Search Results (877)

The news media in Gulf countries are among the most important institutions shaping the public’s understanding of climate action. This study aims to explore the longitudinal developments, thematic framing, and sentiment tones of Omani English-language newspapers’ sustainability discourse during the period 2015–2025. The study applied a multi-stage computational method to analyze a deduplicated corpus of 709 news articles from two English-language dailies of Oman: Times of Oman and Oman Daily Observer for a time period from 2015 to 2025. Latent Dirichlet Allocation (LDA) topic modeling was used to group topics into five types of macro frames. Results show that the focus of the Omani media is very localized, and the dominant frames are “Corporate ESG, Business & Green Investment” and “Clean Energy Policy, Research & Institutional Governance”. Furthermore, a sentiment analysis of headlines using VADER showed clearly defined sentiment polarity; domestic economic transition headlines were characterized by high positive polarity, while global climate crisis headlines had a higher negative polarity. The study concludes that the press in Oman is in a strategic process of “nationalizing” the climate change issue, moving from the conventional disaster discourse to making sustainability a local opportunity for economic diversification, technological modernization, and national resilience. Full article

This paper presents a resilience-driven probabilistic multiobjective framework for reactive power planning in islanded microgrids under extreme contingencies, explicitly integrating sustainability objectives and alignment with the United Nations Sustainable Development Goals (SDGs). The proposed planning framework simultaneously optimizes technical reliability, economic viability, environmental sustainability, and social resilience using the IEEE 33-bus distribution system as a representative test network. Uncertainties associated with solar irradiance, wind speed, and load demand are modeled using the Two-Point Estimation Method (2PEM), while the Non-dominated Sorting Genetic Algorithm II (NSGA-II) determines Pareto optimal planning solutions for five reactive power support strategies. The results demonstrate that planning solutions optimized for grid-connected operation are not necessarily the most effective under islanded conditions. Within the adopted multi-criteria evaluation framework, the dedicated D-STATCOM strategy achieves the highest overall normalized performance, providing 87.2% load preservation, 93.7% critical-load protection, and an 8.7 h representative survival time, while reducing total load shedding to 12.8% and eliminating high-risk shedding events (>30%). Furthermore, it decreases event-related economic losses by more than 75% and achieves the lowest environmental impact, with a 62.5% reduction in life-cycle CO₂ emission intensity relative to the conventional grid baseline. A normalization sensitivity analysis confirms that the comparative ranking of the investigated strategies remains unchanged under alternative normalization methods, demonstrating the robustness of the proposed evaluation framework. From a sustainability perspective, the proposed framework contributes to SDG 7 (Affordable and Clean Energy) through reliable low-carbon microgrid operation, SDG 9 (Industry, Innovation and Infrastructure) through resilient power system planning, SDG 11 (Sustainable Cities and Communities) by enhancing the continuity of critical urban services, SDG 13 (Climate Action) through reduced life-cycle emissions, and SDG 8 (Decent Work and Economic Growth) by supporting local employment associated with distributed energy deployment. Full article

For millennia, farmers in cassava’s homeland in the neotropics selected varieties suited to their climate, soils, biological environments, management systems, and nutritional needs. These actions were fundamental to the crop’s success as a reliable staple energy source. After the arrival of Europeans in the New World, these varieties spread first to Africa and later to Asia, where they were further selected for local conditions and needs. The founders of the International Center for Tropical Agriculture in Cali, Colombia, recognized the importance of legacy landrace varieties as sources of genetic diversity for breeding to support tropical production systems, improve nutrition, and boost income. One of earliest activities of the new center, beginning in 1969, was to organize the collection of cassava landraces to establish a genetic resource for breeding purposes at the center’s headquarters in the Cauca Valley. We describe the multifaceted understanding about this remarkable heritage through thorough evaluation over a diverse range of environments and the creation of new varieties aimed at adding value across the crop’s diverse agro-ecologies and uses. CIAT breeders, along with a broad coalition of partners, have targeted demands from growers, processors, and consumers throughout the tropics. Latin American germplasm remains the foundational source of novel, high-value traits that differentiate cassava products across markets, underpinning the continued gains in productivity, quality, and end-use performance. Full article

Regional climate models (RCMs) remain affected by limited spatial resolution and systematic biases relative to observations, posing a major challenge for the provision of actionable climate information at the local scale. Bridging the gap between coarse, biased model outputs and site-specific climate needs is therefore both urgent and an active area of research. Here, we present a diagnosis of near-surface air temperature biases, based on the vertical structure of the atmospheric column, in an ensemble of EURO-CORDEX simulations over a complex Alpine region. We show how the conventional and commonly applied temperature bias correction, aimed at removing the discrepancy between the model representation of orography and the actual terrain elevation, still leaves biases of a magnitude comparable to or even greater than the applied correction. After the orographic correction, residual biases originate from both free-atmosphere temperature biases and low-level biases. Each contribution to the TAS bias is quantified through an inter-model seasonal analysis. This study highlights the limitations of RCMs in representing complex orography and boundary-layer conditions in Alpine regions. It also provides interpretative keys for a better understanding of the underlying site-specific physical causes of TAS biases and cautions against the potential pitfalls of a straightforward application of bias-correction methods. Full article

Amid growing water stress in British Columbia caused by climate change, population growth, and rising demand, local governments in Canada are adopting water demand management and conservation policies as part of broader climate adaptation efforts to promote sustainable water use and optimize efficiency. Our objective was to understand the actions local governments are taking to address climate-related impacts on the water sector. Thus, the study assesses the implementation status and progress of water demand management in British Columbia by surveying water managers from 94 local jurisdictions across the province. Our analysis focuses on four key dimensions: prioritization, planning, investment, and implementation of water demand management policies. Results reveal that 84% of surveyed jurisdictions have water efficiency/conservation plans, and 55% of household water connections are metered. Commonly implemented measures include mandatory water restrictions, volumetric pricing, leak detection, and public education campaigns, with regional policies proving more prevalent than municipal-level initiatives. Despite notable progress, implementation remains uneven, with gaps in resource allocation and many household connections still without water meters. Tracking the progress of implementing best practices in water conservation is an essential part of local climate adaptation. Full article

As global environmental challenges intensify and the imperative to mitigate extreme climate change grows increasingly urgent, the sustainable development of economic and social systems has emerged as a fundamental pathway for addressing environmental challenges. Smart manufacturing, as a core technology of the new round of technological revolution, can drive green transformation and enhance sustainable development performance. Using a sample of A-share listed manufacturing corporations from 2009 to 2023, this study systematically investigates the impact, underlying mechanisms, and heterogeneity of smart manufacturing on the sustainable development performance of manufacturing corporations from the perspective of local environmental regulation stringency. The results show that corporate smart manufacturing significantly improves sustainable development performance. This conclusion remains robust after addressing endogeneity concerns and conducting a series of robustness checks. Mechanism analysis reveals that corporate smart manufacturing operates via three primary channels: the data asset accumulation effect, the green technology innovation effect, and the information environment improvement effect. Furthermore, the moderating role of local environmental regulation stringency follows an inverted U-shaped trajectory, suggesting that regulatory stringency facilitates the smart manufacturing–performance nexus only up to a certain threshold, beyond which it becomes counterproductive. Heterogeneity analysis further shows that the positive effect is particularly pronounced among private corporations, large-scale corporations, those located in the eastern region, and those situated in key environmental protection cities. By uncovering the micro-level mechanisms through which smart manufacturing affects sustainable performance, this study offers novel theoretical insights and actionable policy implications for leveraging intelligent technologies to advance manufacturing sustainability, both in China and globally, in support of the nation’s carbon peaking and carbon neutrality commitments. Full article

This study analyzes trends in 18 temperature- and precipitation-related indices recommended by the Expert Team on Climate Change Detection and Indices (ETCCDI) across mainland Portugal over the most recent 46-year complete period (1980–2025), using ERA5-Land reanalysis data. Prior to the computation of the indices, daily ERA5-Land extreme temperature and precipitation data were validated against ground-based observations, showing good agreement and confirming the suitability of the reanalysis dataset for assessing climate extremes at the national scale. The results reveal a consistent and statistically significant warming signal across the study area, with the strongest increases observed in southern and inland regions. The frequency of extreme heat events has intensified markedly since the early 2000s. In contrast, precipitation-related indices display high spatial variability and trends with limited statistical significance, reflecting both genuine climatic heterogeneity and the inherent challenges of reproducing rainfall extremes in complex terrain using reanalysis data. The combined interpretation highlights an emerging climatic asymmetry—robust and spatially coherent warming versus localized and uncertain precipitation trends—underscoring distinct regional vulnerabilities. The findings provide actionable insights for climate services, supporting the design of region-specific adaptation strategies in Portugal, especially the need to address increasing heat stress in southern regions and the growing exposure to short-duration heavy rainfall in northern areas. Full article

Climate-resilient land-use planning increasingly requires methods that can translate heterogeneous climate-risk knowledge into actionable territorial recommendations. This study develops the Hazard–Sector Translation Framework, a planning-oriented method for linking regional hazard pathways with affected territorial systems, land-use domains, recommendation families, planning instruments, and resilience functions. The framework was developed through a qualitative cross-regional synthesis of five Southern European regions: Sicily, Costa del Sol, Osijek-Baranja County, Central Greece, and the Troodos Mountain Range. The analysis identifies how diverse hazard pathways, including heat, drought, water scarcity, pluvial flooding, wildfire, hail, frost risk, and tourism climate-suitability pressures, become actionable through recurring land-use domains. Results show that heterogeneous regional risks converge around five main land-use resilience domains: buildings, agriculture, blue-green infrastructure, transportation, and protected-area conservation, while governance and capacity are treated separately as cross-cutting implementation conditions. The operational matrix demonstrates how region-specific hazard pathways can be converted into traceable recommendation structures without reducing local complexity. The framework does not replace detailed hazard modeling or climate-risk assessment; rather, it provides an intermediate methodological bridge between climate-risk evidence and land-use planning action. The approach is transferable to other regions seeking to organize stakeholder-derived needs and adaptation recommendations into coherent, sector-specific planning responses. Full article

This paper illustrates the theoretical framework, the methodological approach, and the primary findings of a project titled MASSTER (Managing(South)Africa-and-Senegal-Sustainability-Targets-through-Economic-diversification-of-Rural-areas), in which higher educational institutions (HEIs) and organizations from Senegal, South Africa, and Europe collaborate with the aim of addressing the nexus between migration, agriculture and development in Sub-Saharan Africa by co-creating evidence-based training, tools, and actions to foster development and co-development while targeting several Sustainable Development Goals (SDGs), namely Zero-hunger, Good-Health-and-Wellbeing, Gender-Equality, Quality-education, Decent-work-and-economic-growth, Responsible-consumption-and-production, all within the frame of Partnership-for-the-Goals. The MASSTER project employs a transdisciplinary and bottom-up approach based on a cross-sectional study conducted in South Africa and Senegal to identify actual challenges, needs, and resources from farmers (n = 737) and students enrolled in agricultural courses (n = 1.013). Findings underpinned the co-creation of the primary project outcomes: training on agritourism development, farm management, income-generating activities, climate change resilience, and food value chain; toolkits designed to boost the adoption of the Whole of Society Approach and to strengthen cooperation between HEIs and local communities; MASSTER Student-and-alumni-tracking-procedure-with-early-warning-mechanism-for-brain-drain; and a MOOC on critical-thinking-and-empowerment. The MASSTER project can have a relevant impact at local and international levels, providing evidence-based tools to be used by HEIs, stakeholders/policymakers, and the scientific community to actively foster development and co-development. Full article

Drought is a recurring agricultural hazard in Northeast Thailand’s floodplain environments, yet the actual values of remote sensing indices at confirmed drought locations remain poorly characterized. This study characterized six remote sensing indices (NDVI, VCI, SMI, NDMI, MNDWI, NSMI) at 541 agricultural drought-reporting locations in the Chi River Basin, Maha Sarakham Province, across three years representing different ENSO phases (La Niña 2020, El Niño 2023, neutral 2024). Drought-reporting frequency was classified based on village-level alert frequency: high frequency (six alerts, n = 100 villages) and low–moderate frequency (≤3 alerts, n = 441 villages). Sentinel-2 imagery was processed for the January–May dry season. Due to non-independence of observations (repeated measurements and spatial autocorrelation), analyses focused on descriptive statistics and effect sizes (Cohen’s d) rather than formal hypothesis testing. Results revealed remarkably small mean differences between frequency classes (0.008–0.024), with uniformly small effect sizes (Cohen’s d = 0.20–0.22). VCI and MNDWI showed negligible differences (Cohen’s d = 0.124 and 0.094, respectively). Index values at high-frequency locations showed stability across years (CV < 7% for all indices except NDMI), with limited year-to-year variation. SMI and NSMI were perfectly correlated (r = 1.00), indicating mathematical redundancy. Provisional descriptive reference points were derived from the three-year dataset (NDVI ≈ 0.21, VCI ≈ 0.52, SMI ≈ 0.41, MNDWI ≈ −0.33 at high-frequency locations), but these are descriptive summaries only and require validation with longer time series before they can be considered for operational use. These findings demonstrate that individual remote sensing indices have limited discriminatory power in this sandy soil floodplain environment, where local factors—soil properties, topography, and irrigation access—dominate over regional climate forcing. Five policy-relevant observations are proposed, including re-evaluation of threshold-based early warning systems and prioritized irrigation investments based on static vulnerability factors. This study contributes to SDG 2 (Zero Hunger), SDG 6 (Clean Water), and SDG 13 (Climate Action) through improved understanding of drought monitoring limitations in floodplain environments. Full article

The systemic shift in the energy consumption structure from high-carbon fossil fuels to low-carbon clean energy constitutes a critical pathway toward global climate governance and carbon neutrality. However, this sustainable transition is consistently impeded by deep-seated institutional frictions and structural barriers, such as governance fragmentation and carbon lock-in effects embedded in traditional industrial organization. Whether digital government development can overcome these barriers by nurturing resilient business ecosystems and thereby promote a systemic low-carbon energy transition remains an urgent question within sustainable development research. To address this issue, this study integrates digital government development, regional e-commerce ecosystem competitiveness, and the low-carbon transition of the energy consumption structure into a unified analytical and sustainable governance framework. Using panel data from 30 Chinese provinces from 2012 to 2022, we exploit the institutional reform of provincial big data administrations as a quasi-natural experiment to identify the impacts of digital government. Regional e-commerce ecosystem competitiveness is comprehensively evaluated across four sustainable dimensions: ecological innovation capacity, market connectivity, ecological global integration, and inclusive infrastructure. Methodologically, we employ a spatial difference-in-differences model to capture geographic interdependencies alongside a double machine learning framework to handle high-dimensional confounding and nonlinear disturbances. The empirical findings reveal that both digital government development and regional e-commerce ecosystem competitiveness significantly drive the low-carbon transition of the energy consumption structure. The institutional effect of digital government exhibits strong regional embeddedness with localized impacts, whereas e-commerce ecosystem competitiveness generates positive spatial spillovers that accelerate energy optimization in neighboring regions. Crucially, regional e-commerce ecosystem competitiveness serves as a significant partial mediator, constructing a reliable transmission channel from institutional design to market-based decarbonization. Further pathway analysis indicates that market connectivity and inclusive infrastructure function as the primary transmission channels, effectively mitigating transportation energy intensity and bridging the digital-green divide, while the mediating contribution of ecological innovation capacity is relatively constrained due to cross-organizational coordination thresholds. This study clarifies the interactive mechanism between public digital governance and market ecosystem competitiveness in advancing environmental sustainability, thereby offering fresh theoretical insights and actionable policy implications for emerging market economies striving for economic growth and decarbonization. Full article

Against the backdrop of global climate change, enhancing the carbon sink capacity of ecosystems has become one of the key pathways for implementing climate action. This study takes the Western Hills–Yongding River Cultural Belt in Beijing as its study area to systematically investigate the coupling mechanisms between multidimensional spatial pattern factors and the carbon sink capacity of vegetation net primary productivity (NPP). The study employed the morphological spatial pattern analysis (MSPA) method to structurally identify and quantify regional green space landscape elements. By integrating vertical vector data such as building height and canopy height, and incorporating terrain background and human activity intensity indicators, a comprehensive system of driving factors was established, encompassing two-dimensional landscape patterns, three-dimensional spatial structures and multi-dimensional attributes. Based on the Local Climate Zone (LCZ) classification system, a detailed classification of the study area’s land cover was carried out, and eight typical units—encompassing built-up settlements, brownfield sites, forested green spaces and riparian wetlands—were selected as the core objects of analysis. Building on this, multiple non-linear machine learning regression models were constructed to conduct fitting analyses and accuracy validation; the LightGBM model was identified as the optimal fitting model based on the test set coefficient of determination (R²) and root mean square error (RMSE) as core indicators; Furthermore, the SHAP interpretability analysis framework was introduced to systematically reveal the relative importance of each of the driving factors, their positive and negative effects, non-linear response characteristics, and two-factor interaction mechanisms. Based on these findings, category-specific, differentiated spatial regulation strategies for enhancing carbon sinks were proposed, providing scientific support for the optimisation of ecological spaces, the ecological restoration of brownfield sites, and the realisation of carbon sink potential within the study area. Full article

Child malnutrition remains a critical global health challenge, yet most existing studies rely on static risk estimates and overlook the spatial–temporal nature of environmental exposures and localized socioeconomic disparities. To address this gap, we integrated Earth observation-derived environmental indicators, geolocated conflict events, socioeconomic variables, and child health outcomes, and applied a Fixed Effects Two-Stage Least Squares Spatial Durbin Error Model (FE-2SLS-SDEM). We found distinct hotspots of joint vulnerability, where areas experiencing both high conflict intensity and recurrent droughts show significantly higher rates of childhood stunting. High conflict intensity, drought severity, diarrheal prevalence, and inadequate sanitation significantly increase stunting, while maternal and paternal education, improved sanitation, economic development (proxied by nighttime light intensity), and agricultural productivity reduce it. Among these determinants, female education demonstrated the most pronounced inverse relationship with childhood stunting. Additionally, exposure to both drought severity and high conflict intensity independently and in combination worsens childhood stunting not only within affected regions but also in nearby localities. Our results underscore the urgency of geographically targeted, multisectoral, and action-oriented policies aimed at strengthening community and health system capacities to mitigate the converging risks of climate change and conflict on child malnutrition. Full article

Environmental challenges, such as climate change, resource circulation, and biodiversity loss, are closely interrelated with regional issues, including population decline, aging, youth outmigration, and the maintenance of local industries. Addressing these challenges requires not only scientific and technological innovation but also educational and participatory approaches that empower citizens to engage with complex regional issues. This paper conceptualizes co-learning as a framework for environmental education and citizen engagement in transitions toward sustainable and resilient communities. Co-learning is defined as a multi-actor learning process in which participants with diverse forms of knowledge and social positions revise their understandings, relationships, and orientations toward action through dialogic and reflective interaction. The paper organizes co-learning into a conceptual and methodological framework that connects environmental education, citizen engagement, science communication, technology assessment, community-based projects, and policy-oriented learning. It proposes four types of co-learning—interdisciplinary, bridging, project-based, and policy co-learning—and identifies design principles and analytical dimensions for examining learning outcomes and empowerment processes. Illustrative practices in Tanegashima, Japan, help clarify how the proposed framework can connect science, education, and citizen engagement. Full article

This study examines the extent to which energy justice principles are incorporated into local climate governance across six Turkish coastal metropolitan municipalities: Istanbul, Bursa, Kocaeli, Izmir, Antalya, and Trabzon. Using a mixed-methods design that combines qualitative policy analysis with a purpose-built Energy Justice Index (EJI), the study evaluates distributive, procedural, and recognition justice provisions within Climate Action Plans (CAPs) and Sustainable Energy and Climate Action Plans (SECAPs). The findings indicate that local climate planning in Türkiye remains predominantly focused on technical and emissions-oriented objectives, offering comparatively limited attention to democratic participation and the needs of vulnerable populations. Quantitatively, the municipalities exhibit a low mean EJI score of 30.2 out of 100 and a mean Technocracy Gap of 15.7 points, alongside a universal absence of monitoring mechanisms for vulnerable groups (R3 = 0 across all cases). Procedural justice emerged as the weakest dimension, while recognition justice was frequently limited to identifying vulnerable groups without corresponding monitoring. The study contributes to energy justice scholarship by developing a municipal-level assessment framework and introducing the Technocracy Gap as an exploratory indicator of the balance between technical climate ambition and justice-oriented governance provisions. It further proposes a rights-based governance approach for more just local climate planning. Full article