Psychological Carbon Footprint: The Hidden Climate Cost of Negative Emotions

Hong Chen (陈红); Qingqing Sun (孙青青)

Abstract

Mental health and climate change are not independent crises, but rather mutually reinforcing systemic challenges. To quantify the hidden climate costs, this paper introduces and operationalizes the concept of psychological carbon footprint (PCF) for the first time, and develops a multi-level association mining computational model termed “Emotion recognition-behavior association-carbon emission accounting” (EBC). Based on massive comment datasets from three major platforms—Xiaohongshu, Douyin, and Weibo—this study employed both manual annotation and automated scoring to identify 17 types of negative emotions and 13 types of behavioral responses. It then used confidence and lift scores to uncover emotion-behavior association pathways and, by integrating carbon emission coefficients, estimated the average daily associated PCF for different emotion types. The study found that: (1) anger (104.21 kg CO_2e per instance), despair (101.60 kg CO_2e per instance), and feelings of injustice (85.44 kg CO_2e per instanc) constitute a high-emission cluster, whereas the frequently occurring feelings of helplessness and anxiety are associated with lower carbon emissions, demonstrating an asymmetry between frequency and intensity; (2) Although self-harm tendencies and eating disorders occur infrequently, their associated emissions per unit are extremely high (>100 kg CO_2e per instanc), with confidence levels of 0.628 and 0.367, respectively, making them key targets for emission reduction; (3) The spatial distribution of negative emotional intensity is highly coupled with China’s Hu Huanyong Line population density pattern and the economic gradient across the eastern, central, and western regions, with comments reflecting high negative emotional intensity concentrated in the eastern coastal provinces. This study further reveals that affect-oriented platforms, represented by Xiaohongshu, exhibit a bias towards internalising an emotional cocoon, whereas topic-oriented platforms, represented by Weibo, exhibit a bias towards externalising a conflict-driven emotional cocoon. For the first time, this paper systematically operationalises the analytical framework and computational model for quantifying the psychological carbon footprint, uncovering the hidden climate cost of negative emotions. These findings provide deep insights and empirical evidence for developing synergistic strategies that address both mental health and climate mitigation.

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