Search Results (868)

In the context of increasingly frequent and severe natural disasters, scientifically measuring and analyzing the efficiency of natural disaster emergency management in China is of great practical significance for enhancing the performance of the emergency management system and promoting its systematic and high-quality development. This study first applies a super-efficiency SBM-DEA model with undesirable outputs to systematically measure the efficiency of China’s natural disaster emergency management system during the period 2019–2023. Subsequently, the Dagum Gini coefficient and Kernel Density estimation are employed to examine regional disparities and dynamic evolution across eastern, central, western, and northeastern China. Finally, the coefficient of variation and spatial econometric models are applied to test the spatial convergence characteristics of emergency management efficiency. The results indicate that: (1) China’s overall disaster emergency management efficiency remains at a relatively low level and exhibits a fluctuating trend characterized by an initial increase followed by a decline. The regional distribution pattern of emergency efficiency is ranked as “Northeast > Central > West > East”. (2) The average annual contributions of intra-regional disparities, inter-regional disparities, and transvariation density to the overall variation in national emergency management efficiency are 27.58%, 39.90%, and 32.53%, respectively, indicating that inter-regional disparities and transvariation density are the dominant sources of systemic differences among regional subsystems. (3) The national distribution of emergency management efficiency displays a bimodal pattern, indicating polarization; however, the secondary peak is relatively flat, suggesting a weakening trend of provincial-level polarization and a gradual narrowing gap with high-efficiency regions. (4) σ-divergence is observed at the national level and in the central region, while both absolute and conditional β-convergence exist to varying degrees at the national level and across all four regions. Nevertheless, the enhancement of natural disaster emergency management efficiency has not yet realized a system-level transition from convergence in growth rates to convergence in efficiency gaps. In addition, economic development, technological progress, urbanization, and industrial structure exert significantly heterogeneous effects on disaster emergency management efficiency across different regions. Full article

To investigate the load characteristics and rock-breaking efficiency of the hobs on the conical cutterhead, a theoretical model of the hob’s rock-breaking load was established based on the plastic-brittle characteristics of rock, with a verification error of less than 5%. A numerical model of dual-hob rotary rock breaking was developed using ABAQUS 2022 software to comparatively study the influence of penetration depth (P), cutter spacing (S), and rotational speed (V) on the hob’s load behavior and rock-breaking efficiency. The specific energy of rock breaking under various test conditions was obtained through orthogonal experiments. The results indicate that, as the penetration depth increases, the average rock-breaking load of the hob gradually increases, while the specific energy first decreases and then increases. With larger cutter spacing, the average load shows a modest increase, and the specific energy exhibits a gradually rising trend with a diminishing growth rate. As the rotational speed increases, the average load increases slightly, while the specific energy rises with an accelerating growth rate. Range analysis revealed that the order of influence of factors on rock-breaking efficiency is P > S > V. The highest rock-breaking efficiency was achieved at P = 2 mm, S = 60 mm, and V = 7 r/min. At a significance level of 0.05, the penetration depth was found to have a significant effect on specific energy. This study provides a valuable reference for the design of hob layouts and parameter settings of conical cutterheads, contributing to improved rock-breaking efficiency. Full article

This paper proposes an innovative research algorithm “measurement—pattern—driving force—synergy” that determines the eco-efficiency of 83 Russian federal subjects (2000–2019) using the Slacks-Based Measure (SBM) model with non-desired outputs (incorporating comprehensive input indicators such as water resources and electricity input, and dual non-desired outputs of waste gas and wastewater). Combined with hot spot analysis, a gravity center model, and panel Tobit regression, we reveal the temporal-spatial evolution and driving mechanisms of eco-efficiency in resource-based economies. The research finds that the overall eco-efficiency of Russia is at a medium level and shows a dynamic correlation with the economic development stage. In the early stage of the period under review, there was a high degree of synergy, but the efficiency declined during the period of rapid economic growth. Later, it rebounded somewhat in tie with technological progress. Spatially, it presents a special pattern of low efficiency in the western European industrialized regions and high efficiency in the Arctic and Far East peripheral regions, reflecting the spatial heterogeneity of resource-dependent economies and the survival-constrained efficiency feature. The analysis of influencing factors indicates that per capita GDP has a significant positive driving effect on eco-efficiency, but the expansion of residents’ consumption, the improvement of education level and the dependence on foreign trade all have inhibitory effects, highlighting the path dependence of the current growth model on the structure of resource consumption. The research suggests that Russia should implement differentiated spatial governance in the future, promote the green transformation of consumption and trade structures, and strengthen the ecological orientation of the education and scientific research system to achieve a fundamental transformation of regional sustainable development from survival constraints to innovation-driven. Full article

Agricultural biomass recycling efficiency is central to advancing the green and sustainable transition of agriculture. Drawing on panel data for 30 Chinese provinces from 2019 to 2023, this study measures recycling efficiency using a three-stage super-efficiency SBM model with undesirable output and examines its determinants with a panel Tobit model. The second-stage SFA indicates that the effects of external conditions on input slacks are input-specific. In particular, GDP is statistically significant only in the biomass-generation slack equation, whereas topographic relief and rural road network density do not show robust associations with any slack measure once controls are included. After removing the influence of environmental factors and random shocks, the overall national level of agricultural biomass recycling efficiency remains moderate. The national mean Stage 3 efficiency decreased from 0.586 in 2019 to 0.427 in 2022 and recovered to 0.543 in 2023. The five-year average was 0.510, which is close to the Stage 1 average of 0.503. Spatial analysis indicates weak global spatial autocorrelation, with only occasional local clustering. The efficiency centroid oscillated during the study period rather than following a one-way migration path, with a total displacement of 70.05 km. The determinant analysis indicates that the number of specialised agricultural machinery has the most stable positive association with recycling efficiency, while other policy, market, and human capital variables do not show robust significance in the short panel. These findings underline the need to align equipment deployment and collection systems with local terrain and transport conditions, expand machinery leasing and service provision, and strengthen capacity building in low-efficiency regions. Establishing a national information sharing and dispatch platform would facilitate cross-regional resource flows and more efficient allocation, while improving local service outlets would make participation more convenient for farmers and reduce transaction costs. Full article

This study investigates how geopolitical risk shaped the green economic efficiency (GEE) of 19 countries in the G20 group from 2000 to 2022. Using the Super-SBM model, we construct a cross-country measure of GEE and empirically examine both its determinants and underlying mechanisms. The results show that rising geopolitical risk significantly undermines GEE, indicating that external uncertainty disrupts countries’ ability to balance economic growth with environmental performance. Mechanism analysis reveals that geopolitical tensions heighten energy security concerns, leading to increased fossil fuel consumption, and trigger exchange rate depreciation to decrease green economic efficiency. Moreover, foreign direct investment mitigates the adverse effects of geopolitical risk by facilitating technology spillovers and capital inflows. Moreover, geopolitical risks have different impacts on the efficiency of a country’s green economy, varying across levels such as the country’s economic development level, resource endowment, and trade openness. The findings highlight geopolitical risk as a constraint on global green transition. Policymakers should strengthen energy source diversity, stabilize exchange rate environments, and promote FDI to enhance national resilience. Building institutional capacity is essential in sustaining green economic efficiency under rising geopolitical uncertainty. Full article

In the context of rapid digital transformation, smart seaports have emerged as crucial entities for enhancing operational efficiency and promoting sustainable port governance. To achieve sustainable development, the integration of advanced technologies into seaport operations has become essential. However, the existing literature primarily highlights the construction achievements of smart seaports, with limited investigation into the configuration mechanisms that account for variations in efficiency. This study analyzes eight representative smart seaports in China from 2019 to 2024. Based on the Technology–Organizational–Environment (TOE) framework, six condition variables are identified. Comprehensive technical efficiency is measured using the three-stage super-efficiency Slack-Based Measure (SBM) model. Necessary Condition Analysis (NCA) and fuzzy-set Qualitative Comparative Analysis (fsQCA) are then employed to identify the configuration pathways leading to either high or non-high smart seaport operational efficiency. The findings indicate that no single factor is a necessary condition for high efficiency; instead, operational efficiency results from the synergistic interplay of multiple factors. Four distinct configuration pathways that lead to high efficiency are identified. Furthermore, a significant causal asymmetry exists between efficient and inefficient configurations, highlighting the contextual complexity inherent in smart seaport operational efficiency. This study provides a configurational perspective on the operational efficiency of smart seaports in order to offer policy and management insights for sustainable seaport operations. Full article

Based on panel data from 14 prefectures in Xinjiang from 2004 to 2022, this study employs the Super-SBM model and panel threshold regression to assess how urbanization and industrial growth influence industrial water resource utilization efficiency (IWRUE). Xinjiang exhibits a distinct “high-north–low-south” spatial pattern: Urumqi and other northern regions show continuous improvement and Tacheng maintains long-term superiority, while southern areas such as Kizilsu and Hotan remain persistently low. Although IWRUE increases overall, regional trajectories diverge considerably. Two significant thresholds are identified—industrial output value and urbanization rate. Below these thresholds, water consumption strongly suppresses IWRUE, industrial employment exerts a negative effect, and investment plays a positive role. Once the thresholds are exceeded, the negative effect of water consumption weakens, industrial employment turns positive, and investment becomes insignificant. Policy implications suggest that regions below the thresholds should strengthen investment in water-saving technologies and productive capital, whereas regions beyond the thresholds should focus on enhancing labor quality, promoting green innovation and improving refined management to stabilize IWRUE and foster coordinated regional development. Full article

Following the transfer of farmland, new agricultural entities exhibit clearer profit-oriented goals and heightened sensitivity to changes in profitability. Changes in farmland transfer prices significantly affect producers’ crop selection, input choices, technology adoption, farming methods, and intensity. This study establishes a motivation–behavior–outcome analytical framework by integrating producer behavior theory with the mechanism of farmland health formation, suggesting that rising transfer prices may prompt producers to exhibit five types of positive or negative behaviors. The SBM-DEA model is employed to measure the grain green total factor productivity of farmland across 102 counties in China’s Henan Province from 2017 to 2022, reflecting the healthy utilization of farmland. Results from the two-way fixed-effects and threshold effect models reveal an inverted U-shaped relationship, indicating initially positive and later negative impacts of increasing transfer prices on farmland health utilization. GTWR model findings highlight that economic disparities and the pace of price increases dictate the intensity of producers’ positive and negative motivations, while the economic capacity for absorbing shocks and the natural endowment capacity for absorbing shocks influence the likelihood and magnitude of these effects. Government regulation should, therefore, focus on regulating producer interests. Full article

As improving power sector energy efficiency is crucial for China’s sustainable low-carbon transition, this study investigates the impact and spatial spillover effects of carbon market pilot policies. Utilizing panel data from 30 Chinese provinces spanning 2009 to 2022, we employ a super-efficiency Slack-Based Measure (SBM) model alongside a Spatial Difference-in-Differences (SDID) approach to assess policy outcomes. Empirical findings reveal that while the pilot policy significantly improves local power energy efficiency by an average of 10.68%, it exerts a negative spatial spillover on neighboring regions (rho = −0.3802). This disparity occurs because asymmetric resource agglomeration causes the policy-driven “polarization effect” to dominate the “trickle-down effect”. Furthermore, mechanism analyses demonstrate that increasing the clean energy share—primarily driven by technological substitution, fiscal subsidies, and policy guidance—serves as the main transmission channel. However, strict short-term compliance costs induce a “strategic innovation” response, which promotes green utility model patents but crowds out substantive green inventions. Ultimately, this study highlights the necessity of establishing cross-regional carbon market alliances and technology diffusion mechanisms to mitigate regional disparities and foster collaborative carbon reduction, and promote long-term sustainability. Full article

Resource-based cities in the Yellow River Basin are important pillars of national energy security and regional coordinated development, and their green transformation is closely related to the overall strategy of ecological protection and high-quality development in the basin. This study takes 34 resource-based cities within the basin as the research objects and employs a combination of methods, including the Super Slacks-Based Measure (SBM) model, the Malmquist–Luenberger index, the standard deviational ellipse, the Dagum Gini coefficient, and the geographical detector, to systematically analyze the spatio-temporal evolution characteristics and driving mechanisms of green development efficiency from 2012 to 2022. The results indicate that: (1) green development efficiency shows an overall upward trend, forming a pattern of leading performance in the lower reaches, lagging development in the middle reaches, and accelerated catching-up in the upper reaches, with efficiency improvements jointly driven by technical efficiency enhancement and technological progress; (2) the gravity center of efficiency shifts southwestward overall, and interregional disparities constitute the main source of overall differences; (3) economic development level, science and technology investment, fiscal expenditure, and energy intensity are the key driving factors, with significantly strengthened interactions among multiple factors. From the dual perspectives of basin location and the urban life cycle, this study constructs a multidimensional analytical framework that provides a reference for categorized regulation and coordinated regional governance of resource-based cities. Full article

This study aims to evaluate the impact of agricultural land property rights system reform on Agricultural Green Total Factor Productivity (AGTFP) and to uncover its underlying mechanisms. Treating the nationwide rollout of the Three Rights Separation Reform (TRSR) as a quasi-natural experiment, we employ provincial panel data from 2011 to 2023. The Super-SBM model is applied to measure AGTFP, followed by a multi-period Difference-in-Differences framework to identify the causal effects. The results indicate that the TRSR significantly enhances AGTFP, yielding an average improvement of 0.112 units. Mechanism analyses reveal that this gain is achieved through three distinct channels: promoting labor-saving technological progress, optimizing factor allocation efficiency, and facilitating agricultural green transformation. Heterogeneity analyses further demonstrate that the positive effects are more pronounced in plains regions, areas with lower rural per capita income, and jurisdictions with higher agricultural fiscal expenditure. These findings remain robust after a series of robustness and endogeneity tests. This study provides novel institutional evidence on the drivers of AGTFP and offers policy-relevant insights for advancing sustainable agricultural transformation in developing economies. Full article

Taking the rail transit transfer stations in Qingyang, Wuhou, and Chenghua Districts of Chengdu as the research objects, this study constructs a static-dynamic coupled analytical framework by integrating the input-oriented three-stage super-efficiency SBM model and the output-oriented Malmquist-Luenberger (ML) index to systematically evaluate rail transit transfer efficiency. The findings reveal that the transfer efficiency of Chengdu Metro exhibited a fluctuating growth pattern from 2017 to 2023, with significant variations corresponding to periods of network expansion and operational adjustments. Improvements in technical efficiency and management optimization have been key drivers of overall efficiency gains. The three-stage super-efficiency SBM model effectively filters out the impacts of environmental variables and random noise, uncovering inter-station efficiency disparities and resource redundancy issues. Decomposition of the ML index indicates that both technical efficiency and technological progress jointly drive total factor productivity (TFP) changes. On average, technical efficiency has been the more stable and prominent contributor to productivity growth. However, the reasons for TFP declines at certain stations are varied; some under-performed due to lagging technological progress, while others faced constraints in technical or scale efficiencies. The study confirms that the synergistic application of the three-stage model and the ML index can accurately identify bottlenecks and provide theoretical support and practical pathways for optimizing resource allocation and dynamic management in urban rail transit systems. Findings and methods from Chengdu’s practice provide a replicable paradigm for evaluating, planning and optimizing rail transit transfer hubs in Chinese cities at different development stages, and offer empirical references for advancing urban public transport and sustainable development of comprehensive transportation systems. Full article

Global demand for animal protein necessitates sustainable alternatives to soybean meal (SBM). This systematic review evaluated 177 peer-reviewed articles (2002–2023) across 12 categories to analyse the nutritional value of alternative protein sources for ruminant diets and to assess the associated environmental trade-offs. This was achieved through a targeted review, synthesising data from Life Cycle Assessments (LCAs) to create a multi-criteria matrix for ranking sustainability profiles. Results indicate that microalgae, insects, and single-cell proteins exhibit crude protein levels comparable to SBM. Moreover, insects, seaweeds, and animal by-products (ABPs) often present superior essential amino acid profiles and high intestinal digestibility. From an environmental perspective, insects, seaweeds and microalgae offer excellent land-use efficiency and significant enteric methane mitigation (17–74.6%), though current economic viability is hindered by high processing costs and emerging supply chains. Conversely, ABPs and agro-industrial by-products effectively embody circular economy principles, enhancing local system resilience. Ultimately, replacing SBM requires a multi-objective approach through a functional hybridisation model, carefully balancing metabolic efficiency with environmental sustainability. While microalgae, insects, and seaweeds demonstrate promising nutritional and mitigation potential, addressing economic barriers and ensuring biosecurity seems essential. Future LCA frameworks should prioritise bioavailable nutrient metrics to optimise the environmental impact of ruminant production. Full article

Green technological innovation is critical for highly polluting agricultural operations to accelerate green transformation and achieve sustainable growth. A detailed review of these firms’ green technological innovation efficiency, as well as an analysis of the internal and external factors influencing it, can help accelerate their green technological innovation processes. As a result, this study employs firms identified as key polluting units (extremely polluting) by environmental protection authorities from 2018 to 2023 as decision-making entities. After removing ST samples, a final sample of 198 companies was obtained. This study measures and analyses the efficiency of green technological innovation using the Super-efficiency SBM model and the Super-efficiency SBM–Malmquist index model. A random-effects model investigates additional contributing elements. The findings show that the average comprehensive technological efficiency of green innovation in high-pollution agricultural enterprises is 0.29, which is only 75% of the overall efficiency level for all agricultural enterprises, and that this efficiency is distributed regionally as follows: western > central > eastern. External factors, including economic development levels and environmental restrictions, have a considerable detrimental impact on the green technology innovation efficiency of high-pollution agricultural operations. Internally, enterprise scale and equity concentration have a considerable favorable impact. The impact of technological spillover levels, government support, and how well capital is allocated is not significant and needs to be looked at more closely in a more detailed and contextual way. Full article