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The analysis of effects of clean energy power generation
Available online at www.sciencedirect.com Available online at www.sciencedirect.com
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Energyonline Procedia 00 (2018) 000–000 Available onlineatat www.sciencedirect.com Available www.sciencedirect.com Energy Procedia 00 (2018) 000–000
ScienceDirect ScienceDirect
www.elsevier.com/locate/procedia www.elsevier.com/locate/procedia
Energy (2018) 000–000 947–952 EnergyProcedia Procedia152 00 (2017) www.elsevier.com/locate/procedia
Applied Energy Symposium and Forum 2018: Low carbon cities and urban energy systems, CUE2018-Applied Energy andLow Forum 2018: Low carbon andsystems, Applied Energy Symposium andSymposium Forum 2018: carbon cities and urbancities energy CUE2018, 5–7June 2018, Shanghai, China CUE2018, 5–7June Shanghai, ChinaChina urban energy systems, 5–7 2018, June 2018, Shanghai,
The analysis of effects of clean energy power generation The analysis of effectsSymposium of cleanonenergy power The 15th International District Heating andgeneration Cooling
a,* a , Liu Wenjia Liu Hongtao Assessing the feasibility of using the heat demand-outdoor a,* Liu Hongtao , Liu Wenjia a School of Management, Polytechnical University, heat P. R. China temperature function for aNorthwestern long-term district demand forecast School of Management, Northwestern Polytechnical University, P. R. China a a
Abstract I. Abstract
Andrića,b,c*, A. Pinaa, P. Ferrãoa, J. Fournierb., B. Lacarrièrec, O. Le Correc
a
IN+ Center Innovation, Technology and in Policy Research - Instituto Superior Técnico, Rovisco Pais 1, 1049-001 Lisbon, Portugal to As pollution hasfor become a significant issue China, developing clean energy powerAv. generation is one of the main strategies b Veolia Recherche & China, Innovation, 291 Avenue Dreyfous Daniel, 78520 Limay, France As pollution has become a significant issue in developing clean energy power generation is one of the main strategies to promote the sustainable economy. Based on the partially closed input-output model corresponding to 29 industries in 2012, this c Département Systèmes Énergétiques et Environnement - IMT Atlantique, 4 rue Alfred Kastler, 44300 Nantes, France promote the sustainable Based on the corresponding to 29wind industries 2012, this paper categorized powereconomy. generation sectors to partially coal-fireclosed powerinput-output generation,model gas power generation, poweringeneration, paper categorized powerbiomass generation sectors coal-fire and power generation, gas powertogeneration, power generation, photovoltaic generation, energy powertogeneration, nuclear power generation analyze thewind impacts on environment photovoltaic generation, biomass energy and nuclear power to analyze the impacts on environment and social-economic developments. The power resultsgeneration, showed that improving singlegeneration clean energy technology would lead to different and social-economic developments. The results thatofimproving single clean technology would lead to different degrees of positive impacts on environment, but showed the effects GDP development and energy employment are highly correlated with the Abstract degrees of positive costs. impacts onthe environment, but the effects GDP development and employment are highly correlated the various generating For high generating costs, theofclean-energy technologies would contribute to the positivewith effects; various generating costs. For the high generating costs, the clean-energy technologies would contribute to the positive effects; however, the drawbacks of social economic development could overweight the benefits for the low generating costs in short run. District heating networks are commonly addressed in the literature as one of the most effective solutions for decreasing the however, theimproving drawbacks of social economic could overweight the benefits thewhich lowreduce generating costs in short Ingreenhouse addition, diverse energydevelopment generating approaches could promote GDPforgrowth, pollution, and increase gas emissions fromclean the building sector. These systems require high investments are returned through therun. heat In addition, diverse clean energy generating approaches could promote growth, reduce andeconomic increase employment. Finally, this paper provided some policy suggestions for the green-energy supply low-carbon sales. Due improving to the changed climate conditions and building renovation policies,GDP heatpower demand in and thepollution, future could decrease, employment. Finally, this paper developments. prolonging the investment returnprovided period. some policy suggestions for the green-energy power supply and low-carbon economic developments. The main scope of this paper is to assess the feasibility of using the heat demand – outdoor temperature function for heat demand Copyright 2018 Elsevier Ltd. All rights reserved. forecast. © The district of Alvalade, located in Lisbon (Portugal), was used as a case study. The district is consisted of 665 Copyright © 2018 Elsevier Ltd. All rights reserved. Copyright © 2018 Elsevier Ltd. All rights reserved. Selection and peer-review under responsibility of the scientific committee of Applied Energy Symposium and 2018:district Low buildings that vary in both construction period weather scenarios (low, medium, high)Forum and three Selection and peer-review under responsibility ofand thetypology. scientificThree committee of the 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. renovation were developed (shallow, intermediate, deep). To estimate the error, obtained heat demand values were Forum 2018:scenarios Low carbon cities and urban energy systems. carbon citieswith andresults urban energy systems, CUE2018. compared from a dynamic heat demand model, previously developed and validated by the authors.
Keywords:input-output clean energy powerchange generation; macroeconomic effects; environmental China The results showed analysis; that when only weather is considered, the margin of error couldeffects; be acceptable for some applications Keywords:input-output analysis; clean energy power generation; macroeconomic effects; environmental effects; China
(the error in annual demand was lower than 20% for all weather scenarios considered). However, after introducing renovation scenarios, the error value increased up to 59.5% (depending on the weather and renovation scenarios combination considered). 1.The Introduction value of slope coefficient increased on average within the range of 3.8% up to 8% per decade, that corresponds to the 1.decrease Introduction in the number of heating hours of 22-139h during the heating season (depending on the combination of weather and China, with the most energy On consumption and function emission in the increased world, the coal-dependent economy renovation scenarios considered). the other hand, intercept for traditional 7.8-12.7% per decade (depending on the created issues as airmodify pollution, which the significantly theconsidered, sustainable China,many with serious the most energy consumption and emission in the the function world, traditional coal-dependent economy coupled scenarios). Theenvironmental values suggested could such be used to parameters for restricting the scenarios and development. Power industry a typical for resource consumption and created seriousofgeneration environmental issuesis such as airenergy-intensive pollution, whichindustry significantly restricting the sustainable improvemany the accuracy heat demand estimations.
pollutant emission, the coal industry fire generation still contributed to a large proportion that 65% of all typesand of development. Powerand generation is a typical energy-intensive industry for resource consumption generation in 2016. and the coal fire generation still contributed to a large proportion that 65% of all types of pollutant emission, © 2017 TheinAuthors. generation 2016. Published by Elsevier Ltd.
responsibility of the Scientific Committee of The 15th International Symposium on District Heating and * Peer-review Hongtao Liu. under Tel.: 18636891326; E-mail address:[email protected] * Cooling. Hongtao Liu. Tel.: 18636891326; E-mail address:[email protected] Keywords: Heat demand; Forecast; Climate change 1876-6102 Copyright © 2018 Elsevier Ltd. All rights reserved. Selection peer-review under responsibility the scientific 1876-6102and Copyright © 2018 Elsevier Ltd. All of rights reserved. committee of the Applied Energy Symposium and Forum 2018: Low carbon cities and urbanand energy systems, under CUE2018. Selection peer-review responsibility of the scientific committee of the Applied Energy Symposium and Forum 2018: Low carbon cities 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.2018.09.098
948 2
Liu Hongtao et al. / Energy Procedia 152 (2018) 947–952 Hongtao Liu & Wenjia Liu / Energy Procedia 00 (2018) 000–000
National Energy Administration of China published “13th Five-Year" Energy Development Planning in 2017, which pointed that China would reduce coal consumption rate to 58%, and power industry could become the pioneer of the low carbon revolution. The promotion of clean energy technology is a hot topic attracting high attention from many fields in the world. There were some scientific literatures introduced the effects of single clean resource during the past few years [1-5], but few of them mentioned the overall impact of the clean energy. Most of studies emphasised on the social and economic benefits from applying the clean energy and particularly focused on employment, which indicated it could bring both positive and negative effects on employment [6-8]. This paper based on the information appeared on China’s input-output table in 2012 to categorize power generation industry to coal-fire power generation, gas power generation, wind power generation, photovoltaic power generation, biomass energy power generation and nuclear power generation, and built up the input-output table of 28 sectors. Then it developed a partially closed input-output model including an inter-industry matrix of resident income and consumption. The effects of different clean technologies to generate power will be evaluated from following aspects: 1) the impacts on the amount of energy and resource consumptions; 2) the effects on environmental factors such as climate change, acid pollution, and photochemical pollution; 3) the impacts on GDP and employment. In addition, it will provide some suggestions to government making appropriate policies for energy conservation and emission reduction, which will play a significant role in both theoretical and practical applications. 2. Methodology 2.1. Input-output model This paper used partially closed input-output model, extending the A matrix to incorporate the household consumption into the column vectors, and used the labor's reward as the new row vectors.,which is represented by A* .Partially closed input-output model can be obtained as follow:
X (I A * )1Y * Changes in output resulting from changes in final demand as follows:
X (I A * )1 Y * Where ∆Y ∗ represents matrix of changes in final investment, ∆X represents matrix of variation in output.
In the second step, we analysed the environmental impact of clean energy power generation, and based on the IPCC, we used the pollutant emission factors in the process of fossil energy utilization to calculate the emission of various pollutants, including carbon dioxide emissions, sulphur dioxide emissions, nitrogen oxides and dust emissions. This is given as:
Q
n
E k 1
fk
Where 𝐐𝐐 represents matrix of pollutants emissions and k represents the variety of energy of coal, oil, and natural gas. 𝑓𝑓𝑘𝑘 represents the carbon emission coefficient of the kth energy sources. We used 𝛃𝛃 to express direct emission coefficient matrix. The pollutant discharge of unit output is calculated as follows:
F
X
Where ∆𝐅𝐅 represents matrix of variation in pollutant emissions.
In the third step, we analysed the socio-economic impact. The changes of GDP can be defined as follow:
GDP V X
Liu Hongtao et al. / Energy Procedia 152 (2018) 947–952 Hongtao Liu & Wenjia Liu / Energy Procedia 00 (2018) 000–000
949 3
Where 𝐕𝐕 represents value-added of unit output. We used quantity of employment to measure the impact of clean energy power generation on social development. The changes of employment can be calculated as follow:
L
X
Where 𝛄𝛄 represents employment per unit output; ∆𝐋𝐋 represents matrix of change in employment
2.2. Decomposition and consolidation of industries
The common input-output table of 139 industries have contained data of clean energy generation, but the data did not display. It caused inconvenient to analyse the influence of the clean energy power supply. Based on the inputoutput table in 2012, and the electric power industry would be divided into coal-fired power sector, gas power generation, hydropower, wind power, photovoltaic, biomass power generation and nuclear power. The proportion of various energy sources for power generation can be defined as 𝑤𝑤1 to 𝑤𝑤7 . The proportion of the various power generation technologies consumed by each industry is 𝑔𝑔𝑗𝑗𝑗𝑗 . The column vector f various power generation technologies in input-output table can be defined as follows:
g 11 w 1 x g w x 1n 1
g 71 w 7 x g 7n
w 7 x
We assumed that the uses of various power generation are the same. The row vector can be established according to the proportion of various generation technologies. Then we can get the matrix of clean energy power generation. Finally, the RAS method is used to balance the input -output tables. The costs of generating electricity from various sources extracting from the relevant literatures, as shown in Table 1 Table 1 Proportion and cost of generation technology Coal gas wind hydropower photovoltaic power power power
Power generation technology Proportion of generation（%） Cost of generation （yuan /kwh）
biomass power
nuclear power
75.58
2.22
17.17
2.07
0.08
0.63
1.98
0.40
0.51
0.24
0.42
1.00
0.69
0.27
3. The effects of different single types of clean energy power generation 3.1. Social-economic effects This paper based on the perspective of changing final demand of power generation to evaluate the socialeconomic effect and environmental effect by replacing coal fire to clean energy. Since different types of clean energy have different generating costs, when 10 thousand yuan of final demand of coal fire generation is replaced by other clean energy, the added values of final demand of different clean energy are in the following table.2. Table.2 The final demand adjustment of all kinds of clean energy power generation technology (100 million yuan) Coal power
gas power
hydropower
wind power
photovoltaic
-1
1.28
0.61
1.06
2.53
biomass power 1.75
nuclear power 0.68
When clean technologies replace coal fire generation, different clean energy leads to change different rates of GDP
Liu Hongtao et al. / Energy Procedia 152 (2018) 947–952 Hongtao Liu & Wenjia Liu / Energy Procedia 00 (2018) 000–000
950 4
and employment as shown in table.3. Table.3 The variation of GDP and employment by clean technologies Power generation technology GDP change (10000 yuan) Number of changes in employment (person)
wind power
Gas power
hydropower
photovoltaic
biomass power
nuclear power
2861.00
607.59
-3899.00
15341.77
7518.99
-3139.24
139.35
237.12
-151.14
40646.26
1116.69
-129.49
From the Table.3 above, for the generating costs of clean energy technologies less than coal fire generating costs (hydroelectric generation and nuclear power generation), only rising the final demand of clean energy technologies would lead to negative impacts on GDP and employment. However, for those generating costs are greater than coal fire generating costs, such as wind power, gas power, photovoltaic and biomass power, they would have positive impacts on GDP and employment. Particularly, for the photovoltaic power generation, it has much stronger positive effects on employment rate comparing to other clean energy. Therefore, the data showed that if only increase final demand of one type of clean energy, like only increasing investment of hydroelectric power, it has the risk of bringing negative effects on economy and society in the short run. 3.2. Environmental effects Considering to environmental effects, this paper focused on the pollution of fossil energy, which includes emissions of carbon dioxide, sulfur dioxide, nitrogen dioxide, and dust. It could induce many environmental issues like climate change, acid rain, and photochemical pollution. Since greenhouse effect is a very hot topic in China and world, we chose the carbon dioxide emission to evaluate environmental effects from different industries. The method is the same as that using in economic analysis, and the comparison of energy consumption and pollutant discharge is in table.4. Table.4i Energy consumption and pollutant discharge Power generation technology Energy consumption (10000 tons of standard coal) CO2 emissions (10000 tons) SO2 emissions (10000 tons) NOx emissions (10000 tons) Dust emission (10000 tons)
wind power
Gas power
hydropower
photovoltaic
biomass power
nuclear power
-0.426
0.663
-0.857
0.887
0.374
-0.907
-5.167 -0.008 -0.009 -0.003
9.218 0.017 0.021 0.004
-10.389 -0.017 -0.020 -0.005
11.504 0.021 0.026 0.006
3.406 0.007 0.008 0.002
-10.024 -0.016 -0.019 -0.005
From the Table 4 above, for the generating costs less than coal fire generating costs like hydropower and nuclear power, only increasing the final demand of clean energy technology would bring positive effects. For the wind power energy, because its cost is a bit higher than the cost of coal fire generation, it would bring positive impact on environment as well if increase its final demand. On the other hand, for the generating costs which much higher than coal fire, such as gas, solar, and biomass, they would increase the energy consumption and emission compared to coal. However, if evaluate the overall energy intensity and emission releasing intensity, both of fuel gas, photovoltaic and biomass generation would fall slightly. Therefore, it indicated that using cleaning energy will have positive effects for environment in different degrees.
Liu Hongtao et al. / Energy Procedia 152 (2018) 947–952 Hongtao Liu & Wenjia Liu / Energy Procedia 00 (2018) 000–000
951 5
4. The overall effects of technologies of clean energy power generation 4.1. Social-economic effects In order to match the input-output data, this paper adopted the proportion and costs of different clean energy to adjust the final demand of power generation. If decline the final demand of coal fire generation by one billion and increase fuel gas generation, hydroelectric power, wind power, photovoltaic power, biomass power, and nuclear power by 0.092 billion yuan, 0.914 billion yuan, 0.052 billion yuan, 0.004 billion yuan, 0.067 billion yuan, 0.143 billion yuan separately, China’s GDP would increase by 0.27 billion yuan while the employment would increase by 87.72 people. It shows the strong overall positive effects of the various clean energy applications. In terms of the impacts on energy sectors and non-energy sectors, if adopt clean energy technologies for power generation, the most significant two that increase of employment rate are photovoltaic and biomass generation, but the employment rate of coal extraction and coal processing would decline slightly. All other sectors have increasing employment rate comparing to before. For the non-energy sectors, although other service industries have the most significant increase in employment, and the industry of electric appliance, equipment and instrument also have increasing rate in some degree, the employment of transportation, storage, and postal service would have large amount of decease. Overall, adopting multiple clean energy technologies to replace coal fire to generation power could cause the positive effects on employment. 4.2. Environmental effects Applying multiple clean energy technologies of power generation could decline resource consumption and emission, and it is beneficial for coping with climate change, acid pollution, and photochemical pollution, as shown in Table.5. Particularly, the most significant reduction is carbon dioxide, which decrease by 20.96 thousand of tons. Table.5 Variation of pollutant emission Energy consumption (10000 tons of standard coal) -0.213
CO2 emissions (10000 tons)
SO2 emissions (10000 tons)
NOx emissions (10000 tons)
Dust emission (10000 tons)
-2.096
-0.002
-0.002
-0.001
The emission of carbon dioxide is reduced by a great amount of 90.96 thousand tons in coal fire power generation and 7.60 thousand tons in coal extraction and coal processing if adopt multiple clean energy for power generation. Other energy sectors could have a little bit increasing in emission. For the non-energy sectors, the most significant reduction is in metal smelting and processing industry, which is about 0.046 thousand tons. In addition, the carbon emissions of wood processing, paper industry and chemistry products also showed a falling trend. However, other service and non-metal mineral industry would have higher carbon emissions than before. Overall, the multiple clean energy technologies for power generation could bring positive effects on environment. 5. Conclusion With the “green development consideration” becoming a hot topic in China, improving clean energy technologies and transforming to a low-carbon economy have been the internal requirements for China’s sustainable development. This paper reviewed the relevant literature and based on partially closed input-output model to categorize different clean energy and build a complete analysing system to exam the effects of applying clean energy to generate power. Then it measured the effects from the social-economic and environmental angles and provided the results and policy suggestions for decision makers. Introducing the single green energy to generate power will have the different effects on society and economy. If
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Liu Hongtao et al. / Energy Procedia 152 (2018) 947–952 Hongtao Liu & Wenjia Liu / Energy Procedia 00 (2018) 000–000
improve the high-cost green energy, it would bring positive effects; however, the low-cost green energy could bring the negative effects. For the environmental effects, promoting high-cost single clean energy could bring large positive effects while low-cost ones could bring less positive effects. Thus, improving green energy technologies should not only focus on final demand, but also need other approaches to support the power generation development from various perspectives. The method of adopting multiple clean energy is an important approach to change the negative effects. The results showed that China’s GDP and employment rate could be improved, and resource consumption and emission would be declining. Therefore, this approach could bring positive effects on both economy and environment, but it required to be planed and adjusted the weight of different green energy to ensure the most positive impact. The future studies could be focusing on calculating and testing the proportion of the different green energy to maximize the benefits of economic development and minimize the pollutant emission. References: [1] Himanshu Nautiyal, S.K. Singal,Varun, et al. Small hydropower for sustainable energy development in India. Renewable and Sustainable Energy Reviews, 2011, 15:2021-2027 [2] ThapatSilalertruksa, Shabbir H. Gheewala ,KatjaHunecke, et al. Biofuels and employment effects: implications for socioeconomic development in Thailand. Biomass and Bioenergy, 2012, 46:409-418 [3] Seung-HoonYoo, Tae-HoYoo, et al. The Role of the nuclear power generation in the Korean national economy: An input-output analysis. Progress in Nuclear Energy, 2009, 51:86-92 [4] Shin Nagashima, Yohji Uchiyama, Keiichi Okajima. Environment, energy and economic analysis of wind power generation system installation with input-output table. Energy Procedia, 2015, 75:683-690 [5] Blanca Corona, CristinadelaRúa, GuillermoSanMiguel. Socio-economic and environmental effects of concentrated solar power in Spain: A multiregional input output analysis. Solar Energy Materials & Solar Cells, 2016, 156:112-121 [6] Heidi Garrett-Peltier. Green versus brown: Comparing the employment impacts of energy efficiency, renewable energy, and fossil fuels using an input-output model. Economic Modelling, 2017, 46:439-447. [7] Lehr, U.,Lutz,C.,Edler,D .Green jobs? Economic impacts of renewable energy in Germany. Energy Policy, 2012. 47:358–364. [8] Moreno, B.,Lopez,A.J. The effect of renewable energy on employment. The case of Asturias (Spain). Renewable and Sustainable Energy Reviews 2008, 12:732–751.
i
Abbreviations and Notations CO2-Carbon dioxide SO2-sulphur dioxide NOx-Nitrogen oxides
ScienceDirect ScienceDirect
Energyonline Procedia 00 (2018) 000–000 Available onlineatat www.sciencedirect.com Available www.sciencedirect.com Energy Procedia 00 (2018) 000–000
ScienceDirect ScienceDirect
www.elsevier.com/locate/procedia www.elsevier.com/locate/procedia
Energy (2018) 000–000 947–952 EnergyProcedia Procedia152 00 (2017) www.elsevier.com/locate/procedia
Applied Energy Symposium and Forum 2018: Low carbon cities and urban energy systems, CUE2018-Applied Energy andLow Forum 2018: Low carbon andsystems, Applied Energy Symposium andSymposium Forum 2018: carbon cities and urbancities energy CUE2018, 5–7June 2018, Shanghai, China CUE2018, 5–7June Shanghai, ChinaChina urban energy systems, 5–7 2018, June 2018, Shanghai,
The analysis of effects of clean energy power generation The analysis of effectsSymposium of cleanonenergy power The 15th International District Heating andgeneration Cooling
a,* a , Liu Wenjia Liu Hongtao Assessing the feasibility of using the heat demand-outdoor a,* Liu Hongtao , Liu Wenjia a School of Management, Polytechnical University, heat P. R. China temperature function for aNorthwestern long-term district demand forecast School of Management, Northwestern Polytechnical University, P. R. China a a
Abstract I. Abstract
Andrića,b,c*, A. Pinaa, P. Ferrãoa, J. Fournierb., B. Lacarrièrec, O. Le Correc
a
IN+ Center Innovation, Technology and in Policy Research - Instituto Superior Técnico, Rovisco Pais 1, 1049-001 Lisbon, Portugal to As pollution hasfor become a significant issue China, developing clean energy powerAv. generation is one of the main strategies b Veolia Recherche & China, Innovation, 291 Avenue Dreyfous Daniel, 78520 Limay, France As pollution has become a significant issue in developing clean energy power generation is one of the main strategies to promote the sustainable economy. Based on the partially closed input-output model corresponding to 29 industries in 2012, this c Département Systèmes Énergétiques et Environnement - IMT Atlantique, 4 rue Alfred Kastler, 44300 Nantes, France promote the sustainable Based on the corresponding to 29wind industries 2012, this paper categorized powereconomy. generation sectors to partially coal-fireclosed powerinput-output generation,model gas power generation, poweringeneration, paper categorized powerbiomass generation sectors coal-fire and power generation, gas powertogeneration, power generation, photovoltaic generation, energy powertogeneration, nuclear power generation analyze thewind impacts on environment photovoltaic generation, biomass energy and nuclear power to analyze the impacts on environment and social-economic developments. The power resultsgeneration, showed that improving singlegeneration clean energy technology would lead to different and social-economic developments. The results thatofimproving single clean technology would lead to different degrees of positive impacts on environment, but showed the effects GDP development and energy employment are highly correlated with the Abstract degrees of positive costs. impacts onthe environment, but the effects GDP development and employment are highly correlated the various generating For high generating costs, theofclean-energy technologies would contribute to the positivewith effects; various generating costs. For the high generating costs, the clean-energy technologies would contribute to the positive effects; however, the drawbacks of social economic development could overweight the benefits for the low generating costs in short run. District heating networks are commonly addressed in the literature as one of the most effective solutions for decreasing the however, theimproving drawbacks of social economic could overweight the benefits thewhich lowreduce generating costs in short Ingreenhouse addition, diverse energydevelopment generating approaches could promote GDPforgrowth, pollution, and increase gas emissions fromclean the building sector. These systems require high investments are returned through therun. heat In addition, diverse clean energy generating approaches could promote growth, reduce andeconomic increase employment. Finally, this paper provided some policy suggestions for the green-energy supply low-carbon sales. Due improving to the changed climate conditions and building renovation policies,GDP heatpower demand in and thepollution, future could decrease, employment. Finally, this paper developments. prolonging the investment returnprovided period. some policy suggestions for the green-energy power supply and low-carbon economic developments. The main scope of this paper is to assess the feasibility of using the heat demand – outdoor temperature function for heat demand Copyright 2018 Elsevier Ltd. All rights reserved. forecast. © The district of Alvalade, located in Lisbon (Portugal), was used as a case study. The district is consisted of 665 Copyright © 2018 Elsevier Ltd. All rights reserved. Copyright © 2018 Elsevier Ltd. All rights reserved. Selection and peer-review under responsibility of the scientific committee of Applied Energy Symposium and 2018:district Low buildings that vary in both construction period weather scenarios (low, medium, high)Forum and three Selection and peer-review under responsibility ofand thetypology. scientificThree committee of the 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. renovation were developed (shallow, intermediate, deep). To estimate the error, obtained heat demand values were Forum 2018:scenarios Low carbon cities and urban energy systems. carbon citieswith andresults urban energy systems, CUE2018. compared from a dynamic heat demand model, previously developed and validated by the authors.
Keywords:input-output clean energy powerchange generation; macroeconomic effects; environmental China The results showed analysis; that when only weather is considered, the margin of error couldeffects; be acceptable for some applications Keywords:input-output analysis; clean energy power generation; macroeconomic effects; environmental effects; China
(the error in annual demand was lower than 20% for all weather scenarios considered). However, after introducing renovation scenarios, the error value increased up to 59.5% (depending on the weather and renovation scenarios combination considered). 1.The Introduction value of slope coefficient increased on average within the range of 3.8% up to 8% per decade, that corresponds to the 1.decrease Introduction in the number of heating hours of 22-139h during the heating season (depending on the combination of weather and China, with the most energy On consumption and function emission in the increased world, the coal-dependent economy renovation scenarios considered). the other hand, intercept for traditional 7.8-12.7% per decade (depending on the created issues as airmodify pollution, which the significantly theconsidered, sustainable China,many with serious the most energy consumption and emission in the the function world, traditional coal-dependent economy coupled scenarios). Theenvironmental values suggested could such be used to parameters for restricting the scenarios and development. Power industry a typical for resource consumption and created seriousofgeneration environmental issuesis such as airenergy-intensive pollution, whichindustry significantly restricting the sustainable improvemany the accuracy heat demand estimations.
pollutant emission, the coal industry fire generation still contributed to a large proportion that 65% of all typesand of development. Powerand generation is a typical energy-intensive industry for resource consumption generation in 2016. and the coal fire generation still contributed to a large proportion that 65% of all types of pollutant emission, © 2017 TheinAuthors. generation 2016. Published by Elsevier Ltd.
responsibility of the Scientific Committee of The 15th International Symposium on District Heating and * Peer-review Hongtao Liu. under Tel.: 18636891326; E-mail address:[email protected] * Cooling. Hongtao Liu. Tel.: 18636891326; E-mail address:[email protected] Keywords: Heat demand; Forecast; Climate change 1876-6102 Copyright © 2018 Elsevier Ltd. All rights reserved. Selection peer-review under responsibility the scientific 1876-6102and Copyright © 2018 Elsevier Ltd. All of rights reserved. committee of the Applied Energy Symposium and Forum 2018: Low carbon cities and urbanand energy systems, under CUE2018. Selection peer-review responsibility of the scientific committee of the Applied Energy Symposium and Forum 2018: Low carbon cities 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.2018.09.098
948 2
Liu Hongtao et al. / Energy Procedia 152 (2018) 947–952 Hongtao Liu & Wenjia Liu / Energy Procedia 00 (2018) 000–000
National Energy Administration of China published “13th Five-Year" Energy Development Planning in 2017, which pointed that China would reduce coal consumption rate to 58%, and power industry could become the pioneer of the low carbon revolution. The promotion of clean energy technology is a hot topic attracting high attention from many fields in the world. There were some scientific literatures introduced the effects of single clean resource during the past few years [1-5], but few of them mentioned the overall impact of the clean energy. Most of studies emphasised on the social and economic benefits from applying the clean energy and particularly focused on employment, which indicated it could bring both positive and negative effects on employment [6-8]. This paper based on the information appeared on China’s input-output table in 2012 to categorize power generation industry to coal-fire power generation, gas power generation, wind power generation, photovoltaic power generation, biomass energy power generation and nuclear power generation, and built up the input-output table of 28 sectors. Then it developed a partially closed input-output model including an inter-industry matrix of resident income and consumption. The effects of different clean technologies to generate power will be evaluated from following aspects: 1) the impacts on the amount of energy and resource consumptions; 2) the effects on environmental factors such as climate change, acid pollution, and photochemical pollution; 3) the impacts on GDP and employment. In addition, it will provide some suggestions to government making appropriate policies for energy conservation and emission reduction, which will play a significant role in both theoretical and practical applications. 2. Methodology 2.1. Input-output model This paper used partially closed input-output model, extending the A matrix to incorporate the household consumption into the column vectors, and used the labor's reward as the new row vectors.,which is represented by A* .Partially closed input-output model can be obtained as follow:
X (I A * )1Y * Changes in output resulting from changes in final demand as follows:
X (I A * )1 Y * Where ∆Y ∗ represents matrix of changes in final investment, ∆X represents matrix of variation in output.
In the second step, we analysed the environmental impact of clean energy power generation, and based on the IPCC, we used the pollutant emission factors in the process of fossil energy utilization to calculate the emission of various pollutants, including carbon dioxide emissions, sulphur dioxide emissions, nitrogen oxides and dust emissions. This is given as:
Q
n
E k 1
fk
Where 𝐐𝐐 represents matrix of pollutants emissions and k represents the variety of energy of coal, oil, and natural gas. 𝑓𝑓𝑘𝑘 represents the carbon emission coefficient of the kth energy sources. We used 𝛃𝛃 to express direct emission coefficient matrix. The pollutant discharge of unit output is calculated as follows:
F
X
Where ∆𝐅𝐅 represents matrix of variation in pollutant emissions.
In the third step, we analysed the socio-economic impact. The changes of GDP can be defined as follow:
GDP V X
Liu Hongtao et al. / Energy Procedia 152 (2018) 947–952 Hongtao Liu & Wenjia Liu / Energy Procedia 00 (2018) 000–000
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Where 𝐕𝐕 represents value-added of unit output. We used quantity of employment to measure the impact of clean energy power generation on social development. The changes of employment can be calculated as follow:
L
X
Where 𝛄𝛄 represents employment per unit output; ∆𝐋𝐋 represents matrix of change in employment
2.2. Decomposition and consolidation of industries
The common input-output table of 139 industries have contained data of clean energy generation, but the data did not display. It caused inconvenient to analyse the influence of the clean energy power supply. Based on the inputoutput table in 2012, and the electric power industry would be divided into coal-fired power sector, gas power generation, hydropower, wind power, photovoltaic, biomass power generation and nuclear power. The proportion of various energy sources for power generation can be defined as 𝑤𝑤1 to 𝑤𝑤7 . The proportion of the various power generation technologies consumed by each industry is 𝑔𝑔𝑗𝑗𝑗𝑗 . The column vector f various power generation technologies in input-output table can be defined as follows:
g 11 w 1 x g w x 1n 1
g 71 w 7 x g 7n
w 7 x
We assumed that the uses of various power generation are the same. The row vector can be established according to the proportion of various generation technologies. Then we can get the matrix of clean energy power generation. Finally, the RAS method is used to balance the input -output tables. The costs of generating electricity from various sources extracting from the relevant literatures, as shown in Table 1 Table 1 Proportion and cost of generation technology Coal gas wind hydropower photovoltaic power power power
Power generation technology Proportion of generation（%） Cost of generation （yuan /kwh）
biomass power
nuclear power
75.58
2.22
17.17
2.07
0.08
0.63
1.98
0.40
0.51
0.24
0.42
1.00
0.69
0.27
3. The effects of different single types of clean energy power generation 3.1. Social-economic effects This paper based on the perspective of changing final demand of power generation to evaluate the socialeconomic effect and environmental effect by replacing coal fire to clean energy. Since different types of clean energy have different generating costs, when 10 thousand yuan of final demand of coal fire generation is replaced by other clean energy, the added values of final demand of different clean energy are in the following table.2. Table.2 The final demand adjustment of all kinds of clean energy power generation technology (100 million yuan) Coal power
gas power
hydropower
wind power
photovoltaic
-1
1.28
0.61
1.06
2.53
biomass power 1.75
nuclear power 0.68
When clean technologies replace coal fire generation, different clean energy leads to change different rates of GDP
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and employment as shown in table.3. Table.3 The variation of GDP and employment by clean technologies Power generation technology GDP change (10000 yuan) Number of changes in employment (person)
wind power
Gas power
hydropower
photovoltaic
biomass power
nuclear power
2861.00
607.59
-3899.00
15341.77
7518.99
-3139.24
139.35
237.12
-151.14
40646.26
1116.69
-129.49
From the Table.3 above, for the generating costs of clean energy technologies less than coal fire generating costs (hydroelectric generation and nuclear power generation), only rising the final demand of clean energy technologies would lead to negative impacts on GDP and employment. However, for those generating costs are greater than coal fire generating costs, such as wind power, gas power, photovoltaic and biomass power, they would have positive impacts on GDP and employment. Particularly, for the photovoltaic power generation, it has much stronger positive effects on employment rate comparing to other clean energy. Therefore, the data showed that if only increase final demand of one type of clean energy, like only increasing investment of hydroelectric power, it has the risk of bringing negative effects on economy and society in the short run. 3.2. Environmental effects Considering to environmental effects, this paper focused on the pollution of fossil energy, which includes emissions of carbon dioxide, sulfur dioxide, nitrogen dioxide, and dust. It could induce many environmental issues like climate change, acid rain, and photochemical pollution. Since greenhouse effect is a very hot topic in China and world, we chose the carbon dioxide emission to evaluate environmental effects from different industries. The method is the same as that using in economic analysis, and the comparison of energy consumption and pollutant discharge is in table.4. Table.4i Energy consumption and pollutant discharge Power generation technology Energy consumption (10000 tons of standard coal) CO2 emissions (10000 tons) SO2 emissions (10000 tons) NOx emissions (10000 tons) Dust emission (10000 tons)
wind power
Gas power
hydropower
photovoltaic
biomass power
nuclear power
-0.426
0.663
-0.857
0.887
0.374
-0.907
-5.167 -0.008 -0.009 -0.003
9.218 0.017 0.021 0.004
-10.389 -0.017 -0.020 -0.005
11.504 0.021 0.026 0.006
3.406 0.007 0.008 0.002
-10.024 -0.016 -0.019 -0.005
From the Table 4 above, for the generating costs less than coal fire generating costs like hydropower and nuclear power, only increasing the final demand of clean energy technology would bring positive effects. For the wind power energy, because its cost is a bit higher than the cost of coal fire generation, it would bring positive impact on environment as well if increase its final demand. On the other hand, for the generating costs which much higher than coal fire, such as gas, solar, and biomass, they would increase the energy consumption and emission compared to coal. However, if evaluate the overall energy intensity and emission releasing intensity, both of fuel gas, photovoltaic and biomass generation would fall slightly. Therefore, it indicated that using cleaning energy will have positive effects for environment in different degrees.
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4. The overall effects of technologies of clean energy power generation 4.1. Social-economic effects In order to match the input-output data, this paper adopted the proportion and costs of different clean energy to adjust the final demand of power generation. If decline the final demand of coal fire generation by one billion and increase fuel gas generation, hydroelectric power, wind power, photovoltaic power, biomass power, and nuclear power by 0.092 billion yuan, 0.914 billion yuan, 0.052 billion yuan, 0.004 billion yuan, 0.067 billion yuan, 0.143 billion yuan separately, China’s GDP would increase by 0.27 billion yuan while the employment would increase by 87.72 people. It shows the strong overall positive effects of the various clean energy applications. In terms of the impacts on energy sectors and non-energy sectors, if adopt clean energy technologies for power generation, the most significant two that increase of employment rate are photovoltaic and biomass generation, but the employment rate of coal extraction and coal processing would decline slightly. All other sectors have increasing employment rate comparing to before. For the non-energy sectors, although other service industries have the most significant increase in employment, and the industry of electric appliance, equipment and instrument also have increasing rate in some degree, the employment of transportation, storage, and postal service would have large amount of decease. Overall, adopting multiple clean energy technologies to replace coal fire to generation power could cause the positive effects on employment. 4.2. Environmental effects Applying multiple clean energy technologies of power generation could decline resource consumption and emission, and it is beneficial for coping with climate change, acid pollution, and photochemical pollution, as shown in Table.5. Particularly, the most significant reduction is carbon dioxide, which decrease by 20.96 thousand of tons. Table.5 Variation of pollutant emission Energy consumption (10000 tons of standard coal) -0.213
CO2 emissions (10000 tons)
SO2 emissions (10000 tons)
NOx emissions (10000 tons)
Dust emission (10000 tons)
-2.096
-0.002
-0.002
-0.001
The emission of carbon dioxide is reduced by a great amount of 90.96 thousand tons in coal fire power generation and 7.60 thousand tons in coal extraction and coal processing if adopt multiple clean energy for power generation. Other energy sectors could have a little bit increasing in emission. For the non-energy sectors, the most significant reduction is in metal smelting and processing industry, which is about 0.046 thousand tons. In addition, the carbon emissions of wood processing, paper industry and chemistry products also showed a falling trend. However, other service and non-metal mineral industry would have higher carbon emissions than before. Overall, the multiple clean energy technologies for power generation could bring positive effects on environment. 5. Conclusion With the “green development consideration” becoming a hot topic in China, improving clean energy technologies and transforming to a low-carbon economy have been the internal requirements for China’s sustainable development. This paper reviewed the relevant literature and based on partially closed input-output model to categorize different clean energy and build a complete analysing system to exam the effects of applying clean energy to generate power. Then it measured the effects from the social-economic and environmental angles and provided the results and policy suggestions for decision makers. Introducing the single green energy to generate power will have the different effects on society and economy. If
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improve the high-cost green energy, it would bring positive effects; however, the low-cost green energy could bring the negative effects. For the environmental effects, promoting high-cost single clean energy could bring large positive effects while low-cost ones could bring less positive effects. Thus, improving green energy technologies should not only focus on final demand, but also need other approaches to support the power generation development from various perspectives. The method of adopting multiple clean energy is an important approach to change the negative effects. The results showed that China’s GDP and employment rate could be improved, and resource consumption and emission would be declining. Therefore, this approach could bring positive effects on both economy and environment, but it required to be planed and adjusted the weight of different green energy to ensure the most positive impact. The future studies could be focusing on calculating and testing the proportion of the different green energy to maximize the benefits of economic development and minimize the pollutant emission. References: [1] Himanshu Nautiyal, S.K. Singal,Varun, et al. Small hydropower for sustainable energy development in India. Renewable and Sustainable Energy Reviews, 2011, 15:2021-2027 [2] ThapatSilalertruksa, Shabbir H. Gheewala ,KatjaHunecke, et al. Biofuels and employment effects: implications for socioeconomic development in Thailand. Biomass and Bioenergy, 2012, 46:409-418 [3] Seung-HoonYoo, Tae-HoYoo, et al. The Role of the nuclear power generation in the Korean national economy: An input-output analysis. Progress in Nuclear Energy, 2009, 51:86-92 [4] Shin Nagashima, Yohji Uchiyama, Keiichi Okajima. Environment, energy and economic analysis of wind power generation system installation with input-output table. Energy Procedia, 2015, 75:683-690 [5] Blanca Corona, CristinadelaRúa, GuillermoSanMiguel. Socio-economic and environmental effects of concentrated solar power in Spain: A multiregional input output analysis. Solar Energy Materials & Solar Cells, 2016, 156:112-121 [6] Heidi Garrett-Peltier. Green versus brown: Comparing the employment impacts of energy efficiency, renewable energy, and fossil fuels using an input-output model. Economic Modelling, 2017, 46:439-447. [7] Lehr, U.,Lutz,C.,Edler,D .Green jobs? Economic impacts of renewable energy in Germany. Energy Policy, 2012. 47:358–364. [8] Moreno, B.,Lopez,A.J. The effect of renewable energy on employment. The case of Asturias (Spain). Renewable and Sustainable Energy Reviews 2008, 12:732–751.
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Abbreviations and Notations CO2-Carbon dioxide SO2-sulphur dioxide NOx-Nitrogen oxides