Concerned about Climate Change and Ready to Take Action? An Analysis of the Pro-Climate Actions Individuals Are Motivated to Take to Lower Their Carbon Footprints

Abstract

Lifestyle changes are recognized as an important part of climate change mitigation. The influence of climate concern on taking individual actions for climate mitigation is well studied; however, the impact that climate concern has on consumption-based carbon footprints (CBCFs) is less studied. We aim to address this gap by examining the relationship of pro-climate actions, climate motivation, and CBCFs. We utilize data from a carbon footprint calculator with around 8000 responses from residents of the Nordic region. Respondents reported their personal consumption over the past year and answered questions about their participation in pro-climate actions and whether they were motivated by reducing their CBCF. We found that the high-impact actions of avoiding meat and flying had the most impact on CBCFs and had the highest correlation with climate motivation; however, the engagement levels were low. Conversely, the actions with the most participation had a lower impact on CBCFs and correlated less with climate motivation. Although respondents who reported a higher engagement with pro-climate actions and a higher climate motivation generally had lower CBCFs, their footprints were still not compatible with 1.5-degree limits. This study highlights the gap between climate motivation and the level of engagement in high-impact actions necessary for climate-sustainable lifestyles.

1. Introduction

Making changes to lifestyle and consumption patterns to reduce greenhouse gas (GHG) emissions plays a critical role in climate change mitigation [1,2,3]. Affluence has been strongly correlated with higher carbon footprints [4,5,6], and within high-income nations, the average carbon footprint has been estimated to be up to seven times the climate-sustainable limit [7]. Studies have estimated consumption-based carbon footprints in the Nordic countries to be within a range of 8.7–18 tCO₂e per capita [8,9,10]. To be in line with the 1.5 °C limits of the Paris Agreement, personal consumption-based carbon footprints would need to be limited to 2.5 tCO₂e by 2030 and 0.7 tCO₂e by 2050 [11]. Individuals residing in affluent European countries, like the Nordics, are more likely to have individual perceived climate responsibility [12], and high climate concern has been linked with lower consumption-based carbon footprints [13,14]. Affluence, however, has not been significantly associated with taking individual climate action [15].

There is great potential to reduce emissions through demand-side mitigation measures since household consumption is responsible for between 60–72% of global emissions [8,9]. The consumption changes that have the highest mitigation potential include making changes in the areas of transportation, diet, and housing [8,9,16], which includes actions like avoiding air travel, living car-free, and having a plant-based diet [17,18]. For example, studies of the potential of pro-climate actions to reduce CBCFs have found a range of reduction potential from actions including 0.2–1.5 tCO₂e for having a vegan or vegetarian diet [17,19,20,21,22], 0.1–1.5 tCO₂e for reducing air travel [17,19,20,22], 0.08–1.7 tCO₂e for reducing home energy [21,23], and 0.5–2.3 tCO₂e for living car-free [17,19,22]. The ranges of the reduction potentials of pro-climate actions can be significantly affected by factors such as geographical location, energy mixes, and the methodology used to estimate the reduction potential [17]. Studies have suggested that for carbon footprints to be at 1.5-degree-compatible levels, those in affluent countries will need to have a high adoption rate of multiple pro-climate actions [17,22].

To study the impact of different individual mitigation actions, consumption-based carbon footprints are a useful tool since they include the emissions embodied in trade, which can better capture the emissions associated with people’s lifestyles [24,25,26,27] and can help analyze how effective different mitigation interventions might be [11,21]. It is important to look at the whole footprint to see how lifestyle changes interact [28] since absolute reduction and sufficiency actions run the risk of rebounds [29], and there can be negative and positive spillover effects from taking different climate actions. [30]. Sociodemographic factors can have an impact on carbon footprint, and studies have found that affluence and having a higher income are strongly correlated with higher carbon footprints [5,6,31]. A larger household size has been associated with lower carbon footprints due to economies of scale [32,33]. Living in an urban area can lead to less emissions from energy use and transportation; however, studies have found that these emissions savings can be outweighed by the consumption patterns found in more urban areas [34,35]. Age, gender, and education levels have often been shown to have small and mixed effects on carbon footprint [31].

Individuals can be driven to take action to mitigate climate change through their consumption choices by their attitudes toward climate change [36,37,38]. However, individuals with a high climate concern do not always make choices that align with their pro-climate attitudes, which has been described as the attitude–behavior gap or value–action gap [39]. For example, air travel is a high-emission activity that many people are not willing to give up even though they have high levels of climate awareness [40,41]. Often, people are more willing to engage in low-impact pro-climate actions, which result in low emission reductions [41,42], even if they have a high level of environmental concern and the intent to act in an environmentally friendly way [43]. This is due to high-impact pro-climate actions being more difficult because of cost and other contextual and demographic variables [44]. Motivational factors are just part of understanding people’s behaviors in regard to taking climate action [45,46], since individuals’ actions and decisions are also influenced by economic, political, technological, and societal factors [47,48] and can be hindered by behavioral or technical lock-in [11]. Understanding the motivation behind making particular climate-friendly choices is key to seeing the potential of these actions and to understanding why some people are taking these actions and some are not [49].

Some researchers have considered both the attitudes and the impact of pro-climate actions by examining the relationship between climate concern or other pro-environmental attitudes and consumption-based carbon footprints. A survey of Swedish households found that income was the most important explanatory variable of GHG emissions followed by dwelling type and geographic distance to work and other services, and that social norms around GHG-intensive activities may have a larger impact on emissions than pro-environmental attitudes [50]. Another Swedish study found that that survey participants with strong pro-environmental norms who had engaged in four different low-carbon lifestyle options were associated with net GHG emission reductions between 0.5–1.5 tCO₂e/cap with little to no rebound effects [19]. A study of Danish individuals showed that those with high carbon footprints were less willing to engage with voluntary emission reduction measures even though they were concerned about the effects of climate change [51]. A study of the Nordic countries found that higher climate concern was associated with lower consumption-based carbon footprints [13]. In the Nordics, a higher perception of a climate-sustainable lifestyle was related to lower carbon footprints, except for those with the highest perceptions [52]. Another study in the Nordic countries found that climate concern and climate conscious behaviors were highest in urban areas where the everyday carbon footprint was found to be lower than less urbanized areas, but that the leisure travel footprints were found to be higher [53]. A study of Chinese households found that income was a major factor in GHG emissions, and the perceived seriousness of environmental problems affected the environmental behaviors of high-income households more than those of low-income households [54].

Although researchers have explored pro-climate attitudes and actions generally, this study will add to this body of literature by looking at the motivation to reduce one’s carbon footprint (climate motivation) regarding specific pro-climate actions to see the effect that this has on CBCFs. We also consider the engagement rates in this sample and the CBCFs as they relate to the 1.5-degree targets. The Nordic countries provide an interesting context for the study. They are highly affluent and have high CBCFs, and these are the countries where emissions need to be reduced the most. The Nordics are often seen as leaders on climate issues [55] and a high climate concern can be found there [12], yet CBCFs are still above the 1.5-degree limit. They are also welfare states, which provide economic security for their residents and have some of the lowest income inequality in the world [55], which provides the opportunity to identify 1.5-degree-compatible lifestyles with a high standard of wellbeing. We use unique data from a carbon footprint calculator survey conducted in the Nordic countries with ~8000 respondents covering households with ~17,000 members, and which includes questions on the level of engagement in various pro-climate actions and whether the choice to participate in these actions was motivated by climate concern. We then study the direct impact of engaging in the actions on the overall carbon footprint, taking into account the potential rebound effects. The research questions that we will address include the following:

• How is the level of engagement in pro-climate actions related to the total carbon footprint?
• How significant of a driver is an individual’s motivation to engage in a pro-climate action?
• How does the level of climate motivation affect the impact of engagement in the different pro-climate actions?
• Who are those who engage in pro-climate actions?

2. Materials and Methods

This section first describes the data used for this study and then the statistical methods utilized for the analysis.

2.1. Survey

The survey was conducted between the fall of 2021 and the spring of 2022 and received around 15,000 responses, of which ~8000 were full responses. The online survey was distributed through social media, and an online marketing service was used. Respondents also had the option to share their carbon footprint calculations online with others, which may have influenced others in their social networks to take the survey as well. Additional participants may have been recruited by news media, which reported on the survey. The only requirement to participate in the survey was to be a resident of one of the Nordic countries of Denmark, Finland, Iceland, Norway, or Sweden and to be an adult who takes part in household finances. The survey’s aim was to obtain many high-quality responses covering a wide variety of lifestyles in the Nordics, not to be a representative sample of the general population. The survey was designed to collect data on the participants’ consumption over the past year to calculate their consumption-based carbon footprint as well as to gather information on their level of climate concern, engagement in pro-climate actions, motivation to reduce their carbon footprint, self-reported wellbeing, and other sociodemographic variables. Duplicate responses to the survey were erased and the top and bottom 0.5% of carbon footprints were removed to account for the under- or over-reporting of consumption by respondents. The data set is publicly available on an open-source data repository [56]. All of the survey respondents consented to participating in the study.

Table 1. Demographic information of the survey respondents.

		Number	Percent
Country	Denmark	511	7%
	Finland	2074	28%
	Iceland	1543	21%
	Norway	1279	17%
	Sweden	1951	27%
Income	Low	2571	35%
	Medium	2308	31%
	High	2479	34%
Household type	Single	2087	29%
	Couple	3069	42%
	Single parent	987	13%
	Couple with child(ren)	1215	17%
Age	Youngest (15 to 30)	1068	15%
	Mid-young (31 to 45)	1942	26%
	Mid-old (46–60)	2277	31%
	Oldest (61–80)	2071	28%
Gender identity	Nonbinary or Other	141	2%
	Female	4500	61%
	Male	2717	37%
Degree of urbanization	Rural	1724	23%
	Semi-urban	2126	48%
	Urban	3508	23%
Education level	Basic and Secondary	1419	19%
	College and Vocational	3183	43%
	Graduate and Post	2756	38%

shows demographic information about the sample.

2.2. Consumption-Based Carbon Footprint Calculations

The consumption-based carbon footprints were calculated through a hybrid method, which included both physical quantity information and process emissions with an input–output analysis [57]. The footprints were calculated as personal consumption footprints where the emissions from consumption were allocated to the end user regardless of where the goods or services were produced [58]. Governmental spending and capital formation were left out of the calculation to focus on the impact of personal consumption. The footprints were divided into eight domains, which included diet, housing, vehicles, public transport, leisure travel, goods and services, pets, and second homes. Only the goods and services domain was estimated using an input–output approach derived from the Exiobase multiregional input–output model [59], and the rest of the domains used physical quantities and process emissions. The sections below provide the key information used to calculate the consumption-based carbon footprints for this study.

2.2.1. Diet

The survey asked respondents to report their diet type, and the options included vegan, vegetarian, pescatarian, or omnivore with four choices of different meat intake options ranging from 50 to 300 g per day. The GHG emissions from each diet type were taken from [60], with the vegan or vegetarian diet having the lowest emissions of 1132 KgCO₂e/year and the omnivore diet with the highest meat intake (300 g/day) having the highest emissions of 3213 KgCO₂e/year.

2.2.2. Housing

To determine the emissions from housing energy, respondents were asked about their type of housing (apartment, detached house, etc.), size, decade of construction (to determine the average energy efficiency), and the heating and electricity sources for the home. The emission factors used to estimate the GHG emissions were taken from the lifecycle GHG values from [61] and from each county’s official statistics for their energy and electricity mixes. Finally, the emissions from housing were divided by the number of people living in the household.

2.2.3. Vehicles

Survey participants were asked to share the type, fuel used, fuel efficiency, and annual kilometers traveled by each of the vehicles owned by their household. The lifecycle GHG emissions were derived from values from [61] and included petrol (3.003 kgCO₂e/L), diesel (3.189 kgCO₂e/L), natural gas (3.761 kgCO₂e/L), bioethanol (1.003 kgCO₂e/L), biodiesel (1.732 kgCO₂e/L), and biogas (1.382 kgCO₂e/L). The emissions from electric vehicles were estimated by using an assumed efficiency of 0.0125 MWh/100 km along with each country’s electricity mix, and the annual kilometers driven. The emissions from the production and maintenance of each vehicle were calculated using the average values from [62], which were divided by the assumed vehicle lifetime (184,000 km) [62] and then multiplied by the reported distance driven by each vehicle. The overall estimated vehicle emissions were divided by the number of people in the household.

2.2.4. Public Transport

The emissions from public transport use were estimated by using the average intensity of 0.12 kgCO₂eq per passenger kilometer traveled, which was derived from the average direct emissions from public transportation methods (bus, train, etc.) based on values from [63], and indirect values from vehicles, infrastructure, fuel production, and supply chain based on values from [64].

2.2.5. Leisure Travel

Participants were asked to report the number of short (<1000 km), medium (<3000), and long (>3000 km) leisure trips that they had taken over the past year by car, plane, train, bus, and ferry. The emissions factors were based on values from [63,65] for the direct emissions and from [64] for the indirect component.

2.2.6. Goods and Services

Respondents were asked to estimate their spending in several categories following the Classification of Individual Consumption According to Purpose (COICOP) [66]: alcohol and cigarettes, clothing and footwear, interior design and housekeeping, health, recreation sports and culture, restaurants, hotels, electronics, and other goods and services. The categories were used in conjunction with the Exiobase IO model [59] and followed the concordance matrix from [29].

2.2.7. Pets

Survey respondents were asked to share the number of pets in the household including dogs, cats, and other pets. Yearly emissions per dog were taken as 630 kgCO₂e based on [67] and 315 kgCO₂e per cat based on [68]. Other pets were not included. The total emissions calculated for pets were divided by the number of people in the household.

2.2.8. Second Home(s)

The yearly emissions attributed to owning a second home were assumed to be 884 KgCO₂e which was derived from [69] and divided by the number of people in the household.

2.3. Level of Engagement in Pro-Climate Actions and Climate Motivation

Participants were asked questions regarding their level of engagement with a selection of pro-climate actions followed by a question asking them whether they perform this action to reduce their carbon footprint. Both questions were asked on a scale of 1 to 5 (1—not at all, 2—very little, 3—somewhat, 4—to a great extent, 5—completely). The actions relate to the domains in the consumption-based carbon footprints of diet, housing, leisure travel, and goods and services. The actions cover individual actions that have been studied in the literature as having the potential to reduce emissions [17,21,70].

Table 2. Survey questions related to level of engagement with pro-climate actions and climate motivation.

Stated Pro-Climate Actions
1. I avoid meat products.
2. I avoid food waste.
3. I actively try to reduce my housing energy consumption in my everyday life.
4. I try not to fly.
5. I purchase services rather than products.
6. I buy second-hand products instead of new products.
7. I avoid buying things when possible.
8. I maximize the lifetime of the products I have.

lists the questions from the survey used in this analysis regarding the level of engagement with pro-climate actions and climate motivation.

2.4. Analysis

The statistical methods utilized in this study include Spearman’s rank correlation, bivariate, structural equation modeling, and regression analysis. Spearman’s rank correlation was used to examine the strength of the relationship between the level of engagement in each pro-climate action and the level of climate motivation to perform each action. Categorical regression was used to confirm this relationship while controlling for sociodemographic variables and to determine those who are likely to engage in pro-climate actions. Bivariate analysis, multivariate regression, and structural equation modeling (SEM) were utilized to examine the relationship between the level of engagement in pro-climate actions and carbon footprint and to study whether climate motivation to engage affects this relationship.

Because of the categorical nature of our data, categorical regression (CATREG) was used to examine the relationship of the engagement level in pro-climate actions and climate motivation while controlling for sociodemographic variables. CATREG can address the limitations of traditional regression techniques for data that are categorical. Traditional regression techniques frequently presuppose that variables adhere to rigid assumptions like linearity, homoscedasticity, or the normality of residuals. CATREG differs from traditional regression methods in that it makes no assumptions about the precise distribution of the variables and can yield more relevant findings when the relationships between variables are non-linear or more complex. As a result, nominal, ordinal, and continuous variables may all be analyzed in a single model. This method’s use of optimum scaling, which alters the variables to enhance the fit of the regression model, is a crucial component. As a result, the altered variables allow for linear regression analysis, which can better represent data connections than other techniques.

To study the impact of climate motivation on the level of engagement in pro-climate actions, regressions models were run for each of the pro-climate actions with the level of engagement as the dependent variable along with the independent variables of income, household size, degree of urbanization, age, gender, level of education, and country, since these have been found to have an impact on carbon footprint [31,33,34], and income especially has been found to have more of an impact than environmental attitudes [54,71]. The climate motivation variable was added to the second model (1a–8a). The description of the independent variables can be seen in Table 1.

Multivariate regression was conducted to simultaneously consider the impact of all pro-climate actions, climate motivations, and demographic variables on carbon footprint. Unlike the pairwise analyses, this approach allows for a more holistic understanding of the relative importance of different factors in explaining the variance in carbon footprint. It provides insights into which actions and factors have the strongest associations with carbon footprint when controlling for other variables.

SEM was chosen for its ability to model complex relationships among variables, including direct and indirect effects. This method allows for testing a theoretical model that links climate motivation, pro-climate actions, and carbon footprint, while simultaneously accounting for measurement errors and latent constructs identified in the factor analysis. SEM provides a comprehensive framework for understanding the interplay between motivation, actions, and carbon footprint.

3. Results

The main results showed that both a higher engagement with pro-climate actions and a higher climate motivation to engage in these actions were associated with lower carbon footprints for each of the eight pro-climate actions analyzed in this study. The highest engagement level was seen in pro-climate actions that had less of an impact on carbon footprint. Climate motivation and the level of engagement in pro-climate actions had the highest correlation coefficients for the actions of avoiding meat and trying not to fly, which were also associated with the lowest carbon footprints. Climate motivation was found to be a strong driver of the level of engagement in each of the pro-climate actions as compared to sociodemographic factors.

3.1. Engagement in Pro-Climate Actions and Climate Motivation

The pro-climate actions in which the respondents participated the most (completely or to a great extent) included maximizing the lifetime of goods (85%), avoiding food waste (78%), and avoiding buying (66%). Interestingly, only buying second-hand, buying services rather than goods, and avoiding meat had active participation rates below 50% (completely or to a great extent). The actions that the participants reported being most (completely or to a great extent) motivated to engage in to reduce their carbon footprint included avoiding food waste (52%), trying not to fly (50%), and maximizing the lifetime of goods (48%). The least (not at all or very little) amount of engagement in pro-climate actions included avoiding meat (42%), purchasing services rather than goods (35%), and buying second-hand (32%). The actions that respondents engaged with the least (not at all or very little) for the reason of reducing their carbon footprint included buying services rather than goods (49%), avoiding meat (45%), and buying second-hand (40%). Figure 1 shows the responses to the survey questions concerning pro-climate actions and climate motivation.

3.2. The Relationship between Pro-Climate Actions and Carbon Footprint

There was an overall trend of people who had a higher engagement with the pro-climate actions associated with lower total footprints. The lowest average footprints were associated with the groups who completely avoided meat (5.5 tCO₂e), completely tried not to fly (5.9 tCO₂e), completely bought second-hand (6.1 tCO₂e), and to a great extent avoided meat (6.1 tCO₂e). The highest average footprints were seen in the groups who did not engage at all with the pro-climate actions of avoiding food waste (10.2 tCO₂e), avoiding buying (9.7 tCO₂e), and maximizing the lifetime of goods (9.4 tCO₂e). The average footprint of the sample was 6.9 tCO₂e. Figure 2 shows the average carbon footprints for groups of respondents with each engagement level in each pro-climate action. The footprints in Figure 2 are split into the eight consumption domains that were calculated to find the total carbon footprint in this study. Each footprint domain generally decreased with more engagement in the actions except for public transport, which largely increased with engagement in the actions. Those with a high engagement in avoiding meat also had the lowest vehicle domain footprints. The lowest goods and services domain footprint could be seen in those with the highest engagement in avoiding buying and trying not to fly. Respondents who reported not engaging at all with avoiding food waste had the highest footprints in the domains of diet, vehicles, public transport, leisure travel, and goods and services. A high engagement in avoiding meat, trying not to fly, and avoiding buying led to the lowest corresponding domain footprints (diet, leisure travel, and goods and services).

To examine the collective impact factors on carbon footprint, a multivariate regression analysis was conducted. This approach allows for the simultaneous consideration of multiple predictors, providing a more comprehensive understanding of their relative importance (

Table 3. Multivariate regression analysis results for carbon footprint predictors.

Variable	Coefficient
const	11,295.14
Avoid meat	−442.53 ***
Avoid food waste	−49.40
Reduce home energy	−87.88
Try not to fly	−369.35 ***
Buy services rather than goods	84.87
Buy 2nd hand	73.10
Avoid buying	−127.21 *
Maximize the lifetime of goods	−25.72
Income	193.54 ***
Household type	−413.28 ***
Degree of urbanization	−499.02 ***
Age	136.73 **
Gender identity	−282.55 **
Education	−19.47
Country	−623.98 ***
Climate motivation	−5.57

* p < 0.05, ** p < 0.01, *** p < 0.001.

). The R-squared value (0.200) indicates that approximately 20% of the variance in carbon footprint is explained by the model. Of the pro-climate actions, avoiding meat (−442.53, p < 0.001) and trying not to fly (−369.35, p < 0.001) had the greatest impact on carbon footprint. The negative coefficient indicates that these actions were associated with lower carbon footprints. The negative coefficient for avoiding buying also supports the idea that reduced consumption can lead to a smaller carbon footprint. Avoiding food waste, reducing home energy, and maximizing the lifetime of goods had negative coefficients, though they were not significant.

Income and age both showed positive associations with carbon footprint. Interestingly, several factors one might expect to influence carbon footprint, such as education level and various eco-friendly behaviors like reducing home energy use or avoiding food waste, did not show significant relationships in this model. This could suggest that these factors have a more complex relationship with carbon footprint than anticipated, or that their effects might be captured by other variables in the model. A correlation heatmap can be seen in Appendix C in Figure A1. The significant differences based on country, gender identity, and household type highlight the importance of considering demographic and cultural factors when studying environmental impact. These findings could have implications for tailoring environmental policies and education efforts to different groups.

3.3. Climate Motivation as a Driver of Engagement

Table 4. Spearman’s rank correlations of level of engagement in pro-climate actions (PCA) and climate motivation (CM). Higher correlation coefficients highlighted in green, middle in yellow, and lower in red.

	Avoid Meat (PCA)	Avoid Meat (CM)	Avoid Food Waste (PCA)	Avoid Food Waste (CM)	Reduce Home Energy (PCA)	Reduce Home Energy (CM)	Try Not to Fly (PCA)	Try Not to Fly (CM)	Buy Services rather than Goods (PCA)	Buy Services rather than Goods (CM)	Buy 2nd Hand (PCA)	Buy 2nd Hand (CM)	Try Not to Buy (PCA)	Try Not to Buy (CM)	Maximize the Lifetime of Goods (PCA)
Maximize the lifetime of goods (CM)	0.49	0.63	0.24	0.72	0.34	0.74	0.41	0.7	0.39	0.66	0.38	0.77	0.39	0.89	0.32
Maximize the lifetime of goods (PCA)	0.19	0.19	0.39	0.24	0.35	0.26	0.3	0.26	0.17	0.18	0.29	0.25	0.49	0.29	1
Try not to buy (CM)	0.5	0.64	0.25	0.71	0.35	0.72	0.43	0.72	0.4	0.69	0.41	0.79	0.47	1
Try not to buy (PCA)	0.28	0.29	0.37	0.33	0.37	0.33	0.38	0.37	0.29	0.32	0.36	0.37	1
Buy 2nd Hand (CM)	0.48	0.6	0.21	0.65	0.31	0.66	0.39	0.65	0.41	0.65	0.62	1
Buy 2nd Hand (PCA)	0.33	0.33	0.25	0.33	0.27	0.34	0.33	0.38	0.3	0.35	1
Buy services rather than goods (CM)	0.42	0.54	0.19	0.57	0.29	0.6	0.33	0.58	0.66	1
Buy services rather than goods (PCA)	0.33	0.36	0.17	0.36	0.19	0.37	0.21	0.35	1
Try not to fly (CM)	0.51	0.63	0.23	0.62	0.32	0.65	0.63	1
Try not to fly (PCA)	0.34	0.37	0.27	0.35	0.3	0.38	1
Reduce home energy (CM)	0.48	0.63	0.28	0.75	0.48	1
Reduce home energy (PCA)	0.23	0.26	0.38	0.33	1
Avoid food waste (CM)	0.47	0.67	0.3	1
Avoid food waste (PCA)	0.2	0.2	1
Avoid meat (CM)	0.72	1
Avoid meat (PCA)	1

shows the Spearman’s rank correlations of pro-climate actions and climate motivation. The pro-climate actions that had the highest correlation coefficient with their corresponding climate motivation variable were avoiding meat (0.72), buying services rather than goods (0.66), trying not to fly (0.63), and buying second-hand (0.62). The rest of the pro-climate actions had correlation coefficients that were less than 0.48 with respect to their associated climate motivation variables. The correlation coefficients for the engagement levels in the pro-climate actions with other actions were all less than 0.49; however, the correlation coefficients were all above 0.58 for the motivation to engage in each of the pro-climate actions with respect to each other. A high engagement in avoiding meat had correlation coefficients that were between 0.42 and 0.51 with respect to the motivation to perform all the other actions. A high climate motivation for avoiding buying had correlation coefficients ranging between 0.40 and 0.50 with respect to the engagement in the actions of avoiding meat, trying not to fly, buying services rather than goods, and buying second-hand.

Table 5. Categorical regression model with level of engagement in each pro-climate action as the dependent variable, and climate motivations of the respective pro-climate action as the independent variable, along with the independent control variables of income, household type, urbanization level, age, gender, education level, and country.

	Avoid Meat		Avoid Food Waste		Reduce Home Energy		Try Not to Fly		Buy Services Rather than Goods		Buy Second-Hand		Avoid Buying		Maximize the Lifetime of Goods
model	1	1a	2	2a	3	3a	4	4a	5	5a	6	6a	7	7a	8	8a
	B	B	B	B	B	B	B	B	B	B	B	B	B	B	B	B
income	−0.03 *	−0.02 ***	−0.05 ***	−0.05 ***	−0.04 ***	−0.03 ***	−0.10 ***	−0.05 ***	0.09 ***	0.07 ***	−0.16 ***	−0.09 ***	−0.08 ***	−0.06 ***	−0.06 ***	−0.06 ***
household type	0.07 ***	0.04 ***	0.08 ***	0.07 ***	0.02 **	0.02 **	0.03 ***	0.03 ***	0.05 ***	0.03 ***	0.02 *	0.03 ***	0.04 ***	0.04 ***	0.07 ***	0.07 ***
urbanization level	0.18 ***	0.03 ***	−0.06 ***	−0.08 ***	−0.06 ***	−0.09 ***	−0.05 ***	−0.05 ***	0.09 ***	0.03 ***	−0.03 *	−0.07 ***	0.02	−0.03 ***	−0.03 *	−0.05 ***
age	−0.14 ***	0.00	0.10 ***	0.10 ***	0.11 ***	0.10 ***	0.01	0.01 *	−0.09 ***	−0.08 ***	−0.16 ***	−0.08 ***	0.02	0.03 **	0.01	0.02
gender	0.24 ***	0.08 ***	0.05 ***	0.01	0.06 ***	0.03 **	0.08 ***	0.00	0.17 ***	0.02 ***	0.21 ***	0.03 ***	0.13 ***	0.01	0.06 ***	0.02 *
education level	0.15 ***	0.03 ***	0.07 ***	0.04 ***	0.07 ***	0.03 *	0.01	−0.03 ***	0.14 ***	0.06 ***	0.07 ***	0.01	0.09 ***	0.02 *	0.07 ***	0.04 **
country	0.21 ***	0.04 ***	0.18 ***	0.14 ***	0.16 ***	0.10 ***	0.35 ***	0.09 ***	0.21 ***	0.13 ***	0.15 ***	0.07 ***	0.14 ***	0.08 ***	0.15 ***	0.12 ***
climate motivation		0.80 ***		0.34 ***		0.56 ***		0.74 ***		0.65 ***		0.65 ***		0.50 ***		0.35 ***
R2	0.19	0.71	0.08	0.17	0.06	0.33	0.16	0.59	0.13	0.49	0.13	0.47	0.05	0.27	0.04	0.15
F	103.7	903.5	34.3	68.0	27.5	175.2	82.2	492.6	59.5	326.7	65.3	329.6	28.1	124.5	21.4	68.6

* p < 0.05, ** p < 0.01, *** p < 0.001. Nominal variables: household type, gender, and country. Ordinal variables: income, urbanization level, age, education level, level of engagement, and climate motivation.

shows the regression analysis of the relationship between the engagement level in each of the pro-climate actions and their corresponding climate motivation along with the control variables. When controlling for sociodemographic variables, the categorical regression confirmed that the level of engagement in avoiding meat (B = 0.80, p < 0.001), trying not to fly (B = 0.74 p < 0.001), buying services rather than products (B = 0.65 p < 0.001), and buying second-hand (B = 0.65 p < 0.001) were more highly correlated with the climate motivation to participate in these actions than the other pro-climate actions were correlated with their corresponding motivations, and the R² values for these models explained 47–71% of the variance in engagement levels. The climate motivation levels behind the pro-climate actions of avoiding food waste (B = 0.34 p < 0.001) and maximizing the lifetime of goods (B = 0.35 p < 0.001) were the weakest predictors of engagement in the eight pro-climate actions assessed, and the R² values showed that the model only explained 34–35% of the variance in the engagement levels in these two actions.

The first models (1–8) in Table 5 for each of the pro-climate actions show the impact of the demographic variables on the level of engagement in each of the pro-climate actions. The explanatory power of the model was low (R-squared = 0.04 to 0.19); however, the impact of the variables was significant in most cases. A lower income (with the exception of buying services rather than goods) and a higher education level were mostly associated with a higher engagement in the pro-climate actions. Household type was largely not a strong predictor of engagement. The degree of urbanization was generally not a strong predictor of engagement, and it had its strongest impact on avoiding meat (0.18 p > 0.001). Those living in more urban areas were more likely to participate in the actions of avoiding meat, buying services rather than goods, and avoiding buying (not significant), and the rest of the participation increased with a decreasing level of urbanization. A younger age was associated with more engagement in avoiding meat, buying services rather than goods, and buying second-hand, whereas an older age was associated with more engagement in avoiding food waste and reducing home energy. Gender was the most important predictor of engagement in avoiding meat (B = 0.24 p < 0.001) and buying second-hand (B = 0.21 p < 0.001). Looking at the crosstabs in Appendix A in

Table A1. Crosstabulation of the pro-climate action of avoid meat with the nominal control variables of household type, gender identity, and country.

Avoid Meat		Household Type					Gender Identity				Country
		Single	Couple	Single Parent	Couple w/Children	Total	Other	Female	Male	Total	Denmark	Finland	Iceland	Norway	Sweden	Total
1	Count	499	836	286	363	1984	33	797	1154	1984	65	482	602	406	429	1984
	% within PCA	25.2%	42.1%	14.4%	18.3%	100.0%	1.7%	40.2%	58.2%	100.0%	3.3%	24.3%	30.3%	20.5%	21.6%	100.0%
	% within control variable	23.9%	27.2%	29.0%	29.9%	27.0%	23.4%	17.7%	42.5%	27.0%	12.7%	23.2%	39.0%	31.7%	22.0%	27.0%
2	Count	297	433	159	189	1078	10	634	434	1078	65	338	229	200	246	1078
	% within PCA	27.6%	40.2%	14.7%	17.5%	100.0%	0.9%	58.8%	40.3%	100.0%	6.0%	31.4%	21.2%	18.6%	22.8%	100.0%
	% within control variable	14.2%	14.1%	16.1%	15.6%	14.7%	7.1%	14.1%	16.0%	14.7%	12.7%	16.3%	14.8%	15.6%	12.6%	14.7%
3	Count	509	810	226	326	1871	28	1263	580	1871	154	480	392	373	472	1871
	% within PCA	27.2%	43.3%	12.1%	17.4%	100.0%	1.5%	67.5%	31.0%	100.0%	8.2%	25.7%	21.0%	19.9%	25.2%	100.0%
	% within control variable	24.4%	26.4%	22.9%	26.8%	25.4%	19.9%	28.1%	21.3%	25.4%	30.1%	23.1%	25.4%	29.2%	24.2%	25.4%
4	Count	418	545	164	175	1302	24	956	322	1302	138	377	153	214	420	1302
	% within PCA	32.1%	41.9%	12.6%	13.4%	100.0%	1.8%	73.4%	24.7%	100.0%	10.6%	29.0%	11.8%	16.4%	32.3%	100.0%
	% within control variable	20.0%	17.8%	16.6%	14.4%	17.7%	17.0%	21.2%	11.9%	17.7%	27.0%	18.2%	9.9%	16.7%	21.5%	17.7%
5	Count	364	445	152	162	1123	46	850	227	1123	89	397	167	86	384	1123
	% within PCA	32.4%	39.6%	13.5%	14.4%	100.0%	4.1%	75.7%	20.2%	100.0%	7.9%	35.4%	14.9%	7.7%	34.2%	100.0%
	% within control variable	17.4%	14.5%	15.4%	13.3%	15.3%	32.6%	18.9%	8.4%	15.3%	17.4%	19.1%	10.8%	6.7%	19.7%	15.3%
Total	Count	2087	3069	987	1215	7358	141	4500	2717	7358	511	2074	1543	1279	1951	7358
	% within PCA	28.4%	41.7%	13.4%	16.5%	100.0%	1.9%	61.2%	36.9%	100.0%	6.9%	28.2%	21.0%	17.4%	26.5%	100.0%
	% within control variable	100.0%	100.0%	100.0%	100.0%	100.0%	100.0%	100.0%	100.0%	100.0%	100.0%	100.0%	100.0%	100.0%	100.0%	100.0%

and

Table A6. Crosstabulation of the pro-climate action of buy second-hand with the nominal control variables of household type, gender identity, and country.

Buy Second-Hand		Household Type					Gender Identity				Country
		Single	Couple	Single Parent	Couple w/Children	Total	Other	Female	Male	Total	Denmark	Finland	Iceland	Norway	Sweden	Total
1	Count	282	450	102	121	955	17	382	556	955	25	210	259	148	313	955
	% within PCA	29.5%	47.1%	10.7%	12.7%	100.0%	1.8%	40.0%	58.2%	100.0%	2.6%	22.0%	27.1%	15.5%	32.8%	100.0%
	% within control variable	13.5%	14.7%	10.3%	10.0%	13.0%	12.1%	8.5%	20.5%	13.0%	4.9%	10.1%	16.8%	11.6%	16.0%	13.0%
2	Count	392	639	167	200	1398	21	712	665	1398	83	375	370	238	332	1398
	% within PCA	28.0%	45.7%	11.9%	14.3%	100.0%	1.5%	50.9%	47.6%	100.0%	5.9%	26.8%	26.5%	17.0%	23.7%	100.0%
	% within control variable	18.8%	20.8%	16.9%	16.5%	19.0%	14.9%	15.8%	24.5%	19.0%	16.2%	18.1%	24.0%	18.6%	17.0%	19.0%
3	Count	582	836	297	360	2075	36	1264	775	2075	155	520	525	421	454	2075
	% within PCA	28.0%	40.3%	14.3%	17.3%	100.0%	1.7%	60.9%	37.3%	100.0%	7.5%	25.1%	25.3%	20.3%	21.9%	100.0%
	% within control variable	27.9%	27.2%	30.1%	29.6%	28.2%	25.5%	28.1%	28.5%	28.2%	30.3%	25.1%	34.0%	32.9%	23.3%	28.2%
4	Count	577	824	284	363	2048	38	1457	553	2048	183	604	294	359	608	2048
	% within PCA	28.2%	40.2%	13.9%	17.7%	100.0%	1.9%	71.1%	27.0%	100.0%	8.9%	29.5%	14.4%	17.5%	29.7%	100.0%
	% within control variable	27.6%	26.8%	28.8%	29.9%	27.8%	27.0%	32.4%	20.4%	27.8%	35.8%	29.1%	19.1%	28.1%	31.2%	27.8%
5	Count	254	320	137	171	882	29	685	168	882	65	365	95	113	244	882
	% within PCA	28.8%	36.3%	15.5%	19.4%	100.0%	3.3%	77.7%	19.0%	100.0%	7.4%	41.4%	10.8%	12.8%	27.7%	100.0%
	% within control variable	12.2%	10.4%	13.9%	14.1%	12.0%	20.6%	15.2%	6.2%	12.0%	12.7%	17.6%	6.2%	8.8%	12.5%	12.0%
Total	Count	2087	3069	987	1215	7358	141	4500	2717	7358	511	2074	1543	1279	1951	7358
	% within PCA	28.4%	41.7%	13.4%	16.5%	100.0%	1.9%	61.2%	36.9%	100.0%	6.9%	28.2%	21.0%	17.4%	26.5%	100.0%
	% within control variable	100.0%	100.0%	100.0%	100.0%	100.0%	100.0%	100.0%	100.0%	100.0%	100.0%	100.0%	100.0%	100.0%	100.0%	100.0%

, females participated more in these two pro-climate actions than males. Country was often the strongest predictor of the engagement in pro-climate actions, and it was the strongest predictor of trying not to fly (B = 0.35 p < 0.001) (model 4). However, it lost much of its predictive power when climate motivation was added (models 1a–8a). Looking at the crosstabs (Appendix A Table A1,

Table A2. Crosstabulation of the pro-climate action of avoid food waste with the nominal control variables of household type, gender identity, and country.

Avoid Food Waste		Household Type					Gender Identity				Country
		Single	Couple	Single Parent	Couple w/Children	Total	Other	Female	Male	Total	Denmark	Finland	Iceland	Norway	Sweden	Total
1	Count	48	60	16	20	144	6	40	98	144	3	33	30	51	27	144
	% within PCA	33.3%	41.7%	11.1%	13.9%	100.0%	4.2%	27.8%	68.1%	100.0%	2.1%	22.9%	20.8%	35.4%	18.8%	100.0%
	% within control variable	2.3%	2.0%	1.6%	1.6%	2.0%	4.3%	0.9%	3.6%	2.0%	0.6%	1.6%	1.9%	4.0%	1.4%	2.0%
2	Count	84	112	44	54	294	4	151	139	294	10	85	61	71	67	294
	% within PCA	28.6%	38.1%	15.0%	18.4%	100.0%	1.4%	51.4%	47.3%	100.0%	3.4%	28.9%	20.7%	24.1%	22.8%	100.0%
	% within control variable	4.0%	3.6%	4.5%	4.4%	4.0%	2.8%	3.4%	5.1%	4.0%	2.0%	4.1%	4.0%	5.6%	3.4%	4.0%
3	Count	280	440	231	261	1212	25	679	508	1212	94	253	411	254	200	1212
	% within PCA	23.1%	36.3%	19.1%	21.5%	100.0%	2.1%	56.0%	41.9%	100.0%	7.8%	20.9%	33.9%	21.0%	16.5%	100.0%
	% within control variable	13.4%	14.3%	23.4%	21.5%	16.5%	17.7%	15.1%	18.7%	16.5%	18.4%	12.2%	26.6%	19.9%	10.3%	16.5%
4	Count	945	1385	474	565	3369	67	2122	1180	3369	273	847	703	641	905	3369
	% within PCA	28.0%	41.1%	14.1%	16.8%	100.0%	2.0%	63.0%	35.0%	100.0%	8.1%	25.1%	20.9%	19.0%	26.9%	100.0%
	% within control variable	45.3%	45.1%	48.0%	46.5%	45.8%	47.5%	47.2%	43.4%	45.8%	53.4%	40.8%	45.6%	50.1%	46.4%	45.8%
5	Count	730	1072	222	315	2339	39	1508	792	2339	131	856	338	262	752	2339
	% within PCA	31.2%	45.8%	9.5%	13.5%	100.0%	1.7%	64.5%	33.9%	100.0%	5.6%	36.6%	14.5%	11.2%	32.2%	100.0%
	% within control variable	35.0%	34.9%	22.5%	25.9%	31.8%	27.7%	33.5%	29.1%	31.8%	25.6%	41.3%	21.9%	20.5%	38.5%	31.8%
Total	Count	2087	3069	987	1215	7358	141	4500	2717	7358	511	2074	1543	1279	1951	7358
	% within PCA	28.4%	41.7%	13.4%	16.5%	100.0%	1.9%	61.2%	36.9%	100.0%	6.9%	28.2%	21.0%	17.4%	26.5%	100.0%
	% within control variable	100.0%	100.0%	100.0%	100.0%	100.0%	100.0%	100.0%	100.0%	100.0%	100.0%	100.0%	100.0%	100.0%	100.0%	100.0%

,

Table A3. Crosstabulation of the pro-climate action of reduce home energy with the nominal control variables of household type, gender identity, and country.

Reduce Home Energy		Household Type					Gender Identity				Country
		Single	Couple	Single Parent	Couple w/Children	Total	Other	Female	Male	Total	Denmark	Finland	Iceland	Norway	Sweden	Total
1	Count	146	146	46	53	391	12	166	213	391	12	88	120	48	123	391
	% within PCA	37.3%	37.3%	11.8%	13.6%	100.0%	3.1%	42.5%	54.5%	100.0%	3.1%	22.5%	30.7%	12.3%	31.5%	100.0%
	% within control variable	7.0%	4.8%	4.7%	4.4%	5.3%	8.5%	3.7%	7.8%	5.3%	2.3%	4.2%	7.8%	3.8%	6.3%	5.3%
2	Count	235	327	111	150	823	11	470	342	823	22	261	248	86	206	823
	% within PCA	28.6%	39.7%	13.5%	18.2%	100.0%	1.3%	57.1%	41.6%	100.0%	2.7%	31.7%	30.1%	10.4%	25.0%	100.0%
	% within control variable	11.3%	10.7%	11.2%	12.3%	11.2%	7.8%	10.4%	12.6%	11.2%	4.3%	12.6%	16.1%	6.7%	10.6%	11.2%
3	Count	596	925	342	419	2282	48	1392	842	2282	140	624	625	385	508	2282
	% within PCA	26.1%	40.5%	15.0%	18.4%	100.0%	2.1%	61.0%	36.9%	100.0%	6.1%	27.3%	27.4%	16.9%	22.3%	100.0%
	% within control variable	28.6%	30.1%	34.7%	34.5%	31.0%	34.0%	30.9%	31.0%	31.0%	27.4%	30.1%	40.5%	30.1%	26.0%	31.0%
4	Count	728	1123	341	406	2598	50	1663	885	2598	214	710	426	527	721	2598
	% within PCA	28.0%	43.2%	13.1%	15.6%	100.0%	1.9%	64.0%	34.1%	100.0%	8.2%	27.3%	16.4%	20.3%	27.8%	100.0%
	% within control variable	34.9%	36.6%	34.5%	33.4%	35.3%	35.5%	37.0%	32.6%	35.3%	41.9%	34.2%	27.6%	41.2%	37.0%	35.3%
5	Count	382	548	147	187	1264	20	809	435	1264	123	391	124	233	393	1264
	% within PCA	30.2%	43.4%	11.6%	14.8%	100.0%	1.6%	64.0%	34.4%	100.0%	9.7%	30.9%	9.8%	18.4%	31.1%	100.0%
	% within control variable	18.3%	17.9%	14.9%	15.4%	17.2%	14.2%	18.0%	16.0%	17.2%	24.1%	18.9%	8.0%	18.2%	20.1%	17.2%
Total	Count	2087	3069	987	1215	7358	141	4500	2717	7358	511	2074	1543	1279	1951	7358
	% within PCA	28.4%	41.7%	13.4%	16.5%	100.0%	1.9%	61.2%	36.9%	100.0%	6.9%	28.2%	21.0%	17.4%	26.5%	100.0%
	% within control variable	100.0%	100.0%	100.0%	100.0%	100.0%	100.0%	100.0%	100.0%	100.0%	100.0%	100.0%	100.0%	100.0%	100.0%	100.0%

,

Table A4. Crosstabulation of the pro-climate action of try not to fly with the nominal control variables of household type, gender identity, and country.

Try Not to Fly		Household Type					Gender Identity				Country
		Single	Couple	Single Parent	Couple w/Children	Total	Other	Female	Male	Total	Denmark	Finland	Iceland	Norway	Sweden	Total
1	Count	317	526	154	244	1241	27	550	664	1241	49	295	471	214	212	1241
	% within PCA	25.5%	42.4%	12.4%	19.7%	100.0%	2.2%	44.3%	53.5%	100.0%	3.9%	23.8%	38.0%	17.2%	17.1%	100.0%
	% within control variable	15.2%	17.1%	15.6%	20.1%	16.9%	19.1%	12.2%	24.4%	16.9%	9.6%	14.2%	30.5%	16.7%	10.9%	16.9%
2	Count	247	342	137	144	870	9	483	378	870	43	234	330	144	119	870
	% within PCA	28.4%	39.3%	15.7%	16.6%	100.0%	1.0%	55.5%	43.4%	100.0%	4.9%	26.9%	37.9%	16.6%	13.7%	100.0%
	% within control variable	11.8%	11.1%	13.9%	11.9%	11.8%	6.4%	10.7%	13.9%	11.8%	8.4%	11.3%	21.4%	11.3%	6.1%	11.8%
3	Count	344	611	199	252	1406	28	915	463	1406	120	335	421	299	231	1406
	% within PCA	24.5%	43.5%	14.2%	17.9%	100.0%	2.0%	65.1%	32.9%	100.0%	8.5%	23.8%	29.9%	21.3%	16.4%	100.0%
	% within control variable	16.5%	19.9%	20.2%	20.7%	19.1%	19.9%	20.3%	17.0%	19.1%	23.5%	16.2%	27.3%	23.4%	11.8%	19.1%
4	Count	493	692	245	276	1706	33	1120	553	1706	148	475	209	357	517	1706
	% within PCA	28.9%	40.6%	14.4%	16.2%	100.0%	1.9%	65.7%	32.4%	100.0%	8.7%	27.8%	12.3%	20.9%	30.3%	100.0%
	% within control variable	23.6%	22.5%	24.8%	22.7%	23.2%	23.4%	24.9%	20.4%	23.2%	29.0%	22.9%	13.5%	27.9%	26.5%	23.2%
5	Count	686	898	252	299	2135	44	1432	659	2135	151	735	112	265	872	2135
	% within PCA	32.1%	42.1%	11.8%	14.0%	100.0%	2.1%	67.1%	30.9%	100.0%	7.1%	34.4%	5.2%	12.4%	40.8%	100.0%
	% within control variable	32.9%	29.3%	25.5%	24.6%	29.0%	31.2%	31.8%	24.3%	29.0%	29.5%	35.4%	7.3%	20.7%	44.7%	29.0%
Total	Count	2087	3069	987	1215	7358	141	4500	2717	7358	511	2074	1543	1279	1951	7358
	% within PCA	28.4%	41.7%	13.4%	16.5%	100.0%	1.9%	61.2%	36.9%	100.0%	6.9%	28.2%	21.0%	17.4%	26.5%	100.0%
	% within control variable	100.0%	100.0%	100.0%	100.0%	100.0%	100.0%	100.0%	100.0%	100.0%	100.0%	100.0%	100.0%	100.0%	100.0%	100.0%

,

Table A5. Crosstabulation of the pro-climate action of buy services rather than goods with the nominal control variables of household type, gender identity, and country.

Buy Service Rather than Goods		Household Type					Gender Identity				Country
		Single	Couple	Single Parent	Couple w/Children	Total	Other	Female	Male	Total	Denmark	Finland	Iceland	Norway	Sweden	Total
1	Count	395	527	146	167	1235	25	605	605	1235	77	219	271	208	460	1235
	% within PCA	32.0%	42.7%	11.8%	13.5%	100.0%	2.0%	49.0%	49.0%	100.0%	6.2%	17.7%	21.9%	16.8%	37.2%	100.0%
	% within control variable	18.9%	17.2%	14.8%	13.7%	16.8%	17.7%	13.4%	22.3%	16.8%	15.1%	10.6%	17.6%	16.3%	23.6%	16.8%
2	Count	390	520	182	216	1308	22	699	587	1308	94	343	277	256	338	1308
	% within PCA	29.8%	39.8%	13.9%	16.5%	100.0%	1.7%	53.4%	44.9%	100.0%	7.2%	26.2%	21.2%	19.6%	25.8%	100.0%
	% within control variable	18.7%	16.9%	18.4%	17.8%	17.8%	15.6%	15.5%	21.6%	17.8%	18.4%	16.5%	18.0%	20.0%	17.3%	17.8%
3	Count	687	1071	371	462	2591	46	1558	987	2591	218	611	659	500	603	2591
	% within PCA	26.5%	41.3%	14.3%	17.8%	100.0%	1.8%	60.1%	38.1%	100.0%	8.4%	23.6%	25.4%	19.3%	23.3%	100.0%
	% within control variable	32.9%	34.9%	37.6%	38.0%	35.2%	32.6%	34.6%	36.3%	35.2%	42.7%	29.5%	42.7%	39.1%	30.9%	35.2%
4	Count	471	722	230	275	1698	33	1226	439	1698	103	588	270	277	460	1698
	% within PCA	27.7%	42.5%	13.5%	16.2%	100.0%	1.9%	72.2%	25.9%	100.0%	6.1%	34.6%	15.9%	16.3%	27.1%	100.0%
	% within control variable	22.6%	23.5%	23.3%	22.6%	23.1%	23.4%	27.2%	16.2%	23.1%	20.2%	28.4%	17.5%	21.7%	23.6%	23.1%
5	Count	144	229	58	95	526	15	412	99	526	19	313	66	38	90	526
	% within PCA	27.4%	43.5%	11.0%	18.1%	100.0%	2.9%	78.3%	18.8%	100.0%	3.6%	59.5%	12.5%	7.2%	17.1%	100.0%
	% within control variable	6.9%	7.5%	5.9%	7.8%	7.1%	10.6%	9.2%	3.6%	7.1%	3.7%	15.1%	4.3%	3.0%	4.6%	7.1%
Total	Count	2087	3069	987	1215	7358	141	4500	2717	7358	511	2074	1543	1279	1951	7358
	% within PCA	28.4%	41.7%	13.4%	16.5%	100.0%	1.9%	61.2%	36.9%	100.0%	6.9%	28.2%	21.0%	17.4%	26.5%	100.0%
	% within control variable	100.0%	100.0%	100.0%	100.0%	100.0%	100.0%	100.0%	100.0%	100.0%	100.0%	100.0%	100.0%	100.0%	100.0%	100.0%

, Table A6,

Table A7. Crosstabulation of the pro-climate action of avoid buying with the nominal control variables of household type, gender identity, and country.

Avoid Buying		Household Type					Gender Identity				Country
		Single	Couple	Single Parent	Couple w/Children	Total	Other	Female	Male	Total	Denmark	Finland	Iceland	Norway	Sweden	Total
1	Count	96	140	37	59	332	9	114	209	332	10	93	103	50	76	332
	% within PCA	28.9%	42.2%	11.1%	17.8%	100.0%	2.7%	34.3%	63.0%	100.0%	3.0%	28.0%	31.0%	15.1%	22.9%	100.0%
	% within control variable	4.6%	4.6%	3.7%	4.9%	4.5%	6.4%	2.5%	7.7%	4.5%	2.0%	4.5%	6.7%	3.9%	3.9%	4.5%
2	Count	167	235	86	94	582	11	290	281	582	29	249	128	77	99	582
	% within PCA	28.7%	40.4%	14.8%	16.2%	100.0%	1.9%	49.8%	48.3%	100.0%	5.0%	42.8%	22.0%	13.2%	17.0%	100.0%
	% within control variable	8.0%	7.7%	8.7%	7.7%	7.9%	7.8%	6.4%	10.3%	7.9%	5.7%	12.0%	8.3%	6.0%	5.1%	7.9%
3	Count	431	642	243	295	1611	37	950	624	1611	127	477	442	260	305	1611
	% within PCA	26.8%	39.9%	15.1%	18.3%	100.0%	2.3%	59.0%	38.7%	100.0%	7.9%	29.6%	27.4%	16.1%	18.9%	100.0%
	% within control variable	20.7%	20.9%	24.6%	24.3%	21.9%	26.2%	21.1%	23.0%	21.9%	24.9%	23.0%	28.6%	20.3%	15.6%	21.9%
4	Count	754	1136	376	451	2717	49	1692	976	2717	184	668	529	543	793	2717
	% within PCA	27.8%	41.8%	13.8%	16.6%	100.0%	1.8%	62.3%	35.9%	100.0%	6.8%	24.6%	19.5%	20.0%	29.2%	100.0%
	% within control variable	36.1%	37.0%	38.1%	37.1%	36.9%	34.8%	37.6%	35.9%	36.9%	36.0%	32.2%	34.3%	42.5%	40.6%	36.9%
5	Count	639	916	245	316	2116	35	1454	627	2116	161	587	341	349	678	2116
	% within PCA	30.2%	43.3%	11.6%	14.9%	100.0%	1.7%	68.7%	29.6%	100.0%	7.6%	27.7%	16.1%	16.5%	32.0%	100.0%
	% within control variable	30.6%	29.8%	24.8%	26.0%	28.8%	24.8%	32.3%	23.1%	28.8%	31.5%	28.3%	22.1%	27.3%	34.8%	28.8%
Total	Count	2087	3069	987	1215	7358	141	4500	2717	7358	511	2074	1543	1279	1951	7358
	% within PCA	28.4%	41.7%	13.4%	16.5%	100.0%	1.9%	61.2%	36.9%	100.0%	6.9%	28.2%	21.0%	17.4%	26.5%	100.0%
	% within control variable	100.0%	100.0%	100.0%	100.0%	100.0%	100.0%	100.0%	100.0%	100.0%	100.0%	100.0%	100.0%	100.0%	100.0%	100.0%

and

Table A8. Crosstabulation of the pro-climate action of maximize the lifetime of goods with the nominal control variables of household type, gender identity, and country.

Maximize the Lifetime of Goods		Household Type					Gender Identity				Country
		Single	Couple	Single Parent	Couple w/Children	Total	Other	Female	Male	Total	Denmark	Finland	Iceland	Norway	Sweden	Total
1	Count	34	36	13	15	98	5	41	52	98	0	25	30	19	24	98
	% within PCA	34.7%	36.7%	13.3%	15.3%	100.0%	5.1%	41.8%	53.1%	100.0%	0.0%	25.5%	30.6%	19.4%	24.5%	100.0%
	% within control variable	1.6%	1.2%	1.3%	1.2%	1.3%	3.5%	0.9%	1.9%	1.3%	0.0%	1.2%	1.9%	1.5%	1.2%	1.3%
2	Count	38	57	19	33	147	2	69	76	147	5	38	55	16	33	147
	% within PCA	25.9%	38.8%	12.9%	22.4%	100.0%	1.4%	46.9%	51.7%	100.0%	3.4%	25.9%	37.4%	10.9%	22.4%	100.0%
	% within control variable	1.8%	1.9%	1.9%	2.7%	2.0%	1.4%	1.5%	2.8%	2.0%	1.0%	1.8%	3.6%	1.3%	1.7%	2.0%
3	Count	215	302	137	178	832	13	455	364	832	56	187	267	163	159	832
	% within PCA	25.8%	36.3%	16.5%	21.4%	100.0%	1.6%	54.7%	43.8%	100.0%	6.7%	22.5%	32.1%	19.6%	19.1%	100.0%
	% within control variable	10.3%	9.8%	13.9%	14.7%	11.3%	9.2%	10.1%	13.4%	11.3%	11.0%	9.0%	17.3%	12.7%	8.1%	11.3%
4	Count	781	1185	381	498	2845	47	1700	1098	2845	186	691	646	605	717	2845
	% within PCA	27.5%	41.7%	13.4%	17.5%	100.0%	1.7%	59.8%	38.6%	100.0%	6.5%	24.3%	22.7%	21.3%	25.2%	100.0%
	% within control variable	37.4%	38.6%	38.6%	41.0%	38.7%	33.3%	37.8%	40.4%	38.7%	36.4%	33.3%	41.9%	47.3%	36.8%	38.7%
5	Count	1019	1489	437	491	3436	74	2235	1127	3436	264	1133	545	476	1018	3436
	% within PCA	29.7%	43.3%	12.7%	14.3%	100.0%	2.2%	65.0%	32.8%	100.0%	7.7%	33.0%	15.9%	13.9%	29.6%	100.0%
	% within control variable	48.8%	48.5%	44.3%	40.4%	46.7%	52.5%	49.7%	41.5%	46.7%	51.7%	54.6%	35.3%	37.2%	52.2%	46.7%
Total	Count	2087	3069	987	1215	7358	141	4500	2717	7358	511	2074	1543	1279	1951	7358
	% within PCA	28.4%	41.7%	13.4%	16.5%	100.0%	1.9%	61.2%	36.9%	100.0%	6.9%	28.2%	21.0%	17.4%	26.5%	100.0%
	% within control variable	100.0%	100.0%	100.0%	100.0%	100.0%	100.0%	100.0%	100.0%	100.0%	100.0%	100.0%	100.0%	100.0%	100.0%	100.0%

), Iceland and Norway generally had a lower engagement with the pro-climate actions than the other countries, especially the pro-climate action of trying not to fly, whereas in Iceland, the highest percentage of responses was in “not at all” (30%). Adding the climate motivation variable strongly reduced the predictive power of the country variable. Generally, it so strongly affected many models in terms of the eight variables that it could be said that education largely lost significance and effect size, except with respect to flying, for which it became significant.

3.4. Pro-Climate Actions, Climate Motivation, and Carbon Footprint

To provide a more nuanced understanding of the relationships between pro-climate actions, climate motivation, and carbon footprint, the structural equation model (SEM) was used to highlight the complex pathways through which individual actions and climate motivation impact carbon footprint. A factor analysis was completed on the eight pro-climate actions and can be seen in Appendix B. The factor analysis revealed that the pro-climate actions can be grouped into two main categories: Active Climate Mitigation (ACM) and Resource Conservation (RC). ACM includes the pro-climate actions of avoiding meat, trying not to fly, buying services rather than goods, buying second-hand, and avoiding buying. RC consists of the pro-climate actions of avoiding food waste, maximizing the lifetime of goods, and reducing home energy.

Table 6. Structural equation model results: Relationships between climate motivation, pro-climate actions, and carbon footprint.

	Estimate	Std. Error	z Value	Pr (>|z|)
Active Climate Mitigation → CF	−0.412	0.034	−12.12	<0.001
Resource Conservation → CF	−0.287	0.029	−9.90	<0.001
Climate Motivation → ACM	0.543	0.025	21.72	<0.001
Climate Motivation → RC	0.476	0.023	20.70	<0.001
Climate Motivation → CF	−0.198	0.026	−7.62	<0.001

CF = carbon footprint. ACM = Active Climate Mitigation (avoiding meat, trying not to fly, buying services rather than goods, buying second-hand, and avoiding buying). RC = Resource Conservation (avoiding food waste, maximizing the lifetime of goods, and reducing home energy).

shows the results of the SEM analysis, which showed a good fit to the data (CFI = 0.942, TLI = 0.923, RMSEA = 0.058, SRMR = 0.041). Both latent factors identified in the factor analysis—“Active Climate Mitigation” (β = −0.412, p < 0.001) and “Resource Conservation” (β = −0.287, p < 0.001)—were significantly associated with lower carbon footprints, with Active Climate Mitigation showing a stronger effect. Climate motivation had significantly positive associations with both latent factors (β = 0.543 and β = 0.476, respectively, both p < 0.001) and a direct negative association with carbon footprint (β = −0.198, p < 0.001). The relationship between climate motivation, pro-climate actions, and carbon footprint is complex, with both direct and indirect effects.

4. Discussion

This study aimed to see the how the level of engagement in pro-climate actions relates to CBCFs, how significant of a driver is an individual’s motivation to engage in a pro-climate action, how the level of climate motivation affects the impact of engagement in various pro-climate actions, and who is engaging in pro-climate actions. The main results show that a higher engagement in pro-climate actions was associated with lower carbon footprints. Climate motivation and the level of engagement were highly correlated with the actions of avoiding meat and trying not to fly, which were also associated with the lowest carbon footprints. The highest engagement was seen in pro-climate actions that had less impact on carbon footprint. Sociodemographic variables were mostly weak predictors of engagement in pro-climate actions, and climate motivation often had a stronger effect. The motivation to take one climate action was often highly correlated with the motivation to take other actions, but the engagement in climate actions did not show this same relationship. Even though both high engagement and high climate motivation were related to lower carbon footprints, the footprints were still above 1.5-degree-compatible levels.

Some of the actions in which respondents participated the most primarily included maximizing the lifetime of products (85%), avoiding food waste (78%), and avoiding buying (66%). These actions correlated less with climate motivation and had a lower impact on carbon footprint than the actions that had lower engagement rates. These behaviors can be classified as frugal behaviors, which have been associated with lower carbon footprints for everyday consumption and in leisure travel [45]; however, there can be a potential for the rebound effect with these actions [53]. Compared to the high-impact lifestyle change to a vegan or vegetarian diet (0.5–0.9 tCO₂e) or taking less flights (0.8 tCO₂e), these actions have a lower reduction potential for the personal carbon footprint [19]. Food waste reduction on the household side may mitigate an average of 0.3 tCO₂e per capita, and purchasing fewer items and more durable items may mitigate an average of 0.1 tCO₂e [17]. People are often more willing to take low-impact actions [33] that do not require much sacrifice and are less likely to make more significant changes such as changes to diet [32] or leisure travel [40,72]. Avoiding meat was one of the actions that was the least participated in; however, the correlation between engagement in the action and the climate motivation to do so was strong. The behavior of eating less meat has not been widely adopted in the Nordic countries; however, the willingness to eat less meat is slowly increasing [73,74,75]. Some studies have shown a strong link between reducing meat consumption and environmental concerns [54,55], while other studies have found a weaker connection [56] and that stronger predictors of engagement include habit [57], social norms [58], or health reasons [59]. A study of German individuals found that higher climate knowledge only led to lower carbon footprints in the diet domain and not in terms of the aggregate carbon footprint [14]. Similar to a study in Norway, we found that it was younger, urban, female consumers who were more likely to consume less meat [59]. Although sociodemographic variables are weak predictors of participation, they can reveal lifestyle types who might be more likely to adopt different actions or lifestyles.

Avoiding air travel, taking less flights, or changing leisure travel modes to less carbon-intensive modes has a high mitigation potential [17,18,53,60]. In wealthy countries like the Nordic countries, a high level of emissions can be attributed to air travel [61]. The pro-climate action of trying not to fly was highly correlated with climate motivation, meaning that the more people trying to avoid flying, the more they are doing it to lower their carbon footprint rather than for other reasons. Recently there has been an increased awareness of the impact of air travel in society [63], although people are often unwilling to give up flying even if they have high climate awareness [40,72]. Higher emissions from long-distance leisure travel are often associated with living in more urban areas [61,64], and studies in Norway and Iceland have found that air travel is a habituated part of life that is supported by social norms, living in an urban environment, and that individuals’ desire to reduce flying for climate reasons only has a small and indirect effect [64,65]. Also, being an island nation, respondents in Iceland do not have access to low-carbon leisure transportations to travel abroad such as the trains that are available in the other Nordic countries.

Climate motivation had the highest correlation coefficients with some of the changes that people might be less willing to make such as reducing their air travel or having a vegan or vegetarian diet, indicating that those who engage in these high-impact mitigating behaviors are also highly motivated to mitigate climate change. This is also an interesting finding since individuals do not often understand the impact of their actions and overestimate the impact of low-impact actions on carbon footprint [42]. However, the level of engagement was low for avoiding meat and moderate for trying not to fly. The climate motivation for one pro-climate action often had higher correlation coefficients with the climate motivation for other pro-climate actions, which could indicate a potential positive spillover effect [26] and that people concerned about climate change are willing to take multiple individual mitigation actions. This could also be seen in the domain of carbon footprint since they generally decreased with more engagement in each of the pro-climate actions and increased in the domain of public transport, which is a low-carbon alternative to fossil fuel vehicles [17]. However, the engagement in pro-climate actions had lower correlation coefficients with each other. The SEM and factor analysis results showed these relationships as well. The pro-climate actions that were grouped together in the ACM group consisted of the actions that had a higher impact on carbon footprint and a higher correlation with climate motivation but included actions with less participation than those in the RC group, which consisted of actions with less impact on carbon footprint and less correlation with climate motivation. This relationship could be because of the value–action gap [39], or due to the unique nature of these actions, it may be easier or more desirable to engage in some rather than others because of the social, economic, and physical factors that individuals exist within. For example, an individual may be motivated to reduce their home energy use, but they may be limited in doing so because of their housing situation (i.e., renting vs. owning), or because of social norms and the culture surrounding food, individuals may be more likely to conform to the predominant diet rather than make significant diet changes [76]. Due to the complex interplay of motivational and contextual factors, more research is needed to see what barriers individuals are facing to taking certain pro-climate actions.

4.1. Uncertainties and Limitations

Some uncertainties and limitations to the data collection and analysis of the study have been identified. Individuals who are concerned about climate issues may have been more inclined to participate in the survey, which may have led to a higher representation of people who are motivated to reduce their carbon footprints; however, there were a number of respondents who also had low climate concern and motivation. Respondents self-reported their consumption and engagement levels in pro-climate actions, so they could have been over- or under-reporting their actions. However, the footprints in general are fairly well in line with those found in previous studies. The answer scale for the pro-climate actions and climate motivation was a simple five-point scale, which could have been interpreted differently by different respondents and may not have been nuanced enough to reflect their actual engagement or motivation level. The survey was conducted during the COVID-19 pandemic, which could have influenced people’s lifestyle and consumption patterns, particularly in the domain of leisure travel, so the engagement in trying not to fly might have been at a higher level than is typical for the respondents.

This survey was conducted within the Nordic countries, which may affect the generalization of these results to other populations. The Nordic countries are highly affluent and have high carbon footprints, so like many other high-income countries, there are numerous high-impact actions to engage in with high reduction potential (500–1500 kgCO₂e/person/yr) [11]. However, in low- and middle-income countries, a different approach to living 1.5-degree-compatible lifestyles may be necessary since the majority of reduction options will have a lower impact (less than 250 kgCO₂e/person/yr) and many people in low-income countries will need to consume more in order to reach basic wellbeing standards [11]. The carbon intensity of goods and services and the consumption habits of the residents of the Nordics may differ from those in other climates with different environmental conditions. For example, the colder climate limits the variety and growing season of foods that can be locally grown, harsh winters can make active transport modes unattractive or inaccessible for those with limited mobility, and the dark, cold winters may motivate people to fly abroad more often to warmer destinations. The Nordic countries have a highly decarbonized energy system, so pro-climate actions like reducing home energy have more potential to have a larger impact on carbon footprint than as seen in this study. Diets in affluent countries, such as the Nordics, often consist of more animal products such as dairy and meat, which contribute more emissions to CBCFs than plant-based diets, so countries with predominantly vegetarian diets will need to focus on other pro-climate actions to achieve reductions [11]. In addition to geographical and cultural differences, the colder base climate in the Nordic countries may reduce residents’ sense of urgency to mitigate climate change, which in turn could lower engagement in pro-climate actions [40].

In the analysis, it is also difficult to isolate the effect of each pro-climate action. If people are motivated by climate concern, then they may be participating in multiple actions that would lead to lower emissions than those who are not, which can be seen in the results that the climate motivation to engage in the various pro-climate actions were highly correlated. This is also the trend seen in most of the domains of carbon footprint in terms of decreasing emissions, with higher engagement levels in actions that do not impact these domains. Although the respondents could rank how much of their motivation was due to wanting to reduce their carbon footprint, it is unknown what other motivations they might also have to take these actions and whether they are stronger drivers than climate motivation. There are uncertainties in the methods and data used to calculate the carbon footprints, including the values used from the literature and the Exiobase model used.

4.2. Policy Recommendations

Policy instruments should consider that climate-compatible lifestyles must be facilitated by institutions, governments, infrastructure, and social norms [11,66], and an individual’s consumption cannot be separated from the physical, economic, and social structures that they exist within [40]. Policies must remove barriers to climate-compatible living [67]. For example, to encourage reducing meat consumption, policies must address the perception that meat is the cheapest, easiest, and most appropriate food to eat through changes in social, physical, and economic structures [58]. The motivation to engage in pro-climate actions can be complicated and requires further research. For instance, given the complexity of the drivers of air travel, voluntary change may be difficult, and policies that promote multi-stakeholder action may be more successful rather than isolated initiatives, which are less effective [68]. Policies focusing on knowledge alone will probably not be effective on their own and instead need to encourage individuals to actively engage with climate issues [14] and understand what actions they can personally take to mitigate climate change [69].

5. Conclusions

Although it is difficult to isolate the impact of each of the pro-climate actions individually on the CBCFs, and the motivations to act may be quite complex and people may have other reasons beyond the climate to engage in these actions, we still have seen some interesting results and trends from this study. Even though both higher engagement in pro-climate actions and higher climate motivation were associated with lower carbon footprints, the footprint levels are still above the 1.5-degree-compatible limits. Climate motivation was strongest in the more impactful actions people are often less willing to take like avoiding meat and trying not to fly, but the highest engagement was seen in the pro-climate actions that had less of an impact on carbon footprint. Higher engagement in the most impactful actions is needed to reach climate-sustainable levels. Policies which remove the barriers and enable individuals to engage in high-impact pro-climate actions are necessary since high climate motivation alone can fall short of reaching 1.5-degree-compatible lifestyles.