Trond Husby (p)

Local air quality in European cities is receiving increased interest in policy and research. The European Environment Agency estimates that exposure to particulate matter, ozone and nitrogen dioxide was responsible for nearly 500.000 premature deaths across the EU28 in 2012. At the same time, recent smog events have gained substantial media attention, sometimes prompting drastic policy interventions such as the two-day ban on diesel cars entering Oslo in January 2017. In order to develop appropriate policy responses there is a need for a thorough understanding of the main sources of pollution.

Traditionally, air pollution modelling has focused primarily on the technology behind emissions: conventional source apportionment methods typically tries to attribute emissions from transport to different vehicle types and combustion technology. However, the conventional methods neglect an important driver of emissions - namely citizens' daily activities. We address this shortcoming, putting citizens' daily activities at the centre of our study. More specifically, we focus on how citizens' daily activities affect pollution levels through travel behaviour. However, citizens' decisions to do not occur in a vacuum. Commuting generally takes place between locations with a high density of homes and locations with a high density of work places; the choice of commuting mode is constrained by available parking lots, public transport accessibility and so on. Tracking the emissions from daily activities of citizens requires a fine-grained analysis that places citizens' decisions in the spatial context of the city.

We present ongoing work with an activity-based model of the metropolitan region of Amsterdam (MRA). Activity-based models are grounded in behavioral theories about how people schedule their daily activities in the presence of constraints. Decisions encompass where to participate in activities, when to participate in activities, and how to get to these activities. The presentation will be divided into two parts. First we present the basis of the activity-based model, namely the synthetic population of individuals allocated into households and dwellings located within the MRA. We discuss the underlying data sources, the methodology used for the population synthesis and the fit between the synthetic population and observed micro data. Second, we describe the structure of the activity-based model, presenting data sources and initial results of calculations of emissions based on activities.

Aline Magalhaes (p), Gilvan Guedes, Edson Domingues, Isabel Ferreira, Renata Barreto

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Stuart McIntyre (p)

A large body of literature has explored the production side of the energy sector at a regional level, and more generally geographic variation in electricity production and employment. Meanwhile, little attention appears to have been paid to exploring whether regional differences exist on the consumer side. In this paper we begin by exploring the available aggregate data for each UK region to better understand whether there exists evidence of differences in household behaviour with respect to energy consumption and use. Given differences in the composition of households across UK regions, we then estimate a demand model which controls for observable characteristics of households to explore whether there is any evidence of regional differences between how households' electricity and gas demands respond to changes in energy prices and household income. This is done using the UK Living Cost and Food (LCF) survey and estimate a Quasi-Almost Ideal Demand System. Evidence on whether there are these regional differences is important given the wide variety of energy-economy-environment modelling that takes place at a regional level, particularly in the UK. More generally, evidence of regional variation in how households respond to changing energy prices has important implications for the use of price (through applying a tax on the use of energy) as an instrument of energy policy. In the UK, there are a number of dimensions of energy policy which, while intended to be spatially blind, have a distinct spatially differentiated impact. This makes understanding any regional differences important in understanding the impact of these policies.