家庭用水使用アプリで節水促進(Home Water-use App Improves Water Conservation)

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2025-05-23 カリフォルニア大学リバーサイド校(UCR)

カリフォルニア大学リバーサイド校の研究によると、スマート水道メーターと連携したアプリ「Dropcountr」を使うことで、家庭の水使用量が平均6%、高使用世帯では最大12%削減可能であることが判明した。アプリはユーザーにリアルタイムの使用量や漏水警告を通知し、近隣世帯との比較などの情報を提供。行動経済学のナッジ理論を応用し、節水を促進する設計。調査はカリフォルニア州フォルサム市で2013~2019年にかけて実施された。

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高頻度分析と家庭の水消費: 不均一な影響の推定 High-frequency analytics and residential water consumption: Estimating heterogeneous effects

Mehdi Nemati, Steven Buck, Hilary Soldati
Resource and Energy Economics  Available online: 14 May 2025
DOI:https://doi.org/10.1016/j.reseneeco.2025.101500

家庭用水使用アプリで節水促進(Home Water-use App Improves Water Conservation)

Highlights

  • High-frequency online Home Water Use Reports (HWURs) lead to a 6.2 % reduction in water consumption for a typical household enrolled in the program.
  • We estimate significant heterogeneity in the treatment effects depending on the day of the week, the content of push notifications, and the baseline consumption levels.
  • Our analysis indicates that leak alerts are particularly effective at reducing water consumption immediately after they are issued
  • We illustrate how mean reversion can bias a naïve panel estimator for heterogeneous treatment effects when heterogeneity arises from the outcome variable.

Abstract

This paper estimates how high-frequency online Home Water Use Reports (HWURs) affect household-level water consumption. The HWURs under the study share social comparisons, consumption analytics, leak alerts, and conservation information to residential accounts, primarily through digital communications. The data utilized in this paper is a daily panel dataset that tracks single-family residential households from January 2013 to September 2019. We found a 6.2 % reduction in average daily household water consumption for a typical household enrolled in the program. We estimate heterogeneous treatment effects by the day of the week, the content of push notifications, and baseline consumption quintile. For the latter, we provide an illustrative test to emphasize how mean reversion can severely bias a naïve panel data estimator for heterogeneous treatment effects when the source of heterogeneity is the outcome variable. We also find evidence that leak alerts effectively reduce water consumption immediately following the alert.

1603情報システム・データ工学
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