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Explores carbon emissions and balance in Shaanxi Province, highlighting benefits for ecological planning.
Land use carbon emissions (LUCE) is crucial for achieving carbon neutrality and sustainable development goals. How to coordinate ecological and economic benefits and formulate targeted regional carbon balance strategies based on local conditions remains to be explored. Therefore, Shaanxi Province in China was selected as the case for this study due to its rich ecological and energy resources and its significant differences. LUCE was calculated for each county in the region from 2000 to 2020. The contribution of carbon ecological capacity was quantified by calculating the carbon ecological support coefficient (ESC). The economic output per unit of carbon emission was measured using the carbon economic contribution coefficient (ECC). Based on the above, an optimized functional zoning for LUCE and carbon balance was constructed. Results showed that LUCE in Shaanxi Province increased from 2,088.18 × 10 4 tonnes in 2000 to 19,330.99 × 10 4 tonnes in 2020, with central region accounting for 64.81% of the total, northern region 22.92%, and southern region 12.27%. About 53.27% of counties maintained ecological deficits (ESC < 1) in the Northern Shaanxi Energy Zone and the Guanzhong Belt, while 56.07% exhibited an imbalance between economic output and carbon emissions (ECC < 1) in the Northern periphery and the Southern Ecological Zone. Economic growth was positively correlated with LUCE and negatively with ESC, but had a relatively small relationship with ECC and carbon absorption. Subsequently, all counties in Shaanxi Province were classified into five functional types: Carbon Sink Zones, Low-Carbon Economic Zones, Economic Development Zones, Carbon Intensity Control Zones and High-Carbon Optimization Zones, and a targeted strategy was proposed for each functional type. These results could provide a practical and transferable framework for supporting carbon neutrality planning and sustainable land use management in ecologically sensitive regions.
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Mu et al. (2026) studied this question.
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