Search Results (125)

ESG has profoundly influenced asset pricing and resource allocation in capital markets. However, existing research largely focuses on the economic consequences of absolute ESG levels, with limited attention paid to how the gap between corporate ESG performance and aspiration levels—the ESG performance aspiration gap—is related to the behavior of information intermediaries in the capital market. Using a sample of A-share non-financial listed companies on the Shenzhen Stock Exchange from 2013 to 2024, this paper empirically examines the relation between the ESG performance aspiration gap and institutional investors’ site visits and its potential underlying channel. The findings are as follows. First, the ESG performance aspiration gap is positively correlated with institutional investors’ site visits. This conclusion remains robust after a series of robustness tests. Second, the mechanism analysis is consistent with information asymmetry serving as a potential channel linking the ESG performance aspiration gap and institutional investors’ site visits. Third, the moderating effect analysis shows that both marketization level and analyst coverage negatively moderate the positive relation between the ESG performance aspiration gap and institutional investors’ site visits. This paper extends the research boundaries of the economic consequences of ESG and institutional investors’ information search behavior, providing a new theoretical explanation for how the capital market responds to the dynamic changes in corporate ESG performance. It also offers policy implications for improving the ESG information disclosure system and enhancing the information efficiency of the capital market. Full article

RFID is usually used for product recognition, object-level tracking, inventory visibility, logistics monitoring, authentication, and automated data capture for various supply chain industries. RFID can enable traceability, authentication (anti-counterfeiting), order allocation, inventory control, warehouse efficiency, logistics monitoring, safety management and sustainability information in apparel, fashion, and product-based supply chains. The current evidence, however, is quite diverse, ranging from fashion supply chains and manufacturing analytics to inventory systems, logistics, asset tracking, environmental monitoring, worker safety, privacy, cyber and other aspects of digital transformation. The purpose of the scoping review is to chart evidence on RFID deployment in apparel, fashion, textile, and transferable product contexts, focusing on traceability, anti-counterfeiting, product visibility, and implementation conditions in apparel, fashion, and textile supply chains, as well as in sustainability contexts. The review process followed scoping review guidance and its protocol. Evidence was found in the following formats: EndNote XML/export records, Covidence screening (title/abstract), Covidence screening outputs (included study exports), and uploaded full-text PDFs. Documents were classified by source type, study design, context, technology focus, purpose, key findings, relevance to sustainability, limitations, and role in the synthesis. The results show that RFID evidence is strongest in product identification, item-level visibility, secure inventory search, manufacturing data capture, logistics tracking, asset traceability, and operational efficiency. Full article

State Council Document No. 43 and the revised Budget Law legally disclaimed Chinese local governments’ responsibility for the debts of the enterprises they own. Across 22,233 firm-year observations on 1686 listed firms from 2009 to 2023, the effective cost of debt of local state-owned firms fell by 0.33 percentage points relative to private firms, 5.5 percent of its mean. The direction was not obvious in advance: withdrawing a guarantee that lenders had been pricing should have made credit dearer, while the debt swap enacted alongside the disclaimer replaced high-cost vehicle liabilities with low-cost provincial bonds and eased the balance sheets standing behind those firms. Central state-owned enterprises, whose support the reform left untouched, serve as a falsification group and show no statistically distinguishable change. The estimate passes the joint pre-trend test, survives matching, entropy balancing and twenty specification changes, and operates through the interest paid rather than the quantity of debt, which identifies a price effect. Investment rose by 0.76 percentage points of assets, but no improvement in investment efficiency was detected, and an equivalence test cannot exclude a small one. A cross-sectional pattern consistent with fiscal relief does not survive correction for multiple testing, so that channel is reported as suggestive rather than identified. Hardening the budget constraint of local governments lowered the debt servicing burden of the firms they own without directing the freed resources toward more productive use, so the sustainability gain is fiscal rather than allocative. Full article

Portfolio reinforcement learning (RL) commonly represents each action as a complete asset-weight vector, causing the action dimension and exploration difficulty to grow with the investment universe. This study proposes FrontierStep-RL, which replaces the direct N-dimensional action with two bounded variables: a frontier coordinate and a rebalancing step. At each decision date, rolling estimates of expected returns and covariance define a regularized efficient frontier. A cost–risk-aware coordinate organizes the frontier using normalized local changes in predicted volatility and one-way turnover. The coordinate selects a frontier-supported target portfolio, while the step controls how far the pre-trade portfolio moves toward that target. We evaluate FrontierStep-RL on FF49, FF100, and FNSPID-50 against traditional strategies, controlled direct-weight RL policies, and recent portfolio-management methods. FrontierStep-RL achieves net Sharpe ratios of 0.75 on both FF49 and FNSPID-50 while maintaining comparatively low volatility, drawdown, and turnover. In the 100-asset setting, it achieves 0.68, compared with 0.52 for the strongest direct-weight baseline, and completes all runs. At a transaction cost of 50 basis points, it retains net Sharpe ratios of 0.559 and 0.568. The results support fixed-dimensional target selection and controlled execution for scalable, transaction-cost-aware portfolio RL. Full article

Against the backdrop of promoting common prosperity, enhancing farmers’ income and improving its structural composition have become critical policy concerns. Using panel data from 30 provincial administrative units in mainland China covering the period 2013–2022, this study empirically examines the impact of agricultural new-quality productive forces (ANQP) on the differentiated structure of farmers’ income. The results show that ANQP has a limited effect on Wage, Operating and Transfer income components but significantly increases property income. This finding suggests that the positive effect of ANQP on farmers’ income is likely to be associated with the appreciation of agricultural assets and the extension of agricultural industrial chains. In addition, a significant threshold effect of agricultural fiscal investment is identified only for property income. When fiscal expenditure remains within an appropriate range, ANQP exerts a stronger positive influence on farmers’ property income, whereas excessive fiscal investment reduces marginal benefits and may even hinder resource allocation efficiency. These findings imply that optimizing agricultural fiscal policies, strengthening technological progress, and adopting region-specific development strategies are essential to effectively promote farmers’ income growth and advance the goal of common prosperity. Full article

Sustainable agricultural development requires regional agricultural systems to balance output growth, resource efficiency, ecological protection, and long-term resilience. In mountainous and ecologically sensitive regions, identifying the development constraints and statistically derived marginal contribution thresholds of agriculture is essential for promoting green transformation and sustainable land use. Taking Yunnan Province, China, as a representative plateau mountainous agricultural region, this study uses provincial annual data from 1990 to 2023 to quantitatively identify the key drivers and threshold characteristics of agricultural development under multidimensional constraints. A multidimensional indicator system was constructed covering fiscal and investment support, agricultural production inputs, rural infrastructure, and labor and population conditions. Ridge regression was employed to address multicollinearity among explanatory variables, Bootstrap approximate inference was used to improve the robustness of coefficient estimation, and the SHAP interpretation framework was introduced to rank key driving factors and identify marginal contribution thresholds. By integrating ridge regression, Bootstrap approximate inference, SHAP-based contribution ranking, and threshold identification, the proposed framework advances prior agricultural sustainability studies by linking coefficient-based factor analysis with interpretable marginal contribution thresholds under conditions of high multicollinearity and multidimensional resource constraints. The results show that agricultural development in Yunnan is characterized by multidimensional resource and infrastructure constraints. Rural electricity consumption, total reservoir storage capacity, fixed asset investment in agriculture, forestry, animal husbandry and fisheries, local public fiscal budget expenditure, and agricultural population generally act as positive supporting factors. Rural electricity consumption is the most stable and core driver across the aggregate and three sectoral models. In contrast, pesticide and fertilizer inputs show significant negative associations in most models, suggesting that future agricultural development in Yunnan is unlikely to be sustainably supported by continued expansion of high-intensity chemical inputs. Sectoral heterogeneity is also evident: agriculture and animal husbandry are more dependent on energy, water resources, and mechanization, whereas forestry shows a more distinct operational structure. The SHAP dependence analysis identifies several statistically derived marginal contribution thresholds, including rural electricity consumption of approximately 6.055 billion kWh, total reservoir storage capacity of approximately 10.395 billion m³, total agricultural machinery power of approximately 19.8324 million kW, pesticide use of approximately 37,500 tons, and fertilizer application of approximately 1.5238 million tons. These values should be interpreted as empirical transition points in the modeled marginal contributions rather than definitive biophysical ecological limits. They indicate that the sustainability-related constraint structure of agricultural development in Yunnan is not a single output ceiling but a composite interval shaped by infrastructure support capacity, factor allocation conditions, and the declining marginal contribution of high-intensity chemical inputs. The findings provide directional quantitative evidence for sustainable agricultural governance, agricultural green transformation, and differentiated policy discussion in mountainous agricultural regions and offer reference implications for advancing SDG 2 and SDG 15 through the coordination of food-related production, resource use efficiency, and ecosystem conservation. The identified thresholds should be interpreted as model-derived marginal contribution transition points rather than operational policy cutoffs or directly enforceable ecological standards. Full article

This study develops a cooperative multi-agent deep reinforcement learning (MARL) framework for simulation-based cost-overrun mitigation in smart grid construction projects under dynamic engineering uncertainty. Modern smart grid construction involves digital substations, renewable-energy-connected facilities, flexible transmission assets, intelligent monitoring systems, and geographically distributed contractors; therefore, cost escalation is driven by sequential interactions among procurement, schedule execution, equipment deployment, supervision, weather, logistics, and price volatility. The proposed framework models procurement management, construction scheduling, equipment allocation, and supervision-control units as decentralized agents embedded in a calibrated construction simulation environment. The environment is parameterized from 42 smart grid construction projects in Henan Province, China and generates disturbance scenarios involving weather efficiency loss, transportation delay, market-price volatility, labor shortage, and supply-chain interruption. A hybrid DQN–PPO mechanism represents mixed decision structures: value-based DQN modules handle discrete managerial choices such as task acceleration, supplier switching, and procurement timing, whereas PPO modules adjust continuous resource-allocation and recovery-intensity decisions. A hierarchical reward function combines local departmental objectives with project-level penalties for cost overrun, schedule delay, idle resources, recovery expenditure, safety risk, and environmental impact. The experimental protocol uses 30 paired random seeds, nonparametric bootstrap confidence intervals, Holm-adjusted Wilcoxon signed-rank tests, and comparison with deterministic optimization, rolling-horizon MPC, stochastic/robust optimization, single-agent DRL, MAPPO, MADDPG/MATD3, QMIX, and HAPPO baselines. The proposed framework achieves a mean cost-overrun rate of 6.83% and a mean schedule deviation of 16.82 days, reducing cost overrun by 18.7% and schedule deviation by 21.4% relative to rule-based construction management under the reported disturbance settings. The calibrated simulation evidence establishes a statistically evaluated decision-support framework for coordinated construction cost control and provides an artifact-level reproducibility pathway through configuration files, random-seed lists, anonymized synthetic benchmarks, and aggregated logs. Full article

Ports play a crucial role in global maritime logistics systems, and evaluating their operational efficiency is essential for improving resource allocation. However, existing studies often treat port operations as a black box, limiting the understanding of internal mechanisms and sources of inefficiency. Using panel data from 17 Chinese port-listed companies from 2018 to 2023, this study applies a network slacks-based measure (SBM) model to evaluate operational efficiency within a two-stage framework. The service stage transforms labor, fixed assets, and berth length into cargo and container throughput, while the profitability stage converts these outputs into revenue and net profit. The results show that overall operational efficiency remains relatively low. Profitability-stage efficiency is generally lower than service-stage efficiency, indicating that inefficiency mainly arises from the limited ability to convert operational performance into financial outcomes. Significant differences are also observed across regions and operational scales. To further capture persistence, this study evaluates the dynamic performance of efficiency by considering both growth and fluctuation over time. The findings indicate that most inefficient ports exhibit weak persistence, reflecting limited capacity to maintain stable efficiency improvements. This study provides a stage-based analytical framework for identifying inefficiency sources and offers insights for enhancing both efficiency and the long-term stability of port operations. Full article

Based on China’s provincial panel data from 2000 to 2022, this paper constructs a measurement model of the spatial misallocation of fossil energy and investigates its impact on regional economic development, its transmission mechanism, as well as the moderating effect of new energy development. The results show that: (1) Spatial misallocation of fossil energy significantly hinders economic development, and this conclusion is robust under a variety of robustness checks; (2) The inhibitory effect of fossil energy spatial misallocation on economic development is most pronounced in the central region, regions with insufficient energy allocation, and in the context of coal misallocation; (3) New energy development not only exerts a positive driving effect on economic development, but also weakens the negative impact of fossil energy misallocation; after crossing a critical threshold, its effect shifts from inhibition to promotion; (4) Fixed-asset investment, industrial structure, and energy efficiency play negative mediating roles. The negative indirect effects of these three variables superimpose on the negative direct effect of fossil energy spatial misallocation, further strengthening the impediment to economic development. This study provides a basis for optimizing fossil energy allocation and promoting the coordinated development of traditional and new energy sources. Full article

The rapid growth of the sharing economy has improved resource utilization in car-sharing, yet it has also sharpened market competition and diversified user demand. A persistent obstacle is the low coordination efficiency between asset-heavy operating companies and traffic-driven platforms, whose misaligned objectives waste social resources. This paper uses differential game theory to analyze their dynamic coordination strategies and benefit allocation mechanisms. The Nerlove–Arrow model captures the evolution of brand goodwill, while the company’s decisions on station layout, vehicle dispatch, and pricing, together with the platform’s advertising investment, form the core decision variables in a two-party game framework linking the asset side and the traffic side. Compared with the non-cooperative Nash equilibrium, the cooperative mode removes the double marginalization effect, strengthens the investment incentives of both parties, and raises the system’s steady-state goodwill and total profit, achieving a Pareto improvement. To ground the cooperative framework in rigorous theory, we supply a verification theorem confirming that the linear candidate value functions satisfy the Hamilton–Jacobi–Bellman equations over the entire admissible state space. A formal proof of instantaneous rationality ensures that neither party falls into a cooperation trap on the horizon $[0,T]$, and the asymptotic stability of the steady-state goodwill trajectory is established. We further endogenize the revenue-sharing coefficient through a generalized Nash bargaining model that admits asymmetric bargaining structures, and introduce a Stackelberg leadership benchmark as a third comparative regime. Sensitivity analyses with respect to the discount rate and user heterogeneity confirm the robustness of the findings. A dedicated discussion section bridges the gap between idealized parameterization and data-driven calibration, describing practical pathways via A/B testing, user churn metrics, and econometric estimation of demand parameters. The results offer a scientific decision-making reference for strategic cooperation in the car-sharing industry. Full article

Urban Unmanned Aerial Vehicle (UAV) logistics operations are frequently constrained by the intersection of limited battery endurance and dynamic ground traffic. When mobile depots are delayed by congestion, onboard drone fleets experience extended idling periods, leading to constrained sortie generation and reduced asset utilization. To address this bottleneck, this paper introduces a traffic-predictive multi-UAV dispatch framework for deterministic day-ahead planning under modeled urban operating conditions. By coupling a count-derived macroscopic speed surrogate learned using XGBoost with a Particle Swarm Optimization (PSO)–Mixed-Integer Linear Programming (MILP) optimization architecture, the framework synchronizes mobile depot trajectories with forecasted low-congestion windows and pre-allocates endurance-feasible parallel aerial sorties. Controlled computational experiments across 30 synthetic routing instances demonstrate the potential value of this approach within the stated modeling assumptions. Compared to baseline clustered deployments, the traffic-aware framework raises mean fleet utilization from 0.43 to 0.63—a 46.2% relative improvement driven by temporal compression of the mission window rather than an absolute increase in flight hours. Furthermore, the proposed framework reduces total mission completion time by 69.87% relative to the conventional truck-only baseline, while achieving a 29.58% incremental gain over static speed drone deployments. These findings suggest that incorporating predictive ground traffic information into day-ahead UAV scheduling can improve modeled fleet efficiency; however, field validation with measured route-level speeds, real delivery demand, and operational constraints remains necessary before deployment-level claims can be made. Full article

As a non-rivalrous, replicable, and non-consumable production factor, data offers conditions for resource-efficient value creation, and the conversion from scattered data resources into measurable data assets sits at the center of firm competitiveness and sustainable allocation of digital factors. How artificial intelligence supports this conversion, and how executive cognition shapes its strength, are taken up within a framework drawing on the resource-based view, dynamic capability, and upper-echelons theory. Using 24,251 firm-year observations from Chinese A-share listed firms over 2012–2022, panel fixed-effects estimation yields a positive association between AI and data asset formation, stable across instrumental-variable estimation, propensity score matching, Heckman correction, and alternative measures of both variables. AI deepens data mining capability through stronger research and development investment and widens data-carrying capacity through expanded digital infrastructure, with the two channels opening up the relationship. Cognitive flexibility improves the fit between AI and shifting business scenarios, while cognitive complexity supports balanced allocation of technological resources across competing constraints; both characteristics strengthen the main association. The pattern is more pronounced among state-owned enterprises and firms in eastern and central regions, with industry differences less clear-cut. The findings inform differentiated policy design for sustainable digital development in emerging-market settings. Full article

Under China’s dual-carbon agenda, a central question is whether the Low-Carbon City (LCC) pilot policy reduces energy intensity, whether this effect can be credibly interpreted as causal, and under which conditions and through which channels it operates. Using a balanced panel of 282 prefecture-level and higher-level cities from 2006 to 2023, this study develops a problem-oriented framework that integrates effect identification, credibility validation, and heterogeneity and mechanism analysis. The average treatment effect is estimated using staggered difference-in-differences, while dynamic effects are identified with interaction-weighted and imputation-based event-study estimators, and selection concerns are further addressed through propensity score matching difference-in-differences and a battery of stability checks. The results show that the LCC pilot policy reduces urban energy intensity, with the baseline estimate implying a decline of about 15–16%, and that the policy effect accumulates over time rather than appearing immediately. This finding remains stable across alternative specifications, placebo tests, and matched-sample estimation. The policy effect is stronger in cities with higher initial energy intensity and higher levels of economic development. Mechanistic evidence indicates that adjustments in fixed asset investment and changes in AI-related resource allocation are two observable channels associated with the decline in energy intensity. By focusing on energy intensity as a process-oriented performance indicator, this study provides more direct evidence on the energy-efficiency consequences of low-carbon urban governance and clarifies the conditional and structural foundations of policy effectiveness. Full article

As global industrialization and energy demands rise, excessive reliance on fossil fuels escalates carbon emissions, making clean energy alternatives an urgent priority for sustainable development. As a key transition pathway, wind and solar power can be converted into hydrogen via electrolyzers for electricity generation, thermal supply, or natural gas synthesis. This enables flexible multi-energy coordination and improves overall renewable energy utilization efficiency. However, conventional electrolyzer scheduling approaches typically assume fixed hydrogen production efficiency, failing to account for dynamic variations in operating conditions, efficiency attenuation, and lifetime degradation under fluctuating renewable inputs. This inadequacy compromises the long-term sustainability of green hydrogen systems. To address these challenges, this paper proposes a hybrid AEL-PEM electrolyzer power allocation and operating condition array rotation strategy. Piecewise linear models are established to characterize the efficiency and full life cycle degradation of both electrolyzer types across normal operation, overload, and start–stop transitions. A mixed-integer linear programming (MILP) model is formulated with an objective function incorporating energy purchase costs, start–stop penalty costs, and electrolyzer lifetime degradation costs, and is solved using the Gurobi solver. Simulation validation is conducted using a 24 h typical summer day dataset with a 15 min resolution. Three comparative schemes are evaluated to verify the strategy’s effectiveness in minimizing total system operation costs and enhancing renewable energy utilization efficiency through optimized operating condition management. Results demonstrate that the proposed strategy reduces total system costs by 23%, entirely eliminates renewable energy curtailment, and balances electrolyzer lifespan degradation across all units, collectively advancing the economic efficiency, asset sustainability, and long-term operational reliability of green hydrogen systems. Full article

Market indices serve as a benchmark for performance comparison, guide asset allocation decisions, and reflect overall market sentiment and economic conditions, thereby influencing investment strategies by representing a segment of the market. Unquestionably, investor sentiment impacts price movement. In this paper, the objectives were to study the effectiveness of the Money Flow Index (MFI) in enhancing the performance of predictive analysis by capturing market psychology, developing an investment strategy, and analyzing the performance of the method mentioned. This study applies machine learning algorithms with technical indicators and optimizes portfolio allocation based on three notable market indices in Southeast Asia (SEA): SET50 in Thailand, STI in Singapore, and VN30 in Vietnam. Firstly, we combined technical indicators with machine learning—Support Vector Classifier (SVC), Random Forest (RF), and Extreme Gradient Boosting (XGBoost)—by comparing datasets with and without MFI over the period from 2013 to 2023. The results showed that XGBoost with MFI delivered the best predictive performance across three indices. These findings indicate that MFI significantly enhances prediction accuracy, even during volatile market conditions (COVID-19). Additionally, the predictions were integrated into the Markowitz Mean-Variance (MV) model to construct an optimal portfolio, which was then benchmarked against an equal-weight portfolio (1/N). Ultimately, the findings demonstrate that incorporating the machine learning predictions into the MV framework efficiently generates wealth. Full article