Bibliographic record
Abstract
In Chapter 1, I report the results of evaluating the hourly impact of a behavioral intervention tested in a randomized controlled trial. Under the program, a randomly selected group of households in Alberta was provided visual information on their home heat loss. I find that the households conserve the same amount of electricity during peak and off-peak electricity demand hours, i.e. the intervention has failed to target peak times, and accounting for the intraday distribution of the electricity savings is not important when measuring the social benefits of the program. As a policy recommendation, the study suggests implementing retail electricity prices fluctuating within a day coupled with information feedback on households’ electricity usage. Chapter 2 assesses realized energy and air leakage changes in homes constructed before and after new building energy code adoptions in three Canadian provinces: Ontario, New Brunswick, and Alberta. We find no electricity or air leakage reductions attributable to more stringent code requirements, and there is no evidence that natural gas consumption declined after a code change. Instead, a generalized improvement in residential electricity consumption and air leakage rates is observable several years before new code adoptions, depending on the province. These preexisting trends in electricity usage and air leakage may point to changes in building industry practice preceding new building code adoptions, though further investigation is required to assess the drivers of these changes. The estimated energy savings are also not in line with ex-ante engineering projections. In Chapter 3, we use regression discontinuity design to examine the effects of the congestion pricing policy introduced on the San Francisco-Oakland Bay Bridge on July 1, 2010. The study finds that the new road toll, which led to a decline in rush hour traffic volume on the bridge, was associated with a moderate uptake in public transit ridership, but it did not affect traffic-related local air pollution and respiratory illness incidence in the bridge vicinity, in contrast with the past work on the topic in other settings. This points to the importance of considering the heterogeneous place-based factors that drive the welfare effects of environmental policy.
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.028 | 0.005 |
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".