Search Results (12,427)

Forest volume is an important information for assessing the economic value and carbon sequestration capacity of forest resources and serves as a key indicator for energy flow and biodiversity. Although remote sensing technology is applied to estimate volume, optical remote sensing data have limitations in capturing forest vertical height information and may suffer from reflectance saturation. While LiDAR data can provide more detailed vertical structural information, they come with high processing costs and limited observation range. Therefore, improving the accuracy of volume estimation through multi-source data fusion has become a crucial challenge and research focus in the field of forest remote sensing. In this study, we integrated Sentinel-2 multispectral data, Resource-3 stereoscopic imagery, UAV-based LiDAR data, and field survey data to quantitatively estimate the forest volume in Saihanwula Nature Reserve, located in Inner Mongolia, China, on the southern part of Daxing’anling Mountains. The study evaluated the performance of multi-source remote sensing features by using recursive feature elimination (RFE) to select the most relevant factors and applied four machine learning models—multiple linear regression (MLR), k-nearest neighbors (kNN), random forest (RF), and gradient boosting regression tree (GBRT)—to develop volume estimation models. The evaluation metrics include the coefficient of determination (R²), root mean square error (RMSE), and relative root mean square error (rRMSE). The results show that (1) forest Canopy Height Model (CHM) data were strongly correlated with forest volume, helping to alleviate the reflectance saturation issues inherent in spectral texture data. The fusion of CHM and spectral data resulted in an improved volume estimation model with R² = 0.75 and RMSE = 8.16 m³/hm², highlighting the importance of integrating multi-source canopy height information for more accurate volume estimation. (2) Volume estimation accuracy varied across different tree species. For Betula platyphylla, we obtained R² = 0.71 and RMSE = 6.96 m³/hm²; for Quercus mongolica, R² = 0.74 and RMSE = 6.90 m³/hm²; and for Populus davidiana, R² = 0.51 and RMSE = 9.29 m³/hm². The total forest volume in the Saihanwula Reserve ranges from 50 to 110 m³/hm². (3) Among the four machine learning models, GBRT consistently outperformed others in all evaluation metrics, achieving the highest R² of 0.86, lowest RMSE of 9.69 m³/hm², and lowest rRMSE of 24.57%, suggesting its potential for forest biomass estimation. In conclusion, accurate estimation of forest volume is critical for evaluating forest management practices and timber resources. While this integrated approach shows promise, its operational application requires further external validation and uncertainty analysis to support policy-relevant decisions. The integration of multi-source remote sensing data provides valuable support for forest resource accounting, economic value assessment, and monitoring dynamic changes in forest ecosystems. Full article

The transportation sector, as a major source of carbon emissions, plays a crucial role in the realization of dual carbon goals worldwide. In this study, an improved least absolute shrinkage and selection operator (LASSO) is used to identify six key factors affecting transportation carbon emissions (TCEs) in China. Aiming at the spatio-temporal characteristics of transportation carbon emissions, a CNN-BiLSTM neural network model is constructed for the first time for prediction, and an improved whale optimization algorithm (EWOA) is introduced for hyperparameter optimization, finding that the prediction model combining spatio-temporal characteristics has a more significant prediction accuracy, and scenario forecasting was carried out using the prediction model. Research indicates that over the past three decades, TCEs have demonstrated a rapid growth trend. Under the baseline, green, low-carbon, and high-carbon scenarios, peak carbon emissions are expected in 2035, 2031, 2030, and 2040. The adoption of a low-carbon scenario represents the most advantageous pathway for the sustainable progression of China’s transportation sector. Consequently, it is imperative for China to accelerate the formulation and implementation of low-carbon policies, promote the application of clean energy and facilitate the green transformation of the transportation sector. These efforts will contribute to the early realization of dual-carbon goals with a positive impact on global sustainable development. Full article

Wind power has gained attention as a vital renewable energy source capable of reducing emissions and serving as an effective alternative to fossil fuels. Floating wind farms could significantly enhance the energy capacities of Mediterranean countries. However, location selection for offshore wind farms (OWFs) is a challenge for renewable energy policy and marine spatial planning (MSP). To address these issues, this study considers the marine space of Greece to propose a GIS-based multi-criteria decision-making (MCDM) framework employing the Analytic Hierarchy Process (AHP) to identify suitable sites for OWFs. The approach assesses 19 exclusion criteria encompassing legislative, environmental, safety, and technical constraints to determine the eligible areas. Subsequently, 10 evaluation criteria are weighted to determine the selected areas’ level of suitability. The study considers baseline conditions (1981–2010) and future climate scenarios based on RCP 4.5 and RCP 8.5 for two horizons (2011–2040 and 2041–2070), integrating projected wind velocities and sea level rise to evaluate potential shifts in suitable areas. Results indicate the central and southeastern Aegean Sea as the most suitable areas for OWF deployment. Climate projections indicate a modest increase in suitable areas. The findings serve as input for climate-resilient MSP seeking to promote sustainable energy development. Full article

Purpose: Following George Floyd’s death, the push for law enforcement accountability policies has intensified. Despite robust legislative action, challenges in enacting and implementing meaningful reforms persist. This study analyzes police accountability policies (PAP) in the U.S. from 2020 to 2022, identifying barriers and facilitators through expert perspectives in enforcement oversight, policy advocacy, and community engagement. Methods: The study used a dual approach: analyzing 226 police accountability bills from all 50 U.S. states, D.C., and Puerto Rico via the National Conference of State Legislatures database, and categorizing them into six key areas such as training, technology use, and certification. Additionally, a survey was conducted among experts to identify the challenges and drivers in passing police accountability legislation. Findings: A legislative analysis showed that although 48 states passed police accountability laws, California, New Jersey, Oklahoma, and Colorado have made significant strides by passing multiple pieces of legislation aimed at enhancing law enforcement accountability and ensuring better policing practices. The most common policies focused on training and technology, enacted by 16 and 12 states, respectively. However, crucial certification and decertification policies were adopted in just 13 states, highlighting the inconsistent implementation of measures critical for police accountability and transparency. The survey identified several barriers to passing PAP, including inadequate support from local governments (72.7%). Structural exclusion of poor and minority communities from policing resources was also a significant barrier (54.5%). Facilitators included community support (81.8%) and a cultural shift in policing towards viewing officers as “guardians” rather than “warriors” (63.6%). Conclusions: While some progress has been made in passing PAP, considerable gaps remain, particularly in enforcement and comprehensive reform. Resistance from law enforcement institutions, lack of community support, and structural inequalities continue to impede the adoption of effective PAP. Full article

Although organizational resilience is well established, refining the systematic quantitative evaluation of health systems resilience (HSR) remains an ongoing opportunity for advancement. Research either focuses on individual HSR indicators, such as social welfare policy, public expenditure, health insurance, healthcare quality, and technology, or broadly examines socio-economic factors, highlighting the need for a more comprehensive methodological approach. This study employed the Slacks-Based Measure (SBM) within Data Envelopment Analysis (DEA) to analyze efficiency by maximizing outputs. It systematically examined key HSR factors across countries, providing insights for improved policymaking and resource allocation. Taking a five-year (2016–2020) dataset that covered 55 to 56 countries and evaluating 17 indicators across governance, health systems, and economic aspects, the paper presents that all sixteen top-ranked countries with a perfect efficiency score of 1 belonged to the high-income group, with ten in Europe, highlighting regional HSR differences. This paper concludes that adequate economic resources form the foundation of HSR and ensure stability and sustained progress. A properly supported healthcare workforce is essential for significantly enhancing health systems and delivering quality care. Last, effective governance and the equitable allocation of resources are crucial for fostering sustainable development and strengthening HSR. Full article

Family empowerment is a key component of effective family-centered practices in healthcare, mental health, and educational services. The Family Empowerment Scale (FES) is the most commonly used instrument to evaluate empowerment in families raising children with emotional, behavioral, or developmental disorders. Despite its importance, the FES for diverse populations, especially Latinx parents, has rarely been evaluated using innovative psychometric approaches. In this study, we evaluated key dimensions and psychometric evidence of the Family Empowerment Scale (FES) for 96 Latinx parents of children with intellectual and developmental disabilities (IDD) in the United States using an exploratory graph analysis (EGA). The EGA identified a five-dimensional structure, and EGA models outperformed the original CFA 3-factor models for both parents of children with autism and other disabilities. This study identified distinct, meaningful dimensions of empowerment that reflect both shared and unique empowerment experiences across two Latinx parent groups. These insights can inform the design of culturally responsive interventions, instruments, and policies that more precisely capture and boost empowerment in Latinx families. This study contributes to closing a gap in the literature by elevating the voices and experiences of Latinx families by laying the groundwork for more equitable support systems in special education and disability services. Full article

Urban flooding has significantly impacted the livelihoods of households and communities worldwide. It highlights the urgency of focusing on both flood preparedness and adaptation strategies to understand the community’s perception and adaptive capacity. This study investigates the levels of risk perception, flood preparedness, and adaptive capacity, while also exploring the inter-relationships among these factors within the context of urban flooding in Malaysia. A quantitative approach was employed, involving a structured questionnaire administered to residents in flood-prone urban areas across Greater Kuala Lumpur, Malaysia. A total of 212 responses were analysed using descriptive statistics, categorical index classification, and Spearman correlation analysis. The findings indicate that residents generally reported high levels of risk perception and preparedness, although adaptive capacity exhibited greater variability, with a mean score of 3.97 (SD = 0.64). Positive associations were found among risk perception, flood preparedness, and adaptive capacity. This study contributes to the existing knowledge by providing evidence on community resilience and highlighting key factors that can guide flood management policies and encourage adaptive planning at the community level. Full article

The mining industry (MI) in mineral-rich regions is pivotal for economic growth but is challenged by significant pollution and emissions. This study examines Guangxi, a representative region in China, in light of the country’s “Dual Carbon” goals. We quantified carbon emissions from the MI from 2005 to 2021, employing the generalized Divisia index method (GDIM) to analyze the factors driving these emissions. Additionally, a system dynamics (SD) model was developed, integrating economic, demographic, energy, environmental, and policy variables to assess decarbonization strategies and the potential for carbon decoupling. The key findings include the following: (1) Carbon accounting analysis reveals a rising emission trend in Guangxi’s MI, predominantly driven by electricity consumption, with the non-ferrous metal mining sector contributing the largest share of total emissions. (2) The primary drivers of carbon emissions were identified as economic scale, population intensity, and energy intensity, with periodic fluctuations in sector-specific drivers necessitating coordinated policy adjustments. (3) Scenario analysis showed that the Emission Reduction Scenario (ERS) is the only approach that achieves a carbon peak before 2030, indicating that it is the most effective decarbonization pathway. (4) Between 2022 and 2035, carbon decoupling from total output value is projected to improve under both the Energy-Saving Scenario (ESS) and ERS, achieving strong decoupling, while the resource extraction shows limited decoupling effects often displaying an expansionary connection. This study aims to enhance the understanding and promote the advancement of green and low-carbon development within the MI in mineral-rich regions. Full article

This study proposes a novel Deep Reinforcement Learning (DRL) policy network structure for portfolio management called PortRSMs. PortRSMs employs stacked State-Space Models (SSMs) for the modeling of multi-scale continuous regime shifts in financial time series, striking a balance between exploring consistent distribution properties over short periods and maintaining sensitivity to sudden shocks in price sequences. PortRSMs also performs cross-asset regime fusion through hypergraph attention mechanisms, providing a more comprehensive state space for describing changes in asset correlations and co-integration. Experiments conducted on two different trading frequencies in the stock markets of the United States and Hong Kong show the superiority of PortRSMs compared to other approaches in terms of profitability, risk–return balancing, robustness, and the ability to handle sudden market shocks. Specifically, PortRSMs achieves up to a 0.03 improvement in the annual Sharpe ratio in the U.S. market, and up to a 0.12 improvement for the Hong Kong market compared to baseline methods. Full article

Water governance in Europe faces mounting challenges from climate change, demographic pressures, and aging infrastructure—especially in Southern regions increasingly affected by drought and institutional fragmentation. In contrast, Nordic countries such as Denmark and Sweden exhibit coherent, integrated governance systems with strong regulatory oversight. This study introduces the Water Governance Maturity Index (WGMI), a document-based assessment tool designed to evaluate national water governance across five dimensions: institutional capacity, operational effectiveness, environmental ambition, equity, and climate adaptation. Applying the WGMI to eight EU countries—four Nordic and four Southern—reveals a persistent North–South divide in governance maturity. Nordic countries consistently score in the “advanced” or “model” range, while Southern countries face systemic gaps in implementation, climate integration, and territorial inclusion. Based on these findings, the study offers actionable policy recommendations, including the establishment of independent regulators, strengthening of river basin coordination, mainstreaming of climate-water strategies, and expansion of affordability and participation mechanisms. By translating complex governance principles into measurable indicators, the WGMI provides a practical tool for benchmarking reform progress and supporting the EU’s broader agenda for just resilience and climate adaptation. Unlike broader frameworks like SDG 6.5.1, the WGMI’s document-based, dimension-specific approach provides granular, actionable insights for governance reform, enhancing its utility for EU and global policymakers. Full article

With the rapid growth of elevator installations, conventional scheduled maintenance struggles to meet the dual demands of ensuring operational safety and cost control. This study proposes an innovative on-demand maintenance method that aligns with the Chinese policy directives on elevator maintenance reform. First, we conduct a historical fault cause analysis to identify the root causes of elevator entrapment incidents. Next, we establish an entrapment prediction model based on our historical data. Then, we design an elevator entrapment risk index report according to the prediction results. Finally, we formulate an on-demand maintenance plan that combines insights from the report with the conclusions of the cause analysis. Field implementation and comparative experiments demonstrate that the proposed on-demand maintenance method outperforms the scheduled one. The result shows significant reductions in accident and maintenance workload, justifying the practical value of this approach for the industry. Full article

(1) Background: This article aims to understand the forms and elements through which the inhabitants of the city of Medellin have configured their subjectivity in the context of the application of neoliberal policies in the last two decades. In this way, we can approach the frameworks of understanding that constitute a fundamental part of the individuation processes in which the incorporation of their subjectivities is evidenced in neoliberal contexts that, in the historical process, have been converging with authoritarian, antidemocratic and neoconservative elements. (2) Method: A qualitative approach with a hermeneutic-interpretative paradigm was used. In-depth semi-structured interviews were conducted with 41 inhabitants of Medellín who were politically identified with right-wing or center-right positions. Data analysis included thematic coding to identify patterns of thought and points of view. (3) Results: Participants associate success with individual effort and see state intervention as an obstacle to development. They reject redistributive policies, arguing that they generate dependency. In addition, they justify authoritarian models of government in the name of security and progress, from a moral superiority, which is related to a negative and stigmatizing perception of progressive sectors and a negative view of the social rule of law and public policies with social sense. (4) Conclusions: The naturalization of merit as a guiding principle, the perception of themselves as morally superior based on religious values that grant a subjective place of certainty and goodness; the criminalization of expressions of political leftism, mobilizations and redistributive reforms and support for policies that establish authoritarianism and perpetuate exclusion and structural inequalities, closes roads to a participatory democracy that enables social and economic transformations. Full article

Background/Objectives: Strengthening antimicrobial stewardship (AMS) programs is an invaluable intervention in the ongoing efforts to contain the threat of antimicrobial resistance (AMR), particularly in low-resource settings. This study evaluates the impact of the Telementoring, Education, and Advocacy Collaboration initiative for Health through Antimicrobial Stewardship (TEACH AMS), which uses the virtual Extension for Community Healthcare Outcomes (ECHO) learning model to enhance AMS capacity in Kenya, Ghana, and Malawi. Methods: A mixed-methods approach was used, which included attendance data collection, facility-level assessments, post-session and follow-up surveys, as well as focus group discussions. Results: Between September 2023 and February 2025, 77 virtual learning sessions were conducted, engaging 2445 unique participants from hospital-based AMS committees and health professionals across the three countries. Participants reported significant knowledge gain, and data showed facility improvements in two core AMS areas, including the implementation of multidisciplinary ward-based interventions/communications and enhanced monitoring of antibiotic resistance patterns. Along those lines, participants reported that the program assisted them in improving prescribing and culture-based treatments, and also evidence-informed antibiotic selection. The evidence of implementing ward-based interventions was further stressed in focus group discussions, as well as other strengthened practices like point-prevalence surveys, and development or revision of stewardship policies. Substantial improvements in microbiology services were also shared by participants, particularly in Malawi. Other practices mentioned were strengthened multidisciplinary communication, infection prevention efforts, and education of patients and the community. Conclusion: Our findings suggest that a virtual case-based learning educational intervention, providing structured and tailored AMS capacity building, can drive behavior change and strengthen healthcare systems in low resource settings. Future efforts should aim to scale up the engagements and sustain improvements to further strengthen AMS capacity. Full article

Climate change poses an escalating threat to cultural heritage (CH), driven by intensifying climate-related hazards and systemic vulnerabilities. In response, risk assessment frameworks and methodologies (RAFMs) have emerged to evaluate and guide adaptation strategies for safeguarding heritage assets. This study conducts a state-of-the-art (SotA) review of 86 unique RAFMs using a Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA)-guided systematic approach to assess their scope, methodological rigor, alignment with global climate and disaster risk reduction (DRR) frameworks, and consistency in conceptual definitions of hazard, exposure, and vulnerability. Results reveal a growing integration of Intergovernmental Panel on Climate Change (IPCC)-based climate projections and alignment with international policy instruments such as the Sendai Framework and United Nations Sustainable Development Goals (UN SDGs). However, notable gaps persist, including definitional inconsistencies, particularly in the misapplication of vulnerability concepts; fragmented and case-specific methodologies that challenge comparability; and limited integration of intangible heritage. Best practices include participatory stakeholder engagement, scenario-based modeling, and incorporation of multi-scale risk typologies. This review advocates for more standardized, interdisciplinary, and policy-aligned frameworks that enable scalable, culturally sensitive, and action-oriented risk assessments, ultimately strengthening the resilience of cultural heritage in a changing climate. Full article

Strengthening climate resilience constitutes a foundational approach through which cities adapt to climate change and mitigate associated environmental risks. However, research on the influence of economic policy environments on climate resilience remains limited. Guided by institutional theory and dynamic capability theory, this study employs a panel dataset comprising 272 Chinese cities at the prefecture level and above, covering the period from 2009 to 2023. It constructs a composite index framework for evaluating the green business environment (GBE) and urban climate resilience (UCR) using the entropy weight method. Employing a two-way fixed-effect regression model, it examined the impact of GBE optimization on UCR empirically and also explored the underlying mechanisms. The results show that improvements in the GBE significantly enhance UCR, with green innovation (GI) in technology functioning as an intermediary mechanism within this relationship. Moreover, climate policy uncertainty (CPU) exerts a moderating effect along this transmission pathway: on the one hand, it amplifies the beneficial effect of the GBE on GI; on the other hand, it hampers the transformation of GI into improved GBEs. The former effect dominates, indicating that optimizing the GBE becomes particularly critical for enhancing UCR under high CPU. To eliminate potential endogenous issues, this paper adopts a two-stage regression model based on the instrumental variable method (2SLS). The above conclusion still holds after undergoing a series of robustness tests. This study reveals the mechanism by which a GBE enhances its growth through GI. By incorporating CPU as a heterogeneous factor, the findings suggest that governments should balance policy incentives with environmental regulations in climate resilience governance. Furthermore, maintaining awareness of the risks stemming from climate policy volatility is of critical importance. By providing a stable and supportive institutional environment, governments can foster steady progress in green innovation and comprehensively improve urban adaptive capacity to climate change. Full article