Behavioral, Information, and Monetary Interventions to Reduce Energy Consumption in Households: A Living Systematic Review and Network Meta‐Analysis
Published June 8, 2026 | Version v2
Dataset Open

Behavioral, Information, and Monetary Interventions to Reduce Energy Consumption in Households: A Living Systematic Review and Network Meta‐Analysis

Authors/Creators

  • 1. ROR icon Potsdam Institute for Climate Impact Research

Contributors

Researcher:

  • 1. ROR icon University of British Columbia

Description

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 223 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.

 

Methods

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:
    1. Monetary: Pricing, rebates, and financial rewards.
    2. Information: Energy audits, tips, and awareness campaigns.
    3. Feedback: Smart meters, home displays, and redesigned utility bills.
    4. Social Comparison: Benchmarking household use against peers (e.g., Home Energy Reports).
    5. Motivation: Goal setting, commitments, and gamification.
  • Methodological Framework: The data includes about 700 effect sizes 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.

Series information

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 upto 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.

Files

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Additional details

Related works

Is new version of
Publication: 10.1002/cl2.1424 (DOI)
Dataset: https://github.com/tarunMkhanna/LSR-HouseholdEnergy (URL)

Software

Repository URL
https://github.com/tarunMkhanna/LSR-HouseholdEnergy