  This study preprocessed 2000-2019 energy consumption data for 46 key Sichuan
industries using matrix normalization. DBSCAN clustering identified 16 feature
classes to objectively group industries. Penalized regression models were then
applied for their advantages in overfitting control, high-dimensional data
processing, and feature selection - well-suited for the complex energy data.
Results showed the second cluster around coal had highest emissions due to
production needs. Emissions from gasoline-focused and coke-focused clusters
were also significant. Based on this, emission reduction suggestions included
clean coal technologies, transportation management, coal-electricity
replacement in steel, and industry standardization. The research introduced
unsupervised learning to objectively select factors and aimed to explore new
emission reduction avenues. In summary, the study identified industry
groupings, assessed emissions drivers, and proposed scientific reduction
strategies to better inform decision-making using algorithms like DBSCAN and
penalized regression models.
