A wavelet coherence approach to zoning supply-demand matching of carbon sequestration service

Abstract

Carbon sequestration (CS) is an important regulating service provided by natural ecosystem which plays an important role in mitigating global climate change. However, there is often spatial mismatch between the carbon sequestration supply and demand (CSSD), which makes it difficult to reduce carbon emissions and increase carbon sinks to achieve local carbon balance. Therefore, it is important to clarify the optimal scale to explore spatial matches and mismatches between CSSD and delimit spatial units for implementing effective carbon-focused management policies. Taking Hunan Province, China as an example, we evaluated CSSD in 2001 and 2017, and identified the optimal scale of spatial matching based on wavelet coherence analysis. The results showed that from 2001 to 2017, CS supply in Hunan Province increased by 6.45 %, while CS demand increased by 261.11 %. 8.40 km was identified as the optimal scale of CSSD spatial matches and mismatches, and Hunan Province could be divided into 3231 spatial units including four types according to the combination of CSSD, i.e. High supply-High demand, Low supply-Low demand, High supply-Low demand and Low supply-High demand. Based on the type changes of spatial units from 2001 to 2017, it was found that the key areas in need of ecological restoration were located in the east side of Xuefeng Mountains and the west side of Luoxiao Mountains, which could support accurate ecosystem monitoring and management under the background of improving the ‘one map’ of territorial space in Hunan Province. Based on wavelet coherence analysis, this study provided a spatial zoning approach for sustainable land use management, with a special focus on carbon sequestration supply and demand.