Two essays analysing pollution from agriculture : alternatives for assessing indirect effects
Bibliographic record
Abstract
Given the importance of agricultural GHG mitigation strategies, this thesis addresses, both theoretically and empirically, the indirect effects of GHG mitigation in agriculture. The first Chapter is focused on the "ancillary" physical effects of GHG mitigation, specifically in the case of water quality. Chapter two provides an adaptation of a theoretical/graphical framework that can be used to analyze the indirect effects of GHG mitigation strategies. The analysis in Chapter one develops watershed and provincial estimates of water quality co-effects of GHG mitigation strategies by linking a water quality model to a national level agricultural sector model. The Canadian Economic and Emissions Model of Agriculture (CEEMA) is used as the agricultural model. Its output is used as input for the Agricultural Non-Point Source Pollution Model (AGNPS). The output of AGNPS is then assessed using the British Columbia (BC) Water Quality Guidelines. Results from Chapter one show that around 28% of the water in the Okanagan watershed is below desirable standards. The provincial results were obtained for the lower part of BC. They show that the basins along the main rivers contain water that is barely suitable for aquatic life. In the case of the Okanagan watershed under a $25/tonne carbon equivalent price scenario there is around a 4% decrease in the total pollutant loadings ending up in the water. The biggest decrease is in Nitrogen, around 7%, with TSS being around 6%, and Phosphorous being insignificantly under 1% change. The results show that the water quality ancillary effects of GHG mitigation strategies are existent and can be quantified and targeted accordingly. The analysis done in Chapters two, although different from the analysis in Chapter one, presents an example of the possible microfoundations for some of the effects quantified in Chapter one and allows us to see how farmers react to different scenarios caused by the presence of a carbon equivalent price. The two assumed scenarios are: An overall increase of the prices of all inputs and an increase only on the N-based fertilizer price. I show how a farmer will react to these changes focusing on his risk attitudes. Chapter two uses the state contingent approach with the case of a farmer that produces a certain crop and is faced with uncertainty caused by two states of the environment and by input use. Using state contingency I develop a diagrammatic framework to analyze input transformation and two scenarios assumed to be caused by the presence of a carbon equivalent price and the resulting effects on non point source pollution. This type of framework is relatively new in the literature and discusses intuition that has not been presented before to analyze GHG mitigation. Both analyses done on this thesis, although radically different, show that when doing policy analysis on GHG mitigation they have to be targeted according to research done on their overall effects. Failing to do proper policy analysis could prove to be resource and time consuming not achieving the desired effects. Analyzing policies aimed to reduce GHG emissions must include both the direct and indirect effects caused by their adoption. These analyzes have to include the effects that geographic, climatic, and other aspects will have on their outcome. If these outcomes are not correctly assessed, they could lead to failed objectives in reducing GHG emissions and improving the environment.
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 | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.007 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.010 | 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".