Abstract
Circular Economy (CE) is increasingly considered vital in achieving environmental sustainability. Assessing progress is a crucial part of implementing CE policies and managing CE practices [1]. Both scholars and practitioners have thus developed numerous CE indicators varying in scope and objectives. Achieving environmental sustainability is a matter of the society as a whole, but tangible actions happen at much smaller levels. However, there is a lack of correspondence between existing macro- (society-, region-, etc.) and micro- (product-, company-, etc.) level indicators [2]. The ultimate objective of this research is to determine what kind of micro-level CE indicators can be utilized to achieve macro-level benefits, and how. In order to accomplish this, we analyze micro-level indicators from the perspective that circularity does not ensure environmental sustainability, and circularity achieved at the micro-level is potentially insignificant if not counterproductive toward circularity and/or sustainability at the macro-level. Then, we propose agent-based modelling (ABM) as an effective approach to investigate the complex relationship between circular actions enabled by indicators at the micro-level and the environmental implications at the macro-level. Preliminarily, we created a list of 67 CE indicators and found at least 10 measuring “circularity” at the product- or the company-level. Among these, two major patterns were identified: 1) the combination of assessments of multiple CE elements and 2) the assessment of economic value. Some indicators, in effect, combine the assessment results of engagement levels of a product or company in multiple aspects of the CE, possibly but not necessarily using scores from more specific indicators (e.g. recycling rates) and calculating the weighted average. Others primarily focus on how long the economic value of a product is sustained for but not on specifically how. We suggest that these indicators could present a satisfactory score, recognizing the existence of a circular measure itself, even when the ultimate, overall outcome of the measure was environmentally insufficient or counterproductive. This is especially plausible when an LCA-based approach is not involved. Additionally, for “combination” indicators, weights perfectly calibrated at one particular point in time may yield erroneous and misleading results under changing or different conditions. A problem with “economic-value” indicators is that the economic value of materials do not necessarily correlate with the amount or quality of the materials, while environmental impacts do. Furthermore, consumers and other products/companies play a significant role in the real world. For example, environmentally conscious consumers may pay a premium for more circular products but unknowingly induce a rebound effect on the whole; recycling between multiple companies may be more environmentally effective, even if it negatively impacts the individual circularity scores of the companies. Dynamic factors outside the scope of the product or company in question are not considered by micro-level circularity indicators, but substantial in the actual process of achieving macro-level sustainability. We argue that ABM is an effective approach to cope with the complexity entailing the utilization of CE indicators as outlined above and to evaluate and develop CE indicators as an enabler of environmental sustainability. ABM is a type of computational modelling that simulates how the actions of autonomous individuals in a system result in the characteristics of the system as a whole. Our research shows that ABM can test micro-level indicators for environmental consequences at the macro-level, with consideration of consumer responses and corporate decision-making. [1] Saidani, et al. (2019). A taxonomy of circular economy indicators. J. Clean. Prod., 207, pp.542-559. [2] Moraga, et al. (2019). Circular economy indicators: What do they measure? Resour Conserv Recycl, 146, pp.452–461.
