Long-term Dynamics of Acequia Population
in New Mexico
Yining Bai, Saeed P. Langarudi, and Alexander (Sam) Fernald
Water Resources Research Institute
New Mexico State University
Y. Bai---ynb@ nmsu.edu, Saeed L.-- lang@nmsu.edu, Alexander F.--- afernald@ nmsu.edu
August 14, 2017
Abstract
There is evidence that traditional irrigation communities in New Mex-
ico, United States, which are also known as acequias, benefit the state’s
socio-economic-hydrologic systems in many respects. Despite their long-
term resi to their arid envi , these ities have been
declining since 1980s. This paper identifies ydrologic
mechanisms responsible for such decline and explores potential challenges
that it may impose to both rural and urban life in New Mexico. Results
show that current trend of urban growth is not sustainable. Turning the
trend is shown to be extremely difficult, though. Sensitive areas of the
socio-economic:
em are identified and directions for future rese
ditionally, the presented model provides a basis for future modeling in the
political economy of acequias.
1 Introduction
Acequiag!}a network of ity managed irrigation systems—are key to sus-
tainable extraction and distribution of water resources in the state of New Mex-
ico, United States since the 16" century. By diverting water throughout the
floodplain, acequias provide the society with human (c.g., domestic water), agri-
culture (¢.g., crop irrigation and livestock water), ecologic (c.g., riparian habi-
tat support), and hydrologic (e.g., enhanced surface water-groundwater con-
nectivity; aquifer recharge) benefits. However, system dynamics simulations,
performed by [Turner et al] and summarized in Figure[1] show that pop-
ulation of acequia community has been declining since 1980s. In fact, many
acequia members, while still living in the rural areas, abandon the traditional
practices in pursuit of higher incomes provided by external job opportunities in
urban areas.
tic
1The term acequia is derived from Arabic as-saqiya, meaning water conduit.
Person
100
oO
QaOMReAMRHRE SAMAR AAMAS
RRRRRRRRRRRHRRRR SESS
@ Observed eee Predicted
Figure 1: Estimated ion of acequia c ity vs. actual data
Historical data from Decennial Census, U.S. Census Bureau (retrieved from
strated in Figure[2|show that rural population, although grow-
ing in total numbers, has been declining significantly relative to urban popula-
tion over the last century.
Considering the benefits that traditional agricultural activities provide for
ecology of New Mexico, disappearance of rural population, in general, and ac
quia community, in particular, might cause serious challenges to the state. The
challenges may be even harder if we take into account chronic drought that the
state has been facing with over the past decade. Sustainability of the acequia
community is indeed critical for the state to weather the future challenges.
The model that is developed in this paper predicts the potential challenge:
Prediction of the system’s behavior from 1900 to 2300 is shown in Figur
Despite significant dec
ine of rural population until mid 21° century, the ur-
banization trend decelerates and even reversed during 2030s and 2040s. Rural
population then is stabilized at about 37%.
Reason for such a change in urbanization trend could be explained by over-
shoot and dec
ine of urban capital. Exponential growth of urban capital until
2050s takes place due to the increasing urbanization trend and the economic
water and other e
Consequently, natural capital declines. Urban growth which relies on s
of natural capital decelerates and eventually goes negative
pport
capital decay be-
comes greater than investment). As urban capital declines, pressure on natural
capital decreases so a new steady state will be reached at a lower level than the
500,000 100
450,000 |
400,000 |
350,000 |
300,000 |
250,000 |
200,000 | .*
150,000 4
Number of Residents (person)
100,000 4
50,000 4
1900 1920 1980 1960 1980 2000
Time (year)
+ Rural Population — - — Rural Percentage of Population
Figure 2: Historical trend of rural population in New Mexic
economic prime in 2050s.
Causal structure sible for the presented behavior is discussed in further
, in fact, an effort to develop a generic model to
of acequia community and potential problems it may create
for the state of New Mexico. The model is used to generate insights that may be
helpful for alleviating the problems. To build confidence in the model’s output,
validation tests performed and sensitivity analysis conducted are reported in
Outcomes of the analysi iscussed in Section [J] And, Section
the paper.
Section
conclud
2 Model Structure
The model that is developed in this paper stands on the shoulder of a pre
ously developed system dynamics model by [Fernald et al]
Most of the causal relationships of the current model
from their work. However, there are some fundamental differences
the models. Most importantly, dynamics of urbanization and land and water
markets were exogenous to the original model p. 6). In
the current model, however, these dynamics are included as endogenous mecha-
nisms. We believe that these mechanisms are key to explain decline of acequias.
Another important difference between the models is their simulation time range.
The simulation range of the origianl model is (1969,2007). The current model,
are adoptec
between
900 2000 2100 2200 2300
Year
—— Urban Capital --- Natural Capital
~ Groundwater - - -Rural Population
Figure 3: Distribution of population for the base mulation
on the other hand, has a much wider simulation range spanning four centuries
(1900,2300). In other words, the original model does not attempt to predict the
future behavior of the system while the current model does. Finally, the origi-
nal model uses many historical time series as exogenous drivers of the model’s
s deprecated by prominent system dynamicists:
stem dynamics models must be “com-
pletely endogenous with no external time series to drive [them].” Therefore, the
current model exclusively relies on endogenous structures.
The model is organized in 4 modules as shown in Figure[J] Modules are “pop-
ulation,” “farming,” “capital,” and “hydrology.” “Population” module affects
“capital” and “farming” by providing workforce. It also impacts “hydrolog
by consuming water. “Capital” has impact on the distribution of “population”
through urbanization process as well as on the “hydrology” system through
urban demand for water. activities influence distribution of “popu-
lation” by affecting farmers’ attachment to the land. It also determines demand
for irrigation water which impacts the “hydrology” sy
ing” might affect the “capital” module by changing the pace of urbanization.
Finally, “hydrology” determines level of water availability for all other modules,
thus affecting water use in indus
module is described in the following subsections in further details.
rm-
em. In addition, “
rial, agricultural, and residential sectors. Each
hydrology
Figure 4: Module view of the acequia model
2.1 Population
hows the-structure of “population” module. Population here is broken
down into three categories:
farming forming =
ness effect of natural capital
onurban atractivenaes
capital.urban investment
Figure 5: Structure of the population module
e Farmers work in the agricultural sector and r
are primary contributors to the acequia a
de in rural areas. They
ivit
Urban farmers, although living in rural areas, have some occupation in
urban areas. They may contribute to the acequia activities too but with
a lower-than-normal rate.
Urban population live in urban areas and primarily work in industry
and (or) busin
The model does not track absolute numbers for each stock of population;
instead, their relative values are computed. In other words, each stock of popu-
lation represents a percentage of total population. Therefore, growth (decline)
of total population is not addressed by this mode]
Acequia farme find external jobs in urban are:
in farming and traditional activities. This process is captured by the flow rate
“farmers finding external job.” rive or negative depending
on attractiveness of external jobs. *Bxternal job attractivenes: a function
of prospects of farming relative to income opportunities in urban areas which is
represented by “urban workforc “Urban workforce gap,” in turn, shows
discrepancy between supply and demand] of workforce.
Farmers can permanently move to urban areas through the rate of “migration
to cities.” Migration happens if urban areas are more attractive than rural are
“Urban attractiveness” is assumed to be a function of three major factors. F
factor is urban development. As cities develop, more amenities will be available
for citizens. In other words, material standard of living in cities increas
That means better healthcare provision, access to higher education, modern
entertainment, etc. Second factor is the natural capital that supports urban
areas. Natural capital provides services to urban areas without which urban life
could become incredibly difficult. Preventing floods and sand storms, providing
pleasant landscape and aesthetic features, and absorbing pollutants are a few
examples of such services [Groenfeldt][2006) [Fleming et al.| 2014). Third fac
affecting urban attractiven availability of water. While urban and natural
capital may impact attractiveness with some time delays, water availability, a
a function of urban water supply-demand ratio, is a crucial factor that impacts
immediately. Indeed, no city can live without water.
and become active
gap.
the urban |:
2.2 Capital
Structure of “capital” module is shown in Figure[6] Urban capital is an aggre
tion of all sorts of equipment, facilities, constructions, infrastructure, tc. that
support production and consumption in urban areas. Urban capital increases
by “urban investment” and declines through “urban capital decay.”
This could be considered as a limitation of the model and is open for future modeling
efforts.
SWhen the
sively on farr
Workforce demand is implicit in the model and is calculated from “urban investment.”
te is negative, “urban farmers” quit their urban occupations and work exclu-
Urban capital
residential land
In development :
Urban capital decay
ban capital
dovelopment
natural capital decay
fect of urban capt
‘on natural capital
Figure 6: Structure of the capital module
Urban investment expands if there are enough “residential land,” “work-
and “natural capital.” Natural capital also affects life cycle of urban
capital. It helps to prevent many ecological hardships such as flooding and sand
storms which may impose significant damage to infrastructure and amenities in
urban setting
Natural eanital itself could grow and decline independent of human inter-
umed that it remains in a steady
is occurred. Natural capital regenerates
could be affected by the level of groundwater. In f
water plays a key role in maintenance of natural c
(Groenfeldt] [2006] [Fernald and Guldan}
Therefor, it is assumed that level of groundwater Sanne
ural capital regeneration rate. Urban capital also has a direct negative impact
on natural capital through cons ion of natural resources. To simplify, this
impact is aggregated in formulation of natural capital regeneration rate. There
are other factors influencing natural capital as well but they are out of the
boundary of this study.
ate if no human
by a cons‘
it is shown that ground-
ant rate which
2.3 Farming
“farm land, ierigation waeziag vand “active
is a function of water supply-demand
ratio. Total supply of water for irrigation is equal to summation of “actual ditch
delivery” and “irrigation pumping” which will be explained in Section4] Total
demand of irrigation is determined by the land that is used for farming, “farm
land.”
=
Figure 7: Structure of the farming module
Total land is assumed to be constant and is distributed to three differ
states. “Farm land” represents percentage of total land that is
farming. “Fallow land” represents percentage of total land that is left fallow.
And, “residential land” represents the remaining land which could be used for
residential, industrial, or business purposes
sired land use (“land to be used”) declines. Farms that remain fallow for a long
time might be sold depending on two factors. First is the pressure from the
urban population. If distribution of population moves toward urban areas then
more “residential land” would be needed. That increases pressure on farmers to
sell their lands—through a price mechanism that is implicit in the model. The
pressure could be manifested by inflated land prices, for example. As more land
is sold “residential land” increases, thus, “land sale fraction” declines. Second
factor affecting farm sale is “attachment to land.” Farmers who have spent
many years doing farming on their land, become attached to it and feel a ps:
chological barrier to sell their farm lands. T t delay,
the sale processes [Beedell and Rehman] “At
tachment to land” may also affect “urba: pate
in acequia traditional farming « ,
participation” [Mayagoitia ct all]
Farms could become fallow if de-
pay Haare
Mayagoitia et al.
farmers’ willingnes
ities which is called here as pie farme
[Fernald et al.][2012) [Turner ct al.|
2.4 Hydrology
The hydrology system, as shown in Figure] consists of surface “water” s
tem and “groundwater”
Kilometers) represents all transforming water on the surface and available water
em. Stock of (surface) water (measured in Cubic
in channels including main river channel and artificial ditches. There are two
rates flowing out of “water.” One is “ditch delivery” which is total farmers’
withdrawal for irrigation use. The other is “outflow” whic
streams out of the em of surface water.
07 roy
Figure 8: Structure of the hydrology module
“Ditch delivery” is usually equal to total “water right” of farmers. However,
it may be bounded by availability of “water”: “ditch delivery” would decline
if level of water declines. Water right could also change based on adequacy of
water for irrigation. These changes, nonetheless, may be subject to very long
delays.
Main input to stock of “water” is “inflow” which includes precipitation, up-
stream flow, and returning water (from upland back to lower reach). Another
input is “returning flow” which includes scepage of irrigation after evaporation
returns to the river. A fraction of “baseflow” returns to the surface water de-
pending on level (thickness) of groundwater. As level of groundwater increases,
a larger fraction of baseflow flows back to the surface water.
ion flow that streamflows back from surround-
Baseflow is groundwater rec
ing groundwater. Other than baseflow, there are two other outflows from
groundwater: “urban pumping,” and “irrigation pumping.” “Urban pump-
ing” represents amount of water that is extracted from groundwater for urban
dential, industrial, etc.) use and depends on “urban water demand” and
availability of groundwater. If the level of groundwater is sufficient for current
demand, then water will be pumped out as much as needed; otherwise, the
extraction will be limited.
Urban demand for water is a function of “urban population” and “urban cap-
s of urbanization require higher levels of water consumption.
1 per unit of urbanization is assumed as constant. Since pop-
Effect of Localization on the Car Market Under Intense Sanctions;
A System Dynamics Approach
Babak Barazandeh!, Mohammadhussein Rafieisakhaei, Amirbahador Moosavi®, Kaveh Bastani!
Abstract—In recent years, Iran’s economy has been under
sever sanctions. The sanctions were increased by United Nations
Security Council (UNSC) after 2012 which caused rapid changes
in the price of all imported materials. As the result, price of the
imported parts and materials of car manufacturing companies
rapidly increased. After this sudden jump in the prices, the
government decided to import technology and adjust it with
regards to the economic, social, and political conditions of the
country which is termed as ization. In_ this
activities of the target countries. As an example of this type of
sanction, the limitations imposed to Myanmar since 1993
or the bans imposed to India at 1998 ®) or the economic
sanction of Zimbabwe in 2001 [10] could be mentioned.
One of the strictest of this type of sanctions was the recent
economic sanction on Iran and more specifically on energy
sector As a result of this sanctions, Iran encountered
paper, we first propose a model describing effect of sanctions on
Iran’s energy market and relate these sanctions to the market
of car manufacturing companies. Then we model the effect of
technology localization in stabilizing the car prices in long term.
I. INTRODUCTION
In the long history of the relationship among governments
of different countries, there have been always countries with
opposing ideas. Sometimes in competitions and interactions
among countries, front runner countries unified with their
alliances to force the other one or group to change their
political behavior, taken actions could be diplomatic bans
(i. military interventions or economic sanction: For
clarifying the subject, North Korea is the country which has
been experienced international sanctions since Korean War in
, or the economic sanctions against Cuba, Sudan and
several other countries are the famous historic examples [5].
After creation of the United Nations in 1945, which got to-
gether the countries from all over the world, unanimous sanc-
tions are imposed more reasonably and more organized (6)
With leading of United Nations, main intention of sanctions
to force the hostile country to cooperate with rest of the world
to prevent the probable wars [7]. In most of the cases, the
only substitute for entering direct military interventions is to
impose economic sanctions to provoke and force the countries
for getting along with international rules. Besides the sanction
with s in its oil export which was the main revenue
source of the country, value of Rial, Iranian currency, dropped
massively to one third of its value and inflation rate went
over 40% during a period of 6 month: These sanctions
caused immense fluctuations and uprisings in the price of
common merchandise (4). One of the boldest uprisings was
in the price of manufacturing material and equipment for car
industries which was doubled in less than 6 months and caused
a stagnancy in the market
Despite all the damages to the economy of Iran during the
period of sanctions, recent agreement of Iranian government
that is called Joint Comprehensive Plan of Action (JCPOA)
with P5+1 countries, including United States, United
Kingdom, France, China, Russia and Germany has brought
the hopes back to the market of Iran with over 80 Million
ready-to-buy consumer. Based on these agreement all the
economic sanctions imposed against Iran after 2006 were
lifted, international interactions and activities of country will
become normal, banking system got connected to Society for
Worldwide Interbank Financial Telecommunication(SWIFT)
again and oil export limitation was removed. It is worth to
mention that still a part of unilateral sanctions of United States
against Iran is in place but these sanctions are mostly following
persons and not whole economy of country }
Historically Iran used to import main parts and equipment
from foreign partners for its car industries but during the
sanctions period, Iranian government tried to encourage local
imposed by UN, there are examples of sanctions imposed
unilaterally or multilaterally. As an example, During past
decades United States has imposed some unilateral economic
sanctions on a variety of countries. All these sanctions whether
or not forced by UN are called “International Sanctions” along
the lines of this paper.
One of the most powerful sanctions among the over men-
tioned different types is economic sanction, which forces
limitations on the trade stream and international economic
1B. Barazandeh and K. Bastani are with the Department of Indus-
trial & Systems Engineering, Virginia Tech, 2M. Rafieisakhaei is with the
Department of Electrical and Computer Engineering, Texas A&M Univer-
sity, 3A. Moosavi is with IFP School. {Corresponding author:
babak7@vt .edu}
to spend more on R&D researches and produce
most of the requisites of industry domestically, this policy and
actions of country is referred with the term of Localization”
in this paper. Although, localization had almost no effect on
stabilizing of market and controlling the prices in short term
but in the mid-term and long term it can strongly reduce the
dependency of Iranian car industry to abroad and as a result
this could prevent future price fluctuation in the market
Besides this, oil price is always one of the most impo:
tant drivers of market in Iran [19]. Before sanctions 80%
of country’s revenue was made up by oil and oil product
exportation (20). During past several years, due to international
economic sanctions and pressures on oil and gas industry of
Tran and also because of global oil price crisis |, revenue
ulation is measured in relative terms (percentage), it should be safe to assume
no relative evolution in water conservation technologies.
Urban consumption is not the only usage of groundwater, though. Agri-
culture is the other user and is controlled by “irrigation adequacy.” If current
supply of water is not adequate for irrigation demands, “desired pumping for
irrigation” would increase and vice versa. Not as much as irrigation water that
is desired may be extracted though. Similar to urban use, this pumping require-
ment might face with a limit which is imposed by “groundwater availability”
indicator. Availability of ground water is a function of groundwater demand
relative to total groundwater available. If demand relative to available ground-
“availability” indicator decline
Finally, groundwater could be recharged through seepage from irrigation.
In fact, a fraction of water that is used for irrigation (“actual ditch delivery”)
could return to the aquifer. That fraction is dependent on thickness (level)
of groundwater. As level of groundwater increases “recharge fraction” declines
[Lutz et al McMahon et al
*Ketual ditch delivery® is equal
water increases then the
itch water transfer” subtracted from
“ditch delivery” i.e. amount of water that remains for irrigation after transfers
al water right policie , farmers
and local rural residents can transfer their water right to other users in order
for the state to improve efficiency of water consumption. Water trans!
ar if there is a demand pressure from urban areas and if there is adequate
water for irrigation.
ers may
3 Model Validation
There are many
Confidence building in a model's output is a gradual pro
idati 2 [Forrester and
i$ paper has been sub-
5 including dimer ional-cora , integration error,
extreme conditions, behavior noma surprise behavior, and sensitivity anal:
ysis. Current ve successfully pass these tests. Boundary
adequacy , and parameter
also conducted but‘oaly at a rudimentary level
ment tests are
3.1 Behavior reproduction
The model, although fi till a generic model that should
be able to addi of family member problems with similar ¢
ssed by the acequia problem. We know that historical fit is :
models [Forrester] [1973] [Forrester and care
. As [Forrester] [2013] p. 30] has written:
-d on New Mexico, i
acl
as pos
for model validity of such gene
Sterman|
comprehensive analysis, which as our next step, are still needed to
¢ confidence in the model’s results.
There is no reason that a generic model should reproduce any
specific historical time series. Instead, it should generate the kind
of dynamic behavior that is observed in the systems that are being
represented. If one runs the model with different noise sequences one
will get simulations that have the same character, but not the same
values at different points in time. Likewise, the time series from
an actual economy represent only one of a multitude of detailed
behaviors that might have occurred if the random effects in the real
system had been different. In other words, historical data from a
real economy should be interpreted as only one of a multitude of
possible data histories.
However, qualitative replication of historical bel
ary to build confidence in a model’s output
simulation of the model for the variable “rura
avior (reference mode) is
Hence, base
pop which repre-
as the main variable of
run
sents percentage of population living in rural areas
the model—is compared with the historical data in Figure J] As we can see,
the model reproduces historical decline of rural population relative to urban
population reasonably welf®]
3.2 Sensitivity analysis
To test sensitivity of the model to its parameters, 3 simulation runs are pe
formed for cach parameter. Run 1 represents the base case with default param-
eter value. Run 2 represents the simulation with a lower-than-default value of
the parameter. And, Run 3 represents the simulation with a higher-than-default
value of the parameter. The sensitivity analy reveals that the model’s be-
havior is sensitive to the following parameters.
3.2.1. Capital life
This parameter represents average life cycle of urban capital such as infrastruc-
ture, equipment, technology, construction, etc. which support production and
consumption activities in urban areas. Default value of the parameter is
to be 20 years. Variation range is (10,40). Figure[L0|shows the results for two key
variables of the model (“rural population” and “urban capital”). Longer cap-
ital life—equivalent of a system with cheaper capital maintenance—generat
disastrous outcome. Urban capital collapses dramatically after 2050s and con-
sequently, urban population moves (almost completely) to rural areas
pital life—representing a system with more expensive capital maintenance—
yields a smoother outcome, although with a lower steady state level of urban
capital at the end of simulation. There is no collapse is this case. However,
rural population almost disappears which may not be a desired outcome.
assumed
SCurrently, we are collecting data for other variables of the model so that a more compre-
hensive model calibration becomes possible in our future modeling efforts.
7Complete results are reported in the document that is submitted along with the paper as
supporting material,
100
i=]
s
Ss
pe §
a
{o}
ia
Sg
°
e
we)
2
oO
2
oO
a
1900 1922 1944 1966 1988 2010
Year
——Rural Population (simulated)
~Rural Population (historical data)
; Rural population as a percentage of total population (simulated vs.
ical data)
Rural Population Urban Capital
Percent
Unit of Capital
Year
Runt ~~ Run2 —Run3
Figure 10: Sensitivity of the model’s behavior to “capital life”
3.2.2 Urban investment exponent
Urban investment is a form of Cobb-Douglas function as shown in Equation
Elasticity of the function could be adjusted by a parameter (represented hei
by a), default value of which is assumed to be 0.5.
investment = (production factors)" (1)
The model is extremely sensitive to this parameter. As shown in Figure
a small variation in the parameter dramatically changes the model’s behav-
ior. Therefore, a more precise modeling should employ a carefully estimated
production function for the urban investment.
Rural Population Urban Capital
re tom
Percent
Unit of Capital
2
° ..
1900 ‘2000 100 2200 200
Year
Runt == Run 2 Runs
Figure 11: Sensitivity of the model’s behavior to “urban investment exponent”
Implication of this test is very similar to the previous one: more expensive
investment (i.e. smaller a) generates
inv
moother urban growth while cheaper
stment (i.e. larger @) leads to aggressive overshoot and decline of the urban
capital.
3.2.3. Farming exponent
‘arming exponent” represents ela:
indic cequia traditional agricultural activ:
tivity at all) to 1 (maximum level of activities
parameter is shown in Figur
‘ity of the “farming” function. “Farming”
Tt changes from 0 (no ac-
). Sensitivity of the model to this
Default value of the parameter is 0.5 with
variation range of (0.1,0.9). The model is not sensitive to higher valu
parameter but it is to the lower values (j0.5). When the value is lower,
er to 1, thus repre:
nting a more resistant acequia
stains the rural population at a higher level with no
capital grows at a much lower rate,
in turn.
major upheaval. Urban
3.2.4 Water demand constant
This parameter represents amount of water that is needed per unit of urban
capital-population. Default value of the parameter is 0.010 Cubi
per year and it varies within the range (0.005,0.020). Figure
model
> Kilometers.
hows the
's response to the variation. As we can see, the sensitivity is considerable.
13
Rural Population Urban Capital
a Som
q S
é 3
5
‘Sa, amos 30m as feP ae ae ae ee
ear Year
runt =~ Run? — Ruma runt =~ Run? —Run3
Figure 12: Sensitivity of the model’s behavior to “farming exponent”
Interestingly, however, more efficient use of water in urban areas will not change
the final steady state value of the urban capital significantly. This case causes
an even more violent upheaval in urban development and leads to demise of
rural population. In fact, more efficient use of water helps the urban capital
grow faster. The accelerated urban growth continues until the natural capital
declines to critical point where further growth becomes very difficult. Urban
capital starts to collapse but urban population do not move back to rural areas
because agriculture, and thus rural life, is almost extinguished now.
Rural Population Urban Capital
Percent
Unit of Capital
900 7000 2100 7200 7300
Year
Runt =) = Run 2 Run
Figure 13: Sensitivity of the model’s behavior to “water demand constant”
4 Discussion
Our study shows that current urban growth in New Mexico may not be a sus-
tainable trend. Excessive growth of urban population and capital is predicted
4
to irreversibly damage natural capital through decline of groundwater resources
which have remained intact due to acequias’ sustainable water management
practices over centuries. These results are achieved from a relatively simple
model in absence of population growth and long-term drought that have been
the case over the past decade. In other words, unsustainable behavior of the
em is caused by its internal mechanisms that are inherent in it and not by
external, uncontrollable factors
Urban dapital life, urban investment, farting; aid water demand constant,
are identified as the sensitive points of the model. For example, investment
to improve efficiency of water use in urban areas turns out to be an abortive
solution for the decline of water resources which causes the urban capital to
fail. This potential solution may even deteriorate the collapse by accelerating
the growth process and thu ing a more sever overshoot and decline.
In general, policies that promote urban growth may work in the short- and
mid-term but they undermine natural capital in the long-run and cause the ur-
ban capital to collapse. The average gain of the society from the urban growth
over the long-run might be significant though. However, there is a political
oice that the society must make between two cases: more significant mate
andard of living with higher levels of consumption vs. more stable
able) material standard of living with higher quality—but lower level
sumption. Experimentation with the model reveals that it is almost impo!
to achieve both simultancously.
of traditional agriculture in general, and acequias in particular, is
not only a problem for the traditional communities, identity of societies, and
the cultur@§] but also a bigger challenge for the urban and modern settings.
Urban population cannot survive without maintenance of the natural capital—
a service that has been provided by acequias for many years [Fernald ct al,
cequ culture enhances vegetative cover and diversity,
support wildlife habitat, recharge shallow aquifers, sequester carbon, improve
air and water quality, retain storm-water flow, and control flooding. They also
provide nutrient cycling and soil formation, ecotourism and environmental edu-
cation, extension of the lurlgation son, and aesthetic enrichment in ecological
scape diversity Furthermore, acequias provide a
atiltival id SOCaT Some .p settlements and
s}
au
Demis
land:
nexus
Joeal cultures Spatininig iiiajor pexiods of political devélopnient rout 1998 ta the
modern period
_ Our analysis
quias are allowed to transfor thete irrigation
eventually be used for non-agricultural ac . Urbanization is alleged to play
a key role in emergence of such transfei dential lands so
farming activities decline. Less water will be needed for irrigation then, thus the
issues have actually been excluded from our analysis. Nonetheless, one could easily
> that inclusion of these factors would strengthen the argument posed by this paper.
unused water will be transfered. These transfers are deemed to explain parts
oll their water so there will be less
It, so land sale
creates a vicious
of the urbanization trend as well. Farm«
water available for irrigation. Farm lands become fallo
s a more attractive choice
positive feedback loop that causes the acequias community to decline. The pol-
icy that is tested here tries to break this ¢ feedba
possibility of water right transfers. Figure[I4]shows the impact of the policy on
the model’s behavior.
becom
loop by eliminating
0
0
0
0
1900 2000 2100 2200 2300
Year
—— Urban Capital —-- Natural Capital
- Groundwater - - -Rural Population
Figure 14: Impact of water transfer ban on long-term dynamics of the acequia
model
Although the policy revives the rural population but it also causes the urban
capital to collapse. In fact, water shortage in urban areas reduces attractiveness
of urban life. Collapse of urban life is harmful for the rural population. Through
external jobs, urban capital provides added income, independent of farming,
which could be considered as a coping strategy for acequias to weather periods
of drought. In fact, external jobs have a positive impact on acequias’ survival by
providing additional sources of income Moreover, urban
growth creates demand for the farmers’ products. Indeed, a balance between
urban and rural growth must be achieved in order to maximize aggregate social
welfare.
Another interesting finding is that acequias’ lifestyle is mainly based on the
16
preservation of savings rather than on the production of profit. Their traditional
agriculture is to pass a way of life on to their offspring. From the farmer-rancher
standpoint, their agricultural operations are successful as long as they do not
create debt . 3014]. The equation of “farming” in our
model follows this general rule and is fundamentally different from formulation
of “urban investment” in which the concept of growth is dominant. It is not
difficult to show that how the model would behave differently if the formulation
of “farming” was based on “growth” rather than on “maintenance.” It will
be an interesting topic for future research to investigate the role of farmers’
pro-sustainability mindset in their historical decline. In other words, we argue
that acequia community would not have been declined as much as they have
if their mental model targeted economic growth instead of sustainability. And
that could, of course, create another problem: growth of a rural population as
destructive (toward natural capital) as the urban population. And, that would
not have been sustainable either. Origins of this particular mentality is not
clear. It might be due to nature of agriculture sector that is not as profitable
as industrial and service sectors; so, over years, expectation of farmers have
been adjusted to the situation. Or, maybe some more fundamental ii
responsible for the phenomenon.
sues are
5 Conclusion
In this paper a generic model of acequia population dynamics is de
jously developed system dynamics model [Fernald et al.|
The goal was to provide a basis for future modeling of acequia
ation dynamics and to predict qualitative behavior of the system over the
long-term. More precisely, we hypothesized that current decline of traditional
agricultural activities as practiced by acequia community in New Mexico could
be detrimental not only to the rural population but also to the urban settings.
Simulation outputs support our hypothesis. It is shown that any attempt
to promote urban growth will help prosperity of urban population in the short-
and mid-term but with the expense of a disastrous collapse in the long-run. Our
analysis also identific ive areas of the system. This will help future studies
to focus on high impact points of the problem. The points include urban capital
life, urban investment function, farming function, and water demand constant.
Although the model has been subject to many validation tests but there
are some limitations to the work. First, the model does not include population
growth. Rural and urban population change as a percentage of total popula-
tion, Inclusion of population growth could generate more interesting results.
Nevertheless, we believe that it will not change implication of our results.
Second, demographics of the population is an important aspect of the prob-
lem which is also excluded from this analysis for the sake of simplicity. For
example, younger people tend to be more open to migration. As they get older
they become more sensitive to morality of farming and agriculture, thus more re-
sistant to the temptation of migration. These factors affect dynamics of acequia
on a pre
17
population. Future modeling efforts could break down the current population
structure so that demographics of the problem is also taken into consideration.
Finally, a comprehensive policy analysis has been avoided in this paper.
This is mainly because the model is generic and not appropriate to prescribe
tailored policies. Initial settings are set so that the model starts in equilibrium
(not shown in the paper) and replicates the historical behavior. Many of the
parameters, however, could be estimated by collecting real data from formal
databases. A more careful estimation of parameters is required so that the
analy
model becomes more reliable for a real-world poli
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of country has dropped and at the same time, share of oil
revenues on annual budget changed. This policy change put
the main pressure on people and industries for increasing and
improving taxing system. State-controlled car industry is part
of this regulative alteration. The massive 80 million market of
Tran and government support from domestic car industries has
made the car market as one of the most attractive businesses
in Iran.
In this paper, following our previous work:
have used Systems Dynamics
market and its dependency to the oil price to catch the
fluctuation in the market. Besides, we have modeled the effect
of the localization on this market to see and predict its mid and
long term impacts in car market as an example of important
industrial sector in Iran.
Il. MODEL AND CAUSAL LOOPS
In this part, we will describe the overall model with the
casual loops inside the model. Briefly, our model consists of
three different sub models that are connected with each other
to make the main model. First sub model is the oil market
model that has the main supply and demand loops. Second sub
model is the car market model which consists of three model
namely demand, supply and sanctions. The final sub model
is the sub model of the localization which consists of policy
making, cost reduction and technology complexity loops. In
the following, each sub model and its relation to other sub
models will be described in details.
A. Model of the Oil Market
In our model, the oil market briefly consists of one main
model and two sub model. Main model describes the balance
between supply and demand and the sub models describe the
effective parameters of the oil demand and supply that are
described in detail in the following.
1) Main Model: Like any other commodity in the free
market, oil market is strongly affected by supply and demand.
The increased price will motivate oil producing countries to
increase their supply and will decease the demand of oil in
the market. In our model, the pure value of the supply and
demand does not directly affect the oil price while they affect
it by Demand to Supply Ratio(DSR). A ratio smaller than one
represents the saturated market where there is more oil in the
market than the demand which will decrease the price, while
a ratio greater than one represents great demand and shortage
of supply in the market which will lead to price increase.
It is obvious that demand and supply are not independent
parameters and are affected by external factors. The effective
factors on supply and demand that will be described in the next
sections are briefly shown as effective factors on oil demand
and supply. Figure [I] represents the over mentioned relation
among the parameters.
We have used linear regression ( OilPrice =
Slope * DSR + Intercept ) to find the relation between
DSR and oil price. We have used the historical quarterly data
between first quarter of 2013 till forth quarter of 2015
Effective Factors on
Oil Demand
‘Oil Price
- +
Oil Demand Oil Supply
4 Demand to
Supply Ratio
Effective Factors on
Oil Supply
Fig. 1: Main Model of the Oil Market
Due to the existence of sudden and short-term changes in
the oil price which is temporary and not the real trend of
the market, we have used least absolute deviation regression
instead of the ordinary least square which is robust to the
outliers. Table[f]represents the result of our regression analysis.
2) Supply Sub Model: In our model, as shown in Figure
we have divided the oil producing countries into four different
categories; OPEC members, Russia, US and other countries.
This division is due to their production policy. OPEC members
try to set their policies with each other and they supply oil
to the market as OPEC oil basket. We have considered Iran
separately in the model since its production share changes
rapidly in the market due to the different sanctions imposed
to its supply.
Russia is considered separately since they usually have
their independent policy of production. On the other hand,
the international sanctions on the economy of Russia as the
third greatest oil producing county due to Ukraine cri:
caused to model its production individually to catch its supply
accurately.
US oil production is considered separately too, due to its
independent production policy and Shale oil production which
increased US oil supply greatly BI. The other producing
countries usually have stable production policy and we have
considered their production as one parameter.
3) Demand Loop: As it is shown in Figure[3} like the sup-
ply sub model, we have divided the oil consuming countries
according to their policies. Countries with the same economic
policy are considered as one parameter. On 1960, a group
of countries signed an to create an organi: to
coordinate their policies. This group of countries are called
OECD (Organisation for Economic Co-operation and Devel-
opment)
[Regression Analysis
| Least Absolute Deviation(LAD) | =179.0
[ [Ordinary Least Square =296.4
TABLE I: Regression Analysis for DSR and Oil Price
[Intercept | Stope_]
1795]
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21
Spectons Other OPEC
Members Oil Supply
+
i OPEC Supply
Russia Oil
Supply Nyt Iran's Oil
Oil Supply Supply
se
+ <
US Oil Supply Other Countries Sanctions
Oil Supply
Fig. 2: Supply Sub Model of the Oil Market
On May of 2008, Brazil, Russia, India and China, as the
major emerging economies, came to an agreement to help
each other economically and follow a coordinated economic
policies. After 2010, South Africa was added to this group
and now member of this group are called BRICS (Brazil,
Russia,India, China and South Africa) ij. In our model
we have considered their demand as “BRICS countries oil
demand”. The remaining countries that are not member of
OECD or BRICS are grouped together and modeled as ”Other
Countries Oil Demand”.
OECD Countries
Oil Demand
a
Oil Demand
+, +
wf \_ ane Countries
Other Countries Oil Demand
Oil Demand
Fig. 3: Demand Sub Model of the Oil Market
B. Model of the Car Market
In this section we will describe the model of the car market
in details. The car market has a main balancing loop describing
effect of supply and demand in determining price of the car.
The other effective parameters in the market like sanctions, oil
price and working capital to debit ratio are parametrized in a
sub model that are described in the following.
1) Main Model : Like any other commodity in the free
market, car is affected by demand and supply. Car market
specially in a closed economy like Iran is affected directly
from demand and supply. In our model, increasing the demand
for a fixed value of supply will increase the price and the
increased supply for a fixed value of demand will decrease
the price. This relation is shown in Fig.
C a, ff Supply
Car Price
a
External Factor a \
on Diemanid External Factor
on Supply
Effective Factors
on Car Price
Fig. 4: Main Loop of Car Price
2) Sub Model: Most of the manufacturing companies in
the world import some of their needed parts from external
sources and rarely a company produces all of its needed parts
inside one manufacturing site. For example, Airbus has sites
all around the world including France, Germany, Spain, China,
US, Japan and India that work closely together to have a
high quality final products . The same thing applies for
Tran’s car This causes a dep to the foreign
currency specially US dollar which is the main currency used
in the international trades
Tran’s economy and subsequently its manufacturing com-
panies are strongly related to the exported oil since it is the
main source for foreign currency. As the result, oil market will
have a great effect on the exchange dollar rate(relative value
of a currency unit against US dollar). In our model, we have
modeled this dependency through the oil price and supply. Oil
supply is an important factor since it could be greatly reduced
by international sanctions which will reduce the total foreign
currency gained by oil dependent countries for a fixed value of
oil price. In a country with high inflation like Iran, it is normal
for the prices to change through time [ but this changing
rate could be reduced by different factors. One of these factors
is manufacturing productivity Productivity by definition
is the effectiveness of a company in changing resources into
Efficient manufacturing systems can reduce the
changing rate of a merchandise price
modeled the effect of productivity on this change rate.
The other important factors in price changing rate are
working capital and the debt of the manufacturing company.
Having high debt forces the manger of the companies to
slightly start increasing the price to pay their debts while
having high working capital shows the right policy 50) of
the company and sometimes this even could lead to decrease
the changing rate in order to attract more customers. In another
words, in this situation, companies focus to increase their
revenue by increasing their selling instead of increasing their
price. We have modeled this trade-off between working capital
and the debt by working capital to debt ratio. The higher this
ratio, the lower price change rate.
In a high inflation country like Iran that its economy is
highly dependent to oil production and has been under sever
economic sanctions, one of the most important factors in
balancing the price will be the localization factor. When a
country is under sanctions and has a lot of problems in gaining
foreign currency and importing needed parts for its industry,
they start to localize their industry by producing all of their
needed materials inside the county. This option is completely
effective (in long-term) for Iran due to its different mines and
natural resources.
After Iran’s sever sanction in recent years, the prices start
increasing rapidly and meanwhile the government started
investing on the industry localization. This is the main reason
for price balancing after 2 years of sanctions while almost all
other factors are the same.
Exchange Dollar Rate
Exchange Dollar Rate
—eForma Price
Fig. 6: Iran’s Exchange Dollar Rate increase, Formal Rate Vs.
Free Market Rate
C. Model of the Localization
Nowadays, most companies are strongly related to each
other and cooperate with each other improve their economy
and the quality for the final product. On the other hand,
some technologies are more stable in one company in a
special country that industries prefer to have its related part
to be produced in that company. However, in some countries
like Iran, due to the sanctions, cooperating is not easy for
companies so they prefer to produce their needed parts inside
the country which is called as localization. Localization can
make the country more stable for further sanctions.
In the following part, we introduce several variables that
affect the rate and amount of localization in a developing coun-
try. Then we model the relationships between these parameters
and their effects on technology i The
model consists of one main model and a sub model that are
connected to each other to model the localization and the way
it stabilize the industry.
1) Main Model of Localization : The main model in
localization consists of dependency on foreign country, need
to import new technology, financial support for R&D, R&D
infrastructure, capacity of technology development, R&D ac-
tivity, i ly Pp ry, rate of yy
ization and tect ry Country that is de-
pendent to foreign country needs to import more new technolo-
gies. Import more technology, will reduce the financial support
for R&D. On the other hand, having for support for R&D will
increase the R&D infrastructure that will lead to more capacity
of technology development and R&D activity respectively. On
the other hand, increasing the R&D activities will increase
the technol e by country i that will
increase the expertise on technical knowledge. This increase
in the expertise will increase technology localization that will
decease dependency to foreign country. Figure B] shows the
over mentioned relations.
Dependency on
Foreign Country
c .
Need to Import
New Technology Technology Localization
+
Financial Support Rate of Technology
for R&D Localization
+ +
R&D Expertise on
Infrastructure Technical Knowledge
+ +
Capacity of
Technology Independently Developed Technology
Development fe
R&D Activity
Fig. 8: Main Loop of Localization
‘Supporting,
iia
Potiey Proper Laws for
‘Technology Transfer.
Technology Localizati
a Complesity of
Technology
Expertise on
Technical Knowledge
Proper Model for
Technology Transfer
Proper Afsitude
Newly Developed:
Fig. 9: Main Loop of Localization
Effecive Factors
On Localization
Car Production Mannitectinias PEC Oil
by Productivity Supply
é AL Exchange
‘company's Dollar Rate
Debt a :
e Car Price Sanctions
ae” Rat
Working Capital to ne
Debt Rati = il Pri
r ng Car Demand oe
Company's oe
Working Capital
Fig. 5: Main Loop of Car Price
<Global Oil
Price> Economic
3 Tran's Oil es Sanctions
Other OPEC Supply
5. , Countries Oil Supply
Other Countries
Oil Supply
US Oil Supply
+ Supply
4 Global Oil
Suppl Global Oil Price
upp he ba a
Oil Demand to
Supply Ratio
fa
Global Oil
Demand -
+ F
OPEC Oil
USD Exchange
Rate
+
Iran's Oil +
Revenues Price of Material
Used in Cars
Car ce
Car orcaton to
_ q
Car vin Ss ar Demand
Fig. 7: Car Price Change
2) Sub Model: The sub model in localization consists of
supporting policy, proper laws for technology transfer, proper
model for technology localization, proper attitude, newly
developed technology, expertise on technical knowledge and
complexity of technology which is shown in Fig. iO}
The complexity of technology will decrease the rate for
localization. On the other hand, supporting policy and proper
attitude to for localization will improve the model for tech-
nology transfer.
III. SIMULATION RESULTS
For evaluating the model, we have simulated the price in
Vensim PLE for the car Pride” produced by ’Saipa” auto
manufacturing which is the second largest auto manufacturing
company in Iran. The real data for the price changes is
between 2001 and 2015 i i
connecting model. In the real data, after initi tion of the
sanctions on the oil market, the car price starts to climb to
nearly triple its initial price, which was nearly flat before
sanctions. Moreover, the damped oil price fluctuations are
propagated with some delay to the car price. By the way, after
a sudden jump in price, the government started to localize the
industry which showed its effect in stabilizing the market in
long term. Figure compares the simulations results with
real data. As it is seen form the figure, the model represents
the trend of the market. The effect of localization is also clear
in stabilizing the market which is reflected in the simulation.
Car Price Change, Real Data Vs. Simulated
S15
Ul re
i
2003 2005-2007 2009 2015
Year
Fig. 10: Car Price Change
2011 2018 2017
IV. CONCLUSION
This paper focus on modeling effect of sanctions on Iran’s
economy whose revenues is heavily dependent on the exports
of the sanctioned product. Our main focus was on the Iranian
car industry and we showed that due to the sanctions, there
was a shortage in the foreign currency which led to an increase
in the price of materials in the supply chain of the car industry
and as the result, there was a great jump in the price at the
beginning of the sanctions. However, in long term, due to
investment of government on industry localization, the prices
become more stable. In the paper, after developing a model
for the oil market which was the main sanctioned product for
Tran, we develop a model for the car market and relate it to
the sanctions of the oil market. To describe the stability of the
market in long term, we propose a model to explain effect of
localization on the market. Our model explains and follows
the historical trend of the market.
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