THE ROLE OF ECONOMIC SUPPORT MEASURES IN THE DEVELOPMENT OF GREEN CONSTRUCTION: INTERNATIONAL EXPERIENCE
Bibliographic record
Abstract
Различные человеческие факторы привели к значительному увеличению выбросов парниковых газов, ведущих к драматическому изменению климата, истощению природы в городских и пригородных районах, а также потере биоразнообразия. В наши дни одной из мер, направленных на уменьшение всех этих отрицательных тенденций в городских ландшафтах, является строительство экологически чистых зданий. Для стимулирования зеленого строительства разные страны разработали специальные стандарты и меры государственной поддержки, способствующие привлечению девелоперов и физических лиц к участию в проектах по строительству зеленых зданий. Основная цель этой статьи проанализировать и показать разницу между мерами экономической поддержки, разработанными ведущими странами в области экологически чистого строительства, такими как США, Канада, ОАЭ, Китай и Великобритания. Результаты этого исследования показывают некоторые из лучших практик, которые могут быть приняты во внимание правительствами других стран для продвижения зеленого строительства и оказания положительного воздействия на окружающую среду. Various human factors have led to a significant increase in greenhouse gas emissions causing dramatic climate change, nature depletion in urban and peri-urban areas, and biodiversity loss. Nowadays, one of measures aimed to reduce all these negative trends in cityscapes is the construction of eco-friendly buildings. In order to boost green construction, different countries developed special guidelines and state support measures promoting the idea of participation in green projects among construction companies and individuals. Main purpose of this article is to analyze and show the difference between economic support measures developed by the leading countries in green construction such as the USA, Canada, the UAE, China and the United Kingdom. The results of this study show some of the best practices to be considered by the governments of other countries to promote construction of eco-friendly buildings and make a positive impact on the global 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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".