Modeling Greenhouse Gas Emissions from Agriculture

This study analyzes the series of annual emissions of greenhouse gases (GHGs) from agriculture in the European Union countries for 32 years. The outliers, autocorrelation, and change points were detected for each series and the Total one using the boxplot, autocorrelation function (ACF), and Pettit, Hubert, and CUSUM tests. The existence of a monotonic trend in the data series was checked against the randomness by the Mann–Kendall test; further, the slope of the linear trend was determined by Sen’s nonparametric approach and classical regression. The best distribution was fitted for each data series. The results indicate that most series present aberrant values (indicating periods with high emissions), are autocorrelated, and have a decreasing tendency over time (showing the diminishing of GHG emissions from agriculture during the study period). The distributions that best fit the individual series were of Wakeby, Johnson SB, Burr, and Log-logistic type. The Total series has a decreasing trend, presents a second-order autocorrelation, and is right-skewed. An ARIMA(1,1,2) model was built and validated for it and was used for the forecast.