Data Structure The synthesized data is structured around three primary dimensions: Interventions, Study Characteristics, and Outcome Measures. Interventions: The review classifies policy instruments into five core categories: Monetary: Pricing, rebates, and financial rewards. Information: Energy audits, tips, and awareness campaigns. Feedback: Smart meters, home displays, and redesigned utility bills. Social Comparison: Benchmarking household use against peers (e.g., Home Energy Reports). Motivation: Goal setting, commitments, and gamification. Methodological Framework: The data includes 663 effect sizes harmonized to Cohen’s d. We use a multilevel random-effects model to account for dependencies within studies and an NMA to evaluate the relative efficacy of intervention "packages". Risk of Bias (ROB): Each study is coded for quality using a modified framework from the Center for Environmental Evidence (CEE), covering internal and external validity. What can I use this data for? This dataset is designed for researchers, climate policy analysts, and practitioners to: Evaluate Policy Efficacy: Compare the average effectiveness of different intervention types (e.g., Monetary vs. Behavioral). Design Intervention Packages: Identify which combinations of interventions (e.g., combining information with social comparison) yield the highest energy savings. Assess Evidence Quality: Filter findings based on the Risk of Bias to ensure policy recommendations are built on robust evidence. Track Living Evidence: As a "living" review, this dataset serves as a pilot for continually updated climate policy evidence.

Summary Figures The record includes key visualizations derived from the Network Meta-Analysis: Rankograms: Visualizing the probability of an intervention being the most effective. Network Geometry Plots: Showing the relationships and evidence loops between different intervention categories. Forest Plots: Displaying average effect sizes (Cohen's d) across different subgroups and ROB levels. Code Availability The analysis was performed using the metafor package in R. Repository: https://github.com/tarunMkhanna/LSR-HouseholdEnergy Main Scripts: Includes code for the fixed-effects model, NMA implementation, and machine learning screening logs. Changelog v1.2 (Current): Update includes literature published through January 2025 and January 2026, adding roughly --- new documents to the initial pool and ....(more). v1.1: Update includes literature published through (month) 2020?? and December 2024, adding roughly 53,000 new documents to the initial pool and nearly doubling the number of effect sizes used for synthesis. v1.0: Initial meta-analysis based on Khanna et al. (2021) methods, covering 122 studies.

General Description This record contains the data and supporting materials for the Living Systematic Review (LSR) and Network Meta-Analysis (NMA) titled "Behavioral, Information, and Monetary Interventions to Reduce Energy Consumption in Households.". As climate targets require urgent demand-side action, this project provides a rigorous, up-to-date synthesis of how different policy interventions, such as nudges, financial rewards, and feedback can impact residential energy use. The dataset harmonizes evidence from over 213 relevant studies spanning 40 countries and involving more than 6.5 million households. By employing machine learning (ML) for literature screening and an NMA to compare intervention packages, this review offers a "solution-oriented" map for policymakers to understand what works, to what extent, and in what combination.