Global Warming: Predicting OPEC Carbon Dioxide Emissions from Petroleum Consumption Using Neural Network and Hybrid...

Area of Science:

• Environmental Science
• Climate Change Research
• Energy Economics

Background:

• Global warming, driven by greenhouse gas emissions like carbon dioxide (CO2) from petroleum, causes significant environmental and economic damage.
• Policy makers are increasingly concerned about climate change impacts, including extreme weather events and rising temperatures.
• Existing CO2 emission prediction methods have limitations, particularly for the Organization of the Petroleum Exporting Countries (OPEC).

Purpose of the Study:

• To develop an accurate predictive model for OPEC's CO2 emissions stemming from petroleum consumption.
• To address the limitations of current CO2 emission forecasting techniques.
• To provide a tool for OPEC nations to manage and reduce their carbon footprint.

Main Methods:

• Data on OPEC CO2 emissions was sourced from the Energy Information Administration.
• An Artificial Neural Network (ANN) was selected for its predictive capabilities.
• A hybrid optimization technique, combining cuckoo search and accelerated particle swarm optimization, was used to train the ANN for improved performance.

Main Results:

• The developed model accurately predicts OPEC CO2 emissions over various future timeframes (3, 6, 9, 12, and 16 years).
• The hybrid ANN model demonstrates superior accuracy and speed compared to existing state-of-the-art prediction methods.
• The model provides reliable forecasts crucial for environmental policy development.

Conclusions:

• Accurate OPEC CO2 emission predictions can guide economic restructuring in member countries to meet climate targets.
• The findings support efforts to reduce global warming by aligning with international benchmarks like the Kyoto Protocol.
• The study discusses policy implications for sustainable development and emission reduction strategies in OPEC nations.