Forecasting greenhouse gas emissions in the Korean shipping industry using the least squares adjusted with pseudo data
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The problem of greenhouse gas emissions is considered as one of the most important problems in global environmental issues. In the shipping industry, the problem of greenhouse gas emissions is also a critical issue, and International Maritime Organization published 3rd report on greenhouse gas in 2014. Studies on greenhouse gas emissions in the shipping industry have mainly focused on policy issues or an estimation of greenhouse gas emissions from fuel consumption. In this paper, we forecast greenhouse gas emissions using nonparametric statistical methods. Among the nonparametric statistical methods, local regression with one-sided kernel function is used to forecast a future value. However, setting a future value as an unknown in the optimization procedure gives the difference between the test error and the training error. We propose the adjusted least squares with pseudo data to reduce this difference and to get a more accurate forecast. Theoretical bases on our method and empirical results are presented comparing the various types of pseudo data.
Abstract
The problem of greenhouse gas emissions is considered as one of the most important problems in global environmental issues. In the shipping industry, the problem of greenhouse gas emissions is also a critical issue, and International Maritime Organization published 3rd report on greenhouse gas in 2014. Studies on greenhouse gas emissions in the shipping industry have mainly focused on policy issues or an estimation of greenhouse gas emissions from fuel consumption. In this paper, we forecast greenhouse gas emissions using nonparametric statistical methods. Among the nonparametric statistical methods, local regression with one-sided kernel function is used to forecast a future value. However, setting a future value as an unknown in the optimization procedure gives the difference between the test error and the training error. We propose the adjusted least squares with pseudo data to reduce this difference and to get a more accurate forecast. Theoretical bases on our method and empirical results are presented comparing the various types of pseudo data.
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