| 中国城市碳排放强度的时空演变、动态跃迁及收敛趋势 |
| 摘要点击 2616 全文点击 270 投稿时间：2023-05-15 修订日期：2023-07-07 |
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| 中文关键词 城市碳排放强度 时空演变 动态跃迁 空间收敛 |
| 英文关键词 urban carbon emission intensity spatio-temporal evolution dynamic transition spatial convergence |
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| 中文摘要 |
| 合理控制城市碳排放强度对于中国实现碳达峰、碳中和目标愿景以及应对气候变化的意义重大.采用夜间灯光数据反演得到2001~2020年城市碳排放量,测算碳排放强度,并对城市碳排放强度的时空演变、动态跃迁及收敛趋势进行研究.结果表明：①中国城市碳排放强度持续下降,从2001年的2.79 t·万元-1降至2020年的0.88 t·万元-1,年均降幅5.94%.各大区域城市碳排放强度的差距存在收敛特征.空间分布上,城市碳排放强度的高值区集中在东北地区,以及内蒙古、宁夏和陕西等地,南北地区差异拉大,中南部与东部地区的碳排放强度降幅明显,高低集聚层次分明.②中国城市碳排放强度的global Moran's I较高,均值为0.436,空间自相关性显著.碳排放强度以城市自身与邻域城市均未发生跃迁的类型为主,不同类型间时空跃迁的概率较低,凝聚度指数高达82.57%.跃迁类型的稳态较高,碳排放强度的时空演变存在空间锁定效应和“俱乐部趋同”现象.③城市碳排放强度的σ收敛不显著,但存在绝对β收敛和条件β收敛.绝对β收敛速度差异明显,全国的收敛速度为3.137%.东部和西部地区的收敛速度略低,分别仅有3.043%和3.050%.④条件β收敛速度有所加快.东部地区增幅最大,为3.772%；东北地区的收敛速度提升小,仅有0.098%；中部和西部的收敛速度增长处于中间水平,分别为0.486%和0.661%.人均GDP提升、人口空间集聚和外资引进带来低碳技术的变革、财政资金对R&D的投入倾斜促进了碳排放强度的空间收敛. |
| 英文摘要 |
| Rational control of urban carbon emission intensity was of great significance for China to achieve the goal of carbon peak and carbon neutrality, to combat tackle climate change. This study used nighttime lighting data to invert urban carbon emissions in China from 2001 to 2020, calculated carbon emission intensity, and used spatial autocorrelation and convergence test models to study the spatiotemporal evolution, dynamic transition, and convergence trend of urban carbon emission intensity in China. The results showed that： ① during the research period, urban carbon emission intensity in China continued to decrease, from 0.279 tons per thousand yuan in 2001 to 0.088 tons per thousand yuan in 2020, with an average annual decrease of 5.94%. There was a convergence characteristic in the differences in carbon emission intensity among major regional cities. In terms of spatial distribution, the high value areas of urban carbon emission intensity were concentrated in provinces such as Northeast China, Inner Mongolia, Ningxia, and Shaanxi. The difference between the northern and southern regions was widening, and carbon emission intensity in the central, southern, and eastern regions had decreased significantly, with clear levels of high and low agglomeration. ② The global Moran's I of carbon emission intensity in Chinese cities was relatively high, with an average of 0.436, indicating significant spatial autocorrelation. The probability of spatio-temporal transition between different types was relatively low, with a spatial cohesion index of 82.57%. The spatial stability of the transition type was relatively high, and there was a spatial locking effect and ‘club convergence’ phenomenon in the spatiotemporal evolution of carbon emission intensity. ③ The σ convergence in China and the four major regions was not significant, but absolute β convergence and conditions of β convergence existed. The convergence speed of absolute β was different, and the rate of convergence nationwide was 3.137%. The rate of convergence in the eastern and western regions was slightly lower, at only 3.043% and 3.050%, respectively. The frequent flow of factors such as people, funds, and information in the western region led to a higher convergence rate of urban carbon emission intensity. ④ The rate of conditional β convergence had accelerated. The growth rate in the eastern region was the highest, at 3.772%. The rate of convergence in Northeast China increased slightly, only by 0.098%. The growth rate of convergence in the central and western regions was in the middle range, at 0.486% and 0.661%, respectively. The impact of factors such as economic level, industrial structure, population density, foreign investment, scientific research investment, and road network density on urban carbon emission intensity showed significant heterogeneity. The increase in per capita GDP, population spatial agglomeration, and the transformation of low-carbon technologies brought about by foreign investment, as well as the inclination of fiscal investment in R&D, all had a positive effect on the convergence of urban carbon emission intensity. |
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