From Lab to Market: Environmental Due Diligence using LCA and IAM tools

Understanding the environmental implications of new technologies in the early stages of development is critical. It shapes investment decisions and has become a prerequisite for securing funding, similar to the financial due diligence (DD) process. However, early-stage lifecycle assessment (LCA) and ex-ante LCA methods that predict a mature technology’s environmental impacts provide limited understanding of market potential. It is even harder to use those methods when considering investments across different technological domains and sectors. Therefore, a mix of assessment approaches, including early-stage LCA and feasibility analyses with a consequential approach and integrated assessment models (IAMs), is key to justifying investment in early-stage technology implementation. 

In this study, we introduce an environmental due diligence process (DD) for the evaluation of new products and technologies within their applied market space. We combine different industrial ecology tools and innovation management methods in support of investment decision-making in early development stages. We showcase the applicability of the environmental DD framework using the case of construction materials. We examine the potential of lower carbon cement production and illustrate the source of data, the market assessment process, the DD process and the results. We then discuss the potential and challenges in using this data for performing national assessment impacts using integrated assessment models such as the MESSAGEix-Buildings and MESSAGEix-Materials developed by IIASA.  

Our research is important because it interconnects industrial ecology, impact investing and integrated assessment models. It proposes a new DD process, generic enough to adapt across industries, TRL stages, and market sectors. This approach is robust and sufficiently established to lower the barrier for using LCA data, creating a high impact for environmental considerations in early design processes while modeling large scale implications across sectors.