Search Results (235)

Innovation-driven labor markets play a pivotal role in economic development, yet significant disparities exist in how efficiently countries transform innovation inputs into labor market outcomes. This study addresses the critical gap in benchmarking multi-stage innovation efficiency by developing an integrated framework combining Data Envelopment Analysis (DEA) Super Slack-Based Measure (Super-SBM) for static efficiency evaluation and the Malmquist Productivity Index (MPI) for dynamic productivity decomposition, enhanced with cooperative game theory for robustness testing. Focusing on the top 20 innovative economies over a 5-year period, we analyze key inputs (Innovation Index, GDP, trade openness) and outputs (labor force, unemployment rates), revealing stark efficiency contrasts: China, Luxembourg, and the U.S. demonstrate optimal performance (mean scores > 1.9), while Singapore and the Netherlands show significant underutilization (scores < 0.4). Our results identify a critical productivity shift period (average MPI = 1.325) driven primarily by technological advancements. This study contributes a replicable, data-driven model for cross-domain efficiency assessment and provides empirical evidence for policymakers to optimize innovation-labor market conversion. The methodological framework offers scalable applications for future research in computational economics and productivity analysis. Full article

The COVID-19 pandemic triggered unprecedented economic disruptions, raising critical questions about the resilience and adaptability of macroeconomic productivity across countries. This study examines the impact of COVID-19 on macroeconomic efficiency and productivity dynamics in 37 OECD countries using quarterly data from 2018Q1 to 2024Q4. By employing a Slack-Based Measure Data Envelopment Analysis (SBM-DEA) and the Malmquist Productivity Index (MPI), we decompose total factor productivity (TFP) into efficiency change (EC) and technological change (TC) across three periods: pre-pandemic, during-pandemic, and post-pandemic. Our framework incorporates both desirable (GDP) and undesirable outputs (inflation, unemployment, housing price inflation, and interest rate distortions), offering a multidimensional view of macroeconomic efficiency. Results show broad but uneven productivity gains, with technological progress proving more resilient than efficiency during the pandemic. Post-COVID recovery trajectories diverged, reflecting differences in structural adaptability and innovation capacity. Regression analysis reveals that stringent lockdowns in 2020 were associated with lower productivity in 2023–2024, while more adaptive policies in 2021 supported long-term technological gains. These findings highlight the importance of aligning crisis response with forward-looking economic strategies and demonstrate the value of DEA-based methods for evaluating macroeconomic performance beyond GDP. Full article

Focusing on the dielectric elastomer actuator (DEA), this paper proposes a backstepping implicit inverse adaptive control scheme with creep direct inverse compensation. Firstly, a novel fractional-order creep Krasnoselskii–Pokrovskii (FCKP) model is established, which effectively captures hysteresis behavior and creep dynamic characteristics. Significantly, this study pioneers the incorporation of the fractional-order method into a hysteresis-coupled creep model. Secondly, based on the FCKP model, the creep direct inverse compensation is developed to combine with the backstepping implicit inverse adaptive control scheme, where the implicit inverse algorithm avoids the construction of the direct inverse model to mitigate hysteresis. Finally, the proposed control scheme was validated on the DEA system control experimental platform. Under both single-frequency and composite-frequency conditions, it achieved mean absolute errors of 0.0035 and 0.0111, and root mean square errors of 0.0044 and 0.0133, respectively, demonstrating superior tracking performance compared to other control schemes. Full article

Renewable energy stands as a pivotal solution to environmental concerns, prompting substantial research and development endeavors to promote its adoption and enhance energy efficiency. Despite the recognized environmental superiority of renewable energy systems, there is a lack of globally standardized indicators specifically focused on renewable energy efficiency. This study aims to develop and apply a non-parametric data envelopment analysis (DEA) indicator, termed the Renewable Energy Indicator (REI), to measure environmental performance at the national level and to identify differences in renewable energy efficiency across countries grouped by development status and income level. The REI incorporates new factors such as agricultural methane emissions (thousand metric tons of ${\mathrm{CO}}_2$ equivalent), PM2.5 air pollution exposure (µg/m³), and aspects related to electricity, including consumption (as % of total final energy consumption), production from renewable sources, excluding hydroelectric (kWh), and accessibility in rural and urban areas (% of population with access), aligning with the emerging paradigm outlined by the United Nations. By segmenting the REI into global, developmental, and income group classifications, this study conducts the Mann–Whitney U test and the Kruskal–Wallis H tests to identify variations in renewable energy efficiency among different country groups. Our findings reveal top-performing countries globally, highlighting both developed (e.g., Sweden) and developing nations (e.g., Costa Rica, Sri Lanka). Central and North European countries demonstrate high efficiency, while those facing political and economic instability perform poorly. Agricultural-dependent nations like Australia and Argentina exhibit lower REI due to significant methane emissions. Disparities between developed and developing markets underscore the importance of understanding distinct socio-economic dynamics for effective policy formulation. Comparative analysis across income groups informs specific strategies tailored to each category. Full article

In the context of increasingly complex and dynamic maritime logistics, seaports serve as critical nodes for intermodal transport, energy distribution, and global trade. Ensuring the safe and uninterrupted operation of port infrastructure—particularly berths—is vital for maintaining supply chain resilience. This study explores the impact of multiple risk categories on berth efficiency in a seaport, aligning with the growing emphasis on maritime safety and risk-informed decision-making. A two-stage methodology is adopted. In the first phase, the DEA CCR input-oriented model is employed to assess the efficiency of selected berths considered as Decision Making Units (DMUs). In the second phase, the Analytical Hierarchy Process (AHP) is used to categorize and quantify the impact of four major risk classes—operational, technical, safety, and environmental—on berth efficiency. The results demonstrate that operational and safety risks contribute 63.91% of the composite weight in the AHP risk assessment hierarchy. These findings are highly relevant to contemporary efforts in maritime risk modeling, especially for individual ports and port systems with high berth utilization and vulnerability to system disruptions. The proposed integrated approach offers a scalable and replicable decision-support tool for port authorities, port operators, planners, and maritime safety stakeholders, enabling proactive risk mitigation, optimal utilization of available resources in a port, and improved berth performance. Its methodological design is appropriately suited to support further applications in port resilience frameworks and maritime safety strategies, being one of the bases for establishing collision avoidance strategies related to an individual port and/or port system, too. 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

In the era of new productive forces, the efficient utilization of industrial land in high-tech zones is critical for fostering technological innovation, intelligent manufacturing, and green development. However, constrained by limited land reserves, inefficient stock utilization, and sluggish industrial upgrading, high-tech zones must establish a scientific early warning mechanism for industrial land redevelopment. This study constructs a four-tier early warning system (normal, alert, warning, and response) based on three key dimensions: enterprise life cycle, enterprise–park compatibility, and industrial land use efficiency. Using the Jinan High-Tech Zone as a case study, this study conducts an empirical analysis of 360 industrial land parcels from 2020 to 2022, employing DEA, fixed effects models, GIS visualization, and MCDA methods. The results indicate a strong correlation between enterprise life cycle and land use efficiency, with significant spatial differentiation in enterprise–park compatibility. Efficient land use is concentrated in areas with well-defined functions and high industrial agglomeration. This study identifies 360 land use scenarios, with 12% classified as normal, 28% requiring monitoring, 52% requiring optimization, and 8% necessitating redevelopment. Based on these findings, a “warning–monitoring–regulation” closed-loop management model is proposed, providing decision-making support for dynamic land optimization and sustainable development in high-tech zones. Full article

The construction of ecological civilization is intrinsically connected to green development. The green development of forestry enterprises serves as a key approach to achieving this goal. The research purpose of this paper is to explore the realization path of green development of forestry enterprises. First, an improved CRITIC (Criteria Importance Through Intercriteria Correlation)–entropy weight method was used to construct a reasonable input-output indicator system. Next, a three-stage data envelopment analysis (DEA) model was employed to evaluate the comprehensive technical efficiency of green development across 33 forestry enterprises in China, using panel data from 2017 to 2022. Finally, the study explored various configurational pathways for achieving green development by integrating the Technology–Organization–Environment (TOE) framework with dynamic qualitative comparative analysis (QCA). The findings reveal that green development in forestry enterprises is shaped by the interplay of multiple factors. Four distinct configurations were identified as instrumental in driving high green development. These configurations could be classified into two categories: the environment–organization synergistic development model and the technology–organization dual-driven model. This study provides empirical insights into the complex configurational relationships underlying green development in forestry enterprises, offering valuable guidance for optimizing development strategies. Full article

Based on the urgency of education for sustainable development (ESD), it is crucial to explore ESD implementation efficiency. Since ESD is closely related to social resources, it is necessary to explore which resources can improve ESD implementation efficiency. This study employs dynamic qualitative comparative analysis (QCA) to explore the multiple development pathways of external resources supporting the implementation efficiency of ESD in 31 provinces of China from 2017 to 2022. The results indicate that abundant basic resource- (Type I), complementary technology–culture resource- (Type II), and culture–information technology educational resource-driven (Type III) approaches are the main pathways to achieve high ESD execution efficiency. A key contribution of this study is its emphasis on the role of modern information technology in ESD. The insights garnered from this study can guide educators in leveraging information resources effectively to optimize ESD outcomes. Full article

This paper proposes a novel hybrid metaheuristic algorithm (MHA) for maximum power point tracking (MPPT), integrating particle swarm optimization (PSO), the differential evolution algorithm (DEA), and the grey wolf optimizer (GWO). The proposed method is inspired by the structural phases of the white shark optimizer (WSO), a recently introduced MHA. This study evaluates the MPPT performance of WSO and compares it with the proposed hybrid approach to provide insights into optimal MPPT selection. The key contributions include an in-depth analysis of the WSO framework, benchmarking its performance against the hybrid model. Both algorithms are implemented in an MPPT system and assessed based on tracking speed, accuracy, and adaptability. The results indicate that the WSO achieves a faster convergence due to its biologically inspired design, whereas the hybrid model, despite requiring additional coordination time, ensures comprehensive search space exploration. Notably, the proposed method excels in dynamic tracking efficiency, which is crucial for accurately following time-varying P-V curves. This study underscores the trade-off between tracking speed and efficiency, demonstrating that while WSO is advantageous for rapid tracking, the hybrid approach enhances overall MPPT performance under dynamic conditions. These findings offer valuable insights for optimizing MPPT strategies in renewable energy systems. Full article

Enhancing production efficiency in major sugarcane-producing regions is of strategic significance for ensuring the security of China’s sugar industry and promoting its industrial upgrading. Using the DEA–Malmquist–Tobit modeling framework, this study dynamically evaluates production efficiency from 2011 to 2023, spanning China’s 12th to 14th Five-Year Plan periods, with a focus on the primary sugarcane-producing regions: Guangdong, Guangxi, Yunnan, and Hainan. Results indicate a U-shaped fluctuation in national comprehensive technical efficiency, with a historical low in 2022 due to a collapse in scale efficiency, pinpointing scale management as the central constraint. Regionally, Guangdong consistently maintained optimal dual efficiency. Yunnan stabilized its efficiency through rigid policy mechanisms. Guangxi experienced setbacks due to competition between eucalyptus and sugarcane cultivation, while Hainan faced a precipitous decline in scale efficiency following industry exits. Total factor productivity (TFP) analysis revealed that stagnation in technological advancement was the primary cause of productivity decline, leading to asynchronous regional technology diffusion and subsequent reliance on scale adjustments. During the 12th Five-Year Plan, Hainan led in TFP growth but experienced a sharp downturn in the 13th period due to policy tightening. In contrast, Guangdong achieved notable TFP growth in the 14th period through technological breakthroughs, whereas Yunnan lagged behind Guangxi due to technological inertia. Analysis of the driving mechanisms showed that urbanization rates significantly boosted efficiency through intensified land use. Full article

This study evaluated the efficiency performance of the world’s top ten seed-producing companies from 2016 to 2022, exploring the interplay between asset scale, technological innovation, and resource allocation in the context of the third global wave of seed industry mergers and growing external uncertainties. Against the backdrop of rising sustainability demands and low-carbon transitions, optimizing firm-level efficiency has become central in balancing economic performance with environmental responsibility. Using Data Envelopment Analysis (DEA) and the Malmquist Productivity Index (MPI), in this study, we conducted a comprehensive static and dynamic assessment of firm efficiency. The results reveal considerable heterogeneity across firms and over time. Corteva’s overall technical efficiency (OTE) rose from 0.57 in 2018 to 0.91 in 2022, reflecting successful post-merger integration and digital innovation. DLF achieved an OTE = 1.00 in 2020 and 2022, indicating stable specialization on an optimal scale. In contrast, Bayer’s OTE dropped from 0.72 in 2016 to 0.36 in 2022, underscoring the challenges of resource integration after large-scale mergers. In terms of productivity dynamics, Corteva exhibited a sharp EFFCH surge to 1.7041 in 2018–2019, reflecting a phase of rapid efficiency recovery following its post-merger restructuring. Syngenta also demonstrated strong managerial improvement, with its EFFCH reaching 1.3759 in 2017–2018 and maintaining positive momentum thereafter. Over the entire period, Syngenta recorded the highest cumulative growth in efficiency (up 40.76%), while Bayer showed a significant decline (−28.33%), highlighting contrasting integration outcomes. On the technological front, DLF stood out with a TECHCH increase of 34.67%, suggesting that innovation remained the key driver of long-term productivity gains, particularly among firms that avoided aggressive mergers. These findings emphasize the importance of aligning technological investment with scalable and resilient operational structures to achieve sustainable efficiency. This study offers empirical guidance for policymakers and strategic planners seeking to strengthen the seed industry’s role in green transformation, while also providing a framework applicable to other capital-intensive sectors undergoing structural transition. Full article

While existing research has focused on logistics carbon emissions, understanding spatiotemporal emission cost dynamics and drivers remains limited. This study bridges three gaps through methodological advances: (1) Applying the Non-Radial Directional Distance Function (NDDF) to measure Marginal Carbon Abatement Costs (MCAC), overcoming traditional Data Envelopment Analysis (DEA) model’s proportional adjustment constraints for provincial heterogeneity; (2) Pioneering dual-dimensional MCAC analysis integrating temporal trends (2013–2022) with spatial autocorrelation; and (3) Developing a spatial Durbin error model with time-fixed effects capturing direct/indirect impacts of innovation and infrastructure. Based on provincial data from 2013–2022, our findings demonstrate a U-shaped temporal trajectory of MCAC with the index fluctuating between 0.3483 and 0.4655, alongside significant spatial heterogeneity following an Eastern > Central > Northeastern > Western pattern. The identification of persistent high-high/low-low clusters through local Moran’s I analysis provides new evidence of spatial dependence in emission reduction costs, with these polarized clusters consistently comprising 70% of Chinese cities throughout the study period. Notably, the spatial econometric results reveal that foreign investment and logistics infrastructure exert competitive spillover effects, paradoxically increasing neighboring regions’ MCAC, a previously undocumented phenomenon in sustainability literature. These methodological advancements and empirical insights establish a novel framework for spatial cost allocation in emission reduction planning. Full article

In various fields, planar 2R underactuated robots have garnered significant attention due to their numerous applications. To guarantee the stable control of these robots, a simple control strategy is presented in this paper, and we utilize the intelligent optimization algorithm to enhance the controller parameters. Initially, a comprehensive dynamic model is developed for the robot with its control properties described. Subsequently, we design a PD controller to control the movement of the planar 2R underactuated robot. The differential evolution algorithm (DEA) is used to optimize the parameters of the PD controller to obtain the best control effect and make each link reach the target state. The findings from the simulation demonstrate the efficacy of the approach, and the designed strategy shows a higher stability and convergence rate, highlighting its important contribution to the field of underactuated robots. Full article

This study proposes a dynamic bit-level encoding algorithm (DEA) and introduces the S+DEA compression framework, which enhances compression efficiency by integrating the DEA with image segmentation as a preprocessing step. The novel approaches were validated on four different datasets, demonstrating strong performance and broad applicability. A dedicated data structure was developed to facilitate lossless storage and precise reconstruction of compressed data, ensuring data integrity throughout the process. The evaluation results showed that the DEA outperformed all benchmark encoding algorithms, achieving an improvement percentage (IP) value of 45.12, indicating its effectiveness as a highly efficient encoding method. Moreover, the S+DEA compression algorithm demonstrated significant improvements in compression efficiency. It consistently outperformed BPG, JPEG-LS, and JPEG2000 across three datasets. While it performed slightly worse than JPEG-LS in medical images, it remained competitive overall. A dataset-specific analysis revealed that in medical images, the S+DEA performed close to the DEA, suggesting that segmentation alone does not enhance compression in this domain. This emphasizes the importance of exploring alternative preprocessing techniques to enhance the DEA’s performance in medical imaging applications. The experimental results demonstrate that the DEA and S+DEA offer competitive encoding and compression capabilities, making them promising alternatives to existing frameworks. Full article