An Input-Output Analysis with an Environmentally Adjusted Agricultural and Forestry Sector in Bangladesh
Bibliographic record
Abstract
Traditional national income accounting methods does not make an allowance for the environmental damages incurred while producing the current output. Agriculture is no longer considered to be an environmentally friendly activity. In Bangladesh, efforts to feed an ever-increasing population have meant unsustainable farming practices and a steady depletion of the resource base. The quality of the environment has been degraded and ability of the future generations compromised. This calls for “greening” of GDP by deducting negative changes in environmental quality in national income calculation. This paper begins by developing an environmental account for Bangladesh's agricultural sector. This is done by collecting physical data on depletion and degradation of environmental resources and then estimating their costs by appropriate valuation techniques. The environmentally adjusted agricultural sector is then integrated with a Social Accounting Matrix. Finally, sectoral products for agriculture and forestry are estimated net of environmental cost.
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How this classification was reachedexpand
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".