A review of barriers to full-scale deployment of emissions-reduction technologies
Bibliographic record
Abstract
Innovative clean technologies are part of the solution to reducing greenhouse gas emissions in both Canada and Alberta, particularly in the latter’s petroleum industry. However, while governments and their agencies may provide policies and financial support, proponents of cleantech still face numerous barriers to full deployment and commercialization. To navigate the innovation and funding process successfully, it’s crucial for proponents to know the factors that impact the effective commercialization of cleantech innovations. They must also understand the role policies play in either supporting or hindering favourable outcomes. Start-ups require support that focuses on innovation with a strong commercial potential, while scale-ups need to rely on proven strengths if they want to obtain private sector support for growth. Granting agencies and governments have an important role in supporting innovation. More clearly demonstrating and communicating their due diligence around funding decisions justifies expenditure of public money. Moreover, their decisions can and should send a signal to private sector financiers whether a certain innovation represents a good investment. Due diligence equally works to signal financiers when a specific project does not merit investment. The need to find innovative solutions to reducing emissions may seem pressing, but the race should not be to the swiftest. De-risking for commercialization means that a proponent must firmly establish that the technology works, is economically feasible and can attain sufficient market penetration for a return on investment to the prospective financier, as well as provide socio-economic and environmental benefits. Trying to simplify or speed up the stages of innovation and the funding process means proponents can be exposed to incompletely proven and riskier technologies, which can damage credibility with financiers. A balance must be struck between the financier’s wish to expedite the de-risking process and the need to avoid inadequate de-risking which can jeopardize the project and its funding at a later stage. Distinctions must also be made between firm-level support, which allows a company more flexibility in pursuing or cancelling projects, and project-level supports, in which the funding is specifically targeted for use in the development of a particular innovation and has a defined end point. Cleantech innovation in Alberta faces added hurdles associated with a post-2014 economic downturn that has reduced some firms’ cash flows and has made firms, as well as government, less inclined to support cleantech innovations. This situation makes it crucial for innovation proponents seeking funding to distinguish clearly between a proposed project’s economic and environmental benefits. A technology whose primary benefit is reducing emissions is susceptible to changes in emissions pricing or regulations, and thus is not an attractive candidate for investors. An innovation that primarily reduces costs but offers a secondary environmental benefit is a better investment because it is much less sensitive to policy changes. Alberta innovators must make sure they emphasize the economic benefits, and do their due diligence and careful de-risking if they want to surmount the added obstacles. Cleantech innovation doesn’t have to become a casualty of the provincial economic environment if the proper steps in the innovative and fiscal processes are conscientiously followed.
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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 | 0.008 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".