- Email: [email protected]
Sectoral dynamics and natural resource management
Journal of Economic Dynamics & Control 26 (2002) 1481 – 1498 www.elsevier.com/locate/econbase
Sectoral dynamics and natural resource management Anantha Kumar Duraiappah International Institute for Sustainable Development (IISD), 161, Portage Avenue, Winnipeg, Canada
Abstract In order to analyze, understand and prescribe natural resource management strategies, the decision making framework should ideally capture the dynamics of inter-dependency between the economic and ecological systems in an integrated manner. However, the inclusion of two complex systems within a single integrated framework makes many of the present analytical tools redundant. Computational sectoral models on the other hand are ideally suited to meet this challenge. In this paper, I present an integrated sectoral model for the shrimp sector in Thailand. The paper describes how the dynamics of economics and the natural system are captured and the complexity that arises from this integration. Some preliminary results demonstrate how sectoral strategies can change when the environmental costs of sectoral strategies are taken into account during the planning stage. ? 2002 Elsevier Science B.V. All rights reserved. Keywords: Non-linear; Optimization; Integrated; Computational; GAMS
Foreword I met David Kendrick in the fall of 1986 as a graduate student at the University of Texas at Austin. It was only natural (there are not that many David Kendrick’s out there) that he became ;rst my dissertation supervisor, then a friend and now a mentor. I would like to thank the editors for giving me this opportunity to contribute to this very timely special issue in his honor. David introduced me to the world of mathematical modeling techniques for economic planning and to numerical computational techniques for solving these models. His work in sectoral planning as well as in multi-sectoral economic growth models laid the foundation for many of the integrated economic– ecological systems I have developed over the years. The ;rst model, an integrated climate change-economic growth model was formulated while I was a doctoral student under David. His suggestions and help were invaluable in my attempt to formulate E-mail address: [email protected] (A.K. Duraiappah). 0165-1889/02/$ - see front matter ? 2002 Elsevier Science B.V. All rights reserved. PII: S 0 1 6 5 - 1 8 8 9 ( 0 1 ) 0 0 0 8 1 - 1
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and solve what at that time seemed to be a highly non-linear and complex model. Frustrations and at many times sheer hopelessness were always overcome by David’s clear thoughts and reasoning towards a solution. 1. Introduction Investment decisions in the past primarily focused on the evaluation of individual projects based on either cost-bene;t or rate of return analysis. It is a practice still accepted as the modus operandi. However, David Kendrick and Ardy Stoutjesdijk (1978) argued as far back as in the late 1970s that attention should be redirected from just the evaluation of projects towards the design of projects. The primary force driving their argument for design versus evaluation is the advantage design allows in capturing explicitly the inter-dependency that can occur across a number of individual projects with respect to the following economic decisions: • • • • • • •
timing of investments, location of investments, size of investments, choice of technologies, economies of scale, mix of ;nal products, options of exports and imports.
For example, the decision to build a steel mill will be inEuenced not only by the choice of technologies but also on the location of the investments, the size of the investment and the possibilities of exporting or importing inputs and outputs to supplement domestic surplus or shortages, respectively. These were issues that conventional cost-bene;t or rate of return analysis was not capable of incorporating in the evaluation process. Why then is conventional cost-bene;t and rate of return techniques still the modus operandi for investment decisions? The formulation and solution of sectoral models was a non-trivial task in the 1970s when they were ;rst introduced. These models required a certain degree of specialized knowledge in mathematical modeling and computational techniques. The state-of-art in both ;elds was still at infancy at that point in time. Although great strides have been made in both areas over the last two decades, the adoption of sectoral models as an investment tool has been limited. Much of this inertia can be traced to the perceived complexity of these models and basically it was much simpler to compute cost-bene;t or rate of return analysis to select or reject a project. However, not deterred by this reluctance to use sectoral models, Kendrick began to develop prototype software to simplify the mathematical formulation of a sectoral model (Kendrick 1990, 1991). Although the beta versions proved successful, the response by the project investment crowd was limited. The time commitment required for sectoral modeling was still considered too high by investment specialists. Moreover, with the recent increasing awareness of environmental repercussions of investment decisions, it was much straightforward to extend the conventional cost-bene;t
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or rate of return frameworks to incorporate environmental costs of investment projects. The extension of sectoral models to include environmental dynamics posed a slightly more non-trivial task. However, if we take a good look at the dynamics of environmental degradation caused by investment projects, the case for sectoral designs is actually strengthened. The strong inter-dependencies among the various economic factors described earlier in the paper and environmental factors make it imperative that a design approach be taken versus the conventional assessment of individual projects. For example, in the case of the steel sector, the decision to invest in a steel mill at a particular location is now not only dependant on the economic factors highlighted earlier but also on the following factors: • The air dispersion dynamics of the location. • The distance the mill is from human habitats. • Physical damages done by the investments. This high degree of inter-dependency among economic and environmental variables strengthens the case for using sectoral modeling to guide investment policies. The methodology used in developing a design for an industry or sector is described in detail in Kendrick (1967) and Kendrick and Stoutjesdijk (1978) while models developed speci;cally for the steel and fertilizer sector can be found in Kendrick and Stoutjesdijk (1978) and Choksi et al. (1981), respectively. In this paper, I shall draw on the basic building blocks of the sectoral model methodology illustrated in Kendrick and Stoutjesdijk (1978) and then extend the model by including environmental resource bases and incorporating the inter-dependencies among the economic and environmental variables within a single design process. The structure of the paper is as follows. In Section 2 a brief overview of the critical factors underlying a shrimp sector is presented. In Section 3, the sectoral model for the shrimp sector in Thailand is provided. Some preliminary results are provided in Section 4 to demonstrate the uniqueness of the sectoral model in investment and natural resource management decisions. The paper ends with some concluding remarks. 2. Shrimp sector dynamics I shall not go into too much detail on the dynamics of the shrimp sector but only provide a brief overview of the sector to give the reader an idea of the critical components driving the system. The major problem shrimp sectors face in a large number of developing countries is the issue of sustainability. Shrimp sectors just do not survive. The normal pattern for shrimp sectors is a boom in the ;rst three to ;ve years and then go bust as diseases hit the sector (Patmasiriwat et al., 1998). The main reason why the shrimp sector in most of these countries has survived at all is the fact that the sector practices slash and burn techniques by moving to new locations when a bust occurs. Many theories have been put forward to explain why diseases happen. However, there is an emerging consensus among shrimp experts that low quality of water together with reducing resiliency among shrimps as the primary reasons for diseases to
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occur (Briggs and Funge-Smith, 1994; Phillips, 1994; Chiu, 1988; Wongsaengchan, 1990; Boyd and Musig, 1992). The quality of water and the issue of resiliency are in turn inEuenced largely by the management techniques adopted by the farms (Dierberg and Kiattisimkul, 1996). The economics of shrimp production at an individual farm level based on conventional cost-bene;t or rate of return analysis tends to favor the adoption of very high stocking rates for juvenile shrimps, the use of high technology and minimize the use of land (Funge-Smith and Aeron-Thomas, 1995; Thongrak et al., 1997). The use of high technology entails the use of arti;cial feeding strategy, the use of antibiotics to prevent diseases and machinery to increase the level of dissolved oxygen in the ponds. However, the success of these initiatives has been limited and the incidence of diseases has yet to decrease signi;cantly (Funge-Smith and Briggs, 1994; Dierberg and Kiattisimkul, 1996; Flaherty and Karnjanakesorn, 1994). In order to provide some new insight into the problem, this paper attempts to approach the problem from a sectoral perspective. The underlying dynamics of the sector demonstrates strong inter-dependencies among a number of critical factors— inter-dependencies that conventional tools for investment miss. The brief overview of the sector presented above suggest that in order to design an investment strategy for the shrimp sector, three critical factors need to be addressed. The ;rst is land use; the second is water management; and the third is a combination of stocking density and feeding intensity. Land or more speci;cally, soil type is an important factor in shrimp production. Land with clay soils is known to be less suitable for shrimp production than land with alluvial soils. Although the former can be used substantial additional costs will need to be incurred in order to prepare the soil for shrimp production. The decision to use clay soils will primarily be determined by the additional costs of preparation versus the bene;ts from using the land 1 and the degree of land scarcity. A unique strength of the sectoral modeling approach is that the cost of scarcity of land is computed explicitly within the decision-making framework through shadow pricing. Water or more speci;cally brackish 2 water management poses a slightly more complex challenge. Clean brackish water is an essential ingredient for shrimp production. Dirty water reduces resistance levels in shrimps therefore making them more susceptible to diseases. Dirty water also carries viruses and bacteria that can cause high mortality in shrimp production. The unique characteristic of brackish water that makes it diKerent to land is that brackish water is sourced from a common source—the coast and can be technically de;ned as a public good. The maintenance of the quality of the water system is therefore dependant on all the farms within a speci;ed geographical region. In essence, the quality of the water system is determined by the number of farms and the technology adopted by the various farms in the region. Each individual farm can control the quality of the water to a certain extent by adopting an open or closed water management strategy. In the case of the former, the farm relies completely on the common water system. In the case of the 1
Clay solid are usually found near the coast and therefore the cost of sourcing water from the coast is much lower than for lands further inland. 2 Shrimp production needs water of a certain salinity level.
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closed system, the farm has facilities to clean water before using it for production and it also limits the frequency of sourcing water from the common system. The farm relies on the reuse of water within the farm. This option naturally entails higher cost, as land has to be appropriated for storing, cleaning and recycling of water within the farm. There also additional costs from chemicals needed to clean the water before usage. The ;nal factor is stocking density and feeding intensity. Stocking density is the number of juvenile shrimp that is stocked per unit area while feeding intensity is amount of arti;cial feed used per unit area. The standard assumption would be to adopt high stocking and feeding densities in order to obtain high ;nal yields. However, there is a downside to high stocking densities and feeding intensities. Stocking densities beyond a critical point are positively correlated to high mortality rates. The critical point is in turn determined by a host of economic and environmental factors. Stocking densities beyond the critical point create high stress environments caused by over-crowding and together with high feeding intensities produce higher amount of fecal discharge that create unsanitary conditions that are ideal for viral and bacterial outbreaks. The three factors 3 put together in various permutations then form the management strategies available to the sector. Each combination or pro;le can be perceived as a process or recipe for shrimp production. The activity level of each pro;le then determines the amount of inputs and outputs produced. The prices of the inputs used and the outputs produced then give us the revenues and costs of each pro;le. The model is formulated to ;nd the optimal number of farms within speci;ed geographical regions that employ a certain combination of management strategies that maximize net bene;ts. This is done by developing a non-linear programming model of the sector.
3. The sectoral model for the shrimp sector 3.1. Description of the model The objective in the shrimp sector model is to maximize net bene;ts. Bene;ts come from the sale of shrimps while costs are presented in two categories. Direct costs are primarily production cost and are comprised of ;xed and variable cost. Indirect cost is the second cost category and components are the removal of waste products and the opportunity cost of land abandoned by shrimp farms. 4 The model is solved to ;nd the set of management strategies adopted by an optimal number of farms that will maximize net bene;ts without violating land constraints. In order to capture critical turning points in the use of inputs, the sector is presented as a non-linear programming model.
3 I shall refer to the combination of stocking intensity and feeding intensity as a single factor as both items are jointly developed. 4 Lands abandoned by shrimp farms can rarely be used for other agricultural activities due to the high salinity content of the soils.
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3.2. Model nomenclature One of the ;rst rules David Kendrick stressed in economic modeling is the attention a modeler must pay to nomenclature and style (Kendrick, 1984). It is therefore convenient to think in terms of sets of regions, land types, farm techniques, water management options, commodities and time periods for shrimp sectoral models. It is also convenient to think in sets as it makes the transition process from the mathematical model to the computational model in the general algebraic modeling system (GAMS) smooth and transparent (Kendrick et al., 1992). In order to avoid the curse of dimensionality, superscripts in this model are used to facilitate the use of variable names longer than just a single letter. Subscripts are used to denote the set domain over which an operation is to be executed. Sets R D P W C T
regions land type farm techniques water management options commodities time periods
Variables z x u la lu lug lur lb lc lp
activity levels ;nal shipment raw material purchases land available for shrimp farming demand for land by shrimp farming land used for cultivation of shrimps land actually used for shrimp farming land abandoned by shrimp farms land converted to shrimp farming productivity drop in land caused by drop in survival rates
Parameters A L P IC TC s fi si fmd
technology matrix land availability prices initial conditions terminal conditions survival rate feed intensity seed intensity farm density
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xs r dc idc n
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sludge production revenue direct cost indirect cost number of farms
3.3. Model equations Raw material purchases by respective farms: ucrpdwt = acpdw zrpdwt ;
c ∈ CFV; p ∈ P; d ∈ D; w ∈ W; t ∈ T; r ∈ R:
(1)
Eq. (1) computes the amount of raw materials used by the sector. The CFV set is the commodity sub-set which consists of only raw materials. These are: (1) energy; (2) feed; (3) seed; and (4) chemicals. The z variable tells us the activity level in region r, using process p located in land type d and using water management option w in time t. The coeMcients in the A matrix (acpdw ) were computed from data collected from a farm survey. The A matrix can be considered as the “heart” of the model. Total land use: a“tarea ”pdw zrpdwt = lurpdwt ;
r ∈ R; p ∈ P; w ∈ W; d ∈ D; t ∈ T:
(2)
Land use by shrimp farms is equal to the demand, which is denoted by the purchase level that in turn is determined by the activity levels. This area covers ponds used for shrimp production, water cleaning ponds, plus land used for infrastructure. Total grow out area: lug rpdwt = a“garea ”pdw zrpdwt ;
r ∈ R; p ∈ P; d ∈ D; w ∈ W; t ∈ T:
(3)
In this equation, we compute the total area covered by ponds used for actual shrimp farming. Land type covered by shrimp farms in each region: lurpdwt ; r ∈ R; d ∈ D; t ∈ T: (4) lur rdt = p∈P w∈W
Eq. (4) computes the total land of type d in each of the regions that is used by shrimp farms. Land use constraint: a lur rdt 6 lrdt ;
r ∈ R; d ∈ D; t ∈ T:
(5)
The amount of land used by shrimp farms has to be less than the area under shrimp farming. Land accumulation: lardt+1 = lardt − lbrdt+1 + lcrdt+1 ;
r ∈ R; d ∈ D; t ∈ T:
(6)
The land under shrimp farming is accumulative and depends on the level in the previous period minus land abandoned plus land converted.
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Land use balance equation: b LatQrdt = lardt + lau rdt + lrdt ;
r ∈ R; d ∈ D; t ∈ T:
(7)
The total amount of land under shrimps plus land under alternative uses plus abandoned land must be equal to total amount of land available. This equation can be interpreted as an identity or balance equation. The data for the total amount of land available for each soil type and in each region were computed based on GIS information. This is reEected under the category of parameters L. Land abandoned by shrimp farms: lurpdwt lprpdwt : (8) lbrdt = p∈P w∈W
The amount of land abandoned depends on the productivity drop witnessed on the farms. Productivity drop: lprpdwt = e−srpdwt =pconst pgrad ;
r ∈ R; p ∈ P; d ∈ D; w ∈ W; t ∈ T:
(9)
The degree of productivity drop is determined primarily by the survival rate. The lower the survival rate, the higher the productivity drops. The pconst and pgrad reEect the rate and magnitude of the impact of the survival rate has on the productivity drop experienced by the farms. In many ways, we can use varying ;gures for pconst and pgrad to capture risk taking behavior on the part of the various shrimp farms. The ;gures used in this study were 0.12 and 2.3007, respectively, and these coeMcients were derived from a calibration process whereby the underlying premise is that farms get abandoned when the survival rate of shrimps falls below 60%. Natural shrimp production level: p = a“shrimp”pdw zripdwt ; xrpdwt
r ∈ R; p ∈ P; d ∈ D; w ∈ W; t ∈ T:
(10)
The natural shrimp production level denotes the harvest level, which can be experienced if no diseases occur. The output level used in the technology matrix is net of the natural mortality rate. Actual shrimp production levels: p o = xrpdwt srpdwt ; xrpdwt
r ∈ R; p ∈ P; d ∈ D; w ∈ W; t ∈ T:
(11)
The actual shrimp harvested is net of mortality rates caused by controllable factors that are described in detail in the survival equation below. Shrimp survival rate: i i ln srpdwt = a1 ln frpdwt + a2 ln srpdwt + a3 ln frtmd + a4
i ln frpdwt i ln srpdwt
+ a5 zrpd“close”t
i i + a6 (ln frtmd )2 + a7 (ln frpdwt )2 + a8 (ln srpdwt )2 ;
r ∈ R; p ∈ P; d ∈ D; w ∈ W; t ∈ T:
(12)
A modi;ed form of a translog function is used for the shrimp survival rate. The function was econometrically estimated using survey data from a sample size of 350
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farms (Duraiappah et al., 2000). Various functional forms were used but the above function provided the best ;t. See the appendix for a schematic illustration of the function and how it captures the unique characteristics of shrimp survival. Feed intensity: u“feed”rpdwt i frpdwt ; r ∈ R; p ∈ P; d ∈ D; w ∈ W; t ∈ T: (13) = lug rpdwt Feed intensity is primarily computed as the total amount of feed purchased by the farms divided by the grow-out area used by the respective farms. Seed intensity: u“seed”rpdwt i srpdwt = ; r ∈ R; p ∈ P; d ∈ D; w ∈ W; t ∈ T: (14) lug rpdwt Seed intensity is computed in a similar manner as the feed intensity. Number of farms: lurpdwt nrpdwt = ; r ∈ R; p ∈ P; d ∈ D; w ∈ W; t ∈ T: a“tarea ”pdw
(15)
The number of farms is equal to the total land under use by the various farm categories divided by the unit area required by a hypothetical farm. We assume that each category as described by the technology matrix is representative of a farm. Farm density: ug p∈P d∈D w∈W lrpdwt md frt = ; r ∈ R; t ∈ T: (16) Q d∈D lat rd The farm density is computed based on the ratio of the number of grow out ponds in operation to total area in the region. Sludge production: s = a“sludge”pdw zrpdwt ; xrpdwt
r ∈ R; p ∈ P; d ∈ D; w ∈ W; t ∈ T:
(17)
Sludge production is dependent on the farm category adopted. The total amount is in turn determined by the actual production levels on each respective farm. The pro9t function: (r − dc − idc ) t t t = : (18) t (1 + i) t∈T The pro;t function for each farmer is equal to revenues minus costs. The revenues are from the sale of the shrimps while the costs are comprised of the following components: direct and indirect costs. Within the ;rst component, we further clarify between ;xed and variable costs. Fixed costs will be land costs and capital costs. Variable cost components will be feed, chemicals, fry, energy. The indirect costs will be oK-site environmental costs such as sludge disposal and the opportunity costs incurred from land conversion as well as land abandonment. Revenues: o tr = xrpdwt p“shrimp” : (19) p∈P d∈D w∈W r∈R
Revenue is equal to actual harvest of shrimps multiplied by the price. Prices are ;xed.
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Direct costs: tdc =
uripdwct pc :
(20)
p∈P d∈D w∈W c∈CFV ∪“capital” r∈R
Direct cost is equal to ;xed costs plus variable costs. The CFV is a sub-set of commodities that depend on the production levels. These would be feed, fry, energy, chemicals, etc. Capital is included in the direct cost category and is classi;ed in the model as a commodity. Indirect costs: tidc = a“sludge”pdw zrpdwt p“sludge” p∈P d∈D w∈W r∈R
+
lcrdt p“opport” +
d∈D r∈R
lbrdt p“opport” :
(21)
d∈D r∈R
Indirect costs primarily relate to the environmental costs, which are caused by the shrimp sector. We begin by computing the costs of sludge disposal. The next two components are the opportunity costs foregone when land is converted to shrimp farming and when shrimp farms are abandoned. 4. Some preliminary results In this section, results from a couple of model simulations are presented to highlight some of the strengths sectoral models have over conventional cost-bene;t and rate of return analysis. The results presented in this study will be for only one region, although the model presented in the previous section had the capability of handling a number of regions. The version presented in this study does not have any inter-regional behavioral relationships. Fig. 1 shows the management pro;les chosen under present prices and when environmental waste is dumped into the common water system. No attention is paid to the opportunity cost of land abandoned by the shrimp sector. The declining number of farms mimics quite closely the present trend in the shrimp sector (Dierberg and Kiattisimkul, 1996). Relatively high stocking rates with open water exchange systems are adopted throughout the sector. Open water exchange systems are chosen because they require less land and are less costly to operate because they do not use as much chemical inputs for cleaning water as closed systems demand. The choice of stocking rates suggests an even more interesting analysis of trade-oKs occurring in the background. The medium high stocking rate (75 pl=m2 ) is chosen instead of the very high stocking rate (100 pl=m2 ) because of the relatively lower mortality rates under the former system (see Fig. 2). The trade-oK that occurs here is ;nal output levels determined by higher stocking rate but higher mortality rate versus lower stocking rate but higher survival rate. It can also be seen from Fig. 1, that the management pro;les change over time with some substitution from medium high stocking rates to very high stocking rates
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Fig. 1. Management pro;les under present prices and when environmental waste is dumped into the common water system.
HHI.OP
MHI.OP 0.47
% survival rate
0.46 0.45 0.44 0.43 0.42 0.41 0.4 0
2
4
6
Time (crops) Fig. 2. Survival rates under management pro;les.
8
10
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Fig. 3. Management pro;les when environmental waste is disposed properly and when opportunity cost of abandoned land is included in total cost.
beginning to occur in period eight. The switch can be partly explained by the lower diKerentials in survival rates between the two stocking rates. For example, if very high stocking rates were adopted earlier in the time period, then the subsequent higher mortality rates would have outweighed the ;nal output levels that may have been harvested. The possibility of allowing a combination of pro;les within a time period as well as over time periods is one of the unique strength of sectoral models. The decreasing number of farms observed in Fig. 1 suggests that the sectoral design is not sustainable in the long run. The total area under shrimp farming is declining— caused by a rapid increase in land abandonment which in turn is caused by high (low) mortality (survival) rates. The farm density at the beginning of the time period is approximately 65% of total land area but drops to 40% by the end of the time period. And at the end of the period, approximately 41% of total land area is classi;ed as abandoned. The high rate of farm abandonment is caused by a succession of low survival rates. If the sector is allowed to operate under present conditions, a collapse is inevitable. A second simulation was run but with the cost of environmental externalities caused by the sector internalized within the decision-making framework. As mentioned in the previous section, the environmental costs or indirect costs in this study comprise of two components. The ;rst is the cost of disposing the waste created by the sector and the second cost category is the opportunity cost of land abandoned by the shrimp sector. Fig. 3 tells us that the rate of farms abandoned becomes negligible and levels oK to about 80,000 farms by the end of the time period. The second observation is that closed water exchange systems are chosen in place of open water systems as observed in the previous simulation. The third observation is that a combination of stocking densities is adopted on what at ;rst observation seemed to be based on soil characteristics but on a more detailed analysis revealed land scarcity as the driving factor.
A.K. Duraiappah / Journal of Economic Dynamics & Control 26 (2002) 1481 – 1498 MHI.CS
HHI.CS
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MHI.OP
Survival Rate
% survival rate
70
60
50
40 0
2
4
6
8
10
Time (crops)
Fig. 4. Survival rates under various management pro;les.
The primary reason for the switch to closed systems is the need to minimize the rate of land abandonment. There is now a cost for abandoning land and in order to reduce the rate of abandonment, the mortality (survival) rate has to be decreased (increased). One way of increasing survival rates is by using the closed water system (see Fig. 4). However, because closed water exchange systems require larger tracts of land, the model prescribes a higher stocking rate (100 pl=m2 ) in a majority of cases. The only exception is in the case of non-acidic clay soils. This exception highlights some of the dynamics underlying the shrimp sector and strengthens the case for capturing the inter-dependency among the critical variables in the design of a sector. The reason for choosing medium high stocking rates is because of the larger acreage available under non-acidic clay soils. 5 The larger acreage reduces land scarcity as a binding factor. Moreover, if very high stocking rates were adopted in these soils, the levels of waste production will also increase due to the larger acreage under production and subsequently the cost of disposal. These costs will then need to be weighed against the foregone bene;ts incurred through the higher mortality rates that will occur; not only for that particular soil type but also for the other soil categories because the ensuing water quality is inEuenced by the management pro;le of all farms in the region. The last observation we would like to discuss is the number of farms. The number of farms in the second simulation is about 33% lower than in the ;rst simulation. This result tells us that with internalization of externalities, there is pressure to reduce and stabilize total eTuent production and the most cost-eKective manner is by controlling the number of farms. The high pro;table pro;le of the shrimp sector makes it diMcult for policymakers to discourage shrimp farming (Primavera, 1993; Masae and Rakkheaw, 1992). The best strategy would be to make sure that the sector is monitored and motivated to pursue 5
Non-acidic clay soils forms the largest section of lands available.
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140 120
U.S$/ha
100 80 60 40 20 0 75pl/m2- close water system
100pl/m2- close water system Pond Attributes
75pl/m2- open water system
Fig. 5. Farm permit prices for various management pro;les.
sustainable farming strategies. We turn our attention to some policy tools that can be used to motivate the sector to adopt sustainable strategies. A number of options are available. One alternative would be to implement an eTuent emission tax on farms that pollute above a pre-speci;ed emission limit. Subsidies can of course be given to farms that emit less than the limit. The drawback with this instrument is that it leaves open the following two critical issues: (1) environmental costs caused by soil salinization on both abandoned and adjourning lands; and (2) the number of farms within speci;ed geographical boundaries. A second option would be to introduce a system of price diKerentiated farm permits. Farm permits achieve two objectives. First, by specifying the total number of permits, authorities can control the actual number of farms within speci;ed geographical boundaries. Second, the price of the permit can be set based on farm management practices as well as on soil characteristics, i.e.—a diKerentiated permit price system based on spatial and technical properties. Fig. 5 gives the permit price that needs to be collected from the respective farms based on soil type, stocking density and the water system adopted. These prices were computed by dividing the environmental cost caused by the farms adopting a certain farm technique by the total number of farms using that technique. Farms that adopt the open system pay the highest permit price, while farms adopting lower stocking rates combined with closed water systems pay the lowest permit price. All other combinations fall between these two ends of the spectrum. An interesting observation to note here is the inverse relationship between farm permit prices and survival rates. The lower the price of the permit, the higher is the survival rate. The unique characteristic of the pricing system lies in its eMciency improvement properties—high polluting management strategies pay a higher price than those strategies which exert lower pressures on the environment. By imposing a price diKerentiated
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permit system based on environmental pressures, it forces, or more appropriately “motivates”, the sector to adopt management strategies that increase net pro;ts. The pro;ts under simulation two were observed to increase by more than a factor of two to those in simulation one. This at ;rst glance may seem contradictory, as one would expect that with external costs included, net pro;ts go down. However, on the contrary, with external costs internalized, a search for techniques to reduce mortality rates is initiated. This in turn produces a combination of techniques and farm density, which increases the overall pro;ts of the sector.
5. Discussion and conclusions Formulating a sectoral design is a non-trivial exercise as demonstrated by this study. Do the bene;ts of having such a model to guide the designing process outweigh the costs incurred in formulating such a model? I believe it is. The results presented in the previous section demonstrate a high degree of inter-dependency among the following critical factors. • • • • • • • •
Land scarcity. Stocking densities. Water-management options. Number of farms. Timing of management pro;les. Cost of waste disposal. Opportunity cost of abandoned land. Survival rates.
Conventional investment evaluation techniques would have looked each management pro;le in isolation. However, as demonstrated in the previous section, choosing a high stocking rate not only inEuences the mortality rates of that stocking rate, but also the mortality rates of other stocking rates. This comes from the common good property of the water system that is captured implicitly through the survival function. Another critical factor that inEuences management pro;les is the number of farms. A majority of studies on the shrimp sector have acknowledged the important role the number of farms plays in determining the sustainability of the sector (Dierberg and Kiattisimkul, 1996; Potaros, 1995). However, there is a lack of an analytical tool to address this factor and individual project evaluation techniques fall short of achieving this objective. In conclusion, the one main diKerence between a sectoral design approach and conventional cost-bene;t or rate of return analysis is the degree of scope in capturing the inter-dependencies among the critical factors of the system under consideration. Once, inter-dependencies are captured explicitly within a single decision-making framework, the choice set expends from the evaluation of a single project or investment to a set of projects that make up a comprehensive investment strategy for the sector.
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90
Survival Rate (Percent)
80 70 60 50 40 30 20 10
13
4
0 r /A
d
ee
7
S n(
L
12
rea)
10
9
7
6 eed/A
)
ea
10
Ln(F
Fig. 6. Shrimp survival rate as function of feed and seed for close water exchange system.
90 80
Survival Rate (Percent)
70 60 50 40 30 20 10
13
0
4
)
10
rea)
7
12
eed/A
10
Ln(F
9
7
6
1496
ea
r /A
ed
Se
( Ln
Fig. 7. Shrimp survival rate as function of feed and seed for open system.
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6. For further reading The following reference is also of interest to the reader: Kendrick et al., 1984. Appendix Shrimp survival rates as a function of feed and seed for close water exchange system and the open system are shown in Figs. 6 and 7. References Boyd, C.D., Musig, Y., 1992. Shrimp pond eTuent: observations of the nature of the problem on commercial farms. Proceedings of the Special Session on Shrimp Farming, World Aquaculture Society Annual Meeting, Baton Rouge, LA, 195 –197. Briggs, M.R.P., Funge-Smith, S.J., 1994. Unsustainable shrimp culture. A review of causes and potential solutions from experience in Thailand. In: Briggs, M.R.P. (Ed.), Development of strategies for sustainable shrimp farming. Final Report to the Overseas Development Administration, Research Project R4751, Institute of Aquaculture, University of Stirling. Chiu, Y.N., 1988. Water quality management for intensive prawn ponds. In: Chiu, Y.N., Santos, L.M., Juliano, R.O. (Eds.), Technical Considerations for the Management and Operation of Intensive Prawn Farms. U.P. Aquaculture Society, Iioilo City, Philippines, pp. 102–128. Choksi, A., Kendrick, D.A., Meeraus, A., Stoutjesdijk, A., 1981. La Programmation des Investissements Industriels. Economica, Paris. Dierberg, F.E., Kiattisimkul, W., 1996. Issues, impacts, and implications of shrimp aquaculture in Thailand. Environmental Management 20 (5), 649–666. Duraiappah, A.K., Israngkura, A., Sombat Sae-Hae, X., 2000. Sustainable shrimp farming: estimations of a survival function. CREED Working paper No. 31, International Institute for Environment and Development (IIED), London. Flaherty, M., Karnjanakesorn, C., 1994. Marine shrimp aquaculture and natural resource degradation in Thailand. Environmental Management 19, 27–37. Funge-Smith, S.J., Briggs, M.R.P., 1994. The origins and fate of solids and suspended solids in intensive marine shrimp ponds in Thailand. In: Briggs, M.R.P. (Ed.), Development of Strategies for Sustainable Shrimp Farming. Final Report to the Overseas Development Administration, UK, Research Project R4751, Institute of Aquaculture, University of Stirling, UK. Funge-Smith, S.J., Aeron-Thomas, M., 1995. The economic factors and risks inEuencing the sustainability of Thai intensive shrimp farms. Institute of Aquaculture, University of Stirling, UK. Kendrick, D.A., Stoutjesdijk, A., 1978. The Planning of Industrial Programs: A Methodology. Johns Hopkins University Press, Baltimore, MD. Kendrick, D.A., 1990. A production model construction system: PM statement to math programming. Journal of Economic Dynamics and Control 14 (2), 219–236. Kendrick, D.A., 1991. A graphical interface for production and transportation system modeling: PTS. Computer Science in Economics and Management 4, 229–236. Kendrick, D.A., 1967. Investment Planning and Economic Integration. The Economics of Planning 7 (1), 48–72. Kendrick, D.A., Meeraus, A., Alatorre, J., 1984. The Planning of Investment Programs in the Steel Sector. The Johns Hopkins University Press, Baltimore. Kendrick, D.A., 1984. Style in multisectoral modeling. In: Hugh Hallet, A.J. (Ed.), Applied Decision Analysis and Economic Behaviour. Martinus NijhoK Publishers, Dordrecht, pp. 329–360. Kendrick, D.A., Brooke, A., Meeraus, A., 1992. GAMS: Release 2.25: A Users Guide, Scienti;c Press Series. Boyd and Fraser Publishing Co, Danvers, MA.
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Masae, A., Rakkheaw, S., 1992. Social aspects of artisanal ;sheries and shrimp farming in Pak Phanang Bay. Asian Fisheries Social Science Research Network: Coastal Resources Institute, Price Songkhla University, Hat Yai, Thailand. Patmasiriwat, D., Kuik, O., Pednekar, S., 1998. The shrimp aquaculture sector in Thailand: a review of economic development and trade issues. CREED Working Paper. No IIED, London. Phillips, M.J., 1994. Shrimp culture and the environment. Unpublished paper prepared for Aquaculture Development in Southeast Asia ’94 (ADSEA ’94). 26 –28th July, 1994, Hoilo City, Philippines, Network for Aquaculture Centers in Asia-Paci;c. Kasetsart University, Bangkok 10900, Thailand. Potaros, M., 1995. Thailand, FAO report on a regional study and workshop on the environmental assessment and management of aquaculture development. Bangkok, Thailand. Primavera, J.H., 1993. A critical review of shrimp pond culture in the Philippines. Reviews in Fisheries Science 1 (2), 151–201. Thongrak, S., Prato, T., Chiayvareesajja, S., Kurtz, W., 1997. Economic and water quality evaluation of intensive shrimp production systems in Thailand. Agricultural Systems 53, 121–141. Wongsaengchan, A., 1990. Impacts of intensive shrimp cultivation on water quality in the Songkhla Basin, Thailand. Ph.D. Dissertation, University of Tennessee, Knoxville.
Sectoral dynamics and natural resource management Anantha Kumar Duraiappah International Institute for Sustainable Development (IISD), 161, Portage Avenue, Winnipeg, Canada
Abstract In order to analyze, understand and prescribe natural resource management strategies, the decision making framework should ideally capture the dynamics of inter-dependency between the economic and ecological systems in an integrated manner. However, the inclusion of two complex systems within a single integrated framework makes many of the present analytical tools redundant. Computational sectoral models on the other hand are ideally suited to meet this challenge. In this paper, I present an integrated sectoral model for the shrimp sector in Thailand. The paper describes how the dynamics of economics and the natural system are captured and the complexity that arises from this integration. Some preliminary results demonstrate how sectoral strategies can change when the environmental costs of sectoral strategies are taken into account during the planning stage. ? 2002 Elsevier Science B.V. All rights reserved. Keywords: Non-linear; Optimization; Integrated; Computational; GAMS
Foreword I met David Kendrick in the fall of 1986 as a graduate student at the University of Texas at Austin. It was only natural (there are not that many David Kendrick’s out there) that he became ;rst my dissertation supervisor, then a friend and now a mentor. I would like to thank the editors for giving me this opportunity to contribute to this very timely special issue in his honor. David introduced me to the world of mathematical modeling techniques for economic planning and to numerical computational techniques for solving these models. His work in sectoral planning as well as in multi-sectoral economic growth models laid the foundation for many of the integrated economic– ecological systems I have developed over the years. The ;rst model, an integrated climate change-economic growth model was formulated while I was a doctoral student under David. His suggestions and help were invaluable in my attempt to formulate E-mail address: [email protected] (A.K. Duraiappah). 0165-1889/02/$ - see front matter ? 2002 Elsevier Science B.V. All rights reserved. PII: S 0 1 6 5 - 1 8 8 9 ( 0 1 ) 0 0 0 8 1 - 1
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and solve what at that time seemed to be a highly non-linear and complex model. Frustrations and at many times sheer hopelessness were always overcome by David’s clear thoughts and reasoning towards a solution. 1. Introduction Investment decisions in the past primarily focused on the evaluation of individual projects based on either cost-bene;t or rate of return analysis. It is a practice still accepted as the modus operandi. However, David Kendrick and Ardy Stoutjesdijk (1978) argued as far back as in the late 1970s that attention should be redirected from just the evaluation of projects towards the design of projects. The primary force driving their argument for design versus evaluation is the advantage design allows in capturing explicitly the inter-dependency that can occur across a number of individual projects with respect to the following economic decisions: • • • • • • •
timing of investments, location of investments, size of investments, choice of technologies, economies of scale, mix of ;nal products, options of exports and imports.
For example, the decision to build a steel mill will be inEuenced not only by the choice of technologies but also on the location of the investments, the size of the investment and the possibilities of exporting or importing inputs and outputs to supplement domestic surplus or shortages, respectively. These were issues that conventional cost-bene;t or rate of return analysis was not capable of incorporating in the evaluation process. Why then is conventional cost-bene;t and rate of return techniques still the modus operandi for investment decisions? The formulation and solution of sectoral models was a non-trivial task in the 1970s when they were ;rst introduced. These models required a certain degree of specialized knowledge in mathematical modeling and computational techniques. The state-of-art in both ;elds was still at infancy at that point in time. Although great strides have been made in both areas over the last two decades, the adoption of sectoral models as an investment tool has been limited. Much of this inertia can be traced to the perceived complexity of these models and basically it was much simpler to compute cost-bene;t or rate of return analysis to select or reject a project. However, not deterred by this reluctance to use sectoral models, Kendrick began to develop prototype software to simplify the mathematical formulation of a sectoral model (Kendrick 1990, 1991). Although the beta versions proved successful, the response by the project investment crowd was limited. The time commitment required for sectoral modeling was still considered too high by investment specialists. Moreover, with the recent increasing awareness of environmental repercussions of investment decisions, it was much straightforward to extend the conventional cost-bene;t
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or rate of return frameworks to incorporate environmental costs of investment projects. The extension of sectoral models to include environmental dynamics posed a slightly more non-trivial task. However, if we take a good look at the dynamics of environmental degradation caused by investment projects, the case for sectoral designs is actually strengthened. The strong inter-dependencies among the various economic factors described earlier in the paper and environmental factors make it imperative that a design approach be taken versus the conventional assessment of individual projects. For example, in the case of the steel sector, the decision to invest in a steel mill at a particular location is now not only dependant on the economic factors highlighted earlier but also on the following factors: • The air dispersion dynamics of the location. • The distance the mill is from human habitats. • Physical damages done by the investments. This high degree of inter-dependency among economic and environmental variables strengthens the case for using sectoral modeling to guide investment policies. The methodology used in developing a design for an industry or sector is described in detail in Kendrick (1967) and Kendrick and Stoutjesdijk (1978) while models developed speci;cally for the steel and fertilizer sector can be found in Kendrick and Stoutjesdijk (1978) and Choksi et al. (1981), respectively. In this paper, I shall draw on the basic building blocks of the sectoral model methodology illustrated in Kendrick and Stoutjesdijk (1978) and then extend the model by including environmental resource bases and incorporating the inter-dependencies among the economic and environmental variables within a single design process. The structure of the paper is as follows. In Section 2 a brief overview of the critical factors underlying a shrimp sector is presented. In Section 3, the sectoral model for the shrimp sector in Thailand is provided. Some preliminary results are provided in Section 4 to demonstrate the uniqueness of the sectoral model in investment and natural resource management decisions. The paper ends with some concluding remarks. 2. Shrimp sector dynamics I shall not go into too much detail on the dynamics of the shrimp sector but only provide a brief overview of the sector to give the reader an idea of the critical components driving the system. The major problem shrimp sectors face in a large number of developing countries is the issue of sustainability. Shrimp sectors just do not survive. The normal pattern for shrimp sectors is a boom in the ;rst three to ;ve years and then go bust as diseases hit the sector (Patmasiriwat et al., 1998). The main reason why the shrimp sector in most of these countries has survived at all is the fact that the sector practices slash and burn techniques by moving to new locations when a bust occurs. Many theories have been put forward to explain why diseases happen. However, there is an emerging consensus among shrimp experts that low quality of water together with reducing resiliency among shrimps as the primary reasons for diseases to
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occur (Briggs and Funge-Smith, 1994; Phillips, 1994; Chiu, 1988; Wongsaengchan, 1990; Boyd and Musig, 1992). The quality of water and the issue of resiliency are in turn inEuenced largely by the management techniques adopted by the farms (Dierberg and Kiattisimkul, 1996). The economics of shrimp production at an individual farm level based on conventional cost-bene;t or rate of return analysis tends to favor the adoption of very high stocking rates for juvenile shrimps, the use of high technology and minimize the use of land (Funge-Smith and Aeron-Thomas, 1995; Thongrak et al., 1997). The use of high technology entails the use of arti;cial feeding strategy, the use of antibiotics to prevent diseases and machinery to increase the level of dissolved oxygen in the ponds. However, the success of these initiatives has been limited and the incidence of diseases has yet to decrease signi;cantly (Funge-Smith and Briggs, 1994; Dierberg and Kiattisimkul, 1996; Flaherty and Karnjanakesorn, 1994). In order to provide some new insight into the problem, this paper attempts to approach the problem from a sectoral perspective. The underlying dynamics of the sector demonstrates strong inter-dependencies among a number of critical factors— inter-dependencies that conventional tools for investment miss. The brief overview of the sector presented above suggest that in order to design an investment strategy for the shrimp sector, three critical factors need to be addressed. The ;rst is land use; the second is water management; and the third is a combination of stocking density and feeding intensity. Land or more speci;cally, soil type is an important factor in shrimp production. Land with clay soils is known to be less suitable for shrimp production than land with alluvial soils. Although the former can be used substantial additional costs will need to be incurred in order to prepare the soil for shrimp production. The decision to use clay soils will primarily be determined by the additional costs of preparation versus the bene;ts from using the land 1 and the degree of land scarcity. A unique strength of the sectoral modeling approach is that the cost of scarcity of land is computed explicitly within the decision-making framework through shadow pricing. Water or more speci;cally brackish 2 water management poses a slightly more complex challenge. Clean brackish water is an essential ingredient for shrimp production. Dirty water reduces resistance levels in shrimps therefore making them more susceptible to diseases. Dirty water also carries viruses and bacteria that can cause high mortality in shrimp production. The unique characteristic of brackish water that makes it diKerent to land is that brackish water is sourced from a common source—the coast and can be technically de;ned as a public good. The maintenance of the quality of the water system is therefore dependant on all the farms within a speci;ed geographical region. In essence, the quality of the water system is determined by the number of farms and the technology adopted by the various farms in the region. Each individual farm can control the quality of the water to a certain extent by adopting an open or closed water management strategy. In the case of the former, the farm relies completely on the common water system. In the case of the 1
Clay solid are usually found near the coast and therefore the cost of sourcing water from the coast is much lower than for lands further inland. 2 Shrimp production needs water of a certain salinity level.
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closed system, the farm has facilities to clean water before using it for production and it also limits the frequency of sourcing water from the common system. The farm relies on the reuse of water within the farm. This option naturally entails higher cost, as land has to be appropriated for storing, cleaning and recycling of water within the farm. There also additional costs from chemicals needed to clean the water before usage. The ;nal factor is stocking density and feeding intensity. Stocking density is the number of juvenile shrimp that is stocked per unit area while feeding intensity is amount of arti;cial feed used per unit area. The standard assumption would be to adopt high stocking and feeding densities in order to obtain high ;nal yields. However, there is a downside to high stocking densities and feeding intensities. Stocking densities beyond a critical point are positively correlated to high mortality rates. The critical point is in turn determined by a host of economic and environmental factors. Stocking densities beyond the critical point create high stress environments caused by over-crowding and together with high feeding intensities produce higher amount of fecal discharge that create unsanitary conditions that are ideal for viral and bacterial outbreaks. The three factors 3 put together in various permutations then form the management strategies available to the sector. Each combination or pro;le can be perceived as a process or recipe for shrimp production. The activity level of each pro;le then determines the amount of inputs and outputs produced. The prices of the inputs used and the outputs produced then give us the revenues and costs of each pro;le. The model is formulated to ;nd the optimal number of farms within speci;ed geographical regions that employ a certain combination of management strategies that maximize net bene;ts. This is done by developing a non-linear programming model of the sector.
3. The sectoral model for the shrimp sector 3.1. Description of the model The objective in the shrimp sector model is to maximize net bene;ts. Bene;ts come from the sale of shrimps while costs are presented in two categories. Direct costs are primarily production cost and are comprised of ;xed and variable cost. Indirect cost is the second cost category and components are the removal of waste products and the opportunity cost of land abandoned by shrimp farms. 4 The model is solved to ;nd the set of management strategies adopted by an optimal number of farms that will maximize net bene;ts without violating land constraints. In order to capture critical turning points in the use of inputs, the sector is presented as a non-linear programming model.
3 I shall refer to the combination of stocking intensity and feeding intensity as a single factor as both items are jointly developed. 4 Lands abandoned by shrimp farms can rarely be used for other agricultural activities due to the high salinity content of the soils.
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3.2. Model nomenclature One of the ;rst rules David Kendrick stressed in economic modeling is the attention a modeler must pay to nomenclature and style (Kendrick, 1984). It is therefore convenient to think in terms of sets of regions, land types, farm techniques, water management options, commodities and time periods for shrimp sectoral models. It is also convenient to think in sets as it makes the transition process from the mathematical model to the computational model in the general algebraic modeling system (GAMS) smooth and transparent (Kendrick et al., 1992). In order to avoid the curse of dimensionality, superscripts in this model are used to facilitate the use of variable names longer than just a single letter. Subscripts are used to denote the set domain over which an operation is to be executed. Sets R D P W C T
regions land type farm techniques water management options commodities time periods
Variables z x u la lu lug lur lb lc lp
activity levels ;nal shipment raw material purchases land available for shrimp farming demand for land by shrimp farming land used for cultivation of shrimps land actually used for shrimp farming land abandoned by shrimp farms land converted to shrimp farming productivity drop in land caused by drop in survival rates
Parameters A L P IC TC s fi si fmd
technology matrix land availability prices initial conditions terminal conditions survival rate feed intensity seed intensity farm density
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xs r dc idc n
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sludge production revenue direct cost indirect cost number of farms
3.3. Model equations Raw material purchases by respective farms: ucrpdwt = acpdw zrpdwt ;
c ∈ CFV; p ∈ P; d ∈ D; w ∈ W; t ∈ T; r ∈ R:
(1)
Eq. (1) computes the amount of raw materials used by the sector. The CFV set is the commodity sub-set which consists of only raw materials. These are: (1) energy; (2) feed; (3) seed; and (4) chemicals. The z variable tells us the activity level in region r, using process p located in land type d and using water management option w in time t. The coeMcients in the A matrix (acpdw ) were computed from data collected from a farm survey. The A matrix can be considered as the “heart” of the model. Total land use: a“tarea ”pdw zrpdwt = lurpdwt ;
r ∈ R; p ∈ P; w ∈ W; d ∈ D; t ∈ T:
(2)
Land use by shrimp farms is equal to the demand, which is denoted by the purchase level that in turn is determined by the activity levels. This area covers ponds used for shrimp production, water cleaning ponds, plus land used for infrastructure. Total grow out area: lug rpdwt = a“garea ”pdw zrpdwt ;
r ∈ R; p ∈ P; d ∈ D; w ∈ W; t ∈ T:
(3)
In this equation, we compute the total area covered by ponds used for actual shrimp farming. Land type covered by shrimp farms in each region: lurpdwt ; r ∈ R; d ∈ D; t ∈ T: (4) lur rdt = p∈P w∈W
Eq. (4) computes the total land of type d in each of the regions that is used by shrimp farms. Land use constraint: a lur rdt 6 lrdt ;
r ∈ R; d ∈ D; t ∈ T:
(5)
The amount of land used by shrimp farms has to be less than the area under shrimp farming. Land accumulation: lardt+1 = lardt − lbrdt+1 + lcrdt+1 ;
r ∈ R; d ∈ D; t ∈ T:
(6)
The land under shrimp farming is accumulative and depends on the level in the previous period minus land abandoned plus land converted.
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Land use balance equation: b LatQrdt = lardt + lau rdt + lrdt ;
r ∈ R; d ∈ D; t ∈ T:
(7)
The total amount of land under shrimps plus land under alternative uses plus abandoned land must be equal to total amount of land available. This equation can be interpreted as an identity or balance equation. The data for the total amount of land available for each soil type and in each region were computed based on GIS information. This is reEected under the category of parameters L. Land abandoned by shrimp farms: lurpdwt lprpdwt : (8) lbrdt = p∈P w∈W
The amount of land abandoned depends on the productivity drop witnessed on the farms. Productivity drop: lprpdwt = e−srpdwt =pconst pgrad ;
r ∈ R; p ∈ P; d ∈ D; w ∈ W; t ∈ T:
(9)
The degree of productivity drop is determined primarily by the survival rate. The lower the survival rate, the higher the productivity drops. The pconst and pgrad reEect the rate and magnitude of the impact of the survival rate has on the productivity drop experienced by the farms. In many ways, we can use varying ;gures for pconst and pgrad to capture risk taking behavior on the part of the various shrimp farms. The ;gures used in this study were 0.12 and 2.3007, respectively, and these coeMcients were derived from a calibration process whereby the underlying premise is that farms get abandoned when the survival rate of shrimps falls below 60%. Natural shrimp production level: p = a“shrimp”pdw zripdwt ; xrpdwt
r ∈ R; p ∈ P; d ∈ D; w ∈ W; t ∈ T:
(10)
The natural shrimp production level denotes the harvest level, which can be experienced if no diseases occur. The output level used in the technology matrix is net of the natural mortality rate. Actual shrimp production levels: p o = xrpdwt srpdwt ; xrpdwt
r ∈ R; p ∈ P; d ∈ D; w ∈ W; t ∈ T:
(11)
The actual shrimp harvested is net of mortality rates caused by controllable factors that are described in detail in the survival equation below. Shrimp survival rate: i i ln srpdwt = a1 ln frpdwt + a2 ln srpdwt + a3 ln frtmd + a4
i ln frpdwt i ln srpdwt
+ a5 zrpd“close”t
i i + a6 (ln frtmd )2 + a7 (ln frpdwt )2 + a8 (ln srpdwt )2 ;
r ∈ R; p ∈ P; d ∈ D; w ∈ W; t ∈ T:
(12)
A modi;ed form of a translog function is used for the shrimp survival rate. The function was econometrically estimated using survey data from a sample size of 350
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farms (Duraiappah et al., 2000). Various functional forms were used but the above function provided the best ;t. See the appendix for a schematic illustration of the function and how it captures the unique characteristics of shrimp survival. Feed intensity: u“feed”rpdwt i frpdwt ; r ∈ R; p ∈ P; d ∈ D; w ∈ W; t ∈ T: (13) = lug rpdwt Feed intensity is primarily computed as the total amount of feed purchased by the farms divided by the grow-out area used by the respective farms. Seed intensity: u“seed”rpdwt i srpdwt = ; r ∈ R; p ∈ P; d ∈ D; w ∈ W; t ∈ T: (14) lug rpdwt Seed intensity is computed in a similar manner as the feed intensity. Number of farms: lurpdwt nrpdwt = ; r ∈ R; p ∈ P; d ∈ D; w ∈ W; t ∈ T: a“tarea ”pdw
(15)
The number of farms is equal to the total land under use by the various farm categories divided by the unit area required by a hypothetical farm. We assume that each category as described by the technology matrix is representative of a farm. Farm density: ug p∈P d∈D w∈W lrpdwt md frt = ; r ∈ R; t ∈ T: (16) Q d∈D lat rd The farm density is computed based on the ratio of the number of grow out ponds in operation to total area in the region. Sludge production: s = a“sludge”pdw zrpdwt ; xrpdwt
r ∈ R; p ∈ P; d ∈ D; w ∈ W; t ∈ T:
(17)
Sludge production is dependent on the farm category adopted. The total amount is in turn determined by the actual production levels on each respective farm. The pro9t function: (r − dc − idc ) t t t = : (18) t (1 + i) t∈T The pro;t function for each farmer is equal to revenues minus costs. The revenues are from the sale of the shrimps while the costs are comprised of the following components: direct and indirect costs. Within the ;rst component, we further clarify between ;xed and variable costs. Fixed costs will be land costs and capital costs. Variable cost components will be feed, chemicals, fry, energy. The indirect costs will be oK-site environmental costs such as sludge disposal and the opportunity costs incurred from land conversion as well as land abandonment. Revenues: o tr = xrpdwt p“shrimp” : (19) p∈P d∈D w∈W r∈R
Revenue is equal to actual harvest of shrimps multiplied by the price. Prices are ;xed.
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Direct costs: tdc =
uripdwct pc :
(20)
p∈P d∈D w∈W c∈CFV ∪“capital” r∈R
Direct cost is equal to ;xed costs plus variable costs. The CFV is a sub-set of commodities that depend on the production levels. These would be feed, fry, energy, chemicals, etc. Capital is included in the direct cost category and is classi;ed in the model as a commodity. Indirect costs: tidc = a“sludge”pdw zrpdwt p“sludge” p∈P d∈D w∈W r∈R
+
lcrdt p“opport” +
d∈D r∈R
lbrdt p“opport” :
(21)
d∈D r∈R
Indirect costs primarily relate to the environmental costs, which are caused by the shrimp sector. We begin by computing the costs of sludge disposal. The next two components are the opportunity costs foregone when land is converted to shrimp farming and when shrimp farms are abandoned. 4. Some preliminary results In this section, results from a couple of model simulations are presented to highlight some of the strengths sectoral models have over conventional cost-bene;t and rate of return analysis. The results presented in this study will be for only one region, although the model presented in the previous section had the capability of handling a number of regions. The version presented in this study does not have any inter-regional behavioral relationships. Fig. 1 shows the management pro;les chosen under present prices and when environmental waste is dumped into the common water system. No attention is paid to the opportunity cost of land abandoned by the shrimp sector. The declining number of farms mimics quite closely the present trend in the shrimp sector (Dierberg and Kiattisimkul, 1996). Relatively high stocking rates with open water exchange systems are adopted throughout the sector. Open water exchange systems are chosen because they require less land and are less costly to operate because they do not use as much chemical inputs for cleaning water as closed systems demand. The choice of stocking rates suggests an even more interesting analysis of trade-oKs occurring in the background. The medium high stocking rate (75 pl=m2 ) is chosen instead of the very high stocking rate (100 pl=m2 ) because of the relatively lower mortality rates under the former system (see Fig. 2). The trade-oK that occurs here is ;nal output levels determined by higher stocking rate but higher mortality rate versus lower stocking rate but higher survival rate. It can also be seen from Fig. 1, that the management pro;les change over time with some substitution from medium high stocking rates to very high stocking rates
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Fig. 1. Management pro;les under present prices and when environmental waste is dumped into the common water system.
HHI.OP
MHI.OP 0.47
% survival rate
0.46 0.45 0.44 0.43 0.42 0.41 0.4 0
2
4
6
Time (crops) Fig. 2. Survival rates under management pro;les.
8
10
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Fig. 3. Management pro;les when environmental waste is disposed properly and when opportunity cost of abandoned land is included in total cost.
beginning to occur in period eight. The switch can be partly explained by the lower diKerentials in survival rates between the two stocking rates. For example, if very high stocking rates were adopted earlier in the time period, then the subsequent higher mortality rates would have outweighed the ;nal output levels that may have been harvested. The possibility of allowing a combination of pro;les within a time period as well as over time periods is one of the unique strength of sectoral models. The decreasing number of farms observed in Fig. 1 suggests that the sectoral design is not sustainable in the long run. The total area under shrimp farming is declining— caused by a rapid increase in land abandonment which in turn is caused by high (low) mortality (survival) rates. The farm density at the beginning of the time period is approximately 65% of total land area but drops to 40% by the end of the time period. And at the end of the period, approximately 41% of total land area is classi;ed as abandoned. The high rate of farm abandonment is caused by a succession of low survival rates. If the sector is allowed to operate under present conditions, a collapse is inevitable. A second simulation was run but with the cost of environmental externalities caused by the sector internalized within the decision-making framework. As mentioned in the previous section, the environmental costs or indirect costs in this study comprise of two components. The ;rst is the cost of disposing the waste created by the sector and the second cost category is the opportunity cost of land abandoned by the shrimp sector. Fig. 3 tells us that the rate of farms abandoned becomes negligible and levels oK to about 80,000 farms by the end of the time period. The second observation is that closed water exchange systems are chosen in place of open water systems as observed in the previous simulation. The third observation is that a combination of stocking densities is adopted on what at ;rst observation seemed to be based on soil characteristics but on a more detailed analysis revealed land scarcity as the driving factor.
A.K. Duraiappah / Journal of Economic Dynamics & Control 26 (2002) 1481 – 1498 MHI.CS
HHI.CS
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MHI.OP
Survival Rate
% survival rate
70
60
50
40 0
2
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6
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10
Time (crops)
Fig. 4. Survival rates under various management pro;les.
The primary reason for the switch to closed systems is the need to minimize the rate of land abandonment. There is now a cost for abandoning land and in order to reduce the rate of abandonment, the mortality (survival) rate has to be decreased (increased). One way of increasing survival rates is by using the closed water system (see Fig. 4). However, because closed water exchange systems require larger tracts of land, the model prescribes a higher stocking rate (100 pl=m2 ) in a majority of cases. The only exception is in the case of non-acidic clay soils. This exception highlights some of the dynamics underlying the shrimp sector and strengthens the case for capturing the inter-dependency among the critical variables in the design of a sector. The reason for choosing medium high stocking rates is because of the larger acreage available under non-acidic clay soils. 5 The larger acreage reduces land scarcity as a binding factor. Moreover, if very high stocking rates were adopted in these soils, the levels of waste production will also increase due to the larger acreage under production and subsequently the cost of disposal. These costs will then need to be weighed against the foregone bene;ts incurred through the higher mortality rates that will occur; not only for that particular soil type but also for the other soil categories because the ensuing water quality is inEuenced by the management pro;le of all farms in the region. The last observation we would like to discuss is the number of farms. The number of farms in the second simulation is about 33% lower than in the ;rst simulation. This result tells us that with internalization of externalities, there is pressure to reduce and stabilize total eTuent production and the most cost-eKective manner is by controlling the number of farms. The high pro;table pro;le of the shrimp sector makes it diMcult for policymakers to discourage shrimp farming (Primavera, 1993; Masae and Rakkheaw, 1992). The best strategy would be to make sure that the sector is monitored and motivated to pursue 5
Non-acidic clay soils forms the largest section of lands available.
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140 120
U.S$/ha
100 80 60 40 20 0 75pl/m2- close water system
100pl/m2- close water system Pond Attributes
75pl/m2- open water system
Fig. 5. Farm permit prices for various management pro;les.
sustainable farming strategies. We turn our attention to some policy tools that can be used to motivate the sector to adopt sustainable strategies. A number of options are available. One alternative would be to implement an eTuent emission tax on farms that pollute above a pre-speci;ed emission limit. Subsidies can of course be given to farms that emit less than the limit. The drawback with this instrument is that it leaves open the following two critical issues: (1) environmental costs caused by soil salinization on both abandoned and adjourning lands; and (2) the number of farms within speci;ed geographical boundaries. A second option would be to introduce a system of price diKerentiated farm permits. Farm permits achieve two objectives. First, by specifying the total number of permits, authorities can control the actual number of farms within speci;ed geographical boundaries. Second, the price of the permit can be set based on farm management practices as well as on soil characteristics, i.e.—a diKerentiated permit price system based on spatial and technical properties. Fig. 5 gives the permit price that needs to be collected from the respective farms based on soil type, stocking density and the water system adopted. These prices were computed by dividing the environmental cost caused by the farms adopting a certain farm technique by the total number of farms using that technique. Farms that adopt the open system pay the highest permit price, while farms adopting lower stocking rates combined with closed water systems pay the lowest permit price. All other combinations fall between these two ends of the spectrum. An interesting observation to note here is the inverse relationship between farm permit prices and survival rates. The lower the price of the permit, the higher is the survival rate. The unique characteristic of the pricing system lies in its eMciency improvement properties—high polluting management strategies pay a higher price than those strategies which exert lower pressures on the environment. By imposing a price diKerentiated
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permit system based on environmental pressures, it forces, or more appropriately “motivates”, the sector to adopt management strategies that increase net pro;ts. The pro;ts under simulation two were observed to increase by more than a factor of two to those in simulation one. This at ;rst glance may seem contradictory, as one would expect that with external costs included, net pro;ts go down. However, on the contrary, with external costs internalized, a search for techniques to reduce mortality rates is initiated. This in turn produces a combination of techniques and farm density, which increases the overall pro;ts of the sector.
5. Discussion and conclusions Formulating a sectoral design is a non-trivial exercise as demonstrated by this study. Do the bene;ts of having such a model to guide the designing process outweigh the costs incurred in formulating such a model? I believe it is. The results presented in the previous section demonstrate a high degree of inter-dependency among the following critical factors. • • • • • • • •
Land scarcity. Stocking densities. Water-management options. Number of farms. Timing of management pro;les. Cost of waste disposal. Opportunity cost of abandoned land. Survival rates.
Conventional investment evaluation techniques would have looked each management pro;le in isolation. However, as demonstrated in the previous section, choosing a high stocking rate not only inEuences the mortality rates of that stocking rate, but also the mortality rates of other stocking rates. This comes from the common good property of the water system that is captured implicitly through the survival function. Another critical factor that inEuences management pro;les is the number of farms. A majority of studies on the shrimp sector have acknowledged the important role the number of farms plays in determining the sustainability of the sector (Dierberg and Kiattisimkul, 1996; Potaros, 1995). However, there is a lack of an analytical tool to address this factor and individual project evaluation techniques fall short of achieving this objective. In conclusion, the one main diKerence between a sectoral design approach and conventional cost-bene;t or rate of return analysis is the degree of scope in capturing the inter-dependencies among the critical factors of the system under consideration. Once, inter-dependencies are captured explicitly within a single decision-making framework, the choice set expends from the evaluation of a single project or investment to a set of projects that make up a comprehensive investment strategy for the sector.
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90
Survival Rate (Percent)
80 70 60 50 40 30 20 10
13
4
0 r /A
d
ee
7
S n(
L
12
rea)
10
9
7
6 eed/A
)
ea
10
Ln(F
Fig. 6. Shrimp survival rate as function of feed and seed for close water exchange system.
90 80
Survival Rate (Percent)
70 60 50 40 30 20 10
13
0
4
)
10
rea)
7
12
eed/A
10
Ln(F
9
7
6
1496
ea
r /A
ed
Se
( Ln
Fig. 7. Shrimp survival rate as function of feed and seed for open system.
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6. For further reading The following reference is also of interest to the reader: Kendrick et al., 1984. Appendix Shrimp survival rates as a function of feed and seed for close water exchange system and the open system are shown in Figs. 6 and 7. References Boyd, C.D., Musig, Y., 1992. Shrimp pond eTuent: observations of the nature of the problem on commercial farms. Proceedings of the Special Session on Shrimp Farming, World Aquaculture Society Annual Meeting, Baton Rouge, LA, 195 –197. Briggs, M.R.P., Funge-Smith, S.J., 1994. Unsustainable shrimp culture. A review of causes and potential solutions from experience in Thailand. In: Briggs, M.R.P. (Ed.), Development of strategies for sustainable shrimp farming. Final Report to the Overseas Development Administration, Research Project R4751, Institute of Aquaculture, University of Stirling. Chiu, Y.N., 1988. Water quality management for intensive prawn ponds. In: Chiu, Y.N., Santos, L.M., Juliano, R.O. (Eds.), Technical Considerations for the Management and Operation of Intensive Prawn Farms. U.P. Aquaculture Society, Iioilo City, Philippines, pp. 102–128. Choksi, A., Kendrick, D.A., Meeraus, A., Stoutjesdijk, A., 1981. La Programmation des Investissements Industriels. Economica, Paris. Dierberg, F.E., Kiattisimkul, W., 1996. Issues, impacts, and implications of shrimp aquaculture in Thailand. Environmental Management 20 (5), 649–666. Duraiappah, A.K., Israngkura, A., Sombat Sae-Hae, X., 2000. Sustainable shrimp farming: estimations of a survival function. CREED Working paper No. 31, International Institute for Environment and Development (IIED), London. Flaherty, M., Karnjanakesorn, C., 1994. Marine shrimp aquaculture and natural resource degradation in Thailand. Environmental Management 19, 27–37. Funge-Smith, S.J., Briggs, M.R.P., 1994. The origins and fate of solids and suspended solids in intensive marine shrimp ponds in Thailand. In: Briggs, M.R.P. (Ed.), Development of Strategies for Sustainable Shrimp Farming. Final Report to the Overseas Development Administration, UK, Research Project R4751, Institute of Aquaculture, University of Stirling, UK. Funge-Smith, S.J., Aeron-Thomas, M., 1995. The economic factors and risks inEuencing the sustainability of Thai intensive shrimp farms. Institute of Aquaculture, University of Stirling, UK. Kendrick, D.A., Stoutjesdijk, A., 1978. The Planning of Industrial Programs: A Methodology. Johns Hopkins University Press, Baltimore, MD. Kendrick, D.A., 1990. A production model construction system: PM statement to math programming. Journal of Economic Dynamics and Control 14 (2), 219–236. Kendrick, D.A., 1991. A graphical interface for production and transportation system modeling: PTS. Computer Science in Economics and Management 4, 229–236. Kendrick, D.A., 1967. Investment Planning and Economic Integration. The Economics of Planning 7 (1), 48–72. Kendrick, D.A., Meeraus, A., Alatorre, J., 1984. The Planning of Investment Programs in the Steel Sector. The Johns Hopkins University Press, Baltimore. Kendrick, D.A., 1984. Style in multisectoral modeling. In: Hugh Hallet, A.J. (Ed.), Applied Decision Analysis and Economic Behaviour. Martinus NijhoK Publishers, Dordrecht, pp. 329–360. Kendrick, D.A., Brooke, A., Meeraus, A., 1992. GAMS: Release 2.25: A Users Guide, Scienti;c Press Series. Boyd and Fraser Publishing Co, Danvers, MA.
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Masae, A., Rakkheaw, S., 1992. Social aspects of artisanal ;sheries and shrimp farming in Pak Phanang Bay. Asian Fisheries Social Science Research Network: Coastal Resources Institute, Price Songkhla University, Hat Yai, Thailand. Patmasiriwat, D., Kuik, O., Pednekar, S., 1998. The shrimp aquaculture sector in Thailand: a review of economic development and trade issues. CREED Working Paper. No IIED, London. Phillips, M.J., 1994. Shrimp culture and the environment. Unpublished paper prepared for Aquaculture Development in Southeast Asia ’94 (ADSEA ’94). 26 –28th July, 1994, Hoilo City, Philippines, Network for Aquaculture Centers in Asia-Paci;c. Kasetsart University, Bangkok 10900, Thailand. Potaros, M., 1995. Thailand, FAO report on a regional study and workshop on the environmental assessment and management of aquaculture development. Bangkok, Thailand. Primavera, J.H., 1993. A critical review of shrimp pond culture in the Philippines. Reviews in Fisheries Science 1 (2), 151–201. Thongrak, S., Prato, T., Chiayvareesajja, S., Kurtz, W., 1997. Economic and water quality evaluation of intensive shrimp production systems in Thailand. Agricultural Systems 53, 121–141. Wongsaengchan, A., 1990. Impacts of intensive shrimp cultivation on water quality in the Songkhla Basin, Thailand. Ph.D. Dissertation, University of Tennessee, Knoxville.