Abstract

Social scientists and psychologists who study environmental issues need to improve their measures to capture relevant pro-environmental behaviors to reduce greenhouse gas emissions – the main driver of climate change. They also need to identify meaningful predictors for these behaviors, which go beyond mere statistical significance. In this large representative study of the Austrian population (N = 1,083), we aim at addressing both issues. We focus on relevant and specific energy-related behavioral intentions (traveling, electricity consumption and heating) and test a set of preregistered social-psychological predictors in path models, followed by an exploratory machine-learning approach. We show that a combination of some prominent predictors – perceived behavior control, consideration of future and immediate consequences, and willingness to sacrifice – accounts for only 20 to 30% of variance in behavioral intentions. We suggest that future studies confirm our results in other cultures and set even higher qualitative benchmarks for measures and predictors.

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