Evidence map›Paper›PMID 24583270›Full record

ArticleEnvironmental health perspectives2014

Estimating the health effects of greenhouse gas mitigation strategies: addressing parametric, model, and valuation challenges.

Justin V Remais, Jeremy J Hess, Kristie L Ebi, Anil Markandya, John M Balbus, Paul Wilkinson, Andy Haines, Zaid Chalabi

Abstract read
In one paragraph

Article in Environmental health perspectives, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
20citing papers in PubMed, 1 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

20 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Review
  3. Review
  4. Article
  5. Article
  6. Review
  7. Article
  8. Science Policy to Advance a Climate Change and Health Research Agenda in the United States.International journal of environmental research and public health · 2021
    Article
  9. Article
  10. Article
  11. Integrating Public Health into Climate Change Policy and Planning: State of Practice Update.International journal of environmental research and public health · 2019
    Review
  12. Evaluating Health Co-Benefits of Climate Change Mitigation in Urban Mobility.International journal of environmental research and public health · 2018
    Article
  13. The Political Economy of Health Co-Benefits: Embedding Health in the Climate Change Agenda.International journal of environmental research and public health · 2018
    Article
  14. Climate change impacts on human health over Europe through its effect on air quality.Environmental health : a global access science source · 2017
    Review
  15. Article
  16. Review
  17. The Role of Health Co-Benefits in the Development of Australian Climate Change Mitigation Policies.International journal of environmental research and public health · 2016
    Article
  18. Climate change, cash transfers and health.Bulletin of the World Health Organization · 2015
    Article
  19. Using a social justice and health framework to assess European climate change adaptation strategies.International journal of environmental research and public health · 2014
    Article
  20. Article
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

8 authors.

Justin V RemaisDepartment of Environmental Health, Rollins School of Public Health, Emory University, Atlanta, Georgia, USA.
Jeremy J Hess
Kristie L Ebi
Anil Markandya
John M Balbus
Paul Wilkinson
Andy Haines
Zaid Chalabi

Funding

Models for improving surveillance of environmentally-mediated infectious diseasesK01AI091864 · NIAID · UNIVERSITY OF CALIFORNIA BERKELEY · PI REMAIS, JUSTIN V · 2011 to 2015
$643k
NIAID NIH HHS K01 AI091864NIAID NIH HHS K01AI091864
6 · The paper itself

Abstract

backgroundPolicy decisions regarding climate change mitigation are increasingly incorporating the beneficial and adverse health impacts of greenhouse gas emission reduction strategies. Studies of such co-benefits and co-harms involve modeling approaches requiring a range of analytic decisions that affect the model output.

objectiveOur objective was to assess analytic decisions regarding model framework, structure, choice of parameters, and handling of uncertainty when modeling health co-benefits, and to make recommendations for improvements that could increase policy uptake.

methodsWe describe the assumptions and analytic decisions underlying models of mitigation co-benefits, examining their effects on modeling outputs, and consider tools for quantifying uncertainty. DISCUSSION: There is considerable variation in approaches to valuation metrics, discounting methods, uncertainty characterization and propagation, and assessment of low-probability/high-impact events. There is also variable inclusion of adverse impacts of mitigation policies, and limited extension of modeling domains to include implementation considerations. Going forward, co-benefits modeling efforts should be carried out in collaboration with policy makers; these efforts should include the full range of positive and negative impacts and critical uncertainties, as well as a range of discount rates, and should explicitly characterize uncertainty. We make recommendations to improve the rigor and consistency of modeling of health co-benefits.

conclusionModeling health co-benefits requires systematic consideration of the suitability of model assumptions, of what should be included and excluded from the model framework, and how uncertainty should be treated. Increased attention to these and other analytic decisions has the potential to increase the policy relevance and application of co-benefits modeling studies, potentially helping policy makers to maximize mitigation potential while simultaneously improving health.

Indexed as

Climate ChangeEnvironmental MonitoringGreenhouse EffectHumansModels, Theoretical

Identifiers

PMID24583270
PMCPMC4014758

What OpenQuestion holds

Textmetadata
LicenceCC0
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.