# Do perceived natural environment and eco-anxiety relate to mental health and quality of life? Findings from a representative adult sample

Aglaé Drion, Pierre O. Jacquet, Philip J. Batterham, Kimberly Brown

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Aglaé Drion, Pierre O. Jacquet, Philip J. Batterham, Kimberly Brown. Do perceived natural environment and eco-anxiety relate to mental health and quality of life? Findings from a representative adult sample. Wellbeing, Space and Society, 2024, 7, 10.1016/j.wss.2024.100220. hal-04831747

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# Do perceived natural environment and eco-anxiety relate to mental health and quality of life? Findings from a representative adult sample

Aglaé Drion<sup>a,b</sup>, Pierre O. Jacquet<sup>a,c,d</sup>, Kimberly Brown<sup>e</sup>, Philip J. Batterham<sup>b,*</sup>

## ARTICLE INFO

**Keywords:**

- Environment
- Climate change
- Mental health
- Well-being
- Quality of life

## ABSTRACT

Using data representing the Australian community (n=1083), this study examines whether there is a link between the way individuals perceive their natural living environment and their mental health state. Linear mixed model regressions are used to assess the associations of environmental (perception of environmental problems and eco-anxiety) and non-environmental variables on mental health and quality of life. A small significant association between eco-anxiety and quality of life was found but no significant effects of environmental variables were obtained on mental distress. Our findings suggest that non-environmental explanatory variables, in particular the socioeconomic situation, drive more variance in mental health.

# 1. Introduction

## Background

According to the recent Burden of Disease and Injury in Australia study (Australian Government, 2022), mental and substance use disorders were the 4th leading disease group in Australia in 2022, accounting for about 12% of the total burden, although suicide deaths are not included in this estimate. According to the WHO (World Health Organization, 2022), mental health “is a state of mental well-being that enables people to cope with the stresses of life, realize their abilities, learn well and work well, and contribute to their community. Mental health conditions include mental disorders and psychosocial disabilities as well as other mental states associated with significant distress, impairment in functioning, or risk of self-harm”. Taking care of mental health is thus of great importance for preventing not only the emergence of problematic common mental disorders (CMD), but also for reducing morbidity and mortality, and maximising “personal, community and socio-economic development”. This has been even more evident in the context of the COVID-19 pandemic, in which the rising incidence of mental health problems, mostly in young people, has been highlighted (COVID-19 Mental Disorders Collaborators, 2021).

In order to improve global mental health, it is essential to consider

which factors are most influential, and to identify emerging threats to population mental health. Among them, we find a mixture of biological (genetic heritability, gene by environment interactions, biological age, etc.) and social factors and cultural factors (socio-economic status, education level, adverse experience, quality of social relations, living environment, etc.) (Wainer and Chesters, 2021). In this paper, the focus is on another class of risk factors that have been relatively overlooked so far (Busby Grant et al., 2023), the (dys)regulation of natural environments in which people live.

Several hypotheses about how the natural environment can impact directly or indirectly on mental health have been proposed. These hypotheses often focus on peoples' positive and negative psychological experiences and responses to the natural environment, which includes their contact with animals, plants, landscapes and wilderness experiences (Frumkin, 2001), which may be distinct from effects of the built environment. For example, there is strong evidence that exposure to preserved and well-regulated natural environments correlates with mental health benefits (see (Pearson and Craig, 2014) for a meta-analysis), although evidence for a general influence of natural environments on mental health is more mixed (Sui et al. 2022). Hartig et al. (2014) observed that contact with nature (e.g., the availability of urban parks, the amount of green space in and near residential areas, and the preservation of peri-urban natural areas) could reduce the risk of

<footnote><sup>a</sup> Institut du Psychotraumatisme de l'Enfant et de l'Adolescent, Conseil Départemental Yvelines et Hauts-de-Seine et Centre Hospitalier de Versailles, Versailles, France</footnote>

<footnote><sup>b</sup> Centre for Mental Health Research, College of Health and Medicine, The Australian National University, Canberra, Australia</footnote>

<footnote><sup>c</sup> Centre de recherche en épidémiologie et en santé des populations, INSERM U1018, UVSQ, Université Paris-Saclay, Paris, France</footnote>

<footnote><sup>d</sup> LNC2, INSERM U960, Département d'études Cognitives, Ecole normale supérieure, PSL Research University, Paris, France</footnote>

<footnote><sup>e</sup> Health Research Institute, University of Canberra, Canberra, Australia</footnote>

<footnote>* Corresponding author at: Centre for Mental Health Research, The Australian National University, 63 Eggleston Road, Canberra Australia 2601.
E-mail address: philip.batterham@anu.edu.au (P.J. Batterham).</footnote>
illnesses involving chronic stress, and have short-term restorative effects for the treatment of mental health conditions such as depression. Small but significant effects of green space have also been observed in recent longitudinal and cross-sectional cohort studies (Geary et al., 2023; Sun et al., 2023).

While much of the quantitative research in this area has focused at the group or population level, there is evidence suggesting that there may be individual factors that may help explain the mechanisms by which the natural environment can impact on mental health. For example, two studies focusing on environmental perceptions found that individuals who reported environmental problems had a higher prevalence of CMD (Rocha et al., 2012) and that neighborhood satisfaction fully mediated the effect between perceived environmental characteristics and mental health (Leslie and Cerin, 2008). These studies suggest that there might be predictable differences in the way people experience and respond to the natural environment, which might further give rise to differences observed at the group level. Importantly, such a differential susceptibility is unlikely to be adequately accounted for by ecological studies where whole groups are assumed to have similar exposures and responses. One may therefore expect individual differences to be associated with: a) levels of exposure to degradation to the natural environment, b) how people perceive these changes, and potentially, c) how strongly characteristics of the natural environmental may influence mental health and wellbeing for different groups. Collection of individual-level data on exposures and outcomes has the advantage of allowing examination of differences at the individual level, which is not possible otherwise (Echeverria et al., 2008).

Other alternative associations between the natural environment and mental health are possible. Researchers have proposed the idea that people who care about the natural environment are those who can benefit from socioeconomic capital (that is, those who are richer, more educated, and have better living conditions overall; Givens and Jorgenson, 2011), a factor that protects against mental health issues (Kivimaki et al., 2020). According to this view, the association between environmental awareness and mental health is only indirect, correlational and mediated by individuals' socioeconomic status (Berry et al., 2010). For instance, it may be the case that people with higher socioeconomic status have greater means to engage in long-term cooperative behaviours (Farkas et al., 2022; Lettinga, Jacquet et al., 2020; Pepper and Nettle, 2017) like pro-environmental behaviour, or that people who have experienced life adversities or disadvantage may see environmental problems to be less salient to their health and wellbeing because they have more urgent needs to satisfy (Lee, 2006). Following this reasoning, we may expect greater perception of environmental problems to correlate with better mental health and quality of life. However, this may not be direct and may be fully moderated by socioeconomic status.

A concept that has received little attention so far but that can help better understand the relationship between the natural environment and mental health is “eco-anxiety”. Research on eco-anxiety suggests that this psychological construct is primarily associated with people’s subjective perceptions of the climate, rather than being based on objective experiences of climate-change related events (Clayton, 2020). This makes eco-anxiety a valuable target for researchers: in comparison to natural disaster experiences, eco-anxiety is likely to impact on a larger portion of the population.

There is no specific diagnosis of eco-anxiety to date, but the Australian College of Mental Health Nurses (Usher et al., 2019) defined it as a “self-reported specific form of anxiety relating to stress and distress by environmental changes and our knowledge of them”. However, it is difficult to define such a mental state, since it can be expressed in various ways: everyday episodes of sadness, despair or even grief, sudden panic attacks, anger, feelings of low self-worth, etc. (Arcanjo, 2019). The way that the living environment and its potential related problems are perceived by individuals is very subjective. In spite of this, only a few studies have investigated how much individual perceptions of the environment relate to mental health outcomes (Boluda-Verdu et al.,

2022).

As the effects of climate change accelerate, and with increasing mental health concerns, there is a need to better understand the relationship between the natural environment and people's mental health and quality of life. Specifically, more research understanding the types of factors at an individual level that may help explain this relationship, including concepts related to people's subjective interpretation of climate-change and the natural environment more generally.

### Objective and hypotheses

The present work thus aims to study the impact of variables reflecting the subjective perception of people's local natural environments on their mental health and quality of life (N = 1083). Based on evidence that extreme climate, weather events and environmental degradation lead to poorer mental health and well-being, we hypothesise that an increased sensitivity to environmental problems (i.e. eco-anxiety) is associated with a poorer mental health and degraded quality of life, possibly because it increases stress that further triggers more elevated anxiety levels (Pearson and Craig, 2014; Rocha et al., 2012; McFarlane et al., 1997; Batterham et al., 2022b). Further, socio-economic status (SES) may play a role in predicting not only mental health and wellbeing outcomes, but also perception of environmental problems and pro-environmental behaviours. Cooper and Stewart (2015) indeed suggest that financial struggles might have a strong (relative to the environmental and other explanatory variables) negative effect on mental health and quality of life and explain all of the variation. Batterham et al. (2022a) showed that indicators of financial worry or struggle appear to be significantly associated with poor mental health and wellbeing outcomes among Australian farmers. Our analyses also attempt to explore these relationships.

We also account for other potentially explanatory variables, with a specific interest in age, education and gender. Salinas-Jiménez et al. (2011) found that education had a significant positive effect on quality of life independently of its effect on income, thus identifying a consumption component. Gender may also impact on levels of quality of life (Giusta et al., 2011) and psychological distress. The roles of these sociodemographic characteristics are important to consider as they may influence environmental concerns (Newman and Fernander, 2015).

## 2. Method

### 2.1. Participants and procedure

Between November and December 2020, quota sampling was performed to recruit a sample that was representative of the Australian adult population on the basis of age group, gender, location (State/Territory population) and remoteness categories (urban, regional or rural classification). Qualtrics Research Services (a service which facilitates the development and distribution of surveys) was used to recruit participants from market research panels, inviting panel members to answer a cross-sectional online survey. In order to avoid self-selection bias, survey invitations did not include specific details about the content of the survey. The inclusion criteria were the following: living in Australia, being more than 18 years old, and knowing how to read and write in English well enough to complete the survey. Participants provided written (online) informed consent and had the possibility to withdraw from the study at any time up until completion and none of the questions were mandatory except demographic questions. Respondents who took less than 7 min for survey completion or failed multiple attention checks were excluded as a quality control measure. Participants were paid for completing surveys via QRS by a chosen reward (typically gift cards or airline reward programs of around AU$2–4 value).

A total of 1094 eligible participants completed the survey. This corresponded to a view rate of 31% and a completion rate of 45%
(n=3509 participants indeed entered the survey, including n=117 who did not consent, n=939 who did not meet demographic quotas, and n=1359 who did not complete the survey or failed reliability checks).

## 2.2. Measures

The survey comprised 70 questions which took between 20 and 30 min to complete. Its usability and technical functioning were tested by the authors before it was launched. Items that were included in these analyses were the demographic and socio-economic characteristics, environmental data, quality of life and mental health variables (a description of the scales can be found in the Supplementary Table A.6). All data were self-reported, and characteristics were consistent with the Australian population: 788 (72%) were living in urban areas, with 197 (18%) in regional and 105 (10%) in rural areas; 558 (51%) women, 528 (48%) men and 4 (0.3%) non-binary participants.

**Demographic and socio-economic indicators.** The demographic and socio-economic characteristics used included gender (men, women or other), age, highest educational qualification (primary school, year 10 equivalent, year 12, certificate level I-IV, diploma/associate degree, bachelor degree, graduate diploma/certificate, master degree, or doctoral degree), area of residence (metropolitan area, regional area, or rural and remote area), and financial stress (not at all, a little, somewhat, quite a lot, considerably, or extremely) which was coded into two categories: participants reporting no financial stress ("not at all" and "a little") taken as the reference group, and participants reporting greater financial stress categorised into another group.

**Environmental indicators.** Data on the natural environmental included self-reported data on the state of the participant's local natural environment, which used a bespoke scale of seriousness of environmental problems in the participant's community. Eight items covered: rainfall change or drought, temperature change, reduced biodiversity, unusual/ extreme weather patterns, reduced trees and vegetation, erosion or poor soil quality, poor air quality and poor water quality, and were rated on a 4-point Likert scale from "not at all" to "extremely serious". The scale had strong internal consistency in the current sample (alpha = 0.95). Environmental attitudes were assessed using the New Environmental Paradigm Scale (Dunlap, 2010), which examines personal concerns about climate based on the level of agreement with 15 statements about the relations between humans and nature. Positively worded items were reverse-coded, resulting in a total score ranging 15-75 with higher scores indicating greater eco-anxiety. This measure was used as a proxy for eco-anxiety and had good internal consistency (alpha = 0.87).

**Quality of life indicator.** Quality of life was assessed using the EUROHIS-QOL, an 8-item scale that includes items about health and social satisfaction (e.g., health satisfaction, everyday energy, satisfaction about daily activity, relationship qualities, living space conditions). The scale had a strong internal consistency (alpha = 0.89).

**Mental health indicator.** Finally, mental health was assessed by asking people about current or past mental health diagnoses (anxiety, depression, bipolar disorder), and using validated scales to assess depression (PHQ-9) (Spitzer et al., 1999), generalised anxiety (GAD-7) (Spitzer et al., 2006) and psychological distress (Distress Questionnaire-5 (DQ5) (Batterham et al., 2016). The present analyses focused on the DQ5 as the mental health outcome, as it is a brief scale of psychological distress that has been shown to be accurate in identifying a range of common mental disorders. It consists of 5 items rated on a 5-point frequency scale, examining the participant's symptoms over the last 30 days. The scale displayed strong internal consistency (alpha = 0.94).

Supplementary Table A.6 summarizes the data included in the study.

## 2.3. Analysis

A first step of data pre-processing led to a data set of 1083 participants and initial exploratory analyses examining the univariate associations of eco-anxiety and environmental perception with mental health

were used to ensure there was no evidence for curvilinear relationships. Then, separate model comparison procedures using linear mixed models were performed to identify the model that best explained each of the two outcome variables, mental health (DQ5, refer to as LMED models), and quality of life (EUROHIS-QOL, refer to as LME models). Linear mixed models included random intercepts that accounted for individual random variation in the model. No random slopes were included as it was expected that each explanatory variable would have consistent effects on the two outcomes. A visual inspection of the residuals showed that the assumptions of linearity, homoscedasticity and normality were approximately satisfied (see Supplementary Materials A.3). The reference models included sociodemographic variables (education, finance, age and gender) as predictors of either mental health or quality of life. Six alternative models of increasing complexity, which incrementally incorporated main effects of the environmental variables to test the primary hypothesis that environmental perceptions and eco-anxiety would be associated with mental health and wellbeing. Subsequently, interaction terms were incrementally incorporated to assess the secondary hypothesis of whether financial stress influenced environmental effects and to explore whether the environmental variables influenced each other. Finally, we added rurality to the model to assess whether findings were consistent across urban and rural settings. Models were compared to the reference model using Chi-squared difference tests. Table 1 summarizes the structure of these models. Alternative orderings made negligible difference to model selection.

Best-fitting models were selected on the basis of Chi-squared difference tests and their parameter estimates were summarized. Robust estimators of standard error were used to account for non-normality of residuals. Finally, to ensure that findings were robust to potential biases such as the risk of overfitting, all model coefficients, standard errors and their 95% confidence intervals were re-estimated using a non-parametric bootstrapping procedure (1000 random samplings with replacement for each model). This approach does not rely on normality assumptions and allows for the calculation of uncertainty in any kind of distribution (see Supplementary Materials C.1).

## 3. Results

The fit indices of the reference and alternative models for mental health (LMED model set) and quality of life (LME model set) are presented in Tables 2 and 3 respectively. For each outcome we next describe

<table><caption>**Table 1**
Reference and alternative models for DQ5 psychological distress.</caption><thead><tr><th>Model</th><th>Dependent variables</th><th>Independent variables</th></tr></thead><tbody><tr><td>LMED0 | LME0</td><td>DQ5 | Quality of life</td><td>education, age, gender, financial stress</td></tr><tr><td>LMED1 | LME1</td><td>DQ5 | Quality of life</td><td>education, age, gender, financial stress, eco-anxiety</td></tr><tr><td>LMED2 | LME2</td><td>DQ5 | Quality of life</td><td>education, age, gender, financial stress, eco-anxiety, environmental perception</td></tr><tr><td>LMED3 | LME3</td><td>DQ5 | Quality of life</td><td>education, age, gender, financial stress, eco-anxiety, environmental perception, eco-anxiety*financial stress</td></tr><tr><td>LMED4 | LME4</td><td>DQ5 | Quality of life</td><td>education, age, gender, financial stress, eco-anxiety, environmental perception, eco-anxiety*financial stress, environmental perception*finance</td></tr><tr><td>LMED5 | LME5</td><td>DQ5 | Quality of life</td><td>education, age, gender, financial stress, eco-anxiety, environmental perception, eco-anxiety*financial stress, environmental perception*financial stress, environmental perception*eco-anxiety</td></tr><tr><td>LMED6 | LME6</td><td>DQ5 | Quality of life</td><td>education, age, gender, financial stress, eco-anxiety, environmental perception, eco-anxiety*financial stress, environmental perception*financial stress, environmental perception*eco-anxiety, rurality</td></tr></tbody></table>
<table><caption>**Table 2**
Model comparison with chi-square difference tests for DQ5 psychological distress.</caption><thead><tr><th>Model</th><th>Number of parameters</th><th>AIC</th><th>BIC</th><th>Loglikelihood</th><th>Deviance</th><th>Chi-square χ2</th><th>Degrees of freedom Df</th><th>p</th></tr></thead><tbody><tr><td>LMED0</td><td>7</td><td>6509.4</td><td>6544.3</td><td>-3247.7</td><td>6495.4</td><td></td><td></td><td></td></tr><tr><td>LMED1</td><td>8</td><td>6511.0</td><td>6550.9</td><td>-3247.5</td><td>6495.0</td><td>0.3561</td><td>1</td><td>.5507</td></tr><tr><td>LMED2</td><td>9</td><td>6511.2</td><td>6556.0</td><td>-3246.6</td><td>6493.2</td><td>1.8524</td><td>1</td><td>.1735</td></tr><tr><td>LMED3</td><td>10</td><td>6511.8</td><td>6561.6</td><td>-3245.9</td><td>6491.8</td><td>1.4020</td><td>1</td><td>.2364</td></tr><tr><td>LMED4</td><td>11</td><td>6513.8</td><td>6568.6</td><td>-3245.9</td><td>6491.8</td><td>0.0081</td><td>1</td><td>.9284</td></tr><tr><td>LMED5</td><td>12</td><td>6515.5</td><td>6575.3</td><td>-3245.7</td><td>6491.5</td><td>0.2856</td><td>1</td><td>.5930</td></tr><tr><td>LMED6</td><td>13</td><td>6517.3</td><td>6582.1</td><td>-3245.7</td><td>6491.3</td><td>1.1416</td><td>1</td><td>.7067</td></tr></tbody></table>

Notes: bold cells indicate the best fitting model

<table><caption>**Table 3**
Model comparison with chi-square difference tests for quality of life.</caption><thead><tr><th></th><th>Number of parameters</th><th>AIC</th><th>BIC</th><th>Loglikelihood</th><th>Deviance</th><th>Chi-square χ2</th><th>Degrees of freedom df</th><th>p</th></tr></thead><tbody><tr><td>LME0</td><td>7</td><td>6771.2</td><td>6806.1</td><td>-3378.6</td><td>6757.2</td><td></td><td></td><td></td></tr><tr><td>LME1</td><td>8</td><td>6764.3</td><td>6804.2</td><td>-3374.1</td><td>6748.3</td><td>8.8754</td><td>1</td><td>.00289</td></tr><tr><td>LME2</td><td>9</td><td>6766.2</td><td>6811.1</td><td>-3374.1</td><td>6748.2</td><td>0.0923</td><td>1</td><td>.76122</td></tr><tr><td>LME3</td><td>10</td><td>6766.1</td><td>6815.9</td><td>-3373.1</td><td>6746.1</td><td>2.1043</td><td>1</td><td>.14689</td></tr><tr><td>LME4</td><td>11</td><td>6768.0</td><td>6822.9</td><td>-3373.0</td><td>6746.0</td><td>0.0539</td><td>1</td><td>.81642</td></tr><tr><td>LME5</td><td>12</td><td>6769.9</td><td>6829.7</td><td>-3373.0</td><td>6745.9</td><td>0.1196</td><td>1</td><td>.72945</td></tr><tr><td>LME6</td><td>13</td><td>6768.3</td><td>6833.1</td><td>-3371.2</td><td>6742.3</td><td>3.6014</td><td>1</td><td>.05773</td></tr></tbody></table>

Notes: bold cells indicate the best fitting model

results of the reference model, and proceed with a description of the best fitting model, as assessed by our model comparison procedure (Table 1).

Results of the baseline model for mental health (LMED0) revealed that all the explanatory variables apart from gender were significantly associated with the mental health outcome (Table 4): greater education (estimate = -0.196; SE = 0.082; t = -2.378; p = 0.018; 95% CI -0.353, -0.038; Cohen's d = -0.066) and older age (estimate = -0.090; SE = 0.009; t = -9.756; p < 0.001; 95% CI -0.108, -0.072; Cohen's d = -0.136) were associated with lower scores on the DQ5 (i.e., less psychological distress), whereas higher financial stress (estimate = 2.656; SE = 0.336; t = 7.909; p < 0.001; 95% CI 2.000, 3.290; Cohen's d = 0.717) linked with higher DQ5 scores (i.e., more psychological distress). The comparison procedure (Table 2) shows that no other model fit the data better. The addition of the other regression terms – simple effect of eco-anxiety and simple effect of environmental perception, interaction effects – did not increase the amount of explained variance with respect to the baseline model. We then tested the robustness of the parameters to sampling noise using a 95% CIs bootstrap resampling method. All the effects described above proved robust to these tests.

We then proceeded with the model set taking the quality of life as outcome. The comparison model procedure (Table 3) showed that the only model that fit the data better than the reference model was the LME1 model, which incorporated “eco-anxiety” as an additional main effect. Hence, adding more regression terms – whether under the form of simple effects or interaction effects – did not yield a better fit. In the LME1 model, greater eco-anxiety (estimate = -0.051; SE = 0.017; t = -2.977; p = 0.003; 95% CI -0.085, -0.016; Cohen's d = -0.080) had a negative association with quality of life (Table 5). In addition, as expected, quality of life was associated with greater education (estimate = 0.415; SE = 0.093; t = 4.465; p < 0.001; 95% CI 0.230, 0.578; Cohen's d = 0.120) and less financial stress (estimate = -5.691; SE = 0.379; t = -15.015; p < 0.001; 95% CI -6.420, -4.960; Cohen's d = 0.425). All these results were replicated after applying the bootstrapping procedure to control for slope outliers and sampling noise (see Supplementary Materials C.1).

<table><caption>**Table 4**
Estimation of the parameters of the best-fitting model for mental health (DQ5), LMED0.</caption><thead><tr><th>Fixed effects</th><th>Estimate</th><th>SE</th><th>df</th><th>t</th><th>p</th></tr></thead><tbody><tr><td>Intercept</td><td>14.580</td><td>0.734</td><td>1075</td><td>19.860</td><td>&lt;0.001</td></tr><tr><td>Education</td><td>-0.196</td><td>0.082</td><td>1075</td><td>-2.378</td><td>0.018</td></tr><tr><td>Age</td><td>-0.090</td><td>0.009</td><td>1075</td><td>-9.756</td><td>&lt;0.001</td></tr><tr><td>Gender</td><td>0.002</td><td>0.317</td><td>1075</td><td>0.007</td><td>.994</td></tr><tr><td>Financial stress</td><td>2.656</td><td>0.336</td><td>1075</td><td>7.909</td><td>&lt;0.001</td></tr></tbody></table>

<table><caption>**Table 5**
Estimation of the parameters of the best-fitting mixed model for quality of life, LME1.</caption><thead><tr><th>Fixed effects</th><th>Estimate</th><th>SE</th><th>df</th><th>t</th><th>p</th></tr></thead><tbody><tr><td>Intercept</td><td>29.199</td><td>1.216</td><td>1074</td><td>24.004</td><td>&lt;0.001</td></tr><tr><td>Education</td><td>0.403</td><td>0.093</td><td>1074</td><td>4.353</td><td>&lt;0.001</td></tr><tr><td>Age</td><td>0.040</td><td>0.010</td><td>1074</td><td>3.817</td><td>&lt;0.001</td></tr><tr><td>Gender</td><td>-0.015</td><td>0.359</td><td>1074</td><td>-0.041</td><td>.968</td></tr><tr><td>Financial stress</td><td>-5.681</td><td>0.378</td><td>1074</td><td>-15.043</td><td>&lt;0.001</td></tr><tr><td>Eco-anxiety</td><td>-0.051</td><td>0.017</td><td>1074</td><td>-2.977</td><td>.003</td></tr></tbody></table>

## 4. Discussion

In the context of increasingly worrying climate change and of a rising incidence of mental health problems in the population (COVID-19 Mental Health Disorders Collaborators, 2021), this study aimed to provide evidence of an association between the relationship of individuals to their natural environment and their mental health. Using linear-mixed models and a model comparison procedure, we found that neither the way in which an individual perceives their natural environment's climatic problems, nor the anxiety it generates for them, seem to have a significant impact on mental health. Mental health was instead influenced by other variables, such as socio-economic status, education level and age. However, quality of life was significantly but modestly influenced by behaviours and beliefs towards the environment, which was the indicator of eco-anxiety used in our study.

### Interpretation of the results

The statistical analyses indicated that only age, education and finance stress had significant associations with mental health in this study. Effect sizes were small, with the exception of a moderate effect of financial stress on mental health. These effects were in the hypothesized direction: greater education, older age and less financial stress were associated with less susceptibility to mental health problems. The absence of effects of environmental variables on mental health was
inconsistent with the hypotheses made, although consistent with the small or null effects observed in previous studies (Sui et al. 2022). It may be the case that only extreme levels of environmental degradation have direct mental health impacts (Palinkas and Wong, 2020). Most of the evidence for this direct link comes from studies using objective data about weather and increasing natural disasters. There is also the possibility of indirect effects through higher likelihood of economic disruption and conflict as environmental degradation and climate change accelerates. Mantler and Logan, 2015 moreover highlight definitional and methodological issues which challenge the evidence found in other studies about a direct impact of environmental degradation on mental health.

The results we obtained for quality of life were slightly different, as a small but significant portion of the variance was explained by environmental variables. Indeed, in addition to a small effects of education and financial stress, eco-anxiety had a small but significant effect on quality of life. Overall, our results were in the expected direction: greater education and less financial stress were associated with higher quality of life; whereas eco-anxiety was detrimental for quality of life. However, the absence of significant interaction effects between eco-anxiety and SES indicated that the negative link between eco-anxiety and quality of life was not dependent on socio-economic status. Future research could further examine whether socio-economic status mediates the role of eco-anxiety on quality of life, notably by leading people with a higher SES to put a greater emphasis on the future rather than on present (Berry et al., 2010; Farkas et al., 2022; Lettinga, Jacquet et al., 2020; Pepper and Nettle, 2017).

These observations show that environmental concerns may influence quality of life and well-being, even if they do not link with mental health symptoms. Although mental health and quality of life are correlated (better mental health is associated with higher quality in life, see Supplementary Materials A.2), the drivers of these outcomes are somewhat distinct. It may be important to take into account environmental considerations in order to improve quality of life, but negative appraisals of the environment do not seem to be sufficient to directly influence mental health symptoms, at least in the short time scale of this study and in the general population. We must however note that, for both outcome variables, the effect of finance stress was more reliable than the other effects (greater confidence in the parameter estimates). This is in line with the existing literature showing a preferential effect of SES and living conditions on mental health and life satisfaction. Indirect effects exerted by other factors not measured here (e.g., biological factors and food supply could also explain variance in quality of life (Jäbrink-Shgal and Andreasson, 2020)) may also be influenced by contextual factors such as SES. A more holistic examination of social, biological, psychological and environmental determinants of quality of life should be examined in future research, such as the types of environmental values people hold, and their sense of place and attachment to natural environments. While these have been explored in previous studies (Marshall et al., 2019), there is little research on how SES may mediate the relationship between the natural environment and human wellbeing.

## Strengths and limitations

This study has some strengths. We used a large national sample, representative of the Australian population on the bases of age group, gender and location. We performed a rigorous statistical analysis by comparing successive models that included variables selected on the basis of theory and evidence. To our knowledge, no previous study has examined links between the natural environment and individual mental health in a representative sample. The findings should however be interpreted in the context of the limitations of the research.

The first limitation is that the study was cross-sectional, which does not allow us to assess whether relationships are causal, nor their direction. The possibility of reverse causality is thus a potential problem, because people who had a worse mental health or quality of life might

perceive their environment as more problematic or be more eco-anxious. So, concerning the link between eco-anxiety and quality of life observed, even if it is consistent with previous literature, it is not clear whether eco-anxiety drives lower quality of life. It could indeed be that because people are less satisfied in life for many reasons, they could be more sensitive to sources of stress overall, including climate problems.

Moreover, to our knowledge at the time of data collection, no precise assessment scales for eco-anxiety exist, in sharp contrast with mental health and quality of life assessment scales. As noted, the variable labelled "eco-anxiety" that we used to measure people's beliefs or behaviors regarding climate was only a proxy indicator. Consequently, one cannot be certain whether this proxy allows us to efficiently identify people who experience eco-anxiety. A specific problem may indeed not be a great source of stress for one person, but it could be for others. Usher et al., 2019 explained that when becoming aware of environmental problems, some people are spurred to act while others become overwhelmed and anxious. Note however that, in our data, elevated levels of eco-anxiety were weakly but significantly linked with an increased likelihood of suffering from anxiety disorder, hence confirming the capacity of our measure to capture this trait. There were also differences between the eco-anxiety measure, which had a focus on general perceptions of the natural environment, and the measure of environmental perceptions, which had a more targeted focus on the natural environment in the individual's local community. It is possible that general perceptions of or attitudes towards the environment may differ depending on whether a local or global context is the focus, and further validation research may examine how the framing of scales influences responses. The concept of mental health is also broad and difficult to estimate, varying from one study to another. Among the multiple existing indicators, we choose to use the DQ5 scale. In order to give more evidence for the lack of impact of environmental variables on mental health, specific diagnostic categories of mental ill health could alternatively be examined as outcomes.

A lack of universal definitions of eco-anxiety, along with diverse measures of mental health, quality of life, environmental problems and environmental perceptions makes comparison between different research on this topic quite challenging, and limits consensus (Spitzer et al., 1999; Shaefer-McDaniel et al., 2010). It also raises questions about the replicability of these kinds of studies. The definitional problems may also explain why no significant effects for the interactions between the perceived environmental state and eco-anxiety/attitudes toward environment were obtained, contrary to hypotheses. Using other ways to measure these characteristics might lead to the theoretically expected outcome. These putative associations may also differ depending on the living environment of the individual, such as whether they live in a rural environment where nature is more present, or more generally their sense of connection they have with the natural environment. This hypothesis would thus merit further research with other validated scales in communities that are more susceptible to environmental degradation. Our examination of individual perceptions of the natural environment may also differ from studies that examine access to or use of the natural environment.

Some remarks about the data must also be raised. The data were collected in Australia in late 2020, not long after the major fires of 2019–2020 (Jalaludin et al., 2020), and in the middle of the COVID-19 pandemic. This might lead to biases in the results of the survey: even if only a small proportion of the sample was directly impacted by fires, there was extensive media coverage of the fires that would have been likely to impact on the perception of climate across participants and its impact on their mental health and quality of life might be influenced by such events (Boluda-Verdu et al., 2022). Because of the pandemic, mental health and quality of life in the sample may have been worse than in another time period. Moreover, it may be the case that younger people are more concerned about climatic issues and thus may be more eco-anxious (Schneider-Mayerson and Ling, 2020), so adolescent samples may provide different outcomes. Despite providing a representative
sample on the basis of demographic characteristics, it is possible that the sample included people with greater interest in mental health or environment than the general population. It should also be noted that it is not possible to fully account for self-selection of people into the neighbourhoods in which they live.

Regarding variable selection, some variables well known to have an impact on mental health and quality of life were not accounted for, including ethnicity or cultural background, socio-economic position in childhood, stressful life events, presence of physical health problems, lack of social support and substance use (Rocha et al., 2012). Controlling for more parameters in the survey might thus have provided more reliable conclusions, although at the expense of parsimony. Moreover, self-reported survey answers could be influenced by expectancies and other biases such as social desirability, despite the anonymity of the data, or inaccuracies in understanding terminology such as differences between rural and regional locations. There is also a possibility that order effects may have influenced responding, although the scales of interest were separated throughout the survey. In addition, the sample was recruited from market research panels, which may have influenced the composition with respect to factors other than age, gender and location.

Finally, as noted in the results section, the homogeneity and normality of the residuals were not observed in the regression models. This was handled by the use of robust estimators in the regression models and by the bootstrapping procedure, with similar outcomes.

## Implications and further research

Although no direct associations with mental health were found, it is possible that ongoing exposure to environmental degradation could lead to declines in mental health in the longer term. It could indeed be important to follow the potential evolution of the impact of environmental problems on mental health in longitudinal study designs. Such studies could test if a long exposure to environmental problems could lead to changes in mental health, while examining the potential evolution of the impact of environment on mental health in the context of growing climate change. This hypothetical long-term effect has been discussed by (Batterham et al., 2022b), who suggested that any direct health benefits of engaging in environmental sustainability projects may only occur in the long term.

Furthermore, previous literature suggests that environmental concerns are more likely to have an effect in the case of extreme climate events such as natural disasters and droughts (Batterham et al., 2022a). Because of the increased frequency and severity of such events, along with anticipatory anxiety about the possibility of change, clinicians should be aware of both of these potential impacts, with disaster agencies prepared for increasing mental health concerns in the wake of such climate-related disasters. Future research in these directions may be more productive in understanding the positive and negative impacts of disaster responses on mental health outcomes, rather than quantifying the weak, indirect associations between mental health and environmental variation outside of disasters in the general population. Additionally, future research should also seek to understand the factors that may make individuals and groups more vulnerable to the negative impacts of environmental degradation and climate-change related events. This would help ensure a more targeted approach to addressing the negative mental health impacts of climate change.

The small effect of environmental concerns on quality of life may suggest a role for policy to develop and implement services designed to prevent these impacts and improve well-being. If a strong enough link was identified within a vulnerable community, potential solutions may include encouraging physical activity in green spaces, improving air quality, making cities more green, developing public health education, personal engagement in environmental conservation, or promotion of positive psychological outcomes associated with climate change (Palinkas and Wong, 2020; Geary et al. 2023; Sun et al., 2023).

It is also unclear whether similar results would be obtained in countries other than Australia. The place of nature in Australia may be quite different than in European countries for example, with the Australian natural environment potentially being more psychologically salient and more prone to disasters. It may therefore be worthwhile to replicate this study in another country, and consider how the findings might vary in lower and middle-income nations.

Finally, the findings could also lead to research in other fields, for example, by examining how people might experience negative emotional and mental states as a result of climate change impacts, consistent with the biophilia hypothesis introduced by Wilson (Kellert and Wilson, 1993). This hypothesis posits that humans have an innate connection with the natural world and derive psychological and well-being benefits from this affiliation.

## 5. Conclusion

This study aimed to provide evidence of a potential link between the relationship of individuals to their natural environment and their mental health. Findings indicate that neither the way in which an individual perceives their natural living environment's conditions, nor the anxiety generated by environmental problems appeared to have any significant impact on mental health. Mental health was instead influenced by other contextual variables such as socio-economic level mainly, education level and age. A small effect of behaviours and beliefs towards the environment, indicative of eco-anxiety, was observed for levels of quality of life. This legitimises the inclusion of environmental variables in public policy considerations to improve individual well-being. This small effect also makes it possible to hypothesise that subjective environmental variables may influence mental health in the longer term or during disaster.

More precise definitions of the concepts used in this study such as perception of the environment and eco-anxiety, along with additional research using other means of estimating the relationship between environment and mental health may be important for future research in this area.

## Ethics statement

The work described was carried out in accordance with The Code of Ethics of the World Medical Association (Declaration of Helsinki). The ethical aspects of the study were approved by The Australian National University Human Research Ethics Committee (ANU HREC protocol number 2020/593).

## CRediT authorship contribution statement

**Aglaé Drion:** Writing – original draft, Methodology, Investigation, Formal analysis, Conceptualization. **Pierre O. Jacquet:** Writing – review & editing, Supervision, Resources, Methodology. **Kimberly Brown:** Writing – review & editing, Methodology. **Philip J. Batterham:** Writing – review & editing, Conceptualization, Supervision, Resources, Methodology, Investigation, Funding acquisition.

## 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.

## Acknowledgement

P.O.J. is supported by the Agence Nationale de la Recherche grant ANR-22-CE28-0012-01 eLIFUN (JCJC). The study was supported by funding from the National Health and Medical Research Council, grant 1138713.
## Supplementary materials

Supplementary material associated with this article can be found, in the online version, at doi:10.1016/j.wss.2024.100220.

## References

Australian Government, 2022. Australian Burden of Disease Study 2022. Australian Institute of Health and Welfare. https://www.aihw.gov.au/getmedia/d9ae4bfa-df27-4e3c-9846-ba452befbac5/aihw-bod-37.pdf.aspx?inline=tru.

Arcanjo, M., 2019. Eco-Anxiety: Mental Health Impacts of Environmental Disasters and Climate Change. Climate Institute. https://wmich.edu/sites/default/files/attach-ments/u304/2021/Eco-Anxiety-Mental-Health-Impacts-of-Environmental-Disasters-and-Climate-Change.pdf.

Batterham, P.J., Brown, K., Calear, A.L., Lindenmayer, D., Hingee, K., Poyser, C., 2022a. The FarmWell study: examining relationships between farm environment, financial status and the mental health and wellbeing of farmers. Psychiatry Res. Commun. 2, 100036.

Batterham, P.J., Sunderland, M., Carragher, N., Calear, A.L., Mackinnon, A.J., Slade, T., 2016. The distress questionnaire- 5: population screener for psychological distress was more accurate than the K6/K10. J. Clin. Epidemiol. 71, 35-42.

Batterham, P.J., Brown, K., Trias, A., Poyser, C., Kazan, D., Calear, A.L., 2022b. Systematic review of quantitative studies assessing the relationship between environment and mental health in rural areas. Austr. J. Rural Health 30, 306-320.

Berry, H.L., Bowen, K., Kiellstrom, T., 2010. Climate change and mental health: a causal pathways framework. Int. J. Public Health 55, 123-132.

Boluda-Verdu, I., Senent-Valero, M., Cases-Escolano, M., Matijasevich, A., Pastor-Valero, M., 2022. Fear for the future: Eco-anxiety and health implications, a systematic review. J. Environ. Psychol. 84, 101904.

Busby Grant, J., Batterham, P.J., Calear, A.L., Grant, W.J., Christensen, H., 2023. Recognising the anxiogenic environment as a driver of youth anxiety. Lancet Child Adolesc. Health 5, 303-305.

Clayton, S., 2020. Climate anxiety: Psychological responses to climate change. J. Anxiety. Disord. 74, 102263.

Cooper, K., Stewart, K., 2015. Does Money in Adulthood Affect Adult outcomes? Joseph Rowntree Foundation. https://www.basw.co.uk/system/files/resources/basw3122-3-70.pdf.

COVID-19 Mental Disorders Collaborators, 2021. Global prevalence and burden of depression and anxiety disorders in 204 countries and territories in 2020 due to the COVID-19 pandemic. Lancet 398, 1700-1712.

del Mar Salinas-Jiménez, M., Artés, J., Salinas-Jiménez, J., 2011. Education as a positional good: a life satisfaction approach. Soc. Indic. Res. 103, 409-426.

Dunlap, R.E., 2010. The new environmental paradigm scale: from marginality to worldwide use. J. Environ. Educ. 14, 3-18.

Echeverría, S., Diez-Roux, A.V., Shea, S., Borrell, L.N., Jackson, S., 2008. Associations of neighbourhood problems and neighbourhood social cohesion with mental health and health behaviours: the multi-ethnic study of atherosclerosis. Health and Place 14, 853-865.

Farkas, B.C., Chambon, V., Jacquet, P.O., 2022. Do perceived control and time orientation mediate the effect of early life adversity on reproductive behaviour and health status? Insights from the European Value Study and the European Social Survey. Humanit. Soc. Sci. Commun. 9, 52.

Frumkin, H., 2001. Beyond toxicity: Human health and the natural environment. Am. J. Prev. Med. 20, 234-240.

Geary, R.S., Thompson, D., Mizen, A., Akbari, A., Garrett, J.K., Rowney, F.M., Watkins, A., Lyons, R.A., Stratton, G., Lovell, R., Nieuwenhuijsen, M., 2023. Ambient greenness, access to local green spaces, and subsequent mental health: a 10-year longitudinal dynamic panel study of 2.3 million adults in Wales. Lancet Planet. Health 7, e809-e818.

Giusta, M.D., Jewell, S.L., Kambhampati, U.S., 2011. Gender and Life Satisfaction in the UK. Fem. Econ. 17, 1-34. https://www.thelancet.com/journals/lanch-i/article/PIIS2352-%204642(23)00023-8/fulltext.

Givens, J.E., Jorgenson, A.K., 2011. The effects of affluence, economic development, and environmental degradation on environmental concern: a multilevel analysis. Organ. Environ. 24, 74-91.

Hartig, T., Mitchell, R., de Vries, S., Frumkin, H., 2014. Nature and health. Ann. Rev. Public Health 35, 207-228.

Jábrink-Shgal, E., Andreasson, A., 2020. The gut microbiota and mental health in adults. Curr. Opin. Neurobiol. 62, 102-114.

Jajaludin, B., Johnston, F., Vardoulakis, S., Morgan, G., 2020. Reflections on the Catastrophic 2019-2020 Australian Bushfires. Innovation 1, 100010.

Kellert, S.R., Wilson, E.O., 1993. The Biophilia Hypothesis. Island Press.

Kivimäki, M., Batty, G.D., Pentti, J., Shipley, M.J., Sipilä, P.N., Nyberg, S.T., Suominen, S.B., Oksanen, T., Stenholm, S., Virtanen, M., Marmot, M.G., 2020. Association between socioeconomic status and the development of mental and physical health conditions in adulthood: a multi-cohort study. Lancet Public Health 5, e140-e149.

Lee, K.N., 2006. Urban sustainability and the limits of classical environmentalism. Environ. Urban. 18, 9-22 (2006).

Leslin, E., Cerin, E., 2008. Are perceptions of the local environment related to neighbourhood satisfaction and mental health in adults? Prev. Med. 47, 273-278.

*Lettinga*, N., Jacquet*, P.O., André, J.B., Baumard, N., Chevallier, C., 2020. Environmental harshness is associated with lower investment in collective actions. PLoS One 16, 236715.

Mantler, A., Logan, A.C., 2015. Natural environments and mental health. Adv. Integr. Med. 2, 5-12.

N. A., Marshall, N.A., Thiault, L., Beeden, A., Beeden, R., Benham, C., Curnock, M.I., Diedrich, A., Gurney, G.G., I., Jones, Marshall, P.A., Nakamura, N., Pert, P., 2019. Our environmental value orientations influence how we respond to climate change. Front. Psychol. 10, 938.

McFarlane, A.C., Claver, J.R., Bookless, C.L., 1997. Psychiatric morbidity following a natural disaster: an Australian bushfire. Soc. Psychiatry Psychiatr. Epidemiol. 32, 261-268.

Newman, T.P., Fernandes, R., 2015. A reassessment of factors associated with environmental concern and behavior using the 2010 General Social Survey. Environ. Educ. Res. 22, 153-175.

Palinkas, L.A., Wong, M., 2020. Global climate change and mental health. Curr. Opin. Psychol. 32, 12-16.

Pearson, D.G., Craig, T., 2014. The great outdoors? Exploring the mental health benefits of natural environments. Front. Psychol. 5, 1178.

Pepper, G.V., Nettle, D., 2017. The Behavioural Constellation of deprivation: Causes and Consequences. Cambridge University Press.

Rocha, K., Pérez, K., Rodriguez-Sanz, M., Obiols, J.E., Borrell, C., 2012. Perception of environmental problems and common mental disorders (CMD). Soc. Psychiatry Psychiatr. Epidemiol. 47, 1675-1684.

Shaefer-McDaniel, N., O'Brien Caughy, M., O'Campo, P., Gearey, W., 2010. Examining methodological details of neighbourhood observations and the relationship to health: a literature review. Soc. Sci. Med. 70, 277-292.

Schneider-Mayerson, M., Ling, L.K., 2020. Eco-reproductive concerns in the age of climate change. Clim. Change 163, 1007-1023.

Spitzer, R.L., Kroenke, K., Williams, J.B., 1999. Validation and utility of a self-report version of PRIME-MD: the PHQ primary care study. JAMA 282, 1737-1744.

Spitzer, R.L., Kroenke, K., Williams, J.B., Löwe, B., 2006. A brief measure for assessing generalized anxiety disorder: the GAD-7. Arch. Intern. Med. 166, 1092-1097.

Sui, Y, Ettema, D., Helbich, M., 2022. Longitudinal associations between the neighborhood social, natural, and built environment and mental health: A systematic review with meta-analyses. Health Place 77, 102893.

Sun, Y., Molitor, J., Benmarhnia, T., Avila, C., Chiu, V., Slezak, J., Sacks, D.A., Chen, J.C., Getahun, D., Wu, J., 2023. Association between urban green space and postpartum depression, and the role of physical activity: a retrospective cohort study in Southern California. Lancet Region. Health-Am. 21, 100462.

Usher, K., Durkin, J., Bhullar, N., 2019. Eco-anxiety: How thinking about climate change-related environmental decline is affecting our mental health. Int. J. Ment. Health Nurs. 28, 1233-1234.

Wainer, J., Chesters, J., 2021. Rural mental health: neither romanticism nor despair. Austr. J. Rural Health 8, 141-147.

World Health Organization (2022). *Mental Health*. https://www.who.int/news-room/fact-sheets/detail/mental-health-strengthening-our-response.