Nature-Based Solutions for Carbon Neutral Climate Resilient Buildings and Communities: A Review of Technical Evidence, Design Guidelines, and Policies
Bibliographic record
Abstract
The building sector is responsible for nearly 40% of the total global direct and indirect CO2 emissions. Urban green infrastructure, which includes features such as urban trees, vegetation, green roofs, and green facades, are examples of nature-based solutions often employed as municipal climate mitigation and adaptation strategies. This approach offers a range of cost-effective strategies for reducing municipal CO2 emissions and presents compelling public policy co-benefits such as improved urban livability and enhanced environmental conditions. For municipalities to confidently deploy these solutions at a scale necessary to achieve climate benefits, acquiring knowledge of quantifiable and demonstrated outcomes is an essential requirement. The objectives of this paper are to (1) provide a comprehensive analysis of the advantages and limitations of nature-based solutions (NBS) to address the challenge of reducing CO2 emissions; (2) evaluate existing design guidelines and policies as may be available across Canada, and that that support the of implementation of NBS in urban agglomerations; (3) identify knowledge gaps and research needs to address challenges to the implementation of NBS. In this review, suggestions and requirements as presented in these documents are examined while giving due consideration to the scientific evidence available in research papers. It was found that the adoption of NBS can contribute to carbon neutral communities through reduced building energy consumption and carbon sequestration. Supportive guidelines and policies have been developed, or are in development, to promote the implementation of NBS at the city scale, despite challenges in assessing, quantitatively, their impact due to uncertainties in data, methods, and scale. Nonetheless, existing research provides sufficient evidence to qualify the measures and suggestions outlined in the guidelines and policies described in this paper.
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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 | 0.016 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".