Measuring Corporate Energy Transition Through Web‐Based Evidence and Large Language Models

ABSTRACT This paper proposes a scalable method to measure firm‐level engagement with the energy transition using corporate website disclosures and large language models (LLMs). We construct a restrictive, policy‐aligned rubric that codes evidence of (i) energy efficiency improvements, (ii) decarbonization and emissions‐reduction strategies, and (iii) renewable energy use or procurement, as well as the renewable sources involved conditional on adoption. Applying this framework to a large panel of Spanish firms observed in 2023 and 2025, we show that LLMs, when constrained by transparent decision rules, can extract reliable and reproducible indicators from heterogeneous web content while limiting the risk of classifying generic sustainability narratives as implementation. Empirically, corporate energy transition engagement is increasing but remains limited in scope: Only a minority of firm‐year observations disclose actions. Disclosures are strongly asymmetric across dimensions, with renewable energy actions dominating, energy efficiency appearing less frequently, and decarbonization strategies remaining rare. Among renewable adopters, the reported energy mix is highly concentrated in solar. Adoption and disclosure vary by firm size and sector, and joint patterns indicate that integrated multidimensional transition strategies are uncommon. Overall, the proposed approach provides a conservative but robust benchmark for near real‐time monitoring of disclosed corporate energy‐transition engagement at scale.