Editing IPCC:AR6/WGIII/Chapter-8 (section)

== 8.7 Knowledge Gaps ==

<div id="h1-8-siblings" class="h1-siblings"></div>

While there is growing literature on urban NBS, which encompasses urban green and blue infrastructure in cities, there is still a knowledge gap regarding how these climate mitigation actions can be integrated in urban planning and design, as well as their mitigation potential, especially for cities that have yet to be built. In moving forward with the research agenda on cities and climate change science, transformation of urban systems will be critical; however, understanding this transformation and how best to assess mitigation action remain key knowledge gaps ( [[#Butcher-Gollach--2018|Butcher-Gollach 2018]] ; [[#Pathak--2018|Pathak and Mahadevia 2018]] ; [[#Rigolon--2018|Rigolon et al. 2018]] ; [[#Anguelovski--2019|Anguelovski et al. 2019]] ; [[#Buyana--2019|Buyana et al. 2019]] ; [[#Trundle--2020|Trundle 2020]] ; [[#Azunre--2021|Azunre et al. 2021]] ).

There is a key knowledge gap in respect to the potential of the informal sector in developing country cities. Informality extends beyond illegality of economic activities to include housing, locally developed off-grid infrastructure, and alternative waste management strategies. Limited literature and understanding of the mitigation potential of enhanced informal sector is highlighted in the key research agenda on cities from the Cities and Climate Change Science Conference ( [[#Prieur-Richard--2018|Prieur-Richard et al. 2018]] ).

City-level models and data for understanding of urban systems is another knowledge gap. With increased availability of open data systems, big data and computing capacities, there is an opportunity for analysis of urban systems ( [[#Frantzeskaki--2019|Frantzeskaki et al. 2019]] ).

While there is much literature on urban climate governance, there is still limited understanding of the governance models and regimes that support multi-level decision-making for mitigation and climate action in general. Transformative climate action will require changing relationships between actors to utilise the knowledge from data and models and deepen understanding of the urban system to support decision-making.

<div id="8.7.1" class="h2-container"></div>

<span id="covid-19-and-cities"></span>
=== 8.7.1 COVID-19 and Cities ===

<div id="h2-33-siblings" class="h2-siblings"></div>

The COVID-19 pandemic has disrupted many aspects of urban life while raising questions about urban densities, transportation, public space, and other urban issues. The impact of COVID-19 on urban activity and urban GHG emissions may offer insights into urban emissions and their behavioural drivers and may include structural shifts in emissions that last into the future. The science is unclear as to the links between urban characteristics and COVID-19, and involves multiple aspects. For example, some research shows higher COVID-19 infection rates with city size (e.g., [[#Dalziel--2018|Dalziel et al. 2018]] ; [[#Stier--2021|Stier et al. 2021]] ), as well as challenges to epidemic preparedness due to high population density and high volume of public transportation ( [[#Layne--2020|Layne et al. 2020]] ; [[#Lee--2020|Lee et al. 2020]] ). Other research from 913 metropolitan areas shows that density is unrelated to COVID-19 infection rates and, in fact, has been inversely related to COVID-19 mortality rates when controlled for metropolitan population.

Densely populated counties are found to have significantly lower mortality rates, possibly due to such advantages as better health care systems, as well as greater adherence to social-distancing measures ( [[#Hamidi--2020|Hamidi et al. 2020]] ). Sustainable urbanisation and urban infrastructure that address the SDGs can also improve preparedness and resilience against future pandemics. For example, long-term exposure to air pollution has been found to exacerbate the impacts of COVID-19 infections ( [[#Wu--2020b|Wu et al. 2020b]] ), while urban areas with cleaner air from clean energy and greenspace, can provide advantages.

Some studies indicate that socio-economic factors, such as poverty, racial and ethnic disparities, and crowding are more significant than density in COVID-19 spread and associated mortality rate ( [[#Borjas--2020|Borjas 2020]] ; [[#Maroko--2020|Maroko et al. 2020]] ; [[#Lamb--2021|Lamb et al. 2021]] ). The evidence for the connection between household crowding and the risk of contagion from infectious diseases is also strong. A 2018 World Health Organization (WHO) systematic review of the effect of household crowding on health concluded that a majority of studies of the risk of non-tuberculosis infectious diseases, including flu-related illnesses, were associated with household crowding ( [[#Shannon--2018|Shannon et al. 2018]] ).

Though preliminary, some studies suggest that urban areas saw larger overall declines in emissions because of lower commuter activity and associated emissions. For example, researchers have explored the COVID-19 impact in the cities of Los Angeles, Baltimore, Washington, DC, and San Francisco Bay Area in the United States. In the San Francisco region, a decline of 30% in anthropogenic CO 2 was observed, which was primarily due to changes in on-road traffic ( [[#Turner--2020|Turner et al. 2020]] ). Declines in the Washington, DC/Baltimore region and in the Los Angeles urban area were 33% and 34%, respectively, in the month of April 2020 compared to previous years ( [[#Yadav--2021|Yadav et al. 2021]] ).

At the global scale COVID-related lockdown and travel restrictions reduced daily CO 2 emissions by –17% in early April 2020 compared to 2019 values ( [[#Le%20Quéré--2020|Le Quéré et al. 2020]] ; [[#Liu--2020b|Liu et al. 2020b]] ), though subsequent studies have questioned the accuracy of the indirect proxy data used ( [[#Oda--2021|Oda et al. 2021]] ). Research at the national scale in the United States found that daily CO 2 emissions declined –15% during the late March to early June time period ( [[#Gillingham--2020|Gillingham et al. 2020]] ). Research in China estimated that the first quarter of 2020 saw an 11.5% decline in CO 2 emissions relative to 2019 ( [[#Zheng--2020|Zheng et al. 2020]] ; [[#Han--2021|Han et al. 2021]] ). In Europe, estimates indicated a –12.5% decline in the first half of 2020 compared to 2019 ( [[#Andreoni--2021|Andreoni 2021]] ). Rebound to pre-COVID trajectories has been evidenced following the ease of travel restrictions ( [[#Le%20Quéré--2021|Le Quéré et al. 2021]] ). It remains unclear to what extent COVID resulted in any structural change in the underlying drivers of urban emissions.

Changes in local air pollution emissions, particularly due to altered transportation patterns, have caused temporary air quality improvements in many cities around the world (see critical review by [[#Adam--2021|Adam et al. 2021]] ). Many outdoor air pollutants, such as particulates, nitrogen dioxide, carbon monoxide, and volatile organic compounds declined during national lockdowns. Levels of tropospheric ozone, however, remained constant or increased. A promising transformation that has been observed in many cities is an increase in the share of active travel modes such as cycling and walking ( [[#Sharifi--2020|Sharifi and Khavarian-Garmsir 2020]] ). While this may be temporary, other trends, such as increased rates of teleworking and/or increased reliance on smart solutions that allow remote provision of services provide an unprecedented opportunity to transform urban travel patterns ( [[#Belzunegui-Eraso--2020|Belzunegui-Eraso and Erro-Garcés 2020]] ; [[#Sharifi--2020|Sharifi and Khavarian-Garmsir 2020]] ).

Related to the transport sector, the pandemic has resulted in concerns regarding the safety of public transport modes, which has resulted in significant reductions in public transport ridership in some cities ( [[#Bucsky--2020|Bucsky 2020]] ; [[#de%20Haas--2020|de Haas et al. 2020]] ) while providing opportunities for urban transitions in others (Newman AO 2020). Considering the significance of public transportation for achieving low-carbon and inclusive urban development, appropriate response measures could enhance health safety of public transport modes and regain public trust ( [[#Sharifi--2020|Sharifi and Khavarian-Garmsir 2020]] ). Similarly, there is a perceived correlation between the higher densities of urban living and the risk of increased virus transmission ( [[#Hamidi--2020|Hamidi et al. 2020]] ; [[#Khavarian-Garmsir--2021|Khavarian-Garmsir et al. 2021]] ).

While city size could be a risk factor with higher transmission in larger cities ( [[#Hamidi--2020|Hamidi et al. 2020]] ; [[#Stier--2021|Stier et al. 2021]] ), there is also evidence showing that density is not a major risk factor and indeed cities that are more compact have more capacity to respond to and control the pandemic ( [[#Hamidi--2020|Hamidi et al. 2020]] ). Considering the spatial pattern of density, even distribution of density can reduce the possibility of crowding that is found to contribute to the scale and length of virus outbreak in cities. Overall, more research is needed to better understand the impacts of density on outbreak dynamics and address public health concerns for resilient cities.

Cities could seize this opportunity to provide better infrastructure to further foster active transportation. This could, for example, involve measures, such as expanding cycling networks and restricting existing streets to make them more pedestrian- and cycling-friendly contributing to health and adaptation co-benefits, as discussed in [[#8.2|Section 8.2]] ( [[#Sharifi--2021|Sharifi 2021]] ). Strengthening the science–policy interface is another consideration that could support urban transformation (Cross-Chapter Box 1 in Chapter 1).

<div id="8.7.2" class="h2-container"></div>

<span id="scenarios"></span>
=== 8.7.2 Scenarios ===

<div id="h2-34-siblings" class="h2-siblings"></div>

The urban share of global emissions is significant and is expected to increase in the coming decades. This places emphasis on the need to expand development of urban emissions scenarios within climate mitigation scenarios ( [[#Gurney--2021|Gurney et al. 2021]] , 2022). The literature on globally comprehensive analysis of urban emissions within the existing IPCC scenario framework remains very limited, curtailing understanding of urban emissions tipping points, mitigation opportunities and overall climate policy complexity. A review of the applications of the SSP-RCP scenario framework also recommended downscaling global SSPs to improve the applicability of this framework to regional and local scales ( [[#O’Neill--2020|O’Neill et al. 2020]] ). This remains an urgent need and will require multidisciplinary research efforts, particularly as net-zero-emissions targets are emphasised.

<div id="8.7.3" class="h2-container"></div>

<span id="urban-emissions-data"></span>
=== 8.7.3 Urban Emissions Data ===

<div id="h2-35-siblings" class="h2-siblings"></div>

Though there has been a rapid rise in quantification and analysis of urban emissions, gaps remain in comprehensive global coverage, particularly in the Global South, and reliance on standardised frameworks and systematic data are lacking ( [[#Gurney--2021|Gurney and Shepson 2021]] ; [[#Mueller--2021|Mueller et al. 2021]] ). The development of protocols by ( [[#BSI--2013|BSI 2013]] ; [[#Fong--2014|Fong et al. 2014]] ; [[#ICLEI--2019b|ICLEI 2019b]] ) that urban areas can use to organise emissions accounts has been an important step forward, but no single agreed-upon reporting framework exists ( [[#Lombardi--2017|Lombardi et al. 2017]] ; [[#Chen--2019b|Chen et al. 2019b]] ; [[#Ramaswami--2021|Ramaswami et al. 2021]] ). Additionally, there is no standardisation of emissions data and limited independent validation procedures ( [[#Gurney--2021|Gurney and Shepson 2021]] ). This is partly driven by the recognition that urban emissions can be conceptualised using different frameworks, each of which has a different meaning for different urban communities ( [[#8.1.6.2|Section 8.1.6.2]] ). Equally important is the recognition that acquisition and analysis of complex data used to populate urban GHG inventory protocols remains a barrier for local practitioners ( [[#Creutzig--2019|Creutzig et al. 2019]] ). The limited standardisation has also led to incomparability of the many individual or city cluster analyses that have been accomplished since AR5. Finally, comprehensive, global quantification of urban emissions remains incomplete in spite of recent efforts ( [[#Moran--2018|Moran et al. 2018]] ; [[#Zheng--2018|Zheng et al. 2018]] ; [[#Harris--2020|Harris et al. 2020]] ; [[#Jiang--2020|Jiang et al. 2020]] ; [[#Wei--2021|Wei et al. 2021]] ; [[#Wiedmann--2021|Wiedmann et al. 2021]] ).

Similarly, independent verification or evaluation of urban GHG emissions has seen a large number of research studies (e.g., [[#Wu--2016|Wu et al. 2016]] ; [[#Sargent--2018|Sargent et al. 2018]] ; [[#Whetstone--2018|Whetstone 2018]] ; [[#Lauvaux--2020|Lauvaux et al. 2020]] ). This has been driven by the recognition that self-reported approaches may not provide adequate accuracy to track emissions changes and provide confidence for mitigation investment ( [[#Gurney--2021|Gurney and Shepson 2021]] ).

The most promising approach to independent verification of urban emissions has been the use of urban atmospheric monitoring (direct flux and/or concentration) as a means to assess and track urban GHG emissions ( [[#Davis--2017|Davis et al. 2017]] ). However, like the basic accounting approach itself, standardisation and practical deployment and scaling is an essential near-term need.

<div id="frequently-asked-questions" class="h1-container"></div>

<span id="frequently-asked-questions-faqs"></span>