| 不同空间尺度下中国土地利用碳排放时空演变特征及影响因素分析 |
| 摘要点击 701 全文点击 96 投稿时间：2025-05-01 修订日期：2025-07-25 |
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| 中文关键词 不同空间尺度 地均碳排放量 夜间灯光数据 土地利用/覆被变化（LUCC）数据 LMDI模型 碳排放系数法 |
| 英文关键词 different spatial scales carbon emissions per land area nighttime lighting data land use/cover change（LUCC）data LMDI model carbon emission coefficient method |
| DOI 10.13227/j.hjkx.202505003 |
| 作者 | 单位 | E-mail | | 张紫涵 | 新疆大学地理与遥感科学学院, 乌鲁木齐 830017 | zhangzihan@stu.xju.edu.cn | | 夏楠 | 新疆大学地理与遥感科学学院, 乌鲁木齐 830017
自然资源部荒漠-绿洲生态监测与修复工程技术创新中心, 乌鲁木齐 830002
新疆大学绿洲生态重点实验室, 乌鲁木齐 830017 | xn_gis@xju.edu.cn | | 唐玉倩 | 新疆大学地理与遥感科学学院, 乌鲁木齐 830017 | | | 孙思帆 | 新疆大学地理与遥感科学学院, 乌鲁木齐 830017 | | | 徐战江 | 新疆大学地理与遥感科学学院, 乌鲁木齐 830017 | | | 马勇刚 | 新疆大学地理与遥感科学学院, 乌鲁木齐 830017
自然资源部荒漠-绿洲生态监测与修复工程技术创新中心, 乌鲁木齐 830002
新疆大学绿洲生态重点实验室, 乌鲁木齐 830017 | |
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| 中文摘要 |
| 为了探究中国土地利用碳排放的动态演进及影响因素在不同空间尺度下的特征，基于中国30 m土地利用/覆被变化（LUCC）数据集和DMSP/OLS夜间灯光数据，综合运用碳排放系数法、空间自相关、变异系数法、SLOPE趋势分析、重心迁移分析和LMDI模型法，从省级、市级和县级这3个尺度，对中国土地利用碳排放时空演变特征及影响因素进行了对比分析. 结果表明：①3种尺度下地均碳排放量均呈现出“东南高、西北低”的分异特征，并由“点状核心”演变为“区域聚集”. 省级和市级尺度下地均碳排放量持续增加，而县级尺度地均碳排放量先增加后减少，增长速率均呈下降趋势. ②3种尺度下地均碳排放量局部空间分布模式均以正相关为主，LL型占主导. 空间尺度越小，对碳排放聚集性和异质性的表达越精细. ③3种尺度下地均碳排放量变化趋势均表现为：东部>西部，沿海>内陆，均以缓慢增长型和较慢增长型为主. 随着空间尺度的减小，其他类型变化趋势的数量快速增加. ④3种尺度下的重心位置和移动轨迹存在明显差异：省级尺度下重心位于江苏省和安徽省并向西南方向迁移；市级尺度重心位置位于安徽省和河南省，而县级尺度重心始终位于河南省商丘市内，两种尺度下的重心均呈“7”字形路径向西北方向移动. ⑤在不同尺度和阶段下，不同因素对土地利用碳排放的作用方式和强度存在显著差异. 3种尺度下，过去20 a间均表现为：能源效率对土地利用碳排放的抑制作用最强，经济发展对土地利用碳排放的促进作用最强. 研究对于制定不同空间尺度下的差异化减排政策，助力“双碳”目标的实现具有重要意义. |
| 英文摘要 |
| In this study， to investigate the dynamic evolution and driving factors of carbon emissions resulting from land use in China across multiple spatial scales， we employed the 30-meter resolution China land use/cover change （LUCC） dataset in conjunction with DMSP/OLS nighttime light data. Utilizing a combination of the carbon emission coefficient method， spatial autocorrelation analysis， coefficient of variation， SLOPE trend analysis， gravity center migration analysis， and the logarithmic mean Divisia index （LMDI） method， a comparative analysis was conducted to examine the spatiotemporal evolution and determinants of land use carbon emissions at the provincial， municipal， and county levels. The key findings are as follows： ① Across all three spatial scales， per-unit land carbon emissions exhibited a spatial pattern characterized by “high in the southeast and low in the northwest，” evolving from a “point-core” to a “regional aggregation” structure. At the provincial and municipal levels， per-unit carbon emissions have shown a consistent upward trend， whereas at the county level， emissions first increased and then declined， with the growth rate showing a downward trajectory. ② At all scales， the local spatial distribution of per-unit land carbon emissions was predominantly characterized by positive spatial autocorrelation， with the “low-low” （LL） clustering type being the most prevalent. Finer spatial scales revealed more detailed patterns of carbon emission clustering and spatial heterogeneity. ③ Spatially， the trends in per-unit land carbon emissions followed the pattern： eastern > western， coastal > inland， with “slow growth” and “relatively slow growth” being the dominant trend categories. As the spatial resolution increased （i.e.， scale decreased）， the diversity of trend types became more pronounced. ④ Significant differences were observed in the spatial location and movement trajectories of the carbon emission gravity centers across the three scales. At the provincial level， the gravity center was located between Jiangsu and Anhui provinces， moving southwestward； at the municipal level， it shifted between Anhui and Henan provinces； while at the county level， it remained within Shangqiu City in Henan Province， tracing a northwestward “7-shaped” path. ⑤ The nature and intensity of the factors influencing land use carbon emissions varied considerably across spatial scales and temporal stages. Over the past two decades， energy efficiency has exerted the most significant inhibitory effect on carbon emissions across all scales， whereas economic development has been the most prominent driving force. These findings provide critical insights for formulating scale-specific emission reduction strategies and offer valuable guidance for advancing China's “dual carbon” goals of carbon peaking and carbon neutrality. |
