With the rapid and vigorous growth of forest tourism, the irresponsible environmental behavior of tourists has caused enormous strain on forests’ ecological systems. Carrying out environmental education in forest parks is conducive to promoting the sustainable development of forest tourism. To explore the impact of human–place emotion on environmental education effects, this study took Fuzhou National Forest Park as an example to construct a structural equation model composed of landscape perception, environment interpretation, place attachment, and the effects of environmental education (EEE). The relationship between the four elements and the mechanism of action was clarified. A questionnaire was used with 480 visitors. Statistical analysis showed that: (1) The value of scientific research and education (0.774) influences landscape perception. Reliability (0.770) and tangibility (0.718) contribute to environmental interpretation. Place identification and dependence are represented by environmental identity (0.771) and are activity-dependent (0.792), respectively. Knowledge (0.860) and behavior (0.869) are essential factors in driving the EEE. (2) Place attachment and environment interpretation had a significant positive impact on the environmental education effect (p < 0.001), and there was no direct effect between landscape perception and EEE. (3) Landscape perception and environmental interpretation indirectly influence EEE with place attachment as full and partial mediators, respectively. This paper aims to provide theoretical support for better synergistic growth of forest park ecology, economy, and environment.
This paper examines computational merits provided by assumptions made in scientific modeling, especially regression, by trying to exhibit abstractly a model deprived of those assumptions. It shows that the principle of Occam's Razor has been mistakenly used as model developers' justification to keep scientific models "as simple as possible", and that the cost of inflating computability is truncation of model robustness.
In this paper we argue that a couple of taken-for-granted methods employed in studying behavioral facts of human risk preference are mistakable. We call for within-subjects experiment design and propose a simple statistical method that might be used to test the validity of using sample mean in interpreting as well as generalizing risk preferences
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