The rebound effects of switching to vegetarianism. A microeconomic analysis of Swedish consumption behavior

Grabs, Janina

doi:10.1016/j.ecolecon.2015.04.030

Cited by 59 publications

(38 citation statements)

References 44 publications

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“…[]). From this income‐based split of EXIOBASE household final consumption, we estimated product‐specific total expenditure elasticities and marginal spending shares

{boldy}^{m a r}

using the log‐log model (Haque ; Grabs ), where

{boldy}^{m a r}

sums to 1 for each column. Finally, the additional final demand due to the substitution effect

y^{sub}

is estimated by (note that the calculation is done for each product of the system o ) (equation ):

\begin{matrix} true \\ right y_{o}^{s u b} & = & left p ((), bold1 boldy - bold1 {boldy}^{r e d}) \frac{y_{o}^{m a r}}{\sum_{g} y_{g}^{m a r}} \forall ((), normalo \in normalg) \\ right y_{o}^{sub} & = & left 0 \forall ((), normalo \notin normalg) \end{matrix}

…”

Section: Methodsmentioning

confidence: 99%

“…Penetration rate -assumed uptake rate per sector/product of q t y ) final demand -column vector of size (z); t A j ) interindustry structure -column vector of size (z) for each effected industry j; t s ) efficiency improvements -row vector of size (z) Tisserant et al [2017]). From this income-based split of EX-IOBASE household final consumption, we estimated productspecific total expenditure elasticities and marginal spending shares y mar using the log-log model (Haque 2006;Grabs 2015), where y mar sums to 1 for each column. Finally, the additional final demand due to the substitution effect y sub is estimated by (note that the calculation is done for each product of the system o) (equation 5):…”

Section: Consumption Pattern Change or Consumption Reduction And Rebmentioning

confidence: 99%

See 1 more Smart Citation

Prioritizing Consumption‐Based Carbon Policy Based on the Evaluation of Mitigation Potential Using Input‐Output Methods

Wood¹,

Moran²,

Stadler³

et al. 2017

J of Industrial Ecology

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SummaryCarbon footprints aim to engage consumers in contributing to climate-change mitigation. Consumption-oriented policy measures attempt to cause voluntary or incentivized interventions that reduce environmental impact through the supply chain by utilizing demand drivers. A large body of life cycle assessment studies describe how specific actions can reduce the environmental footprint of an individual or household. However, these assessments are often conducted with a narrow focus on particular goods and processes. Here, we formalize a counterfactual method and operational tool for scoping the potential impact of such actions, focusing on economy-wide impact. This "quickscan" tool can model shifts and reductions in demand, rebound effects (using marginal expenditure), changes in domestic and international production recipes, and reductions in the environmental intensity of production. This tool provides quick, macro-level estimates of the efficacy of consumer-oriented policy measures and can help to prioritize relevant policies. We demonstrate the method using two case studies on diet and clothing using the EXIOBASE3 multiregional input-output database, giving spatially explicit information on where environmental impact reductions of the interventions occur, and where impacts may increase in the case of rebounds. Keywords:carbon footprints climate policy industrial ecology input-output analysis life cycle assessment (LCA) rebound Supporting information is linked to this article on the JIE website

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“…[]). From this income‐based split of EXIOBASE household final consumption, we estimated product‐specific total expenditure elasticities and marginal spending shares

{boldy}^{m a r}

using the log‐log model (Haque ; Grabs ), where

{boldy}^{m a r}

sums to 1 for each column. Finally, the additional final demand due to the substitution effect

y^{sub}

is estimated by (note that the calculation is done for each product of the system o ) (equation ):

\begin{matrix} true \\ right y_{o}^{s u b} & = & left p ((), bold1 boldy - bold1 {boldy}^{r e d}) \frac{y_{o}^{m a r}}{\sum_{g} y_{g}^{m a r}} \forall ((), normalo \in normalg) \\ right y_{o}^{sub} & = & left 0 \forall ((), normalo \notin normalg) \end{matrix}

…”

Section: Methodsmentioning

confidence: 99%

Section: Consumption Pattern Change or Consumption Reduction And Rebmentioning

confidence: 99%

Prioritizing Consumption‐Based Carbon Policy Based on the Evaluation of Mitigation Potential Using Input‐Output Methods

Wood¹,

Moran²,

Stadler³

et al. 2017

J of Industrial Ecology

View full text Add to dashboard Cite

show abstract

“…Moreover, [Bjelle et al, 2018] examined a set of 34 possible behavioral actions to be undertaken in Norwegian households; they found that people could potentially reduce their carbon footprint by 58%. In Sweden, Grabs [2015] calculated that switching to a vegetarian diet can save 16% of energy use and lower greenhouse gas emissions by 20% related to their dietary consumption, with corresponding energy RE of 96% and GHG rebounds of 49%. However, this study only focused on income effects.…”

Section: Energy Efficiency Improvement As Preference: Change In Consumentioning

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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“…Several studies also highlight that the rebound effect is heterogeneous. For example, Grabs (2015) investigated the rebound effects associated with a shift to a vegetarian diet. The analysis was performed for different income deciles in Sweden, and the study found that the GHG rebound effect was 87% in the lowest income decile, while only 25% in the highest one (Grabs et al 2015).…”

Section: Focus On the Effectiveness Of Interventions: The Role Of Thementioning

confidence: 99%

“…For example, Grabs (2015) investigated the rebound effects associated with a shift to a vegetarian diet. The analysis was performed for different income deciles in Sweden, and the study found that the GHG rebound effect was 87% in the lowest income decile, while only 25% in the highest one (Grabs et al 2015). Based on the German Mobility Panel, a household-level travel survey, Frondel and colleagues (2012) investigated rebound effects in private transport and found that the rebound effect following efficiency improvements is significantly higher in households with low mileage than in households with high vehicle mileage.…”

Section: Focus On the Effectiveness Of Interventions: The Role Of Thementioning

confidence: 99%

Perspectives on quantifying and influencing household metabolism

Harder

Dombi

Peters

2016

Journal of Environmental Planning and Management

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Household metabolism is a concept that is concerned with the analysis of stocks and flows of energy, matter, and information at the household scale. This paper starts by providing a brief overview of the concept of household metabolism. Rather than attempting an in-depth review and analysis of this field, the article subsequently maps the contributions and perspectives of a broad variety of research traditions that have an interface with the concept of household metabolism. Next, this paper highlights a number of controversial issues connected to household metabolism, and studies at the interface of household metabolism. Finally, this paper argues that the concept of household metabolism can provide valuable help in diagnosing misalignments between enacted system dynamics and expressed societal goals, and can help to design research that facilitates their alignment.

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The rebound effects of switching to vegetarianism. A microeconomic analysis of Swedish consumption behavior

Cited by 59 publications

References 44 publications

Prioritizing Consumption‐Based Carbon Policy Based on the Evaluation of Mitigation Potential Using Input‐Output Methods

Prioritizing Consumption‐Based Carbon Policy Based on the Evaluation of Mitigation Potential Using Input‐Output Methods

The Rebound Effect and its Representation in Energy and Climate Models

Perspectives on quantifying and influencing household metabolism

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