Public Policy and Incentives for Socially Responsible New Business Models in Market-Driven Real Estate to Build Green Projects
Bibliographic record
Abstract
The construction industry and the built environment accounts for 38% of global greenhouse gases. Significant efforts are being implemented across stakeholder categories to provide supportive guidelines and ways to address the negative impact; however, market developers need to be engaged to create the scale of impact due to large portfolios. Unfortunately, the short-term interests of private developers in real estate are to maximize profits and not to invest in long-term climate mitigation strategies. This paper will address the barriers and opportunities to incentivize, regulate real estate developers, and account for the market to adopt the lens of the B-Corp movement’s triple bottom line business practices, using business to address social and environmental challenges. Academically, accepted theories addressed through a literature review will be analyzed by a socially-oriented developer in Montreal and demonstrated through an eco-district case study. This study will identify the key stakeholders and address the life cycle thinking process to tackle the carbon impacts in the building development sector through the lens of real estate developers. This literature review will be complemented by the empirical study of one of the authors being a private developer, to link academic best practices with the market realities of real estate development. The findings of the process will outline possible solutions to real estate development that suggest cities have the opportunity to play the role of an educator, mediator, regulator, and incentivizing body to private real estate developers. Generally, critical factors of collaboration and capacity building through business modelling lists of barriers and opportunities could promote positive adoption opportunities for large-scale green development projects with a high impact on climate mitigation strategies, which could transform how the construction industry adapts to building green and socially inclusive communities.
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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 | 0.018 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.009 |
| Scholarly communication | 0.015 | 0.008 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.009 | 0.005 |
| Insufficient payload (model declined to judge) | 0.016 | 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".