The promotion of pro-environmental behavior is regarded as very important in solving environmental problems. The Value-Belief-Norm (VBN) theory usually emphasizes internal factors; however, we have transformed this theory by including the environmental knowledge as an external factor. The results showed that action-related environmental knowledge was related to the ecological worldview and directly influenced the private sphere behavior. The ecological worldview, which in this paper was operationalized as environmental concern, had a direct effect on public sphere behavior and an indirect effect on private behavior through awareness of behavioral consequences. Thus, in this paper we revealed how specific environmental knowledge influenced pro-environmental behavior. We also suggest that it is important to educate people about local and global environmental problems, about the impact of behavior on the environment not only in private but also in the public sphere, and to foster the ecocentrism, as well. In addition, we revealed the meaning and necessity of education for environmental citizenship.

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A meta-analysis was carried out to investigate the relationship between connectedness with nature, environmental identity, and environmental self-identity. Through meta-analyzing these relationships, we are able to assess the true estimate of their magnitude. The results revealed a strong correlation between measures of connectedness with nature and environmental identity (r = .75 [0.67, 0.83], k = 11) as well as environmental self-identity (r = .57 [.31, .84], k = 5). Further moderation analysis indicated that the relationship between connectedness with nature and environmental identity is different for graphical and questionnaire instruments used for assessing connectedness with nature; the aggregated correlation for graphical instruments (r = .62 [.56, .67], k = 9) was significantly lower than for questionnaires (r = .82 [.74, .91], k = 9). We suggest revisiting the various instruments assessing human-nature relatedness to maximize unique variance among them.