Demande et potentiel de demande (2/2)

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En provenance du cours de École des Ponts ParisTech

Mobilités et véhicules électriques

33 notes

École des Ponts ParisTech

Mobilités et véhicules électriques

33 notes

L’objectif de ce MOOC est d’aider ses apprenants, quel que soit leur profil, leur formation ou leur pays d’origine, à construire une réponse personnelle à des questions comme celles-ci : - Les voitures électriques seront-elles les dernières à circuler dans les métropoles du XXI° siècle ? - Le gain environnemental associé à l’électrification des véhicules justifie-t-il des investissements massifs dans les infrastructures de charge ? - Le véhicule électrique est-il réservé aux riches des pays du Nord ? Grâce à ce MOOC, les participants pourront acquérir des connaissances issues des sciences de l’ingénieur, de la sociologie, des sciences de l’environnement, de l’économie, de la géographie et des sciences politiques pour évaluer, analyser et mettre en œuvre le déploiement des véhicules électriques là où il est pertinent. Le Groupe Renault et les écoles de ParisTech collaborent depuis bientôt 15 ans sur des sujets liés à la mobilité durable. Ensemble, ils ont créé deux Masters (Transport et développement durable en 2004, Mobilité et véhicules électriques en 2010) et l’Institut de la Mobilité Durable (IMD), en 2009, pour accompagner les mutations en cours. Le MOOC Mobilités et véhicules électriques est issu de cette histoire commune et a été élaboré à partir d'un cours délivré au sein du Master Mobilités et Véhicules Électriques, piloté par Arts et Métiers ParisTech en partenariat avec Ensta ParisTech, Mines ParisTech et l’École des Ponts ParisTech. Ce cours est en français, cependant des sous-titres en anglais sont disponibles pour toutes les vidéos et vous pouvez écrire vos messages dans le forum en anglais si vous le désirez./ This course is given in French, nevertheless English subtitles are available for all videos and you can write your posts in the forum in English if desired.

À partir de la leçon

Chapitre 3 - Analyse économique

<p>Nous utiliserons dans ce chapitre l'analyse économique pour éclairer le choix entre voiture thermique et électrique. </p><p>Après avoir présenté dans les <em>Points méthode</em> quelques outils et concepts d'analyse économique, nous utiliserons ce cadre analytique pour évaluer les coûts et les gains associés à l'électrification du parc automobile, du point de vue des acheteurs comme de celui de la société, en tenant compte à la fois du coût des batteries, des infrastructures de charge, des gains en termes environnementaux et des modifications de recettes fiscales pour l'État.<br/> Puis nous évoquerons les enjeux macroéconomiques du véhicule électrique, avant de nous intéresser à la demande et au potentiel de demande de véhicule électrique à l'échelle microéconomique.</p><p>Le chapitre se conclut bien entendu par une évaluation sous forme de quiz. Cette évaluation ne porte que sur les vidéos de cours et est notée.</p>

Point méthode - Analyse désagrégée du potentiel de demande en VE des ménages2:52

Point méthode - Modèle de choix d'équipement des ménages2:36

Demande et potentiel de demande (1/2)9:01

Demande et potentiel de demande (2/2)10:51

Rencontrer les enseignants

Virginie BOUTUEIL
Researcher
LVMT (City Mobility Transport Lab), École des Ponts ParisTech
Émeric FORTIN
Head of the Master in Transport and Sustainable Development
Direction of Studies, École des Ponts ParisTech

-This video is the second of two videos on the analysis

of demand and potential demand for electric vehicles

using the concepts and tools of economics.

The goal of this video is twofold, firstly, knowing the tools

for evaluating the potential demand for electric vehicles,

and secondly, providing a critical perspective on the analyses

and forecasts of the deployment of electric vehicles.

Assessing the potential demand for electric vehicles

for a given territory or population, i.e. the level of demand

that a product may expect to reach in the future on this territory

or in this population, proves very difficult.

This is notably due to the complexity of interactions

within mobility systems in general, and electric ones in particular,

to the uncertainty about the main parameters

affecting the conditions of deployment of electric mobility,

energy prices for example, and to the complex and uncertain game

of the parties involved in this deployment.

The results of assessing the potential demand

are therefore uncertain, all the more so

as the considered geographic scope is large,

and the projected time horizon is remote.

In 2009, estimates of the potential demand for electric vehicles

ranged from 8 to 50% of new global vehicle sales by 2030,

and from 10 to 90% of new global vehicle sales by 2050.

Two types of approaches may be used for assessing the potential demand

for electric vehicles, each type corresponding

to different goals.

The first type, aggregate approaches to potential demand,

is essentially used to identify long-term trends

in vehicle purchase behavior, therefore in the market shares

of innovative technology such as electric vehicles,

generally on broad scopes.

However, it does not inform about the characteristics

of potential buyers, or about their preferences.

Other methods, that we will refer to as disaggregate approaches

to potential demand, are used to identify the characteristics

of potential buyers, their location if necessary,

their preferences, and so on.

Aggregate analyses of potential demand for electric vehicles

consider demand from a group as a whole,

for example all French households,

and study what part of this whole is compatible with the characteristics

of the new product or service considered.

Diffusion models fall into this category.

The Bass diffusion model, developed in the 1960s,

and widely used in marketing and management sciences,

links the diffusion of a new product or service

firstly to an inclination of potential buyers to innovation,

and secondly to an inclination of the same buyers

to imitate those having already purchased the product or service.

Analyses of compatibility of practices

generally provide an assessment of potential demand

for electric vehicles based on the segmentation

of daily journeys into distance classes.

If two thirds of company vehicles drive less than 100 km a day

and the range of the electric vehicle is 100 km, then the electric vehicle

may be a practical alternative for two thirds of company vehicles.

Finally, cost-effectiveness analyses provide an assessment

of potential demand for electric vehicles

based on the comparison of purchase and usage costs

of these vehicles compared with those of a reference vehicle, often thermal.

Here, for a given annual mileage, we understand

that the higher purchase cost of the electric vehicle

may be paid off after a number of years of ownership

given the lower usage costs.

The same reasoning could apply for a fixed number of years of ownership

and be used to determine beyond which annual mileage

the electric vehicle becomes cost-effective

compared to its thermal rival.

We recognize the aggregate analyses that we discussed

to the fact that they do not inform on the individual characteristics

of the potential buyers of electric vehicles.

They cannot be used to identify the socio-professional category,

income group, age, or place of residence

of these potential buyers, nor can they be used to distinguish

different preferences among these buyers.

The target groups that are identified by aggregate analyses

are identified at a very aggregate level.

Explicitly or implicitly, aggregate analyses

are based on assumptions, for example on future energy prices,

electric vehicle ranges, and so on.

And the results of these analyses may prove very sensitive

to the levels chosen by the analyst for some of these parameters.

Any result of aggregate analyses of compatibility or cost-effectiveness

that does not come with a sensitivity analysis to the main parameters

should be considered at least with caution.

Some aggregate analyses of potential demand,

if they are based on averaged or partial data,

may also lead the analyst to underestimate

the exceptional uses of the vehicle, such as holiday journeys,

and the weight that such journeys may have

in the choice of vehicle at the time of purchase.

Finally, aggregate approaches, usually relying on a limited number

of choice parameters, for example average daily journeys,

or total ownership costs, or a combination of both,

leave out many choice factors, notably those that may reflect

the diversity of preferences within the members of the considered group,

in terms of access to parking and charging infrastructure,

but also in terms of self-image, of comfort, of technical performance,

of environmental performance, and so on.

Disaggregate approaches to potential demand

are generally used in scientific literature

to identify the individual characteristics

of potential buyers of electric vehicles,

their location, and so on, rather than to predict

long-term diffusion trends.

Unlike aggregate approaches, they take into account

individual characteristics of potential buyers,

whether they are individuals, households, or companies,

and may reflect different preferences and purchase motivations

among these buyers.

The first category of disaggregate approaches

to potential demand that we will discuss here

is that of models of vehicle choice.

These models may be based either on detailed data

on potential buyers, their current vehicle,

their current travel practices, usually collected

through revealed preference surveys, notably household travel surveys,

or on data from surveys placing potential buyers

in a hypothetical situation of choice of a new vehicle,

then called stated preference surveys.

Models of vehicle choice may provide an assessment

of potential demand for electric vehicles

based on the various choices made by various types of buyers,

more or less young, more or less rich, more or less urban, and so on,

between targeted characteristics of the vehicles, such as their price,

range, comfort, and so on.

Models of vehicle choice

as we saw, are used to identify the individual characteristics

of potential buyers, and to locate potential demand.

They are also used to quantify the choices that they make

between different attributes of the vehicles presented to them.

A choice between purchase and usage cost,

between total ownership costs and range,

between cost and environmental performance, and so on.

Note however that these models rely on data costly to produce.

Furthermore, for models based on revealed preference surveys,

the issue arises of the transferability

of the observed choices to choices in the presence

of vehicles with all new characteristics.

And for models based on stated preference surveys,

the issue arises of the transferability to real life

of the choice made in a hypothetical situation,

maybe in a situation of partial information.

A second category of disaggregate analyses

of potential demand that we will discuss here

is analysis by constraint.

Here we will apply it to the particular case of households.

The principle of analysis disaggregated by constraint

is as follows: with a detailed database of households,

their vehicles, and their mobility, for example a household travel survey,

first is considered the population of the households as a whole,

then is applied a series of selection filters,

corresponding to as many criteria

of compatibility with the electric vehicle.

For example, we may consider that only motorized households

are likely to buy an electric vehicle.

We may then consider that among them, only those with access

to a parking space where they will be able to install

a charging station are likely to take the leap.

We will then study the compatibility of their journeys

with the range of the electric vehicle,

and finally we will check if the electric vehicle

is economically beneficial to them.

Like models of vehicle choice, analyses disaggregated by constraint

may be used to identify the individual characteristics

of potential buyers, and to locate potential demand.

These analyses may also be used to assess the relative weight

of the various constraints, and may help public authorities

or vehicle manufacturers to identify the principal barriers

to the deployment of electric vehicles,

parking, range, cost, and so on.

However, like models of vehicle choice,

analyses disaggregated by constraint are based on costly data,

notably data from large-scale surveys.

Analyses by constraint are sensitive to the assumptions of the analyst,

for example on the way to process households with one vehicle

and households with several vehicles.

Finally, analyses by constraint only take into account

a limited number of dimensions in the choice of vehicle,

and generally assume the substitution, all other things being equal,

of a thermal vehicle by an electric vehicle.

In conclusion, you understood

that there is no magic formula for assessing potential demand

for electric vehicles.

There is a variety of approaches, more or less adapted

depending on the objectives, and on the available data.

Regardless of the approach, assumptions are made,

explicitly or implicitly, about the parameters of the analysis,

and these assumptions influence, sometimes significantly,

the estimated potential demand.

There is no objective assessment of potential demand,

at best these assessments are transparent and discussed,

for example through a sensitivity analysis.

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