Search Results (7,040)

The low-carbon transition (LCT) of manufacturing clusters is a critical pathway to addressing bottlenecks in global climate governance and promoting sustainable economic development in developing countries. Accurately measuring the level of this transition and clarifying its dynamic trends are of great significance. Drawing on the economic rationale of a low-carbon economy, this study constructs a comprehensive evaluation indicator system and employs the entropy-weighted CRITIC-grey relational TOPSIS method to measure the LCT levels of China’s four major industrial bases from 2013 to 2023. Combined with convergence analysis, the Theil index, mechanism analysis, and policy scenario simulation, it systematically analyzes the characteristics of disparities and the underlying mechanisms. The study’s results show that low-carbon technology is the core driver of the LCT of the four major industrial bases. The LCT levels of the four major industrial bases have generally increased, with some bases exhibiting a catch-up effect internally. The overall disparity among the four major industrial bases has widened, primarily driven by intra-base differences. Specifically, the Beijing–Tianjin–Tangshan industrial base displays polarization characteristics, while the Central-Southern Liaoning industrial base shows a relatively low-level equilibrium. The transition of resource-based cities lags, mainly constrained by rigid industrial structures and insufficient investment in technology. Industrial structure optimization plays a certain role in improving resource-based regions, whereas technological innovation has a more pronounced effect in developed regions. This study constructs a comprehensive analytical framework of “measurement–evolution–mechanism–simulation,” which refines the quantitative evaluation system for the LCT of manufacturing clusters. The findings provide empirical support for formulating differentiated low-carbon policies for manufacturing clusters and optimizing coordinated emission reduction pathways, while also offering a reference paradigm for similar research in other developing countries. Full article

As artificial intelligence (AI) increasingly reshapes the global technological and economic landscape, understanding and forecasting national AI competitiveness has become an important yet challenging task. Unlike conventional Analytic Hierarchy Process (AHP)–Entropy-based evaluation methods and machine learning approaches that treat indicators as isolated or weakly connected features, this study proposes an integrated framework that explicitly represents inter-indicator dependencies as a structured global topology. Based on an Input–Process–Output–Environment (IPOE) system with 24 indicators for 10 major economies during 2016–2025, AHP–Entropy, XGBoost, Design of Experiments (DOE), and Bayesian networks are combined to identify dependency pathways among indicators. These structural relations are embedded into a graph neural network (GNN) for competitiveness assessment, while a Dynamic GNN-ARIMA module is developed to project future competitiveness trajectories under limited samples. Building on these projections, a multi-objective fund allocation optimization model is constructed and solved via the NSGA-II algorithm to reduce policy volatility while maintaining future AI competitiveness with a strategic investment of RMB 500 billion. Results show that the U.S. remains the clear leader, followed by China, while mid-tier economies show noticeable reshuffling. Under the Min-Variance strategy with the investment, China is projected to significantly narrow the gap with the United States, reaching a comparable level of competitiveness. Full article

Population aging has become a significant global phenomenon, particularly in developing countries where urban systems are not fully prepared to support older adults. Although the concept of age-friendly cities has been widely promoted, many municipalities still lack an integrated database that links health, social, economic, and environmental dimensions to support policymaking and built-environment improvement. This study aims to develop and validate a four-dimensional healthy aging database framework for assessing age-friendly built environments and public facilities at the municipal level. A mixed-methods approach was employed, combining quantitative surveys, built-environment assessments, and functional ability evaluation using the Barthel Activities of Daily Living Index. The study was conducted in Rangsit Municipality, Thailand, as a case study. The results demonstrate that the proposed framework can systematically integrate multidimensional aging data and identify priority areas for housing improvement, public facility modification, and community services. The framework also supports evidence-based decision-making and place-based policy implementation for age-friendly urban development. This study contributes a practical database framework and assessment tool that can assist local governments in evidence-based, inclusive, and sustainable urban development and aging societies. Full article

Provider payment reform is widely regarded as an important policy instrument for improving medical service efficiency, while empirical evidence on mixed provider payment systems remains limited. Taking China’s Sanming healthcare reform as a case, this study examines the effects of a mixed provider payment system that combines global budgets with diagnosis-related group (DRG)-based payment, referred to as the “double-bundling” reform, on medical service performance. Using a balanced panel dataset of public medical institutions in Sanming from 2014 to 2023, we exploit the staggered rollout of the reform as a quasi-natural experiment and estimate its effects using a staggered difference-in-differences approach. The results show that the reform significantly reduced the inpatient-to-outpatient admission ratio while increasing average length of stay and bed occupancy rate. These findings suggest that the reform was associated with higher admission thresholds, fewer potentially avoidable hospitalizations, and improved bed utilization within county-level medical institutions. Additional results indicate that the reform contributed to outpatient cost containment without a statistically significant increase in the average cost per inpatient admission. Overall, the evidence suggests that the provider payment reform helped strengthen cost-control incentives and improve the alignment between expenditure restraint and service delivery efficiency within vertically integrated county-level medical alliances. This study provides empirical evidence from China for the design of mixed provider payment reforms in integrated delivery systems. Full article

This study aims to enhance the precision of humanoid robots in imitating complex human “walking–grasping” coordinated movements. Addressing limitations in sample efficiency and reward function design in Generative Adversarial Imitation Learning (GAIL), we propose the Similarity Reward-Augmented Generative Adversarial Imitation Learning (SRA-GAIL) framework. The method integrates plantar thin-film resistive pressure sensors to measure the real-time pressure distribution at four key points on both feet, combined with roll/pitch angle data acquired from JY901S inertial measurement units (IMUs). A Lagrangian constraint optimization strategy is employed to achieve gait stability control based on the zero moment point (ZMP). Simultaneously, a visual similarity evaluation module is established using human demonstration trajectories captured by a Logitech C920E camera, augmented by grip force feedback from flexible thin-film pressure sensors on the hands. This enables the design of a multimodal sensor-fused similarity reward function. By incorporating Lagrangian constraint optimization and a maximum entropy reinforcement learning framework, Similarity Reward-Augmented Generative Adversarial Imitation Learning synchronously optimizes gait stability control—guided by zero moment point (ZMP) and roll/pitch data—and vision-based trajectory similarity evaluation. These components address motion stability constraints and trajectory similarity metrics, respectively, generating biomechanically plausible gait strategies. A spatiotemporal attention mechanism parses human motion trajectory features to drive the end-effector for high-precision trajectory tracking. To validate the proposed method, an imitation learning experimental system was constructed on a physical XIAOLI humanoid robot platform, integrating inertial measurement units (IMUs), plantar pressure sensors, and a vision system. Quantitative evaluations were conducted across multiple dimensions, including robot platform analysis, walking stability, object grasping success rates, and end-effector trajectory similarity. The results demonstrate that, compared to Generative Adversarial Imitation Learning (GAIL) and behavioral cloning, Similarity Reward-Augmented Generative Adversarial Imitation Learning achieves a stable object grasping success rate of 93.7% in complex environments, with a 23.8% improvement in sample efficiency. The method maintains a 96.5% compliance rate for zero moment point (ZMP) trajectories within the support polygon, significantly outperforming baseline approaches. This effectively addresses the bottleneck in robot policies adapting to dynamic changes in real-world environments. Full article

Protecting high-standard farmland is pivotal for sustainable land utilization and long-term regional food security. However, delineating high-standard farmland in metropolitan areas often neglects the influences of future urban expansion and farmland morphology, critical factors for enhancing farmland productivity. To address this, this study established a systematic evaluation framework for high-standard farmland delineation. It employed the patch-generating land use simulation model to forecast the probability of future urban expansion while employing the analytic hierarchy process and entropy weight method to calculate combined weights for evaluating farmland suitability. An ant colony optimization algorithm was implemented to improve farmland suitability and morphological compactness, thereby scientifically delineating high-standard farmland. Results from Guangzhou reveal that farmland area decreased between 2000 and 2020, primarily driven by urban expansion. The delineated high-standard farmland covers 682.18 km², achieving dual optimization of farmland suitability and compactness. The results are predominantly located within permanent basic farmland and grain production functional zones. This finding aligns with previous studies and existing plans, demonstrating the methodology’s superiority. Furthermore, this study categorizes Guangzhou’s high-standard farmland into four grades and proposes targeted policy recommendations. In summary, this study presents a new and scientific approach for high-standard farmland delineation, offering valuable policy support for sustainable farmland management. Full article

The construction sector in Saudi Arabia is under increasing pressure to enhance productivity and technological capability in line with Vision 2030, yet the adoption of artificial intelligence (AI) remains uneven. This study investigates the multi-level barriers affecting AI adoption in the Saudi construction industry using a sequential explanatory design that combines large-scale survey analysis with Interpretive Structural Modeling (ISM) and MICMAC classification. Data were collected from 181 construction professionals through a structured questionnaire covering eight constructs and 50 measurement items. Descriptive statistics reveal moderate AI utilization with a clear preference for analytics-driven applications over physical automation technologies. Perceptual rankings identify trust deficits and workforce capability gaps as prominent concerns. However, the ISM hierarchy uncovers a different structural reality: limited government support emerges as the root driver, cascading through cost and leadership constraints into workforce deficiencies, attitudinal resistance, and ultimately data ecosystem challenges. This perception–structure divergence highlights the risk of prioritizing visible symptoms over foundational causes. The MICMAC analysis further confirms the dominance of policy and strategic drivers within the adoption system. The study contributes by providing one of the first hierarchical mappings of AI adoption barriers in the Saudi construction context and offers a phased intervention roadmap for policymakers and industry leaders. The findings emphasize that sustainable AI diffusion in government-influenced construction ecosystems requires coordinated action across regulatory, organizational, and human capital dimensions rather than isolated technical investments. Full article

Industry 4.0 is transforming the way companies manufacture, improve, and distribute products, moving toward fast, intelligent, and flexible manufacturing, which will bring about fundamental changes in enterprises’ production capabilities. The Flexible Job Shop Scheduling Problem (FJSP) allows a single job to be divided into multiple operations, each of which can be processed on multiple machines. Due to its high flexibility and complexity, traditional scheduling methods are difficult to meet the needs of dynamic production. Dispatching rules struggle to effectively perceive the global precedence relationships among jobs and the distribution of machine workloads; metaheuristic approaches suffer from slow iterative convergence; existing deep reinforcement learning methods often employ a single policy network to handle both operation sequencing and machine assignment in a coupled manner, which tends to cause training instability and slow convergence. This paper proposes a deep reinforcement learning model that integrates Multi-Proximal Policy Optimization (MPPO) and Dual Attention Network (DAN) to address the FJSP. The model uses the operation message attention block and machine message attention block of DAN to capture the dependency relationships between operations and the dynamic competitive relationships between machines, respectively, and extract deep features. At the same time, MPPO designs dual actor networks to handle operation sequencing and machine assignment decisions separately, and combines a centralized critic to optimize the policy. This balances exploration and exploitation and improves training stability. Experiments are conducted based on the SD1 and SD2 datasets. In FJSP instances of four scales, the model is compared with PPO-DAN, PPO-HGNN, traditional scheduling rules, and OR-Tools. The results show that the algorithm reduces makespan by up to 4.2% on SD1 and 10.1% on SD2. Moreover, it achieves better performance than traditional scheduling rules. Its comprehensive performance is superior to that of the comparison methods, verifying its effectiveness and practical application potential in solving the FJSP. Full article

Accurate trajectory tracking is fundamental to the autonomous operation of unmanned aerial vehicles (UAVs) in complex tasks. While proximal policy optimization (PPO) has shown strong potential in UAV control, its performance is highly sensitive to hyperparameter configuration, and manual tuning is time-consuming due to complex interparameter coupling. This paper proposes CCO–PPO, a framework integrating the cuckoo catfish optimizer (CCO) with PPO for automatic hyperparameter optimization in UAV trajectory tracking. The problem is formulated as a Markov decision process with a 20-dimensional state space, and the CCO performs offline search over a four-dimensional hyperparameter space. Evaluated across seven test environments covering diverse trajectory geometries, wind disturbances, sensor noise, and large-scale scenarios, CCO–PPO achieves the lowest tracking error in all cases. Performance gains over baseline PPO increase monotonically with task complexity, reaching 18.8% under combined wind disturbance and sensor noise, with statistically significant advantages in 85.7% of pairwise comparisons against baseline PPO, SAC, and TD3. Ablation studies confirm that joint optimization of all four hyperparameters is essential under high-disturbance conditions, and comparisons with Bayesian optimization validate the CCO’s superior cross-seed stability. These results demonstrate that metaheuristic hyperparameter optimization substantially enhances policy robustness in high-disturbance UAV trajectory tracking scenarios. Full article

As the global population ages rapidly, the built environment has become increasingly critical for the health of older adults. In China, although the government has continuously promoted age-friendly retrofitting of old residential communities, these communities often face low usage rates after renovation. This study evaluated the age-friendliness of eight old residential units in Dalian, China, using the EVOLVE 2010 (Evaluation of Older Adults’ Living Environment) tool and combined semi-structured interviews with older residents. The results analyzed environmental impacts on older adults’ health across three domains: diet, exercise, and emotion. The findings reveal systemic deficiencies in housing units, external spaces, and urban planning, with limited physical accessibility being the predominant concern. Improving spatial functionality or environmental optimization alone is insufficient to enhance health outcomes; instead, expanding the range of activities and improving access to various locations contribute to increased self-esteem, confidence, and engagement in beneficial behaviors such as physical exercise and a healthy diet. The study highlights a gap between current renovation practices and the actual needs of older adults, emphasizing that accessibility should be prioritized in age-friendly design. These findings provide evidence-based policy implications for promoting healthy aging through urban renewal. Full article

Against the policy backdrop of high-quality development and the “Dual Carbon” goals, corporate environmental responsibility and green governance have emerged as core drivers of corporate value creation and resource allocation in capital markets. However, in practice, corporate environmental disclosure has increasingly degenerated into an impression management tool. Using a sample of China’s A-share listed companies from 2011 to 2024, this paper combines text analysis of annual reports with green patent data to systematically examine the impact of performance feedback on corporate strategic environmental decoupling, drawing upon the behavioral theory of the firm and legitimacy theory. The findings are as follows: First, negative performance feedback significantly increases corporate greenwashing propensity, whereas positive performance feedback significantly strengthens corporate brownwashing behavior. Second, government regulation amplifies the costs of falsifying environmental information, significantly suppressing the positive impact of negative performance feedback on greenwashing, but exacerbating the positive impact of positive performance feedback on brownwashing. Conversely, media attention amplifies the benefits of corporate green performances, significantly strengthening the catalytic effect of negative performance feedback on greenwashing, while effectively suppressing the positive impact of positive performance feedback on brownwashing. Third, heterogeneity analysis reveals that the impact of performance feedback on corporate strategic decoupling in environmental disclosure is more pronounced among non-state-owned enterprises, firms facing high industry competitive pressure, and those in heavily polluting industries. By integrating greenwashing and brownwashing into a unified analytical framework, this study expands the research boundaries of corporate environmental disclosure and strategic behaviors. Furthermore, it deepens the application contexts of the behavioral theory of the firm within non-financial disclosure, deconstructs the myth of homogeneous governance effects under legitimacy pressure, and provides vital implications for investors, policymakers, and fund managers. Full article

South Asia has experienced a persistent rise in per capita carbon dioxide emissions despite growing policy attention to low-carbon development. Against this background, this study examines how economic growth, energy intensity, renewable energy, urbanization, and trade openness shape per capita carbon dioxide emissions in six South Asian countries over the period 1990–2023. Grounded in the STIRPAT framework, the analysis combines fixed-effect estimation with two-step system generalized method of moments to address unobserved heterogeneity, endogeneity, and emissions persistence. The results show that economic growth remains strongly carbon-intensive, with gross domestic product per capita exhibiting a near-proportional elasticity with emissions. Energy intensity significantly increases emissions, while renewable energy reduces them. Urbanization exerts a positive but smaller effect, whereas trade openness remains statistically fragile. The findings also indicate strong emission persistence, underscoring the importance of early intervention. The study contributes to the regional environmental literature by providing an integrated and dynamic assessment of South Asia’s growth–energy–emissions nexus and by introducing a composite policy-support dimension into the empirical framework. The results offer practical implications for energy efficiency reform, renewable expansion, and climate-sensitive urban policy in developing economies. Full article

This study examines whether fiscal decentralization operates as a strategic macroeconomic risk-management instrument and whether its effectiveness depends on institutional quality. Using a balanced panel of 27 European Union member states over 2008–2023, a composite fiscal decentralization index combining expenditure and revenue autonomy is constructed, and a dynamic specification is estimated using a two-step System-GMM estimator. Output volatility is measured as a five-year rolling standard deviation of real GDP growth. The results indicate that fiscal decentralization exhibits a statistically significant effect on volatility whose direction depends on governance quality. Institutional quality directly reduces volatility, and the interaction between decentralization and institutional quality is negative and highly significant. A critical institutional threshold of 1.865 (WGI estimate scale), above which decentralization reduces output volatility, is identified. These findings indicate that decentralization functions as a conditional risk-management mechanism embedded within institutional capacity. The results provide policy-relevant insights into EU fiscal architecture design in an era of recurrent macroeconomic shocks. Full article

Environmental economics and policy research has paid limited attention to interfirm spillover effects on firm-level performance. This study addresses this gap by distinguishing between the direct and spillover effects of environmental regulation and firm-specific resources on firm performance. Using panel data for Korean manufacturing firms, we estimate a dynamic spatial Durbin model (SDM) that accounts for both temporal persistence and spatial dependence. The empirical results provide clear evidence. First, environmental regulation and firm-specific factors—including intellectual capital, physical capital, and organizational slack—exert statistically significant positive direct effects on firms’ sustainable growth rate (SGR). Second, interaction effects are crucial: environmental regulation significantly enhances SGR when combined with organizational slack, highlighting the importance of internal resource conditions. Third, spatial spillover effects are identified only under specific configurations. Environmental regulation generates positive spillover effects when interacting jointly with intellectual capital, physical capital, and organizational slack, rather than as an independent driver. Similarly, physical capital produces spillover effects through its interactions with other firm resources. Importantly, these effects vary across firms. Spillover effects are more pronounced in firms with high absorptive capacity, whereas they are weaker or insignificant in firms with low absorptive capacity. Overall, the findings indicate that environmental regulation affects firm performance primarily through resource complementarities and conditional spatial interactions, offering policy implications for more targeted regulatory design Full article

The Beijing–Tianjin–Hebei (BTH) coordinated-development strategy provides a county-level setting for examining how transport-led regional restructuring reshaped the relationship between human activity and land–environment conditions. Using a balanced panel of 200 county-level units from 2010 to 2020, we work with two linked subsystems: the human-activity subsystem (H), which combines transport integration and economic upgrading, and the land–environment subsystem (L), which combines land-use transition and ecological response. Pooled entropy weighting, a coupling-coordination index, spatial autocorrelation analysis, and fixed-effects differential-response models are used to trace temporal change, spatial clustering, and post-2014 heterogeneity within BTH. Mean coupling coordination (D) rose from 0.5430 to 0.6012, but the increase came mainly from the rise of H, while L changed only slightly. Positive spatial autocorrelation persisted throughout the period. Counties in the Beijing–Tianjin ring kept higher absolute coordination levels, yet after 2014, they improved more slowly than non-ring counties because land–environment adjustment lagged behind changes within H. Relative to key ecological function zones, agricultural counties—and to a lesser extent urbanized counties—posted faster gains in D, again mainly through H. The results show that in BTH, regional integration did not move the two subsystems in lockstep: transport reorganization and economic upgrading advanced faster than land–environment adjustment, so durable county coordination still depended on land governance, ecological regulation, and policies matched to territorial functions. Full article