| Owing to the fast-economic development in China,the accelerating progress of urbanization necessitates the massive input of construction materials.Urban road system as an important part of urban infrastructures,road construction area expands in multiples,which relies heavily on the consumptions of natural resources.Therefore,it is very necessary to calculate the material storage and to study the material metabolism of urban road system.The current research on material metabolism of urban road systems is lacking,or material flow analysis(MFA)is mostly conducted from a global and national level,and it is difficult to analyze a specific urban road system.Therefore,this article is committed to building a material metabolism model and environmental impact assessment system for urban road systems.Using full convolutional neural networks to extract road network structure from high-resolution remote sensing images,this study established a regional urban road system material stock model,and evaluaed the spatio-temporal evolution of material metabolism.Moreover,the methods of life cycle analysis(LCA)and data envelopment analysis(DEA)were introduced,and a systematic environmental effect evaluation system of urban road systems was established in this study.Firstly,using the regional MFA method,a regional urban road system material stock model based on the high-resolution remote sensing image is constructed.A full convolutional neural network is used to extract the road vector network of the area to be studied by semantic segmentation of the high-resolution remote sensing images.The urban road system is simplified into a "node,line,cross-section" model,using the "bottom-up" MFA method,the material within the system were calculated.The material stock space-time analysis was carried out with the urban road system of Nanjing as an example.Results revealed that: 1)The total material stock within Nanjing's urban road system is increasing year by year,from 819 million tons in2014 to 1,777 million tons in 2021,an increase of 1.17 times.2)40% of material stock within Nanjing's urban road system is concentrated in arterial road,and aggregate is the main constituent(more than 92%)of the urban road system.3)Its spatial distribution characteristics show that the material stock was concentrated in the center area,and gathered along with the expressway.Then,combining the established MS model of urban road system and LCA methodology,the environmental impact of the urban road system including raw material production and transportation phases was analyzed and quantified.The result showed that: 1)the environmental impact generated by the urban road system continues to intensify,the most serious environmental impact caused by the arterial road(more than 40%).2)the top three materials that cause the most serious environmental impact are aggregate,cement,asphalt.These three materials cause more than 80% of the environmental impact.Last,based on the constructed MFA-LCA method for urban road system,the DEA model was introduced.The DEA model was improved by adding non-desired outputs to both the target and limiting equations.And environmental efficiency indicators were put forward based on the MFA-LCA-DEA methodology.Through a quantitative environmental efficiency analysis of the urban road system in Nanjing,we found out that the environmental efficiency of expressways is significantly improved,and the environmental efficiency index reaches 100% after 2018.While the environmental efficiency of arterial,collector and branch road were low.In particular,the environmental efficiency of arterial and collector road was lower than 35%,the environmental efficiency of branch road is about 45%.This study achieved the construction of the urban road system material metabolism model and the environmental impact assessment system,obtained the regional urban road system MFA model,and quantified the environmental efficiency of the urban road system.Which can be used to evaluate the status quo of the road network as well as to optimize urban road planning,which also has positive influence on decision makings on the implementation of the sustainable management of urban road systems. |
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