School of Architecture, Harbin Institute of Technology; Key Laboratory of Cold Region Urban and Rural Human Settlement Environment Science and Technology, Ministry of Industry and Information Technology, Harbin 150006, China.
School of Art and Design, Heilongjiang Institute of Technology, Harbin 150050, China.
Int J Environ Res Public Health. 2023 Jan 1;20(1):829. doi: 10.3390/ijerph20010829.
This paper constructs a county-level carbon emission inversion model in Northeast China. We first fit the nighttime light data of the Visible Infrared Imaging Radiometer Suite (VIIRS) with local energy consumption statistics and carbon emissions data. We analyze the temporal and spatial characteristics of county-level energy-related carbon emissions in Northeast China from 2012 to 2020. At the same time, we use the geographic detector method to analyze the impact of various socio-economic factors on county carbon emissions under the single effect and interaction. The main results are as follows: (1) The county-level carbon emission model in Northeast China is relatively more accurate. The regression coefficient is 0.1217 and the determination coefficient R of the regression equation is 0.7722. More than 80% of the provinces have an error of less than 25%, meeting the estimation accuracy requirements. (2) From 2012 to 2020, the carbon emissions of county-level towns in Northeast China showed a trend of increasing first and then decreasing from 461.1159 million tons in 2012 to 405.752 million tons in 2020. It reached a peak of 486.325 million tons in 2014. (3) The regions with higher carbon emission growth rates are concentrated in the northern and coastal areas of Northeast China. The areas with low carbon emission growth rates are mainly distributed in some underdeveloped areas in the south and north in Northeast China. (4) Under the effect of the single factor urbanization rate, the added values of the secondary industry and public finance income have higher explanatory power to regional emissions. These factors promote the increase of county carbon emissions. When fiscal revenue and expenditure and the added value of the secondary industry and per capita GDP interact with the urbanization rate, respectively, the explanatory power of these factors on regional carbon emissions will be enhanced and the promotion of carbon emissions will be strengthened. The research results are helpful for exploring the changing rules and influencing factors of county carbon emissions in Northeast China and for providing data support for low-carbon development and decision making in Northeast China.
本文构建了东北地区县级碳排放反演模型。首先，我们将夜间灯光数据与当地能源消费统计数据和碳排放数据进行拟合。然后，分析了 2012 年至 2020 年东北地区县级能源相关碳排放的时空特征。同时，利用地理探测器方法分析了各种社会经济因素在单一效应和交互作用下对县级碳排放的影响。主要结果如下：（1）东北地区县级碳排放模型较为准确，回归方程的回归系数为 0.1217，决定系数 R 为 0.7722，80%以上的省份误差在 25%以内，满足估计精度要求。（2）2012 年至 2020 年，东北地区县级城镇碳排放呈先增后减趋势，从 2012 年的 461.1159 百万吨增加到 2020 年的 405.752 百万吨，2014 年达到峰值 486.325 百万吨。（3）碳排放增长率较高的地区集中在东北地区的北部和沿海地区，碳排放增长率较低的地区主要分布在东北地区南部和北部一些欠发达地区。（4）在单一因素城市化率的作用下，第二产业增加值和公共财政收入对区域排放的解释力较高，这些因素促进了县级碳排放的增加。当财政收支和第二产业增加值与人均 GDP 与城市化率相互作用时，这些因素对区域碳排放的解释力增强，促进了碳排放的增强。研究结果有助于探索东北地区县级碳排放的变化规律和影响因素，为东北地区低碳发展和决策提供数据支持。
