摘要
量化和预测用地的碳排放是实现规划控碳的前提和基础。基于城镇建设用地分类体系,从城市用地建筑能源消费的碳排放核算视角,提出以"用地"作为碳排放的核算终端和核算单元,基于PCA-BP神经网络建立规划用地碳排放预测模型来预测用地碳排放。将调研获得的样本地块的碳排放数据作为因变量,以其用地特征指标(包括:容积率、建筑单体数量、用地面积、建筑密度、建筑高度、用地类型、用地兼容性、人口密度)作为自变量,建立用地碳排放预测模型。以长兴县老城区为实例,应用该模型预测用地碳排放,从模型预测结果来看,该方法能较准确地预测用地的碳排放,为城市的低碳规划和碳排放管控提供了量化依据。
Because the carbon emission intensity of urban construction land is much higher than that of other land use types,and the carbon emissions produced by different land use methods are quite different,it is of great significance to make rational use of spatial planning control means and adjust land use at the spatial planning level with“land use”as the breakthrough point,so as to achieve carbon control and low-carbon development.Due to the constraints of weak foundation,few references and difficult measurement,the domestic research on the quantification of carbon emissions from planned land is generally lagging behind,and it is impossible to reveal the relationship between carbon emissions from land use and the scale,type and spatial characteristics of specific land plots from the perspective of land use units.Quantifying and forecasting the carbon emissions of land use is the premise and foundation for realizing planning and controlling carbon.However,at present,the barriers of quantitative analysis of structural emission reduction data in spatial planning are difficult to break,and there is still a lack of relatively objective and effective tools to measure the carbon emissions of urban land.In view of this deficiency,from the perspective of carbon emission accounting for building energy consumption of urban land,a land-use carbon emission prediction method is proposed,which takes“land use”as the carbon emission accounting terminal and accounting unit,and then uses PCA-BP neural network to establish a planning land-use carbon emission prediction model to predict land-use carbon emissions.The core of the land-use carbon emission prediction model based on PCA-BP neural network is to analyze the known sample data by statistical analysis method,and establish a mapping relationship,so as to analyze the unknown data,and replace the artificial consideration of various non-linear relationships which cannot be estimated by data with machine learning internal intelligent logic.The carbon emission data of sample land
作者
闫凤英
刘思娴
张小平
YAN Fengying;LIU Sixian;ZHANG Xiaoping
出处
《西部人居环境学刊》
CSCD
2021年第6期1-7,共7页
Journal of Human Settlements in West China
基金
国家重点研发计划资助项目(2018YFC0704700)
国家自然科学基金项目(51878441)。
关键词
用地碳排放
碳排放预测
用地指标
BP神经网络
主成分分析(PCA)
Carbon Emissions for Land Use
Forecasting Carbon Emission
Land Index
BP Neural Network
Principal Component Analysis
