Greenhouse Gas Emission Estimation and Risk Assessment for Sectors

Abstract

Today, rapid population growth, industrialization, and energy consumption cause a serious increase in global greenhouse gas (GHG) emissions. This increase poses significant threats to environmental sustainability, human health, and ecosystem balance. This study aims to examine Türkiye's greenhouse gas emissions for the period 1990–2021 based on energy, industrial processes and product use (IPPU), agriculture, and waste sectors; to make forward-looking estimates for CO₂, CH₄, and N₂O gases, and to analyze the risks associated with these emissions. In the study, emission estimates were made using Exponential Smoothing and ARIMA models from time series analysis methods. ARIMA (1,1,1) model provided more accurate forecasts with low error rates, and it was observed that the highest increase was in CO₂ emissions, and it was determined that especially the energy sector made the main contribution to this increase. Regular increases were also detected in CH₄ and N₂O gases. Following the estimation results, risk factors for the environmental and social impacts of greenhouse gases were defined. Risk factors were determined by literature review and expert opinions; Health problems caused by CO₂, ecosystem damage, and risks such as access to drinking water have been identified as priority threats. The impact of N₂O on the ozone layer and the consequences of CH₄ on agriculture and livestock are also prominent findings. As a result, it is seen that sectoral greenhouse gas emissions in Türkiye will continue to increase with the current trend, and especially the energy and agriculture sectors play leading roles in this increase. This situation reveals the need for urgent interventions in terms of greenhouse gas control and climate policies at the national level. The study is an important guide for policymakers, environmental planners, and academic researchers in terms of both emission estimation and risk analysis.

Keywords

• Greenhouse gas emissions
• ARIMA method
• exponential smoothing method
• risk analysis

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