| Literature DB >> 34203161 |
Lan Zhang1, Guowen Huang1, Yongtao Li2, Shitai Bao2.
Abstract
Landsenses ecology has been widely applied in research into sustainable consciousness and behavior and the notion of landsense creation realizes the unity of the macro physical senses and micro psychological perceptions. However, a great deal of current research about landsenses ecology has concentrated on the dimension of the physical senses, while there have been relatively few studies on the dimension of its psychological perception. This paper begins by clarifying the concept of self and explaining out that the psychological perception mechanism of landsense creation represents a process of guiding people to know themselves and realize their ecological self. It then utilizes the example of low-carbon discourse to explore the factors contributing to the resonance of ecological self-vision. Our results show that the perceived self-efficacy, environmental concern and environmental knowledge triggered by ecological discourse are the main factors contributing to the resonance of sustainable vision, thus clarifying the indicators of landsenses ecology at the level of psychological perception. Our purpose is to effectively guide the landsense creation of harmonious discourse and promote people to engage in potentially more sustainable behavior.Entities:
Keywords: ecolinguistics; ecological self; harmonious discourse analysis; landsenses ecology; linguistic landsense
Mesh:
Substances:
Year: 2021 PMID: 34203161 PMCID: PMC8296938 DOI: 10.3390/ijerph18136914
Source DB: PubMed Journal: Int J Environ Res Public Health ISSN: 1660-4601 Impact factor: 3.390
Figure 1The psychological perception process of landsense creation.
Items for factor analysis.
| Code | Variables | 1 | 2 | 3 | 4 | 5 |
|---|---|---|---|---|---|---|
|
| Do you understand the concept of low-carbon lifestyles after reading this text? | not at all | not very | uncertain | very | extremely |
|
| Do you understand the significance of low-carbon lifestyles after reading this text? | not at all | not very | uncertain | very | extremely |
|
| Do you know what should be done for a low-carbon lifestyle after reading this text? | not at all | not very | uncertain | very | extremely |
|
| Do you know what you can do for a low-carbon lifestyle after reading this text? | not at all | not very | uncertain | very | extremely |
|
| Do you think it is important to live a low-carbon life after reading this text? | not at all | not very | uncertain | very | extremely |
|
| Does this text give you a strong sense of environmental urgency? | not at all | not very | uncertain | very | extremely |
|
| Does this text give you a strong sense of social responsibility? | not at all | not very | uncertain | very | extremely |
|
| Do you think low carbon is closely related to daily life after reading this text? | not at all | not very | uncertain | very | extremely |
|
| Do you think personal behavior is closely related to environmental protection after reading this text? | not at all | not very | uncertain | very | extremely |
|
| Do you aspire to a low-carbon lifestyle? | not at all | not very | uncertain | very | extremely |
|
| Will you feel guilty about wasteful behavior later? | not at all | not very | uncertain | very | extremely |
|
| Will you pay attention to the details of low-carbon lifestyles in the future? | not at all | not very | uncertain | very | extremely |
Test of homogeneity of variances.
| Levene Statistic | df1 | df2 | Sig. |
|---|---|---|---|
| 2.014 | 2 | 176 | 0.137 |
Robustness tests of the equality of means.
| Statistic a | df1 | df2 | Sig. | |
|---|---|---|---|---|
| Brown–Forsythe | 28.213 | 2 | 153.064 | 0.000 |
a. Asymptotically F distributed.
ANOVA.
| Sum of Squares | df | Mean Square | F | Sig. | |
|---|---|---|---|---|---|
| Between Groups | 21.772 | 2 | 10.886 | 29.033 | 0.000 |
| Within Groups | 65.993 | 176 | 0.375 | ||
| Total | 87.765 | 178 |
Multiple Comparisons.
| (I) Groups | (J) Groups | Mean Difference (I–J) | Std. Error | Sig. | 95% Confidence Interval | |
|---|---|---|---|---|---|---|
| Lower Bound | Upper Bound | |||||
| Group 1 | Group 2 | −0.68000 * | 0.11725 | 0.000 | −0.9114 | −0.4486 |
| Group 3 | 0.09391 | 0.11373 | 0.410 | −0.1305 | 0.3184 | |
| Group 2 | Group 1 | 0.68000 * | 0.11725 | 0.000 | 0.4486 | 0.9114 |
| Group 3 | 0.77391 * | 0.10809 | 0.000 | 0.5606 | 0.9872 | |
| Group 3 | Group 1 | −0.09391 | 0.11373 | 0.410 | −0.3184 | 0.1305 |
| Group 2 | −0.77391 * | 0.10809 | 0.000 | −0.9872 | −0.5606 | |
*. The mean difference is significant at the 0.05 level.
Figure 2Factor analysis results: Scree plot.
Total variance explained.
| Component | Initial Eigenvalues | Extraction Sums of Squared Loadings | Rotation Sums of Squared Loadings | ||||||
|---|---|---|---|---|---|---|---|---|---|
| Total | % of Variance | Cumulative % | Total | % of Variance | Cumulative % | Total | % of Variance | Cumulative % | |
| 1 | 4.732 | 39.437 | 39.437 | 4.732 | 39.437 | 39.437 | 3.653 | 30.441 | 30.441 |
| 2 | 1.768 | 14.730 | 54.167 | 1.768 | 14.730 | 54.167 | 2.283 | 19.024 | 49.464 |
| 3 | 1.195 | 9.956 | 64.123 | 1.195 | 9.956 | 64.123 | 1.759 | 14.659 | 64.123 |
| 4 | 0.934 | 7.784 | 71.907 | ||||||
| 5 | 0.798 | 6.648 | 78.556 | ||||||
| 6 | 0.716 | 5.969 | 84.525 | ||||||
| 7 | 0.469 | 3.911 | 88.436 | ||||||
| 8 | 0.417 | 3.472 | 91.908 | ||||||
| 9 | 0.314 | 2.621 | 94.529 | ||||||
| 10 | 0.283 | 2.357 | 96.886 | ||||||
| 11 | 0.218 | 1.819 | 98.705 | ||||||
| 12 | 0.155 | 1.295 | 100.000 | ||||||
Extraction method: Principal component analysis.
Rotated Component Matrix (showing only loads greater than 0.5).
| Component | |||
|---|---|---|---|
| 1 | 2 | 3 | |
| 0.832 | |||
| 0.754 | |||
| 0.717 | |||
| 0.667 | |||
| 0.632 | |||
| 0.580 | |||
| 0.527 | |||
| 0.887 | |||
| 0.850 | |||
| 0.522 | 0.602 | ||
| 0.896 | |||
| 0.569 | |||
Extraction Method: Principal Component Analysis.; Rotation Method: Varimax with Kaiser Normalization; a. Rotation converged in 5 iterations.
Correlations.
| F1 | F2 | F3 | F | |||
|---|---|---|---|---|---|---|
| Spearman’s rho | F1 | Correlation Coefficient | 1.000 | |||
| Sig. (2-tailed) | 0.000 | |||||
| F2 | Correlation Coefficient | −0.015 | 1.000 | |||
| Sig. (2-tailed) | 0.840 | 0.000 | ||||
| F3 | Correlation Coefficient | −0.009 | −0.007 | 1.000 | ||
| Sig. (2-tailed) | 0.900 | 0.922 | 0.000 | |||
| F | Correlation Coefficient | 0.768 ** | 0.342 ** | 0.400 ** | 1.000 | |
| Sig. (2-tailed) | 0.000 | 0.000 | 0.000 | 0.000 |
**. Correlation is significant at the 0.01 (2-tailed).