Super-efficiency Slack-based Measure Research Articles

Evaluation of Urban Low-carbon Development Efficiency: Evidence from 30 Cities in China

Abstract With the acceleration of global urbanization, the issues faced by urban development are becoming increasingly apparent. Some countries and regions have placed less emphasis on low-carbon emissions in the development process and prioritized urban development. However, blindly pursuing rapid urbanization has led to excessive urban carbon emissions, which outweigh the gains. Therefore, based on panel data from 30 cities in China from 2010 to 2019, this study combines the super-efficiency slacks-based measure (SBM) model and the Malmquist index, taking urban carbon emissions as undesirable outputs, to evaluate urban development efficiency. Through a multidimensional comparative analysis, this study reveals the developmental challenges encountered by various cities. The results show that: statically, the overall urbanization progress of these 30 cities is commendable, but significant fluctuations are observed in the development trend. The mean super-efficiency of cities in the eastern coastal areas is relatively high, whereas other regions exhibit instances of ineffective mean super-efficiency. Dynamically, there are few periods of efficiency improvement during 2010-2019, and there is an imbalance in the sources of efficiency improvement. Shanghai and Hangzhou exhibit the best continuous dynamic improvement status, with small fluctuations in dynamic efficiency, whereas Guiyang has the worst dynamic efficiency improvement status, indicating poor urban development. This study underscores the urgent need for a balanced approach to urban development that incorporates low-carbon initiatives to ensure sustainable progress and mitigate adverse environmental impacts.

Effects of Industrial Structure on the Green Utilization Efficiency of Urban Land: A Case Study of the Bohai Rim Region, China

The green utilization of urban land is a critical component of regional high-quality development. Enhancing the green utilization efficiency of urban land (GUEUL) is of great significance to improving the quality of ecological environment and achieving a green lifestyle and low-carbon production. With the process of urbanization, the industrial structure is constantly adjusted, which has an impact on the structure and efficiency of urban land utilization. Taking 43 cities in the Bohai Rim region as an example, the super-efficiency slack-based measure model with undesirable outputs (super-SBM-undesirable model) was employed to evaluate the GUEUL from 2006 to 2021, and the panel quantile model was used to explore the impact of industrial structure on GUEUL. The results showed that the GUEUL in the Bohai Rim region appeared a fluctuating upward trend from 0.664 in 2006 to 0.837 in 2021. High-value zones shifted from western Liaoning province and southern Shandong province to a “C”-shaped belt around the coastline and expanded continuously, while low-value zones decreased significantly. Furthermore, the influence of industrial structure on GUEUL was significantly positive, but there were significant differences among different efficiency levels. Industrial structure upgrading exerted the most significant effect on GUEUL improvement in low-value zones, while industrial structure rationalization was dominant in high-value zones, and the influence of industrial structure technologization was more significant in medium-low-value zones. Therefore, differentiated industrial structure adjustment policies should be formulated based on the actual condition of each city to improve GUEUL.

Can the Development of Renewable Energy Improve Total-Factor Carbon Emissions Efficiency? Evidence from 30 Provinces in China

Abstract The global temperature is exceeding 1.5°C above pre-industrial levels due to the increasing carbon emissions, and developing renewable energy is expected to be one of the most effective solutions. However, whether the development of renewable energy contributes to curbing the carbon emissions while maintaining economic development is still under investigation. Using the total-factor carbon emission efficiency (TFCEE), this paper first measures the carbon emission abatement and economic development of 30 provinces in China from 2005 to 2019. Then, based on the super-efficiency slacks-based measure (SE-SBM) model, the panel threshold models and spatial Durbin models are established to comprehensively investigate the impact of RED on TFCEE. The findings reveal that: (1) RED significantly improves TFCEE. For every 1% increase in RED, TFCEE experiences a rise ranging from 0.020% to 0.035%. (2) The beneficial effect of RED on TFCEE increases with economic restructuring and technological progress. (3) The indirect impact of RED on TFCEE through spatial spillover is significantly greater than its direct effect. Potential transmission mechanisms for this spatial spillover effect are the cross-regional mobilization of renewable electricity and the diffusion and absorption of low-carbon knowledge and technologies. The above conclusions provide empirical evidence for China and other developing countries to formulate appropriate energy transformation strategies. Keywords: Renewable energy development; Total-factor carbon emissions efficiency; Transmission mechanism; Spatial spillover effect; Nonlinear effect.

Spatial Spillover Effects and Correlation Network Analysis of Green Construction Development Efficiency in China

Green construction has been implemented globally to address resource depletion and environmental degradation. The effectiveness of green construction development depends on the efficiency of resource utilization and the common progress of all regions. However, existing studies have paid limited attention to green construction development efficiency (GCDE) and its spatial correlation. This study uses the super-efficiency slacks-based measure (Super-SBM) model, spatial econometric models, and social network analysis (SNA) method to reveal the spatial distribution, spatial spillover effects, and spatial correlation of GCDE across 30 provinces in China. The results indicate significant disparities in GCDE among regions, with the eastern region exhibiting considerably higher GCDE than the western and northeastern regions. There is evidence of spatial spillover effects, and the spatial correlation of GCDE is strengthening. Provinces with high GCDE occupy central positions in the GCDE network and exert spillover effects on other provinces, while underdeveloped provinces have opportunities to benefit from regional cooperation. The findings provide policy insights for the implementation and enhancement of green construction development actions. Moreover, they provide Chinese experience for green construction practices in other areas of the world.

What were the spatiotemporal evolution characteristics and influencing factors of global land use carbon emission efficiency? A case study of the 136 countries

Nations worldwide must collaborate to tackle the challenge of improving carbon emission efficiency and optimizing land use effectively. This study extends the study area from provinces and cities, regional city clusters, and countries to 136 major countries in the world, and shows the differences between countries. The paper analyzed the spatiotemporal evolution characteristics and influencing factors of land use carbon emission efficiency (LCEE) in these 136 countries from 2000 to 2019 using the super-efficiency slacks-based measure data envelopment analysis (Super-SBM DEA) model, global malmquist index, spatial autocorrelation measurement tools and Spatial Durbin model. It can help to promote low-carbon and green development while simultaneously improving the global LCEE. The findings indicate that: (1) Over the study period, there is a trend toward a decline in the LCEE worldwide. (2) The primary cause of the worldwide LCEE’s declining trend during the study period is the reduction in technical efficiency. (3) The low-low cluster type accounts for more, while the high-high agglomeration type accounts for less and exhibits a sluggish growth pattern. On the whole, there is a phenomenon of non-aggregation and non-uniformity. It is necessary to increase the driving effect of high-high cluster radiation. (4) Economic development level, investment level, population size, public service level and environmental sustainability level all have a significant impact on LCEE, while industrial development level has no significant impact on LCEE. From a global perspective, the radiation driving role of countries with high LCEE should be increased. Improve the efficiency and technological level of land use emission in light of local conditions, and promote independent innovation in green and low-carbon technologies. This is conducive to the establishment of long-term low-carbon growth.

Spatial differentiation and driving factors of scientific and technological innovation efficiency under the background of the digital economy

Scientific and technological innovation (STI) activities are facilitated under the influence of the digital economy and depend on digital elements to support innovation activities. This study measures digital element inputs and outputs using data related to the digital economy and estimates STI efficiency in 29 provincial regions in mainland China from 2010 to 2021 employing the global super-efficiency slacks-based measure (SBM) model. Then, spatial characteristics and driving forces of STI efficiency are quantified using spatial autocorrelation and geographic detectors. The results reveal that STI efficiency exhibits a fluctuating, W-shaped pattern, and the spatial correlation effect shows a gradually declining trend from 2010 to 2021. The findings reveal that the core drivers of STI efficiency are digital financial development, e-commerce development, digital access level, and related factors. Therefore, strengthening inter-regional innovation links, promoting the construction of 5 G mobile internet and accelerating the integration of digital industries into traditional industries are recommended.

Measurement of Tourism Ecological Efficiency and Analysis of Influencing Factors under the Background of Climate Change: A Case Study of Three Provinces in China’s Cryosphere

Against the backdrop of climate change and the “dual carbon” goals, enhancing the ecological efficiency of cryospheric tourism is crucial not only for the high-quality development of the tourism industry itself but also for the protection of the ecological environment and the promotion of green sustainable development in the cryospheric region. In light of this, this study, taking climate change as its background and based on the perspective of carbon emission constraints, integrates multidimensional factors such as “climate change, carbon emission constraints, and cryospheric resources” into a unified measurement framework to construct a model for evaluating the ecological efficiency of tourism in the cryosphere. Specifically, the model considers inputs, expected outputs, and unexpected outputs. Subsequently, employing the super-efficiency slack-based measure (SBM) model, this study measures the tourism ecological efficiency (TEE) of three provinces (Xinjiang, Qinghai, Tibet) in the cryosphere from 2013 to 2021 and utilizes the Malmquist–Luenberger index and gray correlation model to reveal their dynamic changes, efficiency decomposition, and influencing factors. The results indicate that: (1) The overall mean of TEE in the cryosphere is between 0.2428 and 1.2142, Over the study period, the average annual growth rate and corresponding confidence interval were 14.74%, (−8.61%, 64.23%), showing a significant fluctuating growth trend. Among them, Xinjiang stands out, with its mean scores ranging from 0.2418 to 1.6229, surpassing the overall average level of the cryosphere. (2) During the study period, the overall dynamic efficiency of tourism ecology in the cryosphere increased by 21.54%, driven by the synergy of technological progress (TC), pure technical efficiency (PET), and scale efficiency (SE). For each province, the dynamic efficiency of tourism ecology has improved, but to varying degrees. (3) Regarding the driving factors of TEE in the cryosphere, each driving factor is closely related to TEE, ranked from large to small as follows: carbon emission structure, level of economic development, infrastructure, intensity of technological input, industrial structure, resource endowment, and environmental regulation. This article holds theoretical and practical significance for promoting the high-quality development of polar tourism and achieving synergistic progress between the economy and environment.

The threshold effect and driving path of the rapid transit system on tourism eco-efficiency in China

Rapid transit is important for the green development of China’s tourism industry because it profoundly affects the optimal structural layout and rational allocation of elements that impact regional tourism. Based on provincial panel data from 2009 to 2020, the entropy weight method, tourism carbon emission measurement model, Super efficiency slacks-based measure model and threshold regression model were used to determine the impact of the rapid transit system on tourism eco-efficiency in China. The findings showed that rapid transit system has increased steadily, while tourism eco-efficiency has fluctuated significantly, and the relative development characteristics of rapid transit system and tourism eco-efficiency gradually changed from “lagging rapid transit system” to “lagging tourism eco-efficiency.” In addition, the rapid transit system and the highway and civil aviation have a nonlinear relationship with tourism eco-efficiency, while the high-speed railway has not passed the threshold effect test, and at the regional level, the impacts of rapid transit system have heterogeneous characteristics. As the deepening construction of rapid transit system, its threshold effect on tourism eco-efficiency will change from positive “V” to positive “U” type, promoting the more efficient development of environmentally friendly tourism.

Has the Digital Economy Improved the Urban Land Green Use Efficiency? Evidence from the National Big Data Comprehensive Pilot Zone Policy

The advancement of the big data industry is playing a pivotal role in urban land management refinement. Recently, China initiated a big data strategy, establishing national big data comprehensive pilot zones (NBDCPZs) across diverse regions. These initiatives present substantial opportunities for enhancing the urban land green use efficiency (ULGUE). Consequently, in this study, we utilized the super-efficiency slack-based measure (SBM) model with undesirable outputs to assess the ULGUEs across 281 prefecture-level cities in China from 2006 to 2021. Subsequently, leveraging the NBDCPZ establishment as a quasi-natural experiment, we employed the difference-in-differences (DID) method to empirically explore the impact of the NBDCPZ policy on the ULGUE for the first time. The findings revealed the following: (1) The implementation of the NBDCPZ policy significantly enhances the ULGUE; (2) the effects are mediated through mechanisms such as fostering technological innovation, mitigating resource misallocation, and promoting industrial agglomeration; (3) the heterogeneity analysis emphasizes the increased policy effectiveness in cities characterized by fewer natural resources, lower economic growth pressures, stable development stages, and moderate digital infrastructure and human capital levels; and (4) further analysis demonstrates the significant positive spillover effects of the NBDCPZ policy on the ULGUEs of neighboring non-pilot cities, with a diminishing impact as the proximity between pilot and non-pilot cities decreases. Overall, this study contributes to the literature on the relationship between the digital economy and land utilization, offering valuable insights for achieving sustainable urban development.

Evaluation of Urban–Rural Total Factor Flow Efficiency Based on Multiple Symbiosis: Insights from 27 Provinces in China

The rational flow of production factors is crucial for promoting benign interactions between urban and rural areas. To unveil the intrinsic mechanisms of factor flow pathways promoting mutual symbiosis between urban and rural areas, this study, based on symbiosis theory, takes total factor flow including land, technology, capital, and labor as inputs and urban–rural symbiosis level as output. Utilizing the Super-Efficiency Slack-Based Measure (SBM) model, this study calculates the urban–rural total factor flow efficiency of 27 provinces in China from 2011 to 2021 and explores specific improvement directions of urban–rural factor flow based on projection analysis. This study revealed the following findings: (1) The overall efficiency of urban–rural total factor flow in China shows a fluctuating upward trend but has not yet reached an effective state. There are significant regional disparities, with 8 provinces such as Guangdong and Fujian reaching Pareto optimality, while the remaining 19 provinces exhibit varying degrees of inefficiency. (2) Provinces with insufficient symbiotic production are mainly concentrated in the central and western regions and the northeast region, with 14 provinces including Inner Mongolia showing the inadequate transformation of urban–rural symbiosis. However, except for Hainan, the situation is gradually improving in other regions annually. (3) There is input redundancy in total factor, where land, labor, and capital redundancy are the main reasons for the inefficiency of urban–rural total factor flow in China. However, trends show that the redundancy of land, labor, and capital elements is improving annually, while technology redundancy is worsening. (4) Through a comprehensive analysis of input redundancy, output deficiency, symbiosis coefficient, and efficiency, this study categorizes the impact of factor flow on urban–rural symbiosis level into basic matching, redundancy, and comprehensive scarcity types. The research provides scientific guidance for promoting sustainable development through the rational flow of total factors and offers valuable insights for similar countries.

Spatiotemporal evolution characteristics and influencing factors of the crop water use efficiency in watersheds based on the water footprint.

Climate change has exacerbated the contradiction between water scarcity and sustainable agricultural development. Assessing the crop water use efficiency and its influencing factors could provide a decision-making reference to realize Sustainable Development Goal 2. By analyzing the temporal and spatial evolution characteristics of the crop water footprint, the blue water footprint, green water footprint, and grey water footprint were introduced into the super efficiency slack-based measure model to evaluate the crop water use efficiency in basins. The influence of the driving factors was examined by using the geographic detector model. The situation in the provinces along the Yellow River Basin from 2005 to 2020 was used as a verification case. The results indicated that (1) during the study period, crop water use in the basin was mainly based on the blue water footprint, accounting for approximately 55% of the total water footprint, the grey water footprint, accounting for approximately 30% of the total water footprint, and the green water footprint, accounting for the lowest proportion, at approximately 15%. (2) The crop water use efficiency exhibited a spatial distribution pattern of high values in the east and low values in the west, with obvious upstream provinces<midstream provinces<downstream provinces regional characteristics. (3) The driving factors of crop water use efficiency changes could be ranked as follows: effective irrigation rate (0.75) > disposable income of rural residents (0.71) > population urbanization rate (0.65) > degree of agricultural mechanization (0.63) > agricultural disaster rate (0.61). Furthermore, the interaction effects between the driving factors were greater than the effects of the single factors. The study provides an important reference for understanding the changes, driving mechanisms, and impacts of crop water use efficiency in basin areas. It promotes green agricultural transformation and development to address climate change and alleviate the pressure on water resources.

Analysis of dynamic relationship between agricultural products circulation service industry and circulation efficiency in less developed regions: based on data from Western China

The market circulation system for agricultural products in less-developed regions lags behind; it fails to meet the requirements for efficient circulation. Therefore, it is crucial to explore the dynamic relationship between the Agricultural Products Circulation (APC) service industry and circulation efficiency. Using panel data from 29 regions in China, the entropy method measured the Development Level (DL) of the APC service industry, while the non-oriented super-efficiency Slacks-Based Measure (SBM) model was employed to assess the APC efficiency. A Panel Vector Autoregressive (PVAR) model was built for estimation. The impulse response function and variance decomposition were utilized to analyze the differences in the DL of the APC service industry, circulation efficiency, and their interaction mechanism across the Western, Central, and Eastern regions of China. The findings indicate that the DL of the APC service industry and APC efficiency in the less-developed regions of the West have a positive driving effect on each other. However, the magnitude of this effect was asymmetric and transient. The APC service industry’s DL and APC efficiency are more dependent on their inertia, and no efficient two-way feedback mechanism has been formed. This study provides a reference for the construction and sustainable development of modernized APC systems in less-developed regions.

Provincial-scale assessment of vulnerability and resilience to drought in China

Frequent droughts have caused severe disaster losses in China. Such events can be minimized by enhancing the country's resilience and reducing its vulnerability, where this can ensure socioeconomic stability and sustainable development. Evaluating the vulnerability and resilience to drought is thus crucial for effectively managing the risk of disasters and promoting sustainable socioeconomic development. In this study, we constructed a comprehensive framework to assess the spatiotemporal characteristics of China's vulnerability and resilience to drought at the provincial scale from an input-output perspective by using the Super-efficiency Data Envelopment Analysis (DEA) model and the Super-efficiency Slacks-Based Measure DEA (SBM-DEA) model. This study focused on drought drivers, the disaster-forming environment, drought bearers, disaster intensity, and recovery. The results showed that the vulnerability to drought of 42 % of China's provinces decreased from 2010 to 2022, that of only 29 % of the provinces increased, while the status of a majority of provinces improved in general. The center of gravity of the vulnerability to drought moved toward the southwest over time and a spatial clustering of vulnerability was observed, with High-High clusters moving from the north to the south. Moreover, the resilience to drought declined in 36 % of provinces and increased in only 20 %, reflecting poor resilience overall. The center of gravity of China's overall resilience to drought moved northward, with a relatively stable spatial pattern and prominent clusters of Low-Low resilience indicating a pressing need for improvement. Areas with high vulnerability and low resilience were concentrated in inland western and eastern regions, and this highlights the importance of drought prevention and mitigation in provinces like Xinjiang, Inner Mongolia, Jiangxi, and Fujian. The findings here provide valuable insights for mitigating the risk of drought and promoting sustainable socioeconomic development.

The Impact of the Carbon Emission Trading Shadow Price on the Green Total Factor Productivity of the Power Industry in China

To mitigate the problem of global climate change, governments have taken measures to reduce greenhouse gas emissions. Carbon emission trading has gradually attracted attention as a market-oriented option. Power industry panel data from 30 provinces in China were used for an empirical analysis in this study. The super-efficiency Slack-Based Measure (SBM) model was used to calculate the shadow price of carbon trading and the green total factor productivity (GTFP), and the Ordinary Least Squares (OLS) regression model was used to quantitatively analyze the correlation between the shadow price of carbon trading and the GTFP of the power industry. The results showed that the shadow price of carbon trading had a significantly negative impact on the GTFP of the power industry; therefore, it needs to be improved and perfected. Through a further analysis using the heterogeneity test, it was found that there were problems in the current carbon trading price mechanism. In the face of the above problems, we offer suggestions for improvement from the perspectives of the government and companies. This study helps deepen the understanding of carbon trading prices and the GTFP in the power industry, and it provides a reference for formulating more effective carbon trading policies and corporate green management strategies.

Impact of rural digital economy development on agricultural eco-efficiency: evidence from mainland China

The integration of the digital economy with rural development is of great significance as it plays a pivotal role in mitigating carbon emissions and environmental pollution in agriculture, thereby contributing to the evolution of agriculture in a green and sustainable manner. This study aims to examine the impact and mechanisms of rural digital economy development (RDED) on agricultural eco-efficiency (AEE). Specifically, based on provincial-level panel data from China spanning from 2011 to 2021, we evaluate China’s AEE by employing the super-efficiency slacks-based measure (Super SBM) model, taking into account the positive externality of agricultural carbon sinks. Then we analyze the impact and mechanisms of RDED on AEE using the two-way fixed effects model. The findings indicate that: (1) RDED significantly promotes AEE, and this conclusion remains robust even after being tested by replacing the explained variable, altering the sample interval, and including more control variables; (2) RDED can significantly drive AEE in the midwestern regions of China, but the promotion effect on the eastern region has not been fully demonstrated. Additionally, the promotion effect in southern China is greater than that in northern China; (3) agricultural science and technology investment partially mediates the impact of RDED on AEE. Moreover, agricultural science and technology innovation has a positive moderating effect on the relationship between RDED and AEE. Lastly, this study provides new evidence and policy recommendations for developing countries, such as China, to proactively facilitate the coordinated development of the rural digital economy and agricultural ecology, and attain green and sustainable ecological agriculture.

Network Centrality and Road Freight Efficiency: A Spatial Econometric Analysis of South Korea’s Transport Regions

Purpose - The purpose of this research is to investigate the road freight efficiency in 15 regions of South Korea using a super-efficiency SBM model and to understand the role of network centrality in influencing this efficiency. It aims to analyze how in-degree and out-degree centrality within the road freight network impacts the efficiency of road freight and how these effects manifest spatially across different regions. Design/Methodology – The study applied a super-efficiency Slacks-Based Measure (SBM) model to evaluate the efficiency of road freight across various regions. It incorporated two indices, indegree and out-degree centrality, to measure the network centrality of regions within the road freight network. Furthermore, it utilized the Spatial Durbin Model (SDM) to decompose and analyze the direct and indirect effects of network centrality on road freight efficiency, considering different spatial weight matrix. Findings – The study found that road freight efficiency in a region positively correlates with its centrality within the road transport network, indicating regions that are central to the network tend to utilize their inputs more effectively and improve transport efficiency. It also discovered positive spillover effects of network centrality, suggesting that a region’s central role in the network benefits not only itself but also adjacent regions. Originality/value - The research is valuable for its application of the super-efficiency SBM and SDM models to the context of road transport networks in South Korea, a relatively underexplored area in transportation studies. It contributes original insights into the spatial dynamics of road freight efficiency, emphasizing the role of network centrality and its variable effects across different regions.

Impacts of Low-Carbon Pilot Policies on the Land Green Use Efficiency in Adjacent Non-Pilot Cities: An Empirical Study Based on 257 Prefecture-Level and above Cities in China

In the context of global climate change, the low-carbon city pilot policy has become an important strategy to promote green development. Based on the panel data from 257 prefecture-level and above cities in China, this study utilized the Super-Efficiency SBM (Slacks-Based Measure) to measure the land green use efficiency and analyzes the impact of the policy on adjacent non-pilot cities using a difference-in-differences model. The findings indicate that the implementation of low-carbon pilot policies can significantly improve the land green use efficiency in adjacent non-pilot cities, which can be primarily ascribed to the spillover effect and catfish effect. A heterogeneity analysis further revealed the positive effects of the policies in the eastern region and non-resource-based cities. This study provides valuable references for relevant legal provisions on environmental regulation and for continuously monitoring and evaluating the policy effects to achieve sustainable development goals.

Evaluation of China’s Marine Aquaculture Sector’s Green Development Level Using the Super-Efficiency Slacks-Based Measure and Global Malmquist–Luenberger Index Models

Given China’s rapidly expanding marine aquaculture industry, the associated ecological issues have garnered widespread attention. Therefore, it is crucial to speed up the green growth of marine aquaculture in order to save the environment and use resources sustainably. In order to statically assess and dynamically analyze the green development efficiency levels of marine aquaculture in nine coastal provinces of China from 2012 to 2021, this study uses the non-expected output super-efficiency Slacks-Based Measure model and the Global Malmquist–Luenberger index method. Additionally, it integrates input–output redundancy rates to analyze the causes of efficiency loss. Static efficiency primarily reflects whether a region’s inputs and outputs at a given point in time reach an effective efficiency level, while the level of dynamic efficiency mainly gauges the dynamic changes in the efficiency of green production. The results show that, from 2012 to 2021, China’s marine aquaculture industry’s average static efficiency of green output was 0.705. The southern marine economic zone exhibited the highest static efficiency value in the green development of marine aquaculture, displaying a stepped distribution pattern of “south–north–east” in decreasing order. The input–output redundancy analysis reveals that the primary causes of static efficiency loss in China’s marine aquaculture industry are attributed to varying degrees of redundant inputs and carbon emission outputs. Looking through the lens of the GML index, the annual average growth rate of the green total factor productivity in China’s marine aquaculture stands at 11.1%, with an annual average change in technical efficiency of 1.8%, while the annual average change in technological progress amounts to 9.1%, suggesting that technological advancement is the primary driver of the rise in green total factor productivity in China’s marine aquaculture sector. According to the study, in order to encourage China’s marine aquaculture industry to grow sustainably, efforts should be made not only to accelerate technological advancements but also to enhance technical efficiency. Policies that are specifically designed for the local environment should be developed to support the sustainable development of the marine aquaculture sector and to make resource allocation easier.

Unveiling the Patterns and Drivers of Ecological Efficiency in Chinese Cities: A Comprehensive Study Using Super-Efficiency Slacks-Based Measure and Geographically Weighted Regression Approaches

Urban ecological efficiency stands as a pivotal indicator that mirrors the level of sustainable development within cities. To unravel the sustainable development status of Chinese cities and illuminate the factors impacting the diverse developments among them, this study leveraged the super-efficiency SBM (slacks-based measure) model to assess the ecological efficiency of 284 prefectural-level and above cities across China in 2019, divulging their spatial distribution. Furthermore, a GWR (geographically weighted regression) model was also employed to scrutinize the factors influencing the ecological efficiency of these cities. Key findings include: (1) The mean ecological efficiency of Chinese cities in 2019 stood at 0.555, signaling moderate urban sustainability, with southern cities outperforming their northern counterparts. (2) A pronounced spatial clustering of ecological efficiency was evident, featuring positive spillover effects around high-efficiency cities and conversely, negative spillover effects around low-efficiency cities. (3) Economic development and population density positively influenced urban ecological efficiency, while urbanization levels exhibited a negative impact. The influences of industrial structure, technological level, and opening-up level varied, showcasing both positive and negative impacts contingent upon the spatial disposition of the cities. Hence, policymakers are advised to recognize the spatial nuances in the impacts of distinct factors on urban ecological efficiency and tailor measures accordingly to fortify urban sustainability.

Assessing the impact of prefabricated buildings on urban green total factor energy efficiency

Prefabricated buildings (PB) are assembled using prefabricated components, known for their cost-effectiveness and high efficiency. They are seen as an effective means to achieving sustainable development goals and mitigating climate change. This paper examines the impact of prefabricated buildings on urban green total factor energy efficiency (GTFEE) using panel data from 278 cities in China. The analysis is conducted through the application of a super-efficiency slack-based measure (SBM) model and a difference-in-differences model. The results of the study indicate that the promotion of PB can significantly improve urban GTFEE; there is heterogeneity in the improvement of urban GTFEE by PB; the innovation effect, industrial effect and marketization effect of PB are the main ways to improve urban GTFEE. Specifically, PB exerts an innovation effect by promoting technological innovation. It influences consumer and investor demand, leading to increased activity in the secondary industry, thereby creating an industrial effect. Moreover, PB facilitates the marketization effect by phasing out outdated industries, encouraging autonomous enterprise innovation, and fostering the growth of new advanced enterprises. This study reveals the critical role of PB in urban GTFEE improvement. It clarifies its mechanism of action, which provides a reference for urban sustainable development strategies and energy efficiency.