Decreasing the greenhouse gas (GHG) emissions from the residential sector is critical to the low-carbon transition. Applying information and communication technologies to power systems makes it possible to reduce GHG emissions in the residential sector, for example through the development of smart homes. Smart homes are more energy efficient and thus, they may be prone to the rebound effect (RE), (i.e., an increase in demand following the introduction of more efficient technology). Moreover, because the electricity's environmental impacts, cost and demand all vary over time, the potential for RE may also fluctuate. Accounting for these temporal aspects could therefore provide more insights into how and why potential RE may occur in smart homes, especially with regard to households' behaviours. In this study, an agent-based model is used to simulate standard and smart home electricity consumption. Life cycle assessment and environmentally extended input-output tables are used to calculate the households' electricity consumption and RE GHG emissions during the simulations. Results show that, while indirect RE in smart homes is low (about 5% in the simulations), the choice of metric used for smart electricity management is key to maximize the GHG emissions reductions of smart homes. When smart homes perform load shifting based on an economic rather than environmental signal, RE increases by almost five-fold. Moreover, certain periods, such as weekdays or the winter season, lead to more significant RE. Thus, considering factors that decrease RE could enable smart homes to reach their full potential contribution to sustainability.

减少居民部门的温室气体排放对于低碳转型至关重要。将信息和通信技术应用于电力系统可以减少住宅领域的温室气体排放，例如通过发展智能家居。智能家居的能源效率更高，因此，它们可能容易受到反弹效应（RE）的影响（即，引入更高效的技术后需求增加）。此外，由于电力对环境的影响，成本和需求随时间而变化，因此可再生能源的潜力也可能会波动。因此，对这些时间方面的考虑可以提供更多有关智能家庭中潜在RE的方式和原因的见解，尤其是在家庭行为方面。在这个研究中，基于代理的模型用于模拟标准和智能家居的电力消耗。生命周期评估和环境扩展的投入产出表用于在模拟过程中计算家庭的电力消耗和RE GHG排放。结果表明，尽管智能家居中的间接可再生能源较低（在模拟中约为5％），但选择用于智能电力管理的度量标准是最大化智能家居减少温室气体排放的关键。当智能家居根据经济信号而非环境信号执行负载转移时，可再生能源增加了近五倍。此外，某些时段（例如工作日或冬季）会导致更显着的RE。因此，考虑减少可再生能源的因素，可使智能家居充分发挥其对可持续发展的潜在贡献。