Multi-agent-based Simulation for Policy Evaluation of Carbon Emissions
To effectively control air pollution, China has established regional carbon emissions trading markets based on cap and trade in seven pilot provinces and cities. Government agencies that manage the market want to know that whether the policies of carbon e
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Abstract. To effectively control air pollution, China has established regional carbon emissions trading markets based on cap and trade in seven pilot provinces and cities. Government agencies that manage the market want to know that whether the policies of carbon emissions are effective and appropriate. Under cap and trade, the emissions trading system, made up of the government, emissions exchange and participating enterprises, is nonlinear, dynamic, uncertain, and decision-decentralized. Also, there exist interactive influences among participants and multiple decision objectives in the emissions trading system. So, this research applied a multi-agent modeling and simulation approach to build such a simulation model for the regional emissions trading system, and then analyzed the policies related to total emissions control, emission quotas allocation, emission quotas auction, and excessive emission fines. Keywords: Policy evaluation Cap and trade Emissions trading system Multi-agent modeling and simulation Analysis of variance
1 Introduction Due to over-developed human activities, unrestricted greenhouse gas emissions have been a primary cause for the global warming that caused significant environmental changes (Zhang et al. 2011; IPCC 2007). Over the years, there have been some positive progresses in coping with climate change. In China, the government has committed to cut its CO2 emissions (Yi et al. 2011), and is aggressively pushing for a compulsory carbon emission reduction program (EN 2010). In 2011, Beijing, Guangdong, Shanghai, Tianjin, Chongqing, Hubei and Shenzhen were determined as the first carbon emissions trading pilot provinces and cities. Unified national carbon emissions trading market will run in 2016, and the quotas are allocated by the state. On November 12, 2014, the Chinese government promised in the sino-us joint statement on climate change: China plans to make carbon dioxide emissions reach peak around 2030, and will try to reach peak at an early date. There has been a rich body of literature related to the study of cap and trade application or emissions trading system. Many are qualitative studies that used a descriptive approach to discuss the risks and allocating initial emission allowance to © Springer Science+Business Media Singapore 2016 L. Zhang et al. (Eds.): AsiaSim 2016/SCS AutumnSim 2016, Part III, CCIS 645, pp. 265–272, 2016. DOI: 10.1007/978-981-10-2669-0_29
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generating companies (Yi et al. 2011; Wei et al. 2012; James and Chen 2012) and the design of mechanism for carbon-credit trade (Robert et al. 2012). Some papers focused on optimization issues related to the carbon-credit resource planning from a production system perspective (Chen et al. 2013). Mathematical programming models (e.g. LP, NLP, MIP) in particular have been developed to optimizing resource decisions for single manufacturer (Kockar et al. 2009; Wang et al. 2012; Chang et al. 2012). These studies demonstrated that cap and trade conditions generate significant impact on the resource selec
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