مسیر بهینه برای استراتژیک چین ذخیره نفت: تجزیه و تحلیل برنامه ریزی پویا
|کد مقاله||سال انتشار||مقاله انگلیسی||ترجمه فارسی||تعداد کلمات|
|25760||2012||6 صفحه PDF||سفارش دهید||4710 کلمه|
Publisher : Elsevier - Science Direct (الزویر - ساینس دایرکت)
Journal : Energy Economics, Volume 34, Issue 4, July 2012, Pages 1058–1063
This paper proposes a dynamic programming model to explore the optimal stockpiling path for China's strategic petroleum reserve before 2020. The optimal oil acquisition sizes in 2008–2020 under different scenarios are estimated. The effects of oil price, risks and elasticity value on inventory size are further investigated. It is found that the optimal stockpile acquisition strategies are mainly determined by oil price and total inventory costs. While oil supply disruption is not considered, China's optimal stockpile acquisition rate increases from 19.2 to 52 million barrels from 2008 to 2020. If an oil supply disruption occurs, the oil acquisition rate will be reduced significantly. However, it may not be a good strategy to interrupt oil reserve activities in order to minimize the total costs for the entire planning period. Highlights ► A dynamic programming model for explore the optimal path of China's strategic petroleum reserve till 2020. ► China's optimal acquisition sizes increase from 19.2 to 52 million barrels. ► Sensitivity analysis of optimal stockpile size under different disruption conditions and elasticity values. JEL classifications C61; Q41
China's fast economic growth over the past three decades greatly boosted its demand for oil consumption. According to NBSC (2008), China's annual oil consumption was 360 million tons in 2008 and more than 50% of that relied on imports. Compared to the 169 million tons in 1995, this figure has become doubled in thirteen years. It is estimated that till 2020 China's annual oil demand will be about 560–600 million tons, while the annual domestic oil supply is only about 200–220 million tons. The oil supply-demand gap will reach 340 to 400 million tons, which implies that over 60% of the oil demand will have to be dependent on imports. China's growing dependence on overseas oil supplies, which are mainly from the Middle East and West Africa, has resulted in a major concern about its future oil supply security. This concern was further enhanced by the insufficient commercial oil storage capacity in China. In order to safeguard its oil supply security, China government has also established its strategic petroleum reserve (SPR) by following international practices. SPR is an emergency oil storage maintained by a country to ensure its energy and economic security (Wei et al., 2008 and Wu and Wei, 2009). It is generally agreed that SPR is an effective and powerful measure against the negative effects due to oil supply interruption. The largest SPR holder in the world is the United States with a reported capacity of 727 million barrels (Taylor and van Doren, 2005). Japan is the second largest SPR holder with a capacity of 579 million barrels in 2003. China's target is to build a SPR equivalent to 100 days' net oil imports by 2020. Since earlier 2004, China government has been preparing for the establishment of its SPR. By the end of 2008, all the four stockpile bases of its first SPR project had been finished with a capacity about 1000 to 1200 million tons (Xue and Qiao, 2009). Owing to the practical significance of SPR, researchers have begun to study SPR issues with modeling techniques since the first world oil crisis. Several earlier studies, e.g. Nordhaus, 1974 and Tolley and Wilman, 1977, proposed to use the two-period model to identify a single “optimal stockpile size”. By considering the mutual influences among different decision-makers, several researchers have also employed game theoretical models to study the SPR issue. Balas (1981) developed a short-run game between oil importing nations and a politically motivated cartel that takes advantage of disruptions to inflict economic losses on importing nations. Hogan (1983) established a Stackelberg game model for examining the interactions between two oil consuming countries. Murphy et al. (1987) proposed a discrete time Nash game for modeling the inventory interactions between two nations or two aggregates of nations. Some discrete models, e.g. decision tree, have also been used for quantifying the optimal SPR. For instance, Samouilidis and Berahas (1982) established a decision tree model considering both crude oil and refined oil inventories. Recently, Wei et al. (2008) developed a decision tree model to analyze China's optimal stockpile size under different scenarios. Some non-linear optimization models have also been proposed for solving the SPR problem. Kuenne et al. (1979) used an optimization model, which includes a GNP response function and crude oil supply reductions, to determine the optimal drawdown trajectories for SPR during an embargo. Hubbard and Weiner (1986) estimated an equation for private oil inventory behavior in the United States and discussed public–private interactions in stockpiling. Zweifel and Bonomo (1995) developed an optimal reserve model that takes into account multiple risks for oil and gas. In addition, the optimal stockpile size has been qualitatively analyzed in many previous studies including Kata, 1981, Jenkins-Smith and Weimer, 1985 and Taylor and van Doren, 2006. As a well established operations research technique, dynamic programming (DP) has also gained popularity in estimating the optimal SPR. Teisberg (1981) developed a DP model to estimate the optimal acquisition and sale strategies for the SPR of the United States. Chao and Manne (1983) extended the study by Teisberg (1981) to the case with a broader conception of oil disruption cost and the state space. Oren and Wan (1986) proposed a stochastic DP approach to quantifying the optimal size, fillup and drawdown rates for the SPR of the United States under various assumptions. Recently, several studies have also used DP to study China's SPR. For example, Wu et al. (2008) quantified the impact of the uncertain in world oil prices on the optimal stockpile acquisition strategies of China's SPR for the periods 2007–2010 and 2011–2020. Zhang et al. (2009) further analyzed the optimal size of China's SPR and the best acquisition and drawdown strategies. The above studies on China's SPR mainly dealt with the estimation of the optimal SPR size or acquisition and drawdown strategies. Once the inventory size planned and the time limit are specified, the issue on how to stockpile must be addressed. As described earlier, China's SPR target is to have an oil reserve equivalent to about 100 days of net imports before 2020. What kinds of reserve strategies should China follow to realize its SPR objective in a cost-effective manner within the specified time? Which factors will affect China's oil reserve strategies? How to affect? This paper attempts to answer these questions by exploring China's optimal stockpiling path for attaining its SPR target. More specifically, we propose a DP model and use it to analyze the optimal stockpile acquisition strategies for China from 2008 to 2020, taking into account the impacts of different factors such as oil price, cost and risks. The remainder of this paper is organized as follows. In Section 2, we propose a cost function-based DP model for analyzing China's oil reserve strategies. The implementation of the DP model is described in Section 3. Section 4 presents the main results and findings of our empirical analysis using the DP model. Section 5 concludes this study with some policy recommendations.
نتیجه گیری انگلیسی
This paper proposes a DP model for quantifying the optimal path for attaining China's target of having a SPR equivalent to 100 days of net imports by 2020. First, we calculate the optimal oil stockpile sizes from 2008 to 2020 in normal state. Then the cases with different disruption sizes are studied and the relevant strategies are formulated. Finally, we carry out a sensitivity analysis of the time path of optimal stockpile size and inventory size with respect to elasticity value in normal state. Based on our modeling results, we may make the following policy recommendations for attaining China's SPR target. First, China should adopt a moderately increasing storage strategy in normal state, and the optimal stockpile acquisition rate increases from 19.2 to 52 million barrels in 2008–2020. When a supply disruption or oil shock occurs, the government should cut down its oil acquisition size. However, from the viewpoint of minimizing the total costs in the whole planning period, it may not be appropriate to interrupt the oil reserve activities. Our sensitivity analysis results with respect to elasticity value show that smooth reserve policies are preferred and more oil needs to be stored in the beginning in order to reduce the vulnerability cost. However, when elasticity is high, more aggressive acquisition policies are preferred while the inventory should kept at a low level in the beginning to reduce the inventory holding cost. This study assumes that China's oil inventory does not affect international oil price. However, with the increase in oil imports by China, its SPR size will be further expanded and may have certain impacts on international oil prices. How it will affect the optimal path of China's SPR is a potential topic worth further investigating. In addition, game behaviors between exporting and importing countries as well as speculative behaviors in oil markets are not considered this study. Further research may be carried out to explore China's optimal SPR path by considering these factors altogether.