Green national accounting: the case of Chile's mining sector
Bibliographic record
Abstract
This article uses the welfare foundations for the usual net domestic product (NDP) income measure of the traditional National Accounts System (NAS) provided by Weitzman (1976, 2000), and the propositions of Hartwick (1993) and Hamilton (1994a) to correct this measure in order to obtain a green (sustainable) measure of economic income. It estimates green measures of the economic income of Chile's mining sector for the period 1977–1996. Different methodologies regarding the valuation of mining resources are employed, and exploration expenditures in the mining sector are included to empirically estimate the green measures of income. The results clearly show that the usual income measures of the traditional NAS overestimated the economic income generated by the Chilean mining sector during the period by 20–40 per cent, and its rate of growth by 3–20 per cent. Moreover, this overestimation has increased in recent years. These empirical results are remarkably similar when different methodologies are used to calculate green measures of the mining sector's economic income. The empirical evidence produced in this work, together with the one provided by other studies, leads to the conclusion that Chile's outstanding recent economic growth has not delivered the amount of economic income recorded by its NAS, since a significant part of it corresponded to depreciation of the country's natural capital.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".