Search Results (703)

The intermittent and lumpy demand of spare parts requires the choice of the right forecasting model among a variety of existing methods. Spare parts have an uneven lifecycle and mean time to failure for each individual item. As a result, they have a varied time of replacement, and consequently, a varied demand. This paper introduces a multi-cost function optimization approach that dynamically selects and adjusts forecasting models tailored to each spare part. The performance comparisons of the various demand forecasting methods led us to a new forecasting model, the Sfiris–Koulouriotis (SK) method, suited for highly lumpy and intermittent demand. A scaled version of the novel Stock-Keeping Unit-oriented Prediction Error Costs metric is also introduced. The composite negative-binomial–Bernoulli probability distribution for the stock control leveraged the replenishment policy. The best safety stock level is calculated for each individual item. Empirical validation in the automotive industry demonstrated that our approach significantly reduces safety stock while maintaining service levels, offering practical benefits for inventory management. Full article

Biodiversity loss poses a threat to corporate performance and social welfare. Biodiversity disclosure enables investors to evaluate firms’ biodiversity status. However, it remains unclear whether and how biodiversity disclosure affects capital market efficiency. In this paper, we employ a binary variable derived from a word-frequency analysis of annual reports to determine whether a firm has disclosed biodiversity information. Using a panel of Chinese listed companies from 2011 to 2022, we provide robust evidence that Companies that disclose biodiversity information have experienced sustained improvements in stock liquidity. Furthermore, the effect is significantly amplified after the 2020 UN Biodiversity Summit, suggesting that investors respond positively to biodiversity disclosure. Channel analysis reveals that higher inventory turnover reinforces this positive effect, while greater financing constraints and higher management ownership weaken it. Heterogeneity analysis further indicates that this effect is more pronounced among firms with higher environmental information asymmetry, lower supply chain transparency, and lower patient capital. This study sheds light on how biodiversity disclosure affects market efficiency and offers important insights for future research and policy. Full article

This review analyzes how recent electric-vehicle LCAs have been carried out, emphasizing goals and scope, functional units, system boundaries (cradle-to-grave and well-to-wheel), and attributional versus consequential modeling rather than reporting outcomes. Using a systematic search of studies mainly from 2018–2025, it maps common tools and data sources (Ecoinvent, GREET, GaBi, and regional inventories) and summarizes LCIA practices, underscoring the need to report versions, regionalization, and assumptions transparently for comparability. Uncertainty studies are uneven: sensitivity and scenario analyses are common, while probabilistic approaches (e.g., Monte Carlo) are less used, indicating room for more consistent, multi-parameter uncertainty analysis. The results show that outcomes are context-dependent: BEVs deliver the largest life-cycle GHG cuts on low-carbon grids with improved battery production and end-of-life management; PHEVs and HEVs act as transitional options shaped by real-world use; and FCEV benefits depend on low-carbon hydrogen. Vehicle-integrated photovoltaics and solar-powered vehicles are promising yet under-studied, with performance tied to local irradiance, design, and grid evolution. Future research suggests harmonized reporting, more regionalized and time-aware modeling, broader probabilistic uncertainty, and comprehensive LCAs of VIPV/SPV and circular pathways to support policy-ready, comparable results. Full article

This study utilizes data from the eighth and ninth Chinese National Forest Inventories of Qinghai Province to establish a climate-sensitive transfer matrix growth model for natural forests in Qinghai Province. The model considers tree species diversity (S_d), size diversity (D_c), mean annual temperature (MAT), and mean annual precipitation (MAP) and their impacts on tree growth, mortality, and recruitment. Additionally, the forest stand growth and development were predicted under different climate scenarios (RCP2.6, RCP4.5, RCP8.5) for the next 50 years. The results show that the number of Qinghai spruce (Picea crassifolia Kom.) and White birch (Betula platyphylla Sukaczev) trees per hectare gradually decreases, but the stock volume continues to increase. The number of trees per hectare remains relatively stable (from 2235 to 855), with stock volume increasing annually for the first 30 years of the simulation and then stabilizing (from 76.96 to 798.02). Other tree species groups exhibit a continuous annual increase. Comparing the changes in stock volume and tree numbers under three different climate scenarios, there was no significant difference, and the overall trend remained similar. The finding fills a gap in the research on climate-sensitive transfer matrix growth models for natural forests in Qinghai Province. Compared to single-tree and whole-stand models, this model can predict forest stand growth more quickly and effectively, providing a reliable reference for future forest management. It helps formulate policies to address climate change and promote the sustainable development of forest health. This achievement will contribute to a better understanding of future forest stand growth trends, offering valuable insights for sustainable forest management. Full article

Urban air pollution in Kazakhstan poses persistent risks; this study synthesizes measured concentrations, source evidence, and policy responses to inform mitigation in cold, inversion-prone cities. We compile national monitoring (Kazhydromet), community PM_2.5 sensors, emissions inventories and recent CEMS provisions, and appraise modeling approaches (Gaussian screening, Eulerian CTMs, and data-driven forecasting). Seasonal descriptive comparisons are performed for Astana using 56,944 observations (2023–2024), partitioned into heating and non-heating periods, and published receptor apportionment is integrated. Across major cities, annual PM_2.5 generally exceeds WHO guidelines and winter stagnation drives episodes. In Astana, the heating season means rose relative to non-heating equivalents—PM_2.5 12.3 vs. 10.6 μg m⁻³ (+16%) and SO₂ 21.9 vs. 14.8 μg m⁻³ (+23%)—while NO was unchanged; higher means but lower medians indicate episodic winter peaks. Receptor analyses attribute large shares of PM_2.5 to traffic (spark-ignition engines 30% and diesel 7%) and coal-related contributions including secondary nitrate (15%), consistent with power/heat and vehicle dominance. Evidence supports prioritizing clean heating (coal-to-gas and efficiency), transport emission controls, and dense monitoring to enable accountability within Kazakhstan’s Environmental Code and decarbonization strategy. A tiered modeling workflow can quantify intervention impacts and deweather trends; the near-term focus should be on reducing winter exposures. Full article

In inventory management, business organizations gradually face challenges due to the complexities of managing perishable goods whose value diminishes over time. In such circumstances, interval’s bounds estimated business policy can be adopted to study a non-deterministic inventory model incorporating decay, preservation technology, and financial incentives, viz. advanced payments and fixed discounts. This study explores an interval Economic Order Quantity (EOQ) model incorporating advanced payment with discount options under preservation technology framework in interval environment. In this model, the demand rate is expressed as a convex combination of linear and power patterns of the selling price. The present model is formulated mathematically using interval differential equations and interval mathematics. Then, the corresponding interval-valued average profit of the model is obtained. In order to optimize the corresponding interval optimization problem, C-U optimization technique is developed. Employing the C-U optimization technique, the said interval optimization problem is converted into crisp optimization problems. Then, these problems are solved numerically by Wolfrom MATHEMATICA-11.0 software and validated with the help of two numerical examples. Finally, sensitivity analyses have been performed to study the impact of known inventory parameters on optimal policy. Full article

The growing prominence of environmental, social, and governance (ESG) considerations has introduced new challenges for firms worldwide. While ESG practices are often framed as long-term drivers of competitiveness, uncertainty surrounding their regulatory requirements has created significant operational risks. The primary objective of this study is to examine how ESG uncertainty (ESG) affects inventory management in listed firms. The study analyzed data from Chinese A-share listed companies over the period 2010 to 2024. A series of econometric estimations, including fixed effect models, two-stage least squares (2SLS), and system GMM, were employed to ensure the robustness of the results and to address issues of heteroscedasticity, endogeneity, and dynamic effects. The empirical results consistently revealed that ESG uncertainty exerted a significant negative effect on inventory management. Firms facing greater unpredictability in ESG-related requirements experienced disruptions in supply chain coordination, difficulties in demand forecasting, and inefficiencies in inventory turnover. Beyond this, larger firms and those with higher environmental expenditures exhibited weaker inventory efficiency, while debt ratio, cost of capital, and firm performance were positively associated with improved inventory outcomes. For corporate managers, the study highlighted the importance of embedding sustainability considerations into inventory strategies and adopting flexible procurement systems, predictive analytics, and stronger governance mechanisms. The findings underscored the broader societal need for clarity and stability in ESG regulations. For this, reducing policy unpredictability could enable firms to align sustainability commitments with operational efficiency, thereby improving competitiveness while minimizing waste and resource misallocation. This study was among the first to empirically establish the link between ESG uncertainty and inventory management, bridging the gap between sustainability research and operational efficiency. Full article

This work presents optimized decision tree-based ensemble machine learning models for predicting and quantifying the effects of greenhouse gas (GHG) emissions in Bangladesh. It aims to identify policy implications in response to significant environmental changes. The models analyze the emissions of CO₂, N₂O, and CH₄ from sectors including energy, industry, agriculture, and waste. We consider many parameters, including energy consumption, population, urbanization, gross domestic products, foreign direct investment, and per capita income. The data covers the period from 1971 to 2019. The model is trained using 80% of the dataset and validated using the remaining 20%. The hyperparameters, such as the number of estimators, maximum samples, maximum depth, learning rate, and minimum samples leaf, were optimized via particle swarm optimization. The models were tested, and their forecasts were extended till 2041. An examination of feature importance has identified energy consumption as a critical factor in greenhouse gas emissions, acknowledging the positive effects of clean energy in accordance with the clean development mechanism. The results demonstrate a robust model performance, with an R² score of approximately 0.90 for both the training and testing datasets. The bagging decision tree model showed the lowest mean squared error of 151.3453 and the lowest mean absolute percentage error of 0.1686. The findings of this study will help decision-makers understand the complex connections between socioeconomic conditions and the elements that contribute to greenhouse gas emissions. The discoveries will enable more precise monitoring of national greenhouse gas (GHG) inventories, allowing for focused efforts to mitigate climate change in Bangladesh. Full article

The aim of this study was to rigorously quantify and analyse the concentrations of atmospheric pollutants (PM₁₀, PM_2.5, SO₂, NO₂, CO, H₂S, and O₃) emitted by artisanal brick kilns in Juliaca City, Peru. The AERMOD dispersion model and a network of low-cost sensors (LCSs) were employed to characterise air quality at specific receptor sites. A georeferenced inventory of kiln operations was created to determine their parameters and operational intensity, providing a foundation for estimating emission factors and rates. Data were obtained from the United States Environmental Protection Agency (EPA) and supplemented with locally gathered meteorological records, which were processed for integration into the AERMOD model. The findings revealed that brick kilns are a principal source of atmospheric pollution in the region, with carbon monoxide (CO) emissions being especially pronounced. The LCSs facilitated the identification of pollutant concentrations at various locations and enabled the quantification of the specific contribution of brick production to ambient aerosol levels. Comparative assessments determined that these sources account for approximately 85% of CO emissions within the study area, underscoring a significant adverse impact on air quality and public health. Background pollutant levels, emission rates, spatial distributions, and concentration patterns were analysed within the assessment zones, resulting in solid model performance. These results provide a sound scientific basis for the formulation and implementation of targeted environmental mitigation policies in urban areas and the outskirts of Juliaca. Full article

Achieving carbon neutrality requires a comprehensive understanding of terrestrial carbon dynamics, particularly the capacity of ecosystems to act as carbon sinks. This study quantified the temporal and spatial variability of net primary production (NPP) and net ecosystem production (NEP) across South Korea from 2010 to 2024, assessing long-term carbon sink trends and their implications for carbon neutrality and nature-based solutions (NbSs). Using the Carnegie–Ames–Stanford Approach (CASA) model driven by Moderate Resolution Imaging Spectroradiometer (MODIS) satellite data and climate variables, we estimated ecosystem carbon fluxes at high spatial and temporal resolutions. In 2024, national NPP totaled 78.63 Mt CO₂ yr⁻¹, with a mean value of 1956.63 t CO₂ ha⁻¹ yr⁻¹. High productivity was concentrated in upland forests of Gangwon-do, Mt. Jirisan, and northern Gyeongsangbuk-do, where favorable vegetation indices and climatic conditions enhanced photosynthesis. Lower productivity occurred in urbanized areas and intensively farmed lowlands. Heterotrophic respiration (RH) was estimated at 15.35 Mt CO₂ yr⁻¹, with elevated rates in warm, humid lowlands and reduced values in high-elevation forests. The resulting NEP in 2024 was 63.29 Mt CO₂ yr⁻¹, with strong sinks along the Baekdudaegan Range and localized negative NEP pockets in lowlands dominated by urban development or agriculture. From 2010 to 2024, the spatially averaged NPP increased from 1170 to 1543 g C m⁻² yr⁻¹, indicating a general upward trend in ecosystem productivity. However, interannual variability was influenced by climatic fluctuations, land-cover changes, and data masking adjustments. These findings provide critical insights into the spatiotemporal dynamics of terrestrial carbon sinks in South Korea, offering essential baseline data for national greenhouse gas inventories and the strategic integration of NbSs into carbon-neutral policies. Full article

In alignment with the United Nations’ Sustainable Development Goals (SDGs) and the global pursuit of net-zero emissions, higher education institutions (HEIs) are increasingly expected to demonstrate robust climate accountability and effective decarbonization strategies. This three-year longitudinal study presents a comprehensive assessment of greenhouse gas (GHG) emissions at a higher education institution, employing the ISO 14064-1:2018 framework to strengthen inventory design, boundary delineation, and data governance protocols. Findings indicate that purchased electricity constitutes the largest share; however, fugitive refrigerant leakage and Scope 3 activities—particularly commuting and business travel—represent substantial and often underestimated components of the institution’s carbon footprint. Methodological refinements, including the incorporation of updated emission factors coefficients and enhanced data verification, have revealed the sensitivity of GHG inventories to both policy reforms and behavioral changes, as well as institutional policy reforms. The study also demonstrates that targeted refrigerant management and low-carbon mobility initiatives can generate measurable mitigation effects, even under conditions of expanding campus activity. Beyond the institution-specific results, this research proposes a replicable framework that integrates ISO 14064-1 compliance with data quality assurance and digital verification tools. This framework provides HEIs globally with a structured pathway to enhance reporting credibility, develop evidence-based mitigation strategies, and accelerate progress toward carbon neutrality. These insights underline the strategic role of universities in advancing sector-wide climate leadership and contributing to sustainable development transitions. Full article

Conditional automated driving delegates routine control to automation while keeping drivers responsible for supervision and timely takeovers. In this context, safety and usability hinge on calibrated trust, a state between overtrust and distrust that aligns reliance with actual system capabilities. We investigated how calibrated trust relates to concurrent behavior during conditional automation in a driving-simulator study (n = 26). After a brief familiarization block, drivers completed four takeover request (TOR) exposures while performing a non-driving-related task (NDRT). Trust was assessed with a validated multi-item inventory. NDRT engagement was operationalized as successful Surrogate Reference Task (SuRT) clicks per second, and takeover behavior was indexed by TOR reaction time (TOR-RT) from TOR onset to the first valid control input. The results showed that higher trust was associated with greater ND RT throughput during automated driving, whereas TOR-RT did not change significantly across repeated exposures, consistent with familiarization. In this sample, we did not observe a systematic penalty in TOR-RT associated with higher trust; however, confidence-interval benchmarks indicate that modest delays cannot be ruled out. This suggests that, after brief onboarding, calibrated trust can coexist with timely safety-critical responses within the limits of our design. These findings tentatively support interface and training strategies that promote calibrated trust (e.g., predictable TOR policies, transparent capability boundaries, and short onboarding) to help drivers navigate between overtrust and distrust. Full article

Motor vehicle emissions are a major air quality concern in Chinese cities. However, traditional population-based emission inventory methods fail to capture the spatial and temporal variations in emissions for effective policy design. This study proposes a high-resolution approach for traffic flow prediction and vehicle emission inventory estimation, using Jinan City, China, as a case study. We leverage multi-source geospatial data and employ a two-fold random forest model to predict hourly traffic flow at a road-segment level. Speed-aligned emission factors were then combined with these data to calculate hourly and road-level vehicle emission estimates. Compared to traditional methods, our approach offers substantial improvements: (1) improved spatiotemporal resolution; (2) enhanced accuracy of traffic flow prediction; and (3) support for more effective vehicle emission control strategies. Results show that heavy-duty vehicles, particularly freight trucks operating on inter-regional corridors through Jinan, contribute 78% more to NO_X emissions than local light-duty vehicles. These transient emissions are typically overlooked in static inventories but constitute a significant source of urban pollution. This study offers valuable insights for combining geospatial data and machine learning to improve the accuracy and resolution of vehicle emission inventories, supporting urban air quality policy and planning. Full article

Monitoring air pollution remains a significant challenge for both environmental policy and public health, particularly in parts of Eastern Europe where industrial structures are undergoing transition. In this paper, we examine long-term air quality trends in Poland between 1990 and 2023, drawing on multiple sources: satellite observations (from 2019 to 2025), ground-based stations, and official national emission inventories. The analysis focused on sulfur dioxide (SO₂), nitrogen dioxide (NO₂), and particulate matter (PM₁₀, PM_2.5). Data were obtained from the Sentinel-5P TROPOMI sensor, processed through Google Earth Engine, and monitored by the Chief Inspectorate of Environmental Protection (GIOŚ, Warsaw, Poland) and the National Inventory Report (NIR, Warsaw, Poland), compiled by KOBiZE (The National Centre for Emissions Management, Warsaw, Poland). The results show a decline in emissions. SO₂, for instance, dropped from about 2700 kilotons in 1990 to under 400 kilotons in 2023. Ground-based measurements matched well with inventory data (correlations around 0.75–0.85), but the agreement was noticeably weaker when satellite estimates were compared with surface monitoring. In addition to analyzing emission trends, this study examined the relationship between pollution levels and meteorological conditions across major Polish cities from 2019 to mid-2024. Pearson’s correlation analysis revealed strong negative correlations between temperature and pollutant concentrations, especially for SO₂, reflecting the seasonal nature of pollution peaks during colder months. Wind speed exhibited ambiguous relationships, with daily data indicating a dilution effect (negative correlations), whereas monthly averages revealed positive associations, likely due to seasonal confounding. Higher humidity was consistently linked to higher pollution levels, and precipitation showed weak negative correlations, likely influenced by seasonal weather patterns rather than direct atmospheric processes. These findings suggest that combining different monitoring methods, despite their quirks and mismatches, provides a fuller picture of atmospheric pollution. They also point to a practical challenge. Further improvements will depend less on sweeping industrial reform and more on shifting everyday practices, like how homes are heated and how people move around cities. Full article

Industrial heritage has emerged in recent decades as a distinctive category within cultural heritage, though its aesthetic significance remains underexplored. Unlike traditional monuments with long historical resonance, industrial remains are often recent, standardized, and seemingly devoid of unique cultural symbolism. Yet, in China—where industrial production expanded massively under both demographic pressures and the Maoist planned economy—these sites now constitute one of the world’s largest inventories of heritage. This study builds on earlier discussions of heritage aesthetics by systematically analyzing the foundations of aesthetic value in industrial heritage, combining historical, functional, and identity-driven perspectives. Drawing on long-term field research, archival documentation, and policy analysis, it examines how adaptive reuse projects—from Beijing’s 798 Art District to Shougang Park and the reconfigured factories of Shanghai and Wuhan—redefine the visual and social significance of former industrial sites. The methodology integrates heritage aesthetic theory with case-based evidence to assess three key components: technological–historical traces, landscape transformation, and collective memory. Results indicate that aesthetic value rarely arises from static preservation but is constructed through refunctionalization, where industrial ruins acquire renewed meaning as cultural parks, creative hubs, or community spaces. Moreover, large-scale Chinese practices reveal that industrial heritage possesses not only visual appeal but also profound identity-based resonance for generations shaped by the “factory managing community.” By situating industrial heritage within the broader aesthetic system of cultural heritage, this research demonstrates that its value lies in the synthesis of function, memory, and landscape, and that China’s experience provides a compelling framework for rethinking global approaches to industrial heritage aesthetics. Full article