Abstract Aging prestressed concrete (PC) structures, particularly those utilizing unbonded tendons, are susceptible to long-term deterioration mechanisms such as creep, shrinkage, and prestress loss, which manifest in complex damage forms including surface cracking and FRP debonding. Traditional inspection techniques are often labor-intensive and insufficiently accurate in detecting early-stage or subvisible defects. This study proposes a novel artificial intelligence (AI)-driven framework for automated defect detection and classification in prestressed concrete beams based on visual and structural response data. This work examines progressive damage in newly cast laboratory prestressed concrete beams under controlled loading, using synchronized high-resolution vision and vibration measurements, and evaluates specimen-disjoint generalization and edge-oriented deployability. A hybrid deep learning architecture combining YOLOv8 for crack localization, Swin Transformer for damage severity classification, and CNN–Transformer models for time-series vibration analysis is developed. The results demonstrate that the proposed system achieves a crack detection accuracy of 96.3%, a severity classification F1-score of 93.1%, and can localize damage with a mean IoU of 0.82. This work presents an integrated, aging-aware multimodal AI framework for damage assessment in prestressed concrete beams with unbonded tendons, combining real-time vision-based crack/debonding localization with synchronized vibration/strain analysis to provide a scalable alternative to traditional SHM methods. Graphical Abstract

This study aimed to develop an authentic mathematics assessment for elementary schools in the form of a testlet-based instrument integrated with deep learning principles and the Adiwiyata context within the framework of transformative Islamic education in the Special Region of Yogyakarta. Employing basic research with an embedded mixed-methods design, the development process adapted the Plomp model and the instrument development framework of Oreondo and Antonio. Data were collected through teacher needs surveys, expert validation, readability testing, and field trials involving 462 fifth-grade students from elementary schools and Islamic elementary schools. Quantitative analyses included Content Validity Index (CVI), Aiken’s V, Cronbach’s Alpha, Classical Test Theory (CTT), and Item Response Theory (IRT) using the 2-PL Graded Response Model. The results indicate that the developed instruments meet acceptable psychometric standards, with Aiken’s V values ranging from 0.75 to 1.00 and high internal consistency for both the testlet instrument (Cronbach’s Alpha = 0.845) and the environmental awareness questionnaire (Cronbach’s Alpha = 0.850). Item analysis shows adequate discrimination and a structured progression of difficulty, although one item exhibited low discrimination in the 2-PL GRM, highlighting the importance of IRT-based diagnostics for testlet refinement. Descriptive findings reveal that students demonstrate high levels of environmental awareness, particularly in the knowledge and attitude dimensions, while mathematical achievement remains low on non-routine items. Correlation analysis shows no significant relationship between environmental awareness and mathematical ability. Methodologically, this study contributes a validated and contextually grounded assessment framework that integrates expert judgment, reliability analysis, and complementary CTT–IRT procedures. Theoretically, the findings reconceptualize authentic assessment as a diagnostic bridge rather than a direct causal link between affective values and cognitive performance, demonstrating that environmental concern functions as a potential cognitive resource only when explicitly activated within mathematical tasks.

Clear cell renal cell carcinoma (ccRCC) exhibits strong heterogeneity and variable therapeutic responses. PANoptosis, an integrated form of inflammatory programmed cell death, may influence tumor immunity and prognosis, yet its role in ccRCC remains unclear. Multi-omics data from TCGA database were analyzed to characterize PANoptosis-related genes (PRG), define molecular and gene subtypes, and construct a prognostic PRG score using Cox and LASSO regression. Immune infiltration, drug sensitivity, and predicted immunotherapy response were evaluated. Single-cell RNA sequencing analysis was used to map PRG expression across cell populations. In vitro experiments were performed to validate RBCK1 function in ccRCC. 14 PRG showed marked CNV alterations and differential expression. Three PRG molecular subtypes displayed distinct survival outcomes and immune landscapes. A three-gene PRG score (RIPK1, PYCARD, RBCK1) independently stratified prognosis and correlated with immune infiltration, mutation burden, and therapy sensitivity. Lower scores predicted better immunotherapy response and higher drug sensitivity. Single-cell analysis revealed broad PRG expression across macrophages, epithelial cells, endothelial cells, and stem-like cells. RBCK1 was significantly upregulated in ccRCC and promoted proliferation and migration, while its knockdown inhibited tumor cell growth. We delineated the PANoptosis landscape in ccRCC and developed a robust PRG score with strong prognostic and immunological relevance. RBCK1 functions as a key oncogenic regulator and potential therapeutic target. These findings offer a valuable framework for precision risk assessment and treatment optimization in ccRCC.

Resilience to climate change is a complex concept, especially in metropolitan areas where diverse services and stakeholders interact. Promoting sustainable climate adaptation, a resilience assessment method focused on regional areas and nature-based solutions is presented, along with its open-access, web-based platform, supporting resilience assessment, planning, and monitoring. Floods, droughts, heat or cold waves, windstorms, and forest fires can be assessed. A framework for holistic assessment and other framework, addressing critical infrastructure, are integrated. Four resilience dimensions are assessed: organizational (governance, social aspects, finance); spatial (exposure, impacts, and mapping); functional (service management, interdependencies); and physical (infrastructure robustness, redundancy). Strategic services comprise, e.g., water, waste, and natural areas. Resilience capacities, e.g., to prevent, respond, and recover from disruptions, are also assessed. The paper emphasizes new developments and assessment. Practical step-by-step guidance aligned with assessment purposes is included, aiming to address observed limitations (e.g., fragmented service provision, communication silos, data constraints). Overall results of a Spanish metropolitan area (AMB) and an exploratory application to an Austrian rural case (SLR) are also presented. Following the guidelines, AMB progressed from an essential to a comprehensive assessment. Overall, almost 1/3 of the metrics are advanced or progressing. SLR assessed its resilience capabilities regarding electrical infrastructure.

Abstract The detection of incipient and minor cracks in prestressed reinforced concrete (RC) structures is crucial in ensuring safety of existing bridges. However, traditional structural health monitoring (SHM) often fails to provide reliable and effective early detection. As a matter of fact, literature SHM applications typically investigated moderate-to-severe cracking conditions and often developed criteria that are likely to depend on investigated scenarios. Aiming at addressing the abovementioned literature gap, this study evaluates the effectiveness of acoustic emission (AE) testing for early crack identification in post-tensioned RC girders. AE tests are carried out during cyclic and monotonic four-point bending tests on different specimens up to failure. Multiple AE analysis methods are systematically implemented considering literature methods and novel method specifications (MSs). The evolution of key AE features is examined, and several indicators are further analyzed through a blind assessment framework. The complete AE dataset of AE data is made publicly available. AE activity trends and their potential correlation with observed mechanical damage are identified and discussed. Among the investigated indicators, relative acoustic entropy shows particular promise for early crack detection. The study systematically compares the application of multiple assessment methods, identifying strengths and weaknesses and outlining potential SHM criteria. The findings demonstrate that AE testing, when combined with suitable MSs and damage criteria, offers a viable path for reliable SHM. This paper lays the groundwork for development of robust damage detection criteria.

Measuring the resilience of historic areas is challenging due to their heterogeneity in scale, heritage type, multi-hazard exposure, and socio-cultural context, creating the need for a flexible framework aligned with the latest Intergovernmental Panel on Climate Change (IPCC) approaches. This study introduces the SHELTER framework, which takes the historic area as its primary unit of analysis while enabling a cross-scalar assessment, from artefact/building scale to urban and transregional contexts. Developed through a co-creation strategy and an extensive literature review, the framework integrates indicators for multidimensional, cross-scale, and systemic resilience assessment and monitoring. The indicators span hazards such as heatwaves, earthquakes, floods, subsidence, and wildfires and capture exposure and vulnerability, the latter being understood as the sensitivity and coping, adaptive, and transformative capacities of communities. Refinement using the RACER methodology yielded a concise yet comprehensive shortlist of indicators, providing both general overviews and specific insights tailored to historic environments. The framework’s efficacy was tested across five case studies, demonstrating adaptability and suitability in diverse historic areas. Overall, SHELTER moves beyond a traditional focus on physical vulnerability and risk management, offering a replicable, holistic set of resilience indicators that supports consistent assessment and monitoring while respecting the singularities of historic settings.

Assessing functional abilities in stroke rehabilitation is essential, combining subjective self-reports with objective clinical evaluations. This study aimed to compare self-reported impairments from stroke patients with rehabilitation team evaluations using the ICF stroke core set at 3 time points: 1 month post-discharge, after 6 months, and 12 months post-diagnosis. Additionally, the study sought to identify ICF subdomains most impacting health-related quality of life (HRQOL) as measured by EQ-5D. This longitudinal, retrospective observational study included consecutive 118 stroke patients at the Satahospital Rehabilitation Unit (2021-2022). Results showed that, 1 month after discharge, patients rated their functioning higher than team assessments, particularly in cognitive domains. By 12 months, patients' self-reports indicated lower functioning than team evaluations, with discrepancies diminishing over time. Objective assessments revealed significant improvements in mobility, self-care, and cognitive functions, while patients reported progress in life activities and social interactions but little change in physical or cognitive domains. Depression levels and self-care ability (washing) were the strongest predictors of improved HRQOL. These findings reveal that patients initially overestimate their abilities, influenced by a lack of awareness and emotional factors, while rehabilitation teams provide more objective evaluations and individualized rehabilitation. Integrated assessment frameworks combining subjective and objective perspectives are crucial to optimizing rehabilitation outcomes.

Following large-scale threatening events, a key challenge is to rapidly establish which species have been most affected and are in need of urgent conservation. For data-poor taxa, such assessments are challenging. In Australia, invertebrates represent over 90% of faunal diversity and are critical for ecosystem function, yet most are undescribed, and, of the described, most are poorly known. Thus, it is important to have a way to estimate susceptibility to major disturbance of data-deficient taxa. We developed a novel trait-based method for assessing the impact of a major wildfire on invertebrates. We applied it to 1220 species that showed high distributional overlap with the 2019-2020 Australian megafires. We estimated susceptibility based on the microhabitat species occupy, their life-history and ecological traits, and mechanisms that account for key data uncertainties (number of usable occurrence records, availability of traits data, and recency of taxonomic work). We found 748 species likely to be of potential conservation concern following the megafires; 169, 579, and 454 were highly, moderately, and mildly threatened by a major fire, respectively. Most species (867) were associated with poor or very poor data quality. Of the 867 poorly known species, 97 were most at risk from a major fire. Our approach is generalizable to other data-deficient taxa and to major disturbance events globally and can be used to improve representation of poorly known species in conservation assessments and threat mitigation decisions. If the uncertainties and knowledge gaps we identified are addressed, it is likely risk prediction could be improved.

Intensive swine production contributes significantly to the global protein supply but generates considerable environmental pressure, particularly through greenhouse gas emissions and surplus slurry management. Anaerobic digestion (AD), especially (co-AD), has been widely investigated as a mitigation strategy to enhance renewable energy generation and nutrient recovery. This systematic review synthesizes life cycle assessment (LCA) studies published between 2019 and 2025 that evaluated AD systems treating swine slurry, following the PRISMA 2020 guidelines. Across diverse methodological approaches and regional contexts, the literature consistently shows that AD can reduce global warming potential compared with conventional slurry management, with stronger environmental benefits when biogas is efficiently valorized and when swine slurry is co-digested with complementary organic substrat. Co-AD emerges as a key mitigation option by improving biogas yields, process stability, and overall environmental performance while also enabling better utilization of external organic waste. However, the results remain highly sensitive to operational factors such as methane leakage, digestate management, energy efficiency, and substrate selection. This review highlights the methodological inconsistencies among LCA studies and underscores the need for harmonized assessment frameworks and improved emission data. Overall, co-AD represents a promising pathway for enhancing the environmental sustainability of swine production systems when integrated into optimized, context-specific management strategies.

Against the backdrop of rapid urbanization and climate change, energy burden inequity arises between existing and new residential buildings due to generational differences in building envelopes. This study develops a demand-side energy burden equity assessment framework based on energy simulations of typical existing and new apartments in representative cities across China’s five major climate zones. The framework integrates multi-climate conditions, long-term evolution under different Shared Socioeconomic Pathways, and adaptable retrofit implications. Results indicate that demand-side energy burden inequity is widespread but structurally heterogeneous across climate zones, with the largest disparity observed in heating-dominated regions (up to 95.69 kWh/m2 in Harbin). Under future warming, three scaling pathways emerge: convergence in heating-dominated regions (up to −27%), divergence in cooling-dominated and mixed regions (up to +382%), and offsetting effects driven by heating–cooling structural shifts in cold regions (up to −5%). Retrofit analysis shows that combined envelope upgrades achieve substantial inequity reduction (88–152%), though with diminishing marginal returns, while single targeted measures already yield high benefits in cooling-dominated and mild regions (74% and 83%, respectively). The findings provide differentiated and forward-looking evidence to support equity-oriented interventions in urban residential retrofitting and policy design.

Humpback whales produce a wide variety of frequency-modulated vocalizations, called song units. Modeling and synthesis of these units form the basis for many bio-inspired applications, including underwater covert communication and naturalistic playback experiments. Conventional synthesis methods are based on fundamental frequency contour modeling of single-segment signals, which exhibit limitations in terms of synthesis flexibility and similarity. To address the above limitations, this paper proposes a humpback whale song unit synthesis method based on small-sample training. Fundamental frequency contours and line spectral pairs are extracted from humpback whale song units collected in marine environments to construct the training dataset. Using these parameters, a hidden Markov model (HMM) is established for parameter training, and probability density functions are obtained for each HMM state. To address high-frequency jitter in generated fundamental frequency contours, a parameter generation method that combines dynamic feature constraints with variational mode decomposition denoising is introduced, yielding smoother fundamental frequency curves. For enhanced synthesis flexibility, state duration modification and fundamental frequency modification methods are proposed based on parameter distributions. Finally, the generated parameters are converted into time-domain waveforms using a linear predictive coding-pitch vocoder. To comprehensively evaluate the synthesis performance, an assessment framework based on statistical parametric analysis and t-distributed stochastic neighbor embedding is established. Simulation results demonstrate that the proposed humpback whale song unit synthesis system achieves superior flexibility and similarity compared to the conventional approach based on single whistles modeling, ultimately enhancing performance in bio-inspired applications.

Agricultural productivity currently faces challenges such as soil fertility issues, climatic instability, pests and diseases, and anthropization. This drives a shift towards sustainable agricultural practices, including biopreparations—products derived from living organisms or their metabolites that serve as biofertilizers, biopesticides, biostimulants, or biodegradation agents. Among these, the genus Bacillus is a primary candidate for sustainable agriculture; however, this review primarily covers rhizosphere-isolated organisms referred to as plant growth-promoting rhizobacteria. Bacillus strains possess a suite of direct and indirect mechanisms to promote plant development and biocontrol, as well as to tolerate various abiotic stresses. This review aims to describe all the mechanisms attributed to strains of this genus and their impact on different crops to promote plant growth, hormonal regulation (indole-3-acetic acid (IAA), abscisic acid (ABA), and ethylene), tolerance to abiotic stresses such as drought, heavy metals, salinity and heat stress, as well as resistance to pests and diseases. Furthermore, this work analyzes quantitative data regarding yield improvements and the environmental variables that influence the consistency of Bacillus performance in the field. Finally, to provide a balanced perspective, the review incorporates future directions in research on biosafety and risk assessment frameworks.

Background: Lifestyle challenges such as prolonged sitting, irregular dietary habits, high stress levels, and lack of physical activity have become increasingly common among working professionals. All these factors contribute to the risk of chronic diseases such as diabetes, heart disease, obesity, and high blood pressure, which in turn result in reduced work performance and quality of life and may further affect health services access through increase healthcare needs. The teaching environment, like many other work environments, is mentally, emotionally, and practically demanding, and it puts extra pressure on those who work in it. Academicians, who devote themselves to guiding young minds, often make unhealthy daily choices and face significant work-related stress, which can lead to serious long-term health problems. This review highlights that health and well-being are shaped not by a single factor such as diet, work patterns, or habits, but by their combined effect. Methods: A study of around 113 studies has highlighted that academicians usually feel drained and physically exhausted. Result: The factors like prolonged fasts, insufficient water intake, long-standing hours, long and continuous talking, and extended periods in the sitting position have added to their stress levels at the workplace. The most critical finding is that these factors do not affect in isolation but impact as a combined interaction. These issues influence each other, thus increasing the vulnerability to lifestyle disorders. Conclusions: This critical problem can be addressed with a Multimodal Assessment Framework that integrates teachers’ data on dietary habits, workplace ergonomics, sleep quality, and levels of physical activity. The presented work also proposes a statistical technique with an Artificial Intelligence (AI) model, and generates Vulnerability Quotient (VQ) that show lifestyle disease-related exposure of the teachers, which may be further used to provide remedial interventions. These insights can further guide institutions and policymakers to design healthier, supportive, and sustainable teaching environments.

This study proposes a modelling framework for simulating cyclist–vehicle interactions at urban intersections characterised by geometric constraints and variable visibility conditions. A Digital Model (DM) of the intersection geometry was developed in SUMO, complemented by a custom behavioural model calibrated using experimental trajectory data to capture cyclists’ and drivers’ perception–reaction and braking behaviour. These two components were combined to simulate scenarios with varying visibility conditions and perception-triggered braking responses in severe conflict situations. Results show that reduced visibility significantly reduces temporal safety margins, with over 50% of all simulated interactions yielding differential time-to-arrival (TTA2) values below 2 s. Furthermore, obstructed conditions lead to higher- and more-dispersed relative crossing speeds (DV), typically increasing by 0.5–1.0 m/s compared to unobstructed conditions. Simulation data confirmed that clear visibility promotes anticipatory and adaptive user behaviour, whereas limited sightlines reduce braking availability and increase the likelihood and severity of conflicts, with distributions conditioned by the intersection’s geometry. The ability to generate detailed synthetic datasets of cyclist–vehicle interactions, often not obtainable through field observation, demonstrates the potential of the proposed framework for safety assessment. This approach supports the evaluation of mitigation strategies, including C-ITS-based solutions, and provides a basis for developing predictive AI models to enhance the safety of vulnerable road users.

Abstract Introduction Altered wildfire regimes, exacerbated by unsustainable management, threaten natural ecosystem recovery post‐fire. Effective restoration requires timely fire impact assessments and tailored, evidence‐based management. While fire databases and Environmental Impact Assessment (EIA) frameworks partially support decision‐making, a holistic platform linking assessment, planning, and operational actions is still lacking. Objectives Our goal was to develop and test a web‐based Post‐Fire Spatial Decision Support System (PF‐SDSS) that facilitates decision‐making across three post‐fire management levels: problem definition, strategic planning, and operational management. Methods PF‐SDSS integrates satellite imagery with high‐resolution cartography in a participatory multi‐criteria analysis (MCA), using server‐ and cloud‐based computing for real‐time analyses. The generated soil erosion risk (SER) and vegetation recovery potential (VRP) maps underpin rule‐based restoration prioritization and recommendations that provide site‐specific practices derived from a comprehensive literature review. Field validation (Spearman's correlation), sensitivity analysis (MCA weight variations), and usability evaluation (System Usability Scale [SUS] method) assessed the system's performance. Results PF‐SDSS is freely available online, with a demonstration for Ávila Province, Spain. Validation showed significant correlations for SER ( ρ = 0.56) and VRP ( ρ = 0.42). Sensitivity analysis confirmed MCA robustness under 20% weight variations, and the 75% SUS score indicated satisfactory usability and acceptance among end‐users. Conclusions This study automated the post‐wildfire management planning cycle within a modular framework. The EIA module supports problem definition by mapping fire impacts. The strategic planning module identifies priority areas and sets site‐specific management objectives. The operational planning module offers spatially oriented, evidence‐based management alternatives.

Evaluation and modeling of environmental stressors affecting enteric microbial survival in soil: Implications for wastewater reuse and risk management.

Regenerative agriculture (RA) offers a critical pathway for climate change mitigation and adaptation, yet its implementation is often hindered by conceptual ambiguity and a lack of standardized assessment frameworks. This study employs a comparative systemic analysis, integrated with a Failure Mode and Effects Analysis (FMEA) framework, to evaluate the resilience of medium-sized RA farms (50–200 ha)—a segment representing the professional backbone of European agriculture—under varying infrastructural and policy conditions. By synthesizing recent standardized metrics from the global literature, the research constructs three operational contexts: Context A (Integrated High-Performance), characterized by robust support and digital monitoring; Context B (Transitional/Fragmented), reflecting partial adoption with limited resources; and Context C (Maladaptive), representing systemic barriers. The results reveal a significant “Resilience Gap” between theoretical potential and practical reality. Specifically, the analysis identifies that ecological practices alone (e.g., cover cropping, no-till) are insufficient to guarantee economic resilience without the support of Monitoring, Reporting, and Verification (MRV) systems. In transitional contexts, the inability to verify ecosystem services prevents farmers from accessing financial buffers, rendering the system vulnerable to climate shocks. This study concludes that enhancing RA resilience requires a paradigmatic shift from practice-based subsidies to outcome-based incentives, underpinned by accessible MRV technologies and standardized socio-economic indicators.

Understanding the coupling mechanisms between vegetation phenology and carbon productivity is essential for assessing ecosystem responses to climate change and guiding sustainable grassland management. This study focuses on stable alpine grasslands on the southern slope of the Qilian Mountains from 2001 to 2020, a climatically sensitive but relatively under-investigated transition zone on the northeastern Tibetan Plateau. We utilized MODIS NDVI time-series (MOD13Q1) and the latest PML V2 gross primary productivity (GPP) product at 500 m resolution to quantify changes in the start (SOS), end (EOS), and length (LOS) of the growing season. A pixel-wise linear regression approach was applied to evaluate the sensitivity of GPP to phenological metrics, explicitly characterizing how much GPP changes in response to unit shifts in SOS, EOS and LOS. Compared with previous studies that mainly described large-scale correlations between phenology and GPP or relied on coarser GPP products, this study provides a pixel-level, sensitivity-based assessment of phenology–carbon coupling in alpine grasslands using a long-term, phenology–GPP dataset tailored to the Qilian alpine region. The results revealed trends of earlier SOS, delayed EOS, and extended LOS, accompanied by a gradual increase in GPP. However, phenology–GPP coupling exhibited notable spatial heterogeneity. In mid- and low-altitude areas, extended growing seasons enhanced GPP, whereas high-altitude zones showed limited or even negative responses, likely due to climatic constraints such as cold stress and thermal–moisture mismatches. To better understand these spatial differences, we constructed a three-dimensional phenology–GPP sensitivity space and applied k-means clustering to delineate three ecological functional zones: (1) high carbon sink potential, (2) ecologically fragile regions, and (3) neutral buffers. This sensitivity-based functional zonation moves beyond traditional correlation analyses and provides a process-oriented and spatially explicit framework for ecosystem service assessment, carbon sink enhancement and adaptive land-use strategies in sensitive mountain environments.

Masonry retaining walls constitute an essential component of historic and urban infrastructure in seismic regions; however, their seismic performance remains insufficiently quantified due to material heterogeneity, limited tensile capacity, and complex soil–structure interaction. This study investigates the seismic response of historic stone masonry retaining walls using a finite element-based anisotropic macro-modeling approach. The analysis focuses on the perimeter retaining walls of Emirgan Grove in Istanbul, which represent culturally significant heritage structures constructed from natural limestone and cement–lime mortar. Material properties were defined based on experimental test results and representative values reported in the literature, while composite anisotropic behavior was incorporated into the numerical models. Static loads, earth pressures, and seismic actions were applied in accordance with the Turkish Building Earthquake Code (TBEC-2018) using the equivalent static earthquake load method. Representative wall segments with heights of 2.5 m, 3.5 m, 4.0 m, and 6.30 m were analyzed. The numerical results show that maximum compressive stresses reached approximately 0.48 MPa, remaining well below the allowable limit of 4.50 MPa, while maximum tensile stresses of about 0.28 MPa did not exceed the allowable tensile limit of 1.00 MPa. In contrast, shear stresses locally reached approximately 0.25 MPa, exceeding the allowable shear limit of 0.10 MPa, particularly along the soil–wall interface in taller walls. Sliding stability was satisfied in all cases, whereas overturning and shear behavior governed seismic vulnerability. These findings confirm that wall height is the primary parameter controlling seismic response and demonstrate the effectiveness of the proposed framework for preservation-oriented seismic safety assessment of historic masonry retaining walls.

Electric vehicles (EVs) represent a cornerstone of sustainable urban mobility, yet their environmental benefits in coal-dependent grids like Bengaluru's require renewable energy integration and AI-driven efficiencies. This study employs a comprehensive Life Cycle Assessment (LCA) framework, augmented by machine learning scenario analysis, to quantify emission reductions from solar-powered charging and AI-optimized operations. Primary data from a stratified survey of 300 Bengaluru EV stakeholders, analysed through t-tests, multiple regression, and structural equation modelling (SEM), demonstrate 38% lower lifecycle GHG emissions and 15% reduced operational energy use. Environmental consciousness significantly predicts willingness-to-pay (WTP), moderated by infrastructure and cost barriers. These results advocate targeted policies for AI-renewable ecosystems, positioning Bengaluru as a model for India's net-zero urban transport ambitions by 2035.