Measuring the Rebound Effect with Micro Data

Measuring the rebound effect with micro data

The rebound effect is the causal impact of an increase in fuel efficiency on the demand for car kilometres. The rebound effect has been studied in several different sectors (for a survey, see Greening et al. (2000)). Some recent studies have extensively focused on passenger transportation using macro data (see, Small and Van Dender (2007); Hymel et al. (2010)). However, the rebound effect is es...

Measuring congestion in data envelopment analysis with common weights

Context-Dependent Data Envelopment Analysis-Measuring Attractiveness and Progress with Interval Data

Data envelopment analysis (DEA) is a method for recognizing the efficient frontier of decision making units (DMUs).This paper presents a Context-dependent DEA which uses the interval inputs and outputs. Context-dependent approach with interval inputs and outputs can consider a set of DMUs against the special context. Each context shows an efficient frontier including DMUs in particular l...

Measuring and testing spatial mass concentration of micro-geographic data

The question of measuring and testing industrial spatial concentration based on microgeographic data with distance based methods has been addressed by Arbia (1996), Espa et al.(2010), Duranton-Overman (2005) and Marcon-Puech (2010). We discuss the basic requirements for such measures introduced by Duranton and Overman (2005) and we propose four additional requirements. We also discuss the null ...

The Rebound Effect for Passenger Vehicles

Increasingly stringent fuel economy standards will reduce per-mile driving costs and may raise vehicle miles traveled, which is referred to as the rebound effect. All previous estimates impose at least one of three behavioral assumptions: (a) fuel economy is uncorrelated with other vehicle attributes; (b) fuel economy is uncorrelated with attributes of other vehicles owned by the household; and...