1. Introduction
The changing climate poses a constant reminder of the need to mitigate and adapt to its negative impacts at the global policy level and at the individual level [1]. Although global, and local policies are clearly necessary to ensure a healthy climate and healthy environment, individual behaviors also matter [2]. One way to halt or prevent the changing climate is via pro-environmental behaviors. These refer to behaviors which aim to pose limited harm to the environment or even benefit it. Pro-environmental behaviors vary widely and include behaviors such as limited consumption of meat products, energy preservation, green energy use, limited consumption of disposable products, reliance on public transportation, and recycling. Research has highlighted multiple barriers to pro-environmental behaviors [3]. These include limited awareness and knowledge, higher financial costs, lack of time, or high cost and effort [4].
Past research has attempted to classify pro-environmental behaviors into subgroups [5,6,7]. The rationale is that by identifying somewhat more homogenous subgroups of individuals who engage in similar ways with regards to the environment, one can improve targeted interventions to increase pro-environmental behaviors. This line of research tended to classify individuals mainly as those who are highly engaged in pro-environmental behaviors versus those who do not engage in such behaviors [5,6,7]. To date, little attention has been given to the actual pro-environmental behaviors in constructing this classification. Instead, research has addressed the different behaviors as equivalent in nature and distinguished between individuals mainly based on their level of engagement.
2. Correlates of Pro-Environmental Behaviors
There are several well-known demographic variables, which correlate with pro-environmental behaviors. Age has been associated with pro-environmental behaviors with research showing that younger persons, more so than older persons, are concerned about the changing environment [8]. However, younger persons are less likely to engage in pro-environmental behaviors [9]. Gender is another relevant variable, with research consistently documenting women to be more environmentally cautious and more likely to engage in pro-environmental behaviors [10]. Moreover, even at the societal level, countries that are ruled by women are more likely to advocate for environmental policies [11]. This has been attributed to the fact that women are brought up as potential carers and, therefore, are more likely to be altruistic in their behaviors [12]. Men, on the other hand, are socialized to be independent and competitive. These qualities make women more concerned about the environment than men [13]. Education is yet another consistent predictor of pro-environmental behaviors. Research has shown that people of higher levels of education are more likely to engage in pro-environmental behaviors [9].
Reasons for limited engagement in pro-environmental behaviors may vary. On the one hand, one can find reasons such as disbelief in climate change or a sense of indifference [14]; on the other hand, reasons such as limited information or knowledge may also arise [15]. There is a distinction between these two types of reasons for lack of engagement: the first represents a view that “I don’t care because climate change is not real or insignificant” [16,17], whereas the second represents the view of “I am lacking adequate information and knowledge and would have acted if more information were provided” [18]. Clearly, climate change deniers or skeptics are less likely to engage in pro-environmental behaviors [19] and likely hold the former view of indifference and lack of belief about the current climate situation. However, there is also a relationship between climate change knowledge and engagement, with those who have limited knowledge being less likely to engage in pro-environmental behaviors [18]. The two different explanations for limited pro-environmental behaviors likely require different interventions, as convincing someone that climate change is real is different from instructing a person with information concerning relevant pro-environmental behaviors. It is expected that the association of these varied reasons for inaction with pro-environmental behaviors would differ, with those who disbelieve in the effects of the changing climate being less likely to engage in pro-environmental behaviors compared with those who lack knowledge or awareness of pro-environmental behaviors.
3. The Present Study
Research has shown country variability in the engagement in pro-environmental behaviors. People in affluent countries are more likely to engage in pro-environmental behaviors, whereas those in countries that experience high levels of inequality are less likely to engage in pro-environmental behaviors [20]. In contrast to many developed countries (e.g., Northern Europe), where the topic of pro-environmental behaviors has received substantial attention [21], research on the topic in Israel has been lagging. The lack of attention is probably not coincidental but rather represents the limited attention given to environmental issues in Israeli society at large and by the Israeli government [22,23]. Past research has found that the Israeli public has limited knowledge and understanding concerning various forms of pro-environmental behaviors [24]. Moreover, the public tends to assign the responsibility for the current situation to the government, corporations, and industries, minimizing the role of individuals [24]. Nevertheless, just like the rest of the world, Israel is impacted by the changing climate and, thus, experiences more frequent and severe heat waves, long periods of drought, low air quality, and a reduction in its biodiversity [25].
The present study was designed to examine pro-environmental behaviors in a country that tends to disregard environmental issues both at the policy level and at the level of the public [22,23]. We also examined demographic characteristics that possibly correlate with pro-environmental behaviors as well as reasons for lack of engagement. This is important because it raises awareness of the topic. It is also important to examine whether research on pro-environmental behaviors conducted in Western countries that have shown concern for environmental issues can be generalized to other countries such as Israel.
As in past research, we expected the typology of pro-environmental behaviors to largely represent the degree of pro-environmental behaviors, rather than posing a clear differentiation based on the type of behaviors one is engaged in [6,26]. We also expected pro-environmental behaviors to be associated with advanced age. In addition, we expected women and those of higher levels of education to be more likely to engage in pro-environmental behaviors. We also expected pro-environmental behaviors to correlate with the reasons people give for limited engagement in pro-environmental behaviors. Specifically, we expected that those who are less likely to engage in pro-environmental behaviors are more likely to endorse varied reasons for limited engagement.
4. Methods
Sample and procedure. The study was approved by the ethics committee of the PI’s university. All participants signed an online consent and received information about the study prior to participation. A sample of 647 participants was recruited via a survey company. Respondents were sent a link via email and received financial compensation for completing the survey in February 2023. The sample was limited to individuals over the age of 18 and was designed to target equal numbers of men and women. The average age of participants was 52.11 (SD = 19.47) and almost evenly divided by gender. See Table 1 for more details.
Measures
Pro-environmental behaviors. A total of 10 items were selected to represent a variety of pro-environmental behaviors. The items were based on a review of existing literature in the field [10,27,28]. Cronbach’s alpha = 0.70. Although the selected items were not identical to the items proposed by existing scales [29,30], they covered similar domains.
Demographic information. Age, gender, and education were gathered based on self-report.
Reasons for limited engagement in pro-environmental behaviors. Four items were selected to represent reasons for limited engagement in pro-environmental behaviors. Two of the items concerned limited knowledge and understanding, whereas the other two addressed perceived limited responsibility and indifference toward the changing climate. Participants were instructed to endorse all explanations they found relevant for their lack of engagement in pro-environmental behaviors. Each of these explanations was examined as a separate variable in the present study.
Analysis. Latent class analysis is used to identify subgroups or clusters of related cases within a heterogeneous population. The idea is that latent class analysis can identify more homogenous population groups (latent classes) within otherwise heterogeneous data. The method detects subgroups of participants based on similar response patterns on a set of variables or indicators of the overall construct [31]. In the present study, the 10 pro-environmental behaviors were used as categorical dependent variables in the mixture modeling procedure in Mplus [32] to detect the number of potential subgroups that can be inferred from the data and to model observed variables within subgroups [31].
The overall goal is to achieve an adequate model fit with the lowest number of subgroups, as this represents the most parsimonious solution [31]. The analysis started with a single-subgroup solution and increased the number of subgroups based on the improvement in model fit. Lower values of the Akaiake information criteria (AIC) and the Bayesian information criteria (BIC) indicate better-fitting models. Both the AIC and the BIC indices represent indices of model fit. They provide a balance of model complexity against the sample size [33]. Using the Lo-Mendell-Rubin adjusted likelihood ratio test, difference tests were calculated in order to determine whether an additional subtype improves the fit of the model [34]. A significant p-value suggests that the model provides a better fit to the data compared with a model with one less subgroup. In addition, entropy scores (how well latent classes are separated from each other) were assessed, with a higher entropy score (closer to 1), indicating better prediction. A good fitting model is expected to result in a high probability of classification of a case to only one of the classifications. To ensure the models’ stability, the starting values based on the local maximum in the iteration process were specified to vary [35]. Once the number of subgroups was determined, latent class membership was used as a between-subject variable to examine correlates of the latent subgroups. For continuous variables, we used the One-Way Analysis of Variance (ANOVA), followed by post hoc Bonferroni corrections to examine differences between the subgroups. For categorical variables, we used chi-square statistics.
Recommended steps in the development and testing of a typology:
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Decide on the sampling frame: who is the target sample of the study?
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Consider the minimum sample size required for the analysis.
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Decide on the contents which make up the construct that is being examined by the study.
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Consider the minimum number of indicators required for a meaningful analysis of subgroups across the construct of relevance.
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Clean the data and examine for missing values and outliers.
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Conduct latent class analysis. For more information on latent class analysis [33].
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Decide on the number of classes based on fit statistics, with lower BIC and AIC indicating improved models (compared with models of fewer classes/profiles). These data are examined against entropy scores, which indicate how well the model defines the classes. A score > 0.8 represents an acceptable solution, while the Lo–Mendell–Rubin test adjusted likelihood ratio is significant. The goal is to reach as few classes as statistically possible.
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Examine the correlates of the new class solution.
5. Results
Latent class analysis. The three-subgroup solution had the best fit indices as indicated in Table 2. The AIC and BIC indices of the three-class solution demonstrated a reduction compared with the two-class solution. This was accompanied by a higher entropy score, suggesting the uniqueness of the three-subgroup solution. In addition, the LMR-A p-value was significant, indicating that this solution was substantially better than a two-subgroup solution. A four-subgroup solution resulted in somewhat better-fit indices than the three-subgroup solution, but the LMR-A p-value was non-significant, suggesting that a fourth profile was unnecessary. In addition, in the four-subgroup solution, two of the subgroups were small, suggesting the possibility of low power and precision [36]. Because the decision concerning the number of subgroups is not purely statistical but is also theoretically grounded [32,37], the three-subgroup solution was chosen and is detailed in the text. See Table 2.
5.1. The Three-Subgroup Solution
The three-subgroup solution consisted of a large (387; 59.8%) subgroup that had medium-level scores on all pro-environmental behaviors, and two smaller subgroups of individuals who reported high engagement (113; 17.5%) and low engagement (146; 22.6%) in pro-environmental behaviors, respectively. Significant differences in all 10 items were found across the three subgroups. These are reported in Table 3. Waste recycling and avoiding disposable products were the most common pro-environmental behaviors the sample engaged in, and the use of renewable energies and isolation of the house were the least common pro-environmental behaviors.
Figure 1 provides a visual illustration of the cluster solution.
5.2. Bivariate Correlates
Once a three-subgroup solution was established, its demographic correlates as well as reasons for lack of engagement in pro-environmental behaviors were examined. See Table 4 for details. Older persons were significantly more likely to belong to the high pro-environmental engagers group, compared with the medium and low engagers groups. In addition, more educated individuals were more likely to belong to the high or medium engagers group compared with the low engagers group. There were no significant differences in the gender distribution across the three subgroups. The low-engagement group was significantly more likely to express doubts regarding the changing climate and view the changing climate as not within their responsibility. This group was significantly less likely to endorse reasons for limited engagement due to a lack of information or clarity about what needs to be done.
6. Discussion
The present study identified a classification of individuals engaged in pro-environmental behaviors. The classification was examined against demographic characteristics and reasons for limited engagement in pro-environmental behaviors. The study is important, given the fact that it was conducted in a society that pays limited attention to environmental issues [22]. In such a society, we would expect a high percentage of people who present with minimal or no pro-environmental behaviors. Hence, the study can be used to increase awareness of the topic as well as to better identify means to engage individuals in pro-environmental behaviors.
Our findings point to a classification by level of engagement in pro-environmental behaviors rather than by the type of behavior one is engaged in. The largest group was the one that engaged in pro-environmental behaviors at a medium level, whereas the low- and high-engagement groups were smaller. This classification is consistent with past research conducted in other countries, which has classified people based on the degree of pro-environmental behaviors they engaged in [5,6]. The two most common pro-environmental behaviors people engaged in were waste recycling and avoiding disposable products, whereas the least common behaviors people engaged in were use of renewable energies and isolating the house. A qualitative study conducted with older persons in Israel has found that they were most aware of recycling and avoiding disposables but reported limited acknowledgment of other ways to preserve the environment [24]. The present study provides further evidence of the need to increase attention to and awareness of various forms of pro-environmental behaviors the public can engage in, beyond the most popular ones reported here. Such an intervention to increase societal awareness should be implemented, regardless of the cluster one is classified into.
As expected [9], more educated and older people were more likely to be classified into the high engagers group. This finding is somewhat unexpected, given the current discourse which tends to blame older persons for their lack of action to prevent the current climate situation and their substantial carbon footprint over time [38]. Nonetheless, the present findings support other studies that have shown that older persons are more likely to engage in pro-environmental behaviors compared with younger persons [9], despite the fact that the latter report higher levels of eco-anxiety [8].
In contrast to expectations [39], our findings resulted in non-significant gender differences in pro-environmental behaviors. This finding stands in contrast to other studies that have consistently shown that women are more likely to engage in pro-environmental behaviors and report greater environmental concern [11,39]. Because the level of public awareness of environmental issues and the government’s attention to the topic are limited in Israel [22,23], it is possible that the correlates of pro-environmental behaviors are somewhat different in this country compared with other Western countries that show greater attention to pro-environmental issues.
An unexpected finding concerns the differential endorsement of reasons for limited engagement in pro-environmental behaviors. Those individuals who were classified into the least engaged subgroup tended not to worry about the changing climate and not to view pro-environmental behaviors as within their responsibility and used these arguments to explain their lack of involvement. In contrast, compared with the other two subgroups (medium and high engagement), they were less likely to state that limited information or unclear knowledge about the changing climate prevented them from engaging in pro-environmental behaviors. These findings are important because they suggest that different interventions are required to increase pro-environmental behaviors in different subgroups. Whereas those who already engage in pro-environmental behaviors are more likely to benefit from education about varied pro-environmental behaviors one can engage in, those who demonstrate limited engagement in pro-environmental behaviors are more likely to benefit from interventions that address their disbelief in the changing climate and their limited sense of responsibility for the climate situation.
The theory of planned behavior [40] suggests that three different types of beliefs are associated with the likelihood of engaging in a behavior. These include behavioral beliefs associated with the estimated likelihood of the outcomes, given the engagement in a particular behavior; normative beliefs, which form the motivation to engage in a particular behavior; and control beliefs, which represent the degree of self-efficacy one feels with regard to their ability to perform the behavior. This theory points to the complexity of determinants which potentially result in differential behavioral engagement and, therefore, may require different types of interventions. Consistently, past research has identified six types of interventions which can be linked to 12 different determinants of pro-environmental behaviors [41]. Thus, such an approach clearly acknowledges the importance of matching interventions to the characteristics of the particular population group. For instance, providing monetary incentives and nudges increases engagement in pro-environmental behaviors [42]. However, this is likely more effective for those individuals who do not worry about the changing climate or do not view it as being within their responsibility. Such legal interventions might be beneficial in ensuring that the costs associated with using pro-environmental behaviors are reduced, so that individuals are more likely to experience a financial incentive for being pro-environmental [43,44]. Those who lack information about pro-environmental behaviors, on the other hand, might benefit from a different set of interventions that are more informative and educational in nature.
Despite its contribution, the present study has some limitations that should be acknowledged. First, this is a cross-sectional study that cannot provide information about cause and effect. In addition, this is a non-representative sample of individuals with access to the internet and basic technological skills. It is possible that people who are less technologically savvy will respond differently to questions about the changing climate given the association of pro-environmental behaviors with education [9]. Hence, given the reliance on the internet for data collection, it is possible that the present sample is more knowledgeable about pro-environmental behaviors than the general Israeli population. It is also important to note that the study was conducted in Israel, a country that demonstrates limited awareness of climate issues [22,23]. Hence, once again, the findings may not be generalized to other countries. Transitioning from self-report to objective data on pro-environmental behaviors is desirable, as this can potentially allude to trends that are less biased by social desirability. In addition, assessing psychological variables as possible determinants of pro-environmental behaviors is desirable and may assist in better tailoring future interventions. Nevertheless, this study is important for several reasons. First and foremost, the study points to different methods that should be used to increase engagement in pro-environmental behaviors in those who already engage in such behaviors versus those who show low levels of engagement. The study also points to certain pro-environmental behaviors that deserve more public attention, given the overall limited public engagement. Finally, the study corroborates past research conducted in other Western countries, thus stressing the known association between education and pro-environmental behaviors [9] and questioning the public discourse that blames older persons for their lack of engagement in pro-environmental behaviors [38].
The study was approved by the ethics committee of the school of social work at Bar Ilan University. #012307, February 2023.
Respondents confirmed their consent to participate in the study online.
The data presented in this study are available on request from the corresponding author.
The author declares no conflicts of interest.
Footnotes
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Study variables (N = 647).
| Variable Name | Frequency (%)/Mean (SD) |
|---|---|
| Age | 52.11 (19.47) |
| Gender | |
| Women | 324 (50.1%) |
| Education (1–5) | 3.60 (0.96) |
| Pro-environmental behaviors | |
| Reduce energy consumption | 200 (30.9%) |
| Reduce water consumption | 141 (21.8%) |
| Waste recycling | 451 (69.7%) |
| Avoid disposable products | 431 (66.6%) |
| Consume seasonal foods | 127 (19.6%) |
| Consume environmentally friendly products | 265 (41.0%) |
| Use alternative transportation (e.g., public transportation, bicycles) | 146 (22.6%) |
| Purchase a hybrid or electric car | 125 (19.3%) |
| Use renewable energies | 85 (13.1%) |
| Isolate the house | 95 (14.7%) |
| Reasons for lack of engagement | |
| Limited information | 375 (58.0%) |
| Unclear what needs to be done | 367 (57.5%) |
| Do not worry about the changing climate | 118 (18.2%) |
| It is not my responsibility | 80 (12.4%) |
Fit indices of competing models (N = 647).
| Model | Loglikelihood | AIC | BIC | Entropy | Mean Probability of Profile Membership | LMR-A p-Value |
|---|---|---|---|---|---|---|
| 1-subgroup | −3491.06 | 7002.12 | 7046.84 | |||
| 2-subgroup | −3230.27 | 6502.54 | 6596.46 | 0.65 | Subgroup 1: 0.90 | <0.01 |
| Subgroup 2: 0.87 | ||||||
| 3-subgroup | −3167.40 | 6398.79 | 6541.91 | 0.73 | Subgroup 1: 0.89 | <0.001 |
| Subgroup 2: 0.84 | ||||||
| Subgroup 3: 0.88 | ||||||
| 4-subgroup | −3148.14 | 6382.27 | 6547.58 | 0.75 | Subgroup 1: 0.83 | 0.28 |
| Subgroup 2: 0.88 | ||||||
| Subgroup 3: 0.76 | ||||||
| Subgroup 4: 0.89 |
Lower AIC (Akaike Information Criterion) and BIC (Bayesian Information Criterion) indicate a better fit of the model. A higher entropy score suggests that the different profiles are more distinguishable, with a value of 1 indicating perfect classification and 0 indicating no differentiation. The addition of another profile is deemed justified when the LMR-A (Lo–Mendell–Rubin test adjusted likelihood ratio) p-value is significant.
Pro-environmental behaviors across the three-subgroup solution.
| Pro-Environmental Behavior | Medium Pro-Environmental Engagement (387; 59.8%) | High Pro-Environmental Engagement (113; 17.5%) | Low Pro-Environmental Engagement (146; 22.6%) | p-Value |
|---|---|---|---|---|
| Reduce energy consumption | 105 (26.4%) | 93 (86.9%) | 2 (1.4%) | <0.001 |
| Reduce water consumption | 80 (20.2%) | 56 (52.3%) | 5 (3.5%) | <0.001 |
| Waste recycling | 311 (78.3%) | 104 (97.2%) | 36 (25.2%) | <0.001 |
| Avoid disposable products | 330 (83.1%) | 101 (94.4%) | 0 (0%) | <0.001 |
| Consume seasonal foods | 49 (12.3%) | 74 (69.2%) | 4 (2.8%) | <0.001 |
| Consume environmentally friendly products | 160 (40.3%) | 97 (90.7%) | 8 (5.6%) | <0.001 |
| Use alternative transportation (e.g., public transportation, bicycles) | 95 (23.9%) | 48 (44.9%) | 3 (2.1%) | <0.001 |
| Purchase a hybrid or electric car | 63 (15.9%) | 59 (55.1%) | 3 (2.1%) | <0.001 |
| Use renewable energies | 37 (9.3%) | 48 (44.9%) | 0 (0%) | <0.001 |
| Isolate the house | 47 (11.8%) | 47 (43.9%) | 1 (0.7%) | <0.001 |
Demographic correlates and reasons for lack of engagement across the three-subgroup solution.
| Medium Pro-Environmental Engagement (387; 59.8%) | High Pro-Environmental Engagement (113; 17.5%) | Low Pro-Environmental Engagement (146; 22.6%) | p-Value | |
|---|---|---|---|---|
| Age | 53.42 (19.50) | 60.33 (16.90) | 42.31 (17.31) | <0.001 |
| Gender (female) | 210 (52.9%) | 48 (44.9%) | 66 (46.2%) | 0.19 |
| Education | 3.66 a (0.87) | 3.87 a (0.78) | 3.21 (1.18) | <0.001 |
| Reasons for lack of engagement | ||||
| Limited information | 246 (62.0%) | 70 (65.4%) | 56 (41.3%) | <0.001 |
| Unclear what needs to be done | 239 (60.2%) | 66 (61.7%) | 62 (43.4%) | <0.001 |
| Do not worry about the changing climate | 57 (14.4%) | 14 (13.1%) | 47 (32.9%) | <0.001 |
| It is not my responsibility | 40 (10.1%) | 11 (10.3%) | 29 (20.3%) | 0.001 |
a means that the two values are not significantly different from each other.
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