Can Private Vehicle-augmenting Technical Progress Reduce Household and Total Fuel Use?

Figus, Gioele; Swales, John; Turner, Karen

doi:10.1016/j.ecolecon.2017.10.005

Cited by 10 publications

(6 citation statements)

References 17 publications

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“…Overall, we consider the lack of a well-functioning market to be a moderate-level risk, but the impact could be signi cant if the effects of behaviour change programmes are not sustained or not scalable. Macroeconomic modelling (Figus et al, 2018) suggests that reductions in fossil fuel use for private transport will not be achieved through technical e ciency improvements, but probably require either travel mode switching or wholesale substitution of fossil fuel by renewables. The three high-level risks are optimism bias arising in the innovation category, changing policy and regulatory framework occurring in the political category, and operational failure in the technical category.…”

Section: Stages 2-3: Nine Moderate-level Risksmentioning

confidence: 99%

Energy Demand Reduction as a Supply Chain: What are the Risks?

Axon

Darton

2023

Preprint

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Demand Reduction is a strategy with the potential to make a significant contribution to the energy supply/demand balance. Its two major themes are improving the energy efficiency of devices (appliances and processes) and changing people’s behaviour towards using less energy. In our analysis of a nation’s energy security, we treat Demand Reduction as an additional fuel which delivers ‘negafuel’, allowing a particular level of energy services to be met at a lower volume of supply than would be possible in its absence. In common with other fuels, negafuel is delivered by a supply chain with linked stages, all encountering risks of various types. A comprehensive survey of these risks in a case study of the UK, shows that Demand Reduction belongs to a middle-ranking group of fuels in terms of overall risk. High-level risks encountered include the difficulty of assessing and delivering potential energy savings, the rate of building construction at the highest energy efficiency standards, optimism bias, changing policy and regulation, and operational failure (both of technology and policy). Assessing the risk of Demand Reduction as a supplied negafuel focuses attention on specific risks requiring mitigation, facilitating design of better policy and more effective commercial products.

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Section: Stages 2-3: Nine Moderate-level Risksmentioning

confidence: 99%

Energy Demand Reduction as a Supply Chain: What are the Risks?

Axon

Darton

2023

Preprint

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“…In equation (3), Γ p and Γ l are the elasticities of energy in production and employment, both with respect to a change in labour efficiency. Using results given in Figus et al [25], these input-use elasticities can be expressed as functions of the elasticity of demand for the product (η), the elasticity International Journal of Energy Research of substitution between labour and energy in production (σ), and the share of labour in production (s):…”

Section: Theoretical Backgroundmentioning

confidence: 99%

Examining the Interplay of Labour Productivity Policies and Industrial-Energy-Environmental Policy Goals

Ross,

McGregor,

Swales

et al. 2023

International Journal of Energy Research

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The academic and policy-oriented literature increasingly quantifies the wider effects of energy policy on the macroeconomy. However, the spillovers from economic policies to energy use are less frequently recognised, meaning that many policymakers who strive to improve productivity whilst simultaneously targeting emission reduction fail to consider these interactions. This paper addresses this issue by using simulation results generated by introducing an exogenous increase in labour productivity in a computable general equilibrium model calibrated with data from the United Kingdom. Theoretical analysis suggests that increasing labour productivity can have positive or negative effects on employment levels and energy use. However, the simulation results show that in the context of a developed open economy, improved labour efficiency will increase employment, a key policy objective, but simultaneously increase energy use. A key policy implication is that this work highlights the need for policy frameworks that explicitly acknowledge and quantify the interconnections between national economic policy strategies and energy policy objectives. Secondly, it shows that in practice, policies to reduce carbon emissions need to be strengthened alongside policies to improve productivity successfully implemented.

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“…(3) Revised models assumed perfect competition, except Figus et al [2017b], Figus et al [2018]. For (4) and (5), mobile representation of capital between national sectors, investments, and labor increase gradually.…”

Section: Macroeconomic Simulation Modelsmentioning

confidence: 99%

“…For (4) and (5), mobile representation of capital between national sectors, investments, and labor increase gradually. (6) Recent models are not only dynamic, such that they capture consumer's responsiveness [Figus et al, 2017b], [Figus et al, 2018] [Chang et al, 2018], [Bye et al, 2018], [Duarte et al, 2018], including consumer response to price changes in time, but are also regional-specific (or spatial CGE models) [Helgesen et al, 2018]. (7) To represent energy and non-energy goods, CES/Cobb douglas functions are commonly used and inputs in the energy sector are usually modeled as Leontief composites, with no possibility of substitution, in RE studies assessed in this overview.…”

Section: Macroeconomic Simulation Modelsmentioning

confidence: 99%

“…More recently, heterogeneity has played an important role in studies, disaggregating specific energy-intensive and less energy-intensive energy services (e.g. public vs. private transport or fossil fuel-vs. renewable-sourced heating), and including the representation of durable goods/investments within energy service sectors could provide more precise policy advice [Ryan et al, 2017], [Figus et al, 2018]. Figure 3 shows RE magnitudes obtained in ex-ante studies examined in this review.…”

Section: Ex-post Studiesmentioning

confidence: 99%

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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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Can Private Vehicle-augmenting Technical Progress Reduce Household and Total Fuel Use?

Cited by 10 publications

References 17 publications

Energy Demand Reduction as a Supply Chain: What are the Risks?

Energy Demand Reduction as a Supply Chain: What are the Risks?

Examining the Interplay of Labour Productivity Policies and Industrial-Energy-Environmental Policy Goals

The Rebound Effect and its Representation in Energy and Climate Models

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