Discovering Geographical Flock Patterns of CO₂ Emissions in China Using Trajectory Mining Techniques

Area of Science:

• Environmental Science
• Data Science
• Geospatial Analysis

Background:

• Carbon dioxide (CO2) emissions are a primary driver of climate change.
• Effective climate policies require understanding specific emission patterns.
• Existing trajectory analysis methods can be adapted for geographical data.

Purpose of the Study:

• To extend the concept of flock patterns to geographical CO2 emission data.
• To develop a spatiotemporal graph (STG)-based approach for discovering these patterns.
• To identify actionable insights for CO2 emission reduction policies.

Main Methods:

• Generating attribute trajectories from CO2 emission data.
• Constructing spatiotemporal graphs (STGs) from these trajectories.
• Deriving and discovering eight types of geographical flock patterns based on attribute values and duration.

Main Results:

• The proposed STG approach effectively identifies geographical flock patterns in CO2 emissions.
• A case study in China (province and regional levels) validated the method's effectiveness.
• Discovered patterns offer insights into emission dynamics.

Conclusions:

• The STG-based method is a powerful tool for analyzing geographical CO2 emission patterns.
• Findings can inform targeted policy-making for coordinated carbon emission control.
• This research contributes to a better understanding of emission behaviors for climate action.