5.1. Study Area
The interviews were conducted in and around Cusco. This semi-arid high-mountain region is located in the southern Peruvian Andes. Its highest peaks are around 6000 m above sea level and have been covered by glaciers for centuries. The climate is characterized by 2 seasons: The rainy season between November and March, and the dry season between April and October. The temperatures are relatively consistent throughout the year, with an average high of 19–21 °C and an average low of 0–6.5 °C. To reflect the region’s heterogeneity in terms of degree of urbanization (e.g., population), socioeconomic conditions (e.g., most relevant economic sectors, access to electricity, presence of paved roads), and environmental conditions (e.g., proximity to flood-prone rivers, elevation above sea level), we interviewed respondents in 5 locations (
Figure 1).
Cusco. The region’s center, Cusco, stretches over 4 districts and is home to almost 400,000 people [
68]. The city’s elevation is around 3400 m above sea level. A considerable part of the population works in agriculture, especially corn and native tubers, as well as in tourism and industry. Within our study, the sample of Cusco represents a highly urbanized population with relatively low exposure and vulnerability to effects of climate change.
Izcuchaca and Huacarpay. Izcuchaca is a rural town, located in the district of Anta (estimated population: 1000 inhabitants), situated at 3345 m above sea level. Its main economic sustenance is based on bio-gardens, the vegetable trade, and the raising of small animals, with tourism remaining a rare activity. According to the technical reports of the National Institute of Civil Defense [
69], it is located in a geological high-danger zone as it is prone to landslides.
Huacarpay is located in the Lucre district in the Quispicanchi province, south of Cusco (515 inhabitants; [
68]), at an approximate altitude of 3020 m above sea level. Its main economic activity is tourism related to the Huacarpay Wetland. The town is located in a geological danger zone prone to landslides and, additionally, to flooding of the Lucre River [
70]. The town experienced a severe flooding event in 2010, which forced the population to be relocated. However, the inhabitants have since returned to the flood-risk areas.
In our study, both towns represent rural locations with similar climatic conditions as Cusco, although they are economically less well-off and probably both more exposed and vulnerable to the consequences of climate change. Whereas Izcuchaca is particularly challenged economically, Huacarpay is more exposed to flooding and has already experienced such catastrophic events. Note that the samples were drawn only from the villages Izcuchaca and Huacarpay, not the entire districts.
Urubamba. With 13,942 inhabitants [
68], Urubamba is the largest town in the Sacred Valley (Valle Sagrado). The town is located relatively low at 2870 m. The Vilcanota (or Urubamba) River as well as 2 smaller rivers, which carry the runoff of the surrounding snow-capped mountains including Chicón, run through the town. These rivers have repeatedly caused damage due to flooding. During the dry season, the town often suffers from water scarcity and droughts. Whereas a large part of the town’s population works in agriculture, it has also a thriving tourist industry. This location represents a relatively urbanized population, which, in contrast to Cusco, is more vulnerable and particularly exposed to effects of climate change. In fact, it is considered a high-risk area prone to flooding [
71].
San Isidro de Chicón. San Isidro is the population center of the Chicón Basin, which is a side valley of the Sacred Valley (Valle Sagrado) directly connecting the center of Urubamba with the snow-capped Chicón (5530 m). In spite of the closeness to the economic center Urubamba, the valley is poorly developed and holds only about 584 inhabitants [
68]. Climatically, Urubamba and Chicón are directly connected, and when flooding hits the Chicón Basin, parts of Urubamba are also affected. The last catastrophic event happened in 2010, when a part of the Chicón glacier broke and caused a flood wave that reached deep into Urubamba, causing heavy damage. People in Chicón mainly live from arable farming and livestock production. Because there is hardly any tourism in Chicón, it is economically less well-off than Urubamba. This is visible, for example, in the lack of asphalt roads and only limited access to electricity. The level of education is lower and the influence of indigenous culture stronger, with many people having Quechua as their first language. Therefore, this sample—though of limited size—represents the counterpoint to Cusco: A rural population highly vulnerable and exposed to consequences of climate change.
5.2. Sample and Procedures
After thorough piloting with members of the public, trained local interviewers conducted tablet-assisted, structured face-to-face interviews in Spanish between May 2016 and January 2017. Three considerations guided the target sample size. First, the sample needed to be large enough to perform the planned analyses with sufficient statistical power. Of all analyses planned within this project, the analysis that required the largest sample was a structural equation model (not reported here). This analysis required a sample size of approximately 900 respondents (see [
8,
28]). Second, the sample should be broadly representative of the general population of the research area in terms of age, gender, education, income, and income. Representativeness ensures that the samples are not biased toward specific sociodemographic characteristics, some of which have been found to be related to perceptions of climate change [
8,
72]. Moreover, representativeness is important to make descriptive claims (e.g., ‘X% think that …’). To achieve a broadly representative sample, 1067 respondents are necessary (margin of error: ±3%, confidence interval: 95%). Third, we had to consider the limited resources of this project. In sum, although the ideal sample size would have been at least 1000 participants for each question asked, we could only interview 1804 people in total and secure a final overall sample size of 1316.
Members of the general public in the Cusco region were selected by a random route procedure [
73]. In each of the 5 study locations, interviewers started from roads that were previously selected on maps. From there, they went in all available directions and asked in every second house if someone 16 years or older was willing to participate, irrespective of whether the house was a private home, a business, or a farm (which most houses in rural areas were). They followed this sampling strategy until they had interviewed 4 people in each direction. People younger than 18 years were included to account for the fact that many future impacts of climate change will be experienced by young people and because it provided more cases to compare the very young people (<20 years) with those older than 20 years. According to ethics regulations in Peru, people between 16 and 18 years are allowed to complete surveys with the consent of their parents, which was obtained before the interviews. According to Hoffmeyer-Zlotnik [
73], random route samples are representative for the specific geographic area sampled, even though people refusing an interview bias the sample. Although no data were available to check the representativeness of the sample at the level of the 5 locations, some comparisons with the population of the Department of Cusco were possible [
68]. This showed that people in the age category 20–29 were overrepresented (difference in relative proportion: 11%), and the distributions of the remaining age categories were very similar (difference <5%). Moreover, people with a university degree were overrepresented (difference: 26%), and those with no formal or primary education level were underrepresented (difference: 8%, 12%). Those who learned Quechua as their first language were underrepresented in our sample (difference: 23%), while native Spanish speakers were overrepresented (difference: 25%). With respect to gender and religion, the composition of the sample was very similar to the official statistics (difference <3%; [
68]). So, while not perfectly representative, particularly due to the bias toward higher educated native Spanish speakers, we deem the sample a valid basis for drawing conclusions regarding perceptions of climate change in the region. The differences between under- and overrepresented groups will be investigated in the analyses.
The survey included a broad range of topics, and most questions were presented in a closed-ended format. Because completing all questions would have lasted 4 hours, we created different versions with overlapping sets of topics. This enabled us to cover all topics without putting too much strain on respondents. The interviews typically took between 50 min and 90 min to complete.
To avoid influencing answers to the open questions at the beginning of the interview, interviewers said that they were interested in the interviewees’ “opinions”, and no information about the content of the survey was provided until the open questions were given and entered into a tablet. Interviewers were instructed to adhere to the item wordings and to explain unclear words to the participants without influencing the answers. For example, “climate” and “weather” were difficult to distinguish for many participants, as the same word (el clima) is commonly used for both. Key concepts, such as climate change, the data-gathering procedure (e.g., the answering scales), and, of course, the interview itself (to obtain the informed consent of the participants), were carefully explained by the interviewers.
Of the 3609 people approached, 1804 (50.0%) agreed to start the interview. To ensure good data quality, we excluded 163 respondents who found it difficult to understand the questions (e.g., because their first language was not Spanish or because the interview setting was too loud), who rushed through the questions responding “Don’t know” most of the time, who were overly distracted (e.g., because they were serving clients at the same time), or who did not want to complete the interview. Further, 325 cases were removed based on a check of the entire database (i.e., also items that were not used in the presented analyses) regarding item discrimination. Cases that regularly provided almost the same answers to very different constructs were excluded. The final sample included 1316 respondents (see
Table 1 for its sociodemographic characteristics).
5.3. Measures
The survey contained a broad range of questions on people’s perceptions of climate change and possible ways to respond (including willingness to help others and accept help from others, behavioral intentions, and policy support with respect to both mitigation and adaptation). This paper focuses exclusively on aspects of climate change perceptions. We used open- and closed-answer formats. For the latter type, categorical counts and Likert scales were used. The Likert scales had 5 answer options if the answers were unipolar (i.e., running from a neutral value to an extreme) and 9 options in the case of bipolar items (i.e., running from an extreme to the opposite extreme). This way, the resolution for answers on Likert scales was constant throughout the questionnaire. The interviewer provided a printed scale and explained the meaning of the extreme and neutral values, and the participants could indicate their answer by pointing to a specific value (
Figure 2). The graphics had different forms of symbolizing the more/less concept of the scales and, before asking the first question using scales, the interviewers explained the concept and the graphics.
Most important issue. To contextualize respondents’ perceptions of climate change in the broader context of their everyday experiences and preoccupations, the first question in the survey asked them to describe, in their own words, what they thought would “be the most important problem Peru will face in the next 20 years” [
46].
Associations with climate change. To explore existing perceptions of climate change that were not biased by the survey questions, we then asked approximately half of the respondents (n = 711, 54.0%) to name the “first ideas, pictures or feelings” that came to mind when they thought about climate change [
46,
74]. Note that this question was asked before the interviewers explained the concept of climate change. If respondents did not understand what the question was about, the interviewers related the concept to everyday experiences, such as having heard about it on TV. If the participant still felt unable to reply, the interviewer continued with the next question. A formal introduction of the concept was provided only later in the questionnaire.
Personal experiences of climate-related events. Next, respondents were asked how frequently they had personally experienced 5 types of single climate-related events with potentially catastrophic effects in the last 5 years in their area: (1) Droughts and water shortages, (2) storms or heavy rainfall that led to destruction, (3) severe and unusual flooding, (4) mudslides or avalanches, and (5) diseases or pests that had previously been uncommon in their region. Five answer options were provided: “Never”, “Once”, “Twice”, “Three times”, and “More than three times.” Respondents could also answer with “Don’t know”, “Don’t want to say”, and “Don’t remember”, which we collapsed into the single category, “Refused.” The specific contents of this and other questions relating to environmental changes were selected based on the impacts described by the IPCC [
2]. The goal was to select events that are of high relevance in the investigated area, but also in other parts of the world to allow comparisons with future studies.
Perceptions of environmental and societal changes. Participants then indicated how much they thought 18 types of environmental and societal phenomena had changed in the last 10 years. The 5 phenomena investigated in the previous questions about personal experiences were also assessed here. However, the focus was on perceptions (which can be based on own experiences or not) of changes (and not the phenomena themselves). For example, a person might have experienced the last flooding event as not too severe but perceive that flooding events are becoming more and more severe. In addition to the phenomena for which personal experiences were assessed, 12 other changes were investigated, including agricultural yields and the melting of glaciers. The 9 answer options matched the content of the questions (e.g., −4 = “Much less frequent” vs. 4 = “Much more frequent” for frequency of rain).
Self-assessed knowledge about climate change. A single question was used to gauge respondents’ subjective level of knowledge about climate change: “Have you ever heard about climate change, global warming, or the greenhouse effect? How much do you think you know about this phenomenon?” (0 = “Never heard about it”, 4 = “I’m an expert on this topic”). After participants answered this question, the interviewer explained the concept of climate change as used in the survey.
Beliefs about the reality and the causes of climate change. Participants indicated to what extent they believed that climate was changing (1) locally and (2) globally. Answer options ranged from “Certainly not changing” (−4), to “I am totally unsure” (0), to “Certainly changing” (4). Participants who at least considered that the climate might be changing (i.e., with scores higher than −4 on both previous questions) then indicated on a 9-point scale whether they believed it was caused “Only by natural processes” (−4), “Equally by natural processes and human activities” (0), or “Only by human activities” (4).
Psychological distance. To keep the questionnaire length manageable, we included only the spatial dimension of psychological distance, which is the most widely researched dimension and therefore the most suitable for comparisons with other research [
75,
76,
77,
78]. Respondents were asked how they thought different places, ranging from their immediate environment to the whole world, would be “affected by consequences of climate change due to global warming, such as droughts, flooding, diseases, or mudslides and avalanches.” Answer options ranged from “Not affected at all” (0) to “Strongly affected” (4).
Expectations about future changes. To investigate respondents’ expectations about how things might change in the future, respondents were asked to indicate how much and in what direction climate change would affect 18 dimensions of the natural and human environment. These questions were presented with 9-point Likert scales that matched the content of the questions (e.g., “Will strongly decrease” vs. “Will strongly increase” for questions about the extent of changes, and “Will strongly deteriorate” vs. “Will strongly improve” for questions about qualitative changes).
Worry about climate change. Finally, respondents indicated how worried they were about climate change [
8,
46]. The response options ranged from “Not worried at all” (0) to “Very worried” (4).
5.4. Analyses
Analyses of closed-ended questions. To gain a better understanding of how people in the Cusco region generally perceive climate change, we first examined the perceptions of the entire sample (percentages and means). In a second step, we investigated whether perceptions varied for different sociodemographic groups (e.g., women vs. men). Because the conditions for ANOVA were not met (normal distribution, homogeneity of variance) for many of the dependent variables, we used Kruskal–Wallis analyses of variance by ranks, with the perception variables (personal experiences, perceived past and expected future changes, knowledge, beliefs, psychological distance, and worry) as dependent measures and the sociodemographic variables as independent variables. Because some categories only included very few people and to facilitate interpretation, we combined some of the subcategories of the sociodemographic variables before conducting the analyses. The conditions for using this nonparametric method were met: The dependent variable was at least ordinal, the observations between groups were independent, and the independent variable had 2 or more levels. In total, we ran 572 Kruskal–Wallis tests, of which 108 resulted in statistically significant (
p < 0.05) results. To identify the rank means that differ significantly, we used Dunn’s test for pairwise multiple comparisons with Holm’s correction for multiple group comparisons. The Kruskal–Wallis tests and the pairwise comparisons were conducted using the R package rstatix [
79]. To reduce the results to a manageable number, we tried to identify patterns of results that had at least 3 statistically significant differences. To avoid overinterpreting randomly occurring effects, single results that did not align into a pattern were reported as exceptions. Detailed results can be found in the
Supplementary Materials.
Analyses of open-ended questions. The analysis of open-ended questions involved 4 steps. First, we prepared the data by converting all words to lowercase, correcting spelling errors, removing function words (words with relatively little semantic meaning such as “the” or “at”), and standardized some terms (e.g., using the infinitive for frequent verbs). Second, we analyzed the frequency of single words. Third, to gain a deeper understanding of the meaning of these frequencies, we explored how much they co-occurred with other words [
80]. Fourth, we translated the results from Spanish to English.
To conduct the analyses and prepare this article, we used the statistical software R and R Studio [
81,
82] and several R packages [
83,
84,
85,
86,
87,
88,
89,
90,
91].