标题： 利用机器学习工具评估气候游说影响的数学建模、分析和仿真 显示英文标题

标题： Mathematical Modeling, Analysis and Simulation Utilizing Machine Learning Tools for Assessing the Impact of Climate Lobbying

摘要： 气候变化政策和立法对国内外应对当今紧迫的环境挑战具有重大影响。此类气候立法的有效性与当选官员之间的复杂动态密切相关，这种动态很大程度上受到游说活动不懈努力的影响。本项目旨在开发一种新型的 compartmental 模型，以预测美国气候立法的发展轨迹。通过理解围绕议会投票、游说影响以及美国国会内部竞选捐款流动的动态，我们旨在通过全面研究《美国清洁能源与安全法案》（ACESA）来验证我们的模型。我们的模型能够很好地捕捉不同立法派系之间的非线性动态，包括温和派、坚定支持者和强烈反对者的动态，最终形成丰富的最终投票结果动态。我们对该模型进行了稳定性分析，并从公开的游说记录和现有的大量文献中估计参数。数值验证针对2009年重要的ACESA投票，连同当代研究，强调了该模型作为理解气候游说动态的有力工具的潜力。我们还分析了旨在指导未来立法努力以追求有效气候行动的模型路径。 显示英文摘要

摘要： Climate policy and legislation has a significant influence on both domestic and global responses to the pressing environmental challenges of our time. The effectiveness of such climate legislation is closely tied to the complex dynamics among elected officials, a dynamic significantly shaped by the relentless efforts of lobbying. This project aims to develop a novel compartmental model to forecast the trajectory of climate legislation within the United States. By understanding the dynamics surrounding floor votes, the ramifications of lobbying, and the flow of campaign donations within the chambers of the U.S. Congress, we aim to validate our model through a comprehensive case study of the American Clean Energy and Security Act (ACESA). Our model adeptly captures the nonlinear dynamics among diverse legislative factions, including centrists, ardent supporters, and vocal opponents of the bill, culminating in a rich dynamics of final voting outcomes. We conduct a stability analysis of the model, estimating parameters from public lobbying records and a robust body of existing literature. The numerical verification against the pivotal 2009 ACESA vote, alongside contemporary research, underscores the models promising potential as a tool to understand the dynamics of climate lobbying. We also analyse the pathways of the model that aims to guide future legislative endeavors in the pursuit of effective climate action.