Worker mobility and the purchase of low CO2 emission vehicles in France: a datamining approach.

AuthorBoroumand, Raphael Homayoun
  1. Introduction

    The emissions of greenhouse gases (GHG) from transport have grown substantially in recent years, and projections for 2020-2030 are pessimistic (Perez-Suarez and Lopez-Menendez, 2015). In many countries of the European Union (EU) such as France, this growth may even thwart the aim of complying with the 3x20 environmental rule1; it is a central component of the EU's growth strategy for 2020 (EU, 2010). Moreover, it is a long-established finding that CO2 emissions from the most polluting vehicles are directly responsible for a growing number of cancers and respiratory diseases (Dockery et al., 1993; Fullerton et al., 2008). During the year 2012 alone, 7 million premature deaths were attributed to air pollution exposure (WHO, 2014). Particular attention must also be devoted to the emission of fine particles from diesel vehicles (Longhin et al., 2016). Studies linking health and air pollution report that particulate matter (PM) and black carbon emissions from incomplete fuel combustion are responsible for long-term mortality (Bond et al., 2013), and cindynics2 has provided insights on this matter that can be incorporated into risk analysis models (Fann et al, 2012; Smith et al., 2013).

    Designed as a component of the new environmental science, the study of territories and, specifically, the negative externalities related to car use in urban areas have already generated good results and helped to provide strong conclusions (Parry et al., 2007). Since the 1990s3, the environmental protection of territories has fully included new binding practices for reducing waste, protecting the environment and maintaining an optimal quality of life, which includes the preservation of air quality (IPCC, 2014).

    The dominant stream of literature focuses on describing the positive externalities associated with control over transport. Indeed, while industry, agriculture and services are all responsible for the growth of CO2 emissions, the most important component of air quality degradation comes from transport (Curiel-Esparza et al., 2016; Rustemoglu and Andrs, 2016; Davydova-Belitskaya and Skiba, 2003).

    Studies that go deeper into identifying the causes of carbon emissions stress the key role played by factors such as population change (Schellnhuber and SvirejevaHopkins, 2008), economic growth per capita, regional energy intensity, the contribution of regional fuel mix and energy and carbon intensity (Gonzalez et al., 2014; Remuzgo and Sarabia, 2015). Other specific papers over the 1995-2015 period strictly demonstrate the strong relationship between the per capita income of urban territorial units and the pollution burden they generate (4): Boyce, 1994; Torras and Boyce, 1998; Scruggs, 1998; Boyce et al., 1999; McGranahan and Satterthwaite, 2002; Hedenus and Azar, 2005; White, 2007; Kovacs et al., 2013; Zwickl et al., 2014; Holian and Kahn, 2015.

    If we restrict our attention to the analysis of air pollution factors in territorial units (regions or sub-regional units), in addition to socioeconomic data collection to establish a typography of transport users' polluting practices in urban areas (Buchs and Schnepf, 2013; Chancel, 2014), it is crucial to have data on the composition of the private car fleet (Agyemang-Bonsu et al., 2010; Kholod et al, 2016). This difficulty is not always easy to overcome because state censuses of private parking facilities, such as the COPERT 44 databases for Europe and parts of America and Asia, are heterogeneous and incomplete due to their practice of compiling public data that are not published on a regular basis. Alternative approaches proposed in various studies such as photographs and videos of roads and parking lots (Kholod et al., 2016) provide valuable additional information, but their accuracy is insufficient if the aim is to evaluate the level of pollution caused by a country's passenger car fleet. For example, data from the COPERT 4 file distinguish among passenger cars, light commercial vehicles, trucks, buses, motorcycles and mopeds. However, the file contains no data on the tax horsepower or engine types of passenger cars. However, several studies claim that among the factors affecting pollution from transport emissions in urban areas, it is most important to consider the composition of the fleet and parking restrictions (Kholod et al., 2016), if possible through composite indicators (Kilkis, 2016) or multidisciplinary studies (Venkatesh et al., 2014).

    Only a public census over a long period of time enables the effective application of data relating to fleets and behavior to measure progress in addressing air pollution from transport. Recall that a 2010 estimate indicated that the share of transport in total global greenhouse gas emissions was 23%, with the corresponding figure for the US being 25% (Sims et al., 2014). Automobile manufacturers in the EU have already implemented innovations to significantly reduce the fuel consumption of passenger cars (Fontaras and Dilara, 2012). However, the overall volume of fuel combustion and corresponding pollution from transport may continue to grow if forecasts indicating a substantial increase in the number of vehicles per resident of a territorial unit, city, region or state continue to hold (IEA/OECD, 2010). Indeed, forecasts from the IPCC indicate that the number of vehicles will double by 2030 and triple by 2050, bringing the total number of light-duty vehicles worldwide to 2 billion (Kahn Ribeiro et al., 2007). Given these uncertainties, two political interventions can be recommended: first, to encourage lower-emissions transport choices related to individual mobility as carpooling, widespread public transportation use, rail system use, and alternative scenarios (Vermote et al., 2013); and, second, promote technological innovation to optimize vehicles and make them less polluting (Palencia et al., 2012). However, in the 1990s, the EU initiated a new policy: moving from a petrol-driven (5) car market to a diesel-driven one. Studies have demonstrated that the potential gains from this industrial policy choice are small and overcompensated by, first, the massive CO2 emissions attributed to the associated supply chain and, second, the resulting increase in black carbon particles because the first generation of diesel vehicles were not equipped with particulate filters (Cams and Helmers, 2013). Worse, the dieselization of the European fleet allowed buyers of passenger cars with diesel engines to affect future savings on their vehicle purchases. With an average of -35% in fuel combustion efficiency for a type-A diesel vehicle compared to gasoline vehicles of the same type, buyers can afford to buy more powerful and therefore more polluting cars (Schipper and Fulton, 2009).

    On a purely behavioral level, studies seeking to better understand the determinants of the choice of commuter transport modes are numerous. Some incorporate into model inputs regarding the possible degree of leisure during the trip, comfort, ease of postponing travel or the cost related to the daily commute to work (Wang et al. 2013; Chowdhury et al., 2015; Peer et al., 2016). Others identify the quality and the security of route changes as levers that could encourage the use of public transport instead of private cars (Kingham et al., 2001). It appears that commuters' age and income influence passenger car purchasing behavior and, therefore, their choice of transport mode (Liu et al., 2016). However, if the ease of access to transport infrastructure and the route practicability are clearly identified as factors affecting the satisfaction of workers with mobility requirements (Wang and Wang, 2016), the literature rarely includes the exact distribution of daily commuting methods used by a population based on geographic data at the level of the territorial unit. There are studies demonstrating the difficulties in commuting to work related to geographical factors (Mackett and Thoreau, 2015) and others that offer a high degree of detail on the modes of transport used daily by commuters (St-Louis et al., 2014). However, few studies are able to combine behavioral data, national vehicle fleet data and geographical data.

    In an original approach using datamining and clustering, justified in section 2.2, our paper fills a major gap in the literature by providing a highly detailed study and results regarding the level of pro-environmental behavior by French commuters when making a car purchase. Global and national studies highlighting the cause and effect relationship between fuel combustion and the environmental and health problems are now sufficiently numerous and precise to promote environmental awareness among consumers. However, few of these studies are directly applicable to France, where the administrative division of the territory is an intricate and historical legacy with high impact in the implementation of energy policies. Our working hypothesis is that in spite of the great geographical and behavioral heterogeneity of the French territorial units, it is possible to establish a new basic system to implement environmental and industrial policies respectful of the diversity of the population (i.e., departmental units in our paper). The first objective of this research is to document and highlight the specific behaviors and trends among the French in terms of labor mobility, that is to say, mobility linked to job requirements (distance, frequency, infrastructure, etc.). The second objective of this paper is to shape a non-driven geographical classification of French departmental territorial units, based on both mobility behavior and passenger car fleet composition.

    The paper is structured as follows. Section 1 discusses the level of detail available in extant studies on worker mobility. Section 2 describes the conceptual framework and specifies the data and methodology. Section 3 develops the empirical results and economic discussions. Section 4 concludes...

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