| 摘要: |
| 厘清城市空间碳排放分布对推动低
碳转型至关重要。本研究以寒地滨海城市大
连为例，融合10 m 分辨率SDGSAT-1 微光
影像与百度慧眼数据，构建冬季街区碳排
放空间化模型。通过空间自相关分析发现：
碳排放水平与强度在用地类型上呈现相似
规律（商业、公共服务用地为高排放区，临
山沿海居住区为低碳潜力区），但空间分布
存在显著差异。进一步结合土地利用数据揭
示：高排放街区多集中于城市核心区，低碳
潜力街区呈带状沿海分布。相关性分析表
明，街区面积、容积率、建筑高度及到海距
离是关键影响因素。研究提出基于“空间要
素—用地类型—减排潜力”的差异化减排策
略，为寒地滨海城市精准降碳提供科学依
据，助力大连实现“双碳”目标。 |
| 关键词: 寒地滨海城市 街区碳排放 空间
化模拟 SDGSAT-1 卫星微光影像 百度
慧眼 |
| DOI：10.13791/j.cnki.hsfwest.20240205003 |
| 分类号: |
| 基金项目: |
|
| Spatialized simulation and analysis of winter carbon emissions in blocks based on highresolutionremote sensing data and social perception data: A case study of cold coastal cityDalian |
|
LIU Daiyun,LI Jinzuo,CAI Jun,ZHOU Haibo
|
| Abstract: |
| Cities are the epicenters of human activities, industrial production, transportation, and
construction development, and are the focal points of high energy consumption and carbon emissions.
Statistics indicate that 80% of global greenhouse gas emissions stem from urban areas. Clarifying the
distribution of carbon emissions in urban space has important reference value for promoting lowcarbon
economy and comprehensive green transformation of social development. As an important
cold coastal city in the Bohai Bay region, Dalian is affected by the ocean and its climate, and the
spatial carbon emissions of the city in different seasons are also characterized by regional
characteristics. In this paper, it combines high-resolution remote sensing and social perception data,
SDGSAT-1 Glimmer Imager (10 m) and Baidu Map Wise Eye Date, to spatially simulate the carbon
emissions of the cold coastal city blocks in the winter, utilizing spatial autocorrelation analysis to
identify the spatial distribution of key carbon emission blocks and low-carbon emission potential
blocks, combining land use data to explore the patterns of land use types in corresponding blocks of
cold coastal cities, and finally determining the key spatial elements affecting blocks carbon emissions
through correlation analysis. The results indicate that the carbon emission spatialization simulation
method employed in this paper can reasonably and effectively simulate the carbon emission pattern
and detailed differences. This method can be employed to study the spatial distribution of carbon
emissions across different time periods. When viewed from the perspective of land use classification,
the level of blocks carbon emissions in winter and the results of carbon emission intensity exhibit
similarities, yet spatial distribution perspectives reveal discrepancies. The high carbon emission levels
and intensities of city blocks in winter are predominantly associated with land designated for
commercial services, public management and public services, and transportation. The city blocks with
low carbon emission levels and intensity are primarily residential areas. Blocks exhibit spatial
clustering characteristics from both the levels and intensities of carbon emissions, with the spatial
clustering characteristics being more pronounced from the perspective of carbon emission intensity.
Key blocks for high carbon emission are primarily commercial and service land, as well as public
management and public service land, while blocks with low carbon emission potential are mainly
residential areas adjacent to mountains and coastal areas. The area of the block, the floor area ratio of
the block, and the average height of the building have different degrees of impact on the carbon
emissions of the block. In key carbon emission blocks, there is a significant positive correlation
between block area, total building surface area, and block carbon emission levels. Similarly, in blocks
with low carbon emission potential, there is also a significant positive correlation between block area,floor area ratio, total building surface area, and block carbon emission levels. This indicates that an increase in the aforementioned indicators will lead to higher
carbon emission levels in both types of typical blocks. In key carbon emission blocks, there is a significant positive correlation between floor area ratio, average
building height, and block carbon emission intensity. Similarly, in blocks with low carbon emission potential, there is also a significant positive correlation
between floor area ratio, average building height, total building surface area, and block carbon emission intensity. This indicates that an increase in the
aforementioned indicators will lead to higher carbon emission levels in both types of typical blocks. And the low-carbon development focus of the block can be
divided according to the shortest distance from the block to the sea. On this basis, a more accurate carbon emission reduction optimization strategy is
constructed to help Dalian achieve the “Carbon Peaking and Carbon Neutrality Goals” in an orderly and efficient manner. |
| Key words: cold coastal city blocks carbon emission spatialization simulation SDASAT-1 Glimmer Imager Baidu Map Wise Eye |
