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Global efforts to address climate change, including the United Nations Framework Convention on Climate Change (UNFCCC) and the Sustainable Development Goals (SDGs), underscore the need to understand how local communities experience vulnerability and build adaptive responses. Such understanding is essential for designing effective, contextually grounded strategies to mitigate the adverse effects of climate variability. According to the Intergovernmental Panel on Climate Change (IPCC, 2014), adaptation entails adjustments in natural or human systems in response to actual or expected climate stimuli, aimed at moderating harm or exploiting beneficial opportunities. These responses span behavioral, institutional, and structural changes, encompassing shifts in livelihood strategies, land use, and ecosystem management (Gupta et al., 2022; Sultana et al., 2023; Hossain et al., 2018).

While adaptation research has traditionally emphasized technological and ecological dimensions, increasing scholarly attention is now directed toward the social drivers of vulnerability and adaptive capacity (Adger and Kelly, 1999; Raymond and Robinson, 2013). Gender, in particular, has emerged as a critical axis along which climate impacts are experienced and negotiated. Women, especially in agrarian settings, are often disproportionately affected by climate change due to their reliance on natural resources and their constrained access to land, finance, education, and institutional support systems (Perez et al., 2015; Hackfort and Burchardt, 2018; Ngigi et al., 2017). However, framing women solely as victims risks obscuring their agency and the complex socio-cultural and economic structures that shape adaptation processes.

Feminist political ecology critiques such reductionist framings, advocating for a more nuanced exploration of how gender intersects with other social identities and power relations to shape adaptation pathways (Mollett and Faria, 2013; Goldman et al., 2018; Carey et al., 2016). This perspective challenges the essentialist portrayal of women as a uniform group, emphasizing instead the heterogeneity within gender categories shaped by caste, class, age, marital status, and household roles (Kabeer, 2003; Bürkle, 2023; Sultana, 2014). Recent empirical work reveals that adaptive behaviors are not only gendered but also shaped by overlapping and often unequal social positions. For instance, women's strategies in coping with water scarcity, livelihood shifts, or resource management are mediated by household structure, life stage, social capital, and access to information (Rakib et al., 2017; Haeffner et al., 2018; Gautier et al., 2016).

Intersectionality theory, originally articulated by Crenshaw (1991), offers a powerful conceptual framework for analyzing how multiple forms of social stratification interact to shape individual and collective vulnerability. Applied to climate adaptation, this framework enables a deeper understanding of how social identities intersect with institutional and structural forces to shape behavioral responses to environmental risks (Thomas et al., 2019; Kaijser and Kronsell, 2014; Wolf and Moser, 2011). Yet, despite its theoretical potential, empirical applications of intersectionality in behavioral climate research remain underdeveloped. Particularly lacking are studies that connect intersectional insights with models that explain why individuals act the way they do in the face of climate stress.

In this context, behavioral science offers valuable theoretical tools. Among these, the Theory of Planned Behavior (TPB) has gained prominence for its ability to predict and explain individual actions based on attitudes, subjective norms, and PBC (Ajzen, 1991). TPB has been successfully applied in environmental behavior studies, accounting for a substantial portion of variance in adaptation-related decisions (Gansser and Reich, 2023; Arora and Sahney, 2018; Leng et al., 2011). However, its traditional formulation often overlooks structural inequalities, psychosocial stressors, and local cultural dynamics—factors particularly relevant to women in marginal agrarian settings (Prasad, 2024; Ahmed et al., 2025; Elkins, 2022).

To enhance TPB's contextual relevance, scholars have proposed extended models that incorporate situational and affective variables, thereby offering a more holistic understanding of decision-making in high-risk, resource-constrained environments (Gansser and Reich, 2023; Sapry and Ahmad, 2024). Nevertheless, few studies have attempted to integrate intersectionality with TPB to examine how adaptive intentions among women are shaped not just by internal motivations but also by external structures of constraint and opportunity.

This study addresses that gap by applying an extended TPB framework to examine climate adaptation behaviors (ABs) among rural women in Uttarakhand, India. The region's ecological fragility, combined with deep-rooted gender asymmetries and socio-economic marginalization, presents a compelling case for analyzing how behavioral intentions (BIs) are shaped by intersecting identities and psychosocial dynamics.

This approach contributes to ongoing efforts to develop empirically grounded, theoretically robust frameworks that capture the complexity of adaptive decision-making in climate-vulnerable communities. It offers both conceptual refinement and practical relevance by linking behavioral theory with intersectional insights in climate adaptation research.

The collected data were entered into a Microsoft Excel spreadsheet and analyzed using SMART PLS software, utilizing the PLS-SEM approach. Structural Equation Modeling (SEM) enables the analysis of multiple relationships between dependent and independent variables, and has become increasingly central to research in the social and behavioral sciences (Benitez et al., 2020), due to its flexibility and improved handling of measurement error (Hooman, 2014). Two primary SEM approaches exist: covariance-based SEM (CB-SEM) and variance-based SEM (PLS-SEM). In this study, we employed variance-based SEM, specifically Partial Least Squares Path Modeling (PLS-PM), using SMART PLS and R software. PLS-PM is well-suited for predictive modeling, theory development, and situations involving complex models, smaller sample sizes, or data that violate normality assumptions (Seyyed Abbaszadeh et al., 2012; Henseler et al., 2016).

Partial Least Square Structural Equation Modeling models consist of two components: the structural (inner) model, which captures relationships among latent constructs, and the measurement (outer) model, which links latent variables to their corresponding manifest indicators. In the present study, all latent constructs were modeled using reflective measurement models, i.e., each latent variable was assumed to cause the variation in its observed indicators, and the indicators were expected to be correlated (Hair et al., 2021). Constructs such as BI, Adoption Behavior (AB), Subjective Norms (SN), RP, and Self-Efficacy and Trust (SET) were each measured reflectively through multiple indicators derived from validated scales. Accordingly, validity and reliability of the reflective measurement models were evaluated using criteria such as indicator reliability (outer loadings >0.7), internal consistency reliability (Cronbach's alpha and Composite Reliability), convergent validity [Average Variance Extracted (AVE) >0.5], and discriminant validity (using the Fornell-Larcker criterion and HTMT ratios). Latent variables, which represent abstract theoretical concepts such as perceived institutional trust or BI, were inferred through observable indicators such as clarity of roles or willingness to comply. These manifest variables play a crucial role in understanding the underlying constructs and identifying the most influential factors (Abugre and Sackey, 2022). The combination of inner and outer models in PLS-PM facilitated a robust analysis of the conceptual framework and helped pinpoint the key drivers and interrelationships among the studied variables.

The analysis followed a two-stage approach using PLS-SEM. The first stage involved testing the measurement model to ensure that the constructs were valid and reliable. In the second stage, the structural model was evaluated to test the hypothesized relationships and their practical implications. This section narrates both the empirical validation and interpretation of the model, connecting directly with the hypotheses framed in Table 1.

In the fragile socio-ecological landscape of Uttarakhand, the need for effective climate change adaptation strategies remains urgent. The region's vulnerability arises from its mountainous terrain, heavy dependence on agriculture, and increasing exposure to climate variability (Maikhuri et al., 2017; Sati, 2015; Kumar et al., 2021). These geographic and environmental constraints are further compounded by socio-economic transitions, which influence both the availability of resources and the agency of rural communities to adapt. Within this context, rural women emerge as central actors in climate resilience owing to their intimate engagement with agricultural practices and natural resource management. Despite persistent barriers—including limited access to technology, information, and decision-making forums—women in Uttarakhand have become key implementers of grassroots-level adaptation strategies (Sharma et al., 2019; Kori, 2024).

This study employed PLS-SEM using TPB framework to explore the underlying socio-psychological drivers of climate AB. The TPB framework posits that BI is shaped by three core constructs: PBC (operationalized as self-efficacy), SN, and attitudes or outcome expectations (represented by RP). PLS-SEM enabled the dissection of these latent constructs and their interrelations, offering a theoretically grounded and statistically rigorous approach to understanding rural women's AB (Al Mamun et al., 2023).

The empirical findings provide robust support for the hypothesized model, confirming the centrality of BI in predicting AB. Among the constructs, risk perception (RP → BI: β = 0.719) emerged as the most influential predictor of BI. This finding substantiates the TPB's assertion that behavioral expectations are significantly shaped by perceived outcomes, particularly in high-risk environments. In the context of Uttarakhand, climate-related threats such as glacial melt, flash floods, and crop failures have made these risks highly salient. However, it is not only awareness but also the immediacy and frequency of climate-related events that likely elevate perceived risk. This suggests that communication strategies should go beyond information dissemination to include experiential learning and localized narratives that heighten risk salience and contextual relevance.

Self-efficacy (SET → BI: β = 0.609) was another strong determinant of BI, reaffirming its importance in both TPB and social cognitive theory (Schwarzer and Fuchs, 1995). The positive relationship suggests that when women perceive themselves as capable of responding effectively to climate threats, they are more likely to form intentions to act. However, self-efficacy in this context is not a purely individual construct—it is shaped by access to resources, institutional support, and social networks. Therefore, efforts to enhance women's adaptive capacity must incorporate structural enablers such as land rights, financial inclusion, and access to extension services, which can convert perceived control into actual agency.

The role of SN was multifaceted. Social norms significantly influenced both BI (SN → BI: β = 0.338) and RP (SN → RP: β = 0.655), suggesting that individual adaptation is not only shaped by personal beliefs but also by the expectations and practices of the wider community. Furthermore, the strong relationship between SN and self-efficacy (SN → SET: β = 0.573) highlights the interdependence between perceived support and personal confidence. These findings indicate that rural women's adaptive behaviors are embedded within a network of collective meaning-making and social validation. In practical terms, this implies that adaptation interventions that leverage community platforms—such as women's self-help groups, cooperatives, and village institutions—are likely to yield more sustainable outcomes than those targeting individuals in isolation.

While BI was a significant predictor of actual behavior (BI → AB: β = 0.328), the effect size was modest (f² = 0.034), pointing to a well-documented gap between intention and action in behavioral science literature. This gap may be attributed to a range of contextual barriers such as time poverty, gendered labor burdens, lack of institutional support, and entrenched social hierarchies. These findings suggest that while psychological readiness is necessary, it is not sufficient. The TPB, while valuable, may benefit from integration with complementary frameworks such as the Transtheoretical Model or Protection Motivation Theory to better account for constraints beyond volition.

The explanatory power of the model was substantial, with R² values of 0.570 for RP, 0.530 for BI, 0.349 for AB, and 0.328 for SET. These figures confirm that the socio-psychological constructs account for a considerable portion of the variance in AB. The model fit was also confirmed through the SRMR (0.047) and NFI (0.928), indicating strong empirical alignment between observed data and theoretical expectations.

The effect size (f²) analysis further enriches the understanding of practical significance. Social norms exerted the strongest effects on RP (0.671) and self-efficacy (0.488), signaling that the influence of the community is both profound and multidimensional. Interestingly, SET → BI (f² = 0.410) and RP → BI (f² = 0.281) also had considerable effects, suggesting that both internal confidence and external threat perception play key roles in motivating intention. These results point toward the importance of dual strategies: one that enhances women's capacity to act (through skills, knowledge, and support), and another that enhances their motivation to act (by addressing perceptions of climate risk and urgency).

These findings align with global literature documenting the indispensable role of women in climate adaptation, particularly in ecologically vulnerable regions such as the Himalayas, Andes, and Pamirs (Wester and Lama, 2019; Goli et al., 2020; Prakash et al., 2024). Women's roles as custodians of agro-biodiversity and managers of natural resources position them at the forefront of adaptive strategies. However, our study provides a more granular understanding by demonstrating how variation in access to assets, education, and institutional support influences adaptation choices. Women with higher socio-economic status were found to adopt proactive and strategic responses, including technological innovations and diversification, while those from marginalized households tended to rely on reactive, short-term coping mechanisms (Ragasa et al., 2014; Najjar et al., 2023).

The intersection of gender, class, and institutional access highlights the inequitable distribution of adaptive capacity. As shown by Bhattarai et al. (2015) and Price (2018), systemic inequalities constrain marginalized women's ability to transform awareness into action. These findings suggest that climate adaptation policies must adopt a rights-based and intersectional approach, recognizing the differentiated needs and capacities of women based on their social positioning. Efforts to enhance ecological resilience through agro-biodiversity management and community-based adaptation must be complemented by measures that dismantle structural barriers, ensure gender-responsive governance, and build inclusive systems of support.

The use of PLS-SEM allowed for exploration of the interplay between cognitive, emotional, and social dimensions of AB. The model not only confirmed existing theoretical relationships but also revealed new pathways—particularly the mediating role of BI and the amplifying influence of SN. The insights generated highlight the value of integrating behavioral theory into climate adaptation research, especially in diverse and dynamic socio-ecological settings like Uttarakhand.

This study provides critical insights into the socio-psychological underpinnings of climate ABs among rural women in Uttarakhand, highlighting the intersection of gender, environmental vulnerability, and behavioral intent. The study draws the TPB and reveals that women's ABs are shaped by a constellation of factors—most notably RP, self-efficacy, and SN. While women play a pivotal role in managing agro-ecological systems and sustaining household livelihoods, their adaptive responses are constrained by structural barriers such as unequal access to land, limited financial autonomy, and exclusion from formal decision-making processes. The study affirms that adaptation is neither uniform nor gender-neutral. Women with stronger risk awareness and higher self-efficacy were more likely to develop BIs that translated into adaptive practices. However, these behaviors were not realized equally across the population. Variations in adaptation are significantly influenced by socio-economic status, community support, and institutional exposure, making a strong case for intersectional and context-specific policy design.

Based on the empirical findings and structural relationships identified, several policy-level recommendations can be made:

While this study provides valuable insights, several limitations should be acknowledged: