Tai-Kuei Yu

This research collects corporate social responsibility (CSR) reports from stock exchange companies in Taiwan and employs text mining technologies to analyze the relationship and document similarity between CSR reports from various industries and the Sustainable Development Goals (SDGs). The methods used include natural language processing (NLP), TF-IDF weighting, principal component analysis (PCA), and document similarity assessment. The study applies sub-items of selected SDG terms to screen feature words, build the TF-IDF matrix, analyze the CSR report contents using PCA, and utilize cosine similarity to compare the similarity between CSR reports and SDG sub-items. A total of 225 feature words were identified based on SDG sub-items, with the top 60 feature words (26.7%) accounting for 77.9% of the total TF-IDF weights, aligning with the Pareto principle. Analyzing 370 CSR reports from selected stock exchange companies (0050 ETF), unique and representative feature words and explained variations were identified. Each rotated principal component allowed the identification of corresponding SDG sub-items through specific feature words. The high diversity of feature words resulted in low and unique explained variance for each rotated principal component. Document similarity comparisons between CSR reports and SDG sub-items revealed confidence levels indicating the degree of alignment between CSR reports and SDG sub-items. For the natural language segmentation process and automatic document classification of CSR reports, the assistance of domain experts is recommended to ensure accurate and consistent segmentation and classification results.

Facing the big data wave, this study applied artificial intelligence to cite knowledge and find a feasible process to play a crucial role in supplying innovative value in environmental education. Intelligence agents of artificial intelligence and natural language processing (NLP) are two key areas leading the trend in artificial intelligence; this research adopted NLP to analyze the research topics of environmental education research journals in the Web of Science (WoS) database during 2011–2020 and interpret the categories and characteristics of abstracts for environmental education papers. The corpus data were selected from abstracts and keywords of research journal papers, which were analyzed with text mining, cluster analysis, latent Dirichlet allocation (LDA), and co-word analysis methods. The decisions regarding the classification of feature words were determined and reviewed by domain experts, and the associated TF-IDF weights were calculated for the following cluster analysis, which involved a combination of hierarchical clustering and K-means analysis. The hierarchical clustering and LDA decided the number of required categories as seven, and the K-means cluster analysis classified the overall documents into seven categories. This study utilized co-word analysis to check the suitability of the K-means classification, analyzed the terms with high TF-IDF wights for distinct K-means groups, and examined the terms for different topics with the LDA technique. A comparison of the results demonstrated that most categories that were recognized with K-means and LDA methods were the same and shared similar words; however, two categories had slight differences. The involvement of field experts assisted with the consistency and correctness of the classified topics and documents.

The effects of climate change have already begun to threaten biological diversity and human societies, and these effects will continue to grow over time. To face the challenges related to mitigation and adaptation will require an educated and motivated citizenry. From the perspective of green education, providing knowledge related to climate change and promoting pro-environmental behaviors is imperative. In this study, we assess current levels of knowledge, risk perception and types of pro-environmental behaviors. We administered a questionnaire to 1118 university students in Taiwan evaluating climate change knowledge (15 items), risk perception (23 items), and attitudes toward behavioral change (33 items). Factor analyses were conducted to identify the underlying latent variables for risk perception and obstacles to behavioral change, and ANOVA tests were performed to identify significant associations between three different levels of climate change knowledge and responses to the risk perception and obstacles-to-change items. We found that higher levels of knowledge significantly predicted greater perceptions of risk related to biodiversity threats and increased public costs. In terms of behavior, students with lower levels of knowledge were significantly more likely to find uncertainties related to climate change to be a greater obstacle to engaging in pro-environmental behaviors. Higher levels of knowledge clearly allow individuals to better assess the threats posed by climate change and reduces the perceived level of uncertainty related to climate change and the impact of pro-environmental behaviors. Our results suggest that Taiwan’s efforts to implement climate change related information in the public schools and in the university system have been effective and that such efforts should be broadened to reach the public as a whole.

This study developed a model that examined the relationship between undergraduate students’ beliefs, norms and pro-environment behavioral intentions in the context of global climate change (GCC). The model was further evaluated to determine whether latent variables, such as sustainability value, environmental concern, social norms, perceived risk, pro-environmental attitude, as defined by the theory of planned behavior and value-belief-norm theory, significantly influenced students’ intentions towards pro-environmental behavior. The research model was empirically tested using data collected form 275 undergraduate students. Empirical results found support for four interaction effects of personality traits and the related latent variables of environmental attitude, including sustainability value, social norms, environmental concern and perceived risk. The impact of undergraduate students’ environmental attitudes was moderated by personality traits. The findings of this research offer policy makers and enterprises better understandings of undergraduate students’ attitudes and behavioral intentions towards GCC and promote the visibility of this issue.