The Impact of Government Climate Narrative Plot on Public Pro-Environmental Behavior Intentions: A Survey Experiment

The Impact of Government Climate Narrative Plot on Public Pro-Environmental Behavior Intentions: A Survey Experiment | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article The Impact of Government Climate Narrative Plot on Public Pro-Environmental Behavior Intentions: A Survey Experiment Lin Dong, Zuobao Wang, Yuqiang Zhang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6230248/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Narratives have emerged as a critical policy tool for nudging public behaviors and intentions, garnering increasing attention from policymakers, scholars, and practitioners. However, minimal research has examined how distinct plots of climate narratives influence pro-environmental behavior intentions (PEBI). This study aims to fill this gap by examining the interplay between emotional framing (positive vs. negative) and temporal framing (occurred vs. not occurred) in government climate narrative plots, as well as their influence on PEBI through risk and benefit perception pathways. Using experimental data from a sample of 638 Chinese citizens, we found that negative framing narratives outperformed positive framing ones in enhancing PEBI by heightening risk perception. However, mediation analysis revealed that the advantage of negatively framed narratives was partially offset by their reduced benefit perception. Moreover, moderated mediation analysis demonstrated that, under an occurred temporal framing, positive framing narratives were more effective than negative framing ones in enhancing benefit perception, thereby promoting PEBI. These findings enrich the theoretical framework of causal pathways linking climate narratives to PEBI, offering robust empirical evidence to guide policymakers in crafting refined climate communication strategies. Social science/Environmental studies Social science/Social policy pro-environmental behavior narrative plot policy narrative climate change experimental survey Figures Figure 1 Figure 2 1. Introduction Global climate change creates severe impacts on both ecosystems and human society, significantly threatens to impede the progress toward sustainable global development (IPCC, 2023). Studies have shown that climate changes are a widespread impact caused by numerous human activities, such as continuous burn of fossil fuels, deforestation, and other similar activities (Howlett & Rawat, 2019; Trenberth, 2018; Vlek & Steg, 2007). Numerous public behaviors contribute to mitigating climate change (Capstick, Whitmarsh, Poortinga, Pidgeon, & Upham, 2015; Creutzig et al., 2018). For example, people can reduce energy consumption by using energy-efficient appliances, improving building insulation, and minimizing unnecessary electricity use (Dietz, Gardner, Gilligan, Stern, & Vandenbergh, 2009), as well as shift consumption patterns by purchasing energy-efficient products, supporting refurbished goods, and reducing meat consumption (Poore & Nemecek, 2018). In addition, individuals can further contribute to mitigating climate change by participating in environmental volunteer activities and engaging in decision-making behaviors (Liverani, 2014; Wolske & Stern, 2018). Clearly, collective efforts from the public are essential for mitigating climate change (O'Brien, 2012). However, climate environmental issues are inherently collective problems, and most people lack motivation or awareness to proactively engage in pro-environmental behaviors(Lewis Jr, Green, Duker, & Onyeador, 2021). They often perceive such behaviors as too complex or costly, or they believe that individual efforts are futile in the face of widespread inaction, leading to a “free-rider” phenomenon (Z. Liu & Lei, 2024). Research indicates that government narratives have emerged as a powerful tool in shaping public attitude and prosocial behavior (Adobor, 2024; Braddock & Dillard, 2016; Mao & Nishide, 2025). Narratives, which refer to stories that individuals use and tell, can significantly influence behavior by evoking emotional resonance and facilitating cognitive restructuring. For environmental issue, research has also suggested that narratives can promote pro-environmental behavior (Moezzi, Janda, & Rotmann, 2017). On one hand, narratives translate complex environmental challenges—such as climate mitigation policies or ecological restoration frameworks—into relatable terms that align with the public’s lived experiences, thus enhancing comprehension of technical information (Herman, 2003). On the other hand, when audiences identify with narrative roles, the stories evoke affective responses (e.g., urgency from fear or empowerment from hope), which catalyze behavioral shifts by aligning individual intention with sustainability goals (Morris et al., 2019). Nonetheless, the potential of narratives’ actual effectiveness remains controversial. Some scholars suggest that the efficacy of narratives may be influenced by various factors, particularly the plot structure and its alignment with the audience’s values (Jones & McBeth, 2010; O' Donovan, 2018). Moreover, the overuse of narratives may lead to “narrative fatigue” or “information overload,” which could, in turn, weaken the narrative’s effect (Freeman, 2015). Consequently, in government climate narratives, optimizing narrative plots is critical for enhancing public behavior intentions. Narrative plot design—the structural arrangement of context, characters, and moral imperatives that establishes policy problems and provides causal explanations within stories (Jones & McBeth, 2010)—serves as one of the critical determinant of public cognition and behavioral responses (Shenhav, 2015). Through its information architecture and emotional delivery mechanisms, narrative plots shape individuals’ mental representations of complex environmental issues by activating cognitive schemas and affective evaluations (S. Brown & Tu, 2020; Dahlstrom, 2014). Yet, there is a notable gap in systematic research exploring the impact of plot framing within climate narratives. Firstly, the causal pathways through which narrative plots transform behavioral intentions remain poorly understood. The mediating roles between narrative emotional framing (positive vs. negative) and pro-environmental behavior intentions (PEBI) outcomes require rigorous empirical validation. Next, existing studies predominantly examine emotional framing or temporal framing (occurred vs. not occurred) in isolation, overlooking their interactive effects PEBI through differentiated cognitive pathways. And this siloed approach hinders the identification of optimal narrative plot outcomes. Finally, mainstream research focuses disproportionately on developed economies (Af Malmborg, 2022; Crow & Jones, 2018), neglecting the unique narrative mechanisms operating in transitional economies where state-led environmental governance predominates. These limitation constrain policymakers’ ability to design culturally attuned climate communication strategies, particularly in Global South contexts where rapid industrialization intersects with sustainability imperatives. To address these deficiencies, this study systematically evaluates the influence of varying narrative plot frame combinations on PEBI. It pursues a threefold theoretical advancement: (1) testing the mediating roles of risk perception and benefit perception within the narrative influence chain; (2) elucidating the interactive effects of emotional framing and temporal framing on PEBI; and (3) extending the explanatory scope of narrative policy theory to non-Western, vertically governed systems. The findings offer practical implications by providing a foundation for governments to craft phased, context-adapted climate communication strategies, while also contributing valuable insights for behavioral information interventions. As a representative of developing countries, China’s early development was predominantly driven by an economic growth-focused model, which prioritized rapid industrialization and urbanization at the expense of environmental sustainability, leading to severe environmental degradation (Yuan et al., 2020). Since 2013, however, China has undergone a significant transformation in its development approach, shifting toward an ecologically prioritized model through the prioritization of ecological restoration. This transition is exemplified by the implementation of policies such as the “ Ecological Civilization ” framework and the promotion of narratives like “ clear waters and green mountains are as good as mountains of gold and silver ,” aimed at aligning public behavior with sustainability goals (Dai & Zeng, 2021). Unlike Western nations, which largely addressed industrial pollution decades ago, China is still in a pivotal phase of environmental governance. This phase is characterized by ongoing policy implementation and public exposure to climate risks, such as smog and extreme weather (Kostka & Zhang, 2018). These tangible experiences, coupled with China’s unique context as a transitioning economy where rapid industrialization intersects with sustainability imperatives, provide a distinctive empirical foundation to investigate which government climate narrative framing interventions are effective in promoting PEBI, particularly in transitioning economies facing similar developmental and environmental challenges. Building on this context, this study employs a 2×2 between-subjects experimental design to investigate how emotional framing and temporal framing in Chinese government climate narratives influence PEBI, with a focus on the mediating roles of risk and benefit perception. The remainder of this paper is structured as follows: Section 2 develops hypotheses linking narrative framing to PEBI through perceptual mediators; Section 3 details the experimental methodology, including participant randomization and stimulus design; Section 4 presents hypothesis testing results; Section 5 discusses theoretical implications for narrative-driven behavioral interventions; Section 6 concludes with limitations and future research directions. 2. Literature Review and Research Hypotheses 2.1. Government Narratives and Narrative Structures Narrative has emerged as a core topic in interdisciplinary research, encompassing fields such as psychology, linguistics, neuroscience, and management. The narrative theory, which synthesizes findings from multiple disciplines, posits that narratives play a crucial role in the construction and updating of cognitive patterns in the brain (Herman, 2003). By immersing individuals in the storyline, narratives are more capable than other forms of information in shaping people’s attitudes, intentions, and behaviors (Holt & Thompson, 2004). Among others, the plot is a crucial component of narrative framework, as it links a series of events, experiences, or actions cohesively, forming a meaningful whole (Fischer & Forester, 1993). Importantly, the narrative plot also serves a logical attribution function (McBeth, Shanahan, Arnell, & Hathaway, 2007). By stating and explaining causal relationships, the plot provide clarity and coherence, which help audiences understand the sequence of events and their interconnections (Stone, 2002). This attribution function serves as a foundational through which narratives shape individuals’ understanding of complex phenomena and foster their engagement with the issues at hand (Bandola-Gill & Smith, 2021). In recent years, a growing body of research has focused on the distinct effects of emotional and temporal framing within narrative plots on individual perception and behavior. First, several studies have investigated the influence of narrative emotion—including both positive and negative narratives—on public behavior across various domains (Dunlop, Wakefield, & Kashima, 2008). For instance, emotionally arousing persuasive messages tend to be better recalled, and perceived as more effective, than less emotional messages, both in the field of health communication (Dillard & Peck, 2001; Pechmann & Reibling, 2006), and in consumer marketing (Edson Escalas, Chapman Moore, & Edell Britton, 2004). Regarding climate issues, some studies suggest that governments and experts should raise public awareness of climate change through negatively framed narratives (Dales, Padfield, & Bridge, 2024). For example, some scholars argue that environmental behaviors should be promoted by presenting stories about the “imminent risks of climate change” (Bosone, Chevrier, & Martinez, 2023; Spence, Poortinga, & Pidgeon, 2012). Conversely, other studies contend that, despite the considerable potential of negative emotions in climate change communication to raise awareness, they are not effective tools for motivating genuine individual participation (O'Neill & Day, 2009; Richter, Gabe-Thomas, Queirós, Sheppard, & Pahl, 2023). In contrast, narratives employing a positive emotional framework are more effective in fostering sustained public engagement in sustainable behaviors (Neef et al., 2023). These conflicting findings highlight the complexity of emotional framing effects, suggesting that the efficacy of narratives may depend on contextual factors. Second, a limited number of studies investigated the effects of narrative time on individual behavior. Within the temporal framework of narrative plots, scholars have categorized them into “occurred” (past-focused) and “not occurred” (future-oriented) framing (Ruff, Stelmach, & Jones, 2022). Research suggests that emphasizing past versus future events may shape public perceptions of climate issues and influence behavioral intentions (Jie, Lapinski, & Peng, 2018; Y. Wang, Thier, Lee, & Nan, 2023). Additional studies have found that temporal framing affects individuals' perceptual judgments and decision-making by modifying their psychological distance from events (Konstantinidis, Dai, & Newell, 2025; Trope & Liberman, 2010). Furthermore, a growing body of research has explored the relationship between perception and pro-environmental behavior, with a particular focus on the roles of perception perception (Jia & Wang, 2024; Latif et al., 2023). For instance, O'Connor, Bord, and Fisher (1999) found that risk perception effectively accounts for behavioral intentions related to climate change mitigation. Bradley, Babutsidze, Chai, and Reser (2020) using samples from Australia and France, demonstrated that risk perception indirectly predicts pro-environmental behavior. Similarly, research on benefit perception has highlighted its role in shaping attitudes and behaviors toward sustainability (Huijts, Molin, & Steg, 2012). For example, individuals are more likely to adopt renewable energy technologies when they perceive tangible benefits, such as cost savings or environmental improvements (Steg, Bolderdijk, Keizer, & Perlaviciute, 2014), and consumers often prefer sustainably produced goods over cheaper alternatives manufactured under environmentally harmful conditions (Bechtel, Genovese, & Scheve, 2019). These findings underscore the critical roles of risk and benefit perception as potential mediating mechanisms through which narratives can influence public behavior, providing a robust theoretical foundation for examining the causal pathways between narrative framing and PEBI in the context of climate change communication. Nevertheless, while the aforementioned studies demonstrate that specific narrative plots and perceptions serve as effective tools for promoting certain public behaviors and intentions, few studies have systematically investigated how the interplay between emotional and temporal frameworks within government climate narratives shapes PEBI through perceptual pathways. Given the critical role of pro-environmental behavior in mitigating global climate change, investigating how emotional frameworks interact with temporal frameworks to influence public PEBI via perception emerges as a compelling and valuable research question. In response to these gaps, this study investigated how emotional framing in government climate narratives shapes PEBI through perception pathways, moderated by temporal framing. This study provides new perspectives for understanding the relationship between narrative cognition and behavior intentions in the context of mitigating global climate change, and reveals how narrative strategies can be optimized to promote pro-environmental behavior. By focusing on emotional and temporal framing, and systematically exploring the interactions of different framing strategies, this research deepens and enriches previous studies that primarily focused on the effects of single framing strategies, thus complementing research in the field of policy narrative. 2.2. The Emotional Framing of Narrative Plots and Public Pro-Environmental Behavior Intentions In governmental climate narratives, the deployment of distinct emotional framing shapes the construction of climate issues differently, potentially influencing PEBI. Currently, research findings on the influence of emotions framing of narrative plot on public behavioral intentions present two distinct perspectives. On one hand, recent studies suggest that negative emotions narratives can effectively foster changes in individuals’ behavioral and intentions (Morris et al., 2019). Skeirytė and Liobikienė (2025) conducted an analysis of environmental behaviors and found that individuals’ pro-environmental actions are driven by emotions such as anxiety and sadness, with the frequency of these behaviors increasing alongside heightened levels of such emotions. Similarly, Bretter and Pangbourne (2025) identified a positive correlation between emotions like shame, sadness, and unease and the extent of individuals’ adoption of sustainable transportation modes. Furthermore, Morris et al. (2019) demonstrated that emotional narratives emphasizing negative value outcomes were more successful in encouraging pro-environmental behaviors compared to neutral informational narratives. On the other hand, some research suggests that positive emotions exert a beneficial influence on individuals’ sustainable behaviors and intentions (Taufik, Bolderdijk, & Steg, 2016; Zelenski & Desrochers, 2021). For instance, a study on transportation mode choices demonstrated that positive emotions associated with active travel options, such as walking, positively influenced preferences for these modes (Zavareh, Mehdizadeh, & Nordfjærn, 2020). And, positive emotions were found to predict how reliably individuals seek out information sources regarding sustainable transportation (Manca & Fornara, 2019). In addition, in the domain of consumption decisions, research indicates that positive emotions play a significant role in guiding preferences for green products (Kao & Du, 2020). Specifically, Kao and Du (2020) revealed that environmental advertisements eliciting positive emotions improved consumers’ attitudes toward green products, advertised brands, and purchase intentions. Furthermore, recent work by Husain-Naviatti (2025) suggests that climate change communication strategies emphasizing hope and empathy—such as narratives of human resilience in overcoming climate-related challenges—may prove more effective in inspiring action than approaches relying solely on fear. Overall, both negative and positive emotions appear capable of influencing behavioral intentions, though the relative efficacy of each emotional frame may depend on the specific domain of intervention. In the context of climate narratives, where climate change is a complex issue deeply intertwined with personal life, individuals may exhibit greater sensitivity to negative emotional narratives (Tversky & Kahneman, 1991). Therefore, we hypothesize that negative narratives may hold a comparative advantage in triggering immediate PEBI: H1 The main effect of emotional framing on public attitudes is significant. Compared to plot focusing on positive emoiton, negative emotional narratives plot are more effective in enhancing PEBI. 2.3. The Mediating Role of Risk Perception and Benefit Perception Risk perception refers to an individual’s subjective assessment of the likelihood and severity of potential threats (Renn & Rohrmann, 2000). Empirical evidence demonstrates a robust positive association between risk perception and attitudes and behavioral intentions of individuals (Maddux & Rogers, 1983; Zhang, Du, Huang, Mao, & Jiao, 2024). For instance, Maartensson and Loi (2022) found that heightened risk perception of climate change significantly amplified individuals' willingness to adopt mitigation behaviors. Similarly, Shao, Xian, Lin, and Small (2017) analyzed coastal county data in the United States, revealing that individuals with elevated perceptions of flood risk exhibited stronger support for flood management policies. Similar findings have been observed in China. Such as, Tian and Jiang (2025) explored the determinants of ecological protection behavior among residents and found a positive correlation between ecological risk perception and both the willingness and behavior engage in ecological protection. These findings collectively underscore risk perception’s pivotal role as a cognitive mediator in environmental decision-making processes. Building on this foundation and considering the potential main effect of emotional framing on PEBI, we hypothesize that risk perception mediates the relationship between emotional framing and PEBI: H2 Risk perception mediates the relationship between emotional framing and PEBI, such that negative framing increases risk perception, which in turn enhances PEBI. Benefit perception refers to an individual’s subjective evaluation of the positive outcomes associated with adopting a specific behavior (Bolderdijk, Steg, Geller, Lehman, & Postmes, 2012). Empirical studies across diverse contexts underscore benefit perception as a critical antecedent to attitude formation and behavioral engagement (Felgendreff et al., 2025; Rickard, Yang, Liu, & Boze, 2021). For instance, the meta-analysis by Schulte, Scheller, Sloot, and Bruckner (2022) on residential photovoltaic adoption intentions revealed a strong correlation between perceived benefits and willingness to adopt solar energy systems. Similarly, Bolderdijk et al. (2012) found that perceptions of economic and social benefits significantly increased public support for pro-environmental actions. These findings collectively highlight benefit perception’s dual role as both a cognitive motivator and a behavioral catalyst in environmental decision-making. Building on this evidence and considering the potential influence of emotional framing on PEBI, we hypothesize that benefit perception mediates the relationship between emotional framing and PEBI: H3 Benefit perception mediates the relationship between emotional framing and PEBI, such that positive framing increases benefit perception, which in turn enhances PEBI. 2.4. The Moderating Role of Narrative Temporal Framing Time serves as a critical informational framework in narrative communication, influencing public perception and judgment (H. Kim, Rao, & Lee, 2009). It also exerts persuasive effects on behavioral intentions (Huang & Xu, 2024). In reality, individuals tend to perceive future threats more intensely, as the uncertainty surrounding future events amplifies attention and anticipation of potential risks (Kahneman & Tversky, 1979). Conversely, when reflecting on past events, individuals are more likely to evaluate outcomes based on concrete experiences, leading to more specific behavioral expectations (Banerjee, 2024). Based on these insights, we posit that under negative and not occurred framing amplifies the contextualization and uncertainty of potential threats, thereby more effectively driving the indirect effect of risk perception on behavioral intentions. In contrast, under positive and occurred framing provides more tangible and visible incentives, enhancing public confidence and expectations regarding the benefits of pro-environmental behavior, thus driving PEBI. Accordingly, we propose the following hypotheses: H4 The temporal frame moderates the indirect effect of emotional framing on behavioral intentions through risk perception and benefit perception. H4a The temporal frame moderates the indirect effect of emotional framing on PEBI through risk perception. Under negative emotional narratives, not occurred framing strengthens the indirect effect of risk perception on PEBI. H4b The temporal frame moderates the indirect effect of emotional framing on PEBI through benefit perception. Under positive emotional narratives, occurred framing strengthens the indirect effect of benefit perception on PEBI. Based on the above analysis and hypotheses, the theoretical model of this study is proposed as shown in Fig. 1 . 3. Research Methodology To test the hypotheses outlined above, we conducted an online survey experiment that follows a 2 (positive vs. negative) × 2 (occurred vs. not occurred) between-subject design. Four experimental conditions were generated by this design as intervention groups, allowing for the measurement of participants’ risk perception, benefit perception, PEBI, and demographic information. The experiment was administered through an online survey. This approach enabled precise control over participants’ exposure to the narrative interventions, ensuring standardized presentation of stimuli and minimizing interference from external factors (Chandler, Mueller, & Paolacci, 2014; J. Kim, 2024). By adopting this approach, we aims to isolate the effects of emotional and temporal framing on risk and benefit perceptions, as well as their subsequent influence on PEBI. 3.1. Participants The experiment was conducted on Credamo ( www.credamo.com ), a professional survey platform affiliated with ESOMAR, hosting a diverse sample pool of over 3 million participants from various regions, age groups, and educational backgrounds across China, ensuring sample representativeness. Participants were randomly assigned to experimental conditions using the platform’s randomization function. The sample size was determined based on calculations using G*Power software. The experimental design followed a 2×2 between-subjects design. The effect size (f) was set at 0.25 (indicating a medium effect size), with statistical power set at 0.80 and a significance level of 0.05. The calculation indicated that a minimum sample size of 128 participants was required. 3.2. Materials Based on a 2×2 experimental design, four distinct climate narrative scenarios were developed. All experimental stimuli were presented in a written narrative format, with each narrative centered on the theme of climate change. The intervention materials were developed by adapting content from official media reports on climate change and ecological restoration, published between 2018 and 2023, and sourced from state-endorsed media outlets, including People’s Daily and Xinhua News Agency (Cao, 2019; Schaub, 2021) 1 . The adaptation process followed specific principles: negative narratives incorporated fear and urgency inducing terms (e.g., ‘disaster,’ ‘crisis’;) (Randall, 2009), while positive narratives emphasized hopeful and optimistic terms (e.g.,‘achievements,’ ‘progress’) (Schneider, Zaval, & Markowitz, 2021). Temporal framing was distinguished through linguistic markers: ‘occurred’ narratives used past-tense expressions (e.g., ‘has caused,’ ‘has achieved’) to describe historical climate events, while ‘not occurred’ narratives employed future-tense expressions (e.g.,‘will,’ ‘future’) to project future scenarios, aligning with prior temporal framing research (Chuanshen & Yunzhe, 2023; Hernes & Obstfeld, 2022). To minimize selection bias, two researchers independently screened the materials, achieving high inter-rater reliability (Cohen’s Kappa = 0.82). A pilot study (n = 30) was conducted to validate the intervention materials, ensuring they elicited the intended cognitive responses while avoiding information overload (Lang, 2006). The specific content of the four conditions is as follows: The negative-occurred framing condition focuses on stories detailing the negative impacts of climate change that have already occurred, highlighting the detrimental effects on the national ecological environment and the lives of citizens. The negative-not occurred framing condition centers on potential future climate risks, emphasizing the uncertainty and challenges these threats could pose to China’s ecological environment and economic development. The positive-occurred framing condition emphasizes the positive outcomes of climate governance achieved by the central government, showcasing how policies and mechanisms innovations have effectively mitigated carbon emissions. The positive-not occurred framing condition envisions a future shaped by national climate governance strategies and actions, portraying a scenario of improved living conditions through proactive government planning and efforts (for detailed content, see the Appendix A). 3.3. Measure This study employed a 7-point Likert scale (1 = strongly disagree, 7 = strongly agree) to assess variables, with detailed items presented in Appendix A. Pro-environmental Behavior Intentions (PEBI) was measured using 3 items, designed to assess participants’ PEBI after the experiment. The items were adapted from the study by Sun, Ma, and Wei (2023) on the influence of gratitude on pro-environmental behavior. Risk Perception (RP) was measured using 3 items, aimed at assessing participants’ risk perception after reading the experimental materials. The items were based on the study by Van Valkengoed, Perlaviciute, and Steg (2024) on the relationship between climate change risk perception and adaptation behavior. Benefit Perception (BP) was measured using 3 items, adapted from the research by S. Wang, Wang, Lin, and Li (2019) on public perceptions of nuclear energy, and how benefit perception influences public acceptance. 3.4. Procedure The experimental procedure consisted of the following steps: First, participants were presented with the experiment instructions and an initial questionnaire. Before the experiment began, the questionnaire reminded participants to carefully read the instructions on the page and follow the provided guidance. Participants were informed that the purpose of the experiment was to investigate the impact of narrative framing on PEBI. All participants voluntarily consented to participate in the experiment and completed an informed consent form, which outlined their rights and responsibilities within the study, including the right to withdraw and privacy protection measures. Upon completion of the task, participants who finished the experiment were awarded a monetary incentive to increase participation rates and enhance the reliability of the data. Next, the presentation of stimulus materials took place. Participants in the experimental groups were instructed to read specific government climate change narratives. The intervention materials were presented as short texts, derived from official media reports at the central levels, ensuring authenticity and authority. To ensure that participants carefully read the materials, a countdown timer was set on the experiment page, and attention check questions were embedded to filter out participants who did not engage seriously. Participants in the control group were directed to the questionnaire section directly, without exposure to any government climate change narrative intervention. In total, 638 valid participants were included in the data analysis. The participants had diverse educational backgrounds and household registrations, with an average age of 31 years. Of the participants, 96.6% held a bachelor's degree or higher, and 90.8% were registered as urban residents (See Appendix B for detailed statistics). 3.5. Data analysis methods First, SPSS 27.0 was used to conduct statistical analysis on the demographic characteristics of the sample data. A one-way analysis of variance (ANOVA) was performed to test the balance of sample characteristics, including means, F-values, and p-values, to ensure the complete random assignment of participants. Second, SPSS 27.0 was employed to perform descriptive statistics and correlation analysis on the main variables, providing a preliminary understanding of the direct relationships between variables. Independent samples t-tests and ordinary least squares (OLS) regression analyses were conducted to explore the direct effects of emotional framing and temporal framing on PEBI, thereby testing H1. The PROCESS Model 4 was utilized to analyze parallel mediation effects, investigating the roles of risk perception and benefit perception in the relationship between emotional framing (negative vs. positive) and PEBI. Path coefficients for each generated pathway were calculated, and the Bootstrap resampling method was applied to test the stability of the mediation effects, thereby testing H2 and H3. Third, the PROCESS Model 7 was used to conduct moderated mediation analysis, verifying the moderating role of temporal framing on the mediation effects between emotional framing and PEBI, as well as their relationships with risk perception and benefit perception. The Bootstrap resampling method was again employed to test the stability of the moderated mediation effects, thereby testing H4. 4. Results To ensure complete random assignment in the study, we conducted a one-way analysis of variance (ANOVA) to compare the differences between the experimental and control groups on initial variables. The results revealed no statistically significant differences between the groups in terms of gender, age, education level, residence, environmental awareness level (EAL), and prior pro-environmental behavior experience (EBE). This indicates that random assignment effectively balanced the groups on key baseline variables, minimizing the potential influence of confounding variables and ensuring the internal validity of the experimental results (see Table 1 ). Table 1 Results of Balance Test Variable Treatment group F p 1 2 3 4 Gender 0.721 0.685 0.717 0.693 0.225 0.879 Age 1.045 1.070 1.112 1.058 0.162 0.922 Education 2.909 3.049 2.987 2.968 1.089 0.353 Residence 0.110 0.105 0.072 0.079 0.659 0.578 EAL 4.032 4.009 4.009 3.945 1.403 0.241 EBE 0.448 0.510 0.474 0.476 0.387 0.762 PEBI 6.104 5.788 6.189 5.882 8.711 0.000 Note: To maintain consistency throughout the article, all reported values are presented to three decimal places. The same applies hereafter. 4.1. Main Effect Analysis We investigated the impact of the emotional and temporal framing of government narratives on the PEBI (see Table 2 ). An independent samples t-test revealed that, under the emotional framing condition, participants in the negative framing group exhibited significantly higher PEBI than those in the positive framing group, indicating a significant positive effect of emotional framing on the public’s PEBI (t = 4.963, p < 0.001). This showed that negative narratives focusing on environmental “destruction” and “crisis” are more effective in enhancing the public’s PEBI. Therefore, Hypothesis H1 is supported. Table 2 Main Effect Analysis Results Group N Mean SD t p Emotion Frame Negative Emotion 306 6.146 0.658 4.963 0.000*** Positive Emotion 332 5.841 0.883 4.2. Mediation Effect Analysis To further examine the differential impact of emotional framing in narratives, regression analysis was conducted to explore the influence of emotional framing on mediator variables and the dependent variable, as well as to investigate potential mediation effects (see Table 3 ). The results of Model 1 indicated that negative emotion (Emot = 0) significantly increased participants’ risk perception compared to positive emotion (Emot = 1) (b = -1.577, p < 0.001). The results of Model 2 indicated that positive emotion significantly increased participants' benefit perception compared to negative emotion (b = 1.067, p < 0.001). The results of Model 3 demonstrated that emotional framing had a significant negative impact on PEBI (b = -0.375, p < 0.001). Positive emotion, compared to negative emotion, exerted a weaker positive influence on PEBI. This result further validated Hypothesis H1. Table 3 Regression Results Model (1) Model (2) Model (3) Model (4) Risk-per Ben-per PEBI PEBI Emot -1.577*** (-20.467) 1.067*** (14.515) -0.375*** (-5.023) -0.320*** (-3.905) Gender 0.044 (0.527) 0.070 (0.878) 0.219*** (3.320) 0.200** (3.150) Age 0.052 (1.179) 0.059 (1.415) 0.047*** (4.343) 0.132*** (3.968) Edu − .205*** (-3.513) 0.098 + (1.764) 0.062 (1.027) 0.053 (1.188) Area -0.020 (-0.145) 0.025 (0.194) -0.135 (-1.250) -0.137 (-1.326) EAL 0.007 (0.075) -0.023 (-0.265) 0.057 (0.798) -0.061 (-0.884) EBE − .032* (-0.411) 0.159* (2.158) 0.178** (2.947) 0.151* (2.590) RP 0.123*** (4.079) BP 0.196*** (6.230) _cons 6.552*** 15.581 4.270*** 10.643 5.388*** (16.323) 3.748*** (9.428) N 638 638 638 638 adj.R 2 0.408 0.256 0.091 0.162 Note:The coefficients are unstandardized, with t-values in parentheses. +p < 0.10, *p < 0.05, **p < 0.01, ***p < 0.001. Model 4 considered the effects of the mediator variables risk perception (RP) and benefit perception. The results indicated that both risk perception (b = 0.123, p < 0.001) and benefit perception (b = 0.196, p < 0.001) had significant positive effects on PEBI, suggesting that higher levels of risk and benefit perception were associated with greater PEBI. After including the mediators in Model 4, the coefficient of emotional framing on PEBI decreased in magnitude from − 0.375 in Model 3 to -0.320 (SE = 0.061, p < 0.001). This reduction reflects the mediating roles of risk perception and benefit perception, which collectively explained a portion of the total effect of emotional framing on PEBI. Based on these results, it was inferred that risk perception and benefit perception may serve as mediators in the relationship between emotional framing and PEBI. To further analyze the mediating role of risk perception and benefit perception, we conducted a mediation analysis using Model 4 of the PROCESS macro with 5,000 Bootstrap samples (Hayes, 2013). The results (see Table 4 ) revealed a significant total effect of emotional framing on PEBI (b = -0.301, SE = 0.061, p < 0.001), indicating that negative framing (Emot = 0), compared to positive framing (Emot = 1), was more effective in enhancing PEBI overall. The mediation analysis identified two distinct pathways. First, the path “Emotional Framing → Risk Perception → PEBI” exhibited a significant indirect effect (b = -0.190, SE = 0.048, p < 0.001). Negative indirect effect demonstrates that negative framing, compared to positive framing, was more effective in enhancing PEBI by increasing risk perception. Second, the path “Emotional Framing → Benefit Perception → PEBI” showed a significant positive indirect effect (b = 0.207, SE = 0.039, p < 0.001), indicating that positive framing, compared to negative framing, was more effective in enhancing PEBI by increasing benefit perception. Consequently, the findings support Hypothesis H2 and H3. In addition, the underlying mechanisms varied across the two pathways. In the risk perception path, the direct effect of emotional framing on PEBI was not significant after accounting for risk perception (b = -0.111, SE = 0.077, p = 0.152), indicating full mediation. In contrast, in the benefit perception path, the direct effect remained significant after accounting for benefit perception (b = -0.508, SE = 0.068, p < 0.001), suggesting partial mediation. These results highlight that negative framing primarily enhances PEBI through risk perception, whereas the positive indirect effect of benefit perception in positive framing partially offsets its overall weaker effect. Table 4 Mediation Effect Results Mediating Variable Type Effect SE 95% CI (LL) 95% CI (UL) p RP Total Effect (Emot > PEBI) -0.301 0.061 -0.420 -0.182 0.000 Direct Effect (Emot > PEBI) -0.111 0.077 -0.262 0.041 0.152 Indirect Effect (Emot →RP → PEBI) -0.190 0.048 -0.291 -0.100 BP Total Effect (Emot → PEBI) -0.301 0.061 -0.420 -0.182 0.000 Direct Effect (Emot → PEBI) -0.508 0.068 -0.642 -0.375 0.000 Indirect Effect (Emot → BP →PEBI) 0.207 0.039 0.133 0.287 4.3. Moderation Effect Analysis PROCESS Model 7 was used to analyze the impact of the emotional framing of government narratives on PEBI through mediator variables, with a focus on testing the moderating role of temporal framing in the indirect effects (see Table 5 ). The analysis revealed that the moderating variable, temporal framing, had a significant effect on the indirect effects of emotional framing under different conditions. The moderating effect was clearly supported when benefit perception served as the mediator, while no significant support was found when risk perception was the mediator. Specifically, when risk perception served as the mediator, the analysis showed that the indirect effect of emotional framing on PEBI was significant under both temporal framing conditions. In the occurred framing condition (narrative time = 0), the indirect effect was was significant (b = -0.176, SE = 0.048). In the not occurred framing condition (narrative time = 1), the indirect effect was also significant (b = -0.203, SE = 0.053). Although the indirect effects were significant under both temporal framing conditions, the index of moderated mediation was not significant (b = -0.027, SE = 0.019). Therefore, Hypothesis H4a was not supported. When benefit perception served as the mediator, moderated mediation was statistically supported. As illustrated in Fig. 2 and Table 6t, under the occurred temporal framing condition, the indirect effect was positive and significant (b = 0.300, SE = 0.054). Under the not occurred temporal framing condition, the indirect effect remained significant but weaker (b = 0.132, SE = 0.029). The index of moderated mediation was significant (b = -0.168, SE = 0.041), indicating that temporal framing attenuated the strength of the emotional framing → benefit perception → PEBI pathway, that is, under positive emotional narratives, occurred framing strengthens the indirect effect of benefit perception on PEBI. Therefore, Hypothesis H4b was supported. Table 5 Moderated mediation results Moderating Variable Type Effect SE 95% CI (LL) 95% CI (UL) RP Direct Effect (Emot > PEBI) -0.112 0.078 -0.265 0.041 Conditional Indirect Effect (Occurred) -0.176 0.048 -0.275 -0.086 Conditional Indirect Effect (Not Occurred) -0.203 0.053 -0.311 -0.101 Index of Moderated Mediation -0.027 0.019 -0.066 0.008 BP Direct Effect (Emot > PEBI) -0.515 0.068 -0.649 -0.381 Conditional Indirect Effect (Occurred) 0.300 0.054 0.199 0.412 Conditional Indirect Effect (Not Occurred) 0.132 0.029 0.080 0.192 Index of Moderated Mediation -0.168 0.041 -0.257 -0.097 5. Discussion Recent studies have shown that, compared to traditional information framing, emotional narratives and their role interactions are more effective in stimulating prosocial behavior (Barraza, Alexander, Beavin, Terris, & Zak, 2015; Loewenstein, 2010). However, research on the influence of narrative plots on PEBI remains limited. From a narrative plot perspective, we explore the potential mechanisms through which emotional and temporal framing in government climate narratives shape PEBI. The findings indicate that the emotional framing within government climate narratives significantly affects the PEBI, with risk perception and benefit perception playing a mediating role between emotional framing and PEBI. Moreover, the temporal framing moderates the effect of emotional framing on PEBI through benefit perception. First, the results indicate that negative framing narratives outperform positive framing ones in enhancing PEBI. Specifically, negative emotional framing significantly increased the public's willingness to engage in PEBI compared to positive framing. This result supports our hypothesis, posited within the context of governmental climate narratives, that individuals that individuals exhibit greater sensitivity to negative emotional narratives in governmental climate communication (Weiner, 2000). This finding aligns with loss aversion theory, which suggests that individuals are more motivated to avoid losses than to pursue equivalent gains (Kahneman & Tversky, 1979), as well as with prior research demonstrating that fear-based narratives effectively drive pro-environmental actions by heightening risk perception (Skeirytė & Liobikienė, 2025). By emphasizing climate threats—such as extreme heatwaves and floods—negative narratives likely amplified the perceived urgency and severity of climate risks, thereby prompting stronger intentions to adopt protective behaviors (Gilbert, Fiske, & Lindzey, 1998). However, mediation analysis revealed that the advantage of negative framing narratives was partially offset by their lower benefit perception. This suggests that, while negative framing effectively heightened risk perception, it simultaneously diminished the perceived benefits of engaging in pro-environmental behaviors (e.g., individuals may perceive their actions as insufficient to address the scale of the climate crisis). Benefit perception, a critical driver of behavioral intentions (Bolderdijk et al., 2012; Spence & Pidgeon, 2010), plays a significant mediating role: the indirect effect of negative framing on PEBI through reduced benefit perception counteracts part of its direct positive effect. This trade-off underscores a dual-pathway mechanism in narrative persuasion: negative narratives primarily generate urgency through risk perception, but their effectiveness is tempered by diminished benefit perception, potentially leading to feelings of helplessness or emotional fatigue (O’Neill & Day, 2009). In contrast, positive framing fosters confidence in the efficacy of individual actions encouraging individuals to believe they can achieve benefits through their actions (Chadwick, 2015), thus promoting a focus on long-term benefits and solutions. In addition, the impact of narratives on PEBI is fully mediated by risk perception (i.e., narrative → risk perception → intention). This may stem from the framing of climate change by governmental entities as a “risk” or “damage,” leading individuals to prioritize risk avoidance. This singular focus on threat may result in risk perception being the dominant mediating pathway, as individuals prioritize risk avoidance over other considerations(Spence & Pidgeon, 2010). Conversely, positive framing (e.g., “achieved emissions reduction achievements”) indirectly promotes intentions by enhancing benefit perception. Positive framing may also influence PEBI through other mechanisms, such as narrative persuasion and environmental attitudes (J.-X. Liu, 2023; S. Liu & Yang, 2023). Finally, this study demonstrates that the causal pathways linking emotional framing, benefit perception, and PEBI vary depending on the temporal framing employed. Specifically, when temporal framing is “occurred,” positive emotional framing significantly outperforms negative emotional framing in promoting PEBI through benefit perception. This finding suggests that past-focused positive narratives have a greater capacity to shape behavioral intentions, aligning with Kahneman's Dual-System Theory. This theory posits that human cognition is governed by two systems: System 1, characterized by fast, emotional, and intuitive processing, and System 2, which involves slow, analytical, and deliberative processing (Evans, 2008; Kannengiesser & Gero, 2019). In daily decision-making, System 1 often dominates due to its efficiency, while System 2 is typically activated in situations requiring careful reasoning or when confronting novel and complex scenarios(Turel & Qahri-Saremi, 2016). Consequently, in “occurred” scenarios, which are concrete and experiential, System 1 thinking likely predominates, rendering benefit-focused narratives more persuasive by tapping into immediate reward-seeking mechanisms. Conversely, in “not occurred” scenarios, which are abstract and hypothetical, System 2 thinking may prevail, enabling risk-based narratives to gain an advantage through cognitive elaboration of long-term consequences. 6. Implications 6.1. Implications for theory In recent decades, governments worldwide have increasingly prioritized public engagement in environmental governance—a central theme in public administration, environmental policy, and behavioral science research. While narratives are widely recognized as more effective than conventional information dissemination in fostering pro-environmental behaviors, the mechanisms through which emotional and temporal framing interact to shape behavioral intentions remain underexplored. Our study addresses this gap by offering three key theoretical contributions: First, we extend the analytical framework of narrative theory. Previous research on narratives and behavior has predominantly focused on the influence of a single narrative character or plot on behavior. However, as Hineline (2018) has argued, the influence of narratives on behavior is a complex, systematic process involving the interaction of multiple factors. In this regard, our study constructs a comprehensive theoretical framework at the micro-level, integrating narrative emotional framing, temporal framing, perception, and behavioral intentions. This framework elucidates the conditions, mechanisms, and reasons behind the effectiveness of government climate narratives in promoting PEBI. By examining the interaction between emotional and temporal framing and incorporating perception into the entire model, this study further enriches the literature on narratives and behavior. Although our contextual design focused on a specific environmental issue (i.e., climate change), it also provides a multidimensional, systematic analytical tool for future research, offering important references for studies in other fields. Second, we break through the long-standing dominance of the “risk central paradigm” in climate communication by empirically validating the role of benefit perception in how narratives influence PEBI. The findings reveal that positive emotional narratives significantly enhance PEBI by increasing benefit perception, with an effect strength comparable to that of the risk pathway. Correspondingly, this result contests the prevailing risk-centered climate discourse and aligns with perspectives suggesting that an overemphasis on threats can have counterproductive effects (Aronson, 2014; Witte & Allen, 2000), thereby affirming the strategic importance of benefit perception in promoting public behavior. Climate change should not be solely defined as a “risk,” and policymakers should balance risk warnings with benefit-focused messaging to avoid narrative fatigue. Finally, this study extends narrative theory by broadening its applicability across diverse contexts. Existing research has largely concentrated on developed countries (Lyytimäki, 2023; Merry & Mattingly, 2023), whereas our findings demonstrate that narrative tools can effectively align public behavior with sustainability agendas even in transitioning economies like China. By designing effective narrative plots, governments can address implementation barriers unique to rapid industrialization contexts. This expands the scope of narrative theory across multiple institutional and cultural settings while providing policymakers with actionable tools to foster sustainable behaviors. 6.2. Implications for Practice Our study holds significant theoretical value and offers practical insights for designing effective climate narrative strategies. Government communication can enhance its impact by strategically employing emotional framing in climate narratives. Specifically, positive emotional framing is particularly effective when addressing past climate change contexts, as it fosters public confidence in future progress by highlighting the tangible benefits of environmental actions—e.g., showcasing successful case studies to demonstrate actionable outcomes. Conversely, negative emotional framing is more effective for highlighting potential climate change scenarios, as it heightens public risk perception and motivates preventive actions against environmental threats. However, as psychologists warn against viewing emotion as a simple lever for behavior (Chapman, Lickel, & Markowitz, 2017), we do not believe that narratives with purely negative or positive framing are the only solution. Since individuals typically respond more positively to information that aligns with their existing belief systems (K. Brown et al., 2019), different narrative structures may be more effective for different individuals. For example, differentiated narrative strategies can be designed based on group cultural and educational backgrounds. As environmental awareness deepens within society, the role of climate narratives may become increasingly important (Wynes & Nicholas, 2017). Our findings suggest that emotion and time can serve as effective frameworks in intervention activities and can be an essential component of government environmental communication, warranting careful consideration in policy design and communication. 7. Research limitations and future prospects Several limitations of the present research should be addressed. First, our experimental design focused exclusively on text-based government climate narratives, omitting audiovisual or multimodal formats. This choice was deliberate to isolate the effects of emotional and temporal framing without confounding variables introduced by visual or auditory stimuli (Geise & Baden, 2015). However, while this approach enhanced internal validity, it limits generalizability to real-world contexts where multimodal narratives dominate public communication. Future research should further comparing text-based narratives with audiovisual formats to assess how sensory engagement moderates framing effects. Second, we focused on measuring immediate behavioral intentions rather than long-term behavioral changes. Longitudinal tracking was infeasible due to resource constraints, yet our findings provide foundational insights into the cognitive-affective mechanisms that precede action—a critical first step for designing field experiments. Future research could track real-world behaviors (e.g., energy consumption, policy support) over time to evaluate the persistence of narrative-driven intentions, thereby gaining a deeper understanding. Declarations Ethical Approval: This study received ethical approval from institutional ethics committee on March 18, 2024. All procedures involving human participants were conducted in accordance with the ethical standards outlined in the Declaration of Helsinki . The study ensured complete anonymity and voluntary participation, with no collection of sensitive personal information. Informed consent ： Informed consent was obtained from all participants through an online process during the data collection between October 19, 2024, and December 27, 2024. The study utilized the Credamo platform to distribute questionnaires, which included an introductory section clearly outlining the study’s purpose, procedures, and voluntary nature. Participants were required to provide their consent by agreeing to proceed before accessing the formal questionnaire. Consent to Participate: The authors give their permission to participate. Consent to Publish: The authors consent to publish this article in your journal and to transfer copyright to the publisher once the paper has been accepted. Funding: This research was supported by the National Social Science Fund of China under the project (Project No. 24CZZ068); the Key Project of the Liaoning Provincial Social Science Fund (Project No. L22AGL010); the Fundamental Research Funds for the Central Universities (Project No. N2314009); and the Liaoning Provincial Economic and Social Development Research Project of 2021 (Project No. 2023lslybkt-052). Competing Interests: The authors declare no conflicts of interest. The opinions expressed here are those of the authors and do not necessarily reflect the position of the government of China or of any other organization. Availability of data and materials: The datasets generated during the current study are available from the corresponding author upon reasonable request. Acknowledgements We express our sincere gratitude to the participants who assisted with data collection and questionnaire distribution, whose invaluable contributions were essential to the successful completion of this study. We also gratefully acknowledge the financial support provided by the National Social Science Fund of China, the Liaoning Provincial Social Science Fund, the Fundamental Research Funds for the Central Universities, and the Liaoning Provincial Economic and Social Development Research Project. References Adobor, H. (2024). How do sensemaking and climate change education affect climate engagement at the grassroots level? A study of five communities in Southeastern Ghana. Climatic Change, 177 (3). doi:https://doi.org/10.1007/s10584-024-03701-w Af Malmborg, F. (2022). Narrative dynamics in European Commission AI policy—Sensemaking, agency construction, and anchoring. Review of Policy Research, 40 (5), 757-780. doi:https://doi.org/10.1111/ropr.12529 Bandola-Gill, J., & Smith, K. (2021). Governing by narratives: REF impact case studies and restrictive storytelling in performance measurement. Studies in Higher Education, 47 , 1-15. doi:https://doi.org/10.1080/03075079.2021.1978965 Banerjee, A. (2024). The effect of heterogeneity and risk on co-operation: Experimental evidence. Journal of Behavioral and Experimental Economics, 109 . Barraza, J. A., Alexander, V., Beavin, L. E., Terris, E. T., & Zak, P. J. (2015). The heart of the story: peripheral physiology during narrative exposure predicts charitable giving. Biol Psychol, 105 , 138-143. doi:https://doi.org/10.1016/j.biopsycho.2015.01.008 Bechtel, M. M., Genovese, F., & Scheve, K. F. (2019). Interests, norms and support for the provision of global public goods: The case of climate co-operation. British Journal of Political Science, 49 (4), 1333-1355. doi:https://doi.org/10.1017/S0007123417000205 Bolderdijk, J., Steg, L., Geller, E. S., Lehman, P., & Postmes, T. (2012). Comparing the effectiveness of monetary versus moral motives in environmental campaigning. Nature Climate Change, 3 , 413-416. doi:https://doi.org/10.1038/nclimate1767 Bosone, L., Chevrier, M., & Martinez, F. (2023). When narratives speak louder than numbers: the effects of narrative persuasion across the stages of behavioural change to reduce air pollution. Frontiers in Psychology, 14 . doi:https://doi.org/10.3389/fpsyg.2023.1072187 Braddock, K., & Dillard, J. P. (2016). Meta-analytic evidence for the persuasive effect of narratives on beliefs, attitudes, intentions, and behaviors. Communication monographs, 83 (4), 446-467. doi:https://doi.org/10.1080/03637751.2015.1128555 Bradley, G. L., Babutsidze, Z., Chai, A., & Reser, J. P. (2020). The role of climate change risk perception, response efficacy, and psychological adaptation in pro-environmental behavior: A two nation study. Journal of Environmental Psychology, 68 , 101410. doi:https://doi.org/10.1016/j.jenvp.2020.101410 Bretter, C., & Pangbourne, K. (2025). Emotions as antecedents of sustainable travel behaviour. Travel Behaviour and Society, 38 . doi:https://doi.org/10.1016/j.tbs.2024.100903 Brown, K., Adger, W. N., Devine-Wright, P., Anderies, J. M., Barr, S., Bousquet, F., . . . Quinn, T. (2019). Empathy, place and identity interactions for sustainability. Global Environmental Change, 56 , 11-17. doi:https://doi.org/10.1016/j.gloenvcha.2019.03.003 Brown, S., & Tu, C. (2020). The shapes of stories: A “resonator” model of plot structure. Frontiers of Narrative Studies, 6 (2), 259-288. doi:https://doi.org/10.1515/fns-2020-0016 Cao, Z. (2019). Research on national policy narrative in the changes of China's rural land rights policy. (Doctoral dissertation), Jilin University, Jilin. Available from Cnki Capstick, S., Whitmarsh, L., Poortinga, W., Pidgeon, N., & Upham, P. (2015). International trends in public perceptions of climate change over the past quarter century. Wiley Interdisciplinary Reviews-Climate Change, 6 (1), 35-61. doi:https://doi.org/10.1002/wcc.321 Chandler, J., Mueller, P., & Paolacci, G. (2014). Nonnaïveté among Amazon Mechanical Turk workers: Consequences and solutions for behavioral researchers. Behavior research methods, 46 , 112-130. doi:https://doi.org/10.3758/s13428-013-0365-7 Chapman, D. A., Lickel, B., & Markowitz, E. M. (2017). Reassessing emotion in climate change communication. Nature Climate Change, 7 (12), 850-852. doi:https://doi.org/10.1038/s41558-017-0021-9 Chuanshen, Q., & Yunzhe, L. (2023). The Role of Narrative in Facial Recognition Risk Communication: A Survey Experiment. China Public Administration Review, 5 (3), 31-56. doi:https://doi.org/10.1016/j.im.2024.103963 Creutzig, F., Roy, J., Lamb, W. F., Azevedo, I. M. L., de Bruin, W. B., Dalkmann, H., . . . Weber, E. U. (2018). Towards demand-side solutions for mitigating climate change. Nature Climate Change, 8 (4), 268-271. doi:https://doi.org/10.1038/s41558-018-0121-1 Crow, D. A., & Jones, M. D. (2018). Narratives as tools for influencing policy change. Practical Lessons from Policy Theories, 46 (2). doi:https://doi.org/10.1332/030557318X15230061022899 Dahlstrom, M. F. (2014). Using narratives and storytelling to communicate science with nonexpert audiences. Proceedings of the National Academy of Sciences, 111 (4), 13614-13620. doi:https://doi.org/10.1073/pnas.1320645111 Dai, J., & Zeng, F. (2021). Environmental Risk Communication in China: Actors, Issues, and Governance . London: Routledge. Dales, A., Padfield, R., & Bridge, G. (2024). Critical sustainability stories: How communicators can translate the complexity of the climate emergency and shape public narratives. Geoforum, 150 . doi:https://doi.org/10.1016/j.geoforum.2024.103963 Dietz, T., Gardner, G., Gilligan, J., Stern, P., & Vandenbergh, M. (2009). Household actions can provide a behavioral wedge to rapidly reduce U.S. carbon emissions. Proceedings of the National Academy of Sciences of the United States of America, 106 , 18452-18456. doi:https://doi.org/10.1073/pnas.0908738106 Dillard, J. P., & Peck, E. (2001). Persuasion and the structure of affect: Dual systems and discrete emotions as complementary models. Human Communication Research, 27 (1), 38-68. doi:https://doi.org/10.1111/j.1468-2958.2001.tb00775.x Dunlop, S., Wakefield, M., & Kashima, Y. (2008). Can You Feel It? Negative Emotion, Risk, and Narrative in Health Communication. Media Psychology, 11 , 52-75. doi:https://doi.org/10.1080/15213260701853112 Edson Escalas, J., Chapman Moore, M., & Edell Britton, J. (2004). Fishing For Feelings? Hooking Viewers Helps! Journal of Consumer Psychology, 14 (1), 105-114. doi:https://doi.org/10.1207/s15327663jcp1401&2_12 Evans, J. S. B. (2008). Dual-processing accounts of reasoning, judgment, and social cognition. Annu. Rev. Psychol., 59 (1), 255-278. doi:https://doi.org/10.1146/annurev.psych.59.103006.093629 Felgendreff, L., Rebitschek, F. G., Shamsrizi, P., Geiger, M., Jenny, M. A., & Betsch, C. (2025). Explaining seasonality increases perceived effectiveness of influenza vaccination: An experimental study. British Journal of Health Psychology, 30 (1). doi:https://doi.org/10.1111/bjhp.12770 Fischer, F., & Forester, J. (1993). The Argumentative Turn in Policy Analysis and Planning : Duke University Press. Freeman, M. (2015). Narrative as a mode of understanding: Method, theory, praxis. The handbook of narrative analysis , 19-37. Geise, S., & Baden, C. U. (2015). Putting the Image Back Into the Frame: Modeling the Linkage Between Visual Communication and Frame-Processing Theory. Communication Theory, 25 , 46-69. doi:https://doi.org/10.1111/comt.12048 Gilbert, D. T., Fiske, S. T., & Lindzey, G. (1998). The handbook of social psychology, Vols. 1-2, 4th ed. In D. T. Gilbert, S. T. Fiske, & G. Lindzey (Eds.), The handbook of social psychology, Vols. 1-2, 4th ed. (pp. 680-740): McGraw-Hill. Hayes, A. F. (2013). Introduction to mediation, moderation, and conditional process analysis: A regression-based approach. Guilford Press, New York, NY, US. Herman, D. (2003). Narrative theory and the cognitive sciences. Narrative Inquiry, 11 , 1-34. doi:https://doi.org/10.1075/ni.11.1.01her Hernes, T., & Obstfeld, D. (2022). A temporal narrative view of sensemaking. Organization Theory, 3 (4), 26317877221131585. doi:https://doi.org/10.1177/26317877221131585 Hineline, P. (2018). Narrative: why it’s important, and how it works. Perspectives on Behavior Science, 41 . doi:https://doi.org/10.1007/s40614-018-0137-x Holt, D., & Thompson, C. (2004). Man‐of‐Action Heroes: The Pursuit of Heroic Masculinity in Everyday Consumption. Journal of Consumer Research, 31 , 425-440. doi:https://doi.org/10.1086/422120 Howlett, M., & Rawat, S. (2019). Behavioral Science and Climate Policy. Oxford Research Encyclopedia of Climate Science . Huang, G., & Xu, J. (2024). Disentangling the Effects of Temporal Framing on Risk Perception, Attitude, Behavioral Intention, and Behavior: A Multilevel Meta-Analysis. Communication Research, 51 , 3-27. doi:https://doi.org/10.1177/00936502221102102 Huijts, N. M. A., Molin, E. J. E., & Steg, L. (2012). Psychological factors influencing sustainable energy technology acceptance: A review-based comprehensive framework. Renewable and Sustainable Energy Reviews, 16 (1), 525-531. doi:https://doi.org/10.1016/j.rser.2011.08.018 Husain-Naviatti, A. (2025). The power of human narrative: Inspiring action on climate change. Environmental Science & Policy, 163 . doi:10.1016/j.envsci.2024.103954 IPCC. (2023). Climate change 2023: The physical science basis: Summary for policymakers . Retrieved from https://www.ipcc.ch/report/sixth-assessment-report-cycle/ Jia, F. L., & Wang, W. (2024). City-level sustainable development impacts on environmental literacy: feelings toward nature, environmental knowledge, and pro-environmental behavior. Environmental Education Research, 30 (8), 1263-1278. doi:10.1080/13504622.2024.2315573 Jie, Z., Lapinski, M. K., & Peng, W. (2018). Crafting messages to promote water conservation: Using time‐framed messages to boost conservation actions in the United States and China. Journal of Applied Social Psychology, 48 , 248-256. doi:https://doi.org/10.1111/jasp.12509 Jones, M. D., & McBeth, M. K. (2010). A Narrative Policy Framework: Clear Enough to Be Wrong? Policy Studies Journal, 38 , 329-353. doi:https://doi.org/10.1111/j.1541-0072.2010.00364.x Kahneman, D., & Tversky, A. (1979). Prospect Theory: An Analysis of Decision under Risk. Econometrica, 47 (2), 263-291. doi:https://doi.org/10.2307/1914185 Kannengiesser, U., & Gero, J. S. (2019). Design thinking, fast and slow: A framework for Kahneman’s dual-system theory in design. Design Science, 5 , e10. doi:https://doi.org/10.1017/dsj.2019.9 Kao, T.-F., & Du, Y.-Z. (2020). A study on the influence of green advertising design and environmental emotion on advertising effect. Journal of Cleaner Production, 242 , 118294. doi:https://doi.org/10.1016/j.jclepro.2019.118294 Kim, H., Rao, A., & Lee, A. (2009). It's time to vote: the effect of matching message orientation and temporal frame on political persuasion. Journal of Consumer Research, 35 , 877-889. doi:https://doi.org/10.1086/593700 Kim, J. (2024). Why do experiments fail? Six practical suggestions for successful online experiments. Journal of Travel Research, 63 (7), 1835-1844. doi:https://doi.org/10.1177/00472875231209983 Konstantinidis, E., Dai, J. Y., & Newell, B. (2025). Risk, Time, and Psychological Distance: Does Construal Level Theory Capture the Impact of Delay on Risk Preference? Journal of Experimental Psychology-General, 154 (2), 552-573. doi:https://doi.org/10.1037/xge0001647 Kostka, G., & Zhang, C. (2018). Tightening the grip: environmental governance under Xi Jinping. Environmental Politics, 27 (5), 769-781. doi:https://doi.org/10.1080/09644016.2018.1491116 Lang, A. (2006). The limited capacity model of mediated message processing. Journal of Communication, 50 (1), 46-70. doi:https://doi.org/10.1111/j.1460-2466.2000.tb02833.x Latif, B., Gaskin, J., Gunarathne, N., Sroufe, R., Sharif, A., & Hanan, A. S. (2023). Climate change risk perception and pro-environmental behavior: the moderating role of environmental values and psychological contract breach. Social Responsibility Journal . doi:https://doi.org/10.1108/SRJ-02-2023-0084 Lewis Jr, N. A., Green, D. J., Duker, A., & Onyeador, I. N. (2021). Not seeing eye to eye: Challenges to building ethnically and economically diverse environmental coalitions. Current Opinion in Behavioral Sciences, 42 , 60-64. doi:https://doi.org/10.1016/j.cobeha.2021.02.025 Liu, J.-X. (2023). The influence of narrative transportation on university students' environmental intentions: A serial mediation of empathy with nature and environmental Attitudes. Journal of Cleaner Production, 431 , 139763. doi:https://doi.org/10.1016/j.jclepro.2023.139763 Liu, S., & Yang, J. Z. (2023). Narrative persuasion and psychological distance: Analyzing the effectiveness of distance-framed narratives in communicating ocean plastic pollution. Risk Anal, 43 (10), 2099-2113. doi:https://doi.org/10.1111/risa.14111 Liu, Z., & Lei, Y. (2024). Shaping public support for nudge-based decarbonization policies through policy narrative: The role of victim type, victim number and narrativity. Journal of Cleaner Production, 449 . doi:https://doi.org/10.1016/j.jclepro.2024.141797 Liverani, A. (2014). Climate Change and Individual Behavior. World Bank Policy Research Working Paper . Loewenstein, G. F. (2010). Insufficient Emotion: Soul-searching by a Former Indicter of Strong Emotions. Emotion Review, 2 , 234 - 239. doi:https://doi.org/10.1177/1754073910362598 Lyytimäki, J. (2023). Storylines nailing or failing sustainability: energy, mining and mobility as narrative arenas for societal transition. Sustainable Development, 31 (1), 170-179. doi:https://doi.org/10.1002/sd.2381 Maartensson, H., & Loi, N. M. (2022). Exploring the relationships between risk perception, behavioural willingness, and constructive hope in pro-environmental behaviour. Environmental Education Research, 28 (4), 600-613. doi:https://doi.org/10.1080/13504622.2021.2015295 Maddux, J. E., & Rogers, R. W. (1983). Protection motivation and self-efficacy: A revised theory of fear appeals and attitude change. Journal of Experimental Social Psychology, 19 (5), 469-479. doi:https://doi.org/10.1016/0022-1031(83)90023-9 Manca, S., & Fornara, F. (2019). Attitude toward sustainable transport as a function of source and argument reliability and anticipated emotions. Sustainability, 11 (12), 3288. Mao, W. Y., & Nishide, Y. (2025). Crafting the messages for environmental NGOs: how narrative medium and mode shape different engagement levels. Humanities & Social Sciences Communications, 12 (1). doi:10.1057/s41599-025-04470-2 McBeth, M. K., Shanahan, E. A., Arnell, R. J., & Hathaway, P. L. (2007). The Intersection of Narrative Policy Analysis and Policy Change Theory. Policy Studies Journal, 35 , 87-108. doi: https://doi.org/10.1111/j.1541-0072.2007.00208.x Merry, M., & Mattingly, H. (2023). Framing the climate crisis: Dread and fatalism in media and interest group responses to IPCC reports. Review of Policy Research . doi:https://doi.org/10.1111/ropr.12539 Moezzi, M., Janda, K. B., & Rotmann, S. (2017). Using stories, narratives, and storytelling in energy and climate change research. Energy Research & Social Science, 31 , 1-10. doi:https://doi.org/10.1016/j.erss.2017.06.034 Morris, B. S., Chrysochou, P., Christensen, J. D., Orquin, J. L., Barraza, J., Zak, P. J., & Mitkidis, P. (2019). Stories vs. facts: triggering emotion and action-taking on climate change. Climatic Change, 154 (1-2), 19-36. doi:https://doi.org/10.1007/s10584-019-02425-6 Neef, N. E., Fusswinkel, S., Roos, C., Frank, L., Shihepo, K., & Richter, I. (2023). Optimistic narrative future visions: a communication tool for promoting sustainable (plastic) behavior. Frontiers in Psychology, 14 . doi:https://doi.org/10.3389/fpsyg.2023.1252895 O' Donovan, K. (2018). Does the Narrative Policy Framework Apply to Local Policy Issues? Politics & Policy, 46 , 532-570. doi:https://doi.org/10.1111/polp.12265 O'Brien, K. (2012). Global environmental change II: From adaptation to deliberate transformation. Progress in Human Geography, 36 (5), 667-676. doi:https://doi.org/10.1177/0309132511425767 O'Connor, R. E., Bord, R. J., & Fisher, A. (1999). Risk perceptions, general environmental beliefs, and willingness to address climate change. Risk Analysis, 19 (3), 461-471. doi:https://doi.org/10.1023/A:1007004813446 O'Neill, S., & Day, S. (2009). "Fear won't do it": promoting positive engagement with climate change through visual and Iconic representations. Science Communication - SCI COMMUN, 30 , 355-379. doi:https://doi.org/10.1177/1075547008329201 Pechmann, C., & Reibling, E. T. (2006). Antismoking advertisements for youths: an independent evaluation of health, counter-industry, and industry approaches. Am J Public Health, 96 (5), 906-913. doi:https://doi.org/10.2105/ajph.2004.057273 Poore, J., & Nemecek, T. (2018). Reducing food's environmental impacts through producers and consumers. Science, 360 (6392), 987-992. doi:https://doi.org/10.1126/science.aaq0216 Randall, R. (2009). Loss and climate change: the cost of parallel narratives. Ecopsychology, 1 . doi:https://doi.org/10.1089/eco.2009.0034 Renn, O., & Rohrmann, B. (2000). Cross-Cultural Risk Perception: A Survey of Empirical Studies . Richter, I., Gabe-Thomas, E., Queirós, A. M., Sheppard, S. R. J., & Pahl, S. (2023). Advancing the potential impact of future scenarios by integrating psychological principles. Environmental Science & Policy, 140 , 68-79. doi:https://doi.org/10.1016/j.envsci.2022.11.015 Rickard, L. N., Yang, J. Z., Liu, S. X., & Boze, T. (2021). Fish Tales: How Narrative Modality, Emotion, and Transportation Influence Support for Sustainable Aquaculture. Science Communication, 43 (2), 252-275. doi:https://doi.org/10.1177/1075547020987555 Ruff, J., Stelmach, G., & Jones, M. (2022). Space for stories: legislative narratives and the establishment of the US Space Force. Policy Sciences, 55 . doi:https://doi.org/10.1007/s11077-022-09455-5 Schaub, S. (2021). Public contestation over agricultural pollution: a discourse network analysis on narrative strategies in the policy process. Policy Sciences, 54 (4), 783-821. doi:https://doi.org/10.1007/s11077-021-09439-x Schneider, C., Zaval, L., & Markowitz, E. (2021). Positive emotions and climate change. Current Opinion in Behavioral Sciences, 42 , 114-120. doi:https://doi.org/10.1016/j.cobeha.2021.04.009 Schulte, E., Scheller, F., Sloot, D., & Bruckner, T. (2022). A meta-analysis of residential PV adoption: the important role of perceived benefits, intentions and antecedents in solar energy acceptance. Energy Research & Social Science, 84 , 102339. doi:https://doi.org/10.1016/j.erss.2021.102339 Shao, W., Xian, S., Lin, N., & Small, M. J. (2017). A sequential model to link contextual risk, perception and public support for flood adaptation policy. Water Research, 122 , 216-225. doi:https://doi.org/10.1016/j.watres.2017.05.072 Shenhav, S. (2015). Analyzing social narratives . New York: Routledge. Skeirytė, A., & Liobikienė, G. (2025). Emotions related to climate change, and their impact on environmental behaviour. Journal of Cleaner Production, 488 , 144659. doi:https://doi.org/10.1016/j.jclepro.2025.144659 Spence, A., Poortinga, W., & Pidgeon, N. (2012). The psychological distance of climate change. Risk Anal, 32 (6), 957-972. doi:https://doi.org/10.1111/j.1539-6924.2011.01695.x Steg, L., Bolderdijk, J. W., Keizer, K., & Perlaviciute, G. (2014). An integrated framework for encouraging pro-environmental behaviour: The role of values, situational factors and goals. Journal of Environmental Psychology, 38 , 104-115. doi:https://doi.org/10.1016/j.jenvp.2014.01.002 Stone, D. A. (2002). Policy paradox : the art of political decision making . New York: W.W. Norton. Sun, J., Ma, B., & Wei, S. (2023). Same gratitude, different pro-environmental behaviors? Effect of the dual-path influence mechanism of gratitude on pro-environmental behavior. Journal of Cleaner Production, 415 . doi:https://doi.org/10.1016/j.jclepro.2023.137779 Taufik, D., Bolderdijk, J. W., & Steg, L. (2016). Going green? The relative importance of feelings over calculation in driving environmental intent in the Netherlands and the United States. Energy Research & Social Science, 22 , 52-62. doi:https://doi.org/10.1016/j.erss.2016.08.012 Tian, X. Z., & Jiang, Y. L. (2025). Exploring behavioral determinants of residents' ecological conservation in rural tourism development. Scientific Reports, 15 (1), 13. doi:https://doi.org/10.1038/s41598-025-85571-5 Trenberth, K. E. (2018). Climate change caused by human activities is happening and it already has major consequences. Journal of energy & natural resources law, 36 (4), 463-481. doi:https://doi.org/10.1080/02646811.2018.1450895 Trope, Y., & Liberman, N. (2010). Construal-level theory of psychological distance. Psychol Rev, 117 (2), 440-463. doi:https://doi.org/10.1037/a0018963 Turel, O., & Qahri-Saremi, H. (2016). Problematic use of social networking sites: Antecedents and consequence from a dual-system theory perspective. Journal of Management Information Systems, 33 (4), 1087-1116. doi:https://doi.org/10.1080/07421222.2016.1267529 Tversky, A., & Kahneman, D. (1991). Loss aversion in riskless choice: a reference-dependent model. Quarterly Journal of Economics, 106 , 1039-1061. doi: https://doi.org/10.2307/2937956 Van Valkengoed, A., Perlaviciute, G., & Steg, L. (2024). From believing in climate change to adapting to climate change: The role of risk perception and efficacy beliefs. Risk Analysis, 44 (3), 553-565. doi:https://doi.org/10.1111/risa.14193 Vlek, C., & Steg, L. (2007). Human behavior and environmental sustainability: Problems, driving forces, and research topics. Journal of Social Issues, 63 (1), 1-19. doi:https://doi.org/10.1111/j.1540-4560.2007.00493.x Wang, S., Wang, J., Lin, S., & Li, J. (2019). Public perceptions and acceptance of nuclear energy in China: The role of public knowledge, perceived benefit, perceived risk and public engagement. Energy Policy, 126 , 352-360. doi:https://doi.org/10.1016/j.enpol.2018.11.040 Wang, Y., Thier, K., Lee, S., & Nan, X. (2023). Persuasive effects of temporal framing in health messaging: a meta-analysis. Health Commun, 39 , 1-14. doi:https://doi.org/10.1080/10410236.2023.2175407 Weiner, B. (2000). Intrapersonal and interpersonal theories of motivation from an attributional perspective. Educational Psychology Review, 12 (1), 1-14. doi:https://doi.org/10.1023/A:1009017532121 Wolske, K. S., & Stern, P. C. (2018). Contributions of psychology to limiting climate change: Opportunities through consumer behavior. In S. Clayton & C. Manning (Eds.), Psychology and Climate Change (pp. 127-160): Academic Press. Wynes, S., & Nicholas, K. A. (2017). The climate mitigation gap: education and government recommendations miss the most effective individual actions. Environmental Research Letters, 12 (7). doi:https://doi.org/10.1088/1748-9326/aa7541 Yuan, J., Lu, Y., Wang, C., Cao, X., Chen, C., Cui, H., . . . Du, D. (2020). Ecology of industrial pollution in China. Ecosystem Health and Sustainability, 6 (1), 1779010. doi:https://doi.org/10.1080/20964129.2020.1779010 Zavareh, M. F., Mehdizadeh, M., & Nordfjærn, T. (2020). Active travel as a pro-environmental behaviour: An integrated framework. Transportation Research Part D: Transport and Environment, 84 , 102356. doi:https://doi.org/10.1016/j.trd.2020.102356 Zelenski, J. M., & Desrochers, J. E. (2021). Can positive and self-transcendent emotions promote pro-environmental behavior? Current opinion in psychology, 42 , 31-35. doi:https://doi.org/10.31234/osf.io/adhx2 Zhang, Y., Du, Q., Huang, Y. L., Mao, Y. Y., & Jiao, L. D. (2024). Decoding determinants of pro-environmental behaviors of higher education students: insights for sustainable future. International Journal of Sustainability in Higher Education . doi:https://doi.org/10.1108/ijshe-03-2024-0166 Footnotes These outlets are widely recognized as the most authoritative media in China, blending political authority with narrative elements, making them a critical data source for policy narrative research. For instance, Cao (2019) analyzed narrative features in China’s agricultural property rights policy changes using materials from People’ s Daily and other media outlets. Similarly, McComas et al. (1999) analyzed how media constructed narratives around global warming based on reports from The New York Times and The Washington Post. Schaub (2021) examined how advocacy coalitions employed narrative strategies to influence Germany’s agricultural fertilizer policy, utilizing reports from the authoritative German outlet Frankfurter Allgemeine Zeitung. Additional Declarations No competing interests reported. Supplementary Files 20250305SupplementaryMaterialsAppendixAB.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6230248","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":449391109,"identity":"5d816c6b-91ba-4210-90c6-f25f6e35d923","order_by":0,"name":"Lin Dong","email":"","orcid":"","institution":"Northeastern University","correspondingAuthor":false,"prefix":"","firstName":"Lin","middleName":"","lastName":"Dong","suffix":""},{"id":449391110,"identity":"7c6da4e9-28e4-42bf-917e-82ad152e3f1a","order_by":1,"name":"Zuobao Wang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAsklEQVRIiWNgGAWjYFACHsYHCRU2PPz8DcRrYTZ4cCZNRnLGAeK1sEk+bDtsY9CQQKQGg/NnDxsksJ3nMWA4wPjhYw4xWg6cS3yQwHObx5y5gVly5jYitJgd7DE2SJC4zWPZcICNmZcoLYd5zCQSDM7xGBxIIFbLMZCWhAMkaLE/w5dskHAgmUdyxsFm4vwi2X/24MOf/+zs+fmbD374SIwWJMDYQJr6UTAKRsEoGAW4AQBJNDfeCNraYwAAAABJRU5ErkJggg==","orcid":"","institution":"Northeastern University","correspondingAuthor":true,"prefix":"","firstName":"Zuobao","middleName":"","lastName":"Wang","suffix":""},{"id":449391111,"identity":"0fd0bc82-0cc4-4a0d-888b-e83d6dcd7b03","order_by":2,"name":"Yuqiang Zhang","email":"","orcid":"","institution":"Yunnan University","correspondingAuthor":false,"prefix":"","firstName":"Yuqiang","middleName":"","lastName":"Zhang","suffix":""}],"badges":[],"createdAt":"2025-03-15 04:08:15","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6230248/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6230248/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":81658962,"identity":"2e849e1e-8fd9-45a7-841e-1eec640dd3fc","added_by":"auto","created_at":"2025-04-29 20:19:00","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":36090,"visible":true,"origin":"","legend":"\u003cp\u003eConceptual Framework of the Interplay Between Emotional Framing, Temporal Framing, and PEBI via Risk Perceptions and Benefit Perceptions\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-6230248/v1/edcdce3517695587d4c92dfd.png"},{"id":81658963,"identity":"18f365a9-183a-4b70-a3ef-07f6fe825273","added_by":"auto","created_at":"2025-04-29 20:19:00","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":88385,"visible":true,"origin":"","legend":"\u003cp\u003eThe Influence of Benefit Perception on the PEBI at Two Levels of Temporal Framing\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-6230248/v1/53760b2cc715332536a08551.png"},{"id":84405192,"identity":"dcd05d0e-3096-4c3c-bed3-db6af699133c","added_by":"auto","created_at":"2025-06-11 14:17:09","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1164404,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6230248/v1/8d3e82ff-5e6f-45a2-9fd5-07e43f3265b9.pdf"},{"id":81658964,"identity":"c0be14b3-3ed2-4d05-b378-a100eb7ac06e","added_by":"auto","created_at":"2025-04-29 20:19:00","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":46719,"visible":true,"origin":"","legend":"","description":"","filename":"20250305SupplementaryMaterialsAppendixAB.docx","url":"https://assets-eu.researchsquare.com/files/rs-6230248/v1/bedc3de64ce14ff7a6ebe740.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"The Impact of Government Climate Narrative Plot on Public Pro-Environmental Behavior Intentions: A Survey Experiment","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eGlobal climate change creates severe impacts on both ecosystems and human society, significantly threatens to impede the progress toward sustainable global development (IPCC, 2023). Studies have shown that climate changes are a widespread impact caused by numerous human activities, such as continuous burn of fossil fuels, deforestation, and other similar activities (Howlett \u0026amp; Rawat, 2019; Trenberth, 2018; Vlek \u0026amp; Steg, 2007). Numerous public behaviors contribute to mitigating climate change (Capstick, Whitmarsh, Poortinga, Pidgeon, \u0026amp; Upham, 2015; Creutzig et al., 2018). For example, people can reduce energy consumption by using energy-efficient appliances, improving building insulation, and minimizing unnecessary electricity use (Dietz, Gardner, Gilligan, Stern, \u0026amp; Vandenbergh, 2009), as well as shift consumption patterns by purchasing energy-efficient products, supporting refurbished goods, and reducing meat consumption (Poore \u0026amp; Nemecek, 2018). In addition, individuals can further contribute to mitigating climate change by participating in environmental volunteer activities and engaging in decision-making behaviors (Liverani, 2014; Wolske \u0026amp; Stern, 2018). Clearly, collective efforts from the public are essential for mitigating climate change (O'Brien, 2012). However, climate environmental issues are inherently collective problems, and most people lack motivation or awareness to proactively engage in pro-environmental behaviors(Lewis Jr, Green, Duker, \u0026amp; Onyeador, 2021). They often perceive such behaviors as too complex or costly, or they believe that individual efforts are futile in the face of widespread inaction, leading to a \u0026ldquo;free-rider\u0026rdquo; phenomenon (Z. Liu \u0026amp; Lei, 2024).\u003c/p\u003e \u003cp\u003eResearch indicates that government narratives have emerged as a powerful tool in shaping public attitude and prosocial behavior (Adobor, 2024; Braddock \u0026amp; Dillard, 2016; Mao \u0026amp; Nishide, 2025). Narratives, which refer to stories that individuals use and tell, can significantly influence behavior by evoking emotional resonance and facilitating cognitive restructuring. For environmental issue, research has also suggested that narratives can promote pro-environmental behavior (Moezzi, Janda, \u0026amp; Rotmann, 2017). On one hand, narratives translate complex environmental challenges\u0026mdash;such as climate mitigation policies or ecological restoration frameworks\u0026mdash;into relatable terms that align with the public\u0026rsquo;s lived experiences, thus enhancing comprehension of technical information (Herman, 2003). On the other hand, when audiences identify with narrative roles, the stories evoke affective responses (e.g., urgency from fear or empowerment from hope), which catalyze behavioral shifts by aligning individual intention with sustainability goals (Morris et al., 2019).\u003c/p\u003e \u003cp\u003eNonetheless, the potential of narratives\u0026rsquo; actual effectiveness remains controversial. Some scholars suggest that the efficacy of narratives may be influenced by various factors, particularly the plot structure and its alignment with the audience\u0026rsquo;s values (Jones \u0026amp; McBeth, 2010; O' Donovan, 2018). Moreover, the overuse of narratives may lead to \u0026ldquo;narrative fatigue\u0026rdquo; or \u0026ldquo;information overload,\u0026rdquo; which could, in turn, weaken the narrative\u0026rsquo;s effect (Freeman, 2015). Consequently, in government climate narratives, optimizing narrative plots is critical for enhancing public behavior intentions.\u003c/p\u003e \u003cp\u003eNarrative plot design\u0026mdash;the structural arrangement of context, characters, and moral imperatives that establishes policy problems and provides causal explanations within stories (Jones \u0026amp; McBeth, 2010)\u0026mdash;serves as one of the critical determinant of public cognition and behavioral responses (Shenhav, 2015). Through its information architecture and emotional delivery mechanisms, narrative plots shape individuals\u0026rsquo; mental representations of complex environmental issues by activating cognitive schemas and affective evaluations (S. Brown \u0026amp; Tu, 2020; Dahlstrom, 2014). Yet, there is a notable gap in systematic research exploring the impact of plot framing within climate narratives. Firstly, the causal pathways through which narrative plots transform behavioral intentions remain poorly understood. The mediating roles between narrative emotional framing (positive vs. negative) and pro-environmental behavior intentions (PEBI) outcomes require rigorous empirical validation. Next, existing studies predominantly examine emotional framing or temporal framing (occurred vs. not occurred) in isolation, overlooking their interactive effects PEBI through differentiated cognitive pathways. And this siloed approach hinders the identification of optimal narrative plot outcomes. Finally, mainstream research focuses disproportionately on developed economies (Af Malmborg, 2022; Crow \u0026amp; Jones, 2018), neglecting the unique narrative mechanisms operating in transitional economies where state-led environmental governance predominates. These limitation constrain policymakers\u0026rsquo; ability to design culturally attuned climate communication strategies, particularly in Global South contexts where rapid industrialization intersects with sustainability imperatives.\u003c/p\u003e \u003cp\u003eTo address these deficiencies, this study systematically evaluates the influence of varying narrative plot frame combinations on PEBI. It pursues a threefold theoretical advancement: (1) testing the mediating roles of risk perception and benefit perception within the narrative influence chain; (2) elucidating the interactive effects of emotional framing and temporal framing on PEBI; and (3) extending the explanatory scope of narrative policy theory to non-Western, vertically governed systems. The findings offer practical implications by providing a foundation for governments to craft phased, context-adapted climate communication strategies, while also contributing valuable insights for behavioral information interventions.\u003c/p\u003e \u003cp\u003eAs a representative of developing countries, China\u0026rsquo;s early development was predominantly driven by an economic growth-focused model, which prioritized rapid industrialization and urbanization at the expense of environmental sustainability, leading to severe environmental degradation (Yuan et al., 2020). Since 2013, however, China has undergone a significant transformation in its development approach, shifting toward an ecologically prioritized model through the prioritization of ecological restoration. This transition is exemplified by the implementation of policies such as the \u0026ldquo;\u003cem\u003eEcological Civilization\u003c/em\u003e\u0026rdquo; framework and the promotion of narratives like \u0026ldquo;\u003cem\u003eclear waters and green mountains are as good as mountains of gold and silver\u003c/em\u003e,\u0026rdquo; aimed at aligning public behavior with sustainability goals (Dai \u0026amp; Zeng, 2021). Unlike Western nations, which largely addressed industrial pollution decades ago, China is still in a pivotal phase of environmental governance. This phase is characterized by ongoing policy implementation and public exposure to climate risks, such as smog and extreme weather (Kostka \u0026amp; Zhang, 2018). These tangible experiences, coupled with China\u0026rsquo;s unique context as a transitioning economy where rapid industrialization intersects with sustainability imperatives, provide a distinctive empirical foundation to investigate which government climate narrative framing interventions are effective in promoting PEBI, particularly in transitioning economies facing similar developmental and environmental challenges.\u003c/p\u003e \u003cp\u003eBuilding on this context, this study employs a 2\u0026times;2 between-subjects experimental design to investigate how emotional framing and temporal framing in Chinese government climate narratives influence PEBI, with a focus on the mediating roles of risk and benefit perception. The remainder of this paper is structured as follows: Section \u003cspan refid=\"Sec2\" class=\"InternalRef\"\u003e2\u003c/span\u003e develops hypotheses linking narrative framing to PEBI through perceptual mediators; Section \u003cspan refid=\"Sec7\" class=\"InternalRef\"\u003e3\u003c/span\u003e details the experimental methodology, including participant randomization and stimulus design; Section 4 presents hypothesis testing results; Section 5 discusses theoretical implications for narrative-driven behavioral interventions; Section 6 concludes with limitations and future research directions.\u003c/p\u003e"},{"header":"2. Literature Review and Research Hypotheses","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1. Government Narratives and Narrative Structures\u003c/h2\u003e \u003cp\u003eNarrative has emerged as a core topic in interdisciplinary research, encompassing fields such as psychology, linguistics, neuroscience, and management. The narrative theory, which synthesizes findings from multiple disciplines, posits that narratives play a crucial role in the construction and updating of cognitive patterns in the brain (Herman, 2003). By immersing individuals in the storyline, narratives are more capable than other forms of information in shaping people\u0026rsquo;s attitudes, intentions, and behaviors (Holt \u0026amp; Thompson, 2004).\u003c/p\u003e \u003cp\u003eAmong others, the plot is a crucial component of narrative framework, as it links a series of events, experiences, or actions cohesively, forming a meaningful whole (Fischer \u0026amp; Forester, 1993). Importantly, the narrative plot also serves a logical attribution function (McBeth, Shanahan, Arnell, \u0026amp; Hathaway, 2007). By stating and explaining causal relationships, the plot provide clarity and coherence, which help audiences understand the sequence of events and their interconnections (Stone, 2002). This attribution function serves as a foundational through which narratives shape individuals\u0026rsquo; understanding of complex phenomena and foster their engagement with the issues at hand (Bandola-Gill \u0026amp; Smith, 2021).\u003c/p\u003e \u003cp\u003eIn recent years, a growing body of research has focused on the distinct effects of emotional and temporal framing within narrative plots on individual perception and behavior. First, several studies have investigated the influence of narrative emotion\u0026mdash;including both positive and negative narratives\u0026mdash;on public behavior across various domains (Dunlop, Wakefield, \u0026amp; Kashima, 2008). For instance, emotionally arousing persuasive messages tend to be better recalled, and perceived as more effective, than less emotional messages, both in the field of health communication (Dillard \u0026amp; Peck, 2001; Pechmann \u0026amp; Reibling, 2006), and in consumer marketing (Edson Escalas, Chapman Moore, \u0026amp; Edell Britton, 2004). Regarding climate issues, some studies suggest that governments and experts should raise public awareness of climate change through negatively framed narratives (Dales, Padfield, \u0026amp; Bridge, 2024). For example, some scholars argue that environmental behaviors should be promoted by presenting stories about the \u0026ldquo;imminent risks of climate change\u0026rdquo; (Bosone, Chevrier, \u0026amp; Martinez, 2023; Spence, Poortinga, \u0026amp; Pidgeon, 2012). Conversely, other studies contend that, despite the considerable potential of negative emotions in climate change communication to raise awareness, they are not effective tools for motivating genuine individual participation (O'Neill \u0026amp; Day, 2009; Richter, Gabe-Thomas, Queir\u0026oacute;s, Sheppard, \u0026amp; Pahl, 2023). In contrast, narratives employing a positive emotional framework are more effective in fostering sustained public engagement in sustainable behaviors (Neef et al., 2023). These conflicting findings highlight the complexity of emotional framing effects, suggesting that the efficacy of narratives may depend on contextual factors.\u003c/p\u003e \u003cp\u003eSecond, a limited number of studies investigated the effects of narrative time on individual behavior. Within the temporal framework of narrative plots, scholars have categorized them into \u0026ldquo;occurred\u0026rdquo; (past-focused) and \u0026ldquo;not occurred\u0026rdquo; (future-oriented) framing (Ruff, Stelmach, \u0026amp; Jones, 2022). Research suggests that emphasizing past versus future events may shape public perceptions of climate issues and influence behavioral intentions (Jie, Lapinski, \u0026amp; Peng, 2018; Y. Wang, Thier, Lee, \u0026amp; Nan, 2023). Additional studies have found that temporal framing affects individuals' perceptual judgments and decision-making by modifying their psychological distance from events (Konstantinidis, Dai, \u0026amp; Newell, 2025; Trope \u0026amp; Liberman, 2010).\u003c/p\u003e \u003cp\u003eFurthermore, a growing body of research has explored the relationship between perception and pro-environmental behavior, with a particular focus on the roles of perception perception (Jia \u0026amp; Wang, 2024; Latif et al., 2023). For instance, O'Connor, Bord, and Fisher (1999) found that risk perception effectively accounts for behavioral intentions related to climate change mitigation. Bradley, Babutsidze, Chai, and Reser (2020) using samples from Australia and France, demonstrated that risk perception indirectly predicts pro-environmental behavior. Similarly, research on benefit perception has highlighted its role in shaping attitudes and behaviors toward sustainability (Huijts, Molin, \u0026amp; Steg, 2012). For example, individuals are more likely to adopt renewable energy technologies when they perceive tangible benefits, such as cost savings or environmental improvements (Steg, Bolderdijk, Keizer, \u0026amp; Perlaviciute, 2014), and consumers often prefer sustainably produced goods over cheaper alternatives manufactured under environmentally harmful conditions (Bechtel, Genovese, \u0026amp; Scheve, 2019). These findings underscore the critical roles of risk and benefit perception as potential mediating mechanisms through which narratives can influence public behavior, providing a robust theoretical foundation for examining the causal pathways between narrative framing and PEBI in the context of climate change communication.\u003c/p\u003e \u003cp\u003eNevertheless, while the aforementioned studies demonstrate that specific narrative plots and perceptions serve as effective tools for promoting certain public behaviors and intentions, few studies have systematically investigated how the interplay between emotional and temporal frameworks within government climate narratives shapes PEBI through perceptual pathways. Given the critical role of pro-environmental behavior in mitigating global climate change, investigating how emotional frameworks interact with temporal frameworks to influence public PEBI via perception emerges as a compelling and valuable research question. In response to these gaps, this study investigated how emotional framing in government climate narratives shapes PEBI through perception pathways, moderated by temporal framing. This study provides new perspectives for understanding the relationship between narrative cognition and behavior intentions in the context of mitigating global climate change, and reveals how narrative strategies can be optimized to promote pro-environmental behavior. By focusing on emotional and temporal framing, and systematically exploring the interactions of different framing strategies, this research deepens and enriches previous studies that primarily focused on the effects of single framing strategies, thus complementing research in the field of policy narrative.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2. The Emotional Framing of Narrative Plots and Public Pro-Environmental Behavior Intentions\u003c/h2\u003e \u003cp\u003eIn governmental climate narratives, the deployment of distinct emotional framing shapes the construction of climate issues differently, potentially influencing PEBI. Currently, research findings on the influence of emotions framing of narrative plot on public behavioral intentions present two distinct perspectives. On one hand, recent studies suggest that negative emotions narratives can effectively foster changes in individuals\u0026rsquo; behavioral and intentions (Morris et al., 2019). Skeirytė and Liobikienė (2025) conducted an analysis of environmental behaviors and found that individuals\u0026rsquo; pro-environmental actions are driven by emotions such as anxiety and sadness, with the frequency of these behaviors increasing alongside heightened levels of such emotions. Similarly, Bretter and Pangbourne (2025) identified a positive correlation between emotions like shame, sadness, and unease and the extent of individuals\u0026rsquo; adoption of sustainable transportation modes. Furthermore, Morris et al. (2019) demonstrated that emotional narratives emphasizing negative value outcomes were more successful in encouraging pro-environmental behaviors compared to neutral informational narratives.\u003c/p\u003e \u003cp\u003eOn the other hand, some research suggests that positive emotions exert a beneficial influence on individuals\u0026rsquo; sustainable behaviors and intentions (Taufik, Bolderdijk, \u0026amp; Steg, 2016; Zelenski \u0026amp; Desrochers, 2021). For instance, a study on transportation mode choices demonstrated that positive emotions associated with active travel options, such as walking, positively influenced preferences for these modes (Zavareh, Mehdizadeh, \u0026amp; Nordfj\u0026aelig;rn, 2020). And, positive emotions were found to predict how reliably individuals seek out information sources regarding sustainable transportation (Manca \u0026amp; Fornara, 2019). In addition, in the domain of consumption decisions, research indicates that positive emotions play a significant role in guiding preferences for green products (Kao \u0026amp; Du, 2020). Specifically, Kao and Du (2020) revealed that environmental advertisements eliciting positive emotions improved consumers\u0026rsquo; attitudes toward green products, advertised brands, and purchase intentions. Furthermore, recent work by Husain-Naviatti (2025) suggests that climate change communication strategies emphasizing hope and empathy\u0026mdash;such as narratives of human resilience in overcoming climate-related challenges\u0026mdash;may prove more effective in inspiring action than approaches relying solely on fear.\u003c/p\u003e \u003cp\u003eOverall, both negative and positive emotions appear capable of influencing behavioral intentions, though the relative efficacy of each emotional frame may depend on the specific domain of intervention. In the context of climate narratives, where climate change is a complex issue deeply intertwined with personal life, individuals may exhibit greater sensitivity to negative emotional narratives (Tversky \u0026amp; Kahneman, 1991). Therefore, we hypothesize that negative narratives may hold a comparative advantage in triggering immediate PEBI:\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eH1\u003c/strong\u003e \u003cp\u003e \u003cem\u003eThe main effect of emotional framing on public attitudes is significant. Compared to plot focusing on positive emoiton, negative emotional narratives plot are more effective in enhancing PEBI.\u003c/em\u003e \u003c/p\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3. The Mediating Role of Risk Perception and Benefit Perception\u003c/h2\u003e \u003cp\u003eRisk perception refers to an individual\u0026rsquo;s subjective assessment of the likelihood and severity of potential threats (Renn \u0026amp; Rohrmann, 2000). Empirical evidence demonstrates a robust positive association between risk perception and attitudes and behavioral intentions of individuals (Maddux \u0026amp; Rogers, 1983; Zhang, Du, Huang, Mao, \u0026amp; Jiao, 2024). For instance, Maartensson and Loi (2022) found that heightened risk perception of climate change significantly amplified individuals' willingness to adopt mitigation behaviors. Similarly, Shao, Xian, Lin, and Small (2017) analyzed coastal county data in the United States, revealing that individuals with elevated perceptions of flood risk exhibited stronger support for flood management policies. Similar findings have been observed in China. Such as, Tian and Jiang (2025) explored the determinants of ecological protection behavior among residents and found a positive correlation between ecological risk perception and both the willingness and behavior engage in ecological protection. These findings collectively underscore risk perception\u0026rsquo;s pivotal role as a cognitive mediator in environmental decision-making processes. Building on this foundation and considering the potential main effect of emotional framing on PEBI, we hypothesize that risk perception mediates the relationship between emotional framing and PEBI:\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eH2\u003c/strong\u003e \u003cp\u003e \u003cem\u003eRisk perception mediates the relationship between emotional framing and PEBI, such that negative framing increases risk perception, which in turn enhances PEBI.\u003c/em\u003e \u003c/p\u003e \u003c/p\u003e \u003cp\u003eBenefit perception refers to an individual\u0026rsquo;s subjective evaluation of the positive outcomes associated with adopting a specific behavior (Bolderdijk, Steg, Geller, Lehman, \u0026amp; Postmes, 2012). Empirical studies across diverse contexts underscore benefit perception as a critical antecedent to attitude formation and behavioral engagement (Felgendreff et al., 2025; Rickard, Yang, Liu, \u0026amp; Boze, 2021). For instance, the meta-analysis by Schulte, Scheller, Sloot, and Bruckner (2022) on residential photovoltaic adoption intentions revealed a strong correlation between perceived benefits and willingness to adopt solar energy systems. Similarly, Bolderdijk et al. (2012) found that perceptions of economic and social benefits significantly increased public support for pro-environmental actions. These findings collectively highlight benefit perception\u0026rsquo;s dual role as both a cognitive motivator and a behavioral catalyst in environmental decision-making. Building on this evidence and considering the potential influence of emotional framing on PEBI, we hypothesize that benefit perception mediates the relationship between emotional framing and PEBI:\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eH3\u003c/strong\u003e \u003cp\u003e \u003cem\u003eBenefit perception mediates the relationship between emotional framing and PEBI, such that positive framing increases benefit perception, which in turn enhances PEBI.\u003c/em\u003e \u003c/p\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4. The Moderating Role of Narrative Temporal Framing\u003c/h2\u003e \u003cp\u003eTime serves as a critical informational framework in narrative communication, influencing public perception and judgment (H. Kim, Rao, \u0026amp; Lee, 2009). It also exerts persuasive effects on behavioral intentions (Huang \u0026amp; Xu, 2024). In reality, individuals tend to perceive future threats more intensely, as the uncertainty surrounding future events amplifies attention and anticipation of potential risks (Kahneman \u0026amp; Tversky, 1979). Conversely, when reflecting on past events, individuals are more likely to evaluate outcomes based on concrete experiences, leading to more specific behavioral expectations (Banerjee, 2024).\u003c/p\u003e \u003cp\u003eBased on these insights, we posit that under negative and not occurred framing amplifies the contextualization and uncertainty of potential threats, thereby more effectively driving the indirect effect of risk perception on behavioral intentions. In contrast, under positive and occurred framing provides more tangible and visible incentives, enhancing public confidence and expectations regarding the benefits of pro-environmental behavior, thus driving PEBI. Accordingly, we propose the following hypotheses:\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eH4\u003c/strong\u003e \u003cp\u003e \u003cem\u003eThe temporal frame moderates the indirect effect of emotional framing on behavioral intentions through risk perception and benefit perception.\u003c/em\u003e \u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eH4a\u003c/strong\u003e \u003cp\u003e \u003cem\u003eThe temporal frame moderates the indirect effect of emotional framing on PEBI through risk perception. Under negative emotional narratives, not occurred framing strengthens the indirect effect of risk perception on PEBI.\u003c/em\u003e \u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eH4b\u003c/strong\u003e \u003cp\u003e \u003cem\u003eThe temporal frame moderates the indirect effect of emotional framing on PEBI through benefit perception. Under positive emotional narratives, occurred framing strengthens the indirect effect of benefit perception on PEBI.\u003c/em\u003e \u003c/p\u003e \u003c/p\u003e \u003cp\u003eBased on the above analysis and hypotheses, the theoretical model of this study is proposed as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"3. Research Methodology","content":"\u003cp\u003eTo test the hypotheses outlined above, we conducted an online survey experiment that follows a 2 (positive vs. negative) \u0026times; 2 (occurred vs. not occurred) between-subject design. Four experimental conditions were generated by this design as intervention groups, allowing for the measurement of participants\u0026rsquo; risk perception, benefit perception, PEBI, and demographic information. The experiment was administered through an online survey. This approach enabled precise control over participants\u0026rsquo; exposure to the narrative interventions, ensuring standardized presentation of stimuli and minimizing interference from external factors (Chandler, Mueller, \u0026amp; Paolacci, 2014; J. Kim, 2024). By adopting this approach, we aims to isolate the effects of emotional and temporal framing on risk and benefit perceptions, as well as their subsequent influence on PEBI.\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e3.1. Participants\u003c/h2\u003e \u003cp\u003eThe experiment was conducted on Credamo (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e\u003ca href=\"http://www.credamo.com\" target=\"_blank\"\u003ewww.credamo.com\u003c/a\u003e\u003c/span\u003e\u003cspan address=\"http://www.credamo.com\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), a professional survey platform affiliated with ESOMAR, hosting a diverse sample pool of over 3\u0026nbsp;million participants from various regions, age groups, and educational backgrounds across China, ensuring sample representativeness. Participants were randomly assigned to experimental conditions using the platform\u0026rsquo;s randomization function. The sample size was determined based on calculations using G*Power software. The experimental design followed a 2\u0026times;2 between-subjects design. The effect size (f) was set at 0.25 (indicating a medium effect size), with statistical power set at 0.80 and a significance level of 0.05. The calculation indicated that a minimum sample size of 128 participants was required.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e3.2. Materials\u003c/h2\u003e \u003cp\u003eBased on a 2\u0026times;2 experimental design, four distinct climate narrative scenarios were developed. All experimental stimuli were presented in a written narrative format, with each narrative centered on the theme of climate change. The intervention materials were developed by adapting content from official media reports on climate change and ecological restoration, published between 2018 and 2023, and sourced from state-endorsed media outlets, including \u003cem\u003ePeople\u0026rsquo;s Daily\u003c/em\u003e and \u003cem\u003eXinhua News Agency\u003c/em\u003e(Cao, 2019; Schaub, 2021)\u003csup\u003e1\u003c/sup\u003e. The adaptation process followed specific principles: negative narratives incorporated fear and urgency inducing terms (e.g., \u0026lsquo;disaster,\u0026rsquo; \u0026lsquo;crisis\u0026rsquo;;) (Randall, 2009), while positive narratives emphasized hopeful and optimistic terms (e.g.,\u0026lsquo;achievements,\u0026rsquo; \u0026lsquo;progress\u0026rsquo;) (Schneider, Zaval, \u0026amp; Markowitz, 2021). Temporal framing was distinguished through linguistic markers: \u0026lsquo;occurred\u0026rsquo; narratives used past-tense expressions (e.g., \u0026lsquo;has caused,\u0026rsquo; \u0026lsquo;has achieved\u0026rsquo;) to describe historical climate events, while \u0026lsquo;not occurred\u0026rsquo; narratives employed future-tense expressions (e.g.,\u0026lsquo;will,\u0026rsquo; \u0026lsquo;future\u0026rsquo;) to project future scenarios, aligning with prior temporal framing research (Chuanshen \u0026amp; Yunzhe, 2023; Hernes \u0026amp; Obstfeld, 2022). To minimize selection bias, two researchers independently screened the materials, achieving high inter-rater reliability (Cohen\u0026rsquo;s Kappa\u0026thinsp;=\u0026thinsp;0.82). A pilot study (n\u0026thinsp;=\u0026thinsp;30) was conducted to validate the intervention materials, ensuring they elicited the intended cognitive responses while avoiding information overload (Lang, 2006). The specific content of the four conditions is as follows:\u003c/p\u003e \u003cp\u003eThe \u003cem\u003enegative-occurred framing\u003c/em\u003e condition focuses on stories detailing the negative impacts of climate change that have already occurred, highlighting the detrimental effects on the national ecological environment and the lives of citizens.\u003c/p\u003e \u003cp\u003eThe \u003cem\u003enegative-not occurred framing\u003c/em\u003e condition centers on potential future climate risks, emphasizing the uncertainty and challenges these threats could pose to China\u0026rsquo;s ecological environment and economic development.\u003c/p\u003e \u003cp\u003eThe \u003cem\u003epositive-occurred framing\u003c/em\u003e condition emphasizes the positive outcomes of climate governance achieved by the central government, showcasing how policies and mechanisms innovations have effectively mitigated carbon emissions.\u003c/p\u003e \u003cp\u003eThe \u003cem\u003epositive-not occurred framing\u003c/em\u003e condition envisions a future shaped by national climate governance strategies and actions, portraying a scenario of improved living conditions through proactive government planning and efforts (for detailed content, see the Appendix A).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e3.3. Measure\u003c/h2\u003e \u003cp\u003eThis study employed a 7-point Likert scale (1\u0026thinsp;=\u0026thinsp;strongly disagree, 7\u0026thinsp;=\u0026thinsp;strongly agree) to assess variables, with detailed items presented in Appendix A. Pro-environmental Behavior Intentions (PEBI) was measured using 3 items, designed to assess participants\u0026rsquo; PEBI after the experiment. The items were adapted from the study by Sun, Ma, and Wei (2023) on the influence of gratitude on pro-environmental behavior.\u003c/p\u003e \u003cp\u003eRisk Perception (RP) was measured using 3 items, aimed at assessing participants\u0026rsquo; risk perception after reading the experimental materials. The items were based on the study by Van Valkengoed, Perlaviciute, and Steg (2024) on the relationship between climate change risk perception and adaptation behavior.\u003c/p\u003e \u003cp\u003eBenefit Perception (BP) was measured using 3 items, adapted from the research by S. Wang, Wang, Lin, and Li (2019) on public perceptions of nuclear energy, and how benefit perception influences public acceptance.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e3.4. Procedure\u003c/h2\u003e \u003cp\u003eThe experimental procedure consisted of the following steps: First, participants were presented with the experiment instructions and an initial questionnaire. Before the experiment began, the questionnaire reminded participants to carefully read the instructions on the page and follow the provided guidance. Participants were informed that the purpose of the experiment was to investigate the impact of narrative framing on PEBI. All participants voluntarily consented to participate in the experiment and completed an informed consent form, which outlined their rights and responsibilities within the study, including the right to withdraw and privacy protection measures. Upon completion of the task, participants who finished the experiment were awarded a monetary incentive to increase participation rates and enhance the reliability of the data.\u003c/p\u003e \u003cp\u003eNext, the presentation of stimulus materials took place. Participants in the experimental groups were instructed to read specific government climate change narratives. The intervention materials were presented as short texts, derived from official media reports at the central levels, ensuring authenticity and authority. To ensure that participants carefully read the materials, a countdown timer was set on the experiment page, and attention check questions were embedded to filter out participants who did not engage seriously. Participants in the control group were directed to the questionnaire section directly, without exposure to any government climate change narrative intervention. In total, 638 valid participants were included in the data analysis. The participants had diverse educational backgrounds and household registrations, with an average age of 31 years. Of the participants, 96.6% held a bachelor's degree or higher, and 90.8% were registered as urban residents (See Appendix B for detailed statistics).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e3.5. Data analysis methods\u003c/h2\u003e \u003cp\u003eFirst, SPSS 27.0 was used to conduct statistical analysis on the demographic characteristics of the sample data. A one-way analysis of variance (ANOVA) was performed to test the balance of sample characteristics, including means, F-values, and p-values, to ensure the complete random assignment of participants.\u003c/p\u003e \u003cp\u003eSecond, SPSS 27.0 was employed to perform descriptive statistics and correlation analysis on the main variables, providing a preliminary understanding of the direct relationships between variables. Independent samples t-tests and ordinary least squares (OLS) regression analyses were conducted to explore the direct effects of emotional framing and temporal framing on PEBI, thereby testing H1. The PROCESS Model 4 was utilized to analyze parallel mediation effects, investigating the roles of risk perception and benefit perception in the relationship between emotional framing (negative vs. positive) and PEBI. Path coefficients for each generated pathway were calculated, and the Bootstrap resampling method was applied to test the stability of the mediation effects, thereby testing H2 and H3.\u003c/p\u003e \u003cp\u003eThird, the PROCESS Model 7 was used to conduct moderated mediation analysis, verifying the moderating role of temporal framing on the mediation effects between emotional framing and PEBI, as well as their relationships with risk perception and benefit perception. The Bootstrap resampling method was again employed to test the stability of the moderated mediation effects, thereby testing H4.\u003c/p\u003e \u003c/div\u003e"},{"header":"4. Results","content":"\u003cp\u003eTo ensure complete random assignment in the study, we conducted a one-way analysis of variance (ANOVA) to compare the differences between the experimental and control groups on initial variables. The results revealed no statistically significant differences between the groups in terms of gender, age, education level, residence, environmental awareness level (EAL), and prior pro-environmental behavior experience (EBE). This indicates that random assignment effectively balanced the groups on key baseline variables, minimizing the potential influence of confounding variables and ensuring the internal validity of the experimental results (see Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eResults of Balance Test\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cem\u003eVariable\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"4\" nameend=\"c5\" namest=\"c2\"\u003e \u003cp\u003eTreatment group\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cem\u003eF\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eGender\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.721\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.685\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.717\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.693\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.225\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.879\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eAge\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.045\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.070\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.112\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.058\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.162\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.922\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eEducation\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.909\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.049\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.987\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.968\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.089\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.353\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eResidence\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.110\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.105\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.072\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.079\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.659\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.578\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eEAL\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4.032\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4.009\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4.009\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3.945\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.403\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.241\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eEBE\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.448\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.510\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.474\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.476\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.387\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.762\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003ePEBI\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e6.104\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5.788\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e6.189\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e5.882\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e8.711\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003eNote: To maintain consistency throughout the article, all reported values are presented to three decimal places. The same applies hereafter.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e4.1. Main Effect Analysis\u003c/h2\u003e \u003cp\u003eWe investigated the impact of the emotional and temporal framing of government narratives on the PEBI (see Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). An independent samples t-test revealed that, under the emotional framing condition, participants in the negative framing group exhibited significantly higher PEBI than those in the positive framing group, indicating a significant positive effect of emotional framing on the public\u0026rsquo;s PEBI (t\u0026thinsp;=\u0026thinsp;4.963, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). This showed that negative narratives focusing on environmental \u0026ldquo;destruction\u0026rdquo; and \u0026ldquo;crisis\u0026rdquo; are more effective in enhancing the public\u0026rsquo;s PEBI. Therefore, Hypothesis H1 is supported.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eMain Effect Analysis Results\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGroup\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMean\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSD\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003et\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003ep\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEmotion Frame\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNegative Emotion\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e306\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e6.146\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.658\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e4.963\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.000***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePositive Emotion\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e332\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5.841\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.883\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003e4.2. Mediation Effect Analysis\u003c/h2\u003e \u003cp\u003eTo further examine the differential impact of emotional framing in narratives, regression analysis was conducted to explore the influence of emotional framing on mediator variables and the dependent variable, as well as to investigate potential mediation effects (see Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). The results of Model 1 indicated that negative emotion (Emot\u0026thinsp;=\u0026thinsp;0) significantly increased participants\u0026rsquo; risk perception compared to positive emotion (Emot\u0026thinsp;=\u0026thinsp;1) (b = -1.577, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). The results of Model 2 indicated that positive emotion significantly increased participants' benefit perception compared to negative emotion (b\u0026thinsp;=\u0026thinsp;1.067, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). The results of Model 3 demonstrated that emotional framing had a significant negative impact on PEBI (b = -0.375, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Positive emotion, compared to negative emotion, exerted a weaker positive influence on PEBI. This result further validated Hypothesis H1.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eRegression Results\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eModel (1)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eModel (2)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eModel (3)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eModel (4)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRisk-per\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBen-per\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePEBI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003ePEBI\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eEmot\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-1.577***\u003c/p\u003e \u003cp\u003e(-20.467)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.067***\u003c/p\u003e \u003cp\u003e(14.515)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.375***\u003c/p\u003e \u003cp\u003e(-5.023)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.320***\u003c/p\u003e \u003cp\u003e(-3.905)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eGender\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.044\u003c/p\u003e \u003cp\u003e(0.527)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.070\u003c/p\u003e \u003cp\u003e(0.878)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.219***\u003c/p\u003e \u003cp\u003e(3.320)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.200**\u003c/p\u003e \u003cp\u003e(3.150)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eAge\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.052\u003c/p\u003e \u003cp\u003e(1.179)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.059\u003c/p\u003e \u003cp\u003e(1.415)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.047***\u003c/p\u003e \u003cp\u003e(4.343)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.132***\u003c/p\u003e \u003cp\u003e(3.968)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eEdu\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.205***\u003c/p\u003e \u003cp\u003e(-3.513)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.098\u003csup\u003e+\u003c/sup\u003e\u003c/p\u003e \u003cp\u003e(1.764)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.062\u003c/p\u003e \u003cp\u003e(1.027)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.053\u003c/p\u003e \u003cp\u003e(1.188)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eArea\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.020\u003c/p\u003e \u003cp\u003e(-0.145)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.025\u003c/p\u003e \u003cp\u003e(0.194)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.135\u003c/p\u003e \u003cp\u003e(-1.250)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.137\u003c/p\u003e \u003cp\u003e(-1.326)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eEAL\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.007\u003c/p\u003e \u003cp\u003e(0.075)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.023\u003c/p\u003e \u003cp\u003e(-0.265)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.057\u003c/p\u003e \u003cp\u003e(0.798)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.061\u003c/p\u003e \u003cp\u003e(-0.884)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eEBE\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.032*\u003c/p\u003e \u003cp\u003e(-0.411)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.159*\u003c/p\u003e \u003cp\u003e(2.158)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.178**\u003c/p\u003e \u003cp\u003e(2.947)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.151*\u003c/p\u003e \u003cp\u003e(2.590)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eRP\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.123***\u003c/p\u003e \u003cp\u003e(4.079)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eBP\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.196***\u003c/p\u003e \u003cp\u003e(6.230)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003e_cons\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6.552***\u003c/p\u003e \u003cp\u003e15.581\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.270***\u003c/p\u003e \u003cp\u003e10.643\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.388***\u003c/p\u003e \u003cp\u003e(16.323)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.748***\u003c/p\u003e \u003cp\u003e(9.428)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eN\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e638\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e638\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e638\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e638\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eadj.R\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.408\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.256\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.091\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.162\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003eNote:The coefficients are unstandardized, with t-values in parentheses. +p\u0026thinsp;\u0026lt;\u0026thinsp;0.10, *p\u0026thinsp;\u0026lt;\u0026thinsp;0.05, **p\u0026thinsp;\u0026lt;\u0026thinsp;0.01, ***p\u0026thinsp;\u0026lt;\u0026thinsp;0.001.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eModel 4 considered the effects of the mediator variables risk perception (RP) and benefit perception. The results indicated that both risk perception (b\u0026thinsp;=\u0026thinsp;0.123, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and benefit perception (b\u0026thinsp;=\u0026thinsp;0.196, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) had significant positive effects on PEBI, suggesting that higher levels of risk and benefit perception were associated with greater PEBI. After including the mediators in Model 4, the coefficient of emotional framing on PEBI decreased in magnitude from \u0026minus;\u0026thinsp;0.375 in Model 3 to -0.320 (SE\u0026thinsp;=\u0026thinsp;0.061, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). This reduction reflects the mediating roles of risk perception and benefit perception, which collectively explained a portion of the total effect of emotional framing on PEBI. Based on these results, it was inferred that risk perception and benefit perception may serve as mediators in the relationship between emotional framing and PEBI.\u003c/p\u003e \u003cp\u003eTo further analyze the mediating role of risk perception and benefit perception, we conducted a mediation analysis using Model 4 of the PROCESS macro with 5,000 Bootstrap samples (Hayes, 2013). The results (see Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e) revealed a significant total effect of emotional framing on PEBI (b = -0.301, SE\u0026thinsp;=\u0026thinsp;0.061, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), indicating that negative framing (Emot\u0026thinsp;=\u0026thinsp;0), compared to positive framing (Emot\u0026thinsp;=\u0026thinsp;1), was more effective in enhancing PEBI overall.\u003c/p\u003e \u003cp\u003eThe mediation analysis identified two distinct pathways. First, the path \u0026ldquo;Emotional Framing \u0026rarr; Risk Perception \u0026rarr; PEBI\u0026rdquo; exhibited a significant indirect effect (b = -0.190, SE\u0026thinsp;=\u0026thinsp;0.048, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Negative indirect effect demonstrates that negative framing, compared to positive framing, was more effective in enhancing PEBI by increasing risk perception. Second, the path \u0026ldquo;Emotional Framing \u0026rarr; Benefit Perception \u0026rarr; PEBI\u0026rdquo; showed a significant positive indirect effect (b\u0026thinsp;=\u0026thinsp;0.207, SE\u0026thinsp;=\u0026thinsp;0.039, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), indicating that positive framing, compared to negative framing, was more effective in enhancing PEBI by increasing benefit perception. Consequently, the findings support Hypothesis H2 and H3.\u003c/p\u003e \u003cp\u003eIn addition, the underlying mechanisms varied across the two pathways. In the risk perception path, the direct effect of emotional framing on PEBI was not significant after accounting for risk perception (b = -0.111, SE\u0026thinsp;=\u0026thinsp;0.077, p\u0026thinsp;=\u0026thinsp;0.152), indicating full mediation. In contrast, in the benefit perception path, the direct effect remained significant after accounting for benefit perception (b = -0.508, SE\u0026thinsp;=\u0026thinsp;0.068, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), suggesting partial mediation. These results highlight that negative framing primarily enhances PEBI through risk perception, whereas the positive indirect effect of benefit perception in positive framing partially offsets its overall weaker effect.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eMediation Effect Results\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMediating Variable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eType\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eEffect\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSE\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e95% CI (LL)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e95% CI (UL)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003ep\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTotal Effect\u003c/p\u003e \u003cp\u003e(Emot\u0026thinsp;\u0026gt;\u0026thinsp;PEBI)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.301\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.061\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.420\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-0.182\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDirect Effect\u003c/p\u003e \u003cp\u003e(Emot\u0026thinsp;\u0026gt;\u0026thinsp;PEBI)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.111\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.077\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.262\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.041\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.152\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIndirect Effect\u003c/p\u003e \u003cp\u003e(Emot \u0026rarr;RP \u0026rarr; PEBI)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.190\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.048\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.291\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-0.100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTotal Effect\u003c/p\u003e \u003cp\u003e(Emot \u0026rarr; PEBI)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.301\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.061\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.420\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-0.182\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDirect Effect\u003c/p\u003e \u003cp\u003e(Emot \u0026rarr; PEBI)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.508\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.068\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.642\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-0.375\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIndirect Effect\u003c/p\u003e \u003cp\u003e(Emot \u0026rarr; BP \u0026rarr;PEBI)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.207\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.039\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.133\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.287\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003e4.3. Moderation Effect Analysis\u003c/h2\u003e \u003cp\u003ePROCESS Model 7 was used to analyze the impact of the emotional framing of government narratives on PEBI through mediator variables, with a focus on testing the moderating role of temporal framing in the indirect effects (see Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). The analysis revealed that the moderating variable, temporal framing, had a significant effect on the indirect effects of emotional framing under different conditions. The moderating effect was clearly supported when benefit perception served as the mediator, while no significant support was found when risk perception was the mediator.\u003c/p\u003e \u003cp\u003eSpecifically, when risk perception served as the mediator, the analysis showed that the indirect effect of emotional framing on PEBI was significant under both temporal framing conditions. In the occurred framing condition (narrative time\u0026thinsp;=\u0026thinsp;0), the indirect effect was was significant (b = -0.176, SE\u0026thinsp;=\u0026thinsp;0.048). In the not occurred framing condition (narrative time\u0026thinsp;=\u0026thinsp;1), the indirect effect was also significant (b = -0.203, SE\u0026thinsp;=\u0026thinsp;0.053). Although the indirect effects were significant under both temporal framing conditions, the index of moderated mediation was not significant (b = -0.027, SE\u0026thinsp;=\u0026thinsp;0.019). Therefore, Hypothesis H4a was not supported.\u003c/p\u003e \u003cp\u003eWhen benefit perception served as the mediator, moderated mediation was statistically supported. As illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e and Table\u0026nbsp;6t, under the occurred temporal framing condition, the indirect effect was positive and significant (b\u0026thinsp;=\u0026thinsp;0.300, SE\u0026thinsp;=\u0026thinsp;0.054). Under the not occurred temporal framing condition, the indirect effect remained significant but weaker (b\u0026thinsp;=\u0026thinsp;0.132, SE\u0026thinsp;=\u0026thinsp;0.029). The index of moderated mediation was significant (b = -0.168, SE\u0026thinsp;=\u0026thinsp;0.041), indicating that temporal framing attenuated the strength of the emotional framing \u0026rarr; benefit perception \u0026rarr; PEBI pathway, that is, under positive emotional narratives, occurred framing strengthens the indirect effect of benefit perception on PEBI. Therefore, Hypothesis H4b was supported.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eModerated mediation results\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModerating Variable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eType\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eEffect\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSE\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e95% CI (LL)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e95% CI (UL)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDirect Effect (Emot\u0026thinsp;\u0026gt;\u0026thinsp;PEBI)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.112\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.078\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.265\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.041\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eConditional Indirect Effect (Occurred)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.176\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.048\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.275\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-0.086\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eConditional Indirect Effect (Not Occurred)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.203\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.053\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.311\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-0.101\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIndex of Moderated Mediation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.027\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.019\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.066\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.008\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDirect Effect (Emot\u0026thinsp;\u0026gt;\u0026thinsp;PEBI)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.515\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.068\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.649\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-0.381\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eConditional Indirect Effect (Occurred)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.300\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.054\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.199\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.412\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eConditional Indirect Effect (Not Occurred)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.132\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.029\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.080\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.192\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIndex of Moderated Mediation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.168\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.041\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.257\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-0.097\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"5. Discussion","content":"\u003cp\u003eRecent studies have shown that, compared to traditional information framing, emotional narratives and their role interactions are more effective in stimulating prosocial behavior (Barraza, Alexander, Beavin, Terris, \u0026amp; Zak, 2015; Loewenstein, 2010). However, research on the influence of narrative plots on PEBI remains limited. From a narrative plot perspective, we explore the potential mechanisms through which emotional and temporal framing in government climate narratives shape PEBI. The findings indicate that the emotional framing within government climate narratives significantly affects the PEBI, with risk perception and benefit perception playing a mediating role between emotional framing and PEBI. Moreover, the temporal framing moderates the effect of emotional framing on PEBI through benefit perception.\u003c/p\u003e \u003cp\u003eFirst, the results indicate that negative framing narratives outperform positive framing ones in enhancing PEBI. Specifically, negative emotional framing significantly increased the public's willingness to engage in PEBI compared to positive framing. This result supports our hypothesis, posited within the context of governmental climate narratives, that individuals that individuals exhibit greater sensitivity to negative emotional narratives in governmental climate communication (Weiner, 2000). This finding aligns with loss aversion theory, which suggests that individuals are more motivated to avoid losses than to pursue equivalent gains (Kahneman \u0026amp; Tversky, 1979), as well as with prior research demonstrating that fear-based narratives effectively drive pro-environmental actions by heightening risk perception (Skeirytė \u0026amp; Liobikienė, 2025). By emphasizing climate threats\u0026mdash;such as extreme heatwaves and floods\u0026mdash;negative narratives likely amplified the perceived urgency and severity of climate risks, thereby prompting stronger intentions to adopt protective behaviors (Gilbert, Fiske, \u0026amp; Lindzey, 1998).\u003c/p\u003e \u003cp\u003eHowever, mediation analysis revealed that the advantage of negative framing narratives was partially offset by their lower benefit perception. This suggests that, while negative framing effectively heightened risk perception, it simultaneously diminished the perceived benefits of engaging in pro-environmental behaviors (e.g., individuals may perceive their actions as insufficient to address the scale of the climate crisis). Benefit perception, a critical driver of behavioral intentions (Bolderdijk et al., 2012; Spence \u0026amp; Pidgeon, 2010), plays a significant mediating role: the indirect effect of negative framing on PEBI through reduced benefit perception counteracts part of its direct positive effect. This trade-off underscores a dual-pathway mechanism in narrative persuasion: negative narratives primarily generate urgency through risk perception, but their effectiveness is tempered by diminished benefit perception, potentially leading to feelings of helplessness or emotional fatigue (O\u0026rsquo;Neill \u0026amp; Day, 2009). In contrast, positive framing fosters confidence in the efficacy of individual actions encouraging individuals to believe they can achieve benefits through their actions (Chadwick, 2015), thus promoting a focus on long-term benefits and solutions.\u003c/p\u003e \u003cp\u003eIn addition, the impact of narratives on PEBI is fully mediated by risk perception (i.e., narrative \u0026rarr; risk perception \u0026rarr; intention). This may stem from the framing of climate change by governmental entities as a \u0026ldquo;risk\u0026rdquo; or \u0026ldquo;damage,\u0026rdquo; leading individuals to prioritize risk avoidance. This singular focus on threat may result in risk perception being the dominant mediating pathway, as individuals prioritize risk avoidance over other considerations(Spence \u0026amp; Pidgeon, 2010). Conversely, positive framing (e.g., \u0026ldquo;achieved emissions reduction achievements\u0026rdquo;) indirectly promotes intentions by enhancing benefit perception. Positive framing may also influence PEBI through other mechanisms, such as narrative persuasion and environmental attitudes (J.-X. Liu, 2023; S. Liu \u0026amp; Yang, 2023).\u003c/p\u003e \u003cp\u003eFinally, this study demonstrates that the causal pathways linking emotional framing, benefit perception, and PEBI vary depending on the temporal framing employed. Specifically, when temporal framing is \u0026ldquo;occurred,\u0026rdquo; positive emotional framing significantly outperforms negative emotional framing in promoting PEBI through benefit perception. This finding suggests that past-focused positive narratives have a greater capacity to shape behavioral intentions, aligning with Kahneman's Dual-System Theory. This theory posits that human cognition is governed by two systems: System 1, characterized by fast, emotional, and intuitive processing, and System 2, which involves slow, analytical, and deliberative processing (Evans, 2008; Kannengiesser \u0026amp; Gero, 2019). In daily decision-making, System 1 often dominates due to its efficiency, while System 2 is typically activated in situations requiring careful reasoning or when confronting novel and complex scenarios(Turel \u0026amp; Qahri-Saremi, 2016). Consequently, in \u0026ldquo;occurred\u0026rdquo; scenarios, which are concrete and experiential, System 1 thinking likely predominates, rendering benefit-focused narratives more persuasive by tapping into immediate reward-seeking mechanisms. Conversely, in \u0026ldquo;not occurred\u0026rdquo; scenarios, which are abstract and hypothetical, System 2 thinking may prevail, enabling risk-based narratives to gain an advantage through cognitive elaboration of long-term consequences.\u003c/p\u003e"},{"header":"6. Implications","content":"\u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003e6.1. Implications for theory\u003c/h2\u003e \u003cp\u003eIn recent decades, governments worldwide have increasingly prioritized public engagement in environmental governance\u0026mdash;a central theme in public administration, environmental policy, and behavioral science research. While narratives are widely recognized as more effective than conventional information dissemination in fostering pro-environmental behaviors, the mechanisms through which emotional and temporal framing interact to shape behavioral intentions remain underexplored. Our study addresses this gap by offering three key theoretical contributions:\u003c/p\u003e \u003cp\u003eFirst, we extend the analytical framework of narrative theory. Previous research on narratives and behavior has predominantly focused on the influence of a single narrative character or plot on behavior. However, as Hineline (2018) has argued, the influence of narratives on behavior is a complex, systematic process involving the interaction of multiple factors. In this regard, our study constructs a comprehensive theoretical framework at the micro-level, integrating narrative emotional framing, temporal framing, perception, and behavioral intentions. This framework elucidates the conditions, mechanisms, and reasons behind the effectiveness of government climate narratives in promoting PEBI. By examining the interaction between emotional and temporal framing and incorporating perception into the entire model, this study further enriches the literature on narratives and behavior. Although our contextual design focused on a specific environmental issue (i.e., climate change), it also provides a multidimensional, systematic analytical tool for future research, offering important references for studies in other fields.\u003c/p\u003e \u003cp\u003eSecond, we break through the long-standing dominance of the \u0026ldquo;risk central paradigm\u0026rdquo; in climate communication by empirically validating the role of benefit perception in how narratives influence PEBI. The findings reveal that positive emotional narratives significantly enhance PEBI by increasing benefit perception, with an effect strength comparable to that of the risk pathway. Correspondingly, this result contests the prevailing risk-centered climate discourse and aligns with perspectives suggesting that an overemphasis on threats can have counterproductive effects (Aronson, 2014; Witte \u0026amp; Allen, 2000), thereby affirming the strategic importance of benefit perception in promoting public behavior. Climate change should not be solely defined as a \u0026ldquo;risk,\u0026rdquo; and policymakers should balance risk warnings with benefit-focused messaging to avoid narrative fatigue.\u003c/p\u003e \u003cp\u003eFinally, this study extends narrative theory by broadening its applicability across diverse contexts. Existing research has largely concentrated on developed countries (Lyytim\u0026auml;ki, 2023; Merry \u0026amp; Mattingly, 2023), whereas our findings demonstrate that narrative tools can effectively align public behavior with sustainability agendas even in transitioning economies like China. By designing effective narrative plots, governments can address implementation barriers unique to rapid industrialization contexts. This expands the scope of narrative theory across multiple institutional and cultural settings while providing policymakers with actionable tools to foster sustainable behaviors.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003e6.2. Implications for Practice\u003c/h2\u003e \u003cp\u003eOur study holds significant theoretical value and offers practical insights for designing effective climate narrative strategies. Government communication can enhance its impact by strategically employing emotional framing in climate narratives. Specifically, positive emotional framing is particularly effective when addressing past climate change contexts, as it fosters public confidence in future progress by highlighting the tangible benefits of environmental actions\u0026mdash;e.g., showcasing successful case studies to demonstrate actionable outcomes. Conversely, negative emotional framing is more effective for highlighting potential climate change scenarios, as it heightens public risk perception and motivates preventive actions against environmental threats.\u003c/p\u003e \u003cp\u003eHowever, as psychologists warn against viewing emotion as a simple lever for behavior (Chapman, Lickel, \u0026amp; Markowitz, 2017), we do not believe that narratives with purely negative or positive framing are the only solution. Since individuals typically respond more positively to information that aligns with their existing belief systems (K. Brown et al., 2019), different narrative structures may be more effective for different individuals. For example, differentiated narrative strategies can be designed based on group cultural and educational backgrounds. As environmental awareness deepens within society, the role of climate narratives may become increasingly important (Wynes \u0026amp; Nicholas, 2017). Our findings suggest that emotion and time can serve as effective frameworks in intervention activities and can be an essential component of government environmental communication, warranting careful consideration in policy design and communication.\u003c/p\u003e \u003c/div\u003e"},{"header":"7. Research limitations and future prospects","content":"\u003cp\u003eSeveral limitations of the present research should be addressed. First, our experimental design focused exclusively on text-based government climate narratives, omitting audiovisual or multimodal formats. This choice was deliberate to isolate the effects of emotional and temporal framing without confounding variables introduced by visual or auditory stimuli (Geise \u0026amp; Baden, 2015). However, while this approach enhanced internal validity, it limits generalizability to real-world contexts where multimodal narratives dominate public communication. Future research should further comparing text-based narratives with audiovisual formats to assess how sensory engagement moderates framing effects.\u003c/p\u003e \u003cp\u003eSecond, we focused on measuring immediate behavioral intentions rather than long-term behavioral changes. Longitudinal tracking was infeasible due to resource constraints, yet our findings provide foundational insights into the cognitive-affective mechanisms that precede action\u0026mdash;a critical first step for designing field experiments. Future research could track real-world behaviors (e.g., energy consumption, policy support) over time to evaluate the persistence of narrative-driven intentions, thereby gaining a deeper understanding.\u003c/p\u003e "},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthical Approval:\u003c/strong\u003e This study received ethical approval from institutional ethics committee on March 18, 2024. All procedures involving human participants were conducted in accordance with the ethical standards outlined in the Declaration of \u003cem\u003eHelsinki\u003c/em\u003e. The study ensured complete anonymity and voluntary participation, with no collection of sensitive personal information.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eInformed consent\u003c/strong\u003e\u003cstrong\u003e：\u003c/strong\u003eInformed consent was obtained from all participants through an online process during the data collection between October 19, 2024, and December 27, 2024. The study utilized the \u003cem\u003eCredamo\u003c/em\u003e platform to distribute questionnaires, which included an introductory section clearly outlining the study’s purpose, procedures, and voluntary nature. Participants were required to provide their consent by agreeing to proceed before accessing the formal questionnaire.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to Participate:\u003c/strong\u003e The authors give their permission to participate.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to Publish:\u003c/strong\u003e The authors consent to publish this article in your journal and to transfer copyright to the publisher once the paper has been accepted.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding:\u003c/strong\u003e This research was supported by the National Social Science Fund of China under the project (Project No. 24CZZ068); the Key Project of the Liaoning Provincial Social Science Fund (Project No. L22AGL010); the Fundamental Research Funds for the Central Universities (Project No. N2314009); and the Liaoning Provincial Economic and Social Development Research Project of 2021 (Project No. 2023lslybkt-052).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting Interests:\u003c/strong\u003e The authors declare no conflicts of interest. The opinions expressed here are those of the authors and do not necessarily reflect the position of the government of China or of any other organization.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials:\u003c/strong\u003e The datasets generated during the current study are available from the corresponding author upon reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe express our sincere gratitude to the participants who assisted with data collection and questionnaire distribution, whose invaluable contributions were essential to the successful completion of this study. We also gratefully acknowledge the financial support provided by the National Social Science Fund of China, the Liaoning Provincial Social Science Fund, the Fundamental Research Funds for the Central Universities, and the Liaoning Provincial Economic and Social Development Research Project.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAdobor, H. (2024). How do sensemaking and climate change education affect climate engagement at the grassroots level? A study of five communities in Southeastern Ghana. \u003cem\u003eClimatic Change, 177\u003c/em\u003e(3). doi:https://doi.org/10.1007/s10584-024-03701-w\u003c/li\u003e\n\u003cli\u003eAf Malmborg, F. (2022). Narrative dynamics in European Commission AI policy\u0026mdash;Sensemaking, agency construction, and anchoring. \u003cem\u003eReview of Policy Research, 40\u003c/em\u003e(5), 757-780. doi:https://doi.org/10.1111/ropr.12529\u003c/li\u003e\n\u003cli\u003eBandola-Gill, J., \u0026amp; Smith, K. (2021). Governing by narratives: REF impact case studies and restrictive storytelling in performance measurement. \u003cem\u003eStudies in Higher Education, 47\u003c/em\u003e, 1-15. doi:https://doi.org/10.1080/03075079.2021.1978965\u003c/li\u003e\n\u003cli\u003eBanerjee, A. (2024). The effect of heterogeneity and risk on co-operation: Experimental evidence. \u003cem\u003eJournal of Behavioral and Experimental Economics, 109\u003c/em\u003e.\u003c/li\u003e\n\u003cli\u003eBarraza, J. A., Alexander, V., Beavin, L. E., Terris, E. T., \u0026amp; Zak, P. J. (2015). The heart of the story: peripheral physiology during narrative exposure predicts charitable giving. \u003cem\u003eBiol Psychol, 105\u003c/em\u003e, 138-143. doi:https://doi.org/10.1016/j.biopsycho.2015.01.008\u003c/li\u003e\n\u003cli\u003eBechtel, M. M., Genovese, F., \u0026amp; Scheve, K. F. (2019). Interests, norms and support for the provision of global public goods: The case of climate co-operation. \u003cem\u003eBritish Journal of Political Science, 49\u003c/em\u003e(4), 1333-1355. doi:https://doi.org/10.1017/S0007123417000205\u003c/li\u003e\n\u003cli\u003eBolderdijk, J., Steg, L., Geller, E. S., Lehman, P., \u0026amp; Postmes, T. (2012). Comparing the effectiveness of monetary versus moral motives in environmental campaigning. \u003cem\u003eNature Climate Change, 3\u003c/em\u003e, 413-416. doi:https://doi.org/10.1038/nclimate1767\u003c/li\u003e\n\u003cli\u003eBosone, L., Chevrier, M., \u0026amp; Martinez, F. (2023). When narratives speak louder than numbers: the effects of narrative persuasion across the stages of behavioural change to reduce air pollution. \u003cem\u003eFrontiers in Psychology, 14\u003c/em\u003e. doi:https://doi.org/10.3389/fpsyg.2023.1072187\u003c/li\u003e\n\u003cli\u003eBraddock, K., \u0026amp; Dillard, J. P. (2016). Meta-analytic evidence for the persuasive effect of narratives on beliefs, attitudes, intentions, and behaviors. \u003cem\u003eCommunication monographs, 83\u003c/em\u003e(4), 446-467. doi:https://doi.org/10.1080/03637751.2015.1128555\u003c/li\u003e\n\u003cli\u003eBradley, G. L., Babutsidze, Z., Chai, A., \u0026amp; Reser, J. P. (2020). The role of climate change risk perception, response efficacy, and psychological adaptation in pro-environmental behavior: A two nation study. \u003cem\u003eJournal of Environmental Psychology, 68\u003c/em\u003e, 101410. doi:https://doi.org/10.1016/j.jenvp.2020.101410\u003c/li\u003e\n\u003cli\u003eBretter, C., \u0026amp; Pangbourne, K. (2025). Emotions as antecedents of sustainable travel behaviour. \u003cem\u003eTravel Behaviour and Society, 38\u003c/em\u003e. doi:https://doi.org/10.1016/j.tbs.2024.100903\u003c/li\u003e\n\u003cli\u003eBrown, K., Adger, W. N., Devine-Wright, P., Anderies, J. M., Barr, S., Bousquet, F., . . . Quinn, T. (2019). Empathy, place and identity interactions for sustainability. \u003cem\u003eGlobal Environmental Change, 56\u003c/em\u003e, 11-17. doi:https://doi.org/10.1016/j.gloenvcha.2019.03.003\u003c/li\u003e\n\u003cli\u003eBrown, S., \u0026amp; Tu, C. (2020). The shapes of stories: A \u0026ldquo;resonator\u0026rdquo; model of plot structure. \u003cem\u003eFrontiers of Narrative Studies, 6\u003c/em\u003e(2), 259-288. doi:https://doi.org/10.1515/fns-2020-0016\u003c/li\u003e\n\u003cli\u003eCao, Z. (2019). \u003cem\u003eResearch on national policy narrative in the changes of China's rural land rights policy.\u003c/em\u003e (Doctoral dissertation), Jilin University, Jilin. Available from Cnki\u003c/li\u003e\n\u003cli\u003eCapstick, S., Whitmarsh, L., Poortinga, W., Pidgeon, N., \u0026amp; Upham, P. (2015). International trends in public perceptions of climate change over the past quarter century. \u003cem\u003eWiley Interdisciplinary Reviews-Climate Change, 6\u003c/em\u003e(1), 35-61. doi:https://doi.org/10.1002/wcc.321\u003c/li\u003e\n\u003cli\u003eChandler, J., Mueller, P., \u0026amp; Paolacci, G. (2014). Nonna\u0026iuml;vet\u0026eacute; among Amazon Mechanical Turk workers: Consequences and solutions for behavioral researchers. \u003cem\u003eBehavior research methods, 46\u003c/em\u003e, 112-130. doi:https://doi.org/10.3758/s13428-013-0365-7\u003c/li\u003e\n\u003cli\u003eChapman, D. A., Lickel, B., \u0026amp; Markowitz, E. M. (2017). Reassessing emotion in climate change communication. \u003cem\u003eNature Climate Change, 7\u003c/em\u003e(12), 850-852. doi:https://doi.org/10.1038/s41558-017-0021-9\u003c/li\u003e\n\u003cli\u003eChuanshen, Q., \u0026amp; Yunzhe, L. (2023). The Role of Narrative in Facial Recognition Risk Communication: A Survey Experiment. \u003cem\u003eChina Public Administration Review, 5\u003c/em\u003e(3), 31-56. doi:https://doi.org/10.1016/j.im.2024.103963\u003c/li\u003e\n\u003cli\u003eCreutzig, F., Roy, J., Lamb, W. F., Azevedo, I. M. L., de Bruin, W. B., Dalkmann, H., . . . Weber, E. U. (2018). Towards demand-side solutions for mitigating climate change. \u003cem\u003eNature Climate Change, 8\u003c/em\u003e(4), 268-271. doi:https://doi.org/10.1038/s41558-018-0121-1\u003c/li\u003e\n\u003cli\u003eCrow, D. A., \u0026amp; Jones, M. D. (2018). Narratives as tools for influencing policy change. \u003cem\u003ePractical Lessons from Policy Theories, 46\u003c/em\u003e(2). doi:https://doi.org/10.1332/030557318X15230061022899\u003c/li\u003e\n\u003cli\u003eDahlstrom, M. F. (2014). Using narratives and storytelling to communicate science with nonexpert audiences. \u003cem\u003eProceedings of the National Academy of Sciences, 111\u003c/em\u003e(4), 13614-13620. doi:https://doi.org/10.1073/pnas.1320645111\u003c/li\u003e\n\u003cli\u003eDai, J., \u0026amp; Zeng, F. (2021). \u003cem\u003eEnvironmental Risk Communication in China: Actors, Issues, and Governance\u003c/em\u003e. London: Routledge.\u003c/li\u003e\n\u003cli\u003eDales, A., Padfield, R., \u0026amp; Bridge, G. (2024). Critical sustainability stories: How communicators can translate the complexity of the climate emergency and shape public narratives. \u003cem\u003eGeoforum, 150\u003c/em\u003e. doi:https://doi.org/10.1016/j.geoforum.2024.103963\u003c/li\u003e\n\u003cli\u003eDietz, T., Gardner, G., Gilligan, J., Stern, P., \u0026amp; Vandenbergh, M. (2009). Household actions can provide a behavioral wedge to rapidly reduce U.S. carbon emissions. \u003cem\u003eProceedings of the National Academy of Sciences of the United States of America, 106\u003c/em\u003e, 18452-18456. doi:https://doi.org/10.1073/pnas.0908738106\u003c/li\u003e\n\u003cli\u003eDillard, J. P., \u0026amp; Peck, E. (2001). Persuasion and the structure of affect: Dual systems and discrete emotions as complementary models. \u003cem\u003eHuman Communication Research, 27\u003c/em\u003e(1), 38-68. doi:https://doi.org/10.1111/j.1468-2958.2001.tb00775.x\u003c/li\u003e\n\u003cli\u003eDunlop, S., Wakefield, M., \u0026amp; Kashima, Y. (2008). Can You Feel It? Negative Emotion, Risk, and Narrative in Health Communication. \u003cem\u003eMedia Psychology, 11\u003c/em\u003e, 52-75. doi:https://doi.org/10.1080/15213260701853112\u003c/li\u003e\n\u003cli\u003eEdson Escalas, J., Chapman Moore, M., \u0026amp; Edell Britton, J. (2004). Fishing For Feelings? Hooking Viewers Helps! \u003cem\u003eJournal of Consumer Psychology, 14\u003c/em\u003e(1), 105-114. doi:https://doi.org/10.1207/s15327663jcp1401\u0026amp;2_12\u003c/li\u003e\n\u003cli\u003eEvans, J. S. B. (2008). Dual-processing accounts of reasoning, judgment, and social cognition. \u003cem\u003eAnnu. Rev. Psychol., 59\u003c/em\u003e(1), 255-278. doi:https://doi.org/10.1146/annurev.psych.59.103006.093629\u003c/li\u003e\n\u003cli\u003eFelgendreff, L., Rebitschek, F. G., Shamsrizi, P., Geiger, M., Jenny, M. A., \u0026amp; Betsch, C. (2025). Explaining seasonality increases perceived effectiveness of influenza vaccination: An experimental study. \u003cem\u003eBritish Journal of Health Psychology, 30\u003c/em\u003e(1). doi:https://doi.org/10.1111/bjhp.12770\u003c/li\u003e\n\u003cli\u003eFischer, F., \u0026amp; Forester, J. (1993). \u003cem\u003eThe Argumentative Turn in Policy Analysis and Planning\u003c/em\u003e: Duke University Press.\u003c/li\u003e\n\u003cli\u003eFreeman, M. (2015). Narrative as a mode of understanding: Method, theory, praxis. \u003cem\u003eThe handbook of narrative analysis\u003c/em\u003e, 19-37.\u003c/li\u003e\n\u003cli\u003eGeise, S., \u0026amp; Baden, C. U. (2015). Putting the Image Back Into the Frame: Modeling the Linkage Between Visual Communication and Frame-Processing Theory. \u003cem\u003eCommunication Theory, 25\u003c/em\u003e, 46-69. doi:https://doi.org/10.1111/comt.12048\u003c/li\u003e\n\u003cli\u003eGilbert, D. T., Fiske, S. T., \u0026amp; Lindzey, G. (1998). The handbook of social psychology, Vols. 1-2, 4th ed. In D. T. Gilbert, S. T. Fiske, \u0026amp; G. Lindzey (Eds.), \u003cem\u003eThe handbook of social psychology, Vols. 1-2, 4th ed.\u003c/em\u003e (pp. 680-740): McGraw-Hill.\u003c/li\u003e\n\u003cli\u003eHayes, A. F. (2013). \u003cem\u003eIntroduction to mediation, moderation, and conditional process analysis: A regression-based approach.\u003c/em\u003e Guilford Press, New York, NY, US.\u003c/li\u003e\n\u003cli\u003eHerman, D. (2003). Narrative theory and the cognitive sciences. \u003cem\u003eNarrative Inquiry, 11\u003c/em\u003e, 1-34. doi:https://doi.org/10.1075/ni.11.1.01her\u003c/li\u003e\n\u003cli\u003eHernes, T., \u0026amp; Obstfeld, D. (2022). A temporal narrative view of sensemaking. \u003cem\u003eOrganization Theory, 3\u003c/em\u003e(4), 26317877221131585. doi:https://doi.org/10.1177/26317877221131585\u003c/li\u003e\n\u003cli\u003eHineline, P. (2018). Narrative: why it\u0026rsquo;s important, and how it works. \u003cem\u003ePerspectives on Behavior Science, 41\u003c/em\u003e. doi:https://doi.org/10.1007/s40614-018-0137-x\u003c/li\u003e\n\u003cli\u003eHolt, D., \u0026amp; Thompson, C. (2004). Man‐of‐Action Heroes: The Pursuit of Heroic Masculinity in Everyday Consumption. \u003cem\u003eJournal of Consumer Research, 31\u003c/em\u003e, 425-440. doi:https://doi.org/10.1086/422120\u003c/li\u003e\n\u003cli\u003eHowlett, M., \u0026amp; Rawat, S. (2019). Behavioral Science and Climate Policy. \u003cem\u003eOxford Research Encyclopedia of Climate Science\u003c/em\u003e.\u003c/li\u003e\n\u003cli\u003eHuang, G., \u0026amp; Xu, J. (2024). Disentangling the Effects of Temporal Framing on Risk Perception, Attitude, Behavioral Intention, and Behavior: A Multilevel Meta-Analysis. \u003cem\u003eCommunication Research, 51\u003c/em\u003e, 3-27. doi:https://doi.org/10.1177/00936502221102102\u003c/li\u003e\n\u003cli\u003eHuijts, N. M. A., Molin, E. J. E., \u0026amp; Steg, L. (2012). Psychological factors influencing sustainable energy technology acceptance: A review-based comprehensive framework. \u003cem\u003eRenewable and Sustainable Energy Reviews, 16\u003c/em\u003e(1), 525-531. doi:https://doi.org/10.1016/j.rser.2011.08.018\u003c/li\u003e\n\u003cli\u003eHusain-Naviatti, A. (2025). The power of human narrative: Inspiring action on climate change. \u003cem\u003eEnvironmental Science \u0026amp; Policy, 163\u003c/em\u003e. doi:10.1016/j.envsci.2024.103954\u003c/li\u003e\n\u003cli\u003eIPCC. (2023). \u003cem\u003eClimate change 2023: The physical science basis: Summary for policymakers\u003c/em\u003e. Retrieved from https://www.ipcc.ch/report/sixth-assessment-report-cycle/\u003c/li\u003e\n\u003cli\u003eJia, F. L., \u0026amp; Wang, W. (2024). City-level sustainable development impacts on environmental literacy: feelings toward nature, environmental knowledge, and pro-environmental behavior. \u003cem\u003eEnvironmental Education Research, 30\u003c/em\u003e(8), 1263-1278. doi:10.1080/13504622.2024.2315573\u003c/li\u003e\n\u003cli\u003eJie, Z., Lapinski, M. K., \u0026amp; Peng, W. (2018). Crafting messages to promote water conservation: Using time‐framed messages to boost conservation actions in the United States and China. \u003cem\u003eJournal of Applied Social Psychology, 48\u003c/em\u003e, 248-256. doi:https://doi.org/10.1111/jasp.12509\u003c/li\u003e\n\u003cli\u003eJones, M. D., \u0026amp; McBeth, M. K. (2010). A Narrative Policy Framework: Clear Enough to Be Wrong? \u003cem\u003ePolicy Studies Journal, 38\u003c/em\u003e, 329-353. doi:https://doi.org/10.1111/j.1541-0072.2010.00364.x\u003c/li\u003e\n\u003cli\u003eKahneman, D., \u0026amp; Tversky, A. (1979). Prospect Theory: An Analysis of Decision under Risk. \u003cem\u003eEconometrica, 47\u003c/em\u003e(2), 263-291. doi:https://doi.org/10.2307/1914185\u003c/li\u003e\n\u003cli\u003eKannengiesser, U., \u0026amp; Gero, J. S. (2019). Design thinking, fast and slow: A framework for Kahneman\u0026rsquo;s dual-system theory in design. \u003cem\u003eDesign Science, 5\u003c/em\u003e, e10. doi:https://doi.org/10.1017/dsj.2019.9\u003c/li\u003e\n\u003cli\u003eKao, T.-F., \u0026amp; Du, Y.-Z. (2020). A study on the influence of green advertising design and environmental emotion on advertising effect. \u003cem\u003eJournal of Cleaner Production, 242\u003c/em\u003e, 118294. doi:https://doi.org/10.1016/j.jclepro.2019.118294\u003c/li\u003e\n\u003cli\u003eKim, H., Rao, A., \u0026amp; Lee, A. (2009). It's time to vote: the effect of matching message orientation and temporal frame on political persuasion. \u003cem\u003eJournal of Consumer Research, 35\u003c/em\u003e, 877-889. doi:https://doi.org/10.1086/593700\u003c/li\u003e\n\u003cli\u003eKim, J. (2024). Why do experiments fail? Six practical suggestions for successful online experiments. \u003cem\u003eJournal of Travel Research, 63\u003c/em\u003e(7), 1835-1844. doi:https://doi.org/10.1177/00472875231209983\u003c/li\u003e\n\u003cli\u003eKonstantinidis, E., Dai, J. Y., \u0026amp; Newell, B. (2025). Risk, Time, and Psychological Distance: Does Construal Level Theory Capture the Impact of Delay on Risk Preference? \u003cem\u003eJournal of Experimental Psychology-General, 154\u003c/em\u003e(2), 552-573. doi:https://doi.org/10.1037/xge0001647\u003c/li\u003e\n\u003cli\u003eKostka, G., \u0026amp; Zhang, C. (2018). Tightening the grip: environmental governance under Xi Jinping. \u003cem\u003eEnvironmental Politics, 27\u003c/em\u003e(5), 769-781. doi:https://doi.org/10.1080/09644016.2018.1491116\u003c/li\u003e\n\u003cli\u003eLang, A. (2006). The limited capacity model of mediated message processing. \u003cem\u003eJournal of Communication, 50\u003c/em\u003e(1), 46-70. doi:https://doi.org/10.1111/j.1460-2466.2000.tb02833.x\u003c/li\u003e\n\u003cli\u003eLatif, B., Gaskin, J., Gunarathne, N., Sroufe, R., Sharif, A., \u0026amp; Hanan, A. S. (2023). Climate change risk perception and pro-environmental behavior: the moderating role of environmental values and psychological contract breach. \u003cem\u003eSocial Responsibility Journal\u003c/em\u003e. doi:https://doi.org/10.1108/SRJ-02-2023-0084\u003c/li\u003e\n\u003cli\u003eLewis Jr, N. A., Green, D. J., Duker, A., \u0026amp; Onyeador, I. N. (2021). Not seeing eye to eye: Challenges to building ethnically and economically diverse environmental coalitions. \u003cem\u003eCurrent Opinion in Behavioral Sciences, 42\u003c/em\u003e, 60-64. doi:https://doi.org/10.1016/j.cobeha.2021.02.025\u003c/li\u003e\n\u003cli\u003eLiu, J.-X. (2023). The influence of narrative transportation on university students' environmental intentions: A serial mediation of empathy with nature and environmental Attitudes. \u003cem\u003eJournal of Cleaner Production, 431\u003c/em\u003e, 139763. doi:https://doi.org/10.1016/j.jclepro.2023.139763\u003c/li\u003e\n\u003cli\u003eLiu, S., \u0026amp; Yang, J. Z. (2023). Narrative persuasion and psychological distance: Analyzing the effectiveness of distance-framed narratives in communicating ocean plastic pollution. \u003cem\u003eRisk Anal, 43\u003c/em\u003e(10), 2099-2113. doi:https://doi.org/10.1111/risa.14111\u003c/li\u003e\n\u003cli\u003eLiu, Z., \u0026amp; Lei, Y. (2024). Shaping public support for nudge-based decarbonization policies through policy narrative: The role of victim type, victim number and narrativity. \u003cem\u003eJournal of Cleaner Production, 449\u003c/em\u003e. doi:https://doi.org/10.1016/j.jclepro.2024.141797\u003c/li\u003e\n\u003cli\u003eLiverani, A. (2014). Climate Change and Individual Behavior. \u003cem\u003eWorld Bank Policy Research Working Paper\u003c/em\u003e.\u003c/li\u003e\n\u003cli\u003eLoewenstein, G. F. (2010). Insufficient Emotion: Soul-searching by a Former Indicter of Strong Emotions. \u003cem\u003eEmotion Review, 2\u003c/em\u003e, 234 - 239. doi:https://doi.org/10.1177/1754073910362598\u003c/li\u003e\n\u003cli\u003eLyytim\u0026auml;ki, J. (2023). Storylines nailing or failing sustainability: energy, mining and mobility as narrative arenas for societal transition. \u003cem\u003eSustainable Development, 31\u003c/em\u003e(1), 170-179. doi:https://doi.org/10.1002/sd.2381\u003c/li\u003e\n\u003cli\u003eMaartensson, H., \u0026amp; Loi, N. M. (2022). Exploring the relationships between risk perception, behavioural willingness, and constructive hope in pro-environmental behaviour. \u003cem\u003eEnvironmental Education Research, 28\u003c/em\u003e(4), 600-613. doi:https://doi.org/10.1080/13504622.2021.2015295\u003c/li\u003e\n\u003cli\u003eMaddux, J. E., \u0026amp; Rogers, R. W. (1983). Protection motivation and self-efficacy: A revised theory of fear appeals and attitude change. \u003cem\u003eJournal of Experimental Social Psychology, 19\u003c/em\u003e(5), 469-479. doi:https://doi.org/10.1016/0022-1031(83)90023-9\u003c/li\u003e\n\u003cli\u003eManca, S., \u0026amp; Fornara, F. (2019). Attitude toward sustainable transport as a function of source and argument reliability and anticipated emotions. \u003cem\u003eSustainability, 11\u003c/em\u003e(12), 3288.\u003c/li\u003e\n\u003cli\u003eMao, W. Y., \u0026amp; Nishide, Y. (2025). Crafting the messages for environmental NGOs: how narrative medium and mode shape different engagement levels. \u003cem\u003eHumanities \u0026amp; Social Sciences Communications, 12\u003c/em\u003e(1). doi:10.1057/s41599-025-04470-2\u003c/li\u003e\n\u003cli\u003eMcBeth, M. K., Shanahan, E. A., Arnell, R. J., \u0026amp; Hathaway, P. L. (2007). The Intersection of Narrative Policy Analysis and Policy Change Theory. \u003cem\u003ePolicy Studies Journal, 35\u003c/em\u003e, 87-108. doi: https://doi.org/10.1111/j.1541-0072.2007.00208.x\u003c/li\u003e\n\u003cli\u003eMerry, M., \u0026amp; Mattingly, H. (2023). Framing the climate crisis: Dread and fatalism in media and interest group responses to IPCC reports. \u003cem\u003eReview of Policy Research\u003c/em\u003e. doi:https://doi.org/10.1111/ropr.12539\u003c/li\u003e\n\u003cli\u003eMoezzi, M., Janda, K. B., \u0026amp; Rotmann, S. (2017). Using stories, narratives, and storytelling in energy and climate change research. \u003cem\u003eEnergy Research \u0026amp; Social Science, 31\u003c/em\u003e, 1-10. doi:https://doi.org/10.1016/j.erss.2017.06.034\u003c/li\u003e\n\u003cli\u003eMorris, B. S., Chrysochou, P., Christensen, J. D., Orquin, J. L., Barraza, J., Zak, P. J., \u0026amp; Mitkidis, P. (2019). Stories vs. facts: triggering emotion and action-taking on climate change. \u003cem\u003eClimatic Change, 154\u003c/em\u003e(1-2), 19-36. doi:https://doi.org/10.1007/s10584-019-02425-6\u003c/li\u003e\n\u003cli\u003eNeef, N. E., Fusswinkel, S., Roos, C., Frank, L., Shihepo, K., \u0026amp; Richter, I. (2023). Optimistic narrative future visions: a communication tool for promoting sustainable (plastic) behavior. \u003cem\u003eFrontiers in Psychology, 14\u003c/em\u003e. doi:https://doi.org/10.3389/fpsyg.2023.1252895\u003c/li\u003e\n\u003cli\u003eO' Donovan, K. (2018). Does the Narrative Policy Framework Apply to Local Policy Issues? \u003cem\u003ePolitics \u0026amp; Policy, 46\u003c/em\u003e, 532-570. doi:https://doi.org/10.1111/polp.12265\u003c/li\u003e\n\u003cli\u003eO'Brien, K. (2012). Global environmental change II: From adaptation to deliberate transformation. \u003cem\u003eProgress in Human Geography, 36\u003c/em\u003e(5), 667-676. doi:https://doi.org/10.1177/0309132511425767\u003c/li\u003e\n\u003cli\u003eO'Connor, R. E., Bord, R. J., \u0026amp; Fisher, A. (1999). Risk perceptions, general environmental beliefs, and willingness to address climate change. \u003cem\u003eRisk Analysis, 19\u003c/em\u003e(3), 461-471. doi:https://doi.org/10.1023/A:1007004813446\u003c/li\u003e\n\u003cli\u003eO'Neill, S., \u0026amp; Day, S. (2009). \"Fear won't do it\": promoting positive engagement with climate change through visual and Iconic representations. \u003cem\u003eScience Communication - SCI COMMUN, 30\u003c/em\u003e, 355-379. doi:https://doi.org/10.1177/1075547008329201\u003c/li\u003e\n\u003cli\u003ePechmann, C., \u0026amp; Reibling, E. T. (2006). Antismoking advertisements for youths: an independent evaluation of health, counter-industry, and industry approaches. \u003cem\u003eAm J Public Health, 96\u003c/em\u003e(5), 906-913. doi:https://doi.org/10.2105/ajph.2004.057273\u003c/li\u003e\n\u003cli\u003ePoore, J., \u0026amp; Nemecek, T. (2018). Reducing food's environmental impacts through producers and consumers. \u003cem\u003eScience, 360\u003c/em\u003e(6392), 987-992. doi:https://doi.org/10.1126/science.aaq0216\u003c/li\u003e\n\u003cli\u003eRandall, R. (2009). Loss and climate change: the cost of parallel narratives. \u003cem\u003eEcopsychology, 1\u003c/em\u003e. doi:https://doi.org/10.1089/eco.2009.0034\u003c/li\u003e\n\u003cli\u003eRenn, O., \u0026amp; Rohrmann, B. (2000). \u003cem\u003eCross-Cultural Risk Perception: A Survey of Empirical Studies\u003c/em\u003e.\u003c/li\u003e\n\u003cli\u003eRichter, I., Gabe-Thomas, E., Queir\u0026oacute;s, A. M., Sheppard, S. R. J., \u0026amp; Pahl, S. (2023). Advancing the potential impact of future scenarios by integrating psychological principles. \u003cem\u003eEnvironmental Science \u0026amp; Policy, 140\u003c/em\u003e, 68-79. doi:https://doi.org/10.1016/j.envsci.2022.11.015\u003c/li\u003e\n\u003cli\u003eRickard, L. N., Yang, J. Z., Liu, S. X., \u0026amp; Boze, T. (2021). Fish Tales: How Narrative Modality, Emotion, and Transportation Influence Support for Sustainable Aquaculture. \u003cem\u003eScience Communication, 43\u003c/em\u003e(2), 252-275. doi:https://doi.org/10.1177/1075547020987555\u003c/li\u003e\n\u003cli\u003eRuff, J., Stelmach, G., \u0026amp; Jones, M. (2022). Space for stories: legislative narratives and the establishment of the US Space Force. \u003cem\u003ePolicy Sciences, 55\u003c/em\u003e. doi:https://doi.org/10.1007/s11077-022-09455-5\u003c/li\u003e\n\u003cli\u003eSchaub, S. (2021). Public contestation over agricultural pollution: a discourse network analysis on narrative strategies in the policy process. \u003cem\u003ePolicy Sciences, 54\u003c/em\u003e(4), 783-821. doi:https://doi.org/10.1007/s11077-021-09439-x\u003c/li\u003e\n\u003cli\u003eSchneider, C., Zaval, L., \u0026amp; Markowitz, E. (2021). Positive emotions and climate change. \u003cem\u003eCurrent Opinion in Behavioral Sciences, 42\u003c/em\u003e, 114-120. doi:https://doi.org/10.1016/j.cobeha.2021.04.009\u003c/li\u003e\n\u003cli\u003eSchulte, E., Scheller, F., Sloot, D., \u0026amp; Bruckner, T. (2022). A meta-analysis of residential PV adoption: the important role of perceived benefits, intentions and antecedents in solar energy acceptance. \u003cem\u003eEnergy Research \u0026amp; Social Science, 84\u003c/em\u003e, 102339. doi:https://doi.org/10.1016/j.erss.2021.102339\u003c/li\u003e\n\u003cli\u003eShao, W., Xian, S., Lin, N., \u0026amp; Small, M. J. (2017). A sequential model to link contextual risk, perception and public support for flood adaptation policy. \u003cem\u003eWater Research, 122\u003c/em\u003e, 216-225. doi:https://doi.org/10.1016/j.watres.2017.05.072\u003c/li\u003e\n\u003cli\u003eShenhav, S. (2015). \u003cem\u003eAnalyzing social narratives\u003c/em\u003e. New York: Routledge.\u003c/li\u003e\n\u003cli\u003eSkeirytė, A., \u0026amp; Liobikienė, G. (2025). Emotions related to climate change, and their impact on environmental behaviour. \u003cem\u003eJournal of Cleaner Production, 488\u003c/em\u003e, 144659. doi:https://doi.org/10.1016/j.jclepro.2025.144659\u003c/li\u003e\n\u003cli\u003eSpence, A., Poortinga, W., \u0026amp; Pidgeon, N. (2012). The psychological distance of climate change. \u003cem\u003eRisk Anal, 32\u003c/em\u003e(6), 957-972. doi:https://doi.org/10.1111/j.1539-6924.2011.01695.x\u003c/li\u003e\n\u003cli\u003eSteg, L., Bolderdijk, J. W., Keizer, K., \u0026amp; Perlaviciute, G. (2014). An integrated framework for encouraging pro-environmental behaviour: The role of values, situational factors and goals. \u003cem\u003eJournal of Environmental Psychology, 38\u003c/em\u003e, 104-115. doi:https://doi.org/10.1016/j.jenvp.2014.01.002\u003c/li\u003e\n\u003cli\u003eStone, D. A. (2002). \u003cem\u003ePolicy paradox : the art of political decision making\u003c/em\u003e. New York: W.W. Norton.\u003c/li\u003e\n\u003cli\u003eSun, J., Ma, B., \u0026amp; Wei, S. (2023). Same gratitude, different pro-environmental behaviors? Effect of the dual-path influence mechanism of gratitude on pro-environmental behavior. \u003cem\u003eJournal of Cleaner Production, 415\u003c/em\u003e. doi:https://doi.org/10.1016/j.jclepro.2023.137779\u003c/li\u003e\n\u003cli\u003eTaufik, D., Bolderdijk, J. W., \u0026amp; Steg, L. (2016). Going green? The relative importance of feelings over calculation in driving environmental intent in the Netherlands and the United States. \u003cem\u003eEnergy Research \u0026amp; Social Science, 22\u003c/em\u003e, 52-62. doi:https://doi.org/10.1016/j.erss.2016.08.012\u003c/li\u003e\n\u003cli\u003eTian, X. Z., \u0026amp; Jiang, Y. L. (2025). Exploring behavioral determinants of residents' ecological conservation in rural tourism development. \u003cem\u003eScientific Reports, 15\u003c/em\u003e(1), 13. doi:https://doi.org/10.1038/s41598-025-85571-5\u003c/li\u003e\n\u003cli\u003eTrenberth, K. E. (2018). Climate change caused by human activities is happening and it already has major consequences. \u003cem\u003eJournal of energy \u0026amp; natural resources law, 36\u003c/em\u003e(4), 463-481. doi:https://doi.org/10.1080/02646811.2018.1450895\u003c/li\u003e\n\u003cli\u003eTrope, Y., \u0026amp; Liberman, N. (2010). Construal-level theory of psychological distance. \u003cem\u003ePsychol Rev, 117\u003c/em\u003e(2), 440-463. doi:https://doi.org/10.1037/a0018963\u003c/li\u003e\n\u003cli\u003eTurel, O., \u0026amp; Qahri-Saremi, H. (2016). Problematic use of social networking sites: Antecedents and consequence from a dual-system theory perspective. \u003cem\u003eJournal of Management Information Systems, 33\u003c/em\u003e(4), 1087-1116. doi:https://doi.org/10.1080/07421222.2016.1267529\u003c/li\u003e\n\u003cli\u003eTversky, A., \u0026amp; Kahneman, D. (1991). Loss aversion in riskless choice: a reference-dependent model. \u003cem\u003eQuarterly Journal of Economics, 106\u003c/em\u003e, 1039-1061. doi: https://doi.org/10.2307/2937956\u003c/li\u003e\n\u003cli\u003eVan Valkengoed, A., Perlaviciute, G., \u0026amp; Steg, L. (2024). From believing in climate change to adapting to climate change: The role of risk perception and efficacy beliefs. \u003cem\u003eRisk Analysis, 44\u003c/em\u003e(3), 553-565. doi:https://doi.org/10.1111/risa.14193\u003c/li\u003e\n\u003cli\u003eVlek, C., \u0026amp; Steg, L. (2007). Human behavior and environmental sustainability: Problems, driving forces, and research topics. \u003cem\u003eJournal of Social Issues, 63\u003c/em\u003e(1), 1-19. doi:https://doi.org/10.1111/j.1540-4560.2007.00493.x\u003c/li\u003e\n\u003cli\u003eWang, S., Wang, J., Lin, S., \u0026amp; Li, J. (2019). Public perceptions and acceptance of nuclear energy in China: The role of public knowledge, perceived benefit, perceived risk and public engagement. \u003cem\u003eEnergy Policy, 126\u003c/em\u003e, 352-360. doi:https://doi.org/10.1016/j.enpol.2018.11.040\u003c/li\u003e\n\u003cli\u003eWang, Y., Thier, K., Lee, S., \u0026amp; Nan, X. (2023). Persuasive effects of temporal framing in health messaging: a meta-analysis. \u003cem\u003eHealth Commun, 39\u003c/em\u003e, 1-14. doi:https://doi.org/10.1080/10410236.2023.2175407\u003c/li\u003e\n\u003cli\u003eWeiner, B. (2000). Intrapersonal and interpersonal theories of motivation from an attributional perspective. \u003cem\u003eEducational Psychology Review, 12\u003c/em\u003e(1), 1-14. doi:https://doi.org/10.1023/A:1009017532121\u003c/li\u003e\n\u003cli\u003eWolske, K. S., \u0026amp; Stern, P. C. (2018). Contributions of psychology to limiting climate change: Opportunities through consumer behavior. In S. Clayton \u0026amp; C. Manning (Eds.), \u003cem\u003ePsychology and Climate Change\u003c/em\u003e (pp. 127-160): Academic Press.\u003c/li\u003e\n\u003cli\u003eWynes, S., \u0026amp; Nicholas, K. A. (2017). The climate mitigation gap: education and government recommendations miss the most effective individual actions. \u003cem\u003eEnvironmental Research Letters, 12\u003c/em\u003e(7). doi:https://doi.org/10.1088/1748-9326/aa7541\u003c/li\u003e\n\u003cli\u003eYuan, J., Lu, Y., Wang, C., Cao, X., Chen, C., Cui, H., . . . Du, D. (2020). Ecology of industrial pollution in China. \u003cem\u003eEcosystem Health and Sustainability, 6\u003c/em\u003e(1), 1779010. doi:https://doi.org/10.1080/20964129.2020.1779010\u003c/li\u003e\n\u003cli\u003eZavareh, M. F., Mehdizadeh, M., \u0026amp; Nordfj\u0026aelig;rn, T. (2020). Active travel as a pro-environmental behaviour: An integrated framework. \u003cem\u003eTransportation Research Part D: Transport and Environment, 84\u003c/em\u003e, 102356. doi:https://doi.org/10.1016/j.trd.2020.102356\u003c/li\u003e\n\u003cli\u003eZelenski, J. M., \u0026amp; Desrochers, J. E. (2021). Can positive and self-transcendent emotions promote pro-environmental behavior? \u003cem\u003eCurrent opinion in psychology, 42\u003c/em\u003e, 31-35. doi:https://doi.org/10.31234/osf.io/adhx2\u003c/li\u003e\n\u003cli\u003eZhang, Y., Du, Q., Huang, Y. L., Mao, Y. Y., \u0026amp; Jiao, L. D. (2024). Decoding determinants of pro-environmental behaviors of higher education students: insights for sustainable future. \u003cem\u003eInternational Journal of Sustainability in Higher Education\u003c/em\u003e. doi:https://doi.org/10.1108/ijshe-03-2024-0166\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Footnotes","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003e These outlets are widely recognized as the most authoritative media in China, blending political authority with narrative elements, making them a critical data source for policy narrative research. For instance, Cao (2019) analyzed narrative features in China\u0026rsquo;s agricultural property rights policy changes using materials from People\u0026rsquo; s Daily and other media outlets. Similarly, McComas et al. (1999) analyzed how media constructed narratives around global warming based on reports from The New York Times and The Washington Post. Schaub (2021) examined how advocacy coalitions employed narrative strategies to influence Germany\u0026rsquo;s agricultural fertilizer policy, utilizing reports from the authoritative German outlet Frankfurter Allgemeine Zeitung.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"pro-environmental behavior, narrative plot, policy narrative, climate change, experimental survey","lastPublishedDoi":"10.21203/rs.3.rs-6230248/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6230248/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eNarratives have emerged as a critical policy tool for nudging public behaviors and intentions, garnering increasing attention from policymakers, scholars, and practitioners. However, minimal research has examined how distinct plots of climate narratives influence pro-environmental behavior intentions (PEBI). This study aims to fill this gap by examining the interplay between emotional framing (positive vs. negative) and temporal framing (occurred vs. not occurred) in government climate narrative plots, as well as their influence on PEBI through risk and benefit perception pathways. Using experimental data from a sample of 638 Chinese citizens, we found that negative framing narratives outperformed positive framing ones in enhancing PEBI by heightening risk perception. However, mediation analysis revealed that the advantage of negatively framed narratives was partially offset by their reduced benefit perception. Moreover, moderated mediation analysis demonstrated that, under an occurred temporal framing, positive framing narratives were more effective than negative framing ones in enhancing benefit perception, thereby promoting PEBI. These findings enrich the theoretical framework of causal pathways linking climate narratives to PEBI, offering robust empirical evidence to guide policymakers in crafting refined climate communication strategies.\u003c/p\u003e","manuscriptTitle":"The Impact of Government Climate Narrative Plot on Public Pro-Environmental Behavior Intentions: A Survey Experiment","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-04-29 20:18:55","doi":"10.21203/rs.3.rs-6230248/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"db7be381-733d-4a0e-8567-18678c00fcc6","owner":[],"postedDate":"April 29th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":47820090,"name":"Social science/Environmental studies"},{"id":47820091,"name":"Social science/Social policy"}],"tags":[],"updatedAt":"2025-06-11T14:08:52+00:00","versionOfRecord":[],"versionCreatedAt":"2025-04-29 20:18:55","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6230248","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6230248","identity":"rs-6230248","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}