Analysis of Environmentally Certified Residential Developments in Poland
Bibliographic record
Abstract
Abstract The scale of environmental pressure caused by the construction sector has prompted a search for new technical solutions to minimize the negative impact of this part of the national economy on the environment. The drive to evaluate the adopted solutions has led to the creation of building assessment systems - environmental certificates. The aim of the article is both to introduce the certification processes used in the Polish residential property market and to indicate the locations of residential investments that have obtained the certificates in question. The paper takes a closer look at environmental certification using multi-criteria building assessment systems, i.e. BREEAM, LEED and HQE. The time scope of the presented research covered the years 2016 to the first quarter of 2022. The research carried out allowed an upward trend to be observed in the number of environmental certificates awarded, indicating the use of green building principles for residential properties. It is certain that there has been a development of this type of investment in Poland in the recent years. An analysis of the spatial distribution of the surveyed investments shows that location clusters have formed - green (low-emission) residential investments are distributed in the largest cities in Poland, which are characterized by a strictly defined consumer profile.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".