Evidence map›Paper›PMID 40634556›Full record

ArticleScientific reports2025

Agent based modeling of energy consuming rights trading for low carbon transformation in China.

Feng Han, Xin Li, Xiaohan Bi, Jie Gao, Zihao Cao, Dong Yang, Feng Shi, Yongfei Jia

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

8 authors.

Feng HanInstitute of Science and Technology for Development of Shandong, Qilu University of Technology (Shandong Academy of Sciences), Jinan, 250100, China. hanfeng@qlu.edu.cn.
Xin LiInstitute of Science and Technology for Development of Shandong, Qilu University of Technology (Shandong Academy of Sciences), Jinan, 250100, China.
Xiaohan BiInformation Research Institute of Shandong Academy of Sciences, Qilu University of Technology (Shandong Academy of Sciences), Jinan, 250100, China.
Jie GaoInstitute of Science and Technology for Development of Shandong, Qilu University of Technology (Shandong Academy of Sciences), Jinan, 250100, China.
Zihao CaoInstitute of Science and Technology for Development of Shandong, Qilu University of Technology (Shandong Academy of Sciences), Jinan, 250100, China.
Dong YangInstitute of Science and Technology for Development of Shandong, Qilu University of Technology (Shandong Academy of Sciences), Jinan, 250100, China.
Feng ShiSchool of Environmental Science and Engineering, Qilu University of Technology (Shandong Academy of Sciences), Jinan, 250100, China.
Yongfei JiaThink Tank Center, Qilu University of Technology (Shandong Academy of Sciences), Jinan, 250100, China.

Funding

Humanities and Social Science Project of the Ministry of Education of China 24YJCZH081Key R&D Program (Soft Science Project) of Shandong Province 2024RKY0404National Natural Science Foundation of China 42471317Taishan Scholar Program of Shandong Province tsqn202312232
6 · The paper itself

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.

Indexed as

Agent-based modelingEnergy-consuming rightsLow-carbon transformationMarket-oriented allocationPolicy simulation

Identifiers

PMID40634556
PMCPMC12241347

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.