Generating societal value from natural capital on corporate-owned land: a real estate case study from Mauritius
Bibliographic record
Abstract
Land-use change is a major driver of climate change and biodiversity loss. Natural Capital Accounting (NCA) is emerging as a tool to assess impacts on and dependencies from nature across scales. This makes NCA a valuable tool for assessing the environmental impacts of business operations and value chains. While previously used in sectors like mining and forestry, NCA applications in real estate are scarce, and prospective modelling is increasingly needed to guide future land use decisions aligned with the Kunming-Montreal Global Biodiversity Framework. This study applied NCA to three land-use scenarios for a 1300-ha corporate estate in Mauritius, a Small Island Developing State. Mauritius is constrained by size wherein land use change dynamics favour real estate development on privately-owned land to attract foreign investment. Models aligned with the UN System of Environmental Economic Accounting - Ecosystem Accounting (SEEA-EA) show that a financially maximizing urban scenario over 2025-2049 would slightly improve watershed ecosystem condition but halve societal ecosystem service value relative to a do-nothing scenario. In contrast, business value is increased 37-fold. A low-density agro-residential scenario would grow business value 20-fold, societal value 3-fold, and markedly improve ecosystem condition across the estate. The findings emphasize the need to assess both biophysical and monetary dimensions, as focusing solely on ecosystem condition may mislead stakeholders about societal benefits. The study demonstrates NCA's potential to complement existing environmental assessments, support land use planning at national and subnational levels, and inform sustainable development strategies that balance business and societal value. Observations also address nature-related risks and opportunities, including sustainability-linked finance and biodiversity offsets, and highlight future research needs on the combined effects of climate and land-use changes, as well as cultural value considerations.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".