Evaluating the economic impacts of crop yield change and sea level rise induced by climate change on Taiwan's agricultural sector

AGRICULTURALECONOMICS

Agricultural Economics 43 (2012) 205–214

Evaluating the economic impacts of crop yield change and sea level riseinduced by climate change on Taiwan’s agricultural sector

Ching-Cheng Changa,∗, Chi-Chung Chenb, Bruce McCarlc

aInstitute of Economics, Academia Sinica, Department of Agricultural Economics, National Taiwan University, and APEC Research Center for Typhoon andSociety, No. 128, Sec 2, Academia Road, Taipei 115, Taiwan

bDepartment of Applied Economics, National Chung-Hsing University, No. 250, Kuo-Kuang Road, Taichung 402, TaiwancDepartment of Agricultural Economics, Texas A&M University, Mail Stop 2124, College Station, TX 77854, USA

Received 2 December 2009; received in revised form 17 April 2011; accepted 6 September 2011

Abstract

This article investigates the effects of sea level rise and climate change induced crop yield alterations on Taiwan as well as possible adaptationstrategies. For sea level rise of up to 5 meters, as much as 4.9% of total acreage and 16% of rice acreage would be lost. The empirical findingsshow that the sea level damages range from NT$ 0.84 to 4.10 billion while crop yield losses range from NT$ 1.79 to 2.55 billion. We investigatealternative adaptation strategies finding crop yield technological progress and tariff reduction could significantly mitigate these effects.

JEL classifications: C61, Q11, Q54

Keywords: Sea level rise; Stochastic agricultural sector model; Adaptation strategy

1. Introduction

The agricultural sector is highly vulnerable to climate changeand climate variability. Climate change is expected to causeselected areas to experience losses in agricultural productiv-ity, primarily due to reductions in crop yields (Rosenzweiget al., 2002). The economic impacts of such change on agri-culture has been examined by Adams et al. (1990), Kaneet al. (1992), Easterling et al. (1993), Kaiser et al. (1993),Rosenzweig and Parry (1994), Darwin et al. (1995), Adamset al. (1998), Lewandrowski and Schimmelfennig (1999) Chang(2002), Reilly et al. (2002a, 2002b), McCarl (2006), and USClimate Change Science Program (2008) among others.

Sea level rise (SLR) due to climate change is also a long-term threat to portions of society including agriculture. Therate of SLR has been accelerating with the 100 year average

∗Corresponding author. Tel.: +886-2-27822791; fax: 886-2-27853946.E-mail address: [email protected] (C.-C. Chang).

Data Appendix Available Online

A data appendix to replicate main results is available in the online version ofthis article. Please note: Wiley-Blackwell is not responsible for the contentor functionality of any supporting information supplied by the authors. Anyqueries (other than missing material) should be directed to the correspondingauthor for the article.

being 1.8 mm per year and the 1993–2003 periods showing anaverage of 3.1 mm per year (Bindoff et al., 2007; Church andWhite, 2006; Douglas, 1997). Some predict yet larger rates forthe future. For instance, Raper and Braithwaite (2006) projectannual SLR caused by melting mountain glaciers and ice-caps will fall between 0.046 and 0.051 meters by 2100. Meieret al. (2007) estimate an additional 0.1 to 0.25 meters of SLR by2100 due to glacier and ice cap melting. The IntergovernmentalPanel of Climate Change fourth assessment report (IPCC, 2007)projects 0.18 to 0.59 meters SLR without consideration of icemelting by 2100. Rahmstorf (2007) projects a cumulative SLRof 0.5 to 1.4 meters by 2100. Dasgupta et al. (2009) project 1 to3 meters of rise but indicates as much as 5 meters is possible ifthe unexpected rapid breakup of Greenland ice cover and WestAntarctic ice sheet occurs.

SLR would be costly and would affect coastal areas in a va-riety of ways, including flooding, potential loss of life, damageto property, coastal erosion, changes in surface and ground wa-ter quality, decreasing agricultural and aquaculture productionthrough land inundation, and damages to transportation infras-tructure. Darwin and Tol (2001) estimated the direct economicdamages of SLR in the global scale. Bosello et al. (2007) useda static computable general equilibrium model to estimate bothdirect and indirect impacts and found substantial global welfare

c© 2011 International Association of Agricultural Economists DOI: 10.1111/j.1574-0862.2011.00577.x

206 C.-C. Chang et al. / Agricultural Economics 43 (2012) 205–214

losses. However, studies of agricultural implications have beenlimited because most of them only concentrate on coastal pro-tection implications. This study extends the previous studies byevaluating the economic impacts of simultaneous SLR and cropyield effects on the Taiwanese agricultural sector. Possible off-sets from adopting alternative adaptation strategies includingcrop mix adjustments, trade liberalization, and technologicalimprovement on yield performance are also examined.

2. Agricultural analysis of climate change

In retrospect, three different scales of economic models havebeen used including farm level, national level, and global levelto study the agricultural impacts from climate change. In thefarm scale, Easterling et al. (1993) applied the Erosion Pro-ductivity Impact Calculator (EPIC) model to simulate grainproduction outcome before and after adopting farm manage-ment measures to cope with climate change. However, they didnot consider economic impacts. Kaiser et al. (1993)’s studyintegrated a farm-level economic model with atmospheric andagronomic components to simulate crop yield alterations dueto climate change and then estimated the economic impacts us-ing a reduced-form model. The empirical results indicated thatgrain farmers in southern Minnesota could adopt maturing cul-tivars and crop mix adjustment to effectively offset warming.However, market responses such as price alterations cannot bereflected by the farm-level assessment by nature.

A national-level model could be found in the studies byAdams et al. (1990), Adams et al. (1995), Reilly (1995), andChang (2002) applied the agricultural sector model to esti-mate the economic impacts of climate change. The major ad-vantages of an agricultural sector model as compared withfarm-level model are that prices are treated endogenously, pro-ducers/consumers’ responses to market price signals are consid-ered, and trade activities are included along with governmentpolicy interventions. However, such a model is of a partialequilibrium nature which assumes that other factors in non-agricultural sector are constant.

In the global scale, Rosenzweig and Parry (1994), Darwinet al. (1995), and Reilly (1995) assessed the potential impactsof climate on world food markets. The model they adopted wasmore general which could capture the global economic effectsand display the distributional effects across the world. However,the global model is usually not able to reflect detailed regionalresponses. In comparison, sector model analysts have lookedinto the size of the international adjustments under climatechange and generally assume that on a global scale internationaltrade volumes are not greatly affected (McCarl, 2006; Reilly,1995). This assumption will be maintained herein.

3. Effects of sea level rise on agricultural cropland

To estimate the effects of SLR on the inundation of agri-cultural cropland, we use SLR scenarios from Dasgupta et al.

(2009) wherein the rise ranges from 1 to 5 meters. Dasguptaet al. combine geographic information system (GIS) softwareand coastal terrain models with data sets on country and coast-line boundary, elevation, population, agriculture and urban ex-tent, and wetland coverage. SLRs of 1 to 5 meters are foundto inundate 0.39% to 2.10% of global cropland. Their resultsshow that Egypt, Japan, Myanmar, South America, Taiwan, andVietnam are the most vulnerable regions.

Taiwan is vulnerable to SLR due to its geophysical character-istics. It has a total area of 3.6 million hectares. The land risesfrom the sea to a mountain range of 3,000 meters within 50–60kilometers. The location of this mountain chain determines thedistribution of arable land. Total cultivated area is 0.82 millionhectares, or 24% of the total land area. The major agriculturalproduction zones are located in the central and south-west re-gions. These production zones are low lying and amount to atleast 60% of the country’s total. As SLRs by 1 meter, about 1%(or 8,651 hectares) of agricultural zones will be inundated asshown in the Appendix. The extreme case of 5-meter SLR willresult in a loss of 4.91% in total (or 40,378 hectares).

4. Climate change effects on crop yields

To estimate the effects of climate change on crop production(i.e., crop yield effects), a two-step process is adopted followingthe work done in Taiwan by Chang (2002). The first step is toestimate the effects of climate conditions on crop yield whilethe second step is to combine these effects with future climatechange scenarios in Taiwan.

Chang (2002) used county-level panel data to estimate cropyield response models in which both climate and non-climatevariables are included. This study adopted Chang’s estimationoutcomes as shown in Table 1. The elasticity estimates in Table1 reflect how crop yields are altered when temperature or pre-cipitation levels change in percentage terms in different season.These elasticities are then used to calculate the effects of climatechange on different crop yields before entering the agriculturalsector model as an exogenous process of productivity change.

Next, the IPCC (2007) temperature and precipitation projec-tions have to be downscaled before climate change scenariosare chosen. The downscaled results for the A2 and B2 scenariosunder the Canadian CGCM, Hadley’s HADCM, and Germany’sECHAM are listed in Table 2. The outcomes from HADCM fallin the middle between the CGCM and ECHAM and thus arechosen here. In other words, the scenarios in this study involve(a) 1% increase in temperature with 6% increase in precipita-tion, and (b) 6% increase in temperature with 9% increase inprecipitation.

In turn, the percentage change in crop yields under these twoclimate change scenarios is calculated and shown inTable 3.Table 3 shows both positive and negative results on crop yields,in which yields for rice, sweet-potatoes, adzuki bean, cucum-ber, watermelon, mushroom, bananas, grapefruit, mango, lo-quat, apple, papaya, sugar apple, passion fruit, and coconut will

C.-C. Chang et al. / Agricultural Economics 43 (2012) 205–214 207

Table 1Chang (2002)’s estimates of crop yield responses to temperature and precipitation

Temperature Precipitation

Spring Summer Fall Winter Spring Summer Fall Winter

Rice 0.08 0.00 −0.20 −0.05 −0.01 −0.08 0.03 0.01Corn 0.00 0.08 −0.78 0.37 0.00 −0.04 −0.06 0.00Wheat 0.04 0.15 0.16 −0.36 0.00 0.07 −0.05 0.00Sorghum −0.12 −1.11 1.77 1.50 −0.04 0.10 0.51 0.00Soybeans −0.25 −0.05 0.26 −0.23 −0.05 −0.12 0.51 0.11Peanuts −0.17 0.40 0.10 −0.13 −0.01 −0.08 0.00 0.06Adzuki bean −0.34 −0.54 0.73 −1.34 0.00 0.07 −0.27 0.08Sweet potatoes 0.16 0.20 −0.67 0.26 0.00 −0.03 −0.21 −0.01Potatoes −0.06 −0.25 −0.38 1.12 0.00 0.33 −0.36 0.09Tea 0.02 0.59 −1.18 0.64 0.02 −0.24 0.02 0.01Sugarcane −0.10 −0.23 0.04 −0.33 0.03 0.35 −0.26 0.06Sesame 0.32 −0.51 −1.49 0.38 −0.11 0.00 0.45 −0.07Radish 0.01 0.02 0.09 −0.04 0.00 0.07 0.03 0.02Carrot 0.15 −0.26 1.54 0.15 −0.01 0.06 −0.28 0.02Ginger 0.18 0.41 0.45 −0.12 0.00 0.07 0.02 0.00Scallion 0.07 −0.17 1.31 0.59 0.00 −0.03 −0.11 0.03Garlic bulb −0.05 −0.38 −1.04 1.48 0.01 0.29 0.06 0.23Leek 0.06 0.64 −0.72 0.75 0.00 −0.11 −0.14 0.01Bamboo 0.09 0.06 0.46 0.26 −0.01 −0.07 −0.09 −0.07Asparagus −0.09 −1.05 −0.44 0.79 −0.04 −0.06 0.41 0.04Water bamboo −0.09 −1.05 −0.44 0.79 −0.04 −0.06 0.41 0.04Cabbage 0.08 −0.07 0.05 0.16 −0.02 0.17 −0.30 0.01Cauliflower 0.08 −0.07 0.05 0.16 −0.02 0.17 −0.30 0.01Cucumber −0.06 −0.11 −0.16 0.12 −0.01 0.03 −0.22 −0.01Bitter 0.04 −0.11 −0.16 0.12 −0.01 0.03 −0.22 −0.01Tomato 0.05 −0.09 0.72 0.72 −0.09 −0.16 0.21 −0.02Pea 0.19 −0.09 0.17 0.31 −0.04 −0.05 −0.04 −0.05Watermelon 0.15 −0.51 1.01 −0.90 −0.01 −0.08 0.13 −0.05Cantaloupe 0.02 0.03 0.65 −0.31 0.01 0.25 −0.64 0.03Mushroom 0.46 0.02 −0.96 0.83 −0.01 0.06 0.19 0.02Banana −0.23 −0.50 −0.22 −0.13 −0.04 −0.07 0.00 0.00Pineapple 0.12 −0.27 −1.32 −0.55 −0.01 0.17 −0.07 −0.01Ponkan 0.18 −0.59 0.60 0.00 0.01 0.23 −0.32 0.04Tankan 0.12 −0.07 1.07 0.55 0.04 −0.24 0.03 0.04Wentan 0.20 0.03 0.54 −0.01 0.04 0.04 0.00 0.06Liucheng 0.27 −0.26 −0.19 0.06 0.01 0.12 0.21 −0.04Lemon −0.22 0.10 −0.50 −0.26 0.02 0.02 0.15 0.03Grapefruit −0.16 0.41 −1.24 −0.10 0.01 −0.14 0.12 −0.06Mango −0.16 0.24 0.74 −0.18 −0.07 −0.38 −0.40 0.06Betel −0.21 0.44 −1.76 0.01 −0.03 0.27 −0.18 0.01Guava −0.07 0.24 0.09 0.15 0.02 0.09 −0.05 0.03Wax apple −0.07 0.84 0.46 1.37 −0.01 −0.12 0.02 0.04Grape −0.03 0.01 −1.39 0.41 0.02 0.35 0.29 0.03Loquat 0.04 −0.12 −0.08 0.37 −0.01 −0.12 −0.15 −0.10Plum 0.23 0.23 −0.26 0.12 −0.01 −0.05 0.26 −0.06Peach 0.24 −0.18 0.38 0.10 0.02 −0.10 0.06 0.06Persimmons 0.20 −0.53 0.34 0.71 0.01 −0.13 0.17 0.02Apricot −0.24 −0.17 −0.60 0.65 0.02 0.20 0.09 0.00Liche 0.18 −0.29 0.95 0.04 −0.01 0.00 −0.27 −0.07Carambolas −0.06 −0.84 0.39 0.04 −0.01 0.00 0.23 −0.04Pear −0.06 0.34 −0.65 −0.16 0.01 −0.19 0.30 −0.04Apple −0.15 −0.13 −2.72 −0.38 0.01 −0.04 −0.18 −0.05Papaya 0.04 −0.33 0.37 −0.56 0.02 −0.14 0.00 −0.07Sugar apple −0.10 −0.21 0.36 −0.85 −0.06 −0.12 0.07 −0.14Passion fruit −0.24 0.13 −0.48 −0.73 −0.05 0.06 −0.20 0.06Coconut 0.04 −0.07 0.10 −0.53 0.01 0.06 −0.26 −0.10


Table 2Change in 2055 Taiwan climate under various climate model projections

GCMS CGCM HADCM ECHAM

SRES A2 B2 A2 B2 A2 B2

Temperature 18% −3% +1% +6% +6% +7%Precipitation −3% −3% +9% +10% −4% −14%

Note: The numbers in this table represent the percentage change in the 2055projections relative to average conditions during the years 1961 to 1990. Forinstance, under CGCM A2 case temperature is projected to increase by 18% in2055 as compared with the average temperature from year 1961 to 1990.

decline; while yields for potatoes, carrot, ginger, scallion, gar-lic bulb, bitter, ponkan, tankan, wentan, liucheng, guava, plum,peach, persimmons, and apricot will rise.

5. Sector modeling of climate change

To evaluate the economic impacts of climate change as man-ifest through SLR and crop yields, a stochastic version of Tai-wanese Agricultural Sector Model (TASM) was used followingthe procedures proposed by Adams et al. (1990) and a num-ber of subsequent studies. Below we explain the basic modelstructure followed by the SLR additions.

5.1. Basics of TASM

TASM is a multi-product partial equilibrium model basedon the previous work of Baumes (1978), McCarl and Spreen(1980), and Chang et al. (1992). The empirical structure hasbeen adapted to Taiwan and used in a number of climate-related studies (e.g., Chang 2002; Chen and Chang 2005).TASM simulates market equilibrium under the assumption ofperfect competition with individual producers and consumersas price-takers. It incorporates price-dependent product demandand input supply curves. Algebraically TASM can be depictedas follows.

Suppose that there exist I agricultural crop commoditieswhich are produced in K regions through production activitiesXik(i = 1, 2, . . . , I; k = 1, 2, . . . , K). Each activity Xik de-picts production on a hectare of land. Total production in eachregion can be calculated by multiplying per hectare yield Yik

times hectares produced Xik . For product demand, we assumeall commodities are sold in the wholesale markets. The whole-sale level demand functions are assumed to be integrable andcan be represented by the following inverse demand functions:

PQi = ψ(Qi) i = 1, 2, . . . , I, (1)

where Qi is the total quantity of consumption and PQi is the

average wholesale price of commodity i.

Table 3Percentage changes in crop yields under HADGCM projections

Group Products A2 B2 Group Products A2 B2

Rice Rice −0.71 −1.66 Vegetables Cantaloupe 2.64 4.65Cereal Corn −1.24 −3.02 Mushroom −2.87 −9.91

Wheat 0.15 0.09 Fruits Banana −2.04 −7.51Sorghum 7.16 17.90 Pineapple −1.35 −11.40

Pulses Soybeans 3.76 2.86 Ponkan −0.28 0.55Peanuts −0.03 0.95 Tankan 0.55 8.79Adzuki bean −2.63 −10.13 Wentan 2.02 5.94

Roots Sweet-Potatoes −2.26 −2.76 Liucheng 2.52 2.25Potatoes 0.97 3.17 Lemon 1.02 −3.11

Special Tea −1.65 −1.45 Grapefruit −1.69 −7.19Cane for Process 1.03 −1.87 Mango −6.46 −4.12Cane for Fresh 1.03 −1.87 Betel −0.94 −8.43Sesame 1.14 −5.07 Guava 1.25 3.38

Vegetables Radish 1.14 1.67 Wax apple 1.96 14.86Carrot −0.31 7.34 Grape 5.23 0.91Ginger 1.71 6.38 Loquat −3.22 −2.59Scallion 0.81 9.69 Plum 1.55 3.27Garlic bulb 5.25 5.89 Peach 0.84 3.53Leek −1.44 1.96 Persimmons 1.32 4.97Bamboo −1.20 2.90 Apricot 2.40 0.87Asparagus 2.37 −1.18 Liche −2.36 1.66Water Bamboo 2.37 −1.18 Carambolas 1.13 −1.05Cabbage −1.02 −0.05 Pear 0.11 −2.53Cauliflower −1.02 −0.05 Apple −5.72 −22.86Cucumber −1.90 −2.69 Papaya −2.13 −4.74Bitter 0.89 7.80 Sugar Apple −2.99 −7.26Tomato −1.03 1.69 Passion Fruit −2.47 −9.21Pea −0.38 −1.65 Coconut −3.05 −5.64Watermelon −2.72 −1.10


In input markets, each production activity applies two re-gional inputs (land and labor) and N inputs purchased from thenon-farm sector (such as fertilizer and chemicals). The pricesof N purchased inputs are assumed exogenous. However, theprices of the regional inputs are endogenously determined bythe derived demand from the production activities and regionalsupply functions. Regional supply functions are integrable andof the form as follows:

P Lk = αk(Lk) k = 1, 2, . . . , K, (2)

P Rk = βk(Rk) k = 1, 2, . . . , K, (3)

where P Lk , P R

k are rental price of cropland and the wages ofrural labor, respectively, while Lk,Rk are the cropland andagricultural employment, respectively.

The objective function which maximizes the sum of con-sumers’ plus producers’ surplus is used to simulate a perfectlycompetitive market equilibrium following Samuelson (1952)and Takayama and Judge (1964). It is defined as the area be-tween the product demand and factor supply curves to the leftof their intersection as follows:

Max :∑

i

∫ψ(Qi) dQi −

∑i

∑k

CikXik

−∑

k

∫αk(Lk) dLk −

∑k

∫βk(Rk) dRk. (4)

The constraints are:

Qi −∑

k

YikXik ≤ O ∀i, (5)

∑i

Xik − Lk ≤ O ∀k, (6)

∑i

fikXik − Rk ≤ O ∀k, (7)

where Cik is the purchased input cost in region k used inproducing the ith commodity, Yik is per hectare yield ofith commodity produced in region k, and fik is the de-mand for the regional input in region k. Terms Qi and Xik

are endogenous variables while Cik , Yik , and fik are knownparameters.

5.2. Modeling farm support and trade policies

Domestic policy in Taiwan supports rice prices and imposescropland set-asides. Incorporating such policies in TASM re-quires addition of two sets of variables. The first set reflectsthe government rice purchasing program that provides farmerswith a guaranteed price which is above the market equilibriumprice. Letting P G

i be the weighted government guaranteed pur-chase price and QG

i the total amount of government purchases.

The farm revenue realized from the government rice purchaseprogram (P G

i ∗ QGi ) is added into the objective function as an

additional farm revenue source while it removes rice from themarket place up to the amount allowed.

The second set of policy variables relates to the set-asideprogram. Farmers receive a subsidy of P L per hectare whenthey participate in this program with ALk representing the set-aside acreage in region k. The total area being set aside is about280,000 hectares where the payment farmer received is aboutNT$ 90,000 per hectare pre year.

Regarding trade policies, with a self-sufficiency ratio of32.4%, Taiwan imports many agricultural products each yearunder two types of arrangements: tariff and tariff rate quota(TRQ). Wheat, feed grains, pulse, meat, and most horticulturalproducts are imported with low import tariffs, but rice, fluidmilk, and a few selected sensitive products are imported by theTRQ system for protecting domestic producers.

5.3. Modeling uncertainty of crop yields

The model will be modified into a stochastic one since cropyields are uncertain due to climate conditions. The stochasticmodel basically follows the stochastic programming with re-course (SPR) formulation as discussed in Lambert et al. (1995)and Chen and McCarl (2000). It contains more than one state ofnature to define crop yield variability, but commodity demandis the same under each state of nature. Farmers are assumedto make planting decisions before actual yields are revealed.Revenue outcome under each state of nature is explicitly addedinto the objective function to accommodate the variability as-sociated with planting decisions. Thus, a two-stage approachis embedded where farmers make decisions on crop plantingacreages in the first stage while a supply–demand balance con-straint ensures market clearance at each state of nature in thesecond stage.

The mathematical forms of the stochastic TASM with sub-script s denoting the state of nature from climate conditions areas follows.

MAX∑

s

ρ(s) ∗{ ∑

i

∫ψ(Qis) dQis

−∑

k

∫αk(Lk) dLk −

∑k

∫β(Rk) dRk −

∑i

∑k

CikXik

+∑

i

∑i

∫ED

(QM

is

)dQM

is

+∑

i

∫EXED(TRQsi) dTRQis −

∑i

∫ES

(QX

i

)dQX

i

+∑

i

[taxi ∗ QM

is + outtaxi ∗ TRQis

]}

+∑

i

P Gi ∗ QG

i +∑

k

P L ∗ ALk, (8)


Subject to:

Qis + QXis + QG

i − ∑k

Yiks ∗ (1 + CCYIELDi) ∗ Xik

− (QM

is + TRQis

) ≤ 0 ∀i, s, (9)∑

i

Xik + ALk − Lk ∗ (1 − SLR) ≤ 0 ∀k, (10)

∑i

fikXik − Rk ≤ O ∀k, (11)

where beyond the definitions above

ρ(s) Probability of state of nature s,Qis Quantity demanded for product i under state of

nature sQG

i Government purchases quantity for pricesupported product i

QMis Import quantity demanded for product i under

state of nature sQX

is Export quantity for product i under state ofnature s

ED(QMis ) Inverse import demand function for ith

commodityES(QX

is ) Inverse export supply function for ith commodityTRQis Import quantity exceeding the quotaEXED(TRQis ) Inverse excess demand curve of product i that

the import quantity is exceeding quotataxi Import tariff for product iouttaxi Out-of-Quota tariff for product iYiks Per hectare yield of ith commodity produced

under state of nature s in region kP L Set-Aside subsidySUBj Subsidy on planting energy crop jALk Set-Aside acreage in region kCCYIELDi Percentage change of ith crop yield due to

climate changeSLR Percentage change of cropland due to SLR

The objective function (8) maximizes total expected con-sumers’ and producers’ surpluses incorporating the market dis-tortion effects from the domestic and trade policies. The firstline aggregates the domestic surplus by subtracting the areasunder the domestic supply curves from the areas under the do-mestic demand. The second line summarizes the net welfarefrom trading activities which is the areas under the excess de-mand curves minus the areas under the excess supply curves.The third line represents custom revenues under the regular tar-iffs and TRQs. The fourth line is the producers’ revenues fromgovernment price supports and set-aside programs.

Equation (9) is the supply–demand balance constraint foreach commodity under each state of nature. The first threeterms give total demand which include domestic demand (Qis),export demand (QX

is), and government purchases (QGi ). The

last two terms in (9) represent the supply side which includedomestic production (

∑k Yiks ∗ (1 + CCYIELDi) ∗ Xik) and

imports (QMis + T RQis). Note that the domestic production

of each crop will be influenced by the climate-induced yield

changes through the productivity estimates of CCYIELDi asshown in Table 3. Equation (10) shows that agricultural cropsand set-aside acreage are competing with each other and the to-tal endowment Lk in each region will be influenced by sea levelintrusion of SLR in magnitude. Equation (11) is the constrainton farm labor.

5.4. Empirical specification of TASM

The TASM depicts the production of 75 primary commodi-ties which include 60 traditional crops, 5 floral crops, 7 livestockspecies, and 3 types of forests, and 27 secondary commodities.They are produced in 15 subregions aggregating into four ma-jor production and processing regions. The total value of theseprimary commodities accounts for more than 85% of Taiwan’stotal agricultural product value. Sub-regional production activ-ities are specified in the model for each commodity. Crop andlivestock mixes activities and constraints are also specified atthe sub-regional level, but the input markets for cropland, pas-ture land, forest land, and farm labor are specified at the regionallevel.

The data sources largely come from published governmentstatistics and research reports, which include the Taiwan Agri-cultural Yearbook (Council of Agriculture), Production Costand Income of Farm Products (Agricultural and Food Agency),Taiwan Agricultural Prices Monthly (Council of Agriculture),Taiwan Agricultural Prices and Costs Monthly (Council ofAgriculture), and Taiwan Agricultural Trade Statistics (Coun-cil of Agriculture). Demand elasticities of agricultural productscome from Chang (2002). The empirical model is validatedbased on the comparison between the equilibrium solution andactual statistics. The year 2007 was chosen as the baseline toconstruct the database, and both the total production and pricesare used as the basis for validation.

6. Scenario design

The simulations of climate change effects incorporate bothsea-level-induced land losses and climate-induced crop yieldchanges. First, the percentage losses in cropland estimated byDasgupta et al. (2009) are used to reduce the arable land endow-ment in Eq. (10) under 1–5 meters SLR. Second, estimates oncrop yield changes are based on Chang (2002) and a family ofIPCC Special Report on Emissions Scenarios (SRESs) as shownin Table 3. The percentage yield changes are incorporated intothe model through CCYIELD in Eq. (9).

The definition for each scenario is shown as follows:

• BASE: This scenario ignores the SLR and crop yield effects.• Sea Level Rise: This family of scenarios subjects the model

to lost acreage due to SLR ranging from 1 to 5 meters.• Crop Yield Effects: Estimates of the effects of climate

change on crop yields from HADGCM and two SRES sce-narios (A2 and B2) are included. Comparing A2 and B2, A2


assumes higher population projections and thus is associatedwith higher food demand than B2.

• Sea Level Rise and Crop Yield Effects Simultaneously:Both the effects of SLR and climate change on rice productionare simultaneously considered.

• Adaptation Strategies: Two adaptation strategies are sim-ulated, one on crop yield technology improvement and theother on trade liberalization in the form of tariff reductions.In particular we examine the ability of such actions to off-set the climate change effects although we do not incorporatethe costs of such activities because there are many limitationson estimating these costs (see discussion in McCarl (2007),section 1.3). Note the model can also adjust the crop mix.

7. Results on effects of climate change

The economic impacts of SLR on total welfare and produc-tion values are shown in the top rows of Table 4. The cropyield effects are shown in the bottom rows of Table 4, followedby the combined effects of SLR and crop yield effects. Boththe agricultural production values and welfare decrease as theSLRs. The annual total social welfare losses range from NT$0.85 billion to NT$ 4.12 billion as SLRs from 1 to 5 meters.The corresponding losses on production values are estimatedto be 0.22% up to 1.30% in total. The warming-induced cropyield effects also affect production values and welfare nega-tively. Welfare losses under B2 scenario are much larger thanthose under A2 scenario. The combination effects of sea levelrice and crop yield effects range from NT$ 2.66 billion up toNT$ 5.77 billion under the HADGCM-B2 crop yield scenario.

Details on the effects on prices and production are shown inTable 5. Due to the double protection from price support andimport policies, rice production and prices are only slightly af-fected by SLR and crop yield effects. The most significantly af-fected crop groups are pulse (e.g., peanuts, soybeans), specialtycrops (e.g., sugarcane), and fruits (e.g., waxapples, mangos)because they are cultivated in the coastal regions. As the SLRbecomes more extreme in the form of salt water intrusion onmost coastal arable land, crops grown on the higher elevatedareas (e.g., tea, betel nuts) will also be affected due to migra-tion of crops into the inland areas. Substantial supply shortageand price hike will be inevitable if there are no proper copingmeasures.

8. Adapting to climate change

The climate change damages may require adaptations. Welook at three adaptation approaches, one on farmer’s crop-mixchoices and the other two on government policies. In particu-lar there might be investments to develop better heat-resistant,drought- and salinity-tolerant crop varieties, or cropping prac-tices with new flood control infrastructure. Also, Taiwan as anet food importing country could rely on international tradeby reducing trade barriers. Thus we examine the amount ofcrop yield improvement and tariff reduction needed to mitigateclimate change damages.

The model as solved allows adaptation in the form of cropmix adjustments as crop yield effects and SLRs occur. To ex-amine the value and extent of such adaptations, we compare themodel solutions where crop mix can adjust with those where

Table 4The impacts of SLR and crop yield changes on production and welfare (Unit: Million NT$,%)

2007 Baseline Sea level rise

One meter Two meters Three meters Four meters Five meters

Production value 285,560 −635 −1,249 −1,778 −2,826 −3,720(−0.22) (−0.44) (−0.62) (−0.99) (−1.30)

Producers’ surplus 150,740 809 730 310 −170 1,123(0.54) (0.48) (0.21) (−0.11) (0.74)

Consumers’ surplus 2153,773 −1,664 −2,215 −2,555 −3,029 −5,245(−0.08) (−0.10) (−0.12) (−0.14) (−0.24)

Total welfare 2304,513 −855 −1,485 −2,245 −3,199 −4,123(−0.10) (−0.14) (−0.18)

(−0.04) (−0.06)

2007 Baseline Crop yield effects Combining sea level and crop yield effects

A2 B2 One meter Two meters Three meters Four meters Five meters& B2 & B2 & B2 & B2 & B2

Production value 285,560 −342 −4,617 −2,378 −2,511 −3,080 −4,055 −4,941(−0.12) (−1.62) (−0.83) (−0.88) (−1.08) (−1.42) (−1.73)

Producers’ surplus 150,740 −520 −4,312 −5,062 −3,834 −2,942 −2,982 −3,621(−0.34) (−2.86) (−3.36) (−2.54) (−1.95) (−1.98) (−2.40)

Consumers’ surplus 2153,773 −371 2,464 2,395 588 −1,029 −1,914 −2,151(−0.02) (0.11) (0.11) (0.03) (−0.05) (−0.09) (−0.10)

Total welfare 2304,513 −891 −1,848 −2,666 −3,246 −3,971 −4,896 −5,772(−0.04) (−0.08) (−0.12) (−0.14) (−0.17) (−0.21) (−0.25)

Note: The numbers in parenthesis represent the percentage change with respect to 2007 Baseline.


Table 5Economic impacts of SLR and HADGCM crop yield effects on prices and production for selected crop groups (Unit: NT$/ton, ton,%)

Crop groups 2007 Baseline Sea level rise Crop yield effects

————— Percentage change from base case —————

Level One meter Two meters Three meters Four meters Five meters A2 B2

Rice Price 23.90 −2.14 −0.89 −0.95 −0.84 −0.83 0.67 0.65Production 1,206,676 0.10 0.09 0.07 0.05 0.03 0.06 −1.22

Cereal Price 13.04 −6.06 −7.40 −6.54 −1.19 1.85 −7.34 −0.59Production 114,449 −0.24 −0.48 −0.69 −1.51 −4.02 1.17 2.12

Pulses Price 41.45 6.13 4.17 3.56 −1.81 −2.28 1.47 −7.40Production 57,299 −0.67 −0.79 −0.57 −0.70 −0.85 −0.20 −1.64

Root Price 11.05 −3.23 −1.03 0.02 8.56 12.67 9.77 10.61Production 255,905 0.09 0.04 −0.08 −0.30 −0.30 −0.17 0.22

Special Price 6.22 7.10 7.37 9.90 9.91 14.30 3.72 −1.67Production 826,286 −2.77 −4.16 −7.09 −8.17 −10.18 0.96 0.75

Vegetables Price 20.28 0.70 1.06 1.65 1.84 2.11 −0.04 −12.80Production 1,866,535 −0.41 −0.66 −0.98 −1.46 −1.83 0.15 0.51

Fruits Price 23.79 2.24 2.11 2.20 3.57 6.29 −1.24 4.33Production 2,377,538 −0.58 −1.02 −1.42 −2.44 −3.14 −0.87 −3.54

Note: The numbers under Baseline are the absolute levels while the numbers under Sea Level Rise and Crop Yield Effects represent the percentage change fromBaseline.

Table 6Welfare effects of possible adaptation strategies (Unit: NT$ Million)

Combinations of SLR and crop yield effects

One Two Three Four Fivemeter meters meters meters meters& B2 & B2 & B2 & B2 & B2

I. Without adaptation strategy−2,666 −3,246 −3,971 −4,896 −5,772

II. Crop yield improvement in terms of % increase in all crop yields1% increase −1,235 −1,822 −2,537 −3,455 −4,3342% increase 199 −384 −1,095 −2,000 −2,8673% increase 1,546 990 295 −607 −1,4614% increase 2,847 2,310 1,634 746 −1115% increase 4,140 3,612 2,961 2,089 1,265III. Trade liberalization in form of import tariff reduction5% Cut −535 −1,135 −1,863 −2,792 −3,67610% Cut 518 −71 −791 −1,718 −2,59715% Cut 1,860 1,269 549 −372 −1,24720% Cut 2,873 2,285 1,568 642 −23325% Cut 3,846 3,259 2,542 1,620 745

crop mix cannot adjust. Meanwhile, the total area of crop mixis proportionally reduced by the loss of land implied by thesea level intrusion scenarios. In the case of a 5 meter SLR,this comparison shows that crop mix adjustments can onlyreduce the welfare losses by 0.57% which is approximatelyNT$ 13 billion. The crop mix adjustment is primarily in theform of substituting rice with root crops and some specialtycrops.

Next, we examine policy-based adjustments to address thelevel of needed offset from productivity enhancement. To dothis, we simulate the consequences of independently increasingall crop yields (productivity) by 1% to 5%. The results on wel-fare are listed in Table 6. We find that this adaptation strategy

could potentially offset the welfare losses. If there is no adapta-tion, the agricultural damages due to climate change range fromNT$ 3 to 6 billion. However, if all crop yields are improved by2%, there is a gain of 0.2 billion under the 1 meter rise scenario.Higher yield gains are required to offset the larger SLR levels,with crop yields increases of 3%, 4%, and 5% to offset theimplications of 3, 4, and 5 meter SLRs, respectively. Finally,we examine trade liberalization through trade barriers reduc-tion finding that import tariff reductions of 10%, 15%, 20%,and 25% can also mitigate the negative SLR impacts when cropyield effects are factored in.

9. Concluding remarks

In this article we examine what SLR would do to Tai-wan’s agriculture while considering crop-yield induced effects.The methodology involves first developing estimates of the ef-fects of climate change on crop yields and adopting data fromDasgupta et al. on how SLR would affect cropland acreage.Second, these effects are incorporated into a Taiwanese agri-cultural sector model to evaluate the economic impacts andwelfare implications. Finally, the offsetting effects of possibleadaptation strategies adopted by farmers and government areexamined from which several findings arise.

First the Taiwanese agricultural sector is sensitive to the sim-ulated climate change effect. The specialty crops and fruits arethe most sensitive crops to SLR while root crops and fruits arethe major affected crops by climate-related crop yield changes.The combined effects of SLR and crop yield changes reducethe production values by NT$ 2 to 5 billions and total socialwelfare by NT$ 3 to 6 billions under the B2 HADGCM cropyield predictions.


In the face of this adaptations may well be in order. Thecrop mix adjustment embedded in our model is estimated toreduce the damage of SLR and crop yield effects in a substantialmanner but cannot offset the whole damages. Therefore, otheradaptation strategies are also examined in the form of yieldenhancing technological progress and lowering import tariffs.The crop yield enhancement involves adapting varieties andfarm practices to enhance heat, salinity, and drought tolerance.Policy makers may wish to consider such an option given thelikely inevitability of a substantial degree of climate change(see arguments in Rose and McCarl 2008) as it will take timeto develop and require R&D investment (the costs of which wehave ignored; see McCarl 2007 for discussion).

On the other hand, we also find trade liberalization can off-set the potential damages from climate change although thisrequires an increase in a country’s dependency on foreign foodsupplies. Similar results can be found in many Asian countrieswhere suitable land resources are very limited. However, thereis a wide range of uncertainties regarding the direct and indi-rect impact of climate change on the global food productions.Thus, funding for adaptations should be targeted in promotingcomplementarities between import and domestic production tooffset the negative impact of climate change. Finally, while weonly study Taiwan, other countries such as Japan, Vietnam,Bangladesh, Myanmar, Egypt, and countries in South Americaand West Africa are also vulnerable to SLR and may need tobegin considering adaptive actions.

Appendix: Percentage of agricultural land lost by SLRscenario Unit:%

One Two Three Four Fivemeter meter meter meter meter

Bangladesh 0.65 1.51 3.33 6.41 10.03Brazil 0.09 0.18 0.32 0.49 0.65Central America 0.24 0.48 0.89 1.36 1.90China 0.62 1.03 1.54 2.14 2.74East Africa 0.03 0.07 0.13 0.18 0.23Egypt 9.28 12.02 14.89 17.70 20.84India 0.10 0.18 0.32 0.54 0.78Indonesia 0.72 1.35 2.37 3.81 5.60Japan 0.83 2.26 4.48 7.66 11.85Korea DPR 0.16 0.32 0.69 1.32 2.16Korea Rep 0.49 0.76 1.26 1.90 2.73Myanmar 1.48 2.43 4.31 7.53 11.13Other Asia 0.17 0.36 0.82 1.51 2.43Pakistan 0.08 0.22 0.45 0.78 1.16Philippines 0.37 0.59 0.97 1.46 2.02South America 3.19 6.66 10.56 14.03 17.66Taiwan 1.05 1.81 2.72 3.85 4.91Thailand 0.22 0.63 1.53 2.85 4.28USA 5.00 10.00 14.00 17.00 19.00Vietnam 7.14 12.26 17.15 20.85 23.43West Africa 2.19 4.16 7.03 9.71 11.89

References

Adams, R.M., Bryant, K.J., McCarl, B.A., 1995. Value of improved long rangeweather information. Contemporary. Econ. Pol. 13, 10–19.

Adams, R.M., Hurd, B., Lenhart, S., Leary, N., 1998. The effects of globalwarming on agriculture: an interpretative review. J. Clim. Res. 11, 19–30.

Adams, R.M., Rosenzweig, C., Ritchie, J., Peart, R., Glyer, J.D., McCarl, B.A.,Curry, B., Jones, J., 1990. Global climate change and U.S. agriculture.Nature 345(May), 219–224.

Agricultural and Food Agency, various issues, Taiwan agriculturalproduction cost survey report. Available at: http://agrcost.afa.gov.tw:9000/agrcost/yearreport/index-report.html

Baumes, H., 1978. A partial equilibrium sector model of us agriculture open totrade: a domestic agricultural and agricultural trade policy analysis. Ph.D.dissertation, Purdue University, West Lafayette, IN.

Bindoff, N.L., Wellebrand, J., Artale, V., Cazenave, A., Gregory, J.M., Gulev,S., Hanawa, K., Le Quere, C., Levitus, S., Nojiri, U., Shum, C.K., Talley,L.D., Unnikrishnan, A.S., 2007. Observations: oceanic climate change andsea level. Climate change 2007: the physical science basis. Contribution ofworking group I to the fourth assessment report of the IntergovernmentalPanel on Climate Change, Cambridge University Press.

Bosello, F., Roson, R., Tol, R.S.T., 2007. Economy-wide estimates of the im-plications of climate change: sea level rise. Env. Res. Econ. 37, 549–571.

Chang C.C., 2002. The potential impacts of climate change on Taiwan’s agri-culture. Agr. Econ. 27, 51–64.

Chang, C.C., McCarl, B.A., Mjelde, J., Richardson, J., 1992. Sectoral implica-tions of farm program modification. Am. J. Agr. Econ. 74, 38–49.

Chen, C.C., Chang, C.C., 2005. The impact of weather on crop yield distributionin Taiwan: some new evidence from panel data models and implications forcrop insurance. J. Agr. Econ. 33, 503–511.

Chen, C.C., McCarl, B.A., 2000. The value of ENSO information: considerationof uncertainty and trade. J. Agr. Res. Econ. 25, 368–385.

Church, J., White, N., 2006. A 20th century acceleration in global sea-levelrise. Geophy. Res. Lett. 33, L01602.

Council of Agriculture, various issues, Production cost and incomeof farm products Statistics. Available at: http://www.coa.gov.tw/view.php?catid=207

Council of Agriculture, various issues, Taiwan agricultural prices monthly.Available at: http://www.coa.gov.tw/view.php?catid=7820.

Council of Agriculture, various issues, Taiwan agricultural trade statistics.Available at: http://agrapp.coa.gov.tw/TS2/TS2Jsp/Index.jsp

Council of Agriculture, various issues, Taiwan agricultural yearbook, Availableat: http://www.coa.gov.tw/view.php?catid=207.

Darwin, R.F., Tol, R.S.J., 2001. Estimates of the economic effects of sea levelrise. Env. Res. Econ. 19, 113–129.

Darwin, R., Tsigas, M., Lewandrowski, J., Raneses, A., 1995. world agricul-ture and climate change: economic adaptation. Report No. AER-709, USDepartment of Agriculture, Economic Research Service, Washington, DC.

Dasgupta, S., Laplante, B., Meisner, C., Wheeler, D., Yan, J., 2009. The impactof sea level rise on developing countries: a comparative analysis. Clim.Change 93, 9–388.

Douglas, B.C., 1997. Global sea rise: a redetermination. Surv. Geophys. 18,279–292.

Easterling III, W.E., Crosson, P.R., Rosenberg, N.J., McKenney, M.S., Katz,L.A., Lemon, K.M., 1993. Agricultural impacts of and response to cli-mate change in the Missouri–Iowa–Nebraska–Kansas (MINK) region. Clim.Change 24, 23–61.

Intergovernmental Panel on Climate Change, 2007. Climate Change 2007:Impacts, Adaptation and Vulnerability. Cambridge University Press, Cam-bridge, UK.

Kaiser, H.M., Riha, S.J., Wilks, D.S., Rossiter, D.G., Sampath, R., 1993. Afarm-level analysis of economic and agronomic impacts of gradual globalwarming. Am. J. Agr. Econ. 75, 387–398.


Kane, S., Reilly, J., Tobey, J., 1992. An empirical study of the economic effectsof climate change on world agriculture. Clim. Change 21, 17–35.

Lambert, D.K., McCarl, B.A., He, Q., Kaylen, M.S., Rosenthal, W., Chang,C.C., Nayda, W.I., 1995. Uncertain yields in sectoral welfare analysis: anapplication to global warming. J. Agr. App. Econ. 27, 423–435.

Lewandrowski, J., Schimmelpfennig, D., 1999. Economic implications of cli-mate change for us agriculture: assessing recent evidence. Land. Econ. 75,39–57.

McCarl, B.A., 2006. US Agriculture in the climate change squeeze:Part 1: Sectoral sensitivity and vulnerability. National Environmen-tal Trust. (Available at: http://agecon2.tamu.edu/people/faculty/mccarl-bruce/papers/1303Agriculture in the climate change squeez1.doc).

McCarl, B.A., 2007. Adaptation options for agriculture, forestry andfisheries: a report to the UNFCCC secretariat financial and techni-cal support division. (Available at: http://unfccc.int/files/cooperation_and_support/financial_mechanism/application/pdf/mccarl.pdf).

McCarl, B.A., Spreen, T.H., 1980. Price endogenous mathematical program-ming as a tool for sector analysis. Am. J. Agr. Econ. 62, 87–102.

Meier, M.F., Dyurgerov, M.B., Rick, U.K., O’Neel, S., Pfeffer, W.T., Anderson,R.S., Anderson, S.P., Glazovsky, A.F., 2007. Glaciers dominate eustatic sea-level rise in the 21st century. Science 317, 1064–1067.

Rahmstorf, S., 2007. A semi-empirical approach to projecting future sea levelrise. Science 315, 368–370.

Raper, S.C.B., Braithwaite, R.J., 2006. Low sea level rise projections frommountain glaciers and icecaps under global warming. Nature 439, 311–313.

Reilly, J., 1995. Climate change and global agriculture: recent findings andissues. Am. J. Agr. Econ. 77, 727–733.

Reilly, J.M., Hrubovcak, J., Graham, J., Abler, D.G., Darwin, R., Hollinger,S.E., Izaurralde, R.C., Jagtap, S., Jones, J.W., Kimble, J., McCarl, B.A.,Mearns, L.O., Ojima, D.S., Paul, E.A., Paustian, K., Riha, S.J., Rosenberg,N.J., Rosenzweig, C., Tubiello, F., 2002a. Changing Climate and ChangingAgriculture. Cambridge University Press, New York, NY.

Reilly, J.M., Tubiello, F., McCarl, B.A., Abler, D.G., Darwin, R., Fuglie, K.,Hollinger, S.E., Izaurralde, R.C., Jagtap, S., Jones, J.W., Mearns, L.O.,Ojima, D.S., Paul, E.A., Paustian, K., Riha, S.J., Rosenberg, N.J., Rosen-zweig, C., 2002b. US agriculture and climate change: New results. Clim.Change 57, 43–69.

Rose, S.K., McCarl, B.A., 2008. Greenhouse gas emissions, stabilization andthe inevitability of adaptation: challenges for agriculture. Choices 23, 15–18.

Rosenzweig, C., Parry, M.L., 1994. Potential impacts of climate change onworld food supply. Nature 13, 133–138.

Rosenzweig, C., Tubiello, F.N., Goldberg, R., Mills, E., Bloomfield, J., 2002.Increased crop damage in the US from excess precipitation under climatechange. Global Env. Change 12, 197–202.

Samuelson, P.A., 1952. Spatial price equilibrium and linear programming. Am.Econ. Rev. 42, 283–303.

Takayama, T., Judge, G.G., 1964. Equilibrium among spatially separated mar-kets: a reformulation. Econometrica 32, 510–524.

United States Climate Change Science Program (USCCSP), 2008. The Effectsof Climate Change on Agriculture, Land Resources, Water Resources, andBiodiversity in the United States, US Dept of Agriculture, Washington, DC.

Documents

Evaluating the economic impacts of crop yield change and sea level rise induced by climate change on Taiwan's agricultural sector