Co-production of techno-economic forecasting for energy policy-making

Techno-economic forecasts are central tools for energy policy-making and energy system modelling. Recent studies on energy system models, scenarios, and techno-economic forecasts have focused on participatory approaches. They highlight uncertainties in long-term forecasts and the technocracy of long-term energy planning as areas for future research. We examine techno-economic forecasts as a co-production process, which is not yet fully understood. The study contributes to this knowledge gap by analyzing the Danish Energy Agency's work on techno-economic forecasts for energy technology, collectively known as ”Technology Catalogues”. The empirical basis comprises an analysis of internal and external policy documents, as well as 32 semi-structured interviews. The interviews are with Danish stakeholders from academia, NGOs, industry, and public authorities . Our analysis shows that co-production of techno-economic forecasts (in a Danish context) enhances the perceived legitimacy and credibility of techno-economic forecasts. Yet, the study also reveals challenges related to maintaining transparency, trust and stakeholder motivation. Furthermore, sector-specific dynamics shape the nature and depth of stakeholder engagement, with larger and more centralized actors exerting greater influence in some technology areas, while resource constraints limit participation in others. The study shows that technology-specific approaches and focus on co-production processes, rather than perfect forecasts, are necessary to ensure the transparency, comparability and trustworthiness of techno-economic forecasts, which make them suitable for long-term energy policy making. • Co-production when forecasting enhances legitimacy but includes key tensions. • Techno-economic forecasting of technologies should reflect sector-specific stakeholder dynamics and asymmetries. • Emphasis for governmental techno-economic forecasting should be on participatory processes rather than perfect numbers. • The findings could be used as a guide for designing credible and transparent forecasting that underpins energy policy.