Cross-Cultural Differences In Perceptions of The Environmental Impact of Generative Ai

This study addresses a critical gap in the literature on sustainable infor-mation systems by empirically investigating how national culture shapes perceptions of the environmental impact of generative artificial intelligence (GenAI). Based on a survey of 434 university students from France, Portu-gal, the United States, and China, the research employs statistical analyses to test for significant cross-national differences. The results reveal pro-nounced disparities. For instance, while 70% of French respondents ex-press a desire to know the carbon footprint of their AI queries, only 45% of Chinese respondents share this concern. Conversely, Portuguese (72%) and American (62%) students show significantly greater acceptance of envi-ronmental usage quotas than their French (41%) and Chinese (40%) coun-terparts. Analysis of Variance (ANOVA) confirms that country of residence is a statistically significant predictor of environmental concern and atti-tudes toward regulatory measures. These findings challenge universalist "one-size-fits-all" approaches to digital sustainability policy. The study concludes that promoting sustainable AI on a global scale requires context-sensitive strategies aligned with local cultural values, institutional frame-works, and technological ecosystems. We contribute to the Green IS dis-course by integrating cross-cultural theory with the emerging debate on "frugal AI."