| 交通运输业碳排放效率时空演变及趋势预测 |
| 摘要点击 1755 全文点击 169 投稿时间：2023-05-30 修订日期：2023-07-12 |
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| 中文关键词 交通运输业 碳排放效率 超效率SBM模型 空间马尔科夫链模型 时空演变 趋势预测 |
| 英文关键词 transportation industry carbon emission efficiency super efficient SBM model spatial Markov chain model spatio-temporal evolution trend prediction |
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| 中文摘要 |
| 为探索周围邻域对交通运输业碳排放效率演变的影响,分析了交通运输业碳排放效率的时空演变特征,并对其长期演变趋势进行预测.首先,采用超效率SBM模型测度我国30个省份的交通运输业碳排放效率,利用核密度估计方法分析其随时间演变的特征；其次,构建传统与空间马尔可夫概率转移矩阵进一步分析了交通运输业碳排放效率的时空演变特征；最后,针对不同类型的省份提出相关建议.结果表明：①交通运输业碳排放效率的平均水平低于0.6,但呈逐年递增的趋势,且区域差异逐渐缩小.②各省份交通运输业碳排放效率至少有71%的概率维持原有状态,且短期内难以实现跨阶段转移.③地理空间格局影响交通运输业碳排放效率的演变过程,当某省份与碳排放效率高的省份相邻时,转移到高效率的概率增加,而与碳排放效率低的省份相邻时则相反.此外,在空间溢出效应的影响下,交通运输业碳排放效率的演变逐渐呈现“俱乐部收敛”的趋势.④从长期演变趋势来看,各省份交通运输业碳排放效率向高水平转移的可能性增大,呈现出由低到高递增的态势,同时“俱乐部收敛”现象逐渐消失. |
| 英文摘要 |
| In order to explore the influence of the surrounding neighborhood on the evolution of the carbon emission efficiency of the transportation industry, the temporal and spatial evolution characteristics of the carbon emission efficiency of the transportation industry were analyzed, and its long-term evolution trend was predicted. Firstly, the carbon emission efficiency of the transportation industry in 30 provinces of China was assessed using the highly efficient SBM model. Subsequently, the characteristics of carbon emission efficiency over time were analyzed using the nuclear density estimation method. Furthermore, the temporal and spatial evolution characteristics of carbon emission efficiency within the transportation industry were analyzed by utilizing both the traditional and spatial Markov probability transition matrix. Finally, relevant recommendations for different types of provinces were presented based on the above analysis. The results indicated that： ① the average carbon emission efficiency level within the transportation industry remained below 0.6. However, there was a gradual annual increase observed in this efficiency, and the regional disparities were gradually diminishing. ② There was a minimum probability of 71% that the carbon emission efficiency of the transportation industry in each province would maintain its original state, and it was difficult to achieve cross-stage transfer in the short term. ③ The evolution of carbon emission efficiency within the transportation industry was influenced by geographical and spatial patterns. When a province was neighboring a province with high carbon emission efficiency, there was an increased probability of transitioning towards higher efficiency. Conversely, when adjacent to a province with low carbon emission efficiency, the probability of transferring to higher efficiency decreased. Moreover, due to the impact of the spatial spillover effect, the evolution of carbon emission efficiency within the transportation industry gradually exhibited a “club convergence” trend. ④ From the perspective of long-term evolution, the possibility of transferring the carbon emission efficiency of the transportation industry in various provinces to a high level increased and exhibited an increasing trend from low to high. Simultaneously, the phenomenon of “club convergence” gradually disappeared. |
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