Bibliographic record
Abstract
This case challenges students to critically evaluate ExxonMobil’s climate disclosure strategy following its January 2022 net zero pledge, which notably omits Scope 3 emissions. It explores whether the company’s public commitments reflect a meaningful shift in business strategy or a continuation of legacy practices under a revised narrative. Students examine whether ExxonMobil’s continued fossil fuel investments, its acquisition of Pioneer Natural Resources, and its rejection of climate-related shareholder proposals in 2023 are consistent with its stated climate commitments. These developments occurred alongside or shortly after the net zero pledge, prompting questions about the alignment between disclosure and strategic direction. The case also considers how ExxonMobil’s long-standing posture toward climate science may influence stakeholder perceptions of its current strategy. In addition, it highlights the technical and conceptual challenges involved in measuring emissions, assessing risk, and aligning disclosure practices with evolving reporting frameworks. Through this analysis, students explore the financial, operational, and reputational dimensions of ESG reporting. The case is suitable for upper-level undergraduate and MBA courses in financial reporting, corporate governance, and sustainability accounting. It is designed to develop students’ ability to interpret ESG disclosures, assess climate-related risk, and evaluate the credibility of strategic commitments in the context of evolving stakeholder expectations and regulatory change.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.010 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.008 | 0.006 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".