspective of GI promoting sustainable development, the findings of this paper provide valuable recommendations for formulating GI and sustainable development policies. [ABSTRACT FROM AUTHOR]

• ·The three-stage super efficiency SBM-DEA model and Malmquist index was used to measure carbon emission efficiency, index and decomposition terms of land use. • ·LUCE and its spatial and temporal pattern of urban agglomerations in the YREB were discussed from static and dynamic perspectives respectively. • ·Environmental variables such as level of economic development, urbanization rate, and industrial structure all have significant effects on input slack value. • ·The PTE of the MRYR was relatively low due to insufficient resources in low-carbon technology, while the low TE of the CC is due to its insufficient SE. • ·TC was the main factor promoting the improvement of LUCE in the whole basin, and the trend changes of LUCE in the three urban agglomerations was closely related to their management level, technological level, and adjustment of element structure. Exploring regional differences in land use carbon emission efficiency (LUCE) and the path of collaborative emission reduction in the Yangtze River Economic Belt (YREB) are of immense importance to achieve the sustainable regional development. Firstly, based on the estimation method of land use carbon emissions, this paper scientifically calculates the carbon emission of land use of the Yangtze River Delta urban agglomeration (YRD), the middle reaches of the Yangtze River urban agglomeration (MRYR), and the Chengdu-Chongqing urban agglomeration (CC) from 2010 to 2021 in the YREB. And the carbon emission of land use of each city in urban agglomerations was obtained, which was regarded as the unexpected output in the efficiency evaluation index system. Secondly, the three-stage SBM-DEA model was used to reveal the LUCE and its spatiotemporal characteristics in different regions from a static perspective. The empirical results of the second stage SFA showed that the level of economic development, urbanization rate, industrial structure, non-agricultural conversion rate of agricultural land, the level of scientific and technological innovation and government intervention had significant effects on LUCE. By comparing the efficiency values and their decompositions in the first and third stage of various urban agglomerations, it was found that after eliminating environmental and random factors, the average ranking of comprehensive technical efficiency (TE) of land use carbon emission changed from YRD > MRYR > CC to YRD > CC > MRYR due to the cross influence of pure technical efficiency (PTE) and scale efficiency (SE). Thirdly, Malmquist index model was used to analyze the change of total factor productivity index (TFPch) and its spatiotemporal pattern from a dynamic perspective. It was found that the TFPch, technological progress index (TC), pure technical efficiency index (PEC) and scale efficiency index (SEC) of land use carbon emission of the three major urban agglomerations all showed varying degrees of increase after adjustment. Finally, to further analyze the impact of technological progress, PTE and SE on LUCE, a regression model was constructed with LUCE of each city in urban agglomeration as the dependent variable, and TC, PEC and SEC as independent variables. It was concluded that technological progress was the main factor to promote the improvement of LUCE in the whole YREB. The trend of changes of LUCE in various urban agglomerations was closely related to its management level, technology level and factor structure adjustment. The above conclusions provide support for the coordinated emission reduction path of urban agglomerations in the YREB from the perspective of land use, which is conducive to actively and steadily promoting the realization of carbon peak and carbon neutrality goals. [ABSTRACT FROM AUTHOR]

To examine the intricate relationship between digitalization development and sustainable development, this study commences its analysis by employing the entropy method to establish an integrated indicator of digitalization development among advanced nations. Subsequently, we utilize a panel dataset spanning a decade from 2010 to 2020, encompassing 36 developed countries, to estimate Green Total Factor Productivity (GTFP) growth. We establish a fixed effects model, a mediation effect model and a threshold regression model to explore the relationship between the two variables. The empirical findings of this research reveal a significant positive association between the progress and enhancement of digitalization and the advancement of sustainable development (GTFP). Furthermore, this research elucidates the underlying mechanism by which digitalization development indirectly fosters the advancement of green productivity. This facilitation occurs through the promotion of technological innovation, the enhancement of human capital, and tupgrades in the industrial framework. Notably, our study finds the non-linear relation between digitalization development and GTFP growth, suggesting that the magnitude of impact on environmental performance is contingent on the scale of technological innovation, the growth of human capital, and the level of financial development. • The GTFP and digitalization among OECD countries are investigated; • Digitalization has direct effect on GTFP in OECD countries; • Digitalization has indirect effects on GTFP in OECD countries; • There is nonlinear relationship between GTFP and digitalization. [ABSTRACT FROM AUTHOR]

Digital development is critical for achieving coordinated regional economic growth, energy conservation, and emissions reduction, which makes it a viable method for achieving carbon neutrality and peaks. This study investigates the impact of the digital economy on urban total factor productivity and carbon emissions, as well as its mechanism, from 2011 to 2019 by developing a digital economy evaluation index system. We find that the growth of the digital economy can increase urban total factor productivity while lowering carbon emissions and also has a significant spatial spillover effect on regional economic growth, energy conservation, and emissions reduction. The main influencing mechanism is that the digital economy accelerates marketization by encouraging industrial structure upgrades and technological progress, which results in higher total factor productivity and lower energy consumption. In terms of the spatial distribution, the digital economy promotes total factor productivity more in the central and western regions and large cities and promotes carbon emission reduction less in the eastern regions and smaller cities. The findings extend the benefits of the digital economy beyond economic and social welfare benefits and serve as a model for achieving a win–win situation in terms of both economic growth and carbon emissions reduction. [ABSTRACT FROM AUTHOR]

Artificial intelligence (AI) has been proved to be an important engine of green economic development, yet how it will affect the internal structure of green economy is unknown. The aim of this study is to examine the impact and its mechanism of AI on green total factor productivity (GTFP) from the internal-structure perspective, by using provincial panel data of China from 2009 to 2021 and global Malmquist index. The main research results show that: (1) the development of AI contributes to China's GTFP growth. And this effect is more significant in undeveloped areas; (2) AI promotes China's GTFP growth mainly by improving resource allocation efficiency, while it exerts little impact through the paths of technological progress and scale efficiency; (3) the transmission mechanism of AI on GTFP varies greatly among China's three main regions. In the eastern region, AI improves GTFP mainly by both advancing technological progress and improving resource allocation efficiency, while in central region AI contributes to GTFP growth mainly through technological progress. Compared with the eastern and central regions, AI in the western region plays a stronger impact on GTFP through the channel of improving scale efficiency. This study helps to understand the pathways of artificial intelligence affecting the transformation of green economic growth and formulate differentiated regional policies in light of local conditions. • This paper studies the effects of AI on China's GTFP from internal-structural perspective. • GTFP is decomposed into three internal factors by the global Malmquist index. • AI has positive effect on GTFP through different impacts on internal factors. • Improving resource allocation efficiency is the most critical channel of AI on GTFP. • AI plays a more significant role in promoting GTFP in less developed areas. [ABSTRACT FROM AUTHOR]

Green transformation is currently a major challenge for both developed and developing economies, while the role of environmental insurance (EI) in green productivity in developing economies remains unclear. Using firm-level data from Chinese A-shape listed companies, this study investigates the relationship between EI on green total factor productivity (GTFP) along the period 2010–2019. To do so, a slack-based measurement model integrated with Global Malmquist-Luenberger approach (SBM-GML) is applied in GTFP calculation, and then a time-varying difference-in-difference (DID) setup is employed for empirical analysis, as well as its combination with propensity score matching method (PSM-DID) to overcome the sample self-selection bias. We find that EI significantly promotes firm's GTFP by about 0.22 units, which is mainly driven by the improvement of technical efficiency rather than technological progress. Further analysis exploring channels suggests that EI promotes GTFP by alleviating financing constraints and stimulating green innovation. However, the heterogeneity analysis shows that both financing constraint mechanism and technological innovation mechanism are mainly observed in heavily polluting and non-state-owned enterprises (non-SOEs). Our findings provide a new insight for policy-makers that utilizing environmental insurance can promote green productivity, but which should be focused more on those enterprises who are difficult to absorb external financing and technological innovation resources. • The relationship between EI and GTFP in the context of China was investigated for the first time. • The promoting effect of EI on GTFP is mainly reflected in the improvement of technical efficiency. • EI promotes GTFP by alleviating financing constraints and simulating technological innovation. • Both financing constraints and technological innovation mechanisms exist in non-SOEs and heavily polluting enterprises. [ABSTRACT FROM AUTHOR]

The question of whether environmental regulation fosters technological innovation and green development, as a nuanced extension of the Porter hypothesis, constitutes a focal point in contemporary research. Despite this attention, the literature often omits a multifaceted evaluation framework for green development and fails to consider multiaspectual environmental regulation and technological innovation. This study develops a comprehensive model of green total factor productivity (GTFP), situating the Chinese economy within an economy–environment–health nexus. The extended Crépon–Dugeut–Mairesse model is employed to revisit the "strong", "weak", and "narrow" Porter hypotheses. The analysis reveals that formal environmental regulation exerts a crowding-out effect on research and development (R&D), whereas informal environmental regulation exhibits a facilitating effect, corroborating the narrow version of the Porter hypothesis. Both categories of regulation contribute to substantial innovation. Following the incorporation of R&D factors, heterogeneity in the "weak" Porter hypothesis emerges in the Chinese context, contingent upon specific types of environmental regulation and technological innovation. Environmental regulation positively influences GTFP, affirming the "strong" Porter hypothesis, primarily through the vector of technical progress change. A developmental trajectory to enhance GTFP is thus articulated: judicious environmental regulation leads to R&D, which in turn fosters innovation quality, subsequently affecting the technical progress change index and ultimately GTFP. Correspondingly, policy recommendations are delineated across three dimensions: judicious environmental regulation, targeted innovation support, and regional coordination. [Display omitted] • A GTFP evaluation framework within the economy–environment–health is constructed. • The "strong", "weak" and "narrow" Porter hypotheses are retested by the CDM model. • Multidimensional regulation, innovation, and productivity are investigated. • There are innovation-driven interactions and regional heterogeneity. • Reasonable regulation–R&D–innovation quality–GTC–GTFP is a key development path. [ABSTRACT FROM AUTHOR]