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The comprehensive evaluation on resource environmental bearing capacity of central cities in the Delta-A case study on City

WANG Kui-feng1,2,3*, XU Meng1, CHEN Xiao-man4

1 Institute and Laboratory of Geological Sciences, Key Laboratory of Geological Process and Resource Utilization of Metallic Minerals in Shandong, Key Laboratory of Gold Mineralization Process and Resource Utilization Subordinate to the Ministry of Land and Resource, 250013, . 2 Key Laboratory of Assessing Resource Environmental bearing capacity, Ministry of Land and Resources (Chinese Academy of Land and Resource Economics, China University of Geosciences in Beijing), Beijing 101149, China. 3 School of Civil Engineering, Shandong University, Jinan 250061, China. 4 Guangdong Research Center for Geoanalysis, Guangzhou, Guangdong Province 510080, China.

Abstract: Dongying City, which is the most important central city in the Yellow River Delta, is located in the estuary of the Yellow River. With a short land formation time, ecological environment is very weak in this area. To realize the sustainable economic development of the Yellow River Delta, resource environment and resource environmental bearing capacity (REBC) must be improved. This study builds assessment system of regional REBC through resource and economic characteristics in Yellow River Delta and uses principal component analysis (PCA) method to evaluate REBC of five counties and districts in Dongying City in 2011-2015 on the dimensions of time and space. Results show that, on the time dimension, is ranked first, Dongying second for four years and Hekou and Kenli districts with lower ranks in 2012-2015, indicating that more attention needs to be paid to REBC of Hekou and Dongying districts and these two districts should be included into key monitoring areas. From space scale, REBC in five counties and districts has been gradually improving. In order to further develop REBC in Dongying City, measures such as intensifying protection of urban ecological environment and developing circular economy, etc. should be implemented.

Keywords: Resource environmental bearing capacity; Dongying City; Comprehensive evaluation; Yellow River Delta

national strategy. REBC refers to that on the Introduction premise that the natural ecological environment is not damaged and a good ecological system maintains, the economy and population scale that With the acceleration of economic develop- natural resources and environmental capacity in ment and industrialization, the dependence on certain geographical space can hold, and it reflects resources and environment is increasing day by day, the relationship between socio-economic develop- which inevitably has a great influence on them in ment as well as resource exploitation and turn, thus affecting the harmonious development of environmental protection. The evaluation of REBC human and nature. Building a resource-conserving includes resource endowments and environmental and environment-friendly society based on resource capacity, and the matching degree of resource environmental bearing capacity (REBC), with environmental supporting capacity and socio- natural law as its principle and the sustainable economic development (WANG Kui-feng and XU development as its goal has been risen to the Meng, 2017; WANG Kui-feng, 2016; LI Na and

*WANG Kui-feng (1981-), male, from Shandong province, post WANG Kui-feng, 2016; HUANG Bing-jie and doctor, senior engineer, engaged in geological environment and QIAO Lu, 2012; HUANG Jie, 2014; WANG geological resources research. E-mail: [email protected]. Yun-xi, 2015; WANG Wei, 2012; ZHOU Wei et al. 354http://gwse.iheg.org.cn http://gwse.iheg.org.cn Journal of GrouJoundrwnalt eor fS Grcieouncend awnadte Er nSgcineeiencrieng and VEonl.g5i n eeNori.n4g D ecVo. l.20175 N o.4 Dec. 2017

2015). Comprehensive REBC evaluation of the Yellow Dongying City is an important resource and the River Delta is conducted with county as the basic core city of the Blue Economic Zone on Shandong unit. Under the principles of being typical, Peninsula and Efficient Ecological Economic Zone representative, scientific and practical, a evaluation in the Yellow River Delta, as well as an important indicator system composed of factors which can oil industrial base and typical oil and gas resource reflect the regional resource environmental quality city in East China. With a short land formation time, from different aspects and have statistical ecological environment and REBC are very weak in significance is established to make sure that the Dongying City, a central city in the Yellow River indicator can objectively reflect the real situation of Delta. To realize the sustainable economic REBC. development of the Yellow River Delta, REBC must be improved. At present, researches related to 1.2 Establishment comprehensive REBC evaluation of oil and gas resource cities which coordinated land and marine The comprehensive REBC evaluation system development have not been published. This paper consists of target and indicator levels. The target establishes evaluation system model and evaluation level refers to target of REBC evaluation. As methods of REBC based on resource environmental resource environment mutually coordinates with and economic development characteristics of oil socio-economic development, the indicator system and gas resource cities in the Yellow River Delta should consider economic and social indicators, so and its coastal zone, offering an objective that the final target level should include resource evaluation basis for deciding regional strategy of environmental indicator and socio-economic sustainable development (GAO Yan-liang, 2011; indicator. The indicator level is composed of WANG Cun-long et al. 2014; YAN Shi-qiang, comprehensive, scientific and representative 2005). indicators that can reflect the characteristics and conditions of a certain aspect of an object independently. Resource environmental indicator as well as socio-economic indicator in this study is 1 Establishment of indicator system shown in Table 1.

1.1 Principles

Table 1 The comprehensive REBC evaluation indicator system in central cities of the Yellow River Delta

Indicator Target level Indicator level properties N1 richness of oil and gas resources, N2 per capita arable land, N3 land development intensity (ratio of construction land), N4 available water, N5 recycling rate of industrial water, N6 the effective utilization coefficient of irrigation water, N7 standard-reaching rate of industrial effluent , N8 harmless Supporting treatment rate of urban garbage, N9 standard-reaching rate of centralized indicators Resource drinking water source, N10 rate of good air quality, N11comprehensive environmental utilization rate of solid waste, N12 urban per capita green area, N13 forest indicator coverage rate, N14 relative proportion of wetland, N15 relative area of ecological protection zones. N16 annual average concentration of PMIO, N17 annual average concentration Pressure of sulfur dioxide, N18 annual average concentration of nitrogen dioxide, N19 indicators development intensity of coastline area, N20 stability of regional crustal structure, N21 risk of geological disasters, N22 oil pollution in water and soil. N23 ratio of investment on comprehensive ecological improvement in GDP, Socio-economic Supporting N24 per capita GDP, N25 ratio of fiscal revenue in GDP, N26 growth rate of indicator indicators fixed asset investment, N29 urban per capita disposable income, N30 per capita living space, N31 gross industrial output value. http://gwse.iheg.org.cn 355 JouJournalr noaf lGr ofou Grnoudwnadtwera Stecri eSncceien acend a Endn gEineengineeringr ing V o l.V5o l .5N o .N4 o .4D ec D. ec2017. 2017

crustal structure, risk of geological disasters and oil 2 Data sources, evaluation method pollution in water and soil. Supporting indicators and empirical analysis refer to all the socio-economic indicators and other resource environmental indicators. Within the determined range, greater supporting indicators and 2.1 Data sources smaller pressure indicators are better. The The indicators within the resource environ- standardization of indicator data adopts Z-score mental system include qualitative and quantitative Standardization (standardization of standard ones, and the indicators of socio-economic deviation) in the SPSS software. What should be development system are all quantitative ones. The noted is that negative indicators should adopt quantitative indicators are mainly consulted with reciprocal or reverse assignment. relevant data provided from Statistical Yearbook of Shandong Province, Dongying Statistical Yearbook, 2.4 Calculation of comprehensive Statistical Yearbook of Dongying City as well as its evaluation indicator counties and districts, Statistical Bulletin of National Economic and Social Development in The results of IBM SPSS Statistics 21’s Dongying City, China Environmental Statistics correlation analysis show that most of the indicators Yearbook, Bulletin of Water Resources in have significant linear correlation, which indicates Dongying City and Environment Bulletin of that there are information overlaps. Therefore, this Dongying City from 2011 to 2016. The qualitative study adopts PCA method to evaluate REBC in indicators are mainly graded from 1 to 5 by survey Dongying City from 2011 to 2015. During analysis, reports (GAO Yan-liang, 2011; SONG Jie-kun et al. features of time and space dimensions area reflected: 2006; WAN Hong, 2010; WANG Cun-long et al. On the one hand, REBC of 5 different counties and 2014; ZHENG Ke-fang et al. 2015) of various districts of Dongying city in the same year are industries (such as land, environmental protection, evaluated; on the other hand, analysis and forestry, marine and others), which have relatively evaluation of REBC of same area from 2011 to high reliability. 2015 are conducted. Accuracy and breadth of evaluation results can be improved through these two dimensions. 2.2 Evaluation method

The principal component analysis (PCA) 2.4.1 Time-scale calculation of REBC in method has advantages of reducing workload in Dongying City indicator selection, eliminating mutual influences PCA method in SPSS Statistics 21 software is between the various indicators, simplifying weight adopted to comprehensively evaluate and analyze determination as well as being more objective and REBC of 5 different counties and districts of reasonable, so that this study chooses PCA method Dongying City in the same year from 2011 to 2015 to analyse comprehensive REBC of central cities (CHEN Xian-peng, 2015; WANG Qin-mei and (Dongying City) in Yellow River Delta. YANG Jun-ge, 2015) according to time scale (in different regions in the same year). 2.3 Standardized data processing Taking data of counties and districts in As the original data are different in dimension, Dongying City in 2011 as an example, main standardizing the original data to be comparable is components with corresponding value greater than necessary. Among all the indicators, pressure 1 are extracted. The total load of 4 principal indicators are negative, including annual average components reaches 100%, among which the first concentration of PMIO, annual average principal component accounts for 44.469%, the concentration of sulfur dioxide, annual average second for 24.404%, the third for 17.797%, and the concentration of nitrogen dioxide, development fourth for 13.329% (Table 2). intensity of coastline area, stability of regional

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Table 2 Total variance

Initial value Extraction sums of squared loadings Components Total Variance % Accumulated % Total Variance % Accumulated % 1 13.341 44.469 44.469 13.341 44.469 44.469 2 7.321 24.404 68.873 7.321 24.404 68.873 3 5.339 17.797 86.671 5.339 17.797 86.671 4 3.999 13.329 100.000 3.999 13.329 100.000

The initial loading values of the above four components are results of factor scores multiplied by principal components can be directly gained by running arithmetic square roots of corresponding variances. SPSS 21, and the results are shown in Table 3. The table below shows principal factor scores and Factor scores can be displayed in the data scores of principal components (Table 4). window through SPSS. Scores of principal

Table 3 Coefficient matrix of scores of principal components Components 1 2 3 4 Richness of oil and gas resources -.792 .500 .348 -.043 Per capita arable land -.432 -.820 .337 -.167 Land development intensity .417 .575 -.704 .016 Total water consumption .649 -.719 -.169 .182 Recycling rate of industrial water .602 -.095 .585 -.536 The effective utilization coefficient of irrigation water .879 -.043 -.115 -.461 Standard-reaching rate of industrial effluent .867 -.033 .492 .070 Rate of good air quality -.338 .328 .478 -.741 Annual average concentration of PMIO -.657 .695 .181 .229 Annual average concentration of sulfur dioxide -.698 .688 -.119 -.158 Annual average concentration of nitrogen dioxide -.968 .167 -.163 .091 Comprehensive utilization rate of solid waste .620 .333 -.681 -.200 Urban per capita green area .661 .642 .065 .382 Forest coverage rate .957 .002 .225 .185 Relative proportion of wetland -.750 .430 .454 -.217 Development intensity of coastline area .573 -.241 -.591 .514 Stability of regional crustal structure .716 .592 -.086 -.359 Risk of geological disasters .069 .797 -.592 .103 Ecological protection areas -.798 -.083 .532 .271 Oil pollution in water and soil .656 .530 -.062 .533 Ratio of investment on comprehensive ecological improvement .845 .315 .410 -.138 in GDP Per capita GDP .134 -.359 .687 .617 Ratio of fiscal revenue in GDP -.310 .109 .560 .761 Growth rate of fixed asset investment .658 .520 .526 -.144 Total amount of actual utilization of foreign investment .773 .545 .261 .196 Total imports and exports .910 .080 .386 .128 Urban per capita disposable income -.157 .962 .028 .223 Per capita living space -.405 -.465 -.528 .584 Gross industrial output value .806 -.176 .435 .360 Engel coefficient -.582 .650 .216 .438

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Table 4 Principal factor scores and scores of principal components 2011 Factor score 1 Factor score 2 Factor score 3 Factor score 4 Dongying 0.123 1.425 -1.058 0.184 Hekou -1.024 0.431 1.058 -0.920 Kenli -0.723 -0.614 0.109 1.513 Lijin 0.073 -1.184 -0.988 -0.903 Guangrao 1.551 -0.058 0.880 0.126 Score of principal Score of principal Score of principal Score of principal 2011 component 1 component 2 component 3 component 4 Dongying 0.448 3.856 -2.445 0.369 Hekou -3.742 1.167 2.444 -1.839 Kenli -2.642 -1.661 0.251 3.025 Lijin 0.268 -3.204 -2.283 -1.807 Guangrao 5.667 -0.157 2.032 0.252

Table 5 The comprehensive REBC evaluation scores of five counties and districts in Dongying City in 2011

Counties/districts Guangrao Dongying Kenli Hekou Lijin Comprehensive scores 2.876823 0.754511 -1.13214 -1.18944 -1.30975

Fig. 1 The histogram and spatial distribution map of comprehensive REBC evaluation scores of 5 counties and districts in Dongying City in 2011 The comprehensive score Z for each assessed Similarly, SPSS software can be used to area is calculated according to the following calculate and analyze REBC indicator values, formula: standard values, correlation coefficient matrix, initial values, variance percentages, cumulative Z = r1Z1 + r2Z2 +r3Z3 + r4Z4 variance percentages and coefficient matrix of The r1, r2, r3 and r4 in the formula represent the variance percentages of scores of the principal principal components’ scores of five counties and districts in Dongying City in 2012-2015. The component 1-4 respectively. Z1, Z2, Z3 and Z4 represent scores of the principal component 1-4 comprehensive REBC evaluation scores of five respectively. The comprehensive REBC evaluation counties and districts in Dongying City in scores of five counties and districts in Dongying 2011-2015 are shown in Table 6 and Fig. 2 below. City in 2011 are shown in Table 5 and Fig. 1.

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Fig. 2 The histogram and spatial distribution map of comprehensive REBC evaluation scores of 5 counties and districts in Dongying City in 2012-2015 http://gwse.iheg.org.cn http://gwse.iheg.org359.cn JouJournalr noaf lGr ofou Grnoudwnadtwera Stecri eSncceien acend a Endn gEineengineeringr ing V o l.V5o l .5N o .N4 o .4D ec D. ec2017. 2017

Table 6 The comprehensive REBC evaluation scores of 5 counties and districts in Dongying City in 2011-2015

Counties/districts Guangrao Dongying Lijin Kenli Hekou Year 2012 2.687754 1.281894 -0.49708 -1.61743 -1.85514 2013 3.016594 0.89938 -0.60019 -1.09191 -2.22387 2014 3.271676 -0.30782 -0.04278 -0.70891 -2.21216 2015 1.94325 1.612147 -0.33069 -0.64663 -2.57808

different years from 2011 to 2015 according to 2.4.2 Spatial calculation of REBC in Dongying spatial scale (in same region in different years). The City specific process is no longer detailed, and the analysis results are shown in Table 7 and Fig. 3 With the same steps in the 2.4.1, PCA method in below. SPSS Statistics 21 software is adopted to comprehensively evaluate and analyze each of 5 counties or districts’ REBC in Dongying City in

Fig. 3 The histogram of comprehensive REBC evaluation scores of five counties and districts in Dongying City in 2012-2015

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Table 7 The comprehensive REBC evaluation scores of 5 counties and districts in Dongying City in 2012-2015 Year Counties 2011 2012 2013 2014 2015 /districts -5.23827 -1.12642 0.971348 2.104649 3.288695 -4.27886 -1.30879 0.837206 2.291416 2.459021 -4.35459 -2.48918 0.0528 2.775687 4.015284 Guangrao county -3.06568 -2.32066 -0.49504 1.790666 4.090685 -4.1742 -2.04615 -1.91153 3.746075 4.385766

relatively lower output per unit result in lower 3 Comprehensive REBC evaluation of comprehensive scores. In conclusion, these three aspects are the short boards and the disadvantages Dongying City that affect the comprehensive evaluation scores. 2) The scores of comprehensive REBC 3.1 Results evaluation scores of five counties and districts in The scores and histogram of comprehensive Dongying city in 2012-2015 together with analysis REBC evaluation scores of five counties and on time and space dimensions show dynamic districts in Dongying City in 2012-2015 (in variability and regional differences of these data. As different regions in the same year) show that among a whole, the comprehensive evaluation scores of five years, Guangrao County is ranked first, REBC in five counties and districts have been Dongying district second for four years and Hekou gradually improving year by year, which indicates and Kenli districts with lower ranks in 2012-2015, stronger REBC, better socio-economic deve- indicating that more attention needs to be paid to lopment and higher awareness of environmental REBC of Hekou and Dongying districts and these protection. However, from the spatial distribution, two districts should be included into key monitoring favorable management and policies including areas. increasing investment and developing unused land The scores and histogram of comprehensive should be implemented in Hekou and Kenli districts REBC evaluation scores of five counties and to promote economic development. As for whole districts in Dongying City in 2012-2015 (in same Dongying City, although the REBC of two districts region in different years) show that REBC in five are increasing year by year, these two districts still counties and districts has been gradually improving have weak REBC, which should draw attention of year by year and growth speed is also on rise, relative administrative departments. Monitoring indicating positive development of REBC in five negative indicators such as pollution of oil, gas, soil counties and districts in Dongying City. and water, development of unused land as well as agriculture-source pollution should be enhanced. 3.2 Reasons

1) From the perspective of indicator setting and 4 Suggestions and solutions data analysis, the comprehensive REBC scores of Hekou district and Kenli district are lower. This 1) As a typical city of oil and gas resources, may be due to that these two counties are main oil measures should be implemented including fields with higher pollution level of oil and gas, optimizing and upgrading industrial structure of oil water as well as soil in large area in Dongying City; field, rapidly developing circular economy, meanwhile, these two districts have relatively lower building the base with important modern port socio-economic output and limited investment on services and advanced manufacturing industry as comprehensive ecological improvement; in well as integrating developing Dongying port and addition, relatively large unused land and lower oil-related industrial clusters. http://gwse.iheg.org.cn 361 JouJournalr noaf lGr ofou Grnoudwnadtwera Stecri eSncceien acend a Endn gEineengineeringr ing V o l.V5o l .5N o .N4 o .4D ec D. ec2017. 2017

2) Land and marine resources should be under systemic dynamic model-A case study of rational exploitation. Specific measures should be Yiwu City in the Zhejiang Province. taken on different land resources with most strict Hangzhou: Zhejiang University. farmland protection system and policies. Restrictive GAO Yan-liang. 2011. Evaluation on the protection of important protected ecological areas, ecological environment and the sustainable efficient agricultural land and other important envelopment research of Dongying City. ecological areas should be implemented. For Tianjin: Tianjin University. industries such as offshore shoal development, salt HUANG Bing-jie, QIAO Lu. 2012. The evaluation and chemistry should be under strict restriction and of the coordination degree between ecological supervision. What’s more, strengthening coastal environment and economic development in ecological protection and marine pollution control the Yellow River Delta-A case study on should be conducted, and indicators of quantity and Dongying City. Henan Science, 30(8):1153- 1156. quality of natural resources, spatial structure of natural ecology, REBC and others should be HUANG Jie. 2014. Carrying capacity analysis on included in the comprehensive socio-economic resources and environment in Central Plains evaluation system. urban agglomeration. Wuhan: Central China University. 3) In the process of REBC assessment, from the natural population growth, population pressure is LI Na, WANG Kui-feng. 2016. Evaluation of not very heavy in the Yellow River Delta, so that coordinated development of regional favorable policies should be carried out to introduce resources and economy around Shandong high-level personnel to developing high-tech Peninsula urban agglomerations. Journal of Groundwater Science and Engineering, 4(3): industries and enhance the technological and 220-230. economic development of the Yellow River Delta. 4) Adhering to combination of prevention and SONG Jie-kun, LI Dian-wei. 2006. A study on treatment, continuous efforts should be made to transition and sustainable development of mineral areas: An example of Dongying City, strengthen prevention and control of water and soil Shandong Province. Journal of China pollution, especially unfavorable factors such as University of Petroleum (Edition of Social water and soil pollution of oil fields together with Sciences), 22(1):36-39. unused saline-alkali soil. Studying reasons of The Key Laboratory of Resource Environmental ground crude oil and improving treatment and Bearing Capacity Evaluation of Ministry of management of produced water as well as Land and Resources. 2014. Evaluation utilization of saline-alkali soil are necessary. monitoring and the idea of warning Developing treatment and control capabilities of concerning carrying capacity on resource and local enterprises, especially departments of environment. Natural Resource Economics of petrochemical industry is another solution. China, 317(4):20-24. WANG Cun-long, XIE Song-shi, et al. 2014. Soil Acknowledgements environment contamination situation of the Dongying oil-gas exploitation area. Geo- This study is jointly funded by The National physical and Geochemical Exploration, Natural Science Fund Project (41602356), Open 38(6):1252-1259. Projects of Key REBC Laboratories supported by WANG Kui-feng, XU Meng. 2017. The evaluation the Ministry of Land and Resources (Number: of the coordination degree between resource CCA2016.08), Shandong Provincial Geological environment and economic development in Prospecting Fund Project (Prospecting number in the Yellow River Delta-A case study on Shandong Province: 2013(55); 2016(07) ). Dongying City. Earth and Environmental Science, 64(1):12-44. References WANG Kui-feng. 2016. Evaluation of water resources carrying capacity of Shandong CHEN Xian-peng. 2015. Evaluation of resource Peninsula, China. Journal of Groundwater and environment carrying capacity of land Science and Engineering, 4(2):120-130. based on principal component analysis and

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WANG Qin-min, YANG Jun-ge. 2015. Com- : China University of Petroleum. prehensive evaluation about new urbanization level of Guantian Economic Zone: Based on YAN Shi-qiang. 2005. Comprehensive study on PCA analysis. Journal of Xi’an University of eco-geologic environment in the Yellow Finance and Economics, 28(1):30-36. River Delta. Changchun: Jilin University. WANG Wei. 2012. Research on comprehensive ZHENG Ke-fang, TIAN Tian, ZHANG Hai-ning. evaluation of water and soil resources 2015. A nearshore resource environmental carrying capacity in the Yellow River Delta. bearing capacity evaluation method research Tai’an: Shandong Agricultural University. is reviewed. Marine Information, (1): 30-35. WANG Yun-xi. 2015. Research on regional ZHOU Wei, YUAN Guo-hua, LUO Shi-xing. 2015. economic coordinated envelopment based on The idea concerning monitoring and early resource environment capacity-Takeing Yue warning of carrying capacity of resources and and Yu as an example. Hangzhou: Zhejiang the environment in making coordinated University. development plans for land and sea in Guangxi Zhuang Autonomous Region. WAN Hong. 2010. Research on wetland Natural Resource Economics of China, (10): information extraction and analysis of the 8-12. Yellow River Delta based on RS and GIS.

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