AI-assisted longitudinal comparison of scenario knowledge representation in IPCC synthesis reports

Unofficial AI-generated summary based on the public title and abstract. Not an official translation.

🔬研究者:Demonstrates a replicable NLP-based method for longitudinal content analysis of assessment reports, useful for scholars studying knowledge evolution in climate science.

🏛政策担当者:Highlights how scenario representation has shifted, cautioning against over-reliance on CDR and emphasizing policy and institutional conditions for technology deployment.

This paper examines how successive Intergovernmental Panel on Climate Change (IPCC) synthesis reports represent scenario knowledge when they are queried through the same scenario-based questions. The purpose is not to test whether the reports predict the same future, because IPCC scenarios are conditional what-if explorations rather than forecasts. Instead, the paper asks how the evidentiary, causal, and uncertainty structure of the reports changes across assessment cycles. We analyze six synthesis reports from the First Assessment Report to the Sixth Assessment Report, using a retrieval-augmented generation (RAG) pipeline and layered analytical prompts. Four theoretically selected pillars organize the comparison: mitigation-adaptation pathway divergence, emerging technologies and scale-up constraints, compound socio-economic risks and governance stress, and carbon dioxide removal feasibility with policy lock-in risks. The results show substantial continuity in the high-level logic of climate assessment: all reports treat delayed mitigation as increasing future risk and all reports caution that technological potential depends on policy, finance, infrastructure, and institutional conditions. The strongest change lies in representation. Early reports often rely on qualitative conditional reasoning; later reports increasingly use quantitative signposts, calibrated uncertainty language, integrated socio-economic pathways, and explicit treatment of limits to adaptation and net-zero pathways. For example, carbon dioxide removal is nearly absent as a policy-relevant pathway in the early assessments, appears in AR5 as a model-dependent negative-emissions assumption, and becomes in AR6 a necessary but limited component of net-zero pathways whose overuse can create mitigation-deterrence and lock-in risks. The paper contributes a replicable method for auditing longitudinal consistency in large assessment corpora and a substantive account of how IPCC scenario knowledge has moved from descriptive climate futures toward integrated, uncertainty-calibrated, and policy-conditioned knowledge representation.

• semanticscholar https://journals.plos.org/climate/article/file?id=10.1371/journal.pclm.0000965&type=printablefirst seen 2026-07-18 07:17:52