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powerful and to the point’, ‘this project will be carried out sustainably or even ‘this fish is caught sustainably from the sea’.

Apparently, people don’t want to be concrete and precise. partly because they are afraid too much openness might lead to strategic actions from others involved, but partly also because they simply don’t know. it is much easier to announce your solution as being the best and defend your choice with general things like ‘it is sustainable’ or ‘i’ve been looking into it very well and i can tell you i’m impressed’ instead of giving open, clear, and explicit arguments. However, this approach will lead to a lot of discussion as others also announce and defend their chosen alternative. by asking the ‘what does this mean?’ question you force yourself and others to be precise, concrete, and talk in terms of measurable things. This might not completely remove the discussion about what to do, but at least reduces this discussion to the hard-to-measure issues.

2.4 What does the system look like? – Causal diagram

So far, we have been very explicit about the problem, and expressed it in terms of dilemmas and tensions from different actor perspectives. Also, we have identified criteria (using a goal tree) so we can measure to what extent the goals of the actors involved are reached with a certain alternative. These criteria, of course, represent the tensions and dilemmas in the different problem statements, so we can see whether an alternative also actually solves these problems (i.e., decouples the factors that may be conflicting; that cause the dilemma). if the dilemmas in your problem statements are not present in your goal trees, go back and change the goal trees, or improve the problem statements with the information you gathered making the goal trees. This is the iterative, non-linear character of these analyses, as we discussed in Chapter 1.

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A certain action will be an interesting alternative for a specific actor if it is ‘doing something that solves his problem’. now that the problems held by different actors are represented in criteria using the goal tree technique, an alternative becomes ‘doing something that influences the scores on the criteria’.

basically, anything that influences the scores on the criteria can be treated as an alternative. needless to say not all of these alternatives are practical, not even ‘good’ (i.e., influencing criteria in the desired direction of change). However, how ‘good’ certain alternatives are is a matter of interpreting their scores on the criteria. First step is to find out what influences the scores on the criteria.

practically, let’s take a criterion from our well-balanced set (derived from all the goal trees we made for the actors involved) and ask two questions: ‘what factors influence this criterion?’ and ‘what factors are influenced by this criterion?’ note that the questions we asked when constructing a goal tree were definition questions (‘what does it mean?’), while these two questions are about causal relations (‘what influences?’).

look at the figure below to distinguish the two types of questions. The answer to the question ‘what factor influences this criterion?’ question are factors like factor A and b. if factors A and b change, then the value of the criterion (which is also a factor) also changes. The answer to the question ‘what factors are influenced by this criterion?’ question are factors like C and D. if the criterion changes in value, as a result, factors C and D also change.

Factors A and b are causally related to the criterion (and not the other way around!) The criterion is causally related to the factors C and D.


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Factor A

Factor B

Factor C

Factor D

Criterion

What factors areinfluenced by the

criterion?

What factors influencethe criterion?

imagine the goal is ‘cheap petrol’, so the criterion becomes ‘petrol price’ in [€/litre]. Factors in the category of ‘Factors A and b’ in the left of the figure could be ‘crude oil price’ in [€/barrel], ‘demand for petrol’ in [litre/week] and ‘petrol production’ [litre/week]. Factors in the ‘C and D’ category on the right could be ‘average distance travelled by car’ in [km/week]. This would look like:

+

_

+ _Petrol price[€/litre]

Demand forpetrol

[litre/week]

Petrolproduction[litre/week]

Averagedistance

travelled by car[km/week]

Crude oil price[€/barrel]


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When the crude oil price increases, it is likely that the petrol price also increases, which is why there is a + near the arrow head of the arrow between crude oil price and petrol price. please note this causal relation is noT the other way around: a higher crude oil price is not caused by a higher petrol price. When the petrol price increases you can expect that people in the end will travel less by car, which is why there is a – sign near the arrowhead that causally links those two factors. (experience tells us that the petrol price must rise seriously before people really drive less. Studies show people rather buy more fuel-efficient cars than drive less. However, this is just an example for illustration.)

Are the four arrows in this figure representing all causal links between the presented factors? What happens when, on average, people drive less kilometres in their cars? They will use less petrol and the demand for petrol decreases! And what will that do to petrol production? it will, in the end, also decrease. So, the figure should look like:

+

_

+ _Petrol price[€/litre]

Demand forpetrol

[litre/week]

Petrolproduction[litre/week]

Averagedistance

travelled by car[km/week]

Crude oil price[€/barrel]

+

+

When people drive more, there is a greater demand for petrol and the price might go up. This increased price may cause people to drive less, so the demand for petrol reduces. This principle of feedback


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is very common to all complex multi-actor problems. in fact, if you draw a causal diagram without feedback loops, you should seriously think of where you missed important information about your system. Feedback loops are almost always present!

According to this rather simple causal diagram, the petrol price is influenced by three factors. if we, in dealing with a problem, are interested in a lower petrol price, then all actions that are lowering the crude oil price, lowering the demand for petrol and/or are increasing the petrol production, are alternatives to us. in this simple example we could compare the alternatives to each other, looking at the extent to which they lower the petrol price.

of course, many other criteria matter as well, that is why we made the effort to create all these goal trees for all actors involved to come to a well-balanced set of criteria. Your complete causal diagram, including all important criteria from the goal trees you designed, will be much larger.

let’s take a closer look at the elements in the causal diagram we have just sketched. The formulation in the circles is special. notice for instance that we see no verbs, so, no actions are present in the causal diagram. Also we don’t see words like ‘more, ‘higher’, ‘lower’, nor do we see normative statements like ‘good’, ‘sustainable’ or ‘better’. What we see are words that together describe a characteristic of our problem, so, a characteristic of the system we are studying.

A characteristic of our system in the example is what a litre of petrol costs for somebody in that system. or the amount of petrol that is produced every week in this system.. We call these ‘factors’: characteristics of the system. but there is more; we see that to every factor in the causal diagram, a unit is attached. ‘petrol price’, for instance, is measured in [€/litre], and ‘petrol production’ in [litre/week]. That means that a factor is a characteristic of a system that can be


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measured (i.e., that can take a value) and that this value can constantly change in time under the influence of other factors.

A factor like ‘the weather’ is definitely a characteristic of some systems and will be highly relevant for many problems. However, the ‘value’ of this ‘factor’ cannot increase or decrease. in our simple causal diagram, the value of the factor ‘petrol price’ increases (e.g., from €1.30 per litre to €1.90) when the value of the factor ‘crude oil price’ increases (e.g., from €50 per barrel, to €110). but how can ‘the weather’ increase? Although important for many problems, this factor cannot be used in a causal diagram in this form. ‘The weather’ is more a general concept that we can represent by other, more concrete factors, like ‘wind speed’ in [km/h] or ‘amount of rain’ (precipitation) in [mm/m2/year].

We discussed actions and alternatives earlier in this section and concluded that every action influencing the value of one of the criteria in the causal diagram is a potential alternative, as it changes the score on that criterion.

The causal diagram is very useful in connecting the alternatives to the criteria. its usefulness is that is shows how (i.e., through what mechanism) the alternatives eventually change the value of the criteria. it makes it possible to estimate the effects of different alternatives on the set of criteria that is used. Therefore, it makes it possible that a client, in the end, can compare the alternatives and choose one to solve the problem. in this way, the causal diagram, like all other techniques presented in this book, helps the client decide. in other words, it facilitates the decision-making process.

What can help you practically when constructing a causal diagram is to identify factors using a table with three columns: ‘concepts’, ‘factors’ and ‘units’. most of the time when you start studying a system, you have some abstract ideas about things that are important. if the problem is about alternative energy sources, you quickly might think about things like


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‘climate’, ‘electricity usage’, or ‘environmental awareness’. These factors are not yet explicit, operational factors, but mere concepts. put these in column 1, as in the table below.

Concepts Factors Units

Climate

electricity usage

environmental awareness

now think of factors that can represent these three rather abstract concepts. For climate you might think about ‘temperature’, ‘precipitation’, ‘wind’ and for electricity usage you might think about ‘demand’, ‘price’ and ‘availability’. For environmental awareness, this is harder, but you might come up with – based on literature research, for example – ‘willingness to pay more for eco-friendly products’ or another factor that influences the criteria in your system. put these in column 2.


Climate Temperature

precipitation

Wind

electricity usage Demand

price

Availability

environmental awareness Willingness to pay more for eco-friendly products

Amount of awareness

now think about the units for these factors. Temperature can be measured in degrees Celsius. but it changes throughout the day and from day to day. What is the temperature we want to measure? probably we want to know the average over the year, so the unit becomes [˚C] and we rename the factor in ‘average temperature of the region’. For rain


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the same can be done, although we might not be interested as much in averages, but more in the total amount: ‘precipitation’ measured in [mm/m2/year]. it is the exercise of thinking in good units that makes you formulate the factors more precisely and explicitly! below, a possible result is shown. pay attention to the units of ‘willingness to pay more for eco-friendly products’ and ‘amount of awareness’. These percentages cannot be determined precisely, but it is possible to make estimates or use the results of a survey.

The units you choose are dependent on the problem you are analyzing: rainfall might be measured in [mm/m2/year] when you are looking at the climate, but when you are researching safety of water basins, or the amount of water the soil can handle, you are more interested in heavy rainfall, for example in [mm/m2/hour].


Climate Average temperature [˚C]

precipitation [mm/m2/year]

Average wind speed [km/h]

electricity usage Demand for electricity [kWh/year]

price of electricity [€/kWh]

Downtime of electricity supply

[minute/year]

environmental awareness

Willingness to pay more for eco-friendly products

[% (€/€)]

Amount of awareness [% (aware people/total number of people)]

if you had started to create a causal diagram using the first column concepts, it would result in a model without much practical value. The causal relations would be either vague or impossible to determine.


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The exercise with the units made the factors more explicit. Using these explicit factors of the second column makes creating a causal diagram simple and straightforward. And, especially, easy to communicate to your client and stakeholders, and thus easy to improve with their inputs.

The point with soft factors such as ‘environmental awareness’ is that everyone feels it has an influence, but it is too vague and ambiguous to discuss, and the causal relations around it make it very difficult to think of alternatives that may influence the criteria. The soft factors also make it impossible to make calculations on the effects of the alternatives. making these soft factors more explicit will help in the debate on which alternatives are available to deal with the problem.

What are commonly made mistakes and their effects when designing causal diagrams?Badly formulated factors in the diagram – The problems we are facing are very complex, involve many actors with their different perceptions and often concern abstract notions and concepts like ‘motivation’, ‘sustainability’ and ‘integral approach’. it is not always easy to translate these abstractions in factors the way we introduced them in this Section, as characteristic of a system that can have a (dynamic) value. What often happens is that such abstract notions (like the concepts in the first column in the earlier table) become the centre of an impractical star shaped causal diagram. Half of the factors influence this ‘abstract notion’, while the other half is only influenced by it. it looks, for instance, like the figure below:


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_

Motivation ofworkers

Salary

Working hours

Workplacecondition

+

Production

Illness leave

+

+

_

This diagram does not help to clarify how the system works. Clearly, somebody is interested in production numbers and in the illness leave in a company. instead of figuring out what mechanism influences these two important criteria, the analyst came up with an abstract concept (motivation) that has to represent this mechanism that the client is actually looking for. next step is that some general ‘motivators’ are introduced to make it look like a complete diagram. What is the added value of such a diagram compared to what the client already knows or assumes?

This diagram does not do more than reinforce the idea of the client that ‘motivation’, whatever that is, is the key to the solution of the problem. Will the addition of this diagram lead to a wider solution space than ‘money’, ‘working conditions’, ‘holidays’ or similar factors? Does the client, after receiving this diagram, understand more about what happens in the organisation and what mechanisms are present that can be influenced? Although some clients like to have their own ideas reinforced, this diagram creates no added value to the problem beyond common sense.


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Star or line shaped diagrams and unconnected diagram parts – We have already discussed the issue of star shaped causal diagrams above. Some other shapes can also be seen often, like the line shaped diagrams or separated pieces (islands) of a diagram that somehow should form a coherent whole.

+

__

line shaped diagrams are long causally related chains of factors that are not connected to any other factor in the diagram. The analyst seems to follow a certain line of reasoning that he was not able to leave. An example is:

Rate of salary[€/month]

Amount ofmoney on

bank account[€]

Number ofexpenditures[expenditure/

week]

Number ofconsumer goods

possessed[good]

Amount ofspace le� in

the a�ic[m2]

+ + + _

The system is represented in such a way that no other thing seems possible to happen than this, and only this, causal chain. Apparently, the analyst could not think about other possibilities present in the highly complex problems we study. When you recognise long one-factor-on-one-other-factor chains in a causal diagram, be aware. in this example, the designer of this chain diagram literally copied the client’s opinion about how the system works (and, thus, also what should be done about the problem appearing in this system).


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Clients often like to see their thoughts being reinforced, so they can say, ‘i told you i was right’. Sometimes they even hire consultants or analysts to only reinforce their ideas! However, clients also often have a single actor perspective on the problem, which can be one of the reasons why the problem persists. Here is where you, as analyst, can be of substantial added value.

Too large causal diagrams – You cannot model everything. The goal for us is to explore the system (to be able to find alternatives) and to communicate the behaviour of the system to the client and other actors. if you make the causal diagram too large, it will be very difficult to read and understand. A rule of thumb: limit yourself to 15 to 20 factors maximum.

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Sara, Peter and WalterMeet Sara. Thirty years of age and with five years of working experience, she works as a policy officer for the Ministry of Transport of her national government. Sara is pretty good at what she does, but definitely not a star in analysing situations or coming up with new creative solutions to problems. That is unfortunate, because this morning her supervisor Peter walks in with a challenging project.‘Good morning Sara, how are you doing? I hope you are fine, because I have an exciting project for you. As you know, in the past few years there have been a number of policies in place to stimulate the use of electric vehicles (EVs), but the latest monitoring report has shown a smaller effect than we hoped for. The adoption of EVs just doesn’t seem to gain pace. The minister has said that we must look into existing and new stimulation possibilities for EVs, since she wants to reach higher adoption levels in line with the goals that were set internationally five years ago. Right now, we are just falling behind. Luckily, other countries are not doing much better. However, our minister wants to be a frontrunner. She asked for a list of concrete proposals and what they would cost. What I want to ask is if you can find out what those concrete proposals should be. The minister said she didn’t want to stick to the old-fashioned subsidy packages, but something a little bit more innovative. To be honest, I have no idea what that should be, but I am sure you will come up with something. Next week I have a short meeting with the assistant of the minister, so it would be great if we could have something to give back to him then. I believe that is all for now. If you have any questions, do let me know, you know where to find me. Good luck Sara! I am sure you’ll do great on this one.’

Sara sits a little discomforted, puzzled almost, in her chair. ‘Stimulation of EVs? What does that mean? What exactly does he want me to do?’ Sara shakes her head. She has no idea what will make her perform well on this job. This assignment could be a great

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opportunity to show what she is capable of, but why does it have to be a creative idea? Sara firmly believes she is not good at that at all. She knows quite a bit about alternative energy sources and that EVs exist, but how they work exactly or what the problems are… She hasn’t a clue.

But Sara is determined to do well on this project. She immediately gets up and walks to the office of her supervisor. She knocks on his door and enters. ‘Oh wow, that is fast Sara. How can I help you?’ Peter replies. ‘Well, just after you gave this challenging project, I was wondering if you can send me the monitoring report you mentioned, and what effects we are trying to reach – how exactly do we know if we succeed in stimulating EVs?’ Peter sways back in his chair. ‘Hmm. Good question, Sara, you immediately hit the nail on the head! This is one of the points that puzzle me in a lot of new policies: when they are implemented as a result of a political decision, no one is clear about what goals exactly should be reached with the policy. As if it is easier to implement that way… This makes it very hard to monitor the effectiveness of that policy. Of course, we do have plans for the effects we try to reach, but not specifically for the policy measures for EVs that are in place right now. Let me think about what you should know. Have a seat by the way.’

Sara opens a little notebook that she carries with her and waits. ‘Okay. First about why we started stimulating EVs: because our country will need to lower its CO2 emissions and cars are a big producer of CO2 emissions and particulate matter. The minister decided that more EVs instead of regular cars could be one of ways to achieve this. Also, because we must decrease our reliance on fossil fuels, the government has considered stimulating alternatives to fossil fuels. I heard that in a few weeks the minister will have an important meeting with her colleagues from other countries and they will together form a strategy for the coming years. EVs are one of the

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alternatives. Now, as I said, the adoption of EVs in our country is far too low, therefore our minister wants concrete measures. At least, that’s how I understood it.

Then, about when we know that we have achieved our goal: an increase of EVs is the best way to measure this, I guess. Of course, take into account the costs of stimulating that here. The minister has been very clear that we will need to stay within the budget of this year and that the budget will certainly decrease next year because of the current financial situation. Oh, and now you ask: the minister said she would favour options that involve local policy makers, institutions or entrepreneurs. I am not sure how we could do that, but take it into account if you can. Does this answer your questions? I’ll send you any reports I can find on this topic by the end of the day, ok?’ Sara nods. ‘Thank you Peter. This makes it clearer for me. I’m starting my analysis right away.’ Peter smiles and Sara speeds back to her own desk. Based on this short conversation, she makes a simple goal tree on the back of her paper. This may be useful during the rest of her work.

Sara turns the page of her notebook and stares. Now she knows what she has to achieve and why, but she still has no clue how to do this. Luckily, Sara remembers a tool that could prove to be useful in situations like this. At the top of her blank page, Sara writes ‘EV adoption causal diagram’. At the centre she writes ‘number of EVs’ since Peter said he considers increasing the number of EVs to be the goal for the policy, with the ultimate goal to decrease CO2 emissions and become less dependent on fossil fuels. But let’s get to that later.

‘What influences the number of EVs?’ Sara ponders, ‘The ‘number of charging points’ and the ‘distance to the nearest charging point’ are important to owners of EVs, so these factors should be OK to make sure a region is EV-friendly. We do have to take into account that the more EVs there are, the more charging points we need. The price and

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the so-called ‘Total Cost of Ownership’ are, of course, also important, because if EVs and their maintenance are too expensive, no one will buy one. I think the ‘coolness’ of EVs also plays a role in the adoption of these cars: their image has to be attractive to a large group of people. I am not sure yet how to incorporate this, but let’s just put it somewhere in the causal diagram.’

‘Now, let’s dig a little deeper.’ Sara opens her browser and searches for ‘Causes number of EVs’. After opening a few websites, she finds some useful information:

Range Perception: Probably no other single factor has had as much influence over the history of the electric car as range. The early EV was great in the cities, but most could not venture too far out of town where there might be no electricity at all, let alone a charging station of some sort.[…]The range perception problem is still with the EV today. Some people call it Range Anxiety. However, as with most anxieties, poor perception is part of the problem. […] Today, the Tesla claims 244 miles on a charge, though uphill against the wind on a cold day in the fast lane, for example, you will most likely get less.Source: http://www.evsroll.com/History_of_electric_car.html, visited on 28 may 2012

The demonstration trial involves 340 vehicles […] being tested on everyday journeys by real-life users. This analysis provides a valuable initial insight into the first three-month ‘adaptation phase’ […]Range anxiety: Prior to the trial 100% […] said they would be more concerned about reaching their destination with an EV than they would with their normal car. After three months this dropped significantly, by 35%.

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The drop in range anxiety is in part due to the increased understanding of vehicle capabilities, driving techniques and journey planning. Charging data also shows users gained more confidence in their journey distance over the three months, with an eight per cent increase in users allowing their batteries to drop below 50% before plugging in.Source: initial Findings from the Ultra-low Carbon vehicle Demonstrator programme, Technology Strategy board, http://www.innovateuk.org/_assets/pdf/press-releases/ulcv_reportaug11.pdf, visited on 28 may 2012

‘This is important!’ Sara notices. She quickly writes down both ‘driving range of EVs’ and the perception of that range. ‘Hmm, let’s put that differently: ‘perceived driving range in relation to real range’. That last one can be influenced by the real range, but is also influenced by the length of time people own an EV, as was shown in the study in the United Kingdom. But what if we can allow for people to use, or experience an EV, without having to own one? That would make it more attractive!’ Sara had just written ‘EV possession time’, but quickly changes it into ‘EV experience time’. ‘This also allows for people renting EVs, or taking EV-taxis, etcetera, and still gaining the important experience. Nice!’

Other websites mention the rising fossil fuel prices as a main driver for EV ownership. ‘A higher fossil fuel price will not lead automatically to a higher number of EVs, though.’ Sara thinks. ‘It must have to do with both the fossil fuel price and the electricity price, and with the fuel efficiency of EVs and regular cars.’ She starts drawing at the bottom of the causal diagram, introducing the factors ‘% (EV costs/km) / (regular car costs/km)’, ‘electricity price’ and ‘fuel efficiency of EVs (kWh/100km)’ for the EV-part, and ‘fossil fuel price’ and ‘fuel efficiency of regular cars (litre/100km)’ to represent the regular car costs/km. Sara looks puzzled at the plus and minus signs she has

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to put next to the arrows; ‘This is quite a complex one, I will have to check these relations again later.’ Thinking about possibilities to lower the percentage in ‘% (EV costs/km) / (regular car costs/km)’, Sara adds two factors that influence the fossil fuel price. ‘The ‘fossil fuel price’ is influenced by the ‘crude oil price’, which we cannot change, but also by the ‘excise tax on fossil fuels’ that our government controls. This might be an opportunity to make fossil fuels less attractive and EVs more attractive.’

As Sara continues to work on her diagram, she slowly starts to lose pace. Although her understanding of why people aren’t driving EVs is expanding, she feels she is missing out on an important part of the problem field. She has not incorporated CO2 emissions yet, ‘How to do this? This is not directly related to the number of EVs, but more to the amount people drive…’

At that moment, Sara’s older colleague Walter walks by. Fifty-four years of age, Walter is an experienced engineer who now works for the department of Transport as a policy advisor. He is always friendly, but can be very critical at the same time. ‘Sara, good morning! Tell me, why do you look so puzzled?’ ‘Hi Walter. I am fine, but indeed I am a little puzzled. I made this causal diagram for a new project Peter has assigned to me. The minister wants to know how to increase the number of EVs in innovative ways, and we think the goals behind it are to decrease CO2 and particulate matter emissions, and to decrease the dependency on fossil fuels. Right now, I am stuck! I need to incorporate CO2 emissions, but I have to find a way to do that correctly, and it has a relation with the amount people drive, not with the number of EVs…’

‘Alright, Sara. It does sound complex; let me see what you have been working on so far.’ Sara shows Walter her causal diagram and waits while Walter studies her work carefully.

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‘Now you know that I don’t know a lot about EVs, Sara. I do know how the electromagnetic motor works, but I have no idea why people wouldn’t want to buy them. I guess your fi ndings so far make sense. I think you are on your way. If I could make one suggestion: you could include a factor that represents the proportion of kms driven by EVs relative to the total amount of kms driven in a year, and relate all other factors on fuel use and emissions to that.’ Sara lightens up. ‘That is a great idea, Walter!’ Sara grabs her pen and starts drawing new factors. ‘That is the solution to linking the EVs to CO2 emissions in a sensible way. The point is that I also read about the fact that many EVs are being used as a second car, and if they drive only a small number of kms, they are not very benefi cial.’ ‘Excellent!’ Walter smiles, ‘I have the feeling my single suggestion got you back on track again. I have to run now, my appointment started fi ve minutes ago. Good luck Sara!’ As Walter speeds off to his offi ce, Sara continues to work on her causal diagram.

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6666

The factor ‘% (EV kms)/(total car kms)’ is very useful, and Sara directly links it to the use of fossil fuels and electricity. These factors are also dependent on the total distance travelled (otherwise there would just be a ratio), and of course on the efficiency of both EVs and regular cars, which were already in the causal diagram. ‘If people drive less kms, or if regular cars become much more efficient, CO2 emissions will decrease as well.’

The day is coming to an end, and Sara adds the final factor to her causal diagram, at least for now. ‘If the electricity for the EVs is generated using fossil fuels like coal or oil, we are not making any progress at all!’

The next morning, Sara runs into her supervisor Peter at the coffee machine. ‘Good morning Sara, how are you? Still starting up on that project, I presume?’ ‘Well, to be honest, I may already have something exciting. Here, let me show you what I have.’ Sara quickly gets her notebook and shows Peter her causal diagram.

Together they walk through all steps in Sara’s line of thought. While Peter watches, Sara points out places where alternatives could be applied: ‘To start off old-fashioned, the ‘selling price of an EV’ can be influenced with subsidies, but as you said, the minister wants something more spectacular. Still, we should keep it in mind as a possibility, perhaps in addition to another action. Then there are the ‘number of charging points’ and the ‘average distance to the nearest charging point’. We can influence that quite easily, and it is very important to EV owners. We can also try to influence the ‘coolness of EVs’: by this I mean the attractiveness of these cars to people. We could get stars to become ambassadors of the EV, or something like that.’

‘If we then move to the bottom left part of the causal diagram, we first see the ‘perceived driving range’ which can be influenced by

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‘EV experience time’. This is very interesting; I’ll send you the report in which this is supported. We can influence this by, for example, stimulating rental EVs, or EV taxis. Then also entrepreneurs are involved, and I really feel this might have a huge impact on the success of EVs, although of course I still have to do more research.’

‘Below that we have the economic part of the causal diagram. Basically, the costs per km will be in favour of the EV or in favour of the regular car. To steer this in the ‘right’ direction, our minister can consider increasing the excise tax on fossil fuels. She can also support research in the field of fuel efficiency of EVs, but that will be a more long-term investment, and I think that is also out of reach for our minister – although she might advise this to the Minister of Innovation.’

‘And finally to the part that is perhaps most important: the emissions. I forgot to include ‘particulate matter emissions’, but the factor ‘CO2 emissions´ can be broadened to include that as well. There are two points of application in this part: ‘distance travelled by cars’ and ‘% of fossil fuel generated electricity’. The first is quite obvious, since discouraging driving – while providing good alternatives, of course – will in the end decrease CO2 emissions. The latter is perhaps not considered that much, but if we do not do anything about polluting electricity generation, EVs will not help us in lowering CO2 emissions. I am going to do some more work on it today to write down these possible alternatives and to research their effects. I will get back to you later, when I have some more things to show you.’ Sara grabs her coffee, almost cold by now, picks up her notebook, and starts walking back to her desk. Peter calls her back just in time: ‘Sara, I am excited. You moved way faster than I expected. Ideas like these are exactly the creative things we are looking for. Please continue to work like this, and definitely show me more of those diagrams you are working with. It makes your line of thought much clearer to me! You are doing a great job. Keep that up!’


ch 3

Documents

Transcript of ch 3