A Dynamic Analysis to Evaluate the Environmental Performance of Cities in China
Abstract
:1. Introduction
2. Methodology
2.1. Production Possibility Set
2.2. Malmquist-Luenberger Index
3. Empirical Results
3.1. Data and Sources
3.2. Environmental Performance under the Meta-Frontier
3.3. Environmental Performance under the Group Frontier
3.4. Comparison between Different Frontiers
3.5. Comparison between the ML Index and GDP
4. Discussions
- (1)
- The ML indexes in 166 cities under the meta-frontier and 185 cities under the group frontier are more than 1, which indicates that the urban environmental performances in these cities were improved during the observation period. In most years the ML indexes under the group frontier were higher than that under the meta-frontier in different types of cities. This overestimation under the group frontier for high energy intensity cities is more obvious. Because production technologies in this type of city were not advanced, they had a lower technological frontier. This also means it is difficult to match the group frontier to the meta-frontier by improving the technological efficiency. However, narrowing the technology gap between different types of cities is a more efficient method to improve the urban environmental performance in China.
- (2)
- It is notable that the EC and TC indexes of cities with higher ML growth also remained with an increasing trend basically and the gap between these two indexes was smaller relatively. This indicates in these cities that resource allocation and structure were relatively reasonable, and technological innovation abilities were stronger. However, in these cities with decreasing environmental performances, at least one of its EC or TC index remained at negative growth which means the resource allocation and structure were unreasonable and the technological levels were lower. Thus, the balance between efficient improvement and technological promotion is significant for the overall increase in environmental performance.
- (3)
- Economic policies and crisis have important influences on urban environmental performances. For example, in 2006 and 2012 the ML index had a significant increase. In this period, China published the Chinese eleventh five-year plan (2006–2010) and Environmental government policies such as “Air Pollution Control Action Plan” and “PM2.5 Monitoring”. All of these policies related to industrial structure and resources conservation promoting technological innovation vigorously. The findings are partly in line with previous research [39], government economic plans can lead to a significant change of the environmental situation. Additionally, in 2008 the ML index and its decomposed EC and TC experienced a decreasing trend when the global financial crisis occurred. Thus, strategic policies and huge crises should be considered as external factors when we evaluate environmental performance.
- (4)
- In most cities, the ML growth rates were significantly smaller than GDP growth rates and even totally opposite trends existed between these two indexes. This indicates it is not enough to evaluate the economic development of a city only by GDP. The energy consumption and the environmental performance are also important indicators to evaluate urban development from the sustainable perspective. So it is necessary to rethink the achievement of urban development by combining economy-related indicators and environment-related indicators.
5. Conclusions
Acknowledgements
Conflicts of Interest
References
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Types | Clusters | Mean | Number | Cities (Partly) |
---|---|---|---|---|
Low Energy Intensity | 1 | 0.04 | 198 | Ankang, Baise, Bayanzhuoer, Bengbu, Baicheng, Bazhong, Binzhou, Baoji, Cangzhou, Baoding |
Medium Energy Intensity | 2 | 0.11 | 78 | Anqing, Baiyin, Anshun, Baotou, Anshan, Anyang, Changzhou, Benxi, Beijing, Quzhou |
High Energy Intensity | 3 | 0.43 | 9 | Jinchang, Jiayuguan, Panzhihua, Shizuishan, Tongchuan, Wuhai, Xining, Yangquan, Zhongwei |
City-Meta | Cluster | EC | TC | ML | City-Group | Cluster | EC | TC | ML |
---|---|---|---|---|---|---|---|---|---|
Hefei | 1 | 1.142 | 1.138 | 1.300 | Hefei | 1 | 1.142 | 1.157 | 1.321 |
Songyuan | 1 | 1.199 | 1.071 | 1.283 | Shenyang | 1 | 1.098 | 1.185 | 1.301 |
Huanggang | 1 | 1.166 | 1.074 | 1.252 | Songyuan | 1 | 1.237 | 1.040 | 1.287 |
Huhehaote | 1 | 1.172 | 1.067 | 1.250 | Huhehaote | 1 | 1.172 | 1.073 | 1.258 |
Shenyang | 1 | 1.098 | 1.137 | 1.249 | Huanggang | 1 | 1.166 | 1.072 | 1.250 |
Pingliang | 1 | 0.797 | 0.915 | 0.729 | Pingliang | 1 | 0.797 | 0.900 | 0.717 |
Xiamen | 2 | 1.118 | 1.073 | 1.199 | Chongqing | 2 | 1.050 | 1.144 | 1.201 |
Chongqing | 2 | 1.119 | 1.046 | 1.170 | Anyang | 2 | 1.126 | 1.056 | 1.189 |
Zhengzhou | 2 | 1.124 | 1.039 | 1.168 | Nanjing | 2 | 1.124 | 1.033 | 1.162 |
Nanjing | 2 | 1.109 | 1.020 | 1.130 | Leshan | 2 | 1.083 | 1.071 | 1.161 |
Haikou | 2 | 1.034 | 0.996 | 1.130 | Baotou | 2 | 1.074 | 1.075 | 1.155 |
Yichun | 2 | 0.992 | 0.748 | 0.742 | Liangyang | 2 | 0.810 | 1.022 | 0.828 |
Shizuishan | 3 | 1.006 | 0.989 | 0.994 | Shizuishan | 3 | 0.984 | 1.155 | 1.136 |
Panzhihua | 3 | 1.076 | 0.918 | 0.988 | Wuhai | 3 | 1.021 | 1.072 | 1.095 |
Jiayuguan | 3 | 0.991 | 0.992 | 0.982 | Panzhihua | 3 | 1.014 | 1.016 | 1.030 |
Yangquan | 3 | 0.902 | 0.940 | 0.849 | Tongchuan | 3 | 1.000 | 1.000 | 1.000 |
Jinchang | 3 | 0.947 | 0.934 | 0.884 | Xining | 3 | 1.005 | 0.994 | 0.999 |
Tongchuan | 3 | 0.999 | 0.926 | 0.925 | Jinchang | 3 | 0.995 | 0.989 | 0.985 |
City | Index | 2005–2006 | 2006–2007 | 2007–2008 | 2008–2009 | 2009–2010 | 2010–2011 | 2011–2012 | 2012–2013 | Mean |
---|---|---|---|---|---|---|---|---|---|---|
HIGH–meta | EC | 0.995 | 0.914 | 1.067 | 1.054 | 1.096 | 0.997 | 0.993 | 0.996 | 1.014 |
TC | 0.659 | 1.126 | 1.039 | 1.045 | 0.964 | 1.019 | 1.027 | 1.039 | 0.990 | |
ML | 0.656 | 1.030 | 1.109 | 1.101 | 1.056 | 1.017 | 1.019 | 1.035 | 1.004 | |
MEDIUM–meta | EC | 0.989 | 0.966 | 1.098 | 1.020 | 1.063 | 0.984 | 0.966 | 0.937 | 1.003 |
TC | 0.670 | 1.096 | 1.046 | 1.026 | 0.997 | 1.042 | 1.000 | 1.105 | 0.998 | |
ML | 0.663 | 1.059 | 1.148 | 1.047 | 1.060 | 1.025 | 0.966 | 1.036 | 1.001 | |
LOW–meta | EC | 0.855 | 0.933 | 1.076 | 0.993 | 1.030 | 0.962 | 0.976 | 0.966 | 0.974 |
TC | 0.696 | 1.171 | 0.952 | 0.966 | 0.967 | 1.099 | 1.192 | 1.149 | 1.024 | |
ML | 0.595 | 1.093 | 1.023 | 0.959 | 0.995 | 1.058 | 1.163 | 1.110 | 0.997 | |
HIGH–group | EC | 0.941 | 0.938 | 1.081 | 1.005 | 1.071 | 1.031 | 0.994 | 1.023 | 1.011 |
TC | 0.776 | 1.125 | 1.023 | 1.038 | 0.975 | 1.018 | 1.026 | 1.027 | 1.001 | |
ML | 0.73 | 1.056 | 1.106 | 1.043 | 1.043 | 1.05 | 1.02 | 1.05 | 1.012 | |
MEDIUM–group | EC | 1.007 | 1.006 | 1.1 | 0.984 | 1.091 | 1.047 | 0.972 | 0.969 | 1.022 |
TC | 0.937 | 1.098 | 1.1 | 1.083 | 0.989 | 1.013 | 1.018 | 1.036 | 1.034 | |
ML | 0.944 | 1.105 | 1.21 | 1.066 | 1.078 | 1.06 | 0.989 | 1.004 | 1.057 | |
LOW–group | EC | 0.768 | 1.147 | 1.134 | 0.968 | 0.97 | 0.927 | 0.982 | 1.003 | 0.987 |
TC | 0.99 | 0.971 | 1.23 | 1.092 | 1.145 | 1.124 | 0.964 | 1.08 | 1.074 | |
ML | 0.76 | 1.113 | 1.394 | 1.058 | 1.11 | 1.041 | 0.946 | 1.083 | 1.061 |
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Wang, L.; Xue, X.; Shi, Y.; Wang, Z.; Ji, A. A Dynamic Analysis to Evaluate the Environmental Performance of Cities in China. Sustainability 2018, 10, 862. https://doi.org/10.3390/su10030862
Wang L, Xue X, Shi Y, Wang Z, Ji A. A Dynamic Analysis to Evaluate the Environmental Performance of Cities in China. Sustainability. 2018; 10(3):862. https://doi.org/10.3390/su10030862
Chicago/Turabian StyleWang, Luqi, Xiaolong Xue, Yue Shi, Zeyu Wang, and Ankang Ji. 2018. "A Dynamic Analysis to Evaluate the Environmental Performance of Cities in China" Sustainability 10, no. 3: 862. https://doi.org/10.3390/su10030862