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Will Trump's coal revival plan work? - Comparison of results based on the optimal combined forecasting technique and an extended IPAT forecasting technique

Qiang Wang, Shuyu Li and Rongrong Li

Energy, 2019, vol. 169, issue C, 762-775

Abstract: Discussions about United States President Trump's coal revival plan are dominated by qualitative analyses, few quantitative analyses have been conducted. To fill the research gap, this study analyzes the future coal demand of the United States from a market perspective. Both time-series and econometric forecasting techniques are developed to quantify the change of total coal consumption and coal consumption of electricity (sharing over 90% of total coal consumption) in the United States. The proposed time-series forecasting techniques are based on metabolic grey model, Autoregressive Integrated Moving Average Model-grey model, and induced ordered weighted geometric averaging operator. The mean absolute percent error of the proposed technique is less than 1%, indicating the proposed forecasting technique provides reliable information. The forecasting results obtained by time-series model show coal consumption and coal demand for electricity sector in U.S. will continue to decline in the next decade. The proposed econometric forecasting technique is based on the IPAT identity, grey model and Vector Auto-Regression. The combination econometric forecasting technique is used simultaneously to analyze the impact of various internal factors on coal consumption. The results from the proposed econometric technique also show the decline trend of coal demand in the U.S. Thus, the results from the time-series and econometric forecasting technique are consistent. Based on the quantitative analyses, this study contend that Trump's policy is unlikely to revive the coal industry.

Keywords: Trump's energy policy; Coal revival; Quantitative analysis time-series forecasting model; Econometric forecasting technique (search for similar items in EconPapers)
Date: 2019
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Handle: RePEc:eee:energy:v:169:y:2019:i:c:p:762-775