Search Results (307)

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

Against the backdrop of global demographic transition and widening regional disparities, population shrinkage and population aging have become critical constraints on regional development, posing severe challenges to the socioeconomic resilience of shrinking areas. Taking China’s three northeastern provinces as the study area, this paper investigates the impacts of population aging on county-level socioeconomic resilience and its spatiotemporal heterogeneity. Based on population census and socioeconomic data from 143 counties in Northeast China during 2010–2020, an evaluation index system of socioeconomic resilience is constructed using the entropy weight method. Grey relational analysis, Tobit regression models, and geographically and temporally weighted regression (GTWR) are employed to conduct empirical tests. The results indicate that most counties simultaneously experience population decline and deep aging, and their interaction forms an intensified negative feedback mechanism that constrains the improvement of socioeconomic resilience. Compared with other shrinking regions in China—such as selected counties in the Yangtze River Delta and resource-rich counties in central and western China—Northeast China is distinguished by a unique set of compounded pressures, driven by the simultaneous and mutually reinforcing trends of sustained population decline and deep aging. Population aging exhibits a strong correlation with socioeconomic resilience across all dimensions, with the most pronounced association observed in transformation capacity. Population density also plays an important role, although its correlation strength is relatively weaker. Tobit regression results further confirm that population aging significantly suppresses socioeconomic resilience, whereas population density exerts a positive effect, with notable differences across various types of shrinking counties. GTWR analysis reveals significant spatial heterogeneity in the impacts of these factors on socioeconomic resilience. Overall, this study provides robust empirical evidence for formulating targeted policies and enhancing sustainable development capacity in shrinking and aging regions. Full article

This research studied the role of the fisheries sector, specifically pond-based grouper aquaculture, in coastal Lamongan, Indonesia, which is crucial for coastal food security and economy. Despite relatively high productivity, technical efficiency was not optimal because of its limited livelihood assets, which include human, natural, social, financial, and physical capital. The gap in ownership of these assets has resulted in technical efficiency variations across farmers and has affected both their livelihoods and environmental sustainability. Previous research has mostly focused on capture fisheries or non-grouper species, leaving a critical gap regarding the linkage between livelihood assets and technical efficiency in pond-based grouper aquaculture. This research measured livelihood asset levels, technical efficiency, and the effect of assets on efficiency, using quantitative data from 83 respondents representing the total 105 grouper farming households in coastal Lamongan. Livelihood assets were assessed through scoring and index analysis, technical efficiency was estimated using Stochastic Frontier Analysis (SFA), and the determinants of inefficiency were examined through Tobit regression with robust standard errors. The results found that the average livelihood asset index was 0.47 (moderate), with financial capital being the weakest component. Technical efficiency averaged 0.83, indicating efficient use of inputs while still allowing room for improvement. Natural capital (land area and water resources) and financial capital (income and savings) significantly affected technical inefficiency, whereas human, social, and physical capital did not. These findings emphasize the importance of strengthening the financial capital and the management of natural resources optimally to promote the efficiency and sustainability of grouper aquaculture in coastal Lamongan, Indonesia. Full article

Socialized agricultural services (SASs) play a crucial role in enhancing grain production capacity, promoting environmentally friendly and sustainable practices, and integrating smallholder farmers into modern agriculture. Our study applies the Super-Efficiency SBM (slack-based measure) model to measure the green grain production efficiency (GGPE) of farmers. We then employ the Tobit model, the threshold regression model, and the moderated effect model to empirically analyze the influence of SASs on farmers’ GGPE. SASs are found to significantly enhance farmers’ GGPE. Furthermore, the relationship between the scale of utilization of farmers’ services (SUFS) and GGPE exerts a single-threshold effect. Among government environmental regulations (GERs), both constrained and guided regulation measures significantly positively moderate the relationship between SASs and farmers’ GGPE. We recommend that governments tailor SASs to address specific farmer needs; develop differentiated support policies for service utilization scales based on local conditions; and strengthen the environmental regulatory framework through a multi-pronged approach, by reinforcing constrained environmental regulations, refining incentive environmental regulations, and deepening guided environmental regulations, to consistently elevate farmers’ GGPE. Full article

Against the backdrop of global energy transition and sustainable development, advancing the new energy industry has become a critical pathway for optimizing energy structures and achieving the dual carbon goals. However, while China’s new energy sector has experienced rapid growth, it has also exposed a series of challenges, including insufficient innovation momentum, irrational resource allocation, and low conversion rates of R&D outcomes. To delve into the root causes and propose improvement pathways, this study selected 76 listed new energy enterprises from 2021 to 2023 as samples. It comprehensively employed the DEA-BCC model, Malmquist productivity index, and Tobit regression model to conduct empirical analysis across three dimensions: static, dynamic, and influencing factors. The findings revealed: firstly, during the study period, overall static efficiency remained low, with only about 32.90% of enterprises operating efficiently. Efficiency decomposition indicated that low and unstable pure technical efficiency constrained overall efficiency gains. In contrast, while scale efficiency was relatively high, its growth was sluggish, and some enterprises exhibited significant scale irrelevance. Secondly, dynamic total factor productivity exhibited fluctuating growth primarily driven by technological progress. However, declining technical efficiency—particularly the deterioration of scale efficiency—indicated that while the new energy industry advanced technologically and expanded in scale, its management capabilities had not kept pace. This mismatch among the three factors trapped the industry in a “high investment, low efficiency” dilemma. Thirdly, regression analysis of influencing factors indicated that corporate governance and market competitiveness were pivotal to innovation efficiency: the proportion of independent directors and revenue growth rate exerted significant positive impacts, while equity concentration showed a significant negative effect. Firm size had a weaker influence, and government support did not demonstrate a significant positive impact. Accordingly, this paper proposes pathways to enhance innovation efficiency in new energy enterprises, including optimizing corporate governance structures, formulating differentiated subsidy policies, and improving the innovation ecosystem. The findings of this study not only provide empirical references for the innovative development of the new energy industry but also offer theoretical support for relevant policy formulation. Full article

Bananas underpin Uganda’s food security and rural economy, but productivity is declining due to emerging pests, diseases, and declining soil fertility. To address these challenges, hybrid stress-tolerant banana varieties (HBVs) have been developed and released, but their adoption remains uneven across the country. This study analyzes the spatial distribution and determinants of HBV adoption and intensity in Uganda, providing new insights to inform scaling strategies. A cross-sectional survey of 624 banana-farming households was conducted across 24 districts in both traditional and non-traditional banana-growing regions. Data were analyzed using descriptive statistics and a Tobit regression model to capture both the binary decision to adopt and the intensity of adoption, measured as the number of HBV mats planted. Results showed significant regional variation; adoption was highest in Northern Uganda (73.9%) and lowest in Central and Southwestern regions (≈24%). Education and land size positively influenced adoption, while reliance on planting materials from fellow farmers consistently reduced adoption intensity across all regions. Gender and household structure also shaped adoption patterns, with male and married farmers more likely to plant larger areas of HBVs. The findings highlight the need for regionally tailored interventions, including strengthening formal seed systems, enhancing farmer knowledge, and addressing gender gaps in technology access. Strengthening institutional seed channels and extension support can accelerate HBV scaling and contribute to resilient banana production in Uganda. Full article

We develop a unified likelihood-based framework for limited outcomes built on the two-piece normal family. The framework includes a censored specification that accommodates boundary inflation, a doubly truncated specification on $(0,1)$ for rates and proportions, and a survival formulation with a log-two-piece normal baseline and Gamma frailty to account for unobserved heterogeneity. We derive closed-form building blocks (pdf, cdf, survival, hazard, and cumulative hazard), full log-likelihoods with score functions and observed information, and stable reparameterizations that enable routine optimization. Monte Carlo experiments show a small bias and declining RMSE with increasing sample size; censoring primarily inflates the variability of regression coefficients; the scale parameter remains comparatively stable, and the shape parameter is most sensitive under heavy censoring. Applications to HIV-1 RNA with a detection limit, household food expenditure on $(0,1)$, labor-supply hours with a corner solution, and childhood cancer times to hospitalization demonstrate improved fit over Gaussian, skew-normal, and beta benchmarks according to AIC/BIC/CAIC and goodness-of-fit diagnostics, with model-implied censoring closely matching the observed fraction. The proposed formulations are tractable, flexible, and readily implementable with standard software. Full article

Technological innovation in energy enterprises constitutes a pivotal component in realizing the transition to a green economy. In recent years, the complexity and volatility of the international economic landscape have significantly amplified economic policy uncertainty (EPU) across nations, which is poised to exert a profound influence on the technological innovation activities of energy enterprises. This study employs the Tobit regression method to investigate the relationship between EPU and corporate technological innovation (CTI), based on data from Chinese listed energy companies spanning the period of 2007 to 2018. Empirical results indicate that EPU exerts a significant positive influence on technological innovation for energy enterprises. Furthermore, we employed a Fisher permutation test to further elucidate the heterogeneity of this impact across various sub-industries, enterprise ownership types, and governance mechanisms. Specifically, EPU has a more pronounced promoting effect on technological innovation for traditional energy enterprises, non-state-owned enterprises, and enterprises with high failure tolerance. Against the backdrop of increasing global EPU, the findings of this study offer certain implications for governmental industrial policies and corporate governance mechanisms. Full article

The global shift towards sustainable energy and stringent climate policies has underscored the need for decarbonizing energy systems, electrifying transportation, and transforming supply chains. In this context, China’s new energy vehicle (NEV) industry, as the largest global producer and consumer of automobiles, is pivotal in advancing energy substitution and achieving carbon reduction goals. This study investigates the energy efficiency and supply chain transformation within China’s NEV sector, leveraging panel data from 12 representative provinces over the period 2017–2023. Employing a robust analytical framework that integrates the DEA-BCC model, Malmquist index, and Tobit regression, the study provides a dynamic and regionally differentiated assessment of NEV industry efficiency. The results reveal significant improvements in total factor energy efficiency, predominantly driven by technological progress. R&D intensity, infrastructure development, and environmental regulation are identified as key enablers of efficiency, while excessive government intervention tends to hinder performance. The findings offer valuable empirical insights and policy recommendations for optimizing China’s NEV industry in the context of energy system transformation and sustainable industrial development. Full article

Evaluating rural households’ subjective well-being (SWB) and identifying its determinants is crucial for rural sustainable development. This study takes Diqing Prefecture in the Tibetan region of China as a case, aiming to address two key research questions: (1) How do livelihood assets affect subjective well-being (SWB)—directly or indirectly—through the mediating role of the agricultural-income proportion? (2) Do these effects vary across different livelihood strategies? A questionnaire survey was administered to 489 randomly selected rural households in mid-2022. Two index systems were constructed: one for livelihood assets based on the Sustainable Livelihood Framework and another for SWB based on the Millennium Ecosystem Assessment. A subgroup Tobit regression model was utilized to analyze the heterogeneous effects. The results revealed deficiencies in SWB regarding basic material for a good life and health. Human, financial, and social assets are positively associated with SWB. However, natural assets directly negatively impact SWB across dimensions of basic material, security, and freedom, although the negative effect is masked by the mediating effect of farming livelihood strategies. Notably, human assets’ positive influence significantly strengthens with the agricultural income proportion rising. Whether physical, financial, and social assets positively affect SWB depends on farm work participation. These evidence-based findings contribute to a better understanding of the heterogeneous role of sustainable livelihoods in affecting rural households’ subjective well-being and highlight the need for policymakers to design diverse, targeted policies to support rural development. Full article

The shipping service industry plays a pivotal role in enhancing port competitiveness and fostering urban economic growth, yet limited studies systematically integrate its spatial temporal dynamics with the processes driving port–city synergy. This study constructs a three-dimensional analytical framework encompassing port operations, urban economic development, and shipping service industry agglomeration. Using data from 13 port cities in Jiangsu Province (2015–2023), we apply the entropy weight method, coupling coordination degree model, relative development model, and panel Tobit regression to evaluate interaction intensity, coordination patterns, and influencing factors. Results reveal a clear spatial gradient in coupling coordination, higher in southern Jiangsu and lower in the north, driven by disparities in economic foundations, port capacities, and service industry structures. In most cities, port operations and urban economies lag behind shipping service industry agglomeration, reflecting the predominance of low- and mid-end services. Port construction level, cargo and container throughput, economic development, openness, fixed asset investment, and population density significantly promote coordination, whereas R&D capacity shows no significant effect. The findings advance understanding of port–city service interlinkages and provide targeted policy recommendations for differentiated regional development, infrastructure enhancement, and upgrading toward high-end shipping services, with implications for maritime regions worldwide. Full article

Although corporate tax avoidance strategies may increase internal funding that supports innovation, they can also undermine it by weakening governance and encouraging short-term financial objectives. However, the overall impact remains theoretically contested, with insufficient empirical research available. This study examines the relationship between corporate tax avoidance and innovation, focusing on the moderating role of environmental, social, and governance (ESG) practices. Using a panel dataset of 12,408 firm-year observations of South Korean listed companies from 2014 to 2023, Tobit regression analyses reveal a statistically significant negative association between tax avoidance and innovation. Notably, this negative relationship is significantly mitigated in ESG-engaged firms, particularly those with stronger ESG performance. Further analysis indicates that these moderating effects are driven primarily by the social and governance domains. These findings suggest that ESG practices can offset the detrimental effects of tax avoidance by strengthening governance and stakeholder alignment. This study underscores the importance of integrating ESG principles into corporate tax strategies to support long-term innovation and sustainable corporate development. Full article

An in-depth and comprehensive evaluation of carbon emission efficiency (CEE) is essential for promoting high-quality development and achieving the “dual-carbon” goals. This study applies a super-efficiency slacks-based measure (Super-SBM) model with carbon emissions treated as an undesirable output to measure provincial CEE and the Malmquist–Luenberger (ML) index across 30 provinces and major comprehensive economic zones in China from 2010 to 2023. Efficiency trends for 2024–2025 are projected using a hybrid Autoregressive Integrated Moving Average (ARIMA)–Long Short-Term Memory (LSTM) approach. Furthermore, CEE patterns are examined at both national and regional levels, and the relationships between CEE and potential drivers are analyzed using Tobit regressions. Combining the regression outcomes with short-term forecasts, this study provides a forward-looking perspective on the evolution of CEE and its associated factors. The results indicate that (1) China’s CEE demonstrates a generally fluctuating upward trajectory, with the southern coastal and eastern coastal regions maintaining the highest efficiency levels, while other regions remain relatively lower. (2) The temporal changes in CEE across economic zones correspond to variations in technical efficiency and technological progress, with the latter contributing more prominently to overall improvement. (3) CEE shows significant associations with multiple factors: population density, economic development, technological advancement, government intervention, and environmental regulation are positively associated with efficiency, whereas urbanization tends to correlate negatively. Based on these findings, policy implications are discussed to promote differentiated pathways for enhancing CEE across China’s regions. Full article

Predicting delays in commercial flights is an increasing challenge due to rising air traffic demand, which generates additional costs and operational complexity. This study synthesizes and evaluates machine learning approaches for flight delay predictions, aiming to identify the most accurate prediction logic and assess the role of sample size in model performance. A systematic literature review was conducted, followed by a meta-analysis of 1077 studies published between 2015 and 2025. The studies were classified by prediction logic (binary classification or regression) and evaluated in terms of model effectiveness using Data Envelopment Analysis and Tobit regression to determine the influence of explanatory variables. The results show that binary classification approaches achieved higher average accuracy than regression models did, with confidence intervals validating their relative effectiveness. Furthermore, findings indicate that the use of more complex models does not guarantee improved predictive performance, suggesting that researchers should prioritize robust variable selection rather than constantly adopting increasingly complex methods. This work provides a comprehensive overview of machine learning methods for flight delay predictions and highlights implications for optimizing airport operations and enhancing passenger experience through the adoption of more reliable predictive strategies. Full article

The increasing need to optimize efficiency in port container terminals has led to the transition of operations from manual to automated or semi-automated processes. Automation involves integrating or gradually adopting digital technologies and equipment that reduce human intervention, enhance productivity, safety and sustainability. This study investigates the impact of automation on port efficiency through a comparative analysis of 20 container ports in the wider Mediterranean region, using a two-stage modeling approach. In the first stage, Data Envelopment Analysis (DEA) is applied under constant and variable returns to scale to estimate port efficiency using infrastructure, equipment, and container throughput data. The second stage employs Tobit regression to assess the effect of automated operations or systems on port efficiency, including variables such as the automation index, TEUs per employee, TEUs per ship (call) and revenue. A key contribution of this study is the development of a methodological framework for qualitatively classifying and evaluating these ports based on their level of automation, the introduction of digital technologies or equipment, and investments in new technologies. The results indicate that automation alone does not necessarily lead to higher efficiency unless it is effectively integrated into operations accompanied by adequate staff training and supported by gradual investment strategies. By contrast, cargo intensity (TEUs per call), highlights the importance of vessel size and cargo concentration in improving port performance. Full article