Assessment of the biomass power generation industry in China

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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“…Zhao and Yan [19] Assessing the strengths, weaknesses, opportunities and threats (SWOT) of the biomass power generation industry of China.…”

Section: Purposes Of Study Solution Methods Uncertain Factorsmentioning

confidence: 99%

“…Since straw-based power generation is carbon-central [18,19,[34][35][36][37], the investors of the straw-based power generation project can get revenue from selling CO 2 emission allowance. This study assumes the carbon emission trading scheme is effective.…”

Section: Revenue From Electricity Salementioning

confidence: 99%

Biomass Power Generation Investment in China: A Real Options Evaluation

et al. 2016

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“…The strengths and weaknesses of the project are internal characteristics and are controllable while opportunities and threats are external factors but can react at a determining moment in their favor [18] The implementation of the SWOT analysis allows understanding the strengths of a project and to exploit its opportunities and plan based on them. Also, it contributes to recognize treat or avoid the weaknesses and protect against any threat known [19]. The SWOT methodology was applied to evaluate the internal and external factors afecting the utilization of wheat straw as biofuel in Mexico.…”

Section: Swot Analysismentioning

confidence: 99%

SWOT Analysis Applied to Wheat Straw Utilization as a Biofuel in Mexico

Montero¹,

García²,

Coronado³

et al. 2017

Frontiers in Bioenergy and Biofuels

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Wheat is one of the main crops worldwide with a production of 733 million of tons by 2015. By 2013, the wheat grain production in Mexico was 3,357,307 t. Wheat straw is generated as a biomass waste once the wheat is harvested. However, the agricultural biomass waste has acquired international relevance as a source of bioenergy. The utilization of bioenergy has signiicant environmental beneits, and also economic beneits because the biomass waste is valorized as biofuel. The use of wheat straw as raw material for any productive process presents diverse factors that must be considered. Among those factors are the low density of biomass, handling and high transportation cost, an atractive heating value, and the physicochemical characterization. Therefore, the aim of this work was to apply the SWOT analysis to wheat straw utilization as a biofuel in Mexico. The main indings highlighted an estimation of 4,612,950.23 t of wheat straw generated. The experimental results of proximate analysis were 64.42% volatile mater, 19.49% ixed carbon and 16.09% ash. The higher heating was 14.86 MJ/kg. An energy potential of 69 PJ per agricultural cycle was calculated, equivalent to 19% of the biomass energy share reported in Mexico's National Energy Balance, by 2014.

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“…DEA methods showed their advantage in evaluating the efficiency of the biomass power generation industry, and domestic scholars started to apply them to China's own data. Zhao chose SWOT to analyze the industry state of the biomass power generation of China and thought that the advantage lied in the increasing electricity demand and changing electricity price, yet, the industry was sensitive to local policy and the local industrial environment [6]. Kautto also compared the regional and national biomass power plan, finding that if the regional plan can cooperate with national development better, the whole biomass industry efficiency would take a step toward a higher level [7].…”

Section: Literature Review and Emerged Concernsmentioning

confidence: 99%

Biomass Power Generation Industry Efficiency Evaluation in China

Qin

Tao

2014

Sustainability

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Abstract:In this paper, we compare the properties of the traditional additive-based data envelopment analysis (hereafter, referred to as DEA) models and propose two generalized DEA models, i.e., the big M additive-based DEA (hereafter, referred to as BMA) model and the big M additive-based super-efficiency DEA (hereafter, referred to as BMAS) model, to evaluate the performance of the biomass power plants in China in 2012. The virtues of the new models are two-fold: one is that they inherited the properties of the traditional additive-based DEA models and derived more new additive-based DEA forms; the other is that they can rank the efficient decision making units (hereafter, referred to as DMUs). Therefore, the new models have great potential to be applied in sustainable energy project evaluation. Then, we applied the two new DEA models to evaluate the performance of the biomass power plants in China and find that the efficiency of biomass power plants in the northern part of China is higher than that in the southern part of China. The only three efficient biomass power plants are all in the northern part of China. Furthermore, based on the results of the Wilcoxon-Mann-Whitney rank-sum test and the Kolmogorov-Smirnov test, there is a great technology gap between the biomass power plants in the northern part of China and those in the southern part of China.

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Assessment of the biomass power generation industry in China

Cited by 110 publications

References 6 publications

Biomass Power Generation Investment in China: A Real Options Evaluation

Biomass Power Generation Investment in China: A Real Options Evaluation

SWOT Analysis Applied to Wheat Straw Utilization as a Biofuel in Mexico

Biomass Power Generation Industry Efficiency Evaluation in China

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