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辽宁省土地利用变化对碳排放量的影响分析
引用本文:关伟,;吴如馨.辽宁省土地利用变化对碳排放量的影响分析[J].云南地理环境研究,2014(3):1-6.
作者姓名:关伟  ;吴如馨
作者单位:[1]辽宁师范大学海洋经济与可持续发展中心,辽宁大连116029; [2]辽宁师范大学城市与环境学院,辽宁大连116029
基金项目:国家科技支撑计划项目(2008BAH31806);辽宁经济社会发展立项课题(2013lslktzijjx-45).
摘    要:根据联合国政府间气候变化专门委员会的报告,土地利用方式不当是造成碳排放量增加的主要原因。研究不同土地利用方式的碳排放量,对衡量一个国家或地区的绿色经济发展水平以及量化国际环保事务中的责任具有重大意义。运用IPCC的碳排放量模型计算辽宁省2002~2011年的碳排放量。结合R/S分析法对辽宁省的碳排放趋势进行预测。采用多元回归方法对不同土地利用类型碳排放量变化与碳排放总量变化的关系进行分析。结果表明:(1)辽宁省未来十年的碳排量将呈现下降趋势;(2)林地是最主要的碳汇,建设用地是最主要的碳源;(3)林地的碳排放变化趋势对碳排放总量变化趋势影响最大。

关 键 词:土地利用类型  碳排放  R/S分析  多元回归分析  辽宁省

ANALYSIS OF EFFECTS OF CHANGES OF LAND USE PATTERNS ON CARBON EMISSIONS IN LIAONING PROVINCE
Institution:GUAN Wei, WU Ru -xin (1. Research Center for Marine Economy and Sustainable Development, Liaoning Normal University, Dalian 116029, China; 2. Collage of Urban And Environment, Liaoning Normal University, Dalian 116029, China)
Abstract:According to the report from lntergovernmental Panel on Climate Change, the improper usage of land is one of the main reasons of carbon emission. Therefore, it makes a significant sense to do a better research about carbon emission on different land use patterns, so that we can measure the green economy development level of a country or a region and quantify the responsibilities in international environmental affairs. Calculating the total car- bon emission in Liaoning Province between 2002 and 2011 by using formulas which are related to carbon emission given by IPCC. The results showed that: ( 1 ) carbon emission in Liaoning Province in at least ten years from now on will show downtrend in accordance with R/S analysis. (2) woodland is the biggest carbon source, and con- struction land is the most important carbon sink. (3) the variation tendency of carbon emission of woodland has a significant influence on that total carbon emission which can be reached by using multiple regression analysis to analyze carbon emission of different land use patterns and the total emission.
Keywords:land use patterns  carbon emission  R/S analysis  Multiple regression analysis  Liaoning Province
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