A Random Forest-Based Analysis And Predictive Modeling Of Factors Influencing Youth Climate Action - Info and Reading Options
By Jia tingrui, Li Shilong and Jing-Ying Wu
“A Random Forest-Based Analysis And Predictive Modeling Of Factors Influencing Youth Climate Action” Metadata:
- Title: ➤ A Random Forest-Based Analysis And Predictive Modeling Of Factors Influencing Youth Climate Action
- Authors: Jia tingruiLi ShilongJing-Ying Wu
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- Internet Archive ID: osf-registrations-fby9z-v1
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This study systematically integrates 13 core psychological predictors—place attachment, place dependence, social connection, nature connectedness, altruistic values, egoistic values, biosphere values, environmental efficacy, environmental responsibility, climate risk perception, climate change beliefs, pro-environmental behavior, and environmental self-identity—based on the Value-Belief-Norm theory, Attachment Theory, Protection Motivation Theory, and Environmental Spillover Effect Theory. Unlike previous climate action studies that predominantly focus on single theoretical frameworks, this study attempts to merge multiple theoretical perspectives to address multidimensional psychological mechanisms. These include cognitive factors (e.g., climate risk perception), emotional factors (e.g., place attachment), behavioral intentions (e.g., environmental efficacy), and identity-related factors (e.g., environmental self-identity), with the aim of filling the gap in collaborative theoretical analysis. This study employs a mixed-methods framework to predict youth climate action levels. First, a random forest classification model was developed using 17 integrated predictor variables. Model hyperparameters (mtry, ntree, min_n) were optimized via five-fold cross-validation, and generalizability was assessed using out-of-bag (OOB) error. Variable importance was ranked to identify key predictors. Second, Lasso regression was applied to perform sparse selection of highly important variables, eliminating redundancy through the L1 regularization path and yielding a parsimonious predictor set. Finally, logistic regression was used as a baseline model to compare predictive performance (AUC, accuracy) with the random forest and to interpret the direction and significance of linear effects. Model robustness was evaluated using permutation tests and stratified sampling. The findings were further interpreted within theoretical frameworks such as the Value-Belief-Norm theory and Protection Motivation Theory, shedding light on the synergistic mechanisms driving youth climate action.
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"A Random Forest-Based Analysis And Predictive Modeling Of Factors Influencing Youth Climate Action" is available for download from The Internet Archive in "data" format, the size of the file-s is: 0.11 Mbs, and the file-s went public at Sat May 03 2025.
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- Added Date: 2025-05-03 08:05:46
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