Comparison of Different Methods of Forecasting the Trend of Greenhouse Gas Emissions from Iran's Agricultural Sector

Pezeshk, Fatemeh; Zare Mehrjerdi, Mohammad Reza; Amirtaimoori, Somayeh; Mirzaei Khalilabadi, Hamid Reza; Naghavi, Somayeh

doi:10.22111/jneh.2023.46339.1980

Comparison of Different Methods of Forecasting the Trend of Greenhouse Gas Emissions from Iran's Agricultural Sector

Document Type : Research Article

Authors

¹ MSc, Department of Agricultural Economics, Faculty of Agriculture, Shahid Bahonar University of Kerman, Kerman, Iran

² Professor, Department of Agricultural Economics, Faculty of Agriculture, Shahid Bahonar University of Kerman, Kerman, Iran

³ Associate Professor, Department of Agricultural Economics, Faculty of Agriculture, Shahid Bahonar University of Kerman, Kerman, Iran

⁴ Associate Professor, Department of Agricultural Economics, Faculty of Agriculture, University of Jiroft, Jiroft, Iran

10.22111/jneh.2023.46339.1980

Abstract

The emission of greenhouse gases is the main cause of global warming and environmental problems. The agricultural sector is one of the major sources of emissions so one-third of the greenhouse gas emissions in the world are related to agricultural systems. Today, forecasting is considered an important planning tool for policymakers. Forecasting the amount of greenhouse gas emissions can show a picture of the future for policymakers and help them in making strategic decisions. There are various methods for predicting variables. This study aims to compare the methods of forecasting and the emission trend of the most important greenhouse gases (methane, carbon dioxide, and nitrogen oxide) from Iran's agricultural sector. The necessary statistics and information were collected annually between 1990 and 2019 from the websites of the Food and Agriculture Organization of the United Nations, the World Bank, and the energy balance sheet of the Ministry of Energy, and to predict the variables from univariate methods of autoregressive integrated moving average (ARIMA), single exponential smoothing with trend, double exponential smoothing with trend, Holt-Winters multiplicative and Holt-Winters additive and multivariate model of vector autoregressive were used. Based on the research findings, methods of Holt-Winters additive, artificial neural network, and single exponential smoothing with trend provided the best forecast for methane gas, carbon dioxide, and nitrogen oxide, respectively. The results showed that the emission trend of methane and nitrogen oxide gases will be downward and the amount of carbon dioxide emission will be upward. The results can help predict the greenhouse gases released from the agricultural sector and apply appropriate policies accordingly.

Keywords

Main Subjects

Environmental Contamination

References

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