东北老工业基地绿色发展评价及障碍因素分析

以东北老工业基地11个典型城市为研究对象,从资源利用、产业绿色化、经济发展质量、环境保护、绿色人居5方面构建指标体系,分析东北老工业基地绿色发展水平时空演变特征,运用障碍度模型揭示其绿色发展水平提升的障碍因子。研究表明：东北老工业基地城市间绿色发展水平差异显著,高值区始终分布在沈阳、长春、大连、哈尔滨。研究期内,高水平类型城市数量增加,中高、低水平城市数量减少,以四大城市为核心的绿色发展水平空间格局更加明显。科学技术支出占GDP比重、政府财政自给率、环境保护支出占GDP比重、人均GDP、规模以上工业企业成本费用利润率是影响东北老工业基地绿色发展水平提升的主要障碍因子。

Green Development Level and the Obstacle Factors of Old Industrial Base in Northeast China

Currently, both developed and developing countries emphasize the green development of their economy, which is also the focus against the context of the revitalization of Old Industrial Base(OIB). This article takes OIB in Northeast China as a case study to show its green development condition by evaluating its green development level and identifying its obstacle factors. Firstly, this article introduced the features of green development of OIB based on the existing literature both domestic and abroad. Secondly, we established a comprehensive index system which consists of five aspects: 1) The capacity of resource utilization; 2) The green industry; 3) The quality of economic development; 4) The environment protection and treatment; 5) The green residence environment. This index system includes 25 evaluation indices. Thirdly, the article analyzed the spatial distribution and regional differences of the green development level of 11 typical old industrial cities in Northeast China. The results are as following: First, we find that green development level of Shenyang and Changchun are much higher than other cities in 2014, the next are Dalian and Harbin, the rest cities are lower than these four cities. Second, these 11 cities can be divided into four categories by calculating the standard deviation of their green development level. We find that Shenyang, Changchun, Dalian are in the first group, Harbin is in the second group, Fushun is in the fourth group, and the other cities belong to the third group. Third, the capacity of green development has increased significantly from 2008 to 2014, but the polarization phenomena are even apparent. It gradually formed a green development level pattern in the space that took the four major cities as the center. The index of coefficient of variation shows that regional differences and spatial polarization of green development among old industrial cities tend to decrease. Fourth, based on the factor analysis of the obstacle degree, we find that the development of local economy and green industry is the major factor of rule hierarchy that affects green development in general. Their negative influence on green development becomes stronger over time. From the view of index hierarchy, we can see that the top five obstacle indicators didn’t change during the study period, shows a stable status. The ratio of technology expenditure to GDP are the major obstacle factors that affect green development level, and the major influences of each index on each city’s green development level show an obvious regional characteristic.