Measurement and EvaluationResearch on China’s GreenGrowth Capacity
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With the spread of global financial crisis and the challenge brought by the climate change， to develop green economy has become the global consensus. A number of developed countries and regions have taken the concept of green growth as their critical strategy to overcome financial crisis and achieve economic development. As the second largest economy entity， nine out of ten of china’s GDP growth is at the cost of environment. China urgently needs to transform its economic growth pattern to achieve green growth. At present， the research on green growth capacity in China is rare， however， the way to measure and evaluate green growth capacity of China is of significant importance for the achievement of green growth. For the record of that， this article took as the research object the green growth capacity of 30 provinces and municipalities of mainland China， designed the evaluation index for the green growth capacity of China， and used two ways of analyzing― factor analysis and cluster analysis to measure and evaluate the green growth capacity of various regions of China and hope that this article can provide a reference for the orientation of China’s green economy development.
1. Selection of Green Growth Capacity Evaluation Index
The essence of complex evaluation index system of green growth capacity is to use specific indicators to statistically describe and comprehensively evaluate all the fields included in green growth and the concept of green growth. Based on the related fields and concept of green growth， the measurement of green growth could reflect the development and change of regional economy， environment and social system.
To evaluate the sustainable development of different countries， the UN Commission on sustainable development established a universal framework of sustainable development index system， which is constituted with four major sub-systems： societyl， economy， environment and institution. As the green growth is just a subset of sustainable development， this article， based on the framework of economy system， environment system， society system and institute system， referring to the concept of green growth， selects index to evaluate the green growth capacity.
According to the particular and popular green growth index of authoritative institutions like the UN Commission on Sustainable Development and the World Bank， combining with the statistical status quo of China， concerning of the measurability， comparability， availability and universality of the index and taking into consideration of the actual development situation， this article select 20 specific variables as the evaluation index of green growth capacity of China： CO2 productivity （ten thousand Yuan/ton）， energy productivity （ten thousand Yuan/ton of standard coal）， proportion of non-fossil energy of total energy consumption （%），productivity of industrial solid wastes （Yuan/ton） ， use ratio of industrial solid wastes （%）， land productivity （ten thousand Yuan/hectare）， water resource productivity （Yuan/m3）， ratio of environment & energy department employment of total employment （%）， amount of fresh water per capita （m3/person）， forest coverage （%）. Change ratio of cultivated land （%）， ratio of wetland （%）， ratio of protected area of a region （%）， PMIO （mg/m3）， air quality compliance rate （%）， urban sewage treatment rate （%）， GDP per capita （Yuan）， ratio of R&D funds on environment and energy （%）， ratio of investment on environment pollution control of GDP （%）.
2. Empirical Analysis
2.1 data collection and analysis
The data used in this article is from “China Statistical Yearbook （2011）”， website of National Bureau of Statistics of China， “China Statistical Yearbook on Environment 2011”， “China Statistical Yearbook on Energy 2011” etc. As the lack of direct monitoring data on carbon emission， this article estimated the CO2 emission according to the energy consumption， based on the IPCC fourth assessment report which said that CO2 emission of fossil fuel burning accounted for 95.3% of the total carbon emission. The non-continuous data of land productivity， forest coverage， ratio of R&D funds on energy and environment etc was collected in the most recent year； another part of data， like CO2 productivity， was estimated through energy productivity.
Using SPSS 18.0， at first， this article processed the sample data of 2010 of the selected 30 provinces and municipalities with normalization and dimensionless； then， using factor analysis this article described the green growth capacity of different regions， based on which， factor score model was constructed to obtain the score and ranking of the green growth capacity of different regions； finally， this article classified and evaluated the capacity through cluster analysis.
2.2 Factor Analysis
Factor analysis is a way to reduce dimension and simplify data. Through studying the independence of various variables， factor analysis explores the basis structure of the observed data and describes the basic data structure using the “abstract” variables. Besides， using the factor score model， the overall ranking of observed data can be resulted.
Before factor analysis， this article firstly positively processed the contrary indicator “density of PMIO”； then using SPSS 18.0， the article eliminated dimension and standardized the sample of original data； after that， this article calculated the correlation and tested the validity of the data. The 20 indicator variables were detected with KMO and Bartlett sphericity test with KMO value as 0.741， Bartlett test of sphericity 497.446 and significance level 0.01， which indicated the data was suitable for factor analysis. Using Virmax orthogonal rotation method summarized five common factors， the cumulative contribution rate of which was 76.064%. Considering the meanings of the indicator variables represented by each common factor， the summarized five common factors were named as economic efficiency （F1）， land productivity （F2）， ecological safety （F3）， environmental quality （F4）， and green industry development （F5）， as indicated in Table 1：
2.3 Comprehensive Evaluation
The proportion of the variance contribution of the five common factors of the total variance contribution as weights then got weighted average， i.e. the comprehensive score F of regional green growth capacity. The formula is：
F=（F1*20.311+F2*17.465+F3*14.968+F4*13.593+F5*9.727）/76.064
（1） By calculating the comprehensive scores of various observed data， the score and ranking of each region’s green growth capacity could be reached， as shown in Table 2， which reads that Shanghai had the strongest green growth capacity and was ranked the first of 30 provinces and municipalities， followed by Tianjin municipal city and Guangdong Province as the second and the third. The three province of Xinjiang， Qinghai and Gansu were ranked the end of the list， with the week capacity.
Ranking of green growth capacity is highly consistent with the economic development. The 13 provinces and municipalities （apart from Chongqing） with higher scores are all in the east of China. These regions scored higher in F1 and F3， which indicated that they possessed high economic productivity and safer and more suitable environment. Besides， due to developed economy， sound finance， quickly upgrading industrial structure， high proportion of tertiary industry， these regions encountered earlier the constraints of resources and environment on economic development； therefore， they brought resource utilization and environment under greater protection and possessed more experience， which resulted with higher green economy development.
2.4 Cluster Analysis
To reveal the individual and common features of the factors， which can reflect green growth capacity of different regions， this article used the scores of the 5common factors as variables， with the inner-group association as the method， and squared Euclidean distance as the metric interval standard， to carry out cluster analysis of the green growth capacity of the 30 provinces and municipalities， which were clustered into 5 categories.
Cat.1， Guangdong. The feature of this category is that high economic productivity， improved environment quality and developed green industries. However， with rapid economic growth and high concentration of people， pressure of resources and environment is stressful. So the environment amenity is in general， land resources are scare and the land utilization efficiency is not high.
Cat.2， Shanghai， Tianjin， Beijing， Zhejiang， Jiangsu， Inner Mongolia， Shandong， Ningxia， Chongqing， Shanxi， Shaanxi and Hebei. The feature of this category is that they have relatively complete environment infrastructure and scored comparatively higher in ecological safety. The economic strength of Shanghai drives the improvement of resource utilization rate. Its green industry has began to develop， but still composes small share in total economy. Tianjin， Beijing， Zhejiang and Jiangsu comprise a part of category 2. These areas are characterized by high economic productivity and pay more attention to economic growth efficiency and environment protection. However， long-term of extensive growth has resulted serious environment pollution. Land utilization rate and green industry development are not good. Inner Mongolia， Shandong， Ningxia， Chongqing， Shanxi， Shaanxi and Hebei comprise the last part of this category. Advantage in resources of these areas lays a solid foundation for green industry development. But the low level of technology limits their economic productivity. Land utilization and plan is not reasonable and environment quality of these areas is lower than the national average.
Cat.3， Hainan， Jilin， Liaoning， Heilongjiang， and Qinghai. Ecological safety scored lowest， but green industry development scored comparatively high. These areas are featured with single industry structure， high proportion of resource-dependent industries， and limited economic strength， which determined their resource and energy utilization rate. Protection of environment and ecology is weak and green industry is underdeveloped.
Cat.4， Guangxi， Fujian， Jiangxi， Hunan， Guizhou， Yunnan， Anhui， Henan， Hubei， and Sichuan. These areas scores generally low in economic efficiency， land productivity， green industry development. These areas are featured with comfortable environment and abundant resources. Especially Guangxi， Fujian， Jiangxi， Hunan， Guizhou and Yunnan scored comparatively high in environment quality. But the low economic efficiency resulted in the low rate of resource and energy utilization and weak foundation of green industry.
Cat.5， Xinjiang and Gansu. These two areas scored low in all the common factors. They are economically less developed. With the multi-constraints of resources， environment， economic development and technology， they are under huge pressure for green growth and improvement in all aspects.
3 conclusion and suggestion
With the 30 provinces and municipalities as the research objects， the article established green growth capacity index system， carried out factor analysis and cluster analysis to evaluate the green growth capacity of different areas of China. The result is presented：
（1） The result of factor analysis shows that， economic efficiency， land productivity， ecological safety， environment quality， and green industry development are the main factors influencing green growth capacity of different areas of China. Through evaluating the capacity， we found that the comprehensive ranking of green growth capacity is highly consistent with economic development of different areas with higher green growth capacity in the east part than the west part. Shanghai， Tianjin and Guangdong have comparatively stronger capacity in green growth， while Xinjiang， Qinghai and Gansu are weak in that capacity，
（2） The result of cluster analysis shows that， green growth capacity of different areas can be classified into five categories and the areas of each category are suggested to take corresponding strategies to enhance their capacity. Guangdong in Cat.1 should explore its potential in the field of research， innovation and environment protection to improve land utilization. Shanghai， Beijing and Tianjin of Cat.2 should continue to promote the adjustment of industrial structure and increase the share of green industry. The other areas of Cat.2 should increase educational and financial investment on green technology to improve resource productivity. Areas of Cat.3 should accelerate green innovation， and eliminate backward industries to transit from resource-dependent area to green industry areas. Areas of Cat.4 should strive to develop sustainable new energy industry. Areas of Cat.5 should provide financial and policy support to share the fruits of development and protect environment while developing their economy.
In conclusion， each area should， based on its actual situation and the objective differences from other areas， accelerate the transformation of economic development， improve resource utilization rate， encourage the development of green industry， promote the unity of “green” and “growth” to realize the coordinated development economy， population， resource and environment.
（Wu Chunyou， Director of the Institution of Ecological Planning and Development Research of Dlian University of Technology， researching on ecological planning and development；
Lu Xiaoli， Deputy Director of the Institution of Ecological Planning and Development Research of Dlian University of Technology， researching on environment management；
Zheng Hongna， postgraduate of Management and Economics Department of Dalian University of Techonology， researching on environment management.）