Frontiers in Energy Research (Aug 2023)
Credit rating- and credit score-based carbon emission quota trading model of city dwellers
Abstract
Introduction: The reduction of electricity-related carbon emissions by city dwellers (CDs) is important for China to achieve low-carbon development and sustainable energy transformation. Due to the lack of incentives for reduction, electricity-related carbon emissions from CDs are increasing year by year. To this end, this paper proposes an electricity-related carbon emission quota trading model that integrates a credit rating and credit score system, particularly for motivating CDs to actively participate in carbon emission reduction.Methods: With the history of electricity bill payment data, the density-based spatial clustering of applications with noise (DBSCAN) algorithm is used to cluster CDs, forming different clusters of CDs with different sensitivity levels to carbon emission quota prices. Thereafter, based on the total carbon emission quota and tiered electricity prices from the power company, incentive rules according to the classification result and credit scores of CDs are formulated. Under certain conditions, a leader–follower Stackelberg game between CDs and the power company is built to determine the base price of the carbon emission quota, and thereby, referring to the credit scores of CDs, floating carbon emission quota prices are offered to them in the final settlement.Results: The simulation results for an actual community in a city in China show that the proposed method can considerably reduce the carbon emissions.Discussion: The proposed credit rating and credit score system outperforms the asymmetric Nash negotiation method in terms of promoting carbon emission reduction.
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