# Climate knowledge matters: A causal analysis of knowledge and individual carbon emissions

Florian Fizaine, Guillaume Le Borgne

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## To cite this version:

Florian Fizaine, Guillaume Le Borgne. Climate knowledge matters: A causal analysis of knowledge and individual carbon emissions. Journal of Environmental Management, 2025, 385, pp.125604. 10.1016/j.jenvman.2025.125604. hal-05079084

HAL Id: hal-05079084

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Submitted on 10 Jul 2025

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Research article

# Climate knowledge matters: A causal analysis of knowledge and individual carbon emissions

Florian Fizaine<sup>a, ID</sup>, Guillaume Le Borgne<sup>a,b,*</sup>

<figure><img src="image_4.png" /></figure>

## ARTICLE INFO

JEL codes:

C3

D83

D91

Q54

Q58

Keywords:

Carbon footprint

Climate knowledge

Literacy

Causal inference

Instrumental variables

Pro-environmental behavior

## ABSTRACT

In this paper, we investigate the causal link between individuals' objectively assessed knowledge of climate change and their personal carbon footprints. Using a novel survey of 780 participants, we comprehensively measure perceived and actual climate knowledge, as well as individual beliefs, intentions, and behaviors. We find that individuals tend to overestimate their climate knowledge, with those possessing lower actual knowledge exhibiting the highest overestimation. While simple correlations indicate a weak negative relationship between objective knowledge and carbon footprint, our instrumental variable approach reveals a substantially stronger causal effect: individuals with greater objectively assessed climate knowledge tend to exhibit significantly lower carbon footprints. This effect varies across footprint subcomponents, showing strong proportional reductions in transport-related emissions, moderate reductions in food-related emissions, and no discernible effect in housing, miscellaneous and digital consumption. Our results highlight the importance of addressing knowledge gaps as a pathway to enhancing climate action at the individual level. By using a refined knowledge scale and accounting for confounding variables, we provide robust evidence that increasing factual climate knowledge can meaningfully contribute to lowering carbon footprints - by up to 1 ton of CO₂-equivalent per year. These findings call for targeted educational interventions that go beyond raising awareness to actively improving public understanding of effective mitigation strategies.

## 1. Introduction

Climate change (CC), a global phenomenon with uneven impacts across regions, is an established reality supported by extensive scientific research. Despite clear evidence of its adverse effects, international political progress remains insufficient, as acknowledged by stakeholders (e.g., "COP27 marks a small step towards climate justice, but much more needs to be done for the planet", Ursula von der Leyen, President of the European Commission, November 2022). Greenhouse gas emissions, the primary drivers of anthropogenic global warming, require concerted reduction efforts from states, businesses, and citizens alike.

Household consumption—spanning housing, transportation, food, clothing, and digital use—accounts for nearly two-thirds of global emissions (UNEP, 2020). While political and industrial decisions heavily influence household consumption patterns, individual actions represent a crucial leverage point for reducing emissions. Achieving per capita emission reductions, particularly among those with the highest carbon footprints, is critical to meeting global climate targets. As emphasized by

Chuvieco et al. (2021), the majority of personal emissions are shaped by lifestyle choices, though external factors such as income and occupation remain major determinants. Still, knowledge-based internal drivers can play a complementary role, especially in domains where behavior is more discretionary, like food and clothing.

Reducing individual carbon footprints requires navigating a complex web of socio-economic, cultural, psychological, and contextual factors. These influence not only direct behaviors but also perceptions of climate change, intentions to act, and perceived abilities to implement change (Grothmann and Patt, 2005; Whitmarsh et al., 2020). Awareness and belief in climate change are necessary but insufficient drivers of action. Besides, climate contrarianism and skepticism continue to pose significant barriers to behavioral change (Hornsey and Fielding, 2020; Coan et al., 2021).

While beliefs and intentions have been extensively studied, recent works emphasize the critical role of knowledge in shaping climate-related behavior. Knowledge encompasses both perceived knowledge (self-assessed understanding) and assessed knowledge (objectively

<footnote><sup>a</sup> IREGE, Université Savoie Mont-Blanc, France</footnote>

<footnote><sup>b</sup> UMR MOISA, INRAE Centre Occitanie, Montpellier, France</footnote>

<footnote>* Corresponding author. IREGE, Université Savoie Mont-Blanc, France.</footnote>

<footnote>E-mail address: guillaume.le-borgne@univ-smb.fr (G. Le Borgne).</footnote>
measured factual understanding). Stoutenborough and Vedlitz (2014) highlight that perceived knowledge often reflects subjective confidence rather than actual understanding. Johnson et al. (2024) demonstrate persistent cognitive biases in carbon footprint estimations, suggesting that people misjudge the impact of their actions due to faulty mental models. In parallel, Kretschmer (2024) shows that even simple informational nudges—such as presenting rankings of high-emission activities—can help correct certain misperceptions about the most impactful climate actions. However, she also finds that despite this additional information, significant underestimations persist, particularly in the domain of food.

At the same time, studies like Cordero et al. (2020), Moselhy (2024), and Schleich et al. (2024) offer evidence that educational interventions and carbon literacy—defined by the latter as a combination of knowledge and engagement—can foster meaningful reductions in emissions. Notably, Schleich et al. (2024) find that while engagement plays a central role in lowering overall carbon footprints, factual knowledge is particularly linked to diet-related emissions. This distinction underscores the need for differentiated strategies to address specific behavioral components.

Furthermore, knowledge also matters in shaping public support for climate policies. Douenne and Fabre (2020) show that in the French context, low acceptance of carbon taxation is partly driven by limited and often biased knowledge about the policy's effects. When individuals are better informed—especially about the distributional fairness and environmental effectiveness of such measures—support increases significantly, suggesting a vital role for informational interventions. For their part, Zhang et al. (2024) show that assessed public knowledge exhibits an inverted U-shaped relationship with supportive attitudes toward climate change policies, contrary to the literature, which had previously found a positive and monotonous link. However, their findings are correlational in nature and do not establish causality. Eventually, Wong-Parodi and Berlin Rubin, 2022 show that subjective climate knowledge is correlated with pro-environmental intentions, such as support for a carbon tax or the intention to purchase an electric vehicle. However, their approach remains focused on declarative attitudes and relies exclusively on perceived knowledge. To better understand and influence actual behaviors, it is crucial to use an objective and multidimensional measure of climate knowledge—one that can meaningfully predict concrete outcomes such as individual carbon emissions.

More broadly, despite growing interest in the role of knowledge, existing studies often focus on isolated dimensions—such as behavioral intentions or support for policy—without linking them to concrete emission outcomes. Moreover, many rely on correlational approaches, limiting causal interpretation. To date, no research has systematically examined the independent effect of factual climate knowledge on individual carbon footprints using both granular behavioral data and a robust identification strategy.

Specifically, to our knowledge, no study to date offers a comprehensive investigation that simultaneously integrates.

1. A multidimensional and operational measure of objectively assessed climate knowledge;

2. A detailed and disaggregated carbon footprint for each respondent;

3. A rich set of belief, concern, and social norm variables;

4. A causal estimation strategy relying on instrumental variables to assess the independent impact of knowledge on behavior.

By filling this gap, our paper provides a unique contribution to the literature on environmental behavior and carbon literacy.

The rest of this article is structured as follows. Section 2 reviews relevant literature and derives hypotheses. Section 3 outlines our empirical strategy and dataset. Section 4 presents the results. Section 5 discusses implications, and Section 6 concludes with policy recommendations.

## 2. Literature review and hypotheses development

Understanding the link between knowledge and climate-related behavior requires a solid theoretical grounding in both environmental psychology and behavioral economics. This section first distinguishes between different types of climate knowledge and their behavioral relevance, and then introduces two influential theoretical frameworks—Value-Belief-Norm Theory and Protection Motivation Theory—that guide our empirical approach.

### 2.1. Perceived vs. objective knowledge of climate change

Recent research stresses the need to differentiate between perceived (subjective) knowledge—what individuals believe they know—and objectively assessed knowledge—what they actually know based on factual accuracy (Zhang et al., 2024; Stoutenborough and Vedlitz, 2014). This distinction is crucial: individuals with limited actual knowledge often overestimate their competence, a pattern consistent with the Dunning-Kruger effect (Kruger and Dunning, 1999). Moreover, Douenne and Fabre (2020) emphasize that gaps in factual understanding help explain limited support for climate policies, highlighting the importance of addressing both types of knowledge. Our dataset allows us to directly measure both forms and explore their behavioral implications.

We hypothesize.

**H1a.** Perceived and assessed knowledge of climate change are not equivalent.

**H1b.** The higher an individual's level of assessed knowledge on climate change, the closer their perceived knowledge level is to their assessed level.

### 2.2. Why knowledge matters for climate behavior

The Knowledge Deficit Model (KDM) posits that a lack of factual knowledge is a key barrier to pro-environmental behavior, and that greater factual knowledge enables individuals to align their actions with expert recommendations (Stoutenborough and Vedlitz, 2014). While not explicitly framed within the KDM, recent empirical findings lend support to its core assumption—for example, Schleich et al. (2024) report a negative correlation between assessed knowledge and diet-related carbon footprints, as well as between carbon engagement (interest in climate information) and overall carbon footprints. However, the KDM has also been criticized for overlooking the role of social and psychological mediators in shaping behavior. To address this, our research draws on two broader behavioral frameworks—Value-Belief-Norm (VBN) theory and Protection Motivation Theory (PMT) — to incorporate essential individual-level determinants of behavior and their influence on resulting carbon footprints.

### 2.3. Value-Belief-Norm Theory (VBN)

VBN theory (Stern, 2000) describes a causal chain in which individual values influence ecological beliefs, which then activate personal norms that guide behavior. For example, individuals with strong biospheric values and high awareness of environmental consequences are more likely to adopt sustainable actions. This model helps position climate knowledge as a foundation for belief formation and norm activation.

Our data include several belief-related constructs such as perceived social and descriptive norms, concern about climate change, and perceived temporal/geographical proximity of climate impacts—all of which function as mediators or moderators of knowledge-based effects.
## 2.4. Protection Motivation Theory (PMT)

PMT provides a complementary perspective by focusing on how individuals appraise climate threats and their capacity to respond. It includes two main components: 1) threat appraisal, involving the perceived severity and vulnerability to climate change; 2) coping appraisal, involving perceived self-efficacy and response efficacy.

Recent empirical work underscores the growing use of Protection Motivation Theory to explain climate-related behaviors. For example, Frantz et al. (2024) applied PMT to assess behavioral intentions in both climate change deniers and acknowledgers, finding that threat and efficacy appraisals significantly predicted mitigation intentions across both groups—even when belief in climate change was absent. This supports PMT's robustness in contexts marked by ideological division and highlights the utility of incorporating both individual and collective dimensions of threat and efficacy. Similarly, Crosman et al. (2019) emphasized the importance of efficacy beliefs—specifically self-efficacy and response efficacy—in shaping public perceptions of actionable strategies to mitigate climate change risks. These studies illustrate the practical relevance of PMT in climate research and justify its integration into our theoretical model.

Our survey therefore includes items aligned with PMT dimensions: perceived difficulty in reducing one's emissions, the expected effectiveness of personal action, and locus of control (i.e., whether mitigation is seen as the responsibility of citizens or governments).

## 2.5. Integrating frameworks: toward a causal interpretation

The Value-Belief-Norm (VBN) theory and Protection Motivation Theory (PMT) provide a robust conceptual foundation for understanding the psychological mechanisms that influence climate-related behaviors. These frameworks suggest that knowledge can shape behavior through multiple channels: a) by reinforcing beliefs and personal norms (VBN); b) by amplifying perceived severity and self-efficacy (PMT); c) or by directly enabling individuals to identify and prioritize the most impactful mitigation actions.

However, while these frameworks help explain behavioral heterogeneity, they do not resolve a central question: Does factual climate knowledge causally reduce individual carbon footprints, or is it merely correlated with pro-environmental behavior? To answer this, we go beyond associative models and employ an instrumental variable (IV) strategy to isolate the exogenous variation in assessed knowledge. This approach allows us to estimate the independent causal impact of factual knowledge while controlling for key psychological factors (e.g., values, concern, norms, efficacy beliefs) and socio-demographic variables.

Isolating this effect is crucial for several reasons.

### 1. Knowledge as a Catalyst for Action:

Values and beliefs often remain abstract unless translated into actionable insights. An individual may strongly value environmental protection but fail to understand which behaviors—such as reducing air travel or meat consumption—carry the highest carbon intensity. Knowledge can thus act as a bridge between motivation and action.

### 2. Correcting Cognitive Biases:

Research has shown that people with low factual knowledge tend to overestimate their understanding (Zhang et al., 2024), leading to misinformed behavioral choices. Our approach directly tackles this overconfidence by measuring actual (not self-reported) knowledge and isolating its behavioral impact.

### 3. Capturing Behavior-Specific Effects:

The influence of knowledge may differ across behavioral domains.

For instance, Schleich et al. (2024) find strong links between knowledge and food-related emissions, but weaker effects in domains like housing. Including a comprehensive set of psychological variables allows us to better understand these domain-specific variations.

### 4. Addressing Structural Barriers:

While motivation is essential, structural constraints—such as income, infrastructure, or availability of alternatives—can limit behavioral change. However, knowledge can help identify feasible, high-impact options even within such constraints, making it a pragmatic driver of change.

From a methodological perspective, our IV regression framework offers a significant advancement over standard OLS or structural equation modeling. While SEM is useful for exploring latent constructs and indirect pathways, it cannot establish causal directionality. In contrast, the IV approach addresses issues of endogeneity, reverse causality, and omitted variable bias, providing stronger empirical grounds for policy-relevant conclusions.

We thus formally test the following hypothesis.

H2. Higher levels of assessed climate knowledge causally reduce individual carbon footprints.

To rigorously test this hypothesis, it is essential to rely on accurate and behaviorally sensitive measures of individual emissions. The next subsection outlines the methodological tools used to assess carbon footprints at the individual level, which form the dependent variable in our empirical analysis.

## 2.6. Methodologies for carbon footprint assessment

Accurate carbon footprint assessments are critical for understanding individual emissions. Two main approaches—top-down and bottom-up—offer contrasting strengths. The top-down approach uses macro-economic tools like Exiobase to estimate average footprints from national data but cannot capture individual or intra-population variations (Douenne and Fabre, 2020). In contrast, the bottom-up approach uses expenditure data to calculate emissions, providing detailed insights into individual consumption patterns (Starr et al., 2023).

Individual carbon footprint calculators represent a practical application of the bottom-up methodology. These tools assess emissions across categories such as food, transport, and housing, offering a detailed breakdown of behavior-driven emissions (Burgui-Burgui and Chuvieco, 2020). Chuvieco et al. (2021), for example, found significant variations in emissions for specific domains based on knowledge levels, though total emissions often remained unaffected. These findings highlight the importance of comprehensive and precise measures to link knowledge and behavior.

Despite their utility, footprint calculators alone face limitations in driving behavioral change. Awareness of carbon footprints does not necessarily translate into reduced emissions, especially in high-impact areas like housing and travel (Büchs et al., 2018). Combining insights from VBN and PMT frameworks with advanced footprint calculators offers a promising pathway to bridge this gap, linking knowledge and intentions to measurable outcomes.

These methodological choices provide a solid foundation for empirically linking knowledge levels to actual climate-related behaviors. In the following section, we present the dataset, survey design, and identification strategy that enable us to estimate this relationship while accounting for key psychological and structural factors.

## 3. Methodology and data

### 3.1. Study design and data collection

To do so, we performed a survey on 780 peoples between March and
May 2022. These peoples have not been randomly selected, they came from the entourage of second-year Bachelor in Business students at the Annecy University Institute of Technology (Savoie Mont-Blanc University). In other words, each student grants us access to the data collected within their surroundings. This approach allows us to measure peer effects within each individual's environment and use them as a variable for causal identification—a strategy that would not have been feasible with traditional representative surveys. In addition, this allowed performing a very long survey (more than 30 min) on people on a large range of topics (see Fig. 1 and Supplementary materials A-B). Obviously, this method has limitations, as our sample (prior to correction and weighting) does not fully align with the demographic characteristics of the French population (see Supplementary materials C). Nonetheless, our results strongly correspond with the findings of a limited number of studies that conduct carbon footprint surveys (ADEME, 2023) and examine French climate change contrarianism within a nationally representative French population (Cordonier, 2022; Bentolila et al., 2023).<fnref n="1" />

More formally, the survey is subdivided into four main parts (see Fig. 1). In the first part, we ask peoples about their environmental behavior. We use the ADEME<fnref n="2" /> carbon footprint calculator providing total individual footprint but also the subcategory of the footprint (associated with food, home, transport, numeric and miscellaneous consumption). In a second part, we retrieved the characteristics of the individuals (sex, age, level of diploma, income ...). In a third part, we ask peoples about their perceived knowledge relative to climate change<fnref n="3" /> but we also check their objective knowledge relative to climate change. This

approach is the originality of our research. In addition, we also separate questions checking the “*know what*” (theoretical knowledge) from the “*know how*” (practical knowledge). In other words, we check how individuals understand the issue relative to climate change but also whether they hold main keys to act individually in reducing their carbon footprint. Lastly, in the fourth part, we assess the beliefs (environmental concern, perceived behavioral control and efficiency, descriptive and subjective norms, perceived social geographical and temporal distance to the issue) and intention of peoples through a several ordered questions (Likert scale).

In the following two subsections, we introduce the main descriptive statistics of our sample (a) but also the methodologies performed in order to identify correlational relationships (b).

## 3.2. Individual perceptions and intention

The individual perceptions of the phenomenon of climate change is evaluated on several aspects: 1 - the individual's perceived temporal (TempDist) and geographical (GeoDist) distances from climate change. 2 - perceived social norm (SocNorm) and descriptive norm (DesNorm). 3 - individual perceived efficacy as regards climate change (PercEff) and best perceived sources/levers of climate change mitigation (Tech vs Suffi, Citiz vs State). It should be noted that, on average, the individuals surveyed see climate change as already present (average of 2.35 on a scale of 1-7 for DistTemp), but in regions still rather far from their own (average of 5.47 on a scale of 1-7 for GeoDist1). Main descriptive statistics of variables included in this study are given in Table C2 of supplementary materials.

In addition, the intention to act in order to limit one's carbon footprint is measured by the following item: "I do my best to have as small a carbon footprint as possible" 1: Strongly disagree to 7: Strongly agree. It is, in average (4.33), just above the middle of the scale.

## 3.3. Perceived, actual, general and practical knowledge of people

The survey includes 10 objective questions relative to the climate change. These questions check the objective knowledge held by the individuals. The range of these questions are large and covers several important areas where behavior has a large contribution to individual carbon footprint (see Tables 1–2). Our questions are more concerned with mapping knowledge than with metric knowledge (Panzone et al., 2020), the former being more operational and useful for everyday choices and trade-offs. The success to the different questions is highly variable ranging from 17.50 % for question 4–82.11 % for question 1. Overall, we observe that peoples held a medium understanding of what is climate change, what causes climate change and whether this phenomenon is due to human activities (questions 1–3). Conversely, peoples are more deprived when we asking them about the best action to reduce their carbon footprint. For instance, only 17.5 % of people identify that transport of food does not represent the essential part of carbon footprint due to alimentation. Priorization of action by their impact in terms of carbon footprint is also a very difficult exercise where people do not particularly excel. In question 6, among five actions we require to nominate the most important action in order to reduce carbon footprint. Again, less than 30 % of people identify the correct answer.

The use of multiple correspondent analysis demonstrates the high degree of correlation between the answers provided by the individuals (see Fig. 2). In other words, peoples who answered correctly to Q10 tends to also provided good answers for Q5 and Q9. Moreover, the first correspondent axis resumes nearly 80 % of the inertia and most of questions are well describe by it. The unique exception is question 6, mainly expressed through the second component. Questions 4 seems also to follow a different pattern. More especially, we see that the zero position of these questions is very close to the origin. This means that answering falsely to these questions is not adequately described by the two main component of the MCA. The low score of all people to these

<figure><img src="image_5.png" /><figcaption>Fig. 1. Timeline of our survey and the various aspects assessed.</figcaption></figure>

<footnote n="1">¹ See Supplementary materials F for a comparison of our results on the prevalence of climate change contrarianism with those of other representative French surveys.</footnote>

<footnote n="2">² ADEME is the French environmental agency.</footnote>

<footnote n="3">³ “Overall do you think you have a good understanding of the challenges of global warming?”</footnote>
<table><caption>**Table 1**
Topics covered by "our exam" of actual knowledge on climate change.</caption><thead><tr><th></th><th>Q1</th><th>Q2</th><th>Q3</th><th>Q4</th><th>Q5</th><th>Q6</th><th>Q7</th><th>Q8</th><th>Q9</th><th>Q10</th></tr></thead><tbody><tr><td>Right answer (%)</td><td>82.11 %</td><td>63.84 %</td><td>51.35 %</td><td>17.50 %</td><td>69.50 %</td><td>29.34 %</td><td>38.61 %</td><td>33.59 %</td><td>48.26 %</td><td>56.50 %</td></tr><tr><td>Wrong answer (%)</td><td>17.89 %</td><td>36.16 %</td><td>48.65 %</td><td>82.50 %</td><td>30.50 %</td><td>70.66 %</td><td>61.39 %</td><td>66.41 %</td><td>51.74 %</td><td>43.50 %</td></tr><tr><td>Includ. Share of "I don't know"</td><td>7.98 %</td><td>8.24 %</td><td>12.23 %</td><td>34.88 %</td><td>-</td><td>8.62 %</td><td>22.78 %</td><td>9.52 %</td><td>12.10 %</td><td>6.82 %</td></tr><tr><td>Reality of Climate Change</td><td></td><td>Causes of CC</td><td>Anthropogenic origin of CC</td><td>Food Footprint</td><td>Food Footprint</td><td>Hierarchy of impact</td><td>Effort required</td><td>Home footprint</td><td>Home footprint</td><td>Transport footprint</td></tr></tbody></table>

questions could explain this.

In Fig. 3, we show the average score (on a hundred scale) on people for perceived knowledge on climate change (SubjKnow), but also for actual knowledge on climate change (ObjKnow1) without Q4 and Q4/Q6 (resp. ObjKnow2 and ObjKnow3). In the nutshell, we observe that perceived knowledge score is higher than actual tested knowledge. If we compute the level of overestimation of the actual knowledge (ratio of perceived knowledge over the actual knowledge), we observe that people in the lowest quartile of actual knowledge tend to highly overestimate their actual knowledge in comparison to people in higher quartile of actual knowledge (Fig. 4). This effect is known as the Dunning-Kruger effect (Kruger and Dunning, 1999). Nonetheless, the coefficient of correlation between the two variables is quite low but positive (0.15–0.3) and significant (spearman’s rho and Kendall’s tau) showing that competent peoples are also people who tend to declare to be the more competent. Additionally, boxplots of actual and perceived knowledge across quartiles of actual knowledge confirm this finding (Fig. 5). Individuals with low actual knowledge significantly overestimate their abilities, whereas those with high actual knowledge tend to assess their abilities more accurately, albeit with a slight underestimation of their true level of knowledge.

## 3.4. Carbon footprint

We describe here the results relative to the individual carbon footprint of our sample. The average individual carbon footprint of our sample is 7.84 tons of CO2eq per year (median of 7.2 – see Figs. 6 and 7 for more detail). This figure is quite close to the average of the ADEME national representative survey for French population (8.2 – median of 7.3) performed in 2023 with the same tool but also to other estimations at the macro-scale (8.6 for the average French population for ADEME and 8.2 for French ministry MTES in 2020).

Transport (30 %), food (29 %) and home (16.5 %) are the main contributors to the individual carbon footprint (Figs. 8 and 9). The variance of carbon footprint relative to transport seems higher than other subcategories of carbon footprint. This could be due to the very wide range of transport constraints and lifestyles of respondents (Fig. 8). The proportion of carbon footprint attributed to transport increases with higher levels of total carbon footprint (Fig. 9), a trend also observed in other studies (Pottier et al., 2020).

## 3.5. Methodology and modelling strategy

To examine the effect of climate change knowledge on individual carbon emissions, we implement a multi-step empirical strategy combining clustering techniques and econometric modeling.

First, we use a Hierarchical Ascendent Clustering (HAC) procedure to identify groups of respondents sharing similar beliefs about climate change. The clustering is based on responses to a series of items measuring climate concern, perceived temporal and spatial distance, perceived effectiveness, attribution of responsibility, and social norms. The resulting clusters capture belief-based social environments that

reflect how individuals perceive climate change and position themselves ideologically in relation to it.

We then estimate the relationship between two distinct forms of climate knowledge—self-perceived knowledge and objectively measured knowledge—and individuals’ carbon footprints. The carbon footprint is computed for each participant using detailed survey data on consumption behaviors, matched with emission factors across major domains (transport, food, housing, digital activities and miscellaneous consumption).

While ordinary least squares (OLS) regressions allow us to assess the correlation between knowledge and carbon emissions, these estimates may suffer from endogeneity, due to factors such as reverse causality (e. g., individuals with lower emissions becoming more informed), or omitted variables (e.g., structural constraints like access to low-carbon alternatives, health-related mobility limitations, or time availability). To address these issues, we turn to an instrumental variable (IV) approach using two-stage least squares (2SLS) estimation.

Our instruments are constructed from the average level of climate knowledge and climate beliefs (identified through the HAC procedure) within the respondent’s own peer cluster. Crucially, when computing these cluster-level averages, we exclude the focal respondent’s own values, ensuring that the instrument captures peer influence rather than individual characteristics. This approach relies on the assumption that an individual’s exposure to the informational environment of their belief-based peer group affects their level of objective knowledge, while remaining exogenous to the unobserved factors that influence their personal carbon footprint.<fnref n="4" />

The underlying logic is that individuals are likely to acquire information through their immediate social environment, while the average knowledge or belief in that environment is plausibly unrelated to their unobserved behavioral constraints or preferences, provided that relevant covariates are accounted for. The model also includes measures of the network’s socioeconomic composition (e.g., median income and education levels), indicators of average behavior within the network (e. g. average carbon footprint), and variables describing the respondent’s physical environment, such as urban density and real estate prices. These controls allow us to isolate the influence of social exposure to climate knowledge from other potential channels such as shared material conditions, social norms, or access-based constraints. This modeling strategy enables us to disentangle the roles of perceived and objective knowledge. It also accounts for unobserved heterogeneity and potential reverse causality. By combining belief clustering with an instrumental variable design, we aim to estimate the causal impact of climate knowledge on individual behavior with a high degree of internal

<footnote n="4">⁴ Our instruments satisfy the three key empirical requirements for a valid IV strategy. We conduct tests for under-identification and over-identification (using the Hansen J-statistic), as well as the Kleibergen-Paap rk Wald F-statistic to confirm instrument strength. Fig. 3 summarizes the identification strategy. The instrument (average knowledge and beliefs within the peer cluster, excluding the respondent) affects individual knowledge but is assumed to be unrelated to unobserved determinants of emissions.</footnote>
<caption>**Table 2**</caption>

Detailed results from the questions used to construct the actual carbon knowledge score.

<table><thead><tr><th>Item</th><th>Measurement of actual carbon knowledge</th><th>Share of responses</th></tr></thead><tbody><tr><td>Q1</td><td>Question 1 COO: understanding and knowledge of the problem: In your opinion, is global warming a fact proven and demonstrated by the scientific community?</td><td></td></tr><tr><td>(i)</td><td>Yes (x)</td><td>82.11 %</td></tr><tr><td>(ii)</td><td>No</td><td>3.73 %</td></tr><tr><td>(iii)</td><td>There is as yet no scientific consensus on the subject</td><td>6.18 %</td></tr><tr><td>(iv)</td><td>I don't know</td><td>7.98 %</td></tr><tr><td>Q2</td><td>Question 2 COO: understanding and knowledge of the problem: What do you think is the main cause of global warming?</td><td></td></tr><tr><td>(i)</td><td>Depletion of the ozone layer</td><td>23.04 %</td></tr><tr><td>(ii)</td><td>Greenhouse gas emissions (x)</td><td>63.84 %</td></tr><tr><td>(iii)</td><td>Variations in solar radiation</td><td>2.57 %</td></tr><tr><td>(iv)</td><td>Aerosol emissions</td><td>2.32 %</td></tr><tr><td>(v)</td><td>I don't know</td><td>8.24 %</td></tr><tr><td>Q3</td><td>Question 3 COO: understanding and knowledge of the problem: In your opinion, is global warming due to natural causes (outside mankind)?</td><td></td></tr><tr><td>(i)</td><td>Yes</td><td>15.44 %</td></tr><tr><td>(ii)</td><td>No (x)</td><td>51.35 %</td></tr><tr><td>(iii)</td><td>The question is not clear-cut</td><td>20.98 %</td></tr><tr><td>(iv)</td><td>I don't know</td><td>12.23 %</td></tr><tr><td>Q4</td><td>Question 4 COO: Managing the food problem: On average, food transport accounts for most of the carbon impact of our food.</td><td></td></tr><tr><td>(i)</td><td>Yes</td><td>47.62 %</td></tr><tr><td>(ii)</td><td>No (x)</td><td>17.50 %</td></tr><tr><td>(iii)</td><td>I don't know</td><td>34.88 %</td></tr><tr><td>Q5</td><td>Question 5 COO: Managing the food problem: Which of these meals has the lowest carbon content?</td><td></td></tr><tr><td>(i)</td><td>Starter: Red cabbage and apple salad, Main course: Cantonese rice, Dessert: Apple compote (x)</td><td>69.50 %</td></tr><tr><td>(ii)</td><td>Starter: Tzatziki, Main course: steak and French fries, Dessert: pear tart</td><td>7.85 %</td></tr><tr><td>(iii)</td><td>Starter: Greek-style vegetables, Main course: chicken with rice, Cheese platter</td><td>22.65 %</td></tr><tr><td>Q6</td><td>Question 6 COO: Prioritization problem management: Which action do you think would have the greatest impact on a French person's annual carbon footprint?</td><td></td></tr><tr><td>(i)</td><td>Become a vegetarian</td><td>18.28 %</td></tr><tr><td>(ii)</td><td>Reduce Internet streaming time by 4 h a day</td><td>8.88 %</td></tr><tr><td>(iii)</td><td>Give up a flight from Paris to New York (x)</td><td>29.34 %</td></tr><tr><td>(iv)</td><td>Reduce heating temperature by one degree</td><td>22.65 %</td></tr><tr><td>(v)</td><td>Go on vacation by train rather than by car</td><td>12.23 %</td></tr><tr><td>(vi)</td><td>I don't know</td><td>8.62 %</td></tr><tr><td>Q7</td><td>Question 7 COO: Managing the scale of the effort required: According to the IPCC, in order to contain global warming to +2 °C compared with the pre-industrial era, the French should, on average, :</td><td></td></tr><tr><td>(i)</td><td>Maintain their carbon footprint at current levels</td><td>2.32 %</td></tr><tr><td>(ii)</td><td>Halve carbon footprint</td><td>20.21 %</td></tr><tr><td>(iii)</td><td>Divide carbon footprint by 5 (x)</td><td>38.61 %</td></tr><tr><td>(iv)</td><td>Divide carbon footprint by 10</td><td>16.09 %</td></tr><tr><td>(v)</td><td>I don't know</td><td>22.78 %</td></tr><tr><td>Q8</td><td>Question 8 COO: Managing the habitat problem: Which of these actions is the most effective in reducing the carbon footprint of your home?</td><td></td></tr><tr><td>(i)</td><td>Lower your heating temperature by 1 °C</td><td>24.32 %</td></tr><tr><td>(ii)</td><td>Replace your fossil fuel heating with a low-carbon heating system (x)</td><td>33.59 %</td></tr><tr><td>(iii)</td><td>Turn off all electrical and electronic appliances in the home</td><td>23.17 %</td></tr><tr><td>(iv)</td><td>Systematically switch off lights when leaving the room</td><td>9.40 %</td></tr><tr><td>(v)</td><td>I don't know</td><td>9.52 %</td></tr><tr><td>Q9</td><td>Question 9 COO: Managing the habitat problem: At the level of a French home, which heating method emits the most CO<sub>2</sub>?</td><td></td></tr><tr><td>(i)</td><td>Oil-fired boiler (x)</td><td>48.26 %</td></tr><tr><td>(ii)</td><td>Electric convector heating</td><td>7.85 %</td></tr><tr><td>(iii)</td><td>Wood-fired heating</td><td>18.40 %</td></tr><tr><td>(iv)</td><td>Gas-fired boiler</td><td>9.52 %</td></tr><tr><td>(v)</td><td>Heat pump electric heating</td><td>3.86 %</td></tr><tr><td>(vi)</td><td>I don't know</td><td>12.10 %</td></tr><tr><td>Q10</td><td>Question 10 COO: Transport problem management: In terms of transport for a 100 km journey, which solution is the most efficient to emit the least CO<sub>2</sub>?</td><td></td></tr><tr><td>(i)</td><td>Carpooling with an internal combustion engine car</td><td>16.99 %</td></tr><tr><td>(ii)</td><td>Train (x)</td><td>56.50 %</td></tr><tr><td>(iii)</td><td>Single electric car</td><td>17.50 %</td></tr><tr><td>(iv)</td><td>Individual combustion car</td><td>2.19 %</td></tr><tr><td>(v)</td><td>I don't know</td><td>6.82 %</td></tr></tbody></table>

<figure><img src="image_6.png" /><figcaption>**Fig. 2.** MCA on the 10 questions from the actual knowledge assessment. Note: Question 6 yields notably different results compared to the other questions.</figcaption></figure>

<figure><img src="image_7.png" /><figcaption>**Fig. 3.** Distribution of score for SubjKnow, ObjKnow1, ObjKnow2 (without Q6) and ObjKnow3 (without Q4-Q6).</figcaption></figure>

<figure><img src="image_8.png" /><figcaption>**Fig. 4.** Degree of overestimation of actual knowledge by quartile of actual knowledge.</figcaption></figure>
<figure><img src="image_9.png" /><figcaption>Fig. 5. Changes in perceived and actual knowledge levels across actual knowledge quartiles.</figcaption></figure>

<figure><img src="image_10.png" /><figcaption>Fig. 6. Carbon footprint results for our sample compared to the ADEME average from a nationally representative survey of the French population.</figcaption></figure>

<figure><img src="image_11.png" /><figcaption>Fig. 7. Density function of the individual carbon footprint.</figcaption></figure>

validity.

Formally, our estimation strategy can be summarized by the following system of equations:

$$ \ln(CF)_i = \alpha + \beta_1 \ln(ObjKnow)_i + \sum_{s=1}^{m} \gamma_z X_{z,i} + \varepsilon_i \quad (1) $$

<figure><img src="image_12.png" /><figcaption>Fig. 8. Boxplot for individual carbon footprint and its subcategories.</figcaption></figure>

<figure><img src="image_13.png" /><figcaption>Fig. 9. Decomposition of carbon footprint into several subcomponents and by carbon footprint level. Source: Authors' calculations.</figcaption></figure>

$$ \ln(ObjKnow)_i = \alpha + \sum_{z=1}^{m} \pi_z X_{z,i} + \sum_{L=1}^{n} \pi_L X_{L,i} + \nu_i \quad (2) $$

Where $\ln(CF)$ is the dependent variable for the ith observation, $\ln(ObjKnow)$ represents the endogenous regressor, $X_{z,i}$ represents the included exogenous regressors, and $X_{L,i}$ represents the excluded exogenous regressors, $X_{z,i}$ and $X_{L,i}$ are collectively called the instruments. ui and vi are zero-mean error terms, and the correlations between ui and the elements of vi are presumably nonzero. Fig. 10 briefly summarizes our instrumental regression strategy.

## 4. Results

In this section, we will briefly describe our results related to the clustering of individuals' beliefs (4.1). Then, we will move on to the results regarding the connection between perceived and actual knowledge, as well as their association with other socioeconomic characteristics (4.2). In section 4.3, we offer an instrumental regression strategy to identify a causal relationship between objective knowledge and individual carbon footprint.

### 4.1. Clusterisation on people's beliefs

In a first step, we offer to explore the possibility of clustering individuals according to their subjective relationships with CC (beliefs, norms, concern ...) in order to observe how these groups correlate to objective knowledge and individual carbon footprints.
<figure><img src="image_14.png" /><figcaption>Fig. 10. Overview of the instrumental variable strategy.</figcaption></figure>

A condition imposing a minimum of three groups was set for the Hierarchical Ascendent Clustering (HAC), in order to obtain something more useful than the dichotomy "good performers" vs "bad performers". Based on the Euclidean distance for dissimilarities and an inertia criterion for tree truncation, the HAC leads to an optimal classification into three groups. Clustering dendogram of dissimilarities is available in the supplementary materials D. Then, a k-means classification was conducted, with the initial barycenters of the three classes obtained from the HAC, as is usually done.

The intra-class variance is 71 %, the inter-class variance 29 %, suggesting that these are not three clearly distinct classes, but more likely a continuum from one class to another. However, as exposed hereafter, the three groups show significant differences, including on intention and behavior.

The table of coordinates of the barycenters of the groups helps to describe these classes. ANOVAS were conducted to test the significance of the differences between the classes for the variables involved in the clustering as well as for the carbon footprint intention variables (see Supplementary materials D). The three groups can be described as follows.

* Class 3 consists of individuals with low concern about climate change (CC), perceiving it as a distant issue both in time and space. This group also has the highest share of climate change contrarians (43 %), compared to 15 % in Class 1 and 7.5 % in Class 2. Unlike the other two groups, members of Class 3 are more inclined to believe that technological progress is a more effective solution to climate change than sufficiency-based measures, which focus on lifestyle changes rather than technological innovation (Tech vs. Suffi).

* Class 2 represents the most concerned individuals. They see climate change as imminent and geographically close to home, perceive the strongest social norms around climate action, and have the highest belief in the effectiveness of individual actions.

* Class 1 is nearly as concerned as Class 2 but holds a more moderate view on the effectiveness of individual actions. Instead, they believe that addressing climate change is primarily the responsibility of governments (Fig. 11 - Citizen vs. State).

The categorical variable corresponding to these classes is then used in our models explaining the carbon footprint. In this way, we take into

<figure><img src="image_15.png" /><figcaption>Fig. 11. Box plot for the three clusters at the questions TechvsSuffi (technical progress - 1 vs sufficiency 7) and CitizvsState (citizens- 1 vs States - 7).</figcaption></figure>

account, in a unique and unified way, concerns, social norms, perceived distance from the phenomenon, locus of control and personal effectiveness.

## 4.2. Connections and profiles related to perceived and actual knowledge

In this section, we describe the profiles associated with different forms of climate change knowledge and how these forms are interconnected.

Although perceived and actual knowledge are positively correlated on average, the correlation remains poor ($r^2$ = 0.04). Using OLS regressions, Table 3 illustrates how perceived and factual knowledge correlate with control variables and with each other. Contrary to the findings of Stoutenborough and Vedlitz (2014), we identify a positive and significant relationship between perceived and actual knowledge (columns 2 and 4). This indicates that, all else being equal, higher actual knowledge corresponds to higher perceived knowledge. On average, individuals who are highly knowledgeable about climate change are aware of their expertise.
<table><caption>**Table 3**
Main Results from OLS Multiple Regression on various forms of individual knowledge (actual, perceived and Perceived-to-Actual Knowledge Ratio).</caption><thead><tr><th rowspan="2">VARIABLES</th><th>(1)</th><th>(2)</th><th>(3)</th><th>(4)</th><th>(5)</th><th>(6)</th><th>(7)</th></tr><tr><th>Objknow3 (log)</th><th>Objknow3 (log)</th><th>Subjknow (log)</th><th>Subjknow (log)</th><th>Overestim. Know</th><th>Overestim. Know</th><th>Overestim. Know</th></tr></thead><tbody><tr><td>Subj. Knowledge (log)</td><td></td><td>0.1311**<br>(0.0625)</td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Objknow3 (log)</td><td></td><td></td><td></td><td>0.0830**<br>(0.0374)</td><td></td><td></td><td></td></tr><tr><td>Quartile 2 Objknow3</td><td></td><td></td><td></td><td></td><td></td><td>-1.1731***<br>(0.1412)</td><td>-1.2424***<br>(0.1102)</td></tr><tr><td>Quartile 3 Objknow3</td><td></td><td></td><td></td><td></td><td></td><td>-1.3902***<br>(0.1421)</td><td>-1.5132***<br>(0.1010)</td></tr><tr><td>Quartile 4 Objknow3</td><td></td><td></td><td></td><td></td><td></td><td>-1.5244***<br>(0.1420)</td><td>-1.6591***<br>(0.1018)</td></tr><tr><td>Woman</td><td>-0.0909**<br>(0.0406)</td><td>-0.0854**<br>(0.0415)</td><td>-0.0493<br>(0.0352)</td><td>-0.0422<br>(0.0352)</td><td>0.1119<br>(0.1058)</td><td></td><td>0.0350<br>(0.0858)</td></tr><tr><td>30-44 Years</td><td>-0.0722<br>(0.0786)</td><td>-0.0516<br>(0.0795)</td><td>-0.0527<br>(0.0563)</td><td>-0.0456<br>(0.0562)</td><td>0.0989<br>(0.1554)</td><td></td><td>0.0407<br>(0.1203)</td></tr><tr><td>45-59 Years</td><td>0.0542<br>(0.0551)</td><td>0.0605<br>(0.0572)</td><td>-0.0423<br>(0.0472)</td><td>-0.0463<br>(0.0476)</td><td>-0.1872<br>(0.1272)</td><td></td><td>-0.1439<br>(0.1013)</td></tr><tr><td>60+ Years</td><td>0.0597<br>(0.0737)</td><td>0.0663<br>(0.0746)</td><td>-0.0452<br>(0.0558)</td><td>-0.0500<br>(0.0556)</td><td>-0.2382<br>(0.1511)</td><td></td><td>-0.2373**<br>(0.1142)</td></tr><tr><td>PCS sup.</td><td>-0.0686<br>(0.0511)</td><td>-0.0757<br>(0.0535)</td><td>0.0042<br>(0.0518)</td><td>0.0129<br>(0.0525)</td><td>0.2741*<br>(0.1562)</td><td></td><td>0.2194*<br>(0.1321)</td></tr><tr><td>High.Sch.</td><td>0.2614***<br>(0.0753)</td><td>0.2288***<br>(0.0735)</td><td>0.1043**<br>(0.0507)</td><td>0.0836<br>(0.0513)</td><td>-0.2969*<br>(0.1607)</td><td></td><td>-0.0041<br>(0.1304)</td></tr><tr><td>High.Sch.+2</td><td>0.3006***<br>(0.0812)</td><td>0.2690***<br>(0.0798)</td><td>0.1620***<br>(0.0585)</td><td>0.1372**<br>(0.0586)</td><td>-0.3685**<br>(0.1699)</td><td></td><td>-0.0481<br>(0.1237)</td></tr><tr><td>High.Sch.+3/+4</td><td>0.3001***<br>(0.0784)</td><td>0.2693***<br>(0.0773)</td><td>0.0812<br>(0.0585)</td><td>0.0612<br>(0.0601)</td><td>-0.4372***<br>(0.1515)</td><td></td><td>-0.1081<br>(0.1152)</td></tr><tr><td>Master/PhD</td><td>0.4963***<br>(0.0968)</td><td>0.4856***<br>(0.0989)</td><td>-0.0351<br>(0.0965)</td><td>-0.0758<br>(0.0973)</td><td>-0.8794***<br>(0.2061)</td><td></td><td>-0.3400**<br>(0.1343)</td></tr><tr><td>Income + 30 k$ per year</td><td>0.0913<br>(0.0577)</td><td>0.0818<br>(0.0583)</td><td>0.0850**<br>(0.0355)</td><td>0.0762**<br>(0.0367)</td><td>-0.0975<br>(0.1370)</td><td></td><td>-0.0034<br>(0.0940)</td></tr><tr><td>Intention (log)</td><td>0.0792<br>(0.0525)</td><td>-0.0096<br>(0.0519)</td><td>0.3615***<br>(0.0532)</td><td>0.3541***<br>(0.0527)</td><td>0.5502***<br>(0.1136)</td><td></td><td>0.6839***<br>(0.1095)</td></tr><tr><td>Beliefs - Class2</td><td>0.0645<br>(0.0493)</td><td>0.0579<br>(0.0491)</td><td>0.0814**<br>(0.0330)</td><td>0.0749**<br>(0.0336)</td><td>-0.0527<br>(0.1086)</td><td></td><td>-0.0135<br>(0.0743)</td></tr><tr><td>Beliefs - Class3</td><td>-0.3792***<br>(0.0702)</td><td>-0.3438***<br>(0.0709)</td><td>-0.1485***<br>(0.0487)</td><td>-0.1183**<br>(0.0539)</td><td>0.4160***<br>(0.1521)</td><td></td><td>0.0514<br>(0.1185)</td></tr><tr><td>Density town (log)</td><td>0.0142<br>(0.0158)</td><td>0.0101<br>(0.0161)</td><td>0.0164<br>(0.0132)</td><td>0.0156<br>(0.0134)</td><td>-0.0203<br>(0.0451)</td><td></td><td>-0.0178<br>(0.0380)</td></tr><tr><td>Real estate price (log)</td><td>-0.0101<br>(0.0630)</td><td>-0.0111<br>(0.0628)</td><td>-0.0863*<br>(0.0502)</td><td>-0.0820<br>(0.0516)</td><td>0.0353<br>(0.1450)</td><td></td><td>0.0642<br>(0.1098)</td></tr><tr><td>Network. Med. Educ = 2</td><td>-0.1241<br>(0.0796)</td><td>-0.0886<br>(0.0734)</td><td>-0.0771<br>(0.0859)</td><td>-0.0685<br>(0.0852)</td><td>0.0273<br>(0.1524)</td><td></td><td>-0.0459<br>(0.1359)</td></tr><tr><td>Network. Med. Educ = 3</td><td>-0.2047**<br>(0.1030)</td><td>-0.1651*<br>(0.0985)</td><td>-0.1069<br>(0.0906)</td><td>-0.0885<br>(0.0874)</td><td>0.1695<br>(0.1745)</td><td></td><td>0.0397<br>(0.1439)</td></tr><tr><td>Network. Med. Educ = 4</td><td>-0.1908*<br>(0.0983)</td><td>-0.1488<br>(0.0960)</td><td>-0.0588<br>(0.1017)</td><td>-0.0466<br>(0.1030)</td><td>0.1562<br>(0.2015)</td><td></td><td>-0.0040<br>(0.1613)</td></tr><tr><td>Network. Med. Educ = 5</td><td>-0.2316*<br>(0.1267)</td><td>-0.2115*<br>(0.1227)</td><td>0.0559<br>(0.1096)</td><td>0.0752<br>(0.1098)</td><td>0.2648<br>(0.2403)</td><td></td><td>-0.1743<br>(0.1718)</td></tr><tr><td>Network. Med. Inc. = 2</td><td>0.0500<br>(0.0870)</td><td>0.0332<br>(0.0865)</td><td>0.1170*<br>(0.0622)</td><td>0.1154*<br>(0.0615)</td><td>0.1092<br>(0.1678)</td><td></td><td>0.1033<br>(0.1102)</td></tr><tr><td>Network. Med. Inc. = 3</td><td>0.0401<br>(0.0939)</td><td>0.0234<br>(0.0947)</td><td>0.1424**<br>(0.0644)</td><td>0.1385**<br>(0.0651)</td><td>0.2248<br>(0.1915)</td><td></td><td>0.2371*<br>(0.1210)</td></tr><tr><td>Network. Med. Inc. = 4</td><td>-0.0465<br>(0.0934)</td><td>-0.0679<br>(0.0953)</td><td>0.1776**<br>(0.0755)</td><td>0.1806**<br>(0.0773)</td><td>0.4348*<br>(0.2469)</td><td></td><td>0.4266**<br>(0.1932)</td></tr><tr><td>Network. Med. Inc. = 5</td><td>0.0560<br>(0.1145)</td><td>0.0577<br>(0.1243)</td><td>-0.0016<br>(0.0665)</td><td>-0.0046<br>(0.0692)</td><td>-0.1165<br>(0.2197)</td><td></td><td>-0.1821<br>(0.1715)</td></tr><tr><td>Constant</td><td>-0.8433*<br>(0.5034)</td><td>-0.6269<br>(0.5018)</td><td>-0.4488<br>(0.4087)</td><td>-0.4087<br>(0.4150)</td><td>0.4851<br>(1.1523)</td><td>2.3467***<br>(0.1407)</td><td>0.9468<br>(0.8553)</td></tr><tr><td>Observations</td><td>768</td><td>756</td><td>762</td><td>756</td><td>768</td><td>770</td><td>768</td></tr><tr><td>R-squared</td><td>0.2744</td><td>0.2547</td><td>0.2897</td><td>0.2925</td><td>0.1478</td><td>0.3545</td><td>0.4807</td></tr></tbody></table>

Robust standard errors in parentheses, ***p < 0.01, **p < 0.05, *p < 0.1.

Nonetheless, we observe significant differences in the profiles associated with each form of knowledge. Actual knowledge is linked to being male, having a high level of education, and holding beliefs aligned with climate change concerns (fewer individuals in belief class 3). Conversely, perceived knowledge is associated with intermediate education levels, high income, a strong self-declared intention to address climate change, and beliefs that align with climate change concerns (fewer individuals in belief classes 1 and 3).

Lastly, regarding the perceived-actual knowledge gap, we find that

individuals with high levels of intention to act on climate change and beliefs misaligned with climate change concerns (e.g., low concern, strong faith in technological solutions, or perceiving climate change as a distant phenomenon) tend to exhibit a larger gap.

All these analyses show that perceived knowledge and objective knowledge on climate change are not equivalent - which validates hypothesis H1a - and therefore not substitutable in the studies.

We can further explore the relationship between actual knowledge and individuals' ability to accurately assess their climate change literacy
(i.e., the difference between perceived and actual knowledge). Using OLS regression (Table 3 – columns 6–7), we demonstrate that individuals with higher levels of actual knowledge in climate change literacy are less likely to overestimate their own knowledge. This effect becomes more pronounced as the level of actual knowledge increases and remains robust after including control variables. These findings provide evidence supporting the Dunning-Kruger effect and Hypothesis H1b.

### 4.3. Causal effect identification of objective knowledge

When switching to the link between knowledge hold by economic agents and their behavior, the relationship seems at best weak (see Table 4 - OLS regressions).

Performing multiple regression methods shows that carbon footprint elasticity to objective knowledge is negative but low and not always significant at 5 % of risk (see Table 4). Our results ranges from -0.0563 to -0.0837 %. This means the more individuals tend to hold high objective knowledge on CC the less are their individual carbon footprint.

The use of alternative scores of objective knowledge (without Q4, and Q4-Q6) demonstrates a higher and more robust correlation to the total individual carbon footprint. Nonetheless, the effect of objective knowledge is no longer significant at the disaggregate scale (sub-categories of carbon footprint in Table 4). The use of perceived knowledge often estimated in marketing studies is fruitless, displaying a low and non-significant positive elasticity (Table 4 - column 1).

Now, our aim is to quantify the causal impact of knowledge on individuals' carbon footprints using the instrumental variable method. In this final approach, we create exogenous variation in individual objective knowledge using two instruments: the average knowledge and dominant beliefs within the individual's cluster. These instruments meet all empirical requirements associated with the instrumental variables regression strategy (including tests for underidentification, over-identification, and weak instruments).

We observe significant differences compared to models based on the OLS method (see Table 5 and Fig. 12). First, the elasticity of carbon footprint with respect to objective knowledge increases from -0.0837 to -0.2077 while remaining statistically significant at the 1 % level – thus validating Hypothesis H2. On average, a 1 % increase in an individual's level of objective knowledge results in a 0.2 % reduction in their individual carbon footprint.

Second, while the OLS method fails to identify an effect on the subcomponents of the carbon footprint, our IV strategy reveals two significant effects of objective knowledge. We find a strong and negative elasticity of transport-related carbon footprint to objective knowledge (-0.7) and a moderate effect on food-related carbon footprint (-0.17).

Conversely, we do not identify any impact on other subcomponents of the individual carbon footprint (home, miscellaneous and digital emissions).

Additionally, focusing on the coefficients for control variables at the 10 % significance level, individuals in higher socio-professional categories exhibit a 9.5 % higher carbon footprint compared to those in lower socio-professional categories (column 1). This result hold when controlling for individual income. When looking at columns 2 and 4, this result seems to come from the transport and home carbon footprint where higher socio-professional categories are correlated to higher carbon footprint. We also find a positive and significant link between individual income and individual carbon footprint (columns 1 and 2). Moreover, women seem more inclined to have a lower carbon footprint than men do (except for home and digital carbon footprint). This result is in line with other studies (Cordonier, 2022; ADEME, 2023). According to Table 5, this is mostly due to food and transport carbon footprint (see also ADEME, 2023). Conversely, women have a higher carbon footprint than men when switching to the carbon footprint associated with miscellaneous consumption.

Regarding the declared intention, Table 5 reveals a significant yet slightly negative correlation only with the carbon footprint of food, along with a weak negative correlation with digital and miscellaneous consumption. When controls are applied, we observe only a negative correlation between belief category 2 and the carbon footprint associated with food carbon footprint.

The population density of an individual's town of residence appears to capture a small but expected negative coefficient with carbon footprint. This is primarily driven by a negative correlation with transport- and home-related carbon footprints, slightly offset by a positive and significant correlation with digital carbon footprint. In addition, real estate prices in the individual's location show strong positive coefficients in four out of six specifications. Nevertheless, we assume that this variable captures the primary impact of the individual's economic environment while simultaneously accounting for undeclared responses in the income variable.

Interestingly, the mean carbon footprint of the respondent's network emerges as a significant predictor of individual emissions, with elasticities ranging from 0.3 to 0.5 in four out of six specifications. This result echoes earlier findings in behavioral economics and psychology (e.g., Allcott, 2011; Nolan et al., 2008), which highlight the powerful role of descriptive norms in shaping environmental behaviors. Similar effects have been observed across domains, including regional peer behaviors (Binder et al., 2019) and close social influence (Salazar et al., 2013). While our primary focus remains on the cognitive determinants of carbon-intensive behavior, this finding confirms that behavioral spill-overs from one's social environment may rival or exceed the impact of

<caption>**Table 4**</caption>

Main results from OLS multiple regression on individual carbon footprint and various subcomponents.

<table><thead><tr><th>Dep. Var. (log)</th><th>(1)</th><th>(2)</th><th>(3)</th><th>(4)</th><th>(5)</th><th>(6)</th><th>(7)</th><th>(8)</th><th>(9)</th></tr><tr><th>VARIABLES</th><th>CF</th><th>CF</th><th>CF</th><th>CF</th><th>CFtrans</th><th>CFfood</th><th>CFhome</th><th>CFmisc</th><th>CFdig</th></tr></thead><tbody><tr><td>SubjKnow(log)</td><td>0.0491<br>(0.0438)</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>ObjKnow1 (log)</td><td></td><td>-0.0563<br>(0.0380)</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>ObjKnow3 (log)</td><td></td><td></td><td>-0.0837**<br>(0.0377)</td><td>-0.0633*<br>(0.0343)</td><td>-0.1852<br>(0.1212)</td><td>-0.0066<br>(0.0357)</td><td>-0.0782<br>(0.1108)</td><td>0.0561<br>(0.1036)</td><td>0.0031<br>(0.0848)</td></tr><tr><td>Individual Socio-Economic Controls</td><td>YES</td><td>YES</td><td>YES</td><td>YES</td><td>YES</td><td>YES</td><td>YES</td><td>YES</td><td>YES</td></tr><tr><td>Individual beliefs</td><td>YES</td><td>YES</td><td>YES</td><td>YES</td><td>YES</td><td>YES</td><td>YES</td><td>YES</td><td>YES</td></tr><tr><td>Individual intention</td><td>YES</td><td>YES</td><td>YES</td><td>YES</td><td>YES</td><td>YES</td><td>YES</td><td>YES</td><td>YES</td></tr><tr><td>Characteristics of the individual's environment</td><td>YES</td><td>YES</td><td>YES</td><td>YES</td><td>YES</td><td>YES</td><td>YES</td><td>YES</td><td>YES</td></tr><tr><td>Socio-economic median of the individual's network</td><td>YES</td><td>YES</td><td>YES</td><td>YES</td><td>YES</td><td>YES</td><td>YES</td><td>YES</td><td>YES</td></tr><tr><td>Mean carbon footprint of the individual's network</td><td>NO</td><td>NO</td><td>NO</td><td>YES</td><td>NO</td><td>NO</td><td>NO</td><td>NO</td><td>NO</td></tr><tr><td>Observations</td><td>762</td><td>772</td><td>768</td><td>764</td><td>758</td><td>768</td><td>767</td><td>761</td><td>756</td></tr><tr><td>R-squared</td><td>0.2106</td><td>0.2070</td><td>0.2124</td><td>0.2467</td><td>0.2184</td><td>0.2808</td><td>0.1519</td><td>0.1388</td><td>0.1276</td></tr></tbody></table>

Robust standard errors in parentheses, ***p < 0.01, **p < 0.05, *p < 0.1.
<table><caption>**Table 5**
Main results from the instrumental regression methodology applied to actual knowledge. Note: Auxiliary regressions are available in the Supplementary materials E.</caption><thead><tr><th>Dep. Var. (log)</th><th>(1)</th><th>(2)</th><th>(4)</th><th>(5)</th><th>(6)</th><th>(7)</th></tr><tr><th>VARIABLES</th><th>CF</th><th>CFtrans</th><th>CFfood</th><th>CFhome</th><th>CFmisc</th><th>CFdig</th></tr></thead><tbody><tr><td>ObjKnow3 (log)</td><td>-0.2077**<br>(0.0832)</td><td>-0.6950**<br>(0.2834)</td><td>-0.1707**<br>(0.0711)</td><td>0.1775<br>(0.3063)</td><td>0.1416<br>(0.2871)</td><td>-0.3113<br>(0.3259)</td></tr><tr><td>Woman</td><td>-0.1170***<br>(0.0326)</td><td>-0.3930***<br>(0.1085)</td><td>-0.1553***<br>(0.0369)</td><td>-0.0290<br>(0.0905)</td><td>0.2171***<br>(0.0717)</td><td>-0.0129<br>(0.0880)</td></tr><tr><td>30-44 Years</td><td>-0.0930**<br>(0.0434)</td><td>-0.2004*<br>(0.1209)</td><td>-0.0819*<br>(0.0471)</td><td>0.0399<br>(0.1128)</td><td>-0.1714**<br>(0.0776)</td><td>0.1508<br>(0.1166)</td></tr><tr><td>45-59 Years</td><td>-0.0007<br>(0.0389)</td><td>-0.0056<br>(0.1094)</td><td>-0.0075<br>(0.0461)</td><td>0.0939<br>(0.0996)</td><td>-0.2082***<br>(0.0762)</td><td>0.0486<br>(0.0863)</td></tr><tr><td>60+ Years</td><td>-0.0451<br>(0.0463)</td><td>-0.4449***<br>(0.1366)</td><td>-0.0568<br>(0.0448)</td><td>0.6086***<br>(0.1279)</td><td>-0.3792***<br>(0.1042)</td><td>-0.3478***<br>(0.1191)</td></tr><tr><td>PCS sup.</td><td>0.0949**<br>(0.0460)</td><td>0.2681**<br>(0.1338)</td><td>0.0093<br>(0.0560)</td><td>0.2455***<br>(0.0937)</td><td>0.0070<br>(0.0686)</td><td>-0.0157<br>(0.1149)</td></tr><tr><td>High.Sch.</td><td>0.0683<br>(0.0599)</td><td>0.2582<br>(0.1735)</td><td>-0.0083<br>(0.0528)</td><td>-0.0064<br>(0.1612)</td><td>0.0005<br>(0.1249)</td><td>-0.0111<br>(0.1377)</td></tr><tr><td>High.Sch.+2</td><td>0.0864<br>(0.0605)</td><td>0.2990*<br>(0.1616)</td><td>0.0427<br>(0.0558)</td><td>0.0015<br>(0.1651)</td><td>0.0301<br>(0.1073)</td><td>0.1014<br>(0.1592)</td></tr><tr><td>High.Sch.+3/+4</td><td>0.0732<br>(0.0610)</td><td>0.3672**<br>(0.1765)</td><td>0.0196<br>(0.0645)</td><td>-0.0560<br>(0.1974)</td><td>0.1335<br>(0.0978)</td><td>-0.0644<br>(0.2019)</td></tr><tr><td>Master/PhD</td><td>-0.0640<br>(0.0960)</td><td>0.1522<br>(0.2731)</td><td>-0.1677<br>(0.1170)</td><td>-0.4594*<br>(0.2386)</td><td>0.0277<br>(0.1644)</td><td>-0.0704<br>(0.2607)</td></tr><tr><td>Income + 30 k€ per year</td><td>0.0888***<br>(0.0335)</td><td>0.2837***<br>(0.1049)</td><td>0.0331<br>(0.0442)</td><td>0.1105<br>(0.0993)</td><td>-0.0141<br>(0.0783)</td><td>0.1048<br>(0.1138)</td></tr><tr><td>Intention (log)</td><td>-0.0589<br>(0.0369)</td><td>0.0054<br>(0.1166)</td><td>-0.1640***<br>(0.0329)</td><td>0.0918<br>(0.1077)</td><td>-0.1553*<br>(0.0867)</td><td>-0.1536*<br>(0.0800)</td></tr><tr><td>Beliefs - Class2</td><td>-0.0431<br>(0.0374)</td><td>0.0358<br>(0.0967)</td><td>-0.0745**<br>(0.0379)</td><td>0.0600<br>(0.0897)</td><td>-0.1275*<br>(0.0764)</td><td>-0.2093*<br>(0.1082)</td></tr><tr><td>Beliefs - Class3</td><td>-0.0469<br>(0.0568)</td><td>-0.1967<br>(0.1751)</td><td>-0.0331<br>(0.0513)</td><td>0.0849<br>(0.1568)</td><td>0.0972<br>(0.1385)</td><td>-0.2458<br>(0.1582)</td></tr><tr><td>Density town (log)</td><td>-0.0223**<br>(0.0088)</td><td>-0.0634**<br>(0.0295)</td><td>-0.0061<br>(0.0101)</td><td>-0.0435*<br>(0.0253)</td><td>0.0189<br>(0.0218)</td><td>0.0691***<br>(0.0251)</td></tr><tr><td>Real estate price (log)</td><td>0.0934***<br>(0.0345)</td><td>0.3479***<br>(0.0944)</td><td>0.0746*<br>(0.0432)</td><td>0.0098<br>(0.1042)</td><td>0.1881***<br>(0.0728)</td><td>-0.1877**<br>(0.0881)</td></tr><tr><td>Network. Med. Educ = 2</td><td>-0.0848*<br>(0.0464)</td><td>-0.3177**<br>(0.1605)</td><td>-0.0195<br>(0.0521)</td><td>-0.0122<br>(0.1640)</td><td>0.0206<br>(0.0919)</td><td>0.2329<br>(0.1915)</td></tr><tr><td>Network. Med. Educ = 3</td><td>-0.1138**<br>(0.0548)</td><td>-0.3307*<br>(0.1700)</td><td>-0.1013*<br>(0.0566)</td><td>0.0774<br>(0.1950)</td><td>-0.0724<br>(0.0771)</td><td>0.4639**<br>(0.2043)</td></tr><tr><td>Network. Med. Educ = 4</td><td>-0.0905<br>(0.0710)</td><td>-0.4273**<br>(0.2034)</td><td>-0.1012<br>(0.0753)</td><td>0.2450<br>(0.1956)</td><td>-0.1278<br>(0.1080)</td><td>0.0106<br>(0.2414)</td></tr><tr><td>Network. Med. Educ = 5</td><td>0.0884<br>(0.0764)</td><td>-0.1083<br>(0.1844)</td><td>-0.1383<br>(0.1009)</td><td>0.8845***<br>(0.2330)</td><td>-0.1455<br>(0.1439)</td><td>0.7567***<br>(0.2531)</td></tr><tr><td>Network. Med. Inc. = 2</td><td>-0.1300***<br>(0.0364)</td><td>-0.3340***<br>(0.1105)</td><td>-0.0646<br>(0.0473)</td><td>-0.2370*<br>(0.1236)</td><td>0.0063<br>(0.0795)</td><td>0.0277<br>(0.1624)</td></tr><tr><td>Network. Med. Inc. = 3</td><td>-0.0596<br>(0.0367)</td><td>-0.1219<br>(0.1149)</td><td>-0.0563<br>(0.0491)</td><td>-0.2501*<br>(0.1362)</td><td>-0.0022<br>(0.1048)</td><td>-0.1951<br>(0.1418)</td></tr><tr><td>Network. Med. Inc. = 4</td><td>-0.0956*<br>(0.0500)</td><td>-0.2587*<br>(0.1348)</td><td>-0.0461<br>(0.0661)</td><td>-0.2641<br>(0.1799)</td><td>0.1231<br>(0.1210)</td><td>-0.2754<br>(0.1776)</td></tr><tr><td>Network. Med. Inc. = 5</td><td>-0.0478<br>(0.0409)</td><td>-0.1677<br>(0.1579)</td><td>0.1626**<br>(0.0800)</td><td>-0.2567<br>(0.2002)</td><td>0.2114<br>(0.1630)</td><td>0.4433**<br>(0.2045)</td></tr><tr><td>Network. av. CF (log)</td><td>0.3379***<br>(0.0917)</td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Network. av. CF transport (log)</td><td></td><td>0.3363***<br>(0.1266)</td><td></td><td></td><td></td><td></td></tr><tr><td>Network. av. CF food (log)</td><td></td><td></td><td>0.3003***<br>(0.0937)</td><td></td><td></td><td></td></tr><tr><td>Network. av. CF home (log)</td><td></td><td></td><td></td><td>0.1250<br>(0.1139)</td><td></td><td></td></tr><tr><td>Network. av. CF misc. (log)</td><td></td><td></td><td></td><td></td><td>0.4734***<br>(0.1034)</td><td></td></tr><tr><td>Network. av. CF dig. (log)</td><td></td><td></td><td></td><td></td><td></td><td>0.1019<br>(0.1237)</td></tr><tr><td>Constant</td><td>0.7832***<br>(0.2969)</td><td>-2.2364***<br>(0.7397)</td><td>0.2771<br>(0.3311)</td><td>-0.0570<br>(0.9046)</td><td>-1.7277**<br>(0.7323)</td><td>-0.6324<br>(0.8651)</td></tr><tr><td>Underidentification test (Kleibergen-Paap)</td><td>9.183**<br>(36.35)</td><td>8.98**<br>(32.599)</td><td>11.031**<br>(30.826)</td><td>10.847**<br>(46.299)</td><td>10.640**<br>(55.749)</td><td>10.279**<br>(47.149)</td></tr><tr><td>Weak identification test (Kleibergen-Paap)</td><td>2.444</td><td>0.847</td><td>2.179</td><td>2.974</td><td>2.32</td><td>1.795</td></tr><tr><td>Overidentification test (Hansen J statistic)</td><td>764</td><td>754</td><td>764</td><td>763</td><td>757</td><td>752</td></tr><tr><td>R-squared</td><td>0.2158</td><td>0.1853</td><td>0.2575</td><td>0.1345</td><td>0.1686</td><td>0.1175</td></tr></tbody></table>

Robust standard errors in parentheses, ***p < 0.01, **p < 0.05, *p < 0.1. Note: We use three instruments by intersecting the average objective knowledge within the individual's cluster with the cluster's dominant belief category.
<figure><img src="image_16.png" /><figcaption>**Fig. 12.** Marginal effects of actual knowledge on carbon footprint based on OLS and IV regressions. Source: authors' calculations.</figcaption></figure>

information alone.

These results pave the way for a more in-depth discussion of the role of objective knowledge in reducing individual carbon footprints, which is the focus of the following section, along with related policy recommendations.

# 5. General discussion

This study investigates the nuanced relationship between individuals' objective knowledge of climate change and their associated carbon footprints, focusing on the mechanisms that drive pro-environmental behavior. Our analysis makes three significant contributions: (1) a robust and nuanced measurement of objective climate knowledge, and its comparison with the subjective knowledge level of individuals (2) causal inference (between knowledge and carbon footprint) through instrumental variable regressions that control for a comprehensive set of socio-economic and behavioral variables (3) all this using a precise assessment of carbon footprints and their sub-components using a detailed carbon footprint calculator.

## 5.1. Knowledge and carbon footprint components

Our results establish a significant negative relationship between objectively assessed knowledge and total carbon footprint, with substantial heterogeneity across footprint subcomponents. Specifically, our causal estimate suggests that increasing knowledge from a very low score (13 %) to full knowledge (100 %) would lead to an average reduction of 3 tonnes of CO₂ equivalent per year (see Fig. 12). A more realistic improvement—from the observed average knowledge score (~50 %) to full knowledge—would still result in an average reduction of 1 [0.2–1.6] tonnes of CO₂ equivalent per year. This reduction is comparable to the average annual impact of home-related emissions (1.3 t CO₂ eq.) or the emissions from driving between 5000 and 9000 km. While this alone is insufficient to meet climate targets, it constitutes a meaningful contribution and serves as a crucial lever for fostering further pro-environmental intentions and behaviors.

Our findings reveal that factual climate knowledge has a measurable and causal impact on individuals' carbon emissions, with this impact not being uniform across consumption domains. Specifically — in line with prior studies (e.g. Schleich et al., 2024; Chuvieco et al., 2021) — we find that knowledge has a particularly strong effect in the domain of transport, a moderate effect in food-related choices, and a negligible influence in housing and digital consumption. This heterogeneity can be explained by the level of agency individuals have in each domain. In transport and food, individuals have more control over their choices, such as reducing air travel, switching to public transportation, or altering dietary habits. In contrast, housing-related emissions are

heavily influenced by structural constraints—such as building energy performance, heating systems, or the feasibility of retrofitting—which are less responsive to individual knowledge in the short term. Similarly, digital emissions, being less visible and harder to directly link to individual behaviors, show weaker associations with knowledge.

These findings resonate with the conclusions of Johnson et al. (2024), who show that people systematically misjudge the carbon impact of various actions. High-impact behaviors such as flying or eating meat are often underestimated, while low-impact behaviors like recycling are overestimated. This suggests that even if individuals are informed about climate change, they need more detailed, concrete knowledge about the impact of their specific actions across different sectors. Kretschmer (2024) further underscores this point, demonstrating that informational nudges, such as ranking household actions, can improve carbon literacy but are rarely sufficient for sustained behavioral change. Our findings extend her work by showing that deeper, objectively assessed climate knowledge exerts a stronger and more consistent influence on emissions reduction.

Additionally, we confirm gender-based differences in carbon footprints, with women exhibiting a significantly lower footprint than men (-11.7 %), consistent with previous findings (Kanyama et al., 2021; Chuvieco et al., 2021; Guillén et al., 2019; Schleich et al., 2024). Our estimates fall within the range of previous studies (-3 % to -16 %). However, unlike Chuvieco et al. (2021) and Schleich et al. (2024), who relied on relatively basic measures of objective knowledge, we employ a more granular measure incorporating both fundamental climate science and individual-level mitigation knowledge questions. Yet, contrary to some prior findings suggesting that women possess greater climate change mitigation knowledge, we align with Douenne and Fabre (2020) in observing the opposite: men tend to score higher on our objective knowledge scale.

## 5.2. Causal evidence and methodological innovations

A key strength of our study lies in its ability to establish a causal link between objective knowledge and carbon footprint reduction. By employing instrumental variable regressions that leverage exogenous variation in individuals' social environments, we demonstrate that the estimated effect of knowledge on behavior is significantly stronger when accounting for endogeneity. This reinforces the importance of moving beyond simple correlations to derive robust causal inferences.

Our findings also reinforce the gap between perceived and objective knowledge, a phenomenon well-documented in climate change research (Stoutenborough and Vedlitz, 2014; Zhang et al., 2024). Many individuals with low objective knowledge overestimate their understanding—a result consistent with the Dunning-Kruger effect (Kruger and Dunning, 1999). This highlights the need for interventions that not only improve objective knowledge but also correct overconfidence and misinformation.

## 5.3. Social and structural constraints

Finally, our study underscores the interplay between individual agency and structural constraints in shaping carbon footprints. While knowledge and intention have meaningful effects on behavior, they are not equally influential across all domains of consumption. The lack of significant effects for certain high-emission categories, such as housing, suggests the presence of external constraints—financial, infrastructural, and regulatory—that limit the capacity of individuals to act on their knowledge and intentions. This confirms prior findings by Douenne and Fabre (2020) and reinforces the necessity of systemic policy interventions to complement individual behavioral change. Knowledge alone is necessary but insufficient for meaningful and lasting reductions in carbon footprints.

Moreover, our results align with those of Nordman et al. (2024), who illustrate how targeted professional advice and institutional support can
significantly reduce carbon footprints, particularly in domains like food services. Their findings, together with ours, reinforce the notion that while knowledge is important, its effectiveness is enhanced when it is coupled with external support structures and context-sensitive interventions.

## 5.4. Policy implications and contributions

Our study underscores the critical role of factual carbon knowledge in reducing individual carbon footprints. Consistent with prior research on carbon literacy (Schleich et al., 2024), our findings support the need for targeted educational campaigns that go beyond general awareness to emphasize actionable and context-specific knowledge. To maximize their effectiveness, such campaigns should be tailored to specific behavioral domains (e.g., food, transportation) and take into account the real and perceived constraints that individuals face in the corresponding domain.

While the Knowledge Deficit Model is widely considered outdated, our findings highlight that knowledge still has an effect of its own that should not be overlooked. Knowledge remains a tangible and actionable lever for change, as it can be directly addressed through education, information campaigns, awareness initiatives, and citizen workshops. Enhancing people's knowledge remains crucial, as our study demonstrates a significant causal link between assessed carbon knowledge and individual carbon footprints. We confirm that both general and practical knowledge of climate change are, on average, low—leaving considerable room for impact. Moreover, in a dynamic and reciprocal process, knowledge, beliefs, and intentions shape one another. Besides, Russell and Ashkanasy, 2021 emphasize the importance of emotion in climate messaging. Their experimental findings suggest that affective engagement can enhance cognitive responses and thus foster stronger behavioral commitments. This highlights a potential complementarity between rational information provision and emotionally resonant interventions. The integration of emotional appeal with factual, rational knowledge could therefore be an effective way to deepen commitment to climate action and facilitate lasting behavior change.

Moreover, the work by Cordero et al. (2020) provides a useful benchmark for the impact of educational interventions. Their intensive educational program led to individual emission reductions on the order of 2.86 tons of CO2 per year—comparable to the magnitude of effects estimated in our model. This convergence supports the credibility and practical relevance of our findings, underlining the potential for scaled educational interventions to contribute meaningfully to national decarbonization strategies. Kretschmer (2004) suggests that individuals struggle to incorporate new information into their decision-making when it is presented without prior context or integration into existing mental models. Knowledge appears to matter most when paired with the capacity for behavior adjustment—confirming once again that literacy must go hand-in-hand with feasibility.

Altogether, our results point to the need for integrated climate education programs that not only disseminate factual knowledge but also engage affective pathways and address structural barriers. Knowledge is a necessary condition for action, and to a certain extent it is sufficient in itself to significantly influence behavior. Nevertheless, it becomes most effective when reinforced by enabling environments and motivational cues. Thus, combining informative campaigns with emotionally resonant messaging and structural support will be crucial to maximizing their impact and driving sustainable behavioral change.

## 5.5. Limitations

Although this research has sought to overcome a number of limitations identified in the literature, it also has its own limitations.

A first limitation concerns the sample of respondents. The data collection method certainly made it possible to isolate the effect of knowledge in an original way, via knowledge of the clusters of the

interviewers' entourage. However, it remains true that this sample is not representative of the French population. We lack insight into the extent of sample selection bias, as individuals are not randomly selected from the French population. On the other hand, it's improbable to conduct data collection via a polling company due to the extensive nature of the survey (comprising more than 90 questions, with an average survey time of 26 min), and primarily because of the network structure of the data for the peer effects identification. In order to minimize the consequences of this limitation, statistical adjustments have been made as part of the analyses in this article.

The second limitation is that we are not testing an application or measure that would increase people's knowledge. We are not in a position to say whether it is easy or not to persistently increase the general knowledge of the population about global warming. The question of the method, frequency and distribution channel of this increase in knowledge remains open, in order to find the most effective way of increasing people's knowledge.

## 5.6. Future research

A number of research avenues are opening up as a result of this work.

Firstly, it would seem appropriate to try and confirm this initial causal result using other methodologies: randomized controlled trials, differences in differences, regression on discontinuity, etc.

We would then need to study how beliefs and knowledge, as well as the socio-economic characteristics of individuals, can affect the way in which people go about reducing their carbon footprint (hierarchy of action levers).

In addition, in order to clarify the disparate results on the sub-categories of the carbon footprint, it would be interesting to question individuals on the elements (transport, food, etc.) for which they think they can reduce their EC and on those for which they don't think they can (we are measuring here an overall perceived difficulty and not per element). Some carbon calculators, like the one used in this experiment, offer respondents an extension of the simulation, allowing them to virtually decide which practices to modify to achieve a given footprint reduction. Research has already shown the value of this type of extension (Burgui-Burgui and Chuvieco, 2020). On the one hand, it puts individuals in a perspective of modifying their practices. On the other hand, it enables them to see concretely, for each sub-category, what a carbon footprint reduction of a given level would imply in terms of changes. In addition, although this simulation of changes does not remove the constraints on the individual, it does let them decide and arbitrate for themselves the changes that are possible and consented to. The question of the impact on actual behavior, in the short and long term, remains to be studied and could be a fruitful avenue of research.

Furthermore, in order to take into account reciprocal causalities and the evolution of individuals over time, we would like to propose a longitudinal follow-up of interviewees. This would enable us to see how knowledge influences concerns and beliefs (and vice-versa, since concerns can motivate a search for information), but also behaviors (which can themselves influence concerns, positively or negatively).

Finally, a number of individuals - including those with a high level of concern and knowledge about CC - now seem to be giving up on climate change, believing that no matter how they behave, "we won't make it". Grothmann and Patt (2005) have already noted the negative impact of fatalism (along with denial and wishful thinking) on individual intention to adapt behavior to the presence and prospect of climate change. It therefore seems crucial to quantify this defeatism at present, and to study the extent of its effect on behavior. Defeatism is not the same as climate change contrarianism, although the behavioural consequences of both can be similar. Combating defeatism does not rely on the same solutions as combating climate contrarianism, and this opens up the necessary research perspectives. More broadly, it seems relevant to evaluate the optimism (resp. the pessimism) of the respondents regarding the future of their relatives, of humanity (relative to the CC or
more globally). Optimism and pessimism can be brakes or motors for action. The possibility of crossing pessimism/optimism and action/inaction in a semantic square seems to be a relevant avenue.

## 6. Conclusion

This paper demonstrates that climate knowledge plays a causal role in reducing individuals' carbon emissions. This result builds on a robust methodological approach combining a multidimensional and operational measure of objectively assessed climate knowledge, a detailed and disaggregated carbon footprint for each respondent, a rich set of belief, concern, and social norm variables, and a causal estimation strategy using instrumental variables to isolate the independent effect of knowledge on behavior.

Our findings reveal that factual climate knowledge is often overestimated by individuals with lower actual competence—a pattern consistent with the Dunning-Kruger effect. However, once exogenous variation in knowledge is isolated, we find a substantial and significant impact on total carbon footprint, especially in high-emission areas such as transport and food.

Importantly, improving climate knowledge is not merely a matter of raising awareness; it also involves correcting persistent misperceptions about which actions are most effective. Moreover, knowledge supports—but does not substitute for—psychological drivers such as concern, social norms, and perceived efficacy.

These findings carry key policy implications. Educational efforts should focus on developing concrete, actionable knowledge rather than relying solely on abstract and/or generic messages. Carbon literacy should be systematically integrated into both formal education and public outreach. Finally, interventions that combine knowledge-building with motivational and structural support—such as better access to low-carbon options or clearer labeling—are likely to be most effective.

While prior studies have shown that climate education (Cordero et al., 2020; Moselhy, 2024) or framing (Kretschmer, 2024) can foster pro-environmental attitudes, few have established a clear causal link between actual climate knowledge and individual carbon emissions. This paper contributes to filling that gap.

## CRediT authorship contribution statement

**Florian Fizaine:** Writing – original draft, Visualization, Validation, Project administration, Methodology, Investigation, Formal analysis, Data curation, Conceptualization. **Guillaume Le Borgne:** Writing – original draft, Validation, Methodology, Investigation, Formal analysis, Conceptualization.

## Declaration of competing interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

## Appendix A. Supplementary data

Supplementary data to this article can be found online at https://doi.org/10.1016/j.jenvman.2025.125604.

## Data availability

Data will be made available on request.

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