Search Results (721)

Rapid urban expansion in tropical coastal cities has intensified vegetation moisture stress, compromising urban resilience and ecological stability. This study investigates the spatial drivers of the Moisture Stress Index (MSI) in Makassar City, Indonesia, by integrating biophysical indicators and land-use characteristics through multi-scale regression analyses. Utilizing dry-season satellite composites (May–August 2025), the research derived Normalized Difference Vegetation Index (NDVI), Land Surface Temperature (LST), and Normalized Difference Built-up Index (NDBI). MSI was modeled using Ordinary Least Squares (OLS) and Geographically Weighted Regression (GWR) across 240 m, 480 m, and 960 m grids. Results indicate that MSI is highly sensitive to urban morphology and land-use configuration. High moisture stress was concentrated in commercial–industrial and dense residential zones characterized by extreme population densities exceeding 28,000 people/km² and elevated NDBI. In contrast, agricultural zones and open/green spaces provided significant cooling and moisture retention. Comparative performance analysis reveals that the local GWR model significantly outperformed the global OLS model, achieving a substantial reduction in AICc (−10,475.81) and resolving significant spatial autocorrelation to achieve random residuals (z-score = 1.55). The study further confirms that NDBI is the most robust biophysical predictor of MSI. Spatial heterogeneity analysis demonstrated that land-use influences are geographically contingent, with institutional areas showing varied effects based on campus design and canopy presence. These findings emphasize the necessity of scale-aware, climate-adaptive urban planning and demonstrate that GWR provides a high-fidelity tool for identifying neighborhood-level “micro-hotspots” overlooked by global modeling frameworks. Full article

Anaerobic digestion under a Waste-to-Energy (WtE-AD) framework represents a sustainable alternative for managing organic waste and generating bioenergy in developing countries. However, most life cycle assessment (LCA) studies implicitly assume stable operation, overlooking the environmental implications of process instability. In practice, large-scale WtE-AD plants are frequently affected by methanogenic inhibition events that reduce methane production and compromise their technical, economic, and environmental performance. This study—Part 2 of a two-paper series—addresses this gap by quantifying, from a life cycle perspective, the environmental consequences of recurrent methanogenic inhibition events in large-scale WtE-AD systems, complementing the techno-economic analysis presented in Part 1. Large-scale WtE-AD plants were modeled using design equations based on treatment capacity (60–200 t d⁻¹), considering scenarios with up to ten inhibition events over a 25-year operational period. The LCA was conducted in accordance with ISO 14040:14044 standards, defining as the functional unit one ton of co-digested fruit and vegetable residues with meat industry wastes, under an attributional approach with system boundary expansion and evaluating midpoint indicators through the ReCiPe 2016 method. Results show that inhibition events increase greenhouse gas emissions by up to 400% (from 28.1 to 138.6 kg CO₂ eq t⁻¹ of waste treated), while plants with capacities above 125 t d⁻¹ exhibit environmental credits (negative emission balances), demonstrating greater environmental resilience. Electricity substitution from the Mexican grid generated savings of up to 0.624 kg CO₂ eq kWh⁻¹, although the magnitude of the benefits strongly depends on the regional electricity mix. This dependency was further explored through comparative electricity mix scenarios representative of different levels of power sector decarbonization, allowing the sensitivity of WtE-AD environmental performance to regional grid characteristics to be assessed. Compared to landfill disposal (1326 kg CO₂ eq t⁻¹), WtE-AD plants significantly reduce impacts across all assessed categories. By explicitly integrating operational instability into an industrial-scale LCA framework, this work highlights the importance of evaluating methanogenic inhibition events from a life cycle perspective, providing key insights for the design of more sustainable and resilient WtE-AD processes within a Latin American context. Full article

The environmental profile of the electricity grid varies throughout the year, especially with the introduction of renewables, given their high level of discontinuity. Additionally, positive energy districts emerged as a solution for decarbonizing the building sector, thanks to high levels of self-consumed electricity and the benefits of exporting electricity to the national grid. Most life cycle assessment studies assume a retrospective and static background system for these evaluations. This study presents a prospective attributional life cycle assessment of the Italian national electricity grid for 2024–2040. Hourly factors were derived from a cost-optimization energy model, plus ecoinvent and PREMISE for background modeling. The model projects a sharp decline in fossil-based generation and a significant expansion of solar photovoltaics and wind, which together exceed half of national production by 2040. A total of 16 impact categories were evaluated, revealing decreasing trends in climate change (255 to 141–100 gCO₂-eq/kWh_e) and others, and rising temporal variability in mineral/metal resource depletion and land use due to renewable intermittency. Applying the method to a positive energy district in Bologna shows that time-resolved factors offer clearer insights than annual averages, especially for season-dependent impacts, and demonstrate substantial reductions in impact by 2040, alongside notable differences between consuming and exporting electricity. Full article

Rapid land use and land cover (LULC) changes in densely populated watersheds pose serious challenges to the sustainability of ecosystem services (ES), yet their spatially explicit economic consequences remain insufficiently understood. This study analyzes the spatio-temporal dynamics of LULC and ecosystem service values (ESVs) in the Citarum Watershed, Indonesia, one of the country’s most critical and intensively transformed watersheds. Multi-temporal Landsat imagery from 2003, 2013, and 2023 was classified using a Random Forest algorithm, while future LULC conditions for 2043 were projected using a Multi-layer Perceptron–Markov Chain (MLP–MC) model under three scenarios: Business-as-Usual (BAU), Protecting Paddy Field (PPF), and Protecting Forest Area (PFA). ESVs were quantified at multiple spatial scales (county, 250 m grids, and 100 m grids) using both the Traditional Benefit Transfer (TBT) method and a Spatial Benefit Transfer (SBT) approach that integrates biophysical indicators with socio-economic variables. The contribution of LULC transitions to ESV dynamics was further assessed using the Ecosystem Service Change Intensity (ESCI) index. The results reveal substantial historical forest and shrubland losses, alongside rapid expansion of settlements and dryland agriculture, indicating intensifying anthropogenic pressure on watershed functions. Scenario analysis shows continued degradation under BAU, limited mitigation under PPF, and improved forest retention under PFA; although settlement expansion persists across all scenarios. Total ESV declined from USD 2641.33 million in 2003 to USD 1585.01 million in 2023, representing a cumulative loss of 46.13%. Projections indicate severe ESV losses under BAU and PPF by 2043, while PFA substantially reduces, but does not eliminate economic degradation. ESCI results identify forest and shrubland conversion to settlements and dryland agriculture as the dominant drivers of ESV decline. These findings demonstrate that integrating multi-scenario LULC modeling with spatially explicit ESV assessment provides a more robust basis for ecosystem-based spatial planning and supports sustainable watershed management under increasing development pressure. Full article

Urbanization in mountainous regions alters hydrologic systems, yet the spatial patterning of residential (RA) and non-residential (NRA) areas in response to hydrologic constraints remains poorly quantified. In this study, we analyzed how such constraints shaped the distinct locational logic of RA and NRA expansion in the mountainous Kunming Core Region (KCR), Southwest China, from 1975 to 2020. Using the Global Human Settlement Layer (GHS-BUILT-S) built-up fraction data and its functionally classified RA and NRA layers at 100 m resolution, we quantified multi-decadal urban land changes via regression and centroid migration analyses. Six hydrologic factors, namely altitude, slope, surface roughness, distance to river (DTR), depth to water table (DTWT) and vegetation root depth (VRD), were derived from global terrain, groundwater, and rooting depth datasets, and harmonized to a common grid. Results show a two-phase urbanization pattern: moderate, compact growth before 1995 followed by rapid, near-exponential expansion, dominated by RA. RA consistently clustered in hydrologically favorable zones (low–moderate roughness, mid-altitudes, lower slopes, proximal rivers, shallow–moderate DTWT, moderate VRD), whereas NRA expanded into more hydrologically variable terrain (higher roughness, intermediate DTR, deeper DTWT, higher altitudes, deeper VRD). Contribution-weighting analysis revealed a temporal shift in dominant drivers: for RA, from river proximity and slope in 1975 to terrain roughness in 2020; for NRA, from vegetation root depth and moderate topography to root depth plus altitude. Geographic centroids of both RA and NRA migrated northeastward, indicating coordinated yet functionally distinct peri-urban and corridor-oriented growth. These findings provide a hierarchical, factor-based framework for integrating hydrologic constraints into risk-informed land-use planning in topographically complex basins. Full article

The prevention and control of desertification in northern China is currently in a critical stage of transitioning from large-scale governance to precise adaptation. Identifying potential risk areas during the ecological restoration process is a scientific prerequisite for achieving long-term governance. This study focuses on the typical ecologically fragile area of Ordos City, where high-resolution grazing pressure grid data and a night-time light index were innovatively integrated into the assessment system to develop a desertification sensitivity evaluation framework that couples climatic, vegetative, soil, and human activity (CVSH) factors. Compared to linear models, the CVSH framework enhances dynamic assessment accuracy by coupling human activity indicators, particularly addressing the policy lag effect inherent in PSR models. The study systematically tracked the temporal and spatial differentiation process of desertification sensitivity from 2000 to 2024, finding that the spatial pattern shows a significant “the west is high while the east is low” concentration, and the time series has experienced a phased turning point of “first suppression then growth”. Mechanism analysis indicates that climate aridification and vegetation degradation are the dominant stress factors, while intense human activities have significantly exacerbated the vulnerability of local ecosystems through nonlinear interactions, leading to the re-expansion of high-sensitivity zones after 2018, with their area proportion increasing sharply from 15.52% to 30.07%. This study reveals the fragility of ecological engineering effectiveness and the complexity of risk evolution under the combined influence of climate fluctuations and human interference, providing a direct scientific picture and decision support for achieving differentiated ecological risk management and sustainable land management in different regions. Full article

The rapid expansion of Internet of Things (IoT)-enabled smart grids has intensified the need for reliable fault detection and autonomous self-healing under non-stationary operating conditions characterized by frequent concept drift. To address the limitations of static and single-strategy adaptive models, this paper proposes H-CLAS, a novel Hybrid Continual Learning for Adaptive Self-healing framework that unifies regularization-based, memory-based, architectural, and meta-learning strategies within a single adaptive pipeline. The framework integrates convolutional neural networks (CNNs) for fault detection, graph neural networks for topology-aware fault localization, reinforcement learning for self-healing control, and a hybrid drift detection mechanism combining ADWIN and Page–Hinkley tests. Continual adaptation is achieved through the synergistic use of Elastic Weight Consolidation, memory-augmented replay, progressive neural network expansion, and Model-Agnostic Meta-Learning for rapid adaptation to emerging drifts. Extensive experiments conducted on the Smart City Air Quality and Network Intrusion Detection Dataset (NSL-KDD) demonstrate that H-CLAS achieves accuracy improvements of 12–15% over baseline methods, reduces false positives by over 50%, and enables 2–3× faster recovery after drift events. By enhancing resilience, reliability, and autonomy in critical IoT-driven infrastructures, the proposed framework contributes to improved grid stability, reduced downtime, and safer, more sustainable energy and urban monitoring systems, thereby providing significant societal and environmental benefits. Full article

Rising global electricity demand and the expansion of distribution networks require a critical assessment of component-level greenhouse gas contributions. Distribution transformers, although indispensable, have significant life-cycle carbon impacts due to the use of materials, manufacturing, and in-service losses. This study conducts a life-cycle assessment of a single-phase, 75 kVA oil-immersed distribution transformer manufactured in Newfoundland, one of the provinces with the cleanest, hydro-dominated grids in Canada, and evaluates it over a 40-year lifespan. Using a cradle-to-use boundary, the analysis quantifies embodied emissions from raw material extraction, manufacturing, and transportation, alongside operational emissions derived from empirically measured no-load and load losses. All the data are collected directly during the manufacturing process, ensuring high analytical fidelity. The energy efficiency of the transformer is analyzed in MATLAB version R2023b using measured no-load and load losses to generate efficiency, load characteristics under various operating conditions. Under varying load factor scenarios and based on Newfoundland’s 2025 grid intensity of 18 g CO₂e/kWh, the lifetime operational emissions are estimated to range from 0.19 t CO₂e under no-load operation to 4.4 t CO₂e under full-load conditions. A linear regression-based decarbonization model using Microsoft Excel projects grid intensity to reach net-zero around 2037, two years beyond the provincial target, indicating that post-2037 transformer losses will remain energetically relevant but carbon-neutral. Sensitivity analysis reveals that temporary overloading can substantially elevate lifetime emissions, emphasizing the value of smart-grid-enabled load management and optimal transformer sizing. Comparative assessment with fossil fuel-intensive provinces across Canada demonstrates the dominant influence of grid generation mix on life-cycle emissions. Additionally, refurbishment scenarios indicate up to 50% reduction in cradle-to-gate emissions through material reuse and oil reclamation. The findings establish a scalable framework for integrating grid decarbonization trajectories, life-cycle carbon modelling, and circular-economy strategies into sustainable distribution network planning and transformer asset management. Full article

With the large-scale integration of power electronic converters, non-linear loads, and renewable energy generation, voltage and current waveform distortion in modern power systems has become increasingly severe, making harmonic resonance amplification and non-stationary distortion more prominent. Accurate and robust harmonic-level prediction and detection have become essential foundations for power quality monitoring and operational protection. However, traditional harmonic analysis methods remain highly dependent on pre-designed time–frequency transformations and manual feature extraction. They are sensitive to noise interference and operational variations, often exhibiting performance degradation under complex operating conditions. To address these challenges, a Unified Physics-Transformer-based harmonic detection scheme is proposed to accurately forecast harmonic levels in offshore wind farms (OWFs). This framework utilizes real-world wind speed data from Bozcaada, Turkey, to drive a high-fidelity electromagnetic transient simulation, constructing a benchmark dataset without reliance on generative data expansion. The proposed model features a Feature Tokenizer to project continuous physical quantities (e.g., wind speed, active power) into high-dimensional latent spaces and employs a Multi-Head Self-Attention mechanism to explicitly capture the complex, non-linear couplings between meteorological inputs and electrical states. Crucially, a Multi-Task Learning (MTL) strategy is implemented to simultaneously regress the Total Harmonic Distortion (THD) and the characteristic 5th Harmonic (H5), effectively leveraging shared representations to improve generalization. Comparative experiments with Random Forest, LSTM, and GRU systematically evaluate the predictive performance using metrics such as root mean square error (RMSE) and mean absolute percentage error (MAPE). Results demonstrate that the Physics-Transformer significantly outperforms baseline methods in prediction accuracy, robustness to operational variations, and the ability to capture transient resonance events. This study provides a data-efficient, high-precision approach for harmonic forecasting, offering valuable insights for future renewable grid integration and stability analysis. Full article

As the three-phase four-bridge inverter (3P4B) can effectively compensate for the unbalanced three-phase loads in the grid, it is an important converter option for distributed generation grid connection. As in a three-phase three-bridge inverter (3P3B), the wide variation in grid impedance also poses instability issues for 3P4B. This issue has been well-addressed for 3P3B, which can be seen as a differential-mode circuit. However, 3P4B has an extra common-mode circuit, and the solution to the instability problem has not been investigated so far. To address this issue, this paper first analyzes the mechanism of 3P4B common-mode circuit instability and discovers its stability range difference from its differential-mode circuit. Then, an equivalent control delay compensator is independently introduced into the common-mode loop, which extends its stable range. This paper also conducts a detailed analysis of the control delay compensator’s impacts on the common-mode control loop and proposes a quantitative design method for the compensator accordingly. Experimental results validate that the proposed method effectively mitigates common-mode loop instability even under a wide range of grid impedance variations. Full article

Driven by the global energy transition, the rapid expansion of photovoltaic (PV) capacity—particularly in China’s “sand-Gobi-desert” mega-bases—demands highly efficient collection technologies. DC collection, offering low losses, compactness, and high reliability, is emerging as a critical solution for large-scale integration. This paper provides a comprehensive review of PV DC step-up collection systems. First, it analyzes typical network architectures, compares AC versus DC schemes, and examines design constraints imposed by DC bus voltage levels. Second, control strategies are summarized across device, equipment, and system levels. Third, based on engineering practices in ultra-large-scale bases, key challenges regarding fault detection, efficiency optimization, economic viability, and grid code compatibility are identified alongside representative solutions. Finally, future trends in high-voltage hardware maturation, protection bottlenecks, real-time artificial intelligence, and specialized standardization are proposed. This study serves as a vital reference for the topology design and engineering standardization of PV DC collection systems. Full article

This article examines public preferences for a proposed West Coast submarine high-voltage direct current (HVDC) transmission network in South Korea, installed by trenching and burying the cables in the seabed, which is essential for facilitating renewable energy integration and ensuring a stable electricity supply to the Seoul Metropolitan Area. The purpose of this study is to estimate South Korean households’ willingness to pay (WTP) for the proposed West Coast submarine HVDC network using contingent valuation (CV), thereby assessing its social acceptability amid renewable energy integration challenges. Employing a CV survey with a nationally representative sample of 1000 households conducted from late May to late June 2025, the research applies the one-and-one-half-bound spike model to address zero WTP responses and incorporates socio-demographic covariates to account for preference heterogeneity. The analysis estimates an average monthly WTP of KRW 1832 (USD 1.33) per household for the HVDC infrastructure. Results demonstrate statistically significant public support for the submarine HVDC project despite its high capital investment and potential electricity rate increases. These findings underscore notable consumer acceptance and provide valuable welfare insights for policymakers, reinforcing the prioritization of this project within South Korea’s energy transition framework. This paper contributes to the field of energy infrastructure valuation by advancing methodological approaches and offering policy-relevant recommendations for sustainable grid expansion. Full article

The persistent expansion in the intensity and scope of human disturbance has become a key driver of global biodiversity loss, affecting wildlife behavior and population stability across multiple dimensions. As a characteristic symbiotic assemblage in the subalpine forest ecosystems of Sichuan, the giant panda (Ailuropoda melanoleuca), red panda (Ailurus fulgens), and forest musk deer (Moschus berezovskii) exhibit significant research value in their responses to human disturbance. However, existing studies lack systematic analysis of multiple disturbances within the same protected area. This study was conducted in the Sichuan Liziping National Nature Reserve, where infrared camera traps were deployed using a kilometer-grid layout. By integrating spatiotemporal pattern analysis and Generalized Additive Models (GAM), we investigated the characteristics of human disturbance and the response strategies of the three species within their habitats. The results show that: (1) A total of seven types of human disturbance were identified in the reserve, with the top three by frequency being cattle disturbance, goat disturbance, and walking disturbance; (2) Temporally, summer and winter were high-occurrence seasons for disturbance, with peaks around 12:00–14:00, while the giant panda exhibited a bimodal diurnal activity pattern (10:00–12:00, 14:00–16:00), the red panda peaked mainly at 8:00–10:00, and the forest musk deer preferred crepuscular and nocturnal activity—all three species displayed activity rhythms that temporally avoided peak disturbance periods; (3) Spatially, giant pandas were sparsely distributed, red pandas showed aggregated distribution, and forest musk deer exhibited a multi-core distribution, with the core distribution areas of each species spatially segregated from high-disturbance zones; (4) GAM analysis revealed that the red panda responded most significantly to disturbance, the giant panda showed marginal significance, and the forest musk deer showed no significant response. This study systematically elucidates the spatiotemporal differences in responses to multiple human disturbances among three sympatric species within the same landscape, providing a scientific basis for the management of human activities, habitat optimization, and synergistic biodiversity conservation in protected areas. It holds practical significance for promoting harmonious coexistence between human and wildlife. Full article

The shift towards the integration of and transition to renewable energy has led to an increase in renewable energy communities (RECs) and smart grids (SGs). The significance of these RECs is mainly energy self-sufficiency, energy independence, and energy autonomy. Despite this, in low- and middle-income countries and regions like Pakistan and the Middle East, SGs and RECs are still in their initial stage. However, they have potential for green energy solutions rooted in their unique geographic and climatic conditions. SGs offer energy monitoring, communication infrastructure, and automation features to help these communities build flexible and efficient energy systems. This work provides an overview of Pakistani and Middle Eastern energy policies, goals, and initiatives while aligning with European comparisons. This work also highlights technical, regulatory, and economic challenges in those regions. The main objectives of the research are to ensure that residential service sizes are optimized to maximize the economic and environmental benefits of green energy. Furthermore, in line with SDG 7, affordable and clean energy, the focus in this study is on the development and transformation of energy systems for sustainability and creating synergies with other SDGs. The paper presents insights on the European Directive, including the amended Renewable Energy Directive (RED II and III), to recommend policy enhancements and regulatory changes that could strengthen the growth of RECs in Asian countries, Pakistan, and the Middle East, paving the way for a more inclusive and sustainable energy future. Additionally, it addresses the main causes that hinder the expansion of RECs and SGs, and offers strategic recommendations to support their development in order to reduce dependency on national electric grids. To perform this, a perspective study of Pakistan’s indicative generation capacity by 2031, along with comparisons of energy capacity in the EU, the Middle East, and Asia, is presented. Pakistan’s solar, wind, and hydro potential is also explored in detail. This study is a baseline and informative resource for policy makers, researchers, industry stakeholders, and energy communities’ promoters, who are committed to the task of promoting sustainable renewable energy solutions. Full article

Switching to electric vehicles (EVs) could help reduce air pollution in cities. This is especially important for cities in Russia that have grown quickly because of industry, like those in Siberia, where environmental problems are particularly acute. However, several factors continue to hinder the rapid expansion of EVs on the market, such as an additional strain on the energy infrastructure, which threatens to cause power outages. This study proposes a model for estimating the electricity consumption by EVs in the largest Russian cities, taking into account the technical characteristics of the EV fleet and climatic conditions. The calculations indicate that if 15% of the current car fleet are replaced by EVs, electricity consumption in the 16 largest cities in Russia would increase by 2.2 TWh per year in total. The estimated additional demand in particular cities varies between 33 mln and 769 mln kWh per year, depending on the number of vehicles and the local climate. Furthermore, we conducted an intra-day simulation of electricity consumption from EVs in a conditional Russian city with a population of over one million people. Three scenarios for the power grid load have been developed: (A) the maximum scenario, in which all EVs have a battery level of 0%; (B) the medium scenario, where EVs’ state of charge is distributed between 0% and 100%, and (C) the minimum scenario, involving charging scheduling that allows only EVs with a battery level of 20% or less to charge. The findings show that replacing just 15% of the car fleet with electric vehicles will trigger an increase in current daily household urban consumption of 28.4% in scenario (C), 75.6% in scenario (B) and 141.8% in scenario (A). Consequently, even in Russia’s largest cities, the further proliferation of EVs requires large-scale investments in power infrastructure. An additional 1 mln kWh used by EVs per day may require $160.7 mln investments in energy facilities and urban distribution networks. These findings highlight the necessity of a more thorough cost–benefit analysis of widespread electric vehicle adoption in densely populated urban areas. Full article