This article analyses the energy transition trajectories of 25 African countries, combining machine learning and econometric methods. Initially, a Dynamic Time Warping (DTW)-based partitioning approach is used to divide countries into three groups with distinct socio-economic and energy profiles. The Light Gradient Boosting Machine (LightGBM) model is then used to evaluate the significance of macroeconomic and structural variables. A Panel Autoregressive Distributed Lag (Panel ARDL) model is then applied to each group to examine the short- and long-term relationships between macroeconomic and structural variables and renewable energy consumption. The results demonstrate consistency in the importance of variables identified by a Long Short-Term Memory (LSTM) model and their long-term effects within the Panel ARDL framework, thereby showcasing the robustness of the approach. The analysis reveals different dynamics: the first group is hindered by macroeconomic vulnerabilities such as financial instability and high debt, whereas the second group enjoys more favourable conditions. These results provide a basis for developing policies tailored to the specific contexts of each group to accelerate the energy transition in Africa. Thus, the study contributes to a better understanding of the key factors and helps to guide sustainable development strategies on the continent.

The catastrophic 2021 eruption of Mount Semeru severely degraded local ecosystems, necessitating rapid pioneer vegetation establishment to restore landscape stability. While conventional satellite imagery often obscures critical micro-scale ecological interactions, ultra-high-resolution unmanned aerial vehicles (UAVs) provide an unprecedented tool for assessing post-disaster resilience. This study aimed to quantify the multitemporal recovery rate and model the micro-topographic constraints dictating the spatial heterogeneity of pioneer colonization. A 127.08-ha primary lahar corridor within Volcanic Hazard Zone III was mapped using UAVs (2.7 cm/pixel) over a three-year period (2022, 2023, 2025). Vegetation cover was extracted using the Excess Green (ExG) index and automated Otsu thresholding. The methodology was rigorously validated using 300 multitemporal ground-truth points, yielding an overall accuracy of 93.00%, an F1-score of 0.933, and a Cohen’s Kappa coefficient of 0.860, confirming exceptional classification reliability. Post-classification spatial analysis revealed a net positive vegetation expansion rate of +1.88 ha/year, indicating a successful transition from initial colonization to ecological stabilization. However, recovery was profoundly heterogeneous, governed entirely by geomorphological constraints rather than uniform macro-climatic factors. Population-based zonal statistics identified significant ecological preferences for higher peripheral elevations (mean 3,081.0 m), gentler slopes acting as natural seed traps (45.5°), and Southwest-facing aspects that provide essential micro-climatic buffering against equatorial desiccation. These findings offer a strategic paradigm shift for environmental management, recommending that artificial restoration and erosion control interventions in active stratovolcanoes be precisely targeted at these highly resilient micro-topographical hotspots to accelerate the formation of a natural biological shield.

This study assesses the degree to which adaptive governance principles are incorporated into four regional development planning documents of North Sumatra Province, aiming to manage the increasing energy crises and climate threats. Four aspects of adaptive governance are included in the analysis using a document-based evaluation framework: (1) reactivity to uncertainty; (2) scenario-based projection; (3) flexibility of development indicators; and (4) institutional coordinating capacity. The 2025–2045 Regional Long-Term Development Plan (Rencana Pembangunan Jangka Panjang Daerah, RPJPD), the 2025–2029 Technocratic Draft of the Regional Medium-Term Development Plan (Rencana Pembangunan Jangka Menengah Daerah, RPJMD), the 2024–2026 Regional Development Plan (Rencana Pembangunan Daerah, RPD), and the Development Planning Agency’s 2024–2026 Strategic Plan are among the documents examined. Document content was categorized using a qualitative thematic coding process in relation to the assessment matrix. The findings show that planning is still technocratic and linear, lacking institutional tools to facilitate adaptive decision-making, risk-responsive indicators, and alternative scenarios. This disparity indicates that the planning tools required to anticipate crisis-driven disruptions in the energy and environmental sectors are not aligned with the sustainability agenda. In order to improve provincial readiness and hasten the achievement of Sustainable Development Goals (SDGs) 7, 13, and 16, the study suggests integrating risk-based planning, dynamic performance indicators, and cross-sector institutional platforms.

In this research, both Synthetic Aperture Radar (SAR) and optical satellite data are used independently to evaluate the urban expansion, thermal change (Land Surface Temperature (LST)/Urban Heat Island (UHI)), and particulate air quality (PM$_{10}$) ten-year forecasts for Dehradun, Uttarakhand, India for the decade 2014–2024. The Sentinel-1 C-band SAR data (backscatter amplitude $\sigma^{\circ}$ and interferometric coherence $\gamma$) provide a cloud-penetrating and structurally-based evaluation of urban extent. The data used include Landsat-8 OLI/TIRS, which are used for supervised classification of land use and land cover (LULC) by maximum likelihood. The maximum likelihood classification (MLC) and LST retrieval using Mono-Window Algorithm (MWA). The both sensors are processed through independent analytical pipelines and offering corroborating lines of evidence for urban change. The results show important environmental changes: There was an increase in urban area of +28.3 km$^2$ (+3.9%). agricultural land increased by +55.9 km$^2$ (+8.15%), while forest cover declined sharply by -83.1 km$^2$ (-12.05%)—figures with area adjusted uncertainty estimates. The mean LST increased by $\sim$+3.0 °C over the study transect, with mean annual air temperature rising from 21.22 °C to 24.21 °C (+14.1%), which is in line with the rise in temperature at the station, confirming intensification of the Surface Urban Heat Island (SUHI). The concentration of PM$_{10}$ at all the three Central Pollution Control Board (CPCB) monitoring stations it was consistently found that levels of SO$_2$ exceeded the national annual standard for India (60 $\mu$g/m$^3$) by 2.5–2.7 times throughout the study period. These are analyzed in a framework of Environmental Impact Assessment (EIA), which links to Identified land cover change to environmental impact pathways, management implications and policies Himalayan Mountain cities suggestions for urban planning, green space protection and air quality control.

In this study we have evaluated three advanced water treatment technologies in laboratory conditions, electrochemical (EC), fluidized bed (FB) and nanocomposite-based systems. The performance of the three technologies were evaluated based on several characteristics, such as pollutant removal efficiency, operating cost (USD/m$^3$), specific energy consumption (kWh/kg), throughput (kg/h), space-time yield (STY, kg/m$^3$$\cdot$h) and energy utilization efficiency (kg/kWh). The results show that the nanocomposite system offers the best treatment efficiency (93.17% removal efficiency and very low variability (standard deviation = 0.78%), showing good stability and reliability of the process. We found that nanocomposite system had moderate operating cost of 0.109−0.116 USD/m$^3$ and specific energy consumption of 3.60−6.52 kWh/kg, with an average value of 4.70 kWh/kg. Also, it has the highest STY (0.94 kg/m$^3$$\cdot$h) and high energy utilization efficiency (0.2776 kg/kWh). In contrast, the FB system has the lowest average operating cost (0.1016 USD/m$^3$), lowest average specific energy consumption (4.20 kWh/kg) and the best energy utilization efficiency (0.2493 kg/kWh) and is the most economical option even with the lowest pollutant removal efficiency. The EC system provided the best removal efficiency (91.32%), but the highest operating cost (0.1242 USD/m$^3$) and energy consumption (6.50 kWh/kg) of the other technologies. In analysis of variance (ANOVA) and Tukey’s Honestly Significant Difference (HSD) tests, there was significant difference between all the technologies ($p$ $<$ 0.05). The nanocomposite system achieved 5.39% removal efficiency and the FB system was able to have better energy utilization than the EC and nanocomposite technology. In general, the nanocomposite technology was the best in terms of treatment efficiency, energy efficiency, and operational cost optimization and the FB system is the best choice for large-scale applications.

The expansion of the shipbuilding industry in coastal areas contributes substantially to economic development while simultaneously posing significant risks to air quality and community health. This study analyzed the concentration and spatial distribution of air pollutants and assessed non-carcinogenic health risks in the shipyard industrial area of Batam City. Air quality measurements were conducted for PM$_{2.5}$, SO$_2$, NO$_2$, CO, and Pb parameters at multiple receptor points at different distances from the emission source. The field measurement data were then integrated with dispersion modeling using the American Meteorological Society/Environmental Protection Agency Regulatory Model (AERMOD) based on local meteorological conditions. Health risk was evaluated using the Hazard Quotient (HQ) approach with reference to national air quality standards. Pollutant concentrations decreased consistently with increasing distance from emission sources, with PM$_{2.5}$ exhibiting the widest and most persistent spatial distribution. Although most pollutant concentrations remained below regulatory thresholds, PM$_{2.5}$ yielded HQ values exceeding 1.0 across all receptor distances up to 2000 m, indicating significant non-carcinogenic health risk at all observed distances. Model validation demonstrated strong spatial agreement between measured and simulated concentrations ($R^2$ $>$ 0.84), with a consistent tendency toward underestimation of absolute values. The integration of spatial dispersion modeling with health risk assessment offers a comprehensive analytical framework for air quality management and public health protection in coastal industrial settings.