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Uncertainty modeling of CCS investment strategy in China’s power sector
Applied Energy 87 (2010) 2392–2400
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Applied Energy journal homepage: www.elsevier.com/locate/apenergy
Uncertainty modeling of CCS investment strategy in China’s power sector Wenji Zhou a, Bing Zhu a,b,*, Sabine Fuss b, Jana Szolgayová b,c, Michael Obersteiner b, Weiyang Fei a a
Department of Chemical Engineering, Tsinghua University, Beijing 100084, PR China International Institute for Systems Analysis (IIASA), Schlossplatz 1, A-2361 Laxenburg, Austria c Department of Applied Mathematics and Statistics, Faculty of Mathematics, Physics and Informatics, Comenius University, Bratislava, Slovakia b
a r t i c l e
i n f o
Article history: Received 29 September 2009 Received in revised form 22 January 2010 Accepted 22 January 2010 Available online 13 February 2010 Keywords: Carbon capture and storage Uncertainty Real options Energy investment Chinese climate policy
a b s t r a c t The increasing pressure resulting from the need for CO2 mitigation is in conflict with the predominance of coal in China’s energy structure. A possible solution to this tension between climate change and fossil fuel consumption fact could be the introduction of the carbon capture and storage (CCS) technology. However, high cost and other problems give rise to great uncertainty in R&D and popularization of carbon capture technology. This paper presents a real options model incorporating policy uncertainty described by carbon price scenarios (including stochasticity), allowing for possible technological change. This model is further used to determine the best strategy for investing in CCS technology in an uncertain environment in China and the effect of climate policy on the decision-making process of investment into carbon-saving technologies. Ó 2010 Elsevier Ltd. All rights reserved.
1. Introduction According to the report from the International Energy Agency (IEA) [1], China ranks the first place in terms of CO2 emissions by fuel combustion among countries in the world. In recent years, due to the rapid growth of high energy-consumption sectors such as power, steel, cement and chemical industries, China’s CO2 emissions have risen dramatically. A key characteristic of China’s energy structure is that coal dominates in energy use, accounting for about 70% in total energy consumption [2], while oil and natural gas constitute a relatively small part only.1 This situation is expected to last for several decades in the future, mainly due to China’s considerable coal reserves. This fact together with the increasingly perceivable need to decrease the global CO2 emissions imposes great pressure on policy makers and therefore poses huge challenges to China both with regard to energy security and climate change. Carbon capture and storage (CCS) is considered as an important approach to control CO2 emissions caused by fossil fuel consumption. The power industry is one of the main CO2 emissions sectors, in particularly in China. More than 80% of electricity is generated by coal combustion [2], with relatively low-efficiency coal-fired
* Corresponding author. Address: Department of Chemical Engineering, Tsinghua University, Beijing 100084, PR China. Tel./fax: +86 10 62782520. E-mail address: [email protected] (B. Zhu). 1 According to 2007 data, the shares of coal, petroleum and natural gas are 73%, 21%, and 3%, respectively. With respect to renewable energy, hydro power is most developed, accounting for 3%. Nuclear and wind power are under fast development, but still constitute very small shares. 0306-2619/$ - see front matter Ó 2010 Elsevier Ltd. All rights reserved. doi:10.1016/j.apenergy.2010.01.013
technology. This indicates that to reduce CO2 emissions in China, the power industry needs to be considered as one of the first sources. Installing CCS devices in power plants is an attractive option, as it enables further use of abundant coal resources, while at the same time cutting emissions from that combustion by a considerable amount. However, the high cost of CCS and the uncertainty associated with its technological development are obstacles to a fast diffusion of this technology, particularly in developing countries like China. The Chinese government has been strengthening related R&D efforts and some demonstration projects are under construction.2 From the energy companies’ pointof-view, CCS installation is still associated with high cost and could only be considered if it was profitable for the whole value chain. This, however, remains highly uncertain due to the lack of knowledge about the direction of future climate policy. China’s power industry is now confronted by big challenges from increasingly strict environmental policies aiming at the reduction of pollutants. With respect to environmental regulation, the requirement of environmental protection has become much
2 CO2 capture technology in China has been applied in some industrial sectors such as ammonia, hydrogen and petroleum for several decades. However, R&D of largescale capture in the power sector just started a few years ago. In 2005, CCS technology was listed in the National Outlines for Medium and Long-term Planning for Scientific and Technological Development (2006–2020), which significantly spurred relevant research. In July 2008, the first demonstration carbon capture project in China has been completed in the Beijing Thermal Power Plant owned by Huaneng Group, with a capacity of 3000 tons of CO2 captured per year. In the meantime, some other demonstration projects with larger capture capacity are also at different stages of completion.
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stricter than before. For example, power plants are compelled to install desulfurization devices by regulation issued in recent years. However, there is currently not such a stringent requirement for China’s power industry to reduce greenhouse gas emissions. China is included in the clean development mechanism (CDM),3 however, and some energy enterprises have benefited from CDM projects. It is still hard to predict whether and when the situation will change and what kinds of policy instruments will be adopted in the post-Kyoto period. Moreover, in a cap-and-trade market, the CO2 price will fluctuate. In the face of such an uncertain environment, strategic decisions on investment into CO2 mitigation technology cannot be based on traditional discounted cash flow (DCF) analysis because companies possibly choose to delay the decision rather than immediately make a now-or-never decision by using DCF, as the investment is irreversible, involves high sunk costs and its payoff is uncertain. In order to capture these specific characteristics of the investment decision, real options analysis (ROA) has therefore been chosen for the analysis [3]. Generally, ROA is designed to take three important factors into account: the irreversibility of the investment, the uncertainty surrounding the future cash flows from the investment (here through volatile CO2 price processes), and the opportunity of timing the investment flexibly (here through adding the CCS module earlier or later in the planning period) [4]. Investment strategy in the power sector has been analyzed by using real options methods before, but only a few of them focus on climate change policies, and it is hard to find any discussion on this issue for developing countries like China. Laurikka [5] presents a simulation model implementing ROA to assess the impact of emissions trading scheme on integrated coal gasification combined cycle (IGCC). The study simulates three types of stochastic variables: the price of electricity, the price of fuel and the price of emission allowances. Yang et al. [6] value real options using a dynamic programming approach for technology investment choices under uncertain climate policy. Three cases for gas, coal and nuclear power investments are considered. Energy prices and the CO2 price are set to change randomly; for the latter one, a price jump was incorporated to represent policy-related shocks. All these studies are based on a specific emissions trading system, which cannot be applied directly to the case of China. Furthermore, the absence of technological improvement in those studies implies neglecting one of the most important drivers for low-carbon technology adoption. Kumbarog˘lu et al. [7] integrate technological learning curves into a real options framework to appraise renewable energy technologies. Fuss and Szolgayová [8] use a real options model with stochastic technical change and stochastic fossil fuel prices, to investigate their impact on replacement investment decisions in the electricity sector. Unlike the studies above, this paper attempts to model and analyze climate policy uncertainty in China’s energy industry under consideration of technological change and establish a real options model to obtain CCS investment strategies from the point-of-view of a typical energy enterprise, hence providing policy implications for CO2 mitigation in the coming post-Kyoto period. The paper consists of six sections. Following this introduction, Section 2 presents the methodological framework of the real options model used in the study. In Section 3, the factors of policy uncertainty and technological progress are analyzed. Section 4 describes the features of three representative types of technologies considered. These are a pulverized coal power plant, a wind farm and an IGCC plant. The
3 The clean development mechanism is an arrangement under the Kyoto protocol allowing industrialized countries with a greenhouse gas reduction commitment (Annex 1 countries) to invest in projects that reduce emissions in developing countries as an alternative to more expensive emission reduction in their own countries.
data used and the assumptions are explained in detail. The scenarios generated by varying underlying model parameters are analyzed in Section 5. Finally, the policy implications of the results from this real options model and its features as a policy analysis tool are presented in the conclusion. 2. Model description A typical CCS system consists of three parts: capture, transport and storage. The capture part contains chemical devices such as absorber and desorber, accounting for 70–80% of the total cost [9]. These devices can be in-built, when a new modern plant is constructed, or they can be added to an existing plant by retrofitting it at higher cost. In the model we consider an investor maximizing the sum of his expected discounted profits over the planning period, who faces uncertain climate policy. The real options model determines the optimal timing of investing into a CCS module given that a coal plant already exists. Several possible types of coal plants (and corresponding CCS systems) are analyzed separately. It also derives the corresponding profit distribution resulting from optimal investment. Several scenarios for CO2 price development (involving both deterministic and stochastic processes) were implemented to reflect different possible policy outcomes and examine their impact on CO2 mitigation technology investment behavior. In Fig. 1, we show an overview of price assumptions used in the model. Only the CO2 price is assumed to be uncertain, all other prices are modeled as deterministic. The motivation for an uncertain fluctuating carbon price CO2 price can be seen for example in a policy resulting in CO2 credits or allowances being traded amongst firms. We consider the planning horizon equal to the lifetime of the power plant, i.e. 30 years and that the decisions can be done on a yearly basis. The investor faces an optimization problem of timing the decision to invest into the CCS module so that the sum of discounted expected future profits is maximized. Let xt denote the state that the system is currently in year t, i.e. it tells whether the basic plant, the CCS module or both have been built and whether the CCS module is currently running, let at be the action (i.e. the control) which the decision-maker chooses to undertake in year t. Possible actions are to either build the CCS module (which is feasible only in case it has not been built yet) or do nothing. xt+1 depends only on the action at and xt, with at as an element from the set of feasible actions. The yearly profit p for a given state x and actions a can be expressed as:
pðx; a; Pc Þ ¼ qe ðaÞPe qc ðaÞPc qf ðaÞPf OMCðaÞ cðaÞ; c
ð1Þ c
where P represents the price of CO2 (or CO2 credit when P is negative), according to which energy companies could either be penalized by paying for CO2 emissions required by a stringent policy, or benefit from selling CO2 credits in a CDM-type market. The variable is created to describe a possible climate policy variation in the future. qe and qf refer to annual quantities of electricity output and fuel consumption, Pe and Pf represent prices of electricity and fuel respectively. OMC refers to operational and maintenance cost, and c denotes the cost associated with the undertaken action. In case the action is to build the CCS module, it is equal to the expenditure for retrofitting an existing plant with a CCS module. In case the action is to do nothing, this cost is equal to zero. Based on this profit function, the investor’s optimization problem can be formulated as follows:
max c
30 P
at ðxt ;P t Þ2Aðxt Þ t¼1
s:t:
ert E
pðxt ; at ðxt ; Pct Þ; Pct Þ
xtþ1 ¼ Fðxt ; at ðxt ; Pct ÞÞ
ð2Þ
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V 30 ðx; PÞ ¼ 0 V t ðx; Pct Þ ¼ max pðx; a; Pct Þ þ er E V tþ1 ðFðx; aÞ; P ctþ1 Þjx; Pct
ð3Þ
a2AðxÞ
where the optimum action for each state and price in each year is obtained as the argument that maximizes the term on the right side of the Bellman equation. The equation indicates that the value of the project in year t is composed of two parts: one is the immediate profit p(xt, at, P ct ), and the other is the expected and discounted value of next year, which is also called continuation value by Dixit and Pindyck [3]. To calculate E[Vt+1(F(x, a),Pctþ1 )|x, P ct )], we discretize the carbon prices and use the Monte Carlo simulations. The Monte Carlo simulations approach can be easily implemented and despite the general disadvantage that a large amount of simulations are required to obtain a sufficiently reliable results [10], it has proven to remain efficient in this framework for a rather high degree of complexity and delivers the same results as the partial differential equations approach [5]. The solution of the recursive optimization part is a multidimensional table containing the optimum action at for every t, x, and Pct . These optimum actions are further referred to as ‘‘strategies”. In order to analyze the final outcome, possible price paths are simulated and discretized subsequently (so a relatively fine price grid is needed in order to obtain precise results). The corresponding decisions are extracted from the output. In this way we can derive both the profit distribution and the frequency with which the CCS investment option is exercised. 3. Impact factors modeling 3.1. Deterministic variables Electricity price and technology cost are modeled as deterministic variables, from several reasons. The electricity price does not have an impact on the optimum decisions in the model, since the output of the power plant both with and without CCS is the same. Therefore, there would be no added value in a stochastic electricity price. On top of this, in China, the electricity pricing is regulated by the National Development and Reform Commission (NDRC), which makes it rather stable compared to price evolution in Western markets. To simulate the future trend, historical information of China’s on-grid price4 was collected from [11,12], as shown in the 4
On-grid price refers to the electricity price sales from individual power plants to the national power grid.
700
700
600
600
500
500
yuan/MWH
together with an assumption on the CO2 price. In the maximization problem, A is the set of feasible actions for a given state xt, r is discount rate, and E[] denotes the expected value. The investor’s problem is thus to determine the optimal investment strategy fat ðxt ; Pct ÞgTt¼1 . Since the model is formulated as a optimum control model with discrete time on a finite horizon, it can be solved by dynamic programming. The value function can be calculated recursively by the Bellman equation:
yuan/MWH
Fig. 1. Framework of the real options model.
400 300
400 300
200
200
100
100
0 1978 1985
2008
0
5
10 15
year
20 25
30
year
Fig. 2. Trend of on-grid electricity price.
left panel of Fig. 2. Historical data showed that after the long period of central planning, the 1985 reform in China’s power industry resulted in a growing electricity price. Based on current policy condition in China’s power industry, we assume that in the coming decades the trend will continue, and follow an exponential process with parameters obtained from historical data fitting, as shown in the panel of Fig. 2. Although the real development of cost for immature but promising technologies like CCS is very volatile, in some cases even ‘‘negative learning” phenomena occur, in our model we consider the technology cost as exogenous and deterministic. According to several studies [13–15], the cost improves over time due to the reduction in input factor prices, financing cost or improvements in organizational efficiency. This effect is explained by the technological progress and learning-by-doing, where the concept of a learning curve is widely used, indicating that the development of marginal or average unit cost is a function of cumulative production or capacity. To introduce directly such a functional form into our profit equation is not feasible5, hence we need to formulate technical change as a function of time rather than cumulative installed capacity. Technology cost is assumed constant in the scenarios without technological improvement. For the scenario that takes technical change into account, only modern technologies such as IGCC and CCS are considered to experience decreasing costs. The downward trend is assumed to follow an exponential process as shown in Fig. 3.
5 Remember that this is a plant-level analysis and the investment option gets exercised only once, after which learning from installing more is not an option anymore.
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8
500
x 109
450
7
IGCC with CCS
-0.01*t
400
cost(t)=cost(0)*e
350 6 300 5
IGCC without CCS
250 200
4
150 3 100 2
50 0
1
-50 0
5
10
15
20
25
30
year
5
10
15
20
25
30
year Fig. 4. Simulation of future trend of CO2 price.
Fig. 3. Decreasing capital costs of IGCC with and without CCS module.
3.2. Stochastic variable To investigate how uncertainty in climate policy for the coming decades affects energy technology investment behavior, the CO2 price is chosen to be stochastic, which could mimic subsequent adaptation of policy by the government or fluctuations due to imperfections in carbon markets in case an emission trading scheme-type (ETS) system would be chosen as regulatory framework. As mentioned above, this variable denotes either the price for a CO2 allowance (or a CO2 tax) or the credit granted for saving CO2, which depends on the scenario. Since the CO2 price is believed to increase, once the government commits to a GHG target [16,17], a geometric brownian motion (GBM)6 is employed, which is described by the following equation: c
dP ¼ lP c dt þ rPc dz
ð4Þ
where the parameter l is the drift, representing the positive trend of the CO2 price, r represents the volatility and dz is the increment of a Wiener process. Fig. 4 shows a CO2 price trajectory in a ‘‘carbon penalty” scenario, in which the emission of CO2 will be penalized starting from year 4, which would correspond to the post-Kyoto period. 4. Technology and data 4.1. Technology description Three types of technologies are examined in this paper: a conventional pulverized coal power plant, an IGCC plant and a wind farm of comparable size.7 The first type, the conventional coal power plant, is one of most widely implemented technologies in China’s power sector. The second one, IGCC, represents more advanced 6 GBM process is applicable to situations where the price trends follow the exponential curve. In fact, many of the market prices don’t meet this requirement, for example, energy prices in long run [3]; however, in a 30 years time scale, it is reasonable to assume a GBM process for the prices according to historical data [3]. Since the history of carbon trading market is rather short, there is no reliable historical series yet from which to extract that info empirically, thus in most researches carbon prices are assumed to follow GBM processes, for example [18], for the simplicity of the model, in this research, we make the same assumption. 7 CCS investment is not relevant for wind power. The profits for wind technology are nevertheless computed, so as to have a benchmark to compare the coal-fired power plants to. This enables us to form an idea about the profitability of renewables compared to traditional fossil-fuel-fired technologies.
and more efficient power generation technologies, which have not been installed in China yet.8 Because of China’s large wind energy potential and favorable policy environment, China’s wind power has experienced rapid growth in recent years, and it is recognized as a very promising renewable technology for the future. With respect to CO2 mitigation, these three types of technologies can be viewed as three different approaches to reduce carbon emissions, which are carbon capture, energy efficiency improvement, and renewable energy. There are three main technologies currently proposed for CO2 capture. In post-combustion capture, most of the CO2 from the combustion products are removed before vented to the atmosphere. The most commercially advanced methods use wet scrubbing with aqueous amine solution [19]. Pre-combustion capture involves removal of CO2 prior to combustion, to produce hydrogen-rich fuel gas which could be used in many applications such as IGCC. The separation process typically uses a physical solvent such as methanol or glycol. Because CO2 is present in much higher concentrations in syngas than in post-combustion flue gas, CO2 capture should be less expensive for pre-combustion capture than for post-combustion capture. Oxygen-combustion uses nearly pure oxygen instead of air, resulting in a flue gas that is mainly CO2 and H2O and thus easily to be separated. Normally, a range of other options for capturing and separating CO2 including, for example, ionic liquid and membrane processes offer the potential for a stepwise reduction in the cost and energy needed for CO2 capture. For the conventional coal power plant, we assume that the CCS module can be added to the existing plant. IGCC is more efficient than the conventional plant and can be retrofitted with CCS at a relatively lower cost due to its higher concentration of CO2 in its flue gas. The third way of CO2 mitigation, renewable energy is represented by wind power. Details of each technology are described below.
4.1.1. Conventional pulverized coal power plant There are various kinds of power generation technologies among pulverized coal (PC) plants. Currently in China, the power sector strategically chooses ultra-supercritical (USC) PC and supercritical (SC) PC plants for new capacity additions coupled with pollution control technologies, and CFB (supercritical circulating 8 However, several IGCC demonstration projects are under construction or are at the stage of planning.
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Table 1 Power plant data for PC, IGCC and wind.
a
Parameters
PC
PC + CCS
IGCC
IGCC + CCS
Wind
Installed capacity (MW) Electricity output (MWh) Capital cost (yuan) O&M costa (yuan/year) Electricity cost (yuan/MWh) CO2 emissions (ton/year) Coal consumption (ton)
650 3.6 106 2.6 109 2.1 108 276 28,80,000 12,24,000
800 3.6 106 5.2 109 4.1 108 433 459,200 14,96,000
685 3.6 106 5.14 109 2.82 108 360 25,20,000 1188000
725 3.6 106 7.45 109 4.1 108 479 410,400 1254000
1500 3.6 106 1.1 1010 4.5 108 650 0 0
In O&M cost here, fuel cost is separated for PC and IGCC.
fluidized bed, which is another advanced power generation technology) as supplement. According to the national industry policy, 600 MW and larger-capacity units are required for thermal power generation in the coming years [20]. And a developing trend of clean and high-efficiency power generation technologies is inevitable. Therefore, we choose supercritical PC as the representative technology for the conventional coal power plant. When an existing PC plant is retrofitted with CO2 capture, the major new technological units that get added to the original system are: (1) the absorption process: the flue gas exiting the flue gas desulfurization (FGD) system is introduced into an inhibited chemical or physical absorber–stripper system, and (2) the CO2 compression process: to facilitate the transport of CO2 captured from flue gas, gaseous CO2 needs to be compressed by a CO2 compression unit, which will result in extra electric power consumption and represent another parasitic load. Adding of these devices will result in a reduction of net electric efficiency. 4.1.2. IGCC IGCC is an advanced power generation system that combines coal gasification with a highly efficient cycle. It is composed of two main parts, one of which is called coal gasification. The other part is a combined cycle of gas–steam to generate electricity. Generally speaking, compared to conventional coal-fired power plants, IGCC is not only more energy-efficient, but also environmentally sounder. Pollutants like SO2, NOx and particulate matter emissions from an IGCC system are relatively minor [20]. Due to the high concentration of CO2 in the flue gas, which could be captured using the pre-combustion method, it will also be less costly to retrofit the IGCC plant with CCS. According to [21], the capital expense and operational expense of an IGCC unit are about 80–90% of those of pulverized coal combustion power plants.
and operational costs but performs better in energy and environmental terms. Wind power, representing renewable energy in this study, has the highest capital costs, but near-zero fossil fuel consumption and CO2 emissions.
4.2. Data and assumptions We collect the data required for the model and for unavailable data; some assumptions are made on the basis of data reported by related literature [20–24]. The parameters are shown in Table 1, where the electricity output for each type of power plant is normalized. Table 1 shows that wind power is most expensive in terms of investment and operational costs, while IGCC ranks second, and PC is the cheapest alternative. It is also shown that to produce the same amount of electricity, the required capacity for the power plant with CCS is larger than for those without CCS, indicating that installing and running the CCS module will cause extra energy consumption or partial losses of the electricity produced. Comparing the additional capital and O&M costs for retrofitting of the PC plant to those of the IGCC plant, we can see that the costs for the latter one are less. The reason for this is mainly because it is easier to retrofit an IGCC plant with CCS than a PC plant, as explained earlier in this section. In addition, the coal price is assumed to be constant, since we do not want to confuse fuel price effects with the impact of policy uncertainty.10
5. Scenario analysis
4.1.3. Wind power A significant advantage of wind power is its zero fuel cost. In fact, almost no fossil fuel is needed in the whole generation process (except during the construction phase of the actual wind mill). This means that the electricity produced by the wind farm hardly causes greenhouse gases or other pollutants. On the other hand, it has shortcomings as well. For example, the strength of the wind is not constant and it varies from zero to storm force. This means that wind turbines do not produce the same amount of electricity all the time. There will be times when they produce no electricity at all.9 To summarize, all of the three technologies have their advantages and disadvantages. PC has low capital and operational expenses, but the energy- and environment-related costs are comparatively at low grade. In addition, the cost of retrofitting with CCS is higher than that of IGCC, which has higher capital
Two scenarios of different types of climate policy and one scenario focusing on technological improvement are developed to investigate the energy investor’s behavior under different circumstances. There are numerous studies and proposals for the design of post-2012 climate regime [25,26]. These involve suggestions to improve financial channels or to enhance market mechanisms to facilitate developing countries’ participation in global GHG abatement actions. Starting from this, we build one scenario with a carbon credit market and one scenario involving a carbon penalty. In the first scenario, a carbon credit market is designed and this represents a relatively loose policy constraint, under which China’s energy companies have an option to participate in carbon credit trading on a voluntary basis. This is similar to the current CDM framework. The second scenario, on the contrary, reflects a stringent policy, where energy companies are obligated to reduce the CO2 emissions, which need to be covered by purchasing CO2 allowances in market. Considering technological progress, the third scenario emphasizes the influence of decreasing carbon capture cost on companies’ investment decision.
9 However, we are not considering differences between base and peak load technologies in this model and want to emphasize again that this is a technology representative for renewable energy in general.
10 In addition, from a more technical perspective, the amount of different stochastic prices for each technology in a real options framework using our methodology, which can be included, is limited by the degree of computational complexity this creates.
W. Zhou et al. / Applied Energy 87 (2010) 2392–2400 Table 2 Reference settings for carbon credit market scenario. Variable
Expression
P ct
P ct
P et
Value of parameters
= 56 (t 6 3) dPc = lPc dt + rPc dz (t > 3)
l = 0.02, r = 0.02, with a starting price
P et = P e0 exp(let)
le = 0.02
P c4 = 200
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The value of being able to install CCS indicated by CCS option value rises for the PC plant, as the volatility of the carbon credit price increases. In addition, the comparison of IGCC and PC in this regard shows that the IGCC plant will benefit more from retrofitting because both capital and O&M costs are much lower for CCS module installed on IGCC. So the expected profit of IGCC with CCS is closer to that of PC with CCS compared to the case without CCS. This indicates vast potential for IGCC development in a CO2 reduction scenario.
5.1. Carbon credit market We assume that a new market for trading carbon credits will be created upon expiration of the current CDM policy. This is assumed to occur within 4 years from now. The market might emerge from a policy framework, which is similar with CDM, but will include trade among enterprises within China. Thus, a specific energy company will benefit from selling carbon credits obtained by implementing low-carbon technologies. CCS is also assumed to be incorporated into the market.11 The only stochastic variable in this scenario, the CO2 credit price, reflects fluctuations in the credit market. The year before the policy is enacted, the CO2 credit price is assumed to be constant at 56 yuan/ton CO2. This is the standard price in the current CDM market. Afterwards, the price jumps to 200 yuan/ton CO2 and follows a GBM process subsequently as shown in Table 2. The results for the reference case for the PC plant include the optimal timing of investment in CCS and the corresponding profit distributions as presented in Fig. 5. CCS option values are calculated, to reflect the value gained by having the possibility to install CCS in the face of policy uncertainty. Similarly, Blyth et al. [18] show that policy uncertainty creates a risk premium by increasing the payoffs required from the project in order to justify proceeding with the project immediately rather than waiting. The results show that the optimal timing to add the CCS module centers in the first 2 years after the introduction of the policy. The average profit is 10.8 billion yuan. The CCS module thus gets installed almost immediately, since the CO2 credit price is very high. We therefore also examine the decisions given a lower starting price, implying that a future CDM market will not be so attractive to investors. In another experiment, a larger volatility is considered as well. Table 3 shows the results for the three plant types. The most important findings can be summarized as follows: Wind power, favored by the high CO2 credit price and zero fuel cost, is the most profitable technology for power generation among the three technologies, even though its capital cost is highest at present. IGCC is the least profitable type of technology because of its high capital and O&M cost, and PC lies in the middle of the two. If the carbon credit price is too low, there is no incentive for companies to install CCS modules. As shown in the results, both the PC and the IGCC plants will not be retrofitted with CCS in the scenario with low prices. For wind power, the expected profit will be substantially decreased, but it still ranks highest among the three, although the gap shrinks substantially. Higher risk in the market has significant impacts on investment behavior. As the volatility increases, the investment will be postponed, which is the typical options effect described by Dixit and Pindyck [3] and verified in applications to energy investments under price uncertainty by e.g. Fleten et al. [27].
11
CCS is not included in CDM projects currently, although the issue is still under discussion. In this article, we assume that credits from carbon capture can be traded in the market.
5.2. Carbon penalty The carbon penalty scenario implies a much more stringent policy. It is similar to the European Union Emission Trading Scheme (EU-ETS), in which a CO2 emission cap is set and then the corresponding amount of permits to be issued is determined on the basis of the cap. Companies that need to increase their emissions beyond their allowance must buy credits from those who pollute less, so that the total emissions add up to the cap. We conducted several experiments: (1) the policy is enacted in year 4 versus the case where it starts only in year 10; and (2) we increased the volatility of the GBM to reflect larger fluctuations within the market (or externally inflicted by adjustments of policy through the government). The results for the PC plant are shown in Table 4. The major findings are: As the year of starting the policy is delayed, the investment into CCS will also occur later. The expected profit is obviously considerably higher, since no CO2 payments are made before the policy is enacted. In addition, shifting the policy start to a later date slightly slows down the adoption time: in the second scenario the CCS module is adopted 2.3 years after the policy enactment on average, but in the previous scenario the average adoption time was 1.5 years after the introduction of the carbon price. The reason for this effect is that the remaining planning horizon is much shorter when the carbon price is introduced later, so the trigger price has to be higher than in the case where the policy is introduced more in the beginning. Similar to the carbon credit market scenario, increasing volatility of the carbon penalty price (when the starting time of the policy is in year 4) will result in a postponement of CCS installation by 2 years. Not surprisingly, compared to the carbon credit market scenario, expected profits in the carbon penalty scenario are substantially lower, implying that the payment for CO2 reduction will become a heavy burden on energy companies. However, those who tend to invest earlier into carbon reduction technologies have the potential to make higher profits in a favorable policy environment such as CDM.12 5.3. Technological improvement The impact from technological change on investment behavior was determined by testing for different cost-decreasing rates. The main findings from the results presented in Table 5 are: An increase in the rate of technical change leads to a rise in expected profits and earlier investment into CCS for both of IGCC and PC plants. These results illustrate that technological 12 Note that such analysis is beyond the scope of this paper, since real options modeling rests on the assumption of a risk-neutral decision-maker, so early up-front investments in order to hedge against an unfavorable future or to take advantage of favorable circumstances can never be an outcome of this model.
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Profit distribution
Timing of CCS Investment 1500 6000 1000 4000 500
2000
0
0
10
20
0 0.9
30
1
1.1
1.2
year
1.3 10
x 10
Electricity price
CO2 credit price 0 -100
Yuan/tCO2
Yuan/MWh
600 500 400
-200 -300 -400
300
0
10
20
0
30
10
year
20
30
year
Fig. 5. Profit and timing of CCS investment in PC case.
Table 3 Numeric results for three types of power plants in different carbon credit market conditions.
a
Lower carbon credit price (starting price = 56, r = 0.02)
Reference (starting price = 200, r = 0.02)
Higher uncertain carbon credit market (starting price = 200, r = 0.05)
PC Optimal timing to invest in CCS Expected profit (unit: Yuan) CCS option valuea
No 1 1010 0
Year 5.5 1.08 1010 7.9 108
Year 7.6 1.09 1010 8.5 108
IGCC Optimal timing to invest in CCS Expected profit (unit: Yuan) CCS option value
No 6.7 109 0
Year 4 9.2 109 2.5 109
Year 4 9.2 109 2.5 109
Wind Expected profit (unit: Yuan)
1.3 1010
1.9 1010
1.9 1010
CCS option value denotes that compared to the strategy that CCS module would be never installed, the extra profit made by optimized real option strategy.
Table 4 Optimal timing of CCS investment and expected profit with different penalty conditions for PC case.
PC Optimal timing to invest in CCS Expected profit (unit: Yuan)
Later policy start (starting year = 10, r = 0.02)
Reference (starting year = 4, r = 0.02)
Higher uncertain penalty (starting year = 4, r = 0.05)
Year 12.3 4.2 109
Year 5.5 1.35 109
Year 7.6 1.42 109
improvement can provide a significant incentive for CCS adoption. The IGCC plant will benefit more than the PC plant from technical change. The CCS module is mostly added in the very beginning of the new policy regime.
6. Policy implication and concluding remarks Even though it is one of the major, potential CO2 mitigation measures, CCS needs to consume extra energy to reduce carbon
emissions, which makes it less cost-effective than renewable energy alternatives and energy efficiency approaches. As a result, much debate has arisen on whether CCS should be further developed considering its high cost and energy inefficiency, which is especially important for developing countries like China. Water consumption in carbon capture operations may become another concern considering water shortage being a common problem in China. On the other hand, China still lacks integrated technologies for transportation, injection, monitoring and risk control, as well as robust knowledge about storage potentials. Large-scale leakage
W. Zhou et al. / Applied Energy 87 (2010) 2392–2400 Table 5 Results of different technology change rates scenarios for PC and IGCC. Technical change (OMCt = OMC0 eat, a = 0.01)
No technical change (a = 0)
PC Optimal timing for CCS Expected profit (Yuan) CCS option value
Year 4 1.4 109 8.4 108
Year 5.5 1.35 109 7.9 108
IGCC Optimal timing for CCS Expected profit (Yuan) CCS option value
Year 4 1.0 109 2.54 109
Year 4 8.9 108 2.49 109
Technology change rate
and geological disaster are also of concern in relation to CCS deployment in China, according to a survey [28]. These issues might become main barriers and hamper the large-scale deployment of CCS in China. Currently, most focus is put on energy efficiency improvements in China’s climate policy. In the national 11th five-year plan (from 2005 to 2010), energy-saving and pollutant-reducing actions are promoted and their implementation becomes a criterion for the assessment of government officials’ performance. The energy conservation law has also taken effect since August 2008. These policies give an incentive to enterprises to reduce energy consumption and thereby reduce CO2 emissions as well. However, these policies are not directly targeted at CO2 emissions and energy companies are not under any obligation to conduct such actions. Most of the projects aiming at CO2 mitigation in China are under the umbrella of CDM. The CDM projects in China involve renewable energy such as wind power, energy efficiency improvements of e.g. power plants. The trading price is actually very low with about 50– 70 yuan per ton [29]. Whether developing countries with large emissions like China will be faced with commitments to CO2 reduction similar to Annex I countries some time in the future is still not clear given the current situation of international climate negotiation. The objective of this study has been to analyze investment strategies for CCS in China’s energy sector in an uncertain environment. To carry out the corresponding experiments, three kinds of technologies – a conventional pulverized coal power plant, an IGCC plant and a wind farm – have been selected as the representatives of the three approaches for CO2 mitigation (i.e. energy efficiency improvement, renewable energy, and carbon capture and storage) in China. Of these technologies, CCS modules can be added to the former two. To incorporate the uncertain factors into the decision process, a real options model has been developed, and scenarios to mimic various possible policy outcomes and rates of technical change have been analyzed and discussed. The key findings from the model and scenario analysis can be summarized as follows: (1) Flexibility in CCS investment decision making has an economic value, and it increases with an increase in CO2 price uncertainty. Some studies, as for example [30], present similar results. Moreover, this research illustrates that higher uncertainty with respect to the carbon market will increase the CCS option value, i.e., the economic value of the possibility to retrofit existing plants with CCS. (2) The responding time of companies to install CCS to climate policy is shorter when the policy is implemented in the beginning of the planning period. The result proves a conclusion from [18] by using a different approach. In particular, [18] (Energy Policy 2007, vol. 35, p. 5772) state that ‘‘the closer in time a company is to a change in policy, the greater the policy risk will be, and the greater the impact on investment decisions”. The real reason is that if the policy is
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enacted later, then the remaining planning horizon is shorter and then a higher price is needed to trigger the CCS investment. (3) The profitability of renewable energy will increase significantly in the long term. In a world more constrained by climate policy, the zero-CO2-emission advantage of renewable energy will improve the economics compared to fossil-fuelconsuming power plants. Technologies with zero fuel cost (e.g. wind, solar or hydro power) benefit even more because of zero fuel cost.13 For IGCC, the results show that its profit is not so competitive, except under more stringent climate policies. However, other advantages of IGCC, such as lower emission levels of other pollutants, have not been considered in this analysis. Therefore, a broader framework needs to be developed to assess this technology more comprehensively. (4) For China’s power industry, the expected profits of IGCC plants and conventional coal-fired power plants will not be very different when considering technical change. CCS should then be integrated in newly-built and more efficient power plants such as IGCC, which is due to their lower capture cost. Therefore, the three approaches to reduce carbon emissions in the electricity sector (CCS, energy efficiency and renewable energy) should always be considered in combination with respect to the overall strategy for CO2 mitigation. Furthermore, given the rather low cost of IGCC in Western countries, technology transfer could enable China’s power industry to implement CCS at a larger scale and more cost-effectively under a more stringent climate policy regime.
Acknowledgements The first author is grateful for the support from National Natural Science Foundation of China (NFSC) to participate in IIASA’s Young Scientist Summer Program (YSSP) when part of the work was conducted. Financial supports from NFSC (No. 20876087), China’s National Hi-Tech R&D Program (No. 2008AA062301) and Ph.D. Programs Foundation of Ministry of Education of China (No. 20080030049) are also gratefully acknowledged. At IIASA, the research leading to these results has received funding from the European Community’s Seventh Framework Programme (FP7) under grant agreement No. 212535, Climate Change – Terrestrial Adaptation and Mitigation in Europe (CC-TAME), www.cctame.eu. References [1] International Energy Agency. CO2 emissions from fuel combustion, 2009 ed., 2009. [2] China National Bureau of Statistics, China National Energy Administration. China energy statistics yearbook 2008. China Statistics Press; 2008. [3] Dixit A, Pindyck R. Investment under uncertainty. Princeton: Princeton University Press; 1994. [4] Fuss S, Szolgayová J, Obersteiner M, et al. Investment under market and climate policy uncertainty. Appl Energy 2008;85:708–21. [5] Laurikka H. Option value of gasification technology within an emissions trading scheme. Energy Policy 2006;18:3916–28. [6] Yang M, Blyth W, Bradley R, et al. Evaluating the power investment options with uncertainty in climate policy. Energy Econ 2008;30(4):1933–50. [7] Kumbarog˘lu G, Madlener R, Demirel M. A real options evaluation model for the diffusion prospects of new renewable power generation technologies. Energy Econ 2008;30:1882–908. [8] Fuss S, Szolgayová J. Fuel price and technological uncertainty in a real options model for electricity planning. Appl Energy; 2009. doi:10.1016/ j.apenergy.2009.05.020. [9] Intergovernmental panel on climate change. Special report on carbon dioxide capture and storage; 2005. [10] Copeland T, Antikarov R. Real options: a practitioners’ guide. Thomson; 2003. 13 Note that this is different for biofuels, since the fuel in this case does not come at zero cost.
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[11] Lam PL. Pricing of electricity in China. Energy 2004;29(2):287–300. [12] China Electric Power Publishing Committee. China electric power yearbook. Beijing: China Electric Power Publishing; 1999. [13] Riahi K, Rubin ES, Taylor MR, et al. Technological learning for carbon capture and sequestration technologies. Energy Econ 2004;26:539–64. [14] Rubin ES, Taylor MR, Yeh S, et al. Learning curves for environmental technology and their importance for climate policy analysis. Energy 2004;29(9–10):1551–9. [15] McDonald A, Schrattenholzer L. Learning rates for energy technologies. Energy Policy 2001;29:255–61. [16] Daskalakis J, Psychoyios D, Markellos N. Modeling CO2 emission allowance prices and derivatives: evidence from the European Trading Scheme. J Bank Finance 2009;33(7):1230–41. [17] Abadie ML, Chamorro MJ. European CO2 prices and carbon capture investments. Energy Econ 2008;30(6):2992–3015. [18] Blyth W, Bradley R, Bunn D, et al. Investment risks under uncertain climate change policy. Energy Policy 2007;35:5766–73. [19] Gibbins J, Chalmers H. Carbon capture and storage. Energy Policy 2008;36:4317–22. [20] Zhao LF, Xiao YH, Gallagher KS, et al. Technical, environmental, and economic assessment of deploying advanced coal power technologies in the Chinese context. Energy Policy 2008;36:2709–18. [21] Rubin ES, Chen C, Rao A. Cost and performance of fossil fuel power plants with CO2 capture and storage. Energy Policy 2007;35:4444–54.
[22] Huanga Y, Rezvania S, McIlveen D, Minchenerb A et al. Techno-economic study of CO2 capture and storage in coal fired oxygen fed entrained flow IGCC power plants. Fuel Process Technol; 2008. [23] Riahi K, Rubin E, Schrattenholzer L. Prospects for carbon capture and sequestration technologies assuming their technological learning. Energy 2004;29:1218–309. [24] REN21. Renewables 2007 global status report. Paris: REN21 Secretariat and Washington (DC): World watch Institute; 2008. [25] Aldy JE, Stavins RN. Designing the post-Kyoto climate regime: lessons from the harvard project on international climate agreements. Cambridge (Mass): Report for Harvard Project on International Climate Agreements, Belfer Center for Science and International Affairs, Harvard Kennedy School, November 24, 2008. [26] Michaelowa A, Tangen K, Hasselknippe H. Issues and options for the post-2012 climate architecture – an overview. Int Environ Agreements 2005;5:5–24. [27] Fleten SE, Maribu K, Wangensteen I. Optimal investment strategies in decentralized renewable power generation under uncertainty. Energy 2007;32:803–15. [28] Reiner D, Liang X. Opportunities and hurdles in applying CCS technologies in China – with a focus on industrial stakeholders. Energy Procedia 2009;1:4827–34. [29] Wang C, Fu P, Chen JN. Contribution of clean development mechanism to the mitigation of greenhouse gas emissions. J Tsinghua Univ 2008;48(3):357–61. [30] Sekar RC. Carbon dioxide capture from coal-fired power plants: a real options analysis. MSc Thesis, MIT; 2005.
Contents lists available at ScienceDirect
Applied Energy journal homepage: www.elsevier.com/locate/apenergy
Uncertainty modeling of CCS investment strategy in China’s power sector Wenji Zhou a, Bing Zhu a,b,*, Sabine Fuss b, Jana Szolgayová b,c, Michael Obersteiner b, Weiyang Fei a a
Department of Chemical Engineering, Tsinghua University, Beijing 100084, PR China International Institute for Systems Analysis (IIASA), Schlossplatz 1, A-2361 Laxenburg, Austria c Department of Applied Mathematics and Statistics, Faculty of Mathematics, Physics and Informatics, Comenius University, Bratislava, Slovakia b
a r t i c l e
i n f o
Article history: Received 29 September 2009 Received in revised form 22 January 2010 Accepted 22 January 2010 Available online 13 February 2010 Keywords: Carbon capture and storage Uncertainty Real options Energy investment Chinese climate policy
a b s t r a c t The increasing pressure resulting from the need for CO2 mitigation is in conflict with the predominance of coal in China’s energy structure. A possible solution to this tension between climate change and fossil fuel consumption fact could be the introduction of the carbon capture and storage (CCS) technology. However, high cost and other problems give rise to great uncertainty in R&D and popularization of carbon capture technology. This paper presents a real options model incorporating policy uncertainty described by carbon price scenarios (including stochasticity), allowing for possible technological change. This model is further used to determine the best strategy for investing in CCS technology in an uncertain environment in China and the effect of climate policy on the decision-making process of investment into carbon-saving technologies. Ó 2010 Elsevier Ltd. All rights reserved.
1. Introduction According to the report from the International Energy Agency (IEA) [1], China ranks the first place in terms of CO2 emissions by fuel combustion among countries in the world. In recent years, due to the rapid growth of high energy-consumption sectors such as power, steel, cement and chemical industries, China’s CO2 emissions have risen dramatically. A key characteristic of China’s energy structure is that coal dominates in energy use, accounting for about 70% in total energy consumption [2], while oil and natural gas constitute a relatively small part only.1 This situation is expected to last for several decades in the future, mainly due to China’s considerable coal reserves. This fact together with the increasingly perceivable need to decrease the global CO2 emissions imposes great pressure on policy makers and therefore poses huge challenges to China both with regard to energy security and climate change. Carbon capture and storage (CCS) is considered as an important approach to control CO2 emissions caused by fossil fuel consumption. The power industry is one of the main CO2 emissions sectors, in particularly in China. More than 80% of electricity is generated by coal combustion [2], with relatively low-efficiency coal-fired
* Corresponding author. Address: Department of Chemical Engineering, Tsinghua University, Beijing 100084, PR China. Tel./fax: +86 10 62782520. E-mail address: [email protected] (B. Zhu). 1 According to 2007 data, the shares of coal, petroleum and natural gas are 73%, 21%, and 3%, respectively. With respect to renewable energy, hydro power is most developed, accounting for 3%. Nuclear and wind power are under fast development, but still constitute very small shares. 0306-2619/$ - see front matter Ó 2010 Elsevier Ltd. All rights reserved. doi:10.1016/j.apenergy.2010.01.013
technology. This indicates that to reduce CO2 emissions in China, the power industry needs to be considered as one of the first sources. Installing CCS devices in power plants is an attractive option, as it enables further use of abundant coal resources, while at the same time cutting emissions from that combustion by a considerable amount. However, the high cost of CCS and the uncertainty associated with its technological development are obstacles to a fast diffusion of this technology, particularly in developing countries like China. The Chinese government has been strengthening related R&D efforts and some demonstration projects are under construction.2 From the energy companies’ pointof-view, CCS installation is still associated with high cost and could only be considered if it was profitable for the whole value chain. This, however, remains highly uncertain due to the lack of knowledge about the direction of future climate policy. China’s power industry is now confronted by big challenges from increasingly strict environmental policies aiming at the reduction of pollutants. With respect to environmental regulation, the requirement of environmental protection has become much
2 CO2 capture technology in China has been applied in some industrial sectors such as ammonia, hydrogen and petroleum for several decades. However, R&D of largescale capture in the power sector just started a few years ago. In 2005, CCS technology was listed in the National Outlines for Medium and Long-term Planning for Scientific and Technological Development (2006–2020), which significantly spurred relevant research. In July 2008, the first demonstration carbon capture project in China has been completed in the Beijing Thermal Power Plant owned by Huaneng Group, with a capacity of 3000 tons of CO2 captured per year. In the meantime, some other demonstration projects with larger capture capacity are also at different stages of completion.
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stricter than before. For example, power plants are compelled to install desulfurization devices by regulation issued in recent years. However, there is currently not such a stringent requirement for China’s power industry to reduce greenhouse gas emissions. China is included in the clean development mechanism (CDM),3 however, and some energy enterprises have benefited from CDM projects. It is still hard to predict whether and when the situation will change and what kinds of policy instruments will be adopted in the post-Kyoto period. Moreover, in a cap-and-trade market, the CO2 price will fluctuate. In the face of such an uncertain environment, strategic decisions on investment into CO2 mitigation technology cannot be based on traditional discounted cash flow (DCF) analysis because companies possibly choose to delay the decision rather than immediately make a now-or-never decision by using DCF, as the investment is irreversible, involves high sunk costs and its payoff is uncertain. In order to capture these specific characteristics of the investment decision, real options analysis (ROA) has therefore been chosen for the analysis [3]. Generally, ROA is designed to take three important factors into account: the irreversibility of the investment, the uncertainty surrounding the future cash flows from the investment (here through volatile CO2 price processes), and the opportunity of timing the investment flexibly (here through adding the CCS module earlier or later in the planning period) [4]. Investment strategy in the power sector has been analyzed by using real options methods before, but only a few of them focus on climate change policies, and it is hard to find any discussion on this issue for developing countries like China. Laurikka [5] presents a simulation model implementing ROA to assess the impact of emissions trading scheme on integrated coal gasification combined cycle (IGCC). The study simulates three types of stochastic variables: the price of electricity, the price of fuel and the price of emission allowances. Yang et al. [6] value real options using a dynamic programming approach for technology investment choices under uncertain climate policy. Three cases for gas, coal and nuclear power investments are considered. Energy prices and the CO2 price are set to change randomly; for the latter one, a price jump was incorporated to represent policy-related shocks. All these studies are based on a specific emissions trading system, which cannot be applied directly to the case of China. Furthermore, the absence of technological improvement in those studies implies neglecting one of the most important drivers for low-carbon technology adoption. Kumbarog˘lu et al. [7] integrate technological learning curves into a real options framework to appraise renewable energy technologies. Fuss and Szolgayová [8] use a real options model with stochastic technical change and stochastic fossil fuel prices, to investigate their impact on replacement investment decisions in the electricity sector. Unlike the studies above, this paper attempts to model and analyze climate policy uncertainty in China’s energy industry under consideration of technological change and establish a real options model to obtain CCS investment strategies from the point-of-view of a typical energy enterprise, hence providing policy implications for CO2 mitigation in the coming post-Kyoto period. The paper consists of six sections. Following this introduction, Section 2 presents the methodological framework of the real options model used in the study. In Section 3, the factors of policy uncertainty and technological progress are analyzed. Section 4 describes the features of three representative types of technologies considered. These are a pulverized coal power plant, a wind farm and an IGCC plant. The
3 The clean development mechanism is an arrangement under the Kyoto protocol allowing industrialized countries with a greenhouse gas reduction commitment (Annex 1 countries) to invest in projects that reduce emissions in developing countries as an alternative to more expensive emission reduction in their own countries.
data used and the assumptions are explained in detail. The scenarios generated by varying underlying model parameters are analyzed in Section 5. Finally, the policy implications of the results from this real options model and its features as a policy analysis tool are presented in the conclusion. 2. Model description A typical CCS system consists of three parts: capture, transport and storage. The capture part contains chemical devices such as absorber and desorber, accounting for 70–80% of the total cost [9]. These devices can be in-built, when a new modern plant is constructed, or they can be added to an existing plant by retrofitting it at higher cost. In the model we consider an investor maximizing the sum of his expected discounted profits over the planning period, who faces uncertain climate policy. The real options model determines the optimal timing of investing into a CCS module given that a coal plant already exists. Several possible types of coal plants (and corresponding CCS systems) are analyzed separately. It also derives the corresponding profit distribution resulting from optimal investment. Several scenarios for CO2 price development (involving both deterministic and stochastic processes) were implemented to reflect different possible policy outcomes and examine their impact on CO2 mitigation technology investment behavior. In Fig. 1, we show an overview of price assumptions used in the model. Only the CO2 price is assumed to be uncertain, all other prices are modeled as deterministic. The motivation for an uncertain fluctuating carbon price CO2 price can be seen for example in a policy resulting in CO2 credits or allowances being traded amongst firms. We consider the planning horizon equal to the lifetime of the power plant, i.e. 30 years and that the decisions can be done on a yearly basis. The investor faces an optimization problem of timing the decision to invest into the CCS module so that the sum of discounted expected future profits is maximized. Let xt denote the state that the system is currently in year t, i.e. it tells whether the basic plant, the CCS module or both have been built and whether the CCS module is currently running, let at be the action (i.e. the control) which the decision-maker chooses to undertake in year t. Possible actions are to either build the CCS module (which is feasible only in case it has not been built yet) or do nothing. xt+1 depends only on the action at and xt, with at as an element from the set of feasible actions. The yearly profit p for a given state x and actions a can be expressed as:
pðx; a; Pc Þ ¼ qe ðaÞPe qc ðaÞPc qf ðaÞPf OMCðaÞ cðaÞ; c
ð1Þ c
where P represents the price of CO2 (or CO2 credit when P is negative), according to which energy companies could either be penalized by paying for CO2 emissions required by a stringent policy, or benefit from selling CO2 credits in a CDM-type market. The variable is created to describe a possible climate policy variation in the future. qe and qf refer to annual quantities of electricity output and fuel consumption, Pe and Pf represent prices of electricity and fuel respectively. OMC refers to operational and maintenance cost, and c denotes the cost associated with the undertaken action. In case the action is to build the CCS module, it is equal to the expenditure for retrofitting an existing plant with a CCS module. In case the action is to do nothing, this cost is equal to zero. Based on this profit function, the investor’s optimization problem can be formulated as follows:
max c
30 P
at ðxt ;P t Þ2Aðxt Þ t¼1
s:t:
ert E
pðxt ; at ðxt ; Pct Þ; Pct Þ
xtþ1 ¼ Fðxt ; at ðxt ; Pct ÞÞ
ð2Þ
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V 30 ðx; PÞ ¼ 0 V t ðx; Pct Þ ¼ max pðx; a; Pct Þ þ er E V tþ1 ðFðx; aÞ; P ctþ1 Þjx; Pct
ð3Þ
a2AðxÞ
where the optimum action for each state and price in each year is obtained as the argument that maximizes the term on the right side of the Bellman equation. The equation indicates that the value of the project in year t is composed of two parts: one is the immediate profit p(xt, at, P ct ), and the other is the expected and discounted value of next year, which is also called continuation value by Dixit and Pindyck [3]. To calculate E[Vt+1(F(x, a),Pctþ1 )|x, P ct )], we discretize the carbon prices and use the Monte Carlo simulations. The Monte Carlo simulations approach can be easily implemented and despite the general disadvantage that a large amount of simulations are required to obtain a sufficiently reliable results [10], it has proven to remain efficient in this framework for a rather high degree of complexity and delivers the same results as the partial differential equations approach [5]. The solution of the recursive optimization part is a multidimensional table containing the optimum action at for every t, x, and Pct . These optimum actions are further referred to as ‘‘strategies”. In order to analyze the final outcome, possible price paths are simulated and discretized subsequently (so a relatively fine price grid is needed in order to obtain precise results). The corresponding decisions are extracted from the output. In this way we can derive both the profit distribution and the frequency with which the CCS investment option is exercised. 3. Impact factors modeling 3.1. Deterministic variables Electricity price and technology cost are modeled as deterministic variables, from several reasons. The electricity price does not have an impact on the optimum decisions in the model, since the output of the power plant both with and without CCS is the same. Therefore, there would be no added value in a stochastic electricity price. On top of this, in China, the electricity pricing is regulated by the National Development and Reform Commission (NDRC), which makes it rather stable compared to price evolution in Western markets. To simulate the future trend, historical information of China’s on-grid price4 was collected from [11,12], as shown in the 4
On-grid price refers to the electricity price sales from individual power plants to the national power grid.
700
700
600
600
500
500
yuan/MWH
together with an assumption on the CO2 price. In the maximization problem, A is the set of feasible actions for a given state xt, r is discount rate, and E[] denotes the expected value. The investor’s problem is thus to determine the optimal investment strategy fat ðxt ; Pct ÞgTt¼1 . Since the model is formulated as a optimum control model with discrete time on a finite horizon, it can be solved by dynamic programming. The value function can be calculated recursively by the Bellman equation:
yuan/MWH
Fig. 1. Framework of the real options model.
400 300
400 300
200
200
100
100
0 1978 1985
2008
0
5
10 15
year
20 25
30
year
Fig. 2. Trend of on-grid electricity price.
left panel of Fig. 2. Historical data showed that after the long period of central planning, the 1985 reform in China’s power industry resulted in a growing electricity price. Based on current policy condition in China’s power industry, we assume that in the coming decades the trend will continue, and follow an exponential process with parameters obtained from historical data fitting, as shown in the panel of Fig. 2. Although the real development of cost for immature but promising technologies like CCS is very volatile, in some cases even ‘‘negative learning” phenomena occur, in our model we consider the technology cost as exogenous and deterministic. According to several studies [13–15], the cost improves over time due to the reduction in input factor prices, financing cost or improvements in organizational efficiency. This effect is explained by the technological progress and learning-by-doing, where the concept of a learning curve is widely used, indicating that the development of marginal or average unit cost is a function of cumulative production or capacity. To introduce directly such a functional form into our profit equation is not feasible5, hence we need to formulate technical change as a function of time rather than cumulative installed capacity. Technology cost is assumed constant in the scenarios without technological improvement. For the scenario that takes technical change into account, only modern technologies such as IGCC and CCS are considered to experience decreasing costs. The downward trend is assumed to follow an exponential process as shown in Fig. 3.
5 Remember that this is a plant-level analysis and the investment option gets exercised only once, after which learning from installing more is not an option anymore.
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8
500
x 109
450
7
IGCC with CCS
-0.01*t
400
cost(t)=cost(0)*e
350 6 300 5
IGCC without CCS
250 200
4
150 3 100 2
50 0
1
-50 0
5
10
15
20
25
30
year
5
10
15
20
25
30
year Fig. 4. Simulation of future trend of CO2 price.
Fig. 3. Decreasing capital costs of IGCC with and without CCS module.
3.2. Stochastic variable To investigate how uncertainty in climate policy for the coming decades affects energy technology investment behavior, the CO2 price is chosen to be stochastic, which could mimic subsequent adaptation of policy by the government or fluctuations due to imperfections in carbon markets in case an emission trading scheme-type (ETS) system would be chosen as regulatory framework. As mentioned above, this variable denotes either the price for a CO2 allowance (or a CO2 tax) or the credit granted for saving CO2, which depends on the scenario. Since the CO2 price is believed to increase, once the government commits to a GHG target [16,17], a geometric brownian motion (GBM)6 is employed, which is described by the following equation: c
dP ¼ lP c dt þ rPc dz
ð4Þ
where the parameter l is the drift, representing the positive trend of the CO2 price, r represents the volatility and dz is the increment of a Wiener process. Fig. 4 shows a CO2 price trajectory in a ‘‘carbon penalty” scenario, in which the emission of CO2 will be penalized starting from year 4, which would correspond to the post-Kyoto period. 4. Technology and data 4.1. Technology description Three types of technologies are examined in this paper: a conventional pulverized coal power plant, an IGCC plant and a wind farm of comparable size.7 The first type, the conventional coal power plant, is one of most widely implemented technologies in China’s power sector. The second one, IGCC, represents more advanced 6 GBM process is applicable to situations where the price trends follow the exponential curve. In fact, many of the market prices don’t meet this requirement, for example, energy prices in long run [3]; however, in a 30 years time scale, it is reasonable to assume a GBM process for the prices according to historical data [3]. Since the history of carbon trading market is rather short, there is no reliable historical series yet from which to extract that info empirically, thus in most researches carbon prices are assumed to follow GBM processes, for example [18], for the simplicity of the model, in this research, we make the same assumption. 7 CCS investment is not relevant for wind power. The profits for wind technology are nevertheless computed, so as to have a benchmark to compare the coal-fired power plants to. This enables us to form an idea about the profitability of renewables compared to traditional fossil-fuel-fired technologies.
and more efficient power generation technologies, which have not been installed in China yet.8 Because of China’s large wind energy potential and favorable policy environment, China’s wind power has experienced rapid growth in recent years, and it is recognized as a very promising renewable technology for the future. With respect to CO2 mitigation, these three types of technologies can be viewed as three different approaches to reduce carbon emissions, which are carbon capture, energy efficiency improvement, and renewable energy. There are three main technologies currently proposed for CO2 capture. In post-combustion capture, most of the CO2 from the combustion products are removed before vented to the atmosphere. The most commercially advanced methods use wet scrubbing with aqueous amine solution [19]. Pre-combustion capture involves removal of CO2 prior to combustion, to produce hydrogen-rich fuel gas which could be used in many applications such as IGCC. The separation process typically uses a physical solvent such as methanol or glycol. Because CO2 is present in much higher concentrations in syngas than in post-combustion flue gas, CO2 capture should be less expensive for pre-combustion capture than for post-combustion capture. Oxygen-combustion uses nearly pure oxygen instead of air, resulting in a flue gas that is mainly CO2 and H2O and thus easily to be separated. Normally, a range of other options for capturing and separating CO2 including, for example, ionic liquid and membrane processes offer the potential for a stepwise reduction in the cost and energy needed for CO2 capture. For the conventional coal power plant, we assume that the CCS module can be added to the existing plant. IGCC is more efficient than the conventional plant and can be retrofitted with CCS at a relatively lower cost due to its higher concentration of CO2 in its flue gas. The third way of CO2 mitigation, renewable energy is represented by wind power. Details of each technology are described below.
4.1.1. Conventional pulverized coal power plant There are various kinds of power generation technologies among pulverized coal (PC) plants. Currently in China, the power sector strategically chooses ultra-supercritical (USC) PC and supercritical (SC) PC plants for new capacity additions coupled with pollution control technologies, and CFB (supercritical circulating 8 However, several IGCC demonstration projects are under construction or are at the stage of planning.
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Table 1 Power plant data for PC, IGCC and wind.
a
Parameters
PC
PC + CCS
IGCC
IGCC + CCS
Wind
Installed capacity (MW) Electricity output (MWh) Capital cost (yuan) O&M costa (yuan/year) Electricity cost (yuan/MWh) CO2 emissions (ton/year) Coal consumption (ton)
650 3.6 106 2.6 109 2.1 108 276 28,80,000 12,24,000
800 3.6 106 5.2 109 4.1 108 433 459,200 14,96,000
685 3.6 106 5.14 109 2.82 108 360 25,20,000 1188000
725 3.6 106 7.45 109 4.1 108 479 410,400 1254000
1500 3.6 106 1.1 1010 4.5 108 650 0 0
In O&M cost here, fuel cost is separated for PC and IGCC.
fluidized bed, which is another advanced power generation technology) as supplement. According to the national industry policy, 600 MW and larger-capacity units are required for thermal power generation in the coming years [20]. And a developing trend of clean and high-efficiency power generation technologies is inevitable. Therefore, we choose supercritical PC as the representative technology for the conventional coal power plant. When an existing PC plant is retrofitted with CO2 capture, the major new technological units that get added to the original system are: (1) the absorption process: the flue gas exiting the flue gas desulfurization (FGD) system is introduced into an inhibited chemical or physical absorber–stripper system, and (2) the CO2 compression process: to facilitate the transport of CO2 captured from flue gas, gaseous CO2 needs to be compressed by a CO2 compression unit, which will result in extra electric power consumption and represent another parasitic load. Adding of these devices will result in a reduction of net electric efficiency. 4.1.2. IGCC IGCC is an advanced power generation system that combines coal gasification with a highly efficient cycle. It is composed of two main parts, one of which is called coal gasification. The other part is a combined cycle of gas–steam to generate electricity. Generally speaking, compared to conventional coal-fired power plants, IGCC is not only more energy-efficient, but also environmentally sounder. Pollutants like SO2, NOx and particulate matter emissions from an IGCC system are relatively minor [20]. Due to the high concentration of CO2 in the flue gas, which could be captured using the pre-combustion method, it will also be less costly to retrofit the IGCC plant with CCS. According to [21], the capital expense and operational expense of an IGCC unit are about 80–90% of those of pulverized coal combustion power plants.
and operational costs but performs better in energy and environmental terms. Wind power, representing renewable energy in this study, has the highest capital costs, but near-zero fossil fuel consumption and CO2 emissions.
4.2. Data and assumptions We collect the data required for the model and for unavailable data; some assumptions are made on the basis of data reported by related literature [20–24]. The parameters are shown in Table 1, where the electricity output for each type of power plant is normalized. Table 1 shows that wind power is most expensive in terms of investment and operational costs, while IGCC ranks second, and PC is the cheapest alternative. It is also shown that to produce the same amount of electricity, the required capacity for the power plant with CCS is larger than for those without CCS, indicating that installing and running the CCS module will cause extra energy consumption or partial losses of the electricity produced. Comparing the additional capital and O&M costs for retrofitting of the PC plant to those of the IGCC plant, we can see that the costs for the latter one are less. The reason for this is mainly because it is easier to retrofit an IGCC plant with CCS than a PC plant, as explained earlier in this section. In addition, the coal price is assumed to be constant, since we do not want to confuse fuel price effects with the impact of policy uncertainty.10
5. Scenario analysis
4.1.3. Wind power A significant advantage of wind power is its zero fuel cost. In fact, almost no fossil fuel is needed in the whole generation process (except during the construction phase of the actual wind mill). This means that the electricity produced by the wind farm hardly causes greenhouse gases or other pollutants. On the other hand, it has shortcomings as well. For example, the strength of the wind is not constant and it varies from zero to storm force. This means that wind turbines do not produce the same amount of electricity all the time. There will be times when they produce no electricity at all.9 To summarize, all of the three technologies have their advantages and disadvantages. PC has low capital and operational expenses, but the energy- and environment-related costs are comparatively at low grade. In addition, the cost of retrofitting with CCS is higher than that of IGCC, which has higher capital
Two scenarios of different types of climate policy and one scenario focusing on technological improvement are developed to investigate the energy investor’s behavior under different circumstances. There are numerous studies and proposals for the design of post-2012 climate regime [25,26]. These involve suggestions to improve financial channels or to enhance market mechanisms to facilitate developing countries’ participation in global GHG abatement actions. Starting from this, we build one scenario with a carbon credit market and one scenario involving a carbon penalty. In the first scenario, a carbon credit market is designed and this represents a relatively loose policy constraint, under which China’s energy companies have an option to participate in carbon credit trading on a voluntary basis. This is similar to the current CDM framework. The second scenario, on the contrary, reflects a stringent policy, where energy companies are obligated to reduce the CO2 emissions, which need to be covered by purchasing CO2 allowances in market. Considering technological progress, the third scenario emphasizes the influence of decreasing carbon capture cost on companies’ investment decision.
9 However, we are not considering differences between base and peak load technologies in this model and want to emphasize again that this is a technology representative for renewable energy in general.
10 In addition, from a more technical perspective, the amount of different stochastic prices for each technology in a real options framework using our methodology, which can be included, is limited by the degree of computational complexity this creates.
W. Zhou et al. / Applied Energy 87 (2010) 2392–2400 Table 2 Reference settings for carbon credit market scenario. Variable
Expression
P ct
P ct
P et
Value of parameters
= 56 (t 6 3) dPc = lPc dt + rPc dz (t > 3)
l = 0.02, r = 0.02, with a starting price
P et = P e0 exp(let)
le = 0.02
P c4 = 200
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The value of being able to install CCS indicated by CCS option value rises for the PC plant, as the volatility of the carbon credit price increases. In addition, the comparison of IGCC and PC in this regard shows that the IGCC plant will benefit more from retrofitting because both capital and O&M costs are much lower for CCS module installed on IGCC. So the expected profit of IGCC with CCS is closer to that of PC with CCS compared to the case without CCS. This indicates vast potential for IGCC development in a CO2 reduction scenario.
5.1. Carbon credit market We assume that a new market for trading carbon credits will be created upon expiration of the current CDM policy. This is assumed to occur within 4 years from now. The market might emerge from a policy framework, which is similar with CDM, but will include trade among enterprises within China. Thus, a specific energy company will benefit from selling carbon credits obtained by implementing low-carbon technologies. CCS is also assumed to be incorporated into the market.11 The only stochastic variable in this scenario, the CO2 credit price, reflects fluctuations in the credit market. The year before the policy is enacted, the CO2 credit price is assumed to be constant at 56 yuan/ton CO2. This is the standard price in the current CDM market. Afterwards, the price jumps to 200 yuan/ton CO2 and follows a GBM process subsequently as shown in Table 2. The results for the reference case for the PC plant include the optimal timing of investment in CCS and the corresponding profit distributions as presented in Fig. 5. CCS option values are calculated, to reflect the value gained by having the possibility to install CCS in the face of policy uncertainty. Similarly, Blyth et al. [18] show that policy uncertainty creates a risk premium by increasing the payoffs required from the project in order to justify proceeding with the project immediately rather than waiting. The results show that the optimal timing to add the CCS module centers in the first 2 years after the introduction of the policy. The average profit is 10.8 billion yuan. The CCS module thus gets installed almost immediately, since the CO2 credit price is very high. We therefore also examine the decisions given a lower starting price, implying that a future CDM market will not be so attractive to investors. In another experiment, a larger volatility is considered as well. Table 3 shows the results for the three plant types. The most important findings can be summarized as follows: Wind power, favored by the high CO2 credit price and zero fuel cost, is the most profitable technology for power generation among the three technologies, even though its capital cost is highest at present. IGCC is the least profitable type of technology because of its high capital and O&M cost, and PC lies in the middle of the two. If the carbon credit price is too low, there is no incentive for companies to install CCS modules. As shown in the results, both the PC and the IGCC plants will not be retrofitted with CCS in the scenario with low prices. For wind power, the expected profit will be substantially decreased, but it still ranks highest among the three, although the gap shrinks substantially. Higher risk in the market has significant impacts on investment behavior. As the volatility increases, the investment will be postponed, which is the typical options effect described by Dixit and Pindyck [3] and verified in applications to energy investments under price uncertainty by e.g. Fleten et al. [27].
11
CCS is not included in CDM projects currently, although the issue is still under discussion. In this article, we assume that credits from carbon capture can be traded in the market.
5.2. Carbon penalty The carbon penalty scenario implies a much more stringent policy. It is similar to the European Union Emission Trading Scheme (EU-ETS), in which a CO2 emission cap is set and then the corresponding amount of permits to be issued is determined on the basis of the cap. Companies that need to increase their emissions beyond their allowance must buy credits from those who pollute less, so that the total emissions add up to the cap. We conducted several experiments: (1) the policy is enacted in year 4 versus the case where it starts only in year 10; and (2) we increased the volatility of the GBM to reflect larger fluctuations within the market (or externally inflicted by adjustments of policy through the government). The results for the PC plant are shown in Table 4. The major findings are: As the year of starting the policy is delayed, the investment into CCS will also occur later. The expected profit is obviously considerably higher, since no CO2 payments are made before the policy is enacted. In addition, shifting the policy start to a later date slightly slows down the adoption time: in the second scenario the CCS module is adopted 2.3 years after the policy enactment on average, but in the previous scenario the average adoption time was 1.5 years after the introduction of the carbon price. The reason for this effect is that the remaining planning horizon is much shorter when the carbon price is introduced later, so the trigger price has to be higher than in the case where the policy is introduced more in the beginning. Similar to the carbon credit market scenario, increasing volatility of the carbon penalty price (when the starting time of the policy is in year 4) will result in a postponement of CCS installation by 2 years. Not surprisingly, compared to the carbon credit market scenario, expected profits in the carbon penalty scenario are substantially lower, implying that the payment for CO2 reduction will become a heavy burden on energy companies. However, those who tend to invest earlier into carbon reduction technologies have the potential to make higher profits in a favorable policy environment such as CDM.12 5.3. Technological improvement The impact from technological change on investment behavior was determined by testing for different cost-decreasing rates. The main findings from the results presented in Table 5 are: An increase in the rate of technical change leads to a rise in expected profits and earlier investment into CCS for both of IGCC and PC plants. These results illustrate that technological 12 Note that such analysis is beyond the scope of this paper, since real options modeling rests on the assumption of a risk-neutral decision-maker, so early up-front investments in order to hedge against an unfavorable future or to take advantage of favorable circumstances can never be an outcome of this model.
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Profit distribution
Timing of CCS Investment 1500 6000 1000 4000 500
2000
0
0
10
20
0 0.9
30
1
1.1
1.2
year
1.3 10
x 10
Electricity price
CO2 credit price 0 -100
Yuan/tCO2
Yuan/MWh
600 500 400
-200 -300 -400
300
0
10
20
0
30
10
year
20
30
year
Fig. 5. Profit and timing of CCS investment in PC case.
Table 3 Numeric results for three types of power plants in different carbon credit market conditions.
a
Lower carbon credit price (starting price = 56, r = 0.02)
Reference (starting price = 200, r = 0.02)
Higher uncertain carbon credit market (starting price = 200, r = 0.05)
PC Optimal timing to invest in CCS Expected profit (unit: Yuan) CCS option valuea
No 1 1010 0
Year 5.5 1.08 1010 7.9 108
Year 7.6 1.09 1010 8.5 108
IGCC Optimal timing to invest in CCS Expected profit (unit: Yuan) CCS option value
No 6.7 109 0
Year 4 9.2 109 2.5 109
Year 4 9.2 109 2.5 109
Wind Expected profit (unit: Yuan)
1.3 1010
1.9 1010
1.9 1010
CCS option value denotes that compared to the strategy that CCS module would be never installed, the extra profit made by optimized real option strategy.
Table 4 Optimal timing of CCS investment and expected profit with different penalty conditions for PC case.
PC Optimal timing to invest in CCS Expected profit (unit: Yuan)
Later policy start (starting year = 10, r = 0.02)
Reference (starting year = 4, r = 0.02)
Higher uncertain penalty (starting year = 4, r = 0.05)
Year 12.3 4.2 109
Year 5.5 1.35 109
Year 7.6 1.42 109
improvement can provide a significant incentive for CCS adoption. The IGCC plant will benefit more than the PC plant from technical change. The CCS module is mostly added in the very beginning of the new policy regime.
6. Policy implication and concluding remarks Even though it is one of the major, potential CO2 mitigation measures, CCS needs to consume extra energy to reduce carbon
emissions, which makes it less cost-effective than renewable energy alternatives and energy efficiency approaches. As a result, much debate has arisen on whether CCS should be further developed considering its high cost and energy inefficiency, which is especially important for developing countries like China. Water consumption in carbon capture operations may become another concern considering water shortage being a common problem in China. On the other hand, China still lacks integrated technologies for transportation, injection, monitoring and risk control, as well as robust knowledge about storage potentials. Large-scale leakage
W. Zhou et al. / Applied Energy 87 (2010) 2392–2400 Table 5 Results of different technology change rates scenarios for PC and IGCC. Technical change (OMCt = OMC0 eat, a = 0.01)
No technical change (a = 0)
PC Optimal timing for CCS Expected profit (Yuan) CCS option value
Year 4 1.4 109 8.4 108
Year 5.5 1.35 109 7.9 108
IGCC Optimal timing for CCS Expected profit (Yuan) CCS option value
Year 4 1.0 109 2.54 109
Year 4 8.9 108 2.49 109
Technology change rate
and geological disaster are also of concern in relation to CCS deployment in China, according to a survey [28]. These issues might become main barriers and hamper the large-scale deployment of CCS in China. Currently, most focus is put on energy efficiency improvements in China’s climate policy. In the national 11th five-year plan (from 2005 to 2010), energy-saving and pollutant-reducing actions are promoted and their implementation becomes a criterion for the assessment of government officials’ performance. The energy conservation law has also taken effect since August 2008. These policies give an incentive to enterprises to reduce energy consumption and thereby reduce CO2 emissions as well. However, these policies are not directly targeted at CO2 emissions and energy companies are not under any obligation to conduct such actions. Most of the projects aiming at CO2 mitigation in China are under the umbrella of CDM. The CDM projects in China involve renewable energy such as wind power, energy efficiency improvements of e.g. power plants. The trading price is actually very low with about 50– 70 yuan per ton [29]. Whether developing countries with large emissions like China will be faced with commitments to CO2 reduction similar to Annex I countries some time in the future is still not clear given the current situation of international climate negotiation. The objective of this study has been to analyze investment strategies for CCS in China’s energy sector in an uncertain environment. To carry out the corresponding experiments, three kinds of technologies – a conventional pulverized coal power plant, an IGCC plant and a wind farm – have been selected as the representatives of the three approaches for CO2 mitigation (i.e. energy efficiency improvement, renewable energy, and carbon capture and storage) in China. Of these technologies, CCS modules can be added to the former two. To incorporate the uncertain factors into the decision process, a real options model has been developed, and scenarios to mimic various possible policy outcomes and rates of technical change have been analyzed and discussed. The key findings from the model and scenario analysis can be summarized as follows: (1) Flexibility in CCS investment decision making has an economic value, and it increases with an increase in CO2 price uncertainty. Some studies, as for example [30], present similar results. Moreover, this research illustrates that higher uncertainty with respect to the carbon market will increase the CCS option value, i.e., the economic value of the possibility to retrofit existing plants with CCS. (2) The responding time of companies to install CCS to climate policy is shorter when the policy is implemented in the beginning of the planning period. The result proves a conclusion from [18] by using a different approach. In particular, [18] (Energy Policy 2007, vol. 35, p. 5772) state that ‘‘the closer in time a company is to a change in policy, the greater the policy risk will be, and the greater the impact on investment decisions”. The real reason is that if the policy is
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enacted later, then the remaining planning horizon is shorter and then a higher price is needed to trigger the CCS investment. (3) The profitability of renewable energy will increase significantly in the long term. In a world more constrained by climate policy, the zero-CO2-emission advantage of renewable energy will improve the economics compared to fossil-fuelconsuming power plants. Technologies with zero fuel cost (e.g. wind, solar or hydro power) benefit even more because of zero fuel cost.13 For IGCC, the results show that its profit is not so competitive, except under more stringent climate policies. However, other advantages of IGCC, such as lower emission levels of other pollutants, have not been considered in this analysis. Therefore, a broader framework needs to be developed to assess this technology more comprehensively. (4) For China’s power industry, the expected profits of IGCC plants and conventional coal-fired power plants will not be very different when considering technical change. CCS should then be integrated in newly-built and more efficient power plants such as IGCC, which is due to their lower capture cost. Therefore, the three approaches to reduce carbon emissions in the electricity sector (CCS, energy efficiency and renewable energy) should always be considered in combination with respect to the overall strategy for CO2 mitigation. Furthermore, given the rather low cost of IGCC in Western countries, technology transfer could enable China’s power industry to implement CCS at a larger scale and more cost-effectively under a more stringent climate policy regime.
Acknowledgements The first author is grateful for the support from National Natural Science Foundation of China (NFSC) to participate in IIASA’s Young Scientist Summer Program (YSSP) when part of the work was conducted. Financial supports from NFSC (No. 20876087), China’s National Hi-Tech R&D Program (No. 2008AA062301) and Ph.D. Programs Foundation of Ministry of Education of China (No. 20080030049) are also gratefully acknowledged. At IIASA, the research leading to these results has received funding from the European Community’s Seventh Framework Programme (FP7) under grant agreement No. 212535, Climate Change – Terrestrial Adaptation and Mitigation in Europe (CC-TAME), www.cctame.eu. References [1] International Energy Agency. CO2 emissions from fuel combustion, 2009 ed., 2009. [2] China National Bureau of Statistics, China National Energy Administration. China energy statistics yearbook 2008. China Statistics Press; 2008. [3] Dixit A, Pindyck R. Investment under uncertainty. Princeton: Princeton University Press; 1994. [4] Fuss S, Szolgayová J, Obersteiner M, et al. Investment under market and climate policy uncertainty. Appl Energy 2008;85:708–21. [5] Laurikka H. Option value of gasification technology within an emissions trading scheme. Energy Policy 2006;18:3916–28. [6] Yang M, Blyth W, Bradley R, et al. Evaluating the power investment options with uncertainty in climate policy. Energy Econ 2008;30(4):1933–50. [7] Kumbarog˘lu G, Madlener R, Demirel M. A real options evaluation model for the diffusion prospects of new renewable power generation technologies. Energy Econ 2008;30:1882–908. [8] Fuss S, Szolgayová J. Fuel price and technological uncertainty in a real options model for electricity planning. Appl Energy; 2009. doi:10.1016/ j.apenergy.2009.05.020. [9] Intergovernmental panel on climate change. Special report on carbon dioxide capture and storage; 2005. [10] Copeland T, Antikarov R. Real options: a practitioners’ guide. Thomson; 2003. 13 Note that this is different for biofuels, since the fuel in this case does not come at zero cost.
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