Search Results (5,883)

The purpose of this study is to explore how Zimbabwean firms use Environmental Management Accounting (EMA) and climate risk disclosure amid climate policy uncertainty and how managers perceive these practices as relevant to organisational resilience and long-term sustainability within a volatile institutional and macroeconomic context. The study was couched in the interpretivist research philosophy and adopted the inductive research approach. A case study research design, which aligns with a qualitative research design, was chosen for the study. The study employed in-depth interviews with management accountants, finance executives, and industry leaders across firms in Harare. The study adopted the cross-sectional time horizon and analysed data using thematic analysis to develop insights into the role of EMA and climate risk disclosure in times of policy uncertainty. The findings suggest that participants perceived climate policy uncertainty as influencing organisational efforts to reconfigure management accounting practices through greater environmental performance monitoring, adaptive budgeting, and scenario-based planning. The findings of this study suggest that organisational actors interpreted climate policy uncertainty as a condition requiring greater flexibility in budgeting, environmental monitoring, and strategic planning. Participants in this study associated EMA with improved environmental cost visibility and more adaptive approaches to investment appraisal and risk management under uncertain policy conditions. Similarly, participants perceived climate risk disclosure as increasingly crucial for strengthening organisational legitimacy, stakeholder confidence, and institutional credibility. While respondents linked sustainability-oriented accounting adaptation to broader organisational resilience and long-term sustainable growth aspirations, these relationships were understood through managerial perceptions and organisational experiences rather than as directly measurable macroeconomic outcomes. The study contributes to the sustainability accounting literature by providing qualitative, context-sensitive insights into how managers in an emerging economy interpret climate policy uncertainty and adapt EMA and climate risk disclosure practices within volatile institutional conditions. The study further contributes by integrating sensemaking theory and institutional theory to explain how organisational interpretations of uncertainty shape sustainability-oriented accounting adaptation and perceptions of organisational resilience. It is therefore recommended that the regulatory institutional pillar be strengthened to reduce uncertainty and enhance the EMA’s strategic adaptation. Full article

Delirium is a common and serious condition among neurological patients, and the overlap between delirium symptoms and neurological disorders complicates both diagnosis and management. Despite its clinical impact, guidance for delirium management in neurological settings remains limited. This qualitative study aimed to investigate healthcare professionals’ perceptions of delirium management in a Danish neurological hospital setting. Focus group interviews were conducted with five multidisciplinary healthcare professional groups. Maximum variation sampling was used to capture diverse perspectives, and 24 healthcare professionals from the same neurological department participated. Data were analyzed using reflexive thematic analysis. Three themes were identified: (1) delirium care practices in an acute neurological setting; (2) interdisciplinary collaboration in delirium care; and (3) responsibility for delirium care. The findings highlight challenges related to prioritization, mono-professional practices, and organizational structures that shape how responsibility for delirium management is understood and enacted. Overall, the study illustrates the complexity of delirium management within multidisciplinary neurological teams and suggests the need for context-sensitive approaches that support collaboration and clarify responsibilities in clinical practice. Full article

Methoxyflavones, along with chalcones and phenolic acids, belong to natural polyphenols. Apart from their antioxidant function, in plants, they act as pigments and are a part of plant chemical defense system. A member of this group is pedalitin, which expresses a range of interesting biological properties. The compound acts as an anticancer agent, strengthens the immune system, improves insulin sensitivity, protects internal organs, and also shows anti-inflammatory and neuroprotective activity. Due to the constant need for alternative therapies for treatment and prevention of lifestyle diseases, O-methylated flavonoids, including pedalitin, may serve as natural lead molecules in designing new structures with significant pharmacological potential. This review summarizes the literature reports on obtaining pedalitin and assessing its biological activity, also in the context of further functionalization of this compound and potential new applications. Full article

Background/Objectives: Chronic primary pain (CPP) is a new diagnostic category including chronic pain conditions lacking clinical signs or a clear etiopathogenetic origin. These disorders may share a common neural mechanism known as central sensitization, where nociceptive neurons become hyper-responsive to standard or subthreshold pain stimuli, resulting in pain hyper-sensitivity. In this context, non-invasive brain stimulation (NIBS) appears to be a promising tool for improving CPP symptoms by targeting maladaptive brain activity and connectivity. To date, the effects of NIBS on CPP symptoms remain unexplored. To fill this gap, we conducted a meta-analysis, investigating the effect of NIBS in improving the three core symptoms of CPP, namely pain intensity, emotional distress, and functional disability. Methods: Following PRISMA guidelines, we screened four databases up to February 2025 for English-language, peer-reviewed randomized clinical trials that included CPP patients treated with NIBS and reported pre/post or follow-up scores on validated measures of at least one core symptom. Quality of life was examined as an additional outcome. Results: Fifty-four studies were included, with 1371 participants receiving real stimulation and 1103 sham. Findings highlighted that real stimulation improved CPP symptoms immediately after treatment and at one-month follow-up. Meta-regressions showed that longer CPP duration reduced short-term effects on emotional distress and diminished all outcomes at one-month follow-up. Conclusions: Further research is needed to establish standardized NIBS protocols for CPP management, to investigate the effectiveness at longer follow-up periods, and to test whether combining NIBS with other interventions enhances treatment effectiveness and durability. Full article

Non-attributed graph sequence offers a powerful formalism for modeling the structural dynamics of complex systems—such as social networks, urban infrastructures, and document transmission pathways—where vertex interactions evolve over time without explicit attribute information. Mining association rules from such sequences to uncover recurring topological patterns have attracted growing interest. Yet two fundamental challenges remain: (1) how to effectively encode edge-level temporal dynamics in non-attributed settings, and (2) how to perform efficient and semantically meaningful temporal association rule mining under structural uncertainty. To address these within a systems-oriented framework, we propose two novel algorithms: the weighted temporal association rule mining algorithm and the fuzzy weighted temporal association rule mining algorithm. The first algorithm introduces time-dependent numerical weights to quantify the strength and persistence of vertex connectivity, integrating them into support and confidence measures to capture both the intensity and evolution of interactions. The second algorithm extends this by incorporating fuzzy set theory, modeling ambiguous or context-sensitive relationships (e.g., indistinct links or weakly correlated vertices) and generating fuzzy-weighted rules that enhance interpretability for real-world system analysis. Evaluated through five comprehensive experiments across diverse datasets and scales using standard metrics (support, confidence, rule count, running time), our methods produce more selective rule sets and achieve lower computational times compared to the classical Apriori algorithm. The proposed approaches thus establish a robust, data-driven foundation for analyzing temporal evolution and structural uncertainty in dynamic complex systems—providing a generalizable methodology applicable beyond domain-specific constraints. Full article

Background: Dedicated artificial intelligence (AI) systems for fracture detection already exist, yet general-purpose multimodal models are increasingly accessible to clinicians despite not being developed or formally validated as medical devices. Their behavior in focused orthopedic imaging tasks remains insufficiently characterized. Purpose: To characterize how two accessible general-purpose multimodal models interpret AP pelvis radiographs with hip fractures, focusing on context dependence, overconfidence, and complementary error patterns within a surgically confirmed positive-only cohort. This was a behavioral characterization study of a fracture-positive cohort, not a diagnostic accuracy evaluation. Methods: In April 2026, we retrospectively studied 214 surgically confirmed hip fractures on AP pelvis radiographs using two general-purpose multimodal models under six prompting conditions. In runs A–D, the models were explicitly told that a hip fracture was present and were asked to classify it; in runs E–F, they were not told whether a hip fracture was present. Each image was rerun de novo in a separate chat session through vendor APIs using a fixed base prompt and no image preprocessing. We recorded hip-fracture detection, correct laterality, coarse fracture pattern, intracapsular displacement, AO/OTA grading, subtrochanteric identification, and self-reported confidence. Because the cohort contained hip fractures only, we report fracture-detection rates and classification performance within a positive-only cohort rather than full diagnostic-accuracy metrics. Results: Using the more conservative endpoint of hip-fracture detection with correct laterality, GPT-5.4 was correct in 79.0% and 86.4% of cases in runs E and F, whereas Gemini was correct in 80.4% and 93.5%, respectively. When outputs from both models were combined, this endpoint reached 89.7% in run E and 96.7% in run F, indicating complementary rather than redundant error patterns. Incorrect laterality cues markedly degraded performance, from 90.7% to 66.4% in GPT-5.4 and from 97.7% to 57.0% in Gemini. Performance remained limited for treatment-relevant subtyping, particularly AO/OTA grading and subtrochanteric identification. Both models frequently remained highly confident when wrong, and self-reported confidence did not reliably distinguish correct from incorrect outputs. Conclusions: Accessible general-purpose multimodal models showed partial capability for coarse hip-fracture interpretation, but they remained context-sensitive, unreliable for treatment-relevant subtyping, and highly confident even when incorrect. Their complementary error patterns are hypothesis-generating rather than evidence of clinical readiness. On the basis of these findings, we do not support unvalidated or uncontrolled clinical use of such models. As access to these tools expands, explicit usage boundaries, minimum performance expectations, repeated local revalidation, and sustained human oversight become increasingly necessary. Full article

[d=LE]Transitions of control between automated driving systems and human drivers remain safety-relevant and cognitively demanding moments in human–automation interaction. Recent studies show that transition performance depends not only on takeover timing or response speed but also on traffic complexity, driver readiness, automation limitations, trust calibration, and situational-awareness recovery. As in-vehicle interaction evolves toward conversational and agentic AI assistance, takeover support also becomes a problem of governing how natural-language AI systems communicate with the driver under uncertainty.Transitions of control between automated driving systems and human drivers remain safety-relevant and cognitively demanding moments in human-automation interaction. Recent studies suggest that transition performance should not be assessed only through takeover timing or response speed since control resumption quality also depends on traffic complexity, driver readiness, automation limitations, and situational awareness recovery. [d=LE]This paper proposes a digital-twin-mediated framework for human-aware takeover support in automated driving. In this framework, the companion AI is treated as an assumed LLM-based in-vehicle conversational or agentic assistant used as an advisory interaction component. The contribution is defined at the architectural level: human, vehicle, and context/road digital twins provide structured semantic state abstractions through a semantic state interface exposing confidence, freshness, provenance, and consistency metadata, while a trustworthy companion AI (TCAI) layer grounds, constrains, validates, and governs companion AI output proposals before HMI delivery.This paper motivates and defines a trustworthy companion AI (TCAI) layer for human-aware transition support in automated driving. The TCAI is conceived as a bounded, supervised, and explainable advisory agent that supports the driver without entering the safety-critical vehicle-control loop. It reasons over structured semantic state abstractions derived from a human digital twin, a vehicle digital twin, and a context/road digital twin, exposing driver readiness, automation capability, and contextual urgency in a form that supports traceable, uncertainty-aware, and degradation-aware assistance. [d=LE]Building on the research on driver-state monitoring, adaptive HMI, trust calibration, explainability, conversational assistance, and human assistance systems (HASs), the framework coordinates advisory interaction across vigilance support, contextual explanation, trust-calibrating communication, and directive handover guidance. The TCAI layer combines bounded reasoning, human-factor-derived guardrails, state-consistency management, dynamic explanation-depth control, trust-dynamics modeling, graded watchdog veto handling, mandatory access-control assumptions, and deterministic fallback. Safety-critical vehicle-control and minimum risk condition (MRC) functions remain assigned to the deterministic vehicle-control stack, while the authorized output path of the TCAI layer is validated HMI delivery.Building on the research on driver-state monitoring, adaptive HMI, trust calibration, explainability, and conversational assistance, we propose a conceptual architecture in which the TCAI coordinates multimodal assistance across different interaction conditions, including vigilance support, contextual explanation, trust-calibrating communication, and directive handover guidance. The companion does not actuate the vehicle; its outputs are constrained by runtime governance, policy enforcement, and deterministic fallback mechanisms. [d=LE]The paper concludes with a validation agenda and technical roadmap covering planned transitions, urgent handovers, degraded or adversarial conditions, temporal fusion of driver-state evidence, phase-sensitive HMI policies, trust-calibration trajectories, driver veto and partial-disabling mechanisms, and staged simulator-to-vehicle evaluation. Although motivated by SAE Level 3 automation, the framework may also inform fallback-related Level 4 scenarios in which human and automated agency must be managed under uncertainty.The paper concludes with a research roadmap for validating the proposed architecture under planned transitions, urgent handovers, and degraded or adversarial conditions. Although motivated by SAE Level 3 automation, the approach may also inform fallback-related Level 4 scenarios. Full article

Background/Objectives: Pulses are nutrient-dense, low-glycaemic legumes rich in fibre and bioactive compounds that may modulate carcinogenesis through effects on diet quality, metabolism, and the gut microbiome. This scoping review mapped human evidence on pulses in relation to cancer risk reduction and related mechanistic and survivorship-relevant outcomes. Methods: Following the Preferred Reporting Items for Systematic reviews and Meta-Analyses extension for Scoping Reviews (PRISMA-ScR) and Joanna Briggs Institute (JBI) Population, Concept and Context (PCC) guidance, we searched CENTRAL, Scopus and PubMed (2014–31 December 2025), supplemented by backward and forward citation tracking, for English-language human studies in which pulses were a defined exposure or intervention and cancer-specific clinical outcomes or biomarkers were reported. Exposures are described using the original ‘legume’ terminology, with pulse-specific interpretation restricted to FAO-defined pulses or clearly dry pulse forms and to pulse-dominant legume intake where the constituent items were predominantly pulses but preparation was not specified. Results: After screening 1244 records, 15 studies met the inclusion criteria, comprising five case–control studies, five 4-week randomised controlled trials (RCTs), one 8-week randomised crossover trial, one controlled feeding study, two prospective cohort studies, and one other prospective study. Observational data from a single pooled case–control study suggest that higher pulse-dominant legume intake is compatible with modestly lower colorectal cancer risk, although the findings are mixed and often attenuate after adjustment for lifestyle and dietary confounders. Evidence for breast and oesophageal cancer and all-cancer mortality is limited, frequently subgroup-specific or highly sensitive to confounder control, and survivorship endpoints are represented mainly by short-term mechanistic and feasibility trials in colorectal cancer survivors rather than by long-term clinical outcomes. Notably, five of these navy bean interventions were conducted by a single research group using similar protocols, which constrains the independence of replication. Conclusions: Pulses can be considered practical components of cancer-protective dietary patterns, especially for colorectal cancer, but the heterogeneity of study designs, short-term interventions, limited sample sizes, and lack of preparation-specific exposure data preclude firm causal inferences; longer-term, rigorously designed trials and detailed observational work are needed to refine pulse-based recommendations for cancer risk reduction and to clarify any role in survivorship care. Full article

Background: Public healthcare systems face persistent workforce retention challenges that undermine service continuity, organizational resilience, and public value creation. Although leadership is frequently identified as a relevant lever, the literature remains theoretically fragmented and often treats leadership effects as direct and context-free. Methods: This review adopts a PRISMA-guided systematic literature review as a theory-building strategy. Searches were conducted in Web of Science, Scopus, and PubMed using combinations of terms related to leadership, organizational commitment, job satisfaction, turnover intention, and retention in healthcare settings. The review identified 640 records, removed 372 duplicates, screened 268 titles and abstracts, assessed 90 full-text records for eligibility, and retained 30 peer-reviewed studies for configurative synthesis. The analysis combined thematic synthesis with configurative mapping to identify mechanisms, recurring patterns, and contextual contingencies. Results: The review shows three consistent patterns. First, leadership is linked to retention predominantly through organizational commitment, especially affective and normative commitment, rather than through direct effects. Second, institutional and organizational conditions—particularly red tape and working conditions—shape the strength of leadership–commitment relationships. Third, workforce heterogeneity, including generational differences, affects how leadership practices and organizational environments are interpreted, although these dynamics are rarely theorized explicitly in the literature. Conclusions: The article develops a governance-sensitive integrative framework in which people-centered leadership influences turnover intentions indirectly through organizational commitment, while red tape and working conditions operate as contextual moderators. By embedding leadership within Public Administration and governance theory, the review clarifies the literature’s main explanatory gap and provides a foundation for comparative empirical testing and for more sustainable workforce strategies in public healthcare systems. Full article

Visually impaired individuals face significant challenges in autonomous mobility and situational awareness. Most existing assistive technologies address isolated tasks, such as object recognition or text reading, while failing to capture broader environmental context. This work addresses this limitation by proposing a scene-sensitive, low-cost assistive system that delivers holistic situational information. We present Vocal-Eyes, an intelligent smart glasses platform that provides periodic audio descriptions of the surrounding environment. The system employs a cloud-based neural processing pipeline in which visual features are extracted using a Transformer-based architecture. Relational context is modeled through scene graph generation, and scene graphs are translated into natural language via a graph-to-text module. A lightweight hardware prototype captures visual data locally, while computationally intensive processing is offloaded to the cloud to reduce power consumption. The experimental results show that relational, scene-based narration produces more coherent and informative descriptions than object-centric approaches while maintaining acceptable periodic latency. Cost analysis further indicates that Vocal-Eyes is significantly more affordable than comparable commercial smart glasses solutions. These results demonstrate that Transformer-based scene understanding with cloud-assisted processing is an effective and practical approach for developing accessible, context-aware assistive technologies for visually impaired users. Full article

Neighbourhood-scale sustainable urban mobility assessment requires analytical tools that evaluate walking, cycling, and public transport together rather than as separate modes. Existing studies often rely on single-mode indicators or aggregated urban-scale measures, which limit their ability to reveal micro-scale spatial inequalities and multimodal performance imbalances. This study addresses this gap by developing an integrated Geographic Information Systems (GIS)–Analytic Hierarchy Process (AHP)–correlation–clustering framework for six sample areas in Kayseri, Türkiye. The framework evaluates three main criteria—walkability, bikeability, and public transport accessibility—through ten sub-criteria. In addition, seven land-use and urban design variables are used to examine built environment relationships. A 100 × 100 m grid-based spatial database was created; criteria weights were determined using AHP; mobility scores were examined through correlation analysis; and spatial mobility typologies were identified using K-means clustering. The findings indicate that development density and land-use diversity support walkability. However, similar density patterns do not automatically improve cycling performance or public transport integration. The clustering results reveal persistent modal imbalances, even in areas with medium-to-high overall performance. The study demonstrates that density alone is insufficient for multimodal sustainability and offers an adaptable decision-support framework for context-sensitive neighbourhood planning. Full article

The sustainable transformation of idle built environments represents a critical pathway for advancing low-carbon development and achieving carbon neutrality targets. This study examines how idle rural building stocks may contribute to sustainable built environment systems through rural building repurposing and regeneration strategies. It introduces the concept of Green Rural Centers (GRCs), multifunctional facilities formed through the adaptive reuse of idle buildings that integrate low-carbon design, community services, and local economic functions. Within the proposed framework, GRCs are conceptually characterized as facilities that may: (1) achieve 50–70% reductions in operational energy demand through passive and renewable measures, (2) incorporate two or more community-oriented functions (e.g., education, governance, cultural services), and (3) demonstrate embodied carbon savings of ≥40% compared to demolition-and-rebuild scenarios. Grounded in fieldwork from Fujian Province, China, and aligned with national policies, the study evaluates spatial transformation, carbon mitigation, and institutional integration. Using a mixed-methods approach that combines scenario-based carbon-reduction estimation and appraisal, spatial analysis, comparative case studies, and policy evaluation, the findings indicate that retrofitting 30% of approximately 68,000 idle rural schools could achieve approximately 734,400 metric tons of cumulative CO₂ reduction by 2060 under the baseline scenario. Under conservative and ambitious implementation conditions, the estimated cumulative reductions are approximately 408,000 and 1,224,000 metric tons of CO₂, respectively. Sensitivity analysis shows that moderate improvements in retrofit quality or implementation rates significantly amplify emissions reduction outcomes. Beyond environmental performance, the proposed framework may also support community resilience, decentralized service provision, and socio-economic revitalization. This research reframes idle building stock as a strategic asset within sustainable built environment systems, policy-relevant exploratory framework potentially adaptable to comparable rural contexts. This study contributes to the sustainable built environment discourse by demonstrating how underutilized rural building stocks can function as broader low-carbon rural regeneration systems. Full article

Rural public art is increasingly used to improve living environments and reactivate place-based culture in rural communities. However, existing evaluations remain fragmented and provide limited support for assessing intervention effectiveness and formulating targeted strategies. To address this gap, this study constructs a multidimensional evaluation system for rural public art interventions and empirically tests it through case studies of 11 villages in Songyang County, China. The system covers three dimensions: material space creation, cultural heritage and innovation, and the reconstruction of social relations. A mixed-methods approach was adopted, combining literature review, field investigation, expert consultation, AHP weighting, and Kano demand classification. The results support the validity of the proposed evaluation system and identify cultural heritage preservation and transmission, basic and cultural facilities, funding safeguards, spatial accessibility, cultural affinity, and local cultural aesthetic compatibility as stable priority indicators. The comparison between expert weighting and stakeholder sensitivity further reveals differences between strategic importance and locally perceived demand. This study provides an operational evaluation system for assessing rural public art interventions and translates the evaluation results into targeted strategies, offering empirical support for more sustainable and context-sensitive rural public art practices. Full article

Background/Objectives: Labor pain is a multidimensional experience associated with physiological, cultural, psychological, and contextual factors. This study aimed to examine the association of personality-related characteristics, cultural beliefs, obstetric characteristics, and proxy indicators of post-disaster context with labor pain perception among women giving birth in Hatay after the 2023 Türkiye earthquakes. Methods: This prospective observational study was conducted with 314 women admitted to Hatay Training and Research Hospital between February and June 2025. Participants were between 38 and 42 gestational weeks, had a singleton healthy fetus, were admitted in active labor, and were expected to give birth vaginally. Data were collected using a researcher-developed questionnaire, the Ten-Item Personality Inventory, and the Visual Analog Scale. Labor pain was assessed at 6 cm, 8 cm, and full cervical dilatation (10 cm). Results: VAS scores increased significantly across cervical dilatation points, from 5.04 ± 0.81 at 6 cm to 7.01 ± 0.82 at 8 cm and 8.06 ± 0.93 at full cervical dilatation (10 cm). Repeated-measures ANOVA showed a significant within-person increase in pain intensity across the three assessment points, F(2, 626) = 996.444, p < 0.001, partial η² = 0.761. Age was not significantly correlated with VAS pain score at full cervical dilatation. In exploratory unadjusted comparisons, VAS scores at full cervical dilatation differed according to education level, official marriage status, previous birth history and mode, attendance at antenatal education, and praying to relieve labor pain. In the multivariable regression model, higher Extraversion and higher education level were associated with lower VAS scores, whereas attendance at antenatal education, greater importance given to traditional rules, previous assisted vaginal/cesarean birth, and current place of residence were independently associated with VAS scores. Conscientiousness was not significantly associated with VAS scores in the adjusted model. Earthquake experience was not significantly associated with VAS scores. Conclusions: Labor pain perception was associated with selected sociodemographic, obstetric, and cultural characteristics. The findings support the importance of individualized, culturally sensitive, and trauma-informed midwifery care in disaster-affected regions. Personality-related findings should be interpreted cautiously because the corrected reliability analysis showed low internal consistency for Agreeableness, Emotional Stability, and Openness to Experience, although Extraversion showed high internal consistency and Conscientiousness showed relatively better but still limited internal consistency. Disaster-related findings should also be interpreted cautiously because post-disaster context was assessed using only limited proxy indicators; current place of residence was independently associated with VAS scores in the adjusted model, whereas earthquake experience was not. Because of the observational design, causal interpretations cannot be made. Full article

Rural household energy-saving behavior is central to low-carbon development in ecologically fragile plateau regions. This study explores whether climate risk perception promotes household energy-saving behavior, through which livelihood capital mechanisms this effect operates, and which livelihood capital configurations support high levels of such behavior. Drawing on survey data from 315 rural households in Xining, China, a sustainable livelihood framework is integrated with the pressure–state–response model, and PLS-SEM, an ANN, and fsQCA are applied. The integrated framework regards climate risk perception as external pressure, livelihood capital as the household livelihood state, and energy-saving behavior as the behavioral response. The sustainable livelihood framework identifies the multidimensional resource conditions of rural households, whereas the pressure–state–response model specifies the causal sequence through which perceived climate pressure affects livelihood states and induces behavioral responses. The results show that climate risk perception significantly promotes energy-saving behavior. Physical, human, and social capital exert positive effects, whereas natural and financial capital exert negative effects. Moreover, natural, financial, and social capital significantly mediate the link between climate risk perception and energy-saving behavior. Multi-group analysis shows that physical capital matters more for agriculture-dominated households than non-farm households. The ANN results identify social and human capital as the strongest predictors, and the fsQCA results show that high levels of energy-saving behavior arise not from any single condition but from multiple capital configurations, in which social capital is consistently central. Energy conservation under climate risk is therefore best understood as a multidimensional, nonlinear adaptation process embedded in household livelihood structures rather than a response to any single factor. These findings extend rural energy-saving research by linking climate pressure, livelihood conditions, and configurational decision logic in a plateau socio-ecological context. Policy interventions should combine energy-efficient infrastructure, targeted financial incentives, community-based diffusion, and livelihood-sensitive support for rural households. Full article