Cargando…

Climate action for health and wellbeing in cities: a protocol for the systematic development of a database of peer-reviewed studies using machine learning methods

Cities produce more than 70% of global greenhouse gas emissions. Action by cities is therefore crucial for climate change mitigation as well as for safeguarding the health and wellbeing of their populations under climate change. Many city governments have made ambitious commitments to climate change...

Descripción completa

Detalles Bibliográficos
Autores principales: Belesova, Kristine, Callaghan, Max, Minx, Jan C, Creutzig, Felix, Turcu, Catalina, Hutchinson, Emma, Milner, James, Crane, Melanie, Haines, Andy, Davies, Michael, Wilkinson, Paul
Formato: Online Artículo Texto
Lenguaje:English
Publicado: F1000 Research Limited 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8022210/
https://www.ncbi.nlm.nih.gov/pubmed/33860107
http://dx.doi.org/10.12688/wellcomeopenres.16570.1
_version_ 1783674893773045760
author Belesova, Kristine
Callaghan, Max
Minx, Jan C
Creutzig, Felix
Turcu, Catalina
Hutchinson, Emma
Milner, James
Crane, Melanie
Haines, Andy
Davies, Michael
Wilkinson, Paul
author_facet Belesova, Kristine
Callaghan, Max
Minx, Jan C
Creutzig, Felix
Turcu, Catalina
Hutchinson, Emma
Milner, James
Crane, Melanie
Haines, Andy
Davies, Michael
Wilkinson, Paul
author_sort Belesova, Kristine
collection PubMed
description Cities produce more than 70% of global greenhouse gas emissions. Action by cities is therefore crucial for climate change mitigation as well as for safeguarding the health and wellbeing of their populations under climate change. Many city governments have made ambitious commitments to climate change mitigation and adaptation and implemented a range of actions to address them. However, a systematic record and synthesis of the findings of evaluations of the effect of such actions on human health and wellbeing is currently lacking. This, in turn, impedes the development of robust knowledge on what constitutes high-impact climate actions of benefit to human health and wellbeing, which can inform future action plans, their implementation and scale-up. The development of a systematic record of studies reporting climate and health actions in cities is made challenging by the broad landscape of relevant literature scattered across many disciplines and sectors, which is challenging to effectively consolidate using traditional literature review methods. This protocol reports an innovative approach for the systematic development of a database of studies of climate change mitigation and adaptation actions implemented in cities, and their benefits (or disbenefits) for human health and wellbeing, derived from peer-reviewed academic literature. Our approach draws on extensive tailored search strategies and machine learning methods for article classification and tagging to generate a database for subsequent systematic reviews addressing questions of importance to urban decision-makers on climate actions in cities for human health and wellbeing.
format Online
Article
Text
id pubmed-8022210
institution National Center for Biotechnology Information
language English
publishDate 2021
publisher F1000 Research Limited
record_format MEDLINE/PubMed
spelling pubmed-80222102021-04-14 Climate action for health and wellbeing in cities: a protocol for the systematic development of a database of peer-reviewed studies using machine learning methods Belesova, Kristine Callaghan, Max Minx, Jan C Creutzig, Felix Turcu, Catalina Hutchinson, Emma Milner, James Crane, Melanie Haines, Andy Davies, Michael Wilkinson, Paul Wellcome Open Res Study Protocol Cities produce more than 70% of global greenhouse gas emissions. Action by cities is therefore crucial for climate change mitigation as well as for safeguarding the health and wellbeing of their populations under climate change. Many city governments have made ambitious commitments to climate change mitigation and adaptation and implemented a range of actions to address them. However, a systematic record and synthesis of the findings of evaluations of the effect of such actions on human health and wellbeing is currently lacking. This, in turn, impedes the development of robust knowledge on what constitutes high-impact climate actions of benefit to human health and wellbeing, which can inform future action plans, their implementation and scale-up. The development of a systematic record of studies reporting climate and health actions in cities is made challenging by the broad landscape of relevant literature scattered across many disciplines and sectors, which is challenging to effectively consolidate using traditional literature review methods. This protocol reports an innovative approach for the systematic development of a database of studies of climate change mitigation and adaptation actions implemented in cities, and their benefits (or disbenefits) for human health and wellbeing, derived from peer-reviewed academic literature. Our approach draws on extensive tailored search strategies and machine learning methods for article classification and tagging to generate a database for subsequent systematic reviews addressing questions of importance to urban decision-makers on climate actions in cities for human health and wellbeing. F1000 Research Limited 2021-03-05 /pmc/articles/PMC8022210/ /pubmed/33860107 http://dx.doi.org/10.12688/wellcomeopenres.16570.1 Text en Copyright: © 2021 Belesova K et al. http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution Licence, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Study Protocol
Belesova, Kristine
Callaghan, Max
Minx, Jan C
Creutzig, Felix
Turcu, Catalina
Hutchinson, Emma
Milner, James
Crane, Melanie
Haines, Andy
Davies, Michael
Wilkinson, Paul
Climate action for health and wellbeing in cities: a protocol for the systematic development of a database of peer-reviewed studies using machine learning methods
title Climate action for health and wellbeing in cities: a protocol for the systematic development of a database of peer-reviewed studies using machine learning methods
title_full Climate action for health and wellbeing in cities: a protocol for the systematic development of a database of peer-reviewed studies using machine learning methods
title_fullStr Climate action for health and wellbeing in cities: a protocol for the systematic development of a database of peer-reviewed studies using machine learning methods
title_full_unstemmed Climate action for health and wellbeing in cities: a protocol for the systematic development of a database of peer-reviewed studies using machine learning methods
title_short Climate action for health and wellbeing in cities: a protocol for the systematic development of a database of peer-reviewed studies using machine learning methods
title_sort climate action for health and wellbeing in cities: a protocol for the systematic development of a database of peer-reviewed studies using machine learning methods
topic Study Protocol
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8022210/
https://www.ncbi.nlm.nih.gov/pubmed/33860107
http://dx.doi.org/10.12688/wellcomeopenres.16570.1
work_keys_str_mv AT belesovakristine climateactionforhealthandwellbeingincitiesaprotocolforthesystematicdevelopmentofadatabaseofpeerreviewedstudiesusingmachinelearningmethods
AT callaghanmax climateactionforhealthandwellbeingincitiesaprotocolforthesystematicdevelopmentofadatabaseofpeerreviewedstudiesusingmachinelearningmethods
AT minxjanc climateactionforhealthandwellbeingincitiesaprotocolforthesystematicdevelopmentofadatabaseofpeerreviewedstudiesusingmachinelearningmethods
AT creutzigfelix climateactionforhealthandwellbeingincitiesaprotocolforthesystematicdevelopmentofadatabaseofpeerreviewedstudiesusingmachinelearningmethods
AT turcucatalina climateactionforhealthandwellbeingincitiesaprotocolforthesystematicdevelopmentofadatabaseofpeerreviewedstudiesusingmachinelearningmethods
AT hutchinsonemma climateactionforhealthandwellbeingincitiesaprotocolforthesystematicdevelopmentofadatabaseofpeerreviewedstudiesusingmachinelearningmethods
AT milnerjames climateactionforhealthandwellbeingincitiesaprotocolforthesystematicdevelopmentofadatabaseofpeerreviewedstudiesusingmachinelearningmethods
AT cranemelanie climateactionforhealthandwellbeingincitiesaprotocolforthesystematicdevelopmentofadatabaseofpeerreviewedstudiesusingmachinelearningmethods
AT hainesandy climateactionforhealthandwellbeingincitiesaprotocolforthesystematicdevelopmentofadatabaseofpeerreviewedstudiesusingmachinelearningmethods
AT daviesmichael climateactionforhealthandwellbeingincitiesaprotocolforthesystematicdevelopmentofadatabaseofpeerreviewedstudiesusingmachinelearningmethods
AT wilkinsonpaul climateactionforhealthandwellbeingincitiesaprotocolforthesystematicdevelopmentofadatabaseofpeerreviewedstudiesusingmachinelearningmethods