Climate change is impacting us in many different ways. Some, like increasing temperature, hot nights, and heavier rainfall, are well-known, and we can get ourselves prepared better for them. However, because modern cities like Hong Kong are intricately connected, there are many nonlinear links between different parts of society, and climate change's impact on one area may trigger unforeseen changes in another area. In this project, we shall try to find out more about these potentially significant, but less predictable climate change impacts.

In particular, we shall use machine learning to analyze historical news and research databases to identify examples of nonlinear climate change (CC) impacts. We shall use natural language processing to identify and categorize CC risks into a Climate Adaptation and REsilience (CARE) database and develop risk estimates for different physical, social/economic, political changes that may enhance or reduce the exposure or vulnerability to the identified impact examples.