Dealing with Carbon: Environmental Policy, Organizations, and Technological Responses to Climate Change
Bibliographic record
Abstract
This dissertation examines three interconnected dimensions of environmental governance: how contested technologies gain policy legitimacy, how entrepreneurs construct and moralize new environmental markets, and how expertise shapes American energy policy discourse. Through mixed-methods research including computational analysis of Senate communications (2000-2023), interviews with industry stakeholders, and analysis of policy hearings, I reveal the long-term institutional and narrative processes underlying technological responses to climate challenges. The first paper documents fundamental shifts in American energy policy deliberations, tracking the increasing influence of business interests and changing patterns of expertise in Senate hearings. My analysis reveals how partisan priorities shape which problems receive attention and how institutional constraints determine which voices dominate policy discourse. The second paper introduces "insulated consensus” technologies, demonstrating how certain solutions persist not through effectiveness but through alignment with dominant political narratives about technological innovation. Carbon capture exemplifies how technologies that preserve existing infrastructures receive disproportionate support regardless of outcomes. Together, these papers contribute to an understanding of current policy and the actions of market actors related to addressing environmental issues challenges. The third paper investigates entrepreneurial efforts to establish carbon markets, showing how startups navigate moral tensions while creating new commodities from environmental problems. Through interviews with 17 industry actors, I reveal how entrepreneurs constitute emergent markets through cultural classifications that justify their work despite technological uncertainties and opposition.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".