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Record W2153239776 · doi:10.5539/jsd.v5n3p84

An Input-Output Analysis with an Environmentally Adjusted Agricultural and Forestry Sector in Bangladesh

2012· article· en· W2153239776 on OpenAlexvenueno aff
Shamim Shakur, A. K. Enamul Haque

Bibliographic record

VenueJournal of Sustainable Development · 2012
Typearticle
Languageen
FieldEnvironmental Science
TopicEnvironmental Impact and Sustainability
Canadian institutionsnot available
Fundersnot available
KeywordsAgricultureSocial accounting matrixEnvironmental degradationNatural resource economicsEnvironmentally friendlyBusinessNational accountsAgricultural economicsEnvironmental qualityValuation (finance)Environmental accountingAllowance (engineering)DamagesPopulationResource (disambiguation)Consumption (sociology)EconomicsGeographyAccountingOperations managementEcology

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.037
Threshold uncertainty score0.790

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.002
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.008
GPT teacher head0.212
Teacher spread0.204 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations3
Published2012
Admission routes1
Has abstractyes

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