Harmonizing and Analyzing Multi-Sectoral Dynamics at Flexible Spatial Scales
Planning and decision making around infrastructure needs often happen at scales relevant to individual resources, for example by river basins for water or grid-regions for electricity. However, these sectors are highly inter-connected and a holistic multi-sectoral approach, accounting for dynamics across scales and sectors, would lead to a more efficient system. To facilitate holistic cross-sectoral decision-making, a team of researchers led by scientists at the U.S. Department of Energy’s Pacific Northwest National Laboratory, have developed an open-source modeling platform, called Metis, that combines global human and Earth system dynamic tools with local datasets. This platform allows users to explore and evaluate impacts of different global and regional influences on the evolution of local resource interactions.
The importance of holistic multi-sectoral modeling at multiple scales is well established in the literature, but implementation in practice remains a challenge. Metis provides an efficient modeling platform to easily analyze and evaluate the impacts of different global and regional influences across sectors at finer spatial scales than typically modeled in the Global Change Analysis Model (GCAM).
Metis is an open-source R package hosted on GitHub. To overcome the challenge of data scarcity that is typical of regional planning studies evaluating multi-sector dynamics, Metis provides users with default data sets describing energy, water, and land supplies and demands for the region of interest. These default data sets are built from GCAM outputs that are downscaled to a region of interest. Metis then aggregates the downscaled data and integrates multi-sectoral data across modeling tools at variable spatial scales. Metis functions collectively allowing users to compare, manipulate, and harmonize multi-sector data at user-specified spatial scales. Users can also identify and quantify sectoral inter-linkages via Metis. Each Metis function can also be used independently to support an array of other research applications, such as spatial analysis and data visualization.