| 黄河流域交通运输碳排放时空特征及影响因素 |
| 摘要点击 284 全文点击 14 投稿时间：2025-04-17 修订日期：2025-07-28 |
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| 中文关键词 交通运输碳排放 黄河流域 影响因素 最优参数地理探测器（OPGD） 时空地理加权回归（GTWR）模型 |
| 英文关键词 transportation carbon emissions the Yellow River Basin influencing factors optimal parameter geographic detector（OPGD） geographically and temporally weighted regression（GTWR）model |
| DOI 10.13227/j.hjkx.202504219 |
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| 中文摘要 |
| 随着“双碳”目标稳步推进，深入探究黄河流域交通碳排放特征及驱动因素对推动流域低碳转型与高质量发展具有重要意义. 基于2010~2022年流域64个地级市（州、盟）的交通碳排放数据，综合运用空间自相关分析、标准差椭圆、最优参数地理探测器（OPGD）和时空地理加权回归（GTWR）等方法，探究交通碳排放的时空格局及其驱动机制. 结果表明：①黄河流域交通碳排放呈增长趋势，空间上呈现“东高西低”格局，标准差椭圆重心始终在长治市并向东南方向迁移. ②OPGD识别出货运量、人口规模、城镇化水平和对外开放水平为影响交通碳排放的主导驱动因子，因子间交互作用以非线性增强和双因子增强为主. ③GTWR结果显示，货运量、人口规模和城镇化对多数城市交通碳排放具有显著正向驱动效应，而对外开放水平则呈现负向抑制作用. 据此，提出构建跨区域碳交易机制、优化城市空间布局和推进智能交通建设等建议，可为黄河流域交通碳减排提供科学依据. |
| 英文摘要 |
| As the “dual carbon” goals steadily advance， investigating the characteristics and driving factors of transportation carbon emissions in the Yellow River Basin is of great significance for promoting low-carbon transition and high-quality development. Based on transportation carbon emission data of 64 prefecture-level cities （states and leagues） in the basin from 2010 to 2022， we comprehensively applied spatial autocorrelation analysis， standard deviational ellipse， optimal parameter geographical detector （OPGD）， and geographically and temporally weighted regression （GTWR） to explore the spatio-temporal patterns and driving mechanisms of transportation carbon emissions. The findings reveal that： ① Transportation carbon emissions in the Yellow River Basin showed an increasing trend， with a spatial pattern of “high in the east， low in the west.” The centroid of the standard deviational ellipse consistently remained in Changzhi City while shifting southeastward. ② OPGD identified freight volume， population size， urbanization level， and openness level as the primary driving factors， with interactions primarily exhibiting nonlinear enhancement and bifactorial enhancement. ③ GTWR results showed that freight volume， population size， and urbanization had significant positive driving effects on transportation carbon emissions in most cities， while the openness level demonstrated a negative inhibitory effect. Accordingly， we propose establishing a cross-regional carbon trading mechanism， optimizing urban spatial layouts， and advancing intelligent transportation systems， which could provide a scientific basis for reducing transport carbon emissions in the Yellow River Basin. |
