Is the rival city always greener? An analysis of the indicators for European Green Capital Award shortlisted and applicant cities,
2010–2024
Is the rival city always greener? An analysis of the indicators for European Green Capital Award shortlisted and applicant cities,
2010–2024
Author(s): Dalma Schmeller, Dávid SümeghySubject(s): Social Sciences, Economy, Geography, Regional studies
Published by: Központi Statisztikai Hivatal
Keywords: European Green Capital Award; environmental sustainability; green cities; environmental indicators
Summary/Abstract: The European Green Capital Award (EGCA) has been given to cities that can serve as role models for other cities in responding to environmental challenges with innovative solutions and contributing to the development of more sustainable and healthier cities. This study examines 100 of the 110 cities that applied for the award by the round of 2024 based on quantitative data that could measure the environmental awareness of those cities. The variables were selected in line with the topics of the EGCA call for proposals. Exploratory data analysis (EDA) was used to reveal the differences between the two groups, finalists and applicants who were nonshortlisted. Based on Mann‒Whitney U tests and chi-square tests, the values of the finalists were convincingly more favorable for only 10 variables. To identify the variables with the strongest relationship with the outcome of the application, a logistic regression was performed after a dimension reduction carried out with multiple factor analysis (MFA). The model can be applied with high accuracy mainly in the category of nonshortlisted candidates (there are several erroneous estimates for the winners), which suggests that other, nonmeasurable criteria are also influencing factors. The model, with some limitations, can also be used by cities that also want to compete in the future to assess their chances before submitting their application.
Journal: Regional Statistics
- Issue Year: 14/2024
- Issue No: 02
- Page Range: 197-228
- Page Count: 32
- Language: English