B-ETS: A Trusted Blockchain-based Emissions Trading System for

Vehicle-to-Vehicle Networks

Lam Duc Nguyen

1 a

, Amari N. Lewis

2 b

, Israel Leyva-Mayorga

1 c

Amelia Regan

2 d

and Petar Popovski

1 e

Department of Electronic System , Aalborg University, Denmark

Department of Computer Science, University of California, Irvine, U.S.A.

Keywords:

V2V, Distributed Ledger Technologies, Blockchain, Emission Allowances Trading.

Abstract:

Urban areas are negatively impacted by Carbon Dioxide (CO

) and Nitrogen Oxide (NO

) emissions. In

order to achieve a cost-effective reduction of greenhouse gas emissions and to combat climate change, the

European Union (EU) introduced an Emissions Trading System (ETS) where organizations can buy or receive

emission allowances as needed. The current ETS is a centralized one, consisting of a set of complex rules.

It is currently administered at the organizational level and is used for ﬁxed-point sources of pollution such as

factories, power plants, and reﬁneries. However, the current ETS cannot efﬁciently cope with vehicle mobility,

even though vehicles are one of the primary sources of CO

and NO

emissions. In this study, we propose

a new distributed Blockchain-based emissions allowance trading system called B-ETS. This system enables

transparent and trustworthy data exchange as well as trading of allowances among vehicles, relying on vehicle-

to-vehicle communication. In addition, we introduce an economic incentive-based mechanism that appeals to

individual drivers and leads them to modify their driving behavior in order to reduce emissions. The efﬁciency

of the proposed system is studied through extensive simulations, showing how increased vehicle connectivity

can lead to reduction of the emissions generated from those vehicles. We demonstrate that our method can be

used for full life-cycle monitoring and fuel economy reporting. This leads us to conjecture that the proposed

system could lead to important behavioural changes among the drivers.

1 INTRODUCTION

Typical passenger vehicles emit about 4.6 metric

tons of carbon dioxide CO

per year. The Euro-

pean Union’s Emission Trading System (EU-ETS) is

the world’s ﬁrst major carbon trading market with

the main goal to combat climate change and reduce

Greenhouse Gas (GHG) emissions in a cost effec-

tive way. The EU-ETS works on a Cap-and-Trade

(CAP) principle which allows companies that gener-

ate point source emissions to receive or buy emission

allowances, which can be traded as needed (Com-

mision, 2015). The process of our B-ETS CAP pro-

gram is described in Figure 1, where it is seen that

it is based on a complex centralized method of trad-

https://orcid.org/0000-0003-0161-3055

https://orcid.org/0000-0001-9685-4403

https://orcid.org/0000-0002-7116-397X

https://orcid.org/0000-0003-4220-2148

https://orcid.org/0000-0001-6195-4797

ing among the organizations involved. The ﬁrst step

in CAP is to make a centralized decision (by a regu-

latory agency or some other collective entity) on the

aggregate quantity of emissions allowed. Allowances

are then written in accordance with this quantity, after

which they are distributed among the sources respon-

sible for the emissions.

Since 2018, the EU-ETS began penalizing vehi-

cle manufacturers for exceeding the targets for ﬂeet-

wide emissions for new vehicles sold in any given

year. The manufacturers are required to pay an excess

emissions premium for each newly registered car. A

penalty of e95 must be paid for each gram per km

above the target (Commision, 2015) and the target

of CO

for the 2020-2021 period is set to 95 grams

per km. In this work, we address the need for a new

trusted and distributed system which can audit emis-

sions at the vehicle-level.

The emerging Distributed Ledger Technologies

(DLTs) brought a new era of distributed peer-to-peer

applications and guarantees trust among involved par-

Nguyen, L., Lewis, A., Leyva-Mayorga, I., Regan, A. and Popovski, P.

B-ETS: A Trusted Blockchain-based Emissions Trading System for Vehicle-to-Vehicle Networks.

DOI: 10.5220/0010460501710179

In Proceedings of the 7th International Conference on Vehicle Technology and Intelligent Transport Systems (VEHITS 2021), pages 171-179

ISBN: 978-989-758-513-5

171

CAP CAP

Surplus of

Emission

Allowances

Shortage of

Emission

Allowances

Emission Allowances

Credit

Emission Trading

Sale Purchase

EMITTER A

EMITTER B

Carbon Market

EAB(A) EAB(B)

Excess

Available

Distributed Ledger & Smart Contract

Transactions

B-ETS

Standard CAP

Figure 1: B-ETS general architecture.

ties. The terms DLT and Blockchain will be used in-

terchangeably throughout this paper, Blockchains are

a type of DLTs, where chains of blocks are made up of

digital pieces of information called transactions and

every node maintains a copy of the ledger. In DLTs,

the authentication process relies on consensus among

multiple nodes in the network (Nguyen et al., 2020).

Each record has a timestamp and cryptographic signa-

ture; the system is secure and maintains a transaction

ledger that is immutable and traceable. Ultimately,

the goal of applying Blockchain technology to the

transportation industry is to provide a fully distributed

ETS system that can encourage direct communication

between producers and consumers. A primary reason

to embrace new DLTs is to bypass the administrative

pitfalls that have plagued current emissions monitor-

ing systems. Security is another aspect that motivates

this approach. For instance, data pollution attacks are

incredibly dangerous, these attacks typically occur in

centralized systems and involve an adversary trying

to modify the content of the packets and then forward

the corrupted messages to neighboring nodes. The in-

tegration of Blockchain in individual carbon trading

will accelerate the involvement of the public in car-

bon trading and sensitize society to individual level

carbon footprints.

Current V2V approaches have limitations such

as: the need for trusted third-party entities, security

hardware, higher communication and storage over-

head, high implementation costs, and issues related

to the conﬁdentiality of data. Studies (Zheng et al.,

2017), (Alsabaan et al., 2013), (Li et al., 2018) have

strictly considered Vehicle-to-Infrastructure (V2I) ap-

proaches incorporating additional resources such as

On-board Units (OBUs) and Roadside Units (RSUs).

Eckert et al. develop a carbon Blockchain framework

for Smart Mobility Data-Market as a trading system

for CO

in the form of carbon tokens in (Eckert et al.,

2019). The evaluation is done on the user and vehicu-

lar levels. Pan et al. outlined some advantages of the

use of Blockchain in ETS namely safety and reliabil-

ity, efﬁciency, convenience, openness and inclusive-

ness (Pan et al., 2019). That work was not concerned

with V2V networks or mobile carbon emissions trad-

ing, but it did introduce the concept of personal car-

bon emissions trading which could be applied in ve-

hicular networks.

In this study, we ﬁrst tackle the challenges of the

current EU-ETS system by proposing a distributed

emissions allowance trading system called B-ETS.

The system creates an account for the emissions gen-

erated from each vehicle and allows exchanges among

vehicles in a trusted manner based on Blockchain

and Smart Contracts. In B-ETS, each vehicle acts as

a light client in the global Blockchain network and

manages its own Emission Allowance Balance (EAB)

which is reset at the beginning of each day. The EAB

data is recorded transparently and immutably in the

distributed ledger. It should be noted that we use one

day as our unit of time without loss of generality. Any

other unit (a week, a month) could be used if that

seemed more suitable.

Then, we introduce an economic incentive-based

mechanism which attracts drivers to change their driv-

ing behavior in order to reduce emissions. Each vehi-

cle’s generated emissions are calculated and the data

are recorded immutably in the distributed ledger. If

the emission level is higher than the deﬁned thresh-

old, the EAB will be reduced. If the EAB goes to

zero, the driver needs to buy credits in the form of

EAB from others.

The proposed V2V-based allowance trading sys-

tem would not replace the in-service ﬂeet-wide moni-

toring required by the EU-ETS plan. Rather, it would

complement that plan by making it the responsibility

of drivers to meet personal emissions targets. That is,

without individualized feedback, drivers cannot mea-

sure the environmental impacts of their actions. Fur-

thermore, without incentives, they might not be will-

ing to contribute to environmental sustainability.

Given the proposed B-ETS system, vehicles par-

ticipating in the program will be inﬂuenced by the

economic incentive. Drivers are more prone to be-

have better when their EAB and driving privileges

are at stake. If drivers contribute to lower emissions

(i.e., demonstrate healthy driving habits), their EAB

will increase or remain positive. Essentially, drivers

VEHITS 2021 - 7th International Conference on Vehicle Technology and Intelligent Transport Systems

172

want to avoid having to purchase credits from others

or having a negative EAB balance as this could lead

to driving restrictions.

Our mechanism can be compared to the trafﬁc

point penalty system in the U.S., Canada and other

countries. As punishment for committing trafﬁc vio-

lations, the drivers risk the suspension or revocation

of their license based on a point-record mechanism

in place. As a result, the Department of Motor Vehi-

cles (DMV) can revoke the driver’s license of that per-

son and they are not allowed to drive any motor vehi-

cle. In order to mitigate the social cost of license sus-

pensions, point-removal systems exist for most point-

record drivers licenses (Dionne et al., 2011). In con-

trast, our system proposes a daily (or weekly or other

period as appropriate) record of associated driving be-

haviors with vehicle emissions data and individual ac-

counts.

The execution of the smart contract guarantees

trust among vehicles and driving habits, (e.g, avoid

idling, speeding, etc) and CO

levels. Vehicles in the

system are alerted via rules deﬁned in the smart con-

tract to reduce emissions (EEA, 2019) (Zheng et al.,

2017).

Our solution to reducing vehicle CO

emissions

involves the use of DLT-enabled emissions monitor-

ing, which could be applicable to any market world-

wide. In this work, we focus on the EU, but, our

method can comply with regulations in China and

could be implemented in the US to measure life-

cycle Corporate Average Fuel Efﬁciency (CAFE)

standards.

The contributions of this study are described as

follows:

• First, we propose a distributed Blockchain-based

emission trading system named B-ETS that will

meet the requirements of the EU-ETS plan for

reducing vehicular emissions. B-ETS overcomes

the disadvantages of current centralized ETS sys-

tems and provides a trustworthy approach for ex-

changing data in vehicle-to-vehicle networks.

• Second, we introduce an economic incentive-

based system which motivates drivers to reduce

fuel consumption and pollution. Based on the au-

tonomous execution of smart contracts, the incen-

tive mechanism is guaranteed to work in a trusted

and distributed manner.

• Third, realizing the lack of communication and

computation analysis in Blockchain-enabled ve-

hicle networks, we present a theoretical model to

derive the communication efﬁciency of the pro-

posed system B-ETS.

Distributed Ledger

NOx

CO2

Road

V2V

EAB Trading

Transactions

Figure 2: Blockchain-enabled vehicular emission trading

system.

The remainder of this paper is organized as follows.

In the next section, we present the system model and

analysis. In section III, the performance evaluation is

outlined including our results. Finally, in section IV,

we provide our conclusion and plan future work.

2 SYSTEM MODEL AND

ANALYSIS

2.1 Blockchain as a Ledger for VANET

The system operates within periods of duration T . In

this section, we describe the two major system com-

ponents: the vehicles and the distributed ledger, fol-

lowed by the selected model for CO

emissions. Ta-

ble 1 presents the nomenclature used throughout the

paper.

2.1.1 Vehicles

Let V be the set of vehicles in the system. An On-

Board-Unit (OBU) is installed in each vehicle i ∈ V

in the Blockchain-based VANET. The OBU performs

light tasks, including collection and transmission data

to other vehicles according to the IEEE 802.11p com-

munication standard, and provides support to passen-

gers and drivers.

Within each system period of duration T , the CO

emission monitoring system takes samples of the av-

erage CO

emissions per km in each vehicle i and up-

dates the ledger. The CO

is sampled at ﬁxed intervals

of duration T

< T hours. The sample taken by vehi-

cle i at time t ∈

{

0,T

,2T

,... ,T

}

hours is denoted as

(t) and consists of the taken measurement, the vehi-

cle ID i, and a timestamp, generated as a function of t.

B-ETS: A Trusted Blockchain-based Emissions Trading System for Vehicle-to-Vehicle Networks

173

Table 1: Nomenclature.

Symbols Descriptions

T Considered system period [hours]

V Set of vehicles

i Vehicle i ∈ V

sampling period

(t) Average CO

emissions per km for

vehicle i at time t

(t) Emission allowance balance of

vehicle i at time t ∈ [0, T )

(t) Penalty/tax for vehicle i at time t

(t) Incentive (subsidy) for vehicle i at time t

total

Total allowed latency

trans

Communication latency

comp

Blockchain veriﬁcation latency

R Communication data rate [packets/s]

(t) Speed of vehicle i at time t [km/h]

Blockchain block size in bits

i j

(t) Relative speed between i and j at time t

i j

Communication Range between i and j

i, j

(t) Allowances sold by j to i at time t

T Maximum allowed CO

emissions gener-

ated by vehicles per km.

The amount of CO

generated at the vehicles is reset

to zero at the beginning of each period of duration T ,

hence, ε

(0) = 0.

2.1.2 Distributed Ledger

The distributed ledger records the data exchange his-

tory grouped into blocks and linked together chrono-

logically. To minimize the cost of storage, the sens-

ing data could be hashed and stored at more power-

ful nodes, and only the hash of data is recorded to

the blockchain. Next, a conﬁrmation message is sent

back to conﬁrm that the data has been added to the

ledger as presented in Figure 4. We assume that the

data services (e.g., data storage, trading and task dis-

patching) are implemented on top of a permissionless

Blockchain (Nguyen et al., 2021).

In a permissionless blockchain, any peer can join

and leave the network at any time as a reader or writer.

Permissionless Blockchains are open and decentral-

ized with no central authority. Bitcoin and Ethereum

are instances of permissionless Blockchains. In con-

trast, in permissioned Blockchains a central author-

ity decides and attributes the right to individual peers

to participate in the write or read operations of the

blockchain. Examples of these include Hyperledger

Fabric and R3 Corda (Wust and Gervais, 2018).

The sensing data are formatted into transactions

of ﬁxed size. To enhance efﬁciency, only the digest of

each transaction is stored on the chain, and the content

of the transactions are stored by each consensus node

off-chain or at the IPFS storage.

2.1.3 Emissions Model

The amount of CO

generated from vehicles depends

on various factors such as: national average age dis-

tributions, vehicle activity speeds, operating modes,

vehicle-miles traveled, starts and idling, temperatures,

maintenance, anti-tampering programs, and average

gasoline fuel properties in that calendar year (EPA,

2018). The calculation of emissions in our simula-

tions are based on the Handbook Emission Factors for

Road Transport V3.1 (HBEFA), the model was im-

plemented by extracting the data from HBEFA and

ﬁtting them to a continuous function obtained by sim-

plifying the function of the power the vehicle engine

must produce to overcome the driving resistance force

(Krajzewicz et al., 2015).

2.2 Emission Allowances Trading

2.2.1 Traditional Cap-and-Trade

Traditionally, cap-and-trade commonly refers to gov-

ernmental regulations and programs in place to limit

the levels of CO

emissions as a result of industry ac-

tivity. As brieﬂy mentioned, the EU-ETS works on a

cap and trade principle, where the cap is a dynamic

limitation, set on the total amount of GHG emitted by

installations covered by the system. Within the sys-

tem, companies receive or buy emission allowances

which can be traded. Although, vehicular emissions

were not initially considered, in 2006, researchers at

MIT joint program on the science and policy of global

change introduced the implementation of a cap-and-

trade policy for vehicles. Their central conclusion in-

dicated that there are important efﬁciency gains to be

realized by including transport emissions under the

CAP and by integrating pre-existing programs, such

as CAFE, and cap-and-trade systems (Ellerman et al.,

2006).

2.2.2 B-ETS Framework

Our B-ETS framework considers an economy where

vehicles produce goods over a system period

[0,T ] hours. Therefore, each vehicle i acts as a wal-

let in the Blockchain network and its EAB at time t

is denoted as B

(t) ∈ R. In the system, the updates

to the EAB are triggered by the sampling of the CO

emissions of the vehicles, hence, the system operates

at speciﬁc times t ∈ {0, T

,2T

,. .. }. At the beginning

of each period of duration T , the EAB of each vehicle

i is reset to a pre-deﬁned value B

(0). So, the EAB

VEHITS 2021 - 7th International Conference on Vehicle Technology and Intelligent Transport Systems

174

0 20 40 60 80 100

Average Speed (mph)

500

1000

1500

2000

CO2 (g/mi)

Congestion

Mitigation

Traffic Smotthing

Speed

Management

Figure 3: CO

emissions (grams/mile) as a function of av-

erage speed (mph) (Cappiello et al., 2002).

cannot be accumulated between subsequent periods.

However, if i were to hold on to this initial allowance

endowment until the end of the period, it would be

able to offset the system’s cap by up to B

(0) units of

emissions credits. This is the cap aspect in our B-ETS

scheme.

The EAB pertains to an individual account

in which the allowances are used and exchanged

amongst vehicles for environmental sustainability. In

order to offset penalties, the vehicles with low bal-

ances may engage in buying allowances from vehi-

cles that expect to meet demand with fewer emissions

than their own cap. This is our trade aspect of B-ETS

framework.

Remark 1: A CAP program is only feasible in sce-

narios where the vehicles have a positive allowance

balance at the beginning of the periods. Hence, the

following inequality must hold:

(0) > 0 for all i ∈ V . (1)

The maximum allowed CO

emissions generated

by vehicles per km is denoted as T (which is deﬁned

as a rule in smart contract). If ε

(t) > T , then our ini-

tial smart contract is executed to generate an alert to i

to reduce vehicle speed as a direct solution to reduce

amount of generated CO

, and the ﬁne p

(t) will be

deducted from its EAB. In contrast, the subsidy s

(t)

will be endowed to i for maintaining the CO

emis-

sions below T .

The values of p

(t) and s

(t) are considered as

taxes and subsidies for vehicle i that depend on their

behavior. The incentive may help encourage the

driver to control their driving behavior to avoid gen-

erating CO

higher than the allowed standard. The

driver needs to choose between receiving an incen-

tive by reducing amount of emissions or being ﬁned

due to overloaded generated emissions. The penalties

and subsidies are computed based on the theoretical

model presented in (Fullerton and West, 2000) which

depends on various vehicular factors.

In order to increase the subsidies and reduce the

penalties, the drivers can follow strategies deﬁned in

smart contracts. For example, Figure 3 shows that

is a function of average speed. First, we observe

that very low average speeds generally represent stop

and start driving periods, and vehicles traveling in

short distances, in these cases, the emission rates are

quite high. In this period, the smart contract deﬁnes

rules to increase trafﬁc speeds and reduce congestion

by, for instance avoiding high trafﬁc roads to reduce

emissions. Second, when the speed of the vehicle is

too high, it demands high engine loads which require

more fuel, leading to higher CO

emission rates. The

techniques to manage high speeds are implemented in

the contracts which recommends the drivers to simply

reduce their speeds. Consequently, moderate speeds

of around 40 to 60 mph are ideal speeds which re-

duce emissions and will give the drivers incentive to

improve their balances.

In addition, the EAB can be traded among vehi-

cles based on predeﬁned smart contracts. Whenever

(t) < 0, there will be a red alert issued to i for hav-

ing a negative-balance. This alert is in the form of

penalties, or restricted road access to zero-balance ve-

hicles. In this cases, the vehicles can either wait until

the next period for their EAB of to be reset or buy

the EAB from other vehicles. We consider the case

of vehicles exchanging EAB on-road via execution

of smart contract and distributed ledger. For this, let

i, j

(t) be the amount of allowances sold by vehicle j

from vehicle i at time t. These operations are recorded

in the distributed ledger.

Remark 2: The vehicle j cannot sell more al-

lowances e

i j

(t) than it actually owns. In other words,

i cannot buy more than is actually available. Hence,

i, j

(t) ≤ B

(t),for all j ∈ V , t ∈ [0,T ) (2)

2.2.3 Operation

The operation of the vehicle’s emission allowance

trading is performed in the following steps:

Step 1. Publishing Data. Each vehicle i ∈

V computes its own average generated CO

emis-

sions, namely, ε

(t) as shown in Figure 2 for t ∈

{0,T

,2T

,. .. ,T }. These values are published to light

ledger version of each vehicle and synchronized with

the full ledger stored in DLT full nodes.

Step 2. Emission Control. The generated CO

emis-

sions data is recorded in the ledger, and the smart con-

tract with the predeﬁned rules is executed. These rules

are characterized by two categories namely maximum

emissions and actions: warnings, alerts and re-

minders. The published CO

data is formatted and

arranged into blocks to be veriﬁed through a consen-

sus process. If ε

(t) > T , the smart contract issues

an alert message to i to control its driving behavior

B-ETS: A Trusted Blockchain-based Emissions Trading System for Vehicle-to-Vehicle Networks

175

Confirmation

Ask for EAB, e

i,j

(t)

Agreement

Buying EAB e

i,j

(t) from j

Selling EAB to i

Settlement

(t) B

(t)

+ e

i,j

(t)

Mining

DLT Miners

(t) B

(t)

- e

i,j

(t)

(t) B

(t-T

)

- p

(t)

(t) B

(t-T

)

(t)

Figure 4: Communication System.

and p

(t) is deducted from B

(t) via smart contract.

Hence, the ledger is updated with the value B

(t) ←

(t − T

) − p

(t). In contrast, if j has maintained a

safe speed and emitted reasonable amounts of CO

, it

receives an incentive s

(t) to its balance. Hence, the

ledger is updated with B

(t) ← B

(t − T

) + s

(t).

Step 3. Emission Allowance Trading. After receiv-

ing a conﬁrmation with the required action from the

smart contract, if B

(t) < 0, then i needs to re-charge

its EAB by buying emission allowances from other

vehicles. For example, i makes an agreement with j to

buy an amount of emission allowances e

i, j

(t). Then,

i sends the buying request for the amount e

i, j

(t) to

execute a smart contract. Next, j updates the smart

contract with a selling request and e

i, j

(t).

Step 4. Settlement. Finally, the EAB of each vehicle

is updated and settled as B

(t) ← B

(t) + e

i, j

(t) and

(t) ← B

(t)− e

i, j

(t).

In this paper, we focus on the efﬁciency of V2V

communication between vehicles for exchanging data

and trading EAB. We study these in terms of end-

to-end latency which includes the transmission la-

tency among vehicles and computation latency of

Blockchain validation processes.

2.3 Joint Communication and

Computation Model

In this section, we deﬁne the total available time for

communication between two vehicles and the impact

of the Blockchain computation latency.

0 25 50 75 100 125 150

−1

= 10 m

= 50 m

= 100 m

= 500 m

= 1000 m

Relative speed kv

(t)k (km/h)

Upper bound for L

total

with

constant relative speed (s)

Figure 5: Upper bound fr the total latency L

total

for com-

munication between vehicles with constant speed.

Let (x

(t),y

(t)) denote the position of vehicle i at

time t. If communication is initiated at time t, the

time in which two vehicles, namely i and j, are avail-

able for communication is deﬁned by 1) their commu-

nication range r

i j

2) their positions (x

(t),y

(t)) and

(t),y

(t)), 3) their relative speed, given by vector

i j

(t) = v

(t) − v

(t) km/h. Clearly, to initiate com-

munication at time t, the distance between the vehi-

cles must be

i, j

(t) =

(t)− x

(t))

+ (y

(t)− y

(t))

≤ r

i j

(3)

Then, the total time for V2V communication between

vehicles i and j at time t is given as

total

(t) = min

`∈R





` > t, d

i, j

(`) ≥ r

i j



−t. (4)

It is immediate to see that L

total

→ ∞ when

i j

)k → 0 for all t

∈ [t,`]. This implies that when-

ever both vehicles move in the same direction and

with near equal speed, they will have a long time

total

to communicate and exchange messages. Fur-

thermore, it can be seen that, the upper bound for

total

seconds for the case where the relative speed

i j

) km/h remains constant for all t

∈ [t, `] is

total

≤

7.2r

i j

(t)k

(5)

Figure 5 illustrates the upper bound for L

total

with

several values of kv

i j

(t)k.

The time needed to complete a trade between two

vehicles i and j in B-ETS can be divided into two

parts. First, the communication between vehicles,

simply denoted as L

trans

, and, second, the time needed

for the veriﬁcation process in the distributed ledger,

denoted as L

comp

. Hence, a trade is completed suc-

cessfully if and only if

total

≥ L

trans

+ L

comp

. (6)

VEHITS 2021 - 7th International Conference on Vehicle Technology and Intelligent Transport Systems

176

From there, we deﬁne the probability of successful

data trading as

success

= Pr(L

comp

+ L

trans

≤ L

total

) (7)

The latency for the communication between vehi-

cles i and j, denoted simply as L

trans

, is a function of

the amount of data that must be exchanged and the

effective data rate selected for communication R in

packets per second. The data that must be exchanges

is deﬁned by the block size of the Blockchain, de-

noted as S

. On the other hand, the effective data

rate R is determined by the implemented protocol, the

wireless conditions (e.g., fading, noise, interference,

and number of active devices), and the modulation

and coding scheme; where the latter determines the

instantaneous data rate. The implemented protocol

for communication is the IEEE 802.11p standard and

the wireless environment are given in Section 3. Nev-

ertheless, we can approximate the latency for com-

munication by assuming that the effective data rate

remains constant throughout the trade as

trans

≈

. (8)

The formulations to calculate L

comp

are presented

in the following.

2.3.1 Blockchain Computation Latency

We consider a Blockchain-based VANET network

that includes a subset of vehicles M ⊆ V that work as

miners. These miners start their Proof-of-work (PoW)

mechanism computation at the same time and keep

executing the PoW process until one of the miners

completes the computational task by ﬁnding the de-

sired hash value(Nakamoto, 2019). When a miner i

executes the computational task for the POW of cur-

rent block, the time period required to complete this

PoW can be formulated as an exponential random

variable W

whose distribution is f

(w, i) = λ

−λ

in which λ

= λ

presents for the computing speed

of a miner, P

is power consumption for computation

of a miner, and λ

is a constant scaling factor. Once a

miner completes its PoW, it will broadcast messages

to other miners, so that other miners can stop their

PoW and synchronize the new block.

For the PoW computation, we are interested in

ﬁnding the time in which the ﬁrst miner i∗, among

all the M = |M | miners, ﬁnds out the desired hash

value. This is the time for the fastest PoW computa-

tion among miners and denoted by the random vari-

able W

i∗

. By assuming {W

} are i.i.d. random vari-

ables, we can calculate the complementary cumula-

Table 2: Smart contract execution costs.

Smart Contracts Gas Ether USD

UserAuthority 159430 15.9·10

−5

0.0723

RecordData 152443 15.2·10

−5

0.0692

AlertControl 213924 21.3·10

−5

0.0971

Incentive 224934 22.4·10

−5

0.1021

RecordData 276394 27.6·10

−5

0.1254

EABTransfer 246374 24.6·10

−5

0.1118

* 1 Ether = 10

Gwei; 1 USD = 4,182,471.9949 Gwei

tive probability distribution of W

i∗

Pr(W

i∗

> w) = Pr



min

i∈M

) > w



∏

i∈M

Pr(W

> w)

= (1 − Pr(W

< w))

, s.t. i ∈ M . (9)

Hence, L

comp

is the average computational latency of

the fastest miner i∗, calculated as

comp

∞

(1 − Pr(W

≤ w))

dw =

∞

−λ

(10)

Now we can calculate the communication latency as

trans

+ L

comp

Note that it can occur that the communication de-

lay exceeds the available communication time L

total

In such a case, a proposed transactions with poten-

tially valid PoW solution must be abandoned. Hence,

ﬁnding a valid puzzle solution does not guarantee that

the proposed transactions will be ﬁnally accepted by

the network because of the propagation delay. In such

cases, a Blockchain fork can only be adopted as the

canonical Blockchain state when it is ﬁrst dissemi-

nated across the network. In scope of this research,

to simplify, we do not address the problem of fork,

please refer to (Wang et al., 2019) for more detail.

3 PERFORMANCE EVALUATION

In this section, we analyze the performance of our

proposed B-ETS system.

In order to emulate a realistic vehicle network as

presented in Figure 2, a combination of micro sim-

ulators, network libraries and open-source vehicular

network simulators is employed. Speciﬁcally, SUMO

(Krajzewicz et al., 2015), OMNET++ which runs in

parallel via a proxy TCP connection, and Veins. The

IEEE 802.11p standard is used for communication be-

tween vehicles and a simple path loss model is se-

lected. In each simulation, 120 vehicles are generated

and located randomly. The CO

emissions are cal-

culated reading the Trafﬁc Control Interface (TraCI)

commands from SUMO. Ethereum is deployed as a

B-ETS: A Trusted Blockchain-based Emissions Trading System for Vehicle-to-Vehicle Networks

177

0 20 40 60 80 100 120 140

Time (s)

CO2 (mg/s) * 10

CO2 standard

CO2 DLT-based

0 20 40 60 80 100 120 140

Time

0.5

1.5

2.5

3.5

NOx (mg/s)

NOx standard

NOx DLT-based

(a) CO2 Emission

0 20 40 60 80 100 120 140

Time

CO2 (mg/s) * 10

CO2 standard

CO2 DLT-based

0 20 40 60 80 100 120 140

Time (s)

0.5

1.5

2.5

3.5

NOx (mg/s)

NOx standard

NOx DLT-based

(b) NOx Emission

0.5 1 1.5 2 2.5 3 3.5

Latency (s)

0.2

0.4

0.6

0.8

Emperical CDF

Conventional System

DLT-based System

Figure 6: Performance Evaluation. (a) and (b): The CO

and NOx emission generated in standard and DLT based

systems; (c) Communication latency between standard and

Blockchain-based system.

ledger in the experiments by using local Ganache plat-

form.

The computational efforts to execute smart con-

tracts in Blockchain are measured in units of gas. The

currency for Ethereum is Ether (ETH). In our simu-

lations, the transaction costs and execution costs are

converted to ETH and USD, see Table 2. The ETH

gas station was used to estimate the costs, the price

is generated using a static average of 20 Gwei, where

one Ether is equivalent to 10

Wei. The transaction

costs are the costs associated with sending the con-

tract codes to the Ethereum blockchain, dependent on

the size of the contract.

The amount of CO

generated from vehicles is

dependent upon various factors such as: speed, age

of vehicles, etc. We ran two separate experiments to

compare the amount of emissions generated between

a standard CAP system and a Blockchain-based sys-

tem when the driving behavior is controlled. Figure 6

illustrates the generated CO

and NO

, along with the

V2V communication latency for the standard and the

DLT-based trading. In the DLT-based trading, vehi-

cles follow deﬁned rules such as dropping their speed

in the smart contract. In Figure 6 we observe that the

amount of CO

and NO

generated from DLT-based

system is lower than conventional system. These re-

sults prove that our system has the ability to reduce

the overall CO

emitted from vehicles on the network.

In B-ETS, the transactions exchanged between ve-

hicles are encrypted, and veriﬁed before attached in

the distributed ledger. Therefore, the trusted record-

ing and trading data is guaranteed in comparison with

standard system. However, because of extra veriﬁca-

tion steps in Blockchain, the time to complete a trans-

action between vehicles is higher. This is a trade-off

between trust and latency in Blockchain-based sys-

tems.

4 CONCLUSION

In this paper, we ﬁrst proposed a Blockchain-based

Emission Trading System, called B-ETS, to support

the accounting and monitoring of emissions in ve-

hicular networks. B-ETS provides a trustworthy and

transparency for accounting the emissions generated

from vehicles. The vehicles can exchange their emis-

sion allowances through autonomous smart contracts

in a trusted manner. We introduce an economic in-

centive scheme based on smart contracts to encourage

drivers to behave in environmentally friendly ways.

This work provides a mechanism for policy mak-

ers, vehicle manufacturers and the EU-ETS to enforce

the carbon emissions regulations in a more efﬁcient,

secure manner as well as to perform full life-cycle

analysis of vehicles. Using the proposed method

could result in vehicle manufacturer savings, ensur-

ing that they are not subject to excess emissions fees

at the end of the year through the continuous monitor-

ing and reporting of CO

The next stage of this work involves further

analysis of the current system in two ways. First,

VEHITS 2021 - 7th International Conference on Vehicle Technology and Intelligent Transport Systems

178

we will include the analysis of more pollutants such

as Particulate Matter (PM

), Carbon Monoxide (CO),

Sulfur Dioxide (SO

) into B-ETS. Then, we will ad-

dress the limitations of this work by diversifying the

vehicles on the network, thereby incorporating other

types of vehicles (other than passenger vehicles), such

as: buses, vans and trucks.

ACKNOWLEDGEMENTS

This work has been in part supported by the European

Union’s Horizon 2020 program under Grant 957218

IntellIoT, the Independent Research Fund Denmark

(DFF) under Grants Nr. 8022-00284B (SEMIOTIC)

and Nr. 9165-00001B (GROW), and the National Sci-

ence Foundation Graduate Research Fellowship un-

der Grant DGE-1839285.

REFERENCES

Alsabaan, M., Naik, K., and Khalifa, T. (2013). Optimiza-

tion of fuel cost and emissions using V2V communi-

cations. IEEE Transactions on Intelligent Transporta-

tion Systems, 14(3):1449–1461.

Cappiello, A., Chabini, I., Nam, E. K., Lue, A., and

Abou Zeid, M. (2002). A statistical model of vehi-

cle emissions and fuel consumption. In Proceedings.

The IEEE 5th International Conference on Intelligent

Transportation Systems, pages 801–809. IEEE.

Commision, E. (2015). Road transport: reducing CO2

emissions from vehicles. (Accessed on 10/28/2020).

Dionne, G., Pinquet, J., Maurice, M., and Vanasse, C.

(2011). Incentive mechanisms for safe driving: a com-

parative analysis with dynamic data. The review of

Economics and Statistics, 93(1):218–227.

Eckert, J., L

opez, D., Azevedo, C. L., and Farooq, B.

(2019). A blockchain-based user-centric emission

monitoring and trading system for multi-modal mo-

bility. arXiv preprint arXiv:1908.05629.

EEA (2019). Do lower speed limits on motor-

ways reduce fuel consumption and pollutant emis-

sions? https://www.eea.europa.eu/themes/transport/

speed-limits-fuel-consumption-and/. (Accessed on

10/28/2020).

Ellerman, A. D., Jacoby, H. D., and Zimmerman, M. B.

(2006). Bringing transportation into a cap-and-trade

regime.

EPA (2018). Estimated U.S. average vehicle emissions rates

per vehicle by vehicle type using gasoline and diesel

— bureau of transportation statistics. https://www.bts.

gov/content/. (Accessed on 10/28/2020).

Fullerton, D. and West, S. (2000). Tax and subsidy com-

binations for the control of car pollution. Technical

report, National Bureau of Economic Research.

Krajzewicz, D., Behrisch, M., Wagner, P., Luz, R., and

Krumnow, M. (2015). Second generation of pollutant

emission models for sumo. In Modeling mobility with

open data, pages 203–221. Springer.

Li, L., Liu, J., Cheng, L., Qiu, S., Wang, W., Zhang,

X., and Zhang, Z. (2018). Creditcoin: A privacy-

preserving blockchain-based incentive announcement

network for communications of smart vehicles. IEEE

Transactions on Intelligent Transportation Systems,

19(7):2204–2220.

Nakamoto, S. (2019). Bitcoin: A peer-to-peer electronic

cash system. Technical report, Manubot.

Nguyen, L. D., Kalor, A. E., Leyva-Mayorga, I., and

Popovski, P. (2020). Trusted wireless monitoring

based on distributed ledgers over NB-IoT connectiv-

ity. IEEE Communications Magazine, 58(6):77–83.

Nguyen, L. D., Leyva-Mayorga, I., Lewis, A. N., and

Popovski, P. (2021). Modeling and analysis of data

trading on blockchain-based market in iot networks.

IEEE Internet of Things Journal, pages 1–1.

Pan, Y., Zhang, X., Wang, Y., Yan, J., Zhou, S., Li, G., and

Bao, J. (2019). Application of blockchain in carbon

trading. Energy Procedia, 158:4286–4291.

Wang, W., Hoang, D. T., Hu, P., Xiong, Z., Niyato, D.,

Wang, P., Wen, Y., and Kim, D. I. (2019). A survey on

consensus mechanisms and mining strategy manage-

ment in blockchain networks. IEEE Access, 7:22328–

22370.

Wust, K. and Gervais, A. (2018). Do you need a

blockchain? In Proceedings IEEE Crypto Valley Con-

ference on Blockchain Technology (CVCBT), pages

45–54.

Zheng, F., Li, J., van Zuylen, H., and Lu, C. (2017).

Inﬂuence of driver characteristics on emissions and

fuel consumption. Transportation Research Procedia,

27:624–631.

B-ETS: A Trusted Blockchain-based Emissions Trading System for Vehicle-to-Vehicle Networks

179