Limited access to energy in rural areas undermines the quality of life and hinders the short-term economic growth in a community. It is therefore essential to identify the evolution of technological tools, the social factors, and the current development in the forms of energy commercialization. Using a bibliometric approach and systematic review, this study aimed to conduct case studies in rural communities that implemented decentralized and sustainable energy systems. The methodology involved: i) A bibliometric analysis under the mapping of co-occurrence by keywords and trend topics using scientific databases like Scopus and Web of Science (WoS); ii) The Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) method; and iii) A systematic review using the Mixed Methods Appraisal Tool (MMAT). A total of 259 articles from rural communities were analyzed from year 1979 to 2024 to prove that biomass, prevailing throughout history, is the most feasible source of energy generated during implementation; the analysis also provided a better understanding of its utilization mechanisms. Bioenergy accounted for 36% of the scientific contribution, primarily out of its widespread availability and the diversity of methods for harnessing energy from this resource. The energy transition of the last two decades was reflected in renewable energy sources (29%), energy mix (18%), and solar energy (9%), relegating conventional energy to only 2%. This study discovered that the research areas of hydropower and wind energy were influenced by the feasibility and social acceptability of their respective projects. Meanwhile, the use of blockchain, exerting an impact on the traceability of decentralized energy trading, advocated a proposal for change in current markets to strengthen the sustainability of projects, streamline processes, and back up information. To sum up, this study examined the utilization and implementation of renewable energy in decentralized energy projects, thereby contributing to energy autonomy and optimized resource utilization.
There was incomplete literature on the threshold effect of interest rates on investment, particularly investment by source of capital. This study investigated key macroeconomic factors, such as lending interest rates, inflation, exchange rates, growth in gross domestic product (GDP) and money supply, together with their impact on the proliferation in public capital, private capital, foreign direct investment, and total investment in Vietnam. Threshold regression (TR) was applied to analyze secondary data spanning from year 1996 to 2022; it was discovered that the threshold of interest rate was significant only for the public investment model across four funding sources. Although the threshold test of interest rates was not statistically significant for three of the funding sources, the threshold values of interest rate influenced investment in ownership ranked from low to high, i.e., foreign direct investment, public investment, total investment, and lastly private investment. The gap in the literature and the findings in this study highlighted the response of investment with different ownership to macroeconomic changes, especially in emerging economies like Vietnam. The results illustrated that lending interest rates and inflation negatively impacted private investment, which was subject to the effect of monetary tightening. However, these factors had minimal effects on total investment and foreign direct investment. Public investment and foreign direct investment are primarily influenced by fiscal policies. As regards private investment, it reacts more strongly to changes in exchange rate than foreign direct investment; policy adjustments are therefore recommended to weather the periods of economic instability and high interest rates.
Climate change poses severe challenges to small-scale fisheries, which require critical adaptation strategies. This study developed a model of climate change adaptation among small-scale fishermen in Bengkulu Province, Indonesia, using a framework that links poverty, livelihood vulnerability, and adaptive capacity. This study contributes novel empirical evidence on how these factors interact to shape adaptive behavior in small-scale fisheries within a developing country context. Data was collected from a survey of 700 fishing households selected by quota sampling. The direct and indirect relationships among socioeconomic variables and adaptation strategies were examined using path analysis in Statistical Package for the Social Sciences (SPSS) and Analysis of Moment Structures (AMOS). The findings revealed that poverty had a significantly adverse effect on the adaptive capacity of fishermen, limiting their capability to respond effectively to climate stressors. Consequently, a majority of fishermen relied on low-cost and easily implemented strategies, such as adjusting fishing times and shifting fishing grounds. Fishing experience, vessel capacity, fishing distance, and the type of fishing gear, in contrast, showed significantly positive effects on adaptation. These results underscore that economic constraints weaken adaptive capacity, while technical assets and practical knowledge enhance resilience. The policy implications highlighted the imperative to strengthen fishermen’s institutions, update fleets, establish cooperatives, diversify fishing gear, and provide accessible digital climate information services. Such governmental interventions are crucial for enhancing adaptive capacity, supporting the sustainable management of fisheries, and improving the economic resilience of coastal communities.
Urban building energy modeling (UBEM) is essential for understanding energy consumption and developing sustainable policies at the city scale. However, current UBEM approaches overlook spatial and temporal interactions and lack generalizability across diverse urban contexts. This study introduces a hybrid framework that integrates physics-based simulations with machine learning based residual learning to enhance prediction accuracy using real energy consumption data. The methodology incorporates GIS-supported data collection and processing. Multiple ML models were applied to predict monthly consumption and validate their performance. Meanwhile, a physics-based model is used to simulate hourly energy consumption. The best performing ML model was later used for daily residual learning to calibrate physics-based simulation outputs. The framework was tested on residential buildings connected to the District Heating Network in Turin, Italy. Results showed LGBM achieved the highest performance with a R2 of 0.883 and a MAPE below 15% in most months. Residual learning reduced daily prediction error in 80% of cases, with up to 75% improvement in extreme cases. After model calibration, 65% of buildings achieved a daily MAPE below 30%, and 55% fell below 20%, demonstrating consistent error reduction across varied building types and consumption levels. This confirms the effectiveness of the hybrid approach in enhancing accuracy and reliability at the urban scale.
This study investigated sustainable tourism practices in the aviation sector by assessing how passenger awareness and carbon offset pricing could be integrated into travel behaviors. With the International Civil Aviation Organization (ICAO) Carbon Emissions Calculator, the analysis covered five Thai Airways routes from Thailand to Shanghai, Guangzhou, Beijing, Kunming, and Chengdu. The calculated offset costs per passenger ranged between 6.55 and 36.99 CNY, which were derived by applying a benchmark of 95 CNY/tCO2e (≈ 445 THB) to per-passenger emissions. These proposed offset contributions were not obtained from evidence of direct survey on the offset cost per passenger. On the other hand, the benchmark selected was based on the estimate in the international literature, anticipated price trends, and the goal of encouraging broader participation. The findings prioritized the importance of consistent terminology, explicit standards, and collaborative policies between public and private stakeholders to strengthen travelers’ engagement in carbon offset programs.
Urban air pollution remains a persistent challenge in the Global South, where rapid urbanization, limited monitoring infrastructure, and weak regulatory frameworks hinder effective environmental governance. In Lima, Peru—one of the most polluted capitals in Latin America—elevated PM2.5 and PM10 concentrations continue to pose serious threats to public health and sustainable urban development. Traditional Air Quality Index (AQIs), such as the U.S. EPA standard, often struggle to account for data uncertainty, pollutant interactions, and spatial heterogeneity. To address these gaps, this study introduces a novel AQI based on grey systems theory, applying a grey clustering framework enhanced with center-point triangular whitenization weight functions (CTWF). The model was specifically designed to handle ambiguous data and overlapping pollution categories. It was applied to daily PM2.5 and PM10 data from nine monitoring stations across metropolitan Lima, with validation conducted against both Peru’s national air quality standards and the U.S. EPA AQI. Results showed that the proposed index outperformed conventional methods under uncertain conditions, revealing critical spatial disparities often missed by traditional models. Beyond diagnostic accuracy, the index offers a scalable and transferable tool for urban planners and decision-makers to support targeted interventions, inform policy development, and advance Sustainable Development Goals—specifically SDG 3 (Good Health and Well-Being) and SDG 11 (Sustainable Cities and Communities).
Overurbanization poses environmental challenges that threaten human health and biodiversity. Nature-Based Solutions (NBS) enhance urban livability, restore biodiversity, and provide vital Ecosystem Services (ES), such as mitigating the Urban Heat Island (UHI) effect. This study evaluates environmental monitoring at Marco Biagi Park (Reggio Emilia, Italy) as part of the Life City AdapT3 project. Following the introduction of micro-forests, rural edges, tree rows, and a wetland, data were collected to assess local climate mitigation and carbon storage. Microclimatic effects were analyzed using satellite images (Landsat 8) and on-site measurements. Between 2021-2024, summer Land Surface Temperature (LST) decreased in post-intervention period by 2.1℃. Air temperature in urban forest areas averaged 1.2℃ lower, while humidity increased by 10% compared to built-up areas. Using the i-Tree model, it was estimated that Marco Biagi Park stored 332.20 kg of carbon in 2024 and 825.20 kg in 2025—representing a 148.4% increase in just one year. Species of the Quercus genus, Prunus avium and Tilia platyphyllos contributed 58.26% to this carbon storage in 2025. Findings highlight NBS effectiveness in improving urban microclimates and carbon sequestration, reinforcing their role in sustainable city planning.
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