Search Results (1,259)

Synergistic governance of pollution and carbon reduction is a critical pathway for promoting the comprehensive green transformation of economic and social development. Based on panel data from 30 Chinese provinces over the period 2006–2023, this study employs the super-efficiency SBM-DEA model to measure the synergistic efficiency of air pollution and carbon reduction (SE), and applies the Dagum Gini coefficient and spatial autocorrelation analysis to reveal its spatiotemporal differentiation characteristics. The modified gravity model and social network analysis are used to characterize the evolutionary patterns of the spatial network of synergistic efficiency of air pollution and carbon reduction (SEN). Furthermore, a two-way fixed-effects model and the Spatial Durbin Model are constructed to empirically examine the impact of new quality productivity (NQP) on SE and its spatial spillover effects. The main findings are as follows: First, SE exhibits an overall declining trend, with a clear gradient pattern of “East > West > Central > Northeast” and significant positive spatial agglomeration. Second, the linkage intensity of SEN has steadily increased, and inter-provincial synergistic connections have grown closer; however, the core–periphery structure remains stable, network density fluctuates at a persistently low level, and a multi-center balanced pattern has yet to emerge. Third, NQP significantly enhances SE, but green technological innovation exhibits a suppression effect along the transmission pathway. Fourth, the driving effect of NQP on SE demonstrates notable regional and resource-endowment heterogeneity, while also showing a significant positive spatial spillover effect. These findings offer policy implications for advancing regionally tailored synergistic governance of pollution and carbon reduction and for optimizing the structural configuration of inter-provincial synergistic networks. Full article

Improving water resource use efficiency (WRUE) is essential for achieving sustainable water management under increasing socioeconomic and environmental pressures. This study investigates the spatiotemporal evolution, associated factors, and spatial transition characteristics of WRUE across 11 provincial-level administrative regions in the Yangtze River Basin during 2010–2024. An integrated framework combining the super-efficiency SBM-window DEA model, Malmquist–Luenberger index, GeoDetector, and conventional and spatial Markov chain models was developed to characterize efficiency dynamics, productivity changes, explanatory factors, and state-transition pathways. The results showed that WRUE exhibited an overall fluctuating upward trend with a clear spatial gradient of lower reaches > middle reaches > upper reaches. The mean ML index was 1.004, indicating that technological change (TC) was the main contributor to productivity improvement. Urbanization rate, water use per CNY 10,000 of GDP, industrial water-use share, and primary-industry share exhibited relatively high explanatory power, and their interactions enhanced explanatory power. Markov analysis revealed strong persistence in WRUE states, while transition probabilities differed across spatial neighborhood conditions. Assuming stable transition probabilities, the high-efficiency state would reach a steady-state probability of 0.8155. These findings provide insights for differentiated water resource management and coordinated regional development. Full article

This study examines the efficiency of Fiji’s banking sector from 2000 to 2025 using a two-stage analytical framework. First, data envelopment analysis (DEA) under the variable returns to scale (VRS) assumption is employed to estimate the efficiency scores of seven financial institutions: five commercial banks—four foreign-owned and one locally owned—and two locally owned non-bank financial institutions. Second, a two-limit Tobit model is used to investigate whether the estimated efficiency scores are associated with credit risk, return on assets (ROA), return on equity (ROE), bank size, foreign ownership, loan-loss provisions relative to net income, and real GDP growth. The balanced panel comprises 182 institution-year observations over 26 years. The DEA results indicate a high mean efficiency score of 0.923, although meaningful variation is observed across institutions and over time. Foreign-owned banks record a marginally higher mean efficiency score than locally owned institutions (0.924 compared with 0.921); however, foreign ownership is not statistically significant in the multivariate Tobit model. ROA has a positive and statistically significant association with efficiency, whereas ROE has a negative and statistically significant association, suggesting that asset profitability and equity profitability capture distinct balance-sheet and capital-structure channels. Bank size is positively associated with efficiency, while credit risk, loan-loss provisioning, and real GDP growth are statistically insignificant. Overall, the findings suggest that Fiji’s financial institutions operate relatively close to the estimated best-practice frontier. Nevertheless, the small number of institutions and the resulting dense DEA frontier warrant cautious interpretation. The study concludes that policies aimed at strengthening institutional efficiency should prioritise cost discipline, productive asset utilisation, appropriate capital management, and technology diffusion rather than ownership status alone. Full article

Appropriate-scale operation is important for integrating ordinary farm households into modern agriculture, yet the appropriate scale may differ substantially across producer types. Using micro-level data from the nationally representative 2021 China Household Finance Survey, this study develops an analytical framework integrating efficiency and resilience perspectives to compare ordinary farm households and family farms. Efficiency is the core empirical dimension and is assessed using cost–benefit analysis, the DEA-BCC model, and multivariate econometric methods. Resilience is treated as a theoretical perspective and a constraint on scale decisions; it is interpreted indirectly through the scale responses of the two groups to disaster shocks rather than measured using a standalone resilience index. The results identify distinct efficiency-based appropriate-scale ranges: 1–3 mu (approximately 0.07–0.20 hm²) for ordinary farm households and 100–200 mu (approximately 6.67–13.33 hm²) for family farms, with lower net profit per unit area at intermediate scales. Economic rationality and institutional constraints show marked heterogeneity across producer types: commercialization, land titling, subsidies, and credit access are more strongly associated with the scale expansion of family farms, whereas non-farm employment opportunities play a more decisive role for ordinary farm households; household labor and social networks contribute to scale expansion in both groups, and their between-group differences are not statistically significant. Disaster impact is associated with a significant reduction in the operational scale of ordinary farm households, whereas in the subgroup estimates the operational scale of family farms shows no significant association with disaster impact, suggesting that family farms are better able to maintain their operational scale. Digital technology also operates through differentiated pathways: it primarily alleviates information and factor constraints among ordinary farm households while strengthening efficiency, financial access, and organizational coordination among family farms. These findings support differentiated scale and digital-agriculture policies in economies characterized by heterogeneous farm structures. Full article

This study measures construction waste management efficiency (CWME) in 27 EU member states (2016–2025) using a three-stage super-efficiency SBM-DEA model, constructs a spatial correlation network via a modified gravity model, and employs social network analysis and random forest to investigate network structure and driving mechanisms. Results reveal persistent cross-country heterogeneity and a widening East–West efficiency divide. The CWME network exhibits small-world characteristics, with CONCOR analysis partitioning member states into four blocks: Northern and Baltic states serve as the dominant Net Spillover bloc, whereas Southern periphery countries remain Net Beneficial recipients whose membership contracted from five to three by 2025. Random forest identifies government environmental protection expenditure, unit labour cost, and population density as the top three driving factors, with unit labour cost and government environmental protection expenditure exhibiting the strongest negative marginal effects on CWME, while urbanization rate shows a near-neutral relationship. These findings provide empirical support for policy interventions targeting the structural economic determinants of CWME. By integrating efficiency measurement, spatial network analysis, and machine learning-based driver identification, this study offers a comprehensive framework for diagnosing regional disparities in construction waste governance and informing cross-regional collaborative policy design. Full article

As emerging economies navigate the global “twin transition”, decoupling industrial growth from environmental degradation has become an urgent imperative. This study investigates the fundamental role of digital infrastructure in driving Industrial Green Innovation Efficiency (IGIE). Utilizing panel data from 30 Chinese provinces spanning 2012 to 2022, we first measure the environment-adjusted IGIE employing a three-stage global SBM-DEA model to mitigate external environmental interferences and statistical noise. Subsequently, a two-way fixed-effects model, fortified by a Bartik-type instrumental variable (IV-2SLS) approach, is constructed to identify causal impacts. The descriptive results reveal that China’s overall IGIE exhibits a fluctuating upward trajectory, although absolute efficiency levels remain low. Furthermore, significant regional variations characterize the national landscape, manifesting as Eastern leadership, Central catch-up, Western improvement, and Northeastern volatility. Crucially, empirical baseline estimations confirm that digital infrastructure exerts a robust positive effect on IGIE. Mechanism analyses further demonstrate that this efficiency enhancement is primarily driven by the promotion of digital inclusive finance and localized technological diffusion. These findings theoretically enrich the understanding of digital empowerment and provide a practical blueprint for policymakers globally to formulate targeted infrastructure investments and differentiated regional transformation strategies. Full article

Improving natural resource total factor productivity (NTFP) is sential for easing resource and environmental constraints, advancing high-quality development, and supporting China’s dual-carbon goals. This study aims to evaluate the static efficiency, intertemporal productivity change, regional heterogeneity, and hierarchical frontier differences in environmentally adjusted natural resource total factor productivity in China’s aggregated agriculture, forestry, animal husbandry, and fishery sector from 2008 to 2020. To achieve this aim, we apply a non-radial, non-oriented undesirable-output SBM-DEA model under constant returns to scale, national, regional, and province-specific frontier comparisons. A Global Malmquist–Luenberger index is further used to decompose productivity change into efficiency change and technical change. The analysis shows an overall upward trajectory in national mean NTFP, a persistent eastern advantage, and co-movement among provincial, regional, and national frontier indices, while the dynamic productivity change indicates that productivity improvement was driven mainly by technical change rather than efficiency change. Sensitivity analyses using alternative undesirable-output specifications and cluster-based groupings support the robustness of the main conclusions and inform more targeted discussion and policy implications. Full article

Background/Objectives: Health systems involve numerous stakeholders interconnected through nonlinear relationships. While Data Envelopment Analysis (DEA) has been widely used to measure health system efficiency, conventional estimates may exhibit finite-sample bias. An important question, therefore, concerns how health system performance can be measured more reliably, and what factors explain cross-country differences in efficiency. This study introduces an integrated framework that combines Two-Stage Bootstrap DEA with machine learning to assess the performance of the health systems of 26 OECD countries using 2022 data. Methods: In the first step, technical efficiency scores are computed using an output-oriented constant returns to scale (CRS) DEA model. Subsequently, bias-corrected efficiency estimates are derived using the Bootstrap procedure proposed by Simar and Wilson. In the second step, truncated regression analysis and machine learning-based partial dependence analysis, the latter validated through leave-one-out cross-validation, are employed to investigate the determinants of efficiency. Results: The Bootstrap procedure reveals statistically significant differences from conventional DEA results, and bias-corrected results indicate that South Korea, Canada, and the United States achieve the highest efficiency levels. The findings show that tobacco use prevalence has a significantly negative association with health system efficiency and alcohol consumption exhibits a negative, threshold-type pattern, while GDP per capita and out-of-pocket health expenditure display more complex, nonlinear effects. Furthermore, the scenario analysis indicates that a 10% reduction in tobacco use yields the largest predicted single-intervention improvement, while combined interventions produce additional but sub-additive gains. Conclusions: The proposed framework presents a transparent and validated approach for assessing and explaining health system performance, generating findings relevant to the development of evidence-based health policy. Full article

Marine fisheries play a vital role in ensuring food supply and sustaining livelihoods in coastal regions of China. However, the expansion of aquaculture has led to increasing carbon emissions and mounting pressure on resources and the environment, making the improvement of ecological efficiency a critical issue for the sustainable development of the industry. From the perspective of carbon emissions as undesirable output, this paper employs the DEA-SBM model and the GML index to measure the ecological efficiency of marine fisheries across nine coastal provinces in China from 2006 to 2023. Furthermore, using fixed-effects models, mediation-effect models, and grouped regression models, this study empirically examines the impacts of fishermen’s income and environmental regulations on the ecological efficiency of marine fisheries and their transmission mechanisms. The results indicate that ecological efficiency exhibits fluctuating trends across provinces, with significant inter-provincial disparities. Fishermen’s income has a significant positive effect on ecological efficiency, while environmental regulations show a significant negative effect. Digitalization level significantly promotes ecological efficiency, whereas fishery disaster losses significantly inhibit it. Technological adoption intention plays a partial mediating role in the pathways through which both fishermen’s income and environmental regulations affect ecological efficiency. Significant regional heterogeneity is observed, with the eastern coastal region exhibiting the strongest effects of various factors and the northern coastal region showing the weakest. Accordingly, this paper proposes differentiated enhancement pathways for ecological efficiency from four dimensions, including technological innovation-driven development, industrial structure optimization, environmental policy regulation, and regional coordinated governance, with the aim of providing theoretical foundations and policy references for the low-carbon transformation and sustainable development of marine fisheries in China’s coastal areas. Full article

This study examines how the quality of corporate environmental information disclosure, jointly with governance structure characteristics, shapes the input–output efficiency of green innovation in Chinese A-share listed firms, and whether industry- and region-level conditions moderate that relationship. Drawing on a panel of 2847 firms spanning 2014–2024, we construct a multidimensional disclosure quality index through content analysis across completeness, verifiability, quantification depth, and forward-looking commitment, and measure green innovation efficiency through a super-efficiency slacks-based DEA model accommodating undesirable outputs. A three-level hierarchical linear model partitions variance across firm, industry, and provincial layers and permits the disclosure–efficiency slope to vary with industry regulation intensity and provincial marketization. The results indicate that higher disclosure quality is associated with greater green innovation efficiency, a link we attribute to financing-constraint relief, reputational accumulation, and intensified external monitoring, offered as interpretive channels rather than as separately tested mediators. Board independence, environmentally experienced executives, and institutional shareholding amplify the conversion, while ownership concentration dampens it. Cross-level evidence shows that industry regulation intensity and regional marketization further steepen the firm-level slope. Findings remain stable across alternative measurement, restricted sampling, propensity score matching, and instrumental variable identification. The analysis offers a multilevel reframing of disclosure–innovation research and informs the design of mandatory disclosure rules, governance reform, and green finance infrastructure in transitioning economies. Full article

Climate-related hazards pose increasing challenges to socio-economic sustainability, compelling firms to improve the efficiency of green investment while maintaining profitability and environmental responsibility. Using Chinese A-share listed firms as the research sample, this study measured corporate green investment efficiency using an output-oriented SBM-DEA framework and examined the effect of management climate risk perception using a two-way fixed-effects panel model. The results indicated that stronger executive awareness of climate risks significantly improved corporate green investment efficiency. Green technology innovation partially mediated the relationship between management climate risk perception and green investment efficiency. Climate policy uncertainty inhibited the positive relationship between management climate risk perception and green investment efficiency, whereas organizational resilience enhanced it. The heterogeneity analysis indicated that this positive effect was more pronounced in non-technology-intensive and non-heavy-polluting industries. This study can serve as a reference for enterprises seeking to address climate risks and develop systematic climate risk management and prevention systems. Full article

Reducing poverty and income inequality is a major objective of contemporary social policy. Considerable financial resources are spent on social protection to support this purpose. Since these resources are limited, their efficient use is important for improving poverty and income inequality outcomes. The aim of this study is to assess the efficiency and productivity of social protection expenditure in eleven Central and Eastern European (CEE) EU Member States through Data Envelopment Analysis (DEA) and the Malmquist Productivity Index (MPI). Three input-oriented DEA models are estimated for 2016–2023, including two poverty-oriented models and one income-distribution-oriented model. The results indicate high efficiency and scale efficiency across countries, but favourable results in both poverty-oriented models are not necessarily accompanied by similar results in the income-distribution-oriented model. MPI decomposition shows that productivity dynamics in the poverty-oriented models reflect mainly technical efficiency changes before the COVID-19 pandemic and technological change thereafter, whereas productivity dynamics in the income-distribution-oriented model reflect mainly technical efficiency changes throughout the period. Full article

Against the backdrop of increasing global food security pressures and tightening resource–environment constraints, enhancing grain production efficiency has become a focal international concern. Based on panel data from 16 cities in Shandong Province, China, spanning 2013 to 2022, this study employs the DEA–Malmquist index, SBM model, and Spatial Durbin model to measure grain production efficiency and analyze its spatiotemporal evolution and influencing factors. The findings reveal that Shandong’s grain production efficiency has generally improved but exhibits a spatial differentiation pattern of “higher in Western Shandong, lower in Eastern Shandong,” with significant positive spatial correlation and agglomeration effects. Mechanization significantly boosts efficiency, while urbanization, excessive fertilizer and pesticide use, and labor surplus exert notable negative impacts. This research clarifies the spatial spillover mechanisms and key constraints of efficiency, providing scientific evidence and practical guidance for optimizing agricultural resource allocation, promoting regional collaborative innovation, and formulating differentiated food security policies in Shandong Province and other regions with similar natural–economic conditions. Full article

The embedded finance paradigm is fundamentally restructuring digital economies by seamlessly integrating financial services into non-financial digital infrastructures. This study dynamically evaluates the productivity frontiers of Sea Limited’s embedded fintech operations (SeaMoney) across six core geographic markets (Indonesia, Thailand, Vietnam, the Philippines, Malaysia, and Brazil) over the 2023–2026 temporal horizon. Employing a rigorous Panel Data Envelopment Analysis (DEA) Malmquist Productivity Index framework, the research measures systemic performance by analyzing Sales & Marketing (S & M) Expenses and the undesirable Non-Performing Loan (NPL) rate as inputs, against Gross Loan Outstanding as the primary credit output. Before model execution, robust isotonicity was empirically validated using Pearson correlation matrices. The empirical findings reveal profoundly robust systemic performance across the global ecosystem, driven primarily by overarching algorithmic innovations captured by the Technical Change (TC) index. However, this technological boundary exhibits a stabilizing deceleration over time, indicative of a maturing ecosystem transitioning from explosive, frontier-shifting innovation to optimized refinement. Furthermore, localized managerial optimization, measured by the Efficiency Change (EC) index, displays significant regional heterogeneity. While markets like Brazil demonstrated aggressive late-stage efficiency spikes, and core Southeast Asian markets (such as Vietnam and the Philippines) maintained highly stable, competitive trajectories, other regions, such as Thailand, experienced notable managerial regression. This regression signals severe internal frictions in optimizing local marketing budgets against rising credit defaults. Managerial Implications: These findings provide critical strategic insights for orchestrators of the multinational e-commerce ecosystem. The empirical evidence suggests that relying exclusively on centralized technological scaling, such as unified platform infrastructure and global AI architectures, is insufficient for sustained operational growth. To maintain a competitive advantage, operations executives must deploy hyper-localized resource-allocation and customer-acquisition frameworks tailored to specific regional market dynamics and consumer behavior. Sustainable scaling in cross-border digital commerce requires a precise dynamic equilibrium: leveraging robust global technological infrastructure while executing highly adaptive, market-specific operational and marketing optimizations to maximize customer lifetime value (CLV), eliminate customer acquisition waste, and streamline localized transaction and engagement cycles. Full article

This study develops the Fuzzy Cross-Efficiency Network Design (FCEND) framework—an integrated Geographic Information System (GIS) and Data Envelopment Analysis (DEA) approach for logistics network design under deep uncertainty. Unlike conventional methods that ignore spatial equity and data credibility, FCEND combines GIS-based suitability mapping (30 m resolution, incorporating slope, land use, and floodplains), hybrid efficiency scores (${\mathrm{\Phi }}_{\mathrm{j}}$) integrating Cross-Efficiency DEA (CEDEA) peer evaluation with Fuzzy DEA (FDEA) uncertainty modeling, and a multi-objective function Z(S) = α·Efficiency(S) + β·H(S) − γ·Gini(S) that balances demand-weighted efficiency, portfolio-dependent criterion diversity (represented by the entropy term H(S)), and spatial equity. Applied to Iran’s staple food commodity network—85 million people across 1.65 million km²—FCEND identifies an optimal 15-node portfolio spanning 15 provinces with 74% direct population coverage within 150 km. The portfolio achieves a Gini coefficient of 0.298, and 9 of 15 nodes with excellent rail connectivity, while capturing strategically vital nodes (Borujerd, Bandar Abbas, Zahedan) overlooked by conventional approaches. Nine core sites with stability scores (f_j = 1.0) demonstrate perfect stability across all uncertainty scenarios. The framework’s modular architecture is conceptually transferable to emerging economies, as illustrated through adaptation to Vietnam (70% parameter swap). By integrating GIS-based spatial analysis, peer evaluation, fuzzy uncertainty, portfolio-dependent entropy, and equity constraints within a unified optimization framework, FCEND offers a transferable methodology for evidence-based logistics infrastructure planning—contributing directly to the United Nations Sustainable Development Goals (SDGs): SDG 2 (Zero Hunger), SDG 9 (Resilient Infrastructure), SDG 10 (Reduced Inequalities), and SDG 13 (Climate Action). Full article