Influence of Rebound Effect on Energy Saving in Smart Homes

Chen, Ko-jung; Lu, Ta-Ping; Rau, Pei-Luen Patrick; Huang, Ding-Long

doi:10.1007/978-3-319-92252-2_21

Cited by 4 publications

(4 citation statements)

References 15 publications

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“…The types of sufficiencyoriented PSS in this review primarily aim to reduce private and/or single-use consumption through optimization of product ownership as a sharing system, a leasing system, or a mix of both. One PSS, smart home assistance (Chen et al, 2018), encourages consumers to question consumption. The inclusion of RE in these studies either 1) describe the potential for RE to occur in qualitative terms (Iran & Müller, 2020;Harris et al, 2021;Siderius & Poldner, 2021); 2) anticipate RE behavior in the calculation of the environmental impact of the PSS (modeling the behavior exogenously) in ex-ante simulations (Briceno et al, 2005;Chen & Kockelman, 2016;Menon & Mahanty, 2015;Tsuji et al, 2015); or 3) estimate the RE magnitude ex-post using environmental accounting methods (Amutani et al, 2020;Font Vivanco et al, 2015;Yin et al, 2018;Warmington-Lundström & Laurenti, 2020).…”

Section: Resultsmentioning

confidence: 99%

“…This kind of effect is also called price or income effect (Metic & Pigosso, 2022). An example of a re-spending effect is using the monetary savings of a smart-home PSS to consume more energy through increased household temperatures or brightness (Chen et al, 2018). Over half of the RE mechanisms (53.33%, 8 of 15 instances) are related to re-spending of financial resources (Briceno et al, 2005;Chen & Kockelman, 2016;Menon & Mahanty, 2015;Yin et al, 2018;Iran & Müller, 2020).…”

Section: Rebound Triggers and Mechanismsmentioning

confidence: 99%

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Uncovering rebound effects of sufficiency-oriented product-service systems: a systematic review

Andrew,

van den Bergh,

Pigosso

2024

Proc. Des. Soc.

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The discourse surrounding sustainable consumption and production has evolved to encompass sufficiency strategies in addition to efficiency and effectiveness. Product-service systems (PSSs) can promote sufficiency by replacing traditional product-intensive systems with dematerialized services and changes in ownership structures. Sufficiency-oriented PSS may, however, generate rebound effects which offset potential sufficiency benefits or even result in backfire. This paper examines the connection between sufficiency-oriented PSS and rebound reviewing 12 empirical studies addressing rebound.

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Section: Resultsmentioning

confidence: 99%

Section: Rebound Triggers and Mechanismsmentioning

confidence: 99%

Uncovering rebound effects of sufficiency-oriented product-service systems: a systematic review

Andrew,

van den Bergh,

Pigosso

2024

Proc. Des. Soc.

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“…(2016) point out, for many low‐income households the problem is not (only) thermally poor dwellings but (also) high fuel prices, often due to regressive fuel price policies (cf., Haar, 2019). Also, there are clear cases where poorer households still have cold homes after retrofitting because they see this as an opportunity to save money by keeping the thermostat set on low (Chen et al., 2018).…”

Section: Review Results: the Rebound Effect In The Context Of Sustain...mentioning

confidence: 99%

Rebound effect and sustainability science: A review

Vivanco

Galvin

Santarius

et al. 2022

J of Industrial Ecology

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Rebound effects have been historically studied through narrow framings which may overlook the complexity of sustainability challenges, sometimes leading to badly informed conclusions and policy recommendations. Here we present a critical literature review of rebound effects in the context of sustainability science in order to (1) map existing rebound research which goes beyond mainstream approaches, (2) unveil and classify current knowledge gaps in relation to sustainability science, (3) outline a research agenda, and (4) provide a knowledge base to support the design of effective policies toward sustainable development. We analyzed the literature in accordance with seven criteria for sustainable assessment: boundary orientedness, comprehensiveness, integratedness, stakeholder involvement, scalability, strategicness, and transparency. Our review identified three main issues: (1) the failure to address the multidimensionality of rebound effects, whereby both negative and positive outcomes may arise simultaneously, (2) the shift toward absolute rebound metrics which enables the contextualization of its effect with respect to science and policy goals, and (3) a general lack of attention to behavioral effects. We conclude that addressing these issues will help rebound research gain explanatory power and relevance for key decision‐makers. We envision that with better alignment with sustainability science, future rebound research could help elucidate trade‐offs in policies, including why certain strategies such as those based on the circular economy might fall short of expectations, and why achieving key goals and targets such as the sustainable development goals is so challenging. This knowledge is crucial for promoting a prioritization of actions and a concrete transition toward sustainability.

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“…Similarly, smart appliances can generate energy use from new end-uses. Both sorts of rebound effects are typically in the order of 10-20 % (Chen, Li, Lu, Rau, & Huang, 2018;Hymel, Small, & Dender, 2010); hence they reduce the effectiveness of technological efficiency improvements without disqualifying them.…”

Section: Optimizing Existing Infrastructure With ML Is Insufficientmentioning

confidence: 99%

Machine learning for geographically differentiated climate change mitigation in urban areas

Milojevic-Dupont

Creutzig

2021

Sustainable Cities and Society

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Artificial intelligence and machine learning are transforming scientific disciplines, but their full potential for climate change mitigation remains elusive. Here, we conduct a systematic review of applied machine learning studies that are of relevance for climate change mitigation, focusing specifically on the fields of remote sensing, urban transportation, and buildings. The relevant body of literature spans twenty years and is growing exponentially. We show that the emergence of big data and machine learning methods enables climate solution research to overcome generic recommendations and provide policy solutions at urban, street, building and household scale, adapted to specific contexts, but scalable to global mitigation potentials. We suggest a meta-algorithmic architecture and framework for using machine learning to optimize urban planning for accelerating, improving and transforming urban infrastructure provision. climate change mitigation a distant goal, but also making comparisons between local-scale studies difficult (Lamb, Callaghan, Creutzig, Khosla, & Minx, 2018). The IPCC's AR5 reports knowledge gaps on urban climate action (IPCC, 2014): there is too little understanding of the magnitude of the emissions reductions from altering urban form, and emissions savings from integrated infrastructure and land use planning. New analyses are required both to understand relationships (

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Influence of Rebound Effect on Energy Saving in Smart Homes

Cited by 4 publications

References 15 publications

Uncovering rebound effects of sufficiency-oriented product-service systems: a systematic review

Uncovering rebound effects of sufficiency-oriented product-service systems: a systematic review

Rebound effect and sustainability science: A review

Machine learning for geographically differentiated climate change mitigation in urban areas

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