Table 2
Strengths and weaknesses of MFA.
Strengths | Weaknesses | |
---|---|---|
Time, temporality | Foresight, the future based on modeling derived from MFA (like IoS models). Deals also with the past (time series since beginning/middle of 20th century) due to the long lifetimes of some goods (e.g. construction, machines, industrial equipment). | The IoS model is a phenomenological model, not a model based on a mechanism described in details. Need for more work in this area, based on outside scientific disciplines. |
Scale, space | Large systems, institutions, large companies or industry associations | Too large for most companies. They consider that it is not “their business”! |
Scope | 1/3 of the Mendeleev table already covered. | Missing elements and materials, such as polymers, composites, missing goods and parts. Focus on “pure” elements is also a weakness. Need to take co-elements into account such as alloying/tramp elements in metals, impurities in ores (true at mining, use and recycling levels) |
Access to data | Somewhere in the datasphere but (for a long time) kept safely inside the knowledge base of a research team. Supplementary materials and open data policies are slowly removing this habit. | No commercially or publicly available database, except for MFAc. Therefore, newcomers have a high barrier of entry. Moreover, data reconciliation remains a rather “hidden”, “hush-hush” reality. |
Standardization | No | No |
Achievements | Foresight studies on future materials need, construction, etc. | Not enough estimates of recycling rates nor of stocks (anthropogenic mines, hibernating stock, etc.) |
Missing elements | Social dimension Reuse, lean and frugal design rarely studied Economics (MFC, similar to LCC?) MFA at a smaller scale, e.g. addressing industrial symbiosis Integration of MFA as a strategic management tool inside business outfits |
|
Evolutions? | Automatic creation of MFAs, based on algorithms, automatic collection of data in the Big Data sphere, Artificial Intelligence, etc. |
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