Measuring greenhouse gas (GHG) emissions across different types of land is essential for urban planning during the transition to low-carbon cities. Urban land identification, based on remote sensing images and points of interest (POIs), has the potential to narrow down the statistical unit, helping achieve spatial visualization of GHG emissions below the city level and distinguish emission characteristics of different land-use types. Considering land type as a classification standard for GHG emissions, we constructed the modified STRIPAT-PLS model by identifying land-use types, reconstructing land-use GHG emissions (LUGEs) inventory, and extracting driving indexes, which calculated driving force and LUGEs that were missed in historical years. It deepened the depth and precision of GHG emissions research and provided a reference for LUGE reduction and land management below the city level where missing historical data on GHG inventories. The results showed that: 1) the modified STRIPAT-PLS model achieved a simulation accuracy of 94.9%, demonstrating the effectiveness of our research approach in depicting GHG emissions across different land-use types. 2) Land scale, socio-economic development, and industrial development were key factors that impacted agricultural land, residential land, and airport respectively. 3) Airport was the most significant carbon contributor, while industrial witnessed the highest growth in emission intensity; 4) the spatial analysis indicated decreasing differences in GHG emissions and an overall increase in emission intensity. In addition, the research proposed emission reduction priorities and strategies for each land type to promote low-carbon goals in airport cities.

中文翻译：

---

基于土地利用变化的城市温室气体排放特征——以空港新城为例

测量不同类型土地的温室气体 (GHG) 排放对于向低碳城市转型期间的城市规划至关重要。基于遥感图像和兴趣点（POI）的城市土地识别具有缩小统计单位范围的潜力，有助于实现城市以下温室气体排放的空间可视化，区分不同土地利用类型的排放特征。以土地类型作为温室气体排放分类标准，通过识别土地利用类型、重构土地利用温室气体排放（LUGE）清单、提取驱动指数，构建了修正的STRIPAT-PLS模型，计算出驱动力和LUGE错过了历史岁月。深化了温室气体排放研究的深度和精度，为缺乏温室气体清单历史数据的城市以下的LUGE减排和土地管理提供了参考。结果表明：1）修改后的STRIPAT-PLS模型的模拟精度达到94.9%，证明了我们的研究方法在描述不同土地利用类型的温室气体排放方面的有效性。 2）土地规模、社会经济发展和产业发展分别是影响农用地、住宅用地和机场的关键因素。 3）机场碳排放贡献最大，工业排放强度增幅最大； 4）空间分析表明温室气体排放差异缩小，排放强度总体增加。此外，研究还提出了每种土地类型的减排重点和策略，以促进机场城市的低碳目标。