Three Essays on Environmental and Resource Economics
Bibliographic record
Abstract
This thesis consists of three essays on the topics related to environment and resource economics. In the first Chapter studies the role of air pollution in worker productivity among tree planters in Canada. To do so, we acquired confidential payroll data from one of Canada’s largest tree-planting companies, which includes information on worker output (trees planted), hours worked, and the location data of planting activity. We link worker data with hourly ambient air pollution data (PM2.5 concentrations), as well as worker experience, planting piece-rate, and weather (temperature, precipitation, and wind speed). We find that pollution reduces worker productivity: a 10-unit increase in daily PM2.5 reduces productivity by around 3.82% or $6.90 in daily earnings. We explore whether the effect of pollution is dependent on worker productivity, job difficulty, or the presence of incentives. The results suggest that the effect of pollution is more acute for more difficult jobs, but the effect does not depend on worker productivity or the presence of incentives. Finally, we corroborate the main results using an alternative measure of air pollution based on satellite data, and various other robustness checks. Chapter two investigates the impact of minimum wages on worker productivity using evidence from Canadian tree planters. The primary analysis detects an overall productivity-improving effect, showing that every 1% increase in the minimum wage level can raise the productivity of all tree planters by an average of 0.48%. Heterogeneous effect analysis shows that this impact varies based on planter experience and skill: it remains positive but diminishes as planters gain experience; and while the effect is negative for the least productive planters, it is positive for more skilled workers. Additionally, we assess the effect of minimum wage on labor supply, as well as conduct contract-level analysis by aggregating planter-level data to the contract level. Lastly, various productivity measures are employed to test the robustness of our main findings. Chapter three study the effects of the stringency of COVID-19 containment policies on air pollution and exposure disparities among social groups in Canada. We use daily air pollution data and the COVID-19 policy stringency index from Oxford University’s COVID-19 Government Response Tracker. We also estimate the monetary value of a change in policy stringency using the Air Quality Benefits Assessment tool developed by Health Canada and the measure of the value of mortality and morbidity risk reduction. We find that more stringent COVID-19 policies in 2020 reduced seven air pollutant levels (PM2.5, NO2, SO2, CO, PM10, NOX, and NO), but increased O3. A higher exposure of Indigenous groups to CO and low-income groups to PM2.5, NO2, NOX, and NO remained despite the overall reduction in air pollution due to the pandemic policies. Furthermore, a 10% increase in policy stringency would have resulted in air quality improvements valued at approximately $7 billion for 2022. The findings suggest that policies restricting human activities can improve environmental quality, and valuation of these measures can be used to inform policy, but the elimination of air pollution exposure gaps may require more targeted interventions to tackle the underlying factors for such disparities.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.017 | 0.004 |
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".