Search Results (505)

Achieving urban climate neutrality and interim mitigation targets requires rapid demand-side emission reductions, yet current user-centred interventions remain fragmented, are often concentrated on low-impact actions, and rarely provide a traceable basis for comparing outcomes, validity conditions, and equity implications across contexts. This paper reframes demand-side mitigation as a design problem of “use infrastructures”: integrated configurations of communication, product-technology, services, interaction, and governance that make low-carbon choices practicable within everyday routines. We introduce the Design Evidence Link (DEL) as a traceability device supporting ex ante configuration (selection and orchestration of levers) and ex post verification (monitoring, attribution of outcomes, and trade-off control). Through a design-led comparative analysis of nine international cases in high-impact sectors (household energy, ground mobility, food systems, and circular economy/materials), we derive and consolidate a shared extraction and coding protocol that links determinants (barriers and enablers) to design requirements and decision-grade metrics (carbon impact, adoption, continuity, and equity), explicitly qualifying uncertainty and evidence levels. Cross-case results show that effective interventions rely less on isolated information and more on coordinated action packages that reduce cognitive and economic frictions, enhance data credibility through standards and accountability, and embed follow-up mechanisms that support behavioural continuity. DEL also surfaces recurring validity conditions and failure modes (digital exclusion, trust erosion, rebound, and lock-in), translating them into operational criteria for policy and design. Compared with behaviour-change or theory-of-change framings, DEL focuses on the observable orchestration of integrated conditions of use and on the explicit grading of evidence. It should therefore be read as a structured analytical–operational framework for ex ante and ex post assessment, whose transferability remains conditional on source quality, contextual prerequisites, and the limits of the selected cases. Full article

Railway transport plays a crucial role in sustainable and low-carbon mobility; however, the safety and resilience of railway systems are increasingly challenged by aging infrastructure, spatial inequality, and intensifying climate extremes. These challenges are particularly evident in Serbia, where railway stations in rural and peripheral areas often lack adequate safety infrastructure, accessibility, and climate-adaptive design, especially affecting women and other vulnerable passengers. The aim of this study is to develop a GIS-based spatial framework for assessing gender-sensitive railway safety under combined sociospatial and environmental pressures. The analysis integrates multiple geo-information sources, including railway infrastructure data, passenger statistics, safety incidents, and climate hazard indicators such as floods, heatwaves, heavy snowfall, and windstorms. Geographic Information System (GIS) techniques, including kernel density estimation, buffer and zonal statistics, spatial interpolation, and spatial regression, were applied to evaluate spatial safety patterns and environmental risks. The results reveal pronounced regional disparities, with southern and eastern Serbia representing the most vulnerable areas due to inactive stations, poor lighting, limited digital connectivity, and frequent exposure to extreme weather events. Rural railway stations are frequently located in climate risk zones, and many do not meet the minimum safety infrastructure standards. Based on these findings, this study recommends strengthening station lighting and surveillance systems, improving digital connectivity and emergency accessibility, and integrating climate-resilient infrastructure planning into railway modernization strategies. Overall, the findings highlight the importance of combining GIS-based spatial analysis, climate hazard assessment, and gender-sensitive planning to support safer, more inclusive, and climate-resilient railway infrastructure in Serbia. Full article

The global shift toward electric vehicles (EVs) has accelerated as governments pursue low-carbon transport systems and sustainable mobility transitions. In emerging economies such as Thailand, however, consumer adoption remains influenced by a complex interplay of policy incentives, perceived benefits, and charging-related uncertainties. This study investigates the determinants of EV adoption intention by integrating ecosystem and policy support with perceived value and perceived risk within a unified analytical framework. Grounded in customer perception theory and technology adoption perspectives, this research addresses the fragmented treatment of these factors in prior studies. Data were collected from 400 respondents with prior EV experience and analyzed using structural equation modeling to examine both direct and mediated relationships. The findings reveal that ecosystem and policy support significantly strengthen adoption intention, primarily by enhancing perceived value and reducing perceived risk. These results highlight the pivotal role of perception-based mechanisms in translating policy initiatives into consumer commitment. The study suggests that effective EV promotion in Thailand and similar emerging markets requires coordinated ecosystem development, clear policy communication, and reliable charging infrastructure to sustain long-term adoption momentum. Full article

Clarifying the spatiotemporal evolution and driving mechanisms of bike-sharing-to-metro feeder trips (BSMF) is key to optimizing urban public transport’s first-and-last-mile connectivity and advancing low-carbon development. Existing studies on BSMF mostly ignore spatiotemporal heterogeneity, lack in-depth exploration of multi-factor interaction effects, and have subjective stratification or model specification bias, which hinder the accurate depiction of BSMF’s complex evolutionary patterns. Taking Xi’an as a case with 126 metro stations as analysis units, this study integrates multi-source data including shared bike trip records, metro network and built environment attributes to address the above issues. A framework combining kernel density estimation, spatial autocorrelation analysis, Optimal Parameter Geographic Detector (OPGD) and Geographically and Temporally Weighted Regression (GTWR) models (OPGD-GTWR) is constructed to identify BSMF’s spatiotemporal patterns, screen key influencing factors and reveal their spatiotemporal heterogeneity and interactive mechanisms. Results show Xi’an’s BSMF trips feature a “double-peak and double-valley” temporal tidal pattern and core-periphery spatial agglomeration. The OPGD-GTWR model (R² = 0.853) outperforms traditional models in capturing spatiotemporal heterogeneity. These findings provide empirical evidence and refined references for shared mobility resource allocation, bike-metro integration improvement and transit-oriented urban planning. Full article

Evaluating urban mobility transitions is essential to determine whether local transport interventions support broader sustainability goals. Cities increasingly implement initiatives to promote public transport, active mobility, and low-carbon transport systems. Still, assessing their impact on city-scale structural change remains challenging. Existing evaluation approaches often rely on project-level monitoring or fragmented indicators, which limits cross-city comparison and the assessment of long-term system transformation. This paper proposes a dual-track methodology to evaluate sustainable urban mobility interventions. The first track uses city-defined key performance indicators to capture local implementation processes, governance dynamics, and perceived outcomes. The second track relies on publicly available open data to assess city-scale changes in mobility indicators, including public transport accessibility, cycling infrastructure provision, and traffic-related air pollution. The methodology is applied to ten European cities using open data and satellite-based environmental indicators. Results indicate that while cities report progress at the project level, external indicators show limited short-term structural change in city-wide mobility systems. These findings highlight the value of open data as an independent evaluation layer that contextualises local results and supports transparent assessment of urban mobility transitions. Full article

As a sustainability-oriented mode of education, cross-border digital education has distinct advantages, including a low carbon footprint associated with decreased student and staff commute times and expanded accessibility for disadvantaged learners. However, the intrinsic mechanisms by which globally mobile talent, including international students and transnational professionals, utilize their global skills and networks to create sustainable EdTech entrepreneurial initiatives need further investigation. Based on dynamic capability theory and resource orchestration logic, this study examines how human and social capital shape entrepreneurial engagement through resource integration capability (RIC) via PLS-SEM analysis of data collected from 318 transnationally mobile actors. The study finds that neither form of capital has a direct association on entrepreneurial entry; instead, both are associated with entrepreneurial entry indirectly through RIC, allowing mobile talent to combine and allocate knowledge, networks, and digital technologies across institutional and cultural boundaries. The study examines how cross-border EdTech entrepreneurship works towards creating inclusive and equitable quality education, as well as global partnerships, through scalable, adaptable, and low-carbon educational services, while meeting objectives 4 and 17 of the UN Sustainable Development Goals. This study reveals the transformation process centered around RIC, highlighting the need to create innovative ecosystems that transition from talent attraction to talent empowerment. The findings underline the importance of RIC in translating global mobility into sustainable digital education solutions. Full article

The rapid growth of electric vehicles (EVs) is reshaping transport systems and accelerating the sustainable digital transformation of smart mobility. EV battery-swapping, delivered through platform-based, data-driven service networks, offers a low-carbon alternative to conventional refueling and plug-in charging by shortening replenishment time and enabling centralized battery management. However, the behavioral mechanisms driving user adoption of this digitally enabled infrastructure remain insufficiently understood. This study develops a socio-technical system (STS) model in which social and technical drivers influence users’ intention to adopt EV battery-swapping services via the dual mediation of perceived trust and perceived risk. Using a three-stage mixed-methods design that combines a PRISMA-based literature review, expert interviews with user-journey mapping, and a large-scale user survey, the study identifies six social and technical antecedents of EV battery-swapping adoption. Based on 565 valid responses from EV users in the Beijing–Tianjin–Hebei region, partial least squares structural equation modeling and multi-group analysis are employed to test the proposed framework. The results show that all six antecedents significantly affect perceived trust and perceived risk, which in turn mediate their impacts on adoption intention, with notable heterogeneity across income and usage-frequency groups. The findings provide a mechanism-based extension of STS theory for digitally mediated battery-swapping infrastructure by showing how socio-technical conditions shape adoption via trust and risk, and they offer actionable implications for operators and policymakers to build secure, user-centered swapping services within intelligent transport systems. Full article

The increasing reliance on motorised traffic has led to significant environmental, health and urban mobility challenges for pedestrians and cyclists. Despite growing awareness of the benefits of active travel, including improved public health, reduced carbon emissions, and enhanced urban liveability, many cities struggle to implement effective interventions that prioritise non-motorised mobility due to inadequate infrastructure, safety concerns and car-oriented policies. It is essential to introduce strategic interventions, such as traffic calming measures, dedicated cycle lanes, pedestrian-friendly infrastructure and policy reforms to encourage sustainable mobility choices. This paper examined the impacts of bicycle and pedestrian infrastructure schemes on sustainability and Low Traffic Neighbourhoods (LTNs) at the Trafford Road corridor in Greater Manchester and Wood Street in Wakefield city centre, respectively. Most of the projected trips from the hypothetical office building will occur on the western and northern leg of the Haden Circus roundabout, with approximately 50% on the inward traffic of the western leg towards the roundabout and approximately 40% going outwards. The bicycle infrastructure scheme in the Trafford Road corridor observed an increase of up to 34% in bicycle traffic flow. On the other hand, the pedestrian infrastructure scheme on Wood Street caused a gradual increase in bicycle traffic on Wood Street from 174 to 356 per hour but had an insignificant influence on the pedestrian flow. Many United Kingdom (UK) councils have proposed traffic calming schemes in the city centre to enhance accessibility for pedestrians and cyclists, improve urban air quality and promote business and economic development. This paper examines how the schemes increase pedestrian and cyclist footfall within the traffic-calming zone while increasing traffic on adjacent roads. Restricting motorised traffic to prioritise cycling and walking improves public health, reduces pollution, enhances road safety, boosts local economies, and creates more liveable urban spaces, all while promoting sustainable and efficient transportation. Full article

Sustainable food production systems based on the use of biofertilizers and soil improvers are proposed to mitigate agricultural-related environmental impacts and address the climate crisis. In particular, plant growth-promoting microbes (PGPM) and biochar (Char) have been reported to improve plant growth, soil quality, and crop yield; however, their effects on food quality remain debated. In this study, untargeted metabolomics based on ultra-high performance liquid chromatography–ion mobility–high-resolution mass spectrometry (UHPLC-IMS-HRMS) and proton nuclear magnetic resonance spectroscopy (¹H-NMR) are proposed to achieve a comprehensive investigation of the effects of Char, PGPM and Char+PGPM on durum wheat. A total of 88 metabolites were annotated by UHPLC-IMS-HRMS, mainly belonging to carbohydrates, flavones, flavonoids, glycerophospholipids, and glycolipids, while 30 compounds were annotated by ¹H-NMR, mostly amino acids and short-chain carboxylic acids. The two datasets were merged with the gluten protein content dataset by using low- and mid-level data fusion approaches, obtaining models that exhibit excellent classification performance. Integrated analysis highlighted that the combined Char+PGPM treatment induced metabolic changes across multiple chemical classes, including enrichment of flavonoids and lipids, and downregulation of carbohydrate metabolites, suggesting a redistribution of carbon resources and modulation of secondary metabolism with potential implications on wheat grain quality. Full article

Accurate measurement of soil CO₂ flux is essential for understanding terrestrial carbon dynamics and quantifying greenhouse gas emissions from soil. However, the complexity and high cost of traditional measurement equipment limit its wide adoption in agriculture and other terrestrial ecosystems, including grasslands and managed field environments. In this paper, we developed a low-cost, automated soil CO₂ flux chamber for soil CO₂ flux monitoring. The flux chamber utilizes a commercially available MH-Z19 NDIR CO₂ sensor (Winsen Electronics Technology Co., Ltd., Zhengzhou, China), integrated with a Raspberry Pi microcontroller (Raspberry Pi Ltd., Cambridge, UK; manufactured by Sony UK Technology Centre, Pencoed, Wales, UK) for automated data collection and remote monitoring. The collected data are wirelessly transmitted to a computer or mobile device for real-time display. The total material cost of the system is less than $162. Side-by-side field measurements with a commercial LI-COR 8200-01S chamber (LI-COR Biosciences, Lincoln, NE, USA) showed that CO₂ fluxes measured by the low-cost chamber were consistently lower than those measured by the commercial instrument, averaging approximately 0.75–0.80 times the LI-COR values, indicating systematic underestimation in magnitude, while showing strong linear agreement (R² ≈ 0.98–0.99) across repeated field measurements. This indicates that the system reliably tracks relative changes in soil CO₂ flux, although a systematic bias in magnitude is present. This affordable and user-friendly chamber improves accessibility for researchers and field practitioners, enabling practical monitoring of soil CO₂ flux in applications where cost and portability are critical. Full article

Autonomous driving technology is a core enabler for new energy vehicle industrial upgrading and a critical pillar for achieving sustainable development goals (SDGs), especially sustainable urban mobility, low-carbon transportation, and efficient intelligent transportation systems (ITS). However, unstable nighttime low-light perception severely restricts autonomous driving deployment, hindering sustainable transportation development—rooted in visual feature degradation and cross-modal imbalance that impair 3D object detection (autonomous driving’s core perception technology). To address this and advance sustainable autonomous driving, this paper proposes a Bird’s-Eye View (BEV)-based multi-modal 3D object detection approach tailored for nighttime scenarios, integrating low-light adaptive components while preserving the original BEV pipeline. Without modifying core inference, it enhances low-light robustness and cross-modal fusion stability, ensuring reliable perception for sustainable autonomous driving operation. Extensive experiments on the nuScenes nighttime subset quantify performance via rigorous metrics (NDS, mAP, mATE). Results show the method outperforms BEVFusion with negligible parameter/inference overhead, achieving 1.13% NDS improvement. This validates its effectiveness and provides a sustainable technical tool for autonomous driving perception, promoting new energy vehicle popularization, optimizing urban ITS efficiency, reducing perception-related accidents and carbon emissions, and directly contributing to transportation and socio-economic sustainability. Full article

This study investigates how firms in high-emission sectors progress along the low-carbon transition by analysing the joint dynamics of Management Quality (MQ) and Carbon Performance (CP) using probabilistic modelling and explainable machine-learning methods. Digitalisation is conceptualised as the increasing use of data-driven and algorithmic tools in corporate governance, sustainability monitoring, and regulatory oversight, enabling a more granular assessment of corporate transition pathways across multiple time horizons. Using annual Transition Pathway Initiative data for 175 firms over the period 2018–2025, we apply discrete-time Markov chains to capture state persistence and directional mobility in MQ and CP, while Hidden Markov Models uncover latent performance regimes shaping firms’ transition trajectories across three decarbonisation horizons (2028, 2035, and 2050). To enhance interpretability and policy relevance, CatBoost-based feature importance analysis identifies governance, emissions-related, and sector-specific drivers of transitions between states. The results indicate a steady and highly persistent improvement in Management Quality, reflecting cumulative consolidation of governance structures, while Carbon Performance evolves more slowly and heterogeneously, with only moderate convergence emerging toward the 2050 horizon. Latent-regime estimates reveal a gradual shift from volatile, low-performance pathways toward more stable transition regimes over time. From a policy perspective, the findings suggest that governance improvements alone are insufficient to ensure timely emission reductions, highlighting the need for digitally enabled, sector-specific regulatory incentives and enforcement mechanisms targeting realised Carbon Performance. Full article

Urban sustainable mobility requires understanding how people travel, which modes they use, and what impacts these choices generate. This study proposes a smart mobility analytics framework that integrates GPS traces, dynamic traffic variables, and machine learning to infer transport modes and sustainability metrics in Cuenca, Ecuador. Geospatial and kinematic data were collected at 1 Hz from 50 participants over four working weeks, yielding 8.99 million samples across five modes: walking, cycling, tram, bus, and private vehicles. A compact subset of physical and spatial predictors, derived from speed, acceleration, jerk, longitudinal forces, and distance to public transport routes, was selected using the Football Optimization Algorithm. A classification tree trained with a 70/15/15 train–validation–test split achieved an overall accuracy of 84.2%, with class precisions of about 99% for pedestrian and bicycle, 93% for tram, 76% for private vehicles, and 64% for bus. The classified trajectories show that walking and cycling account for approximately 65% of total travel time but only 2% of total distance and 1.7% of CO₂ emissions, whereas motorized modes generate more than 98% of emissions. Buses contribute nearly four times more CO₂ than private vehicles, despite carrying a larger passenger volume. The proposed framework delivers detailed, policy-relevant indicators to support low-carbon urban transport strategies. Full article

Carbon dioxide injection is one of the most advanced and commercially proven methods of enhanced hydrocarbon recovery, and CO₂ injection has been shown to be very effective in conventional oil reservoirs and is gaining attention in gas, unconventional, and coalbed methane reservoirs. The advantages of CO₂ injection lie in the favorable phase properties and interactions with reservoir fluids, such as swelling, reduction in oil viscosity, reduction in interfacial tension, and miscible displacement in favorable cases. But the low viscosity and density of CO₂ compared to the reservoir fluids result in unfavorable mobility ratios and gravity override, resulting in sweep efficiency limitations. This review offers a broad and EOR-centric evaluation of the various CO₂ injection methods for a broad array of reservoir types, such as depleted oil reservoirs, gas reservoirs for the purpose of gas recovery, tight gas/sands, as well as coalbed methane reservoirs. Particular attention will be given to the use of mobility control/sweep enhancement techniques such as water alternating gas (CO₂-WAG), foam-assisted CO₂ injection, polymer-assisted WAG processes, as well as hybrid processes that combine the use of CO₂ injection with low salinity or engineered waterflood. Further, recent developments in compositional simulation, fracture-resolving simulation, hysteresis modeling, and data-driven optimization techniques have been highlighted. Operational challenges such as injectivity reduction, asphaltene precipitation, corrosion, and conformance problems have been reviewed, along with the existing methods to mitigate such issues. Finally, key gaps in the current studies have been identified, with an emphasis on the development of EHR processes using CO₂ in complex and low-permeability reservoirs, enhancing the resistance of chemical and foam methods in realistic conditions, and the development of reliable methods for optimizing the process on the field scale. This review article will act as an aid in the technical development process for the implementation of CO₂ injection projects for the recovery of hydrocarbons. Full article

The growing demand for carbon-based energy materials requires sustainable alternatives to fossil fuels. This study explored the production and characterization of char obtained from refuse-derived fuel (RDF) and biomass blended pellets in varying proportions (0%, 15%, 25%, 50%, and 100% RDF). The objective was to evaluate their potential as high-energy-density solid fuels while addressing operational challenges related to ash behavior. Chars were produced at 400 °C for one hour in a muffle furnace in closed crucibles. A set of analytical techniques (calorimetry, infrared spectroscopy, thermogravimetry, inductively coupled plasma, and X-ray fluorescence) was employed to assess physicochemical properties. RDF content strongly affected mass yield, energy yield, and thermochemical behavior. Among the tested formulations, char with 50 and 25% of RDF (C_RDF50:BW50 and C_RDF25:BW75) ignited at lower temperatures (≈150 °C) and showed high flammability (C) values (1.97–2.03 × 10⁻⁵), indicating greater flammability. They also reached higher combustion temperatures (716–746 °C), suggesting improved thermal stability during the final combustion stage. Both chars presented increased high heating values (18–19 MJ/kg, dry basis) and a few surface functional groups. This supports a lower devolatilization rate, meaning that although ignition is easy, combustion remains stable and controllable. All chars showed very high acid–base indices, indicating a strong tendency for ash melting. However, low slag viscosity and alkalinity values suggest viscous, poorly mobile slag, reducing adhesion and buildup on reactor surfaces. This study combines thermogravimetric combustion analysis with ash chemistry–based slagging and fouling indices to provide an integrated assessment of the operational behavior of RDF–biomass-derived char fuels. The results highlight the technical feasibility of chars produced from RDF and biomass blended pellets, whose thermal properties make them promising candidates for use as solid fuels. Full article