Agri-food CO2 Emission Dataset - Forecasting ML

The agricultural CO2 emission dataset has been constructed by merging and reprocessing approximately a dozen individual datasets from the Food and Agriculture Organization (FAO) and data from IPCC. These datasets were, cleaned, preprocessed and merged together to create a comprehensive and cohesive dataset for analysis and forecasting purposes.

The dataset, as demonstrated in the notebook, describes CO2 emissions related to agri-food, which amount to approximately 62% of the global annual emissions.

Indeed, the emissions from the agri-food sector are significant when studying climate change. As the dataset shows, these emissions contribute to a substantial portion of the global annual emissions. Understanding and addressing the environmental impact of the agri-food industry is crucial for mitigating climate change and developing sustainable practices within this sector.

For a better understanding of the dataset, I have written a notebook where I perform an analysis of the relationship between emissions, climate change and geografic Area. Additionally, I provide an example of regression to predict the percentage variations in temperatures.

Dataset Features:

• Savanna fires: Emissions from fires in savanna ecosystems.
• Forest fires: Emissions from fires in forested areas.
• Crop Residues: Emissions from burning or decomposing leftover plant material after crop harvesting.
• Rice Cultivation: Emissions from methane released during rice cultivation.
• Drained organic soils (CO2): Emissions from carbon dioxide released when draining organic soils.
• Pesticides Manufacturing: Emissions from the production of pesticides.
• Food Transport: Emissions from transporting food products.
• Forestland: Land covered by forests.
• Net Forest conversion: Change in forest area due to deforestation and afforestation.
• Food Household Consumption: Emissions from food consumption at the household level.
• Food Retail: Emissions from the operation of retail establishments selling food.
• On-farm Electricity Use: Electricity consumption on farms.
• Food Packaging: Emissions from the production and disposal of food packaging materials.
• Agrifood Systems Waste Disposal: Emissions from waste disposal in the agrifood system.
• Food Processing: Emissions from processing food products.
• Fertilizers Manufacturing: Emissions from the production of fertilizers.
• IPPU: Emissions from industrial processes and product use.
• Manure applied to Soils: Emissions from applying animal manure to agricultural soils.
• Manure left on Pasture: Emissions from animal manure on pasture or grazing land.
• Manure Management: Emissions from managing and treating animal manure.
• Fires in organic soils: Emissions from fires in organic soils.
• Fires in humid tropical forests: Emissions from fires in humid tropical forests.
• On-farm energy use: Energy consumption on farms.
• Rural population: Number of people living in rural areas.
• Urban population: Number of people living in urban areas.
• Total Population - Male: Total number of male individuals in the population.
• Total Population - Female: Total number of female individuals in the population.
• total_emission: Total greenhouse gas emissions from various sources.
• Average Temperature °C: The average increasing of temperature (by year) in degrees Celsius,

Importance and Context:

The agricultural sector contributes to approximately, how i'll demostrate in my notebook, 62% of the total global CO2 emissions, making it a significant contributor to climate change. This dataset plays a crucial role in understanding and monitoring the impact of agricultural activities on CO2 emissions. By leveraging machine learning techniques, it enables the forecasting of future emissions, allowing policymakers and researchers to develop targeted strategies and interventions for sustainable agricultural practices. This dataset serves as a valuable resource for climate scientists, environmental researchers, and policymakers striving to mitigate the environmental impact of the agricultural sector.

Author note:

• CO2 is recorded in kilotonnes (kt): 1 kt represents 1000 kg of CO2.
• The feature "Average Temperature C°", which can be used as the target for machine learning models, represents the average yearly temperature increase. For example, if it is 0.12, it means that the temperature in that specific location increased by 0.12 degrees Celsius.
• Forestland is the only feature that exhibits negative emissions due to its role as a carbon sink. Through photosynthesis, forests absorb and store carbon dioxide, effectively removing it from the atmosphere. Sustainable forest management, along with afforestation and reforestation efforts, further contribute to negative emissions by increasing carbon sequestration capacity.
• If you prefer Fahrenheit to Celsius, here is the formula: °F = (°C x 9/5) + 32

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