Discussion on Behavioural Features Representation

The four behavioural features identified in the 14 ‘E+T’ models reviewed are presented in Table 5.3, while advantages and disadvantages of the various methodologies are discussed in the remainder of this section. As summarized in Figure 5.3 below, 12 of the 14 models reviewed include a

representation of modal choice, 9 represent technology choice, 5 of them model driving pattern and only one deals with new mobility trends.

4 _{The European Environment Agency showed that values close to 2 for car occupancy have been reached in some European}

countries in 2008. Hence, the value has been chosen to represent carpooling as an important phenomenon in the two countries

5 _{The analysis has been performed with the bottom-up optimization energy model TIMES-DK for the period 2015-2050 for}

Figure 5.3: Distribution of the modelling approaches across the four behavioural features

This analysis underlines the reason behind the easier applicability of concepts as technology choice and modal choice with respect to fewer implementations of driving pattern and new mobility trends. Among the reviewed studies, hybrid models have the potential to capture all the four behavioural features, although the scope of the analysis determines the appropriate level of detail for each characteristic. Hybrid models, e.g., CIMS (Horne et al., 2005), IMACLIM-R (Waisman et al., 2013) and ReMIND (Pietzcker et al., 2014), specifically build a framework able to investigate trends across different systems or knowledge domains. Therefore, they are meant for carrying out cross-

disciplinary analyses and give answers to research and policy questions from a broader perspective. To limit model complexity, these gains could come at the expense of a more aggregated

representation of reality. On the other hand, bottom-up and top-down models often address a specific energy and transport policy issue, e.g. as for EPPA (Karplus et al., 2013), ESME (Pye and Daly, 2015) and UKTCM (Brand et al., 2012). In this case, models can provide robust insights on a certain phenomenon, as the future potential for modal shift (Girod et al., 2012) or the acceptance and penetration of electric vehicles in the transport sector (Bunch et al., 2015).

Table 5.3: Representation of transport behavioural features in energy and transport models Model Name Technology Choice Modal Choice Driving Pattern New Mobility Trends Modelling Approach Reference BLUE Intangible costs/benefits Hurdle rates

- - - H (Li and Strachan,

2016)

CIMS MNL MNL - MNL H (Horne et al., 2005)

COCHIN- TIMES

Disutility

costs -

Driving

profiles - BU (Bunch et al., 2015)

ECLIPSE - TTB - - H (Turton, 2008)

EPPA Nested CES Nested CES E - TD (Karplus et al., 2013)

ESME - TTB - - BU (Pye and Daly, 2015)

GCAM NMNL LM - - BU (Kyle and Kim, 2011,

Mishra et al., 2013) IMACLIM-R - TTB CES E - H (Waisman et al., 2013) Irish TIMES

CA-TIMES - TTB, TTI - - BU (Daly et al., 2014)

MESSAGE- TRANSPORT Disutility costs MNL Driving profiles - BU (McCollum et al., 2016) PRIMES- TREMOVE NMNL CES Driving profiles -- BU (E3MLab/ICCS, 2014)

ReMIND - Nested CES - - H (Pietzcker et al.,

2010)

TRAVEL NMNL NMNL - - BU (Girod et al., 2012)

UKTCM LM E - - BU (Brand et al., 2012)

Notes: NMNL: nested multinomial logit model, MNL: multinomial logit model, LM: logit model, TTB: Standard budget and

time budget constraints, TTI: travel time investment, E: elasticities, CES: constant elasticities of substitution.

Energy system models are usually technology-rich, thus allowing a precise description of the technical, environmental and economical characteristics of the technologies taken into

consideration. Nonetheless, in determining the optimal shares to fulfil the travel service demand, traditionally these models only regard the life-cycle costs of the technology (actualized investment, operation and maintenance and fuel costs), disregarding that vehicle preferences are highly

heterogeneous and based on many non-economic aspects. McCollum et al. (2016), recognising that low-carbon future transport scenarios have been explored so far without adequately considering any

behavioural aspect, have recently addressed the topic of behavioural realism of vehicle choice in IAMs. Four main methodologies were identified that are generally used to represent technology choice in a more behaviourally realistic way: (i) discrete choice models, (ii) constant elasticities of substitution, (iii) disutility costs and (iv) hurdle rates. A common trait of all these approaches is that they attempt to capture consumer behaviour when choosing a transport technology by including some non-monetary parameters that affect a consumer’s decisions.

5.4.3.1 Discrete Choice Models

The most common approach observed in the literature to introduce technology choice is through discrete choice models. In this review, 5 models have been found to use this methodology, as shown in Table 5.3. Discrete choice models calculate the probability of an individual's choice from a finite set of alternatives. The selection of an alternative is determined through the principle of random utility maximisation, which assumes that individuals aim at maximising their utility when making a choice. Each alternative is characterised by means of both monetary and non-monetary parameters that influence people’s choice.

As an example, Equation 5.1 illustrates the standard formula for a multinomial logit model (MNL) - a form of discrete choice modelling, which is used in the global transport model TRAVEL (Girod et al., 2012). This model calculates passenger transport shares and it is part of the energy model TIMER, which is in turn included in the wider IAM framework IMAGE (Van Sluisveld et al., 2016). Equation 5.1 calculates the fleet composition within a travel mode 𝑚 at each time period 𝑡 and region 𝑟 for each vehicle 𝑣. The share of each alternative is calculated by comparing its cost with that of all the competing technologies within an exponential function that uses 𝜆 as a calibration factor.

𝑆ℎ𝑎𝑟𝑒𝑟,𝑣,𝑡= 𝑒(𝜆 ×𝐶𝑜𝑠𝑡𝑟,𝑣,𝑡) ∑ 𝑒(𝜆 ×𝐶𝑜𝑠𝑡𝑟,𝑣,𝑡) 𝑖 (5.1)

In TRAVEL, the cost characterising each technology is calculated as shown in Equation 5.2.

𝐶𝑜𝑠𝑡𝑟,𝑣,𝑡=

𝐴𝑑𝑑𝑇𝑒𝑐ℎ𝐶𝑜𝑠𝑡𝑠_𝑣,𝑡+ 𝐸𝑛𝑒𝑟𝑔𝑦𝐶𝑜𝑠𝑡𝑠_{𝑟,𝑣,𝑡}+ 𝑁𝑜𝑛𝐸𝑛𝑒𝑟𝑔𝑦𝐶𝑜𝑠𝑡𝑠_𝑟,𝑡 𝑙𝑜𝑎𝑑_𝑟,𝑡

The total cost is the sum of three addends:

i. Investment into vehicles (𝐴𝑑𝑑𝑇𝑒𝑐ℎ𝐶𝑜𝑠𝑡𝑠𝑣,𝑡)

ii. Energy costs accounting for vehicle efficiency and energy prices (𝐸𝑛𝑒𝑟𝑔𝑦𝐶𝑜𝑠𝑡𝑠𝑟,𝑣,𝑡) iii. Non-energy costs related to vehicle purchase and maintenance, which simulate the

increased willingness to pay, associated with higher levels of income (𝑁𝑜𝑛𝐸𝑛𝑒𝑟𝑔𝑦𝐶𝑜𝑠𝑡𝑠𝑟,𝑡)

In the hybrid model CIMS (Horne et al., 2005), an MNL model for vehicle choice is developed from stated preference surveys where respondents are asked to choose among four vehicle types defined by attributes such as capital costs, operating costs, fuel availability, express lane access, emissions and power. The gathered data serves to build the utility functions for each vehicle type j, as visible from Equation 5.3:

𝑈_𝑗= 𝛽⃗⃗⃗⃗ × 𝑋_𝑗 ⃗⃗⃗ + 𝐴𝑆𝐶_{𝑗 𝐽} (5.3)

The vector of attributes 𝑋⃗⃗⃗ , is multiplied by the weighting coefficient vector 𝛽𝑗 ⃗⃗⃗⃗ , with the variable 𝑗 Alternative Substitution Constant ASC representing a specific constant for the alternative technology 𝑗. To represent this function in CIMS, market shares are computed according to Equation 5.4, where the utility functions are translated into capital costs (𝐶𝐶𝑗), operating costs (𝑂𝐶𝑗) and non-financial costs (travel time and comfort) per each mode 𝑖𝑗 with a private discount rate 𝑟, and market heterogeneity exponent 𝑣: 𝑆ℎ𝑎𝑟𝑒𝑗= 𝑒𝑈𝑗 ∑ 𝑒𝑈𝑗 𝑗 = [𝐶𝐶_𝑗×_{1 − (1 + 𝑟)}𝑟 _−𝑛+ 𝑂𝐶_𝑗+ 𝑖_𝑗]−𝑣 ∑ [𝐶𝐶_{𝑗 𝑗}×_{1 − (1 + 𝑟)}𝑟 _−𝑛+ 𝑂𝐶_𝑗+ 𝑖_𝑗]−𝑣 (5.4)

As demonstrated by the two examples reported above, discrete choice models are effective for introducing non-monetary parameters that affect individuals’ decisions. Among the most commonly used intangible costs included in such models are technical risk of immature technologies, model availability, acceptance factors (to simulate accelerated market diffusion), density of

recharging/refuelling infrastructure and range limitations.

Normally, the estimation of the model parameters and the calibration require data from a survey and a statistical analysis of the surveyed data. The time and cost of the data collection are a function of the number of alternatives to be included in the model. Discrete choice models find large-scale application in simulation programmes, where parameters statistically inferred from the survey simulate consumer behaviour. Conversely, optimization models are often based on linear

programming methods, hence model linking or a linearization procedure are required for the integration of the discrete choice models.

5.4.3.2 Constant elasticities of substitution

Another method of representing transport technology choice in the literature is that of using constant elasticities of substitution (CES). The CES between two input parameters of a utility function measure the constant percentage response of the relative marginal product of the two parameters to a percentage change of the proportion of the parameters. In the CGE model EPPA (Karplus et al., 2013), the original nesting structure described in (Paltsev et al., 2004) has been extended to include the possibility of substitution between conventional ICE vehicles and alternative fuelled vehicles (AFV), as shown in Figure 5.4.

Figure 5.4: Representation of passenger vehicle choice in EPPA. Source: (Karplus et al., 2013)

CES regulate the choice between the transport categories, based on fuel costs, powertrain costs and a fixed factor, the latter accounting for different constraints on the adoption of alternative vehicles. Constraints on adoption include the gradual fleet turnover, dynamic changes in the relative cost of alternative technologies with respect to the existing technology, and fixed costs associated with reaching a stable production and obtaining wide market acceptance. The main advantage of CES is that capturing consumer behaviour in technology choice only requires the inclusion of additional input factors to capital and labour in the standard production functions. One problem associated with this method is that CES are generally the result of an educated guess or a literature review, since they cannot be calculated empirically. Moreover, they typically find application in top-down macroeconomic models and thus the integration in conventional energy models requires the adoption of soft linking or the use of a hybrid modelling approach.

5.4.3.3 Disutility Costs

The incorporation of disutility costs allows for considering the (often non-monetary) discomfort costs encountered by consumers when adopting a specific transport technology. Electric vehicles (EV) offer a common example, wherein the users could associate EVs with lack of refuelling

infrastructure, range anxiety and scarce vehicle model variety. McCollum et al. (2012) provide a first example of the use of disutility costs in a linear programming model. In this case, technology choice is limited to fuel choice, including inconvenience costs for non-liquid fuels. A much more extensive use of disutility costs is offered by COCHIN-TIMES (Bunch et al., 2015) and MESSAGE-TRANSPORT (McCollum et al., 2016). The two studies apply the same methodology in different model

frameworks: a linear programming tool (TIMES/MESSAGE) has been transformed to be able to replicate the output of MA3_{T, an MNL model designed to estimate the choice probabilities of an} array of technologies for different consumer groups (Lin, 2015, Lin and Greene, 2010). By adding some extra features such as heterogeneity of population, disutility terms, and calibration parameters, the optimization framework is used as a “simulation-like” model. Heterogeneity is introduced to overcome the traditional concept of “mean representative decision-agent” (McCollum et al., 2014) and to take into account that distinct consumer groups are characterized by different preferences towards vehicle adoption and operation. In Bunch et al. (2015) and McCollum et al. (2016), consumers are differentiated along several dimensions: settlement pattern (urban, suburban and rural), attitude towards technology adoption (early adopter, early majority and late majority) and vehicle usage intensity (modest driver, average driver and frequent driver). Then, disutility costs are included to reflect that the different classes of transport users have varying preferences and comfort perceptions towards refuelling and recharging station accessibility, range anxiety and model availability.

While in MESSAGE-TRANSPORT disutility costs are homogeneous within each consumer group, the “unobserved consumer heterogeneity” is represented through distribution functions (called “clones”) in COCHIN-TIMES (Bunch et al., 2015), thus bringing the model results closer to the simulation model MA3_{T. This approach allows overcoming sharp technology penetration. However,} the modelling complexity grows significantly, requiring high-level computational capacity.

Acknowledging that actors have varying sensitivities to cost differentials when making investment decisions, Li and Strachan (2016) include market heterogeneity and intangible costs/benefits in the dynamic stochastic socio-technical simulation model BLUE. This chapter recognises the combination

of transport users’ heterogeneity and disutility costs as the most advanced and effective way to improve the behavioural realism of vehicle choice in optimization models.

5.4.3.4 Hurdle rates

Hurdle rates are higher discount factors associated with new or not fully commercial technologies. Hurdle rates account for the higher investment risk, uncertainty and imperfect knowledge perceived by the consumer, thus simulating the hesitancy to invest in a newer technology over an established technology (Mallah and Bansal, 2011). With respect to the transport system, the application of higher discount rates on less mature, more uncertain technologies is a traditional method to model vehicle choice. A simple approach consists of having technology-specific hurdle rates, while a more sophisticated method considers consumer-specific rates. In the model BLUE, Li and Strachan (2016) associate different hurdle rates to reflect actors’ different attitudes towards investment risk. Horne et al. (2005) explicitly include the variable discount rate as part of the MNL formulation within the model CIMS, to simulate people´s varying behaviour in vehicle purchasing decisions. The UKTCM model (Brand et al., 2012) distinguishes three main market segments for cars: private, fleet and business car buyers. Higher hurdle rates are associated to private vehicles to emphasize the higher total upfront costs confronted by the private consumer with respect to the fleet or business buyer. The allocation of variable hurdle rates to technology and consumer groups is a simple and generally applicable methodology in energy and transport models. The difficult calibration procedure and sole reliance on literature values for the determination of the discount rates represents a limitation for this approach.