Actors, Frames and Arguments: A Multi-Decade Computational Analysis of Climate Discourse in Financial News using Large Language Models

TL;DR

This study analyzes two decades of financial news to understand how climate discourse has evolved, revealing shifts in framing and dominant voices, using large language models and a novel analytical pipeline.

Contribution

It introduces a replicable LLM-based framework for longitudinal media analysis and uncovers how financial elites' framing of climate issues has changed over time.

Findings

Pre-2015 coverage emphasized risk and regulation.

Post-Paris Agreement discourse focused on economic opportunity.

Financial institutions became dominant voices in climate discourse.

Abstract

Financial news media shapes trillion-dollar climate investment decisions, yet discourse in this elite domain remains underexplored. We analyze two decades of climate-related articles (2000-2023) from Dow Jones Newswire using an Actor-Frame-Argument (AFA) pipeline that extracts who speaks, how issues are framed, and which arguments are deployed. We validate extractions against 2,000 human-annotated articles using a Decompositional Verification Framework that evaluates completeness, faithfulness, coherence, and relevance. Our longitudinal analysis uncovers a structural transformation: pre-2015 coverage emphasized risk and regulatory burden; post-Paris Agreement, discourse shifted toward economic opportunity and innovation, with financial institutions becoming dominant voices. Methodologically, we provide a replicable paradigm for longitudinal media analysis with LLMs; substantively, we reveal how financial elites have internalized and reframed the climate crisis across two decades.