- Email: [email protected]
The economic and environmental impact analysis of replacing fossil energy with electricity in Guangxi—based on Input-Output model
Available online at www.sciencedirect.com Available online at www.sciencedirect.com
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Energy Procedia 00 (2018) 000–000 Available online www.sciencedirect.com Available online atatwww.sciencedirect.com Energy Procedia 00 (2018) 000–000
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www.elsevier.com/locate/procedia www.elsevier.com/locate/procedia
Energy (2018) 000–000 841–846 EnergyProcedia Procedia152 00 (2017) www.elsevier.com/locate/procedia
Applied Energy Symposium and Forum 2018: Low carbon cities and urban energy systems, Applied Energy Symposium andSymposium Forum 2018: carbon cities and urbancities energy CUE2018-Applied Energy andLow Forum 2018: Low carbon andsystems, CUE2018, 5–7 June 2018, Shanghai, China CUE2018, 5–7 June 2018,2018, Shanghai, ChinaChina urban energy systems, 5–7 June Shanghai,
The economic and environmental impact analysis of replacing fossil The economic andInternational environmental impact analysis fossil The 15th Symposium on District Heating of andreplacing Cooling energy with electricity in Guangxi—based on Input-Output model energy with electricity in Guangxi—based on Input-Output model Assessing the feasibility ofb,*, using demand-outdoor a a a Xu Yan , Shuai Han , Yuanyuan Cheng Xiqiaothe Linheat , Lijuan Qinaa, Wanlu Wuaa, Bo a a b, a Xu Yan , Shuai Han , Yuanyuan Cheng *, Xiqiao Lin , Lijuan , Wanlu forecast Wu , Bo a temperature function for a long-term demand Zenga district heatQin Zeng *, A. Pina , P. Ferrão , J. Fournier ., B. Lacarrière , O. Le Corre
Electric Power Grid Co., a,b,c Power Research a Institute of Guangxi a b Ltd., Nanning, 530023,cChina c a b Electric Power Economics Research Institute of Guangxi Grid Co., Ltd.,University, Nanning, 530023, China Center for Energy Research, School ofPower Economics, Xiamen Xiamen,China 361005, China b China Center for Energy Economics Research, School of Economics, Xiamen University, Xiamen, 361005, China a 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 a
I. Andrić
Abstract Abstract The implementation of replacing fossil energy with electricity is an effective measure to prevent and control haze, and it plays an The implementation of replacing fossil energy with electricity is an effective measure to prevent and control haze, and it plays an extremely Abstract important role in realizing the sustainable development of the economy. Guangxi Power Grid Co., Ltd. responds extremely important role call in realizing the sustainable of the of economy. Power Co.,during Ltd. responds positively to the national and proposes to promote development the implementation electricityGuangxi substitution in Grid Guangxi the 13th positively toThis thepolicy national call and proposes to promote thepollution implementation of electricity substitution in Guangxi duringBased the 13th Five-Year. can reduce both the environmental and Guangxi’s dependence on foreign fossil energy. District heating networks are commonly addressed in the literature as one of the most effective solutions for decreasing on the Five-Year. policy can reduce bothLtd., the the environmental pollution substitution and Guangxi’s dependence on fossil energy. on the plan of This Guangxi Power Grid effects These of electricity implementation areforeign studied in this paperBased bythe using greenhouse gas emissions from Co., the building sector. systems require high investments which are returned through heat the plan of Guangxi(IO) Power Grid Co., Ltd.,The the main effectsconclusions of electricity implementation areincrement studied inwill this paperthe byprices using 2012 table of Guangxi. aresubstitution as follows: 1. electricity price sales.Input-Output Due to the changed climate conditions and building renovation policies, heat demand in the futureaffect could decrease, 2012 Input-Output (IO)and table of Guangxi. The main conclusions are as follows: 1. electricity price incrementposition will affect the prices ofprolonging other sectors, CPI GDP deflator, which means that the electricity sector occupies an important in Guangxi’s the investment return period. of other sectors, CPI and GDP deflator, which means the sector an important position in Guangxi’s economy; electricity substitution reduces use ofthat andelectricity indeed, but–occupies the electricity generation structure hasdemand been The main2. scope of this paper is to assess the the feasibility ofcoal using theoilheat demand outdoor temperature function for heat economy; 2. electricity substitution reduces the use ofofcoal and oilstill indeed, butcertain the electricity generation structure has been dominated by thermal power so far, so the consumption electricity implies environmental costs. forecast. The district of Alvalade, located in Lisbon (Portugal), was used as a case study. The district is consisted of 665 dominated by thermal power so far, so the consumption of electricity still implies certain environmental costs. buildings that vary in both construction period and typology. Three weather scenarios (low, medium, high) and three district Copyright 2018 Elsevier All rights reserved.intermediate, deep). To estimate the error, obtained heat demand values were renovation© wereLtd. developed (shallow, Copyright © scenarios 2018 Elsevier Elsevier Ltd. All rights reserved. Copyright © 2018 Ltd. All rights reserved. Selection and peer-review under responsibility of the model, scientific committee of Applied Energy Symposium and Forum 2018: Low comparedand withpeer-review results fromunder a dynamic heat demand previously developed validated by the authors. Selection responsibility of the scientific committee of theand CUE2018-Applied Energy Symposium and Selection and peer-review under responsibility of the scientific committee of Applied Energy Symposium and Forum 2018: Low carbon cities and urban energy systems, CUE2018. The results that when onlyurban weather change is considered, the margin of error could be acceptable for some applications Forum 2018: showed Low carbon cities and energy systems. carbon cities and urban energy systems, CUE2018. (the error in annual demand was lower than 20% for all weather scenarios considered). However, after introducing renovation Keywords: electricity substitution; environmental pollution; Input-Output table scenarios, the error value increased up to 59.5% (depending on the weather and renovation scenarios combination considered). Keywords: electricity substitution; environmental pollution; Input-Output table The value of slope coefficient increased on average within the range of 3.8% up to 8% per decade, that corresponds to the decrease in the number of heating hours of 22-139h during the heating season (depending on the combination of weather and renovation scenarios considered). On the other hand, function intercept increased for 7.8-12.7% per decade (depending on the coupled scenarios). The values suggested could be used to modify the function parameters for the scenarios considered, and improve the accuracy of heat demand estimations. © 2017 The Authors. Published by Elsevier Ltd. Peer-review under responsibility of the Scientific Committee of The 15th International Symposium on District Heating and * Corresponding author. Cooling. * E-mail Corresponding address:author. [email protected]
E-mail address: [email protected] Keywords:Copyright Heat demand; Forecast; Climate 1876-6102 © 2018 Elsevier Ltd. Allchange rights reserved. 1876-6102 Copyright © 2018 Elsevier Ltd. All of rights reserved. committee of the Applied Energy Symposium and Forum 2018: Low carbon cities Selection and peer-review under responsibility the scientific Selection peer-review responsibility of the scientific committee of the Applied Energy Symposium and Forum 2018: Low carbon cities and urbanand energy systems, under CUE2018. and urban energy systems, CUE2018. 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. 10.1016/j.egypro.2018.09.188
842 2
Xu Yan et al. / Energy Procedia 152 (2018) 841–846 Author name / Energy Procedia 00 (2018) 000–000
1. Introduction Environmental pollution not only endangers people’s health, but also reflects China’s current coal-based energy consumption structure and the unsustainability of industrial development path which is high-consumption, highemission, and high-pollution. At present, the proportion of electricity to coal and the electrification level in China is low. A large amount of fossil energy consumption is one of the main factors that cause serious haze. The implementation of replacing fossil energy with electricity is a method of using electricity to replace burning coal and fuel in the terminal energy consumption [1]. So, it is an effective measure to prevent and control haze, and it plays an extremely important role in realizing the sustainable development of the economy. Guangxi is located in South China and its fossil resources are scarce. The consumption of energy depends on resources transported from outside the province for a long time since there is a big gap between energy consumption and production. Compared with other resources, Guangxi is a large province with hydropower in China. Electricity is clean, safe and convenient compared with the traditional fossil energy. Increasing the proportion of electricity consumption will not only reduce the use of fossil energy and alleviate the shortage of resources, but also reduce emissions of greenhouse gases and air pollutants. Therefore, it is an effective way to achieve the goal of green and sustainable development in Guangxi. The rapid development of China’s economy depends on the supply of cheap coal for a long time. At the same time, the problem of environmental pollution is becoming more and more serious. The pressure of environmental degradation faced by government will accelerate the reform of energy structure [2]. Some scholars point out that the main measure to control fog and haze is to promote the adjustment of energy structure and technological progress [3]. Some scholars also think that it’s necessary to combine the energy structure adjustment with the industrial structure optimization [4-5]. Other scholars believe that the development of natural gas is an effective measure to adjust the energy structure and improve the environmental quality [6-7]. But it must be pointed out that there is a certain limit on the development of natural gas because of the feature of China’s energy resources: a lot of coal and very little oil and gas. So, the shortage occurs easily in the peak period of gas use [8-9]. Therefore, the electricity consumption and the sustainable development of the economy have become the new research goals of many scholars [10-11]. Input-Output (IO) analysis was first proposed by W.Leontief in the 1930s. It is a method to study the relationship among input factors, outputs and related economic sectors. At present, many scholars have applied IO analysis to the study of energy and environment. For example, some scholars use the IO method to analyze the carbon emissions of various industries [12-14], and further analyze the impact of carbon tax on CO 2 emissions [15-16]. Some scholars also study the problems related to carbon intensity [17] and energy prices [18-19] through IO method. In this paper, the IO model is used to analyze the economic and environmental impact of electricity substitution in Guangxi. 2. Methodology and data 2.1. The influence of electricity price increment The IO Price Impact Model is used to analyse the effect of price change among different sectors. So, we can analyse the influence of electricity price fluctuation on all other sectors which are associated with its production. Referring to Lin & Wang (2009) [20], if we want to calculate the effect of price change we can use the following model:
P j
n
a P , i 1
ij
i
a X ij X j , i, j 1, 2, ..., n ij
(1)
In Eq.(1), P is the price change of one department, and aij is the directly consumption coefficient in the IO table. aij reflects the production technology structure of the economy and society, which is usually assumed to remain unchanged in the short term. Eq.(1) can be expanded from j = 1 to j = n-1: P1 a11 P1 a21 P2 a31 P3 an1Pn
P2 a12 P1 a22 P2 a32 P3
an 2 Pn
Pn 1 a1, n 1 P1 a2, n 1 P2 a3, n 1 P3
an , n 1Pn
(2)
Xu Yan et al. / Energy Procedia 152 (2018) 841–846 Author name / Energy Procedia 00 (2018) 000–000
843 3
Then the above n-1 equations can be rewritten in matrix form: an 1,1 P1 P1 a11 a21 a31
P2 Pn 1
an1 a an 1,2 P 2 n 2 Pn an 1, n 1 Pn 1 an , n 1
a12 a22 a32 a1, n 1 a2, n 1 a3, n 1
(3)
So, we finally have:
P1 an1 P2 an 2 Pn ( I AnT1, n 1 ) 1 Pn 1 an , n 1
(4)
In Eq.(4), I is a n-1 order identity matrix, and AnT1, n 1 is a transport matrix of direct consumption coefficient matrix which removes the n-th row and the n-th column. 2.2. The changes of CPI and GDP deflator Through Section 2.1, we can get the final price change of each sector. Then, we can calculate the change of CPI and GDP deflator through the final resident consumption and final output value in the IO table. Referring to Lin & Wang (2009) [20], the specific formula is as follows:
PX , j X n
j 1
Index
j
j
n
j 1
1, 2,
,n
(5)
j
∆Index indicates the change of price index. If Index is CPI, then X j is the final resident consumption in the j-th sector. If Index is GDP deflator, then X j is the final output value in the j-th sector. Eq.(5) is under the condition that the price is not regulated by the government. However, if government controls the price, it often caused an exogenous impact on the supply and demand of goods. Therefore, it is necessary to analyse the condition that the market price is regulated by the government. For example, firstly the price change of the n-th sector is Pn , and it will cause price changes of the other n-1 sectors, which is ( P11 , P21 , , Pn1 2 , Pn11 ) . If n-2 and n-1 sectors are regulated by the government, which means Pn12 Pn11 0 , we should remove the influence of these 2 2 2 3 3 3 two sectors which is (P1 , P2 , , Pn 3 ) and (P1 , P2 , , Pn 3 ) . The specific formula is:
(P Index n 3 j 1
1 j
Pj2 Pj3 ) X j Pn X n
n j 1
Xj
(6)
2.3. The change of output When the output of one sector changes, it will directly cause other sectors’ output to follow change because these sectors will use the products of that sector as input in the production. After the output of all these sectors changes, the second round of variation will start indirectly. As a result of all these impacts among sectors, the final equilibrium will be reached. The specific equation can be expressed as follows. n
X
ij
Yi X i , i 1, 2,
,n
(7)
j 1
Eq.(7) can be rewritten in matrix:
AX Y X X ( I A) 1 Y
(8)
Xu Yan et al. / Energy Procedia 152 (2018) 841–846 Author name / Energy Procedia 00 (2018) 000–000
844 4
In Eq.(8), X is the total output matrix, Y is the final demand matrix and ( I A) 1 is called the Leontief inverse matrix. Therefore, we can get the final output changes through final demand changes. 2.4. Data This paper mainly uses the 2012 IO table (42 sectors) of Guangxi as the basis for analysis. And the electricity demand increment data comes from “Guangxi Power Grid Co., Ltd. 13th Five-Year Electricity Substitution Planning” which is not listed in this paper for reasons of space. We can calculate that the total amount of coal and oil reduction are 1 million 232 thousand and 750 tons standard coal, and 920 thousand and 590 tons standard coal respectively. From the 2016 statistical yearbook of Guangxi, we can see that the total consumption of electricity, coal and oil are 16 million 398 thousand and 800 tons standard coal, 44 million 927 thousand and 800 tons standard coal, and 17 million 602 thousand and 900 tons standard coal respectively in 2015. Compared with 2015, the electricity demand increases by about 7%, the coal demand decreases by about 2.74%, and the oil demand decreases by about 5.23% through the implementation of electricity substitution. 3. Results 3.1. The changes of price and the fluctuation of CPI and GDP deflator We simulate the price changes under the assumption of different price fluctuation of electricity sector which are 10%, 15% and 20% respectively. All calculations are done by Matlab software and the specific results are not listed in this paper for reasons of space. Therefore, we only describe the results briefly. When the electricity sector price changes by 10%, 15% and 20%, there are 5, 14 and 20 sectors respectively which prices have increased more than 1%. The largest price change of the sector is Water production and supply, and the smallest is Petroleum and natural gas production products. We can see that the price changes in different sectors are different. These sectors which are more vulnerable to electricity price fluctuations use a larger amount of electricity as input in production process. The fluctuation of CPI and GDP deflator are calculated in two cases. The first one is free price fluctuations and the second one is to consider about oil price controlled by the government. The results are shown in Fig.1 and Fig.2.
Fig.1 The change of CPI index
Fig.2 The change of GDP deflator
From Fig.1 and Fig.2 we can see that the greater the fluctuation of the electricity price, the greater the change in CPI or GDP deflator. Compared to no regulation, the change will decrease when the oil price is regulated. We can also find that the change of the GDP deflator is greater than CPI. This is because GDP reflects the value of all the final products in the whole society, while CPI only reflects the value of household consumption. So, GDP will be more affected by price fluctuations. 3.2. The changes of output When electricity demand increases by 7%, the output of electricity sector increases by 11.4%, which is far beyond 7%. This is because the increase of electricity demand not only leads to the electricity output increment directly, but
Xu Yan et al. / Energy Procedia 152 (2018) 841–846 Author name / Energy Procedia 00 (2018) 000–000
845 5
also increases the output of other sectors. However, these sectors also need to use electricity in the production process, so it will lead the output of the electricity sector to increase indirectly. What’s more, we find that the output growth of coal sector is far more than the average growth of other sectors, which is 1.91%. This can be explained by the power generation structure in Guangxi. As we can see from Fig.3, the proportion of thermal power in Guangxi accounts for a large share. So, from the current situation of power generation structure in Guangxi, the growth of electricity is often accompanied by a large amount of coal consumption, and the usage of electricity also implies certain environmental cost. Due to the shortage of fossil energy in Guangxi, if the proportion of thermal power does not decrease, the increase of electricity consumption will also lead to the increase of dependence on fossil energy.
Fig.3 Thermal power and hydropower generation ratio in Guangxi Source: China Energy Statistical Yearbook 2016 & 2017
3.3. Environmental effect The Intergovernmental Panel on Climate Change (IPCC) provides a series of standards for measuring greenhouse gas emissions. Some of the emission factors are shown in Table 1. Table 1. Greenhouse gas emission factors Energy
carbon dioxide
methane
nitrous oxide
raw coal
-4
3.9610 Kg/Kcal
-9
4.1910 Kg/Kcal
6.2810-9Kg/Kcal
crude oil
3.0710-4Kg/Kcal
1.2810-8Kg/Kcal
2.5110-9Kg/Kcal
Source: IPCC
We can calculate that the output of coal and oil will increase by 1.91% and 0.16% respectively with the increase of electricity demand in Section 3.2. Therefore, considering the two opposite influences, the total effect caused by electricity substitution is: the net decrease of coal output is 0.83%, which is 372 thousand and 900 tons standard coal, and the net decrease of oil output is 5.07%, which is 892 thousand and 470 tons standard coal. The results of emissions reduction are shown in Table 2. Table 2. Greenhouse gas emissions raw coal
crude oil
Net output reduction (ten thousand tons)
37.29
89.247
Net heat value reduction (kcal)
2.61031012
6.24731012
Reduction of carbon dioxide emissions (10 thousand tons)
103.368
191.792
Reduction of methane emissions (tons)
10.937
79.965
Reduction of nitrous oxide emissions (tons)
16.393
15.681
4. Conclusions and policy recommendations Electricity substitution can effectively reduce the emissions of greenhouse gas by reducing the consumption of
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Xu Yan et al. / Energy Procedia 152 (2018) 841–846 Author name / Energy Procedia 00 (2018) 000–000
fossil energy. But we must also realize that the proportion of thermal power is still very large in Guangxi. Although replacing fossil energy with electricity can reduce the consumption of coal and oil to some degree, the production of electricity can also lead to the increment of coal consumption. Therefore, the beneficial effects of electricity substitution on the environment are very limited. Replacing fossil energy with electricity is a necessary measure for a more harmonious development of the economy and society. It can not only promote the development of economy and society, but also realize the reform of the energy structure and reduce the environmental pollution. Our paper shows that the implementation of replacing fossil energy with electricity can indeed reduce greenhouse gas effectively. However, increasing the consumption of electricity will also stimulate coal consumption because of the electricity generation structure in Guangxi. In order to implement the electricity substitution policy better, we must increase the use of clean energy such as hydropower, wind power, nuclear power, solar power and so on, which means that changing the power generation structure in Guangxi is the most effective way for energy saving and emission reduction. What’s more, increasing clean energy can also alleviate the shortage of resources in Guangxi effectively and reducing the dependence of foreign energy resources. Guangxi should take advantage of their water resources to develop hydropower vigorously. Only in this way the demand for coal will decrease in electricity sector, and the environmental pollution will be reduced. Reference [1] Guidance on the promotion of electrical energy substitution; 2016. [2] Lin B, Li J. Transformation of China’s Energy Structure under Environmental Governance Constraints: A Peak Value Analysis of Coal and Carbon Dioxide. Social Sciences in China 2015; 09:84-107. (in Chinese). [3] Wei W, Ma X. Optimal Policy for Energy Structure Adjustment and Haze Governance in China. China Population, Resources and Environment 2015; 07(25): 6-14. (in Chinese). [4] He L, Yang P, Meng Y, Kong Y. Contribution Potential of Optimizing Energy Structure to Low Carbon Shandong. China Population, Resources and Environment 2015; 06(25): 89-97. (in Chinese). [5] Ma L, Zhang X. The Spatial Effect of China’s Haze pollution and the Impact from Economic Change and Energy Structure. China Industrial Economics 2014; 04:19-31. (in Chinese). [6] Wu X. On Restructuring Energy. Journal of East China Jiaotong University 2003; 55-58. (in Chinese). [7] Zhang X, Lou F, Zhang X. Carbon emissions and economic impacts of natural gas price regulation in China: based on the incomplete competition CGE model. China Population, Resources and Environment 2016; 07(26): 76-84. (in Chinese). [8] Han J. The path of energy structure adjustment “soft landing”: developing clean coal using, solving energy difficulties and practicing energy revolution. Management World 2016; 02:3-7. (in Chinese). [9] Jiang Y, Tang X, Ni W, Wang J, Hu S. The relationship between Beijing PM2.5 and heating heat source in winter and its treatment measures. Energy of China 2014; 36(1):7-13. (in Chinese). [10] Zhang C, Zhou K, Yang S, Shao Z. On electricity consumption and economic growth in China. Renewable and Sustainable Energy Reviews 2017; 76:P353-368. [11] Sarwar S, Chen W, Waheed R. Electricity consumption, oil price and economic growth: Global perspective. Renewable and Sustainable Energy Reviews 2017; 76:9-18. [12] Wang A, Feng Z, Meng B. Measure of Carbon Emissions and Carbon Transfers in 30 Provinces of China. The Journal of Quantitative & Technical Economics 2017; 08(34):89-104. (in Chinese). [13] He Y. The Measurement and Application of Industry Complete Carbon Emissions. Statistical Research 2012; 29 (3):67-72. (in Chinese). [14] Peng J, Xie R, Lai M. Energy-related CO2 emissions in the China’s iron and steel industry: A global supply chain analysis. Resources, Conservation and Recycling 2016; 129:P392-401. [15] Pan W. The Impacts of Carbon Tax on the Sectoral and the Regional Competitiveness in China. The Journal of Quantitative & Technical Economics 2015; 06:3-20. (in Chinese). [16] Yang C, Wang F, Men M. Analyzing the Impacts of Carbon Taxes on CO2 Emissions and Macroeconomics. Statistical Research 2011; 28(7):45-54. (in Chinese). [17] Wang F, Feng G. Elasticities of China’s Carbon Intensity with Respect to Industrial Development, Energy Efficiency and Intermediate Input Coefficients. The Journal of Quantitative & Technical Economics 2012; 05:50-62. (in Chinese). [18] Ren Z. The Potential and Actual Impact of Energy Price Increase to General Price Level in China. Economic Research Journal 2012; 08:5969. (in Chinese). [19] Valadkhani A, Babacan A, Dabir-Alai P. The impacts of rising energy prices on non-energy sectors in Australia. Economic Analysis and Policy 2014; 44(4):386-395. [20] Lin B, Wang F. Impact of Energy Price Increase on General Price Level in China: A Study Based on Input-Output Model and Recursive SVAR Model. Economic Research Journal 2009; 12:66-79(in Chinese).
ScienceDirect ScienceDirect
Energy Procedia 00 (2018) 000–000 Available online www.sciencedirect.com Available online atatwww.sciencedirect.com Energy Procedia 00 (2018) 000–000
ScienceDirect ScienceDirect
www.elsevier.com/locate/procedia www.elsevier.com/locate/procedia
Energy (2018) 000–000 841–846 EnergyProcedia Procedia152 00 (2017) www.elsevier.com/locate/procedia
Applied Energy Symposium and Forum 2018: Low carbon cities and urban energy systems, Applied Energy Symposium andSymposium Forum 2018: carbon cities and urbancities energy CUE2018-Applied Energy andLow Forum 2018: Low carbon andsystems, CUE2018, 5–7 June 2018, Shanghai, China CUE2018, 5–7 June 2018,2018, Shanghai, ChinaChina urban energy systems, 5–7 June Shanghai,
The economic and environmental impact analysis of replacing fossil The economic andInternational environmental impact analysis fossil The 15th Symposium on District Heating of andreplacing Cooling energy with electricity in Guangxi—based on Input-Output model energy with electricity in Guangxi—based on Input-Output model Assessing the feasibility ofb,*, using demand-outdoor a a a Xu Yan , Shuai Han , Yuanyuan Cheng Xiqiaothe Linheat , Lijuan Qinaa, Wanlu Wuaa, Bo a a b, a Xu Yan , Shuai Han , Yuanyuan Cheng *, Xiqiao Lin , Lijuan , Wanlu forecast Wu , Bo a temperature function for a long-term demand Zenga district heatQin Zeng *, A. Pina , P. Ferrão , J. Fournier ., B. Lacarrière , O. Le Corre
Electric Power Grid Co., a,b,c Power Research a Institute of Guangxi a b Ltd., Nanning, 530023,cChina c a b Electric Power Economics Research Institute of Guangxi Grid Co., Ltd.,University, Nanning, 530023, China Center for Energy Research, School ofPower Economics, Xiamen Xiamen,China 361005, China b China Center for Energy Economics Research, School of Economics, Xiamen University, Xiamen, 361005, China a 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 a
I. Andrić
Abstract Abstract The implementation of replacing fossil energy with electricity is an effective measure to prevent and control haze, and it plays an The implementation of replacing fossil energy with electricity is an effective measure to prevent and control haze, and it plays an extremely Abstract important role in realizing the sustainable development of the economy. Guangxi Power Grid Co., Ltd. responds extremely important role call in realizing the sustainable of the of economy. Power Co.,during Ltd. responds positively to the national and proposes to promote development the implementation electricityGuangxi substitution in Grid Guangxi the 13th positively toThis thepolicy national call and proposes to promote thepollution implementation of electricity substitution in Guangxi duringBased the 13th Five-Year. can reduce both the environmental and Guangxi’s dependence on foreign fossil energy. District heating networks are commonly addressed in the literature as one of the most effective solutions for decreasing on the Five-Year. policy can reduce bothLtd., the the environmental pollution substitution and Guangxi’s dependence on fossil energy. on the plan of This Guangxi Power Grid effects These of electricity implementation areforeign studied in this paperBased bythe using greenhouse gas emissions from Co., the building sector. systems require high investments which are returned through heat the plan of Guangxi(IO) Power Grid Co., Ltd.,The the main effectsconclusions of electricity implementation areincrement studied inwill this paperthe byprices using 2012 table of Guangxi. aresubstitution as follows: 1. electricity price sales.Input-Output Due to the changed climate conditions and building renovation policies, heat demand in the futureaffect could decrease, 2012 Input-Output (IO)and table of Guangxi. The main conclusions are as follows: 1. electricity price incrementposition will affect the prices ofprolonging other sectors, CPI GDP deflator, which means that the electricity sector occupies an important in Guangxi’s the investment return period. of other sectors, CPI and GDP deflator, which means the sector an important position in Guangxi’s economy; electricity substitution reduces use ofthat andelectricity indeed, but–occupies the electricity generation structure hasdemand been The main2. scope of this paper is to assess the the feasibility ofcoal using theoilheat demand outdoor temperature function for heat economy; 2. electricity substitution reduces the use ofofcoal and oilstill indeed, butcertain the electricity generation structure has been dominated by thermal power so far, so the consumption electricity implies environmental costs. forecast. The district of Alvalade, located in Lisbon (Portugal), was used as a case study. The district is consisted of 665 dominated by thermal power so far, so the consumption of electricity still implies certain environmental costs. buildings that vary in both construction period and typology. Three weather scenarios (low, medium, high) and three district Copyright 2018 Elsevier All rights reserved.intermediate, deep). To estimate the error, obtained heat demand values were renovation© wereLtd. developed (shallow, Copyright © scenarios 2018 Elsevier Elsevier Ltd. All rights reserved. Copyright © 2018 Ltd. All rights reserved. Selection and peer-review under responsibility of the model, scientific committee of Applied Energy Symposium and Forum 2018: Low comparedand withpeer-review results fromunder a dynamic heat demand previously developed validated by the authors. Selection responsibility of the scientific committee of theand CUE2018-Applied Energy Symposium and Selection and peer-review under responsibility of the scientific committee of Applied Energy Symposium and Forum 2018: Low carbon cities and urban energy systems, CUE2018. The results that when onlyurban weather change is considered, the margin of error could be acceptable for some applications Forum 2018: showed Low carbon cities and energy systems. carbon cities and urban energy systems, CUE2018. (the error in annual demand was lower than 20% for all weather scenarios considered). However, after introducing renovation Keywords: electricity substitution; environmental pollution; Input-Output table scenarios, the error value increased up to 59.5% (depending on the weather and renovation scenarios combination considered). Keywords: electricity substitution; environmental pollution; Input-Output table The value of slope coefficient increased on average within the range of 3.8% up to 8% per decade, that corresponds to the decrease in the number of heating hours of 22-139h during the heating season (depending on the combination of weather and renovation scenarios considered). On the other hand, function intercept increased for 7.8-12.7% per decade (depending on the coupled scenarios). The values suggested could be used to modify the function parameters for the scenarios considered, and improve the accuracy of heat demand estimations. © 2017 The Authors. Published by Elsevier Ltd. Peer-review under responsibility of the Scientific Committee of The 15th International Symposium on District Heating and * Corresponding author. Cooling. * E-mail Corresponding address:author. [email protected]
E-mail address: [email protected] Keywords:Copyright Heat demand; Forecast; Climate 1876-6102 © 2018 Elsevier Ltd. Allchange rights reserved. 1876-6102 Copyright © 2018 Elsevier Ltd. All of rights reserved. committee of the Applied Energy Symposium and Forum 2018: Low carbon cities Selection and peer-review under responsibility the scientific Selection peer-review responsibility of the scientific committee of the Applied Energy Symposium and Forum 2018: Low carbon cities and urbanand energy systems, under CUE2018. and urban energy systems, CUE2018. 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. 10.1016/j.egypro.2018.09.188
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Xu Yan et al. / Energy Procedia 152 (2018) 841–846 Author name / Energy Procedia 00 (2018) 000–000
1. Introduction Environmental pollution not only endangers people’s health, but also reflects China’s current coal-based energy consumption structure and the unsustainability of industrial development path which is high-consumption, highemission, and high-pollution. At present, the proportion of electricity to coal and the electrification level in China is low. A large amount of fossil energy consumption is one of the main factors that cause serious haze. The implementation of replacing fossil energy with electricity is a method of using electricity to replace burning coal and fuel in the terminal energy consumption [1]. So, it is an effective measure to prevent and control haze, and it plays an extremely important role in realizing the sustainable development of the economy. Guangxi is located in South China and its fossil resources are scarce. The consumption of energy depends on resources transported from outside the province for a long time since there is a big gap between energy consumption and production. Compared with other resources, Guangxi is a large province with hydropower in China. Electricity is clean, safe and convenient compared with the traditional fossil energy. Increasing the proportion of electricity consumption will not only reduce the use of fossil energy and alleviate the shortage of resources, but also reduce emissions of greenhouse gases and air pollutants. Therefore, it is an effective way to achieve the goal of green and sustainable development in Guangxi. The rapid development of China’s economy depends on the supply of cheap coal for a long time. At the same time, the problem of environmental pollution is becoming more and more serious. The pressure of environmental degradation faced by government will accelerate the reform of energy structure [2]. Some scholars point out that the main measure to control fog and haze is to promote the adjustment of energy structure and technological progress [3]. Some scholars also think that it’s necessary to combine the energy structure adjustment with the industrial structure optimization [4-5]. Other scholars believe that the development of natural gas is an effective measure to adjust the energy structure and improve the environmental quality [6-7]. But it must be pointed out that there is a certain limit on the development of natural gas because of the feature of China’s energy resources: a lot of coal and very little oil and gas. So, the shortage occurs easily in the peak period of gas use [8-9]. Therefore, the electricity consumption and the sustainable development of the economy have become the new research goals of many scholars [10-11]. Input-Output (IO) analysis was first proposed by W.Leontief in the 1930s. It is a method to study the relationship among input factors, outputs and related economic sectors. At present, many scholars have applied IO analysis to the study of energy and environment. For example, some scholars use the IO method to analyze the carbon emissions of various industries [12-14], and further analyze the impact of carbon tax on CO 2 emissions [15-16]. Some scholars also study the problems related to carbon intensity [17] and energy prices [18-19] through IO method. In this paper, the IO model is used to analyze the economic and environmental impact of electricity substitution in Guangxi. 2. Methodology and data 2.1. The influence of electricity price increment The IO Price Impact Model is used to analyse the effect of price change among different sectors. So, we can analyse the influence of electricity price fluctuation on all other sectors which are associated with its production. Referring to Lin & Wang (2009) [20], if we want to calculate the effect of price change we can use the following model:
P j
n
a P , i 1
ij
i
a X ij X j , i, j 1, 2, ..., n ij
(1)
In Eq.(1), P is the price change of one department, and aij is the directly consumption coefficient in the IO table. aij reflects the production technology structure of the economy and society, which is usually assumed to remain unchanged in the short term. Eq.(1) can be expanded from j = 1 to j = n-1: P1 a11 P1 a21 P2 a31 P3 an1Pn
P2 a12 P1 a22 P2 a32 P3
an 2 Pn
Pn 1 a1, n 1 P1 a2, n 1 P2 a3, n 1 P3
an , n 1Pn
(2)
Xu Yan et al. / Energy Procedia 152 (2018) 841–846 Author name / Energy Procedia 00 (2018) 000–000
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Then the above n-1 equations can be rewritten in matrix form: an 1,1 P1 P1 a11 a21 a31
P2 Pn 1
an1 a an 1,2 P 2 n 2 Pn an 1, n 1 Pn 1 an , n 1
a12 a22 a32 a1, n 1 a2, n 1 a3, n 1
(3)
So, we finally have:
P1 an1 P2 an 2 Pn ( I AnT1, n 1 ) 1 Pn 1 an , n 1
(4)
In Eq.(4), I is a n-1 order identity matrix, and AnT1, n 1 is a transport matrix of direct consumption coefficient matrix which removes the n-th row and the n-th column. 2.2. The changes of CPI and GDP deflator Through Section 2.1, we can get the final price change of each sector. Then, we can calculate the change of CPI and GDP deflator through the final resident consumption and final output value in the IO table. Referring to Lin & Wang (2009) [20], the specific formula is as follows:
PX , j X n
j 1
Index
j
j
n
j 1
1, 2,
,n
(5)
j
∆Index indicates the change of price index. If Index is CPI, then X j is the final resident consumption in the j-th sector. If Index is GDP deflator, then X j is the final output value in the j-th sector. Eq.(5) is under the condition that the price is not regulated by the government. However, if government controls the price, it often caused an exogenous impact on the supply and demand of goods. Therefore, it is necessary to analyse the condition that the market price is regulated by the government. For example, firstly the price change of the n-th sector is Pn , and it will cause price changes of the other n-1 sectors, which is ( P11 , P21 , , Pn1 2 , Pn11 ) . If n-2 and n-1 sectors are regulated by the government, which means Pn12 Pn11 0 , we should remove the influence of these 2 2 2 3 3 3 two sectors which is (P1 , P2 , , Pn 3 ) and (P1 , P2 , , Pn 3 ) . The specific formula is:
(P Index n 3 j 1
1 j
Pj2 Pj3 ) X j Pn X n
n j 1
Xj
(6)
2.3. The change of output When the output of one sector changes, it will directly cause other sectors’ output to follow change because these sectors will use the products of that sector as input in the production. After the output of all these sectors changes, the second round of variation will start indirectly. As a result of all these impacts among sectors, the final equilibrium will be reached. The specific equation can be expressed as follows. n
X
ij
Yi X i , i 1, 2,
,n
(7)
j 1
Eq.(7) can be rewritten in matrix:
AX Y X X ( I A) 1 Y
(8)
Xu Yan et al. / Energy Procedia 152 (2018) 841–846 Author name / Energy Procedia 00 (2018) 000–000
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In Eq.(8), X is the total output matrix, Y is the final demand matrix and ( I A) 1 is called the Leontief inverse matrix. Therefore, we can get the final output changes through final demand changes. 2.4. Data This paper mainly uses the 2012 IO table (42 sectors) of Guangxi as the basis for analysis. And the electricity demand increment data comes from “Guangxi Power Grid Co., Ltd. 13th Five-Year Electricity Substitution Planning” which is not listed in this paper for reasons of space. We can calculate that the total amount of coal and oil reduction are 1 million 232 thousand and 750 tons standard coal, and 920 thousand and 590 tons standard coal respectively. From the 2016 statistical yearbook of Guangxi, we can see that the total consumption of electricity, coal and oil are 16 million 398 thousand and 800 tons standard coal, 44 million 927 thousand and 800 tons standard coal, and 17 million 602 thousand and 900 tons standard coal respectively in 2015. Compared with 2015, the electricity demand increases by about 7%, the coal demand decreases by about 2.74%, and the oil demand decreases by about 5.23% through the implementation of electricity substitution. 3. Results 3.1. The changes of price and the fluctuation of CPI and GDP deflator We simulate the price changes under the assumption of different price fluctuation of electricity sector which are 10%, 15% and 20% respectively. All calculations are done by Matlab software and the specific results are not listed in this paper for reasons of space. Therefore, we only describe the results briefly. When the electricity sector price changes by 10%, 15% and 20%, there are 5, 14 and 20 sectors respectively which prices have increased more than 1%. The largest price change of the sector is Water production and supply, and the smallest is Petroleum and natural gas production products. We can see that the price changes in different sectors are different. These sectors which are more vulnerable to electricity price fluctuations use a larger amount of electricity as input in production process. The fluctuation of CPI and GDP deflator are calculated in two cases. The first one is free price fluctuations and the second one is to consider about oil price controlled by the government. The results are shown in Fig.1 and Fig.2.
Fig.1 The change of CPI index
Fig.2 The change of GDP deflator
From Fig.1 and Fig.2 we can see that the greater the fluctuation of the electricity price, the greater the change in CPI or GDP deflator. Compared to no regulation, the change will decrease when the oil price is regulated. We can also find that the change of the GDP deflator is greater than CPI. This is because GDP reflects the value of all the final products in the whole society, while CPI only reflects the value of household consumption. So, GDP will be more affected by price fluctuations. 3.2. The changes of output When electricity demand increases by 7%, the output of electricity sector increases by 11.4%, which is far beyond 7%. This is because the increase of electricity demand not only leads to the electricity output increment directly, but
Xu Yan et al. / Energy Procedia 152 (2018) 841–846 Author name / Energy Procedia 00 (2018) 000–000
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also increases the output of other sectors. However, these sectors also need to use electricity in the production process, so it will lead the output of the electricity sector to increase indirectly. What’s more, we find that the output growth of coal sector is far more than the average growth of other sectors, which is 1.91%. This can be explained by the power generation structure in Guangxi. As we can see from Fig.3, the proportion of thermal power in Guangxi accounts for a large share. So, from the current situation of power generation structure in Guangxi, the growth of electricity is often accompanied by a large amount of coal consumption, and the usage of electricity also implies certain environmental cost. Due to the shortage of fossil energy in Guangxi, if the proportion of thermal power does not decrease, the increase of electricity consumption will also lead to the increase of dependence on fossil energy.
Fig.3 Thermal power and hydropower generation ratio in Guangxi Source: China Energy Statistical Yearbook 2016 & 2017
3.3. Environmental effect The Intergovernmental Panel on Climate Change (IPCC) provides a series of standards for measuring greenhouse gas emissions. Some of the emission factors are shown in Table 1. Table 1. Greenhouse gas emission factors Energy
carbon dioxide
methane
nitrous oxide
raw coal
-4
3.9610 Kg/Kcal
-9
4.1910 Kg/Kcal
6.2810-9Kg/Kcal
crude oil
3.0710-4Kg/Kcal
1.2810-8Kg/Kcal
2.5110-9Kg/Kcal
Source: IPCC
We can calculate that the output of coal and oil will increase by 1.91% and 0.16% respectively with the increase of electricity demand in Section 3.2. Therefore, considering the two opposite influences, the total effect caused by electricity substitution is: the net decrease of coal output is 0.83%, which is 372 thousand and 900 tons standard coal, and the net decrease of oil output is 5.07%, which is 892 thousand and 470 tons standard coal. The results of emissions reduction are shown in Table 2. Table 2. Greenhouse gas emissions raw coal
crude oil
Net output reduction (ten thousand tons)
37.29
89.247
Net heat value reduction (kcal)
2.61031012
6.24731012
Reduction of carbon dioxide emissions (10 thousand tons)
103.368
191.792
Reduction of methane emissions (tons)
10.937
79.965
Reduction of nitrous oxide emissions (tons)
16.393
15.681
4. Conclusions and policy recommendations Electricity substitution can effectively reduce the emissions of greenhouse gas by reducing the consumption of
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Xu Yan et al. / Energy Procedia 152 (2018) 841–846 Author name / Energy Procedia 00 (2018) 000–000
fossil energy. But we must also realize that the proportion of thermal power is still very large in Guangxi. Although replacing fossil energy with electricity can reduce the consumption of coal and oil to some degree, the production of electricity can also lead to the increment of coal consumption. Therefore, the beneficial effects of electricity substitution on the environment are very limited. Replacing fossil energy with electricity is a necessary measure for a more harmonious development of the economy and society. It can not only promote the development of economy and society, but also realize the reform of the energy structure and reduce the environmental pollution. Our paper shows that the implementation of replacing fossil energy with electricity can indeed reduce greenhouse gas effectively. However, increasing the consumption of electricity will also stimulate coal consumption because of the electricity generation structure in Guangxi. 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