Opportunities and barriers for a crop-based energy sector in Ontario
Bibliographic record
Abstract
This study investigates the existing opportunities and barriers for expanding the crop-based energy sector in Ontario. The investigation takes place at a time when growing concerns about sustainability--environmental, social, and economic--are encouraging the exploration of alternatives to energy systems based on fossil fuels, and concerns around the future viability of rural communities are making agriculturally-based and rural-based energy production systems attractive to many. To explore opportunities and barriers for the crop-based energy sector, this thesis addresses the question: What is the political-economic context within which the crop-based energy sector operates in Ontario? Taking an institutional approach, the study involved 26 interviews with individuals whose organizations influence Ontario's crop-based energy sector (that includes the biofuels ethanol and biodiesel), developed a model outlining relationships between the crop-based energy sector and other sectors of the economy, as well as the state, and implemented a survey of Ontario Members of Provincial Parliament's perspectives on biofuels. This research examines the balance of power of knowledge, production, security, finance, and technology for Ontario's crop-based energy sector. The overall balance of power currently rests with the petroleum sector. Through force field analysis, the study also identifies the key opportunities and barriers for the growth and development of the biofuels sector. These opportunities include climate change and rural development agendas, and the barriers include the petroleum sector, cost of production, and some sectors of the state. A few overarching conclusions emerge from this research: (1) Change in Ontario's crop-based energy sector is driven foremost by political and economic forces; (2) Climate change is the most significant driving force for the development and expansion of Ontario's crop-based energy sector; (3) Production cost and resistance from the petroleum sector are the most significant restraining forces for the development and expansion of Ontario's crop-based energy sector; and (4) Regulatory and/or financial commitments will be required for Ontario's crop-based energy sector to expand. Based on these findings and conclusions, the thesis offers recommendations for both research and practice, focusing on activities that will address areas of concern for Ontario's crop-based energy sector and promote its growth and development.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".