江苏省工业碳排放时空分异及减排策略

TEMPORAL-SPATIAL DIFFERENTIATION AND EMISSION REDUCTION STRATEGY OF JIANGSU’S INDUSTRIAL CARBON EMISSION

研究工业碳排放的时空分布特征对于制定碳减排策略、促进工业低碳化转型具有重要意义。根据2010—2019年江苏省各市规模以上工业企业原煤、焦炭、天然气、汽油、煤油、柴油和燃料油等主要能源消费数据,采用碳排放系数法核算13个地级市工业碳排放量,利用ArcGis软件绘制工业碳排放时空分布图;采用空间自相关分析法,并利用Geoda软件计算工业碳排放的全局Moran’s I指数,分析其全局空间自相关性;为测度工业碳排放局部空间聚类情况,利用Geoda软件绘制LISA聚类图;采用空间重心模型画出2010—2019年碳排放重心转移轨迹图,对碳排放的时空分布特征进一步进行分析;最后提出了碳减排策略。结果表明:(1)10年间,江苏省工业碳排放总量稳中有降,单位工业生产总值碳排放量下降趋势明显,碳减排效果显著;(2)工业碳排放各区域之间存在显著差异,较高值区和较低值区占主体,且南北差异大,总体上呈南高北低,因此各市在制定碳减排措施时应避免“一刀切”,将苏南地区作为碳减排重点控制区域;(3)江苏省工业碳排放在空间分布上具有显著的正相关性,碳排放在空间上呈现聚集性,苏南地区为高-高聚集区,苏北地区(徐州市除外)为低-低聚集区,大部分市域工业碳排放局部空间集聚性不显著;(4)碳排放重心整体变化不大,10年间重心点均位于扬州市内,表明江苏省工业碳排放南高北低的局面未被打破。

Temporal-spatial distribution of industrial carbon emission is key to making carbon emission strategy and to promoting industrial low carbon transformation. This paper, according to Jiangsu’s 2010 to 2019 industrial coal, coking coal, natural gas, gasoline, kerosene, diesel oil and fuel oil data, uses carbon emission coefficient to estimate industrial carbon emission in Jiangsu’s thirteen prefectures, and applies ArcGIS to draw the temporal-spatial distribution map. Spatial auto-correlation method is used to calculate the overall Moran’s index of industrial carbon emission by means of Geoda software with LISA clustering map worked out. Spatial core model is used to map carbon emission core migration tracks from 2010 to 2019.Finally, this paper presents suggestions on carbon reduction. The results show a stably decreasing industrial carbon emission over the decade in Jiangsu province, obviously in unit industrial GDP, suggesting a good achievement in carbon reduction. However, industrial carbon emission varies with regions, majorities with relatively high and low emissions, and high in the south and low in the north. Each prefecture should make differentiated carbon reduction measures to avoid rigidly uniform, with emphasis on southern Jiangsu. The spatial distribution of Jiangsu’s carbon emission is of outstandingly positive spatial correlation showing high-high clustering in the south and low-low clustering in the north(except Xuzhou city), but not conspicuous in most cities. Carbon emission core has little changes, always within Yangzhou city, verifying that Jiangsu’s carbon emission is still south-high-north-low.