Assessment of water quality trading market performance through regulating agricultural nonpoint sour by Journal of Research in Ecology

Journal of Research in Ecology

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Original Research

Journal of Research in Ecology

Assessment of water quality trading market performance through regulating agricultural nonpoint sources (findings from an analytical case study of Gharesoo watershed in Iran Authors: Emad Mahjoobi, Mojtaba Ardestani and Amin Sarang

Institution: Department of Environmental Engineering, Graduate Faculty of Environment, University of Tehran.

Corresponding author: Emad Mahjoobi

ABSTRACT: Agricultural Nonpoint Sources (NPS) are widely believed to decrease pollution for a much lower unit cost than Point Sources (PS) and they could be the main way of potential cost savings in a Water Quality Trading (WQT) program. However, their sporadic nature and inherent uncertainties make the trading challenging. This study focused on an assessment of involving regulated agricultural NPS into WQT market through the context of Agricultural Cooperatives (AC) for defining Total Maximum Daily Load (TMDL) limits in Gharesoo watershed in the west of Iran. Accordingly, a methodology was proposed to pinpoint location-based trading ratios as well as an environmental penalty cost to achieve a more well-designed market structure. Additionally, a trading algorithm was developed to create a detailed pattern benchmark based on which all potential trades among PS/NPS could be determined. Results showed that regulating NPS in the Gharesoo watershed Total Phosphorus (TP) trading market led to higher trading volume, participation rate, and total exchange value. Moreover, it could save the total cost of implementing the TPTMDL in this watershed compared to the Command and Control approach and the time when merely PS are regulated. Finally, it was revealed that expanding the scale of farmers and farmlands through AC context can decrease the inherent uncertainties of NPS and make them easier to be regulated. Besides, larger credit packages could be created and the performance of trading market is enhanced. Keywords: Agricultural nonpoint sources; Gharesoo watershed; Water quality trading Abbreviations

Email Id:

NPS = Non Point Sources; BMP = Best Management Practices; WQT = Water Quality; Trading; PS = Point Sources; TMDL = Total Maximum Daily Load; AC = Agricultural Cooperative; TP = Total Phosphorus; TN = Total Nitrogen; TV = Trading Volume; PR = Participation Rate; TEV = Total Exchanged Value; ME = Market Efficiency

Article Citation: Emad Mahjoobi, Mojtaba Ardestani and Amin Sarang Assessment of water quality trading market performance through regulating agricultural nonpoint sources (findings from an analytical case study of Gharesoo watershed in Iran) Journal of Research in Ecology (2016) 4(2): 267-288 Dates: Received: 26 Sep 2016 Web Address: http://ecologyresearch.info/ documents/EC0164.pdf

Journal of Research in Ecology An International Scientific Research Journal

Accepted: 30 Sep 2016

Published: 13 Oct 2016

This article is governed by the Creative Commons Attribution License (http://creativecommons.org/ licenses/by/4.0), which gives permission for unrestricted use, non-commercial, distribution and reproduction in all medium, provided the original work is properly cited.

267-288 | JRE | 2016 | Vol 4 | No 2

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Mahjoobi et al., 2016 NPS are challenging due to the fact that they cannot be

INTRODUCTION Surface water pollution remains a critical problem

different

parts

the

world.

directly and clearly measured by source (Nielsen, 2012;

This

Shortle and Horan, 2008). This problem could be

phenomenon is the direct outcome of rapid development

addressed by using an approximation of reductions in

of social economies, so that the health of many aquatic

NPS pollution instead of actual reductions. On the other

ecosystems are seriously endangered by the incremental

hand, due to the sporadic nature and inherent

growth of pollution loads (Jones and Vossler, 2014;

uncertainties of NPS, transaction costs which are related

Zhang et al., 2015). In the meantime, agricultural Non

to information and traders searching, bargaining,

Point Source (NPS) pollution plays a major part to this

decision making, monitoring and enforcement will be

problem and contributes to the nutrient enrichment of

higher for NPS than those for trading among PS

streams and decreases water quality level as a result

(Shabman and Stephenson, 2007; Zhou et al., 2016).

(Duncan, 2014; Howden et al., 2013). Best Management

For a few decades, emission limits have been

Practices (BMPs) are being introduced to reduce nutrient

applied to industrial and municipal PS as regulated

transport from agricultural lands to water bodies.

sectors to achieve water pollution control goals. In

However, creative economic incentives are demanded to

contrast, agricultural NPS have been addressed through a

encourage farmers to apply BMPs for their lands and

range of strategies that accentuate voluntary adoption of

activities (Corrales et al., 2014).

BMPs. These strategies generally have failed to comply

Water Quality Trading (WQT) programs have

with water-quality goals (Ribaudo, 2009; Shortle, 2013).

received escalating attention as an acknowledged

One reason is that when NPS are unregulated,

effective instrument to meet increasingly stringent

environmental authorities consider only PS to develop

nutrient water quality goals at an overall lower cost,

TMDL program for a watershed. In this case, ignoring

since, they allow reallocation of additional reductions in

the impact of NPS on water quality leads to more

loads to sources with relatively lower marginal

stringent load limits. Achieving these limits needs in its

abatement costs (Jamshidi et al., 2014; Ribaudo and

turn adaptation of the most advanced technologies with

Savage, 2014). Originally, such programs could be

higher control costs (Ribaudo et al., 2014). Therefore, it

applied to any contaminant in water and they entail

is incumbent upon authorities to set targets of pollution

trades among Point Sources (PS), NPS, or between PS/

loads both for PS and NPS for implementation of the

NPS (Shortle, 2013). In a common PS/NPS trading

TMDL provisions (Ribaudo, 2009; Shortle, 2013).

program, a regulated PS may be required to reduce

Establishment of Agricultural Cooperative (AC)

pollution discharges owing toTotal Maximum Daily

in each rural district, involving all the small landowners

Load (TMDL) limits by purchasing credits from

scattered in that region, allowed to conducting trade

unregulated NPS that would otherwise have to be

among rural districts rather than between farmers. Rural

provided through enhanced treatment technologies

district is the smallest administrative division which

(Ribaudo and Gottlieb, 2011).

contains a collection of adjacent villages, places and

Agricultural NPS are widely believed to be able

farms and it is homogeneous in terms of social,

to decrease pollution for a much lower unit cost than PS

economic, cultural and natural conditions. Therefore,

(Houtven et al., 2012; Wang et al., 2004). In other

organizing ACs facilitates offering services and planning

words, NPS are the radix of potential cost savings in a

in the system. Measurement of actual emissions from

PS/NPS trading program. Nevertheless, trades involving 268

Journal of Research in Ecology (2016) 4(2): 267-288

Mahjoobi et al., 2016 NPS will not be possible unless each AC invests in

quality is directly related to the socioeconomic

development of its rural district drainage network.

development in this watershed, and it is of great

It is expected that by founding agricultural

importance to the sustainable development of adjacent

cooperatives, a large number of agricultural landsâ&#x20AC;&#x2122;

coastal areas. Due to increasing industrial, agricultural

owners are organized in the form of a legal identity.

and population growth, Gharesoo river has become

Consequently, transaction costs of the market will

seriously polluted so that the pollution level often

reduce, actual emissions from these lands will possibly

exceeds the standard limits.

be measured, and restrictions on them will be regulated

The main identified polluting sources were

and imposed. Therefore, these cooperatives as large-

domestic, industrial, and agricultural ones. This area

scale farm entities may be the suitable agents for the

receives the effluents from six cities comprising

offset system,

less uncertainty and

residential population of 0.5 to 800 thousand people, and

transaction costs. They could act as aggregators and

849 villages in 20 rural districts comprising residential

work with groups of farmers to provide a sufficient

population of 0.1 to 24.7 thousand people. Two industrial

supply of credits so that the needs of large PS buyers are

towns, three slaughterhouses, two fish farms, a refinery,

met.

a soft drink factory, and a sugar factory were also in the

which have

This study was an attempt to appraise the economic and environmental benefits of regulating NPS

vicinity of the streamlines. Figure 1a shows the location of these polluting sources in the watershed.

in a market for implementing a Total Phosphorus (TP)

In this study, all the irrigated lands in every rural

environmental credit trading program. Hence, the

district were integrated as an agricultural cooperative.

influence of regulating NPS polluters through context of

Accordingly, there are 19 agricultural cooperatives

agricultural cooperatives upon defining TMDL limits in

covering an irrigated land area from 100 to 17000

Gharesoo watershed in the west of Iran was investigated

hectares discharging into the river (Figure 1b). In order

and its outcomes in the trading discharge market

to estimate the quantity and quality of steady-state

performance such as supply of available credits,

agricultural return water, a couple of essential parameters

participation rate and market efficiency were also

were determined including: cropping pattern, the average

examined.

water use per hectare, export coefficients of TP and Total

Additionally, a trading algorithm which provides

Nitrogen (TN) for each crop, and conversion coefficients

a detailed pattern benchmark was developed. This

to calculate the volume of the return water (Alvarez-

algorithm identifies potential buyers and sellers and then

Cobelas et al., 2008; Donahue, 2013).

distinguishes exactly who should trade with whom and

To simulate water quality, a justified segment of

based on which credit price. Such information is useful

Gharesoo river, between the confluence of Marak and

when it comes to setting up a trading framework and

Ravansar tributaries flowing toward watershedâ&#x20AC;&#x2122;s output

evaluating an actual market performance.

in Ghourbaghestan hydrometric station, with a length of about 64 Km was selected (Figure 1c). Therefore,

MATERIALS AND METHODS

effluents in Marak, Ravansar and Razavar subbasins

Study area

were aggregated based on the types of emission sources.

This research has been carried out on an

Table 1 shows all the PS and NPS affecting the river.

analytical case study of Gharesoo watershed covering an

The TN - TP loads accumulated from domestic,

area of about 5324.5 Km in the west of Iran. Water Journal of Research in Ecology (2016) 4(2): 267-288

industrial, and agricultural sources discharging into the 269

270

Figure 1. a) Location of polluting sources in Gharesoo watershed; b) Integrated rural districts as agricultural c) Gharesoo river

cooperatives;

Mahjoobi et al., 2016

Journal of Research in Ecology (2016) 4(2): 267-288

Mahjoobi et al., 2016 Table 1. Discharger Type and Specifications Emission source

Type

Q (m3/s)

Distance of effluent to the terminus (Km)

Effluent concentrations

RV-D RV-I RV-A

Urban & Rural Industry Agriculture

0.0612 0.0034 0.8110

64 64 64

BOD5 (Kg/d) 1110.39 279.26 0.0

TN (Kg/d) 260.8 33.3 878.48

TP (Kg/d) 60.38 8.09 156.25

MA-D

Urban & Rural

0.0077

167.54

40.1

12.86

MA-I

Industry

0.0144

1469.41

114.92

27.02

343.9

66.43

MA-A

Agriculture

0.3672

0.0

MD-A

Agriculture

0.6265

64 to 31

0.0

383.71

81.9

BD-A

Agriculture

0.3098

64 to 31

0.0

204.55

41.67

FF-I BD-D RZ-A

Industry Rural Agriculture

0.0342 0.0033 0.1967

49 40 37

27572.7 114.15 0.0

1490.42 27.4 467.88

335.34 9.13 88.75

RZ-D

Urban & Rural

0.1267

3117.1

731.86

168.2

SS-I

Industry

0.1264

56667.49

77.92

49.59

Industry Industry Agriculture Agriculture Urban Rural Rural

0.0147 0.0058 0.0410 0.0321 2.0515 0.0045 0.0016

22 19 19 to 0 19 to 0 16 10 7

7.09 500 0.0 0.0 13727.88 156.1 56.73

0.0 0.0 30.51 24.76 6801.19 37.46 13.61

0.0 0.0 5.79 4.6 887.13 12.49 4.54

KR-I BI-I DF-A GH-A KR-D DF-D GH-D

river was 66.14% - 57.16%, 14.35% - 20.79%, and

trading (Caplan and Sasaki, 2009; Zhang et al., 2013).

19.51% - 22.05%, respectively.

Therefore, the treatment processes for PS are classified

To simulate and calibrate the river condition

into four groups with respect to their efficiency and total

utilizing QUAL2Kw software, a wide variety of inputs

control cost

including: 1) AP such as conventional

including long-term statistical data and average sampling

activated sludge, extended aeration, trickling filter, and

results of hydrometric stations, characteristics of the

sand filtration; 2) BP such as modified Ludzackâ&#x20AC;&#x201C;

cross sections and meteorological data were used

Ettinger, A/O, and four-stage Bardenpho; 3) CP such as

(Chapra and Pelletier, 2008). Results of water quality

A2/O, University of Cape Town, five-stage Bardenpho,

modeling show that the water quality of most of the river

sequencing batch reactors, step feed integrated fixed-film

reaches does not meet the required standard.

activated sludge and Johannesburg; and 4) DP such as

Control cost functions

Virginia initiative plant, modified University of Cape

It is expected that the water quality improves

Town, oxidation ditch, and membrane bioreactors; with 0

along the river if a suitable TP-TMDL program is

-15, 15-50, 50-75, and 75-90 TP percentage removal,

defined. However, there are different methods of

respectively (US EPA, 2007; George et al., 2003; Jiang

effective wastewater treatment for reducing phosphorus

et al., 2004).

concentration. Discrete nature of technology steps affects

The total control cost equations of treatment

decisions made by polluters about abatement and/or

processes for PS were derived as a function of two main

Journal of Research in Ecology (2016) 4(2): 267-288

271

Mahjoobi et al., 2016 variables, i.e. the spectrum of discharge flows and TP

Cost (MAC) of nonpoint sources’ BMPs per hectare as

removal percentage according to the following steps. At

TP removal percentage (R) was calculated as Eq. 2.

first, five functions were fitted by adding a polynomial

MAC = 0.0017R3 - 0.2223R2 + 10.0702R

trend line to the diagrams developed by Jiang et al.,

Then, since there were three BMP categories, the

2004. They presented five diagrams for five ranges of

area bounded by the MAC equation was integrated

discharge flows, i.e. 1, 10, 20, 50, 100 MGD to estimate

between the start and end of the removal intervals to

the total cost of TP removal based on the TP removal

calculate the Average Annual Cost per hectare (AAC) of

percentages. Afterward, all five derived cost equations

each BMP group (Eq. 3).

(2)

were adjusted to a baseline year (i.e. 2010). Due to the fact that there were four treatment process categories and there was a need to have some equations calculating the

AAC(2010$)=

average cost of each process, the area bounded by the

95.63 for AN 154.10 for BN 177.22 for CN

(3)

graph of each polynomial equation was integrated between the start and end of the removal intervals. This

Total maximum daily loads and evironmental penalty

process was done for all five equations. Finally, there

cost

were five average removal costs for each removal

The

TMDL

program

was

developed

using

categories for five discharge flows. Under these

simulation results to set limits on pollutant loads by

conditions, the flow level

is the primary source of

estimating the assimilative capacity of the river

variation in across technology options (Eq. 1) (Suter et

(Copeland, 2012; Duncan, 2014). In this process, it was

al., 2013).

assumed that all the polluters should uniformly reduce 3

in which ‘Q’ is in the MGD unit (1 MGD = 0.0438m /s).

their loads to inhibit the possible biases among

Additionally, BMPs are also classified into three

stakeholders (Ashtiani et al., 2015) and also, the

145.19XQ -26490.78XQ +2018409.87X Q for AP 3

321.24XQ -58597.74XQ +5340803.27XQ for BP TCC(2010$)=

363.66XQ3 -66954.71XQ2 +6390320.97XQ for CP 367.93XQ3 -69403.77XQ2 +6936800.88XQ for DP

(1)

condition of water quality in all reaches must meet at least the Iranian standards of discharge to surface waters which are DO ≥ 5 mg/L, BOD 5 ≤ 30 mg/L, TSS ≤ 40 mg/ L, TN ≤ 11.3 mg/L and TP ≤ 1.96 mg/L. To determine the amount of environmental penalty cost (PP) for polluters who exceed the TMDL limits, the following

groups with respect to their efficiency and total cost to

equation was considered.

control the pollution load of NPS including: 1) AN such

PP = Average Total Control Cost to Meet the TMDL Limit

as Diversion Systems; 2) BN such as Reduced Tillage

Total Abatement Needed Under TMDL

X Safety Factor

Systems; and 3) CN such as Terrace Systems and Filter Strips;

with

0-30,

30-55,

and

55-75

(4) Safety factor (1.25 for this watershed) was

percentage removal, respectively (Schary and Fisher-

applied to ensure that water quality is kept at the

Vanden, 2004; Waidler et al., 2011; Wainger et al.,

standard level and reductions are consistent with the

2013). Performance range and the average annual cost of

TMDL program.

various BMPs per hectare were determined according to

Impact factors and trading ratios

the previous related studies (Rees et al., 2015; Zhou et

Both the level of emissions and the location and

al., 2009). Following costs adjustment, the Mean Annual

transfer characteristics of them affect on the extent and

272

Journal of Research in Ecology (2016) 4(2): 267-288

Mahjoobi et al., 2016 spatial pattern of the damage on environment (Hung and

in which ‘IFi’ is a dimensionless impact factor of polluter

Shaw, 2005; Zhang et al., 2013). This heterogeneity

‘i’ , ‘δTPji’ is the value of TP concentration (mg/L)

among emission sources complicates trading and lack of

reduced in reach ‘j’ due to one unit reduction (Kg/d) of

attention to that could bring about the potential risk of

the estimated loads of TSS, BOD5, TN and TP for

spatial hot spots (Obropta et al., 2008; Zhang et al.,

polluter ‘i’, ‘n’ is the total number of polluters, ‘m’ is

2013).

the total number of reaches, and ‘zi’ is the counter of the

WQT program developers typically use trade ratios

first reach of the river affected by polluter ‘i’ while the

which are the exchange rates at which pollution

reaches were sorted in ascending order from terminus to

reductions from one source can be traded for pollution

headwater.

from another source to interpret spatial and source

The IF expresses the relative impacts of load

heterogeneities of the emission sources (Zhang et al.,

reductions achieved by each of the polluters on the water

2013). Most of the PS/NPS trade ratios developed in

quality of the river. Based on model estimations of the

WQT programs are almost always uniform across all

relative damage, in the next step, trading ratios among

sources and they usually considerably exceed 1:1

the polluters can be calculated by dividing their impact

(Shortle and Horan, 2008). Applying a single trade ratio

factors and used as a mean to urge the WQT program to

to all PS/NPS trades uniformly lessens the efficiency of

a socially cost-effective outcome and avoid the

the WQT programs (Shortle, 2013). Also, high Trading

contraventions of predetermined TMDL limits (Sado et

Ratios (TR) could be mentioned as a key to impediment

al., 2010; Zhang et al., 2013). As a result of the

refraining existing markets from achieving all potential

implementation of this approach, the trading ratios show

benefits of the transaction (Fowlie and Muller, 2013;

lower figures compared with when one checkpoint is

Holland and Yates, 2015). Since the impacts of emission

considered to control pollution level. So, it is expected

sources on the quality of checkpoints are totally

that these obtained values to be politically acceptable for

dependent on their distance (Hung and Shaw, 2005;

stakeholders and it can assist decision makers to

Wittmann, 2014), an approach is presented

for

incentivize the WQT program and encourage early

determining location-based trading ratios by considering

participation to ensure that water quality benefits are

the impact of polluters on the quality of all the river

maximized (Shortle, 2013).

length. In this approach, the first step is carrying out the

Trading algorithm

sensitivity analysis to find the impact factors of each emission sources on the river quality by Eq. 5:

   m  TP  ji  n   m  zi   j  ki  TP   ji   i 1   IFi       n  m  TPji   n   m  zi     i 1  j  ki  TPji     i 1   

Figure 2 illustrates the algorithm of the estimation of TMDL implementation’s costs in a watershed before and after applying trading conditions. All the parameters are defined in Appendix (Table A1). RESULTS AND DISCUSSION

(5)

TMDL limits were calculated in two scenarios: 1) abatement requirements were only applied to the PS as regulated polluters; and 2) load limits were considered for both PS and NPS by assuming rural districts as agricultural

cooperatives.

This

developed

TMDL

program indicates that 85 percent of whole TP load for Journal of Research in Ecology (2016) 4(2): 267-288

273

Mahjoobi et al., 2016

Figure 2. The TMDL implication and trading algorithm 274

Journal of Research in Ecology (2016) 4(2): 267-288

Mahjoobi et al., 2016 all PS in Scenario 1 and 35 percent of whole TP load for

load allocations were done only for PS; NPS don’t need

both PS and NPS in Scenario 2 must be removed to

to reduce their discharge. In Scenario 2, all the PS except

satisfy constraints and keep water quality at the standard

KR-I, BI-I, KR-D, SS-I and RV-D have a preference to

level.

select the process BP and all the NPS decided to adopt

Based on cost functions derived in the previous

process BN owing to lower costs of compliance with

section, the average total control cost of treatment

TMDL limits. Consequently, by applying Command and

processes for 85% and 35% TP reduction are $204.09

Control approach in this watershed, the total cost of

million and $164.79 million, respectively. In addition,

TMDL implementation of Scenario 1 and Scenario 2

the environmental penalty cost for the first and second

which respectively regulate PS and both PS and NPS

scenarios is $525/Kg and $800/Kg of TP, respectively

equaled to $193.18 million and $136.77 million. It was

(according to Eq. 4).

noted

Technical

and

economical

characteristics

that

regulating

NPS

through

agricultural

cooperative as well as PS decreased the total cost of

polluters under the TMDL program in two scenarios are

TMDL program by about 29.2% in comparison with the

shown in Appendix (Tables A2-A5). Without a trading

first scenario.

program, polluters in each scenario have to meet their target

loads

through

adopting

either

By applying the proposed trading algorithm in

treatment

Figure 2, each credits’ price that is more than the average

technologies or BMPs. They have flexibility in deciding

control cost of a unit of pollution for sellers and is also

how to reduce their loads. In this case, each of the

less than the penalty price is introduced to the market.

polluters determines its optimal process type based on

Therefore, the result of every potential trade was

the minimum total cost associated with different options

analyzed by its potential gains or losses. Table 2 shows

(Figure 2: Non-Trade Condition), which are marked in

fifteen trading opportunities as all the possible trades

bold letters in the columns of TC. As it can be seen, in

among sources in Scenario 1. KR-D and SS-I are the

both scenarios, KR-I does not play any role in the

buyers with total credit requirements of 796.21 Kg/d and

program because the TP load of KR-I is zero and also the

there are eight potential sellers in this scenario. The total

concentration of other variables i.e. BOD, TSS, and TN

supply of the available credits are equaled to 32.59 Kg/d.

are under the standard limits. On the contrary, although

The minimum and maximum bid prices of these credits

the TP load of BI-I is zero similar to KR-I, it is required

were $58.84/Kg and $494.32/ Kg respectively.

to select one of the treatment levels to meet the standards

The proposed algorithm indicated that the most

for its other parameters (TSS and BOD5). Consequently,

profitable trades are performed first and also, the

it should adopt at least the second level of treatment (i.e.

stopping criterion of a transaction is either the

BP) in Scenario 1 with 85% TP reduction limit and the

completion of a buyer’s credit requirement or when a

first level of treatment (i.e. AP) in Scenario 2 with 35%

seller runs out of credit supply (Figure 2: Trade

TP reduction limit. It is rational that KR-D and SS-I

Condition). Thus, Table 3 reveals transactions carried out

prefer to pay the environmental penalty for their

among sources in Scenario 1. The results specified that

discharge in Scenario 1 due to higher average control

32.59 Kg/d of TP loads which are worth $2.72 million

cost of different options (see Appendix -Table A2). By

are exchanged among eight sellers and SS-I as buyer.

regulating both PS and NPS in Scenario 2, RV-D also

Accordingly, trading amongst sources resulted in a

prefers to pay the penalty. Other PS in Scenario 1 choose

3.23% reduction of the total cost ($186.94 million) by

the process DP based on their total cost. Moreover, since Journal of Research in Ecology (2016) 4(2): 267-288

275

Mahjoobi et al., 2016 Table 2. The possible trades between sources in scenario 1 Buyer

Credits required (Kg/d)

KR-D

-754.06

implementing

the

FF-I DF-D BD-D

Credits supplied (Kg/d) 16.77 0.62 0.46

Suggested credit price ($/Kg) 58.84 172.7 181.75

RV-I

0.4

328.58

RZ-D MA-I

8.41 1.35

378.73 417.81

MA-D

0.64

468.41

TMDL

program

Seller

Gharesoo

watershed.

Buyer

SS-I

Credits required (Kg/d)

-42.15

FF-I DF-D BD-D

Credits supplied (Kg/d) 16.77 0.62 0.46

Suggested credit price ($/Kg) 60.39 177.26 186.54

RV-I

0.4

337.25

RZ-D MA-I MA-D BI-I

8.41 1.35 0.64 3.93

388.72 428.82 480.75 494.32

Seller

in Scenario 1 implies that regulating both PS and NPS in Gharesoo watershed decreases the credit demand while

The presence of many stakeholders with different pollution control cost could insure achieving economic

increasing the credit supply in the market about 0.5 and 4.5 times, respectively.

efficiency benefits of trading (Ribaudo et al., 2014).

Table 5 indicates all the transactions carried out

Thus, it is expected that programs which regulate both

among the trading possibilities in Scenario 2. Totally,

PS and NPS will promote higher volumes of trading due

175.72 Kg/d of TP of $19.99 million value were

to the expansion of those who can participate in the

exchanged among the participants. SS-I offset its TMDL

market. Table 4 shows all the possible trades among

limits through buying credits from FF-I worth $0.71

polluters in Scenario 2. The results showed that trading

million. In contrast, KR-D compensated 156.44 Kg/d of

opportunities have increased by 260% compared to

the total 310.49 Kg/d TP load reduction by paying

Scenario 1. KR-D, SS-I and RV-D are the buyers with

$18.88 million through trading with others including

total credit requirements of 348.98 Kg/d. The total

three industries, four domestics and seven agricultural

supply of the available credits are equaled to 178.83 Kg/

cooperatives. It also paid $44.98 million as an

d. The minimum and maximum bid prices of these

environmental penalty for its remaining discharge load.

credits were $71.25/Kg and $780.98/Kg, respectively.

Similarly, RV-D paid $0.4 million for 1.93 Kg/d of the

Comparing of these values with the corresponding values

total 21.13 Kg/d TP load reduction through trading with

Table 3. Transactions carried out between sources in scenario 1 (1) (2) (3) = (1) * (2) * 365 / 10^6 Transaction priority Buyer Seller Credits traded Credit price Transaction cost (M$) (Kg/d) ($/Kg)

276

SS-I

FF-I

16.77

60.39

0.37

2 3 4

SS-I SS-I SS-I

DF-D BD-D RV-I

0.62 0.46 0.4

177.26 186.54 337.25

0.04 0.031 0.05

SS-I

RZ-D

8.41

388.72

1.193

6 7

SS-I SS-I

MA-I MA-D

1.35 0.64

428.82 480.75

0.211 0.113

SS-I

BI-I

3.93

494.32

0.709

Journal of Research in Ecology (2016) 4(2): 267-288

Mahjoobi et al., 2016

Buyer

Credits required (Kg/d)

KR-D

-310.49

SS-I

-17.36

Table 4. The possible trades between sources in scenario 2 Credits Suggested Credits Seller supplied credit price Buyer required Seller (Kg/d) ($/Kg) (Kg/d) FF-I 50.3 109.94 GH-D RZ-A 17.75 198.1 MA-A RV-A 31.25 251.91 DF-D MD-A 0.92 264.34 BD-D SS-I -17.36 BD-A 1.16 278.9 DF-A GH-D 0.68 307.22 GH-A MA-A 13.29 314.8 RV-I DF-D 1.87 322.89 RZ-D BD-D 1.37 339.81 FF-I DF-A 8.33 342.47 RZ-A GH-A 16.38 372.79 RV-A RV-I 1.21 614.34 MD-A RZ-D 25.23 706.16 BD-A MA-I 4.05 780.98 MA-A RV-D -21.13 FF-I 50.3 112.84 DF-A RZ-A 17.75 203.32 GH-A BI-I 3.11 236.09 RV-I RV-A 31.25 258.55 MA-I MD-A 0.92 271.31 MA-D BD-A 1.16 286.25

Credits supplied (Kg/d) 0.68 13.29 1.87 1.37 8.33 16.38 1.21 25.23 50.3 17.75 31.25 0.92 1.16 13.29 8.33 16.38 1.21 4.05

Suggested credit price ($/Kg) 315.32 323.1 331.4 348.77 351.5 382.62 630.54 724.78 71.25 128.37 163.25 171.3 180.73 204 221.93 241.58 398.11 506.09

1.93

567.46

MA-D and it also reimbursed $5.61 million as an

the total cost will decrease down to $85.45 million which

environmental penalty for the remaining discharge load.

is equaled to 37.52% reduction in costs compared to the

Considering the trading program completely applicable

command and control approach.

in this watershed through regulating both PS and NPS, Table 5. Transactions carried out between sources in scenario 2 (1)

(2)

(3) = (1) * (2) * 365 / 10^6

Credits traded (kg/d)

Credit price ($/kg)

Transaction cost (M$)

FF-I

17.36

112.84

0.715

KR-D

FF-I

32.94

109.94

1.322

KR-D KR-D

RZ-A RV-A

17.75 31.25

198.1 251.91

1.283 2.873

KR-D

MD-A

0.92

264.34

0.089

KR-D

BD-A

1.16

278.9

0.118

KR-D

GH-D

0.68

307.22

0.076

8 9

KR-D KR-D

MA-A DF-D

13.29 1.87

314.8 322.89

1.526 0.221

KR-D

BD-D

1.37

339.81

0.17

KR-D

DF-A

8.33

342.47

1.042

KR-D

GH-A

16.38

372.79

2.229

RV-D

MA-D

1.93

567.46

0.4

14 15 16

KR-D KR-D KR-D

RV-I RZ-D MA-I

1.21 25.23 4.05

614.34 706.16 780.98

0.272 6.503 1.16

Transaction priority

Buyer

Seller

SS-I

2 3 4

Journal of Research in Ecology (2016) 4(2): 267-288

277

Mahjoobi et al., 2016 The success of water quality markets are typically

CONCLUSION

appraised along the extent of cost efficiency obtained

In this study, it was examined how integration of

through trading (Caplan and Sasaki, 2014). To allow a

small irrigated lands distributed in every rural district

better comparison of market performance in different

through an agricultural cooperative would be used to

scenarios, trading activity was described by the

manage water quality by enforcing load limits for

following factors in this study; Trading Volume (TV)

regulating agricultural pollution. The TMDL program

which is the total number of traded credits, Participation

was defined using a simulation model with primary

Rate (PR) which is the number of polluters who do trade

objectives of reducing nutrient loads from agricultural

over the total number of sources, Total Exchanged Value

sources and calculating more robust environmental

(TEV) which is the monetary value of TV, Total Cost

penalties and location-based trading ratios between each

(TC), and Market Efficiency (ME) which is measured by

pair of polluters. A trading algorithm was proposed to

an index of pollution control cost savings calculated by

create a benchmark pattern of trading for a potential TP

dividing trading to the non-trading condition (Nguyen et

trading market in Gharesoo watershed by assessing all

al., 2013).

possible trades among PS/NPS. The performance of the

Table 6 shows these parameters in two scenarios.

WQT market was investigated through two scenarios of

Results showed that TV increased about 5.5 times due to

defining TMDL limits. Regarding the results, it was

regulating NPS in the TMDL program. Additionally, the

concluded that if scattered irrigated lands integrate as an

PR grew by 100% in the market. Increasing the number

agricultural cooperative, the inherent uncertainties of

of potential buyers and sellers as well as the way of

these

determining the trading ratios were two drivers leading

restrictions on their activities will be set. Subsequently, it

to these growing changes in the market performance. The

will result in the larger credit packages and lower

calculated trading ratios among sources varied from 0.9

transaction costs.

sources

could

decrease

and

environmental

to 1.58 in the performed transactions based on our

Furthermore, the market parameters implied that

proposed methodology. These trading ratios along with

regulating both PS and NPS in the WQT program could

considerable differences in the reduction of the cost of

increase TV, PR, and the value of transactions. Finally, it

pollution of PS an NPS improved the attractiveness of

was inferred that increasing the scale of farmers through

the market. Meanwhile, TEV surged about 7.35 times.

agricultural cooperative context can play a major role in

Finally, regulating both PS and NPS to develop

amplifying the performance of PS/NPS trading market.

TMDL program and well-design trading market could

Additionally, the proposed trading algorithm provided

enhance the cost of improving river quality in the

local water managers and decision makers with detailed

Gharesoo watershed by about 54.3% relative to the time

roadmap of an optimal trading pattern within the

that only PS were regulated.

watershed.

Table 6. Market parameters in two scenarios Scenario 2: Market Scenario 1: Regulating both PS parameters Regulating PS and NPS TV (ton/yr) 11.89 64.14 PR (%) 45 90 TEV (M$) 2.72 19.99 TC (M$) 186.94 85.45 ME (%) 3.23 37.52 278

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281

Mahjoobi et al., 2016 APPENDIX Supporting information associated with this article can be found in this appendix. Table A1. Definition of parameters used in proposed algorithm Parameter

Definition

Parameter

Definition

Discharge level of polluter

Impact factor of polluter

Current load of polluter

Target load of polluter Coefficients of total control cost function of

Potential treatment efficiency of process Irrigated land area of polluter

process Total reduction needed of polluter

Total reduction achieved of polluter

due

to selection of process

Potential surplus reduction of polluter

due to

selection of process

Total control cost of polluter

due to Average annual cost per hectare of process

selection of process Average control cost of polluter

due to

selection of process Total penalty cost of polluter

due to

selection of process due to

Total cost of polluter

selection of process Optimum cost of polluter different processes

Incremental control cost of polluter

due to selection of

process among

Incremental control cost of polluter optimum process

Potential surplus reduction of polluter optimum process in

Total control cost of polluter process

in optimum

Trading ratio between seller

and

Average control cost of polluter process

in optimum

Non-trading condition cost of system

Credit price offered by seller

to buyer

buyer Market cost of polluter

282

Total market cost of system

Journal of Research in Ecology (2016) 4(2): 267-288

Mahjoobi et al., 2016 Table A2. Technical and economical characteristics of PS under the TMDL program in scenario 1 Polluter

GH-D

DF-D

KR-D

BI-I

KR-I

SS-I

RZ-D

CLTP

TLTP

TRN

Process

TRA

TCC

ICC

ACC

PSR

TPC

(%)

(MGD)

(kg/d)

Type

(kg/d)

(M$)

($/kg)

(M$)

0.0

-3.86

0.739

0.68

0.074

299.5

-3.18

0.609

0.683

2.27

0.197

237.76

-1.59

0.304

0.501

3.4

0.236

189.66

-0.45

0.087

0.323

4.08

0.256

181.66

171.57

0.23

0.0

0.256

0.0

-10.61

2.034

1.87

0.205

299.24

-8.74

1.675

1.880

6.24

0.541

237.59

-4.37

0.838

1.379

9.37

0.648

189.53

-1.25

0.239

0.887

11.24

0.703

181.54

171.46

0.62

0.0

0.703

6.62

6.29

5.99

6.11

6.12

6.15

5.9

0.0369

0.1015

46.824

0.1321

0.3352

2.8849

2.8907

4.54

12.49

887.13

0.0

49.59

168.2

0.68

1.87

133.0 7

0.0

7.44

25.23

3.86

10.61

754.06

0.0

42.15

142.97

0.0

-754.06

144.5

133.07

51.33

1056.9

-620.99

119.0

180.33

443.56

154.58

954.79

-310.49

59.499

214.08

665.35

189.76

781.37

-88.71

206.76

798.41

210.41

764.5

722.03

44.36

0.0

210.41

0.0

3.11

0.266

234.6

3.11

0.0

0.266

3.93

0.704

491.19

3.93

0.0

0.704

4.34

0.843

532.16

4.34

0.0

0.843

4.75

0.915

527.78

4.75

0.0

0.915

0.0

0.674

0.0

0.674

0.0

1.784

0.0

1.784

0.0

2.135

0.0

2.135

0.0

2.318

0.0

2.318

0.0

-42.15

8.077

7.44

5.606

2064.8

-34.71

6.652

12.258

24.8

14.928

1649.4

-17.36

3.326

18.254

37.19

17.887

1317.6

-4.96

0.95

18.837

44.63

19.443

1263.7

1193.5

2.48

0.0

19.443

0.0

-142.97

27.396

25.23

5.617

609.94

-117.74

22.562

28.179

84.10

14.957

487.26

-58.87

11.281

26.238

126.15

17.922

389.23

-16.82

3.223

21.145

151.38

19.481

373.32

352.58

8.41

0.0

19.481

Journal of Research in Ecology (2016) 4(2): 267-288

283

Mahjoobi et al., 2016 Table A2 (Continue) Polluter

BD-D

FF-I

RV-D

RV-I

MA-D

MA-I

284

IF (%)

5.98

5.26

3.88

CLTP

TLTP

TRN

Process

TRA

TCC

ICC

ACC

PSR

TPC

(MGD)

(kg/d)

Type

(kg/d)

(M$)

($/kg)

(M$)

0.0742

0.7809

1.3973

0.0771

0.1749

0.3282

9.13

335.34

60.38

8.09

12.86

27.02

1.37

50.3

9.06

1.21

1.93

4.05

7.76

285.04

51.32

6.87

10.93

22.96

0.0

-7.76

1.487

1.37

0.15

299.35

-6.39

1.225

1.375

4.57

0.396

237.67

-3.20

0.612

1.009 0.649

6.85

0.474

189.59

-0.91

0.175

8.22

0.515

181.59

171.51

0.46

0.0

0.515

0.0

-285.04

54.621

50.3

1.56

84.98

-234.74

44.982

46.542

167.67

4.135

67.57

-117.37

22.491

26.626

251.5

4.95

53.92

-33.53

6.426

11.376

301.81

5.375

51.66

48.79

16.77

0.0

5.375

0.0

-51.32

9.834

9.06

2.769

837.64

-42.26

8.099

10.868

30.19

7.349

666.96

-21.13

4.05

11.399

45.28

8.799

532.38

-6.04

1.157

9.956

54.34

9.558

510.26

481.91

3.02

0.0

9.558

0.0

-6.87

1.317

1.21

0.155

351.0

-5.66

1.085

1.24

4.04

0.411

278.68

-2.83

0.542

0.954

6.07

0.492

222.3

-0.81

0.155

0.647

7.28

0.534

212.93

201.1

0.4

0.0

0.534

0.0

-10.93

2.095

1.93

0.352

500.21

-9.0

1.725

2.078

6.43

0.932

397.22

-4.5

0.863

1.795

9.65

1.116

316.88

-1.29

0.247

1.362

11.58

1.211

303.54

286.67

0.64

0.0

1.211

0.0

-22.96

4.4

4.05

0.66

445.97

-18.91

3.624

4.283

13.51

1.747

354.26

-9.46

1.812

3.559

20.26

2.09

282.63

-2.7

0.518

2.608

24.31

2.269

270.75

255.71

1.35

0.0

2.269

Journal of Research in Ecology (2016) 4(2): 267-288

Mahjoobi et al., 2016 Table A3. Technical and economical characteristics of NPS under the TMDL program in scenario 1 Polluter

RZ-A

RV-A

MA-A

GH-A

DF-A

BD-A

MD-A

IF (%)

5.90

3.88

4.0

4.2

CLTP

TLTP

TRN

Process

TRA

TCC

ICC

ACC

PSR

TPC

(MGD)

(kg/d)

Type

(kg/d)

(M$)

($/kg)

(M$)

4.4887

18.509

8.3801

14.299

7.070

0.9353

0.7319

88.75

156.25

66.43

81.9

41.67

5.79

4.6

88.75

156.25

66.43

81.9

41.67

5.79

4.6

0.0

Journal of Research in Ecology (2016) 4(2): 267-288

0.0

26.62

1.374

141.37

26.62

0.0

1.374

48.81

2.214

124.26

48.81

0.0

2.214

66.56

2.546

104.8

66.56

0.0

2.546

0.0

46.87

2.022

118.19

46.87

0.0

2.022

85.94

3.258

103.88

85.94

0.0

3.258

117.18

3.747

87.61

117.18

0.0

3.747

0.0

19.93

1.074

147.69

19.93

0.0

1.074

36.53

1.731

129.82

36.53

0.0

1.731

49.82

1.991

109.48

49.82

0.0

1.991

0.0

24.57

1.616

180.24

24.57

0.0

1.616

45.04

2.605

158.42

45.04

0.0

2.605

61.42

2.995

133.61

61.42

0.0

2.995

0.0

12.5

0.756

165.58

12.5

0.0

0.756

22.92

1.217

145.54

22.92

0.0

1.217

31.25

1.4

122.74

31.25

0.0

1.4

0.0

1.74

0.09

141.6

1.74

0.0

0.09

3.18

0.145

124.46

3.18

0.0

0.145

4.34

0.166

104.97

4.34

0.0

0.166

0.0

1.38

0.068

134.21

1.38

0.0

0.067

2.53

0.109

117.96

2.53

0.0

0.109

3.45

0.125

99.49

3.45

0.0

0.125

285

Mahjoobi et al., 2016 Table A4. Technical and economical characteristics of PS under the TMDL program in scenario 2 Polluter

GH-D

DF-D

KR-D

BI-I

KR-I

SS-I

RZ-D

286

IF (%)

6.62

6.29

5.99

6.11

6.12

6.15

5.9

CLTP

TLTP

TRN

Process

TRA

TCC

ICC

ACC

PSR

TPC

(MGD)

(kg/d)

Type

(kg/d)

(M$)

($/kg)

(M$)

0.0

-1.59

0.464

0.68

0.074

299.5

-0.91

0.265

0.339

2.27

0.197

339.66

237.76

0.68

0.0

0.197

3.4

0.236

406.42

189.66

1.82

0.0

0.236

4.08

0.256

441.18

171.57

2.5

0.0

0.256

0.0369

0.1015

46.824

0.1321

0.3352

2.8849

2.8907

4.54

12.49

887.13

0.0

49.59

168.2

2.95

8.12

576.63

0.0

32.23

109.33

1.59

4.37

310.4 9

0.0

17.36

58.87

0.0

-4.37

1.276

1.87

0.205

299.24

-2.5

0.729

0.934

6.24

0.541

339.42

237.59

1.87

0.0

0.541

9.37

0.648

406.14

189.53

0.0

0.648

11.24

0.703

440.89

171.46

6.87

0.0

0.703

0.0

-310.49

90.66

133.07

51.33

1056.9

-177.43

51.81

103.14

443.56

154.58

1364

954.79

133.07

0.0

154.58

665.35

189.76

1674.4

781.37

354.85

0.0

189.76

798.41

210.41

1856.6

722.03

487.92

0.0

210.41

0.0

3.11

0.266

234.6

3.11

0.0

0.266

3.93

0.704

491.19

3.93

0.0

0.704

4.34

0.843

532.16

4.34

0.0

0.843

4.75

0.915

527.78

4.75

0.0

0.915

0.0

0.674

0.0

0.674

0.0

1.784

0.0

1.784

0.0

2.135

0.0

2.135

0.0

2.318

0.0

2.318 5.068

0.0

-17.36

5.07

7.44

5.606

2064.8

-9.92

2.9

8.502

24.8

14.928

2356.3

1649.4

7.44

0.0

14.928

37.19

17.887

2823.4

1317.6

19.84

0.0

17.887

44.63

19.443

3069.1

1193.5

27.27

0.0

19.443

0.0

-58.87

17.19

25.23

5.617

609.94

-33.64

9.82

15.44

84.10

14.957

696.08

487.26

25.23

0.0

14.957

126.15

17.922

834.07

389.23

67.28

0.0

17.922

151.38

19.481

906.64

352.58

92.51

0.0

19.481

Journal of Research in Ecology (2016) 4(2): 267-288

Mahjoobi et al., 2016 Table A4 (Continue) Polluter

BD-D

IF (%)

5.98

CLTP

TLTP

TRN

Process

TRA

TCC

ICC

ACC

PSR

TPC

(MGD)

(kg/d)

Type

(kg/d)

(M$)

($/kg)

(M$)

0.0742

9.13

5.94

3.2

0.0

-3.2

0.93

0.933

1.37

0.15

299.35

-1.83

0.53

0.683

4.57

0.396

339.52

237.67

1.37

0.0

0.396

6.85

0.474

406.26

189.59

3.65

0.0

0.474

8.22

0.515

441.01

171.51

5.02

0.0

0.515

34.27

34.272

FF-I

RV-D

RV-I

MA-D

MA-I

5.26

3.88

0.7809

1.3973

0.0771

0.1749

0.3282

335.34

60.38

8.09

12.86

27.02

217.97

39.25

5.26

8.36

17.56

117.37

21.13

2.83

4.5

9.46

Journal of Research in Ecology (2016) 4(2): 267-288

0.0

117.37

50.3

1.56

84.98

-67.07

19.58

21.144

167.67

4.135

96.53

67.57

50.3

0.0

4.135

251.5

4.95

115.54

53.92

134.14

0.0

4.95

301.81

5.375

125.47

48.79

184.44

0.0

5.375

0.0

-21.13

6.17

6.171

9.06

2.769

837.64

-12.08

3.53

6.295

30.19

7.349

952.79

666.96

9.06

0.0

7.349

45.28

8.799

1140.8

532.38

24.15

0.0

8.799

54.34

9.558

1239.2

481.91

33.21

0.0

9.558

0.0

-2.83

0.83

0.827

1.21

0.155

351.0

-1.62

0.47

0.628

4.04

0.411

398.11

278.68

1.21

0.0

0.411

6.07

0.492

476.36

222.3

3.23

0.0

0.492

7.28

0.534

517.12

201.1

4.45

0.0

0.534

0.0

-4.5

1.31

1.315

1.93

0.352

500.21

-2.57

0.75

1.103

6.43

0.932

567.46

397.22

1.93

0.0

0.932

9.65

1.116

679.03

316.88

5.15

0.0

1.116

11.58

1.211

737.16

286.67

7.07

0.0

1.211

0.0

-9.46

2.76

2.761

4.05

0.66

445.97

-5.4

1.58

2.237

13.51

1.747

506.09

354.26

4.05

0.0

1.747

20.26

2.09

605.64

282.63

10.81

0.0

2.09

24.31

2.269

657.54

255.71

14.86

0.0

2.269

287

Mahjoobi et al., 2016 Table A5. Technical and economical characteristics of NPS under the TMDL program in scenario 2 Polluter

RZ-A

RV-A

MA-A

GH-A

DF-A

BD-A

MD-A

288

IF (%)

5.90

3.88

4.0

4.2

CLTP

TLTP

TRN

Process

TRA

TCC

ICC

ACC

PSR

TPC

(MGD)

(kg/d)

Type

(kg/d)

(M$)

($/kg)

(M$)

0.0

-31.06

9.07

26.62

1.374

141.37

-4.44

1.3

2.67

48.81

2.214

195.27

124.26

17.75

0.0

2.214

66.56

2.546

224.57

104.8

35.5

0.0

2.546

0.0

-54.69

15.97

15.968

46.87

2.022

118.19

-7.81

2.28

4.303

85.94

3.258

163.25

103.88

31.25

0.0

3.258

117.18

3.747

187.74

87.61

62.5

0.0

3.747

0.0

-23.25

6.79

6.789

19.93

1.074

147.69

-3.32

0.97

2.044

36.53

1.731

204

129.82

13.29

0.0

1.731

49.82

1.991

234.6

109.48

26.57

0.0

1.991

0.0

-28.66

8.37

24.57

1.616

180.24

-4.09

1.2

2.812

45.04

2.605

248.95

158.42

16.38

0.0

2.605

61.42

2.995

286.3

133.61

32.76

0.0

2.995

4.4887

18.509

8.3801

14.299

7.070

0.9353

0.7319

88.75

156.25

66.43

81.9

41.67

5.79

4.6

57.69

101.56

43.18

53.23

27.09

3.76

2.99

31.06

54.69

23.25

28.66

14.58

2.03

1.61

0.0

-14.58

4.26

4.259

12.5

0.756

165.58

-2.08

0.61

1.364

22.92

1.217

228.7

145.54

8.33

0.0

1.217

31.25

1.4

263.01

122.74

16.67

0.0

1.4

0.0

-2.03

0.59

0.592

1.74

0.09

141.6

-0.29

0.08

0.174

3.18

0.145

195.59

124.46

1.16

0.0

0.145

4.34

0.166

224.93

104.97

2.32

0.0

0.166

0.0

-1.61

0.47

1.38

0.068

134.21

-0.23

0.07

0.135

2.53

0.109

185.37

117.96

0.92

0.0

0.109

3.45

0.125

213.18

99.49

1.84

0.0

0.125

Journal of Research in Ecology (2016) 4(2): 267-288