Search Results (1,226)

The global transition to electric mobility is crucial for reducing transportation-related emissions, although there is a scarcity of empirical research on customer adoption psychology in transition economies in Central Asia. This study investigates the economic and structural drivers of electric vehicle purchase intention in the Republic of Uzbekistan. Data collected from prospective customers across large city hubs were analyzed using a dual hierarchical multiple linear regression model, supported by an empirical bootstrapping procedure with 2000 resamples, based on the rational choice theory and bounded rationality. The structural model shows that baseline socio-demographics explain insignificant initial variance ($R^2$ = 0.105); however, the integration of primary theoretical constructs yields a significant incremental variance change ($\Delta R^2$ = 0.096), explaining 20.1% of the total variance. Inferential tracking confirms that government incentives are the only statistically significant driver of the purchase intention (p = 0.009). Conversely, purchase cost (p = 0.251) and charging infrastructure (p = 0.475) lack direct significance. However, partial collinearity and infrastructure expectation effects systematically change these localized contact points. The study concludes that consumer intent in this emerging marketplace is primarily anchored to macro-level institutional policy signaling rather than immediate vehicle-specific characteristics or current physical network constraints. Full article

This study investigates the relationship between renewable energy (RE) expansion and economic diversification in Egypt over 1990–2023 using a nonlinear autoregressive distributed lag (NARDL) framework. Egypt’s fossil fuel share stands at approximately 93% of primary energy supply, yet the country has committed to a 42% renewable electricity target by 2035. Despite quadrupling utility-scale RE capacity from 2.8 GW to 11.2 GW between 2015 and 2023, the Economic Diversification Index (EDI) has remained broadly stagnant. The bounds test confirms long-run cointegration (F = 6.760), exceeding small-sample critical values at the 1% level. Long-run estimates reveal that positive RE shocks are associated with lower diversification (θ⁺ = −0.571, p = 0.035) and negative shocks exhibit a statistically similar adverse effect (θ⁻ = −0.271, p = 0.024). Oil rents exhibit a positive long-run association (β = 0.145, p = 0.003). The error-correction term (−0.569) indicates approximately 57% annual adjustment. The Wald test provides marginal evidence against long-run symmetry (F = 2.999, p = 0.097). To complement the Granger causality analysis and address small-sample concerns, we additionally implement the Toda and Yamamoto augmented VAR procedure, which confirms robust unidirectional temporal precedence from LRE to LEDI (χ² = 23.48, p < 0.001) without reverse feedback (χ² = 2.25, p = 0.133). These patterns are interpreted through the lens of the Green Dependency Paradox—a conceptually distinct framework characterized by three mechanisms absent from classical resource curse theory: technology-mediated capital flight, procurement-induced deindustrialization, and policy-reversible lock-in operating under conditions of high import content, absent local content mandates, and fragmented industrial policy coordination. A tri-phase, evidence-grounded policy framework is proposed. All findings are explicitly conditional on Egypt’s current institutional context. Full article

The European 2035 decarbonisation framework rests on a conditional premise—that higher renewable-electricity penetration accelerates battery electric vehicle (BEV) adoption—yet it has not been tested at the panel level. The question is timely: the December 2025 Automotive Package would soften the 2035 target from 100 to 90 percent CO₂ reduction and permit continued production of plug-in hybrids beyond 2035, while the Alternative Fuels Infrastructure Regulation (AFIR) imposes binding charging-coverage targets from 2025 onwards. We assemble an annual panel of 31 European economies over 2015–2024 (310 country-year observations) and combine a two-way fixed-effects baseline on five disaggregated powertrain shares, an interaction model with public charging coverage as a moderator, and a Hansen-style threshold panel. The within-country BEV-share coefficient on renewable-electricity penetration is statistically null (β = +0.18, p = 0.247), rejecting the linear premise. The plug-in hybrid share, by contrast, responds positively and unconditionally (β = +0.36, p = 0.001)—a “PHEV paradox” of compositional response. The BEV channel, by contrast, is conditional on infrastructure: its marginal effect rises with public charging coverage and is positive only in the upper part of the charging distribution (interaction β₃ = +0.13, p = 0.027). A formal Hansen-style threshold test in the renewable share does not reject the linear specification (sup-F = 0.73, bootstrap p = 0.97), so the BEV conditionality is identified through the charging-coverage interaction. The findings characterise a two-speed European transition. The first channel reflects compliance-led PHEV hedging; the second reflects BEV charging network complementarity enabled by AFIR-mandated coverage. Subsidy rebalancing away from PHEV eligibility, strict AFIR enforcement, and PHEV utility-factor reform are necessary policy levers for the 2035 framework to deliver full electrification rather than the partial electrification that current incentives yield. Full article

Small rural businesses face significant challenges due to geographical constraints, transportation costs, small market size, and low population density. On top of that, the energy crisis that arose after the start of the 2022 Russia–Ukraine war and the sanctions imposed by the EU and the US have created a stifling energy environment. The latter has exposed the businesses to the risk of energy poverty. The current study examines energy poverty within three business sectors that are prominent in the Greek countryside. These are entities firstly involved in the processing, manufacturing, and standardization of agricultural products; secondly, involved in the trade of agricultural products; and lastly, certain businesses operating in the tourist area. More specifically, this research examines the energy needs and energy obligations of these businesses as well as the energy efficiency of their facilities by simultaneously exploring the impact of European and national energy policies on addressing energy poverty in rural micro-businesses. To detect the opinions, experiences, perceptions, estimations, and expectations of entrepreneurs who maintain these businesses in rural areas, a qualitative approach was adopted utilizing personal in-depth interviews. Overall, fifteen micro-entrepreneurs were interviewed. Findings revealed that energy costs for rural businesses are becoming a major issue for their survival. Moreover, they have a substantial effect on their operational costs, exceeding other expenses and leading to an increase in energy poverty. These findings have also been confirmed by statistical data. Energy costs for small businesses range from 15% to 35% depending on the business, and during peak periods or crises, they exceed 40%. In addition, fees and taxes account for over 40% of electricity bills. Full article

With the increasing penetration of electric vehicles (EVs), multi-virtual power plant (multi-VPP) systems face growing challenges in coordinating heterogeneous flexible resources, managing stochastic EV charging and discharging behaviors, and maintaining distribution network security. This paper develops an integrated collaborative scheduling strategy for multi-VPPs with EV cluster participation. In the proposed framework, EV clusters, energy storage systems, and distributed generation units are coordinated under distribution-network operational constraints. The regulation capability of EV clusters is characterized by considering state of charge (SOC) dynamics, charging/discharging power limits, arrival and departure times, vehicle availability, and user travel requirements and is further embedded into the scheduling decision space of each VPP. To coordinate operational economy and nodal voltage security, a voltage-security-aware optimization objective is formulated and transformed into a Markov game. A multi-agent deep reinforcement learning (MADRL) method is then adopted to learn coordinated scheduling policies among multiple VPP agents. Case studies show that the proposed method achieves stable convergence after approximately 3500 training episodes, with a normalized reward exceeding 0.92, and outperforms TD3, DDPG, and PPO in terms of convergence speed and training stability. The scheduling results further indicate that the proposed strategy effectively coordinates EV clusters and energy storage systems, maintains nodal voltages within safe limits, and improves the operational performance of multi-VPP systems. These results demonstrate the applicability of the proposed framework for secure and economic collaborative scheduling in distribution networks. 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

Jordan is rapidly adopting renewable energy and electric vehicles (EVs), positioning itself as a leader in the Middle East’s energy transition. However, challenges in maintaining grid stability are rising. Time-of-Use (ToU) electricity tariffs hold promise in promoting demand-side flexibility; however, their impact in developing countries remains underexplored. This study investigates the effects of ToU tariffs on Jordan’s residential and transport sectors using historical data under a static demand assumption to isolate the direct tariff-design effect. Our results reveal that ToU tariffs may disproportionately burden low-income households, with electricity bills rising by 67% to 158%. In the transport sector, even grid-friendly EV charging results in a significant rise in bills, up to 130%. These findings raise equity concerns and highlight the need for tailored ToU structures. We conclude our study by discussing the policy implications of our findings and offer actionable insights for policymakers to ensure equitable access to affordable energy in Jordan and other developing countries facing similar challenges. Full article

As a typical model of urban green and sustainable micro-transportation, shared electric bicycles play a crucial role in short and medium-distance travel as well as in connecting with public transportation. To respond to the national concept of low-carbon travel, this study takes users of urban shared electric bicycles in Kunming, Yunnan Province as the research sample and distributes questionnaires online through the Wenjuanxing platform to conduct an investigation into the factors influencing residents’ short-term and long-term continuance usage intention of shared electric bicycles. The results of the ordered logit model show that: at the level of personal attributes, the number of family-owned electric bicycles exerts a negative impact on residents’ short-term and long-term willingness to continue using shared electric bicycles. In terms of travel attributes, travel frequency has a positive impact on residents’ long-term continuance usage intention of shared electric bicycles, while exerting little influence on their short-term continuance usage intention. As for the original travel modes, groups with the habit of walking show a rejection of shared electric bicycles. From the perspective of attitudinal perceptions, independent variables reflecting instantaneity characteristics such as riding speed, unlocking speed and easy electric bicycle returning have a promoting effect on residents’ short-term continuance usage intention; independent variables reflecting sustainability characteristics such as good endurance capacity contribute to residents’ long-term continuance usage intention, while the overall travel comfort has a positive effect on the continuance usage intention across all time periods. Based on the model results, this paper puts forward policy recommendations from four aspects to promote urban residents’ continuance usage intention of shared electric bicycles. Full article

The transition of thermal power systems toward lower-carbon electricity raises a critical strategic question: whether to rely on cross-border electricity imports or reactivate domestic nuclear capacity under supply constraints. This study examines the trade-offs between these alternatives within a sustainable finance framework. A contingent-claim model is developed in which a life insurer provides long-term financing to a biomass-energy supplier, a thermal power plant, and a nuclear power plant operating under carbon-pricing regulation. The framework links electricity-market decisions with financial risk valuation, allowing the joint effects of biomass utilization, carbon regulation, electricity imports, and nuclear-security risks to be evaluated. The results show that biomass integration and tighter carbon regulation reduce short-term profitability in thermal generation but support long-run decarbonization. Cross-border electricity imports improve system flexibility and reduce operational volatility, strengthening the financial position of thermal producers. In contrast, nuclear-security disruptions significantly increase default risk for nuclear assets, reflecting their exposure to operational and regulatory uncertainty. By integrating energy-transition strategies with contingent-claim valuation, the analysis highlights the role of financial intermediation in shaping investment incentives and risk allocation in the electricity sector. The findings suggest that coordinated policies combining market integration, low-carbon transition strategies, and stable financing mechanisms can enhance system resilience. Full article

Electric scooters (e-scooters) have rapidly integrated into university transportation networks; however, there is limited empirical understanding of users’ and non-users’ perceptions, which is essential for developing effective and inclusive policies. This study addresses this gap by analyzing the differential perceptions of e-scooter adoption, safety, and policy preferences at Louisiana State University (LSU). A quantitative, cross-sectional survey was administered to 1036 respondents (592 users and 444 non-users). Statistical analyses, including Chi-square tests and Binary Logistic Regression, were used to identify key perceptual differences and behavioral predictors of e-scooter usage. Results show that users were predominantly male undergraduates, with speed (90%) and convenience (61%) as the primary motivators. Users were over 12 times more likely to perceive e-scooters as safer than walking. In contrast, non-users cited frequent scooter misplacement (84%) as their top barrier to adoption. Logistic regression confirmed that concern about misplacement (Odds Ratio = 0.076) and support for restrictive policies were strong negative predictors of use, while belief in safety and low cost were positive predictors. These findings may help inform campus micromobility policy discussions. The strong negative perceptions associated with scooter misplacement suggest that designated parking hubs and geofencing strategies could help improve campus operations and pedestrian accessibility. In addition, because safety perception was identified as an important predictor of e-scooter use, targeted safety awareness and educational initiatives may help improve rider behavior and address perceived operational safety concerns. This strategy ensures a balance between user adoption incentives and the safety/accessibility needs of the entire university community. Full article

Accurate short-term electricity load forecasting is crucial for day-ahead scheduling and secure operation of power systems. However, electricity load series exhibit significant non-stationarity, with complex coupling between low-frequency trends and high-frequency fluctuations, making it difficult for conventional forecasting models to simultaneously characterize the overall trend and stochastic disturbances. To address this issue, this paper proposes a short-term load forecasting model based on STL decomposition and CNN-BiLSTM optimized by deep reinforcement learning. First, the original load series is decomposed into trend, seasonal, and residual components using the STL algorithm. Second, a dual-channel parallel forecasting architecture is constructed: the linear channel uses a linear regression model to predict the trend and seasonal components, thereby characterizing the low-frequency variations in the load; the nonlinear channel uses a CNN-BiLSTM framework optimized by deep reinforcement learning to predict the high-frequency residual component, and this process is formulated as a Markov decision process. Specifically, the attention-based CNN-BiLSTM serves as the policy network, and its forecasting strategy is dynamically optimized under the guidance of a reward function to enhance the modeling capability for high-frequency stochastic fluctuations. Finally, the load forecasting results for the next 24 h are obtained through dual-channel result reconstruction. Experimental results based on the ERCOT system-level load data show that the proposed model achieves superior forecasting performance, with a root mean square error of 976.4 MW and a mean absolute percentage error of 1.81%. Further multi-season testing, meteorological perturbation analysis, fair comparison under the same STL preprocessing, and ablation experiments demonstrate that the proposed model maintains good forecasting performance under different seasonal scenarios, meteorological input errors, and fair experimental settings, thereby validating its effectiveness for short-term load forecasting. Full article

The optimal reconfiguration of electrical distribution feeders is a fundamental strategy for reducing active power losses and improving voltage profiles, yet it remains a challenging mixed-integer nonlinear programming (MINLP) problem due to the combinatorial explosion of radial topologies and the nonlinearities introduced by power flow equations. This paper proposes a novel master–slave methodology that integrates a Chu and Beasley genetic algorithm (CBGA) with a minimum spanning tree (MST)-based repair mechanism to address these challenges. In the master stage, the CBGA explores the binary space of switching decisions via steady-state population management, duplicate elimination, and stagnation restart policies. A key contribution lies in the MST-based repair procedure, which ensures that every individual generated by crossover and mutation is projected onto a feasible radial and connected configuration, effectively confining the search to the constrained solution space without recourse to penalty functions. A systematic weight-design rule preserves the Hamming distance between infeasible offspring and repaired solutions, minimizing the distortion of genetic information. The slave stage evaluates each candidate topology using a successive approximations power flow solver, assessing electrical feasibility and computing active power losses. The proposed methodology is validated on multiple test feeders, ranging from small 9- and 24-bus networks to large-scale benchmarks including 33-, 69-, 84-, 136-, and 415-bus systems. A comparison against the deterministic sequential switch opening method (SSOM) and a specialized tabu search demonstrates that the CBGA-MST consistently matches the best-known optima in the literature, achieving loss reductions of up to 9.63% compared to SSOM on the 415-bus system. A statistical analysis over 100 independent runs confirms the algorithm’s robustness, with zero standard deviation for networks of up to 69 buses and a standard deviation of only 2.99 kW (0.51%) for the 415-bus system. The findings confirm that the proposed approach offers superior scalability, robustness, and solution quality, positioning it as a practical and effective tool for distribution system operators seeking to enhance network efficiency under peak load conditions. Full article

This paper estimates an extended Cobb–Douglas production function for five major economies (China, the EU, India, the Russian Federation, and the USA) over the period of 1990–2023, incorporating electricity production from renewable, non-renewable, and nuclear sources as discrete production inputs. To capture complex properties in time series, a comprehensive econometric strategy is adopted, which combines linearity tests, multiple detection of structural changes, linear and nonlinear unit root tests, autoregressive distributed lag (ARDL) bounds testing for cointegration, error correction modelling, and error correction model (ECM)-based Granger causality. The results confirm the presence of mixed orders of integration, nonlinear dynamics, and structural instability across countries, justifying the use of the ARDL framework. The bounds test reveals a long-run cointegrating relationship between output, capital, labour, and energy inputs in all five economies. Long-run elasticities differ significantly across countries, highlighting strong structural heterogeneity. The short-term dynamics show that energy shocks have asymmetric and country-specific effects on output, while the error correction terms confirm convergence towards the long-run equilibrium, with the fastest adjustment observed in the EU and the slowest in the US. The causality results support the hypothesis of growth-led energy in China, India and the Russian Federation, while two-way feedback is observed in the EU and the US. These findings suggest that energy policy cannot be uniform across countries and must be aligned with domestic production structures, technological intensity, and energy transition stages. Full article

Maintaining aging equipment is essential for ensuring the reliability and performance of power substations, which are critical components of electric power systems. Preventive maintenance (PM) policies are widely used in this context, but their definition is often based on fixed schedules that do not explicitly account for system degradation and failure dynamics. This paper proposes a structured decision-making framework for evaluating the extension of PM intervals in complex engineering systems. The approach integrates Reliability Block Diagram (RBD) modeling with an imperfect maintenance representation based on the Generalized Renewal Process (GRP) and a Kijima model, allowing the representation of cumulative degradation effects. A decision criterion combining system reliability and the expected number of failures is defined to assess the feasibility of maintenance strategies. The framework is applied to a power substation case study, with emphasis on the transmission line subsystem, considering different PM extension scenarios and parameter uncertainty through sensitivity analysis. The results show that maintenance interval extension must be treated as a constrained problem, in which reductions in maintenance effort must be balanced against increased degradation and failure risk. For the presented case study, only moderate extensions are found to be consistently feasible across all evaluated conditions. The proposed approach provides a systematic and practical method for supporting maintenance planning under uncertainty, enabling consistent and transparent evaluation of maintenance strategies. Full article

This study examines patterns of digital inclusion in Ziguinchor, Senegal, using household survey data combined with spatial indicators of infrastructure and access. We construct a Digital Inclusion Index (DII) capturing realized digital practices and a Composite Digital Access Score (CDAS) reflecting enabling conditions across six domains, including technological equipment, electricity, affordability, and spatial access. The results reveal substantial variation in digital inclusion across quartiers, with strong associations between inclusion outcomes and infrastructural and socioeconomic conditions, particularly electricity reliability, device quality, and mobility constraints. A key finding is the coexistence of near-universal smartphone ownership with relatively low levels of internet use, indicating a pronounced gap between access and effective engagement. This divergence suggests that device ownership alone is insufficient to ensure meaningful digital participation. A typology combining DII and CDAS further highlights mismatches between realized use and enabling conditions, identifying groups of “under-utilizers” and “over-achievers.” The findings are consistent with multidimensional digital divide frameworks and point to the importance of both structural conditions and user capabilities. Given the cross-sectional design, results should be interpreted as conditional associations rather than causal effects. The study contributes a place-based analytical framework for diagnosing digital inclusion gaps in secondary cities and provides evidence to inform targeted, context-specific policy interventions. Full article