A real options–based CCS investment evaluation model: Case study of China’s power generation sector

Zhu, Lei; Fan, Ying

doi:10.1016/j.apenergy.2011.04.005

Cited by 158 publications

(53 citation statements)

References 40 publications

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“…At the international level, experts from scientific institutions and non-governmental organisations (NGOs) expect a later large-scale availability than previously assumed due to the low carbon pricing level, delayed demonstration projects and a lack of public acceptance in potential storage regions [36][37][38][39][40][41]. Although CO 2 capture is currently undergoing substantial development and several CCS demonstration projects have been launched in China [9], a lack of business cases and the uncertainties in climate change policy [10] are hampering the launch of commercial technology. We therefore choose the year 2030 as the start of operation of largescale CCS projects in the "base case".…”

Section: Commercial Availability Of Ccs Technologymentioning

confidence: 99%

“…There were therefore very few onedimensional assessments, most of which focused on public acceptance and life cycle analysis (LCA). An increasing number of authors refer to the uncertainties and challenges faced by CCS, such as increased energy and water consumption, inadequate storage capacities or potential CO 2 leakages, which could hamper the large-scale deployment of CCS [8][9][10][11].…”

Section: Introductionmentioning

confidence: 99%

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Prospects of carbon capture and storage (CCS) in China’s power sector – An integrated assessment

2015

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Objective The aim of the present article is to conduct an integrated assessment in order to explore whether CCS could be a viable technological option for significantly reducing future CO 2 emissions in China.Methods In this paper, an integrated approach covering five assessment dimensions is chosen.Each dimension is investigated using specific methods (graphical abstract). ResultsThe most crucial precondition that must be met is a reliable storage capacity assessment based on site-specific geological data. Our projection of different trends of coal-based power plant capacities up to 2050 ranges between 34 and 221 Gt of CO 2 that may be captured from coal-fired power plants to be built by 2050. If very optimistic assumptions about the country's CO 2 storage potential are applied, 192 Gt of CO 2 could theoretically be stored as a result of matching these sources with suitable sinks. If a cautious approach is taken, this figure falls to 29 Gt of CO 2 . In practice, this potential will decrease further with the impact of technical, legal, economic and social acceptance factors. Further constraints may be the delayed commercial availability of CCS in China; a significant barrier to achieving the economic viability of CCS due to a currently non-existing nation-wide CO 2 pricing scheme that generates a sufficiently strong price signal; an expected life-cycle reduction rate of the power plant's greenhouse gas emissions of 59 to 60%; and an increase in most other negative environmental and social impacts. Conclusion and practice implicationsMost experts expect a striking dominance of coal-fired power generation in the country's electricity sector, even if the recent trend towards a flattened deployment of coal capacity and reduced annual growth rates of coal-fired generation proves to be true in the future. In order to reduce fossil fuel-related CO 2 emissions to a level that would be consistent with the long-term climate protection target of the international community to

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Section: Commercial Availability Of Ccs Technologymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Prospects of carbon capture and storage (CCS) in China’s power sector – An integrated assessment

2015

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“…The method and process resemble some previous studies [41][42][43][44][45][46]. The details of the solution process are as follows:…”

Section: Model Solutionmentioning

confidence: 99%

“…This study uses MATLAB software to solve the model [41,42]. The investment value and the most likely optimal investment time are employed to reflect the optimal investment strategy.…”

Section: Base Case Analysismentioning

confidence: 99%

“…These indicate that the government should continue raising the subsidy. In addition, the most likely optimal investment time in the constant pattern and the growth pattern is the same because the impact of the change pattern of the subsidy on the most likely optimal investment time is not even [14,18,39,41]. The National Development and Reform Commission (NDRC) has proposed that the subsidy should be reduced by 2% per year [52].…”

Section: Implications Of Subsidy Policymentioning

confidence: 99%

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Biomass Power Generation Investment in China: A Real Options Evaluation

et al. 2016

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This paper proposes a real options model for evaluating the biomass power generation investment in China. The uncertainties in the market price of electricity, CO 2 price and straw price are considered. Meanwhile the dynamic relationship between installed capacity and fuel cost, as well as the long-term reduction of subsidy are described. Two scenarios, i.e., with the carbon emission trading scheme existent and non-existent, respectively, is built to empirically analyze the investment of a 25-MW straw-based power generation project. The results show that investors should undertake the investment in 2030 under two scenarios. Investment values are 14,869,254.8 and 37,608,727 Chinese Yuan (RMB), respectively. The implementation of the carbon emission trading scheme theoretically helps improve investment value and advance the most likely optimal investment time. However, the current CO 2 price is not sufficient to advance the most likely optimal investment time. The impacts of several factors, including subsidy policy, CO 2 price, straw price, installed capacity, correlation structure and the validity period of investment, on the optimal investment strategy are also examined. It is suggested that governments take some measures, including increasing subsidy, setting the growth pattern of subsidy and establishing and perfecting a nationwide carbon trading market, to improve the investment environment and attract more investments.

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Study on the investment timing of carbon capture and storage under different business modes

Wang

Qie

2018

Greenhouse Gases

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In this paper, the optimal investment timing decision in carbon capture and storage (CCS) is studied under different business modes. Two analytical models of the optimal CCS investment threshold are presented: one is the ‘single mode,’ meaning that CCS investment is performed only by the power producer; the other is the ‘cooperation mode,’ referring to CCS investment can be conducted by cooperation between the power producer and the CCS operator. In the single mode, this study pictures the investment decision for a CCS project based on real options theory considering uncertainties in carbon prices, government incentives, and CCS investment costs. On this basis, a model for the cooperation mode is proposed by integrating real options theory and game theory to analyze the CCS investment threshold. The results of a numerical example show that CCS investment in cooperation mode requires a much larger threshold than in single mode. Moreover, the CCS investment threshold is positively affected by carbon price volatility and the transfer payments coefficients, but negatively affected by government incentives and learning effects. These conclusions provide a theoretical foundation for decision making regarding CCS investment and related policy making. © 2018 Society of Chemical Industry and John Wiley & Sons, Ltd.

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A real options–based CCS investment evaluation model: Case study of China’s power generation sector

Cited by 158 publications

References 40 publications

Prospects of carbon capture and storage (CCS) in China’s power sector – An integrated assessment

Prospects of carbon capture and storage (CCS) in China’s power sector – An integrated assessment

Biomass Power Generation Investment in China: A Real Options Evaluation

Study on the investment timing of carbon capture and storage under different business modes

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