Resident Carbon Credit Incentive Decision-Making Method to Promote Valley Filling

Establishing a reasonable, feasible, and directional incentive carbon points operation model for residents is an effective means to enhance the low-carbon awareness of residents. This paper introduces dynamic carbon emission factors and power characteristics of carbon valley, and a carbon credit-oriented incentive decision-making method for residents to promote valley filling is proposed. Firstly, based on the carbon quota allocation model, considering three factors: monthly carbon quotas, basic carbon emission and incentive carbon credits, a residential carbon credits-oriented incentive model is proposed to promote carbon valley filling. Secondly, the carbon valley electricity consumption ratio is established, and the K-means algorithm is used to cluster residents. Then, considering the constraints, such as the average monthly carbon credits of residents, the proportion of below-zero credit residents and difference between the mean carbon credits of different clusters, a resident carbon credit decision-making model is constructed, and the Grey Wolf Optimization is utilized to solve it. Finally, a numerical example is given to verify the rationality and target incentive of the proposed model.

I. Introduction

The carbon market, as a crucial policy to combat climate change, can contribute to achieving peak carbon and carbon-neutral strategic goals [1]–[2]. The carbon trade method has effectively addressed local air pollution [3]. Recent research has focused on firm-level carbon trade [4]–[6]. Bayer and Aklin [4] confirmed that a carbon market can be effective if it is a credible institution, and firms might cut emissions even though market prices are low. Yu et al. [5] pointed out that the implementation of the market-based climate policies improves the firm's financial performance. However, so far, studies have yet to be conducted on the carbon trade on the residential side. Wang et al. [6] estimated marginal abatement cost curves using a database of more than two million firms covering over 500 four-digit industries and suggested that the next sector be included in China's emissions trading scheme.