山东省能源消费碳排放时空格局研究

Research on the Spatiotemporal Pattern of Carbon Emissions from Energy Consumption in Shandong Province

摘要: 能源消耗和碳排放增加，加剧气候变化，准确估算碳排放和了解碳排放空间分布，是节能减排的基础。传统的碳排放估算仅通过统计数据计算省或区域碳排放量，由于统计数据的缺失，无法估算小空间尺度的碳排放。为了更好地了解市级碳排空间分布，文章利用NPP-VIIRS夜间灯光数据，采用时空地理加权回归模型，建立夜间灯光与能源消费碳排放的关系模型，得到山东省市级能源消费碳排放空间分布图。结果表明：GTWR模型估算，能源消费碳排放具有较高的精度，能够展现山东省碳排放的空间分布特征。总体空间上，山东省碳排放呈增长趋势，主要分布在北部、南部、东部；碳排放热点主要分布在东部的日照、潍坊、青岛、烟台；分布重心在潍坊。研究结果可为山东省减排政策提供支撑。

Abstract: The increase in energy consumption and carbon emissions has aggravated climate change. Accurately estimating carbon emissions and understanding the spatial distribution of carbon emissions are the basis for energy conservation and emission reduction. Traditional carbon emission estimation only calculates provincial or regional carbon emissions through statistical data. Due to the lack of statistical data, it is impossible to estimate carbon emissions at a small spatial scale. In order to better understand the spatial distribution of carbon emissions at the municipal level, this paper uses NPP-VIIRS night light data and a spatiotemporal geographic weighted regression model to establish a relationship model between night light and energy consumption carbon emissions, and obtains a spatial distribution map of energy consumption carbon emissions at the municipal level in Shandong Province. The results show that the GTWR model estimates energy consumption carbon emissions with high accuracy and can show the spatial distribution characteristics of carbon emissions in Shandong Province. In terms of overall space, carbon emissions in Shandong Province are on the rise, mainly distributed in the north, south and east; carbon emission hotspots are mainly distributed in Rizhao, Weifang, Qingdao and Yantai in the east; the distribution center is in Weifang. The research results can provide support for Shandong Province’s emission reduction policies.