3.2. Perspectives of the Priority Policy/Practice Areas and Interventions
presents the perceptions of respondents about the prioritized policy/practice areas and interventions (columns 1–5 correspond to the Likert-scale scores). The results clearly reflect both the prioritisation of traditional approaches to sustainable development, and the markedly low interest in new policy/practice areas and interventions. In particular, the top five priorities mostly include reactive and corrective approaches related to the economy such as (a) improving infrastructure (Me = 5; 62.8% of the respondents scored 5), (b) stimulating economic growth (Me = 5; 63.7% of the respondents scored 5), and (c) revitalising degraded areas through building and infrastructure modernisation and renovation (Me = 4 points; 45.0% of respondents scored 5). A median score of 5 was also obtained for some social policy/practice areas and interventions such as the development of social policies (Me = 5; 53.7% of respondents scored 5). Though rated lower, the group of the five most important priorities also contains approaches related to the environment such as promoting environmental protection and the safe and responsible use of natural resources (Me = 4; 40.2% of respondents scored 5). Areas related to more novel approaches or emerging sustainability issues such as (a) implementing smart city concepts (Me = 3, M = 3.15), (b) responding to global trends (technological revolution) (Me = 3, M = 3.11) or (c) improving the integration of minorities (Me = 3, M = 3.01), were relegated almost to the bottom of the municipal priorities.
Subsequently we test whether there are any patterns in the prioritisation of policy/practice areas and interventions by the mayors (and their executive teams). We analyse the internal structure of these priority perceptions to evaluate the homogeneity of the different components, i.e., the 11 variables describing urban policy/practice areas and interventions used to tackle urban sustainability challenges. We use Exploratory Factor Analysis (EFA) below to identify distinct sub-dimensions among respondents’ perceptions regarding the prioritized policy/practice areas and interventions (Section 2.3
). In other words, EFA is used to determine the latent structure of respondents’ perspectives and reduce the original set of variables (i.e., priorities) to a small set of underlying factors (i.e., clusters of priorities) that reflect the essential information of the variables. In our study these factors essentially capture the main—from the point of view of tackling urban sustainability challenges—groups (clusters) of policy/practice areas and intervention, which explain the relationship between the 11 original measureable priority areas and interventions.
identifies three clusters of policy/practice areas and interventions (three factors), namely “new perspectives” (W1), “traditionalist perspective” (W2), “reactive perspectives” (W3). These factors reflect a relatively significant part of the focus of their individual variables, but at the same time they also reflect distinct overarching priority approaches. When taking into consideration the percentage of variance explained by a particular factor it is possible to determine its significance for tackling urban sustainability challenges. In this context, W1 (“new perspectives towards urban sustainability priorities”) is the main factor for contemporary Polish cities and has the highest significance for tackling urban sustainability challenges in the country.
The first factor (W1) includes three of the less traditional types of policy/practice areas and interventions: namely (a) “responding to global trends (e.g., technological revolution)”, (b) “improving the integration of minorities (e.g., ethnic, religious, cultural), and (c) promoting diversity and tolerance within local communities” and “implementing smart city concepts”. This factor explains the variance of the latent variable at 40.22%. Respondents giving higher scores on the priorities captured by first factor (thus resulting in a higher value for W1 index), tend to prioritize policy/practice areas and interventions related to the new urban challenges.
The second factor (W2) contains the largest number of policy/practice areas and interventions: namely (a) “promoting environmental protection and the safe and responsible use of natural resources”; (b)“developing social policies that ensure the appropriate housing, health care, education, and cultural needs of vulnerable social groups”, (c) “shaping municipal closed-loop economies (i.e., circular economy approaches)”, (d) “solving political and administrative problems”, (e) “encouraging economic growth/employment, attracting investors and creating investment opportunities”. This factor explains 11.73% of the latent variable. Respondents giving a higher score for this factor (thus resulting in a higher value for W2 index) tend to prioritize more traditional policy/practice areas and interventions, which implies that they rather focus on some of the more traditional challenges of sustainable urban development.
The third factor (W3) consists of three policy/practice areas and interventions: namely (a) “revitalising run-down areas through the modernisation and renovation of buildings and infrastructure”; (b) “revitalising degraded areas through investments in human and social capital” and (c) “improving local infrastructure, communication and transport”. This factor explains nearly 10% of the variance of the latent variable, and reflects a rather reactive perspective towards urban sustainability priorities.
Both KMO and Bartlett’s sphericity test indicate the good properties of the 11 variables, in terms of the adequacy for EFA use (Table 4
) (Section 2.3
). Additionally, for all variables, the VIF is below 10, and even lower than 2.5. Factor loadings marked in bold exceed the value of 0.5 and are positive, which not only does it indicate a high positive correlation between the included variables weighing a given factor, but also confirms that their inclusion is appropriate. Each of these factors is characterised by high reliability, and therefore can be used to construct W1, W2 and W3 indices. The indices describe in a one-dimensional way each of the different sub-dimensions of tackling urban sustainability challenges. Scores for the W1 and W3 indices can range between 3 and 15, while for the W2 index scores can range between 5 and 25. For the studied cities, only the index W2 does not reach the minimum value possible (i.e., the results are not below 12 points), see Table 5
Regarding the W1 index, 25% of respondents have scored 8 or below (Q1), 50% have scored 9 or more. and 25% have scored 11 or more (Q3) (Table 5
). The average rating is 9.27 with a relatively good similarity of the results across the entire sample (STD=2.74). The distribution is close to normal, as suggested in the histogram (Figure 3
), the value of kurtosis and the coefficient of skewness (both close to zero) (Table 5
). The above suggest that the respondents’ perceptions of priority policies/practices and interventions from the initial sub-dimension are at a moderate level, with small diversity in relation to the mean.
Similar conclusions can be drawn and for the W2 index. Even though the value and the range of variability of W2 is higher, the results suggest that respondents’ opinions are also poorly diversified in the way they prioritise the “traditionalist perspective towards urban sustainability priorities”. For half of the most typical cities in our sample, the score of the W2 index falls within a very short interval (i.e., between 20–23 points), and has a low standard deviation (Table 5
). In this case, a slightly stronger negative skewness is observed (i.e., the occurrence of atypically low assessments, Figure 3
Conversely, the W3 index exhibits a stronger negative skewness. In particular, 25% of respondents have scored 12 or less (Q1), 50% 13 or more (Me) and 25% 15 or more (Q3), with the maximum score being at the same level as for the W1 (i.e., 15 points). The average rating is 13.03 with a large similarity of results in the entire population (STD = 1.90). These scores suggest that priorities related to regeneration are important, but for some municipalities the significance is lower than for most municipalities (i.e., the range between minimum and the first quartile equals to 9 points, while the range is 14 points).
However, in order to compare the importance of the intervention clusters according to respondents’ responses (and taking into account the different number of interventions constituting the three indices) it is better to calculate the indices as a mean rather than as the sum of individual ratings (i.e., the indices range of these indices is from 1 to 5). Then, policies/practices/interventions in the W1 index are the lowest priority among mayors and their executive teams, as the median score is 3 compared to 4.2 for W2, and 4.33 for W3. Furthermore, it also has the lowest mean: for W1 M = 3.09, STD = 0.91; for W2 M = 4.22, STD = 0.52; for W3 M = 4.33, STD = 0.64). Both the standard deviation and the range (including quadrant range) suggest that the most homogeneous cluster of policy/practices areas and interventions in Polish cities occurs in the second analysed sub-dimension, i.e., “traditionalist perspective towards urban sustainability priorities”.
The policy/practice areas and interventions related to urban regeneration (W3) are the most prioritized among Polish mayors and their executive teams. The mean and median rating is 4.33 and, the value of quartile 3 (Q3) corresponds to the maximum score (5 points). This rating relates to 30% of the sampled cities, while for W2 and W1 it relates only to 9% and 3% of the cities respectively. Thus, this area, though relatively less significant from the perspective of tackling urban sustainability challenges (i.e., the lowest contribution to the explanation of the latent variable), has been prioritized the most among mayors (and their executive teams).
Correlations between the three calculated three indices are statistically significant and positive. Both the scatterplot (Figure 3
) and the value of the correlation coefficient (right part of Table 4
) confirm the relatively strong relationship between indices W1 and W2 (r = 0.591, p
< 0.001*). Although, the correlation between other indices is also statistically significant and positive, it is slightly weaker. This suggests that more modern approaches are more typically prioritised for these municipalities in which mayors (and their executive teams) prioritise also traditional policy/practice areas and interventions.
3.3. Effect of Municipality Characteristics on the Prioritization of Policy/Practice Areas and Interventions
The prioritization of policy/practice areas and interventions is, to a certain extent, related to the characteristics of municipalities. Table 6
shows the relationship between the 11 priorities and the type of municipality (Table 6
) and its wealth as measured by budget revenues (Table 7
). The column headings (score 1–5) in both tables correspond to the priority levels elicited through the Likert scale (Section 2.2
The results suggest that the municipality type and affluence is significantly associated with the prioritization of policy/practice areas and interventions related to the stimulation of economic growth, the implementation of smart city concepts, and the revitalisation of degraded areas (Table 6
). First of all, the prioritization of “encouraging economic growth/employment, attracting investors, and creating investment opportunities” increases with the affluence of the municipality. In particular the highest priority is given in cities with district rights (87.5%; score 5 points) and cities with revenues of PLN 100–500 million (82.6%; score 5 points). The lowest priority is given in the most affluent cities (i.e., >500 PLN million, 66.7%; score 5 points), which can be possibly explained by the recent economic development in the largest (and richer) Polish cities.
Municipality affluence is also significantly related to the prioritization of the smart city concept, with higher priority given in municipalities with district rights (Table 6
) and revenues of over PLN (Polish zlotys) 100 million (Table 7
). In particular seven out of ten cities with district rights, and about six out of ten municipalities with a budget of over PLN 100 million consider the implementation of smart city concepts as an important priority (i.e., rating it at least 4 on the 5-point Likert scale).
Revitalisation, especially when connected with investing in human and social capital, is the third policy/practice area and interventions whose prioritization is differentiated by municipality characteristics. From an urban development perspective, its significance is emphasised especially in cities with district rights (Table 6
), and with high budget revenues (Table 7
). This prioritization might reflect that only such municipalities have the necessary financial resources to undertake revitalization initiatives.
When analyzing indices with municipality characteristics, we find significant differences only for W3 (in F-test p = 0.006). The prioritisation of resource regeneration is the highest for cities with district rights (M = 13.81, STD = 1.35), then for urban municipalities (M = 13.25, STD = 1.67), and finally for urban-rural municipalities (M = 12.82, STD = 2.03). However, only cities with district rights and urban-rural municipalities differ significantly in statistical terms. Again, this possibly results from the much greater experience of cities with district rights in the implementation of revitalisation projects and programmes, as well as the larger scale of infrastructure investments.
In summary, our results suggest that most of the municipality characteristics do not affect the prioritization of most policy/practice areas and interventions. This reflects to some extent other studies (e.g., [50
]) that emphasised that other factors, such as the quality of leadership, administrative efficiency, and human or social capital, are significant when pursuing sustainable development (Section 3.4