A contribution-driven weighted grey relational analysis model and its application in identifying the drivers of carbon emissions

Abstract

The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.A number of Grey Relational Analysis (GRA) models have been developed, but their practical application could yields inconsistent or contradictory results in some situations, complicating decision-making. To address this issue, the framework for determining the Core Model Confidence Set in Grey Relational Analysis (Core GRA-MCS) is presented, and a contribution-driven weighted GRA (CDWGRA) model is proposed. First, the concept of the stability coefficient of GRA models is introduced based on the Kendall coefficient (KC). This stability coefficient quantifies the consistency of the set in system analysis. Next, a framework for determining the Core GRA-MCS is established. This framework uses the stability coefficient, Borda count, and Deng's grey relational degree to identify a subset of GRA models that reliably represent the system's characteristics. For the models in Core GRA-MCS, a weighted aggregation is performed using Deng's grey relational degree as the weight, forming the CDWGRA model. The model provides a unified approach to synthesizing results from multiple GRA models. Finally, the proposed model is used to identify the drivers of carbon emissions in the Yellow River Basin, China. The analysis identifies six key driving factors: Primary Industry, Tertiary Industry, Urbanization Rate, Urban Disposable Income, Natural Gas consumption, and Primary Electricity and Other Energy. These factors highlight the influence of economic activity, energy structure, industrial structure, and social development on regional carbon emissions. The comparative analysis and stability analysis show that the CDWGRA model improves the consistency and reliability of GRA-based analysis, confirming its validity and utility in studying complex systems