辽宁省碳排放影响因素及情景预测

Influencing factors and scenario forecasting of carbon emissions in Liaoning Province, China

辽宁省是用能与碳排放大省,其碳排放管理对我国碳达峰与碳中和目标实现至关重要。为明晰辽宁省碳排放驱动因素及排放趋势,本研究以辽宁省1999—2019年碳排放数据为基础,运用STIRPAT模型,分析人口、城镇化率、人均GDP、第二产业增加值占比、单位GDP能源消耗量和煤炭消费量占比6个因素对辽宁省碳排放量的影响,分别将各因素设置高、中、低3种发展模式,组合得到9种情景,预测不同情景下辽宁省碳排放变化趋势。结果表明:辽宁省碳排放的主要驱动因素是人均GDP,主要抑制因素是单位GDP能耗;9种情景下,辽宁省碳达峰年在2020—2055年之间波动,碳排放峰值在5.44~10.88亿吨。中增长高减排情景下,辽宁省可通过优化能源结构和控制能源消费强度,在不影响经济发展的前提下实现2030年碳达峰目标,达峰年二氧化碳碳排放量为6.11亿吨,是辽宁省的最优碳排放情景。本研究可为辽宁省碳减排工作寻求最佳路径,为实现碳达峰与碳中和目标提供参考依据。

Liaoning is a province with large energy consumption and carbon emissions. Management of carbon emissions in Liaoning Province is crucial to realizing China’s carbon peaking and carbon neutrality goals. To clarify the driving factors and trends of carbon emissions in Liaoning Province, we analyzed the impacts of six factors on carbon emissions in Liaoning Province through STIRPAT model based on carbon emission data from 1999 to 2019. The impact factors included population, urbanization rate, per-capita GDP, secondary industry ratio, energy consumption per unit GDP, and coal consumption ratio. Nine forecasting scenarios with three economic and population growth models and three emission reduction models were set up, and their carbon emission trends under the above nine forecasting scenarios were predicted. The results showed that the main driving factor of carbon emissions in Liaoning Province was per-capita GDP, and that the main inhibitor was energy consumption per unit GDP. The carbon peak year in Liaoning Province would fluctuate between 2020 and 2055 under the nine forecasting scenarios, with peak values ranging from 544 to 1088 million tons CO2. The medium economic development growth and high carbon emission reduction scenario would be the optimal carbon emission scenario in Liaoning Province. Under this forecasting scenario, Liaoning Province could achieve carbon peak(611 million tons CO2) by 2030 without affec-ting economic development through optimizing energy structure and controlling the intensity of energy consumption. Our results would be helpful for seeking the best path for carbon emission reduction in Liaoning Province and providing a reference for its realization of carbon peaking and carbon neutrality goals.