REDD+机制下中国森林碳减排区域影响因素研究  

Regional Impact Factors of Reduction in Forest Carbon Emissions of China Under REDD+

作　　者：盛济川[1,2,3] 周慧[2] 苗壮[4,3] 

机构地区：[1]南京信息工程大学气象灾害预报预警与评估协同创新中心,江苏南京210044 [2]南京信息工程大学中国制造业发展研究院,江苏南京210044 [3]中国社会科学院城市发展与环境研究所,北京100028 [4]泰州学院经济与管理学院,江苏泰州225300

出　　处：《中国人口·资源与环境》2015年第11期37-43,共7页China Population,Resources and Environment

基　　金：国家自然科学基金项目"减少砍伐和退化造成的排放机制(REDD+)影响毁林行为的传导路径及权利平等性研究"(编号:71303123);教育部人文社科基金项目"全球森林减排背景下中国REDD+影响毁林行为减缓的传导路径及政策评估方法研究"(编号:13YJCZH148);教育部哲学社会科学发展项目<中国制造业发展研究报告>(编号:13JBG004);中国博士后科学基金项目"不对称信息;补贴模式;不确定性与REDD+项目绩效"(编号:2015M570209);中国制造业发展研究院开放课题"REDD+机制下的补贴政策对于木材加工业收益的影响及其动态绩效研究"(编号:SK20140090-2);江苏高校优势学科建设工程项目

摘　　要：森林对CO2的吸收是碳捕捉和碳储存的一种重要途径,因砍伐和森林退化造成的温室气体排放已成为全球变暖的第二大主因。因此联合国气候变化框架公约在2007年引入了"减少砍伐和退化所致排放量"(REDD)机制。作为REDD机制的扩展,"REDD+机制"被定义为"采取各种政策方法和积极的激励措施,以帮助发展中国家减少砍伐和森林退化,同时还包括森林保护、森林的可持续经营以及增加森林碳汇。本文分析了中国森林碳减排量的潜在影响因素,通过建立全局回归模型识别出关键的影响因素,在此基础上通过检验发现全局回归模型具有空间非稳定性,并建立了地理加权回归模型对关键影响因素的空间异质性进行了分析。研究结果表明:中国森林碳减排量主要受人均地区生产总值、人口自然增长率、人口密度、农业总产值以及林业总产值等五个因素影响。人均地区生产总值对森林碳减排水平提升具有阻碍作用,并且呈现出东北向西南递减的趋势;而人口密度同样具有阻碍作用,并呈现出从西向东递增的趋势;人口自然增长率对于森林碳减排水平提升在东北区域具有促进作用,而在西南区域则具有阻碍作用;农业发展对森林碳减排水平提升具有促进作用,并具有从东向西递减的趋势;林业发展对森林碳减排水平提升具有促进作用,呈现出从西南向东北递减的趋势。最后针对这些空间异质性提出有针对性的政策工具供给,为中国今后的REDD+机制设计提供决策依据。The absorption of CO2 by forest land is a critical mean of carbon capture and storage. Greenhouse gas emissions from deforestation and forest degradation have become the second major cause of global warming. Owing to these high forest emissions, in 2007 the United Nations Framework Convention on Climate Change （UNFCCC） introduced the Reducing Emissions from Deforestation and Degradation （ REDD ）. An extension of this mechanism, titled REDD ＋ , includes plans for forest protection, sustainable management of forests and enhancement of forest carbon sinks. The potential impact factors of reduction in forest carbon emissions of China are firstly analyzed, and key impact factors are identified by global regression model. Because the model is spatially unstable through statistical test, the geographical weighted regression model is used to analyze spatial heterogeneity of the key factors. The results show that reduction in forest carbon emissions of China is mainly affected by GDP per capita, natural population growth rate, population density, gross output value of agriculture and forestry. The GDP per capita with a negative effect on reduction in forest carbon emissions shows a decreasing trend from the northeast to the southwest, while the population density with a negative effect shows an increasing trend from the west to the east. The natural population growth has a positive effect on reduction in forest carbon emissions in the northeast region, and has a negative effect in the southwest region. Agricultural development can increase reduction in forest carbon emissions, and shows a decreasing trend from the east to the west. Forestry development plays an important promotional role in reduction in forest carbon emissions, showing a decreasing trend from the southwest to the northeast. In order to provide the decision- making basis for REDD ＋ design of China in the future, some policy tools targeting on the spatial heterogeneity are finally proposed.

关 键 词：REDD+ 毁林 气候变化 地理加权回归 

分 类 号：X196[环境科学与工程—环境科学]