Households are responsible for a significant share of global greenhouse emissions. Hence, academic and policy discourses highlight behavioral changes among households as an essential strategy for combating climate change. However, formal models used to assess economic impacts of energy policies face limitations in tracing cumulative impacts of adaptive behavior of diverse households. The past decade has witnessed a proliferation of agent-based simulation models that quantify behavioral climate change mitigation relying on social science theories and micro-level survey data. Yet, these behaviorally-rich models usually operate on a small scale of neighborhoods, towns, and small regions, ignoring macro-scale social institutions such as international markets and rarely covering large areas relevant for climate change mitigation policy. This paper presents a methodology to scale up behavioral changes among heterogeneous individuals regarding energy choices while tracing their macroeconomic and cross-sectoral impacts. To achieve this goal, we combine the strengths of top-down computable general equilibrium models and bottom-up agent-based models. We illustrate the integration process of these two alien modeling approaches by linking data-rich macroeconomic with micro-behavioral models. Following a three-step approach, we investigate the dynamics of cumulative impacts of changes in individual energy use under three behavioral scenarios. Our findings demonstrate that the regional dimension is important in a low-carbon economy transition. Heterogeneity in individual socio-demographics (e.g. education and age), structural characteristics (e.g. type and size of dwellings), behavioral and social traits (e.g. awareness and personal norms), and social interactions amplify these differences, causing nonlinearities in diffusion of green investments among households and macro-economic dynamics.

家庭在全球温室气体排放中占很大比例。因此，学术和政策讨论强调了家庭之间的行为变化，将其作为应对气候变化的一项重要策略。但是，用于评估能源政策的经济影响的正式模型在追踪不同家庭适应行为的累积影响方面面临局限性。在过去的十年中，见证了基于代理的仿真模型的激增，该模型基于社会科学理论和微观调查数据来量化缓解气候变化的行为。但是，这些行为丰富的模型通常在小规模的社区，城镇和小区域中运行，而忽略了诸如国际市场之类的宏观社会机构，很少涉及与减缓气候变化政策有关的大片地区。本文提出了一种方法，可以在能源选择方面扩大异类个体的行为变化，同时追踪他们的宏观经济和跨部门影响。为了实现此目标，我们结合了自上而下的可计算一般均衡模型和自下而上的基于代理的模型的优势。我们通过将数据丰富的宏观经济与微观行为模型联系起来，说明了这两种外来建模方法的集成过程。遵循三步法，我们研究了三种行为情景下个体能源使用变化累积影响的动态。我们的发现表明，区域范围在低碳经济转型中很重要。各个社会人口统计资料中的异质性（例如教育程度和年龄），结构特征（例如房屋的类型和大小），