Substitutability of natural and human capitals: lessons from a simple exploratory model
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Most ecosystem services (ES) are co-produced, to varying degrees, by interactions between people and ecosystems. Although ES research has tended to emphasize the role of ecosystems, or natural capital, in ES provision, the need for a deeper understanding of the role of human-derived capitals, like technology, labour, and management, is increasingly being recognized. Understanding the capacity for, and limitations of, human-derived capitals to enhance or substitute for natural capital is important for environmental decision-making, especially for decisions about when to promote conservation of natural capital to provide ecosystem services and when to employ technological alternatives. From the perspective of long-term sustainable ecosystem management, such decisions are further complicated by dynamics and interactions between different types of capital. We created a simple simulation model to compare how different assumptions around the temporal dynamics and interactions between natural and human-derived capitals affect long-term outcomes of different management choices on ES provision. We found that the extent to which different capitals are substitutable in the long-term depends on how individual capitals change over time and how different capitals interact with each other, and that replicating the near-term function of natural capital does not necessarily mean human-derived capitals are a viable long-term substitute. With an understanding of the dynamics and interactions of natural and human-derived capitals, it is possible to determine general long-term ES management strategies that are more likely to produce the desired benefits.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it