Data-driven neighborhood-level carbon emission accounting models and decarbonization strategies: Empirical study on Central Shenyang City

Abstract

Neighborhood is the basic unit for fine-grained management of urban carbon emissions and the best place to apply low-carbon concepts and technologies. However, accurately calculating carbon emissions at this level remains a challenge, complicating the identification of effective decarbonization strategies. This research proposes utilizing urban land use at the neighborhood level as a decarbonization unit and taking Central Shenyang City as an example to measure the carbon emission characteristics of different sectors by integrating multi-source data. Then, based on the carbon emission intensity of the lands, establishing a baseline for decarbonization and measuring its effectiveness accordingly. The results show that in Central Shenyang City, 90.99 % of CO₂ emissions come from the building and transportation sectors, with carbon sinks offsetting a mere 2.20 % of these emissions. Commercial land has the highest overall level of carbon emissions per unit area, while industrial land records the highest per capita carbon emissions. In addition, there is a significant imbalance in the spatial distribution of carbon emissions among various sectors. Through a graded decarbonization strategy based on the baseline indicators, an overall decarbonization of 19.79 % in carbon emissions is achieved. This study introduces a data-driven model with potential applications in other regions.