城市能源碳排放估算方法探究

A method for city-level energy-related CO_2 emission estimation

作为目前世界上最大的碳排放国家,中国在2015年巴黎气候变化大会上做出承诺,到2030年碳排放量要达到峰值并且单位GDP排放要在2005年水平上下降60%～65%.但现阶段中国碳排放数据主要集中在省级和国家层面,城市作为碳减排措施实施的主要区域,由于基础数据缺乏,长久以来没有完整的碳排放清单.为解决这一问题,本文构建了一套城市级CO_2排放估算方法.该方法从各省能源平衡表(EBT)出发,采取从省级到市级的比例分配方法,选取最为贴近城市碳排放的指标数据,对42个地级市2012年的能源消费型碳排放情况进行估算,并与中国高分辨率碳排放数据(CHRED)进行对比,发现差异均在10%以内,验证了该方法的准确性.同时揭示了此类自上而下的估算方法所带来的区域性差异,并且进一步分析了采用不同来源的化石燃料的排放因子所可能导致的不确定性,建议之后的研究在进行中国城市碳排放核算时采取最恰当的本地化化石燃料排放因子.本文为获得在时间尺度和空间尺度上均连续的中国城市碳排放数据提供了参考方法和合理思路,也能为在城市层面制定科学的碳减排措施提供可靠的数据支撑.

As the world's largest CO2 emitter,China made the commitment at the Paris Climate Change Conference in 2015 to reach carbon emissions peak by 2030,and its' CO2 emissions intensity (CO2 emissions per GDP)will drop by 60%to 65% based on 2005 levels.However,at present,carbon emission studies in China are mainly concentrated at the provincial and national levels.As the main role for implementing carbon reduction measures,due to the lack of basic data,cities don't have complete carbon emission inventories.To solve this problem,this paper constructed a set of city-level CO2 emission estimation methods.The method starts with provincial energy balance tables (EBTs),and used a top-bottom approach to estimate energy consumption and carbon emissions in cities.We chose the distribution coefficient that is most suitable for the relationship between the province and city, and conducted validations for 42.prefecture-level cities' energy-related CO2 emissions in 2012.The estimation results were compared with China High Resolution Emission Database (CHRED)and we found that the differences were all within 10%,which verified the accuracy of the method.At the same time,we studied the regional differences brought about by the method and the uncertainties that resulted from the use of different sources of fossil fuel emission factors.It is suggested that the subsequent studies should use the most appropriate local fossil fuel emission factors for city-level CO2 emissions accounting.We believe our method can provide reasonable ideas for compiling carbon emission inventories for Chinese cities that are continuous in both time and space scales,and also can provide data support for city carbon reduction measures.