Carlo Simulation
6. Main Conclusions and Recommendations 1. Main Conclusions
As a major carbon emissions-generating industry in China, the electric power industry is a major target of China’s energy conservation and emissions reduction plan. Therefore, it is important to find a suitable path toward carbon emissions reduction in the power industry. First, this paper applies the GDIM model to the electric power industry from 2000–2015 to examine the factors that shape carbon emissions levels. To set the future annual average rate of change of major influencing factors according to government policy objectives, we used the Monte Carlo method to simulate the potential average annual change rate in carbon emissions from 2017 to 2030 for the power industry. The main conclusions of this study are as follows.
1. Under the reality of China’s rapid economic development and thermal power generation as the main force, the output scale was the most important factor leading to the increase of carbon emissions in the power industry, and the cumulative increase of carbon emissions in 2000–2015 reached 996.89 million tons. Energy consumption scales and population size were also important factors for the increase of carbon emissions in the power industry, and the cumulative increase of carbon emissions reached 664.340 million tons and 65.204 million tons, respectively. Because fossil energy consumption still accounts for a large proportion in the energy structure of China’s power industry, energy intensity and carbon intensity effects of energy consumption have great carbon emission reduction potential. The energy intensity had reduced the carbon emissions by 29.4241 million tons in 2000–2015, and carbon intensity of energy consumption achieved a breakthrough in carbon emissions from positive to negative effects. In the future, the two are expected to become the main factors to promote carbon emission reduction in the power sector. At present, the power industry in China should focus on the optimization of energy structure to further reduce carbon emissions in the power industry.
2. In different scenarios, changes in carbon emissions of the power industry are different from 2017 to 2030. In the baseline scenario, the maximum probability of a potential annual growth rate
of carbon emissions is expected to be between 1.9% and 2.2%. Under the low-carbon scenario and technological breakthrough scenario, the maximum probability of the potential annual drop rate of carbon emissions are expected to be between 1.6–2.1% and 1.9–2.4%, respectively. Thus, in the current state of development, China’s power industry carbon emissions will likely continue to increase. If there is a breakthrough in the low carbon policy and energy-saving and emission-reduction technologies, the carbon emissions of the power industry are expected to reduce. Under the technological breakthrough scenario, the power industry is predicted to experience the strongest reduction in carbon emissions and thus, technological breakthroughs are considered to be the main path for future carbon emission reduction development in the power industry.
The summary of the main conclusions in this paper can be seen in Table7.
6.2. Recommendations
1. Promote green economics and reduce total energy consumption. Accelerate the transformation of economic development modes; focus on the quality of economic growth; and gradually adopt new economic models that limit energy consumption, pollution and emissions generation while upgrading industrial structures to change the structure of electricity consumption.
In addition, change the traditional method of coal combustion, realize secondary processing of coal, and reduce end-use energy consumption, especially in energy intensive sectors, such as steel manufacturing.
2. Improve public awareness of energy conservation issues and optimize energy consumption structures. Encourage energy conservation policies, formulate relevant energy efficiency standards and norms, enhance public awareness of energy saving and emissions reduction, and reduce electricity consumption. At the same time, limit thermal power production using coal and improve the ratio of natural gas to fossil energy use. To protect the environment, develop solar energy, hydropower, wind energy, and low-carbon energy generation options by encouraging low-carbon energy power generation and reduce carbon emissions by the power industry.
3. Encourage the use of low-carbon equipment and boost technological innovation research and development. At present, the international investment in innovation and research and development continues to increase. In 2017, the European Union issued the third phase of
“Horizon 2020” [73], which comprehensively proposed the goals of climate action and sustainable development, and invested 2.205 billion euros to support the research and innovation of energy technology. As the world’s largest carbon emitter, China should upgrade energy-saving and emissions-reduction equipment in the power industry, strengthen international exchanges and cooperation in environmental technologies, and increase investment in innovative technologies for emissions reduction on the basis of adhering to a series of low-carbon development measures.
At the same time, the government must encourage enterprises to adopt incentives and invest more in continuous research and development in new energy savings and emissions-reducing technologies as well as production equipment. Low-carbon technologies must be developed to realize further reductions in electric carbon emissions by the power industry.
4. Explore the market potential of biomass power generation. Based on Molina’s research [74–76], and combined with the current situation of China’s agriculture, biomass power generation should be vigorously developed. The utilization of agricultural and forestry wastes by biomass power generation not only eliminates methane emissions from biomass stacking or landfill fermentation, but also promotes the development of carbon emissions reduction in the power industry, which fully embodies the concept of a circular economy. The government should adopt an active incentive mechanism to enhance the enthusiasm and market activity of biomass power generation industry to a greater extent.
Table7.Mainconclusionsofthispaper. Factor
TheContributionofVariousDecompositionFactorstotheChanges ofCarbonEmissionsinChina’sPowerIndustry(104Tons) EffectDegreeofInfluence StageContributionsCumulative Contributions2000–20052005–20102010–2015 Outputscale36,025.2518,955.548,338.6599,689.402PositiveBiggest Factor decomposition
Energyconsumptionscale19,851.9225,119.5719,654.4966,434.07PositiveBigger Populationsize1391.7851981.0172311.956524.081PositiveSmall Percapitacarbonemissions35,211.6426,653.958941.39965,413.974PositiveBigger Energyintensity−142.48−386.081−974.895−2942.41NegativeSmaller Carbonintensityofoutput972.67728616.858−27,798.9−20,934.45Double-sidedBigger PercapitaGDP−9734.24−1970.54−9152.2−9254.794NegativeSmall Carbonintensityofenergyconsumption11,404.792698.82−9878.23171.344Double-sidedBigger SimulationresultsoftheaverageannualgrowthrateofcarbonemissionsinChina’spowerindustryunderdifferentscenariosfrom2017to2030 CharacteristicsofthescenarioThemostprobablevalue BaselinescenarioAccordingtothecurrentdevelopmenttrend1.9%to2.2% Scenario forecastsLow-carbonscenarioFollowthepathofgreendevelopment−2.1%to−1.6% TechnicalbreakthroughscenarioStrengthentechnologicalR&Dinvestment−2.4%to−1.9%
Author Contributions:L.Z. was mainly responsible for the writing of the full text. X.M. conceived and designed the study. L.H., P.S., and Y.Z. built the models of the paper.
Acknowledgments:This paper is supported by Humanities and Social Sciences Research Project of Ministry of Education (18YJC910013), National Natural Science Foundation (71573034), Liaoning Economic and Social Development Research Project (2018lslktzd-010), Liaoning Social Science Fund (L17CTJ001, L17BJY042), China Postdoctoral Science Foundation special fund (2017T100180), China Postdoctoral Science Foundation (2016M601318), and Research Project of Dongbei University of Finance and Economics (DUFE2017Q16), National Social Science Foundation Key Project (17ATJ002), National Statistical Research Projects (2016LY42).
Conflicts of Interest:The authors declare no conflict of interest.
References
1. Global Warming. Available online:https://www.pishu.cn/psbk/psgd/zyhj/383725.shtml(accessed on 30 August 2018).
2. Ge, Q.S.; Wang, F.; Wang, S.W.; Cheng, B.B. Determination and uncertainty of seven questions about global warming.China Popul. Resour. Environ.2014,24, 1–6.
3. Blunden, J.; Arndt, D.S.; Baringer, M.O. State of the Climate in 2010. Bull. Am. Meteorol. Soc. 2011,92, S1–S236. [CrossRef]
4. Fu, Y. The causes and effects of global warming.J. Cap. Norm. Univ.2007,06, 11–21.
5. Liu, Y.; Zheng, Y.Y.; Ge, Q.S.; Wang, F. China’s greenhouse gas emission changes under the background of low carbon development and its contribution to global emission reduction.Resour. Sci.2017,39, 2399–2407.
6. Global Carbon Project Releases “Global Carbon Budget 2016”, Global Carbon Emissions Increase Continued to Decline for the Third Year. Available online:http://cnc-fe.cast.org.cn/(accessed on 15 December 2017).
7. “Global Carbon Budget Report”: Strong Global Carbon Emissions Rise 2017 | China Meteorological Administration. Available online:http://m.cma.gov.cn/2011xwzx/2011xqhbh/2011xdtxx/201711/t20171122_
456610.html(accessed on 15 December 2017).
8. Energy Bureau Released “The Eleventh Five-Year Plan” and “The Twelfth Five-Year Plan”. Available online:
http://www.gov.cn/xinwen/2017-01/05/content_5156795.htm#1(accessed on 20 November 2017).
9. Du, X.W. Interpretation and reflection on China’s “Strategy of Energy Production and Consumption Revolution (2016–2030)”.China Econ. Trade Herit.2017,15, 44–45.
10. Zhang, W. Sampling and Separation Performance of Coal Fly Ash in Power Plant. Master’s Thesis, Beijing Jiaotong University, Beijing, China, 2010.
11. Yu, Y.H.; Lv, D.W. China’s power industry emissions reduction policy—An empirical analysis based on power demand function.Technol. Econ.2017,36, 110–118.
12. Cheng, H.Z. China’s small hydropower and rural electrification.Small Hydro Power2001,17, 1–6.
13. Han, D.; Zhao, Y. Talking about new energy power generation and its development prospects.Tech. Wind 2018,30, 123.
14. National Energy Administration. Available online:http://www.nea.gov.cn/(accessed on 30 August 2018).
15. Bhat, I.K.; Prakash, R. LCA of renewable energy for electricity generation systems—A review.Renew. Sustain.
Energy Rev.2009,13, 1067–1073.
16. Lenzen, M. Life cycle energy and greenhouse gas emissions of nuclear energy: A review.Energy Convers. Manag.
2008,49, 2178–2199. [CrossRef]
17. Quesada-Rubio, J.M.; Villar-Rubio, E.; Madrid-Salvador, V.; Molina-Moreno, V. The gap between CO2emissions and allocation rights in the Spanish industry.Environ. Eng. Manag. J.2010,9, 1161–1164. [CrossRef]
18. Quesada-Rubio, J.M.; Villar-Rubio, E.; Mondéjar-Jiménez, J.; Molina-Moreno, V. Carbon dioxide emissions vs. allocation rights: Spanish case analysis.Int. J. Environ. Res.2011,5, 469–474.
19. Mikayilov, J.I.; Galeotti, M.; Hasanov, F.J. The impact of economic growth on CO2emissions in Azerbaijan.
J. Clean. Prod.2018,197, 1558–1572. [CrossRef]
20. Bollen, J.C.; Toet, A.; Vries, H.D. Evaluating cost-effective strategies for meeting regional CO2targets.
Glob. Environ. Chang.1996,6, 359–373. [CrossRef]
21. Remuzgo, L.; Sarabia, J.M. International inequality in CO2emissions: A new factorial decomposition based on Kaya factors.Environ. Sci. Policy2015,54, 15–24. [CrossRef]
22. Zhu, Q.; Peng, X.Z.; Lu, Z.M.; Wu, K.Y. Factor decomposition and empirical analysis of China’s carbon emissions from energy consumption.Resour. Sci.2009,31, 2072–2079.
23. Xia, C.; Li, Y.; Ye, Y.; Shi, Z.; Liu, J. Decomposed driving factors of carbon emissions and scenario analyses of low-carbon transformation in 2020 and 2030 for Zhejiang province.Energies2017,10, 1747. [CrossRef]
24. Kopidou, D.; Diakoulaki, D. Decomposing industrial CO2emissions of southern European Countries into production- and consumption-based driving factors.J. Clean. Prod.2017,112, 4159–4172. [CrossRef]
25. Mahony, T.O. Decomposition of Ireland’s carbon emissions from 1990 to 2010: An extended Kaya identity.
Energy Policy2013,59, 573–581. [CrossRef]
26. Wang, F.; Wu, L.H.; Yang, C. Study on the driving factors of the growth of carbon emissions in China’s economic development.J. Econ. Res.2010,45, 123–136.
27. Wang, J.S.; He, C.F. Consumption of energy, economic growth and CO2emissions in China: A decomposition analysis based on LMDI.Resour. Environ. Yangtze Basin2010,19, 18–23.
28. Du, L.; Wei, C.; Cai, S. Economic development and carbon dioxide emissions in China: Provincial panel data analysis.China Econ. Rev.2012,23, 371–384. [CrossRef]
29. Dong, F.; Yang, Q.L.; Long, R.Y.; Cheng, S.B. Chinese carbon decomposition and dynamic simulation.
China Popul. Resour. Environ.2015,25, 1–8.
30. Ali, H.; Sanjaya, S.; Suryadi, B.; Weller, S.R. Analysing CO2emissions from Singapore’s electricity generation sector: Strategies for 2020 and beyond.Energy2017,124, 553–564. [CrossRef]
31. Sun, W.; Meng, M.; He, Y.; Chang, H. CO2emissions from china’s power industry: Scenarios and policies for 13th five-year plan.Energies2016,9, 825. [CrossRef]
32. Wu, Q.; Peng, C. Scenario analysis of carbon emissions of china’s electric power industry up to 2030.Energies 2016,9, 988. [CrossRef]
33. Cai, W.; Wang, C.; Wang, K.; Zhang, Y.; Chen, J. Scenario analysis on CO2emissions reduction potential in China’s electricity sector.Energy Policy2007,35, 6445–6456. [CrossRef]
34. Ari, I.; Koksal, M.A. Carbon dioxide emission from the Turkish electricity sector and its mitigation options.
Energy Policy2011,39, 6120–6135. [CrossRef]
35. Hou, J.C.; Tan, Z.F. Decomposition of influencing factors on CO2 emissions from power production.
Electr. Power2011,44, 39–42.
36. Steenhof, P.A.; Weber, C.J. An assessment of factors impacting Canada’s electricity sector’s Ghg emissions.
Energy Policy2011,39, 4089–4096. [CrossRef]
37. Chen, X.K.; Zhou, T.R.; Li, X.; Kang, C.Q.; Chen, Q.X. Analysis on decomposition of carbon emission structure and contribution to low carbon in power system.Autom. Electr. Power Syst.2012,36, 18–25.
38. Huo, M.L.; Han, X.Y.; Shan, B.G. An empirical analysis of the influencing factors of carbon emissions intensity in China’s power industry.Electr. Power2013,46, 122–126.
39. Shrestha, R.M.; Anandarajah, G.; Liyanage, M.H. Factors affecting CO2emission from the power sector of selected countries in Asia and the Pacific.Energy Policy2009,37, 2375–2384. [CrossRef]
40. Malla, S. CO2emissions from electricity generation in seven Asia-pacific and North American Countries:
A decomposition analysis.Energy Policy2009,37, 1–9. [CrossRef]
41. Zhang, N.; Zhou, P.; Choi, Y. Energy efficiency, CO2emission performance and technology gaps in fossil fuel electricity generation in Korea: A Meta-Frontier Non-Radial Directional Distance Function analysis.
Energy Policy2013,56, 653–662. [CrossRef]
42. Hu, J.; Kahrl, F.; Yan, Q.; Wang, X. The impact of China’s differential electricity pricing policy on power sector CO2emissions.Energy Policy2012,45, 412–419. [CrossRef]
43. Özer, B.; Görgün, E.; ˙Incecik, S. The scenario analysis on CO2emission mitigation potential in the Turkish electricity sector: 2006–2030.Energy2013,49, 395–403. [CrossRef]
44. Liu, Z.; Zhu, K.W.; Yan, J.M.; Shi, Y.R. Design and analysis of carbon emissions reduction scenarios in the power industry.Power Syst. Technol.2012,36, 1–7.
45. Intergovernmental Panel on Climate Change (IPCC). 2006 IPCC Guidelines for National Greenhouse Gas Inventories. Available online: https://wenku.baidu.com/view/4eb6dad4195f312b3169a5a9.html (accessed on 8 November 2017).
46. National Bureau of Statistics of People’s Republic of China. China Energy Statistical Yearbook.
2016. Available online:http://www.stats.gov.cn/tjsj/tjcbw/201706/t20170621_1505833.html(accessed on 10 November 2017).
47. National Bureau of Statistics of People’s Republic of China. China Statistical Yearbook. 2011–2016.
Available online:http://www.stats.gov.cn/tjsj/ndsj/(accessed on 10 November 2017).
48. Zheng, S.M. Political cycle, five years planning and environmental pollution—Taking industrial sulfur dioxide emission as an example.Cass J. Political Sci.2016,2, 80–94.
49. Lin, B.Q.; Liu, X.Y. Carbon emissions in China’s urbanization: Influencing factors and emission reduction strategy.Econ. Res. J.2010,45, 66–78.
50. IMF World Economic Outlook (WEO) update, January 2017: A Shifting Global Economic Landscape. Available online:http://www.imf.org/external/pubs/ft/weo/2017/update/01/(accessed on 23 December 2017).
51. Niu, J.M.; Dai, R.F.; Dong, W.J.; Ban, J.H.; Hu, C.Y. China’s future CO2emissions scenarios under the US new deal background climate prediction.Adv. Clim. Chang. Res.2018,01, 95–105.
52. Morgan Stanley: Avoid the Banking Crisis, We Are Optimistic about China! Available online:http://oversea.
huanqiu.com/article/(accessed on 25 December 2017).
53. China’s Carbon Pricing Is Expected to Reach 10–25 Yuan/Ton in 2020–2030. Available online:http://www.
tanpaifang.com/tanqiquan/2014/1115/40146.html(accessed on 26 December 2017).
54. General Office of the State Council of the People’s Republic of China issued “Action Plan for Energy Development Strategy (2014–2020)”.Constr. Sci. Tech.2014,22, 6.
55. Hou, J.C.; Shi, D. Study on the driving factors of carbon emissions changes in China’s power industry.
China Ind. Econ.2014,06, 44–56.
56. People’s Republic of China National Economic and Social Development Twelfth Five-Year Plan.People’s Daily 2011,1, 1–56.
57. Vaninsky, A. Factorial decomposition of CO2emissions: A Generalized Divisia Index Approach.Energy Econ.
2014,45, 389–400. [CrossRef]
58. China Electricity Council. Condense together the past to create a new brilliant power industry—A Summary of China electricity council service industry development since 2010.China Power Enterp. Manag.2016, 12–15.
59. China Electricity Council.China’s Power Industry Annual Development Report 2016; China Electronic Production Equipment Industry Association: Beijing, China, 2016; pp. 18–23.
60. Zeng, M.; Yao, J.; Zhao, X.L. Effects of carbon emissions trading on the power industry. China Power Enterp. Manag.2014,5, 23–26.
61. Lin, B.; Wang, X. Promoting energy conservation in China’s Iron & Steel Sector.Energy2014,73, 465–474.
62. Wu, Y.Z.; Shen, J.; Zhang, X. The Impact of urbanization on carbon emissions in developing countries:
A Chinese study based on the U-kaya method.J. Clean. Prod.2016,135, 589–603. [CrossRef]
63. Salakhutdinov, R.; Mnih, A. Bayesian probabilistic matrix factorization using Markov chain Monte Carlo.
In Proceedings of the 25th International Conference on Machine Learning, Helsinki, Finland, 5–9 July 2008;
pp. 880–887.
64. Frieze, A.; Kannan, R.; Vempala, S. Fast Monte-Carlo algorithms for finding low-rank approximations.
J. ACM2004,51, 1025–1041. [CrossRef]
65. Veach, E. Optimally combining sampling techniques for Monte Carlo rendering. In Proceedings of the 22nd Annual Conference on Computer Graphics and Interactive Techniques, Los Angeles, CA, USA, 6–11 August 1995; pp. 419–428.
66. Procopiuc, C.M.; Jones, M.; Agarwal, P.K.; Murali, T.M. A Monte Carlo algorithm for fast projective clustering. In Proceedings of the 2002 ACM SIGMOD International Conference on Management of Data, Madison, WI, USA, 2–6 June 2002; pp. 418–427.
67. Shao, S.; Zhang, X.; Zhao, X.R. Empirical decomposition and peak path of carbon emissions in China’s manufacturing industry—Generalized Divisia Index decomposition and dynamic scenario analysis.
China Ind. Econ.2017,34, 44–63.
68. Ramírez, A.; Keizer, C.D.; Sluijs, J.P.V.D.; Olivier, J.; Brandes, L. Monte Carlo analysis of uncertainties in the netherlands greenhouse gas emission inventory for 1990–2004.Atmos. Environ.2008,42, 8263–8272.
[CrossRef]
69. Wang, C.K.; Cui, W.J. Construction, intensity, scale and electricity carbon emissions: A study based on LMDI decomposition method.Sci. Technol. Manag.2015,35, 220–241.
70. Huh, J.H.; Seo, K. Hybrid advanced metering infrastructure design for micro grid using the game theory model.Int. J. Softw. Eng. Appl.2015,9, 257–268. [CrossRef]
71. Huh, J.H.; Otgonchimeg, S.; Seo, K. Advanced metering infrastructure design and test bed experiment using intelligent agents: Focusing on the plc network base technology for smart grid system.J. Supercomput.2016, 72, 1862–1877. [CrossRef]
72. Huh, J.H. Smart Grid Test Bed Using OPNET and Power Line Communication. Available online:
https://ieeexplore.ieee.org/document/7801748/(accessed on 5 September 2018).
73. Horizon 2020. Available online: http://ec.europa.eu/programmes/horizon2020/en/area/energy (accessed on 5 September 2018).
74. Molina-Moreno, V.; Núñez-Cacho Utrilla, P.; Cortés-García, F.J.; Peña-García, A. The Use of Led Technology and Biomass to Power Public Lighting in a Local Context: The Case of Baeza (Spain).Energies2018,11, 1783.
[CrossRef]
75. Nuñez-Cacho, P.; Górecki, J.; Molina-Moreno, V.; Corpas-Iglesias, F.A. What Gets Measured, Gets Done:
Development of a Circular Economy Measurement Scale for Building Industry.Sustainability2018,10, 2340.
[CrossRef]
76. Villar Rubio, E.; Quesada Rubio, J.M.; Molina Moreno, V. Convergence Analysis of Environmental Fiscal Pressure Acrosss EU-15 Countries.Energy Environ.2015,26, 789–802. [CrossRef]
© 2018 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
energies
Article