In recent years the analysis of US climate policy on the electricity sector has predominantly deployed electricity planning or capacity expansion models that use deterministic or equilibrium optimization methods. While uncertainty in key input assumptions is considered, it is usually restricted to scenario analysis. In this study we combine time-series econometric forecasting methods with an equilibrium electricity system-expansion model. The goal is to produce statistically rigorous distributions of outcomes, rather than rely upon individually selected scenarios.We apply these techniques to the case of the US Inflation Reduction Act (IRA) in the context of the western US electricity grid. The most significant power sector financial incentives are tax credits applied to eligible zero-carbon and storage resources. Our results indicate that the impact of the IRA, in terms of additional investment in low-carbon resources, depends heavily on the realization of key exogenous variables. However, the net effect of the IRA is to sharply narrow the range of future carbon emissions, largely by eliminating states of the world where investment in natural gas resources would otherwise be optimal.
Authors
- Acknowledgements & Disclosure
- This research was partially supported supported by a grant from the California Air Resources Board (CARB). Bushnell also received support from NSF Grant 2330450. We thank Wuzheqian Zhao, Tengda Gong, and Jessica Lyu for outstanding research support. The statements and conclusions in this paper are those of the authors and not necessarily those of the any supporting institution. The views expressed herein are those of the authors and do not necessarily reflect the views of the National Bureau of Economic Research.
- DOI
- https://doi.org/10.3386/w32830
- Pages
- 38
- Published in
- United States of America
Table of Contents
- Introduction 3
- Modeling The Inflation Reduction Act 5
- Electricity Market Investment Model 7
- Model Specification 7
- Model Calibration 9
- Investment in New Generation 10
- Data and Forecasts 12
- Results 17
- Conclusion 22
- Dynamic Factor Model 26
- Obtaining the factors 26
- VAR on the factors 27
- Forecasting the factors 27
- Forecasting the individual variables 28
- Comparison to Traditional Time Series Method: Vector Error Correction 29
- Details of electricity market investment model 33
- Model Specification 33
- Market demand 34
- Fossil-fired generation costs and emissions 34
- Transmission network 35
- Hydro, renewable and other generation 36
