2. RapportRIVMWaardevGroen.pdf

Steps 1. For every grid cell, estimate the percentage of green space within a 1000m buffer (X1) 2. For every grid cell, estimate the population (X2) 3. For every grid cell, estimate the minutes cycled per week per individual for commuting purposes (X3): 103.4 + (0.83 * X1 * 100) Page 72 of 79

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4. For every grid cell, estimate the total amount of minutes cycled per week for commuting purposes (X4): X2 * X3 * (0.63 * 0.06)

2

2

Probability that people will cycle for commuting purpose = (0.63 * 0.23). Percentage of people employed (0.63) times the percentage of commuting done by bike (0.23) in 2017 (www.ois.amsterdam.nl)

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RIVM Letter report 2019-0021

Indicator: Avoided premature deaths from cycling to-from work Unit: lives/year Source: Maas et al. (2008); HEAT Tool (Kahlmeier et al., 2017); Kelly et al. (2014) Based on the calculation obtained on cycling for commuting purposes, the number of avoided premature deaths from cycling is calculated. This is done through the implementation of the HEAT Tool, a tool developed by the World Health Organization, which measures the effects of walking and cycling on human health. Steps 1. For every grid cell, estimate the percentage of green space within a 1000m buffer (X1) 2. For every grid cell, estimate the population (X2) 3. For every grid cell, estimate the minutes cycled per week per individual for commuting purposes (X3) 103.4 + (0.83 * X1 * 100) 4. For every grid cell, estimate the reduced risk of all-cause mortality (RRM) for individuals cycling for commuting purposes (X4). Note: A cap of 45% RRM should be implemented. (X3 / 100) * (1 - 0.9) 3 5. For every grid cell, estimate the sum of all individual RR’s (X5) X2 * X 4 6. Estimate the sum of sum of X5 values from all grid cells (X6). 7. Estimate Amsterdam’s average RRM due to cycling for commuting purposes (X7). X6 𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇 8. Calculate the total avoided premature deaths due to cycling for commuting purposes (X8) ��

1

1−X7

3

× 0.006(𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇)� − 0.006(𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇) � × (0.63 × 0.23)

4

. 5

Relative risk of mortality (RR) of cyclists against non-cyclists = 0.9 (Kelly et al., 2014) MR = Mortality rate Amsterdam 2017 = 0.006 (allecijfers.nl) Probability that people will cycle for commuting purpose = (0.63 * 0.23). Percentage of people employed (0.63) times the percentage of commuting done by bike (0.23) in Amsterdam in 2017 (www.ois.amsterdam.nl) 4 5

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RIVM Letter report 2019-0021

Indicator: Avoided premature deaths from cycling to-from work Unit: lives/year. €/year Source: Maas et al. (2008); HEAT Tool (Kahlmeier et al., 2017); Kelly et al. (2014); Gemeente Amsterdam One way to calculate the monetary benefits of cycling, in this case tofrom work, is to translate minutes cycled by individuals to the number of avoided premature deaths, and to multiply this to the statistical value of a life (WHO, 2017). Various estimates for the value of a statistical life exist, such as the European default values (for 2015) of €2.132 million (WHO European Region), €2.891 million (EU27 countries) or €2.877 million (EU28 countries including Croatia) can also be used (WHO, 2017). Steps 1. For every grid cell, estimate the percentage of green space within a 1000m buffer (X1) 2. For every grid cell, estimate the population (X2) 3. For every grid cell, estimate the minutes cycled per week per individual for commuting purposes (X3) 103.4 + (0.83 * X1 * 100) 4. For every grid cell, estimate the reduced risk of mortality (RRM) for individuals cycling for commuting purposes (X4). Note: A cap of 45% RRM should be implemented. (X3 / 100) * (1 - 0.9) 6 5. Add the RRM’s for the entire population: 5.1 For every grid cell, estimate the sum of all individual RRM’s (X5) X2 * X 4 5.2 Estimate the sum of sum of X5 values from all grid cells (X6).

6 Estimate Amsterdam’s average RRM due to cycling for commuting purposes (X7)

7

8

X6

𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇

× (0.63 × 0.23)

7

Estimate the total avoided premature deaths due to cycling for commuting purposes (X8) �

1

1−X7

× 0.006 (𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇)� − 0.006 (𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇)

8

Estimate the economic value of avoided premature deaths due to cycling for commuting purposes X8 * VSL

6

Relative risk of mortality (RR) of cyclists against non-cyclists = 0.9 (Kelly et al., 2014) Probability that people will cycle for commuting purpose = (0.63 * 0.23). Percentage of people employed (0.63) times the percentage of commuting done by bike (0.23) in Amsterdam in 2017 (www.ois.amsterdam.nl) 8 MR = Mortality rate Amsterdam 2017 = 0.006 (allecijfers.nl) 7

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RIVM Letter report 2019-0021

A2.3 Recreation Indicator: Visits to recreation areas Unit: visits/year Source: Gemeente Amsterdam Recreation was measured by looking at the number of visits to parks and other green recreational areas. The stepwise procedure adopted is described hereunder. Business-As-Usual 1. To calculate the increase in the number of visits to parks for the Business-As-Usual scenario, data provided by the City of Amsterdam on the number of visits to 22 parks, for the years 1996 and 2013, was used. This dataset included two types of parks: ‘neighbourhood parks’ (buurtparken) and ‘city parks’ (stadsparken). It was estimated that for neighbourhood parks, the average yearly percentage increase in visits to these parks was around 3%, whereas for ‘city parks’ (stadsparken), the number of visits increased by 9% per year on average. For each of the 22 parks, its respective percentage growth rate was used to predict the number of visits in 2025. Linear growth was assumed, due to the likelihood of obtaining exorbitant and unrealistic visitation predictions using compounded growth rates, and the lack of evidence suggesting non-linear growth rates. 2. Additional data was provided by the Municipality of Amsterdam on recreational visits to additional recreational areas only for the year 2013.

For areas categorized as ‘city parks’, linear visitation growth was assumed, with an average yearly percentage growth rate of 9%. For all other recreational areas (e.g. neighbourhood parks, ecological areas, recreational areas) a linear, average yearly percentage growth rate of 3% was assumed. 3. For parks where no data on the number of recreational visits has been recorded, the average number of visits per m2 was estimated based on the average number of visits per m2 to neighbourhood parks for the years 2016 and 2025, obtained by collapsing the datasets from Steps 1 and 2 above. 4. The values calculated above for all parks were summed up, to estimate the number of visits to recreational areas during the years 2016 and 2025. Scenarios 1. The scenarios Metropolitan Landscape and Urban Parks involve improvements in management for various recreational areas. For areas where management remains the same, the original growth rates and recreational visit numbers calculated were assumed. For areas where enhancement in management takes place, a 5% linear, average yearly increase is assumed since 2020, provided that the original or previously extrapolated growth rate is equal to or lower than 5%. In this case, it is assumed that the implementation of improved management will not take place until the year 2020.

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RIVM Letter report 2019-0021

Indicator: Visitation expenditures Unit: EUR/year Source: Provincie Noord Holland (2016) The expenditures per visitor were estimated based on the report ‘Report on Visitations to Nature and Recreation Areas North Holland 2016’ (Bezoekersonderzoek natuur- en recreatiegebieden Noord Holland 2016), published by the North Holland Province (Provincie Noord Holland, 2016). It is assumed that, on average, every visit generates €4.69 in associated expenditures. The average expenditure/visit value is the average of all estimates available within the report for recreation areas which fall within Amsterdam. The number of visits per scenario is multiplied by the reference euro value, to obtain the total expenditure associated with visits to recreational areas.

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RIVM Letter report 2019-0021

A2.4 Water storage Indicator: Reduced rainwater in sewers Unit: m3/year Source: SBRCURnet (www.sbrcurnet.nl) In the Netherlands, an average 874mm precipitation occurs every year. Research on water retention by green roofs shows that grass, herbs and mosses can retain between 40-60% rainfall per year. Based on expert judgment, a reference percentage rate for water (rainfall) storage of 55% is assumed. Hence the reduced amount of rainwater in sewers due to the availability of green space was estimated at 0.481m3 per m2 of green area. This value was only estimated for inhabited areas, since these are the areas where the contribution of green to the reduction of rainwater in sewers is most prominent. The contour of inhabited areas was defined using the population centres (bevolkingskernen).

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RIVM Letter report 2019-0021

Indicator: Reduced water treatment costs Unit: €/year Source: SBRCURnet (www.sbrcurnet.nl) Rainfall stored by vegetation would otherwise end up in sewers, incurring higher treatment costs. The associated sewage treatment costs are estimated at €0.78/m3/year. Hence, the value of water stored vegetation in m3/year is multiplied by €0. 78 to obtain the monetary benefit, or the reduced sewage cost, associated with reduced rainfall entering sewers.

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