Natural Gas Prices and Coal Displacement: Evidence from Electricity Markets
Bibliographic record
Abstract
We examine the environmental impact of the post-2005 natural gas glut in the United States due to the shale gas boom.Our focus is on quantifying short-term coal-to-gas switching decisions by different types of electric power plants in response to changes in the relative price of the two fuels.In particular, we study the following entities: investor-owned utilities (IOUs) and independent power producers (IPPs) in restructured markets coordinated by Independent System Operators, as well as IOUs in traditional vertically-integrated markets.Using alternative data aggregations and model specifications, we find that IOUs operating in traditional markets are more sensitive to changes in fuel prices than both IOUs and IPPs in restructured markets.We attribute our findings to differences in available gas-fired generating capacity with the most cost-efficient technology: electricity generators reduced their rate of investment in the restructured markets post restructuring.The heterogeneity in the response of fuel consumption to prices has implications for carbon dioxide (CO2) emissions for the entities considered.Using simple back-of-the-envelope calculations, the almost 70% drop in the price of natural gas between June 2008 and the end of 2012 translates to as much as 33% reduction in CO2 emissions for IOUs in traditional markets, but only up to 19% for IOUs in restructured markets.
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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 | 0.001 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 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".