For those of you who want to get more techy, below we have set out the methodologies applied in a little more detail. Alternatively, do visit our FAQs for some of the more frequently asked questions about Greenkeeper and its functionality.

A revealed preference model predicts greenspace demand based on observed visits.

The model uses a random utility framework, which considers each observed choice as a discrete, utility maximising decision. Choices are not to visit a greenspace or visiting any of the greenspaces in the choice set. Demand for greenspaces is estimated based on individual characteristics, site characteristics, distance and alternative greenspaces.

Green space visitors achieve physical health benefits from undertaking activity in green spaces, including running, cycling and playing sports.
The resulting improvements for individual long-term health outcomes are estimated and measured in terms of improved quality and length of life. The valuation of these improvements is based on surveys of how much individuals are willing to pay to improve their quality and length of life.

Visitors to green spaces receive mental wellbeing benefits, measured in self-reported increases in life satisfaction.

We value the increase in life satisfaction by estimating the amount a person would need to be compensated for the loss of life satisfaction if they were unable to visit green spaces.

Local amenity value measures people’s preference for living near greenspaces.
This is expressed by their willingness to pay more for housing that is located near greenspaces than they would pay for the same house if it were further away from it. The uplift of house prices near greenspaces is used to measure the value of that space as a local amenity and contribution to the attractiveness of a residential area.

Trees in green spaces capture and store atmospheric carbon dioxide.

We use a national dataset to calculate tree canopy cover within greenspaces and then estimate the amount of carbon sequestered. The value of this is then based upon the price of untraded carbon.

Urban trees and vegetation remove pollutants from the air.
Using spatial data on air quality, information on land cover by greenspace and average pollutant removal by land cover types, we estimate the amount of pollutants removed by vegetation in urban green spaces each year.

Categories: Methodologies