A Multi-Model Benchmark for designing behavioral policies to reduce energy consumption and air pollutants

Energy consumption is a major driver of air pollution and environmental degradation. In Iran, despite various policy-making efforts, the rise in energy consumption and the failure to achieve conservation goals demonstrate the need to use behavioral policy tools in public decision-making. This study aims to examine how to advance behavioral policies to reduce energy consumption and reduce the emission of atmospheric pollutants. Monthly average data were used to analyze the correlations between meteorological parameters, energy consumption, and atmospheric pollutant concentrations. A SARIMA model was developed in Python to forecast energy demand and greenhouse gas emissions for the next five years and establish criteria for scenario writing. Moreover, 4 desirable scenarios were designed using the Cross-Impact Balance Analysis (CIB) method. Based on a systematic literature review and expert interviews, 8 measures, 9 policy instruments, and 5 evaluation criteria were identified. Then, the Multipol method was used to evaluate the proposed actions, policies, and scenarios based on the identified criteria. The results showed how policy instruments and behavioral measures are aligned with different future scenarios. Also, the desired scenarios were linked with a specific set of prioritized behavioral actions and policies. The present study provides policymakers with an effective decision-making framework for selecting the most appropriate interventions and behavioral policies for optimal energy management and air pollution reduction.