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、倡导团体、媒体行为者及政治决策者之间的复杂互动。尽管基于智能体的模型已在环境政策与社会生态系统研究中得到广泛应用，但现有方法多侧重于孤立分析制度动力学或个体行为机制。本文提出一种模块化的多层级智能体架构，在统一仿真框架内将基于经验认知的决策模型与战略性制度行为相结合。该架构整合了以下组件：(i) 通过HUMAT与MOA框架操作化的动机导向型个体决策机制；(ii) 基于人口同质性网络的社会嵌入影响过程；以及(iii) 面向环境非政府组织、媒体智能体及政治行为者的制度策略模块。政治决策由多重信号聚合生成，包括专家意见输入、公众动员、政党立场及媒体框架。该模型设计支持通过基于调查数据构建的合成群体及生活实验室利益相关方互动形成的制度参数进行经验校准，并支撑气候相关土地利用治理过程的场景探索性分析。本文不呈现实证结果，而是聚焦于该模型的架构设计原则、模块化结构及集成逻辑。我们探讨了这种多层级方法如何为民主气候治理建模做出贡献，并概述了其泛化路径与未来验证方向。