# Incorporating New Technologies in EEIO Models
## 1 Introduction  
New technologies may be developed to provide new goods and services with the same function as existing products but with reduced environmental impacts, while these new technologies may have been developed with the intention of being environmentally preferable and may further be marketed to potential consumers under that assumption, detailed third-party assessments are needed to provide a more quantitative assessment of them in respect to the status quo and to avoid so-called green washing. Various governmental and standards bodies have pointed to life cycle assessment (LCA) as a comprehensive method to evaluate the potential environmental impacts of goods and services [1, 2]. However, LCA faces challenges in effectively evaluating new technologies that have not yet been implemented at production scale [3, 4]. The underlying models for LCA, life cycle inventory (LCI) models, may be constructed using traditional process-based LCI [5], environmentally extended input-output-based LCI [5, 6, 7] or hybrid approaches that integrate the two [5, 8, 9]. As EEIO models (also known as Input-Output LCA models) embed the full structure of the underlying economy represented in the input-output table, EEIO models have an inherent advantage of comprehensiveness and completeness when it is of interest to understand the impacts of new products and new technologies across the whole economy. EEIO models allow us to understand the environmental impacts of products and services in our current economy, without the limitation of an explicit boundary, which is characteristic of process-based LCA methodology. As of this, EEIO models may be a good candidate to assess environmental impacts for new products under development. This could support policy and industry decision making. However, a limitation of EEIO models is that, since these technologies are not yet part of the economy, they are not represented in current economy Input-Output (IO) tables. Nevertheless, various attempts have been made using IO models to understand the potential of new technologies, considering them as new industries in the model. There are two main ways to incorporate new industries into IO models [5; 7; 10]. The first is through changes in the final demand vector. [PERSON] and colleagues [11] developed a methodology to analyze consumer-based policy by modifying the final demand vector, considering both the decrease in the consumption of a good of interest (e.g., meat) and a substitution and income effect that results from different prices. [PERSON] [12] evaluated the economic output and employment of renewable energy in the US using a synthetic industry approach using the demand vector. Similarly, [PERSON] and colleagues [13] investigated increased demand for wind farms in the US to understand their socio-economic impacts. This method is easy to implement and the data requirements are manageable. However, while this method includes the impact in the economy due to the increased demand of the inputs required for the new technologies, it does not include the impacts due to other sectors potentially buying the new product [7]. Another approach is to add new sectors to the IO tables by augmenting them by adding a new commodity and the new technologies. This allows incorporation of both the effect of increased demand for inputs and also the consumption of the new product by existing sectors. The first known models to augment IO tables to model new technologies were both created in the 1970s to perform energy scenario analysis at a national scale in the United States. The first was a model created by [PERSON] to assess the economy-wide effects of coal gasification and gas turbine topping cycle technologies but intended for the broader prospective analysis of new technologies [14]. The second was a model created for energy scenario analysis that was developed at the University of Illinois Center for Advanced Computation in the mid 1970s [15].