Estimation of global rebound effect caused by energy efficiency improvement

Wei, Taoyuan; Liu, Yang

doi:10.1016/j.eneco.2017.05.030

Cited by 134 publications

(38 citation statements)

References 23 publications

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“…In Model 2, we find that households with central heating systems equipped with programmable thermostats, a technical approach to managing residential energy consumption, consume 170% more natural gas than those without this type of equipment. This result, which is consistent with earlier findings by Adua et al (2016) and other studies documenting rebound effects (Brookes 1990, Brannlund et al 2007, Greening et al 2000, Wei and Liu 2017, pertain to Model 3 too: homes with central heating systems equipped with programmable thermostats consume about 8% more total residential energy (electricity and natural gas) than homes without this type of equipment. Several other indicators of efficiency innovations are not related to energy consumption, which, in itself, is anticipated under the political-economy perspective.…”

Section: Efficiency Innovationssupporting

confidence: 91%

“…They suggest technological innovations may help, but only marginally. The marginality of these impacts may be a function of the well-documented rebound effect that often accompanies efficiency improvement in resource utilization (see Brookes 1990, Alcott 2005, Brannlund et al 2007, Greening et al 2000, Wei and Liu 2017.…”

Section: Discussionmentioning

confidence: 99%

“…Yet, whether or not energy efficiency actually leads to the expected results remains inconclusive. Substantial research, including William Stanley Jevons's groundbreaking work on coal in the 1860s, suggests that efficiency gains in the use of a resource often paradoxically lead to increased consumption (Jevons 1906, Brookes 1990, 2000, Greening et al 2000, Clark and Foster 2001, Alcott 2005, Brannlund et al 2007, York et al 2005, Adua et al 2016, Wei and Liu 2017. Jevons (1906, p 140) stressed that 'it is wholly a confusion of ideas to suppose that the economical (efficient) use of fuel is equivalent to diminished consumption.…”

Section: Introductionmentioning

confidence: 99%

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Even for the environment, context matters! States, households, and residential energy consumption

Adua

Clark

2019

Environ. Res. Lett.

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This study adopts a multi-level approach to examine the extent to which state-and household-level factors shape residential energy consumption in the United States, focusing on efficiency improvement and affluence. Analyzing the 2009 Residential Energy Consumption Survey, state-level energy efficiency data from the American Council for an Energy-Efficient Economy (ACEEE), and other sources, we find that state context significantly influences energy consumption at the household level. Households in states scoring high on energy efficiency consume significantly less residential energy than those in states scoring low on the measure. At the household level, the analysis reveals mixed relationships between investment in energy efficiency technologies and residential energy consumption, as some measures of efficiency technology are negatively related to residential energy consumption, others are positively related to it. In regard to affluence, state-level measures do not emerge as significant predictors of residential energy consumption. At the household level, however, affluence drives residential energy consumption, which, in turn, is a significant driver of carbon dioxide emissions. Our study makes an important contribution to the social scientific literature on energy consumption, illuminating distinct relationships at different levels. To the best of our knowledge, this is the first study that simultaneously examines the impacts of factors measured at both the household (micro) and state (meso) levels.

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Section: Efficiency Innovationssupporting

confidence: 91%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Even for the environment, context matters! States, households, and residential energy consumption

Adua

Clark

2019

Environ. Res. Lett.

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“…Taking both sides into account, studies validating elasticities with historical data and the use of more sophisticated methods (i.e. causality identification) and sensitivity analyses would improve the reliability of studies [Saunders, 2013], [Wei and Liu, 2017], . Explicit and endogenous representations of energy efficiency improvements could also reduce bias in estimates [Hunt et al, 2014], [Witajewski-Baltvilks et al, 2017].…”

Section: Combined Insightsmentioning

confidence: 99%

The Rebound Effect and its Representation in Energy and Climate Models

Montero

Löschel²,

Madlener

2018

SSRN Journal

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Standard-Nutzungsbedingungen:Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden.Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen.Sofern die Verfasser die Dokumente unter Open-Content-Lizenzen (insbesondere CC-Lizenzen) zur Verfügung gestellt haben sollten, gelten abweichend von diesen Nutzungsbedingungen die in der dort genannten Lizenz gewährten Nutzungsrechte. AbstractIn this paper, we review the state-of-the-art and common practice of energy and climate modeling visa-vis the rebound literature, in particular regarding how macroeconomic energy and climate models quantify and include energy and greenhouse gas rebound effects. First, we focus on rebound effects in models of costless energy efficiency improvement that hold other attributes constant (zero-cost breakthrough), and an energy efficiency policy that may be bundled with other product changes that affect energy use (policy-induced efficiency improvement) (Gillingham et al. 2015). Second, we examine macroeconomic studies focusing on energy efficiency both in industry and in private households. Third, we go through a general theoretical revision from micro-to macroeconomic levels (the aggregation level) to include a review of the so-called meso-level studies (focused on the analysis of the production side). From 118 recent studies along the aggregation level, out of which 25 compute rebound calculations, we find that the average energy rebound effect is 58% with a standard deviation of 58%, and when we include green house gas rebound calculations, the magnitude is of the order of 43% with a standard deviation of 55%. Finally, we argue that the rebound effect is a phenomenon that requires a sound understanding of the complex interactions from different dimensions (e.g. aggregation level, heterogeneity, climate, energy conservation and economic growth), and we provide some ideas and motivations for future research. JEL ClassificationE13, Q410, Q430, Q48, Q540, R13

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“…For this reason, the benchmarking results of the SDEWES Index for the present sample of 18 SEE cities and any cities in the city pairs are compared to broader contextual factors. Such contextual factors are assessed based on urban hierarchy according to population [136] and the development of the country in which the city is located. Other factors include the set of local characteristics, opportunities, and concerns [137].…”

Section: City Pairings and Pattern Identificationmentioning

confidence: 99%

Benchmarking South East European Cities with the Sustainable Development of Energy, Water and Environment Systems Index

Kılkış¹

2017

J. sustain. dev. energy water environ. syst.

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Tools that can benchmark cities, including cities in South East Europe, are necessary to enable the comparison and diffusion of more sustainable practices for urban systems. The "Sustainable Development of Energy, Water and Environment Systems Index" provides a composite indicator for benchmarking city performance based on 7 dimensions and 35 main indicators. In this research work, the Index is applied to a new sample of 18 cities in South East Europe for which data is collected, normalized, and aggregated. Klagenfurt (3.08), Velenje (3.06) and Pécs (3.01) are found to be the top three cities in the sample while an average city receives an index score of 2.85. The results are further compared to reference averages and evaluated based on the mean simulated values of 10,000 Monte Carlo experiments. The results are interpreted in quartiles for pioneering, transitioning, solution-seeking, and challenged cities. The results are then applied within a benchmarking tool of the Index that supports policy learning to trigger collaboration between cities and further used to match cities according to a search algorithm based on index performance. In addition, the results are compared to urban hierarchy as well as development contexts and mapped onto the spatial dimension as an initial step for enabling a "Sustainable Development of Energy, Water and Environment Systems Future City Network". The paper concludes with a set of four proposed steps to enable decision-makers and urban planners in using the Sustainable Development of Energy, Water and Environment Systems Index in support of more sustainable urban systems.

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Estimation of global rebound effect caused by energy efficiency improvement

Cited by 134 publications

References 23 publications

Even for the environment, context matters! States, households, and residential energy consumption

Even for the environment, context matters! States, households, and residential energy consumption

The Rebound Effect and its Representation in Energy and Climate Models

Benchmarking South East European Cities with the Sustainable Development of Energy, Water and Environment Systems Index

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