# Residential greenness trajectories and child emotional and behavioral difficulties in early childhood: results from ELFE mother-child cohort

Eloi Chazelas, Devin Parker, Florencia Barreto-Zarza, Marie-aline Charles, Maria Melchior

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## To cite this version:

Eloi Chazelas, Devin Parker, Florencia Barreto-Zarza, Marie-aline Charles, Maria Melchior. Residential greenness trajectories and child emotional and behavioral difficulties in early childhood: results from ELFE mother-child cohort. Environmental Research, 2025, 286, pp.122965. 10.1016/j.envres.2025.122965. hal-05299146

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# Residential greenness trajectories and child emotional and behavioral difficulties in early childhood: results from ELFE mother-child cohort

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

Eloi Chazelas<sup>a, *, †</sup>, Devin Parker<sup>a ‡</sup>, Florencia Barreto-Zarza<sup>a,b,c ‡</sup>, Marie Aline Charles<sup>d,e ‡</sup>, Maria Melchior<sup>a</sup>

## ARTICLE INFO

**Keywords:**

Child mental health

Residential greenness

Emotional and behavioral difficulties

Prospective cohort

## ABSTRACT

**Background:** Scientific evidence suggests that residential greenness benefits children's mental health. This study examined relationships between residential greenness trajectories and children's emotional and behavioral difficulties in a representative sample of French children.

**Method:** Data from 10,092 children from the ELFE cohort were analyzed from birth to age 5. Residential greenness was measured based on the Normalized Difference Vegetation Index at 2 months, 3.5 and 5.5 years- old, with trajectories identified using group-based trajectory modeling. Children's mental health was assessed using parent-reported Strengths and Difficulties Questionnaire at age 5.5. Associations between greenness tra- jectories and SDQ scores were assessed via regression models weighted on propensity scores.

**Findings:** Three residential greenness trajectories were identified. Children exposed to average and high greenness had fewer internalizing difficulties compared to the low greenness group (β = -0.11, 95%CI = -0.22, 0.00, P = 0.05; β = -0.15, 95%CI = -0.27, -0.03, P = 0.02, respectively). Children exposed to average and high greenness showed fewer peer relationship problems (β = -0.13, 95%CI = -0.24, -0.02, P = 0.03; β = -0.15, 95%CI = -0.27, -0.02, P = 0.02, respectively). However, children in the high greenness group had increased levels of hyperactivity/inattention symptoms (β = 0.12, 95%CI = 0.01, 0.24, P = 0.03). These associations were only statistically significant among children from low-income families.

**Interpretation:** Higher greenness exposure throughout childhood is associated with lower levels of internalizing difficulties, particularly peer relationships. However, it is also linked to increased hyperactivity/inattention symptoms. This is particularly the case among children from low-income families. These results suggest that exposure to greenness can predict certain aspects of children's emotional well-being and will require replication in other large-scale cohorts.

## 1. Introduction

There is a growing body of evidence suggesting the positive impact of exposure to green spaces on health, especially in urban areas (World Health Organization Regional Office For and Europe, 2016). The benefits include mental well-being, decreased cardiovascular morbidity and mortality, as well as a lower risk of obesity and type 2 diabetes.

Green spaces may contribute to better health through three possible mechanisms (Markevych et al., 2017): a) mitigation of the effects of environmental factors (e.g. air pollution, noise and heat), whereby plants can reduce ambient CO₂ levels, air pollution and traffic noise, and reduce heat by absorbing solar radiation; b) restoration of neuro-cognitive functions by relieving stress and helping regain attention; c) promotion of physical activity and social cohesion. Exposure to greenness has also been associated with lower screen time, which may act as

<footnote><sup>a</sup> Sorbonne University, INSERM U1136, Pierre Louis Institute of Epidemiology and Public Health (IPLESP), Department of Social Epidemiology, Mental health and Addictions (ESSMA), F75012, Paris, France</footnote>

<footnote><sup>b</sup> Faculty of Psychology, University of the Basque Country (UPV/EHU), San Sebastian, Spain</footnote>

<footnote><sup>c</sup> Environmental Epidemiology and Child Development Group, Biodonostia Health Research Institute, San Sebastian, Spain</footnote>

<footnote><sup>d</sup> French Institute for Demographic Studies (Ined), French Institute for Medical Research and Health (Inserm), French Blood Agency, ELFE Joint Unit, Paris, France</footnote>

<footnote><sup>e</sup> French Institute for Medical Research and Health, UMR 1153 Centre for Research in Epidemiology and Statistics Paris University (CRESS), Team Early Life Research on Later Health, Villejuif, France</footnote>

<footnote>* Corresponding author. Sorbonne University, INSERM U1136, Pierre Louis Institute of Epidemiology and Public Health (IPLESP), Department of Social Epidemiology, Mental health and Addictions (ESSMA), F75012, Paris, France.</footnote>

<footnote>E-mail address: eloi.chazelas@inserm.fr (E. Chazelas).</footnote>
Abbreviations

CI Confidence Interval

CO₂ Carbon dioxide

DAG Directed Acyclic Graph

ELFE Etude Longitudinale Française depuis l'Enfance

EPDS Edinburgh Postnatal Depression Scale

NDVI Normalized Difference Vegetation Index

OR Odds Ratio

PS Propensity Scores

SDQ Strengths and Difficulties Questionnaire

an intermediary pathway linking nature contact with improved health outcomes (Oswald et al., 2020).

Access to green spaces may be particularly important among economically deprived communities, children, pregnant women and senior citizens with limited mobility patterns (World Health Organization Regional Office For and Europe, 2016). Yet, as part of the “urban exposome” (Robinson et al., 2018), greenness may influence health through mechanisms of environmental inequality, similar to well-documented pathways involving air and noise pollution. Indeed, access to greenness is often unequally distributed, with low-income neighborhoods typically offering less and lower-quality greenness (Klompmaker et al., 2023; Hoffimann et al., 2017; Schüle et al., 2017; Adélaïde et al., 2024). A key question is therefore how residential greenness can contribute to children’s health and development, and to what extent it contributes to social inequalities in this area.

Several systematic reviews and meta-analyses have studied the relations between greenness and children’s mental health and development. While some experimental research suggest short-term improvement and/or restoration of cognitive performance after contact with nature or green spaces (walks, visits ...) (Nguyen and Walters, 2024; Buczyłowska et al., 2023), observational research on mental health and behavior is inconclusive, notably due to the high level of heterogeneity and risk of bias (Buczyłowska et al., 2023; Zare Sakhvidi et al., 2022; Luque-García et al., 2022). Neighborhoods may change over time, and people relocate, yet observational research in this topic generally relied on cross-sectional data (Zare Sakhvidi et al., 2022; Luque-García et al., 2022). Longitudinal research that takes into account changes in circumstances is needed (Helbich et al., 2019).

The aim of this study was to investigate the relationships between residential greenness trajectories and children’s emotional and behavioral difficulties in a nationally representative sample of French children.

## 2. Methods

### 2.1. Study population

Data for this study come from the ELFE (Etude Longitudinale Française depuis l'Enfance) birth cohort, which aims to understand how children's environment affects their development, health and socialization, from the intra-uterine period to adolescence (Charles et al., 2020). The study recruited 18,289 babies born in 2011 from 320 maternity units throughout France. Designed to be nationally representative, ELFE provides survey weights for each wave allowing to correct for non-representativeness and attrition (simultaneous calibration was used based on 6 contextual variables and other variables associated with attrition, as previously described (Siméon)). Analyses can therefore be weighted to maintain representativeness. Follow-up questionnaires included telephone interviews with parents when the child was 2 months, 1 and 2 years old, and with one parent at 3.5 and 5.5 years old. Details on the research design and questionnaires have previously been

published (Charles et al., 2020).

Exclusion criteria for the present statistical analyses include twins, children in foster care or with profound hearing impairment at 5 years. Finally, children with missing data on the study's exposure and outcome variables were excluded (Appendix 1).

Attrition is expected in cohort studies and may result from participant fatigue, changes in contact information, reduced interest over time, or life events. Socioeconomic differences may also contribute to selective loss to follow-up. Appendix 2 compares included with excluded participants.

### 2.2. Greenness exposure

Participants' addresses were collected at 2 months, 3.5, and 5.5 years old, and geocoded. To represent realistic residential areas, “walkable buffers” were modeled using the R package `osrm`, based on Open-StreetMap, to calculate areas within a 500m walking distance from the address.

The Normalized Difference Vegetation Index (NDVI) (Tucker, 1979) was applied to quantify vegetation cover, with values ranging from -1 (water, snow) to 1 (forests, etc.). Landsat rasters covering all France (30m resolution) for years corresponding to 2 months, 3.5 years and 5.5 years (2011, 2014, 2016) were provided by a partner organization (http://www.meteory.eu/). For a given year, several rasters taken at different times may cover the same point: in this case, the maximum value is used (taking the average could be biased by seasonality and the passage of clouds). For each buffer and each time point, the mean NDVI from collected rasters was calculated.

To identify trajectories of residential greenness during childhood and account for participants' neighborhood changes (either residential moves or evolution of greenness due to land-use changes), group-based trajectory modeling (R package `gbmt`) was used. It identifies distinct groups (trajectories) that share similar trends in a multivariate time series. Three time points (2 months, 3.5, and 5.5 years) were included, and children with fewer than two points were excluded for reliability. The best-fitting model was selected based on the Bayesian information criterion after testing models with varying numbers of trajectories (Nagin and Odgers, 2010).

### 2.3. Outcome

Children's emotional and behavioral difficulties were assessed using the validated French version of the Strengths and Difficulties Questionnaire (SDQ) (Goodman, 2001; d'Acremont and Linden, 2008) and completed by parents when children were 5.5 years. Four negative subscales of 5 items each were studied: symptoms of hyperactivity/inattention, conduct problems, emotional symptoms, and peer relationship problems. Each subscale scores 0 to 10, with higher scores indicating more problematic behaviors or symptoms. The subscales are combined into 2 scales: external (conduct problems + hyperactivity/inattention symptoms) and internal (emotional symptoms + peer relationship problems) scales. Because not all subscales were fully reported in the supplementary analyses, each SDQ linear variable was standardized (centered and scaled) to facilitate comparisons between subscales.

Scores can be studied as continuous outcomes or as categories applying previously validated thresholds, and both methods were tested in this study. According to existing guidelines (Goodman et al., 1998), the normal, borderline, and abnormal score ranges are as follows.

- Hyperactivity/inattention: normal (0–5), borderline (6), abnormal (7–10)

- Conduct problems: normal (0–2), borderline (3), abnormal (4–10)

- Emotional symptoms: normal (0–3), borderline (4), abnormal (5–10)

- Peer relationship problems: normal (0–2), borderline (3), abnormal (4–10).
## 2.4. Statistical analysis

As previously described, to ensure representativity of children born in metropolitan France in 2011, our statistical analyses were weighted considering general contextual properties as well as characteristics associated with parents' participation in the study (Siméon).

In case of a large number of covariates or limited overlap in their distributions between groups, regression adjustment can become unstable or lead to extrapolation issues. Propensity Score (PS) methods focus on balancing covariates, improving robustness and ensuring better group comparison. Multinomial PS based on a gradient boosted model were calculated using the R package *twang*. The Kolmogorov-Smirnov statistic below 0.1 and absolute standardized mean differences below 0.2 were used as indicators of balance between groups (McCaffrey et al., 2013). Graphical assessment of covariate distribution is shown and commented in detail in Appendix 3. Comparisons of absolute standardized mean differences between treatment groups on the pretreatment covariates, before and after weighting, was presented in Fig. 2S: for all covariates, an absolute standardized mean difference below 0.2 was obtained after weighting, suggesting adequate balance. Because PS weighting is suggested to eliminate a greater proportion of the systematic differences in baseline characteristics between treatment groups than covariate adjustment using the PS (Austin, 2011), we then performed crude regression models weighted by PS and survey weights.

A Directed Acyclic Graph (DAG) is presented in Appendix 4a. It was refined iteratively by comparing implied conditional independencies with observed correlations using dagittylocalTests and following Ankan and colleagues procedure, the methodology is described in Appendix 4b (Ankan et al., 2021). In our final statistical model, the following covariates (assessed when children were two months old unless otherwise stated) were used to generate PS: child's sex, exact age when the SDQ was reported (months), birth weight (<2500 vs ≥ 2500g), premature birth (<37 vs ≥ 37 weeks of amenorrhea), child's place of residency (with both parents; with his mother only; other), number of siblings living in the house; parental characteristics: age at child's birth (one variable/parent), each parent's education level (secondary school or lower; secondary education at high school level; higher education [Bac + 2 to +5]; ≥5 years after high school), family income per unit of consumption (<1630 vs ≥ 1630€/month, median income 2011), employment (neither partner has a professional activity; both; only one), financial difficulties (5-modality scale), parental migratory status (not of French nationality; of French nationality descended from 1 or 2 immigrant parents; other French parent, one variable/parent), maternal psychological difficulties during pregnancy (binary), parental postnatal depression ascertained using the Edinburgh Postnatal Depression Scale (EPDS) dichotomized at a threshold of 13 and summed up for both parents (Matthey et al., 2006) (neither parent at risk of depression, one parent at risk, both parents are at risk).

On average, 3.4 % of data on study covariates were missing, with a maximum of 14.2 % for parental postnatal depression. To handle missing data, Multiple Imputation by Chained Equations method by fully conditional specification was applied (15 imputed datasets) (Sterne et al., 2009). For each imputed dataset, the corresponding PS was calculated and regression was performed before pooling the coefficients.

## 2.5. Mediation analysis

Total screen time reported at 3.5 years old was used as a mediator of the associations between residential greenness and children's emotional and behavioral difficulties. The methodology is detailed in Appendix 5.

## 2.6. Interaction and stratification analyses

Interaction between family income and greenness trajectories were explored. Linear regression models were stratified by child sex; family income per unit of consumption (<1630 vs ≥ 1630€/month), according

to rural/urban areas (<2000 vs ≥ 2000 inhabitants), and according to residential mobility during follow-up (yes/no).

## 2.7. Supplementary analyses

A complete case analysis and a minimally adjusted model (propensity scores based on: children sex, exact age when the SDQ was reported, family income, parental education, employment, age, and migratory status) were tested.

Logistic regressions considering SDQ thresholds for each subscale and comparing with a normal score were performed. In addition, a sensitivity analysis using SDQ scores provided by teachers when children were 4 years old was performed. However, due to time constraints of the telephone interviewers to gather data, only 4 out of 5 items from the hyperactivity/inattention subscale and 3 of 5 items from the emotional symptoms' subscale were included in the teacher questionnaire. Other subscales were not studied. Associations between residential greenness and the two SDQ subscales reported by teachers were evaluated in the sub-sample of children assessed at 4 years old (n = 4718).

For more coherence with the literature, we tested different greenness buffers sizes (100m, 300m, 500m, and 1600m) at 2 months of age instead of the trajectories (1600m buffers representing a 20-min walk).

# 3. Results

## 3.1. Description of the study population

A total of 10,092 children (51.2 % boys - 48.8 % girls) were included in the analyses. Group-based trajectory modelling identified 3 clusters of residential greenness trajectories. Fig. 1 illustrates the average NDVI per time point for each trajectory cluster. Table 1 describes the characteristics of children and their families according to residential greenness trajectories.

Greenness trajectory 1 (n = 3311; 32.8 %) corresponded to participants whose residential greenness was lowest during follow-up. Trajectory 2 (n = 3545; 35.1 %) and trajectory 3 (n = 3236; 32.1 %) corresponded to average and high residential greenness, respectively. Compared to participants with the lowest residential greenness, those in trajectories 2 and 3 were more likely to live with both parents (Table 1). Families with greater residential greenness exposure were more likely to have an average educational level and a lower income per consumption unit. A higher proportion of families exposed to higher residential greenness (trajectory 2 and 3) reported that their financial situation was "tight, need to be careful" compared to those from trajectory 1. Parents in families with greater residential greenness exposure were more likely to be of French nationality with no immigrant background compared to those characterized by low residential greenness. Families with greater residential greenness exposure were less likely to have a parent at risk for postnatal depression compared to those with low residential greenness. On average, 3.1 %, 2.5 % and 2.1 % of data on study covariates were missing for participants of greenness trajectories 1, 2 and 3, respectively (data not tabulated).

## 3.2. Associations between greenness trajectories and children's emotional and behavioral difficulties

Table 2 shows the associations between residential greenness trajectory and SDQ scores from linear regression models weighted by PS. Compared to children with a low residential greenness trajectory (trajectory 1), those with a higher residential greenness trajectory (trajectory 3) demonstrated significantly lower levels of internalizing symptoms (β = -0.15, 95%CI = -0.27, -0.03, P = 0.02). Children with an average greenness trajectory were shown to have lower, borderline significant levels of internalizing symptoms (β = -0.11, 95%CI = -0.22, 0.00, P = 0.05). No significant associations were observed with
<figure><img src="image_5.png" /><figcaption>Fig. 1. Mean residential NDVI (500m walkable buffer) per residential greenness trajectory, ELFE cohort study, France, 2011–2016.</figcaption></figure>

levels of externalizing symptoms. When considering SDQ subscales, children on average and high greenness trajectories had lower levels of peer relationships problems ($\beta$ = -0.13, 95%CI = -0.24, -0.02, P = 0.03 and $\beta$ = -0.15, 95%CI = -0.27, -0.02, P = 0.02, respectively). Children with high residential greenness had higher levels of symptoms of hyperactivity/inattention ($\beta$ = 0.12, 95%CI = 0.01, 0.24, P = 0.03). Children in an average greenness trajectory had lower levels of conduct problems ($\beta$ = -0.11, 95%CI = -0.21, 0.00, P = 0.04). A lower, but not significant association with emotional symptoms was observed (trajectory 3 vs 1: $\beta$ = -0.10, 95%CI = -0.22, 0.02, P = 0.09).

### 3.3. Mediation analysis

After removing participants without data on screen time, 10,178 children were included in the mediation analysis. Screen time was not found to be a mediator of the relation between greenness and SDQ scores (Appendix 5). Greenness exposure was associated with peer relationship problems through mechanisms independent of total screen time.

### 3.4. Interaction and stratification analyses

An interaction between family income and greenness trajectory was detected for internalizing symptoms (trajectory 2: P for interaction = 0.07, trajectory 3: P = 0.03). When stratifying by family income (Table 3), associations observed for children's levels of internalizing symptoms remained statistically significant only among children from low income families (trajectory 3 vs 1: $\beta$ = -0.19, 95%CI = -0.34, -0.03, P = 0.02). This was also observed for peer relationship problems (trajectory 3 vs 1: $\beta$ = -0.18, 95%CI = -0.34, -0.03, P = 0.02), and the association was borderline statistically significant for symptoms of hyperactivity/inattention (trajectory 3 vs 1: $\beta$ = 0.15, 95%CI = 0.00, 0.29, P = 0.05). Among high income families, effect sizes were smaller and not significant for the studied outcomes.

Appendix 6 shows results from stratification by residential mobility during follow-up. Associations observed for children's levels of internalizing symptoms were statistically significant only among children who did not experience residential mobility during follow-up (trajectory 3 vs 1: $\beta$ = -0.23, 95%CI = -0.37, -0.09, P < 0.01; trajectory 2 vs 1: $\beta$ = -0.15, 95%CI = -0.28, -0.02, P = 0.03). This was also observed for peer relationship problems and emotional symptoms subscales. The same trend was observed among children from moving families, but with no significant effect and lower effect size. High residential greenness was significantly associated with higher levels of externalizing symptoms only among children who experienced residential mobility

during follow-up (trajectory 3 vs 1: $\beta$ = 0.28, 95%CI = 0.11, 0.46, P < 0.01). This was also observed for symptoms of hyperactivity/inattention and conduct problems.

Appendix 7 shows the results from analyses stratified on children's sex. Associations with internalizing symptoms and hyperactivity/inattention were in the same direction but did not reach statistical significance, probably due to a lack of statistical power. Regarding peer relationship problems, results were in favor of a positive effect for both strata, although not always significant. Appendix 8 describes results from stratification by rural/urban area. Results were consistent across both strata, except for internalizing symptoms, which was in the same direction as the main analyses but not significant in both groups, likely due to insufficient statistical power.

### 3.5. Supplementary analyses

Results from the minimally adjusted model are shown in Appendix 9. Significant positive effects were observed for internalizing scores and peer relationship problems, while a negative, non-significant effect was found for the hyperactivity subscale. Results from complete case analysis are shown in Appendix 10. The same trends as in the main analyses were observed, but they did not reach statistical significance, likely due to limited statistical power (n = 8005 participants).

Appendix 11 shows associations between greenness trajectories and SDQ when considering SDQ thresholds for each subscale and comparing with a normal score. Children within the high trajectory were less likely to have a borderline score for peer relationship problems (OR = 0.72, 95% CI = 0.54–0.96, P = 0.02), but more likely to have a borderline level of symptoms of hyperactivity/inattention (OR = 1.34, 95% CI = 1.03–1.74, P = 0.03). No statistically significant associations were observed regarding abnormal thresholds, though effects were in the same directions as the borderline score.

Appendix 12 shows that high greenness exposure at 2 months was associated with lower levels of children's emotional symptoms as reported by teachers ($\beta$ = -0.18, 95%CI = -0.35, -0.02, P = 0.03). High residential greenness was associated with lower levels of hyperactivity/inattention symptoms, though non-significant ($\beta$ = -0.11, 95% CI = -0.28, 0.06, P = 0.2).

Appendix 13 shows associations between different buffer sizes of residential greenness at 2 months (100m, 300m, 500m, and 1600m) and children's emotional and behavioral difficulties. The results were consistent across buffer sizes, except for 1600m, where most effects were in the same direction but not significant.
<caption>**Table 1**</caption>

Baseline characteristics of the study population, ELFE cohort study, France, 2011–2016. Values represent numbers (percentages) unless stated otherwise.

<table><thead><tr><th>Characteristics<sup>a</sup></th><th></th><th>All participants (n = 10,092)</th><th>Greenness trajectory 1 (n = 3311)</th><th>Greenness trajectory 2 (n = 3545)</th><th>Greenness trajectory 3 (n = 3236)</th></tr></thead><tbody><tr><td rowspan="2">Sex</td><td>Male</td><td>5170 (51.2)</td><td>1636 (49.4)</td><td>1825 (51.5)</td><td>1709 (52.8)</td></tr><tr><td>Female</td><td>4922 (48.8)</td><td>1675 (50.6)</td><td>1720 (48.5)</td><td>1527 (47.2)</td></tr><tr><td rowspan="3">Child's age at 5 year assessment (in months)</td><td>&lt;2500 g</td><td>66.5 (1.8)</td><td>66.5 (1.8)</td><td>66.5 (1.8)</td><td>66.5 (1.8)</td></tr><tr><td>≥2500 g</td><td>333 (3.3)</td><td>104 (3.1)</td><td>126 (3.6)</td><td>103 (3.2)</td></tr><tr><td>Missing</td><td>9549 (94.6)</td><td>3108 (93.9)</td><td>3364 (94.9)</td><td>3077 (95.1)</td></tr><tr><td rowspan="3">Birth weight, binary</td><td>Missing</td><td>210 (2.1)</td><td>99 (3.0)</td><td>55 (1.6)</td><td>56 (1.7)</td></tr><tr><td>No, ≤ 37 weeks of amenorrhea</td><td>9009 (89.3)</td><td>2945 (88.9)</td><td>3172 (89.5)</td><td>2892 (89.4)</td></tr><tr><td>Yes, &gt;37 weeks of amenorrhea</td><td>936 (9.3)</td><td>292 (8.8)</td><td>338 (9.5)</td><td>306 (9.5)</td></tr><tr><td rowspan="3">Premature birth</td><td>Missing</td><td>147 (1.5)</td><td>74 (2.2)</td><td>35 (1.0)</td><td>38 (1.2)</td></tr><tr><td>With both parents</td><td>9665 (95.8)</td><td>3122 (94.3)</td><td>3388 (95.6)</td><td>3155 (97.5)</td></tr><tr><td>With his mother only</td><td>271 (2.7)</td><td>125 (3.8)</td><td>102 (2.9)</td><td>44 (1.4)</td></tr><tr><td rowspan="3">Child's place of residency</td><td>Other</td><td>10 (0.1)</td><td>7 (0.2)</td><td>3 (0.1)</td><td>0 (0.0)</td></tr><tr><td>Missing</td><td>146 (1.4)</td><td>57 (1.7)</td><td>52 (1.5)</td><td>37 (1.1)</td></tr><tr><td>No</td><td>6243 (61.9)</td><td>1903 (57.5)</td><td>2210 (62.3)</td><td>2130 (65.8)</td></tr><tr><td>Yes</td><td>3849 (38.1)</td><td>1408 (42.5)</td><td>1335 (37.7)</td><td>1106 (34.2)</td></tr><tr><td>Mean (SD) number of siblings living in the house<sup>b</sup></td><td></td><td>0.8 (0.9)</td><td>0.7 (0.9)</td><td>0.8 (0.9)</td><td>0.8 (0.9)</td></tr><tr><td>Mean (SD) mother's age at child's birth<sup>b</sup></td><td></td><td>31.4 (4.6)</td><td>31.6 (4.7)</td><td>31.2 (4.6)</td><td>31.3 (4.5)</td></tr><tr><td>Mean (SD) father's age at child's birth<sup>b</sup></td><td></td><td>33.8 (5.8)</td><td>34.2 (6.1)</td><td>33.6 (5.8)</td><td>33.5 (5.5)</td></tr><tr><td rowspan="5">Mother's educational level</td><td>Secondary school or lower</td><td>428 (4.2)</td><td>153 (4.6)</td><td>173 (4.9)</td><td>102 (3.2)</td></tr><tr><td>Secondary education (high school)</td><td>2702 (26.8)</td><td>735 (22.2)</td><td>998 (28.2)</td><td>969 (29.9)</td></tr><tr><td>Higher education (Bac + 2 to +5)</td><td>4406 (43.7)</td><td>1279 (38.6)</td><td>1542 (43.5)</td><td>1585 (49.0)</td></tr><tr><td>≥5 years after high school</td><td>2405 (23.8)</td><td>1082 (32.7)</td><td>780 (22.0)</td><td>543 (16.8)</td></tr><tr><td>Missing</td><td>151 (1.5)</td><td>62 (1.9)</td><td>52 (1.5)</td><td>37 (1.1)</td></tr><tr><td rowspan="5">Father's educational level</td><td>Secondary school or lower</td><td>588 (5.8)</td><td>210 (6.3)</td><td>210 (5.9)</td><td>168 (5.2)</td></tr><tr><td>Secondary education (high school)</td><td>3292 (32.6)</td><td>805 (24.3)</td><td>1168 (32.9)</td><td>1319 (40.8)</td></tr><tr><td>Higher education (Bac + 2 to +5)</td><td>2900 (28.7)</td><td>841 (25.4)</td><td>1069 (30.2)</td><td>990 (30.6)</td></tr><tr><td>≥5 years after high school</td><td>2366 (23.4)</td><td>1053 (31.8)</td><td>771 (21.7)</td><td>542 (16.7)</td></tr><tr><td>Missing</td><td>946 (9.4)</td><td>402 (12.1)</td><td>327 (9.2)</td><td>217 (6.7)</td></tr><tr><td rowspan="3">Income per consumption unit</td><td>&lt;1630€/month</td><td>4723 (46.8)</td><td>1393 (42.1)</td><td>1684 (47.5)</td><td>1646 (50.9)</td></tr><tr><td>≥1630€/month</td><td>4914 (48.7)</td><td>1742 (52.6)</td><td>1698 (47.9)</td><td>1474 (45.6)</td></tr><tr><td>Missing</td><td>455 (4.5)</td><td>176 (5.3)</td><td>163 (4.6)</td><td>116 (3.6)</td></tr><tr><td rowspan="4">Parental employment</td><td>Both parents working</td><td>7133 (70.7)</td><td>2165 (65.4)</td><td>2491 (70.3)</td><td>2477 (76.5)</td></tr><tr><td>One parent unemployed</td><td>2375 (23.5)</td><td>883 (26.7)</td><td>847 (23.9)</td><td>645 (19.9)</td></tr><tr><td>Both parents unemployed</td><td>283 (2.8)</td><td>133 (4.0)</td><td>103 (2.9)</td><td>47 (1.5)</td></tr><tr><td>Missing</td><td>301 (3.0)</td><td>130 (3.9)</td><td>104 (2.9)</td><td>67 (2.1)</td></tr><tr><td rowspan="7">Financial difficulties</td><td>"Comfortable"</td><td>1599 (15.8)</td><td>627 (18.9)</td><td>533 (15.0)</td><td>439 (13.6)</td></tr><tr><td>"It's okay"</td><td>4238 (42.0)</td><td>1335 (40.3)</td><td>1503 (42.4)</td><td>1400 (43.3)</td></tr><tr><td>"It's tight, need to be careful"</td><td>3327 (33.0)</td><td>986 (29.8)</td><td>1190 (33.6)</td><td>1151 (35.6)</td></tr><tr><td>"Struggling"</td><td>601 (6.0)</td><td>232 (7.0)</td><td>202 (5.7)</td><td>167 (5.2)</td></tr><tr><td>"Can't manage without going into debt"<sup>a</sup></td><td>114 (1.1)</td><td>44 (1.3)</td><td>38 (1.1)</td><td>32 (1.0)</td></tr><tr><td>"Prefer not to answer"</td><td>7 (0.1)</td><td>1 (0.0)</td><td>3 (0.1)</td><td>3 (0.1)</td></tr><tr><td>Missing</td><td>206 (2.0)</td><td>86 (2.6)</td><td>76 (2.1)</td><td>44 (1.4)</td></tr><tr><td rowspan="4">Mother's migratory status</td><td>Not of French nationality</td><td>874 (8.7)</td><td>428 (12.9)</td><td>317 (8.9)</td><td>129 (4.0)</td></tr><tr><td>French nationality descended from 1 or 2 immigrant parents</td><td>998 (9.9)</td><td>411 (12.4)</td><td>367 (10.4)</td><td>220 (6.8)</td></tr><tr><td>Other French Mother</td><td>8036 (79.6)</td><td>2397 (72.4)</td><td>2793 (78.8)</td><td>2846 (87.9)</td></tr><tr><td>Missing</td><td>184 (1.8)</td><td>75 (2.3)</td><td>68 (1.9)</td><td>41 (1.3)</td></tr><tr><td rowspan="4">Father's migratory status</td><td>Not of French nationality</td><td>296 (2.9)</td><td>147 (4.4)</td><td>111 (3.1)</td><td>38 (1.2)</td></tr><tr><td>French nationality descended from 1 or 2 immigrant parents</td><td>1430 (14.2)</td><td>591 (17.8)</td><td>543 (15.3)</td><td>296 (9.1)</td></tr><tr><td>Other French Father</td><td>7293 (72.3)</td><td>2166 (65.4)</td><td>2529 (71.3)</td><td>2598 (80.3)</td></tr><tr><td>Missing</td><td>1073 (10.6)</td><td>407 (12.3)</td><td>362 (10.2)</td><td>304 (9.4)</td></tr><tr><td rowspan="3">Mother psychological difficulties during pregnancy</td><td>No</td><td>8768 (86.9)</td><td>2854 (86.2)</td><td>3074 (86.7)</td><td>2840 (87.8)</td></tr><tr><td>Yes</td><td>1226 (12.1)</td><td>396 (12.0)</td><td>448 (12.6)</td><td>382 (11.8)</td></tr><tr><td>Missing</td><td>98 (1.0)</td><td>61 (1.8)</td><td>23 (0.6)</td><td>14 (0.4)</td></tr><tr><td rowspan="4">Parental postnatal depression risk (EPDS scale)</td><td>Neither parent at risk</td><td>7786 (77.2)</td><td>2437 (73.6)</td><td>2750 (77.6)</td><td>2599 (80.3)</td></tr><tr><td>One parent at risk</td><td>835 (8.3)</td><td>286 (8.6)</td><td>273 (7.7)</td><td>276 (8.5)</td></tr><tr><td>Both parents are at risk</td><td>34 (0.3)</td><td>11 (0.3)</td><td>14 (0.4)</td><td>9 (0.3)</td></tr><tr><td>Missing</td><td>1437 (14.2)</td><td>577 (17.4)</td><td>508 (14.3)</td><td>352 (10.9)</td></tr><tr><td>Mean (SD) NDVI at 2 months (500m walkable buffer)<sup>b</sup></td><td></td><td>0.5 (0.1)</td><td>0.3 (0.1)</td><td>0.5 (0.1)</td><td>0.6 (0.1)</td></tr><tr><td>Mean (SD) NDVI at 3.5 years (500m walkable buffer)<sup>b</sup></td><td></td><td>0.5 (0.1)</td><td>0.4 (0.1)</td><td>0.5 (0)</td><td>0.7 (0.1)</td></tr><tr><td>Mean (SD) NDVI at 5.5 years (500m walkable buffer)<sup>b</sup></td><td></td><td>0.5 (0.1)</td><td>0.4 (0.1)</td><td>0.5 (0.1)</td><td>0.7 (0.1)</td></tr><tr><td>Mean (SD) SDQ external scale score</td><td></td><td>5.2 (3.4)</td><td>5.1 (3.4)</td><td>5.2 (3.4)</td><td>5.3 (3.4)</td></tr><tr><td>Mean (SD) SDQ internal scale score</td><td></td><td>3.2 (2.6)</td><td>3.3 (2.7)</td><td>3.2 (2.6)</td><td>3.2 (2.6)</td></tr><tr><td>Mean (SD) SDQ hyperactivity/inattention subscale score</td><td></td><td>3.3 (2.4)</td><td>3.2 (2.4)</td><td>3.3 (2.4)</td><td>3.4 (2.4)</td></tr><tr><td>Mean (SD) SDQ conduct problems subscale score</td><td></td><td>1.9 (1.6)</td><td>1.9 (1.6)</td><td>1.9 (1.5)</td><td>2 (1.5)</td></tr><tr><td>Mean (SD) SDQ emotional problems subscale score</td><td></td><td>2.1 (1.9)</td><td>2.1 (1.9)</td><td>2.1 (1.9)</td><td>2 (1.9)</td></tr><tr><td>Mean (SD) SDQ peer problems subscale score</td><td></td><td>1.2 (1.3)</td><td>1.2 (1.4)</td><td>1.2 (1.3)</td><td>1.1 (1.3)</td></tr></tbody></table>

<footnote><sup>a</sup> At 2 months unless otherwise stated.</footnote>

<footnote><sup>b</sup> Number of siblings, mother's age, father's age and Mean NDVI at 2 months, 3.5 and 5.5 years were respectively missing for 140, 40, 324, 153, 635 and 124 participants.</footnote>
<caption>**Table 2**</caption>

Associations between residential greenness trajectories and children's emotional and behavioral difficulties (assessed by the SDQ), ELFE cohort study, France, 2011–2016.

<table><thead><tr><th>SDQ scale</th><th>Greenness trajectory</th><th>β (95 % CI)</th><th>P value</th></tr></thead><tbody><tr><td rowspan="3">Externalizing symptoms</td><td>trajectory 1</td><td>Reference</td><td></td></tr><tr><td>trajectory 2</td><td>-0.03 (-0.13, 0.08)</td><td>0.6</td></tr><tr><td>trajectory 3</td><td>0.08 (-0.04, 0.20)</td><td>0.2</td></tr><tr><td rowspan="3">Internalizing symptoms</td><td>trajectory 1</td><td>Reference</td><td></td></tr><tr><td>trajectory 2</td><td>-0.11 (-0.22, 0.00)</td><td>0.05</td></tr><tr><td>trajectory 3</td><td>-0.15 (-0.27, -0.03)</td><td>0.02</td></tr><tr><td rowspan="3">Symptoms of hyperactivity/inattention</td><td>trajectory 1</td><td>Reference</td><td></td></tr><tr><td>trajectory 2</td><td>0.03 (-0.07, 0.13)</td><td>0.5</td></tr><tr><td>trajectory 3</td><td>0.12 (0.01, 0.24)</td><td>0.03</td></tr><tr><td rowspan="3">Conduct problems</td><td>trajectory 1</td><td>Reference</td><td></td></tr><tr><td>trajectory 2</td><td>-0.11 (-0.21, -0.00)</td><td>0.04</td></tr><tr><td>trajectory 3</td><td>-0.01 (-0.13, 0.10)</td><td>0.8</td></tr><tr><td rowspan="3">Emotional symptoms</td><td>trajectory 1</td><td>Reference</td><td></td></tr><tr><td>trajectory 2</td><td>-0.06 (-0.16, 0.05)</td><td>0.3</td></tr><tr><td>trajectory 3</td><td>-0.10 (-0.22, 0.02)</td><td>0.09</td></tr><tr><td rowspan="3">Peer relationship problems</td><td>trajectory 1</td><td>Reference</td><td></td></tr><tr><td>trajectory 2</td><td>-0.13 (-0.24, -0.02)</td><td>0.03</td></tr><tr><td>trajectory 3</td><td>-0.15 (-0.27, -0.02)</td><td>0.02</td></tr></tbody></table>

Linear regression weighted on propensity scores.

SDQ = Strengths and Difficulties Questionnaire, β = Adjusted regression coefficient, CI = Confidence Interval.

<caption>**Table 3**</caption>

Associations between residential greenness trajectory and children's emotional and behavioral difficulties (assessed by the SDQ), stratification by income per unit of consumption, ELFE cohort study, France, 2011–2016.

<table><thead><tr><th rowspan="2">SDQ scale</th><th rowspan="2">Greenness trajectory</th><th colspan="2">Low income<sup>a</sup></th><th colspan="2">High income</th></tr><tr><th>β (95 % CI)</th><th>P value</th><th>β (95 % CI)</th><th>P value</th></tr></thead><tbody><tr><td rowspan="3">Externalizing symptoms</td><td>trajectory 1</td><td>Reference</td><td></td><td>Reference</td><td></td></tr><tr><td>trajectory 2</td><td>0.01 (-0.12, 0.14)</td><td>0.9</td><td>0.01 (-0.15, 0.16)</td><td>0.9</td></tr><tr><td>trajectory 3</td><td>0.11 (-0.04, 0.26)</td><td>0.1</td><td>0.06 (-0.11, 0.22)</td><td>0.5</td></tr><tr><td rowspan="3">Internalizing symptoms</td><td>trajectory 1</td><td>Reference</td><td></td><td>Reference</td><td></td></tr><tr><td>trajectory 2</td><td>-0.11 (-0.25, 0.03)</td><td>0.1</td><td>0.05 (-0.09, 0.18)</td><td>0.5</td></tr><tr><td>trajectory 3</td><td>-0.19 (-0.34, -0.03)</td><td>0.02</td><td>0.04 (-0.11, 0.18)</td><td>0.6</td></tr><tr><td rowspan="3">Symptoms of hyperactivity/inattention</td><td>trajectory 1</td><td>Reference</td><td></td><td>Reference</td><td></td></tr><tr><td>trajectory 2</td><td>0.05 (-0.09, 0.18)</td><td>0.5</td><td>0.08 (-0.06, 0.22)</td><td>0.3</td></tr><tr><td>trajectory 3</td><td>0.15 (0.00, 0.29)</td><td>0.05</td><td>0.09 (-0.05, 0.24)</td><td>0.2</td></tr><tr><td rowspan="3">Conduct problems</td><td>trajectory 1</td><td>Reference</td><td></td><td>Reference</td><td></td></tr><tr><td>trajectory 2</td><td>-0.05 (-0.19, 0.08)</td><td>0.4</td><td>-0.10 (-0.26, 0.06)</td><td>0.2</td></tr><tr><td>trajectory 3</td><td>0.01 (-0.13, 0.16)</td><td>0.9</td><td>-0.02 (-0.19, 0.14)</td><td>0.8</td></tr><tr><td rowspan="3">Emotional symptoms</td><td>trajectory 1</td><td>Reference</td><td></td><td>Reference</td><td></td></tr><tr><td>trajectory 2</td><td>-0.05 (-0.18, 0.09)</td><td>0.5</td><td>0.06 (-0.07, 0.20)</td><td>0.3</td></tr><tr><td>trajectory 3</td><td>-0.13 (-0.28, 0.03)</td><td>0.1</td><td>0.05 (-0.08, 0.19)</td><td>0.5</td></tr><tr><td rowspan="3">Peer relationship problems</td><td>trajectory 1</td><td>Reference</td><td></td><td>Reference</td><td></td></tr><tr><td>trajectory 2</td><td>-0.15 (-0.30, -0.00)</td><td>0.05</td><td>-0.00 (-0.14, 0.14)</td><td>0.9</td></tr><tr><td>trajectory 3</td><td>-0.18 (-0.34, -0.03)</td><td>0.02</td><td>-0.00 (-0.16, 0.15)</td><td>0.9</td></tr></tbody></table>

Linear regression weighted on propensity scores.

SDQ = Strengths and Difficulties Questionnaire, β = Adjusted regression coefficient, CI = Confidence Interval.

<sup>a</sup> Low income: <1630€/month (n = 4723); High income: ≥1630€/month (n = 4914).

# 4. Discussion

This study found that children with higher exposure to residential greenness from birth to age 5 had fewer internalizing problems, particularly fewer peer relationships problems, as reported by their parents and teachers. However, higher greenness was also associated with increased levels of hyperactivity/inattention reported by parents, whereas no consistent or significant trend was observed in teacher reports at age 4. Notably, the associations between greenness exposure and children's internalizing symptoms were only statistically significant among children from low-income families, with no clear trend observed among high income families. These results suggest that children's patterns of exposure to residential greenness are related to levels of psychological difficulties, and warrant further research in relation to early life developmental outcomes.

## 4.1. Comparison with other studies

Previous studies highlight the positive impact of greenness on children's psychological well-being and socioeconomic disparities. A cross-sectional study from the European Helix project found that children aged 6–11 years from less deprived areas living within 300m of a major greenspace had improved working memory (Fernandes et al., 2023). In the UK Millennium Cohort Study, disadvantaged children with greater access to green spaces had fewer emotional problems between ages 3 and 5, but this effect did not persist beyond age 5 (Flouri et al., 2014). In a Lithuanian cross-sectional study, children aged 4 to 6 living near city parks had better mental health (Balseviciene et al., 2014). Another UK cross-sectional study focusing on 4-year-olds in deprived areas found a significant association between NDVI and both total and internalizing difficulties, but only among South Asian children, suggesting that greenspace benefits may vary across ethnic groups due to cultural or environmental factors (McEachan et al., 2018).
There are mixed findings related to proximity to greenspace and children's hyperactivity symptoms. Similarly to our results, the European Helix project found that living near a major greenspace increased hyperactivity symptoms in deprived areas (Fernandes et al., 2023). However, a Belgian study followed children aged 6-12 years over three years and found an association between greenness and reduced levels of hyperactivity symptoms (Van Aart et al., 2018). In Germany, two cross-sectional analyses showed that 10-year-old children living farther from urban green spaces had higher rates of hyperactivity and peer relationship problems (Markevych et al., 2014), and that 5-7-year-old children with less access to green spaces had higher levels of hyperactivity symptoms (Zach et al., 2016).

In the Project Viva cohort (USA), exposure to greenness from birth to adolescence showed no association with total SDQ scores. However, maximum greenness exposure in early childhood was linked to better outcomes, emphasizing the importance of early exposure (Jimenez et al., 2021). Similarly, an American study suggest a protective effect of greenness exposure on internalizing symptoms on early (but not middle) childhood, and no effect on external difficulties (Towe-Goodman et al., 2024). In the Longitudinal Study of Australian Children (LSAC), authors found that moderate greenspace exposure was associated with lower internalizing and externalizing difficulties, but higher exposure offered no additional benefits (Feng et al., 2017). At age 12-13, greenspace was linked to improved total and internalizing symptoms, though results varied depending on whether information was reported by parents, children, or teachers (Feng and Astell-Burt, 2017).

## 4.2. Interpretation and mechanisms

Different frameworks have been proposed to envision the possible mechanisms linking greenness exposure and health, notably those by Markevych et al. (2017) and Nieuwenhuijsen et al. (2017). While largely overlapping, these models highlight complementary perspectives on restorative, physical, social, and environmental pathways. Our findings can be interpreted within either of these frameworks.

The association observed between residential greenness and children's reduced levels of internalizing difficulties may be explained by positive neighborhood social cohesion (Luque-García et al., 2022). Indeed, a gradual association with peer relationship problems was observed, which is supported by a previous study showing a mediating role of social interactions on relations between greenness and pro-social behaviors (Putra et al., 2021). Even moderate exposure has demonstrated a positive effect, suggesting that small changes in residential environment could make a difference in children's psychological well-being.

One possible explanation for our unexpected finding of a negative association between residential greenness and children's levels of hyperactivity/inattention symptoms is that greater access to green space promotes physical activity, which could be mistaken by parents for symptoms of hyperactivity/inattention, especially in young children. This suggests that parents may have mistaken higher physical activity for higher levels of symptoms of hyperactivity/inattention, or that the impact of greenness may diminish in a more structured, social environment, such as school. Indeed, when the SDQ was reported by teachers, no consistent or significant trend on children's hyperactivity/inattention symptoms were observed, though only 9 out of 10 items of the subscale were assessed, and the analyses were performed in a subsample of 4718/10,092 participants, which may have reduced the statistical power. Also, a previous study compared the SDQ informants (teacher, parents, child) and found that parent-reported outcomes led to stronger effect sizes than teacher and children informants (Feng and Astell-Burt, 2017).

Previous research has shown that physical activity can mediate the association between greenness and children's prosocial behaviors (Putra et al., 2021). This highlights the importance of considering physical activity as a mediator, which we could not consider in the current study.

Another explanation might be that proximity to green spaces is linked to increased pesticide exposure, which has been hypothesized to be associated with attention deficit disorder, with or without hyperactivity (Roberts et al., 2019). Of note, higher residential greenness was significantly associated with increased hyperactivity/inattention symptoms only in children who experienced residential mobility during follow-up. This could be explained by the fact that moving represents a stressful event, making these children more likely to develop such symptoms.

Associations between residential greenness and children's levels of internalizing difficulties, peer relationship problems, and hyperactivity subscales were strongest in low-income families. This could be explained by the fact that low-income families can be less mobile and spend more time in the vicinity of their homes (Rozynek et al., 2022). Thus, children's activities are more directly related to the neighborhood environment. Conversely, higher income families may allow themselves more time to regularly move further from their home, in order to benefit from higher quality green spaces or varied types of out-of-school activities. Also, associations between residential greenness and children's levels of internalizing difficulties and peer relationship problems were only statically significant among children who did not experience residential mobility during follow-up, but the same trend were observed among moving families.

We cannot exclude the potential mediating role of other environmental factors (Markevych et al., 2017; Nieuwenhuijsen et al., 2017). For example, it is possible that increased exposure to greenness resulted in reduced noise and air pollution exposures, which in turn may have affected children's levels of emotional and behavioral difficulties. Additionally, one study reported an overall negative relationship between greenness and walkability (Shuvo et al., 2021), though the impact of walkability on children's mental health remains unclear. Future research should therefore consider these additional environmental factors to better understand the mechanisms involved (restoration/instauration role of greenness, or mitigation of other environmental factors), but this was not possible in the current study due to the unavailability of relevant covariates.

Lastly, we did not find support for a mediating role of screen time; other research should examine the ways in which actual use of green spaces may be related to children's screen use and psychological well-being.

## 4.3. Strengths and limitations

The primary strength of this study includes the large, nationally representative sample of children, which ensures the generalizability of our findings. Indeed, as the ELFE cohort was designed to be nationally representative and provides survey weights for each wave, our findings are more likely to be generalizable to children born in France in 2011. These weights help correct for non-representativeness and attrition using simultaneous calibration based on six contextual variables and others associated with attrition, as previously described (Siméon). Second, the prospective assessment of the exposure enabled precise tracking of children's environment over time, strengthening the argument for a direct association between greenness and children's psychological health. However, causality cannot be definitively established in an observational study. We modeled the outcome both as a continuous measure and using predefined thresholds, offering a comprehensive understanding of the variable within the general population while allowing classification into diagnostic categories. Another strength lies in the use of dual informants (parents and teachers) for the outcome assessment, though this was not available for all complete SDQ subscales.

Children's psychological difficulties were measured using a validated questionnaire, but we were not able to rely upon expert assessments. Nevertheless, the SDQ has been shown to be valid and robust and has widely been used to ascertain children's behavior in epidemiological studies (Goodman, 2001, d'Acremont and Linden, 2008). Limitations
also include the inability to distinguish between different types of greenness (natural vs built), which may have varying impacts on children's difficulties. However, stratification by rural/urban environment did not change the overall results. It is likely that urban greenness is more 'built,' while rural greenness is more natural, suggesting that both types of greenness might have similar effects on children. Greenness is known to be less accessible in urban areas, however even small increases could have positive effects, as supported by previous research (Markevych et al., 2014). Although missing data posed a limitation, it was addressed using multiple imputation to reduce potential bias and preserve statistical power (Li et al., 2015). Given that 10,092 participants were retained from the initial 18,289, potential selection bias due to attrition is partially addressed through the use of survey weights provided by the ELFE cohort, which help account for both non-representativeness and participant dropout over time. In addition, no substantial differences were observed between included and excluded participants (Appendix 2).

Finally, we favored a comprehensive model in the main analyses, as it adjusts for a wide set of covariates identified as potential confounders based on the DAG, including all variables associated with the outcome. While a smaller set of true confounders was also tested in sensitivity analyses, the comprehensive model aligns with previous guidance (Austin, 2011) for regression analyses, supporting adjustment for all outcome-related covariates to reduce the risk of residual confounding.

## 5. Conclusion

The results of this observational study based on a large prospective representative cohort of French children provides new data regarding associations between residential greenness and children's emotional and behavioral difficulties. Higher greenness exposure through childhood was associated with fewer internalizing problems, particularly peer relationships, by the age of 5 years. Notably, these associations were only statistically significant among children from low-income families, with no clear trend observed among high income families. Higher greenness was also linked with increased symptoms of hyperactivity/inattention reported by parents. These results need replication in other large-scale representative cohorts. In order to better understand the mechanisms involved, there is need for further prospective studies and mediation analyses.

### CRediT authorship contribution statement

**Eloi Chazelas:** Writing – review & editing, Writing – original draft, Methodology, Investigation, Formal analysis, Data curation, Conceptualization. **Devin Parker:** Writing – review & editing, Methodology.

**Florencia Barreto-Zarza:** Writing – review & editing, Methodology.

**Marie Aline Charles:** Writing – review & editing, Validation, Data curation, Conceptualization. **Maria Melchior:** Writing – review & editing, Supervision, Methodology, Conceptualization.

### Ethics

Ethical approval and written informed consent were obtained from each participant before enrolment in the original cohort. Regulatory authorities that oversee ethical data collection in France approved the ELFE: Comité de Protection des Personnes (CPP no. IDFIX-11-024; Comité National Informatique et Libertés, (CNIL no. 910504); and CNIS no. 2011X716AU.

### Declaration of interests

The authors declare no competing interest.

### Funding

ERC Consolidator grant RESEDA (Horizon Europe, 101001420).

The cohort ELFE was supported by the National Research Agency Investment for the Future programme (ANR-11-EQPX-0038); French National Institute for Research in Public Health (IRESF TGIR, 2009–01 programme); Ministry of Higher Education and Research; Ministry of Environment; Ministry of Health; French Agency for Public Health; Ministry of Culture; and National Family Allowance Fund.

### Role of the funding source

The funder of the study had no role in study design, data collection, data analysis, data interpretation, or writing of the report.

### 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.

### Acknowledgements

We thank all the volunteers of the ELFE cohort. The ELFE survey is a joint project between the French Institute for Demographic Studies (INED) and the National Institute of Health and Medical Research (INSERM), in partnership with the French blood transfusion service (Etablissement français du sang, EFS), Santé publique France, the National Institute for Statistics and Economic Studies (INSEE), the Direction générale de la santé (DGS, part of the Ministry of Health and Social Affairs), the Direction générale de la prévention des risques (DGPR, Ministry for the Environment), the Direction de la recherche, des études, de l'évaluation et des statistiques (DREES, Ministry of Health and Social Affairs), the Département des études, de la prospective et des statistiques (DEPS, Ministry of Culture), and the Caisse nationale des allocations familiales (CNAF), with the support of the Ministry of Higher Education and Research and the Institut national de la jeunesse et de l'éducation populaire (INJEP). Via the RECONAI platform, it receives a government grant managed by the National Research Agency under the "Investissements d'avenir" programme (ANR-11-EQPX-0038 and ANR-19-COHO-0001).

We also thank our partner Meteory (https://www.meteory.eu/) for providing the NDVI French rasters.

## Appendix A. Supplementary data

Supplementary data to this article can be found online at https://doi.org/10.1016/j.envres.2025.122965.

### Data availability

The data that has been used is confidential.

### References

Adélaïde, L., Hough, I., Seyve, E., Kloog, I., Fifre, G., Launoy, G., et al., 2024.

Environmental and social inequities in continental France: an analysis of exposure to heat, air pollution, and lack of vegetation. J. Expo. Sci. Environ. Epidemiol. 34 (6), 962–972.

Ankan, A., Wortel, I.M.N., Textor, J., 2021. Testing graphical causal models using the R package "dagitty". Curr Protoc 1 (2), e45.

Austin, P.C., 2011. An introduction to propensity score methods for reducing the effects of confounding in observational studies. Multivariate Behav. Res. 46 (11), 399–424.

Balseviciene, B., Sinkariova, L., Grazuleviciene, R., Andrusaityte, S., Uzdanaviciute, I., Dedele, A., et al., 2014. Impact of residential greenness on preschool children's emotional and behavioral problems. Int. J. Environ. Res. Publ. Health 11 (7), 6757–6770.
Buczyłowska, D., Zhao, T., Singh, N., Jurczak, A., Siry, A., Markevych, I., 2023. Exposure to greenspace and bluespace and cognitive functioning in children – a systematic review. *Environ. Res.* 222, 115340.

Charles, M.A., Thierry, X., Lanoe, J.L., Bois, C., Dufourg, M.N., Popa, R., et al., 2020. Cohort profile: the French national cohort of children (ELFE): birth to 5 years. *Int. J. Epidemiol.* 49 (2), 368-369.

d'Acremont, M., Linden, MV der, 2008. Confirmatory factor analysis of the strengths and difficulties questionnaire in a community sample of French-speaking adolescents. *Eur. J. Psychol. Assess.* 24 (1), 1–8.

Feng, X., Astell-Burt, T., 2017. The relationship between neighbourhood green space and child mental wellbeing depends upon whom you ask: multilevel evidence from 3083 children aged 12–13 years. *Int J Environ Res Public Health* 14 (3), 235.

Feng, X., Astell-Burt, T., 2017. Residential green space quantity and quality and child well-being: a longitudinal study. *Am. J. Prev. Med.* 53 (5), 616–624.

Fernandes, A., Krog, N.H., McEachan, R., Nieuwenhuijsen, M., Julvez, J., Márquez, S., et al., 2023. Availability, accessibility, and use of green spaces and cognitive development in primary school children. *Environ. Pollut.* 334, 122143.

Flouri, E., Midouhas, E., Joshi, H., 2014. The role of urban neighbourhood green space in children's emotional and behavioural resilience. *J. Environ. Psychol.* 40, 179-186.

Goodman, R., 2001. Psychometric properties of the strengths and difficulties questionnaire. *J. Am. Acad. Child Adolesc. Psychiatry* 40 (11), 1337-1345.

Goodman, R., Meltzer, H., Bailey, V., 1998. The strengths and difficulties questionnaire: a pilot study on the validity of the self-report version. *Eur child adolesc psychiatry.* sept 7 (3), 125-130.

Helbich, M., Hagenauer, J., Roberts, H., 2019. Relative importance of perceived physical and social neighborhood characteristics for depression: a machine learning approach. *Soc. Psychiatr. Psychiatr. Epidemiol.*

Hoffimann, E., Barros, H., Ribeiro, A.I., 2017. Socioeconomic inequalities in green space quality and accessibility—evidence from a Southern European City. *Int. J. Environ. Res. Publ. Health* 14 (8), 916.

Jimenez, M.P., Aris, I.M., Rifas-Shiman, S., Young, J., Tiemeier, H., Hivert, M.F., et al., 2021. Early life exposure to greenness and executive function and behavior: an application of inverse probability weighting of marginal structural models. *Environ. Pollut.* 291, 118208.

Klompmaker, J.O., Hart, J.E., Bailey, C.R., Browning, M.H., Casey, J.A., Hanley, J.R., et al., 2023. Racial, ethnic, and socioeconomic disparities in multiple measures of blue and green spaces in the United States. *Environ. Health Perspect.* 131 (1), 017007.

Li, P., Stuart, E.A., Allison, D.B., 2015. Multiple imputation: a flexible tool for handling missing data. *JAMA* 314 (18), 1966–1967.

Luque-García, L., Corrales, A., Lertxundi, A., Díaz, S., Ibarluzea, J., 2022. Does exposure to greenness improve children's neuropsychological development and mental health? A navigation guide systematic review of observational evidence for associations. *Environ. Res.* 206, 112599.

Markevych, I., Tiesler, C.M.T., Fuertes, E., Romanos, M., Dadvand, P., Nieuwenhuijsen, M.J., et al., 2014. Access to urban green spaces and behavioural problems in children: results from the GINIplus and LISAplus studies. *Environ. Int.* 71, 29-35.

Markevych, I., Schoerier, J., Hartig, T., Chudnovsky, A., Hystad, P., Dzhambov, A.M., et al., 2017. Exploring pathways linking greenspace to health: theoretical and methodological guidance. *Environ. Res.* 158, 301–317.

Matthey, S., Henshaw, C., Elliott, S., Barnett, B., 2006. Variability in use of cut-off scores and formats on the Edinburgh postnatal depression scale – implications for clinical and research practice. *Arch Womens Ment Health* 9 (6), 309–315.

McCaffrey, D.F., Griffin, B.A., Almirall, D., Slaughter, M.E., Ramchand, R., Burgette, L.F., 2013. A tutorial on propensity score estimation for multiple treatments using generalized boosted models. *Stat. Med.* 32 (19), 3388-3414.

McEachan, R.R.C., Yang, T.C., Roberts, H., Pickett, K.E., Arseneau-Powell, D., Gidlow, C. J., et al., 2018. Availability, use of, and satisfaction with green space, and children's mental wellbeing at age 4 years in a multicultural, deprived, urban area: results from the born in Bradford cohort study. *Lancet Planet. Health* 2 (6) e244-54.

Nagin, D.S., Odgers, C.L., 2010. Group-based trajectory modeling in clinical research. *Annu. Rev. Clin. Psychol.* 6, 109-138.

Nguyen, L., Walters, J., 2024. Benefits of nature exposure on cognitive functioning in children and adolescents: a systematic review and meta-analysis. *J. Environ. Psychol.* 96, 102336.

Nieuwenhuijsen, M.J., Khreis, H., Triguero-Mas, M., Gascon, M., Dadvand, P., 2017. Fifty shades of green: pathway to healthy urban living. *Epidemiology.* janv 28 (1), 63–71.

Oswald, T.K., Rumbold, A.R., Kedzior, S.G.E., Moore, V.M., 2020. Psychological impacts of “screen time” and “green time” for children and adolescents: a systematic scoping review. *PLOS ONE.* 4 sept 15 (9), e0237725.

Putra, I.G.N.E., Astell-Burt, T., Cliff, D.P., Vella, S.A., Feng, X., 2021. Do physical activity, social interaction, and mental health mediate the association between green space quality and child prosocial behaviour? *Urban Forestry & Urban Greening.* 1 sept 64, 127264.

Roberts, J.R., Dawley, E.H., Reigart, J.R., 2019. Children's low-level pesticide exposure and associations with autism and ADHD: a review. *Pediatr. Res.* 85 (2), 234–241.

Robinson, O., Tamayo, I., de Castro, M., Valentin, A., Giorgis-Allemand, L., Hjertager Krog, N., et al., 2018. The urban exposome during pregnancy and its socioeconomic determinants. *Environmental Health Perspectives.* juill 126 (7), 077005.

Rozynek, C., Schwerdtfeger, S., Lanzendorf, M., 2022. The influence of limited financial resources on daily travel practices. A case study of low-income households with children in the Hanover region (Germany). *J. Transport Geogr.* 100, 103329.

Schüle, S.A., Gabriel, K.M.A., Bolte, G., 2017. Relationship between neighbourhood socioeconomic position and neighbourhood public green space availability: an environmental inequality analysis in a large German city applying generalized linear models. *Int. J. Hyg Environ. Health* 220 (4), 711-718.

Shuvo, F.K., Mazumdar, S., Labib, S.M., 2021. Walkability and greenness do not walk together: investigating associations between greenness and walkability in a large Metropolitan city context. *Int. J. Environ. Res. Publ. Health* 18 (9), 4429.

Simón T. ELFE Survey: Weighting National Survey Data.

Sterne, J.A.C., White, I.R., Carlin, J.B., Spratt, M., Royston, P., Kenward, M.G., et al., 2009. Multiple imputation for missing data in epidemiological and clinical research: potential and pitfalls. *BMJ* 338, b2393.

Towe-Goodman, N., McArthur, K.L., Willoughby, M., Swingler, M.M., Wychgram, C., Just, A.C., et al., 2024. Green space and internalizing or externalizing symptoms among children. *JAMA Netw. Open.* 7 (4), e245742.

Tucker, C.J., 1979. Red and photographic infrared linear combinations for monitoring vegetation. *Remote Sens. Environ.* 8 (2), 127-150.

Van Aart, C.J.C., Michels, N., Sioen, I., De Decker, A., Bijnens, E.M., Janssen, B.G., et al., 2018. Residential landscape as a predictor of psychosocial stress in the life course from childhood to adolescence. *Environ. Int.* 120, 456-463.

World Health Organization Regional Office For Europe. 2016. Urban green spaces and health. Disponible sur: https://iris.who.int/handle/10665/345751. (Accessed 16 September 2024).

Zach, A., Meyer, N., Hendrowarsito, L., Kolb, S., Bolte, G., Nennstiel-Ratzel, U., et al., 2016. Association of sociodemographic and environmental factors with the mental health status among preschool children—Results from a cross-sectional study in Bavaria, Germany. *Int. J. Hyg Environ. Health* 219 (4), 458–467.

Zare Sakhvidi, M.J., Knobel, P., Bauwelinck, M., de Keijzer, C., Boll, L.M., Spano, G., et al., 2022. Greenspace exposure and children behavior: a systematic review. *Sci. Total Environ.* 824, 153608.