Analyzing Ontario’s Climate Change Mitigation and Transportation Planning for a Low - Carbon Economy
Bibliographic record
Abstract
This study is based on review and analysis of Ontario’s climate change mitigation and transportation which is eventually leading to transform into a low-carbon economy. This study is compiled into three sections. The first section provides a clear scenario of Canada’s Greenhouse Gas (GHG) emission trend over the years, the federal government’s role in GHG emission reduction and Canada’s international commitment to climate change mitigation measures along with funding. The second section focuses on a review and analysis of Ontario’s GHG emission reduction especially, in the transportation sector based on four regulatory instruments (i.e. Green Energy Act of 2009, Ontario Climate Change Strategy 2015, Ontario's Five Years Climate Change Action Plan and, Climate Change Mitigation and Low-carbon Economy Act, 2016). My analysis and arguments are focused on Ontario’s GHG emission reduction target for 2014, 2020, 2030 and 2050 to examine whether Ontario’s GHG emission reduction proceedings are heading in the right direction. The third section is an analytical review on Metrolinx’s electrification program for the Regional Express Rail (RER) system in the Greater Toronto and Hamilton areas (GTHA). Emerging issues of the province regarding GHG emission trends, progress in emission reduction, current ways and means to reduce transport sector’s GHG emissions were identified based on the results and findings of the study. Finally, a set of coherent measures have been recommended for GHG emission reduction and climate change mitigation measures applicable to Canada and Ontario. Emission reduction trend under ‘Cap and trade System’ suggests that Ontario’s GHG emission reduction target for the year 2020, 2030 and 2050 may not be achievable in one hand, on the other hand the newly elected Ontario government’s (2018) decisions on abandoning all renewable energy programs along with federal government’s controversial decision on purchasing oil pipeline will further jeopardy the transformation process to a low-carbon economy.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.007 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".