Identifying the optimal node group of carbon emission efficiency correlation network in China based on pinning control theory

Improving carbon emission efficiency (CEE) levels is essential for achieving the 2030 carbon emission peak target. It is unrealistic to raise CEE levels in all regions in the short term, due to its high macroeconomic costs that have a significant impact on overall economic development. A more pragmatic strategy is controlling only some provinces to take the lead in raising CEE levels and indirectly drive other provinces based on network connectivity until the goal of region-wide CEE improvement, which will greatly save policy and economic costs. From the fresh perspective of complex network pinning control theory, this study contributes to propose the optimal node group screening theory to achieve the precise identification of provinces with radiation-led effect in China’s inter-provincial CEE correlation networks in 2012 and 2020. The following conclusions are obtained: (1) CEE levels are spatially heterogeneous, showing the distribution characteristics of higher levels in the east and south, and lower levels in the west and north of China. (2) The optimal pinned node group contains high-, medium- and low-efficiency provinces, and is geographically dispersed. (3) In the pinned node group, low-efficiency provinces radiate downstream to provinces with high betweenness centrality, which is an important “driver” of CEE improvement. (4) Compared with other node selection strategies, the pinned node group screening algorithm designed can achieve better network connectivity by controlling fewer provinces. This work provides a theoretical basis and methodological reference for policy makers to implement policies accurately and smooth the CEE impact pathway.