Is clean growth really all that clean? A review of the cumulative effects associated with renewable energy clean growth initiatives in Canada and beyond
Bibliographic record
Abstract
Clean growth (also known as green capitalism, green economy, or green growth) is an economic theory in which economic expansion occurs in an environmentally and ecologically sustainable manner. Technologies such as hydropower, wind energy, small nuclear reactors, and solar power have been championed as important aspects of the transition and transformation needed to address the climate crisis. Although many of these projects are inherently more environmentally friendly than traditional approaches to energy development, the cumulative effects of clean growth technologies have not been reviewed before. Cumulative effects (the additive impacts of multiple minor stresses such as many small wind turbines or their complex synergistic effects) can create significant threats to the environment. This review aimed to understand the individual and cumulative effects of clean growth technologies, and provide examples of how these cumulative effects are being quantified and mitigated in Canada and beyond. In general, cumulative effects in clean growth technologies are understudied with little knowledge regarding how they are impacting the environment and society. Understanding the societal and cultural impacts of multiple clean growth projects, in addition to the environmental impacts, particularly in traditional Indigenous lands and territories, is a necessity prior to undertaking further development. We recommend investing in long-term monitoring of clean growth technologies as well as ensuring adequate baseline data to better understand potential impacts. Furthermore, we recommend that impact assessments consider long-term and future stressors to allow managers to understand how cumulative effects will manifest in a changing world. Ensuring the use of consistent terminology and varied indicators and/or endpoints for research involving cumulative effects and clean growth, and considering the activity in its broader context (e.g., considering spatial and temporal aspects), will enable more robust research and more comprehensive understanding of impacts. Finally, we suggest documenting best practices for evaluating and monitoring the cumulative effects in clean growth to support decision makers.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.015 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".