| Literature DB >> 31805141 |
Abstract
What relationship with nature shapes children's desire to protect the environment? This study crosses conventional disciplinary boundaries to explore this question. I use qualitative and quantitative methods to analyse experiential, psychological, and contextual dimensions of Human-Nature Connection (HNC) before and after children participate in a project of nature conservation. The results from the interviews (N = 25) suggest that experiential aspects of saving animals enhance children's appreciation and understanding for animals, nature, and nature conservation. However, the analysis of children's psychological HNC (N = 158) shows no statistical difference before and after children participate in the project. Analysing the third dimension-children's contextual HNC-provides further insights. Including children's contextual relations with home, nature, and city, not only improves the prediction of their desire to work for nature, but also exposes a form of Human-Nature Disconnection (HND) shaped by children's closeness to cities that negatively influence it. Overall, combining experiential, psychological, and contextual dimensions of HNC provides rich insights to advance the conceptualisation and assessment of human-nature relationships. People's relationship with nature is better conceived and analysed as systems of relations between mind, body, culture, and environment, which progress through complex dynamics. Future assessments of HNC and HND would benefit from short-term qualitative and long-term quantitative evaluations that explicitly acknowledge their spatial and cultural contexts. This approach would offer novel and valuable insights to promote the psychological and social determinants of resilient sustainable society.Entities:
Mesh:
Year: 2019 PMID: 31805141 PMCID: PMC6894778 DOI: 10.1371/journal.pone.0225951
Source DB: PubMed Journal: PLoS One ISSN: 1932-6203 Impact factor: 3.240
Fig 1Diagrams used for analysis of explicit connectedness with nature and contextual HNC.
The four diagrams used to assess children’s: self-nature closeness (i.e. INS) (a), self-city closeness (b), home-nature closeness (c), home-city closeness (d).
Results of t-test analysis (means, t, df, p-values, effect size d) for differences in psychological and contextual HNC due to baseline, due to impact of the SP, and due to gender.
| Psychological HNC | |||
| MC = 0.66±0.12 | MPRE = 0.59±0.15 | MM = 0.60±0.12 | |
| MT = 0.61±0.12 | MPOST = 0.59±0.14 | MF = 0.65±0.12 | |
| t(99) = 1.96 p = .053 | p>.1 | t(212) = 2.84 p < .01 | |
| d = .39 | d = .39 | ||
| MC = 0.63±0.25 | MPRE = 0.79±0.18 | MM = 0.68±0.22 | |
| MT = 0.76±0.21 | MPOST = 0.81±0.19 | MF = 0.78±0.19 | |
| t(136) = 3.22 p < .01 | p>.1 | t(279) = 3.86 p < .001 | |
| d = .55 | d = .46 | ||
| MC = 0.71±0.16 | MPRE = 0.73±0.18 | MM = 0.67±0.17 | |
| MT = 0.73±0.18 | MPOST = 0.74±0.15 | MF = 0.78±0.13 | |
| p>.1 | p>.1 | t(280) = 5.81 p < .001 | |
| d = .69 | |||
| MC = 0.63±0.22 | MPRE = 0.63±0.22 | MM = 0.61±0.17 | |
| MT = 0.64±0.22 | MPOST = 0.65±0.19 | MF = 0.68±0.22 | |
| p>.1 | p>.1 | t(279) = 2.87 p < .01 | |
| d = .34 | |||
| NA | NA | MM = 0.60±0.38 | |
| MF = 0.81±0.29 | |||
| t(138) = 3.88 p < .001 | |||
| d = .66 | |||
| Contextual HNC | |||
| MC = 0.58±0.28 | MPRE = 0.62±0.25 | MM = 0.57±0.27 | |
| MT = 0.63±0.25 | MPOST = 0.59±0.21 | MF = 0.62±0.21 | |
| p>.1 | p>.1 | t(274) = 1.76 p = .079 | |
| d = .21 | |||
| MC = 0.56±0.25 | MPRE = 0.63±0.22 | MM = 0.59±0.25 | |
| MT = 0.54±0.26 | MPOST = 0.65±0.19 | MF = 0.54±0.23 | |
| p>.1 | p>.1 | t(279) = 2.19 p = .029 | |
| d = .26 | |||
| MC = 0.55±0.27 | MPRE = 0.48±0.27 | MM = 0.56±0.27 | |
| MT = 0.48±0.27 | MPOST = 0.53±0.24 | MF = 0.51±0.24 | |
| p>.1 | p>.1 | t(276) = 1.68 p = .094 | |
| d = .20 | |||
MC: mean control group; MT: mean treatment group; MM: mean male; MF: mean female; MPRE: mean group before SP; MPOST: mean group after SP; NA: Not Available
p-values legend: 0.1>p>0.01: reported (light green)—p<0.01: green—p<0.001: dark green
Fig 2Word clouds of themes for city, home, and nature.
The themes are reported more than three times. The size of the words is weighted for how many times the theme reoccurs in children’s answers.
Fig 3Correlation table and principal component analysis.
(a) Spearman correlation table for all quantitative methods employed. Crossed elements are non-significant (p>.1). The strength of correlations is reported in the upper triangle. (b) Two-dimensional visualization of coordinates obtained from the principal component analysis for children’s desire to work for nature.
Results of principal component analysis.
| Human-Nature Connection | |||||
| 0.643 | 0.198 | 0.834 | 0.172 | -0.030 | |
| 0.646 | 0.185 | 0.781 | 0.185 | -0.250 | |
| 0.586 | 0.204 | 0.648 | 0.156 | -0.202 | |
| 0.727 | 0.227 | 0.594 | 0.174 | 0.249 | |
| 0.481 | 0.169 | 0.143 | 0.210 | 0.897 | |
| Human-Nature Disconnection | |||||
| 0.587 | 0.227 | -0.402 | 0.766 | -0.174 | |
| 0.554 | 0.231 | -0.250 | 0.856 | -0.018 | |
| % of variance | 33.178 | 21.191 | 14.299 | ||
Means (M), standard deviations (SD), factor loadings, and % of variance explained.
Fig 4Three structural equation models to predict children’s desire to work for nature.
a) CTN model; b) HNC model; c) HND-HNC model. Latent variables are in circles, measured variables in rectangles, and the lines show standardized parameter estimates.
children
’s positive relationship with nature improves the fit of the data for all indices (see RMSEA, chisq/df, AIC, and BIC in Table 3 between CTN and HNC model) (vii). However, across all indices, the best model to predict children’s desire to work for nature is the HND-HNC model. In this model, self-city and home-city closeness are indicators of a relationship with nature that is negatively linked to children’s desire to work for nature (viii). Additionally, in the HND-HNC model the percentage of explained variance for children’s desire to work for nature passes from 32.7% (CTN model), and 31.7% (HNC model), to 47.2% (HND-HNC model).Fit indices for the four models studied.
| .000 | .054 | .195 | .270 | |
| .749 | . 958 | .982 | .987 | |
| .909 | .972 | .975 | .965 | |
| 13 | 4 | 7 | 16 | |
| .159 | .102 | .058 | .039 | |
| 4.23 | 2.32 | 1.41 | 1.18 | |
| -202.0 | -277.5 | -354.4 | ||
| -170.7 | -237.9 | 298.1 | ||
| 32.7 | 31.7 | 47.2 | ||
| .000 | .054 | .195 | .270 | |
| .749 | . 958 | .982 | .987 | |
| .909 | .972 | .975 | .965 | |
| .159 | .102 | .058 | .039 | |
| 13 | 4 | 7 | 16 | |
| 4.23 | 2.32 | 1.41 | 1.18 | |
| -202.0 | -277.5 | -354.4 | ||
| -170.7 | -237.9 | 298.1 | ||
| 32.7 | 31.7 | 47.2 | ||
Acceptable indices are highlighted in green, the darker the green the better the fit.
1 p-value: acceptable ≥ 0.05
2 CFI: acceptable ≥ 0.90
3 GIF: acceptable ≥ 0.90
4 RMSEA: acceptable ≤ 0.08
5 chisq/df: acceptable ≤ 3.0
6 Lowest AIC and BIC, highest chi-square/df, and highest R2 work for nature indicate the best fitting model