Quantitative Energy Prediction based on Carbon Emission Analysis by DPR Framework

TL;DR

This paper introduces a combined DBSCAN and Elastic Net framework for analyzing complex, multicollinear data to predict energy consumption and carbon emissions across industries, providing actionable insights.

Contribution

It presents a novel analytical framework integrating clustering and regression for multifactorial emission analysis, addressing structural complexity and multicollinearity.

Findings

Identified 16 emission categories from Chinese industry data

Quantified emission drivers and characteristics for each category

Demonstrated framework's capacity for actionable emission insights

Abstract

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.