Coastal resource-environmental carrying capacity assessment: A comprehensive and trade-off analysis of the case study in Jiangsu coastal zone, eastern China

Ocean and Coastal Management 186 (2020) 105092

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Feature Article

Coastal resource-environmental carrying capacity assessment: A comprehensive and trade-off analysis of the case study in Jiangsu coastal zone, eastern China Rongjuan Liu a, b, Lijie Pu a, b, *, Ming Zhu a, b, Sihua Huang a, b, Yu Jiang a, b a b

School of Geographic and Oceanographic Science, Nanjing University, Xianlin Road 163, 210023, Nanjing, China Key Laboratory of Coastal Zone Exploitation and Protection, Ministry of Natural Resources, Xianlin Road 163, 210023, Nanjing, China

A R T I C L E I N F O

A B S T R A C T

Keywords: Coastal zone Resource-environment carrying capacity evaluation Entropy weight method Analytic hierarchy process Trade-off Spatiotemporal variation

The coastal zone is the key area for sustainable scientific research and realizing the strategic development of China’s economic and ecological goals. As one of the regions with the highest human activity, the coordinated development of its ecological environment and social economy cannot be ignored. Based on the study of the relationship between ecological environment quality and socio-economic development, this paper proposed the Coastal Resource-environmental Carrying Capacity (CRECC) which including three subsystems (the Resource’s system, the Ecological system, and the Socio-economical system), as well as 6 first-level indices and 20 s-level indicators. The entropy weight method and analytic hierarchy process were used to calculate the average weight, and the changing rate of carrying capacity value was analyzed based on the ordinary least square method. Taking the coastal zone of Jiangsu Province as the study area, we calculated the changes of comprehensive/subsystems’ carrying capacity from 2000 to 2015, and the trade-off relationship within the subsystem was analyzed. From 2000 to 2015, the value of CRECC, the Resource’s system, and the Socio-economical system decreased while the Ecological system’s value showed a volatility rise. Overall, the regional carrying capacity level was low. How­ ever, positive environmental management measures have significantly improved the status of the ecological environment and helped to alleviate the environmental pressure brought by population growth. the Ecological system played an important role in the change of regional carrying capacity. Vegetation coverage, coastal water environment quality index, tidal flat reclamation area, habitat, and ecological land proportion are significant influencing factors. This also indicated that the landscape ecological status, offshore water quality and utilization of space resources in Jiangsu coastal zone aren’t sustainable. Therefore, it is recommended to improve the ecological resilience of coastal zones, such as implementing spatial planning of tidal flat resources, and con­ trolling pollutant discharge, etc. in order to promote the regional sustainable development.

1. Introduction As a transitional zone between marine and terrestrial ecosystems, the coastal zone is an important entry point for sustainable scientific research (Broman and Rob�ert, 2017; Pradhan et al., 2017; Yuan et al, 2019; Díaz et al., 2018). Many international organizations and national institutions have launched coastal development strategies and policies in a bid to achieve coastal ecosystem sustainability. For example, the Future Earth Coast pay more attention to collaborative multi-disciplinary technology to enhance sustained and flexible devel­ opment of coastal zones in the future (Ramesh et al., 2015). The US National Ocean Policy and the European Union (EU) Maritime Strategy

emphasized the use of integrated ecosystem-based management to maintain a good marine environment and meet the needs of humans, marine, and coastal areas. In addition, China has established a “moni­ toring and forecasting system for resource-environment carrying ca­ pacity” based on a terrestrial and marine evaluation to promote ecological civilization construction. Currently, many scholars concerned about the comprehensive �k et al., 2016). They assessment of the environment and the economy (Ha are also trying to achieve sustainable development by revealing sub­ stantial conflicts between socio-economic development and resource-environment systems now (Madeira et al., 2018; Sowman and Raemaekers, 2018). Carrying capacity assessment is conducive to

* Corresponding author. School of Geographic and Oceanographic Science, Nanjing University, Xianlin Road 163, 210023, Nanjing, China. E-mail addresses: [email protected] (R. Liu), [email protected] (L. Pu). https://doi.org/10.1016/j.ocecoaman.2020.105092 Received 29 July 2019; Received in revised form 19 December 2019; Accepted 31 December 2019 Available online 14 January 2020 0964-5691/© 2020 Elsevier Ltd. All rights reserved.

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promote the coordinated development of the ecological environment and human activities. It can not only calculate the optimal quantity of factors but also promotes the fairness and sustainability of resource consumption to meet environmental thresholds. The widely accepted definition of carrying capacity is the maximum number of individuals of a population that the environment can support (Leopold, 1943; Mwa­ lyosi, 1991; Seidl and Tisdell, 1999). It originated from the physical concept and was applied to the field of ecology in 1870 (Gabb, 1873). Among the many derivative concepts of “carrying capacity”, the Ecological Carrying Capacity (ECC) has been widely studied and applied earlier and is regarded as a function that reflects the characteristics between the area and the species organism (Sun T et al., 2018). After the middle of the 20th century, the research object of carrying capacity changed from organism or natural system to human, the concept of the population carrying capacity, land resource carrying capacity and water resource carrying capacity emerged. Then the research content concentrated on exploring environmental carrying capacity and ecosystem resilience threshold (Brinson and Bradshaw, 1984; Bloom et al., 1991; Cohen, 1995; Feng et al., 2018). The Resource-environment Carrying Capacity is the extension concept of the above research. Liu (1995) has defined the Resource-environment Carrying Capacity as “an environmental carrying capacity including comprehensive factors (such as atmospheric resources, water resources, land resources, marine or­ ganisms), atmospheric environment, water environment dilution self-purification capacity, and other carrying capacity can be called ‘resource-environmental comprehensive carrying capacity’”. The Resource-environment Carrying Capacity has comprehensive charac­ teristics and has a great advantage in regional resource-environmental basic evaluation or factor evaluation, which helps to reveal human’s influence on environmental profit and loss (Feng et al., 2018). With the development of the marine economy, coastal development and pollution have changed and affected marine ecosystems. A quanti­ tative analysis of the coastal resource-environmental carrying capacity is indispensable to determine influencing factors (Nyima, 2015; Yang and Ding, 2018). However, the research of that needs to consider the universality of regional carrying capacity research and the particularity of marine and coastal resources and is still in the exploratory stage (Sowman, 1987; Silva et al., 2007; Vanclay, 2012; Rani et al., 2015; Sowman and Raemaekers, 2018). The approach for coastal resource-environment carrying capacity studies mainly includes the following three categories: (1) Model-based method. Ecological Foot­ print (Galli et al., 2012; Peng et al., 2019), Emergy evaluation (Jung et al., 2018), and State-space method (Tang, 2015; Nakajima and Ortega, 2016), etc. These methods can be used to characterize the quantitative relationship between carrying capacity and various factors. However, the Ecological Footprint method often deviates from the actual situation due to factor properties, the state space method has certain limitations in determining the ideal carrying capacity value (Shi et al., 2019). (2) The method based on an analytical framework. EU proposed a series of watershed influencing factors based on the P–S-R framework, including pollution sources, hydrological conditions, bio­ logical and resource utilization (Borja et al., 2006). China has put for­ ward a list of monitoring and forecasting system for marine resource-environment carrying capacity. The fixed evaluation list based on constructing a theoretical framework was proposed, but the factors affecting regional characteristics may be ignored. Moreover, the vertical and horizontal relationship of the influencing factors is unclear (Wang et al., 2017). (3) Multi-dimensional evaluation index system (Williams and Lemckert, 2007; Xiao et al., 2019). This method is rela­ tively flexible and more in line with the actual situation. To construct a comprehensive evaluation index system for coastal resources and environmental carrying capacity, many factors must be considered comprehensively, such as coastal resource consumption, ecological environment and whether it is damaged, as well as related economic development and environmental management, etc. The eval­ uation results need to effectively reflect the carrying capacity of the

coastal zone. This article aims to accurately answer the following questions through quantitative evaluation results: To what extent can the carrier (ecosystem) of the regional environment carry human ac­ tivities? And how to quantify the natural environment change is impacting the regional resource and environmental carrying capacity under the influence of natural changes and human intervention? These problems need to be explored further. By constructing the conceptual framework of coastal resource-environment carrying capacity, this study established an evaluation index system and expounded the tradeoff relation within the subsystem. Then we selected the coastal zone of Jiangsu Province for the study area, which can be used as an important reference for the coastal resource-environment carrying capacity assessment. 2. Methodology 2.1. Coastal resource-environment carrying capacity assessment model 2.1.1. Conceptual framework The coastal zone consists of land area, tidal flat and off-shore zone. It is a “natural-society-economy” complex ecosystem with significant human-land interaction. The comprehensive carrying capacity of coastal resource-environment is affected by the ecological environment, natural resources and human society simultaneously (Fig. 1). The Coastal Resource-Environment Carrying Capacity (CRECC) is constructed based on the conceptual framework with three subsystems: Resource’s system: Resource’s system was designed to provide basic materials for ensuring the normal operation of human survival, production, and construction. The indicators related to the exploitation of coastal space and biological resources, and the quantity of currently available resources, which reflected the carrying capacity of coastal resources for human activities. Ecological system: The environmentally basic system for regional sustainable development. In order to provide a good ecological envi­ ronment for human, animals, and plants. The ecological system regu­ lates environment issues through appropriate management decisions. Indicators include the ecological status index that reflects natural characteristics and environmental management index that represent ecological resilience affected by human activities. Different from the resource system, the ecological system not only reflect the carrying ca­ pacity of the coastal ecosystem to human activities, but also pay atten­ tion to the tolerance and assimilation ability of occupation, destruction, and pollution of environmental space by the corresponding economic scale (Arrow et al., 1996; Clark and Tilman, 2017; Feng et al., 2018). Socio-economical system: A social support system that obtains economic benefits through regional development while providing technology and infrastructure support. the socio-economical system is the main driver of economic expansion and sustainable development and the carrying objects of the ecosystem. Indicators composed of population growth, urban expansion, and coastal economic development. 2.1.2. Trade-off within subsystems Coastal resources and ecosystems have both land and sea charac­ teristics. Vegetation, hydrology, and topography are important factors for the source of resource-environment carrying capacity. Population growth and coastal economic development can be regarded as a source of pressure for carrying capacity, that is, carrying objects. In general, the ecological environment itself has the ability to adapt to external disturbances, called ecological resilience (Holling, 1973). The threshold of ecological resilience is limited, whereas external pressure caused by human activities can be continuously increased (Ma et al., 2017). The natural environment will maintain stable if the pres­ sure is within the ecological resilience threshold. On the one hand, overfishing may lead to depletion of the fishery resource, tidal flat reclamation may affect coastal and marine ecological habitats, but 2

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Fig. 1. Conceptual framework of resource and environmental carrying capacity in coastal zones (CRECC).

its resilience (Holling, 1973; Rani et al., 2015; Koroglu et al., 2019). It is mainly reflected in the coastal marine environment, and biodiversity. E1 consists of four three-level indicators: vegetation coverage, habitat, coastal biodiversity index, and coastal water environment quality index. E2- Environmental management: Under the premise of maintain­ ing the ecological resilience of the coastal zone, the role of environ­ mental management measures cannot be ignored. The indicators relate to environmental expenditure, ecological land proportion, total indus­ trial wastewater discharge, and comprehensive utilization rate of in­ dustrial solid. It reflects the government’s investment in ecological protection, the regulation of industrial pollutants and the occupation of ecological land.

resource regeneration, excavation of alternative resources, and local planning strategies will reduce the negative impact of resource devel­ opment. On the other hand, although population growth and coastal economic development have increased the pressure on the environment, various ecological environmental protection measures including soil improvement and water pollution control have obvious positive effects on improving ecological resilience. Therefore, the CRECC、the Re­ source’s system and the Ecological system can be regarded as an elastic and trade-off mechanism. The natural environment and human activ­ ities are mutually constrained and influential. 2.1.3. Indicators system and data Based on the scientific and feasible criteria, this study constructed a “top-down” multi-dimensional CRECC evaluation index system (Table 1). According to the evaluation framework, the system consists of three dimensions, each of which contains two first-level indicators. Then refer to the relevant literature to extract indicators related to each dimension (Liu et al., 2006; Nobre et al., 2010; Wang et al., 2018). We eliminated some of the indicators that are difficult to obtain data, and finally determined 20 indicators. The meanings of the indicators at each level are described as follows:

(3) Socio-economical system S1- Population: Population growth and urbanization rates are the most direct phenomena of human activity and can reflect the changes in coastal carrying objects (Ma et al., 2017). S2- Economic development: The economic growth brought about by the coastal and marine industries has greatly supported the life of the coastal population. The higher the value of the S2 indicator, the more reasonable the economic structure and the lower the pressure on the carrier (Sun C et al., 2018). Indicators include per capita GDP, marine economic output, science, and technology innovation ability. Then we collected the necessary data for each indicator (Table 2).

(1) Resource’s system R1-Resource exploitation: Measure the current status of the major resource’s utilization in economic development activities in the coastal zone. Indicators include Land-use intensity index, tidal flat reclamation area, and Million GDP energy consumption. R2 -Available resources: Measure the natural status of coastal zone resources, and reflect the carrying relationship between coastal natural resources and social-economic activities by comparing the differences with R1. The indicator consists of coastline length, per capita cultivated area, NPP, and fishery resources.

2.2. Assessment methods Applying multiple metrics for a comprehensive assessment usually involves three steps: standardization of indicators, determination of indicator weights, and calculation. 2.2.1. Standardization of indicator data The extreme standard method was used here to eliminate the dimensional and magnitude difference of the original data so that the index value is between [0, 1]. There are 12 positive indicators and 8 negative indicators.

(2) Ecological system E1- Ecological status: As an ecologically sensitive area, the coastal zone should attach great importance to its ecological environment and 3

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Table 1 Evaluation index system of CRECC. No.

Attributesa

Units

Definition

Methods

Land-use intensity index

x1

–

/

L ¼ 100 �

Tidal flat reclamation area

x2

–

km2

The extent to which human activities affect the land system Reflecting the reclamation and utilization of tidal flat

Million GDP energy consumption Coastline length

x3

–

x4

þ

Per capita cultivated area

x5

þ

Net primary productivity of vegetation (NPP)

x6

þ

gC/m2

Fishery resources

x7

þ

104t

Vegetation cover-age

x8

þ

%

Habitat

x9

þ

%

Coastal biodiversity index

x10

þ

/

Coastal water environment quality index Environmental expenditure

x11

þ

/

x12

þ

Ecological land proportion

x13

þ

%

Total industrial wastewater discharge Comprehensive utilization rate of industrial solid

x14

–

104t

x15

þ

%

Population density

x16

–

Urbanization rate

x17

–

capita/ km2 %

Indicators the Resource’s system

Resource exploitation (R1)

Available resources (R2)

the Ecological system

Ecological status (E1)

Environmental management (E2)

the Socioeconomical system

Population (S1)

Economic development (S2)

Per capita GDP

x18

–

Marine economic output

x19

–

Science and technology innovation ability

x20

þ

tce/104 yuan km2

km2/ capita

Reflecting the species and individual quantity of zooplankton, benthic flora, and fauna in the off-shore zone Reflecting water pollution in off-shore zone

104 yuan

4

10 yuan/ capita 108 yuan t

a

Reflecting regional energy consumption Reflecting the changes of coastline under the influence of natural and human activities Reflecting the utilization of arable land and the land potential for food cultivation Reflecting the status of regional plant growth and its response to environmental changes Reflecting the total amount of available aquatic animals and plants resource Measuring vegetation status and regional eco-environment quality Reflecting the change of habitat area in coastal zone

Reflecting the government’s financial support for local environmental protection Reflecting the rate of land use type mainly providing ecosystem services Regional discharge of industrial wastewater and government management Comprehensive utilization of industrial solid waste as a percentage of industrial solid waste production Reflecting the regional population distribution Reflecting the structure level of urban and rural Reflecting the level of regional economic development Reflecting the economic development level of the regional Marine industry Reflecting the development level of coastal science and technology

Pn

i¼1 Ai

� Ci ; L 2 ½100; 500�b

With reference to the land use data and tidal flat reclamation planning documents of Jiangsu Province, the tidal flat reclamation areas of different years were extracted cased on support vector machine and manual methods. All the Kappa coefficient were greater than 0.8. (Zhao et al., 2015) Comprehensive energy consumption(tons of standard coal, tce)/GDP(104 yuan) ① Remote sensing image extraction; ②Coastline length calculating based on Calculate geometry tools in ArcGIS 10.5; ③Statistics of change information Total cultivated land area at the end of the year/total population at the end of the year ① Image (MOD17A3) clipping and extraction ②Statistics of change information Marine catch þ total maricultural (including fish, shellfish, algae, crustaceans, etc.) ① Image (MOD13Q1) clipping and extraction; ② VFC ¼ (NDVI - NDVIsoil)/ (NDVIveg - NDVIsoil)c(Qi et al., 2000) ① Information extraction from Remote sensing image and Jiangsu Environmental quality bulletin; ② Tj ¼ Sj/S2000d Pn e H’ ¼ j 1 pk � log2 pk ; pk ¼ mk � M

Data extraction from Jiangsu Province Marine Environmental Quality Bulletin Data extraction from Jiangsu Statistical Yearbook Ecological land area � total land area*100% (Long et al., 2015) Data extraction from Jiangsu Statistical Yearbook Data extraction from Jiangsu Statistical Yearbook Population/area (km2) Urban permanent population/permanent population *100% GDP (104 yuan)/permanent population Data extraction from Jiangsu Statistical Yearbook Proportion of investment in scientific research*0.6 þ Proportion of people engaged in scientific research*0.4

“þ”means positive indicators, “-”means negative indicators. L is land-use intensity index, Ai is value of land-use intensity index for grade i(i represents 5 land use intensity grades, Ai ​ ranges 1 to 5: “1”for tidal flat and saline, “2” for forest and grassland, “3”for river surface, ditch, road land, facbility agricultural land and riser of terrace, “4” for cultivated land and pit pond water surface; “5” for construction land), Ci is the percentage of grade i-th land-use area in the study area, n is land-use degree grading number, more details can be found in (Xu and Pu., 2014). c VFC is vegetation fractional coverage, NDVIsoil and NDVIveg are NDVI values in areas without vegetation cover and pure vegetation pixel. d Tj is the habitat holding rate, Sj was the habitat area in 2008 or 2015 in j -th evaluation object, S2000 was the initial habitat area in j -th evaluation object. e H’ is the coastal biodiversity index of evaluation object j, mk is the number of k-th species, M is the total number of all species, and Pk is the proportion of k species individuals of the total individuals. b

4

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Table 2 Data sources of 20 indicators.

, wi1 ¼ 1

Indicators

Data name

Data source

Data type

Data period

x1, x5 and x13

Land-use data

Vector

2000, 2008 and 2015

x2, x4 and x9

Landsat7 ETM, Landsat8 OLI MOD17A3

Resource and Environmental Data Cloud Platform (http://www.resdc. cn/) Geospatial Data Cloud (htt p://www.gscloud. cn) https://www.nasa. gov/

Grid

2000, 2008 and 2015

Grid

from 2000 to 2014 (Data of 2015 was obtained by linear regression analysis based on 2000–2014 data series due to data sources) 2000, 2008 and 2015 2000, 2008 and 2015

x6

x8

MOD13Q1

x3, x7, x12, x16, x17, x18, x19 and x20 x10 and x11

Statistics data

x14 and x15

Statistics data Statistics data

https://www.nasa. gov/ hhJiangsu Statistical Yearbookii

Grid

hhJiangsu Marine Environmental Quality Bulletinii hhJiangsu Environmental quality bulletinii

txt

For positive indicators: Nij ¼ （Xij For negative indicators: Nij ¼ （Ximax

txt

txt

� Ximin ） （Ximax � Xij ） （Ximax

Where Hi and wi1 were the entropy and weight of i-th indicator. The AHP firstly compared the 20 s-level indicators, then the l~9 ratio scale method was used to construct the comparison matrix. And the maximum eigenvalue and its eigenvectors of the comparison matrix were determined (Clark and Tilman, 2017). The relative weight wi2 of each factor was thus obtained. Finally, weight wi (the average value of wi1 and wi2 ) of each index were shown in Table 3. Carrying capacity results were calculated by: X Sid ¼

2.2.3. Simple linear regression Ordinary least square method could simulate the changing trend of the carrying capacity value of each region, and estimated the regression coefficient of the time series and the carrying capacity sequence value: n⋅ θslope ¼

ði⋅Ci Þ

i¼1

n⋅

n P

i2

n n P P i⋅ Ci i¼1 i¼1 � n �2 P i

(7)

i¼1

Where θslope was the changing trend, n was the time series, Ci was the i-th annual carrying capacity value. If θslope >0, the carrying capacity raised, θslope <0, the carrying capacity raised reduced. The larger the value of θslope , the greater the changing trend. Results of CRECC, the Resource’s system, the Ecological system, and the socio-economical system were divided into four levels: lower (in an over-loading state), low (in an adjacent fully loaded state, which needs to be improved), high (under loading basically), and higher (under loading) by the natural breakpoint method in the ArcGIS 10.5 platform. Then we calculated the change rate of each region’s carrying capacity over 15 years based on the ordinary least square method based on the Raster Calculator tool in the toolbox of ArcGIS.

(1) (2)

3. Model application and results 3.1. Study area ~ � 200 N) is located Jiangsu Province (116� 180 E~121� 570 E, 30� 450 N35 at the center of the eastern coast, China, which is an important part of the Yangtze River Delta. The eastern coast of Jiangsu consists of 13 counties with extensive tidal flats and waters (Fig. 2). The length of coastline in Jiangsu coastal area is 953.9 km, with a tidal flat area of about 6520.6 km2, accounting for 1/4 of the total area of China’s tidal flats. It is one of the most complex areas of the sedimentary dynamic environment in China. Significant land-sea interactions and abundant tidal flat resources are important features of the coastal areas of Jiangsu Province (Xu et al., 2014; Liu et al., 2018). Regional human activities – such as the construction of ports and wind farms, large-scale reclamation (1986 km2 reclamation area in 1973–2015), and introduction of Spar­ tina alterniflora (178.42 km2 in 1986–2015) –have greatly changed equilibrium state of the coastal environment (Song et al., 2013; Peng et al., 2019; Yu et al., 2019). In the process of development and utili­ zation of coastal resources, the land use is gradually transformed from the natural state to the artificial state. Meanwhile, the coastal zone’s ecological health has been affected.

i¼1

Where fij was the proportion of i-th index, m was the sample’s number. � fij ⋅ ln fij ⋅ ln m

n P

i¼1

Ximin ）

(6)

Where Sid was the carrying capacity value of each sub-system for object j, ws was the weight of each subsystem, and CRECCi was the compre­ hensive value.

2.2.2. Weights determination Quantify the relative importance of indicators, that is, determination of reasonable weights is important for guaranteeing the objectivity of the assessment results (Mikulic et al., 2015). Improved entropy weighting method combined with the analytic hierarchy process (AHP) was used to determine the weight of each index. The entropy weight method is an objective weighting method that used the information entropy to calculate the entropy weight according to the degree of variation of each index. Then, the weight of each index was corrected by the entropy weight, so that the objective index weight was obtained. Generally speaking, if the information entropy of an in­ dicator is smaller, it indicates that the index is more variability, and the more information is provided, the greater the role that can be played in the comprehensive evaluation, and the greater the weight (Guiau, 1971; Sun L et al., 2017). Therefore, the information entropy of each indicator is calculated by the following steps, and the contribution value of each index to the comprehensive evaluation is obtained. , m fij ¼ Nij X (3) N

m X

3 X � Nij � wi ; CRECCi ¼ ðSid � ws Þ d¼1

2008 and 2015 (Data gap for 2000) 2000, 2008 and 2015

Ximin ）

(5)

Hi i¼1

where Ximax and Ximin were the maximum and minimum values of the ith indicator of j-th evaluation object respectively, ​ Xij ​ and ​ Nij were the actual and normalized value of i-th indicator.

Hi ¼

n X

Hi

(4)

i¼1

5

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Table 3 Weights of each first-class and second-class indicators. Indicators R1 R2

E1

x1 x2 x3 x4 x5 x6 x7 x8 x9 x10

Entropy weights

AHP weights

Average weights

0.046 0.042 0.008 0.038 0.045 0.007 0.011 0.170 0.037 0.001

0.042 0.102 0.021 0.052 0.044 0.057 0.027 0.050 0.103 0.061

0.044 0.072 0.015 0.045 0.044 0.032 0.019 0.110 0.070 0.031

Indicators E2

S1 S2

3.2. Results

x11 x12 x13 x14 x15 x16 x17 x18 x19 x20

Entropy weights

AHP weights

Average weights

0.094 0.058 0.109 0.063 0.014 0.046 0.064 0.060 0.065 0.025

0.105 0.042 0.028 0.060 0.015 0.068 0.028 0.027 0.058 0.012

0.099 0.050 0.068 0.062 0.015 0.057 0.046 0.044 0.061 0.019

was calculated (Figs. 3–4, Table 4). The results showed that value of CRECC showed a descending trend. The Resource’s system and the Socio-economical system decreased by 3.4% and 66.5% while the Ecological system increased by 22.1% from 2000 to 2015. CRECC of north and central coastal areas maintained a

3.2.1. Spatiotemporal variation of CRECC and three subsystems Based on the CRECC evaluation model, the carrying capacity value of 13 counties along the coast of Jiangsu Province in 2000, 2008 and 2015

Fig. 2. Location of the study area (The tidal flat refers to the tidal zone between the high tide level and the low tide level along the coast. The data came from Land-use data of Jiangsu Province). 6

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relatively high level with a gradually increasing trend, and of the southern coastal areas was low. More than half of the overall area’s CRECC showed a downward trend. From 2000 to 2015, the carrying capacity value of Resource’s system in 8 counties gradually improved. The increasing rate in Rudong, Sheyang, and Ganyu was highest, and the declining rate in Tongzhou and Lianyun was largest. The Ecological system’s carrying capacity value decreased from 0.496 to 0.345 and rose to 0.606 after 2008, but most area’s carrying capacity was at a low level. Only in the northern coastal zones (Guanyun, Xiangshui, and Binhai), the Ecological system’s carrying capacity improved to a high level in 2015. The carrying ca­ pacity results in 10 counties showed a slow upward trend, with the highest in Xiangshui, Guanyun, and Binhai. As a carrying object sub­ system, the Socio-economical system has experienced a downward trend in the overall study area due to the rapid increase in economic devel­ opment in recent years. And the carrying capacity value had changed from the high level to the low level.

rate)、x18 (per capita GDP) and x19 (marine economic output) had placed a huge burden on the overall coast. Meanwhile, the advancement of x20 (science and technology innovation ability) had alleviated the deterioration of the coastal ecological environment. 3.2.3. Correlation and trade-off analysis within each subsystem According to the evaluation results, the relationship between the subsystems and the indicators was analyzed. Method of chapter 2.2 was applied to calculate the weights of the Socio-economical system, Resource-Ecological system, and 6 first-level indicators. There was a negative correlation between the socio-economical system and the Resource-Ecological system in Qidong, Rudong, Dong­ tai, Dafeng, and Sheyang. In these areas, carrying capacity value of the Resource’s system and Ecological system is low. In addition, the other eight counties showed similar values of the socio-economical system of about 0.54–0.59, while values of the Resource-Ecological system varied in a range of 0.14 (from 0.41 to 0.55). In these areas, the socioeconomical system value distribution is concentrated, and the Re­ source’s system and the Ecological system value are relatively high (Fig. 6) (Fig. 7). By analyzing the changing characteristics of the 6 first-level in­ dicators in 2000–2015, the trade-off within the Resource’s system and the Ecological system subsystem were obtained. It is found that the value of R1- Resource exploitation and R2-Available resources in the Re­ source’s system showed an opposite trend in 2000–2015. At the same time, we found that R1- Resource exploitation is in line with the trend of the Socio-economical system, and both show a gradual downward trend. But the value change of the Resource’s system is 0.495-0.499-0.512, which is relatively stable. On the other hand, the trade-off between E1- Ecological status and E2- Environmental management within the Ecological system is also obvious. The x8 (vegetation coverage) and x11 (coastal water environ­ ment quality index) values were significantly reduced, and the indicator gradually rebounded after 2008 but still did not achieve good improvement. In the meantime, E2- Environmental management maintained a positive contribution to the Ecological system under the

3.2.2. Variation of each index Fig. 5 showed each indicator’s values from 2000 to 2015. In the Resource’s system, the value of x1 (land-use intensity index) and x2 (tidal flat reclamation area) gradually decreased from 2000 to 2015 with increased pressure on regional resource utilization. The value of x3(million GDP energy consumption)、x4 (coastline length)、x5 (per capita cultivated area)、x6 (NPP) and x7 (fishery resources) fluctuated within a certain range. In the Ecological system, the value of x8 (vegetation coverage)、x11 (coastal water environment quality index) and x14 (total industrial wastewater) reduced greatly and the coastal water environment not optimistic as a whole. At the same time, the value of x9 (habitat)、x10 (coastal biodiversity index)、x12 (environmental expenditure)、x13 (ecological land proportion) and x15 (comprehensive utilization rate of industrial solid) had increased. With the rapid development of the economy, the value of the socioeconomical system development index has changed greatly. The rapid decline in the value of x16 (population density)、x17 (urbanization

Fig. 3. Spatial distribution of comprehensive carrying capacity in Jiangsu coastal area from 2000 to 2015. 7

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Fig. 4. The change rate of comprehensive carrying capacity in Jiangsu coastal area from 2000 to 2015(The same row in the histogram represents one category respectively. In one category, blue represents a decreasing change rate and red represents an increasing change rate. The longer the stripe is, the greater the change rate.). (For interpretation of the references to colour in this figure legend, the reader is referred to the Web version of this article.) Table 4 The annual change rate of CRECC and carrying capacity of three systems in the study area (CRECC_s、the Resource’s system_s、the Ecological system_s and the socio-economical system_s means annual change rate of CRECC and three sys­ tems respectively.). Ganyu Lianyun Guanyun Xiangshui Binhai Sheyang Dafeng Dongtai Haian Rudong Tongzhou Haimen Qidong

CRECC_s

Resource_s

Ecological_s

0.005 0.001 0.007 0.014 0.005 0.013 0.016 0.006 0.017 0.001 0.046 0.035 0.009

0.038 0.047 0.01 0.017 0.029 0.047 0.03 0.021 0.026 0.036 0.052 0.001 0.019

0.025 0.004 0.05 0.064 0.034 0.027 0.054 0.008 0.001 0.016 0.02 0.014 0.024

Socio-economical _s 0.042 0.004 0.031 0.046 0.043 0.044 0.046 0.06 0.045 0.04 0.06 0.014 0.041

significant improvement of x12 (environmental expenditure) and other indicators. Although E1- Ecological status has a weighting value of 0.678 in this subsystem, E2- Environmental management significantly improved the ecological environment of the subsystem to maintain a

Fig. 5. Values of the 20 indicators of CRECC in Jiangsu coastal area from 2000 to 2015 (The indicator values shown in the figure are their normalized values.). 8

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slow increase of the Ecological system since 2000.

factors to the ecological carrying capacity of coastal zones. A variety of evaluation models in coastal areas has been widely used in various aspects of ecology, land, tourism, and population to assess the carrying capacity of regional resource-environment for social-economic activities. These evaluation models provided an important reference for regional sustainable development (Williams and Lemckert, 2007; Lin et al., 2011; Shi et al., 2013; Peng et al., 2019). Compared with the model that requires a higher mathematical statistics basis, CRECC is relatively simple and convenient to apply. As a multi-dimensional index evaluation method, the role of natural factors and the interference of human activities are taken into account when selecting indicators. CRECC also focuses on exploring the consumption of resources and the environment, and the balancing effects of regulatory measures on these negative impacts. Yang and Ding (2018) and Peng et al. (2019) respectively used the relative resource carrying capacity model and the ecological footprint theory to calculate the carrying capacity of Jiangsu Province. In their research results, it can be seen that most coastal areas in Jiangsu Province have large resource carrying pressure, but the ecological carrying capacity has improved. This is very similar to the results of this study. In the context of global change, the Jiangsu coastal zone is also facing the problem of balance between climate change and economic devel­ opment. CRECC provided a clear evaluation index system that reflects the actual resource and environmental carrying capacity of the coastal zone. Based on the evaluation results, CRECC could find short-boards in the process of regional sustainable development, improve the accuracy of decision-makers on coastal management, and enhance people’s awareness of the carrying capacity of coastal resource-environment.

4. Discussion and suggestions 4.1. Discussion From 2000 to 2015, CRECC of Jiangsu is basically in a downward trend, and about half of the region is at an overload level. In addition, the Socio-economical system has the largest rate of decline. According to our results, the major factor causing the decline of that is possibly the rapid growth of coastal population and marine development activities. In 2015, the population density in Jiangsu coastal areas was as high as 500 people per km2, about 3.4 times the average population density (146 capita/km2) in China. At the same time, the total economic output value of the study area increased from 11.4 billion Yuan to 39.5 billion Yuan. Similar results were obtained in Chinese scholars’ research on the marine ecology of Zhejiang Province and the coastal resources and environmental carrying capacity of Qingdao (Ma et al., 2017; Wang et al., 2017). In addition, Armono et al. (2017) also proved that the increase in the number of tourists will lead to the degradation of coastal resources and the reduction of ecosystem services in the case study of the ecotourism capacity of the Barulan National Park, Indonesia. The Ecological system has the largest contribution rate to CRECC, and the trends of E1- Ecological status and E2-Environment manage­ ment showed significant differences. The rising trend of E2-Environment management reflected the growing financial support and policy ratio­ nality of the Jiangsu government in recent years. This is consistent with the Chinese government’s calling for ecological civilization construction and the balance between economic development and environmental protection. In addition, the carrying capacity value of E1- Ecological status showed a turning point in 2008 after a declining trend. This is mainly due to the fact that x8 (vegetation coverage) and x11 (coastal water environment quality index) showed a turning point in 2008 after a decline at the same time. In the coastal areas of Jiangsu Province, there was a clear transition from resource-based industries to green industries in 2007–2014 (Ren and Sun, 2017). The reduction of extensive in­ dustries reduced the use of resources and industrial pollution, which may be one of the reasons for this phenomenon. Furthermore, the contribution rate of x8(vegetation coverage) and x11(coastal water environment quality index) to E1- Ecological status reached 55.9% and 30.4% respectively. The actual numerical fluctuation of the index was amplified because of the extreme value standardization and weight method. To a certain extent, good ecosystem conditions play an important role in maintaining the increase of CRECC, improvement of environmental pollution and coastal vegetation also are important

4.2. Implications and suggestions Based on the evaluation results and the contribution rate of all levels of indicators to CERCC, this study found that: (1) x8(vegetation coverage), x11(coastal water environment quality index), x2(tidal flat reclamation area), x9(habitat) and x13(ecological land proportion) are key indicators of CRECC; (2) The carrying capacity bottleneck of each dimension of Jiangsu coastal zone is different, and there are spatial differences in CRECC of 13 counties. Accordingly, the following suggestions are put forward: (1) The local government should strictly restrain the tidal flat reclamation, and standardize environmental impact assessment and marine-use demon­ stration. The three major benefits of economic, social and ecological must be achieved at the same time in the use of tidal flats area; (2) From the perspective of reducing industrial pollution, it should be done: in­ crease government environmental protection efforts and control

Fig. 6. The Socio-economical system and Resource-Ecological system index scatter plot. 9

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Fig. 7. Changing trend 6 first-class indicators from 2000 to 2015.

industrial pollutant emissions. According to the constraints of resources and environment in different regions, total energy consumption should be rationally controlled. In addition, the green development and clean low-carbon utilization of traditional energy security should be pro­ moted. The environmental supervision department shall raise the pollutant discharge standards and clean production evaluation stan­ dards for papermaking, printing, dyeing, chemical, building materials, nonferrous metals, tanning, and other industries. At the same time, it is necessary to develop organic agriculture, ecological agriculture and pay attention to narrowing the gap in regional spatial development during the process of industrial transforming and upgrading. (3) The cultivated land protection area shall be demarcated around the inhabited area, and it is strictly forbidden to occupy. A certain proportion of ecologically significant land must be preserved, such as farmland, grassland, wood­ land, nature reserves, important animal migration sites, and so on. (4) Strengthen the construction of green infrastructure in residential areas, and plant saline-tolerant vegetation, such as perennial biennial root and bulbous herbs, and some low-lying shrubs in the tidal flat area to improve vegetation coverage.

Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. Acknowledgements This work was supported by the marine science and technology innovation special projects of Jiangsu Province, China (Grant No. HY2018-3) and the National Natural Science Foundation of China (Grant No. 41871083). References Armono, H.D., Rosyid, D.M., Nuzula, N.I., et al., 2017. Carrying capacity model applied to coastal ecotourism of Baluran national Park, Indonesia. In: Cities 2016 International Conference: Coastal Planning for Sustainable, vol. 79. Maritime Development. https://doi.org/10.1088/1755-1315/79/1/012004. Arrow, K., Bolin, B., Costanza, R., et al., 1996. Economic growth, carrying capacity, and the environment. Science 1, 104–110. Bloom, J.D., Resnick, M., Ulwelling, J.J., et al., 1991. Environmental management and planning in urban regions - are there differences between growth and shrinkage? Am. J. Psychiatry 148, 1366–1370. � Galparsoro, I., Solaun, O., et al., 2006. The European Water Framework Borja, A., Directive and the DPSIR, a methodological approach to assess the risk of failing to achieve good ecological status. Estuar. Coast Shelf Sci. 66, 84–96. Brinson, Mark M., Bradshaw, et al., 1984. Nutrient assimilative capacity of an alluvial floodplain swamp. J. Appl. Ecol. 21, 1041. Broman, G.I., Rob�ert, K.H., 2017. A framework for strategic sustainable development. J. Clean. Prod. 140, 17–31. Clark, M., Tilman, D., 2017. Comparative analysis of environmental impacts of agricultural production systems, agricultural input efficiency, and food choice. Environ. Res. Lett. 12, 064016. Cohen, J.E., 1995. Population growth and earth’s human carrying capacity. Science 269, 341–346. Díaz, S., Pascual, U., Stenseke, M., et al., 2018. Assessing nature’s contributions to people. Science 359, 270–272. https://doi.org/10.1126/science.aap8826. Feng, Z., Sun, T., Tamartash, R., et al., 2018. The progress of resources and environment carrying capacity: from single-factor carrying capacity research to comprehensive research. J. Resour. Ecol. 9, 125–134. Gabb, W.M., 1873. On the topography and geology of santo Domingo. Trans. Am. Philos. Soc. 15, 49–259. Galli, A., Kitzes, J., Niccolucci, V., et al., 2012. Assessing the global environmental consequences of economic growth through the Ecological Footprint: a focus on China and India. Ecol. Indicat. 17, 99–107. Guiau, S., 1971. Weighted entropy. Rep. Math. Phys. 2, 165–179. H� ak, T., Janou�skov� a, S., Moldan, B., 2016. Sustainable Development Goals: a need for relevant indicators. Ecol. Indicat. 60, 565–573. https://doi.org/10.1016/j. ecolind.2015.08.003. Holling, C.S., 1973. Resilience and stability of ecological systems. Annu. Rev. Ecol. Evol. Syst. 4, 1–23. Jung, C., Kim, C., Kim, S., et al., 2018. Analysis of environmental carrying capacity with emergy perspective of Jeju Island. Sustainability 10. https://doi.org/10.3390/ su10051681.

5. Conclusion This study proposed a new CRECC evaluation model, which is used to assess the carrying capacity of the coastal ecological environment to social-economic development. The interaction mechanism inside the subsystem and the influence of key indicators on the regional CRECC are analyzed. The model has great advantages in reflecting the balance between the consumption of regional resource-environment and human control measures. From 2000 to 2015, the economic development and resource utili­ zation of the Jiangsu coastal zone are under great pressure, and there are spatial differences in the carrying capacity of each dimension in different regions. The ecosystem carrier responds strongly to the pres­ sure of the carrying object. Moreover, improving the vegetation coverage of the coastal zone, enhancing the water environment quality of the coastal waters, controlling the reclamation activities of the tidal flat, and protecting the habitat and other ecological land are the key measures to increase the carrying capacity of the coastal zone. In addi­ tion, this study found that the Resource’s system and the Ecological system presented a trade-off mechanism. This trade-off relationship enables the relevant departments to pay more attention to the rela­ tionship between the socio-economic system and the natural ecosystem in the coastal management process. Policymakers need to develop tar­ geted solutions to regional sustainable development bottlenecks and reasonable and feasible planning solutions from the perspective of improving the ecological resilience of the coastal zone and strength­ ening human intervention. 10

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