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ORIGINAL RESEARCH
Prediction and Path Planning Framework
of X City’s Carbon Emissions Based
on the Long Short-Term Memory
Network Model
1
School of Management, Shanghai University, No. 99, Shangda Road, Shanghai 200444, China
2
College of Engineering, University of Bahrain, Sakheer, P.O. Box 32038, Kingdom of Bahrain
Submission date: 2024-04-28
Final revision date: 2024-11-24
Acceptance date: 2024-12-29
Online publication date: 2025-03-10
Publication date: 2026-01-30
Corresponding author
Pol. J. Environ. Stud. 2026;35(1):1195-1208
KEYWORDS
TOPICS
ABSTRACT
Climate change requires urgent action to reduce greenhouse gas emissions. In response to the urgent
need for accurate carbon emission forecasting to support global and national carbon neutrality
goals, this paper presents a predictive framework for carbon emissions in City X, utilizing the Long
Short-Term Memory (LSTM) network model. The study integrates the Kaya model and the Logarithmic
Mean Divisia Index (LMDI) for precise carbon accounting and identifies the key factors influencing
emissions. Additionally, it employs logistic regression, ARIMA, and the least squares method
to forecast population, GDP, and energy consumption, respectively. The LSTM model is innovatively
applied to predict regional carbon emissions and offer policy recommendations for achieving carbon
neutrality. The study presents three distinct scenarios for dual carbon targets, offering valuable insights
for governments’ green policy development and advancing both theoretical and practical approaches
to sustainable urban planning.
CONFLICT OF INTEREST
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
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