Search Results (1,885)

Electromobility is a key direction in the development of sustainable transport. The aim of the study was to determine its level of development and to identify and assess factors associated with the perception of electromobility among residents of the Lubusz Voivodeship. Additionally, an attempt was made to identify user segments with differing perceptions of this phenomenon. The analysis covered the years 2010–June 2026, with a survey conducted at the turn of 2025/2026. The level of electromobility development was assessed based on quantitative indicators, while resident opinions were collected via a survey. Linear regression and cluster segmentation were used to analyze the data. Relationships between variables were examined, focusing on factors associated with the price acceptance of electric vehicles. The regression results indicated that, of the eight predictors included in the model, only four—infrastructure quality, charging time, vehicle range, and environmental awareness—exhibited statistically significant associations with price acceptance. Cluster analysis allowed for the identification of diverse user segments with different preferences, motivations, and barriers related to electromobility. The results can form the basis for designing public policies, marketing strategies, and investment activities in the area of sustainable transport. Full article

Urban accessibility is increasingly shaped by the interaction between physical mobility, digital accessibility, and social relationships. However, most existing urban simulation models primarily focus on physical transportation and rarely incorporate digital accessibility or social interaction mechanisms. This limitation restricts the ability of conventional models to capture new behavioral patterns associated with digital service adoption and changing urban lifestyles. To address this gap, this study modifies a multi-layer Social Dynamics Simulation (SDS) model by incorporating three interdependent network layers: a real network representing physical accessibility, a virtual network representing digital accessibility, and a social network representing interpersonal relationships. The study introduces an integrated accessibility index that combines physical and digital accessibility based on a probabilistic service choice framework estimated using survey data (n = 6210). The proposed model is applied to a virtual city experiment to examine how digital service usage and social interaction preferences influence long-term urban dynamics. Additionally, the study compares scenarios with and without digital accessibility. The 30-year simulation results indicate that digital accessibility partially relaxes spatial constraints imposed by transportation networks, enabling households to maintain acceptable service access even in locations with lower physical accessibility. However, physical accessibility remains a dominant factor shaping residential concentration around transit nodes. The results further demonstrate that digital service substitution can reduce travel demand while reinforcing accessibility differences across population groups. Furthermore, scenario simulation results show that, with an increase in digital service adoption, high-accessibility zones lose population, while low-accessibility peripheral zones gain population, revealing a sharp dose–response relationship between digital service penetration and urban decentralization. By incorporating the service choice model and digital accessibility, this study provides a new analytical tool, grounded in shopping-related digital substitution, for evaluating sustainable urban development, equity in service access, and emerging smart city policies. Full article

Safe walking and cycling are part of sustainable mobility, but aggregate collision totals do not show whether speed policies reach vulnerable road users (VRUs). Wales reduced the default speed limit on restricted roads from 30 to 20 mph in September 2023. To avoid mechanical reclassification after the reform, roads posted at either 20 or 30 mph were pooled. A 2022Q1–2025Q4 panel compared 22 Welsh and 289 English local authorities using Poisson and ordinary least squares (OLS) fixed-effects models, supplemented by policy-level and spatial analyses. Combined pedestrian and cyclist casualties declined by 19.4% in the Poisson model and 26.0% when the OLS coefficient was scaled to the Welsh pre-reform mean. Poisson reductions were 22.2% for pedestrians, 25.7% for child VRUs and 14.8% for those killed or seriously injured; corresponding OLS outcomes survived false discovery rate control. The police-force placebo yielded a two-sided p value of 0.086. School-context estimates did not survive multiplicity adjustment, while current road status provided descriptive rather than segment-level causal evidence. Mean speed declined by 3.56 mph at 42 selected sites. The findings indicate lower recorded casualties among groups central to active travel. They do not establish lower risk per journey or isolate all Wales-specific changes, but they support transparent monitoring of whether sustainable transport policies reach vulnerable groups. Full article

Against the backdrop of China’s dual-carbon goals—peaking carbon emissions by 2030 and achieving carbon neutrality by 2060—capacity optimization in the steel industry can no longer rely solely on aggregate output reduction. Instead, governance must shift toward a multidimensional approach that combines scale control, structural adjustment, and coordinated regional allocation. This study develops a quantifiable and interpretable assessment model for capacity-adjustment pressure. Monthly provincial crude steel output is used as a high-frequency proxy for capacity utilization and production adjustment. Additive time-series decomposition is applied to extract three components from monthly output—trend, residual, and volatility—which respectively represent structural evolution, short-term deviations, and exposure to shocks. The model further incorporates multidimensional variables, including downstream steel demand, resource and transport constraints, scrap steel ratio, and policy constraints. On this basis, a two-stage hybrid assessment framework is developed. In the first stage, extreme gradient boosting (XGBoost) is used to learn nonlinear relationships and derive data-driven feature importance. In the second stage, a composite pressure index is constructed and transformed into a standardized 0–100 score through a robust rank-based mapping mechanism. Dual thresholds are then used to generate three policy recommendations: maintaining current capacity, structural optimization, and capacity reduction. The results show that production trends and volatility intensity are the primary drivers of capacity-adjustment pressure, while pronounced spatial heterogeneity requires highly localized strategies. The classification assigns 25 provinces to maintaining current capacity, 3 to structural optimization, and 3 to targeted capacity reduction. Finally, integrating SHapley Additive exPlanations (SHAP) enhances model interpretability and provides a quantitative basis for shifting from indiscriminate capacity suppression toward differentiated, region-specific capacity governance, thereby supporting the sustainable low-carbon development of the global steel industry. Full article

Sustainability policies introduced under the European Green Deal have strengthened climate-related disclosure requirements for agri-food companies. In particular, the Corporate Sustainability Reporting Directive requires in-scope companies to transparently disclose information on their environmental performance. However, the academic literature on decarbonisation in the olive oil sector remains fragmented. This systematic literature review synthesises findings by considering cultivation, milling and retail, and waste management as interconnected stages of the olive oil supply chain and by developing a matrix linking decarbonisation strategies to the relevant European Sustainability Reporting Standards (ESRS) environmental, social and governance (ESG) topics. Following the PRISMA protocol, 42 peer-reviewed studies from Scopus and Web of Science were included in the final synthesis, covering cultivation (RQ1), milling and retail (RQ2), and waste management (RQ3). The cultivation stage represents an important part of the emission profile of the chain while also offering potential for carbon sequestration through sustainable management practices, such as reduced tillage, cover crops, organic amendments and biochar application. In the downstream stages, the mill and its retail interface rely on a different set of measures, including two-phase extraction, rooftop photovoltaic systems, thermal recovery from pits, and lighter bottles transported in bulk. Waste management also offers opportunities to recover value from pomace, mill wastewater and pruning waste through biogas, biochar, compost or phenolic extracts. The potential for a net-negative carbon balance is context-dependent and varies with system boundaries, the balancing period, functional units, and the methods used to account for carbon sequestration. The matrix offers a clear classification of decarbonisation strategies and ESRS topics, opening valuable avenues for upcoming studies to extend its practical utility. Full article

This perspective examines the interactions among microplastics (MPs), emerging contaminants (ECs), climate change, and extreme hydrological events. Droughts, water scarcity, heatwaves, intense rainfall, and floods can alter the occurrence, mobilisation, transport, fate, and risks of MPs and associated ECs across water, soil, and groundwater systems. The analysis focuses on the context-dependent potential of MPs to act as vectors of ECs, soil–water interactions, and the potential influence of MPs on greenhouse gas (GHG) emissions. When these stressors co-occur, their combined effects may be additive, synergistic, or antagonistic. Accordingly, the “perfect storm” framing is used here to describe the potential for mutually reinforcing and cascading risks rather than to imply that synergy occurs universally. An integrated perspective therefore helps anticipate worst-case scenarios and move beyond the fragmented assessment of individual stressors. This paper discusses sustainable mitigation and adaptation strategies, including advanced wastewater treatment, water reuse, climate-resilient water management, infrastructure adapted to increasing hydrological variability, climate-smart agriculture, and safer alternatives to conventional plastics and chemicals. Effective implementation combines technological innovation with monitoring, governance, policy action, and public awareness. By framing the microplastics–contaminants–water–soil–climate nexus as an interconnected sustainability challenge, this work aims to support environmental resilience and progress toward the Sustainable Development Goals. Full article

China has proposed dual carbon goals to advance its sustainable development. As one of the country’s major sources of carbon emissions, effective emission control of the transportation sector is central to achieving these goals. There is a lack of research on the optimal allocation of emission reduction targets across transportation sub-sectors from the perspective of total cost-minimization. This paper develops a carbon emission reduction allocation model based on the Integrated MARKAL-EFOM System (TIMES) framework. The model identifies the optimal allocation scheme across different transportation sectors and technology pathways in order to minimize the total abatement cost. A marginal abatement cost curve is constructed to evaluate the cost-effectiveness and reduction potential of various schemes. Using Fujian Province in China as a research case, the results show that from 2022 to 2030, urban passenger transportation (UPT), intercity passenger transportation (IPT), and freight transportation (FT) are expected to undertake 52.7%, 11.1%, and 36.2% of the total carbon reduction potential, corresponding to emission reductions of 13.488 million tons, 2.845 million tons, and 9.255 million tons, respectively. Compared with equal allocation and turnover-proportional allocation schemes, the TIMES-based optimization reduces the overall abatement cost by 22.13 billion yuan. The model effectively minimizes the total system cost while achieving the carbon reduction targets, and the results provide a reference for formulating carbon emission mitigation policies. Full article

Automated surface defect detection in hot-rolled steel is a prerequisite for real-time quality control, yet most deployed inspection systems operate in isolation from Industrial Internet of Things (IIoT) infrastructure. This paper reports a laboratory-scale proof of concept with two contributions. The first is a segmentation study on the Severstal dataset using a leakage-free, defect-stratified split of 1886 test images. Because a trivial all-background predictor already attains 96.66% pixel accuracy, performance is reported through Dice, IoU, precision, recall, and F1 with 95% confidence intervals. A compact from-scratch U-Net (0.49 M parameters) reaches a Dice of 0.416 at 38.6 ms per image, an ImageNet-pretrained DeepLabV3+ model reaches 0.677 at 46.3 ms and 37 times the parameters, and a classical Otsu baseline reaches 0.060, bracketing an explicit accuracy-versus-footprint design space rather than a single recommended model. The second contribution is architectural: a three-layer IIoT architecture whose messaging layer is empirically characterized on a Raspberry Pi broker over 158,500 messages. A factorial experiment isolates the transport configuration of the broker, rather than that of the publisher, as the determinant of end-to-end latency, yielding a seventeen-fold reduction. The layer sustains 1920 messages per second without loss, and a deliberate broker outage shows that MQTT delivery guarantees are semantic rather than temporal, motivating an application-level message-expiry policy. Embedded inference deployment is identified as the primary next step. Full article

Emission reduction remains the foundation of air-pollution control, yet the relationship between reduced emissions and improved ambient air quality is frequently non-proportional. This mismatch is not an exception to atmospheric behavior but a consequence of coupled chemical, meteorological, transport, and removal processes. Here, we critically synthesize global evidence for nonlinear air-quality responses to emission controls, with particular attention to fine particulate matter (PM_2.5) and ozone (O₃). We distinguish five response forms that are directly relevant to policy: near-linear, sublinear, superlinear, threshold, and sign-reversal behavior. Ozone provides the clearest example because the response to nitrogen oxides (NOx) and volatile organic compounds (VOCs) depends on the prevailing photochemical regime and can change as emissions decline. PM_2.5 responses are likewise nonlinear because precursor controls alter atmospheric oxidation capacity, gas-particle partitioning, aerosol water, and interactions among nitrate, sulfate, ammonium, and secondary organic aerosol. Aerosol reductions can further modify photolysis and boundary-layer processes, linking PM_2.5 control to O₃ production. Regional transport and background concentrations attenuate or redistribute the benefits of local controls, while meteorological variability changes both chemical sensitivity and the realized concentration response. These mechanisms imply that sustainable air-quality management cannot be evaluated solely by tonnes of emissions avoided. Instead, policy performance should be assessed along the complete pathway from emission reduction to ambient concentration, exposure, health, climate, ecosystem, equity, and economic outcomes. We propose a sustainability-oriented framework in which control strategies are evaluated for atmospheric effectiveness, multipollutant coherence, spatial equity, climate compatibility, and robustness across changing chemical and meteorological regimes. The evidence supports adaptive, coordinated, and regionally integrated control portfolios rather than fixed single-pollutant reduction ratios. Full article

Modern agriculture’s integration of Internet of Things (IoT), Industrial Control Systems (ICSs), and data analytics boosts productivity but introduces significant cybersecurity and data governance challenges. Existing scholarship is divided between policy-focused governance and technical attack analyses, hindering the development of comprehensive, enforceable defenses. This paper introduces an integrated framework that bridges normative data governance in smart farming (SF) with empirical, honeynet-derived threat intelligence. Drawing on the authors’ previous systematic review of SF data governance and a honeynet simulating agricultural IoT/ICS, the study maps governance challenges to quantitative attack indicators from honeynet logs, classifying each pairing as directly supported by telemetry, indirectly supported by telemetry, or not observable using the current methodology. The findings show that the services flagged as governance concerns face sustained attack pressure: the honeynet recorded brute-force attempts against SSH/Telnet on simulated irrigation controllers (149,000 events), connection and login attempts targeting SMB (Server Message Block) and MQTT (Message Queuing Telemetry Transport) on automated machinery (67,156), credential-guessing attempts against management services (14,937), and ICS protocol probes (11,532). Geographic and protocol distributions reveal that legacy industrial protocols and weakly authenticated management interfaces, both highlighted as governance concerns, constitute the primary attack surface. This evidence supports a tiered governance model integrating protocol-level controls, identity governance, and data-sharing policy, demonstrating that effective SF cybersecurity requires empirically calibrated rather than purely policy-driven frameworks. The proposed framework offers actionable guidance for aligning technical defenses with data governance obligations. This work contributes a new methodological protocol (cross-evidentiary mapping), an empirically calibrated tiered framework, and a coherent research agenda at the intersection of governance and measurement, serving as a template for similar analyses in other critical infrastructure sectors. Full article

Marine pollution in the Philippine archipelago poses increasing threats to biodiversity, ecosystem services, and human health due to rapid urbanization, population growth, and inadequate waste management. As a core region of the Coral Triangle, the Philippines is particularly vulnerable to pollutants transported across interconnected marine ecosystems. This review synthesizes current knowledge on the occurrence, sources, analytical approaches, risk assessment, and knowledge gaps associated with conventional and emerging major marine pollutants, including oil, heavy metals, polycyclic aromatic hydrocarbons, persistent organic pollutants, pharmaceuticals and personal care products, per- and polyfluoroalkyl substances, plastics, and additional stressors such as nutrients, pathogens, and shipping-related contaminants. Available evidence indicates widespread contamination of coastal waters, sediments, and marine biota, with pollution hotspots commonly associated with urbanized coastlines, river systems, industrial zones, shipping routes, aquaculture areas, and tourism centers. Emerging pollutants such as plastics and microplastics are increasingly detected even in remote environments, highlighting limitations in current monitoring and management frameworks. However, significant gaps remain in long-term monitoring, standardized methodologies, human health risk assessment, and interdisciplinary integration. This review emphasizes the need for harmonized monitoring programs, improved waste management infrastructure, strengthened regulatory enforcement, expanded analytical capacity, and integrated science-based policies to support sustainable marine management and ocean governance in the Philippines. Full article

This study examines the dynamic connectedness and spillover transmission mechanisms among tourism-related exchange-traded funds (ETFs), clean energy markets, oil, geopolitical risks, and tourism transportation using daily percentage returns from 24 March 2017 to 16 March 2026. We employed a battery of econometric methodologies, including the R²-decomposed connectedness approach, a TVP-VAR framework to examine the extent and nature of connectedness and distinguish contemporaneous and lagged spillovers, and the DCC-GARCH specification to assess dynamic conditional correlations. The data exhibit substantial volatility and non-normality, supporting these dynamic econometric approaches. The results reveal that connectedness varies considerably over time and tends to intensify during periods of major economic and geopolitical uncertainty. PEJ emerges as the dominant overall net transmitter of shocks, primarily through lagged spillovers, while Transportation and Clean Energy act as contemporaneous transmitters but become net receivers in the lagged horizon. The decomposition further shows that contemporaneous linkages are relatively weak, while lagged effects are stronger, suggesting that shock transmission occurs gradually rather than instantaneously. The dynamic correlations are time-varying, and ETFs display asymmetric behavior across different market conditions. Overall, this study highlights the time-varying nature of interactions between tourism and clean energy assets, driven by energy market dynamics and geopolitical risks, and provides useful implications for portfolio diversification, risk management, and policy decisions in interconnected tourism and sustainable financial markets. Full article

New Light Rail Transit (LRT) systems are frequently promoted as a means of reducing car dependence and supporting urban sustainability; however, their effectiveness depends on how different traveller segments respond to LRT relative to competing modes. This study examines whether car owners and non-car owners exhibit distinct preference structures and policy sensitivities towards a proposed LRT system in Kerman, Iran. A stated-preference mode-choice survey conducted in November 2023 generated 1916 valid choice observations, including 955 observations from car owners and 961 from non-car owners, based on 736 completed questionnaires. Multinomial Logit (MNL), Nested Logit (NL), and Mixed Logit (MXL) models were estimated separately for each segment. Policy implications were assessed using elasticities, marginal effects, and scenario-based simulations. The results show that, for car owners, the MXL specification provides a slightly better statistical fit, indicating modest preference heterogeneity, while the main behavioural conclusions remain consistent across model structures. For non-car owners, the NL model performs best, revealing clearer substitution between public-transport modes (bus/LRT) and on-demand services (taxi/ride-hailing). Although LRT shows a negative baseline preference, particularly among car owners, its uptake increases substantially with improved LRT travel-time performance and higher private-car operating costs. Overall, improving LRT travel-time competitiveness is critical for both groups, while attracting car owners additionally requires coordinated measures that raise the generalised cost of private-car use and strengthen the relative advantage of LRT. These findings highlight the importance of segment-sensitive policy design for maximising the contribution of LRT to sustainable urban transport and long-term urban sustainability. Full article

Mobility-as-a-Service (MaaS) has emerged as a promising approach to urban transportation by integrating multiple mobility services into a single digital platform for trip planning, booking, and payment. More recently, Artificial Intelligence (AI) has expanded the capabilities of MaaS, enabling more efficient data processing, predictive analytics, personalized services, and intelligent decision support. This narrative review examines the current state of research on AI-enabled MaaS from both technological and socioeconomic perspectives. The analysis covers five major research areas: data integration and interoperability, predictive systems and demand forecasting, AI-enabled decision support for policy and planning, fairness and ethical AI, and cybersecurity and privacy protection. The findings show that successful implementation depends not only on advances in AI algorithms but also on high-quality interoperable data, effective governance, regulatory support, public trust, and collaboration among stakeholders. The review concludes that the main challenges facing AI-enabled MaaS are no longer primarily technical but organizational, institutional, and social. Future research should focus on trustworthy and explainable AI, privacy-preserving learning, standardized evaluation methods, fairness-aware optimization, resilient cybersecurity, and long-term assessments of MaaS impacts on sustainable urban mobility. Full article

Rural cultural tourism has become an important pathway for rural revitalization in underdeveloped regions, yet its spatial organization and driving mechanisms remain insufficiently understood. This study investigates the spatial patterns, functional typology, and driving mechanisms—understood as statistically significant spatial associations—of 300 policy-designated rural cultural tourism villages in Gansu Province, China, using an integrated geospatial analytical framework incorporating the Average Nearest Neighbor Index, Kernel Density Estimation (KDE), the Lorenz Curve, Global Moran’s I, and the Geo-detector model. The results reveal a significant clustered distribution characterized by a pronounced “high-density southeast–low-density northwest” pattern. Five functional village types exhibit distinct spatial concentration characteristics associated with differences in resource endowments and development orientations. Geo-detector analysis identifies transportation accessibility, economic development, and topographic conditions as the factors most strongly associated with spatial heterogeneity. Interaction effects among natural, socioeconomic, and policy variables are also detectable. These findings advance the understanding of the spatial organization of rural cultural tourism and demonstrate the value of GIS-based spatial analysis for supporting evidence-based rural revitalization planning and sustainable territorial development in ecologically fragile regions. Full article