Agent based modeling of energy consuming rights trading for low carbon transformation in China
Abstract The market-oriented allocation of Energy-Consuming Rights allows enterprises to trade legally obtained energy consumption quotas on public platforms within regional caps, improving energy efficiency and promoting sustainable development. As an institutional innovation, Energy-consuming righ...
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| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2025-07-01
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| Series: | Scientific Reports |
| Subjects: | |
| Online Access: | https://doi.org/10.1038/s41598-025-10838-w |
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| Summary: | Abstract The market-oriented allocation of Energy-Consuming Rights allows enterprises to trade legally obtained energy consumption quotas on public platforms within regional caps, improving energy efficiency and promoting sustainable development. As an institutional innovation, Energy-consuming rights trading (ECRT) addresses China’s energy and environmental challenges while fostering green transformation. This study employs agent-based modeling to simulate the dynamic interactions among enterprises, governments, and trading platforms under dual constraints of energy consumption and carbon emissions. The results show that: (1) ECRT reduces total energy consumption by 8.1% and carbon emissions by 9.8% compared to regions without trading markets, demonstrating its effectiveness in energy conservation and emission reduction. (2) Paid quotas outperform free quotas, reducing energy consumption by 7.9% and carbon emissions by 6.8%, while stabilizing trading prices and enhancing market activity. (3) High-quality development scenarios improve energy efficiency by 4.4% and reduce carbon emissions by 5%, aligning better with the “dual-carbon” goals. These findings provide valuable insights for optimizing ECRT mechanisms and advancing green, low-carbon economic transformation. Policy recommendations include refining quota allocation methods, enhancing market participation, and integrating ECRT with carbon trading systems to maximize efficiency. |
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| ISSN: | 2045-2322 |