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The role of digital technology in structural change of three major industries has been widely recognized, but its influence on the internal cleanliness change of the industry has not been confirmed. Based on panel data of 36 industrial sub-sectors in China from 2002 to 2020, this paper discusses the mechanism of digital technology driving the cleanliness of industrial structure from a heterogeneity perspective. The theoretical model indicates that variations in output growth across different industries can be attributed to total factor productivity (TFP) and factor distortions, and the heterogeneous contribution of digital technology to TFP and factor distortions leads to the change of industrial structure. The empirical results show that digital technology can not only promote innovation-driven development by increasing total factor productivity, but also help factor-driven development by reducing factor distortion. Heterogeneity tests show that digital technology contributes to the cleanliness of industrial structure, and polluting industries are mainly driven by factors, while cleaning industries are mainly driven by innovation. Therefore, it is necessary to correct the application direction of digital technology in time to better empower innovative development and clean production. [ABSTRACT FROM AUTHOR]

In the face of global economic development and increasing energy demands, the efficient allocation of Research and Development (R&D) resources emerges as a universal concern. This study illuminates the complex relationship between R&D factor mismatch, production losses, and changes in regional energy intensity. Utilizing factor mismatch accounting framework and the STIRPAT model, we systematically assess the impact of spatial mismatches in R&D factors on R&D output and regional energy intensity. The analysis unfolds in three steps, first, the estimation of the degree of spatial R&D factor mismatch and the resulting output gap; second, the calculation of R&D output loss as a function of spatial mismatch coefficient and output share per location; and third, the examination of changes in energy intensity induced by R&D factor mismatch in terms of Total Factor Productivity (TFP), factor mismatch, and output structure. Our findings reveal that R&D factor mismatches can hinder energy intensity reduction, and that regional disparities in the process of factor market marketization significantly contribute to variations in regional energy intensity. These insights emphasize the critical role of efficient R&D resource allocation in promoting energy conservation and productivity, providing substantial implications for policy-making in sustainable growth and energy efficiency across diverse economies. [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]

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]

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]