The PISA studies provide unique opportunities to investigate the competencies and attitudes of 15-year-olds across the world. Past research investigating environmental awareness (EA) in PISA 2006 found associations between EA and science-related competencies and attitudes. Investigating EA in the PISA studies may have important implications for education for sustainable development (ESD): results may show which factors should be considered in educational interventions to enhance students’ EA. Cross-national analyses of EA may provide insights into the predictors of EA on a local, national or international level. This study investigates the individual, school, and country level predictors of EA in PISA 2015 (365,194 students, 12,594 schools, 53 countries). The multi-level regression analysis on EA reveals that most of the variance is located at the student level. On the individual level, variables related to science learning in school are associated with EA across all countries. This study also compares the degrees of EA in the 2006 and 2015 populations. The results show similar degrees of EA in 2006 and 2015. Altogether, the study provides cross-country evidence on important aspects that should be addressed in successful ESD programs.

Occupation coding is the classification of information on occupation that is collected in the context of demographic variables. Occupation coding is an important, but a tedious task for researchers in social science and official statistics that calls for automation. Due to the complexity of the task, currently, researchers carry out hand-coding or computer-assisted coding. However, we argue that, with the rise of transformer-based language models, hand-coding can be displaced by models, such as BERT or GPT3. Hence, we compare these models with state-of-the-art encoding approaches, showing that language models have a clear advantage in Cohen’s kappa compared to related approaches, but also allow for flexible fine-grained coding of single digits. Taking into consideration the hierarchical structure of the occupational group, we also develop an approach that achieves better performance for the classification of different single digit combinations.