Image Framing and Risk Perceptions of Climate Change in Indonesia: An Analysis of Nature Versus
by Lukas Pechacek Student ID: 11954922
Graduate School of Communication Master’s programme Communication Science
Supervisor: Katjana Gattermann February 3rd, 2022 Word count: 7476
Climate change is currently one of the most pressing issues societies around the globe are facing. Especially developing nations are often at the forefront of the battle. Given the importance of climate change in contemporary times, the way the topic is communicated and how the public dissects information surrounding the issue has been on scholarly radar since the beginning of the century. Images especially are beginning to play an increasing part not only visualizing but highlighting aspects of climate change that the public then uses to form its own perceptions of the issue. This research constitutes the first known study that takes visual elements from images portraying human and natural aspects of climate change and examines the effect these image frames have on attitudes regarding climate change among the Indonesian public. Emotions are also considered in this research, assessing whether fear, anger and sadness will act as mediators between viewing human versus natural aspects of climate change and resulting risk perceptions. The survey experiment finds that while there are no differences in risk perceptions of climate change between the two frames, images at least partially play role driving higher risk perceptions when compared to the control condition, in which images were not present. The same could be observed when adding emotions as mediators. While there are no differences between frames, sadness is a
significant mediator between viewing images and risk perceptions. Future research in climate change communication should examine these frames more closely to determine what aspects of climate change images are a more influential driver in risk formation in developing countries.
Key words: Image frames, climate change, risk perceptions, emotions, Indonesia
At first glance, the image used in the article by the Indonesian news outlet Kompas, seems quite peaceful. A picturesque, tiny little island full of greenery right next to the mainland. The photograph shows the exposed reef, practically connecting the island to the mainland and embodies happiness and relaxation. One could just walk over to the island from the pier. A blue sky with scattered white clouds and a lot of palm trees fill the remainder of the picture. It could be a holiday postcard. Yet, the image tells a very different story. The article is on the small islands along the Aceh-Papua coast, that are practically disappearing due to rising sea levels caused by climate change (Wibawa, 2021).
Climate change is one of society’s most significant challenges. A global phenomenon, that is felt across many sectors of the economy as well as wider society. Even though climate change is a challenge for society worldwide, the extent to which it is viewed as a risk differs considerably (Kim & Wolinsky-Nahmias, 2014). Indonesian public risk perceptions in this context are integral the wider debate to climate change for a variety of reasons. Seventy percent of the 200 million that are directly affected by rising sea levels live in only eight countries in Asia (Kulp & Strauss, 2019). Indonesia falls under these countries that are affected by rising sea levels (Buchholz, 2020). Moreover, the country is affected by climate change impacts such as natural disasters, carbon emissions and rising sea levels (Sulistyawati, et al., 2019).
While the rich research on images and cognitive processing covers a multitude of angles (e.g., Powell, et al., 2015, Frohmann, 1992, Kapferer, 2010), there is a lack of research focusing on the effect of online imagery on salient issues such as climate change.
Furthermore, there remains an unclear element of linking visuals of salient issues in news content and the framing effect of such visuals. While analyses of visual communications of
climate change have been conducted before (e.g.: Hansen & Machin, 2013, Duan et al., 2017), there remains a lack of research on the effects on audiences such visual
representations of salient topics can have (Hansen & Machin, 2013). Within imagery this is surprising, given that choices on scale, colour, or focal points of views on images can promote particular ways of perceiving the issue when it is portrayed at the expense of other marginalized ways of perception (Wardekker & Lorenz, 2019).
This study aims to examine whether image frames of human or natural made
occurrences of climate change will lead to emotional responses among the Indonesian public, in turn influencing risk perceptions of climate change. Previous research has indicated the relevancy of emotions as mediators (e.g., Lecheler, et al., 2013). It should be noted here that the scientific consensus on climate change is that the issue at large is human caused.
Regarding the mentioned frames however, they are classified as those resulting from biological and physiological processes of nature because of human activity, and frames in which either humans or directly human manufactured structures and situations are portrayed.
As such, the former can include images of ecosystems, biodiversity, or weather patterns. The latter could include for example traffic jams, people or city smog. While climate change research has identified a range of image frames in news media, (e.g., O’Neill, 2013, Rebich- Hespanha, et al., 2015), the study of human and nature aspects within imagery has not been conducted before. This is surprising, given that combating climate change often involves the cooperation between nature and humans (Cowan, et al., 2010). Using these frames, this study aims to add to the debate of humans versus nature within the realm of climate change and assess whether attitudes differ depending on highlighted aspects of visuals.
Climate change and resulting risk perceptions are unique not only in the sense that the phenomenon is unprecedented in terms of scale, but also time, by being an ongoing event spanning across centuries (van der Linden, 2015). As such, human caused climate change can
be categorized as an evolutionary and novel risk (Griskevicius et al., 2012). Moreover, there are different, country dependent risk perceptions. Whereas most of Europe, the UK and Australia perceive climate change to be a serious global issue (Pidgeon, 2012,
Eurobarometer, 2014), in the US and China the issue is seen as less dangerous (Leiserowitz et al., 2014). More broadly, research by Kim & Wolinsky-Nahmias (2014) has indicated that climate change is perceived as a more serious problem in developing countries compared to developed countries. Public risk perceptions are crucial in that they often drive policy decision as well as motivate certain social, economic and political actions to be taken by governments to address these risks (Leiserowitz, 2006). There is also an element of
uncertainty surrounding climate change. Scientific aspects, unfortunately, are often subject to debate and thus events relating to climate change are often framed as uncertain (Hasibuan et al., 2020). Previous research has also shown that understanding risk perceptions can be an important aspect in driving public behaviours to reduce climate change (e.g: Leiserowitz, 2006, Semenza et al., 2008, Spence et al., 2012). As such, risk perceptions are important in extending and understanding the scientific debates on climate change communication and possible adaptation strategies.
This study will use a survey-experiment based approach to test framing effects onto patters of risk perceptions. Images will be presented, illustrating either human or naturally occurring aspects of climate change. Emotions of fear, anger and sadness will be measured and how these influence risk perceptions on the topic. These frames will be used to assess public opinions and risk perceptions on climate change and as such add not only to the research on climate change communication and image framing but also onto research on risk perceptions resulting from climate change imagery. As such, the overarching research questions of this thesis are:
RQ1: Which type of news media framing (natural or human aspects) of climate change images produces stronger risk perceptions on the topic in Indonesia?
RQ2: To what extent do negative emotions when viewing climate change imagery influence risk perceptions on climate change?
The power of the image
In today’s news environment, images are always in our presence. The image and graphic material has not only become much better in terms of sheer quality, but also considering how smartphones can get this quality straight into the consumers own hands underlines the growing accessibility and presence of images in current times.
When it comes to conveying information, images play a crucial role in news media.
They are perceived and cognitively processed faster than text whilst also arousing a higher exposure with the information portrayed in an image (Lang et al., 1999, Quinn et al., 2007, Zillmann et al., 2001). As the image superiority effect describes it, images are better at fostering stronger memory as images are remembered better than language due to the factual imagery they represent (Nelson et al., 1976, Paivio, 1991). Photographs in particular have the ability to convey links between reality and the image through the image’s visible objectivity (Messaris & Abraham, 2001). Especially in the communication of complex scientific and technological topics with the public, images are often used for purposes of representation, fostering scientific development in line with public understanding (Pauwels, 2006).
These observations were also expanded into the realm of media and communication theory, where news images lead to a superior recall affect over text (e.g.: Newhagen &
Reeves, 1992). Eye tracking studies highlight this amplifying effect of images showing that images are the most common point of entry into newspaper pages (Garcia & Stark, 1991).
While the mentioned research does not include the importance of online news images, it still sets the foundation of this paper in asserting the importance of images and psychological processes of meaning and memory.
Contemporary framing of climate change
Frames help the public dissect issues and provide a narrative element to help the public understand societal issues and potential solutions. In a classical definition by Entman (1993), framing entails the selection of specific aspects of a medium and tailoring these to a communication context to highlight certain aspects and thus influence the interpretation of the viewer. The current research aims to bridge visual framing techniques and personal risk perceptions through experienced emotions. Building on findings in which images portraying certain aspects of climate change impact the level of salience on the issue felt by the audience (O’Neill, et al., 2013) and research on frames of climate change texts alongside images and maps (Spence & Pidgeon, 2010), this research aims to show that images framing climate change in the form of human related aspects produce different levels of risk perceptions compared to frames portraying aspects of climate change in a more natural form. Given the research on image effects on global crises, it becomes of interest to test what aspects of an image evoke higher risk perceptions among viewers.
In fact, framing mechanisms have also shown to be different between countries (Vu, et al., 2019). Importantly, distinctions exist in the way frames appeared to be used between developing and developed countries. Richer countries tend to frame climate change from a more scientific perspective, aimed specifically at domestic politics and in turn being a drive for policy adaptation, justified by superior financial resources that can be allocated to scientific discovery. In contrast, poorer countries tend to focus more on natural aspects of climate change and the impacts of natural occurrences such as severe weather patterns in
Southeast Asia (Vu et al., 2019). Specifically, images portraying impacts of climate change successfully captivate people’s minds (O’Neill et al., 2013). Moreover, images framed as pollution capture people’ attention and make the issue more salient (O’Neill et al., 2013).
Images depicting energy futures go even further in fostering a sense of self efficacy while images with celebrities rather undermined the saliency of climate change (O’Neill et al., 2013). Pollution and celebrities are aspects this research would consider human aspects of climate change, providing a hint that at least pollution aspects may lead to higher risk perceptions. Also, when framed as a distant issue, climate change impacts are not perceived as more severe than in a local frame (Spence & Pidgeon, 2010). While distant frames will not be directly used as part of this research, they play an indirect role. For instance, a picture of Greta Thunberg may seem distant to the Indonesian public due to her mainly being present in Europe, thus creating a disassociation with the image and potentially producing lower risk perceptions of climate change when viewed. Such an image would fall under the human aspects frame of this research. In other words, it is not a representation of a natural, physical element of the environment.
In line with framing, a further theory guiding this paper is exemplar theory. This cognitive theory asserts that when an unfamiliar stimulus enters the mind, similarities are drawn from memory with every previous exemplar from any relevant category (Nosofsky, 1986). Contextualizing the theory to this study, the public’s perceived feelings as the exemplar, can be categorized according to the level of personal connection with the issue portrayed on an image. As such, exemplars that are affected personally by imminent dangers of climate change such as rising sea levels in Indonesia, may thus have higher risk
perceptions. The way certain images on climate change are framed may then lead to a categorization of personal experiences that can lead to different levels of risk perceptions.
While personal experiences are not tested in this research, they are also characterized by experienced emotion, which will be discussed in the next chapter.
Climate change risk perceptions can be attributed to the notion of human perception being subject to what Pidgeon et al. (2003) would consider a distinction between actual, real- world threats and a subjective, perceptual experience of those threats. The latter referring to risk being a human invention that cannot exist without personal experiences ingrained in human minds from the cultures and environment surrounding such perceptual thought (Slovic, 1992). In the case of Indonesia, its vulnerability to natural disasters, rising sea levels and extreme weather makes the effects of framing on risk perceptions more distinct when such frames are those depicting natural impacts of climate change because of the country’s exposure to these aspects. In line with the dangers of climate change and exemplar theory, Lorenzoni (2006) argues that public perceptions of risk are the result of not only scientific descriptions of danger but also personal and psychological experiences such as imagery, emotion and worldviews. Such findings are further supported by O’Connor et al. (1999), who assert that those at more risk to potentially harmful environmental events are likely to have higher risk perceptions. Indonesia’s population is certainly at risk to natural impacts of climate change, making this a likely cause for higher risk perceptions.
While important interpretations of how the public engages with climate change imagery can be drawn out, studies thus far remain limited to Anglophone countries, allowing for further research to be conducted in non-English speaking, developing parts of the world.
However, there is enough evidence to suggest the potential different frames of climate change can have on risk perceptions of climate change. This research aims to extend this literature by assessing the effects of a hereto overlooked classification of frames on attitudes towards climate change. Furthermore, images of pollution provide hint that these may lead to higher risk perceptions. However, this research considers exemplar theory as a baseline and the
theoretical argument of risk perceptions being the product of personal worldviews and exposure to natural impacts of climate change a stronger driver for risk perceptions in Indonesia.
Using the aforementioned research as a guideline, while contextualizing framing patterns of climate change in developing countries that tend to focus more on the naturally occurring aspects, the following hypothesis is derived:
H1: Both frames, human and natural aspects of climate change, generate high risk
perceptions of climate change in Indonesia, with the natural aspects frame being a stronger influence than the human aspects frame.
The mediating role of emotions
Dual processing theory posits that there are two risk processing systems that underline how we understand risk. The cognitive and experiental processing system (Epstein, 1994).
The former being a rational, logical and analytical way of understanding reality. The latter being a fast and intuitive model, understanding reality through metaphors and imagery
(O’Neill et al., 2013). Drawing on Leiserowitz’s (2006) idea that articulates the importance of the experiental processing system in climate change imagery, images of climate change aspects can then influence the emotions that are felt towards the issue. Indeed, Leiserowitz’s (2006) findings indicate that risk perception is greatly affected by emotional factors, making public risk assessments reliant on the experiental processing system.
Framing can be used to convey messages that give rise to particular emotions. While framing does not directly lead to a prediction of behaviour, it can evoke an emotion that becomes the mediator, such as emotions of hope or worry leading to support of climate change mitigation policies (Leiserowitz, 2014). Emotions have also been a predictor of concerns over terrorism threats (Powell, et al., 2015), another grand societal issue.
Research by Leviston et al. (2014) indicates that specifically images of natural disasters, which in this research would fall under the natural aspect frame, elicited high arousal and a negative emotional effect. More generally, climate change imagery evoked negative effects in audiences (Leviston et al., 2014). By many, certain images and frames such as those
depicting a stranded polar bear, a smokestack rising or the glacier that is melting and breaking apart, have become to be considered as some of the most iconic visualizations of climate change (Rebich-Hespanha & Rice, 2016). These are not only reproduced across a range of different media outlets but have also been proven to foster a strong emotional response (Hariman & Lucaites, 2007).
Furthermore, frames of climate change have been shown to instil emotional responses that in turn led to advocacy behaviour on the issue (Nabi, et al., 2018). However, previous research has measured emotional responses pertaining to societal issues as a general
construct, rather than single images of the issue at hand. This study aims to make emotions a more objective mediator, pertaining to single images, thus directly measuring the effect of imagery itself.
In this study, participants’ level of fear, sadness and anger are measured in response to image frames emphasizing human versus natural aspects of climate change. As climate change is being increasingly referred to as a global crisis or emergency, negative frames have come to dominate media discourse (Feldman & Hart, 2021). Lead on by the valence
dimension framework in which emotions are summarized into positive and negative emotions (e.g.: Russel, 2003, Watson et al., 1999), it would then seem plausible to assume that negative frames can lead to negative emotions (Lecheler et al., 2013). This is also supported by more empirical evidence in which negative emotions such as anger and fear are attributed to negative political attitudes and behaviour (Huddy et al., 2007, Valentino et al., 2011).
Previous research goes even further in showing that image frames of the impacts of nuclear
waste arouse fearful emotions and higher risk perceptions of the issue (Slovic et al., 1991).
Anger, caused by a negative situation where it is possible to assign responsibility to a specific actor (in the case of this research, human aspects of climate change), should in turn motivate a person to confront the problem. (Frijda, 1988). Such findings indicate that these emotions, while both negative, differ in intensity towards certain issue, leading to different outcomes and attitudes (Dillard & Peck, 2006, Lerner et al., 2003). In fact, negative emotions have proven to be a strong influence on risk perceptions (Loewenstein, et al., 2001). Particularly fear, sadness and anger have been shown to influence risk perceiving behaviour (Dunlop, et al., 2008).
Bearing in mind these findings and the potential for different effects of the mediating role of emotions on risk perceptions, the following hypothesis is derived.
H2a: Both frames positively influence negative emotions, leading to higher risk perceptions.
Fear as an emotional response to crises arises when the source of the danger posed by the crisis cannot be attributed to a specific actor. Rather, the source is presented as an
ambiguous threat where no individual can be perceived to be in control (Erhardt et al., 2021).
Such threats have been present across recent history in the form of other crises. For example, pandemics such as Covid19, SARS or swine flu have been shown to evoke fear as they are often seen as uncertain events with uncertain outcomes (Taylor, 2019). Furthermore, fear can also intensify risk perceptions and in turn leading to protective behaviour against threats (Lerner, et al., 2003). Climate change, with its uncertainty and rather novel risk can be a driver for triggering a fearful response. Based on the experiences humankind has made with uncertain crises, images eliciting aspects caused by natural processes should evoke a fearful response, leading to higher risk perceptions. Natural aspects in this sense are those frames in
which nature is the sole actor in governing its own destiny, where direct human-controlled action is impossible. The following hypothesis is therefore derived:
H2b: A frame conveying natural aspects of climate change positively influences a fearful emotional response, leading to higher risk perceptions of climate change.
Next, it is assumed that when situational crises emerge where blame can be attributed to a specific actor, an angry emotional response emerges. This is due to the causes of crises being attributed to the role specific actors play in the crisis (Erhardt, et al., 2021). Actors here refer to humans in the form of world leaders, institutions, corporations, governments. In other words, impacts of climate change that are not a product of natural processes. These concrete actors can then be blamed of the conditions of the crisis, making it seem rather controllable to an extent and easier to assert blame (Erhardt et al., 2021). In the context of this study, anger then is a product of a frame eliciting climate change aspects as being a crisis that can be controlled by specific actors. As such, the following hypothesis is derived:
H2c: A frame conveying human aspects of climate change positively influences an angry emotional response, leading to higher risk perceptions of climate change.
Finally, the last negative emotion to be assessed in this research is sadness. This emotion is relevant as impacts of climate change can often directly affect certain groups and thus evoke feelings of sadness when shown on the news. This emotion is not directly related to blame of a perpetrator but rather represents compassion (Solloway et al., 2013). In this sense, sadness also relates to a feeling of lack of control and as such having the potential to have a greater effect on risk perceptions (Lerner & Keltner, 2001). It also presents a crisis with the need to reflect on it and avoid future mistakes to improve the situation (e.g.:
Schwarz, 1990, Lerner & Keltner, 2001). Here, sadness would reflect the natural aspects frame more than the human aspects frame. Yet, sadness has also been shown to evoke a
feeling of unselfishness by acting as a caring and sympathy provoking emotion (e.g., Solloway, et al., 2013). As such, human aspects on climate change may be seen as a
motivation for reducing impacts and contributing to a better climate in the future. From this angle, the following hypothesis is derived:
H2d: Both frames positively influence a sad emotional response, leading to higher risk perceptions of climate change.
To test this study’s hypotheses, an online survey experiment was conducted between the dates of the 13th of December 2021 and the 27th of December 2021, allowing for a data collection period of two weeks. Participants were randomly assigned to the three conditions of the experiment. The three experimental conditions are the two different image frames, natural and human aspects of climate change, plus one control condition in which participants will only be shown questions related to the impacts of climate change.
Participants were recruited in Indonesia across multiple provinces. A total of 162 participants were recruited. Participants were recruited through academic as well as personal contacts of the researcher. To gain a broader perspective, personal contacts of the researcher were encouraged to forward the online survey to their friends, family, and co-workers. This is to ensure that participants was recruited through a diverse range of educational and economic backgrounds. The final sample included Indonesian adults between the ages of 16 and 70 years old (M=26.2, SD=7.14) with 34.6% representing males, 61.1% females and 3.1%
preferring not to disclose their gender. Participants also represented various education levels, with 16% having completed a master’s degree, 45.1% having completed a bachelor’s degree
and 37.7% having completed secondary education. The sample also seems to be generally informed about current events in Indonesia and around the world, with 52.2% indicating they follow the news every other day, 27.2% indicating they follow the news every day of the week and 20.4% indicating they do not follow the news. Moreover, 74.1% follow news on climate change from Indonesia and around the world and 25.9% indicating they do not follow environmental news.
For the two experimental conditions, each condition contains six images in order to control for any single image effects. To test this study’s hypotheses, images were selected from Indonesian and international media outlets such as Kompas, The Guardian or Reuters.
All images relate to climate change and have been published alongside online news articles that over the last five years. This is to ensure that the images come from credible news segments and are relevant to the contemporary debate on climate change. The images
pertaining to the nature frame clearly show natural processes without human presence such as melting ice, species threatened by extinction, extreme weather or a combination of these topics. The images pertaining to the human frame were chosen on the basis that they clearly show humans or situations of direct human influence such as protests, polluted beaches, deforestation or uncontrolled urbanization (see appendix 2). Moreover, images were chosen that conveyed these aspects in a clear and almost dramatic fashion to underline the intensity of climate change. Most images of both image frames also illustrated aspects directly relatable to Indonesia itself, such as deforestation or damage done by a typhoon. Across conditions, 51 participants were randomly exposed to the natural frame, 55 the human frame and 56 the control condition.
The survey experiment was translated into standard Indonesian and distributed online through Qualtrics. Participants were first asked about standard demographic questions such as age, gender, province of origin and level of education. The survey then proceeded to ask participants about their news watching habits and whether they follow news on issues of climate change.
For the two experimental conditions, participants were first informed about the procedure of the survey. They were then shown six images of climate change aspects pertaining to the experimental frames assessed and were then asked questions about their emotions and risk perceptions of climate change. When viewing the images one by one participants were asked to indicate the level of intensity (“Please rate the intensity of this picture”; 1 = not intense, 10 = very intense). This was to ensure participants interacted with the images rather than scrolling through the survey. Participants were then asked to choose on a scale from 1 (natural aspects) to 7 (human aspects) what aspects of climate change they think the images they have just seen belong to. This question served as the manipulations check and ensured that the images participants in the survey have received are reflective of the frames as the researcher has intended. As in the experimental conditions, participants in the control condition were also presented with questions asking about their felt risk
perceptions and emotional sentiments about climate change but were not shown any images.
At the end of the survey all participants were debriefed about the study and given the option to submit their data for analysis.
Emotions, the mediating variable of this study is a latent concept. Individuals often refer to emotions to make sense of events happening around them. Furthermore, emotions in
a heuristic way are necessary to make judgments about events of potential consequence (Oh et al., 2020). Thus, to measure the latent concepts of this study, a multiple indicator measure was proposed using a 5-point Likert scale, ranging from, I strongly disagree (1), to I strongly agree (5), with an option for a neutral choice (3). The scale is put forward to assess the intensity of emotions on level of anger, fear and sadness about climate change. For fear the survey presented two items on what level participants feel fearful and frightened about climate change (e.g., “I feel fearful about climate change” and “I feel frightened about climate change”). For anger participants were presented with two items on what level they feel angry and furious about the impacts of climate change. For sadness participants were presented with two items to indicate what level of sadness and devastation they relate to on climate change. An overall variable, Emotions (M=3.96, SD=.72) was computed for negative emotions to test H2a. The two items for each emotion were computed into one variable pertaining to each emotion to be used for the analysis to test H2b, H2c and H2d, resulting in the variables fear (M=4.06, SD=.94), angry (M=3.86, SD=.83) and sad (M=3.95, SD=.85).
The measure of risk perception draws on measures developed by Leiserowitz (2006) and Bord et al. (2000). 6 items were used to measure risk perceptions of climate change aspects shown on selected images. The items asked respondents how likely they think they themselves and society at large will be impacted by the threats of climate change.
Respondents were also asked how serious they rate the threat of climate change being to Indonesia’s biodiversity, air quality, their own health, and future generations of Indonesia (e.g., “Climate change is a threat to Indonesia’s air quality”). The dependent variable was operationalized using a 5-point Likert scale ranging from, I strongly disagree (1), to I strongly agree (5), with an option for a neutral choice (3). The six items form a reliable scale
(Cronbach’s α=.87). To create one dependent variable for risk perceptions, the mean scores for each of the items measuring risk perceptions were taken from each condition. The means
were then computed, making up the score of the final dependent variable for risk perceptions, Risk Perceptions (M=4.38, SD=.58).
The independent variable in this study is distinguished as Condition. For the manipulations check, the variable Condition was recoded into the Framing variable, with cases from the control group being defined as missing. The Framing variable was created to reflect each of the image frames (1=Natural frame, 2=Human frame). The variable MC was also created, taking the scores from each of the frame’s manipulations check.
To test the manipulations check, an independent samples T-test was conducted, with the Framing variable as the grouping variable and the MC variable as the test variable.
Results show that participants from the human aspects frame (M=5.65, SD=1.39) score higher than participants from the natural aspects frame (M=4.33, SD=2.03). The mean difference (of -1.31) is statistically significant, t (104) = -3.94, p < .001, 95% CI [-1.99, - .66], and
represents a moderate to strong effect, d = .76. As such, the images were perceived by participants in the way the researcher has intended.
To test H1, a one-way ANOVA test was run with the Condition variable as the independent variable and Risk Perceptions as the dependent variable. To test H2b, the Framing variable was recoded into a dummy variable, Nature frame (1=Natural Frame, 0=Human frame) to be used as the independent variable. To test H2c, the values of the Framing variable were once more recoded (1=Human frame, 0=Natural frame), becoming human frame. To test, H2a and H2d, the Condition variable was also recoded into a dummy variable, Images shown, representing whether framing was used or not (1= human or nature frame, 0=no frame). A PROCESS mediation analysis with 5,000 bootstrap samples using the mediators Emotions, fear, angry and sad was conducted to test their effect on the dependent variable of Risk Perceptions.
H1 states that both frames generate high risk perceptions of climate change in Indonesia, with the natural aspects frame being a stronger influence than the human aspects frame. The one-way analysis of variance shows that the effect of the experimental condition on risk perceptions of climate change is statistically significant, F (2, 159) = 4.24, p = .016.
This represents a large effect size, eta² = .23. The experimental conditions thus explain 23%
of the variance in climate change risk perceptions (table I and II in appendix). The post-hoc test (with Bonferroni correction) shows that participants from the nature frame condition (M=4.56, SD=.44) are more risk perceiving than participants from the human frame (M=4.37, SD=.6) and control condition (M=4.24, SD=.63). The mean difference between the natural frame and control condition is statistically significant (Mdifference = –.32, p = .013, 95% CI [.05, - .58]). However, there is no significant mean difference between the natural and human frame (Mdifference = .19, p = .278, 95% CI [-.08, - .45]). There was also no significant mean difference between the human frame and control condition (Mdifference = .13, p = .660, 95% CI [.13, - .39]). As such, while there is a partial effect of having images versus not being
subjected to viewing images, true to the natural frame versus the control condition, there is no statistically significant difference between the two experimental conditions. As such, H1 is rejected.
H2a states that negative emotions positively influence climate change risk perceptions resulting from frames depicting natural and human aspects of climate change. Results
indicate that the frames are significant predictors of negative emotions (b=.42, SE = .12, p
<.001). When looking at the direct effect of the two frames on risk perceptions, there is no significant relationship (b=.-.003, SE = .07, p = .968). However, when including the
mediator, the indirect effect of the two frames on risk perceptions is positive and significant,
as indicated by the bootstrap results (b=.22, SE = .07, CI = .1; .37). Hence, H2a is retained (see table IV in appendix).
Figure 1: Mediation model illustrating negative emotions mediating the relationship between framing and risk perceptions.
b = .42, p < .001 b = .53, p < .001
b = -.00, p = .968
H2b states that out of the two frames, a frame conveying natural aspects of climate change positively influences a fearful response, leading to higher risk perceptions of climate change. Results indicate that the frame is not a significant predictor of fear (b=.20, SE = .16, p = .214), and that fear is a significant predictor of risk perceptions. However, bootstrap results show that the indirect effect is positive but not significant since the confidence intervals contain 0 (b=.08, SE = .07, CI = -.04; .23). Hence, hypothesis H2b is rejected (see table III in appendix).
Images shown Risk Perceptions
Figure 2: Mediation model illustrating fear mediating the relationship between the nature frame and risk perceptions.
b = .20, p = .214 b = .38, p < .001
b = .11, p = .195
H2c states that out of the two frames, a frame conveying human aspects of climate change positively influences an angry emotional response, leading to higher risk perceptions.
Using the Human frame dummy (1=Human frame, 0=Nature frame), results indicate that there is no significant support for this hypothesis. Although anger is a significant predictor of risk perceptions (b=.40, SE = .06, p <.001), bootstrap results show that the indirect effect of anger from the human aspects frame on risk perceptions is positive but not significant as the confidence intervals contain 0 (b=.05, SE = .06, CI = -.05; .18). As such, H2c is rejected (see table III in appendix).
Figure 3: Mediation model illustrating anger mediating the relationship between the human frame and risk perceptions.
b = .13, p = .353 b = .40, p < .001
b = .14, p = .128
Nature frame Risk Perceptions
H2d states that both frames positively influence a sad emotional response, leading to higher risk perceptions of climate change. Results indicated that both frames when compared to the control condition are predictors of a sad emotional response (b=.44, SE = .14, p = .002). When examining the direct effect of the two frames on risk perceptions, there is no significant relationship (b=.03, SE = .08, p = .667). However, when including the mediator, the indirect effect of the two frames on risk perceptions is positive and significant (b=.19, SE
= .07, CI = .07; .33). Hence, H2d is retained (see table IV in appendix).
Figure 4: Mediation model illustrating sadness mediating the relationship between framing and risk perceptions.
b = .44, p = .002
b = .43, p < .001
b = .03, p = .667
The results of the mediation analyses conform with the results of the ANOVA testing H1. There is no significant support for differences between the two frames. Interestingly however, the analyses give indications that the pictures did have significant effects on risk perceptions. Furthermore, when incorporating emotions, the indirect effect of using image frames versus not using the image frames is also significant, supporting the role of frames in fostering stronger risk perceptions. While the predictions pertaining to the difference between frames cannot be confirmed with these results, the effect of the pictures partially show to be playing an influential role in forming attitudes towards climate change.
Risk Perceptions Sad
23 Discussion and conclusion
There has been a large body of literature assessing the effect of framing on people’s opinions about climate change (e.g., Feldman & Hart, 2021, Ford & King, 2015, Gifford &
Comeau, 2011). The research of the effects of image frames is less prevalent (e.g., Bolsen et al., 2019, Leiserowitz, 2006, O’Neill et al., 2013). Even less research has focused on framing in the realm of climate change in Indonesia and developing countries in general. This study aimed to close some of these gaps by providing an assessment of framing patterns and its effects on risk perceptions in the developing world, taking Indonesia as a case study.
Particularly, this study has shifted focus on the sole effect of images, ignoring other framing mechanisms such as texts or combinations of text and image. The frames assessed have in such a way also not been part of any known research before. Additionally, effects of negative emotions were tested, measuring how influential they are in driving risk perceptions of climate change.
While respondents exposed to the natural frame had on average the highest risk perceptions, results show that there is no significant difference between risk perceptions for respondents exposed to a natural aspects frame compared to those exposed to a human aspects frame. However, results also show that there are significant effects of using images versus not having images in the experiment. This is in line with previous literature that points to the amplifying effect of images (e.g., Messaris & Abraham, 2001, Garcia & Stark, 1991).
At least partly, this research also contributes to existing findings in which images are shown to successfully captivate people’s minds (e.g., Wardekker & Lorenz, 2018, Nelson et al., 1976, Paivio, 1991)
The frames assessed however did not provide this research with any support regarding their effects on risk perceptions. This is contrary to what was expected. It was expected that
due to the overarching presence of natural phenomena of climate change such as increasing biodiversity loss, rising sea levels, severe weather patterns as well as the prevalent framing of natural aspects in developing countries (Vu et al., 2019), risk perceptions towards images depicting these aspects would be higher.
The analyses show risk perceptions to be significantly higher when comparing the natural frame versus no images at all. This effect could not be observed when comparing the human frame to the control condition. The true effect of images in the realm of this research thus remains debateable. Adding mediators to the analyses provided an interesting nuance to the results. No significant effect could be observed for the two frames in fostering an angry and fearful response. It was expected that the human aspects frame would be mediated by an angry emotional response, leading to higher risk perceptions, due to its portrayal of climate change aspects that are directly attributable to human influence. However, unlike in previous research where anger is the result of attributing blame to a subject responsible for a crisis (e.g., Erhardt, et al. 2021), this research cannot confirm such findings. Furthermore, it was expected that fear, resulting from the risk of climate change being uncertain and ambiguous where individuals cannot be held responsible, would be a mediator for the natural frame. It was expected that by observing aspects of climate change that are part of natural processes, fear would be a strong mediator. However, contrary to previous research where fear played a part in influencing political attitudes (e.g., Erhardt, et al. 2021, Taylor, 2019), such findings could also not be found in this research. A possible explanation for these results could be that the measures of emotions were operationalized by the general emotional state participants felt towards climate change. The true effect of emotions could have been examined if these related directly to climate change impacts in Indonesia. However, mediation results also show that when comparing the frames to the control condition, emotions do show to be playing a significant part in influencing higher risk perceptions. This also holds true when adding
sadness as a mediator. These aspects of the results prove to be a powerful amplifier for image effects, perhaps making climate change look more intense, leading to a stronger response in the viewer. However, there were no differences between the two image frames resulting in stronger risk perceptions among the Indonesian public.
This study has several limitations. The sample, albeit sufficiently large, is relatively broad. This is due to the survey experiment having been distributed by the academic as well as social contacts of the researcher. This study was also conducted in a single country, begging the question whether the results are representative to other developing countries. A further limitation of this study are the negative tones of the images as well as emotions. The stimuli included some highly negative impacts caused by climate change and as such it would seem plausible that risk perceptions stemming from these images will also be largely skewed towards the higher end of the scale. Future research should thus not only look at including more frames but also more positive emotions and draw analyses from there. While climate change is a complicated phenomenon with many negative connotations, there are also frames highlighting the progress humans have made, which could lead to different outcomes
regarding attitudes (Hart & Feldman, 2016). A further useful addition to compare the true framing effect of images would be to use text frames as an element of comparison or in conjunction with image frames. Research has shown that framing effects are much stronger when there is a text frame present (e.g., Powell, et al., 2015, Feldman & Hart, 2018).
Furthermore, moderators such as economic status, level of education or prior beliefs and attitudes could have also made framing effects stand out.
Before concluding, some implications of this study should be considered. The study at hand sheds light on the importance of images in forming attitudes towards climate change.
While human and nature frames did not lead to significant differences in risk perceptions, the frames should not be overlooked. The frames add to the conversation of nature versus
humans in climate change. While it should be implied that climate change is a human caused phenomena, nature also plays its part without direct human influence. Rising sea levels are natural processes indirectly linked to human activity. However, it shows the sheer power of nature governing itself. This is contrary to other climate change impacts, such as polluted beaches where responsibility can be directly attributed to an actor and where said actor can put a halt to such destruction. The manipulations check of this study proved that the frames functioned in the way the researcher has intended, as such validating their use. However, future research should follow up on these whilst making crucial changes to the research design. The broad sample of this study may account for the indifference between the two frames when assessing risk perceptions. Perhaps a larger sample over a particular segment of the population could yield significant results. It would have been interesting to focus on a subgroup of Indonesia. For instance, people working in a specific industry that is affected by climate change such as farmers. Risk perceptions might have been different as certain groups may be more exposed to aspects of climate change pertaining to one of the frames. Future research should thus incorporate and compare different audiences and draw conclusions about framing effects from there.
In this way, this research aimed to highlight which aspects of climate change are perceived to be riskier to personal and societal health. The frames of this research should at least partially keep playing a part in communication research. While this study did not address attitudes on mitigation strategies, it forms part of a steppingstone, as adaptive behaviours are a follow up of perceived risk perceptions (e.g., Leiserowitz, 2006). Previous research has shown that risk perception is a predictor of preventative behaviour (e.g., O’Neill, et al., 2013, De Dominicis et al., 2015). The frames could give governing bodies an
indication of what aspects are stronger predictions of risk perceptions and with this knowledge adaptation strategies can be implemented that address the public’s concerns.
Along with the power images have shown to have in attitude formation in this research, these frames, while perhaps improved and readapted, can pave the way in driving climate change research, attitude formation and in the next step, possible behavioural implications in climate change adaptation.
In conclusion, this study found that climate change images amplify the social psychology that is at work in forming public risk perceptions. It also highlights the ways in which image frames are processed how such processes add to the formation of risk
perceptions. Developing countries such as Indonesia are at the forefront of the fight whilst at the same time often lacking the necessary resources to combat and mitigate the issue
compared to its developed counter parts. The power images have can thus offer less
developed countries a further mechanism in framing the impacts of climate change to drive up risk perceptions and as such put into place effective mitigation strategies.
Alves, R. (2021, July 13). The UN Convention on Biological Diversity aims to provide solutions to help humans live “in harmony with nature” in places like the Amazon forest in Brazil. [Photograph]. News.Un.Org. https://news.un.org/en/story/2021/07/1095772
Beawiharta. (2021, November 4). A view of deforestation on Indonesia’s Sumatra
island [Photograph]. Reuters.Com. https://www.reuters.com/business/cop/indonesia-signals- about-face-cop26-zero-deforestation-pledge-2021-11-04/
Bolsen, Toby, Risa Palm, and Justin T. Kingsland. "Counteracting climate science
politicization with effective frames and imagery." Science Communication 41.2 (2019): 147- 171.
Bord, R. J., O'connor, R. E., & Fisher, A. (2000). In what sense does the public need to understand global climate change?. Public understanding of science, 9(3), 205.
Buchholz, K. (2020, February 11). Rising Sea Levels Will Threaten 200 Million People by 2100. Statista Infographics. https://www.statista.com/chart/19884/number-of-people- affected-by-rising-sea-levels-per-country/
Buol, R. A. (2021, November 2). Salah satu sudut pantai di pulau Miangas, Kabupaten Kepulauan Talaud, Sulawesi Utara [Photograph]. Kompas.
Cowan, C., Epple, C., Korn, H., Schliep, R., & Stadler, J. (2010). Working with nature to tackle climate change. Bonn: Bundesamt für Naturschutz.
Dillard, J. P., & Peck, E. 2006. Persuasion and the structure of affect. Dual systems and discrete emotions as complementary models. Human Communication Research, 27, 1, 38–68.
De Dominicis, S., Fornara, F., Cancellieri, U. G., Twigger-Ross, C., & Bonaiuto, M. (2015).
We are at risk, and so what? Place attachment, environmental risk perceptions and preventive coping behaviours. Journal of Environmental Psychology, 43, 66-78.
Duan, R. (2018). Visual Communication of Climate Change: The Effect of Construal Level.
Michigan State University.
Dunlop, S., Wakefield, M., & Kashima, Y. (2008). Can you feel it? Negative emotion, risk, and narrative in health communication. Media Psychology, 11(1), 52-75.
Ellis-Petersen, H. (2021, November 5). Indonesia says Cop26 zero-deforestation pledge it signed ‘unfair.’ The Guardian. https://www.theguardian.com/world/2021/nov/05/indonesia- says-cop26-zero-deforestation-pledge-it-signed-unfair
Entman, R. M. (1993). Framing: Toward clarification of a fractured paradigm. Journal of Communication, 43(4), 51-58.
Epstein, S. (1994). Integration of the cognitive and the psychodynamic unconscious.
American psychologist, 49(8), 709.
Erhardt, J., Freitag, M., Filsinger, M., & Wamsler, S. (2021). The Emotional Foundations of Political Support: How Fear and Anger Affect Trust in the Government in Times of the Covid‐19 Pandemic. Swiss Political Science Review.
European Commission. (2014). Special Eurobarometer 409–Climate Change.
Feldman, L., & Hart, P. S. (2018). Is there any hope? How climate change news imagery and text influence audience emotions and support for climate mitigation policies. Risk
Analysis, 38(3), 585-602.
Feldman, L., & Hart, P. (2021). Upping the ante? The effects of “emergency” and “crisis”
framing in climate change news. Climatic Change, 169(1), 1-20.
Ford, J. D., & King, D. (2015). Coverage and framing of climate change adaptation in the media: A review of influential North American newspapers during 1993–2013.
Environmental Science & Policy, 48, 137-146.
Frohmann, B. (1992). The power of images: a discourse analysis of the cognitive viewpoint. Journal of documentation.
Frijda N. H. (1988). The laws of emotion. The American psychologist, 43(5), 349–358.
Garcia, M. R., & Stark, P. M. (1991). Eyes on the News The Poynter Institute for Media Studies, St. Petersburg, Florida, USA.
Gifford, R., & Comeau, L. A. (2011). Message framing influences perceived climate change competence, engagement, and behavioral intentions. Global Environmental Change, 21(4), 1301-1307.
Griskevicius, V., Cantú, S. M., & Van Vugt, M. (2012). The evolutionary bases for
sustainable behavior: Implications for marketing, policy, and social entrepreneurship. Journal of Public Policy & Marketing, 31(1), 115-128.
Hammerschlag, A. (2021, October 26). A male loggerhead sea turtle glides through the pristine waters off the coast of Sal, Cape Verde. [Photograph]. Theguardian.Com.
Hansen, A., & Machin, D. (2013). Researching visual environmental communication.
Environmental Communication: A Journal of Nature and Culture, 7(2), 151-168.
Hariman, R., & Lucaites, J. L. (2007). No caption needed: Iconic photographs, public culture, and liberal democracy. University of Chicago Press.
Hart, P. S., & Feldman, L. (2016). The impact of climate change–related imagery and text on public opinion and behavior change. Science Communication, 38(4), 415-441.
Hasibuan, A. M., Gregg, D., & Stringer, R. (2020). Accounting for diverse risk attitudes in measures of risk perceptions: A case study of climate change risk for small-scale citrus farmers in Indonesia. Land Use Policy, 95, 104252.
Helber, S. (2021, December 6). Dalam foto udara yang diambil dengan pesawat tak berawak ini, air banjir mengelilingi rumah-rumah yang rusak akibat badai, Selasa, 31 Agustus 2021, di Paroki Lafourche, La., saat warga berusaha memulihkan diri dari dampak Badai
Ida [Photograph]. Kompas.Com.
Huddy, L., Feldman, S., & Cassese, E. (2007). On the distinct political effects of anxiety and anger. I WR Neuman, GE Marcus, AN Crigler & M. MacKuen (red.), The affect effect.
Dynamics of empotion in political thinking and behavior.
Ilustrasi sampah plastik mencemari pantai. (2021, October 25). [Photograph]. Kompas.Com.
Kapferer, J. (2010). Introduction: Images of Power and the power of images. Social Analysis, 54(2), 1-8.
Kim, S. Y., & Wolinsky-Nahmias, Y. (2014). Cross-national public opinion on climate change: The effects of affluence and vulnerability. Global Environmental Politics, 14(1), 79- 106.
Kirschhoffer, B. (2021, May 6). A recent study has blamed the lurch in the Earth’s axis and poles on the melting of the world’s glaciers, especially from the polar regions. [Photograph].
Theguardian.Com. https://www.theguardian.com/environment/2021/may/06/how-melting- glaciers-have-accelerated-a-shift-in-earths-axis
Kulp, S. A., & Strauss, B. H. (2019). New elevation data triple estimates of global vulnerability to sea-level rise and coastal flooding. Nature communications, 10(1), 1-12.
Lang, A., Potter, R. F., & Bolls, P. D. (1999). Something for nothing: Is visual encoding automatic?. Media Psychology, 1(2), 145-163.
Lecheler, S., Schuck, A. R., & De Vreese, C. H. (2013). Dealing with feelings: Positive and negative discrete emotions as mediators of news framing effects.
le Moyne, R. (2021, September 2). Pollution fills the skyline of the Chinese city of Shanghai at dusk. [Photograph]. News.Un.Org. https://news.un.org/en/story/2021/09/1099042
Leiserowitz, A. (2006). Climate change risk perception and policy preferences: The role of affect, imagery, and values. Climatic change, 77(1), 45-72.
Leiserowitz, A., Maibach, E., Roser-Renouf, C., Feinberg, G., Rosenthal, S., & Marlon, J.
(2014). Climate change in the American mind: Americans’ global warming beliefs and attitudes in November, 2013. Yale University and George Mason University. New Haven, CT: Yale Project on Climate Change Communication, 3-34.
Lerner, J. S., & Keltner, D. (2001). Fear, anger, and risk. Journal of personality and social psychology, 81 (1), 146.
Lerner, J. S., Gonzalez, R. M., Small, D. A., & Fischhoff, B. (2003). Effects of fear and anger on perceived risks of terrorism: A national field experiment. Psychological science, 14(2), 144-150.
Leviston, Z., Price, J., & Bishop, B. (2014). Imagining climate change: The role of implicit associations and affective psychological distancing in climate change responses. European Journal of Social Psychology, 44(5), 441-454.
Loewenstein, G. F., Weber, E. U., Hsee, C. K., & Welch, N. (2001). Risk as feelings. Psychological bulletin, 127(2), 267.
Lorenzoni, I., Leiserowitz, A., de Franca Doria, M., Poortinga, W., & Pidgeon, N. F. (2006).
Cross‐national comparisons of image associations with “global warming” and “climate change” among laypeople in the United States of America and Great Britain. Journal of risk research, 9(03), 265-281.
McCarty, J. A., & Shrum, L. J. (2001). The influence of individualism, collectivism, and locus of control on environmental beliefs and behavior. Journal of Public Policy &
Marketing, 20(1), 93-104.
Messaris, P., & Abraham, L. (2001). The role of images in framing news stories. In Framing public life (pp. 231-242). Routledge.
Nabi, R. L., Gustafson, A., & Jensen, R. (2018). Framing climate change: Exploring the role of emotion in generating advocacy behavior. Science Communication, 40(4), 442-468.
Nelson, D. L., Reed, V. S., & Walling, J. R. (1976). Pictorial superiority effect. Journal of experimental psychology: Human learning and memory, 2(5), 523.
Newhagen, J. E., & Reeves, B. (1992). The evening's bad news: Effects of compelling negative television news images on memory. Journal of Communication, 42(2), 25-41.
Nogier, S. (2021, August 17). Parts of the Var riverbed have dried up owing to low water levels and recent hot temperatures in Carros, southern France. [Photograph].
Theguardian.Com. https://www.theguardian.com/environment/2021/aug/17/global-water- crisis-will-intensify-with-climate-breakdown-says-report
Nosofsky, R. M. (1986). Attention, similarity, and the identification–categorization relationship. Journal of experimental psychology: General, 115(1), 39.
O'Connor, R. E., Bard, R. J., & Fisher, A. (1999). Risk perceptions, general environmental beliefs, and willingness to address climate change. Risk analysis, 19(3), 461-471.
Oh, S.-H., Lee, S. Y., & Han, C. (2021). The Effects of Social Media Use on Preventive Behaviors during Infectious Disease Outbreaks: The Mediating Role of Self-relevant Emotions and Public Risk Perception. Health Communication, 1–10.
O’Neill, S. (2020). More than meets the eye: a longitudinal analysis of climate change imagery in the print media. Climatic Change, 163(1), 9-26.
O’Neill, S. J., Boykoff, M., Niemeyer, S., & Day, S. A. (2013). On the use of imagery for climate change engagement. Global environmental change, 23(2), 413-421.
Paivio, A. (1991). Images in mind: the evolution of a theory. Harvester Wheatsheaf.
Pauwels, L. (Ed.). (2006). Visual cultures of science: rethinking representational practices in knowledge building and science communication. UPNE.
Pidgeon, N., Kasperson, R. E., & Slovic, P. (Eds.). (2003). The social amplification of risk.
Cambridge University Press.
Pidgeon, N. (2012). Public understanding of, and attitudes to, climate change: UK and international perspectives and policy. Climate Policy, 12(sup01), S85-S106.
Powell, T. E., Boomgaarden, H. G., De Swert, K., & de Vreese, C. H. (2015). A Clearer Picture: The Contribution of Visuals and Text to Framing Effects. Journal of Communication, 65(6), 997–1017. https://doi.org/10.1111/jcom.12184
Quinn, S. A. R. A. H., Stark, P., & Edmonds, R. (2007). Eyetracking the news: A study of print and online reading. The Poynter Institute.
Rebich-Hespanha, S., & Rice, R. E. (2016). Climate and sustainability| dominant visual frames in climate change news stories: Implications for formative evaluation in climate change campaigns. International Journal of Communication, 10, 33.
Rebich-Hespanha, S., Rice, R. E., Montello, D. R., Retzloff, S., Tien, S., & Hespanha, J. P.
(2015). Image themes and frames in US print news stories about climate change.
Environmental Communication, 9(4), 491-519.
Russell, J. A. (2003). Core affect and the psychological construction of emotion.
Psychological review, 110(1), 145
Sandberg, F. (2021, November 30). Greta Thunberg [Photograph]. Kompas.
Schwarz, N., Higgins, E. T., & Sorrentino, R. M. (1990). Handbook of motivation and cognition: Foundations of social behavior. New York, NY: Guilford, 527-561.
Semenza, J. C., Hall, D. E., Wilson, D. J., Bontempo, B. D., Sailor, D. J., & George, L. A.
(2008). Public perception of climate change: voluntary mitigation and barriers to behavior change. American journal of preventive medicine, 35(5), 479-487.
Slovic, P., Flynn, J. H., & Layman, M. (1991). Perceived risk, trust, and the politics of nuclear waste. Science, 254(5038), 1603-1607.
Slovic, P. (1992). Perception of risk: Reflections on the psychometric paradigm.
Smith, N., & Leiserowitz, A. (2014). The role of emotion in global warming policy support and opposition. Risk Analysis, 34(5), 937-948.
Solloway, T., Slater, M. D., Chung, A., & Goodall, C. E. (2013). Anger, Sadness, and Fear Responses to Crime and Accident News Stories. Journal of media psychology.
Spence, A., & Pidgeon, N. (2010). Framing and communicating climate change: The effects of distance and outcome frame manipulations. Global Environmental Change, 20(4), 656- 667.
Spence, A., Poortinga, W., & Pidgeon, N. (2012). The psychological distance of climate change. Risk Analysis: An International Journal, 32(6), 957-972.
Statista. (2021, November 5). Largest global emitters of carbon dioxide by country 2020.
Stevenson, A. (2020, December 17). A kookaburra surveys its destroyed home after a bushfire passed through Wallabi Point, NSW. [Photograph]. Wwf.Org.Au.
Stevenson, K. T., King, T. L., Selm, K. R., Peterson, M. N., & Monroe, M. C. (2018).
Framing climate change communication to prompt individual and collective action among adolescents from agricultural communities. Environmental Education Research, 24(3), 365- 377.
Sulistyawati, S., Mulasari, S. A., & Sukesi, T. W. (2018). Assessment of knowledge
regarding climate change and health among adolescents in Yogyakarta, Indonesia. Journal of environmental and public health, 2018.
Taylor, S. (2019). The psychology of pandemics: Preparing for the next global outbreak of infectious disease. Cambridge Scholars Publishing.
Tiburzy, R. (2021, October 12). lustrasi perubahan iklim dari emisi karbon dapat menjadi pandemi baru lagi [Photograph]. Kompas.Com.
Valdez, D. (2021, September 12). Damaged buildings and debris are seen after Typhoon Chanthu passed through Sabtang, Batanes, Philippines, in this September 12,
2021 [Photograph]. Reuters.Com. https://www.reuters.com/world/asia-pacific/strong- typhoon-cuts-power-causes-flooding-northern-philippines-2021-09-12/
Valentino, N. A., Brader, T., Groenendyk, E. W., Gregorowicz, K., & Hutchings, V. L.
(2011). Election night’s alright for fighting: The role of emotions in political participation.
The Journal of Politics, 73(1), 156-170.
Van der Linden, S. (2015). The social-psychological determinants of climate change risk perceptions: Towards a comprehensive model. Journal of Environmental Psychology, 41, 112-124.
Vu, H. T., Liu, Y., & Tran, D. V. (2019). Nationalizing a global phenomenon: A study of how the press in 45 countries and territories portrays climate change. Global Environmental Change, 58, 101942.
Wardekker, A., & Lorenz, S. (2019). The visual framing of climate change impacts and adaptation in the IPCC assessment reports. Climatic Change, 156(1), 273-292.
Watson, D., Wiese, D., Vaidya, J., & Tellegen, A. (1999). The two general activation systems of affect: Structural findings, evolutionary considerations, and psychobiological evidence.
Journal of personality and social psychology, 76(5), 820.
Weston, P. (2021, October 29). Komodo dragon in danger of extinction as sea levels rise. The Guardian. https://www.theguardian.com/environment/2021/sep/04/komodo-dragon-climate- crisis-sea-levels-rise-extinction-aoe
Wibawa, S. W. (2021, November 2). Akibat Perubahan Iklim, Pulau Kecil Sepanjang Aceh- Papua Nyaris Tenggelam Halaman all - Kompas.com. KOMPAS.Com.
Zillmann, D., Knobloch, S., & Yu, H. S. (2001). Effects of photographs on the selective reading of news reports. Media Psychology, 3(4), 301-324.
38 Appendix 1: Tables
Squares df Mean Square F Sig.
Between Groups 2.72 2 1.36 4.24 .016
Within Groups 50.86 159 .320
Total 53.57 161
Table II: Multiple comparisons table illustrating comparisons between experimental conditions on risk perceptions of climate change.
Dependent Variable: Risk Perceptions Bonferroni
(I) Condition Variable
(J) Condition Variable
(I-J) Std. Error Sig.
95% Confidence Interval Lower
Bound Upper Bound
Natural Human .19 .11 .278 -.08 .45
Control .32 .11 .013 .05 .58
Human Natural -.19 .11 .278 -.45 .08
Control .13 .11 .660 -.13 .39
Control Natural -.32* .11 .013 -.58 -.05
Human -.13 .11 .660 -.39 .13
*. The mean difference is significant at the 0.05 level.