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Evaluation of the ecological civilization index of China based on the double benchmark progressive method
Accepted Manuscript Evaluation of the ecological civilization index of China based on the double benchmark progressive method Linbo Zhang, Jiao Yang, Daiqing Li, Haijiang Liu, Yuxi Xie, Ting Song, Shanghua Luo PII:
S0959-6526(19)30572-4
DOI:
https://doi.org/10.1016/j.jclepro.2019.02.173
Reference:
JCLP 15903
To appear in:
Journal of Cleaner Production
Received Date: 9 October 2018 Revised Date:
12 February 2019
Accepted Date: 16 February 2019
Please cite this article as: Zhang L, Yang J, Li D, Liu H, Xie Y, Song T, Luo S, Evaluation of the ecological civilization index of China based on the double benchmark progressive method, Journal of Cleaner Production (2019), doi: https://doi.org/10.1016/j.jclepro.2019.02.173. This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.
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Title page
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Evaluation of the Ecological Civilization Index of China based on the
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Double Benchmark Progressive Method
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Linbo Zhanga, Jiao Yanga*, Daiqing Lia, Haijiang Liub, Yuxi Xiec, Ting Songa,
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Shanghua Luoa
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a Chinese Research Academy of Environmental Sciences, Beijing 100012, China
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b China National Environmental Monitoring Center, Beijing 100012, China
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c Beijing Normal University, Beijing 100875, China
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*Corresponding author. Tel.: +86-010-84915171. E-mail: [email protected]
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Daiqing Li ([email protected]);
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Haijiang Liu ([email protected]);
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Yuqian Xie ([email protected]);
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Ting Song ([email protected]);
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Shanghua Luo ([email protected])
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Zhang linbo ([email protected]);
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Evaluation of the Ecological Civilization Index of China based on the
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Double Benchmark Progressive Method
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Abstract: Since 2013, the Chinese government has issued a set of action plans
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catered toward the ecological civilization construction. To quantify and evaluate the
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ecological civilization development, this study has introduced the ecological
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civilization index of China (ECI), using different standardization methods (extreme
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standardization, single-benchmark progressive standardization, and double benchmark
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progressive standardization), to evaluate ecological civilization development on
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city-level (337 cities) of China.
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The issues of China’s ecological civilization
construction were analyzed. The evaluation results suggest that in the double
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benchmark progressive method, every evaluation score has its practical meaning and
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is therefore suitable for policy making. This study obtained an average ECI score of
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61.16, for 337 cities. This is China’s overall ECI score, which is classified as C-grade.
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Only 4.75% of cities’ green environment (GE) score obtained A-grade, while 53.41%
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of cities’ GE scored below the passing level, suggesting that the quality of the ecology
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and environment is neglected in the development of China eco-civilization. This study
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provides valuable knowledge for both the ecological and environment management
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and eco-civilization construction.
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Keywords: eco-civilization, eco-civilization index of China, eco-civilization
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development evaluation, double-benchmark progressive method
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1. Introduction
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Eco-civilization construction was introduced by the Chinese government in the
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edition on the sustainable development of China, which is on the premises of ecology
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preservation and environment improvement to strengthen the economic and social
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development. Since 2013, the Chinese government has issued a set of action plans
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catered toward ecological civilization construction. Since eco-civilization construction
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has been written into the Constitution of the People’s Republic of China,
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eco-civilization construction is a long-term and national task (Xi, 2018). However, it
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remains unclear how the ecological civilization development can be quantified and
ACCEPTED MANUSCRIPT evaluated. Yan et al. (2010) published their first annual report on China’s provincial
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eco-civilization indices in 2010, where they provided the corresponding results on the
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ecosystem vitality, environmental quality, social development, and the total
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eco-civilization progress of provinces during 2005-2008. Subsequently, Yan et al.
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(2015) published the Annual Report on China’s Provincial Eco-Civilization Index
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(ECI 2015), where they point out significant differences in the level of ECI among
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provinces, the overall decline of the national ECI. In the evaluation report, Hainan
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province achieved the highest score, and Hebei province showed the lowest score.
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The authors of the Green Development Index Report of China—Regional Comparison,
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which considers green economic growth, resources and environment, and government
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policy, have proposed a green development index for the evaluation of the sustainable
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development potential (Li et al., 2014; Li et al., 2017). In the report, 30 province-level
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regions and 100 cities have been evaluated, the results showed that Beijing achieved
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the highest score of the 30 provinces, while Haikou achieved the highest score among
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the 100 cities. In response to the need for unified assessment of eco-civilization
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performance of different regions, the Green Development Index System and the
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Assessment Target of Ecological Civilization Construction (NDRC 2016), a tool for
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assessing government performance, proposed by the Chinese government. Because of
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shortage of data, the evaluation just only been reported on provincial-wide.
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Meanwhile, several researches have evaluating the trends of China’s ecological
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civilization construction using a novel indicator system, with the aim to elaborate to
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the psychological problem and conflict between humans and the environment (Zhang
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et al. 2017). So far, most of reports analyzed the eco-civilization on national scale,
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province scale, as well as a part of cities, with extreme standardization method for
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normalization. However, due to the large differences in geography, economy, and
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social as well as cultural history of China, different regions exhibit different levels of
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eco-civilization. It is important to evaluate the eco-civilization level on a city-wide,
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even county-wide level, and make it a long-term focus to reflect the regional or
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national eco-civilization development level.
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Normalization is an indispensable step in comprehensive assessment. Extreme
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Yan et al., 2010; Yeo, 2000) have often been used by scholars. Several studies realized
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normalization and dimensionless through weighting and aggregation, using methods
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such as Criteria Importance Through Intercriteria Correlation (CRITIC) (Jahan, 2012)
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Among these methods, the threshold method using Benchmarking evaluation of each
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upper and lower limits, builds the evaluation of indicators’ realization degree for
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nondimensional treatment, which can yield practical meaning for evaluation. Each
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score from the evaluation with the benchmarking method conveys the practical
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meaning and policy issues, as well as easy to interannual comparison.
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In this article, we set an eco-civilization indicator framework to evaluate the
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level of ecological civilization development in 337 cities (not including the Hong
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Kong Special Administrative Region, Macau Administrative regions, and Taiwan) in
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China in 2015. In addition, a standardization method was proposed, and named the
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double-benchmark progressive method (DBD), to investigated the differences among
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this method, the standard deviation method, and the single-benchmark progressive
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method. Our specific objectives were: (1) to compare different standardization
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methods; (2) to set up the ecological civilization index of China (ECI) and to evaluate
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ecological civilization development on city-level of China; (3) to analyze and reveal
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issues of China’s ecological civilization construction.
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2. Indicators framework, methodology and data
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2.1. The ECI Framework
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The ecological civilization index (ECI) is composed of 20 indicators that reflect
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city-level environmental, economic and social data. These indicators are combined
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into four themes (green environment, green production, green lifestyle, and green
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instrument). Each of the themes combines one to three indexes, and an additional a
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regulation index (Environmental risk events), allover nine indexes (ecological quality
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index, environmental quality index, industry optimization index, industry efficiency
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index, urban-rural coordination index, green consumption index, pollution control
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index, construction performance index, and regulation index).
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2.2.Weight of the indicators
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These weightings are generally set according to 30 experts who are engaged on
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ecology, economy, and environment protection research. Since the weight ratio should
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fit the key purpose of the ecological civilization construction policy issue,
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environmental health and ecology quality should be more important than other
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indicators in this evaluation. Therefore, slight adjustments to integrate this weighting
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have be made. The detailed weight ratios are shown in Table 1.
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Please insert Table 1 here 2.3. Data
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The ECI uses primary and secondary data from multilateral organizations,
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government agencies, and academic collaborations. The original data of the
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socio-economic indicators are given from the statistical data of China's Statistical
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Yearbook and the Urban Statistical Yearbook. The statistics on environmental
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protection, pollution control and pollution governance are obtained from remote
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sensing data, environmental monitoring data and other industry statistics, in addition
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to relevant research results, such as habitat quality index and per capita ecological
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footprint.
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The surface water quality index (MEP, 2016) represents the city water quality index (CWQI) (Eq. (1))
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Where
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=
×
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(
)
×
(1)
represents the water quality of the city, !"
represents
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the water quality of rivers,
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represents the number of cross sections of a river, and N represents the number of lake
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monitoring points.
represents the water quality of lakes, M
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The water pollution intensity is the ratio of industrial chemical oxygen demand
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(COD), ammonia nitrogen, and gross domestic product (GDP). The air pollution
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intensity is the ratio of industrial SO2, NOx, ash, and GDP, and the environmental risk
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index is based on the national emergency plan for environmental emergencies (Eq.
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(2)).
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ERI =−3×A−2×B−1×C−0.5×D
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where ERI represents the environmental risk index, and A, B, C, and D represent
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the number of mega environmental events, the number of significant environmental
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events, the number of environmental events, and the number of general environmental
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events, respectively.
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(2)
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The ecological quality index (Eq. (3)) (MEP, 2015) and the per capita ecological
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footprint (Hu and He, 2000) (Borucke et al., 2013) (Eq. (4)) originate from scientific
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research studies.
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EI=511.26×
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(0.35×Aforest+0.21×Agrass+0.28×Awet+0.11×Acrop+0.05×Aunused)/S
(3)
where EI represents the ecological quality index, Aforest represents the area of
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forest, Agrass represents the area of grassland, Awet represents the area of wetland, Acrop
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represents the area of cropland, Aunused represents the area of unused land, and S
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represents the total area.
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EF = ∑()* &' +
(4)
where EF represents the ecological footprint (ha), i represents the land use types
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(including fossil energy land, arable land, grassland, forest, construction land, waters),
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EPi represents the global annual average ecological productivity (kg/ha), Ci represents
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the resource consumption, and EQi is equal to a quantization factor.
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The remainder of the index data were obtained from the statistical yearbook. 2.4. Methodology
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Data standardization is conducted scale data in a small specific interval, which
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should be used in specific comparison and evaluation indexes that makes it easier to
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compare and weigh the indexes of different units. In this article, three types of
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standardization methods were used: Extreme standardization, Single-benchmark, and
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Double-benchmark.
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(1) Extreme standardization
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Extreme standardization is a linear transform of the original data, which restrict
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the results to the interval [0,1]. To compare the results to the other standardization
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method, the interval is expended 100 times, e.g. [0,100]. the specific formula is as
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follows:
,- =
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/ 0 1234 (/ 0 )
2 67/ 0 81234 (/ 0 ) . 2 67/ 81/ 0 0 2 67/ 0 81234 (/ 0 )
, :ℎ<= > - ?@ A BC@?D?E< ?=F?GADCH
, :ℎ<= > - ?@ A =
(5)
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where, Aij represents the value of the indicator data after normalization, Xij
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represents the original value of the indicator before normalization, max(Xij) represents
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the maximum value of the indicator before normalization; min(Xij) represents the
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minimum value of the indicator before normalization, i represents the year, and j
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represents the indicator’s sequence.
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(2) Benchmark progressive method
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The transformed data are used to calculate performance indicators. A
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proximity-to-target methodology was used previously (Hsu et al. 2016) (Fig. 1),
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which assesses how close each city is to an identified policy target (see specific see
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Equations (6) and (7)). The targets are high performance benchmarks that are
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primarily defined by international or national policy goals or established scientific
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thresholds. The benchmarks for air the quality index, for example, are based on air
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quality standard targets established by the China National Environmental Monitoring
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Centre, the benchmarks of which are widely accepted. Thereby, to confirm how close
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or far cities are to passing level and excellence level, two targets were set in this
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methodology, one of them is passing target, and the other one is excellent target,
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which is called “Double-Benchmark” (Fig. 2). The target is specified in Equation (8)
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A3K
MNO
= LP(MNO)
, 0 ≤ X3K ≤ S(X3K )
1, X 3K ≥ S(X3K )
1− A3K = d
MNO 1P7MNO 8 P7MNO 8
, X3K is the positive indicator (6)
, X3K ≥ S(X3K )
1, 0 ≤ X3K ≤ S(X3K )
A3K = 7X3K − S!(M3K) 8 × 7P
(Pf 1P )
f(gNO) 1P (gNO) 8
, X3K is the positive indcator (7) + S! (8)
if A3K < 0, the value is 0; if A3K > 100, the value is 100
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where A3K represents the value of the indicator data after normalization, X3K
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represents the original value of the indicator before normalization; Sij represents the
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benchmark in Sing-benchmark method, which is set to the A value of the indicator in
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this study; Sj(M3K) represents the benchmark A of the indicator, S!(M3K) represents
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benchmark C of the indicator; Sj represents the value of the indicator corresponding
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corresponding to benchmark C (90 points);
to benchmark A (60 points), and S! represents the value of the indicator (Pf 1P )
7Pf(gNO) 1P (gNO) 8
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represents the change in
the value along with the increase or decrease in each indicator, where i represents the
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year, and j represents the indicator’s sequence.
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Please insert Fig. 1 here
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Please insert Fig. 2 here
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(3) Selection criteria for data in the ECI
The selection of values A and C is mainly based on relevant industry standards in
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China. Foreach department, the relevant planning, or other requirements are set by the
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government, the status quo of the cities is indicated at the same level of development
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in China and abroad, as well as Word Bank data. For several indicators with no
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reference, the target value can be set according to the statistical distribution feature of
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each index. Since a high-performance benchmark can be determined through an
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analysis of the best-performing cities, the original data were selected at 60% and 90%
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of the overall position of the indicator’s value (Table 2).
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Please insert Table 2 here
(4) Calculation the ECI The ECI including three levels: nation-wide, province-wide, and city-wide.
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Based on the year of assessment in 2015, with 337 provinces (autonomous regions /
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municipalities) as the evaluation unit, this study adopted the standardized method as
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dimensionless normalization, using the analytic hierarchy process (Gan et al., 2017) to
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assign weight to each index, and the comprehensive weighted index method to
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evaluate the development level of eco-civilization. Finally, the levels of
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B-grade (70≤K＜80), (60≤K＜70), and D-grade (K＜60). According to Kába 2010,
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an overall index was constructed that aggregates the information provided by all the
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indicators considered. The province-wide ECI is the mean of all cities in that province
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and the national-wide ECI is the mean of all cities in China (337 cities). The formula
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for the calculation of ECI are shown in the followings:
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city-wide ECI：
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(9)
province-wide ECI： EcoP =
∑o pq & n r
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EcoC = ∑()* , ∙
(10)
m represents the number of provinces. ECI of China： EcoN =
0
∑ pq & n -
j represents the number of cities.
(11)
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Please insert Table 3 here
(5) Principle component analysis
Normalized principle component analysis (PCA) was performed (Grimaldi et al.,
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2014) to account for functional divisions (Table 4) and urban agglomerations (Table
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5). The separation of functional divisions was investigated according to (Jie, 2015).
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All metrics were in accord with normal distribution through the normal distribution
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detection (Shapiro–Wilk’s normality test P<0.05).
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3. Results and Discussions
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3.1. ECI evaluation using Single-Benchmark progressive, Double-Benchmark
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progressive, and range method
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The results of the ECI evaluation provided an account of eco-civilization
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development levels and are available for each city of China (337 cities) (Appendix 1).
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results are not significantly different among the three methods, and the ranking of the
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cities from the three methods were similar. The mean value and SD of ECI of 337
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cities from the DBD method are higher than that from RM and SBD methods (Table
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4). From the analysis of possibility density function (PDF), the curve of ES and SBD
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almost coincide, which indicates that the results from ES and SBD have little
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discrepancy and the results were higher than from the other two methods.
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The ES and SBD methods were widely used by many researchers (Phillis et al.
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2017) (Pollesch and Dale, 2016). They indicated that DBD method are reliable to
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evaluate ECI, and they provide information of the situation of environment, economy,
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life of people, and infrastructure of each city. Thereby the scores calculated by DBD
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method can indicates that how close or far cities are to passing targets and excellence
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targets.
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Please insert table 5 here
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Please insert Fig. 3 here
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3.2. Analysis city-wide eco-civilization in China According to the evaluation (Table 6), the mean of 337 cities’ ECI score is 61.16,
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as the China’s overall ECI score, which is classified as C-grade. Only one of the
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city-level regions (Huangshan) achieved A-grade (prefecture-level city and
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province-level municipality), 43 cities obtained B-grade, 150 cities obtained C-grade,
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and 143 cities were D-grade. In total, 42.43% of China’s cities failed this evaluation
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(below to the C-grade). 167 cities’ scores were higher than average (61.16), and 194
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cities achieved the passing level. These results indicate that the overall ECI was
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obviously lower than the national target and international level.
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The green life domain had the highest score ((mean = 64.48, grade C), followed
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by the green production (mean = 63.38, grade C), green instrument (mean= 63.30
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grade C) and green environment domain (mean =57.17, grade D). Only 16 cities, 4.75%
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of China’s cities, obtained A-grade of green environment, and 180 cities were D-grade
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which was below the passing-level (Table 6). In summary, the ecological environment
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was the short board of the ecological civilization development level of China.
Please insert Fig. 4 here The spatial distribution shows that, the ECI in the southeast is superior to that of
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the northwest, which is correspond to the report of Yan et al. (2015); the southeast has
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an obvious advantage of economic efficiency and green instrument, while the
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northwest shows a pattern of economic weakness. Additionally, due to the higher
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forest stand quality and climatic conditions, the high scoring cities of green
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environment were mainly in the south and northeast of China. The high scores cities
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of green life were mainly in the east and middle, and Tibet, which indicates more
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coordinated development between urban and rural, as well as humans and naturel. The
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high scoring cities of green production and green instrument were much more
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scattered over the nation, which indicated that green development and environment
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governance were became national consensus, otherwise, a set of steps had been taken.
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Please insert Fig. 5 here
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According to the PCA analysis, the first two axis of a PCA performed on the CEI
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metrics accounted for 65.83% of the total variance. The first PCA axis (38.59% of
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total variance) contrasted key ecological functional areas and main production area of
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agricultural products, whereas the second PCA axis tended to optimal development
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zone and key development zone. The key ecological areas and main production of
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agricultural products were associated with higher green environment scores. In
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contrast, higher green production scores and green instrument scores were observed in
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optimal both the development zone and the key development zone.
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Please insert Fig. 6 here
Fig. 7 shows a relationship between cities’ GE score and economic development
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(GDP per capita) of 74 main cities in China suggesting that most of the cities are
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suffer from an imbalance between environmental and the economic development. The
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top ten cities of ECI are come from Zhejiang (six cities), Fujian (one city),
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Guangdong province (two cities) and Hunan (one city) (shown in green of Fig. 6).
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Green environment scores are dispersed in their relationship with GDP per capita. 36
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35.58 (Beijing) to 73.93 (Hangzhou). Lishui (located in Zhejiang province) is the only
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city, whose environment score (85.88) and GDP per capita score (83.76) are both high
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among the 74 cities. Many wealthy cities, such as Beijing, Tianjin, Zhenzhou,
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Hohehot, Shenyang, Zhenjiang, and Jiaxing (GE scores below 40), underperform on
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environmental performance relative to similar economic peers. It is indicated that as
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cities develop, more focus and efforts should be paid to environmental improvement
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and ecologic preservation.
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4. Conclusions and implications
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4.1. Conclusions
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Please insert Fig. 7 here
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The purpose of this paper, was to address a set of regulating and supporting ECI
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on each city in China. We analyzed the ecology quality, environment quality, economy,
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life of urban and rural people, and environmental protection measures and instruments
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of cities in China, using a new standardized method (the Double benchmark
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progressive).
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The main results suggest that the advantage of the Double Benchmark
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Progressive method is that every evaluation score has its practical meaning. Not only
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can the score of different cities be compared but each index score in this index system
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can be compared with each other. Furthermore, with a passing line, the
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eco-civilization level of the city can be easily classified, which is suitable for policy
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making.
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These evaluation results suggest that the overall ECI was obviously lower than
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both the national target and international level and indicated ecology and environment
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quality are the short board of development of China eco-civilization. As green
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environmental scores are dispersed in their relationship with GDP per capita, many
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wealthy cities underperform on environmental performance relative to their economic
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development. The construction between the environment and the development of
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economy development was acute and imbalance in many cities of China.
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efficiency, carbon dioxide emissions and the soil environment quality. Thus, the
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ecological civilization development level of the whole area cannot be fully improved.
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Therefore, it is an important measure that can promote the construction of ecological
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civilization in China to strengthen the statistics of relevant ecological civilization
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indicators and strengthening the accounting of ecological assets.
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4.2. Implications
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Ecology and environmental quality were still the short board of China’s
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eco-civilization, despite measures that have been taken. This indicates that
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environment improvement lags behind protection measures. As much of research have
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reported the water shortage (Gong et al., 2018) and air pollution (Li et al., 2018), it
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has become an urgent issue of the pollution and resources shortage in the north China
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plain recent years. In this evaluation, the results also showed that eco-civilization
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development was at an imbalance among the cities of China. Cities in the south and
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east obtained higher scores than in the north and west, and the North China Plain
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gathered most lower score cities. In the North China Plain, enormous population
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pressure, climatic conditions that are not conducive to the spread of pollution, lack of
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water resources, and a non-environmentally friendly industrial structure are be
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responsible for the worse eco-civilization development performance. Therefore,
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eco-civilization development performed worse in the main production area of
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agricultural products than in the other three functional zone, according to this
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evaluation, which indicates environment quality and living conditions of rural people
337
need be improved. Several of the key ecological functional zone showed low score of
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eco-civilization, especially in the west of China, where appropriate policies need be
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implemented, such as ecological compensation and ecological emigration. In terms of
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the optimal development zone, air quality and water quality are the most important
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problems, which need more efficient measurements. It is worth noting that surface
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water quality score was much lower than air quality score, which means that water
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pollution was an urgent problem in most cities of China. As the air pollution issue
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received more focus, surface water quality must not be ignored.
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Acknowledgments This research was supported by the Construction and application demonstration
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of regional ecological quality comprehensive monitoring technology system
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(2017YFC0503806) and the Strategic Issues on Eco-Civilization Construction
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(2015-ZD-16-05-01) program. We are grateful to China National Environmental
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Monitoring Centre for providing data support.
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Gan, X., et al., 2017. "When to use what: Methods for weighting and aggregating
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of ecological civilization. Studies Dialectics Nature 25, 114e118 (in Chinese).
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Zhu, C.G., Jiang, B., 2009. The theoretical construction and positive test of the index
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Table 1 Indicator system and weight ratio of the eco-civilization index
409
Table 2 Selection criteria of targets
410
Table 3 Classification of eco-civilization development levels
411
Table 4 Information of functional divisions
412
Table 5 Statistical analysis on ECI of different methods
413
Table 6 Results of ECI of cities. (the score is average of the 337 cities’ evaluation
414
results)
415
Fig. 1 Single-benchmark method
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Fig. 2 Double-benchmark method
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Fig. 3 Possibility density function of ECI on different methods.
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Fig. 4 ECI classification distribution of China
419
Fig. 5 Four theme scores distribution of China. (a) green environment scores
420
distribution; (b) green production scores distribution; (c) green life scores distribution;
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(d) green instrument scores distribution
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Fig. 6 Results of normalized principal component analysis (PCA) on the four
423
functional zone metrics at the city scale. (a): correlation of variables with the first-two
424
PCA axes (each arrow points in the direction of highest value for a given variable); (b)
425
distribution of variance among PCA axes; (c) associated PCA factorial map plots
426
grouped by functional zone classes. (GE: green environment, GP: green production,
427
GL: green life style, GI: green instruments, ODZ: optimal development zone, KDZ:
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key development zone, APZ: main production area of agricultural products, EFZ:
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ecological functional zone)
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Fig. 7 GDP per capita score of the region versus green environment score. (the top-ten
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of ECI in 74 cities are shown in green)
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ACCEPTED MANUSCRIPT Table 1 Indicator system and weight ratio of eco-civilization index Quota
Unit
Ecological quality index (0.50)
Ecological land quality (EQI) (1.00)
/
Air quality index (AQI) (0.50)
/
Surface water quality index (CWQI)* (0.50)
/
Per capita GDP (per GDP) (0.50)
yuan
Proportion of the tertiary industry’s added value (PTI) (0.50)
%
GDP per constructive land (GPCL) (0.30)
yuan per square kilometer
Water pollution intensity (WPI) (0.25)
kg/ten thousand yuan
Environmenta l quality index (0.50) Industry optimization index (0.60)
Green Production (0.25)
Industry efficiency index (0.40)
Air pollution intensity (API) (0.25)
kg/ten thousand yuan
Fertilizer per crop area (FPCA) (0.20)
ton/ha
Urbanization (UZT) (0.30)
%
Per capita disposable income of urban residents (PCDI) (0.30)
yuan
Income proportion of urban and rural residents (IPUR) (0.40)
/
Per capita park green area (PCPA) (0.45)
ha/ten thousand people
Green coverage rate of built-up area (GCRB) (0.55)
%
Per capita ecological footprint (PCEF) (1.00)
globe ha
Urban waste water treatment rate (WWT) (0.50)
%
Harmless treatment rate of urban solid waste (TRSW) (0.50)
%
Natural reserve area ratio (NRAR) (0.40)
%
Decrease rate of unit GDP energy consumption (DEC) (0.60)
%
Environmental risk events (ERE)
/
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Urban-rural coordination index (0.35)
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Green Environment (0.40)
Index
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Theme
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Green Life (0.15)
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Urban human settlements index (0.35)
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Green consumption index (0.30)
Green Infrastructure (0.20)
Pollution control index (0.50) Construction performance index (0.50)
Regulation index 433 434
*data listed in parentheses indicate weight ratio
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Table 2 Selection criteria of targets Units
Targets
Targets basis
1
Ecological land quality
/
A：80
Statistical distribution characteristics
2
Air quality index
/
C：50 A：50
Air Quality Standards (GB3095-2012)
3
Surface water quality index
/
C：100 A：10
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Surface Water Environmental Quality Standard (GB3838-2002) and Statistical distribution characteristics
Per capita GDP
RMB
A：60000
5
Proportion of the tertiary industry’s added value
%
C：20000 A：60
GDP per constructive land
RMB/m2
Division of Five types of regions and economies according to the Word Bank A value is the proportion of the tertiary industry’ added value of the high-income country and C value originates from the proportion of the third industry in the later period of industrialization
C：40 A：520 C：270
Statistical distribution characteristics
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C：20 4
Water pollution intensity
g/RMB
A：0.015
Statistical distribution characteristics
8
Air pollution intensity
g/RMB
C：0.04 A：0.2
Statistical distribution characteristics
9
Fertilizer per crop area (FPCA)* Urbanization
t/hm2
C：0.5 A：0.18
%
C：0.45 A：80
Per capita disposable income of urban residents
RMB
11
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7
10
C：60 A：100000 C：18000
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Indicators
A value originates from the high-income country and C value originates from the Action plan for zero increase in fertilizer use by 2020 by Chinese Ministry of Agriculture
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A value originates from 13th Five-Year planning of China’s national economy and C value originates from high-income country standard by the World Bank A value originates from the high-income country and C value originates from the basic standards for building a well-off society in overall completion
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Income proportion of urban and rural residents
/
A：1.8
Per capita park green area
m2
Green coverage rate of built-up area
Targets basis Statistical distribution characteristics
C：2.2
%
C：7.5
A value originates from the Indicators of national ecological civilization construction demonstration city and C value originates from Evaluation Standard of urban landscape greening of China (GB50563-2010)
A：40
Evaluation Standard of urban landscape greening of China (GB50563-2010)
A：13
C：36 15
Per capita ecological footprint
globe ha
A：1
Statistical distribution characteristics
C：2 %
A：95 C：85
Harmless treatment rate of urban solid waste
%
18
Natural reserve area ratio
%
A：20
19
Decrease rate of unit GDP energy consumption
%
C：12 A：9
436
A：95 C：85
New national urbanization planning (2014-2020)
A value originates from the Indicators of national ecological civilization construction demonstration city and C value originates from the high-income country
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A value originates from the New national urbanization planning (2014-2020); C value originates from the Indicators of national ecological civilization construction demonstration city
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Urban waste water treatment rate
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Targets
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Units
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Indicators
C：3.9
A value originates from the Action plan for energy saving and low carbon development (2015-2015) and C value was defined according to statistical distribution characteristics
ACCEPTED MANUSCRIPT Table 3 Classification of eco-civilization development levels Gradation
Standard
Situation of eco-civilization development
A
K≥80
The development level of ecological civilization is excellent. All fields can be in the leading level of China, or can reach the advanced level of the world, without obvious short boards.
70＜K≤60
D
438
The development level of ecological civilization is good. Most of the indicators reach the advanced level of China, but there are still obvious deficiencies and constraints in some aspects.
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C
80＜K≤70
The level of the development of ecological civilization has reached the standard, and all fields are basically able to meet the requirements of the state, but the development of various field is not balanced, and there are still large gaps in several of the indicators.
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B
K <60
The development of ecological civilization was below the standard, and there are prominent shortcomings or constraints in various fields.
*K is the score of ECI
439
Table 4 Information of functional divisions
Number of cities Proportional Area (%)
Main production areas of agricultural products
Key ecological functional zone
26
98
105
109
2.03
12.51
21.65
63.82
10.58
31.77
34.81
22.84
9.72
5.71
3.80
3.91
74.05
59.34
47.44
46.21
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Population proportion (%) Per capita GDP (ten thousand yuan) Urbanization (%)
Key development zone
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Optimal development zone
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440
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437
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Table 5 Statistical analysis on ECI of different methods Methods
n
Mean
Median
Maximum
Minimum
Std. Deviation
337
56.73
56.58
74.51
37.27
6.78
SBM
337
56.73
56.58
74.52
37.27
6.78
DBM
337
61.40
61.40
81.22
38.46
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ES
7.51
442
*ES: extreme standardization, SBM: single-benchmark progressive standardization, DBM: double
443
benchmark progressive standardization, Std. Deviation: Standard Deviation
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Table 6 Results of ECI of cities. (the score is average of the 337 cities’ evaluation
446
results) Score Green
57.17
Number (%)
Environment Green
63.38
Number (%)
Green
64.48
Life
Instrument
61.16
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ECI
63.30
B-grade
C-grade
D-grade
16
69
72
180
4.75
20.47
21.36
53.41
39
39
109
150
11.57
11.57
32.34
44.50
Number
12
104
115
106
(%)
3.56
30.86
34.12
31.45
Number
25
93
106
116
(%)
7.42
27.60
30.56
34.42
Number
1
43
150
143
(%)
0.30
12.76
44.51
42.43
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Green
A-grade
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Production
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445
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Fig. 1 Single-benchmark method
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449
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447
M AN U
450 451
Fig. 2 Double-benchmark method
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452
453 454
Fig 3. Possibility density function of ECI on different methods. ES: extreme
455
standardization; DBM: double-benchmark method.
Fig. 4 ECI classification distribution in China
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(c)
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(b)
458 459
Fig. 5 Four theme scores distribution in China. (a) green environment scores
461
distribution; (b) green production scores distribution; (c) green life scores distribution;
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(d) green instrument scores distribution
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(a)
GE
(c)
GL GP
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GI
EFZ
ODZ
KDZ
(b)
APZ
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Eigenvalues
Scores and classes
Fig 6. Results of normalized principal component analysis (PCA) on the four functional zone metrics at the city scale. (a) correlation of variables with the first-two
TE D
PCA axes (each arrow points in the direction of highest value for a given variable); (b) shows the distribution of variance among PCA axes; (c) associated PCA factorial map plots grouped by functional zone classes. (GE: green environment, GP: green
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production, GL: green life style, GI: green instruments, ODZ: optimal development zone, KDZ: key development zone, APZ: main production area of agricultural
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products, EFZ: ecological functional zone)
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Fig 7. GDP per capita score of the region versus green environment score. (the top-ten
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of ECI in 74 cities shown green)
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Highlights: China’s overall level of the ecological civilization development is not optimistic. China’s ecological civilization development is uneven in spatial distribution. Ecology and environmental quality were still the short board of China’s Eco-civilization construction. The ecological civilization development in the main production areas of agricultural products lags other functional areas.
S0959-6526(19)30572-4
DOI:
https://doi.org/10.1016/j.jclepro.2019.02.173
Reference:
JCLP 15903
To appear in:
Journal of Cleaner Production
Received Date: 9 October 2018 Revised Date:
12 February 2019
Accepted Date: 16 February 2019
Please cite this article as: Zhang L, Yang J, Li D, Liu H, Xie Y, Song T, Luo S, Evaluation of the ecological civilization index of China based on the double benchmark progressive method, Journal of Cleaner Production (2019), doi: https://doi.org/10.1016/j.jclepro.2019.02.173. This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.
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Title page
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Evaluation of the Ecological Civilization Index of China based on the
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Double Benchmark Progressive Method
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Linbo Zhanga, Jiao Yanga*, Daiqing Lia, Haijiang Liub, Yuxi Xiec, Ting Songa,
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Shanghua Luoa
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a Chinese Research Academy of Environmental Sciences, Beijing 100012, China
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b China National Environmental Monitoring Center, Beijing 100012, China
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c Beijing Normal University, Beijing 100875, China
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*Corresponding author. Tel.: +86-010-84915171. E-mail: [email protected]
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Daiqing Li ([email protected]);
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Haijiang Liu ([email protected]);
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Yuqian Xie ([email protected]);
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Ting Song ([email protected]);
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Shanghua Luo ([email protected])
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Zhang linbo ([email protected]);
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Evaluation of the Ecological Civilization Index of China based on the
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Double Benchmark Progressive Method
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Abstract: Since 2013, the Chinese government has issued a set of action plans
4
catered toward the ecological civilization construction. To quantify and evaluate the
5
ecological civilization development, this study has introduced the ecological
6
civilization index of China (ECI), using different standardization methods (extreme
7
standardization, single-benchmark progressive standardization, and double benchmark
8
progressive standardization), to evaluate ecological civilization development on
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city-level (337 cities) of China.
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The issues of China’s ecological civilization
construction were analyzed. The evaluation results suggest that in the double
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benchmark progressive method, every evaluation score has its practical meaning and
12
is therefore suitable for policy making. This study obtained an average ECI score of
13
61.16, for 337 cities. This is China’s overall ECI score, which is classified as C-grade.
14
Only 4.75% of cities’ green environment (GE) score obtained A-grade, while 53.41%
15
of cities’ GE scored below the passing level, suggesting that the quality of the ecology
16
and environment is neglected in the development of China eco-civilization. This study
17
provides valuable knowledge for both the ecological and environment management
18
and eco-civilization construction.
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Keywords: eco-civilization, eco-civilization index of China, eco-civilization
20
development evaluation, double-benchmark progressive method
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1. Introduction
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Eco-civilization construction was introduced by the Chinese government in the
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edition on the sustainable development of China, which is on the premises of ecology
24
preservation and environment improvement to strengthen the economic and social
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development. Since 2013, the Chinese government has issued a set of action plans
26
catered toward ecological civilization construction. Since eco-civilization construction
27
has been written into the Constitution of the People’s Republic of China,
28
eco-civilization construction is a long-term and national task (Xi, 2018). However, it
29
remains unclear how the ecological civilization development can be quantified and
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31
eco-civilization indices in 2010, where they provided the corresponding results on the
32
ecosystem vitality, environmental quality, social development, and the total
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eco-civilization progress of provinces during 2005-2008. Subsequently, Yan et al.
34
(2015) published the Annual Report on China’s Provincial Eco-Civilization Index
35
(ECI 2015), where they point out significant differences in the level of ECI among
36
provinces, the overall decline of the national ECI. In the evaluation report, Hainan
37
province achieved the highest score, and Hebei province showed the lowest score.
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The authors of the Green Development Index Report of China—Regional Comparison,
39
which considers green economic growth, resources and environment, and government
40
policy, have proposed a green development index for the evaluation of the sustainable
41
development potential (Li et al., 2014; Li et al., 2017). In the report, 30 province-level
42
regions and 100 cities have been evaluated, the results showed that Beijing achieved
43
the highest score of the 30 provinces, while Haikou achieved the highest score among
44
the 100 cities. In response to the need for unified assessment of eco-civilization
45
performance of different regions, the Green Development Index System and the
46
Assessment Target of Ecological Civilization Construction (NDRC 2016), a tool for
47
assessing government performance, proposed by the Chinese government. Because of
48
shortage of data, the evaluation just only been reported on provincial-wide.
49
Meanwhile, several researches have evaluating the trends of China’s ecological
50
civilization construction using a novel indicator system, with the aim to elaborate to
51
the psychological problem and conflict between humans and the environment (Zhang
52
et al. 2017). So far, most of reports analyzed the eco-civilization on national scale,
53
province scale, as well as a part of cities, with extreme standardization method for
54
normalization. However, due to the large differences in geography, economy, and
55
social as well as cultural history of China, different regions exhibit different levels of
56
eco-civilization. It is important to evaluate the eco-civilization level on a city-wide,
57
even county-wide level, and make it a long-term focus to reflect the regional or
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national eco-civilization development level.
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Normalization is an indispensable step in comprehensive assessment. Extreme
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61
Yan et al., 2010; Yeo, 2000) have often been used by scholars. Several studies realized
62
normalization and dimensionless through weighting and aggregation, using methods
63
such as Criteria Importance Through Intercriteria Correlation (CRITIC) (Jahan, 2012)
64
Among these methods, the threshold method using Benchmarking evaluation of each
65
upper and lower limits, builds the evaluation of indicators’ realization degree for
66
nondimensional treatment, which can yield practical meaning for evaluation. Each
67
score from the evaluation with the benchmarking method conveys the practical
68
meaning and policy issues, as well as easy to interannual comparison.
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In this article, we set an eco-civilization indicator framework to evaluate the
70
level of ecological civilization development in 337 cities (not including the Hong
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Kong Special Administrative Region, Macau Administrative regions, and Taiwan) in
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China in 2015. In addition, a standardization method was proposed, and named the
73
double-benchmark progressive method (DBD), to investigated the differences among
74
this method, the standard deviation method, and the single-benchmark progressive
75
method. Our specific objectives were: (1) to compare different standardization
76
methods; (2) to set up the ecological civilization index of China (ECI) and to evaluate
77
ecological civilization development on city-level of China; (3) to analyze and reveal
78
issues of China’s ecological civilization construction.
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2. Indicators framework, methodology and data
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2.1. The ECI Framework
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The ecological civilization index (ECI) is composed of 20 indicators that reflect
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city-level environmental, economic and social data. These indicators are combined
83
into four themes (green environment, green production, green lifestyle, and green
84
instrument). Each of the themes combines one to three indexes, and an additional a
85
regulation index (Environmental risk events), allover nine indexes (ecological quality
86
index, environmental quality index, industry optimization index, industry efficiency
87
index, urban-rural coordination index, green consumption index, pollution control
88
index, construction performance index, and regulation index).
89
2.2.Weight of the indicators
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91
These weightings are generally set according to 30 experts who are engaged on
92
ecology, economy, and environment protection research. Since the weight ratio should
93
fit the key purpose of the ecological civilization construction policy issue,
94
environmental health and ecology quality should be more important than other
95
indicators in this evaluation. Therefore, slight adjustments to integrate this weighting
96
have be made. The detailed weight ratios are shown in Table 1.
97
Please insert Table 1 here 2.3. Data
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The ECI uses primary and secondary data from multilateral organizations,
100
government agencies, and academic collaborations. The original data of the
101
socio-economic indicators are given from the statistical data of China's Statistical
102
Yearbook and the Urban Statistical Yearbook. The statistics on environmental
103
protection, pollution control and pollution governance are obtained from remote
104
sensing data, environmental monitoring data and other industry statistics, in addition
105
to relevant research results, such as habitat quality index and per capita ecological
106
footprint.
107
The surface water quality index (MEP, 2016) represents the city water quality index (CWQI) (Eq. (1))
109
Where
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=
×
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(
)
×
(1)
represents the water quality of the city, !"
represents
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the water quality of rivers,
112
represents the number of cross sections of a river, and N represents the number of lake
113
monitoring points.
represents the water quality of lakes, M
114
The water pollution intensity is the ratio of industrial chemical oxygen demand
115
(COD), ammonia nitrogen, and gross domestic product (GDP). The air pollution
116
intensity is the ratio of industrial SO2, NOx, ash, and GDP, and the environmental risk
117
index is based on the national emergency plan for environmental emergencies (Eq.
118
(2)).
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ERI =−3×A−2×B−1×C−0.5×D
120
where ERI represents the environmental risk index, and A, B, C, and D represent
121
the number of mega environmental events, the number of significant environmental
122
events, the number of environmental events, and the number of general environmental
123
events, respectively.
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(2)
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The ecological quality index (Eq. (3)) (MEP, 2015) and the per capita ecological
125
footprint (Hu and He, 2000) (Borucke et al., 2013) (Eq. (4)) originate from scientific
126
research studies.
127
EI=511.26×
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(0.35×Aforest+0.21×Agrass+0.28×Awet+0.11×Acrop+0.05×Aunused)/S
(3)
where EI represents the ecological quality index, Aforest represents the area of
130
forest, Agrass represents the area of grassland, Awet represents the area of wetland, Acrop
131
represents the area of cropland, Aunused represents the area of unused land, and S
132
represents the total area.
134
EF = ∑()* &' +
(4)
where EF represents the ecological footprint (ha), i represents the land use types
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(including fossil energy land, arable land, grassland, forest, construction land, waters),
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EPi represents the global annual average ecological productivity (kg/ha), Ci represents
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the resource consumption, and EQi is equal to a quantization factor.
139 140
The remainder of the index data were obtained from the statistical yearbook. 2.4. Methodology
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Data standardization is conducted scale data in a small specific interval, which
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should be used in specific comparison and evaluation indexes that makes it easier to
142
compare and weigh the indexes of different units. In this article, three types of
143
standardization methods were used: Extreme standardization, Single-benchmark, and
144
Double-benchmark.
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(1) Extreme standardization
146
Extreme standardization is a linear transform of the original data, which restrict
147
the results to the interval [0,1]. To compare the results to the other standardization
ACCEPTED MANUSCRIPT 148
method, the interval is expended 100 times, e.g. [0,100]. the specific formula is as
149
follows:
,- =
151
/ 0 1234 (/ 0 )
2 67/ 0 81234 (/ 0 ) . 2 67/ 81/ 0 0 2 67/ 0 81234 (/ 0 )
, :ℎ<= > - ?@ A BC@?D?E< ?=F?GADCH
, :ℎ<= > - ?@ A =
(5)
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where, Aij represents the value of the indicator data after normalization, Xij
153
represents the original value of the indicator before normalization, max(Xij) represents
154
the maximum value of the indicator before normalization; min(Xij) represents the
155
minimum value of the indicator before normalization, i represents the year, and j
156
represents the indicator’s sequence.
157
(2) Benchmark progressive method
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The transformed data are used to calculate performance indicators. A
159
proximity-to-target methodology was used previously (Hsu et al. 2016) (Fig. 1),
160
which assesses how close each city is to an identified policy target (see specific see
161
Equations (6) and (7)). The targets are high performance benchmarks that are
162
primarily defined by international or national policy goals or established scientific
163
thresholds. The benchmarks for air the quality index, for example, are based on air
164
quality standard targets established by the China National Environmental Monitoring
165
Centre, the benchmarks of which are widely accepted. Thereby, to confirm how close
166
or far cities are to passing level and excellence level, two targets were set in this
167
methodology, one of them is passing target, and the other one is excellent target,
168
which is called “Double-Benchmark” (Fig. 2). The target is specified in Equation (8)
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A3K
MNO
= LP(MNO)
, 0 ≤ X3K ≤ S(X3K )
1, X 3K ≥ S(X3K )
1− A3K = d
MNO 1P7MNO 8 P7MNO 8
, X3K is the positive indicator (6)
, X3K ≥ S(X3K )
1, 0 ≤ X3K ≤ S(X3K )
A3K = 7X3K − S!(M3K) 8 × 7P
(Pf 1P )
f(gNO) 1P (gNO) 8
, X3K is the positive indcator (7) + S! (8)
if A3K < 0, the value is 0; if A3K > 100, the value is 100
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173
where A3K represents the value of the indicator data after normalization, X3K
174
represents the original value of the indicator before normalization; Sij represents the
175
benchmark in Sing-benchmark method, which is set to the A value of the indicator in
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this study; Sj(M3K) represents the benchmark A of the indicator, S!(M3K) represents
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benchmark C of the indicator; Sj represents the value of the indicator corresponding
179
corresponding to benchmark C (90 points);
to benchmark A (60 points), and S! represents the value of the indicator (Pf 1P )
7Pf(gNO) 1P (gNO) 8
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represents the change in
the value along with the increase or decrease in each indicator, where i represents the
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year, and j represents the indicator’s sequence.
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Please insert Fig. 1 here
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Please insert Fig. 2 here
184
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(3) Selection criteria for data in the ECI
The selection of values A and C is mainly based on relevant industry standards in
186
China. Foreach department, the relevant planning, or other requirements are set by the
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government, the status quo of the cities is indicated at the same level of development
188
in China and abroad, as well as Word Bank data. For several indicators with no
189
reference, the target value can be set according to the statistical distribution feature of
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each index. Since a high-performance benchmark can be determined through an
191
analysis of the best-performing cities, the original data were selected at 60% and 90%
192
of the overall position of the indicator’s value (Table 2).
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Please insert Table 2 here
(4) Calculation the ECI The ECI including three levels: nation-wide, province-wide, and city-wide.
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Based on the year of assessment in 2015, with 337 provinces (autonomous regions /
197
municipalities) as the evaluation unit, this study adopted the standardized method as
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dimensionless normalization, using the analytic hierarchy process (Gan et al., 2017) to
199
assign weight to each index, and the comprehensive weighted index method to
200
evaluate the development level of eco-civilization. Finally, the levels of
ACCEPTED MANUSCRIPT eco-civilization development were classified into five grades, namely A-grade (K≥80),
202
B-grade (70≤K＜80), (60≤K＜70), and D-grade (K＜60). According to Kába 2010,
203
an overall index was constructed that aggregates the information provided by all the
204
indicators considered. The province-wide ECI is the mean of all cities in that province
205
and the national-wide ECI is the mean of all cities in China (337 cities). The formula
206
for the calculation of ECI are shown in the followings:
207
city-wide ECI：
211 212 213 214 215 216
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(9)
province-wide ECI： EcoP =
∑o pq & n r
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EcoC = ∑()* , ∙
(10)
m represents the number of provinces. ECI of China： EcoN =
0
∑ pq & n -
j represents the number of cities.
(11)
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Please insert Table 3 here
(5) Principle component analysis
Normalized principle component analysis (PCA) was performed (Grimaldi et al.,
218
2014) to account for functional divisions (Table 4) and urban agglomerations (Table
219
5). The separation of functional divisions was investigated according to (Jie, 2015).
220
All metrics were in accord with normal distribution through the normal distribution
221
detection (Shapiro–Wilk’s normality test P<0.05).
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Please insert Table 4 here
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3. Results and Discussions
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3.1. ECI evaluation using Single-Benchmark progressive, Double-Benchmark
225
progressive, and range method
226
The results of the ECI evaluation provided an account of eco-civilization
227
development levels and are available for each city of China (337 cities) (Appendix 1).
ACCEPTED MANUSCRIPT According to the T-test of the results of the ECI on different method, the calculated
229
results are not significantly different among the three methods, and the ranking of the
230
cities from the three methods were similar. The mean value and SD of ECI of 337
231
cities from the DBD method are higher than that from RM and SBD methods (Table
232
4). From the analysis of possibility density function (PDF), the curve of ES and SBD
233
almost coincide, which indicates that the results from ES and SBD have little
234
discrepancy and the results were higher than from the other two methods.
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The ES and SBD methods were widely used by many researchers (Phillis et al.
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2017) (Pollesch and Dale, 2016). They indicated that DBD method are reliable to
237
evaluate ECI, and they provide information of the situation of environment, economy,
238
life of people, and infrastructure of each city. Thereby the scores calculated by DBD
239
method can indicates that how close or far cities are to passing targets and excellence
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targets.
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Please insert table 5 here
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Please insert Fig. 3 here
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3.2. Analysis city-wide eco-civilization in China According to the evaluation (Table 6), the mean of 337 cities’ ECI score is 61.16,
245
as the China’s overall ECI score, which is classified as C-grade. Only one of the
246
city-level regions (Huangshan) achieved A-grade (prefecture-level city and
247
province-level municipality), 43 cities obtained B-grade, 150 cities obtained C-grade,
248
and 143 cities were D-grade. In total, 42.43% of China’s cities failed this evaluation
249
(below to the C-grade). 167 cities’ scores were higher than average (61.16), and 194
250
cities achieved the passing level. These results indicate that the overall ECI was
251
obviously lower than the national target and international level.
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The green life domain had the highest score ((mean = 64.48, grade C), followed
253
by the green production (mean = 63.38, grade C), green instrument (mean= 63.30
254
grade C) and green environment domain (mean =57.17, grade D). Only 16 cities, 4.75%
255
of China’s cities, obtained A-grade of green environment, and 180 cities were D-grade
256
which was below the passing-level (Table 6). In summary, the ecological environment
ACCEPTED MANUSCRIPT 257 258
was the short board of the ecological civilization development level of China.
Please insert Fig. 4 here The spatial distribution shows that, the ECI in the southeast is superior to that of
260
the northwest, which is correspond to the report of Yan et al. (2015); the southeast has
261
an obvious advantage of economic efficiency and green instrument, while the
262
northwest shows a pattern of economic weakness. Additionally, due to the higher
263
forest stand quality and climatic conditions, the high scoring cities of green
264
environment were mainly in the south and northeast of China. The high scores cities
265
of green life were mainly in the east and middle, and Tibet, which indicates more
266
coordinated development between urban and rural, as well as humans and naturel. The
267
high scoring cities of green production and green instrument were much more
268
scattered over the nation, which indicated that green development and environment
269
governance were became national consensus, otherwise, a set of steps had been taken.
270
Please insert Fig. 5 here
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According to the PCA analysis, the first two axis of a PCA performed on the CEI
272
metrics accounted for 65.83% of the total variance. The first PCA axis (38.59% of
273
total variance) contrasted key ecological functional areas and main production area of
274
agricultural products, whereas the second PCA axis tended to optimal development
275
zone and key development zone. The key ecological areas and main production of
276
agricultural products were associated with higher green environment scores. In
277
contrast, higher green production scores and green instrument scores were observed in
278
optimal both the development zone and the key development zone.
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Please insert Fig. 6 here
Fig. 7 shows a relationship between cities’ GE score and economic development
281
(GDP per capita) of 74 main cities in China suggesting that most of the cities are
282
suffer from an imbalance between environmental and the economic development. The
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top ten cities of ECI are come from Zhejiang (six cities), Fujian (one city),
284
Guangdong province (two cities) and Hunan (one city) (shown in green of Fig. 6).
285
Green environment scores are dispersed in their relationship with GDP per capita. 36
ACCEPTED MANUSCRIPT cities achieved 100 scores on GDP per capita, and their GE scores dispersed from
287
35.58 (Beijing) to 73.93 (Hangzhou). Lishui (located in Zhejiang province) is the only
288
city, whose environment score (85.88) and GDP per capita score (83.76) are both high
289
among the 74 cities. Many wealthy cities, such as Beijing, Tianjin, Zhenzhou,
290
Hohehot, Shenyang, Zhenjiang, and Jiaxing (GE scores below 40), underperform on
291
environmental performance relative to similar economic peers. It is indicated that as
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cities develop, more focus and efforts should be paid to environmental improvement
293
and ecologic preservation.
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4. Conclusions and implications
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4.1. Conclusions
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Please insert Fig. 7 here
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The purpose of this paper, was to address a set of regulating and supporting ECI
298
on each city in China. We analyzed the ecology quality, environment quality, economy,
299
life of urban and rural people, and environmental protection measures and instruments
300
of cities in China, using a new standardized method (the Double benchmark
301
progressive).
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The main results suggest that the advantage of the Double Benchmark
303
Progressive method is that every evaluation score has its practical meaning. Not only
304
can the score of different cities be compared but each index score in this index system
305
can be compared with each other. Furthermore, with a passing line, the
306
eco-civilization level of the city can be easily classified, which is suitable for policy
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making.
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These evaluation results suggest that the overall ECI was obviously lower than
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both the national target and international level and indicated ecology and environment
310
quality are the short board of development of China eco-civilization. As green
311
environmental scores are dispersed in their relationship with GDP per capita, many
312
wealthy cities underperform on environmental performance relative to their economic
313
development. The construction between the environment and the development of
314
economy development was acute and imbalance in many cities of China.
ACCEPTED MANUSCRIPT Due to the lack of data, our study does not reflect the energy utilization
316
efficiency, carbon dioxide emissions and the soil environment quality. Thus, the
317
ecological civilization development level of the whole area cannot be fully improved.
318
Therefore, it is an important measure that can promote the construction of ecological
319
civilization in China to strengthen the statistics of relevant ecological civilization
320
indicators and strengthening the accounting of ecological assets.
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4.2. Implications
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Ecology and environmental quality were still the short board of China’s
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eco-civilization, despite measures that have been taken. This indicates that
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environment improvement lags behind protection measures. As much of research have
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reported the water shortage (Gong et al., 2018) and air pollution (Li et al., 2018), it
326
has become an urgent issue of the pollution and resources shortage in the north China
327
plain recent years. In this evaluation, the results also showed that eco-civilization
328
development was at an imbalance among the cities of China. Cities in the south and
329
east obtained higher scores than in the north and west, and the North China Plain
330
gathered most lower score cities. In the North China Plain, enormous population
331
pressure, climatic conditions that are not conducive to the spread of pollution, lack of
332
water resources, and a non-environmentally friendly industrial structure are be
333
responsible for the worse eco-civilization development performance. Therefore,
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eco-civilization development performed worse in the main production area of
335
agricultural products than in the other three functional zone, according to this
336
evaluation, which indicates environment quality and living conditions of rural people
337
need be improved. Several of the key ecological functional zone showed low score of
338
eco-civilization, especially in the west of China, where appropriate policies need be
339
implemented, such as ecological compensation and ecological emigration. In terms of
340
the optimal development zone, air quality and water quality are the most important
341
problems, which need more efficient measurements. It is worth noting that surface
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water quality score was much lower than air quality score, which means that water
343
pollution was an urgent problem in most cities of China. As the air pollution issue
344
received more focus, surface water quality must not be ignored.
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Acknowledgments This research was supported by the Construction and application demonstration
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of regional ecological quality comprehensive monitoring technology system
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(2017YFC0503806) and the Strategic Issues on Eco-Civilization Construction
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(2015-ZD-16-05-01) program. We are grateful to China National Environmental
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Monitoring Centre for providing data support.
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Reference:
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Borucke, M., Moore, D., Cranston, G., et al., 2013. Accounting for demand and
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supply of the biosphere's regenerative capacity: The National Footprint Accounts’
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of ecological civilization. Studies Dialectics Nature 25, 114e118 (in Chinese).
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Zhu, C.G., Jiang, B., 2009. The theoretical construction and positive test of the index
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ACCEPTED MANUSCRIPT Capition Titles:
408
Table 1 Indicator system and weight ratio of the eco-civilization index
409
Table 2 Selection criteria of targets
410
Table 3 Classification of eco-civilization development levels
411
Table 4 Information of functional divisions
412
Table 5 Statistical analysis on ECI of different methods
413
Table 6 Results of ECI of cities. (the score is average of the 337 cities’ evaluation
414
results)
415
Fig. 1 Single-benchmark method
416
Fig. 2 Double-benchmark method
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Fig. 3 Possibility density function of ECI on different methods.
418
Fig. 4 ECI classification distribution of China
419
Fig. 5 Four theme scores distribution of China. (a) green environment scores
420
distribution; (b) green production scores distribution; (c) green life scores distribution;
421
(d) green instrument scores distribution
422
Fig. 6 Results of normalized principal component analysis (PCA) on the four
423
functional zone metrics at the city scale. (a): correlation of variables with the first-two
424
PCA axes (each arrow points in the direction of highest value for a given variable); (b)
425
distribution of variance among PCA axes; (c) associated PCA factorial map plots
426
grouped by functional zone classes. (GE: green environment, GP: green production,
427
GL: green life style, GI: green instruments, ODZ: optimal development zone, KDZ:
428
key development zone, APZ: main production area of agricultural products, EFZ:
429
ecological functional zone)
430
Fig. 7 GDP per capita score of the region versus green environment score. (the top-ten
431
of ECI in 74 cities are shown in green)
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ACCEPTED MANUSCRIPT Table 1 Indicator system and weight ratio of eco-civilization index Quota
Unit
Ecological quality index (0.50)
Ecological land quality (EQI) (1.00)
/
Air quality index (AQI) (0.50)
/
Surface water quality index (CWQI)* (0.50)
/
Per capita GDP (per GDP) (0.50)
yuan
Proportion of the tertiary industry’s added value (PTI) (0.50)
%
GDP per constructive land (GPCL) (0.30)
yuan per square kilometer
Water pollution intensity (WPI) (0.25)
kg/ten thousand yuan
Environmenta l quality index (0.50) Industry optimization index (0.60)
Green Production (0.25)
Industry efficiency index (0.40)
Air pollution intensity (API) (0.25)
kg/ten thousand yuan
Fertilizer per crop area (FPCA) (0.20)
ton/ha
Urbanization (UZT) (0.30)
%
Per capita disposable income of urban residents (PCDI) (0.30)
yuan
Income proportion of urban and rural residents (IPUR) (0.40)
/
Per capita park green area (PCPA) (0.45)
ha/ten thousand people
Green coverage rate of built-up area (GCRB) (0.55)
%
Per capita ecological footprint (PCEF) (1.00)
globe ha
Urban waste water treatment rate (WWT) (0.50)
%
Harmless treatment rate of urban solid waste (TRSW) (0.50)
%
Natural reserve area ratio (NRAR) (0.40)
%
Decrease rate of unit GDP energy consumption (DEC) (0.60)
%
Environmental risk events (ERE)
/
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Urban-rural coordination index (0.35)
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Green Environment (0.40)
Index
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Theme
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Green Life (0.15)
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Urban human settlements index (0.35)
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Green consumption index (0.30)
Green Infrastructure (0.20)
Pollution control index (0.50) Construction performance index (0.50)
Regulation index 433 434
*data listed in parentheses indicate weight ratio
ACCEPTED MANUSCRIPT
Table 2 Selection criteria of targets Units
Targets
Targets basis
1
Ecological land quality
/
A：80
Statistical distribution characteristics
2
Air quality index
/
C：50 A：50
Air Quality Standards (GB3095-2012)
3
Surface water quality index
/
C：100 A：10
SC
Surface Water Environmental Quality Standard (GB3838-2002) and Statistical distribution characteristics
Per capita GDP
RMB
A：60000
5
Proportion of the tertiary industry’s added value
%
C：20000 A：60
GDP per constructive land
RMB/m2
Division of Five types of regions and economies according to the Word Bank A value is the proportion of the tertiary industry’ added value of the high-income country and C value originates from the proportion of the third industry in the later period of industrialization
C：40 A：520 C：270
Statistical distribution characteristics
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C：20 4
Water pollution intensity
g/RMB
A：0.015
Statistical distribution characteristics
8
Air pollution intensity
g/RMB
C：0.04 A：0.2
Statistical distribution characteristics
9
Fertilizer per crop area (FPCA)* Urbanization
t/hm2
C：0.5 A：0.18
%
C：0.45 A：80
Per capita disposable income of urban residents
RMB
11
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7
10
C：60 A：100000 C：18000
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Indicators
A value originates from the high-income country and C value originates from the Action plan for zero increase in fertilizer use by 2020 by Chinese Ministry of Agriculture
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435
A value originates from 13th Five-Year planning of China’s national economy and C value originates from high-income country standard by the World Bank A value originates from the high-income country and C value originates from the basic standards for building a well-off society in overall completion
ACCEPTED MANUSCRIPT
14
Income proportion of urban and rural residents
/
A：1.8
Per capita park green area
m2
Green coverage rate of built-up area
Targets basis Statistical distribution characteristics
C：2.2
%
C：7.5
A value originates from the Indicators of national ecological civilization construction demonstration city and C value originates from Evaluation Standard of urban landscape greening of China (GB50563-2010)
A：40
Evaluation Standard of urban landscape greening of China (GB50563-2010)
A：13
C：36 15
Per capita ecological footprint
globe ha
A：1
Statistical distribution characteristics
C：2 %
A：95 C：85
Harmless treatment rate of urban solid waste
%
18
Natural reserve area ratio
%
A：20
19
Decrease rate of unit GDP energy consumption
%
C：12 A：9
436
A：95 C：85
New national urbanization planning (2014-2020)
A value originates from the Indicators of national ecological civilization construction demonstration city and C value originates from the high-income country
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A value originates from the New national urbanization planning (2014-2020); C value originates from the Indicators of national ecological civilization construction demonstration city
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Urban waste water treatment rate
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Targets
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Units
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Indicators
C：3.9
A value originates from the Action plan for energy saving and low carbon development (2015-2015) and C value was defined according to statistical distribution characteristics
ACCEPTED MANUSCRIPT Table 3 Classification of eco-civilization development levels Gradation
Standard
Situation of eco-civilization development
A
K≥80
The development level of ecological civilization is excellent. All fields can be in the leading level of China, or can reach the advanced level of the world, without obvious short boards.
70＜K≤60
D
438
The development level of ecological civilization is good. Most of the indicators reach the advanced level of China, but there are still obvious deficiencies and constraints in some aspects.
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C
80＜K≤70
The level of the development of ecological civilization has reached the standard, and all fields are basically able to meet the requirements of the state, but the development of various field is not balanced, and there are still large gaps in several of the indicators.
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B
K <60
The development of ecological civilization was below the standard, and there are prominent shortcomings or constraints in various fields.
*K is the score of ECI
439
Table 4 Information of functional divisions
Number of cities Proportional Area (%)
Main production areas of agricultural products
Key ecological functional zone
26
98
105
109
2.03
12.51
21.65
63.82
10.58
31.77
34.81
22.84
9.72
5.71
3.80
3.91
74.05
59.34
47.44
46.21
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Population proportion (%) Per capita GDP (ten thousand yuan) Urbanization (%)
Key development zone
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Optimal development zone
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440
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Table 5 Statistical analysis on ECI of different methods Methods
n
Mean
Median
Maximum
Minimum
Std. Deviation
337
56.73
56.58
74.51
37.27
6.78
SBM
337
56.73
56.58
74.52
37.27
6.78
DBM
337
61.40
61.40
81.22
38.46
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ES
7.51
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*ES: extreme standardization, SBM: single-benchmark progressive standardization, DBM: double
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benchmark progressive standardization, Std. Deviation: Standard Deviation
SC
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Table 6 Results of ECI of cities. (the score is average of the 337 cities’ evaluation
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results) Score Green
57.17
Number (%)
Environment Green
63.38
Number (%)
Green
64.48
Life
Instrument
61.16
AC C
ECI
63.30
B-grade
C-grade
D-grade
16
69
72
180
4.75
20.47
21.36
53.41
39
39
109
150
11.57
11.57
32.34
44.50
Number
12
104
115
106
(%)
3.56
30.86
34.12
31.45
Number
25
93
106
116
(%)
7.42
27.60
30.56
34.42
Number
1
43
150
143
(%)
0.30
12.76
44.51
42.43
EP
Green
A-grade
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Production
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Fig. 1 Single-benchmark method
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Fig. 2 Double-benchmark method
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TE D
452
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Fig 3. Possibility density function of ECI on different methods. ES: extreme
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standardization; DBM: double-benchmark method.
Fig. 4 ECI classification distribution in China
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Fig. 5 Four theme scores distribution in China. (a) green environment scores
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distribution; (b) green production scores distribution; (c) green life scores distribution;
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(d) green instrument scores distribution
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(a)
GE
(c)
GL GP
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GI
EFZ
ODZ
KDZ
(b)
APZ
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Eigenvalues
Scores and classes
Fig 6. Results of normalized principal component analysis (PCA) on the four functional zone metrics at the city scale. (a) correlation of variables with the first-two
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PCA axes (each arrow points in the direction of highest value for a given variable); (b) shows the distribution of variance among PCA axes; (c) associated PCA factorial map plots grouped by functional zone classes. (GE: green environment, GP: green
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production, GL: green life style, GI: green instruments, ODZ: optimal development zone, KDZ: key development zone, APZ: main production area of agricultural
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products, EFZ: ecological functional zone)
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Fig 7. GDP per capita score of the region versus green environment score. (the top-ten
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of ECI in 74 cities shown green)
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Highlights: China’s overall level of the ecological civilization development is not optimistic. China’s ecological civilization development is uneven in spatial distribution. Ecology and environmental quality were still the short board of China’s Eco-civilization construction. The ecological civilization development in the main production areas of agricultural products lags other functional areas.