Optimizing Collection and Pretreatment Methods for Cost-Effective and Low-CO 2 Emission Biomass Supply Chains

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To tackle this issue, we devised a biomass supply chain model encompassing collection, pretreatment, storage, and transportation phases. We examined the effects of various collection and pretreatment method combinations on supply-chain costs and CO 2 emissions. The model's validity was confirmed using Heilongjiang and Zhejiang as representatives of northern and southern regions. Mechanized collection with shredding-baling proved to be the most economical, costing 226.6 y/t in Heilongjiang and 217.7 y/t in Zhejiang within a 60 km collection radius. For CO 2 emissions, mechanized collection with kneading emerged as the optimal choice. With governmental subsidies, mechanized collection with kneading became the preferred option, considering both cost and CO 2 emissions. By incorporating agricultural cooperatives, costs were further reduced by up to 70 y/t in Heilongjiang and 65 y/t in Zhejiang. This model facilitates the cost-effective collection of straw for ethanol production in biomass-scattered China. Biomass decentralization combining collection and pretreatment methods is key to achieving site-specific biomass supply. Furthermore, the model can be adapted for acquiring biomass feedstock in other sectors and offers insights for biomass procurement in diverse regions. Cellulosic ethanol Straw Supply chain Costs CO2 emissions Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Introduction As global fossil energy consumption rises, worsening climate change, biofuels have become an essential alternative to fossil fuels in the future[ 1 , 2 ]. Ethanol has been used as a partial substitute for gasoline in several regions to alleviate energy shortages[ 3 , 4 ]. Utilizing lignocellulose (agricultural and forestry residues, energy grasses) as raw material not only avoids the food competition problem in the first-generation biorefineries but also brings a certain positive impact on CO 2 emission reduction[ 5 , 6 ]. Over the past decade, extensive research has been conducted on developing technologies for producing cellulosic ethanol from lignocellulosic materials, notably focusing on the dilute acid pretreatment process for cellulosic ethanol production developed by the National Renewable Energy Laboratory (NREL)[ 7 – 9 ]. To meet the demand for second-generation cellulosic ethanol feedstock, large-scale farms, exemplified by the United States, have shifted to cultivating energy crops to ease energy demand pressures[ 10 – 12 ]. However, in China, where there is high food demand and limited cultivation of energy crops, the primary feedstock for second-generation cellulosic ethanol remains agricultural waste. Moreover, China's intricate topography results in decentralized distribution of agricultural waste, leading to challenges in its collection, storage, and transportation[ 13 – 16 ]. Consequently, bioethanol plants in China are predominantly small-scale, relying on nearby agricultural residues as feedstock. Internationally, appropriate biomass supply chain models have been developed by several studies for different countries and regions focusing on collection, transportation, storage, and pretreatment[ 17 , 18 ]. Teresa et al. created a biomass supply chain model for northern Sweden to assess the costs and CO 2 emissions of harvesting forest residues and transporting them to fuel plants, which is a common model. In a different study[ 19 ]. R. L. Machado et al. designed a bioethanol supply chain model for sugarcane cultivation, ethanol processing, storage, transportation, and distribution to meet market demands. Their approach optimized cultivation areas and sizes to reduce transportation distances and enhance cost efficiency. However, this model is not suitable for China due to its reliance on decentralized straw as a biomass feedstock, unlike Brazil's centralized sugarcane cultivation[ 20 ]. Kapil M. Gumte et al. developed a comprehensive biomass supply chain model for India, covering all supply chain stages, with a focus on analyzing the cost implications of rail and road transportation modes. However, rail transportation of biomass remains challenging to implement in China at present[ 21 ]. Previous studies in China have mainly focused on straw acquisition in specific small areas, lacking suitable supply chain models for high-demand bioethanol feedstocks[ 22 – 24 ]. The decentralized and regionally varying distribution of agricultural residues in China underscores the need for a supply chain model with broad applicability. Enhancing vehicle transportation capacity is a key strategy in addressing long-distance transportation challenges arising from dispersed distribution[ 25 , 26 ]. In rural China, to increase the transportation efficiency, farmers typically utilize baling equipment to compress the straw density[ 27 , 28 ]. Particularly, for feed mills requiring larger quantities of straw, the straw is compacted in rural areas into high-density large square bales before transport.[ 29 , 30 ]. Indeed, preprocessing biomass into high-density forms before transportation is an effective method to reduce transportation costs[ 31 , 32 ]. In addition, the Chinese government has introduced a series of policy measures in the face of the CO 2 emissions caused by straw burning, and local governments have also established targets for local straw utilization[ 33 – 35 ], particularly with some regions requiring that the comprehensive utilization rate of straw in the local area should reach more than 90%, and corresponding subsidy policies have been established to increase the motivation of farmers and enterprises to utilize straw. It is worth noting that the Chinese government, in response to current agricultural challenges, has introduced an agricultural cooperative model to address the current problems of fragmented rural crop cultivation and low utilization of agricultural equipment[ 36 , 37 ]. In 2019, the Chinese government recognized 25 exemplary agricultural cooperative models, of which 9 were designated as regional benchmarks. The integration of large-scale agricultural residue supply chains into these cooperative models has the potential to improve cost efficiency through optimized resource utilization and reduced transportation costs. In this study, we developed a straw supply chain model for bioethanol feedstock in China, addressing challenges associated with decentralized straw biomass collection. The model integrates collection, storage, pretreatment, and transportation stages. Two collection methods (manual and mechanical) and four pretreatment methods (baling, Kneading, Crushing-baling, and Pelletizing) are considered to optimize the supply chain cost in a typical Chinese region. The model's applicability is validated using crop residues from Heilongjiang Province and Zhejiang Province, representing northern and southern agricultural regions. Optimal supply chain scenarios are identified for these regions. Additionally, regional policy subsidies and agricultural cooperative models in China are integrated to reduce input costs in the studied regions. Materials and Methods Figure1 illustrates the methodological framework for this analysis, encompassing data analysis on agricultural residues suitable for cellulosic ethanol production in representative regions, the establishment of a supply chain model, and an assessment of suitability and feasibility in a typical agricultural region in China. Figure 1 Supply chain model construction of cellulosic ethanol plant feedstock straw, economic and environmental feasibility analysis process Estimating quantities of agricultural residues We selected two large agricultural provinces, Heilongjiang and Zhejiang, as representatives of the northern and northern regions of China, respectively, for assessing the potential agricultural residues available for cellulosic ethanol production. Data on annual crop production for eight common crops (rice, corn, and wheat) and five indigenous crops (oilseed rape and tobacco) were extracted from the 2023 statistical yearbooks of the selected provinces. The total straw yield in each region was determined based on the straw-to-grain ratio specific to each crop. All straw generated was assumed to be utilized off-site, considering losses during the supply chain, with a straw availability coefficient set at 0.85. Detailed calculations are provided in Appendix S1. Cellulosic ethanol feedstock supply chain model We analyze the straw supply chain, which serves as the primary source of raw material for cellulosic ethanol production, as illustrated in Fig. 1. The key stages include biomass collection, biomass pre-treatment (comprising storage site pre-treatment and comminution pre-treatment before cellulosic ethanol production), storage and transportation. Figure 2 offers comprehensive details for each component. Fig 2. The eight straw supply chain models established and the abbreviations therein A straw collection model (S2.1) was devised to determine the collection radius of straw mass suitable for cellulosic ethanol production in two specific regions. Moreover, the collected straw must be transported to a storage point for further preservation. Hence, it is essential to calculate the collection radius for the storage point as well. The collection radius was computed for storage stations of 10,000 t and 50,000 t capacities to facilitate a comparative analysis aimed at identifying the optimal storage station size. The biomass transportation model mainly consists of the quantification of fossil fuel consumption and the costs of the straw transportation process. In addition to the obvious relationship between fuel consumption and the distance of straw transportation, and vehicle transportation capacity, it is also affected by the way of straw pre-treatment. The cost required for vehicle operation, generally includes fuel costs, labor assistance costs, vehicle purchase costs, and repair and maintenance costs. In addition, in this study, since transportation involves large trucks that need to be involved in the loading and unloading process, the time loss and cost of the straw loading and unloading process were further quantified. We quantified the expenses associated with straw storage at the site, encompassing the stacking costs post-baling at the storage location and the supplementary stacking expenses following the transportation of straw in bulk to the collection and storage site during the harvest season. This study examines three primary pretreatment methods: Kneading, Crushing-baling, and Pelletizing. Biomass baling, produced using automatic pick-up balers, falls into four categories. Varied pretreatment methods can impact transportation, storage, and crushing procedures in cellulosic ethanol facilities. Our focus is on quantifying the costs and CO2 emissions associated with the pretreatment process. Assessment and analysis Costs and CO 2 emissions of straw supply processes During the straw collection process, we made four assumptions. Firstly, we assumed that the distance between each collection and storage point is negligible in a large number of points, although this was directly overlooked. Secondly, we assumed that the purchase price of straw is consistent between the southern and northern regions. Thirdly, we assumed that the distribution of straw is uniform in both regions and is not influenced by topographical factors. Lastly, we assumed the use of the same type of machinery for straw collection in both regions in the model, regardless of variations in regional subsidies for agricultural machinery. Subsequently, the costs of eight supply chain models, as depicted in Fig. 2, were quantified based on S2. These models were generated by varying the sizes of collection and storage stations in the two regions. Based on the CO 2 model constructed by S3, we quantified the CO 2 emissions of eight supply chain modes at the scale of 10000 t and 50000 t collection and storage station in the two regions, which provides a reference for the appropriate scale of cellulosic ethanol plant construction in the two regions under the comprehensive consideration of cost and environmental impact. Cost analysis of collection in subsidized areas Subsidy policies have been developed in various provinces of China to stimulate farmers and enterprises to utilize straw, but there are significant differences in subsidy standards. For Heilongjiang and Zhejiang, the subsidy policies are quite different, as Heilongjiang mainly subsidizes counties with major arable land in the Southeast and Northwest regions, and the subsidies have no standards and are based on land area, whereas Zhejiang Province subsidizes the utilization of straw across the whole province but sets the subsidy standards, and the detailed information is shown in S1.2. Therefore, we chose the circle range in Fig. S1 as the subsidized area for the analysis of straw supply chain, where we assumed that the distribution of straw in the circular area is uniform. We calculate the costs of the eight supply chain modes considering the subsidies, and provide appropriate supply chain models for the two regions, as well as a reference for other regions. Straw supply under the agricultural cooperative model Based on the current state of agricultural development in China, the Chinese government has promoted the establishment of agricultural cooperatives between regional enterprises and rural villages to collectivize Chinese agriculture and thereby increase the income of corporate farmers. We assume that the agricultural cooperative model is used to establish bases in villages and collect pre-treated straw using farmers' farm vehicles and equipment for collecting straw for making feed, etc. The company then buys the pre-treated straw directly from the bases in the countryside at a higher price without having to buy its equipment or collect the straw from the fields, but this process requires the company's trucks to transport it deeper into the villages, which adds a certain distance. Therefore, in our calculations, the transportation cost of the tractor in the field is excluded, the pretreatment cost is converted to the farmer's selling price of straw, the truck transportation distance is directly recorded as the distance from the field to the cellulosic ethanol plant, and the collection area is still the subsidized area of straw. Results Costs and CO 2 emissions of straw supply chain Heilongjiang and Zhejiang were selected as representative regions of northern and southern China, with their straw distributions detailed in Table S1 .1. Heilongjiang and Zhejiang have total straw productions of 9381 t and 719 t, respectively. Based on Eq S1, the straw density was determined as 50.57 t/km² for Heilongjiang and 18.02 t/km² for Zhejiang, which were used for subsequent cost and CO 2 emission calculations. Furthermore, since the cost for a 10,000 t storage station is lower compared to a 50,000 t station (as shown in the appendix ), a 10,000 t storage station size was selected. Cost analysis The primary constraint affecting the viability of the straw supply chain model is cost. This section examines the costs of eight supply chain models for Heilongjiang and Zhejiang, as illustrated in Fig. 3. For quantities below 10,000 t, the processes involve baling and transporting the straw to cellulosic ethanol plants, leading to two initial cost curves representing manual and mechanical collection. It is noteworthy that at the initial stage of the curve, the supply chain costs for manual and mechanical collection in both regions demonstrate contrasting trends as the collection radius expands. This disparity arises because, at lower collection volumes, manual collection necessitates only the adjustment of labor force, while mechanical collection entails higher expenditures on equipment procurement, which are amortized over increasing collection volumes. Ultimately, mechanical collection proves to be more economical than manual collection, with the point of intersection indicating the level at which their costs are equivalent. The intersection radius is below 5 km, implying that manual collection is more suitable for small-scale straw processing and utilization. Subsequently, starting from collection radius of 7 km in Heilongjiang and 11 km in Zhejiang, the costs of supply chain modes increase as the collection radius expands. The cost escalation for manual collection modes is notably higher compared to mechanical collection modes. This difference is mainly attributed to the low efficiency of manual collection, which stands at 3 t/day in this study, reflecting real-world conditions, and the high labor costs in China amounting to 200 yuan/day, as per research findings, and showing a continuous upward trend. Despite the potential for creating rural job opportunities, manual collection costs more than double that of mechanical collection, rendering it economically unviable. Among the eight supply chain modes evaluated, model VI emerges as the most cost-effective option for both Heilongjiang and Zhejiang regions. This model integrates mechanical collection (AC) with storage station crushing-baling (SBG), making it particularly suitable for cellulosic ethanol plants. Cellulosic ethanol production necessitates two pretreatment stages: biomass pulverization for particle size reduction, followed by cellulose extraction through either physicochemical or biological means. Models III and VI offer pre-crushed straw, enabling direct utilization for cellulose extraction post bale breaking, while other modes require additional crushing at the bioethanol facility, resulting in increased energy consumption and costs.However, as the collection radius increases, the cost of model VIII gradually becomes lower than that of model IV and approaches that of model VI, particularly in Zhejiang. This is mainly because the transportation cost of granulated pretreated straw is lower than that of other pretreatment types, and with increasing collection radius and transportation distance, the higher pretreatment and crushing costs are gradually offset by the lower transportation costs. At a 300 wt collection volume, model VIII becomes the lowest-cost option. In general, the cost trends of the straw supply chain models in both regions are generally similar, with costs below 400 yuan/t in Heilongjiang and below 450 yuan/t in Zhejiang. The difference in costs is primarily attributed to variations in straw distribution, as the lower straw density in Zhejiang leads to increased transportation distances. Specifically, within a 100 km collection radius, the lowest costs were 239.2 yuan/t in Heilongjiang and 253.1 yuan/t in Zhejiang, considering an 80 yuan/t straw purchase price. These findings highlight the effectiveness of the pretreatment and compression density strategies in reducing expenses related to long-distance transportation. Moreover, these pretreatment models offer a practical approach for regions to procure biomass raw materials according to local circumstances and availability, avoiding the high costs of indiscriminate biomass acquisition. However, there is still a problem to be noted here. Although mechanical collection is a more appropriate collection method, there are still some rural areas in China where manual collection is used, which will convert high collection cost into straw purchase price. Therefore, improving farmers 'mechanized production through machine purchase subsidies is a powerful means of straw utilization. Fig 3. Unit cost versus collection radius for eight straw supply chain models (a: Heilongjiang; b: Zhejiang) CO 2 emissions analysis In this section, we analyze the CO 2 emissions of eight supply chain modes following S3, as illustrated in Fig. 4 . Both regions exhibit a consistent upward trend with slight variations. Within a collection radius ranging from 20 km to 60 km, mode VII (pelleting) demonstrates the highest CO 2 emissions in both regions. This is primarily attributed to the substantial energy consumption during the pelleting process and the resultant CO 2 emissions. However, with an expanding collection radius, mode I (baling) gradually surpasses pelleting in terms of emissions. This shift occurs because the low density of baled straw diminishes truck loading capacity, necessitating more transportation trips and consequently leading to higher CO 2 emissions. Among the eight modes, mode IV stands out with the lowest emissions, contrasting mode VI, which boasts the lowest cost. This distinction arises from the ease of pulverization of kneaded pretreated straw in mode IV, eliminating the need for bale loading and unloading as required in mode VI. Consequently, mode IV exhibits reduced energy consumption and CO 2 emissions. Comparing the two regions, Zhejiang's straw supply chain notably emits a significantly higher amount of CO 2 compared to Heilongjiang. This disparity is primarily attributed to the more dispersed distribution of straw in Zhejiang. Despite employing similar pretreatment methods, the extended transportation distances in Zhejiang contribute to elevated CO 2 emissions, aligning with the findings of the cost analysis. This observation underscores the model's applicability across diverse regions in China. Additionally, this study examines China's global leadership in the new energy industry, focusing on new energy vehicles and new energy agricultural tractors. Despite advancements in various models of new energy agricultural tractors, the adoption of electric trucks remains limited. In S5, we analyze the costs and CO 2 emissions of the supply chain using electric tractors for straw transportation. Our findings indicate that electric tractors lead to a reduction in supply chain costs. The advantages of electric equipment are more noticeable for smaller collection volumes. For instance, at a collection radius of 100 km, costs in Heilongjiang and Zhejiang decrease by 5.3 y/t and 7.9 y/t, respectively, while CO 2 emissions decrease by 1.03 kg CO 2 /t and 1.77 kg CO 2 /t. This trend is primarily due to tractors playing a more significant role in transportation at smaller collection radius. However, as the collection radius expands, the reliance on truck transportation increases, diminishing the cost-saving and CO 2 emission benefits of electric tractors. The main costs and CO 2 emissions are associated with the truck transportation stage. The potential replacement of traditional trucks with electric or hydrogen-powered vehicles in the future could substantially contribute to cost and emission reductions. Given the short field transportation distances and the widespread distribution of biomass in China, emissions from field transportation are relatively low, thus limiting the potential economic and environmental advantages of electric tractors. Consequently, the development and deployment of electric trucks will play a pivotal role in significantly reducing biomass supply chain costs and CO 2 emissions in the future. In summary, considering both economic and environmental factors, processing straw as a bioethanol feedstock by direct crushing and baling at pretreatment bases in Heilongjiang and Zhejiang regions emerges as the optimal approach. This study focuses on the secondary pretreatment stage of a cellulosic ethanol plant, necessitating adjustments solely to the parameters related to ethanol in secondary pretreatment when evaluating biomass utilization for feed and paper production, among other applications. In conclusion, the establishment of appropriate straw supply chain models in regions like China, characterized by low straw distribution density, a large population, and abundant labor resources, holds significant importance. The biomass supply chain model proposed in this study, which integrates manual and mechanical access and compares various pretreatment methods, aligns effectively with the prevailing conditions in China.compares different pretreatment methods is undoubtedly in line with the actual situation in China. Straw supply chain costs with government subsidies Given the environmental impact of straw burning, the Chinese government has implemented straw utilization policies, and provincial governments have introduced corresponding subsidy policies. This chapter examines the impact of these policies on supply chain costs in Heilongjiang and Zhejiang, as outlined in Section 2.3.2. The cost implications of subsidized straw supply chain models are presented in Fig. 5. In Heilongjiang, upon the application of an 80 y/t subsidy, all supply chain costs within a 100 km storage radius decrease to below 300 y/t. Particularly, following the subsidy, the cost disparity between model IV and model VI in Heilongjiang diminishes, especially at smaller collection radius. As the subsidy does not influence CO2 emissions, model IV is deemed more favorable for low collection volumes, considering both cost and emissions. In Zhejiang, the subsidy policy involves a threshold: subsidies commence when straw utilization surpasses 1000 t, covering 50% of the costs, and escalate when utilization exceeds 10,000 t, with a maximum limit of 50,000 yuan. For the cellulosic ethanol industry, the straw supply chain constitutes less than 10% of total costs, but the Zhejiang government requires that over half of the subsidy be allocated to the supply chain. Here, we calculate the subsidy at an average of 50% of costs. Notably, all supply chain expenses in Zhejiang are under 250 yuan/t, lower than those in Heilongjiang, underscoring the significance of the policy for the straw industry's advancement. Despite similarities in trends between Zhejiang and Heilongjiang, model IV emerges as the preferred option for small-scale collection in terms of balancing costs and CO 2 emissions. With the aim of promoting straw utilization, most Chinese regions have introduced subsidy schemes, with the exception of Heilongjiang and Zhejiang. These subsidies play a pivotal role in facilitating straw processing and utilization. In the examined regions, the application of subsidies leads to the preference of model IV over model VI as the optimal supply chain framework for straw used in cellulosic ethanol production when the collection radius is below 50 km, taking into account both financial and environmental considerations. This further underscores the significance of our model, emphasizing the influence of biomass pretreatment methods on supply chain expenses. Figure 5. Eight supply chain models under subsidies (a: Heilongjiang; b: Zhejiang) Straw Supply Chain Costs in Agricultural Cooperatives Ethanol plants typically do not receive subsidies for purchasing biomass pretreatment equipment like crushers. Conversely, farmers stand to benefit from substantial subsidies, often exceeding 30% of the equipment's cost, if they acquire such machinery. This would lead to a notable decrease in supply chain expenses. Moreover, many farmers already possess similar equipment, such as crushers, negating the necessity for redundant acquisitions and further reducing costs. The Chinese government advocates for the formation of agricultural cooperatives to improve resource utilization, boost farmers' incomes, and cut down corporate expenditures. In this study, we introduces an agricultural cooperative model that integrates a straw supply chain, detailed in Section 2.3.3. The findings for Heilongjiang and Zhejiang, illustrated in Fig. 6 , exhibit significant cost savings. In Heilongjiang, the cost of each supply chain model within the cooperative framework drops below 250 y/t, with a maximum reduction of 70 y/t compared to conventional models. Similarly, in Zhejiang, the maximum cost reduction amounts to 65 y/t. These savings can be divided between enterprises and farmers, effectively raising farmers' incomes and stimulating straw supply. The cooperative model not only streamlines costs but also aligns with national objectives of sustainable resource utilization and rural economic advancement. In China, farmers in nearly all regions own their own agricultural vehicles, and many also possess crop harvesting and handling equipment, as well as feed processing facilities such as guillotine choppers, shredders, and pelletizers. By strategically integrating these existing resources into the straw supply chain model, significant cost reductions can be achieved. As outlined in this study, farmers can lease their equipment to cooperatives or directly participate in straw collection and processing, creating additional income streams. This cooperative model not only aligns with national goals of sustainable agricultural development, rural economic growth, and efficient resource utilization but also serves as a valuable reference for other regions in China. By leveraging existing resources and fostering collaboration between farmers and enterprises, this model provides a scalable and economically viable solution for straw utilization. Conclusion Accessing biomass feedstock poses a significant challenge for the ethanol industry, especially in regions such as China, where biomass distribution is decentralized. This study developed a comprehensive biomass supply chain model that includes collection, pretreatment, storage, and transportation stages. By taking into account two primary cost constraints - collection and pretreatment methods, the study assessed various combinations of two collecting methods and four pretreatment methods to facilitate cost-effective and low-emission access to biomass for cellulosic ethanol feedstock. The study identified the optimal supply chain model for Heilongjiang and Zhejiang, representing northern and southern China, respectively, focusing on cost minimization and CO 2 emissions reduction. In these regions, employing mechanical collection followed by baling after shredding demonstrated the lowest cost at 226.6 yuan per ton and 217.7 yuan per ton within a collection radius of 60 km. Furthermore, utilizing mechanical collection followed by kneading showed the lowest CO 2 emissions at 21.48 kgCO 2 /t straw and 35.01 kgCO 2 /t straw in Heilongjiang and Zhejiang, respectively. Heilongjiang achieved lower costs and emissions due to its high straw distribution density. In Heilongjiang, the current subsidy policy provides 80 y/t, while in Zhejiang, it covers half of that amount, favoring the mechanical collection - kneading combination for reduced emissions and CO 2 emissions. Taking into account the existing agricultural cooperative model in China, cost savings can reach up to 70 y/t and 65 y/t in Heilongjiang and Zhejiang, respectively. The biomass supply chain model developed in this study enables cost-effective access to straw, a cellulosic ethanol feedstock, from various regions through decentralized biomass distribution in China. Emphasizing the combination of collection and pretreatment methods as crucial research aspects allows for locally tailored biomass supply solutions across different regions. Nevertheless, selecting the suitable combination of collection and pretreatment methods is essential, considering the varying distribution of biomass across regions, and should be based on empirical data prior to implementation. Declarations Funding This work was partially funded by the National Key Research and Development Program of China (2022YFB4201901). Data Availability The datasets generated during and/or analysed during the current study are available from the corresponding author on reasonable request Author Contributions Changliu He: Conceptualization, Methodology, Investigation, Formal analysis, Data curation, Writing - original draft. Xi Zhao: Supervision, Writing - review & editing. Lei Zheng: Supervision, Writing - review & editing. Jiayu Xin: Supervision, Writing - review & editing. 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Sustainability 11(6). https://doi.org/10.3390/su11061762 Xinxin L, Zuliang S, Jiuchen W, Rongfeng J Review on the Crop Straw Utilization Technology of China. ate 2020. https://doi.org/10.11648/j.ajese.20200404.12 Wang Y-j, Bi Y-y, Gao C-y (2010) The Assessment and Utilization of Straw Resources in China. Agricultural Sci China 9(12):1807–1815. https://doi.org/10.1016/S1671-2927(09)60279-0 Hong J, Ren L, Hong J, Xu C (2016) Environmental impact assessment of corn straw utilization in China. J Clean Prod 112:1700–1708. https://doi.org/10.1016/j.jclepro.2015.02.081 Guo L, Wang D, Tabil LG, Wang G (2016) Compression and relaxation properties of selected biomass for briquetting. Biosyst Eng 148:101–110. https://doi.org/10.1016/j.biosystemseng.2016.05.009 Poddar S, Kamruzzaman M, Sujan SMA, Hossain M, Jamal MS, Gafur MA, Khanam M (2014) Effect of compression pressure on lignocellulosic biomass pellet to improve fuel properties: Higher heating value. Fuel 131:43–48. https://doi.org/10.1016/j.fuel.2014.04.061 Sun D, Ge Y, Zhou Y (2019) Punishing and rewarding: How do policy measures affect crop straw use by farmers? An empirical analysis of Jiangsu Province of China. Energy Policy 134:110882. https://doi.org/10.1016/j.enpol.2019.110882 Huang L, Zhu Y, Wang Q, Zhu A, Liu Z, Wang Y, Allen DT, Li L (2021) Assessment of the effects of straw burning bans in China: Emissions, air quality, and health impacts. Sci Total Environ 789:147935. https://doi.org/10.1016/j.scitotenv.2021.147935 Li H, Miao Z, Zhang B (2022) Review and prospect of comprehensive straw utilization and government policy in China. Chin J Popul Resour Environ 20(4):402–406. https://doi.org/10.1016/j.cjpre.2022.11.010 Zheng S, Wang Z, Awokuse TO (2012) Determinants of Producers' Participation in Agricultural Cooperatives: Evidence from Northern China. Appl Economic Perspect Policy 34(1):167–186. https://doi.org/10.1093/aepp/ppr044 Zhong Z, Jiang W, Li Y (2023) Bridging the gap between smallholders and modern agriculture: Full insight into China's agricultural cooperatives. J Rural Stud 101:103037. https://doi.org/10.1016/j.jrurstud.2023.103037 Supplementary Files AbstractGraph.docx SupportingInformation.docx Cite Share Download PDF Status: Published Journal Publication published 20 Jun, 2025 Read the published version in BioEnergy Research → Version 1 posted Editorial decision: Accept as is 18 May, 2025 Reviewers agreed at journal 19 Apr, 2025 Reviewers invited by journal 19 Apr, 2025 Editor assigned by journal 31 Mar, 2025 First submitted to journal 21 Mar, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-5446256","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":445212541,"identity":"d57fd093-a7ed-4d11-b3dc-82c346af73a0","order_by":0,"name":"Changliu He","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA/UlEQVRIiWNgGAWjYBACfmbmww8kDCTs2JiZj0HFEvBrkWxvSzOwqLBJ5mNnSwPyDQhrMThzxkCi4kwa4zx+HjPitDDcSDAwuNl2mJmNmefbY56aPwz87DkGDD934NbBOCMh4eHMtsN8bMy82415jhkwSPa8MWDsPYNbC7NEwgFjSbAtvNukeRsMGAxu5BgwM7bh1sImkdgg/bftMGMbM88zsBZ7Qlp4eA4zSEgAvQ/UwgaxRYKAFgn2NjZgkNkkszGzmRvOOWbMI3HmWcHBXjxa7A/zfwZHpXz/4WcP3tTIyfG3J2988BOPFkyXgogDJGgYBaNgFIyCUYAFAADGf0jP81Ra8gAAAABJRU5ErkJggg==","orcid":"","institution":"Beijing University of Chemical Technology","correspondingAuthor":true,"prefix":"","firstName":"Changliu","middleName":"","lastName":"He","suffix":""},{"id":445212542,"identity":"3bda9454-7a20-455d-b361-1722bd251018","order_by":1,"name":"Xi Zhao","email":"","orcid":"","institution":"Beijing University of Chemical Technology","correspondingAuthor":false,"prefix":"","firstName":"Xi","middleName":"","lastName":"Zhao","suffix":""},{"id":445212543,"identity":"2838d987-0e9d-466e-ad8f-6e8adb38ccd9","order_by":2,"name":"Lei Zheng","email":"","orcid":"","institution":"Beijing University of Chemical Technology","correspondingAuthor":false,"prefix":"","firstName":"Lei","middleName":"","lastName":"Zheng","suffix":""},{"id":445212544,"identity":"f0fe9c75-7eec-4e2c-9c6d-d50abb98650c","order_by":3,"name":"Jiayu Xin","email":"","orcid":"","institution":"Beijing University of Chemical Technology","correspondingAuthor":false,"prefix":"","firstName":"Jiayu","middleName":"","lastName":"Xin","suffix":""},{"id":445212545,"identity":"b00329ea-c34c-4eb9-9b45-a067a531a4fb","order_by":4,"name":"Huimin Yun","email":"","orcid":"","institution":"Beijing University of Chemical Technology","correspondingAuthor":false,"prefix":"","firstName":"Huimin","middleName":"","lastName":"Yun","suffix":""},{"id":445212546,"identity":"74e66b28-7fa8-4223-910f-f0d58e88207a","order_by":5,"name":"Xu Zhang","email":"","orcid":"","institution":"Beijing University of Chemical Technology","correspondingAuthor":false,"prefix":"","firstName":"Xu","middleName":"","lastName":"Zhang","suffix":""}],"badges":[],"createdAt":"2024-11-13 10:40:14","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5446256/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5446256/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s12155-025-10854-8","type":"published","date":"2025-06-20T15:57:06+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":81231382,"identity":"590b9214-3a2e-4297-9c36-924b555bfa56","added_by":"auto","created_at":"2025-04-23 17:52:42","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":89347,"visible":true,"origin":"","legend":"\u003cp\u003eSupply chain model construction of cellulosic ethanol plant feedstock straw, economic and environmental feasibility analysis process\u003c/p\u003e","description":"","filename":"Picture1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5446256/v1/001c0a26840264c9f77ae17a.jpg"},{"id":81231385,"identity":"28293b0c-b18c-40e8-94b0-8aec54ee81fe","added_by":"auto","created_at":"2025-04-23 17:52:42","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":112735,"visible":true,"origin":"","legend":"\u003cp\u003eThe eight straw supply chain models established and the abbreviations therein\u003c/p\u003e","description":"","filename":"Picture2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5446256/v1/aec43a31cc01d10cb3ebe20d.jpg"},{"id":81231381,"identity":"4e1c44a1-d248-4770-a37d-094d1b6fb809","added_by":"auto","created_at":"2025-04-23 17:52:42","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":65926,"visible":true,"origin":"","legend":"\u003cp\u003eUnit cost versus collection radius for eight straw supply chain models (a: Heilongjiang; b: Zhejiang)\u003c/p\u003e","description":"","filename":"Picture3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5446256/v1/eb196d8ae5303a0c5f4f2c46.jpg"},{"id":81232289,"identity":"32e0208b-c038-4b9d-a4ee-f4da2982e2c2","added_by":"auto","created_at":"2025-04-23 18:00:42","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":67302,"visible":true,"origin":"","legend":"\u003cp\u003eRelationship between CO\u003csub\u003e2\u003c/sub\u003e emissions and collection radius for eight straw supply chain models (a: Heilongjiang; b: Zhejiang)\u003c/p\u003e","description":"","filename":"Picture4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5446256/v1/3e3689bcdbc4f4020ecf90dd.jpg"},{"id":81231384,"identity":"5aef7201-45a4-4eef-b543-d06e1701d183","added_by":"auto","created_at":"2025-04-23 17:52:42","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":72184,"visible":true,"origin":"","legend":"\u003cp\u003eEight supply chain models under subsidies (a: Heilongjiang; b: Zhejiang)\u003c/p\u003e","description":"","filename":"Picture5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5446256/v1/8381d1e69d311f04d46ef6e0.jpg"},{"id":81231387,"identity":"4a1cabc0-d928-471b-bcf4-7a9a37028991","added_by":"auto","created_at":"2025-04-23 17:52:42","extension":"jpg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":72184,"visible":true,"origin":"","legend":"\u003cp\u003eCosts of eight supply chain models under the agricultural cooperative model (a: Heilongjiang; b: Zhejiang)\u003c/p\u003e","description":"","filename":"Picture6.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5446256/v1/3a29b3c9c77b1cb703bf7b2d.jpg"},{"id":85231288,"identity":"e5b27bbc-c844-4f43-846d-6a15ea32d7cb","added_by":"auto","created_at":"2025-06-23 16:04:50","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1109021,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5446256/v1/c98ee83e-7275-4b82-af84-f42b641cfb5a.pdf"},{"id":81231383,"identity":"b950e0ae-b135-4aa9-af16-3169402d63ef","added_by":"auto","created_at":"2025-04-23 17:52:42","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":127539,"visible":true,"origin":"","legend":"","description":"","filename":"AbstractGraph.docx","url":"https://assets-eu.researchsquare.com/files/rs-5446256/v1/50fd694d18d9828271082235.docx"},{"id":81231394,"identity":"f66f90d2-36d2-4eac-b702-93fe9fd2a847","added_by":"auto","created_at":"2025-04-23 17:52:43","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":2609602,"visible":true,"origin":"","legend":"","description":"","filename":"SupportingInformation.docx","url":"https://assets-eu.researchsquare.com/files/rs-5446256/v1/9e0c9c0bff79b9a4a1514281.docx"}],"financialInterests":"","formattedTitle":"Optimizing Collection and Pretreatment Methods for Cost-Effective and Low-CO 2 Emission Biomass Supply Chains","fulltext":[{"header":"Introduction","content":"\u003cp\u003eAs global fossil energy consumption rises, worsening climate change, biofuels have become an essential alternative to fossil fuels in the future[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Ethanol has been used as a partial substitute for gasoline in several regions to alleviate energy shortages[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Utilizing lignocellulose (agricultural and forestry residues, energy grasses) as raw material not only avoids the food competition problem in the first-generation biorefineries but also brings a certain positive impact on CO\u003csub\u003e2\u003c/sub\u003e emission reduction[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Over the past decade, extensive research has been conducted on developing technologies for producing cellulosic ethanol from lignocellulosic materials, notably focusing on the dilute acid pretreatment process for cellulosic ethanol production developed by the National Renewable Energy Laboratory (NREL)[\u003cspan additionalcitationids=\"CR8\" citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. To meet the demand for second-generation cellulosic ethanol feedstock, large-scale farms, exemplified by the United States, have shifted to cultivating energy crops to ease energy demand pressures[\u003cspan additionalcitationids=\"CR11\" citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. However, in China, where there is high food demand and limited cultivation of energy crops, the primary feedstock for second-generation cellulosic ethanol remains agricultural waste. Moreover, China's intricate topography results in decentralized distribution of agricultural waste, leading to challenges in its collection, storage, and transportation[\u003cspan additionalcitationids=\"CR14 CR15\" citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. Consequently, bioethanol plants in China are predominantly small-scale, relying on nearby agricultural residues as feedstock.\u003c/p\u003e \u003cp\u003eInternationally, appropriate biomass supply chain models have been developed by several studies for different countries and regions focusing on collection, transportation, storage, and pretreatment[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Teresa et al. created a biomass supply chain model for northern Sweden to assess the costs and CO\u003csub\u003e2\u003c/sub\u003e emissions of harvesting forest residues and transporting them to fuel plants, which is a common model. In a different study[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. R. L. Machado et al. designed a bioethanol supply chain model for sugarcane cultivation, ethanol processing, storage, transportation, and distribution to meet market demands. Their approach optimized cultivation areas and sizes to reduce transportation distances and enhance cost efficiency. However, this model is not suitable for China due to its reliance on decentralized straw as a biomass feedstock, unlike Brazil's centralized sugarcane cultivation[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Kapil M. Gumte et al. developed a comprehensive biomass supply chain model for India, covering all supply chain stages, with a focus on analyzing the cost implications of rail and road transportation modes. However, rail transportation of biomass remains challenging to implement in China at present[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. Previous studies in China have mainly focused on straw acquisition in specific small areas, lacking suitable supply chain models for high-demand bioethanol feedstocks[\u003cspan additionalcitationids=\"CR23\" citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. The decentralized and regionally varying distribution of agricultural residues in China underscores the need for a supply chain model with broad applicability. Enhancing vehicle transportation capacity is a key strategy in addressing long-distance transportation challenges arising from dispersed distribution[\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. In rural China, to increase the transportation efficiency, farmers typically utilize baling equipment to compress the straw density[\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. Particularly, for feed mills requiring larger quantities of straw, the straw is compacted in rural areas into high-density large square bales before transport.[\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. Indeed, preprocessing biomass into high-density forms before transportation is an effective method to reduce transportation costs[\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. In addition, the Chinese government has introduced a series of policy measures in the face of the CO\u003csub\u003e2\u003c/sub\u003e emissions caused by straw burning, and local governments have also established targets for local straw utilization[\u003cspan additionalcitationids=\"CR34\" citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e], particularly with some regions requiring that the comprehensive utilization rate of straw in the local area should reach more than 90%, and corresponding subsidy policies have been established to increase the motivation of farmers and enterprises to utilize straw.\u003c/p\u003e \u003cp\u003eIt is worth noting that the Chinese government, in response to current agricultural challenges, has introduced an agricultural cooperative model to address the current problems of fragmented rural crop cultivation and low utilization of agricultural equipment[\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. In 2019, the Chinese government recognized 25 exemplary agricultural cooperative models, of which 9 were designated as regional benchmarks. The integration of large-scale agricultural residue supply chains into these cooperative models has the potential to improve cost efficiency through optimized resource utilization and reduced transportation costs.\u003c/p\u003e \u003cp\u003eIn this study, we developed a straw supply chain model for bioethanol feedstock in China, addressing challenges associated with decentralized straw biomass collection. The model integrates collection, storage, pretreatment, and transportation stages. Two collection methods (manual and mechanical) and four pretreatment methods (baling, Kneading, Crushing-baling, and Pelletizing) are considered to optimize the supply chain cost in a typical Chinese region. The model's applicability is validated using crop residues from Heilongjiang Province and Zhejiang Province, representing northern and southern agricultural regions. Optimal supply chain scenarios are identified for these regions. Additionally, regional policy subsidies and agricultural cooperative models in China are integrated to reduce input costs in the studied regions.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cp\u003e \u003c/p\u003e \u003cp\u003eFigure1 illustrates the methodological framework for this analysis, encompassing data analysis on agricultural residues suitable for cellulosic ethanol production in representative regions, the establishment of a supply chain model, and an assessment of suitability and feasibility in a typical agricultural region in China.\u003c/p\u003e \u003cp\u003eFigure\u0026nbsp;1 Supply chain model construction of cellulosic ethanol plant feedstock straw, economic and environmental feasibility analysis process\u003c/p\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eEstimating quantities of agricultural residues\u003c/h2\u003e \u003cp\u003eWe selected two large agricultural provinces, Heilongjiang and Zhejiang, as representatives of the northern and northern regions of China, respectively, for assessing the potential agricultural residues available for cellulosic ethanol production. Data on annual crop production for eight common crops (rice, corn, and wheat) and five indigenous crops (oilseed rape and tobacco) were extracted from the 2023 statistical yearbooks of the selected provinces. The total straw yield in each region was determined based on the straw-to-grain ratio specific to each crop. All straw generated was assumed to be utilized off-site, considering losses during the supply chain, with a straw availability coefficient set at 0.85. Detailed calculations are provided in \u003cspan refid=\"Sec16\" class=\"InternalRef\"\u003eAppendix\u003c/span\u003e S1.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eCellulosic ethanol feedstock supply chain model\u003c/h3\u003e\n\u003cp\u003eWe analyze the straw supply chain, which serves as the primary source of raw material for cellulosic ethanol production, as illustrated in Fig.\u0026nbsp;1. The key stages include biomass collection, biomass pre-treatment (comprising storage site pre-treatment and comminution pre-treatment before cellulosic ethanol production), storage and transportation. Figure\u0026nbsp;2 offers comprehensive details for each component.\u003c/p\u003e \u003cp\u003e Fig 2. The eight straw supply chain models established and the abbreviations therein\u003c/p\u003e \u003cp\u003eA straw collection model (S2.1) was devised to determine the collection radius of straw mass suitable for cellulosic ethanol production in two specific regions. Moreover, the collected straw must be transported to a storage point for further preservation. Hence, it is essential to calculate the collection radius for the storage point as well. The collection radius was computed for storage stations of 10,000 t and 50,000 t capacities to facilitate a comparative analysis aimed at identifying the optimal storage station size.\u003c/p\u003e \u003cp\u003eThe biomass transportation model mainly consists of the quantification of fossil fuel consumption and the costs of the straw transportation process. In addition to the obvious relationship between fuel consumption and the distance of straw transportation, and vehicle transportation capacity, it is also affected by the way of straw pre-treatment. The cost required for vehicle operation, generally includes fuel costs, labor assistance costs, vehicle purchase costs, and repair and maintenance costs. In addition, in this study, since transportation involves large trucks that need to be involved in the loading and unloading process, the time loss and cost of the straw loading and unloading process were further quantified.\u003c/p\u003e \u003cp\u003eWe quantified the expenses associated with straw storage at the site, encompassing the stacking costs post-baling at the storage location and the supplementary stacking expenses following the transportation of straw in bulk to the collection and storage site during the harvest season.\u003c/p\u003e \u003cp\u003eThis study examines three primary pretreatment methods: Kneading, Crushing-baling, and Pelletizing. Biomass baling, produced using automatic pick-up balers, falls into four categories. Varied pretreatment methods can impact transportation, storage, and crushing procedures in cellulosic ethanol facilities. Our focus is on quantifying the costs and CO2 emissions associated with the pretreatment process.\u003c/p\u003e\n\u003ch3\u003eAssessment and analysis\u003c/h3\u003e\n\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eCosts and CO\u003csub\u003e2\u003c/sub\u003e emissions of straw supply processes\u003c/h2\u003e \u003cp\u003eDuring the straw collection process, we made four assumptions. Firstly, we assumed that the distance between each collection and storage point is negligible in a large number of points, although this was directly overlooked. Secondly, we assumed that the purchase price of straw is consistent between the southern and northern regions. Thirdly, we assumed that the distribution of straw is uniform in both regions and is not influenced by topographical factors. Lastly, we assumed the use of the same type of machinery for straw collection in both regions in the model, regardless of variations in regional subsidies for agricultural machinery. Subsequently, the costs of eight supply chain models, as depicted in Fig.\u0026nbsp;2, were quantified based on S2. These models were generated by varying the sizes of collection and storage stations in the two regions.\u003c/p\u003e \u003cp\u003eBased on the CO\u003csub\u003e2\u003c/sub\u003e model constructed by S3, we quantified the CO\u003csub\u003e2\u003c/sub\u003e emissions of eight supply chain modes at the scale of 10000 t and 50000 t collection and storage station in the two regions, which provides a reference for the appropriate scale of cellulosic ethanol plant construction in the two regions under the comprehensive consideration of cost and environmental impact.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eCost analysis of collection in subsidized areas\u003c/h3\u003e\n\u003cp\u003eSubsidy policies have been developed in various provinces of China to stimulate farmers and enterprises to utilize straw, but there are significant differences in subsidy standards. For Heilongjiang and Zhejiang, the subsidy policies are quite different, as Heilongjiang mainly subsidizes counties with major arable land in the Southeast and Northwest regions, and the subsidies have no standards and are based on land area, whereas Zhejiang Province subsidizes the utilization of straw across the whole province but sets the subsidy standards, and the detailed information is shown in S1.2. Therefore, we chose the circle range in Fig. \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e as the subsidized area for the analysis of straw supply chain, where we assumed that the distribution of straw in the circular area is uniform. We calculate the costs of the eight supply chain modes considering the subsidies, and provide appropriate supply chain models for the two regions, as well as a reference for other regions.\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eStraw supply under the agricultural cooperative model\u003c/h2\u003e \u003cp\u003eBased on the current state of agricultural development in China, the Chinese government has promoted the establishment of agricultural cooperatives between regional enterprises and rural villages to collectivize Chinese agriculture and thereby increase the income of corporate farmers. We assume that the agricultural cooperative model is used to establish bases in villages and collect pre-treated straw using farmers' farm vehicles and equipment for collecting straw for making feed, etc. The company then buys the pre-treated straw directly from the bases in the countryside at a higher price without having to buy its equipment or collect the straw from the fields, but this process requires the company's trucks to transport it deeper into the villages, which adds a certain distance. Therefore, in our calculations, the transportation cost of the tractor in the field is excluded, the pretreatment cost is converted to the farmer's selling price of straw, the truck transportation distance is directly recorded as the distance from the field to the cellulosic ethanol plant, and the collection area is still the subsidized area of straw.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eCosts and CO\u003csub\u003e2\u003c/sub\u003e emissions of straw supply chain\u003c/h2\u003e \u003cp\u003eHeilongjiang and Zhejiang were selected as representative regions of northern and southern China, with their straw distributions detailed in Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e.1. Heilongjiang and Zhejiang have total straw productions of 9381 t and 719 t, respectively. Based on Eq S1, the straw density was determined as 50.57 t/km\u0026sup2; for Heilongjiang and 18.02 t/km\u0026sup2; for Zhejiang, which were used for subsequent cost and CO\u003csub\u003e2\u003c/sub\u003e emission calculations. Furthermore, since the cost for a 10,000 t storage station is lower compared to a 50,000 t station (as shown in the \u003cspan refid=\"Sec16\" class=\"InternalRef\"\u003eappendix\u003c/span\u003e), a 10,000 t storage station size was selected.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eCost analysis\u003c/h2\u003e \u003cp\u003eThe primary constraint affecting the viability of the straw supply chain model is cost. This section examines the costs of eight supply chain models for Heilongjiang and Zhejiang, as illustrated in Fig.\u0026nbsp;3. For quantities below 10,000 t, the processes involve baling and transporting the straw to cellulosic ethanol plants, leading to two initial cost curves representing manual and mechanical collection. It is noteworthy that at the initial stage of the curve, the supply chain costs for manual and mechanical collection in both regions demonstrate contrasting trends as the collection radius expands. This disparity arises because, at lower collection volumes, manual collection necessitates only the adjustment of labor force, while mechanical collection entails higher expenditures on equipment procurement, which are amortized over increasing collection volumes. Ultimately, mechanical collection proves to be more economical than manual collection, with the point of intersection indicating the level at which their costs are equivalent. The intersection radius is below 5 km, implying that manual collection is more suitable for small-scale straw processing and utilization.\u003c/p\u003e \u003cp\u003eSubsequently, starting from collection radius of 7 km in Heilongjiang and 11 km in Zhejiang, the costs of supply chain modes increase as the collection radius expands. The cost escalation for manual collection modes is notably higher compared to mechanical collection modes. This difference is mainly attributed to the low efficiency of manual collection, which stands at 3 t/day in this study, reflecting real-world conditions, and the high labor costs in China amounting to 200 yuan/day, as per research findings, and showing a continuous upward trend. Despite the potential for creating rural job opportunities, manual collection costs more than double that of mechanical collection, rendering it economically unviable. Among the eight supply chain modes evaluated, model VI emerges as the most cost-effective option for both Heilongjiang and Zhejiang regions. This model integrates mechanical collection (AC) with storage station crushing-baling (SBG), making it particularly suitable for cellulosic ethanol plants. Cellulosic ethanol production necessitates two pretreatment stages: biomass pulverization for particle size reduction, followed by cellulose extraction through either physicochemical or biological means. Models III and VI offer pre-crushed straw, enabling direct utilization for cellulose extraction post bale breaking, while other modes require additional crushing at the bioethanol facility, resulting in increased energy consumption and costs.However, as the collection radius increases, the cost of model VIII gradually becomes lower than that of model IV and approaches that of model VI, particularly in Zhejiang. This is mainly because the transportation cost of granulated pretreated straw is lower than that of other pretreatment types, and with increasing collection radius and transportation distance, the higher pretreatment and crushing costs are gradually offset by the lower transportation costs. At a 300 wt collection volume, model VIII becomes the lowest-cost option.\u003c/p\u003e \u003cp\u003eIn general, the cost trends of the straw supply chain models in both regions are generally similar, with costs below 400 yuan/t in Heilongjiang and below 450 yuan/t in Zhejiang. The difference in costs is primarily attributed to variations in straw distribution, as the lower straw density in Zhejiang leads to increased transportation distances. Specifically, within a 100 km collection radius, the lowest costs were 239.2 yuan/t in Heilongjiang and 253.1 yuan/t in Zhejiang, considering an 80 yuan/t straw purchase price. These findings highlight the effectiveness of the pretreatment and compression density strategies in reducing expenses related to long-distance transportation. Moreover, these pretreatment models offer a practical approach for regions to procure biomass raw materials according to local circumstances and availability, avoiding the high costs of indiscriminate biomass acquisition. However, there is still a problem to be noted here. Although mechanical collection is a more appropriate collection method, there are still some rural areas in China where manual collection is used, which will convert high collection cost into straw purchase price. Therefore, improving farmers 'mechanized production through machine purchase subsidies is a powerful means of straw utilization.\u003c/p\u003e \u003cp\u003e Fig 3. Unit cost versus collection radius for eight straw supply chain models (a: Heilongjiang; b: Zhejiang)\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eCO\u003csub\u003e2\u003c/sub\u003e emissions analysis\u003c/h2\u003e \u003cp\u003eIn this section, we analyze the CO\u003csub\u003e2\u003c/sub\u003e emissions of eight supply chain modes following S3, as illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e4\u003c/span\u003e. Both regions exhibit a consistent upward trend with slight variations. Within a collection radius ranging from 20 km to 60 km, mode VII (pelleting) demonstrates the highest CO\u003csub\u003e2\u003c/sub\u003e emissions in both regions. This is primarily attributed to the substantial energy consumption during the pelleting process and the resultant CO\u003csub\u003e2\u003c/sub\u003e emissions. However, with an expanding collection radius, mode I (baling) gradually surpasses pelleting in terms of emissions. This shift occurs because the low density of baled straw diminishes truck loading capacity, necessitating more transportation trips and consequently leading to higher CO\u003csub\u003e2\u003c/sub\u003e emissions. Among the eight modes, mode IV stands out with the lowest emissions, contrasting mode VI, which boasts the lowest cost. This distinction arises from the ease of pulverization of kneaded pretreated straw in mode IV, eliminating the need for bale loading and unloading as required in mode VI. Consequently, mode IV exhibits reduced energy consumption and CO\u003csub\u003e2\u003c/sub\u003e emissions. Comparing the two regions, Zhejiang's straw supply chain notably emits a significantly higher amount of CO\u003csub\u003e2\u003c/sub\u003e compared to Heilongjiang. This disparity is primarily attributed to the more dispersed distribution of straw in Zhejiang. Despite employing similar pretreatment methods, the extended transportation distances in Zhejiang contribute to elevated CO\u003csub\u003e2\u003c/sub\u003e emissions, aligning with the findings of the cost analysis. This observation underscores the model's applicability across diverse regions in China.\u003c/p\u003e \u003cp\u003eAdditionally, this study examines China's global leadership in the new energy industry, focusing on new energy vehicles and new energy agricultural tractors. Despite advancements in various models of new energy agricultural tractors, the adoption of electric trucks remains limited. In S5, we analyze the costs and CO\u003csub\u003e2\u003c/sub\u003e emissions of the supply chain using electric tractors for straw transportation. Our findings indicate that electric tractors lead to a reduction in supply chain costs. The advantages of electric equipment are more noticeable for smaller collection volumes. For instance, at a collection radius of 100 km, costs in Heilongjiang and Zhejiang decrease by 5.3 y/t and 7.9 y/t, respectively, while CO\u003csub\u003e2\u003c/sub\u003e emissions decrease by 1.03 kg CO\u003csub\u003e2\u003c/sub\u003e/t and 1.77 kg CO\u003csub\u003e2\u003c/sub\u003e/t. This trend is primarily due to tractors playing a more significant role in transportation at smaller collection radius. However, as the collection radius expands, the reliance on truck transportation increases, diminishing the cost-saving and CO\u003csub\u003e2\u003c/sub\u003e emission benefits of electric tractors. The main costs and CO\u003csub\u003e2\u003c/sub\u003e emissions are associated with the truck transportation stage. The potential replacement of traditional trucks with electric or hydrogen-powered vehicles in the future could substantially contribute to cost and emission reductions.\u003c/p\u003e \u003cp\u003eGiven the short field transportation distances and the widespread distribution of biomass in China, emissions from field transportation are relatively low, thus limiting the potential economic and environmental advantages of electric tractors. Consequently, the development and deployment of electric trucks will play a pivotal role in significantly reducing biomass supply chain costs and CO\u003csub\u003e2\u003c/sub\u003e emissions in the future. In summary, considering both economic and environmental factors, processing straw as a bioethanol feedstock by direct crushing and baling at pretreatment bases in Heilongjiang and Zhejiang regions emerges as the optimal approach. This study focuses on the secondary pretreatment stage of a cellulosic ethanol plant, necessitating adjustments solely to the parameters related to ethanol in secondary pretreatment when evaluating biomass utilization for feed and paper production, among other applications. In conclusion, the establishment of appropriate straw supply chain models in regions like China, characterized by low straw distribution density, a large population, and abundant labor resources, holds significant importance. The biomass supply chain model proposed in this study, which integrates manual and mechanical access and compares various pretreatment methods, aligns effectively with the prevailing conditions in China.compares different pretreatment methods is undoubtedly in line with the actual situation in China.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eStraw supply chain costs with government subsidies\u003c/h2\u003e \u003cp\u003eGiven the environmental impact of straw burning, the Chinese government has implemented straw utilization policies, and provincial governments have introduced corresponding subsidy policies. This chapter examines the impact of these policies on supply chain costs in Heilongjiang and Zhejiang, as outlined in Section 2.3.2. The cost implications of subsidized straw supply chain models are presented in Fig.\u0026nbsp;5. In Heilongjiang, upon the application of an 80 y/t subsidy, all supply chain costs within a 100 km storage radius decrease to below 300 y/t. Particularly, following the subsidy, the cost disparity between model IV and model VI in Heilongjiang diminishes, especially at smaller collection radius. As the subsidy does not influence CO2 emissions, model IV is deemed more favorable for low collection volumes, considering both cost and emissions. In Zhejiang, the subsidy policy involves a threshold: subsidies commence when straw utilization surpasses 1000 t, covering 50% of the costs, and escalate when utilization exceeds 10,000 t, with a maximum limit of 50,000 yuan. For the cellulosic ethanol industry, the straw supply chain constitutes less than 10% of total costs, but the Zhejiang government requires that over half of the subsidy be allocated to the supply chain. Here, we calculate the subsidy at an average of 50% of costs. Notably, all supply chain expenses in Zhejiang are under 250 yuan/t, lower than those in Heilongjiang, underscoring the significance of the policy for the straw industry's advancement. Despite similarities in trends between Zhejiang and Heilongjiang, model IV emerges as the preferred option for small-scale collection in terms of balancing costs and CO\u003csub\u003e2\u003c/sub\u003e emissions.\u003c/p\u003e \u003cp\u003eWith the aim of promoting straw utilization, most Chinese regions have introduced subsidy schemes, with the exception of Heilongjiang and Zhejiang. These subsidies play a pivotal role in facilitating straw processing and utilization. In the examined regions, the application of subsidies leads to the preference of model IV over model VI as the optimal supply chain framework for straw used in cellulosic ethanol production when the collection radius is below 50 km, taking into account both financial and environmental considerations. This further underscores the significance of our model, emphasizing the influence of biomass pretreatment methods on supply chain expenses.\u003c/p\u003e \u003cp\u003eFigure\u0026nbsp;5. Eight supply chain models under subsidies (a: Heilongjiang; b: Zhejiang)\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eStraw Supply Chain Costs in Agricultural Cooperatives\u003c/h2\u003e \u003cp\u003eEthanol plants typically do not receive subsidies for purchasing biomass pretreatment equipment like crushers. Conversely, farmers stand to benefit from substantial subsidies, often exceeding 30% of the equipment's cost, if they acquire such machinery. This would lead to a notable decrease in supply chain expenses. Moreover, many farmers already possess similar equipment, such as crushers, negating the necessity for redundant acquisitions and further reducing costs. The Chinese government advocates for the formation of agricultural cooperatives to improve resource utilization, boost farmers' incomes, and cut down corporate expenditures. In this study, we introduces an agricultural cooperative model that integrates a straw supply chain, detailed in Section 2.3.3. The findings for Heilongjiang and Zhejiang, illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e6\u003c/span\u003e, exhibit significant cost savings. In Heilongjiang, the cost of each supply chain model within the cooperative framework drops below 250 y/t, with a maximum reduction of 70 y/t compared to conventional models. Similarly, in Zhejiang, the maximum cost reduction amounts to 65 y/t. These savings can be divided between enterprises and farmers, effectively raising farmers' incomes and stimulating straw supply. The cooperative model not only streamlines costs but also aligns with national objectives of sustainable resource utilization and rural economic advancement.\u003c/p\u003e \u003cp\u003eIn China, farmers in nearly all regions own their own agricultural vehicles, and many also possess crop harvesting and handling equipment, as well as feed processing facilities such as guillotine choppers, shredders, and pelletizers. By strategically integrating these existing resources into the straw supply chain model, significant cost reductions can be achieved. As outlined in this study, farmers can lease their equipment to cooperatives or directly participate in straw collection and processing, creating additional income streams. This cooperative model not only aligns with national goals of sustainable agricultural development, rural economic growth, and efficient resource utilization but also serves as a valuable reference for other regions in China. By leveraging existing resources and fostering collaboration between farmers and enterprises, this model provides a scalable and economically viable solution for straw utilization.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003eAccessing biomass feedstock poses a significant challenge for the ethanol industry, especially in regions such as China, where biomass distribution is decentralized. This study developed a comprehensive biomass supply chain model that includes collection, pretreatment, storage, and transportation stages. By taking into account two primary cost constraints - collection and pretreatment methods, the study assessed various combinations of two collecting methods and four pretreatment methods to facilitate cost-effective and low-emission access to biomass for cellulosic ethanol feedstock. The study identified the optimal supply chain model for Heilongjiang and Zhejiang, representing northern and southern China, respectively, focusing on cost minimization and CO\u003csub\u003e2\u003c/sub\u003e emissions reduction. In these regions, employing mechanical collection followed by baling after shredding demonstrated the lowest cost at 226.6 yuan per ton and 217.7 yuan per ton within a collection radius of 60 km. Furthermore, utilizing mechanical collection followed by kneading showed the lowest CO\u003csub\u003e2\u003c/sub\u003e emissions at 21.48 kgCO\u003csub\u003e2\u003c/sub\u003e/t straw and 35.01 kgCO\u003csub\u003e2\u003c/sub\u003e/t straw in Heilongjiang and Zhejiang, respectively. Heilongjiang achieved lower costs and emissions due to its high straw distribution density. In Heilongjiang, the current subsidy policy provides 80 y/t, while in Zhejiang, it covers half of that amount, favoring the mechanical collection - kneading combination for reduced emissions and CO\u003csub\u003e2\u003c/sub\u003e emissions. Taking into account the existing agricultural cooperative model in China, cost savings can reach up to 70 y/t and 65 y/t in Heilongjiang and Zhejiang, respectively. The biomass supply chain model developed in this study enables cost-effective access to straw, a cellulosic ethanol feedstock, from various regions through decentralized biomass distribution in China. Emphasizing the combination of collection and pretreatment methods as crucial research aspects allows for locally tailored biomass supply solutions across different regions. Nevertheless, selecting the suitable combination of collection and pretreatment methods is essential, considering the varying distribution of biomass across regions, and should be based on empirical data prior to implementation.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was partially funded by the National Key Research and Development Program of China (2022YFB4201901).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets generated during and/or analysed during the current study are available from the corresponding author on reasonable request\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eChangliu He: Conceptualization, Methodology, Investigation, Formal analysis, Data curation, Writing - original draft. Xi Zhao: Supervision, Writing - review \u0026amp; editing. Lei Zheng: Supervision, Writing - review \u0026amp; editing. Jiayu Xin: Supervision, Writing - review \u0026amp; editing. Huimin Yun: Conceptualization, Supervision, Writing - review \u0026amp; editing, Funding acquisition. Xu Zhang: Conceptualization, Supervision, Writing - review \u0026amp; editing.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDeclarations\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eConflict of Interest The authors declare no competing interests.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eRossi LM, Gallo JMR, Mattoso LHC, Buckeridge MS, Licence P, Allen DT (2021) Ethanol from Sugarcane and the Brazilian Biomass-Based Energy and Chemicals Sector. 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To tackle this issue, we devised a biomass supply chain model encompassing collection, pretreatment, storage, and transportation phases. We examined the effects of various collection and pretreatment method combinations on supply-chain costs and CO\u003csub\u003e2\u003c/sub\u003e emissions. The model's validity was confirmed using Heilongjiang and Zhejiang as representatives of northern and southern regions. Mechanized collection with shredding-baling proved to be the most economical, costing 226.6 y/t in Heilongjiang and 217.7 y/t in Zhejiang within a 60 km collection radius. For CO\u003csub\u003e2\u003c/sub\u003e emissions, mechanized collection with kneading emerged as the optimal choice. With governmental subsidies, mechanized collection with kneading became the preferred option, considering both cost and CO\u003csub\u003e2\u003c/sub\u003e emissions. By incorporating agricultural cooperatives, costs were further reduced by up to 70 y/t in Heilongjiang and 65 y/t in Zhejiang. This model facilitates the cost-effective collection of straw for ethanol production in biomass-scattered China. Biomass decentralization combining collection and pretreatment methods is key to achieving site-specific biomass supply. Furthermore, the model can be adapted for acquiring biomass feedstock in other sectors and offers insights for biomass procurement in diverse regions.\u003c/p\u003e","manuscriptTitle":"Optimizing Collection and Pretreatment Methods for Cost-Effective and Low-CO 2 Emission Biomass Supply Chains","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-04-23 17:52:37","doi":"10.21203/rs.3.rs-5446256/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Accept as is","date":"2025-05-18T09:59:18+00:00","index":"","fulltext":""},{"type":"reviewerAgreed","content":"","date":"2025-04-20T03:04:57+00:00","index":0,"fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-04-19T19:46:39+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-03-31T09:37:00+00:00","index":"","fulltext":""},{"type":"submitted","content":"BioEnergy Research","date":"2025-03-21T08:25:57+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bioenergy-research","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bere","sideBox":"Learn more about [BioEnergy Research](https://www.springer.com/journal/12155)","snPcode":"12155","submissionUrl":"https://submission.nature.com/new-submission/12155/3","title":"BioEnergy Research","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"1d29c00c-3da3-42f7-8dd0-4fbcb20f4a1e","owner":[],"postedDate":"April 23rd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-06-23T15:59:03+00:00","versionOfRecord":{"articleIdentity":"rs-5446256","link":"https://doi.org/10.1007/s12155-025-10854-8","journal":{"identity":"bioenergy-research","isVorOnly":false,"title":"BioEnergy Research"},"publishedOn":"2025-06-20 15:57:06","publishedOnDateReadable":"June 20th, 2025"},"versionCreatedAt":"2025-04-23 17:52:37","video":"","vorDoi":"10.1007/s12155-025-10854-8","vorDoiUrl":"https://doi.org/10.1007/s12155-025-10854-8","workflowStages":[]},"version":"v1","identity":"rs-5446256","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5446256","identity":"rs-5446256","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}