China has pledged to cap its carbon emission by 2030 and achieve carbon neutrality by 2060, making knowledge about how the Chinese general public understands climate change crucial and timely. This article reports findings from surveys of climate change perceptions in six Chinese cities (∼40 million people). We identify 10 distinct mental images and 37 subcategories that represent a wide spectrum of perceptions of climate change among the Chinese public. The results reveal that people tend to conflate climate change with air pollution and seasonal weather changes. Although skepticism is not prominent, voices for action are also limited. Furthermore, climate change perceptions are heterogenous across regions and demographic groups. Respondents from developed cities are less likely to conflate climate change with local weather. People living in polluted regions tend to equate climate change with air pollution. Well‐educated, high‐income, and young residents are more aware of the scientific dimensions of climate change and its consequences. Females and the elderly think more about health implications and how to adapt. Compared to Western countries, opinions about climate change in China are less polarized and controversial, probably due to different political realities and media framings. This study provides an updated picture of climate change perceptions among the Chinese general public and recommends targeted and multi‐level communication strategies for policymakers.

Industrial enterprises are major sources of contaminants, making their regulation vital for sustainable development. Tracking contaminant generation at the firm-level is challenging due to enterprise heterogeneity and the lack of a universal estimation method. This study addresses the issue by focusing on hazardous waste (HW), which is difficult to monitor automatically. We developed a data-driven methodology to predict HW generation using wastewater big data which is grounded in the availability of this data with widespread application of automatic sensors and the logical assumption that a correlation exists between wastewater and HW generation. We created a generic framework that used representative variables from diverse sectors, exploited a data-balance algorithm to address long-tail data distribution, and incorporated causal discovery to screen features and improve computation efficiency. Our method was tested on 1024 enterprises across 10 sectors in Jiangsu, China, demonstrating high fidelity (R² = 0.87) in predicting HW generation with 4,260,593 daily wastewater data.