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Wildfires pose significant threats to ecosystems, economies, and human health. The integration of remote sensing (RS), geospatial information systems (GIS), and artificial intelligence (AI) has emerged as a powerful approach for addressing wildfire-related challenges. However, existing review studies typically focus on specific wildfire tasks and lack a comprehensive synthesis of how geospatial data and supervised AI techniques interact across the full wildfire management cycle. Therefore, this study aims to provide a meta-analysis review of the integration of RS, GIS, and supervised AI methods in wildfire science. This study follows the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) framework to systematically analyze 449 peer-reviewed journal articles published between 1994 and 2024. The review examines various wildfire-related tasks, data sources, algorithmic approaches, spatial scales, performance metrics, and other aspects used in wildfire geospatial AI (GeoAI) studies. The results reveal a strong concentration of research on tasks such as burned area mapping (BAM), wildfire detection, and susceptibility mapping, while critical areas, such as fuel mapping, wildfire vulnerability, and post-fire recovery, remain underexplored. The analysis also identifies a dominant use of traditional machine learning (ML) algorithms, such as Random Forest (RF), and an increasing adoption of deep learning (DL) models, particularly convolutional neural networks (CNNs). Furthermore, the geographic distribution of studies highlights significant global disparities, with most research conducted in high-income regions, while wildfire-prone areas in developing regions remain underrepresented. The review also reveals limited adoption of advanced AI techniques, including transfer learning, transformer architectures, Geo-foundation AI models, and explainable AI (XAI). These findings provide a comprehensive synthesis of GeoAI applications in wildfire management and highlight critical methodological, geographic, and application-level gaps. Addressing these gaps through improved data accessibility, adoption of advanced AI methods, and increased research focus on underrepresented wildfire tasks and regions will be essential for developing scalable, interpretable, and globally applicable wildfire management systems. Full article

To improve the low-carbon economic operation of integrated energy systems under wind power uncertainty, this paper develops an optimal scheduling model for an integrated energy system coupling oxygen-enriched combustion with hydrogen–ammonia–carbon utilization pathways. The proposed framework integrates oxygen-enriched combustion, electrolysis-based hydrogen production, methanation, hydrogen fuel cells, ammonia synthesis, urea synthesis, captured CO₂ utilization, reward–penalty ladder-type carbon trading, and IGDT-based wind power uncertainty scheduling. A deterministic scheduling model is first established to minimize the total operating cost, and Information Gap Decision Theory is then introduced to formulate risk-averse and opportunity-seeking scheduling strategies under wind power uncertainty. Simulation results show that, compared with the post-combustion carbon capture scenario and the conventional coal-fired scenario, the proposed system reduces the total operating cost by 3.37% and 8.03%, respectively, and reduces the wind curtailment cost by 40.2% and 57.0%, respectively. Compared with the post-combustion carbon capture scenario, carbon emissions are reduced by 17.7%. The hydrogen–ammonia–urea chain generates approximately 15.68 × 10⁴ CNY of urea revenue and improves carbon resource utilization. Under an IGDT deviation factor of 0.03, the risk-averse strategy increases the total operating cost by approximately 10.30 × 10⁴ CNY to enhance operational robustness, while the opportunity-seeking strategy reduces the total operating cost by approximately 10.30 × 10⁴ CNY and decreases carbon emissions by 19.6 t. These simulation results verify the effectiveness of the proposed scheduling framework under the designed case study. The proposed framework can improve the low-carbon economy, renewable energy accommodation, carbon resource utilization, and adaptability to wind power uncertainty of the studied integrated energy system. Full article

Against the backdrop of global energy transition and multiple uncertainties, enhancing energy resilience has become a core priority for China’s pursuit of secure and sustainable development. Using Chinese provincial panel data from 2011 to 2019, this study applies a two-way fixed effects model, mediation effect tests, and interaction term analysis to empirically investigate the relationship between industrial structure, green finance, and energy resilience. The main findings are as follows. First, the increases in gross regional product (GRP) and the added value of the secondary and tertiary sectors significantly enhance energy resilience. Second, heterogeneity analysis indicates that in regions with a high level of green finance, both GRP and the secondary sector’s added value exhibit stronger positive effects on energy resilience, whereas in regions with lower levels of green finance, the tertiary sector’s added value contributes more significantly to energy resilience improvement. In areas with high coal dependency, the secondary sector’s added value shows a significantly positive effect on energy resilience. Increases in industrial and construction industry added value significantly enhance energy resilience, suggesting that the expansion of the secondary industry contributes positively to the stability and resilience of the energy system. Third, the mechanism analysis shows that green finance contributes to energy resilience partly through the optimization of the energy consumption structure. Specifically, by effectively curbing coal consumption and, to a lesser extent, fuel oil production, green finance reduces the structural dependence of the economy on high-carbon energy. By contrast, channels such as electricity generation yield weaker and less robust evidence. These findings suggest that energy resilience is fundamentally shaped by the interplay of industrial structure, financial intermediation, and energy structure adjustment. Therefore, policy should shift from single instruments to integrated governance, synergizing industrial policy, green finance, and energy optimization to bolster energy resilience. Full article

Geopolitical shocks affect small open economies through multiple, correlated channels, yet applied CGE analyses typically impose the timing and persistence of those shocks by assumption. This paper develops a two-stage SVAR–CGE framework that links econometrically identified shock dynamics to general-equilibrium welfare evaluation for Thailand. First, a seven-variable narrative SVAR estimated on monthly data for 2000–2025, identified using the Caldara–Iacoviello Geopolitical Risk Index, is used to recover the persistence of five transmission channels: oil prices, shipping costs, exchange rates, tourism demand, and private investment. Second, these estimated persistence parameters discipline the shock paths in a 22-sector recursive comparative-static CGE model calibrated to Thailand’s 2025 Social Accounting Matrix and simulated over three annual periods using a present-value integral transformation. Under the baseline shock bundle, GDP declines by 3.18% and CPI increases by 5.49%, with welfare losses exhibiting a bimodal distributional pattern—largest for Q1 through consumption-share exposure and for Q4 through tradeable-sector intensity—departing from the monotonically regressive pattern in single-channel analyses. Policy simulations show that targeted transfers calibrated to income rank dominate a universal fuel subsidy on fiscal efficiency, welfare effectiveness (welfare multiplier 1.377 vs. 0.334), and progressivity (1.00 vs. 0.94), at half the fiscal cost (1.48% vs. 2.97% of baseline GDP). An additional bimodal-targeting scenario (S4) at identical fiscal cost underperforms income-rank targeting on all metrics, confirming the latter as the robust second-best instrument under LES preferences with strong MPC heterogeneity. These rankings are supported by the central calibration of a 9-point sensitivity grid, with partial corroboration at off-baseline configurations. The paper contributes by showing that empirically disciplining inter-annual shock dynamics in CGE analysis can materially alter policy conclusions under correlated multi-channel external shocks, shifting the preferred response from sector-specific price subsidies toward demand-side household transfers. Full article

This study investigates whether the optimal utilization of the biomass potential contained in municipal solid waste (MSW) can support the implementation of circular economy (CE) principles and contribute to climate policy objectives, particularly the reduction in greenhouse gas (GHG) emissions in the waste management sector. The analysis evaluates whether waste-to-energy recovery can support the objectives of the European Green Deal, including a 55% reduction in GHG emissions by 2035 and the achievement of climate neutrality by 2050. The assessment was conducted for two MSW streams generated in a Polish municipality: separately collected biowaste and residual MSW remaining after meeting European reuse and recycling targets. The study summarizes the results of detailed experimental investigations of the physicochemical and fuel properties of these waste streams. Proven and commercially available energy recovery technologies, including anaerobic digestion (AD) of biowaste and incineration of residual waste, were analyzed. GHG emissions were assessed using a life cycle assessment (LCA) approach, taking into account both direct emissions and avoided emissions resulting from the substitution of conventional energy and fertilizer production. The experimental results revealed significant variability in the biodegradability and energy potential of individual biowaste fractions, with the highest biogas yields observed for kitchen waste. Residual waste exhibited a considerable calorific value and a significant share of renewable energy due to its biomass content. The results indicate that the share of renewable energy in electricity generated from waste is expected to increase from 46.1% in 2025 to 49.9% in 2040. In relation to the total electricity demand of the analyzed city, energy recovered from waste accounts for 1.8 ± 0.3% in 2025 and 1.3 ± 0.2% in 2040. Scenario-based modeling demonstrated that the target system, maximizing energy recovery from both biowaste and residual waste, achieves a consistently negative GHG emission balance throughout the analyzed period (2025–2040), ranging from −72 ± 15 kg CO₂-eq/ton in 2025, through the most favorable value of −81 ± 17 kg CO₂-eq/ton in 2035, to −57 ± 12 kg CO₂-eq/ton in 2040, expressed per ton of total managed biowaste and residual waste. Full article

Predictive energy management strategies (PEMSs) have attracted increasing attention in hybrid electric vehicles (HEVs) for improving fuel economy and powertrain efficiency using anticipated driving information. For PEMS, data-driven velocity prediction is widely used to capture complex driving patterns from historical trajectories and future traffic priors, but often lacks kinematic awareness, leading to physical causality violations and long-horizon state drift. To address these issues, this paper proposes a physics-informed PEMS, where a Physics-Informed Spatio-Temporal Network (PI-STN) provides control-oriented velocity information for an MPC-based energy management controller. Specifically, to address pseudo-motion in velocity prediction under standstill conditions, a global zero-speed gating mechanism is introduced; to suppress acceleration/deceleration trends that violate vehicle kinematic causality, a causal penalty is designed; and to mitigate temporal phase misalignment between data-driven predictions and physical motion priors, a Differentiable Kalman Filter (DKF) is incorporated. At each receding horizon step, the PI-STN-predicted velocity sequence is converted into future power demand through longitudinal vehicle dynamics and used by MPC for engine–battery power allocation under SOC and engine transient constraints. Under the same tested conditions, the proposed strategy reduces engine power fluctuation by 15.1% compared with BiLSTM-Transformer, and achieves an equivalent fuel consumption of 323.74 g, outperforming Transformer-KF by 3.12%. Full article

To meet the goals of the Paris Agreement of avoiding dangerous climate change, decarbonisation of the global economy needs to proceed around three to five times faster over the coming decade than over the past two decades. This poses a great challenge for policy. Carbon pricing has often been put forward as the most efficient, or cost-effective, policy for achieving decarbonisation. This paper uses a stylised agent-based model to investigate whether implementing non-equilibrium dynamics and endogenous innovation results in more effective emission reductions for carbon tax compared with emission trading schemes. We find that the implementation of a carbon price is not policy-agnostic and that a carbon tax achieves faster emissions reduction, lower cumulative emissions, and lower cumulative (potentially wasted) investment in fossil fuel assets than a cap-and-trade policy with the same average carbon price. While a comparison between carbon pricing and alternative policies is outside the scope of this paper, we consider the broader policy implications that may be drawn from a new theoretical explanation for the difference in performance of the alternative carbon pricing approaches, and suggest that the traditional view that policy should aim to minimise the marginal emissions abatement cost is mistaken. Full article

Life expectancy is a key indicator of public health and sustainable development in Gulf Cooperation Council (GCC) countries, where rapid economic growth, urbanization, and fossil-fuel dependence create environmental and health challenges. This study examines the determinants of life expectancy in six Gulf Cooperation Council countries from 2000 to 2023, focusing on death rates, renewable energy consumption, gross domestic product (GDP) per capita growth, government health expenditure, and carbon dioxide (CO₂) emissions. The empirical strategy combines cross-sectional dependence and slope heterogeneity tests, second-generation panel unit root tests, panel cointegration analysis, and a dynamic System Generalized Method of Moments (System GMM) estimator, with Driscoll–Kraay fixed-effects estimates used for robustness. The results show that higher death rates significantly reduce life expectancy, whereas renewable energy consumption and government health expenditure improve longevity. GDP per capita growth has a modest positive effect, while CO₂ emissions negatively affect life expectancy, confirming the adverse public health consequences of environmental degradation. Robustness checks support the reliability of the main findings. Overall, the evidence highlights the need for integrated policies that combine clean energy transition, stronger environmental regulation, preventive healthcare investment, and sustainable urban development to improve long-term health outcomes in resource-dependent economies in the region. Full article

The transition to a hydrogen-based energy economy emphasizes the potential of hydrogen as a fuel for plug-in hybrid electric vehicles (PHEVs). The performance of a hydrogen engine within a PHEV depends on the choice of its operating modes, which influence both efficiency and emissions. This study proposes a method for developing engine operating lines (EOLs) on engine maps based on minimizing nitrogen oxide (NOx) emissions while considering constraints on maximum engine power. A total of 15 EOLs are proposed for configurations with both constant and variable maximum engine power. Using mathematical modeling of PHEV operation under the Worldwide Harmonized Light Vehicles Test Cycle (WLTC), the impact of EOL selection on engine characteristics, as well as on battery and generator parameters, is analyzed. For a comprehensive evaluation of EOL effectiveness, five criteria are introduced, considering fuel energy consumption, NOx emissions, wear, mechanical fatigue, and noise, vibration, and harshness (NVH). The Analytic Hierarchy Process (AHP) and the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) are applied to determine the weighting factors of the criteria and to rank the proposed EOLs, thereby identifying the most efficient configurations. The results show that, for the base hydrogen engine configuration, selecting appropriate operating modes alone enables NOx emissions to be reduced significantly below Euro 6 limits, without any hardware modifications or exhaust aftertreatment. Full article

The article presents the results of experimental investigations and regression-based analysis on the influence of a water injection system on the fuel efficiency and performance of diesel engines used in agricultural machine–tractor units. The need to improve fuel economy and operating efficiency is driven by increasing energy costs and the growing demand for more efficient agricultural machinery. The study focuses on the application of water injection into the intake manifold as a method for improving fuel consumption and engine operating performance under laboratory and field conditions. Experimental investigations were carried out on a Deutz 1000.3 W diesel engine under laboratory and field conditions. The engine was tested on a dynamometer, and field tests were performed using a Deutz-Fahr Agrolux 80 (SDF Group, Treviglio, Italy) tractor powered by the tested Deutz engine and coupled with an Amazone D9-20 Super (AMAZONEN-WERKE H. DREYER SE & Co., KG, Hasbergen-Gaste, Germany) seeder. The seeder was used as a trailed agricultural implement and did not have its own engine. The optimal water-to-fuel ratio was found to be 27–32%, ensuring a balance between increased engine power and reduced fuel consumption. The use of water injection reduced fuel consumption by 10–15%, increased effective engine power by up to 19%, and improved traction performance under field conditions. The novelty of this study lies in the adaptation and experimental evaluation of intake water injection for an agricultural diesel engine operating as part of a machine–tractor unit. Particular attention was paid to the relationship between the water-to-fuel ratio, fuel consumption, engine performance, and traction characteristics under laboratory and field conditions. The results demonstrate that water injection provides a cost-effective and technically feasible solution for improving fuel economy and operating performance of agricultural diesel engines without requiring significant modifications to existing designs. Potential environmental benefits may be associated with reduced diesel fuel consumption. However, emissions were not the main experimental focus of the present study and should be investigated separately in future work. Full article

The energy demand, depletion of fossil fuels, generation of plastic waste, and final disposal of waste cooking oil (WCO) have become major concerns due to industrialization and population growth, creating significant environmental challenges. These challenges have encouraged the development of sustainable alternatives for the valorization of residual feedstocks. On the one hand, global energy consumption continues to increase, promoting the search for alternative fuel sources; on the other hand, the improper disposal of plastic waste has motivated the development of recycling technologies for plastic residues that are difficult to recycle through conventional routes. Moreover, WCO is commonly discharged into drainage systems, contributing to water contamination. Therefore, this study evaluates the alkaline-assisted co-processing of waste cooking oil with crude and distilled expanded polystyrene (EPS) pyrolysis fractions to obtain liquid products with potential application as fuel blendstock components. Specifically, the work explores the co-valorization of WCO with two aromatic hydrocarbon fractions derived from EPS pyrolysis: crude EPS pyrolysis oil and its distillate fraction. These EPS-derived streams are evaluated as residual hydrocarbon co-feeds for the alkaline-assisted processing of WCO into liquid fuel-like products. The influence of the catalyst loading, WCO-to-EPS-derived fraction mass ratio, and EPS-derived fraction type was analyzed based on the liquid product yield. Furthermore, first-generation vegetable oils were tested under selected conditions to compare their behavior with WCO and assess the applicability of the process to different lipid feedstocks. Finally, the fuel-related properties of the obtained liquid products were evaluated through the density, kinematic viscosity, and heating value, and compared with commercial fuel specifications. The results showed liquid product yields up to 92%, kinematic viscosity values within the range of international fuel specifications under selected conditions, and heating values above 40 MJ/kg. However, the density values indicated limitations for direct use as standalone fuels; therefore, the obtained products should be considered as potential fuel blendstock components requiring further blending and chemical characterization studies. Full article

To address the issues of increased energy consumption and reduced engine efficiency in plug-in hybrid electric vehicles (PHEVs) under low-temperature conditions due to cabin heating demands, this paper investigates the coupling characteristics between the powertrain system and the cabin thermal management system and proposes an adaptive energy management strategy tailored for low-temperature environments. First, a comprehensive model incorporating vehicle dynamics, the engine, and the passenger compartment thermal management system was established. The impact of different ambient temperatures and equivalent factors on the system’s energy consumption characteristics was then quantitatively analyzed under WLTC conditions. Based on this, an adaptive strategy for minimizing equivalent fuel consumption that accounts for cabin heating demand was designed. By using real-time cabin heating demand and engine waste heat power as state feedback, the equivalent factor is dynamically adjusted to coordinate the allocation of power between propulsion and heating. Simulation and hardware-in-the-loop test results indicate that the optimized strategy, by promoting early engine engagement and improving waste heat recovery efficiency, reduces PTC energy consumption by 0.47 kWh under −20 °C WLTC conditions, decreases additional fuel consumption caused by low temperatures by approximately 59%, and improves the vehicle’s equivalent fuel economy by 4.6%, while effectively maintaining passenger compartment thermal comfort. This study contributes to sustainable transportation by reducing low-temperature-induced energy waste, lowering equivalent fuel consumption, and promoting efficient use of engine waste heat, thereby supporting carbon emission reduction goals in hybrid electric vehicle operations. Full article

With the in-depth advancement of the “dual carbon” strategy, Proton Exchange Membrane Fuel Cells (PEMFCs), as efficient and clean energy conversion devices, show great potential in the fields of transportation power and stationary power generation. For multi-stack fuel cell systems, a hierarchical optimization strategy based on Pareto decoupling and real-time correction is presented to achieve system efficiency improvement and balanced management of stack aging. Firstly, the Forgetting Factor Recursive Least Square (FFRLS) method is adopted to online identify the parameters of the system’s net output power-efficiency curve. Furthermore, in the steady-state layer, the Arithmetic Optimization Algorithm (AOA) is used to construct an efficiency-optimal candidate solution set. The Dijkstra algorithm is combined to search for the optimal power gradient path, generating a reference power table. In the dynamic layer, with the reference power table as the basis, the AOA algorithm is used to take efficiency optimization as the goal. Load fluctuations are suppressed in real time through strong constraints, realizing the balance between dynamic efficiency and operational stability. This method ensures the stable operation of the system and significantly improves the overall economy and adaptability of power allocation. Simulation results show that this strategy can effectively improve the overall operating efficiency of the system, slow down the stack aging rate, and ensure the stable operation of the system. Full article

The reduction in driving range during winter remains a major barrier to the widespread adoption of battery electric buses (BEBs), as battery performance degradation and increased Heating, Ventilation and Air Conditioning (HVAC) energy demand significantly raise total energy consumption. This study investigates the use of proton exchange membrane fuel cells (PEMFCs) as auxiliary power units for range-extended electric buses (FC-REEBs) under low-temperature conditions to address this challenge. A comprehensive dynamic model was developed in MATLAB/Simulink 2025a version, integrating a fuel cell system, lithium-ion battery, power conversion unit, vehicle dynamics, and cabin thermal model. The model was evaluated under the World Harmonized Vehicle Cycle (WHVC) to compare three fuel cell operation strategies defined by fuel cell capacity and operating modes for cabin heating and battery charging. Performance was compared in terms of SOC variation, fuel cell loading patterns, hydrogen consumption, and equivalent fuel economy. Results indicate that the high-capacity strategy improves SOC stability but increases hydrogen consumption and reduces overall efficiency. In contrast, the strategy prioritizing cabin heating with minimal battery charging effectively utilizes waste heat and achieves the highest equivalent fuel economy. These findings highlight key trade-offs among different operating strategies and demonstrate that fuel cells can significantly enhance BEB efficiency and driving performance in cold environments while reducing battery load. Full article

The current study investigates the relationships among environmental variables, energy sector characteristics, and the resilience of the financial sector using a panel dataset of OECD countries covering 2004–2021. For that purpose, information from the World Bank Global Financial Development Database and Sovereign ESG Data was used, along with the indicator of financial stability—bank capitalization, represented by the capital-to-asset ratio. This work uses an integrated empirical framework that includes panel regressions, clustering techniques, and machine learning models. The findings from fixed-effects panel regression indicate that methane emissions, PM2.5 air pollution, and energy dependence are negatively correlated with bank capitalization, whereas renewable energy consumption is positively correlated. Contrariwise, fossil fuel consumption is positively correlated with the dependent variable, perhaps indicating the financial conditions prevailing at the moment, but not accounting for the long-run sustainable perspective. Robustness checks, such as excluding major economies, using lagged specifications, and adding control variables, confirm the robustness of the main empirical relationships, yet the results need to be interpreted conditionally. Through clustering analysis, various regimes are observed across the sample, each characterized by different combinations of environmental, energy, and financial features. On the other hand, the machine learning results obtained using K-Nearest Neighbors and Random Forest algorithms are consistent with the regression analysis, revealing non-linearities in the data. Full article