Bibliographic record
Abstract
The landmark Paris Agreement to address climate change, officially entered into force in November 2016 and has now been ratified by 185 of 197 Parties to the Convention. The agreement sets a course for all countries to limit global temperature rise to below 20 C and preferably to below 1.50 C. The latest report of the International Panel on Climate Change (IPCC) has provided warnings that global warming is becoming irreversible and the adverse impacts of climate change on human societies is calamitous. The IPCC report also carries a positive message that it is still possible to limit global warming to a 1.5 °C increase and described several technology pathways that countries could use to reduce their emissions. But are the technology pathways promoted by the IPCC and being pursued by countries well-founded and coherent such that they have a possibility of achieving the desired net zero emissions by 2050? Are countries developing the right strategies and taking immediate action to address decarbonization of their energy systems? What are the policy-relevant indicators on how fast and by how much emissions can be reduced? Are there monumental changes in the energy system driven by technology, competitiveness and social innovation that will fundamentally impact climate policy? To address the above questions, this study will review the history of climate change agreements and will examine the IPCC illustrative strategies to limit temperature increase to 1.50 C. Discussion will also be centered on emerging technology pathways for displacing fossil fuels including nuclear energy, renewable energy (non-biomass), bioenergy, natural gas as a ‘bridge fuel’, carbon capture utilization and storage and CO2 retention and negative emissions. It will be shown that despite enthusiastic support for climate mitigation there are many serious policy and engineering obstacles to the series of pathways required for greenhouse gas reductions by mid-century. We argue that emissions from bioenergy should be treated in the same way as emissions from fossil fuels and this leaves many developed countries in a deep hole for reducing emissions. Based on the analysis in this study, recommendations are made for Canada to pursue strategic policy directions and the design of unique and rational innovation programs.
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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 | 0.014 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.004 | 0.022 |
| Scholarly communication | 0.017 | 0.024 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.011 | 0.019 |
| Insufficient payload (model declined to judge) | 0.010 | 0.005 |
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".