(680f) Screening and Ranking Eco-Innovations for Sustainable Circularity: Hotspot and Sensitivity-Based Optimization

Given the urgency of action towards mitigating climate change and reducing material consumption, there are a plethora of innovative technologies, supply-chains and policy-actions being proposed. These are targeted towards towards reducing direct emissions and natural resource uptake. Recently, innovations are also being targeted towards improving the circularity of the system, with the intention to decouple technological systems from fossil-based ‘linear’ economies [1]. It is essential to screen these alternatives based on potential for improving the current product value-chains through direct implementation and novel synergies. Here, the value-chain of a product consists of its entire life-cycle, including extraction of natural resources, manufacturing, usage and end-of life; and the eco-innovations can target any of these sectors. Once a manageable subset of innovations are screened, it would be of interest to industrial and governmental stakeholders to rank these ‘eco-innovations’ based on their scope of adoption and achieving a sustainable and circular value-chain. As part of this work, we have developed a novel working algorithm which relies on a multi-objective optimization routine to screen and rank eco-innovations from a database of conceptual options. The algorithm is implemented for a case study of grocery bags with plastic packaging innovations, compiled by our collaborators at the Global Kaiteki Center.

Limited amount of research has been done to use Life Cycle Assessment (LCA) to evaluate emerging options for sustainability and circularity. Prospective LCA research focuses on performing a deeper analysis of a singular emerging technology by performing predictive scenario analysis for foreground and background emissions in the future [2]. While extremely useful to estimate the actual environmental benefits of a small-scale technology in nascent stages, it is computationally demanding and requires considerable effort. This makes it unviable for evaluating numerous innovative solutions and the incredibly large design space generated due to combinatorial synergies.

We undertake the task of performing preliminary evaluation of a large number of innovative alternatives using a hierarchical screening and ranking algorithm. A methodology to do this does not exist in literature, and experts usually rely on qualitative estimates, heuristics and experience to favor one eco-innovation over other while structuring an investment strategy. Since the algorithm relies on a multi-objective ‘superstructure’ optimization formulation [3], it efficiently explores the innovative alternatives and ranks them based on their potential to improve sustainability and circularity objectives. The steps of the algorithm are summarized below:

1. Optimize the conventional value-chain

2. Find hotspot activities and sensitive stages of the value-chain, as targets for promising eco-innovations

3. Screen conceptual eco-innovations based on value-chain stage and readiness for adoption

4. Model these innovations and optimize the new superstructure network to determine benefits of innovation

5. Rank the screened eco-innovations based on potential to create win-win solutions

In addition to describing the algorithm, this talk will delve into the methodology employed for screening eco-innovations, namely the hotspot analysis and sensitivity-based optimization on conventional value-chains. The hotspot analysis approach employs life cycle allocation and accounting to identify largest contributors to greenhouse gas emissions and material leakages within the value-chain. In contrast, the sensitivity-based approach finds optimal perturbations in the value-chain operation that can bring about the largest improvement in sustainability and circularity objectives [4]. This approach exploits the computational structure of LCA, especially the technology and intervention matrices that quantify the national average operation of processes in the product life-cycle. The sensitivity-based approach defines binary variables indicating whether or not each non-zero cell in the technology matrix is allowed to perturb by a factor. Thus, the design allows for pathway selection and pre-defined number of such perturbations (according to stakeholder preference). The objective functions are still formulated as life-cycle environmental impact (for sustainability) and circularity [5]. The outcome of this approach is a set of optimal perturbations which can lead to win-win solutions, i.e. improvement of both objectives simultaneously. In this talk, we will also discuss the applicability of this novel approach for networks such as cradle-to-cradle life-cycle network of grocery bags and the network representing the entire chemical and material industry [6].

Identifying hotspots and sensitive activities can not only advance the screening step of the proposed algorithm, but can also guide future research and development in critical sectors of the value-chain. This talk will demonstrate the screening of innovations from the plastics packaging domain that can benefit the grocery bags network. It will compare and contrast the inferences from hotspot analysis (a retrospective approach) and the sensitivity-based optimization method (a prospective approach), for several such networks. Ultimately, the algorithm results will be presented in the form of eco-innovation ranking for packaging innovations based on several win-win criteria, such as a ‘utopia point’ shift. We believe that the novel algorithm developed to screen and rank innovations can be beneficial to corporations and governments which are looking to invest limited resources for adoption of eco-innovations. The screening step is itself expected to be very useful for identifying critical life cycle activities where eco-innovation research must be directed to.

References

1. De Jesus, Ana, et al. "Eco-innovation in the transition to a circular economy: An analytical literature review." Journal of Cleaner Production 172 (2018): 2999-3018.

2. Arvidsson, Rickard, et al. "Environmental assessment of emerging technologies: recommendations for prospective LCA." Journal of Industrial Ecology 22.6 (2018): 1286-1294.

3. Vyom Thakker and Bhavik R. Bakshi. "Toward sustainable circular economies: A computational framework for assessment and design." Journal of Cleaner Production 295 (2021): 126353.

4. Vyom Thakker and Bhavik R. Bakshi. “Guiding Innovationsand Value-chain Improvements Using Life-Cycle Design for Sustainable Circular Economy.” Computer Aided Chemical Engineering (2022) – under review.

5. Vyom Thakker and Bhavik R. Bakshi. "Designing Value Chains of Plastic and Paper Carrier Bags for a Sustainable and Circular Economy." ACS Sustainable Chemistry & Engineering 9.49 (2021): 16687-16698.

6. Sen, Amrita, George Stephanopoulos, and Bhavik Bakshi. "Mapping Anthropogenic Carbon Mobilization Through Chemical Process and Manufacturing Industries." PSE 2021+ Conference Proceedings (2022).