Introduction

Agriculture is central to food security and economic development, contributing nearly 4% to global Gross Domestic Product (GDP)1. As the global population is projected to grow by over 50% by 2050, there will be a need for a 35–56% increase in food production to meet this rising demand2. However, the sector faces increasing challenges due to the unpredictable nature of CC, manifesting in extreme weather events such as excessive rainfall, flooding, droughts, and temperature anomalies3,4,5,6,7,8,9. These shifts threaten not only agricultural production but also food security and the livelihoods of millions worldwide10,11,12. Agriculture is both vulnerable to CC impacts and a major contributor to global GHG emissions13ranking second only to fossil fuel combustion as a source of emissions14. Activities such as fertilizer and pesticide application, coupled with energy-intensive processes, contribute significantly to GHG emissions15. The rise in GHG emissions has profound consequences for rural livelihoods and agricultural productivity16. Hence, achieving sustainable food production necessitates reducing GHG emissions from agriculture and enhancing resilience to climate-related risks17.

GHGs like carbon dioxide (CO2), methane (CH4), and nitrous oxide (N2O) are pivotal in global warming and subsequent CC18. These gases trap heat within the Earth’s atmosphere, exacerbating global temperature rise19. Agriculture, as a primary emitter of these gases, is responsible for approximately 16.2 billion tons of GHG emissions annually20. Emissions vary significantly across farms based on production methods, region, and management practices21. Of the total agricultural GHG emissions, CH4 and N2O contribute 32% and 14%, respectively. Rice cultivation, in particular, is one of the largest human-induced sources of GHGs, contributing approximately 10% of total emissions in the agricultural and food systems20. It is the second-largest emitter of CH4 globally22with flooded rice fields generating substantial amounts of both methane and nitrous oxide, both of which have high global warming potentials23. Furthermore, rice fields require significantly more water than other major crops, such as wheat and maize, making water management a crucial challenge in rice production24. With growing concerns over water scarcity, enhancing the efficiency of water and nutrient use in rice farming is essential for improving productivity while reducing environmental impacts25. The use of flood irrigation, in particular, fosters methanogenic bacteria activity, increasing methane emissions, a potent GHG26,27with serious implications for world warming28,29.

Despite its environmental footprint, rice remains a crucial food crop, providing sustenance for over half of the global population30,31. Rice is cultivated on over 164 million hectares globally, with nearly 86% of this area located in Asia31. It constitutes a primary food source for more than 50% of the global population, providing over 20% of daily caloric intake30,32. Asia accounts for 90% of global rice production, making it central to the region’s food security33. However, Asia is also the leading emitter of GHGs from agriculture, contributing 6.8 gigatons of CO2 equivalent, which represents 42% of the sector’s global emissions. Notably, countries like India and China, as major rice producers, are responsible for 47% and 56% of CH4 and N2O emissions from rice cultivation, respectively33.

It is widely acknowledged that farmers can respond to the impacts of CC through two primary strategies: mitigation and adaptation. Mitigation focuses on reducing GHGs emissions by adopting low-carbon agricultural technologies34while adaptation involves adjusting farming practices to the new environmental conditions brought about by CC35,36. Both strategies aim to enhance agricultural sustainability and productivity in the face of CC37,38. Although most research has predominantly examined adaptation behaviors, exploring the underlying factors that drive farmers’ adaptive responses39limited attention has been given to mitigation behaviors, particularly the adoption of low-carbon practices, at the micro level40. Moreover, few studies have integrated both adaptation and mitigation strategies in a comprehensive analysis41. This research, hence, tries to fill this gap by examining the factors influencing both adaptation and mitigation behaviors among Iranian rice farmers at the micro level. To achieve this, the following specific objectives were pursued.

  1. 1.

    Assessing the explanatory power of the Theory of Planned Behavior (TPB) in identifying factors affecting the implementation of climate change adaptation and mitigation measures by farmers.

  2. 2.

    Investigating the effectiveness of the Value-Belief-Norm (VBN) theory in analyzing the psychological and social factors affecting farmers’ decision-making to implement climate change adaptation and mitigation strategies.

  3. 3.

    Comparative and integrated analysis of the TPB and VBN theories in order to more comprehensively examine the individual, attitudinal, and normative components affecting the implementation of climate change adaptation and mitigation measures by farmers.

  4. 4.

    Providing policy solutions based on research evidence to promote sustainable agricultural planning and improve the level of acceptance of adaptation and mitigation measures among the implementation of climate change adaptation and mitigation measures by farmers.

CC and agricultural production

Agriculture and CC are intrinsically linked, with CC posing significant risks to agricultural production through phenomena such as droughts, floods, and temperature anomalies, all of which hinder crop growth and reduce agricultural yields42. Agricultural activities, along with forestry and land use, account for approximately 31% of global GHGs emissions33. Addressing the impacts of CC on agriculture involves two primary strategies: adaptation and mitigation13. Adaptation, which is the immediate response of farmers, seeks to reduce the susceptibility of both ecological and human systems to the adverse effects of CC43. Mitigation, on the other hand, focuses on reducing GHGs emissions from agricultural practices by adopting low-carbon production methods44representing an indirect response from farmers41.

Low-carbon management measures can play a crucial role in reducing emissions35,45and the combination of both adaptation and mitigation strategies is essential for enhancing agricultural productivity and minimizing the impacts of CC46. CC adaptation strategies typically involve practices that help farmers respond to climate triggers, such as early planting or adjusting livestock management in areas affected by insufficient rainfall47. Mitigation strategies, however, encompass a broader range of technological and management innovations, including soil conservation, water and fertilizer efficiency, and improving soil fertility. These practices aim to optimize resource use, boost farmer resilience, and decrease GHG emissions48,49. The advantages of these combined approaches include increased carbon sequestration, improved input efficiency, and enhanced resilience of agricultural systems, particularly for smallholders facing both current and future climate risks50.

Both adaptation and mitigation are indispensable for ensuring food security within the agricultural sector51. Farmers’ decisions to mitigate and adapt to CC are significantly influenced by their perceptions of its effects52with CC awareness playing a major role in shaping their response choices53. These perceptions are further shaped by household characteristics, historical experiences with local climate patterns—particularly how CC affects agricultural productivity—and the knowledge farmers acquire, alongside the socio-cultural and geographical contexts of their farming practices54. Understanding climate variability involves complex psychological elements, including knowledge, norms, beliefs, and attitudes54. To explain the factors influencing behavior, psychological theories including the Theory of Planned Behavior (TPB) and moral norm-based models, including the Norm Activation Model (NAM), Value-Belief-Norm (VBN), and Value-Identity-Personal Norm (VIP) models, are commonly employed55. Among these, the VBN model has been shown to be particularly effective in explaining both adaptation and mitigation behaviors13. Consequently, this study adopts both the TPB and VBN frameworks to investigate the factors influencing adaptation and mitigation behaviors among farmers (Fig. 1).

TPB

The TPB, originally introduced by Ajzen56is extensively applied to understand environmentally responsible behaviors57. As an evolution of the Theory of Reasoned Action (TRA) by Ajzen & Fishbein58TPB was developed to overcome TRA’s limitations, particularly in contexts where individuals may not have full volitional control over their actions. Unlike TRA, which focuses on attitudes and subjective norms, TPB incorporates another additional component—perceived behavioral control—reflecting an individual’s understanding of their ability to conduct a given behavior56. This addition allows TPB to account for both internal factors (attitudes), social factors (subjective norms), and external factors (perceived behavioral control) that collectively influence behavior56. The key advantage of TPB is its comprehensive approach, considering a range of factors that can be applied to various behavioral contexts59. TPB has proven effective in different agricultural and environmental domains, such as water saving techniques60safe application of chemical fertilizer61biodiversity conservation62 and forest conservation63. This highlights its versatility as an analytical tool for understanding and predicting individual behavior. According to TPB, behavioral intention is the primary predictor of behavior64. Behavioral intentions reflect the degree of effort individuals are willing to invest in a behavior and their motivation to engage in it. As a general rule, stronger intentions correlate with a higher likelihood of performing the behavior56. Consequently, intention serves as the direct precursor to behavior, indicating how ready or willing an individual is to engage in a specific action56,65. In this sense, intention represents the driving force behind the behavior66. The hypotheses of this section will be analyzed in the following.

Attitude → Intention.

Attitude in TPB is conceptualized as a person’s general evaluation of a particular behavior, which can be positive or negative, and reflects their belief about the potential outcomes or consequences of the behavior67. A positive attitude toward a behavior typically leads to a corresponding intention to conduct that behavior68,69. When individuals hold a positive view of a new behavior, they are more likely to abandon old behaviors in favor of the new one, as a favorable attitude increases the possibility of participating in the behavior70,71,72. Research consistently supports that positive attitudes are strongly linked to the formation of behavioral intentions63,73,74. Accordingly, we hypothesize that rice farmers who have a positive attitude toward adaptation and mitigation actions in response to CC will be more likely to intend to get involved in these actions. This section’s hypotheses are as follows:

Hypothesis

a: Attitude positively and significantly affects rice farmers’ intention to adopt adaptation behaviors in response to CC.

Hypothesis

b: Attitude positively and significantly affects rice farmers’ intention to adopt mitigation behaviors in response to CC.

SN → intention.

Subjective Norm (SN) indicates a person’s perception of the expectations that a reference group has concerning a specific behavior75. This concept reflects the social pressure exerted on an individual to conform to the perceived norms of others76. People often feel motivated to engage in behaviors that they believe are valued by important others, which, in turn, influences their decisions to act67,77. The social influence to adopt a particular behavior is shaped through two types of normative beliefs: injunctive and descriptive78. Injunctive norms represent the approval or disapproval expressed by reference groups, such as family, peers, or coworkers, while descriptive norms indicate perceptions about how commonly others engage in a given behavior78. The influence of peers, neighboring farmers, agricultural advisors, and extension agents can significantly guide farmers’ decision-making79. According to Ajzen56SNs are linked to normative rewards, which individuals receive when they conform to the expectations of others. Empirical studies have indicated a positive relationship between SNs and behavior34,60,67,80,81. Thus, this study hypothesizes that if rice farmers perceive that influential figures in their lives expect them to engage in adaptation and mitigation behaviors in response to CC, their intentions to adopt these behaviors will likely be influenced.

Hypothesis

a: SNs positively and significantly affects the intention of rice farmers to use adaptation behaviors in response to CC.

Hypothesis

b: SNs positively and significantly affects the intention of rice farmers to use mitigation behaviors in response to CC.

PBC → Intention.

PBC is also a vital factor in shaping individuals’ intentions. It denotes one’s perception of how easy or difficult it is to execute a behavior, based on internal abilities and external support or barriers—such as access to resources, necessary skills, and social cooperation82,83,84. It encompasses beliefs about the availability of resources, such as time, money, and expertise, which facilitate or hinder the intended behavior85. People who believe they possess greater control over these facilitating or hindering factors tend to develop stronger intentions to perform the behavior in question86. According to TPB, PBC directly influences behavioral intention56a relationship that has been supported by numerous studies60,83,87,88,89,90,91. In this study, therefore it is posited that if rice farmers perceive themselves as having the necessary resources and knowledge to implement adaptation and mitigation strategies for CC, their intentions to engage in these behaviors will be positively influenced.

Hypothesis

a: PBC positively and significantly influences the intention of rice farmers to adopt adaptation behaviors in response to CC.

Hypothesis

b: PBC positively and significantly influences the intention of rice farmers to adopt mitigation behaviors in response to CC.

Expanded TPB

TPB is a flexible framework that can be expanded by modifying its core structure and incorporating additional variables to enhance its explanatory power and predictive accuracy for individual behavior56. Ajzen56as one of the key proponents of this theory, emphasized that TPB can be refined by integrating new constructs to improve its applicability across diverse contexts However, while TPB effectively explains behavioral intentions, it does not92. explicitly account for motivational drivers93. Moreover, existing TPB constructs do not fully capture the variance in behavior and intention, highlighting the need for additional explanatory factors60. Consequently, there is growing scholarly discussion on refining TPB by incorporating new psychological and contextual determinants80. Expanding TPB by integrating complementary theories offers a more holistic perspective on behavioral intentions, particularly in the agricultural sector, where multiple cognitive and contextual factors influence decision-making94. Such expansions allow for a deeper understanding of the psychological mechanisms driving behavior95. Given TPB’s potential for refinement, integrating additional constructs can enhance its predictive capability and reliability92. Many studies have extended TPB by introducing novel variables18,60,80,96,97 or by integrating it with other psychological models98,99,100. In this study, TPB is extended through its integration with the Value-Belief-Norm (VBN) theory, which is particularly relevant for examining adaptation and mitigation behaviors46,55. VBN theory offer a structured framework for understanding the hierarchical relationships between values, beliefs, and norms in shaping behavior101. This theory is considered an effective normative model due to its comprehensive approach, emphasis on psychological dimensions—especially intrinsic beliefs and values—and its capacity to link human behavior with environmental concerns102. Additionally, VBN uniquely incorporates an ecological worldview, emphasizing the interconnectedness between humans and nature103. Therefore, this study employs VBN as a complementary framework to enhance TPB’s explanatory power.

VBN theory

The Norm Activation Model (NAM) is a well-established framework for predicting pro-social behaviors that yield positive externalities101. Building on NAM, Stern104 introduced the VBN theory, integrating personal values and the New Ecological Paradigm (NEP) to quantify extensive environmental concerns and their impact on human behavior. VBN theory forms the basis for a causal model comprising personal values—such as altruistic, biospheric, and egoistic orientations—awareness of consequences (AC), ascription of responsibility (AR), and personal norms (PN) within the NEP framework104. It highlights the fundamental relationship between human values and environmental responsibility, illustrating how awareness of negative results and a sense of moral obligation shape pro-environmental behavior82,102. This model posits a hierarchical structure in which values influence beliefs, which in turn shape norms and behaviors. It also accommodates additional contextual variables that may further modulate these psychological mechanisms105. The following sections further explore the interconnections between these theoretical perspectives and their implications for adaptation and mitigation behaviors.

Altruistic value (AV) → NEP.

AV reflects an individual’s concern for the well-being of others and society106. It encompasses selfless behavior, motivated by personal ethical beliefs rather than self-interest107. The interplay between moral norms and AC of one’s actions fosters pro-social behavior108. A person’s moral framework defines their perception of what is ethically right and just, independent of external opinions109. Consequently, altruistic individuals prioritize intrinsic rewards over extrinsic ones, though this does not entirely preclude personal benefits110. AV is closely associated with a commitment to justice, social harmony, and equity111. Individuals with AV are generally inclined toward pro-environmental beliefs and behaviors, as these values emphasize collective well-being over personal gain112. They often focus on societal benefits, aiming to enhance community welfare through their actions. However, AV-oriented individuals may sometimes overlook the interests of non-human species113. Given these characteristics, this study posits that rice farmers with AV are more likely to exhibit a positive attitude toward adopting adaptation and mitigation behaviors. Thus, the hypothesis is formulated as follows:

Hypothesis (4)

Individuals with AV exert a significant positive influence on their NEP, shaping their intention to adopt adaptation and mitigation behaviors.

Biospheric value (BV) → NEP.

BV refers to environmental values that prioritize the conservation of natural resources114,115. While some studies do not differentiate between AV and BV15,116BV fundamentally differs from AV in that it places greater emphasis on environmental well-being rather than human welfare117. Individuals with BV demonstrate heightened environmental awareness and a deep concern for the preservation of natural ecosystems, including flora and fauna118. BV plays a significant role in shaping a person’s environmental identity, which subsequently influences their pro-environmental behaviors119. This value system urges individuals to consider the well-being of both human and non-human species when making behavioral decisions, thereby fostering greater environmental consciousness120. Rooted in ecological preservation, BV underscores the necessity for humans to coexist harmoniously with nature. Those who uphold BVs are acutely aware of the ecological repercussions of their actions121. Given these attributes, this study hypothesizes that rice farmers with BV will exhibit a favorable disposition toward adopting adaptation and mitigation strategies in response to CC. Accordingly, the hypothesis is stated as follows:

Hypothesis (5)

Individuals with BV exert a significant positive influence on their NEP, shaping their intention to adopt adaptation and mitigation behaviors.

Egoistic values (EV) are characterized by an individual’s tendency to evaluate environmental issues based on personal costs and benefits. Those with strong EV orientations are likely to resist environmentally protective behaviors if they perceive them as economically burdensome102. Grounded in self-interest, individuals driven by EV prioritize personal gain—particularly financial advantages—over collective well-being113. These individuals are often described as “economic beings” due to their preference for maximizing personal benefits113. Consequently, EV-oriented individuals tend to exhibit lower levels of environmental concern, as their decision-making processes are predominantly influenced by personal interests. According to these theoretical considerations, present attempt hypothesizes that rice farmers with EV will be less inclined to adopt adaptation and mitigation behaviors in response to CC. The hypothesis is articulated as follows:

Hypothesis (6)

Individuals with EV exert a significant negative influence on their NEP, shaping their intention to adopt adaptation and mitigation behaviors.

NEP → AC.

NEP serves as a foundational tool for evaluating individuals’ environmental attitudes and their inclination towards pro-environmental actions98,122. NEP is anchored in three key principles that assess environmental perspectives: (1) the belief that humans are a part of nature, not its masters (opposing anthropocentrism), (2) the recognition of the fragility of nature’s balance, where human interventions can disturb ecological harmony, and (3) the understanding that Earth’s capacity is finite, necessitating controlled development to ensure sustainability123. The broad adoption of NEP by scholars across various cultural contexts attests to its efficacy as a reliable framework for understanding environmental attitudes and guiding behaviors122. The strength of NEP lies in its ability to integrate worldview, attitudes, and behaviors124. Empirical studies consistently demonstrate that individuals scoring higher on the NEP scale tend to exhibit stronger environmental connections, a deeper commitment to environmental preservation, and heightened concern for ecological issues. Moreover, such individuals are more likely to consider the environmental consequences of their measures125. Values are crucial in shaping behavior102 and serve as the cornerstone of environmental actions, influencing individuals’ AC125. Unlike beliefs, which can be malleable, values are stable and enduring, driving behavior over time89. Numerous studies have established a positive association between NEP and AC105,126. Given this, it is hypothesized that individuals with stronger NEP scores will exhibit greater awareness of the environmental effects of their behaviors and demonstrate a higher likelihood of adopting adaptation and mitigation practices at the farm level.

Hypothesis (7)

NEP has a significant positive impact on individuals’ AC, influencing their intention to adopt adaptation and mitigation behaviors.

AC → PN.

AC pertains to individuals’ recognition of the positive outcomes associated with pro-social and pro-environmental behaviors, as well as the negative repercussions when such actions are avoided13. AC involves understanding the harmful environmental impacts of one’s actions and the broader consequences for others when specific behaviors are neglected125. It reflects how people perceive the adverse consequences of their environmental choices127. The stronger the AC, the greater the moral obligation individuals feel to act, which, in turn, enhances the activation of personal norms (PNs) for engaging in environmentally friendly behaviors128. AC plays a pivotal role in fostering PN, which refers to the internalized beliefs and attitudes about the interconnectedness between people and environment123. Based on the VBN model, beliefs are closely tied to personal norms, meaning that those who hold pro-environmental beliefs are more likely to participate in actions that align with these norms129. Increased awareness of the environmental impact of individual’s behaviors fosters a sense of responsibility, which enhances the activation of PN130. In line with these findings, Zhang et al.13 demonstrated that AC significantly contributes to the activation of PN in relation to pro-environmental behaviors. Therefore, this study hypothesizes that rice farmers with heightened AC regarding their behaviors are more likely to develop PN, thereby adopting adaptation and mitigation practices.

Hypothesis (8)

AC has a significant positive impact on activating PN among rice farmers, influencing the adoption of adaptation and mitigation behaviors.

AC → AR.

In the context of pro-environmental behaviors, AC reflects a person’s understanding of the negative impacts of their measures and the associated sense of responsibility114. Ascription of Responsibility (AR) reflects the personal recognition of guilt for irresponsible behavior or, conversely, the acknowledgment of responsibility for positive outcomes resulting from pro-environmental actions Existing research has consistently indicated a significant association127. between AC and AR, showing that individuals who are more aware of the consequences of their performances tend to create a greater sense of responsibility131. AC enhances peoples’ perception of the impact their behaviors have on others, which, in turn, strengthens their AR and reinforces their commitment to act101. Given this, it is hypothesized that individuals with heightened AC will be more likely to experience a stronger sense of responsibility, which will influence their willingness to adopt adaptation and mitigation behaviors.

Hypothesis (9)

AC has a significant positive impact on the AR of rice farmers, influencing their intention to adopt adaptation and mitigation behaviors.

AR → PN.

AR is widely recognized as a important factor in activating PNs in pro-environmental contexts132. AR reflects an individual’s sense of responsibility for the positive outcomes that result from their actions127. Various research have shown the role of AR in the activation of PN, underscoring its importance in fostering pro-environmental behavior88,114,133. For instance, study by Savari and Khaleghi67 on forest conservation revealed that AR plays a significant role in motivating PN related to conservation efforts. Similarly, Han and Hyun114 emphasized that AR is essential for activating PN for pro-environmental actions. In this study, it is hypothesized that rice farmers who feel a stronger sense of responsibility for their actions will be more likely to activate PN and engage in adaptation and mitigation practices.

Hypothesis (10)

AR has a significant positive impact on the PN of rice farmers, influencing their adoption of adaptation and mitigation behaviors.

PN → Intention.

PN demonstrate an individual’s internalized moral commitment to performing a particular behavior, and have consistently been shown to predict pro-environmental actions134. PN are grounded in an individual’s perception of what is morally right and carry a sense of moral obligation to act114,135. Based on the VBN theory, PNs significantly affect the likelihood of engaging in environmentally friendly behavior35,136. Previous research has confirmed the significant role of PNs in shaping behavioral intentions46,136,137. For instance, Wang et al.34 found that PNs positively influence individuals’ intentions to adopt environmental practices. Similarly, Rezaei et al.138 showed that PN is a key determinant of agricultural producers’ intention to utilize integrated pest management. In line with these findings, Savari et al.139 identified PN as a major factor in farmland owners’ intention to use safe activities for chemical fertilizer use. This research hypothesizes that rice farmers who possess strong PN towards environmental protection will demonstrate stronger intentions to engage in adaptation and mitigation behaviors to address CC.

Hypothesis (11a)

PN significantly and positively influence rice farmers’ intention to adopt adaptation behaviors in response to CC.

Hypothesis (11b)

PN significantly and positively influence rice farmers’ intention to adopt mitigation behaviors in response to CC.

TPB-VBN integration

Although the TPB is widely applied in understanding human behavior, it has faced critiques regarding its capacity to fully explain behavioral intentions140. Studies highlight that TPB often overlooks the influence of social contexts and the dynamic interactions between individuals, which are critical components in shaping behavior141. Additionally, the TPB tends to disregard the role of individuals’ value systems and worldviews55 while focusing primarily on rational motivations, often neglecting the influence of norms80. In contrast, research underscores the importance of both rational and norm-based motivations in driving pro-environmental actions119. The VBN theory, however, offers a holistic perspective on the drivers of pro-environmental behavior by integrating both rational and norm-driven motivations82. By combining TPB with VBN, a more holistic understanding of the decision-making process surrounding the adoption of behaviors, such as adaptation and mitigation strategies in the context of CC, can be achieved55,111. This study seeks to find the relationships between four key variables—AC, Attitude, PNs, and SNs—through the integration of the TPB and VBN frameworks.

AC → Attitude.

Numerous studies have emphasized the significant impact of AC on individuals’ attitudes toward pro-environmental behaviors67,114,142. When individuals recognize the consequences of their actions, it shapes their perspectives and attitudes, leading to more protective attitudes towards the environment67. Generally, protective attitudes arise when individuals become aware of the environmental impacts of their behavior, prompting them to adopt more sustainable responses to environmental challenges143. While attitude alone may not suffice in altering behavior, a heightened AC of one’s actions often fosters a stronger commitment to engaging in pro-environmental behavior144. For example, Savari and Khaleghi67 found that increased awareness of environmental consequences directly influences individuals’ attitudes toward conservation efforts. Similarly, Ren et al.145 showed that individuals with greater environmental awareness tend to hold more favorable attitudes toward environmentally responsible conduct, particularly in tourism contexts. Building on this, it is hypothesized that if rice farmers become more aware of the environmental outcomes of their actions, they will develop more favorable attitudes towards adopting adaptation and mitigation strategies to reduce their environmental impact.

Hypothesis (12)

AC has a significant positive impact on rice farmers’ attitudes toward adopting adaptation and mitigation behaviors.

SN → PN.

The influence of SNs on PNs has been well-documented in the literature114,139,146,147,148. SNs, present within a community, have a crucial role in forming individual PN through social pressures89. If farmers perceive pro-environmental behaviors as socially acceptable or expected, they may feel a moral obligation to adopt these behaviors149. Unlike PN, which focuses on personal responsibility, SN reflects broader social expectations and pressures regarding specific actions67. These norms guide individuals in determining the appropriateness of their behavior in a social context150. For instance, Savari et al.139 found that SN can activate PN in Iranian farmers, particularly in relation to reducing fertilizer use as part of sustainable agricultural practices. Based on this, it is hypothesized that if rice farmers perceive social pressure to adopt adaptation and mitigation behaviors, they will be more likely to internalize these behaviors within their PNs.

Hypothesis (13)

SN has a significant positive effect on rice farmers’ PN regarding the adoption of adaptation and mitigation behaviors.

Fig. 1
Fig. 1
Full size image

Conceptual framework of the research.

Materials and methods

To test the theoretical framework of the research, Smart PLS software version 3 (https://parsmodir.com/soft/smartpls) was used. The results are presented in Figs. 3, 4 and 5.

Study area

This research was conducted in Shushtar County (see Fig. 2). To define the spatial role of the study area, geographic information system (GIS) analysis was performed using ArcGIS 10.5. (https://www.esri.com). The area is characterized by an arid climate, where average annual precipitation remains significantly lower than the national mean151,152. Much of the county is comprised of desert and semi-arid terrain, and the scope of such land has gradually increased over time153. Over the last decade, the region has witnessed a temperature rise exceeding 4 °C, with maximum temperatures often reaching above 55 °C during the hottest months. Shushtar holds a vital role in Iran’s agricultural sector, generating around 550,000 tons of crops annually. The county includes over 75,000 hectares of agricultural lands, encompassing both irrigated and rainfed systems. It leads Khuzestan Province in rice production, with approximately 32,000 hectares dedicated to this crop. Notably, about 95% of this area utilizes water-intensive practices such as transplanting and flooded irrigation. In recent years, rice cultivation in the region has grown by over 15%. However, water scarcity poses a significant challenge for the local farming community, as river water availability has declined due to excessive extraction for agricultural use153. Combined with intensifying heat stress, these water shortages underscore the urgent need for implementing both adaptation and mitigation strategies in the agricultural practices of Shushtar.

Fig. 2
Fig. 2
Full size image

(Source: Research findings)

The location of Shushtar County in Iran and Khuzestan Province.

Target population and sample size

The target population refers to the entire set of individuals, objects, or phenomena that are the subject of study in a research investigation154. In this study, the statistical population include all rice cultivators in Shushtar County. The sample size was determined using the Krejcie and Morgan155 table, which recommended a total sample size of 385 individuals. The decision to use this table was based on three key factors: (1) Accuracy: The table is based on reliable statistical formulas, ensuring robust results; (2) Flexibility: It can be applied to populations of varying sizes and confidence levels; and (3) Ease of calculation: It simplifies sample size calculations, particularly when the population variance is unknown156.

Participants

In this research, participants had an average age of 49.46 years, with a standard deviation of 17.29. Around one-third of the participants had completed middle school, while approximately 8% held a university degree. The mean size of farmland was 12.02 hectares, and the mean farming background was 10.12 years. In terms of farmers’ knowledge about adaptation and mitigation strategies, 63.52% and 73.78% had low to moderate levels of knowledge, respectively. Additionally, 61.24% of the respondents reported never having participated in any training programs related to these strategies.

Stratified sampling method

To ensure representative sampling, a stratified random sampling approach with proportional allocation was adopted. Based on the regional classification provided by the Iranian Statistics Center, Shushtar County was initially segmented into several distinct zones. From each of these zones, two rural districts were randomly chosen. Subsequently, two villages were randomly selected from each of the identified rural districts, culminating in a total of 12 villages where data collection was carried out. Stratified sampling is a probability sampling technique that enhances the representativeness of the sample by dividing the population into homogeneous subgroups or strata. Each stratum shares specific characteristics, and random samples are drawn from each group157. This approach helps ensure the accurate representation of the population and reduces sampling errors, ultimately improving the precision of the study’s results (Berndt, 2020).

Survey design

The research questionnaire was developed following a comprehensive review of previous studies related to adaptation, mitigation, water conservation, CC, and pro-environmental behaviors. This review allowed for the identification of relevant constructs and measurement items employed by prior scholars to evaluate variables within the TPB and VBN frameworks. Based on this review, the following constructs were included in the questionnaire: adaptation intention (3 items), mitigation intention (3 items), PNs (4 items), AR (4 items), attitude (AT) (3 items), AC (3 items), SNs (3 items), PBC (4 items), AV (3 items), BV (3 items), EV (3 items). To assess respondents’ opinions, a 5-point Likert scale was used (1 = Very Low to 5 = Very High), which helps minimize potential statistical issues158.

Validity of the study instrument

The validity of a questionnaire refers to its ability to accurately measure the intended variables. In other words, validity determines whether the instrument effectively captures what it is designed to assess159. In present research, two key approaches were employed to evaluate validity: (1) Face validity: It assesses whether the questionnaire items are relevant and appropriate for measuring the intended constructs. This form of validity ensures that the instrument appears logically sound and conceptually aligned with the research objectives from both expert and non-expert perspectives160. It serves as an essential preliminary evaluation, often conducted by specialists in the field159. In this study, a panel of experts from CC, social sciences, rural development, psychology, and environmental sciences reviewed the questionnaire. Based on their insights, necessary revisions were implemented before final approval. (2) Construct validity: It examines whether the measurement tool accurately reflects the theoretical concept it is designed to assess. This type of validity ensures that the results align with relevant theoretical frameworks and hypotheses (Strauss & Smith, 2009). One of the widely accepted methods for evaluating construct validity is through the Average Variance Extracted (AVE)158. As presented in Table 1, since the AVE values exceed the recommended threshold of 0.5, the research instrument demonstrates satisfactory construct validity.

Reliability of the research tool

Reliability is a fundamental technique for assessing the quality of a measurement tool, reflecting its ability to produce stable and consistent results across repeated applications161. In this study, reliability was examined using two key measures: (1) Cronbach’s Alpha: This statistic evaluates the internal consistency of the questionnaire, indicating how well individual items within a construct correlate with one another. A Cronbach’s Alpha value close to 1 suggests strong internal reliability162. Although this method is widely used, it primarily assesses internal consistency and does not account for other reliability aspects163. A value above 0.7 is generally considered acceptable, indicating a reliable measurement instrument164. (2) Composite Reliability (CR): It evaluates whether the measurement tool consistently captures the intended constructs, ensuring stability and accuracy in the obtained results163. A composite reliability value exceeding 0.6 is deemed acceptable165. Based on the findings presented in Table 1, both Cronbach’s Alpha and composite reliability values meet the required thresholds, confirming that the research instrument possesses strong validity and reliability.

Ethical statement

Informed consent

was obtained from all individuals who participated in the study. All materials and methods were carried out according to instructions and regulations, and this research has been approved by a committee at Agricultural Sciences and Natural Resources University of Khuzestan, Mollasani, Iran. All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards.

Data analysis

In this study, data analysis was conducted employing SPSS and Smart PLS. Although the sample size technically warranted the use of covariance-based SEM, the study opted for variance-based SEM using SmartPLS due to three critical considerations: (i) Model complexity: The research framework included both reflective and formative constructs, as well as intricate indirect relationships. SmartPLS is particularly well-suited for such configurations. (ii) Data non-normality: Covariance-based techniques are sensitive to data normality assumptions. Given the dispersion and non-normal distribution of variables in this study, variance-based SEM provided a more robust analytical approach. (iii) Predictive orientation: The primary aim of this research was to predict behavioral outcomes related to flood protection decisions, rather than merely explain correlations. SmartPLS offers enhanced capabilities for predictive modeling and handling mediating effects. Therefore, SmartPLS was selected not only for its flexibility and tolerance of data irregularities but also for its superior performance in exploring both direct and indirect influences among key variables.

SEM was utilized as an advanced statistical technique to evaluate the relationships between observed variables and latent constructs. Compared to conventional statistical methods, SEM allows for a more comprehensive analysis of complex relationships166. Latent variables represent theoretical constructs that cannot be measured directly, instead, they are inferred through a multiple measurable indicators167. In contrast, observed variables are directly measurable data points that serve as input for statistical analysis. These observed variables play a crucial role in quantifying latent constructs, ensuring a more robust and insightful analysis168. The measurement model assesses the associations between observed indicators and their respective latent constructs. Each latent variable is assessed based on multiple observed variables, with factor loadings indicating the strength of these associations169. The structural model, on the other hand, investigate causal relationships between latent variables, identifying both direct and indirect effects within the framework167. To conduct SEM analysis, Smart PLS was utilized, a widely adopted software designed for Partial Least Squares (PLS) modeling. This approach is particularly suitable for analyzing complex models, especially in cases with smaller sample sizes or non-normally distributed data170. One of the significant benefits of Smart PLS is its capability to handle multi-variable models efficiently while offering a user-friendly graphical interface. This feature allows researchers to design, test, and refine their models with ease167. Additionally, Smart PLS provides visual representations of models and analytical results in an intuitive format, enhancing interpretability163. Furthermore, to assess potential multicollinearity, Variance Inflation Factor (VIF) values were calculated for each indicator. A conservative threshold of < 5 was applied, in alignment with statistical best practices, ensuring minimal redundancy and independence among constructs165.

Results

Evaluation of measurement models

The assessment of the measurement model involved four main steps: (i) assessing the unidimensionality of the markers, (ii) examining the validity and reliability of the study tool, (iii) evaluating the discriminant validity between constructs, and (iv) analyzing the Heterotrait-Monotrait Ratio (HTMT) of the variables. Hypothesis testing could only proceed after the measurement model had been validated. The following sections present the findings from each of these stages.

Unidimensionality: To assess unidimensionality, two primary factors were considered: factor loadings and t-values168. A marker is considered reliable if its factor loading exceeds 0.50 and is statistically significant at the 1% level166. According to the findings presented in Table 1, all indicators met these thresholds, confirming their validity as measures of the latent variables.

Validity and reliability

The validity and reliability of the questionnaire was confirmed when Cronbach’s alpha exceeded 0.70, composite reliability (CR) was above 0.60, and the average variance extracted (AVE) exceeded 0.50168,170. As indicated in Table 1, all items of the questionnaire met these criteria, ensuring that the instrument was both valid and reliable.

Furthermore, to ensure the independence of the constructs within the model, multicollinearity was assessed through the calculation of VIF values for all indicators. The results revealed that all VIF values remained below the recommended threshold of 5, indicating the absence of significant multicollinearity among the constructs. This outcome affirms that the indicators are free from redundancy and that each construct uniquely and reliably represents distinct dimensions of the phenomena under investigation.

Table 1 Assessment of the fit of the research measurement model.
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Discriminant validity

To evaluate discriminant validity, the square root of the AVE was compared to the correlations among the latent constructs. Following the criterion established by Fornell and Larcker158discriminant validity is confirmed when the square root of AVE for a construct is greater than its correlation with other constructs. The current study found AVE square roots ranging from 0.723 to 0.829, all of which exceeded the inter-construct correlations (ranging from 0.265 to 0.596), thus supporting discriminant validity (Table 2).

Table 2 Discriminant validity of research constructs.
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Heterotrait-Monotrait ratio (HTMT)

The HTMT ratio was used as an additional method to evaluate discriminant validity by assessing the similarity of indicators within each construct168. According to Henseler et al.171HTMT values should remain below 0.85 to indicate acceptable discriminant validity. The analysis showed that all constructs met this criterion, thereby providing further confirmation of discriminant validity (Table 3).

Table 3 HTMT rate for research constructs.
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Assessment of the structural model

TPB model for predicting adaptation and mitigation behaviors

The findings from the estimation of the TPB model for predicting farmers’ intention to adopt adaptation and mitigation behaviors are illustrated in Fig. 3. The findings indicate that all variables in the TPB—namely, attitude, SN, and PBC—significantly influenced the adoption of adaptation and mitigation behaviors in response to CC. Specifically, the TPB model accounted for 42.6% of the variance in the intention to engage in adaptation behaviors and 32.4% of the variance in the intention to adopt mitigation strategies.

Fig. 3
Fig. 3
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(Source: Research findings)

Results of the TPB model in the context of intention to adopt adaptation and mitigation behaviors.

4.2.2. VBN model for predicting adaptation and mitigation behaviors

The findings from the VBN model estimation for predicting farmers’ intention to adopt adaptation and mitigation behaviors are presented in Fig. 4. The findings suggest that the following values were significant predictors of the NEP: AV (β = 0.185, p < .001), BV (β = 0.106, p < .001), and EV (β = -0.614, p < .001). These values accounted for 37% of the variance in NEP. Additionally, significant relationships were found between NEP (β = 0.120, p < .001), AC (β = 0.534, p < .001), AR (β = 0.603, p < .001) and PNs. Furthermore, the results indicated that environmental PNs were significant predictors of farmers’ intention to engage in adaptation and mitigation behaviors. Notably, the effect of PN on mitigation behaviors (β = 0.659, p < .001) was stronger than its effect on adaptation behaviors (β = 0.613, p < .001).

Fig. 4
Fig. 4
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(Source: Research findings)

Results of the VBN model on the intention to adopt adaptation and mitigation behaviors.

VBN-TPB model for predicting adaptation and mitigation behaviors

The results from the combined VBN-TPB model are shown in Fig. 5. The findings indicate that this integrated model offers a more comprehensive explanation of adaptation and mitigation behaviors than the individual models. Specifically, the combined model enhanced the explanatory power of the mediating variables, including Attitude, NEP, AC, AR, PN, and the dependent variables (adaptation and mitigation behaviors). This suggests that the constructed model is superior to the separate models, as changes in any of the variables lead to more substantial changes in the dependent variables. Moreover, the results highlighted that, among the independent variables, PN had the strongest influence on farmers’ intention.

Fig. 5
Fig. 5
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(Source: Research findings)

Results of the VBN-TPB model on the intention to adopt adaptation and mitigation behaviors.

Hypothesis testing

To examine the study hypotheses, path coefficients and t-statistics were calculated, following the approach proposed by Hair et al.172. A bootstrapping algorithm with 4000 and 3000 samples was employed in PLS analysis to evaluate the significance of the coefficients (Table 4). Results demonstrated that the framework used in this study is highly powerful, as all hypotheses were statistically significant. The only variation observed was in the t-values, which depend on the sample size; however, the significance of the parameters remained unchanged. Moreover, the findings indicated that the variables in this study accounted for 67.6% and 64.2% of the variance in the intention to use adaptation and mitigation behaviors, respectively.

Table 4 Results of examining research hypotheses.
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Discussion

This study investigates the factors affecting the use of adaptation and mitigation behaviors against CC among rice farmers in Iran. Both the VBN and TPB models were employed individually and in combination. The results from evaluating these models separately reveal that, compared to the VBN model, the TPB model demonstrates greater explanatory strength regarding farmers’ intentions to adopt adaptation behaviors. This outcome aligns with findings from Zhang et al. (2024), indicating that rational decision-making models better explain adaptation behaviors. Specifically, the TPB variables—attitude, SN, and PBC—accounted for 42.6% and 32.4% of the variance in the intention to adopt adaptation and mitigation behaviors, respectively. These findings suggest that the TPB, as a rational theory, is particularly effective in explaining behaviors related to adaptation13. Within the TPB framework, Attitude was identified as the most influential predictor of both adaptation and mitigation behaviors among rice farmers, supporting Hypotheses 1a and 1b. This result corroborates previous research by Mazana et al.144Panwanitdumrong and Chen73Savari and Khaleghi67and Cammarata et al.74all of which highlight the importance of attitude in shaping individuals’ intentions to adopt new behaviors. This finding suggests that people with a desirable environmental attitude are more likely to participate in environmentally appropriate actions. An individual’s behavior is heavily affected by their assessment of the desirability of that behavior—the more favorable the attitude, the higher the likelihood of adopting the behavior63,98,173,174. In the case of rice farmers, a positive evaluation of the potential outcomes of adaptation and mitigation strategies increases the likelihood of their adoption143. Consequently, promoting positive attitudes toward adaptation and mitigation strategies is essential, not only to improve farmers’ agricultural performance but also to enhance their resilience to CC55. This is particularly important considering that a substantial number of rice cultivators possess limited awareness regarding these strategies, making them more vulnerable to the adverse effects of CC.

The second most significant factor influencing farmers’ intention to engage in adaptation and mitigation behaviors, according to the TPB model, was SNs (supporting Hypotheses 2a and 2b), which reflects the social influence or norms that can facilitate behavior change34,60,67,80,81. While an individual’s positive attitude toward adaptation or mitigation may encourage behavioral change, social norms can either support or hinder such actions34. People often rely on their reference groups to guide their decisions, and societal expectations regarding what should be done can form percon behavior81. The interplay between personal beliefs about expected outcomes, societal expectations, and confidence in one’s ability to perform a behavior is key to forming behavioral intentions175. Specific SNs, which reflect perceived consequences and social approval, are crucial determinants of actions with subjective value176. Therefore, fostering adaptation and mitigation behaviors through trusted, socially recognized individuals could play a crucial role in promoting behavior change among rice farmers.

The third significant variable within the TPB model influencing farmers’ intention to adopt adaptation and mitigation behaviors was PBC, supporting Hypotheses 3a and 3b. PBC is composed of two key elements: self-efficacy and resource availability11,132,177,178. Self-efficacy reflects an individual’s belief in their capability to successfully conduct an action or use a new technology83,87. Those with higher self-efficacy are more inclined to take on challenging and innovative behaviors, often being the first to try new approaches82. However, in developing countries, where literacy rates are often lower, individuals may struggle with self-efficacy, limiting their capacity to adopt complex adaptation and mitigation strategies, even when these practices offer clear benefits98. Addressing this gap through targeted training programs could enhance both the knowledge and practical talents necessary to implement these strategies89. The second component of PBC is the availability of necessary resources for adopting adaptation and mitigation behaviors. In many instances, these strategies demand substantial financial and material resources, which can be a significant barrier for farmers in developing countries who may lack the funds to implement them60. Therefore, it is crucial for governments to provide financial support and incentives to help farmers overcome these resource constraints and adopt climate-smart practices.

When comparing the models, the VBN model showed greater explanatory power for CC mitigation behaviors than the TPB model. The variables in the VBN model accounted for 36.7% and 43.5% of farmers’ intentions to adopt adaptation and mitigation behaviors, respectively. This suggests that in the context of long-term strategies like CC mitigation, the role of individual values and perspectives is critical. In the VBN model, individuals are divided into three value groups: egoistic, altruistic, and biospheric105. For initiatives aimed at reducing carbon emissions, focusing on psychological factors such as altruistic, biospheric, and egoistic values, along with PNs, can significantly influence behavioral change13. Further analysis of these values reveals that individuals with AV and BV are inclined to view society and nature as interconnected, believing that it is their responsibility to maintain this balance (supporting H4, H5). Research has shown that individuals with these values tend to be more concerned about CC and are proactive in seeking solutions to mitigate its impacts127. These individuals feel a strong sense of responsibility toward societal and environmental well-being, often preferring strategies that help maintain the status quo for future generations179. On the other hand, individuals with an EV tend to downplay or ignore the significance of CC and are less likely to support risk management and mitigation strategies180. These individuals typically prioritize personal gain over the collective good, leading to behaviors that contribute to resource overuse, such as excessive reliance on chemical fertilizers, water, and other agricultural inputs (supporting H6).

Another key hypothesis in the VBN model examined the influence of the NEP on AC, which has been supported by various studies105,126,181 (supporting H7). Research consistently indicates that individuals who align with a higher NEP are more likely to exhibit favorable environmental attitudes, such as a deep sense of connection with nature, a firm belief in the importance of environmental conservation, and an increased concern for environmental issues. These individuals are also more inclined to reflect on the environmental consequences of their actions125. Numerous studies have highlighted NEP as a crucial factor in encouraging pro-environmental behaviors, with individuals who hold a higher NEP being more conscious of environmental degradation and more likely to consider the environmental effect of their behavior3. In the context of rice farmers, it can be posited that those with a stronger NEP are more likely to be aware of the environmental outcomes of their farming practices and thus more inclined to adopt both adaptation and mitigation strategies on their farms.

Furthermore, AC emerged as another influential variable in the VBN model, significantly impacting PNs and AR in the adoption of adaptation and mitigation behaviors (supporting H8, H9). This finding suggests that awareness of the environmental outcomes of one’s behaviors plays a crucial role in shaping pro-environmental behavior. When individuals become aware of the potential harmful results of their actions, they tend to become more attuned to environmental issues125,182. AC is particularly important in daily activities within agricultural communities, where it can promote sustainable practices and reduce environmental harm183. For rice farmers, recognizing the negative consequences of excessive resource use—such as water, chemical fertilizers, and pesticides—can increase their likelihood of adopting mitigation behaviors in response to CC. AC serves as a critical trigger for the activation of PN and AR127fostering a moral commitment to sustainable practices. This moral commitment, in turn, influences their willingness to implement adaptation and mitigation strategies13. In essence, increased AR elevates personal responsibility, which directly shapes behavioral intentions82.

Moreover, the results indicated that AR can significantly affect PNs in the adoption of adaptation and mitigation behaviors by rice farmers (supporting H10). AR reflects an individual’s perception of accountability for the environmental outcomes of their actions127. It is rooted in the belief that individuals have a duty to act to reduce harmful environmental impacts, such as CC and pollution184. This sense of responsibility can influence behavior through PN, encouraging individuals to take proactive steps toward mitigating environmental damage185. When rice farmers recognize that their actions—such as overusing water, improper irrigation practices, excessive chemical application, and lack of soil conservation—contribute to CC, this realization can drive them to adopt more sustainable practices186,187,188. Thus, if farmers understand that their current climate challenges are partly the result of their past behaviors, they are more likely to take responsibility and implement adaptation and mitigation strategies189.

The results of the integrated VBN-TPB model revealed that the proposed theory demonstrates substantial explanatory power in this context. Specifically, the model was able to account for 64.2% and 67.6% of the variance in farmlands’ intentions to adopt adaptation and mitigation strategies, respectively. These findings suggest that implementing both adaptation and mitigation strategies in parallel can effectively reduce the impacts of climate variability on agricultural systems. However, it is essential to recognize that the process and outcomes of psychological factors influencing these behaviors may vary depending on the nature of the perceived or actual consequences. For example, carbon emission reduction behaviors in agricultural production generate externalities that benefit a broader community, with such benefits becoming apparent only after long periods. In contrast, adaptation behaviors tend to develop both personal and farm resilience, thereby decreasing susceptibility to CC impacts. Moreover, the advantages of adaptation strategies are typically observable within a shorter time frame and directly influence the well-being of farmers13. The most influential variable in the model was PNs (supporting H11a, H11b). Analyzing this result, it can be argued that moral norms are deeply ingrained emotional drives that compel individuals to take actions aligned with their personal values182. These norms have a considerable impact on the evaluation of behaviors, either positive or negative. When individuals view a action positively, the likelihood of engaging in that action increases. In the case of rice farmers, if they evaluate adaptation and mitigation strategies favorably, this will significantly influence their intention to adopt these practices134. Moreover, the VBN-TPB model suggests that AC also influences Attitude (supporting H12), a finding consistent with studies by Han and Hyun114Makanyeza et al.142and Savari and Khaleghi67. This implies that an individual’s AC of their actions can shape their evaluation of those behaviors. Typically, protective attitudes emerge when individuals identify the potential consequences of their behaviors, leading them to respond more appropriately to environmental challenges143. For instance, a research by Hamid et al.190 demonstrated that farmers’ awareness of the harmful effects of excessive nitrogen use led to more responsible attitudes toward its application. Therefore, it is essential for rice farmers to become aware of the adverse consequences of behaviors such as overuse of water and fertilizers. This awareness will, in turn, help develop more favorable attitudes toward adopting adaptation and mitigation practices. Finally, the last hypothesis of the study proposed that SNs influence PNs (supporting H13). Several studies have demonstrated that social pressures can contribute to the internalization of norms114,139,146,147,148. This finding indicates that the more social pressure rice farmers feel to adopt adaptation and mitigation behaviors, the more likely their PNs will be affected. Consequently, they are more likely to adjust their behaviors to align with societal expectations. This indicates that SNs acts as a social influence, guiding individuals toward adopting behaviors that meet societal norms and expectations150.

Policy implication

To effectively foster adaptation and mitigation behaviors among rice farmers, it is recommended that policymakers design and implement integrated behavioral interventions that align with the psychological drivers identified through the TPB and VBN frameworks. These interventions should:

  1. (i)

    . Leverage local values and environmental norms (VBN) to inspire ethical responsibility and collective motivation toward sustainable farming practices.

  2. (ii)

    Enhance perceived behavioral control and positive attitudes (TPB) through training, success stories, and easy-to-access resources that make adaptation methods seem feasible and beneficial.

  3. (iii)

    Combine incentives with social influence, using farmer cooperatives, regional champions, and peer-led workshops to normalize and promote climate-friendly practices.

  4. (iv)

    Tailor communication strategies to match farmers’ belief systems and cultural context, ensuring that policies resonate emotionally and practically at the community level.

  5. (v)

    Institutionalize support mechanisms, such as subsidies for low-emission technologies, adaptive crop management systems, and climate-resilient infrastructure, to reduce barriers to implementation.

Conclusion and limitations

The present attempt aimed to explore the factors influencing Iranian rice farmers’ intention to adopt adaptation and mitigation strategies in response to CC. This study was grounded in the TPB and VBN framework. The findings revealed that while the TPB model demonstrated a stronger explanatory power in relation to adaptation behaviors, the VBN model was more effective in explaining mitigation behaviors. Overall, the integrated TPB-VBN model accounted for 64.2% and 67.6% of the variance in rice cultivators’ willingness to adopt adaptation and mitigation strategies, respectively. Notably, PNs emerged as the most effective factor driving farmers’ intentions in both contexts. However, there are three key limitations to this study: (1) The research primarily focused on farmers’ intentions, which, based on the TPB, is the most significant predictor of behavior but may not always accurately reflect actual behavior. Future studies should evaluate the actual behaviors of rice farmers to better understand the translation of intentions into practice. (2) While the integrated TPB-VBN framework accounted for a significant share of the variance in farmers’ behavioral intentions, a portion of variance remains unaccounted for. To strengthen the model’s predictive capability, future research should consider incorporating additional influential variables that may further refine and expand the existing conceptual framework. (3) The third limitation pertains to the respondent selection process. It is possible that people with higher environmentally-friendly attitudes were more likely to participate in the survey, potentially introducing selection bias. To mitigate this issue, future research should ensure a more representative sample to avoid such biases and provide a more accurate reflection of the broader farmer population.