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Table 1: Input maps applied to estimate reduction of healthcare and labour costs due to vegetation.

Input

Unit

Short description

Source

Inhabitants

# inhabitants per dam2

Shows the number of inhabitants per dam2

RIVM 2017

Agricultural crop parcels

Categories for crop types

Yearly updated cadastral map of agricultural parcels with information on crop types per parcel

RVO 2013

Vegetation cover

% cover per dam2

The percentage of a cell that is covered by vegetation (low vegetation, bushes and shrubs and trees combined)

RIVM 2017

Percentage non-green area

% non-cover per dam2

Percentage of a cell that is not covered by vegetation (inverse of the vegetation cover map)

VITO

 

Build-up of the model

 

To determine the health effects of vegetation, the percentage of vegetation within a one km radius around every cell needs to be calculated. This can be done in two ways − one calculation includes agricultural areas surrounding cities and towns, one excludes agricultural areas. The calculation is done as followed:

...

Table 2: Output maps resulting from application of the ‘green and healthcare model’.

Output

Unit

Short description

Amount of vegetation in a 1 km radius

% vegetation cover

The percentage of urban green in a 1 km radius around the cell

Health effects of urban green on urban living environment

Reduced doctor’s visits dam-2 vegetation yr-1

The effect a specific green area has on the reduction of doctor’s visits by inhabitants in the surrounding area.

Avoided health costs due to vegetation

€ dam-2 yr-1

The reduction of public health costs as a result of vegetation in the surroundings of homes.

Avoided health-related labour costs due to vegetation

€ dam-2 yr-1

The reduction of labour costs due to better health of employees as a result of vegetation in the surroundings of their homes.

 

References

 

  • KPMG, 2012. ‘Groen, gezond en productief. The Economics of Ecosystems & Biodiversity (TEEB NL): natuur en gezondheid’.
  • Maas J., 2008. Vitamin G: Green environments, healthy. environments, Proefschrift ter verkrijging van de graad van docoraat Universiteit Utrecht, Utrecht, 2008.
  • Remme, R., De Nijs, T., Paulin, M., 2008. Natural Capital Model, Technical documentation of the quantification, mapping and monetary valuation of urban ecosystem services, RIVM, RIVM Report 2017-0040.
  • RIVM, 2003 onwards. Cijfertool kosten van ziekten met cijfers uit de ‘kosten van ziektestudie’.
  • RVO 2013. Basisregistratie Gewaspercelen (BRP), 2013. Available at http://www.nationaalgeoregister.nl/geonetwork/srv/dut/catalog.search#/metadata/%7B25943e6e-bb27-4b7a-b240-150ffeaa582e%7D
  • Steenbeek R., Hooftman W., Geuskens G., Wevers C., 2010. Objectiveren van gezondheidsgerelateerde non-participatie en de vermijdbare bijdrage van de gezondheidszorg hieraan. TNO, TNO-rapport 2010.171/13738.01.01.

...

Table 1: Input maps applied to estimate the retention of particulate matter (PM10) by vegetation.

Input

Unit

Short description

Source

LCEU map

Ecosystem unit classes

Ecosystem unit classes map for the Netherlands in 2013

CBS 2017

Concentration of PM₁₀

μg m-3

Concentration of PM₁₀ in 2016

RIVM 2016

Trees

% cover dam-2

Percentage of a cell that is covered by trees higher than 2.5 meters

RIVM 2017

Bushes and shrubs

% cover dam-2

Percentage of a cell that is covered by bushes and shrubs between 1 and 2.5 meters high

RIVM 2017

Low vegetation

% cover dam-2

Percentage of a cell that is covered by vegetation that is lower than 1 meter

RIVM 2017

Percentage non-green area cell

% cover dam-2

Percentage of a cell that is not covered by vegetation

RIVM 2017

 

Build-up of the model

 

The deposition velocity depends on the type of vegetation and land cover. The type of vegetation is based on the maps with the percentage of trees, shrubs and low vegetation/grass. The land cover is taken from the LCEU map. The average deposition velocity of a grid cell is estimated as:

...

Table 2: Average deposition velocities for various vegetation types (De Nocker et al., 2016).

Vegetation type

Deposition velocity (m s-1)

no vegetation1

0.0 - 0.2

deciduous forest

0.5

coniferous forest

0.7

shrubs & bushes

0.3

meadows & grassland

0.2

arable land

0.2

Water

0.1

low natural vegetation

0.2

low-stem orchard

0.2

mixed forest

0.6

1The value depends on the type of land cover assigned in the LCEU map. All built-up areas in the LCEU map receive value 0.0, water and forest area 0.1 and agriculture 0.2

...

Table 3: Output map generated for the co-benefit retention of particulate matter (PM10) by vegetation.

Output map

Unit

Short description

Monetary value of PM10 retention by vegetation

€  dam-2 yr-¹

The reduction in healthcare costs due to the retention of PM₁₀ by vegetation

 

References

  • Abhijith, K.V., Kumar, P., Gallagher, J., McNabola, A., Baldauf, R., Pilla, R., Broderick, B., Di Sabatino, S., Pulvirenti, B., 2017. Air pollution abatement performances of green infrastructure in open road and built-up street canyon environments - A review. Atmospheric Environment 162, p71-86.
  • Baldauf, R., 2017. Roadside vegetation design characteristics that can improve local, near-road air quality. Transportation Research Part D 52 p354–361.
  • CE-Delft, 2017. Handboek Milieuprijzen 2017. Methodische onderbouwing van kengetallen gebruikt voor waardering van emissies en milieu-impact. Publicatienummer: 17.7A76.64, Delft.
  • CE-Delft, 2014. Externe en infrastructuurkosten van verkeer. Een overzicht voor Nederland in 2010. Publicatienummer: 14.4485.35, Delft.
  • De Nocker L en Viaene P., 2016. Methode ecosysteemdienst fijn stof afvang, ECOPLAN. VITO.
  • HEATCO, 2006. Developing Harmonised European Approaches for Transport Costing and Project Assessment (HEATCO). Deliverable D5: Proposal for Harmonised Guidelines, Stuttgart: IER, University of Stuttgart.
  • Janhall, S., 2015. Review on urban vegetation and particle air pollution – Deposition and dispersion. Atmospheric Environment 105, p130-137.
  • Remme, R., De Nijs, T., Paulin, M., 2008. Natural Capital Model, Technical documentation of the quantification, mapping and monetary valuation of urban ecosystem services, RIVM, RIVM Report 2017-0040.
  • RIVM, 2017. Grootschalige concentratie- en depositiekaarten Nederland. Rapportage 2016. RIVM Rapport 2016-0068. RIVM, Bilthoven, the Netherlands.

...

Table 1: Input maps applied to estimate the influence of vegetation and water on residential property values.

Input

Unit

Short description

Source

LCEU map

Ecosystem unit classes

Ecosystem unit classes map for the Netherlands in 2016

CBS 2017

Inhabitants

# inhabitants dam-2

Shows the number of inhabitants per cell

RIVM 2017

Property Value

€

Average property value per neighbourhood 2015 (Dutch: WOZ)

CBS 2016

Vegetation

% cover dam-2

Shows the percentage of a cell that is covered by vegetation (low vegetation, bushes and shrubs and trees combined).

RIVM 2017

Open Water

land use class

Selection of water classes from Top10Water

RIVM 2017

Percentage non-green area cell

% cover dam-2

Percentage of a cell that is not covered by vegetation

RIVM 2017

 

Build-up of the model

 

The influence of vegetation and water on residential property values is estimated based on the

...

Table 2: Fraction of increase in property value given different amenities of vegetation and water (Luttik & Zijlstra, 1997 and Ruijgrok, 2006).

Types of vegetation and water                   

Fraction of property value increase

View of a tree line

 

0.05

View of a park or water

 

0.08

Proximity to a park or water

0.06

Open water

0.12

 

Currently, the presence of multiple types of green or water are not accounted for. The highest fraction increase that is available is applied: open water, view on park or water, proximity to park or water respectively.

...

Table 3: Output map generated for the co-benefit ‘influence of vegetation & water on residential property values’.

Output map

Unit

Short description

Increase in property value

€

The increase in property value due to surrounding vegetation, open water and parks.

  

References

  • CBS, 2017. Ecosystem Unit map, 2013. Available at https://www.cbs.nl/en-gb/background/2017/12/ecosystem-unit- map.
  • CBS, 2016. Gemeente, wijk- en buurtenkaart 2015. Available at http://www.cbsinuwbuurt.nl/#sub- buurten2015_gemiddelde_WOZwoningwaarde.
  • Czembrowski P. & Kronenberg J., 2016. Hedonic pricing and different urban green space types and sizes: Insights into the discussion on valuing ecosystem services. Landscape and Urban Planning 146, 11-19.
  • Daams M.N., Sijtsma, F.J., van der Vlist, A.J., 2016. The effect of natural space on nearby property prices; accounting for perceived attractiveness. Land Economics 92:3 389-410.
  • Franco S.F. & Macdonald J.L., 2017. Measurement and valuation of urban greenness: Remote sensing and hedonic applications to Lisbon, Portugal. Regional Science and Urban Economics 76, 156-180.
  • Luttik J. & Zijlstra M., 1997. Woongenot heeft een prijs; Het waardeverhogend effect van een groene en waterrijke omgeving op huizenprijzen. Wageningen SC-DLO (Rapport 562).
  • Remme, R., De Nijs, T., Paulin, M., 2008. Natural Capital Model, Technical documentation of the quantification, mapping and monetary valuation of urban ecosystem services, RIVM, RIVM Report 2017-0040.
  • Ruijgrok E.C.M., Smale A.J., Zijlstra R., Abma R., Berkers R.F.A., Németh A.A., Asselman N., de Kluiver P.P., de Groot D., Kirchholtes U., Todd P.G., Buter E., Hellegers P.J.G.J. and Rosenberg F.A., 2006. Kentallen Waardering Natuur, Water, Bodem en Landschap, Hulpmiddel voor de MKBA. Witteveen+Bos, commissioned by Ministerie van Landbouw Natuurbeheer en Voedselkwaliteit, The Hague.

...

Table 1: Input maps applied to estimate the increase in physical activity by the population due to surrounding vegetation in a 300m buffer.

Input

Unit

Short description

Source

Inhabitants

# inhabitants dam-2

Shows the number of inhabitants per cell

RIVM (2017)

Vegetation cover

% cover dam-2

The percentage of a cell that is covered by vegetation (low vegetation, bushes and shrubs and trees combined)

RIVM (2017)

 

Build-up of the model

 

To determine increase in physical activity due to vegetation, the percentage of vegetation within a 300m radius around every cell needs to be calculated. The calculation is done as followed:

...

Table 2: Percentage of vegetation within a buffer of 300m and changes in the amount of minutes that individuals invest in outdoor physical activity. (Source: Klompmaker et al., 2018)

Percentage of green space

(quintiles – NDVI)

Minutes extra spent on outdoor physical activity (95% CI)

≤ 40

0

45-50

3.5

51-55

9.4

53–59

18.6

 

Finally, the amount of increase in physical activity by the population due to surrounding vegetation is calculated as followed:

...

Table 3: Output map generated for the co-benefit increase in physical activity due to surrounding vegetation.

Output map

Unit

Short description

Increase in time spend on physical activity

Min dam-2

The increase in the amount of time spend on physical activity by the population based on the surrounding vegetation in a 300m buffer.

 

References

...

Table 1: Input maps applied to estimate the benefits of increased cycling to and from work due to vegetation.

Input

Unit

Short description

Source

Inhabitants

# inhabitants dam-2

Shows the number of inhabitants per cell

RIVM (2017)

Vegetation cover

% cover per dam-2

The percentage of a cell that is covered by vegetation (low vegetation, bushes and shrubs and trees combined)

RIVM (2017)

 

Figure 1: Relationship between the percentage of green space (vegetation cover) and the minutes people engage in cycling for commuting purposes

...

Table 2: Output map generated for the co-benefit of increased cycling for commuting purposes due to vegetation.

Output map

Unit

Short description

Monetary benefit of increased cycling for commuting purposes

€ dam-2

The monetary benefits of increased time spend on cycling for commuting purposes due vegetation in a 1 km buffer.

 

References

  • Kahlmeier S., Götschi T., Cavill N., Castro Fernandez A., Brand, David Rojas Rueda C., Woodcock J., Kelly P., Lieb C., Oja P., Foster, Harry Rutter C., Racioppi F., 2017. Health economic assessment tool (HEAT) for walking and for cycling. Methods and user guide on physical activity, air pollution, injuries and carbon impact assessments. World Health Organization.
  • Kelly P., Kahlmeier S., Götschi T., Orsini N., Richards J., Roberts N., Scarborough, P., Foster C., 2014. Systematic review and meta-analysis of reduction in all-cause mortality from walking and cycling and shape of dose response relationship. International journal of behavioral nutrition and physical activity. 11(1). 132.
  • Maas J., Verheij. R. A., Spreeuwenberg. P., & Groenewegen. P.P., 2008. Physical activity as a possible mechanism behind the relationship between green space and health: a multilevel analysis. BMC public health. 8(1). 206.

...

Table 1: Input maps applied to estimate carbon sequestration by vegetation.

Input

Unit

Short description

Source

LCEU map

LCEU land use classes

Land cover class map

CBS 2016

Agricultural crop parcels

Land cover types for crops

Types of crops found on arable fields

RVO 2013

Groundwater level from the soil map*

Groundwater level in cm

Spatial information on groundwater level and soil structure to roughly 1 metre depth

Alterra 2006

BOFEK2012 map*

Soil biophysical units

Defines areas with similar soil characteristics and hydrological activity (BOFEK2012)

 Alterra 2016

Min & Max Groundwater level

Groundwater level in cm

Defines maximum and minimum average groundwater levels

NHI 2016

Trees

% cover dam-2

Percentage of a cell that is covered by trees higher than 2.5 meters

RIVM

*The original maps have been supplemented with data from TNO (2015), so that the maps fully cover urban areas.

...

Table 2: Wood increment (m3 ha-1 yr-1) per soil texture and drainage class combination for three forest types.

Soil texture/drainage

Texture

Drainage

 

 

very dry

dry

moist-wet

wet

Mixed forest

class/class

1

2

3

4

peat & sandy soils

1

4

6

6

5

loamy sand soils

2

5

8

8

6

(sandy) loam soils

3

3

11

10

7

(heavy) clay soils

4

3

9

10

6

Coniferous forest

class/class

very dry

dry

moist-wet

wet

peat & sandy soils

1

7

9

7

2

loamy sand soils

2

8

10

8

2

(sandy) loam soils

3

4

10

7

2

(heavy) clay soils

4

4

8

6

0

Deciduous forest

class/class

very dry

dry

moist-wet

wet

peat & sandy soils

1

4

6

6

5

loamy sand soils

2

5

8

8

6

(sandy) loam soils

3

3

11

10

7

(heavy) clay soils

4

3

9

10

6

 

Table 3: Reclassification of the soil classes from the BOFEK map (soil physical properties) into soil texture classes.

BOFEK Code

Texture

BOFEK Code

Texture

BOFEK Code

Texture

BOFEK Code

Texture

101

V

303

S

321

S

412

E

102

V

304

Z

322

Z

413

E

103

V

305

Z

323

Z

414

E

104

V

306

Z

324

Z

415

U

105

V

307

S

325

S

416

L

106

V

308

S

326

Z

417

L

107

V

309

Z

327

Z

418

E

108

V

310

Z

401

E

419

E

109

V

311

Z

402

E

420

E

110

V

312

S

403

E

421

E

201

U

313

S

404

U

422

U

202

E

314

S

405

U

501

E

203

V

315

S

406

L

502

L

204

V

316

S

407

E

503

U

205

Z

317

S

408

L

504

L

206

Z

318

S

409

L

505

L

301

Z

319

S

410

E

506

L

302

Z

320

Z

411

E

507

A

 

Table 4: Classification of soil texture classes into four texture groups and two texture types.

Texture class

Code

Texture group

Code

Texture type

Code

A: loam soils

1

(sandy) loam soils

3

Heavy

2

E: clay

2

(heavy) clay soils

4

Heavy

2

L: sandy loam soils

3

(sandy) loam soils

3

Heavy

2

P: light sandy loam soils

4

loamy sand soils

2

Light

1

S: loamy sand soils

5

loamy sand soils

2

Light

1

U: heavy clay soils

6

(heavy) clay soils

4

Heavy

2

V: peat

7

peat & sandy soils

1

Heavy

2

Z: sandy

8

peat & sandy soils

1

Light

1

 

Soil drainage

Input maps with the average minimum (GLG) and maximum (GHG) groundwater level (NHI,2006) are reclassified into nine soil drainage classes, according to Finke et al. (2010) as given in Figure 1. As the groundwater level maps do not cover the Wadden islands in the north of the Netherlands, the groundwater level from the soil map is reclassified into the same nine hydrological classes according to a reclassification table based on expert judgement (available on request). In both cases, a distinction has been made between two texture types: light soils and heavy soils as defined in Table 4. The nine drainage classes have been regrouped into four drainage groups according to Table 5 in order to estimate the annual increment.

...

Table 5: Information from ‘Drainage group’ knowledge table necessary for reclassification.

Drainage class

Description

Drainage group

Code

A

excessively drained soils (very dry)

Very dry

1

B

well-drained soils (dry)

Dry

2

C

moderately well-drained soils (medium dry)

Dry

2

D

insufficiently drained soils (moderately wet)

Moist-wet

3

E

rather poorly drained soils with groundwater permanently (wet)

Moist-wet

3

F

poorly drained soils with groundwater permanently (very wet)

Wet

4

G

extremely poorly drained soils (very wet)

Wet

4

H

poorly drained soils with backwater (temporary groundwater) (very wet)

Moist-wet

3

I

rather poorly drained soils with backwater (temporary groundwater) (wet)

Wet

4

 

Calculating carbon sequestration by woody vegetation

...

Table 6: Output maps generated for the co-benefit carbon sequestration by vegetation.

Output map

Unit

Short description

Annual wood production

kg dam-2 yr-1

The amount of wood produced annually by woody vegetation

Annual carbon sequestration

kg dam-2 yr-1

The amount of carbon that is stored by woody vegetation annually by uptake of CO2.

Monetary value of stored carbon

€ dam-2 yr-1

The monetary value of the amount of carbon that is stored annually by woody vegetation.

  

References

  • Aertsens J., De Nocker L., Lauwers H., Norga K., Simoens I., Meiresonne L., Turkelboom F., Broekx S., 2012. Daarom groen! Waarom u wint bij groen in uw stad of gemeente. Studie uitgevoerd in opdracht van: ANB – Afdeling Natuur en Bos.
  • Anthoff, D. & Tol, R.S.J., 2013. The uncertainty about the social cost of carbon: a decomposition analysis using fund. Climatic Change 117:3, 515-530.
  • Alterra 2006. Bodemkaart van Nederland 1:50000. Available at http://www.nationaalgeoregister.nl/geonetwork/srv/dut/catalog.search#/metadata /1032583a-1d76-4cf8-a301-3670078185ac?tab=contact
  • Alterra 2016. Bodemfysische Eenhedenkaart (BOFEK 2012). Available at https://www.wur.nl/nl/show/Bodemfysische-Eenhedenkaart-BOFEK2012.htm
  • CBS 2017. Ecosystem Unit map, 2013. Available at https://www.cbs.nl/en-gb/background/2017/12/ecosystem-unit-map
  • Demey, A., Baeten, L., and Verheyen, K., 2013. Opbouw methodiek prijsbepaling hout. ANB. Brussels, Belgium.
  • Finke, P.A., Van de Wauw, J. and Baert, G., 2010. Ontwikkelen en uittesten van een methodiek voor het actualiseren van de drainageklasse van de bodemkaart van Vlaanderen. Universiteit Gent, vakgroep Geologie en Bodemkunde.
  • Liekens, I., Van der Biest, K., Staes, J., De Nocker, L., Aertsens, J., & Broekx, S., 2013. Waardering van ecosysteemdiensten, een handleiding. VITO. Mol.
  • NHI, 2006. GxG kaarten, Gemiddelde Grondwaterstanden van modelversie LHM 3.0.2 voor 1998-2006. Available at http://www.nhi.nu/nl/index.php/data/
  • Nordhaus, W.D., 2017. Revisiting the social cost of carbon. PNAS 114:7, 1518-1523.
  • RVO 2013. Basisregistratie Gewaspercelen (BRP), 2013. Available at http://www.nationaalgeoregister.nl/geonetwork/srv/dut/catalog.search#/ metadata/%7B25943e6e-bb27-4b7a-b240-150ffeaa582e%7D
  • Schelhaas, M.J., Clerkx, A.P.P.M., 2015. Het Nederlandse bos in cijfers: resultaten van de 6e Nederlandse Bosinventarisatie. Vakblad Natuur Bos Landschap 12, 23-27.
  • Schelhaas, M.J., Clerkx, A.P.P.M., Daamen, W.P., Oldenburger, J.F., Velema, G., Schnitger, P., Schoonderwoerd, H. & Kramer, H., 2014. Zesde Nederlandse Bosinventarisatie. Methoden en basisresultaten. Wageningen, Alterra Wageningen UR, Alterra-rapport 2545.
  • TNO, 2015. Oppervlaktegeologie, geologische kaart onder INSPIRE. Available at http://www.nationaalgeoregister.nl/geonetwork/srv/dut/catalog.search#/metadata /80630ee7-3a15-4ea0-bdc0-a8aebfa2f204?tab=relations
  • Van de Walle I., van Camp N., Perrin D., Lemeur R., Verheyen K., van Wesemael B., Laitat E., 2005. Growing stock-based assessment of the carbon stock in the Belgian forest biomass. Annals of Forest Science 62: 1-12.
  • Vandekerkhove, K., De Keersmaeker, L., Demolder, H., Esprit, M., Thomaes, A., Van Daele, T., & Van der Aa, B., 2014. Hoofdstuk 13 - Ecosysteemdienst houtproductie. In M. Stevens (Ed.), Natuurrapport - Toestand en trend van ecosystemen en ecosysteemdiensten in Vlaanderen. Brussels: Instituut voor Natuur-en Bosonderzoek.
  • Wolf, J., Beusen, A.H.W., Groenendijk, P., Kroon, T., Rötter, R., van Zeijts, H., 2003. The integrated modeling system STONE for calculating nutrient emissions from agriculture in the Netherlands. Environmental Modelling & Software 18:7, 597-617.


Additional relevant co-benefits

Currently, seven co-benefits are calculated in the Green Benefit Planner extension in the Climate Adaptation Support Tool. These benefits are quantified both physically and monetarily. There are a number of co-benefits that cannot be quantified yet, but are important regarding climate adaptation measures (also see the CBA report1). Additional co-benefits that might be relevant to policy assignments are given below:

 

Biodiversity

At present, biodiversity cannot be quantified in the Green Benefit Planner. However, adding green and blue due to climate adaptation measures can have a positive effect on animal and plant biodiversity in municipalities. The increase in biodiversity, on its turn, can have positive effects on the delivery of other relevant co-benefits/ecosystem servicers such as pollination.

 

Recreation

Currently, only the increase in physical activity in green environments and the health care benefits of cycling for commuting purposes due to green are quantified. Green, however, can also contribute to other forms of recreation. These other forms of recreation are not quantified in the tool, but can be an important co-benefit to climate adaptation measures and contribute in the decision making process of applying these measures.

 

Social cohesion

Increasing the amount of green in the environment by means of climate adaptation measures offers possibilities for creating meeting places for local residents. This can increase the social cohesion and liveability of neighbourhoods.

 

Experience value

Changing the public space by means of climate adaptation measures can increase the attraction of an area, especially with ‘green’ and ‘blue’ measures. Thus, these measures can improve the experience value of the public space, which can have positive effects on local residents in and around this area.

 

Noise reduction

Green has a dampening effect on noise disturbance caused by traffic, for instance. The reduction in noise disturbance reduces stress of local residents that live in and around areas where  ‘noise dampening’ green is implemented.

 

Changing subsidence / CO2 emissions of peat

If climate adaptation measures change the ground water level in peat areas, they have an effect on the emission of CO2 and subsidence of these peat soils.

 

Pollination

The implementation of green can enhance or increase the habitat for different insect species that contribute to pollination. In the last years, the numbers of these pollinators have dropped significantly, thereby threatening the production of crops and other plant species. Creating habitat for these species through climate adaptation measures can positively contribute to the survival of these species.

 

Increase in investment costs

The investment costs are determined by the measures that are implemented, which can differ per scenario.

 

Changes in management costs of green and grey

Changes in land use from grey/paved functions to green functions result in changes in management costs.

 

[1]https://ruimtelijkeadaptatie.nl/actueel/actueel/nieuws/2020/onderzoeken-nkwk-kbs-2019/