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...
Autores principales: | , , , , , , , , , , |
---|---|
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 |