Structured identification of response options to address environmental health risks at the Agbogbloshie electronic waste site
Bibliographic record
Abstract
Abstract Electronic waste (e-waste) is a growing problem across low- and middle-income countries. Agbogbloshie (Accra, Ghana) is among the world's largest and most notorious e-waste sites, with an increasing number of studies documenting a range of environmental health risks. The present study aimed to provide national, regional, and international stakeholders with a summary of expert opinion on the most pressing problems arising from e-waste activities at Agbogbloshie, as well as suggested solutions to address these problems. Structured interviews were performed between April and September 2015 that used a Logical Framework Approach as a scoping exercise to gauge problems and benefits of e-waste recycling, and the Delphi methodology to identify response options. Stakeholders (n = 19) from 15 institutions were interviewed with 2 rounds of a Delphi Poll: open-ended interviews followed by an electronic questionnaire in which experts ranked various proposed response options based on health, environmental, social, and economic benefit and feasibility. The goal was to prioritize potential interventions that would address identified problems at Agbogbloshie. Experts identified the most beneficial and feasible options in decreasing rank order as follows and prefaced by the statement “it is recommended that”: 1) there be further research on the health effects; 2) e-waste workers be given appropriate personal protective equipment; 3) the Ministry of the Environment, Science, Technology and Innovation re-visit Ghana's Hazardous Waste Bill; 4) e-waste workers be involved in the planning process of interventions and are be kept informed of any results; and 5) there be increased education and sensitization on hazards related to e-waste for both workers and the general public. These solutions are discussed in relation to ongoing dialogue at the international level concerning e-waste recycling interventions, with strengths and weaknesses examined for the Ghanaian context. Integr Environ Assess Manag 2017;13:980–991. ©2017 SETAC Key Points This is one of the first studies to systematically identify options to address the environmental health concerns associated with e-waste, even though the activity is escalating worldwide. Key response options included a need for more research, the use of personal protective equipment, passing national laws, involvement of the workers themselves in the planning of interventions, and education. The outcome of the study has direct and immediate relevance to a number of active discussions at the national, regional, and international levels concerning the assessment and management of e-waste.
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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 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.002 |
| 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".