Please wait a minute...
Submit  |   Chinese  | 
Advanced Search
   Home  |  Online Now  |  Current Issue  |  Focus  |  Archive  |  For Authors  |  Journal Information   Open Access  
Submit  |   Chinese  | 
Engineering    2017, Vol. 3 Issue (4) : 512 -517
Research |
An Empirical Study on China’s Energy Supply-and-Demand Model Considering Carbon Emission Peak Constraints in 2030
Jinhang Chen()
China Datang Corporation, Beijing 100033, China

China’s energy supply-and-demand model and two related carbon emission scenarios, including a planned peak scenario and an advanced peak scenario, are designed taking into consideration China’s economic development, technological progress, policies, resources, environmental capacity, and other factors. The analysis of the defined scenarios provides the following conclusions: Primary energy and power demand will continue to grow leading up to 2030, and the growth rate of power demand will be much higher than that of primary energy demand. Moreover, low carbonization will be a basic feature of energy supply-and-demand structural changes, and non-fossil energy will replace oil as the second largest energy source. Finally, energy-related carbon emissions could peak in 2025 through the application of more efficient energy consumption patterns and more low-carbon energy supply modes. The push toward decarbonization of the power industry is essential for reducing the peak value of carbon emissions.

Keywords Carbon emission      Peak      Energy supply and demand      Model      Scenario     
Corresponding Authors: Jinhang Chen   
Just Accepted Date: 15 August 2017   Issue Date: 13 September 2017
E-mail this article
E-mail Alert
Articles by authors
Jinhang Chen
Cite this article:   
Jinhang Chen. An Empirical Study on China’s Energy Supply-and-Demand Model Considering Carbon Emission Peak Constraints in 2030[J]. Engineering, 2017, 3(4): 512 -517 .
URL:     OR
1   Wang F, Wu L, Yang C. Driving factors for growth of carbon dioxide emissions during economic development in China. Econ Res J 2010;(2):123–36. Chinese.
2   Chai Q. The decomposition of China’s carbon dioxide emission peak. China Policy Rev 2015;(7):54–6. Chinese.
3   Qu S, Guo C. Forecast of China’s carbon emissions based on STIRPAT model. China Popul Resour Environ 2010;20(12):10–5. Chinese.
4   Jiang K, He C, Zhuang X, Liu J, Gao J, Xu X, et al.Scenario and feasibility study for peaking CO2 emission from energy activities in China. Adv Clim Change Res 2016;12(3):167–71. Chinese.
5   Ma D, Chen W. Analysis of China’s 2030 carbon emission peak level and peak path. China Popul Resour Environ 2016;26(5):1–4. Chinese.
6   Yang X, Fu L, Ding D. Issues on regional CO2 emission peak measurement: Taking Beijing as an example. China Popul Resour Environt 2015;25(10):39–44. Chinese.
7   Liu Q, Li Q, Zheng X. The prediction of carbon dioxide emissions in Chongqing based on fossil fuel combustion. Acta Scienciae Circumstantiae 2017;37(4):1582–93. Chinese.
8   Cheng L, Xing L. Analysis of requirement and impact of power development under the peak carbon emissions in 2030. Electric Power 2016;49(1):174–7.
9   Guo S. Industrial carbon peak management in the stage of climate change. Energ Conserv Environ Prot 2016;(7):50–3. Chinese.
10   Goldstein G, Tosato G, editors. Global energy systems and common analyses. Report. Paris: International Energy Agency; 2008 Jun.
11   Energy Information Administration (EIA). The national energy modeling system: An overview. Report. Washington, DC: EIA; 2009 Oct. Report No.: DOE/EIA–0581.
12   International Atomic Energy Agency (IAEA). Model for Analysis of Energy Demand (MAED-2): User's manual. Vienna: IAEA; 2006 Jan.
13   National Institute for Environmental Studies. Asia-Pacific Integrated Model. Tokyo: National Institute for Environmental Studies; 1997 Mar.
14   Stockholm Environment Institute (SEI). LEAP user guide. Boston: SEI; 2006 Mar.
[1] Kan Li, Lin Zhang, Heyan Huang. Social Influence Analysis: Models, Methods, and Evaluation[J]. Engineering, 2018, 4(1): 40 -46 .
[2] Le-Dong Zhu, Xiao-Liang Meng, Lin-Qing Du, Ming-Chang Ding. A Simplified Nonlinear Model of Vertical Vortex-Induced Force on Box Decks for Predicting Stable Amplitudes of Vortex-Induced Vibrations[J]. Engineering, 2017, 3(6): 854 -862 .
[3] Xuejie Gao, Filippo Giorgi. Use of the RegCM System over East Asia: Review and Perspectives[J]. Engineering, 2017, 3(5): 766 -772 .
[4] Tianjun Zhou, Xiaolong Chen, Bo Wu, Zhun Guo, Yong Sun, Liwei Zou, Wenmin Man, Lixia Zhang, Chao He. A Robustness Analysis of CMIP5 Models over the East Asia-Western North Pacific Domain[J]. Engineering, 2017, 3(5): 773 -778 .
[5] Patcharapit Promoppatum, Shi-Chune Yao, P. Chris Pistorius, Anthony D. Rollett. A Comprehensive Comparison of the Analytical and Numerical Prediction of the Thermal History and Solidification Microstructure of Inconel 718 Products Made by Laser Powder-Bed Fusion[J]. Engineering, 2017, 3(5): 685 -694 .
[6] Wentao Yan, Ya Qian, Weixin Ma, Bin Zhou, Yongxing Shen, Feng Lin. Modeling and Experimental Validation of the Electron Beam Selective Melting Process[J]. Engineering, 2017, 3(5): 701 -707 .
[7] Dongdong Gu, Chenglong Ma, Mujian Xia, Donghua Dai, Qimin Shi. A Multiscale Understanding of the Thermodynamic and Kinetic Mechanisms of Laser Additive Manufacturing[J]. Engineering, 2017, 3(5): 675 -684 .
[8] Robert S. Pierce, Brian G. Falzon. Simulating Resin Infusion through Textile Reinforcement Materials for the Manufacture of Complex Composite Structures[J]. Engineering, 2017, 3(5): 596 -607 .
[9] Han-Xiong Li, Haitao Si. Control for Intelligent Manufacturing: A Multiscale Challenge[J]. Engineering, 2017, 3(5): 608 -615 .
[10] Xiaobo Luo, Meihong Wang. Improving Prediction Accuracy of a Rate-Based Model of an MEA-Based Carbon Capture Process for Large-Scale Commercial Deployment[J]. Engineering, 2017, 3(2): 232 -243 .
[11] Ian David Lockhart Bogle. A Perspective on Smart Process Manufacturing Research Challenges for Process Systems Engineers[J]. Engineering, 2017, 3(2): 161 -165 .
[12] Pedro A. Castillo Castillo, Pedro M. Castro, Vladimir Mahalec. Global Optimization of Nonlinear Blend-Scheduling Problems[J]. Engineering, 2017, 3(2): 188 -201 .
[13] Francesco Rossi, Simone Colombo, Sauro Pierucci, Eliseo Ranzi, Flavio Manenti. Upstream Operations in the Oil Industry: Rigorous Modeling of an Electrostatic Coalescer[J]. Engineering, 2017, 3(2): 220 -231 .
[14] Nan Xing, Jianping Li, Lanning Wang. Multidecadal Trends in Large-Scale Annual Mean SATa Based on CMIP5 Historical Simulations and Future Projections[J]. Engineering, 2017, 3(1): 136 -143 .
[15] Eric P. Xing,Qirong Ho,Pengtao Xie,Dai Wei. Strategies and Principles of Distributed Machine Learning on Big Data[J]. Engineering, 2016, 2(2): 179 -195 .
Copyright © 2015 Higher Education Press & Engineering Sciences Press, All Rights Reserved.