The Impact of ESG Performance on Investors' Decision-Making in the Real Estate Industry: Based on Green Building Certification and Facility Management
Bibliographic record
Abstract
With the global climate problem escalating, the real estate investor's environmental awareness is gradually rising, and financial returns are no longer the only consideration for investors, who are increasingly interested in the real estate industry's performance on the environmental level. This paper focuses on the impact of real estate practices in green building certification and facility management on investors' economic returns and environmental considerations. The study shows that improved environmental, social and governance (ESG) performance not only increases property values and rent levels, but also improves tenant satisfaction and lease renewal rates, which in turn enhances the competitiveness of the real estate market, and thus ESG performance improvement has a positive effect on investors. However, the existing leadership in energy and environmental design (LEED) certification for green building certification still has some limitations in terms of actual energy efficiency assessment and LEED's assessment criteria, which need to be further optimized. In addition, the introduction of edge computing in this paper is superficial and weakly referential to the practice of real estate industry, but it can be used as a general direction to enhance ESG performance.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".