Email Outreach Successfully Attracts Attention but Advocacy Techniques Do Not Further Improve Policymaker Engagement with Climate Science

Email Outreach Successfully Attracts Attention but Advocacy Techniques Do Not Further Improve Policymaker Engagement with Climate Science | 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 Email Outreach Successfully Attracts Attention but Advocacy Techniques Do Not Further Improve Policymaker Engagement with Climate Science Riley Loria, Jessica Pugel, Matthew Goldberg, Rebecca Bascom, and 4 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4607745/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 01 Feb, 2025 Read the published version in Communications Earth & Environment → Version 1 posted You are reading this latest preprint version Abstract One of the most challenging aspects of climate change mitigation today is not identifying solutions but reaching political leaders with climate scientists’ existing solutions. Although there is substantial research on climate change communication, such research rarely focuses on one of the most impactful groups: policymakers. It is essential to test theoretically sound methods to increase lawmaker attention to research evidence. In a series of four rapid-cycle randomized controlled email trials (N = 6642–7620 per trial), we test three common and theoretically derived advocacy tactics to increase engagement with a climate change fact sheet sent via email (i.e., a norms manipulation, a number focused manipulation, and emotional language manipulation). In all four trials, the control message increased engagement more than the messages using advocacy tactics, measured by fact sheet clicks. This demonstrates the importance of testing communication methods within the appropriate populations, especially a population with significant influence over climate policy. Scientific community and society/Scientific community/Policy Scientific community and society/Social sciences/Psychology Scientific community and society/Social sciences/Communication Scientific community and society/Social sciences/Climate change/Climate-change mitigation Figures Figure 1 Figure 2 Figure 3 Introduction There is broad and enduring consensus among environmental scientists that global climate change is caused by human activity and is a massive threat to life across the planet 1 .Decades of evidence indicate that climate change impacts virtually every part of the Earth, such as the health of marine life, decline in fertility of soil, and increased frequency of extreme weather events 2,3 . Though scientists across disciplines have developed effective methods to mitigate many of the impacts and causes of climate change, the implementation of such strategies has been slow 4,5 . One major challenge is reaching political leaders with the appropriate research to execute the existing solutions 6,7 . This makes research on effective communication about climate change and mitigation strategies vital. Though there is substantial scientific work on effective science communication of policy issues and the climate crisis, most of such research is focused on the general public. This work rarely focuses on policymakers (who we define here as legislators and their staff), despite their disproportionately high power, influence over policy, and ability to shape societal norms 8,9 . Legislators have both greater direct climate policy influence and sway over public opinion than does the general public 10 . As such, science communicators’ time and effort may be more effectively used by learning ways to directly engage with policymakers via primary communication like email 11,12 . By catering strategic communication about climate change to this population, we might improve both the reach and effect of such messaging. However, because of their high level of impact, there is also greater competition for the attention of policymakers 11 . To best make use of communication resources, we must evaluate effective methods of attention capture and engagement, specifically among legislators and their staff. Research suggests that message framing effects are highly context dependent 13 . Due to the somewhat malleable nature of framing effects, testing the effectiveness of a particular engagement tactic in its intended context is essential. While communication researchers have developed numerous theoretically sound tactics to increase message engagement, relatively few have tested the tactics among legislators. Not only are legislators a vital population to understand but they may be more challenging than other populations to successfully engage via common advocacy tactics. Past work from our team has shown mixed results of messaging strategies on engagement in a policymaker audience 12 . Policymakers, who are frequently inundated with persuasive messages, may be particularly challenging to reach. Given both the uniqueness of this group and their ability to help generate massive change, it is essential to test communication techniques in this population. In the following sections, we outline three common advocacy tactics designed to increase engagement, which the current project tests among legislators. While each of these strategies is strongly supported by theory and in experimental settings, they have rarely, if ever, been empirically assessed in this population. Firstly, an enormous body of work in the behavioral sciences finds that social norms impact attitudes and consequentially, behavior around issues like climate change 14–17 . However, the role of norms in the specific context of understanding policymakers' behavior may be unique. The behaviors associated with policy choices may be more complex than those often measured in norms experiments with the public (e.g., convincing people to adjust their thermostats for energy conservation via messaging about the energy usage of their neighbors). It is also plausible that, as publicly elected officials, policymakers may be particularly sensitive and attentive to norms. However, it is also plausible that for policymakers who are constantly inundated with information about public opinion, messages cuing social norms may not stand out or be attention grabbing, and thereby may not evoke engagement. We see substantial evidence for the influence of norms in general, but little is known about the effectiveness of norms as a framing technique in this context and for this audience. Secondly, numbers or statistics and the format in which the numbers are presented may influence message impact or processing 18,19 . For example, past research on persuasion suggests that a quantitative message can evoke less elaboration, meaning that people less deeply process messages that emphasize numbers 20 . Based on this work we might expect numbers to evoke less engagement. However, individual differences in numeracy, or ease of numerical processing, also predict how effective numbers are as advocacy tactics; appeals to statistics likely improves message attention for those with high numeracy 21–23 . Given the relevance of statistics, budget plans, and quantitative information generally for policy roles, we might expect high numeracy and number-oriented processing among policymakers. Because of the evidence of variability in the effect of numbers on message attention and because the numeracy of policymakers has not been empirically tested, message testing focused on how to best use quantitative information in this group is essential. Finally, appeal to emotion is another well-established communication tactic which merits further testing among policymakers. One way in which emotional appeals can influence message effectiveness is via attention 24,25 . Increased attention garnered by emotionally evocative messages may motivate deeper processing and more enduring retention of a message 26 . While a variety of research finds that, in many instances, appeals to evocative emotions like fear or sympathy can garner attention and engagement, these appeals still represent a framing strategy with effects that are audience and context dependent. Various researchers have found evidence suggesting that people can become desensitized to emotional appeals. For example, there is evidence for desensitization to fear appeals in public health campaigns. On the opposite end of emotions, compassion fatigue, or burnout associated with jobs that require consistent caretaking is a well-documented phenomenon 27–29 . Given the exposure policymakers and their staff have to emotional appeals from groups like advocates and lobbyists, this population could experience desensitization to emotional appeals. Accordingly, science communicators need to empirically test the effectiveness of appeals to emotion. Current Work and Results Our work is uniquely positioned to test the effectiveness of these advocacy techniques for disseminating climate change information in the high-impact context of science communication with policymakers. Acknowledging the competition for these individuals’ attention and their political influence, the current project assesses the effectiveness of various communication strategies to better allocate limited resources among science communicators. We use the SciComm Optimizer for Policy Engagement (SCOPE) model to compare engagement metrics for climate change fact sheet emails using some traditional advocacy methods (i.e., norms, numbers, and emotions) to a standard control email subject and body 9,12,27 . SCOPE is a replicable model designed to quantitatively assess strategies to increase the reach of science among policymakers via email using rapid-cycle randomized controlled trials (see Fig. 1 ). Though advocacy tactics have shown meaningful effects on climate change beliefs in experimental trials with the public, it is unclear if these effects will generalize to policymakers and to this messaging context 28 . The current study aims to test the effects of traditional advocacy tactics on engagement with climate change research among policymakers. The research team developed four fact sheets containing current and policy-relevant research on the impact of climate change to be sent out to state legislators and their staff via email. We conducted four messaging trials, each comparing different messaging techniques to a control message. Within each trial, the fact sheets were kept constant while the content and subject line of the emails were manipulated. In Trial one, selected legislators and staff received one of two emails with distinct bodies and subject lines that tested the use of norms. The norms manipulation message integrated the recipients’ state’s metric for the percentage of the population who 1) were worried about climate change, and 2) thought their local officials should do more to address climate change. These data were collected and made available by the Yale Program on Climate Change Communication (YPCCC). The control email did not include these public opinion polling numbers and had a simple subject line. Although recipients in states with a higher percentage of people worried about climate change did in fact click more frequently (IRR = 1.04, SE = 0.01, z = 2.78, p = .006), those who received the norm email clicked about half as many times as those who received the control (IRR = 0.48, SE = 0.07, z = -5.30, p < .001). Trial two replicated this norm vs. no norms manipulation with an additional set of conditions. Two conditions included numbers in the email body and the other two did not, resulting in four distinct email groups. In this trial, the presence of numbers did not cause a difference in clicks on the fact sheet (IRR = 1.03, SE = 0.16, z = 0.17, p = .87, 95%CI[0.76, 1.39]), nor was there a significant interaction between language and numbers condition (IRR = 1.20, SE = 0.37, z = 0.60, p = − .55, 95%CI[0.66, 2.20]). However, there was a significant impact of the language used. Specifically, the message that was framed using no norms language was clicked 1.57 times as frequently as the email using norms language (IRR = 1.57, SE = 0.24, z = 2.94, p = .003, 95%CI[1.16, 2.13]). For replies to the emails, there was also a significant main effect of language. Those who received the “no norms” language were about 5.5 times as likely to reply (170 vs. 32 replies) as those who received the “norms” email (OR = 5.51, SE = 1.07, z = 8.79, p < .001, 95%CI[3.77, 8.06]). Note that this may be inflated due to autoreplies; the norms email did not prompt as many autoreplies as the no norms one did. In contrast to Trial one, including the percentage of people in their state reported to support regulating CO2 as a pollutant in the email body did not significantly affect fact sheet clicks, nor did it moderate the impact of the numbers’ presence. Trial three used three email bodies to further test the role of numbers. In the first, no numbers were included at all, instead describing the risks of climate change with general terms (e.g., “increasing”). In the second, “soft” numbers were used, which described the magnitude of the risks concretely, but without specific absolute numbers (e.g., “double”, “one in eight families”). The final email condition used “hard” numbers, which describe the risks with specific numbers (e.g., “increase by 2.6x”, “41 million Americans”). The subject line remained constant across conditions. There was no significant effect of the number condition on how many times the fact sheet was clicked when comparing no numbers to soft numbers, (IRR = 1.24, SE = 0.23, z = 1.17, p = .25, 95%CI[0.86, 1.78]) or when comparing hard numbers to no numbers (IRR = 1.09, SE = 0.20, z = 0.44, p = .660, 95%CI[0.75, 1.57]). Number condition also had no effect on whether the recipient replied (chi2( 2 ) = 0.49, p = .78). Finally, in Trial four, three email bodies and subject lines were crafted to vary the frequency and intensity of emotional words but keep constant the informational content. The no emotion message described the impact of extreme heat using detached, medical language (e.g., “For example, it’s estimated that more than 1,000 deaths in the US will be from extreme heat this year.”). The second used a moderate amount of emotion (e.g., “As a result, this year, the families of more than 1,000 Americans will lose their loved ones.”), and the third used a high amount of emotionally charged words (e.g., “As a result, this year alone , over 1,000 Americans will be killed prematurely - robbing them of living a full life.”). There was no significant effect of emotion level on number of times they clicked the fact sheet when comparing high emotion to no emotion, (IRR = 0.75, SE = 0.13, z = -1.70, p = .089, 95%CI[0.54, 1.04]) or when comparing medium emotion to no emotion, (IRR = 0.91, SE = 0.15, z = -0.56, p = .058, 95%CI[0.66, 1.26]). There was also no effect of emotion condition on whether replied to the email (chi2( 2 ) = 1.51, p = .47). Table 1 Descriptive Results N Fact Sheet Clicks (ŷ) Replies Trial 1 6642 482 (0.07) 148 Control 3319 325 (0.10) 118 Norms 3323 157 (0.05) 30 Trial 2 7477 621 (0.08) 202 Norms, Numbers 1870 125 (0.07) 14 Norms, No Numbers 1866 116 (0.06) 18 No Norms, Numbers 1877 180 (0.10) 82 No Norms, No Numbers 1864 200 (0.11) 88 Trial 3 7575 462 (0.06) 297 No numbers 2525 139 (0.06) 93 Soft numbers 2526 172 (0.07) 113 Hard numbers 2524 151 (0.06) 91 Trial 4 7620 809 (0.11) 404 Low emotion 2534 303 (0.12) 138 Medium emotion 2549 278 (0.11) 124 High emotion 2537 228 (0.09) 142 Note. ŷ = predicted value of clicks (the mean) Table 2 Results of negative binomial models testing the effects of email body on number of clicks on the fact sheet IRR (95% CI) SE z p Trial 1 Norm (ref. Control) 0.48 (0.37, 0.63) 0.07 -5.30 < .001 Trial 2 No Norms (ref. Norms) 1.57 (1.16, 2.13) 0.24 2.94 .003 No numbers (ref. Numbers) 1.02 (0.76, 1.39) 0.16 0.17 .87 Trial 3 Soft numbers (ref. no numbers) 1.24 (0.86, 1.78) 0.23 1.14 .25 Hard numbers (ref. no numbers) 1.09 (0.75, 1.57) 0.20 0.44 .66 Trial 4 Medium emotion (ref. no emotion) 0.91 (0.66, 1.26) 0.15 -0.56 .58 High emotion (ref. no emotion) 0.75 (0.54, 1.04) 0.13 -1.70 .089 Note. IRR = incident ratio interval; CI = confidence interval; SE = standard error Discussion Across all four trials, our messages gain a substantial amount of policymaker engagement. While click rates and response rates can be highly context dependent and there is little past work on expected click rates among policymakers, based on prior email data collected by our organization, rates in these trials were good (as high as 11%) 30 . However, we do not find evidence that the theoretically supported communication tactics tested here evoke more engagement among policymakers than simple control messages. At best, the advocacy tactic and control conditions elicited roughly the same level of engagement, and at worst, the advocacy emails received substantially less engagement. The ineffectiveness of these theoretically sound communication strategies demonstrates the importance of testing tactics for disseminating climate change information within the unique contexts in which the methods might be used. The massive influence policymakers have over climate change legislation makes testing messages in this population especially vital 29 . We offer several explanations for the weakness of these advocacy tools among legislators. Trials one and two attempted to manipulate norms, alluding to concern among constituents about climate threats. Interestingly, in both trials we found that emails appealing to norms garnered significantly less engagement than the standard control message. Despite the substantial body of past work demonstrating the impact that social norms have on individuals’ attitudes and behavior, reporting that a high percentage of citizens were concerned about a climate issue did not evoke increased engagement 15 . Though the reason for this effect is not entirely clear, one possible explanation is the directness and simplicity of the control message and subject line compared to the norms message made it appear more authentic. This may be particularly appealing to this population, given they receive a high volume of messages. Interestingly, the actual percentage of citizens worried about climate change in a state did predict greater message engagement, suggesting that policymakers share similar levels of concern about climate change to their constituents. Even advocacy tactics that are based on well-established psychological phenomena can fall short in populations that are desensitized to their use, making continuous testing essential. Trials two and three found no impact of either the inclusion of numbers or any impact of the type of numbers included on engagement. Individual differences in numeracy may partly account for these findings. Work on numeracy suggests it may moderate how effective numbers are for facilitating engagement, meaning that what is an effective engagement tactic for some, is not effective or even repelling to others 22,23,31 .So, while quantitative messages may increase engagement among some policymakers or congressional staff, it might decrease engagement for others, suppressing effects. Additionally, while policymakers may be more inclined towards the use of numbers in some contexts (e.g., budget choices, polling), negative perceptions of scientists as cold may make numbers ineffective or even aversive in others 32,33 .This insight suggests the use of numbers in climate change information campaigns may not be worth the additional investment in this specific context, given their lack of impact. Again, we see the importance of repeated testing of specific messaging strategies. Lastly, Trial four manipulated the intensity and frequency of emotionally valanced language. Again, we found no significant differences in engagement by the level of emotion included in the email body. While in many instances emotional appeals can be effective tools for capturing attention and thereby increasing engagement, this is not universally true. Legislators may encounter frequent emotional appeals and be desensitized to them, making messages less attention grabbing 34,35 . Appeals to negative emotion may even result in less attention if the aversive emotion evokes a desire for avoidance or escape. Research, including in climate change advocacy, finds evidence of context dependent disengagement with threatening messages 36 . The perceived authenticity of emotions also plays an important role in their efficacy as messaging strategies. Past work finds that authentically evoked emotions may drive engagement with science about racism and public health, but emotional appeals perceived as inauthentic were ineffective or even counter-productive 37 . Though this project offers valuable insight into the effectiveness of the examined communication strategies, it is important to note its limitations. Perhaps most importantly, this project can only speak to the effectiveness of the particular stimuli included in our trials. We operationalize and manipulate norms, numbers, and emotions, each in one specific way. As such, we cannot conclusively say that no version of these manipulations would be effective among lawmakers. For example, we do not cover a range of emotions, focusing on negative valence, and cannot argue that other emotion manipulations (e.g., positive emotion-based stimuli), would be ineffective. It is also worth noting that the engagement we track in these trials is limited to email behavior. This kind of engagement does not necessarily translate into other actions. However, despite these limitations, our email trials suggest that we are consistently able to get the attention of policymakers on essential scientific findings. Work targeting email engagement with policymakers may serve as a key steppingstone to other kinds of meaningful engagement with climate research and policy. Despite the strong theoretical basis for the three strategies tested across four trials in the current research, they were ineffective for our target population of state legislators. These results demonstrate that we should not assume common persuasion and engagement tactics will improve engagement among legislators. Future research should assess any planned messaging tactics within the appropriate population and researchers should be aware that it may be difficult and unfruitful to try to improve baseline engagement with such tactics. Anyone aiming to reach policymakers about issues as important as climate change mitigation should approach standard advocacy strategies with a healthy dose of skepticism. When interacting with policymakers, science communicators may be better off expending their efforts on things such as increasing the diversity of ways and mediums to contact, influence, or engage policymakers rather than attempting to refine the ideal message. In this group, a purposeful and direct message seems to be best. Declarations "IRB approval and oversight was provided by the Pennsylvania State University ethics board." References Ripple, W. J., Wolf, C., Newsome, T. M., Barnard, P. & Moomaw, W. R. World scientists’ warning of a climate emergency. BioScience 70 , 8–100 (2020). Bernai, R. R. Managing the risks of extreme events and disasters to advance climate change adaptation. Econ. Energy Environ. Policy 2 , 101–113 (2013). Grimm, N. B. et al. The impacts of climate change on ecosystem structure and function. Front. Ecol. Environ. 11 , 474–482 (2013). Fawzy, S., Osman, A. I., Doran, J. & Rooney, D. W. Strategies for mitigation of climate change: a review. Environ. Chem. Lett. 18 , 2069–2094 (2020). Wright, L. & Fulton, L. Climate change mitigation and transport in developing nations. Transp. Rev. 25 , 691–717 (2005). McHugh, L. H., Lemos, M. C. & Morrison, T. H. Risk? Crisis? Emergency? Implications of the new climate emergency framing for governance and policy. Wiley Interdiscip. Rev. Clim. Change 12 , e736 (2021). Wiest, S. L., Raymond, L. & Clawson, R. A. Framing, partisan predispositions, and public opinion on climate change. Glob. Environ. Change 31 , 187–198 (2015). Goldberg, M. H., Gustafson, A., Ballew, M. T., Rosenthal, S. A. & Leiserowitz, A. Identifying the most important predictors of support for climate policy in the United States. Behav. Public Policy 5 , 480–502 (2021). Scott, T. et al. Cutting through the noise during crisis by enhancing the relevance of research to policymakers. Evid. Policy 19 , 178–195 (2023). Van Boven, L. & Sherman, D. K. Elite influence on public attitudes about climate policy. Curr. Opin. Behav. Sci. 42 , 83–88 (2021). Goldberg, M. H. & Gustafson, A. A Framework for Understanding the Effects of Strategic Communication Campaigns. Int. J. Strateg. Commun. 17 , 1–20 (2023). Scott, J. T. et al. SciComm Optimizer for Policy Engagement: a randomized controlled trial of the SCOPE model on state legislators’ research use in public discourse. Implement. Sci. 18 , 1–13 (2023). Chong, D. & Druckman, J. N. A theory of framing and opinion formation in competitive elite environments. J. Commun. 57 , 99–118 (2007). Ajzen, I. The theory of planned behavior. Organ. Behav. Hum. Decis. Process. 50 , 179–211 (1991). Cialdini, R. B. et al. Managing social norms for persuasive impact. Soc. Influ. 1 , 3–15 (2006). Additional Declarations There is NO Competing Interest. Supplementary Files OnlineMethods.docx Cite Share Download PDF Status: Published Journal Publication published 01 Feb, 2025 Read the published version in Communications Earth & Environment → 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. 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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-4607745","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":324602479,"identity":"a38f26cb-d7c7-46b1-b44f-b0f5f2ba0623","order_by":0,"name":"Riley Loria","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA9ElEQVRIie3Pv6rCMBTH8RMPeJdC1yP+eYZAoRX0YRThurh16eBQEdrFB3DwJfoIEtClLyAuBcHJodOlIMhNIi5Co6NgvkP5EfKhBMBm+8QQY/ntywGs0AOAq3MDYYqQuoNcj5cEHgSgSW+RIGWLkwPUC1I8R1WkhsgKiAbjuIZ0BFt6kngd0fQPq1yO/DfkkE9rCSFL2pKM1wj+kSVywMwnlggTSa938vMXauJeJLkZSYJ34vioCam/xEaybG04eYRO2FJvITqHfLSberXEFdvyEg175O6zstJjkhXlfNCtI6qGw5+PRobrKla9uGCz2Wxf3j9EkEcCAXnlZgAAAABJRU5ErkJggg==","orcid":"","institution":"University of Colorado Boulder","correspondingAuthor":true,"prefix":"","firstName":"Riley","middleName":"","lastName":"Loria","suffix":""},{"id":324602480,"identity":"62030f02-1031-4dc5-8d57-ce3825a000d3","order_by":1,"name":"Jessica Pugel","email":"","orcid":"","institution":"Evidence-to-Impact Collaborative, Pennsylvania State University","correspondingAuthor":false,"prefix":"","firstName":"Jessica","middleName":"","lastName":"Pugel","suffix":""},{"id":324602481,"identity":"ec76988e-bed3-4745-9b47-568d1fb4e8fb","order_by":2,"name":"Matthew Goldberg","email":"","orcid":"https://orcid.org/0000-0003-1267-7839","institution":"Yale University","correspondingAuthor":false,"prefix":"","firstName":"Matthew","middleName":"","lastName":"Goldberg","suffix":""},{"id":324602482,"identity":"5c53b3ee-8b3b-45dd-853a-2dca95ba41fa","order_by":3,"name":"Rebecca Bascom","email":"","orcid":"","institution":"Department of Medicine; Department of Public Health Sciences, Pennsylvania State University","correspondingAuthor":false,"prefix":"","firstName":"Rebecca","middleName":"","lastName":"Bascom","suffix":""},{"id":324602483,"identity":"f83e4ff0-6a3c-4bf8-be50-93e85ad60a5b","order_by":4,"name":"Deborah Halla","email":"","orcid":"","institution":"Evidence-to-Impact Collaborative, Pennsylvania State University","correspondingAuthor":false,"prefix":"","firstName":"Deborah","middleName":"","lastName":"Halla","suffix":""},{"id":324602484,"identity":"e21df75f-1d0b-4fc3-8efe-1f8b29744c3e","order_by":5,"name":"Taylor Scott","email":"","orcid":"","institution":"Evidence-to-Impact Collaborative, Pennsylvania State University","correspondingAuthor":false,"prefix":"","firstName":"Taylor","middleName":"","lastName":"Scott","suffix":""},{"id":324602485,"identity":"6898dd1b-2af3-4808-a062-49e672078f17","order_by":6,"name":"Max Crowley","email":"","orcid":"","institution":"Evidence-to-Impact Collaborative, Pennsylvania State University","correspondingAuthor":false,"prefix":"","firstName":"Max","middleName":"","lastName":"Crowley","suffix":""},{"id":324602486,"identity":"1621ab7e-7423-4839-a880-40fe456471d3","order_by":7,"name":"Elizabeth Long","email":"","orcid":"","institution":"Evidence-to-Impact Collaborative, Pennsylvania State University","correspondingAuthor":false,"prefix":"","firstName":"Elizabeth","middleName":"","lastName":"Long","suffix":""}],"badges":[],"createdAt":"2024-06-19 19:30:08","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4607745/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4607745/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1038/s43247-025-02055-0","type":"published","date":"2025-02-01T05:00:00+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":63023963,"identity":"a75fb46b-3a80-477d-913e-3cd27e059219","added_by":"auto","created_at":"2024-08-22 08:05:48","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":25107,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eThe SCOPE Model\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e\u003cstrong\u003eNote.\u003c/strong\u003e\u003c/em\u003e\u003cem\u003e CQI = continuous quality improvement.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4607745/v1/81b9a7ea6cfc48753b843bf1.jpg"},{"id":63022241,"identity":"58d1458c-880f-4b06-ab18-3967eddbfaad","added_by":"auto","created_at":"2024-08-22 07:49:48","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":38206,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eTotal fact sheet clicks by condition across trials\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-4607745/v1/161d09cb746f1f780f04f939.png"},{"id":63022246,"identity":"3b8765c1-8d04-4818-b74e-049a85054115","added_by":"auto","created_at":"2024-08-22 07:49:49","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":69189,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eForest plot for incidence ratio intervals of fact sheet clicks by email condition across all trials. An incident ratio of 1 indicates no difference in click rates between conditions. Only the norms versus control conditions in trials 1 and 2 showed substantial differences in click rates by condition such that in both trials, the control condition received substantially more clicks than the norms condition.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-4607745/v1/6c4455274936ad87eb794783.png"},{"id":75251873,"identity":"528c773f-1695-48d4-9650-d220e3c3bfac","added_by":"auto","created_at":"2025-02-02 08:06:09","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":665908,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4607745/v1/5a335021-a8d9-4576-84bf-66bae44c4f25.pdf"},{"id":63023259,"identity":"469a17b2-b985-40d0-aed2-97ee0723ec37","added_by":"auto","created_at":"2024-08-22 07:57:48","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":24101,"visible":true,"origin":"","legend":"","description":"","filename":"OnlineMethods.docx","url":"https://assets-eu.researchsquare.com/files/rs-4607745/v1/96e7dc1063acc2fb76420efc.docx"}],"financialInterests":"There is \u003cb\u003eNO\u003c/b\u003e Competing Interest.","formattedTitle":"Email Outreach Successfully Attracts Attention but Advocacy Techniques Do Not Further Improve Policymaker Engagement with Climate Science","fulltext":[{"header":"Introduction","content":"\u003cp\u003eThere is broad and enduring consensus among environmental scientists that global climate change is caused by human activity and is a massive threat to life across the planet\u003csup\u003e1\u003c/sup\u003e.Decades of evidence indicate that climate change impacts virtually every part of the Earth, such as the health of marine life, decline in fertility of soil, and increased frequency of extreme weather events\u003csup\u003e2,3\u003c/sup\u003e. Though scientists across disciplines have developed effective methods to mitigate many of the impacts and causes of climate change, the implementation of such strategies has been slow\u003csup\u003e4,5\u003c/sup\u003e.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eOne major challenge is reaching political leaders with the appropriate research to execute the existing solutions\u003csup\u003e6,7\u003c/sup\u003e. This makes research on effective communication about climate change and mitigation strategies vital. Though there is substantial scientific work on effective science communication of policy issues and the climate crisis, most of such research is focused on the general public. This work rarely focuses on policymakers (who we define here as legislators and their staff), despite their disproportionately high power, influence over policy, and ability to shape societal norms\u003csup\u003e8,9\u003c/sup\u003e. \u0026nbsp; Legislators have both greater direct climate policy influence and sway over public opinion than does the general public\u003csup\u003e10\u003c/sup\u003e. As such, science communicators\u0026rsquo; time and effort may be more effectively used by learning ways to directly engage with policymakers via primary communication like email\u003csup\u003e11,12\u003c/sup\u003e. By catering strategic communication about climate change to this population, we might improve both the reach and effect of such messaging. However, because of their high level of impact, there is also greater competition for the attention of policymakers \u003csup\u003e11\u003c/sup\u003e. To best make use of communication resources, we must evaluate effective methods of attention capture and engagement, specifically among legislators and their staff.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eResearch suggests that message framing effects are highly context dependent\u003csup\u003e13\u003c/sup\u003e. Due to the somewhat malleable nature of framing effects, testing the effectiveness of a particular engagement tactic in its intended context is essential. While communication researchers have developed numerous theoretically sound tactics to increase message engagement, relatively few have tested the tactics among legislators. Not only are legislators a vital population to understand but they may be more challenging than other populations to successfully engage via common advocacy tactics. Past work from our team has shown mixed results of messaging strategies on engagement in a policymaker audience\u003csup\u003e12\u003c/sup\u003e. Policymakers, who are frequently inundated with persuasive messages, may be particularly challenging to reach. Given both the uniqueness of this group and their ability to help generate massive change, it is essential to test communication techniques in this population. In the following sections, we outline three common advocacy tactics designed to increase engagement, which the current project tests among legislators. While each of these strategies is strongly supported by theory and in experimental settings, they have rarely, if ever, been empirically assessed in this population.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFirstly, an enormous body of work in the behavioral sciences finds that social norms impact attitudes and consequentially, behavior around issues like climate change\u003csup\u003e14\u0026ndash;17\u003c/sup\u003e. However, the role of norms in the specific context of understanding policymakers\u0026apos; behavior may be unique. The behaviors associated with policy choices may be more complex than those often measured in norms experiments with the public (e.g., convincing people to adjust their thermostats for energy conservation via messaging about the energy usage of their neighbors). It is also plausible that, as publicly elected officials, policymakers may be particularly sensitive and attentive to norms. However, it is also plausible that for policymakers who are constantly inundated with information about public opinion, messages cuing social norms may not stand out or be attention grabbing, and thereby may not evoke engagement. We see substantial evidence for the influence of norms in general, but little is known about the effectiveness of norms as a framing technique in this context and for this audience.\u003c/p\u003e\n\u003cp\u003eSecondly, numbers or statistics and the format in which the numbers are presented may influence message impact or processing\u003csup\u003e18,19\u003c/sup\u003e. For example, past research on persuasion suggests that a quantitative message can evoke less elaboration, meaning that people less deeply process messages that emphasize numbers\u003csup\u003e20\u003c/sup\u003e. Based on this work we might expect numbers to evoke less engagement. However, \u0026nbsp;individual differences in numeracy, or ease of numerical processing, also predict how effective numbers are as advocacy tactics; appeals to statistics likely improves message attention for those with high numeracy\u003csup\u003e21\u0026ndash;23\u003c/sup\u003e. Given the relevance of statistics, budget plans, and quantitative information generally for policy roles, we might expect high numeracy and number-oriented processing among policymakers. Because of the evidence of variability in the effect of numbers on message attention and because the numeracy of policymakers has not been empirically tested, message testing focused on how to best use quantitative information in this group is essential.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFinally, appeal to emotion is another well-established communication tactic which merits further testing among policymakers. One way in which emotional appeals can influence message effectiveness is via attention\u003csup\u003e24,25\u003c/sup\u003e. Increased attention garnered by emotionally evocative messages may motivate deeper processing and more enduring retention of a message\u003csup\u003e26\u003c/sup\u003e. While a variety of research finds that, in many instances, appeals to evocative emotions like fear or sympathy can garner attention and engagement, these appeals still represent a framing strategy with effects that are audience and context dependent. Various researchers have found evidence suggesting that people can become desensitized to emotional appeals. For example, there is evidence for desensitization to fear appeals in public health campaigns. On the opposite end of emotions, compassion fatigue, or burnout associated with jobs that require consistent caretaking is a well-documented phenomenon\u003csup\u003e27\u0026ndash;29\u003c/sup\u003e. Given the exposure policymakers and their staff have to emotional appeals from groups like advocates and lobbyists, this population could experience desensitization to emotional appeals. Accordingly, science communicators need to empirically test the effectiveness of appeals to emotion.\u0026nbsp;\u003c/p\u003e"},{"header":"Current Work and Results","content":"\u003cp\u003eOur work is uniquely positioned to test the effectiveness of these advocacy techniques for disseminating climate change information in the high-impact context of science communication with policymakers. Acknowledging the competition for these individuals\u0026rsquo; attention and their political influence, the current project assesses the effectiveness of various communication strategies to better allocate limited resources among science communicators. We use the SciComm Optimizer for Policy Engagement (SCOPE) model to compare engagement metrics for climate change fact sheet emails using some traditional advocacy methods (i.e., norms, numbers, and emotions) to a standard control email subject and body\u003csup\u003e9,12,27\u003c/sup\u003e. SCOPE is a replicable model designed to quantitatively assess strategies to increase the reach of science among policymakers via email using rapid-cycle randomized controlled trials (see Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Though advocacy tactics have shown meaningful effects on climate change beliefs in experimental trials with the public, it is unclear if these effects will generalize to policymakers and to this messaging context\u003csup\u003e28\u003c/sup\u003e. The current study aims to test the effects of traditional advocacy tactics on engagement with climate change research among policymakers.\u003c/p\u003e \u003c/p\u003e \u003cp\u003eThe research team developed four fact sheets containing current and policy-relevant research on the impact of climate change to be sent out to state legislators and their staff via email. We conducted four messaging trials, each comparing different messaging techniques to a control message. Within each trial, the fact sheets were kept constant while the content and subject line of the emails were manipulated.\u003c/p\u003e \u003cp\u003eIn Trial one, selected legislators and staff received one of two emails with distinct bodies and subject lines that tested the use of norms. The norms manipulation message integrated the recipients\u0026rsquo; state\u0026rsquo;s metric for the percentage of the population who 1) were worried about climate change, and 2) thought their local officials should do more to address climate change. These data were collected and made available by the Yale Program on Climate Change Communication (YPCCC). The control email did not include these public opinion polling numbers and had a simple subject line. Although recipients in states with a higher percentage of people worried about climate change did in fact click more frequently (IRR\u0026thinsp;=\u0026thinsp;1.04, SE\u0026thinsp;=\u0026thinsp;0.01, z\u0026thinsp;=\u0026thinsp;2.78, p\u0026thinsp;=\u0026thinsp;.006), those who received the norm email clicked about half as many times as those who received the control (IRR\u0026thinsp;=\u0026thinsp;0.48, SE\u0026thinsp;=\u0026thinsp;0.07, z = -5.30, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001).\u003c/p\u003e \u003cp\u003eTrial two replicated this norm vs. no norms manipulation with an additional set of conditions. Two conditions included numbers in the email body and the other two did not, resulting in four distinct email groups. In this trial, the presence of numbers did not cause a difference in clicks on the fact sheet (IRR\u0026thinsp;=\u0026thinsp;1.03, SE\u0026thinsp;=\u0026thinsp;0.16, z\u0026thinsp;=\u0026thinsp;0.17, p\u0026thinsp;=\u0026thinsp;.87, 95%CI[0.76, 1.39]), nor was there a significant interaction between language and numbers condition (IRR\u0026thinsp;=\u0026thinsp;1.20, SE\u0026thinsp;=\u0026thinsp;0.37, z\u0026thinsp;=\u0026thinsp;0.60, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;.55, 95%CI[0.66, 2.20]). However, there was a significant impact of the language used. Specifically, the message that was framed using no norms language was clicked 1.57 times as frequently as the email using norms language (IRR\u0026thinsp;=\u0026thinsp;1.57, SE\u0026thinsp;=\u0026thinsp;0.24, z\u0026thinsp;=\u0026thinsp;2.94, p\u0026thinsp;=\u0026thinsp;.003, 95%CI[1.16, 2.13]).\u003c/p\u003e \u003cp\u003eFor replies to the emails, there was also a significant main effect of language. Those who received the \u0026ldquo;no norms\u0026rdquo; language were about 5.5 times as likely to reply (170 vs. 32 replies) as those who received the \u0026ldquo;norms\u0026rdquo; email (OR\u0026thinsp;=\u0026thinsp;5.51, SE\u0026thinsp;=\u0026thinsp;1.07, z\u0026thinsp;=\u0026thinsp;8.79, p\u0026thinsp;\u0026lt;\u0026thinsp;.001, 95%CI[3.77, 8.06]). Note that this may be inflated due to autoreplies; the norms email did not prompt as many autoreplies as the no norms one did. In contrast to Trial one, including the percentage of people in their state reported to support regulating CO2 as a pollutant in the email body did not significantly affect fact sheet clicks, nor did it moderate the impact of the numbers\u0026rsquo; presence.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eTrial three used three email bodies to further test the role of numbers. In the first, no numbers were included at all, instead describing the risks of climate change with general terms (e.g., \u0026ldquo;increasing\u0026rdquo;). In the second, \u0026ldquo;soft\u0026rdquo; numbers were used, which described the magnitude of the risks concretely, but without specific absolute numbers (e.g., \u0026ldquo;double\u0026rdquo;, \u0026ldquo;one in eight families\u0026rdquo;). The final email condition used \u0026ldquo;hard\u0026rdquo; numbers, which describe the risks with specific numbers (e.g., \u0026ldquo;increase by 2.6x\u0026rdquo;, \u0026ldquo;41\u0026nbsp;million Americans\u0026rdquo;). The subject line remained constant across conditions. There was no significant effect of the number condition on how many times the fact sheet was clicked when comparing no numbers to soft numbers, (IRR\u0026thinsp;=\u0026thinsp;1.24, SE\u0026thinsp;=\u0026thinsp;0.23, z\u0026thinsp;=\u0026thinsp;1.17, p\u0026thinsp;=\u0026thinsp;.25, 95%CI[0.86, 1.78]) or when comparing hard numbers to no numbers (IRR\u0026thinsp;=\u0026thinsp;1.09, SE\u0026thinsp;=\u0026thinsp;0.20, z\u0026thinsp;=\u0026thinsp;0.44, p\u0026thinsp;=\u0026thinsp;.660, 95%CI[0.75, 1.57]). Number condition also had no effect on whether the recipient replied (chi2(\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e)\u0026thinsp;=\u0026thinsp;0.49, p\u0026thinsp;=\u0026thinsp;.78).\u003c/p\u003e \u003cp\u003eFinally, in Trial four, three email bodies and subject lines were crafted to vary the frequency and intensity of emotional words but keep constant the informational content. The no emotion message described the impact of extreme heat using detached, medical language (e.g., \u0026ldquo;For example, it\u0026rsquo;s estimated that more than 1,000 deaths in the US will be from extreme heat this year.\u0026rdquo;). The second used a moderate amount of emotion (e.g., \u0026ldquo;As a result, this year, the families of more than 1,000 Americans will lose their loved ones.\u0026rdquo;), and the third used a high amount of emotionally charged words (e.g., \u0026ldquo;As a result, \u003cem\u003ethis year alone\u003c/em\u003e, over 1,000 Americans will be killed prematurely - robbing them of living a full life.\u0026rdquo;). There was no significant effect of emotion level on number of times they clicked the fact sheet when comparing high emotion to no emotion, (IRR\u0026thinsp;=\u0026thinsp;0.75, SE\u0026thinsp;=\u0026thinsp;0.13, z = -1.70, p\u0026thinsp;=\u0026thinsp;.089, 95%CI[0.54, 1.04]) or when comparing medium emotion to no emotion, (IRR\u0026thinsp;=\u0026thinsp;0.91, SE\u0026thinsp;=\u0026thinsp;0.15, z = -0.56, p\u0026thinsp;=\u0026thinsp;.058,\u003c/p\u003e \u003cp\u003e95%CI[0.66, 1.26]). There was also no effect of emotion condition on whether replied to the email (chi2(\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e)\u0026thinsp;=\u0026thinsp;1.51, p\u0026thinsp;=\u0026thinsp;.47).\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\u003eDescriptive Results\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFact Sheet Clicks (ŷ)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eReplies\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTrial 1\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e6642\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e482 (0.07)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e148\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eControl\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3319\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e325 (0.10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e118\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNorms\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3323\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e157 (0.05)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTrial 2\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e7477\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e621 (0.08)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e202\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNorms, Numbers\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1870\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e125 (0.07)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNorms, No Numbers\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1866\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e116 (0.06)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo Norms, Numbers\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1877\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e180 (0.10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e82\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo Norms, No Numbers\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1864\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e200 (0.11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e88\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTrial 3\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e7575\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e462 (0.06)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e297\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo numbers\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2525\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e139 (0.06)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e93\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSoft numbers\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2526\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e172 (0.07)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e113\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHard numbers\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2524\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e151 (0.06)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e91\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTrial 4\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e7620\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e809 (0.11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e404\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLow emotion\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2534\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e303 (0.12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e138\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMedium emotion\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2549\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e278 (0.11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e124\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigh emotion\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2537\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e228 (0.09)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e142\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003e\u003cb\u003eNote.\u003c/b\u003e ŷ = predicted value of clicks (the mean)\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \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\u003eResults of negative binomial models testing the effects of email body on number of clicks on the fact sheet\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=\"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=\"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\u003eIRR (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSE\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ez\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTrial 1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNorm (ref. Control)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.48 (0.37, 0.63)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-5.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTrial 2\u003c/b\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\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo Norms (ref. Norms)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.57 (1.16, 2.13)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.003\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo numbers (ref. Numbers)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.02 (0.76, 1.39)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.87\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTrial 3\u003c/b\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\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSoft numbers (ref. no numbers)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.24 (0.86, 1.78)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.25\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHard numbers (ref. no numbers)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.09 (0.75, 1.57)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.66\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTrial 4\u003c/b\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\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMedium emotion (ref. no emotion)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.91 (0.66, 1.26)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.58\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigh emotion (ref. no emotion)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.75 (0.54, 1.04)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-1.70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.089\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003e\u003cb\u003eNote.\u003c/b\u003e IRR\u0026thinsp;=\u0026thinsp;incident ratio interval; CI\u0026thinsp;=\u0026thinsp;confidence interval; SE\u0026thinsp;=\u0026thinsp;standard error\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e "},{"header":"Discussion","content":"\u003cp\u003eAcross all four trials, our messages gain a substantial amount of policymaker engagement. While click rates and response rates can be highly context dependent and there is little past work on expected click rates among policymakers, based on prior email data collected by our organization, rates in these trials were good (as high as 11%)\u003csup\u003e30\u003c/sup\u003e. However, we do not find evidence that the theoretically supported communication tactics tested here evoke more engagement among policymakers than simple control messages. At best, the advocacy tactic and control conditions elicited roughly the same level of engagement, and at worst, the advocacy emails received substantially less engagement. The ineffectiveness of these theoretically sound communication strategies demonstrates the importance of testing tactics for disseminating climate change information within the unique contexts in which the methods might be used. The massive influence policymakers have over climate change legislation makes testing messages in this population especially vital\u003csup\u003e29\u003c/sup\u003e. We offer several explanations for the weakness of these advocacy tools among legislators.\u003c/p\u003e \u003cp\u003eTrials one and two attempted to manipulate norms, alluding to concern among constituents about climate threats. Interestingly, in both trials we found that emails appealing to norms garnered significantly less engagement than the standard control message. Despite the substantial body of past work demonstrating the impact that social norms have on individuals\u0026rsquo; attitudes and behavior, reporting that a high percentage of citizens were concerned about a climate issue did not evoke increased engagement\u003csup\u003e15\u003c/sup\u003e. Though the reason for this effect is not entirely clear, one possible explanation is the directness and simplicity of the control message and subject line compared to the norms message made it appear more authentic. This may be particularly appealing to this population, given they receive a high volume of messages. Interestingly, the actual percentage of citizens worried about climate change in a state did predict greater message engagement, suggesting that policymakers share similar levels of concern about climate change to their constituents. Even advocacy tactics that are based on well-established psychological phenomena can fall short in populations that are desensitized to their use, making continuous testing essential.\u003c/p\u003e \u003cp\u003eTrials two and three found no impact of either the inclusion of numbers or any impact of the type of numbers included on engagement. Individual differences in numeracy may partly account for these findings. Work on numeracy suggests it may moderate how effective numbers are for facilitating engagement, meaning that what is an effective engagement tactic for some, is not effective or even repelling to others\u003csup\u003e22,23,31\u003c/sup\u003e.So, while quantitative messages may increase engagement among some policymakers or congressional staff, it might decrease engagement for others, suppressing effects. Additionally, while policymakers may be more inclined towards the use of numbers in some contexts (e.g., budget choices, polling), negative perceptions of scientists as cold may make numbers ineffective or even aversive in others\u003csup\u003e32,33\u003c/sup\u003e.This insight suggests the use of numbers in climate change information campaigns may not be worth the additional investment in this specific context, given their lack of impact. Again, we see the importance of repeated testing of specific messaging strategies.\u003c/p\u003e \u003cp\u003eLastly, Trial four manipulated the intensity and frequency of emotionally valanced language. Again, we found no significant differences in engagement by the level of emotion included in the email body. While in many instances emotional appeals can be effective tools for capturing attention and thereby increasing engagement, this is not universally true. Legislators may encounter frequent emotional appeals and be desensitized to them, making messages less attention grabbing\u003csup\u003e34,35\u003c/sup\u003e. Appeals to negative emotion may even result in less attention if the aversive emotion evokes a desire for avoidance or escape. Research, including in climate change advocacy, finds evidence of context dependent disengagement with threatening messages\u003csup\u003e36\u003c/sup\u003e. The perceived authenticity of emotions also plays an important role in their efficacy as messaging strategies. Past work finds that authentically evoked emotions may drive engagement with science about racism and public health, but emotional appeals perceived as inauthentic were ineffective or even counter-productive\u003csup\u003e37\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThough this project offers valuable insight into the effectiveness of the examined communication strategies, it is important to note its limitations. Perhaps most importantly, this project can only speak to the effectiveness of the particular stimuli included in our trials. We operationalize and manipulate norms, numbers, and emotions, each in one specific way. As such, we cannot conclusively say that no version of these manipulations would be effective among lawmakers. For example, we do not cover a range of emotions, focusing on negative valence, and cannot argue that other emotion manipulations (e.g., positive emotion-based stimuli), would be ineffective. It is also worth noting that the engagement we track in these trials is limited to email behavior. This kind of engagement does not necessarily translate into other actions. However, despite these limitations, our email trials suggest that we are consistently able to get the attention of policymakers on essential scientific findings. Work targeting email engagement with policymakers may serve as a key steppingstone to other kinds of meaningful engagement with climate research and policy.\u003c/p\u003e \u003cp\u003eDespite the strong theoretical basis for the three strategies tested across four trials in the current research, they were ineffective for our target population of state legislators. These results demonstrate that we should not assume common persuasion and engagement tactics will improve engagement among legislators. Future research should assess any planned messaging tactics within the appropriate population and researchers should be aware that it may be difficult and unfruitful to try to improve baseline engagement with such tactics. Anyone aiming to reach policymakers about issues as important as climate change mitigation should approach standard advocacy strategies with a healthy dose of skepticism. When interacting with policymakers, science communicators may be better off expending their efforts on things such as increasing the diversity of ways and mediums to contact, influence, or engage policymakers rather than attempting to refine the ideal message. In this group, a purposeful and direct message seems to be best.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u0026nbsp;\"IRB approval and oversight was provided by the Pennsylvania State University ethics board.\"\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eRipple, W. J., Wolf, C., Newsome, T. M., Barnard, P. \u0026amp; Moomaw, W. R. World scientists\u0026rsquo; warning of a climate emergency. \u003cem\u003eBioScience\u003c/em\u003e \u003cstrong\u003e70\u003c/strong\u003e, 8\u0026ndash;100 (2020).\u003c/li\u003e\n\u003cli\u003eBernai, R. R. Managing the risks of extreme events and disasters to advance climate change adaptation. \u003cem\u003eEcon. Energy Environ. Policy\u003c/em\u003e \u003cstrong\u003e2\u003c/strong\u003e, 101\u0026ndash;113 (2013).\u003c/li\u003e\n\u003cli\u003eGrimm, N. B. \u003cem\u003eet al.\u003c/em\u003e The impacts of climate change on ecosystem structure and function. \u003cem\u003eFront. Ecol. 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Influ.\u003c/em\u003e \u003cstrong\u003e1\u003c/strong\u003e, 3\u0026ndash;15 (2006).\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"nature-portfolio","isNatureJournal":true,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"","title":"Nature Portfolio","twitterHandle":"","acdcEnabled":false,"dfaEnabled":false,"editorialSystem":"ejp","reportingPortfolio":"","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-4607745/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4607745/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eOne of the most challenging aspects of climate change mitigation today is not identifying solutions but reaching political leaders with climate scientists’ existing solutions. Although there is substantial research on climate change communication, such research rarely focuses on one of the most impactful groups: policymakers. It is essential to test theoretically sound methods to increase lawmaker attention to research evidence. In a series of four rapid-cycle randomized controlled email trials (N = 6642–7620 per trial), we test three common and theoretically derived advocacy tactics to increase engagement with a climate change fact sheet sent via email (i.e., a norms manipulation, a number focused manipulation, and emotional language manipulation). In all four trials, the control message increased engagement more than the messages using advocacy tactics, measured by fact sheet clicks. This demonstrates the importance of testing communication methods within the appropriate populations, especially a population with significant influence over climate policy.\u003c/p\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e","manuscriptTitle":"Email Outreach Successfully Attracts Attention but Advocacy Techniques Do Not Further Improve Policymaker Engagement with Climate Science","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-08-22 07:49:44","doi":"10.21203/rs.3.rs-4607745/v1","editorialEvents":[],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"communications-earth-and-environment","isNatureJournal":true,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"commsenv","sideBox":"Learn more about [Communications Earth and Environment](https://www.nature.com/commsenv/)","snPcode":"","submissionUrl":"","title":"Communications Earth \u0026 Environment","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"ejp","reportingPortfolio":"Communications Series","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"16e8701d-d6be-45fd-9f5b-a19ffb56dd0b","owner":[],"postedDate":"August 22nd, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":34332302,"name":"Scientific community and society/Scientific community/Policy"},{"id":34332303,"name":"Scientific community and society/Social sciences/Psychology"},{"id":34332304,"name":"Scientific community and society/Social sciences/Communication"},{"id":34332305,"name":"Scientific community and society/Social sciences/Climate change/Climate-change mitigation"}],"tags":[],"updatedAt":"2025-02-02T08:06:04+00:00","versionOfRecord":{"articleIdentity":"rs-4607745","link":"https://doi.org/10.1038/s43247-025-02055-0","journal":{"identity":"communications-earth-and-environment","isVorOnly":false,"title":"Communications Earth \u0026 Environment"},"publishedOn":"2025-02-01 05:00:00","publishedOnDateReadable":"February 1st, 2025"},"versionCreatedAt":"2024-08-22 07:49:44","video":"","vorDoi":"10.1038/s43247-025-02055-0","vorDoiUrl":"https://doi.org/10.1038/s43247-025-02055-0","workflowStages":[]},"version":"v1","identity":"rs-4607745","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4607745","identity":"rs-4607745","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}