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http://dx.doi.org/10.1016/j.net.2019.01.016

Policy implication of nuclear energy's potential for energy optimization and CO2 mitigation: A case study of Fujian, China  

Peng, Lihong (College of the Environment & Ecology, Xiamen University)
Zhang, Yi (College of the Environment & Ecology, Xiamen University)
Li, Feng (College of the Environment & Ecology, Xiamen University)
Wang, Qian (Science Research Department, Lanzhou University)
Chen, Xiaochou (Information and Network Center, Xiamen University)
Yu, Ang (College of the Environment & Ecology, Xiamen University)
Publication Information
Nuclear Engineering and Technology / v.51, no.4, 2019 , pp. 1154-1162 More about this Journal
Abstract
China is undertaking an energy reform from fossil fuels to clean energy to accomplish $CO_2$ intensity (CI) reduction commitments. After hydropower, nuclear energy is potential based on breadthwise comparison with the world and analysis of government energy consumption (EC) plan. This paper establishes a CI energy policy response forecasting model based on national and provincial EC plans. This model is then applied in Fujian Province to predict its CI from 2016 to 2020. The result shows that CI declines at a range of 43%-53% compared to that in 2005 considering five conditions of economic growth in 2020. Furthermore, Fujian will achieve the national goals in advance because EC is controlled and nuclear energy ratio increased to 16.4% (the proportion of non-fossil in primary energy is 26.7%). Finally, the development of nuclear energy in China and the world are analyzed, and several policies for energy optimization and CI reduction are proposed.
Keywords
$CO_2$ intensity reduction; Energy structure optimization; Nuclear energy; $CO_2$ intensity energy policy response forecasting model;
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1 C.C. Chang, A multivariate causality test of carbon dioxide emissions, energy consumption and economic growth in China, Appl. Energy 87 (2010) 3533-3537.   DOI
2 Statistical review of world renewable energy [online], http://databank.worldbank.org/data/reports.aspx?source=2&series=EG.USE.CRNW.ZS&country= (Accessed 23 September 2015).
3 Y. Fan, L.C. Liu, G. Wu, H.T. Tsai, Y.M. Wei, Changes in carbon intensity in China: empirical findings from 1980-2003, Ecol. Econ. 62 (2007) 683-691.   DOI
4 S. Liang, T. Zhang, What is driving $CO_2$ emissions in a typical manufacturing center of South China? The case of Jiangsu Province, Energy Policy 39 (2011) 7078-7083.   DOI
5 P. Peters Glen, L. Weber Christopher, G. Dabo, H. Klaus, China's growing $CO_2$ emissions-a race between increasing consumption and efficiency gains, Environ. Sci. Technol. 41 (2007) 5939-5944.   DOI
6 C. Wang, J. Chen, Z. Ji, Decomposition of energy-related $CO_2$ emission in China: 1957-2000, Energy 30 (2005) 73-83.   DOI
7 B. Lin, X. Lei, Carbon emissions reduction in China's food industry, Energy Policy 86 (35) (2015) 483-492.   DOI
8 X. Yin, W. Chen, J. Eom, L.E. Clarke, S.H. Kim, P.L. Patel, S. Yu, G.P. Kyle, China's transportation energy consumption and $CO_2$ emissions from a global perspective, Energy Policy 82 (2015) 233-248.   DOI
9 2015 Energy Review of China. [online] http://www.bp.com/zh_cn/china/reports-and-publications/_bp_2015.html (Accessed 30 December 2017).
10 13th Five-Year Plan of Electric Power Development. [online] http://www.sdpc.gov.cn/zcfb/zcfbghwb/201612/P020161222570036010274.pdf (Accessed 21 January 2018).
11 2016 Statistical Yearbook of Fujian Province. [online] http://www.stats-fj.gov.cn/tongjinianjian/dz2016/index-cn.htm (Accessed 20 February 2017).
12 L. Nan, Z. Ma, J. Kang, Changes in carbon intensity in China's industrial sector: decomposition and attribution analysis, Energy Policy 87 (2015) 28-38.   DOI
13 K. Feng, K. Hubacek, D. Guan, Lifestyles, technology and $CO_2$ emissions in China: a regional comparative analysis, Ecol. Econ. 69 (2009) 145-154.   DOI
14 B. Lin, K. Du, Energy and $CO_2$ emissions performance in China's regional economies: do market-oriented reforms matter? Energy Policy 78 (2015) 113-124.   DOI
15 Nuclear Share of Power Generation in, 2015 [online], https://www.iaea.org/PRIS/WorldStatistics/NuclearShareofElectricityGeneration.aspx (Accessed 15 October 2017).
16 Q. Wang, X. Chen, Regulatory failures for nuclear safety-the bad example of Japan-implication for the rest of world, Renew. Sustain. Energy Rev. 16 (2012) 2610-2617.   DOI
17 Ermis, Midilli, Dincer, A.M. Rosen, Artificial neural network analysis of world green energy use, Energy Policy 35 (2007) 1731-1743.   DOI
18 Power Reactor Information System [PRIS], China, 2017 [online], https://www.iaea.org/pris/CountryStatistics/CountryDetails.aspx?current=CN (Accessed 15 October 2017).
19 Y.H. Yang, L. Peng, X.B. Chen, X.Z. Liu, A GA-BP neural network model for predicting the temperature of slabs in the reheating furnace, Appl. Mech. Mater. 58-60 (2011) 1371-1377.   DOI
20 Y.S. Murat, H. Ceylan, Use of artificial neural networks for transport energy demand modeling, Energy Policy 34 (2006) 3165-3172.   DOI
21 W.G. Zong, W.E. Roper, Energy demand estimation of South Korea using artificial neural network, Energy Policy 37 (2009) 4049-4054.   DOI
22 Y. Wang, S. Liang, Carbon dioxide mitigation target of China in 2020 and key economic sectors, Energy Policy 58 (2013) 90-96.   DOI
23 B. Zhu, K. Wang, J. Chevallier, W. Ping, Y.M. Wei, Can China achieve its carbon intensity target by 2020 while sustaining economic growth? Ecol. Econ. 119 (2015) 209-216.   DOI
24 Y.G. Kim, J. Yoo, W. Oh, Driving forces of rapid $CO_2$ emissions growth: a case of Korea, Energy Policy 82 (2015) 144-155.   DOI
25 Z.L. Ding, Rough analysis of $CO_2$ emissions reduction targets in China by 2020, Shanxi Energy Conserv. 3 (2010) 1-5 (in Chinese).
26 W.B. Lin, J. Su, H.X. Zhou, China's energy demand forecast in new economic growth: 2015-2030, Acad. Res. 3 (2016) 106-112 (in Chinese).
27 K.H. Kim, K.H. Sul, J.E. Szulejko, S.D. Chambers, X. Feng, M.H. Lee, Progress in the reduction of carbon monoxide levels in major urban areas in Korea, Environ. Pollut. 207 (2015) 420-428.   DOI
28 R. Wang, W. Liu, L. Xiao, L. Jian, W. Kao, Path towards achieving of China's 2020 carbon emission reduction target-A discussion of low-carbon energy policies at province level, Energy Policy 39 (2011) 2740-2747.   DOI
29 X. Su, W. Zhou, F. Sun, K. Nakagami, Possible pathways for dealing with Japan's post-Fukushima challenge and achieving $CO_2$ emission reduction targets in 2030, Energy 66 (2014) 90-97.   DOI
30 Z. Ming, Y. Liu, S. Ouyang, S. Hui, C. Li, Nuclear energy in the Post-Fukushima Era: research on the developments of the Chinese and worldwide nuclear power industries, Renew. Sustain. Energy Rev. 58 (2016) 147-156.   DOI
31 F. Dellano-Paz, A. Calvo-Silvosa, S.I. Antelo, I. Soares, The European low-carbon mix for 2030: the role of renewable energy sources in an environmentally and socially efficient approach, Renew. Sustain. EneCrgy Rev. 48 (2015) 49-61.   DOI
32 T. Ma, P.A. Ostergaard, H. Lund, H. Yang, L. Lu, An energy system model for Hong Kong in 2020, Energy 68 (2014) 301-310.   DOI
33 Q. Li, Y.N. Wei, Y. Dong, Coupling analysis of China's urbanization and carbon emissions: example from Hubei Province, Nat. Hazards 81 (2016) 1333-1348.   DOI
34 Statistical Review of World Energy June 2016, Primary Energy. [online] http://www.bp.com/content/dam/bp/pdf/energy-economics/statistical-review-2016/bp-statistical-review-of-world-energy-2016-primary-energy.pdf (Accessed 25 December 2016).
35 Y. Hao, Y.M. Wei, When does the turning point in China's $CO_2$ emissions occur? Results based on the Green Solow model, Environ. Dev. Econ 20 (2014) 1-23.   DOI
36 $CO_2$ emissions from fuel combustion. [online] http://www.iea.org/bookshop/729-$CO_2$_Emissions_from_Fuel_Combustion (Accessed 13 September 2016).
37 Sino-Us Joint Statement on Climate Change. [online] http://www.gov.cn/xinwen/2014-11/13/content_2777663.htm (Accessed 23 September 2016).
38 S. Niu, Y. Liu, Y. Ding, Q. Wei, China's energy systems transformation and emissions peak, Renew. Sustain. Energy Rev. 58 (2016) 782-795.   DOI