Carbon Trading Scheme in the People’s - Recherche en IA spécialisée au MIT

Carbon Trading Scheme in the People’s
Republic of China: Evaluating the
Performance of Seven Pilot Projects
Xing Chen and Jintao Xu∗

The People’s Republic of China (RPC) launched seven emissions trading
scheme (ETS) pilot projects in 2013–2014 to explore a cost-effective approach
for low-carbon development. The central government subsequently announced
its plans for the full-ﬂedged implementation of ETS in the entire PRC in late
2017. To ensure the success of ETS in the PRC, it is necessary to gain a better
understanding of the experiences and lessons learned in the pilot projects. Dans
this paper, we provide a policy overview of the seven pilot projects, y compris
policy design, legislative basis, and market performance. We use the synthetic
control method to evaluate the carbon mitigation effect of each of the seven
ETS pilots. Our ﬁndings are that success has been limited and uneven across
the pilot projects, which warrants deeper evaluation of the differences between
them and caution in scheme expansion. Results from the analysis also shed
light on policy improvements that can beneﬁt the nationwide development of
ETS.

Mots clés: cap-and-trade, climate change, emissions trading schemes, synthetic
control method
Codes JEL: Q51, Q54, Q56

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je. Introduction

The need to address climate change and reduce greenhouse gas (GHG)
emissions has become the consensus of the world’s major countries. Policy makers
are employing or contemplating the use of market-based instruments for climate
politique. Au cours des dernières années, cap-and-trade schemes have commanded attention in
discussions related to climate change. The theoretical attraction of cap-and-trade is
its potential to reduce emissions at lower cost than conventional, direct regulations
such as mandated technologies or performance standards.

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∗Xing Chen: Doctoral student, College of Environmental Science and Engineering, Peking University. E-mail:
xingc@pku.edu.cn; Jintao Xu (corresponding author): Professeur, National School of Development, Peking
University. E-mail: xujt@nsd.pku.edu.cn. This paper has beneﬁted greatly from suggestions made by participants
at the Asian Development Review Conference in Seoul in August 2017. We would also like to thank the managing
editor and two anonymous referees for helpful comments and suggestions. ADB recognizes “China” as the People’s
Republic of China. La clause de non-responsabilité habituelle s'applique.

Revue du développement en Asie, vol. 35, Non. 2, pp. 131–152
https://doi.org/10.1162/adev_a_00117

© 2018 Asian Development Bank and
Asian Development Bank Institute.
Publié sous Creative Commons
Attribution 3.0 International (CC PAR 3.0) Licence.

132 Revue du développement en Asie

In October 2011,

the National Development and Reform Commission
(NDRC) of the People’s Republic of China (RPC) designated seven provinces
and cities—Beijing, Chongqing, Guangdong, Hubei, Shanghai, Shenzhen, et
Tianjin—as regional pilot projects in a carbon emissions trading scheme (ETS)
that is consistent with the logic of a cap-and-trade system (NDRC 2011). Le
Government of the PRC also established a nationwide carbon emissions trading
market at the end of 2017. While there is a wide agreement among economists as
to the potential advantages of market-based instruments, there is much debate as to
whether ETS is the best policy option for the PRC.

This article provides an overview and analysis of the PRC’s carbon market
based on 4 years of pilot testing. We introduce the background and market
performances of ETS, along with a comparison of the seven pilot projects in
different regions of the PRC. We use the synthetic control method to evaluate
emissions reduction achievements at the regional level. Challenges that exist in the
PRC carbon market are identiﬁed and policy recommendations to further improve
the market are also provided.

The rest of this paper is organized as follows. The next section lays
out the legislation that supports the PRC’s ETS system and analyzes existing
règlements. Section III focuses on the actual market performances of the seven
pilot projects. Section IV evaluates the carbon mitigation effect of the seven
pilot projects based on the synthetic control method. The ﬁnal section provides
highlights of key conclusions from the analysis, the main challenges to successful
implementation of ETS, and policy recommendations to support the program’s
expansion nationwide.

II. Policy Overview

Theoretically, ETS affects total GHG emissions by creating a market for
emissions permits allocated to individual emitters under an aggregate emissions
cap. The regulatory authority stipulates the total allowable quantity of emissions.
Ce faisant, the level of scarcity of allowable GHG emissions is determined; total
allowable emissions are then divided into a certain number of emissions permits
that are allocated to individual emitters based upon certain rules. Recognizing
differences in marginal costs of implementing the permits by different individual
emitters, the trading of permits is allowed and an equilibrium price for the permits
emerges. This equilibrium price provides a signal as to the level of scarcity of
the emissions permits, guiding individual emitters (ﬁrms most likely) to choose
between reducing or increasing GHG emissions, and to identify technologies
corresponding to their choices. De plus, an effective ETS achieves the set cap
with minimum social costs.

Dans 2009, the PRC pledged to reduce by 2020 the intensity of carbon dioxide
(CO2) emissions per unit of gross domestic product (PIB) by 40%–45% from

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Carbon Trading Scheme in the People’s Republic of China 133

Tableau 1. Legislative Basis of Seven Emissions Trading Scheme Pilots

Pilot Province or City

Legal Document

Beijing

Resolution on Beijing to Carry Out Carbon Trade Pilot under the Premise

Shanghai

Guangdong

Shenzhen

Tianjin

Hubei

Chongqing

of Strictly Controlling Total Carbon Emissions (Beijing Municipal
People’s Congress Standing Committee) (31 Décembre 2013)

Shanghai Carbon Emission Management Interim Guidelines (Shanghai
Municipal People’s Government Order No. 10) (18 Novembre 2013)
Guangdong Province Carbon Emission Management Interim Guidelines
(Guangdong Provincial People’s Government Order No. 197) (15
Janvier 2014)

Regulation on Carbon Emission Management for the Shenzhen Special

Economic Zone (Shenzhen Municipal People’s Congress) (30 Décembre
2012)

Notice on Issuing the Interim Measures on Carbon Emissions Trading in
Tianjin (General Ofﬁce of Tianjin Municipal People’s Government) (21
May 2013)

Hubei Province Carbon Emissions and Trade Management Interim

Measures (Hubei Provincial Government Order No. 371) (25 Avril
2014)

Chongqing Carbon Emission and Trade Management Interim Measures
(Chongqing Municipal People’s Government 41st Executive Meeting)
(27 Mars 2014)

Sources: All data are from the following local municipal government websites. For Beijing, http://www.bjrd
.gov.cn/zdgz/zyfb/jyjd/201312/t20131230_124249.html;
for Shanghai, http://www.shanghai.gov.cn/nw2/nw2314
/nw2319/nw2407/nw31294/u26aw37414.html; for Guangdong, http://zwgk.gd.gov.cn/006939748/201401/t20140117
_462131.html; for Shenzhen, http://www.sz.gov.cn/zfgb/2013/gb817/201301/t20130110_2099860.htm; for Tianjin,
http://qhs.ndrc.gov.cn/qjfzjz/201312/t20131231_697047.html; for Hubei, http://fgw.hubei.gov.cn/ywcs2016/qhc/zg
_gzdt/bgs_wbwj/201404/t20140425_76918.shtml; and for Chongqing, http://www.cq.gov.cn/publicinfo/web/views
/Show!detail.action?sid=3874934.

2005 levels. In December 2011, the PRC suggested for the ﬁrst time in its 12th
Five-Year Plan for National Economic and Social Development to “gradually
establish a carbon emissions trading market” as a way to control GHG emissions.
The GHG Emissions Control Work Schedule for the 12th Five-Year Plan speciﬁcally
points to establishing a “carbon emissions trading pilot” and developing the PRC’s
“overall program for [un] carbon trading market.” This indicates that the PRC’s
carbon trading policy will follow the principle of “ﬁrst pilot at the local level, alors
scale up.” In October 2011, a Notice on Conducting Carbon Trading Pilots by the
NDRC conﬁrmed seven designated pilots. The pilot provinces and cities established
an institutional basis for carbon trading and ofﬁcially launched trading in
2013–2014. Building on the pilot experience, the PRC accelerated the construction
of a national carbon trading market, which began operation on 19 Décembre 2017.
Establishing the legal basis of ETS is an important prerequisite for successful
implementation. The legal documents listed in Table 1 carry different legal weight:
some are resolutions passed by the local People’s Congress Standing Committees,
while others are government orders. In addition to legal tools, some pilot ETSs also

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134 Revue du développement en Asie

use administrative methods such as conﬁscation of the following year’s permits and
public shaming to promote compliance among ﬁrms.

The science, rationality, and effectiveness associated with ETS design is also
critical for the success of each pilot and of the future national carbon market. Dans
addition to learning from existing markets globally, including the European ETS
and the Regional GHG Initiative in the United States, the PRC has conducted
in-depth studies into its own conditions in order to establish locally appropriate
marchés. Dans l'ensemble, the cap-and-trade schemes in the PRC’s seven pilots have the
following characteristics:

• Executive entity. In most cases, the executive entity is the local Development
and Reform Commission, generally its department in charge of resources,
environment, and energy. Local ﬁnance bureaus and other departments provide
support to implementation.

• Industries regulated. Almost all energy-intensive industries are covered in the
pilot trading schemes, including electricity, steel, cement, and chemicals. Le
Beijing, Shanghai, and Tianjin ETSs also include the construction and services
industries.

• Government intervention. Government intervention in the carbon market
includes emissions data collection, emissions permit allocation and auction,
and interventions in market pricing when necessary. Par exemple,
le
Beijing, Shanghai, and Shenzhen ETSs put forward market conditions and
methodologies to regulate prices for emissions permits.

• Permit allocation methodology. All pilots adopted the historical emissions
method to allocate permits, while the benchmarking method was used for
new facilities and certain industries. The Guangdong, Hubei, et Shenzhen
ETSs conducted auctions for emissions permit allocation. Other pilots generally
distributed permits free of charge.

• Transaction and reporting thresholds. The pilots also set market access
conditions for companies involved in the trading scheme and announced
thresholds for emissions reporting for other large emitters that were not covered
by the trading system (referred as “reporting companies”). These companies
must report their emissions to the government on an annual basis so the latter
can determine whether they should be included in the future carbon trading
scheme or not. The emissions of companies involved in carbon trading (referred
to as “emissions control companies”) account for about 60% of total regional
emissions in Guangdong and Tianjin, and for more than 40% in other pilot
régions (Tableau 2).

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Carbon Trading Scheme in the People’s Republic of China 135

Tableau 2. Carbon Emissions Cap and Number of
Covered Companies in Seven Pilots

Pilot
Province
or City

Beijing
Shanghai
Guangdong
Shenzhen
Tianjin
Hubei
Chongqing

Carbon
Emissions Cap
(CO2 million
tons/year)

Number of
Covered
Companies

Proportion
of Allowance
in Total
Emissions

70
510
350
30
150
120
100

490
191
202
635
114
138
242

40.0%
57.0%
58.0%
40.0%
60.0%
35.9%
39.5%

CO2 = carbon dioxide.
Remarques: Most pilots increased their number of covered companies each
année. The information in this table is for the initial year of each pilot. All
pilots were launched in 2013, except for Hubei and Chongqing, lequel
were launched in 2014.
Source: Carbon Emissions Allowance Management Rules (Interim)
published by the local government in each pilot region.

Tableau 3. Trading Indicators of the Seven Emissions Trading Scheme Pilots

Pilot Province
or City

Shenzhen
Beijing
Shanghai
Guangdong
Tianjin
Hubei
Chongqing
Average

Starting Date Active Ratioa

19 Jun 2013
28 Nov 2013
19 Dec 2013
19 Dec 2013
26 Dec 2013
2 Apr 2014
16 Jun 2014

…

90%
69%
63%
71%
52%
96%
18%
66%

Average
Trading Price
(CNY/ton CO2)

Average
Trading
Volume
(ton/day)

Share of
Total
Volume

47
50
25
26
22
21
20
30

18,604
7,869
11,819
34,399
3,450
50,163
6,644
18,992

16%
6%
9%
26%
3%
35%
5%
…

CNY = yuan, CO2 = carbon dioxide.
aActive ratio refers to the number of days that have trading volume divided by the total number of trading
jours.
Source: China Carbon Trading Platform. http://k.tanjiaoyi.com/ (accessed June 30, 2017).

III. Market Performance

The effectiveness of ETS pilots relies not only on institutional design, mais
also on the actualities of policy implementation. The intermediate effects of the
carbon trading policy can be observed through the market performances of the seven
ETS pilots. This study selected carbon price and trading volume to evaluate market
performance of each of the seven pilots (Tableau 3).

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136 Revue du développement en Asie

Chiffre 1. Historical Trend of Carbon Prices in the Seven Emissions Trading Scheme Pilots

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CNY = yuan.
Source: China Carbon Trading Platform. http://k.tanjiaoyi.com/ (accessed June 30, 2017).

UN.

Carbon Price

Price ﬂuctuations were observed to be normal in most ETS pilots. Dans l'ensemble,
the price ﬂuctuated wildly in 2013; subsequently, price ﬂuctuations were smaller.
This is because in the early stage of ETS in the PRC, there was only one pilot
project (Zhao et al. 2016). With the subsequent launching of other ETS pilots, le
price ﬂuctuations moderated (Chiffre 1).

Price differences are obvious among the seven ETS pilots, with the overall
average price in the review period (June 2013–June 2017) being CNY30 per ton.
The average price was CNY50 per ton in Beijing, which ranks the highest, followed
by Shenzhen at CNY47 per ton. The average price in Chongqing in June 2017 était
CNY20 per ton, which ranks the lowest. The lowest price observed in Beijing during
the review period was CNY44 per ton, which is still higher than the national average
price and the peak price observed in Chongqing.

As can be seen from Figure 2, among the seven pilot ETSs, prices ﬂuctuated
the most in Tianjin and Shenzhen, while price movements in Hubei ETS were
relatively small. Cependant, when approaching the compliance deadline (mainly in

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Carbon Trading Scheme in the People’s Republic of China 137

Chiffre 2. Peak Price, Average Price, and Lowest Price of the Seven Emissions Trading
Scheme Pilots

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CNY = yuan.
Source: Authors’ calculations based on data from China Carbon Trading Platform. http://k.tanjiaoyi.com/ (accessed
Juin 30, 2017).

June or July each year), the carbon price always rose.1 Although price ﬂuctuations
are common in the early stage of ETSs globally, excessive price ﬂuctuations are not
conducive to reﬂecting the actual cost of carbon emissions. They create huge risks
for market participants and uncertainty for the covered companies.

Trading Volume

Another characteristic of ETS pilots is their low trading volumes, lequel
seems to be a big challenge for the PRC’s carbon market. There is little or even
no trading volume on most days. Cependant, the trading volume of each ETS pilot
increased sharply before its respective compliance period (Chiffre 3).

Although the overall size of the PRC’s seven pilot carbon markets is
substantial, companies were not enthusiastic in participating in carbon trading, comme
evidenced by the ratio of cumulative trading volumes to carbon emissions cap. Zhao
et autres. (2016) depicted this phenomenon: for the Shenzhen ETS, the cumulative
trading volume only accounted for 5.6% of the carbon emissions cap, which is
the highest among the seven ETS pilots. In the other six pilots, the ratios were

1When approaching compliance deadline, all participants must ensure that they have followed the procedural

steps or they may get penalized.

138 Revue du développement en Asie

Chiffre 3. Historical Trend of Trading Volumes in the Seven Emissions Trading
Scheme Pilots

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Source: Authors’ calculations based on data from China Carbon Trading Platform. http://k.tanjiaoyi.com/ (accessed
Juin 30, 2017).

4.9% in Hubei, 3% in Beijing, 2% in Shanghai, and less than 1% in each of the
other three pilot locations. The total cumulative trading volume of seven ETS pilots
only accounted for 1.4% of the total carbon emissions cap. Ainsi, the ratio is rather
low and most of the allowances have not been traded. It is partly because of policy
uncertainty during the pilot period of ETS, so it is difﬁcult for covered companies to
stay cautious. En outre, during the pilot period, covered companies are pressured
by other existing energy conservation policies (par exemple., energy saving targets) so ETS
may not substantially inﬂuence companies’ behavior.

Comparing compliance across the seven pilot ETSs—from the start date
until the end date of the trading period—reveals that the Hubei, Guangdong, et
Shenzhen ETSs had the most active trading. From the perspective of secondary
the design and operation of the Hubei ETS has been
market performance,
réussi. Par 30 Juin 2017, cumulative nationwide trading volume was 114.6
million tons, among which the cumulative trading volume of the Hubei ETS was
40.4 million tons, or about 35% of the total. The Guangdong and Shenzhen ETSs
also both had a larger trading volume than either the Beijing, Shanghai, Chongqing,
or Tianjin ETSs (Chiffre 4). En même temps, Tianjin and Chongqing seems to be far
behind the average level, reﬂected by fairly low market liquidity in the two places.

Carbon Trading Scheme in the People’s Republic of China 139

Chiffre 4. Trading Volume Shares of the Seven Emissions Trading Scheme Pilots

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Source: Authors’ calculations based on data from China Carbon Trading Platform. http://k.tanjiaoyi.com/ (accessed
Juin 30, 2017).

IV. Emissions Reduction Achievements

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With important

implications for global climate change mitigation,

le
development of ETS in the PRC has attracted increasing attention in recent
années. Before ETS was actually implemented, many researchers discussed the
mechanism design and regional linkages from a theoretical perspective. With the
establishment of ETS in the PRC, researchers began evaluating its emissions
reduction achievements. Dans cette section, we focus on the carbon mitigation effect
of the seven pilot ETSs.

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UN.

Research on Emissions Trading Scheme Impact Assessment

Perhaps because the PRC’s ETS pilots only covered a short amount of time
from preparation to commissioning, most attention has been paid to estimating the
potential (ex ante) impacts of the regional ETSs and the hypothetical nationwide
ETS (Jiang et al. 2016). There are few studies conducting an impact assessment
from ex post empirical perspectives.

140 Revue du développement en Asie

Many researchers have used computable general equilibrium (CGE) ou
CGE-based models to assess the PRC’s upcoming national ETS. Tang and Wu
(2013) established an interregional CGE model to simulate social welfare impacts
of different climate policies. They found that ETS can moderate the economic and
social welfare losses regardless of the allocation of emissions permits. Liu et al.
(2013) used a Sino-TERMCO2 model to investigate carbon abatement effects of
separated and linked markets. Their results showed that the linked market can
improve social welfare and reduce carbon emissions intensity, but this system may
distribute welfare more unevenly among different industries.

As current

research mainly focuses on individual ETS pilots, un
comprehensive comparison of all seven pilots is needed. For the Shanghai ETS,
Par exemple, Zhou (2015) simulated the economic impacts and cobeneﬁts under
alternative employment conditions. The results illustrated that a double dividend
from carbon emissions trading is available if the labor released from ETS-
affected sectors is absorbed immediately. Otherwise, GDP will decrease 1.5%–2.4%
compared to the baseline. For the Guangdong ETS, Wang et al. (2015) used
a GD_CGE model to simulate the effects of carbon mitigation through ETS
under the carbon intensity target. The results show that with an abatement target,
implementation of ETS can reduce abatement costs and decrease GDP losses, lequel
constitutes a cost-effective way to achieve carbon reduction. Ren, Dai, and Wang
(2015) used a dynamic two-region CGE model and the results showed that if a
carbon trading policy is implemented in a low-carbon scenario GDP declines 0.8%
relative to the baseline. For the Hubei ETS, Tan, Liu, and Wang (2016) used a
multiregional general equilibrium model (Term-CO2) to simulate economic and
environmental impacts. The results showed that the carbon emissions of Hubei
are reduced by 1% and GDP declines slightly by 0.06%. For the Tianjin ETS,
Liu et al. (2017) simulated the impacts on the economy and environment using a
Term-CO2 model. The results showed that carbon emissions decrease by 0.62% et
GDP declines a marginal 0.04%.

To sum up, these studies showed that ETS has the potential to lower the total
economic costs and social welfare losses caused by carbon emissions abatement
in the PRC, but its impact signiﬁcantly varies by province and sector. Parmi
these studies, cependant, only a small number of such impact assessments have been
conducted and most of them have drawn qualitative conclusions. Empirical ex post
impact assessments of the pilot ETSs are needed to guide the operation of the PRC’s
nationwide ETS (Jiang et al. 2016).

B. Method and Data

To estimate the effects of events and policy interventions at the aggregate
level, researchers often use comparative case studies. In such studies, chercheurs
estimate the evolution of aggregate outcomes (in this case, CO2 emissions) for a

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Carbon Trading Scheme in the People’s Republic of China 141

unit affected by an occurrence of the event and compare it to the evolution of the
same aggregates estimated for some control group of unaffected units. Cependant, it
is difﬁcult to estimate the carbon mitigation effect of carbon trading because of the
lack of solid control groups in this case.

The synthetic control method is used for effect estimation in settings where
a single unit (par exemple., state, country, or ﬁrm) is exposed to an event or intervention.
The synthetic control method was ﬁrst introduced and implemented in Abadie and
Gardeazabal (2003). Other comparative studies include investigating the economic
impacts of German reuniﬁcation (Abadie, Diamond, and Hainmueller 2015) et
the local impacts of nuclear power facilities (Ando 2015). There is also a series of
research focusing on the PRC. Liu and Fan (2013) examine the economic impacts
of the PRC’s house property tax pilot program in Chongqing. Zhang, Zhong, et
Faire (2016) used this same method to answer the question of whether the Olympic
Games improved air quality in Beijing.

Based on Abadie, Diamond, and Hainmueller (2011), we use a simple model
providing a rationale for the use of the synthetic control method. Suppose that
we observe J + 1 régions, and the ﬁrst region is exposed to the intervention of
interest, so that we have J remaining regions as potential controls. Suppose Y N
est
it
the outcome that would be observed for region i at time t without treatment for
units i = 1, 2, . . . J. + 1, and time periods t = 1, 2, . . . T . Let T0 be the number
of preintervention periods, avec 1 < T0 < T . Let Y I it be the outcome that would be observed for region i at time t with treatment in periods T0 + 1 to T. We assume that the intervention has no effect on the outcome before the implementation period, so for all i in period t ∈ {1, 2, . . . T0}, we have Y N it be the it effect of the intervention for unit i at time t, and let Dit be an indicator that takes a value of 1 if unit i is exposed to the intervention at time t and zero otherwise. The observed outcome for unit i at time t is it . Let αit = Y I − Y N = Y I it (1) (2) + Ditαit Yit = Y N it We aim to estimate (α1T0+1, . . . α1T ). For t > T0,

α1t = Y I
1t

− Y N
1t

= Y1t − Y N
1t
it is observed, to estimate α1t we just need to estimate Y N

Because Y I

1t ; suppose

that Y N
it

is given by a factor model:

= ∂t + θtZi + λtμi + εit

Y N
it
where Zi is a (r × 1) vector of observed covariates (not affected by the intervention),
∂t is an unknown common factor with constant factor loading across units, λt is
un (1 × F ) vector of unknown parameters, μi is a (F × 1) vector of unobserved
factor loadings, and the error term εit represents unobserved transitory shocks at
the regional level with zero mean.

(3)

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142 Revue du développement en Asie

, . . . w∗

We have to estimate Y N

it . Consider a (J × 1) vector of weights W ∗ =
(w∗
j+1) such that WJ ≥ 0 for j = 2, … , J. + 1 and w2 + · · · + wJ +1 = 1.
2
Each value of the vector W ∗ represents a potential synthetic control, c'est, un
particular weighted average of control regions. The value of the outcome variable
for each synthetic control indexed by W ∗ is

J. +1(cid:2)

j=2

J. +1(cid:2)

j=2

w jYit = ∂t + θt

J. +1(cid:2)

j=2

w jZ j + λt

J. +1(cid:2)

j=2

w jμi +

J. +1(cid:2)

w jεit

Suppose that there are W ∗ = (w∗
2

, . . . w∗

w∗

jYjt = Y11, . . .

J. +1(cid:2)

j=2

w∗

jYjT0

= Y1T0 and

(cid:3)

T0
je = 1

λ(cid:5)

t λt is nonsingular, alors

Y N
1t

−

J. +1(cid:2)

j=2

w∗

jYjt =

T0(cid:2)

J. +1(cid:2)

w∗
j

λt

(cid:4)

T0(cid:2)

λ(cid:5)

t λt

j=2

s=1

je = 1

j=2

(cid:5)−1

j=2
j+1)(cid:5) such that
J. +1(cid:2)

w∗

j Z j = Z1

(cid:6)

λ(cid:5)

ε jsεis

(cid:7)

−

(cid:6)
ε jt − εit

(cid:7)

w∗
j

J. +1(cid:2)

j=2

(6)

J. +1
j=2
J. +1
j=2

Abadie, Diamond, and Hainmueller (2011) proved that, under the general
condition, the right-hand side of equation (6) will approach zero. Par conséquent,
(cid:3)
it , where T0 < t ≤ T . So α1t = Yit − (cid:3) w∗ jYjt is the unbiased estimation of Y N w∗ jYjt is the unbiased estimation of α1t. Take Hubei as an example. We construct the synthetic Hubei as a weighted average of potential control provinces, with weights chosen so that the resulting synthetic Hubei best reproduces the values of a set of predictors of CO2 emissions in Hubei before the carbon trading system was implemented (i.e., before 2013). Because the synthetic Hubei is meant to reproduce the CO2 emissions that would have been observed for Hubei in the absence of a carbon trading pilot, we discard from the donor pool provinces that adopted a carbon trading system during our sample period. Therefore, Beijing, Chongqing, Guangdong, Shanghai, and Tianjin are excluded from the donor pool. Finally, our donor pool includes the remaining 24 provinces. Our outcome variable of interest is annual CO2 emissions, calculated based on energy consumption at the provincial level. We use annual provincial-level data of energy consumption during the 1995–2015 period from the China Statistical Yearbook (National Bureau of Statistics of China 2016). Since seven pilots went into effect beginning in late 2013, we mark 2013 as the treatment year. This gives us 17 years of preintervention data. Our sample period begins in 1995 because it is the ﬁrst year for which data on energy consumption are available for all our control provinces. It ends in 2015 because newer data have not yet been published. Based (4) (5) l D o w n o a d e d f r o m h t t p : / / d i r e c t . m i t . / e d u a d e v / a r t i c e - p d l f / / / / 3 5 2 1 3 1 1 6 4 4 1 1 3 a d e v _ a _ 0 0 1 1 7 p d / . f b y g u e s t t o n 0 9 S e p e m b e r 2 0 2 3 Carbon Trading Scheme in the People’s Republic of China 143 Table 4. Social and Economic Characteristics of Actual and Synthetic Emissions Trading Scheme Pilots Pilot Province or City Hubei Hubei Beijing Beijing Shanghai Shanghai Tianjin Tianjin Chongqing Chongqing Guangdong Guangdong (real) (synthetic) (real) (synthetic) (real) (synthetic) (real) (synthetic) (real) (synthetic) (real) (synthetic) GDP per Capita (CNY) Share of Secondary Industry in Total GDP (%) 13,703.1 13,746.8 42,862.0 17,454.5 47,094.2 21,964.8 37,534.3 14,944.7 13,678.4 13,286.3 24,596.6 24,505.5 42.6 42.8 29.9 33.5 46.5 50.4 52.8 48.9 44.2 44.3 48.4 49.0 CNY = yuan, GDP = gross domestic product. Source: Authors’ calculations. on the method proposed by the Intergovernmental Panel on Climate Change, we calculate annual CO2 emissions for all provinces. We choose our predictors of CO2 emissions based on Auffhammer and Carson (2008): GDP per capita and share of secondary industry in total GDP. Using the techniques described above, we construct a synthetic for each of Beijing, Chongqing, Guangdong, Hubei, Shanghai, and Tianjin that mirrors the values of the predictors of CO2 emissions for themselves before the introduction of a carbon trading system. We estimate the carbon mitigation effect of carbon trading pilots as the difference in CO2 emissions between Hubei and its synthetic version after 2013. We then perform a series of placebo studies that conﬁrm that our estimated effects for carbon trading pilots are unusually large relative to the distribution of the estimate that we obtain when we apply the same analysis to the provinces in the donor pool. Then we repeat the process for Beijing, Chongqing, Guangdong, Shanghai, and Tianjin. C. Results As explained above, we construct the synthetic Hubei as the convex combination of provinces in the donor pool that most closely resemble Hubei in terms of prepilot values of CO2 emissions predictors. The results are displayed in Table 4, which compares the pretreatment characteristics of the actual Hubei with that of the synthetic Hubei, as well as comparisons for the other ﬁve pilots. Table 5 displays the weight for each synthetic pilot. Because social and economic characteristics vary substantially across provinces, different synthetic results emerge. Generally, the closer to the average l D o w n o a d e d f r o m h t t p : / / d i r e c t . m i t . / e d u a d e v / a r t i c e - p d l f / / / / 3 5 2 1 3 1 1 6 4 4 1 1 3 a d e v _ a _ 0 0 1 1 7 p d / . f b y g u e s t t o n 0 9 S e p e m b e r 2 0 2 3 144 Asian Development Review Table 5. Synthetic Weight for Each Synthetic Pilot Province Anhui Fujian Gansu Guangxi Guizhou Hainan Hebei Heilongjiang Henan Hunan Inner Mongolia Jiangsu Jiangxi Jilin Liaoning Qinghai Shaanxi Shandong Shanxi Sichuan Xinjiang Yunnan Zhejiang Hubei_ weight Beijing_ weight Shanghai_ weight 0.036 0.025 0.019 0.033 0.025 0.096 0.026 0.018 0.045 0.059 0.024 0.057 0.025 0.017 0.024 0.013 0.020 0.027 0.017 0.322 0.021 0.025 0.029 0 0 0 0 0 0.682 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.318 0 0 0 0 0 0 0 0 0 0 0.018 0 0 0 0 0.352 0 0 0 0 0 0 0.629 Source: Authors’ calculations. Tianjin_ Chongqing_ Guangdong_ weight weight weight 0 0 0 0 0 0 0 0 0 0 0 0 0 0.566 0 0.381 0 0 0 0 0 0 0.052 0.027 0.030 0.047 0.031 0.042 0.080 0.020 0.034 0.015 0.023 0.036 0.012 0.034 0.040 0.026 0.301 0.036 0.010 0.039 0.020 0.043 0.036 0.020 0.003 0.008 0.003 0.003 0.003 0.133 0.003 0.003 0.003 0.004 0.011 0.052 0.003 0.003 0.007 0.003 0.003 0.005 0.003 0.003 0.004 0.003 0.734 level of the whole country, the better the synthetic result is. For example, Hubei, Guangdong, and Tianjin each have a better synthetic result than either Shanghai, Beijing, or Chongqing. Figure 5 plots the trends in CO2 emissions in Hubei and the synthetic Hubei. Since 2013, CO2 emissions in Hubei and synthetic Hubei have differed notably, indicating Hubei reduced CO2 emissions by about 59.5 million tons in 2015 due to the carbon trading scheme. Figure 6 shows that before 2013 the synthetic data ﬁt the actual data quite well for Guangdong. After 2013, the gap between the CO2 emissions of Guangdong and that of synthetic Guangdong emerges, which shows a deviation from the synthetic data. Guangdong (including Shenzhen) reduced CO2 emissions by 37.1 million tons in 2015. From the perspective of trading indicators, Hubei and Guangdong (including Shenzhen) had more active trading than the other ﬁve pilots. The synthetic results support our expectation that more trading volume results in more emissions reduction. Figure 7 plots the trends in CO2 emissions in Tianjin and synthetic Tianjin. Since 2013, CO2 emissions in Tianjin seemed to have outpaced those in synthetic l D o w n o a d e d f r o m h t t p : / / d i r e c t . m i t . / e d u a d e v / a r t i c e - p d l f / / / / 3 5 2 1 3 1 1 6 4 4 1 1 3 a d e v _ a _ 0 0 1 1 7 p d / . f b y g u e s t t o n 0 9 S e p e m b e r 2 0 2 3 Carbon Trading Scheme in the People’s Republic of China 145 Figure 5. Carbon Dioxide Emissions in Hubei versus Synthetic Hubei l D o w n o a d e d f r o m h t t p : / / d i r e c t . m i t . Source: Both actual emissions and synthetic emissions are authors’ estimates based on data from the National Bureau of Statistics of China. 2016. China Statistical Yearbook, 1995–2015. Beijing. Figure 6. Carbon Dioxide Emissions in Guangdong versus Synthetic Guangdong / e d u a d e v / a r t i c e - p d l f / / / / 3 5 2 1 3 1 1 6 4 4 1 1 3 a d e v _ a _ 0 0 1 1 7 p d . / f b y g u e s t t o n 0 9 S e p e m b e r 2 0 2 3 Source: Both actual emissions and synthetic emissions are authors’ estimates based on data from the National Bureau of Statistics of China. 2016. China Statistical Yearbook, 1995–2015. Beijing. 146 Asian Development Review Figure 7. Carbon Dioxide Emissions in Tianjin versus Synthetic Tianjin l D o w n o a d e d f r o m h t t p : / / d i r e c t . m i t . / e d u a d e v / a r t i c e - p d l f / / / / 3 5 2 1 3 1 1 6 4 4 1 1 3 a d e v _ a _ 0 0 1 1 7 p d . / f b y g u e s t t o n 0 9 S e p e m b e r 2 0 2 3 Source: Both actual emissions and synthetic emissions are authors’ estimates based on data from the National Bureau of Statistics of China. 2016. China Statistical Yearbook, 1995–2015. Beijing. Tianjin. The result is expected given the lack of liquidity in the Tianjin trading market. However, we cannot get good synthetic results before 2013 for Beijing, Chongqing, and Shanghai. Therefore, we cannot say anything about their respective performances yet and are still searching for a better synthetic control strategy for these three municipalities. The existing results are included in the Appendix. D. Robustness Test The empirical results in the last section reveal a gap between the CO2 emissions of Hubei and those of synthetic Hubei. In this context, we will use a placebo test to check the statistical signiﬁcance of our results. For example, are there any other provinces among the donor pool that show a gap between real CO2 emissions and synthetic CO2 emissions when these provinces are viewed as a treatment group? We iteratively apply the synthetic control method to estimate the impact of the carbon trading pilot on every other province. We can get the difference between real emissions and synthetic emissions, and then we divide the difference by real emissions. Before doing this test, we need to exclude the provinces that do not ﬁt the original CO2 emissions data before 2012. The bad ﬁt before the treatment means that the gap after the treatment may not be caused by the treatment itself. Therefore, Carbon Trading Scheme in the People’s Republic of China 147 Figure 8. Distribution of Carbon Emissions Forecast Changes in Guangdong, Hubei, and Other Provinces l D o w n o a d e d f r o m h t t p : / / d i r e c t . m i t . / e d u a d e v / a r t i c e - p d l Source: Both actual emissions and synthetic emissions are authors’ estimates based on data from the National Bureau of Statistics of China. 2016. China Statistical Yearbook, 1995–2015. Beijing. we exclude the provinces whose mean squared prediction error before 2012 are larger than 100. Finally, we obtain nine provinces as a potential control group. In Figure 8, each gray line represents the difference in CO2 emissions between each province in the donor pool and its respective synthetic version. The estimated gap for Hubei is unusually large relative to the distribution of the gaps for other provinces in the donor pool. Similarly, Guangdong shows the same pattern since 2013, when its ETS went into effect. V. Conclusions Generally, the low trading volume in the PRC’s carbon trading market shows that it is not liquid enough to be well functioning. The sharp increase in trading volume before the compliance period shows that the covered companies are unenthusiastic about ETS and view it as a routine government inspection that they must comply with. At the same time, the prohibition on cross-provincial trade reduces the attractiveness of the PRC carbon market to investors, especially institutional investors. The awareness and participation of companies in the carbon market is still relatively low (China Emissions Exchange 2014). The high trading volume in f / / / / 3 5 2 1 3 1 1 6 4 4 1 1 3 a d e v _ a _ 0 0 1 1 7 p d / . f b y g u e s t t o n 0 9 S e p e m b e r 2 0 2 3 148 Asian Development Review the month before the compliance period shows that companies still treat carbon emissions trading as a means of compliance rather than an investment. Because the PRC’s carbon market is still in an early stage of development, companies’ views of carbon asset values will evolve with the development and maturing of the carbon market. The seven pilot ETSs have achieved different emissions reduction results. Hubei stands out in many aspects as its ETS pilot has been very inﬂuential. Based on our synthetic result, Hubei reduced emissions by 59.5 million tons in 2015 through its carbon trading scheme. Guangdong (including Shenzhen) also performed reasonably well, reducing emissions by 37.1 million tons in 2015. On the other hand, Tianjin did not notably reduce its carbon emissions. For policy recommendations, we suggest the following measures. The expansion of coverage of the PRC’s ETS is necessary. For now, most sectors covered by ETS are energy-intensive sectors such as petroleum processing, electricity, and steel. However, for Beijing, Shanghai, and Shenzhen, where energy- intensive sectors account for a low share of emissions, it is difﬁcult to see an active market with a limited coverage. In these three pilots, the transport sector accounts for a large share of total emissions. According to China Emissions Exchange (2014), the transport sector accounted for 27.9% of Shenzhen’s total emissions in 2010. Therefore, including the transport sector in the nationwide ETS is sensible. Allowing multiple products to activate the ETS system. The PRC’s seven ETS all use a single spot product, which may limit the liquidity of carbon markets. On the contrary, for the European Union ETS, forward trading accounts for 80%–90% of the shares traded while spot trading accounts for only about 10% (Aatola, Ollikainen, and Toppinen 2013). We suggest that authorities investigate the possibility to permit necessary derivative products in carbon trading markets. Shanghai and Shenzhen both have a stock exchange and therefore an advantage in promoting ﬁnancial innovation compared with other pilots. Shanghai and Shenzhen should seize the chance to become the largest carbon futures exchange centers in the world. Improving market transparency is the foundation to releasing market signals. Not all pilots clearly post information about each covered company. At the same time, although the carbon price and trading volume for each day are posted online, we ﬁnd it difﬁcult to know the exact trading parties. Improving market transparency would help enhance the efﬁcacy of the system. Despite the limited success of the ETS program, this has been extended nationwide, starting December 2017. There are two main differences between the nationwide scheme and the seven pilot programs. First, the supervision of the nationwide ETS program has shifted from NDRC to the newly established Ministry of Ecology and Environment. Second, the power generation sector is the only sector in the nationwide scheme, including 1,700 power companies. The coverage of only the power sector is due to two reasons. One is that the power sector is typically l D o w n o a d e d f r o m h t t p : / / d i r e c t . m i t . / e d u a d e v / a r t i c e - p d l f / / / / 3 5 2 1 3 1 1 6 4 4 1 1 3 a d e v _ a _ 0 0 1 1 7 p d . / f b y g u e s t t o n 0 9 S e p e m b e r 2 0 2 3 Carbon Trading Scheme in the People’s Republic of China 149 energy intensive and accounts for a large share of the PRC’s carbon emissions. The other reason is that the power sector has better data. If the initial strategy to include only the power sector goes smoothly, the central authorities intend to extend the market to cover more sectors. Finally, the PRC began implementing environmental taxes on 1 January 2018. 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Based on the Synthetic Control Method.” Environmental Economics and Policy Studies 18 (1): 21–39. Zhao, Xin-gang, Gui-wu Jiang, Dan Nie, and Hao Chen. 2016. “How to Improve the Market Efﬁciency of Carbon Trading: A Perspective of China.” Renewable and Sustainable Energy Reviews 59: 1229–45. Zhou, Shenglv. 2015. “Economic and Environmental Impacts of the Shanghai Carbon Emissions Trading: Based on CGE Model Analysis.” Advances in Climate Change Research 11: 144– 52. l D o w n o a d e d f r o m h t t p : / / d i r e c t . m i t . / e d u a d e v / a r t i c e - p d l f / / / / 3 5 2 1 3 1 1 6 4 4 1 1 3 a d e v _ a _ 0 0 1 1 7 p d / . f b y g u e s t t o n 0 9 S e p e m b e r 2 0 2 3 Carbon Trading Scheme in the People’s Republic of China 151 Appendix Figure A.1. Comparison of Emissions, Beijing l D o w n o a d e d f r o m h t t p : / / d i r e c t . m i t . / e d u a d e v / a r t i c e - p d l f / Source: Both actual emissions and synthetic emissions are authors’ estimates based on data from the National Bureau of Statistics of China. 2016. China Statistical Yearbook, 1995–2015. Beijing. Figure A.2. Comparison of Emissions, Chongqing / / / 3 5 2 1 3 1 1 6 4 4 1 1 3 a d e v _ a _ 0 0 1 1 7 p d . / f b y g u e s t t o n 0 9 S e p e m b e r 2 0 2 3 Source: Both actual emissions and synthetic emissions are authors’ estimates based on data from the National Bureau of Statistics of China. 2016. China Statistical Yearbook, 1995–2015. Beijing. 152 Asian Development Review Figure A.3. Comparison of Emissions, Shanghai Source: Both actual emissions and synthetic emissions are authors’ estimates based on data from the National Bureau of Statistics of China. 2016. China Statistical Yearbook, 1995–2015. Beijing. l D o w n o a d e d f r o m h t t p : / / d i r e c t . m i t . / e d u a d e v / a r t i c e - p d l f / / / / 3 5 2 1 3 1 1 6 4 4 1 1 3 a d e v _ a _ 0 0 1 1 7 p d / . f b y g u e s t t o n 0 9 S e p e m b e r 2 0 2 3
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