Managing bioethanol supply chain resiliency: a risk-sharing model to mitigate yield uncertainty risk

Pages1510-1527
DOIhttps://doi.org/10.1108/IMDS-09-2017-0429
Date13 August 2018
Published date13 August 2018
AuthorFei Ye,Gang Hou,Yina Li,Shaoling Fu
Managing bioethanol supply chain
resiliency: a risk-sharing model to
mitigate yield uncertainty risk
Fei Ye, Gang Hou and Yina Li
School of Business Administration, South China University of Technology,
Guangzhou, China, and
Shaoling Fu
College of Economics and Management, South China Agricultural University,
Guangzhou, China
Abstract
Purpose The purpose of this paper is to propose a risk-sharing model to coordinate the decision-making
behavior of players in a cassava-based bioethanol supply chain under random yield and demand
environment, so as to mitigate the yield and demand uncertainty risk and improve the bioethanol supply
chain resiliency and performance.
Design/methodology/approach The decision-making behavior under three models, namely, centralized
model, decentralized model and risk-sharing model, are analyzed. An empirical test of the advantages and
feasibility of the proposed risk-sharing model, as well as the test of yield uncertainty risk, risk-sharing
coefficients and randomly fluctuating cassava market price on the decision-making behavior and
performances are provided.
Findings Though the proposed risk-sharing model cannot achieve the supply chain performance in the
centralized model, it does help to encourage the farmers and the company to increase the supply of cassava
and achieve the Pareto improvement of both players compared to the decentralized model. In particular, these
improvements will be enlarged as the yield uncertainty risk is higher.
Practical implications The findings will help decision makers in the bioethanol supply chain to
understand how to mitigate the yield uncertainty risk and improve the supply chain resiliency under yield
and demand uncertainty environment. It will also be conducive to ensure the supply of feedstock and the
development of the bioethanol industry.
Originality/value The proposed risk-sharing model incorporates the yield uncertainty risk, the random
market demand and the hierarchical decision-making behavior structure of the bioethanol supply chain in
the model.
Keywords Cassava, Bioethanol supply chain, Risk-sharing, Yield uncertainty
Paper type Research paper
1. Introduction
As a promising alternative to fossil fuels and a solution to reduce the greenhouse gas
(GHG) emissions, bioethanol is playing an increasingly important role in global society in
the past decades (Li and Hu, 2014; Ba et al., 2016; Meyer et al., 2016). This issue is
especially important in China, the largest primary energy consumer and emitter of GHG in
the world (Ye et al., 2018). In recent years, China has launched programs to promote the
production of bioethanol to meet the ever-increasing energy consumption and to achieve
the GHG emissions reduction target. Figure 1 shows that Chinas production of bioethanol
is increasing rapidly in previous years. However, bioethanol is still a source of renewable
energy that is generally underutilized in China. The production of bioethanol in 2015 is
only 2.3m tonnes, which is still significantly lower than the 10m tonnes target in 2020
(Ye et al., 2017).
Industrial Management & Data
Systems
Vol. 118 No. 7, 2018
pp. 1510-1527
© Emerald PublishingLimited
0263-5577
DOI 10.1108/IMDS-09-2017-0429
Received 28 September 2017
Revised 3 February 2018
30 March 2018
Accepted 2 April 2018
The current issue and full text archive of this journal is available on Emerald Insight at:
www.emeraldinsight.com/0263-5577.htm
The study is supported by National Natural Science Foundation of China (71771090; 71471066;
71371006; 71420107024).
1510
IMDS
118,7
To promote the development of bioethanol industry, the sufficient supply of feedstock is
extremely important. But Chinas bioethanol industry is suffering from an undersupply of
agricultural feedstock for bioethanol production (Ye et al., 2018). Meanwhile, China used to
rely heavily on consumable grain crops, such as corn and wheat, to produce bioethanol.
However, after the 20072008 global food crisis claiming to be related to bioethanol
production and the accompanying food vs fuel debate (Timilsina and Shrestha, 2011), the
Chinese Government abolished the government subsidy on corn-based bioethanol industry
in 2016 ( Jin, 2014). Alternatively, the production of bioethanol from non-edible feedstock
becomes the Chinese Government key support project, within which cassava is the most
important and attractive one due to its high-starch content and good environment
adaptability (Leng et al., 2008; Jakrawatana et al., 2016). Despite the increasing production of
cassava every year, the demand for cassava in China, however, still far exceeds the supply
and, consequently, relies heavily on the import from other countries, as shown in Figure 2.
Therefore, how to encourage the production of cassava to ensure sufficient feedstock for
bioethanol production has become a serious challenge for the development of bioethanol
industry in China. A couple of risks deeply influence this issue. On the one hand, the
undersupply of feedstock is due to the scattered and low intensive cultivation by small-scale
farmers with little use of mechanized planting (Liu et al., 2013), as well as the uncertainties
related to weather variability, which directly caused yield uncertainty and the mismatch
between farmerssupply and companys demand. On the other hand, considering the short
shelf life of bioethanol, strict storage and transportation environment and stochastic market
demand (Meyer et al., 2016), the bioethanol supply chain is riskier than traditional supply
chain. Moreover, the target to maximize their own expected profit by farmers and
2.50
2.00
1.50
Million tonnes
1.00
0.50
0.00
2003
2004
2005
2006
2007
2008
2009
2010
2011
2012
2013
2014
2015
Figure 1.
Chinas bioethanol
production quantity
Million tonnes
2003
2004
2005
2006
2007
2008
2009
2010
2011
2012
2013
2014
2015
8
7
5
4
3
2
0
1Figure 2.
Chinas cassava
import quantity
1511
Bioethanol
supply chain
resiliency

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