TY  - EJOU
AU  - Zhang, Junwei 
AU  - Liu, Pei 
AU  - Li, Huihang 
AU  - Huang, Guokang 
AU  - Yuan, Bozheng 
AU  - Wei, Wenjing 
AU  - Zhang, Xiaoshun 

TI  - Assessment of Carbon Reduction Potential Driven by High Energy Consumption Enterprises’ Electricity Usage Behavior
T2  - Energy Engineering

PY  - 2026
VL  - 123
IS  - 5
SN  - 1546-0118

AB  - Addressing global climate challenges necessitates urgent low carbon transitions in high energy consuming enterprises (HECEs). This study proposes a comprehensive framework to assess their carbon reduction potential (CRP) by integrating electricity usage behavior analysis and dynamic carbon emission factor (DCEF) prediction. HECEs are classified into “electricity reduction” and “electricity transfer” categories based on load characteristics, enabling tailored optimization strategies. The framework employs machine learning to predict DCEFs, capturing real time variations in grid carbon intensity. A low carbon optimization model is then formulated to minimize emissions while adhering to production requirements and grid constraints, solved efficiently by a swarm intelligence algorithm (Chinese Pangolin Optimizer). Case studies demonstrate the framework’s effectiveness in quantifying emission reduction pathways and guiding differentiated strategies for HECEs. User A and User C, designated as “electricity transfer” types, achieved significant carbon reductions of 9.3% and 8.4% respectively through optimizing electricity usage timing. User B, categorized as “electricity reduction,” realized a notable 6.7% carbon reduction by cutting electricity consumption, exceeding its predefined target. This highlights substantial CRP achievable via DCEF guided behavioral optimization. The Chinese Pangolin Optimizer (CPO) algorithm exhibits superior convergence and computational efficiency compared to the Genetic Algorithm, ensuring robust and stable low carbon solutions. This systematic approach prioritizes behavioral and operational adjustments, fostering sustainable development aligned with global climate goals.
KW  - High energy consuming enterprises; carbon reduction potential; electricity usage behavior; dynamic carbon emission factor

DO  - 10.32604/ee.2025.072462