Utility rebates, emissions spillovers and lobbying : essays on environmental economics
Bibliographic record
Abstract
The first essay, co-authored with Sumeet Gulati, estimates the increase in the market share of ENERGY STAR-qualified appliances attributed to utility rebates in the US. Results show that a dollar increase in the rebate leads to a 0.3% increase in the share of ENERGY STAR-qualified clothes washers while the effect is not significant for dishwashers and refrigerators. Assuming a redemption rate of 40%, the cost of a megawatt hour saved is lower than the estimated cost of building and operating an additional power plant and the average on-peak spot price. Therefore, rebate programs for ENERGY STAR clothes washers are a cost-effective way to reduce energy demand. In the second essay I analyse the presence of pollution spillovers by looking at emission levels and changes in emissions. I use a spatial autoregressive (SAR) model with geographic distance and industry distance weight matrices as well as an extension of the SAR model that uses the two weight matrices simultaneously to exploit the variation in the toxicity-weighted emission levels and emission changes in a large sample of manufacturing facilities in Canada. I find that, compared to OLS results, these spatial linkages exist and are stronger for within sector linkages than geographic linkages. In the third essay I use firm-level characteristics to predict the lobbying and abatement decision of firms in a model with two non-cooperating firms. There are three sources of firm heterogeneity, viz. the marginal cost of production, the emission intensity and the marginal cost factor of abatement. The decision to lobby or abate or do both depends on the cost-effectiveness of lobbying against that of abating. I find that a firm will abate and not lobby if its effective marginal abatement cost, which depends on output, is lower than a threshold value.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 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".