Through the looking glass: Environmental health economics in low and middle income countries ✶
Bibliographic record
Abstract
Abstract Human interactions with the environment can profoundly impact many outcomes – health being chief among them. While the nature of environmental risks changes across time and space, the burden of disease attributable to environmental risk hovers stubbornly around one quarter of the total global disease burden. Further, environmental risks are particularly damaging to the health of children, but also to the elderly and the impoverished in low and middle income countries (LMICs). This chapter highlights the ways in which economics provides analytical insight about the human–environment relationship and about potential ways to prevent diseases. Specifically, we contend that the household production framework – which focuses on the beneficiary and households – helps us understand when and how households will avert environmental risks. While economists have been mostly on the sidelines of environmental health research, there is a growing literature from LMICs that examines three aspects of reduction in household environmental risks: (i) how households value these risk reductions, (ii) what factors drive household adoption of environmental health technologies, and (iii) what are the impacts of these technologies on household health. At the risk of simplification, our review of this literature finds relatively low values for environmental risk reductions, which is mirrored by limited adoption of environmental health technologies and, accordingly, disappointing impact on health. Economists have made less progress in linking the literatures on valuation, adoption and impacts with each other. We conclude by explaining why the next wave of research should focus on these links and on multiple risks, environmental disasters, and political economy of the supply of interventions.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".