Prediction of Anthropogenic Greenhouse Gas Emissions via Manure Management in Indonesia and Alternative Policies for Indonesian Livestock Development

سال انتشار: 1402
نوع سند: مقاله ژورنالی
زبان: انگلیسی
مشاهده: 78

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شناسه ملی سند علمی:

JR_JREE-10-3_009

تاریخ نمایه سازی: 3 آبان 1402

چکیده مقاله:

The percentage of production and utilization of hydrocarbon resources from the livestock sector has raised concerns regarding the worldwide issue of global warming. A total of CH۴ emissions from enteric fermentation and waste management has been estimated at ۷۸.۳ %. Meanwhile, N۲O emissions are ۷۵-۸۰ % of total agricultural emissions. This raises questions about the extent of global warming due to increased CO۲ resulting in changes in weather and global warming. This research aims to predict Green House Gas (GHG) emissions from manure management and present policy alternatives for Indonesian livestock development. Secondary data was taken from a related website (fao.org) with coverage throughout Indonesia from ۱۹۶۱ to ۲۰۲۱, containing ۱۲,۴۸۰ rows and ۵ column features including item, Element, Year, Unit, and Value emission. LSTM and GRU are used to predict the trend of emission from manure management to provide alternative policies on greenhouse gas mitigation in Indonesia. The results showed that based on ۱۵ types of livestock that emit GHG emissions, ۳ types of livestock produce the highest emissions from ۱۹۶۱ to ۲۰۲۱: (a) cattle, (b) cattle and non-dairy, and (c) poultry. Significant reduction in the emission of carbon dioxide (CO۲eq) in ۲۰۲۰ is indicated by reduced public consumption and hampered supply chains with large-scale social restrictions (covid-۱۹ pandemic policy). Based on these results, fertilizer storage duration can be used as a policy to reduce CO۲eq emissions, hence it is desired that fertilizer management techniques can be properly regulated. Mitigation can also be accomplished by utilizing livestock waste as biogas and upgrading animal feed additives with chitosan or potassium nitrate.

نویسندگان

Nur Aini

Department of Management, Faculty of Economic and Business, Ahmad Dahlan Institute of Technology and Business, Indonesia.

Widi Hastomo

Department of Management, Faculty of Economic and Business, Ahmad Dahlan Institute of Technology and Business, Indonesia.

Ratna Yulika Go

Department of Information Technology, Faculty of Computer Science, Universitas Indonesia, Indonesia.

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