Next Article in Journal
Food System vs. Sustainability: An Incompatible Relationship in Mexico
Next Article in Special Issue
Sustainability Science Communication: Case Study of a True Cost Campaign in Germany
Previous Article in Journal
The Innovative Entrepreneurial Marketing Journey and Sustainable Development of Southeast Asian Immigrants
Previous Article in Special Issue
Dietary Behavior as a Target of Environmental Policy: Which Policy Instruments Are Adequate to Incentivize Plant-Based Diets?
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Sustainability
Volume 16
Issue 7
10.3390/su16072816
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

Gender and Socioeconomic Influences on Ten Pro-Environmental Behavior Intentions: A German Comparative Study

by
Paul Schulz
1,*,
Susanne Nicolai
1,
Samuel Tomczyk
2,
Silke Schmidt
2,
Philipp Franikowski
3 and
Susanne Stoll-Kleemann
1
1
Institute of Geography and Geology, Department of Sustainability Science and Applied Geography, University of Greifswald, 17489 Greifswald, Germany
2
Institute of Psychology, Department of Health and Prevention, University of Greifswald, 17489 Greifswald, Germany
3
The Institute for Educational Quality Improvement (IQB), Humboldt-University of Berlin, 10099 Berlin, Germany
*
Author to whom correspondence should be addressed.
Sustainability 2024, 16(7), 2816; https://doi.org/10.3390/su16072816
Submission received: 21 December 2023 / Revised: 12 March 2024 / Accepted: 26 March 2024 / Published: 28 March 2024
(This article belongs to the Special Issue Transformation to Sustainability and Behavior Change)
Browse Figure
Review Reports Versions Notes

Abstract

:
Pro-environmental behaviors (PEBs) such as climate-friendly mobility and eating habits hold great promise in terms of reducing greenhouse gas (GHG) emissions and, thus, are important goals for addressing climate change from a population perspective. Yet, sociodemographic correlates and differences in PEB intentions have to be considered in designing messages and behavior change interventions. This study implemented a quota-sampling survey (N = 979, 511 women, 468 men, age M = 50.4, SD = 17.2) of the German population and found that, overall, participants exhibit strong intentions to engage in various PEBs, with the exception of cycling and adopting a vegetarian diet. Moreover, women displayed higher intentions to engage in PEBs compared to men, particularly in adopting a vegetarian diet. The relationships between socioeconomic status (SES) and PEB intentions, as well as the combined effects of gender and SES, were inconsistent for different PEB intentions. We conclude that on a population level, intention-building interventions are necessary for vegetarianism and cycling, while for the other PEBs, interventions may focus on closing the intention–behavior gap. There is a need to further research the interplay of different PEBs in diverse groups and for interventional studies targeting the discrepancy in eating habits across genders.

1. Introduction

Tackling current environmental crises such as climate change, land use change, pollution, and many more requires an ecological transformation of society [1]. One goal that Germany shares with the European Union, as well as with other nations and international organizations, is the reduction in greenhouse gas (GHG) emissions to net zero by the middle of the century [2]. However, in 2021, Germany still emitted 756 million t CO2-eq, which is, on average, 9.09 t CO2-eq per person [3]. The unit CO2-eq is used to describe the amount of CO2 that would have a similar effect on climate change to that of a different GHG or a mixture of GHGs [1,4]. While the political and economic spheres need to implement ways to reduce GHG emissions, individual behavioral changes are necessary as well. There is potential for engaging in pro-environmental behaviors (PEBs) such as reducing one’s individual GHG footprint in the sectors of energy, transport and mobility, waste, and food. Additionally, PEBs can have positive side effects for the individual. For example, reducing energy consumption saves money, cycling instead of using a car comes with health benefits [5,6], and so does reducing one’s meat consumption [7]. The impact of PEBs in different sectors and their potential for individual behavior change shall be discussed below.

1.1. Sections of Pro-Environmental Behavior

Energy. Many household devices do not only use power when in use but also in standby mode [8,9], which is a small amount for each single device but combines to make up 3–16% of residential electricity use [10]. Thus, turning devices off completely when not in use is an effective way to reduce standby power consumption [11]. Another effective energy-saving behavior is lowering the room temperature. Different heating behaviors can account for a 51% variance in heat power consumption [12]. In a Danish study, the difference in heating energy consumption between families who live in similar buildings but have different heating behavior could vary between 4000 kWh/a and 14,600 kWh/a [13]. Doing laundry contains further energy-saving potential. Lowering the average washing temperature in western Europe from 41.7 °C to 36 °C alone would save 1.7 TWh/a [14]. Washing a load at 30 °C instead of 60 °C requires only a third of the energy [15]. Lastly, individual consumers can switch to a green electricity provider, i.e., a company that produces electricity only or partly from renewable energy sources [16].
Transport and Mobility. Relevant individual mobility behaviors comprise travel (e.g., using trains instead of plane rides) and daily commute (e.g., using public instead of private transport or using a bicycle instead of motorized transport). Using publicly available online flight trackers to estimate the amount of flights in 2019, one study found that 937 million tons of CO2 was produced by civil aviation alone [17]. While there might be technical solutions to reduce flight emissions, there are two simple and effective behavioral solutions: First is switching to an environmentally less harmful mode of travel, such as the railway. For instance, if Finland replaced all short-haul flights with railway service, emissions could be reduced by 95% [18]. Second is refraining from travel altogether. Apparently, almost half of air travel lacks an important reason, according to the travelers themselves, when asked to rate the importance of their flights [19]. For daily commutes, an environmentally friendly option is switching from motorcar to bicycle: if just 5% of New Zealand’s vehicle kilometers were traveled by bicycle instead of a car, that would reduce the country’s transport sector emissions by 0.4% [20]. If the cycling behavior of all EU-27 countries approximated Denmark’s, this could account for 12–26% of the EU’s 2050 emission reduction goal for the transport sector [21]. Research from the UK points out that electric bikes greatly reduce greenhouse gas emissions when used instead of cars: in a case study of one UK medical practice, 748 kg CO2 per year was saved by using an e-bike instead of a car for commuting and house visits [22].
Waste. A way to reduce unnecessary waste and be more environmentally friendly is choosing durable products instead of using plastic bags or other single-use plastics (e.g., when shopping). Plastic packaging is associated with 68 environmentally hazardous chemicals [23], and residual plastic bags pollute marine ecosystems [24,25]. Products with unnecessarily short lifetimes cause adverse environmental effects due to having to be produced more often. For example, the fast fashion sector causes water waste, chemical pollution, and a high GHG footprint, and there are calls for a change to slow fashion with more durable materials [26,27]. In addition, long-lasting products effectively act as carbon storage. One estimate found that, globally, 11.5 Gt C is stored in wood, plastic, and bitumen products [28].
Food. Livestock farming contributes 1.85 Gt CO2, that is, 5% of yearly human CO2 production [29]. From 2005 to 2007, the yearly GHG emissions from red meat production amounted to 4 Gt CO2-eq, and they are projected to further increase [29]. Accordingly, eating less or no meat and dairy is one of the best opportunities to reduce food-related emissions [30]. In fact, with 3.81 kg CO2-eq per day, a vegetarian is responsible for only half the GHG emissions of a person who eats 100 g of meat daily [31].

1.2. PEB Intentions in the Population

According to a framework for promoting PEBs, the first step is identifying which behavior should be changed [32]. In light of the variety of aforementioned PEBs, it is necessary to investigate which behaviors are most promising (in terms of GHG emission reduction) and most prevalent in the general population to inform policymakers and design PEB-related messages and campaigns. Therefore, it is helpful to know which, and to what extent, PEB intentions are already present in the population. Adapting the theory of planned behavior, intentions are direct predictors of behavior [33]. Two meta-analyses found intentions to belong to the strongest predictors of PEB and showed that intentions were a mediator for all other factors influencing PEB [34,35]. Intentions are informed by emotional, cognitive, and motivational predispositions towards a behavior, which are influenced by individual experiences, psychosocial processes, and situational and environmental factors that shape its translation into behavioral action. For example, a person might have a joyful memory of eating vegetarian cuisine (emotional predisposition), a desire to be healthier (motivational predisposition), and a belief that vegetarianism is a healthy food choice (cognitive disposition), but the cafeteria might have only a few meatless choices (situational barrier), or their friends might think little of vegetarianism (psychosocial pressure). Intentions have been used to explain pro-environmental behavior numerous times [36]. If existing intentions for a behavior are already strong, future research can be focused on interventions to combat the intention–behavior gap [37], which also applies for PEB intentions [38]. In contrast, if behavior intentions are weak, further research on constructs predicting the intention is necessary.
This study has two goals: The first is to show the extent of PEB intentions in the German population in order to determine the need for population-wide intervention planning. The second is to analyze how gender and socioeconomic variables are associated with PEB intentions to tailor messages and interventions targeting specific subgroups of the population.
The results of research on the association of PEB intentions with gender and other socioeconomic characteristics are ambivalent: while multiple studies and reviews concur that women show more PEBs than men [39,40,41], other studies show differing effects of education level, income, and occupation, which are often grouped together as socioeconomic status (SES), on PEBs. In a representative Swiss study, participants with a higher income emitted more CO2 and showed fewer PEB intentions, while their years of education were positively correlated with PEB intentions [42]. A German study on sustainable diets showed positive effects of education on PEBs but a higher meat consumption in low- and middle-income levels and no effect of occupation [43]. Nevertheless, reviews conclude that environmentalism is associated with high income and high education and, therefore, high SES [39,40,44].

1.3. Hypotheses

We expect, for this German sample, that PEB intentions will be higher for women than men (Hypothesis 1) and higher for participants with higher SES than for participants with lower SES (Hypothesis 2). Additionally, we expect a combined effect, namely, that PEB intentions will be higher for women with higher SES and lower for men with lower SES (Hypothesis 3). The effects of other sociodemographic variables on PEB intentions will be examined in an exploratory approach.

2. Materials and Methods

2.1. Sample

We used SoSciSurvey to prepare and conduct the survey and the panel service meinungsplatz.de to recruit N = 1100 German participants of at least 18 years of age, which is the legal age in Germany. They were quota sampled to be representative of the German population in distribution of sex and age groups, according to official government statistics [45]. Participants provided their informed consent for the survey and were paid the panel service’s standard fee. Data were collected in September 2020. We excluded n = 121 speedsters (i.e., participants with a mean relative completion speed factor > 2.0 [46]), resulting in a final sample of 979 participants (511 women, 468 men, age M = 50.4, SD = 17.2, range = 18–84 years) that remained representative of the German population, as has been reported before in another study using the same dataset [47]. See this other study for additional supplementary material about the sample. A third study using data from the same survey filtered the data differently and, therefore, used a different sample [48].

2.2. Scales and Measures

Sociodemographic variables included age (in years) and gender (1: female, 2: male, 3: diverse, 4: prefer not to say), with participants who answered 3 and 4 being excluded from the questionnaire, as there were no data available to calculate representative quota for those answers. Other sociodemographic variables were the following: highest educational degree (1: still in school, 2: no school graduation, 3: graduation after 8 to 9 years of school, 4: graduation after 10 years of school, 5: university of applied science entrance qualification, 6: A-level, that is, university entrance qualification, 7: university of applied science degree, 8: university degree, 9: other, with option to specify), occupation (1: school student, 2: university student, 3: currently in job training, 4: employed, 5: unemployed, 6: civil servant, 7: in retirement, 8: housewife/househusband, 9: volunteering year, 10: self-employed, 11: other, with option to specify), household net income per month (1: less than 400 €, 2: 401–800 €, 3: 801–1200 €, …, 10: 3601–4000 €, 11: more than 4000 €), area of residence (1: center of a city, more than 100,000 inhabitants; 2: city, more than 100,000 inhabitants; 3: town, between 20,000 and 100,000 inhabitants; 4: small town, less than 20,000 inhabitants; 5: village). The PEB intentions for the next year were assessed by one item each, as listed in Table 1. The items were generated based on discussions among experts on sustainability science who were associated with the working group. The full survey contained further scales that are not analyzed here [47].

2.3. Statistical Analyses

We used Microsoft Excel 2016 and R version 4.1.2 [49]. We imported the data with readxl [50] and utilized the packages jmv [51], corrplot [52], afex [53], emmeans [54], rstatix [55], corrr [56], effectsize [57], and tidyverse (including dplyr, tidyr, and ggplot2) [58] for the statistical analyses. Excel was used to estimate the SES score; R was used for all further analyses.
We estimated the SES score, according to Lampert [59], based on current job, household net income, and highest educational degree. For answers given on highest educational degree and current job, which were not listed in [59], SES scores were assigned according to the consensual decision of the authors. For retirees, SES scores could not be estimated correctly, as no information on their former job was available. Therefore, retirees will be excluded from all analyses involving SES.
For descriptive statistics, we calculated sample statistics and the average strength of the PEB intentions along with their standard error mean.
For each analysis, participants with missing data were excluded. In order to include SES in the ANOVA, the sample was split into quintiles (i.e., 20% quantiles), based on their SES score; then, we assigned the samples to three SES groups: high SES for the highest quintile (20% of the sample), low SES for the lowest quintile (20% of the sample), and middle SES for the other three quintiles (60% of the sample). Descriptive statistics of the resulting subgroups can be seen in Table 2. As the variances in PEB intentions were not homogeneous and the groups’ n sizes varied, Welch’s ANOVA tests were used [60]. As Welch’s ANOVA is a univariate test, we combined the two factors SES and gender and directly compared the six resulting cells. In the case of a significant result, a Games–Howell post-hoc test was used.
For the explorative approach, first, a within-subjects ANOVA with post-hoc Bonferroni-corrected pairwise t-tests was performed to show differences between the PEB intentions. Second, we estimated Pearson correlations between PEB intentions and sociodemographic variables. We collapsed area of residence into the variable big city (1: living in a city above 100,000 inhabitants, 2: living in a smaller town or village) and highest educational degree into the variable A-level (1: having A-level or higher degree, 2: not having A-level) and then estimated point-biserial correlations with the resulting dichotomous variables.

3. Results

Overall, most participants had attended school for at least 10 years before graduation (32%) or receiving a university degree (31%, see Table 2). Most participants were employed (42%) or retired (30%). While earning less than 800 € was rare, the remaining income levels were spread between 14% and 22%. Notably, more men than women had a university degree (36% vs. 27%), and more men than women were in the highest income class (22% vs. 12%). SES groups differed, for instance, in that 98% of the high-SES group had a university degree but only 26% of the middle-SES and 1% of the low-SES group. The frequencies of the high-income group showed a similar trend. Participants with high SES were the only subgroup with a majority (61%) living in a big city. Regarding PEB intentions, the participants reported moderate to high intentions for all behaviors except for vegetarianism (M = 2.70) and cycling (M = 3.45, see Table 3).

3.1. Hypotheses Testing

The hypotheses were tested for each PEB intention separately (see Table 4). There are no significant group differences in intentions for waste reduction, durability, room temperature, green electricity, and bicycle use. While the ANOVA for energy saving is significant, no pairwise comparison is significant. Therefore, the hypotheses on higher PEB intentions for women than men (Hypothesis 1), for high-SES over low-SES participants (Hypothesis 2), and for women with high SES over men with lower SES (Hypothesis 3) are rejected for these six behavioral intentions.
However, there are significant effects for the remaining PEB intentions. First, in accordance with Hypothesis 3, the intention to buy regional products is significantly higher for women with high SES than for men with middle SES and men with low SES. Second, in line with Hypothesis 1, women with low SES have higher intentions of not flying than men with middle or high SES. Additionally, men with low SES have a stronger intention of not flying than men with middle SES and men with high SES. Third, there is one significant difference in the intention for low laundry temperature that corresponds to Hypothesis 1, i.e., women with middle SES have stronger intentions than men with middle SES. Fourth, in line with Hypothesis 1, there are significant differences in intention for vegetarianism. Women with low and middle SES each have stronger intentions than men with middle SES and men with high SES. However, some of these findings contrast with Hypothesis 3, as the combined effects of gender and SES oppose the assumed directions (see Table 4).

3.2. Explorative Approach

Across all participants, mean values of PEB intentions rank as follows from strongest to weakest: (1) waste reduction, (2) regional products, (3) durability, (4) energy saving, (5) no flying, (6) low laundry temperature, (7) low room temperature, (8) green electricity, (9) cycling, (10) vegetarianism (see Table 3). The within-subjects ANOVA, corrected for sphericity with the Greenhouse–Geisser method, shows significant differences between the PEB intentions (F(7.47, 7304.59) = 222.12, p < .001, η2 = .15). Post-hoc Bonferroni-corrected pairwise t-tests show that most PEB intentions differ significantly, with p < .001 from each other, with the following two exceptions: the intention durability is significantly stronger than the intention energy saving, with only p < .01, and there is no significant difference between the intentions of no flying, low laundry temperature, low room temperature, and green electricity.
The correlation matrix between sociodemographic variables and PEB intentions is shown in Figure 1. It is notable that most PEB intentions are significantly correlated with each other. Regarding sociodemographic variables, male gender is negatively correlated with all PEB intentions except durability, low room temperature, and green electricity, with which there is no significant correlation. Meanwhile, SES score is correlated negatively with no flying but positively with low room temperature. Age is correlated negatively with low laundry temperature and vegetarianism but positively with waste reduction, regional products, durability, not flying, and low room temperature.

4. Discussion

This study examined different PEB intentions in the German population and investigated their association with sociodemographic characteristics. Intentions are among the strongest predictors of behavior [33,34,35]. We assumed SES and female gender to be positively associated with PEB intentions, and these hypotheses were partly supported by the data. For waste reduction, durability, room temperature, green electricity, energy saving, and using a bicycle, all hypotheses had to be rejected, as no group differences were found. However, as can be seen in Table 4, intentions to buy regional products were significantly higher in women with high SES than men with low SES. Intentions of not flying were significantly higher in women with high SES than in men with middle and high SES. Intentions to eat a vegetarian diet were significantly higher in women with low and middle SES than in men with middle and high SES.
As shown in Table 3, across all participants, the highest intentions were observed for waste reduction, regional products, durability, and energy saving. Interventions promoting these behaviors should focus on closing the intention–behavior gap [37] and might consider aspects of habits that can override intentions [61]. The lowest intentions were related to cycling and vegetarianism. To promote these PEBs, interventions should be intention building; for this, factors of the theory of planned behavior [33], as well as values and identities [62], should be implemented. Regardless of gender, a higher SES score went along with more flying and lower room temperature.
Regarding Hypothesis 1, that is, PEB intentions are higher in women, Figure 1 shows significant negative correlations for seven out of ten PEB intentions (waste reduction, regional products, energy saving, no flying, low laundry temperature, cycling, vegetarianism) with male gender. Therefore, we see the male gender as impeding PEB. This is in line with the current literature, e.g., [63,64] and could be explained with social role theory. Social role theory posits that people’s behavior conforms to their gender roles, as they are rewarded for conforming and penalized for deviating from these roles [65]. In a previous study, men who engaged in pro-environmental activities were described as being feminine and mocked for their behavior [66]; thus, it can be assumed that PEB is seen as female rather than male. Deviating from the perceived male role and behavior can lead to social consequences such as social distancing [65]. Hence, the association of PEB with the female gender might hinder men, as they fear social penalties when behaving pro-environmentally. Adding to this interpretation, it has been shown that identity has a high impact on pro-environmental intentions [62].
Another reason for a higher PEB engagement in women might be their higher levels of environmental concern [63], which might be connected to higher risk perception and thus motivate PEB change, in accordance with behavior change theory, which has been empirically proven numerous times, e.g., in [33,34,35]. An additional explanation, though less empirically proven, is social role theory. Following social role theory, it is possible that such concern might activate stereotypes of women being more caring [67] and thus lead to gendered perceptions of PEB. However, this hypothesis requires further research.
Moreover, previous research has investigated gender differences in PEB across the public and private spheres. Accordingly, findings that men engage more in PEB in the public space are rather heterogeneous and inconsistent, whereas the findings that women engage more in PEB in the private sphere are highly consistent [64]. As the PEB intentions of this article are completely in the private domain, our findings are in line with this general trend. For future research, the impact of gender and role perceptions and expectations on PEB in the population deserves more attention, for instance, across policy domains and social context, as women are also underrepresented in places of (political) power and thus have fewer opportunities to demonstrate PEB in these places [64].
Hypothesis 2, i.e., a higher SES being associated with higher PEB, could not be supported. On the contrary, a higher SES was associated with more flying. Flying can provide comfort to consumers, which may make it more appealing if they can afford it. Therefore, an ethical discourse and incentive must be created to compensate for this. Accordingly, environmentally friendly behavior must become “the easy behavior” and be made more appealing [68,69]. Conversely, a higher SES was associated with intentions to lower room temperature. The reason for this might be that participants with a higher SES have more opportunity to do so, because, in general, the GHG emissions of housing were found to be higher in households with a higher income (see Table 2; the only area in which higher income had lower GHG emissions was the food domain [70]). Moreover, higher income provides the opportunity to properly insulate houses to regulate room temperature. Since opportunity is a key component of behavior change theory, this also corresponds to previous research, e.g., [33,34,35].
Lastly, Hypothesis 3, that is, PEB intentions will be higher for women with high SES and lower for men with low SES, was supported for buying regional products and reducing the number of flights per year. Therefore, future PEB-related communication should address both groups differently: women with higher SES should receive support for behavioral implementation since they already report higher intentions, and men and low SES groups might benefit from tailored information to increase PEB intentions first. For vegetarianism, however, women of low and middle SES showed higher vegetarian intentions than men with middle and high SES. It seems that gender-based differences were not equalized by socioeconomic resources, highlighting the need for targeted interventions. This finding is complemented by another study showing that the proportion of vegetarians was highest among women of low SES, while it was lowest among men in this SES group [71]. This indicates that low SES may exacerbate gender-based differences in PEB as opposed to high SES. Nevertheless, looking at education as one aspect of SES showed that as the level of education increased, a higher proportion of both women and men usually ate a vegetarian diet [71]. Taken together, more research is needed to examine the mechanisms that connect different indicators of SES, gender roles, and PEBs in the population and to understand the higher proportion of PEBs, such as practicing a vegetarian diet, in women despite lower SES and how this could be applied to also reach men and support them in enacting dietary behavior change. The exploratory analyses showed that waste reduction, buying regional products, durability, and green electricity correlate with each other. These PEBs, therefore, might build a more general cluster of “conscious consumption”. In consumer psychology, conscious consumption decisions are differentiated from unconscious impulse purchases using either Wason and Evans’ dual process theory [72] or Bittmann’s contingency approach [73], according to which four main goals are relevant for purchasing decisions: (a) making an accurate decision, (b) effort avoidance, (c) good justifiability to oneself and others, and (d) avoiding negative emotions. However, we have not yet found empirical support for such clusters of pro-environmental consumption in the scientific literature.
Most of the PEB intentions in this study showed rather high levels (mean scores above 4 out of 5) except for cycling and vegetarianism. According to the low-cost hypothesis [74], perceived behavioral costs for these two PEBs might be higher than for other PEBs. Regarding cycling, these behavioral costs might mean less comfort in biking, loss of a car as a status symbol, and more dangerous travel due to unsafe or nonexistent bike lanes. Moreover, as bikes are an individual mode of traveling, this also excludes the social aspect of travel by car (e.g., for families). Therefore, future research should examine different aspects of cycling in everyday life, its association with GHG reductions, and how it can be integrated into different living situations, e.g., [75].
Behavioral costs in regard to giving up meat consumption might be taste preferences, culinary traditions (habits), and social norms [76]. However, these behavioral costs might have sunken over the last decades. Although reliable estimates of vegetarians in society before 2000 are rare, it is supposed that this number has been growing since the 1970s. Estimates of the number of vegetarians in Germany between 1990 and 2016 range from 2% to 10%, with a tendency of more people becoming vegetarians within the last 30 years [71]. This might be due to the increased availability, quality, and lower price of vegetarian replacement products in supermarkets [77]. This observation corresponds to the aspect of opportunity in behavior change theory. Moreover, since many replacement products have a similar taste and texture compared to meat products, culinary traditions might be easily continued without meat. However, so far, the visibility and level of self-organization of vegetarians have increased more than the actual number of vegetarians [78]. According to a systematic review, people who are willing to change or have already changed their meat consumption are a minority, showing again the low intentions for vegetarianism [76].
Despite being unpopular, eating a vegetarian diet has a high impact, as livestock farming contributes 5% of yearly human CO2 production [29], and the phosphorus and nitrogen input caused by livestock causes further environmental problems [79]. Moreover, meat consumption fosters a loss of biodiversity [80]. Reducing meat consumption is accompanied by health improvements [29]. Therefore, more research should focus on meat-related behavior change and find ways to build stronger intentions to reduce meat consumption and encourage a vegetarian diet.
A first approach—considering our findings and the social role theory—might be to disentangle PEB from gender roles to provide men the opportunity to behave pro-environmentally without having to fear social punishment, thus lowering perceived behavioral costs. Secondly, parts of the population with high SES should be addressed with tailored interventions regarding flying and more environmentally friendly modes of travel for everyday life. This should be accompanied by environmental prevention, such as providing dedicated bike lanes or parking spots close to building entries at the workplace, reward systems for green travel options, and, overall, additional and safer bike lanes. Both aspects, gender and SES, should be considered when designing PEB-related messaging and advertisement campaigns. Moreover, reducing gender and income inequality in society might produce more female leaders and more diverse SES leaders who could act as pro-environmental role models and have the power to make political pro-environmental decisions.

5. Conclusions

The aim of this study was to investigate which PEBs are the most promising (regarding GHG reduction) and most prevalent in the general population to identify further steps to address climate change. In this quota-sampled online survey of the German population, we compared the intentions for 10 different PEBs, offering a starting point for future research on PEB interventions, which might be developed by governments, activist groups, or researchers. We were able to show that, in general, PEB intentions are quite high, with the exceptions of cycling and vegetarianism. The implication is that interventions for those two PEBs should focus on intention building, while, for the other PEBs, they can focus on closing the intention–behavior gap. Women showed higher PEB intentions than men, especially in terms of vegetarianism. Following our interpretation, in line with social role theory, men could be addressed by depicting vegetarianism as a male behavior. Participants with higher SES were more likely to lower their room temperature but less likely to change their air travel. Therefore, these subgroups need to be targeted by different interventions. Vegetarianism, though being very impactful, was the most unpopular PEB intention. Therefore, more research and intervention programs on meat reduction as well as air travel are urgently needed, in particular, regarding gender and SES differences. As gender roles are highly associated with PEB and, possibly, with the expression of SES differences, a first step might be disentangling this connection. Future research should use our results and implications when developing new PEB interventions, both for choosing which behavior to focus on and how to address it.

6. Limitations

This study is cross-sectional; therefore, the results cannot be interpreted causally. The selection of items measuring PEB intentions followed an expert panel, yet further validation is recommended, for instance, to differentiate between different types of behaviors (e.g., a cluster of conscious consumption behaviors). More elaborate methods such as finite mixture modeling might be appropriate to examine clusters of behaviors and their associations in future research. Moreover, gender differences and the low intentions regarding vegetarianism compared to other PEBs could be investigated using qualitative methods. In addition, we performed several correlation analyses leading to a cumulative alpha error probability. Finally, we examined PEB intentions but not current behaviors, so we are mindful of the intention–behavior gap. Future research should thus incorporate more behavioral measures (e.g., via field studies and observations) to complement research into PEB in the general population.

Author Contributions

Conceptualization, P.S., S.T., S.S. and S.S.-K.; methodology, S.N. and P.F.; software, P.S.; validation, P.S. and P.F.; formal analysis, P.S.; investigation, P.S.; resources, S.S.-K.; data curation, S.N. and P.F.; writing—original draft preparation, P.S. and S.N.; writing—review and editing, P.S., S.N., S.T., S.S., P.F. and S.S.-K.; visualization, P.S.; supervision, S.T., S.S. and S.S.-K.; project administration, S.S.-K., S.S. and S.T. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Institutional Review Board Statement

The survey was designed and conducted in accordance with the Code of Ethics of the World Medical Association (“World Medical Association Declaration of Helsinki”, 2013). All participants provided their informed consent to participate prior to the start of the survey and were compensated for their participation. They were informed about the aim of the study and that they could terminate their participation at any time without negative consequences. Ethical review and approval were not required for these studies, in accordance with German legislation and institutional requirements. The approval of the university’s data protection officer has been obtained. The study was not preregistered.

Informed Consent Statement

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

Data Availability Statement

Data and R Script are available here: https://www.doi.org/10.17605/OSF.IO/KXN92.

Conflicts of Interest

The authors declare no conflicts of interest.

References

  1. IPCC. Synthesis Report of the IPCC Sixth Assessment Report (AR6): Longer Report; IPCC: Geneva, Switzerland, 2023. [Google Scholar]
  2. BMUB. Klimaschutzplan 2050—Klimaschutzpolitische Grundsätze und Ziele der Bundesregierung; Bundesministerium für Umwelt, Naturschutz und Reaktorsicherheit: Berlin, Germany, 2016.
  3. Umweltbundesamt; Bundesministerium für Wirtschaft und Klima. Treibhausgasemissionen Stiegen 2021 um 4,5 Prozent; 2022. Available online: https://www.umweltbundesamt.de/presse/pressemitteilungen/treibhausgasemissionen-stiegen-2021-um-45-prozent (accessed on 20 December 2023).
  4. IPCC. Climate Change 2022: Mitigation of Climate Change. Contribution of Working Group III to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change; Shukla, P.R., Skea, J., Slade, R., Al Khourdajie, A., van Diemen, R., McCollum, D., Pathak, M., Some, S., Vyas, P., Fradera, R., et al., Eds.; Cambridge University Press: Cambridge, UK; New York, NY, USA, 2022. [Google Scholar] [CrossRef]
  5. Götschi, T.; Garrard, J.; Giles-Corti, B. Cycling as a Part of Daily Life: A Review of Health Perspectives. Transp. Rev. 2016, 36, 45–71. [Google Scholar] [CrossRef]
  6. De Hartog, J.J.; Boogaard, H.; Nijland, H.; Hoek, G. Do the health benefits of cycling outweigh the risks? Environ. Health Perspect. 2010, 118, 1109–1116. [Google Scholar] [CrossRef] [PubMed]
  7. Willett, W.; Rockström, J.; Loken, B.; Springmann, M.; Lang, T.; Vermeulen, S.; Garnett, T.; Tilman, D.; DeClerck, F.; Wood, A.; et al. Food in the Anthropocene: The EAT–Lancet Commission on healthy diets from sustainable food systems. Lancet 2019, 393, 447–492. [Google Scholar] [CrossRef] [PubMed]
  8. Ajay-D-Vimal Raj, P.; Sudhakaran, M.; Philomen-D-Anand Raj, P. Estimation of Standby Power Consumption for Typical Appliances. J. Eng. Sci. Technol. Rev. 2009, 2, 71–75. [Google Scholar]
  9. Roth, K.; Lim, B. Residential Consumer Electronics Energy Consumption in 2013. In Proceedings of the ACEEE Summer Study on Energy Efficiency in Buildings, Pacific Grove, CA, USA, 17–22 August 2014. [Google Scholar]
  10. Gerber, D.L.; Meier, A.; Liou, R.; Hosbach, R. Emerging Zero-Standby Solutions for Miscellaneous Electric Loads and the Internet of Things. Electronics 2019, 8, 570. [Google Scholar] [CrossRef]
  11. Gram-Hanssen, K.; Gudbjerg, E. Standby consumption in households—by means of communication or technology? In Proceedings of the ACEEE’s 2006 Summer Study on Energy Efficiency in Buildings “Less is More”: En Route to Zero Energy Buildings, Pacific Groove, CA, USA, 13–18 August 2006. [Google Scholar]
  12. Gill, Z.M.; Tierney, M.J.; Pegg, I.M.; Allan, N. Low-energy dwellings: The contribution of behaviours to actual performance. Build. Res. Inf. 2010, 38, 491–508. [Google Scholar] [CrossRef]
  13. Gram-Hanssen, K. Residential heat comfort practices: Understanding users. Build. Res. Inf. 2010, 38, 175–186. [Google Scholar] [CrossRef]
  14. Pakula, C.; Stamminger, R. Energy and water savings potential in automatic laundry washing processes. Energy Effic. 2015, 8, 205–222. [Google Scholar] [CrossRef]
  15. Rüdenauer, I.; Grießhammer, R.; Götz, K.; Birzle-Harder, B. PROSA Waschmaschinen, Öko-Institut e.V.: Freiburg, Germany, 2004.
  16. Hast, A.; Syri, S.; Jokiniemi, J.; Huuskonen, M.; Cross, S. Review of green electricity products in the United Kingdom, Germany and Finland. Renew. Sustain. Energy Rev. 2015, 42, 1370–1384. [Google Scholar] [CrossRef]
  17. Quadros, F.D.A.; Snellen, M.; Sun, J.; Dedoussi, I.C. Global Civil Aviation Emissions Estimates for 2017–2020 Using ADS-B Data. J. Aircr. 2022, 59, 1394–1405. [Google Scholar] [CrossRef]
  18. Baumeister, S.; Leung, A. The emissions reduction potential of substituting short-haul flights with non-high-speed rail (NHSR): The case of Finland. Case Stud. Transp. Policy 2021, 9, 40–50. [Google Scholar] [CrossRef]
  19. Gössling, S.; Hanna, P.; Higham, J.; Cohen, S.; Hopkins, D. Can we fly less? Evaluating the ‘necessity’ of air travel. J. Air Transp. Manag. 2019, 81, 101722. [Google Scholar] [CrossRef]
  20. Lindsay, G.; Macmillan, A.; Woodward, A. Moving urban trips from cars to bicycles: Impact on health and emissions. Aust. N. Z. J. Public Health 2011, 35, 54–60. [Google Scholar] [CrossRef] [PubMed]
  21. Blondel, B.; Mispelon, C.; Ferguson, J. Cycle More Often 2 Cool Down The Planet!—Quantifying CO2 Savings of Cycling; ECF: Brussels, Belgium, 2011. [Google Scholar]
  22. Pierce, J.M.T.; Nash, A.B.; Clouter, C.A. The in-use annual energy and carbon saving by switching from a car to an electric bicycle in an urban UK general medical practice: The implication for NHS commuters. Environ. Dev. Sustain. 2013, 15, 1645–1651. [Google Scholar] [CrossRef]
  23. Groh, K.J.; Backhaus, T.; Carney-Almroth, B.; Geueke, B.; Inostroza, P.A.; Lennquist, A.; Leslie, H.A.; Maffini, M.; Slunge, D.; Trasande, L.; et al. Overview of known plastic packaging-associated chemicals and their hazards. Sci. Total Environ. 2019, 651, 3253–3268. [Google Scholar] [CrossRef] [PubMed]
  24. Schnurr, R.E.J.; Alboiu, V.; Chaudhary, M.; Corbett, R.A.; Quanz, M.E.; Sankar, K.; Srain, H.S.; Thavarajah, V.; Xanthos, D.; Walker, T.R. Reducing marine pollution from single-use plastics (SUPs): A review. Mar. Pollut. Bull. 2018, 137, 157–171. [Google Scholar] [CrossRef] [PubMed]
  25. Xanthos, D.; Walker, T.R. International policies to reduce plastic marine pollution from single-use plastics (plastic bags and microbeads): A review. Mar. Pollut. Bull. 2017, 118, 17–26. [Google Scholar] [CrossRef]
  26. Niinimäki, K.; Peters, G.; Dahlbo, H.; Perry, P.; Rissanen, T.; Gwilt, A. The environmental price of fast fashion. Nat. Rev. Earth Environ. 2020, 1, 189–200. [Google Scholar] [CrossRef]
  27. Centobelli, P.; Abbate, S.; Nadeem, S.P.; Garza-Reyes, J.A. Slowing the fast fashion industry: An all-round perspective. Curr. Opin. Green Sustain. Chem. 2022, 38, 100684. [Google Scholar] [CrossRef]
  28. Lauk, C.; Haberl, H.; Erb, K.-H.; Gingrich, S.; Krausmann, F. Global socioeconomic carbon stocks in long-lived products 1900–2008. Environ. Res. Lett. 2012, 7, 34023. [Google Scholar] [CrossRef]
  29. Godfray, H.C.J.; Aveyard, P.; Garnett, T.; Hall, J.W.; Key, T.J.; Lorimer, J.; Pierrehumbert, R.T.; Scarborough, P.; Springmann, M.; Jebb, S.A. Meat consumption, health, and the environment. Science 2018, 361, aam5324. [Google Scholar] [CrossRef]
  30. Garnett, T. Where are the best opportunities for reducing greenhouse gas emissions in the food system (including the food chain)? Food Policy 2011, 36, S23–S32. [Google Scholar] [CrossRef]
  31. Scarborough, P.; Appleby, P.N.; Mizdrak, A.; Briggs, A.D.M.; Travis, R.C.; Bradbury, K.E.; Key, T.J. Dietary greenhouse gas emissions of meat-eaters, fish-eaters, vegetarians and vegans in the UK. Clim. Chang. 2014, 125, 179–192. [Google Scholar] [CrossRef]
  32. Steg, L.; Vlek, C. Encouraging pro-environmental behaviour: An integrative review and research agenda. J. Environ. Psychol. 2009, 29, 309–317. [Google Scholar] [CrossRef]
  33. Ajzen, I. The theory of planned behavior. Organ. Behav. Hum. Decis. Process. 1991, 50, 179–211. [Google Scholar] [CrossRef]
  34. Klöckner, C.A. A comprehensive model of the psychology of environmental behaviour—A meta-analysis. Glob. Environ. Chang. 2013, 23, 1028–1038. [Google Scholar] [CrossRef]
  35. Bamberg, S.; Möser, G. Twenty years after Hines, Hungerford, and Tomera: A new meta-analysis of psycho-social determinants of pro-environmental behaviour. J. Environ. Psychol. 2007, 27, 14–25. [Google Scholar] [CrossRef]
  36. Yuriev, A.; Dahmen, M.; Paillé, P.; Boiral, O.; Guillaumie, L. Pro-environmental behaviors through the lens of the theory of planned behavior: A scoping review. Resour. Conserv. Recycl. 2020, 155, 104660. [Google Scholar] [CrossRef]
  37. Sheeran, P.; Webb, T.L. The Intention-Behavior Gap. Soc. Personal. Psychol. Compass 2016, 10, 503–518. [Google Scholar] [CrossRef]
  38. Hassan, L.M.; Shiu, E.; Shaw, D. Who Says There is an Intention–Behaviour Gap? Assessing the Empirical Evidence of an Intention–Behaviour Gap in Ethical Consumption. J. Bus. Ethics 2016, 136, 219–236. [Google Scholar] [CrossRef]
  39. Gifford, R.; Nilsson, A. Personal and social factors that influence pro-environmental concern and behaviour: A review. Int. J. Psychol. 2014, 49, 141–157. [Google Scholar] [CrossRef] [PubMed]
  40. Li, D.; Zhao, L.; Ma, S.; Shao, S.; Zhang, L. What influences an individual’s pro-environmental behavior? A literature review. Resour. Conserv. Recycl. 2019, 146, 28–34. [Google Scholar] [CrossRef]
  41. Vicente-Molina, M.A.; Fernández-Sáinz, A.; Izagirre-Olaizola, J. Environmental knowledge and other variables affecting pro-environmental behaviour: Comparison of university students from emerging and advanced countries. J. Clean. Prod. 2013, 61, 130–138. [Google Scholar] [CrossRef]
  42. Bruderer Enzler, H.; Diekmann, A. All Talk and No Action? An Analysis of Environmental Concern, Income and Greenhouse Gas Emissions in Switzerland. Energy Res. Soc. Sci. 2019, 51, 12–19. [Google Scholar] [CrossRef]
  43. Klink, U.; Mata, J.; Frank, R.; Schüz, B. Socioeconomic differences in animal food consumption: Education rather than income makes a difference. Front. Nutr. 2022, 9, 993379. [Google Scholar] [CrossRef] [PubMed]
  44. Grandin, A.; Guillou, L.; Abdel Sater, R.; Foucault, M.; Chevallier, C. Socioeconomic status, time preferences and pro-environmentalism. J. Environ. Psychol. 2022, 79, 101720. [Google Scholar] [CrossRef]
  45. Federal Statistical Office. Bevölkerungsstand—Bevölkerung nach Nationalität und Geschlecht (Quartalszahlen): Ergebnisse der Bevölkerungsfortschreibung auf Grundlage des Zensus 2019. Available online: https://www.destatis.de/DE/Themen/Gesellschaft-Umwelt/Bevoelkerung/Bevoelkerungsstand/Tabellen/bevoelkerung-altersgruppen-deutschland.html (accessed on 2 March 2023).
  46. Leiner, D.J. Too Fast, too Straight, too Weird: Non-Reactive Indicators for Meaningless Data in Internet Surveys. SRM 2019, 13, 229–248. [Google Scholar] [CrossRef]
  47. Stoll-Kleemann, S.; Nicolai, S.; Franikowski, P. Exploring the Moral Challenges of Confronting High-Carbon-Emitting Behavior: The Role of Emotions and Media Coverage. Sustainability 2022, 14, 5742. [Google Scholar] [CrossRef]
  48. Nicolai, S.; Franikowski, P.; Stoll-Kleemann, S. Predicting Pro-environmental Intention and Behavior Based on Justice Sensitivity, Moral Disengagement, and Moral Emotions—Results of Two Quota-Sampling Surveys. Front. Psychol. 2022, 13, 914366. [Google Scholar] [CrossRef]
  49. R Core Team. R: A Language and Environment for Statistical Computing; R Foundation for Statistical Computing: Vienna, Austria, 2023. [Google Scholar]
  50. Wickham, H.; Bryan, J. _readxl: Read Excel Files_. R Package Version 1.4.3. 2023. Available online: https://CRAN.R-project.org/package=readxl (accessed on 2 March 2024).
  51. Selker, R.; Love, J.; Dropmann, D.; Moreno, V. _jmv: The ‘jamovi’ Analyses_. R Package Version 2.3.4. 2022. Available online: https://CRAN.R-project.org/package=jmv (accessed on 2 March 2024).
  52. Wei, T.; Simko, V. R Package ‘corrplot’: Visualization of a Correlation Matrix (Version 0.92). 2021. Available online: https://github.com/taiyun/corrplot (accessed on 2 March 2024).
  53. Singmann, H.; Bolker, B.; Westfall, J.; Aust, F.; Ben-Shachar, M. _Afex: Analysis of Factorial Experiments_. R Package Version 1.3-0. 2023. Available online: https://CRAN.R-project.org/package=afex (accessed on 2 March 2024).
  54. Lenth, R. _Emmeans: Estimated Marginal Means, aka Least-Squares Means_. R Package Version 1.8.8. 2023. Available online: https://CRAN.R-project.org/package=emmeans (accessed on 2 March 2024).
  55. Kassambara, A. _rstatix: Pipe-Friendly Framework for Basic Statistical Tests_. R Package Version 0.7.2. 2023. Available online: https://CRAN.R-project.org/package=rstatix (accessed on 2 March 2024).
  56. Kuhn, M.; Jackson, S.; Cimentada, J. _corrr: Correlations in R_. R Package Version 0.4.4. 2022. Available online: https://CRAN.R-project.org/package=corrr (accessed on 2 March 2024).
  57. Ben-Shachar, M.; Lüdecke, D.; Makowski, D. effectsize: Estimation of Effect Size Indices and Standardized Parameters. J. Open Source Softw. 2020, 5, 2815. [Google Scholar] [CrossRef]
  58. Wickham, H.; Averick, M.; Bryan, J.; Chang, W.; McGowan, L.; François, R.; Grolemund, G.; Hayes, A.; Henry, L.; Hester, J.; et al. Welcome to the Tidyverse. J. Open Source Softw. 2019, 4, 1686. [Google Scholar] [CrossRef]
  59. Lampert, T.; Kroll, L.; Müters, S.; Stolzenberg, H. Messung des sozioökonomischen Status in der Studie zur Gesundheit Erwachsener in Deutschland (DEGS1). Bundesgesundheitsblatt Gesundheitsforschung Gesundheitsschutz 2013, 56, 631–636. [Google Scholar] [CrossRef]
  60. Kohr, R.L.; Games, P.A. Robustness of the Analysis of Variance, the Welch Procedure and a Box Procedure to Heterogeneous Variances. J. Exp. Educ. 1974, 43, 61–69. [Google Scholar] [CrossRef]
  61. Kurz, T.; Gardner, B.; Verplanken, B.; Abraham, C. Habitual behaviors or patterns of practice? Explaining and changing repetitive climate-relevant actions. WIREs Clim. Chang. 2015, 6, 113–128. [Google Scholar] [CrossRef]
  62. Gatersleben, B.; Murtagh, N.; Abrahamse, W. Values, identity and pro-environmental behaviour. Contemp. Soc. Sci. 2014, 9, 374–392. [Google Scholar] [CrossRef]
  63. Hunter, L.M.; Hatch, A.; Johnson, A. Cross-national gender variation in environmental behaviors. Soc. Sci. Q. 2004, 85, 677–694. [Google Scholar] [CrossRef]
  64. Kennedy, E.H.; Kmec, J. Reinterpreting the gender gap in household pro-environmental behaviour. Environ. Sociol. 2018, 4, 299–310. [Google Scholar] [CrossRef]
  65. Swim, J.K.; Gillis, A.J.; Hamaty, K.J. Gender Bending and Gender Conformity: The Social Consequences of Engaging in Feminine and Masculine Pro-Environmental Behaviors. Sex Roles 2020, 82, 363–385. [Google Scholar] [CrossRef]
  66. Rome, A. ‘Political Hermaphrodites’: Gender and Environmental Reform in Progressive America. Environ. Hist. 2006, 11, 440–463. [Google Scholar] [CrossRef]
  67. Bloodhart, B.; Swim, J.K.; Dicicco, E. “Be Worried, be VERY Worried:” Preferences for and Impacts of Negative Emotional Climate Change Communication. Front. Commun. 2019, 3, 423854. [Google Scholar] [CrossRef]
  68. Thøgersen, J. Consumer behavior and climate change: Consumers need considerable assistance. Curr. Opin. Behav. Sci. 2021, 42, 9–14. [Google Scholar] [CrossRef]
  69. Ullström, S.; Stripple, J.; Nicholas, K.A. From aspirational luxury to hypermobility to staying on the ground: Changing discourses of holiday air travel in Sweden. J. Sustain. Tour. 2023, 31, 688–705. [Google Scholar] [CrossRef]
  70. Bruderer Enzler, H.; Diekmann, A. Environmental Impact and Pro-Environmental Behavior: Correlations to Income and Environmental Concern; ETH Zurich, Chair of Sociology No. 9. 2015. Available online: https://econpapers.repec.org/paper/etswpaper/9.htm (accessed on 2 March 2024).
  71. Robert Koch-Institut. Verbreitung der Vegetarischen Ernährungsweise in Deutschland; RKI-Bib1; Robert Koch-Institut: Berlin, Germany, 2016.
  72. Wason, P.C.; Evans, J. Dual processes in reasoning? Cognition 1974, 3, 141–154. [Google Scholar] [CrossRef]
  73. Bittman, M.; England, P.; Sayer, L.; Folbre, N.; Matheson, G. When Does Gender Trump Money? Bargaining and Time in Household Work. Am. J. Sociol. 2003, 109, 186–214. [Google Scholar] [CrossRef]
  74. Diekmann, A.; Preisendörfer, P. Green and Greenback. Ration. Soc. 2003, 15, 441–472. [Google Scholar] [CrossRef]
  75. Stewart, G.; Anokye, N.; Pokhrel, S. Improving population levels of physical activity through integration into everyday life: A before and after analysis of the Cycling City and Towns programme. Lancet 2016, 388, S106. [Google Scholar] [CrossRef]
  76. Sanchez-Sabate, R.; Sabaté, J. Consumer Attitudes Towards Environmental Concerns of Meat Consumption: A Systematic Review. Int. J. Environ. Res. Public Health 2019, 16, 1220. [Google Scholar] [CrossRef]
  77. Statistisches Bundesamt. Pressemitteilung Nr. N 025 vom 9. Mai 2022. 2022. [Google Scholar]
  78. Beardsworth, A.; Keil, T. Sociology on the Menu: An Invitation to the Study of Food and Society; Routledge: London, UK, 1997; ISBN 9781134823178. [Google Scholar]
  79. Bouwman, L.; Goldewijk, K.K.; van der Hoek, K.W.; Beusen, A.H.W.; van Vuuren, D.P.; Willems, J.; Rufino, M.C.; Stehfest, E. Exploring global changes in nitrogen and phosphorus cycles in agriculture induced by livestock production over the 1900–2050 period. Proc. Natl. Acad. Sci. USA 2013, 110, 20882–20887. [Google Scholar] [CrossRef]
  80. Stoll-Kleemann, S.; Schmidt, U.J. Reducing meat consumption in developed and transition countries to counter climate change and biodiversity loss: A review of influence factors. Reg. Environ. Chang. 2017, 17, 1261–1277. [Google Scholar] [CrossRef]
Sustainability 16 02816 g001
Figure 1. Correlation matrix with correlation coefficients. Significant correlations (p < .05) are in black. All correlations with male gender (versus female gender), big city (versus living in a small town or village), and A-Level (versus having no A-level) are point-biserial correlations; the remainder are Pearson correlations. N = 979, except SES correlations, which are estimated without retirees, n = 674.
Figure 1. Correlation matrix with correlation coefficients. Significant correlations (p < .05) are in black. All correlations with male gender (versus female gender), big city (versus living in a small town or village), and A-Level (versus having no A-level) are point-biserial correlations; the remainder are Pearson correlations. N = 979, except SES correlations, which are estimated without retirees, n = 674.
Sustainability 16 02816 g001
Table 1. PEB Intention Items (previously published in [47]).
Table 1. PEB Intention Items (previously published in [47]).
PEB IntentionsItems
To what extent do these statements apply to you?
In 2021, I would like to (continue to) …
Waste reduction… buy mainly waste-avoiding products (e.g., using no plastic bags for fruit and vegetables).
Regional products… buy regional products rather than products transported over long distances.
Durability… pay more attention to the long term than to the price when making new purchases (e.g., electrical appliances, clothing).
Energy saving… switch off my electrical appliances completely (i.e., not only put them on standby).
No flying (R)… take trips by plane.
Low laundry temperature (R)… wash my laundry mainly at 60 °C or higher.
Low room temperature (R)… heat my apartment to more than 22 °C in the winter months.
Electricity… purchase green electricity.
Cycling (R)… mainly use the car for short distances (up to 20 km).
Vegetarianism… eat a vegetarian diet.
Note: (R) signifies reversed items. Answers on a 6-point Likert response scale from 1 = “does not apply to me at all” to 6 = “fully applies to me”.
Table 2. Absolute and Relative Frequencies of Sociodemographics of Sample and Subgroups.
Table 2. Absolute and Relative Frequencies of Sociodemographics of Sample and Subgroups.
AllGenderSocioeconomic Status a
WomenMenLowMiddleHigh
N979511468141426109
w = 83
m = 58		w = 229
m = 197		w = 46
m = 63
Mean age (SD)50.36 (17.17)49.40 (17.37)51.41 (16.90)35.96 (15.79)43.68 (13.88)47.27 (12.72)
Highest educational degree
 Graduation after 8–9 years100 (0.10)52 (0.10)48 (0.10)29 (0.21)12 (0.03)0 (0.00)
 Graduation after 10 years317 (0.32)182 (0.36)135 (0.29)44 (0.31)157 (0.37)0 (0.00)
 Graduation after 11–13 years b242 (0.25)131 (0.26)111 (0.24)63 (0.45)144 (0.34)2 (0.02)
 (Applied) university degree b305 (0.31)137 (0.27)168 (0.36)2 (0.01)111 (0.26)107 (0.98)
 Other b15 (0.02)9 (0.02)6 (0.01)3 (0.02)2 (0.00)0 (0.00)
Occupation
 Still in training b112 (0.11)69 (0.14)43 (0.09)58 (0.41)51 (0.12)0 (0.00)
 Employed416 (0.42)214 (0.42)202 (0.43)46 (0.33)300 (0.70)67 (0.61)
 Unemployed20 (0.02)7 (0.01)13 (0.03)18 (0.13)2 (0.00)0 (0.00)
 Civil servant33 (0.03)10 (0.02)23 (0.05)0 (0.00)10 (0.02)23 (0.21)
 In retirement296 (0.30)151 (0.30)145 (0.31)
    Housewife/househusband25 (0.03)24 (0.05)1 (0.00)9 (0.06)16 (0.04)0 (0.00)
 Self-employed66 (0.07)33 (0.06)33 (0.07)1 (0.01)45 (0.11)18 (0.17)
 Other b11 (0.01)3 (0.01)8 (0.02)6 (0.04)2 (0.00)1 (0.01)
Income
 <400 €20 (0.02)9 (0.02)11 (0.02)12 (0.09)2 (0.00)0 (0.00)
 401–800 €42 (0.04)30 (0.06)12 (0.03)27 (0.19)3 (0.01)0 (0.00)
 801–1600 € b192 (0.20)118 (0.23)74 (0.16)63 (0.45)41 (0.10)0 (0.00)
 1601–2400 € b218 (0.22)114 (0.22)104 (0.22)31 (0.22)108 (0.25)1 (0.01)
 2401–3200 € b206 (0.21)105 (0.21)101 (0.22)7 (0.05)137 (0.32)7 (0.06)
 3201–4000 € b137 (0.14)73 (0.14)64 (0.14)1 (0.01)67 (0.16)38 (0.35)
 >4000 €164 (0.17)62 (0.12)102 (0.22)0 (0.00)68 (0.16)63 (0.58)
Area of Residence
 Big city b418 (0.43)219 (0.43)199 (0.43)59 (0.42)176 (0.41)66 (0.61)
 Town or rural b561 (0.57)292 (0.57)269 (0.57)82 (0.58)250 (0.59)43 (0.39)
Note: w = women, m = men. Relative frequencies in brackets. a SES estimates are without retirees, n = 674. b See full items in Section 2.2. Answers combined as follows: highest educational degree: graduation after 11–13 years, 5, 6; (applied) university degree: 7, 8; other, 1, 2, 9; occupation: still in training, 1, 2, 3; other, 9, 11; income: according to amounts; area of residence: big city, 1, 2; town or rural, 3, 4, 5.
Table 3. PEB Intention Means and Standard Deviations.
Table 3. PEB Intention Means and Standard Deviations.
PEB IntentionsAllGenderSocioeconomic Status a
WomenMenLowMiddleHigh
Waste reduction5.08 (1.22)5.23 (1.15)4.92 (1.27)5.06 (1.29)5.01 (1.24)5.05 (1.15)
Regional products4.85 (1.27)5.01 (1.21)4.68 (1.30)4.80 (1.30)4.76 (1.23)4.93 (1.22)
Durability4.66 (1.32)4.63 (1.38)4.70 (1.25)4.53 (1.48)4.65 (1.25)4.76 (1.28)
Energy saving4.64 (1.48)4.81 (1.42)4.46 (1.52)4.72 (1.48)4.61 (1.42)4.57 (1.56)
No flying4.24 (1.85)4.39 (1.78)4.08 (1.92)4.57 (1.64)3.95 (1.88)3.61 (1.84)
Low laundry temp.4.16 (1.68)4.35 (1.65)3.95 (1.69)4.31 (1.56)4.27 (1.58)4.47 (1.57)
Low room temp.4.13 (1.65)4.11 (1.63)4.15 (1.68)4.11 (1.65)4.01 (1.61)4.43 (1.69)
Electricity4.10 (1.72)4.11 (1.71)4.08 (1.73)3.96 (1.67)4.15 (1.65)4.25 (1.74)
Cycling3.45 (1.88)3.63 (1.87)3.25 (1.87)3.75 (1.91)3.35 (1.81)3.43 (1.92)
Vegetarianism2.70 (1.77)3.02 (1.85)2.34 (1.60)3.06 (1.89)2.82 (1.76)2.72 (1.72)
Note: Temp. = temperature. N = 979. Standard deviations in brackets. a SES estimates are without retirees, n = 674.
Table 4. Significant Post-Hoc Test Comparisons of PEB Intentions.
Table 4. Significant Post-Hoc Test Comparisons of PEB Intentions.
IntentionF-Testpη2Group ComparisonsSig.
Waste reductionF(5, 192.48) = 2.21.05.016
Regional productsF(5, 191.85) = 4.23**.028w high SES> m low SES*
> m middle SES**
DurabilityF(5, 189.74) = 1.37.24.010
Energy savingF(5, 189.73) = 2.92*.022no significance
No flyingF(5, 193.06) = 4.69***.030w low SES> m middle SES*
> m high SES**
m low SES> m middle SES*
> m high SES**
Low laundry temp.F(5, 190.30) = 3.09*.023w middle SES> m middle SES*
Low room temp.F(5, 189.48) = 1.44.21.012
ElectricityF(5, 190.42) = 1.35.25.001
CyclingF(5, 188.76) = 2.18.06.016
VegetarianismF(5, 190.98) = 5.93***.042w low SES> m middle SES**
> m high SES**
w middle SES> m middle SES**
> m high SES**
Note: w = women, m = men, temp. = temperature, Sig. = significance level: * p < .05, ** p < .01, *** p < .01. Games–Howell post-hoc group comparisons were only calculated when ANOVAs were significant. Only significant group comparisons are shown.
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Share and Cite

MDPI and ACS Style

Schulz, P.; Nicolai, S.; Tomczyk, S.; Schmidt, S.; Franikowski, P.; Stoll-Kleemann, S. Gender and Socioeconomic Influences on Ten Pro-Environmental Behavior Intentions: A German Comparative Study. Sustainability 2024, 16, 2816. https://doi.org/10.3390/su16072816

AMA Style

Schulz P, Nicolai S, Tomczyk S, Schmidt S, Franikowski P, Stoll-Kleemann S. Gender and Socioeconomic Influences on Ten Pro-Environmental Behavior Intentions: A German Comparative Study. Sustainability. 2024; 16(7):2816. https://doi.org/10.3390/su16072816

Chicago/Turabian Style

Schulz, Paul, Susanne Nicolai, Samuel Tomczyk, Silke Schmidt, Philipp Franikowski, and Susanne Stoll-Kleemann. 2024. "Gender and Socioeconomic Influences on Ten Pro-Environmental Behavior Intentions: A German Comparative Study" Sustainability 16, no. 7: 2816. https://doi.org/10.3390/su16072816

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