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<p>Integrated assessment and energy system models have long been criticized for their limited interpretability of results, particularly by policymakers who find it difficult to use model results for policy-making due to non-linear model drivers and implicit assumptions. This underscores the urgent need to systematically attribute model outcomes for effective decision support. Here, we present a novel post-attribution framework combining error diagnostics, machine learning, and econometric analysis to disentangle the impacts of model inputs, structural inertia, and implicit assumptions. This framework is applied to post-evaluate global scenarios in terms of energy system and demand sector across mainstream Integrated Assessment Models (IAMs), identifying the discrepancies sources between models regarding energy transition. We find that the largest discrepancies in model inputs stem from energy demand variables, while historical model fingerprints in economic and energy supply variables are relatively minor, although the latter’s input errors can have a delayed impact on long-term emissions. Significant differences in decarbonization pathways across models, largely driven by model preferences and technological assumptions such as technological inertia, investment cost, and maturity timelines, underscore the importance of considering modeling preferences in IAMs when attributing emission pathway differences. Our study paves the way for interpreting IAM ensembles results through machine learning, identifying the deep drivers of result discrepancies, and supporting model development and policy decision-making.</p>

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Leveraging machine learning to reveal transparency in integrated assessment model ensembles

  • 期刊论文

  • 发表日期：

    2026年4月25日

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  • Hongbo Duan,
  • Yixin Sun,
  • Yun Tang,
  • Gokul Iyer,
  • Xiyu Li

摘要

Integrated assessment and energy system models have long been criticized for their limited interpretability of results, particularly by policymakers who find it difficult to use model results for policy-making due to non-linear model drivers and implicit assumptions. This underscores the urgent need to systematically attribute model outcomes for effective decision support. Here, we present a novel post-attribution framework combining error diagnostics, machine learning, and econometric analysis to disentangle the impacts of model inputs, structural inertia, and implicit assumptions. This framework is applied to post-evaluate global scenarios in terms of energy system and demand sector across mainstream Integrated Assessment Models (IAMs), identifying the discrepancies sources between models regarding energy transition. We find that the largest discrepancies in model inputs stem from energy demand variables, while historical model fingerprints in economic and energy supply variables are relatively minor, although the latter’s input errors can have a delayed impact on long-term emissions. Significant differences in decarbonization pathways across models, largely driven by model preferences and technological assumptions such as technological inertia, investment cost, and maturity timelines, underscore the importance of considering modeling preferences in IAMs when attributing emission pathway differences. Our study paves the way for interpreting IAM ensembles results through machine learning, identifying the deep drivers of result discrepancies, and supporting model development and policy decision-making.