*3.2. Pro-Environmental Behaviors*

#### 3.2.1. Results

There were significant differences in the PEBs of different groups, extending to the spatial dimension. Respondents within the low education category had the lowest clothing and produce related PEB factor scores, while the high education category had the highest (Figure 3a). PEB factor scores regarding household energy use did not differ greatly depending on education level. The household type with the highest PEB factor scores in all three categories was families. Women, in general, seemed to have higher PEB scores in all three categories, although the most variance was in the clothing category. The higher the income category of the respondents, the lower their PEB factor score in the clothing category. An opposite trend was found in the produce category, where the wealthiest respondents bought the most organic, local, and package free produce. Regarding household energy, very little variance was found, but in general, the wealthier respondents were less likely to make a conscious decision of reducing household energy consumption.

**Figure 3.** (**a**) Mean pro-environmental behavior (PEB) factor scores by categories; (**b**) Hot spot (Getis-Ord Gi\*) map of factor scores of PEB factor 1: clothing (n = 831). Areas highlighted in red have values higher than the regional average, and areas highlighted in blue have values lower than the regional average.

Most notable is that the residents of the pedestrian-oriented zone had relatively high PEB factor scores related to clothing and produce purchases, which is also reflected in the spatial analysis. Local indicators of spatial association show that high values of the factors related to produce and clothing purchases cluster in central parts of the pedestrian-oriented zones of Helsinki (Figures 3b and 4b). We found no significant spatial association of the factor related to household energy and heating saving (Figure 4a and Table 3).

**Figure 4.** (**a**) Hot spot (Getis-Ord Gi\*) map of factor scores of PEB factor 2: household energy (n = 831). Areas highlighted in red have values higher than the regional average, and areas highlighted in blue have values lower than the regional average.; (**b**) hot spot (Getis-Ord Gi\*) map of factor scores of PEB factor 3: produce (n = 831). Areas highlighted in red have values higher than the regional average, and areas highlighted in blue have values lower than the regional average.


**Table 3.** Results of the spatial analyses of PEB factor scores.

As can be seen in Table 4, the only independent variable that influenced all three PEB categories was PEA, all of which were positive and had relatively large effect sizes and impacts on R<sup>2</sup> (model 1 improved from R2 = 0.100 to R2 = 0.298 in model 1a; model 3 improved from R2 = 0.013 to R2 = 0.203 in model 3a).

**Table 4.** Multiple linear regression of clothing, household energy, and produce related pro-environmental behavior factor scores, with education level, household type, income category, gender, pro-environmental attitude, and zones as dependent variables.


Notes. \**p* < 0.05. \*\**p* < 0.01. \*\*\**p* < 0.001. <sup>1</sup> Model 1: PEB regarding clothing as a dependent variable. Education level, household type, income category, and gender as independent variables. 1a: PEA added as an independent variable. 1b: zones added as an independent variable. Model 2: PEB regarding household energy-saving as a dependent variable. Education level, household type, income category, and gender as independent variables. 2a: PEA added as an independent variable. 2b: zones added as an independent variable. Model 3: PEB regarding the purchase of produce as a dependent variable. Education level, household type, income category, and gender as independent variables. 3a: PEA added as an independent variable. 3b: zones added as an independent variable.

The wealthy residents were less likely to buy environmentally-friendly clothing (Table 4), which could be due to the purchasing of second-hand clothing being a part of our clothing measure. Education level had a significant effect on PEBs related to clothing only when attitudes and urban zones were not included (model 1), indicating that it only affects the model through attitudes. Household types affected the PEB clothing model (models 1, 1a, and 1b). Families were more likely to buy environmental, ethical, or second-hand clothing. Women had positive coefficients throughout the models, which suggest that they not only had more environmental concern, but also were more likely to take care of the kind of clothing they did purchase. There was no influence of geographical location on the

models, despite spatial clustering of the factor scores, which suggests geographical clustering was due to patterns in PEAs.

Models 2, 2a, and 2b confirmed the small household energy variance found between income groups in the bivariate analysis (Figure 3a); none of the coefficients were statistically significant. A high level of income had a significantly positive effect on PEBs regarding produce and significant negative coefficients in the clothing models; the more affluent population is more likely to take care when purchasing food, but less likely to think about the environmental effects related to clothing. Residents of the car-oriented zones were more likely to save heating energy than the residents of pedestrian-oriented zones (model 2b), despite the lack of a spatial association of this variable (Figure 4a).

Gender lost significance when attitudes were added to the produce model, which suggests that it only affects the produce purchases through attitudes. Residents of the car-oriented zones were less likely to engage in PEBs related to produce purchases than residents of the pedestrian-oriented zones. Spatial autocorrelation and residual analysis was performed on models 1b, 2b, and 3b (see Appendix D, Table A6). No spatial autocorrelation was found, using global Moran's I with a threshold of *p* < 0.05, but the residuals of the clothing model (1b) showed signs of heteroskedasticity, exhibiting more variance with higher predicted values. As a result, the regression was run again using robust standard errors to see if the coefficients held their significance. The *p* values of these models were very similar and no coefficients lost or gained significance, indicating that our initial models predicted the significance adequately. Although OLS might not provide the best possible fit for the data, it still provided unbiased estimation of which variables influence the dependent variable, which was the primary goal of our analysis.

#### 3.2.2. Discussion

The regression (Table 4) showed that PEA had a significant positive effect on all three PEB categories, which suggests that the attitude-behavior gap related to household energy-saving and the purchase of produce and clothing was small in our results. Value–belief–norm models have been more successful at explaining these low-cost, "good intention" behaviors than ones that have larger behavioral restrictions, such as limiting car-use [67]. Interestingly, PEAs had the least effect on household energy-related PEBs of the three categories. The effect was still quite large and significant, which is in line with other studies [39,42,43]. This could indicate that it is easier and more accessible to install secondary heating or control personal energy use in detached houses in the suburbs than in apartment buildings in the centers, as suggested by Kyrö et al. [64].

The only other variable that had a significant positive relationship with the energy PEB factor score was the residential zone, where the residents of the more sparsely populated areas were more likely to minimize household energy use. However, this is likely due to only the single-family house residents in the car-oriented zones paying directly for their heating, whereas those living in apartment buildings pay it as a part of the housing management fee or rent, having no monetary incentive to reduce usage [68,69]. Furthermore, in HMA, over 80% of households are connected to district heating, covering virtually all apartment buildings, while electricity is used for heating in the low-rise outer fringe areas [23]. Electricity is more expensive than district heating, which in turn could lead to less energy use due to monetary reasons.

The effect of zones on PEBs related to produce might be due to characteristics of the urban surroundings, which differ in availability of organic, package free, and local produce. Suburban residents may find it more difficult to practice sustainable consumption than their urban counterparts [70]. Overall, the higher the income category of respondents, the less likely they were to have high PEB scores related to household energy and clothing (Figure 3a), which is in line with several papers that state a positive correlation between income and carbon footprints related to consumption [27,49,50,71].

Multiple linear regression performed on the data split by zones showed that the relationships between PEB and PEA did not differ notably between residential zones, as the coefficients for PEA in all three zones were similar in size and significance (see Table A2 in Appendix C).
