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postgraduate thesis: Strategic investment of greentech projects in China

TitleStrategic investment of greentech projects in China
Authors
Advisors
Advisor(s):Chu, LK
Issue Date2014
PublisherThe University of Hong Kong (Pokfulam, Hong Kong)
Citation
Qin, H. [覃涵]. (2014). Strategic investment of greentech projects in China. (Thesis). University of Hong Kong, Pokfulam, Hong Kong SAR. Retrieved from http://dx.doi.org/10.5353/th_b5223982
AbstractWith the implementation of the pilot Emission Trading Schemes (ETSs) in China since 2013, there emerges a new opportunity for investing in Chinese green technology (greentech) projects. Apart from trading the international CERs (Certified Emissions Reductions), investors nowadays can also trade the CCERs (Chinese Certified Emissions Reductions) in the domestic carbon market. However, the pricing of CCERs is different from that of CERs due to the distinct climate policies in China, thus making the investment decisions in Chinese greentech projects a complicated problem. This study is, to our knowledge, the first attempt to evaluate greentech projects under uncertain climate policies in developing countries like China. To identify the investment environment of Chinese greentech projects, a qualitative research on the development of climate policies and greentech market is first conducted. Based on the study of international climate polices and carbon markets, the pricing mechanisms of carbon assets (carbon allowances and carbon offsets) are investigated. Furthermore, China’s climate polices, including the energy policies and emission reduction policies, are analysed in detail. In addition, both the administrative and economic instruments used in China’s climate policies are investigated. Then, business opportunities and challenges in greentech market are investigated. It is shown that there is substantial demand for greentech projects, and the uncertainties embedded a Chinese greentech project in power sector mainly come from the CCER price and electricity price. To integrate the different stochastic price processes, a real-options-based greentech investment (ROGI) model is developed to derive the investment options value and the optimal investment timing. Considering the possible interventions of the government, the CCER price is modelled as mean-reverting process with jump and cap-floor. In addition, an equivalent electricity price is developed and also described by a mean-reverting process. To solve the ROGI model with various uncertainties involved, a least-square Monte Carlo (LSM) approach is developed. The proposed LSM algorithm is built within the dynamic programming framework. It is shown that the optimal investment strategy can be characterized by a continuation region. The proposed ROGI model is verified by a real case of wind power project investment in China. Sensitive analysis is conducted to examine the effects of the model parameters on the investment decisions. Different climate policy scenarios are then tested, including carbon price jump, carbon price cap and floor, and carbon tax. Several interesting findings are concluded from the results analysis in this study. First, it is found that investors are relatively insensitive to the jump in the CCER price, the volatility of the CCER price, or the mean-reverting speed. Second, the CCER price floor set by the government can effectively encourage greentech investments while the price cap is not a major concern of investors. Finally, the long-term growth rate of the CCER price as well as the carbon tax rate can also plays a significant role in investment decisions.
DegreeDoctor of Philosophy
SubjectGreen technology - Capital investments - China
Dept/ProgramIndustrial and Manufacturing Systems Engineering
Persistent Identifierhttp://hdl.handle.net/10722/206685

 

DC FieldValueLanguage
dc.contributor.advisorChu, LK-
dc.contributor.authorQin, Han-
dc.contributor.author覃涵-
dc.date.accessioned2014-11-25T03:53:18Z-
dc.date.available2014-11-25T03:53:18Z-
dc.date.issued2014-
dc.identifier.citationQin, H. [覃涵]. (2014). Strategic investment of greentech projects in China. (Thesis). University of Hong Kong, Pokfulam, Hong Kong SAR. Retrieved from http://dx.doi.org/10.5353/th_b5223982-
dc.identifier.urihttp://hdl.handle.net/10722/206685-
dc.description.abstractWith the implementation of the pilot Emission Trading Schemes (ETSs) in China since 2013, there emerges a new opportunity for investing in Chinese green technology (greentech) projects. Apart from trading the international CERs (Certified Emissions Reductions), investors nowadays can also trade the CCERs (Chinese Certified Emissions Reductions) in the domestic carbon market. However, the pricing of CCERs is different from that of CERs due to the distinct climate policies in China, thus making the investment decisions in Chinese greentech projects a complicated problem. This study is, to our knowledge, the first attempt to evaluate greentech projects under uncertain climate policies in developing countries like China. To identify the investment environment of Chinese greentech projects, a qualitative research on the development of climate policies and greentech market is first conducted. Based on the study of international climate polices and carbon markets, the pricing mechanisms of carbon assets (carbon allowances and carbon offsets) are investigated. Furthermore, China’s climate polices, including the energy policies and emission reduction policies, are analysed in detail. In addition, both the administrative and economic instruments used in China’s climate policies are investigated. Then, business opportunities and challenges in greentech market are investigated. It is shown that there is substantial demand for greentech projects, and the uncertainties embedded a Chinese greentech project in power sector mainly come from the CCER price and electricity price. To integrate the different stochastic price processes, a real-options-based greentech investment (ROGI) model is developed to derive the investment options value and the optimal investment timing. Considering the possible interventions of the government, the CCER price is modelled as mean-reverting process with jump and cap-floor. In addition, an equivalent electricity price is developed and also described by a mean-reverting process. To solve the ROGI model with various uncertainties involved, a least-square Monte Carlo (LSM) approach is developed. The proposed LSM algorithm is built within the dynamic programming framework. It is shown that the optimal investment strategy can be characterized by a continuation region. The proposed ROGI model is verified by a real case of wind power project investment in China. Sensitive analysis is conducted to examine the effects of the model parameters on the investment decisions. Different climate policy scenarios are then tested, including carbon price jump, carbon price cap and floor, and carbon tax. Several interesting findings are concluded from the results analysis in this study. First, it is found that investors are relatively insensitive to the jump in the CCER price, the volatility of the CCER price, or the mean-reverting speed. Second, the CCER price floor set by the government can effectively encourage greentech investments while the price cap is not a major concern of investors. Finally, the long-term growth rate of the CCER price as well as the carbon tax rate can also plays a significant role in investment decisions.-
dc.languageeng-
dc.publisherThe University of Hong Kong (Pokfulam, Hong Kong)-
dc.relation.ispartofHKU Theses Online (HKUTO)-
dc.rightsCreative Commons: Attribution 3.0 Hong Kong License-
dc.rightsThe author retains all proprietary rights, (such as patent rights) and the right to use in future works.-
dc.subject.lcshGreen technology - Capital investments - China-
dc.titleStrategic investment of greentech projects in China-
dc.typePG_Thesis-
dc.identifier.hkulb5223982-
dc.description.thesisnameDoctor of Philosophy-
dc.description.thesislevelDoctoral-
dc.description.thesisdisciplineIndustrial and Manufacturing Systems Engineering-
dc.description.naturepublished_or_final_version-
dc.identifier.doi10.5353/th_b5223982-

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