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IntroductionPollutants from farming and rural life have a serious negative impact on water and soil. The advantages of green finance in financial support and credit management can guide the green transformation of agricultural production, thus protecting the ecological environment.MethodsBased on panel data from 30 provinces in China from 2005 to 2021, the study used inventory analysis, entropy method and two-way fixed effect model to analyze the impact of green finance on agricultural non-point source pollution (ANSP) and the mediating mechanism.ResultsANSP has experienced an inverted U-shaped trend of first rising and then falling, and the rural ecological environment is improving. At the time level, the ANSP produced by agricultural production in China experienced an inverted U-shaped evolution trend of first increasing and then decreasing during the sample period. At the spatial level, ANSP has a significant low regional differentiation. The ANSP of the central region and the coastal economic development area is higher than that of the western region. The results of linear regression show that green finance has a negative effect on ANSP. Promoting green finance in rural areas can effectively guide the green transformation of agriculture and reduce the dependence of crop cultivation on fertilizers and pesticides. This result held even after the elimination of endogeneity and after various tests. Land transfer and environmental supervision are important intermediary mechanisms. Under the influence of these two economic variables, the role of green finance in pollution reduction can be fully brought into play. However, there is regional heterogeneity in the negative effects of green finance on ANSPs. Its effect on pollution reduction is stronger in major grain-producing areas and regions with well-developed digital financial infrastructure.DiscussionThis study confirms once again that green finance has a negative effect on ANSP, and the findings help to decouple the development of the agricultural economy from emissions. The outstanding contribution of this study is the discovery of the role of land transfer and government environmental regulation. I order to give full play to the role of green finance in pollution reduction, this study provides relevant policy measures. These measures are intended to improve the green finance system and business model in agriculture and rural areas.

Abstract Sustainable Development Goal 8 proposes the promotion of full and productive employment for all. Intelligent production factors, such as robots, the Internet of Things, and extensive data analysis, are reshaping the dynamics of labour supply and demand. In China, which is a developing country with a large population and labour force, analysing the impact of artificial intelligence technology on the labour market is of particular importance. Based on panel data from 30 provinces in China from 2006 to 2020, a two-way fixed-effect model and the two-stage least squares method are used to analyse the impact of AI on employment and to assess its heterogeneity. The introduction and installation of artificial intelligence technology as represented by industrial robots in Chinese enterprises has increased the number of jobs. The results of some mechanism studies show that the increase of labour productivity, the deepening of capital and the refinement of the division of labour that has been introduced into industrial enterprises through the introduction of robotics have successfully mitigated the damaging impact of the adoption of robot technology on employment. Rather than the traditional perceptions of robotics crowding out labour jobs, the overall impact on the labour market has exerted a promotional effect. The positive effect of artificial intelligence on employment exhibits an inevitable heterogeneity, and it serves to relatively improves the job share of women and workers in labour-intensive industries. Mechanism research has shown that virtual agglomeration, which evolved from traditional industrial agglomeration in the era of the digital economy, is an important channel for increasing employment. The findings of this study contribute to the understanding of the impact of modern digital technologies on the well-being of people in developing countries. To give full play to the positive role of artificial intelligence technology in employment, we should improve the social security system, accelerate the process of developing high-end domestic robots and deepen the reform of the education and training system.

Amidst rapidly evolving market conditions, relying solely on internal information and knowledge for innovation has proven inadequate. The emergence of the digital economy injected renewed vitality into enterprise innovation. However, existing studies have predominantly concentrated on the digital transformation intrinsic to enterprises, neglecting the impact of partners' digital transition on enterprises’ innovation performance. From the perspective of supply chain spillovers, this study evaluates the influence of digital transitions in upstream and downstream enterprises on the innovation activities of midstream enterprises. The results show that the digital transformation of enterprises within the supply chain exhibits a transmission effect, and the digital transformation of upstream and downstream enterprises significantly promotes the innovation performance of midstream enterprises. The mechanistic analysis revealed that the digital transformation of upstream and downstream enterprises mainly enhances the resilience of the supply chain by improving its efficiency, optimizing the match between supply and demand, and stabilizing the relationship between supply and demand, thus promoting the innovation level of midstream enterprises. Further analysis shows that improvements in the innovation output of midstream enterprises are reflected in the quality of innovation. When the upstream and downstream firms belong to the same industry, the supply chain spillover effect of digital transformation is more evident, and simultaneously, the digital collaboration of the supply chain can promote the innovation of midstream enterprises.

Introduction: Achieving peak carbon dioxide emissions and carbon neutrality is an extensive and profound systematic economic and social change. Through market-oriented financial means, green finance has moved forward the effective governance port, curbed polluting investment and promoted technological progress such as green low-carbon, energy conservation and environmental protection, which has become a powerful starting point to support the practice of low-carbon development.Methods: Based on the panel data of 30 provinces in China (except Tibet, Hongkong, Macau and Taiwan Province) from 2004 to 2021, this paper calculates the development level of green finance in China provinces by using entropy weight method, and on this basis, uses mathematical statistical model to verify the impact of green finance and its sub-dimensions on carbon emissions and the regulatory effect of heterogeneous environmental regulation tools.Results: The results show that the development of green finance has a significant inhibitory effect on carbon emissions during the investigation period, and there is a time lag effect. After a series of robustness tests and considering endogenous problems, this conclusion still holds. From the results of heterogeneity analysis, the carbon emission reduction effect of green credit is the most obvious, and the impact of green finance on carbon emission is slightly different in different regions. Besides, Command-controlled environmental regulation tools and public participation environmental regulation tools play a positive regulatory role in the transmission path of green finance’s impact on carbon emissions, but market-driven environmental regulation tools cannot effectively enhance the carbon emission reduction effect of green finance development.Discussion: The research results of this paper provide a basis for the government to formulate flexible, accurate, reasonable and appropriate green financial policies, help to strengthen the exchange and cooperation between regions in reducing carbon and fixing carbon, and actively and steadily promote China’s goal of “peak carbon dioxide emissions, carbon neutrality”.

Introduction: Strengthening the early warning of greenhouse gas emissions from agriculture is an important way to achieve Goal 13 of the Sustainable Development Goals. Agricultural carbon emissions are an important part of greenhouse gases, and accelerating the development of green and low-carbon agriculture is of great significance for China to achieve high-quality economic development and the goal of “carbon neutrality in peak carbon dioxide emissions”.Methods: By measuring the total agricultural carbon emissions in China and seven administrative regions from 2000 to 2021, the paper analyzes the influencing factors of agricultural carbon emissions in China by using STIRPAT environmental pressure model, and on this basis, predicts the peak trend of agricultural carbon emissions in China under different development scenarios by using the extreme learning machine model optimized by genetic algorithm.Results: The results showed that the extreme learning machine model improved by the genetic algorithm can overcome the shortcoming that the extreme learning machine model is easy to fall into the local optimal solution, thus obtaining higher prediction accuracy. At the same time, from 2000 to 2021, the total agricultural carbon emissions in China showed a continuous fluctuation trend, and due to the constraints of the agricultural economic level, agricultural industrial structure, and agricultural human capital, the agricultural carbon emissions showed spatial differentiation. It is worth noting that, in the context of green development, the agricultural carbon emissions of the seven regions in China all have the potential to achieve the “peak carbon dioxide emissions” goal in 2030, with only a slight difference at the peak.Discussion: The research results of this paper provide evidence for the government to formulate flexible, accurate, reasonable and appropriate agricultural carbon reduction policies, which is helpful to strengthen the exchanges and cooperation of regional agricultural and rural carbon reduction and fixation, and actively and steadily promote China's agriculture to achieve the goal of “peak carbon dioxide emissions carbon neutrality”.

Intelligent manufacturing offers opportunities for green technology innovation at a low cost, helps eliminate outdated equipment, and improves pollution efficiency, which is essential for environmental protection. From the perspective of the industrial sector, based on the data of 269 Chinese cities, this study empirically examines the impacts of intelligent manufacturing on ecological environment using the fixed effect model and unconditional quantile model. The research results show that intelligent manufacturing can significantly reduce the concentration of PM2.5 in the air and environmental pollution by optimizing resource allocation during production and operation. The results of the external mechanism show that green technological innovation plays a positive mediating role in the impact mechanism of intelligent manufacturing to improve air quality. The marginal impact of intelligent manufacturing on environmental pollution is nonlinear, and the main trend is the M-type distribution trend of increasing first, then decreasing, and then rising. In particular, intelligent manufacturing has a greater welfare effect on the ecological environment at the lower quantiles. The results provide a viable path for the government to actively plan a strategic layout to improve the ecological environment and achieving the goal of high-quality economic development.

Air pollution severely threatens people’s health and sustainable economic development. In the era of the digital economy, modern information technology is profoundly changing the way governments govern, the production mode of enterprises, and the living behavior of residents. Whether digital technology can bring ecological welfare needs to be further studied. Based on panel data from 269 Chinese cities from 2006 to 2021, this study empirically examines the impact of digital technology on air pollution by using the two-way fixed effect model. The results show that digital technology will significantly reduce the concentration of fine particles in the air and help protect the atmospheric environment. The results are still valid after using the interactive fixed effect model and the two-stage least square method after the robustness test and causality identification. Digital technology can also reduce the air pollution by promoting green innovation, improving energy efficiency, and easing market segmentation. The effect of digital technology on reducing the concentration of fine particles in the air is heterogeneous. Digital technology plays a more substantial role in reducing pollution in resource-based cities and areas with a high degree of modernization of the commodity supply chain. The positive effect of digital technology in reducing air pollution is affected by the amount of air pollutants emitted. When the concentration of PM2.5 in the air is high, the role of digital technology in protecting the atmosphere will be strongly highlighted. This research is a beneficial exploration of protecting the atmospheric environment by using digital technology while building an ecological civilization society. The conclusion will help urban managers, the public, and business operators entirely use modern equipment such as 5G, remote sensing, and the Internet of Things in their respective fields to protect the atmospheric environment.

IntroductionPromoting the development of digital technology is an important step in meeting the challenge of global climate change and achieving carbon peaking and carbon neutrality goals.MethodsBased on panel data of Chinese cities from 2006 to 2020, this paper used econometrics to investigate the impact and mechanism of digital technology on carbon emissions.ResultsThe results showed that digital technology can significantly reduce carbon emission intensity and improve carbon emission efficiency. These results remained robust after changing the estimation method, adding policy omission variables, replacing core variables, and solving the endogeneity problem. Digital technology can indirectly reduce carbon emissions by promoting green technological innovation and reducing energy intensity, and it plays a significant role in the carbon emission reduction practices of carbon emission trading policies and comprehensive national big data pilot zones. The replicability, non-exclusivity, and high mobility of digital technology help to accelerate the spread of knowledge and information between different cities, which leads to a spillover effect on carbon emission reductions. Our unconditional quantile regression model results showed that digital technology’s carbon emission reduction effect continuously decreases with increases in carbon dioxide emissions.DiscussionThe results of this paper provide evidence for the potential use of digital technology in achieving the goal of carbon neutrality, which is of great significance for achieving high-quality innovation and promoting the green transformation of the economy and society.

The innovation and development of enterprises are influenced by both internal management and external economic policy uncertainty. This study examines methods for enterprises to ensure the development of sustainable innovation under economic policy uncertainty. Specifically, this study examines the effect of national economic policy uncertainty as a threshold variable on enterprise financial flexibility in a sustainable innovation model using the micro data of non-financial companies in China's A-share market from 2007 to 2019. Through an empirical analysis of the mechanism of the effect of enterprise financial flexibility on innovation, this paper discusses the possible ''adaptive effect'' of financial flexibility under policy uncertainty. Using a threshold effect model, this study examines the coefficient change of the negative effect of enterprise financing constraints on sustainable innovation caused by fluctuations in economic policy uncertainty and records the threshold value of the economic policy uncertainty index. The results show that a rise in policy uncertainty has a negative impact on enterprise innovation, and enterprises can actively adjust their flexible financial reserve to gain an adaptive effect under the influence of policy uncertainty. The results indicate a simultaneous threshold effect based on economic policy uncertainty in a model of enterprise financing constraints and sustainable innovation. Accordingly, the economic policy uncertainty index can be divided into a “reasonable area” and “warning area,” to indicate the need for enterprises to take countermeasures to deal with the fluctuations caused by government economic policies appropriately.

Some environmental stressors lead to the onset of depression via inhibiting hippocampal BDNF expression, but other environmental stressors-induced depression exhibits no change in BDNF expression. The underlying mechanisms behind the divergence remain unknown. In this study, depression-like behaviors were induced in rats by maternal deprivation (MD) and chronic unpredictable stress (CUPS). Depression-like behaviors were tested by open field test, forced swimming test, and sucrose consumption test. BDNF and miR-16 expressions in the hippocampus were examined by real-time PCR. MD and CUPS rats crawled less distance, exhibited decreased vertical activity, and produced more fecal pellets than control rats in the open field test. However, MD rats crawled less distance and produced significantly less fecal pellets than CUPS rats. In the forced swimming and sucrose consumption tests, CUPS and MD rats exhibited longer floating time and consumed less sucrose than control rats, but MD rats exhibited shorter floating time and consumed less sucrose than CUPS rats. MD but not CUPS rats showed lower BDNF mRNA and higher miR-16 expression than control rats. In MD rats, BDNF mRNA expression negatively correlated with the expression of miR-16. BDNF expression positively correlated with the total distance rats crawled and vertical activity in the open field test while miR-16 expression negatively correlated the two behaviors. BDNF positively correlated with sucrose preference rate while miR-16 negatively correlated with sucrose preference rate of the sucrose consumption test. Our study suggests that MD and CUPS induced different depression-like behaviors in rats. Depression induced by MD but not CUPS was significantly associated with upregulation of miR-16 and possibly subsequent downregulation of BDNF in hippocampus.

A common conundrum discussed in economic research revolves around the fact that nations endowed with plentiful natural resources often exhibit a lower gross domestic product (GDP). This conundrum is commonly called the "resource curse", where most empirical studies about the effects primarily focused on developed economies. At the same time, limited data is available regarding a burgeoning oil-exporting nation like the Republic of Yemen. This research endeavor aims to investigate the relationship between oil price Changes and Yemen's economic growth. Utilizing annual data spanning from 1990 to 2019, the study employs the auto-regressive distributed lag (ARDL) model to establish the long-term connection between oil price volatility and economic growth over both short and long timeframes. This study's outcomes indicate that oil price Changes have a significant positive relationship with Yemen's economic growth in both the long and short run. Oil rents show a significant negative relationship with economic growth in both the long and short run. The results of GLM, RLS, and GMM robustness checks are consistent with our model results. Based on these findings, we suggest that Yemen should diversify its economy by investing in agriculture and tourism, and focus on human capital, education, and research and development. These steps could reduce the economy's dependence on oil and enhance sustainable economic growth. These empirical insights and suggestions are particularly useful for policymakers as they help build sound external and economic policies to sustain long-term economic growth. [ABSTRACT FROM AUTHOR]

Peroxymonosulfate-based electrochemical advanced oxidation processes (PMS-EAOPs) have great potential for sustainable water purification, so an in-depth understanding of its catalytic mechanism is imperative to facilitate its practical application. Herein, the performance enhancement and mechanism of electroenhanced PMS activation by single-atom Fe catalyst modified carbon felt was investigated. Compared with the anode, the cathode exhibited faster bisphenol A degradation (kcathode = 0.073 vs. kanode = 0.015 min-1), increased PMS consumption (98.8 vs. 10.3%), and an order of magnitude reduction of Fe dissolution (0.068 vs. 0.787 mg L-1). Mass transfer is a key factor limiting PMS activation, while the electrostriction of water in the hydrophobic region caused by cathode electric field (CEF) significantly increased mass transfer coefficient (km, cathode = 1.49 x 10-4 vs. km, anode = 2.68 x 10-5 m s-1). The enhanced activation of PMS is a synergistic result between electroactivation and catalyst-activation, which is controlled by the applied current density. ¹O2 and direct electron transfer are the main active species and activation pathway, which achieve high degradation efficiency over pH 3 to 10. Density functional theory calculations prove CEF increases the adsorption energy, lengthens the O-O bond in PMS, and promotes charge transfer. A flow-through convection unit achieves sustainable operation with high removal efficiency (99.5% to 97.5%), low electrical energy consumption (0.15 kWh log-1 m-3), and low Fe leaching (0.81% of the total single atom Fe). This work reveals the critical role of electric fields in modulating Fenton-like catalytic activity, which may advance the development of advanced oxidation processes and other electrocatalytic applications. [ABSTRACT FROM AUTHOR]