The Political Economy of Convertible Firms: Electric Utilities and Automakers in Climate Politics
Bibliographic record
Abstract
Electric utilities and automakers stand at the crossroads of the fossil fuel past and the clean energy future. Their flexibility in energy use combined with market power and political influence means electricity and car firms are seen as the key ‘convertible’ industries which may be won over from opposing to supporting ambitious climate policy. However, there is confusion about the conditions under which this pro-climate ‘flipping’ by firms happens. The purpose of this dissertation is to answer the question: what explains variation in convertible firm positions on climate policy? The project unfolds in three main phases. First, I identify varying explanations in the academic literature for firm political preference formation and change. These explanations tend to prioritise one of three different levels of corporate identity: firm-level, national-level or sector-level characteristics. In the second phase of the project, I create a novel empirical measure of the climate policy stances of 13 electric utilities and 12 automakers between 2005 and 2021. My chosen measure leverages climate-relevant discourse contained in 1,630 corporate earnings calls to generate a climate sentiment score with 403 firm-year observations. In the third and final phase I use a mixture of quantitative and qualitative techniques to investigate the determinants of convertible firm climate policy preferences. I test firm-level, national-level and sector-level hypotheses using a mixture of descriptive statistics and regression analysis. In two case study chapters, I investigate puzzling climate behaviour by E.ON and Iberdrola in the electricity sector, and Fiat and Volkswagen in the auto sector, to better understand the logic behind their policy preferences. My results suggest that convertible firms with cleaner emissions profiles tend to adopt more pro-climate policy positions, particularly in the electricity sector. However, sometimes even leaders in material decarbonisation adopt oppositional policy stances, either because they have collaborative rather than competitive relations with more polluting firms or because their chosen technologies are being disadvantaged by policy. While decarbonisation leaders in convertible sectors may still be effective partners in green coalitions, pro-climate actors should be conscious of the complexities surrounding the political strategies of these firms.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".