Bibliographic record
Abstract
Construction and real estate have been central to the debates on sustainable development. However, the dominant definition of sustainability in construction and real estate remain centred on the environmental dimension. The 2030 Agenda and its Sustainable Development Goals (SDGs) offer new opportunities for the building sector to expand its focus. The available literature utilizes the existing green ratings, sustainability assessment tools and standards as the basis for investigating how construction and buildings can contribute to the 2030 Agenda for Sustainable Development. However, less focus was placed on exploring the broad intersection between the building sector, on the one hand, and the SDGs and their targets on the other. This paper uses a multi-step methodology to analyze the potential role of construction and real estate in the 2030 Agenda. The paper identifies SDG targets that depend (directly or indirectly) on construction and real estate activities, and reveals that 17% of the SDG targets are directly dependent and 27% of the targets are indirectly dependent on these sectors’ activities. The identified targets are analyzed and are found to be related to all 17 goals—with the largest contributions to SDGs 11, 6, and 7. The results of the analysis are mapped and illustrated in order to provide insights to academics, practitioners and governments. This research contributes to the literature on the implementation of the 2030 Agenda. It also exposes the synergistic possibilities, and the partnerships required, to make use of the potential role of construction and real estate in the implementation of the UN Agenda.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.012 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.004 | 0.013 |
| Scholarly communication | 0.010 | 0.007 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".