Bioenergy; China; R package; Straw feedstock supply chain; Bio-energy; Bioenergy conversion; Conversion plants; Feedstock supply; Global environment; Integrated modeling; Supply costs; Waste Management and Disposal; Economics and Econometrics
Abstract :
[en] With the growing concerns of fossil fuel scarcity and its negative impacts on global environment, bioenergy as an alternative energy source has attracted more attention as climate change mitigation. As one of the largest carbon emission and agricultural production countries in the world, China has abundant straw resources and great potential for energy utilization, and the number of straw-to-energy business projects is increasing dramatically. Correspondingly, there is a strong demand to design a stable and sustainable straw feedstock supply chain. However, due to the uncertainty of system boundary and neglect of potential risks, the procurement cost for bioenergy conversion plants (BCP) is varied significantly. Model is a critical approach in strengthening the understanding that leads to promoting supply efficiency. Therefore, an open-source & GIS-enabled linear programming model, named StrawFeed, is proposed to simulate the operation of straw feedstock supply. The costs of raking, baling, loading and transporting have been investigated as components in the StrawFeed model. The model is applied to case analysis of corn straw supply in Nongan county, a major corn production region in Northeast China. The results illustrate that the straw supply cost could be 172 CNY/ton, and the reasonable profit allocation mechanism could achieve the triple-win solution among farmers, brokers, and BCP. Furthermore, the challenges and opportunities for optimization are investigated with scenario analysis, based on unique circumstances and supporting policies in China. Unfavorable weather could delay the available working day, and thereby cost would increase up to 13%. The optimized scheme of straw utilization could achieve better environmental and ecological benefits, but the transportation distance for straw supply has to be expanded and the increased cost would be up to 53%. Cross-regional operation of agricultural machinery and machine procurement subsidies could reduce the cost by 18% and 5% respectively. This model is helpful to estimate accurate supply cost and deploy sustainable straw feedstock supply, which could contribute to assisting investors and policymakers for bioenergy industry in China.
Disciplines :
Business & economic sciences: Multidisciplinary, general & others
Author, co-author :
Wang, Shu ; Université de Liège - ULiège > TERRA Research Centre ; Institute of Agricultural Resources and Regional Planning, Chinese Academy of Agricultural Sciences, Beijing, China
Yin, Changbin; Institute of Agricultural Resources and Regional Planning, Chinese Academy of Agricultural Sciences, Beijing, China ; Research Center for Agricultural Green Development in China, Beijing, China
Jiao, Jian ; Université de Liège - ULiège > TERRA Research Centre ; Institute of Agricultural Resources and Regional Planning, Chinese Academy of Agricultural Sciences, Beijing, China
Yang, Xiaomei; Institute of Agricultural Resources and Regional Planning, Chinese Academy of Agricultural Sciences, Beijing, China
Shi, Boyang ; Université de Liège - ULiège > TERRA Research Centre ; Institute of Agricultural Resources and Regional Planning, Chinese Academy of Agricultural Sciences, Beijing, China
Richel, Aurore ; Université de Liège - ULiège > TERRA Research Centre > Smart Technologies for Food and Biobased Products (SMARTECH)
Language :
English
Title :
StrawFeed model: An integrated model of straw feedstock supply chain for bioenergy in China
This research was funded by Major Program of National Philosophy and Social Science Foundation of China (No. 18ZDA048 ), the earmarked fund for Modern Agro-industry Technology Research System-Green Manure ( CARS-22-G25 ), Fundamental Research Funds for Central Non-profit Scientific Institution (No. 1610132021002 ). The China Scholarship Council (CSC) is gratefully acknowledged for financial support of the PhD study of Mr. Shu Wang.
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