Abstract
Conditional forecasts of US economic and energy sector activity are developed using information from a dynamic, data-rich environment. The forecasts are conditional on a path for carbon dioxide emissions outlined in the US Environmental Protection Agency’s Clean Power Plan (CPP) and are estimated based on a factor-augmented autoregressive framework. Results suggest that overall growth will be slower under the CPP than it would otherwise; however, economic growth and CO2 reductions can be achieved simultaneously. There are little differences between unconditional (business-as-usual) and conditional forecasts of the variables in the early part of the forecast period; the impacts of the CPP are small while the constraints on carbon dioxide are less stringent. The results serve as a data-driven complement to structural analyses of policy change in the energy sector.
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Notes
In addition to the causal structure outlined in Fig. 2, forecasts are generated from a model where the direction of flow from CO2 to factor 4 is reversed (i.e., factor 4 to CO2). Forecasting results are robust to this specification (industrial production has 3.97% annual growth in the conditional case and 3.92% annual growth in the unconditional case; real personal income 3.27% and 3.43%).
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The views expressed in this paper are those of the authors and do not necessarily reflect the position of the Federal Reserve Bank of Chicago, the Federal Reserve System, or Texas A&M University.
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Binder, K.E., Mjelde, J.W. Projecting impacts of carbon dioxide emission reductions in the US electric power sector: evidence from a data-rich approach. Climatic Change 151, 143–155 (2018). https://doi.org/10.1007/s10584-018-2297-9
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DOI: https://doi.org/10.1007/s10584-018-2297-9