The vast majority of climate policies fail to significantly reduce emissions and so make little difference to stopping climate change, suggesting that governments must work much harder to identify ways to actually shift the needle. Nicolas Koch at the Mercator Research Institute on Global Commons and Climate Change in Berlin and his colleagues discovered this by assessing the impact of 1500 climate policies put into force between 1998 and 2022, covering 41 countries across six continents. They began by using machine learning to identify moments in which a country's emissions dropped significantly, relative to a control group of other nations not included in the analysis. The researchers found 69 of these emissions "breaks" and compared them with a database compiled by the Organisation for Economic Co-operation and Development (OECD) that tracks what types of climate policies were enacted when. While matching policy shifts to emission changes isn't an exact science, the team was able to attribute 63 of these breaks to one or more policy interventions within a two-year interval around the break, in order to allow for lagged or anticipated effects.

Inspired by the dual-process theory of human cognition, we introduce DUMA, a novel conversational agent framework that embodies a dual-mind mechanism through the utilization of two generative Large Language Models (LLMs) dedicated to fast and slow thinking respectively. The fast thinking model serves as the primary interface for external interactions and initial response generation, evaluating the necessity for engaging the slow thinking model based on the complexity of the complete response. When invoked, the slow thinking model takes over the conversation, engaging in meticulous planning, reasoning, and tool utilization to provide a well-analyzed response. This dual-mind configuration allows for a seamless transition between intuitive responses and deliberate problem-solving processes based on the situation. We have constructed a conversational agent to handle online inquiries in the real estate industry. The experiment proves that our method balances effectiveness and efficiency, and has a significant improvement compared to the baseline.