Search Results (477)

This study examines Malaysian consumers’ perceptions toward purchasing electric vehicles (EVs) in the context of increasing global efforts to promote sustainable mobility and reduce carbon emissions. Despite government initiatives and incentives to encourage EV adoption in Malaysia, uptake remains relatively slow compared to more developed markets, highlighting the need to understand consumer attitudes and concerns. A qualitative research design was adopted using semi-structured interviews with five purposively selected participants who had awareness or experience related to vehicle ownership decisions. Thematic analysis was used to analyse the data in relation to financial considerations, charging infrastructure, social influence, and service reliability. The findings indicate that financial concerns, particularly high purchase costs and battery-related uncertainties, are the most significant barriers to EV adoption. Charging infrastructure limitations, including insufficient charging stations, long charging times, and lack of home charging access, further reduce consumer confidence. Social influence from peers, family, and online platforms plays a dual role in shaping both positive and negative perceptions, while service reliability concerns, especially regarding battery durability and after-sales support, affect trust in EV ownership. The study concludes that EV adoption in Malaysia is influenced by interconnected economic, technological, and social factors, suggesting that coordinated improvements in affordability, infrastructure, consumer awareness, and service support are essential to accelerate adoption. This study contributes novel qualitative evidence by revealing how financial, infrastructural, social, and service-related factors collectively shape Malaysian consumers’ EV purchase perceptions, extending previous survey-based research in the Malaysian context. Full article

Transport decarbonization through electric mobility depends not only on how many charging stations are deployed but where, and whether expansion balances accessibility, grid readiness, land-use protection and regional equity. Türkiye, targeting net-zero by 2053 with electric car sales exceeding 10% of the market in 2024, shows a highly uneven charging network: provincial provision ranges from 9.0 to 155.6 points per 100,000 inhabitants, with the least-served half of the population holding only 22.3% of installed capacity (Gini = 0.311). This study develops a GIS-based multi-criteria framework treating charging expansion as a sustainability-constrained planning problem. Six criteria, namely population, GDP, transformer and transmission-line proximity, road-network proximity, and city-centre proximity, were harmonized to a 100-m grid via fuzzy membership functions, with an exclusion mask protecting sensitive land uses. Three weighting scenarios were compared: equal weights (EVCSI-A), Random Forest-derived weights (EVCSI-B), and expert AHP weights (EVCSI-C). Road accessibility (41.12%) and economic capacity (29.84%) dominated existing placement, explaining ~71% of feature importance, stable across algorithms and bootstrap replicates. National results reveal an efficiency–equity trade-off: EVCSI-B concentrates suitability in metropolitan corridors, EVCSI-A preserves broader coverage, and EVCSI-C reinforces metropolitan bias. Central and Eastern Anatolia remain underserved. We recommend sustainability-constrained screening followed by grid-capacity verification, positioning EVCSI as a transferable equity-monitoring tool supporting SDG 7, 9, 11 and 13. Full article

Electric vehicle (EV) remanufacturing is critical to circular economy development. However, consumers suffer from battery-induced range anxiety, limited transferable factory warranties for pre-owned EVs, and low trust in remanufactured components due to unstandardized aftermarket supervision. To mitigate these quality concerns, secondhand EV retailers have adopted a full original-price refund guarantee. While existing studies primarily focus on subsidies, carbon policies, and quality disclosure, few explore refund guarantee mechanisms in EV remanufacturing, especially their threshold effects and interactions with pure and hybrid component sourcing strategies. This study addresses this gap by establishing six game-theoretic models and deriving market equilibria via backward induction. We systematically analyze how failure rates, procurement costs, consumer-perceived quality, and guarantee strength affect market performance, optimal sourcing strategies, and social welfare, with numerical simulations validating our theoretical results. The findings reveal a universal threshold effect: refund guarantees boost sales only beyond a critical strength level, whereas weak guarantees fail to cover compensation costs and may even reduce social welfare. In particular, refund guarantees significantly improve social welfare under pure-remanufactured sourcing. This work offers actionable insights for remanufacturers, consumers, and regulators. Firms can optimize guarantee efficiency by simplifying claim processes, while policymakers can implement differentiated warranty regulations and tiered incentives. Full article

As electric vehicles become widespread, home EV chargers have emerged as a key interface between household energy use and daily mobility. However, their design optimization remains insufficiently informed by large-scale user feedback. This study develops a review-driven, interpretable machine learning framework to identify design priorities for home EV chargers. Of the 26,763 reviews collected from the JD e-commerce platform, 23,893 were retained after cleaning. BERTopic extracted raw topics, which were consolidated into ten design dimensions through independent coding, inter-coder agreement assessment, and consensus adjudication. A structured large language model protocol then transformed the reviews into evidence-constrained, aspect-level semantic proxy variables representing evaluative direction and intensity. Coding reliability was evaluated against dual-coder annotations, while a matched absence-as-zero specification examined sensitivity to the treatment of unmentioned dimensions. Platform ratings were subsequently introduced as the prediction target, and repeated data partitions and cross-model SHAP comparisons were used to assess partition- and model-level stability. Charging Performance, Operational Stability, Perceived Product Quality, and Operational Convenience and Portability consistently ranked as the most important factors associated with platform-rated satisfaction. In contrast, Installation Friendliness and After-sales Service showed asymmetric attribution patterns characterized by stronger low-value penalties than high-value gains. The framework supports translating online review evidence into product-level design priorities, while emphasizing that SHAP identifies predictive associations rather than causal effects. Full article

High penetrations of renewable generation are increasing the need for long-duration energy storage capable of intertemporal balancing and reserve provision. However, the market value of heterogeneous storage portfolios under shared grid-connection constraints remains insufficiently quantified. This study develops a risk-averse day-ahead co-bidding model for a hybrid pumped-hydro and compressed-air energy storage (CAES) portfolio participating jointly in energy and spinning-reserve markets. Monte Carlo sampling and scenario reduction are used to represent price uncertainty, while conditional value-at-risk (CVaR) captures downside-profit risk. Shared point-of-common-coupling (PCC) constraints explicitly couple electricity sales, purchases, and reserve offers. Compared with homogeneous pumped-hydro expansion, replacing the equivalent incremental pumped-hydro capacity with CAES increases the cumulative reserve bid by 65.71%, while expected profit decreases by 1.17% and raw-scenario back-test CVaR remains nearly unchanged, decreasing by only 0.05%. Relative to the unconstrained hybrid-storage case, the shared PCC constraints reduce expected profit, raw-scenario back-test CVaR, and reserve bids by 1.01%, 1.34%, and 18.62%, respectively. Scenario-reduction sensitivity and synthetic price–spread analyses indicate that the main operating mechanisms remain stable within the assumed scenario-generation framework, while sensitivity analyses reveal diminishing returns from CAES expansion and saturation of PCC-related profit gains near 5000 MW. Because all price scenarios are synthetic and neither historical nor independent out-of-sample market data are used, these analyses constitute model-based robustness tests rather than seasonal or real-market validation. The findings support the coordinated configuration of heterogeneous storage, grid-interface capacity, and risk preferences, but should be interpreted as market-bidding-level comparative evidence under the adopted equivalent CAES representation rather than as market-specific profitability forecasts. Full article

While the adoption of electric vehicles can reduce CO₂ emissions, the extent of this reduction depends on the growth of the EV market. Sigmoid growth models, such as logistic or Gompertz function models, can be used to predict expected EV sales trends; however, their quality has not yet been comprehensively analyzed, as this would require looking into the future to compare today’s predictions with future data. Since this is obviously not possible, this study takes a retrograde approach. It uses the available historical data to create forecasts that are then compared with the actual values from subsequent years. For example, a forecast based on data from 2010 to 2014 can be compared with the values achieved in years from 2015 to 2025. The quality of the functions is assessed using fit indices. Among the ten distinct functions tested, including two equivalent Gompertz functions, and under the stated saturation assumptions, the Gompertz family offers the most stable retrospective forecasts of EV stock (prediction period MAPE of 16.0% for the global data, against 15.1% for the generalized logistic, which performs comparably). The generalized logistic attains marginally better global point accuracy, whereas Gompertz is preferred for its greater stability across forecast origins and its more interpretable parameters. Full article

The rapid growth of graphics processing unit (GPU)-accelerated AI workloads has made data centers significant sources of medium-grade waste heat, creating both a sustainability challenge and an urban decarbonization opportunity. This paper presents the Cascaded Exergy-Economic Valorization (CEEV) framework, a three-stage system that converts data-center waste heat through (1) an organic Rankine cycle for GPU liquid-cooling loops at 65–85 °C; (2) a transcritical CO₂ heat pump, upgrading residual heat to 75–90 °C; and (3) thermochemical energy storage using SrBr₂·6H₂O for seasonal heat banking. The framework introduces two metrics: the Exergy Value Index (EVI, $/kJ) and the Levelized Cost of Stored Heat (LCSH, $/kWhth). Results for a 10 MW liquid-cooled data center across three climate zones show cascade exergy utilization of 31.2–38.7%, operational cost reductions of 15–25%, 20-year NPV of $2.2–8.4 million, and payback periods of 5.8–7.8 years. The simpler HP (heat pump) +TCES (thermochemical energy storag) configuration achieves higher deterministic Net Present Value (NPV) because it preserves the full waste-heat temperature for the heat pump; however, the full three-stage cascade becomes preferable when electricity prices exceed approximately $50/MWhe, when revenue diversification is valued, or when real-options flexibility is important. Real-options analysis shows that traditional NPV undervalues cascaded waste-heat recovery investments by 18–32%. Even without carbon credit revenue, NPV remains positive at $1.6–6.1 million, confirming that district-heating sales and electricity revenue alone can support investment. The CEEV framework advances sustainable data-center development by providing quantifiable tools for waste-heat performance assessment, supporting policy instruments such as the EU Energy Efficiency Directive and the German EnEfG, and aligning with SDGs 7, 9, 11, and 13. Full article

High electricity prices remain a substantial barrier to electric vehicle (EV) diffusion. To address this challenge, we propose a bundled sales model that integrates EVs with distributed, operationally off-grid photovoltaic (PV) systems for self-consumption. Using a sequential game-theoretic framework and scenario analysis calibrated to U.S. and German data, we show that, within the calibrated scenarios and declared system boundaries, bundling accelerates EV adoption and reduces modeled oil dependency, measured as the physical volume of fossil fuel displaced by the bundled fleet. In Germany, bundling increases oil-dependency reduction by 7.8 percentage points, to 34.5%, relative to the traditional unbundled model. The bundled model also delivers stronger decarbonization, yielding incremental lifecycle emission reductions of 11% in the U.S. and 29% in Germany under the declared system boundary. Three insights follow. First, bundling is especially advantageous in markets with high grid tariffs, strong solar irradiance, or falling PV costs. Second, decoupling EV charging from carbon-intensive grids promotes household energy self-sufficiency and helps households become more resilient energy prosumers. Third, the threshold analysis indicates that the model is already viable in high-tariff markets such as Germany, while declining battery costs are likely to trigger a tipping point in lower-tariff markets such as the U.S., supporting a gradual diffusion pattern from suburbs to cities. These findings identify a viable pathway for low-carbon transport transitions through synergistic EV–solar integration. Full article

As climate change intensifies, transitioning the construction sector away from fossil fuels is vital to reducing global greenhouse gas emissions and localized urban pollution. This paper assesses the economic feasibility of electrifying construction machinery by developing an Annualized Cost of Ownership framework that incorporates mobile charging solutions, internalizes environmental and public health operational externalities (CO₂, PM_2.5, NO_x, and SO₂), and relies on Monte Carlo simulation with Cholesky decomposition to capture the interdependencies among cost drivers. We analyze twenty distinct models of excavators and wheel loaders—the two largest contributors to construction-machinery emissions—comprising functionally equivalent diesel and battery-electric variants. Our results show that several compact electric models are already cost-competitive even without internalizing environmental and public health operational externalities. When these are accounted for, the economic advantage of electric machinery increases, particularly in denser urban areas where local air pollution damages are severe. While projected battery cost reductions further lower electric ownership costs, the magnitude of this effect is modest. However, the weak penetration of electric construction equipment in the US underscores that targeted policy interventions—such as point-of-sale rebates, green procurement mandates, tax credits, charging infrastructure subsidies, or the creation of low-emission zones and noise ordinances that advantage electric construction machinery—are needed to accelerate market adoption. These measures are particularly critical in densely populated urban areas, where internalizing local air pollution and public health externalities significantly amplifies the economic value of zero-emission machinery. Full article

With increasing penetration of distributed energy resources, peer-to-peer (P2P) electricity trading has attracted attention for locally utilizing surplus renewable energy. This paper proposes a distributed consensus-based P2P electricity trading method that explicitly considers prosumer equipment operation constraints. Each prosumer autonomously solves a daily scheduling problem considering electricity demand, PV generation, battery operation, grid purchase and sale, and P2P trades with neighboring prosumers. P2P prices and desired trading quantities are iteratively adjusted through local information exchange. After convergence, bidirectional trades are converted into net one-way trades, and the final feasible daily schedule is obtained by re-optimizing with fixed trading quantities. Numerical simulations were conducted for six low-voltage prosumers using annual residential demand data and a representative daily PV generation profile. In the base case, the proposed method reduced annual electricity cost by 13.7% compared with the no-P2P case, while its total cost was only 2.3% higher than that of the centralized benchmark. Unlike the centralized benchmark, which increased costs for some prosumers, the proposed method reduced costs for all prosumers. Wheeling-charge sensitivity analysis showed that the charge affects P2P trading volume and benefit allocation. Future work will address tariff design, PV uncertainty, scalability, and distribution-network constraints. Full article

This paper examines the state of the academic literature on the development of after-sales and maintenance services for electric vehicles (EVs), highlighting their critical yet underexplored role in the transition to electrified mobility. Against the backdrop of rising EV sales, this study investigates how service ecosystems influence long-term adoption. A systematic review was conducted to identify recurring themes, barriers, and proposed solutions related to EV maintenance and after-sales systems. The findings indicate that, despite lower mechanical complexity compared to internal combustion vehicles, EVs generate new service demands due to their reliance on electronics, software, and high-voltage systems. Key barriers to EV adoption include high purchase costs, limited charging infrastructure, and shortages of skilled technicians, which collectively affect consumer confidence beyond the point of acquisition. The analysis shows that after-sales services constitute both a technical and economic bottleneck in large-scale EV diffusion. The existing literature predominantly emphasizes theoretical solutions, such as digitalized maintenance and data-driven business models, with limited focus on practical implementation strategies. This paper concludes that sustainable EV adoption depends not only on technological and infrastructural progress but also on workforce adaptation, proposing a transitional management framework to support independent workshops in shifting toward fully electric service operations. Full article

Despite growing scholarly attention to electric vehicle adoption in Southeast Asia, no study has systematically compared battery electric vehicle (BEV) readiness across Thailand, Lao PDR, and Vietnam using a unified demand–supply framework. This paper develops and applies a seven-dimension BEV Country Readiness Assessment framework encompassing supply-side factors (parts and materials sourcing, manufacturing, and after-sales support), demand, and enabling environment (legal and regulatory, government support, and market development), with maturity scores (1–5) assigned across 16 sub-dimensions based on national statistics, industry reports, and primary field research conducted between August 2024 and March 2026. Thailand scores highest overall (3.43/5.0) as a regional production hub with balanced readiness; Vietnam follows (3.26/5.0) with the highest demand score driven by VinFast’s ecosystem; and Lao PDR scores lowest (1.95/5.0) yet exhibits a notably high EV/ICE registration ratio driven by fleet-led adoption in a very small absolute market. The findings reveal complementary rather than competitive roles within the regional BEV value chain, with energy security, supply chain dependency on China, and institutional capacity identified as critical determinants of long-term readiness. Full article

Program KBLBB was implemented to reduce carbon emissions and mitigate climate change by 2030. Total sales of Battery Electric Vehicles (BEVs) in Indonesia until June 2025 are 107,428, with the increase in sales resulting in a proportional rise in EV battery waste. EV battery waste requires comprehensive policy recommendations for its management, as in Indonesia. The goal of this research is to develop a sustainable assessment for an EV battery waste management model that addresses environmental, economic, and social perspectives. The assessment is carried out using the End-of-Waste framework model, Reuse, with recycling technology hydrometallurgy for Nickel Manganese Cobalt (NMC) and Lithium Ferro Phosphate (LFP) batteries. The results show that the environmental impacts of waste from NMC batteries are 20% smaller than those of LFP batteries, with 80% of the impacts. The total cost of waste from LFP batteries is lower than that of NMC batteries. The S-LCA risk score shows the same results for waste from NMC and LPF batteries: a very high risk for actual female employment, unequal remuneration, no collective bargaining indicators, and no right to organize. Sensitivity analysis results for EV battery waste management model for NMC batteries with hydrometallurgy, collection level of 30%, and recovery rate of 85%. Full article

The growing penetration of electric logistics vehicles (ELVs) poses a significant challenge to electric utility site selection. This paper addresses the problem of joint site selection for electric logistics vehicle charging and swapping stations (CSSs). First, a joint site selection model is introduced to characterize the problem, and an improved genetic algorithm (IGA) is designed to solve this model. Derived from the standard genetic algorithm (SGA), the IGA incorporates local search operations, evolutionary inversion operations, and an elitist preservation strategy to enhance performance. On this basis, small-scale numerical simulations are conducted to determine the optimal parameters, thereby guaranteeing optimal algorithmic efficiency. Subsequently, large-scale numerical simulations are performed, with key indicators recorded including the optimal routing length, battery replenishment frequency, number of stations, number of ELVs, and solution time. Finally, analysis across three efficiency levels demonstrates that joint siting improves distribution efficiency by 39.38%, increases grid electricity sales by 46.89%, and reduces total transportation costs by 26.28%, with the optimization scheme validated across six different numerical scenarios. Overall, the joint site selection proposed in this paper has enhanced the benefits of relevant stakeholders and provided a reference for building a low-carbon transportation chain. Full article

The current study aimed to investigate complex links among a large set of anxiety-related variables and identify targets for well-being interventions in a large sample of male and female vocational education training students. In total, 28 psychological constructs, such as self-esteem, parental pressure and dissatisfaction and motivation, were assessed in four groups of VET students (mode age: 16). The sample included 3069 females in ASSP schools (nursing and caring); 2108 females and 1772 males in Commerce schools (sales and management); and 2262 males in MELEC schools (electricity and maintenance). We used Gaussian Graphical models (GGMs) that allow for building sparse models of links among multiple variables and detecting targets for interventions via the identification of the most central nodes. We showed gender differences in absolute means for some variables (higher self-esteem and math grades in males; higher anxiety and error sensitivity, but stronger endorsement of mastery approach achievement goals in females), as well as in network structure. GGMs suggested that the key nodes were self-reported math competence for females in the ASSP group, self-regulation for females in Commerce, and mastery approach goals for males in both MELEC and Commerce groups, and that these should be differentially targeted by educational interventions in these populations. Full article