Search Results (698)

Mobility hubs promise to reduce car dependence and make multimodal travel work in practice, yet behavioural evidence remains limited when hub improvements coexist with easier car access. This article examines the tension at Rome Trastevere, an urban rail node that gradually acquires mobility-hub functions while facing improved parking access near Piazza della Radio. The empirical analysis combines a pilot survey of 83 users with an on-site stated preference survey of 204 valid respondents. The stated preference instrument uses a route-based feasible-choice design with nine choice sets per experiment: respondents evaluate alternatives among bikes, walking, e-scooters, e-mopeds, public transport, private cars, and shared cars under variations in travel time, travel cost, and search time. The paper estimates a multinomial logit model in Apollo and uses sample enumeration, supported by Monte Carlo simulation, to assess four parking and shared-mobility scenarios and produce confidence intervals around predicted probabilities. Results show that users respond to time, monetary cost, and search friction in coherent and policy-relevant ways. Setting the car parking search time to zero increases predicted car probability only marginally, by about 0.9% relative to the baseline. By contrast, a EUR 1/h increase in parking cost reduces predicted car probability by about 14.7%, while a EUR 1.5/h increase reduces it by about 22.4%. A coordinated scenario combining higher parking cost and lower shared-mode search time produces the lowest predicted car probability and strengthens e-scooter and e-moped alternatives, while public transport remains the dominant option. Findings indicate that parking pricing steers behaviour more clearly than parking convenience destabilizes it in the tested range. The paper shows that mobility-hub performance depends on coordinated access management, including parking regulation, shared-service reliability, and legible multimodal transfer. Full article

The worldwide shift to electric mobility has intensified in recent years owing to heightened apprehensions over greenhouse gas emissions, energy security, and the necessity for sustainable transportation systems. Electric vehicles (EVs) are acknowledged as a viable alternative for diminishing reliance on fossil fuels and enhancing energy efficiency in the transportation sector. While affluent nations have achieved considerable advancements in electric vehicle adoption and charging infrastructure, numerous developing countries still encounter significant technical and infrastructural obstacles that hinder extensive EV integration. In South Africa, these difficulties are exacerbated by ongoing electrical supply limitations, deteriorating transmission and distribution facilities, and recurrent load shedding, which heighten worries about the dependability and stability of the national power grid. The rising adoption of electric vehicles adds extra electrical demands to power systems, especially at the distribution network level, where most of the charging takes place. Disorganized EV charging can substantially modify current load patterns, leading to heightened peak demand, voltage variations, transformer overload, and network congestion. The technical consequences are especially significant in South Africa, where the power grid functions with constricted generation capacity and minimal reserve margins. Various mitigating measures have been suggested to tackle these difficulties, including intelligent charging, demand-side management, time-of-use pricing, and vehicle-to-grid technologies. This paper establishes a basic theoretical framework through an extensive literature review to investigate the technological problems related to electric vehicle adoption in South Africa, while assessing the environmental and economic ramifications for sustainable urban transportation systems. Full article

Weak grid infrastructure and the absence of flexible storage are among the principal barriers to reliable, low-carbon energy access in sub-Saharan transmission systems. This paper proposes a hierarchical multi-objective framework for the optimal siting and sizing of battery energy storage systems (BESSs), applied to the 130-bus Mali transmission network within the EMERGE project. The upper level employs NSGA-II to simultaneously maximize daily price arbitrage revenue and minimize active power losses; the lower level solves a network-constrained DC optimal power flow with thermal branch limits enforced as hard linear inequalities via the Power Transfer Distribution Factor (PTDF) matrix. Over 500 generations, the framework identifies Bus 91 (SIRAKORO II, 150 kV) as the dominant storage location, achieving a maximum daily revenue of approximately €10,033 at a marginal loss increment of $6.7\times {10}^{-3}$ MWh. The resulting Pareto front gives Mali system planners a quantitative tool for trading off private investment returns against grid-level environmental impact, demonstrating that rigorous network-constrained BESS planning is technically tractable and economically viable in the resource-constrained context of sub-Saharan energy transitions. Full article

To promote the sustainable development of agriculture and reduce resource waste, this paper investigates sustainable financing strategies for a green fresh agri-food supply chain. We employ a purely theoretical Stackelberg game model and numerical simulations based on hypothetical parameters to develop three financing models for a supply chain consisting of one capital-constrained farmer and one retailer, considering consumers’ dual sensitivity to product freshness and greenness. Analytical and numerical results reveal that: (1) with low financing rates, internal financing effectively alleviates under investment in preservation, leading to higher wholesale/retail prices. In a green-sensitive market, the resulting price premium compensates for cost increases, avoiding the “low quality–low price” trap under external financing. (2) The retailer’s total profit decreases as the internal financing rate rises; higher interest income cannot offset demand loss caused by reduced preservation effort. Thus, a low- or zero-interest strategy maximizes the retailer’s operational profit. (3) As consumer sensitivity to freshness and greenness increases, profit growth under internal financing displays convexity. However, under extremely high freshness sensitivity, external financing yields stronger marginal incentives, suggesting that retailers should adjust profit allocation in the high-end market. The findings provide theoretical guidance for financing mode selection and practical insights for promoting green agricultural sustainable development. Full article

The rapid growth of the sharing economy has improved resource utilization in car-sharing, yet it has also sharpened market competition and diversified user demand. A persistent obstacle is the low coordination efficiency between asset-heavy operating companies and traffic-driven platforms, whose misaligned objectives waste social resources. This paper uses differential game theory to analyze their dynamic coordination strategies and benefit allocation mechanisms. The Nerlove–Arrow model captures the evolution of brand goodwill, while the company’s decisions on station layout, vehicle dispatch, and pricing, together with the platform’s advertising investment, form the core decision variables in a two-party game framework linking the asset side and the traffic side. Compared with the non-cooperative Nash equilibrium, the cooperative mode removes the double marginalization effect, strengthens the investment incentives of both parties, and raises the system’s steady-state goodwill and total profit, achieving a Pareto improvement. To ground the cooperative framework in rigorous theory, we supply a verification theorem confirming that the linear candidate value functions satisfy the Hamilton–Jacobi–Bellman equations over the entire admissible state space. A formal proof of instantaneous rationality ensures that neither party falls into a cooperation trap on the horizon $[0,T]$, and the asymptotic stability of the steady-state goodwill trajectory is established. We further endogenize the revenue-sharing coefficient through a generalized Nash bargaining model that admits asymmetric bargaining structures, and introduce a Stackelberg leadership benchmark as a third comparative regime. Sensitivity analyses with respect to the discount rate and user heterogeneity confirm the robustness of the findings. A dedicated discussion section bridges the gap between idealized parameterization and data-driven calibration, describing practical pathways via A/B testing, user churn metrics, and econometric estimation of demand parameters. The results offer a scientific decision-making reference for strategic cooperation in the car-sharing industry. Full article

Rail-transit station areas concentrate commercial services, employment, and intensive land development, but their vitality is shaped by multiple built-environment conditions rather than rail accessibility alone. Focusing on 20 stations along the Osaka Metro Midosuji Line in Japan, this study uses Japanese chome units, which are small neighborhood-level address and statistical units, within an 800 m pedestrian catchment as analytical units and measures commercial-service agglomeration intensity, employment intensity, and commercial–employment composite vitality. The composite indicator measures the static co-concentration of commercial-service provision and employment carrying capacity, with pedestrian flow, consumption activity, and dwell time treated as separate dimensions of station-area vitality. Ten station-area environmental variables are examined using ordinary least squares (OLS), Lasso, Random Forest, Back-Propagation (BP) Neural Network, and extreme gradient boosting (XGBoost) models, with Shapley additive explanations (SHAP) applied to interpret variable contributions and nonlinear responses. Results show that nonlinear models generally outperform linear models. Development intensity, officially assessed land price, and network distance to the nearest metro station are the most influential variables, showing threshold, marginal, and non-monotonic effects. Split models indicate that commercial-service agglomeration is more sensitive to rail proximity and street-network conditions, whereas employment intensity is more associated with development intensity and land price. These findings support fine-grained station-area renewal and mixed-function planning. Full article

Demand response (DR) plays a key role in enhancing power system flexibility under increasing renewable penetration, yet most existing approaches rely on aggregate demand models that fail to capture appliance-level heterogeneity. A bilevel programming framework for DR incentive design incorporating non-intrusive load monitoring (NILM)-based flexibility estimation is proposed. A conditional factorial hidden Markov model (CFHMM) is used to disaggregate smart meter data and recover appliance-level consumption patterns, which are then mapped to willingness-to-accept (WTA) values to construct device-informed DR potential functions. These estimates are embedded in a bilevel optimization model, where a retailer determines optimal incentives while accounting for the endogenous impact of demand response on locational marginal prices through market clearing. The model is reformulated as a single-level mixed-integer linear program using Karush–Kuhn–Tucker (KKT) conditions. Case studies using real-world data and the IEEE test system show that the proposed framework produces more effective incentive strategies than aggregate DR modeling, leading to improved DR utilization and higher retailer profitability. Full article

We address this question by jointly modeling the distributional dynamics of the U.S. and Canadian term premia. Our approach combines a flexible marginal specification—the Skewed Generalized Error Distribution—with a flexible bivariate copula (BB7) to capture evolving cross-market dependence. We illustrate the usefulness of this framework by examining December 2024, a period marked by a sharp rise in the U.S. term premium, and track how the forecasted joint distributions evolved throughout this episode. We document a striking change in conditional tail dependence between U.S. and Canadian term premia over this period. While term premia serve as a motivating application, our framework is applicable to a broad class of asset prices and macro-financial variables. Full article

Reliable prediction of carbon allowance prices plays a crucial role in emissions trading systems, particularly for market participation, regulatory compliance, and long-term cost planning. In China, regional carbon markets differ markedly in trading activity, price formation mechanisms, and responsiveness to external signals, which limits the effectiveness of conventional single-model forecasting approaches. This study develops a unified machine learning framework designed to accommodate such cross-market heterogeneity. The framework incorporates a diverse set of explanatory variables, including historical price-based indicators, trading volume information, inter-market linkage signals, and macroeconomic factors. Three ensemble-based learning algorithms-XGBoost, LightGBM, and Random Forest—are implemented, and their outputs are further integrated using a weighted aggregation scheme to improve generalization across markets. The empirical evaluation across seven pilot markets shows that, while LightGBM consistently performs well as a standalone model, the proposed ensemble framework achieves superior stability and adaptability under varying market conditions. The forecasting accuracy is high across all cases, with coefficients of determination above 0.74 and reaching values greater than 0.92 in most markets. Further investigation through feature ablation highlights the heterogeneous role of external information, indicating that predictor importance varies significantly between markets and that no universal feature combination yields optimal performance. Leveraging the forecast outputs, the study also demonstrates practical applications in decision support, including timing strategies for allowance sales and dynamic cost assessment in offshore wind engineering scenarios. By systematically evaluating the marginal contribution of different information groups to predictive uncertainty, the framework offers a flexible tool for managing information-structure uncertainty in fragmented carbon markets. The proposed framework offers an integrated solution that connects predictive modeling with operational and engineering decision on processes, providing a flexible tool for managing uncertainty in fragmented carbon markets. Full article

Following the 2022 disruption of Russian pipeline gas, European countries shifted toward liquefied natural gas (LNG) at markedly different speeds; yet, the drivers of this variation remain poorly understood. This study asks what explains these differences. Using a balanced panel of eight major European gas importers over 2015–2024 (80 observations), the study models the share of LNG in total gas imports as the dependent variable, reversing the conventional approach that treats LNG as an explanatory variable for gas prices. The interaction between the post-2022 structural break and storage fill levels is negative and statistically significant (β = −0.006, p = 0.019 clustered; p = 0.002 Driscoll-Kraay), suggesting that countries with lower storage reserves tended to increase their LNG dependence more strongly. This result is robust across seven of eight specifications and survives time-trend controls and leave-one-country-out analysis. Marginal effects reveal that the storage–LNG relationship was absent before the shock and emerged only after the disruption. Renewable energy penetration emerges as a significant positive predictor. Full article

Given the combined limitations of carbon peaking and carbon neutrality goals, the economic cost of industrial emission reduction in China has become increasingly prominent and regionally differentiated. This study evaluates the shadow prices of CO₂ within the Chinese sector and examines the spatiotemporal evolution of carbon abatement costs across provinces, as well as the underlying influencing mechanisms. To capture the evolution of marginal abatement costs (MAC), we use two non-parametric frameworks based on provincial panel data from 2010 to 2022: slack-based measure (SBM), and the directional distance function (DDF) that accounts for unwanted outcomes. In addition, a fixed effects model with regional and temporal effects was constructed to determine the key determinants of marginal carbon reduction costs. Empirical evidence suggests that: (1) From 2010 to 2022, China’s industrial carbon abatement marginal cost has clearly increased, indicating that emission reduction has gradually shifted from a low-cost stage driven by efficiency improvement to a high-cost stage relying on structural adjustment and advanced technologies. (2) Carbon abatement costs exhibit significant provincial heterogeneity by a small number of high-cost provinces (mainly in developed regions) and a majority of low-cost regions. (3) The industrial carbon emission reduction cost curves in some provinces of China have obvious similar evolution paths, and some areas also show a lagging phenomenon. (4) Carbon emission intensity is the dominant factor influencing abatement costs and presents a significant U-shaped relationship, while urbanization increases cost pressure and trade openness helps reduce abatement costs through structural optimization and technology spillovers. Full article

The IMO Net-Zero Framework and its carbon regulations impose binding constraints on fuel selection and fleet evolution. A techno-economic optimization model is developed to quantify this interaction along the Shanghai–Los Angeles green shipping corridor. The framework integrates vessel-level Mixed-Integer Non-Linear Programming (MINLP) with a Multinomial Logit formulation to simulate fleet diffusion, minimizing Total Cost of Ownership (TCO) over 2026–2050. The results identify a persistent marginal compliance regime driven by the tiered carbon penalty structure. Rather than achieving full compliance, fleets systematically position their Greenhouse Gas Fuel Intensity (GFI) near the penalty threshold, where limited penalties remain economically preferable to high-cost zero-carbon fuels. This behavior sustains fossil LNG as the dominant transitional option and delays the TCO crossover with ammonia until 2043. Under intensified penalties, the crossover advances to approximately 2030, triggering rapid cost escalation for LNG and eliminating the economic viability of drop-in biofuel strategies. Across all scenarios, absolute zero GHG emissions are not achieved due to residual fossil dependence and upstream Well-to-Wake (WTW) emissions. The transition is therefore bounded by the interaction between penalty avoidance behavior and the pace of Power-to-X fuel deployment. These findings indicate that carbon penalty levels determine the timing of decarbonization, while relative fuel prices govern technology selection, with direct implications for corridor-specific fuel infrastructure and investment decisions. 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

Improving window performance is a key strategy for reducing heating energy demand in residential buildings; however, the economic feasibility of upgrading to higher performance grades remains uncertain, particularly for single-detached houses. This study quantitatively evaluates the energy-saving performance and economic feasibility of window performance grade improvements in single-detached houses in South Korea. Heating energy demand was estimated using ECO2-OD (Energy Conservation Optimization Tool for One-zone Dwelling), the national standard simulation tool adopted for the Building Energy Efficiency Certification (BEEC) and Zero Energy Building (ZEB) certification systems. Representative residential prototypes constructed in 1980, 1987, and 2001 were analyzed to reflect differences in envelope performance associated with construction vintage. Window upgrades from the baseline grade to Grades 3, 2, and 1 using polyvinyl chloride (PVC) windows were simulated under consistent building geometry and operating conditions. Heating energy demand reductions were converted into annual energy cost savings using market-based natural gas prices. Economic feasibility was assessed using annual indicator-based metrics—unit cost of energy saving (UCES), payback period (PBP), and return on investment (ROI)—as well as discounted cash flow-based life-cycle metrics, including net present value (NPV) and discounted payback period (DPB). The results show that heating energy demand decreases consistently with improved window performance across all construction years; however, marginal energy savings diminish at higher performance grades, while investment costs increase. Upgrading from the baseline to intermediate performance grades yields the most favorable economic outcomes, particularly for houses constructed in 2001, whereas upgrades to the highest performance grade often fail to achieve economic feasibility when time value is considered. These findings indicate that uniform application of the highest-grade windows may not be economically optimal. Unlike previous studies that mainly focused on thermal performance or case-specific retrofit outcomes, this study compares multiple window performance grades across construction-year-specific single-detached house prototypes under a unified economic evaluation framework. This study highlights the importance of construction year-specific and performance-tiered retrofit strategies and provides quantitative evidence to support cost-effective window retrofit policies for single-detached residential buildings. Full article

This multi-level study investigates the economic resilience of mango farmers during the COVID-19 pandemic and their pesticide management practices under Thailand’s Q-GAP (Quality Good Agricultural Practices) certification standard. Field surveys compared the economic outcomes of 104 certified and 151 non-certified farmers from 2019 to 2023, together with pesticide use practices during the year preceding the 2024 survey. The sample was drawn from three provinces in northern Thailand: Phitsanulok, Phetchabun, and Phichit. The statistical analysis of the collected information produced several key findings. Certified farms achieved significantly higher production and sales than non-certified farms over the five-year period, mainly due to larger farm size and higher prices obtained from premium export market sales. Certified farmers also adopted a wider range of coping strategies during the pandemic, whereas non-certified farmers mainly reduced mango investments related to mango cultivation. Certified farmers reported significantly higher rates of insecticide and fungicide adoption, as well as significantly higher annual pesticide application frequencies across all three pesticide categories. Residue analysis showed no significant difference in organophosphate (OP) residues between the two groups; however, pyrethroid (PY) residues were significantly higher among certified farms. This pattern suggests that certified farmers may apply pesticides more intensively to satisfy the aesthetic requirements of premium export markets. Regression results further showed that herbicide application frequency was the only factor marginally associated with PY-type residue levels among certified farmers, although this finding should be interpreted cautiously because of the weak model fit. Full article