Detection of driving factors and critical thresholds for carbon sequestration capacity in urban agglomerations using a combined causal inference and machine learning approach

The carbon sequestration capacity in urban agglomeration ecosystems is crucial for enhancing scientific understanding of the carbon cycle and promoting sustainable development to mitigate climate change. However, existing studies on driving factors, particularly regarding determining causal mechanisms and critical thresholds for carbon sequestration in urban agglomeration ecosystems remain unclear. To address this knowledge gap, we propose a CMSC framework which integrates causal inference and machine learning methods to reveal the underlying mechanisms and determine the thresholds of drivers affecting carbon sequestration in the Yangtze River Delta urban agglomeration (YRDUA). The underlying driving mechanisms of carbon sequestration were heterogeneous between municipal county and non-municipal county in YRDUA. The thresholds of nighttime light, surface solar radiation downwards, air temperature, total precipitation and population density that impacted carbon sequestration in non-municipal (municipal) counties of YRDUA were 0.04 (0.4) nW·cm−2·sr−1·yr−1, −6.1 × 104 (−5.46 × 104) J·m−2·yr−1, 0.013 (0.017) K·yr−1, 3.64 × 10−5 (2.51 × 10−5) m·yr−1 and −0.04 people·km−2·yr−1, respectively. Furthermore, the long-term (from 2021 to 2100) carbon sequestration dataset with county-level scale in the YRDUA was generated using the causal inference-based machine learning model. In the context of carbon neutrality, we found that the optimal emission scenario for low-carbon sustainable development of YRDUA is SSP3, under which the average carbon sequestration in most counties will exceed 1 × 107 t. Our study provides a constructive basis for a science-based carbon cycle and ecological management in the urban agglomeration of China.