Introduction

This research investigates Nigeria’s progress towards achieving zero routine gas flaring by 2030, a goal set within global initiatives. It addresses the environmental, economic, and health impacts of gas flaring in Nigeria, despite existing legal frameworks.  

Objective

• To predict, using machine learning models, whether Nigeria will achieve its zero flaring target by 2030.  

• To evaluate the predictive capability of developed models.  

• To predict the date of achieving zero flaring and suggest pathways to reach this goal.  

Methodology

Historical data on gas production and flaring volumes from 1958-2022, obtained from the World Bank and other sources, was analyzed using Linear regression.

Assumption

The core assumption is that patterns recognized in the flaring volume’s signature since 1958 are expected to persist over the prediction horizon, provided that the key drivers influencing flaring behavior remain consistent and no major unforeseen policy interventions, technological disruptions, or significant economic/environmental shocks occur.

Model structure

The following model parameters were considered while developing the machine learning model:

• Year                                         

• Gas flared(100 Million cubic meters)         

• Gas Produced(100 Million cubic meters)      

• Oil production (100 Million Barrels)          

• AVERAGE Crude price($) (BARREL)           

• Cost (USD per 100 Billion cubic meters)  

• No. of active Rigs                        

• Gas Flare penalty ($ per 1000 cubic meter)