1. Introduction
The “Kyoto Protocol” under which major industrial countries agreed to reduce their
CO2 emissions by 5.2% against 1990 level from 2008 to 2012 (the first commitment
period) was passed in December 1997 in Kyoto, Japan. The Protocol allowed these
countries to use three types of market mechanisms —joint implementation (JI),
clean development (CDM), and emissions trading (ET) — to fulfill their reduction
commitments. As a result, quota-based carbon emission trading markets were
developed rapidly in these countries, such as EU-ETS. Stern (2008) claimed that
trading mechanism for quota-based carbon emission permits should be promoted
post-Kyoto. However, the question is whether such a mechanism can ensure global
climate protection effectively, efficiently and equally. To answer this question,
scholars have carried out quantitative simulation on two aspects: carbon emission
quotas allocation and the subsequent trading of carbon emission permits.
In the field of carbon emission quotas allocation, Bohm and Larsen’s (1994) study
showed that initial quota allocation by net per capita costs of emission reduction is
beneficial for short-term equity. However, they also showed that initial quota
allocation by the size of population is conducive to the formation of long-term
equity. Janssen and Rothmans (1995) introduced calculations for future regional
emission quotas by subtracting historical regional emissions from total regional
emission quotas within a specified period, and examined the impact of different
quota allocation schemes (e.g. population size, GNP and amount of energy usage on
regional carbon emission quotas). Cramton and Kerr (2002) analyzed the auction of
carbon emission rights, and suggested that auction-style quota allocation schemes
are superior to grandfathering (allocating quotas based on historical output or
emissions). However, these studies primarily focused on the comparison of different
quota allocation principles from the point of view of equity, and lack examination of
the impact of limitations on total emission quotas. Most scholars believe that as the
first legally binding international emissions reduction agreement, Kyoto Protocol’s
effectiveness has been limited（Malakoff, 1997; Najam, 1998）. If this is the case
then global emission reduction schemes must take into account the specific situation
of developing countries, including historic responsibility for emissions, mitigation
actions, and assistance provided to the most vulnerable countries or regions etc.
(Rajan, 1997; Sagar and Kandlikar, 1997) Relative to the internationally renowned
Stern (2008) scheme, Sørensen (2008) scheme, Copenhagen Accord, etc., Chinese
scholars such as Ding et al.(2009), Jiang et al. (2009),Wang et al. (2012) also
launched multifaceted studies on global carbon emission reduction schemes.
In the field of trading carbon emission permits, Nordhaus (1977) pointed out that
economically greenhouse gas emissions are externalities and that major solutions to
the externalities of greenhouse gases include three key approaches: carbon taxes,
carbon trading and emission standards. Zhang (1998) claimed that, with regards to
the control of greenhouse gases, carbon trading is superior to carbon taxes, which
are in turn better than emission standards. Recent studies of carbon trading models
mainly use two types of modeling: computable general equilibrium (CGE) and
agent based simulation (ABS). Manne and Rutherford (1994) used a five-region
prospective model to analyze the impacts of carbon emissions limits on oil prices,
carbon leakage, and carbon trading; Ellerman and Decaux (1998) analyzed
CO2emissions trading under the Kyoto Protocol using marginal abatement curves
derived from Emissions Prediction and Policy Analysis (EPPA, a CGE model
developed by MIT); McKibbin et al. (2000) used a multi-region, multi-sector
intertemporal general equilibrium model to analyze carbon trading and capital flows
under the Kyoto Protocol; Szabo′et al. (2006) created a global cement industry
dynamic simulation model (CEMSIM) to perform simulations on CO2 emissions
trading within the EU and Annex B countries. Unfortunately, these dynamic
computable general equilibrium trade models are all based on a common
assumption that economic growth is always in a steady state of balanced growth. In
reality, this steady state of balanced growth assumption is only valid in a single
region or an area with no trading, and therefore, this assumption is problematic in
multi-regional trade models (Springer, 1999). Compared to CGE, simulation studies
using the ABS method are relatively rare. Mizuta and Yamagata (2001) established
an international greenhouse gas emissions trading model under an auction
mechanism; Chappin (2006) simulated the influence of CO2 trading in the EU on
investments in Dutch electricity generation. Bakam and Matthews (2009) built a
carbon trading system for agricultural department in Scotland. Based on the ABS
method, these studies provide a dynamic market trading simulation mechanism in a
complex economic and social environment, rather than relying on the assumption of
balanced state used in traditional economic theories.
To evaluate the effect of quota-based carbon emission permits trading mechanism
globally, this article constructs a carbon trading model by using ABS modeling
technology, and then develops a global carbon trading system, finally examining the
capital flows of carbon trading and its impact on global climate protection.
Considering that China and other developing countries have played an increasingly
important role in the field of climate change, this article will focus more on these
regions. Section 2 will detail the carbon-trading model and research method,
Section 3 discusses the results of scenarios simulation and finally section 4 sets out
the conclusions.
2. Model and method
Focusing on quota-based carbon emission permit trading mechanisms and ABS
modeling technology, this article establishes a global carbon emission trading model
(CETM) that divides the world into six regions: China (CN), the US (US), the EU
(EU), Japan (JP), the former Soviet Union (FSU), and rest of the world (ROW).The
CETM is divided into two modules - carbon emission quotas allocation (CEQA)
module and carbon emission permits trading (CEPT) module - based upon the
reality that carbon emission quota allocation and carbon emission permit trading are
the two key functions in a quota-based carbon emission permit trading mechanism.
Firstly, the CEQA module computes the global and regional carbon emission quotas,
and then the CEPT module simulates the capital flows of carbon emission permits
trading and determines the equilibrium price of carbon emission permits.
2.1 CEQA module
The goal of the CEQA module is to estimate the carbon emission quotas on both a
global and a regional scale. Considering that there is no consensus criterion on
global carbon emission quotas allocation, the CEQA module adapts two different
methods to calculate the global and regional carbon emission quotas. They are the
target control of atmospheric concentration of CO2 (TCACC) and the global carbon
emission reduction scheme (GCERS).
2.1.1TCACC method
The TCACC method assumes that the CO2 anthropic emissions consist of two major
sources: fossil fuel emissions and land-use emissions. It also assumes that a part of
the CO2 released into the atmosphere will be absorbed by marine and terrestrial
ecosystems (Ding et al., 2009). In this case, given the atmospheric concentrations of
CO2 for a target year, the global carbon emission quotas within a specified period
can be calculated by the following equation.
Q
n
D

f
 D s  *2.12
1 
 * n
(1)
where Q n is the n years of global carbon emission quotas; s and f are the initial
year and target year, respectively; D s and D f are the atmospheric concentrations
of CO2 in the initial year and target year, respectively; n is the number of years
from the initial year to the target year;  is the absorptivity of CO2 (i.e., the ratio
of absorbed CO2 from one unit of CO2 emission by terrestrial and marine
ecosystems); and  is the annual emissions caused by land-use. According to
Canadell et al. (2007), the average absorptivity of CO2 from 2000 to 2006 is 0.54.
Here set 0.54 for  . Research from The Carbon Dioxide Information Analysis
Center (CDIAC) shows little change in CO2 emissions from land-use over the past
50 years, between 1.25-1.70GtC( gigaton carbon) per year (Houghton, 2008). Here
set 1.5GtC per year for  . The constant 2.12 in formula (1) is the conversion rate
from 1ppmv atmospheric concentration of CO2 to the quantity of carbon (Ding et al.,
2009).
Taking no account of historical carbon emissions from the different regions, global
carbon emission quotas can be allocated to each region according to a regional
indicator. Depending on different quota allocation principles, the indicator can be
GDP, population, carbon emissions etc. In this case, the n years of carbon
emission quotas in region i ( Qin ) can be expressed as,
xi * Q n
Q 
xi
n
i
(2)
where xi is the indicator for region i .
Because the n years of carbon emission quotas in region i ( Qin ) are equal to the
sum of the annual carbon emission quotas from the initial year s to the target year
f
Q
i ,t
 Qin (3)
t[ s , f ]
where Qi ,t is the carbon emission quotas for region i at year t , the subscript t is
any year within n years.
Assuming that the annual regional carbon emission quotas reductions are evenly
distributed after the initial year s to the target year f , and the regional carbon
emission quotas are equal to regional carbon emissions at the initial year s , then
Qi ,t  Ei , s  di (t  s )
(4)
where Ei , s is the carbon emissions in region i at the initial year s , d i is the
quantity of annual carbon emission quotas reduction in region i .
According to equation (3) and (4), the carbon emission quotas in region i at year
t ( Qi ,t ) can be expressed as
Qi ,t  Ei ,s   t  s  *
2 Ei , s  2Qin / ( f  s  1)
f s
(5)
However, because of the large differences in development between developed and
developing countries, historical carbon emissions must be considered when
allocating the global carbon emission quotas to each region. After comparing and
analyzing existing international allocation principles of global carbon emission
quotas, Ding et al. (2009) and Wang et al. (2009) highlighted the quota allocation
principle using the equal cumulative carbon emissions per capita, meaning that the
regional cumulative carbon emissions per capita are equal within a certain period,
best reflects the common but differentiated responsibilities and the standards of
fairness and justice. In this case, use equation (6) to replace equation (2)
cq n  cqin 
'
'

t[ h , s )
Ei ,t popi ,t 
Q
t[ s ,t ]
i ,t
popi ,t
(6)
'
where n is the number of years from the historical start year h to the target year
'
f , cqin is the cumulative carbon emissions per capita in region i from the
historical start year h to the target year f , popi ,t is the population in region i
at year t (Population data are obtained fromhttp://databank.worldbank.org/). Here,
cq n  cqin indicates that the cumulative carbon emissions per capita in every
'
'
region are equal.
2.1.2GCERS method
Based on the global carbon emission reduction scheme, the GCERS method
calculates every regional carbon emission quota according to the percentage of
reduction in the target year relative to the reference year. From the point of view of
modeling, the GCERS method is relatively easier than the TCACC. Assuming that
the regional carbon emission quotas are allocated in each year according to a fixed
annual reduction rate, then the carbon emission quotas in region i at year t , Qi ,t
can be expressed as
Qi ,t  Ei , s   f  t  *
Ei , s  1  pi  * Ei ,r
t  s 1
(7)
where Ei ,r is the carbon emissions in region i at the reference year r , pi is the
percentage of carbon emission reductionto the target year in region i .
2.2 CEPT module
The CEPT module is responsible for simulating the trading behavior of carbon
emission permits in each region. By comparing each regional carbon emission
permits with its actual carbon emissions, it calculates each regional carbon emission
permits deficit (or surplus) and determines the equilibrium price of carbon emission
permits.
Calculating the regional carbon emission permits and regional carbon emissions is
the premise of the CEPT module. On the one hand, setting the regional carbon
emission quotas calculated by the CEQA module as the regional carbon emission
permits in the CEPT module. That means
Ri ,t  Qi ,t
(8)
where Ri ,t is the carbon emission permits in region i at time t .
On the other hand, get regional carbon emissions by estimating regional reduction
rates for carbon emissions. By using the abatement cost function proposed by Pizer
(1999), the relationship between the regional reduction rate and marginal abatement
cost is as follow.
MACi ,t 
ai *bi
* bi 1
 i ,t *(1  ci *Tt 2 / 9) i ,t
(9)
where i ,t is the reduction rate of carbon emissions in region i at year t , MACi ,t
is the marginal abatement cost of carbon emissions in region i at year t , ai and
bi are parameters of the abatement cost function in region i , ci is the damage
coefficient of temperature increase on the economies of region i ,
Tt is the
increase of global average temperature since industrialization at time t , and  i ,t is
the carbon emissions intensity in region i at time t .
Given the regional marginal abatement cost, the reduction rate of carbon emissions
in region i at time t , i ,t can be expressed as follow.
i ,t  (b 1)
i
MACi ,t * i ,t *(1  ci *Tt 2 / 9)
ai *bi
(10)
Then the carbon emissions in region i at time t , Ei ,t would be
Ei ,t   i ,t *Yi ,t * 1  ui ,t  (11)
where Yi ,t is the GDP in region i at time t .
By subtracting the carbon emissions from the carbon emission permits, the volume
of carbon emission trading in region i at time t , Ti ,t would be
Ti ,t  Ri ,t  Ei ,t
(12)
For all regions, they have
T
i ,t
 0 (13)
i
The CEPT module assumes that there is only one equilibrium price in carbon
permits trading. That means the marginal abatement costs in all regions will be
equal when the global carbon emission permits equal to the global carbon
emissions.
MACt  Pt *
(14)
where Pt * is the equilibrium price of global carbon emission permits at time t .
At this time, GDP will be adjusted by the following equation.
Yi*,t  Yi,t  Ti ,t * Pt*
(15)
where Yi*,t is the GDP adjusted by carbon emission permits trading in region i at
time t .
It should be noted that this article focuses on the modeling of global carbon trading.
The relationship among carbon emissions, economic system and climate system is
mainly referenced from the LRICE model established by Wang et al. (2010). The
integrated relationship between the CETM and LRICES model is shown in Figure
1.
Insert figure 1 here
2.3Agent-based method
The description above shows that the process for the CETM module involves an
interaction between regions. To simulate this interaction effectively, this article
adapts an agent-based modeling method. According to functions in the CETM
module, it set three types of agents: a global agent, a regional agent, and an observer
agent. The interaction and information transmission among the three types of agents
are shown in Figure 2.
Insert figure 2 here
The global agent is the core of the CETM. It has four major functions: to allocate
global carbon emission quotas among regions; to iteratively send supply
information on global carbon emission permits to the regional agent; to receive
feedback on demand information for carbon emission permits from the regional
agent; to measure whether the supply and demand of global carbon emission
permits is balanced; and search for an equilibrium price for global carbon emission
permits.
Each region in the CETM will be represented by an independent agent to participate
in both the CEQA and CEPT module. Corresponding to regional divisions, the
regional agents are divided into the CN agent, US agent, JP agent, EU agent, FSU
agent, and ROW agent. They have three fundamental functions: to receive
information on the result of quota allocation and the price of global carbon emission
permits from the global agent; to make a rational economic decision based on the
information received and regional economic development; and to send their
decision on permit trading back to the global agent. Here, rational economic
decision-making means that the action of trading permits always occurs when the
regional marginal abatement cost is greater than the price of global carbon emission
permits. Otherwise, the regional agent would prefer to reduce carbon emissions.
The observer agent is in responsible for the statistical analysis of data, including
economic indicators and trading information for the global and regional agents.
In the process of the CETM modeling, the most crucial problem is how to find out
the equilibrium price of global carbon emission permits. To solve this problem, this
study developed a six step approach: (1) the global agent initializes the price of
global carbon emission permits to zero; (2) according to the price of global carbon
emission permits, each regional agent calculates its own reduction rate of carbon
emissions; (3) according to the regional reduction rate of carbon emissions, each
regional agent calculates its own carbon emissions; (4) the global agent adds up all
of the regional carbon emissions to obtain global carbon emissions; (5) the global
agent compares global carbon emission permits with the level of global carbon
emissions, if they are equal, the current price of global carbon emission permits is
the equilibrium price; (6) if the global carbon emission permits is less than the
global carbon emission permits, the global agent slightly increases the price of
global carbon emission permits; otherwise, slightly decreases the price, then returns
to step (2). A flow chart for the above is shown in Figure 3.
Insert figure 3 here
3. Simulation and discussion
Based on the above CETM, this study developed a simulation system (CETS) on
global carbon emission trading to compare and predict the effect of carbon emission
trading on climate protection under different global carbon emission reduction
schemes.To facilitate the study, this article uses the scenario analysis method.
3.1 Scenario set-up
Three scenarios are set as follows:

Scenario 0: The “business as usual” scenario. Assuming that there is no
additional carbon emission reduction scheme for a region, this scenario
provides a comparison baseline for other scenarios.

Scenario 1: The “equal cumulative carbon emissions per capita” scenario.
Considering the differences in historical cumulative carbon emissions among
regions, this scenario uses the TCACC method and adapts the principle of equal
cumulative carbon emissions per capita to allocate the global carbon emission
quotas. According to Ding et al. (2009), this scenario sets 470 ppmv as the
ceiling of the global atmospheric CO2 concentrations. To reflect the principle of
the common but differentiated responsibilities proposed by the United Nations
Framework Convention on Climate Change (UNFCCC), this scenario sets 1990
as the historical start year, 2010 as the initial year and 2050as the target year. It
assumes that regional carbon emissions per capita converge by 2050 and
remain at their 2050 level after 2050.

Scenario 2: The “2°C target” scenario. Based on the GCERS method and the
2°C global carbon emission reduction scheme proposed by Wang et al. (2011),
this scenario divides the course of global carbon emission reduction into three
phases. In the first phase, from 2010 to 2020, regional carbon emissions are
reduced according to the Copenhagen Accord(The reduction scheme of ROW in the
first stage refers to CN). In the second phase, from 2021 to 2050, the carbon
emissions in developed countries (US, EU, and JP) are reduced by 80 percent,
compared to 1990 level. The carbon emissions in FSU are reduced by 50
percent, compared to 1990 level. The carbon emissions in CN and ROW are
reduced by 28 percent and 20 percent respectively, compared to 2005 level. In
the third phase, from 2051 to 2100, the carbon emissions in all regions remain
at their 2050 level. Total emissions reduction will be implemented in CN and
ROW at 2025, instead of intensity reduction before that year.
It’s worth mentioning that CETM can evaluate global carbon emission trading for
all kinds of carbon emission reduction scenarios. But considering that China has
become the world’s largest carbon emitter, here this study focuses on two typical
scenarios proposed by Chinese scholars as examples.
3.2 Analysis of carbon emission quotas allocation
Based on the scenarios introduced in Section 3.1, the global and regional carbon
emission quotas can be calculated by CETS, see table 1.
Insert table 1 here
Table 1 shows the global carbon emission quotas in scenario 0 are the largest both
during 2010-2050 and during 2051-2100, followed by scenario 1 and scenario 2,
and global carbon emission quotas during 2010-2100 in scenario 0 are 1275GtC,
more than twice 619GtC in scenario 1 and 457GtC in scenario 2. In other words,
scenario 1 or scenario 2 would greatly reduce global carbon emissions relative to
scenario 0. Of course, CETM can arrive at other values for global carbon emission
quotas by adjusting the control target of global atmospheric CO2 concentration
under the TCACC method, for example, 450ppmv for 210GtC during 2010-2050,
and by altering the regional carbon emission reduction scheme under the GCERS
method, but to help in understanding global carbon emission quotas, this article
only discusses those cases outlined in scenario 1 and scenario 2.
From a regional point of view, table 1 shows the carbon emission quotas of CN and
ROW during 2010-2050 in scenario 1 are larger than those in scenario 2, while the
opposite happens with US, EU, JP and FSU. Obviously, scenario 1 favors China and
other developing countries (most countries of ROW) more than the developed
countries, when compared with scenario 2.
It is important to note that quota deficits appear in scenario 1, where the carbon
emission quotas of US during 2010-2050 are -10GtC. This suggests that the
historical (1990-2009) carbon emissions of US have overdrawn on its future carbon
emission quotas. In this case, the principle of equal cumulative carbon emissions
per capita would face great difficulty in being accepted by most developed countries
in international negotiations on carbon emission reduction. Actually, historical start
year plays a critical role in the course of calculating regional carbon emission
quotas. For example, if we move the historical start year, 1990 in scenario 1,
backward to 1861, the carbon emission quotas of US during 2010-2050 would
change from -10GtC to -68GtC. Similarly, we can also move the historical start year
forward to 2010, and then we get 15GtC for the carbon emission quotas of the US
during 2010-2050. Simulation result shows that the earlier the historical start year
we set, more divergent regional carbon emission quotas between the developing
countries and the developed countries are obtained. Considering the historical start
year involves differentiated responsibilities of regional historical carbon emissions
that will not be discussed further in this article.
3.3 Analysis of the trading of carbon emission permits
As we know, to date, there is no unified global carbon trading market, so this article
assumes global carbon emission permit trading will commence in 2025. To facilitate
the simulation of that, this study allocates the regional carbon emission quotas
during 2010-2100 to each year with a fixed reduction rate.
From the point of view of global average temperature rise, see figure 4, scenario 0
results in a 2.99°C increase by 2100, which exceeds the “2°C threshold “ proposed
by the IPCC (2007) by almost 1°C, while scenarios 1 and 2 result in a 2.17°C and
1.98°C increase by 2100, respectively. This coincides with the result of global
carbon emission quotas allocation, in which the global carbon emission quotas in
scenario 1arelarger than that in scenario 2.In other word, the less the global quotas
are, the lower the global average temperature would be.
Insert figure 4 here
Due to the fact that there are no carbon emission limits in scenario 0, the following
analysis on price, and volume of carbon emission trading will focus on scenario 1
and scenario 2.
From the point of view of value (see figure 5),the trading prices of carbon emission
in scenario 1 and scenario 2 increase from 84$/tC(the unit price of carbon, represent
US dollar per ton of carbon, where $ is the constant US dollar in 2000) and 184$/tC
by 2025 to 2490$/tC and 3307$/tC by 2100, respectively. There are two factors
contributing to increases in trading price. From the perspective of supply, it makes
sense that an increase in trading price follows a decrease in carbon emission quotas.
Under the assumption of a fixed reduction rate of carbon emission quota in both
scenario 1 and scenario 2,the carbon emission quotas would come down year on
year, thus contributing to increases in trading price. From the demand perspective, it
also makes sense that an increase in trading price follows an increase in demand (or
perceived need) for greater carbon emissions. With future economic growth,
demand for greater carbon emissions will increase, thus contributing to increases in
trading price.
Insert figure 5 here
It should be noted that the trading prices for carbon emission in scenario 2 are
higher than scenario 1. This is because global carbon emission quotas in the former
are less than the latter. It should be note that the trading price of carbon emission by
2100 is quite high,2490$/tC in scenario 1 and 3307$/tC in scenario, requiring that
new reduction technology, such as CCS, must be developed and disseminated to
reduce the impact of carbon emission reduction on the global economy.
Scenario 1 (figure 6) shows that for 2025-2100, only the ROW will have constant
positive trading volumes, whilst the US, JP, and FSU will display constant negative
trading volumes; CN will change from positive to negative in this period, while EU
will change from negative to positive. Scenario 2 (figure 7) shows that for
2025-2100, again, only the ROW will have constant positive trading volume, while
the US and JP will have constant negative trading volume; CN and FSU will change
from positive to negative, while EU will change from negative to positive.
Obviously, countries with positive trading volume (mainly ROW, CN and FSU)
would be carbon emission permit sellers; while countries with negative trading
volume (mainly US, JP and EU) would be carbon emission permit buyers. In other
word, the mechanism of carbon emission trading is helpful for transferring allocated
quotas from developing countriesto developed counties.
Insert figure 6 and 7 here
※ Where positive value means that the carbon emission permits have a surplus after regional
carbon emissions are subtracted from the allocated quotas, creating carbon emission permit
seller; Negative value means that the carbon emission permits have a deficit, creating carbon
emission permit buyer.
Theoretically, the size of trading volumes depends on the gap between carbon
emissions and allocated quotas. The larger the gap is, the more trade there would be.
For regions with surplus quotas, increasing their quotas means increasing their
positive gap, and in turn resulting in an increase in trading volumes; while for
regions with deficit quotas, increasing their quotas mean decreasing their negative
gap, and in turn resulting in a decrease in trading volumes. In other words, even if
global quotas remain constant, differing quota allocations between regions will lead
to different trading volumes.
It is necessary to note that countries may change their trading roles in the future, for
instance, in both scenarios China will change from a permit seller to permit buyer.
Considering the expected rising trading prices, storing quotas for future use may
provecost-efficient. In this case, if quota storage is considered, our model requires
some corresponding improvements, an issue to be pursued in future research..
Insert figure 8 and 9 here
Figure 8 shows, during both 2025-2050or 2051-2100, only the ROW has positive
cumulative trading values in scenario 1. In scenario 2(figure 9) shows that in
addition to ROW, CN and FSU have slightly positive cumulative trading values
during 2025-2050; while it is also only ROW that has positive cumulative trading
values during 2050-2100. These results indicate that developing countries,
especially ROW can receive capital inflow under the implementation of carbon
trading, and developed countries such as US, JP can achieve their reduction
obligations by purchasing developing countries deficit permits. Overall, carbon
emission trading is helpful for both developing countries and developed countries.
For China, the cumulative trading values during 2025-2050 and 2051-2100 are
-0.06 and -38trillion US dollars, respectively, in scenario 1; while 2.8 and -22
trillion US dollars, respectively, in scenario 2.Obviously, scenario 2 is more
beneficial for protecting Chinese economic interests than scenario 1.
From the point of view of the actual regional emissions, although the quota
allocation mechanism assigns all regions’ quotas each year according to a given
principle, the actual regional emissions are still differentiated from the allocated
quotas due to the carbon trading mechanism. When the marginal abatement costs of
a region are higher than the trading price, the region with deficit quotas would
rather purchase the necessary additional quota from those with lower marginal
abatement costs than abate by itself. In this case, under the carbon emissions trading
the relative sizes of the actual regional carbon emissions per capita would display
little change. For example, the actual regional carbon emissions per capita of the US,
FSU, JP and CN in 2100 are still much higher than that of the ROW and EU in both
scenarios 1 and 2 (see figure 10).
Insert figure 10 here
The reasons why actual regional carbon emissions per capita of EU and ROW in
2100 are lower than other regions could be as follows: For the EU, advanced
technology and strong political will to reduce emissions contribute to its future
lower emissions per capita; For the ROW, lower levels of economic development
leads to its lower emission requirements, and in turn result in low future emissions
per capita. Obviously, under the carbon emission trading process, the principle of
“equal cumulative carbon emissions per capita” does not necessarily lead to regions’
actual carbon emissions per capita being equal.
To further evaluate the global impacts of carbon trading, the Ramsey utility
function is used for analysis. Unlike GDP, the Ramsey function considers both the
total GDP and per capita welfare, which is a representation of comprehensive
national power. Wang et al. (2010) described the formula for the Ramsey utility
function, and considering the academic controversy over the discount rate, this
article set 0.015 and 0.001 respectively for the discount rate  (Nordhaus, 2007;
Stern, 2008). Figure 11 shows the change rates of the global cumulative Ramsey
utility in scenarios 1 and 2 with or without carbon trading, relative to that in
scenario 0.
Insert figure 11 here
Figure 11shows that the size of the discount rate cannot change the relative size of
the cumulative change rate of the Ramsey utility. In both scenarios 1 and 2, carbon
emission trading always increases the global Ramsey utility.
4. Conclusions
Based on agent modeling technology, this article establishes a global carbon-trading
model and simulation system. By setting an “equal cumulative carbon emissions per
capita” scenario and a “2°C target” scenario, simulation results shows that:
(1) The results of carbon trading depend on quota allocation. To offset the
numerous historic carbon emissions, developed countries such as US would face
huge quota deficits in scenario 1. In this case, the principle of equal cumulative
carbon emissions per capita may not be accepted easily by these countries.
(2) With a decrease in global carbon emission quotas and an increase demand for
carbon emissions, the global carbon price will rise in the future.
(3) The implementation of carbon trading is helpful for transferring capital mainly
from developed countries to developing countries.
(4)Under the carbon emission trading process, developed countries will purchase a
large number of emissions quotas from developing countries; therefore, the
principle of equal cumulative carbon emissions per capita will not hold. The
cumulative carbon emissions per capita in developed countries will be still much
higher than that in developing countries.
(5) In both scenario 1 and 2, carbon emission trading always increases the global
Ramsey utility.
Reference
Bakam, I., Matthews, R., 2009. Emission trading in agriculture: a study of design options using an agent-based
approach. Mitigation and Adaptation Strategies for Global Change. 14(8), 755-776.
Bohm, P., Larsen, B., 1994, Fairness in a tradable-permit treaty for carbon emission reductions in Europe and
the Former Soviet Union. Environmental and resource economics. 4(3), 219-239.
Canadell, J. G., Le, Q.C and Raupach, M. R., et al., 2007, Contributions to accelerating atmospheric CO2
growth from economic activity, carbon intensity, and efficiency of natural sink. Proceedings of the
National Academy of Sciences of the United States of America, 104: 18866—18870
Chappin, E. J. L., 2006, Carbon Dioxide Emission Trade Impact on Power Generation Portfolio: Agent-based
Modelling to Elucidate Influences of Emission Trading on Investments in Dutch Electricity Generation.
http://www.tudelft.nl/live/binaries/b3f1782a-82d1-45f9-ae2f-e83e318fa1f1/doc/chappin_msc_thesis_report
_final.pdf.
Cramton, P., Kerr, S., 2002, Tradable carbon permit auctions: how and why to auction not grandfather. Energy
Policy. 30(4), 333-345.
Ding, Z., Duan, X. and Ge, Q., et al., 2009, Controlling the Atmospheric CO2 Concentration: National Carbon
Emissions Calculation. Scichina. 39(8), 1009-1027.
Ellerman, A. D., Decaux, A., 1998, Analysis of post-Kyoto CO2 emissions trading using marginal abatement
curves. http://dspace.mit.edu/handle/1721.1/3608.
Houghton, R. A., 2008, Carbon Flux to The Atmosphere from Land-Use Changes 1850—2005. A Compendium
of Data on Global Change. Carbon Dioxide Information Analysis Center, Oak Ridge National Laboratory,
U.S.
Department
of
Energy,
Oak
Ridge,
Tenn.,
USA.
Trends.
http:
//cdiac.ornl.gov/trends/landuse/houghton/houghton.html
Janssen, M., Rotmans, J., 1995, Allocation of fossil CO2 emission rights quantifying cultural perspectives.
Ecological Economics. 13(1), 65-79.
Jiang, K., Hu, X. and Liu, Q, et al., 2009, Projection for Low Carbon Scenerios by 2050. Environmental
Protection. 24(4), 28-30.
Malakoff, D., 1997, Thirty Koyotos needed to control warming. Science. 278(5346), 2048.
Manne, A. S., 1994, Rutherford T F. International trade in oil, gas and carbon emission rights: an intertemporal
general equilibrium Model. The Energy Journal. 15(1), 57-76.
Mckibbin, W. J., Ross, M.T., and Shackleton, R., et al., 2000, Emissions Trading, Capital Flows and the Kyoto
Protocol. http://www.brookings.edu/views/Papers/bdp/Bdp144/bdp144.pdf.
Mizuta, H., Yamagata, Y., 2001, Agent-based simulation and greenhouse gas emissions trading. Proceedings of
the 2001 Winter Simulation Conference, Virginia.
Najam, A., Page, T., 1998, The climate convention: deciphering the kyoto protocol. Environ. Conserv. 25(3),
187-194.
Nordhaus, W. D., 1977, Economic growth and climate: the carbon dioxide problem. American Economic Review.
67(1), 341-346.
Nordhaus, W. D., 2007. A review of the “Stern review on the economics of climate change”. Journal of
Economic Literature. 45(3), 686–702.
Pizer, W. A., 1999. The optimal choice of climate change policy in the presence of uncertainty. Resource and
Energy Economics. 21(3-4), 255-287.
Rajan, M. K., 1997, Global Environmental Politics. Delhi: Oxford University Press.
Sagar, A., Kandlikar, M., 1997, Knowledge, rhetoric and power: international politics of climate change.
Economic and Political Weekly. 32(49), 12-19.
Sørensen, B., 2008, Pathways to climate stabilisation. Energy Policy. 36(9), 3505-3509.
Springer, K., 1999, Climate policy and trade: Dynamics and the steady-state assumption in a multi-regional
framework.
http://www.ifw-kiel.de/ifw_members/publications/climate-policy-and-trade-dynamics-and-the-steady-state
-assumption-in-a-multi-regional-framework/kap952.pdf.
Stern, N., 2008, The economics of climate change. American Economic Review. 98(2), 1-37.
Szabo′, L., Hidalgo, I., and Ciscar, J.C., et al., 2006, CO2 emission trading within the European Union and
Annex B countries: the cement industry case. Energy Policy. 34(1), 72-87.
Wang, Z., Wu, J. and Li, H, 2009, Using simulation to assess climate-change strategies for global participation.
Acta Ecologica Sinica. 29(5), 2407-2417.
Wang, Z., Wu, J. and Zhu, Y., et al., 2010, Research on Climate Change. Beijing：Scinece Press.
Wang, Z., Zhu, Q. and Wu, J., 2011, Research on China's Emission Reduction Scheme for Searching Superiority
with Uncertainty of Climate Change. Bulletin of the Chinese Academy of Science. 26(3), 261-270.
Zhang, Z. X., 1998, Greenhouse gas emissions trading and the world trading system. http://
mpra.ub.uni-muenchen.de/12971/ 1/MPRA_paper_12971.pdf.