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.

Kristine Belesova,Michael Davies,Paul Wilkinson,James Milner,Andy Haines,Felix Creutzig,Max Callaghan,Emma Hutchinson,Jan C Minx,Melanie Crane,Catalina Turcu

doi:10.12688/wellcomeopenres.16570.1

Abstract

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.

Highlights

Cities are responsible for 71% to 76% of global energyrelated carbon emissions, including both consumption and production-related emission (Seto et al, 2014)
In 2015, there were over 10,000 climate actions identified as being undertaken in the 96 cities comprising the C40 cities climate leadership group, with further potential 26,000 actions identified that could be implemented to expand their range of existing climate actions (C40 Cities & Arup, 2015)
Here, we extend the machine-learning methods developed by Lamb et al (2019), and apply them to a set of extensive specialised search strategies developed by our multidisciplinary team to cover studies of both urban climate change mitigation and adaptation actions relevant to human health and wellbeing

Summary

Introduction

Cities are responsible for 71% to 76% of global energyrelated carbon emissions, including both consumption and production-related emission (Seto et al, 2014). To limit global average temperature increase to well below 2°C, CO2 and short-lived climate pollutant emissions need to be reduced to net zero (often abbreviated as the net zero target) within the 50 years – though some suggest that cities should achieve this much earlier (C40 & Arup, 2017). The achievement of this deadline would require climate action at all scales: individual, city, national and international levels triggering rapid transformation of the ways in which urban societies operate. A decline of 4 to 7% (2% to 13%) in global CO2 emissions is projected in 2020 due to the measures taken in response to the COVID-19 pandemic, those are not suitable for the required sustained long-term emission reduction (Belesova et al, 2020b; Forster et al, 2020; Quéré et al, 2020)

Objectives

Findings

Discussion

Conclusion