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Digital finance is a product of emerging technology-enabled innovation in financial services and has a critical impact on the green transformation of the manufacturing industry. We propose a new efficiency measurement model based on the slacks-based measure (SBM) to measure the efficiency of green transformation on regional manufacturing. Chinese interprovincial data from 2010 to 2019 were obtained for the study. In addition, we estimated the effect of digital finance on green transformation of manufacturing using a benchmark panel model. Finally, considering the regional heterogeneity and spatial effects of green transformation efficiency in the manufacturing industry, we constructed a spatial Durbin model based on an economic-geographic nested spatial weight matrix to analyze the spatial influence of digital finance on green transformation in the manufacturing industry. The results show that the green transformation of the manufacturing industry has significant positive spatial spillover effects owing to the existence of competition, demonstration, and economic correlation effects among regions.

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In the context of accelerated development of the digital economy, whether enterprises can drive green total factor productivity (GTFP) through digital technology has become the key to promoting high-quality development of the economy and achieving the goal of "dual-carbon", However, the relationship between digital transformation and GTFP is still controversial in existing studies. Based on the data of 150 listed companies in China's A-share energy industry from 2011 to 2021, this study empirically analyzes the impact of digital transformation on GTFP using a fixed-effect model. The study shows an inverted U-shaped nonlinear effect of digital transformation on enterprises' GTFP, and the conclusion still holds after a series of robustness tests. Mechanism analysis shows that enterprise investment efficiency and labour allocation efficiency play a significant mediating role in the above inverted U-shaped relationship, in which the inverted U-shaped relationship between digital transformation and GTFP mainly stems from the influence of enterprise investment efficiency. Heterogeneity analysis finds that the inverted U-shaped relationship between digital transformation and GTFP of enterprises is more significant in large-scale enterprises, new energy enterprises and enterprises in central and western regions. The study's findings provide important insights for enterprises to promote digital transformation and realize the green and high-quality development of the energy industry.

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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.

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Introduction: The study proposes a method based on difference-in-differences (DID) to improve the resource allocation efficiency of medical and health financial expenditure to better guarantee the health level of enterprise employees. The DEA method is utilized to measure the comprehensive technology, pure technology, and scale as the resource allocation efficiency values of urban medical and health financial expenditure. Methods: The proposed method includes the use of DEA to measure the resource allocation efficiency values of urban medical and health financial expenditure. The benchmark regression model and DID model are used to analyze the impact effect, robustness, and parallel trend of the policy. Results: The study shows that the proposed method effectively evaluates and analyzes the impact of medical comprehensive reform on the resource allocation efficiency of urban medical and health financial expenditure. The comprehensive medical reform can improve the comprehensive efficiency and scale efficiency of urban medical and health financial expenditure, leading to improved resource allocation efficiency of urban employees' medical and health financial expenditure. The results also indicate a significant positive effect on the time trend, which can have a long-term impact and effectiveness. Discussion: The proposed method can provide useful insights into the resource allocation efficiency of medical and health financial expenditure, which can help improve the health level of enterprise employees. The study suggests that comprehensive medical reform can be an effective way to improve resource allocation efficiency and guarantee the health of employees in urban areas. Further research can be conducted to evaluate the impact of medical reform on other aspects of health care, such as quality and accessibility.

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New energy strategies are crucial to address energy and environmental issues, but the energy consumption transition may also affect firm behavior with unintended economic consequences. Using data from A-share listed companies from 2010 to 2019, this paper investigates the impact of energy consumption structure transformation on firms' total factor productivity (TFP) using China's new energy demonstration city (NEDC) policy as a shock. It is found that the NEDC reduces firms' TFP by about 6.4%. This conclusion still holds after a series of robustness and endogeneity tests. According to the channel analysis, NEDC reduces the efficiency of firms' resource allocation and innovation, resulting in efficiency losses. Furthermore, differences in firms' ownership and geographical location make the impact of NEDC on TFP heterogeneous. For example, the hindering effect of NEDC on TFP is more pronounced in private firms and firms in regions with lower marketization. This paper shows that the promotion and application of new energy may have certain economic costs. To better balance the benefits and costs of new energy strategies, the government and other relevant departments should increase policy flexibility and perfection.

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The effectiveness of a health care system is an important factor for improving people's health and quality of life. The purpose of this research is to analyze the efficiency and spatial spillover effects of provincial health systems in China using panel data from 2009 to 2020. We employ the two-stage network DEA model to evaluate their efficiencies and use a spatial econometric model for empirical estimation. The results suggest that the overall efficiency, resource allocation efficiency, and service operation efficiency of health systems in different regions of China generally have fluctuating upward trends, with large differences in efficiency among the various regions. Further analysis reveals that the efficiency of China's health system has a significant spatial spillover effect. The level of economic development, fiscal decentralization and old-age dependency ratio are important factors affecting the health system efficiency. Our findings help to identify the efficiency and internal operating mechanisms of China's health system at different stages, and are expected to contribute to policymakers' efforts to build a high-quality health service system.

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BACKGROUND: In disease mapping, fine-resolution spatial health data are routinely aggregated for various reasons, for example to protect privacy. Usually, such aggregation occurs only once, resulting in 'single-aggregation disease maps' whose representation of the underlying data depends on the chosen set of aggregation units. This dependence is described by the modifiable areal unit problem (MAUP). Despite an extensive literature, in practice, the MAUP is rarely acknowledged, including in disease mapping. Further, despite single-aggregation disease maps being widely relied upon to guide distribution of healthcare resources, potential inefficiencies arising due to the impact of the MAUP on such maps have not previously been investigated. RESULTS: We introduce the overlay aggregation method (OAM) for disease mapping. This method avoids dependence on any single set of aggregate-level mapping units through incorporating information from many different sets. We characterise OAM as a novel smoothing technique and show how its use results in potentially dramatic improvements in resource allocation efficiency over single-aggregation maps. We demonstrate these findings in a simulation context and through applying OAM to a real-world dataset: ischaemic stroke hospital admissions in Perth, Western Australia, in 2016. CONCLUSIONS: The ongoing, widespread lack of acknowledgement of the MAUP in disease mapping suggests that unawareness of its impact is extensive or that impact is underestimated. Routine implementation of OAM can help avoid resource allocation inefficiencies associated with this phenomenon. Our findings have immediate worldwide implications wherever single-aggregation disease maps are used to guide health policy planning and service delivery.

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In China, patients with chronic diseases have complete freedom to choose the medical institutions at which they are treated, which has resulted in wasted medical resources and increased medical expenses. The purpose of this study is to determine the effective mechanisms to incentivise patients with chronic diseases to obtain referrals from community health centres to tertiary hospitals and estimate the funds that could be saved using various mechanisms. Questionnaire research, expert consultations, and data simulation were applied. We surveyed 1824 outpatients at nine tertiary hospitals in Shanghai, and the results showed that the proportion of patients willing to obtain referrals was 48.4%. By increasing the registration fee, reducing the payment ratio of medical insurance, publicising, and rating the quality of community health centres, up to 51.3%, 50.6%, and 65.41% patients with chronic diseases indicated that they would obtain referrals, respectively. According to the 2015 Shanghai outpatient database, the funding that could be saved through these three mechanisms would be 361.67, 356.73, and 461.14 million yuan, respectively. We conclude that referral of patients with chronic diseases could reduce medical expenses and save medical insurance funds. Nevertheless, no single measure can effectively change patient habits. Comprehensive measures need to be applied to guide patient referral actively.

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Objective:To investigate the function of the quality factor in the growth of the total heath expenditure, in order to provide references for making the object of the health policy. Methods: Through the decomposition the identical equation of the growth of the total expenditure on health, to analyze the contribution of medical service quality and its factor. Results: The sustaining improvement of medical service quality in the important factor on the constant growth of the total expenditure on health. Conclusion: The primary objective of health policy is to improve the quality of medical services, and the secondary is to control the expense.