Next Article in Journal
Glycerol-Based Deep Eutectic Solvents for Simultaneous Organosolv Treatment/Extraction: High-Performance Recovery of Antioxidant Polyphenols from Onion Solid Wastes
Next Article in Special Issue
How E-Government Can Help Societies during a Crisis: Implications of UTAUT Model in Lebanon
Previous Article in Journal
Redefining the Use of Vinification Waste By-Products in Broiler Diets
Previous Article in Special Issue
Beyond Personal Responsibility: Analyzing How Attributing Responsibility for Environmental Protection Can Hinder Action
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Sustainability
Volume 14
Issue 23
10.3390/su142315716
sustainability-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles thumb_up
...
Endorse textsms
...
Comment
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

Longitudinal Predictors of Perceived Climate Change Importance and Worry among Italian Youths: A Machine Learning Approach

by
Gabriele Prati
1,*,
Iana Tzankova
2,
Cinzia Albanesi
1 and
Elvira Cicognani
1
1
Department of Psychology “R. Canestrari”, University of Bologna, 47521 Cesena, Italy
2
Department of Education Studies “G. M. Bertin”, University of Bologna, 40126 Bologna, Italy
*
Author to whom correspondence should be addressed.
Sustainability 2022, 14(23), 15716; https://doi.org/10.3390/su142315716
Submission received: 24 October 2022 / Revised: 21 November 2022 / Accepted: 21 November 2022 / Published: 25 November 2022
(This article belongs to the Special Issue What Psychology for a Sustainable Community?)
Browse Figure
Review Reports Versions Notes

Abstract

:
The current study aimed to investigate the longitudinal predictors of perceived importance of climate change and personal worry among Italian youths. Specifically, we used machine learning techniques to examine the predictive importance of a wide range of socio-demographic factors, political perceptions, attitudes on a national and European level (identity, attitudes, tolerance, support for democracy, authoritarianism, nationalism, political trust), efficacy beliefs, social well-being, political interest, and different forms of participation on perceived importance of climate change and personal worry. In this longitudinal study, we collected data using a questionnaire in two waves at a one-year interval—in 2016 and 2017. Participants were 1288 Italian young adults (61.3% were female; 38.7% were male) whose mean age was 19.18 (SD = 3.29) ranging between 15 and 30 years. Breiman’s random forest algorithm performed better than Friedman’s gradient boosting machines algorithm. The random forest algorithm revealed that age, tolerance toward migrants, and tolerance toward refugees were the most important predictors of perceived importance of climate change and personal worry. Other important predictors were national/European identity, political interest, internal political efficacy, nationalism, social well-being, self-efficacy, authoritarianism, anti-democratic attitudes, EU warmth, and online and civic participation.

1. Introduction

Communities around the world are increasingly facing the challenges of extreme weather events and other environmental hazards associated with climate change. Indeed, the Fifth Assessment Report of the Intergovernmental Panel on Climate Change (IPCC; at the time of writing the IPCC is in its Sixth Assessment cycle) highlighted that the human influence on the climate system is clear, and such influence had pervasive effects on human and natural systems [1]. The literature on climate change risk perception has evidenced how the threat can be underestimated in people’s subjective evaluations due to the nature of the abstract and long-term nature of changes related to climate issues [2]. Higher levels of societal awareness and support for climate action and mitigation are needed. Moreover, awareness and perceived risk are among the factors that influence individual motivation to engage in pro-environmental behavior change [3]. For instance, there is evidence that countries’ carbon dioxide emissions correlate negatively with climate change risk perception [4]. However, public perceptions of global climate change are characterized by high levels of uncertainty concerning the dangers posed by climate change and whether to approve any mitigation or adaptation actions and plans [5]. While the scientific evidence concerning the dramatic impact of climate change is solid [1], climate change is still a controversial topic [6]. This is problematic because mitigation and adaptation to climate change factors require public support and engagement [7,8].
To date, most of the research examining worry and perceived importance of climate change’s negative consequences for humans and the environment has focused on adults [9]. However, there are several reasons why climate change is especially relevant for young people [10]. Future generations are more likely to be affected by a failure to deal with climate change [11]. Future generations will also need to find solutions to address the predicted impact of climate change on their lives. For instance, the lives of young people will be affected by the consequences of climate change in terms of security, health, and socioeconomic conditions [9]. According to Fløttum, et al. [12] (p. 1128):
Despite the relevance of climate change for youth, the evidence concerning the predictors of perceived importance of climate change and worry among youth is scant. To address this research gap, in the present study we focus on the predictors of perceived importance of climate change and worry among youth.
Previous studies have used a wide range of measures to assess the general public’s perceptions of climate change risk, employing measures of general concern about the issue [13], perceived likelihood of impacts [14,15,16], perceived seriousness or importance of the issue [17,18,19], personal worry [20,21,22] or a combination of these in a single measure [23,24,25]. However, these aspects can be considered conceptually distinct dimensions of the perception of climate change risk [26]. For example, perceived likelihood of impacts (the cognitive component) and perceived importance of climate change (the evaluative component) may vary differently with socio-economic and attitudinal factors [19]. Personal worry, moreover, is distinguished from general concern and cognitive perceptions as it is an active emotional state more directly related to behavioral responses [26]. There is little empirical research that has considered possible differences between dimensions of climate change risk perception and has investigated which explanatory factors may be specific to each [17,27]. The present study seeks to contribute to empirical evidence by investigating explanatory factors related to two distinct dimensions of climate change risk perception—perceived importance (evaluative dimension) and personal worry about the impact (emotional dimension).
The rest of the Introduction section is structured as follows. First of all, we briefly review the literature on predictors of attitudes and concerns about climate change. Next, we focus on the predictors of climate change importance and worry within the EU context, considering some specific characteristics such as the theoretical tension between European identity and national identity. Finally, we define the main aims of the current study.

1.1. What Predicts Climate Change Importance and Worry?

The beliefs and concerns about climate change among young people tend to be associated with age [9]. There is also evidence of a strong gender divide, with young women more worried about the environment compared to young men [28]. Such gender divide is similar to ones obtained in previous studies involving adults [21,23,29,30]. In addition to gender and age, other socio-demographic variables have been linked to beliefs and concerns about climate change: level of education [21,30,31], belonging to ethnic minorities [30,32,33], socio-economic status [30,34], religiousness [30,35,36], and living in an urban vs. rural area [37].
The challenges related to climate change and the related adaptation and mitigation measures risk exacerbating existing weaknesses in democratic theory and practice, especially in terms of legitimacy challenges [38,39]. Notwithstanding, democracies could offer solutions to climate change issues by fostering empowerment and participatory decision-making [40]. In addition, some researchers argue that democracy is best placed to address the challenges of climate change [40,41,42,43]. Some studies suggest that active citizens, who engage in civic participation, tend to be particularly concerned about climate change and environmental issues [44,45]. Therefore, we may expect an association between views on climate change and democracy support, empowerment, and participation.
Previous research showed that social well-being predicts pro-environmental behavior [46]. Specifically, according to the social outlook hypothesis, the experience of a positive mental state predicts a favorable view of one’s community and society as well as increases the likelihood of prosocial behavior [47]. According to Kaida and Kaida [48], there is a win-win relationship between positive psychological states and pro-environmental behaviors. Given that pro-environmental behavior can be conceptualized as a form of prosocial behavior [46], we added social well-being as a possible predictor of climate change importance beliefs and worry.
Bandura’s theory of self-efficacy (and collective efficacy) has been used to understand people’s reactions to climate change [49]. Specifically, the theory posits that people, groups, and communities are unlikely to address climate change effectively unless they hold a perceived sense of efficacy that their individual and collective efforts can be successful. Indeed, there is evidence that support for reducing the risks of climate change is associated with self-efficacy beliefs [50,51]. Therefore, based on Bandura’s theory of self-efficacy, we may expect an association between self-efficacy and collective efficacy and climate change beliefs.
According to Bodor, et al. [52], a necessary precondition for a sufficient level of collective action is the development of trust between people. Social and institutional trust is associated with climate change beliefs and attitudes [31,53]. Therefore, we may expect that social trust could play a role in predicting climate change importance and worry.

1.2. Potential Predictors of Climate Change Importance and Worry within the EU Context

Previous research revealed that concern for the welfare of all people and nature (i.e., universalism) [54], sense of global citizenship [55], and superordinate identity [56] are important from a psychological point of view because the issue of climate change and its resolution represents a pervasive commons dilemma that requires a sense of “us” as members of a superordinate or higher-order group. In the European context, the idea of Europe can provide a superordinate identity that is potentially able to transcend traditional national identities. The emergence of a European identity is strongly interconnected with the systematic construction of national identity [57,58]. National identities and European identity become salient in relation to climate change issues [59]. The European Union (EU) could be viewed as representative of an ‘Us’ in relation to climate change: According to Olausson [58], “the embryo of a European identity is discursively constructed in the news reporting on climate change.” The EU has indeed emerged as a leader in climate change policy in the last 20 years owing to a gradual “Europeanisation” of national climate policies, which also became an important driver for a wider integration of member states [60]. More recently, the European Council launched a comprehensive package of policies—the European Green New Deal [61]—and adopted a European Climate Law that puts the goal of climate neutral Europe by 2050 in legislation [62]. Recent forms of (neo)nationalism among western European countries reject supranational (e.g., European Union) climate policies as well as international or European Union climate treaties [61,62,63]. There is evidence that the adherence to policies dealing with the issue of climate change is a function of the extent to which citizens identify with the national or supernational (Europe) institutions enacting them [64]. The tension between a climate change agenda which requires a superordinate identity and a nationalist ideology that focuses on the defense of national sovereignty is relevant for climate change views. Indeed, public attitudes consistent with a nationalist ideology are linked to climate change skepticism [63]. Such nationalist ideology clashes not only with international policies to mitigate climate change but also with an increase in climate refugees [65], which is an inevitable consequence of climate change [63]. Indeed, there is evidence that opposition to immigration is associated with climate change skepticism [66]. Therefore, it is possible to hypothesize a role of national and European identification as well as attitudes towards the nation, Europe, and immigration/refugees on views about climate change.

1.3. Purpose of the Present Study

Previous research has evidenced that political attitudes and psychological variables are key factors affecting climate change perceptions, alongside or even more so than sociodemographic characteristics [21,67,68]. Few studies have focused on assessing the predictive importance of different factors on climate change attitudes [67]. Moreover, to our knowledge, no existing studies examine the predictive importance of a wide range of socio-demographic and psychosocial factors on distinct dimensions of climate change risk perception.
In the present study, we examined the psychosocial predictors of perceived importance of climate change and personal worry among Italian youth. There is evidence that climate change risk perception and importance have different predictors [17]. The paper will explore through machine learning techniques the predictive power of socio-demographic factors, political perceptions, attitudes on a national and European level (identity, attitudes, tolerance, support for democracy, authoritarianism, nationalism, political trust), efficacy beliefs, social well-being, political interest, and different forms of participation.

2. Materials and Methods

The data were collected in two waves at a one-year interval–in 2016 and 2017, within the research project CATCH-EyoU funded by the European Union within the H2020 framework program (grant number 64538). It was approved by the Bioethics Committee of the University of Bologna. Adolescents and young adult participants filled out a self-administered questionnaire either online (64.3%) or pencil-and-paper (35.7%). To ensure anonymity and to enable data matching, participants were asked to produce an anonymous code based on instructions that were given for both surveys.

2.1. Procedure

Adolescents (15–19 years old) were approached in six upper secondary schools with different tracks (vocational, technical, or academic) in the Emilia-Romagna region in northern Italy. Further information on the participating schools and the data collection procedure has been previously published [57,69]. All contacted schools were public, ensuring adequate variability between tracks and municipal contexts typical of Italy (large and small cities, rural settings). We did not contact private schools because they tend to include students from families with a high socio-economic background as reported in Italian census data [70]. Convenience sampling was the sampling strategy. School’s headmaster and reference teachers were contacted first. All the schools that were contacted agreed to participate. After a formal agreement, participation in the study was proposed to students. All the students agreed to take part in the study. Students completed the questionnaires on paper or online, during class and under the supervision of a researcher and/or a teacher. All students and, in the case of underage participants, parents completed participation consent forms.
Young adults (20–30 years old) were contacted by e-mail through the office of the University of Bologna (92.7%) and through youth organizations (7.3%). University students attended different courses and had origins in different parts of Italy. The choice of the University was based on convenience and because it was located in the same region as the six upper secondary schools. The students received an invitation to participate in the survey. All participants filled out consent forms before starting the online questionnaire.

2.2. The Context

Emilia-Romagna is situated in the north of Italy and is one of the regions of the country. This region comprises an area of 22,446 km2 and about 4.4 million inhabitants. The population density is equal to 200 inhabitants per km2 in 2019 and closely resembles the national average. Emilia-Romagna is one of the most developed and wealthiest regions in Italy and Europe. Therefore, from a socio-economic perspective, this region is similar to the most developed and wealthiest regions in Italy and Europe and cannot be compared to most other regions. Notwithstanding, the evaluation reports by the six upper secondary schools revealed that student performance in standardized tests is substantially similar to that of national benchmarks [57,69].

2.3. Sample

The sample consisted of 1288 participants (61.3% were female; 38.7% were male), after excluding incomplete responses and those missing important socio-demographic information (10%). The average age was 19.18 (SD = 3.29), ranging between 15 and 30 years old. Details on the representativeness of the sample are available in previous studies [70,71].

2.4. Instrument

The instrument was constructed by the CATCH-EyoU consortium. Adaptation of the questionnaire was done through two independent translations into Italian and a back translation by a third independent translator. Any differences were examined and resolved by the team. The instrument was assessed through a pilot study with 206 participants with the aim of developing a reliable, parsimonious, and valid questionnaire. The pilot study was conducted involving students from different schools but with similar backgrounds. The pilot study revealed that participants encountered no difficulty understanding the items. Moreover, changes to the questionnaire following the pilot study included the shortening of some scales and minor modifications in wording and layout. Participants were able to fill in the survey in 30 min.

2.4.1. Dependent Variables

The dependent measures were assessed at Wave 2 (W2) of the data collection in November 2017–January 2018.
To measure climate change importance, participants were asked to indicate how important they thought it was to deal with the issue of climate change on a 5-point Likert scale (1 = not important at all, 5 = extremely important).
To assess worry about climate change, participants were asked to indicate their agreement with the statement “I am worried about the impact of climate change on my country”. The response scale ranged from 1 = strongly disagree to 5 = strongly agree.

2.4.2. Independent Variables

All independent variables were assessed at Wave 1 (W1) of the data collection in November 2016–January 2017. The measures were based on existing scales and previous studies in the field. Table 1 reports the description, sample items, response scales and reliabilities for all independent variable measures.

2.5. Design

Analyses were conducted using JASP Version 16.3 [89] and the R-package gbm and random Forest. In the analyses, we used two machine learning algorithms—Breiman’s random forest algorithm and Friedman’s gradient boosting machines (GBM) algorithm —because they (1) can analyze predictor variables of different data levels, such as categorical, ordinal, or continuous data and (2) provide variable importance measures that can be employed to rank predictors according to their predictive power. For both the GBM algorithm and the random forest algorithm, we scaled the variables. We conducted different sensitivity to determine the optimal selection of the hyperparameters. Regarding the random forest algorithm, we optimized the error of prediction using a validation data set using a maximum of 5000 possible decision trees to be considered, and we set as 50 the percentage of training data that is used to train each individual tree. As data split preferences, we used 20% of the data as holdout test data and 20% for the purposes of training. Concerning the GBM algorithm, we tested 100 to 5000 trees to fit using shrinkage rates that ranged between 0.01 and 0.001. We chose a shrinkage rate below 0.001 with a maximum of 5000 trees to improve predictive performance. We randomly chose 20% of the data as holdout test data. The accuracy of the model was tested by twenty-fold cross-validation. We evaluated the performance of each model using the Mean Square Error (MSE), the root-mean-square error (RMSE), and the coefficient of determination (R2). The best model from each algorithm was determined based on the lowest RMSE and, in case of a tie, the highest R2. Missing data analysis revealed minimal missing data (i.e., 4.35%). Following recommendations for dealing with missing data [90], given that missing data was minimal (less than 5%), we decided not to impute data.

3. Results

Table 2 shows the correlation coefficients (Pearson’s r) between W1 variables and W2 perceived importance and worry. Only a portion of the W1 variables of interest reported a correlation greater than 0.10—a standard definition of a small effect [91]. Age, Participation at EU level, EU voting intentions, online participation, civic participation, European commitment, European exploration, national exploration, country-competence, political interest, self-concept (political efficacy), collective efficacy (political efficacy), internal political efficacy, tolerance-refugees, and tolerance-immigrants were positively related to both climate change importance and climate change worry. Antidemocratic attitudes, authoritarianism, nationalism, and place of living (countryside) correlated negatively with both climate change importance and climate change worry. While the relationship between gender (male) and climate change importance was not significant, the relationship between gender (male) and climate change worry was significant and negative.
The predictions derived from machine learning algorithms are reported in Table 3. The random forest algorithm reported the lowest RMSE. In addition, the random forest algorithm had the lowest MSE. Finally, the coefficient of determination (R2) was similar across the machine algorithms when predicting climate change importance (about 15% of explained variance), while R2 of the random forest regression model predicting climate change worry was higher (19% of explained variance) compared to the gradient boosting model (6% of explained variance). Therefore, we chose the random forest algorithm, and we calculated the variables importance for each random forest model.
Table 4 displays the total increase in node purity of variables of the established random forest models. A higher increase in node purity meant greater importance of a variable and more contribution of a variable to the variation of the outcome. Age, tolerance toward migrants, and tolerance toward refugees had the highest node purity increase values for both climate change importance and climate change worry.
The relationships between age and both climate change importance and climate change worry are depicted in Figure 1. Climate change importance and climate change worry linearly increase from the age of 15 years to about 23–25 years. At the age of 23–25 years, a plateau is reached. Thereafter, a small decrease in climate change importance and climate change worry was observed.
The second group of variables with high node purity increase values included national/European identity (commitment, exploration and reconsideration dimensions), political interest, internal political efficacy, nationalism, social well-being, self-efficacy, authoritarianism, anti-democratic attitudes, EU warmth, and online and civic participation (Table 4). With the exception of age, the importance of the other socio-demographic variables (e.g., gender, parents’ status, income, citizenship, living arrangements) was lower. In addition, the importance of attitudes toward the country/EU (except for Country warmth), personal empowerment, self-concept (political efficacy), institutional and social trust, EU voting intentions, collective efficacy (political efficacy), protest participation, religiousness, political participation, and participation at EU level was lower. The importance of collective efficacy (political efficacy) was higher in the model predicting climate change importance compared to the model predicting climate change worry, while the importance of internal political efficacy and self-efficacy was slightly higher in the model predicting climate change worry compared to the model predicting climate change importance.

4. Discussion

The aim of the current study was to investigate the longitudinal predictors of climate change importance and worry in a sample of Italian youth. The use of machine learning techniques allowed us to handle a large number of potential predictors without variable deletion. More important, the use of traditional techniques like ordinary least squares (OLS) approaches satisfactorily explain the outcomes in an initial sample but may fail to accurately predict the same outcome in other samples. Indeed, without preventing overfitting, it is difficult to estimate if and how the model can generalize to new samples even if the goodness of fit or the effect size indicates a good performance of the model. Machine learning techniques provide predictive accuracy for out-of-sample data and “an increased focus on prediction, rather than explanation, can ultimately lead us to greater understanding of behavior” [92].
The findings of the present study revealed that age is one of the most important variables predicting climate change importance and worry. Specifically, climate change importance and worry increased from the age of 15 years to the age of 30 years. Previous research demonstrated that although the levels of knowledge regarding different aspects of climate change are positively related to age among youth, the levels of belief and concern regarding climate change tend to decline from 14 to 18 years [9]. Such “adolescent dip” in belief and concern regarding climate change has been found in several studies [93,94,95,96,97]. Different reasons have been put forward to explain such “adolescent dip”: disruptions in the development of prosocial moral reasoning [98], greater adherence to hedonistic values among late adolescents [96], higher feelings of powerlessness to exert change among adolescents [22,99], higher adherence to worldview and cultural values among adolescents [28,100], and higher opportunities to make choices and greater awareness of the costs associated with pro-environmental behaviors and policies [9]. The findings of the present study do not offer support for these assumptions. Age positively predicted climate change importance and worry. We believe that adolescence and young adulthood (i.e., emerging adulthood) is a key developmental stage for climate change importance and worry for three main reasons. First, the development of abstract thinking and complex abstract thinking is particularly important in taking in new information and understanding the complexity and seriousness of climate change as well as complex global problems and risks. Second, among adolescents and young adults, understanding the larger world and its problems represents an important developmental challenge [101,102] Indeed, preparing students for sustainable development goals and active citizenship is gaining more importance to the point that they have been formalized as compulsory learning subjects of civic education, which was introduced as mandatory into the Italian education system in 2019 [87,103]. Third, different psychological and social changes mark adolescence such as emotional separation from parents and social autonomy, development of identity and strong peer relationships, challenging the authority and the social structure of society [104]. For instance, there is evidence that peers play an important role in developing knowledge and beliefs concerning climate change [105,106] and that there are complex interactions, divergent points of view, and conflicts between parents and late adolescents concerning environmental issues [106,107]. Given the complex interaction among these factors, we believe that the relationship between age and climate change importance and worry is multi-directional and contextually bound. Therefore, the phenomenon of adolescent dip might be true for some individuals, contexts, and cultures, but not for all. Indeed, there is evidence that pro-environmental behavior among adolescents is inversely associated with age only in an individualistic culture but not in a collectivistic culture [108]. In terms of the individualism/collectivism dimension, Italy falls between Western countries and Asian countries [109]. Given the role of age, an implication for policymakers is to plan, develop, and provide a range of curriculum-based educational opportunities to early adolescents to help them explore and discuss the issues of climate change in an engaging way.
The perception of the seriousness of climate change is positively correlated with a leftist position on the left–right scale [29,110]. While political orientation has been found to be an important predictor of climate change perceptions, especially in the US and in Western and Northern Europe, in Southern and Eastern European countries, like Italy, the left-right political divide tends to be smaller regarding climate change [20,21]. The meaning of the “left” and “right” labels can vary in different political contexts. A deeper investigation of conservative orientations on social justice and immigration-related issues is needed to gain better insight into what determines individuals’ perceptions about climate change. By including a wide range of political attitudes related to nationalist vs. European and democratic vs. authoritarian orientations, our study provides a more comprehensive look at the political positions that influence climate change perceptions. The findings suggest that among Italian youth immigration-related attitudes are particularly relevant in predicting perceived importance of climate change and personal worry about its impact. We believe that climate-related and immigration-related attitudes are a part of a larger network of ideologies or ideological climates that also include nationalism, authoritarianism, and hostility to the EU [65,66,111,112,113]. According to this network of ideologies, the primary targets of hostility are typically refugees and immigrants, but also environmentalists and climate scientists, as well as the EU as a supranational institution that is directing climate change [111]. The findings of the present study suggest that tolerance toward refugees and migrants are particularly relevant when predicting climate change importance and worry and that young people frame the issue of climate change as an issue of justice, being probably aware that the consequences of climate change disproportionately affect communities in the Global South and that environmental migration is already a reality [114]. It is interesting to note that climate change skepticism and denial predicted by immigration-related attitudes may contribute to one of the effects of climate change: A growth in migration flows [115,116]. In the prediction of climate change importance and worry, authoritarianism, anti-democratic attitudes, nationalism, as well as EU identity and attitudes, seem less important than immigration-related attitudes. Nevertheless, nationalism and national/European identity and attitudes formed the group of most important variables after age, immigration-related attitudes, online participation, and political interest. We may speculate that climate-related attitudes and concerns are associated with different ways of adopting and questioning national and European identity. Both national and European identity exploration were related to the outcome variables, but commitment only to a European identity was associated with climate change importance and worry. Questioning the meaning of European identity concerning climate change may refer to young people’s awareness of the need for a global approach to the issue. Still, it does not mean failing to recognize Europe’s weight in environmental issues: the correlational data also show that the perception of a competent Europe is associated with a reduction in concern about climate issues. Even the data on the European vote, strongly related to climate worry, seems consistent with this understanding, in which Europe is given the mandate to play its part. The national identity dimension seems to work the opposite as nationalist policies do not put climate change at the center of the political agenda; only exploration of this identification in our young sample is related to climate change attitudes.
Online participation and political interest emerged as important factors in predicting climate change importance and worry. Although scholars, policymakers, and practitioners have all called attention to the issue of online misinformation about climate change [117], we believe that the Internet may also provide spaces for ordinary citizens’ engagement in everyday discussions and deliberation about climate change [118,119,120,121]. There is evidence that European young people are less skeptical regarding the possibility of contrasting climate change and that social media platforms have played a pivotal role in supporting young activists in promoting environmental awareness and mobilizing collective action [122]. The findings of this study confirm the important role played by the Internet in youth’s political lives [123]. To foster participation in climate discourse online, the provision of information on climate change from social media and interpersonal conversations about climate change topics have been found useful [124]. We believe there may be an interplay between political interest and online participation: Political interest may stimulate the use of the Internet to get information and to participate in issues related to climate change and vice versa.
Efficacy beliefs and civic participation played a role in the prediction of climate change importance and worry. The finding that prior experience with civic engagement as well as the beliefs that one is capable of performing an action predict engagement with climate change is in line with the theoretical social-cognitive model of political activism for climate change mitigation [125]. The political dimensions of efficacy beliefs are stronger predictors of climate change importance and worry than personal ones, and they also have a stronger association with climate change importance than with worry: we can speculate that having the sense that one can act as a collective works as a coping strategy, reducing the emotional burden of climate change, without undermining its perceived seriousness.
The low importance of social and institutional trust in predicting climate change importance and worry is in line with the findings of previous research [52,126]. There is evidence that the effect size of the relationship between general, or non-domain specific, trust and engagement with climate change is negligible, while trusting scientists and environmental groups strongly correlates with public climate-friendly behaviors [127]. Therefore, future studies may employ different and more relevant trust measures. General trust may not capture the expectations of reciprocity that are key to addressing climate change as a collective action problem. In addition, the findings of the present study confirm the significant, albeit modest, role of social well-being in predicting climate change importance and worry as a form of prosocial behavior [46].
Previous research revealed a strong gender divide, with greater worry about climate change among young women compared to young men [28]. This gender divide was found not only in studies involving young people but also adults [21,23,29,30]. In the present study, gender played a very small or negligible role in predicting climate change importance and worry. We contend that such gender differences tend to disappear when including more proximal variables in the model. Therefore, although gender differences may exist, such as in civic and political engagement and participation [128], their role is modest and may be explained by different mediators. Also, the greater accessibility of the Internet and social media could contribute to closing the gender gap in political participation that, in turn, may contribute to making women’s voices more influential in contrasting climate change: there is evidence that when women are more influential in politics, policies address climate change issues more seriously [129].
We found some differences in the most important predictors between the two outcomes. Collective efficacy (political efficacy) had higher importance in the model predicting climate change importance compared to the model predicting climate change worry. In addition, internal political efficacy and self-efficacy showed a higher importance in the model predicting climate change worry compared to the model predicting climate change importance. This pattern of findings seems to suggest that worry about climate change has more to do with personal beliefs about one’s own capability, while climate change importance is more strongly connected to the capability of members of a community. Therefore, worry about climate change is more individual-oriented, whereas climate change importance is more community-oriented. Despite these small differences, it is interesting to note that the model predicting climate change importance differed slightly from the model predicting climate change worry.
It is important for these findings to be replicated and extended to other European countries to see if equivalent findings emerge. This research has some limitations that need to be acknowledged. First, the sample cannot be considered representative of the population of Italian youth because convenience sampling was used. Although we did not use a representative sample in our study, we do not believe that our findings are biased. Specifically, we examined theoretical relationships between constructs that we believe are not affected by our sampling techniques. Moreover, in our models, we controlled for the effect of many factors, including several socio-demographic variables. Second, the longitudinal design of the research cannot support any causal claims. Third, we acknowledge that we used paper and pencil as well as online surveys. We cannot rule out the hypothesis that a difference in the delivery of a research questionnaire (e.g., method, time, setting) had the potential to influence the answers. Finally, the use of self-report measures may be subject to biases such as recall bias, single-responder bias, and social desirability bias.

5. Conclusions

To our knowledge, this is the first longitudinal study examining the predictive importance of a wide range of socio-demographic and psychosocial factors on perceived importance of climate change and personal worry about its impact among youth. Among Italian adolescents and young adults, age is the most important predictor, and the levels of climate change importance and worry grew from the age of 15 years to the age of 30 years. In addition to age, the findings of the present study highlighted the role of immigration-related attitudes in predicting perceived climate change importance and worry. Although there is no straightforward logical connection between climate change skepticism (or denial) and negative attitudes towards refugees and migrants, we believe that such seemingly unrelated perceptions, attitudes, and beliefs are part of a larger network of ideologies or ideological climates surrounding global issues.

Author Contributions

Conceptualization, G.P.; methodology, G.P., E.C. and C.A., formal analysis, G.P.; data curation, I.T.; writing—original draft preparation, G.P.; writing—review and editing, G.P., I.T. and C.A.; visualization, G.P.; funding acquisition, E.C. and C.A. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by the European Union, Horizon 2020 framework programme, under grant agreement number 649538 for the project CATCH-EyoU—Constructing AcTive CitizensHip with European Youth: Policies, Practices, Challenges and Solution; H2020 Societal Challenges.

Institutional Review Board Statement

The study was conducted in accordance with the Declaration of Helsinki and approved by the Bioethics Committee of the University of Bologna.

Informed Consent Statement

Informed consent was obtained from all subjects involved in the study.

Data Availability Statement

The data that support the findings of this study will be made openly available upon publication in a public data repository that can mint a persistent digital identifier (i.e., a digital object identifier).

Conflicts of Interest

The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.

References

  1. IPCC. Climate Change 2014 Impacts, Adaptation, and Vulnerability Part B: Regional Aspects: Working Group ii Contribution to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change; Cambridge University Press: Cambridge, UK; New York, NY, USA, 2014. [Google Scholar]
  2. Weber, E.U. What shapes perceptions of climate change? WIREs Clim. Change 2010, 1, 332–342. [Google Scholar] [CrossRef]
  3. IPCC. Climate Change 2022: Impacts, Adaptation, and Vulnerability. Contribution of Working Group II to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change; Cambridge University Press: Cambridge, UK, 2022. [Google Scholar]
  4. Luís, S.; Vauclair, C.-M.; Lima, M.L. Raising awareness of climate change causes? Cross-national evidence for the normalization of societal risk perception of climate change. Environ. Sci. Policy 2018, 80, 74–81. [Google Scholar] [CrossRef]
  5. Hagen, B.; Middel, A.; Pijawka, D. European climate change perceptions: Public support for mitigation and adaptation policies. Environ. Policy Gov. 2016, 26, 170–183. [Google Scholar] [CrossRef]
  6. Harth, N.S. Affect, (group-based) emotions, and climate change action. Curr. Opin. Psychol. 2021, 42, 140–144. [Google Scholar] [CrossRef] [PubMed]
  7. Semenza, J.C.; Hall, D.E.; Wilson, D.J.; Bontempo, B.D.; Sailor, D.J.; George, L.A. Public perception of climate change: Voluntary mitigation and barriers to behavior change. Am. J. Prev. Med. 2008, 35, 479–487. [Google Scholar] [CrossRef] [PubMed]
  8. Whitmarsh, L.; O’Neill, S.; Lorenzoni, I. Public engagement with climate change: What do we know and where do we go from here? Int. J. Media Cult. Politics 2013, 9, 7–25. [Google Scholar] [CrossRef] [PubMed]
  9. Lee, K.; Gjersoe, N.; O’Neill, S.; Barnett, J. Youth perceptions of climate change: A narrative synthesis. WIREs Clim. Change 2020, 11, e641. [Google Scholar] [CrossRef] [Green Version]
  10. Han, H.; Ahn, S.W. Youth mobilization to stop global climate change: Narratives and impact. Sustainability 2020, 12, 4127. [Google Scholar] [CrossRef]
  11. Warren, M. Thousands of scientists are backing the kids striking for climate change. Nature 2019, 567, 291–292. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  12. Fløttum, K.; Dahl, T.; Rivenes, V. Young Norwegians and their views on climate change and the future: Findings from a climate concerned and oil-rich nation. J. Youth Stud. 2016, 19, 1128–1143. [Google Scholar] [CrossRef]
  13. Spence, A.; Poortinga, W.; Pidgeon, N. The psychological distance of climate change. Risk Anal. 2012, 32, 957–972. [Google Scholar] [CrossRef] [Green Version]
  14. Akerlof, K.; Maibach, E.W.; Fitzgerald, D.; Cedeno, A.Y.; Neuman, A. Do people “personally experience” global warming, and if so how, and does it matter? Glob. Environ. Change 2013, 23, 81–91. [Google Scholar] [CrossRef]
  15. Hidalgo, M.C.; Pisano, I. Determinants of risk perception and willingness to tackle climate change. A pilot study. PsyEcology 2010, 1, 105–112. [Google Scholar] [CrossRef]
  16. Milfont, T.L. The interplay between knowledge, perceived efficacy, and concern about global warming and climate change: A one-year longitudinal study. Risk Anal. 2012, 32, 1003–1020. [Google Scholar] [CrossRef]
  17. Lo, A.Y.; Chow, A.T. The relationship between climate change concern and national wealth. Clim. Change 2015, 131, 335–348. [Google Scholar] [CrossRef] [Green Version]
  18. Malka, A.; Krosnick, J.A.; Langer, G. The association of knowledge with concern about global warming: Trusted information sources shape public thinking. Risk Anal. 2009, 29, 633–647. [Google Scholar] [CrossRef]
  19. Whitmarsh, L. Are flood victims more concerned about climate change than other people? The role of direct experience in risk perception and behavioural response. J. Risk Res. 2008, 11, 351–374. [Google Scholar] [CrossRef]
  20. Gregersen, T.; Doran, R.; Böhm, G.; Tvinnereim, E.; Poortinga, W. Political orientation moderates the relationship between climate change beliefs and worry about climate change. Front. Psychol. 2020, 11, 1573. [Google Scholar] [CrossRef]
  21. Poortinga, W.; Whitmarsh, L.; Steg, L.; Böhm, G.; Fisher, S. Climate change perceptions and their individual-level determinants: A cross-European analysis. Global Environ. Change 2019, 55, 25–35. [Google Scholar] [CrossRef]
  22. Ojala, M. Regulating worry, promoting hope: How do children, adolescents, and young adults cope with climate change? Int. J. Environ. Sci. Educ. 2013, 8, 537–561. [Google Scholar]
  23. McCright, A.M. The effects of gender on climate change knowledge and concern in the American public. Popul. Environ. 2010, 32, 66–87. [Google Scholar] [CrossRef]
  24. Leiserowitz, A. Climate change risk perception and policy preferences: The role of affect, imagery, and values. Clim. Change 2006, 77, 45–72. [Google Scholar] [CrossRef] [Green Version]
  25. Van der Linden, S. The social-psychological determinants of climate change risk perceptions: Towards a comprehensive model. J. Environ. Psychol. 2015, 41, 112–124. [Google Scholar] [CrossRef]
  26. Van der Linden, S. Determinants and measurement of climate change risk perception, worry, and concern. In The Oxford Encyclopedia of Climate Change Communication; Oxford University Press: Oxford, UK, 2017. [Google Scholar] [CrossRef] [Green Version]
  27. Sundblad, E.-L.; Biel, A.; Gärling, T. Cognitive and affective risk judgements related to climate change. J. Environ. Psychol. 2007, 27, 97–106. [Google Scholar] [CrossRef] [Green Version]
  28. Tranter, B.; Skrbis, Z. Political and social divisions over climate change among young Queenslanders. Environ. Plan. A Econ. Space 2014, 46, 1638–1651. [Google Scholar] [CrossRef]
  29. McCright, A.M.; Dunlap, R.E.; Marquart-Pyatt, S.T. Political ideology and views about climate change in the European Union. Env. Polit. 2016, 25, 338–358. [Google Scholar] [CrossRef]
  30. Milfont, T.L.; Milojev, P.; Greaves, L.M.; Sibley, C.G. Socio-structural and psychological foundations of climate change beliefs. N. Z. J. Psychol. 2015, 44, 17–30. [Google Scholar]
  31. Zhou, M. Public environmental skepticism: A cross-national and multilevel analysis. Int. Sociol. 2014, 30, 61–85. [Google Scholar] [CrossRef]
  32. Elias, T.; Dahmen, N.S.; Morrison, D.D.; Morrison, D.; Morris, D.L. Understanding climate change perceptions and attitudes across racial/ethnic groups. Howard J. Commun. 2019, 30, 38–56. [Google Scholar] [CrossRef]
  33. Schuldt, J.P.; Pearson, A.R. The role of race and ethnicity in climate change polarization: Evidence from a U.S. national survey experiment. Clim. Change 2016, 136, 495–505. [Google Scholar] [CrossRef]
  34. Etale, A.; Ammann, P.; Siegrist, M. The influence of socio-economic status on risk prioritisation. J. Risk Res. 2022, 25, 501–519. [Google Scholar] [CrossRef]
  35. Preston, J.L.; Shin, F. Opposing effects of spirituality and religious fundamentalism on environmental attitudes. J. Environ. Psychol. 2022, 80, 101772. [Google Scholar] [CrossRef]
  36. Morrison, M.; Duncan, R.; Parton, K. Religion does matter for climate change attitudes and behavior. PLoS ONE 2015, 10, e0134868. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  37. Weckroth, M.; Ala-Mantila, S. Socioeconomic geography of climate change views in Europe. Glob. Environ. Change 2022, 72, 102453. [Google Scholar] [CrossRef]
  38. Di Paola, M.; Jamieson, D. Climate change and the challenges to democracy. Univ. Miami Law Rev. 2017, 72, 369–424. [Google Scholar]
  39. Stehr, N. An inconvenient democracy: Knowledge and climate change. Society 2013, 50, 55–60. [Google Scholar] [CrossRef]
  40. Burnell, P. Democracy, democratization and climate change: Complex relationships. Democratization 2012, 19, 813–842. [Google Scholar] [CrossRef] [Green Version]
  41. Niemeyer, S. Democracy and climate change: What can deliberative democracy contribute? Aust. J. Politics Hist. 2013, 59, 429–448. [Google Scholar] [CrossRef]
  42. Stevenson, H.; Dryzek, J.S. Democratizing Global Climate Governance; Cambridge University Press: Cambridge, UK, 2014. [Google Scholar]
  43. Lacelle-Webster, A.; Warren, M.E. Citizens’ assemblies and democracy. In Oxford Research Encyclopedia of Politics; Oxford University Press: Oxford, UK, 2021. [Google Scholar] [CrossRef]
  44. Barkan, S.E. Explaining public support for the environmental movement: A civic voluntarism model. Soc. Sci. Quart. 2004, 85, 913–937. [Google Scholar] [CrossRef]
  45. Lee, T.M.; Markowitz, E.M.; Howe, P.D.; Ko, C.-Y.; Leiserowitz, A.A. Predictors of public climate change awareness and risk perception around the world. Nat. Clim. Change 2015, 5, 1014–1020. [Google Scholar] [CrossRef]
  46. Prati, G.; Albanesi, C.; Pietrantoni, L. Social well-being and pro-environmental behavior: A cross-lagged panel design. Hum. Ecol. Rev. 2017, 23, 123–140. [Google Scholar] [CrossRef]
  47. Carlson, M.; Charlin, V.; Miller, N. Positive mood and helping behavior: A test of six hypotheses. J. Personal. Soc. Psychol. 1988, 55, 211–229. [Google Scholar] [CrossRef] [PubMed]
  48. Kaida, N.; Kaida, K. Pro-environmental behavior correlates with present and future subjective well-being. Environ. Dev. Sustain. 2016, 18, 111–127. [Google Scholar] [CrossRef]
  49. Heald, S. Climate silence, moral disengagement, and self-efficacy: How albert bandura’s theories inform our climate-change predicament. Environ. Sci. Policy Sustain. Dev. 2017, 59, 4–15. [Google Scholar] [CrossRef]
  50. Maltby, K.M.; Simpson, S.D.; Turner, R.A. Scepticism and perceived self-efficacy influence fishers’ low risk perceptions of climate change. Clim. Risk Manag. 2021, 31, 100267. [Google Scholar] [CrossRef]
  51. Bostrom, A.; Hayes, A.L.; Crosman, K.M. Efficacy, action, and support for reducing climate change risks. Risk Anal. 2019, 39, 805–828. [Google Scholar] [CrossRef] [PubMed]
  52. Bodor, Á.; Varjú, V.; Grünhut, Z. The effect of trust on the various dimensions of climate change attitudes. Sustainability 2020, 12, 10200. [Google Scholar] [CrossRef]
  53. Kácha, O.; Vintr, J.; Brick, C. Four Europes: Climate change beliefs and attitudes predict behavior and policy preferences using a latent class analysis on 23 countries. J. Environ. Psychol. 2022, 81, 101815. [Google Scholar] [CrossRef]
  54. Prati, G.; Pietrantoni, L.; Albanesi, C. Human values and beliefs and concern about climate change: A Bayesian longitudinal analysis. Qual. Quant. 2018, 52, 1613–1625. [Google Scholar] [CrossRef] [Green Version]
  55. Prati, G.; Milite, A.; Cicognani, E. International mobility and friendship relationships: Do they contribute to the development of a sense of global citizenship? Commun. Psychol. Glob. Perspect. 2021, 7, 145–159. [Google Scholar] [CrossRef]
  56. Batalha, L.; Reynolds, K.J. ASPIRing to mitigate climate change: Superordinate identity in global climate negotiations. Polit. Psychol. 2012, 33, 743–760. [Google Scholar] [CrossRef]
  57. Prati, G.; Cicognani, E.; Mazzoni, D. Cross-border friendships and collective European identity: A longitudinal study. Eur. Union Politics 2019, 20, 649–669. [Google Scholar] [CrossRef]
  58. Olausson, U. Towards a European identity? The news media and the case of climate change. Eur. J. Commun. 2010, 25, 138–152. [Google Scholar] [CrossRef]
  59. Eckersley, R. National identities, international roles, and the legitimation of climate leadership: Germany and Norway compared. Env. Polit. 2016, 25, 180–201. [Google Scholar] [CrossRef]
  60. Jordan, A.; Huitema, D.; Van Asselt, H.; Rayner, T.; Berkhout, F. (Eds.) Climate Change Policy in the European Union: Confronting the Dilemmas of Mitigation and Adaptation? Cambridge University Press: Cambridge, UK, 2010. [Google Scholar]
  61. European Council. European Council Meeting—Conclusions; European Council: Strasbourg, France, 2019. [Google Scholar]
  62. European Council. Council Adopts European Climate Law; European Council: Strasbourg, France, 2021. [Google Scholar]
  63. Kulin, J.; Sevä, I.J.; Dunlap, R.E. Nationalist ideology, rightwing populism, and public views about climate change in Europe. Env. Polit. 2021, 30, 1111–1134. [Google Scholar] [CrossRef]
  64. Bertolotti, M.; Catellani, P. Agreement with climate change policies: Framing the future and national versus supranational identity. Eur. J. Soc. Psychol. 2015, 45, 847–857. [Google Scholar] [CrossRef]
  65. Eger, M.A.; Valdez, S. From radical right to neo-nationalist. Eur. Political Sci. 2019, 18, 379–399. [Google Scholar] [CrossRef]
  66. Krange, O.; Kaltenborn, B.P.; Hultman, M. Cool dudes in Norway: Climate change denial among conservative Norwegian men. Environ. Sociol. 2019, 5, 1–11. [Google Scholar] [CrossRef]
  67. Beiser-McGrath, L.F.; Huber, R.A. Assessing the relative importance of psychological and demographic factors for predicting climate and environmental attitudes. Clim. Change 2018, 149, 335–347. [Google Scholar] [CrossRef]
  68. Hornsey, M.J.; Harris, E.A.; Bain, P.G.; Fielding, K.S. Meta-analyses of the determinants and outcomes of belief in climate change. Nat. Clim. Change 2016, 6, 622–626. [Google Scholar] [CrossRef] [Green Version]
  69. Tzankova, I.; Prati, G.; Cicognani, E. Profiles of citizenship orientations among youth. YOUNG 2021, 30, 57–79. [Google Scholar] [CrossRef]
  70. ISTAT. Studenti e Scuole dell’Istruzione Primaria e Secondaria in Italia [Students and Schools in Primary and Secondary Education in Italy]; Istituto Nazionale di Statistica: Rome, Italy, 2017. [Google Scholar]
  71. Ministry of Education University and Research. Portale dei Dati dell’Istruzione Superiore [Higher Education Data Portal]; Ministry of Education University and Research: Rome, Italy, 2017.
  72. Crocetti, E.; Schwartz, S.J.; Fermani, A.; Meeus, W. The utrecht-management of identity commitments scale (U-MICS): Italian validation and cross-national comparisons. Eur. J. Psychol. Assess. 2010, 26, 172–186. [Google Scholar] [CrossRef] [Green Version]
  73. Fiske, S.T.; Cuddy, A.J.C.; Glick, P.; Xu, J. A model of (often mixed) stereotype content: Competence and warmth respectively follow from perceived status and competition. J. Personal. Soc. Psychol. 2002, 82, 878–902. [Google Scholar] [CrossRef]
  74. Barrett, M.; Zani, B. (Eds.) . Political and Civic Engagement. Multidisciplinary Perspectives; Routledge: London, UK, 2015. [Google Scholar]
  75. Finkel, S.E.; Sigelman, L.; Humphries, S. Democratic values and political tolerance. In Measures of Political Attitudes; Robinson, J.P., Shaver, P.R., Wrightman, L.S., Eds.; Academic Press: San Diego, CA, USA, 1999; pp. 203–296. [Google Scholar]
  76. Funke, F. The dimensionality of right-wing authoritarianism: Lessons from the dilemma between theory and measurement. Polit. Psychol. 2005, 26, 195–218. [Google Scholar] [CrossRef]
  77. Davidov, E. Measurement equivalnece of nationalism and constructive patriotism in the ISSP: 34 countries in a comparative perspective. Political Anal. 2009, 17, 64–82. [Google Scholar] [CrossRef] [Green Version]
  78. Dahl, V.; Amnå, E.; Banaji, S.; Landberg, M.; Šerek, J.; Ribeiro, N.; Beilmann, M.; Pavlopoulos, V.; Zani, B. Apathy or alienation? Political passivity among youths across eight European union countries. Eur. J. Dev. Psychol. 2018, 15, 284–301. [Google Scholar] [CrossRef]
  79. Schwarzer, R.; Jerusalem, M. Generalized self-efficacy scale. In Measures in Health Psychology: A User’s Portfolio. Causal and Control Beliefs; Weinman, J., Wright, S., Johnston, M., Eds.; NFER-NELSON: Berkshire, UK, 1995; pp. 35–37. [Google Scholar]
  80. Albanesi, C.; Guarino, A.; Zani, B.; Cicognani, E.; Tzankova, I. Civic engagement in a changing world: Does it contribute to the development of global citizenship? TPM-Test. Psychom. Methodol. Appl. Psychol. 2019, 26, 495–511. [Google Scholar]
  81. Russo, S.; Stattin, H. Self-determination theory and the role of political interest in adolescents’ sociopolitical development. J. Appl. Dev. Psychol. 2017, 50, 71–78. [Google Scholar] [CrossRef]
  82. Fischer, A.; Kohr, H. Politisches Engagement [Political Engagement]. ZUMA Informationssystem. Elektronisches Handbuch Sozialwissenschaftlicher Erhebungsinstrumente (Version 6.00); Zentrum für Umfragen, Methoden und Analysen: Mannheim, Germany, 2002. [Google Scholar]
  83. Cicognani, E.; Mazzoni, D.; Albanesi, C.; Zani, B. Sense of community and empowerment among young people: Understanding pathways from civic participation to social well-being. VOLUNTAS Int. J. Volunt. Nonprofit Organ. 2015, 26, 24–44. [Google Scholar] [CrossRef]
  84. Keyes, C.L.M.; Wissing, M.; Potgieter, J.P.; Temane, M.; Kruger, A.; van Rooy, S. Evaluation of the mental health continuum-short form (MHC-SF) in Setswana-speaking South Africans. Clin. Psychol. Psychother. 2008, 15, 181–192. [Google Scholar] [CrossRef]
  85. Petrillo, G.; Capone, V.; Caso, D.; Keyes, C.L.M. The mental health continuum—Short form (MHC–SF) as a measure of well-being in the Italian context. Soc. Indic. Res. 2015, 121, 291–312. [Google Scholar] [CrossRef]
  86. Amnå, E.; Ekström, M.; Kerr, M.; Stattin, H. Codebook: The political Socialization Program; Örebro University: Youth & Society: Örebro, Sweden, 2010. [Google Scholar]
  87. Prati, G.; Mazzoni, D.; Guarino, A.; Albanesi, C.; Cicognani, E. Evaluation of an active citizenship intervention based on youth-led participatory action research. Health Educ. Behav. 2020, 47, 894–904. [Google Scholar] [CrossRef]
  88. Enchikova, E.; Neves, T.; Mejias, S.; Kalmus, V.; Cicognani, E.; Ferreira, P.D. Civic and political participation of European youth: Fair measurement in different cultural and social contexts. Front. Educ. 2019, 4, 16. [Google Scholar] [CrossRef]
  89. JASP Team. JASP, 0.16.3. 2022. [Google Scholar]
  90. Fichman, M.; Cummings, J.N. Multiple imputation for missing data: Making the most of what you know. Organ. Res. Methods 2003, 6, 282–308. [Google Scholar] [CrossRef]
  91. Cohen, J. Statistical Power Analysis for the Behavioral Sciences, 2nd ed.; Erlbaum: Hillsdale, NJ, USA, 1988. [Google Scholar]
  92. Yarkoni, T.; Westfall, J. Choosing prediction over explanation in psychology: Lessons from machine learning. Perspect. Psychol. Sci. 2017, 12, 1100–1122. [Google Scholar] [CrossRef] [PubMed]
  93. Negev, M.; Sagy, G.; Garb, Y.; Salzberg, A.; Tal, A. Evaluating the environmental literacy of Israeli elementary and high school students. J. Environ. Educ. 2008, 39, 3–20. [Google Scholar] [CrossRef]
  94. Olsson, D.; Gericke, N. The adolescent dip in students’ sustainability consciousness—Implications for education for sustainable development. J. Environ. Educ. 2016, 47, 35–51. [Google Scholar] [CrossRef]
  95. Uitto, A.; Juuti, K.; Lavonen, J.; Byman, R.; Meisalo, V. Secondary school students’ interests, attitudes and values concerning school science related to environmental issues in Finland. Environ. Educ. Res. 2011, 17, 167–186. [Google Scholar] [CrossRef]
  96. Uitto, A.; Saloranta, S. The relationship between secondary school students’ environmental and human values, attitudes, interests and motivations. Procedia-Soc. Behav. Sci. 2010, 9, 1866–1872. [Google Scholar] [CrossRef] [Green Version]
  97. Otto, S.; Evans, G.W.; Moon, M.J.; Kaiser, F.G. The development of children’s environmental attitude and behavior. Glob. Environ. Change 2019, 58, 101947. [Google Scholar] [CrossRef]
  98. Eisenberg, N.; Carlo, G.; Murphy, B.; Van Court, P. Prosocial development in late adolescence: A longitudinal study. Child Dev. 1995, 66, 1179–1197. [Google Scholar] [CrossRef] [PubMed]
  99. Ojala, M. Hope and climate change: The importance of hope for environmental engagement among young people. Environ. Educ. Res. 2012, 18, 625–642. [Google Scholar] [CrossRef]
  100. Stevenson, K.T.; Peterson, M.N.; Bondell, H.D.; Moore, S.E.; Carrier, S.J. Overcoming skepticism with education: Interacting influences of worldview and climate change knowledge on perceived climate change risk among adolescents. Clim. Change 2014, 126, 293–304. [Google Scholar] [CrossRef]
  101. Ojala, M. To trust or not to trust? Young people’s trust in climate change science and implications for climate change engagement. Child. Geogr. 2021, 19, 284–290. [Google Scholar] [CrossRef]
  102. Arnett, J.J. The psychology of globalization. Am. Psychol. 2002, 57, 774–783. [Google Scholar] [CrossRef]
  103. Albanesi, C.; Prati, G.; Guarino, A.; Cicognani, E. School citizenship education through YPAR: What works? A mixed-methods study in Italy. J. Adolesc. Res. 2021, 07435584211035564. [Google Scholar] [CrossRef]
  104. Lerner, R.M.; Steinberg, L. Handbook of Adolescent Psychology; Em Lerner, R.M., Steinberg, L., Eds.; John Wiley & Sons, Inc.: Hoboken, NJ, USA, 2009; pp. 524–558. [Google Scholar] [CrossRef]
  105. Corner, A.; Roberts, O.; Chiari, S.; Völler, S.; Mayrhuber, E.S.; Mandl, S.; Monson, K. How do young people engage with climate change? The role of knowledge, values, message framing, and trusted communicators. WIREs Clim. Change 2015, 6, 523–534. [Google Scholar] [CrossRef]
  106. Ojala, M. How do children, adolescents, and young adults relate to climate change? Implications for developmental psychology. Eur. J. Dev. Psychol. 2022, 1–15. [Google Scholar] [CrossRef]
  107. Collins, R. Keeping it in the family? Re-focusing household sustainability. Geoforum 2015, 60, 22–32. [Google Scholar] [CrossRef] [Green Version]
  108. Krettenauer, T.; Wang, W.; Jia, F.; Yao, Y. Connectedness with nature and the decline of pro-environmental behavior in adolescence: A comparison of Canada and China. J. Environ. Psychol. 2020, 71, 101348. [Google Scholar] [CrossRef]
  109. Burton, L.; Delvecchio, E.; Germani, A.; Mazzeschi, C. Individualism/collectivism and personality in Italian and American groups. Curr. Psychol. 2021, 40, 29–34. [Google Scholar] [CrossRef]
  110. Kvaløy, B.; Finseraas, H.; Listhaug, O. The publics’ concern for global warming: A cross-national study of 47 countries. J. Peace Res. 2012, 49, 11–22. [Google Scholar] [CrossRef]
  111. Lockwood, M. Right-wing populism and the climate change agenda: Exploring the linkages. Env. Polit. 2018, 27, 712–732. [Google Scholar] [CrossRef]
  112. Jeffries, E. Nationalist advance. Nat. Clim. Change 2017, 7, 469–471. [Google Scholar] [CrossRef]
  113. Conversi, D. The ultimate challenge: Nationalism and climate change. Natl. Pap. 2020, 48, 625–636. [Google Scholar] [CrossRef] [Green Version]
  114. Fernandes-Jesus, M.; Barnes, B.; Diniz, R.F. Communities reclaiming power and social justice in the face of climate change. Commun. Psychol. Glob. Perspect. 2020, 6, 1–21. [Google Scholar]
  115. Beine, M.; Parsons, C. Climatic factors as determinants of international migration. Scand. J. Econ. 2015, 117, 723–767. [Google Scholar] [CrossRef] [Green Version]
  116. Kelman, I. Imaginary numbers of climate change migrants? Soc. Sci. 2019, 8, 131. [Google Scholar] [CrossRef] [Green Version]
  117. Treen, K.M.d.I.; Williams, H.T.P.; O’Neill, S.J. Online misinformation about climate change. WIREs Clim. Change 2020, 11, e665. [Google Scholar] [CrossRef]
  118. Talpin, J.; Wojcik, S. Deliberating environmental policy issues: Comparing the learning potential of online and face-to-face discussions on climate change. Policy Internet 2010, 2, 61–93. [Google Scholar] [CrossRef]
  119. Cagle, L.E.; Herndl, C. Shades of denialism: Discovering possibilities for a more nuanced deliberation about climate change in online discussion forums. Commun. Des. Q. 2019, 7, 22–39. [Google Scholar] [CrossRef]
  120. Barr, S. Climate forums: Virtual discourses on climate change and the sustainable lifestyle. Area 2011, 43, 14–22. [Google Scholar] [CrossRef]
  121. Schäfer, M.S. Online communication on climate change and climate politics: A literature review. WIREs Clim. Change 2012, 3, 527–543. [Google Scholar] [CrossRef]
  122. Wielk, E.; Standlee, A. Fighting for their future: An exploratory study of online community building in the youth climate change movement. Qual. Sociol. Rev. 2021, 17, 22–37. [Google Scholar] [CrossRef]
  123. Kim, Y.; Russo, S.; Amnå, E. The longitudinal relation between online and offline political participation among youth at two different developmental stages. New Media Soc. 2016, 19, 899–917. [Google Scholar] [CrossRef]
  124. Arlt, D.; Hoppe, I.; Schmitt, J.B.; De Silva-Schmidt, F.; Brüggemann, M. Climate engagement in a digital age: Exploring the drivers of participation in climate discourse online in the context of COP21. Environ. Commun. 2018, 12, 84–98. [Google Scholar] [CrossRef]
  125. Roser-Renouf, C.; Maibach, E.W.; Leiserowitz, A.; Zhao, X. The genesis of climate change activism: From key beliefs to political action. Clim. Change 2014, 125, 163–178. [Google Scholar] [CrossRef] [Green Version]
  126. Carlton, S.J.; Jacobson, S.K. Climate change and coastal environmental risk perceptions in Florida. J. Environ. Manage. 2013, 130, 32–39. [Google Scholar] [CrossRef] [PubMed]
  127. Cologna, V.; Siegrist, M. The role of trust for climate change mitigation and adaptation behaviour: A meta-analysis. J. Environ. Psychol. 2020, 69, 101428. [Google Scholar] [CrossRef]
  128. Stefani, S.; Prati, G.; Tzankova, I.; Ricci, E.; Albanesi, C.; Cicognani, E. Gender differences in civic and political engagement and participation among Italian young people. Soc. Psychol. Bull. 2021, 16, 1–25. [Google Scholar] [CrossRef]
  129. Mavisakalyan, A.; Tarverdi, Y. Gender and climate change: Do female parliamentarians make difference? Europ. J. Polit. Econ. 2019, 56, 151–164. [Google Scholar] [CrossRef]
Sustainability 14 15716 g001 550
Figure 1. Scatterplot depicting the correlation between age and the dependent variables using a loess line as the fit function: (a) Correlation between age and climate change importance; (b) Correlation between age and climate change worry.
Figure 1. Scatterplot depicting the correlation between age and the dependent variables using a loess line as the fit function: (a) Correlation between age and climate change importance; (b) Correlation between age and climate change worry.
Sustainability 14 15716 g001
Table
Table 1. All Independent Variable Measures.
Table 1. All Independent Variable Measures.
MeasureScaleSample ItemsResponse ScaleReliability (𝛼)
Demographics
Age
Gender
Religiousness
National/ethnic minority
Economic situation		n.a.“Does the money your household has cover everything your family needs?”
Years old
Female/Male
1 = Not at all, 4 = Very
Majority/Minority
1 = Not at all, 4 = Fully		n.a.
National/European identityThe Utrecht-Management of Identity Commitments Scale (U-MICS; [72] was used to measure three dimensions of national and European identity: commitment (3 items), exploration (3 items), reconsideration (3 items).Commitment: “I feel strong ties toward Italy/Europe”
Exploration: “I often think about what it means to be Italian/European”
Reconsideration: “My feelings about Italy/Europe are changing”		5-point Likert scale: 1 = strongly disagree, 5 = strongly agreeCountry commitment: 0.84
Country exploration: 0.81
Country reconsideration: 0.70
EU commitment: 0.82
EU exploration: 0.84
EU reconsideration: 0.56
Attitudes toward the country/EUFive items measured the competence (2 items) and warmth (3 items) dimensions of the Stereotype Content Model [73] with reference to the country and the EU.“How would you describe Italy/the European Union? Competent/Incompetent; Efficient/Inefficient; Warm/Cold; Friendly/Unfriendly; Welcoming/Unwelcoming”5-point semantic differential: 1 = Competent, 5 = IncompetentCountry Competence: 0.72
Country Warmth: 0.81
EU Competence: 0.58
EU Warmth: 0.69
Tolerance toward migrantsThree items adapted from previous studies [74] measured tolerance toward migrants’ rights.“Migrants should have the right to maintain their traditions and cultural heritage.”5-point Likert scale: 1 = strongly disagree, 5 = strongly agree0.70
Tolerance toward refugeesThree items adapted from previous studies (ref) measured tolerance toward refugees’ rights.“I feel that our government does not do enough to help refugees.”5-point Likert scale: 1 = strongly disagree, 5 = strongly agree0.72
Antidemocratic attitudesThree items adapted from Finkel, et al. [75] measured support for democracy, freedom of speech and expression.“Instead of needing ‘civil rights and freedoms’ our country needs one thing only: law and order.”5-point Likert scale: 1 = strongly disagree, 5 = strongly agree0.65
AuthoritarianismThree items adapted from Funke [76], measured support for the control of civil liberties.“Instead of needing ‘civil rights and freedoms’ our country needs one thing only: law and order. “5-point Likert scale: 1 = strongly disagree, 5 = strongly agree0.64
NationalismFollowing previous research [77] three items measured nationalist attitudes.“Generally speaking, Italy is a better country than most other countries.”5-point Likert scale: 1 = strongly disagree, 5 = strongly agree0.73
Institutional and social trustThree items derived from previous studies [78] measured trust in the European Union, the national government and in most people.“I trust the European Union.”5-point Likert scale: 1 = strongly disagree, 5 = strongly agree0.58
Self-efficacyFive items from the Generalized Self-Efficacy Scale [79] were used to measure the belief that one can perform novel or difficult tasks, or cope with adversity.“I can always solve difficult problems if I try hard enough.”5-point Likert scale: 1 = strongly disagree, 5 = strongly agree0.81
Political efficacyThe following dimensions of political efficacy were measured: competence self-concept (2 items, [74,80], the belief about one’s own political competence and awareness; collective efficacy (2 items, [74]), the belief that one’s group can have political influence; internal political efficacy (3 items; [81]), the belief in being able competently to participate in political action.
Self-concept: “I feel that I have a pretty good understanding of important societal issues.”
Collective efficacy: “I think that by working together, young people can change things for the better.”
Internal political efficacy: “If I really tried, I could manage to actively work in organizations trying to solve problems in society.”		5-point Likert scale: 1 = strongly disagree, 5 = strongly agreePolitical self-concept: r = 0.61
Collective political efficacy: r = 0.57
Internal political efficacy: 0.82
Political alienationFour items adapted from Fischer and Kohr [82] measured sense of political powerlessness related to Italy and the EU“People like me do not have opportunities to influence the decisions of the European Union/national parliament.”5-point Likert scale: 1 = strongly disagree, 5 = strongly agree0.84
Personal empowermentTwo items adapted from previous studies [80,83], measured personal capacity to act politically.“I am able to look for people, institutions and services that can help me to find solutions to my problems.”5-point Likert scale: 1 = strongly disagree, 5 = strongly agreer = 0.44
Social well-beingFour items from the Mental Health Continuum—Short Form [84,85] assessed perceived social wellbeing.“During the past month, how often did you feel that… Our society is becoming a better place.”5-point Likert scale: 1 = strongly disagree, 5 = strongly agree0.68
Political interestFour items, adapted from previous studies [78,86], measured interest in politics, in societal issues, in European-related topics and in national politics.“How interested are you in politics?”5-point Likert scale: 1 = strongly disagree, 5 = strongly agree0.89
Civic and political participationCivic and Political Participation Scale [87,88] was used, measuring four forms of participation: Political; Civic; Online; and Protest. Participants were asked to report their activities in the last 12 months.Political participation: “Contacted a politician or public official (for example via e-mail)”;
Civic participation:
“Volunteered or worked for a social cause”;
Online participation: “Discussed social or political issues on the internet”
Protest participation: “Taken part in an occupation of a building or a public space”.		5-point Likert scale:
1 = No, 5 = Very often		Political participation: 0.80
Civic participation:
0.70
Online participation: 0.84
Protest participation: 0.66
Participation at EU level18 items adapted from the Civic and Political Participation Scale [85] measured participation related to the EU.“Were any of the activities related to the EU? If yes, then please tick them.”Yes/No0.80
EU voting intentionsAd hoc item measured the intention to vote in the next EU elections.“Will you vote in the next European parliament elections?”Yes/No/I don’t known.a.
Table
Table 2. Correlation Coefficients (Pearson’s r) between W1 Correlates and W2 Climate Change Importance and Climate Change Worry.
Table 2. Correlation Coefficients (Pearson’s r) between W1 Correlates and W2 Climate Change Importance and Climate Change Worry.
W1 CorrelatesClimate Change Importance (W2)Climate Change Worry (W2)
Age0.310 **0.286 **
Gender (male)−0.042−0.132 **
Religiousness−0.0430.005
National/ethnic minority−0.027−0.043
Place of living (countryside)−0.100 **−0.104 **
Household income level (higher income)−0.076 **−0.043
Participation at EU level0.111 **0.105 **
EU voting intentions0.211 **0.246 **
Online participation0.253 **0.256 **
Political participation0.091 **0.099 **
Civic participation0.182 **0.212 **
Protest participation0.0280.027
European commitment0.135 **0.118 **
National commitment−0.004−0.034
European exploration0.264 **0.231 **
National exploration0.214 **0.192 **
European reconsideration0.060 *0.037
National reconsideration0.0290.025
EU—competence−0.058 *−0.078 **
EU—warmth0.071 *0.052
Country—warmth−0.038−0.022
Country—competence0.136 **0.107 **
Tolerance—refugees0.285 **0.266 **
Tolerance—immigrants0.275 **0.261 **
Antidemocratic attitudes−0.182 **−0.183 **
Authoritarianism−0.182 **−0.183 **
Nationalism−0.144 **−0.158 **
Political alienation−0.052−0.043
Self-efficacy0.085 **0.084 **
Personal empowerment0.072 **0.075 **
Political interest0.264 **0.227 **
Institutional and social trust0.106 **0.080 **
Social well-being0.0530.010
Self-concept (political efficacy)0.180 **0.160 **
Collective efficacy (political efficacy)0.225 **0.179 **
Internal political efficacy0.233 **0.208 **
Note. ** Correlation is significant at the 0.01 level (2-tailed); * Correlation is significant at the 0.05 level (2-tailed).
Table
Table 3. Comparison of Machine Learning Algorithms Predicting Climate Change Importance and Climate Change Worry.
Table 3. Comparison of Machine Learning Algorithms Predicting Climate Change Importance and Climate Change Worry.
Validation MSETest MSERMSER2
Climate change importanceGradient boosting0.8090.9860.9930.153
Random forest0.7690.8640.9300.148
Climate change worryGradient boosting0.8370.9400.9700.059
Random forest0.9160.8310.9120.194
Note. MSE = mean squared error; RMSE = root-mean-square error.
Table
Table 4. Importance Rank of Variables (i.e., Total Increase in Node Purity) in Random Forest Model.
Table 4. Importance Rank of Variables (i.e., Total Increase in Node Purity) in Random Forest Model.
Outcome: Climate Change Importance Outcome: Climate Change Worry
Age17.263Age16.775
Tolerance toward refugees15.990Tolerance toward migrants15.187
Tolerance toward migrants15.912Tolerance toward refugees13.925
Online participation15.716Political interest12.832
European exploration14.044Internal political efficacy12.600
Political interest13.442Online participation12.258
European reconsideration12.467European exploration11.763
National exploration11.665National exploration11.31
Internal political efficacy11.105Political alienation11.187
European commitment10.242European commitment11.077
Nationalism10.222Civic participation10.655
Political alienation9.894European reconsideration10.067
National commitment9.436Nationalism9.638
EU warmth9.426Self-efficacy9.574
Civic participation9.352National commitment9.455
Social well-being9.185EU warmth9.364
Self-efficacy9.038Social well-being9.126
National reconsideration9.011Authoritarianism9.043
Collective efficacy (political efficacy)8.963Anti-democratic attitudes8.995
Authoritarianism8.582National reconsideration8.855
Anti-democratic attitudes8.480Country competent8.475
Education of mother/carer8.414Country warmth8.369
Self-concept (political efficacy)8.361EU competent8.281
Place of living8.334Personal empowerment8.118
EU competent8.046Self-concept (political efficacy)7.997
Country competent7.985Institutional and social trust7.722
Education of father/carer7.631EU voting intentions7.504
Country warmth7.504Place of living7.216
Institutional and social trust7.271Collective efficacy (political efficacy)7.160
Personal empowerment6.637Education of father/carer6.959
Having a partner5.274Education of mother/carer6.361
Religiousness4.800Having a partner5.191
EU voting intentions3.876Protest participation4.597
Political participation3.841Religiousness4.359
Protest participation3.682Mother’s occupation status4.235
Mother’s occupation status3.615Household income level3.684
Household income level3.353Political participation2.867
Father’s occupation status3.068Gender2.671
Gender2.054Father’s occupation status2.426
Living with one or both parents/carers2.033Participation at EU level2.132
Having the citizenship1.717Having the citizenship2.129
Participation at EU level1.651Living with one or both parents/carers1.751
Living with friends/roommates1.567National/ethnic majority or minority1.477
National/ethnic majority or minority0.948Living with friends/roommates1.196
Other living arrangements0.387Living alone0.411
Living with partner/spouse0.222Other living arrangements0.240
Living alone0.221Living with partner/spouse0.158
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Share and Cite

MDPI and ACS Style

Prati, G.; Tzankova, I.; Albanesi, C.; Cicognani, E. Longitudinal Predictors of Perceived Climate Change Importance and Worry among Italian Youths: A Machine Learning Approach. Sustainability 2022, 14, 15716. https://doi.org/10.3390/su142315716

AMA Style

Prati G, Tzankova I, Albanesi C, Cicognani E. Longitudinal Predictors of Perceived Climate Change Importance and Worry among Italian Youths: A Machine Learning Approach. Sustainability. 2022; 14(23):15716. https://doi.org/10.3390/su142315716

Chicago/Turabian Style

Prati, Gabriele, Iana Tzankova, Cinzia Albanesi, and Elvira Cicognani. 2022. "Longitudinal Predictors of Perceived Climate Change Importance and Worry among Italian Youths: A Machine Learning Approach" Sustainability 14, no. 23: 15716. https://doi.org/10.3390/su142315716

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop