Airports are the primary venues for aircraft operations during the Landing and Take-off(LTO)phase and constitute a major component of aviation's overall carbon emissions.By combining the International Civil Aviati...Airports are the primary venues for aircraft operations during the Landing and Take-off(LTO)phase and constitute a major component of aviation's overall carbon emissions.By combining the International Civil Aviation Organization(ICAO)standard emission model with an emission inventory compiled according to the proportion of aircraft takeoff and landing operations at airports,this study calculates a long-term series of carbon emissions for all operating airports in China(excluding Hong Kong,Macao,and Taiwan of China)from 2005 to 2022.Using the Kernel Density Analysis tool,the study identifies the agglomeration characteristics and evolutionary trends of airport carbon emissions within the jurisdictions of China's seven air traffic control regions.Furthermore,by integrating the Stochastic Impacts by regression on Population,Affluence,and Technology model with a spatial panel regression model,the influencing factors and spatial effects of airport carbon emissions in China are examined.The findings are as follows.1)From 2005 to 2022,carbon emissions from China's civil aviation airports generally showed a trend of steady growth followed by fluctuating decline;specifically,it increased at an average annual rate of 9.01%from 2005 to 2019 and decreased at an average annual rate of 11.27%from 2020 to 2022.Among China's seven air traffic control regions,the growth rate of airport carbon emissions was more significant in the Southwest Region,Xinjiang Region,Northwest Region,and Northeast Region,while the growth was relatively slow in the three traditional hub airport-concentrated regions(Northern Region,Eastern Region,and Central and Southern Region).2)With 2020 as the dividing line,the proportion of LevelⅠand LevelⅡairports in terms of carbon emissions among the seven air traffic control regions first decreased and then increased,while the proportion of LevelⅢ,LevelⅣ,and LevelⅤairports first increased and then decreased.3)The number of medium-high-density core areas of airport carbon emissions in China increased from 8 to 14,with a relatively stable agglomeration trend.These core areas are mainly distributed east and south of the Hu Huanyong Line.4)Six indicators exert a positive impact on airport carbon emissions in a region,namely urban population density,air passenger traffic by region,per capita Gross Domestic Product(GDP),per capita social consumer goods retail sales,per capita disposable income,and regional international tourism income.In contrast,five indicators exert a negative impact,including the unemployment rate,regional general public budget expenditure,regional general science and technology budget expenditure,Research and Development(R&D)expenditure,and number of patent applications.This study breaks through the limitation of solely using the ICAO model for research,comprehensively presents the trends and spatiotemporal heterogeneity of China's airport carbon emissions over a medium and long time period,and conducts a comparative analysis of the differences before and after the epidemic.It has theoretical guiding significance for comprehensively grasping the overall trend of aviation carbon emissions and positive practical significance for promoting the green development of the civil aviation industry.展开更多
为了有效缓解空中航路网络中大规模航空器集群排放引发的环境问题,研究提出一种基于聚合网络交通流的空中交通绿色优化方法。以航空器集群聚合形成的网络交通流为研究对象,构建空中航路网络交通流动力学模型,用以描述管制干预下网络交...为了有效缓解空中航路网络中大规模航空器集群排放引发的环境问题,研究提出一种基于聚合网络交通流的空中交通绿色优化方法。以航空器集群聚合形成的网络交通流为研究对象,构建空中航路网络交通流动力学模型,用以描述管制干预下网络交通流的动态演化行为。在此基础上,以最小化航空排放所致的全球绝对温变潜力(Absolute Global Temperature Potential, AGTP)为目标,构建空中航路网络交通流绿色优化模型。该模型的维度与大规模航空器集群数量无关,而取决于有限的空域单元数量,显著降低了建模维度。此外,为了进一步削减计算复杂度,通过引入辅助变量约束的方法,将原本的非线性规划问题转化为混合整数线性规划(Mixed-Integer Linear Programming, MILP)问题,并借助Cplex进行求解。最后,以华东区域5个高空管制扇区构成的航路网络为实例,对所提方法开展验证。实例分析结果显示:网络交通流动力学模型能够较好地描述空中交通的动态演化行为,平均推演误差为4.55%;网络交通流绿色管控模型可有效降低航空排放的环境影响,在25 a、50 a、100 a的时间影响尺度下,AGTP分别降低38.73%、47.62%、47.65%。展开更多
针对传统方法难以准确预测复杂工况下的燃油流量,进而影响排放计算精度的问题,提出一种基于神经网络基函数分解的NBEATS-MARS(neural basis expansion analysis for time series with multi-variable adaptive rapid state-transition)...针对传统方法难以准确预测复杂工况下的燃油流量,进而影响排放计算精度的问题,提出一种基于神经网络基函数分解的NBEATS-MARS(neural basis expansion analysis for time series with multi-variable adaptive rapid state-transition)模型。该模型采用多栈分解结构,设计多类型基函数系统,通过基函数分解实现可解释的高精度预测。实验表明:NBEATS-MARS模型方均根误差为59.49,对称平均绝对百分比误差为5.75%,中位数误差仅为0.29%;在爬升巡航下降阶段表现最佳,方均根误差为32.75,对称平均绝对百分比误差为1.87%。基于此构建了综合航空排放计算方法,通过将预测的燃油流量数据作为核心输入,结合发动机排气温度等健康状态参数,实现了二氧化碳、氮氧化物、黑碳和有机碳等多种航空排放物的精确量化。燃油流量预测误差的降低使排放计算不确定性显著减小,巡航阶段排放量计算精度提升至±2%以内。该方法通过提高上游燃油流量预测精度,有效改善了下游航空排放评估的准确性和空间分辨率。展开更多
基金Under the auspices of the National Natural Science Foundation of China(No.42071266)the Third Batch of Hebei Youth Top Talent Project,Hebei Natural Science Foundation(No.D2021205013)。
摘要Airports are the primary venues for aircraft operations during the Landing and Take-off(LTO)phase and constitute a major component of aviation's overall carbon emissions.By combining the International Civil Aviation Organization(ICAO)standard emission model with an emission inventory compiled according to the proportion of aircraft takeoff and landing operations at airports,this study calculates a long-term series of carbon emissions for all operating airports in China(excluding Hong Kong,Macao,and Taiwan of China)from 2005 to 2022.Using the Kernel Density Analysis tool,the study identifies the agglomeration characteristics and evolutionary trends of airport carbon emissions within the jurisdictions of China's seven air traffic control regions.Furthermore,by integrating the Stochastic Impacts by regression on Population,Affluence,and Technology model with a spatial panel regression model,the influencing factors and spatial effects of airport carbon emissions in China are examined.The findings are as follows.1)From 2005 to 2022,carbon emissions from China's civil aviation airports generally showed a trend of steady growth followed by fluctuating decline;specifically,it increased at an average annual rate of 9.01%from 2005 to 2019 and decreased at an average annual rate of 11.27%from 2020 to 2022.Among China's seven air traffic control regions,the growth rate of airport carbon emissions was more significant in the Southwest Region,Xinjiang Region,Northwest Region,and Northeast Region,while the growth was relatively slow in the three traditional hub airport-concentrated regions(Northern Region,Eastern Region,and Central and Southern Region).2)With 2020 as the dividing line,the proportion of LevelⅠand LevelⅡairports in terms of carbon emissions among the seven air traffic control regions first decreased and then increased,while the proportion of LevelⅢ,LevelⅣ,and LevelⅤairports first increased and then decreased.3)The number of medium-high-density core areas of airport carbon emissions in China increased from 8 to 14,with a relatively stable agglomeration trend.These core areas are mainly distributed east and south of the Hu Huanyong Line.4)Six indicators exert a positive impact on airport carbon emissions in a region,namely urban population density,air passenger traffic by region,per capita Gross Domestic Product(GDP),per capita social consumer goods retail sales,per capita disposable income,and regional international tourism income.In contrast,five indicators exert a negative impact,including the unemployment rate,regional general public budget expenditure,regional general science and technology budget expenditure,Research and Development(R&D)expenditure,and number of patent applications.This study breaks through the limitation of solely using the ICAO model for research,comprehensively presents the trends and spatiotemporal heterogeneity of China's airport carbon emissions over a medium and long time period,and conducts a comparative analysis of the differences before and after the epidemic.It has theoretical guiding significance for comprehensively grasping the overall trend of aviation carbon emissions and positive practical significance for promoting the green development of the civil aviation industry.
摘要为了有效缓解空中航路网络中大规模航空器集群排放引发的环境问题,研究提出一种基于聚合网络交通流的空中交通绿色优化方法。以航空器集群聚合形成的网络交通流为研究对象,构建空中航路网络交通流动力学模型,用以描述管制干预下网络交通流的动态演化行为。在此基础上,以最小化航空排放所致的全球绝对温变潜力(Absolute Global Temperature Potential, AGTP)为目标,构建空中航路网络交通流绿色优化模型。该模型的维度与大规模航空器集群数量无关,而取决于有限的空域单元数量,显著降低了建模维度。此外,为了进一步削减计算复杂度,通过引入辅助变量约束的方法,将原本的非线性规划问题转化为混合整数线性规划(Mixed-Integer Linear Programming, MILP)问题,并借助Cplex进行求解。最后,以华东区域5个高空管制扇区构成的航路网络为实例,对所提方法开展验证。实例分析结果显示:网络交通流动力学模型能够较好地描述空中交通的动态演化行为,平均推演误差为4.55%;网络交通流绿色管控模型可有效降低航空排放的环境影响,在25 a、50 a、100 a的时间影响尺度下,AGTP分别降低38.73%、47.62%、47.65%。
摘要针对传统方法难以准确预测复杂工况下的燃油流量,进而影响排放计算精度的问题,提出一种基于神经网络基函数分解的NBEATS-MARS(neural basis expansion analysis for time series with multi-variable adaptive rapid state-transition)模型。该模型采用多栈分解结构,设计多类型基函数系统,通过基函数分解实现可解释的高精度预测。实验表明:NBEATS-MARS模型方均根误差为59.49,对称平均绝对百分比误差为5.75%,中位数误差仅为0.29%;在爬升巡航下降阶段表现最佳,方均根误差为32.75,对称平均绝对百分比误差为1.87%。基于此构建了综合航空排放计算方法,通过将预测的燃油流量数据作为核心输入,结合发动机排气温度等健康状态参数,实现了二氧化碳、氮氧化物、黑碳和有机碳等多种航空排放物的精确量化。燃油流量预测误差的降低使排放计算不确定性显著减小,巡航阶段排放量计算精度提升至±2%以内。该方法通过提高上游燃油流量预测精度,有效改善了下游航空排放评估的准确性和空间分辨率。