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.

Due to the sparsity in knowledge, we investigated the presence of various estrogenic endocrine-disrupting chemicals (EEDCs), including phthalates (PAEs), bisphenol-A (BPA), and nonylphenol (NP), as well as microplastics (MPs) in samples of the most widely consumed fish collected from different estuaries in northern Taiwan. We then proceeded to determine the likely contribution that this exposure has on the potential for health impacts in humans following consumption of the fish. Six hundred fish caught from five river estuaries (producing 130 pooled samples) were analyzed to determine how different factors (such as the river, benthic, pelagic, and migratory species) influence EEDCs’ contamination and the possible impacts on human health following typical consumption patterns. The predominant EEDCs was diethyl phthalates (DEP), bis (2-ethylhexyl) phthalates (DEHP), and di-iso-nonylphthalate (DINP) in fish, present at 52.9 ± 77.3, 45.3 ± 79.8, and 42.5 ± 79.3 ng/g dry weight (d.w.), respectively. Residual levels of NP, BPA, and MPs in the fish were 17.4 ± 29.1 and 1.50 ± 2.20 ng/g d.w. and 0.185 ± 0.338 mg/g d.w., respectively. EEDCs and MPs levels varied widely among the five river estuaries sampled due, in part, to differences in habitat types and the associated diversity of fish species sampled. For DEP, the Lao-Jie River and pelagic environments produced the most severely contaminated fish species, respectively. DEP residues were also associated with the burden of MPs in the fish. Based on our analysis, we predict no substantial direct human health risk by EEDCs based on typical consumption rates of estuarine fish by the Taiwanese people. However, other sources of EEDC exposure cannot be ignored.

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.