Qualitative analysis of fossil fuel discourse

Desai et al.'s document-level comparison of annual reports (2016–2022) shows renewable-energy narratives often mask operational positions that delay climate action, summarized in the Skeptical Science roundup on 16 July 2026.

This post provides a reproducible, AI-assisted workflow to extract themes, quantify messaging shifts across 2016–2022, and export stakeholder-ready evidence for research, policy, and communications teams.

• Desai et al. compared annual reports from ExxonMobil, BP, Shell, and TotalEnergies for 2016–2022 and documented increased renewable-energy messaging alongside lagging operational stances.
• Scalable qualitative methods combine OCR, codebook-driven coding, frequency and co-occurrence analysis, and temporal comparison to surface divergence between rhetoric and action.
• Evidano enables OCR, multilingual inputs, AI-assisted coding, co-occurrence networks, and time-series exports to reproduce the analysis and produce stakeholder-ready outputs.

Desai et al. used document-level textual analysis of annual reports to compare external messaging about renewables against inferred strategic positions. Methods like this generally combine close reading with coding (manual or assisted), timeline comparison (2016–2022), and keyword/theme frequency to surface divergence between rhetoric and action.

• Data inputs: corporate annual reports (PDFs), public statements, and timelines (2016–2022).
• Analytic steps: corpus ingestion → preprocessing (OCR, cleanup) → codebook creation → thematic coding → frequency & co-occurrence analysis → temporal comparison.
• Validation: triangulate textual signals with investment, asset portfolios, and policy activity where available.

Follow these seven steps to reproduce the Desai-style analysis using annual reports from 2016–2022.

Step 1: Collect PDFs of annual reports (2016–2022) and standardize file names.

Step 2: Run OCR and language cleanup; remove boilerplate sections you don't want coded.

Step 3: Seed a codebook from a manual read (e.g., "renewable framing", "investment hedging", "policy deflection").

Step 4: Upload corpus to Evidano and run AI-assisted coding across the codebook; review and correct a 10% sample.

Step 5: Generate frequency tables, co-occurrence networks, and timeline charts to measure framing shifts.

Step 6: Cross-check textual signals with investment data or public filings (spot discrepancies).

Step 7: Package quotes, visuals, and an executive memo for stakeholders.

Desai et al.'s 2016–2022 comparison shows why qualitative, reproducible analysis matters: rhetoric can be weaponized to delay action. If your team needs to scale this kind of analysis (across companies, years, and languages) start with a two-week pilot that follows the 7-step checklist above.

• Ready to try it? See how Evidano ingests reports, runs thematic + frequency analyses, and exports stakeholder-ready visuals: Try Evidano for free.
• Primary source: full roundup and links at Skeptical Science (16 July 2026).