Environmentally
Extended Input–Output Databases (EEIOs)
provide an effective tool for assessing environmental impacts around
the world. These databases have yielded many scientific and policy
relevant insights, especially through the national accounting of impacts
embodied in trade. However, most approaches average out the spatial
variation in different factors, usually at the level of the nation,
but sometimes at the subnational level. It is a natural next step
to connect trade with local environmental impacts and local consumption.
Due to investments in earth observation many new data sets are now
available, offering a huge potential for coupling environmental data
sets with economic models such as Multi-Region Input–Output
(MRIO) models. A key tool for linking these scales are Spatially Explicit
Input–Output (SIO) models, which provide both demand and supply
perspectives by linking producers and consumers. Here we define an
SIO model as a model having a resolution greater than the underlying
input–output transaction matrix. Given the increasing interest
in this approach, we present a timely review of the methods used,
insights gained, and limitations of various approaches for integrating
spatial data in input–output modeling. We highlight the evolution
of these approaches, and review the methodological approaches used
in SIO models so far. We investigate the temporal and spatial resolution
of such approaches and analyze the general advantages and limitations
of the modeling framework. Finally, we make suggestions for the future
development of SIO models.