Super-SBM Model Research Articles

The allocation of carbon resources in marine capture fisheries.

Marine fishery carbon emissions play a significant role in agricultural carbon emissions, making resource allocation a crucial topic for the overall marine ecological protection. This paper evaluates the dynamic iteration method as a research approach with the factors of resource allocation consisting of value assessment, optimization objective, difference between value assessment and objective, and optimization calculation. The paper selects the shadow price from the Super-SBM model as the judgment function for the goal value, aiming for the fairness criterion. From an equity standpoint, the allocation of carbon resources in marine capture fisheries proves to be unreasonable. The fishery model exhibits an excessive supply of carbon resources, resulting in wastage, while the green fishery model faces a relatively limited supply, with a focus on energy conservation and environmental protection. To address this issue, this paper proposes a new method and discusses the corrective results. This result shows that the stabilization point achieved is a short-term equilibrium rather than a long-term one. By rectifying the social contradiction of profit-oriented approaches, this research provides a fresh perspective for economic studies and applications, particularly in industrial layout and resource utilization optimization.

The Impact of Digital Finance on the Green Utilization Efficiency of Urban Land: Evidence from 281 Cities in China

In the era of digital economy, digital finance, as an innovative financial model, plays an important role in driving urban industrial transformation and development, technological innovation, industrial upgrading and sustainable utilization of energy, and has a significant impact on sustainable urban development. At present, in the process of building a new pattern of Chinese-style modernization in China, it is of great significance to improve the green use efficiency of urban land through digital finance and realize the sustainable use of land resources and the sustainable development of the city. The current study employed 281 Chinese cities from 2010 to 2020 as research samples to investigate the effects of technological financing on the productivity of city land green usage. Based on the ideas of responsible growth and efficient urban development, an assessment index system was developed. Comprehensive empirical tests, such as the Super-SBM model, fixed effect model, and mediation effect model, were implemented in the research. The study’s findings indicate that: (1) Throughout the research period, the benchmark model’s regression outcomes demonstrate that digital banking impacts urban land’s green development efficiency, with positive moderating effects offered by environmental legislation; the optimization of industrial structure has not yet played a positive regulating effect. (2) Urban area green usage performance is more clearly impacted by the extent of use and the degree of digitization, according to the regression results of digital financing heterogeneity. The positive effect of online financial services on a city’s green use efficiency occurs mainly in eastern cities and southern cities, given the results of urban development level difference. In light of resource endowment unpredictability, “non-resource cities” stand to gain more from global finance’s encouragement of resource-efficient urban land use than do “resource cities”. The results of the mechanism test indicate that there is a strong mediating influence from digital finance, city land environmental use productivity, and green technological breakthroughs. In consideration of these results, the following measures are suggested in this paper: (1) Persist in advocating for the transformation of traditional finance into online financing. (2) Intensify the impact of significant variables on the environmentally friendly use of urban areas. (3) Encourage technology creativity and execution through the application of technological economics.

A DEA game cross-efficiency based improved method for measuring urban carbon emission efficiency in China.

An accurate evaluation of carbon emission efficiency (CEE) at the city level can provide guidelines for understanding low carbon performance, which is crucial to achieving dual carbon targets. Existing CEE studies focused on national, industrial, and provincial scales while neglecting the city level and failing to consider competing relationships among decision-making units in their measurement models. To fill these gaps, this paper introduces the data envelopment analysis game cross-efficiency model (DEA-GCE) to measure urban CEE performance and compares it with the traditional Super-SBM model using the data from 283 Chinese cities between 2006 and 2019. The results show that (1) the DEA-GCE method provided more intensive and stable results. (2) Overall CEE of Chinese cities declined slightly amidst fluctuations during this period. (3) CEE in cities exhibits spatial clustering characteristics. CEE performance in Northeast China has improved, while CEE in Northwest China continues to lag behind. This study introduced an innovative method for calculating urban CEE and conducted an empirical study of 283 Chinese cities, which has implications for formulation of emission reduction policies.

The Efficiency of China’s Hub Economy and Its Influencing Factors: A Two-Stage Analysis Based on the Super SBM-Malmquist-Tobit Model

Hub economy is a kind of emerging economic form. Developing a hub economy is essential to strengthen domestic and foreign connectivity and build a powerful country in transportation. This paper designs a two-stage analysis framework for the evaluation and impact study of the input-output efficiency of the hub economy based on the super SBM model, Malmquist model, and Tobit model. In the first stage, the Super SBM and Malmquist models are used to measure the static and dynamic efficiency of the hub economy. In the second stage, the Tobit model is used to analyze the factors influencing the efficiency of the hub economy. Among them, the explained variable in the second stage is the measurement result of technical efficiency in the first stage. The empirical results of 30 provinces and cities in China from 2012 to 2021 show that (1) the technical efficiency (TE), pure technical efficiency (PTE), and scale efficiency (SE) of China’s hub economy are 0.585, 0.740, and 0.820, respectively, which do not reach the effective state; (2) the technical efficiency change index (Effch), technical progress change index (Techch), and total factor productivity change index (Tfpch) of China’s hub economy are 0.994, 0.945, and 0.939, respectively, indicating that the corresponding efficiencies show a downward trend; and (3) industrial structure, innovation, and technology are significantly and positively correlated with the efficiency of the hub economy; policy and enterprises are significantly negatively correlated with the efficiency of the hub economy; and education does not correlate with the efficiency of the hub economy.

How Does Artificial Intelligence Impact Green Development? Evidence from China

Artificial intelligence not only changes the production methods of traditional industries but also provides an important opportunity to decouple industrial development from environmental degradation and promote green economic growth. In order to further explore the green value of AI, this paper constructs an indicator of industrial robot penetration at the regional level, based on the idea of Bartik’s instrumental variable, and measures green development efficiency using the improved Super-SBM model. Based on a comprehensive explanation of the influence mechanism, a spatial measurement model and mediating effect model are constructed to test the spatial spillover effect and transmission mechanism between AI and green development. This study shows that (1) there is a significant inverted U shape in the impact of AI on green development; (2) the heterogeneity analysis finds that the structural dividend of AI is more obvious in capital-intensive and technology-intensive areas, which can more fully release its empowering effect on green development; (3) AI can not only directly affect green development but also indirectly affect green development by promoting green technology innovation and optimizing industrial structures, etc.; (4) AI has a significant inverted U-shaped spatial spillover effect on green development, and the development of local AI has a radiation-driven effect on the green development performance of its spatially related areas. The research methodology of this paper can be used for future research, and the results could provide support for the formulation of regional AI applications and green development policies.

Can digital economy truly improve agricultural ecological transformation? New insights from China

AbstractAs the world’s biggest emitter of carbon, China’s agricultural carbon emissions account for 16 to 17%, with agriculture being the second largest contributor to carbon emissions. The development of the digital economy has brought profound changes to agricultural ecology. Therefore, we utilize China’s data experience to construct an agricultural ecological transformation system by setting up a Super-SBM model. Then, based on a dynamic panel threshold model, we explore the nonlinear impact mechanism of the digital economy on agricultural ecological transformation from a low-carbon innovation perspective. Our results reveal that the overall level of China’s agricultural ecological transformation is not high, but in the long-term posture, it represents a relatively steady upward trend, with a significant “wealth gap” between different regions. It is worth noting that there is a significant threshold effect of low-carbon innovation heterogeneity in the impact mechanism of digital economy on agricultural ecological transformation: under lower low-carbon innovation levels, the development of digital economy is unable to effectively promote agricultural ecological transformation. However, as the level of low-carbon technological innovation increases and exceeds the critical value, it stimulates the driving effect of digital economy to some extent, thereby promoting the improvement of agricultural ecological transformation, presenting a “U” shaped relationship. The paper has clarified the differential “new phenomena” in the process of promoting agricultural ecological transformation, providing new insights for achieving “carbon reduction and economic promotion” in developing countries.

Research on spatial-temporal heterogeneity of driving factors of green innovation efficiency in Yangtze River Delta urban agglomeration—empirical test based on the Geographically Weighted Regression model

Firstly, based on the data of 40 cities in the Yangtze River Delta from 2010 to 2019, the paper uses the Unexpected Super-SBM model to estimate the green innovation efficiency of each city. On this basis, the paper establishes a Geographically Weighted Regression model to explore the spatial-temporal heterogeneity of the effects of influencing factors on green innovation efficiency. The findings indicate that: The direction and strength of each influencing element on the green innovation efficiency of the Yangtze River Delta cities are varied at the urban spatial scale. The degree of opening up significantly boosts the green innovation efficiency, but the impact intensity shows spatial differences. The green innovation efficiency is promoted by government technology support, but its intensity tends to weaken. In addition, financial support has a negative impact on most cities. Over time, the impact of economic development level on urban green innovation efficiency has changed from inhibition to promotion, and the impact intensity tends to weaken. Industrial structure and environmental regulation tend to show two effects of promotion and obstruction in different cities from weak inhibition. Industrial structure and environmental regulation tend to show two effects of promotion and obstruction in different cities from weak inhibition. The research conclusion of this paper has important theoretical significance and practical value for accelerating the construction of a green, low-carbon and circular economic system in the Yangtze River Delta region, and achieving the “dual carbon” goal.

Urban land use efficiency and its driving factors in ecologically fragile areas: the case of northwest China

ABSTRACT Urban land serves as the medium for urban economic and residential activity. The degree of intensive and efficient use of urban land and the coordinated growth of the economy and society are significantly impacted by utilization efficiency. Northwest China’s temperature, geography, water, and soil characteristics make less of the region suited for urban development. Thus, it is imperative to investigate Northwest China’s urban land use efficiency (ULUE). This study applies the efficiency loss model to highlight the discrepancy between the optimal and actual values of each input-output variable of the ULUE, and uses the Super-SBM model to compute the ULUE of 39 cities. Additionally, the internal driving elements of the ULUE are examined using the Global Malmquist-Luenberger (GML) index. This study discovered that Northwest China had a low ULUE, with a modest upward trend seen between 2004 and 2018. The overall shape of the region was similar to an ‘N’ curve, with high efficiency values concentrated in the southeast of the region in 2018. When considering the agglomeration impact of urban agglomeration, these results are dependable. Moreover, the efficiency loss model discovered that pollution and wasteful resource usage were the main reasons behind the drop in ULUE in Northwest China. Finally, the GML index and its constituents revealed that the main force behind ULUE’s growth in Northwest China is technological developments. This research provides important policy implications for reducing the gap between eastern and western regions, supporting high-quality development in northwest China, and protecting and intensively using urban land.

Research on the enhancement path of green technology innovation efficiency under the group perspective.

China is at a critical moment of transforming high-speed development to high-quality development, and it is significant to improve the efficiency of green technological innovation. In this paper, under the perspective of two-stage innovation value chain, we construct the evaluation index system of green technology innovation efficiency, adopt the super efficiency SBM model to measure the green technology innovation efficiency of China's high-tech industries, and based on the results obtained, we assume the fuzzy set qualitative comparative analysis method (fs-QCA) based on the group theory to explore the complex causal mechanism and grouping paths of the interaction between enterprises, government and market that affects the green technology innovation efficiency Mechanism and group path. The study results show that (1) enterprise, government, and market are not necessary conditions to influence the efficiency of green technological innovation, and even if a particular party plays a central role, it needs the assistance of other parties. (2) The improvement of green technological innovation efficiency requires the interaction of enterprises, government, and market, and even if any party does not have the core conditions, it can still produce high green technological innovation efficiency. (3) The path of the "innovative compensation" effect is identified, which indicates that enterprises will generate a high level of green innovation efficiency under sufficient investment brought about by the enterprise scale effect and matched with a good level of economic development. (4) The market economy-led pathway suggests that when the market economy is highly developed, firms do not need environmental regulation and government support to generate efficient levels of green technological innovation.

The impact of import expansion on energy efficiency: Perspective of cities' geographical characteristics

Energy is an indispensable propeller for economic growth. Improving energy efficiency not only mitigates energy scarcity, but also represents an imperative option for reducing ecological and environmental strain, addressing the challenges of contemporary global warming. With China's increasing participation in global value chains through imports, the question arises: can import trade contribute to advancing energy efficiency? This matter holds great significance in resolving present-day energy challenges and fostering sustainable economic progress. We employ super-SBM model to measure the energy efficiency of cities; based on the data collected from prefecture-level cities from 2000 to 2016, Our findings indicate that: (1) Import trade significantly enhances cities' energy efficiency. (2) Geographical and climatic conditions significantly impact the energy effect of import trade; the enhancement effect is more significant in eastern and northeastern regions, low-relief areas, low-temperature and low-humidity cities. (3) Import trade not only improves local energy efficiency, but also promotes energy efficiency improvement in neighbouring cities, this spatial spillover effect holds significantly under various spatial matrices. We must fully utilize the promotional impact of import trade to enhance the energy efficiency of cities, and maintain our commitment to executing an active import promotion strategy. Considering the impact of topography, climate, and other geographical factors, we must emphasize the role of geographical conditions in optimizing energy and environment. Furthermore, we should encourage inter-regional economic and trade exchanges and establish a joint governance mechanism for cross-regional collaboration.

The Effects and Driving Factors of Low-Carbon Transition of International Oil Companies: Evidence from a Super-SBM Model

As the main source of energy supply and carbon emissions, the oil and gas industry has entered the comprehensive low-carbon transition stage driven by various factors. Since different oil companies possess distinct understandings of transition paths, the effect of low carbon transition varies greatly. Obviously, it is necessary to evaluate the performance of low-carbon transitions within the oil and gas industry. Therefore, in this paper, 10 major international oil companies are taken as examples, and a super-efficiency slack-based measurement (super-SBM) model is applied in the present study to analyze the efficiency of low-carbon transitions. Furthermore, the logarithmic mean Divisia index (LMDI) is used to decompose the driving factors of carbon emissions and analyze their impact on the low-carbon transition of international oil companies. The obtained results reveal that although major oil companies have taken different measures in low-carbon transition and achieved a year-on-year reduction in carbon emissions, from the perspective of the efficiency of the entire production and operation process, these oil companies are inefficient in carbon emissions and need to adopt more effective low-carbon transition strategies; Moreover, after the further decomposition of the carbon emission driving factors of the 10 companies, it is found that improving the energy consumption intensity and development level of oil companies can effectively improve the effect of low-carbon transition of international oil companies. Drawing on the above findings, this paper puts forward suggestions for the low-carbon transition of energy companies, thus providing theoretical support and guidance for energy companies in different countries to implement low-carbon transition and green development strategies.

Research on Efficiency of Marine Green Aquaculture in China: Regional Disparity, Driving Factors, and Dynamic Evolution

It is imperative to achieve the high-quality development of fisheries and green transformation of mariculture. Based on the data of marine aquaculture fisheries in China from 2006 to 2019, this research uses the Super-SBM (Slacks-Based Measure) model to measure the efficiency of marine green aquaculture in China and analyzes the regional gap, evolution trend, and driving factors of marine green aquaculture efficiency in nine coastal provinces in China. The results are as follows: (1) The Super-SBM measurement results showed that the efficiency of marine green aquaculture in China showed a fluctuating upward trend from 2006 to 2019. The results of nuclear density estimation and Dagum Gini coefficient show that there is an obvious regional gap in the efficiency of marine green aquaculture in China, and the regional gap is increasing with time. The Dagum Gini coefficient decomposition results show that inter-regional differences and supervariable density differences were the main sources of marine green aquaculture efficiency. The results of barycentric elliptic standard deviation show that the gravity center of marine green aquaculture efficiency in China shifted to the south from 2006 to 2019, indicating that the efficiency of marine green aquaculture in southern China has had a high degree of improvement. (2) The spatial Markov chain results show that spatial proximity plays a key role in the state transfer of marine green aquaculture efficiency. The higher the efficiency of marine green aquaculture in neighboring provinces, the greater the effect on improving the efficiency of marine green aquaculture in the province. The state transfer of marine green aquaculture efficiency usually occurs between similar horizontal states, and there is less “leapfrog” transfer. (3) The results of the geographic detector show that the number of professional mariculture practitioners is the core driving factor and plays a leading role in the regional gap in the efficiency of marine green aquaculture in China. The explanatory power of the interaction of the two factors on the regional gap of marine green aquaculture efficiency is more than 90%, which is much higher than the explanatory power of a single factor. The regional gap of marine green aquaculture efficiency is the result of multiple driving factors.

New energy vehicle innovation network, innovation resources agglomeration externalities and energy efficiency: Navigating industry chain innovation

Energy efficiency is critical in managing the economy, resources, and environment. Existing research primarily focuses on the effect of the internal urban innovation resources and their agglomeration externalities on energy efficiency. Importantly, it overlooks the effect of external resources acquired through urban innovation networks, referred to as the ‘borrowing size effect’. This effect becomes particularly pronounced in the innovation-driven energy transformation of the new energy vehicle industry chain. Therefore, drawing on the spatial Durbin and Super-SBM models, this study examined the effects of three representative types of new energy vehicle innovation networks on energy efficiency, and investigated the borrowing size effect of urban innovation networks on innovation resources agglomeration externalities. The research findings revealed that: (1) The entirety and collaboration innovation networks had promising results on energy efficiency; (2) The structure innovation network demonstrated an insignificant impact, but the innovation of upstream, midstream, and downstream in the industry chain individually exhibited a promoting effect; (3) The externalities of the collaboration innovation network demonstrated a positive spatial effect, and a borrowing size effect.

Measurement, dynamic evolution and influencing factors of green development efficiency in western China: Based on ecological-economic-social system.

Based on the ecological-economic-social system, green development efficiency is divided into green ecological efficiency, green economic efficiency and green social efficiency. Their corresponding indicator systems are constructed, and the Super-SBM model, Super-SBM-Undesirable model and kernel density estimation are applied to measure and analyze green development efficiency and its dynamic evolution in western China from 2007 to 2019. Tobit model is constructed and used to empirically analyze the influencing factors of the green development efficiency in western China. The study shows that: (1) green ecological efficiency and green economic efficiency in western China are generally at a low level, and mainly dragged by northwest China, while green social efficiency in western China is generally at a high level, and mainly dragged by southwest China; (2) green ecological efficiency, green economic efficiency and green social efficiency in western China all show a slight trend of first decreasing and then increasing; (3) all three sub-efficiencies of green development in western China have a decreasing trend of absolute difference, right trailing and polarization; (4) the lower green ecological efficiency in western China is due to the negative impacts from the level of government intervention, the level of economic development, and foreign direct investment. The lower green economic efficiency is due to the positive impacts from population density, the level of government intervention, the level of financial development, and foreign direct investment. The higher green social efficiency is due to the positive impacts from population density, the level of financial development, the level of economic development, and the green technological innovation. The study is based on countermeasure recommendations focusing on improving green social efficiency in southwest China, as well as green ecological efficiency and green economic efficiency in northwest China, which are of reference value to promote green development more comprehensively in western China.

Cultivated Land Green Use Efficiency and Its Influencing Factors: A Case Study of 39 Cities in the Yangtze River Basin of China

In recent years, the Chinese government has been paying more and more attention to agricultural development and ecological protection. Improving cultivated land green use efficiency (CLGUE) is becoming a crucial issue in promoting the sustainable development of agriculture. This study aims to study the current situation and influencing factors of agricultural production from the perspective of green utilization efficiency of cultivated land. It takes 39 cities in the upper, middle and lower reaches of the Yangtze River basin in China as an example. The CLGUE values in those 39 cities from 2011 to 2020 were specifically measured, using the Super-SBM model, kernel density estimation and geographic detector method. Their temporal and spatial heterogeneity was described, and the influencing factors were detected at both single and interactive levels. The results showed that (1) from 2011 to 2020, the green utilization efficiency value of cultivated land in the Yangtze River basin showed an upward trend on the whole; (2) there is clear spatial heterogeneity between CLGUE values in the Yangtze River basin cities, and the distribution is as follows: downstream region > midstream region > upstream region; (3) cultivated land resource endowment, socioeconomic development and agricultural production technology are important factors affecting the variability in CLGUE values. However, there are some differences in the degree and direction of influence of different influencing factors on different sample subgroups.

Study of the space–time transition and spatial spillover effects of tourism green production efficiency in the Yangtze River Delta—a reanalysis from the perspective of tourism carbon sinks

Tourism green production efficiency serves as the foundation for assessing the mutual coupling performance of the tourism economy and the ecological environment. In this paper, the tourism carbon sink is included in the measurement framework, and the TGPE of 41 cities in the Yangtze River Delta region from 2011 to 2019 is estimated by the Super-SBM model. Furthermore, kernel density estimate, spatial autocorrelation, Markov chain and spatial Durbin model are further integrated to explore its spatio-temporal evolution process, spatial effects and influencing factors. The results show that 1) TGPE in the Yangtze River Delta has been increasing during the study period. The high-efficiency and low-efficiency areas of the TGPE have a bipolar pattern characterized by “low–low convergence” and “high–high convergence.” 2) There is considerable spatial variation in TGPE from north to south. The number of hot spots and sub-hot spots increases in volatility, whereas the number of sub-cold spots and cold spots decreases. 3) Although cities with low levels of TGPE have a higher probability of moving to the next level, grade transformation across hierarchies is difficult to attain. When considering the factor of adjacent types and the influence of spatial lag on the transfer probability. 4) The positive spatial spillover effects of TGPE is significant. At the same time, economic development level, transport accessibility and tourism industry agglomeration have positive spillover effects on neighboring cities. Conversely, urbanization level and openness level have negative spillover effects.

The green economic impact of a green comprehensive industry agglomeration: An example from the sports industry

The optimization of industrial structure and layout is essential for promoting the high-quality development of the regional economy. As a typical example of a green comprehensive industry, the agglomerations of the sports industry have the potential to release additional green benefits. Consequently, this paper uses the balanced panel data of 30 provinces, municipalities, and autonomous regions in China from 1998 to 2021 as samples and, based on the strategic background of China's dual-carbon target and the re-interpretation of the green economy, uses the Super-SBM model to re-measure the green economic efficiency of each region and applies the dynamic spatial Durbin model and the dynamic panel system GMM model to evaluate the direct effect, mediating mechanism, spatial spillover effect, and heterogeneity effect of sports industry agglomeration on the regional green economic efficiency. Empirical findings indicate that: (1) The improvement of green economic efficiency under China's dual-carbon target has the characteristics of dynamic accumulation, and there is a siphon effect between neighboring regions. (2) The effects of sports industry agglomeration on local green economy efficiency show an "inverted U-shape" with a positive spatial spillover effect on the green economy efficiency of neighboring regions; this conclusion is robust. (3) The green economy effect of sports industry agglomeration is more significant in the central and western regions, regions with strict environmental regulations, and regions with a higher willingness for resident participation in sports due to industrial density, compliance costs, and characteristics of sports industry development. (4) Sports industry agglomeration can promote regional green economy efficiency by escaping natural resource dependence and increasing healthy human capital; technological innovation, rationalization of industrial structure, and labor transfer serve as "inverted U-shaped" mediators between sports industry agglomeration and regional green economy efficiency. This study expands the meso- and spatial-level perspectives of the impact of the agglomeration of green industries and comprehensive industries on green development. It is of great theoretical and practical importance for promoting the construction of a regional green industrial system and the high-quality development of the green economy.

Rural recreation tourism in the Panxi region of China in the context of ecological welfare

Urbanisation has improved living, health and welfare standards but degraded the ecological environment. Tourism models are constantly innovating, and the rural health model is attracting attention to rural recreation, and research on eco-welfare performance and tourism efficiency in ethnic minority rural areas is an important foundation for the construction of ecological civilisation and sustainable development in ethnic minority rural areas. The Panxi region of China has abundant sunshine, mild winters and a good ecological environment, and the rich tourism resources are stimulating rural health tourism. Herein, we used the Super-SBM model to measure eco-welfare performance and rural recreation and tourism efficiency in this region, then used the coupling coordination model to assess coupled co-scheduling of eco-welfare performance and rural health tourism efficiency, the Tobit model was constructed to test the influencing factors of the coupling coordination degree of eco-welfare performance and rural health tourism efficiency in Panxi region. The results show that the growth rate of social fixed asset investment, industrial structure and social development level have a positive effect on the coupling and coordination of eco-welfare performance and rural recreation and tourism efficiency in Panxi, while population density and ecological civilization degree have a negative effect on them. Therefore, the development of rural recreation tourism in Panxi can be promoted by playing a guiding role of the government, promoting the transformation of industrial structure, establishing citizens' awareness of green environmental protection and improving the efficiency of natural resources utilization.

The impact of financial support on forestry green total factor productivity from the perspective of environmental regulation

PurposeThe aim of this article is to research on forestry green total factor productivity and explore the impact of financial support on forestry green total factor productivity.Design/methodology/approachThe methods used in this study are super efficiency SBM model of undesired output and empirical model. SBM model is a kind of Data Envelopment Analysis (DEA). The SBM model with non-expected outputs (slacks-based measure) can be used to deal with the problem of efficiency measurement with multiple input and output variables and can be used to analyze the efficiency of green development of forestry economy.FindingsFirst, the overall green total factor productivity of the authors’ country's forestry has shown a trend of first decline and then an increase from 2008 to 2018, and there are significant spatiotemporal differences; second, financial support has a significant positive impact on forestry green total factor productivity; third, environmental regulation has a significant threshold effect in the process of financial support on forestry green total factor productivity, and the role of financial support shows a trend of first increasing and then decreasing.Originality/valueSecondly, taking the data of 30 provinces and cities in the authors’ country from 2008 to 2018 as the research object, using the super-efficiency SBM-Malmquist index to measure the country's forestry green total factor productivity and analyze its temporal and spatial changes; finally, a dynamic panel model was established to explore the impact of financial support on forestry green total factors quantitative impact on productivity, and adding environmental regulation as a threshold variable to establish a dynamic threshold regression, and found that financial support has a nonlinear impact on forestry green total factor productivity.

Spatiotemporal evolution and influencing factors of water resource green efficiency in the cities of the Yangtze River Economic Belt.

Effectively utilizing water resources, which is a fundamental natural resource and a vital economic resource, directly impacts how a country's economy develops. In this study, the Super-SBM model is used to calculate the city water resource green efficiency (CWRGE) of the Yangtze River Economic Belt (YREB), 108 cities that are prefecture level or higher, from 2006 to 2021. And its temporal and spatial evolution as well as its affecting variables are examined. The results indicate that, as a whole, the YREB's CWRGE has not yet achieved an effective level. The CWRGE in the YREB generally exhibits a trend of "first decreasing and then increasing, then decreasing and then increasing" and shows a "W"-shaped evolution law, and the overall trend is upward. There are just seven cities with effective data envelopment analysis (DEA), namely Changzhou, Hangzhou, Shanghai, Xuzhou, Changde, Changsha, and Yuxi. During the reporting period, the CWRGE of cities of various scales showed significant gaps: mega cities > big cities > small and medium-sized cities. From a regional perspective, the highest rate of CWRGE was found downstream of the YREB cities, then upstream, and the middle was the lowest. Spatial correlation findings demonstrated that both the agglomeration range and the outlier range were distributed, and there were mainly two positive aggregations of space forms ("high-high (H-H) type" and "low-low (L-L) type"), and the spatial distribution changed. The results of the spatiotemporal evolution demonstrate that there are more and more cities with high efficiency, as well as cities with low efficiency. From the results of the Tobit regression model, the CWRGE in the YREB are significantly improved by the economical development level, industrial scale, and water usage structure. While foreign direct investment and environmental regulation have considerable detrimental impacts, the impact of scientific and technological investment is not significant.