Game-Theoretic Incentive Mechanism for Collaborative Quality Control in Blockchain-Enhanced Carbon Emissions Verification

Given the urgency of climate change, many countries have set carbon neutrality targets and adopted cap-and-trade (C&T) systems to regulate carbon emissions. Accurate carbon emission data is crucial for the effective operation of carbon pricing and management systems. Monitoring, Reporting, and Verification (MRV) system is at the core of these systems, facing challenges such as, inefficient verification process, and low-quality carbon emissions verification. Blockchain and smart contracts offer promising solutions to some difficulties, while the quality of carbon emissions verification still needs improvement. Therefore, we propose a blockchain-enhanced carbon emissions verification model to optimize system efficiency and support compliance verification. We employ reputation as the admission criterion, screening reliable and trustworthy verification candidates. We design a game-theoretic incentive mechanism implemented through smart contracts to promote compliance and collaborative quality control among participants. Analysis shows that our scheme drives the game model towards the Nash equilibrium that achieves collaborative quality control. Through security analysis and simulation experiments, we verify the efficacy of our mechanism concerning verification quality and procedural automation, confirming its potential to mitigate malpractices and enhance consistent compliance.

Authors

Institutions

Publication Details

Journal
IEEE Transactions on Network Science and Engineering
Published
2024-09-09
DOI
https://doi.org/10.1109/tnse.2024.3456116
Citations
5
Primary Topic
Blockchain Technology Applications and Security
Type
article
Field-Weighted Citation Impact
3.90

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Game-Theoretic Incentive Mechanism for Collaborative Quality Control in Blockchain-Enhanced Carbon Emissions Verification

Zhihao Zhou, Yunhua He, Ke Xiao, Bin Wu et al.
5 citations
IEEE Transactions on Network Science and Engineering
Blockchain Technology Applications and Security
3.90
article

Game-Theoretic Incentive Mechanism for Collaborative Quality Control in Blockchain-Enhanced Carbon Emissions Verification

Zhihao Zhou, Yunhua He, Ke Xiao, Bin Wu, Chao Wang, Xiuzhen Cheng
article en
5 citations

Abstract

Given the urgency of climate change, many countries have set carbon neutrality targets and adopted cap-and-trade (C&T) systems to regulate carbon emissions. Accurate carbon emission data is crucial for the effective operation of carbon pricing and management systems. Monitoring, Reporting, and Verification (MRV) system is at the core of these systems, facing challenges such as, inefficient verification process, and low-quality carbon emissions verification. Blockchain and smart contracts offer promising solutions to some difficulties, while the quality of carbon emissions verification still needs improvement. Therefore, we propose a blockchain-enhanced carbon emissions verification model to optimize system efficiency and support compliance verification. We employ reputation as the admission criterion, screening reliable and trustworthy verification candidates. We design a game-theoretic incentive mechanism implemented through smart contracts to promote compliance and collaborative quality control among participants. Analysis shows that our scheme drives the game model towards the Nash equilibrium that achieves collaborative quality control. Through security analysis and simulation experiments, we verify the efficacy of our mechanism concerning verification quality and procedural automation, confirming its potential to mitigate malpractices and enhance consistent compliance.

IEEE Transactions on Network Science and EngineeringVol. 11(6)
North China University of Technology (CN), George Washington University (US), Institute of Information Engineering (CN)
National Natural Science Foundation of China
Climate action
Openalex Percentile: Top 6%
Blockchain Technology Applications and Security
3.90
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.