Assessment of pathways to reduce CO2 emissions from passenger car fleets: Case study in Ireland

Applied Energy 189 (2017) 283–300

Contents lists available at ScienceDirect

Applied Energy journal homepage: www.elsevier.com/locate/apenergy

Assessment of pathways to reduce CO2 emissions from passenger car fleets: Case study in Ireland Md. Saniul Alam a,b,⇑, Bernard Hyde b, Paul Duffy b, Aonghus McNabola a a b

Department of Civil, Structural & Environmental Engineering, Trinity College, Dublin, Ireland Environmental Protection Agency, Monaghan, Ireland

h i g h l i g h t s
Integration of models provides a robust estimation of tailpipe CO2 emissions.
Taxation impact of vehicle fleet dieselisation was modelled.
A scenario development approach was proposed for policy analysis.
EV provided the largest cost saving option than that of the other fuel technologies.

a r t i c l e

i n f o

Article history: Received 28 July 2016 Received in revised form 17 November 2016 Accepted 11 December 2016

Keywords: Passenger car CO2 Tank to wheel Well to wheel Scenario

a b s t r a c t This study modelled the Passenger (PC) fleet and other categories of road transport in Ireland from 2015 to 2035 to assess the impact of current and potential greenhouse gas mitigation policies on CO2 emissions. Scenarios included the shift of purchasing towards diesel PCs over gasoline PCs. Scrappage rates were also calculated and applied to the fleet to predict future sales of PCs. Seven future policy scenarios were examined using different penetrations of PC sales for different vehicle technologies under current and alternative bio-fuel obligations. Tank to Wheel (T2W) tailpipe and Well to Wheel (W2W) CO2 emissions, and energy demand were modelled using COPERT 4v11.3 and a recently published W2W CO2 emissions model. A percentage reduction of conventional diesel and petrol vehicles, in different scenarios compared to a baseline scenario in the W2W model was applied to estimate the likely changes in T2W emissions at the tailpipe up to 2035. The results revealed that the biofuel policy scenario was insufficient in achieving a significant reduction of CO2 emissions. However, without a fixed reduction target for CO2 from the road transport sector, the success of policy scenarios in the long run is difficult to compare. The current Electric vehicle (EV) policy in Ireland is required to be implemented to reduce CO2 emissions by a significant level. Results also show that a similar achievement of CO2 emission reduction could be possible by using alternative vehicle technologies with higher abatement cost. However, as EV based policies have not been successful so far, Ireland may need to search for alternative pathways. Ó 2016 Elsevier Ltd. All rights reserved.

1. Introduction National and international governments are combating increasing levels of CO2 emissions from anthropogenic sources in order to keep the rise of average global temperatures below 2 °C [1]. EU member states are required to comply with Greenhouse Gas (GHG) emissions reduction targets under the Effort Sharing Decision (406/2009/EC) (20% below the 2005 levels by 2020 for Ireland for the non-Emissions Trading Sector). However, the latest GHG ⇑ Corresponding author at: Department of Civil, Structural & Environmental Engineering, Trinity College, Dublin, Ireland. E-mail address: [email protected] (M.S. Alam). http://dx.doi.org/10.1016/j.apenergy.2016.12.062 0306-2619/Ó 2016 Elsevier Ltd. All rights reserved.

emission projections suggest that reductions of only 6–11% below 2005 levels will be achieved in Ireland by 2020 under current and planned policies and abatement measures, respectively [2]. In order to meet this target a detailed assessment of existing and planned policies and measures is required. At present, Ireland is targeted to reduce at least 80% of 1990 levels of overall CO2 emissions by 2050 from the electricity generation, built environment and transport sectors [3]. However, the country does not have any specific CO2 emissions reduction target from road transport. In contrast, EPA [2] projected a 10–16% increase in 2014 GHG levels by 2020 in this sector. In 2014, approximately 66% of road transport CO2 emissions originated from passenger cars (PCs) [4]. PCs therefore pose an environmental

284

M.S. Alam et al. / Applied Energy 189 (2017) 283–300

Nomenclature CD CD(All)

CG CG (ALL)

CNG CO2 CO2e COPERT EV FCV GDP GHG HDVs HDVsD HDVsG HEV DF HEV DM HEV GF HEV GM

Conventional Diesel passenger car Conventional Diesel passenger car assuming no other technology except convectional gasoline present in the market Conventional Gasoline passenger car Conventional Gasoline passenger car assuming no other technology except convectional diesel present in the market Compressed Natural Gas Carbon dioxide Carbon dioxide equivalent COmputer Programme to calculate Emissions from Road Transport Battery electric car Fuel Cell Vehicle Gross Domestic Product Greenhouse Gases Heavy Duty Vehicles (P3.5 tonne unladen weight) Heavy Duty Vehicles (Diesel) Heavy Duty Vehicles (Gasoline) Hybrid Electric-Diesel-Full Hybrid Electric-Diesel-Mild Hybrid Electric-Gasoline-Full Hybrid Electric-Gasoline-Mild

threat which is at odds with the objective to reduce CO2 emissions at national level. Recent improvements in vehicle efficiency have reduced the energy requirements of road transport, causing a reduction in CO2 emissions. A change in the technological composition of the future vehicle fleet with low carbon technologies may provide an additional reduction of CO2 emissions. From the technological perspective, current Irish road transport policies are heavily focused on biofuel and electric vehicle (EV) penetration in the fleet, but the success of the EV policy is low to date. The current bio-fuel obligation is 6.4% by volume of fossil fuel increasing to 8.7% in 2017 [5]. For the EV policy, 230,000 PCs were targeted for electrification, which was recently revised to 50,000 for all road transport [6]. Without a detailed assessment of current policies, it is difficult to gauge whether CO2 emission targets will be met. In addition, the assessment of alternative policy options may also be required where shortfalls in targets are identified. This paper aims to assess the impact of the penetration rate of current and proposed vehicle technologies in different future scenarios. We provide an overview of the pathways for emissions reduction in relation to CO2 abatement costs in road transport from 2015 to 2035, using Ireland as a case study. In addition, this paper includes several methodological improvements in the process of emissions estimation and projection of future scenarios. As detailed below this paper includes: improved projection of future vehicle sales using a modified sigmoid curve approach; and incorporation of tank-to-wheel tailpipe emissions, well-to-wheel emissions, and real-world fuel consumption, using the integration of the COPERT model and a recent wellto-wheel model. 2. Relevant research Research covering future transport fleet projections, emissions and policies simultaneously, includes two major steps: projection of activity data; and projection of emissions. For activity data, studies either conduct projections of the total mileage or the share of

HEVD HEVG IC km kt LDVs LPG Mt MMR PC PHEV PHEVD PHEVG PJ t T2W Taxi VIF W2W

Hybrid Electric–Diesel (no distinction between mild and full) Hybrid Electric-Gasoline (no distinction between mild and full) Improved Conventional (gasoline and diesel passenger car) Kilometre Kilo tonne Light Duty Vehicles (63.5 tonne unladen weight) Liquefied Petroleum Gas Mega tonne Monitoring Mechanism Regulation Passenger Car (including taxis) Plug-in-hybrid Plug-in-hybrid Diesel Plug-in-hybrid Gasoline Petajoule Tonne Tank to Wheel (for emissions: tailpipe) Small public service vehicles Variable influential factors Well to Wheel

mileage among vehicle categories, as direct inputs for energy/ emissions modelling [7,8]. Vehicle fleets for different baseline and alternative scenarios are obtained to calculate the mileage share and later conduct modelling of energy consumption and/or emissions [9–13]. Zheng et al. [11] estimated the number of PCs, Light Duty Vehicles (LDVs), and Heavy Duty Vehicles (HDVs) from vehicle ownership, population and per capita consumption data using a Gompertz function. Hao et al. [10] projected future sales using an elasticity based approach where a correlation was made between past vehicle sales and GDP. Lumbreras et al. [14] estimated the future fleet composition by converting future mileage predictions into vehicle numbers. Survived vehicles were calculated using vehicle life curves and age distribution. New vehicle sales in each year were calculated by subtracting the survived vehicles from total projected vehicles for each future year. Li and Jones [15] applied a regression model to estimate PC numbers using GDP and permanent population data. In Ireland, Daly and Ó Gallachó [12] applied a technological fleet model where fleet structure, the profile of activity, and fleet efficiency were segregated by vehicle technology and age. An econometric approach using income elasticity was also applied to estimate total future sales and sales by engine size. Another car fleet model as part of the Irish Sustainable Development Model (ISus) was applied in projections of emissions in Ireland [16]. This obtained total fleet from a macro-economic model called HERMES [17,18]. The total car fleet in HERMES was calculated from disposable income and adult population. The division between Conventional Gasoline (CG) and Conventional Diesel (CD) PCs from the total car fleet in ISus was conducted by a complex breakeven distance methodology where higher uses defined the share of CD PCs. A total fleet size may also be developed using the concept of regression [15] and an improvement in the modelling process may be conducted using the concept of segregation of fleet into major two vehicle categories [18]. Disaggregated future sales of PCs according to the detailed technologies (characterised by improved fuel efficiency and/or use of alternative fuels) is difficult to obtain. The general methodology

M.S. Alam et al. / Applied Energy 189 (2017) 283–300

for technology penetration at a very detailed level is the conversion of the utility driven by the customer’s ability to pay and preference to market share [19,20]. Penetration of technologies is also affected by government policies on taxation and technological innovation. For the assessment of future policies, a targeted share of future technologies from total fleet is generally identified by the policy makers regardless of the future preference stated by customers. The future distribution targeted by policy makers of a fleet for alternative scenarios is an area which is not well defined for a number of technologies in the literature. Different approaches have been applied to distribute futures sales: different share of technologies in different years, a fixed share of yearly sales for the projected period [10,21,22] and application of a sigmoid curve (S-curve) for a single technology to estimate the distribution [23]. Developing alternative scenarios in a robust manner may provide more accuracy in the time series of future emissions projections which is also an area requiring further exploration. In estimating emissions, very few studies have focused on Tank-to-Wheel (T2W) tailpipe emissions or real world fuel consumption in analysing policies on the reduction of CO2 emissions [8,11,23–25]. Projection of tank-to-wheel CO2 emissions under current and future policies is a bi-annual reporting obligation for EU Member States under the Monitoring Mechanism Regulation 525/2013. Zheng et al. [11] estimated tank-to-wheel fuel consumption from vehicle type, vehicle age, fuel type, annual distance travelled, fuel economy and fuel density. Tank-to-wheel GHGs were then estimated from the carbon intensity of the fuel. Smith [8,23] employed a model driven by the speed–time profile of the specified driving cycle for estimating emissions from real world CO2 intensity from fuel from the fleet. Previous research has also focused on disaggregating the fuel consumption pattern in order to improve estimation of emissions. Hennessy and Tol [18] segregated fuel consumption according to the vehicle size and technology type, and included parameters such as age and fuel efficiency to estimate emissions. Hao et al. [10] employed a modelling process whereby energy consumption was decomposed into vehicle sales, survival rate, vehicle use intensity, technology penetration, energy intensity, and alternative fuel use. Where future fleet size, fuel and related modelling parameters are available, many previous investigations have used the COPERT model or a modified COPERT methodology to estimate tank-towheel tailpipe emissions [26,27]. COPERT is widely used in the EU to calculate real-world air pollutant and GHG emissions from the road transport sector [28]. Major inputs for the COPERT model include disaggregated mileage and PC data according to the engine size, mass, fuel, and emissions standard. Driven by policy and measures, such as scrappage rate, biofuel, tax, and reduced mobility, Lumbreras et al. [29] and Lumbreras et al. [14] developed a software tool to process inputs to use in COPERT for the projection of emissions including CO2. Although the COPERT model has a detailed database of emissions factors for existing PC technologies, the coverage is limited for future PC technologies. Thus, a further modification is required to the modelling approach while using COPERT. In the current study, the outputs from a Well to Wheel (W2W) model, developed by Gambhir et al. [9], was integrated with the output of COPERT. This was conducted in order to offset the aforementioned limitations of projecting real world tank-to-wheel tailpipe emissions at a detailed level. The Well-to-wheel model is a flexible approach that can be applied to any region or country by changing the corresponding inputs. The major inputs that this model accounts for include a comprehensive list of fuel technology, engine efficiency, fuel blend, and well-to-wheel emissions factors. Its integration with the COPERT model provides a robust indication of the emissions reduction potential of different policies. The wellto-wheel model was originally developed for various future vehicle

285

technologies for the entire road transport sector. However, the model was customised in this study to apply to the PC sector only. The analytical process involved, the parameters and equations applied in the customised model, and an example of its application, are included in Annex A, B and Supplementary material. In comparison to the COPERT model, this model requires detailed fleet distribution data. The CO2 abatement cost is also an output of the well-to-wheel model. 3. Methodology 3.1. Research framework The methodology in Fig. 1 was adopted in this study and includes five main elements: 1. Fleet projection based on current trends; 2. Alternative PC scenario development; 3. Future emissions scenarios; 4. CO2 emission models, data and parameters; and 5. Results comparisons and interpretation. An overview of the first two elements is described in this section, and in detail in Sections 3.2 and 3.3. 3.1.1. Fleet projection based on current trends The projection of the number of different vehicle categories (PC, LDV, HDV, motorcycle and moped) were estimated based on a regression approach using macro-economic data and historic fleet numbers following a similar approach applied by Li and Jones [15]. Validation was carried out using the Coefficient of determination, R2, and comparison with other models and figures, previously developed for Ireland [16,30,31]. Regression models with more than one independent variable were checked for Variable Influential factors (VIF) in order to remove the effect of multicollinearity. Unlike other vehicle categories, total PCs was calculated by adding estimations from two different CG and CD models. The models included the recent impact of PC dieselisation which is different to that of previously applied methodologies [18,32]. The dieselisation impact was included using a ratio of the CG/CD numbers to the total PCs. The future trend of the ratio was estimated using a logistic function. For the purpose of the analysis, PCs were further segregated into different fuel types using their growth trends. Segregation of the PC fleet according to engine size was based on trend extrapolation from ISus projections [16]. Euro technologies were defined by the year of vehicle registration [33,34]. Future PC sales figures at the level of fuel technology were estimated using scrappage rates [12,14,16–18,35,36]. 3.1.2. Alternative PC scenario development Alternative scenarios were only developed for the PC fleet. Previous investigations have developed scenarios for future PC technology sales using customer preference and choice models [20,37]. In comparison to this preference based approach, this study presents an approach where modellers can set a targeted future sales figure for different vehicle technologies within a time period, and the likely impact of given policies can then be assessed. The scenarios were developed to project what future policies could potentially contribute to reducing CO2 emissions. This concept is comparable to Hickman and Banister [38] who applied a back casting process, where alternative policy packages influenced the modelling process to make alterations from a baseline scenario. Similarly, the TIMES model, a techno-economic equilibrium tool, defines an optimal mix of technologies and fuels when emission reduction targets were set for a particular time period [39]. In the proposed approach, the distribution of targeted future sales for a PC technology over the projected period can be defined. The distribution of technology over the years can be controlled to

286

M.S. Alam et al. / Applied Energy 189 (2017) 283–300

Calculated scrappage Rate

PC: Segregated projection based on Fuel, Size and Euro technology

Category wise projection of road transport fleet

All except PC: Distribution in a category among engine size, fuel and, Euro technology was based on the linear trend extrapolation from the inventory data (Duffy, et al. 2014)

Calculation of new sale and yearly survived PC (only based on Fuel)

Alternative PC scenario development Base Fleet Base scenario with 2nd generation bio-fuel

Base emission scenario

Alternative PC fleet scenarios

Assumption regarding 2nd generation bio-fuel

Projected fuel data

Alternative emissions scenarios

COPERT model Other parameters from national emissions inventory data

Fleet projection under current trends

T2W CO2 emissions (Base)

Average PC mileage (Base)

Alternative PC Scenario development

Other parameters

W2W emissions model

W2W CO2 emissions (Baseline)

Future emissions scenarios

W2W CO2 emissions (Alternative)

CO2 emission models, data and parameters

Results

Fig. 1. Methodology of the study.

some extent using the modeller’s knowledge about the current and future trends of vehicle penetration. The distribution of different vehicle technologies for alternative scenarios in previous investigations, assumed different approaches, such as: different share of technologies in different years; a fixed share of yearly sales for the projected period [10,21,22]; and application of a sigmoid curve (S-curve) for a single technology to estimate the distribution [23]. Using the concept of a set target of future sales within a time period and distribution of sales by a modified S-curve, four alternative PC fleet scenarios were developed in this study. 3.1.3. Future emission scenarios A total of seven emissions scenarios were developed in this study, from the fleet projected under the current trend, and four alternative PC fleet scenarios (see Fig. 2, further described in Annex C). The COPERT models included all categories of vehicles, however the well-to-wheel model only included the PC fleet. At first, the COPERT model was deployed to estimate tank-to-wheel tailpipe emissions for a scenario (COP I) where the fleet projected under the current trend was applied and mileage was adjusted to align with the forecasted fuel use in Ireland [40]. The fuel forecast only provided fuel figures for total gasoline and diesel fossil fuels, and biofuels for the entire road transport sector. Thus, the total road transport fleet (excluding EVs) was required to be applied for estimating emissions in COPERT. Fuel consumption for Liquefied Petroleum Gas (LPG) was derived by adjusted total mileage and LPG emission factors from the year 2014 [4]. COP II also included the fleet data based on the current trends without consideration of future policies, however, a higher percentage of biofuels was assumed in comparison to COP I (12%

constant by volume from 2020 onwards (DCENR [5,41]) and an additional 12% more by 2035 with a steady growth from 2026). COP I provided an emission estimation scenario with the attainment of current bio-fuel policy, whereas COP II provided an optimistic scenario with an increased bio-fuel contribution. The remaining five scenarios were developed using the well-towheel model. In the Baseline scenario, the PC fleet under current trends was included and mileage weighted by the PC fleet was imported from COP II. The total share of biofuel was kept similar to COP II, however, the share of second generation biofuels was included with a slightly adjusted value (see Fig. D1, Annex D). This was conducted to make a consistency in bio-fuel share on energy supply between the inputs to COPERT (for COP II scenario) and the well-to-wheel model. The biofuel share of the well-to-wheel model was slightly raised for gasoline and slightly lowered for diesel based on the energy content of the second generation biofuels against first generation biofuels [42,43]. In Scenarios I–IV, higher percentages of first and second generation biofuels and a higher sale of alternative PC technologies were assumed in the fleet (see Table 1 for the exact percentages of new/alternative technologies used in each scenario). First generation biofuels were assumed to grow 18.5% by volume from 12% in 2020. Second generation biofuels were assumed to be introduced in the fuel share from 2020 onwards, and were assumed to increase to 25% penetration by 2035. Fleet composition in these scenarios were developed using an alternative PC scenario development approach discussed later in this section. In scenario I, the fleet vehicle technologies were considered to be similar to the Baseline scenario except for a higher percentage replacement of conventional vehicles by EVs and Plug-in-hybrids

287

M.S. Alam et al. / Applied Energy 189 (2017) 283–300

W2W Model

COPERT Model

COP I

COP II

Baseline

Fleet Technology

Scenario II

Scenario III

Mileage

Current trend of technology penetraon

Euro technology

Forecasted fuel amount

Scenario IV

Future technologies

Exisng

Technology Penetraon

Fuel

Scenario I

Defined trend

Weighted average mileage

Bio-fuel amount st (1 generaon)

Bio-fuel share st nd (1 and 2 generaon)

Fig. 2. Emission scenarios developed in this study.

Table 1 Penetration of different PC technologies in different Scenarios.

a

Assumption for 2035

Baseline (%)

Scenario I (%)

Scenario II (%)

Scenario III (%)

Scenario IV (%)

CG CD EV HEV GM HEV DM HEV GF HEV DF PHEVG PHEVD LPG CNG FCV

31.86 67.53 0.12 0.45 0.00a 0.00a 0.00a 0.00a 0.00a 0.04 0.00 0.00

13.09 45.80 39.73 0.99 0.01 0.01 0.00 0.14 0.14 0.08 0.00 0.00

25.00 42.00 10.00 2.00 4.00 2.00 4.00 2.50 4.50 2.50 0.50 1.00

15.00 20.00 15.00 6.00 8.00 5.00 6.00 7.00 8.00 5.00 1.00 4.00

6.00 7.00 24.00 8.00 10.00 5.00 8.00 10.00 12.00 5.00 1.00 4.00

Total < 0.001%.

(PHEV). In this scenario a total of 50,000 EV and PHEV by 2020 were assumed, in line with national policy targets. The future growth of these EVs and PHEVs were calculated based on a best-fit curve. This curve was selected as it gave an acceptable share of EV and PHEV by 2035. The split between EV and PHEV in the calculation was considered based on the share in the Baseline scenario. For CG, CD, LPG, Hybrid Electric-Gasoline (HEVG) and Hybrid Electric-Diesel (HEVD), penetration of new sales were adjusted by iteration to create a cumulative fleet total of each category similar to Baseline scenario/COP I/II. The distribution of the new sales of these PC technologies over the years from 2015 to 2035, were estimated where vehicle technologies were aggregated into three groups (Group 1, 2 and 3) based on their existing and future market share, and their year of commencement in the fleet. The setup of the well-to-wheel model was kept similar to the Baseline scenario for comparison purposes. For instance, the total mileage was the same across the scenarios, while the mileage distribution among technologies was different in alternative scenarios. This is how the impact of the technology penetration on CO2 reduction was assessed in the Scenarios II–IV. In these ‘what if’ scenarios, new technologies were considered which might penetrate the Irish market in the projected period. The trends of the

penetration of all the technologies were determined by an approach explained in Section 3.1.2. Essentially the share of estimated new sales in CG and CD PCs fleets were gradually reduced by increasing the share of alternative technologies (see Table 1). These were distributed according to the likely future scenarios. The estimated fleets were then imported into the well-to-wheel model to estimate CO2 emissions for the different scenarios. Finally, the percentage saving from these four alternative scenarios in comparison to the Baseline scenario in the well-to-wheel model was applied to the total emissions figures in COP II to estimate the likely reduction of the tank-to-wheel tailpipe emissions. 3.1.4. CO2 emission models For estimation of CO2 emissions, this paper integrates a tank-towheel model (COPERT 4v11.3, [44]) and a well-to-wheel emissions model [9]. This integration offsets the limitations of projection of real world tank-to-wheel tailpipe emissions from road transport at a detailed level. Martin et al. [24] noted that almost all prominent transport-policy models in practice are lacking in adaptation of detailed vehicle characteristics such as inputs for engine size, vehicle mass, and compression ratio, when calculating real world fuel consumption. On the other hand, studies focused on energy/ emissions modelling in transport for policy formulation did not

288

M.S. Alam et al. / Applied Energy 189 (2017) 283–300

often account for tank-to-wheel tailpipe emissions [8,12,23,35,36,45,46]. In this study, tank-to-wheel tailpipe emission was calculated from COPERT based on engine size and emissions standard. The impact of future vehicle technologies, the future fuel efficiency, the use of electricity and hydrogen and the impact of second generation biofuels on well-to-wheel emissions was calculated from the well-to-wheel model. COPERT includes Euro technology up to Euro-6. Even in the latest version, COPERT 5 (released in September, 30th 2016), had three Euro-6 technologies. However, it does not include any differences in fuel efficiency between these classes [47]. Mellios et al. [48] showed the significant level of improvement in fuel efficiency for gasoline and diesel PCs according to the Euro technologies. This means a higher share of Euro-6 PCs in a fleet would result in lower fuel consumption and emissions. However, the fuel efficiency improvement in the future years is limited to Euro-6 in COPERT. In addition, PC technology classes in COPERT are limited to LPG, CNG, gasoline, diesel and PHEV. The well-to-wheel model on the other hand, accounts for a number of future technologies in the horizon and calculates fuel efficiency on a yearly basis using efficiency improvement rates and fuel efficiency uplifts. The impact of the efficiency improvement and penetration of alternative vehicles are noticeable while relative improvement is calculated and applied in the integrated output. The integration is conducted in a MS Excel based framework (modified from the approach of [9] where COPERT results and fleet data from the scenario development approach are integrated with the well-to-wheel model. The current system is capable of comparing a baseline scenario against an alternative scenario. This integration overcomes the individual limitations of tank-to-wheel and wellto-wheel models. The relative improvement is the percentage reduction from the well-to-wheel model (i.e. baseline vs an alternative scenario) that was applied to the estimated emissions of a COPERT scenario to understand the likely reduction of tank-to-wheel tailpipe emissions. In a similar study, Zheng et al. [11] estimated tank-towheel fuel consumption, and the carbon intensity of the fuel was applied to get tank-to-wheel and well-to-wheel emissions, following Huo et al. [25] for policy analysis. The combined approach proposed here will allow EU member states to report CO2 emissions from road transport in a more robust manner under the EU Monitoring Mechanism Regulation (MMR 525/2013). In addition to the emissions output, the well-to-wheel model also estimated marginal abatement cost, which could indicate the potential of analysing perspective pathways for CO2 reduction. The marginal abatement cost (Euro/tCO2) was calculated at a 5% discount rate, and is the ratio of the cost difference and the CO2 emissions reductions (kt of CO2) achieved through their substitution. In the model, the additional cost and emissions saving in 2035 of each new technology were decomposed into vehicle technology, efficiency and emissions intensity in order to calculate the marginal abatement cost. 3.1.5. Data and parameters Historic vehicle data in this study was collected from the Irish national vehicle bulletins [49]. Macro-economic forecasts were obtained from the Economic and Social Research Institute [31] and are those used in the development of air pollutant emission projections in 2014 for Ireland. The fossil fuel and biofuel consumption data were collected from the Sustainable Energy Authority Ireland [40]. Mileage data was obtained from previous publications [33,34]. In COPERT, country specific modelling parameters such as speed, were obtained from previous work [33,34] and kept constant for the projection period (2015–2035). For estimating emissions, the well-to-wheel model required fuel efficiency, fuel

efficiency improvement, well-to-wheel emissions factors, biofuels share by volume, fleet size and mileage data. To estimate the abatement cost, the model also considered the vehicle’s capital cost, operating cost, fuel cost, discount rate, vehicle median life, share of distance by fuel in PHEV, etc. The original well-to-wheel model was customised for this study and changes were made for fuel efficiency, biofuel share, capital cost, emission factors, and input for mileage and fleet to reflect European conditions [5,12,41,50–52]. 3.2. Fleet projection 3.2.1. Irish transport fleet Since the beginning of detailed vehicle records in 1985, the Irish road transport fleet has been dominated by PCs. In 2014, 84.37% of the total 2.33 million vehicles were PCs, 13.62% were LDVs and HDVs, 1.57% were motorcycles and mopeds, and 0.45% were buses and coaches. Out of 1.96 million PCs, 59.19% were CG, 0.45% were HEVG, 40.32% were CD, and 0.031% were EV and LPG. The combined share of the CG and CD of the total PCs from 1990 to 2014 was 99.97% to 100%. Taxis accounted for a very small fraction of the fleet since 1985 (circa 0.05% for CG and 2.0% for CD in 2014). HEVG PCs have started to be reported in the vehicle bulletin since 2008 which was 0.74% of the CG in 2014. LPG and EV were included since 2010 and reached their maximum number at 58 and 531 respectively in 2014. EVs started penetrating the Irish market in 2009/10 and there were only 36 EVs in the fleet in 2010. HEVD, Hybrid Electric-Gasoline-Full (HEV GF), Hybrid Electric-Diesel-Full (HEV DF), and PHEV have started penetrating in the fleet in 2014, however their total number was only 12. No distinction was made for gasoline and diesel based segregation for last two new categories. Imported PC constituted 2.34% of the total in the last five years. 3.2.2. Fleet projection based on macro-economic forecast The total number of vehicles in a category was projected based on a regression approach where vehicle numbers were regressed against a macroeconomic forecast (see Table D1, Annex D) from ESRI [31]. Previous studies applied disposable income, population, GDP, and previous sales data for the estimation of either total fleet or new sales [10,12,17,18]. Due to the focus of this research, a disaggregated level of fuel-based projection was required for PCs. As noted earlier, close to 100% of PCs were either CG or CD. Thus, the projection based on macro-economic factors for CG and CD in the regression approach provided a fleet number for a scenario where the PC transport sector was fully supported by these two technologies. In other words, models for the CD and CG which were termed as CG(All) and CD(All) provided a fleet size where other technologies were absent and this fleet size was required to sustain economic activity. In reality, other technologies will replace these CG and CD vehicles in future. The impacts of likely changes in the penetration of these new technologies were assessed in the different scenarios described earlier. Modelling CG(All) and CD(All) by regression included historic fleet data, macro-economic forecasts and the ratio of CG to CD numbers. The share of CG was markedly higher in the past, for example in 1990 the share of CG was almost 91%. However the proportion of CG has declined dramatically in recent years because of the indirect effects of government policy [53]. Hennessy and Tol [18] applied a breakeven distance model of fuel choice to model the shift of the PC towards diesel fuel where more than ten variables were included. To represent the dieselisation of the car fleet in this study, only one variable representing the ratio of the CG to the total PC was introduced in model development. Using a logistic growth model for the situation where CG PCs decrease and reach to a plateau, the following equation (Eq. (1)) was applied:

M.S. Alam et al. / Applied Energy 189 (2017) 283–300

Rt ¼

R0  K R0 þ ðK  R0 Þ  expðr 0  tÞ

ð1Þ

where Rt = ratio for the year t; K is the maximum limit = 1, r0 and R0 are the decline rate and ratio of gasoline in 2014; t = Year, counted from 2014 onwards. 3.2.3. Trend based fleet projection Macro-economic factors were too general to explain variation in PC distribution at the technological level. Therefore, a trend-based projection was deployed for the segregation of the PC fleet among vehicle categories based on historic data. The number of PCs other than CG and CD were estimated based on the trends of their growth from historical fleet data. In order to estimate the number of CD and CG PCs from the CG (All) and CD (All), other fuel based PCs were subtracted. This analysis was required to facilitate the calculation of the sales of CG and CD PCs in the future years, as the available disaggregated data could only be applied to calculate scrappage rates for CG and CD vehicles. For this, taxis were also excluded from the total CG and CD PCs. For all trend based projections, best-fit curves were chosen from a list of linear, polynomial, power, logarithmic and exponential models. The selection was conducted based on the highest R2 values and the penetration level that might occur in the Irish context. The historic trend of LPG PC was not consistent and thus the yearly growth rate for EV was applied to project LPG PCs. The same growth rates were also applied to the newly penetrated HEVD, HEV and PHEVs in the market in 2014. The projected EV, LPG, HEV, PHEV vehicles were subtracted from the total CG and CD PC assuming that they replaced CG and CD PCs according to the CG/ CD ratio to the total PC fleet. 3.2.4. Estimation of new sales and survived vehicles Historic survival rates of conventional gasoline and conventional diesel PC fleet data according to the year of registration and new vehicle sales in each year was available up to the year 2014 in the Irish national vehicle bulletin. The scrappage rates were applied to the survived and latest sales figures of the PC of the year 2014 to estimate survived PCs of the year 2014 in 2015. Total survived conventional gasoline PCs in 2015 were subtracted from the projected total in order to estimate the future sale of in 2015. The same process was applied for conventional diesel. This process was repeated each year to 2035 to estimate the total number of the new sales of CG and CD PCs. The share of new PCs and survived vehicles for PCs in each year was applied to the other fuel categories as their sale and survived vehicle numbers could not be determined otherwise. Vehicle scrappage rates for CG and CD PCs were calculated from national statistics in this study. Daly and Ó Gallachó [35,36] estimated survival rates for CG and CD PCs according to engine size for the Irish fleet. Here, scrappage rates were calculated for a single or a number of years observing similar rates of yearly decline. Only the decline rate after the registration of vehicles in each year was considered to eliminate the impact of imported vehicles. Best-fit curves were selected to determine the scrappage rate. It should be noted here that there were insufficient data from 2009 onward to derive these curves and thus the scrappage rate of 2009 was applied from then onwards. 3.3. Alternative scenario development Alternative scenarios were developed based on the concept that the cumulative total (between the years 2015 and 2035) of new sales would be distributed among different technologies of PC that will likely occur due to market demand and policy implementation. This way the researcher or policy maker could quantify the

289

environmental impact of changes in the future fleet. Three steps were involved in this process: (1) define the total number of sales within a projected period for different technologies; (2) define the distribution of the sales over the projected period; (3) calculation of survived vehicles from the sales for each year. In the Baseline scenario, CD contributes the major share of sales in the 2015–2035 period (see Table 1). In Scenario I, sales for different technologies were determined by the current and proposed national policy scenarios. For scenarios II–IV, penetration of Compressed Natural Gas (CNG) was considered from the year 2025. The assumption was based on a recent policy paper on energy indicated building of CNG filling stations would commence from 2025 [54] in response to European legislation (Directive 2014/94/EU). In addition, Fuel Cell Vehicles (FCVs) were considered from the year 2030 onwards. While defining sales of different technologies in scenario II–IV, two previous publications relevant to Ireland were consulted [22,55]. In a scenario developed by OECD/IEA [55] where an 80% chance to keep global temperature below 2 °C was targeted, PC sales for the EU were assumed to have the following share: a decrease in sales of 22% and 15% for CG and CD respectively, and Improved Conventional (IC) PCs by 2035; increase in sales of LPG/CNG, EV and FCV by 9%, 6% and 7% in 2035; increase in sales of HEVG, HEVD, Plug-in-hybrid Gasoline (PHEVG) and Plug-inhybrid Diesel (PHEVD) in between 4 and 13%. SEAI [22] provided an optimistic view compared to the previous assessment where EV and PHEV were assumed to achieve 60% and FCV assumed to achieve 18% of all the PC sales by 2050. This leads to a market share of 46%, 35%, 15% and 4% for IC, EV, PHEV and FCV and an extinction of conventional CG and CD by 2035 in a mean scenario. The market share and sales figures in these studies were indicative of maximum and relative sales share among technologies in 2035 that might be applicable to Ireland. For Scenarios II–IV, the share of the sales for different technologies were gradually increased and decreased to assess their likely impact on emissions reduction. For the yearly distribution of new car sales, the above vehicle categories were grouped into three based on their existing market share and future market penetration capabilities (see Table 2) for Scenarios I–IV. The assumption was that technologies in a group would have the same yearly growth rate in market penetration. The sequencing of the groups (or in case of individual technology) is very important for creating a logical distribution among the technologies over the projection period. For distributing new car sales, the cumulative total of the new sales of the groups were distributed following a fitted curve. Cao and Mokhtarian [19] described an S-curve for new technology penetration where penetration grows slowly in the initial year (start), growing steeply as it reaches its half-way point (middle), and gradually slows as the penetration is close to its saturation level (higher end). The curve was applied by Smith [23] in emissions estimation, where only PHEV penetration was assessed. In scenarios with many PC technologies, the distribution of vehicle types is quite complex where some of these technologies are already in existence and more technologies may arrive in later years in the projection period. A modified equation (Eq. (2)) of the S-curve has been applied in this study to overcome this complexity. In this application, instead of using the curve to define how a technology reaches it’s saturation point in it’s lifetime, the equation is applied to distribute it’s targeted share in a projection period. In the modified equation of the S-curve, the final year and a mid-year (ymid) between the initial and final year can be selected where 50% of the cumulative total can be distributed and the shape of the curve adjusted accordingly using a variable k that controls the steepness of the curve. Having the total number of the sales in a line graph, sales for the first group were distributed in a desired shape over the projected years. The distribution of PC sales

290

M.S. Alam et al. / Applied Energy 189 (2017) 283–300

Table 2 Values of the parameters of the modified S-curve.

a

Group

Remarks

Technology

Scenario II

Scenario III

Scenario IV

1

Healthy market share (existing)

CG and CD

ymid = 2022; k = 0.1 m = 2.1

ymid = 2015; k = 0.17 m = 1.96

ymid = 2015; k = 0.5 m = 1.6

2

Shows a promising growth

HEV and PHEV

ymid = 2035; k = 0.18 m = 2.09

ymid = 2026; k = 0.32 m = 1.08

ymid = 2024; k = 0.36 m = 1.04

3

Very low market share/will commence later

EV, LPG, CNG,FCV

ymida = 2035; ka = 0.5 ma = 2.01 (CNG); 2.1 (FCV)

ymida = 2035; ka = 0.5 ma = 2.01 (CNG); 2.1 (FCV)

ymida = 2035; ka = 0.5 ma = 2.01 (CNG); 2.14 (FCV)

Applicable only for CNG and FCV.

Table 3 Regression models. Categories

Model

Adjusted-R2

Validation-R2

Maximum VIF

CG (All) CD (All) LDV HDVG HDVD Bus Coaches Moped Motor-cycle

3357795.6 + 1097.7 V0 + 1957346.79 V1 276957.9 + 6121.9 V2 + 2208319.45(1  V1) 127,600 + 4.6 V5 + 3252 V4 494.5–0.002 V3  3.9 V4 25,050 + 0.09 V3  761.8 V4 944.4 + 0.01 V3 958.7 + 0.04 V3 + 29.5 V4 475.4 + 0.02 V5 + 7.3 V4 10661.3 + 15.6 V0  161.1 V4

0.97 0.99 0.99 0.98 0.93 0.92 0.96 0.79 0.92

0.98 0.99 0.99 0.99 0.94 0.93 0.97 0.81 0.93

2.3 1.3 1.3 1.3 1.3 – 1.3 1.3 1.1

Here, Population, aged 15–64 in thousands = V0; CG ratio to the total (CD &CD) = V1; GNP per thousand population, at constant (2010) prices, Euro million = V2; GDP, at constant (2010) market prices, Euro million = V3; Unemployment rate, ILO basis = V4; and Personal Consumption of Goods and Services, at constant (2010) market prices, Euro million = V5.

CG(All)-This study

3000

2672 CD(All)-This study

No. of Vehicles ('000)

2500

2,462 CG, ISus, 1990-2013

2000

1559 1500

1470 1202

1000

903

CD, ISus, 1990-2013 Total, This Study Total ISus 2011, 1990-2030 Total, ESRI(2014)

500

CD,ESRI(2014) 0

1990 1993 1996 1999 2002 2005 2008 2011 2014 2017 2020 2023 2026 2029 2032 2035

Year

CG, ESRI(2014)

Fig. 3. PC fleet projected by different models.

was back-calculated from the cumulative total sales derived by the equation (Eq. (2)). Fleet size from the preceding year was required in this process to calculate fleet size of the first year of the time series. The first year of the time series in the analysis was also assumed to be the same as second year for simplicity. In order to get the desired distribution, the steepness of the curve, k was selected by iteration while keeping the ymid fixed. However, values for k largely deviated from the standard value of 1 (see Table 2) in order to facilitate technology penetration either at the higher, or lower end of the S-curve in the projected timeframe (Fig. D2, Annex D). Altering the k value by a large margin, made the cumulative total lower than the desired number. In order to overcome this, the curve was raised by adding a multiplier, m, representing the ratio of the actual cumulative value and cumulative value of the first iteration. The same process was applied for the next group. This approach was applied on n-1 groups/technolo-

Table 4 Regression models for PC fleet. Category

Fleet models

R2

EV HEVG HEVG–Taxi CG–Taxi CD-Taxi EV & PHEV projection for Scenario IV

32.25x21

0.92 0.94 0.93 0.92 0.78 1.00

 24.75x1 + 92.75 4375 ln(x2) + 791.08 55.943x3  66.467 3717 ln(x3) + 14,015 37.024x23  255.76x3 + 16,305 25422 ln(x4) + 531

Note: x = Value for year, x was started counted from 1; for x1 from 2010; x2 from 2008, x3 from 1990 and x4 from 2014.

gies. The remaining group/technologies were assigned the remaining distribution.

CFleety ¼ m 

C 20152035 1 þ ekðyymid Þ

ð2Þ

291

M.S. Alam et al. / Applied Energy 189 (2017) 283–300

3

LPG

COP I/COP II/ Baseline

CNG

Number of Vehicles ('000,000)

2.5

CG CG 0.87

2

CD PHEVG

CG1.09

PHEVD

1.5

HEV GF

CD 1.58

HEV DF

1

HEV GM CD 0.84

HEV DM

0.5

EV FCV

0 2015

2017

2019

2021

2023

2025

2027

2029

2031

2033

2035

Year

(a) Fig. 4a. Fleet composition for the baseline scenario.

LPG

Scenario I

Number of Vehicles ('000,000)

CG 0.32 2.5

CNG CG CD

2.0

CD 0.97

CG 1.08

PHEVG PHEVD

1.5

HEV GF HEV DF

1.0

CD 0.85

EV 1.17

0.5

HEV GM HEV DM

EV 0.02

EV

0.0

2015 2017 2019 2021 2023 2025 2027 2029 2031 2033 2035

Scenario II

3.0

Number of Vehicles ('000,000)

3.0

CNG 2.5

LPG 0.07

2.0

CG 0.64

CD 0.83

0.5

HEV GM HEV DM

EV 0.29

0.0

EV

2015 2017 2019 2021 2023 2025 2027 2029 2031 2033 2035

FCV

3.0

Scenario IV

CNG CG 0.37 CD 0.45

Number of Vehicles ('000,000)

Number of Vehicles ('000,000)

HEV GF

PHEVD 0.12

LPG 0.15 CG CD PHEVG

0.5

PHEVD

HEV DF

LPG

2.5

PHEV D 0.12

1.0

PHEVG

Year

Scenario III

1.5

CD

CD 0.95

1.0

FCV

3.0

CG 1.1

CG

CG 1.1

1.5

Year

2.0

LPG

PHEVD CD 0.82

HEV GF EV 0.45

LPG 0.15

2.5

CNG CG

CG 0.16 2.0

CD 0.13 CG 1.1

CD PHEVG PHEVD

1.5

HEV GM 0.18

HEV GF HEV DF

1.0

HEV GM

CD 0.82 0.5

HEV DF

0.0

LPG

EV 0.65

HEV DM EV

0.0

FCV 2015 2017 2019 2021 2023 2025 2027 2029 2031 2033 2035 Year

2015 2017 2019 2021 2023 2025 2027 2029 2031 2033 2035 Year

(b) Fig. 4b. Fleet composition for different alternative scenarios.

292

M.S. Alam et al. / Applied Energy 189 (2017) 283–300

12000

12000

COP II

COP I 10000

LPG CD

6000

CG 3808

HEV G

CD 4182

8000

CO2 (kt)

8000

CO2 (kt)

10000

HEV G

CD 5015

CG

CD 6000 4000

2000

2000 0 2015

Year

CG

CG 3156

4000

0 2015 2017 2019 2021 2023 2025 2027 2029 2031 2033 2035

LPG

2018

2021

2024

2027

2030

2033

Year Fig. 5. T2W tailpipe CO2 emissions from COPERT model.

12,000

Baseline

CG 3,389 LPG

Emissions / ktCO2

10,000

CNG CG

8,000

CD 6,663

CD PHEVG

6,000

PHEVD HEV GF

4,000

HEV DF HEV GM HEV DM

2,000

EV 2015

FCV 2017

2019

2021

2023

2025

2027

2029

2031

2033

2035

Year

(a) Fig. 6a. Total W2W CO2 emissions figures for Baseline scenario.

Here, CFleety is the cumulative fleet distribution for the year y; C2015–2035 is the cumulative total of new sales from 2015 to 2035 for a group; k = the steepness of the curve, in a standard S-curve k = 1; ymid is the year when half of the total cumulative sales are expected to be distributed between initial year and ymid. For Scenario I, a slight deviation was made from the proposed approach for sales distribution for EV and PHEV which was already defined by a fitted curve. However, sales for these vehicles were removed from the total available sales as described earlier. The LPG and HEV were considered as a group in this scenario and the distribution of the group was made by a modified S-curve having values of ymid = 2035; k = 0.09; m = 2.7. Remaining PC technologies were considered to account for the remaining distribution. In scenarios II–IV, the penetration of the CG and CD in Group 1, started at the higher end of the S-curve and were assumed to be extinct in the medium term. It was assumed that half of the CD and CG will be penetrated by 2022 for scenario II (ymid = 2022), leaving more room for penetration of other technologies from 2015 onwards. Similarly, half of the sales estimated in the period 2015–2035 of Group 2 for scenario II–IV were assumed to occur in 2035, 2026 and 2024 respectively. This means that a higher

share of sales of Group 3 starts late for Scenario II, early for Scenario IV and in between for Scenario III. The remaining group was left with the remaining distribution except for CNG and FCV which were assumed to penetrate in 2025 and 2030, respectively. A cross check was conducted for the cumulative total against values derived from Table 1. In the next step, survived vehicles were calculated from yearly sales estimated from the process above. In the absence of survival rates of each technology, survival rates of CG and CD were applied. Some of the survived vehicles of the older technologies CG, CD and HEV were adjusted based on the Baseline fleet data in order to keep the total vehicles the same as the base year.

4. Result 4.1. Fleet Table 3 represents the road transport fleet models for all the road transport in Ireland. The models had high R2 values and the maximum VIF was 2.3. The models provided a total road transport

293

M.S. Alam et al. / Applied Energy 189 (2017) 283–300

12,000

Scenario I

Emissions / ktCO2

10,000 8,000

CG 4,615 CG 1,219

6,000

CD 4,452

4,000

CD 4,127

2,000

LPG CNG CG CD PHEVG PHEVD HEV GF HEV DF HEV GM HEV DM EV FCV

Scenario II

10,000

CG 2,440 Emissions / ktCO2

12,000

CG 4,680

8,000 6,000

2,000

EV 356

-

-

2015 2018 2021 2024 2027 2030 2033 2015 2018 2021 2024 2027 2030 2033

Year

Year 12,000

12,000

10,000

LPG 566

CG 4,689

8,000

CG 1,450 6,000

CD 1,786 CD 4,329

4,000 2,000

HEV DM 701

LPG CNG CG CD PHEVG PHEVD HEV GF HEV DF HEV GM HEV DM EV FCV

2018

2021

2024

2027

2030

CG 4,680

8,000

LPG 568 CG 590

6,000

CD 522

CD 4,282 4,000 2,000

HEV DM 819

2015 2018 2021 2024 2027 2030 2033

2015

Scenario IV

10,000

Emissions / ktCO2

Scenario III Emissions / ktCO2

CD 4,170

CD 4,382

4,000

LPG CNG CG CD PHEVG PHEVD HEV GF HEV DF HEV GM HEV DM EV FCV

2033

LPG CNG CG CD PHEVG PHEVD HEV GF HEV DF HEV GM HEV DM EV FCV

Year

Year

(b)

Final energy demand / PJ

Fig. 6b. Total W2W CO2 emissions figures for alternative scenarios.

Gasoline

70

50 40

Diesel

59.9

60

CNG 44.1

43.5 41.4

30

Electricity

30.0 24.4

23.6

22.5

17.8

20 10

LPG

35.7

30.5

0.01

2.6

0.3

8.2

10.9

Hydrogen

17.4 8.1

5.0

9.9

7.4

11.7 6.8

5.4

Bio ethanol Bio diesel

0 2015

2035 Baseline 2035 Scenario 2035 Scenario 2035 Scenario 2035 Scenario

Year/Scenario Fig. 7. Total energy requirement figures for baseline and alternative scenarios.

fleet of 3.15 million in 2035. In a previous estimation by NRA [30] a 3.2–3.5 million road transport fleet was projected for different growth rates. The PC numbers were further compared with the ISus estimation and projection by ESRI (Fig. 3). Projected numbers from ISus were available until 2030 which was estimated in 2011 [16]. The R2 values for a comparison between the ISus projection and this study were 0.88, 0.98 and 0.98 for CG(All), CD(All) and total fleet, respectively. ESRI projection was available for every five years from 2020 [31] which showed a similar trend for total vehi-

cles. However, a growth in dieselisation of the PC fleet was also shown which can be observed from the changes in the ratio of CD to CG. Table 4 represents the segregated PC fleet models, based on their current growth trend. Table 4 also presents EV & PHEV penetration using the same approach, however the future fleet size in 2020 was fixed. In this model, a total of 50,000 EV and PHEV by 2020 according to national policy were considered and its growth after 2020 was calculated based on the best-fit curve. This curve

294

M.S. Alam et al. / Applied Energy 189 (2017) 283–300

10.0

Emissions 1990-2014

9.0

COP I

Mt of CO2

8.0 7.0

COP II

6.0

Scenario I

5.0

Scenario II

4.0

Scenario III

3.0 2.0

Scenario IV

1.0 0.0

1990 1993 1996 1999 2002 2005 2008 2011 2014 2017 2020 2023 2026 2029 2032 2035

Year Fig. 8. Total T2W CO2 emissions figures for different scenarios.

Table 5 Abatement cost for all scenarios. For 2035

FCV

EV

HEV DM

HEV GM

HEV DF

HEV GF

PHEVD

PHEVG

CG

CD

LPG

CNG

Scenario I

% of newly registered PC Vehicle share Marginal abatement cost Total emissions increase

0.0% 0.0% 0 0

39.7% 47.0% 515 4338

0.0% 0.0% 186 0

1.0% 0.9% 402 7

0.0% 0.0% 185 0

0.0% 0.0% 345 0

0.1% 0.1% 899 6

0.1% 0.1% 620 5

45.8% 38.9% 18 7

13.1% 12.8% 73 23

0.0% 0.0% 0 0

0.1% 0.1% 110 0

Scenario II

% of newly registered PC Vehicle Share Marginal abatement cost Total emissions increase

1.0% 1.2% 899 64

10.0% 11.7% 405 1070

4.0% 4.1% 252 93

2.0% 2.1% 395 33

4.0% 4.1% 201 94

2.0% 2.1% 370 43

4.5% 4.7% 1022 194

2.5% 2.6% 825 102

42.0% 38.2% 18 8

25.0% 25.7% 74 34

0.5% 0.6% 498 7

2.5% 2.9% 1425 10

Scenario III

% of newly registered PC Vehicle Share Marginal abatement cost Total emissions increase

4.0% 5.0% 824 275

15.0% 18.1% 419 1750

8.0% 7.8% 278 164

6.0% 5.8% 438 105

6.0% 5.8% 223 123

5.0% 4.9% 386 97

8.0% 7.8% 1045 319

7.0% 6.8% 898 267

20.0% 16.0% 18 181

15.0% 14.7% 73 58

1.0% 1.2% 587 12

5.0% 6.0% 533 66

Scenario IV

% of newly registered PC Vehicle Share Marginal abatement cost Total emissions increase

4.0% 5.0% 822 278

24.0% 29.2% 457 2887

10.0% 9.2% 285 193

8.0% 7.3% 441 135

8.0% 7.3% 230 154

5.0% 4.6% 386 92

12.0% 11.0% 1054 456

10.0% 9.2% 935 365

7.0% 4.3% 18 319

6.0% 5.7% 73 132

1.0% 1.2% 733 10

5.0% 6.1% 453 79

Note: % of sale PC (2015–2035); Vehicle Share in 2035; Marginal abatement cost (€/t CO2) in 2035; Total emissions increase (ktCO2/yr) in 2035.

was selected as it gives a more acceptable share of EV and PHEV (51% of the total sales) in 2035 than any other trend based curves. Figs. 4a and 4b represents the fleet composition for PCs from the years 2015 to 2035 for all scenarios. The distribution of new PC sales resulted in different penetration rates for different technologies of the vehicles in terms of their corresponding cumulative total (Fig. D3, Annex D). For Scenario IV, a very fast penetration rate was provided for CG and CD which leads to the extinction of their sale by 2026. This causes small shares of CG & CD in Scenario IV from 2026 onwards. The decay curves applied to estimate the fleet composition can be found in the Fig. D4, Annex D. The total number of new PC sales continued to grow in the projected period, however new PC sales remains 5–6% of the total fleet (Fig. D5, Annex D). 4.2. Emissions and energy Emissions factors applied in COPERT were higher than that of the well-to-wheel model as can be seen in Fig. D6, Annex D. Tank-to-wheel tailpipe emissions estimated from COP-I and COPII scenarios are given in Fig. 5 below. COP-II scenario had emissions lower than that of the COP-I scenario because of higher share of biofuels included. In the COP I scenario, total CO2 emissions reached to 8.86 Mega tonne (Mt) in 2035 compared to 7.37 Mt in the COP II scenario. The effect of 12% additional biofuels caused 17% less emissions in 2035 in COP-II.

Figs. 6a and 6b provide well-to-wheel emissions values for the Baseline and scenarios I–IV. For 2035, the well-to-wheel model provided a figure of 10.19 Mt of CO2 for Baseline scenario which was nearly 28% higher than that of COP-II. This was because of the difference of well-to-wheel emissions factors, weighted average mileage and the use of the second generation biofuel. Scenario I showed a significant share of emissions from EV in comparison to the other scenarios due to the planned penetration of EV according to the EV policy, however, the reduction of the total CO2 in that scenario due to penetration of EV was also noticeable. Total CO2 emissions for 2035 for Scenario I was estimated as 5.80 Mt which is about 43% lower than that of the Baseline scenario. With the inclusion of gradually higher levels of new PC technologies for scenarios II–IV, total well-to-wheel CO2 emissions for 2035 were estimated as 8.46 Mt, 7.27 Mt and 6.01 Mt respectively. The energy required in the Baseline scenario for CD was estimated to be much higher in 2035 in Fig. 7 which represented the effect of dieselisation of the fleet. Fig. 7 represented the energy demand derived from the well-to-wheel model, and thus, the energy demand might be slightly higher in the real world as the weighted average mileage from the COPERT model was applied. Nonetheless, the figure provided an indicative amount of energy required for all technology in 2035 for all scenarios. For instance, the energy requirement for electricity may become 17.8 Petajoule (PJ) in 2035 with a higher penetration rate of EV and PHEV under the Scenario I. Total CO2 emissions was also reduced to a similar level to scenario IV, however, the electricity demand in scenario

M.S. Alam et al. / Applied Energy 189 (2017) 283–300

IV was offset significantly by alternative fuels. The total electricity demand for 2035 in scenario IV was 11.7 PJ. Tank-to-wheel tailpipe CO2 emissions reduction based on the percentage reduction from the well-to-wheel model provided a similar reduction for scenario I and Scenario IV in Fig. 8. With optimistic assumptions of higher levels of biofuel use and technological intervention in these two scenarios, tank-to-wheel tailpipe CO2 emissions would be similar to emissions in 1998 (as per national GHG inventory, [4] by 2035. With only biofuel intervention and current trend of technology penetration, the emissions level in 2035 would reduce to 2006 levels in COP-II scenario. Tank-towheel tailpipe emissions total was 6.12 Mt in scenario II and 5.26 Mt in scenario III in 2035 which is similar to the CO2 emissions levels of 1999 and 2001 respectively. 4.3. Abetment cost Table 5 presents the abatement cost (€/tCO2) per technology, change in CO2 emissions, and PC fleet composition for 2035. In addition, Table 5 presents the percentage of new PC sales from 2015 to 2035. Average abatement costs were €258.12, €291.32, €384.97 and €412.22 per tonne of CO2 for Scenarios I to IV respectively in 2035. The likely reductions of CO2 in these scenarios were 4.4, 1.7, 2.9 and 4.2 Mt for the year 2035. The Scenario I and IV had a similar level of reduction of CO2 emissions, however, the average cost in Scenario IV was almost 60% higher per tonne of CO2 than that of scenario I. Penetration of EV provided the largest cost saving options in comparison to the other fuel technologies which is noticeable from scenario I. The increased level of EV sales also provided slightly higher abatement cost while comparing scenario II to IV and then scenario I in a sequence, however the CO2 saving was much higher with the increase of abatement cost of EV, or with the higher penetration of EV. The penetration was always higher for EV than that of other low carbon technologies in all of the scenarios which also have an impact on the emissions saving. 5. Discussion Chiodi et al. [56] identified biofuel as a key component of the Ireland0 s GHG emissions reduction strategy. However, results from extensive use of biofuels assumed in this study only reduced emissions to 2006 levels in COP-II scenario. The current technology penetration was also not sufficient. Over 99% of the PC fleet was occupied by CG and CD in 2014. In order to achieve the desired level of penetration of EV and PHEV, an annual growth of at least 690% for EV and PHEV is required each year to 2020. Smith [8] identified various challenges to EV penetration and noted that without removing the obstructions in EV penetration, 50–75% of PCs will remain beyond the reach of electrification by 2020. With the current trend, EV and PHEV share in 2020 will be below 0.1% of the total fleet. Daly and Ó Gallachó [12] noted that 40% sales of new EV is required to meet the previously targeted 10% share for EV and PHEV in the national fleet by 2020 [57]. SEAI [22] assumed that if the EV contribution to the PC segment is 60% by 2050 in the medium scenario and this electricity come from 50% renewable energy (wind), emissions from the PC fleet would reduce by about 80% with respect to the reported national emission levels in 2011. In comparison to the Baseline scenario that represents the current growth trend, the previously reported/published emissions reduction figures were found to be optimistic. Energy demands projected by different studies were consistent with the projection made by this study. The projected energy demand for 2035 is approximately 103 PJ for the Baseline sce-

295

nario in this study. Daly and Ó Gallachó [35,36] projected energy demand from PCs will be around 86–94 PJ in 2025. Alternative scenarios developed by Daly and Ó Gallachó [12] showed a 10% EV penetration could reduce the energy demand to nearly 85 PJ, saving approximately 3 PJ from their Baseline scenario, whereas with improved efficiency, demand reduction could reach 75 PJ in 2030. Smith [23] projected that when the mileage share of EV and PHEV reaches 15%-80%, electricity demand would be 3.6–54 PJ in 2030. In the Baseline scenario in this study, the electricity demand was estimated to be 0.01 PJ in 2015 which increase to 4.9–17.8 PJ for different alternative scenarios. Except for EV, none of the alternative fuels projected over 10 PJ in any scenario. The baseline CO2 emissions projection under this study was 8.86 Mt in 2035. Daly and Ó Gallachó [12] projected baseline CO2 emissions in 2020 as 5.68 Mt. For 2030, Smith [23] projected CO2 emissions as 7.8 Mt if mileage grows 2% per annum from 2006 levels and that could further reduce to 4.2 Mt or 3 Mt with scenarios of IC or EV and PHEV respectively. In the scenarios I and IV under this study, the minimum level of CO2 was found to be 4.3 Mt and 4.1 Mt respectively in 2035. This could be achieved by either forced penetration of EV reducing the sale of conventional vehicles and continuing for scenario IV, or implementing policies for introducing all new technologies. The pathway that might be taking place in the future with the current EV policy, future changes in biofuels, and current trend of alternative vehicles (Scenario I) was the most effective policy scenario in terms of CO2 reduction. However, the current status of electrification of the fleet is a considerable distance away from the desired level. Alternative pathways might lead to a similar scenario, however, the penetration of many PC technologies might lead to more uncertainties than that which is faced by electrification. Without a fixed target of CO2 reduction in the transport sector, success, or failures are very difficult to measure. Peng et al. [58] identified PHEV and EV as the main channels for reducing carbon emissions in the long term in China. Similar to that study, Scenario I here was found to be the most attractive. However, in the model, capital and running cost of the infrastructure for EV and FCV was assumed as 50% of the electricity cost, and hydrogen fuels cost were included as a mark-up cost [9]. With actual infrastructure and running cost, EV might become a costly option. Abatement cost for EV in Ireland for reduction of CO2e was estimated to be the highest at €125/tCO2e [59]. PHEVG, EV, and second generation bio-gasoline was found to be an expensive option, where the gasoline mix, HEV and first generation biogasoline were cost effective measures in 2030. The vehicle classification, underlying assumption undertaken by SEAI [59] was different from this study. The current study reports abatement costs (€/tonne of emissions) for CO2 which will be higher than that of abatement costs for CO2e as this includes CO2, CH4 and N2O.

6. Conclusion Road transportation is the second largest contributor to GHG emissions in Ireland. This paper has its own focus to assess the penetration of the different PC technologies, however the methodologies applied here face common uncertainties for forecasted macro-economic data, detailed of mileage splits in urban, rural and highways (as required by COPERT), detailing of vehicle size, modelling process, scrappage rate for different technologies, and finally embedded limitations of the emissions models used. However, the modelling process provides indicative future scenarios at a very detailed level. The results provide valuable information about the current status and future likely scenarios to shape future climate change mitigation policies.

296

M.S. Alam et al. / Applied Energy 189 (2017) 283–300

The study shows that with the current state of the technology penetration and even with higher levels of biofuels, tank-towheel tailpipe CO2 emissions do not reduce to any great extent in Ireland. Higher levels of technological intervention might contribute a lower level of CO2 emissions in future years. Along with extensive use of biofuels, either penetration of different technologies is required, or attention is very much required to focus on implementing current EV policy. However the required growth rate necessary to meet existing 2020 targets seem unrealistic at 690%. The abatement cost figures showed that the PHEV based technology is a less attractive option than that of EVs. Due to the absence of large sources of LPG & CNG fuel, EV & HEV seemed to be the most cost effective options. In addition, this study showed that alternative pathways for emissions reduction can be set with higher cost as the current EV penetration is far away from the desired level. The integration of tank-to-wheel and well-to-wheel models in the present study has enabled the individual limitations of both approaches to be overcome. Tank-to-wheel emission models are limited in the number of fuel categories that can be included (only 4). These also do not include alternative fuels such as electric vehicles or fuel cell vehicles. In addition, the fuel efficiency of vehicles in tank-to-wheel models is limited to the Euro-6 category in COPERT. Therefore, in projecting emissions into the future, improvements in fuel efficiency beyond Euro-6 cannot be included. On the other had the well-to-wheel model does not differentiate between engine size and emission technology class (i.e. Euro standard). The combination of both approaches proposed here, overcomes these limitations and enables EU member states to report CO2 emissions from road transport in a more robust manner under the EU Monitoring Mechanism Regulation (MMR 525/2013). Limitations also exist in the current methodology and projections. For example, the scrappage rate of the proposed alternative vehicle was assumed but is uncertain. Uncertainty also clearly exists regarding the macro-economic forecast data used to enable projections of trends. Technology specific mileage rate could also be included in future when data becomes available rather than using the weighted average here. Acknowledgments This work was supported by the Irish EPA Project 2014-CCRPFS.18.

where Ft,i = energy demand in year t for PC technology i. At = average mileage in year t. Effi,(tn) = Fuel efficiency which is subject to the yearly efficiency improvement and also efficiency uplift for some technologies. 3. W2W emissions:

Et;i ¼ Ft;i  EFj where Et,i = fleet level Well to Wheel (W2W) emissions for technology i in year t. EFj = W2W emissions factors which is subject to fuel blend between fossil and bio fuel in gasoline, diesel vehicles and their hybrid. 4. Trade-off between total W2W & T2W emissions:

T2WT;t;i ¼ ðW2WT;t;b  W2WT;t;i Þ  ðT2WT;t;b =W2WT;t;b Þ Here T2W = Tank to wheel & W2W = Well to wheel. T = total emissions; t = yearly, b = Base scenario; i = Alternative scenario. 5. Cost equations regarding vehicle purchase, fuel and maintenance, emissions saving and cost differential decomposition and their assumption were directly followed by [9]. Annex B For a base case, Emission estimation parameter values were described below. Values were changed for alternative scenarios. To see the changes in alternative scenario and cost figures please consult, Supplementary material. From the following Table B1 fuel blend was calculated. Blended fuel was applied to the W2W emissions factors in Table B2.

Table B1 Share of biofuels. Year

Annex A 1. Vehicle population:
Pk;t ¼ Pp;t  Ps;ðtnÞ where Pk,t = newly registered PC in year t. Pp,t = projected total PC population in year t; estimated from regression models. Ps,(tn) = estimated survived PC population in the previous years (n = 1, 2, . . ., n) which is a function of newly registered PC in the past years and corresponding survival rate/decay rate.
Distributed vehicles in a year for technology i = Pk,t + Ps, (t1) + Ps,(t2) + . . . + Ps,(tn). 2. Energy demand:
Ft;i ¼ ðPk;t  Eff i;t þ Ps;ðt1Þ  Eff i;ðt1Þ þ Ps;ðt2Þ  Eff i;ðt2Þ

þ . . . . . . þ Ps;ðtnÞ  Eff i;ðtnÞ Þ  At

2006 2015 2017 2020 2035

Share of 1st gen biofuels in Gasoline/diesel

Of which 2nd generation

Bio ethanol (%)

Bio diesel (%)

Bio ethanol

Bio diesel

2 6 8 12 12

0 6 8 12 12

0%

0%

12%

12%

Table B2 W2W emissions factor. W2W emissions factor

g CO2/MJ

Gasoline Diesel LPG CNG Bio ethanol (1st gen.) Bio diesel (1st gen.) Bio ethanol (2nd gen.) Bio diesel (2nd gen.) Electricity

99 102 77 91 115 79 4 1 89–166

M.S. Alam et al. / Applied Energy 189 (2017) 283–300 Table B3 Fuel efficiency data for each fuel and vehicle type. Timestamp

Diesel

Gasoline

CNG

LPG

Fuel efficiency (km/MJ) in 2010 Average improvement rate 2011–2030 Average improvement rate 2031–2050

0.4310 1.86%

0.5376 1.37%

0.3840 1.30%

0.4116 1.30%

1.12%

1.08%

1.12%

1.12%

Table B4 Fuel efficiency uplift for low-carbon vehicles over conventional vehicles. Sources: Gambhir et al. [9], SEAI [51], DCENR [5], DCENR [41], Daly and Ó Gallachó [12], Hill et al. [52] and Ou et al. [60]. FCV

2

EV

2.9

Conventional

297

Baseline: Existing PC technologies, Current trend of technology penetration without consideration of policies, mileage from COP II and Biofuel share similar to COP II but includes both first and second generation biofuel. Scenario I: Existing PC technologies, Current trend of technology penetration without consideration of policies except for EV and PHEV, Fleet size according to policy for EV and PHEV, Mileage and Biofuel share similar to Baseline. Scenario II–IV: Future PC technologies, Defined number of technological penetration, Mileage and Biofuel share similar to Baseline. Annex D See Fig. D1.

Mild HEV

Full HEV

Plug-in HEV

LPG/CNG

1.2

1.2

1.5

1

Fuel efficiency (km/MJ) data from Table B3 were applied in the W2W model for four fuel technologies. Fuel efficiency data from alternative PC technology was derived from Table B4. In addition, improvement in the average fuel efficiency was calculated from the following Table B3.

Conceptual diagram of vehicle penetration Condition 1: The projection timeframe in the first figure (Fig. D2a) is n to n + 25 years and, a technology may starts earlier and reach to its saturation point before the projection starts (Curve

Annex C Scenarios-at a glance COP I: Existing vehicle technologies, Current trend of technology penetration without consideration of policies, Mileage according to Euro technology, Forecasted Fuel and Biofuel share according to current legislation (only first generation biofuel). COP II: Existing vehicle technologies, Current trend of technology penetration without consideration of policies, Mileage according to Euro technology, Forecasted Fuel and Changes in Biofuel share (only first generation biofuel).

Fig. D2b. Condition 2: Vehicle penetration starts and within the years in the figure.

Fig. D1. % of biofuel in the total gasoline or diesel (in COP II and W2W baseline scenario).

Fig. D2a. Condition 1: Vehicle penetration starts and ends regardless of the projection time frame.

298

M.S. Alam et al. / Applied Energy 189 (2017) 283–300 Table D1 Macro economic variables applied for fleet projection. 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34

GDP, at constant (2010) market prices, Euro million GNP, at constant (2010) prices, Euro million Unemployment rate, ILO basis Housing Completions, thousands Personal Consumption of Goods and Services, at constant (2010) market prices, Euro million Labour Force, PES, thousands Employment in Agriculture, PES Basis, thousands Employment in Industry, PES Basis, thousands Employment in Traditional Manufacturing, PES Basis, thousands Employment in Food Processing, PES basis, thousands Employment in High Technology, PES Basis, thousands Employment in Manufacturing, PES basis, thousands Employment in Utilities, PES Basis, thousands Employment in Building, PES Basis, thousands Employment in Market Services, PES Basis, thousands Employment in Distribution, PES Basis, thousands Employment in Transport & Communications, PES Basis, thousands Employment in Other Market Services, PES Basis, thousands Employment in Non-Market Services, PES Basis, thousands Employment in Health & Education, PES Basis, thousands Employment in Public Administration, PES Basis, thousands Gross Value Added in Agriculture at Factor Cost, Constant Prices, € million Gross Value Added in Industry at Factor Cost, Constant Prices, € million Gross Value Added in Building and Construction at Factor Cost, Constant Prices, € million Gross Value Added in Market Services at Factor Cost, Constant 2004 Prices, € million Gross Value Added in Non-Market Services at Factor Cost, Constant Prices, € million Exports of Goods and Services, Constant Prices, € million Imports of Goods and Services, Constant Prices, € million Population, thousands Population, aged 15–64, thousands Fuel Tourism Petrol (kt) Fuel Tourism Diesel (kt) Total Petrol (kt) Total Diesel (kt)

Fig. D3. Penetration rate of different PC technology in different scenarios.

Fig. D4. Decay curves for CG and CD.

1) and then, only higher end of the S-curve may be applicable for the projection period. For curve 2, a technology may starts close to the projection timeframe and the lower end of the S-curve starts in the projection period. Curve 3 shows a technology that reaches

its saturation point before the end of the projection period, but that technology continues to penetrate after the projection period. Curve 4 shows a technology starts after the projection starts and ends after the projection period.

299

M.S. Alam et al. / Applied Energy 189 (2017) 283–300

80

200

70

180

COP I: Gasoline & Ethanol

60

COP I:Diesel & BioDiesel

50 40

COP II: Gasoline & Ethanol

30 20

COP II:Diesel & BioDiesel

10

COP I & II : LPG

gCO2/MJ (Well2Whell)

gCO2/MJ (T2W)

Fig. D5. PC sales from 2015 to 2035.

Gasoline and Ethanol, Baseline Diesel and BioDiesel,Baseline

160 140 120

Gasoline and Ethanol, All Scenarios

100 80

Diesel and BioDiesel, All Scenarios

60 40

LPG, All Scenarios

2033

0

2035

2029

2031

2027

2023

2025

2021

2019

2017

2015

0

2015 2017 2019 2021 2023 2025 2027 2029 2031 2033 2035

20 CNG, All Scenarios

Year

Year

Fig. D6. Emission factors for different fuel types in COPERT & W2W model.

Condition 2: When the vehicle penetration starts and finishes in natural order within the nth year in the second figure (Fig. D2b), its penetration follows normal S-curve. However, while a vehicle category captured the targeted share rapidly and consequently become extinct, it follows similar paths of early surge curves. On the other hand, late surge presents a situation where vehicle technology penetrates slowly in the markets, but captured targeted share within the later years. Appendix E. Supplementary material Supplementary data associated with this article can be found, in the online version, at http://dx.doi.org/10.1016/j.apenergy.2016. 12.062. References [1] UNFCCC. Historic Paris agreement on climate change 195 nations set path to keep temperature rise well below 2 degrees celsius, newsroom Available at: http://newsroom.unfccc.int/unfccc-newsroom/finale-cop21/; 2015 [last accessed on 16.03.2016]. [2] EPA. Press releases: Ireland projected to miss its EU greenhouse gas emission reduction targets Available at: http://www.epa.ie/newsandevents/ news/name,59044,en.html#.VubcEvntlBc; 2016 [last accessed on 16.03.2016]. [3] DECLG. National policy position Available at: http://www.environ.ie/sites/ default/files/migrated-files/en/Publications/Environment/Atmosphere/ FileDownLoad%2C37827%2Cen.pdf; 2014 [last accessed on 30.05.2016]. [4] Duffy P, Hanley E, Black K, O’Brien P, Hyde B, Alam MS. National inventory submissions 2015: Ireland, UNFCCC Available at: http://unfccc.int/national_ reports/annex_i_ghg_inventories/national_inventories_submissions/items/ 8812.php; 2015 [last accessed on 16.03.2016]. [5] DCENR-Department of Communication, Energy & Natural Resources. Biofuels Available at: http://www.dcenr.gov.ie/energy/en-ie/Renewable-Energy/Pages/ Biofuels.aspx; 2015 [last accessed on 16.03.2016]. [6] DCENR-Department of Communication, Energy & Natural Resources. National energy efficiency action plan 2014 Available at: http://www.dcenr.gov.ie/

[7]

[8] [9]

[10]

[11]

[12] [13]

[14]

[15]

[16]

[17] [18]

[19]

[20]

energy/SiteCollectionDocuments/Energy-fficiency/NEEAP%203.pdf; 2014 [last accessed on 16.03.2016]. Zhang H, Chen W, Huang W. TIMES modelling of transport sector in China and USA: comparisons from a decarbonization perspective. Appl Energy 2016;162:1505–14. Smith WJ. Can EV (electric vehicles) address Ireland’s CO2 emissions from transport? Energy 2010;35:4514–21. Gambhir A, Tse KCL, Tong D, Martinez-Botas R. Reducing China’s road transport sector CO2 emissions to 2050: technologies, costs and decomposition analysis. Appl Energy 2015;157:905–17. Hao H, Liu Z, Zhao F, Li W, Hang W. Scenario analysis of energy consumption and greenhouse gas emissions from China’s passenger vehicles. Energy 2015;91:151–9. Zheng B, Zhang Q, Borken-Kleefeld J, Huo H, Guan D, Klimont Z, et al. How will greenhouse gas emissions from motor vehicles be constrained in China around 2030? Appl Energy 2015;156:230–40. Daly HE, Ó Gallachó BP. Future energy and emissions policy scenarios in Ireland for private car transport. Energy Policy 2012;51:172–83. Kloess M, Muller A. Simulating the impact of policy, energy prices and technological progress on the passenger car fleet in Austria–a model based analysis 2010–2050. Energy Policy 2011;39:5045–62. Lumbreras J, Borge R, Guijarro A, Lopez JM, Rodríguez ME. A methodology to compute emission projections from road transport (EmiTRANS). Technol Forecast Soc Chang 2014;81:165–76. Li P, Jones SL. Vehicle restrictions and CO2 emissions in Beijing – a simple projection using available data. Transp Res Part D: Transp Environ 2015. Leinert S, Daly HE, Hyde B, Ó Gallachóir BP. Co-benefits? Not always: quantifying the negative effect of a CO2-reducing car taxation policy on NOx emissions. Energy Policy 2013;63:1151–9. Hennessy H, Tol RSJ. The impact of tax reform on new car purchases in Ireland. Energy Policy 2009;39:7059–67. Hennessy H, Tol RSJ. The impact of climate policy on private car ownership in Ireland. Working paper no. 342, ESRI; 2010. Available at: https://www.esri.ie/ pubs/WP342.pdf [last accessed on 16.03.2016]. Cao X, Mokhtarian PL. The future demand for alternative fuel passenger vehicles: a preliminary literature review. Davis (CA, US): University of California; 2003. Available at: http://www.tc.umn.edu/~cao/AQP_Cao.pdf [last accessed on 18.03.2016]. Propfe B, Kreyenberg D, Wind J, Schmid S. Market penetration analysis of electric vehicles in the German passenger car market towards 2030. Int J Hydrogen Energy 2013;38(13):5201–8.

300

M.S. Alam et al. / Applied Energy 189 (2017) 283–300

[21] Saisirirat P, Chollacoop N, Tongroon M, Laoonual Y, Pongthanaisawan J. Scenario analysis of electric vehicle technology penetration in thailand: comparisons of required electricity with power development plan and projections of fossil fuel and greenhouse gas reduction. Energy Proc 2013;34:459–70. [22] SEAI. Electric vehicles roadmap 2011–2015 Available at: http://www.seai.ie/ Publications/Statistics_Publications/SEAI_2050_Energy_Roadmaps/Electric_ Vehicle_Roadmap.pdf; 2012 [last accessed on 18.03.2016]. [23] Smith WJ. Plug-in hybrid electric vehicles—a low-carbon solution for Ireland? Energy Policy 2010;38:1485–99. [24] Martin NPD, Bishop JDK, Choudhary R, Boies AM. Can UK passenger vehicles be designed to meet 2020 emissions targets? A novel methodology to forecast fuel consumption with uncertainty analysis. Appl Energy 2015;157:929–39. [25] Huo H, Wang M, Zhang X, He K, Gong H, Jiang K, et al. Projection of energy use and greenhouse gas emissions by motor vehicles in China: policy options and impacts. Energy Policy 2012;43:37–48. [26] Vanhulsel M, Degraeuwe B, Beckx C, Vankerkom J, De Vlieger I. Road transportation emission inventories and projections –case study of Belgium: methodology and pitfalls. Transp Res Part D 2014;27:41–5. [27] Nielsen O, Winther M, Mikkelsen MH, Gyldenkærne S, Lyck E, Plejdrup M, et al. Projection of greenhouse gas emissions 2009–2030. NERI technical report no. 793; 2010. Available at: http://www.dmu.dk/Pub/FR841.pdf [last accessed on 18.03.2016]. [28] EEA. Explaining road transport emissions: a non-technical guide, Copenhagen; 2016. [29] Lumbreras J, Guijarro A, López JM, Rodríguez E. Methodology to quantify the effect of policies and measures in emission reductions from road transport. WIT Trans Built Environ 2009;107. [30] NRA. National road network indicators 2013 Available at: http://www.tii.ie/tiilibrary/strategic-planning/nra-road-network-indicatiors/NRA-National-RoadNetwork-Indicators-2013.pdf; 2013 [last accessed on 16.03.2016]. [31] ESRI - The Economic and Social Research Institute. Macro-economic forecast data, 2014. Personal communication, August. [32] Ding Y, Shen W, Yang S, Han W, Chai Q. Car dieselization:Asolutionto China’s energy security? Energy Policy 2013;62:540–9. [33] Alam MS, Duffy P, Hyde B, McNabola A. Estimation and back extrapolation of CO2 emissions from the road transport sector: emissions in Ireland, 1990– 2013. In: Longhurst, Capilla, Brebbia, Barnes, editors. 23rd International conference on modelling, monitoring and management of air pollution, Valencia, Spain, 1st–3rd June; 2015. p 67–75. [34] Alam MS, Hyde B, Duffy P, McNabola M. Common air pollutant projections from the Irish Road Transport Sector under the Emissions Ceiling Directive (NECD) and Convention on Long-range Trans boundary Air Pollution (CLRTAP): a preliminary Analysis for 2035, Environ 2015, Sligo, Ireland; 2015. [35] Daly HE, Ó Gallachó BP. Modelling future private car energy demand in Ireland. Energy Policy 2011;39:7815–24. [36] Daly HE, Ó Gallachó BP. Modelling private car energy demand using a technological car stock model. Transp Res Part D: Transp Environ 2011;16:93–101. [37] Struben J, Sterman J. Transition challenges for alternative fuel vehicle and transportation systems. Environ Plan B: Plan Des 2008;6:1070–97. [38] Hickman R, Banister D. Looking over the horizon: transport and reduced CO2 emissions in the UK by 2030. Transp Policy 2007;14:377–87. [39] Ó Gallachóir BP, Chiodi A, Gargiulo M, Deane P, Lavigne D, Rout UK. Irish TIMES energy systems model, climate change research programme - CCRP report, EPA Available at: http://www.epa.ie/pubs/reports/research/climate/ccrpreport. html#.VutJG-KLTX4; 2012 [last accessed on 18.03.2016]. [40] SEAI. Sustainable Energy Authority of Ireland, macro-economic forecast and fuel data, 2014. Personal communication, August. [41] DCENR-Department of Communication, Energy & Natural Resources. Public consultation regarding a proposed increase of the biofuel obligation rate Available at: http://www.dcenr.gov.ie/energy/en-ie/Pages/Consultation/ Public-consultation-regarding-a-proposed-increase-of-the-BiofuelObligation-rate.aspx; 2015 [last accessed on 16.03.2016].

[42] Sims R, Taylor M, Saddler J, Mabee W. From 1st- to 2nd-generation. Biofuel Technologies, Extended Executive Summary IEA/OECD; 2008. p. 1–16. Available at: https://www.iea.org/publications/freepublications/publication/ 2nd_Biofuel_Gen_Exec_Sum.pdf [last accessed on 18.03.2016]. [43] Dineen D, Howley M, Holland M. Energy in transport. Sustainable Energy Authority of Ireland; 2014. [44] Ntziachristos L, Gkatzoflias D, Kouridis C, Samaras Z. COPERT: a European road transport emission inventory model. In: Athanasiadis IN, Mitkas PA, Rizzoli AE, Marx Gómez J, editors. Environmental science engineering. Springer; 2009. p. 491–504. [45] Mustapa SI, Bekhet HA. Analysis of CO2 emissions reduction in the Malaysian transportation sector: An optimisation approach. Energy Policy 2016;89:171–83. [46] Georgopoulou E, Mirasgedis S, Sarafidis Y, Gakis N, Hontou V, Lalas DP, et al. Lessons learnt from a sectoral analysis of greenhouse gas mitigation potential in the Balkans. Energy 2015;92:577–91. [47] EMISIA. COPERT 5 training workshop, Copenhagen, Denmark, 16–18th October; 2016. [48] Mellios G, Hausberger S, Keller M, Samaras S, Ntziachristos L. In: Dilara P, Fontaras G, editors. Parameterisation of fuel consumption and CO2 emissions of passenger cars and light commercial vehicles for modelling purposes. European Union, Joint Research Centre – Institute for Energy and Transport (IET); 2011. ISBN 978-92-79-21050-1; ISSN 1018-5593. [49] DOE, DELG, DEHLG, DOT, DOTTS 1987–2014. Irish Bulletin of Vehicle and Driver Statistics (28 reports). [50] DECC. The UK 2050 calculator, version 3.6: XII. A domestic passenger transport costs; 2014. Available at: http://2050-calculator-tool-wiki.decc.gov.uk/pages/ 63 [last accessed on 18.03.2016]. [51] SEAI. Energy data portal: emission factor Available at: http://www.seai.ie/ Energy-Data-Portal/Emission_Factors/; 2014 [last accessed on 18.03.2016]. [52] Hill N, Varma A, Harries J, Norris J, Kay D. A review of the efficiency and cost assumptions for road transport vehicles to 2050. AEA Technology plc, report: ED 57444 - issue number 2; 2012. [53] Giblin S, McNabola A. Modelling the impacts of a carbon emissiondifferentiated vehicle tax system on CO2 emissions intensity from new vehicles in Ireland. Energy Policy 2009;37:1404–11. [54] DCENR-Department of Communication, Energy & Natural Resources. Ireland’s transition to a low carbon energy future 2015–2030 Available at: http://www. dcenr.gov.ie/energy/SiteCollectionDocuments/Energy-Initiatives/Energy% 20White%20Paper%20-%20Dec%202015.pdf; 2015 [last accessed on 18.03.2016]. [55] OECD/IEA. Energy technology perspectives 2012: pathways to a clean energy system Available at: https://www.iea.org/publications/freepublications/ publication/ETP2012_free.pdf; 2012 [last accessed on 18.03.2016] ISBN: 97892-64-17488-7. [56] Chiodi A, Gargiulo M, Deane JP, Lavigne D, Rout UK, Gallachóir Ó, et al. Modelling the impacts of challenging 2020 non-ETS GHG emissions reduction targets on Ireland’s energy system. Energy Policy 2012;51:172–83. [57] DCENR-Department of Communication, Energy & Natural Resources. Second national energy efficiency action plan to 2020 Available at: http://www. dcenr.gov.ie/energy/SiteCollectionDocuments/Energy-Efficiency/NEEAP%202. pdf; 2011 [last accessed on 16.03.2016]. [58] Peng B, Fan Y, Xu j. Integrated assessment of energy efficiency technologies and CO2 abatement cost curves in China’s road passenger car sector. Energ Convers Manage 2016;109:195–212. [59] SEAI. Ireland’s low-carbon opportunity: an analysis of the costs and benefits of reducing greenhouse gas emissions: technical appendix Available at: http:// www.seai.ie/Publications/Renewables_Publications_/Low_Carbon_ Opportunity_Study/Technical_Appendix.pdf; 2009 [last accessed on 23.03.2016]. [60] Ou X, Zhang X, Chang S. Scenario analysis on alternative fuel/vehicle for China’s future road transport: life-cycle energy demand and GHG emissions. Energy Policy 2010;38:3943–56.