Creating multi‐scale nested MRIO tables for linking localized impacts to global consumption drivers

Abstract

Industrial Ecology Virtual Laboratories (IELabs) enable the construction of national‐to‐local‐scale multi‐regional input–output (MRIO) models. These IELabs have been proven to be especially important for analyzing research questions that warrant sub‐national spatial detail. The field of industrial ecology has clearly progressed from the time of national‐only input–output tables. Here, we present a newly developed tool called NLab—“nested IELab”—that nests sub‐national MRIO tables within global country‐scale MRIOs. This capability allows for the investigation of interactions between sub‐national production and consumption systems, with global systems interlinked via international trade. We provide a technical and mathematical roadmap for construction of nested input–output tables in the NLab, and demonstrate this capability through a real‐world assessment of the Western Australian wine industry. Our results suggest that nested MRIO tables provide an added layer of detail at a regional level, when undertaking consumption‐based footprint assessments, leading to improved assessment of quantification of regional impacts. The NLab presented in this work provides tools for analysis of complex trade linkages between industries at various scales, which has the further potential to open avenues for policy‐makers to analyze the implications of local decisions at a global level, and vice versa.

The PIOLab: Building global physical input–output tables in a virtual laboratory

“…An IELab capability that the PIOLab has not tapped so far is the possibility to create subnational and hierarchically nested multi‐geography IO tables that disaggregate economy‐wide material flows into different states and local regions. Because environmental and socioeconomic conditions can vary significantly within countries, IO models should aim to move from the aggregated national to a more detailed spatial level (Fry et al., 2021; Sun et al., 2019). For example, Towa and colleagues (2020) tested various regionalization approaches in the construction of their globally nested hybrid MRSUT where Belgian regions are represented with more detail.…”

Data innovation in industrial ecology

“…Improving the regionalization and geospatial granularity of IE data has been a long-standing research priority, not least to yield recommendations that are specific and applicable in each locality. This regionalization effort continues in the datasets published in this special issue (Agez et al, 2021;Fry et al, 2021;Towa et al, 2020).…”

“…Notably, to facilitate such data consolidation,Vilaysouk et al (2021) demonstrated the use of a semi-supervised ML approach to the development of a classification framework for material intensity parameters of residential buildings. Their data-driven approach distills a highly heterogeneous range of possible material intensity profiles into a conveniently small number of clusters and their associated probability density functions.IE research is also innovating in the compilation of environmentally extended input-output (EEIO) datasets Fry et al (2021). developed a software tool to nest subnational EEIO tables in global multi-regional EEIO tables (MRIO), augmenting their geographical granularity while maintaining the link to global value chains and their implications.…”

Estimating the dynamic environmental footprints of the global finance and business sector towards sustainable development goals

“…However, the multi‐regional input–output (MRIO) analysis technique, based on SRIO analysis, can characterize economic flows between different economic sectors of various regions and provide a global multinational environmental and socioeconomic impact model that satisfies the final demand of a given region (Bulak & Kucukvar, 2022; Chen et al, 2021). The environmental‐extended multi‐regional input–output (EE‐MRIO) analysis, a typical top‐down approach, measures EF by incorporating trade networks between regional sectors and their associated environmental impacts (Dejuán et al, 2022; Fry et al, 2022; Hu et al, 2021; Mi et al, 2020; Xing et al, 2022; Xu et al, 2022; Yang et al, 2020). Many existing studies have examined EF in various global sectors.…”

“…The environmental-extended multi-regional input-output (EE-MRIO) analysis, a typical top-down approach, measures EF by incorporating trade networks between regional sectors and their associated environmental impacts (Dejuán et al, 2022;Fry et al, 2022;Hu et al, 2021;Mi et al, 2020;Xing et al, 2022;Xu et al, 2022;Yang et al, 2020). Many existing studies have examined EF in various global sectors.…”