DECART: Planning for Decarbonising Transport Sector with

Predictive Analytics - An Irish Case Study

Meghana Kshirsagar

1,2,* a

, Gauri Vaidya

1,2 b

, Shravani Rajguru

, Pruthviraj Jadhav

Hrushabh Kale

, Nishanth Shanmugam

, Conor Ryan

1,2

and Vivek Kshirsagar

Biocomputing and Developmental Systems Group, University of Limerick, Limerick, Ireland

Lero, The Science Foundation Ireland Research Centre for Software, Limerick, Ireland

Department of Computer Science and Engineering, Government College of Engineering, Aurangabad, India

Keywords: Decarbonization, Machine Learning, Time-Series Forecasting, Renewable Energy, Carbon Emissions, Road

Transport.

Abstract: This article explores assessing the impact of the decarbonisation of the transport sector using an evidence-

based approach incorporating data analysis and advanced machine learning (ML) modelling. We investigate

the radical behavioural and societal changes needed for the decarbonisation of the transport sector in Ireland.

We perform a study through our system DECArbonisation in Road Transport (DECART), a suite of statistical

and time series ML models for facilitating policy making, monitoring and advising governments, companies

and organisations in the transport sector. Based on data analysis and through scenario-modelling approaches,

we present alternatives to policy and decision makers to achieve goals in mitigation of carbon emissions in

road transport. The models depict how changes in mobility patterns in road transport affect CO2 emissions.

Through insights obtained from the models, we infer that renewable energy in Ireland has the potential for

meeting the growing electricity needs of electric vehicles. Experimentation is conducted on real-world

datasets such as traffic, motor registrations, and data from renewable sources such as wind farms, for building

efficient ML models. The models are validated in terms of accuracy, based on their potential to capture hidden

insights from real-world events and domain knowledge.

1 INTRODUCTION

Globally in 2020, we saw a dramatic fall in carbon

emissions, due to Covid -19 pandemic which resulted

in periodic lockdowns. This was also true for Ireland

where we saw around 15% decline in carbon

emissions. However, with the resumption of normal

activities and similar mobility patterns like those of

pre-pandemic times, we are once again faced with the

challenge of carbon emissions in transportation.

The statistics of greenhouse gases in Ireland

(Summary by Gas | Environmental Protection

Agency, n.d.) states that the major contributors are

Carbon Dioxide (CO2) and Methane (CH4). CO2

mainly comes from the combustion of fossil fuels and

emissions from the transport sector, contributing 40%

https://orcid.org/0000-0002-8182-2465

https://orcid.org/0000-0002-9699-522X

https://orcid.org/0000-0002-7002-5815

of total fuel powered emissions in Ireland, while CH4

comes from agricultural livestock.

Figure 1: Carbon emissions according to Vehicle types.

Figure 1 (Transport | Energy Statistics In Ireland

| SEAI, n.d.), further breaks down the emissions

resulting from major vehicles in the road

Kshirsagar, M., Vaidya, G., Rajguru, S., Jadhav, P., Kale, H., Shanmugam, N., Ryan, C. and Kshirsagar, V.

DECART: Planning for Decarbonising Transport Sector with Predictive Analytics - An Irish Case Study.

DOI: 10.5220/0011087100003203

In Proceedings of the 11th International Conference on Smart Cities and Green ICT Systems (SMARTGREENS 2022), pages 157-164

ISBN: 978-989-758-572-2; ISSN: 2184-4968

157

transportation in Ireland. We observe that private cars

lead the graph by emitting the maximum Carbon

Dioxide which is then followed by Heavy Goods

Vehicles and Light Goods Vehicles. Therefore,

reducing emissions from transport sector is of the

significant interest.

1.1 Contributions

This research paper examines how the government in

Ireland can provide pathways towards a greener

Ireland by decarbonizing 80% of the country by 2050.

In this paper we:

1. Develop DECArbonisation in Road

Transport (DECART), a Machine Learning

(ML) framework leveraging real-world

datasets such as traffic, motor-registrations,

renewables, etc. to obtain critical and hidden

insights in the decarbonisation of transport

sector;

2. The DECART system will be used to monitor

and plan progress in reducing carbon

emissions caused by transport through

predicting growth of electric vehicles;

3. The DECART system will be able to predict

the potential of renewable energy to match up

with the electricity requirements for the

growing footprint of electric vehicles.

The structure of the research work is as follows:

In the background section, we explore some of the

recent works in decarbonisation using ML. In the

methodology section, we discuss the architecture of

the DECART system and, then we discuss the

evidence-based insights based on the models in

Results section. Finally, we conclude the article with

key insights and recommendations.

2 BACKGROUND

There has been growing interest in the field of

transportation among researchers. Some have focused

on reducing congestion control through cost pricing

models (Kshirsagar et al., 2021) while others have

also explored alternative ways in which renewable

energy can be harnessed through road traffic

(Kshirsagar et al., 2021). Some works also

investigated optimizing the usage of fossil fuel in the

transport sector (Gota et al., n.d.; The Challenge of

Decarbonizing Heavy Transport Samantha Gross

Executive Summary, 2020). Researchers have

harnessed the power of ML to develop forecasting

models to gain valuable insights and understandings

on the impact of carbon emissions in road transport

(de Blas et al., 2020; Fu et al., 2019; Nouni et al.,

2021).

In Ireland, this optimization was facilitated with

the emergence of the carbon tax levied on fuel-based

vehicles based to the age of vehicle and amount of

carbon emitted by the vehicle. For every litre of diesel

in addition to its cost of €1.70/litre, the carbon tax

adds to 10.5 cents and the combination of Value

Added Tax and excise duty adds another 80 cents to

a litre of petrol (What Is the Carbon Tax? |

Bonkers.Ie, n.d.).

Recently, certain initiatives from the government

of Ireland have motivated people towards buying

electric vehicles (EVs). Some of the intangible

benefits; however, can be observed immediately with

the purchase of EVs are:

 The cost of the cheapest EV can be

anything between 15% to 50% higher than the

conventional fuel-based passenger cars. But

certain government grants make an attractive

comparison in prices to conventional cars;

 Reduction in Vehicle Registration Tax

summing up to €5000;

 Reductions in tolls of up to 50% for electric

and 25% for hybrid as compared to fuel-based

vehicles;

 A minimum saving in carbon tax of average

of around €100-€300 and which can extend up to

€1200 depending upon the age of fuel-based cars.

Figure 2: Electricity generation from various sources.

The rising number of EVs will cause an increase

in the electricity demand for sourcing the vehicles.

These growing demands of EVs can be significantly

met with renewable energy sources in Ireland.

Similarly, one such alternative for reduction in carbon

emission, which is growing rapidly in Ireland and

other European countries in the use of fuel cell

electric buses, which are powered by hydrogen

instead of electricity as a means of public transport.

Fuel cell vehicles have more demand due to less

refuelling time (10 mins), as compared to pure

electric (180-240 mins). There have been 100 fuel cell

electric buses active across Europe since 2018

SMARTGREENS 2022 - 11th International Conference on Smart Cities and Green ICT Systems

158

(Gunawan et al., 2021). The recent report (Dublin

Unveils Three New Hydrogen Fuel Cell Buses -

Intelligent Transport, n.d.) shows that this will be

soon active in Dublin County in Ireland as an

initiative for zero carbon emission. However, for both

electric as well as hydrogen fuelled vehicles,

renewable energy sources hold a greater potential as

a clean source of energy.

In the not-too-distant past, fuel was primarily the

main source of energy generation at the cost of

emitting large carbon emissions as observed in Figure

2. But since the past decade, there is a switch to fulfil

electricity needs from renewables as observed in

Figure 2 due to negligible carbon emission.

(Electricity | Energy Statistics In Ireland | SEAI, n.d.).

This suggests that renewables will play a significant

role in sourcing electricity requirements in the near

future.

3 METHODOLOGY

This section describes the proposed system

architecture, datasets used, and the ML models

designed for predictive analytics to gain insights from

the mobility patterns in Ireland.

3.1 Architecture of DECART System

and Datasets Used in

Experimentation

Figure 3: DECART Architecture.

The DECART system comprises of a suite of

regression and time-series forecasting models. This

system, with the evidence-based insights obtained

from the ML models, helps us to understand the

present carbon footprint and plan cautiously for the

future to address the challenge of mitigating carbon

emissions in the transport sector. The pipeline of the

DECART system, with its three phases is as illustrated

in Figure 3.

Phase 1 of the system helps us to understand the

carbon emitting factors for fuel-based vehicles using

ML models. With the insights the models, we propose

some of behavioural changes in the society

contributing towards reducing carbon emission.

Table 1: Dataset details.

Dataset Name Description Source

Phase 1:

Monitoring CO2

emissions from

passenger cars

Quantitative

statistics of

passenger car

(2017-20)

(Monitoring of

CO2 Emissions

from Passenger

Cars, n.d.)

Phase 2:

Daily motor

registrations

Number of EV

registrations in

Ireland

(2016-21)

(SIMI

Motorstats .n.d.)

Phase 3:

EnergyPro

Dataset

Information about

energy produced

every 10 minutes

period from wind

farms spread

across some Irish

counties

(2017 - 2018)

(Energy Pro -

Specialist

Windfarm

Analysts &

Managers, n.d.)

Phase 2, with its time-series forecasting models

predicts the growth of EVs in the near future. Phase 3

presents a case study using real-world dataset to

predict the potential of Ireland to source the growing

number of EVs in the future. The datasets used in

experimentation and the overview of their usage in

individual phases are discussed in Table 1.

3.2 ML Models Used in

Experimentation

This work has made use of regression and time series

forecasting models for predictive analytics.

3.2.1 Regression Models

Regression analysis is the process of mathematical

modeling to deploy a relation between dependent and

independent variables. We have used multivariate

linear regression and random forest regression in our

experimentations.

Multivariate Linear Regression: A mathematical

model that deploys a linear relationship between an

dependent variable, Y and multiple independent

variables X {X

, X

, …, X

} (Schneider et al., 2010).

The linear relation between these variables is

described as in equation (1). The multivariate

regression model is used in Phase 1 of the DECART

DECART: Planning for Decarbonising Transport Sector with Predictive Analytics - An Irish Case Study

159

system with dataset D1. The model predicts CO2

emission from the registration year, engine capacity

and manufacture type of the fuel-based vehicle.

Yab



b



1…b







where,

Y: dependent variable

𝑋



: independent variables

a:constant y  intercept

𝑏



: regression coefficient of the variable 𝑋



Random Forest Regressor: Random Forest

regression is the process of ensembling the

predictions from sub-trees, sampled independently

from the datasets to get accurate results. These

regression models are more robust to noise,

decreasing the generalization errors (Breiman, 2001).

The random forest regressor, too, is used in Phase 1

of the system and gives better accuracy over

multivariate regression model.

3.2.2 Time-series Models

The time series models are used in Phase 2 and 3 of

the system for forecasting the growth of electric

vehicles and estimating amount of energy generated

with wind plants.

A time series model is a mathematical model of

an event with a set of vectors x(t), where t = 0, 1, 2,

..., n measured over a period of time to predict the

patterns of the event in future. The process of fitting

a model to the data points of given time series is

termed as time series analysis (Hornik et al., 1989).

Over the Phases 2 and 3 in DECART system, we have

used following three models for forecasting:

Autoregressive (AR) Time Series Model: In AR

Model, a regression model is used to predict the

values at given time t, from the previous data points.

The equation for AR model is as explained in

equation (2) (Box, 1989). We have used AR model in

forecasting the energy generation from wind plant

with dataset D3 in Phase 3 of the system.



b



b





𝜀







where:



: value of event at time t



: intercept at y  axis



:slope coefficient



: value of event at time t  1



: error term

t: time

Autoregressive Integrated Moving Average

(ARIMA): An ARIMA model used the previous data

points and previous error points in a regression model

to predict the values at given time t. The mathematical

representation of ARIMA models is as in equation (3)

(John & Cochrane, 1997). ARIMA model was used

for predicting the energy generation from wind plants

in Phase 3 which performed better than the AR

model.

𝛗





1L







𝛉





𝛆



,i.e.

1  𝛗













1L







1𝛉









𝛆







where:

p: order of autoregressive part

d: order of integrated part

q: order of moving average part

Seasonal ARIMA (SARIMA): AR and ARIMA

models are used with stationary data, where the trends

in the data does not vary with time. However, for

forecasting the seasonal trends in the data, SARIMA

models are used (Agwata Nyamato et al., 2020). The

data is first converted to stationary format with pre-

processing and then the future values are forecasted

using the following equation (4):





𝐿





𝜑





𝐿



1𝐿







1𝐿







𝑦



Θ



𝐿



𝜃





𝐿



𝜀



𝑖.𝑒.Φ





𝐿





𝜑





𝐿



𝑧



Θ



𝐿



𝜃





𝐿



𝜀







where:

𝑧



: 𝑠𝑒𝑎𝑠𝑜𝑛𝑎𝑙𝑙𝑦 𝑑𝑖𝑓𝑓𝑒𝑟𝑒𝑛𝑐𝑒𝑑 𝑠𝑒𝑟𝑖𝑒𝑠

The dataset D2 for predicting the rate of growth

in electric vehicles in Ireland follows a seasonal trend.

Hence, SARIMA model was designed to fit this data.

ARMAX: In some of the instances of time series

models, the models are not only affected by past

values of the series, but also by the external factors

(Tang et al., 2000). e.g. Effect of change in wind

speed on the generation of wind energy. In such cases,

ARMAX models are used. We have used ARMAX

models for prediction of wind energy in Phase 3 of

DECART system.

Artificial Neural Networks (ANN): ANNs are widely

used along with AR, ARIMA and SARIMA models

for time series forecasting due to their unique

characteristics of not defining any assumption of the

data before training. Due to this significant property,

ANNs can be used for large dataset with greater

accuracy and universal approximations. Single

hidden layer neural networks are generally used for

time series forecasting (Khashei & Bijari, 2010). We

have used ANNs in Phase 2 and 3 of DECART system

time series analysis.

SMARTGREENS 2022 - 11th International Conference on Smart Cities and Green ICT Systems

160

3.2.3 Performance Metrics

The models were validated using performance

metrics such as RMSE and R2 score.

Root Mean Squared Error (RMSE): The Square root

of average squared deviation of forecasted values is

termed as RMSE. This measure gives an overview of

error during forecasting (Chicco et al., 2021). The

mathematical representation of RMSE is as given is

equation (5).

RMSE









y















where:



,y



,y



,…,y



: predicted values







,…,y



: observed values

n: number of observations

R2 Score: R2 score or the coefficient of

determination is the measure of the variance of the

dependent variable with respect to the independent

variable. In simple words, it is the measure of how

well the prediction values lie on the line of regression

(Wright, S., 1921). R2 score is defined as in equation

(6).

𝑅



1

∑



y











∑



 y













6

where:



,y



,y



,…,y



: predicted values







,…,y



: observed values

n: number of observations

4 RESULTS AND DISCUSSIONS

This section discusses several evidence-based

insights gained from the data.

4.1 Machine Learning Models for

Carbon Emission Estimation

Phase 1 of the system uses ML models to understand

the mobility patterns in Ireland and factors

contributing towards maximum carbon emission

from road transport.

The increasing emission of carbon can be

addressed by transitioning towards the alternative

options lowering the carbon emissions. One such

alternative is the use of EVs over fuel-based vehicles.

Although the transition towards electric and hybrid

vehicles will be a gradual process, certain behaviours

adopted in society such as recent inclusion of

hydrogen fuelled public transport, and motivating

society towards maximising the usage of e-bikes can

accelerate in lowering levels of carbon emissions.

In order to predict the growth of carbon tax with

the age of the vehicle, we designed a ML model to

estimate the amount of CO2 emitted from a passenger

vehicle from the following details: {manufacturer

name, year of registration, engine capacity}. Dataset

D1 was used for fitting a regression model to predict

the CO2 emitted. After some preliminary

experiments, we found that the best performing

model was random forest regressor with an R2 score

of 0.86 and RMSE of 0.36490. Overtraining was

avoided by diving the dataset into training and testing

sets in the ratio of 75% and 25% respectively. The

ML model will predict the gradual increase in cost of

maintenance which will be directly proportional to

carbon tax calculated according to age of vehicle.

The dataset was cleaned and pre-processed to fill

in the missing values, converting the categorical

variables into numerical ones, followed by

standardisation and scaling. We used K-Nearest

Neighbour Imputer to fill in the missing values. The

highly correlated features were extracted using

correlation matrix with heat maps which led to

important features for training: {year of registration,

manufacturer name and engine capacity}. The

amount of carbon emitted was the predictor feature.

After training the model, we predicted the carbon

emissions for the top two manufacturers in Ireland,

Audi and Subaru for the vehicles registered in 2017

over a period of next 3 years. For Audi vehicles, the

carbon emission increased by 0.7% in one year, but

increased by 3.71% and 2.56% in the following years.

Similarly for Subaru, the carbon emission was neutral

for one year, however, increased by 3.20% and 1.90%

in the next two years as illustrated in Figure 4.

(

)

Figure 4: Multivariate regression analysis to predict CO2

emissions for two top manufacturers in Ireland – Audi and

Subaru.

DECART: Planning for Decarbonising Transport Sector with Predictive Analytics - An Irish Case Study

161

The levels of carbon emission for the top two

manufacturers, Audi and Subaru were predicted from

the machine learning model which led to predict the

cost of carbon tax of such vehicles taking into

consideration the amount of co2 emitted from such

vehicles and the fact of increasing at €7.50 per tonne

of CO2 per year over the decade (CO2 Emissions

from Cars: Facts and Figures (Infographics) | News

| European Parliament, n.d.).

4.2 Machine Learning for Predicting

the Growth in Electric and Hybrid

Vehicles

The current trends in buying patterns of EVs and their

expected growth in future was analysed in Phase 2 of

the DECART system. The time span used was daily

registration of electric and hybrid vehicles from 2016

to 2021. After preliminary pre-processing over the

data, it was then used to forecast the growth in electric

vehicles over the next couple of years. We predicted

the growth of electric vehicles with two different

models, SARIMA and ANN. The statistical results of

both the models proved that SARIMA model

performed better over ANN. This may be due to the

fact as the data was only for the last five years, as the

trends in the electric vehicles can only be observed

since 2016. As the data increases in future, the

accuracy of the models can be increased. The ANN

model was designed with simple RNN and 100

epochs with 2 dense layers. Dataset D2 was used for

fitting the model divided in the ratio of 85% and 15%

respectively for training and testing to avoid

overtraining. The performance of ANN model was

less with R2 score of -0.57 and 0.046 as compared to

SARIMA models with a R2 score of 0.49 and 0.79 to

forecast the growth in electric and hybrid vehicles

over the next two years.

Figure 5: SARIMA Models for growth in electric vehicles.

Figure 5 illustrates the growth of EVs predicted

by the models. The spikes observed at the start and in

the middle of each year in Figure 5 is due to the dual-

registration system followed by the Irish Government

from 2013 (Has Ireland’s Dual Registration Plate

Changed the Way We Buy Cars, n.d.), where

motorists can purchase a brand-new car with new

number series in either January or July.

This dual registration policy has a huge impact on

the distribution of car sales across the year, where

July sales are almost at par with January sales. This

leads to an inevitable drop in car sales in the months

of Nov-Dec, as people prefer buying a new series.

The models were able to capture all such real-world

events. We can predict from the pattern that the

growth in electric and hybrid vehicles will increase

by 54.41% by the end of 2023. This will lead to a high

demand for electricity to charge the electric vehicles

in future. To meet those demands, we can make use

of the energy generated from renewable sources.

4.3 Machine Learning Models for

Predicting Energy from Wind

Farms

The Phase 3 of DECART system calculates the

potential of wind energy as a renewable source to

cope up with the rising demands in electricity.

To understand the capacity of wind energy’s use

in the transportation sector, the graph from Figure 6

illustrates that Ireland compares favourably with its

European Counterparts in generating onshore wind

energy. According to Wind Europe Annual Statistics

2019 (Wind Energy in Europe in 2019, 2018), Ireland

had the highest share of wind in its electricity mix

(33%), followed by Denmark, Portugal and Germany.

Similarly, according to SEAI, (Renewables | Energy

Statistics In Ireland | SEAI, n.d.), electricity

generation in Ireland in 2020, wind energy

contributed to generate 80% of clean energy.

Figure 6: Comparative analysis of potential of on shore

wind generation of Ireland against Europe.

We fitted the Dataset D3 using three different

models- SARIMA, ARMAX and ANN to predict the

SMARTGREENS 2022 - 11th International Conference on Smart Cities and Green ICT Systems

162

amount of energy generated from the Energy Pro

(Welcome to Energy Pro - Specialist Windfarm

Analysts & Managers, n.d.). The dataset was divided

into 70% and 30% respectively for training and

testing purposes. The experiments showed that ANN

model performed better among the three. Hence, we

then predicted the daily energy generated from one of

the five plants given the information about the energy

generated every 10 mins. The model used the data for

energy generated for every 10 minutes in the last 24

hours and predicted the energy generation in the next

24 hours of the day.

After training the model, the results show that the

average cumulative energy generated in 24 hours or

1440 minutes is 5809 KWh. The average

consumption of electric is 65.26KWh for every 337.8

km(Compare Electric Vehicles - EV Database, n.d.),

then we can safely say that a single plant can source

around 89 electric cars in 24 hours.

According to Wind Energy Ireland (Facts &

Stats, n.d.), there are 300 wind farms in Ireland.

Considering this number, all the 300 wind farms

have the potential to source the transport sector can

be powered through clean energy by utilising wind

farms to power 26,700 electric vehicles in a single

day. Hence, wind farms can play a significant role in

the decarbonisation of the transport sector.

The following Table 2 shows the statistical

analysis of the models used for training the datasets

and their results. The best results are highlighted in

bold.

Table 2: Comparative Analysis of the ML models.

Abbreviations used for Dataset D2 are Electric Vehicles

(EV) and Hybrid Vehicles (HV).

Dataset Model Name R2 Score RMSE

Random Forest

Regresso

0.86 0.36490

SARIMA

0.49 (EV)

0.79 (HV)

ANN

-0.43(EV)

0.60(HV)

549.37(EV)

1385.75(HV)

ARIMA 0.916 57.69

ARMAX 0.67 52.41

ANN 0.918 25.48

5 CONCLUSIONS AND FUTURE

SCOPE

In this research work, we performed an in-depth

analysis on the current emission levels in the transport

sector in Ireland. Regression and time-series

forecasting models were designed on real -world

datasets. Through ML models we were able to gain

critical and hidden insights on the mobility and

behaviour patterns in the transportation sector.

Furthermore, to achieve the ambitious plans of zero

emissions in transportation, we discussed that clean

energy, majorly sourced through wind farms, can be

used to meet the electricity requirements of the rise in

electric cars growth. Future scope of this article

suggests case studies on other potential renewable

energy sources with less or zero carbon emissions for

achieving the goal of decarbonising transport in

Ireland. Other aspects of future scope involve making

the models dynamic to adapt to the changing trends

in the transport sector as well as tuning and refining

the hyperparameters of the machine learning models

with Grammatical Evolution. Finally, we conclude

that decarbonising transportation will highly impact

societies and communities, leading to improved air

quality, lower noise levels, less waste in form of

emissions and thus hugely contributing towards better

health and wellbeing. Similarly, with innovation in

battery life of EVs may lead towards widespread

acceptance of electric vehicles in near future.

ACKNOWLEDGEMENTS

This work is supported by Science Foundation Ireland

grant #16/IA/4605.

REFERENCES

Agwata Nyamato, F., Wanjoya, A., & Mageto, T. (2020).

Comparative Analysis of Sarima and Setar Models in

Predicting Pneumonia Cases in Kenya. International

Journal of Data Science and Analysis, 6(1), 48.

https://doi.org/10.11648/J.IJDSA.20200601.16

Box, G. E. P. (1989). An Unexpected Route to Time Series.

Breiman, L. (2001). Random Forests. Machine Learning

2001 45:1, 45(1), 5–32. https://doi.org/10.1023/

A:1010933404324

Chicco, D., Warrens, M. J., & Jurman, G. (2021). The

coefficient of determination R-squared is more

informative than SMAPE, MAE, MAPE, MSE and

RMSE in regression analysis evaluation. PeerJ

Computer Science, 7, 1–24.

https://doi.org/10.7717/PEERJ-CS.623/SUPP-1

CO2 emissions from cars: facts and figures (infographics)

| News | European Parliament. (n.d.). Retrieved

January 27, 2022, from https://www.europarl.

europa.eu/news/en/headlines/society/20190313STO31

218/co2-emissions-from-cars-facts-and-figures-

infographics

Compare electric vehicles - EV Database. (n.d.). Retrieved

January 27, 2022, from https://ev-database.org

DECART: Planning for Decarbonising Transport Sector with Predictive Analytics - An Irish Case Study

163

de Blas, I., Mediavilla, M., Capellán-Pérez, I., & Duce, C.

(2020). The limits of transport decarbonization under the

current growth paradigm. Energy Strategy Reviews, 32,

100543.https://doi.org/10.1016/J.ESR.2020.100543

Dublin unveils three new hydrogen fuel cell buses -

Intelligent Transport. (n.d.). Retrieved March 8, 2022,

from https://www.intelligenttransport.com/transport-

news/126719/dublin-hydrogen-buses/

Electric Cars & Electric Vehicles (EV) In Ireland | SEAI.

(n.d.). Retrieved January 27, 2022, from

https://www.seai.ie/technologies/electric-vehicles/

Electricity | Energy Statistics In Ireland | SEAI. (n.d.).

Retrieved January 27, 2022, from https://www.seai.ie/

data-and-insights/seai-statistics/key-statistics/

electricity/

Facts & Stats. (n.d.). Retrieved January 28, 2022, from

https://windenergyireland.com/about-wind/facts-stats

Fu, P., Pudjianto, D., Zhang, X., & Strbac, G. (2019).

Evaluating Strategies for Decarbonising the Transport

Sector in Great Britain. 2019 IEEE Milan PowerTech,

1–6. https://doi.org/10.1109/PTC.2019.8810865

Gota, S., Huizenga, C., Peet, K., Medimorec, N., & Bakker,

S. (n.d.). Decarbonising Transport to Achieve Paris

Agreement Targets. Retrieved January 27, 2022, from

https://link.springer.com/article/10.1007%2Fs12053-

018-9671-3

Gunawan, T. A., Williamson, I., Raine, D., & Monaghan,

R. F. D. (2021). Decarbonising city bus networks in

Ireland with renewable hydrogen. International

Journal of Hydrogen Energy, 46(57), 28870–28886.

https://doi.org/https://doi.org/10.1016/j.ijhydene.2020.

11.164

Has Ireland’s dual registration plate changed the way we

buy cars? (n.d.). Retrieved January 27, 2022, from

https://startmonday.ie/blog/ireland-dual-registration-

plate

Hornik, K., Stinchcombe, M., & White, H. (1989).

Multilayer feedforward networks are universal

approximators. Neural Networks, 2(5), 359–366.

https://doi.org/10.1016/0893-6080(89)90020-8

John, °, & Cochrane, H. H. (1997). Time Series for

Macroeconomics and Finance.

Khashei, M., & Bijari, M. (2010). An artificial neural

network (p,d,q) model for timeseries forecasting.

Expert Systems with Applications, 37(1), 479–489.

https://doi.org/https://doi.org/10.1016/j.eswa.2009.05.

044

Kshirsagar, M., Lahoti, R., More, T., & Ryan, C. (n.d.).

GREECOPE: Green Computing with Piezoelectric

Effect. https://doi.org/10.5220/0010445801640171

Kshirsagar, M., More, T., Lahoti, R., Adgaonkar, S., Jain,

S., Ryan, C., & Kshirsagar, V. (2021). GREE-COCO:

Green Artificial Intelligence Powered Cost Pricing

Models for Congestion Control. ICAART 2021 -

Proceedings of the 13th International Conference on

Agents and Artificial Intelligence, 2, 916–923.

https://doi.org/10.5220/0010261209160923

Monitoring of CO2 emissions from passenger cars –

Regulation (EU) No 631/2019 — European

Environment Agency. (n.d.). Retrieved January 27,

2022, from https://www.eea.europa.eu/data-and-

maps/data/co2-cars-emission-16

Nouni, M. R., Jha, P., Sarkhel, R., Banerjee, C., Tripathi,

A. K., & Manna, J. (2021). Alternative fuels for

decarbonisation of road transport sector in India:

Options, present status, opportunities, and challenges.

Fuel, 305, 121583. https://doi.org/10.1016/J.FUEL.

2021.121583

Renewables | Energy Statistics In Ireland | SEAI. (n.d.).

Retrieved January 27, 2022, from https://www.seai.ie/

data-and-insights/seai-statistics/key-statistics/

renewables/

Schneider, A., Hommel, G., & Blettner, M. (2010). Linear

Regression Analysis. Deutsches Arzteblatt, 107(44),

776–782. https://doi.org/10.3238/ARZTEBL.2010.

0776

SIMI Motorstats - the complete online vehicle index for

Ireland | Stats. (n.d.). Retrieved January 27, 2022, from

https://stats.beepbeep.ie/

Summary by gas | Environmental Protection Agency. (n.d.).

Retrieved January 27, 2022, from https://www.epa.ie/

our-services/monitoring--assessment/climate-

change/ghg/summary-by-gas/#d.en.84356

Tang, W. K., Wong, Y. K., & Fung, E. H. K. (2000).

Simulations of ARMA and ARMAX Models for a

Machining Process.

The challenge of decarbonizing heavy transport samantha

gross executive summary. (2020).

Transport | Energy Statistics In Ireland | SEAI. (n.d.).

Retrieved January 27, 2022, from https://www.seai.ie/

data-and-insights/seai-statistics/key-statistics/transport/

Welcome to Energy Pro - Specialist Windfarm Analysts &

Managers. (n.d.). Retrieved January 27, 2022, from

https://www.energypro.ie/

What is the carbon tax? | bonkers.ie. (n.d.). Retrieved

January 27, 2022, from https://www.bonkers.ie/

guides/gas-electricity/what-is-carbon-tax/

Wind energy in Europe in 2019. (2018).

Wright, S. (1921) Correlation and Causation. Journal of

Agricultural Research, 20, 557-585. - References -

Scientific Research Publishing. (n.d.). Retrieved

January 27, 2022, from https://www.scirp.org

SMARTGREENS 2022 - 11th International Conference on Smart Cities and Green ICT Systems

164