Wan Ruxing, Qian Shuangyue, Ruan Jianhui, Zhang Li, Zhang Zhe, Zhu Shuying, Jia Min, Cai Bofeng, Li Ling, Wu Jun, Tang Ling
School of Economics and Management, Beijing University of Chemical Technology, Beijing, 100029, China.
School of Economics and Management, University of Chinese Academy of Sciences, Beijing, 100190, China.
J Environ Manage. 2024 Mar;354:120391. doi: 10.1016/j.jenvman.2024.120391. Epub 2024 Feb 15.
Timely and accurate implementation of carbon emissions (CE) analysis and evaluation is necessary for policymaking and management. However, previous inventories, most of which are yearly, provincial or city, and incomplete, have failed to reflect the spatial variations and monthly trends of CE. Based on nighttime light (NTL) data, statistical data, and land use data, in this study, a high-resolution (1 km × 1 km) monthly inventory of CE was developed using back propagation neural network, and the spatiotemporal variations and impact factors of CE at multiple administrative levels was evaluated using spatial autocorrelation model and spatial econometric model. As a large province in terms of both economy and population, Guangdong is facing the severe emission reduction challenges. Therefore, in this study, Guangdong was taken as a case study to explain the method. The results revealed that CE increased unsteadily in Guangdong from 2013 to 2022. Spatially, the high CE areas were distributed in the Pearl River Delta region such as Guangzhou, Shenzhen, and Dongguan, while the low CE areas were distributed in West and East Guangdong. The Global Moran's I decreased from 2013 to 2022 at the city and county levels, suggesting that the inequality of CE in Guangdong steadily decreased at these two administrative levels. Specifically, at the city level, the Global Moran's I gradually decreased from 0.4067 in 2013 to 0.3531 in 2022. In comparison, at the county level, the trend exhibited a slower decline, from 0.3647 in 2013 to 0.3454 in 2022. Furthermore, the analysis of the impact factors revealed that the relationship between CE and gross domestic product was an inverted U-shaped, suggesting the existence of the inverted U-shaped Environmental Kuznets Curve for CE in Guangdong. In addition, the industrial structure had larger positive impact on CE at the different levels. The method developed in this study provides a perspective for establishing high spatiotemporal resolution CE evaluation through NTL data, and the improved inventory of CE could help understand the spatial-temporal variations of CE and formulate regional-monthly-specific emission reduction policies.
及时、准确地开展碳排放（CE）分析与评估对于政策制定和管理而言至关重要。然而，以往的清单大多是年度、省级或市级的，且并不完整，未能反映出碳排放的空间差异和月度趋势。基于夜间灯光（NTL）数据、统计数据和土地利用数据，本研究利用反向传播神经网络构建了分辨率为1千米×1千米的月度碳排放清单，并运用空间自相关模型和空间计量模型评估了多个行政层面碳排放的时空变化及影响因素。作为经济和人口大省，广东正面临严峻的减排挑战。因此，本研究以广东为例阐释该方法。结果显示，2013年至2022年期间广东的碳排放呈不稳定增长。在空间上，高碳排放区域分布在广州、深圳和东莞等珠江三角洲地区，而低碳排放区域分布在粤西和粤东地区。2013年至2022年期间，市县级层面的全局莫兰指数（Global Moran's I）下降，表明这两个行政层面上广东碳排放的不平等程度在稳步降低。具体而言，在市级层面，全局莫兰指数从2013年的0.4067逐渐降至2022年的0.3531。相比之下，在县级层面，该趋势下降较为缓慢，从2013年的0.3647降至2022年的0.3454。此外，对影响因素的分析表明，碳排放与国内生产总值之间呈倒U形关系，这表明广东存在碳排放的倒U形环境库兹涅茨曲线。此外，产业结构在不同层面上对碳排放有更大的正向影响。本研究中开发的方法为通过夜间灯光数据建立高时空分辨率的碳排放评估提供了一个视角，而改进后的碳排放清单有助于了解碳排放的时空变化并制定区域月度特定减排政策。
