Luo Haizhi, Zhang Yiwen, Liu Zhengguang, Yu Zhechen, Song Xia, Meng Xiangzhao, Yang Xiaohu, Sun Lu
Institute of the Building Environment & Sustainability Technology, School of Human Settlements and Civil Engineering, Xi'an Jiaotong University, Xi'an 710049, China.
School of Architecture and Civil Engineering, Xiamen University, Xiamen 361005, China.
Sci Total Environ. 2024 Dec 20;957:176500. doi: 10.1016/j.scitotenv.2024.176500. Epub 2024 Sep 28.
Point source carbon emissions account for approximately 80 % of total emissions. Investigating the influence of land use and socio-economic indicators on these emissions is crucial for achieving sustainable development goals. Existing research faces challenges such as focusing on specific regions, mixing variables that may exhibit multicollinearity, and lacking sufficient land use information. This study takes China, the largest emitting country, as a case study, utilizing geospatial big data to subdivide land use into 11 categories based on emission sectors. The impacts of land use and socio-economic indicators on different emission sectors are discussed from the perspectives of bivariate and spatial statistical analysis, with spatial hotspots identified. Hierarchical regression is used to evaluate the explanatory power of the indicators and to establish models, and potential carbon reduction strategies are further explored. Key findings reveal: (1) Significant multicollinearity between land use and socio-economic indicators was demonstrated, with land use explaining 57.1 % of emissions compared to 37.4 % explained by socio-economic indicators. The spatial consistency between land use and emissions exceeds 80 %, and the spatiotemporal variability is relatively low, making land use a more advantageous factor in explaining point source carbon emissions. (2) Agricultural mechanization increases emission intensity, but this efficient farming method helps convert surplus plowland, the largest influencing factor (Coefficient = 0.717), into carbon sinks, thereby controlling agricultural emissions. (3) Land intensification helps control the area of industrial land, the main factor influencing industrial emissions (Coefficient = 0.392). It also contributes to the efficient use of carbon reduction technologies and industrial supporting land. (4) Mixed commercial and residential land has the greatest impact on commercial, service, and household emissions. However, its relationship with the economy (Correlation = 0.479) is stronger than its relationship with emissions (Correlation = 0.182), making it more applicable to cities that serve as economic growth hubs.
点源碳排放约占总排放量的80%。研究土地利用和社会经济指标对这些排放的影响对于实现可持续发展目标至关重要。现有研究面临着诸如聚焦特定区域、混合可能存在多重共线性的变量以及缺乏足够土地利用信息等挑战。本研究以最大排放国中国为例，利用地理空间大数据将土地利用根据排放部门细分为11类。从双变量和空间统计分析的角度讨论了土地利用和社会经济指标对不同排放部门的影响，并识别了空间热点。使用层次回归来评估指标的解释力并建立模型，并进一步探索潜在的碳减排策略。主要研究结果表明：(1) 土地利用和社会经济指标之间存在显著的多重共线性，土地利用对排放的解释率为57.1%，而社会经济指标的解释率为37.4%。土地利用与排放之间的空间一致性超过80%，时空变异性相对较低，使得土地利用在解释点源碳排放方面是一个更具优势的因素。(2) 农业机械化增加了排放强度，但这种高效的耕作方式有助于将最大影响因素（系数=0.717）——过剩耕地转化为碳汇，从而控制农业排放。(3) 土地集约化有助于控制工业用地面积，工业用地是影响工业排放的主要因素（系数=0.392）。它还有助于碳减排技术和工业配套用地的高效利用。(4) 商住混合用地对商业、服务和家庭排放的影响最大。然而，它与经济的关系（相关性=0.479）比与排放的关系（相关性=0.182）更强，这使其更适用于作为经济增长中心的城市。
