Improving Data Resolution for Industrial Modeling and Analysis

The industrial sector faces substantial and unique challenges in a deeply decarbonized future. For instance, successfully mitigating emissions will likely involve a combination of several strategies, including energy... [ view full abstract ]

The industrial sector faces substantial and unique challenges in a deeply decarbonized future. For instance, successfully mitigating emissions will likely involve a combination of several strategies, including energy efficiency, material efficiency, and electrification. However, analysis of possible decarbonization scenarios is complicated by the heterogeneity of industrial processes and is limited by the lack of detailed, publicly-available data. This presentation will summarize a novel industrial analysis tool built on the merger of spatially-resolved energy data. The tool will serve as a resource for analysts and policy-makers interested in exploring the potential impacts and interactions of deep decarbonization strategies for industry and their implications for the U.S. economy overall. The analysis framework is flexible and allows for researchers to carry out analysis at multiple spatial and temporal scales. Possible extensions of the tool include analysis of local industry resilience and identification of industrial symbiosis potential.

The tool is comprised of two components that enable the construction of deep decarbonization scenarios based on changes to energy efficiency, fuel choice, and materials use. The first component is a detailed framework for linking industrial sector activity with energy use and associated emissions. This framework is built on sub-state-level industrial energy end use data derived from a number of publicly-available sources, including the U.S. Energy Information Administration (EIA), U.S. Census Bureau, and U.S. Environmental Protection Agency. This newly integrated dataset supports a level of analysis detail that was not previously possible with publicly-available data. The dataset will be made available to all researchers through the NREL Data Catalog, which is linked to other open data repositories like OpenEI and data.gov.

The second component of the tool is a dynamic hybrid input-output model constructed from publicly-available U.S. Bureau of Economic Analysis, EIA, and U.S. Geological Survey data. The model will enable user-defined changes to energy and materials use to be reflected as structural changes in the industrial sector. These model dynamics avoid the limitations associated with fixed technical coefficients. The model’s hybrid structure identifies transactions for energy and select materials in physical units and uses monetary units for the remaining commodities (e.g., wood and paper products, machinery, and computer and electronic products). The use of physical units allows the model to be internally consistent in its accounting of material and energy flows, unlike with the use of monetary units only.

The model is being used to explore several use cases, including reduced material demand and increased alternative fuels and feedstocks. The presentation will describe likely scenario assumptions and implementation within the tool. The discussion will also include limitations of the tool in its current form and recommendations for future improvements.