SCAPE Working Paper 1 AC&TH

8/7/2019 SCAPE Working Paper 1 AC&TH

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Emission Trading Scheme in the Maritime Industry:

An experimental analysis

Anthony Theng Heng China and Thiam Hao Chuab

aAssociate Professor, Department of Economics, National University of Singapore

Email:[email protected]

bManagement Associate, PSA Corporation Ltd., 33 Harbour Drive, #2 Pasir PanjangTerminal Building, Singapore 117606, Port of Singapore Authority

Email:[email protected]

February 2011

Abstract

The International Maritime Organization has proposed the implementation of a carbondioxide emission trading scheme for the industry. Two problems associated with this is thatof high noncompliance rate and the need to reconcile the both IMO and UNFCCCprinciples. This study reports a laboratory experiment to examine two design features of thepotential scheme that are related to the problems identified. Our experimental parametersapproximate the possible allocation method and other features of the maritime industry.Two key findings from this study are, (1) Implementation of the dynamic enforcementmodel reduces both permit noncompliance and report noncompliance relative to the staticenforcement model and (2) The initial allocation of permits, which provides a solution toreconcile the two conflicting principle, impacts on the efficiency of the scheme.

Keywords: Emission Trading Scheme, Experiment, Maritime, Dynamic Enforcement,Initial Allocation
mailto:[email protected]:[email protected]:[email protected]:[email protected]:[email protected]:[email protected]:[email protected]:[email protected]


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1. Introduction

Tradable emission permits (TEP) have been advocated to address the problem of

greenhouse gases emissions (Dales, 1968; Montgomery, 1972; Malik, 1990). It seems this is

a preferred method in reducing negative externalities to the targeted level (Montgomery,

1972; Malik, 1990) even though command and control approaches have been regarded as

less efficient as seldom adhere marginal principles (Hackett, 2006). The maritime sector in

particular, the International Maritime Organization (IMO) is exploring the possibility of

implementing TEP (IMO Marine Environment Protection Committee (MEPC), 2009a).

Carbon dioxide emission by international shipping is estimated at 2.7% of the global

emission in 2007 and is projected to grow by 150% to 250% by 2050 (IMO MPEC, 2009b).

Proposals on a carbon dioxide emission permits trading system (Emission Trading Scheme

or ETS) have been submitted by various countries to MEPC to the IMO Marine

Environment Protection Committee (MEPC) for consideration (IMO MEPC, 2009c).

However two issues need addressing. First, as the scheme encompasses a global

dimension, the United Nations Framework Convention on Climate Changes (UNFCCC)

guiding principle of Common but Differentiated Responsibilities (UNFCCC, 1997a) will

have to be incorporated into the ETS. This conflicts with IMOs principle of treating all

ships in the industry equally (MEPC, 2009d). Hence, this study proposes a solution to

resolve this conflict by varying the initial allocation of the permits. The second issue is the

problem of low compliance rate, a crucial component that impacts the economic and

environmental efficiency of the scheme (Harford, 1978; Stranlund and Dhanda, 1999). This

enforcement problem is more serious in the maritime industry due to a few inherent

characteristics. A unique trait of the sector is the confusion in enforcement role created by

the overlapping ofregulatory responsibilities between the flag state (the state which the ship

is registered in), coastal state (the state whose water the ship is sailing in) and the

classification societies (a non-governmental body that establishes rules for the industry)

(Stopford, 2009). In addition, primary data sources for carbon dioxide emissions comes

from fuel consumption sources such as Bunker Delivery Note and Engine Logbook, can be

manipulated. This study aims to evaluate if the implementation of a dynamic enforcement

model in the industry can increase the efficiency of the scheme.

This study employs experimental techniques to study the implementation of the ETS

in the maritime industry. The lack of literature has led to the employment of a stylized

experiment model which incorporates features of the maritime industry. Treatment variablesin the experiment, such as dynamic enforcement model, are also modeled after policy


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suggestions for the industry. This linkage with the industry ensures a high external validity

of the results. The model incorporates many designs that are relevant to policy makers. For

instance, two different treatment variables were used in our experimental framework to

allow for the testing of theories and models. The first treatment variable is the Harrington

(1988) dynamic enforcement model. The experiment in the study seeks to study its impact

on noncompliance. Our experimental results is consistent with the theories that

noncompliance in this model will decrease relative to the static enforcement model (Harford,

1978). The second treatment variable is the initial allocation of permits. We find that this

initial allocation does impact noncompliance and efficiency, a result that contradicts

standard theories (Montgomery, 1972).

The following section reviews the literature and develops features of the

experimental model which link the model and characteristics of the maritime industries.

Section 3 introduces the design of the experimental setup and describes the experimental

procedure. Section 4 introduces several hypotheses and discussion of the results from the

experiment. The final section concludes.

2. Literature Review and Features of the Model

The main advantage of ETS is its ability to minimize the aggregate cost of the target

abatement level (Godby et al., 1997). Firms with lower abatement cost have an incentive to

sell their permits to firms with higher abatement costs. Mutual benefit from trading permits

exists when this transaction price is between the current marginal abatement costs of both

firms. In equilibrium, the permit price is equal to the marginal cost of abating pollution

across all firms (Montgomery 1972; Buckley et al., 2007). Hence, the total abatement cost

in the system is minimized with this incentive-compatible trade mechanism. Moreover, this

optimum point is attained with minimal administrative burden on the regulator as firms

interact directly in the permit market. In addition, the cap on the maximum emissions in

the ETS allows a clear overall environmental standard to be set. However, these merits are

conditional on many small design factors of the scheme (Cason, 2010). Those factors that

are relevant to the research questions will be incorporated into the experimental model.

2.2 Initial Allocation of Permit

One feature that is of concern to the maritime industry is the initial allocation of

permits. This issue of allocation has generally been considered as a distributional problemrather than one that impacts on efficiency (Kerr, 1999). However, experiments have shown


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that the presence of market power leads both the post-trade permit allocation and the permit

prices to be affected by the initial allocation (Hahn, 1984). In another experiment (Cason,

Gangadharan and Duke, 2003) investigating the impact of market power, the number of

buyers and sellers in the different treatments were changed. In this experiment, increasing

market power is represented by a smaller number of buyers or sellers. However, the

differences in efficiency and prices were found to be insignificant. This is in line with

Smiths (1981) experimental results which showed that the double auction structure is the

most likely to result in competitive equilibrium outcome compared to other institutions.

The initial allocation of permits is one of the two treatment variables in the

experiment. In four of the total sessions, all subjects in the session are allocated the same

number of permits. This uniform pre-trade allocation of permits is in line with the IMO

principle of equality towards all ships so shipping lines are given the same number of

permits regardless of her flag states. In the other four sessions, the allocation of permit

among the subject is as follows: Subjects with lower abatement cost functions are given

more permits than those with higher abatement cost function. This is to model the situation

where ships registered in Non Annex I countries are being allocated more permits. These

non Annex I countries are mostly developing countries (UNFCCC, 1997b) with lower

abatement costs functions. Thus this allocation method reflects their reduced responsibility

in contributing to climate change effects. However, the total number of permits in the

experiment is a constant in all sessions to ensure consistency in the sessions. The number of

buyers and sellers are also kept equal in all sessions to eliminate the effect of market power.

Thus, any differences in efficiency can be attributed to other factors.

2.3 Enforcement Model

Enforcement is crucial to the maritime industry due to potential high noncompliance.

A sizable collection of literature exists to provide a comprehensive discussion on this aspect.

Only those relevant to our model will be discussed here. Using a model of non-compliant

firms, Stranlund and Dhanda (1999) demonstrated that the application of enforcement

efforts should be independent of regulated firms exogenous characteristics. In an ETS,

firms are linked up via the permit market so the marginal compliance cost for each firm for

is equivalent to the permit prices in the market. Thus, the individual firms abatement costs

do not impact their compliance decisions.

Theoretical models have shown that imperfect enforcement, which Stranlund et al.(2008) defines as enforcement efforts that are insufficient to induce full compliance by all


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firms, are not as costly as predicted by standard theory of perfectly competitive firms

(Malik, 1990; Stranlund et al., 2008) They found that aggregate abatement costs can still be

minimized despite some noncompliance. In fact, empirical studies conducted have found

that between 60 to 80 percent of regulated firms in ETS have high compliance rates despite

low penalties and imperfect monitoring (Arora and Cason, 1996, Gangadharan, 2001).

Some of the firms even exceeded the standards they need to keep. Explanations for this

puzzling phenomenon varies from of avoidance of negative impacts on the companys stock

prices (Dasgupta, Laplante and Mamingi, 1997) to the over-compliance serving to increase

costs for their rivals when the regulator increases the overall standard (Salop and Scheffman,

1983).

Another commonly cited explanation is Harringtons (1988) dynamic enforcement

model, which will be the second treatment variable in our experiment. Researches on the

field of enforcement require economic experiments due to the absence of reliable field

information (Cason and Gangadharan, 2004). The differences between two dynamics

enforcement models have been studied (Cason and Gangadharan, 2004; Clark et al., 2004)

but there has been a gap in the literature for the comparison of a dynamic and a static

enforcement model. In this study, the merits of the dynamic enforcement model will be

evaluated relative to the baseline case of a standard static enforcement model (Harford,

1978)

A high noncompliance rate is expected in the ETS for the maritime industry due to

the difficulty of enforcement. Many of these reasons are due to the inherent characteristics

of the industry such as its multinational nature and the blurred division of responsibilities

between the different regulators. Implementation of Harringtons (1988) model is expected

to reduce the noncompliance rate in the industry due to the leverage of the two inspection

groups. Relative to a static enforcement model, the dynamic model actually reduces the

monitoring resources required. As long as IMO remains transparent on the classification of

ships into the inspection groups, this model is in line with its principle of equality towards

all ships, despite the use of two inspection groups. Our subsequent results in the experiment

support the implementation of this enforcement model in the industry.

A brief introduction of the dynamic enforcement model will be provided1

1 Readers are urged to consult the reference study, Harrington (1988), for a more detailed analysis on themodel.

. In this

dynamic repeated-game model, both the regulator and the regulated firm can react to the

other parties actions in the previous round. The regulator devises two inspection groups G1


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and G2 that firms can be classified into. The enforcement variables are harsher in the bad

group G2 than G1. The probability of inspection in group i (pi) and fine per unit in group i (Fi)

are such that p2 > p1 and F2 > F1 respectively. If the firm is in G1 when inspected and is then

found to have violated the rule, they will be classified to G2. If firms are inspected in G2 and

found to be in compliance, they will be reclassified back into G1. This creates a Markov

decision problem from the perspective of the firm.

In a conventional static enforcement model (Harford, 1978; Linder and McCabe,

1984), a firm violates the rules when marginal compliant cost, c is greater than expected

fine pF (c> pF). However, even if c> pF in the dynamic model, Harrington postulated that

the model would actually provides a leverage, L, that makes compliance optimal in G2 even

when c rises to as high as ])p1(1[

)Fp-F(pp

Fp1

11222

22

+ where is the discount factor. An

important assumption underlying the model is the existence of a maximum penalty that can

be imposed. This assumption is often relevant in reality as there are often constraints to the

size of the fine. The marginal cost of compliance, c, in an ETS is the permit price in the

market as the purchase of another permit can legally reduces the abatement effort by one

unit. Hence, a dynamic enforcement model can increase compliance rate relative to a static

model. Modifications to the model have been proposed in subsequent literature such as

Harford (1991) who analysed the equilibrium in a dynamic enforcement model where some

random errors exist in measuring the level of compliances. In our experiment, the baseline

Harrington model will be adopted instead for simplification.

2.4 Other Features

Two other design features relevant to the maritime industry will be incorporated into

the experimental model. The first feature is an injection of uncertainties, in the form of an

abatement shock. This shock element is a simulation of the uncertainties on their eventual

emission level that shipping lines face. This can be attributed to measurement errors

(Carlson and Sholtz, 1994) or uncertainties in the container loads for the period ahead.

Besides Cason and Gangadharan (2006), other ETS experiments that included shock in the

setup use a shorter range of (-1, 0, 1) (Godby et al., 1997).

The second feature is the inclusion of self reporting. The high costs involved with

international monitoring means that regulators from the port state or flag state frequently

have to rely on the reports of shipping lines to determine compliance. Besides injecting

realism into the experimental model, the addition of this feature allows an additional


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dimension of noncompliance to be studied. Noncompliance usually refers to the actual

abatement noncompliance, which is not having sufficient permits for the emission level. In

our experiment, there is an additional measure of noncompliance exists. Report

noncompliance refers to the misrepresentation of the actual emission level. Previous ETS

experiments have found a strong relationship these two forms of violations (Stranlund,

Costelli and Chaves 2005).

3. Experimental Design

Many experimental economists have established a systematic methodology for

conducting experimental studies (Hey, 1991; Bardsley et al., 2010). This study will draw

upon these frameworks and principles in designing and conducting the experiment. The

subjects for the experiment were recruited from the student population of the National

University of Singapore. The justification for the use of students in the experiment is based

on the assumption that all agents are incentive-motivated rational beings. Furthermore, the

inclusion of performance-related incentives for the students is a form of extrinsic motivation

to drive them in maximizing their self interests (Bardsley et al. 2010). The subjects

behaviour can then resemble the behaviour of firms in the real world ETS due to the similar

goal of incentive-driven motivation. In addition, the conversion rate2

A total of 9 experimental sessions were conducted. The first session was a pilot test

and its results were discarded for the analysis. Improvements to the model and experimental

procedure were made with the feedbacks collected from the pilot test. For the remaining 8

sessions, the treatment variables were interacted in a balance 2x2 design as seen in Table 1.

is set such that the

average and maximum earnings of the participants are above that of the wage that they

could have earned for NUS official works in the two hours. This feature is compatible with

the high potential payoff for firms in the real world.

Table 1 Interaction of treatment variables

2 The conversion rate was 1 Singapore dollar = 300 experimental dollars

Initial Allocation of Permits

Uniform Allocation Non Uniform Allocation

Enforcement

Model

Dynamic EnforcementModel

2 Sessions 2 Sessions

Static EnforcementModel

2 Sessions 2 Sessions


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There were a total of two sessions for each interaction of treatment variables. For

instance, the top left box indicates that two sessions were conducted for a setup which every

participant was given the same initial allocation of permits and also subjected to the

dynamic enforcement model. Thus, there are a total of four sessions with uniform initial

allocations of permits and another four sessions with non uniform initial allocations of

permits. Similarly, there are four sessions which the regulators enforcement is done

through the dynamic enforcement model and another four sessions which it is done using

the static enforcement model. Randomization was done both on the sitting position of the

subjects and the choice of the experiment days to conduct each treatment session.

The treatments in the experiment were designed to test the hypotheses related to

total emission and compliance. These will be specifically postulated in the next section.

Although actual technical and environmental terminologies are used in describing the

results and experimental designs, subjects were placed in a more neutral environment to

avoid potential biasness. For example, abatement choice was framed as a production

decision while abatement permits were simply labeled as coupons. The experiment was

entirely programmed and conducted using the University of Zurichs z-Tree program

(Fischbacher, 2007).

In each period, the emission level for each subject is fixed at 10 units. Each permit

allows him to legally emit one unit and avoid the abatement cost for that unit. Thus, if the

subject has no permit at the end of the period, he has to incur abatement costs to clean up

(abate) all 10 units of the emissions to comply by the rule. More generally, the total

number of permits + the actual abatement at the end of every period must be greater than

or equal to 10 units to abide by the rule.

All subjects start each stage with 2000 experimental dollars and a fixed allocation of

permits depending on the treatment sessions that they are in. Subjects were told that the

number of periods is randomly selected by the computer and is between 8 10 although it

has been fixed at 9. Subjects were not told the exact number of periods to avoid them

behaving in non-optimal manner in the last period 3

. The marginal abatement costs functions

for the 8 firms are based on the Cason and Gangadharan (2006) study and shown in Table 2.

3 For sensitivity check, the same analysis was done without the 8th and the 9th. There was no qualitative impacton the results. .


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Table 2 Assigned marginal abatement cost functions

Units of abatementType 1

(Firm 1 and 2)Type 2



(Firm 7 and 8)

1 53 67 27 352 61 70 35 38

3 70 74 44 424 80 79 53 475 91 86 63 541

6 103 95 73 637 116 106 84 748 130 119 98 889 145 134 113 10510 161 151 129 125

Permit Endowment inuniform allocation

4 4 4 4

Permit Endowment innon-uniform allocation

2 3 5 6

1Highlightedin bold are the abatement cost saving based on the initial distribution of permits in the non-

uniform allocation.

In both uniform and non-uniform treatments, a total of 32 permits are endowed in

the system per period. From another perspective, the target emissions level in each period is

equivalent to the total endowment of permits, which is equal to 32. Trading of permits can

reduce the total abatement costs incurred in the system in both treatments. In the optimum

competitive equilibrium, prices of successful transaction of permits are in the interval of 88

to 91 and subjects have an incentive to trade. The post-trading permits holding should be as

follow: type 1 and 2 firms hold 6 permits and 5 permits respectively while type 3 and 4

firms hold 3 permits and 2 permits respectively. In this optimum equilibrium, the total

abatement cost of the system is minimized at 2920.

Each period is divided into a few stages as shown in Figure 1. Stage 1 is a permit

trading stage whereby all 8 subjects participate in a three minutes double auction permit

market. In the second stage, the subjects will enter an abatement target. However, their

actual abatement level can vary from their target abatement level due to a shock that is

randomly drawn from a uniform allocation of (-2, -1, 0, 1 or 2). In stage 3, subjects will be

informed of the magnitude and direction of the shock that they experienced. Their

corresponding actual abatement, and also realized emission, will be indicated. The

abatement cost incurred by each subjects is according to his or her actual abatement level.

Stage 4 is another auction round for subjects to purchase or sell their permits. This is a

reconciliation period that is a common feature in ETS as it allows subjects to finalize their


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permit holdings. The setup of this second auction round is similar to stage 1 except that it

lasts only for two minutes.

Figure 1 Graphical display of the stages in one period

In stage 5, subjects will decide on the emission units to report to the inspector,

which is the computer in the experiment. Then, the inspector will randomly select subjects

for inspection based on the inspection probability. In this experimental model, the regulator

has perfect information on the number of permits that each subject holds, which is a

common feature of most ETS with well-designed permit registries. If the inspector selects a

subject for inspection, he will then know the actual abatement units. Thus, the subject will

be fined if he is not complying by the abatement rule (number of permits + the abatement

units is less than 10 units) or his reported abatement level is different from the true

abatement level. For each unit that the subject differs, he or she will be fined the amount

stipulated and the higher of the two total fines for violation will be used. However, if the

inspector chooses not to inspect the subject, he will rely on the reported unit to determine if

the subject is not complying by the abatement rule (number of permits + the abatement units

is less than 10 units). In the treatment session with static enforcement model, the probability

that the inspector will choose a subject for inspection is 0.5 and the per unit fine for

violation is 150. However, if the session is one with dynamic enforcement level, there are

two inspection groups as mentioned in Section 2.3. If a subject is in group 1 and violates the

rules, he will be moved to group 2. If he is inspected in group 2 and found to be compliant,

he will be moved back to group 1. The probability of inspection and fine for violation in

group 1 is 0.3 and 60 respectively. For group 2, the probability of inspection is 0.6 and the

fine for violation is 150 (A summary of these details are in Table 3.4). All subjects in these

sessions start the experiment in group 1. In the last stage of the period, subjects were told if

they were inspected and the amount of cash that they have at the end of this period. They

are also given a fixed sum of revenue that is known to them. After this display summarystage, the first stage of the next period will then begin.

First Stage: Firstauction round to

buy or sell permits.

Second Stage: Set

Abatement Target

Third Stage:Confirmation of theabatement shock and

actual abatement

Fifth Stage: Reportto inspector on

actual abatement.

Sixth stage: Displayend of periodinformation.

Fourth stage: Secondauction round to buy

or sell permits


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Table 3 Inspection details for the dynamic enforcement model

Inspection Group 1 Inspection Group 2

Inspection Probability is 0.30 Inspection Probability is 0.50

Fine for Violation (Per Unit): 60 Fine for Violation (Per Unit): 150

3.1 Experimental Procedure

The instruction sheets and the colour screenshots, which are included in Section 8.1

and 8.2 (Appendices) respectively, were emailed to the participants one day before their

session. They were notified about this through an SMS to their mobile phone. A copy of the

instruction sheet was given to the subjects on the day of the experiment. The same

instructions were read aloud to them before the session began and they were allowed to

clarify doubts that they had. Moreover, participants had to answer a short quiz correctly

before they were allowed to start the experiment. The quiz was designed to test their

understanding of the model. After the experiment, they were then asked to answer a short

questionnaire before leaving the venue. The experimental dollars that they earned over the

nine periods was converted into Singapore dollars at a rate of Singapore dollar (S$) 1 = 300

experimental dollars. This rate was chosen with the objective of fulfilling the incentive

compatible condition so that the expected earning of the student will be higher than the

official NUS wage rate for undergraduates ($8.64/hour). The minimum and maximumearning of the subjects in each session were S$16 and S$38.50 respectively while the

average earnings was S$21.64. These earnings were credited into the bank accounts of the

participants. Care was taken to ensure that the entire experimental session conveyed a sense

of professionalism. The experiment was held over 3 weeks in March 2010 and each session

lasted around 2 hours.

4. Hypotheses

This experiment was designed to allow for the testing of several hypotheses related

to the design of an ETS, especially one for the maritime industry. The first 4 hypotheses are

specifically related to the compliance behaviour of agents in an ETS. These hypotheses will

test noncompliance on two different dimensions. The first type of noncompliance is on

violations in terms of the actual abatement units, which refers to whether the sum of the

total permits held and actual abatement is equal to or more than 10. The second type of

noncompliance pertains to the report violation and check if the abatement report is equal to

the actual abatement.


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Hypothesis 1: Noncompliance is greater for agents in the static enforcement model

Hypothesis 2: Noncompliance is greater when permit prices are higher.

Hypothesis 3: Noncompliance is not different for agents in the presence of different

marginal abatement cost.

Hypothesis 4: Noncompliance is greater following greater (positive) shocks to emissions.

The first hypothesis concerns the Harrington dynamic enforcement model which

was introduced in section 2.3.The use of a two group inspection model provides a leverage

which increases the compliance rate relative to the one group static enforcement model. In

the static enforcement model, the expected per unit fine for violation is only 75 (0.5 x 150)

and definitely less than the expected permit price interval of 88 to 91. Thus, non compliance

is expected. In the dynamic enforcement model, the expected fine for violation in G2 is the

same (0.5 x 150 = 75) as in the static model, but the expected fine for violation in G 1 is even

lower at 18 (0.3 x 0.6). Hence, noncompliance should be higher in the dynamic enforcement

model due to the lower overall expected fine in G1 and G2. However, Harrington postulates

that the enforcement leverage provided by the two inspection groups makes compliance

optimal for subjects in inspection group 2 even when the permit price rises as high as 132 4

Hypothesis 2 and 3 follows from Stranlund and Dhanda (2004) who postulate that

the permit price in an ETS is the marginal compliance cost for all agents since they can

remain in compliance by obtaining permits in the market. Thus, higher permit prices

correspond to higher marginal compliance costs and should lead to an increase in

noncompliance, as stated in hypothesis 2. Following this argument, noncompliance decision

should be independent of individual marginal abatement cost as stated in hypothesis 3.

Firms are linked up by the permit market and can obtain the permit at the same price. Thus,

the same marginal compliance cost, c, holds for all firms in the market. All the variables

that impact the decision to comply (c, p and F) are independent of the individual abatement

.

Thus, the level of noncompliance is expected to be lower in the dynamic enforcement

model. If the hypothesis is true, the dynamic model increases compliance with a reduced

cost of enforcement relative to the static model. The lowered enforcement cost is due to the

lower probability of inspection which translates to lower resources spent on monitoring.

4 As explained in Section 2.3, the formula to calculate this is])p1(1[

)Fp-F(ppFp

1

11222

22

+ , where =0.83 as

assumed in Cason and Gangadharan (2006) study. This assumption ofis a conservation estimate with the

use of 20% as the rate of discount. Using a lower rate of discount will actually increase both and the

leverage in this model. Hence, the results in the analysis still hold.


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cost. In addition, hypothesis 4 states that a greater emission shock, which is equivalent to a

lower abatement shock in our experiment, will increase noncompliance due to the increase

in permit prices. Thus, this increases the cost of compliance, resulting in lower compliance.

The last 2 hypotheses concern the performance of the scheme when the initial

allocation of permits varies.

Hypothesis 5: Noncompliance is not different when the initial allocation of permit is

changed.

Hypothesis 6: Trading efficiency is not different when the initial allocation of permits is

changed.

A well known result in the ETS literature is that the emission choice of agents is

independent of the initial allocation of permits (Montgomery, 1972; Malik, 1990). However,

Stranlund and Murphy (2004; 2005), consistently uncovered significant differences in the

compliance choice when the initial distribution of permit is varied. Thus, these hypotheses

will be tested in the more realistic simulation of the ETS in this study. In addition,

hypothesis 6 states that the trading efficiency of the system should not be affected by the

initial distribution of permits. This trading efficiency index (Cason and Gangadharan, 2003)

is defined as a ratio of the maximum available gain from trade possible in each period and

the formula is

Cost.AbatementOptimum-costabatementPeriodofStart

CostAbatementPeriodofEnd-CostAbatementPeriodofStart 5

. This measurement of

efficiency is solely confined to the trading possibility and does not concern itself with

welfare benefits as this would require an additional social cost function.

5. Results

5.1 Summary of Results

The discussion of the results will first begin with three tables that summarize the

noncompliance behaviour of the subjects and the total emission level in each period. Table

4 shows the magnitude of noncompliance both in terms of actual abatement violations

(Actual) and report violations (Report). On both dimensions, the mean and median

noncompliances were greater in the static enforcement treatment relative to the dynamic

enforcement treatment. These observations provide evidence for hypothesis 1 which states

5 End of Period Abatement Cost is calculated based on the number of permit held at the end of period


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that a dynamic enforcement model can reduce the noncompliance rate in the scheme. This

will be further studied using regression analysis in the next section.

Table 4: Summary statistics for individual noncompliance

Mean Median Std Dev

Dynamic Enforcement Model (Report) 0.8945 0 1.8021

Static Enforcement Model (Report) 1.7421 1 2.0967

Dynamic Enforcement Model (Actual) 0.9492 0 1.7268

Static Enforcement Model (Actual) 1.7070 1 1.9088

Table 5 compares the total emissions level in the scheme for the two enforcement

models. Both mean and median of the emission level was higher in the static enforcement

model compared to the dynamic enforcement model, giving further evidence for hypothesis

1.6

Table 5 Summary statistics for total emission in a period

Mean Median Std Dev

Dynamic Enforcement Model (Report) 36.5625 36.5 5.9456

Static Enforcement Model (Report) 45.0312 44 6.1721

Dynamic Enforcement Model (Actual) 36.5625 36.5 5.9456

Static Enforcement Model (Actual) 45.0312 44 6.1722

In addition, the data in Table 6suggests that uniform allocations leads to higher non-

compliance, although the difference is minimal.

Table 6 Summary statistics for individual noncompliance

Mean Median Std Dev

Uniform Allocation (Report) 1.4648 0 2.1067

Non Uniform Allocation (Report) 1.1718 0 1.8770

Uniform Allocation (Actual) 1.4375 0 1.9312

Non Uniform Allocation (Actual) 1.2188 1 1.7776

5.2 Multivariate Regression Model

The next part of the result analysis will utilise a multivariate regression model to

allow the impact of multiple influences on the dependent variables to be studied. The main

objective of this investigation was to test the specific hypotheses proposed in the previous

6A nonparametric, univariate Wilcoxons signed-rank test was also conducted for variables in Table 5.2. Inorder to satisfy the statistical independence requirement for such a test, one observation per session was used

for each test. The differences in emission were found to be significant for only some periods. Based on thistest, there is no significant evidence to suggest that the emission levels are different when enforcement modelis changed.


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section and the analysis was performed using the computer program Stata. In the analysis

below, results from the first period were excluded to minimise the learning and price

discovery effects; these exclusions were found to have no qualitative impacts on the any of

the conclusions drawn7

Table 6 and Table 7 present the result of a linear random effect model of the

compliance decisions for the subjects. The choice for the random effect model was to take

into account that our experimental data is a form of panel data where some observations are

from the same subjects, who participated in multiple rounds. Moreover, the number of

subjects is high relative to the number of periods. In addition, the Probit and Tobit models

were employed to study on the compliance decision regarding both reporting

noncompliance and actual abatement noncompliance.

.

Table 6 Random effect probit model on compliance decision

Explanatory Variables

Dependent Variables:

[Probit Model (=1 if Comply)]

Model 1 Model 2

Actual AbatementCompliance

Report Compliance

Dynamic Compliance Model 0.7090 (0.1710) ** 0.8125 (0.1771) **

Uniform Allocation - 0.3932 (0.1844) * - 0.5004 (0.1894) **

Individual Shock 0.2471 (0.0455) ** 0.2630 (0.0466) **

Marginal Cost -0.0242 (0.0050) ** - 0.0258 (0.0051) **Mean Pricea - 0.0331 (0.0143) * - 0.0326 (0.0148) *

Constant 6.4977 (1.5563) ** 6.8037 (1.6007) **

Observations 512 512

Number of Subjects 64 64

Wald Chi-Squared (5) 87.68 94.87

Prob > Wald Chi-Squared (5) 0.0000 0.0000

1.Standard error in parenthesis.2.*: denote significant at the 5% level using a two tailed test.3.**: denote significant at the 1 % level using a two tailed test.4.

a Estimated using IV method

In the Probit model (Model 1 and 2), the dependent variable is the binary

compliance decision (=1 if subject comply) for subject groups 11, 12, 88, in the period t

= 2, 3, , 9. In the Tobit Model (Model 3 to 5), the dependent variable is the magnitude of

noncompliance for the same observations. Usage of the Tobit model was to take into

account the lower limit of 0 for the magnitude of violations. There were a total of 64

7 From the 8th period, there exists a possibility that the session may end after the period. Thus, one can argue

that subjects might have played the game differently. As a form of sensitivity check, regressions of the sameequations were run without data from the 8th and 9th period in each session. Discarding these data produces noqualitative differences in the result that follows.


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participants and each of them went through 8 periods, creating a balance panel data set. The

uniform allocation ( = 1 if the initial allocation of permit is uniform; 0 if it is non-uniform)

and the dynamic enforcement model ( = 1 if the treatment is dynamic enforcement model; 0

if it is static enforcement model) were modeled as dummies. Other explanatory variables

included are the individual shock(s) experienced in that period, the marginal cost (which

captures the highest marginal cost of each agent) and the mean prices of successful permit

transactions. However, this price variable is endogenous as higher compliance rate might

increase the prices in the auction stage. As in the standard instrumental variables technique,

a separate price equation based only on exogenous factors was performed and this predicted

price is then included in the compliance equation. 8

In all 5 models, the coefficients of the dummy variable for the enforcement model

are strongly significant. In addition, the sign of this coefficient is positive in model 1 and 2

and negative in model 3 5, providing strong evidence for hypothesis 1 that the dynamic

enforcement model reduces both the likelihood of agents non-complying and the magnitude

of non-compliance. These results are valid for both report and actual abatement

noncompliance. Using mean value as the base, Model 1 and 2 allows us to estimate the

increase in probability of compliance if dynamic enforcement model is used instead of static

enforcement model. Using the dynamic model increases the probability of compliance by

27 percent

9

The dummy for the initial allocation of permits is also significant in model 1 and 2,

which suggests that the allocation method has an impact on the compliance decisions of the

agents. The negative coefficient means that a change from a non-uniform to a uniform

allocation decreases compliance. Thus, this result refutes hypothesis 5 and suggests that the

allocation method is significant in determining noncompliance. Having a uniform allocation

will, relative to a non uniform allocation, decrease the probability of agents complying by

approximately 15 percent and 18 percent for actual abatement violations and report

violations respectively. An interesting result is that the allocation variable is not significant

in Model 3 and 5. Hence, the initial permit allocation is significant in explaining whether

and 30 percent for actual abatement compliance and report compliance

respectively. This difference is significant and provides evidence for the use of the dynamic

enforcement model in the maritime industry.

8 The exogeneous variables included in the equation are the following: dummy for the initial allocation,dummy for the compliance model, magnitude of total shock in that period and the fine paid by the subject inthe previous period. The dependent variable is the mean transaction price. (Model Quality: Observations: 512,

R-Squared: 0.2663, Prob> Chi(4) =0.000)9 This is done using the standard mean estimation for Probit model. The exact workings are available uponrequest.


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agents decide to comply or not but is unable to account for their magnitude of violation

should they choose not to comply.

Table 7 Random effect probit model on noncompliance

ExplanatoryVariables

Dependent Variable: (Tobit Model with lower limit of 0)

Model 3 Model 4 Model 5Magnitude of

Noncompliance(Actual Abatement

Violation)

Magnitude ofNoncompliance

(Actual AbatementViolation)

Magnitude of

Noncompliance(Report Violation)

DynamicComplianceModel

- 0.6208 (0.2268) ** - 0.7375 (0.2110) ** - 0.7810 (0.2286) **

UniformAllocation

0.4107 (0.2419) 0.3907 (0.24626)

IndividualShock - 0.2095 (0.0523) ** - 0.2263 (0.0500) ** - 0.2451 (0.0569) **

Marginal Cost 0.0250 (0.0068) ** 0.0250 (0.0070) ** 0.0291 (0.0068) **

Mean Pricea 0.0208 (0.0166) 0.0079 (0.0181)

Constant - 3.9804 (1.8800) * - 1.8500 (1.0067) - 3.3119 (1.9949)

Observations 512 512 512

Number ofSubjects

64 64 64

Wald Chi-Squared (5) 50.39 46.18

b

59.03

Prob >Wald Chi-Squared (5)

0.0000 0.0000 c 0.0000

1.Standard error in parenthesis.2.*: denote significant at the 5% level using a two tailed test.3.**: denote significant at the 1 % level using a two tailed test.4.a :Estimated using IV method;5.b :Statistics for Wald Chi-Squared(3) test;6.c :Statistics for Prob >Wald Chi-Squared (3)

Another significant variable in all 5 models is the individual shocks. The positivesign of the coefficient in model 1 and 2 indicates that a higher abatement shock (which is

equivalent to a lower emission shock) will increase compliance. This relationship is also

supported by model 3 5 which suggests that higher abatement shock reduces

noncompliance. Thus, these results provide strong evidence for hypothesis 4.

The marginal cost variable is also significant at the 1 percent significance level in all

5 models. Thus, both the decision of whether to comply and the magnitude of

noncompliance can be explained by the individual marginal cost function. This is not in line


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with Stranlund and Dhandas model which predicts that exogenous firm characteristics

should not impact compliance rate. Thus, hypothesis 3 is rejected.

Hypothesis 2 states that a higher permit price should result in greater noncompliance.

The Probit model on the binary decision to comply supports this. A negative coefficient

indicates a negative relationship between the permit price and compliance decision, as what

the theory predicts. Moreover, this variable is significant at the 5% level, providing support

for hypothesis 2. On the other hand, the variables coefficient in model 3 and 5 is positive,

suggesting the same relationship. However, this variable is not significant, even at the 5

percent level.

It must be added that model 4 was included in the regression to test if the removal of

two insignificant variables (Allocation and Mean price) changes any qualitative results for

the three remaining variables. No such effect was detected.

Table 8 shows the results of a Tobit regression model where the dependent variable

is the trading efficiency. The initial allocation variable is significant in explaining the

difference in trading efficiency, even at the 1 percent level. The negative coefficient implies

that a switch to a uniform allocation will result in lower trading efficiencies. Thus,

hypothesis 6 is rejected as experimental results show the initial allocation has an impact on

the efficiency of trading. The inclusion of model 7 serves the same purpose as model 4 in

checking that the addition of redundant variable10 has no qualitative impact on the result11

5.3 Discussion of Results

.

The results from the experiment provided strong evidence that the dynamic

enforcement model reduces both permit noncompliance and report noncompliance. The

same results are obtained from both the Tobit and Probit model, suggesting that

implementation of the dynamic enforcement model can reduce both noncompliance rate and

the magnitude of noncompliance, should they decide not to comply with the rule. Thus, the

leverage postulated by Harrington is evident and results in lower noncompliance. In

addition, the enforcement cost is also lower in the dynamic model relative to the static

10 The statistics for the F-test that both marginal cost and Individual Shock are jointly insignificant has a p-value of 0.992111 Subjects were asked to rate their own risk aversion in the questionnaire at the end of the experiment. As a

form of robustness check, these variables were calculated. As would be expected with randomization, therewas no significant differences for these variables across the different sessions. Hence, any changes in thedependent variables can be attributed to the explanatory variables being tested.


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Table 8 Random effect Tobit model on trading efficiency

Explanatory Variables

Dependent Variables: Tobit Model

(Upper Limit =1)

Model 6 Model 7

Trading Efficiency Trading Efficiency

Dynamic Compliance Model - 1.6959 (0.3606) ** - 1.7001 (0.3591) **Uniform Allocation - 2.7718 (0.3796) ** - 2.7772 (0.3772) **

Individual Shock 0.0092 (0.0734)Marginal Cost 0.000056 (0.0112)

Mean Pricea - 0.0457 (0.0235) -0.0466 (0.0223) *

Constant 5.4289 (2.7678) * 5.5203 (2.171)Observations 512 512Number of Subjects 64 64Wald Chi-Squared (5) 74.48Prob > Wald Chi-Squared (5) 0.0000

Wald Chi-Squared (3) 74.47

Prob > Wald Chi-Squared (3) 0.00001.Standard error in parenthesis.2.*: denote significant at the 5% level using a two tailed test.3.**: denote significant at the 1 % level using a two tailed test.4.a Estimated using IV method

model in the experiment due to the lower rate of monitoring in the good inspection group,

G1 Hence, a change from the static model to the dynamic enforcement model increases

compliance without incurring higher enforcement costs.

Hypothesis 5, which states that noncompliance is not different for agents in the two

different allocations, is rejected at the 1% level of significance for the Probit model but not

rejected in the Tobit model. Our data shows that a uniform allocation will decrease the

probability of agents complying by approximately 15 percent and 18 percent for actual

abatement violations and report violations respectively. However, it has no impact on the

magnitude of noncompliance should they decide to violate the rules.

Hypothesis 6 is also rejected at the 1% level of significance as there is evidence that

a uniform allocation decreases the trading efficiency relative to a non uniform allocation.

Studies have attributed this change in trading efficiency to market power (Maloney and

Yandle, 1984; Cason, Gangadharan and Duke, 2003). Previous experiments which studied

on market power defined it in terms of the number of buyers and sellers (Cason,

Gangadharan and Duke, 2003). Using this definition, this effect of market power is

eliminated in our experiment as there were 4 buyers and 4 sellers in all sessions.

Results from the regression model also support the other hypotheses that higher

permit prices and higher individual emission shocks increase noncompliance. Theories have


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attributed this linkage to an increase in the cost of compliance (Stranlund and Dhanda,

2004). This was also observed in our experiment.

6. Conclusion

6.1 Reconciling conflicting principles

The ETS is a market-based policy to solve the externality problem of greenhouse

gases emission. However, the efficiency of this scheme is crucially dependent on many

design features. In this study, features related to the maritime industry were studied using an

experimental approach. The unique characteristics of an ETS for the maritime industry are

first identified before developing a stylized experimental model that incorporates these

features. Thus, the results obtained have high external validity and are useful for industries

or countries other than the maritime sector.

In particular, two factors that are of concern to the maritime industry were further

examined. The first objective was to test the impact of the dynamic enforcement model on

noncompliance relative to the baseline static enforcement model. We found strong

experimental evidence that implementation of the dynamic enforcement model reduces

noncompliance. This result has strong implications for the maritime industry. Due to

numerous reasons, high noncompliance is one problem for the potential ETS in the industry.

Due to the global scope of this potential ETS, increasing monitoring efforts is likely to incur

high costs. Thus, this study proposes that the industry can introduce a system similar to the

Harrington dynamic enforcement model. This model allows the IMO to react to the

previous actions of the shipping firms by classifying them into one of the two inspection

groups. Having such a system could potentially reduce noncompliances with the same

enforcement budget. To adhere to the organizations main principle of treating all ships

equally, IMO can increase the transparency of the scheme by announcing and providing

reasons for the classification of shipping lines into the two inspection groups.Next, the initial allocation of permits is a potential solution to the schemes need to

reconcile two conflicting principles. While IMO advocates equality towards all ships,

UNFCCC principle is one of heterogeneous treatment for ships (common but differentiated

responsibility). This is one problem that is currently impeding the implantation of such a

scheme. Thus, an initial allocation where non Annex I countries are given more permits, is

one possible solution to adhere to the UNFCCC principle. Conventional models have

indicated that pre-trading allocations affect only equity but not efficiency (Kerr, 1999). Our

results are inconsistent with these theories as we find that the allocation method impacts on


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noncompliance and trading efficiency. More specifically, a non uniform allocation, which is

based on the UNFCCC principle, increases trading efficiency. Thus, the initial allocation of

permits affects the efficiency of the scheme and this effect should not be overlooked by the

IMO in their allocation of permits to the firms.

6.2 Limitation of Research

The analysis in this study faces several limitations which future researches can be

based upon. Although the model was developed to reflect the situation in the maritime

sector, the specific principle behind the two allocation treatment was kept general. Future

experimental research could isolate the specific allocation feature for a further examination

on this topic. Next, modeling after particular proposals to the IMO MEPC can also be done

to evaluate the feasibility of these proposals. An alternative perspective would be to perform

comparisons of ETS with other market-based environmental instruments such as carbon tax

to determine the most optimal instrument for the industry.

The second limitation is the number of periods in each session. As firms in most

ETS participate in the scheme infinitely, the number of periods should be increased to

reflect this aspect. Thus, further research can explore this possibility and determine if this

has a qualitative impact on the result. The third limitation is the possibility of confounding

factors in the experiment. This can lead to biasness in the results obtained and is a concern

in all experiments. In this study, care has been taken to randomize all possible elements of

the procedure to eliminate this effect. The questions on subjects risk aversion also shows

no significant different across the sessions.

As there is currently a lack of literature on an ETS for the maritime industry, this

study takes on a wider approach to incorporate more factors. This will hopefully motivate

further research into this field. With the increasing call for a market-based environmental

tool to complement the sectors technological efforts to clean up emissions, more in-depth

researches on this field would certainly be useful to the IMO.


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