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China is currently confronted with the dilemma of achieving its green development goals while maintaining economic growth. The National Ecological Demonstration Zones (NEDZs) represent an innovative attempt by local governments to balance economic development with ecological civilisation, potentially offering a solution to this issue. This study calculated the Green Total Factor Productivity (GTFP) for 1925 districts and counties from 1999 to 2018. Using the selection of NEDZs as a quasi-natural experiment, a difference-in-differences model was employed to empirically analyse the impact of NEDZs on GTFP. The results indicated that the establishment of NEDZs led to an average increase in GTFP of 0.2175 compared to the control group. The primary mechanisms involved are innovation, structure, and enforcement. Moreover, the green development effects of the NEDZs exhibited regional heterogeneity. Compared to the western regions, the central and eastern regions showed limited green development effects. Areas with smaller populations, lower human capital, and lower administrative levels demonstrated significant improvements after the implementation of the NEDZ. Further analysis reveals a significant spatial agglomeration pattern of GTFP and the NEDZs exert a strong 'siphon effect' on the GTFP of neighbouring areas. This study provides a new perspective on the GTFP research and offers theoretical and practical evidence for assessing the impact of NEDZs. • The green total factor productivity (GTFP) of 1925 counties are calcuated. • The construction of NEDZs significantly enhances GTFP. • The green development effect of NEDZs exhibits heterogeneity in different types of regions. • Innovation effect, structural effect, and enforcement effect are the main mechanisms. • The NEDZs can reduce GTFP in nearby regions. [ABSTRACT FROM AUTHOR]

The traditional data envelopment analysis (DEA) method relies solely on current and historical performance and is unable to predict future efficiency. To overcome this limitation, we propose a novel prediction model called the input–output memory network (IOMN), which extends the Recurrent Neural Network (e.g., Long Short-Term Memory (LSTM) and Gate Recurrent Unit (GRU) to forecast the input and output variables in future periods. We then combined the IOMN with the DEA-Malmquist index to evaluate the efficiency of Decision-Making Units (DMUs) from the past to the future. To demonstrate the effectiveness of our proposed model, we conducted a case study of 15 research institutions in the Chinese Academy of Sciences (CAS) and compared its performance with those of linear regression (LR) and support vector regression (SVR) methods. The comparative results show that the IOMN significantly outperformed both the LR and SVR methods. Finally, we fed the past and predicted future data into the DEA-Malmquist index to examine the productivity efficiency trends in research institutions over time. Our results indicate that the annual mean of total factor productivity (TFP) continued to fluctuate at approximately 1 during the observation period. In the future, the geometric mean of TFP will decline slightly by 2.3%, the geometric mean of efficiency change (EC) will decline by 5%, and the geometric mean of technical change (TC) will increase by 2.9%. [ABSTRACT FROM AUTHOR]

The optimal design of environmental instruments demands a balance between environmental enhancement and economic growth. Utilizing microdata from the China Environmental Statistics Database and the China Industrial Firm Database, this study employs the difference-in-differences (DD) methodology to explore the dual effects of the SO₂ Emissions Trading Scheme (ETS) on the environmental and economic performance of micro-firms. The findings suggest that: (1) The SO₂ ETS not only induces emission reduction effects among firms in pilot areas but also improves their industrial added value. (2) The SO₂ ETS exhibits heterogeneous impacts across firms of diverse ownership, export status, and size. (3) While the SO₂ ETS prompts firms to advance technologically, boosting desulfurization capacities and subsequently enhancing total factor productivity, it also inadvertently results in companies offsetting some environmental compliance costs by curtailing employee wages. • Reveals the dual impacts of the SO₂ ETS on both environmental and economic performances of micro-firms in China. • Indicates a potential trade-off where firms may reduce employee wages to offset environmental compliance expenditures. • Emphasizes the differential effects of the SO₂ ETS on diverse micro-firms' environmental and economic outcomes. [ABSTRACT FROM AUTHOR]

Energy quota trading is critical to both energy conservation and the achievement of dual-carbon goals. However, little remains to be done on total factor productivity and economic potential for Chinese industry. The purpose of this paper is to counterfactually model and explore the effects of energy quota trading on total factor productivity and economic potential in industrial sector. We propose a command-controlled DEA model and an energy quota trading DEA model through non-parametric linear programming based on two policy strategies of energy consumption control. We then assess total factor productivity and its growth sources for 35 two-digit industrial sector in China, as well as examine economic potential and optimal path forecasts for carbon peaking. The results show that total factor productivity and economic potential for energy quota trading are higher than those for command-controlled energy policy. Energy-saving effect can be achieved for energy quota trading because the total energy input is lower. Additionally, the sources of productivity growth differ in timing. The average economic potential and total factor productivity of different industries differ due to the heterogeneity of the production conditions and scale of development of individual market players. Moreover, the catch-up emission reduction path with an increase of 8% in output and 4.76% in energy consumption is the optimal carbon peaking path under energy quota trading. Our findings shed valuable light on China's efforts to expand energy quota trading for high-quality development in the context of carbon peaking. • Propose a command-controlled DEA model and an energy quota trading DEA model. • Assess TFP and its growth sources for industrial sector in China. • Compare the impact of different energy policies on economic potential and TFP. • Estimate the energy saving and emission reduction potential of energy quota trading. • Examine the optimal energy-saving and emission-reduction path forecasts for carbon peaking.. [ABSTRACT FROM AUTHOR]

Microalgae are photosynthetic microorganisms playing a pivotal role in primary production in aquatic ecosystems, sustaining the entry of carbon in the biosphere. Microalgae have also been recognized as sustainable source of biomass to complement crops. For this objective they are cultivated in photobioreactors or ponds at high cell density to maximize biomass productivity and lower the cost of downstream processes. Photosynthesis depends on light availability, that is often not constant over time. In nature, sunlight fluctuates over diurnal cycles and weather conditions. In high-density microalgae cultures of photobioreactors outdoors, on top of natural variations, microalgae are subjected to further complexity in light exposure. Because of the high-density cells experience self-shading effects that heavily limit light availability in most of the mass culture volume. This limitation strongly affects biomass productivity of industrial microalgae cultivation plants with important implications on economic feasibility. Understanding how photosynthesis responds to cell density is informative to assess functionality in the inhomogeneous light environment of industrial photobioreactors. In this work we exploited a high-sensitivity Clark electrode to measure microalgae photosynthesis and compare cultures with different densities, using Nannochloropsis as model organism. We observed that cell density has a substantial impact on photosynthetic activity, and demonstrated the reduction of the cell's light-absorption capacity by genetic modification is a valuable strategy to increase photosynthetic functionality on a chlorophyll-basis of dense microalgae cultures. • Microalgae biomass is a promising alternative to crops. • The impact of cultivation at scale on photosynthesis is still under-investigated. • The Photosynthesis-Irradiance (PI) relationship is informative. • High-sensitivity oxygen measurements for PI investigation was validated. • The effect of cell density on PI was studied in Nannochloropsis pale mutants. [ABSTRACT FROM AUTHOR]

The low-carbon transformation is the key to China's high-quality economic development. This paper uses the panel data of 278 Chinese cities to measure low-carbon total factor productivity (LCTFP) and examines the impact of environmental protection tax (EPT) reform on LCTFP. EPT significantly promotes LCTFP. The promotion effect is more significant in the lower administrative level and resource-based cities in central and western regions. EPT promotes LCTFP through three channels including green technology innovation (GTI) effect, industrial structure upgrading effect and forcing effect. EPT has a negative spatial spillover effect on LCTFP in neighboring areas. This paper provides important empirical evidence for further advancing the EPT in China. • EPT significantly promotes LCTFP. • The promotion effect is more significant in low administrative level and resource-based cities in central-western region. • EPT promotes LCTFP through green technology innovation, industrial structure upgrading and forcing effect. • EPT has a negative spatial spillover effect on LCTFP in neighboring areas. [ABSTRACT FROM AUTHOR]

Green technology innovation has gained incessant recognition due to rising concerns about environmental sustainability. It has the potential to improve energy efficiency by increasing total factor carbon productivity through mitigating effects. This research probes the asymmetric nexus between green technology innovation and energy efficiency in the top ten green innovator economies (Switzerland, Sweden, Germany, USA, Denmark, Finland, UK, Netherlands, Japan, and Norway). Prior investigations applied panel data approaches to acquire persistent outcomes on the green technology innovation-energy efficiency nexus, even though some countries did not individually demonstrate such a link. Comparatively, this study employs a distinctive 'Quantile-on-Quantile' technique that enables investigators to examine time-series dependence in each country by furnishing an international yet economy-specified understanding of the conjunction between the variables. Estimations disclose that green technology innovation improves energy efficiency in the majority of our selected economies at particular quantiles. It is also demonstrated that the asymmetries between our variables fluctuate among countries, highlighting the need for special care when adopting green innovation, energy efficiency, and sustainable environment policies. • The study analyzes the asymmetric green innovation-energy efficiency nexus. • A novel methodology, 'Quantile-on-Quantile (QQ)', is used. • The data for top-10 green innovator countries is used. • It is found that green innovations improve energy efficiency. [ABSTRACT FROM AUTHOR]

This study aims to examine digital technology adoption and globalization innovation implications on green sustainable economic growth on Asian Pacific selected countries (Malaysia, Indonesia, Singapore, Philippines, Thailand, Japan, India, Korea, China, New Zealand, Australia) via integrating digital technology adoption (digitalization and digitization), globalization, and environmental quality. This study employed the Hausman test to determine the appropriate estimation approach between random effect and fixed effect methods due to the digital adoption index (DAI) data limitation. The Hausman test confirms the random effect method for this analysis and the findings reveal that digital technology adoption promotes economic growth in Asian Pacific countries. Globalization was found an insignificant positive influence on Pacific countries' economic growth. The average growth rate of green total factor productivity (GTFP) as a Technology progress of innovation and environmental integration is positive, this might potentially be described by the high contribution of digital technology adoption and from the beneficial effects of labor, capital, CO2, and globalization positives contribution. The significant contribution of this study is filling the gaps via promoting green sustainable economic growth through inclusion of digital technology adoption and globalization in the growth accounting model that ignored. Technology adoption and globalization internalization plays a remarkable role to enhance innovations and knowledge and technology transfer, enabling companies to access wide-ranging markets, encouraging foreign direct investment, collaboration and smart partnerships along with conveying knowledge within and among the nations in technological creativity, globalization-based technology transfer, and intellectual property development to implement Sustainable Development Goals (SDGs). [ABSTRACT FROM AUTHOR]

The benefits of research and development (R&D) investment extend beyond the undertaking firms and may impact the wider economy through spillovers. This study examines the effect of Multinational Companies (MNCs)' R&D intra-industrial spillovers on total factor productivity (TFP) using firm-level data from Indonesia between 2017 and 2019. To address the cross-sectional dependence, we employed a fixed-effects estimator with Driscoll and Kraay's (1998) standard errors. We also incorporate a cost-based approach with a weighting matrix that integrates human resources into R&D activities to capture the R&D intra-industrial spillover from foreign companies. The findings indicate that intra-industrial R&D spillovers from MNCs negatively impact TFP, suggesting product-market rivalry in the domestic market. Foreign and domestic firms operating in a subsector that relies on similar technologies compete to enhance productivity, resulting in business theft. Increasing firms' capability in human resource absorption, access to foreign inputs, firm size, and market concentration can improve TFP in manufacturing firms. This finding has policy implications for the proportions of R&D spending. The results also support the need to foster open innovation by creating an environment that encourages collaboration, skill development, and local innovation. [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]

• Material and energy resources as driver of carbon emission in the Nordics. • Environmental Kuznets curve (EKC) is valid in the region. • Material productivity mitigates carbon emission in the Nordic region. • Total factor productivity hampers carbon emission in the region. • Renewable energy resources promote environmental quality. The recent report of the European Commission (EC) indicates that the Nordic countries, especially Denmark, Finland, Iceland, Norway, and Sweden have some of the highest and above European Union (EU)'s average consumption per capita of domestic material. While reflecting on the environmental consequence of material uptake, this study examines the role of material productivity, total factor productivity, and renewable energy uptake in the region over the period 1970–2019. With the series of empirical approaches deployed for the panel investigation, the results establish that a gain in material productivity and total factor productivity mitigates carbon emission in the region by a statistically significant amount, thus indicating a positive sign toward green productivity/output and environmental sustainability. This evidence is also complimented by the validity of the environmental Kuznets curve (EKC) i.e., inverted U-shaped nexus between economic output and environmental degradation in the investigation. Moreover, the results reveal that uptake in renewable energy usage also improves environmental quality arising from the energy source's capacity to reduce carbon emission. The findings in this investigation offers relevant policy reference toward improving material and resources efficiency. [ABSTRACT FROM AUTHOR]

I show how the exemption of small-scale emitting firms from emissions pricing results in within-country emissions leakage — an emissions price increase for the regulated firms prompts an increase in the emissions of the unregulated. I use a heterogeneous firm model in which a fixed share of firms is subject to emissions pricing. The firms at the lower part of the productivity distribution benefit from being exempted, such that the higher the emissions price, the more and dirtier firms can survive in the domestic market. Leakage is stronger if firms are exempted only if they emit less than a fixed threshold (as for the EU Emission Trading System) because some firms strategically bunch below the threshold, making the emissions price an even weaker tool to reduce total emissions. In environments with low social costs of emission or high fixed regulatory costs, an exemption may be justified; over time, however, the criteria for exemptions should be adjusted accordingly. • Exempting firms reduces the effectiveness of emissions pricing. • A higher emissions price allows dirtier firms to survive and, additionally, • it increases the market share of exempted firms: total non-regulated emissions rise. • This effect is called within-country leakage in my model. • An exemption can still maximise welfare when regulatory costs crowd out production. • For ambitious emission reduction goals, exemption criteria should be revised over time. • Changes in the social costs of emissions or regulatory costs also require revisions. [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]

While research on carbon productivity is growing rapidly, the role of structural change in green transition remains unexplored due to the scarcity of firm-level emission data. This study addresses this gap by utilizing unique register-based greenhouse gas emission data from Finland's energy-intensive manufacturing firms for 2000–2019. Applying a structural change productivity decomposition, we break down the sector's carbon productivity and green total factor productivity into four components: contributions from non-switching continuing firms, industry-switching continuing firms, the effects of entry and exit, and resource allocation. The findings highlight the important role of structural change in the sector's productivity. Non-switching continuing firms emerged as the key drivers of both carbon and green total factor productivity growth. The contribution of entry and exit was negative during the financial crisis, while inefficient resource allocation significantly hindered productivity growth throughout the study period. These findings emphasize the importance of public subsidies targeted at environmentally efficient firms to enhance their competitiveness under challenging market conditions. Furthermore, the establishment of a stable yet positive carbon price would incentivize less-productive firms to adopt environmentally friendly technologies. • The study uses unique register-based firm-level greenhouse gas emission data. • Green productivity is decomposed into components using structural change decomposition. • Structural change plays a substantial role in the sector's green productivity. • Non-switching continuing firms drive carbon and green total factor productivity growth. • Efficient resource allocation can accelerate green productivity. [ABSTRACT FROM AUTHOR]

Rapid developments in environmental infrastructure have contributed to significant improvements in green total factor productivity, but further investigation is required to provide a detailed assessment to understand the policy mechanisms involved. This paper analyzes environmental progress in China through MMQR, CCEMG, and AMG as empirical strategies for 30 provinces in China. Our empirical results reveal that energy optimization through renewable energy is the most effective channel to improve green total factor productivity, though it is not the only available option. Since environmental regulations, infrastructure development, and green technology innovation also directly impact energy efficiency, adopting these within policy channels will positively impact environmental sustainability. Our empirical approach helps suggest novel environmental policy suggestions. In particular, policymakers must introduce structural changes within energy developments to foster renewable energy. Furthermore, China must increase environmental spending to upgrade its energy infrastructure further and solve ecological issues. These insights offer valuable policy guidance for decision-makers in China and globally, aiming to foster economic and environmental sustainability and achieve zero-carbon transition goals. • The current study analyses the role of green factor productivity in zero carbon transition. • We use 30 chinese provincial datasets from 2005 to 2020. • Energy transition is the most effective channel to improve green total factor productivity. [ABSTRACT FROM AUTHOR]

Motivated by the emerging prospects regarding the integration of artificial intelligence (AI) and cleaner production, we specifically examine the role of AI in affecting enterprises' environmental performance. Based on a theoretical model and firm-level empirical evidence from China, we reveal that: (1) AI significantly reduces pollution emission intensity and enhances the environmental performance of Chinese enterprises; (2) The key mechanisms driving the effects are the improvement of total factor productivity, the increase in fixed investments dedicated to pollution reduction, and the optimization of factor input structure; (3) These effects are more pronounced in enterprises with stronger environmental restriction, higher factor intensity, and lower financing constraints. As enterprises strive to adopt more sustainable practices, our findings not only showcase how AI becomes a linchpin for achieving cleaner production goals while also serving as a foundation for informed policymaking in the pursuit of environmental responsibility. [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]

Osmotic stress in the yeast limits productivity in industrial beer production under very high gravity brewing. This study focused on assessing the protective impacts of eleven plant-derived antioxidant dipeptides (PADs) on the osmotic stress tolerance of lager yeast. The results showed that PADs provided yeast with stress tolerance under osmotic stress. PADs supplementation enhanced cell membrane integrity and reduced oxidative damage. PADs upregulated the expression of SOD2 , PEX11 and CTT1 genes under osmotic stress. Moreover, the volatile compounds contents and antioxidant activities of beers were improved by PADs, suggesting favorable quality characteristics. Especially, Phe-Cys and Leu-His could increase the DPPH radical scavenging activity of beer by 41.92% and 18.78% respectively, compared with control. Therefore, PADs are industrially scalable enhancers to improve the ability of yeast to resist osmotic stress and beer quality during very high gravity brewing. [Display omitted] • PADs enhanced the osmotic stress tolerance of lager yeast. • PADs improved yeast cell membrane integrity and reduced oxidative damage. • PADs supplementation improved beer quality during very high gravity brewing. [ABSTRACT FROM AUTHOR]

Existing research suggests that the application of artificial intelligence (AI) technology in industrial fields can promote the development of a green economy. However, for most developing countries, due to imperfect technological capabilities, human capital constraints and other factors, the impact of industrial intelligence on the green economy still needs to be verified. Based on city panel data from China, we find that industrial intelligence has no significant impact on green total factor productivity (GTFP). This conclusion is still valid after GMM estimation, instrumental variable (IV) estimation and a series of robustness tests. After incorporating the environmental regulation into the analysis framework, the impact of industrial intelligence on GTFP is found to be regulated by the intensity of the environmental regulation; that is, industrial intelligence can improve GTFP only in cities with strong environmental regulation. Further research shows that industrial intelligence can optimize and upgrade the industrial structure, given strict environmental regulations, thereby improving city GTFP. We confirm that the institutional premise of environmental regulation in developing countries is indispensable for industrial intelligence to promote the green economy, which has important theoretical and practical significance. [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]

Amid global climate imperatives and intensified economic competition, pivoting from China's conventional growth paradigms to innovative economic catalysts emerges as pivotal for its transformative agenda. Drawing on panel data from 141 principal urban conglomerates spanning 2011–2021, this investigation delves into the intricate nexus between the digital economy and carbon total factor productivity. Our empirical analysis unveils a U-shaped trajectory characterizing the digital economy - carbon total factor productivity interplay, accompanied by a congruent spatial spillover dynamic. While digital economy fortifies environmental governance mechanisms through amplified data and media channels, such regulatory frameworks, albeit efficacious in emission abatement, may inadvertently impede economic vitality, thus attenuating carbon total factor productivity. Progressing from digital economy's foundational phase to its comprehensive deployment, its reverberations on capital productivity manifest in a U-shaped curve, invigorating local carbon total factor productivity while potentially undermining adjacent regions. This digital economy - carbon total factor productivity interrelation is accentuated in advanced, non-resource-reliant metropolises with subdued innovation propensities. This discourse proffers nuanced policy implications for sculpting digital economy trajectories and bolstering carbon total factor productivity in a sustainable context. [Display omitted] • The digital economy demonstrates a U-shaped spatial spillover impact on carbon total factor productivity. • The digital economy influences carbon total factor productivity through governmental environmental regulations. • The digital economy influences carbon total factor productivity through capital productivity. [ABSTRACT FROM AUTHOR]

Titanium aluminides have been identified as potential materials in the aeronautical sector. However, the high processing costs and their low machinability limit their application. This research evaluates the effectiveness of minimum quantity lubrication technique (MQL) and cryogenic lubrication on the turning process of Ti48Al2Cr2Nb aluminide from an economic and industrial productivity point of view, with the aim of determining the optimum cutting conditions to reduce manufacturing costs and maximize the production rate. To this end, a preliminary experimental analysis will be carried out, determining the tool life as a function of the cutting speed for each lubrication condition (dry, MQL and LN2). The results show that, although both MQL and LN2 conditions allow the tool wear to be reduced, the MQL technique stands out for its higher efficiency and for the possibility of reaching a higher cutting speed. In addition, the high cost of liquid nitrogen implies a 47% additional cost. Consequently, this research finds out that the turning process should be carried out in the cutting speed range of 70–76 m/min under MQL conditions to obtain the highest profit rate. [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]

With China's proposed green development strategy, environmental protection investment needs are growing exponentially, and government funding alone is inadequate to meet the massive demand. To encourage social investment in green industries and achieve green development goals, the authorities implemented the major green finance policy entitled the Guidance on Building a Green Financial System (GBGFS) in late 2016. Whether this policy effectively promotes the development of environmentally friendly (EF) firms is an essential under investigated question. This study employs a difference-in-differences approach to explore whether and how the GBGFS affects EF firms' total factor productivity (TFP) with a sample from 2012 to 2021. Our benchmark regression and robustness tests demonstrate that the GBGFS effectively promotes EF firms' TFP. We also obtain three notable research findings with a number of additional analyses. (a) Although there is a certain lag, the policy effect was obviously enhanced with the passage of time. (b) Our threshold effect analyses reveal that the enhancing effect of the GBGFS on EF firms' TFP only becomes significant when research and development (R&D) investment, profitability (ROA), and government subsidies (GSF) exceed certain values. Further analysis demonstrates that firms with higher ROA and more GSF can effectively increase R&D. (c) Finally, GBGFS implementation has a greater impact on firm TFP in China's more developed region than its less developed regions. The findings suggest that regulatory authorities should persist in enforcing the GBGFS policy and encourage corporate R&D investment. In addition, the central government should design targeted policies based on the characteristics of different regions to improve the policy effect. • The Chinese authorities implemented the GBGFS on August 31, 2016. • The GBGFS effectively promotes EF firms' TFP (this result is robust). • The policy effect gradually expanded as the GBGFS was enforced over time. • The policy effect becomes significant when R&D, ROA and GSF exceed certain values. • The GBGFS has a greater impact in more developed region than less developed regions. [ABSTRACT FROM AUTHOR]

Traditional finance exhibits several limitations in its capacity to drive the advancement of the green economy. In contrast, emerging Digital Inclusive Finance (DIF) uses digitization as a tool to address these shortcomings inherent in traditional financial systems. This research employs panel data encompassing 275 cities in China over the period from 2011 to 2021 to examine the relationship between DIF and Urban Water Efficiency (UWE). The findings of this study demonstrate a compelling positive association between DIF and the enhancement of UWE, intensified by significant spatial spillover effects. These results remain robust across a range of tests. It's worth noting that DIF primarily boosts pure technical efficiency, rather than scale efficiency, which are two important aspects of UWE. Delving deeper into the mechanisms that clarify how DIF reinforces green innovation and enhances the green total factor productivity, thereby contributing to UWE. Further exploration indicates that DIF has a more significant impact on UWE in cities with populations exceeding 5 million, no water resource tax, and strict environmental regulation. The combined efforts of financial and environmental regulation by the Chinese government play a key role in strengthening the positive relationship between DIF and UWE. Consequently, this research provides stronger evidence that financial development enhances environmental efficiency and offers new insights for urban water resource management. [Display omitted] • Digital inclusive finance has a significant positive effect on urban water efficiency. • Digital inclusive finance boosts green innovation, enhancing urban water efficiency. • Digital inclusive finance impact on urban water efficiency shows spatial spillover. • Heterogeneity analysis based on population, water resource tax, and environmental regulation factors. [ABSTRACT FROM AUTHOR]

This study employs stochastic frontier production function approach to measure the spatial differences and temporal variations of urban construction land allocation efficiency (UCLAE) and its decomposed indicators in the eastern, central, and western urban agglomerations of China. The collection of construction land price data and the application of the Kumbhakar total factor productivity decomposition method in this study address the research gap in the field of urban land efficiency, specifically the inadequacy of studying the efficiency of construction land allocation. The findings reveal that the UCLAE in the central urban agglomerations demonstrates a stable or upward trend, while the UCLAE in the western and eastern urban agglomerations shows a significant decline, albeit with a decreasing trend that has been curbed. The evolution of urban construction land allocation and its efficiency in different urban agglomerations in China supports the theories of unbalanced growth and inverted U-shaped development. To enhance the UCLAE in Chinese urban agglomerations, it is necessary to address the issue of imbalanced land allocation. This requires not only focusing on the construction land allocation in each urban agglomeration but also considering workforce allocation in the western region and capital allocation in the eastern region. Furthermore, it is crucial to synergistically improve the resource allocative efficiency and technical efficiency of Chinese urban agglomerations. [ABSTRACT FROM AUTHOR]

Ensuring the coordinated development of carbon emissions and green economy has important implications for China's sustainable development. For examining the coordinated development between carbon emissions and green economy in China and its influencing factors, this study assessed the coupling coordination degree (CCD) between carbon emission intensity (CEI) and green total factor productivity (GTFP) using the coupling coordination degree model (CCDM), revealed the spatiotemporal evolution law of CCD using a spatial correlation model and empirically examined their influencing factors using the spatial Durbin model (SDM) to test influencing factors. The following findings were made: (1) The green economy in China synchronously developed with carbon emission reductions from 2000 to 2020. (2) CCD has global spatial concentration effects, and the objects are divided into three categories: cold spots, hot spots, and random distributions. (3) Government scale expansion, transportation volume increase, industrial agglomeration, and urbanization are all not conducive to CCD, but population density and industrial structure upgrading have opposite effects. To promote the coordinated development of carbon emissions and green economy, we can improve the efficiency of energy utilization and reduce carbon emissions, by using clean energy, adopting clean production technology, increasing non-productive public investment in environmental protection, industrial agglomeration in reasonable space, and improving the moderate population agglomeration. Our findings offer valuable insight for understanding the relationship between carbon emission reduction and green economic development. They will allow authorities to take specific actions to ensure the coordinated development of CEI and GTFP. [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]