Addressing the urgent challenge of climate change requires robust policy alternatives for transitioning to clean energy systems. Using Mexico as a critical case study, this research applies advanced modelling and machine-learning techniques to explore how different policy mixes, such as the integration of clean energy incentives, carbon taxes, and energy efficiency measures, can help achieve ambitious climate goals while balancing costs and societal needs in developing countries. By simulating multiple energy transition pathways, our findings provide policymakers with actionable insights for designing robust, cost-effective policy alternatives that are resilient to a range of uncertainties such as fuel price volatility, technological changes, and changing energy demand. Results demonstrate that combining economic signals (e.g. carbon pricing and subsidies for electric vehicles), system-level support (e.g. expanding transmission and distribution capacity), and performance standards (e.g. industry energy efficiency standards) can effectively reduce greenhouse gas emissions while balancing costs and societal needs. • Multiple policy mixes were analysed using exploratory modelling techniques. • The study reveals the most robust alternatives for Mexico's clean energy future. • Carbon pricing and grid expansion emerge as core policy instruments. • Robust policy alternatives perform reliably across uncertain future scenarios. • Findings inform policy design for long-term clean energy transitions.
Castrejon-Campos et al. (Fri,) studied this question.