Similarity clustering and characteristics analysis of carbon emission pathways based on provincial data from China

This study addresses the challenge of formulating effective carbon emission reduction strategies in China, where regional disparities in economic development and policy implementation hinder the achievement of national carbon neutrality goals. Traditional clustering methods often fail to account for the temporal dynamics of carbon emission data, limiting their applicability. To overcome these limitations, we propose a novel approach that combines Dynamic Time Warping (DTW) with Density-Based Spatial Clustering of Applications with Noise (DBSCAN) to analyze carbon emission pathways across 30 Chinese provinces from 2000 to 2019. Our approach categorizes emission pathways into four types: "high-rising," "low-rising," "tail-declining," and "smoothly fluctuating." The study identifies key drivers of emissions, such as economic growth, industrial structure, and energy consumption, while also highlighting the temporal variability in emission trends. Our findings provide region-specific insights that can guide the development of more targeted and effective carbon reduction policies. This research contributes to the optimization of carbon reduction strategies by integrating dynamic temporal factors into clustering methodologies, thereby offering actionable recommendations for policymakers.