Review: The Economics Landscape for Building Decarbonization
Bibliographic record
Abstract
As efforts to mitigate climate change become increasingly urgent, the need to address the environmental impact of the built environment has gained significant attention. Buildings, as major contributors to Greenhouse Gas (GHG) emissions, have a substantial embodied and operational carbon footprint resulting from their construction materials, practices, and lifetime operation. This paper examines the economic landscape of strategies and policies aimed at reducing the embodied and operational carbon footprint of buildings on a global scale, with specific case studies from various national contexts. It delves into various innovative approaches, including economic analysis techniques, market instruments, market demands, and the role of government incentives to reduce the carbon footprint of buildings. The study highlights the crucial role of government policies, financial incentives, and market forces in promoting sustainable practices and fostering the adoption of low-carbon alternatives. By shedding light on the economic dimensions of reducing the carbon footprint of buildings, this research aims to facilitate informed decision-making by policymakers, engineers, and other stakeholders, ultimately contributing to a more sustainable and climate-resilient built environment.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.012 | 0.003 |
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".