Search Results (5,241)

The middle reaches of the West Liaohe River Basin, a typical semi-arid to semi-humid transition and agro-pastoral ecotone in northern China, exhibit high ecological sensitivity, low resilience, and pronounced fragility. Despite growing concerns, existing studies in this region lack a comprehensive assessment paradigm that effectively couples inherent ecological attributes with nonlinear predictive modeling. To fill this gap, we developed an integrative framework that innovatively combined the SRP conceptual model with a stacking ensemble learning technique. This coupling is methodologically novel because it moves beyond linear assumptions, enables the detection of complex nonlinear response surfaces, and establishes a seamless analytical chain from historical evaluation to future projection. By selecting 13 indicators, including topography, climate, soil, vegetation, and socio-economic factors, the weight was determined by the comprehensive application of the analytic hierarchy process and entropy weight method, and the ecological fragility of the middle reaches of the West Liaohe River Basin from 2000 to 2020 was evaluated at multiple scales. The spatial differentiation driving factors were analyzed using a geographic detector. Therefore, an Ensemble Learning Regression model was used to simulate and predict the ecological fragility pattern in 2030. The results show that from 2000 to 2020, the ecological fragility of the study area showed a decreasing trend overall, with the Ecological Vulnerability Synthetical Index (EVSI) decreasing from 3.48 to 2.68, and the spatial pattern gradually shifting from “high in the northwest, low in the southeast” to “overall stability, local optimization.” The spatial agglomeration of ecological fragility gradually weakened, indicating that high-fragility areas tend to disperse and low-fragility areas expand in contiguous areas, and the ecosystem structure tends to develop towards equilibrium. The driving mechanism shows an evolution characteristic from “soil erosion dominated” to “biological abundance dominated,” with the impact of climate factors first increasing and then stabilizing, and the direct pressure from human activities continuously weakening. Under the assumption that historical trends continue, the ensemble learning model projects that by 2030, the ecological vulnerability pattern will be dominated by Mild and Moderate levels, with the area of extremely vulnerable regions significantly reduced to 0.36%. This study verified the applicability of the SRP model in transitional river basins, and the constructed “evaluation-driving mechanism-prediction” framework can provide a scientific basis for the ecological protection and adaptive management of the West Liaohe River Basin and provide a methodological reference for ecological fragility research in similar areas. However, limitations persist: the indicator system and weight assignment are subject to inherent subjectivity, and the 2030 scenario projection based on the Stacking ensemble learning model relies on the BAU (Business-As-Usual) assumption, which fails to account for abrupt climate extremes or major policy shifts. Future studies should incorporate multi-scenario constraints to reduce predictive uncertainty. Full article

This paper evaluates the extent to which European Union member states show alignment between strategic innovation capacity and sustainable development outcomes. To achieve this objective, indicators were collected from Eurostat for two dimensions: strategic capacity for innovation (public expenditure on research and development, human resources in science and technology, and the higher education graduation rate) and sustainable development outcomes (real GDP per capita, employment rate, risk of poverty or social exclusion, and greenhouse gas emissions). Going beyond traditional literature, we develop an analysis based on grey clustering using multiple scenarios to illustrate the complex, non-linear relationships and structural bottlenecks in member states. The stability of the classifications was further examined through threshold sensitivity testing across all scenarios and through 200,000 weight-perturbation simulations for an illustrative boundary case. The results reveal distinct performance typologies: a resilient group of “systemic leaders” (including Denmark, Sweden, and the Netherlands) demonstrating consistent excellence across all applied prioritization scenarios, and a stagnant core facing structural challenges regarding both innovation and sustainability (such as Romania and Hungary). The dynamic analysis covering 2021–2024 suggests that strong innovation-capacity indicators are not necessarily associated with equally strong sustainability-outcome indicators, while certain economies in Central and Eastern Europe show positive convergence trends. Supported by stability simulations conducted across multiple scenarios, the study highlights significant alignment gaps between innovation-capacity indicators and sustainability-outcome indicators across the European Union and offers public policy recommendations to stimulate sustainable cohesion and technology adoption. Full article

With the widespread application of IoT and AI technologies in the sports equipment manufacturing sector, traditional sports equipment manufacturers are facing challenges such as financial pressures, technological integration barriers, and the gradual reduction in government subsidies during digital transformation. To investigate the intrinsic mechanism of multi-stakeholder collaborative transformation, this paper, based on the bounded rationality assumption, incorporates the government, sports equipment manufacturers, and third-party digital service providers into a unified analytical framework. A tripartite evolutionary game model is constructed, systematically deriving the replication dynamic equations for each stakeholder under different strategies, and analyzing the stability of the system equilibrium point using the Jacobian matrix. This study shows that the sustainability of government subsidies depends on the trade-off between social benefits, administrative costs, and subsidy expenditures; whether manufacturers choose to cooperate is mainly influenced by the cost of self-purchased equipment, digital service fees, operation and maintenance risks, and residual value recovery; and the investment willingness of digital service providers depends on infrastructure construction costs, service revenue, and government support. Further simulation analysis shows that a higher initial willingness to cooperate can accelerate system convergence, a moderate capital interest rate helps balance the incentives of both supply and demand sides, excessively high core equipment prices will inhibit manufacturers’ willingness to cooperate, and reasonable digital service fees are key to achieving stable collaboration. This paper provides a theoretical basis for policy optimization, service pricing, and industrial collaborative governance in the digital transformation of the sports manufacturing industry. Full article

Urban centers today face unprecedented challenges in energy management, emissions, waste disposal, and public services, as nearly 70% of the global population is projected to live in cities by 2050. The complexity and rapid evolution of urban systems underscore the pressing need for stability, innovation, and adaptability, particularly regarding sustainability and digital transformation. AI-powered simulation models have emerged as transformative tools, capable of simplifying, predicting, and managing highly intricate urban systems while offering policymakers valuable insights for strategic planning. However, the integration of AI in smart and sustainable urban development presents critical legal, ethical, and regulatory concerns. This study examines these questions by evaluating existing and emerging frameworks addressing algorithmic transparency, data protection, stakeholder engagement, and sustainable development in prominent urban models including Amsterdam, Copenhagen, Singapore, Tokyo, Bangalore, and Nairobi. Comparative analysis is conducted through a doctrinal desk review, focusing on statutory provisions, international policies (EU, UN-Habitat), ISO Smart City standards, and local governance charters. Key issues addressed include the risk that AI models, if unregulated, become opaque “black boxes” that obscure both decision-making logic and accountability. Without robust standards, there is no guarantee of interoperability, revision, or representation of public interest. Equitable management, access, and inclusive participation are vital to responsible AI frameworks in urban planning. This article advances a comprehensive legal and policy framework for ensuring accountability, transparency, and stability in AI-driven city governance, bridging gaps between technological innovation, urban studies, and regulatory oversight. The proposed governance structure empowers cities to adopt multi-level, authority-driven mechanisms that safeguard the common good while leveraging AI’s potential in sustainable urban transformation. Full article

To achieve dual-carbon goals and advance the sustainable development of the automotive industry, China’s Dual-Credit Policy serves as the core long-term mechanism for the low-carbon transition of the automotive industry. Given the coexistence of fuel vehicles (FVs) and new energy vehicles (NEVs) in China, existing research often overemphasizes production output while neglecting energy consumption control, and focuses predominantly on NEVs at the expense of FV optimization. To address these gaps, this paper treats FV fuel consumption and NEV energy efficiency as core endogenous decision variables. We construct profit-maximizing optimal production decision models for both types of manufacturers under the Dual-Credit Policy. Through mathematical derivation, numerical simulations, and empirical tests using actual industrial parameters, this study verifies the existence and uniqueness of optimal solutions. It clarifies the influence mechanisms of policy and market factors on corporate energy decisions and identifies the rules of strategy dominance. The findings reveal that the optimal fuel consumption decisions of FV manufacturers exhibit distinct piecewise patterns and critical threshold effects. Specifically, credit prices, NEV quotas, and fuel consumption standards determine the dominance of compliant (low-consumption) versus non-compliant (high-consumption) strategies. Furthermore, the policy exerts a significant market-oriented positive incentive on the energy efficiency upgrading of NEV manufacturers, with credit prices, market demand, and R&D costs acting as core constraints. Notably, the transition-guiding effect of the policy has clear effective boundaries, and its efficacy highly depends on the alignment between parameter design and market conditions. This research provides theoretical support for manufacturers to formulate energy-optimized production decisions and offers actionable references for the continuous optimization of the Dual-Credit Policy system and the sustainable low-carbon transformation of China’s automotive sector. Full article

This paper develops explicit equations for setting the reorder point s and the order-up-to level S in a periodic-review (R, s, S) inventory policy in a lost-sales environment. The objective is to support direct policy parameterization from demand level, demand variability, review period, lead time, and type-II unit fill-rate target. A long-horizon discrete-event simulation was combined with exhaustive enumeration of integer policy pairs to construct a policy-consistent reference dataset of five million observations. Symbolic regression was then used to convert this simulation-derived reference map into compact closed-form equations for both policy parameters. Over the full tested domain, the equations achieved R² = 0.941 for the reorder point s and R² = 0.989 for the order-up-to level S. On the common domain where analytical comparison is possible, the proposed equations reduced mean absolute error by approximately 65% for the reorder point and 85% for the order-up-to level. The equations also remain directly evaluable at the finite-horizon zero-lost-sales boundary corresponding to a 100% fill rate, where standard normal-loss logic has no finite safety-factor solution. The study provides an interpretable, auditable equation system for initial estimation of policy parameters for periodic-review lost-sales inventory policies within the tested normal-demand domain. Full article

Banks must balance deposit stability, loan expansion, and regulatory compliance while operating under liquidity constraints and financial risks. This study presents a mathematical model to examine the dynamics of bank deposits and loans under the influence of liquidity mechanisms and regulatory policies. The model proceeds in three stages: a deterministic nonlinear model, a dynamic optimal control model, and a stochastic model. Under the deterministic model, deposit withdrawals are liquidity-dependent, leading to a feedback mechanism in which liquidity improves deposit stability while financing loan growth. The theoretical results demonstrate the model’s positive and bounded solutions and show the existence and local stability of equilibria. Several parameters are based on regulatory policies or calibrated from Indonesian banking data, while the unknown parameters are estimated using the particle swarm optimization (PSO) algorithm. The results show that the proposed model is capable of fitting and predicting the data and has slightly lower mean absolute percentage errors for in-sample and out-of-sample compared with the benchmark model, and achieves comparable directional forecasting performance based on the index of directionality. Sensitivity analysis shows that the capital adequacy ratio supports lending, whereas an increased reserve requirement limits lending. An optimal control approach is developed by considering the reserve and capital requirements as time-varying policy variables. By applying Pontryagin’s maximum principle, we establish the necessary conditions for optimality. Numerical experiments demonstrate that the optimal control regulation enhances financial ratios, particularly the loan-to-deposit and liquidity ratios, at a reasonable cost. Finally, the stochastic model accounts for random variations in withdrawals and credit risks. Simulation-based observations reveal that although the system becomes more volatile, the mean dynamics are close to the deterministic case. Our framework offers a data-based and analytically tractable approach for studying the dynamics of banking variables and the effects of regulatory policies. The proposed model provides a mathematical tool for assessing the long-term effects of regulatory policies on banking performance and can assist bank managers and regulators in designing strategies that balance lending activity and liquidity resilience. Full article

As autonomous driving moves toward large-scale deployment, controllable and efficient simulation testing has become a primary means of ensuring system safety. However, in open-world environments, existing scenario catalogs often fail to cover the full spectrum of potential traffic situations, while rare yet high-risk critical scenarios are even harder to obtain. This scarcity renders traditional random sampling and parameter-sweeping strategies ineffective for identifying unknown risks. This study addresses two core challenges: (1) incomplete scenario catalogs hindering unknown critical scenario recognition and (2) insufficient critical samples, where generated scenarios struggle to balance physical realism and edge case coverage. To tackle the first challenge, we propose an open-world recognition method integrating transformers, random forests, and extreme value theory for precise unseen sample detection. Outlier and validity filtering ensure clustering reliability, and random forest activation patterns cluster unknown samples into meaningful groups to expand the scenario catalog. Experiments show the overall F1_macro improved by 2.3 percentage points over SOTA MDENet, with its clustering accuracy surpassing iterative-AutoNovel by 6.2 percentage points. For the second challenge, we introduce a reinforcement-learning-based maneuver-level generation method. It extracts maneuver semantics from trajectories, constructs a low-dimensional parameter space, and models parameter correlations via a multivariate multimodal distribution. A dual-layer LSTM agent with a composite reward iteratively optimizes policies toward high-risk edge scenarios. The results outperformed RLBE; longitudinal and lateral reconstruction errors were reduced by 32.7% and 15.3%, respectively, while high-risk time steps and the collision rate increased by 4.3% and 5.1%, respectively. Finally, we develop a CARLA-based scenario-driven simulation framework, integrating recognized and generated scenarios into closed-loop testing on high-risk road segments. CAS failure cases validate the generated scenarios’ physical feasibility and extreme challenge. Targeted augmentation of scarce critical scenarios enriches the test library and ensures broader coverage of real-world driving conditions. Full article

Cube-shaped automated storage and retrieval systems (AS/RS) enhance storage density by organizing inventory in a three-dimensional grid. However, they face two operational bottlenecks: (1) digging—the temporary removal and restacking of upper bins to access a target bin—and (2) inefficient idle staging and return policies in multi-AGV operations. We proposed a demand-based digging and bin-placement strategy and a waiting-point (staging) selection policy that considers AGV positions and remaining task times. These control policies are implemented in both rule-based and multi-agent reinforcement learning (MARL) variants. Their performance is evaluated using a Discrete Event System Specification (DEVS) simulation framework. In a 30 × 30 × 4 grid, three experiments demonstrated that deploying five AGVs achieved the best performance within the tested configuration; the demand-based digging and placement strategy achieved a 6.2% reduction in makespan, and the rule-based and MARL staging policies achieved additional reductions of 2.5% and 1.1%, respectively. These results highlight the benefits of jointly optimizing digging and multi-AGV staging and provide practical guidance for control-policy design in cube-shaped AS/RS. Full article

This study focuses on the multi-dimensional uncertainties in synergistic pollution reduction and carbon mitigation pathways for the coal chemical industry in the Yellow River Basin, a region facing extreme water scarcity (only 2% of national water) and fragile ecology, and constructs a Multi-Level Uncertainty Conduction (MLUC) Model integrating data, modeling, and validation. Using 2011–2025 data, Monte Carlo (10,000) simulations quantify the impacts of policy, technology, market, and ecological uncertainties on synergistic benefits. Sobol’ global sensitivity (Saltelli) and Shapley decomposition (14 technologies) identify key drivers and technology contributions. A system dynamics model simulates 2023–2050 pathways under baseline, policy-enhanced, technology breakthrough, and composite uncertainty scenarios. Logarithmic Mean Divisia Index (LMDI-I) decomposition reveals a six-factor driving mechanism for carbon emission changes. Results show that policy uncertainty exerts the largest influence, with a variance contribution of approximately 35%, followed by technology (28%), market (22%), and ecological factors (15%)—the latter primarily reflecting water availability and regional ecological carrying capacity. Critical thresholds are 80 CNY/t CO₂ for carbon capture, utilization and storage (CCUS) viability, and 6.8 CNY/t for green hydrogen substitution. Comprehensive resource utilization is optimal near term, while green hydrogen substitution and CCUS–green hydrogen coupling dominate medium- to- long term. The proposed dynamic threshold response mechanism and technology portfolio strategy could boost synergistic benefits by 22% to 35%. These findings underscore the need for watershed-scale collaborative governance and integrated water–carbon–energy management to ensure robust mitigation under the basin’s constraints. Full article

The entry of JD.com into the food delivery sector and the ensuing subsidy competition have resulted in irrational competition, merchant profit squeezes, and food safety risks in China. This study therefore investigates the collaborative governance mechanisms for food delivery platforms under involutionary competition driven by traffic contestation. A two-agent evolutionary game model between platforms and merchants is developed, and Q-learning simulations are conducted to capture dynamic learning behaviors. The analysis examines the effects of coupon face value, cost-sharing mechanisms, traffic incentives, and government incentive-penalty policies on the strategic choices of both agents. Key findings reveal that merchants are more sensitive than platforms to traffic incentives and government penalties. Traffic-dependent merchants and traffic-independent merchants exhibit significantly different responses to government interventions. The coupon face value demonstrates a threshold effect, where only a reasonable range encourages compliant behavior among both parties. Based on these results, a collaborative governance framework is proposed. For traffic-dependent merchants, the government should focus on regulating platform behaviors and supervising coupon value controls, while platforms should establish a reward-oriented, penalty-supported incentive mechanism. For traffic-independent merchants, the government should strengthen consumer-reporting penalty mechanisms and strictly control collusion risks between platforms and merchants. Platforms should increase inspection frequency and reinforce penalties to prevent, at the source, the decline in product quality and market disorder induced by involutionary competition. This study provides strategic insights for achieving collaborative governance of involutionary competition in platform economies under intense traffic contestation. Full article

A sustainable low-carbon transition requires policy that curbs emissions while accelerating green technological innovation. The EU Carbon Border Adjustment Mechanism (CBAM) imposes carbon costs on high-emission exports; yet, how it shapes exporters’ green innovation remains poorly understood. We develop an open-economy Environmental Dynamic Stochastic General Equilibrium (E-DSGE) model embedding three CBAM transmission channels: cost-driven (higher carbon-intensive production costs), awareness-driven (firms’ forward-looking expectations), and information-enhancement (lower green R&D financing costs). The model decomposes CBAM’s green-innovation effects by jointly endogenizing forward-looking green R&D investment and carbon disclosure quality in general equilibrium. Calibrated to Chinese data and solved in Dynare 7.0, the model is simulated over forty quarters. Under the baseline calibration, simulations suggest a CBAM shock raises green R&D investment by approximately 6.5% at its peak and the green technology level by approximately 12.5% by quarter 40, while brown emission intensity falls by approximately 10%. Within this window the policy carries a net welfare cost of approximately 0.34% of steady-state consumption, concentrated in transition-period labor disutility, with most gains accruing later. Combining CBAM with R&D subsidies modestly reduces the within-window welfare cost and raises long-run green technology. Realizing this sustainability potential requires policy credibility, carbon-information infrastructure, and coordinated innovation support. Full article

Risk assessment frameworks for renewable energy projects are predominantly designed for liberalised electricity markets, leaving state-dominated and single-buyer systems analytically underserved. This systematic literature review (SLR) synthesises 116 peer-reviewed studies (2015–2026) following a PRISMA-compliant, Kitchenham-guided protocol to identify and critically evaluate project-level risks and assessment methodologies across diverse electricity market structures. Three contributions are made: (i) a market-structure-differentiated risk taxonomy showing how risk profiles differ structurally across liberalised, hybrid, and single-buyer markets; (ii) the Integrated Risk Assessment Framework for Renewable Energy Projects (IRAF-REPs), a five-layer architecture connecting market structure context, risk category taxonomy, assessment methods, project lifecycle phases, and risk-register standards (ISO 31000/COSO); and (iii) a structured three-horizon future research agenda. Market/price risk (~68%) and policy/regulatory risk (~58%) dominate the reviewed literature, while counterparty/PPA risk—dominant in single-buyer contexts—is largely absent from quantitative frameworks. Monte Carlo simulation and real options analysis lead quantitative practice in liberalised-market studies; the hybrid Monte Carlo-System Dynamics (MC-SD) combination appears in fewer than 4% of studies despite its conceptual suitability for single-buyer contexts. Five research gaps are identified. Findings advance SDG 7, SDG 13, and SDG 9, with direct governance relevance for Indonesia/PLN and comparable Global South economies. Full article

The digital economy has become an emerging driver of rural industrial revitalization. Based on an input-output model and a production-network framework, this study first constructs time-series input-output tables for the digital economy and urban-rural industries from 2002 to 2021. It then identifies key recipient nodes, urban-rural disparity paths, and priority optimization paths of digital value flows. The results show that urban-rural disparities in digital empowerment have narrowed, but the digital divide between urban and rural industries remains substantial. The direct integration of the digital economy into rural industries is still limited, whereas urban-rural industrial integration plays an important mediating role. At the same time, the urban-rural disparity paths in digitally enabled rural industrial revitalization are mainly concentrated in rural capital allocation, asset services, circulation systems, basic agricultural production, and agricultural science and technology services. The counterfactual simulations show that prioritizing the direct embedding of the digital economy into rural manufacturing, agricultural value chains, and public-service sectors, while improving coordinated transmission between urban and rural industries, can strengthen the overall empowerment effect. These findings provide empirical support for more precise and targeted policies to promote rural industrial revitalization through the digital economy. Full article

Underwater surveillance systems require a multi-AUV-based multi-hop underwater data collection system to deliver data from sensor nodes distributed over a wide underwater area to a buoy. In this system, the node-visit of the data collection AUV and the relay transmission to the relay AUV jointly affect the end-to-end Age of Information (AoI) from each node to the buoy. This paper proposes a discrete soft actor–critic (SAC)-based integrated scheduling policy that jointly determines the node-visit and relay transmission of the data collection AUV within a single decision-making process. The proposed method represents the end-to-end information update process from data collection to buoy update in the state design and derives a relationship showing that the accumulated node-wise max AoI increment corresponds to the Mean Peak AoI and uses this increment as the decision step reward. Computer simulation results show that the proposed method achieves a lower Mean Peak AoI and higher Delivery than conventional methods that determine node visits and relay transmissions separately, including Visit-Only SAC, Age-Greedy threshold, and Age-Gain TX. The cumulative distribution function (CDF) analysis also shows more stable performance across episodes. Full article