Long-term energy scenarios (LTES) have been serving as an important planning tool by a wide range of institutions. This article focuses on how LTES have been used (and also devised in some cases) in the government sector, and specifically how the new challenges and opportunities brought by the aspiration for the clean energy transition change the way that governments use LTES. The information tends to remain tacit, and a gap exists in understanding the way to enhance LTES use and development at the government level. To address this gap, we draw on the experience from national institutions that are leading the improvement in official energy scenario planning to articulate a set of overarching best practices to (i) strengthen LTES development, (ii) effectively use LTES for strategic energy planning and (iii) enhance institutional capacity for LTES-based energy planning, all in the context of new challenges associated with the clean energy transition. We present implementation experience collected through the International Renewable Agency’s LTES Network activities to exemplify these best practices. We highlight that in the context of the broad and complex challenges of a clean energy transition driven by ambitious climate targets, the LTES-based energy planning methodologies need to evolve, reflecting the changing landscapes, and that more effective and extensive use of LTES in government needs to be further encouraged.

This briefing examines how artificial intelligence is entering national energy planning and why its use requires explicit governance, assurance and capacity-building. Its scope covers AI applications in forecasting, scenario exploration, optimisation acceleration, digital twins, generative-AI-enabled workflows, stress testing and decision support for increasingly complex, climate-exposed and cross-sectoral energy systems. It argues that AI can improve planning speed, probabilistic analysis and exploration of deep uncertainty, but should complement—not replace—physics-based models, institutional judgement or public accountability. The key finding is that the principal risks of AI in energy planning are institutional rather than purely technical. Poorly governed AI can create opaque model authority, vendor dependence, loss of public-sector capability, weak traceability, data-governance failures, and erosion of national sovereignty over models, data and decisions. The paper therefore proposes the Shared AI Governance for Energy Systems (SAGES) framework as a non-binding, principles-based assurance architecture for governments, regulators and international partners. SAGES centres on five commitments: AI as an empowering partner rather than a default solution; human and institutional accountability for final decisions; proportional, purposeful and testable trust; respect for national sovereignty and institutional maturity; and collective progress towards just energy futures. It recommends practical implementation through a Secretariat and Working Task Groups covering vendor engagement, demonstration tools, sovereignty and IP safeguards, capacity building and model literacy. The briefing concludes that responsible AI adoption requires traceability from data to decision, stress testing, independent challenge, public-sector capability, and international cooperation that supports national ownership without transferring decision rights away from states.

This best practice brief presents Component 5 of the Data-to-Deal (D2D) framework, which addresses stakeholder engagement. This component focuses on the cross-cutting role of stakeholder engagement in enabling the development of effective climate strategies. Effective stakeholder engagement contributes to the technical design process, fosters political will, and enhances investment-readiness by demonstrating national ownership. Component 5 comprises three sub-components of successful stakeholder engagement: (i) Early and strategic engagement, (ii) Iterative and inclusive engagement mechanisms, and (iii) Institutionalising engagement for long-term ownership. These sub-components are embedded across the D2D framework, strengthening the legitimacy and feasibility of climate transition planning processes. The brief draws on various case studies, particularly from Costa Rica, Eswatini, and Chile, to illustrate the best stakeholder engagement practices for developing transition plans and successfully mobilising the finance required for implementation.