Global warming necessitates urgent greenhouse gas emission reduction measures, with many countries committing to carbon neutrality. China, a major emitter, has pledged carbon neutrality by 2060. Urban areas, with concentrated populations and activities, are primary emission sources. Land-use changes significantly impact carbon balance, affecting both terrestrial ecosystem carbon and anthropogenic carbon emissions. Optimizing land use is crucial for enhancing carbon sinks and mitigating emissions. Two key challenges in establishing low-carbon cities are accurate spatial monitoring of carbon sink/source capacity and developing effective strategies for emission reduction and sink enhancement through land-use control. Existing spatial monitoring methods for terrestrial carbon sinks and anthropogenic carbon emissions have limitations. Terrestrial ecosystem carbon evaluations are often conducted at coarse scales (continental, national, provincial), lacking high resolution. Discrepancies in carbon sink values exist due to varying data sources and models. Anthropogenic carbon emission calculations use 'top-down' and 'bottom-up' methods, each with limitations regarding scale and accuracy. Spatial carbon emissions monitoring is typically at the administrative unit level, with limitations in methods using night-light data or satellite carbon concentration data due to resolution constraints. Land-use and land cover change (LUCC) modelling is a significant research area. Future land-use change simulations are conducted under different scenarios, considering land-use type demands and spatial driving forces. Various models exist (cellular automata, CLUE-S, SLEUTH, land transformation model, ANN, support vector machine), each with advantages and disadvantages. Low-carbon land-use optimization often involves adjusting future land-use structures based on carbon sink/source capacity, typically using linear programming. Existing approaches often focus on urban-scale or specific land-use type control, lacking patch-scale detail. This study addresses these gaps by integrating high-resolution carbon sink/source monitoring and LUCC simulation models.

Numerous studies have explored spatial monitoring of terrestrial carbon sinks and anthropogenic carbon emissions, but inconsistencies exist due to varying methodologies and scales. Large-scale assessments often lack the resolution needed for effective urban planning. 'Top-down' and 'bottom-up' approaches to calculating anthropogenic emissions each have strengths and weaknesses. Night-light data and satellite-based estimations offer spatial insights but suffer from resolution limitations. Similarly, land-use change modeling has seen the development of diverse models (CA, CLUE-S, SLEUTH, etc.), but these frequently lack the integration of high-resolution carbon data at the patch scale, limiting their effectiveness for low-carbon land-use optimization. The need for a high-resolution, spatially explicit approach that integrates carbon monitoring with patch-scale land-use optimization is highlighted by the existing literature.

This study uses Shangyu District, Zhejiang Province, China, as a case study. Data sources include energy consumption data, land-use data (from the third national land survey), night-light remote sensing images (Luojia-01 satellite), industry data, building data, net ecosystem productivity (NEP) data (using a method from Ye and Chuai, 2022), carbon flux from inland water data (Ran et al., 2021), and agricultural input indices. Anthropogenic carbon emissions were calculated using two approaches: 1. Carbon emissions from energy consumption were calculated by multiplying energy consumption by carbon emission coefficients (Equation 1). 2. Carbon emissions from industrial production processes were calculated using Equation 2, considering production of glass, aluminum, cement, steel, lime, and ammonia synthesis. 3. Carbon emissions from agricultural production were calculated using Equation 3, considering energy consumption and agricultural resources. Terrestrial ecosystem carbon sink/source capacity was assessed using the NEP index (Equation 3). The study involved two main explorations: 1. A high-resolution carbon monitoring model, integrating various data sources and methods to generate a net carbon sink/source model. 2. A low-carbon land use model, optimizing land-use structure by integrating net carbon sink/source data and traditional spatial constraints as driving forces for land use change. This involved optimizing land transfer rules and allocating land-use types at the patch scale. The low-carbon land-use structure optimization used a linear programming model (Equations 4-17) to minimize total carbon emissions, subject to constraints on land-use area demands in 2030. The model was implemented using Lingo software. The spatial land-use optimization involved two steps: 1. Optimizing land probability-of-occurrence by integrating traditional spatial factors and carbon sink/source capacity using an ANN algorithm (Equations 18-21). 2. Allocating future land spatially using the FLUS model with an adaptive inertia mechanism (Equations 22-24).

Between 2000 and 2020, Shangyu District experienced a rapid increase in anthropogenic carbon emissions (from 51 × 10⁴ to 265 × 10⁴ t), primarily from the industrial sector (around 80%). Terrestrial ecosystems acted as a carbon sink, but the average annual carbon sink offset only approximately 3% of regional anthropogenic carbon emissions. Spatially, high-intensity carbon emissions were concentrated in the northeast (industrial park) and urban areas, while net carbon sinks were concentrated in the south. From 2000 to 2020, significant land-use change occurred, with a 14.5% change in total land area. The largest change was the transfer-out of cropland (96.44 km²), mainly due to built-up land expansion. Land-use transfer resulted in an annual increase of 77.72 × 10⁴ t in carbon emissions. Land-use structure optimization projected a much smaller increase in carbon emissions (7154 t/year) between 2020 and 2030 compared to the 2000-2020 period. This optimization significantly increased the area of ecological land, leading to increased carbon sinks. Further patch-scale optimization enhanced the carbon sink by 129.59 t C/year. Spatial simulation under different scenarios (considering traditional constraints only vs. including carbon sink/source capacity) showed that integrating carbon capacity significantly improves low-carbon land use outcomes.

The findings highlight the significant impact of land-use change on carbon emissions in Shangyu District. The high-resolution carbon monitoring model and the patch-scale low-carbon land use model developed in this study provide a more effective approach to addressing the challenge of low-carbon land use compared to traditional methods. The integration of carbon sink/source capacity as a constraint in land-use optimization is a key innovation, allowing for more precise adjustments at the patch level. The results demonstrate the considerable potential for reducing carbon emissions through careful land-use planning and management, providing valuable insights for urban planning and land spatial planning. The significant reduction in carbon emissions projection from 2020 to 2030, achieved through land-use structure optimization, showcases the model's effectiveness.

This study makes several key contributions: a high-resolution carbon monitoring model for county-scale assessments, a novel patch-scale low-carbon land use model, and a demonstration of the significant potential for carbon emission reduction through optimized land use. The results demonstrate that integrating high-resolution carbon data and patch-scale optimization significantly improves low-carbon land use planning. Future research should focus on enhancing data accuracy (e.g., incorporating residential vacancy rates and detailed agricultural data), refining the models to consider additional land-use types and complexities, and exploring multi-objective optimization that incorporates economic and biodiversity factors.

While this study presents significant advancements, limitations exist. The top-down approach for calculating anthropogenic carbon emissions may introduce some bias, though the error is likely limited within a relatively homogeneous region. The spatial resolution of carbon emissions simulations for transportation and cropland could be improved. Finally, the low-carbon land use optimization currently focuses on absolute carbon sink/source capacity, without fully considering potential trade-offs with other land use objectives (economic benefits, biodiversity).