Critical Aspects, Motivators and Barriers of Building-Integrated Vegetation
Bibliographic record
Abstract
PurposeGreen buildings which provide improved user health conditions and environmentally responsible applications have gained significant attention, due to the increasing environmental problems, particularly caused by the construction industry at the global scale. However, vegetation is still not sufficiently integrated into buildings, even though numerous benefits of plants have been proven by many studies in literature.This research aims to find out the opinions of professionals and academicians in architecture-related fields regarding the critical aspects, as well as the motivators and barriers faced in BIV applications, namely; green roofs, green walls and interior gardens. Hence, it strives to help increase their application rates by underlining the significant issues to be considered. Design/methodology/approachAs to fulfilling these objectives, a questionnaire survey was conducted on 120 participants with varying professions including architects, landscape designers and civil engineers, from four countries.FindingsThe results of this study pointed out that, healthcare buildings were given the first priority among the building types for applying BIV. Moreover, among the motivator factors, receiving a certificate was found as an important incentive, besides the environmental, social and economic benefits of BIV. Furthermore, although the highly rated barriers were found as ‘the lack of proper regulations’ and ‘lack of demand by the user/client’, the findings showed that the highest responsibility for the implementation of these applications was placed on the architect.Research Limitations/Implications Based on the five major groups of Köppen climate classification system, the case countries were selected as one from each of the four main types, and by neglecting only Polar, as it lacks settlements. By considering diverse levels of development and economic welfare, countries were selected as; Canada (Snow: Humid-Subarctic), Libya (Dry: Desert-arid), Malaysia (Tropical: Tropical-Rain forest) and Turkey (Mild temperate: Mediterranean).Since the study covered four different countries, the survey was conducted by the use of Google Forms software program. This tool enabled the production and distribution of questionnaires, as well as the collection of data based on the responses of the participants. Furthermore, in order to provide consistency among the questionnaires applied in different countries, the survey was conducted in English language, although it was not the native language for a majority of the participants.Moreover, based on studies claiming that participants are more inclined to select the option with the mid-value in a Likert scale, which implies a neutral position, in the questionnaire, these types of questions were constructed with the forced choice method, by keeping the scales with even number of options.Practical ImplicationsIt is expected that the results of this study would be beneficial to both the academicians and professionals involved in the green building industry, as well as to the governmental and/or green building authorities. It is expected that this study will help serve as a guide for the stakeholders to increase the application rates of BIV in the construction industry.Social ImplicationsThe results of this study were also evaluated based on the findings of four case countries and certain conclusions were derived as to their underlying socio-economic and geographical reasons.Originality/value - Although studies on similar subjects have appeared in the literature, there are none which solely focuses on BIV applications by conducting a survey on the mentioned four case countries and compares its findings with the literature and presents an in-depth analysis on the issue.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".