|The objective of this study is to quantify the relation, or relationships, between public urban green infrastructure (PUGI) and citizens’ health in the municipality of Amsterdam. The results show what implications these relationships have on the (planned) PUGI of the year 2050. It is argued in many studies that increased proximity of PUGI has a significant positive impact on, among other things, the prevalence of obesity, loneliness, and mental health problems. Amsterdam has a different urban-fabric than the cities that are the focal point of existing studies (mainly north-America). The null-hypothesis is that within the municipality of Amsterdam the proximity of PUGI is less a determinant for health than in existing studies. Among the reasons to assume this is the skew division of PUGI in Amsterdam, with high-income (central) areas in general having less PUGI than lower-income (outer) areas. This study uses a multiple regression model to substantiate and quantify this relation or the null-hypothesis. Urban neighbourhoods, from which there are 91 in Amsterdam, are the aggregation-unit of choice; and beside PUGI several covariables are considered. The findings show that PUGI has no significant-correlation with obesity, mental health problems and loneliness. The covariables, economic-status and neighbourhood design are important explanatory variables for the health-variables. The analysis of the 2050-PUGI showed that the interventions the municipality propose strongly aim to increase the inclusion of low-SES people into the PUGI. A push which is much needed knowing that low-SES citizens are less healthy in general and make less use of PUGI. The 2050-policy additionally tries to increase the quality of the PUGI by introducing park-concierges, sport-amenities, social events, and functional-diversification of the parks (depending on the neighbourhood it is in). In conclusion, the PUGI that Amsterdam neighbourhoods have in their proximity is currently an insignificant determinator for health. Future studies could be helped by introducing new variables such as fast-food-outlets and cardiovascular diseases, which are currently not openly available on the neighbourhood-level. In addition, the 91 neighbourhoods may be too small of a sample-size for robust statistical modelling; leading to significant outliers within the sample-size that are a nuisance to the model-fit and coefficients. Using a (fine-grained) tessellated aggregation in combination with data on this level would allow for better statistical modelling and more robust outcomes. This data-precision is unavailable for Amsterdam, substantiating the choice for using the neighbourhood as aggregation.