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海岛地区海洋碳汇量核算及碳排放影响因素研究——以辽宁省长海县为例 被引量:8

Calculation of Carbon Sink and Influencing Factors of Carbon Emission in Island Area——A Case Study of Changhai County,Liaoning Province
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摘要 海洋是全球最大的碳汇集聚区。随着气候问题的不断加剧和对节能减排要求的不断提升,包括我国在内的一些国家非常重视碳汇和碳排放的平衡与改善。以辽宁省长海县为例,基于海洋碳循环过程,测度海岛地区海洋碳汇量与碳排放量,并运用STIRPAT模型与岭回归模型对长海县地区碳排放量影响因素进行分析。结果表明:长海县海洋碳汇量走势呈先递增后递减的态势,碳排放量呈波动上升趋势;富裕程度、能源强度和人均旅游产值与碳排放量呈正相关,它们每变化1%,碳排放量将分别增加0. 6358%、0. 3356和0. 2894%。该研究在为海岛地区碳汇量核算提供新思路,测度各因素对碳排放的影响程度,为政策制定提供参考。 The ocean area became the largest carbon sinks area in the world.With the climate issues and the increasing demand of energy saving and reduction,including China,many countries attached great importance to the balance and improvement of carbon sinks and emissions.This paper took Changhai County as an example,based on the ocean carbon cycle process,measured the ocean carbon sink and carbon emissions in the island area.It also used STIRPAT model and ridge regression model to analyze the influence factors of carbon emissions.The results showed that the trend of marine carbon sequestration in Changhai County was increased first and then declined.The trend of carbon emissions was rising.Wealth,energy intensity and per capita tourism output were positively correlated with carbon emissions which would increase 0.6358%,0.3356 and 0.2894%respectively with each per cent change of carbon emissions.This paper tried to provide a new idea for the calculation of carbon sequestration in island areas,to measure the impact of various factors of carbon emissions,and to provide a reference for policy formulation.
作者 刘锴 卞扬 王一尧 刘桂春 张耀光 LIU Kai;BIAN Yang;WANG Yi-yao;LIU Gui-chun;ZHANG Yao-guang(Center for Studies of Marine Economy and Sustainable Development,Liaoning Normal University,Dalian 116029,China)
出处 《资源开发与市场》 CAS 2019年第5期632-637,共6页 Resource Development & Market
基金 国家自然科学基金项目(编号:41571127 41671119) 辽宁省教育厅项目(编号:w201683605 H201783629) 辽宁省社科规划基金项目(编号:L18DJL005) 辽宁省社科联项目(编号:2016lsljdwt-27)
关键词 海洋碳汇 碳排放 海岛 长海县 岭回归 marine carbon sinks carbon emissions islands Changhai County ridge regression model
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