Bioethanol is often presented as a straightforward route away from fossil fuels: grow a crop, convert its sugars or starch into alcohol, and blend the resulting fuel into gasoline. In reality, deciding which crop should supply that chain is far more complicated. Water consumption, drought resilience, land competition, greenhouse-gas emissions, farm income, conversion technology, transportation requirements and market volatility can all point in different directions. A new study published in Clean Technologies and Environmental Policy attempts to resolve that tension with a mathematical decision-support framework that combines expert judgment, uncertainty analysis and sustainability metrics. Its conclusion is both practical and provocative: in Kansas, corn remains the strongest near-term bioethanol feedstock, while grain sorghum may provide a more secure option in regions increasingly exposed to drought.
The research, conducted by Ritu Bhuyan, Naveen Kumar and Juthika Mahanta, addresses a problem that has challenged energy planners for decades. The crop producing the most ethanol is not necessarily the crop that delivers the best environmental or socioeconomic outcome. Corn, for example, benefits from a mature agricultural system, established markets and extensive ethanol-processing infrastructure. Yet it can require substantial water and fertilizer inputs, and it competes directly with food and animal-feed systems. Perennial grasses such as miscanthus and switchgrass may offer environmental advantages, including potential soil protection and lower dependence on annual cultivation, but their conversion technologies, supply chains and commercial markets are less developed. The “best” feedstock therefore depends on how multiple, sometimes conflicting, criteria are weighted.
To capture that complexity, the researchers developed an integrated multi-criteria decision-making model called Entropy-SWARA-TOPSIS. It operates within a hybrid intuitionistic fuzzy framework, a mathematical system designed to represent incomplete, ambiguous or conflicting information. In a conventional rating system, an expert might say that a crop is highly suitable or poorly suited. An intuitionistic fuzzy representation goes further by recording three components: the degree of support for a judgment, the degree of opposition to it and the remaining hesitation or uncertainty. That third component is particularly important in sustainability studies, where data may be incomplete, regional conditions may change and experts may disagree about long-term technological performance.
The model combines three different analytical functions. First, the proposed entropy measure estimates objective criterion weights from the information structure of the evaluations. In information theory, entropy is commonly associated with uncertainty or disorder; in a decision model, it can help identify which criteria provide greater discriminatory power among alternatives. Second, the SWARA method, or Step-wise Weight Assessment Ratio Analysis, incorporates the judgments of specialists who rank criteria according to their perceived importance. This prevents the analysis from relying solely on numerical variation in the dataset while also avoiding a purely subjective ranking. Finally, TOPSIS, the Technique for Order Preference by Similarity to Ideal Solution, ranks each crop according to its distance from a hypothetical best option and a hypothetical worst option. A crop receives a higher score when it is closer to the ideal combination of sustainability characteristics and farther from the negative benchmark.
For the Kansas case study, the investigators consulted a panel of ten domain experts and evaluated potential biomass crops against sixteen sustainability criteria. Although the full article is available through subscription, the reported results identify the broad dimensions considered by the framework: environmental performance, economic practicality, social implications and technical feasibility. This structure reflects the reality of biofuel development. A crop must not only produce fermentable material; it must also be available at scale, fit local agricultural conditions, avoid excessive pressure on water resources, support reliable processing and remain economically defensible for farmers and biorefineries. The analysis is therefore less a simple crop-yield contest than an attempt to measure whether a feedstock can function as part of a resilient regional energy system.
Corn emerged with a closeness coefficient of 0.808, the highest among the assessed alternatives. In TOPSIS, the closeness coefficient generally indicates how near an alternative is to the ideal solution, with larger values representing stronger overall performance. Corn’s result reflects the advantage of an established industrial ecosystem. Kansas farmers already possess substantial experience growing the crop, ethanol plants can process it through familiar first-generation technologies, and co-products such as distillers’ grains can enter livestock-feed markets. Existing infrastructure also reduces the logistical and financial barriers that often prevent promising alternative feedstocks from moving beyond experimental cultivation. The researchers describe corn as the most reliable near-term option, not as a perfect solution, but as the crop most capable of delivering bioethanol under current technological and market conditions.
Grain sorghum ranked second, with a closeness coefficient of 0.612, and the study highlights it as an alternative for areas that experience frequent drought. Sorghum is widely recognized for its ability to perform under relatively dry conditions, making it strategically important across parts of the Great Plains. Its value in the model appears to come not from surpassing corn in every category, but from offering a different risk profile. If water scarcity intensifies or irrigation becomes more expensive, a crop that can maintain useful productivity under water stress may become more attractive even if it has a smaller processing network. The finding also suggests that feedstock policy should not be based exclusively on average yields. Regional climate exposure, year-to-year reliability and the cost of securing water may prove just as important as maximum production in favorable seasons.
Miscanthus and switchgrass were identified as promising candidates whose wider adoption depends heavily on technological maturity. These perennial crops can be cultivated differently from annual grain crops and may provide advantages related to soil cover, erosion control and the use of land that is less suitable for conventional food production. Switchgrass has also been studied as a source of cellulosic ethanol, in which enzymes and other processing steps break down structural carbohydrates such as cellulose and hemicellulose into fermentable sugars. That pathway could expand bioethanol production beyond starch-rich crops, but it is technically demanding. Lignocellulosic biomass contains complex structures that resist enzymatic breakdown, often requiring pretreatment, specialized enzymes and careful management of inhibitors that can interfere with fermentation. Until these systems become more efficient and economical, perennial feedstocks may remain environmentally attractive but commercially limited.
The researchers tested the stability of their conclusions in several ways. Their rankings were consistent across twenty-two comparative multi-criteria decision-making methods, with Spearman rank correlations of at least 0.80 for all method pairs. Spearman’s correlation measures how similarly two methods order a group of alternatives; a value above 0.80 indicates strong agreement in ranking behavior. The model also remained robust when the combination parameter gamma was varied across the full interval from zero to one. This parameter controls how different components of the hybrid decision process are balanced, so stability across that range suggests that the result is not an artifact of one narrowly selected setting. In addition, the model outputs were compared with real-world information from the U.S. Department of Agriculture’s National Agricultural Statistics Service, providing an external check against observed agricultural conditions in Kansas.
Yet the study’s most important message may be its warning against declaring a single crop the universal winner. The authors state that no one feedstock can simultaneously satisfy all sustainability goals under present technological and market conditions. Bioethanol can contribute to United Nations Sustainable Development Goals, including SDG 6.4 on improving water-use efficiency, SDG 7.2 on increasing the share of renewable energy and SDG 13.2 on integrating climate action into national planning. But progress toward one goal can create pressure elsewhere. Expanding corn ethanol may support renewable-energy infrastructure while increasing competition for land, water and grain. Promoting perennial crops may improve environmental performance while demanding new harvesting systems, biorefineries and markets. The study therefore calls for policy intervention rather than a simple replacement of one crop with another. A more durable strategy could combine region-specific feedstock portfolios, drought-sensitive planning, incentives for water conservation, investment in cellulosic conversion and safeguards against food-versus-fuel conflicts.
The framework offers policymakers a way to make those trade-offs visible instead of hiding them inside a single production statistic. Its immediate recommendation is clear: maintain corn as the practical foundation of Kansas bioethanol production, expand consideration of grain sorghum where drought risk is high and continue developing miscanthus and switchgrass for a future in which advanced conversion technologies can unlock their potential. The broader significance reaches beyond Kansas. As climate instability, water scarcity and energy demand reshape agricultural planning, decisions about biofuels will increasingly require tools that can work with uncertainty rather than pretend it does not exist. By combining expert knowledge with formal information measures and comparative ranking, the new approach presents bioethanol not as a one-crop solution, but as a regional systems problem—one in which the most sustainable answer may be a flexible mix of feedstocks matched to local risks and technological realities.
Subject of Research: Sustainable biomass crop selection for bioethanol production and its contribution to the United Nations Sustainable Development Goals
Article Title: Bio-ethanol production pathways achieving sustainable development goals using hybrid intuitionistic fuzzy decision support tool
Article References: Bhuyan, R., Kumar, N. & Mahanta, J. “Bio-ethanol production pathways achieving sustainable development goals using hybrid intuitionistic fuzzy decision support tool.” Clean Technologies and Environmental Policy 28, Article 237 (2026).
Image Credits: AI Generated
DOI: https://doi.org/10.1007/s10098-026-03578-6
Keywords: Intuitionistic fuzzy set, information measure, multi-criteria decision-making, biomass, bioethanol, sustainable development goals, energy-food-water nexus, corn, grain sorghum, miscanthus, switchgrass
