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Improving the logistics industry's green total factor productivity (LGTFP) is a key source of power to achieve its high-quality development (HQD) in China. Technological innovation (TI) and environmental regulation (ER) policies are vital factors that affect the improvement of LGTFP. Although the impact of ERs or TI on LGTFP has been extensively researched, few studies have examined their combined effect. This study utilizes panel data from 30 Chinese provinces during 2006-2020 to estimate LGTFP using the Epsilon-based measure and global Malmquist-Luenberger index. Moreover, this study employs a dynamic spatial Durbin model to explore the influence of TI on LGTFP and its spatial spillovers, as well as the modulatory effect of ER in the relations between TI and LGTFP. The results show that (1) China's LGTFP displays positive spatial spillovers and is influenced by the preceding period's level. (2) In the short term, an improvement in TI levels had a positive effect on local LGTFP, but negative spillovers on neighboring districts. Nevertheless, in the long run, an improvement in TI levels had a notably negative influence on local LGTFP but positive spillovers on adjacent regions. (3) ER had an "inverted U-shaped" modulatory roles with spatial spillovers on the relations between TI and LGTFP in the long-term. These discoveries offer valuable insights into the coordinated development of TI systems and ER policies, enabling the formation of a policy system that aligns with the logistics industry's HQD in China. [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]

This paper analyzes the impact of renewable energy consumption, total factor productivity (TFP), income, imports, and exports on consumption-based CO2 emissions in Turkey from 1990 to 2019, utilizing the Autoregressive Distributed Lagged (ARDL) method. The findings indicated that the use of renewable energy, total factor productivity and exports negatively influence CO2 emissions. On the other hand, the positive impact of income and imports on CO2 emissions was found. The negative impact of renewable energy consumption and total factor productivity highlights the importance of implementing environmentally friendly measures in the energy sector (particularly raising the proportion of renewable energy in overall energy consumption) and technological innovation. [ABSTRACT FROM AUTHOR]

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]

In the new normal period, the trend changes and adjustments of the environment such as international trade, production capacity, labor supply and resource constraints have put forward new requirements for China's industrial development, which have brought new challenges and given new opportunities. In the new normal stage where economic growth continues to decline, industrial growth is still an important support for economic growth. The advancement of industrial technology is the main driving force for improving the total factor productivity of the industrial industry. Therefore, the most important thing to promote industrial growth is to upgrade the level of industrial technology. In response to the above-mentioned problems, this paper analyzed the relationship between industrial technology and industrial output in the new normal environment by using the BP neural network (BPNN) algorithm. The connection between the two has been found, which provided a clear direction for the functional adjustment of economic law. Experimental studies have shown that there is a positive relationship between industrial technological progress and industrial output. When other conditions are the same, and when the non-new normal is selected, industrial output increases by about 0.36% for every 1% increase in industrial technological progress. When choosing to be in the new normal, industrial technological progress has a higher impact on industrial output. For every 1% increase in technological progress, industrial output increases by about 0.39%. [ABSTRACT FROM AUTHOR]

This paper aims to analyze the urban human settlements of 21 megacities and supercities from 2011 to 2020 in China and improve urban human settlements planning and management. First, the data envelopment analysis (DEA)–Malmquist–Tobit model is used to conduct a static and dynamic evaluation of the efficiency of urban human settlements and analyze the main factors that affect it. Then, the coupling coordination degree model is utilized to quantitatively evaluate the coupling coordination degree between urban human settlements and high-quality economic development. Finally, the GM (1, 1) model is established to predict each city's average input–output index during the 14th Five-Year Plan period. It is hoped that this paper could promote sustainable urban development. The empirical results are as follows: (1) the technical efficiency (TE) from CRS DEA of human settlements in China's megacities and supercities is low. Management and technology are the shortcomings in the development of urban human settlements; (2) total factor productivity (TFP) has shown negative growth. Technical progress efficiency (TC) is the key to improving the efficiency of urban human settlements; (3) urban infrastructure has the most significant role when promoting the efficiency of urban human settlements; (4) the coupling degree between the efficiency of human settlements and high-quality economic development is mainly high-level coupling. The degree of coordination is gradually rising; and (5) during the 14th Five-Year Plan period, the investment in human settlements in both types of cities will continue to increase. With the continuous expansion of investment, the output will be imbalanced. [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]

This research evaluates the effects of performance benchmarking for public utilities using the Australian experience, providing empirical support for the significance of mechanism design in implementing revenue-cap regulation. We estimate the efficiency and productivity of Australian electricity distribution providers by utilizing the meta-frontier framework with panel data covering the period from 2007 to 2021. Our findings indicate that introducing benchmarking has facilitated technological progress and reduced efficiency gaps among companies. However, there is no statistically significant difference in the average efficiency between the periods before and after the reform, potentially due to strategic behavior exhibited by network service providers. • The average efficiency levels of AustralianDNSP in period of before- and after- 2012 reform are 0.9230 and 0.9280 respectively. • The introduction of benchmarking technique in 2012 promoted the technological progress. • The strategical behaviors of network service providers has led to no statistically significant improvement in efficiency. • We divide Australian DNSPs′ inefficiency in the two sub-periods into cost gap inefficiency and management inefficiency. [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]