Modeling and forecasting carbon dioxide emissions in China using Autoregressive Integrated Moving Average (ARIMA) models

NYONI, THABANI and MUTONGI, CHIPO (2019): Modeling and forecasting carbon dioxide emissions in China using Autoregressive Integrated Moving Average (ARIMA) models.

Abstract

This research uses annual time series data on CO2 emissions in China from 1960 to 2017, to model and forecast CO2 using the Box – Jenkins ARIMA approach. Diagnostic tests indicate that China CO2 emission data is I (2). The study presents the ARIMA (1, 2, 1) model. The diagnostic tests further imply that the presented best model is stable and hence acceptable for predicting carbon dioxide emissions in China. The results of the study reveal that CO2 emissions in China are likely to increase and thereby exposing China to a plethora of climate change related challenges. 4 main policy prescriptions have been put forward for consideration by the Chinese government.

• Item Type: MPRA Paper
• Language: English
• Keywords: ARIMA model; China; CO2 emissions
• Subjects: C - Mathematical and Quantitative Methods > C5 - Econometric Modeling > C53 - Forecasting and Prediction Methods ; Simulation Methods; Q - Agricultural and Natural Resource Economics ; Environmental and Ecological Economics > Q4 - Energy > Q47 - Energy Forecasting; Q - Agricultural and Natural Resource Economics ; Environmental and Ecological Economics > Q5 - Environmental Economics > Q52 - Pollution Control Adoption and Costs ; Distributional Effects ; Employment Effects; Q - Agricultural and Natural Resource Economics ; Environmental and Ecological Economics > Q5 - Environmental Economics > Q53 - Air Pollution ; Water Pollution ; Noise ; Hazardous Waste ; Solid Waste ; Recycling; Q - Agricultural and Natural Resource Economics ; Environmental and Ecological Economics > Q5 - Environmental Economics > Q54 - Climate ; Natural Disasters and Their Management ; Global Warming

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