Causal inference in spatial environmental economics
Bibliographic record
Abstract
This thesis consists in four papers in spatial and environmental economics, in which causal inference methods are employed to analyse two topics: carbon pricing and deforestation. The dissertation is comprised of two parts. The first evaluates the impacts of the 2008 carbon tax implemented in British Columbia, Canada; the second analyses illegal deforestation in Colombia and REDD+ policies in Indonesia. Chapter one studies the effect of British Columbia’s carbon tax on road transportation CO2 emissions and on carbon leakage due to cross-border fuel shopping in the USA. Using the synthetic control method and its extensions, we find that the tax is associated with a decrease in transportation CO2 emissions. However, this effect is not statistically significant, with no role detected for cross-border fuel shopping. In chapter two, we analyse the impact of the tax on PM2.5 concentrations arising from transportation. We detect a statistically significant effect of the carbon tax on air pollution co-benefits, which is heterogeneously distributed across metropolitan areas. Less polluted, less dense and richer areas see greater reductions in air pollution, identifying a post-tax increase in inequality with respect to pollution exposure. Reductions are driven by a switch in commute mode towards low emissions means of transport, principally public transit. Health gains from the tax are large, and regressively co-vary with income. Chapter three focuses on the effects of Colombia’s 2020 Covid-19 lockdown on forest fires. We find that the lockdown is associated with an upsurge in cumulative fires, which is correlated with the presence of armed groups. Chapter four evaluates the effect of the 2011 Indonesian Moratorium on oil palm, timber, and logging concessions. We find that dryland forest inside the Moratorium experienced, at most, a 0.65% rate of forest cover retention compared to non-Moratorium areas, while no effect is detected for carbon-rich peatland. The implied effective carbon price is below US$ 5/tCO2-eq. Moreover, the Moratorium only contributes 3-4% towards Indonesia’s 2015 Paris commitment of a 29% reduction in deforestation by 2030.
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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 | 0.036 | 0.132 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.002 | 0.007 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.012 | 0.001 |
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".