Evidence map›Paper›PMID 35309248›Full record

ArticleInternational environmental agreements : politics, law and economics2022

CHANS-Law: preventing the next pandemic through the integration of social and environmental law.

Kirsten Davies, Michelle Lim, Tianbao Qin, Philip Riordan

Abstract read
In one paragraph

Article in International environmental agreements : politics, law and economics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
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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

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Kirsten DaviesUniversity of New England Law School, Armidale, Australia.ORCID 0000-0002-6940-4359
Michelle LimMacquarie Law School, Macquarie University, Sydney, Australia.ORCID 0000-0002-0559-0113
Tianbao QinResearch Institute of Environmental Law (RIEL), Wuhan, China.ORCID 0000-0003-4932-9213
Philip RiordanWildlife Without Borders, London, UK.ORCID 0000-0001-6285-8596

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Zoonotic viruses have sacrificed hundreds of millions of people throughout human history. There are currently 1.7 million unidentified viruses estimated to be circulating in mammal and bird populations. It is foreseeable that in the near future, another of these will transmit to people, heralding the start of the next pandemic-one potentially more deadly than COVID-19. At the core of this article is a call for pre-emptive protection of the natural environment and its regenerative systems as the first fundamental step in the prevention of future epidemics and pandemics. While zoonoses originate in nature, the predominant legal discipline, managing these crises, is international health law which is invoked reactively once an outbreak has been reported. In this paper, we identify the need for a legal shift in epidemic and pandemic responses. In particular, we call for the incorporation of international environmental agreements to prevent the initial viral spillover from animal to human populations. We propose a strategy of strengthening existing agreements and a coupling of legal disciplines, such as health and environmental law, emphasizing the need for synergies across legal disciplines to enhance the emergence and management of future pandemics and epidemics. We introduce Coupled Human and Natural Systems (CHANS) Law to frame the required integration across legal instruments to regulate inextricably human-nature connections and advocate for the development of a

Indexed as

COVID-19Epidemics and pandemicsInternational environmental agreementsOne-Health

Identifiers

PMID35309248
PMCPMC8917959

What OpenQuestion holds

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LicenceCC BY
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Registered trials

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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.