Assessing Environmental Education Policy Effectiveness: A Stochastic Dynamic Model of Pro-Environmental Behavior Change in Urban Populations

This research develops a stochastic dynamic model to assess and predict the effectiveness of environmental education policies in shaping pro-environmental behaviors in urban populations. Leveraging pooled data from the International Social Survey Programme (ISSP) Environment modules (1993-2020), we construct a discrete-time Markov chain model with three core states of environmental engagement: low, medium, and high. The transition probabilities between these states are estimated using maximum likelihood methods, and the model is specifically calibrated for the contexts of Paris and Tokyo using municipal open data on policy intensity, educational program coverage, and local environmental indicators. Our methodology involves Monte Carlo simulations to project the long-term evolution of population-level engagement under various policy scenarios, ranging from baseline to intensive intervention. Key findings indicate that a moderate policy scenario could accelerate the attainment of a high-engagement societal threshold by approximately 2.3 years in Paris, while Tokyo demonstrates distinct sensitivity, particularly in transitioning populations from low to medium engagement. The model provides a robust, quantitative tool for urban policymakers, enabling evidence-based optimization of educational strategies and resource allocation to maximize behavioral change. It offers a novel, reproducible framework for cross-cultural comparison and the ex-ante evaluation of environmental education initiatives, highlighting the critical interplay between localized policy levers and generalized behavioral dynamics.