of 6 million Euros. The scenario all incentives results in a cumulated loss of about 4,800 million Euros.
Figure 4.15: Government net revenues per year in million Euros for different incentives and scenarios. Effect of different incentives relative to a base run where no BEV incentives exist after 2015.
Table 4.6: Government cost, market impact and cost per BEV of incentives in 2020. Euro and number of BEVs.
BEV policy Effect, number
of BEVs Budget effect (“cost”),
Euro millions Cost per BEV (‘Cost effectiveness’), Euro
VAT exemption only 7,140 17.76 2,487
Road charges only 1,029 0.75 725
Free Parking only 103 0.21 2,007
Annual Tax only 456 0.54 1,183
Purchase Tax only 1,903 8.40 4,412
Direct subsidies1) 1,160 6.66 5,743
All incentives combined 25,063 53.88 2,150
1) Year 2017, subsidies phased out in 2018
Emission Trading Scheme (EU ETS) is in operation and functions as intended: That means that the cap on CO2 emission is constant independently of increases in the usage of electricity for transport. Any short term effects of accumulated emission credits that potentially cause the system to be less effective, are not taken into account. The result of replacing an ICE vehicle with a BEV driving equally many kilometres per year, will thus be to eliminate the ICE vehicle emission while keeping the emission within the EU ETS unchanged, i.e. a 100% reduction of CO2 emissions.
Even without EU ETS, this assumption still resembles the Norwegian electricity mix quite well, since it almost entirely produced by means of hydropower. The effect on the emissions of the various incentives is shown in figure 4.15.
Figure 4.15: Partial effects of various BEV incentives on tonnes of CO2 emissions.
Figure 4.16 shows the budget cost per tonne of CO2 .13 The cost per tonne is so high because the cost is calculated from the entire BEV fleet. In figure 4.1, it was shown that the Base Run Norway (BRN) also generates a substantial number of BEVs that
13 We here focus on the effect on public budgets, whose main effect is to transfer money to and from public budgets. This is different from resource cost effects, as used in Fridstrøm and Østli (2014).
0 1 000 000 2 000 000 3 000 000 4 000 000 5 000 000 6 000 000
2015 2018 2021 2024 2027 2030 2033 2036 2039 2042 2045
Total CO
2emissions (tonnes)
0 No BEV incentives Only VAT exemption
Only Road Charges exemption Only Parking Charges exemption Only Annual Tax Rebate Only Purchase Tax Exemption Only VAT and Purchase Tax Exemption Only Bus Lane Access
All incentives combined
-600 000 -500 000 -400 000 -300 000 -200 000 -100 000 0
2015 2018 2021 2024 2027 2030 2033 2036 2039 2042 2045
CO
2emissions (tonnes) relative to BRN with no BEV
incentives
Only VAT exemption
Only Road Charges exemption Only Parking Charges exemption Only Annual Tax Rebate Only Purchase Tax Exemption Only VAT and Purchase Tax Exemption Only Bus Lane Access
All incentives combined
also receive incentives, adding to the cost in the numerator but not the number of vehicles in the denominator.
Figure 4.16: Figure shows the budget cost per tonne of CO2.
Figure 4.17 presents the year 2020 costs from the previous figure. Bus lane access is by far the least costly policy for government budgets given the assumption of spare capacity in bus lanes. Most remaining BEV policies remove CO2 at a budget cost of around NOK 30-40,000 per tonne.
Figure 4.17: Year 2020 net public budget cost (including lost government revenues) per tonne of CO2
removed, relative to BRN. NOK.
0 10 000 20 000 30 000 40 000 50 000 60 000 70 000 80 000 90 000 100 000
2016 2019 2022 2025 2028 2031 2034 2037 2040 2043
Cost (NOK) per tonne of CO2
Only VAT exemption Only Road Charges exemption
Only Parking Charges exemption
Only Annual Tax Rebate Only Purchase Tax Exemption
Only VAT and Purchase Tax Exemption
Only Bus Lane Access All incentives combined
5 Scenario assessment
The purpose of a scenario exercise is to support and inform policymaking (Stead and Banister, 2003). While scenarios are not about producing exact forecasts, they are still relevant because they point to ways in which different policy options affect and create different possible futures. In this way they are a tool for strategic policy analysis.
Rather than predicting the future based on extrapolation of past trends, scenarios present and analyse possible futures. The scenario approach acknowledges uncertainty and, importantly, take account of the possibility for rapid change and trend breaks.
Scenario assessment is no exact method. In fact, a large diversity of approaches are reported (van Notten et al., 2003). Banister and Hickman (2013) develop a typology of scenario approaches. For the purpose of our task, three main approaches are particularly relevant:
1. “Forecast based” scenarios are a mix of probable as well as possible futures. The horizon is often short-term, the approach quantitatively oriented and is expert- led.
o The COMPETT approach lies somewhere between short and long term, and the SERAPIS model exercise is essentially quantitative. However, SERAPIS modelling is not intended for forecasting, but rather to analyse alternative futures
2. Explorative approaches, (as opposed to trend extrapolation) which often focus on relevant external factors, i.e. those factors that make a difference for the outcome. The intention is to describe a plausible future state. Expert panels can inform the scenario designs. The perspective is long term.
o The COMPETT approach is primarily inspired by this way of establishing scenarios.
3. Backcasting: This is a normative approach where a desirable future is designed.
Then the exercise identifies possible trajectories between today and this image of the future.
o While this kind of scenario building does not form a part of the scenario assessment in this chapter, this principle can be applied in order to identify the kind of policy measures that are necessary, and their intensity, to reach certain goals