Scholarlink

<p>This study develops a stochastic dynamic model to evaluate the long-term effectiveness of environmental education policies in cities. Leveraging data from the International Social Survey Programme (ISSP, 1993–2020) and municipal open data from Paris and Tokyo, we construct a Markov chain model to simulate how urban populations move between states of low, medium, and high environmental engagement. The model’s transition probabilities are estimated using maximum likelihood and calibrated to the specific policy contexts of each city. Using Monte Carlo simulations, we project population-level engagement over 20 years under four policy scenarios: baseline, moderate, intensive, and targeted. Our results reveal distinct behavioral dynamics. In Paris, a moderate policy intensification could accelerate the time to reach a societal tipping point of 60% high engagement by 2.3 years, while an intensive, broad-based strategy yields the largest overall gains. In contrast, Tokyo shows greater responsiveness to targeted interventions focused on moving individuals from low to medium engagement. The model also highlights a significant challenge of behavioral ‘backsliding’, with approximately one-fifth of highly engaged individuals regressing annually in both cities. The findings provide a quantitative, evidence-based tool for urban policymakers to compare strategies and optimize resource allocation. The study underscores the importance of context-specific policy design and offers a reproducible framework for the ex-ante evaluation of environmental education initiatives.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Assessing environmental education policy effectiveness: a stochastic dynamic model of pro-environmental behavior change in urban populations

  • 期刊论文

  • 发表日期：

    2026年5月22日

查看全文
  • Mohamed Yasser Bounnite

摘要

This study develops a stochastic dynamic model to evaluate the long-term effectiveness of environmental education policies in cities. Leveraging data from the International Social Survey Programme (ISSP, 1993–2020) and municipal open data from Paris and Tokyo, we construct a Markov chain model to simulate how urban populations move between states of low, medium, and high environmental engagement. The model’s transition probabilities are estimated using maximum likelihood and calibrated to the specific policy contexts of each city. Using Monte Carlo simulations, we project population-level engagement over 20 years under four policy scenarios: baseline, moderate, intensive, and targeted. Our results reveal distinct behavioral dynamics. In Paris, a moderate policy intensification could accelerate the time to reach a societal tipping point of 60% high engagement by 2.3 years, while an intensive, broad-based strategy yields the largest overall gains. In contrast, Tokyo shows greater responsiveness to targeted interventions focused on moving individuals from low to medium engagement. The model also highlights a significant challenge of behavioral ‘backsliding’, with approximately one-fifth of highly engaged individuals regressing annually in both cities. The findings provide a quantitative, evidence-based tool for urban policymakers to compare strategies and optimize resource allocation. The study underscores the importance of context-specific policy design and offers a reproducible framework for the ex-ante evaluation of environmental education initiatives.