How might China achieve its 2020 emissions target? A scenario analysis of energy consumption and CO 2 emissions using the system dynamics model Xi Liu a, b , Guozhu Mao a, * , Jing Ren a , Rita Yi Man Li c, d, e , Jianghong Guo f , Ling Zhang g a School of Environmental Science and Engineering, Tianjin University, Tianjin 300072, China b College of Management and Economics, Tianjin University, Tianjin 300072, China c Sustainable Real Estate Research Center, Hong Kong Shue Yan University, North Point, Hong Kong d Department of Economics and Finance, Hong Kong Shue Yan University, North Point, Hong Kong e School of Natural and Built Environments, University of South Australia, Adelaide 5000, Australia f Hydrochina Huadong Engineering Corporation, Hangzhou 310014, China g School of Pharmaceutical Science and Technology, Tianjin University, Tianjin 300072, China article info Article history: Received 2 September 2013 Received in revised form 9 December 2014 Accepted 11 December 2014 Available online 31 December 2014 Keywords: Energy consumption CO 2 emissions System dynamics Scenario analysis abstract According to the Chinese government's CO 2 reduction commitment, the production and consumption of traditional and renewable energy in China from 2001 to 2012, this paper forecasts the energy con- sumption, gross CO 2 emissions and CO 2 emission intensity in China from 2013 to 2020 via system dy- namics simulation. Coupled with the energy system and renewable energy policy factors, the effects of different economic growth rates and policy factors on the energy consumption were estimated. The results showed that in different economic growth rates scenarios, total energy consumption and CO 2 emissions increased by 36,140.75 kWh and more than 10,000 billion kg in 10% GDP growth rate scenario than in the 7% scenario in 2020. The CO 2 emission per GDP decreased by 52% in 2020 as compared to 2005 under the scenario of 10% GDP growth rate. Under different renewable energy policy factors' scenarios, the coal energy consumption decreased by 2.3% in scenario of 5% policy factor as compared to 1% policy factor scenario. The CO 2 emissions reduced by 3321.94 billion kg in 2020 under the scenario of 5% policy factor as compared to that in the scenario of 1% policy factor. The CO 2 emission per GDP reduced by 47e50% based on the 2005 level in different scenarios of renewable energy policies. We found that with an increase in GDP, total energy consumption and CO 2 emissions increased in the base scenario. Higher economic growth rates led to an increase in energy consumption, including both the traditional and renewable energy resources. In stark contrast to total CO 2 emission, however, CO 2 emission intensity decreased with an increase in economic growth rate. The renewable energy policy improved the renewable energy development, reduced CO 2 emissions and CO 2 emission intensity. It was more effective in energy saving and CO 2 reduction than the high growth rate scenarios. The results indicated that China is highly likely to achieve its CO 2 emission reduction goal under different simulated scenarios. Besides, the energy distribution was similar in different scenarios. Coal was the major energy source which amounted to more than 70% of the total energy consumption. Finally, we conclude with important policy implications according to our simulations results. © 2015 Elsevier Ltd. All rights reserved. 1. Introduction In recent years, international communities increasingly recog- nized the severe problems of climate change, leading to an increase in pressure to reduce CO 2 emissions (Zhang et al., 2012). The rapid development in industrialization and urbanization has led to an increase in energy use in China sharply to 3617.32 million tons standard coal equivalent in 2012. It accounts for 21% of the global primary energy consumption and China has surpassed the US as the major contributor of CO 2 emissions (Wang and Liang, 2013). Thus, China is facing an urgent need to reduce energy consumption, CO 2 emissions and improve environmental conditions (Cheng et al., * Corresponding author. Tel.: þ86 135 020 786 77. E-mail address: maoguozhu@tju.edu.cn (G. Mao). Contents lists available at ScienceDirect Journal of Cleaner Production journal homepage: www.elsevier.com/locate/jclepro http://dx.doi.org/10.1016/j.jclepro.2014.12.080 0959-6526/© 2015 Elsevier Ltd. All rights reserved. Journal of Cleaner Production 103 (2015) 401e410