Allocation of carbon emission quotas to Chinese power enterprises

Available online at www.sciencedirect.com Available online at www.sciencedirect.com

ScienceDirect ScienceDirect

Availableonline onlineatatwww.sciencedirect.com www.sciencedirect.com Available Energy Procedia 00 (2018) 000–000 Energy Procedia 00 (2018) 000–000

ScienceDirect ScienceDirect

www.elsevier.com/locate/procedia www.elsevier.com/locate/procedia

Energy (2018) 000–000 115–124 EnergyProcedia Procedia152 00 (2017) www.elsevier.com/locate/procedia

CUE2018-Applied Energy Symposium andLow Forum 2018: Low carbon cities andsystems, Applied and Forum 2018: carbon cities urban Applied Energy Energy Symposium Symposium and Forum5–7 2018: Low carbon cities and and urban energy energy systems, urban energy systems, June 2018, Shanghai, China CUE2018, CUE2018, 5–7 5–7 June June 2018, 2018, Shanghai, Shanghai, China China The carbon 15th International Symposium on District Heating and Cooling Allocation Allocation of of carbon emission emission quotas quotas to to Chinese Chinese power power enterprises enterprises 1,2 the feasibility 1 1 the heat 3 1 1 Assessing of using demand-outdoor Huaping Huaping Sun Sun1,2*, *, Gulzara Gulzara Tariq Tariq1,, Hui Hui Chen Chen1,, Jin Jin Zhu Zhu3,, Yue Yue Liu Liu1 and and Chao Chao Wu Wu1 temperature11 Institute function a long-term heat demand forecast of Industrialfor Economics, Jiangsu University, district Zhenjiang 212013 Jiangsu, China Institute of Industrial Economics, Jiangsu University, Zhenjiang 212013 Jiangsu, China 2 School of Environmental Science and Engineering, Shanghai Jiao Tong University,Shanghai 200240, China 2 School of Environmental Science and Engineering, Shanghai Jiao Tong University,Shanghai 200240, China a,b,c and Management, a Nanjing University a of Aeronautics and b Astronautics, Nanjing,c 211106 Jiangsu, China c 3 College of Economics 3 College of Economics and Management, Nanjing University of Aeronautics and Astronautics, Nanjing, 211106 Jiangsu, China

I. Andrić

a

*, A. Pina , P. Ferrão , J. Fournier ., B. Lacarrière , O. Le Corre

IN+ Center for Innovation, Technology and Policy Research - Instituto Superior Técnico, Av. Rovisco Pais 1, 1049-001 Lisbon, Portugal b Veolia Recherche & Innovation, 291 Avenue Dreyfous Daniel, 78520 Limay, France c Département Systèmes Énergétiques et Environnement - IMT Atlantique, 4 rue Alfred Kastler, 44300 Nantes, France

Abstract Abstract

It is essential to study the allocation of carbon emission quotas to key high-carbon industries. Here, we used the theoretical It is essential to study the allocation of carbon emission quotas to key high-carbon industries. Here, we used the theoretical generation performance standard (GPS) to evaluate the carbon inputs and outputs of the five major Chinese power groups. We generation performance standard (GPS) to evaluate the carbon inputs and outputs of the five major Chinese power groups. We predicted their 2017, 2020, and 2030 performances using a grey prediction model based on 2011–2016 data, and then applied the predicted their 2017, 2020, and 2030 performances using a grey prediction model based on 2011–2016 data, and then applied the GPS model to calculate the target year distributions of carbon emissions, finally employing a data envelopment analysis (DEA) Abstract GPS model to calculate the target year distributions of carbon emissions, finally employing a data envelopment analysis (DEA) model to evaluate the effectiveness of these allocations. The carbon trading evaluations and carbon emission reduction potentials model to evaluate the effectiveness of these allocations. The carbon trading evaluations and carbon emission reduction potentials ofDistrict the five major power groups governmental policy must distinguish these effective groups. The DEA showed that China networks are differed; commonly addressed in the literature as one among of the most solutions for decreasing the of the fiveheating major power groups differed; governmental policy must distinguish among these groups. The DEA showed that China Datang Corporation (in 2017, 2020 and 2030), China Huadian Corporation (in 2017, 2020), China Guodian Corporation (inthe 2017, greenhouse gas emissions from theand building These systems require(inhigh investments which are returned through heat Datang Corporation (in 2017, 2020 2030),sector. China Huadian Corporation 2017, 2020), China Guodian Corporation (in 2017, 2020) and State Power Investment (in 2017, 2020 and 2030) are invalid. Thus, there is much room for improvement in carbon sales.and DueState to the changed climate renovation policies, demand theimprovement future couldindecrease, 2020) Power Investment (inconditions 2017, 2020and andbuilding 2030) are invalid. Thus, thereheat is much roominfor carbon emission efficiency. To ensure reductions in carbon emissions, it is essential to improve the coal consumption efficiencies. China prolonging the investment return period. in carbon emissions, it is essential to improve the coal consumption efficiencies. China emission efficiency. To ensure reductions Datang Corporation, China Huadian Corporation, China Guodian Corporation and State Power Investment should reduce coal Datang Corporation, China Huadian Corporation, ChinaofGuodian Corporation and State Power Investment should coal The main scope of this paper is to Datang assess the feasibility heatInvestment demand – outdoor temperature function for reduce heat demand consumption in 2020; while China Corporation andusing State the Power should reduce coal consumption in 2030. To consumption 2020; while China Datang Corporation and State Power reduceThe coaldistrict consumption in 2030. forecast. Theinemissions district ofvia Alvalade, located inofLisbon wasInvestment used a should casehigher study. is consisted of To 665 reduce carbon rational allocation quotas,(Portugal), the government mustasimpose standards on carbonand energyreduce carbon via rational allocation of and quotas, the government must impose higher on carbonand buildings thatemissions vary support in both construction typology. Three weather scenarios (low,standards medium, threeenergydistrict intensive industries; clean energyperiod by formulating preferential fiscal and tax policies; encouragehigh) the and development of intensive industries; support clean energy by formulating preferential and tax encourage development of renovation scenarios were developed (shallow, intermediate, deep). Tofiscal estimate the policies; error, obtained heat the demand values and were renewable energy technology; encourage enterprises to strengthen technical cooperation; seek breakthroughs in transition renewable energy technology; encourage enterprises to strengthen technical cooperation; seek breakthroughs in transition and compared with results from a dynamicand heatenhance demandthe model, previously validated by the authors. energy emission reduction technology; carbon emissiondeveloped efficiency and of electric power enterprises. energy emission reduction technology; and enhance the carbon emission efficiency of electric power enterprises. The results showed that when only weather change is considered, the margin of error could be acceptable for some applications Copyright © Elsevier Ltd. All rights reserved. Copyright ©in2018 2018 Elsevier Ltd.was All rights than reserved. Copyright © 2018 Elsevier Ltd. Alllower rights reserved. (the errorand annual demand 20% for all weather scenarios considered). However, afterand introducing renovation Selection peer-review under responsibility of the scientific committee of Applied Energy Symposium Forum 2018: Low Selection and peer-review underresponsibility responsibility the scientific committee ofand therenovation CUE2018-Applied Energy Symposium and Selection and peer-review under ofofthe scientific committee of Applied Energy Symposium and Forum 2018: Low scenarios, the error value increased up to 59.5% (depending on the weather scenarios combination considered). carbon cities and urban energy systems, CUE2018. Forum 2018: Low carbon cities and urban energy systems. carbon citiesof andslope urban energy systems, CUE2018. The value coefficient increased on average within the range of 3.8% up to 8% per decade, that corresponds to the Keywords: Power enterprises; carbon emissions; GPS; DEA; grey prediction decrease Power in theenterprises; number ofcarbon heating hours of 22-139h during the heating season (depending on the combination of weather and Keywords: emissions; GPS; DEA; grey prediction renovation scenarios considered). On the other hand, function intercept increased for 7.8-12.7% per decade (depending on the scenarios). The values suggested could be used to modify the function parameters for the scenarios considered, and 1.coupled Introduction 1.improve Introduction the accuracy of heat demand estimations.

With the globalization of the world economy and increases in population, global warming caused by human With the globalization of the world economy and increases in population, global warming caused by human greenhouse emissions, and the increasing environmental problems attributable to production that relies on © 2017 The gas Authors. Published by Elsevier Ltd. greenhouse gas emissions, and the increasing environmental problems attributable to production that relies on carbon consumption, have attracted worldwide attention.ofTo sustainable economiconand social development, Peer-review under responsibility of the Scientific Committee Theachieve 15th International Symposium District Heating and carbon consumption, have attracted worldwide attention. To achieve sustainable economic and social development, China aims to reduce its CO2 emissions by 60–65% in 2030 compared to 2005. Presently, China is constructing a Cooling. China aims to reduce its CO2 emissions by 60–65% in 2030 compared to 2005. Presently, China is constructing a national carbon market. It is thus essential to distribute carbon emission rights appropriately among different regions national carbon market. It is thus essential to distribute carbon emission rights appropriately among different regions Keywords: Heat demand; Forecast; Climate change and key industries. and key industries. * Corresponding author. Tel.: +86 13861351939; fax: +86 511 88792188. * Corresponding author. Tel.: +86 13861351939; fax: +86 511 88792188. E-mail address: [email protected] E-mail address: [email protected] 1876-6102 © 2017 The Authors. Published by Elsevier Ltd. Peer-review under responsibility of the Scientific Committee of The 15th International Symposium on District Heating and Cooling. 1876-6102 Copyright © 2018 Elsevier Ltd. All rights reserved. Selection and peer-review under responsibility of the scientific committee of the CUE2018-Applied Energy Symposium and Forum 2018: Low carbon cities and urban energy systems. 

116

Huaping Sun et al. / Energy Procedia 152 (2018) 115–124 Author name / Energy Procedia 00 (2018) 000–000

2

As environmental concerns come to the fore in China, transition to a green low-carbon economy is imperative to ensure sustainability; companies must further reduce carbon emissions. Allocation of carbon emission quotas to high-carbon industries must be optimized from both economic and environmental perspectives. Here, we develop a method whereby carbon quota allocations are optimized by adjusting the inputs and outputs of power enterprises. First, we predict these values in 2017, 2020 and 2030 for the five major Chinese power generators using the Generation Performance Standard (GPS) model in combination with grey prediction based on 2011–2016 data. We then calculate carbon emission distributions by year using a standard power generation model. Finally, we evaluate the effectiveness of quotas distribution using the data envelopment analysis (DEA) model, and advance suggestions as to how inputs and outputs should be changed. To achieve the desired reductions in emissions, and to distribute carbon emission rights reasonably, the government must impose higher standards on carbon- and energy-intensive industries; support clean energy by formulating preferential fiscal and tax policies; encourage the development of renewable energy technology; further reform the power industry; strengthen governmental technological guidance and policy innovation in terms of energy conservation and emission reductions; urge enterprises to improve technological cooperation; seek breakthrough/leapfrogging energy conservation and emission reduction technologies; and actively enhance the carbon emission efficiency of power enterprises. 2. Literature Review The DEA model of Charnes et al. (1978) (also termed the CCR model), which has been widely used to evaluate carbon emission efficiency both in China and elsewhere, features constant-scale compensation [1]. Banker et al. (1984) later changed the scale compensation, creating the BC2 model [2]. Cook et al. (1999) developed a resource allocation approach based on the DEA model; decision-making unit (DMU) efficiency was aligned with preallocation [3]. Beasley (2003) developed a nonlinear, DEA resource allocation approach considering both inputs and outputs [4], and Lins et al. (2003) presented a zero-sum benefit model based on input/output allocation [5]. Färe et al. (1983) studied the relationships among scale efficiencies, technical efficiencies, and input factors, under both scale return invariability and variability, using a DEA method based on radial distance functions [6]. Ang (2004) addressed the methodological aspects of energy policy assessment [7]. Zhou et al. (2008, 2010) decomposed carbon emission performance and used the Malmquist index to empirically research the 18 largest carbon-emitting economies worldwide [8–9]. Long et al. (2017) used the directional distance function (DDF) and directional relaxation-based measurement (DSBM) to measure the ecological efficiency of the Chinese cement industry [10]. Liu et al. (2018) sought efficient decomposition of primary Chinese, carbon emission quotas [11]. Many other scholars have studied carbon emission allocations in the power industry using various models, including the DEA. Yaisawarng and Klein (1994) [12] used the cost of controlling pollutant emission as an input, and the levels of SO2 and other pollutants as outputs, when studying the technical efficiency of US coal power enterprises in the 1980s. Tyteca (1997) [13] used various DEA models to measure the environmental performance of American thermal power plants, and found that the models prioritized different features to yield significantly different efficiencies. Sueyoshi and Goto (2001) [14] introduced a DEA model based on a relaxation variable, emphasizing dynamic efficiency; Lam and Shiu (2001) [15] combined DEA with regression analysis to analyze the efficiency of heating in China in 1995–1996; outdated technology and inefficient use of labor compromised power generation efficiency. Wang et al. (2013) used a data package model to evaluate the allocation of carbon emissions to power industries based on generator performance, and concluded that the allocation method was effective and fair [16]. Bonacina et al. (2007) employed a competitive edge model to analyze power pricing in a carbon emissionstrading scenario [17]. Zhang (2017) considered the national carbon market to be based on carbon emission quotas when analyzing quantitative relationships among several key indicators [18]. Lin et al. (2015) discussed quota allocation based on historical emissions [19]. Song et al. (2017) adopted the principle of “first allocate on the basis of fairness, and then optimize efficiency” when allocating carbon emission quotas to Chinese provinces [20]. Bian (2010) proposed a DEA method for allocation of pollutant emission quotas [21]. Zeng (2010) evaluated the traditional carbon emission distribution model, reviewing the characteristics of the electric power industry and, after considering fairness, proposing a mechanism of carbon emission allocation that reduced market effects [22]. Li et al. (2012) developed a carbon flow tracing model incorporating both fairness and power transmission characteristics [23]. Song et al. (2013) considered the low-carbon market, low-carbon technology, and low-carbon policy when developing a planning



Huaping Sun et al. / Energy Procedia 152 (2018) 115–124 Author name / Energy Procedia 00 (2018) 000–000

117 3

model for low-carbon power based on regional comparisons of carbon emission quotas [24]. Tan (2013) combined various models to explore how the emission quota allocation mechanism impacted electricity trading and found that, if carbon emission rights were allocated to highly efficient generators, the efficiency of large-capacity units and power transactions would improve [25]. Luo (2014) explored various carbon emission distribution methods and developed a weighted-average method that combined historical and datum distribution methods [26]. Chen et al. (2016) presented an initial carbon emission allocation model for individual and grouped power plants, seeking to optimize power supply, implement national policies, and increase efficiency [27]. Mei et al. (2016) established an overall distribution model that dealt with fairness and efficiency via an iterative approach [28]. Most research on the Chinese power industry has focused on the relationship between economic growth and the distribution of carbon emission quotas, and only rarely on the micro-perspectives of power enterprise inputs and outputs. Here, we use a theoretical method and a grey prediction model of power GPSs to measure the carbon emission potentials of the five major Chinese power generators, and to predict carbon emission efficiencies in target years via DEA evaluation. We also advance policy suggestions in terms of input/output adjustment; these have important theoretical and practical ramifications for the development of an optimized national carbon trading market, by modulating the carbon emission rights of power companies. 3. Research Methodology and Proposed Model 3.1. The Generation Performance Standard The GPS refers to the amount of pollutants produced by a generator unit/power plant/power company per kilowatt-hour (kWh); this reflects emission intensity [1]. The GPS first determines the overall power generation performance in a certain year, and then allocates emission quotas according to the planned power generation capacity of each group in the target year. This method is both scientific and reasonable, and is widely used in the power industry because it considers power generation capacity when allocating carbon quotas. The basic model is: GN =

QN EN

m

(1)

QN = ∑QN ,i

(2)

E N = ∑E N ,i

(3)

i =1 m

i =1

GF =

QF EF m

QF =∑QF ,i i =1

m

(4) (5)

EF = ∑E 'F ,i

(6)

GF < GN

(7)

i =1

The formula for the initial allocation of emission quotas is:

 QF ,i GF EF ,i 

The symbols are explained below in Table 1. Table 1. The symbols used in the proposed model Definition Symbol GN GPS value Current CO2 emission QN Current value of power generated EN Current CO2 emission of enterprise i QN,i Current value of power generation by enterprise i EN,i

（8）

Huaping Sun et al. / Energy Procedia 152 (2018) 115–124 Author name / Energy Procedia 00 (2018) 000–000

118

4

GPS value in the target year The total estimated CO2 emissions by the power industry as stipulated by the Government The estimated value of power generated in the target year The predicted CO2 emissions in the target year by enterprise i The estimated value of power generated by enterprise i in the target year Initial emission quota allocated in the target year to enterprise i A factor determined by capacity, power generation features, and the type of power generated

GF QF EF QF,i E’F,i Q’F,i



3.2. Data envelopment analysis The DEA quantitatively explores relative effectiveness using comparable units (multiple inputs and outputs indicators) via linear programming. DEA projects minimum input and maximum output boundaries using input and output data. The method evaluates the relative effectiveness of similar DMUs. The inputs that we employed were the installed capacity, power consumption, and electricity consumption rates of the five major power groups; the output was power generation. The DMU yielded by DEA combines the inputs and outputs of the five power generators. Finally, a “projection principle” is used to analyze the improvements that inefficient DMUs must make, thus providing managers with important information. We adopt the C2R form of DEA, as follows: (9) min[θ -ε (eT 1S - + eT 2 S + )] = θ * n

∑λ X j =1

j

j

+ S - = θX 0

n

∑λ Y -S j =1

j

n

∑λ j =i

j

j

+

(10)

= Y0

(11)

=1

(12)

λ j ≥ 0, j = 1, , n

(13)

S j + ≥ 0, i = 1,, s

(14)

Si - ≥ 0, i = 1, , m

(15)

Definitions of the symbols are shown in Table 2. Table 2. The DEA symbols Symbol Definition Relative efficiency θ ε A non-Archimedean, infinitely small value eT1 m-dimensional unit column vector SThe difference vector eT2 The s-dimension unit column vector S+ The output difference vector n The number of DMUs to be evaluated The relative target efficiency θ*

λj

n DMU weight combination

Xj X0 Yj Y0

The jth DMU input vector Evaluation of the DMU input The output vector of the jth DMU Evaluation of the DMU output



Huaping Sun et al. / Energy Procedia 152 (2018) 115–124 Author name / Energy Procedia 00 (2018) 000–000

119 5

3.3. Grey prediction model A grey prediction model makes predictions using only small amounts of incomplete information. The model considers predictions of a system that contains both known and unknown (or uncertain) information as a grey process in terms of time, but with clear directionality. Of the four types of grey prediction, we used the sequence method to predict phenomena over time. We obtained data for the five generators over the past 5 years; thus, there were relatively few data points in a short time span, which met the conditions for grey prediction. We employed the following steps:

x（0）(n) from previous years: = {x (0) (1), x (0) (2), x (0) (3), x (0) (4), x (0) (5)}

(1) Obtain a time sequence using data

x (0)

(2) Form a new sequence x(i) employing accumulation: k

x (1) (k ) = ∑x ( 0 ) ( m) m =1

(3) Establish data matrices B, YN:

   B=     

 1 (1) (x (1)  x (1)(2) 1  2   1 (1) (x (2)  x (1)(3) 1  2   1 (1) (x (3)  x (1)(4) 1 2   1 (1) (x (4)  x (1)(5) 1 2  (4) Calculate the parameters a and μ :

YN

 x (0)(2)    x (0)(3)    (0)  x (4)  x (0)(5)  

a  a  (B T B ) 1 B T Y N    

(5) Establish the model. The differential equation of the model is:

dx (1) + ax (1) = μ dt

where a is the development grey number and The time response function is:

xˆ(1)(k  1)  (x (1)(0) 

μ is the endogenous control grey number.

  ak  )  a a

xˆ (1) (k +1) is a cumulative value measured over the previous k + 1 years using the formula: x (0) (k ) = xˆ (1) (k ) - xˆ (1) (k -1)

where

(6) Restore the corresponding value of the kth year. 4. Results and Analysis 4.1. Initial allocations of carbon emission quotas

4.1.1 Generating capacity in target years Using the power generated annually by the five groups in 2011–2016, the Grey model was used to predict the power generation in 2017, 2020 and 2030 (Table 3).:

120

Huaping Sun et al. / Energy Procedia 152 (2018) 115–124 Author name / Energy Procedia 00 (2018) 000–000

6

Table 3. Forecasted power generation by the five major producers, and the total power generated, based on 2011– 2016 data (unit: 100 million kWh) China China China Datang China Guodian State Power Year Huaneng Huadian Total Corporation Corporation Investment Group Corporation 2011 6,046.31 5,080.19 4,178.06 4,770.00 3,259.87 47,130.20 2012 6,092.26 5,060.36 4,323.12 4,898.00 3,493.93 49,875.50 2013 6,493.04 4,940.25 4,612.31 5,332.50 3,678.12 54,316.40 2014 6,461.17 4,968.18 5,008.03 5,014.00 3,805.33 57,944.57 2015 6,146.05 4,788.32 4,837.10 4,837.00 3,807.87 58,145.73 2016 6,216.12 4,760.80 4,919.07 5,052.00 3,969.34 61,425.21 2017 6,252.44 4,682.30 5,170.33 4,971.02 4,084.21 64,772.70 2020 6,233.31 4,472.13 5,644.85 4,916.04 4,451.06 74,643.38 2030 6,127.18 3,837.36 7,564.29 4,737.11 5,929.00 119,762.86 4.1.2 Target CO2 emissions To accurately derive initial carbon emission quotas, we used the Grey model to predict the coal consumption of the five generators, and the national average (Table 4). Table 4. Predicted coal consumption by the five generators [unit: g/(kWh)] China China China China State Power Huadian Guodian Year Huaneng Total Datang Investment Corporation Corporation Group Corporation 2011 318.68 321.47 321.00 323.43 321.77 329.00 2012 316.52 318.89 317.00 319.07 317.07 325.00 2013 312.89 316.59 313.00 316.40 313.50 320.97 2014 310.01 312.86 309.88 312.80 309.73 319.00 2015 305.78 309.62 305.20 310.40 307.50 315.00 2016 302.35 306.97 303.10 308.50 304.90 312.00 2017 299.02 303.85 299.09 305.37 301.53 308.91 2020 288.92 295.01 288.94 297.53 292.81 299.75 2030 257.66 267.35 257.54 272.83 265.53 271.11 Note: The 2011–2016 data were derived from the social responsibility and sustainable development reports of the five major groups; the consumptions amounts in 2017, 2020 and 2030 are predictions. The literature [16] shows that 1 kg of standard coal is converted to 2.493 kg of CO2; this fact, in conjunction with the data in Table 3, allows calculation of the CO2 emissions of the five generators in 2017, 2020 and 2030 (Table 5). Table 5. CO2 emissions by each of the five generators and the total values [unit: g/(kWh)] China China China State Power China Datang Huaneng Huadian Guodian Total Year Investment Corporation Group Corporation Corporation 2017 745.4569 757.4981 745.6314 761.2874 751.7143 770.1126 2020 720.2776 735.4599 720.3274 741.7423 729.9753 747.2767 2030 642.3464 666.5036 642.0472 680.1652 661.9663 675.8772 4.1.3 GPS benchmarking and initial carbon emission quotas in target years Formula (4) shows that the GPS reference values for 2017, 2020 and 2030 can be calculated using national power generation and CO2 emission data. Formula (8) shows that the initial carbon quotas for the five major generators should be as listed in Table 6. Table 6. Initial allocation of carbon emissions to the five power generators in various years (unit: million tons). China Huaneng China Datang China Huadian China Guodian State Power Year Group Corporation Corporation Corporation Investment 2017 481.51 360.59 398.17 382.82 314.53



Huaping Sun et al. / Energy Procedia 152 (2018) 115–124 Author name / Energy Procedia 00 (2018) 000–000

2020 2030

465.80 414.12

334.19 259.36

421.83 511.25

367.36 320.17

121 7

332.62 400.73

4.2. Index selection and data processing CO2 emissions are undesirable outputs. The DEA model uses hyperbolic, directional distance, reciprocal transformation, singular boundary, and input methods to solve the problem of undesirable outputs. When choosing inputs and outputs, the principal consideration is whether the indicators are relevant. Apart from inputting, no effective solution may be available. Therefore, we used relevant inputs, as follows. 4.2.1 Inputs Data from 2011–2016 were used to predict the capacities of the five generators in 2017, 2020 and 2030 using the grey model (Table 7). Table 7. The capacities of the five power generators in 2011–2030 (unit: 10,000 kW). China China Datang China Huadian China Guodian State Power Year Huaneng Corporation Corporation Corporation Investment Group 2011 12,538 11,106 9,410 10,672 7,680.2 2012

13,508

11,380

10,180

12,008

8,007.4

2013

14,224

11,543

11,276

12,279

8,967.78

2014

15,149

12,047

12,254

12,520

9,667.47

2015

16,063

12,717

13,476

13,500

10,740.15

2016

16,554

13,090

14,281

14,295.8

11,662.94

2017

17,613

13,600

15,704

14,774

12,829.54

2020 20,606 15,240 20,214 16,933 16,938.27 2030 34,767 22,275 46,901 26,677 42,762.42 Note: The data for 2011–2016 are derived from the social responsibility and sustainability reports of the five power generators. The capacities for 2017, 2020 and 2030 are predictions. Based on data from 2011–2016, the grey prediction model was used to predict auxiliary power rates by the five generators in 2017, 2020 and 2030 (Table 8). Table 8. Auxiliary power rates of the five generators in 2011–2030 China China Datang China Huadian China Guodian State Power Year Huaneng Corporation Corporation Corporation Investment Group 2011 5.08% 6.12% 5.62% 5.30% 6.38% 2012 4.83% 5.93% 5.18% 5.05% 6.07% 2013 4.59% 5.91% 5.18% 4.92% 5.91% 2014 4.41% 5.73% 5.01% 4.68% 5.72% 2015 4.24% 5.51% 4.79% 4.54% 5.57% 2016 4.11% 3.91% 4.77% 4.62% 5.55% 2017 3.92% 4.25% 4.63% 4.40% 5.36% 2020 3.47% 3.37% 4.31% 4.06% 4.99% 2030 2.31% 1.55% 3.38% 3.12% 3.92% Note: The data for 2011–2016 were derived from the social responsibility and sustainability reports of the five generators. The rates for 2017, 2020 and 2030 are predictions. 4.2.2 Output indicators Using X1 to denote the total installed capacity (unit: 10,000 kW), X2 to denote coal consumption (unit: g/kWh), X3 to denote the auxiliary power rate (%), and X4 to denote the initial CO2 quota (unit: million tons), Y1 is

122

Huaping Sun et al. / Energy Procedia 152 (2018) 115–124 Author name / Energy Procedia 00 (2018) 000–000

8

the power generation (unit: 100 million kilowatts). These data yield the inputs and outputs of the five generators in 2017, 2020 and 2030, as shown in Table 9. Table 9. Inputs and outputs of the five generators China China China China State Power Huadian Index Year Huaneng Guodian Datang Investment Corporation Group Corporation Corporation 2017 17,613 13,600 15,704 14,774 12,829.54 X1/10,000 kW 2020 20,606 15,240 20,214 16,933 16,938.27 2030 34,767 22,275 46,901 26,677 42,762.42 2017 299.02 303.85 299.09 305.37 301.53 X2/g(kWh)-1 2020 288.92 295.01 288.94 297.53 292.81 2030 257.66 267.35 257.54 272.83 265.53 2017 3.92% 4.52% 4.63% 4.40% 5.36% X3/% 2020 3.47% 3.37% 4.31% 4.06% 4.99% 2030 2.31% 1.55% 3.38% 3.12% 3.92% 2017 481.51 360.59 398.17 382.82 314.53 2020 X4/million tons 465.80 334.19 421.83 367.36 332.62 2030 414.12 259.36 511.25 320.17 400.73 2017 6,252.44 4,682.30 5,170.33 4,971.02 4,084.21 Y1/100 million 2020 6,233.31 4,472.13 5,644.85 4,916.04 4,451.06 kW 2030 6,127.18 3,837.36 7,564.29 4,737.11 5,929.00 -10

Applying the data in Table 9 to formulae (9)–(12), and assuming ε = 10 in the Matlab routine, the carbon trading outcomes for the five generators are as shown in Table 10. Table 10. Carbon trading by the five generators n DEA 1 n Technical Scale effectivene DMU Year λ ∑ λj ∑ θ* j efficiency efficiency θ * j =1 j =1 ss 2017 1 1 Effective 1 Constant Effective DMU1 2020 1 1 Effective 1 Constant Effective 2030 1 1 Effective 1 Constant Effective 2017 1 0.7489 Invalid 0.7489 Increasing Invalid DMU2 2020 1 0.7175 Invalid 0.7175 Increasing Invalid 2030 1 0.6438 Invalid 0.6438 Increasing Invalid 2017 1 0.8269 Invalid 0.8269 Increasing Invalid DMU3 2020 1 0.9056 Invalid 0. 9056 Increasing Invalid 2030 1 1 Effective 1 Constant Effective 2017 1 0.7951 Invalid 0.7951 Increasing Invalid DMU4 2020 1 0.7887 Invalid 0.7887 Increasing Invalid 2030 1 1 Effective 1 Constant Effective 2017 1 0.6532 Invalid 0.6532 Increasing Invalid DMU5 2020 1 0.7141 Invalid 0.7141 Increasing Invalid 2030 1 0.7838 Invalid 0.7838 Increasing Invalid Table 10 shows that the DEAs of China Huaneng Group (in 2017, 2020 and 2030), China Huadian Corporation (in 2030), and China Guodian Corporation (in 2030) are effective; while the DEAs of China Datang Corporation (in 2017, 2020 and 2030), China Huadian Corporation (in 2017, 2020), China Guodian Corporation (in 2017, 2020) and State Power Investment (in 2017, 2020 and 2030) are invalid. The economic evaluations calculated by Matlab are shown in Table 11. Table 11. Economic evaluations of China Datang Corporation and State Power Investment Input Output Year DMU X1/ X2/g(kWh-1) X3/% X4/ Y1/



Huaping Sun et al. / Energy Procedia 152 (2018) 115–124 Author name / Energy Procedia 00 (2018) 000–000

10,000 kW

2017

2020 2030

DMU2 DMU3 DMU4 DMU5 DMU2 DMU3 DMU4 DMU5 DMU2 DMU5

Decrement Decrement Decrement Decrement Decrement Decrement Decrement Decrement Decrement Decrement

410.1 1139.5 771 1324.5 456.1 1553.1 681.8 2223.8 516.4 6000.2

79.9 51.8 67.6 106.2 87.7 27.3 69.7 86.5 100.3 63.7

0 0 0 0 0 0 0 0 0 0

million tons 0 0 0 0 0 0 0 0 0 0

123 9

100 million kW 0 0 0 0 0 0 0 0 0 0

5. Conclusions and suggestions 5.1. Conclusions We combined grey prediction and GPS-based DEA models to consider power company inputs and outputs, and to calculate carbon allotment efficiencies and improvements in different years. We found that China Huaneng Group is best in efficiency measures. China Datang Corporation, China Huadian Corporation, China Guodian Corporation and State Power Investment should reduce capacities in 2020; while China Datang Corporation and State Power Investment should reduce capacities in 2030. There is much room for improvement in carbon emission efficiency. To ensure reductions in carbon emissions, it is essential to improve the coal consumption efficiencies. China Datang Corporation, China Huadian Corporation, China Guodian Corporation and State Power Investment should reduce coal consumption in 2020; while China Datang Corporation and State Power Investment should reduce coal consumption in 2030. Power companies vary in their ability to reduce carbon emissions; the government must consider this. Policies and regulations must favor enterprises in regions where carbon emissions can be maximally reduced. For less efficient enterprises, governmental guidance and support are required. Power enterprises differ in terms of efficiency; carbon emission quotas must reflect these points and dynamic adjustments are necessary as enterprises become more efficient. 5.2. Policy Implications If the initial allocations of carbon emissions remain unchanged, the various inputs to power systems do not increase. Rather, carbon emissions should be minimized by reference to guaranteed efficiencies (CO2 output is regarded as a negative input in this article). Therefore, if power companies wish to guarantee output according to their allocated carbon quota, they need to use clean energy for efficient power generation. Our modeling showed that power industry capacity could be greatly improved with investment in research and development focused on improvements in existing capacity (optimization/adjustment). The carbon reduction potentials of the five large enterprises differed by region; each enterprise requires a unique efficiency improvement strategy. First, the government should technically support inefficient power enterprises (i.e., those with inappropriately large carbon quotas) and promote co-operation between such enterprises and efficient producers, focusing on energy-saving and emission reduction technologies. This would dynamically improve carbon emission efficiencies. Also, China's power industry must accept rational allocation of carbon emission rights and actively encourage the use of clean energy and new energy sources. The electric power industry must be transformed to reduce emissions. Furthermore, the government must formulate supportive policies. Energy-saving emission reductions must become more stringent, especially for industries that use coal. Fiscal and taxation policies must favor those who use clean energy, and encourage the development of renewable energy. The government must play a leading role in carbon emission reduction. Finally, China should further reform its electric power industry, optimizing and adjusting all power generators and engaging in policy innovations. The government must support, and provide technical guidance on, energy savings and emission reductions. Old-fashioned high-energy technologies and equipment must be eliminated to improve technical efficiency. Also, new emission reduction technologies (both Chinese and foreign) must be embraced; the traditional modes of operation of electric power enterprises changed; innovation promoted; and

124

Huaping Sun et al. / Energy Procedia 152 (2018) 115–124 Author name / Energy Procedia 00 (2018) 000–000

10

business models optimized to transition from a high-carbon model to a low-carbon, clean green future. Acknowledgements The first author acknowledges the financial support provided by the National Natural Science Foundation of China (No.71774071, 71803068, 71690241, 71573121, 71473233), China Postdoctoral Science Foundation (No.2016M601568, 2017M621637), the Young Academic Leader Project of Jiangsu University (5521380003).

References [1] Charnes et al., “Measuring the efficiency of decision making units”, European Journal of Operational Research, 1978. [2] Banker et al., “Some models for estimating technical and scale inefficiencies in data envelopment analysis”, Management Science, 1984. [3] Cook et al., “Characterizing an equitable allocation of shared costs: a DEA approach”, European Journal of Operational Research, 1999. [4] Beasley J E, “Allocating fixed costs and resources via data envelopment analysis”, European Journal of Operational Research, 2003. [5] Lins et al., “Olympic ranking based on a zero sum gains DEA model”, European Journal of Operational Research, 2003. [6] Färe et al., “The relative efficiency of Illinois electric utilities”, Resources and Energy, 1983. [7] Ang BW, “Decomposition analysis for policy making in energy: which is the preferred method?” Energy Policy, 2004. [8] Zhou et al, “Decomposition of aggregate CO2 emissions: a production theoretical approach”, Energy Economics, 2008. [9] Zhou et al., “Total factor carbon emission performance: A Malmquist Index analysis”, Energy Economics, 2010. [10] Long et al., “Convergence analysis of eco-efficiency of China’s cement manufacturers through unit root test of panel data”, Energy, 2017. [11] Liu et al, “Efficient distribution of carbon emissions reduction targets at the city level: A case of Yangtze River Delta region”, Journal of Cleaner Production, 2018. [12] Yaisawarng et al., “The effects of sulfur dioxide controls on productivity change in the US electric power industry”, The Review of Economics and Statistics, 1994. [13] Tyteca, D, “Linear programming models for the measurement of environmental performance of firms—concepts and empirical results”, Journal of Productivity Analysis, 1997. [14] Sueyoshi et al., “Slack-adjusted DEA for time series analysis: performance measurement of Japanese electric power generation industry in 1984–1993”, European Journal of Operational Research, 2001. [15] Lam et al., “Data envelopment analysis of the efficiency of China’s thermal power generation”, Utilities Policy, 2001. [16] Wang et al., “Efficiency of power industry carbon emission quotas based on data envelopment”, Electric Power, 2013. [17] Bonacina et al., “Electricity pricing under “carbon emissions trading”: A dominant firm with competitive fringe model”, Energy Policy, 2007. [18] Zhang XL, “The quantitative relations of some key indicators in China’s national emission trading system design”, Journal of Environmental Economics, 2017. [19] Lin et al., “Chinese carbon market: current status and future perspective”, Journal of Tsinghua University (Science and Technology), 2015. [20] Song et al., “Provincial allocation of carbon emission quotas under the fusion of fairness and efficiency”, Journal of Arid Land Resources and Environment, 2017. [21] Bian YW, “Pollutant emission quota allocation based on DEA”, Or Transactions, 2010. [22] Zeng et al., “Design and analysis of adjustable carbon emissions allocation mechanism in electricity market”, Power System Technology, 2010. [23] Li et al., “Principle and model for regional allocation of carbon emission from electricity sector”, Power System Technology, 2012. [24] Song et al., “Initial allocation mechanism of carbon emission permits in electric power industry”, Electric Power Automation Equipment, 2013. [25] Tan et al., “Analysis model for the impact of carbon emission right initial allocation on generation right trading”, East China Electric Power, 2013. [26] Luo et al., “Mechanism study on carbon emission trading of power sector”, Environmental Science & Technology, 2014. [27] Chen et al., “Initial allocation model of regional carbon emission rights of power industry”, Science and Technology Management Research, 2016. [28] Mei et al., “A study on incorporating multiple equity principles of power carbon emission allowances allocation based on claims”, Power System Technology, 2016.