Climate governance processes involve complex interactions between heterogeneous citizens, advocacy groups, media actors, and political decision-makers. While agent-based models (ABMs) have been widely used to study environmental policy and socio-ecological systems, many existing approaches focus either on institutional dynamics or individual behavioural mechanisms in isolation. This paper presents a modular multi-level agent-based architecture that integrates empirically grounded cognitive decision models with strategic institutional behaviour within a unified simulation framework. The architecture combines (i) motive-based individual decision-making operationalised through the HUMAT and MOA frameworks, (ii) socially embedded influence processes via demographic homophily networks, and (iii) institutional strategy modules for environmental non-governmental organisations (NGOs), media agents, and politicians. Political decisions emerge from the aggregation of multiple signals, including expert input, public mobilisation, party alignment, and media framing. The model is designed to be empirically calibrated through synthetic populations derived from survey data and and institutional parameters informed through Living Lab stakeholder engagement, and to support scenario-based exploration of climate-relevant land-use governance processes. Rather than presenting empirical results, this paper focuses on the architectural design principles, modular structure, and integration logic of the model. We discuss how this multi-layered approach contributes to the modelling of democratic climate governance and outline pathways for generalization and future validation.

翻译：气候治理过程涉及异质性公民、倡导群体、媒体行动者和政治决策者之间的复杂互动。虽然基于智能体的模型（ABM）已被广泛用于研究环境政策与社会生态系统，但许多现有方法仅孤立地关注制度动态或个体行为机制。本文提出了一种模块化且多层次、基于智能体的架构，该架构将基于经验验证的认知决策模型与战略性制度行为整合在一个统一的模拟框架内。该架构结合了：（i）通过HUMAT和MOA框架操作化的基于动机的个体决策，（ii）通过人口同质性网络实现的社会嵌入影响过程，以及（iii）针对环境非政府组织（NGOs）、媒体智能体和政治家的制度策略模块。政治决策源于多重信号的聚合，包括专家输入、公众动员、政党立场和媒体框架。该模型旨在通过调查数据生成的综合人口以及通过生活实验室利益相关者参与获取的制度参数进行经验校准，并支持基于情景的与气候相关的土地利用治理过程探索。本文不呈现实证结果，而是聚焦于该模型的设计原则、模块化结构和集成逻辑。我们讨论了这种多层次方法如何有助于模拟民主气候治理，并概述了推广与未来验证的路径。