Clean Techn Environ Policy (2015) 17:811–817 DOI 10.1007/s10098-014-0854-0
BRIEF REPORT
Abatement technology investment and emissions trading system: a case of coal-fired power industry of Shenzhen, China Ying Huang • Lei Liu • Xiaoming Ma Xiaofeng Pan
•
Received: 20 May 2014 / Accepted: 14 September 2014 / Published online: 23 September 2014 Ó Springer-Verlag Berlin Heidelberg 2014
Abstract As one of China’s emissions trading system (ETS) pilots, Shenzhen established the first carbon market of China in 2013. With field data collection and benefitcost analysis, this article assesses the abatement technology investment decisions of Shenzhen coal-fired power industry under different carbon price scenarios. The results indicate that Shenzhen ETS constitutes a main driver for the short-term technology investment of the industry, but the long-term stimulation effect appears quite limited, except for the integrated gasification combined cycle technology under high carbon price scenario. Further, the paper proposes the short-term and long-term optimal investment strategy for the industry, and relevant policy suggestions.
Y. Huang X. Ma Key Laboratory for Urban Habitat Environmental Science and Technology, School of Environment and Energy, Peking University Shenzhen Graduate School, Shenzhen, China Y. Huang X. Ma College of Environmental Sciences and Engineering, Peking University, Beijing, China L. Liu School of Public Administration, Sichuan University, Chengdu, Sichuan, China L. Liu (&) Robert Schuman Centre for Advanced Studies, European University Institute, Florence, Italy e-mail:
[email protected] X. Pan Shenzhen Environmental Monitoring Center, Shenzhen, China
Keywords Shenzhen emissions trading system Coal-fired power industry Abatement technology investment Benefit-cost analysis
Introduction In 2005, the first large greenhouse gas emissions trading system in the world, known as EU ETS, was launched by European Union. During past years, EU ETS has succeeded in putting CO2 emissions on a downward path that was clearly different from the evolution of economic activity (Ellerman et al. 2014). Enlightened by the EU ETS and faced by growing pressure to reduce emissions, a number of developed and emerging economies have begun building their carbon markets, including India, Brazil, South Korea, and China, among others (Grubb 2012). As the biggest CO2 emitter of the world, China’s endeavor is undoubtedly of great significance. In October of 2011, China initiated pilot ETS in 5 cities (Beijing, Tianjin, Shanghai, Chongqing, and Shenzhen) and 2 provinces (Guangdong and Hubei). After 2 years of preparation, Shenzhen—one of the leading cities of China in economic growth and political reform, launched the first carbon market in China in June of 2013. Different from traditional cap-and-trade systems that require absolute emissions caps, Shenzhen ETS specifies emissions targets based on intensity, which is similar with the ‘‘Perform Achieve and Trade’’ initiative of India that focuses on energy intensity (Singh 2013). Nevertheless, all the intensity-based targets finally have to be converted to an absolute cap to make the trading of emissions allowances or certified energy saving possible. In this sense, the basic mechanism of Shenzhen ETS is actually the same with EU ETS.
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By promoting CO2 emissions to be an economic factor in the decision making of industries (Chang et al. 2014), ETS aims to cut down emissions and boost industrial technological innovation with lowest social cost (Grubb 2012). When facing emissions cap, enterprises will assess their potential of emissions reduction and compare with the carbon price to make their decisions: to invest in advanced abatement technologies or to purchase allowances (Zhang and Wei 2010). Technological innovation of industrial emitters is proved to be critical not only in reducing emissions (Tursun et al. 2014), but also in saving energy, boosting economy, and increasing the effectiveness of relevant environmental policy (Hasaneen et al. 2014). Therefore, a number of studies, particularly on EU ETS, have investigated the relationship between ETS and industrial technological update. For example, in a case study of German electricity sector, Hoffmann (2007) finds that the EU ETS constitutes a main driver for small-scale investments with short amortization times, while the impact on large-scale investments in power plants or in R&D efforts is limited. Szolgayova et al. (2008) find that too low carbon price caps are detrimental to the adoption of alternative establishments for existing coal-fired power plants. For moderately rising carbon prices, fluctuations frequently lead to investment into carbon capture and storage (CCS), which, however, is often not triggered by deterministic carbon prices. Abadie and Chamorro (2008) assess the option to install a CCS unit of a coal-fired power plant in a carbon-constrained environment and obtain the trigger allowance prices only above which it is optimal to install the capture unit immediately. De Schepper et al. (2014) assess the emissions mitigation cost of solar photovoltaic, grid-powered battery electric vehicles (BEVs), and solar-powered BEVs for a Belgian small- and mediumsized enterprise and find that the current financial stimuli for all the three technologies are excessive when compared to the CO2 market value under the EU ETS. For China’s ETS, including CO2 and SO2, while almost all the prior studies are theoretical analysis, prediction, or mechanism design (Ellerman 2002; Raufer and Li 2009; Fan et al. 2014), there has been no empirical research concerning the current pilot program so far. Taking the coal-fired power industry as a case, which is the largest carbon emitter in Shenzhen, this article will investigate the industrial technologies investment under Shenzhen ETS, including for example, if the industry will choose to invest in abatement technologies and what is the optimal investment strategy to achieve emissions reduction target. Currently, about 80 % of world’s electricity is produced from fossil fuels fired thermal power plants (Erdem et al. 2009). The efficiency of power plants in developing countries is still around 32–35 % lower heating value (Siva Reddy et al. 2014). So, this empirical research, actually the first
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after China’s ETS pilot was initiated, is expected to shed some light on the optimization of policy arrangements for later phases, the emissions reduction strategy for the corporate managers, the research on the impact of carbon market on power sector, and further maybe the reform of electricity market (Fan et al. 2014).
Methodology and data Currently, the power plants of Shenzhen mainly include three types, thermal, nuclear, and incinerator, which generated 18.82 Mt CO2 in 2011 and accounted for about 20 % of the city total (Shenzhen Research Center for Urban Development 2012). The only one coal-fired power plant, Shenzhen Mawan Electric Power Co. ltd. (SMEP), emitted 60 % of CO2 of the sector total with 1840 MW of installed capacity and daily consumption of 14,000t of standard coal (SMEP 2011; Shenzhen Research Center for Urban Development 2012; SMEP 2012a, b; Shenzhen Statistics Bureau and China Statistics Bureau’s Survey Office in Shenzhen 2013). Usually, under ETS, the power plant has three optional operation modes: sticking to usual production mode without investing in any abatement technologies; investing in abatement technologies; or reducing production (Laurikka 2006). But reducing production may not be a feasible option because for the first, the government will take back part of the allowances if a plant reduces too much production; second, the emissions reduction target is set based on intensity (CO2 emissions/ industrial added value), which is in accordance with the climate target of Chinese government, so only reducing production may not decrease carbon intensity but possibly, increase it. In this project, we first investigated the available abatement technologies of the plant with field survey, interview with managers and experts, and document research. Then we assessed the optimal technological investment strategies for the plant with benefit-cost analysis, under different scenarios of allowance prices to compromise the uncertainty of the price variation (Chang 2014). In this paper, we engage the discounted cash flow (DCF) method presented in the work of Laurikka and Koljonen (2006). Accounting method Investment benefit is expressed by the change of net present value (DNPV) (Laurikka and Koljonen 2006): DNPV ¼ NPV2 NPV1 ;
ð1Þ
where NPV2 and NPV1 , respectively, denote the NPV of the plant after and before investment (¥). When
Abatement technology investment and emissions trading system
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DNPV [ 0, the investment is profitable and feasible, and vice versa. The NPV is usually reflected by the net cash flow (NCF). The NCF caused by the investment (CFNET ) is calculated by (Laurikka and Koljonen 2006): CFNET ¼ CFINV CFNOW DCf ;
ð2Þ
where CFINV and CFNOW , respectively, denote the NCF after and before investment (¥); Cf is fixed asset investments (¥) and DCf is its variation. In long-term, the transferring cost of production modes could be ignored (Laurikka 2006). The annual NCF of a power plant can be calculated as (Laurikka and Koljonen 2006): Z CF ¼ PðtÞSðtÞdt; ð3Þ where P(t) is the output capacity of the plant at time t (kW); S(t) is the spark spread at time t (¥/kWh). Before ETS is implemented, the NCF of the plant (CFORI) is calculated as (Laurikka and Koljonen 2006): Z pf CFORI ¼ Pð t Þ p e w dt; ð4Þ gf where pe is on-grid price (¥/kWh), including tax and subsidies se (the plant gets subsidies from the local government for adopting technologies such as flue-gas desulfurization); pf is fuel price (¥/t); gf is the thermal parameter; W consists of, for example, specific operation and maintenance costs (¥/kWh). In addition, the power use by the plant itself (5.2 % of the total power generation) needs to be taken into account by multiplyingpe by (1–5.2 %), which also applies to the following equations. The implementation of ETS will change the NCF in two aspects, without considering transaction costs: (a)
ETS decreases the spark spread (S) by increasing the emissions cost: S ¼ pe
(b)
pf ef pco2 w; gf gf
ð5Þ
where ef is the emissions factor of fuels (CO2e/t); pco2 is the market price of allowances (¥/t). ETS provides extra profits to the plant through free allowances. Under Shenzhen ETS, regulated industries could obtain most allowances for free during 2013–2015, the value of which can be assessed by the amount of allowances in different phases (Ni, tCO2e) multiplying by pco2 . Therefore, under ETS, the NCF of the plant before investing in abatement technologies is
CFNOW ¼
Z pf ef Pð t Þ p e pco2 w dt gf gf þ Ni pco2 :
ð6Þ
Finally, the investment in abatement technologies will improve the thermal efficiency and decrease fuel consumption. Under Shenzhen ETS, in the same phase, the initial free allowances of enterprises will not change with the adoption of abatement technologies. So the NCF after the investment is expressed as Z pf ef CFINV ¼ PðtÞ pe pco w dt gINV gINV 2 þ Ni pco2 ; ð7Þ where gINV is the thermal efficiency of the plant after investing in abatement technologies. Assumptions and parameters Assumptions Analytical period The current policy arrangement (first phase) covers 2013–2015 (short-term). The remaining life of the plant equipment is estimated to be 15 years, so the whole analytical period is set to be 2013–2027 (long-term), in which we assume the second and third phase of Shenzhen ETS will be 2016–2020 and 2021–2027. 2020 is chosen as a cut-point to be in accordance with national and municipal 5-year development plan. During the analytical period, it is assumed Shenzhen ETS is a stable and sustainable market that carbon price is stable and predictable; in the meantime, the government will continue to provide relevant subsidies. The discount rate is assumed to be 10 %. The case of not achieving the emissions reduction target and getting punishment will not be considered.1 Allowances and prices According to the policy design of Shenzhen ETS and electricity sector planning, the free allocated allowances will be reduced gradually. It is thus assumed that the free allowances for the plant will be 80 % of its projected emissions in 2016–2020 and 60 % in 2021–2027. According to observation, the mean, highest, and lowest carbon price from the launch of the market till the start of 1
According to ‘‘The Interim Measures for the Administration of CO2 Emissions Trading of Shenzhen Municipality,’’ if the emissions of an enterprise exceed the limit, it will be fined 3 times of the market value of the exceeded emissions. In addition, the emissions limit of the enterprise for the next year will be cut down by the exceeded amount. There may also be some other informal punishments such as covered by official media. So, the punishment is considerably severe that we assume any rational agent will not break the cap.
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Table 1 The parameters of available abatement technologies Technology
Investment (Billion ¥)
Annual emissions reduction (MtCO2e)
New installed capacity (kW)
Investment period (years)
Annual incremental fixed cost (Million ¥)
I Frequency conversion of condensate pumps
8.70a
12,659a
0
–h
0
0
7,600
II Increase of nameplate capacity
12.00a
13,296a
0
–h
0
0
7,600
III Flow passage transformation of steam turbine
228.00a
244,680a
0
–h
0
0
7,600
IV Transformation for increasing unit safety and economy
108.00a
99,720a
0
–h
0
0
7,600
V Intelligent soot blowing systems for boilers VI Steam cooling system
6.00a
4,155a
0
–h
0
0
7,600
3.00a
1,662a
0
–h
0
0
–
0
–
h
0
0
7,600
a
a
Subsidies (Million ¥)
Annual operation timeg (h)
VII Boiler air preheater transformation
102.00
VIII USC
2.65b
0.328d
600,000b
3
0
0
7,600
c
e
250,000c
3
7f
500
6,460
IX IGCC a
3.00
1.514
Source: SMEP (2012a, b)
b
Source: Liu (2012)
c
Source: China Huaneng (2013) Source: Yu (2013)
d
24,930
e
Source: Fan et al. (2012)
f
Source: Laurikka (2006)
g
The current average operation time of generating units of Shenzhen coal-fired power plant is about 7,600 h per year and keeps stable. USC impacts little on unit operation time, while for IGCC, the availability is usually assessed to be 85 % of the normal time
h
According to SMEP (2011) and SMEP (2012a, b), the investment period is very short (usually 1–3 months) and thus is ignored in the calculation Other key parameters are listed in Table 2
the research is, respectively, about 70, 110, and 30 ¥/tCO2e (June 18, 2013–December 26, 2013),2 based on which we assume three scenarios for carbon price in the whole analytical period. Electricity production According to the prediction of the plant on electricity production in 2013–2015 (SMEP 2012a, b) and government planning, it is anticipated that the electricity production in 2013–2027 will keep stable. Emissions reduction target The emissions reduction target of the plant during 2013–2015 has been set by the government as that the emissions intensity of 2015 should be 0.1tCO2e/104 kWh less than that of 2013. Assuming in 2016–2020, the average annual emissions intensity should be 0.1tCO2e/104 kWh less than in 2013–2015; and in 2021–2027, the average annual emissions intensity should be 0.1 tCO2e/104 kWh less than in 2016–2020.
2
http://www.cerx.cn/Portal/home.seam.
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Parameters The currently available abatement technologies and their cost as well as emissions reduction capability are listed in Table 1. Among the technologies, ultra-super-critical (USC) and integrated gasification combined cycle (IGCC) will add new installed capacity and get government subsidies. For the parameters of USC, we refer to the average performance of the generating units that went into operation in recent years in China (Liu 2012; Yu 2013). For IGCC, we refer to the relevant project of Huaneng Tianjin IGCC Co. ltd. of China, which could reduce 87 % of carbon emissions (China Huaneng 2013).
Result If the plant sticks to the production mode as usual and purchases all the needed allowances, as shown in Table 3, in both short-term (2013–2015) and long-term
Abatement technology investment and emissions trading system
815 Table 4 DNPV of investment in abatement technologies
Table 2 Parameters specification Parameter
Unit
On-grid price (pe)
¥/kWh
Shortterm mean
Longterm mean
0.460
0.450a
b
c
Abatement technology
DNPV without ETS
DNPV under ETS pco2 (¥/tCO2e) 30
70
110
USC on-grid price (ps)
¥/kWh
0.470
0.500
1.36
2.40
3.78
5.17
IGCC on-grid price (pIGCC) Thermal efficiency (gf)e
¥/kWh %
0.573d 39.61
0.650c 39.61
II
-1.43
-0.34
1.11
2.57
III
-33.58
-13.50
13.28
40.05
USC thermal efficiency (gUSC)f
%
42.85
42.85
IV
-28.76
-20.58
-9.67
1.25
IGCC thermal efficiency (gIGCC)g
%
42
42
V
-2.70
-2.36
-1.90
-1.45
Annual non-fuel operation and maintenance cost (W)h
¥/kWh
0.088
0.088
Fuel price (pf)i
¥/t
610
600
Emissions factor of coal (ef)f
tCO2e/t
2.1
2.1
a
The long-term on-grid price of coal electricity would be lower because of decreasing coal price and control of coal-fired power generation
b
USC would get a subsidy of 0.01 ¥/kWh because of denitration
c
It is supposed that the government would increase subsidies to support cleaner technologies
d
Source: Tianjin Development and Reform Commission (TDRC) (2013)
e
Source: SMEP (2012a, b)
f
Source: Yu (2013)
g
Source: Fan et al. (2012)
h
Source: SMEP (2011)
i
Based on current market price (http://www.osc.org.cn/CoalIndex/ chs/new)
Table 3 DNPV of production as usual pco2 (¥/tCO2e)
DNPV (Million ¥) 2013–2015
2016–2020
2020–2027 -289.66
Total
30
-8.41
-188.36
-486.43
70
-19.62
-439.51
-675.87
-1135.00
110
-30.83
-690.65
-1062.08
-1783.57
(2013–2027), the NPV loss will increase drastically with the increase of carbon price, and will be larger and larger due to the stricter emissions reduction targets and fewer free allowances in later stages. The DNPV of investment in different abatement technologies is shown in Table 4. USC and IGCC are not taken into account in short-term analysis because of 3 years’ investment period. According to Table 4, in short-term, without ETS, out of all the abatement technologies, the DNPV of only 1 is positive, i.e., the investment is profitable; under ETS, with the rise of carbon price, the number of profitable technologies starts to increase and achieves 4 in high carbon price
Short-term
Long-term
I
VI
-1.68
-1.54
-1.36
-1.18
VII
-82.19
-80.14
-77.42
-74.69
I
21.56
24.74
26.98
33.21
II
19.78
23.12
25.48
32.02
III
356.90
418.32
461.69
582.09
IV
130.38
155.41
173.09
222.15
3.93
4.98
5.71
7.76
V VI
0.97
1.39
1.68
2.50
VII VIII
-42.41 -904.63
-36.15 -849.16
-27.80 -775.21
-19.46 -701.26
IX
-647.14
-433.99
-149.79
134.42
scenario. In long-term, without ETS, out of all the abatement technologies, the DNPV of 6 is positive; under ETS, with the rise of carbon price, the number of profitable technologies keeps almost constant (with DNPV slightly increase), except for technology IX (IGCC) when carbon price achieves 110 ¥/tCO2e. Evidently, the stimulation of ETS for abatement technologies investment in long-term is pretty limited. Further, the highest acceptable marginal abatement costs (MAC) of the plant under different scenarios are assessed, as shown in Fig. 1. If the MAC of a technology is under this limit, then such technology is investable, and vice versa. According to Fig. 1, in short-term, the DNPV of investment in technology V, VI, and VII is still negative under high carbon price scenario, the lowest carbon prices for the investment in which are, respectively, 237, 369, and 1,205 ¥/tCO2e; in long-term, the DNPV of investment in technology VII and VIII is still negative under high carbon price scenario, the lowest carbon prices for the investment in which are, respectively, 203 ¥/tCO2e (2013–2027 average) and 368 ¥/tCO2e (2016–2027 average). Consequently, under ETS, the optimal short-term investment strategy for the plant should be as follows: when the average carbon price is 30 ¥/tCO2e, to invest in technology I for reduction of 37,977 tCO2e and purchase the remaining gap; when the average carbon price is 70 ¥/ tCO2e, to invest in technology I, II, and III for reduction of 811,905 tCO2e and purchase the remaining gap; when the
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Short Term
Highest Investable MAC (without ETS)
1400
\tCO2e
1200
Highest Investable MAC (pco2=30 )
1000
Highest Investable MAC (pco2= 70)
800 600
Highest Investable MAC (pco2= 110)
400
MAC of technology
200
Investable carbon price
0 I
II
III
IV
800
V
VI
VII
Long Term
700
/tCO2e
600
Highest Investable MAC (without ETS) Highest Investable MAC (pco2=30 )
500
Highest Investable MAC (pco2= 70)
400 300
Highest Investable MAC (pco2= 110)
200
MAC of technology
100
Investable carbon price
0 I
II
III
IV
V
VI VII VIII IX
Fig. 1 The MAC of available technologies and highest acceptable MAC of the plant
average carbon price is 110 ¥/tCO2e, to invest in technology I, II, III, and IV for reduction of 1,111,065 tCO2e and purchase the remaining gap. The optimal long-term investment strategy should be as follows: when the average carbon price is 30 and 70 ¥/ tCO2e, to invest in technology I, II, III, IV, V, and VI for reduction of 5.64 MtCO2e and purchase the remaining gap; when the average carbon price is 110 ¥/tCO2e, to invest in technology I, II, III, IV, V, VI, and IX for reduction of 20.78 MtCO2e and purchase the remaining gap.
the long-term is not notable, i.e., the DNPV remains relatively stable when carbon price increases, except for IGCC technology in high carbon price scenario. The result is similar with the existing studies concerning EU to some extent that ETS is an important factor for the abatement technologies investment of enterprises but would not enhance technological update significantly (Laurikka 2006; Laurikka and Koljonen 2006; Hoffmann 2007; Blanco and Rodrigues 2008; Bonenti et al. 2013). In this sense, the control of free allowances in the second and third phase may be more stringent than assumed in the paper to promote further technological innovation. In addition, necessary and reasonable price floors and ceilings may be set to ensure the effectiveness and efficiency of ETS (Helm 2008). For the industry, it is important to follow relevant abatement technological update, carbon market dynamics, and government policy to maximize payoff and avert unnecessary loss and risk. The ETS of China is still in a test phase. In addition to the officially admitted pilots, a number of other local governments have initiatively started to build own carbon markets. As the leading city in this trend, the research on the Shenzhen case is expected to shed some light on the policy design of the newly markets, including allowances allocation, effect assessment, and price intervention. At last, it has to be noted that NPV is a static method, which implicitly assumes that the investment is irreversible. But actually, the decision makers would adjust strategies at any time when necessary. So, further research could engage more flexible methods to get a more detailed conclusion. Acknowledgments We would like to thank the anonymous reviewers and the editors for their insightful and helpful comments for the substantial improvement of the paper.
References Conclusions and discussions With benefit-cost analysis based on assumed scenarios, the article investigates the carbon abatement technologies investment of Shenzhen coal-fired power industry under Shenzhen ETS of China. It is found that in both long-term and short-term, the impact of ETS on the DNPV of the coal-fired power plant is significant that if the plant operates as usual, the NPV loss could be huge. So, investment in appropriate abatement technologies is essential for the industry when ETS is implemented. In short-term, ETS would efficiently promote the investment of the plant that the DNPV with different technologies is sensitive to the variation of carbon price, while such stimulation effect in
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