Search Results (405)

This study investigates whether financial sector development, and, critically, which dimension of it, is associated with the dual energy transition across eleven Southeast Asian economies over 2004–2020. The empirical strategy combines Pooled OLS with Driscoll–Kraay standard errors, two-way fixed effects, Pooled Mean Group ARDL error correction, and Method-of-Moments quantile regression. The results reveal a stark asymmetry: the Financial Institutions Index is positively and robustly associated with clean cooking access across all estimators. Quantile regressions confirm that the FI association with clean cooking is significant across the entire distribution, with the largest coefficients at the lower quantiles. Sub-sample analysis reveals that the FI–clean cooking relationship is especially pronounced in the frontier Cambodia–Lao PDR–Myanmar–Vietnam–Timor-Leste group, where within-country fixed effects yield a coefficient of 257.54 (p < 0.01). Although these associations do not establish strict causality, the findings are consistent with prioritising deepening institutional banking and digital financial inclusion rather than equity-market development as the primary financial-sector channel associated with lower energy poverty in Southeast Asia, although such policy directions require further micro-level validation. Full article

Green hydrogen—hydrogen produced from renewable electricity—is central to global decarbonization strategies. However, despite their fragile governance, damaged infrastructure, water scarcity, and limited investment security, conflict-affected developing economies remain largely absent from hydrogen research. This study addresses that gap by developing and validating a multi-evidence strategic framework for green-hydrogen (GH₂) adoption in fragile institutional environments, using Palestine as a challenging test case. Methodologically speaking, the framework integrates four evidence streams—barrier prioritization by 45 Palestinian experts using the Analytic Hierarchy Process (AHP); structural modeling of barrier–adoption–sustainability relationships using partial least squares structural equation modeling (PLS-SEM); strategic-pathway ranking using the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS); and an original Sustainable Development Goal (SDG) Contribution Index—externally validated by an independent panel of 120 energy experts across 18 Middle East and North Africa (MENA) countries. Three findings stand out. Firstly, expert perception and structural evidence diverge: technical barriers receive the highest expert weight (56.2%) yet show the weakest structural effect on adoption (β = −0.230), whereas social barriers, weighted lowest by experts (4.8%), rank second in predictive power (β = −0.310). Secondly, Small-Scale Community Production is the most robust deployment pathway, ranked first under every weighting scenario tested. Thirdly, government policy quality acts as a governance multiplier, raising the sustainability returns of adoption by 20.2%, with benefits concentrated in SDGs 7, 13, 8, and 9. Practically speaking, the framework yields seven strategic goals and a phased 2026–2040 roadmap for fragile developing economies. Full article

Choosing the most suitable tractor is a complex and high-stakes decision where technical performance, financial capability, and sustainability considerations must be balanced. However, tractor selection in existing studies lacks objective, sustainability-oriented evaluation frameworks, leaving farmers vulnerable to potentially poor investments with long-term economic, operational, and environmental impacts. Therefore, this research proposes a software-based Decision Support System (DSS) that incorporates objective multi-criteria decision-making (MCDM) models within a management control perspective focused on sustainability and provides a clear, data-driven method for tractor selection for small farmers. Four popular tractor models in Egypt were selected for evaluation based on three criteria related to sustainability: power (C1), purchase price (C2), and availability of maintenance and spare parts (C3). Subsequently, a DSS was implemented using Python, and five MCDM methods—CRITIC, MEREC, Entropy, Standard Deviation (SD), and TOPSIS—were used to select the tractor that best meets sustainability objectives. The findings indicate that tractor T2, which had the lowest purchase price (USD 12,390) and enough power (60 HP), was the best-rated tractor. The impact of each criterion varied by method: C1 was the most important in the Entropy method (0.3657), while C2 was the most important in the CRITIC (0.5552), MEREC (0.3432), and SD (0.5938) weightings. The proposed DSS improves transparency and supports more informed, evidence-based decisions in agricultural mechanization. Overall, the system offers a practical and scalable tool that helps smallholder farmers and policymakers make sustainable tractor choices, contributing to progress toward SDGs 2, 7, 12, and 13. Full article

Sustainable small hydropower (SHP) is central to the clean-energy transition of mountainous Central Asia, yet its long-term reliability depends on a rapidly changing cryosphere. Winter river-ice dynamics—an underappreciated control on run-of-river generation—remain poorly characterized owing to the collapse of in situ hydrometeorological networks since 1991. We use a 112-month Sentinel-1 C-band SAR time series (February 2017–May 2026) over a 5320 km² headwater catchment of the Chu River basin, northern Tien Shan, Kyrgyzstan, to quantify river-ice phenology at 20 m resolution using a per-pixel summer-baseline anomaly approach. Mid-winter (December–February) ice cover declined significantly at −0.51%·yr⁻¹ (p = 0.013; Mann–Kendall p = 0.029), with the 2026 winter recording an unprecedented 2.6–2.8 σ departure from the 2017–2025 climatology. Contrasting the cold 2022 and warm 2026 winters revealed bidirectional climate sensitivity—early breakup versus persistent thin ice—posing distinct SHP hazards. ERA5-Land reanalysis (1992–2026) showed significant winter warming with no precipitation or snowfall trend, indicating thermally forced ice decline. Interpreted within a conceptual Peak Water scenario, this signals a closing window of opportunity for SHP generation, with direct relevance to sustainable water–energy management and the UN Sustainable Development Goals (SDG 7; SDG 13). Our results provide the first decadal, satellite-based evidence of river-ice loss for Central Asian mountain rivers and a transferable monitoring framework to support climate-resilient, sustainable hydropower planning in ungauged basins. Full article

Institutional shuttle fleets with fixed routes and predictable terminal parking are well-suited to charging photovoltaic–battery energy storage system (PV–BESS) charging for sustainable campus mobility. However, siting and sizing are often solved numerically without identifying the physical constraints that determine the optimum. This study develops a sustainability-oriented framework for converting a 10-van diesel shuttle fleet at Rajamangala University of Technology Lanna into an electric fleet supported by distributed PV–BESS charging stations. A centralized one-station layout is compared with a distributed two-station layout, and a closed-form active-constraint sizing rule is derived using Karush–Kuhn–Tucker (KKT) analysis. Results show that the distributed configuration eliminates dead-run travel and provides higher lifecycle value than the centralized case. KKT analysis identifies two binding constraints: the PV rooftop-area limit and the BESS one-day autonomy requirement. Under base-case assumptions, the transition achieves positive lifecycle value and substantial CO₂ reduction relative to the diesel baseline. Monte Carlo analysis confirms financial robustness within the uncertainty ranges, while deterministic stress tests show sensitivity to diesel prices, PV electricity credit values, discount rate, and fleet utilization. The framework provides an interpretable decision-support method for institutional fleet electrification in solar-rich campus settings, contributing to SDGs 7, 11, and 13 through clean-energy adoption, sustainable transportation, and CO₂-emission reduction. Full article

The increasing integration of renewable energy introduces uncertainty in microgrid operation, making effective energy management more challenging. Rolling-horizon optimization is used to address this challenge by enabling periodic decision updates; however, most existing approaches rely on fixed optimization horizons and predetermined update frequencies. When prediction accuracy decay (PAD) is considered in adaptive rolling-horizon approaches, it is represented using a fixed decay value, not an online indicator that compares forecasted and actual renewable generation during operation. This leads to suboptimal re-optimization timing, unnecessary computational effort, excessive battery switching, or delayed corrective actions. To address these limitations, this paper proposes a PAD-driven adaptive rolling horizon (ARH) approach, in which re-optimization is triggered using an online PAD indicator computed from the percentage deviation between forecasted and realized renewable generation data. Re-optimization is activated when the PAD indicator exceeds a predefined threshold, enabling adaptive scheduling updates according to real-time forecasting degradation. The problem is formulated as a robust mixed-integer linear programming (MILP) model of a high renewable penetration microgrid, including battery degradation and switching penalties. The energy self-sufficiency ratio (SSR) is used as a key sustainability performance indicator to assess the extent to which local renewable generation and storage satisfy microgrid demand. The proposed approach is first compared with a fixed rolling-horizon approach using a fixed re-optimization interval of 1 h, where the results show a profit improvement of 10.5%. A sensitivity analysis tested the proposed approach under bounded renewable forecast uncertainty levels up to ±15 and different battery capacities. The results indicate that performance is strongly influenced by forecast accuracy and battery capacity, with higher economic gains under low uncertainty and more conservative operation under high uncertainty. Full article

Aviation contributes approximately 2.4% of global CO₂ emissions and 3.5% of total effective radiative forcing when non-CO₂ effects are included, yet Sustainable Aviation Fuel (SAF) accounts for less than 0.5% of European jet fuel consumption. This paper investigates why the gap between policy ambition and deployment persists, asking (i) how misaligned instruments across ReFuelEU Aviation, RED III, CORSIA, and the UK RTFO impede high-integrity production pathways, and (ii) what convergence mechanisms can reduce fragmentation beyond Hydroprocessed Esters and Fatty Acids (HEFA)-dominated supply. Applying the Multi-Level Perspective framework, the study triangulates comparative policy analysis with a stakeholder survey (n = 45) across SAF producers, airlines, policymakers, and investors. Results identify regulatory fragmentation, capacity constraints, and funding barriers as near-equally weighted obstacles, while disaggregation reveals actor-specific priorities: policymakers emphasise regulatory complexity, airlines emphasise funding, and producers emphasise capacity. Most producers declined to disclose volume projections, interpreted here as strategic ambiguity under regulatory uncertainty. Three convergence mechanisms are proposed: harmonised carbon-intensity registries, standardised book-and-claim accounting, and joint feedstock certification protocols. The findings align aviation decarbonisation with SDGs 7, 9, 12, and 13. Without coherent policy architecture, SAF deployment risks entrenching low-ambition compliance pathways that undermine the EU’s contribution to the 2030 Agenda. Full article

Sustainable agricultural development in rural sub-Saharan Africa increasingly depends on coordinated governance of energy and water resources. Despite the growing deployment of solar photovoltaic water pumping systems (SPVWPS), little is known about how the institutional framework shapes SPVWPS effectiveness for productive agricultural use in rural Tanzania. Drawing on systems thinking concepts, specifically hierarchy, interaction, and interconnectedness, this study analyses the institutional frameworks governing energy and water provision for irrigation and livestock keeping across three rural Tanzanian communities. A mixed-methods design was employed, with qualitative inquiry as the primary mode; 65 household surveys, nine semi-structured interviews with community leaders, SPV developers, and local officials, and seven focus group discussions with farmers and livestock keepers were conducted across the three study areas. National energy and water policy documents, reports, and strategic plans were also reviewed to contextualise the institutional frameworks governing energy and water delivery in rural areas. Findings reveal limited coordination among stakeholders, particularly between NGOs, government agencies (REA, RUWASA, and NIRC), and local communities in the planning and implementation of SPVWP projects. Top-down delivery mechanisms marginalised community feedback, undermining local ownership and limiting the productive use potential of installed systems. This study proposes an integrated institutional framework that combines systems thinking with bottom-up and top-down approaches, explicitly embedding structured feedback mechanisms and aligning stakeholder roles across all governance levels. The framework was validated through interviews with experts in the rural energy and governance field, confirming its practical relevance and applicability to rural energy–water governance. The framework offers actionable guidance for policymakers and development practitioners seeking to strengthen institutional coordination in rural energy–water–agriculture governance, contributing to progress towards SDG 7 and SDG 2 across sub-Saharan Africa. Full article

The building sector is a major contributor to global energy consumption and greenhouse gas emissions, and multi-family residential buildings play an important role in urban decarbonization and the transition toward sustainable cities and societies. This study proposes decarbonization-oriented case studies for selecting heating, ventilation, and domestic hot water systems by integrating environmental, economic, and social criteria aligned with the Sustainable Development Goals (SDGs), particularly SDG 7 and SDG 11. This research compares selected conventional and low-carbon building-level heating, ventilation, and domestic hot water systems, including gas boilers and heat pumps integrated with renewable energy and heat recovery. The evaluation is based on a calculation-based energy performance assessment using a quasi-static monthly heat balance approach, economic indicator analysis, and environmental assessment based on primary, final, and useful energy demand and CO₂ emissions. Cooling energy demand was not included in the assessment because the analyzed scenarios were limited to heating, ventilation, and domestic hot water preparation. Furthermore, the social implications are examined, considering energy affordability, long-term operating costs, and the potential to mitigate energy poverty. The results indicate that low-carbon HVAC systems, particularly heat pump systems integrated with renewable energy sources, significantly reduce CO₂ emissions and primary energy consumption compared to conventional solutions. Although they require a higher initial investment, they can achieve lower life cycle costs over the building’s lifetime. The study concludes that holistic, decarbonization-oriented technologies can support cost-effective, socially responsible pathways toward low-carbon, energy-efficient multi-family residential buildings and sustainable urban development. Full article

Sustainability monitoring has mainly focused on measuring where countries stand today, rather than anticipating where they are headed. This study develops an AI-based forecasting framework to predict national sustainability outcomes and identify countries whose actual paths deviate from predictions. Using 749 World Development Indicators across 184 countries and regions from 2003 to 2022, a Temporal Fusion Transformer (TFT) is developed using data from 2003 to 2017 (training and validation) and evaluated on a held-out 2018 to 2022 test period, with calibrated prediction intervals constructed retrospectively over the test period. Assuming that historical development patterns remain informative over the forecast horizon, the model achieves mean absolute errors of 1.10 for the Sustainable Development Goals Index (SDGI, 0 to 100 scale) and 0.008 for the Human Development Index (HDI, 0 to 1 scale), reducing error by at least 19 percent for SDGI and 60 percent for HDI relative to linear trend and XGBoost baselines. Of 184 countries and regions, 115 (62 percent) are classified as on-track for both indices. Among the rest, 35 show positive SDGI deviations, mostly developing nations in Sub-Saharan Africa and South Asia that are exceeding their forecast trajectories, while 23 show negative HDI deviations concentrated among nations affected by conflict and economic disruption. We find this asymmetric pattern is consistent with a decoupling between goal-level and capability-level sustainability, in which policy-driven SDG indicators can advance while foundational human development in health and income stalls. Our model identifies economic indicators as the dominant predictors of HDI (7 of the top 10), while SDGI prediction draws on a more balanced mix of economic, social, environmental, and institutional indicators. We also find that better governance is associated with lower prediction error for both SDGI (p = 0.004) and HDI (p < 0.001), suggesting that countries and regions with stronger institutions follow more predictable sustainability trajectories. Full article

Landfilling remains the dominant waste disposal method worldwide, particularly in developing countries, posing serious environmental, health, and climate challenges. Inefficient practices, weak regulations, and un-engineered sites contribute to massive greenhouse gas (GHG) emissions and resource loss. Transitioning to a circular economy (CE) offers a transformative path for sustainable waste management. By closing material loops, recovering energy, urban mining, controlling emissions and CE strategies can convert traditional landfills into eco-efficient systems. The integration of artificial intelligence (AI) further enhances this transition, enabling real-time monitoring, predictive management, and optimized resource recovery, thereby maximizing environmental and economic benefits. This review presents a three-level CE framework at micro (individual organizations), meso (industrial networks), and macro (national and international) levels designed to extract maximum value from waste streams and mitigate climate impacts. The proposed strategies demonstrate the potential to drastically reduce GHG emissions, promote clean energy via waste-to-energy routes, and contribute to SDGs 7, 11, 12, 13 and 15. By combining technology, innovation, and strategic management, this work highlights how AI-driven CE approaches can transform landfills from environmental liabilities into engines of sustainability and climate action. In implementing CE strategies at various levels, various challenges including technological, socio-economic, ethical, policy-based, and unintended consequences are encountered which impact sustainability initiatives. This review comprehensively discusses challenges associated with CE implementation and identifies technological advancement, social awareness and data-driven AI/ML-based modeling which could ensure success in circularity and ultimately curb climate change impacts in the long term. Full article

Waste-to-hydrogen (W2H) technology is gaining recognition as a viable pathway for simultaneous waste valorisation and clean energy production in the Gulf Cooperation Council (GCC). However, the water resource implications of hydrogen production pathways in this acutely water-scarce region have received insufficient analytical attention. This paper presents the first systematic comparative analysis of water consumption across grey, blue, green, and waste-to-hydrogen production pathways calibrated to the GCC context, using the ISO 14046 water footprint framework and accounting for the desalination penalty that arises when hydrogen facilities draw on energy-intensive desalinated water. The analysis shows that green hydrogen, widely promoted in GCC national hydrogen strategies, incurs a compound water–energy burden substantially greater than global benchmark figures suggest, with electrolysis requiring 9 to 18 litres of water per kilogram of hydrogen and desalination accounting for 4 to 20 per cent of GCC electricity consumption. In contrast, W2H gasification exhibits considerably more modest water demands at 10 litres per kilogram of hydrogen, with high potential for treated wastewater substitution and co-location with municipal waste infrastructure, positioning it as the most water-compatible near-term hydrogen production pathway for arid GCC economies. Drawing on the water–energy nexus and water governance literature, the paper proposes a Water–Hydrogen Governance Framework comprising four policy pillars: water efficiency standards for hydrogen production facilities, water allocation policy for industrial hydrogen projects, integrated water–energy planning at the national level, and regional GCC coordination on water–hydrogen governance. The framework is aligned with SDGs 6, 7, 13, and 17 and provides a structured and practical tool for GCC governments and development institutions seeking to integrate water security into hydrogen strategy. The findings contribute to the emerging literature on resource-constrained hydrogen deployment and offer a replicable governance model for other arid economies pursuing clean hydrogen transitions. Full article

This paper proposes a contingency-aware, sensitivity-based criterion for the optimal placement of shunt compensation in transmission power systems under N–1 security constraints. Conventional approaches typically rely on post-contingency voltage severity or heuristic optimization techniques, which may fail to capture the system-wide impact of reactive power support during the planning stage. The proposed method integrates contingency severity assessment with a system-wide sensitivity index to support structured and physically interpretable planning decisions. First, a global contingency index is used to identify the most critical operating condition under N–1 scenarios. Based on this condition, a reduced set of candidate buses is selected according to post-contingency voltage magnitudes. These candidates are then ranked using a sensitivity metric defined as the derivative of the contingency index with respect to reactive power injection ($𝜕J/𝜕Q_k$), which quantifies the global effect of local reactive support on system performance. The selected compensation locations are validated through AC optimal power flow simulations, enabling the evaluation of voltage profiles and active power losses under both normal and contingency conditions. The methodology is tested on the IEEE 14-, 30-, and 57-bus transmission systems to assess its scalability and consistency across networks of different sizes. Results show that the bus with the lowest post-contingency voltage is not necessarily the optimal compensation location. Instead, the proposed sensitivity-based criterion identifies buses that provide greater system-wide benefits, including reductions in active power losses and improved voltage recovery. The approach provides a transparent and reproducible planning-oriented decision criterion, supporting improved operational efficiency and aligning with sustainability-oriented objectives in modern power systems. The proposed method provides a reproducible and planning-oriented decision criterion that complements conventional optimization-based approaches. Full article

Achieving sustainable development requires decoupling economic growth from environmental degradation. In this context, this study examines the effects of tourism arrivals on CO₂ intensity and energy intensity, two key indicators of environmental sustainability aligned with SDGs 7 and 13. Panel autoregressive distributed lag (ARDL) and nonlinear ARDL models are employed using a balanced panel of 54 countries over the period 1996–2023. In addition, Wald tests for long-run asymmetry, dynamic multiplier analysis, and Dumitrescu–Hurlin causality tests are applied. The results confirm the existence of stable long-run relationships between tourism arrivals and both CO₂ intensity and energy intensity. In the symmetric framework, tourism growth is associated with significant long-run reductions in CO₂ and energy intensity, while short-run effects are negative and significant only for CO₂ intensity. In the asymmetric framework, positive tourism shocks generate stronger and more persistent reductions in both intensity measures, whereas negative shocks lead to weaker environmental efficiency gains. Moreover, the Wald test shows the existence of long-run asymmetry between positive and negative tourism shocks. In addition, the dynamic multiplier analysis confirms that environmental intensity adjusts gradually over time following tourism shocks. Finally, Dumitrescu–Hurlin causality tests indicate bidirectional Granger causality relationships between tourism arrivals and environmental intensity indicators. The findings are robust to dynamic endogeneity, the COVID-19 shock, and country heterogeneity. Overall, the findings indicate that tourism arrivals contribute to lowering long-term environmental intensity, consistent with relative decoupling and the goals of sustainable tourism development. Full article

The attribution of systemic financial stress to specific market sectors requires metrics that are faithful to the model’s computations, statistically consistent, and connected to a physically meaningful measure of directed information flow. This paper addresses all three requirements through information geometry, contributing to SDGs 7, 8, 9, and 17 through an entropic causal chain linking energy infrastructure failure to financial market stress. We conjecture and empirically verify the Entropy–Saliency Equivalence: Metabolic Saliency is an asymptotically unbiased estimator of the local Kullback–Leibler divergence between stressed and resting sector return distributions, with bias decaying at a parametric rate under Gaussian regularity conditions. The finite-sample bias–variance decomposition of the Kraskov–Stögbauer–Grassberger transfer entropy estimator is derived, establishing a minimax-optimal convergence rate. A novel metric, the Spatio-Temporal Information Flux (STIF), quantifies directed inter-sector stress transmission in bits per trading day, providing a bootstrap-calibrated audit trail aligned with the South African Financial Sector Regulation Act and MiFID II. Empirical validation on the JSE canonical panel (87 securities, 2857 trading days, 2015–2026) with Eskom load-shedding stages as exogenous stress injectors confirms the equivalence ($R^2=0.810$, $\widehat{\rho }=0.90$), with walk-forward $R^2=0.789$ and placebo $R^2=0.081$ ruling out estimation artefacts. The energy sector is identified as the primary stress transmitter during Stage 4+ Eskom events (STIF rising from 0.14 to 0.43 bits/day, directional asymmetry ratio 4.7). Robustness checks confirm stability across non-Gaussian securities and rolling transfer entropy windows. Full article