在我国“碳达峰、碳中和”的绿色低碳发展背景下,对碳排放量的周期性分析与准确预测具有重要的现实意义。以浙江省碳排放量为研究对象,引入变分模态分解方法提取多尺度信息,将套索算法(LeastAbsoluteShrinkageandSelectionOperator,LASSO)和灰色模型GM(1,N)相结合,对浙江省的碳排放量进行深入分析和预测。首先,使用变分模态分解方法对浙江省碳排放量进行数据分解,分析其历史波动的周期性。其次,利用套索算法对影响碳排放的关键因素进行有效筛选,降低数据维度,提取主要特征。最后,结合“十四五”规划与实际发展路径,假设了常态、低碳、惯性发展情景,并采用GM(1,N)模型对浙江省2020—2030年碳排放量进行预测,克服了传统预测方法在处理非线性、小样本数据时的局限性,预测结果更加稳健。结果表明浙江省的碳排放量的主导因素包括第三产业占GDP的比重、私人汽车拥有量、全省固定资产投资、电力消费总量、研发强度、技术市场成交额6个指标。预计到2030年,仅低碳发展情景下的碳排放量达到峰值,为400.28Mt,而常态发展情景的碳排放量为474.23Mt,惯性发展情景为568.77Mt,且常态发展情景和惯性发展情景下的碳达峰量在2030年后仍会有所增长。因此,建议浙江省继续优化产业结构、提升能源效率、增加低碳研发投入,稳扎稳打推进“碳达峰”目标。

Under the green and low-carbon development goal of achieving " carbon peaking and carbonneutrality" in China, cyclical analysis and accurate prediction of carbon emissions are of great impor-tance. This paper investigates carbon emissions in Zhejiang Province. First, the variable mode decom-position method is used to decompose the historical data of carbon emissions in Zhejiang Province, ena-bling an analysis of its cyclicality fluctuations. Second, the LASSO algorithm is employed to identify thekey influencing factors of carbon emissions. Finally, considering the 14th Five-Year Plan and the prov-ince′s development trajectory, three development scenarios (normal, low-carbon, and inertia) are as-sumed, and the GM (1, N) model is used to predict the carbon emissions in Zhejiang Province from2020 to 2030. The analysis reveals that the dominant factors affecting carbon emissions in ZhejiangProvince are the proportion of the third industry in GDP, the number of private cars, the total fixed as-set investment in the province, the total electricity consumption, R&D intensity, and technology marketturnover. Under the low-carbon scenario, carbon emissions are projected to peak at 400.28 Mt in 2030.In contrast, under the normal scenario, carbon emissions are estimated to reach 474.23 Mt, while theinertia development scenario predicts carbon emissions of 568.77 Mt. Furthermore, carbon emissionsare expected to continue rising beyond 2030 in the normal and inertia development scenarios. In light ofthese findings, It is recommended that Zhejiang Province should focus on optimizing its industrial struc-ture, improving energy efficiency, increasing investment in low-carbon research and development, andsteadily advancing the goal of " carbon peaking" .