Research on the Cost Curves and Strategies Related to the Carbon Emission Reduction in China

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1 Researc on te Cost Curves and Strategies Related to te Carbon Emission Reduction in Cina Aiua Luo 1, Zengsun Ruan 2*, Xizen Hu 3 1 Scool of Matematics and Statistics Sout-Central University for Nationalities Wuan, Hubei, , Cina 2 Scool of Science, Wuan Institute of Tecnology Wuan, Hubei, , Cina 3 Te second artillery command college Wuan, Hubei, , Cina Abstract To alleviate te climate cange, te emission reduction of carbon dioxide as become one of te ottest issues corresponding to te field of environment, te cost of carbon emission reduction is a crucial factor needed to be considered wen a country reduces its carbon emissions. In te long run, te reduction mainly depends upon progress of science and tecnology, but in sort period it only depends on restricting te development of sectors wit an intense emission, wereas it will result in a great loss of te economic. In tis paper, two matematical models about marginal cost and incremental cost related to carbon emission reduction are built separately, te variation of costs are displayed via cost curves under equivalent reduction and non-equivalent reduction of CO2 from 2010 to 2020, different strategies to acieve te obective of emission reduction in 2020 are assessed, te results indicate tat te emissions reduction wit non-equivalent mitigation is te most suitable metod for carbon reduction in Cina. Keywords - emission reduction of CO2; reduction cost; mitigation strategies I. INTRODUCTION Cina is eavily dependent on coal as a primary energy resource. In 2007, primary energy consumption reaced 2.66 billion tons of standard coal about 70% of total primary energy consumption, wic led to vast quantities of GHG emissions. As te largest developing country in te world, wit te rapid growt of te economic gross, Cina ad surpassed te United States as te biggest emission country of CO 2 in 2007(from IEA), emission reduction of CO 2 in Cina is of great urgency, being a responsible big country, te government as set te target of reducing its per unit GDP energy consumption by 20% in te end of Before te Copenagen Climate Cange Conference 2009, Cina State Council as declared its goal to reduce its per unit GDP CO 2 emission by 40%-45% by 2020, compared wit But once te abatement started, it will affect te growt of economic, ence evaluating te influence on economic is of great strategic sense. Actually te problem about cost of reduction emission of CO 2 is complicated and callenging, wic as great significance and different ways to calculate. To simplify tis question and witout losing generality. te cost of carbon reduction in tis paper is defined as follows, in te current stage of economic development, considering no progress in tecnology, no canges of energy consumption and emission coefficients, te cost is te loss of GDP only generated by readustment of te industrial structure or restriction te development of te sectors wit a ig emission, in sort, it refers to macroeconomic cost in a sort time to reduce te emission of carbon, including te macroeconomic loss from bot direct and indirect. In economics, marginal cost means te increment of total cost wen per unit of production added or decreased under a given level. Te marginal cost of emission reduction of carbon dioxide ere means te loss of GDP wen per unit emission of CO 2 is cut down under a given level of production. Sadow price means te marginal cost wen anoter product is increased, usually it can be used to express te marginal contribution wen per unit resource increases. By means of sadow price, te marginal cost of emission reduction of CO 2 can be acieved troug solving te dual linear programming problem in tis paper. Incremental cost represents cange of te total cost caused by extra abatement of emission, wic is te product of marginal cost and increment of emission reduction, so marginal cost is te incremental cost wen per unit emission reduction increases. Wen emission reduction of CO 2 is concerned, for te sake of udging te effect on economic compreensively, bot marginal cost and incremental cost sould be considered. Totally, te models related to cost analysis can be classified to bottom-up and top-down metods. Different levels of cost of emission reduction are computed by different models. Based on detailed information of tecnology, te bottom-up model describes te performances and availabilities of every tecnology of energy, mainly includes engineer economic computer models, dynamic energy optimal models and energy systematic simulated models etc. Te top-down model is an DOI /IJSSST.a.16.4B ISSN: x online, print

2 aggregate model of te wole economic system, mainly including macroscopic econometric models, computable general equilibrium model etc.. Te differences of tese two metods come from te definition and connotation of te models. Economic model as te assumption tat te market is valid, so te manufactures ave te minimum cost. But engineer model can reflect te invalid of te real world, ave detailed description and data to assess te differences of cost. Te cost of emission reduction is influenced by many factors, suc as demand and price of energy, etc., tere is considerable amount of literatures on tis topic. Mono K.M [1] reviewed te progress and trends in CCS. Lin B and Liu X. [2] considered te dilemma between economic development and energy conservation from te point of energy rebound. Meng L.etc. [3] analyzed te caracteristics of regional CO 2 emissions, also giving some policies suggestions about emission reduction. Löscel A etc. [4] presented te tecnological uncertainty and cost effectiveness of CO 2 emission reduction. Gul T.etc. [5] implemented scenario analysis troug Global Multiregional MARKAL model (GMM). In view of specific sectors of CO 2 emission, Zang Z.X. [6] analyzed te costeffective of different options of emission reduction in electricity sector. Potential of CO 2 emissions reduction in electricity sector was obtained by Cai W. etc. [7] troug scenario analysis. Li Ko etc. [8] stated te abatement cost in CO 2 emission reduction regarding te supply-side policies for te Taiwan power sector. Zu B.etc. [9] described te emissions reduction potential in cemical industry. For transportation sector, X. Mao M. [10] and W.W.Wang [11] presented te situations and strategies of emission reduction. Elzen. etc. [12] carried out te cost analysis for regimes proposed by different countries. Karky and Skutsc [13] estimated te economic profit troug forest management. Cen [14] simulated te energy consumption and carbon emission till 2050 troug CGE model, and te cost was dollars per ton carbon equivalence, te loss rate of GDP was 0.1% ~2.54%. Te rest of tis paper is structured as follows. Section 2 presents and explains te two models, a programming model about marginal cost of emission reduction, and an evaluation model about incremental cost. Section 3 gives data and assumptions. Section 4 discusses simulation scenarios and results. Section 5 concludes te paper. II. MODEL DESCRIPTION AND FORMULATIONS Based on te caracteristic of production and trades from 1987 to 2009, a model is expressed as follows. n maxv av X 1 Subect to AX Y E I X l X X X ac X C (1) 0 E E 0 I I Were V is te gross domestic product(gdp); X is a column vector of total outputs, X is te total outputs of l department, X and X denote vectors of te upper and lower bounds of total outputs;y is te column vector of end products; E means te column vector of export, E is te vector of upper bound of export; I means te column vector of import, I is te vector of upper bound of import; av represents te coefficient of added value; ac denotes te emission of carbon per unit product of te t department, it is emission coefficients of carbon;c is te actual emission of CO 2. Te plan of conserving energy and reducing emissions as implemented from 2005, to estimate te incremental costs under te influence of te target of reduction from 2011 to 2020, a model to calculate te incremental costs of reduction of emission is K kt Ct, ere K is te t incremental costs of reduction of emission, kt is te marginal cost of reduction of every year, Ct is te quantities of te reduction of CO2 emission. Te baseline of emission can be estimated as follows. Given te growt rate of energy consumption e and te growt rate of GDP g, te percentage of decrease of carbon intensity wen te target of reduction every year from 2011 to 2020, te converting formulation is 1 e, i 1 1 g z (1 ) (1 g) 1, werei is te percentage declined of energy intensity, t denotes te percentage decreased of carbon intensity t, t is te proportion of fossil energy in total amount of consumption of primary energy in te year, t 1 is te proportion of te previous year. z is te increment of emission of CO 2. So te accumulated growt rate of CO 2 emission Z t can be expressed by. Zt (1 zt) (1 zt 1) (1 z1) 1 rt Zt Zt, 1 ere r t is reduction rates of te year, so te corresponding emission of every year is Ct ( rt rt 1) C, were C 0 0 is te emission of te baseline, wic is te emission of III. DATA SOURCES AND PROCESSING According to publications of te National Bureau of Statistics, te comparable prices input-output tables of 1992, 1997, 2002 and 2005 are adopted, te input-output table in 2007 was converted to comparable prices table wic takes te price in2000 as baseline, based on te data of Cina Statistics Yearbook in Te input-output tables of energy consumption are adopted establised by te Cinese Academy of Science. According to formulations and data publised by 2006 IPCC emission inventories of GHG and Energy Statistics DOI /IJSSST.a.16.4B ISSN: x online, print

3 Yearbook, te carbon emission coefficients is te product of carbon content, net eat value and te oxidizing ratio. IV. SIMULATION SCENARIOS AND RESULTS A. Marginal Cost and Macroeconomic Cost for Emission Reduction Different ratio of emission reduction of CO 2 results in different cange of GDP, in anoter way, different ratio of reduction results in different marginal cost of reduction, according to feature of function of marginal cost, te marginal cost is zero if tere is no emission reduction, ten te function of marginal cost of reduction can be set as k=ar 2 +br, were r is te percentage of ratio of emission of CO 2, k is te marginal cost of reduction (unit: yuan per tonne). From regression analysis, te coefficients of te function of marginal cost and te goodness of fitting can be given in table1. Figure 1 and figure 2 indicate te trends of marginal cost of emission reduction wit different quantities and te time trends under same reduction in every year. From tese two graps, te marginal cost declines wit time from absolute value, ten te later te reduction, te lower te cost. To mitigate 0.2 billion tons, marginal cost would be 245yuan per ton, ten decreasing in te following years, it would be 225yuan per ton, 202yuan per ton, 176yuan per ton,152yuan per ton and 138yuan per ton in cronological order. Wit te same reduction every year, te marginal cost is lower wen it s implemented more lately, ten more iger more earlier. So it is advantageous to Cina to postpone te emission reduction. On te one and, wit te rapid growt of economic in Cina, te total emissions of CO 2 increase and te relative ratios of reduction is different wit same reduction eac year. On te oter and, te later te reduction, te more advanced tecnology is available wic can reduce costs. Figure 3 sows te macroeconomic cost under different reduction in every year, it can be seen tat te ratios of reduction cost in GDP descend year by year, te cost increases wit te increasing of reduction. Figure 2 Trends of marginal cost under same reduction in every year Figure 3 Te macroeconomic costs under different reduction of emission TABLE I. REGRESSION COEFFICIENTS AND GOODNESS OF FITTING Te start time wit restriction of emission reduction Year a b Goodness of Fitting Figure 1 Trends of marginal cost under different amount reduction of emission TABLE II. THE INCREMENTAL COSTS TO ACHIEVE THE OBJECTIVES OF REDUCTION IN 2020 Te cost of reducion every year/100 million yuan Year 7.5% 8% 9% 40% DOI /IJSSST.a.16.4B ISSN: x online, print

4 Te cost of reducion every year/100 million yuan Year 7.5% 8% 9% 45% B. Te Incremental Costs to Acieve te Same Reduction from 2011 to 2020 Considering te formula of incremental costs, given tree different growt rates of GDP by7.5%, 8% and 9% separately in ten years, acieve te obective of reduction 40% and 45%. Te cost increase wit te rapid growt of GDP, te extent becomes larger. Meanwile, under te same velocity of GDP development, te cost to acieve reduction 45% is double tan reduction 40% and increases gradually. Te obective of reduction 40% can attain troug appropriate adustment of structure and improvement of efficiency, but te reduction 45% depends on not only ig tecnology costs, but also plentiful social costs. Taking 8% of growt rates of GDP for a example, te ratio of te increment costs in GDP is 6.21% to acieve reduction 45% in 2020, corresponding ratio is 3.23% to acieve reduction 40% in Taking 8% of growt rates of GDP for example, te ratio of incremental costs in GDP is 6.21% in te end of 2020 to acieve te obective reduction of 45%, te corresponding ratio is 3.23% to acieve te obective reduction of 40%. C. Te Incremental Cost Evaluation Under Non- Equivalent Emission Reduction from 2011 to 2020 Te target of reduction in 2020 as attracted extensive attention once it was proposed, problems focus on weter te obective can be acieved or not, te same obective was set for te evaluation of every year. In fact, according to te above analysis, te marginal cost decreased year by year, in oter words, te marginal cost will more less wen te constraints of reduction conduct more late. So te marginal cost and incremental cost to be calculated under te nonequivalent is an important problem, wic as a remarkable teoretical and practical meaning wit respect to te target s acievement in Cina. On te basis of properties of geometrical series, given two metods are increasing year by year and decreasing year by year related to te reduction obective of every year, te non-equivalent reduction obective for every year can be denoted by. t 1 1 t (1 ) (1 0 ),0 t 1 (2) Were is te increased speed of te obective eac year, t is te time orizon, t denotes te reduction target of every year. Assign some value to, ten non-equivalent reduction obective can be acieved for every year witin te time orizon. 10 Here 0.5%, t 10, (1 ) (68%,75%), according t 1 to te formulas above, 68% relates te reduction obective 45%, and 75% relates 40%. Six scenarios are discussed as below. (1) S1: comparing te carbon intensity in 2005, it will be mitigated below 40% in 2020 wit equal 2.89% reduction eac year. (2) S2: comparing te carbon intensity in 2005, it will be mitigated below 45% in 2020 wit equal 3.74% reduction eac year. (3) S3: comparing te carbon intensity in 2005, it will be mitigated below 40% in 2020 wit increasing reduction eac year, mitigation 0.69% in te first year. (4) S4: comparing te carbon intensity in 2005, it will be mitigated below 45% in 2020 wit increasing reduction eac year, mitigation 1.55% in te first year. (5) S5: comparing te carbon intensity in 2005, it will be mitigated below 40% in 2020 wit decreasing reduction eac year, mitigation 5.05% in te first year. (6) S6: comparing te carbon intensity in 2005, it will be mitigated below 45% in 2020 wit decreasing reduction eac year, mitigation 5.87% in te first year. Two graps (Fig.4 and Fig.5) sow te marginal cost and increment cost to acieve te target of emission reduction wit non-equivalent mitigation in every year. Bot lead to te consistent results. It is more economic to set an obective wit nonequivalent and incremental mitigation from 2011 to Except 2020, te marginal cost and incremental cost under non-equivalent and incremental mitigation will be less tan equivalent reduction in eac year. On te contrary, te costs under lessened reduction obective are te maximum, iger tan bot incremental mitigation and equivalent reduction. Te speed of increase of te non-equivalent mitigation is more small, te costs under S3 and S6 approximate te costs under te equivalent reduction, ten te relative cost becomes more ig. Compared wit te equivalent reduction for every year, te superiority of costs reaces te maximum in te middle period of te non-equivalent reduction, especially in te year 2016 and 2017, te differences is te biggest in te ten years. In all tese scenarios, te marginal cost and te incremental cost are te same in te beginning and te end point. t DOI /IJSSST.a.16.4B ISSN: x online, print

5 it is reasonable to implement te reduction wit increase of non-equivalent. Concerning te emission reduction in te long run, te key tecnology of renewable energy and new energy resources sould be developed deeply. Furtermore, to lower te cost, te reduction obective wit loose first tigt later sould be set, developing te service sectors greatly, optimizing te structure of industries; extending new energy automobiles and promoting te structure of consumption. Some key tecnology suc as nuclear energy and carbon capture and storage sould pay more attention. Figure 4 Te marginal cost to acieve te goal of reduction from 2010 to 2020 Figure 5 Te incremental cost of reduction to acieve te goal from 2010 to 2020 From tese analysis and evaluation, non-equivalent reduction for every year sould be te rigt coice to acieve te obective reduction in 2020, wic as te smallest cost in all tese scenarios. Te reduction sould implement wit loose first tigt later. More late to implement te reduction and more lower te marginal cost, so te marginal cost and incremental cost will more lower tan equivalent reduction except Under te same index in te last year, non-equivalent reduction is te most economic way, wic as te smallest cost, little loss of economic and little burdens for every year. V. CONCLUSIONS AND POLICY SUGGESTIONS To estimate te marginal cost and incremental costs of reduction emission of CO 2, a linear programming model and incremental costs model are constructed. Based on te data analysis, tere is a remarkable effect on economic to limit emitting, te cost of unit reduction becomes iger wit te emissions-cut pusing. Te marginal cost as a trend of increasing every year under te same emission reduction, te ratios of macroeconomic cost in GDP ave te same tendency, so it will do good to te development of economic of Cina to cut te emission as late as possible. Meanwile, Acknowledgements Te autors acknowledge te tis work was supported by Natural Science Foundation of Hubei Province of Cina (Grant 2014CFB917). REFERENCES [1] Mono K.M., Hemant K.B., Praci. V. Progress and trends in CO2 capture/separation tecnologies: A review. Energy, vol. 4, no. 6, pp , [2] Lin B., Liu X. Dilemma between economic development and energy conservation: Energy rebound effect in Cina Energy, vol. 4, no. 5, pp , [3] Meng L., Guo J., Cai J., Zang Z. Cina s regional CO2 emissions: Caracteristics, inter-regional transfer and emission reduction policies, Energy Policy, vol. 3, no. 9, pp , [4] Löscel A., VOtto V. M. Tecnological uncertainty and cost effectiveness of CO2 emission reduction, Energy Econom, vol. 31, no. 9, pp.s4-s17, [5] Gul T., Kypreos S., Turton H., Barreto L. An energy-economic scenario analysis of alternative fuels for personal transport using te Global Multi-regional MARKAL model (GMM). Energy, vol. 3, no. 4, pp , [6] Zang Z.X. Cost-effective analysis of carbon abatement options in Cina s electricity sector. Energy Sources, vol. 20, no. 12, pp , [7] Cai W., Wang C., Wang K., Zang Y., Cen J. Scenario analysis on CO2 emissions reduction potential in Cina's electricity sector. Energy Policy, vol. 3, no. 5, pp , [8] Li Ko, Cia-Yon Cen, Jeng-Wen Lai, Yu-Hui Wang. batement cost analysis in CO2 emission reduction costs regarding te supply-side policies for te Taiwan power sector, Energy Policy, vol. 6, no. 1, pp , [9] Zu B., Zou W., Hu S., Li Q. Griffy-Brown C., Jin Y. CO2 emissions and reduction potential in Cina s cemical industry Energy, vol. 35, no. 9, pp , [10] X. Mao M., S. Yang S., Liu Q., Tu J., Jaccard M. Acieving CO2 emission reduction and te co-benefits of local air pollution abatement in te transportation sector of Cina Environ. Sci. Policy, vol. 21, no. 45, pp.1-13, [11] W. W. Wang, M. Zang, M. Zou. Using LMDI metod to analyze transport sector CO2 emissions in Cina. Energy, vol. 36, no. 9, pp , [12] Elzen M. Lucas P, Vuuren D. Abatement costs of post-kyoto climate regimes. Energy Policy, vol. 33, no. 16, pp , [13] Karky B. S., Skutsc M. Te cost of carbon abatement troug community forest management in Nepal Himalaya. Ecological Economics, vol. 69, no. 3, pp , [14] Cen W. Te costs of mitigating carbon emission in Cina: findings from Cina MARKAL-MACRO modeling. Energy policy, vol. 33, no. 7, pp , DOI /IJSSST.a.16.4B ISSN: x online, print