Allocation of Carbon Emission Permits among Industrial Sectors in Liaoning Province 1876 6102 © 2016 The Authors Published by Elsevier Ltd This is an open access article under the CC BY NC ND license[.]
Available online at www.sciencedirect.com ScienceDirect Energy Procedia 104 (2016) 449 – 455 CUE2016-Applied Energy Symposium and Forum 2016: Low carbon cities & urban energy systems Allocation of carbon emission permits among industrial sectors in Liaoning province Hailin Mu1,*, Linlin Li 1, Nan Li1, Zhaoquan Xue1 , Longxi Li1 Key Laboratory of Ocean Energy Utilization and Energy Conservation of Ministry of Education, Dalian University of Technology, Dalian 116024, China Abstract China will establish its national ETS to reduce carbon emissions gradually A reasonable CO emissions allocation method helps to stimulate the emitters actively at the beginning stage The paper takes industrial sectors of Liaoning province as a case to distribute the carbon emission permits Considering the principles of fairness and efficiency, it analyzes the distribution of carbon emission permits among sectors in detail based on the method of CRITIC and fuzzy optimization model The final allocation proportions are profitable to build the future carbon trading market in Liaoning province and provide reference for policy makers ©2016 2016The The Authors Published by Elsevier Ltd © Authors Published by Elsevier Ltd This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/) Selection and/or peer-review under responsibility of CUE Peer-review under responsibility of the scientific committee of the Applied Energy Symposium and Forum, CUE2016: Low carbon cities and urban energy systems Keywords:CO2 emissions allocation, CRITIC and fuzzy optimization, industrial sectors, indicators; Introduction With the highly development of economy, large amounts of CO2 are emitted into the environment Carbon emission trading is an effective way to decrease emissions Its success attracts global attention Many other countries gradually get involved in the trading market China has established a carbon trading market in a number of pilot cities to promote emission reduction, and propose the establishment of a national carbon trading market in 12th Five-Year The initial distribution of allowances is important since it is fundamental for the whole process of carbon emission trading Industrial carbon emissions occupy a large proportion of the total emissions in Liaoning province, because industrial sectors consume abundant fossil energies every year Different sectors have different technology levels, energy consumption structures and emission reduction potentials How to allocate the quota of carbon emissions will have a great influence on the carbon market and the balance of industrial development In the initial stage, most emission allowances are allocated to emitters * Corresponding author Tel: +86 411 84708095 E-mail address: mhldut@126.com (Hailin Mu) 1876-6102 © 2016 The Authors Published by Elsevier Ltd This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/) Peer-review under responsibility of the scientific committee of the Applied Energy Symposium and Forum, CUE2016: Low carbon cities and urban energy systems doi:10.1016/j.egypro.2016.12.076 450 Hailin Mu et al / Energy Procedia 104 (2016) 449 – 455 free of charge The remainder will be auctioned, with the auctioned proportion to increase over time after the first phase A lot of researches have been done on the free initial allocation of CO emissions In literature, the allocation methods can be divided into the following types: indicator optimization, multiobjective optimization, game method and hybrid method [1-2] Kong et al [3] used the Boltzmann method on the enterprise distribution, considering the carbon emissions intensity, historical cumulative emissions and future carbon emissions Song et al [4] established the two stages distribution mechanism based on the regional comparison, considering the actual development of the electric power industry According to the principles of fairness and continuous production, Kong et al [5] established a multiobjective decision model for initial emission permits allocation The information entropy method [6] is also effective and fair, mainly because it is based on industry heterogeneity and has an objective consideration of the needs of the industries’ development and reduction potential Literature of distribution are mainly about the various regions or a particular industry, but among different kind of industries it is relatively few This paper combines the method of CRITIC and Fuzzy optimization selection model, and makes an initial allocation for the industrial sectors in Liaoning province As a kind of objective method of giving weights, the CRITIC method gives different weights for the five indicators: number of employees, historical cumulative emissions, gross industrial productions, carbon emission intensity, consumptions of energy The fuzzy optimization model determines the relative inportance degree of these indicators by Generalized Euclidean distance, and calculates the final allocation proportions among these sectors Principle and method 2.1 Calculation of basic indicators of industrial sectors in Liaoning province Since there is no official monitoring data of carbon dioxide emissions, the paper calculates CO2 emissions as Eq (1) according to the 2006 IPCC Guidelines for National Greenhouse Gas Inventories The calculated energies mainly include crude oil, raw coal, coke, gasoline, kerosene, diesel oil, fuel oil, gas and electricity The data of energy consumptions come from the Liaoning Province Statistical Yearbook (2011-2015) CE ¦ CEi i ¦E i i u CVi u CFi u CRi u M Ee u we (1) Where CE means the total CO2 emissions, i means the different varieties of energies, E means the consumptions of the fossil fuel, CV means the average low calorific value, CF means the coefficient of carbon emissions of the energy, CR means the carbon oxidation rate, M means the equivalent coefficient between the carbon and carbon dioxide, namely 44/12 Ee means the consumption of electricity, and we means the average emission coefficient of electricity we varies in different regions Liaoning Province is located in the northeast region, and its average emission coefficient is 1.096 kg CO2 per kWh according to the Provincial Greenhouse Gas Inventories COE means the total CO2 emission coefficient of the energy The relevant values are as Table According to Classification and Code Standard of National Economy Industry, the paper merges industries of similar consumption pattern into a larger sector [7] The industrial sectors in Liaoning province are divided as Table 451 Hailin Mu et al / Energy Procedia 104 (2016) 449 – 455 Table Different kinds of energy coefficients of carbon dioxide emissions Energy CV CF CR COE Coal 20908 26.37 0.94 1.9003 Coke 28435 29.5 0.93 2.8604 Crude oil 41816 20.1 0.98 3.0202 Fuel oil 41816 21.1 0.98 3.1705 Gasoline 43070 18.9 0.98 2.9251 Kerosene 43070 19.5 0.98 3.0179 Diesel 42652 20.2 0.98 3.0959 Natural gas 38931 15.3 0.99 2.1622 Table 2.Classification of industrial sectors Code Sectors The corresponding industrial departments Mining and Quarrying mining and washing of coal, exaction of petroleum and natural gas, mining and processing of ferrous metal ores, mining and processing of non-ferrous metal ores, mining and processing of nonmetal ores, mining of other ores Food industry processing of food from agricultural products, manufacture of food, manufacture of wine ,beverage , refined tea and tobacco Textile industry manufacture of textile, manufacture of textile wearing apparel, footwear and caps, production of leather , fur ,feather and related products Wood processing processing of timber, manufacture of wood, bamboo, rattan, palm and straw products, manufacture of furniture , paper and paper products, printing and reproduction of recording media ,manufacture of articles for culture, educational and sport activity Processing of petroleum, coking and nuclear fuel processing of petroleum coking, processing of nuclear fuel Chemical industry manufacture of raw chemical materials and chemical products, manufacture of medicines, manufacture of chemical fibers, manufacture of rubber and plastics Manufacture of non-metallic mineral products manufacture of non-metallic mineral products Manufacture of metallic mineral products industry smelting and processing of ferrous metal, smelting and processing of non-ferrous metal, processing of metal products Manufacture of machinery equipment industry manufacture of general purpose machinery, manufacture of special purpose machinery, manufacture of transportation equipment, manufacture of electrical machinery and equipment, manufacture of communication equipment, computers and other electronic equipment, manufacture of measuring instrument for cultural activity and office work, manufacture of art manufacturing, recycling and disposal of waste 10 Production and distribution of electricity, heat, gas and water industry production and distribution of electricity power and heat power, production and distribution of gas, production and distribution of water 452 Hailin Mu et al / Energy Procedia 104 (2016) 449 – 455 The distribution of Liaoning province is studied among ten industrial sectors based on the principles of fairness, efficiency and sustainable development The evaluation indicators include the number of employee in various industrial sectors, the gross industrial productions of different sectors, the carbon dioxide emission intensity, the historical cumulative emissions of each industry, the comprehensive energy consumptions of sectors The number of employees of different sectors, the gross industrial productions and the carbon dioxide emission intensity are all derived from the average data of the years from 2012 to 2014 The gross industrial productions and carbon dioxide emission intensity are calculated by the constant price in 2012.The historical cumulative emissions are calculated for the past five years, and these data are based on the Liaoning Province Statistical Yearbook The historical cumulative emissions and industrial comprehensive consumptions of energy come from guidelines for industrial efficiencies in Liaoning province in 2013 The basic data forms a matrix, and each element in it is represented by aij aij means the values of different indexes, i represents sector, and j represents index Table 3.Basic values of different indexes in different sectors Number of employees Historical cumulative emissions Gross industrial productions (104 ) (104 t) (108 yuan) 317730.33 32187.15 3601.60 1.88 1227.19 126645.33 4815.78 6102.35 0.16 232.66 99306.33 1761.05 1568.08 0.20 56.25 64098.67 2255.11 2053.62 0.22 99.77 88734.67 98374.96 4698.71 4.43 1288.08 168375 29822.98 5512.63 0.97 1320.71 70977.67 26475.77 3764.47 1.54 1538.67 411827.33 104330.35 8954.18 2.38 4600.49 653944 13525.97 14650 0.18 492.74 10 157445 92548.85 1960.65 9.62 2526.36 Sectors Emission intensity (tce/104 yuan) Consumptions of energy (104 tce) 2.2 Calculation of allocation proportion The method of CRITIC is a kind of objective method for weighting [8-9] It gives weights for different indicators by comparing their intensities and conflicts of the data Contrast of intensity describes differences of the same index value in different schemes, and it can be represented by the standard deviation The greater the differences are, the greater the standard deviation is Conflict means the correlations between different indexes, and it is described by the correlation coefficient If the conflict between the indexes is stronger, the correlation coefficient will be smaller In order to eliminate the influence of dimensions, the paper makes a pretreatment for the five indicators For a benefit-type indicator, namely the larger the better, its weight is described as Eq (2) zij aij / max(aij ) (2) For a cost-type indicator, namely the smaller the better, the weight can be described as Eq (3) zij min(aij ) / aij (3) 453 Hailin Mu et al / Energy Procedia 104 (2016) 449 – 455 where aij means the value of different indexes, i represents sector, and j represents index The information of each indicator is taken as Eq (4) Ij V j ¦ (1 r j ) (4) i Where V j means jth index’s standard deviation in different sectors, rj means the correlation coefficient between jth index and other indexes The weights Wj of these indications is calculated as Eq (5) Wj Ij ¦I j (5) The paper determine the membership degrees of the indicators according to the fuzzy optimization model [10-11] The higher value of the four indicators, namely number of employee, gross industrial production, the cumulative historical carbon emissions and energy consumption, can be better, and we call them benefit-type indexes The membership function sij is put as Eq (6) j aij {aij } sij j 1,2, max {aij } {aij } (6) j 1,2, j 1,2, Since the indicator of carbon emission intensity is the opposite, we call it the cost-type index Its membership function is expressed as Eq (7) max {aij } aij sij j 1,2, max {aij } {aij } (7) j 1,2, j 1,2, The optimal set Zb is defined as: a1 max {sij } j 1,2, ° b (8) Z {a1 , a 2} ® {sij } a2 ° j 1,2, ¯ In Eq (8), a1 means the set of the maximum membership degrees for benefit-type indexes a2 means the set of the minimum membership degrees for negative indexes The worst set Zw is defined as: a1 {sij } j 1,2, ° (9) Z w {a1 , a 2} ® max {s } a ij ° j 1,2, ¯ In Eq (9), a1 means the set of the minimum relative membership degrees for positive indexes a2 means the set of the maximum minimum relative membership degrees for negative indexes The relative membership degree is calculated as Eq (10), and Si represents the relative membership degree for ith department 2º ª 10 b ẳ ằ j j -s ij ô Ư êơW= i=1 ằ Si = ô1+ 10 w ô êơW= ẳ ằ Ư j j -s ij ơô i=1 ¼» -1 (10) So the final allocation proportion P for i th department is calculated as Eq (11) 10 Pi Si / ¦ Si i (11) 454 Hailin Mu et al / Energy Procedia 104 (2016) 449 – 455 Result Table 4.Weights of different indicators Index number of employee cumulative historical emissions gross domain production carbon emission intensity consumption of energy Weight 0.134 0.207 0.125 0.356 0.179 The weights of the five indicators varies The indicator of carbon emission intensity has the largest weight value, that is 0.356, and it means that this indicator owns the most information The indicator of cumulative historical emissions’ weight value is 0.207, ranked as the second Weights of indexes of consumptions of energy, number of employees and gross industrial productions are between 0.1 and 0.2 Their weights are somehow similar Table 5.Allocation proportions of industrial sectors according to various methods Sectors By historical cumulative emissions By number of employees By gross industrial productions By Fuzzy Optimization 7.93% 14.72% 6.81% 4.66% 1.19% 5.87% 11.54% 0.76% 0.43% 4.60% 2.97% 0.02% 0.56% 2.97% 3.88% 0.01% 24.22% 4.11% 8.89% 18.94% 7.34% 7.80% 10.43% 2.97% 6.52% 3.29% 7.12% 2.54% 25.69% 19.07% 16.94% 28.75% 3.33% 30.29% 27.71% 11.47% 10 22.79% 7.29% 3.71% 29.88% selection model The specific distribution results of carbon emissions permits is displayed in Table 5, as well as the allocation proportions in accordance with the single principle of historical cumulative emissions, the number of employees and the gross industrial productions Industries of higher proportion are mainly those with more historical cumulative emissions and it is beneficial to the sustainable development The Production and distribution of electricity, heat, gas and water industry is distributed the largest amount of emissions Production of petroleum, coking and nuclear industry, the Manufacture of metallic mineral products industry and the Manufacture of machinery equipment industry are allocated more than 10% of carbon quotas Proportion of Mining and Quarrying industry, Chemical industry and Manufacture of non-metallic mineral products industry are less than 5% Others occupy the least percentages Although the Manufacture of machinery equipment industry has the largest productions and the most employees, it hasn’t been allocated the most proportion It is mainly because the paper has a comprehensive consideration of these indicators, and this sector has a less historical carbon emissions Conclusion According to the weights, the policy makers should have more consideration of different industries' cumulative emissions, energy consumptions and productions in the future allocation In addition, the Hailin Mu et al / Energy Procedia 104 (2016) 449 – 455 paper only uses the objective evaluation method to give weights, but it can be combined with the experts’ grades to give comprehensive weights The calculation of the relative membership degrees lead to a comprehensive evaluation and allocation results for industrial sectors in Liaoning Province The allocation can also be used in all the sectors in the future, not only in the industrial sector. Acknowledgements The authors gratefully acknowledge the financial support from the National Natural Science Foundation of China (71273039) The research has also been supported by the Fundamental Research Funds for the Central Universities (DUT14RC(3)151),China Postdoctoral Science Foundation (2015M571309) and Key Laboratory of Ocean Energy Utilization and Energy Conservation of Ministry of Education, Dalian University of Technology References [1] Gomes E.G, Lins M.P.E Modelling undesirable outputs with zero sum gains data envelopment analysis models J Oper Res Soc 2008,59 : 616–623 [2]J MacKenzie, Hanley N, Kornienko T The optimal initial allocation of pollution permits: a relative performance approach Environ Resour Econ 2008,39: 265–282 [3] Ying K, Dongshan Z On an intra-enterprise carbon quota distribution system China Opening Journal.2013,3:36-41 [4] Xudong S, Juan M, Tieyuan X Initial allocation mechanism of carbon emission permit in electric power industry Electric Power Automation Equipment 2013,33: 44-49 [5] Shoude X, Tongcheng H A multi-objectives decision model of initial emission permits allocation Chinese Journal of Management Science.2003,11: 40-44 [6] Dequn Z, Mei W, Qin Z Entropy-based carbon emission allowance allocation among enterprises in the region Journal of BeiJing institute of technology.2015,17: 16-22 [7] Changxin L Carbon emissions during industrialization process in China : affecting factors , emission reduction potential and forecast Dongbei University of Finance and Economics.2010 [8] Houqiang Y, Ling L Five kinds of enterprise comprehensive evaluation based on the entropy value method and the CRITIC method Journal of Hubei Institute of Technology.2012,32 : 83-84ˊ [9] Dongmei S,Chunxiao L, Chen S, Xuefa S, Lin Z, Wenqiang F.Multiple objective and attribute decision making based on the Subjective and Objective Journal of ShanDong University.2015, 45(4):1-9 [10] Jie Z, Shushu H Regional initial allocation of carbon emission in the power industry based on Fuzzy Optimization and CRITIC Methods Environmental Protection Science.2015,41:62-66 [11] Jie H, Zhongmin X Initial water rights allocation model for basins based on the multilevel and muti-objective fuzzy optimization-A case study in Zhangye Municipality Journal of Glaciology and Geocryology.2013,35:776-782 Biography Mu Hailin, professor, doctorial supervisor, vice-president of School of Energy and Power Engineering, Dalian University of technology The main research direction: Economic, Energy and environment sustainable development strategy, policy and countermeasures; Analysis of CO2 emission reduction etc 455 ... corresponding industrial departments Mining and Quarrying mining and washing of coal, exaction of petroleum and natural gas, mining and processing of ferrous metal ores, mining and processing of non-ferrous... basic indicators of industrial sectors in Liaoning province Since there is no official monitoring data of carbon dioxide emissions, the paper calculates CO2 emissions as Eq (1) according to the 2006... combines the method of CRITIC and Fuzzy optimization selection model, and makes an initial allocation for the industrial sectors in Liaoning province As a kind of objective method of giving weights,