• Journal of Geographical Sciences
  • Vol. 30, Issue 7, 1117 (2020)
Lin HA1、2、3, Jianjun TU2, Jianping YANG1、*, Chunhai XU1, Jiaxing PANG4, Debin LU5, Zuolin YAO6, and Wenyu ZHAO4
Author Affiliations
  • 1State Key Laboratory of Cryospheric Science, Northwest Institute of Eco-Environment and Resources, CAS, Lanzhou 730000, China
  • 2College of Economics and Management, Southwest University, Chongqing 400715, China
  • 3University of Chinese Academy of Sciences, Beijing 100049, China
  • 4College of Earth and Environmental Sciences, Lanzhou University, Lanzhou 730000, China
  • 5Department of Land Management, Zhejiang University, Hangzhou 310058, China
  • 6Institute of Geographic Sciences and Natural Resources Research, CAS, Beijing 100101, China
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    DOI: 10.1007/s11442-020-1773-0 Cite this Article
    Lin HA, Jianjun TU, Jianping YANG, Chunhai XU, Jiaxing PANG, Debin LU, Zuolin YAO, Wenyu ZHAO. Regional eco-efficiency evaluation and spatial pattern analysis of the Yangtze River Economic Zone[J]. Journal of Geographical Sciences, 2020, 30(7): 1117 Copy Citation Text show less

    Abstract

    The environmental ecology of the Yangtze River Economic Zone (YREZ) faces ecological function decline, deterioration and degradation under intense human activities, long-term development and utilization and its economy has developed rapidly over recent decades. Eco-efficiency is considered as a measure of coordinated development of economy, resources, environment and ecology, and is currently considered a very important issue. In this paper, based on the slack-based measure and data envelope analysis model, we take 129 prefecture-level cities of the YREZ as the study unit and measure the eco-efficiency of the YREZ in 2000, 2005, 2010 and 2015, which considers undesired output. The evaluation of the status quo of the regional eco-efficiency development was carried out at provincial, prefectural and city scales. The spatial autocorrelation test model and standard deviation ellipse were used to analyze the spatially distributed characteristics and the evolutionary regularity of eco-efficiency. Our study suggested that the eco-efficiency value varied significantly at different spatiotemporal scales and the overall distribution presented an “N-shaped” pattern, the value is the largest downstream and the smallest upstream. Regional eco-efficiency presented certain volatility in growth and a clear spatial positive agglomeration trend from 2000 to 2015. The spatial distribution of each agglomeration area was also significantly different, forming some high-high agglomeration areas at the center of the shaft with Shanghai and surrounding cities, and some low-low agglomeration areas at the center with middle reaches and upstream cities. The low-high over-aggregation and high-low polarization clusters were fewer. At the same time, with the change of the research period, the degree of positive agglomeration became increasingly pronounced and the eco-efficiency gap of the neighborhood unit reduced. The regional eco-efficiency value of the YREZ presented a spatial distribution pattern in the northeast-southwest axis and the evolutionary pattern of the regional eco-efficiency similarly showed a northeast-southwest orientation.
    $\rho =\text{min}\frac{1-\frac{1}{N}\sum\limits_{n=1}^{N}{\frac{s_{n}^{x}}{x_{{k}'n}^{{{t}'}}}}}{1+\frac{1}{M+I}\left( \sum\limits_{m=1}^{M}{\frac{s_{m}^{y}}{y_{{k}'m}^{{{t}'}}}+\sum\limits_{i=1}^{I}{\frac{s_{i}^{b}}{b_{{k}'i}^{{{t}'}}}}} \right)}$ (1)

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    s.t.$\sum\limits_{t=1}^{T}{\sum\limits_{k=1}^{K}{z_{k}^{t}x_{kn}^{t}+s_{n}^{x}=x_{{k}'n}^{{{t}'}},}}\ n=1,...,N$ (2)

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    $\sum\limits_{t=1}^{T}{\sum\limits_{k=1}^{K}{z_{k}^{t}y_{km}^{t}-s_{m}^{y}=y_{{k}'m}^{{{t}'}},\ m=1,...,M}}$ (3)

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    $\sum\limits_{t=1}^{T}{\sum\limits_{k=1}^{K}{z_{k}^{t}b_{ki}^{t}+s_{i}^{b}=b_{{k}'i}^{{{t}'}},i=1,...,I}}$ (4)

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    $z_{k}^{t}\ge 0s_{n}^{x}\ge 0,s_{m}^{y}\ge 0,s_{i}^{b}\ge 0,k=1,...,K$ (5)

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    $I=\frac{\sum\limits_{i=1}^{\text{n}}{\sum\limits_{j=1}^{n}{{{w}_{ij}}({{x}_{i}}-\bar{x})({{x}_{j}}-\bar{x})}}}{{{\sigma }^{2}}\sum\limits_{i=1}^{n}{{}}\sum\limits_{j=1}^{n}{{{w}_{ij}}}}$ (6)

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    $Z=\frac{I-E(I)}{\sqrt{Var(I)}}$ (7)

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    ${{I}_{i}}={{z}_{i}}\sum\limits_{i\ne j}^{n}{{{w}_{ij}}{{z}_{j}}}$ (8)

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    $SD{{E}_{x}}=\sqrt{\frac{\sum\limits_{i=1}^{n}{{{({{x}_{i}}-\bar{X})}^{2}}}}{n}},$ (9)

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    $SD{{E}_{y}}=\sqrt{\frac{\sum\limits_{i=1}^{n}{{{({{y}_{i}}-\bar{Y})}^{2}}}}{n}},$ (10)

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    $\tan \theta =\frac{\left( \sum\limits_{i=1}^{n}{\tilde{x}_{i}^{2}}-\sum\limits_{i=1}^{n}{\tilde{y}_{i}^{2}} \right)+\sqrt{{{(\sum\limits_{i=1}^{n}{\tilde{x}_{i}^{2}}-\sum\limits_{i=1}^{n}{\tilde{y}_{i}^{2}})}^{2}}+4{{(\sum\limits_{i=1}^{n}{{{{\tilde{x}}}_{i}}{{{\tilde{y}}}_{i}}})}^{2}}}}{2\sum\limits_{i=1}^{n}{{{{\tilde{x}}}_{i}}{{{\tilde{y}}}_{i}}}}$, (11)

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    ${{\sigma }_{x}}=\sqrt{2}\sqrt{\sum\limits_{i=1}^{n}{{{({{{\tilde{x}}}_{i}}cos\theta -{{{\tilde{y}}}_{i}}sin\theta )}^{2}}}/n}$, (12)

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    ${{\sigma }_{y}}=\sqrt{2}\sqrt{\sum\limits_{i=1}^{n}{{{({{{\tilde{x}}}_{i}}\sin \theta -{{{\tilde{y}}}_{i}}\cos \theta )}^{2}}}/n}$, (13)

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    Lin HA, Jianjun TU, Jianping YANG, Chunhai XU, Jiaxing PANG, Debin LU, Zuolin YAO, Wenyu ZHAO. Regional eco-efficiency evaluation and spatial pattern analysis of the Yangtze River Economic Zone[J]. Journal of Geographical Sciences, 2020, 30(7): 1117
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