Search Results (309)

Electric vehicles (EVs) have been the most dependable and feasible choice for decarbonizing road transport over the last decade. To ensure the advancement of EVs and establish them as a sustainable alternative to internal combustion engine (ICE) vehicles, the EV sector and technological growth have largely relied on government subsidies. A significant challenge for EVs is their faster depreciation compared to ICE vehicles, primarily owing to swift technological advancements that propel the market while simultaneously rendering older EV models outdated too soon. Another factor that leads to the quicker depreciation of EVs is subsidies. The anticipated cessation of subsidies is expected to provide the required leverage to mitigate the rapid value decline in EVs, given the larger price disparity between new and used EVs. Batteries, which enable EVs to be a viable option, significantly contribute to the depreciation of EVs. In addition to the potential decline in EV battery performance, advancements in technology and reduced prices provide newer models with improved range at a more affordable cost. The used EV market accurately represents the rapid devaluation of EVs; consequently, the two topics are tightly related. Though it might not be immediately apparent, it seems evident that the pace of depreciation of EVs significantly contributes to the small size of the second-hand EV market. Depreciation is a key factor influencing the used EV market. This manuscript outlines the key aspects of depreciation and sustainability in the EV transition, especially those linked to rapid technological advancements, such as batteries, in addition to subsidies and the used EV market. The objective of this manuscript is to expose and analyze the relation between the drivers of the second-hand EV market, such as the cost of ownership, technology, and subsidies, and, on the other hand, present the interplay perspectives and challenges. Full article

In the context of building a new type of power system, the optimal operation of high-proportion new-energy distribution networks (HNEDNs) is a current hot topic. In this paper, a stochastic distribution robust optimization method for HNEDNs that considers energy-storage refinement modeling is proposed. First, an energy-storage lifetime loss model based on the rainfall-counting method is constructed, and then an optimal operation model of an HNEDN considering energy storage refinement modeling is constructed, aiming to minimize the total operation cost while taking into account the energy cost and the penalty cost of abandoning wind and solar power. Then, a source-load uncertainty model of HNEDN is constructed based on the Wasserstein distance and conditional value at risk (CvaR) theory, and the HNEDN optimization model is reconstructed based on the stochastic distribution robust optimization method; based on this, the multiple linearization technique is introduced to approximate the reconstructed model, which aims to both reduce the difficulty in solving the model and ensure the quality of the solution. Finally, the modified IEEE 33-bus power distribution system is used as an example for case analysis, and the simulation results show that the method presented in this paper, through reducing the loss of life in the battery storage device, can reduce the average daily energy storage depreciation cost compared to an HNEDN optimization method that does not take the energy storage life loss into account; this, in turn, reduces the total operating cost of the system. In addition, the stochastic distribution robust optimization method used in this paper can adaptively adjust the economy and robustness of the HNEDN operation strategy according to the confidence level and the available historical sample data on new energy-output prediction errors to obtain the optimal HNEDN operation strategy when compared with other uncertainty treatment methods. Full article

In this paper, a new type of cogeneration system using LNG cold energy as a cooling source and geothermal energy as a heat source is designed and studied from the perspective of LNG cold energy gradient utilization. The system integrates power generation, cold storage, and district cooling. In order to provide more detailed information, the proposed system was analyzed in terms of energy, exergy, and economy. The effects of separator pressure, LNG pump outlet pressure, the mass flow rate of n-Pentane in ORC-I, liquefaction temperature of R23 in the cold storage module, and pump 5 outlet pressure in the refrigeration module on the performance of the system were also investigated. Additionally, the particle swarm algorithm (PSO) was used to optimize the CCHP system with multiple objectives to determine the system’s optimal operation. The optimization results show that the system’s thermal efficiency, exergy efficiency, and depreciation payback period are 66.06%, 42.52%, and 4.509 years, respectively. Full article

Exchange rate volatility poses significant challenges for emerging markets, influencing trade balances, inflation, and capital flows. South Africa’s Rand is particularly vulnerable to global risk sentiment, gold price fluctuations, and interest rate differentials, yet prior studies often analyse these factors in isolation. This study integrates them within an autoregressive distributed lag framework, using monthly data from 2005 to 2023 to capture both short-term fluctuations and long-term equilibrium effects. The findings confirm that higher global risk sentiment triggers immediate Rand depreciation, driven by capital outflows to safe-haven assets. Conversely, rising gold prices and favourable interest rate differentials stabilise the Rand, strengthening trade balances and attracting capital inflows. These results underscore the interconnected nature of global financial conditions and exchange rate movements. This study highlights the importance of economic diversification, foreign reserve accumulation, and proactive monetary policies in mitigating currency instability in emerging markets. Full article

This paper develops a nonlinear optimization model for the optimal allocation of labor and investment resources in a three-sector open economy. The model is based on the Cobb–Douglas production function and incorporates sectoral interdependencies, capital depreciation, trade balances, and import quotas. The resource allocation problem is formalized as a constrained optimization task, solved analytically using the Lagrange multipliers method and numerically via the golden section search. The model is calibrated using real statistical data from Kazakhstan (2010–2022), an open resource-exporting economy. The results identify structural thresholds that define balanced growth conditions and resource-efficient configurations. Compared to existing studies, the proposed model uniquely integrates external trade constraints with analytical solvability, filling a methodological gap in the literature. The developed framework is suitable for medium-term planning under stable external conditions and enables sensitivity analysis under alternative scenarios such as sanctions or price shocks. Limitations include the assumption of stationarity and the absence of dynamic or stochastic features. Future research will focus on dynamic extensions and applications in other open economies. Full article

Driven by growing environmental awareness and supportive regulatory frameworks, electric vehicles (EVs) are witnessing accelerating market penetration. However, a key consumer concern remains: the economic impact of battery degradation, manifesting as vehicle depreciation and diminished driving range. To alleviate this concern, EV manufacturers commonly offer performance-guaranteed free-replacement warranties, under which batteries are replaced at no cost if capacity falls below a specified threshold within the warranty period. This paper develops a symmetry-informed analytical framework to forecast time-varying aggregate warranty replacement demand (AWRD) and to design optimal battery inventory strategies. By coupling stochastic EV sales dynamics with battery performance degradation thresholds, we construct a demand forecasting model that presents structural symmetry over time. Based on this, two inventory optimization models are proposed: the Service-Level Symmetry Model (SLSM), which prioritizes reliability and customer satisfaction, and the Cost-Efficiency Symmetry Model (CESM), which focuses on economic balance and inventory cost minimization. Comparative analysis demonstrates that CESM achieves superior cost performance, reducing total cost by 20.3% while maintaining operational stability. Moreover, incorporating CESM-derived strategies into SLSM yields a hybrid solution that preserves service-level guarantees and achieves a 3.9% cost reduction. Finally, the applicability and robustness of the AWRD forecasting framework and both symmetry-based inventory models are validated using real-world numerical data and Monte Carlo simulations. This research offers a structured and symmetrical perspective on EV battery warranty management and inventory control, aligning with the core principles of symmetry in complex system optimization. Full article

The exchange rate is among the main variables determining foreign trade, as it affects the prices of both exports and imports. Meanwhile, Okun’s misery index (MI) attempts to synthesize the main issues affecting a society by combining two major macroeconomic variables—unemployment and inflation. This study examines how Mexico’s bilateral real exchange rate index with the United States influences Okun’s misery index from 2005Q1 to 2023Q3. A quantitative analysis considering both the long- and short-run relationship between Okun’s MI and the real exchange rate was performed. The results show a unidirectional relationship between the exchange rate and the misery index in the long term, as indicated by the Toda–Yamamoto test. An unrestricted vector autoregressive model was used for the short-run analysis and found that depreciation increases the MI. A variance decomposition analysis shows that the real exchange rate considerably explains variations in the MI, whereas a historical decomposition analysis suggests that this relationship primarily occurs during periods of crisis. Full article

The evolving macroeconomic landscape, shaped by the global financial crisis and the COVID-19 pandemic, poses significant challenges for economies worldwide. However, Central, Eastern, and Southeastern European (CESEE) countries have demonstrated resilience and rapid recovery during crises, driven by a surge in consumption fueled by domestic credit and robust export growth supported by flexible exchange rates and adaptive monetary policies. Prior to EU accession, substantial foreign direct investment (FDI) during privatization and restructuring facilitated knowledge and technology transfers in CESEE economies. This study examines the interplay of exports, real exchange rates, GDP growth, FDI, inflation, domestic credit, and the human development index (HDI) in the CESEE region from 1995 to 2022, covering the transition period, EU accession, and major crises. Employing a panel ARDL model, we account for asymmetric effects of these variables on exports. The results reveal that GDP, FDI, inflation, domestic credit, and HDI significantly and positively influence exports, with HDI and GDP exerting the strongest effects, underscoring the pivotal roles of human capital and economic growth in enhancing export competitiveness. Conversely, real exchange rate depreciation negatively impacts exports, though non-price factors, such as product quality, mitigate this effect. These findings provide a robust basis for targeted policy measures to strengthen economic resilience and export performance in the CESEE region. Full article

Temporary book-tax differences (BTDs) serve as critical proxies for understanding corporate earnings management and tax planning. However, the drivers of large negative BTDs (LNBTDs)—where book income falls below taxable income—remain underexplored. This study investigates the determinants and components of LNBTDs, focusing on their relationship with deferred tax assets (DTAs) and liabilities (DTLs). Utilizing hand-collected data from the tax disclosures of S&P 500 firms’ 10-K filings (2007–2023), I analyze 4685 firm-year observations to identify specific accounting items driving LNBTDs. Findings reveal that deferred revenue, goodwill impairments, R&D, CapEx, environmental obligations, pensions, contingency liabilities, leases, and receivables are significant contributors, often generating substantial DTAs due to timing mismatches between book and tax recognition. Notably, high-tech industries, like the pharmaceutical, medical, and computers and software industries, exhibit pronounced LNBTDs, driven by upfront revenue recognition for tax purposes and deferred recognition for financial reporting, capitalization, amortization and depreciation effects, and other deferred tax components. Regression analyses confirm strong associations between these components and LNBTDs, with asymmetry in reversal patterns suggesting that initial differences do not always offset symmetrically over time. While prior research emphasizes large positive BTDs and tax avoidance, this study highlights economic and industry-specific characteristics as key LNBTD drivers, with limited evidence of earnings manipulation via deferred taxes. These insights enhance the value relevance of deferred tax disclosures and offer implications for reporting standards, tax policy, and research into BTD dynamics. Full article

Addressing the urgent need for sustainable energy transitions in rural development while achieving the dual carbon goals, this study focuses on resolving critical challenges in agricultural photovoltaic (PV) applications, including land-use conflicts, compound energy demands (electricity, heating, cooling), and financial constraints among farmers. To tackle these issues, a dual-mode cost–benefit analysis framework was developed, integrating two distinct investment models: self-invested construction (SIC), where farmers independently finance and manage the system, and energy performance contracting (EPC), where third-party investors fund infrastructure through shared energy-saving or revenue agreements. Then, an integrated photovoltaic-storage agricultural greenhouse (PSAG) microgrid optimization model is established, synergizing renewable energy generation, battery storage, and demand-side management while incorporating operational mode selection. The proposed model is validated through a real-world case study of a village agricultural greenhouse in Gannan, China, characterized by typical rural energy profiles and climatic conditions. Simulation results demonstrate that the optimal system configuration requires 27.91 kWh energy storage capacity and 18.67 kW peak output, with annualized post-depreciation costs of 81,083.69 yuan (SIC) and 74,216.22 yuan (EPC). The key findings reveal that energy storage integration reduces operational costs by 8.5% compared to non-storage scenarios, with the EPC model achieving 9.3% greater cost-effectiveness than SIC through shared-investment mechanisms. The findings suggest that incorporating an energy storage system reduces costs for farmers, with the EPC model offering greater cost savings. Full article

This study evaluates the impact of grape variety and harvesting method—manual versus mechanized—on must composition, wine quality, and economic performance in the Târnave viticultural area of Romania. Four grape varieties—Pinot Noir, Sauvignon Blanc, Fetească Regală, and Muscat Ottonel—were analyzed. Manual harvesting increased reducing sugars by 4.3–5.1 g/L and decreased titratable acidity by 0.6–0.8 g/L, particularly in Pinot Noir and Muscat Ottonel. Alcohol content was higher by 0.4–0.6 vol% in manually harvested samples, and dry extract increased by 1.0–1.3 g/L. Mechanized harvesting raised catechin concentrations by 15–19 mg/L due to enhanced skin maceration, but also slightly elevated volatile acidity (by ~0.1 g/L). From an economic perspective, labor cost was reduced from 480 lei/ton (approx. EUR 96) for manual harvesting to 120 lei/ton (approx. EUR 24) with mechanization. Fuel and maintenance costs for mechanized equipment averaged 85 lei/ha (EUR 17), and equipment depreciation was estimated at 100 lei/ton (EUR 20). The total harvesting cost per ton decreased from 480–520 lei to 300–320 lei (approx. EUR 96 to EUR 64), representing a ~38% reduction. The study supports a hybrid approach: manual harvesting for sensitive or premium cultivars, and mechanization for cost-efficient, large-scale production, aligning wine quality goals with economic sustainability. Full article

In the past decade, extended reality (XR) has been introduced into healthcare due to several potential benefits, such as scalability and cost savings. As there is no comprehensive study covering all the factors influencing the selection of an XR system in the healthcare and medical domain, a Decision Support System is proposed in this paper to identify and rank factors impacting the performance of XR in this domain from an engineering design perspective. The proposed system is built upon the Supply Chain Operations Reference (SCOR) model supported by a literature survey and experts’ knowledge to extract and identify important factors. Subsequently, the factors are categorized into distinct categories, and their relative importance is specified by Analytic Network Process (ANP) models under a fuzzy environment. Two fuzzy approaches for the ANP models are compared, and the results are analyzed using statistical testing. The computational results show that the ranking agreement between the two fuzzy approaches is strong and corresponds to the fact that both approaches yield the same ranking of primary factors, highlighting the significance of reliability as the topmost factor, followed by responsiveness, cost, and agility. It is shown that while the top three important sub-factors are identical between the two approaches, their relative order is slightly varied. Safety is considered to be the most critical aspect within the reliability category in both approaches, but there are discrepancies in the rankings of accuracy and user control and freedom. Both approaches also consider warranty and depreciation costs as the least significant criteria. Full article

The integration of non-conventional renewable energy sources (NCRES) plays a critical role in achieving sustainable and decentralized power systems. However, accurately assessing the economic feasibility of NCRES projects requires methodologies that account for policy-driven incentives and financing mechanisms. To support the shift towards NCRES, evaluating their financial viability while considering public policies and funding options is important. This study presents an improved version of the Levelized Cost of Electricity (LCOE) that includes government incentives such as tax credits, accelerated depreciation, and green bonds. We apply a flexible investment model that helps to find the most cost-effective financing strategies for different renewable technologies. To do this, we use three optimization techniques to identify solutions that lower electricity generation costs: Teaching Learning, Harmony Search, and the Shuffled Frog Leaping Algorithm. The model is tested in a case study in Colombia covering battery storage, large- and small-scale solar power, and wind energy. Results show that combining smart financing with policy support can significantly lower electricity costs, especially for technologies with high upfront investments. We also explore how changes in interest rates affect the results. This framework can help policymakers and investors design more affordable and financially sound renewable energy projects. Full article

Glycerin has a similar energy value to corn and can partially replace it. However, there is a need to find the ideal level for using glycerin in pig feed without negatively interfering with performance parameters. The objective of this study was to determine the digestibility of glycerin through a mixture (MIX) or inserted on top of feed (ONTOP) using chromium indicator (Cr) and total collection (TC) methods. Ten barrows weighed 42.63 ± 4.23 kg in a 2 × 2 + 1 factorial scheme, with two forms of inclusion of the test ingredient (MIX or ONTOP), two digestibility methods (Cr or TC), and a basal diet (BD). The experimental design was completely randomized, and the evaluation period was used as split-plots, with two repetitions per period, totaling four repetitions per diet. The DE and ME values for glycerin were 3443 and 3356 kcal/kg (by the TC method) and 3411 and 3293 kcal/kg (by the Cr method), respectively (p < 0.05). There was no difference in MIX or ONTOP (p > 0.05), except for the digestibility coefficient (DC) of ethereal extract (p < 0.05). The DC and energy values obtained by Cr were lower than those obtained by TC. The inclusion of the test ingredients in the MIX and ONTOP forms did not present any difference, whereas Cr depreciated the nutritional value of glycerin for pigs. Full article

With the annual increase in carbon emissions and the warming of the global temperature, it is imperative to accelerate the construction of a green, low-carbon, circular economic system. The photocatalytic reduction of CO₂ can convert the emitted CO₂ into valuable carbonaceous products, which is of great significance for alleviating the global CO₂ emission problem. In this study, the literature on the “photocatalytic reduction of CO₂” from two Chinese and foreign databases was used as the analysis sample. From the perspective of net present value, nitride-based catalysts were selected as the research object. An in-depth analysis of the costs and economic benefits of the nitride-based photocatalytic reduction of CO₂ was carried out, considering four factors: catalyst efficiency, light conditions, discount rate, and depreciation period. The analysis results show that with a project duration of 10 years and a discount rate of 10%, the net present values of all the catalysts are negative, indicating that from an economic perspective, investment projects using general catalysts to reduce CO₂ are not feasible under current conditions. However, it is worth noting that when the light conditions are changed and sunlight is used as the light source, the net present values corresponding to the Ta₃N₅/Bi and NiOx/Ta₃N₅ catalysts have turned positive, showing a certain economic feasibility. When the yield is increased to 2.64 times and 6.15 times of the original values, the net present values corresponding to the T-CN/ZIS (refers to ZnIn₂S₄ (ZIS) nanosheets grown in situ on tubular g-C₃N₄ microtubes (T-CNs)) catalyst and the Ta₃N₅ cuboid catalyst turn positive, and only the net present value of the g-C₃N₄/Bi₂O₂[BO₂(OH)] catalyst remains negative. Full article