13-10-2020 / hannes.schwaiger

Exploring the relationship between challenges, people and NBS benefits in REGREEN

By Laurence Jones & David Fletcher, UKCEH

Pressures, demand and opportunity for NBS

In REGREEN, we look at a number of pressures that exert a negative impact on the liveability of cities. These include pressures linked to environmental factors such as: High temperatures, Air, noise & water pollution, Flooding, and access to green and blue space. There are also a set of more socially determined pressures such as Change in urban extent and morphology, Increasing population. Other factors will influence the risk of negative impact, and these include factors such as poverty, level of education & other socio-economic factors which may make certain sectors of the population more vulnerable to risk.

The combination of pressure and contextual factors can be conceptualised simply in a framework that combines supply and demand, to identify opportunities for NBS creation (Figure 1). In risk analysis, Risk is the product of Hazard, Exposure and Vulnerability. Here we rename Risk as ‘demand’ and rename Hazard as ‘pressure’ but the meaning remains the same. In our urban context, demand (risk) is a function of pressure, exposure and vulnerability. Using air pollution as an example, pressure is the level of pollutant concentrations, exposure is measured in the number of people exposed to those high pollutant levels, and vulnerability can be assessed as sectors of society which are particularly at risk from poor air quality. Studies have shown that the poorest social groups are at greater risk of poor health from air quality than more wealthy groups exposed to the same pollutant concentrations. The reasons behind this are complex, but include aspects related to both exposure and vulnerability (Lipfert 2004).

The diagram in Figure 1 represents the urban setting as an integrated social-ecological system. It addresses the importance of considering multiple aspects in order to most efficiently plan and design where NBS will provide most benefit. This takes into account aspects of demand from city dwellers where there is a need to reduce the impacts of a pressure, and the potential for the ecological + urban infrastructure components to provide that benefit. Both the demand and the ability of the NBS to provide the necessary benefit will vary depending on contextual factors.

Therefore, the opportunity for NBS will not be equal in a city, and will vary depending on how the pressure varies, and the contextual factors discussed above such as population density, levels of deprivation, age structure, etc. These ideas are discussed in more detail in Deliverable D2.1.

Spatial and temporal variation in pressures and the ability of NBS to provide a benefit.

As an illustration of some of the spatial and temporal complexity in understanding pressures and the ability of NBS to provide a service, we explore this with an example on air pollution removal.

Air pollution removal by trees can show a strong seasonal pattern. Deciduous trees shed their leaves in autumn, which substantially reduces their potential for removing particulate air pollution from the atmosphere as a result (Beckett et al., 2000). At the same time, the efficiency of pollution removed is greater at higher pollution concentrations. So, trees located in areas with the greatest concentrations of pollutant will remove the most and perform the greatest potential service.  This co-variation in both space and time means that identifying mismatches in potential supply and demand can be important in attempting to maximise the efficiency of NBS, through planning the location and type of NBS. Temporal variation in demand is perhaps less obvious, but can occur for example where there are large seasonal changes in population, such as in University towns, school children going to school during term time, large influxes of tourists, or where there are large seasonal populations of migrant workers.

Figure 1:  Conceptual approach to deriving opportunity for NBS. This incorporates potential supply of corresponding ES, and the demand for mitigating the effects of a particular pressure. Opportunity can arise from a mismatch between supply and demand, but also can be used to target where potential supply will be most efficient at delivering a service.


In our mini case study we use data from Paris City. Detailed mapping of PM2.5 concentrations by Paris authorities shows how the pressure from air pollution varies spatially across the city (Figure 2a). For the same part of Paris, we show the land cover, mapped at 10 m pixel resolution, with separate classes for deciduous and evergreen trees (Figure 2b). Using methods described in Jones et al. (2019) and Fletcher et al. (in review), we calculated the quantity of PM2.5 removed by trees over a period of one year with the pollution removal rate differentiated between evergreen and deciduous trees. This rate also varies according to the concentration of PM2.5. Figure 3 shows how there is greater removal by trees alongside roads and in the bottom right part of the image, where pollution concentrations are greater. Pollution removal in the parkland areas is greater by evergreen trees than for deciduous trees. This illustrates both the spatial and temporal variation in the service. Understanding this variation may be useful for urban planning. For example, if exposure to pressures has a seasonal basis which means that service provision during a particular part of the year is more relevant than looking at a yearly total or average level of service, and particularly for understanding spatial variation in where the most service can be delivered.

Figure 2. Section of Paris, showing a) PM2.5 atmospheric concentration and b) Land Cover classification, with six classes, including differentiated deciduous and evergreen trees.

Figure 3. Modelled PM2.5 removal by trees, with deciduous and evergreen types differentiated, overlaid on satellite imagery.



Lipfert, F.W., 2004. Air pollution and poverty: does the sword cut both ways? Editorial, Journal of Epidemiology and Community Health.

Beckett, K. P., et al. (2000). Particulate pollution capture by urban trees: effect of species and windspeed. Global change biology, 6(8), 995-1003.

Jones, L., et al. (2019). Urban Natural Capital Accounts: developing a novel approach to quantify air pollution removal by vegetation. Journal of Environmental Economics & Policy 8:4, 413-428.