Efficient computation of environmentally extended input–output scenario and circular economy modeling

Abstract

Industrial ecology tools are increasingly being used in ways that require high computational times. In the policy arena, this becomes problematic when practitioners want to live-test various alternatives in a responsive and web-based platform. In research, computational times come into play when analyzing large systems with multiple interventions or when requiring many runs for, for example, Monte Carlo simulations. We demonstrate how the computational time of a number of commonly used industrial ecology tools can be reduced significantly, potentially by multiple orders of magnitude. Our case study was the optimization of scenario calculations in Environmentally Extended Input-Output Analysis (EEIOA). Instead of recalculating the Leontief inverse after individual changes to the interindustry relations, as is done traditionally in EEIOA scenario analysis, we give formulations to find the total value of the change in the environmental indicators in one calculation step. We illustrate these novel formulations both for a simple hypothetical system and for the full EXIOBASE EEIO model. The use of explicit formulas decreases the computational time to the degree that it becomes possible to carry out these analyses in live or web-based environments. For our case study, we find an improvement of up to four orders of magnitude.

Relaxing the import proportionality assumption in multi-regional input-output modelling

“…Theoretically, with our allocation algorithm there exist almost infinite possibilities to construct 'new' Z and Y matrices. The computational limit of 4897 iterations is almost entirely owed to the calculation of the Leontief inverse which is -given the size of EXIOBASE -computational expensive even when solved as a system of linear equations (see also [29]).…”

Study on the development status of the coupling of the digital innovation ecosystem and the digital economy

“…The importance of complementarities to the success of platform ecosystems cannot be ignored, as complementarities are characterized by flexible systems, large numbers, more flexible digital innovation structures, and considerable digital innovation potentials, and the more digital innovations of complementarities, the more value they create for the platform ecosystems through network effects [7][8][9]. Therefore, the issue of how to strengthen the digital innovation of complementors in platform ecosystems has become the key to the development of platform ecosystems [10][11][12]. Theoretically, the research of platform ecosystem exists on the crossfertilization of multiple disciplines, and ecological niche, as the core of the environmental theory, is the basis of the digital innovation behaviors of compliments, but the current research on the ecological niche of complementary platforms is still immature; especially the mechanism of the influence of the environmental niche of complementary platforms on their digital innovations is still unclear, and indepth research is urgently needed [13][14][15].…”

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

“…This is an advantage over monetary IO tables (MIOT), whenever research questions focus more strongly on the biophysical dimension of economic activities. For example, as EEIOA is increasingly used for analyzing circular economy strategies (Aguilar‐Hernandez et al., 2019; Çetinay et al., 2020; Donati et al., 2020; Tisserant et al., 2017; Towa et al., 2020; Wiebe et al., 2019), the reliance on MIOTs has been identified as a major limitation because circularity policies are usually defined in physical units (Aguilar‐Hernandez et al., 2018). Third, by tracing material flows through economic sectors in a mass‐balanced manner, PIOTs open up the black box of ew‐MFA and thereby widen its applicability for policy‐oriented analysis that focuses on specific economic activities, such as manufacturing, construction, household consumption, or public procurement (Altimiras‐Martin, 2014; Giljum & Hubacek, 2009).…”