Spatial and temporal evolution of urban resilience in China and analysis of barriers: Based on a sustainable development perspective.

With the increasing uncertainty of urban security, urban resilience construction with risk awareness and bottom-line thinking has become essential for promoting sustainable urban development. This paper measures China's urban resilience development index (CRDI) based on 284 cities in China (except Tibet) using the entropy method from four dimensions: economic, social, environmental, and infrastructure, and analyzes it by combining coupling coordination degree and barrier factor analysis. We find that: (1) At the national level, CRDI and its sub-dimensions show an increasing trend in time, a decreasing spatial layout from coastal to inland, and a "high-high-low-low" clustering feature in space. (2) At the regional level, the CRDI is from high to low in the east, middle, and west order. The sub-dimensions are from high to low in the order of east, middle, and west for economic, social, and infrastructure resilience and from high to low in the order of east, west, and middle for environmental resilience. (3) To coupling coordination degree, the CRDI index coupling coordination is increasing in time trend but is still on the verge of dissonance. (4) Social resilience is the main obstacle factor. In the indicator layer, human resources, innovation, education, security, living, and environmental protection are the areas where CRDI coordinated development is the key to improvement. Based on the above empirical evidence, this paper proposes countermeasures to optimize urban resilience construction from four perspectives: economic, social, environmental, and infrastructure.

The evolution of renewable energy environments utilizing artificial intelligence to enhance energy efficiency and finance.

The development of a country is inseparable from the material guarantee mainly based on energy, but energy is limited, which may restrict the sustainable development of the country. It is very necessary to accelerate the adoption of programs aimed at switching non-renewable energy sources to ones that are, and giving priority to improving renewable energy consumption and storage capabilities. From the experience of the G7 economies, the development of renewable energy (RE) is inevitable and urgent. The China Banking Regulatory Commission has recently issued a number of directives, such as the "Directives for Green Credit" and "Instructions for Granting Credit to Support Energy Conservation and Emission Reduction," to help businesses that use "renewable energy expand". This article firstly discussed the definition of the "green institutional environment" (GIE) and the construction of the index system. Then, on the basis of clarifying the relationship between the GIE, and RE investment theory, a semi-parametric regression model was constructed to empirically analyze the mode and effect of the GIE. Considering the balance between improving model accuracy and reducing computational complexity, the number of hidden nodes opted in this study is 300 so as to lower the time needed to predict the model. Finally, from the perspective of enterprise scale, the level of GIE played a significant role in promoting RE investment in small and medium-sized enterprises, with a coefficient of 1.8276, while the impact on RE investment in large enterprises had not passed the significance test. Based on the conclusions, the government should focus on building a GIE dominated by green regulatory systems, supplemented by green disclosure and supervision systems, and green accounting systems, and should make reasonable plans for releasing various policy directives. At the same time, while offering full play to the guiding role of the policy, its rationality should also be paid attention to, and the excessive implementation of the policy should be avoided, so that an orderly, and good GIE can be created.

Study on the effect of green financial policies on low-carbon economic development based on evidence from green financial reform and innovation pilot zone.

Green finance is key in supporting industries' green transformation and helping achieve low-carbon economic (LCE) development. This paper constructs an LCE development index using panel data from 30 provinces in China from 2011 to 2020. Based on the establishment of the first five pilot green finance zones in China in 2017 as a quasi-natural experiment, the synthetic control method (SCM) is applied to explore the impact of green finance policies on the level of LCE development and to analyze the mechanism and evaluate the policy effects. The empirical results show that (1) the synthetic analysis unit better fits the development trend before the implementation of the pilot. (2) After the implementation of the pilot reform, the level of LCE development in Zhejiang, Jiangxi, Guangdong, and Guizhou provinces has a more significant enhancement effect, but the enhancement in Xinjiang is not significant, which indicates that the reform effect in Zhejiang, Jiangxi, Guangdong, and Guizhou is significantly better than that in Xinjiang to a certain extent. (3) The samples were statistically significant and passed the placebo and ranking tests. Additionally, this paper analyzes the mechanism of policy effectiveness in terms of sci-tech innovation (STI) and energy consumption structure: green finance as a grip for economic transformation can provide financial support for regional STI and energy consumption structure upgrade and promote the capital flow to green low-energy industries, ultimately achieving sustainable economic development. Based on the above findings, policy insights can be provided for the improvement of green finance pilot regions.

The influence of bank governance structure on green credit.

The introduction of green credit policy provides an important idea to solve the contradiction between economic development and environmental protection. Based on fuzzy-set Qualitative Comparative Analysis (fsQCA) method, from the perspective of bank governance structure, this paper selects ownership concentration, independence of the Board, executive incentive, activity of Supervisory Board, degree of market competition and loan quality as antecedent variables to analyze the path of their impact on green credit. It is found that: (1) The main way to achieve high-level green credit is high ownership concentration and good loan quality. (2) The configuration of green credit has causal asymmetry. (3) Ownership structure is the most important factor affecting green credit. (4) There is a substitution between the low independence of the Board and the low executive incentive. The low activity of Supervisory Board and the poor loan quality are also substitutable to a certain extent. The research conclusion of this paper is helpful to improve the green credit level of Chinese banks and win the green reputation for banks.

Sulfur dioxide emissions curbing effects and influencing mechanisms of China's emission trading system.

The emissions trading system, a crucial and fundamental system reform in the environmental resources field of China, was established to promote the continuous and effective reduction of total emissions of major pollutants. In this context, based on the panel data of 285 Chinese cities (except Tibet) from 2004 to 2018, this paper uses the quasi-experimental method of Difference in Difference to assess the effect of the emissions trading system introduced on sulfur dioxide emissions of China and the transmission mechanism. The article generates several intriguing findings. (1) The emissions trading system has a significant suppressive effect on sulfur dioxide emissions. (2) Mechanistic tests show that the emissions trading system can effectively suppress sulfur dioxide emissions by reducing government intervention, stimulating green patent innovation, and improving resource use efficiency, in which green utility patents have a masking effect. (3) From the east, central and west divisions, the emissions trading system has a significant suppression effect on sulfur dioxide emission in the eastern and central regions, and the eastern region is better than the central region. (4) In terms of factor endowment, the emissions trading system has a significant suppression effect on sulfur dioxide emissions in both resource-based and non-resource-based cities, with non-resource-based cities outperforming resource-based cities; while within resource-based effect exists only in regenerative cities. (5) The emissions trading system has a significant suppression effect on sulfur dioxide emissions in old and non-old industrial base cities in industrial base zoning. The suppression effect in non-old industrial base cities is better than that in old industrial base cities. This paper provides empirical evidence for evaluating the emissions trading system at the provincial level in China and suggests policy recommendations for selecting government tools to effectively curb sulfur dioxide emissions. Although the emissions trading system has made an outstanding contribution to sulfur dioxide emissions reduction, there is still much space for further development of potential emission reductions.

Spatial and temporal evolution of urban economic development efficiency in China's Yangtze River economic belt from the perspective of sustainable development.

Urban economic development is crucial to regional economy and people's life, and enhancing the efficiency of urban economic development is of great significance to boost sustainable and healthy economic and social development. In this paper, from the perspective of sustainable development, data of 104 cities in China's Yangtze River Economic Belt (YREB) from 2004 to 2019 are selected, and the urban resource consumption index and urban pollutant emission index are synthesized as new input-output indicators using the Time Series Global Principal Component Analysis (GPCA), combined with the Global Malmquist-Luenberger (GML) Index Model, Standard Deviation Ellipse (SDE) Model to measure the total factor productivity index of urban economic development in China's YREB and analyze its spatial and temporal evolution. The results show that from 2004 to 2019, the total factor productivity index of urban economic development in China's YREB showed an overall fluctuating upward trend with an average annual growth of 5.8%, and the analysis by decomposing indicators shows that the growth of total factor productivity of urban economic development in China's YREB is mainly influenced by the growth of technological progress. Meanwhile, there are obvious regional differences in the efficiency of urban economic development in China's YREB, with the largest difference in the middle reaches of the Yangtze River, the second largest in the upper reaches, and the smallest in the lower reaches. From 2004 to 2019, the efficiency center of gravity of urban economic development efficiency in the YREB has always been located in the middle reaches of the Yangtze River region. The spatial distribution pattern of urban economic development efficiency in the YREB is dominated by the northeast-southwest direction and tends to be concentrated in the study time period.

Research on coupling coordination and influencing factors between Urban low-carbon economy efficiency and digital finance-Evidence from 100 cities in China's Yangtze River economic belt.

China is a large country with rapid economic expansion and high energy consumption, which implies that the country's overall carbon emissions are enormous. It is vital to increase urban low-carbon economy efficiency (ULEE) to achieve sustainable development of China's urban economy. Digital finance is a significant tool to boost ULEE by providing a convenient and effective funding channel for urban low-carbon economic transformation. Analyzing the coupled and coordinated relationship between ULEE and digital finance is of vital importance for the sustainable development of the urban economy. This paper selects panel data of 100 cities in China's Yangtze River Economic Belt (YEB) in 2011-2019, and analyzes the research methods such as the Global Malmquist-Luenberger index model, coupling coordination degree (CCD) model, standard deviation ellipse model, gray model, and geographic detector by The spatial and temporal distribution, dynamic evolution characteristics and influencing factors of the CCD between ULEE and digital finance are analyzed. The study shows that: (1) the CCD of ULEE and digital finance grows by 3.42% annually, reflecting the increasingly coordinated development of the two systems; (2) The CCD of ULEE and digital finance shows a distribution pattern of gradient increase from the upstream region of Yangtze River to the downstream region, meanwhile, the spatial center of gravity moves mainly in the midstream region; (3) The spatial center of gravity of CCD of ULEE and digital finance is expected to move 22.17 km to the southwest from 2019 to 2040; (4) In terms of influencing factors, the influence of informatization and industrial structure on the CCD increases over time, while the influence of factors such as population development, greening, transportation, and scientific research decreases over time. Finally, this paper proposes policy recommendations for improving the CCD of ULEE and digital finance based on the empirical results.

Industrial green total factor productivity in China's Yangtze River Economic Zone: Temporal and spatial difference analysis based on 108 panel data.

As China's strategic support belt, the green development of industry in the Yangtze River Economic Zone is of great significance to promote the construction of China's ecological civilization, build a modern industrial system and accelerate high-quality economic development. The study of green total factor productivity of industry in the Yangtze River Economic Zone has important theoretical and practical value for exploring the green development path of China's industry. This Paper takes the Yangtze River Economic Zone, a key strategic region in China, as the research object, selects the input and output data of industrial production from 2006 to 2018, based on DEA model. To construct an MML index considering expected and unexpected output, and to quantitatively analyze the changes of industrial GTFP in the Yangtze River Economic Zone. The results show that: (1) During the sample period, the industrial green total factor productivity in the Yangtze River Economic Zone shows the spatial characteristics of differential growth and the temporal characteristics of volatile growth. It shows the fluctuation characteristics of "N" shape. (2) According to the order of "upper, middle, and lower" reaches, the spatial pattern of industrial green total factor productivity is characterized by "lower ladder". But the difference between the upper and middle reaches is small. (3) Cities with higher green total factor productivity and lower green total factor productivity each form the characteristics of "club convergence" of spatial agglomeration. (4) Technological efficiency and technological progress efficiency have heterogeneous effects on different river basins in the upper, middle, and lower reaches, and technological progress efficiency is conducive to promoting the evolution of green total factor productivity to a high level. According to the above empirical results, this paper finally puts forward the policy recommendations to improve the industrial green total factor productivity of the Yangtze River Economic Zone and the policy recommendations to reduce the industrial differences between the Yangtze River Economic Zone.

Innovative Mechanism of Rural Finance: Risk Assessment Methods and Impact Factors of Agricultural Loans Based on Personal Emotion and Artificial Intelligence.

Agricultural finance is in an embarrassing position in the current financial environment, especially during the process of COVID-19. Based on a small-scale peasant economy, it can no longer meet the rapidly rising demand of farmers for agricultural finance. Moreover, there has been a serious disconnection between the financial system of secondary and tertiary industries, and the quality of development needs to be improved urgently. The agricultural loan risk assessment has always been the main problem that we pay great attention to in the innovation of agricultural finance. Agricultural loans are an indispensable element in supporting agricultural development and promoting rural revitalization strategy. However, financial institutions have certain credit risks in reviewing and issuing agricultural loans. This article studies the speech emotion recognition of farmers in loan review to assess loan risk. As for emotional confusion caused by speech segmentation, a special method of data connection between Convolutional Neural Networks (CNNs) and Bidirectional Long Short-Term Memory (Bi-LSTM) Networks is designed, and a variable-length speech emotion recognition model including CNN and Bi-LSTM is designed. Experimental results show that the proposed algorithm can effectively improve the risk assessment of farmers in loan review.

Effects of special fiscal revenue on industrial pollution and the spatial spillover: evidence from 30 provinces in China.

In China, industrial pollution is a prominent source of environmental pollution, and the important goal of sustainable development is to reduce industrial pollutant emissions, while ensuring economic stability. Special fiscal revenue (SFR) is the direct channel of government environmental regulation and the main source of environmental protection investment; it is of great significance to analyze the influence of special revenue on industrial pollution for industrial sustainable development. Therefore, this paper uses panel data from 30 provinces in China from 2005 to 2018 to empirically analyze the impact of SFR on the comprehensive industrial pollution index (CIP) and the spatial spillover effect by combining traditional regression and spatial econometric model. The empirical results show that: (1) The increase in SFR will significantly reduce the level of regional CIP emissions, and it verifies the role of SFR as a channel of government environmental regulation and a major source of special funds for environmental protection. (2) SFR exists a significant negative spatial spillover effect. For every 1% increase in SFR in surrounding provinces, the local CIP will decrease by about 0.448%, reflecting SFR has a stronger inhibition effect on CIP for surrounding areas. (3) According to the analysis of different periods, the main impact of SFR on CIP is after the new round of reforms in 2012. The sources and expenditure channels of SFR are more transparent, indicating that the institutional policies have a significant effect on emission reduction. (4) The analysis of individual heterogeneity finds that the increase of SFR in economically underdeveloped areas has a stronger inhibitory effect on CIP, and the space for technological progress in economically developed areas is small, so it is difficult to inhibit CIP in a short period of time. In addition, the instrumental variable model and robustness test support the above conclusions.

Industrial green total factor productivity based on an MML index in the Yangtze River Economic Belt.

The environmental problems caused by industrial development in the Yangtze River Economic Belt in China have become a bottleneck for urban economic development. Therefore, the measurement of the green efficiency of the urban industry in the Yangtze River Economic Belt can provide a reasonable basis for future industrial green development paths. Based on the DEA model, this study measures the industrial green total factor productivity of 108 cities in the YREB by combining the meta-frontier infrastructure framework and the Malmquist-Luenberger index. The results indicate the following: (1) The industrial green total factor productivity of cities in the Yangtze River Economic Belt declined at first and then increased from 2009 to 2016. The overall performance indicated that the structures of the technological efficiency change index and technological change index were unreasonable. The industrial green total factor productivity of midstream cities is the highest, and the difference between cities is influenced by economic development level, industrial base, transportation convenience, labor quality, and the government's emphasis on green development of industrialization. (2) Different regions have different decomposition efficiency to promote the improvement of ITFP. Considering regional heterogeneity, the industrial green total factor productivity in the middle and lower reaches of the region relies on the advantages of technical efficiency, whereas the upper reaches rely on the improvement of management efficiency. (3) The technology gap in the upper and lower reaches is small, and in the middle reaches is relatively large. Thus, according to the above empirical results, this study finally presents some policy suggestions for industrial green development in different cities of the Yangtze River Economic Belt.