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

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1 Journal Journal of Groundwater of Science Science and and Engineering Vol.5 Vol.5 No.4 No.4 Dec. Dec The comprehensive evaluation on resource environmental bearing capacity of central cities in the Yellow River Delta-A case study on Dongying City WANG Kui-feng 1,2,3*, XU Meng 1, CHEN Xiao-man 4 1 Shandong 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, Jinan , China. 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 , China. 3 School of Civil Engineering, Shandong University, Jinan , China. 4 Guangdong Research Center for Geoanalysis, Guangzhou, Guangdong Province , 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 on the dimensions of time and space. Results show that, on the time dimension, Guangrao County is ranked first, Dongying district second for four years and Hekou and Kenli districts with lower ranks in , 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 Introduction With the acceleration of economic development and industrialization, the dependence on resources and environment is increasing day by day, which inevitably has a great influence on them in turn, thus affecting the harmonious development of human and nature. Building a resource-conserving and environment-friendly society based on resource environmental bearing capacity (REBC), with natural law as its principle and the sustainable development as its goal has been risen to the * WANG Kui-feng (1981-), male, from Shandong province, post doctor, senior engineer, engaged in geological environment and geological resources research. maplewkf@126.com national strategy. REBC refers to that on the premise that the natural ecological environment is not damaged and a good ecological system maintains, the economy and population scale that natural resources and environmental capacity in certain geographical space can hold, and it reflects the relationship between socio-economic development as well as resource exploitation and environmental protection. The evaluation of REBC includes resource endowments and environmental capacity, and the matching degree of resource environmental supporting capacity and socioeconomic development (WANG Kui-feng and XU Meng, 2017; WANG Kui-feng, 2016; LI Na and WANG Kui-feng, 2016; HUANG Bing-jie and QIAO Lu, 2012; HUANG Jie, 2014; WANG Yun-xi, 2015; WANG Wei, 2012; ZHOU Wei et al.

2 Journal of Groundwater Journal of Science Groundwater and Engineering Science and Vol.5 Engineering No.4 Dec. Vol No.4 Dec ). Dongying City is an important resource and the core city of the Blue Economic Zone on Shandong Peninsula and Efficient Ecological Economic Zone in the Yellow River Delta, as well as an important oil industrial base and typical oil and gas resource city in East China. With a short land formation time, ecological environment and REBC are very weak in Dongying City, a central city in the Yellow River Delta. To realize the sustainable economic development of the Yellow River Delta, REBC must be improved. At present, researches related to comprehensive REBC evaluation of oil and gas resource cities which coordinated land and marine development have not been published. This paper establishes evaluation system model and evaluation methods of REBC based on resource environmental and economic development characteristics of oil and gas resource cities in the Yellow River Delta and its coastal zone, offering an objective evaluation basis for deciding regional strategy of sustainable development (GAO Yan-liang, 2011; WANG Cun-long et al. 2014; YAN Shi-qiang, 2005). 1 Establishment of indicator system Comprehensive REBC evaluation of the Yellow River Delta is conducted with county as the basic unit. Under the principles of being typical, representative, scientific and practical, a evaluation indicator system composed of factors which can reflect the regional resource environmental quality from different aspects and have statistical significance is established to make sure that the indicator can objectively reflect the real situation of REBC. 1.2 Establishment The comprehensive REBC evaluation system consists of target and indicator levels. The target level refers to target of REBC evaluation. As resource environment mutually coordinates with socio-economic development, the indicator system should consider economic and social indicators, so that the final target level should include resource environmental indicator and socio-economic indicator. The indicator level is composed of comprehensive, scientific and representative 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 shown in Table Principles Table 1 The comprehensive REBC evaluation indicator system in central cities of the Yellow River Delta Target level Resource environmental indicator Socio-economic indicator Indicator properties Supporting indicators Pressure indicators Supporting indicators Indicator level 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 treatment rate of urban garbage, N9 standard-reaching rate of centralized drinking water source, N10 rate of good air quality, N11comprehensive utilization rate of solid waste, N12 urban per capita green area, N13 forest coverage rate, N14 relative proportion of wetland, N15 relative area of ecological protection zones. N16 annual average concentration of PMIO, N17 annual average concentration of sulfur dioxide, N18 annual average concentration of nitrogen dioxide, N19 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, N24 per capita GDP, N25 ratio of fiscal revenue in GDP, N26 growth rate of fixed asset investment, N29 urban per capita disposable income, N30 per capita living space, N31 gross industrial output value

3 Journal Journal of Groundwater of Science Science and and Engineering Vol.5 Vol.5 No.4 No.4 Dec. Dec Data sources, evaluation method and empirical analysis 2.1 Data sources The indicators within the resource environmental system include qualitative and quantitative ones, and the indicators of socio-economic development system are all quantitative ones. The quantitative indicators are mainly consulted with relevant data provided from Statistical Yearbook of Shandong Province, Dongying Statistical Yearbook, Statistical Yearbook of Dongying City as well as its counties and districts, Statistical Bulletin of National Economic and Social Development in Dongying City, China Environmental Statistics Yearbook, Bulletin of Water Resources in Dongying City and Environment Bulletin of Dongying City from 2011 to The qualitative indicators are mainly graded from 1 to 5 by survey reports (GAO Yan-liang, 2011; SONG Jie-kun et al. 2006; WAN Hong, 2010; WANG Cun-long et al. 2014; ZHENG Ke-fang et al. 2015) of various industries (such as land, environmental protection, forestry, marine and others), which have relatively high reliability. 2.2 Evaluation method The principal component analysis (PCA) method has advantages of reducing workload in indicator selection, eliminating mutual influences between the various indicators, simplifying weight determination as well as being more objective and reasonable, so that this study chooses PCA method to analyse comprehensive REBC of central cities (Dongying City) in Yellow River Delta. 2.3 Standardized data processing As the original data are different in dimension, standardizing the original data to be comparable is necessary. Among all the indicators, pressure indicators are negative, including annual average concentration of PMIO, annual average concentration of sulfur dioxide, annual average concentration of nitrogen dioxide, development intensity of coastline area, stability of regional crustal structure, risk of geological disasters and oil pollution in water and soil. Supporting indicators refer to all the socio-economic indicators and other resource environmental indicators. Within the determined range, greater supporting indicators and smaller pressure indicators are better. The standardization of indicator data adopts Z-score Standardization (standardization of standard deviation) in the SPSS software. What should be noted is that negative indicators should adopt reciprocal or reverse assignment. 2.4 Calculation of comprehensive evaluation indicator The results of IBM SPSS Statistics 21 s correlation analysis show that most of the indicators have significant linear correlation, which indicates that there are information overlaps. Therefore, this study adopts PCA method to evaluate REBC in Dongying City from 2011 to During analysis, features of time and space dimensions area reflected: On the one hand, REBC of 5 different counties and districts of Dongying city in the same year are evaluated; on the other hand, analysis and evaluation of REBC of same area from 2011 to 2015 are conducted. Accuracy and breadth of evaluation results can be improved through these two dimensions Time-scale calculation of REBC in Dongying City PCA method in SPSS Statistics 21 software is adopted to comprehensively evaluate and analyze REBC of 5 different counties and districts of Dongying City in the same year from 2011 to 2015 (CHEN Xian-peng, 2015; WANG Qin-mei and YANG Jun-ge, 2015) according to time scale (in different regions in the same year). Taking data of counties and districts in Dongying City in 2011 as an example, main components with corresponding value greater than 1 are extracted. The total load of 4 principal components reaches 100%, among which the first principal component accounts for %, the second for %, the third for %, and the fourth for % (Table 2)

4 Journal of Groundwater Journal of Science Groundwater and Engineering Science and Vol.5 Engineering No.4 Dec. Vol No.4 Dec Table 2 Total variance Components Initial value Extraction sums of squared loadings Total Variance % Accumulated % Total Variance % Accumulated % The initial loading values of the above four principal components can be directly gained by running SPSS 21, and the results are shown in Table 3. Factor scores can be displayed in the data window through SPSS. Scores of principal components are results of factor scores multiplied by arithmetic square roots of corresponding variances. The table below shows principal factor scores and scores of principal components (Table 4). Table 3 Coefficient matrix of scores of principal components Components Richness of oil and gas resources Per capita arable land Land development intensity Total water consumption Recycling rate of industrial water The effective utilization coefficient of irrigation water Standard-reaching rate of industrial effluent Rate of good air quality Annual average concentration of PMIO Annual average concentration of sulfur dioxide Annual average concentration of nitrogen dioxide Comprehensive utilization rate of solid waste Urban per capita green area Forest coverage rate Relative proportion of wetland Development intensity of coastline area Stability of regional crustal structure Risk of geological disasters Ecological protection areas Oil pollution in water and soil Ratio of investment on comprehensive ecological improvement in GDP Per capita GDP Ratio of fiscal revenue in GDP Growth rate of fixed asset investment Total amount of actual utilization of foreign investment Total imports and exports Urban per capita disposable income Per capita living space Gross industrial output value Engel coefficient

5 Journal Journal of Groundwater of Science Science and and Engineering Vol.5 Vol.5 No.4 No.4 Dec. Dec Table 4 Principal factor scores and scores of principal components 2011 Factor score 1 Factor score 2 Factor score 3 Factor score 4 Dongying Hekou Kenli Lijin Guangrao Score of principal Score of principal Score of principal Score of principal component 1 component 2 component 3 component 4 Dongying Hekou Kenli Lijin Guangrao 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 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 area is calculated according to the following formula: Z = r 1 Z 1 + r 2 Z 2 +r 3 Z 3 + r 4 Z 4 The r 1, r 2, r 3 and r 4 in the formula represent the variance percentages of scores of the principal component 1-4 respectively. Z 1, Z 2, Z 3 and Z 4 represent scores of the principal component 1-4 respectively. The comprehensive REBC evaluation scores of five counties and districts in Dongying City in 2011 are shown in Table 5 and Fig. 1. Similarly, SPSS software can be used to calculate and analyze REBC indicator values, standard values, correlation coefficient matrix, initial values, variance percentages, cumulative variance percentages and coefficient matrix of principal components scores of five counties and districts in Dongying City in The comprehensive REBC evaluation scores of five counties and districts in Dongying City in are shown in Table 6 and Fig. 2 below

6 Journal of Groundwater Science and Engineering Vol.5 No.4 Dec Fig. 2 The histogram and spatial distribution map of comprehensive REBC evaluation scores of 5 counties and districts in Dongying City in

7 Journal Journal of Groundwater of Science Science and and Engineering Vol.5 Vol.5 No.4 No.4 Dec. Dec Table 6 The comprehensive REBC evaluation scores of 5 counties and districts in Dongying City in Year Counties/districts Guangrao Dongying Lijin Kenli Hekou Spatial calculation of REBC in Dongying City With the same steps in the 2.4.1, PCA method in SPSS Statistics 21 software is adopted to comprehensively evaluate and analyze each of 5 counties or districts REBC in Dongying City in different years from 2011 to 2015 according to spatial scale (in same region in different years). The specific process is no longer detailed, and the analysis results are shown in Table 7 and Fig. 3 below. Fig. 3 The histogram of comprehensive REBC evaluation scores of five counties and districts in Dongying City in

8 Journal of Groundwater Journal of Science Groundwater and Engineering Science and Vol.5 Engineering No.4 Dec. Vol No.4 Dec Table 7 The comprehensive REBC evaluation scores of 5 counties and districts in Dongying City in Counties /districts Year Dongying district Hekou district Kenli district Guangrao county Lijin county Comprehensive REBC evaluation of Dongying City 3.1 Results The scores and histogram of comprehensive REBC evaluation scores of five counties and districts in Dongying City in (in different regions in the same year) show that among five years, Guangrao County is ranked first, Dongying district second for four years and Hekou and Kenli districts with lower ranks in , 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. The scores and histogram of comprehensive REBC evaluation scores of five counties and districts in Dongying City in (in same region in different years) show that REBC in five counties and districts has been gradually improving year by year and growth speed is also on rise, indicating positive development of REBC in five counties and districts in Dongying City. relatively lower output per unit result in lower comprehensive scores. In conclusion, these three aspects are the short boards and the disadvantages that affect the comprehensive evaluation scores. 2) The scores of comprehensive REBC evaluation scores of five counties and districts in Dongying city in together with analysis on time and space dimensions show dynamic variability and regional differences of these data. As a whole, the comprehensive evaluation scores of REBC in five counties and districts have been gradually improving year by year, which indicates stronger REBC, better socio-economic development and higher awareness of environmental protection. However, from the spatial distribution, favorable management and policies including increasing investment and developing unused land should be implemented in Hekou and Kenli districts to promote economic development. As for whole Dongying City, although the REBC of two districts are increasing year by year, these two districts still have weak REBC, which should draw attention of relative administrative departments. Monitoring negative indicators such as pollution of oil, gas, soil 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 data analysis, the comprehensive REBC scores of Hekou district and Kenli district are lower. This may be due to that these two counties are main oil fields with higher pollution level of oil and gas, water as well as soil in large area in Dongying City; meanwhile, these two districts have relatively lower socio-economic output and limited investment on comprehensive ecological improvement; in addition, relatively large unused land and lower 4 Suggestions and solutions 1) As a typical city of oil and gas resources, measures should be implemented including optimizing and upgrading industrial structure of oil field, rapidly developing circular economy, building the base with important modern port services and advanced manufacturing industry as well as integrating developing Dongying port and oil-related industrial clusters

9 Journal Journal of Groundwater of Science Science and and Engineering Vol.5 Vol.5 No.4 No.4 Dec. Dec ) Land and marine resources should be under rational exploitation. Specific measures should be taken on different land resources with most strict farmland protection system and policies. Restrictive protection of important protected ecological areas, efficient agricultural land and other important ecological areas should be implemented. For industries such as offshore shoal development, salt and chemistry should be under strict restriction and supervision. What s more, strengthening coastal ecological protection and marine pollution control should be conducted, and indicators of quantity and quality of natural resources, spatial structure of natural ecology, REBC and others should be included in the comprehensive socio-economic evaluation system. 3) In the process of REBC assessment, from the natural population growth, population pressure is not very heavy in the Yellow River Delta, so that favorable policies should be carried out to introduce high-level personnel to developing high-tech industries and enhance the technological and economic development of the Yellow River Delta. 4) Adhering to combination of prevention and treatment, continuous efforts should be made to strengthen prevention and control of water and soil pollution, especially unfavorable factors such as water and soil pollution of oil fields together with unused saline-alkali soil. Studying reasons of ground crude oil and improving treatment and management of produced water as well as utilization of saline-alkali soil are necessary. Developing treatment and control capabilities of local enterprises, especially departments of petrochemical industry is another solution. Acknowledgements This study is jointly funded by The National Natural Science Fund Project ( ), Open Projects of Key REBC Laboratories supported by the Ministry of Land and Resources (Number: CCA ), Shandong Provincial Geological Prospecting Fund Project (Prospecting number in Shandong Province: 2013(55); 2016(07) ). References CHEN Xian-peng Evaluation of resource and environment carrying capacity of land based on principal component analysis and systemic dynamic model-a case study of Yiwu City in the Zhejiang Province. Hangzhou: Zhejiang University. GAO Yan-liang Evaluation on the ecological environment and the sustainable envelopment research of Dongying City. Tianjin: Tianjin University. HUANG Bing-jie, QIAO Lu The evaluation of the coordination degree between ecological environment and economic development in the Yellow River Delta-A case study on Dongying City. Henan Science, 30(8): HUANG Jie Carrying capacity analysis on resources and environment in Central Plains urban agglomeration. Wuhan: Central China University. LI Na, WANG Kui-feng Evaluation of coordinated development of regional resources and economy around Shandong Peninsula urban agglomerations. Journal of Groundwater Science and Engineering, 4(3): SONG Jie-kun, LI Dian-wei A study on transition and sustainable development of mineral areas: An example of Dongying City, Shandong Province. Journal of China University of Petroleum (Edition of Social Sciences), 22(1): The Key Laboratory of Resource Environmental Bearing Capacity Evaluation of Ministry of Land and Resources Evaluation monitoring and the idea of warning concerning carrying capacity on resource and environment. Natural Resource Economics of China, 317(4): WANG Cun-long, XIE Song-shi, et al Soil environment contamination situation of the Dongying oil-gas exploitation area. Geophysical and Geochemical Exploration, 38(6): WANG Kui-feng, XU Meng The evaluation of the coordination degree between resource environment and economic development in the Yellow River Delta-A case study on Dongying City. Earth and Environmental Science, 64(1): WANG Kui-feng Evaluation of water resources carrying capacity of Shandong Peninsula, China. Journal of Groundwater Science and Engineering, 4(2):

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