纳入健康损失的生态补偿额度测算——基于省域尺度

Calculation of Eco-compensation Standard Included Health Loss:Based on Provincial Level

生态补偿额度的测算是生态补偿理论研究与实践的核心问题,以往我国生态补偿额度的测算方法很少考虑因环境污染造成的健康损失,导致生态补偿额度标准偏低。借助修正人力资本法、患病失能法以及潜在减寿年法等方法,通过构建一个包含人力资本、大气污染、水环境污染的计量模型来估算我国各省域的健康损失价值,进而将健康损失纳入生态补偿额度测算范围。结果表明:2017年我国因大气污染造成的健康损失约为9632.92亿元,因水污染造成的健康损失约为681.04亿元,健康损失总价值约占全年GDP的1.218%;健康损失价值最高为江苏省,达1633.26亿元,占GDP比重最高的为天津市,达3.963%;纳入健康损失后的生态补偿额度有19个省份为负值,12个省份的生态补偿额度为正值;生态补偿主体均为经济强省或农业大省,在东部沿海和华北地区呈现空间集聚,生态受偿主体均为生态资源丰富的省份,在西部内陆地区集聚分布,空间上呈现明显的"东部补西部、沿海补内陆"的地域分异规律。

The calculation of eco-compensation standard is the core issue of the eco-compensation research.In the past,scholars seldom consider the health loss of eco-compensation standard calculation caused by environmental pollution.Based on the corrected human capital method,disability adjusted life year(DALY)and potential years of life lost(PYLL),this paper constructs a measurement model of health loss which includes human capital,air pollution and water pollution,and estimate its value at the provincial level in China,so as to include health loss in the scope of eco-compensation.Results show that:1)In 2017,the health loss caused by air pollution in China was about 963.292 billion yuan,the health loss caused by water pollution was about 68.1 billion yuan,and the total value of health loss accounted for 1.218%of the whole GDP.2)The highest value of health loss was Jiangsu Province,which was 163.326 billion yuan,the highest proportion of the whole GDP was Tianjin,which accounted for 3.963%.3)The value of eco-compensation was negative in19 provinces(cities),and 12 provinces(cities)were positive.The areas that need to pay eco-compensation are with strong economy or large agricultural provinces.It showed spatial agglomeration in the east of China and North China.The areas that should be compensated have rich ecological resources,and distributes in the western region,it shows the obvious law of regional differentiation of"eastern regions complements western regions and coastal regions complements inland regions".