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125
5
The Growth of Carbon
Markets in Asia:
The Potential Challenges
for Future Development
Yukun Shi, Sudharshan Reddy Paramati, and Xiaohang Ren
5.1 Introduction
Over the past 20 years, academia and industry practitioners have
persistently paid a lot of attention to studying how to eciently address
global climate change and decrease carbon emissions. Historically,
emission trading programs are widely believed to have played a
prominent role in environmental policy, especially to reduce carbon
emissions. As a result, many regional and national carbon trading
markets emerged, including the Regional Emissions Trading Scheme
in Australia, the People’s Republic of China (PRC), New Zealand, the
Republic of Korea (henceforth Korea), and the United States, and most
notably the Emissions Trading System set up by the European Union
(EU ETS). As far as we are concerned, to date the EU ETS is the major
emission cap-and-trade trading program across the world, accounting
for about $175 billion a year. The history of the EU ETS can be traced
back to 2005; today it allocates tradable emissions permits to more than
12 large power stations and industrial plants in more than 20 countries,
accounting for about half of the EU’s total greenhouse gas (GHG)
emissions in aggregate.
Previous research focused on the impacts of energy, financial
markets, and macroeconomic factors on the EU carbon emission market
(see, for instance, Alberola et al. 2008, Chevallier 2009, Koch 2014).
However, to the best of our knowledge, fewer studies have focused on the
relationship between the carbon emission markets of the PRC and the
EU. As the PRC is the largest GHG emitter, its government has given a lot
126 Ways to Achieve Clean Asia
of attention to setting up carbon trading markets, such as the Shanghai
and the Shenzhen carbon emission markets. Therefore, the growth of
carbon emission markets in the PRC has raised the question: What is
the relationship between regional carbon emission markets? Moreover,
the lessons from the linkages between regional carbon emission markets
could help enhance the carbon emission market cooperation both in
the PRC and around the world and provide additional insight for other
developing countries into the domestic emissions trading system (ETS).
Our main empirical findings suggested the existence of co-integration
between the futures prices in PRC’s carbon emission markets. In
particular, the Shanghai carbon emission market positively aects the
Shenzhen carbon emission market, but a similar vice versa relationship
could not be found. Our findings shed light on the policy formulation
of the carbon emission markets for the PRC government to achieve
sustainable economic growth and meet the carbon reduction targets of
the country.
Since the well-known economic revolution that started in the late
1970s, the economy of the PRC has undergone decades of significant
developments and became the second-largest economy in the world
in 2016, according to the World Bank’s data on gross domestic product
(GDP) at the end of 2017. Despite its undebatable economic success,
a side eect of PRC’s economic progress is the unprecedented large
volume of carbon emissions. As a result, the National Development
and Reform Commission issued “interim measures for voluntary
GHG emissions trading management” in June 2012 to explore the
possibility of a voluntary emissions trading program in the PRC. This
is the country’s initial attempt to reduce carbon emissions and has been
generally regarded as a signal to the public. It lays out the theoretical
foundation for further practical implementation of mandatory carbon
trading mechanisms as well as the establishment of carbon emission
rights exchanges in Shanghai, Shenzhen, and other places in the PRC.
As a matter of fact, the first carbon emission exchange in the PRC was
set up in Shenzhen in June 2013, and Shanghai and other places had
followed suit by the end of November 2013. These have since become the
two most important carbon emission exchanges in the PRC, parallel to
the two most important stock exchanges in the country in Shanghai and
Shenzhen. Given the current prominent status of the PRC’s economy, a
significant reduction in the level of carbon emissions of the PRC stock
market will be expected from these programs.
In theory, the establishment of carbon emission markets will
significantly decrease the level of carbon emissions of the country,
which has milestone implications for its carbon emission reductions. In
practice, however, there was a trivial volume before 2007. To date, to
The Growth of Carbon Markets in Asia: The Potential Challenges for Future Development 127
the best of our knowledge, no paper has discussed the interrelationship
among the futures prices in multiple carbon emission exchanges in
the PRC. However, a large strand of literature has already discussed
the interrelationship between the stock prices in the Shanghai and
Shenzhen stock exchanges. This paper aims to focus on these two most
important carbon emission exchanges. To be specific, it will examine
whether the futures price in the Shanghai carbon emission exchange
aects the futures price in the Shenzhen carbon emission exchange, and
vice versa, considering the exogenous shock mainly from the EU ETS.
This paper will contribute to the existing literature in the following
three ways: First, it is the first to investigate the dynamic linkage of the
futures prices between these two markets. The study results will shed
light on the interrelationship of the futures prices in these two markets
and provide risk management guidelines for the two markets’ investors.
Second, this study applies daily high-frequency data from 3 January 2017
to 9 October 2018. An investigation into the futures price on a lower-
frequency level cannot capture the dynamic influence, while intraday
high-frequency data will suer from much misconstruction noise in this
study. Third, we introduce the autoregressive distributed lag model to
this topic, which integrates short-run adjustment eects with long-run
equilibrium without losing information (see Jalil and Feridun 2011).
5.2 Background
Since the emergence of the EU ETS in 2005, the carbon emission market
has emerged to become a prominent system for mitigating climate
change across the globe. Countries of the Association of Southeast Asian
Nations (ASEAN) have focused their attention on this modern and
global idea of mitigating climate change, having noted the rate at which
emissions continue to increase in the region. Northeast Asian countries
such as the PRC, Japan, and Korea contribute more than one-fifth of
the world’s GDP and more than a quarter of global emissions (Ewing
2016a). Equally, the increase in emissions in Southeast Asia is almost
sprinting forward at the same rate as its economic growth, with nearly
a 5% annual increase from 1990 to 2010 (Raitzer et al. 2015). This surge
has been traced to the relative escalation of emissions in sectors such
as manufacturing and transportation. These two sectors are associated
with the region’s structural change rather than rural or agrarian areas.
There is, therefore, an urgent need to curtail the increase in
emissions, owing to the fact that emissions cause much harm to humanity
and the economy as a whole. Given that, the global carbon finance
needs to be upheld by an order of “weight-carrying” in the second
commitment period of the Kyoto Protocol. To enable the stakeholders in
128 Ways to Achieve Clean Asia
the carbon market to see the opportunities far away, outside the project-
based Clean Development Mechanism (CDM), alternative schemes,
such as allowance-based market mechanisms, may be considered in
the emerging economies in a similar manner to the one that functions
in Europe, i.e., the EU ETS (Grubb 2012, Perdan and Azapagic 2011).
Evidently, there has been significant progress in this market as the trend
has gained popularity among the major Asian economies, including
the PRC, India, Japan, Kazakhstan, and Korea. The PRC, which is the
world’s major growing market, is expected to have a greater potential
for the scale of carbon trading (Jotzo et al. 2013, Wang 2013, Fankhauser
2011, Guan and Hubacek 2010).
Based on the above, we see clearly the significance of carbon
emission mechanisms, which are considered popular instruments in
the domestic and international circuit for enhancing eorts toward
mitigating climate change. These instruments often put a price tag
on emissions that lead to climate-damaging GHGs, which eventually
leads to the promotion of climate change mitigation eorts. More
specifically, two approaches led to the creation of carbon markets,
i.e., the cap-and-trade mechanism and the carbon tax mechanism
(Hartmann 2017).
Cap-and-trade mechanism. The cap-and-trade mechanism is
used by governments or intergovernmental bodies to peg a limit to
GHG emissions in a given period. This mechanism is designed to oer
licenses to both companies and industries to minimize the growth of
pollution (Stavins 2008). The scheme is designed in such a way that if
companies that do not meet their cap buy licenses from others, they will
have a surplus. The technicality here is that it provides an opportunity
to reduce GHGs with a cost-eective platform. However, it is also
widely criticized because it rewards most polluters with windfall profits
while undermining the eorts to reduce pollution and achieve a more
sustainable economy.
Despite this criticism, its popularity remains high, going by the wide
range of potentials, which can engender considerable revenues. These
collected revenues have significant implications for distribution in the
spirit of fairness and economic growth. The possible uses of carbon
revenues could include: (i) counterbalancing the unequal eects of high
energy prices on low-income households in the form of discounts on
electricity bills; (ii) providing transitional assistance (e.g., arranging
or providing financial support for job training) for communities and
individuals whose livelihood depends on fossil fuels; (iii) providing
financial support for communities that face an unequal burden from
carbon emissions from fossil fuels; and (iv) investing in clean/renewable
energies, energy-ecient technologies, and clean vehicles, which all
The Growth of Carbon Markets in Asia: The Potential Challenges for Future Development 129
play an important role in moving the economy from a carbon-intensive
to a clean-energy economy.
In addition, cap and trade in the form of ETSs has progressed faster
and attracted the attention of policy makers and practitioners since
the emergence of the EU ETS in 2005. For instance, in 2014, 18 ETS
programs were initiated across 18 national and subnational jurisdictions.
This initiation was planned for one more country and was also being
considered for implementation in 11 more national and subnational
jurisdictions (World Bank 2014). Subsequently, the implementation and
planning of ETSs spread across all regions except in Africa. The ETSs
also covered nearly 9% of global GHG emissions by 2015. Before 2015,
the World Bank (2014) documented that the majority of implemented
ETS programs have made landslide achievements in recent years in
terms of scope, linkages, and development of approaches. To this end,
the cap-and-trade mechanism could be said to be gaining ground
among European, Asian, and North American countries. Hopefully, this
may also potentially spread to other regions such as sub-Saharan Africa
and Africa.
Carbon tax mechanism. Carbon tax is another mechanism used to
curtail excessive emissions around the world. Carbon tax is a form of
tax whereby each unit of GHG emission gives participating firms (and
households, depending on the scope) an incentive to reduce pollution
(Taschini et al. 2013). Hence, the rate of pollution is reduced, depending
on the chosen level of tax. For these reasons, carbon tax could be assessed
by considering the cost or damage associated with each unit of pollution
and the cost associated with controlling that pollution. Therefore, getting
the tax level correct is the key, even though some firms and households
will most likely opt to pay the taxes, while continuing to pollute the
environment, beyond and above what is optimal for society. If the level
of pollution is high, then the cost will rise higher than necessary to
reduce emissions, and it will have a considerable impact on profits, jobs,
and end-use consumers.
It is also important to highlight here that carbon tax can cause an
increase in business overheads. In this case, companies will be pushed
to find ecient ways to manufacture their products and/or deliver their
services, as this would be beneficial to their bottom-line users. This
idea supports more environment-friendly and creative ideas among
industries. Furthermore, carbon emissions have also been linked to
worsening public health as they have a major impact on the quality of
the air we breathe. However, implementing a carbon tax could improve
air quality by reducing carbon emissions, thereby decreasing the rate
of respiratory issues and the number of asthma attacks (Kramer and
Fraser-Hidalgo 2018).
130 Ways to Achieve Clean Asia
Based on the above, many countries have demonstrated knowledge
of carbon trading via various approaches hinged on the flexibility of
carbon taxes to fit into national circumstances. For instance, in 2014,
over 14 countries implemented carbon tax approaches to fight or reduce
the level of emissions in their countries. Some of these countries took
various approaches in the light of national circumstances (to enable
the use of carbon taxes together with other carbon pricing appliances),
while others turned toward approaches that considered industry
competitiveness (World Bank 2014).
On the other hand, politicians around the world favored the use of
the carbon trading option over the second option, carbon tax. This is
because in the carbon trading scheme, each participant firm is given
a certain emission allowance quota in the primary ETS market, and
then trades with other members in the secondary ETS market for
extra allowance to support its ongoing production or for benefits if the
permitted quote is not used (Tang et al. 2016, Zhang et al. 2015). Usually
in the initial stages of trading, the emission allowance quota is either
given to businesses for free or sold at auction. Therefore, over time
the number of existing permits decreases, putting more pressure on
participating firms to invest more money in production technologies to
reduce the growth of carbon emissions. Hence, this approach will help
firms to come up with more innovations, which will significantly assist
them in reducing the price of new technologies.
5.3 The Growth of Carbon Markets in Asia
Since 2015, the issue of climate change has become a global agenda as
many eorts have been made to meet the Paris Agreement. Specifically,
the participating countries are dedicated and prepared to address the
climate change issue through various means. To date, 175 countries
around the world have ratified the agreement, including, among other
Asian countries, countries in Northeast and Southeast Asia (Ewing
2016a). This section seeks to explore the current status in the Asian
region, particularly in the major economies of Northeast Asia. To
address the increasing concerns around the issue of climate change,
the major economies in Northeast Asia have developed carbon markets.
The growth of carbon markets has been very impressive in the last few
years. More specifically, the number of carbon markets has doubled
since 2012 and the general interest in carbon trading is also on an
upswing. Further, emission pricing is worth $50 billion (Ewing 2016b).
In the following, we discuss three major Northeast Asian economies:
the PRC, Japan, and Korea.
The Growth of Carbon Markets in Asia: The Potential Challenges for Future Development 131
The PRC. The main objective of the Kyoto Protocol is to address the
issue of climate change by introducing a proper market mechanism. It
has been noted in the recent past that the international carbon market is
growing rapidly and is well diversified across various forms of markets,
such as the CDM, voluntary emission reduction, and emission allowance
trading. The PRC is not an exception, as it has challenged itself to build
a more formidable carbon market, having realized the volatile nature of
an economy in which carbon emissions are at an extreme level due to
industrialization. As a result, in 2011 the PRC pledged its support toward
promoting low carbon emission development through carbon trading
where the set target of energy consumption per unit of GDP would drop
by 3.4% in 2014 and 2.32% in 2015 (The Economist 2015). By the end of
September 2017, the market had covered 3,000 entities from more than
20 industry sectors, with the total trading volume reaching 200 million
tons of CO2 equivalent (tCO2e) while the total trading value was about
45.1 billion yuan (CNY) and the price ranged from CNY1–123 /tCO2e
(EDF 2017). This therefore indicates that the carbon markets in the PRC
are gaining momentum and moving toward achieving a low-emission
economy.
As an addendum to the above, huge successes have been recorded in
the reduction of carbon emissions (mainly in the power sector) with an
estimated 0.09 billion tCO2e dierence when compared to the level of
2016. Fossil fuel power generation will reach 4.88 kWh with an average
eciency of 305 gce/kWh by 2020. By contrast, in 2016, non-fossil fuel
power generation increased 0.06 trillion kWh, and this is expected to
decrease to 0.51 trillion tCO2e by 2020 (EDF 2017). This, therefore,
implies that the PRC adopted all carbon mechanisms to achieve its
slated plan.
In the ongoing eort to achieve a stronger carbon market, the PRC is
establishing strong collaboration with other international counterparts
with respect to carbon pricing. For instance, in October 2017, both the
PRC and the EU declared continued collaboration on emissions trading.
On this platform, the EU agreed to continue to support the PRC develop
its national ETS for 3 years (EU 2017). In addition, on 4 December
2017, the PRC and Canadian governments released a joint declaration
on climate change, which basically explains their commitment to
strengthening their collaboration, particularly as regards clean energy
and carbon markets (Government of the PRC 2017).
Several researchers have also considered the price dynamics of
CO2 allowances to explain the position of emissions. Some authors
argue that the PRC’s carbon market is competent. Tang et al. (2017),
for example, oered a multi-agent-based ETS simulation approach to
132 Ways to Achieve Clean Asia
test for a suitable carbon allowance auction design for the PRC’s ETS.
The authors concluded that the ETS has a substantial positive impact
on reducing emissions and improving the energy structure. However,
the authors advised that it adversely aects economic growth. Likewise,
Mu et al. (2018), using a computational general equilibrium (CGE)
framework, document that only the eight sectors planned to be part of
the initial execution of the ETS in the PRC are likely to have a much
larger mitigation cost. However, the mitigation costs can be minimized
by up to 3.3% of real GDP by 2030, but only if other prominent energy-
intensive sectors, which account for about an additional 24.8% of total
emissions, are included in the system.
Japan. The development of Japan’s carbon market can be viewed in
two dierent dimensions. First, the development of the CDM project,
which took eect in 2002–2004 with 12 projects, two of which are
being validated. The projects focus on the decomposition of carbon
emissions, and are estimated to have 4.8 million tCO2e per year
(Mizuno 2004). Secondly, the development of the Japanese Voluntary
Emissions Trading Scheme (JVETS), which was introduced in 2005
by the Ministry of Environment, Japan, and is aimed at reducing the
GHG emission activities in the Japanese companies listed under the
Keidanren Voluntary Action Plan (MOEJ 2012). As of 2012, JVETS had
389 members, which achieved a great reduction of 59,419 tCO2 in their
total emissions. It was noted that the mean trading price was roughly
¥216 ($2.60)/tCO2. In addition, the 389 companies that participated in
JVETS recorded a cumulative emissions decrease of 2,217 million tCO2e,
much higher than the planned target of 1,245 million tCO2e. JVETS
increased further to cover 0.3% of the emissions in 1990 (3.4 million
tCO2/year as observed in the fourth term) (IGES, EDF, and IETA 2016).
Studies have analyzed the ETS linking in Japan. For example,
Wakabayashi and Kimura (2018) point out that the Tokyo ETS alone
cannot reduce carbon emissions but has other factors, such as energy-
saving tools. However, Lu et al. (2013) used a multi-regional model and
found that the carbon leakage ratio in the case of full auctioning in the
ETS of Japan is not very dierent from the grandfathering and output-
based scenarios and does not reduce carbon emissions.
Republic of Korea. In compliance with the Kyoto Protocol, in May
2012 the Korean government passed the “Act on the Allocation and
Trading of Greenhouse Gas Emission Permits.” But the implementation
could not begin until 2014 with the establishment of the ETS’s simple
proposal and formulation of the First National Emission Permit
Allocation Plan. At the beginning, the Ministry of the Environment
decided to allocate about 1,598 million Korean allowance units to
525 companies for 2015 (IETA 2017). The allocation of credits was
The Growth of Carbon Markets in Asia: The Potential Challenges for Future Development 133
successfully completed in August 2016, which was part of the first phase
in the first year of compliance.
The second phase of the Korean ETS was introduced in January
2018 and will run through until 2020 with an estimated emission cap of
538.5 MtCO2 to be considered as against 0.4 MtCO2 less than the previous
year as documented in the National Allocation Plan. At the beginning of
2019, auctioning was considered in the subsectors that failed to meet
the standards for trade intensity and further production-related cost
due to the ETS. They are estimated at 3% of total volume of allowances
auctioned to these subsectors. However, the other subsectors that meet
the standards continue to obtain 100% free allocation (MOE, Republic of
Korea 2018). Basically, in the second phase, free allocation is stretched to
eight subsectors rather than the usual three subsectors, namely, cement,
refinery, and aviation. Moreover, guidelines have also been developed to
allow the use of certified emission reductions generated outside Korea
for compliance.
5.4 The Potential Challenges for the Growth
of Carbon Markets in Asia
Asia is one of the fastest-growing regions of the world. However,
the region is also responsible for a major share of global emissions,
particularly since the Paris Agreement in 2015. Therefore, in this
section, we aim to highlight the challenges that the region is facing to
address the issue of climate change without damaging its economic
development and prosperity.
Inadequate implementation of ETS policy. Adequate policy
implementation is indispensable, particularly when beginning to
establish a carbon market. The reason for this is that the market relies
heavily on it to boost outputs through its ETS policy by binding the
emission cap set to all firms that need to limit their GHG emissions,
or by controlling the number of firms based on their commitment to
reducing emissions. However, such an emission cap in the Asian carbon
market is evidently yet to be introduced. Further, it is also important to
highlight that the market demand is not strong enough to help the new
industry meet its emission reduction targets.
This may be because the Asian carbon market is still very young and
most countries in the region are in the first phase of its implementation.
In the absence of full implementation of the ETS policy, it is well
documented that most existing ETSs do not cover all sectors and only
a few major carbon emitters are included in the ETS policy (Mu et al.
2018). The ETS policy has been driven by chemicals, petrochemicals,
134 Ways to Achieve Clean Asia
iron and steel, construction materials, paper, air transport industries,
nonferrous metals, and electricity. However, the wide sectoral coverage
is to estimate the true picture of mitigation costs as compared with the
limited sectoral design, which can underestimate the mitigation costs.
Limited private financing. Financial institutions has been argued
to play an important role in the functioning of the carbon market (UNEP
Finance Initiative 2011). This does not exclude Asia, where the banking
sector plays the major financing role. However, severe capital adequacy
requirements and maturity mismatches have been attached to lending.
According to Watson et al. (2017), 18 Asian countries received a total
of $3.8 billion in climate funds from 17 multilateral climate funds and
initiatives for 422 projects and programs in 2016. Among the funds
are the PRC’s public climate fund which currently dominate the total
investment in climate finance (Wang et al. 2012). An estimated $294
billion was oered by state-owned banks in 2011 and the government
climate spending was $41 billion, while foreign and domestic bank loans
stood at $10 billion (The Climate Group 2013). Thus, this implies that
the major source of climate finance is public funds. It is also important to
highlight that the current system cannot divert private investments into
the carbon markets. However, this is in contrast to the global practice
where private investment is the major source of finance for climate
mitigation (Stadelmann et al. 2013, Grubb et al. 2011).
Lack of regulatory system. Going by international standards in
carbon-trading management systems, there should be an integrated
institutional setup in which the system takes control of regulating carbon
trading and its functioning. For instance, in the EU, the carbon-trading
management system adopted a two-level organization, which includes
central management and environmental protection departments from
the governments of members (Knight 2010). Further, it clearly specifies
the division of work, and explicitly draws a distinction between rights
and obligations. Moreover, coordination and cooperation are ensured to
achieve high eciency in the operation and management of the carbon-
trading system.
However, this is not applicable in the Asian carbon market where no
specific regulatory institution is responsible for the carbon trade setup.
Because of that, it is dicult to set up a reliable and robust monitoring
system and reporting and authentication mechanisms, which are
the backbone to the success of the carbon market in Europe. So, legal
administration is continuously a problem challenging all walks of life
in the society. Hence, we highlight the fact that currently no regional
regulatory system helps strengthen and smoothen the functioning of the
ETS and its administrative operation. Although the ETS program has
been implemented by the respective countries in the region, there is no
The Growth of Carbon Markets in Asia: The Potential Challenges for Future Development 135
harmonized system, and it is useful to review and adopt a more unified
regulatory system for the region, which will play a key role in this aspect.
Corruption, access to information, and local capacity. Most Asian
countries either operate a central economy or are transiting to a market-
driven economy. Thus, eorts to attract foreign investors are always
faced with problems of corruption and bureaucracy, including poor law
enforcement and poor infrastructures, all of which may impact the ease
of doing business generally and the execution of CDM projects in these
countries. In addition, information on CDM potentials is paramount
in the carbon market because it enhances the viability of the market.
However, this information is often not accessible. For example, Nguyen,
Sousa, and Uddin (2010) argued that national potentials of renewable
energy resources have not been investigated and are often not available
to users. As such, investors and developers are deterred from investing
in the Viet Nam carbon market. As these barriers continue to linger, the
awareness of CDM potentials among enterprises, the private sector,
public entities, and nongovernment organization is limited.
5.5 Evidence from Previous Studies
We can reasonably divide the existing international literature on carbon
emission markets into two segments. One strand of the literature has
analyzed the interactions between carbon and energy markets (see, for
example, Mansanet-Bataller et al. 2007, Alberola et al. 2008, Fezzi and
Bunn 2009, Hintermann 2010). These authors find that energy prices—
i.e., prices of oil, gas, coal, and electricity—are important driving factors
for carbon futures prices of the EU carbon emission market in phase I.
Bredin and Muckley (2011) investigate the association between carbon
and energy prices in phase II using a co-integration method. Using the
Dynamic Conditional Correlation GARCH model, Koenig (2011) and
Chevallier (2012) provide evidence on the dynamics of correlations
between carbon and energy markets.
Another strand of the literature examines the theoretical and
empirical link between the carbon and financial markets. For example,
Daskalakis, Psychoyios, and Markellos (2009) find significant negative
links exist between the EU carbon emission market and equity market.
Gronwald, Ketterer, and Trück (2011) employ the copula method and
find a significant dependence between carbon and stock markets. Koch
(2014) considers a multivariate GARCH model to analyze the price
interactions among the carbon, energy, and financial markets.
According to the seminal review paper by Babatunde, Begum, and
Said (2017), there has been an increasing interest recently in investigating
the climate change mitigation policies of the PRC, especially among
136 Ways to Achieve Clean Asia
academia. Research related to the PRC can be safely divided into three
strands, according to the specific aspect the strand focuses on: (i) the
reduction of CO2 emission intensity, (ii) the development of renewable
energy, and (iii) estimation of the emissions peak.
A vast strand of literature tries to investigate the impact and
evaluation of the PRC’s carbon intensity targets, especially using CGE
models. In a notable proportion of this strand of literature, various
optimization methods have been used to look for the optimal mitigation
cost path given some carbon intensity targets, treating these targets as
emission constraints. In the short run, a noteworthy example is Zhang
et al. (2013), who simulated the statistical and economic influences of
the PRC’s national emission intensity targets over the sample period
of 2010–2015 for the 12th National Five-Year Plan. To their surprise,
they can only identify a considerable welfare loss, which ranges from
about 1.2% to 1.5% via dierent disaggregation technologies of reduction
targets among various Chinese provinces. In contrast, Dai et al. (2011)
identified a much smaller (if not trivial) impact. According to the PRC’s
Copenhagen commitment, compared with its 2005 level, the PRC will
try to reduce its national CO2 emissions intensity by 40%–45% before
the end of 2020. Dai et al. (2011) concluded that even in the extreme
case where the PRC has successfully fulfilled its commitment (which
is unlikely given the current situation that its carbon emissions were
relatively high by the end of 2016), the PRC’s national GDP loss would
only range from about 0.032% to 0.24%.
In the long run, Wang, Wang, and Chen (2009) tried to gauge the
economic impact of the PRC’s long-term emission reduction scenario
up to 2050. In contrast to the extant literature, they argued that foreseen
and unforeseen technological innovations may, to some extent, promote
economic growth in the PRC, improve the energy eciency in the
country, and reduce the carbon intensity there. At the same time, Qi et
al. (2016) also simulated a long-term scenario for the PRC from 2020
to 2050. They assumed a 3% average annual reduction in CO2 intensity
over their sample period, and found that the associated national welfare
loss in the PRC would be only 0.9% in 2030, which would increase to
1.6% in 2050. Considering the possible international/national/regional
design of the associated carbon ETS in the future, other papers have also
tried to gauge the economic impacts of carbon intensity targets in the
PRC and obtained dierent results. For instance, assuming the existence
of a counterfactual global carbon emission market, both Zhang et al.
(2017) and Qi and Weng (2016) attempted to gauge the economic impact
of the PRC’s emission targets by the end of 2020 and 2030, respectively.
At the same time, using the sectoral approach as a complement,
Mu, Wang, and Cai (2017) went one step further and argued that the
The Growth of Carbon Markets in Asia: The Potential Challenges for Future Development 137
international Intended Nationally Determined Contributions (INDC)
could help achieve the committed emission reduction target without
suering from statistically significant economic loss. Focusing on one
province, Guangdong, and assuming that the PRC would fully achieve its
Copenhagen commitment, Wang et al. (2015) attempted to quantify the
economic impacts of the ETS in Guangdong, one of the most developed
provinces of the PRC, by the end of 2020. At the same time, focusing on
Shanghai city and assuming the PRC’s INDC commitments will be fully
achieved by the end of 2030, Wu et al. (2016) performed an empirical
analysis similar to that carried out by Wang et al. (2015). Both Wang et
al. (2015) and Wu et al. (2016) identified a negative economic influence
in the PRC brought about by achieving the PRC’s emission reduction
targets, and provided evidence that the carbon ETS can help mitigate
the associated costs when the PRC moves toward its carbon emission
reduction target.
Another strand of literature focuses on the social, economic, and
environmental impacts of the development of renewable energy in the
PRC, especially in the last couple of decades. A notable example is Qi,
Zhang, and Karplus (2014), who conjectured that the carbon emission
reduction will not be substantial, despite a possibility of many additional
renewable power installations in the second decade of the 21st century.
Focusing on the impact on the employment rate, Cai et al. (2014) found
empirical evidence that more and more jobs would be created due to
the development of renewable and/or new energy in the PRC from
the second decade of the 21st century. Interestingly, Dai et al. (2016)
successfully found more encouraging evidence that carbon emissions
would be reduced without incurring substantial macroeconomic costs
due to the large-scale development of renewable energy in the PRC.
Considering the synergy between emission control and renewable/
new energy, Mittal et al. (2016), Cheng et al. (2016), and Duan et al.
(2016) provided more armative evidence that both the targets of
carbon emission reduction and the development of renewable/new
energy in the PRC may be achieved at the same time without too much
macroeconomic costs.
To sum up, the extant literature up until now has already
documented a range of evidence on the ongoing smooth low-carbon
transition of the PRC economy, and most of the evidence is encouraging
for both economists and policy makers. However, there is still a gap in the
literature, which is the interrelationship among the newly established
regional carbon emission exchanges in the PRC, especially considering
the exogenous impact from the well-established carbon emission
exchanges from outside the PRC. Therefore, we are conducting this
project to investigate the potentially existing interrelationship between
138 Ways to Achieve Clean Asia
arguably the two most important domestic carbon emission exchanges
in the PRC, considering the probable exogenous impact from the
influential European ETS.
5.6 Data and Methodology
5.6.1 Data
This section describes the data in detail. The data set consist of daily
prices for the carbon emission markets of the EU1, Shanghai (SH), and
Shenzhen (SZ)2 over the period of 3 January 2017 to 9 October 2018,
since trading volumes in the Shanghai and Shenzhen carbon emission
markets were trivial before 2017. All commodity prices are expressed in
CNY. Figure 5.1 shows the time series plot for the closing prices in both
the Shanghai and Shenzhen carbon emission markets in terms of daily
frequency. According to Figure 5.1, there are enough sample variations
1 Historical Futures Prices: ECX EUA Futures, Continuous Contract #1. Non-adjusted
price based on spot-month continuous contract calculations. Raw data from
Intercontinental exchange.
2 We consider the main contract SZA-2016 in the Shenzhen carbon market.
Figure 5.1 Daily Closing Prices
Note: This figure displays the time series of the futures prices in the first and second commitment
periods in the Shanghai and the Shenzhen carbon emission markets (from 3 January 2017 to
9October 2018) in subfigures (a) and (b), respectively. To construct a continuous series of futures
prices, the futures contracts switch over on the first day of a new month’s trading for all available
trading months. The horizontal axis is dates while the vertical axis is the futures prices.
Source: (a) China Emissions Exchange http://www.cerx.cn/; (b) Shanghai Environment and Energy
Exchange http://www.cneeex.com/.
1/3/2017 6/3/2017
(a) Shanghai carbon emission market (b) Shenzhen carbon emission market
11/3/2017 4/3/2018 9/3/2018 1/3/2017 6/3/2017 11/3/2017 4/3/2018 9/3/2018
30
35
40
45
10
15
0
5
20
25 30
35
40
50
45
10
15
0
5
20
25
The Growth of Carbon Markets in Asia: The Potential Challenges for Future Development 139
Table 5.1 Descriptive Statistics of the Selected Variables
(After Logarithm)
Variable Minimum Maximum Mean
Standard
Deviation
EU 3.482564 5.299405 4.175204 0.515983
SH 3.208825 3.749975 3.540342 0.127661
SZ 2.941804 3.832980 3.389923 0.179254
EU = European Union, SH = Shanghai, SZ = Shenzhen.
Note: This table presents the descriptive statistics of the main variables in the paper.
Source: Authors.
Table 5.2 Unit Root Test Results
Variable ADF Unit Root Test Phillips-Perron (PP) Unit Root Test
In levels
EU –1.1522 –1.8257
SH –1.5387 –2.5868
SZ –3.5543*** –4.3637***
In first-order dierence
EU –7.2938*** –21.984***
SH –8.2219*** –19.829***
SZ –9.7686*** –23.093***
EU = European Union, SH = Shanghai, SZ = Shenzhen.
Note: This table presents the results from two-unit root tests (i.e., the Augmented Dickey-Fuller (ADF)
and Phillips-Perron (PP) unit root tests), while *, **, and *** indicate the significance at the 10%, 5%, and
1% level, respectively.
Source: Authors.
for both the Shanghai and Shenzhen carbon emission markets over
our sample period in our data set. Interestingly, the Shanghai carbon
emission market has no clear time trend, while that of Shenzhen shows a
declining time trend to some extent. We present the summary statistics in
Tables 5.1 and 5.2, which further supports our previous observation that
there are enough sample variations for both the Shanghai and Shenzhen
carbon emission markets over the sample period in our data set.
140 Ways to Achieve Clean Asia
To begin our investigation, it is necessary to test the“stationarity
of the variables used in this analysis. To determine the order of the
“stationarity” of these series, we consider both the Augmented Dickey-
Fuller (ADF) and Phillips-Perron (PP) unit root tests. The results in Table
5.2 show mixed findings. The null hypothesis that there is a unit root in
levels cannot be rejected for the EU and SH markets but not for the SZ
market at 1% significance level. However, the null hypothesis that there
is a unit root in levels is strongly rejected at the 1% significance level for
all variables when they are applied to the first-order dierences. These
findings indicate that our variables have a mixed order of integration
and that EU and SH are I (1) and SZ is I (0), which makes our empirical
analysis more dicult and hinders many common econometric tools in
this project.
5.6.2 Methodology
One aim of this study is to investigate the relationship between the
PRC’s carbon emission markets, and between those of the EU and the
PRC. Due to the mixed order of integration of our data, we employ
the autoregressive distributed lag (ARDL) approach (see, for example,
Pesaran et al. 2001) to investigate the impact of the EU carbon emission
market on those of the PRC. The ARDL model assumes that the
dependent variable is a function of its own past lagged values as well as
current and past values of other explanatory variables. For the model,
the sample series do not have to be I (1) (see, for example, Pesaran
and Pesaran 1997). In addition, the ARDL model in conditional error
correction form can integrate short-run adjustment eects with long-
run equilibrium without losing information (see, for instance, Jalil and
Feridun 2011). Therefore, we can get more information, especially in
terms of ecient co-integration relationships, by using this approach
(see, for instance, Ghatak and Siddiki 2001). The ARDL (p, q) model can
be specified as follows:
=+
+ ,
+ ,
+
=+
+
+
( )+
=+
+
+
( )+
= 1
=
=
.
where t = max(p,q,r),⋯,T. The optimal lag orders p, q, and r can be
obtained by minimizing the conventional Bayesian information criterion
(BIC). For most cases, we find that the optimal number of lags is t = 1,
which we use in the analysis. As a result, we restrict all of them that
equal one to reduce the noise introduced by the optimal number of lags,
and make sure that each term is on the same page.
The Growth of Carbon Markets in Asia: The Potential Challenges for Future Development 141
To better investigate the direction of the long-run relationship
between the Shanghai and the Shenzhen carbon emission markets in
the PRC, we re-parametrize the above ARDL model in conditional error
correction form, which is as follows:
=+
+ ,
+ ,
+
=+
+
+
( )+
=+
+
+
( )+
= 1
=
=
.
=+
+ ,
+ ,
+
=+
+
+
( )+
=+
+
+
( )+
= 1
=
=
.
where
=+
+ ,
+ ,
+
=+
+
+
( )+
=+
+
+
( )+
= 1
=
=
.
and the long-run coecients
=+
+ ,
+ ,
+
=+
+
+
( )+
=+
+
+
( )+
= 1
=
=
.
and
=+
+ ,
+ ,
+
=+
+
+
( )+
=+
+
+
( )+
= 1
=
=
.
. In these two formulas above, the shortened forms SH,
SZ, and EU stand for futures prices in the Shanghai, the Shenzhen, and
the EU carbon emission markets, respectively. These forms of equation
provide us with an opportunity to look into both the short-run and long-
run relationship at the same time. It is also feasible to include more
control variables if necessary.
5.7 Empirical Results and Discussion
This section presents the results obtained via the model specified in
the previous section. We will empirically examine the impact of the
EU carbon emission market on the PRC’s carbon emission markets.
The main results of our ARDL model are displayed in Tables 5.3 and
5.4. Several conclusions can be drawn from these two tables. The most
important is perhaps that on the interrelationship in the long run as
indicated below:
• According to Panel A in Table 5.3, a 1% growth in the futures price
in the Shanghai carbon emission market increases the futures
price in the Shenzhen carbon emission market by 0.5502% in the
long run, which is strongly statistically significant at the 5% level.
• According to Panel A in Table 4, a 1% increase in the futures
price in the Shenzhen carbon emission market raises the futures
price in the Shanghai carbon emission market by 0.4330% in the
long run, which is only marginally statistically significant at the
10% level.
The above findings indicate that there is a (at least unidirectional)
long-run relationship between the Shanghai and the Shenzhen carbon
142 Ways to Achieve Clean Asia
emission markets. More specifically, the long-run results for the
Shenzhen carbon emission market (Panel A in Table 5.3) show that the
Shanghai carbon emission market plays a substantial role in promoting
the futures price in the Shenzhen carbon emission market, which is
generally positive as expected. In contrast, the EU carbon emission
market is not found to have a statistically significant long-run impact
on the Shenzhen carbon emission market. However, the long-run
results for the Shenzhen carbon emission market (Panel A in Table 5.4)
reveal that the Shenzhen carbon emission market is not a statistically
significant determinant of the Shanghai carbon emission market at the
conventional 5% significance level.
To our surprise, the long-run impact of the EU carbon emission
market on the Shanghai and Shenzhen carbon emission markets is
also not statistically significant at the conventional 5% significance
level. To be specific, for the Shenzhen carbon emission market, the
estimated coecient for InEU is –0.0262 with a p value of 0.2956 in
Panel A of Table 5.3. For the Shanghai carbon emission market, the
estimated coecient for InEU is 0.0035 with a p value of 0.9202 in
Panel A of Table5.4.
Table 5.3 Estimated Autoregressive Distributed Lag Model,
Long-run and Short-run Coecients
(Dependent Variable: InSZ, Autoregressive Distributed Lag [1, 5, 1])
Variable Estimate Std. Err Z Value Pr(>z)
Panel A: Long-run coecients
InEU –0.0262 0.0250 –1.0460 0.2956
InSH 0.5502** 0.2406 2.2870 0.0222
Panel B: Short-run coecients
(Intercept) 1.4073*** 0.3346 4.2060 0.0000
L.∆InSZ –0.0483 0.0492 –0.9810 0.3270
∆InEU –0.0349 0.0395 –0.8840 0.3760
∆InSH 0.0081 0.0996 0.0820 0.9350
ecm(–1) –0.1102*** 0.0241 –4.5710 0.0000
Note: This table reports the estimated coefficients from the ARDL model. The dependent variable is
InSZ. Std.Err denotes the standard error, while Pr(>z) denotes the p value. The appropriate lag length
was selected based on the BIC, while *, **, and *** indicate the significance at the 10%, 5%, and 1% level,
respectively.
Source: Authors.
The Growth of Carbon Markets in Asia: The Potential Challenges for Future Development 143
The short-run coecients are shown in Panel B of Tables 5.3 and
5.4. The short-run impact of the EU carbon emission market is also not
statistically significant on either Shanghai or Shenzhen carbon emission
market at the conventional 5% significance level. To be specific, for
the Shenzhen carbon emission market, the estimated coecient for
∆InEU is –0.0349 with a p value of 0.3760 in Panel B of Table 5.3. For the
Shanghai carbon emission market, the estimated coecient for ∆InEU
is –0.0109 with a p value of 0.5733 in Panel B of Table 5.4. The reason
may be due partly to the characteristics of regional trading for carbon
emission markets in the PRC, which lead to the strong link between the
Shenzhen and Shanghai carbon emission markets, and the insignificant
impact of the carbon emission market of the EU on that of Shenzhen
and Shanghai.
The short-run coecients for the key variables are not statistically
significant at the conventional 5% significance level, which indicates
that the short-term fluctuations in the Shenzhen and Shanghai carbon
emission markets are irrelevant to each other. To be specific, for the
Shenzhen carbon emission market, the estimated coecient of ∆InSH
in Table 5.3 is –0.0483 with a p value of 0.3270 while for the Shanghai
carbon emission market, the estimated coecient of ∆InSZ in Table5.4
Table 5.4 Estimated Autoregressive Distributed Lag Model,
Long-run and Short-run Coecients
(Dependent Variable: InSH, Autoregressive Distributed Lag [3, 1, 1])
Variable Estimate Std. Err Z Value Pr(>z)
Panel A: Long-run coecients
InEU 0.0035 0.0349 0.1000 0.9202
InSZ 0.4340* 0.2620 1.6560 0.0977
Panel B: Short-run coecients
(Intercept) 1.0645*** 0.3188 3.3390 0.0008
L.∆InSH 0.0567 0.0483 1.1740 0.2403
∆InEU –0.0109 0.0193 –0.5630 0.5733
∆InSZ 0.0035 0.0235 0.1470 0.8831
ecm(–1) –0.0498*** 0.0146 –3.3990 0.0007
Note: This table reports the estimated coefficients from the ARDL model. The dependent variable is
InSH. Std.Err denotes the standard error, while Pr(>z) denotes the p value. The appropriate lag length
was selected based on the BIC, while *, **, and *** indicate the significance at the 10%, 5%, and 1% level,
respectively.
Source: Authors.
144 Ways to Achieve Clean Asia
is 0.0035 with a p value of 0.8831. Moreover, in both equations, the
estimated coecient of the error correction term (i.e., ecm[−1] in Tables
5.3 and 5.4) is negative and statistically significant at the 1% level. To
be specific, the estimated coecient of ecm(–1) is –0.1102 and –0.0498
in Tables 5.3 and 5.4, respectively. This negative estimated coecient
of the error correction term implies the speed of adjustment at which
a dependent variable returns to equilibrium following a change in the
long-run equilibrium relationship (see, for example, Sari et al. 2008).
What is more, the results show that the futures price in the Shenzhen
carbon emission market, with a larger (in absolute magnitude) estimated
coecient of the error correction term (i.e., ecm[−1] in Tables 5.3 and
5.4), has a faster adjustment rate than the futures price in the Shanghai
carbon emission market.
Given the above findings, we argue that no significant short-run
relationship exists between the Shanghai and Shenzhen carbon emission
markets, but they will influence each other from the standpoint of
their long-run relationship. Hence, we suggest that policy makers and
government ocials enhance the long-term carbon emission market
cooperation and linkage in the PRC to progressively deepen the domestic
emissions trading system.
Finally, we explore the short-run causalities between the futures
prices in the Shanghai and Shenzhen carbon emission markets using
the Granger causality test. The results of these short-run causalities
are shown in Table 5.5. The research results indicate that no reverse
causality exists between the Shanghai and Shenzhen carbon emission
markets at the conventional 5% statistical significance level. However,
there is a bidirectional relationship between the Shanghai and Shenzhen
carbon emission markets at the 10% statistical significance level. For
Table 5.5 Short-run Granger Causalities
Null Hypothesis F-test Statistic Prob.
The future prices in SZ do not Granger-
cause the future prices in SH
0.7870 0.5017
The future prices in SH do not Granger-
cause the future prices in SZ
1.3967 0.2486
Note: This table reports the short-run Granger causalities using one lag in the estimation. We use *, **, and
*** to denote the significance at the 10%, 5%, and 1% level, respectively.
Source: vice versa. Finally, to our surprise, in both the long run and short run, we find that the futures prices
in the EU ETS market very insignificantly impact the futures prices in both the Shanghai and Shenzhen
carbon emission markets. [PLEASE CHECK SOURCE, THANK YOU]
The Growth of Carbon Markets in Asia: The Potential Challenges for Future Development 145
instance, the p value for rejecting the null hypothesis that the futures
prices in Shenzhen do not Granger-cause the futures prices in Shanghai
is 0.06766, while the p value for rejecting the null hypothesis that the
futures prices in Shanghai do not Granger-cause the futures prices in
Shenzhen is 0. 05761.
These findings have three important implications. First, our results
shed light on the policy formulation of the PRC government on the
carbon emission markets to achieve sustainable economic growth and
meet carbon reduction targets. Second, our results suggest that the
carbon emission markets are, to a very large extent, segmented at the
international level; this calls for international cooperation in terms
of reducing carbon emission targets. Third, the identification of a
significant long-run relationship, but only a marginally significant short-
run relationship, suggests that even at the national level the domestic
carbon emission exchanges in the PRC are to some extent segmented.
This suggests that a unified carbon ETS should be put into eect as soon
as possible.
We have to admit that this study has some limitations. Ideally, we
should have relied on a well-accepted economic theory to investigate
the interrelationship between the Shanghai and Shenzhen carbon
emission markets. Without such a theory, we propose using an ARDL
model with daily high-frequency data from 3 January 2017 to 9 October
2018. The choice of carbon emission exchanges is somewhat arbitrary,
as we simply choose arguably the two most famous ones, in parallel to
the well-established stock trading exchanges in the PRC. Of course,
more exogenous variables may be added. However, we doubt that our
key results will change, due to the robust nature of our methodology.
We believe that it is a fruitful research direction and leave it for future
projects.
Further, we make several policy recommendations regarding the
ecient functioning of the ETS in Asia with special reference to the
PRC, due to its significant contribution to global emissions. More
specifically, we suggest that there are several barriers in the Asian
region, particularly the little support for the carbon market. Therefore,
it is important to establish a strong regulatory system that should aim
to strengthen and support the functioning of the ETS in the region, and
perhaps across the major economies. Policy makers and government
ocials should also give considerable attention to encouraging private
investments into the carbon market. There is also a need to support
public–private partnership investments into clean energies and energy-
ecient technologies, which will have a greater impact on climate
change management (Kutan et al. 2018, Paramati et al. 2016, Paramati
146 Ways to Achieve Clean Asia
et al. 2017). Policy makers, with the cooperation of governments,
should aim to improve the functioning of the bureaucracy, particularly
the departments directly and indirectly linked with climate change
initiatives. All these additional policy initiatives will have a greater
impact on the functioning and performance of the ETS in the region,
as well as in the PRC.
The Growth of Carbon Markets in Asia: The Potential Challenges for Future Development 147
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