Towards COP26 targets: Characteristics and influencing factors of spatial correlation network structure on U.S. carbon emission

Abstract

The COP26 stress on countries to accelerate the transition to low-carbon energy systems. To reveal the driving mechanism of the spatial correlation network of carbon emission in the U.S. and promote the coordinated emission reduction of regional carbon emissions in the U.S., This paper investigates the evolution characteristics of the spatial correlation network structure of 50 states in U.S. carbon emission from 2013 to 2020, with the modified gravity model and social network analysis (SNA), and explores its driving factors by quadratic assignment procedure (QAP) model. The conclusions show that there is an obvious spatial correlation among interstate carbon emissions in the U.S., the network density shows a downward trend, the overall network efficiency shows an increasing trend, and the spatial correlation network presents a “core-edge” structure. Economically developed states generally have a core position in the network and play a controlling role in guiding other states to develop together with them. Among the network plates, 16 states such as Alabama, Indiana, South Carolina, New Hampshire, Kentucky, Tennessee, Maine and North Carolina have always been located in the “Broker” plate, which played the role of intermediary and bridge in the network. Massachusetts, Illinois, New Jersey, Maryland and New York, which are concentrated in the coastal and lake areas, have always been located in the “Net Benefit” plate. Wyoming, Colorado, North Dakota, Alaska, Texas and Nebraska, which are concentrated in areas with relatively abundant energy reserves, have always been located in the “Net Overflow” plate, and carbon emission show an obvious spillover effect in the network. The coastal and lake areas are the main destinations of the spatial spillover of the spatial correlation network of carbon emissions in U.S.. Geographical adjacency, population size, per capita GDP and technology level have a significant impact on the spatial correlation of carbon emissions. The spatial correlation and spillover of carbon emissions among states increase with the higher the similarity of technology level among regions.