All Publications


Y. Yao, “Mitigating uncertainties enables more accurate greenhouse gas accounting for petrochemicals”, Nature Chemical Engineering, vol. 1, no. 4, pp. 273 - 274, 2024.

The carbon footprints of petrochemicals have large uncertainties, challenging decarbonization efforts. Now, a study identifies the main uncertainty sources and strategies for improving the accuracy of greenhouse gas emissions estimations and reporting for petrochemicals.

Nature Chemical Engineering 1(4): 273 - 274
Y. Yao, Schaubroeck, T., Feng, H., Arodudu, O., and Gloria, T. P., “Life cycle sustainability assessment for sustainable development goals”, Journal of Industrial Ecology, 2024.
T. Lee, Yao, Y., Graedel, T. E., and Miatto, A., “Critical material requirements and recycling opportunities for US wind and solar power generation”, Journal of Industrial Ecology, 2024.

Abstract The deployment of renewable energy generation technologies, driven primarily by concerns over catastrophic climate change, is expected to increase rapidly in the United States. Rapid increases in the deployment of wind and solar energy will translate to increases in critical material requirements, causing concern that demand could outstrip supply, leading to mineral price volatility and potentially slowing the energy transition. This study presents a detailed demand-side model for wind and solar in the United States using dynamic material flow analysis to calculate the requirements for 15 elements: Cr, Zn, Ga, Se, Mo, Ag, Cd, In, Sn, Te, Pr, Nd, Tb, Dy, and Pb. Results show that transitioning to a completely decarbonized US energy system by 2050 could require a five-to-sevenfold increase in critical material flow-into-use compared with business as usual (BAU), with some materials requiring much larger increases. Rare earth elements (REEs) could require 60–300 times greater material flows into the US power sector in 2050 than in 2021, representing 13%–49% of the total global REE supply. Te requirements for reaching net zero by 2050 could exceed current supply, posing challenges for widespread deployment of cadmium-telluride solar. We also investigate several strategies for reducing material requirements, including closed-loop recycling, material intensity reduction, and changing market share of subtechnologies (e.g., using crystalline silicon solar panels instead of cadmium telluride). Although these strategies can significantly reduce critical material requirements by up to 40% on average, aggressive decarbonization will still require a substantial amount of critical material.

Y. Yao, Lan, K., Graedel, T. E., and Rao, N. D., “Models for Decarbonization in the Chemical Industry”, Annual Review of Chemical and Biomolecular Engineering, vol. 15, 2024.

Various technologies and strategies have been proposed to decarbonize the chemical industry. Assessing the decarbonization, environmental, and economic implications of these technologies and strategies is critical to identifying pathways to a more sustainable industrial future. This study reviews recent advancements and integration of systems analysis models, including process analysis, material flow analysis, life cycle assessment, techno-economic analysis, and machine learning. These models are categorized based on analytical methods and application scales (i.e., micro-, meso-, and macroscale) for promising decarbonization technologies (e.g., carbon capture, storage, and utilization, biomass feedstock, and electrification) and circular economy strategies. Incorporating forward-looking, data-driven approaches into existing models allows for optimizing complex industrial systems and assessing future impacts. Although advances in industrial ecology–, economic-, and planetary boundary–based modeling support a more holistic systems-level assessment, more efforts are needed to consider impacts on ecosystems. Effective applications of these advanced, integrated models require cross-disciplinary collaborations across chemical engineering, industrial ecology, and economics. Expected final online publication date for the Annual Review of Chemical and Biomolecular Engineering , Volume 15 is June 2024. Please see for revised estimates.

K. Lan, Zhang, B., Lee, T., and Yao, Y., “Soil organic carbon change can reduce the climate benefits of biofuel produced from forest residues”, Joule, 2024.

Summary Because biomass residues do not cause land-use change, soil carbon changes are commonly not considered in life cycle assessments (LCAs) of biofuel derived from forest residues adopted by regulatory agencies. Here, we investigate the impacts of soil organic carbon (SOC) changes caused by removing forest residues in the Southern US on the carbon intensity of biofuels. We show that the average greenhouse gas (GHG) emissions by SOC changes over 100 years are 8.8–14.9 gCO2e MJ−1, accounting for 20.3%–65.9% of life cycle GHG emissions of biofuel. These SOC-associated GHG emissions vary by time frame, site conditions, and forest management strategies. For land management, converting forest residues to biofuel is more climate beneficial than on-land decay or pile burning, depending on fossil fuel substitution and site conditions. Our results highlight the need to include soil carbon assessment in biofuel LCAs, policymaking, and forest management, even when forest residues are used and no land-use change is involved.

K. Lan, Wang, H. Szu- Han, Lee, T., de Assis, C. Abbati, Venditti, R. A., Zhu, Y., and Yao, Y., “A modeling framework to identify environmentally greener and lower-cost pathways of nanomaterials”, Green Chem., 2024.

Producing environmentally benign and economically viable nanomaterials is critical for large-scale applications in energy and other industries. This study presents a modeling framework to identify environmentally greener and lower-cost pathways of large-scale nanomaterial production, which encompasses life cycle assessment, Green Chemistry principles, techno-economic analysis, and eco-efficiency analysis. The framework is demonstrated by case studies of cellulose nanomaterials produced in the U.S. For cellulose nanocrystals, the framework identifies pathways that simultaneously reduce the life-cycle global warming potential (GWP) from 17.7 to 2.6 kgCO2e per dry kg cellulose nanocrystals and the minimum selling price (MSP) from US$7540 to US$4587 per dry t cellulose nanocrystals. For cellulose nanofibrils, the strategies present trade-offs of reducing GWP from 7.8 to 0.1 kgCO2e per dry kg cellulose nanofibrils but increasing MSP slightly from US$2873 to US$2985 per dry t cellulose nanofibrils. Eco-efficiency analysis quantifies the magnitudes of co-benefits and trade-offs between the environmental and economic performance of different production strategies and supports decision making for sustainability-informed process optimization.


B. Zhang, Kroeger, J., Planavsky, N., and Yao, Y., “Techno-Economic and Life Cycle Assessment of Enhanced Rock Weathering: A Case Study from the Midwestern United States”, Environmental Science & Technology, 2023.
H. Szu- Han Wang and Yao, Y., “Machine learning for sustainable development and applications of biomass and biomass-derived carbonaceous materials in water and agricultural systems: A review”, Resources, Conservation and Recycling, vol. 190, p. 106847, 2023.

Biomass-derived materials (BDM) have broad applications in water and agricultural systems. As an emerging tool, Machine learning (ML) has been applied to BDM systems to address material, process, and supply chain design challenges. This paper reviewed 53 papers published since 2008 to understand the capabilities, current limitations, and future potentials of ML in supporting sustainable development and applications of BDM. Previous ML applications were classified into three categories based on their objectives – material and process design, end-use performance prediction, and sustainability assessment. These ML applications focus on identifying critical factors for optimizing BDM systems, predicting material features and performances, reverse engineering, and addressing data challenges for sustainability assessments. BDM datasets show large variations, and ∼75% of them possess < 600 data points. Ensemble models and state-of-the-art neural networks (NNs) perform and generalize well on such datasets. Limitations for scaling up ML for BDM systems lie in the low interpretability of the ensemble and NN models and the lack of studies in sustainability assessment that consider geo-temporal dynamics. A workflow is recommended for future ML studies for BDM systems. More research is needed to explore ML applications for sustainable development, assessment, and optimization of BDM systems.

Z. Zhang, Huang, J., Yao, Y., Peters, G., Macdonald, B., La Rosa, A. Daniela, Wang, Z., and Scherer, L., “Environmental impacts of cotton and opportunities for improvement”, Nature Reviews Earth & Environment, pp. 1–13, 2023.
N. Wu, Lan, K., and Yao, Y., “An integrated techno-economic and environmental assessment for carbon capture in hydrogen production by biomass gasification”, Resources, Conservation and Recycling, vol. 188, p. 106693, 2023.

Bioenergy with carbon capture and storage (BECCS) is a potential solution addressing climate change andregional wildfires, and supporting circular economy. This study investigates the economic and environmental performance of a BECCS pathway implementing carbon capture (CC) in hydrogen production via gasifying forest residues in the American West, by developing a framework that integrates process simulations, techno-economic analysis (TEA), and life cycle assessment (LCA). The results show that forest residue-derived hydrogen is economically competitive ($1.52– 2.92/kg H2) compared with fossil-based hydrogen. Incorporating CC increases environmental impact due to additional energy and chemical consumption, which can be mitigated by the energy self-sufficiency design that reduces CC cost to $75/tonne of CO₂ for a 2,000 dry short ton/day plant, or by using renewable energy such as solar and wind. Compared to electrolysis and fossil-based routes with CC, only BECCS can provide carbon-negative hydrogen and is more favorable regarding human health impact and near-term economics.

B. Zhang, Lan, K., Harris, T. B., Ashton, M. S., and Yao, Y., “Climate-smart forestry through innovative wood products and commercial afforestation and reforestation on marginal land”, Proceedings of the National Academy of Sciences, vol. 120, p. e2221840120, 2023.

Afforestation and reforestation (AR) on marginal land are nature-based solutions to climate change. There is a gap in understanding the climate mitigation potential of protection and commercial AR with different combinations of forest plantation management and wood utilization pathways. Here, we fill the gap using a dynamic, multiscale life cycle assessment to estimate one-century greenhouse gas (GHG) mitigation delivered by (both traditional and innovative) commercial and protection AR with different planting density and thinning regimes on marginal land in the southeastern United States. We found that innovative commercial AR generally mitigates more GHGs across 100 y (3.73 to 4.15 Giga tonnes of CO2 equivalent (Gt CO2e)) through cross-laminated timber (CLT) and biochar than protection AR (3.35 to 3.69 Gt CO2e) and commercial AR with traditional lumber production (3.17 to 3.51 Gt CO2e), especially in moderately cooler and dryer regions in this study with higher forest carbon yield, soil clay content, and CLT substitution. In a shorter timeframe (≤50 y), protection AR is likely to deliver higher GHG mitigation. On average, for the same wood product, low-density plantations without thinning and high-density plantations with thinning mitigate more life cycle GHGs and result in higher carbon stock than that of low-density with thinning plantations. Commercial AR increases the carbon stock of standing plantations, wood products, and biochar, but the increases have uneven spatial distributions. Georgia (0.38 Gt C), Alabama (0.28 Gt C), and North Carolina (0.13 Gt C) have the largest carbon stock increases that can be prioritized for innovative commercial AR projects on marginal land.


K. Lan and Yao, Y., “Feasibility of gasifying mixed plastic waste for hydrogen production and carbon capture and storage”, Communications Earth & Environment, vol. 3, 2022.
Y. Yao, “How does COVID-19 affect the life cycle environmental impacts of U.S. household energy and food consumption?”, Environmental Research Letters, vol. 17, p. 034025, 2022.

The COVID-19 pandemic has reduced travel but led to an increase in household food and energy consumption. Previous studies have explored the changes in household consumption of food and energy during the pandemic; however, the economy-wide environmental implications of these changes have not been investigated. This study addresses the knowledge gap by estimating the life cycle environmental impacts of U.S. households during the pandemic using a hybrid life cycle assessment. The results revealed that the reduction in travel outweighed the increase in household energy consumption, leading to a nationwide decrease in life cycle greenhouse gas emissions (−255 Mton CO2 eq), energy use (−4.46 EJ), smog formation (−9.17 Mton O3 eq), minerals and metal use (−16.1 Mton), commercial wastes (−8.31 Mton), and acidification (−226 kton SO2 eq). However, U.S. households had more life cycle freshwater withdrawals (+8.6 Gton) and slightly higher eutrophication (+0.2%), ozone depletion (+0.7%), and freshwater ecotoxicity (+2.1%) caused by increased household energy and food consumption. This study also demonstrated the environmental trade-offs between decreased food services and increased food consumption at home, resulting in diverse trends for food-related life cycle environmental impacts.

K. Lan and Yao, Y., “Dynamic Life Cycle Assessment of Energy Technologies under Different Greenhouse Gas Concentration Pathways”, Environmental Science & Technology (Cover Paper), vol. 56, pp. 1395-1404, 2022.
D. Echeverria, Venditti, R., Jameel, H., and Yao, Y., “Process Simulation-Based Life Cycle Assessment of Dissolving Pulps”, Environmental Science & Technology, vol. 56, pp. 4578-4586, 2022.
R. Buitrago-Tello, Venditti, R. A., Jameel, H., Yao, Y., and Echeverria, D., “Carbon Footprint of Bleached Softwood Fluff Pulp: Detailed Process Simulation and Environmental Life Cycle Assessment to Understand Carbon Emissions”, ACS Sustainable Chemistry & Engineering, vol. 10, pp. 9029-9040, 2022.
Z. Li, Chen, C., Xie, H., Yao, Y., Zhang, X., Brozena, A., Li, J., Ding, Y., Zhao, X., Hong, M., and others, “Sustainable high-strength macrofibres extracted from natural bamboo”, Nature Sustainability, vol. 5, pp. 235–244, 2022.
Nature Sustainability
Z. Zhang, Martin, K. L., Stevenson, K. T., and Yao, Y., “Equally green? Understanding the distribution of urban green infrastructure across student demographics in four public school districts in North Carolina, USA”, Urban Forestry & Urban Greening, vol. 67, p. 127434, 2022.

Green infrastructure (GI) provides a suite of ecosystem services that are widely recognized as critical to health, well-being, and sustainability on an urbanizing planet. However, the distribution of GI across urban landscapes is frequently uneven, resulting in unequal delivery of these services to low-income residents or those belonging to underserved racial/ethnic identities. While GI distribution has been identified as unequal across municipalities, we investigated whether this was true in public schoolyards within and among urban school districts. We examined schoolyards in four metropolitan areas of diverse socio-economic and demographic compositions in North Carolina, USA to determine if they provided equal exposure to GI, then compared whether this was true of the broader urban landscape. We first classified the land cover of elementary schoolyards and their neighborhoods, then used bivariate and multivariate approaches to analyze the relationships between GI (i.e. tree canopy cover and total GI) and the socioeconomic status and race/ethnicity of the schools and surrounding neighborhoods, respectively. We found that the extent of tree canopy cover and total GI in schoolyards was unrelated to the socioeconomic status and the race/ethnicity of students across the four school districts. In contrast, neighborhoods with lower socioeconomic status and larger populations of underserved race/ethnicity residents had less tree canopy cover and total GI. Although total GI was more evenly distributed in schoolyards, the extent of tree canopy cover and total GI in schoolyards was lower than that in the neighborhoods. This suggests opportunities for school districts to expand GI in schoolyards, leveraging their potential to increase ecosystem services to all children, from increased educational opportunities to improved mental, physical, and environmental well-being.

K. Lan, Zhang, B., and Yao, Y., “Circular utilization of urban tree waste contributes to the mitigation of climate change and eutrophication”, One Earth, vol. 5, pp. 944-957, 2022.

Summary Substantial urban tree waste is generated and underutilized in the US. Circular utilization of urban tree wastes has been explored in the literature, but the life-cycle environmental implications of varied utilization pathways have not been fully understood. Here we quantify the life-cycle environmental benefits of utilizing urban tree wastes at process, state, and national levels in the US. Full utilization of urban tree wastes to produce compost, lumber, chips, and biochar substantially reduces nationwide global warming potential (127.4–251.8 Mt CO2 eq./year) and eutrophication potential (93.9–192.7 kt N eq./year) compared with landfilling. Such benefits vary with state-level locations due to varied urban tree waste availability and types. Process-level comparisons identify the most environmentally beneficial combination as using merchantable logs for lumber and residues for biochar. The results highlight the climate change and eutrophication mitigation potential of different circular utilization pathways, supporting the development of circular bioeconomy in the urban environment.

One Earth 5: 944-957
S. Zargar, Yao, Y., and Tu, Q., “A review of inventory modeling methods for missing data in life cycle assessment”, Journal of Industrial Ecology, vol. n/a, 2022.

Abstract Missing data is the key challenge facing life cycle inventory (LCI) modeling. The collection of missing data can be cost-prohibitive and infeasible in many circumstances. Major strategies to address this issue include proxy selection (i.e., selecting a surrogate dataset to represent the missing data) and data creation (e.g., through empirical equations or mechanistic models). Within these two strategies, we identified three approaches that are widely used for LCI modeling: Data-driven, mechanistic, and future (e.g., 2050) inventory modeling. We critically reviewed the 12 common methods of these three approaches by focusing on their features, scope of application, underlying assumptions, and limitations. These methods were characterized based on the following criteria: “domain knowledge requirement” (both as a method developer and a user), “post-treatment requirement,” “challenge in assessing data quality uncertainty,” “challenge in generalizability,” and “challenge in automation.” These criteria can be used by LCA practitioners to select the suitable method(s) to bridge the data gap in LCI modeling, based on the goal and scope of the intended study. We also identified several aspects for future improvement for these reviewed methods.


M. Liao, Lan, K., and Yao, Y., “Sustainability implications of artificial intelligence in the chemical industry: A conceptual framework”, Journal of Industrial Ecology, vol. 26, no. 1, 2021.

Abstract Artificial intelligence (AI) is an emerging technology that has great potential in reducing energy consumption, environmental burdens, and operational risks of chemical production. However, large-scale applications of AI are still limited. One barrier is the lack of quantitative understandings of the potential benefits and risks of different AI applications. This study reviewed relevant AI literature and categorized those case studies by application types, impact categories, and application modes. Most studies assessed the energy, economic, and safety implications of AI applications, while few of them have evaluated the environmental impacts of AI, given the large data gaps and difficulties in choosing appropriate assessment methods. Based on the reviewed case studies in the chemical industry, we proposed a conceptual framework that encompasses approaches from industrial ecology, economics, and engineering to guide the selection of performance indicators and evaluation methods for a holistic assessment of AI’s impacts. This framework could be a valuable tool to support the decision-making related to AI in the fundamental research and practical production of chemicals. Although this study focuses on the chemical industry, the insights of the literature review and the proposed framework could be applied to AI applications in other industries and broad industrial ecology fields. In the end, this study highlights future research directions for addressing the data challenges in assessing AI’s impacts and developing AI-enhanced tools to support the sustainable development of the chemical industry.

S. Xiao, Chen, C., Xia, Q., Liu, Y., Yao, Y., Chen, Q., Hartsfield, M., Brozena, A., Tu, K., Eichhorn, S. J., Yao, Y., Li, J., Gan, W., Shi, S. Q., Yang, V. W., Ricco, M. Lo, Zhu, J. Y., Burgert, I., Luo, A., Li, T., and Hu, L., “Lightweight, strong, moldable wood via cell wall engineering as a sustainable structural material”, Science (Cover Paper), vol. 374, pp. 465-471, 2021.

Wood is an attractive material for structural applications, but it usually works best as boards or sheets. Xiao et al. have developed a process for engineering hardwood that allows these sheets to be manipulated into complex structures (see the Perspective by Tajvidi and Gardner). The key is to manipulate the cell wall structure by shrinking and blasting open the fibers and vessels by drying and “water-shocking” them. This process creates a window wherein the wood can be manipulated without ripping or tearing. Honeycomb, corrugated, or other complex structures are locked in once the wood dries. —BG Closing and reopening the vessels and fibers in hardwood allows it to be molded into complex shapes. Wood is a sustainable structural material, but it cannot be easily shaped while maintaining its mechanical properties. We report a processing strategy that uses cell wall engineering to shape flat sheets of hardwood into versatile three-dimensional (3D) structures. After breaking down wood’s lignin component and closing the vessels and fibers by evaporating water, we partially re-swell the wood in a rapid water-shock process that selectively opens the vessels. This forms a distinct wrinkled cell wall structure that allows the material to be folded and molded into desired shapes. The resulting 3D-molded wood is six times stronger than the starting wood and comparable to widely used lightweight materials such as aluminum alloys. This approach widens wood’s potential as a structural material, with lower environmental impact for buildings and transportation applications.

Science (Cover Paper) 374: 465-471
K. Lan, Ou, L., Park, S., Kelley, S. S., Nepal, P., Kwon, H., Cai, H., and Yao, Y., “Dynamic life-cycle carbon analysis for fast pyrolysis biofuel produced from pine residues: implications of carbon temporal effects”, Biotechnology for Biofuels, vol. 14, no. 1, p. 191, 2021.

Woody biomass has been considered as a promising feedstock for biofuel production via thermochemical conversion technologies such as fast pyrolysis. Extensive Life Cycle Assessment studies have been completed to evaluate the carbon intensity of woody biomass-derived biofuels via fast pyrolysis. However, most studies assumed that woody biomass such as forest residues is a carbon–neutral feedstock like annual crops, despite a distinctive timeframe it takes to grow woody biomass. Besides, few studies have investigated the impacts of forest dynamics and the temporal effects of carbon on the overall carbon intensity of woody-derived biofuels. This study addressed such gaps by developing a life-cycle carbon analysis framework integrating dynamic modeling for forest and biorefinery systems with a time-based discounted Global Warming Potential (GWP) method developed in this work. The framework analyzed dynamic carbon and energy flows of a supply chain for biofuel production from pine residues via fast pyrolysis.

Y. Yao, Schaubroeck, T., Feng, H., Arodudu, O., and Gloria, T. P., “Life cycle sustainability assessment for sustainable development goals”, Journal of Industrial Ecology, 2021.
Q. Xia, Chen, C., Yao, Y., Li, J., He, S., Zhou, Y., Li, T., Pan, X., Yao, Y., and Hu, L., “A strong, biodegradable and recyclable lignocellulosic bioplastic”, Nature Sustainability, 2021.

Renewable and biodegradable materials derived from biomass are attractive candidates to replace non-biodegradable petrochemical plastics. However, the mechanical performance and wet stability of biomass are generally insufficient for practical applications. Herein, we report a facile in situ lignin regeneration strategy to synthesize a high-performance bioplastic from lignocellulosic resources (for example, wood). In this process, the porous matrix of natural wood is deconstructed to form a homogeneous cellulose–lignin slurry that features nanoscale entanglement and hydrogen bonding between the regenerated lignin and cellulose micro/nanofibrils. The resulting lignocellulosic bioplastic shows high mechanical strength, excellent water stability, ultraviolet-light resistance and improved thermal stability. Furthermore, the lignocellulosic bioplastic has a lower environmental impact as it can be easily recycled or safely biodegraded in the natural environment. This in situ lignin regeneration strategy involving only green and recyclable chemicals provides a promising route to producing strong, biodegradable and sustainable lignocellulosic bioplastic as a promising alternative to petrochemical plastics.

D. Echeverria, Venditti, R., Jameel, H., and Yao, Y., “A general Life Cycle Assessment framework for sustainable bleaching: A case study of peracetic acid bleaching of wood pulp”, Journal of Cleaner Production, vol. 290, p. 125854, 2021.

Bleaching is an important industrial operation that has significant environmental impacts. Many new bleaching technologies have been developed; nonetheless, it is challenging to quantify their potential environmental impacts due to the lack of quantitative information and robust analysis methods across different bleaching agents. This study addresses this gap by developing a general Life Cycle Assessment (LCA) framework that integrates LCA with manufacturing process simulations and lab-scale bleaching experiments. The framework was applied to a case study of Peracetic Acid (PAA), a promising bleaching agent, used in the Total Chlorine-Free (TCF) technology for wood pulp production, compared with the traditional Elemental Chlorine-Free (ECF) using chlorine dioxide. Different PAA synthetic pathways (i.e., using acetic acid or triacetin) and bleaching charges were explored using scenario analysis. Results showed that PAA-based TCF achieves a brightness similar to the conventional ECF technology with lower life-cycle impacts in categories such as global warming and eutrophication. From a process perspective, PAA-based TCF reduces the consumption of energy, water, pulping chemicals, completely avoids the use of chlorinated compounds, and provides enhanced process safety. The source of PAA significantly affects the life-cycle environmental impacts of pulp bleaching. Using PAA synthesized from triacetin rather than acetic acid leads to higher environmental impacts; however, such impacts can be mitigated by reducing excessive use of triacetin (direction for future optimization) or using bio-based glycerin in the production of the triacetin feedstock for PAA production. Although this case study focuses on PAA bleaching for wood pulp, the framework has the potential to be used for other/same bleaching agents in different industrial sectors.

M. Liao and Yao, Y., “Applications of Artificial Intelligence-Based Modeling for Bioenergy Systems: A Review”, GCB Bioenergy, 2021.

Abstract Bioenergy is widely considered a sustainable alternative to fossil fuels. However, large-scale applications of biomass-based energy products are limited due to challenges related to feedstock variability, conversion economics, and supply chain reliability. Artificial intelligence (AI), an emerging concept, has been applied to bioenergy systems in recent decades to address those challenges. This paper reviewed 164 articles published between 2005-2019 that applied different AI techniques to bioenergy systems. This review focuses on identifying the unique capabilities of various AI techniques in addressing bioenergy-related research challenges and improving the performance of bioenergy systems. Specifically, we characterized AI studies by their input variables, output variables, AI techniques, dataset size, and performance. We examined AI applications throughout the life cycle of bioenergy systems. We identified four areas in which AI has been mostly applied, including (1) the prediction of biomass properties, (2) the prediction of process performance of biomass conversion, including different conversion pathways and technologies, (3) the prediction of biofuel properties and the performance of bioenergy end-use systems, and (4) supply chain modeling and optimization. Based on the review, AI is particularly useful in generating data that are hard to be measured directly, improving traditional models of biomass conversion and biofuel end-uses, and overcoming the challenges of traditional computing techniques for bioenergy supply chain design and optimization. For future research, efforts are needed to develop standardized and practical procedures for selecting AI techniques and determining training data samples, to enhance data collection, documentation, and sharing across bioenergy related areas, and to explore the potential of AI in supporting the sustainable development of bioenergy systems from holistic perspectives.

S. Van Schoubroeck, Thomassen, G., Van Passel, S., Malina, R., Springael, J., Lizin, S., Venditti, R. A., Yao, Y., and Van Dael, M., “An integrated techno-sustainability assessment (TSA) framework for emerging technologies”, Green Chem., p. -, 2021.

A better understanding of the drivers of the economic, environmental, and social sustainability of emerging (biobased) technologies and products in early development phases can help decision-makers to identify sustainability hurdles and opportunities. Furthermore, it guides additional research and development efforts and investment decisions, that will, ultimately, lead to more sustainable products and technologies entering a market. To this end, this study developed a novel techno-sustainability assessment (TSA) framework with a demonstration on a biobased chemical application. The integrated TSA compares the potential sustainability performance of different (technology) scenarios and helps to make better-informed decisions by evaluating and trading-off sustainability impacts in one holistic framework. The TSA combines methods for comprehensive indicator selection and integration of technological and country-specific data with environmental, economic, and social data. Multi-criteria decision analysis (MCDA) is used to address data uncertainty and to enable scenario comparison if indicators are expressed in different units. A hierarchical, stochastic outranking approach is followed that compares different weighting schemes and preference structures to check for the robustness of the results. The integrated TSA framework is demonstrated on an application for which the sustainability of a production and harvesting plant of microalgae-based food colorants is assessed. For a set of scenarios that vary with regard to the algae feedstock, production technology, and location, the sustainability performance is quantified and compared, and the underlying reasons for this performance are explored.

K. Lan, Ou, L., Park, S., Kelley, S. S., English, B. C., T. Yu, E., Larson, J., and Yao, Y., “Techno-Economic Analysis of decentralized preprocessing systems for fast pyrolysis biorefineries with blended feedstocks in the southeastern United States”, Renewable and Sustainable Energy Reviews, vol. 143, p. 110881, 2021.

This study evaluated the economic feasibility of fast pyrolysis biorefineries fed with blended pine residues and switchgrass in the Southeastern U.S. with different supply chain design. Previous techno-economic analyses (TEA) have focused on either blended biomass or decentralized preprocessing without investigating the impacts of varied process parameters, technology options, and real-world biomass distribution. This study fills the literature gap by modeling scenarios for different biomass blending ratios, biorefinery and preprocessing site (so-called depot) capacities, and alternative preprocessing technologies. High-resolution, real-world geospatial data were analyzed using Geographic Information Systems to facilitate supply chain design and TEA. For a decentralized system, the minimum fuel selling price (MFSP) of biofuel was $3.92–$4.33 per gallon gasoline equivalent (GGE), while the MFSP for the centralized biorefinery at the same capacities ranged between $3.75–$4.02/GGE. Implementing a high moisture pelleting process depot rather than a conventional pelleting process lowered the MFSP by $0.03–$0.17/GGE. Scenario analysis indicated decreased MFSP with increasing biorefinery capacities but not necessarily with increasing depot size. Medium-size depots (500 OMDT/day) achieved the lowest MFSP. This analysis identified the optimal blending ratios for two preprocessing technologies at varied depot sizes. Counterintuitively, increasing the proportion of higher cost switchgrass reduced the MFSP for large biorefineries (>5000 ODMT/day), but increased the MFSP for small biorefineries (1000–2500 ODMT/day). Although the decentralized systems have a higher MFSP based on current analysis, it has other potential benefits such as mitigated supply chain risks and improved feedstock quality that are difficult to be quantified in this TEA.


K. E. Tomberlin, Venditti, R., and Yao, Y., “Life Cycle Carbon Footprint Analysis of Pulp and Paper Grades in the United States Using Production-Line-Based Data and Integration”, BioResources, vol. 15, no. 2, pp. 3899 - 3914, 2020.

Greenhouse gas (GHG) emission levels are causing concern as climate change risks are growing, emphasizing the importance of GHG research for better understanding of emission sources. Previous studies on GHG emissions for the pulp and paper industry have ranged in scope from global to regional to site-specific. This study addresses the present knowledge gap of how GHG emissions vary among paper grades in the US. A cradle-to-gate life cycle carbon analysis for 252 mills in the US was performed by integrating large datasets at the production line level. The results indicated that one metric ton of paper product created a production weighted average of 942 kg of carbon dioxide equivalent (kg CO2eq) of GHG emissions. Greenhouse gas emissions varied by pulp and paper grade, from 608 kg CO2eq per metric ton of product to 1978 kg CO2eq per metric ton of product. Overall, fuels were the greatest contributor to the GHG emissions and should be the focus of emission reduction strategies across pulp and paper grades. © 2020, North Carolina State University.

BioResources 15(2): 3899 - 3914
M. Liao, Kelley, S., and Yao, Y., “Generating Energy and Greenhouse Gas Inventory Data of Activated Carbon Production Using Machine Learning and Kinetic Based Process Simulation”, ACS Sustainable Chemistry and Engineering, vol. 8, no. 2, pp. 1252 - 1261, 2020.

Understanding the environmental implications of activated carbon (AC) produced from diverse biomass feedstocks is critical for biomass screening and process optimization for sustainability. Many studies have developed Life Cycle Assessment (LCA) for biomass-derived AC. However, most of them either focused on individual biomass species with differing process conditions or compared multiple biomass feedstocks without investigating the impacts of feedstocks and process variations. Developing LCA for AC from diverse biomass is time-consuming and challenging due to the lack of process data (e.g., energy and mass balance). This study addresses these knowledge gaps by developing a modeling framework that integrates artificial neural network (ANN), a machine learning approach, and kinetic-based process simulation. The integrated framework is able to generate Life Cycle Inventory data of AC produced from 73 different types of woody biomass with 250 characterization data samples. The results show large variations in energy consumption and GHG emissions across different biomass species (43.4-277 MJ/kg AC and 3.96-22.0 kg CO2-eq/kg AC). The sensitivity analysis indicates that biomass composition (e.g., hydrogen and oxygen content) and process operational conditions (e.g., activation temperature) have large impacts on energy consumption and GHG emissions associated with AC production. Copyright © 2019 American Chemical Society.

S. Johnson, Echeverria, D., Venditti, R., Jameel, H., and Yao, Y., “Supply Chain of Waste Cotton Recycling and Reuse: A Review”, AATCC Journal of Research, vol. 7, pp. 19-31, 2020.

A comprehensive understanding of the waste cotton supply chain and different end-of-life options is essential to promote cotton recycling and reuse. This study analyzed global and US data to understand the quantity, current sources, and destinations of waste cotton. Globally, 11.6 million metric tons of waste cotton are generated per year during cotton garment production. This study also reviewed different options for recycling both pre-consumer and post-consumer cotton waste via chemical and mechanical processes. Different applications of waste cotton were compared to their virgin counterparts from technical, environmental, and economic perspectives. Unlike most previous studies, this research included applications that are not traditional textile products (e. g., biofuels and composites), shedding light on potential new markets for waste cotton that will not compete with virgin cotton.

K. Lan, Park, S., Kelley, S. S., English, B. C., T. Yu, E., Larson, J., and Yao, Y., “Impacts of Uncertain Feedstock Quality on the Economic Feasibility of Fast Pyrolysis Biorefineries with Blended Feedstocks and Decentralized Preprocessing Sites in the Southeastern United States”, GCB Bioenergy, vol. 12, no. 11, 2020.

This study performs Techno-Economic Analysis and Monte Carlo simulations (MCS) to explore the effects that variations in biomass feedstock quality have on the economic feasibility of fast pyrolysis biorefineries that use decentralized preprocessing sites (i.e., depots that produce pellets). Two biomass resources in the Southeastern U.S., i.e., pine residues and switchgrass, were examined as feedstocks. A scenario analysis was conducted for an array of different combinations, including different pellet ash control levels, feedstock blending ratios, different biorefinery capacities, and different biorefinery on-stream capacities, followed by a comparison with the traditional centralized system. MCS results show that, with depot preprocessing, variations in the feedstock moisture and feedstock ash content can be significantly reduced compared with a traditional centralized system. For a biorefinery operating at 100% of its designed capacity, the minimum fuel selling price (MFSP) of the decentralized system is \$3.97–\$4.39 per gallon gasoline equivalent (GGE) based on the mean value across all scenarios, whereas the mean MFSP for the traditional centralized system was \$3.79–\$4.12/GGE. To understand the potential benefits of highly flowable pellets in decreasing biorefinery downtime due to feedstock handling and plugging problems, this study also compares the MFSP of the decentralized system at 90% of its designed capacity with a traditional system at 80%. The analysis illustrates that using low ash pellets mixed with switchgrass and pine residues generates a more competitive MFSP. Specifically, for a biorefinery designed for 2,000 oven dry metric ton per day, running a blended pellet made from 75% switchgrass and 25% pine residues with 2% ash level, and operating at 90% of designed capacity could make an MFSP between \$4.49–\$4.71/GGE. In contrast, a traditional centralized biorefinery operating at 80% of designed capacity marks an MFSP between \$4.72–\$5.28.

GCB Bioenergy 12(11)
K. Lan, Kelley, S. S., Nepal, P., and Yao, Y., “Dynamic life cycle carbon and energy analysis for cross-laminated timber in the Southeastern United States”, Environmental Research Letters, vol. 15, no. 124036, 2020.

Life Cycle Assessment (LCA) has been used to understand the carbon and energy implications of manufacturing and using cross-laminated timber (CLT), an emerging and sustainable alternative to concrete and steel. However, previous LCAs of CLT are static analyses without considering the complex interactions between the CLT manufacturing and forest systems, which are dynamic and largely affected by the variations in forest management, CLT manufacturing, and end-of-life options. This study fills this gap by developing a dynamic life-cycle modeling framework for a cradle-to-grave CLT manufacturing system across 100 years in the Southeastern United States. The framework integrates process-based simulations of CLT manufacturing and forest growth as well as Monte Carlo simulation to address uncertainty. On 1-ha forest land basis, the net greenhouse gas (GHG) emissions ranges from -954 to -1445 metric tonne CO2 eq. for a high forest productivity scenario compared to -609 to -919 for a low forest productivity scenario. All scenarios showed significant GHG emissions from forest residues decay, demonstrating the strong need to consider forest management and their dynamic impacts in LCAs of CLT or other durable wood products (DWP). The results show that using mill residues for energy recovery has lower fossil-based GHG (59%–61% reduction) than selling residues for producing DWP, but increases the net GHG emissions due to the instantaneous release of biogenic carbon in residues. In addition, the results were converted to 1 m3 basis with a cradle-to-gate system boundary to be compared with literature. The results, 113–375 kg CO2 eq./m3 across all scenarios, were consistent with previous studies. Those findings highlight the needs of system-level management to maximize the potential benefits of CLT. This work is an attributional LCA, but the presented results lay a foundation for future consequential LCAs for specific CLT buildings or commercial forest management systems.


K. Lan and Yao, Y., “Integrating Life Cycle Assessment and Agent-Based Modeling: A Dynamic Modeling Framework for Sustainable Agricultural Systems”, Journal of Cleaner Production, vol. 238, 2019.

As food demand increases, it is critical to develop effective strategies and evaluate their potential in reducing Greenhouse Gas (GHG) emissions and other environmental footprints of large-scale agricultural systems. This study addresses the challenge by developing a dynamic system modeling framework integrating Life Cycle Assessment (LCA), Agent-Based Modeling (ABM), and Techno-Economic Analysis (TEA). LCA and TEA were coupled with dynamic simulation models of crop yields, costs, and prices, allowing for the estimation of life-cycle environmental impacts and profitability of crop planting activities under changing climate and economic conditions. The framework was demonstrated by a case study for an agricultural system, including 1,000 farms in the United States over a 30-year time frame. The results indicated that information exchange among farmers, farmers’ environmental awareness, access to environmental information, and farm size are key factors driving the system’s environmental impacts. The results can provide a broad range of stakeholders (e.g., policymakers, nonprofits, agriculture companies) with insightful information to tailor their strategies for effectively managing the environmental footprints of large-scale agricultural systems. The integrated modeling framework has the potential to address sustainability challenges in other systems that are dynamic, involve human behaviors, and have complex interactions among human and nature systems. © 2019 Elsevier Ltd

M. Liao, Kelley, S. S., and Yao, Y., “Artificial neural network based modeling for the prediction of yield and surface area of activated carbon from biomass”, Biofuels, Bioproducts and Biorefining, vol. 13, no. 4, pp. 1015 - 1027, 2019.

Activated carbon (AC) is an adsorbent material with broad industrial applications. Understanding and predicting the yield and quality of AC produced from different feedstock is critical for biomass screening and process design. In this study, multi-layer feedforward artificial neural network (ANN) models were developed to predict the total yield and surface area of AC produced from various biomass feedstock using pyrolysis and steam activation. In total, 168 data samples identified from experiments in literature were used to train, validate, and test the ANN models. The trained ANN models showed high accuracy (R2 > 0.9) and demonstrated good alignment with the independent experimental data. The impacts of using datasets based on different biomass characterization methods (i.e., ultimate analysis and proximate analysis) were evaluated and compared. Finally, a contribution analysis was conducted to understand the impact of different process factors on AC yield and surface area. © 2019 Society of Chemical Industry and John Wiley & Sons, Ltd. © 2019 Society of Chemical Industry and John Wiley & Sons, Ltd

Y. Yao and Huang, R., “A parametric life cycle modeling framework for identifying research development priorities of emerging technologies: A case study of additive manufacturing”, in Procedia CIRP, 2019, vol. 80, pp. 370 - 375.

Life Cycle Assessment (LCA) has been used to assess the environmental implications of emerging technologies in different manufacturing sectors. However, it is challenging to use the traditional LCA method to model the relationships between Life Cycle Inventory (LCI) data and key technical parameters, preventing further analysis for understanding key driving factors and determining priorities for researchand technology development. Furthermore, the sensitivity analysis of traditional LCA could be misleading for decision making or strategic planning given that the potential/possibility of improving specific parameters are commonly not taken into consideration. In this work, a novel parametric analysis framework was developed to address the methodological challenge. The modeling framework integrates process-based engineering models with LCA, Life Cycle Cost analysis (LCC), and optimization. The framework is demonstrated through a case study of additive manufacturing (AM). © 2019 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license

Procedia CIRP 80: 370 - 375
K. Lan, Park, S., and Yao, Y., “Key issue, challenges, and status quo of models for biofuel supply chain design”, in Biofuels for a More Sustainable Future: Life Cycle Sustainability Assessment and Multi-Criteria Decision Making, 2019, pp. 273 - 315.

Biofuel supply chain (BSC) design is crucial for the sustainable production and distribution of biofuel. Many modeling techniques such as optimization and simulation have been employed to BSC at different regional and temporal scales. This chapter reviews the research efforts made in BSC since 2005 to highlight status quo, challenges, and issues related to BSC modeling and design. The basic concept and components of BSC are first introduced and followed by the review of different modeling techniques at various decision levels of BSC design. Challenges and issues identified throughout this review work are highlighted at the end and future research directions are discussed. © 2020 Elsevier Inc. All rights reserved.

A. Nabinger, Tomberlin, K., Venditti, R., and Yao, Y., “Using a data-driven approach to unveil greenhouse gas emission intensities of different pulp and paper products”, in Procedia CIRP, 2019, vol. 80, pp. 689 - 692.

Life Cycle Assessment (LCA) has been used to evaluate the life-cycle Greenhouse Gas (GHG) emissions of pulp and paper production, and most previous studies rely on process-based models for specific product types (e.g., printing paper), industry-average data, or information from a few mills. In this work, a data-driven approach is used to quantify GHG emissions intensities of different paper products manufactured by the U.S. mills. Facility-level emission data collected from publically available governmental databases and mill-level production data collected from the private sector were integrated to track the GHG emissions for different product lines and paper products in mills (in total, 165 mills were matched and analyzed). The results highlight the ranges of GHG emissions intensities by different product groups and categories, and can be used as a transparent data source for LCA practitioners, policymakers, and the pulp and paper industry to perform further analysis on carbon accounting and strategic planning for GHG mitigation. © 2019 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license

Procedia CIRP 80: 689 - 692
K. Lan, Ou, L., Park, S., Kelley, S. S., and Yao, Y., “Life Cycle Analysis of Decentralized Preprocessing Systems for Fast Pyrolysis Biorefineries with Blended Feedstocks in the Southeastern United States”, Energy Technology, 2019.

Blending biomass feedstock is a promising approach to mitigate supply chain risks that are common challenges for large-scale biomass utilization. Understanding the potential environmental benefits of biofuels produced from blended biomass and identifying driving parameters are critical for the supply chain design. Herein, a cradle-to-gate life cycle analysis model for fast pyrolysis biorefineries converting blended feedstocks (pine residues and switchgrass) with traditional centralized and alternative decentralized preprocessing sites, so-called depots, is explained. Different scenarios are developed to investigate the impacts of parameters such as feedstock blending ratios, biorefinery and depot capacities, preprocessing technologies, and allocation methods. The life-cycle energy consumption and global warming potential (GWP) of biofuel production with depots vary between 0.7–1.1 MJ MJ−1 and 43.2–76.6 g CO2 eq. MJ−1, respectively. The results are driven by biorefinery processes and depot preprocesses. A decentralized design reduces the energy consumption of the biorefinery but increases the overall life-cycle energy and GWP. Such increases can be significantly mitigated by increasing switchgrass content as the energy consumption at the depot is driven largely by the higher moisture content of pine feedstocks. Allocation methods also have a large impact on the results but do not change the major trends and overall conclusions. © 2019 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim


Y. Yao, Marano, J., Morrow, III, W. R., and Masanet, E., “Quantifying carbon capture potential and cost of carbon capture technology application in the U.S. refining industry”, International Journal of Greenhouse Gas Control, vol. 74, pp. 87 - 98, 2018.

Carbon capture (CC) technology is receiving increasing attention as a critical technology for climate change mitigation. Most previous studies focus on the application of CC technology in the power generation sector, while fewer studies have analyzed applications in the refining industry, which is one of the largest greenhouse gas (GHG) emissions sources in the U.S. industrial sector. Unlike the power generation sector, the refining industry has highly distributed CO2 emission sources. In this paper, bottom-up modeling and techno-economic analysis approaches are integrated to quantify the national CO2 emission reduction potential and costs of three types of CC technologies applied to U.S. refineries: (1) pre-combustion, (2) post-combustion, and (3) oxyfuel-combustion. Two scenarios are developed to compare different design strategies for CC systems; one is a distributed design scenario for post-combustion technology, the other is a centralized design scenario for pre-combustion and oxyfuel-combustion technology. The results of the two scenarios are compared, and the trade-offs between different design strategies are highlighted. The results shown in this study provide an intuitive and quantitative understanding of the potential of CC technology to reduce CO2 emissions from the U.S. refining industry. Such information is helpful to policymakers, oil companies, and energy/environmental analysts for strategic planning and systems design to manage future CO2 emissions of refineries. © 2018 Elsevier Ltd

Y. Yao and Masanet, E., “Life-cycle modeling framework for generating energy and greenhouse gas emissions inventory of emerging technologies in the chemical industry”, Journal of Cleaner Production, vol. 172, pp. 768 - 777, 2018.

Assessing the life-cycle energy and environmental impacts of emerging technologies is critical to promote sustainable chemical production because such assessment can provide policy makers with useful insights for future investment and technology deployment; it also provides manufacturers and researchers with quantitative understandings of technology potential, possible bottlenecks, and future Research, Development, and Deployment directions. However, this is a challenging task for emerging technologies due to the lack of inventory data. In this paper, a general and flexible modeling framework was developed to generate the inventory data for new technologies in the chemical industry from a life cycle perspective. The modeling framework is applied to an emerging technology in the U.S. ethylene industry, ethane oxidative dehydrogenation, for demonstration. Broadly, the work described in this paper provides a decision-support method that can be further used by researchers, environment/energy analysts, and policy makers to evaluate the net benefits of innovative technologies, guide early-stage technology development and investment decisions, and conduct strategic planning for meeting energy and emissions reduction goals of the U.S. chemical industry. © 2017 Elsevier Ltd

Y. Yao, Chang, Y., Huang, R., Zhang, L., and Masanet, E., “Environmental implications of the methanol economy in China: well-to-wheel comparison of energy and environmental emissions for different methanol fuel production pathways”, Journal of Cleaner Production, vol. 172, pp. 1381 - 1390, 2018.

The methanol economy concept, which promises to replace fossil fuels as means of energy storage, transportation fuels, and feedstocks of chemical products, has existed for decades, but large-scale applications have been elusive. Currently, China is providing policy support for methanol fuels, thereby taking steps towards the methanol economy concept in the fuels and chemical industries. As China’s methanol focus continues, there are two key questions relevant to policymakers, manufacturers and environmental communities: (1) can methanol fuels realize their expected environmental benefits compared to conventional gasoline when adopted at large scales; (2) are there technology and policy options that should be pursued to ensure the potential environmental benefits associated with methanol manufactured from multiple feedstocks in China? In this study, we developed robust estimates of the primary energy use, greenhouse gas (GHG) emissions, water consumption, and air emissions (SO2 and NOx) associated with methanol fuel life cycle in China. Based on the results, both long-term and short-term implications for promoting methanol fuel in China are discussed. The results and discussions presented in this work provide manufacturers and policymakers with more holistic views of the environmental costs and benefits associated with a potential methanol transition in China, as well as actionable guidance to reduce environmental impacts from a life-cycle perspective. © 2017 Elsevier Ltd


M. A. Hen-Era, Kelley, S. S., and Yao, Y., “A life cycle assessment of torrefaction biomass to displace the coal in the electricity generation”, in International Bioenergy and Bioproducts Conference, IBBC 2017, 2017, vol. 2017-November, pp. 11 - 13.


Y. Yao, Graziano, D. J., Riddle, M., Cresko, J., and Masanet, E., “Prospective Energy Analysis of Emerging Technology Options for the United States Ethylene Industry”, Industrial and Engineering Chemistry Research, vol. 55, no. 12, pp. 3493 - 3505, 2016.

In this study, a bottom-up technology assessment model is constructed and applied to evaluate potential changes in the cradle-to-gate primary energy consumption and greenhouse gas (GHG) emissions of U.S. ethylene production in the future. Three chemical pathways are modeled: conventional natural gas to ethylene, shale gas to ethylene, and crude-oil-based naphtha to ethylene. State-of-the-art technology and five emerging technologies for the production of ethylene from natural gas are evaluated at the process and national levels. The results quantify the primary energy and GHG emissions reductions achievable with state-of-the-art and emerging technologies, highlight the key parameters influencing their reduction potentials, and shed light on the implications of possible feedstock and technology shifts for U.S. ethylene production over the next several decades. The generalized and flexible modeling framework presented can be further used by energy, policy, and environmental analysts for assessing the savings potential of different technologies, making decisions in research and development investment, and strategic planning for meeting energy and emissions reduction goals. © 2015 American Chemical Society.

Y. Chang, Li, G., Yao, Y., Zhang, L., and Yu, C., “Quantifying the water-energy-food nexus: Current status and trends”, Energies, vol. 9, no. 2, pp. 1 - 17, 2016.

Water, energy, and food are lifelines for modern societies. The continuously rising world population, growing desires for higher living standards, and inextricable links among the three sectors make the water-energy-food (WEF) nexus a vibrant research pursuit. For the integrated delivery of WEF systems, quantifying WEF connections helps understand synergies and trade-offs across the water, energy, and food sectors, and thus is a critical initial step toward integrated WEF nexus modeling and management. However, current WEF interconnection quantifications encounter methodological hurdles. Also, existing calculation results are scattered across a wide collection of studies in multiple disciplines, which increases data collection and interpretation difficulties. To advance robust WEF nexus quantifications and further contribute to integrated WEF systems modeling and management, this study: (i) summarizes the estimate results to date on WEF interconnections; (ii) analyzes methodological and practical challenges associated with WEF interconnection calculations; and (iii) points out opportunities for enabling robust WEF nexus quantifications in the future. © 2016 by the authors.

Energies 9(2): 1 - 17


Y. Yao, “Accelerating the development of green technologies for chemical production through multiscale life-cycle technology assessment”, in Education Division 2015 - Core Programming Area at the 2015 AIChE Annual Meeting, 2015, p. 205.
Y. Yao, Graziano, D. J., Riddle, M., Cresko, J., and Masanet, E., “Understanding Variability to Reduce the Energy and GHG Footprints of U.S. Ethylene Production”, Environmental Science and Technology, vol. 49, no. 24, pp. 14704 - 14716, 2015.

Recent growth in U.S. ethylene production due to the shale gas boom is affecting the U.S. chemical industrys energy and greenhouse gas (GHG) emissions footprints. To evaluate these effects, a systematic, first-principles model of the cradle-to-gate ethylene production system was developed and applied. The variances associated with estimating the energy consumption and GHG emission intensities of U.S. ethylene production, both from conventional natural gas and from shale gas, are explicitly analyzed. A sensitivity analysis illustrates that the large variances in energy intensity are due to process parameters (e.g., compressor efficiency), and that large variances in GHG emissions intensity are due to fugitive emissions from upstream natural gas production. On the basis of these results, the opportunities with the greatest leverage for reducing the energy and GHG footprints are presented. The model and analysis provide energy analysts and policy makers with a better understanding of the drivers of energy use and GHG emissions associated with U.S. ethylene production. They also constitute a rich data resource that can be used to evaluate options for managing the industrys footprints moving forward. © 2015 American Chemical Society.

Environmental Science and Technology 49(24): 14704 - 14716


Y. Yao, Chang, Y., and Masanet, E., “A hybrid life-cycle inventory for multi-crystalline silicon PV module manufacturing in China”, Environmental Research Letters, vol. 9, no. 11, 2014.

China is the world’s largest manufacturer of multi-crystalline silicon photovoltaic (mc-Si PV) modules, which is a key enabling technology in the global transition to renewable electric power systems. This study presents a hybrid life-cycle inventory (LCI) of Chinese mc-Si PV modules, which fills a critical knowledge gap on the environmental implications of mc-Si PV module manufacturing in China. The hybrid LCI approach combines process-based LCI data for module and poly-silicon manufacturing plants with a 2007 China IO-LCI model for production of raw material and fuel inputs to estimate ‘cradle to gate’ primary energy use, water consumption, and major air pollutant emissions (carbon dioxide, methane, sulfur dioxide, nitrous oxide, and nitrogen oxides). Results suggest that mc-Si PV modules from China may come with higher environmental burdens that one might estimate if one were using LCI results for mc-Si PV modules manufactured elsewhere. These higher burdens can be reasonably explained by the efficiency differences in China’s poly-silicon manufacturing processes, the country’s dependence on highly polluting coal-fired electricity, and the expanded system boundaries associated with the hybrid LCI modeling framework. The results should be useful for establishing more conservative ranges on the potential ‘cradle to gate’ impacts of mc-Si PV module manufacturing for more robust LCAs of PV deployment scenarios. © 2014 IOP Publishing Ltd.

E. Masanet, Chang, Y., Yao, Y., Briam, R., and Huang, R., “Reflections on a massive open online life cycle assessment course”, International Journal of Life Cycle Assessment, vol. 19, no. 12, pp. 1901 - 1907, 2014.

Purpose: This article summarizes student performance and survey data from a recent massive open online course (MOOC) on life cycle assessment (LCA). Its purpose is to shed light on student learning outcomes, challenges, and success factors, as well as on improvement opportunities for the MOOC and the role of online courses in LCA education in general.Methods: Student survey data and course performance data were compiled, analyzed, and interpreted for 1257 students who completed a pre-course survey and 262 students who completed a post-course survey. Both surveys were designed to assess student learning outcomes, topical areas of difficulty, changing perceptions on the nature of LCA, and future plans after completing the MOOC. Results and discussion: Results suggest that online courses can attract and motivate a large number of students and equip them with basic analytical skills to move on to more advanced LCA studies. However, results also highlight how MOOCs are not without structural limitations, especially related to mostly “locked in” content and the impracticality of directly supporting individual students, which can create challenges for teaching difficult topics and conveying important limitations of LCA in practice. Conclusions: Online courses, and MOOCs in particular, may present an opportunity for the LCA community to efficiently recruit and train its next generations of LCA analysts and, in particular, those students who might not otherwise have an opportunity to take an LCA course. More surveys should be conducted by LCA instructors and researchers moving forward to enable scientific development and sharing of best practice teaching methods and materials. © 2014, The Author(s).

Y. Yao, Graziano, D., Riddle, M., Cresko, J., and Masanet, E., “Greener pathways for energy-intensive commodity chemicals: Opportunities and challenges”, Current Opinion in Chemical Engineering, vol. 6, pp. 90 - 98, 2014.

The chemical industry is poised for significant growth and investment, which presents an opportunity for adoption of greener chemical technologies. This article reviews available and emerging technologies for reducing the fossil fuel demand associated with the ammonia, ethylene, methanol, propylene, and benzene, toluene, and xylenes (BTX) industries. These few energy-intensive commodity chemicals (EICCs) account for around half of the energy use and greenhouse gas (GHG) emissions of the global chemical industry. Available data are harmonized to characterize potential energy use and GHG emissions savings, while technical and economic barriers to adoption are discussed. This information sheds light on the status of future technological options for reducing the impacts of the chemicals industry, and provides quantitative data to industry analysts and policy makers seeking a greater understanding of such options for EICCs. © 2014 Elsevier B.V. All rights reserved.


Y. Yao and You, F., Life cycle energy, environmental and economic comparative analysis of cdte thin-film photovoltaics domestic and overseas manufacturing scenarios, vol. 32. 2013, pp. 733 - 738.

Solar energy is one of the most promising renewable energy alternatives for the replacement of traditional fossil fuels. CdTe photovoltaics (PVs) are thin-film solar cells that have the highest market share among all thin-film technologies. Previous LCA studies of CdTe PVs were based on the data from countries that have similar level of industrialization and strict environmental policies. Thus, to date, no LCA results have explored impacts of dramatic geographic diversity on environmental performance of CdTe PVs. Furthermore, few LCAs for CdTe PVs have taken uncertainty, which is an often overlooked but important aspect, into consideration. In this paper, we apply a “Cradle to Gate” LCA to two scenarios in China and the U.S. respectively and calculate the corresponding energy payback time and life cycle environmental impacts. Then, an uncertainty analysis is undertaken through Monte Carlo simulation. Both deterministic and uncertainty-based results indicate that geographic diversity can drastically change performance of CdTe PVs on environmental sustainability. However, this diversity of production locations has no correlation with other uncertain parameters. Results of uncertainty analysis indicate the influence of each parameter and provide guidance for future optimization of CdTe technology. Finally, comparison between CdTe and other PV technologies is displayed and discussed. © 2013 Elsevier B.V.


B. H. Gebreslassie, Yao, Y., and You, F., “Multiobjective optimization of hydrocarbon biorefinery supply chain designs under uncertainty”, in Proceedings of the IEEE Conference on Decision and Control, 2012, pp. 5560 - 5565.

In this work we propose a bi-criterion, multi-period, stochastic mixed-integer linear programming model that address the optimal design and planning of hydrocarbon biorefinery supply chains under supply and demand uncertainties. The model accounts for diverse conversion technologies, feedstock seasonality and fluctuation, geographical diversity, biomass degradation, demand variation, government incentives and risk management. The objective is simultaneous minimization of the expected annualized cost and the financial risk. The financial risk is measured by conditional value-at-risk. The model simultaneously determines the optimal network design, technology selection, capital investment, production planning, and logistics management decisions. Multi-cut L-shaped decomposition approach is implemented to circumvent the computational burden of solving large scale problems. The capabilities of the proposed modeling framework and solution algorithm are illustrated through the optimal design of the hydrocarbon biorefinery supply chain in the State of Illinois. © 2012 IEEE.

B. H. Gebreslassie, Yao, Y., and You, F., “Design under uncertainty of hydrocarbon biorefinery supply chains: Multiobjective stochastic programming models, decomposition algorithm, and a Comparison between CVaR and downside risk”, AIChE Journal, vol. 58, no. 7, pp. 2155 - 2179, 2012.

A bicriterion, multiperiod, stochastic mixed-integer linear programming model to address the optimal design of hydrocarbon biorefinery supply chains under supply and demand uncertainties is presented. The model accounts for multiple conversion technologies, feedstock seasonality and fluctuation, geographical diversity, biomass degradation, demand variation, government incentives, and risk management. The objective is simultaneous minimization of the expected annualized cost and the financial risk. The latter criterion is measured by conditional value-at-risk and downside risk. The model simultaneously determines the optimal network design, technology selection, capital investment, production planning, and logistics management decisions. Multicut L-shaped method is implemented to circumvent the computational burden of solving large scale problems. The proposed modeling framework and algorithm are illustrated through four case studies of hydrocarbon biorefinery supply chain for the State of Illinois. Comparisons between the deterministic and stochastic solutions, the different risk metrics, and two decomposition methods are discussed. The computational results show the effectiveness of the proposed strategy for optimal design of hydrocarbon biorefinery supply chain under the presence of uncertainties. © 2012 American Institute of Chemical Engineers (AIChE).

AIChE Journal 58(7): 2155 - 2179