Despite the importance of evaluating all mitigation options to inform policy decisions addressing climate change, a comprehensive analysis of household-scale interventions and their emissions reduction potential is missing. Here, we address this gap for interventions aimed at changing individual households’ use of existing equipment, such as monetary incentives or feedback. We have performed a machine learning-assisted systematic review and meta-analysis to comparatively assess the effectiveness of these interventions in reducing energy demand in residential buildings. We extracted 360 individual effect sizes from 122 studies representing trials in 25 countries. Our meta-regression confirms that both monetary and non-monetary interventions reduce the energy consumption of households, but monetary incentives, of the sizes reported in the literature, tend to show on average a more pronounced effect. Deploying the right combinations of interventions increases the overall effectiveness. We have estimated a global carbon emissions reduction potential of 0.35 GtCO₂ yr⁻¹, although deploying the most effective packages of interventions could result in greater reduction. While modest, this potential should be viewed in conjunction with the need for de-risking mitigation pathways with energy-demand reductions.

尽管评估所有缓解方案对于为应对气候变化的政策决策提供信息很重要，但仍缺少对家庭规模干预措施及其减排潜力的全面分析。在这里，我们解决了旨在改变个别家庭对现有设备使用的干预措施的这一差距，例如货币激励或反馈。我们进行了机器学习辅助的系统回顾和荟萃分析，以比较评估这些干预措施在减少住宅建筑能源需求方面的有效性。我们从代表 25 个国家的试验的 122 项研究中提取了 360 个个体效应量。我们的元回归证实，货币和非货币干预都减少了家庭的能源消耗，但货币激励措施的规模与文献报道的一样大，往往显示出更明显的效果。部署正确的干预组合可以提高整体效率。我们估计全球碳减排潜力为 0.35 GtCO₂  yr ^-1，尽管部署最有效的一揽子干预措施可能会导致更大程度的减少。虽然这种潜力不大，但应将这种潜力与降低能源需求的降低风险的缓解途径的需求结合起来考虑。