Search Results (214)

Although organizational resilience is well established, refining the systematic quantitative evaluation of health systems resilience (HSR) remains an ongoing opportunity for advancement. Research either focuses on individual HSR indicators, such as social welfare policy, public expenditure, health insurance, healthcare quality, and technology, or broadly examines socio-economic factors, highlighting the need for a more comprehensive methodological approach. This study employed the Slacks-Based Measure (SBM) within Data Envelopment Analysis (DEA) to analyze efficiency by maximizing outputs. It systematically examined key HSR factors across countries, providing insights for improved policymaking and resource allocation. Taking a five-year (2016–2020) dataset that covered 55 to 56 countries and evaluating 17 indicators across governance, health systems, and economic aspects, the paper presents that all sixteen top-ranked countries with a perfect efficiency score of 1 belonged to the high-income group, with ten in Europe, highlighting regional HSR differences. This paper concludes that adequate economic resources form the foundation of HSR and ensure stability and sustained progress. A properly supported healthcare workforce is essential for significantly enhancing health systems and delivering quality care. Last, effective governance and the equitable allocation of resources are crucial for fostering sustainable development and strengthening HSR. Full article

This study examines the impact of cost stickiness on research and development (R&D) investment and corporate performance in Japanese firms. Additionally, it investigates the moderating effect of managerial overconfidence and financial slack. To do so, we analysed a sample of 4877 observations from Japanese firms listed on the Tokyo Stock Exchange between 2014 and 2020. The results show that cost stickiness generally promotes R&D investment while negatively affecting corporate performance. Further, although managerial overconfidence does not moderate the relationship between cost stickiness and R&D investment, it weakens the negative effect of cost stickiness on corporate performance. Meanwhile, financial slack strengthens the positive impact of cost stickiness on R&D investment, but it does not moderate the relationship between cost stickiness and corporate performance. These findings provide strategic insights into resource allocation behaviour in driving innovation and influencing corporate outcomes in the Japanese market context. Full article

Serverless computing has gained significant attention in recent years. However, the cold start problem remains a major challenge, not only because of the substantial latency it introduces to function execution time, but also because frequent cold starts lead to poor resource utilization, especially during workload fluctuations. To address these issues, we propose a multi-level scheduling solution: the Hybrid Model. This model is designed to reduce the frequency of cold starts while maximizing container utilization. At the global level (across invokers), the Hybrid Model employs a skewness-aware scheduling strategy to select the most appropriate invoker for each request. Within each invoker, we introduce a greedy buffer-aware scheduling method that leverages the available slack (remaining buffer) of warm containers to aggressively encourage their reuse. Both the global and the local schedule are tightly integrated with a prediction component- The Hybrid Predictor- that combines Auto-Regressive Integrated Moving Average ARIMA (linear trends) and Random Forest (non-linear residuals + environment-aware features) for 5-min workload forecasts. The Hybrid Model is implemented on Apache OpenWhisk and evaluated using Azure-like traces and real FaaS applications. The evaluations show that the Hybrid Model achieves up to 34% SLA violation reductions compared to three state-of-the-art approaches and maintains the container utilization to be more than 80%. Full article

Improving forest carbon sink efficiency (FCSE) is the key to mitigating climate change and achieving sustainable forest resource management in China. However, current research on FCSE remains predominantly focused on static perspectives and singular linear effects. Based on panel data from 30 provinces (autonomous regions and municipalities) in China from 2008 to 2022, this study integrated the super-efficiency Slack-Based Measure (SBM)-Malmquist–Luenberger (ML) model, spatial autocorrelation analysis, and dynamic fuzzy set qualitative comparative analysis (fsQCA) to reveal the spatiotemporal differentiation characteristics of FCSE and the multi-factor synergistic driving mechanism. The results showed that (1) the average value of the FCSE in China was 1.1. Technological progress (with an average technological change of 1.21) is the core growth driver, but the imbalance of technological efficiency change (EC) among regions restricts long-term sustainability. (2) The spatial distribution exhibited a U-shaped gradient pattern of “eastern—southwestern”, and the synergy effect between nature and economy is significant. (3) The dynamic fsQCA identified three sustainable improvement paths: the “precipitation–economy” collaborative type, the multi-factor co-creation type, and “precipitation–industry-driven” type; precipitation was the universal core condition. (4) Regional differences exist in path application; the eastern part depends on economic coordination, the central part is suitable for industry driving, and the western part requires multi-factor linkage. By introducing a dynamic configuration perspective, analyzing FCSE’s spatiotemporal drivers. We propose a sustainable ‘Nature–Society–Management’ interaction framework and region-specific policy strategies, offering both theoretical and practical tools for sustainable forestry policy design. Full article

To achieve carbon neutrality targets in the power sector, regions with rich coal and renewable energy resources are facing unprecedented pressure. This paper explores the decarbonization pathway in the power sector in Inner Mongolia, China, under different energy transition scenarios based on the Long-Range Energy Alternatives Planning System (LEAP) model. This includes renewable energy expansion, carbon capture and storage (CCS) applications, demand response, and economic regulation scenarios. Subsequently, a combination of the Logarithmic Mean Divisia Index (LMDI) and Slack-Based Measure Data Envelopment Analysis (SBM-DEA) model was developed to investigate the influencing factors and power generation efficiency in low-carbon electricity. The results revealed that this region emphasizes first developing renewable energy and improving the carbon and green electricity market and then accelerating CCS technology. Its carbon emissions are among the lowest, at about 77.29 million tons, but the cost could reach CNY 229.8 billion in 2060. We also found that the influencing factors of carbon productivity, low-carbon electricity structures, and carbon emissions significantly affected low-carbon electricity generation; their cumulative contribution rate is 367–588%, 155–399%, and −189–−737%, respectively. Regarding low-carbon electricity efficiency, the demand response scenario is the lowest at about 0.71; other scenarios show similar efficiency values. This value could be improved by optimizing the energy consumption structure and the installed capacity configuration. Full article

Green innovation has emerged as a key catalyst for the sustainable growth of logistics enterprises. Green innovation not only helps logistics enterprises reduce operating costs but also enhances their competitiveness and promotes the entire industry’s transformation towards environmental protection and efficiency. However, logistics enterprises encounter technical bottlenecks, capital shortages, and insufficient talent and infrastructure when implementing green innovation. Collaboration between universities and industries serves as a crucial method for logistics companies to access external resources and plays a significant role in promoting technological progress, knowledge transfer, and innovation capability enhancement of enterprises. This research, grounded in the theories of social capital and dynamic capabilities, explores the mechanism from the perspective of resources and capabilities, and examines how university–industry collaboration affects green innovation. This research employs a hierarchical regression model to evaluate the proposed hypotheses. The research results show that university–industry collaboration has a positive impact on social capital, slack resources, and dynamic capabilities, and social capital, slack resources, and dynamic capabilities positively influence green innovation. The research results have certain reference value for logistics enterprises to promote green innovation. Full article

As global climate change intensifies and resources become increasingly scarce, China’s sustainable development of freshwater aquaculture faces unprecedented challenges. This study utilizes panel data from 31 provincial-level regions in mainland China (2000–2023) and innovatively constructs a multi-stage sequential modified slack-based measure data envelopment analysis (MSBM-DEA) model. By endogenizing extreme climate factors within the aquaculture production efficiency framework, this study reveals the dynamic impact of climate change on freshwater aquaculture total factor productivity (TFP). The finding indicates that extreme climate events reduce freshwater aquaculture TFP by 1.66% and technical advancement by 18.9%. The impact varies regionally, with eastern provinces experiencing a maximum TFP decline of 3.1%, while western provinces face a significant drop of 5.2%. The eastern region, supported by technology and policy, shows a relatively strong recovery capacity, whereas the western region suffers more due to resource scarcity and technical lag. To tackle these challenges, this study recommends establishing a climate-adaptive TFP monitoring framework and promoting a dual-driven model of technical innovation and efficiency enhancement to bolster fisheries’ climate resilience. This research provides valuable decision making support for climate adaptation strategies in China’s freshwater aquaculture and serves as empirical evidence and theoretical guidance for other climate-vulnerable regions globally. Full article

The existing research lacks a comprehensive framework to explain the impact of natural environmental change on corporate sustainable development. After analyzing 2010–2023 data from 4816 Shanghai/Shenzhen A-share firms (39,271 firm-year observations), fixed-effects models reveal that natural environmental change improves financial performance but harms environmental–social performance. Absorbed slack resources weaken the positive influence of natural environmental change on financial performance and the negative influence on environmental–social performance; unabsorbed slack resources strengthen the influence of natural environmental change on financial performance but weaken the negative influence on environmental–social performance. Digital transformation diminishes the positive financial effects of natural environmental change. Findings suggest that firms should prioritize strategic slack resource allocation to manage environmental uncertainty, as digital initiatives currently demonstrate limited effectiveness in mitigating these challenges. Full article

A critical natural solution to combat global warming and reduce carbon emission is the forest carbon sink (FCS). Owing to variations in geographic location, policy formulation, and economic development, Chinese provinces exhibit significant disparities in forest carbon sink efficiency (FCSE). Therefore, evaluating and enhancing FCSE and optimizing resource allocation have emerged as pressing issues. This study develops a pioneering analytical framework for the systematic estimation and optimization of FCS resources. It measures FCSE, considering both dynamic and static aspects and adopting a spatial–temporal perspective, utilizing the Malmquist Index and Super Efficiency Slacks-Based Measure to analyze the primary factors influencing FCSE. The Autoregressive Integrated Moving Average method forecasts carbon sink goals for typical regions for the years 2030, 2045, and 2060. To effectively enhance FCSE and rationally optimize FCS resource allocation, this study constructs the Inverse Data Envelopment Analysis. The study’s findings indicate significant disparities in the extremes of the average FCSE across Chinese regions, with a mean value difference of 2.2188. Technological change is the primary driver of advancements in FCSE. To achieve the 2060 carbon sink goal, each input indicator requires a substantial increase. Drawing on insights into the FCS landscape, the study delineates regional disparities and offers a scientific foundation for policymakers to devise strategies and address sustainability concerns regarding FCS. Full article

The digital economy has emerged as a transformative force, creating new opportunities for sustainable development, especially within the marine fisheries sector. This study examines the impact of the digital economy on the green total factor productivity (GTFP) of fisheries in China’s coastal regions from 2011 to 2022. Using panel data from 11 coastal provinces, we employ the Slack-Based Measure (SBM) model and the Global Malmquist–Luenberger (GML) index to assess GTFP and analyze the effects of digital economic development. Our findings indicate the following: (1) the digital economy significantly enhances fishery GTFP, improving both resource efficiency and environmental sustainability; (2) the impact varies across regions, reflecting notable regional heterogeneity in digital infrastructure and adoption; and (3) a threshold effect exists, whereby the influence of the digital economy on GTFP varies depending on the level of digital economic development. This research underscores the dual role of digital technologies in boosting fisheries’ economic productivity while promoting greener, more sustainable practices. This study provides valuable insights for policymakers aiming to integrate digital transformation into the sustainable development of marine fisheries. Full article

In the era of Industry 5.0, human-centric manufacturing necessitates deep integration between workers and intelligent workshop scheduling systems. However, the inherent variability in worker efficiency due to learning and forgetting effects poses challenges to human–machine–logistics collaboration, thereby complicating multi-resource scheduling in smart workshops. To address these challenges, this study proposes a real-time task-driven human–machine–logistics collaborative framework designed to enhance multi-resource coordination in smart workshops. First, the framework incorporates a learning-forgetting model to dynamically assess worker efficiency, enabling real-time adjustments to human–machine–logistics resource states. Second, a task-driven self-organizing approach is introduced, allowing human, machine, and logistics resources to form adaptive groups based on task requirements. Third, a task slack-based matching method is developed to facilitate real-time, adaptive allocation of tasks to resource groups. Finally, the proposed method is validated through an engineering case study, demonstrating its effectiveness across different order scales. Experimental results indicate that, on average, completion time is reduced by no less than 10%, energy consumption decreases by at least 8%, and delay time is reduced by over 70%. These findings confirm the effectiveness and adaptability of the proposed method in highly dynamic, multi-resource production environments. Full article

Prior studies have shown mixed findings on the role of financial slack. This study examines how environmental factors such as munificence, dynamism, and complexity moderate the relationship between financial slack and innovation activity. Using data from Compustat and the Center for Research in Security Prices (CRSP) database on 578 computer-processing firms in innovation-intensive industries in the United States, our results reaffirm that financial slack is a strategic asset that enhances R&D investment. Further, we find that the positive consequences of financially abundant firms pursuing innovation are attenuated in munificent environments where firms increasingly rely on external resources. Similarly, in dynamic environments, unpredictable market changes divert slack resources from long-term R&D investments, further weakening the effect. However, there is no significant difference in complex environments. Our study contributes to the existing literature by integrating different environments and highlighting the importance of balancing internal resources with external environments in shaping innovation strategies. For managers, these findings provide practical guidance for resource allocation strategies to effectively support innovation in varying external environments. Full article

As a scarce strategic resource, the efficient utilization of rare earth resources is crucial for ensuring national economic security and promoting sustainable development. AI, the core engine of the Fourth Technological Revolution, provides a favorable opportunity to drive green technological innovation. Green efficiency in technological innovation has not been adequately studied, and the relationship between green efficiency in the rare earth era and AI is still unclear. Based on the above research gap, this study employs the slack-based measure model to perform both static and dynamic evaluations of green efficiency in technological innovation during the technology development and transformation phases of eight listed Chinese rare earth enterprises from 2017 to 2021. This study aims to provide a policy basis for improving the green efficiency of the rare earth industry and the application of AI in the industrial chain. The findings reveal the following: (1) the green efficiency of technological innovation among these rare earth listed enterprises remains low in both phases, with low pure technical efficiency being a key factor contributing to the overall low green technology innovation efficiency; (2) total factor productivity in the technology development phase exhibits a fluctuating upward trajectory while demonstrating a general downward trend in the achievement transformation phase; and (3) the application of AI significantly enhances the green efficiency of technological innovation during the transformation phase, with a more pronounced impact compared to the technology development phase. This study contributes to the existing literature by extending previous research on AI and green efficiency, particularly within the context of the rare earth industry. The empirical results offer valuable policy recommendations for improving the utilization of rare earth resources and enhancing green technological innovation through AI integration. Full article

Offshore wind energy has become a critical component of global efforts to transition toward low-carbon and sustainable energy systems, and although Taiwan’s advantageous geographical position has accelerated its progress in this domain, many of Taiwan’s upcoming offshore wind projects remain in a pre-construction phase, raising questions about their viability for complementary wave energy integration. To address this challenge, this study proposes a hybrid Cross-Efficiency Slacks-Based Measure (CE-SBM) Data Envelopment Analysis (DEA) model. Thirteen announced offshore wind projects were evaluated using spatial and resource-related input variables and energy-centric output variables. The self-efficiency results from the SBM stage highlighted several projects—most notably Zhu Ting, Wo Neng, and Chu Tin—as highly effective in resource utilization under their own weighting schemes. However, the subsequent cross-efficiency analysis added a consensus-based perspective, revealing a clear performance hierarchy and identifying inefficiencies in projects such as Greater Changhua Northeast and Winds of September. These findings underscore the value of combining DEA-based models with slacks-based and cross-efficiency features to guide multifaceted energy development. By prioritizing projects with robust efficiency profiles, policymakers and stakeholders can expedite Taiwan’s broader adoption of integrated wind–wave energy systems, ultimately fostering a more reliable and sustainable marine energy portfolio. Full article

Against the backdrop of global challenges to food security and China’s push to modernize its agriculture, it is critical to understand how government strategies affect innovation efficiency. This study examines how three modes of intergovernmental competition—fiscal spending competition (strategically increasing public spending to attract resources), tax competition (providing incentives to promote investment), and promotion competition (officials prioritizing short-term projects for promotion)—affect the efficiency of agricultural science and technology innovations across China’s provinces. Utilizing panel data (2000–2021) and a Slack-Based Measure Data Envelopment Analysis (DEA-SBM) model, we find that fiscal spending competition suppresses efficiency, particularly in western regions where infrastructure investments crowd out R&D. Tax competition enhances efficiency, yet its impact is attenuated in central China due to low industrial upgrading. Promotion competition impedes long-term innovation, as frequent official turnover prioritizes short-term projects. Regional heterogeneity highlights eastern China’s market-driven advantages versus central/western regions’ structural constraints. Policy implications advocate for spatially differentiated governance, including R&D tax rebates in the east and cross-regional innovation alliances. This study contributes to fiscal decentralization theory by revealing the nonlinear effects of competition modes on agricultural innovation. Full article