摘要
以最少的自然消耗实现人类福祉最大化,促进可持续的福祉提升,是生态福利绩效概念的核心内涵。本文通过构建生态福利绩效模型,将生态福利绩效模型分解为经济增长的福利效应和经济增长的生态效率两个驱动效应,应用DI指数和DEA-ML指数分别对影响中国31个省域经济增长的福利效应和经济增长的生态效率进行了测算及分析。研究表明:①2006—2016年中国人类发展水平大幅提高,在空间上呈"京津沪率先提升,然后由东向西"拓展态势。②2006—2016年中国人类发展水平的增速明显慢于人均生态足迹增速,使得中国生态福利绩效整体呈下降趋势。③中国人类福祉增长与经济增长相对脱钩,人类福祉增速慢于经济增速。中国31个省域分为中福祉增长和低福祉增长两类,其中内蒙古、黑龙江、广西、海南、贵州、甘肃、青海、宁夏、新疆、西藏、云南11个省域为中福祉增长类型,其他20个省域属于低福祉增长类型。④2006—2016年中国传统全要素生产率和绿色全要素生产率都有不同程度的提高,但绿色全要素生产率一直低于传统全要素生产率,表明经济增长付出的资源环境代价影响了中国经济增长质量。⑤中国生态福利绩效提升整体由福利效应和生态效率共同驱动,根据驱动效应分解和生态福利绩效的变化情况,本文将中国31个省域生态福利绩效分为经济主导提升型、福祉带动提升型、福祉滞后下降型、经济滞后下降型、总体下降型5种类型。本文深化了对生态福利绩效变化的驱动效应的认识,对中国不同类型省域采取差异化的生态福利绩效提升策略提供参考。
It is the core connotation of the concept of ecological well-being performance to maximize human well-being with minimum natural consumption and promote sustainable wellbeing. This study constructed a model of ecological well-being performance and decomposed ecological well-being performance into two driving effects, namely, the well-being effect of economic growth and the ecological efficiency of economic growth. The well-being effect of economic growth and the ecological efficiency of economic growth, which affect the change of ecological well-being performance in 31 provinces of China’s mainland, were calculated and analyzed by using the decoupling index(DI) and the DEA-ML index models respectively. The results show that:(1) From 2006 to 2016, the level of human development in China had been greatly improved, showing the characteristics of leading by Beijing, Tianjin, and Shanghai Municipalities and expanding from east to west.(2) From 2006 to 2016, the growth rate of human development level in China was significantly slower than that of per capita ecological footprint.China’s ecological well-being performance showed a downward trend as a whole.(3) China’s human well-being growth was relatively decoupled from economic growth. Human well-being growth slower than economic growth. China’s 31 provinces are divided into two categories:medium well-being growth and low well-being growth, 11 of which are Inner Mongolia,Heilongjiang, Guangxi, Hainan, Guizhou, Gansu, Qinghai, Ningxia, Xinjiang, Tibet and Yunnan belong to the medium well-being growth, while the other 20 belong to the low well-being growth.(4) From 2006 to 2016, China’s traditional total factor productivity and green total factor productivity improved to varying degrees, but green total factor productivity had always been lower than traditional total factor productivity. It shows that the cost of resources and environment paid by economic growth affects the quality of China’s economic growth.(5) On the whole, the performance improvement of eco
作者
王圣云
韩亚杰
任慧敏
李晶
WANG Shengyun;HAN Yajie;REN Huimin;Li Jing(Research Center of Central China Economic Development,Nanchang University,Nanchang 330047,China;School of Economics and Management.Nanchang University,Nanchang 330031,China;School of Tourism,Nanchang University,Nanchang 330031,China)
出处
《资源科学》
CSSCI
CSCD
北大核心
2020年第5期840-855,共16页
Resources Science
基金
国家自然科学基金项目(41861025)
江西省高校人文社会科学研究规划项目(JJ19118)
2019年江西省研究生优质课程和案例建设项目(生态经济与可持续发展)。
关键词
生态福利绩效
人类发展指数(HDI)
人类福祉
生态足迹
绿色全要素生产率
驱动效应
ecological well-being performance
human development index(HDI)
human well-being
ecological footprint
green total factor productivity
driving effect
