simulating demographic and socioeconomic processes on...

Ecological Modelling 140 (2001) 31–49

Simulating demographic and socioeconomic processes onhousehold level and implications for giant panda habitats

Li An a,*, Jianguo Liu a, Zhiyun Ouyang b, Marc Linderman a,Shiqiang Zhou c, Hemin Zhang c

a Department of Fisheries and Wildlife, 13 Natural Resources Building, Michigan State Uni!ersity, East Lansing, MI 48824, USAb Department of Systems Ecology, Research Center for Eco-En!ironmental Sciences, Chinese Academy of Sciences,

Beijing, PR Chinac Giant Panda Research Center of China, Wolong Nature Reser!e, Wenchuan County, Sichuan Pro!ince, PR China

Abstract

Human activities have significantly affected wildlife habitats. Although the ecological effects of human impactshave been demonstrated in many studies, the socioeconomic drivers underlying these human impacts have seldombeen studied. We developed a household-based, stochastic, and dynamic model that simulates the impacts ofhousehold demographic and socioeconomic interactions on fuelwood use, a key factor affecting the quantity andquality of habitats for the giant pandas (Ailuropoda melanoleuca). Using Wolong Nature Reserve (China) as a casestudy, this model mimics household production and consumption processes and integrates various demographic andsocioeconomic factors. Household interviews conducted in 1998 within the Reserve provided the data for parameter-ization. The simulation results fit well with both the data used in constructing the model and with a set of independentdata. Age structure and cropland area were found to be the most sensitive factors in terms of fuelwood consumption,and thus deserve more attention in panda habitat conservation. This model could help reserve managers tounderstand the interrelationships among local economy, local cultural traditions, and habitat degradation, facilitatingmore scientific and economically efficient policymaking. © 2001 Elsevier Science B.V. All rights reserved.

Keywords: Household; Fuelwood consumption; Modeling; Simulation; Giant Panda; Habitat; Wolong Nature Reserve; Population;Environment; Natural resources; China

www.elsevier.com/locate/ecolmodel

1. Introduction

Human activities have caused serious distur-bances to wildlife populations and their habitats(e.g. Wilson, 1988; Ehrlich, 1995). Previous re-

search has mostly focused on the ecological effectsof human activities, such as the effects of agricul-tural land development on wildlife populationabundance (Freemark and Csizy, 1993; Sietman etal., 1994; Warner, 1994), as well as the influencesof timber logging on biomass (Hollifield and Dim-mick, 1995; Norwood et al., 1995), animal homerange change (Ims et al., 1993; Chang et al., 1995;Linnell and Andersen, 1995), and species distribu-

* Corresponding author. Tel.: +1-517-3535468; fax: +1-517-4321699.

E-mail address: [email protected] (L. An).

0304-3800/01/$ - see front matter © 2001 Elsevier Science B.V. All rights reserved.PII: S 0304 -3800 (01 ) 00267 -8

L. An et al. / Ecological Modelling 140 (2001) 31–4932

tion and composition (Lyon, 1979; Geier-Hayes1989; Skovlin et al. 1989; Nelson et al. 1996).

In addition to the understanding of these eco-logical effects, conservation biologists have alsoobserved that human population size, economicactivities, and attitudes (e.g. attitudes towardschildbirth) interact with each other and ultimatelyresult in ecological effects (Liu et al., 1999a).These interactions and the underlying mecha-nisms, though only intuitively understood(Dompka, 1996; Liu, 1997), have been demon-strated to be critically important in terms of con-servation applications (e.g. effective wildlifemanagement, Liu et al., 1999a,b). Additionally,exploration of these issues has triggered integra-tion of ecology, economics, and modeling tech-niques (e.g. establishment of spatially explicitmodels, Liu, 1993; Liu et al., 1995).

Quantitative computer modeling has been aneffective method for integrating ecological andsocioeconomic factors (Costanza and Daly, 1991;Liu, 1993). It helps to understand the dynamics ofthe processes of interest by ‘mimicking the actualbut simplified forces that are assumed to result ina system’s behavior’ (Hannon and Ruth, 1994).For instance, quantitative computer modeling hasbeen successfully used in studying wildlife popula-tion dynamics (Liu, 1993; Conroy et al., 1995),comparing alternative management strategies (Liuet al., 1994, 1995; Turner et al., 1995), and inter-preting the spatial connectivity of processes withina specific habitat (Hanson et al., 1988). All thesestudies were largely conducted on region or land-scape scales, seldom on the household level, whichis actually the basic unit of human productionand consumption in rural areas and thus deservesmore attention for wildlife conservation. In addi-tion to this scale issue, literature on using model-ing to integrate both attitudes and economicincentives into household level decision processesfor wildlife conservation is not, to our knowledge,available.

We developed a household-based and stochasticdynamic model, named ‘Panda habitat–Demo-graphic, Ecological and Economic Model’(‘PANDA–DEEM’ hereafter), to simulate house-hold level consumption and production processes,demographic and socioeconomic dynamics, and

their interactive effects on habitats for the giantpandas (Ailuropoda melanoleuca). By integratingeconomic, demographic and ecological data into asystems model, PANDA–DEEM is designed todisclose the underlying mechanisms for habitatfragmentation and loss. Because forests are animportant component of panda habitats (Hu etal., 1980; Schaller et al., 1985), PANDA–DEEMtakes fuelwood use quantity as an indicator of thegiant panda habitat disturbances caused by hu-man activities. The more fuelwood is used, themore trees need to be cut and forests destroyed.Previous research shows that fuelwood consump-tion has contributed most to the loss of pandahabitats (Liu et al., 1999a, 2001).

In this paper, we begin with an introduction tothe study area and the model structure. We thenfocus on methods for model parameterizationand model testing (including model verification,model validation, sensitivity analysis, and uncer-tainty analysis). Finally, we present the results ofmodel testing and discuss the implications of themodel.

2. Methods

2.1. Study area

Wolong Nature Reserve (102°52"–103°24"E,30°45"–31°25"N, Fig. 1) is located in Sichuan (oneof the most populated provinces in China) withan area of approximately 2000 km2. Four majorethnic groups, i.e. Han, Tibetan, Chang, and Hui,constitute the total rural population of 4320 peo-ple in 1998, corresponding to a total number of942 households. These households mainly rely ongrowing corn, potato, and vegetables for subsis-tence and use fuelwood as their main energysource for cooking and heating in winter. Pigraising, collection of Chinese medicinal herbs,timber harvesting, transportation, construction oflocal roads and hydropower stations, as well ashouse building are also their main activities be-sides farming. The famous ‘one child policy’,which is strictly implemented in urban areas, doesnot apply to this area populated by minoritygroups (Tibetan, Chang and Hui) due to loose

L. An et al. / Ecological Modelling 140 (2001) 31–49 33

governmental control in rural areas with minoritygroups. The average number of children per cou-ple in Wolong is 2.5 (Liu et al., 1999b).

As the largest of China’s 25 giant panda re-serves, Wolong Nature Reserve was designated in1962 with an area of 200 km2 and expanded toapproximately 2000 km2 in 1975. The years since1975 have witnessed a drastic decline in the giantpanda population — from 145 animals in 1974(Schaller et al., 1985) to 72 animals in 1986 (Chi-na’s Ministry of Forestry and World WildlifeFund, 1989), paralleled with a fast human popula-tion increase (an increase of approximately 70%from 1975 to present). As a national treasure ofChina as well as a precious natural heritage ofconcern to people around the world, this most

endangered species, the giant panda, has beenattracting increasing national and internationalattention. For instance, the World Wildlife Fund(WWF) has adopted the giant panda as its logo.

2.2. Conceptual model and data sources

PANDA–DEEM consists of three submodelsand their interrelationships are demonstrated inFig. 2: (1) submodel DEMOGRAPHY includesthe number of people per household, age struc-ture, and educational level. Local residents’ atti-tudes towards birth and education are key factorsinfluencing the demographic features of thehousehold — specifically, the number of children,and the educational level; (2) submodel ECON-

Fig. 1. The location and elevation of Wolong Nature Reserve in China.


Fig. 2. The conceptual structure of PANDA–DEEM. The dashed arrow and star box are assumed impacts of fuelwoodconsumption on the giant panda habitat.

OMY consists of two main components. One isProduction–and–Consumption of the simulatedhousehold, and the other is Incomes–and–Ex-penses. DEMOGRAPHY determines the extent ofConsumption and provides the necessary labor forProduction. Component Incomes–and–Expensessimulates all the income and expense items thatcould occur in a household with the above demo-graphic, consumptive, and productive characteris-tics. Incomes is mostly determined byDEMOGRAPHY, e.g. availability of labor (peo-ple between 21 and 60 in the model) and educa-tional level of labor (average schooling years oflabor). Incomes is also associated with ECON-OMY (Production specifically). Expenses is mainlydetermined by DEMOGRAPHY, such as numberof people and age structure of the household.Production and Consumption can impact Ex-penses, e.g. growing crops needs chemical fertiliz-ers; (3) submodel FUELWOOD is driven by thefollowing factors: DEMOGRAPHY, Production(fuelwood used for cooking pig fodder), and Con-sumption (fuelwood used for heating in winter andfor household food cooking).

The data for parameterizing PANDA–DEEMwere obtained from the interviews with 50 house-holds, conducted by the authors from June to

August, 1998 in Wolong Nature Reserve. The 50households were randomly selected in WolongTownship (one of the two townships in the Re-serve), the region where most of the pandas in-habit. The questionnaire includes: (1) fuelwooduse; (2) incomes and expenses; (3) production andconsumption; and (4) demography. We also askedthe interviewees about their attitudes towardssome social issues, such as the number of childrenthey prefer to have, the marriage age they prefer,and the schooling years they prefer for theirchildren.

All the interview results were entered into aMicrosoft Access database. The information fromfive of the households was unreliable because theirestimates of fuelwood consumption were inconsis-tent with their consumption and productionneeds. Thus the associated data from these fivehouseholds were excluded from use. From theremaining 45 households, we randomly selected30 households for model development and theother 15 households for model validation.

2.3. Submodels

2.3.1. Submodel DEMOGRAPHYThis submodel is individual-based, simulating


the life history of an individual as he/she goesfrom birth (or the starting point of the simulationif the individual is already in the household, orthe year when the individual moves into thehousehold), through each age group, to death (orthe end point of the simulation if the individual isstill alive, or the time when the individual movesout) in an annual time step. The age-group clas-sification is based on the status of occupationwhich an individual at a certain age is most likelyto take: (1) age 0–5 — infant to pre-schoolperiod; (2) age 6–12 — elementary school period;(3) age 13–15 — middle school period; (4) age16–20 — high school or technical school period;(5) age 21–60 — adult period when people workas main labor force; and (6) age 61 and over —retirement period. The individual stays in that agegroup until the age exceeds the age group’s upperthreshold when the individual moves into next agegroup (Fig. 3).

The model incorporates the following events,possibly taking place in a person’s life, into themodel: birth, death, emigration out of the house-hold through marriage or going to college, andimmigration into the household through marriage(the main legal way to move into the Reserve). Asto the death process, the model creates a random

number (between 0 and 1) for each individualeach year and compares it with the mortalityassociated with the corresponding age group (Fig.4). If the random number is smaller than themortality of the group to which the individualbelongs, then the individual dies; otherwise sur-vives and moves into the next year. This processrepeats every year until the individual dies, movesout of the household, or the simulation ends. Theyearly mortality rates of different age groups ofthe whole Reserve (1994–1996) were used asequivalents to death probabilities for an individ-ual over time: 0.745% for age 0–5 group; 0.090%for age 6–12 group; 0.131% for age 13–15 group;0.196% for age 16–20 group; 0.291% for age21–60 group; and 5.354% for age 61+group(Wolong Nature Reserve, 1994–1996).

There are two major types of emigration: emi-gration through marriage and emigration througheducation. Emigration through marriage is basedon the fact that girls generally tend to move outof the household and live with their husbands,and that boys set up new households after mar-riages. This event is set to occur at the age of 22,the average marriage age calculated from our fielddata. For simplicity, all the children except theyoungest move out of the household when they

Fig. 3. Age groups and the associated events in the life span of an individual.


Fig. 4. Simulation of the death process.

Fig. 5. Emigration and immigration through marriage.

reach 22 years old (Fig. 5). Emigration througheducation is based on the fact that if a youngperson could go to college or technical school hewould find a job in a city instead of staying in thehousehold as a farmer (Liu et al., 1999a,b). Ourdata show that 9.6% of the people in the agegroup 16–20 passed the national entrance exami-

nation and went to colleges or technical schools in1996, thus the probability that an individual inthis age group will go to college and move out isset at 0.096 (Fig. 6).

Immigration into a household is based on thefact that one child (usually the youngest son orthe youngest daughter in case of no son in the


household) stays with his/her parents, bringinghis/her spouse into the household at the time ofmarriage (22 years old on average). The probabil-ity that the youngest children stay with theirparents is 0.941. So his/her spouse immigrates intothe household with the same probability of 0.941.The corresponding flows are shown in Fig. 5.

2.3.2. Submodel ECONOMYThis submodel consists of two components,

Production–and–Consumption, and Incomes–and–Expenses. The type and magnitude of Pro-duction–and–Consumption are closely related tothe typical life style of a rural household inWolong. The amounts of corn, potato, rice, andpork are set to be state variables because theyconstitute the dominant food that local house-holds rely on. Farmers grow potatoes and corn astheir staple crops, exchanging most of the potatoesfor rice, and using most of the corn to feed

Fig. 6. Emigration through education.

Fig. 7. Land-dependent life style of Wolong farmers. A large portion of their land (portion 1 and portion 2) is used to grow pigfodder.


pigs. A special type of radish is planted in winteras complementary fodder for pigs (Fig. 7). Thepork (or bacon) thus produced was mainly forhousehold use, but an increasing portion of it hasbeen used for trade as the number of tourists, themain purchasers of local bacon, has increased overtime. Self-consumed vegetables, including beans,celery, and radishes, are ignored in the modelbecause they are of relatively small quantities.Furthermore these vegetables use a small portionof land (often intercropped with the staple crops orplanted in winter, non-growing season of the staplecrops). The only commercial vegetable, cabbage, istaken into account in the model because it is animportant source of income.

The input to this Production–and–Consumptioncycle includes land and labor. The amount of landis controlled by the government, whereas theavailability of labor is associated with the presenthousehold demography, which in turn is deter-mined by the government birth control policy andthe farmers’ birth attitudes. This Production–and–Consumption cycle impacts the amount of fuelwooduse in two ways: (1) corn and radishes have to becooked before being fed to the pigs; and (2) mostof the food items (e.g. potatoes, vegetables, pork,and rice) have to be cooked for human consump-tion.

The following paragraphs discuss the importantparameters for this submodel: (1) when corn is usedto feed pigs, the input-output weight ratio isapproximately 6:1, implying that the consumptionof 6 units of corn by pigs can bring about 1 unitof pork. The input-output ratio for potato and porkis approximately 12:1; (2) the average productivityof radish is 30 000 kg/ha; the average area forplanting (used as model input) radish is 0.146 ha;and the input–output ratio for potato and pork is13:1; (3) the rate of exchanging potatoes for rice is3.5, i.e. 3.5 units of potatoes are equivalent to 1 unitof rice in the market: (4) the regression betweenhousehold size and household potato use quantityper year is:

Household–Potato–Use = 79.638!Household

–Size"30.656

(n=28, R2=0.476, P=0.000048). (1)

The total production of potato (or corn) isdetermined by its productivity, once the total areaof land is fixed. Corn productivity and potatoproductivity were positively associated with perhectare expense. Because observed values of cornand potato productivity are never below 600 and11 250 kg/ha, respectively, we used a maximalfunction to select a higher value between thecalculated and the lower limit values:

Corn–productivity

=Max (1937.100!LOGN (Per–hectare

–expense)"11254.000, 600.000)

(n=30, R2=0.56, P!0.0001). (2)

Potato– productivity

=Max (5662.900!LOGN (Per–hectare

–expense)"29342.000, 11250.000)

(n=30, R2 = 0.591, P!0.0001). (3)

However, if there is no labor (number of peoplebetween 21 and 60 is zero), we assume the produc-tivity would be reduced by multiplying a Corn–labor– index of 0.8. In the Reserve, we did observethat the households without labor had a lowerproductivity even though they hired relatives andfriends to help them with the farming work. Thisis also the case for potato productivity.

Rice is the staple food in Wolong, but not grownthere. In order to obtain enough rice, local residentsusually use potatoes to exchange for rice from theoutside market. If the rice from the exchange is notenough to meet the household demand (householdsize* per capita need, 148.5 kg per person — anaverage of the selected 30 households in 1997), weassume that extra money will be spent to purchasethe rice needed.

Pork is the basic meat in Wolong. Our interviewshowed that almost every household had enoughpork to eat, so the total quantity of pork demandof each household was equal to household sizetimes per capita need. However, pork is not pur-chased from the market in the case of shortagebecause local residents can reduce pork consump-tion by increasing the consumption of other food,e.g. rice, potatoes, and sometimes meat of wild pigsobtained from poaching. The pork is sold if extra


Fig. 8. Household potential incomes and expenses. See Section 2.3.2 for variable definitions.

quantity exists. The market price for pork was 12Yuan/kg in 1997 and 1998 (1 US$=8.3 Yuan).

Another very important component in the sub-model ECONOMY is Incomes–and–Expenses.The difference between the incomes and expenses,i.e. yearly net income, is set to be a state variablebecause it represents the economic status of thehousehold, and determines (at least affects) house-hold expense decisions. The potential incomesources for a typical household include cabbagesale, herb sale, pork sale, construction of roads,local transportation, and temporary jobs. Thepotential expenses include children’s schooling,purchase of electricity, purchase of rice, farmingfacilities, and subsidiary substance (‘Substance’ inFig. 8) that refers to alcohol, cigarettes, salt,soybean sauce, and so on (Fig. 8).

All the incomes or expenses in the future shouldhave been converted to present values by using adiscount rate if we were interested in the accumu-lated incomes or expenses. However, the amountof fuelwood consumption at a given time, which isour focus in this model, is the function of variousdemographic, income, and expense factors at thattime. As long as the function (the relationshipbetween the annual amount of fuelwood con-sumption and those demographic, income andexpense factors) does not change over time, there

is no need to discount the future money andcalculate its present value. Furthermore, forshort-term simulations presented in this paper, nosignificant differences exist between using and notusing a typical discount rate (5%).

Transportation includes the shipping of localagricultural/forest products to the outside andindustrial/consumptive goods to the reserve.Transportation can provide the highest income(13 250 Yuan on average) if a household is able todo it, but few households can afford it because ofthe high expense to purchase vehicles. This in-come requires three pre-requisite conditions: atleast 3 years of education (the smallest number ofschooling years for those households owningtrucks observed in the survey), at least 20 000Yuan of accumulated savings (half of the price fora small truck — we assume this is necessary forbuying a truck based on our interviews with farm-ers), and enough labor (the number of peoplebetween 21 and 60 is greater than or equal to 1).

Income from temporary jobs refers to the in-come from such jobs as building hydropowerstations, building houses for other residents, andworking in local restaurants. It depends on theavailability of the labor and the average schoolingyears of the labor. If the household head is illiter-ate (all the households with income from tempo-


rary jobs have schooling years larger than 0), or thenumber of people between 21 and 60 (defined aslabor in this model) is zero, the household is notable to obtain this type of income. Otherwise thehousehold gets an average annual income of1421.79 Yuan.

Road construction requires physical power andresults in a relatively low income of 750 Yuan peryear (25 Yuan per day, 30 days per year onaverage). Thus we assume no income arises fromroad construction for a household that has no laboror gets transportation income. Otherwise if therandom variable creates a value less than 0.350(35% of the local farmers obtained road construc-tion income in 1997), the household can earn anaverage of 750 Yuan.

Like road construction, herb collection requiresphysical power and the associated income is rela-tively low. Thus if the household receives incomefrom transportation or no labor exists, herb incomeis set at zero. In addition, because no householdswith nine or more schooling years collect herbs, weset this variable to zero if the educational level isequal to or greater than 9 years. Otherwise if therandom variable creates a value less than 0.47 (14out of 30 households have herb income), the herbincome is set to be the function of the educationallevel (in terms of schooling years of the labor in thehousehold):

Herb– income= "123.100!Education

– level+1093.000

(n=14, R2=0.528, P!0.0001). (4)

Cabbage sale is one of the main income sourcesin Wolong Nature Reserve. Approximately 96% ofthe farmers plant cabbage. We found cabbageincome (Yuan) has a significant positive relation-ship with the cabbage land area (ha):

Cabbage– income=20515.000!Cabbage

– land"161.380

(n=25, R2=0.609, P=0.0000042).(5)

Local residents tend to satisfy their demands forpork/bacon first and sell the extra in the form ofbacon. The conversion rate from pork to bacon is

approximately 0.6–0.7, because pork loses weightdue to water loss when it is processed by smoke.Consequently, bacon has a higher price than pork.The total amount of income, whether from porksale or bacon sale, is almost the same. So baconincome is only set to be a function of the amountof pork available. Specifically, bacon income occursonly if the household has extra pork, which is thedifference between the quantity of pork availablein a given year and the quantity needed by thehousehold. The quantity of pork available in agiven year depends on the amount of corn, radishand potatoes used to feed pigs; the quantity neededby the household is the product of household sizetimes the per capita yearly consumption.

Household expenses include children’s schoolingcosts, electricity costs, purchase of rice, farmingfacilities, and subsidiary substance. In order toobtain the annual average expenses that a house-hold has to spend for students in elementary school,middle school, and high school, we did a pre-inter-view of ten households. The associated results were335 Yuan/year per student, 1842 Yuan/year perstudent, and 5175 Yuan/year per student for ele-mentary school, middle school, and high schoolexpenses, respectively (the corresponding resultsbased on those 30 households that were later usedfor model verification were 355 Yuan/year perstudent, 1750 Yuan/year per student, and 3940Yuan/year per person).

Elementary schooling expense occurs when thereare children between 6 and 12 years old in thehousehold. The Chinese government requires allchildren go to elementary school, so the probabilitythat a household sends their children to elementaryschool (R1) was set at 1. One child in elementaryschool needs an average of 355 Yuan per year forenrollment and textbooks. The total expense forelementary school S1 is as follows:S1 = 355!Number–of–children–aged–6– to

–12. (6)The data from the pre-interview showed that the

average expenses for a middle school student anda high school student were 1842 and 5175 Yuan,respectively, and that not all parents were able to,or wanted to, send their children to middle or highschool, as they spent their limited income first on


basic needs such as food, electricity, and farmingfacilities. Thus we used yearly net income as acriterion to determine whether a household willsend children to middle school and high school.Because the yearly expense for middle school was1842 Yuan, we set three levels of net income,1842, 921, and 500 Yuan, to test what farmerswould do if their net income was enough to pay thetotal (1842 Yuan), a half (921 Yuan), or only asmall portion of the schooling expense (500). Wethen surveyed their willingness to send their chil-dren to middle school and high school at differentlevels of net income. If household net income peryear was between 500 and 921, between 921 and1842, or greater than 1842, 29.1, 51.0, or 68.2% ofthe respondents, respectively, said they would sendtheir children to middle school. In the case that thehousehold yearly net income was below 500 Yuan,we assumed the probability was zero. One pointthat should be made is that since the income levelsof 921 and 1842 were based on results of thepre-interview of the ten households rather than theresults of the 30 households used for model verifi-cation, the probabilities thus obtained should notexactly represent the probabilities for those 30households. We will show later in the Section 3.2that the model outcome is not sensitive to theincome and expense items such as middle schooland high school expenses. Therefore, we still usedthe income levels from the pre-interview and theassociated probabilities in the model.

Similarly, when household net income was be-tween 1000 and 2588, between 2588 and 5175, orgreater than 5175, 15.4, 29.3, or 56.0% of therespondents, respectively, said they would sendtheir children to high school.

If the number of people between 13 and 15 yearsold was equal to or greater than 1 and the randomvariable creates a number less than R2 (R2=0.291, 0.510, or 0.682, the probability that thehousehold would send their children to middleschool), the yearly expenses for middle school wereS2:S2 =Number– of–people–between–13–and

–15!1842, (7)The assignment of values to R2 depends on thecorresponding net income in the previous year. If

a household had a very low net income (less than500) but still wanted to send their children toschool, a binary variable (‘MidSchl Intention’ inthe model, 0 for no and 1 for yes) would be used.We set the default value of this variable to be 0because a household generally does not send theirchildren to school if they have very low net income— their subsistence needs (e.g. food and clothes)should be met first. However, when we want to testwhat would occur for a low-income household thatwants to send their children to middle school, weset it to be 1. In this case as well as other cases,where yearly net income is lower than the averageexpense for middle school (1842), debt will occur(net income is negative).

Similarly, the yearly expenses for high school (S3)are calculated by using Eq. (8) if the randomvariable creates a value smaller than R3 (R3=0.154, 0.293, or 0.560, the probability that thehousehold would send their children to highschool):

S3=Number–of–people–between–16–and

–20!5175, (8)

The assignment of values to R3 depends on thecorresponding net income.

Every household uses electricity for lighting andfor appliances such as TV, but the extents to whichfarmers use electricity differ with per capita income.In light of the fact that no household spent morethan 720 Yuan for electricity in 1997, we used aminimal function to restrict the electricity costs:

Yearly–electricity–expense

=Min (0.109!Per–capita

– income+37.870, 720.000)

(n=30, R2=0.605, P!0.0001). (9)

Rice expense occurs if the amount of rice ex-changed from potatoes is smaller than the amountneeded, which is expressed as follows:

Rice–expense= (Rice–needed"Rice–obtained)

!Rice–price, (10)

Rice–needed=Household–size!Per–capita

–rice–need, (11)


Rice–obtained=Potato– for–rice/Market

–exchange–rate, (12)

where Per–capita–rice–need and Market–ex-change–rate are set at 150 kg per person and 3.5,respectively.

Farming expense, including expenditure onseedlings, chemical fertilizers, land films (used tokeep corn seedlings from freezing damage), andtools, is equal to per hectare expense (Yuan/ha)times land area (ha). Average expense per hectarewas 2026.8 Yuan.

Expenses for subsidiary substance (‘Substance’in Fig. 8), including alcohol/beer, cigarettes, salt,and soybean sauce, are a positive function ofhousehold size:

Substance–expense=83.029!Household

–size"55.673

(n=30, R2=0.466, P!0.0001).(13)

Expenses for others refer to money used forclothes, religious activities, medical services, andsome other social activities (e.g. sending gifts to ahousehold with a newborn baby, giving moneyfor a relative’s wedding). It is a function of yearlyincome:

Other–expense

=630.270!LOGN (Yearly– income)

"3651.400

(n=30, R2=0.562, P!0.0001). (14)

For those households with trucks, we assumedthat they got loans from friends, credit unions, orbanks and would pay back later. If the purchasingexpense is X yuan and the household decides toreturn Y Yuan each year, then this loan returnhas to last for X/Y years. Here X and Y were setto be 40 000 (the price of a new small truck, JianYang, 1999, personal communication) and 5000Yuan, respectively.

Yearly net income is connected to another statevariable called ‘Accumulated–Savings’. Yearlynet income goes into Accumulated–Savings at theend of each year if it is positive. If it is negative,the money in Accumulated–Savings is used to

offset the deficit (Fig. 8). If yearly net incomekeeps negative over time, there is a great possibil-ity that some local residents resort to illegal activ-ities such as clandestine logging and poaching(Jian Yang, 1999, personal communication). Thusthe dynamics of yearly net income deserves exten-sive attention in terms of wildlife conservation.

2.3.3. Submodel FUELWOODHousehold fuelwood consumption, a factor di-

rectly affecting panda habitat, is driven by de-mands from heating, cooking, and pig foddercooking. Fuelwood consumption quantity forheating in a household without a senior person(61 years old or above) was set at a constant (7.23m3 per year, an average of fuelwood consumedfor heating for the households without seniorpeople) because there was no statistically signifi-cant relationship between the quantity of fuel-wood for heating and household size. Whether ornot there is a senior person in the house, doeshowever, affect the amount of fuelwood used forheating. A household with a senior person startsheating from early October to mid-April, lastingapproximately 190 days; while that without asenior person does this from late October to earlyApril, lasting approximately 160 days. Assumingthe quantity of fuelwood use per day is a con-stant, a household with a senior person shouldconsume 19% ((190–160)/160) more fuelwoodthan one without a senior person, i.e. 7.23! (1+19%)=8.60 m3 per year.

The annual amount of fuelwood used for cook-ing is a function of the household size:

Fuelwood– for–cooking=0.467!Household

–size+0.703

(n=30, R2= 0.533, P=0.0000046).

(15)

Fuelwood for pig fodder cooking depends onhow much pork is converted from potatoes, cornand radishes. We calculated the average ratiobetween fuelwood for cooking pig fodder andpork produced as 0.0106 (SE=0.0038) m3/kg,indicating that 1 kg of pork produced consumes0.0106 m3 of fuelwood.


2.4. Model programming

PANDA–DEEM was programmed usingSTELLA 5.0 (High Performance Systems, Inc.,1997). A friendly graphic interface was developedto facilitate the communication between the userand the model.

The initial values of the state variables de-scribed in Section 2.3 were set at the averagevalues of the associated variables. One exceptionis that the initial net income was set at 0 becausewe wanted to exclude the historical effects of netincome.

2.5. Model testing and simulation

Model testing aims at justifying the reliabilityof the model and providing confidence for modelapplication. Four approaches (model verification,model validation, sensitivity analysis, and uncer-tainty analysis) were used to achieve this goal. Asmentioned in Section 2.3, we already obtained theequations and the parameter values for submodelsFUELWOOD and ECONOMY. Submodel DE-MOGRAPHY needs household size and ages ofall the household members as model inputs. Forsimplicity, a concept of ‘typical household’ is usedfor model testing and simulation. A typical house-hold is defined as a household with five people(three generations), one grandparent, two parents,and two children. The reason why we set house-hold size to be five, and chose two children andone senior person, was based on the followingobservations: the average household size of the 30households was 5.2 people, and 40%, 33%, and3% of these 30 households had two, three and onechildren/child, respectively. Thus we chose twochildren, two parents, and one grandparent forthis typical household.

We ran the model for different replicates andthen calculated the average of fuelwood consump-tion amounts. These tests indicated that 50 runscould produce a relatively stable outcome. Be-cause we were most interested in structural inter-actions between different variables in thesubmodels, we decided to use a short time lengthto run the model so that the basic structure ofhousehold demography did not change. A number

of runs showed 4–6 years could keep the agestructure of the typical household unchanged butstill allow for some stochastic processes (e.g.whether a child of 9 years old at the beginning ofsimulation could go to middle school or not bythe end of simulation) to function. We used 5years as the time length. The model can be easilyextended to do long-term (10 years or longer)simulations.

2.5.1. Model !erificationWe first used the data from the 30 households

for model development to verify the model re-sults. This approach can be divided into the fol-lowing steps: (1) set the values of the demographicand socioeconomic variables to be exactly thesame as those in the corresponding household; (2)run the model 50 times (replicates); (3) calculatethe average of each run over time (5 years); (4)calculate the average of all the above 50 replicatesand the associated standard error with respect tothe observed value; (5) repeat steps 1–4 for all the30 households; (6) use paired t-tests to test for thedifference between the simulation results and theobserved fuelwood consumption quantities for allthese 30 households.

2.5.2. Model !alidationThe independent data from the 15 households

that had not been employed for model develop-ment were used to validate the model. We fol-lowed the same procedure used for modelverification in Section 2.5.1.

2.5.3. Sensiti!ity analysisThe purpose of conducting a sensitivity analysis

is to evaluate how the amount of fuelwood con-sumption responds to relatively small changes indifferent components of the model. This processcould help us to identify the most sensitive com-ponents. Also, it serves as an alternative approachto model testing because unusual responses of themodel caused by a small change in one compo-nent may indicate some potential errors in modelstructure or model parameterization. To measurethe sensitivity, we employed the following sensi-tivity index (Jørgensen, 1986):

Sx= (dX/X)/(dP/P), (16)


Table 1Sensitivity indices of socioeconomic parametersa

Parameters SensitivityInitial valueindex

"0.050Elementary school 335expenses (Yuan peryear)

Middle school expenses 1842 "0.060(Yuan per year)

High school expenses 5175 "0.009(Yuan per year)

Truck costs (Yuan) "0.01240 000Rice price (Yuan/kg) "0.0502.6

13 250Transportation income 0.002(Yuan per year)

750Road Income (Yuan 0.003per year)

1421.79 0.011Temporary job income(Yuan per year)

Radish land (ha) 0.146 0.144Potato/corn land (ha) 0.27 0.227

a At the beginning of a simulation, a household had fivepeople (9, 12, 34, 35, and 78 years old).

corn land, and transportation income, see Table1) a 10% increase and examined the model re-sponses (Table 1). We ran the model 50 times andcalculated the average amount of fuelwood con-sumption under each of these conditions. Then wecalculated the Sx values based on Eq. (16).

2.5.4. Uncertainty analysisAnother alternative for model testing is uncer-

tainty analysis, which is defined as a way ‘toidentify how model results vary with large vari-ances in parameters’ (Liu and Ashton, 1998).When a parameter has too much uncertainty (or awide range of values), uncertainty analysis can beemployed to examine how a model responds tothe changes in the associated parameter. We firstexamined how the annual amount of householdconsumption responds to changes in householdsize (from 2 to 10), assuming one senior personexists in all the cases. Then we did the same thingexcept that no senior exists in all the cases.

We also examined how annual amounts ofhousehold consumption respond to the interac-tions between household size and area of corn/potato land (the most sensitive economiccomponent based on our sensitivity analysis). Wechose four household sizes (2, 5, 7, 9 people, withone senior person each) and six levels of corn/potato area: 0.033 (ha, smallest area observed),0.135, 0.270 (the average area from the 30 house-holds), 0.405, 0.540, and 0.675 ha. For each ofthese 24 combinations (four household sizes!sixlevels of land area), we ran the model 50 times

where X is the value of dependent variable ofinterest and P is the parameter value under nor-mal conditions; dX is the change in the dependentvariable caused by dP, the change in parametervalue. A larger Sx indicates a higher sensitivity ofthe dependent variable (here fuelwood consump-tion) to the change in a specific component.

We conducted sensitivity analysis of fuelwoodconsumption in response to changes in socioeco-nomic factors. We gave each of the values of theselected socioeconomic components (e.g. potato/

Fig. 9. Model verification — comparison between model predictions and field observations for fuelwood consumption of the 30households in 1997. The field data associated with these 30 households were used for model construction.


Fig. 10. Model validation — comparison between model predictions and field observations for fuelwood consumption of the 15households in 1997. The field data associated with these 15 households were not used for model construction.

Fig. 11. The relation between amount of fuelwood consumption and household size. Household age structure has impact onfuelwood consumption.

and calculated the amounts of annual fuelwoodconsumption.

3. Results

3.1. Verification and !alidation results

The model outcomes agreed well with the ob-servations (Figs. 9 and 10). The paired t-testsshow that the model results matched well with the30 observations (P=0.233, T= "1.220) inmodel verification. The model outputs also agreedwell with the 15 independent observations (P=

0.1453 T= "1.550) in model validation.

3.2. Sensiti!ity analysis results

Amount of fuelwood consumption was not verysensitive to economic components except for twoitems, i.e. radish land and potato/corn land (Table1). A negative relationship existed betweenamount of fuelwood consumption and expense-re-lated items, suggesting that more expenses in ahousehold would lead to less fuelwood consump-tion if other conditions were kept unchanged.Furthermore, there was a positive relationshipbetween amount of fuelwood consumption and


income-related items, suggesting higher incomeswould lead to more fuelwood consumption.

3.3. Uncertainty analysis results

A household with a senior person consumemore fuelwood than one of the same size withouta senior person and this trend was not obviouswhen the household size was two (Fig. 11). Theannual amount of fuelwood consumption forhouseholds with seniors increase with the house-hold size; whereas the annual amount of fuelwoodconsumption for households without seniors showa slight decline first as the household size increasefrom two to four, and then increase from four toten (Fig. 11).

Amount of fuelwood consumption increasewith the area of corn/potato land at each of thehousehold sizes (Fig. 12). As corn/potato landincrease, the differences among different house-hold sizes became smaller and smaller. In thehousehold with two people: as land area increase,the amount of fuelwood consumption increase ata faster speed in the region around 0.1 ha. Itbecame higher than the amounts of fuelwoodconsumed by a household of five people fromapproximately 0.2 ha, and reached the sameamount of fuelwood consumed by a household ofseven people when the land area came to approx-imately 0.65 ha.

4. Conclusions and discussions

The age structure plays an important role indetermining the household fuelwood use (Fig. 11)regardless of other factors (e.g. household in-come) that have the potential to offset this influ-ence. This can be explained from threeperspectives: (1) households with aged peoplestart heating earlier and end heating later eachyear (Section 2.3.3), and thus need more fuel-wood; (2) a household without a senior person,for instance, may have one more person of laborthan that with a senior, and thus may result inmore income. In Section 3.2, we mentioned thepositive relationship between amount of fuelwoodconsumption and income level. In this sense, ahousehold with a senior person should use lessfuelwood due to lower income; (3) the ultimateamount of fuelwood consumption depends onwhich factor (age structure and more income)dominates under different conditions. Fig. 11shows that age structure plays a dominant role indetermining amount of fuelwood consumption forhouseholds of three people or more. However,economic components may offset the differencesof fuelwood consumption caused by age structureunder some conditions. For instance, a two-per-son household without a senior could have onemore person of labor, thus could earn more in-come. As discussed before, this may slightly in-

Fig. 12. The relationship between annual amount of fuelwood consumption and area of corn/potato land.


crease the amount of fuelwood consumption, thusdecreasing the difference of amount of fuelwoodconsumption between a household with a seniorand that without a senior.

The high sensitivity values for corn/potato landand radish land may result from the land-depen-dent life style (Fig. 7). Due to the natural economyin the Reserve, farmers do not have enough oppor-tunities to earn money from industrial and com-mercial activities. The most feasible way is to raiseas many pigs as possible. By doing this, farmers cannot only meet their own needs for meat consump-tion, but also can make money through selling theirbacon to tourists and local restaurants. However,keeping more pigs requires more fodder to feedthem and more fuelwood to cook the fodder. Asmentioned above, local farmers’ income sources arevery limited, so they have to rely on their landinstead of purchasing from market. Here the land-dependent life style results in the route of ‘moreland — more fodder production — more pigs —more fuelwood for cooking fodder’.

The relatively obtuse responses from income- orexpense-related components, consistent with thisland-dependent life style, are explainable in termsof local farmers’ subsistence demand. Farmersmostly depend on their land for subsistence, butthey still have to trade for the necessary goods suchas chemical fertilizers and land films. So the impactsfrom economic status (incomes and expenses),though small, still exist. The positive relation be-tween fuelwood consumption and the income-re-lated items can be explained in two aspects: (1)higher income can increase their crop productivityby allowing the purchase of more chemical fertiliz-ers, thus resulting in more fodder to feed pigs. Inturn, more fuelwood is needed to cook the fodder;(2) though higher income increases the ability to useelectricity, most of the residents do not tend to doso if some cheaper or free substitutes (here fuel-wood) are easily accessible and their income is nothigh enough to replace fuelwood with the electric-ity. In fact, our survey in summer 1999 showed that78.18% of the 220 interviewees thought that thehigh price of electricity, with respect to their lowincome, was one of the barriers to switching fromfuelwood to electricity for cooking and heating.Similarly, we can explain the negative relationship

between fuelwood consumption and expense-re-lated items because more expenses are equivalentto less income when other conditions stay un-changed.

It may be unusual that the amount of fuelwoodconsumption falls slightly when the household sizeis small (from two to four, Fig. 11). This phe-nomenon can also be explained in terms of theland-dependent life style. When a relatively largearea of cropland is fixed (0.27 ha for corn/potatoand 0.146 ha for radish), a household of small size(e.g. two to four people) has a greater ability togrow more pig fodder because land for growinghuman food is much less than that for a householdof large size. As a result, the fewer people, the morefuelwood for cooking pig fodder. But when thehousehold size reaches a certain threshold (herefour people), this trend will diminish because theadditional people need more food, thus the land forgrowing pig fodder decreases drastically. As aresult, household size plays a more important rolein determining amount of fuelwood consumption,and amount of fuelwood consumption displays apositive relationship with household size.

The results of the uncertainty analysis (Fig. 12)are also consistent with the above-mentioned land-dependent life style. When land area is small,people have no way to grow a large amount offodder to feed pigs, thus the portion of fuelwoodfor cooking pig fodder is small, and the householdsize plays a dominant role in determining annualamount of fuelwood consumption. This is thereason why large differences are shown for house-holds with small areas of corn/potato land. As theland area increases, amount of fuelwood consump-tion for cooking pig fodder takes a dominant rolein determining total amount of fuelwood consump-tion, thus reducing the difference caused by house-hold size. However, it is rare to see a smallhousehold (e.g. two people) with a large amount ofcropland (e.g. 0.6 ha) and a large household (e.g.nine people) with a very small amount of cropland(e.g. 0.1 ha). Our field data show that there is apositive relationship between the amount of crop-land and the household size (Fig. 13). This impliesthat a large household usually has more croplandand consumes more fuelwood than a small onedoes. In Fig. 12, the upper right region may


Fig. 13. The relationship between cropland area and household size (n=30, R2=0.343, P=0.00068).

represent fuelwood consumption by large house-holds, whereas the lower left region may reflectfuelwood consumption by small households.

The significance of this model rests on thefollowing aspects: (1) as a household-basedmodel, its submodel DEMOGRAPHY follows thelife span of each individual in the household, thuscan simulate the household population dynamics;(2) this model contains stochastic components inaddition to those deterministic components, thusis able to simulate some random events at a giventime, such as going to college; (3) the modelprovides a useful tool for analyzing interrelation-ships among various variables in the model,mostly based on field observations. Furthermore,it can be used to study the dynamics of fuelwoodconsumption over time; (4) this model canprovide insights into how subsistence demandsand social attitudes interact with each other andthen impose additive impacts on panda habitatdegradation, which is important to enable localadministrators to make scientifically sound andeconomically reasonable decisions; (5) this modelprovides a good foundation for evaluating theimpacts of fuelwood consumption by all house-holds across the entire Reserve.

Acknowledgements

We thank Jian Yang, Yinchun Tan, and HanXiao for their assistance in data acquirement and

model parameterization. We are also grateful toVicky Markham Dompka, Daniel Rutledge, andGeorge Schaller for their useful comments onearlier drafts of this paper. We owe thanks toJialong Xie who provided assistance in program-ming the model and analyzing the data. We arealso indebted to the financial support from theNational Science Foundation (NSF), National In-stitute of Health (NIH), American Association forthe Advancement of Sciences, the John D. andCatherine T. MacArthur Foundation, MichiganState University, the Natural Science Foundationof China, Ministry of Science and Technology ofChina (G2000046807), China Bridges Interna-tional and the Chinese Academy of Sciences.

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