Testing a multi-scale, metabolic analysis at the sub-national versus national level: A societal ecological exploration of Scotland within the United Kingdom

• We consider the utility of MuSIASEM to offer a tractable and interpretable way to conduct multi-scale and cross-categorical analyses of material and energy flow. • We test two cases, one embedded and subnational to the other: Scotland and the United Kingdom. • MuSIASEM lives up to its claims of delivering multi-level and integrated views of a socioecological organism but its utility can be limited by the ways official statistics are collected and presented. • Particularly, system integration across multiple statistical domains presents challenges. • Policymakers and statistical agencies should reconsider their data collection policies and frameworks. To confront the many crises facing humanity—besides other things—proper analytics and data are necessary. Moreover, policymaking should face the challenge that, whatever the system governed, there is both a broader socioecological context and a diversity within the domain of influence. That is, nearly all sustainability policy decisions are inherently multi-scale and need appropriate problem-defining analytics. Here, we consider the utility of Multi-Scale Integrated Analysis of Societal and Ecosystem Metabolism (MuSIASEM) and its claims to offer an interpretable way to conduct analyses of material and energy flows at the nexus of societal and ecological systems. MuSIASEM claims to facilitate the definition and integration of non-commensurable views of industrial sectors, workforce capacities, land use patterns, energy carriers and uses, gross value added, and other resource flows. In this study, we test an application of MuSIASEM on a sub-national and national case, Scotland and the UK, and find that MuSIASEM can deliver multi-level and integrated views of a socioecological organism. However, its utility can be limited by the siloed ways statistics are collected and presented. We argue that policymakers and statistical agencies should reconsider their frames and representations of ‘what data is needed’ to advance deeper, cross-scale analytics of governance.