Modeling for regional ecosystem sustainable development under uncertainty — A case study of Dongying, China

Science of the Total Environment 533 (2015) 462–475

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Science of the Total Environment journal homepage: www.elsevier.com/locate/scitotenv

Modeling for regional ecosystem sustainable development under uncertainty — A case study of Dongying, China K. Zhang, Y.P. Li ⁎, G.H. Huang, L. You, S.W. Jin MOE Key Laboratory of Regional Energy Systems Optimization, S-C Resources and Environmental Research Academy, North China Electric Power University, Beijing 102206, China

H I G H L I G H T S

G R A P H I C A L

A B S T R A C T

• Superiority–inferiority two-stage stochastic programming (STSP) method is developed. • STSP can tackle uncertainties expressed as fuzzy sets and probability distributions. • STSP is applied to planning regional ecosystem sustainable development of Dongying. • Results show that uncertainty has an important role in planning regional ecosystem. • Results can help identify tradeoff between economic and ecological objective.

a r t i c l e

i n f o

Article history: Received 27 January 2015 Received in revised form 25 June 2015 Accepted 28 June 2015 Available online xxxx Editor: Simon Pollard Keywords: Ecosystem sustainability Land trading Optimization Reclamation projects Superiority–inferiority Uncertainty

a b s t r a c t In this study, a superiority–inferiority two-stage stochastic programming (STSP) method is developed for planning regional ecosystem sustainable development. STSP can tackle uncertainties expressed as fuzzy sets and probability distributions; it can be used to analyze various policy scenarios that are associated with different levels of economic penalties when the promised targets are violated. STSP is applied to a real case of planning regional ecosystem sustainable development in the City of Dongying, where ecosystem services valuation approaches are incorporated within the optimization process. Regional ecosystem can provide direct and indirect services and intangible benefits to local economy. Land trading mechanism is introduced for planning the regional ecosystem's sustainable development, where wetlands are buyers who would protect regional ecosystem components and self-organization and maintain its integrity. Results of regional ecosystem activities, land use patterns, and land trading schemes have been obtained. Results reveal that, although large-scale reclamation projects can bring benefits to the local economy development, they can also bring with negative effects to the coastal ecosystem; among all industry activities oil field is the major contributor with a large number of pollutant discharges into local ecosystem. Results also show that uncertainty has an important role in successfully launching such a land trading program and trading scheme can provide more effective manner to sustain the regional ecosystem. The findings can help decision makers to realize the sustainable development of ecological resources in the process of rapid industrialization, as well as the integration of economic and ecological benefits. © 2015 Elsevier B.V. All rights reserved.

⁎ Corresponding author. E-mail addresses: [email protected] (K. Zhang), [email protected] (Y.P. Li), [email protected] (G.H. Huang), [email protected] (L. You), [email protected] (S.W. Jin).

http://dx.doi.org/10.1016/j.scitotenv.2015.06.128 0048-9697/© 2015 Elsevier B.V. All rights reserved.

K. Zhang et al. / Science of the Total Environment 533 (2015) 462–475

1. Introduction Natural ecosystems provide a variety of direct and indirect services and intangible benefits to humans and other living organisms (Costanza et al., 1997; Daily, 1997). These ecosystem services, as well as their economic value have become a focus of interest for scientists, policy makers, and stakeholders over the last decade (Troy and Wilson, 2006). Effective ecosystem management plays a significant role for promoting regional ecosystem sustainability. Nevertheless, a number of issues such as industrial development, increasing urbanization, population growth, and human-activity expansion, exacerbate the degeneration of ecosystem services; the damaged ecosystem can hinder the socio-economic development over a long-term period. Reclamation, as one potential solution for socio-economic development, changes from natural ecosystems to croplands and urban areas, which can result in reducing biodiversity, altering functional processes, and diminishing provision of ecosystem goods and services to society. For instance, it has been observed that reclamation change into urban sites and industry is detrimental for several ecosystem services such as nutrient cycling, climate regulation, erosion control, recreation opportunities, soil fertility and water availability. Therefore, more effective methods for planning regional ecosystem sustainable development are desired. Previously, a number of approaches were developed for planning regional ecosystems with a sustainable development manner, and thus aided decision makers in formulating effective regional ecosystem management policies (Paetzold et al., 2010; Dagnino and Viarengo, 2014; Healey et al., 2014; Luisetti et al., 2014; Yang and Yang, 2014). For example, Prato (2000) proposed a two-stage hierarchical framework for identifying effective ecosystem management actions, which had the greatest likelihood of achieving a sustainable ecosystem state and provides the most preferred combination of non-ecological attributes. Hein et al. (2006) established an enhanced framework for the valuation of ecosystem services, which was used to support the formulation or implementation of ecosystem management plans. van Oudenhoven et al. (2012) developed a framework (distinguishing between ecosystem properties, ecosystem functions, and ecosystem services) to determine quantitative links between indicators for assessing effects of land management on ecosystem services. Muscolo et al. (2014) employed biological indicators (microbial biomass carbon, water soluble phenols and fluoresce in diacetate hydrolase) to determine both the effect and the relative importance of each indicator on soil quality for evaluating sustainability of forest ecosystem. Among them, one effective method to protect regional ecosystem is through the introduction of land trading, which increases the ecosystem services by encouraging its movement from low to high valued use. Land trading refers to the region trades directly the land with another region who fails to fulfill obligations of the regional ecosystem protection. When annual amounts of effluent discharges make each industrial district violate the regional ecosystem obligation, land trading will then become feasible. The industrial owners then face problems of how many lands are needed to be retired for regional ecosystem by measuring industrial benefits and uncertain penalties from random excess effluent exceeding to given discharge limits. In fact, ecosystems include a multitude of subsystems, where multiple processes and multiple factors considered by decision makers, involving the diversity and complexity of system components, the quantification of system's conditions, the fluctuation of parameters over medium or long-term time horizons, the dynamic variation of system's constrains, the controversy of ecological and social standpoints; moreover, these processes/factors as well as their interactions are of uncertainty features. As a result, the inherent complexities and stochastic uncertainties that exist in practical regional ecosystem management have inevitably placed the issue beyond the conventional deterministic optimization methods. Over the past decades, a number of research efforts were conducted for dealing with various uncertainties in the ecosystem management systems (Ahmed et al., 2004; Miao et al., 2014; You et al., 2014; Zhang et al., 2014; Haddad et al., 2015; Roy et al., 2015). Two-stage stochastic programming

463

(TSP) is effective for dealing with problems in which an analysis of policy scenarios is desired and the uncertainties can be expressed as probabilistic distributions (Ahmed et al., 2004). In TSP, an initial decision must be made before the realization of random variables (the first-stage decision), and then, a corrective action can be taken after random events have taken place (the second-stage decision). This implies that a second-stage decision can be used to minimize “penalties” that may appear, due to some infeasibility. However, TSP has difficulties in reflecting uncertainty existed as fuzzy sets. In fact, in many real-world problems, results produced by optimization techniques can be rendered highly questionable if the modeling inputs cannot be expressed with precision (Li et al., 2011b). Fuzzy programming (FP) can effectively deal with decision problems under fuzzy goal and constraints and handling ambiguous coefficients in the objective function and constraints; nevertheless, the conventional FP could generate a large number of additional constraints and variables, and thus resulted in complicated and time-consuming computation processes (Dong et al., 2014). Superiority–inferiority fuzzy programming (SFP) can directly reflect the relationships among fuzzy parameters through varying superiority and inferiority degrees (instead of various discrete intervals under different α-cut levels), leading to reduced computational requirements and improved practical applicability (Cai et al., 2009; Tan et al., 2010). Therefore, one potential approach for better reflecting uncertainties in the ecologic management systems is to incorporate SFP and TSP within a general framework; this leads to a superiority–inferiority TSP method. The objective of this study is to develop a superiority–inferiority twostage stochastic programming (STSP) model for regional ecosystem management under uncertainty. STSP can deal with uncertainties expressed as fuzzy sets and probability distributions through incorporating ecosystem services valuation into the optimization process. The STSP method is then applied to a real case of regional ecosystem planning in the City of Dongying (China), where a number of ecosystem services are quantitatively evaluated by multiple monetary valuation approaches and the land trading scheme is established to coordinate economic development and ecosystem protection. A multitude of scenarios that are associated with different ecosystem levels will be analyzed, which can help the local decision makers in gaining insight into the tradeoff between ecosystem services values and economic objectives. 2. Methodology When uncertainties of the model's right-hand sides are expressed as random variables and decisions need to be made periodically over time, the problem can be formulated as a two-stage stochastic programming (TSP) model (Ahmed et al., 2004; Li et al., 2011a). A TSP linear model can be formulated as follows: maxf ¼

n1 X

c jx j−

j¼1

n2 X v X

ph e j y jh

ð1aÞ

r ¼ 1; 2; …; m1

ð1bÞ

a0t j y jh ≥wh ;

ð1cÞ

j¼1 h¼1

subject to: n1 X

ar j x j ≤br ;

j¼1 n1 X

at j x j þ

j¼1

x j ≥0; y jh ≥0;

n2 X

t ¼ 1; 2; …; m2 ; h ¼ 1; 2; …; v

j¼1

j ¼ 1; 2; …; n1 j ¼ 1; 2; …; n2 ; h ¼ 1; 2; …; v

ð1dÞ ð1eÞ

where xj represent the first-stage decision variables, which have to be decided before the actual realizations of the random variables; yjh denote the second-stage decision variables, which are related to the

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K. Zhang et al. / Science of the Total Environment 533 (2015) 462–475

recourse actions against any infeasibilities arising due to particular realizations of the uncertainties; wh are random variables with probability levels ph, where h = 1, 2, …, v and ∑ph = 1. Models (1a), (1b), (1c), (1d) and (1e) can effectively deal with uncertainties in the right-hand sides presented as random variables (with known probability distributions) when coefficients in the lefthand sides and in the objective function are deterministic. However, in real-world practical problems, it is often difficult to build a probability distribution due to the lack of data or the high cost for acquiring the data. Various uncertainties may be related to the errors in acquired data, the variations in spatial and temporal units, and the incompleteness or impreciseness of observed information (Freeze et al., 1990; Mendoza et al., 1993). This can lead to dual uncertainties of randomness and fuzziness due to the fact that decision makers express different subjective judgments upon a same problem (Li et al., 2010). Fuzzy programming (FP) is capable of handling uncertainties presented as fuzzy sets, and is effective in reflecting ambiguity and vagueness in resource availabilities that present on the right-hand sides of the model (Inuiguchi et al., 1990). Thus, to deal with uncertain parameters presented as fuzzy sets, Models (2a), (2b) (2c), (2d) and (2e) can be reformulated, respectively, as follows: maxf ¼

n1 X

n2 X v X c jx j− ph e j y jh

j¼1

ð2aÞ

j¼1 h¼1

~ over P~ can be defined as the area of Q ~ larger The total superiority of Q ~ than P. Mathematically, this area can be presented as follows: S¼

~r ; ~r j x j ≤ b a

at j x j þ

j¼1

r ¼ 1; 2; …; m1

ð2bÞ

n2 X

a0t j y jh ≥wh ;

t ¼ 1; 2; …; m2 ; h ¼ 1; 2; …; v

ð2cÞ

j¼1

x j ≥0; y jh ≥0;

0

0

~ ≥ P; ~ if Q otherwise ð5aÞ

I¼

8 Z 1n < :

0

0

n o  o inf s : μ Q~ ðsÞ ≥α − inf t : μ P~ ðt Þ≥α dα ≥0

~ ≥ P; ~ if Q otherwise ð5bÞ

~ over P~ can be defined as: The superiority of Q   Z ~ P~ ¼ S Q;

1 0

n n o  o max 0; sup s : μ Q~ ðsÞ≥α − sup t : μ P~ ðt Þ≥α dα: ð6aÞ

~ to P~ is: Similarly, the inferiority of Q   Z ~ ; P~ ¼ I Q

1 0

n n o  o max 0; inf s : μ Q~ ðsÞ≥α − inf t : μ P~ ðt Þ≥α dα:

ð6bÞ

~ ¼ ðv; c; dÞ Consider two triangular fuzzy sets P~ ¼ ðu; a; bÞ and Q

j¼1 n1 X

:

n o  o sup s : μ Q~ ðsÞ≥α − sup t : μ P~ ðt Þ≥α dα ≥0

~ Similar result can also be obtained for the inferiority degree of P~ to Q:

subject to: n1 X

8 Z 1n <

j ¼ 1; 2; …; n1 j ¼ 1; 2; …; n2 ; h ¼ 1; 2; …; v

ð2dÞ ð2eÞ

~r can be presented as fuzzy sets. To solve Models (2a), where ãrj and b (2b), (2c), (2d), and (2e), an “equivalent” deterministic version can be defined. This can be realized by using superiority and inferiority measure, which could reduce number of constraints and variables than tra~ be a family of ditional measures. According to Van Hop (2007), let H triangular fuzzy numbers which can be defined as follows: n o ~¼ ~ H δ ¼ ðδ; a; bÞ; a; b≥0 ; 8   δ−x > > max 0; 1− if x≤δ; aN0 > > > a > < 1   if a ¼ 0 and=or b ¼ 0 μ ~δ ðxÞ ¼ x−δ > > > max 0; 1− if xNδ; bN0 > > b > : 0 otherwise;

ð3aÞ

  ~ P~ ¼ v−u þ d−b S Q; 2

ð7aÞ

~ can be quantified as: and the inferiority of P~ to Q   ~ ¼ v−u þ c−a : ~ Q I P; 2

ð7bÞ

Based on Eqs. (7a) and (7b), the following formulation can be obtained:  S ~f j ðai Þ;

  ~f ða Þ ≠I ~f ða Þ; j k j k

 ~f ða Þ : j i

ð8Þ

Through introducing superiority–inferiority measure into Models (2a), (2b), (2c), (2d) and (2e), a superiority–inferiority two-stage stochastic programming (STSP) model can be formulated as follows: maxf ¼

n1 X

c jx j−

j¼1

n2 X v X j¼1 h¼1

ph e j y jh −pi

m X

λSi −qi

i¼1

m X

λIi

ð9aÞ

i¼1

subject to: ð3bÞ

0 1 n1 X ~r A ¼ λS ; ~r j x j ; b Sr @ a r

r ¼ 1; 2; …; m1

ð9bÞ

r ¼ 1; 2; …; m1

ð9cÞ

j¼1

where scalars a and b (a, b ≥ 0; a, b ∈ R) are named the left and right spreads, respectively. A crisp (i.e. deterministic) number (δ ∈ R) can be illustrated as a triangular fuzzy set ~δ ¼ ðδ; 0; 0Þ . Based on the above definitions, a method for comparing fuzzy sets can be proposed through measuring superiority and inferiority degree. Beginning with ~ Thus, we have: α-cut levels of two triangular fuzzy sets (P~ and Q). P~ α ¼ μ P~ ðxÞ≥α;

ð4aÞ

~ ¼ μ ~ ðxÞ≥α: Q α Q

ð4bÞ

~ , then supfs : μ ~ ðsÞ≥αg− supfμ ~ ðtÞ≥αg≥0: If P~ α ≤ Q α P Q

~ According to Van Hop, the superiority of Q ~ over P~ can be ~ where P~ ≤ Q. ∈T, quantified as:

0 1 n1 X ~ ~r j x j ; br A ¼ λIr ; Ir @ a j¼1 n1 X

at j x j þ

j¼1

x j ≥0; y jh ≥0; λk ≥0;

n2 X

a0t j y jh ≥wh ;

t ¼ 1; 2; …; m2 ; h ¼ 1; 2; …; v

ð9dÞ

j¼1

j ¼ 1; 2; …; n1 j ¼ 1; 2; …; n2 ; h ¼ 1; 2; …; v k ¼ 0; 1; …; r

ð9eÞ ð9fÞ ð9gÞ

K. Zhang et al. / Science of the Total Environment 533 (2015) 462–475

where pi and qi are penalty coefficients, (pi N 0 and qi N 0). It is noticed that the penalty costs pi and qi are basically determined without any rule. Depending of the application situation, the decision maker can select the suitable value for penalty costs (Van Hop, 2007). Then, solutions of xj and max f can be obtained through Models (9aa), (9b), (9c), (9d), (9e) (9f) and (9g). The solution of the STSP model can be generated. The detailed computational processes for solving the STSP model can be summarized as follows: Step 1 Formulate the STSP model; ~ Step 2 Defined the fuzzy sets of P~ and Q, n o   sup s : μ Q~ ðsÞ≥α − sup μ P~ ðt Þ≥α ≥0 ; n o   inf s : μ Q~ ðsÞ≥α − inf μ P~ ðt Þ≥α ≥0 ;

~ Step 3 Obtain the superiority degree of P~ to Q,   Z ~ P~ ¼ S Q;

1 0

n n o  o max 0; sup s : μ Q~ ðsÞ≥ α − sup t : μ P~ ðt Þ≥α dα ;

~ or obtain the inferiority degree of P~ to Q,   Z ~ P~ ¼ I Q;

1 0

n n o  o max 0; inf s : μ Q~ ðsÞ≥α − inf t : μ P~ ðt Þ≥α dα :

~ ¼ ðv; c; Step 4 Quantify two triangular fuzzy sets (P~ ¼ ðu; a; bÞ and Q dÞ),     ~ P~ ¼ v−u þ d−b Q ~ ≥ P~ ; S Q; 2     ~ ¼ v−u þ c−a Q ~ ≤ P~ : ~ Q I P; 2 Step 5 Select the suitable value for penalty costs pi and qi; n1

~r Þ ¼ λS and Ir ~r j x j ; b Step 6 Solve the solution of max f, subject to Sr ð∑ a r j¼1

n1

~ r Þ ¼ λI ~r j x j ; b ð∑ a r j¼1

Step 7 Solve the STSP model under different mitigation levels of μ; Step 8 Obtain the optimal solution under different μ levels; Step 9 Stop.

465

3. Case study 3.1. Overview of the study system The City of Dongying located in the northern (Bohai Sea) coast of Shandong Province (China), is selected as the study area (as shown in Fig. 1). Its area is around 7923 km2. The population was approximately 2.03 million in 2010. Petroleum industry (Shengli oil field) plays a significant role in the city's economic development. In the past decades, petroleum industry and subsidiaries were the pillar industry of the city, leading to a single industry structure. The gross oil output accounted for more than 90% of gross domestic product (GDP) in Dongying. Recently, the city became the new economic development platform and driving force for economic stimulus in Shandong province, mainly containing three petro-chemical industries (i.e. chemical, plastic and rubber industries) and energy industries (i.e. oil refinery and oil field industries). In 2009, the industrial output was RMB¥ 65.1 billion, where approximately 31.6% of GDP was from the petroleum industry; mechanical and electronic industries were the second pillar industries of the city, occupying about 17.6% of GDP. From the 1990s, the city's land resources could not meet the increasing land demand due to booming urbanization and rapid industry development. A large number of reclamation projects were conducted for acquiring land resources to meet industry development, residents' growth, and human-activity expansion. For example, Donggang district (the residential community of Dongying), with an area of 60 km2 is constructed to be in accordance with a growth rate of population around 1.27% per year. Besides, the coastal land reclamation has played a significant role for the local economy growth, and reclamation has provided a large number of land resources to promote the regional industry development. However, a number of large-scale reclamation projects have brought a series of ecological problems, such as occupying coastal space and deteriorating coastal water quality, as well as endangering the animals and plants (e.g., benthic organisms and mangroves) (Wang et al., 2010a,2010b). For example, phytoplankton, the major producer of organic matter in marine, plays an important role in gas regulation service through photosynthesis. The Shannon–Weaver diversity index of phytoplankton decreased about 50% from 1983 to 2004 (Liang and Zhang, 2011). Land reclamation projects have severely threatened to the surrounding wetlands. Wetlands are one of the three major types of ecosystem on the Earth, and play an important role in maintaining ecological balance (Yang and Chen, 2013), such as protecting biological diversity. The area of seabeach wetland decreased

Fig. 1. The study area.

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K. Zhang et al. / Science of the Total Environment 533 (2015) 462–475

around 50% during the period from 1986 to 2007 (Liu and Qi, 2011). After reclamation, a large number of the natural wetlands are used for urban, port and coastal industry expansion. Because of exceedingly high intensity of exploitation and rapid character, regional ecosystem has become one of the most gravely disturbed regions, and which can block further regional socio-economic development. As a consequence, the wetland area is under severe pressure with the development of land reclamation activities. In order to maintain regional ecosystem sustainability, marketbased control measures are advanced to mitigate the conflict between economic development and eco-environmental protection. Ecocompensation mechanisms have made the local government consider the land reclamation projects costs including the damages to coastal ecosystem. Being regarded as one of the most promising ecocompensation mechanisms for tradeoff between economic benefit and ecosystem services values, land trading scheme is an efficient and economical approach to handle land allocation issues. Moreover, land trading has advantages of protecting regional ecosystem. In order to reduce the costs for pollutant emission, land trading is encouraged between industries and regional ecosystem (i.e. non-pollutant emission). The trading scheme can not only meet the pollutant emission mitigation requirement with a maximum system benefit, but also encourage the development of regional ecosystem services. Under land trading mechanism, a target quantity of land is allocated to each industrial activity. If this quantity is satisfied, it will bring about benefit for the industry. If this quantity exceeds the regulated, the industry will have to take measures to mitigate the pollutant emission. Through the scheme of land trading, each industry can sell directly land to other industries with higher profitability. The land can thus be reallocated to the most efficient industries instead proportionally allocated to each industry. In response to such a regulation, the industries need to optimize land traded amounts to achieve a maximized system benefit while to satisfy the pollutant emission reduction requirement. Therefore, markets for entitlements to extract land resources have even been introduced as a mechanism for land reallocation. It is inevitable to build up a land trading program for maintaining the regional ecosystem sustainability. 3.2. Modeling formulation

Max f ¼

Lt  NBit  PCAit þ

i¼1 t¼1

þ

4 X 2 X

2 X 2 X

NBmt ¼ ½ðPSAt  SAmt Þ þ ðPABt  PAT t  Pβmt Þ þ ðRDBt  RDT t  Rβmt Þ þ ðCP mt  C t Þ þ ð0:73  CP mt  Ot Þ þ ðWP mt  TC t Þ!þ ðWPMmt  PMC t Þ 3 X γq  PIqt  Iq þ ðSP mt  SC t Þ þ ðWSOmt  SOC t Þ þ ZP mt  q¼1

þ ðZP mt  ZCIt Þ þ Vt þ ðYCIt  YP mt Þ þ ðRP mt  ROt  RCIt Þ−KCImt  ð10  1dÞ subject to: (1) Reallocated land resources: 3 X

PCAit þ

i¼1

2 X

ERA jt þ WAt ≤TAt ;

∀t

ð10  2aÞ

j¼1

WAmt ¼

NBmt  WAt ; 4 X NBmt

∀t

ð10  2bÞ

m¼1

(2) Total sewage water permit: ðPW ith PC it −PDW it Þ  PCAit ≥EXPW ith ;  EW jth EC jt −EDW jt  ERA jt ≥EXEW jth ;

∀i;

ð10  2cÞ

t

∀i;

t

ðPW ith PC it −PDW it Þ  PCAit −EXPW ith ≤ ð1−μ Þ  SW it ; ∀i; t

ð10  2eÞ

 EW jth EC jt −EDW jt  ERA jt −EXEW jth ≤ ð1−μ Þ  SW jt ; ∀i; t

ð10  2fÞ

3 X

P F it  PC it  PCAit þ

2 X

E F jt  EC jt  ERA jt

j¼1 0 1 3 2 X X þ@ PDW it  PCAit þ EDW jt  ERA jt A  GW t i¼1

i¼1

≤CSW t þ

ð10  2gÞ

j¼1 4 X

α m ð1−μ Þ  RP mt  ROt  WAmt ;

∀t

m¼1

(5) Sewage water treatment capacity: 2 3 3 2  X X 4 ðPW ith PC it −PDW it Þ  PCAit þ EW jth EC jt −EDW jt  ERA jt 5 i¼1 4 X

j¼1

ð1−μ Þ  WP mt  WAmt þ TPC t ;

∀t

m¼1

ð10  2hÞ

3 X 2 X 3 X Lt  NBmt  WAmt − pith  Lt  DTC t  EXPW ith ð10  1aÞ

2 X 2 X 3 X

ð10  2dÞ

(3) Sewage water discharge:

≤

Lt  NB jt  ERA jt

j¼1 t¼1

m¼1 t¼1

−

ð10  1cÞ

(4) Water resources quantity:

In this study, a number of eco-environmental factors were considered, such as, available resources, pollutant treatment capacities, environmental requirement and ecosystem threshold. Besides, consider a number of petro-chemical/energy pollutants, such as sulfur dioxide (SO2), particulate matter with particle size below 10 μm (PM10) and solid wastes. For ecosystem values, eleven ecosystem services are taken into account (including direct values, carbon sequestration, oxygen release, sewage treatment, air pollutant absorption, solid wastes treatment, nutrient regulation, soil retention, biodiversity, water conversion, recreation and ecotourism). The objective is to achieve a maximized system benefit subject to a number of constraints; decision variables are used to describe the key points of the planed system, representing regional wetland ecosystem services values and industrial benefit. Based on the STSP method, the study problem can be formulated as follows: 3 X 2 X

h   NB jt ¼ ERI jt  EC jt −ERR jt  EC jt −EDW jt  TC t − ES jt  EC jt  η jt  SIt −EDS jt  SC t  −EDPM jt  PMC t −EDSO jt  SOC t

i¼1 t¼1 h¼1

(6) COD-discharge allowance:

p jth  Lt  DTC t  EXEW jth

j¼1 t¼1 h¼1

with

 NBit ¼ PCI it  PC it −PCRit  PC it −PDW it  TC t þ PSit  PC it  ηit  SI t −PDSit  SC t −PDPM it  PMC t −PDSOit  SOC t 

ð10  1bÞ

 GBCODt  ð1−GW t Þ PDW it  PCAit þ EDW jt  ERA jt þPCODit  ðPW it  PC it −PDW it Þ  PCAit  þECOD jt  EW jt  EC jt −EDW jt  ERA jt



4

≤ TCODt þ ∑ ωCODmt  WCODt  ð1−μ Þ  WP mt  WAmt ; m¼1

∀t

ð10  2iÞ

K. Zhang et al. / Science of the Total Environment 533 (2015) 462–475

(7) NH3-N-discharge allowance:

 GBNHt  ð1−GW t Þ PDW it  PCAit þ EDW jt  ERA jt þPNHit  ðPW it  PC it −PDW it Þ  PCAit  þENH jt  EW jt  EC jt −EDW jt  ERA jt



467

(9) SO2-discharge allowance: 2 4

3 3 2  X X ðPSOit  PC it −PDSOit Þ  PCAit þ ESO jt  EC jt −EDSO jt  ERA jt 5 i¼1

4

≤ TNH t þ ∑ ωNH mt  WNHt  ð1−μ Þ  WP mt  WAmt ;

∀t

m¼1

j¼1

≤ TSOt þ

4 X

ð1−μ Þ  WSOmt  WAmt ;

ð10  2lÞ

ð10  2jÞ (8) PM10-discharge allowance: 2 3 3 2  X X 4 ðPPM it  PC it −PDPMit Þ  PCAit þ EPM jt  EC jt −EDPM jt  ERA jt 5 i¼1

≤TPMt þ

j¼1 4 X

∀t

m¼1

ð1−μ Þ  WPM mt  WAmt ;

(10) Solid wastes discharge allowance: 3

2 h   i X X   PSit  PC it  1−ηit −PDSit  PCAit þ ES jt  EC jt  1−η jt −EDS jt  ERA jt i¼1

∀t

≤TPSt þ

m¼1

j¼1 4 X

ð1−μ Þ  SP mt  WAmt ;

∀t

m¼1

ð10  2mÞ

ð10  2kÞ

Optimal ecological land trading planning under uncertainty Energy industries

Natural wetlands Complexities of system Land reclamation policy analysis

Petro-chemical industries

Constructed wetlands Randomness in sewage discharge Economic and industrial data Ecosystem service value assessment

Multiply uncertainties

Ecosystem services values

Radom events

Fuzzy sets

Probability distributions

Possibility distributions

Two-stage stochastic programming (TSP)

Superiority-inferiority fuzzy programming (SFP)

Direct services values

Indirect services values

Market price approach (MPA)

Surrogate market approach (SMA)

Mitigation levels of ecosystem services and sewage discharge permit Regional ecosystem Management of Dongying district

Superiority-inferiority two-stage stochastic programming (STSP) model

Generation of decision alternatives

Fig. 2. Framework of the STSP modeling system.

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K. Zhang et al. / Science of the Total Environment 533 (2015) 462–475

(11) Land reclamation intensity: 0 1 3 2 4 X X X @ PCAit þ ERA jt þ WAmt A=CLt ≤ ð1−μ ÞTRt ; i¼1

Table 1 Modeling inputs for production and product benefit of industries.

(12) Vegetation covering: 3 X ðP FRt  PCAit Þ

þ

2  X

4 X ðPβmt þ Rβmt Þ  WAmt ; E FRt  ERA jt ≤

j¼1

∀t ð10  2oÞ

m¼1

(13) Water demand of river wetland: 4 X

ð1−α m Þ  RP mt  ROt  WAmt ≥

m¼1

E TX Q ; n e¼1 e min

∀t

ð10  2pÞ

(14) Nonnegative constraint: PCAit ; ERA jt ; WAmt ≥0; EXPW ith ; EXEW jth ≥0;

t=1

m¼1

j¼1

ð10  2nÞ

i¼1

Time period

∀t

∀i; j; ∀i; j;

t t:

ð10  2qÞ ð10  2rÞ

The detailed nomenclatures for the variables and parameters are provided in Appendix A of the paper. The planning time horizon is 10 years, the first period is from 2015 to 2020, and the second period is from 2020 to 2025. Fig. 2 summarizes the framework of the STSP modeling system. 3.3. Data acquisition and analysis The representative data are investigated based on a number of government reports and a variety of related literatures. In the study area, available water resources include surface water, groundwater, sea water desalination, and recycled water. The available water resources are designed as 107 × 106 m3 in period 1 and 108 × 106 m3 in period 2. The local decision makers plan to develop and expand sewage treatment plants, and the capacity of sewage treatment will reach to 72.3 × 103 and 200 × 103 m3/year in periods 1 and 2, respectively. According to the city's ecological construction master plan, the concentration of COD discharged has a more stringent restrict for 7 kg/RMB¥ 106; meanwhile, a multitude of policies would be implemented to reduce the industrial air-pollutant emissions and guarantee air quality. For example, the industries making benefit of RMB¥ 10 × 103 has restrict for 2.9 kg of air-pollutant. In addition, the comprehensive reutilization ratio for solid wastes was 89% in 2013 and it is expected to increase up to 90% in this planning period. The data of planned and reclaimed land area are collected from Dongying twelfth five-year plan (2011), Dongying municipality's ecosystem construction master plan (2003–2020) and Dongying land reclamation projects planning outline (2010). Table 1 displays the modeling inputs for the chemical, plastic, rubber, oil refinery and oil field industries. For example, Oil refinery industry will produce 4249 ton/ha · year in period 1 and 4421 ton/ha · year in period 2. Income from oil refinery product is RMB¥ 9000/ton in period 1 and RMB¥ 9150/ton in period 2. To develop value estimates for the regional ecosystem services affected by the proposed reclamation projects, we assemble relevant data from different sources. Table 2 presents the modeling inputs for the benefit of constructed wetland by market price approach (MPA) and surrogate market approach (SMA). Market price approach estimates the economic value of

t=2

Production of petro-chemical industry i per ha (ton/ha · a) Chemical industry 2415 Plastic industry 2500 Rubber industry 1807.25

2925 2810 2100.65

Production of energy industry j per ha (ton/ha · a) Oil refinery industry 4249.766 Oil field industry 4014.2

4421.38 4202.85

Product benefit of petro-chemical industry i (RMB¥/ton) Chemical industry 10,728.43 Plastic industry 9376.19 Rubber industry 15,031.48

11,627.08 9385.20 15,737.69

Product benefit of energy industry j (RMB¥/ton) Oil refinery industry 9000 Oil field industry 4500

9150 4600

ecosystem products or services that are bought and sold in commercial markets. For example, seafood production may be conveniently valued by market price methods. Besides, surrogate market approach is that a market for private goods and services that reflects nonpriced environmental services (Curtis, 2004). The goods or services bought and sold in these surrogate markets permit inferences regarding how much environmental services are valued (Mirovitskaya and Ascher, 2002). For example, the destruction of grassland wetland leads to its release oxygen capacity loss. Specific data used for estimating each of the services values are described as follows. Table 3 shows total industrial sewage discharges under probability levels. Considering the values of regional ecosystem, eleven ecosystem services are taken into account. These services values can be evaluated by two methods: (1) direct market approach is used where market prices of outputs (and inputs) are available, which includes productivity loss and public pricing methods; (2) surrogate market approach is used to establish a surrogate market from which the shadow prices can be derived, including substitute cost, restoration cost and travel cost methods (Wang et al., 2010a,2010b). For example, timber is valued at local market prices net of input costs and extraction costs (Gammage, 1994). Besides, the values of absorbing CO2 and releasing O2 can be evaluated using surrogate market approach. According to the formula of photosynthesis and respiration, when wetland ecosystem absorbs 1 g CO2, 0.73 g O2 can be released (Chen et al., 2004; You et al., 2014); around 81% of COD input was removed due to sedimentation in the ditch and

Table 2 Modeling inputs for benefit of constructed wetland per ha. Ecosystem services values

Time period t=1

Direct use values

Indirect use values

Birds (RMB¥/ha·a) Reed (RMB¥/ha·a) Pasture (RMB¥/ha·a) Carbon sequestration (RMB¥/ha·a) Oxygen release (RMB¥/ha·a) Sewage treatment (RMB¥/ha·a) Water conservation (RMB¥/ha·a) Soil retention (RMB¥/ha·a) Nutrient regulation (RMB¥/ha·a) Solid wastes treatment (RMB¥/ha·a) Air pollutant absorption (RMB¥/ha·a) Biodiversity (RMB¥/ha·a) Recreation and ecotourism (RMB ¥/ha·a)

t=2

6369 6581 1078 1106 1040 1044 1,349,247 1,358,363 333,075 335,325 1663 1730 6,192,173 6,204,954 1,652,112 1,669,021 945,822 964,865 16,629 17,304 5390 5639 5212 5212 2123 3228

K. Zhang et al. / Science of the Total Environment 533 (2015) 462–475

high efficient land use, low pollutant discharge, and prior ecosystem protection. Besides, 1012.8 ha land resources would be purchased by wetlands, which would provide more ecosystem services than smaller or more fragmented bodies of wetlands. In future, wetlands would play a significant role in maintaining a high ecosystem services values. Fig. 4 presents the solutions for values of wetland services (symbols “WC, CS, OR, SR and NR”, denote the services of “water conservation, carbon sequestration, oxygen release, soil retention and nutrient regulation”, respectively). Results indicate that water conservation would generate the highest value, accounting for around 58.9% of the total ecosystem values. This because wetland is a huge reservoir, especially lakes, swamp and reservoirs wetlands. Thus, the service contributes to protecting water regulation and supply, which can generate huge ecobenefit to the system. For meadow and lakes (WA2), the average revenue of water conservation would be RMB¥ 6.24 × 106 per year. In the future, the demand for water supply and wastewater treated in natural areas would continue to increase due to the raising wetland areas. The value of nutrient regulation would increase in period 2. For example, for mud flat (WA1), the benefit of nutrient regulation would be RMB¥ 950 × 103 per year in period 1 and RMB¥ 970 × 103 per year in period 2. The service mainly depends on the structural parts of ecosystem, especially vegetation cover and root system, which is contributed to protect soil organic and inorganic fertility. Fig. 5 provides system benefits under different mitigation levels and sewage discharge permits. The system benefit with the trading is higher than that with non-trading scheme. In addition, the system benefit would decrease as μ is raised. For example, with the trading scheme, the system benefit would decrease from RMB¥ 408.8 × 109 (S1-T) to RMB¥ 405.3 × 109 (S5-T). A higher μ denotes an enforced mitigation level. Maintaining regional ecosystem integrity with being seriously considered (μ = 0.2) can lead to a lower system benefit. Conversely, a plan with consideration of taking full use of the services of regional ecosystem (μ = 0) would result in a higher system benefit; however, when the regional ecosystem crosses a threshold, irreversible loss of critical natural capital will be induced, with enormous costs to the city. This implies that industrial scale could be reduced in order to protect the ecosystem (Table 4).

Table 3 Industrial total sewage discharge under probability levels (units: m3). Level of industrial sewage discharge

Probability

h=1

0.2

h=2

0.6

h=3

0.2

Type

i=1 i=2 i=3 j=1 j=2 i=1 i=2 i=3 j=1 j=2 i=1 i=2 i=3 j=1 j=2

Time period t=1

t=2

16.01 12.35 17.68 14.58 7.58 20.01 15.44 22.10 18.23 9.48 24.01 18.53 26.52 21.87 11.38

16.84 12.29 17.87 14.20 7.49 21.05 15.36 22.34 17.75 9.36 25.26 18.43 26.81 21.30 11.24

accumulation and mineralization in the wetland soil (Meuleman et al., 2003). The biochemical degradation capacity of COD is 0.79 m3/ ha · day, while the average cost for treating sewage is about RMB¥ 5.4/m3. 4. Results analysis 4.1. Results under land trading In this study, ten scenarios corresponding to different trading schemes (i.e., trading and non-trading) were examined. Fig. 3 displays the land area traded between industries (symbols “PC1, PC2, PC3, ER1 and ER2” denote “chemical, plastic, rubber, oil refinery and oil field industries”, respectively) and wetlands (symbols “WA1, WA2, WA3 and WA4” denote “mud flat, meadow and lakes, swamp, constructed wetland”, respectively) under different μ values (i.e., 0, 0.05, 0.1, 0.15 and 0.2). Results indicate that land trading schemes would change with ecosystem services and sewage discharge permits. The positive value represents the amount of seller's market; the negative value represents the amount of buyer's market. For example, under μ = 0 in period 2, the land trading amounts wound be 203.7 ha (PC1), 182.3 ha (PC2), 116.9 ha (PC3), 215.7 ha (ER1), and 294.1 ha (ER2). Among them, oil field (ER2) is the main seller who offers land resources to the others. This is mainly because oil field has relatively higher pollutant emissions than the other industries. This implies that, in order to maintain the regional ecosystem integrity, oil field would be suggested to take a new way of eco-industrialization, which is characterized by high technology,

400

S1

469

4.2. Comparison of trading and non-trading schemes Fig. 6 provides the planning area of each industry obtained through trading and non-trading schemes (when μ = 0.1). The results indicate that land allocation plan under trading scheme is obviously different from that under non-trading. For example, in period 1, the planning area for oil field would be 563.5 ha with non-trading and 241.2 ha

S2

S3

S4

PC1

PC2

S5

Land area(ha)

250

100

-50

-200

-350 PC1

PC2

PC3

ER1

ER2

WA1 WA2 WA3 WA4

PC3

ER1

Period 1 Fig. 3. Land area traded between industries and wetlands.

ER2 Period 2

WA1 WA2 WA3 WA4

K. Zhang et al. / Science of the Total Environment 533 (2015) 462–475

with trading; while the total planning area of all wetlands would be 1539.7 ha with non-trading and 2498.1 ha with trading. Variation in the values of wetlands and industries planning area reflects different policies for planning regional ecosystem and managing industrial activities. Under trading scheme, larger fragmented of bodies of wetlands provide original ecosystem services, increase the circulation of nutrients between ecosystems, improve the ability of the ecosystem to absorb and dilute wastes, and directly mitigate deterioration of the ecological environment and reduction of its ecosystem services. This implies that land trading is likely to maintain ecosystem sustainability and increase economy growth. Excess sewage would occur if the environmental infrastructures from the industries could not meet the industrial pollutant discharges. Fig. 7 shows excess sewage discharges from industries under scenario 3. In period 2, for all industries, the total excess sewage discharges would be 19.7 × 103, 43.4 × 103 and 67.1 × 103 tons under low, medium and high sewage generation levels with non-trading; while under the trading, the total excess sewage discharges would be 0 × 103, 5.7 × 103 and 13.6 × 103 tons, respectively. Fig. 8 presents the amounts of COD from industrial activities on trading and non-trading schemes, indicating that the amount of COD discharged from chemical industry is the highest in period 2. Under the medium sewage discharges, the COD discharges of chemical industry would reduce from 3.04 × 103 tons with non-trading to 1.44 × 103 tons with trading. Trading scheme mitigates the burden of ecosystem sewage and COD purification service. This can thus help minimize the penalty due to sewage excess, and increase stability of the ecosystem components and selforganization. Fig. 9(a) displays the amounts of SO2 from industrial activities on trading and non-trading schemes. Results indicate that the amount of SO2 discharged from oil refinery industry is the highest. In period 1, the SO2 discharges of oil refinery industry would reduce from 2.15 × 103 tons with non-trading to 0.86 × 103 tons with trading. SO2 emissions have severely impact on the regional ecosystem, such as climate change. Fig. 9(b) shows the amounts of solid wastes from

a

6.19 6.12

Benef it (106 RMB¥/ha)

8

6.24 6.16

6 1.35 1.39

4

1.65 0.33

1.36

2

0.95

1.66

0.34

0.95

1.65

1.29

0.34

1.66

0

WA4

0.94

0.32

WA3

0.95

WA2 WC

CS

WA1

OR

SR NR

b

Non-trading

408.8 410

407.9

407.1

406.2

405.3

390

370 370.7

368.1

365.6

363.0

350 S1

S2

S3

S4

360.5 S5

Fig. 5. System benefits under different mitigation levels.

industrial activities on trading and non-trading schemes, indicating that the solid wastes discharges of rubber industry would be 4.79 × 103 tons with non-trading and 3.09 × 103 tons with trading. Comparing the amounts of SO2 and solid wastes under trading and that under non-trading, the efficiency of trading and non-trading would be acquired. Therefore, in order to improve the regional air and soil ecosystem, land trading scheme can efficiently control and mitigate the air pollutant and solid wastes from industries. 5. Discussion Compared with the previous studies, the developed STSP has advantages in uncertainty reflection and policy analysis, particularly when the input parameters are provided as probabilistic distributions and fuzzy sets; while the conventional TSP method can only deal with uncertainties presented as random variables (with known probability distributions). The defuzzifying process of conventional fuzzy programming approaches often utilizes ranking operations or the discretizing process of fuzzy sets via α-levels to defuzzify partly/completely fuzzy stochastic variables, which is to create a large number of additional constraints and variables, and the computation process of the expected value is quite complicated and time consuming. In comparison, the STSP can easily reduce significantly number of additional constraints and variables; meanwhile, the method can be the main reason to simplify our solution process and make the conversion execute more efficiently. This is the first attempt to introduce STSP into Dongying's ecosystem sustainable development planning. In this study, oil filed is one of the major pollutant emissions in the city of Dongying, which could impede the local ecosystem sustainability. Through controlling pollutant emissions with trading scheme in Dongying, solutions obtained provide more practical decision bases for regional ecosystem sustainability. Local decisions could thus analyze various policies that are related to different levels of ecological protection levels when the pre-regulated pollutant generation schemes are violated. There are several assumptions for formulating the STSP model, which may bring some limitations for maintaining ecosystem sustainability in Dongying such that (i) as the main driving force of the regional

6.20 6.24

8

Benef it (106 RMB¥/ha)

Trading

430

System benef it (109 RMB¥)

470

6.11

Table 4 The list of scenarios.

6.16

6

Abbreviation Trading scheme

1.36

1.67

1.37

4 1.34

2

0.34 0.34

1.28

0.33

1.68 1.67

1.68

0.97 0.96

0.32

0

0.97

WC

CS

OR

S1

0.96

WA4

S2

WA3 WA2 WA1

S3 S4

SR NR

Fig. 4. Ecosystem services values of wetlands [(a) period 1 and (b) period 2].

S5

Scenario 1 without mitigation of ecosystem services and sewage discharge permit Scenario 2 with 5% mitigation of ecosystem services and sewage discharge permit Scenario 3 with 10% mitigation of ecosystem services and sewage discharge permit Scenario 4 with 15% mitigation of ecosystem services and sewage discharge permit Scenario 5 with 20% mitigation of ecosystem services and sewage discharge permit

K. Zhang et al. / Science of the Total Environment 533 (2015) 462–475 Period 1

Period 2

471

Planning area (ha)

1500

1000

500

0

NT T

NT T

NT T

NT T

NT T

NT T

NT T

NT T

NT T

PC1

PC2

PC3

ER1

ER2

WA1

WA2

WA3

WA4

Fig. 6. Planning area for each industry and wetland under scenario 3.

economic development, only petro-chemical and energy industries are examined; (ii) because one ecological service may overlap another, the problem of double counting may not be avoided completely; (iii) each wetland in the city has the same ecological structure; if each wetland is considered as different structures such that more complexities would be generated which beyond the model's mathematical expression capacity; (iv) due to the multiple activities or processes with complicated and dynamic interrelationships, the required data for the planning study are extensive; most of that are relatively accurate (deterministic numbers), others are less so (highly uncertain); improving the quality of the input data through further investigation and verification would contribute to enhancing the reliability of the generated solutions. Ecosystem services valuation approaches can quantitatively count the economic values of the services and push the integration of ecological and economic dimensions in decision-making to the foreground. Meanwhile, changes in quality or quantity of ecosystem services have value insofar as they either change the benefits associated with human activities or change the costs of those activities. Some useful information (benefits or costs with human activities) is provided to regional decision makers. Nevertheless, the ecosystem service values calculated using direct market approach and surrogate market approach are all subject to the well-known imperfections, including the questionable assumption of perfect substitutability between ecosystem services and manmade alternatives, which has difficulties in obtaining more accurate ecosystem service functions. Secondly, the total ecosystem service value is not simply the sum of relevant individual service, which may lead to double count or underestimation. Generally, these

Excess discharge (103 tonne)

Low

approaches can be used as an efficient tool for analyzing and visualizing impacts of regional economic strategies, and ecosystem integrity protection measures in an interactive, flexible and dynamic context. 6. Conclusions In this study, a superiority–inferiority two-stage stochastic programming (STSP) method has been developed for planning regional ecosystem under uncertainty. Compared with the conventional mathematical techniques, this method improves upon superiority–inferiority fuzzy programming (SFP) and two-stage stochastic programming (TSP) by allowing uncertainties expressed as fuzzy sets and probability distributions to be effectively incorporated within a general framework; it can be used to analyze various policy scenarios that are associated with different levels of economic penalties when the promised policy targets are violated. Moreover, it can incorporate ecosystem services valuation methods directly into its optimization process, such that an effective linkage between economic values and ecosystem services can be provided. Then the desired systematic strategy with maximized system benefit and ecosystem services under uncertainty can be acquired based on the superiority–inferiority two-stage stochastic programming method. The developed method has been applied to a real case of planning regional ecosystem sustainable development in the City of Dongying, China, where the obtained results indicate that land trading scheme is efficient scheme for ecosystem management. A number of scenarios associated with different mitigation levels of ecosystem services and sewage discharge permits are considered. Results of regional ecosystem

Medium

High

20

10

High Medium Low

0 T

NT PC1

T

NT PC2

T

NT PC3

T

NT ER1

Period 1

T

NT ER2

T

NT PC1

T

NT PC2

T

NT PC3

Period 2

Fig. 7. Excess sewage discharges from industries under scenario 3.

T

NT ER1

T

NT ER2

472

K. Zhang et al. / Science of the Total Environment 533 (2015) 462–475

4

COD (103 tonne/year)

Trading

Non-trading

3

2

1

0 L

M

H

PC1

L

M

H

PC2

L

M

H

L

PC3

M

H

ER1

L

M

H

ER2

L

M PC1

H

L

M

H

L

PC2

M

H

L

PC3

Period 1

M ER1

H

L

M

H

ER2

Period 2 Fig. 8. The amounts of COD from industries.

activities, land use patterns, and land trading schemes have been obtained. Results reveal that oil field is the significant contributor with a large number of pollutant discharges into regional ecosystem, resulting in impeding to regional eco-economic sustainable development. Thus, the petro-chemical and energy industries could take a new way of eco-industrialization, which is characterized by high technology, high

efficient land use, low pollutant discharge, and prior ecosystem protection. In addition, wetlands play an important role in maintaining local sustainable development; moreover, the ecosystem services values (such as wastewater treatment, recreation and ecotourism, soil retention) would bring direct and indirect revenue for local socio-economic development. It is fundamental to meet the land demand of socio-

3

SO2 (103 tonne/year)

Non-trading

a

Trading

2

1

0 PC1

PC2

PC3

ER1

ER2

PC1

PC2

Period 1

PC3

ER1

ER2

Period 2

8

Solid wastes (106 tonne/year)

Non-trading

b

Trading

6

4

2

0 PC1

PC2

PC3 Period 1

ER1

ER2

PC1

PC2

PC3

ER1

Period 2

Fig. 9. The amounts of pollution from industries [(a) SO2 and (b) solid wastes].

ER2

K. Zhang et al. / Science of the Total Environment 533 (2015) 462–475

economic development; besides, protecting regional ecosystem components and self-organization and maintaining its integrity. Finally, land trading mechanism is introduced for planning the regional ecosystem sustainable development, where wetlands are buyers who would purchase the land resources from industries (sellers). Therefore, these findings can help decision makers to realize the sustainable development of ecological resources in the process of rapid industrialization, as well as the integration of economic and ecological benefits. However, there are still some extensive researches to be undertaken. (i) The economic benefit of new industries (such as, port industry) should be considered in the City of Dongying. Improvements would be desirable in further investigations to consider the local port industry. (ii) Some ecosystem services (e.g., genetic sources, esthetics and culture) cannot be easily evaluated by estimating only their economic values; therefore, it is necessary to advance more sophisticated methods to evaluate these services. (iii) The effectiveness of a trading program is very sensitive to trading cost, and trading could fail when the cost is too high; thus the trading cost should be considered as a primary factor for regional ecosystem sustainability panning. Therefore, a manager of regional ecosystem management in the City of Dongying should consider the trading cost, which can improve the efficiency of the ecosystem sustainable development. (iv) The policies of land trading program should be formulated by the relationships between resources availability and regional ecosystem sustainability (e.g., economic development, social progress and ecological protection), since fallacious policies would lead to land trading failure in response to the trading cost and other opportunity costs. Consequently, the current study can contribute not only to provide a starting point for potential land trading mechanism establishes to ecosystem management activities in Dongying, but to the study of the improvements to other coastal regions with a large-scale land reclamation project. Correspondingly, the specific suggestions to the authorities can be summarized as follows: (i) advanced industrial pollutant treatment technologies (e.g., tertiary wastewater treatment and depth processing technologies) should be recommended to further improve pollutant removal efficiency; (ii) the investment for local ecocompensation (e.g., land trading scheme) measure should be further implemented to achieve regional ecosystem sustainable development; and (iii) although it may be infeasible to stop all reclamation projects in this area, it is inevitable that the local decision makers should systematically limit large scale reclamation activities and consider rigorously the regional ecosystem sustainability in the future. Acknowledgments This research was supported by the National Basic Research Program of China (2013CB430406 and 2013CB430401), the National Natural Science Foundation of China (51225904), and the 111 Project (B14008). The authors are grateful to the editors and the anonymous reviewers for their insightful comments and suggestions. Appendix A

f t Lt i j m NBit NBjt NBmt PCit ECjt

expected system benefit (RMB¥) planning period, t = 1, 2 length of planning period (year) petro-chemical industry, i = 1, 2, 3 energy industry, j = 1, 2 wetland, m = 1, 2, 3, 4 are mud flat, meadow and lakes, swamps, constructed wetlands, respectively. benefit of petro-chemical industry i per ha (RMB¥/ha) benefit of energy industry j per ha (RMB¥/ha) benefit of wetland m per ha (RMB¥/ha) production of petro-chemical industry i per ha (ton/ha·a) production of energy industry j per ha (ton/ha·a)

PCIit ERIjt PCRit ERRjt PWith PPMit PSOit PSit EWjth EPMjt ESOjt ESjt PDWit PDPMit PDSOit PDSit EDWjt EDPMjt EDSOjt EDSjt ηit ηjt TCt PMCt SOCt SCt SIt DTCt PSAt PABt RDBt SAmt PATt RDTt Pβmt Rβmt CPmt Ct Ot WPmt RPmt ROt RCIt ZPmt ZCIt γq PIqt

473

product benefit of petro-chemical industry i (RMB¥/ton) product benefit of energy industry j (RMB¥/ton) product cost of petro-chemical industry i (RMB¥/ton) product cost of energy industry j (RMB¥/ton) sewage discharge of per production of petro-chemical industry i with probability of pith (m3/ton) PM10 discharge of per production of petro-chemical industry i (kg/ton) SO2 discharge of per production of petro-chemical industry i (kg/ton) solid wastes discharge of per production of petro-chemical industries i (ton/ton) sewage discharge of per production of energy industries j with probability of pith (m3/ton) PM10 discharge of per production of energy industries j (kg/ ton) SO2 discharge of per production of energy industries j (kg/ ton) solid wastes discharge of per production of energy industries j (ton/ton) amounts of disposing sewage of petro-chemical industries i per ha (m3/ha) amounts of disposing PM10 of petro-chemical industries i per ha (kg/ha) amounts of disposing SO2 of petro-chemical industries i per ha (kg/ha) amounts of disposing solid wastes of petro-chemical industries i per ha (ton/ha) amounts of disposing sewage of energy industries j per ha (m3/ha) amounts of disposing PM10 of energy industries j per ha (kg/ ha) amounts of disposing SO2 of energy industries j per ha (kg/ ha) amounts of disposing solid wastes of energy industries j per ha (ton/ha) comprehensive utilization rate of solid wastes to petrochemical industries i (%) comprehensive utilization rate of solid wastes to energy industries j (%) sewage treatment cost (RMB¥/m3) PM10 treatment cost (RMB¥/ton) SO2 treatment cost (RMB¥/ton) solid wastes treatment cost (RMB¥/ton) recyclable solid wastes benefit (RMB¥/ton) penalty of excess sewage exceeding to discharge permit (RMB¥/m3) benefit from wetland per head (RMB¥/head) product benefit of hay (RMB¥/ton) product benefit of reed (RMB¥/ton) amounts of birds per ha (head/ha) amounts of hay per ha (ton/ha) amounts of reed per ha (ton/ha) meadow coefficient of wetlands m (%) reed coefficient of wetlands m (%) absorbing dioxide ability of wetland m per ha (ton/ha) carbon tax (RMB¥/ton) oxygen price (RMB¥/ton) water purification ability of wetland m per ha (m3/ha) water conservation ability of wetland m per ha (m3/ha) rainfall runoff coefficient (%) water price (RMB¥/m3) soil erosion index of wetland m per ha (ton/ha · a) price of river dredging and cleaning (RMB¥/ton) soil element content q (%) price of fertilizer q (RMB¥/ton)

474

Iq WPMmt WSOmt SPmt Vt YCIt YPmt KCImt PFit EFjt GWt αm CSWt TPCt  TCODt  TNH t TPMt TSOt TPSt PCODit ECODjt PNHit ENHjt WCODt WNHt WPMmt WSOmt ωCODmt ωNHmt GBCODt GBNHt TAt SWit SWjt CLt TRt PFRt PFRt T n Qe min

K. Zhang et al. / Science of the Total Environment 533 (2015) 462–475

soil elements in proportion of fertilizer (%) absorbing PM10 ability of wetland m per ha (kg/ha · a) absorbing SO2 ability of wetland m per ha (kg/ha · a) disposing solid wastes ability of wetland m per ha (ton/ha · a) price of biodiversity per ha (RMB¥/ha) per capital tourist consumption (RMB¥/person) annual tourism arrivals of wetland m per ha (person/ha · a) maintenance costs of wetland m (RMB¥/ha · a) water consumption of per unit production of petro-chemical industries i (m3/ton) water consumption of per unit production of energy industries j (m3/ton) recycling rate of treated water from the sewage treating plants (%) wetland hydrating coefficient regional total available water resources (m3) capacity of sewage treatment plant (m3) allowable amount of industrial COD emissions (kg) allowable amount of industrial NH3-N emissions (kg) allowable amount of industrial PM10 emissions (kg) allowable amount of industrial SO2 emissions (kg) allowable amount of industrial solid wastes emissions (ton) COD emissions of per unit production of petro-chemical industries i (kg/m3) COD emissions of per unit production of energy industries j (kg/m3) NH3-N emissions of per unit production of petro-chemical industries i (kg/m3) NH3-N emissions of per unit production of energy industries j (kg/m3) COD amount of per unit sewage by wetland (kg/m3) NH3-N amount of per unit sewage by wetland (kg/m3) absorbing PM10 ability of wetland m per ha (kg/ha) absorbing SO2 ability of wetland m per ha (kg/ha) removal rate of COD (%) removal rate of NH3-N (%) COD concentration of sewage water discharge in emission standard (kg/m3) NH3-N concentration of sewage water discharge in emission standard (kg/m3) total planning area (ha) sewage discharge permit to petro-chemical industries i (m3) sewage discharge permit to energy industries j (m3) length of the coastline (km) reclamation intensity coefficient vegetation area of petro-chemical industries per ha (ha/ha) vegetation area of energy industries per ha (ha/ha) conversion coefficient, value is 31.536 × 106 statistical number of year (a) minimum monthly average runoff (m3/s)

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