Green housing on social media in China: A text mining analysis

Reducing carbon emissions and promoting energy efficiency are imperative to the harmonious coexistence between humans and nature. Green buildings can help minimize energy drain and become an effective way to realize Sustainable Development Goals. However, promoting green housing (GH) faces greater challenges than implementing other green buildings. Although governments vigorously promote GH, the development of GH in China is still stuck in the rut of excessive authority intervention, and public response remains limited. Therefore, this research crawled massive online textual data and applied text mining to explore dynamic public opinions, temporal patterns of GH-related public concerns with different sentiment tendencies and driving factors of different sentiments. The results reveal that positive public sentiments mainly focus on ecological, environmental, social, and individual benefits, but the topic of individual-level benefits gradually decreased. As for negative sentiment on GH, price-related issues are the prominent reason, and quality-related issues have been discussed extensively in recent years and have become the most concerning issues. Moreover, regulations, technical level, education level, and incentives have significant positive impacts on citizens' positive sentiments toward GH, while these factors have significant negative impacts on citizens’ negative sentiments toward GH. The findings can help planners, engineers, and governmental officers develop a systematic understanding of micro-level opinions and offer new insights for GH policies and governance. © 2023 Elsevier Ltd