Search Results (870)

Promoting the value realization of ecological products is a central issue in practicing the concept that “lucid waters and lush mountains are invaluable assets.” This is particularly urgent for alpine regions, which are vital ecological security barriers but face stringent developmental constraints. This study takes Golog Tibetan Autonomous Prefecture in Qinghai Province as a case study. It establishes a Gross Ecosystem Product (GEP) accounting framework tailored to the characteristics of alpine ecosystems and conducts continuous empirical accounting for the period 2020–2023. The findings reveal that: (i) The total GEP of Golog is immense (reaching 655.586 billion yuan in 2023) but exhibits significant dynamic non-stationarity driven by climatic fluctuations, with a coefficient of variation as high as 11.48%. (ii) The value structure of the GEP is highly unbalanced, with regulatory services contributing over 97.6%. Water conservation and biodiversity protection are the two pillars, highlighting its role as a supplier of public ecological products and the predicament of market failure. (iii) The spatial distribution of GEP is highly heterogeneous. Maduo County, comprising 34% of the prefecture’s land area, contributes 48% of its total GEP, with its value per unit area being 1.68 times that of Gande County, revealing the spatial agglomeration of key ecosystem services. To address the dynamic, structural, and spatial constraints identified by these quantitative features, this paper proposes synergistic realization pathways centered on “monetizing regulatory services,” “precision policy regulation,” and “capacity and institution building”. The aim is to overcome the systemic bottlenecks—“difficulties in measurement, trading, coarse compensation, and weak incentives”—in alpine ecological functional zones. This provides a systematic theoretical and practical solution for fostering a virtuous cycle between ecological conservation and regional sustainable development. Full article

The advancement of automated driving technologies offers potential safety and efficiency gains, yet safety remains the primary barrier to higher-level deployment. Failures in automated driving systems rarely result from a single technical malfunction. Instead, they emerge from coupled organizational, technical, human, and environmental factors, particularly in partial and conditional automation where human supervision and intervention remain critical. This study systematically identifies safety failures in automated driving systems and analyzes how they propagate across system layers and human–machine interactions. A qualitative case-based analytical approach is adopted by integrating the Swiss Cheese model and the SHELL model. The Swiss Cheese model is used to represent multilayer defensive structures, including governance and policy, perception, planning and decision-making, control and actuation, and human–machine interfaces. The SHELL model structures interaction failures between liveware and software, hardware, environment, and other liveware. The results reveal recurrent cross-layer failure pathways in which interface-level mismatches, such as low-salience alerts, sensor miscalibration, adverse environmental conditions, and inadequate handover communication, align with latent system weaknesses to produce unsafe outcomes. These findings demonstrate that autonomous driving safety failures are predominantly socio-technical in nature rather than purely technological. The proposed hybrid framework provides actionable insights for system designers, operators, and regulators by identifying critical intervention points for improving interface design, operational procedures, and policy-level safeguards in autonomous driving systems. Full article

Urban shared parking systems represent a complex socio-technical challenge. Despite vast potential, utilization remains persistently low (<15%), revealing a critical policy failure. To address this, this study develops a dynamic system framework based on Life-Cycle Cost (LCC) and Hamilton-Jacobi-Bellman (HJB) optimization to analyze and calibrate the key policy levers influencing owner participation timing (T*). The model, resolved using finite difference methods, captures the system’s non-linear threshold effects by simulating critical system parameters, including system instability (price volatility, ${\mathsf{\sigma }}_p$), internal friction (management fee, $w_g{g}_t$), and demand signals (transaction ratio, Q). Simulations reveal extreme non-linear system responses: a 100% increase in system instability (${\mathsf{\sigma }}_p$) delays participation by 325.5%. More critically, a 100% surge in internal friction (management fees) delays T* by 492% and triggers a 95% revenue collapse—demonstrating the risk of systemic collapse. Conversely, a 20% rise in the demand signal (Q) advances T* by 100% (immediate participation), indicating the system can be rapidly shifted to a new equilibrium by activating positive feedback loops. These findings support a sequenced calibration strategy: regulators must first manage instability via price stabilization, then counteract high friction with subsidies (e.g., 60%), and amplify demand loops. The LCC framework provides a novel dynamic decision support system for calibrating complex urban transportation systems, offering policymakers a tool for scenario testing to accelerate policy adoption and alleviate urban congestion. Full article

In modern Industry 4.0 environments, real-time scheduling presents a complex challenge requiring both formal correctness guarantees and optimal performance. Background: Traditional approaches fail to provide an optimal integration between formal correctness guaranteeing and optimization, and such failure either produces suboptimal results or a correct result lacking guarantee, and studies have indicated that poor scheduling decisions could cause productivity losses of up to 20–30% and increased operational costs of up to USD 2.5 million each year in medium-scale manufacturing facilities. Methods: This work proposes a new hybrid approach by integrating Extended Time Petri Nets (ETPNs) and Finite-State Automata (FSAs) with formal modeling, abstracting ETPNs by extending conventional Time Petri Nets to deterministic time and priority systems, accompanied by Genetic Algorithms (GAs) to optimize the solution to tackle a multi-objective optimization problem. Our solution tackles indeterministic problems by incorporating suitable priority resolution methods and GA to pursue optimal solutions to very complex scheduling problems and starting accurately from standard real-time scheduling-policy models such as DM, RM, and EDF-EDF. Results: Experimental evaluation has clearly verified performance gains up to 48% above conventional techniques, covering completely synthetic and practical case studies, including 31–48% improvement on synthetic benchmarks, 24% increase on resource allocation, and total elimination of constraint violations. Conclusions: The new proposed hybrid technique is, to a considerable extent, a dramatic advancement within real-time scheduling techniques and Industry 4.0, successfully and effectively integrating optimal correctness guaranteeing and favorable GA-aided optimization techniques, which particularly guarantee optimal correctness to safe-related applications and provide considerable improvements to support efficient and optimal performance, extremely helpful within Industry 4.0. Full article

The COVID-19 pandemic highlighted critical issues in health services and public policy, particularly in long-term care facilities across Canada. Failures in these facilities revolving around chronic underfunding, staffing shortages, inadequate infection control, and inconsistent regulatory oversight, underscore the need to rethink health service interventions, especially considering varying implementation contexts among provinces. The Ontario Long-Term Care COVID-19 Commission Final Report pointed to long-standing systemic issues as the primary causes of the sector’s failures. To explore this issue, a narrative review was conducted with findings indicating that the long-term care crisis in Canada cannot be solved by more privatization, regulation or efficiency measures, as these have contributed to the problem’s root causes. Ontario’s long-term care crisis stems from systemic misalignments in policy, structure and stakeholder dynamics, requiring a shift toward systems thinking and resident-centered care to build an equitable and sustainable long-term care sector. Ultimately, governments must lead a policy redesign that reflects shared responsibility, stakeholder interdependence, and public involvement, offering a model for broader healthcare reform. Full article

The opto-mechanical automated manufacturing process, characterized by stringent process constraints, dynamic disturbances, and conflicting optimization objectives, presents significant control challenges for traditional scheduling and control approaches. We formulate the scheduling problem within a closed-loop control paradigm and propose a novel bi-level intelligent control framework integrating Deep Reinforcement Learning (DRL) and Bayesian Optimization (BO). The core of our approach is a bi-level intelligent control framework. An inner DRL agent acts as an adaptive controller, generating control actions (scheduling decisions) by perceiving the system state and learning a near-optimal policy through a carefully designed reward function, while an outer BO loop automatically tunes the DRL’s hyperparameters and reward weights for superior performance. This synergistic BO-DRL mechanism facilitates intelligent and adaptive decision-making. The proposed method is extensively evaluated against standard meta-heuristics, including Genetic Algorithm (GA) and Particle Swarm Optimization (PSO), on a complex 20-jobs × 20-machines flexible job shop scheduling benchmark specific to opto-mechanical automated manufacturing. The experimental results demonstrate that our BO-DRL algorithm significantly outperforms these benchmarks, achieving reductions in makespan of 13.37% and 25.51% compared to GA and PSO, respectively, alongside higher machine utilization and better on-time delivery. Furthermore, the algorithm exhibits enhanced convergence speed, superior robustness under dynamic disruptions (e.g., machine failures, urgent orders), and excellent scalability to larger problem instances. This study confirms that integrating DRL’s perceptual decision-making capability with BO’s efficient parameter optimization yields a powerful and effective solution for intelligent scheduling in high-precision manufacturing environments. Full article

Background: Maternal mortality in Brazil remains a critical indicator of social and racial inequalities, reflecting structural failures in access to and quality of obstetric care. Black women, particularly those categorized as black or brown, are at a higher risk of dying during pregnancy, childbirth, or the postpartum period. This is the result of the intersection of institutional racism, poverty, and social vulnerabilities. This study aimed to analyze trends and associated factors of maternal mortality among black women in Brazil from 2000 to 2020. Methods: This is a retrospective cohort analytical study using data from the Brazilian Mortality Information System. The sample included women aged 10 to 49 years whose underlying cause of death was classified under ICD-10 codes O00–O99. Descriptive and bivariate analyses were conducted, as well as Poisson and multinomial logistic regressions to estimate adjusted risk ratios according to skin color, education, region, type, and place of death. Results: A total of 40,907 maternal deaths were identified, with 59.2% occurring among black women. The maternal mortality ratio was 39% higher among black women compared to white women and more than double among Indigenous women. Low education, residence in the North and Northeast regions, deaths outside hospital settings, and lack of formal investigation were independently associated with increased risk. Direct obstetric causes accounted for most deaths, with hypertensive disorders and puerperal complications being the leading conditions. Conclusions: Maternal mortality among black women in Brazil reveals deep structural inequalities. Urgent public policies that incorporate an intersectional perspective, addressing race, gender, and class, are necessary to reduce disparities and ensure equitable and dignified maternal healthcare. Full article

Technological innovation in energy enterprises constitutes a pivotal component in realizing the transition to a green economy. In recent years, the complexity and volatility of the international economic landscape have significantly amplified economic policy uncertainty (EPU) across nations, which is poised to exert a profound influence on the technological innovation activities of energy enterprises. This study employs the Tobit regression method to investigate the relationship between EPU and corporate technological innovation (CTI), based on data from Chinese listed energy companies spanning the period of 2007 to 2018. Empirical results indicate that EPU exerts a significant positive influence on technological innovation for energy enterprises. Furthermore, we employed a Fisher permutation test to further elucidate the heterogeneity of this impact across various sub-industries, enterprise ownership types, and governance mechanisms. Specifically, EPU has a more pronounced promoting effect on technological innovation for traditional energy enterprises, non-state-owned enterprises, and enterprises with high failure tolerance. Against the backdrop of increasing global EPU, the findings of this study offer certain implications for governmental industrial policies and corporate governance mechanisms. Full article

Fault-tolerant control in safety-critical industrial systems demands adaptive responses to equipment degradation, parameter drift, and sensor failures while maintaining strict operational constraints. Traditional model-based controllers struggle under these conditions, requiring extensive retuning and dense instrumentation. Recent safe multi-agent reinforcement learning (MARL) frameworks with control barrier functions (CBFs) achieve real-time constraint satisfaction in robotics and power systems, yet assume comprehensive state observability—incompatible with sensor-hostile industrial environments where instrumentation degradation and contamination risks dominate design constraints. This work presents a safety-aware multi-agent deep reinforcement learning framework for adaptive fault-tolerant control in sensor-lean industrial environments, achieving formal safety through learned implicit barriers under partial observability. The framework integrates four synergistic mechanisms: (1) multi-layer safety architecture combining constrained action projection, prioritized experience replay, conservative training margins, and curriculum-embedded verification achieving zero constraint violations; (2) multi-agent coordination via decentralized execution with learned complementary policies. Additional components include (3) curriculum-driven sim-to-real transfer through progressive four-stage learning achieving 85–92% performance retention without fine-tuning; (4) offline extended Kalman filter validation enabling 70% instrumentation reduction (91–96% reconstruction accuracy) for regulatory auditing without real-time estimation dependencies. Validated through sustained deployment in commercial beverage manufacturing clean-in-place (CIP) systems—a representative safety-critical testbed with hard flow constraints (≥1.5 L/s), harsh chemical environments, and zero-tolerance contamination requirements—the framework demonstrates superior control precision (coefficient of variation: 2.9–5.3% versus 10% industrial standard) across three hydraulic configurations spanning complexity range 2.1–8.2/10. Comprehensive validation comprising 37+ controlled stress-test campaigns and hundreds of production cycles (accumulated over 6 months) confirms zero safety violations, high reproducibility (CV variation < 0.3% across replicates), predictable complexity–performance scaling ($R^2=0.89$), and zero-retuning cross-topology transferability. The system has operated autonomously in active production for over 6 months, establishing reproducible methodology for safe MARL deployment in partially-observable, sensor-hostile manufacturing environments where analytical CBF approaches are structurally infeasible. Full article

Financial institutions face increased reputational, regulatory, and ethical risks as the frequency and complexity of Environmental, Social, and Governance (ESG) controversies increase. Whistleblowing mechanisms are essential in the context of institutional resilience and the mitigation of internal governance failures. This study quantifies the exposure of 364 European financial institutions to a variety of ESG controversies to assess the effectiveness of whistleblowing during the fiscal year 2024. A whistleblowing performance index that captures the relative influence of ESG-related risk factors—such as corruption allegations, environmental violations, and executive misconduct—is constructed using a hybrid Multi-Criteria Decision-Making (MCDM) framework that is based on Entropy Weighting and the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS). The results emphasize that the perceived efficacy of whistleblower systems is substantially influenced by the frequency of media-reported controversies and the presence of robust anti-bribery policies. The study provides a data-driven, replicable paradigm for assessing internal governance capabilities in the face of ESG risk pressure. Our findings offer actionable insights for regulators, compliance officers, and ESG analysts who are interested in evaluating and enhancing ethical accountability systems within the financial sector by connecting the domains of financial risk management, corporate ethics, and sustainability governance. Full article

To avoid tourism market failure caused by inappropriate development strategies, a study was conducted on the travel preferences of Generation Z, the future tourism market. The study found that tourism has shifted from comfortable, tranquil, and easily accessible areas to areas with unique views, extreme topography, proximity to nature, and instagrammable features. This shift reflects tourists’ responses and motivations. This study used a mixed-methods approach, involving 28 tourism managers and 147 Generation Z tourists. Quantitative methods, such as questionnaires, examined tourism motivations through push and pull factors, while in-depth interviews, a qualitative method, examined shifts in the tourism space. The results of this study demonstrate a shift away from the classical view that younger generations’ tourism behavior is predominantly hedonistic and consumptive toward an experience-oriented, nature-connected orientation, aligned with the concept of eco-experiential tourism. Rather than indicating a uniform or definitive shift in reflection, this tendency highlights the growing importance of experiential, cultural, and digital values in Generation Z’s travel preferences. Theoretically, the study contributes to refining existing perspectives by emphasizing the social, technological, and ecological dimensions shaping these preferences. In practice, the findings underscore the need for tourism policies that integrate nature conservation, digital innovation, and the strengthening of local culture to support sustainable destinations. Full article

Climate change is one of the greatest challenges of our time, with direct implications for sustainable development, the physical and mental health of populations, and the functioning of health systems. Strengthening the resilience and sustainability of health systems through mitigation and adaptation strategies requires the active involvement of health professionals. This scoping review explores health professionals’ perceptions of climate change and its impacts on public health and health systems, as well as their operational preparedness and the barriers to adaptation. The literature review was conducted in three phases (20 December 2024, 20 January 2025, and 20 March 2025) using the Web of Science, Scopus, and PubMed databases, covering the period 2016–2025 and following PRISMA guidelines. Of the 1888 studies initially identified, 36 met the predefined inclusion and exclusion criteria. The findings showed that while health professionals recognize climate change as a current threat to public health and health systems, they are not adequately prepared to address its impacts. The main barriers to addressing climate change are related to a lack of information and awareness, inadequate training, limited time, lack of supportive leadership, failure to integrate sustainable practices into daily clinical practice and, above all, inadequate funding. Based on these findings, there is an urgent need to develop policies that promote the active participation of health professionals in the design and implementation of climate change mitigation and adaptation strategies. At the same time, there is a need to strengthen research activity through both synchronous and diachronic studies in order to gather information on the sustainability and resilience of health systems. Full article

The true contribution of economics to climate policy is marred by many misconceptions. This paper aims to help non-experts better understand economic insights by progressively introducing key concepts. We start with perfectly functioning markets, where prices act as powerful guides to channel scarce resources toward their best use from a social point of view. We then recognize that, in many real cases, markets fail to deliver their promise because prices are either absent or do not reflect social values. Anthropogenic greenhouse gas emissions constitute a particularly acute type of market failure affecting both the whole world and future generations. The fundamental source of this failure is the absence of a price on emissions, and economic research has followed two major routes. On the one hand, economists run complex simulation models to estimate the missing price, which can be used as a social yardstick to guide policy choices. On the other hand, they evaluate policy instruments able to reorient market forces toward more climate protection. Two instruments with a strong potential to influence behavior deserve particular attention: the carbon tax and cap-and-trade (or emissions trading) systems. Despite practical complications and the lack of international coordination, both policies have recently made encouraging progress. Full article

Ethiopia presents a compelling paradox in sustainable development: despite decades of rapid economic growth, improvements in human well-being have not been commensurate. This study examines the role of global economic integration in resolving this paradox by analyzing the impact of trade openness (TOP) and foreign direct investment (FDI) on human development in Ethiopia from 1991 to 2021. We hypothesize that this paradox arises because the benefits of trade and FDI operate primarily through an income-growth channel, with a weaker direct effect on health and education capabilities. Moving beyond the standard Human Development Index (HDI), we construct a modified index (HDI*) that excludes the income component, allowing us to disentangle direct effects on health and education from indirect effects mediated through economic growth. Using the ARDL bounds testing approach, we find that TOP and FDI have significantly stronger long-run effects on standard HDI (0.343 and 0.214, respectively) than on the non-income HDI* (0.235 and 0.136). This indicates that approximately one-third (31.5%) of TOP’s and over one-third (36.4%) of FDI’s total benefit is income-mediated, while the remainder reflects direct capability enhancement. The analysis further reveals that institutional quality significantly amplifies these benefits, whereas inflation specifically undermines non-income dimensions, highlighting the acute vulnerability of social sectors to macroeconomic instability. We conclude that the Ethiopian paradox stems not from a failure of growth but from its weak translation into direct, sustainable gains in health and education. We recommend policies to strengthen institutional governance, attract FDI into health and education sectors, lower trade barriers for agricultural exports, and use trade agreements to address structural trade deficits and promote sustainable human development. Full article

First Nations children remain dramatically over-represented in Australia’s Out-of-Home Care (OOHC) system, particularly in New South Wales (NSW), which continues to report the highest numbers nationally. This narrative review, grounded in a relational First Nations Standpoint Theory and decolonising research paradigms, to critically examine the systemic, structural, and historical factors contributing to these disproportionalities. Drawing on interdisciplinary evidence across law, criminology, education, health, governance studies, and public policy, the analysis centres Indigenous-authored scholarship and contemporary empirical literature, including grey literature, inquiries, and community-led reports. Findings reveal that the OOHC system reproduces the colonial logics that historically drove the Stolen Generations. Macro-level structural drivers—including systemic racism, Indigenous data injustice, entrenched poverty and deprivation, intergenerational trauma, and Westernised governance frameworks—continue to shape child protection policies and practices. Micro-level drivers such as parental supports, mental health distress, substance misuse, family violence, and the criminalisation of children in care (“crossover children”) must be understood as direct consequences of structural inequality rather than as isolated individual risk factors. Current placement and permanency orders in NSW further compound cultural disconnection, with ongoing failures to implement the Aboriginal and Torres Strait Islander Child Placement Principle (ATSICPP). Contemporary cultural rights and Indigenous Cultural and Intellectual Property (ICIP) frameworks highlight the urgency of restoring Indigenous authority in decision-making processes. The literature consistently demonstrates that cultural continuity, kinship networks, and ACCO-led models are sort to produce stronger long-term outcomes for children. The review concludes that genuine transformation requires a systemic shift toward Indigenous-led governance, community-controlled service delivery, data sovereignty, and legislative reform that embeds cultural rights and self-determination. Without acknowledging the structural drivers and redistributing genuine power and authority, the state risks perpetuating a cycle of removal that mirrors earlier assimilationist policies. Strengthening First Peoples governance and cultural authority is therefore essential to creating pathways for First Nations children to live safely, remain connected to family and kin, and thrive in culture. Full article