How will auctioning impact on the carbon emission abatement cost of electric power generation sector in China?

Applied Energy 168 (2016) 594–609

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How will auctioning impact on the carbon emission abatement cost of electric power generation sector in China? Liwei Liu a, Xiaoru Sun a, Chuxiang Chen a, Erdong Zhao a,b,⇑ a b

School of Economics and Management, North China Electric Power University, Beijing 102206, China Climate Change and Energy Development Research Institute, North China Electric Power University, Beijing 102206, China

h i g h l i g h t s
With 5% allowance auctioned, the electric power generation sector’s marginal abatement cost will increase 0.244 Yuan/kW h.
Allowance auction will increase the expenses by China’s electric power generation sector.
Auctioning as an initial allocation in China’s carbon trading market will be challenging.

a r t i c l e

i n f o

Article history: Received 22 June 2015 Received in revised form 19 January 2016 Accepted 21 January 2016

Keywords: Carbon emission Abatement cost Electric power Allowance auction

a b s t r a c t China will enact a national emissions trading system in 2017. The rules for the initial allocation of emission allowances have not been settled yet. This paper assesses the effect of auctioning as an initial emission allowance allocation methods of the China electricity power generation sector. This study employs a combined model with modified Trans-log production function, dynamic simulation model and multiobjective linear programming to provide more realistic results. After estimating the optimal electric power sources structure and technological structure under different auctioning rates, this paper calculates the abatement cost of the China’s electric power generation sector. When the allowance is free, the carbon shadow price will be 206.12 Yuan/ton. When 5% allowance is auctioned, the price will be 216.91 Yuan/ton. When the auctioning rate increases, the carbon emission abatement cost increases accordingly, as does the growth rate. The results also show that when the allowance auction rate is 5%, the marginal abatement costs for coal-fired power generation and clean power generation are 0.123 Yuan/kW h and 0.121 Yuan/kW h respectively, and thus, the total additional cost of the electric power generation sector will increase by 0.244 Yuan/kW h, which would be a heavy burden to China’s electric power generation sector and the forward progress of auctioning in the near future may be hindered. Ó 2016 Elsevier Ltd. All rights reserved.

1. Introduction Carbon emission trading is a market mechanism aimed at globally promoting greenhouse gas abatement and reducing carbon dioxide emissions. Since 2005, carbon trading has occurred both within and outside the framework set up by the Kyoto Protocol. So far, a number of organized international markets for carbon permits have emerged in European Union, America, Australia, etc. [1]. From 2013 to 2015, China successively launched several pilot plans for carbon trading. The pilot plans represent a landmark in China’s intentions to build a nationwide carbon trading market. ⇑ Corresponding author at: School of Economics and Management, North China Electric Power University, Beijing 102206, China. E-mail address: [email protected] (E. Zhao). http://dx.doi.org/10.1016/j.apenergy.2016.01.055 0306-2619/Ó 2016 Elsevier Ltd. All rights reserved.

By the end of 2014, China’s seven pilot plans for carbon trading had been all launched. At the U.S.–China Summit on Sep 2015, Presidents Xi and Obama issued a joint statement on climate change in which China announced that it will enact a national emissions trading system covering power generation, steel, cement, and other key industrial sectors, as well as implement a ‘‘green dispatch” system to favor low-carbon sources in the electric grid in 2017 [2]. The plan would make China the world’s largest carbon emissions market and reflects its wish to join the international carbon trade market. A cap and trade program for CO2 may cause increased expenses for major emitting firms throughout the economy. The electric power sector in China emits 50% of the national CO2 emissions, ranking top compared with steel and cement industry. In addition, 90% of the electric power industry’s emissions come from

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coal-fired thermal power. The installed capacity of coal-fired thermal power has been increasing rapidly, from 253.01 million kW in 2001 to 709.67 million kW in 2010, and the demand for electricity due to economic development in China is increasing at a rate of 10% per year, its dominant position in energy consumption is difficult to change in a short time [3]. It is estimated the average CO2 emitted by the listed coal-fired power companies in the seven pilots of carbon emission trading will have reached 0.37 bn tons between 2013 and 2015, and that of iron & steel 0.16 bn tons [4]. Currently, China’s emission allowance remains essentially at the free allocation stage. For future China carbon emission trade market designs, a bigger share of auctioning will be considered by the Chinese government as the EU ETS did (the governments could auction up to 5% of allowances in phase I and up to 10% in phase II, and the auctioning rate for the power industry in the EU ETS will be possibly 100% everywhere in the EU after 2020 [5,6]). As the free allowance allocation gradually switches to the auctioning in China’s carbon trade market, such a policy may have a significant impact on the electricity generators. Therefore, a thorough understanding of the potential impact of partial auction (or full auctioning) is essential for market participants and the Chinese government policy makers. The key question to be answered in this paper is to what extent the auctioning rate will affect the electric power generation sector’s marginal abatement cost. 2. Literature review 2.1. Allowance allocation and electric power sector Allowance allocation is one of the most important policy design issues in emission trading and the most controversial aspect of the implementation process, not only with regard to equity and policy performance [7], but also the relative well-being of consumers and producers and the relative profitability of different types of producers [8]. How allowance allocation issues might affect the fundamental operations of electric power markets has been highlighted by the results of many studies, which in general, mainly focus on two fields. One is the emission allocation schemes’ pass-through effect on the electricity price (Table 1). Another is the costeffectiveness of emission allocation schemes (Table 2). Whether governments could or should sell emission allowances, instead of giving them out for free, is one of the most hotly contested topics [9]. Even so, many research results have reached a consensus on the pass-through effect of the emission allocation schemes, namely, either an auction-based allocation or grandfathering (free giveaway) of allowances will both have cost pass-through issues [9–11]. No matter how to allocate allowances, the electricity price will increase and its costs will pass to consumers [12]. Giving allowances away for free would not prevent electricity producers from folding the value of emissions allowances into their bids in the power market [13]. The reason is that consuming the right to emit when producing output is an opportunity cost to the firm [6] regardless of whether the allowances are allocated for free or purchased at an auction or market. A company is expected to add the costs of CO2 emission allowances to its other marginal (variable) costs when making (short-term) production or trading decision [14–16]. Sijm et al. [17] estimate emissions costs almost fully (60–100%) passed through to consumers. Point carbon [18] also estimate during the period 2005–2007, pass-through levels in the power sector varied between 75% and 100% in both Germany, the United Kingdom and Spain, between 0% and 75% in Italy, and between 45% and 65% in Poland. Besides that, some scholars also assess the impact of CO2 allowance prices on the wholesale electricity prices, cost passthrough rate and retail electricity tariff rates in the European countries, USA and Australia [19–22].

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In the field of cost-effectiveness of permits allocation schemes, some research results indicate that either auctioned or free emission permits, higher efficiency to reduce carbon emission can be obtained than without carbon trading [23]. The majority of researches [7,9,12,13,24–27] favor auctioning over grandfathering as the rule for the initial allocation of emission permits and regard it the most powerful policy to reduce emissions in the electricity sector. Whether the cap is tight or slack, all auction formats studied are nearly 100% efficient in allocating emissions allowances, with no systematic differences among them [13]. Zhou et al. [12] find that auctioning is a preferred option for allowances allocation, the incentive of carbon abatement under free allocation is limited and could even worse than without emission trading. Burtraw et al. [24] reveal that the auction approach is about twice as costeffective as grandfathering or generation performance standard, when viewed over a wide range of emission reduction targets. 2.2. Abatement cost estimation methodology of the electric power sector With regard to abatement cost estimation in the electric power sector by means of model establishment, directional distance function combined with other methods [28–30] and production function [31,32] have been often used. Aggregated energyenvironment modeling for energy policy analysis and climate change mitigation assessment, such as LEAP [33], MARKAL [34] could also be found in literature. In addition, there have been the applications of simulation models [35–37], programming models [38,39] and other various methods [40–42] (Table 3). Several approaches have been tried in the above studies. Estimates of marginal abatement costs for reducing carbon emissions derived from major economic-energy models vary widely [43]. Important disadvantages of these models impact the estimation accuracy. For example, marginal abatement cost can be derived through the output distance function, however, the inherent shortcoming in the distance function lies in that it can provide only point estimate of the marginal cost, not the entire marginal abatement cost curve [29]; Limitation to one point in time will cause the estimates to be incapable of capturing differences in the emission pathway [44]; Cobb–Douglas production function only fits for situation with one output and two input factors [32]. Even though marginal abatement cost curves obtained by the aggregated models such as LEAP or MARKAL can provide useful insights for the implementation of a CO2 tax or an emissions trading system, they usually lack technologically detailed representation, not permitting the representation of path dependency of the technological structure [44]. Trans-log production function and multi-objective linear programming are static models and can depict the dynamic process only with the help of simulation method; dynamic simulation model is time-dependent. It can often be run in real time to give a virtual response close to the actual system and provide good substitution possibilities, but it cannot depict the internal changes of estimated target if carbon emission is regarded as a single variable. In order to combine the strengths of several approaches and achieve the high accuracy in estimation, this paper addresses these shortcomings and proposes a new approach to derive Marginal Abatement Cost Curves of electricity generation sector in China through the combination of Trans-log Production Function, Dynamic Simulation Model (a system dynamics model) and Multi-objective Linear Programming. This approach is characterized with representation of path dependency of the sector and technological structure, representation of emission differences from different power sources, representation of substitution possibilities, low uncertainty in assumptions, and no limit in one point in time.

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Table 1 Existing researches on pass-through effect of carbon emission schemes on the electricity price. Author(s)

Year Region published

Methodology

Möst et al. 2011 [10]

EU

Zhou et al. 2008 [12]

Australia

Sijm et al. 2006 [17]

EU

Zachmann 2008 et al. [19]

Germany

Leff [20]

USA

2008

Objective and findings

Agent-based models and optimizing energy system models

Nazifi [21] 2015

Australia

Wild et al. 2012 [22]

Australia

To illustrate the impacts of the EU ETS on the future of power generation and investment decisions in the electrical power industry The emissions allocation scheme has a significant influence on electricity generation planning, power plant investments and the electricity market The Cournot equilibrium model is used to simulate the To find out the impact on generation companies of different design choices Australian electricity market with realistic data regarding the free allocation of permits aspect of the proposed emission trading scheme No matter how to allocate allowances, electricity price will increase and the costs will pass to consumers Empirical and model study To analyze the impact of free allocation of CO2 emission allowances on the price of electricity and the profitability of power generation Estimated emissions costs have been almost fully (60–100%) passed through to consumers An error correction & an autoregressive distributed lag To measure the relationship between CO2 price changes and the model development of wholesale electricity prices The rising prices of emission allowances have a stronger impact on wholesale electricity prices than falling prices, which is an asymmetric cost pass-through A brief overview of the different treatment of emissions To investigate the potential impacts of a cap and trade program for CO2 on prices in regulated and deregulated electricity markets in retail electricity rates the United States The resulting increases in retail electricity rates ranged from as low as 0.05% to as high 41% across three states in US An empirical analysis using daily data from July 2010 to To investigate the interaction between a carbon price signal and wholesale October 2013 electricity spot prices Carbon costs would indeed be fully passed on to wholesale electricity spot prices resulting in higher electricity prices for consumers and potential windfall profits for some generators A DC OPF algorithm used with Australian National Carbon prices will impact on wholesale prices, carbon passthrough rates Electricity Market (NEM) model and on retail electricity tariff rates

Table 2 Existing researches on cost-effectiveness of allowance allocation methods in electric power sector. Author (s)

Year Region published

Methodology

Burtraw et al. [24] Hepburn et al. [9] Zhou et al. [12] Ahn [25]

2001

USA

The Haiku electricity market simulation model

2006

EU

Qualitative analysis

2008

Australia The cost of CO2 is assumed to be a variable cost

2014

Korea

Lin et al. 2011 [26]

China

Liu et al. 2012 [7]

China

Liao et al. 2014 [27]

China

The auction approach is about twice as cost-effective as grandfathering or generation performance standard, when viewed over a wide range of emission reduction targets Auctioning is proved to have dynamic incentives and will not reduce the competiveness of industry

Under ETS, the incentive of carbon abatement under free allocation is limited and could even worse than without emission trading. Auctioning is still a preferred option for allowances allocation A mixed complementarity problem (MCP) model To assess the effect of various initial emission allowance allocation methods of the Korean electricity market While the auction is the most powerful policy to reduce emissions in the electricity sector, giving away permits to all power plants based on a fuel-specific benchmark encourages investment, increases output, and leads to a greater level of welfare from an imperfectly competitive industry Four different allocation methods for sulfur dioxide To analyze the most suitable allowance allocation methods for Fujian power plants allowances are compared in 14 power plants The emissions performance method and production value method are the most suitable methods for Fujian power plants Constructing an artificial market for sulfur dioxide To examine four allowance allocation methods and their impact on an SO2 cap(SO2) emission trading by applying an agent-based and-trade program model The auction allowance allocation method is more efficient and has the lowest total emission control costs among the allocation methods examined The Shaply value At the introduction of experimental stage, free allocation pertaining to grandfathering can be adopted; meanwhile, benchmark should be prepared and adopted at the appropriate time

Specifically, Trans-log production model is selected to estimate the shadow price of carbon, instead of distance function model and Cobb–Douglas production Function. Trans-log Production Function can estimate the shadow price for the whole power generation sec-

tor and has no limitation in single point in time. Carbon emission is integrated into Trans-log production function as a factor input; In the estimation of power source structure, we choose Dynamic Simulation Model, which can dynamically estimate power source

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597

Table 3 Existing researches on carbon emission trading and carbon abatement cost in electric power generation sector. Types of Article methodology Direction distance function

Kwon and Yun [28]

Concrete method Year published & region

Results

1999 Korea

The estimated mean values of the marginal abatement costs for SOx, NOx, TSP and CO2 are 310.6, 146.7, 15482.3 and 3.8 thousand won per ton, respectively, for the period of 1990–1995

Park and Lim 2009 [29] Korea

Chen et al. [30]

2014 China

Duality between the output distance function and the revenue function

Integrating the distance function method and real-option Marginal abatement cost: an average of €14.04/ton CO2 for fossilapproach, and the sensitivity analyses fueled power plants; substantial cost heterogeneity among plants exists, which is sufficient to achieve trading gains in allowance market A dynamic analysis model based on the non-parametric Estimate marginal carbon abatement costs of 28 industries directional distance function (including electricity generation) with high CO2, providing data support for carbon emission abatement and carbon emission price determination in Tianjin

Production function

Aggregated modeling

Considine et al. [31]

2009 Europe

The industry’s fuel switching capabilities and the scope for introducing new technologies are limited in the short run For every 10% rise in carbon and fuel prices, the marginal cost of electric power generation increases by 8% Carbon shadow price is 147.27 Yuan/ton in 2008

Zhou and Du 2013 [32] China

Tran-slog production function model

Islas and Grande[33]

The best SO2-abatement route in terms of costs may result in a 41% reduction in the Mexican electric power sector’s SO2 emissions and would require an investment and incur total costs of $841 and $477 million, respectively Deriving MAC curves (MACC) through the combination of With a focus on the UK and the year 2030, as an important intermediate emissions reduction target, system-wide MAC curves an integrated energy system model, UK MARKAL, and are presented accompanied by a detailed analysis of the power, index decomposition analysis transport, and the residential sectors

2008 Mexico

Kesicki [34] 2012 UK

Simulation model

Generalized Leontief (GL) restricted cost function

LEAP model

Ali et al. [35] 2013 Turkey

Original dynamic simulation model

Chappin et al. [36]

Agent-based model

2009 Europe

Neuhoff et al. [37]

Analysis reveals that a delicate balance of feed-in-tariffs, investment subsidies, carbon prices, faster RES permitting and higher DG allowances help reduce CO2 emissions dramatically below BAU levels. There are mitigation options below 50% of business as usual growth, with common policy options such as feed-in-tariffs, investment subsidies and carbon taxes The effect of CET (CO2 emission-trading) on the decisions of power companies in an oligopolistic market:
A long-term portfolio shift toward less-CO2 intensive power generation is observed
The effect of CET is relatively small and materializes late. CET is not sufficient to outweigh the economic incentives to choose for coal The sheer value of free allocations in a sequentially negotiated trading system makes it hard to avoid some distortionary effects

Numerical simulation

Programming Soloveitchik 2002 model et al. [38] Israel

Multi-objective optimization

Different scenarios of pollutant reduction were analyzed CO2 reduction is 4.7–6.7%, NOx reduction is between 8.0% and 14.2%, additional total cost is $93 million and $176 million, marginal

Tolis and Rentizelas [39]

Others

2011 Greece

Stochastic programming algorithm without recourse

Johnson [40] 2014 Different Regions

Using the preferred elasticity estimate with the temporal and regional variation in RPS (renewable portfolio standards) requirements

Nicholson [41]

2011 Australia

The Australian Government Treasury model

West [42]

2012 Australia

Reconstruct MACC with bottom-up approach and incorporating real options analysis

structure according to the concrete parameters under specific circumstances at different time points, and can effectively reflect the sector details and capture the differences in the emission pathway of power sources and allow possibility of substitution; in the estimation of technology structure of coal-fired power in 2016,

abatement cost is 4.8–6.4 $/ton, and average cost of electricity is $2.48–$2.55 Higher electricity prices lead to higher financial yields of power production, irrespective of the CO2 allowance price level. The combination of electricity prices subsidization with high CO2 allowance prices may provide favorable conditions for investors willing to engage on renewable energy markets
The long-run price elasticity of supply of renewable electricity generation to be 2.67
The marginal cost of abatement from RPSs is at least $11 per ton of CO2 compared to a marginal cost of abatement of $3 per ton in the Regional Greenhouse Gas Initiative Australia could save up to $185 billion net in abatement costs by 2050 if 25 gigawatts of nuclear generation capacity were built instead of building new fossil fuel generators Redefining the relative abatement costs for retrofitting postcombustion CCS technology to coal-fired generators

Multi-objective Linear Programming is used, which is based on the detailed plans in 2015 and actual technological parameters of power generation sector. The advantages lie in two aspects. First, the representation of path dependency of the technological structure is achieved. Second, the uncertainty in assumption is largely

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eliminated due to the actual data and the point in time selected (2016), for wide time span (20 years or more) means more uncertainty in assumptions. The originality of this paper lies in: (1) Majority researches on the marginal abatement cost of electricity generation sector due to the carbon emission trading are carried out surrounding the countries in the EU ETS, Australia, America and Korea. That issue in China has not been intensively and thoroughly calculated and fully discussed yet. To our best knowledge, the marginal cost estimation especially from the angle of auctioning rate for China is also rather few. (2) The marginal abatement costs for both coal-fired thermal power and clean power generation in China under different allocation rates have been estimated, which is insufficient in literature. (3) The combined system model employed in this paper is high in precision. It is robust in the synthesis of static model and dynamics simulation model, usage of top-down approach while considering the representation of sector and technological path dependency, and analysis of cost at both one point in time and different points in time. In the remainder of the paper, Section 3 describes in detail the methodology used to estimate these costs. Section 4 is the analysis process. Section 5 provides the total and marginal abatement costs of the electric power sector in China. Section 6 discusses, and Section 7 concludes. 3. Methodology 3.1. Analysis framework Fig. 1 depicts the analysis framework. Step 1: estimation of the carbon emission allowance price (shadow price) under different auctioning rates. Trans-log Production Function is employed in this estimation. Ridge Regression is used to obtain the coefficients of Trans-log Production Function. Step 2: estimation of the optimal power source structure of China’s electric power sector. Power source structure refers to

the share of coal-fired power, hydropower, wind power, nuclear power, solar power and biomass in the power sector. Dynamic simulation model is used in this step. Step 3: estimation of the optimal technological structure of coalfired thermal power generation; Multi-objective linear programming is employed in this step. Step 4: calculation of total abatement cost and the marginal abatement cost of electricity generation sector. Auctioning rate will influence the market price of the allowances. Since the share of allowances that will be auctioned needs to be subtracted from the total amount of allowances to be allocated for free, therefore, the participating companies will thus receive fewer free allowances than in situation without auctioning. Companies will need to purchase more allowances on the market or at the auction, leading to an increase in overall compliance costs unless proceeds are recycled. In the long run, the overall demand and supply will remain the same, in theory (partial) auctioning would not affect prices compared to a situation without auctioning [6,45,46]. However, the difference in market dynamics in a system with full or partial auctioning compared to a scheme with grandfathering may lead to a different market price. With auctioning, the whole amount to be auctioned may not be available at the start of each compliance cycle but be distributed throughout the compliance cycle, leading to higher market and auction prices [45,47,48]. According to the situation in China, this paper assumes that revenue of auctioning will not be returned to the participants. In addition, based on the research objective, we don’t study the influence of the ‘‘windfall profit” possibly brought by free allowances on the power generation industry and only consider the abatement cost induced by the auctioning. The fluctuations of carbon allowance price will result in the change of share of carbon production factor in the total cost of by the electric power sector and will cause the cost of coal-fired and clean energy power generation to deviate from the current level. The reason for this deviation is that the environmental cost share is added to the normal power generation cost in all types

Step 1 Aucon rate

Carbon emission allowance shadow price

Power source structure

Share of coal-fired power generaon

Index

Methodology

Carbon emission allowance shadow price

Trans-log producon funcon

Step 2

Share of clean energy power generaon

Index

Methodology

Power source structure

Dynamic simulaon model

Step 3 Technological structure in coal-fired generaon

Index

Methodology

Technological Structure of coalfired power generaon

Mul-objecve linear programming

Step 4 Operaon cost of carbon abatement

Investment cost of carbon abatement

Operaon cost of carbon abatement

Investment cost of carbon abatement

Total abatement cost of clean energy power generaon

Total abatement cost of coal-fired generaon

Total abatement cost of electricity generaon sector

Index

Methodology

Total abatement cost of electricity generaon sector Marginal abatement cost of electricity generaon sector

Fig. 1. Analysis framework. Note: Power source structure: Power source structure refers to the share of coal-fired power, hydropower, wind power, nuclear power, solar power and biomass power in the power sector. Technological structure: The proportion of various power generation technology in power generation sector.

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of power plants. Consequently, driven by profit-making, the power generation dispatching in different types of power plants will change, and thereby a new power source structure in the electric power sector will appear. On the basis of the above optimal power source structure, for the power generation side, the optimal technological structure to minimize both the coal-fired power costs and carbon emissions should be determined, with which the investment and operation cost for coal-fired power and clean energy respectively, total cost of carbon abatement in the electricity generation sector could be calculated. And finally, the marginal abatement cost of the electricity generation sector could be obtained.

category, when modeling joint good outputs and bad outputs, carbon emission is usually introduced into the production function as an undesirable output [56,57]. Therefore, the first category method was adopted and carbon dioxide emission is introduced into the production function model parallel to the traditional input factors (capital and labor). The Trans-log production function is modified by introducing the carbon emission allowance amount E into the production function as an input factor to achieve the shadow price of carbon emission allowance. Thus, the following production function can be established:

ln Y t ¼ e þ aK ln K t þ aL ln Lt þ aE ln Et þ aKL ln K t ln Lt þ aKE 2

 ln K t ln Et þ aLE ln Lt ln Et þ aKK ðln K t Þ þ aLL ðln Lt Þ

3.2. Model selection and description 3.2.1. Modified Trans-log production function 3.2.1.1. Trans-log production function. China’s carbon trading market has only been established recently, and it remains in the exploratory stage. The existing carbon price, which reflects neither environmental costs nor actual exploitation costs, is severely distorted and has caused a disconnection between the market supply–demand price and the carbon emission rights price. As a result, a regression of China’s existing annual emission permit prices cannot reasonably describe the changing tendencies of its present value. Therefore, a production function is employed instead of regression to get the shadow price of allowance. There are three kinds of production function mainly used in the current researches: Cobb–Douglas production function, Constant Elasticity Production Function and Trans-log Production Function. The reasons for choosing Trans-log production function is as follows: First, Cobb–Douglas production function is mainly applied in the occasion with one single output and two inputs [49]. It presumes that elasticity of substitution is 1. Therefore, when the elasticity of substitution is not 1, Cobb–Douglas production function will lose efficacy and the result is a biased estimator. What’s more, the neutral appearance of technical progress is the precondition of the Cobb–Douglas production function, but the neutral appearance of technical progress does not exist in most economic production relationships. Therefore, Cobb–Douglas production function is not suitable for our study [50]. Second, Constant Elasticity Production Function is the improved Cobb–Douglas production function and has no unit elasticity constraint. However, because Constant Elasticity Production Function is nonlinear, it is difficult to estimate the parameters in the regression analysis with this function [32]. Thirdly, Trans-log production function is a kind of variable elastic production function model [51]. It not only has the logarithmic term to inputs, also contains the cross terms and square terms of all the input elements, compared with the first two functions, it is more flexible in the substitution and the mode conversion [52]. In addition, the function is not limited by the technological progresses, thus, it is more reliable and suitable. Therefore, this study will use Trans-log production function to express the function relation between the inputs and output. 3.2.1.2. Modified Trans-log production function. Generally, carbon emission is dealt with in the mathematical model analysis based on two main categories. In the first category, carbon emission is introduced into the production function as a factor input, since emissions resulting from the production process can be characterized as ‘‘use of the elimination and disposal services of the ecological system”. Hence, they are ‘‘use of natural resources” and thus an input to production [53]. Refs. [29,32,53–55] provide examples of a theoretical model that treats bad outputs as inputs. In the second

þ aEE ðln Et Þ

2

2

ð1Þ

ln Y: logarithmic GDP in year t; ln L: logarithmic labor input in year t; ln K: logarithmic capital stock in year t; ln E: logarithmic carbon emission allowance in year t; ln K ln L; ln K ln E; ln L ln E: the cross terms; ðln K 2 Þ; ðln L2 Þ; ðln E2 Þ: the quadratic components; a: parameter; e: random disturbance term. Derivation of equation:

dY=Y d ln Y t ¼ ¼ aE þ aKE ln K t þ aLE ln Lt þ 2aEE ln Et dE=E d ln Et

ð2Þ

dY Y ¼  ðaE þ aKE ln K t þ aLE ln Lt þ 2aEE ln Et Þ dE E

ð3Þ

According to the marginal productivity theory, the shadow price of the carbon emission allowance in China PE is equal to the marginal productivity of China’s carbon emissions:

P Et ¼

Yt  ðaE þ aKE ln K t þ aLE ln Lt þ 2aEE ln Et Þ Et

ð4Þ

The estimates of coefficients aE ; aKE ; aLE and aEE in (4) should be obtained to calculate the shadow price of allowance P Et . These coefficients will be got by means of Ridge Regression, Matlab analysis is used to execute Ridge Regression programs. 3.2.1.3. Ridge regression. Ridge regression is a biased estimate regression method for total linear data analysis and is essentially a type of improved least squares estimation method. It is a practical and reliable regression method to determine the regression coefficients at the expense of the unbiasedness of the least squares method, as well as the loss of a portion of the information and reduced accuracy. Ridge value the ridge value K ¼ 0, using 0.1 as a step for ridge regression estimation, until the ridge value K ¼ 1. Then, use the 10 ridge regression coefficients to obtain the best range value. Each value of the input is shown in Table 4. The regression results are shown in Table 5. Finally, a diagram of the results returned by the Matlab analysis is presented in Fig. 2. When K P 0:01, the ridge regression coefficient of each corresponding variable is stable. When K ¼ 0:07, the regression coefficient of the ridge regression equation is as follows:

P Et ¼

Yt  ð0:00211 þ 0:002835 ln K t  0:000665 ln Lt Et þ 2  0:00009 ln Et Þ

ð5Þ

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Table 4 GDP, capital stock, labor force and carbon dioxide emissions in China from 2000 to 2012.

a b c d

Year

GDPa (billion Yuan)

Capital stock Kb (billion Yuan)

Labor force Lc (billion)

Carbon dioxide emissions Ed (million ton)

2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012

9921.455 10,965.517 12,033.269 13,582.276 15,987.834 18,493.737 21,631.443 26,581.031 31,404.543 34,090.281 40,151.280 47,310.405 51,947.010

4922.170 5444.500 6052.040 6824.070 7728.300 8893.550 10,226.810 11,700.970 13,278.930 14,938.760 16,746.350 18,806.150 21,044.080

0.714 0.721 0.725 0.731 0.740 0.756 0.763 0.770 0.776 0.783 0.803 0.805 0.806

3.405 3.488 3.694 4.525 5.288 5.790 6.414 6.792 7.035 7.692 8.287 9.057 9.640

Ref. Ref. Ref. Ref.

[94]. [95]. [94]. [94].

3.2.1.4. Rationality of regression model. According to Table 4 and formula (4), prices of China’s emission allowance from 2000 to 2012 are presented in Table 6 and Fig. 3. The regression estimation result shows that the prices followed a general rising trend from 81.84 Yuan/ton in 2002 to 172.83 Yuan/ton in 2012, although, in 2003, the price decreased slightly. Because China is currently enjoying a rapid rate of development, the marginal productivity of carbon dioxide emissions is still increasing. When an emission peak appears, the marginal productivity will decrease. China’s emission rights shadow price and the average trading price in the EUETS are compared (Table 7 and Fig. 3). It can be seen that China’s emission rights shadow price is higher than that of the CDM primary market but lower than the EUETS trading price. Currently, the technology development of developed countries has reached a bottleneck, and as a result, their marginal abatement costs are far higher than those of technically backward developing countries. That is, developed countries’ emission shadow prices should be higher than those of developing countries. EUETS is a relatively developed carbon trading market, so the trading price is generally representative of the actual market emission rights price. In addition, the growth rate of carbon emission shadow prices in this research remains approximately 5%, in line with the provision in Australia’s Clean Energy Act that specifically fixes the carbon price at 23 Australian Dollars per ton during the initial period and then increases it by 5% each year. Therefore, this method can be applied for the 2016 price estimation. 3.2.2. Dynamic simulation model System dynamics is a system modeling and dynamic simulation methodology for the analysis of dynamic complexity in sociomethodology [58]. The cost minimization system dynamics has

been widely applied to problems in the electric power industry related to carbon mitigation [28,29,38,59–62]. Heuristic optimization algorithms (heuristics for short) seek good feasible solutions to optimization problems in circumstances where the complexity of the problem or the limited time available for its solution [63]. When a problem is computational complex, the major advantages of heuristics consists in the fact that their application does not rely on a set of strong assumptions about the optimization problem [64]. It is found that optimization methodologies that are based on heuristics could assist power generation policy analysts to achieve the goal of minimizing the generation costs [65–67]. Therefore, the cost minimization system dynamics approach in Ali’s research [35] and Gumustas’ research [68] is also used in this paper. In Ali’s [35] research, electricity demand is dispatched to each plant type using a heuristic which mimics load taking/shedding activities in power systems. The heuristic executes demand dispatch to nearly minimize the total annual generation cost. In dispatch, the main principle is that the demand assigned to each power plant is inversely proportional to its generation cost. Namely, at each time period, electricity demand is assigned to power plants starting through the one with cheapest generation alternative [35]. This is what this dispatch heuristic mimics. The description of this model is shown in Appendix A. 3.2.3. Multi-objective linear programming Multi-objective optimization theory is first proposed by the French economist V. Pareto in 1896 [69]. It is also known as multiobjective linear programming [70]. Multi-objective linear programming has been widely used in various fields of science, economic systems, government policy, finance, and logistics [71–73] where optimal decisions need to be taken in the presence of trade-offs between two or more conflicting objectives. Minimizing cost and maximizing performance of a car whilst minimizing fuel consumption and emission of pollutants is an example of multi-objective optimization problems involving two and three objectives, respectively. Because of the contradiction and incommensurability between multiple objectives, it is impossible to reach optimal solution of all objectives, therefore, multi-objective linear programming’s robustness lies in that it can transform non-comparable multi-objective into a single objective to make a better understanding of the optimization and seek effective solutions [74,75]. By applying the Multi-objective Linear Programming (MOLP), the optimal technological structure is achieved, which could allow control over the cost and CO2 emissions of a coal-fired power plant at the lowest level. The objective function utilizes the lowest cost at which the generating capacity would meet the demand. The lowest net present value of the total cost F cost is converted to a formula as follows:

M in F cost ¼ ½A1 þ A2 þ A3 PVIF i;n A1 ¼

ð6Þ

n X ai  ðPn  C 0 Þ

ð7Þ

i¼1

Table 5 Regression coefficients according to different values of k (103). Coefficient

k=0

k = 0.1

k = 0.2

k = 0.3

k = 0.4

k = 0.5

k = 0.6

k = 0.7

k = 0.8

k = 0.9

k=1

e aK aL aE aKL aKE aLE aKK aLL aEE

615.53 5609.6 4848.88 788.7 17583.3 2033.9 1761.9 6636.8 10345.9 351.2

615 9.6 0.3 3.8 7.7 1.4 3.6 11.8 0.6 0.3

614.59 9.665 0.125 3.21 7.545 1.315 3.28 11.35 0.845 0.105

614.12 9.49 0.45 2.88 7.365 1.17 3.12 10.925 1.05 0.025

613.65 9.275 0.715 2.63 7.215 1.025 3.015 10.56 1.25 0.01

613.18 9.06 0.94 2.43 7.075 0.9 2.94 10.24 1.43 0.025

612.71 8.85 1.14 2.26 6.955 0.78 2.885 9.95 1.59 0.055

612.24 8.65 1.315 2.11 6.84 0.67 2.835 9.69 1.74 0.09

611.77 8.465 1.47 1.98 6.735 0.56 2.8 9.45 1.875 0.125

611.305 8.29 1.61 1.85 6.635 0.47 2.765 9.23 1.995 0.17

610.84 8.125 1.735 1.73 6.545 0.38 2.74 9.03 2.105 0.21

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L. Liu et al. / Applied Energy 168 (2016) 594–609

Table 7 Comparison between carbon emission allowance shadow prices in China and average transaction prices in EU ETS.

Fig. 2. Matlab results of regression coefficients according to different values of k.

Year The shadow price of The CDM primary market carbon emission allowance (Yuan/ton) transaction price (Yuan/ton)

The CDM secondary market trading price (Yuan/ton)

EUETS transaction price (Yuan/ton)

2005 94.48 2006 101.14 2007 118.88 2008 137.20

181.03 141.89 172.70 170.23

201.80 176.44 181.11 206.37

58.06 86.16 102.39 116.38

n X 8760  Pi P D

ð10Þ

i¼1

Carbon dioxide emissions: Table 6 Shadow price of carbon emission allowance from 2000 to 2012. Year Shadow price of carbon emission allowance (Yuan/ton)

Year-on-year growth rate (%)

2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012

100.00 108.99 104.75 93.35 102.03 107.09 107.04 117.54 115.41 100.39 110.45 108.99 104.23

A2 ¼

81.14 88.43 92.63 86.47 88.22 94.48 101.14 118.88 137.20 137.74 152.14 165.81 172.83

n X

n X 8760  Ei  Pi 6 M y

8760  Pi  Hi  Pc

ð8Þ

8760  Pi  Ei  X  PEt

ð9Þ

i¼1

A3 ¼

n X

ð11Þ

i¼1

i¼1

F cost : the net present value of the total cost; A1 : additional investment cost in 2016; A2 : coal consumption cost for electricity generation in 2016; A3 : environmental cost in 2016; ai : construction costs of unit capacity of energy i in 2016; Pn : installed capacity of energy i in 2016; C 0 : installed capacity of energy i in 2015; Pi : average power output of energy i in 2016; Hi : coal consumption of per unit of output of energy i in 2016; Pc : coal price; Ei : carbon dioxide emissions of per unit of output of energy i; X: the auction rate of carbon emission allowance; PEt : the shadow price of carbon emission allowance; D: the predicted demand; M y : carbon dioxide emissions constraint.

4. Analysis process 4.1. Estimation of shadow price of allowance

Constrained conditions Electricity demand: The sum of each unit output for each year should not be less than the forecasted demand for coal-fired power after dispatching.

4.1.1. Data 4.1.1.1. GDP. Based on the economic situation and governmental policies, the European Union forecasted that the Chinese economic

Fig. 3. Shadow price of carbon emission allowance from 2000 to 2012.

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L. Liu et al. / Applied Energy 168 (2016) 594–609

growth rate will slow, decreasing from 7.7% in 2013 to 7.3% in 2014, 7.1% in 2015, and 6.9% in 2016 [76]. Based on the above forecast, China’s GDP estimations for the following few years are presented in Table 8. 4.1.1.2. Capital stock. The capital stock—GDP ratio estimation method is often used to determine the capital stock in a base period because China’s fixed capital stock is not reported by the China Statistics Departments. China’s capital stock—GDP ratio in recent years is presented in Table 9. By means of the assets appraisal method, the calculation result show that the arithmetic average of the above ratio is 0.4161 and that the geometrical average is 0.4159. These two values are extremely close, which indicates the stability of this ratio. Base on this estimation, China’s capital stock—GDP ratio in 2016 is forecast to be 0.4160, and the estimated GDP in 2016 is forecast to be 70,404.27 billion Yuan; therefore, China’s capital stock is forecast to be 29,288.17 billion Yuan. 4.1.1.3. Labor force. China’s population between the ages of 15–64 will reach 996 million by 2016, accounting for 73% of the total population [77]. China’s labor force in 2016 will be 80% of this value (ages 16–60 for males and 16–55 for females), or 796.8 million. 4.1.1.4. Carbon emissions. China’s carbon dioxide emissions per unit GDP in 2014 dropped by 5% compared with that in 2013. As is described in the 12th Five-Year Plan, by 2015, China will achieve a decrease in carbon intensity of 17%. It can be inferred from this schedule that in 2015, the carbon intensity should be reduced by at least 3.9–4% [78]. China’s carbon emissions exceeded the sum of those of the European Union and America, reaching 10 billion tonnes of CO2 [79]. In 2013, China’s GDP was 56,884.521 billion Yuan, and its carbon intensity was 0.0001757947 tons per Yuan. Therefore, it can be estimated that in 2016, the carbon intensity will be 0.0001603248 tons per Yuan, and the carbon emissions will be 11.2875 billion tons. 4.1.2. Result Using the above forecast data, the carbon emission rights prices under different allowance auction rate in 2016 can be estimated (Table 10). When the allowance is free, the carbon price will be 206.12 Yuan/ton. When 5% allowance is auctioned, the price will be 216.91 Yuan/ton, and for 10%, it will be 228.89 Yuan/ton. The price of carbon emission rights rises as the auction rate increases. 4.2. Power source structure prediction Carbon emissions are mainly produced by the electricity generation sector. The emission abatement measures implemented by this sector include adjusting energy structure, improving fossil fuel efficiency and applying CO2 capture and storage technology. The power supply structure adjustment, which consists of replacing coal power generation with near-zero-emissions energies, such as low-carbon fuels, renewable energy, and nuclear energy, is the main measure applied to reach the long-term emission abatement target. The fact that China is rich in coal and hydropower resources determines China’s energy generation structure, which will likely remain coal-dominated, followed by hydropower and other lower-volume energies [80].

Table 8 Predicted value of GDP in China from 2013 to 2016. Year

2013

2014

2015

2016

GDP value (billion Yuan)

56,884.52

61,264.63

65,736.95

70,404.27

Table 9 The capital stock—GDP ratio from 2008 to 2012. Year

2008

2009

2010

2011

2012

The capital stock—GDP ratio

0.422835 0.438212 0.417081 0.397506 0.405107

By comparing the sum of the electric power sector’s investment and operating costs under different free allowances, the situation in 2016, when China gradually phases out single free allocations by establishing the carbon trading market, will be studied. The reasonable free allocation percentage will also be determined to minimize the total costs of the electric power sector. Based on the above-mentioned heuristic executes demand dispatch, as well as cost parameters and installed capacities, this chapter calculates the capacity utilization efficiency of different energies in an attempt to reduce the overall annual generation cost. The fundamental principle underlying this model is the ratio of the generation demand and cost of each type of energy; the additional environmental cost is included in the generation cost. 4.2.1. Data The data needed for the power source structure estimation mainly include the estimated power demand in 2016, the estimated carbon emissions and the operating costs of various power generation methods, which mainly consists of the basic operating costs (depreciation cost and fuel cost) and the environmental costs, under different allowance auction rates. The environmental cost is calculated based on the carbon emissions of various power generation methods and the emission rights prices under different allowance auction rates. The formula is as follows: carbon emissions of various power generation methods  emission rights prices under different allowance auction rates  auction rates. 4.2.1.1. Power demand. Lin et al. [81] classify the total power consumption into five parts: household power consumption, agricultural power consumption, industrial power consumption, construction power consumption and services power consumption. China’s power demand in the next ten years is forecasted using a regression fit to historical data. The result shows that the average annual growth rate (AAGR) of total power consumption is approximately 7.8% during the 12th Five-Year Plan period, that the power consumption in 2015 will exceed 6000 billion kW h, that the AAGR will be 6.1% during the 13th Five-Year Plan period, and that the annual power consumption in 2016 will be 6586.90 billion kW h. On the basis of these figures, the estimations are adjusted by analyzing the energy conservation and emission abatement potentials of the power demand side. The most efficient measure of these values is demand side management (DSM) because with the support of government regulations and policies, effective incentives and guiding measures as well as appropriate operating models will be adopted to improve the terminal power efficiency, alter the power consumption model, and decrease power consumption and demand while maintaining productivity. Four main energysaving programs are considered in DSM: lighting power consumption, motor power consumption, transformer power consumption and household power consumption, which save 94.8 TW h, 5.2 TW h, 87.75 TW h, and 31.25 TW h, respectively (Table 11). In totally, 219 billion kW h can be saved in 2016, and the total power consumption in 2016 will be 6367.90164 billion kW h. 4.2.1.2. Carbon emissions. The predicted carbon emissions of various power generation methods in 2016 are shown in Table 12.

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L. Liu et al. / Applied Energy 168 (2016) 594–609 Table 10 Shadow price of carbon emission allowance under different auction rate in 2016 (Yuan/ton). Auction rate Shadow price

0% 206.12

5% 216.9

10% 228.89

15% 242.28

20% 257.34

30% 293.89

The power generation capacities per kilowatt of coal-fired power, hydropower, wind power, nuclear power, solar energy and biomass energy are 975.5 g, 120.35 g, 66.7 g, 70 g, 151.7 g and 106.5 g, respectively.

4.2.1.3. Basic operating cost estimation for coal-fired power plants. The cost of China’s coal-fired power sector mainly includes fuel cost, routine operating cost (water rate, materials expenses, payroll, overhaul cost and so on), depreciation cost and period cost. Among them, fuel cost, depreciation cost are the major contributors, making up approximately 80% of the total cost, while some miscellaneous expenses, such as payroll, water rate and routine overhaul cost, only account for approximately one-fifth of the total cost [82]. The investment cost of coal-fired power is approximately 4000 Yuan per kilowatt, and the daily coal-fired power generating capacity per kilowatt of installed capacity averages approximately 16.4 kW h. Additionally, the service life of a coal-fired power plant is approximately 25 years on average [83]. The total coal-fired power generating capacity per kilowatt over the service life is estimated to be 16:4  365  25  0:8 ðcorrection factorÞ ¼ 120; 000 kW h. Then, by amortization of the total cost, the depreciation expense of coal-fired power is calculated to be approximately 0.033 Yuan per kW h. The fuel cost consists primarily of the coal consumption cost, and the unit cost of coal-fired power increased from 231.52 Yuan per ton at the end of 2006 to 345.92 Yuan per ton at the end of 2010. Because Asian coal-fired plants exhibited a decline in coal demand in 2011, coal inventories remained high, causing the coal price to decline. The Bank of America Merrill Lynch predicted that the coal price will show a rising trend, reaching 82 US dollars per ton [84]. The average coal consumption of different coal-fired power units is approximately 310 g/kW h, and as a result, the coal consumption cost is 0.1568 Yuan per kW h. From the above data, the basic operating cost of coal-fired power can be calculated to be approximately 0.23725 Yuan per kW h.

4.2.1.4. Basic operating cost of hydropower. Compared with coal-fired power, hydropower has higher construction costs but is associated with some superior characteristics, namely, a longer operational period and low operating costs. Once put into operation, hydropower’ operating cost basically consists of only labor costs and unit depreciation costs. The long-term operating cost of hydropower companies is distinctly lower than that of coal-fired power, making hydropower a low-cost method. Currently, the basic operating cost of hydropower in China is approximately 0.065 Yuan per kW h [85].

Table 11 Electricity saved by DSM per year in China. Source: Ref. [90]. Energy saving project Lighting Electric motor Transformer Household electricity Total

Share of total power consumption (%) 12 60 28 100

Electricity saved per year (TW h) 94.8 5.2 87.75 31.25 219

40% 342.58

50% 410.69

60% 512.73

70% 682.56

80% 1021.57

90% 2035.36

100% 22,666.98

Table 12 CO2 emission of different power generation. Power category

CO2 emission (g/kW h)

Coal-fired power Hydropower Wind power Nuclear power Solar power Biomass power

975.5 120.35 66.7 70 151.7 106.5

4.2.1.5. Basic operating cost of wind power. The data predicted above in The Assessment of Wind Power Cost is used to calculate wind power cost. Wind power unit cost determinants include not only the accounting costs, such as depreciation, operation and maintenance costs, financial costs and taxes, but also the opportunity cost of the capital funds used by the project, which is reflected by the internal rate of return of the capital fund. Through reasonable parametric analyses, China’s wind power cost development tendency can be analyzed and forecasted scientifically and rationally, revealing that the basic operating cost of China’s wind power in 2016 will be 0.442 Yuan per kW h [86]. 4.2.1.6. Basic operating cost of nuclear power. The cost of nuclear power generation consists of more than 50–60% fixed-asset investment costs, approximately 20% fuel cost and approximately 15% operating cost. A nuclear power station has almost the same operating cost as a coal-fired power station, but its operation is more reliable, showing the highest number of utilization hours in a single year (8000), with an average of 6000. The service life of nuclear power facilities is approximately 30 years longer than that of coalfired power plants. Additionally, the cost structure of nuclear power enables it to be more competitive in later operation. However, stronger safety measures are needed in nuclear power to protect against nuclear accidents, which increases the generation cost. The results of recent research by American Bloomberg New Energy Finance (BNEF) indicate that the average generation cost of global nuclear power stations is 14 cents per kW h (approximately 0.85 Yuan per kW h) [87]. Considering the social cost of nuclear power and using the global average level as a standard, the estimated basic operating cost of China’s nuclear power is 0.85 Yuan per kW h [88]. 4.2.1.7. Basic operating cost of solar power. The technical term for solar power is photovoltaic (PV) power, and its main underlying principle is the PV effect of converting light energy to electric energy by solar panels. The modules in a PV power generation system include auxiliary materials, such as solar panels, solar brackets, DC distribution boxes, PV controllers, off-grid inverters (gridconnected inverters), and AC distribution boxes. The raw materials for solar power generation do not need to be purchased. Thus, the operating costs of solar power can be calculated as follows: The cost of the complete solar power generation system is calculated to be at least 10.5 Yuan per watt, and the daily PV generating capacity per kilowatt of installed capacity is approximately 3 kW h on average. The theoretical service life of such a system is 20 years, and the total generating capacity per kilowatt over the service life is estimated to be 3  365  20  0:8 ðcorrection-factorÞ ¼ 17; 520 kW h. Thus, by amortization of the system’s total cost, the basic operating cost of solar power is calculated to be 0.599 Yuan per kW h [89].

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4.2.1.8. Basic operating cost of biomass power. The cost of biomass power mainly includes the depreciable cost and fuel cost of biomass power stations. The cost of a biomass power generation system is approximately 10,000 Yuan per kW h, the estimated service life is 25 years, and the average annual utilization period is 4356 h. By amortization of the system’s total cost, the basic operating cost is found to be 0.115 Yuan per kW h. The fuel cost is linearly associated with fuel expense and increases as the fuel expense grows. Two methods are adopted in biomass power generation: direct combustion power generation and biomass with coal power generation. The fuel cost of direct combustion power generation is approximately 0.4 Yuan per kW h, while the basic operating cost of biomass with coal power generation is approximately 0.2 Yuan per kW h [90]. 4.2.2. Result From the above data, the estimated generation operating costs of various types of energy sources under different auction rates are shown in Table 13. Combined with the installed capacities of various types of energy sources (Table 14), the power source structure can thus be predicted (Table 15). 4.3. Coal-fired power technological structure analysis and prediction The main greenhouse gas emission mitigation technological costs in China’s electric power sector are described in this section. Technology parameter estimates for China’s coal-fired power in 2015 are shown in Table 16. Carbon emissions baseline estimates of the unit comprehensive generation capacity under various coal-fired power generation technologies are reported in Table 17. Unit price estimates of various coal-fired power generation technologies in 2016 are shown in Table 18. After calculating the values presented above, a prediction of China’s technological structure for coal-fired power in 2016 is made (Table 19). 5. Final results 5.1. Operation cost of emission abatement The carbon emission operating cost of power generation refers to the emission abatement cost during the generation process. Using the cost-optimization dynamic system discussed above, this paper estimates the optimal power source structure of the power industry and dispatch generating capacity for coal-fired power and clean energy from the power demand in 2016. Then, based on the generating capacity and carbon emission rights price, the

Table 14 Installed power generation capacity in 2016. Source: Ref. [96]. Power category

Installed power generation capacity (million kW)

Coal-fired power Hydropower Wind power Nuclear power Solar power Biomass power Total

960 290 100 40 21 23 1434

Table 15 Power source structure (GWH) in 2016. Auction Coal-fired (%) power

Hydropower Wind power

Nuclear power

Solar energy

Biomass energy

0 5 10 15 20 30 40 50 60 70 80

138.45 138.80 139.15 139.49 139.84 140.51 141.18 141.83 142.47 143.10 143.72

13.29 13.37 13.45 13.53 13.60 13.75 13.90 14.05 14.19 14.33 14.47

7.91 7.94 7.97 8.00 8.04 8.10 8.16 8.22 8.28 8.34 8.40

9.46 9.49 9.53 9.56 9.60 9.67 9.73 9.80 9.86 9.92 9.99

427.06 426.40 425.75 425.10 424.45 423.18 421.93 420.69 419.48 418.29 417.12

40.62 40.78 40.94 41.10 41.26 41.58 41.89 42.20 42.50 42.80 43.09

operating costs of emission abatement for coal-fired power and clean energy under different auction rates can be calculated (Table 20). When the auction rate is 5%, the operation costs for coal-fired power and clean energy will be 41 bn Yuan and 2.4 bn Yuan. 5.2. Investment cost of carbon abatement The coal-fired power carbon abatement investment cost is the estimated additional investment cost needed to achieve the optical technological structure installed capacity in the power industry. According to marginal cost estimates of China’s coal-fired power generation technology (Table 18) and technological structure (Table 19) in 2016, the coal-fired power investment cost of carbon reduction can be calculated (Table 20). For clean energy, the investment cost of emission reduction is the estimated additional investment cost of various clean energies needed to reach the optimal technological structure installed capacity in the power industry. According to the predicted power generation unit operating cost of various clean energies and the clean energy power generation

Table 13 Operation cost of different power generation under different auction rate in 2016. Power category

The basic operation costg (Yuan/kW h)

Environmental cost 0%

5%

10%

15%

20%

30%

40%

50%

60%

70%

80%

90%

100%

0.237 0.065 0.442 0.832 0.599 0.415

0.000 0.000 0.000 0.000 0.000 0.000

0.012 0.002 0.001 0.001 0.002 0.001

0.025 0.003 0.002 0.002 0.004 0.003

0.037 0.005 0.003 0.003 0.006 0.004

0.049 0.006 0.003 0.004 0.008 0.005

0.074 0.009 0.005 0.005 0.012 0.008

0.099 0.012 0.007 0.007 0.015 0.011

0.124 0.015 0.008 0.009 0.019 0.014

0.148 0.018 0.010 0.011 0.023 0.016

0.173 0.021 0.012 0.012 0.027 0.019

0.198 0.024 0.014 0.014 0.031 0.022

0.222 0.027 0.015 0.016 0.035 0.024

0.247 0.031 0.017 0.018 0.038 0.027

Auction rate Coal-fired powera Hydropowerb Wind powerc Nuclear owerd Solar powere Biomass owerf a b c d e f g

Ref. Ref. Ref. Ref. Ref. Ref. The

[82]. [82]. [86]. [87]. [89]. [90]. basic operation cost refers to depreciation cost and fuel cost.

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L. Liu et al. / Applied Energy 168 (2016) 594–609 Table 16 Parameters of coal-fired technology in China in 2015. Technology category

Installed capacity

Total installed capacity in 2015 (million kW)

Coal consumption (g/kW h) The operation coefficient (%) Average power generation (kW) ai Hi

Subcritical

0.30 0.60

402.33 145.32

335 327

78.86 81.88

P1 P2

Supercritical

0.60 0.90

242.20 96.88

315 304

81.88 81.82

P3 P4

Ultra supercritical 0.66 1.00

27.46 45.78

295 288

81.88 88.64

P5 P6

Table 17 Carbon emissions baseline estimates of the unit comprehensive generation capacity of various coal-fired power generation technologies. Category

Technology category

Year Fuel gas Coal-fired

Installed capacity (million kW)

Carbon emission baseline estimates (ton/Ten thousand kW h) Ei 2013 3.8 7.44 7.686 7.951 7.954 8.155 8.218

– 1.00 0.66 0.90 0.60 0.60 0.30

Ultra supercritical Supercritical Subcritical

2014 3.8 7.403 7.647 7.911 7.914 8.114 8.177

2015 3.8 7.366 7.609 7.871 7.875 8.074 8.136

Table 18 Unit price estimates of various coal-fired power generation technologies in China in 2016. Installed capacity

Technology category Marginal abatement cost (Yuan/kW)

Year 2  30 Ten thousand kW 2  60 Ten thousand kW 2  60 Ten thousand kW 2  90 Ten thousand kW 2  66 Ten thousand kW 2  100 Ten thousand kW

Subcritical Subcritical Supercritical Supercritical Ultra supercritical Ultra supercritical

2012 4349 4082 3554 3287 3324 3355

Increased percentage from last year (%) Marginal abatement cost (Yuan/kW) 2013 4394 4123 3646 3367 3242 3334

2014 4439.26 4164.23 3740.07 3448.82 3162.02 3313.00

1.01 1.01 1.03 1.02 0.98 0.99

2015 4484.98 4205.87 3836.56 3532.62 3084.02 3292.12

2016 4531.18 4247.93 3935.54 3618.47 3064.59 3271.38

Table 19 Technological structure in the coal-fired generation in China in 2016: million kW. Auction rate

2  30 2  60 2  60 2  90 2  66 2  10

Ten Ten Ten Ten Ten Ten

thousand thousand thousand thousand thousand thousand

kW kW kW kW kW kW

subcritical subcritical supercritical supercritical ultra supercritical ultra supercritical

0%

5%

10%

15%

200%

30%

40%

50%

60%

70%

80%

90%

100%

253.4 215.9 152.5 131.3 16.96 20.55

253.4 208.6 152.5 128.7 17.3 28.8

253.4 201.3 152.5 126.1 19.0 35.7

253.4 194.3 152.5 123.6 20.9 42.2

253.4 187.5 152.5 121.1 23.0 48.2

253.4 180.9 152.5 118.7 25.3 52.5

253.4 174.6 152.5 116.3 27.8 56.4

253.4 168.5 152.5 114.0 30.6 59.7

253.4 162.6 152.5 111.7 33.7 62.6

253.4 156.9 152.5 109.5 37.1 65.0

253.4 151.4 152.5 107.3 40.8 66.8

253.4 146.1 152.5 105.2 44.8 68.0

253.4 141.0 152.5 103.0 49.3 68.6

dispatching capacities verified by the technological structure, China’s clean energy carbon abatement investment cost for 2016 can be calculated (Table 20). When the auction rate is 5%, the investment cost of carbon emission abatement for coal-fired power and clean energy will be 742.5 bn Yuan and 770.5 bn Yuan respectively. 5.3. Marginal and total abatement costs Emission abatement and unit cost estimates for the power generation industry under different auction rates are shown in Tables 20 and 21, respectively. As shown in Table 21, when the auction rate accounts for 5%, the marginal abatement cost of clean power generation is 0.121 Yuan/kW h, while that of coal-fired power generation is

0.123 Yuan/kW h. That is, with 5% allowance auction, the marginal abatement cost of the electric power sector is 0.244 Yuan/kW h. Moreover, when the auction rate increases from 5% to 80%, the marginal cost of emission reduction for coal-fired power increases from 0.1230 to 0.5694 Yuan/kW h, corresponding to an increase of 373%, while for clean-energy, the marginal cost ranged from 0.1214 to 0.1746 Yuan/kW h, representing a 44% increase. Thus, the auction rate has a greater impact on coal-fired power generation than on clean energy. Table 22 depicts the total abatement cost for power generation and shows that as the free carbon emission allowance decreases, the carbon emission abatement cost increases, as does the growth rate. For example, when the carbon emission allowance auction rate rises from 5% to 10%, the corresponding total cost will increase

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Table 20 Abatement cost of electricity generation sector under different auction rate (billion Yuan). Auction rate (%)

0 5 10 15 20 30 40 50 60 70 80

Abatement cost of coal-fired power generation

Abatement cost of clean energy power generation

Operating costs

Investment cost

Total cost

Operating costs

Investment cost

Total cost

0 41 86.4 136.9 193.4 330.1 511.2 763.4 1139.7 1764.2 3007.8

754.6 742.5 730.2 718.1 706.2 690.8 675.7 660.9 646.3 632.1 618.1

754.6 783.6 816.6 854.9 899.6 1020.9 1186.9 1424.2 1786.1 2396.3 3625.9

0 2.4 5.2 8.3 11.7 20.2 31.6 47.6 71.6 111.8 192.2

761.4 770.5 779.5 788.4 797.3 818.4 839.3 859.9 880.1 899.9 919.5

761.4 772.9 784.7 796.7 809.0 838.6 870.9 907.4 951.7 1011.7 1111.7

Table 21 Marginal abatement cost of electricity generation sector under different auction rates (Yuan/kW h). Auction Marginal abatement rate (%) cost of coal-fired power generation (Yuan/kW h)

Marginal abatement cost of clean energy power generation (Yuan/kW h)

Marginal abatement cost of electricity generation sector (Yuan/kW h)

0 5 10 15 20 30 40 50 60 70 80

0.1185 0.1230 0.1282 0.1343 0.1413 0.1603 0.1864 0.2237 0.2805 0.3763 0.5694

0.2381 0.2444 0.2515 0.2594 0.2683 0.2920 0.3231 0.3662 0.4299 0.5352 0.7440

0.1196 0.1214 0.1232 0.1251 0.1270 0.1317 0.1368 0.1425 0.1494 0.1589 0.1746

Table 22 Abatement cost of electricity generation sector under different auction rate (billion Yuan). Auction rate (%)

Abatement cost of electricity generation sector

Increment under ETS

Increased percentage (%)

0 10 20 30 40 50 60 70 80

1516 1601.2 1708.6 1859.4 2057.7 2331.6 2737.7 3408.0 4737.6

85.2 107.3 150.8 198.2 273.8 406.1 670.3 1329.5

5.62 6.71 8.83 10.66 13.31 17.42 24.48 39.01

from 1556.5 bn Yuan to 1601.3 bn Yuan, an increase of 2.86%. Furthermore, when the auction rate is 20%, the corresponding cost increases by 5.62% compared with that of 10%. When the auction allowance accounts for 80%, the cost increases by 39% compared with that of an auction allowance percentage of 70%, which is a relatively high growth rate and is high enough to exceed the rational scope.

6. Discussion From the results we obtained in this research, we can conclude that if auction is implemented in China’s carbon trading market, it will exert a substantial impact on the electric power sector and become a source of pressure on the electricity generation cost

Abatement cost of electricity generation sector

1516.0 1556.5 1601.3 1651.6 1708.6 1859.5 2057.7 2331.6 2737.8 3408.1 4737.7

and the electricity price. In fact, even when the auction rate is only 5%, increases of 0.123 Yuan/kW h and 0.121 Yuan/kW h, respectively, would create a heavy burden on both coal-fired power generation and clean energy. The reasons are as follows: First, in view of thermal power generation cost, the basic operating cost is 0.237 Yuan/kW h (shown in Table 13). 0.123 Yuan/kW h of the marginal abatement cost for the thermal power is almost 50% of the operation cost of thermal power. Second, currently, power generation enterprises have no decision-making power with regard to electricity price. Chinese Government is in charge of the price setting and adjustment according to the historical level and the need of the newly-added cost [91]. In addition, there is no clear method and rules for power producers to participate in the price setting and adjustment. The power generation enterprises could not submit to the government and regulatory authorities for approval if they want to adjust the electricity rate. Power generation producers are also not able to first adjust the price according to coal price fluctuation, and then report to the government and regulatory authorities for approval as American power producers do. In April 2015, the State Council of China issued a Notation on Lowering the Feed-in Tariff of Coal-fired Power and the Electricity Price for Industrial and Commercial Use. After the adjustment, the feed-in tariff of coal-fired power ranged from 0.35 to 0.50 Yuan/kW h in the various provinces [92]. The marginal abatement cost of coal-fired power, 0.123 Yuan/kW h, is approximately 25–33% of the feed-in tariffs. In this case, with the abatement cost increase and government’s decision to lower the electricity price will dually squeeze the profits of power generation enterprises. Thirdly, according to the data from State Grid, the total power generated in 2015 will be over 63,000 billion kW h [92], only if 0.01 Yuan/kW h increase will incur 630 billion Yuan of the total generation cost increase. Therefore, if the carbon allowances were to be partially auctioned, the Chinese central government should carefully consider the emission reduction cost burden on power generating enterprises. As the auction rate increases, its impact on the electric power sector also increases, especially with regard to coal-fired power. Meanwhile, Fig. 4 reveals the increasing trend of marginal cost of carbon emission reduction for the electric power sector in China. The marginal costs for coal-fired power for auction rates of 5–40% increase from 0.1230 to 0.1864 Yuan/kW h, and for auction rates exceeding 40%, the growth rate of coal-fired power accelerates. Thus, 40% may represent a tipping point, after which the auction rate would incur too much additional cost to be applicable. In addition, the Chinese government intends to establish an integrated carbon trade market in China by 2017. However, currently, shortcomings, such as the lack of a real-time carbon price and dominated spot transactions, indicate that China’s carbon trade market is far from a functional system. Additional, a quick

L. Liu et al. / Applied Energy 168 (2016) 594–609

607

Fig. 4. Marginal abatement cost of electricity generation sector under different auctioning rate.

market integration of China’s carbon market also appears remote [1,93,94]. Because the electric power sector is not well prepared for carbon trading, the carbon trade market remains in its infancy, and the unit cost of carbon abatement remains high suggest a pessimistic outlook for the implementation of allowance auctioning. However, a new round of electricity power system reforms would likely favor power generation enterprises and partial allowance auction. In March 2015, the Central Government of China and State Council’s Suggestions on Furthering Electric Power System Reform was issued. Based on these suggestions, the grid company will be in charge of only the transmission and distribution of electricity, and the electricity price will be exclusively determined by a negotiation between the suppliers and demanders of electricity. More decision-making power may provide more profit-making space for power generation enterprises and possibly transfer the carbon emission abatement cost to the electricity price and eventually to the end consumers, who are the real ratepayers.

7. Conclusion Partially auctioning emission allowances will increase the emission abatement cost of electric power generation enterprises. From the angle of cost burden, the increase of auctioning rate will cause the cost of the electric power generation industry to have the tendency to rise. When only 5% is auctioned, the unit costs for coalfired power and clean energy would be 0.123 and 0.121 Yuan/ kW h, respectively. The total additional abatement cost (0.244 Yuan/kW h) would be shouldered by the power plants and would represent a substantial burden. Both the high cost to be induced by the auction and the immaturity of current carbon trade market in China would hinder the further development of such an auction in the near future. Currently, the cost increase brought about by the carbon trading has not been well estimated and acknowledged by the Chinese society. The estimation method used in this system can carry out cost analysis according to the real-time data and provide a basis for policy formulation of different years. Thus, this result has important implications for regulatory authorities in China. The endowment of permits was observed in pilot carbon trade markets, and allowance auction has not been implemented yet. An emission abatement cost sensitivity analysis based on different percentages of auctioned allowance is necessary to make predictions and serve as a basis for the government to evaluate allowance allocation methods. The development stage of the power generation industry must be carefully considered, and the free allowance should be gradually decreased according the industry’s ability to absorb the emission abatement costs. More importantly, government interfer-

ence should be maximized to cushion the substantial impacts of such auctioning to maintain healthy overall economic development. Acknowledgement This research is supported by Beijing Social Science Fund (Grant No. 15JGB092). Appendix A In [35], electricity demand is dispatched to each plant type using a heuristic which mimics load taking/shedding activities in power systems. The heuristic executes demand dispatch to nearly minimize the total annual generation cost. The main principle is that the demand assigned to each power plant is inversely proportional to its generation cost. This is what this dispatch heuristic mimics. The inverse proportionality between the energy generated by a plant and the ratio of its generation costs is formulated as:

bi /

  ci 1 Rj c j

ð12Þ

The demand–supply ratio D/S and the scalar r1 are used to calculate the fractional generations at each iteration. Iteration 1: Calculate bi1 and d1 :

  ci D   r1 bi1 ¼ 1  S Rj c j

r1 ¼

1 1  ðRj cj sj =ðRj cj ÞðRj sj ÞÞ

ð13Þ

ð14Þ

Note that when D ¼ 0; bi ¼ 0. With dn , the total demand becomes equal to the total generation (a fundamental property of any dispatch algorithm). This condition is formulated in Eq. (20). Eq. (13) in iteration 1 does not guarantee that all bi1 6 1. Hence, in the second iteration, the excess demand, given in Eq. (16), is re-distributed among the power plants that have smaller-thanone fractional generation, and new fractional generations are calculated using Eq. (18). This procedure is repeated until bi1 6 1 for all i. Iterations 2–7: n.1: Define set Xn as the set of power plants with biðn1Þ P 1:

Xn ¼ fj : bjðn1Þ P 1g DDn ¼

X i2Xn

ðbi1  1Þsi

ð15Þ ð16Þ

608

L. Liu et al. / Applied Energy 168 (2016) 594–609

Sn ¼

X

ð17Þ

si

i2Xn

[13]

n.2: Calculate bi;n :

bi;n ¼ bi;n1 þ 1  P

ci j2Xn cj

! 

DDn  rn ; Sn

bi;n ¼ 1; for all i 2 Xn

rn ¼

1  1  RjXn ðcj sj Þ=ðRjXn cj ÞðRjXn sj Þ 

[14]

ð18Þ [15]

ð19Þ

[17]

ð20Þ

Note that the fractions bi;7 satisfy the two conditions below:

X bi Si ¼ D

[16]

ð21Þ

[18]

[19]

[20]

i

[21]

bi;n 6 1; for all ði; nÞ

ð22Þ

bi : fractional electricity generation of plant i; ci : electricity generation cost of plant i ($/MW h); D: wholesale electricity demand (MW h/year); S: total supply from all plant types (MW h/year); si : electricity generation capacity of plant i (MW h/year); rn : multiplicative scalar at iteration n; Xn : set including power plants with larger than one fractional generation (bi) at iteration n – 1; X0n : complementary set of Xn ; DDn : unassigned excess demand from iteration n  1 (MW h/year). Condition (20) is shown to hold for DDn in each iteration n, whereas condition 11 is satisfied by Eqs. (18) and (19) in all iterations. Tests with real data indicate that this algorithm is an adequate representation of annual power generation. A similar dispatch heuristic was used in [68]. References [1] Liu L, Chen C, Zhao Y, et al. China’s carbon-emissions trading: overview, challenges and future. Renew Sustain Energy Rev 2015;49:254–66. [2] Bailey Ronald. China pledges to enact cap-and-trade carbon market in 2017. . [3] Liu L, Zong H, Zhao E, et al. Can China realize its carbon emission reduction goal in 2020: from the perspective of thermal power development. Appl Energy 2014;124(1):199–212. [4] The first report on carbon constrain of china listed companies was issued, the carbon emission of electric power generation companies rank first. China Stock Daily, Sep 30, 2013. [5] Goeree JK, Karen P, Holt CA, et al. An experimental study of auctions versus grandfathering to assign pollution permits. J Eur Econ Assoc 2009;8(2– 3):514–25. [6] Woerdman E, Weishaar SE. Pros and cons of auctioning emission rights: a law and economics perspective. SSRN Electron J 2010:27–43. [7] Liu B, He P, Zhang B, et al. Impacts of alternative allowance allocation methods under a cap-and-trade program in power sector. Energy Policy 2012;47 (10):405–15. [8] Burtraw D, Kahn D, Palmer K, et al. CO2 allowance allocation in the regional greenhouse gas initiative and the effect on electricity investors. Electricity J 2006;19:79–90. [9] Hepburn Cameron, Grubb Michael, Neuhoff Karsten, Matthes Felix, Tse Maximilien. Auctioning of EU ETS phase II allowances: how and why? Climate Policy 2006;6:137–60. [10] Möst D, Genoese M, Eßer-Frey A, et al. Design of emission allocation plans and their effects on production and investment planning in the electricity sector. Emissions Trading 2011:71–84. [11] Nanduri V, Otieno W. Assessing the impact of different auction-based CO2 allowance allocation mechanisms. In: 2011 IEEE power and energy society general meeting. IEEE; 2011. p. 1–7. [12] Zhou X, Dong Z, Liebman A, et al. Potential impact of emission trading schemes on the Australian National Electricity Market. IEEE power & energy society

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