Template-Type: ReDIF-Article 1.0
Author-Name: Sanusi, Olajide Idris
Author-Name-First: Olajide Idris
Author-Name-Last: Sanusi
Author-Email: sanusio@uwgb.edu
Author-Workplace-Name: Department of Accounting and Finance, Cofrin School of Business, University of Wisconsin-Green Bay, Green Bay, Wisconsin, USA
Author-Name: Aliyu, Salisu
Author-Name-First: Salisu
Author-Name-Last: Aliyu
Author-Email: aliyusalisu@abu.edu.ng
Author-Workplace-Name: Department of Computer Science, Ahmadu Bello University, Zaria, Nigeria
Author-Name: Safi, Samir
Author-Name-First: Samir
Author-Name-Last: Safi
Author-Email: ssafi@uaeu.ac.ae
Author-Workplace-Name: College of Business and Economics, United Arab Emirates University, Al Ain, UAE
Title: Geopolitical Risk and Country-Level CO2 Emissions: A Deep Learning Approach Comparing LSTM, CNN and ConvLSTM
Abstract: The investigation in this research employs advanced deep learning methods to analyze the bidirectional relationship between geopolitical risks and CO2 emissions in China, India, and the USA across the timeframe of 1990 to 2019. Data sourced from the Recent GPR Index and CO2 emissions was utilized. The exploration of how geopolitical risks influence climate change provides valuable insights for decision-makers in policymaking roles. The study's outcomes indicate a positive correlation between geopolitical risks and CO2 emissions across all three nations, with both variables exhibiting an ascending trend throughout the studied period. Noteworthy is the consistent superior performance of the Long Short-Term Memory (LSTM) model when compared to the Convolutional Neural Network (CNN) and Convolutional Long Short-Term Memory (ConvLSTM) models in short term prediction. This consistent effectiveness in CO2 emission prediction showcases the strength of the LSTM model. The divergence in model efficacy among the different nations highlights the significance of tailoring CO2 emission prediction models to each country's unique attributes. These insights should significantly influence policymakers as they strategize approaches to mitigate geopolitical risks, curtail CO2 emissions, and to adopt well-informed strategies in combating climate change.
Keywords: Geopolitical Risks, CO2 Emissions, Deep Learning, LSTM Model, Climate Change, Environmental Sustainability
Journal: International Journal of Energy Economics and Policy
Pages: 109-117
Volume: 15
Issue: 4
Year: 2025
Month: 06
DOI: 10.32479/ijeep.17764
File-URL: https://econjournals.com/index.php/ijeep/article/download/17764/8945
File-Format: application/pdf
Handle: RePEc:eco:journ2:v:15:y:2025:i:4:id:17764