北京城市代谢预测研究——基于长短期记忆神经网络模型
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摘要
城市化进程提升促使城市环境污染加剧、能源消耗激增、人口密度过大等问题的深层次原因在于城市代谢失调。为精准预测北京市城市代谢变化趋势,论文通过能源消费量及人类活动时间指标测算了1980—2016年北京市体外能代谢率,表征城市代谢程度。据此运用长短期记忆神经网络模型(LSTM)预测了2017—2022年北京各部门体外能代谢率。结果表明:①基于长短期记忆神经网络的城市代谢预测模型精度较高,能够对北京各部门体外能代谢率进行更为精准的预测;②2017—2022年间,北京第一产业和总体外能代谢率呈下降趋势,其中第一产业在2017年达到峰值,第二、第三产业及生活部门体外能代谢率将呈现增长趋势。③除第一、第三产业和总体外能代谢率外,历史变化的时间扰动幅度先小后大。④对各部门体外能代谢率EMRT的影响贡献度最大的因子为第二产业体外能代谢率EMR2,最小的为第一产业体外能代谢率EMR1。论文研究结果可为政策制定者优化城市管理方案、提升城市综合实力提供理论依据和决策支持。
The underlying causes of aggravating urban environmental pollution, escalating energy consumption,population overcrowding, and other urban environmental problems are imbalances in urban metabolism. In order to accurately predict the trend of urban metabolism changes in Beijing City, the exosomatic metabolic rate of Beijing from 1980 to 2016 was estimated by the indicators of energy consumption and human activity time, and the degree of urban metabolism was characterized. Based on the results, the long short-term memory(LSTM)neural network model was used to predict the exosomatic metabolic rate of various sectors in Beijing from 2017 to 2022. The results show that: 1) the urban metabolic prediction model based on LSTM neural network has high accuracy and can make more accurate prediction on the exosomatic metabolic rate of various sectors in Beijing.2) From 2017 to 2022, the exosomatic metabolic rate of the primary industry and the overall external energy in Beijing show a downward trend, among which the primary industry reached its peak in 2017, and the exosomatic metabolism rates of the secondary and tertiary industries show an increasing trend. 3) Except for the primary industry, tertiary industry and the overall exosomatic metabolic rate, the temporal perturbation of historical change ranged from small to large. 4) The factors that contribute the most to EMRTare EMR2, and the least are EMR1. This study may provide a theoretical basis and decision-making support for policymakers to optimize urban management plans and enhance urban comprehensive strength.
The underlying causes of aggravating urban environmental pollution, escalating energy consumption,population overcrowding, and other urban environmental problems are imbalances in urban metabolism. In order to accurately predict the trend of urban metabolism changes in Beijing City, the exosomatic metabolic rate of Beijing from 1980 to 2016 was estimated by the indicators of energy consumption and human activity time, and the degree of urban metabolism was characterized. Based on the results, the long short-term memory(LSTM)neural network model was used to predict the exosomatic metabolic rate of various sectors in Beijing from 2017 to 2022. The results show that: 1) the urban metabolic prediction model based on LSTM neural network has high accuracy and can make more accurate prediction on the exosomatic metabolic rate of various sectors in Beijing.2) From 2017 to 2022, the exosomatic metabolic rate of the primary industry and the overall external energy in Beijing show a downward trend, among which the primary industry reached its peak in 2017, and the exosomatic metabolism rates of the secondary and tertiary industries show an increasing trend. 3) Except for the primary industry, tertiary industry and the overall exosomatic metabolic rate, the temporal perturbation of historical change ranged from small to large. 4) The factors that contribute the most to EMRTare EMR2, and the least are EMR1. This study may provide a theoretical basis and decision-making support for policymakers to optimize urban management plans and enhance urban comprehensive strength.
引文
李恒.2019.人口集中、城市群对经济增长作用的实证分析:以中国十大城市群为例[J].河南大学学报(社会科学版),59(1):43-52.[Li H.2019.Empirical analysis of the effects of population concentration and urban agglomeration on economic growth.Journal of Henan University(Social Science),59(1):43-52.]
李旋旗,花利忠.2012.基于系统动力学的城市住区形态变迁对城市代谢效率的影响[J].生态学报,32(10):2965-2974.[Li X Q,Hua L Z.2012.Landscape aesthetic assessment based on experiential paradigm assessment technology.Acta Ecologica Sinica,32(10):2965-2974.]
刘晔,耿涌,赵恒心.2011.基于MSIASM方法的中国省级行政区体外能代谢分析[J].生态学报,31(11):3133-3142.[Liu Y,Geng Y,Zhao H X,2011.Societal metabolism for chinese provinces based on multi-scale integrated analysis of societal metabolism(MSIASM).Acta Ecologica Sinica,31(11):43-52.]
宋涛,蔡建明,倪攀,等.2013a.城市新陈代谢研究综述及展望[J].地理科学进展,32(11):1650-1661.[Song T,Cai JM,Ni P,et al.2013.Review and prospects of urban metabolism research.Progress in Geography,32(11):1650-1661.]
宋涛,蔡建明,倪攀,等.2013b.基于能值和DEA的中国城市新陈代谢效率分析[J].资源科学,35(11):2166-2173.[Song T,Cai J M,Pan N,et al.2013.Chinese urban metabolic efficiencies based on emergy and DEA.Resources Science,35(11):2166-2173.]
王天送.2008.社会代谢多尺度综合评估(MSIASM)的基本理论与实践[J].地球科学进展,27(1):63-70.[Wang T.2008.The rationale of multiple-scale integrated assessment of societal metabolism.Advances in Earth Science,27(1):63-70.]
王劲峰,徐成东.2017.地理探测器:原理与展望[J].地理学报,72(1):116-134.[Wang J,Xu C.2017.Geodetector:Principle and prospective.Acta Geographica Sinica,72(1):116-134.]
文琦,施琳娜,马彩虹,等.2018.黄土高原村域多维贫困空间异质性研究:以宁夏彭阳县为例[J].地理学报,73(10):1850-1864.[Wen Q,Shi L,Ma C,et al.2018.Spatial heterogeneity of multidimensional poverty at the village level:Loess Plateau.Acta Geographica Sinica,73(10):1850-1864.]
湛东升,张文忠,余建辉,等.2015.基于地理探测器的北京市居民宜居满意度影响机理[J].地理科学进展,34(8):966-975.[Zhan D,Zhang W,Yu J,et al.2015.Analysis of influencing mechanism of residents'livability satisfaction in Beijing using geographical detector.Progress in Geography,34(8):966-975.]
Abdel-Nasser M,Mahmoud K.2017.Accurate photovoltaic power forecasting models using deep LSTM-RNN[J].Neural Computing&Applications,10:1-14.
Elliot T,Rugani B,Almenar J B,et al.2018.A proposal to integrate system dynamics and carbon metabolism for urban planning[J].Procedia CIRP,69:78-82.
Geng Y,Liu Y,Liu D,et al.2011.Regional societal and ecosystem metabolism analysis in China:A multi-scale integrated analysis of societal metabolism(MSIASM)approach[J].Energy,36(8):4799-4808.
Giampietro M,Mayumi K.1997.A dynamic model of socioeconomic systems based on hierarchy theory and its application to sustainability[J].Structural Change&Economic Dynamics,8(4):453-469.
Giampietro M,Mayumi K.2000.Multiple-scale integrated assessment of societal metabolism:Introducing the approach[J].Population&Environment,22(2):109-153.
Giampietro M,Mayumi K,Munda G.2006.Integrated assessment and energy analysis:Quality assurance in multi-criteria analysis of sustainability[J].Energy,31(1):59-86.
Greff K,Srivastava R K,Koutník J,et al.2017.LSTM:Asearch space odyssey[J].IEEE Transactions on Neural Networks&Learning Systems,28(10):2222-2232.
Hadiye A S,Giampietro M,Aleu A L,et al.2009.Applications of the MuSIASEM approach to study changes in the metabolic pattern of Catalonia[R/OL].Reports on Environmental Sciences.2009-07-27[2019-03-15].http://hdl.handle.net/2072/40522.
Han W,Geng Y,Lu Y,et al.2018.Urban metabolism of megacities:A comparative analysis of Shanghai,Tokyo,London and Paris to inform low carbon and sustainable development pathways[J].Energy,155(15):887-898.
Jiang Q,Tang C,Chen C,et al.2018.Stock price forecast based on LSTM neural network[C]//Xu J,Cooke F,Gen M,et al.Proceedings of the twelfth international conference on management science and engineering management.ICMSEM 2018.Cham:Springer:393-408.
Kumar J,Goomer R,Singh A K.2018.Long short term memory recurrent neural network(LSTM-RNN)based workload forecasting model for cloud datacenters[J].Procedia Computer Science,125:676-682.
Li Y,Wang X,Tian X,et al.2018.Understanding the mechanism of urban material metabolism with ecological network analysis:An experimental study of Wuxi,China[J].Ecological Modelling,367:58-67
Lu Y,Geng Y,Qian Y,et al.2016.Changes of human time and land use pattern in one mega city's urban metabolism:Amulti-scale integrated analysis of Shanghai[J].Journal of Cleaner Production,133(1):391-401.
Ma X,Tao Z,Wang Y,et al.2015.Long short-term memory neural network for traffic speed prediction using remote microwave sensor data[J].Transportation Research Part C,54:187-197
Martín J R,Eisenmenger N,Schandl H.2008.Different trajectories of exosomatic energy metabolism for Brazil,Chile and Venezuela:Using the MSIASM approach[R].Working Papers.Barcelona,Spain:Department of Applied Economics,Universitat Autonoma of Barcelona.
Mayumi K.1991.Temporary emancipation from land:From the industrial revolution to the present time[J].Ecological Economics,4(1):35-56.
Munro D A,Holdgate M W.1991.Caring for the earth:A strategy for sustainable living[M].IUCN,UNEP,WWF:14-15.
Pincetl S,Bunje P,Holmes T.2012.An expanded urban metabolism method:Toward a systems approach for assessing urban energy processes and causes[J].Landscape&Urban Planning,107(3):193-202.
Qi W,Deng X,Chu X,et al.2017.Emergy analysis on urban metabolism by counties in Beijing[J].Physics&Chemistry of the Earth Parts A/B/C,101:157-165.
Ramos-Martin J,Giampietro M,Mayumi K.2007.On China's exosomatic energy metabolism:An application of multiscale integrated analysis of societal metabolism(MSI-ASM)[J].Ecological Economics,63(1):174-191.
Scheidel A.2013.From MuSIASEM theory to practice:Report and reflections from field research in Kampot province,Cambodia[R/OL].Reports on Environmental Sciences.Institute of Environmental Science and Technology(IC-TA).Barcelona,Spain:Autonomous University of Barcelona(UAB).http://www.recercat.net/handle/2072/16100.
Silva-Macher J C.2016.A metabolic profile of peru:An application of multi-scale integrated analysis of societal and ecosystem metabolism(MuSIASEM)to the mining sector's exosomatic energy flows[J].Journal of Industrial Ecology,20(5):1072-1082.
Song T,Cai J M,Chahine T,et al.2014.Modeling urban metabolism of Beijing City,China:With a coupled system dynamics:Emergy model[J].Stochastic Environmental Research&Risk Assessment,28(6):1511-1524.
Tejedor-Flores N,Vicente-Galindo P,Galindo-Villardón P.2017.Sustainability multivariate analysis of the energy consumption of ecuador using MuSIASEM and BIPLOTapproach[J].Sustainability,9(6):984.
Vafa-Arani H,Jahani S,Dashti H,et al.2014.A system dynamics modeling for urban air pollution:A case study of Tehran,Iran[J].Transportation Research Part D Transport&Environment,31(31):21-36.
Wang X,Wu S,Li S.2017.Urban metabolism of three cities in Jing-Jin-Ji urban agglomeration,China:Using the MuSI-ASEM Approach[J].Sustainability,9(8):1481-1502.
Yuan X,Chen C,Lei X,et al.2018.Monthly runoff forecasting based on LSTM-ALO model[J].Stochastic Environmental Research&Risk Assessment,32(8):1-14.
Yan Z,Li Y,Zheng H.2017.Ecological network analysis of energy metabolism in the Beijing-Tianjin-Hebei(Jing-JinJi)urban agglomeration[J].Ecological Modelling,351:51-62.
Yan Z,Zheng H,Yang Z,et al.2016.Urban energy flow processes in the Beijing-Tianjin-Hebei(Jing-Jin-Ji)urban agglomeration:Combining multi-regional input-output tables with ecological network analysis[J].Journal of Cleaner Production,114:243-256.
Zhai M,Huang G,Liu L,et al.2018.Dynamic input-output analysis for energy metabolism system in the Province of Guangdong,China[J].Journal of Cleaner Production,196:747-762.
Zhang J,Zhang Y.2011.Ecological network analysis of an urban energy metabolic system[J].Stochastic Environmental Research&Risk Assessment,25(5):685-695.
李旋旗,花利忠.2012.基于系统动力学的城市住区形态变迁对城市代谢效率的影响[J].生态学报,32(10):2965-2974.[Li X Q,Hua L Z.2012.Landscape aesthetic assessment based on experiential paradigm assessment technology.Acta Ecologica Sinica,32(10):2965-2974.]
刘晔,耿涌,赵恒心.2011.基于MSIASM方法的中国省级行政区体外能代谢分析[J].生态学报,31(11):3133-3142.[Liu Y,Geng Y,Zhao H X,2011.Societal metabolism for chinese provinces based on multi-scale integrated analysis of societal metabolism(MSIASM).Acta Ecologica Sinica,31(11):43-52.]
宋涛,蔡建明,倪攀,等.2013a.城市新陈代谢研究综述及展望[J].地理科学进展,32(11):1650-1661.[Song T,Cai JM,Ni P,et al.2013.Review and prospects of urban metabolism research.Progress in Geography,32(11):1650-1661.]
宋涛,蔡建明,倪攀,等.2013b.基于能值和DEA的中国城市新陈代谢效率分析[J].资源科学,35(11):2166-2173.[Song T,Cai J M,Pan N,et al.2013.Chinese urban metabolic efficiencies based on emergy and DEA.Resources Science,35(11):2166-2173.]
王天送.2008.社会代谢多尺度综合评估(MSIASM)的基本理论与实践[J].地球科学进展,27(1):63-70.[Wang T.2008.The rationale of multiple-scale integrated assessment of societal metabolism.Advances in Earth Science,27(1):63-70.]
王劲峰,徐成东.2017.地理探测器:原理与展望[J].地理学报,72(1):116-134.[Wang J,Xu C.2017.Geodetector:Principle and prospective.Acta Geographica Sinica,72(1):116-134.]
文琦,施琳娜,马彩虹,等.2018.黄土高原村域多维贫困空间异质性研究:以宁夏彭阳县为例[J].地理学报,73(10):1850-1864.[Wen Q,Shi L,Ma C,et al.2018.Spatial heterogeneity of multidimensional poverty at the village level:Loess Plateau.Acta Geographica Sinica,73(10):1850-1864.]
湛东升,张文忠,余建辉,等.2015.基于地理探测器的北京市居民宜居满意度影响机理[J].地理科学进展,34(8):966-975.[Zhan D,Zhang W,Yu J,et al.2015.Analysis of influencing mechanism of residents'livability satisfaction in Beijing using geographical detector.Progress in Geography,34(8):966-975.]
Abdel-Nasser M,Mahmoud K.2017.Accurate photovoltaic power forecasting models using deep LSTM-RNN[J].Neural Computing&Applications,10:1-14.
Elliot T,Rugani B,Almenar J B,et al.2018.A proposal to integrate system dynamics and carbon metabolism for urban planning[J].Procedia CIRP,69:78-82.
Geng Y,Liu Y,Liu D,et al.2011.Regional societal and ecosystem metabolism analysis in China:A multi-scale integrated analysis of societal metabolism(MSIASM)approach[J].Energy,36(8):4799-4808.
Giampietro M,Mayumi K.1997.A dynamic model of socioeconomic systems based on hierarchy theory and its application to sustainability[J].Structural Change&Economic Dynamics,8(4):453-469.
Giampietro M,Mayumi K.2000.Multiple-scale integrated assessment of societal metabolism:Introducing the approach[J].Population&Environment,22(2):109-153.
Giampietro M,Mayumi K,Munda G.2006.Integrated assessment and energy analysis:Quality assurance in multi-criteria analysis of sustainability[J].Energy,31(1):59-86.
Greff K,Srivastava R K,Koutník J,et al.2017.LSTM:Asearch space odyssey[J].IEEE Transactions on Neural Networks&Learning Systems,28(10):2222-2232.
Hadiye A S,Giampietro M,Aleu A L,et al.2009.Applications of the MuSIASEM approach to study changes in the metabolic pattern of Catalonia[R/OL].Reports on Environmental Sciences.2009-07-27[2019-03-15].http://hdl.handle.net/2072/40522.
Han W,Geng Y,Lu Y,et al.2018.Urban metabolism of megacities:A comparative analysis of Shanghai,Tokyo,London and Paris to inform low carbon and sustainable development pathways[J].Energy,155(15):887-898.
Jiang Q,Tang C,Chen C,et al.2018.Stock price forecast based on LSTM neural network[C]//Xu J,Cooke F,Gen M,et al.Proceedings of the twelfth international conference on management science and engineering management.ICMSEM 2018.Cham:Springer:393-408.
Kumar J,Goomer R,Singh A K.2018.Long short term memory recurrent neural network(LSTM-RNN)based workload forecasting model for cloud datacenters[J].Procedia Computer Science,125:676-682.
Li Y,Wang X,Tian X,et al.2018.Understanding the mechanism of urban material metabolism with ecological network analysis:An experimental study of Wuxi,China[J].Ecological Modelling,367:58-67
Lu Y,Geng Y,Qian Y,et al.2016.Changes of human time and land use pattern in one mega city's urban metabolism:Amulti-scale integrated analysis of Shanghai[J].Journal of Cleaner Production,133(1):391-401.
Ma X,Tao Z,Wang Y,et al.2015.Long short-term memory neural network for traffic speed prediction using remote microwave sensor data[J].Transportation Research Part C,54:187-197
Martín J R,Eisenmenger N,Schandl H.2008.Different trajectories of exosomatic energy metabolism for Brazil,Chile and Venezuela:Using the MSIASM approach[R].Working Papers.Barcelona,Spain:Department of Applied Economics,Universitat Autonoma of Barcelona.
Mayumi K.1991.Temporary emancipation from land:From the industrial revolution to the present time[J].Ecological Economics,4(1):35-56.
Munro D A,Holdgate M W.1991.Caring for the earth:A strategy for sustainable living[M].IUCN,UNEP,WWF:14-15.
Pincetl S,Bunje P,Holmes T.2012.An expanded urban metabolism method:Toward a systems approach for assessing urban energy processes and causes[J].Landscape&Urban Planning,107(3):193-202.
Qi W,Deng X,Chu X,et al.2017.Emergy analysis on urban metabolism by counties in Beijing[J].Physics&Chemistry of the Earth Parts A/B/C,101:157-165.
Ramos-Martin J,Giampietro M,Mayumi K.2007.On China's exosomatic energy metabolism:An application of multiscale integrated analysis of societal metabolism(MSI-ASM)[J].Ecological Economics,63(1):174-191.
Scheidel A.2013.From MuSIASEM theory to practice:Report and reflections from field research in Kampot province,Cambodia[R/OL].Reports on Environmental Sciences.Institute of Environmental Science and Technology(IC-TA).Barcelona,Spain:Autonomous University of Barcelona(UAB).http://www.recercat.net/handle/2072/16100.
Silva-Macher J C.2016.A metabolic profile of peru:An application of multi-scale integrated analysis of societal and ecosystem metabolism(MuSIASEM)to the mining sector's exosomatic energy flows[J].Journal of Industrial Ecology,20(5):1072-1082.
Song T,Cai J M,Chahine T,et al.2014.Modeling urban metabolism of Beijing City,China:With a coupled system dynamics:Emergy model[J].Stochastic Environmental Research&Risk Assessment,28(6):1511-1524.
Tejedor-Flores N,Vicente-Galindo P,Galindo-Villardón P.2017.Sustainability multivariate analysis of the energy consumption of ecuador using MuSIASEM and BIPLOTapproach[J].Sustainability,9(6):984.
Vafa-Arani H,Jahani S,Dashti H,et al.2014.A system dynamics modeling for urban air pollution:A case study of Tehran,Iran[J].Transportation Research Part D Transport&Environment,31(31):21-36.
Wang X,Wu S,Li S.2017.Urban metabolism of three cities in Jing-Jin-Ji urban agglomeration,China:Using the MuSI-ASEM Approach[J].Sustainability,9(8):1481-1502.
Yuan X,Chen C,Lei X,et al.2018.Monthly runoff forecasting based on LSTM-ALO model[J].Stochastic Environmental Research&Risk Assessment,32(8):1-14.
Yan Z,Li Y,Zheng H.2017.Ecological network analysis of energy metabolism in the Beijing-Tianjin-Hebei(Jing-JinJi)urban agglomeration[J].Ecological Modelling,351:51-62.
Yan Z,Zheng H,Yang Z,et al.2016.Urban energy flow processes in the Beijing-Tianjin-Hebei(Jing-Jin-Ji)urban agglomeration:Combining multi-regional input-output tables with ecological network analysis[J].Journal of Cleaner Production,114:243-256.
Zhai M,Huang G,Liu L,et al.2018.Dynamic input-output analysis for energy metabolism system in the Province of Guangdong,China[J].Journal of Cleaner Production,196:747-762.
Zhang J,Zhang Y.2011.Ecological network analysis of an urban energy metabolic system[J].Stochastic Environmental Research&Risk Assessment,25(5):685-695.