Analysis By Computation And Evolutionary Scenario Of Urban Residential Community Carbon Footprint In Our Country

Since the end of last century, the issue of global climate change becomes the global hot spot. Limited emissions, reduced emission,carbon trading have become the important topics in all countries. Our country is not the contracting party with the task of reduced emissions under the framework convention. But the Chinese government in consideration of the humanitarian spirit and responsible for all humanity, has publicly declared per unit of GDP carbon emissions by2020’s will drop to less than45%of2005. The goal of China’s carbon reduced emissions has a long way to go.Urban residential community is the main place that people lives, and also an important source of carbon emissions. According to the statistics, the carbon footprint of construction industry has accounted for around30%of all the carbon footprint in our country.The urban residential construction area is at the high speed increase stage. And the construction industry is far from the sustainable development. So the carbon reduced emissions space is huge.This paper targeted to urban residential community carbon footprint in China, carries out the research through literature research, data analysis and calculation, and using cluster analysis, correlation analysis, situational evolution analysis and other methods. First we give the definition and analyse about the related basic theory of urban residential community carbon footprint. We define the urban residential community carbon footprint boundary, functional units and emission sources, and expound the calculation method and the emission factor of different emission sources. Then urban residential community carbon footprint can divide into the building related and the resident related. And we respectively develop models and calculate. On this basis, by using cluster analysis and grey relational degree method, we conclude that the main factors influencing the carbon footprint. Finally using the measured datas, we forecast the urban residential community carbon footprint in our country by different forecasting methods, and set four kinds of situation to the future carbon footprint by adjusting the coefficient of evolution, and get the evolution trends of different situations in different periods.