From an ecological perspective, urban green spaces moderate the impact of human activities
by, for example, absorbing pollutants and releasing oxygen (Hough, 1984, cited in Haughton
and Hunter, 1994), contribute to the maintenance of a healthy urban environment by
providing clean air, water and soil (De Groot, 1994), improve the urban climate and maintain
the balance of the city’s natural urban environment (Stanners and Bourdeau, 1995). They
preserve the local natural and cultural heritage by providing habitats for a diversity of urban
wildlife and conserve a diversity of urban resources. Despite the enormous benefits that urban
green spaces provide there is a serious lack of information about the quantity and quality of
urban green spaces. However, with the new integrated approaches to combine strategic
planning for green spaces with innovative design and delivery and the active involvement of
the community at all stages, urban green spaces can be part of an ‘urban renaissance’
(DTLR, 2001).
3. Evaluation of urban green space policies
The present study investigates urban green spaces from a policy evaluation perspective and
analyses European cities in order to obtain strategic and policy relevant information on the
key features of urban green. The study aims to compare and evaluate the current management
practices in European cities by means of the perception of relevant decision makers regarding
performance of urban green space policies. The data and information used for comparison
and evaluation are based on extensive survey questionnaires filled out by relevant
departments or experts of municipalities in European cities that aim to share their experience
in innovative green space policies and strategies. A recently developed artificial intelligence
method, viz. rough set analysis is deployed to assess and identify the most important factors
that are responsible for successes and failures of urban green space policies. In the next sub-
section (Sub-section 3.1.) we explain the rough set analysis which is a qualitative multivariate
decision-analytical classification method that originates from the artificial intelligence
literature and then next, in the second sub-section (Sub-section 3.2.) we describe our case
study and database. Then, in the next section, we evaluate the empirical results of the rough
set analysis that enable us to compare best practices in European cities to develop relevant
policy recommendations.