Substitutability of natural and human capitals: lessons from a simple exploratory model
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.
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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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".