Quantitative Energy Prediction based on Carbon Emission Analysis by DPR Framework
This provides a method for policymakers to analyze regional carbon emissions, though it is incremental as it combines existing techniques.
The study tackled the problem of analyzing carbon emissions with complex, correlated data by proposing a DBSCAN and Elastic Net framework, applied to Chinese industry data (2000-2019) to identify 16 emission categories and assess drivers.
This study proposes a novel analytical framework that integrates DBSCAN clustering with the Elastic Net regression model to address multifactorial problems characterized by structural complexity and multicollinearity, exemplified by carbon emissions analysis. DBSCAN is employed for unsupervised learning to objectively cluster features, while the Elastic Net is utilized for high-dimensional feature selection and complexity control. The Elastic Net is specifically chosen for its ability to balance feature selection and regularization by combining L1 (lasso) and L2 (ridge) penalties, making it particularly suited for datasets with correlated predictors. Applying this framework to energy consumption data from 46 industries in China (2000-2019) resulted in the identification of 16 categories. Emission characteristics and drivers were quantitatively assessed for each category, demonstrating the framework's capacity to identify primary emission sources and provide actionable insights. This research underscores the global applicability of the framework for analyzing complex regional challenges, such as carbon emissions, and highlights its potential to identify opportunities for emission reduction.