Search Results (112)

In Japan’s hilly and mountainous areas, which cover over 60% of the national land area, issues such as population outflow, aging, and regional decline are intensifying. This study explored sustainable decarbonization pathways by examining two representative regions (Maniwa City and Hidakagawa Town), while accounting for diverse regional characteristics. A bottom-up approach was adopted to calculate energy consumption and CO₂ emissions within residential and transportation sectors. Six future scenarios were developed to evaluate emission trends and countermeasure effectiveness in different regions. The key findings are as follows: (1) in the study areas, complex regional issues have resulted in relatively high current levels of CO₂ emissions in these sectors, and conditions may worsen without intervention; (2) if the current trends continue, per-capita CO₂ emissions in both regions are projected to decrease by only around 40% by 2050 compared to 2020 levels; (3) under enhanced countermeasure scenarios, CO₂ emissions could be reduced by >99%, indicating that regional decarbonization is achievable. This study provides reliable information for designing localized sustainability strategies in small-scale, under-researched areas, while highlighting the need for region-specific countermeasures. Furthermore, the findings contribute to the realization of multiple Sustainable Development Goals (SDGs), particularly goals 7, 11, and 13. Full article

Progressive research on reducing engine emissions is highly valued due to the emissions’ significant environmental and health impacts. This comprehensive comparative study examines the catalytic efficiency of manganese (Mn) and cerium silica (Ce-Si) synthesis catalyst-based molds in a diesel engine using a selective catalytic reduction (SCR) technique with diesel and diesel–plastic oil blend (DPB) (B50). In addition to Fourier transform infrared spectroscopy (FTIR) studies, X-ray diffraction (XRD), scanning electron microscopy (SEM), and the Brunauer–Emmett–Teller (BET) method are utilized to characterize the produced molds before and after exhaust gas passes. The Ce-Si-based mold demonstrates superior redox capacity, better adsorption capacity, and better thermal stability, attributed to enhanced oxygen storage and structural integrity compared to the Mn-based mold. Under minimum load conditions, nitrogen oxide (NO) reduction efficiency peaks at 80.70% for the Ce-Si-based mold in the SCR treatment with DPB fuel. Additionally, significant reductions of 86.84%, 65.75%, and 88.88% in hydrocarbon (HC), carbon monoxide (CO), and smoke emissions, respectively, are achieved in the SCR treatment under optimized conditions. Despite a wide temperature range, Ce-Si-based mold promotes high surface area and superior gas diffusion properties. Overall, the Ce-Si-based mold provides efficient emission control in diesel engines, which paves a path for developing better environmental sustainability. The outcomes contribute to advancing environmental sustainability by supporting the achievement of SDGs 7, 11, and 13. Full article

Petroleum-based insulating liquids have traditionally been used in the electrical industry for cooling and insulation. However, their environmental drawbacks, such as non-biodegradability and ecological risks, have led to increasing regulatory restrictions. As a sustainable alternative, vegetable-based insulating liquids have gained attention due to their biodegradability, non-toxicity to aquatic and terrestrial ecosystems, and lower carbon emissions. Adopting vegetable-based insulating liquids also aligns with United Nations Sustainable Development Goals (SDGs) 7 and 13, which focus on cleaner energy sources and reducing carbon emissions. Despite these benefits, most commercially available vegetable-based insulating liquids are derived from edible seed oils, raising concerns about food security and the environmental footprint of large-scale agricultural production, which contributes to greenhouse gas emissions. In recent years, waste cooking oils (WCOs) have emerged as a promising resource for industrial applications through waste-to-value conversion processes. However, their potential as transformer insulating liquids remains largely unexplored due to limited research and available data. This review explores the feasibility of utilizing waste cooking oils as green transformer insulating liquids. It examines the conversion and purification processes required to enhance their suitability for insulation applications, evaluates their dielectric and thermal performance, and assesses their potential implementation in transformers based on existing literature. The objective is to provide a comprehensive assessment of waste cooking oil as an alternative insulating liquid, highlight key challenges associated with its adoption, and outline future research directions to optimize its properties for high-voltage transformer applications. Full article

Small island developing states (SIDS) face a dual constraint of “environmental vulnerability and energy dependence” in the context of climate change. How to achieve just energy transitions has become a core proposition for SIDS to address. This paper focuses on how SIDS can advance Sustainable Development Goal (SDG) 7 (affordable and clean energy) and Sustainable Development Goal 13 (climate action) through UNCLOS–Paris Agreement integration in energy transitions. Grounded in the theoretical framework of the Multidimensional Vulnerability Index (MVI), this research aims to construct a comprehensive analytical system that systematically examines the energy transition challenges facing SIDS and provide multi-level energy transition solutions spanning from international to domestic contexts for climate-vulnerable SIDS. The research findings reveal that SIDS face a structural predicament of “high vulnerability–low resilience” and the triple challenge of “energy–climate–development”. International climate finance is severely mismatched with the degree of vulnerability in SIDS; the United Nations Convention on the Law of the Sea (UNCLOS) and the Paris Agreement lack institutional synergy and fail to adequately support marine renewable energy development in SIDS. In response to these challenges, this study proposes multi-level solutions to promote the synergistic achievement of SDG 7 and SDG 13: at the international level, improve climate finance rules, innovate financing mechanisms, strengthen technological cooperation, and integrate relevant international legal framework; at the domestic level, optimize the layout of marine renewable energy development, construct sustainable investment ecosystems, and strengthen environmental scientific research and local data governance. Full article

Modular and prefabricated buildings are advantageous in terms of construction, transport, energy efficiency, fixed costs, and the use of environmentally friendly materials. Our research aims to analyze, evaluate, and optimize a lightweight perimeter structure with an integrated active thermal protection (ATP). We have developed a mathematical–physical model of a wall fragment, in which we have analyzed several variants through a parametric study. ATP in the energy function of a thermal barrier (TB) represents a high potential for energy savings. Cold tap water (an average temperature of +6 °C, thermal untreated) in the ATP layer of the investigated building structure increases its thermal resistance by up to 27.24%. The TB’s mean temperature can be thermally adjusted to a level comparable to the heated space (e.g., +20 °C). For the fragment under consideration, optimizing the axial distance between the pipes (in the ATP layer) and the insulation thickness (using computer simulation) reveals that a pipe distance of 150 mm and an insulation thickness of 100 mm are the most suitable. ATP has significant potential in the design of sustainable, resilient, and climate-adaptive buildings, thereby meeting the UN SDGs, in particular the Sustainable Development Goal 7 ‘Affordable and Clean Energy’ and the Goal 13 ‘Climate Action’. Full article

This study investigates how various renewable energy technologies influence national carbon intensity (CO₂ emissions per unit of GDP) across 184 countries over the period 2000–2020. In the context of Sustainable Development Goals (SDG 7 and SDG 13) and the post-Paris-Agreement policy landscape, it addresses the gap in understanding technology-specific decarbonization effects and the role of governance. A dynamic panel framework employing the Dynamic Common Correlated Effects (DCCE) estimator accounts for cross-sectional dependence and temporal persistence, while disaggregating total renewables into hydropower, wind, solar, and geothermal generation. Environmental regulation is incorporated as a moderating variable using the World Bank’s Regulatory Quality index. Empirical results demonstrate that higher renewable generation is associated with statistically significant reductions in carbon intensity, with hydropower showing the most consistent negative effect across all income groups. Solar and geothermal technologies yield substantial carbon-reducing impacts in lower-middle-income settings once supportive policies are in place. Wind exhibits heterogeneous outcomes: positive or insignificant effects in some high- and upper-middle-income panels prior to 2015, shifting toward neutral or negative after more stringent regulation. Interaction terms reveal that stronger regulatory environments amplify renewable-driven decarbonization, particularly for intermittent sources such as wind and solar. Key contributions include (1) a comprehensive global assessment of four disaggregated renewable technologies; (2) integration of regulatory quality into decarbonization pathways, illustrating post-2015 policy moderations; and (3) methodological advancement through a large-sample DCCE approach that captures unobserved common shocks and heterogeneous country dynamics. These findings inform targeted policy measures—such as prioritizing hydropower where feasible, strengthening regulatory frameworks, and tailoring technology strategies—to accelerate low-carbon energy transitions worldwide. Full article

This study examines the effects of artificial intelligence investments, green financing, government stability, and institutional quality on renewable energy consumption from a multidimensional perspective. Using panel data for the period 2014–2023, 15 leading countries in the field of green financing were included in the analysis. The Cross-Sectionally Augmented Autoregressive Distributed Lag (CS-ARDL) method was preferred in the empirical analysis; robustness tests were conducted with Fully Modified OLS (FMOLS) and Dynamic OLS (DOLS) estimators to assess the reliability of the findings. According to the findings, artificial intelligence investments have a significant and positive impact on renewable energy consumption in both the short and long term. Similarly, green financing contributes strongly and statistically significantly by enhancing the feasibility of clean energy projects. Furthermore, stable governments and the effective functioning of institutional structures support this process; both factors are observed to have a positive effect on renewable energy consumption. This study offers concrete policy recommendations in line with the United Nations sustainable development goals (SDGs) 7, 9, 13, and 16. Full article

Sustainable cropland management is essential in maintaining national food security. In the black soil regions of China, which are key areas for commercial grain production, sustainable land use must be achieved urgently. To address the absence of integrated, large-scale, remote sensing-based sustainability frameworks in China’s black soil zones, we developed a comprehensive evaluation system with 13 indicators from four dimensions: the soil capacity, the natural capacity, the management level, and crop productivity. With this system and the entropy weight method, we systematically analyzed the spatiotemporal patterns of cropland sustainability in the selected black soil regions from 2010 to 2020. Additionally, a diagnostic model was applied to identify the key limiting factors constraining improvements in cropland sustainability. The results revealed that cropland sustainability in Heilongjiang Province has increased by 7% over the past decade, largely in the central and northeastern regions of the study area, with notable gains in soil capacity (+15.6%), crop productivity (+22.4%), and the management level (+4.8%). While the natural geographical characteristics show no obvious improvement in the overall score, they display significant spatial heterogeneity (with better conditions in the central/eastern regions than in the west). Sustainability increased the most in sloping dry farmland and paddy fields, followed by plain dry farmland and arid windy farmland areas. The soil organic carbon content and effective irrigation amount were the main obstacles affecting improvements in cropland sustainability in black soil regions. Promoting the implementation of technical models, strengthening investment in cropland infrastructure, and enhancing farmer engagement in black soil conservation are essential in ensuring long-term cropland sustainability. These findings provide a solid foundation for sustainable agricultural development, contributing to global food security and aligning with SDG 2 (zero hunger). Full article

Electric vehicles (EVs) are central to the decarbonisation of transport systems and achievement of the Sustainable Development Goals (such as SDGs 7 and 13, affordable and clean energy and climate action, respectively). This study adopts a hybrid methodological framework, merging panel econometric models with machine learning (ML), to examine the drivers of EV adoption across Africa, China, and the European Union between 2015 and 2023. We analyse the influence of charging station density (CSD), GDP per capita, renewable energy share (RES), urbanisation, and electricity access using both first-difference and fixed-effects models for causal insight and Random Forest, XGBoost, and neural network algorithms for predictive analytics. While CSD emerges as the most significant driver across models, results reveal a paradox—GDP per capita demonstrates a negative relationship with EV adoption in econometric models yet ranks among the top predictive features in ML models. This divergence highlights the limitations of assuming linear causality in high-income settings and underscores the value of combining causal and predictive approaches. SHAP and PCA analyses further illustrate regional disparities, with Africa showing low feasibility scores due to infrastructure and grid limitations. Sub-regional case studies (Kenya, South Africa, Morocco, Nigeria) emphasise the need for tailored, integrated policies that address both energy infrastructure and transport equity. Findings highlight the value of combining interpretable models with predictive algorithms to inform inclusive and region-specific EV transition strategies. Full article

Understanding how sectoral CO₂ emissions shape sustainable development outcomes is essential for designing effective energy and economic strategies within the European Union (EU). This study presents a multidimensional analysis of CO₂ emissions, the contributions of individual sectors, and their connections to the Sustainable Development Goals (SDGs). Using Bayesian network analysis, the research identifies significant interdependencies between emission reductions and progress in sustainable development, highlighting the complex relationship between energy transition, economic growth, and social justice. The findings show that total CO₂ emissions in the EU have decreased since 1990; however, the rate of reduction varies across sectors and member states. The most substantial decreases have been recorded in the energy sector, while industrial processes and agriculture show slower progress. Economic crises, such as the 2008 financial collapse and the COVID-19 pandemic, have led to temporary declines in emissions; however, lasting achievements in sustainability require structural transformations rather than short-term disruptions. The Bayesian model reveals strong connections between emission reductions and progress on clean energy (SDG 7), responsible consumption (SDG 12), and climate action (SDG 13), while also indicating indirect impacts on economic growth (SDG 8) and social equity. This highlights the importance of integrated policymaking to maximise the benefits of sustainable development. This study provides a data-driven foundation for enhancing EU climate strategies, ensuring that emission reductions support environmental goals, economic resilience, and social well-being. Full article

The rapid development of renewable energy necessitates advanced solutions that address the volatility and complexity of modern power systems. This study proposes an AI-driven integrated optimization framework for a Virtual Power Plant (VPP) and Smart Grid, aiming to enhance renewable energy utilization, reduce grid losses, and improve economic dispatch efficiency. Leveraging deep reinforcement learning (DRL), this framework dynamically adapts to real-time grid conditions, optimizing multi-objective functions such as power loss minimization and renewable energy maximization. This research incorporates data-driven decision-making, blockchain for secure transactions, and transformer architectures for predictive analytics, ensuring its scalability and adaptability. Experimental validation using real-world data from the Shenzhen VPP demonstrates a 15% reduction in grid losses and a 22% increase in renewable energy utilization compared to traditional methods. This study addresses critical limitations in existing research, such as data rigidity and privacy risks, by introducing federated learning and anonymization techniques. By bridging theoretical innovation with practical application, this work contributes to the United Nations’ Sustainable Development Goals (SDGs) 7 and 13, offering a robust pathway toward a sustainable and intelligent energy future. The findings highlight the transformative potential of AI in power systems, providing actionable insights for policymakers and industry stakeholders. Full article

This study analyzes the intersection of energy, urban planning, decarbonization, and sustainability as a central axis for addressing urban development challenges in Latin America. A systematic search of the Scopus database selected 509 articles published between 2019 and 2024. The documents were thematically classified into urban planning (274), energy (79), and decarbonization (147), identifying only 10 studies that simultaneously integrate at least two of these dimensions in Latin American contexts. While this sample of 10 articles does not allow for generalizations about the region, the article selects representative cases to contextualize the type of research conducted, rather than offering extrapolable results. An exploratory multivariate analysis was applied to identify patterns, thematic gaps, and convergence trends, including Principal Component Analysis (PCA) to reduce the dimensionality of the set of key concepts and Hierarchical Clustering (HCC) to group terms according to their semantic proximity. These results are complemented by co-occurrence and thematic concentration maps generated from keywords extracted from the selected articles. The findings reveal a low level of integration among the topics analyzed, justifying the need to establish new lines of interdisciplinary research. The study proposes a replicable analytical tool that guides future regional research and contributes to the achievement of the Sustainable Development Goals, especially SDG 7 (Affordable and Clean Energy), SDG 11 (Sustainable Cities and Communities), and SDG 13 (Climate Action). Full article

This paper introduces the Integrated Model for Sustainable Development (IMSD), a theory-driven governance framework that embeds Corporate Diversity Responsibility (CDR) into climate and energy policy to advance systemic equity, institutional resilience, and inclusive innovation. Grounded in Institutional Theory, the Resource-Based View (RBV), and Intersectionality Theory, IMSD unifies fragmented sustainability efforts across five pillars: Climate Sustainability, Social Sustainability (CDR), Governance Integration, Collaborative Partnerships, and Implementation and Monitoring. Aligned with SDGs 7, 10, and 13, IMSD operationalizes inclusive leadership, anticipatory adaptation, and equity-centered decision-making. It addresses the compounded climate vulnerabilities faced by women and marginalized groups in the Global South, integrating insights from Indigenous resilience and intersectional adaptation strategies. Unlike conventional CSR or ESG models, IMSD institutionalizes diversity as a strategic asset and governance principle. It transforms DEIB from symbolic compliance into a catalyst for ethical leadership, legitimacy, and performance in turbulent environments. The model’s modular structure supports cross-sector scalability, making it a practical tool for organizations seeking to align ESG mandates with climate justice and inclusive innovation. Future empirical validation of the IMSD framework across diverse governance settings will further strengthen its applicability and global relevance. IMSD represents a paradigm shift in sustainability governance—bridging climate action and social equity through theory-based leadership and systemic institutional transformation. Full article

This review provides a comprehensive and multidisciplinary overview of recent advancements in solar tracking systems (STSs) aimed at improving the efficiency and adaptability of photovoltaic (PV) technologies. The study systematically classifies solar trackers based on tracking axes (fixed, single-axis, and dual-axis), drive mechanisms (active, passive, semi-passive, manual, and chronological), and control strategies (open-loop, closed-loop, hybrid, and AI-based). Fixed-tilt PV systems serve as a baseline, with single-axis trackers achieving 20–35% higher energy yield, and dual-axis trackers offering energy gains ranging from 30% to 45% depending on geographic and climatic conditions. In particular, dual-axis systems outperform others in high-latitude and equatorial regions due to their ability to follow both azimuth and elevation angles throughout the year. Sensor technologies such as LDRs, UV sensors, and fiber-optic sensors are compared in terms of precision and environmental adaptability, while microcontroller platforms—including Arduino, ATmega, and PLC-based controllers—are evaluated for their scalability and application scope. Intelligent tracking systems, especially those leveraging machine learning and predictive analytics, demonstrate additional energy gains up to 7.83% under cloudy conditions compared to conventional algorithms. The review also emphasizes adaptive tracking strategies for backtracking, high-latitude conditions, and cloudy weather, alongside emerging applications in agrivoltaics, where solar tracking not only enhances energy capture but also improves shading control, crop productivity, and rainwater distribution. The findings underscore the importance of selecting appropriate tracking strategies based on site-specific factors, economic constraints, and climatic conditions, while highlighting the central role of solar tracking technologies in achieving greater solar penetration and supporting global sustainability goals, particularly SDG 7 (Affordable and Clean Energy) and SDG 13 (Climate Action). Full article

In an integrated gasification combined cycle (IGCC), a gasification process produces a gas stream from a solid fuel, such as coal or biomass. This gas (syngas or synthesis gas) resulting from the gasification process contains carbon monoxide, molecular hydrogen, and carbon dioxide (other gaseous components may also be present depending on the gasified solid fuel and the gasifying agent). Separating hydrogen from this syngas stream has advantages. One of the methods to separate hydrogen from syngas is selective permeation through a palladium-based metal membrane. This separation process is complicated as it depends nonlinearly on various variables. Thus, it is desirable to develop a simplified reduced-order model (ROM) that can rapidly estimate the separation performance under various operational conditions, as a preliminary stage of computer-aided engineering (CAE) in chemical processes and sustainable industrial operations. To fill this gap, we present here a proposed reduced-order model (ROM) procedure for a one-dimensional steady plug-flow reactor (PFR) and use it to investigate the performance of a membrane reactor (MR), for hydrogen separation from syngas that may be produced in an integrated gasification combined cycle (IGCC). In the proposed model, syngas (a feed stream) enters the membrane reactor from one side into a retentate zone, while nitrogen (a sweep stream) enters the membrane reactor from the opposite side into a neighbor permeate zone. The two zones are separated by permeable palladium membrane surfaces that are selectively permeable to hydrogen. After analyzing the hydrogen permeation profile in a base case (300 °C uniform temperature, 40 atm absolute retentate pressure, and 20 atm absolute permeate pressure), the temperature of the module, the retentate-side pressure, and the permeate-side pressure are varied individually and their influence on the permeation performance is investigated. In all the simulation cases, fixed targets of 95% hydrogen recovery and 40% mole-fraction of hydrogen at the permeate exit are demanded. The module length is allowed to change in order to satisfy these targets. Other dependent permeation-performance variables that are investigated include the logarithmic mean pressure-square-root difference, the hydrogen apparent permeance, and the efficiency factor of the hydrogen permeation. The contributions of our study are linked to the fields of membrane applications, hydrogen production, gasification, analytical modeling, and numerical analysis. In addition to the proposed reduced-order model for hydrogen separation, we present various linear and nonlinear regression models derived from the obtained results. This work gives general insights into hydrogen permeation via palladium membranes in a hydrogen membrane reactor (MR). For example, the temperature is the most effective factor to improve the permeation performance. Increasing the absolute retentate pressure from the base value of 40 atm to 120 atm results in a proportional gain in the permeated hydrogen mass flux, with about 0.05 kg/m².h gained per 1 atm increase in the retentate pressure, while decreasing the absolute permeate pressure from the base value of 20 bar to 0.2 bar causes the hydrogen mass flux to increase exponentially from 1.15 kg/m².h. to 5.11 kg/m².h. This study is linked with the United Nations Sustainable Development Goal (SDG) numbers 7, 9, 11, and 13. Full article