Using AI/ML to Tackle Climate Change

Local Project Nairobi, Kenya Chapter

Coordinated by the Lead of Kenya, Odhiambo Mitchell,

Status: Completed

Project Duration: 03 Oct 2022 - 24 Nov 2022

Open Source resources available from this project

Project background.

The World Meteorological Organization forecasts that the current greenhouse gas (GHG) emissions trend will increase global temperature by 3-5 degrees C by 2100 (Reuters 2018). This would far overshoot the 2-degree limit pledged by the 2015 Paris climate accord (COP 21) and might have a catastrophic impact (Steffen et al. 2018; World Bank 2012).

In order to track progress towards the global climate targets, the parties that signed the Paris Climate Agreement will regularly report their anthropogenic carbon dioxide (CO2) emissions based on energy statistics and CO2 emission factors. Independent evaluation of this self-reporting system is a fast-growing research topic.

This project aims to study the value of satellite observations of the column CO2 concentrations to estimate CO2 anthropogenic emissions within five years of the Orbiting Carbon Observatory-2 (OCO-2) retrievals over and around Kenya.

The problem.

In order to address potential biases in this self-reporting mechanism, the contribution of independent observation systems is being increasingly sought (IPCC, 2019). Our focus here is on the direct observation of carbon dioxide (CO2) emissions from space and on the quantification of CO2 emissions from this observation independently.

NASA’s second Orbiting Carbon Observatory (OCO-2) polar satellite (Eldering et al., 2017) is one of the best existing instruments for the retrieval of column-averaged dry-air mole fraction of CO2 (XCO2). It observes the clear-sky and sun-lit part of the Earth with the footprints of a few km2 (1.29 km × 2.25 km) gathered in a ~10 km wide swath for each orbit, 35 particularly suitable for informing natural CO2 budget at the continental scales

We intend to use NASA’s OCO2 and OCO3 Satellite data and publicly available data on critical CO2 emitting sectors e.g. power plants, steel mills , cement plants, atmospheric “spillover” from agricultural and forest fires, traffic emissions, demographic and economic variables etc to build an AI model that predicts the contribution from each sector.

This CO2 emissions model could be used to track progress for large and small cities, sub-regions or project areas within Kenya which can be used to support decarbonization efforts

Project goals.

  • Develop a model that can be used to track progress for large and small cities, sub-regions or project areas within Kenya.
  • Provide Insights on Co2 emissions in Kenya over time
  • Provide prediction on project/sectoral Co2 emissions.
  • Create dashboards to visualize xco2 measurements by region/overtime

Project plan.

  • Week 1

    Data Collection
    Data Pre-Processing

  • Week 2

    Data Pre-Processing
    Data Collection

  • Week 3

    Exploratory Data Analysis, Modelling

  • Week 4

    Modelling (cont)

  • Week 5

    Modelling

  • Week 6

    Visualization and documentation

  • Week 7

    Visualization and documentation(cont.)

  • Week 8

    Wrap up

Learning outcomes.

1. Data Collection. 2. Data Cleaning. 3. Data Analysis. 4. Data Visualization. 5. Building AI models

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