Mariam Katsarava
Design and Usage of Line Graphs in the Visualisation of Climate Change Data
Dissertation
Design and Usage of Line Graphs in the Visualisation of Climate Change Data
Dissertation
zur Erlangung des Doktorgrades der
Naturwissenschaften (Dr. rer. nat.) an der Fakultät für Psychologie der FernUniversität in Hagen
Vorgelegt von Mariam Katsarava
Hagen, 2021
Erstgutachter: Prof. Dr. Robert Gaschler, Fernuniversität Hagen
Zweitgutachterin: Dr. Helen Fischer, Max Planck Institute for Human Development
Acknowledgment
I would like to express my sincere gratitude to my supervisor Prof. Dr. Robert Gaschler, whose support, trust, and encouragement enabled me to complete the present dissertation. I am beyond thankful to you for giving me the opportunity to work under your guidance, for sharing your expert knowledge in psychology and for inspiring me in many ways. Special thanks to Dr.
Helen Fischer for her willingness and time to supervise my dissertation. I also want to express my deep appreciation towards my collaborator Dr. Helen Landmann for her valuable suggestions and helpful contribution. Cordial thanks to my colleagues at Fernuniversität in Hagen for providing constructive discussions and insightful feedback. Lastly, to my husband, family and friends, I cannot thank you enough for supporting and motivating me throughout the years.
Publications
1. Katsarava, M., Landmann, H., & Gaschler, R. (under review). No matter how you mark the points on the fever curve – Threatening shapes do not add to threat of climate change
2. Katsarava, M., & Gaschler, R. (in progress). Trend evaluation in climate change data – Numerous advantages of line graphs over numbers
3. Katsarava, M., & Gaschler, R. (under review). Fluctuation in the wind energy supply do not impair acceptance of wind farms
Abstract
Data visualisation has become a substantial part of science and of our daily life. Effective data visualisation strategies are crucial for climate change communication. Despite the large variety of visualisation tools, line graphs with time-series are still one of the forms of data presentation relied on the most. In three journal manuscripts, the experiments of the present dissertation aimed at examining the role of line graphs in climate change communication. The first manuscript tested whether emotion associations of shapes reported in the literature have to be taken into account when designing data graphs. Experiment 1 showed that the triangular symbols, frequently used as point markers in line graphs, do not affect how threatening or arousing the climate change graphs are perceived. However, they affect threat ratings when graphs are not labelled (Experiment 2). The second manuscript tested whether line graphs are consistently preferable to tables across measures such as speed, accuracy, and subjective certainty of trend identification in climate change data. Experiment 3 revealed that this was indeed the case for speed and subjective certainty variables. The third manuscript targeted a domain where line graphs are relevant in finding a way to mitigate climate change. Wind power is a renewable energy characterized by high fluctuation. The variation of wind energy input throughout the day is often communicated via line graphs. Experiment 4 showed that people can consistently judge fluctuation and predictableness of generated wind energy on different days. Importantly, making fluctuation in wind energy supply transparent did not affect the acceptance of wind energy and wind plants. Overall, the results of the experiments included in this dissertation extend our knowledge on how to use line graphs when communicating climate change issues. The results suggest that how we present the data regarding climate change might significantly affect the process-effectivity.
Keywords: line graphs, point markers, tables, climate change communication, acceptance of wind energy
Table of contents
1. Introduction ... 6
1.1 Overview ... 6
1.2 A Brief History of Data Visualisation ... 8
1.3 Perception of Data Graphs ... 8
1.4 Climate Change Communication ... 12
1.4.1 Communication of Climate Change Science ... 12
1.4.2 Data Visualisation in Climate Research and Communication ... 13
1.4.3 Climate Change: Emotions and Behaviours ... 15
1.5 Climate Change ... 16
1.5.1 Temperature Changes ... 16
1.5.2 Climate Change Consequences ... 17
1.5.3 Greenhouse Gasses ... 18
1.6 Renewable Energy ... 19
1.6.1 Overview ... 19
1.6.2 Wind Energy ... 20
1.6.3 Acceptance of Wind Plants ... 21
2. Study Objectives ... 22
2.1 Experiment 1 and Experiment 2 – Shape of Markers ... 22
2.2 Experiment 3 – Trends in Graphs vs. Tables ... 22
2.3 Experiment 4 – Shown Fluctuation and Acceptance ... 23
3. Methods Overview ... 24
4. Results Overview ... 28
5. General Discussion ... 30
5.1 Summary and Discussion ... 30
5. 1.1 Influence of Aposematic Signals on Graph Evaluation ... 30
5.1.2 Graphs versus Tables ... 31
5.1.3 Acceptance of Wind Farms: Fluctuation and Predictability Ratings ... 32
5.1.4 Labelled versus Unlabelled Data ... 32
5.2 Limitations and Future Perspectives ... 33
5.3 Conclusion ... 35
References ... 36
1. Introduction
1.1 Overview
Various visualisation formats have been used throughout the centuries to communicate data in different disciplines. From the beginning of the 19th century, statistical graphs gained attention and then became mainstream in the second half of the century (see Friendly, 2008, for a review). In the 21st century, data graphs became omnipresent in many domains including media, politics, climate change, health, sports, etc.
Due to its interdisciplinary nature in addition to other factors, easily accessible formats of visualisation are especially important for communicating climate data (Helbig et al., 2014;
Neset et al., 2016). Several different formats for data visualisation are used in work on climate change and they have their advantages and indications. For instance, interactive visualisations increase the attractiveness of the climate change research, whereas static visual aids facilitate an exact evaluation of the data (Newell et al., 2016). 3D interactive visualisation formats are also helpful in climate change communication (Schroth et al., 2014). Despite the existence of many elaborate ways to visualize climate data, the mostly used are graphs and tables. Climate change researchers predominantly use 2D formats and more specifically time charts (90%) for presenting climate change data (Nocke et al., 2008). Generally, psychological experiments showed that graphs promote fast decision-making (Benbasat & Dexter, 1986) and are optimal for trend evaluation (Meyer et al., 1999; Vessey, 1991), whereas tables are helpful for detecting exact values (Coll et al., 1994; Meyer et al., 1999).
Climate change communication is a complex process. Various factors including cognitive mechanisms (Hegarty, 2011), audience characteristics (e.g., ethnic identity, social class, gender, and political values; Luo & Zhao, 2019; Pearson et al., 2017), and even attitudes toward climate change (Lewandowsky et al., 2016) might affect how individuals perceive climate change risks or evidence. Thus, identifying effective forms of climate change communication and considering the audience characteristics is crucial in conveying information on climate change and promoting pro-environmental actions (Moser, 2010).
Visualisation of climate change issues is especially important as global warming is increasingly affecting our environment and our daily life. For instance, the years 2016, 2019, and 2020 have been rated as the warmest on record (World Meteorological Organisation [WMO], 2020). Increasing temperature can make floods, droughts, heatwaves, and hot spells more frequent on the global level (Arnell et al., 2019). Rising temperature can negatively affect
the food production system and even endanger food security (Myers et al., 2017), it may hinder economic development (S. Lu et al., 2019), influence biodiversity (Nunez et al., 2019; Pecl et al., 2017), enhance health risks (see Butler, 2018, for a review) and increase mortality due to the extreme heat events (Mora et al., 2017). According to the Intergovernmental Panel on Climate Change (IPCC, 2014), global warming is driven by the excessive emission of greenhouse gasses (carbon dioxide, methane, water vapour, nitrous oxide, and ozone) due to anthropogenic (human-induced) activities. Carbon dioxide (CO2) stays in the atmosphere for an exceedingly long period of time (Archer et al., 2009). Thus, reducing CO2 emissions is a key component in addressing the climate change issue.
Implementation of renewable energy sources is vital for the decarbonisation goal.
Adhering this aim hugely depends on residents’ attitudes toward renewable energy systems (Devine-Wright, 2007). Public attitudes toward green energy sources are mostly positive (Liu, et al., 2013; Ntanos et al., 2018; Stokes & Warshaw, 2017). One of the major sources of green energy is wind power. However, some characteristics of wind energy lower acceptance and hinder the wide implementation of wind energy plants (see Devine-Wright, 2007, for a reviw).
According to the Not in My Backyard (NIMBY) phenomenon, citizens have positive attitudes toward wind energy, but they resist the practical implementation of wind farms in their vicinity (E. C. Larson & Krannich, 2016). Visibility of wind turbines from the home is one of the main reasons for reducing preference for wind farms (Ladenburg, 2014; Pedersen et al., 2009) as the harsh visual intrusion of wind turbines reduces property value (Jensen et al., 2018; Sunak &
Madlener, 2016). The noise produced by the turbines further enhances local resistance (Pedersen et al., 2009). There is also some evidence that wind farms might impair human health (see Knopper et al., 2014, for a review) and biodiversity (Panarella, 2014). Apart from the local effects of wind farms, some less studied characteristics of wind power including low reliability, low predictability, and fluctuating energy supply (Rohrig et al., 2013; Schubert et al., 2014) should also be considered when speaking of acceptance as they are considered as problematic aspects of wind power (Ummels et al., 2007).
Research on data visualisation strategies is strongly needed in climate change research and communication in order to address this issue effectively. In the following theoretical part of this thesis, I will describe a brief history of data visualisation (1.2), perception of data graphs (1.3), climate change communication (1.4), and the climate change issues (1.5). Next, I will review the role of renewable energy sources (1.6) in reducing CO2 emissions, as well as factors hindering the acceptance of wind plants (1.6.3).
1.2 A Brief History of Data Visualisation
In ancient times, different visualisation technologies (e.g., symbols engraved into walls, clay-sheets, etc.) have been used to communicate information to others (Chakravarthi, 1992).
In the middle ages, main formats of data visualisation were rotae (wheel graphs), diagrams, tables, and tree structures (Marchese, 2012). Since the later half of the 18th century, different statistical graphics have been used to communicate quantitative information in various domains like health (e.g., cancer mortality, the spread of disease), astronomy (e.g., movement of the planets), weather (e.g., temperature changes), economics (e.g., price variations for different products, inflation), military (e.g., location and movement of the army), etc. (see Tufte, 2001, for a review). The beginning of the 19th century gave a start to the invention of statistical graphs, which are widely used in the modern world (see Friendly, 2008, for a review). William Playfair (1759-1823) was a pioneer at developing common forms of data visualisation including line graphs with time-series, comparative bar charts, pie charts, and circle diagrams mainly to represent changes and relations between the data in economics (Friendly, 2008). He aimed to replace tables – the conventional form of data presentation at that time – with visual presentations (Tufte, 2001). The development of statistical graphs gradually increased over time and flourished in the second half of the 19th century. Friendly (2008) called this period the
“Golden Age of Statistical Graphics”. However, this interest in graphs drastically decreased in the early 1900s (Friendly, 2008).
Data graphs visually represent quantitative information using numbers, words, coordinate systems, lines, symbols, and colours (Tufte, 2001). In his prominent book about data visualisation, Tufte provided an overview of methods for designing statistical graphs (e.g., by combining the presentation of words, numbers, and pictures) and communicating information.
He underlined three main characteristics of excellent graphs like clarity, precision, and efficiency that are essential to communicate complex ideas. Besides, he deemed the possibility to make data comparisons and foster quantitative thinking as an advantage of graphs. Thus, improvement of data visualisation methods and their accessibility has been central for researchers for quite some time.
1.3 Perception of Data Graphs
Data graphs gained high importance in the 21st century and became ubiquitous in many domains including media, politics, climate change, health, sports, etc. (Glazer, 2011). Thus,
“the ability to evaluate and extract data and meaning from graphical representations of numerical information”, also called graph literacy (Garcia-Retamero & Cokely, 2017, p. 588), has become a crucial skill in the modern world.
Comprehension of data graphs is a complex process and can be significantly influenced by various factors. Friel et al. (2001) differentiate three levels of graph comprehension: an elementary level (extracting values from the graph), an intermediate level (identifying and interpreting relationships in the data), and an advanced level (analysing implicit relationships in the data), also referred as “read the data”, “read between the data”, and “read beyond the data”. In addition to this, Ali and Peebles (2013) added the fourth, pre-elementary level of graph comprehension as about 39% of line graph users either could not interpret the data at all or made erroneous interpretations and thus, could not fit into the elementary level.
Understanding cognitive principles involved in the data assessment is a prerequisite for understanding how to design the graphs properly. For instance, prior knowledge including familiarity with the data content, familiarity with the graphs in general (also known as graph literacy), and individual differences in graph comprehension skills might affect graph evaluation (Garcia-Retamero & Cokely, 2017; Shah & Freedman, 2011). If prior knowledge is incompatible with the information depicted in the graph, participants’ interpretation of the data is systematically false (see Shah & Hoeffner, 2002, for a review). Besides, performance is optimal when there is a cognitive fit between the format of data visualisation and the task type (Vessey, 1991; Vessey & Galletta, 1991). Expectations about the data content can also influence graph comprehension and interpretation. For instance, participants can overestimate correlations between two variables due to their expectations (Freedman & Smith, 1996). In addition, graph-related memory might be biased by expectations about the presented data.
Schemas for graphs that are primarily held by viewers affect the representation of the graphs and can thus define how participants recall the graphs (Shah & Hoeffner, 2002).
Although graphs are in a very general sense effective tools for decision-making (Zacks
& Franconeri, 2020), they can also be a source of inaccurate and biased interpretation. For instance, the mean value in bar graphs was estimated lower than it actually was (Godau et al., 2016). Biases in comprehension occur with scatterplots as well. Inferences made by users highly depend on the characteristics of the scatterplots. Doherty and Anderson (2009) provide an overview of the studies focused on identifying biases in the evaluation of scatterplots. For
instance, users overestimate the strength of correlation with the increasing density of points and with the increasing steepness of the regression slope. Besides, a decreasing number of points on the scatterplot is associated with a lower estimate of the correlation. Additionally, the location of noise (how remotely the data points are located from the model line) is relevant as well for fit estimation in scatterplot (Reimann et al., 2020). Thus, visual features of scatterplots might affect their interpretation. Subsequently, auditory scatterplots might be an alternative to the visual ones, as they are found to be useful for the comprehension of some important aspects of the data (Flowers et al., 1997).
In general, visual characteristics like colour, size, and orientation attract attention and lead to intensive processing of visual input (see Wolfe & Horowitz, 2004, for a review).
Similarly, visual characteristics of the graphs (e.g., colour, format, animation, etc.; Shah &
Hoeffner, 2002) and other aspects of graph design (e.g., orientation of bar graphs) might affect data evaluation (M. H. Fischer et al., 2005). According to Pinker (1990), Gestalt principles (e.g., proximity, similarity, connectedness, continuity, and common fate) can affect graph comprehension. The concept of the Proximity Compatibility Principle (PCP) suggests that information relevant to the same task should be displayed close to each other (Wickens &
Carswell, 1995). Gestalt principles can have positive as well as negative effects on graph comprehension. However, correctly applied gestalt laws like connectedness or proximity in graphs (e.g., by grouping relevant quantitative information and creating visual chunks) can improve trend identification (Shah et al., 1999). Gestalt principles (e.g., law of similarity) can explain the role of colours in graph evaluation. Colours can make variable names more memorable by coupling already well-associated codes together (i.e., “red” for hot). However, colours should be cautiously chosen during designing the graphs as their meaning varies across professions and even cultures and can thus interfere with data interpretation (Brockmann, 1991). If applied correctly, colours can be used for highlighting and separating information, or for grouping the elements (Ali & Peebles, 2013; Shah & Hoeffner, 2002). For instance, the number of correctly interpreted trials was higher in the colour-matching graphs compared to the non-matching line graphs (Ali & Peebles, 2013). Concretely, graph comprehension was facilitated when line points and labels on the x-axis had the same colour (law of similarity) while line and data point colours differed (deliberate violation of the law of connectedness in order to perceptually tease apart two data points connected by the line; Ali & Peebles, 2013).
Similarly, participants needed less time for graph comprehension and for retrieval of graph- related information when data and legend were compatible (Huestegge & Philipp, 2011). These
findings are in line with the theory that gestalt laws can significantly improve or hinder graph comprehension.
Nowadays, there is a plethora of possibilities for data visualisation (bar graphs, line graphs, pie charts, tables, etc.). Although some may appear more attractive or more intuitive than others, scientific evidence could possibly help researchers make informed decisions and choose an appropriate format. Which form of data presentation is optimal, depends on the communication goal. For instance, bar graphs are helpful for discrete data comprehension. Pie charts are good for presenting percentage data, whereas line graphs fit best when the task is a trend evaluation (see Shah & Hoeffner, 2002, for a review).
Despite contradictory study results, animated and interactive data graphs are alternative tools of data communication. Early work found inconsistent evidence for these types of graphs (Shah & Hoeffner, 2002). Morrison and Tversky (2000) could not find strong evidence in favour of animated graphs compared to static graphs, as the former ones were more difficult for participants to comprehend. Likewise, data interpretation was not better for the interactive graphics with point clusters compared to the passive ones (Marchak & Marchak, 1991). Later work, however, emphasized the positive effects of animated and interactive line graphs. For instance, the presentation of animated line graphs was associated with improved risk judgment (Kim & Lakshmanan, 2021). Computational thought, necessary for problem-solving, was stimulated when participants were actively involved in the creation of animated graphs (Barana et al., 2020). Cohen and Hegarty (2007) emphasized the importance of participants’ proficiency in making use of additional information in order to show high performance in interactive visualisation tasks. Considering individual differences may shed light on these controversial findings discussed above.
Similar to the interactive versus static graphs debate, graphs and tables are often compared with each other as well. In terms of their effectiveness, they both have certain advantages and disadvantages. Tables are considered easier to read, however, bar graphs allow a better evaluation of data when the task gets more complicated (Dickson et al., 1986). Graphs are superior for enabling trend-evaluation and decision-making (Meyer et al., 1999; Vessey, 1991), whereas tables help detect exact values and promote correct decisions (Coll et al., 1994;
Meyer et al., 1999). Although graphs may allow faster decision-making than tables (Benbasat
& Dexter, 1986), decision confidence can be higher for tables (Zmud et al., 1983). Overall, which format is preferable depends on the specific task and the aim of data communication (Friel et al., 2001; Schnotz & Bannert, 2003; Schonlau & Peters, 2012; Vessey, 1991).
In sum, graph comprehension might be affected by various factors like task characteristics, reader characteristics, the purpose for using graphs, etc. (Friel et al., 2001).
However, the domain of the graph should also be taken into account when speaking of graph evaluation.
1.4 Climate Change Communication
1.4.1 Communication of Climate Change Science
The majority of climate scientists agree that climate crisis exists and it is anthropogenic (Anderegg et al., 2010; Cook et al., 2016; Powell, 2017). Likewise, the majority of European citizens (with the range of 82.2% – 97.7%) believe that the climate is changing (probably or definitely), that it is human-induced (range 82.7% - 95.7%), and will have harmful impacts (range 58.1% - 87.9%; Poortinga et al., 2018). Although climate change communication was effective in terms of raising awareness regarding the issue, there is yet a big gap between acknowledging the problem and transferring these beliefs and this knowledge into pro- environmental actions (Dietz, 2020; Knutti, 2019; Moser, 2010). Having that in mind, there is a legitimate question to whether climate change communication, in ways widely used by scientists and authors in different reports or articles, is effective and whether they lead to actual changes in human behaviour.
Some characteristics of climate change may shed light on the lack of pro-environmental actions. For instance, the fact that the negative impact of excessive emission of greenhouse gasses and results of environmentally friendly actions are not immediately detectable, makes it difficult for novices (non-experts) to juxtapose temporarily distant, expected consequences of climate change with already existing personal or social needs and demands (Dietz, 2020; Knutti, 2019; Moser, 2010). On the other hand, effective communication strategies may fill this gap and make climate change more tangible for novices. Moser (2010) differentiates three main goals of climate change communication: The first one is to inform and educate individuals about climate change; The second purpose is to achieve some type and level of social engagement and action. This goal entails engagement on different levels including behavioural or political; Lastly, communication strategies should aim at bringing changes in social norms and cultural values, as climate-related values are good predictors of behavioural intentions. To
achieve these goals, many aspects of the communication process should be considered (pp. 37- 38).
Evaluating the audience and tailoring the form of communication to characteristics of the audience are essential for the effective conveyance of information. For instance, characteristics like ethnic identity, social class, gender (Pearson et al., 2017), and political values (Luo & Zhao, 2019) could affect how individuals perceive the same evidence about climate change. According to Luo and Zhao (2019), providing information (e.g., strong climate change evidence) more compatible with already existing beliefs (e.g., high concerns about climate change) of participants led to increased pro-environmental actions in liberals, not in conservatives. Hence, there is no universal approach valid to address every audience. Instead, multiple approaches should be employed to communicate climate change information to a broad and diverse audience.
Regarding climate change, it is particularly important to make the information communicated as personally relevant for novices as possible. It is effective to bring the distant problem home (Moser, 2010, p. 40), to make it more personal by emphasizing local level impacts (Neset et al., 2016; O’Neill & Nicholson-Cole, 2009; Sheppard et al., 2011).
Furthermore, an interactive form of communication (e.g., dialog) could also promote climate change communication (Regan, 2007).
1.4.2 Data Visualisation in Climate Research and Communication
Interest in graphics significantly rose during the Golden Age (from 1860 to 1890 years;
Friendly, 2008). During this period, graphics became an important part of government publications and an influential tool of communication between experts from different disciplines (Friendly, 2008). Data graphs gained even more importance in the modern world.
Nowadays, they are a common form of communication between researchers across the fields as well as when presenting important information to the public (Glazer, 2011).
As in many other disciplines, data visualisation is crucial for climate change communication as well. However, the characteristics of this field can make this process more complex. For instance, research on climate change has become increasingly interdisciplinary (Hellsten & Leydesdorff, 2016). Climate change domain entails communication between researchers from various disciplines (Dale et al., 2010). Thus, data visualisation is a highly needed means for communication in interdisciplinary domains like climate change (Johansson
et al., 2010; Schneider, 2012). According to Helbig et al. (2014), easily accessible data visualisation tools are vital for scientific communication and for the interdisciplinary exchange of the data.
Visualisation is an immensely helpful method for communicating complex data.
Different tools of visualisation have different advantages and disadvantages. For instance, interactive visualisation tools are more effective for evoking interest toward climate change research, whereas static visual aids are helpful for deep evaluation of the data (Newell et al., 2016). Besides, interactive climate change communication strategies significantly affect participants’ attitudes and concerns toward climate change (Herring et al., 2017). Pie charts are effective in increasing viewers’ knowledge about the scientific consensus on climate change (van der Linden et al., 2014). According to Schroth et al. (2014), interactive 3D visualisations can facilitate climate change communication. Apart from various visual aids, climate change can also be communicated using narratives and stories (Moezzi et al., 2017). Showing documentary content (“Years of Living Dangerously”; Bieniek-Tobasco et al., 2019) and film (“The Day after Tomorrow”; Lowe et al., 2006) about climate change increases concerns about the issue.
Despite the very advanced tools for visualisation of climate change (Wibeck et al., 2013), graphs and tables are still the most used formats by climate change communicators. A questionnaire performed in form of face-to-face interviews with 76 climate change researchers revealed that 2D formats and specifically time charts (90%), which is a form of line graphs with time on the x-axis, is still the most used tool for climate data presentation (Nocke et al., 2008).
Reports of the Intergovernmental Panel on Climate Change (IPCC) and its Summary for Policymakers (SPM) mostly provide graphs. For instance, 68 of the 184 pages of the IPCC5 main chapters (page 111 to page 1246) contain data graphs and especially line graphs (IPCC, 2014). As line graphs allow faster decision-making and evaluation of trend direction (Benbasat
& Dexter, 1986; Meyer et al., 1999; Vessey, 1991), they are omnipresent in climate change communication. However, graphs used in IPCC reports can be extraordinarily complex (H.
Fischer et al., 2020; Harolds et al., 2016; McMahon et al., 2015). Whilst climate change experts might be well equipped with meaning-making strategies and thus understand the presented data (Stofer & Che, 2014), novice readers can find it difficult to deal with the uncertainties in IPCC graphs (McMahon et al., 2015). Although relatively intuitive IPCC graphs can be misinterpreted to a minor extent, counter-intuitive graphs are much more prone to incorrect or even opposite interpretations (H. Fischer et al., 2020). Moreover, “if the SPM's graphs fail in
comprehensibility, their messages might lose credibility and be rejected altogether” (Amelung et al., 2016, p. 3). Thus, using graphs with an easy and intuitive design can facilitate information conveyance. Increasing the accessibility of graphs for the public is worth the effort, as civilians play a vital role in climate change mitigation (e.g., by influencing government policies indirectly or by pro-environmental consumption behaviours; Clayton et al., 2015).
Comprehension of the cognitive principles involved in data assessment is a prerequisite for understanding how to design the graphs properly to optimally communicate climate change with both experts and novices (Harolds et al., 2016). For instance, Amelung et al. (2016) suggested improving climate change communication by reducing the number of details in SPM to the main findings and providing cognitively digestible input of information. Also, prior beliefs about climate change can influence data interpretation. In this light, Lewandowsky et al.
(2016) conducted an experiment presenting climate change data with neutral labels (e.g., translated into other domains like economics or demographics) as a means to eliminate the influence of knowledge and beliefs about climate change on the participants’ judgments.
Consequently, participants rated the mainstream scientific interpretations as accurate, whereas contrarian claims, endorsed by climate change sceptics, were judged as inaccurate.
1.4.3 Climate Change: Emotions and Behaviours
Presenting visuals of climate change has a significant influence on climate-related affect (see Metag, 2020, for a review) and increases participants’ concern (Herring et al., 2017).
Perceived saliency of the climate change issue also increased after presenting visuals about climate change impacts (Metag et al., 2016). Making the impact of climate change apparent to participants can boost the intention to change behaviour (Chapman et al., 2016). Likewise, the knowledge one holds about climate change issues, can predict pro-environmental actions (Bord et al., 2000).
Presenting visuals might increase anxiety about risks associated with climate change and even increase the motivation to act (Lowe et al., 2006). However, effective climate change communication should be based on not only evoking threat and fear but also on promoting constructive hope (Ojala, 2018). Content analysis of SPM showed that threat-related information was presented to a greater extent than the efficacy information (Poortvliet et al., 2020). Overwhelming individuals with negative information about climate change can have a counterproductive effect and even lead to apathy and hopelessness. Although fear-inducing messages are helpful for catching attention, providing possibilities for action promotes perception of self-efficacy (O’Neill & Nicholson-Cole, 2009). The study conducted by Niles et
al. (2016) in New Zealand showed that participants’ perception of self-efficacy was associated with intended as well as with actual behaviour. Weak perception of self-efficacy, on the other hand, hinders intended behavioural change but communicating information about successful consequences of the actions might improve perceived efficacy and stimulate action (Bieniek- Tobasco et al., 2019). Thus, climate change communication should be based on building a sense of self-efficacy in the audience in order to foster pro-environmental actions.
Research on data graph perception and usage in the climate and energy domain can be enlightened by different fields of psychology such as emotion (Paper 1), graph comprehension (Paper 2), and acceptance research (Paper 3). There are a multitude of questions to be tackled in experiments. Some are part of this thesis: (1) Do different shapes in data graphs activate emotion? (2) Is trend identification objectively and subjectively better with graphs? (3) Does graphical information on the fluctuation of wind energy threaten acceptance?
As this psychological work targets to improve climate research and communication, an overview of relevant work and findings from climate research will be provided before returning to the genuine psychological contributions.
1.5 Climate Change
1.5.1 Temperature Changes
Global climate has experienced considerable and large-scale changes over the past 100 years. The last three decades have been warmer than any other decade since 1850 (IPCC, 2014).
Among the warmest 10 years (2011-2020) on record, the years 2016, 2019, and 2020 are the top three on this list. In 2016, the global mean temperature was 1.1(±0.1)°C above the pre- industrial level. Similarly, the global mean temperature in 2019 was 1.1(±0.1)°C and in 2020, 1.2(± 0.1)°C higher compared to the pre-industrial level (WMO, 2019; WMO, 2020).
Increased global mean surface temperature causes, among other things, ocean warming and contributes to its acidification as the majority (about 90%) of the Earth surface heat is absorbed by the ocean (WMO, 2019). Its heat-trapping rate significantly increased over the last decades. Moreover, ocean heat content has become an important measurement tool for climate change impacts. In 2019, ocean heat content reached the highest point and supposedly, this process will continue in the future as well. From 2009 to 2018, the oceanic uptake of the annual
CO2 emissions made up about 23% (WMO, 2019; WMO, 2020). Although CO2 absorption lowers its concentration in the atmosphere, it does affect the ocean in a negative way, for instance, by decreasing its pH level, also known as oceanic acidification, which has been the case since the late 1980s with the most drastic changes over the last three decades. Thermal expansion due to the increase in the ocean heat content and ice melting leads to a rise in sea level (WMO, 2019).
Glaciers have shrunk worldwide, especially in Greenland and the Arctic (IPCC, 2014).
Global warming is at least twice as high in the arctic as in other regions leading to the significant loss of ice mass (IPCC, 2014; IPCC, 2018). Melting of ice sheets, especially noticeable over the last two decades, is another contributor to the global sea-level rise (IPCC, 2014; WMO, 2019; WMO, 2020). The global sea level increased by about 0.19 m from 1901 to 2010 (IPCC, 2014). In 2019, sea-level rise peaked, partly due to the significant ice-loss in Greenland and Antarctica (WMO, 2019). A similar trend was present in 2020 as well. Overall, 152 gigatons (Gt) of ice melted in Greenland between September 2019 and August 2020 (WMO, 2020).
Taken together, these two factors - glacier mass loss and ocean thermal expansion – can explain approximately 75% of global sea-level rise (IPCC, 2014).
1.5.2 Climate Change Consequences
The consequences of climate change are manifold and affect human well-being to a great degree. The impacts of climate change occur on the global as well as on the local level.
Globally, floods, droughts, heatwaves, and hot spells may become more frequent in case of significant increase of mean temperature (Arnell et al., 2019). Climate change can affect soil fertility in a direct (i.e., changes in soil properties and composition) and indirect (i.e., soil erosions) ways (Biswal, 2021). Moreover, it may also endanger food security by affecting the entire food production system (Myers et al., 2017).
Biodiversity will be strongly affected by the increased global mean temperature (Nunez et al., 2019; Pecl et al., 2017). Strong negative effects on biodiversity is expected even at the moderate (1-2°C) increase of mean temperature. In order to avoid negative impacts on biodiversity, temperature increase should be kept below 1.5°C (Nunez et al., 2019).
Temperature variation can hinder economic development (S. Lu et al., 2019) and may become a source of peoples’ impoverishment in the long term, especially in poor countries (Tol, 2018). Additionally, anthropogenic climate change is harmful to the health (see Butler, 2018,
for a review) and can even increase the global mortality rate due to extreme heat events (Mora et al., 2017).
To act on the complexity of the climate change issue, the factors contributing to this process should be decoupled and carefully evaluated. For instance, excessive emission of greenhouse gasses by human activities provably drives the climate change issue. The role of greenhouse gas emissions in existing climate change is explained in the following section.
1.5.3 Greenhouse Gasses
Greenhouse gasses including carbon dioxide (CO2), methane (CH4), water vapour (H2O), nitrous oxide (N2O), and ozone (O3) are normally responsible for warming the Earth surface by trapping the heat from the sun, which is called the greenhouse effect. The role of this effect in making our planet habitable is vital (IPCC, 1990). However, the concentration of greenhouse gasses has elevated because of human activities, which causes excessive absorption of solar heat and overheats the Earth surface (IPCC, 1990). Hence, human activities induced the increase of global mean temperature by about 1.0°C (with a range of 0.8°C to 1.2°C) compared to pre-industrial levels (IPCC, 2018). Annual anthropogenic greenhouse gas emissions have increased by 10 gigatons of carbon dioxide equivalent (GtCO2eq) between 2000 and 2010, mostly due to the generation of energy (47 %), industry (30 %), transport (11 %), and building (3 %) sectors (IPCC, 2014).
Although all kinds of greenhouse gases contribute to climate change to some degree, CO2 is a major greenhouse gas and the most influential driver of global warming and climate change issues. The main determinants of intensive CO2 emissions are transportation (Knez et al., 2014; Lizbetin et al., 2018), population size and dynamics (Murtaugh & Schlax, 2009), manufacturing industry, trade, agriculture, tourism industry, energy production, etc. (Balogh &
Jámbor, 2017; IPCC, 2014).
The intensities of CO2 in the atmosphere has increased at least by 40% since the industrial revolution (Kweku et al., 2017). Approximately 50% of cumulative anthropogenic CO2 between 1750 and 2010 was emitted in the last 40 years (IPCC, 2014). Between 2009 and 2018, CO2 emissions grew by 0.9% per year, which is about 34.7 (± 1.8) GtCO2 (WMO, 2019).
In 2018, the globally averaged mole fractions of CO2 were 407.8 (±0.1) parts per million (ppm), which means that globally averaged mole fractions made 147% of the pre-industrial (the 1750s) level. In 2019, the concentration of CO2 reached 410.5 (±0.2) ppm (WMO, 2020). Despite the
mobility-reduction during the COVID-19 pandemic, the real-time data indicate that CO2, CH4,
and N2O concentration continued to increase in 2020 as well (WMO, 2020).
The fact that CO2 is a major greenhouse gas and it remains in the atmosphere for centuries makes this issue even more significant (Archer et al., 2009; IPCC, 2014) and thus, demands immediate measures to be taken. CO2 emissions should be significantly reduced if the goal is to pursue the Paris Climate Agreement and keep the temperature increase well below 2°C (IPCC, 2014). Moreover, the European Union aims at going climate-neutral by 2050. To achieve this objective, CO2 footprints should be minimized and the economy should achieve net-zero greenhouse gas emissions by that time in order to adhere to the European Green Deal (LT-LED, 2020). According to the majority of scientists, Paris Climate Agreement indirectly implies (mainly by defining the goal of maximum increase of temperature by 2100) that net CO2 emissions should reach zero by 2060-2080 (Clémençon, 2016).
If the current emission rates are kept constant, the 1.5°C temperature increase compared to pre-industrial levels (1850 - 1900 average) will be reached between 2030-2052. In order to avoid this scenario, emissions should be drastically reduced by 2030 (Tonko, 2019). The following section provides strategies for moving towards the decarbonisation goal.
1.6 Renewable Energy
1.6.1 Overview
Approximately 47% of the increase in annual greenhouse gas emissions between 2000 and 2010 was due to the energy supply (IPCC, 2014). Many studies (including studies employing Environmental Kuznets Curve estimation) have shown that an increase in the consumption of renewable energy leads to the reduction of CO2 emissions (Balogh & Jámbor, 2017; Bekhet & Othman, 2018; Bekun et al., 2019; Chen et al., 2019; Dong et al., 2018;
Moutinho et al., 2018).
It is projected that “renewable energy and energy efficiency measures can potentially achieve 94% of the required emissions reductions by 2050” (Gielen et al., 2019, p. 41). Thus, a wide implementation of renewable energy sources is considered to be a key factor in pursuing the decarbonisation goal. However, the market for green innovations has not been expanded to the desired level yet (Hammami et al., 2018). For instance, in 2018, only about 26% of global
electricity was generated by renewable energy sources. Noticeably, the number of countries relying on renewables is increasing progressively (Renewables [REN21], 2019).
A wide and successful implementation of renewable energy systems depends partly on financial investment (Steffen, 2018) and, more importantly, on public attitudes toward these green systems (see Devine-Wright, 2007, for a review). In the general sense, public attitudes toward renewable energy sources are predominantly positive (Liu, et al., 2013; Ntanos et al., 2018; Stokes & Warshaw, 2017). For instance, the survey conducted by Eurobarometer (2019) showed that about 92% of respondents deemed the implementation of renewable energy systems to be important.
Wind and solar energy systems are the most favoured renewable energy sources in terms of investments. In 2019, investments in the deployment of wind energy systems increased and outweighed the investments in solar photovoltaic (PV) system installation (REN21, 2020).
Although the wide implementation of wind energy farms is crucial for reducing CO2 emissions, the deployment of wind turbines is sometimes hindered due to the resistance from local residents. The next section provides an overview of wind power, its advantages, and disadvantages, as well as the factors constituting the low public acceptance.
1.6.2 Wind Energy
The wind market increases annually, whereas the prices fall due to the competitive environment, tenders, and auctions worldwide (REN21, 2019). Presently the installation of new wind plants is more cost-effective than the exploitation of already existing coal and gas power systems (REN21, 2020).
The global wind market increased by 19% in 2019 and generated 650 gigawatts (GW) in total. The share of wind power generated onshore (621 GW) significantly outrode the share of wind power produced offshore (29 GW). This fact can be explained by costly technologies related to offshore wind plant implementation (REN21, 2019). Although solar and wind power are both very popular renewable energy sources (REN21, 2019), public attitudes are more positive towards solar energy compared to wind energy (Liebe & Dobers, 2019). There are some characteristics of wind energy plants that are perceived negatively, lower acceptance of wind plants and thus, hinder the wide and timely implementation of wind energy systems (Devine-Wright, 2007). The low acceptance problem can be explained not only by the technological and visual characteristics of wind energy systems (i.e., noise and visibility of turbines) but by the specific flaws of renewable wind energy sources (i.e., less reliability of wind energy power due to its high variations) as well.
1.6.3 Acceptance of Wind Plants
Generally, citizens accept wind plants and comprehend how important wind power is to reduce CO2 emissions and thus mitigate climate change. However, practical implementation of wind energy systems faces significant resistance from the local residents. This contradictory attitude is described by Not in My Backyard (NIMBY) phenomenon. This term describes positive attitudes toward wind energy in general but, when it comes to the practical implementation of wind farms, some citizens do not accept them close to their home (E. C Larson & Krannich, 2016; Sharpton et al., 2020). Apart from the territorial proximity, there are other influential factors affecting acceptance of wind farms including the participation of local citizens in the planning and decision-making process, perceived benefits, fairness, trust in project-developers, etc. (Aitken, 2010; Jones & Eiser, 2009; Sonnberger & Ruddat, 2018). In addition, visibility of wind turbines from the dwelling reduces preference (Ladenburg, 2014), increases annoyance (Pedersen et al., 2009) towards wind farms and may negatively affect property value (Jensen et al., 2018; Sunak & Madlener, 2016). Noise, produced by the turbines, is another factor that promotes local resistance (Pedersen et al., 2009). There is some evidence that wind farms endanger human health (see Knopper et al., 2014, for a review) and biodiversity (Panarella, 2014).
Apart from the factors associated with resistance on the local community level, other less studied components come into play when thinking about acceptance on a larger scale.
Characteristics of wind power including reliability, predictability, and fluctuation in the energy supply might also affect acceptance (Rohrig et al., 2013; Schubert et al., 2014). Variation of wind speed lead to varying availability of wind power, which can be related to fluctuation in energy prices. In addition, electricity demand often does not match the supply. Such discrepancies in demand-supply patterns make prices unstable (Badyda & Dylik, 2017). It is interesting to know how far the fluctuating and (at long forecast intervals) unpredictable supply of wind generated power can hinder acceptance of wind energy.
Having certain attitudes toward climate change or renewable energy sources does not always mean that novices are equipped with a sufficient amount of information and knowledge on the topic (Assefa & Frostell, 2007). The study by Bidwell (2016) showed that participants who received quality information about wind energy showed higher acceptance of wind farms, compared to poorly informed participants.
Apart from delivering relevant information, the form of information communication matters as well. The studies, discussed below, aimed at further examining the effects of different (climate-related vs. neutral framing) visualisation formats on data evaluation, decision-making,
and subjective ratings. The next sections provide a summary of the theoretical rationale and objectives of each study, followed by methods, results, and discussion at the end.
2. Study Objectives
2.1 Experiment 1 and Experiment 2 – Shape of Markers
Experiments 1 and 2 are submitted for publication in the manuscript “No matter how you mark the points on the fever curve – Threatening shapes do not add to threat of climate change”.
Graph comprehension is influenced, to some degree, by affective responses to the data content (Lewandowsky et al., 2016). Visual features like roundness, angularity, simplicity, or complexity can further influence an affective evaluation of the data (X. Lu et al., 2012). For instance, edgy shapes are more associated with arousal than curved shapes (Pacheco et al., 2015). Likewise, edgy, angular or triangular shapes are less liked (Bar & Neta, 2006).
Moreover, they are coupled with unpleasant affect (C. L. Larson et al., 2012). Souchet and Aubret (2016) linked fear of triangular forms to evolutionary psychology. They suggest that fear of snakes, for instance, is in fact fear of aposematic signals (e.g., zig-zag pattern, triangular shape of the head). In their experiment, pictures of smilies, snakes, dogs, etc. carrying triangular or pointy shapes were rated as “mean” compared to the same pictures but without aposematic symbols. The authors suggested that humans have innate preparedness to associate triangular shapes with fear. Although it is relatively well established that triangular shapes catch more attention and induce unpleasant affect than round shapes, it is interesting to examine whether choice of marker shapes might affect graph perception and evaluation of the content. Having that in mind, Experiment 1 of this dissertation aimed at testing the influence of line graphs depicting different geometrical figures (triangles, circles, squares, rhombi, and asterisks as point marker shapes) on subjective ratings (threat and arousal) of climate change data.
To control for the influence of graph content (climate change) on the subjective ratings, we conducted a follow-up experiment (Experiment 2 of the current dissertation) with the same data graphs, but without labels on the graphs. The aim was to evaluate the effect of geometrical figures without interfering with the attitudes toward the climate change issues.
2.2 Experiment 3 – Trends in Graphs vs. Tables
Experiment 3 – “Trend evaluation in climate change data – Numerous advantages of line graphs over numbers” is submitted as a manuscript and waiting for revisions. Line graphs are the most commonly used formats in climate data communication (Nocke et al., 2008). They promote decision-making and evaluation of trends (Benbasat & Dexter, 1986; Meyer et al., 1999; Vessey, 1991). Whereas tables promote detection of exact values (Benbasat & Dexter, 1986; Coll et al., 1994; Meyer et al., 1999) and are associated with higher decision confidence (Zmud et al., 1983). Although graphs are evaluated faster than tables, it remains unclear whether choosing graphs over tables leads to lower or higher subjective certainty. H. Fischer et al. (2020) provided evidence that subjective certainty regarding the answer correctness can be high even if the graph interpretation is incorrect. Effective communication of climate change data requires people to process the presented data quickly, correctly, and at high subjective certainty. Thus, in Experiment 3 we aimed at testing the impact of the format (graph versus table) on speed and accuracy of trend evaluation, as well as on subjective certainty. In addition to this, graphs and tables were presented with labels in one group and without labels in another one. This way, in the unlabelled group, we were able to assess the correctness of trend detection, reaction time, and subjective certainty of the answer without influencing participants’ attitudes toward the climate change domain (Lewandowsky et al., 2016).
2.3 Experiment 4 – Shown Fluctuation and Acceptance
Experiment 4 is submitted as manuscript 3 “Fluctuation in the wind energy supply do not impair acceptance of wind farms” to a peer-reviewed journal. Public attitudes toward wind energy are generally positive, but the deployment of wind parks is often accompanied by resistance from local citizens (Devine-Wright, 2007; Sharpton et al., 2020). Wind power faces several challenges like low reliability, low predictability, and high fluctuation in the energy supply (Rohrig et al., 2013; Schubert et al., 2014), which may reduce acceptance. There is also evidence that public opposition towards wind parks might be determined by a deficiency in knowledge (Bidwell, 2016). Thus, factors affecting wind energy acceptance are manifold and relatively well studied. However, less is known about whether making fluctuation in wind energy supply transparent would also affect acceptance. Having that in mind, Experiment 4 was designed to examine the effects of presenting line graphs with time-series about fluctuation in wind energy supply on acceptance of wind farms. Predictability and fluctuation ratings were assessed after each line graph. In order to control for the potential influence of participants’
attitudes toward wind power and subsequently detect potential biases in predictability and fluctuation ratings, one group received the unlabelled line graphs (cf. Lewandowsky et al., 2016).
The next section provides more information about how all experiments were conducted in order to answer the research questions.
3. Methods Overview
All four experiments included in this dissertation had some characteristics in common.
For instance, they all used line graphs and, in all cases, real data was used to generate the graphs.
Even in the control experiments, where labelling was avoided, the participants were aware that they were dealing with real data (rather than spending their time on fictional data). One graph or table appeared on the screen at a time (rather than e.g., showing multiple graphs or tables on the screen simultaneously). In all cases, the figures were shown in black and white and were created using Microsoft Excel. In all cases, participants could use as much time as needed to respond to the stimulus and questions (though reaction time was measured in some cases, graphs and tables did not disappear automatically from the screen). Responses were indicated by mouse-clicks on rating scales. All experiments were run as online-experiments using the Unipark/Questback system. Participants were mostly students of FernUniversität and highly heterogeneous in terms of age and other characteristics.
In Experiment 1, we analysed the data of 314 participants (Mage = 33.38, SD = 13.56 years; 68% female). Participants were aware that the data graphs they would receive contained data about climate change. They were also instructed that they had to do ratings after the presentation of each graph. After giving informed consent and answering demographic questions about their age, gender, and proficiency in the German language, participants received five different line graphs about climate change (glacier mass balance, global annual ocean temperature, global annual land surface temperature, CO2 emissions from fossil fuel energy sources, and sea level change). Each graph depicted one of five different symbols (triangle, circle, square, rhombus, and asterisk) as point markers. Graphs had the same layout but different data content and different symbols (see Figure 1). Participants received five graphs successively and each graph was followed by two subjective ratings: Threat rating (“how threatening do you consider the scientific evidence in the graph?”; from 1 - not threatening to
5 - very threatening) and arousal rating (“how arousing do you consider the scientific evidence in the graph?”; 1 – calm to 5 – arousing). Each content and each symbol was presented only once per participant and the order of graph presentation was randomized. Each graph content was paired with one of the five symbols using Latin Square (see Table 1). As a result, we obtained five groups, and participants were randomly assigned to one of these five groups.
Figure 1
Presented graph x symbol combinations in the first group.
Group 1
Glacier mass balance
-500
-700 Millimeter
water -900 equivalent (mm.w.e.) -1100
-1300
-1500
2001-2010 2012/13 2014/15 2016/17 Years
Global annual ocean temperature
0,9 0,8 0,7
°C above the0,6 20th century
average 0,5 0,4 0,3 0,2
2011 2012 2013 2014 2015 2016 2017 2018 Years
Global annual land surface temperature
1,5 1,4 1,3 1,2
°C above the 20th century 1,1
average 1 0,9 0,8 0,7
2011 2012 2013 2014 2015 2016 2017 2018 Years
CO2 - emissions from fossil fuel energy sources
38 37,5
37 CO2 - 36,5 emissions
in 36 gigatonnes35,5 35 34,5 34
2011 2012 2013 2014 2015 2016 2017 2018 Years
Sea level
90 85 80 Sea height 75
variation (mm) 70
65 60 55 50
2011 2012 2013 2014 2015 2016 2017 2018 Years
Table 1
Latin Square for balanced graph x symbol combinations across the groups.
Glacier mass balance
Ocean temperature
Land surface temperature
CO2
emissions
Sea level change
Group 1 Triangle Circle Square Rhombus Asterisk
Group 2 Asterisk Triangle Circle Square Rhombus
Group 3 Rhombus Asterisk Triangle Circle Square
Group 4 Square Rhombus Asterisk Triangle Circle
Group 5 Circle Square Rhombus Asterisk Triangle
In Experiment 2, which is a follow up experiment of the previous experiment, we analysed the data of 279 participants (Mage = 30.69, SD = 10.24 years; 76.7% female; 22.9%
male; 0.4% other). We presented the same graphs as in Experiment 1. The only difference was the absence of labels on the graphs. Thus, participants in Experiment 2 were not aware of the data content. This change enabled us to observe the influence of triangles vs other symbols on threat and arousal ratings without potential interference due to participants’ attitudes toward climate change. Except for presenting the label-free graphs in Experiment 2, Experiments 1 and 2 were identical in terms of design and procedure.
In Experiment 3, we analysed the data of 190 participants (Mage = 33, SD = 12.42 years;
58% female). After giving informed consent and answering demographic questions (same as in Experiments 1 and 2), participants received two line graphs and two tables about climate change (glacier mass balance, global annual ocean temperature, CO2 emissions from fossil fuel energy sources, and sea level change). We had two independent groups of participants: The labelled group (received two labelled graphs and two labelled tables) and the unlabelled group (received two unlabelled graphs and two unlabelled tables, see Figure 2). Thus, the second group was not aware of the content of the presented data. Format (graph or table) x content (climate change data) combinations were assigned using a Latin Square. Participants were randomly assigned to the groups. After seeing each graph and table, participants received two tasks. In the first one, they had to detect the trend direction (deciding whether data depicted an ascending or descending trend). At the same time, we measured the time participants needed to make the judgment. In the second task, they had to rate how certain they were about their answer regarding the trend direction (from 1- not certain to 5 - certain). The presentation order of graphs and tables was randomized.
Global Glacier Mass Balance Index
Years Millimeter water equivalent (mm. w.e.)
2001-2010 -740
2011/2012 -659
2012/2013 -858
2013/2014 -751
2014/2015 -1136
2015/2016 -840
2016/2017 -877
2017/2018 -1247
Global Annual Ocean Temperature
Years °C above the 20th century average
2011 0,4
2012 0,45
2013 0,48
2014 0,57
2015 0,74
2016 0,75
2017 0,67
2018 0,66
2011 34,4
2012 35
2013 35,3
2014 35,6
2015 35,5
2016 35,7
2017 36,2
2018 37,1
2011 54,3
2012 64,4
2013 63,8
2014 69,9
2015 82,5
2016 79,8
2017 85,1
2018 88,1
Figure 2
Format and content combinations in the labelled group 1.1 (A) and in the unlabelled group 2.2 (B).
(A) Labelled group 1.1
CO2 emissions in gigatonnes
CO2 Emissions From Fossil Fuel Energy Sources 38
37,5 37 36,5 36 35,5 35 34,5 34
2011 2012 2013 2014 2015 2016 2017 2018 Years
Sea height variation (mm)
Sea Level 90
85 80 75 70 65 60 55 50
2011 2012 2013 2014 2015 2016 2017 2018 Years
(B) Unlabelled group 2.2
-900
2001-2010 2011/12 2012/13 2013/14 2014/15 2015/16 2016/17 2017/18 2011 2012 2013 2014 2015 2016 2017 2018
In Experiment 4, we analysed the data of 218 participants (Mage = 34.30, SD = 11.90 years; 74% female). All participants received a total of 30 line graphs with the amount of wind
-500 0,9
-700
0,8
0,7
0,6
-1100 0,5
0,4 -1300
0,3
-1500 0,2
energy fed into the grid. Each graph presented wind fluctuation (30 days of September 2019) with 5 hour time intervals on the x-axis (from 0:00 to 24:00 o’clock, see Figure 3). All participants rated fluctuation (“The fluctuation throughout the day is very large’’: from 1 - strongly disagree to 5 - strongly agree) and predictability (“The fluctuation throughout the day is well predictable’’: from 1 - strongly disagree to 5 - strongly agree) for each graph. Some participants (only Group 1 and Group 2) received nine items about participants’ acceptance of wind energy in general and the acceptance of wind farms. Participants were randomly assigned to one of the three independent groups. Group 1 (labelled-pre) made acceptance ratings first and then received the line graphs with two subjective fluctuation and predictability ratings.
Group 2 (labelled-post) received the reversed order of tasks (graphs first and acceptance items later). Group 3 (unlabelled) received the unlabelled line graphs with subjective fluctuation and predictability ratings. Thus, Group 3 made subjective ratings without being aware of the graph content.
Figure 3
Examples of the graphs presented in each group.
(A) Group 1 (labelled-pre) and Group 2 (labelled-post)
September 1, 2019 September 20, 2019 September 30, 2019
20000 20000 20000
15000
Wind energy
feed-in
in megawatts 10000
5000
15000
Wind energy feed-in
in megawatts 10000 5000
15000
Wind energy feed-in in megawatts 10000
5000
0
0:00 5:00 10:00 15:00 20:00
Hours
0
0:00 5:00 10:00 15:00 20:00
Hours
0
0:00 5:00 10:00 15:00 20:00
Hours
(B) Group 3 (unlabelled)
20000
15000
10000
5000
20000
15000
10000
5000
20000
15000
10000
5000
0
0:00 5:00 10:00 15:00 20:00 0
0:00 5:00 10:00 15:00 20:00
0
0:00 5:00 10:00 15:00 20:00
4. Results Overview
