摘要
二氧化碳、甲烷等的排放是造成全球温室效应的主要气体,预测温室气体未来的排放是实现碳达峰、碳中和目标的重要基础,对于减缓全球气候变暖、科学制定碳达峰及碳中和路径举措具有重要意义。本文通过对数据种类、数据来源、预测方法介绍碳排放数据预测方式,对常见的碳排放模型如LEAP模型、STIRPAT模型、神经网络模型、组合模型进行综合对比,通过介绍不同情景应用,对比模式应用案例,深化碳排放预测模型的使用方式,助力城市及重点行业编制“双碳”行动方案,减缓全球温室效应。
Emissions of carbon dioxide,methane and other gases are the main causes of global greenhouse effect,and predicting future emissions of greenhouse gases is an important basis for achieving the goal of carbon peak and carbon neutrality,it is of great significance for the mitigation of global warming,the scientific formulation of carbon peak and carbon neutral pathway.This paper introduces the methods of carbon emission data prediction through the data types,data sources and prediction methods,the common carbon emission models,such as LEAP model,STIRPAT model,neural network model and combination model,are comprehensively compared,deepen the use of carbon emission prediction models to help cities and key industries to formulate“Double carbon”action plan to mitigate the global greenhouse effect.
作者
孙晓晨
杨斌彬
刘梦丹
黄戴勇
魏宪峰
Sun Xiaochen;Yang Bingbing;Liu Mengdan;Huang Daiyong;Wei Xianfeng(Shenzhen Green Creating Promotion Center of Living Environment,Shenzhen 518000,China)
出处
《广东化工》
CAS
2023年第2期138-140,131,共4页
Guangdong Chemical Industry
基金
深圳市生态环境局环境科研课题资金项目“福田区碳达峰路径及场景分析研究”。
关键词
温室效应
数据预测
模型应用
影响因素
情景设计
greenhouse effect
data forecast
model application
scenario design
influence factor
