Forecast on China’s Energy Consumption and Carbon Emissions Driven by Micro Innovation
WU Jing1, WANG Zheng1,2, ZHU Qianting3, GONG Yi4
1. Institute of Policy and Management, Chinese Academy of Sciences, Beijing 100190,China; 2. Key Laboratory of Geographical Information Science, Ministry of State Education of China, East China Normal University, Shanghai 200062,China; 3. School of Business Administration, China University of Petroleum, Beijing 102249, China; 4. Beijing Decision-making Consultant Center, Beijing 100089,China
Abstract:This paper integrates input-output model with agent-based simulation, in which an input-output model with 17 sectors is established at the macro economy level, and an agent-based model is developed simulating firms’ innovations in each sector at the micro economy level. The emergency of industrial structure evolution,energy consumption change and carbon emission change at the macro level are driven by innovations of firm agents. Results show that due to the uncertainty of innovation, the peak years of energy and emission are also uncertain. The energy peak year will subject to a normal distribution from 2025 to 2036; while the distribution of emission peak year is also identified as a normal distribution from 2024 to 2033. The year with the maximum probability for energy peak will be 2031 with the probability of 23.57%; and 2029 will be the year with the maximum probability 33.51% for emission peak. Taking the average of 50 simulations, it is indicated that the energy peak will be 5146Mtce in 2029, and the emission peak will be 2.7GtC in 2029.
吴静, 王铮, 朱潜挺, 龚轶. 微观创新驱动下的中国能源消费与碳排放趋势研究[J]. 复杂系统与复杂性科学, 2016, 13(4): 68-79.
WU Jing, WANG Zheng, ZHU Qianting, GONG Yi. Forecast on China’s Energy Consumption and Carbon Emissions Driven by Micro Innovation[J]. Complex Systems and Complexity Science, 2016, 13(4): 68-79.
[1] Aydin G. Modeling of energy consumption based on economic and demographic factors: the case of Turkey with projections[J].Renewable and Sustainable Energy Reviews, 2014, 35: 382-389. [2] Suganthi L, Anand A. Samuel energy models for demand forecasting—a review[J].Renewable and Sustainable Energy Reviews, 2012,16(2): 1223-1240m [3] Parajuli R, stergaard P A, Dalgaard T, et al. Energy consumption projection of Nepal: An econometric approach[J].Renewable Energy, 2014, 63: 432-444. [4] Yuan J, Xu Y, Hu Z, et al. Peak energy consumption and CO2 emissions in China[J].Energy Policy, 2014, 68: 508-523. [5] Uzlu E, Kankal M, Akpinar A, et al. Estimates of energy consumption in Turkey using neural networks with the teaching-learning-based optimization algorithm[J].Energy, 2014, 75: 295-303. [6] Ekonomou L. Greek long-term energy consumption prediction using artificialneural networks[J].Energy, 2010, 35:512-517. [7] Ceylan H, Ozturk HK. Estimating energy demand of Turkey based on economicindicators using genetic algorithm approach[J].Energy Conversion andManagement, 2004,45(15-16):2525-2537. [8] Wing S, Eckaus R S. The implications of historical decline in US energy intensity for long-run CO2 emission projections[J].Energy Policy, 2007, 35(11):5267-5286. [9] 王铮,朱永彬,刘昌新,等. 最优增长路径下的中国碳排放估计[J].地理学报,2010,65(12):1559-1568. Wang Zheng, Zhu Yongbin, Liu Changxin, et al. Integrated projection of carbon emission for China under optimal economic growth path[J].ActaGeographicaSinica, 2010, 65(12): 1559-1568. [10] Vaillancourt K, Alcocer Y, Bahn O, et al. A Canadian 2050 energy outlook: analysis with the multi-regional model TIMES-Canada[J].Applied Energy, 2014, 132: 56-65. [11] Richstein J C, Chappin E J L, de Vries L J. Cross-border electricity market effects due to price caps in an emission trading system: An agent-based approach[J].Energy Policy, 2014,71:139-158 [12] Natarajan S,Padget J, Elliott L. Modelling UK domestic energy and carbon emissions: an agent-based approach[J].Energy and Buildings, 2011, 43(10): 2602-2612. [13] Liu Y. Relationship between industrial firms, high-carbon and low-carbon energy: An agent-based simulation approach[J].Applied mathematics and Computation, 2013, 219: 7472-7479. [14] Zhang B, Zhang Y, Bi J. An adaptive agent-based modeling approach for analyzing the influence of transaction costs on emission trading markets[J].Environmental Modelling &Software, 2011, 26(4): 482-491. [15] Desmarchelier B, Djellal F, Gallouj F. Environmental policies and eco-innovations by service firms: an agent-based model[J].Technological Forecasting and Social Change, 2013, 80(7): 1395-1408. [16] Lee T, Yao R, Coker P. An analysis of UK policies for domestic energy reduction using an agent based tool[J].Energy Policy, 2014, 66: 267-279. [17] Gerst M D, Wang P, Rovenini A, et al. Agent-based modeling of climate policy: an introduction to the ENGAGE multi-level model framework[J].Environmental Modelling & Software, 2013, 44:62-75. [18] Mialhe F, Becu N, Gunnell Y. An agent-based model for analyzing land use dynamics in response to farmer behaviour and environmental change in the Pampanga delta (Philippines)[J].Agriculture, Ecosystems & Environment, 2012, 161: 55-69. [19] Nannen V, van den Bergh J, Eiben A E. Impact of environmental dynamics on economic evolution: a stylized agent-based policy analysis[J].Technological Forecasting &Scial Change,2013, 80: 329-350. [20] 顾高翔,王铮,姚梓璇. 基于自主体的经济危机模拟[J].复杂系统与复杂性科学, 2011, 8(4):27-35. GuGaoxiang, Wang Zheng, Yao Zixuan. Agent-based simulation on economic crisis[J].Complex Systems and Complexity Science, 2011:8(4):27-35. [21] 顾高翔,王铮. 基于三个生产部门的经济危机ABS动力学模拟[J].复杂系统与复杂性科学,2013,10(2): 1-12. GuGaoxiang, Wang Zheng. TABS dynamics simulation of economic crisis based on three production sectors[J].Complex Systems and Complexity Science,2013,10(2):1-12. [22] DosiG, FagioloG ,Napoletano M, et al. Income distribution, credit and fiscal policies in an agent-based Keynesian model[J].Journal of Economic Dynamics and Control, 37(8): 1598-1625. [23] Neveu A R. Fiscal policy and business cycle characteristics in a heterogeneous agent macro model[J].Journal of Economic Behavior & Organization, 2013, 92:224-240. [24] Dosi G, Fagiolo G,Roventini A. An Evolutionary Model of Endogenous Business Cycles[J].Computational Economics,2006, 27: 3-34. [25] 张世伟,冯娟. 经济增长与收入差距:一个基于主体的经济模拟途径[J].财经科学,2007, 226:41-49. Zhang Shiwei, Feng Juan. Economic growth and income inequality: an agent-based economic simulation approach[J].Finance & Economics, 2007,226:41-49. [26] Rixen M, Weigand J. Agent-based simulation of policy induced diffusion of Smart Meters[J].Technological Forecasting and Social Change, 2013, 85:153-167. [27] Sopha B M, Christian A K, Edgar G H. Exploring policy options for a transition to sustainable heating system diffusion using an agent-based simulation[J].Energy Policy,2011, 39(5):2722-2729. [28] Chappin E, Afman M. An agent-based model of transitions in consumer lighting: Policy impacts from the E.U. phase-out of incandescents[J].Environmental Innovation and Societal Transitions, 2013 , 7: 16-36. [29] 刘小平, 黎夏, 叶嘉安. 基于多智能体系统的空间决策行为及土地利用格局演变的模拟[J].中国科学,2006, 36(11): 1027-1036. Liu Xiaoping, Li Xia, Ye Jiaan. Agent-based simulation on spatial decision behavior and evolution of land use change[J].Science in China Ser. D Earth Sciences, 36(11):1027-1036. [30] Arsanjani J J, Helbich M, Noronha Vaz E. Spatiotemporal simulation of urban growth patterns using agent-based modeling: the case of Tehran[J].Cities, 2013, 32: 33-42. [31] Lorentz A, Savona M. Evolutionary micro-dynamics and changes in the economic structure[J].Journal of Evolutionary Economics, 2008, 18(3/4): 389-412. [32] Craxi A,Licata A. Structural change and business cycles: an evolutionary approach[J].Papers on Economics and Evolution,2010,43(2):221-4. [33] 龚轶,顾高翔,刘昌新,等. 技术创新推动下的中国产业结构进化[J].科学学研究,2013,31(8):1252-1259. Gong Yi, GuGaoxiang, Liu Changxin, et al. Chinese industry structure evolution driven by innovation[J].Studies in Science of Science, 2013, 31(8): 1252-1259. [34] 薛俊波. 基于CGE的中国宏观经济政策模拟系统开发及其应用[D].北京:中国科学院科技政策与管理科学研究所,2006. XueJunbo. CGE modelling on China’s macroeconomy[D].Beijing: Institute of Policy and Management, Chinese Academy of Sciences, 2006. [35] 石莹,刘昌新,吴静,等. 欧盟碳减排目标的经济可能性评估[J].世界地理研究,2013,22(3):18-29. Shi Ying, Liu Changxin, Wu Jing, et al. Assessment of economic probability under the EU’s carbon emission reduction objective[J].World Regional Studies, 2013,22(3): 18-29. [36] 朱永彬,王铮,庞丽,等. 基于经济模拟的中国能源消费与碳排放高峰预测[J].地理学报,2009,64(8):935-944. Zhu YongBin, Wang Zheng, Pang Li, et al. Simulation on China’s economy and prediction on Energy consumption and carbon emission under optimal growth path[J].ActaGeographicaSinica, 2009,64(8): 935-944. [37] 姜克隽,胡秀莲. 中国2050年低碳发展情景研究[C]//2050中国能源和碳排放研究课题组. 2050中国能源和碳排放报告. 北京:科学出版社,2009:753-819. Jing Kejun, Hu Xiulian. Low-carbon scenarios by 2050 in China[C]//Research team on China’s energy use and carbon emission. 2050 China Energy and CO2 Emissions Report. Beijing: Science Press, 2009: 753-819. [38] 杜强,陈乔,陆宁. 基于改进IPAT模型的中国未来碳排放预测[J].环境科学学报,2012,32(9):2294-2302. Du Qiang, Chen Qiao, Lu Ning. Forecast of China’s carbon emissions based on modified IPAT model[J].Acta Scientiae Circumstantiae, 2012, 32(9): 2294-2302. [39] 岳超,王少鹏,朱江玲,等. 2050年中国碳排放量的情景预测——碳排放与社会发展Ⅳ[J].北京大学学报(自然科学版),46(4):517-524. Yue Peng, Wang Shaopeng, Zhu Jiangling, et al. 2050 carbon emissions projection for China—Carbon emissions and social development, IV[J].ActaScientiarumNaturaliumUniversitatisPekinensis, 46(4): 517-524. [40] 梁慎宁,郭朝先. 基于STIRPAT模型的中国碳排放峰值预测研究[J].中国人口·资源与环境,2010,20(12):10-15. Qu Shenning, GuoChaoxian. Forecast of China’s carbon emissions based on STIRPAT model[J].China Population Resources and Environment, 20(12): 10-15. [41] 刘昌义,陈孜,渠慎宁. 中国的工业化进程与碳排放峰值[C]//应对气候变化报告——科学认知与政治争锋.王伟光,郑国光. 北京:社会科学文献出版社,2014:139-150. Liu Changyi, Chen Zi, Qu Shenning. China’s emission peak and its industrialization process[C]//Annual Report on Actions to Address Climate Change. Wang Weiguang, Zheng Guoguang. Beijing: Social Sciences Academic Press (China), 2014: 139-150. [42] 陆旸. 中国人口趋势与碳排放峰值[C]//应对气候变化报告——科学认知与政治争锋.王伟光,郑国光. 北京:社会科学文献出版社,2014:183-191. Lu Yang. China’s emission peak and it’s residential consumption[C]//Annual Report on Actions to Address Climate Change. Wang Weiguang, Zheng Guoguang. Beijing: Social Sciences Academic Press (China), 2014: 183-191.