Search Results (942)

The siphon effect between airports has amplified the polarization in passenger throughput, undermining the balanced development and sustainability of airport clusters. The airport siphon effect occurs when one airport attracts a disproportionate share of passengers, concentrating traffic at the expense of others, which affects the overall resilience of the entire airport cluster. To address this issue, this study proposes a siphon index, expands the range of ground transportation options for passengers, and establishes a zero-siphon model to assess the impact of siphoning on the resiliency of airport clusters. Using this framework, four major airport clusters in China were selected as research subjects, with regional aviation accessibility serving as a measure of resilience. The results showed that among the four airport clusters, the siphon effect is most pronounced in the Guangzhou region. To explore the implications of this effect further, three airport disruption scenarios were simulated to assess the resilience of the Pearl River Delta airport cluster. The results indicated that the intensity and timing of disruptive events significantly affect airport cluster resilience, with hub airports being particularly sensitive. This study analyzes the risks associated with excessive route concentration, providing policymakers with critical insights to enhance the sustainability, equity, and resilience of airport clusters. The proposed strategies facilitate coordinated infrastructure development, optimized air–ground intermodal connectivity, and risk mitigation. These measures contribute to building more sustainable and adaptive aviation networks in rapidly urbanizing regions. Full article

Global supply chains often face uncertainties in production lead times, fluctuating exchange rates, and varying tariff regulations, all of which can significantly impact total profit. To address these challenges, this study formulates a multi-country supply chain problem as a Semi-Markov Decision Process (SMDP), integrating both currency variability and tariff levels. Using a Q-learning-based method (SMART), we explore three scenarios: (1) wide currency gaps under a uniform tariff, (2) narrowed currency gaps encouraging more local sourcing, and (3) distinct tariff structures that highlight how varying duties can reshape global fulfillment decisions. Beyond these baselines we analyze uncertainty-extended variants and targeted sensitivities (quantity discounts, tariff escalation, and the joint influence of inventory holding costs and tariff costs). Simulation results, accompanied by policy heatmaps and performance metrics, illustrate how small or large shifts in exchange rates and tariffs can alter sourcing strategies, transportation modes, and inventory management. A Deep Q-Network (DQN) is also applied to validate the Q-learning policy, demonstrating alignment with a more advanced neural model for moderate-scale problems. These findings underscore the adaptability of reinforcement learning in guiding practitioners and policymakers, especially under rapidly changing trade environments where exchange rate volatility and incremental tariff changes demand robust, data-driven decision-making. Full article

Actuators and power electronics are fundamental components of modern control systems, enabling high-precision functionality, enhanced energy efficiency, and sophisticated automation. This study investigates evolving research trends and thematic developments in these areas spanning the last two decades (2005–2024). This study analyzed 1840 peer-reviewed abstracts obtained from the Web of Science database using BERTopic modeling, which integrates transformer-based sentence embeddings with UMAP for dimensionality reduction and HDBSCAN for clustering. The approach also employed class-based TF-IDF calculations, intertopic distance visualization, and hierarchical clustering to clarify topic structures. The analysis revealed a steady increase in research publications, with a marked surge post-2015. From 2005 to 2014, investigations were mainly focused on established areas including piezoelectric actuators, adaptive control, and hydraulic systems. In contrast, the 2015–2024 period saw broader diversification into new topics such as advanced materials, robotic mechanisms, resilient systems, and networked actuator control through communication protocols. The structural topic analysis indicated a shift from a unified to a more differentiated and specialized spectrum of research themes. This study offers a rigorous, data-driven outlook on the increasing complexity and diversity of actuator and power electronics research. The findings are pertinent for researchers, engineers, and policymakers aiming to advance state-of-the-art, sustainable industrial technologies. Full article

Climate warming poses significant challenges for the sustainable management of natural water resources, making efficient planning and usage essential. This study evaluates the water requirements, irrigation demand, and rainfall deficits for two key vegetable crops, carrot and white cabbage, under projected climate scenarios RCP 4.5 and RCP 8.5 for the period 2031–2100. The analysis was conducted for Kraków and Rzeszów Counties in southern Poland using projected monthly temperature and precipitation data from the Klimada 2.0 portal. Potential evapotranspiration (ETp) during the growing season (May–October) was estimated using Treder’s empirical model and the crop coefficient method adapted for Polish conditions. The reference period for comparison was 1951–2020. The results reveal a significant upward trend in water demand for both crops, with the highest increases under the RCP 8.5 scenario–seasonal ETp values reaching up to 517 mm for cabbage and 497 mm for carrot. Rainfall deficits are projected to intensify, especially during July and August, with greater shortages in Rzeszów County compared to Kraków County. Irrigation demand varies depending on soil type and drought severity, becoming critical in medium and very dry years. These findings underscore the necessity of adapting irrigation strategies and water resource management to ensure sustainable vegetable production under changing climate conditions. The data provide valuable guidance for farmers, advisors, and policymakers in planning effective irrigation infrastructure and optimizing water-use efficiency in southern Poland. Full article

Traditional Chinese villages, which have long supported villagers’ comfort level of daily activities, are increasingly affected by global climate change and rural reconstruction, prompting growing research interest in their outdoor microclimate design. This systematic review aims to synthesize and evaluate the outdoor microclimate spatial design mechanism studies in traditional Chinese villages noted for their uniqueness and complexity. Following the PRISMA method, this study was carried out on November 27, 2024, by retrieving studies from the Scopus and CNKI databases and applying predefined inclusion and exclusion criteria; 42 empirical studies were systematically reviewed. It identifies current research trends, summarizes concepts, frameworks, indicators, and methodologies with a focus on the design mechanisms considering scales, contexts, and user groups, and outlines directions for future research. The findings reveal a growing number of publications, with case studies predominantly concentrated on three concepts: physical microclimates, human comfort, and behavioral responses, characterized as distributed in south-east areas. Based on these concepts and their correlations, this study proposes a classification framework based on multiple scales, contexts, and user groups. Within this framework, the study found that relative humidity and PET (physiological equivalent temperature) emerge as the most commonly used indicators, while field measurements, simulations, surveys, and observations are identified as the primary methods. The review further reveals that unique outdoor spatial design characteristics shape physical microclimates, human comfort, and behavior indicators influenced by contexts and users from the macro to the micro scale. Future research should advance existing studies by enriching the current contextual framework and explore more microclimatic factors. This review offers a comprehensive overview and actionable insights for outdoor microclimate design, policymaking, and the promotion of climate adaptation and villagers’ public health in different traditional rural settings. Full article

Accurate forecasting of household waste generation is essential for sustainable urban planning and the development of data-driven environmental policies. Conventional statistical models, while simple and interpretable, often fail to capture the nonlinear and multidimensional relationships inherent in waste production patterns. This study proposes a machine learning-based regression framework utilizing Random Forest and XGBoost algorithms to predict annual household waste generation across four metropolitan regions in South Korea Seoul, Gyeonggi, Incheon, and Jeju over the period from 2000 to 2023. Independent variables include demographic indicators (total population, working-age population, elderly population), economic indicators (Gross Regional Domestic Product), and regional identifiers encoded using One-Hot Encoding. A derived feature, elderly ratio, was introduced to reflect population aging. Model performance was evaluated using R², RMSE, and MAE, with artificial noise added to simulate uncertainty. Random Forest demonstrated superior generalization and robustness to data irregularities, especially in data-scarce regions like Jeju. SHAP-based interpretability analysis revealed total population and GRDP as the most influential features. The findings underscore the importance of incorporating economic indicators in waste forecasting models, as demographic variables alone were insufficient for explaining waste dynamics. This approach provides valuable insights for policymakers and supports the development of adaptive, region-specific strategies for waste reduction and infrastructure investment. Full article

The prediction and management of water quality are critical to ensure sustainable water resources, particularly in regions like Malaysia, where rivers face increasing pollution from industrialisation, agriculture, and urban expansion. This review aims to provide a comprehensive analysis of machine learning (ML) models and statistical methods applied in forecasting and classification of water quality. A particular focus is given to hybrid models that integrate multiple approaches to improve predictive accuracy and robustness. This study also reviews water quality standards and highlights the environmental context that necessitates advanced predictive tools. Statistical techniques such as residual analysis, principal component analysis (PCA), and feature importance assessment are also explored to enhance model interpretability and reliability. Comparative tables of model performance, strengths, and limitations are presented alongside real-world applications. Despite recent advancements, challenges remain in data quality, model interpretability, and integration of spatio-temporal and fuzzy logic techniques. This review identifies key research gaps and proposes future directions for developing transparent, adaptive, and accurate models. The findings can also guide researchers and policymakers towards the development of smart water quality management systems that enhance decision-making and ecological sustainability. Full article

Rapid urbanization has intensified global settlement density, significantly increasing the importance of urban street environmental quality, which profoundly affects residents’ physical and psychological well-being. Traditional methods for evaluating urban environmental quality have largely overlooked dynamic perceptual changes occurring throughout the day, resulting in incomplete assessments. To bridge this methodological gap, this study presents an innovative approach combining advanced deep learning techniques with time-series street view imagery (SVI) analysis to systematically quantify spatio-temporal variations in the perceived environmental quality of pedestrian-oriented streets. It further addresses two central questions: how perceived environmental quality varies spatially across sections of a pedestrian-oriented street and how these perceptions fluctuate temporally throughout the day. Utilizing Golden Street, a representative living street in Shanghai’s Changning District, as the empirical setting, street view images were manually collected at 96 sampling points across multiple time intervals within a single day. The collected images underwent semantic segmentation using the DeepLabv3+ model, and emotional scores were quantified through the validated MIT Place Pulse 2.0 dataset across six subjective indicators: “Safe,” “Lively,” “Wealthy,” “Beautiful,” “Depressing,” and “Boring.” Spatial and temporal patterns of these indicators were subsequently analyzed to elucidate their relationships with environmental attributes. This study demonstrates the effectiveness of integrating deep learning models with time-series SVI for assessing urban environmental perceptions, providing robust empirical insights for urban planners and policymakers. The results emphasize the necessity of context-sensitive, temporally adaptive urban design strategies to enhance urban livability and psychological well-being, ultimately contributing to more vibrant, secure, and sustainable pedestrian-oriented urban environments. Full article

Legume crops are sensitive to shifting environmental conditions, as they depend on a narrow range of climatic stability for growth and nitrogen fixation. This research sought to assess the sustainability of Faba Asturiana (FA) cultivation under current and future climatic scenarios by establishing generalized linear mixed models (GLMMs). Specifically, it aimed to (1) investigate the effects of significant climatic stressors, including higher nighttime temperatures and extended drought periods, on crop viability, (2) analyze future scenarios based on Representative Concentration Pathways (RCP 4.5 and RCP 8.5), and (3) recommend adaptive measures to mitigate threats. Six spatial GLMMs were developed, incorporating variables such as extreme temperatures, precipitation, and the drought duration. Under present-day conditions (1971–2000), all the models exhibited strong predictive performances (AUC: 0.840–0.887), with warm nights (tasminNa20) consistently showing a negative effect on suitability (coefficients: −0.58 to −1.16). Suitability projections under future climate scenarios revealed considerable variation among the developed models. Under RCP 4.5, Far Future, Model 1 projected a 7.9% increase in the mean suitability, while under RCP 8.5, Far Future, the same model showed a 78% decline. Models using extreme cold, drought, or precipitation as climatic stressors (e.g., Models 2–4) revealed the most significant suitability losses under RCP 8.5, with the reductions exceeding 90%. In contrast, comprising variables less affected by severe fluctuations, Model 6 showed relative stability in most of the developed scenarios. The model also produced the highest mean suitability (0.130 ± 0.207) in an extreme projective scenario. The results highlight that high night temperatures and prolonged drought periods are the most limiting factors for FA cultivation. ecological niche models (ENMs) performed well, with a mean AUC value of 0.991 (SD = 0.006) and a mean TSS of 0.963 (SD = 0.024). According to the modeling results, among the variables affecting the current distribution of Protected Geographical Indication-registered AF, prspellb1 (max consecutive dry days) had the highest effect of 28.3%. Applying advanced statistical analyses, this study provides important insights for policymakers and farmers, contributing to the long-term sustainability of PGI agroecosystems in a warming world. Full article

Effective water governance is essential for sustainable development amidst water scarcity challenges in semi-arid regions like Esmeraldas Province, which has substantial agrotourism potential. Yet, fragmented governance and chronic water shortages threaten its viability. Using a mixed-method approach, this study analyzed how sustainable water governance can support agrotourism development in Esmeraldas Province, Ecuador. This study combined policy gaps analysis, stakeholder surveys (policymakers, farmers, community leaders, and tourism operators), and water availability using the Standardized Precipitation Evapotranspiration Index (SPEI) from 1980 to 2022. The results revealed a lack of policy regulation and water infrastructure as the major governance gaps that need more intervention. The survey respondents indicated that water is mainly used for domestic and economic activities and the conservation of natural ecosystems. The SPEI revealed a significant drought trend falling below −3, with severe drought years coinciding with many crop losses and a fall in tourism. This study highlights the interconnection between water governance and agrotourism in Esmeraldas, Ecuador, proposing a strategic framework that incorporates adaptive governance principles and inclusive participation mechanisms, emphasizing targeted capacity building to strengthen water management practices and enhance the Sustainable Development Goals for agrotourism resilience. Full article

Certain industries will bear a disproportionate share of the burden of climate change. Climate change risk perceptions can impact workers’ mental health and well-being; increased climate change risk perceptions are also associated with more favourable adaptive attitudes. It is, therefore, important to understand whether climate risk perceptions differ across workers between industries. We conducted an online survey of British Columbians (16+) in 2021 using social media advertisements. Participants rated how likely they believed their industry (Natural Resources, Science, Art and Recreation, Education/Law/Government, Health, Management/Business, Manufacturing, Sales, Trades) would be affected by climate change (on a scale from “Very Unlikely” to “Very Likely”). Ordinal logistic regression examined the association between occupational category and perceived industry vulnerability, adjusting for socio-demographic factors. Among 877 participants, 66.1% of Natural Resources workers perceived it was very/somewhat likely that climate change would impact their industry; only those in Science (78.3%) and Art and Recreation (71.4%) occupations had higher percentages. In the adjusted model, compared to Natural Resources workers, respondents in other occupations, including those in Art and Recreation, Education/Law/Government, Management/Business, Manufacturing, Sales, and Trades, perceived significantly lower risk of climate change-related industry impacts. Industry-specific interventions are needed to increase awareness of and readiness for climate adaptation. Policymakers and industry leaders should prioritize sectoral differences when designing interventions to support climate resilience in the workforce. Full article

Virtual power plants (VPPs) enable the efficient participation of distributed renewable energy resources in electricity markets by aggregating them. However, the profitability of VPPs is challenged by market volatility and regulatory constraints, such as price caps and imbalance penalties. This study examines the joint impact of varying price cap levels and imbalance penalty structures on the bidding strategies and revenues of VPPs. A stochastic optimization model was developed, where a three-stage scenario tree was utilized to capture the uncertainty in electricity prices and renewable generation output. Simulations were performed under various market conditions using real-world price and generation data from the Korean electricity market. The analysis reveals that higher price cap coefficients lead to greater revenue and more segmented bidding strategies, especially under asymmetric penalty structures. Segment-wise analysis of bid price–quantity pairs shows that over-bidding is preferred under upward-only penalty schemes, while under-bidding is preferred under downward-only ones. Notably, revenue improvement tapers off beyond a price cap coefficient of 0.8, which indicates that there exists an optimal threshold for regulatory design. The findings of this study suggest the need for coordination between price caps and imbalance penalties to maintain market efficiency while supporting renewable energy integration. The proposed framework also offers practical insights for market operators and policymakers seeking to balance profitability, adaptability, and stability in VPP-integrated electricity markets. Full article

Climate change disrupts lives globally and poses significant challenges to mental health. Although several scales assess climate anxiety, many either conflate symptoms with coping responses or fail to adequately capture the core symptomatology of anxiety. Hence, this study aimed to develop and validate the Brief Anxiety Scale for Climate Change (BACC), a self-report measure designed to assess symptoms of climate anxiety. A preliminary pool of 21 items was generated based on the diagnostic criteria for generalized anxiety disorder and climate-related stress. Study 1 (n = 300) explored the factor structure via an exploratory factor analysis while Study 2 (n = 400) independently validated the structure via a confirmatory factor analysis (CFA). Analyses of the internal consistency, content validity, and discriminant validity helped refine the scale to a final 13-item version with two factors: cognitive and functional impairment. The CFA results indicated that all the fit indices met the recommended thresholds, and the final version demonstrated excellent internal consistency (Cronbach’s α = 0.92). Additionally, latent correlations revealed that climate anxiety was moderately associated with generalized anxiety and depression. The BACC was developed to identify individuals in the community who experience climate anxiety beyond an adaptive level, thereby promoting sustainable mental health in the context of climate change. These findings suggest that the BACC is a promising tool for assessing climate anxiety. With better identification, mental health professionals, community practitioners, and policymakers can utilize the scale to develop climate-sensitive public health programs and tailored intervention strategies. Full article

Accurate carbon price forecasting is essential for market stability, risk management, and policy-making. To address the nonlinear, non-stationary, and multiscale nature of carbon prices, this paper proposes a forecasting framework integrating secondary decomposition, two-stage feature selection, and dynamic ensemble learning. Firstly, the original price series is decomposed into intrinsic mode functions (IMFs), using complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN). The IMFs are then grouped into low- and high-frequency components based on multiscale entropy (MSE) and K-Means clustering. To further alleviate mode mixing in the high-frequency components, an improved variational mode decomposition (VMD) optimized by particle swarm optimization (PSO) is applied for secondary decomposition. Secondly, a two-stage feature-selection method is employed, in which the partial autocorrelation function (PACF) is used to select relevant lagged features, while the maximal information coefficient (MIC) is applied to identify key variables from both historical and external data. Finally, this paper introduces a dynamic integration module based on sliding windows and sequential least squares programming (SLSQP), which can not only adaptively adjust the weights of four base learners but can also effectively leverage the complementary advantages of each model and track the dynamic trends of carbon prices. The empirical results of the carbon markets in Hubei and Guangdong indicate that the proposed method outperforms the benchmark model in terms of prediction accuracy and robustness, and the method has been tested by Diebold Mariano (DM). The main contributions are the improved feature-extraction process and the innovative use of a sliding window-based SLSQP method for dynamic ensemble weight optimization. Full article

Background: This review explores the influence of eco-anxiety on sustainable consumption, with a specific focus on the urban context. While the literature on green consumerism continues to expand, the role of emotional and psychological factors, especially eco-anxiety, in shaping sustainable consumption decisions remains underexplored. Most existing studies emphasize cognitive, social, or contextual drivers, often overlooking affective dimensions that may significantly influence consumer behavior. Addressing this gap, the review examines how emotional responses to climate change, such as eco-anxiety, inform and potentially motivate eco-friendly consumption patterns. Understanding these affective pathways offers valuable insights on how individuals and urban communities can effectively adapt to climate change and establish a sustainable consumption culture. Methods: A systematic literature search was conducted in Scopus and Web of Sciences databases, following a predefined keyword strategy, resulting in 56 initial records. We further implemented a supplementary search of gray literature on Google Scholar to search for additional reports. The full-text screening process identified 12 eligible studies based on the following inclusion criteria: quantitative or mixed-methods studies focusing on adult and young adult individuals, including both measures of eco-anxiety and green consumption and assessing their direct or indirect relationship. Results: Findings suggest that eco-anxiety functions as a cognitive–affective motivator for sustainable consumer choices; however, the strength and direction of this influence appear contingent on moderating emotional and psychological variables and cross-cultural and demographic moderators. Discussion: This review highlights the need for urban-focused intervention tailored communication, marketing, and business strategies that address the emotional dimensions of climate change. Policymakers and businesses are encouraged to consider affective drivers as eco-anxiety to promote sustainable consumption stewardship within urban communities. By addressing these psychological responses, urban societies can become more resilient and proactive in confronting climate change challenges. Full article