气候风险与我国巨灾保险发展空间不均衡性实证研究
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摘要
我国气象巨灾频发且各区域气候风险差异显著,巨灾保险作为分散风险的重要手段应当因地制宜,"一刀切"的发展策略导致我国各地巨灾保险发展水平不均衡。基于此,首先应对我国各地区的气候风险进行衡量,从危险性、暴露性和抗风险能力三个角度运用灰色关联度法计算各省份气候风险的关联度,将我国划分为三个风险区域。在此基础上利用Dagum基尼系数对三个风险区巨灾保险发展水平的不均衡进行实证研究。研究发现:(1)风险1区和风险3区之间的巨灾保险发展水平差距最大;(2)风险2区的巨灾保险发展水平最不均衡,风险3区次之,风险1区的巨灾保险发展水平相对均衡;(3)区域间巨灾保险发展不均衡问题相较于区域内更严重。
China's meteorological catastrophes occur frequently and the climate risks vary greatly from region to region.As an important means of diversifying risks, catastrophe insurance should be tailored to local conditions. The "one size fits all" development strategy has led to uneven development of catastrophe insurance across China. Based on this, we first measure the climate risks of various regions in China, and use the grey correlation method to calculate the correlation degree of climate risks among provinces(municipalities and autonomous regions) from the three perspectives of risk, exposure and risk resistance, and divide China into 3 Risk areas. On this basis, the Dagum Gini coefficient is used to empirically study the imbalance of the development level of catastrophe insurance in the three risk areas. The study found that:(1) The development level of catastrophe insurance in risk zone 2 is the most unequal, followed by risk zone 3, and the development level of catastrophe insurance in risk zone 1 is relatively balanced;(2) the development level gap between the risk 1 and the risk 3 is the largest;(3) the unbalanced development of catastrophe insurance between regions is more serious than that in the region.
China's meteorological catastrophes occur frequently and the climate risks vary greatly from region to region.As an important means of diversifying risks, catastrophe insurance should be tailored to local conditions. The "one size fits all" development strategy has led to uneven development of catastrophe insurance across China. Based on this, we first measure the climate risks of various regions in China, and use the grey correlation method to calculate the correlation degree of climate risks among provinces(municipalities and autonomous regions) from the three perspectives of risk, exposure and risk resistance, and divide China into 3 Risk areas. On this basis, the Dagum Gini coefficient is used to empirically study the imbalance of the development level of catastrophe insurance in the three risk areas. The study found that:(1) The development level of catastrophe insurance in risk zone 2 is the most unequal, followed by risk zone 3, and the development level of catastrophe insurance in risk zone 1 is relatively balanced;(2) the development level gap between the risk 1 and the risk 3 is the largest;(3) the unbalanced development of catastrophe insurance between regions is more serious than that in the region.
引文
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[16]邱波,郑龙龙.巨灾风险视角下的我国政策性农业保险效率研究[J].农业经济问题,2016(5):69-76.
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[23]Bouwer L M,Vellinga P.On the flood risk in the Netherlands[M]//Begum S,Stive M J F,Hall J W.Flood risk management in Europe.Berlin:Springer Netherlands,2007.
[24]段胜.巨灾损失指数在巨灾风险综合评估体系中的作用探析[J]保险研究,2012(1):14-20.
[25]张祖荣,马岚.我国省域政策性农业保险发展不平衡的实证分析[J].财经科学,2016(7):20-30.
[26]周延,屠海平.跨区域型台风巨灾保险基金设计[J].中国软科学,2017(6):69-80.
[27]叶明华,孙蓉.农业水旱灾害的分级评估与农业保险的风险分担[J].农村经济,2013(6):3-8.
[28]庹国柱,朱俊生.完善我国农业保险制度需要解决的几个重要问题[J].保险研究,2014(2):44-54.
[29]何小伟,王克.农业保险大灾风险分散机制的财政支持依据及路径选择--以吉林、安徽、四川三省为例[J].农业经济问题,2013(10):36-40.
[30]隋广军,唐丹玲.台风灾害与地区经济差距:粤省证据[J].改革,2012(6):18-25.
[31]余洋.基于保障水平的农业保险保费补贴差异化政策研究--美国的经验与中国的选择[J].农业经济问题,2013(10):29-35.
[32]张伟,郭颂平,罗向明.风险演变、收入调整与不同地理区域农业保险的差异化需求[J].保险研究,2013(10):32-41.
[33]张祖荣.农业保险的保费分解与政府财政补贴方式选择[J].财经科学,2013(5):18-25.
[34]田玲,姚鹏.我国巨灾保险基金规模研究--以地震风险为例[J].保险研究,2013(4):13-21.
[35]王翔,李云仙,李幸.农房地震保险费率厘定研究--以云南省为例[J].保险研究,2015(8):88-97.
[36]Dodo A,Xu N,Davidson R A,et al.Optimizing regional earthquake mitigation investment strategies[J].Earthquake Spectra,2005,21(2):305-327.
[37]Kunreuther H,Michel-Kerjan E,Ranger N.Insuring future climate catastrophes[J].Climatic Change,2013,118(2):339-354.
[38]李心愉,赵景涛,刘忠轶.我国农业保险开展效率研究--基于企业和区域的视角[J].江西财经大学学报,2015(2):69-80.
[39]郑军,汪运娣.我国农业保险差异性财政补贴:地区经济差距与财政支出公平[J].重庆工商大学学报(社会科学版),2017(3):1-8.
[40]孔锋,吕丽莉,方建.农业巨灾风险评估理论和方法研究综述和展望[J].保险研究,2016(9):103-116.
[41]IPCC AR5.Intergovernmental panel on climate change climate change 2001 fifth assessment report(AR5)[M].Cambridge:Cambridge University Press,2001.
[42]刘华军,鲍振,杨骞.中国二氧化碳排放的分布动态与演进趋势[J].资源科学,2013(10):1925-1932.
[43]钟水映,李强谊,徐飞.中国农业现代化发展水平的空间非均衡及动态演进[J].中国人口·资源与环境,2016(7):145-152.
(1)若基尼系数低于0.2表示不均衡程度极低;0.2~0.29表示不均衡程度低;0.3~0.39表示不均衡程度中;0.4~0.59表示不均衡程度高;0.6以上表示不均衡程度极高。
[2]钱凤魁,王文涛,刘燕华.农业领域应对气候变化的适应措施与对策[J].中国人口·资源与环境,2014(5):19-24.
[3]黄颖.我国政策性农业保险财政补贴机制的实践和创新[J]西南金融,2015(1):66-72.
[4]田玲,邢宏洋,高俊.巨灾风险可保性研究[J].保险研究,2013(1):3-13.
[5]Cummins J D.Should the government provide insurance for catastrophes?[J].Federal Reserve Bank of St Louis Review,2006,88(7):337-380.
[6]Jaffee D,Kunreuther H,Michel-Kerjan E.Long Term Property Insurance[J].Journal of Insurance Regulation,2010,28(2):167-187.
[7]Kunreuther H C,Michel-Kerjan E O,Doherty N A,et al.At war with the weather:Managing large-scale risks in a new era of catastrophes[M].Cambridge:The MIT Press,2011.
[8]Erdik M,Durukal E.Earthquake risk and its mitigation in Istanbul[J].Natural Hazards,2008,44(2):181-197.
[9]田玲,吴亚玲,沈祥成.基于CVaR的地震巨灾保险基金规模测算[J].经济评论,2016(4):141-150.
[10]马超群,马宗刚,张小勇.随机利率条件下的巨灾风险债券定价研究[J].中国管理科学,2012(S1):475-480.
[11]郝军章,崔玉杰.基于POT模型的巨灾风险度量与保险模式研究--以地震风险为例[J].数理统计与管理,2016(1):132-141.
[12]Zimbidis A A,Frangos N E,Pantelous A A.Modeling earthquake risk via extreme value theory and pricing the respective catastrophe bonds[J].Astin Bulletin the Journal of the Iaa,200737(1):163-183.
[13]高庆鹏,周振,何新平.政策性农业保险巨灾风险分担模式比较--以北京、江苏、安徽为例[J].保险研究,2012(12):30-37.
[14]庹国柱,王克,张峭,等.中国农业保险大灾风险分散制度及大灾风险基金规模研究[J].保险研究,2013(6):3-15.
[15]张艳,范流通,卜一.政策性农业巨灾保险偿付能力评估--基于云南试点的调查[J].保险研究,2012(12):43-51.
[16]邱波,郑龙龙.巨灾风险视角下的我国政策性农业保险效率研究[J].农业经济问题,2016(5):69-76.
[17]张旭升.中国巨灾风险暴露与巨灾保险赔付不对称实证[J].求索,2010(3):51-53.
[18]李永,范蓓,刘鹃.多事件触发巨灾债券设计与定价研究:以中国台风债券为例[J].中国软科学,2012(3):41-48.
[19]叶明华.农业气象灾害的空间集聚与政策性农业保险的风险分散--以江、浙、沪、皖71个气象站点降水量的空间分析为例(1980-2014)[J].财贸研究,2016(4):32-41.
[20]林毓铭,林博.发展巨灾保险的紧迫性与路径依赖[J].保险研究,2014(2):35-43.
[21]Hoff H,Warner K,Bouwer L M.The role of financial services in climate adaption in developing countries[J].Vierteljahrshefte Zur Wirtschaftsforschung,2005,74(2):196-207.
[22]Botzen W J W,Van den Bergh J C J M,Bouwer L M.Climate change and increased risk for the insurance sector:A global perspective and an assessment for the Netherlands[J].Natural Hazards,2010,52(3):577-598.
[23]Bouwer L M,Vellinga P.On the flood risk in the Netherlands[M]//Begum S,Stive M J F,Hall J W.Flood risk management in Europe.Berlin:Springer Netherlands,2007.
[24]段胜.巨灾损失指数在巨灾风险综合评估体系中的作用探析[J]保险研究,2012(1):14-20.
[25]张祖荣,马岚.我国省域政策性农业保险发展不平衡的实证分析[J].财经科学,2016(7):20-30.
[26]周延,屠海平.跨区域型台风巨灾保险基金设计[J].中国软科学,2017(6):69-80.
[27]叶明华,孙蓉.农业水旱灾害的分级评估与农业保险的风险分担[J].农村经济,2013(6):3-8.
[28]庹国柱,朱俊生.完善我国农业保险制度需要解决的几个重要问题[J].保险研究,2014(2):44-54.
[29]何小伟,王克.农业保险大灾风险分散机制的财政支持依据及路径选择--以吉林、安徽、四川三省为例[J].农业经济问题,2013(10):36-40.
[30]隋广军,唐丹玲.台风灾害与地区经济差距:粤省证据[J].改革,2012(6):18-25.
[31]余洋.基于保障水平的农业保险保费补贴差异化政策研究--美国的经验与中国的选择[J].农业经济问题,2013(10):29-35.
[32]张伟,郭颂平,罗向明.风险演变、收入调整与不同地理区域农业保险的差异化需求[J].保险研究,2013(10):32-41.
[33]张祖荣.农业保险的保费分解与政府财政补贴方式选择[J].财经科学,2013(5):18-25.
[34]田玲,姚鹏.我国巨灾保险基金规模研究--以地震风险为例[J].保险研究,2013(4):13-21.
[35]王翔,李云仙,李幸.农房地震保险费率厘定研究--以云南省为例[J].保险研究,2015(8):88-97.
[36]Dodo A,Xu N,Davidson R A,et al.Optimizing regional earthquake mitigation investment strategies[J].Earthquake Spectra,2005,21(2):305-327.
[37]Kunreuther H,Michel-Kerjan E,Ranger N.Insuring future climate catastrophes[J].Climatic Change,2013,118(2):339-354.
[38]李心愉,赵景涛,刘忠轶.我国农业保险开展效率研究--基于企业和区域的视角[J].江西财经大学学报,2015(2):69-80.
[39]郑军,汪运娣.我国农业保险差异性财政补贴:地区经济差距与财政支出公平[J].重庆工商大学学报(社会科学版),2017(3):1-8.
[40]孔锋,吕丽莉,方建.农业巨灾风险评估理论和方法研究综述和展望[J].保险研究,2016(9):103-116.
[41]IPCC AR5.Intergovernmental panel on climate change climate change 2001 fifth assessment report(AR5)[M].Cambridge:Cambridge University Press,2001.
[42]刘华军,鲍振,杨骞.中国二氧化碳排放的分布动态与演进趋势[J].资源科学,2013(10):1925-1932.
[43]钟水映,李强谊,徐飞.中国农业现代化发展水平的空间非均衡及动态演进[J].中国人口·资源与环境,2016(7):145-152.
(1)若基尼系数低于0.2表示不均衡程度极低;0.2~0.29表示不均衡程度低;0.3~0.39表示不均衡程度中;0.4~0.59表示不均衡程度高;0.6以上表示不均衡程度极高。