Search Results (80,450)

This study contributes to the literature on sustainable international investment by examining how public data openness—treated as a non-rivalrous production factor—enables firms to overcome information asymmetries, reduce financing constraints, and catalyze innovation-driven overseas expansion. Grounded in China’s dual strategic imperatives of digital factor marketization and high-quality outbound investment, we exploit the staggered rollout of municipal public data platforms (2008–2023) as a quasi-natural experiment and apply a firm- and city-level staggered difference-in-differences (DID) design to a sample of A-share listed firms. Our findings demonstrate that public data openness significantly enhances the sustainability of outward investment, measured through improved capital allocation efficiency and long-term resilience. Mechanism tests reveal that innovation capacity and eased financing constraints serve as key transmission channels, with stronger effects observed in firms with higher human capital endowments, robust internal controls, and exposure to technologically dynamic sectors. Heterogeneity analyses further indicate that the sustainability impact is amplified under environmental uncertainty, suggesting that data openness acts as an institutional buffer. The results offer actionable policy implications for aligning digital governance with corporate sustainability goals, and provide empirical grounding for integrating open data infrastructure into national strategies for responsible internationalization. Full article

Selective algorithmic auditing requires deciding which process evidence should receive attention when exhaustive review is infeasible. This Review introduces a four-layer framework that connects process representation, learning, inspection allocation, and governance within a single budgeted sequential decision problem over event streams. Unlike prior reviews centered on predictive process monitoring, explainability, cost analysis, or bibliometric structure, the proposed framework examines how these functions interact when human review, computation, latency, and documentation capacity are constrained. A structured and targeted survey of 89 unique publication families is used to illustrate and critically examine event-log, Petri-net, graph, object-centric, neural, uncertainty-aware, sequential, bandit, reinforcement learning, and audit architecture approaches. The reviewed evidence indicates that substantial bodies of work address the individual layers, but cross-layer evaluation remains fragmented and uses heterogeneous datasets, objectives, and validation protocols. The synthesis identifies five priorities: audit-ready benchmarks, explicit inspection budget protocols, calibrated uncertainty, transfer across organizational contexts, and reproducible governance interfaces. The main contribution is a computational framework and a corpus-bounded research agenda that connects representation, learning, inspection allocation, and governance for selective algorithmic auditing. Full article

The cathode material design is a persistent challenge in the development of next-generation rechargeable batteries. The cathode performance is critically influenced by certain key parameters, i.e., composition, crystal structures, ion transport, and degradation behaviour. Moreover, techno-economic and sustainable considerations also play a pivotal role in the viable cathode material design. In recent years, the integration of static machine learning models with conventional experimental techniques has significantly enhanced the cathode material design. However, the sequential nature of cathode discovery has not been fully captured by these techniques as they do not update their decision strategy based on prior outcomes. In this review, reinforcement learning (RL) as a decision making technique for cathode material design has been evaluated. Firstly, cathode design space, including major cathode families, optimisation objectives, and key material variables have been explored. Afterwards, cathode discovery has been presented in terms of RL states, actions, rewards, policies, environments, constraints, and feedback. The key focus of this review is to analyse how RL can support the composition selection, dopant, and crystal structure optimisation. The review also discusses the current limitations of RL based cathode design including data scarcity, dataset bias, limited cathode specific benchmarks, reward function design, physical validity, and experimental validations. The future directions have been proposed for physics informed and experimentally validated RL infrastructure that integrates the density functional theory, molecular dynamics, artificial intelligence and human expertise. Full article

Consumer trust is critical to the sustainable development of cross-border B2C e-commerce platforms. Accurately evaluating and diagnosing trust weaknesses has become a key concern for both practitioners and researchers. To address the inherent limitations of existing trust evaluation methods, this study proposes an improved AHP-Ridge Regression hybrid model that integrates expert knowledge with actual consumer perception data. First, an improved Analytic Hierarchy Process based on stakeholder-oriented nonlinear programming is employed to optimize the evaluation weights of nine experts, reduce subjective bias, and generate expert prior weights for each dimension and indicator. Second, these prior weights are incorporated as the regularization prior mean of the Ridge Regression model to construct the improved AHP-Ridge Regression model. Based on survey data from 387 valid respondents across five major cross-border platforms (Tmall Global, JD International, Pinduoduo Global, Sam’s Club Global, and CDFG Duty-Free), the model is compared with six baseline models using a 30-times repeated five-fold nested cross-validation. The proposed model achieves the lowest RMSE (0.3179) and highest R² (0.6510) among all compared models, with statistically significant improvements over all baselines (Nadeau–Bengio-corrected p < 0.001, large Cohen’s d effect sizes). However, the improvement over conventional Linear Regression is modest in absolute magnitude (ΔRMSE ≈ 0.0012). The primary value of the proposed model lies not in a dramatic leap in predictive accuracy but in its theoretical grounding, interpretability, and diagnostic capability. Permutation importance analysis reveals that Platform Fluidity, AI Technology Usability, Page Layout & Navigation Clarity, Content Accuracy, and Policy Assurance are the most important predictors of consumer trust. Comprehensive calibration and residual diagnostics (including MAE, normality tests, and heteroscedasticity checks) confirm the model’s predictive reliability. Furthermore, platform-specific diagnostics identify three distinct trust profiles (high-trust benchmark, trust-improvement priority, and mixed-profile platforms), providing managers with actionable insights for resource allocation. This study offers cross-border B2C e-commerce platforms a trust evaluation tool that balances predictive accuracy and interpretability, and provides implications for sustainable platform governance and ESG-oriented management by linking trust diagnostics with platform accountability frameworks. Full article

Adaptive locomotion across continuous terrain transitions remains difficult for hexapod robots because contact timing, body attitude, support height, and load distribution change simultaneously along a route. This paper presents a unified proprioception-driven deep reinforcement learning and central pattern generator (DRL-CPG) framework for terrain-transition locomotion without visual terrain classification, explicit terrain labels, or terrain-specific controller switching. A high-level proximal policy optimization policy maps a 46-dimensional proprioceptive observation to a three-dimensional CPG modulation action comprising oscillation amplitude, swing-phase frequency, and turn modulation. A coupled six-node Hopf oscillator network then expands these modulated parameters into phase-coordinated rhythmic commands, which are mapped to the 18 joint targets of a JetHexa hexapod and executed by a low-level proportional-derivative controller. The observation space contains body linear velocity, body angular velocity, relative joint positions, relative joint velocities, the previous three-dimensional policy action, and inertial measurement unit (IMU)yaw/heading relative to the initial track direction. A continuous route consisting of flat ground, uphill stairs, irregular terrain, downhill stairs, and a recovery segment is defined to evaluate transition-aware locomotion using route completion, velocity-tracking error, lateral deviation, and roll/pitch fluctuation. Compared with the fixed-parameter CPG and end-to-end DRL baselines, the proposed method increased the full-distance success rate at 4.7 m from 9% and 20%, respectively, to 88%, while maintaining smoother velocity, lateral deviation, and roll/pitch responses. The framework preserves the rhythmic prior of CPG control while reducing the exploration burden of reinforcement learning, providing a compact formulation for adaptive hexapod locomotion across terrain transitions. Full article

Across the literature, family involvement is recognized as a key factor for academic progress, inclusive education and personal development. However, family size is rarely examined in bibliometric analyses, despite growing attention to socioeconomic and family-related factors influencing educational participation and inequality. Although empirical evidence does not establish a direct relationship between family size and inclusive education, existing research suggests that inclusive practices are more effective when family-related challenges are acknowledged. This study maps the representation of large families within the literature addressing educational inequality, participation, and inclusion-related processes. A bibliometric and co-occurrence analysis of 395 Web of Science publications (retrieved March 2026) was conducted using VOSviewer and Microsoft Excel. Findings revealed that research emerged in the 1990s and expanded after 2016, with the United States, Ethiopia, and China as leading contributors. Six thematic clusters were identified: socioeconomic participation, child vulnerability, educational outcomes, fertility and gender dynamics, and household-level constraints. Results indicate that large families are unevenly addressed, appearing more frequently in research on educational inequality than in inclusive education. Based on these findings, the study proposes a conceptual framework describing the “lights and shadows” of large family contexts in inclusive education, capturing both constraining and enabling dimensions associated with family structure. Full article

Coverage Path Planning (CPP) for fixed-wing aerial robots remains challenging in dynamic and partially unknown environments due to the need to simultaneously satisfy coverage completeness, kinematic feasibility, and real-time adaptability. Conventional approaches typically rely on pre-defined geometric patterns or simplified motion models, which limit their effectiveness when encountering environmental disturbances or unforeseen obstacles. This paper presents a hybrid real-time CPP framework that integrates an offline coverage strategy with an online optimisation and control scheme. In the offline phase, a back-and-forth coverage pattern is generated based on the geometric properties of the region and sensor characteristics, ensuring full nominal coverage. During execution, this trajectory is adaptively refined using a Model Predictive Control (MPC) formulation augmented by a policy gradient-based update mechanism and an augmented Dubins path smoothing strategy. The MPC framework explicitly accounts for vehicle dynamics, actuator limitations, and obstacle avoidance constraints, while the policy gradient component improves the responsiveness of the optimisation process under rapidly changing conditions. The augmented Dubins formulation enables smooth and dynamically feasible transitions, allowing the vehicle to deviate from and reliably return to the nominal coverage path after disturbance or avoidance manoeuvres. Simulation results in cluttered environments with static and dynamic obstacles demonstrate that the proposed approach achieves improved tracking consistency and smoother trajectories, while maintaining real-time feasibility. Moreover, the proposed approach reduces the maximum computational burden by approximately 0.35 s compared to the conventionally utilized optimization algorithm. These results highlight the potential of the framework for practical deployment in fixed-wing aerial coverage missions operating in uncertain environments. Full article

The extent to which environmental regulation yields a “weak” or “strong” Porter effect is fundamentally shaped by the trade-off between industrial output and productivity improvements, a complex dynamic that requires granular, sub-national empirical evidence. This study employs a continuous multi-period DID approach to evaluate the impact of China’s prohibited breeding zone policy (PBZP) on sustainability indicators—specifically, pig green total factor productivity (PGTFP) and its components, green technology efficiency (GEC) and green technology progress (GTC)—across 371 counties in the Yangtze River Basin from 2013 to 2021. The results indicate that PBZP significantly boosts county-level PGTFP and GTC in the basin, with an effect size of 0.206 for PGTFP and GTC identified as its primary driver, thereby supporting the “strong Porter hypothesis”, as confirmed by robustness and endogeneity tests. Channel exploration shows that PBZP has enhanced PGTFP through capital factor optimization and spatial restructuring. Heterogeneity analysis identifies significant regional differences across the upper, middle, and lower reaches of the Yangtze River. Further analysis reveals a trade-off between the impact of PBZP on PGTFP and the production capacity of pigs. Finally, this study outlines several policy recommendations for fostering new quality productive forces in pig farming by improving PGTFP while safeguarding effective pig production capacity. Full article

Malaysia’s upstream poultry supply chain, comprising broiler farms, hatcheries, and breeder operations, faces persistent barriers to circular economy (CE) adoption despite growing national policy support. Existing literature has focused predominantly on quantitative assessments of CE performance, leaving a critical gap in contextual, institutional-level understanding of why CE practices remain selectively and unevenly implemented. This paper reports findings from an exploratory qualitative study involving nine semi-structured in-depth interviews with informants drawn primarily from institutional and regulatory stakeholder categories: regulatory agencies (Department of Veterinary Services Malaysia, DVS), industry associations (Federation of Livestock Farmers’ Associations of Malaysia, FLFAM), marketing and distribution agencies (Federal Agricultural Marketing Authority, FAMA), research institutions (Malaysian Agricultural Research and Development Institute, MARDI), one private sector operator, and academic informants. Thematic analysis reveals five interconnected themes that collectively explain CE implementation gaps as seen from this institutional vantage point: (1) fragmented internal coordination as the primary operational barrier, (2) policy-practice misalignment between regulatory intent and operational realities, (3) the awareness-to-action deficit in CE conceptualisation, (4) differentiated capability constraints across enterprise scales (reported mainly through an industry-association intermediary rather than smallholders directly), and (5) the digital-circular complementarity gap raised by a minority of informants (4 of 9). The study makes three contributions: it generates an institutional-level, qualitative account of CE barriers and enablers for Malaysia’s upstream poultry sector from an institutional perspective; it introduces the concept of capability-sensitive CE governance, situated relative to related constructs such as the dynamic capabilities view, absorptive capacity, and the Technology–Organisation–Environment (TOE) framework, as a novel theoretical construct grounded in empirical evidence; and it proposes a preliminary Circular Economy Implementation Readiness Model (CEIRM), offered as a basis for future validation rather than a tested framework, that outlines a staged, practical pathway for CE adoption at the enterprise level. Findings have exploratory implications for policymakers, agri-food managers, and researchers engaged in circular transitions in developing-economy food systems. Full article

Waste management is essential for addressing local and global environmental challenges. Failure to manage waste effectively results in several consequences, including environmental degradation, public health risks, and economic costs. Besides the need for adequate and up-to-date waste management systems, citizens’ cooperation is crucial to achieving sustainable waste management in any community, which depends on their waste literacy levels, comprising the knowledge, attitudes, and behaviours required. This study assessed waste literacy among students completing compulsory education in Portugal to provide evidence regarding the outcomes of environmental education policies related to waste management. A representative sample of 1506 freshmen students was surveyed at the start of their undergraduate programmes. An online questionnaire was administered to the sample and the data were analyzed using statistical tests for assessing the influential factors associated with students’ waste knowledge, attitudes and behaviours. Results show that, on average, students demonstrated good levels of knowledge and practices on waste management, together with a predominantly ecocentric worldview. A higher performance in waste management knowledge and practices was observed among younger students, with higher grades and higher paternal educational attainment. Besides these variables, best practices were significantly more prevalent among those with a higher participation in environmental activities and holding membership in environmental organizations. The findings suggest that upon completing compulsory education in Portugal, students display generally high levels of waste literacy, while also revealing specific gaps in knowledge and practices that warrant further educational attention. The study identifies these areas for improvement, highlighting the potential value of more practical, participatory and community-based approaches to waste education. Full article

Ulaanbaatar, Mongolia, relies heavily on coal for winter heating, and continued urban expansion is expected to increase heating demand. This study investigated heating demand and rooftop PV potential in Ulaanbaatar from 2016 to 2025 using Google Earth Engine. Nighttime light intensity, heating degree days (HDD), and solar radiation datasets were integrated to evaluate changes in human activity, climate-driven heating demand, and rooftop solar resources. Electricity-equivalent heating demand, rooftop PV generation, coal displacement potential, and techno-economic performance under different policy scenarios were assessed. The results reveal a clear increase in urban built-up area during the study period, accompanied by rising nighttime light intensity, indicating growing urban activity. Despite relatively stable HDD, urban expansion implies increasing potential heating demands. Under the main rooftop PV deployment scenario, surplus electricity could theoretically offset approximately 10.9% of coal-based heating demand and reduce CO₂ emissions by 1.126 MtCO₂ yr⁻¹ through resistance heating. When the same surplus was converted through heat pumps with COP = 1.86, the theoretical coal-heating offset increased to 20.3%. However, economically viable large-scale deployment remained strongly dependent on feed-in tariff support. These findings indicate that continued urban expansion may lead to higher heating demand in Ulaanbaatar and highlight both the opportunities and structural limitations of rooftop PV for decarbonizing coal-dependent heating systems in cold-climate cities. Full article

This study quantifies five decades (1975–2025) of residential architectural transformation in Delhi (Tier-I) and Patna (Tier-II), India, among middle-income-group households, and evaluates its implications for passive design loss and mechanical cooling dependence. A stratified survey of 1080 middle-income-group (MIG-I and MIG-II) households across six construction decades documents the systematic elimination of passive features: courtyard presence collapsed from 54.44% (Cohort A, 1975–1985) to 1.39% (Cohort C, 2015–2025); load-bearing masonry declined from 47.78% to 7.78%; and houses lacking passive cooling features quadrupled from 10.83% to 40.83%. Correspondingly, households with three or more air conditioning (AC) units rose from 12.78% (Cohort A) to 33.06% (Cohort C). However, 30–35% of households across all cohorts stated that the ‘indoor temperature remains comfortable without AC’, challenging deterministic AC-dependence assumptions. Regional divergence in drivers is evident: globalization dominates in Delhi (59.24%), while lack of traditional awareness dominates in Patna (39.29%), suggesting hierarchical diffusion and indicating a need for differentiated policy responses. Architectural homogenization shows a consistent association with higher AC ownership and electricity consumption, raising concerns about energy insecurity and climate vulnerability. Hybrid design, integrating traditional passive strategies with contemporary spatial needs, offers a pathway toward climate-resilient urban futures. Full article

Maritime Spatial Planning (MSP) is increasingly recognized as the primary governance framework for integrating multiple and often competing maritime activities within shared marine space. This paper examines how the Multi-Use (MU) concept is incorporated into formally adopted MSP frameworks across six Mediterranean EU Member States, namely France, Spain, Italy, Cyprus, Slovenia, and Malta. Drawing on content analysis and document analysis of officially approved national plans, the study develops a multi-criteria analytical framework combining conceptual and spatial dimensions to assess MU integration systematically across national contexts. Mediterranean MSP systems are dominated by soft MU forms, particularly combinations involving tourism, fisheries, underwater cultural heritage, and environmental protection, while hard MU configurations involving offshore renewable energy, aquaculture, and shared infrastructure remain largely at the strategic or pilot stage. Four distinct pathways of MU integration are identified: operational, compatibility-based, strategic coexistence, and administrative coexistence. These pathways reflect different approaches to integrating MU within MSP systems, from explicit multi-use promotion to coexistence-oriented planning. They also indicate that the institutional adaptation of marine governance has not progressed as rapidly as technological developments, particularly in relation to emerging MU configurations. Effective MU integration depends less on formal policy recognition and more on the existence of planning instruments, zoning mechanisms, and cross-sectoral governance arrangements capable of translating strategic objectives into operational practice. Full article

The agricultural sector is increasingly exposed to a complex set of climatic, economic, and institutional pressures, indicating that resilience assessment alone is insufficient; investments must also be strategically directed toward strengthening resilience. In response, this study proposes an integrated framework for assessing the resilience of crop farms and supporting the strategic screening and relative prioritization of investments at the regional level. The framework is applied to Romania, which is administratively organized into eight NUTS-2 (Nomenclature of Territorial Units for Statistics) development regions. The framework combines a Farm Resilience Composite Index (ICRF)—structured around five interconnected pillars (productive, economic, technological, ecological, and organizational)—with a Strategic Resilience Investment Score (SRIS), designed to translate resilience diagnostic outcomes into strategic screening and relative investment priorities. The methodology integrates normalized regional sub-indicators, expert-based weighting, and catch-up gap measures relative to the observed regional benchmark. It further incorporates complementarity and substitution relationships among sub-indicators, thereby capturing the systemic nature of resilience. Robustness is ensured through sensitivity analysis, Monte Carlo simulations, and a leave-one-region-out jackknife procedure. The results indicate that investment priorities are not driven solely by the magnitude of regional deficits. Instead, they emerge at the intersection of catch-up needs, systemic relevance, and the stability of outcomes under uncertainty. Across all regions, production stability, access to the agricultural knowledge and innovation system (AKIS), and farm-level digitalization consistently emerge as cross-regional priorities. Moreover, at least two of the three initial priorities are retained in 55 of the 56 leave-one-region-out comparisons, indicating a high degree of robustness. In contrast, ecological, economic, and organizational indicators exhibit greater territorial specificity. Overall, the ICRF–SRIS framework provides a transparent and analytically grounded tool for supporting place-based agricultural policy and guiding investment allocation in the context of post-2027 Common Agricultural Policy (CAP). Full article

Digital transformation is portrayed both as a lever of territorial convergence and as a driver of polarisation between urban cores and peripheries, yet its effect on regional economic resilience has rarely been measured systematically. This study transposes the Economic Resilience Index framework from the national to the regional scale, building a Regional Resilience Index (R-ERI) for 236 NUTS2 regions of the EU-27 from Eurostat indicators, anchored in the capacities of absorption, recovery, and adaptation and measuring resilience as a capacity rather than as a realised shock trajectory. Two complementary models are estimated: a spatial Durbin panel with two-way fixed effects (2018–2023), spanning the COVID-19 pandemic and 2022 energy shocks, and an exploratory cross-sectional difference model exploiting regional artificial intelligence (AI) adoption data disaggregated by NACE branch (2023–2025). The results show that resilience is strongly spatially autocorrelated (Moran’s I between 0.66 and 0.74; p = 0.001); that digital connectivity generates a positive indirect effect on neighbouring regions despite a negative own-region effect; and that the synergy hypothesis—that digitalisation yields more resilience when combined with traditional sectors—does not hold robustly, the interaction being null in the panel and only marginally positive in the AI layer (p = 0.10). We conclude that digital connectivity is not, on its own, an automatic convergence mechanism, and that cohesion policy should account for each region’s sectoral structure and peripheral position. Full article