# Incorporating New Technologies in EEIO Models
## 5 Conclusions and Discussion
Second, as mentioned in Appendix B, the perfect substitution assumption used here, with a percentage, and the difference in prices between the existing product and the new product generate inconsistencies between economic assumptions and production processes. In reality, the penetration of these new technologies will depend on market interactions and, given price differences, both an income effect and a substitution effect will be present. Thus, the introduction of the new product in this methodology is a simplification of the economy. Similarly, this perfect substitution assumption could be too strong for some new products that we may be interested in analyzing, limiting the applicability of the methodology. For example, if we had considered woody-based ethanol, because of blending limits, it could not be considered a perfect substitute for gasoline. However, it could be a perfect substitute for corn-derived ethanol. Third, there are the well-known limitations of EEIO in terms of the aggregation of products into economic sectors, that the production functions have constant returns to scale and the assumption that all products are made in the same way within an industry, also called the industry technology assumption [5; 7; 12; 41]. Validation and cross-checking of EEIO, hybrid and process-based LCA data and methods is an ongoing research theme and continuing imperative [42; 43; 44]. Future research to improve this methodology could include: (1) To disaggregate similar industries by separating them into similar commodities and the remaining commodities produced. This would allow for a more consistent substitution. (2) To extend the methodology to include secondary products of the new industries. For example, bio-refineries could produce biofuels and bio-chemicals as co-products. In some cases, these bio-chemicals could be key for the economic viability of the bio-refinery. These secondary products could generate broader displacement effects that could have repercussions on future production and therefore on future environmental impacts. Secondary products could be represented using this methodology by iteratively adding one product at a time, as long as the modeler is aware of the uncertainties associated with the substitution assumption each time a new product is added. (3) Comparison of this methodology with hybrid LCA approaches using the same data could identify how different methodologies vary and the circumstances in which the result differ or can be cross-validated [42; 43; 44]. (4) The transformation from monetary units to physical units and vice versa could have an important impact on the results depending on the price used for the transformation. See Appendix B for more information. Here, mixed unit input-output formulations could be considered [7]. **Author Contributions:** Conceptualization, C.G.A.-P. and W.W.I. ; methodology, C.G.A.-P.; software, C.G.A.-P.; validation, C.G.A.-P. and W.W.I; formal analysis, C.G.A.-P.; investigation, C.G.A.-P.; resources, W.W.I. and V.M.T. ; data curation, C.G.A.-P.; writing--original draft preparation, C.G.A.-P.; writing--review and editing, V.M.T., W.W.I. and C.G.A.-P.; visualization, C.G.A.-P.; supervision, V.M.T. and W.W.I. ; project administration, W.W.I. ; funding acquisition, W.I and V.M.T. All authors have read and agreed to the published version of the manuscript. **Funding:** This research was funded by USEPA through the Regional Sustainability and Environmental Sciences (RESES) program. The APC was funded by USEPA's Center for Environmental Solutions and Emergency Response. This document was reviewed in accordance with U.S. Environmental Protection Agency policy and approved for publication. The views expressed in this article are those of the author(s) and do not necessarily represent the views or the policies of the U.S. Environmental Protection Agency.