(749c) Exploring a Computational Framework for Spatially-Explicit Absolute Sustainability Assessment Based on a Multi-Regional Hybrid Approach | AIChE

(749c) Exploring a Computational Framework for Spatially-Explicit Absolute Sustainability Assessment Based on a Multi-Regional Hybrid Approach


Lee, K. - Presenter, The Ohio State University
Chun, S., The Ohio State University
Bielicki, J. M., The Ohio State University
Bakshi, B., Ohio State University
To give more reliable sustainability assessment results to decision-makers, life cycle assessment (LCA) methodologies have been developed in various ways. Some of the developments have focused on how to represent the complete life cycle boundary while utilizing detailed inventory data and how to take account of the spatially heterogeneous inventory data. The former one was accomplished by a multiscale modeling approach (hybrid LCA) that integrates process-based LCA (PLCA) model with environmentally-extended input-output (EEIO) model [1]. The later one has been addressed by developing multi-regional models for each of the PLCA [2] and EEIO [3] models. Multi-regional models are needed to conduct a spatially-explicit sustainability assessment since activities and interventions are different by region. For instance, activities in one region may have different commodity inputs, technological efficiencies, supply chains, and emission coefficients from the other regions. Different demographics by region also require region-specific impact characterization factors. Many existing PLCA and EEIO models such as the U.S. NREL PLCA model and the USEEIO model [4] are based on national average data, and thus, they do not take account of the above regional heterogeneity.

In this work, we explore a computational framework for spatially-explicit absolute LCA (SEA-LCA) utilizing local process models as well as regional/national/global EEIO models. For such multi-regional models, data availability is one of the primary limiting factors because it is challenging to collect region-specific inventory data. To minimize such time-consuming tasks, a hybrid modeling approach that integrates existing databases and models is necessary. The resulting hybrid model consists of multi-regional inventories at multiple spatial scales. If local process data are available at the facility or county scale, such data are included in the hybrid model as local process inventories. If any local process data are not easily available at fine scales, multi-regional EEIO data and national/global EEIO data can be employed to represent the complete life cycle boundary. U.S. EPA has published the U.S. state-level EEIO model [3] and the USEEIO model [4].

Another effort to advance the LCA methodology is to include the supply of ecosystem services in the PLCA model. Techno-ecological synergy in LCA (TES-LCA) model [5] and its regionalized model [6] have been developed to account for ecosystem services in calculating absolute sustainability indicators. Multi-regional ecosystem services can also be included in the hybrid model at multiple scales depending on their data availability. The resulting SEA-LCA model could assess the absolute sustainability of activities at various scales (e.g., local, state-level, national, and global scales). Sustainability indicators at different scales are useful in addressing various stakeholder’s interests and serviceshed scales.

A case study is performed on local activities in the U.S. Midwest. We demonstrate how local process models can be connected to various scale’s economic models. We discuss how sensitive to spatial scales the LCA indicators are and why a spatially-explicit assessment needs to be considered.

Through model integration, the SEA-LCA model advances sustainability assessment methodology by accounting for three aspects: multiple spatial scales, spatial heterogeneity, and ecosystem services. Additional aspects can be considered as well to advance the method further. One could be the consideration of cross-disciplinary effects of market changes and social behavioral changes on the sustainability indicators. The temporal dynamics of inventory data also need to be taken into account. A dynamic computable general equilibrium model and behavioral change model could be potentially integrated with the LCA model to address such additional aspects.


[1] Suh, S. (2004). Functions, commodities and environmental impacts in an ecological–economic model. Ecological Economics, 48(4), 451-467.

[2] Yang, Y., & Heijungs, R. (2017). A generalized computational structure for regional life-cycle assessment. The International Journal of Life Cycle Assessment, 22(2), 213-221.

[3] Yang, Y., Ingwersen, W. W., & Meyer, D. E. (2018). Exploring the relevance of spatial scale to life cycle inventory results using environmentally-extended input-output models of the United States. Environmental modelling & software, 99, 52-57.

[4] Yang, Y., Ingwersen, W. W., Hawkins, T. R., Srocka, M., & Meyer, D. E. (2017). USEEIO: A new and transparent United States environmentally-extended input-output model. Journal of cleaner production, 158, 308-318.

[5] Liu, X., Ziv, G., & Bakshi, B. R. (2018). Ecosystem services in life cycle assessment-Part 1: a computational framework. Journal of Cleaner Production, 197, 314-322.

[6] Liu, X., Ziv, G., & Bakshi, B. R. (2018). Ecosystem services in life cycle assessment-Part 2: Adaptations to regional and serviceshed information. Journal of Cleaner Production, 197, 772-780.