Consumer End-Use Energy Efficiency and Rebound Effects

By Inês Lima Azevedo. 2014

136 citations recorded by the source corpus.

Subfields: Pollution, Renewable Energy, Sustainability and the Environment

View Paper: https://www.semanticscholar.org/paper/eead1a63cd8dabf811ed87dd45291e8906e16e2e

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In Pollution

In Renewable Energy, Sustainability and the Environment

Citations in the corpus

Transforming Consumption: From Decoupling, to Behavior Change, to System Changes for Sustainable Consumption (2015, background): “, strong sustainable consumption researchers argue convincingly for a fuller understanding and appreciation of the logics and decision processes of the actors—consumers, business, and government—and the systemic nature of so-called lock-in of unsustainable activity.”
Tackling Climate Change with Machine Learning (2019, background): “[303] Inês ML Azevedo.”
The dynamics of behavior change: Evidence from energy conservation (2016, background): “Energy rebounds are known and commonly discussed in the iterature on energy efficiency (Gillingham et al., 2013; Azevedo, 2014), but to our knowledge, this is the first direct evidence f a rebound at the appliance level.”
How to deal with the rebound effect? A policy-oriented approach (2016, background): “Other relevant studies include the works of Sorrell (2007), the International Risk Governance Council (IRGC, 2013); Azevedo (2014), Ouyang et al. (2010), Levett (2009), Herring (2011), FreireGonzález and Puig Ventosa (2015).”
A pathway to circular economy: Developing a conceptual framework for complex value assessment of resources recovered from waste (2017, background): “…growth and thus higher energy use at the macro level (Herring, 1999); other documented rebound effects include the introduction of lowenergy light bulbs leading to people counterproductively having the lights switched on for longer, therefore leading to increased energy consumption (Azevedo, 2014).”
Beyond Technology: Demand-Side Solutions for Climate Change Mitigation (2016, background): “In turn, carbon (or fuel) pricing remains a necessary component of an effective mitigation policy portfolio, inter alia, to avoid or reduce rebound effects associated with efficiency improvements (13).”
Aligning artificial intelligence with climate change mitigation (2022, unknown): “This can eat into GHG benefits from efficiency gains or even counteract them 95 .”