Economic effects of mitigation and adaptation technique for global warming in
agricultural sector: Evaluation by computable general equilibrium model
Yoji Kunimitsu (National Institute for Rural Engineering of NARO)

1. Introduction
Agriculture is highly dependent on climate conditions, such as temperature, solar radiation, and precipitation, so futureclimate change will affect food production and may make food supplies vulnerable. Stern (2006) predicted that agriculture incountries at higher latitudes would likely benefit from a moderate level of warming (2–3 ° C), but that even a small amount ofclimate change in tropical regions would cause yields to decline. Japan is located at a relatively high latitude, so it is possiblethat Japanese rice production may benefit from future climate change. However, an increase in yield does not necessarilymean an economic benefit. To measure the economic effects of climate change, we need to evaluate changes in price andquantity with considering market conditions. In this sense, a comprehensive evaluation of the impact of climate change on therice sector in Japan is important, both for making policy decisions and from an academic interest point of view (Watanabe andKume, 2009).
Changes in crop yields can be measured by field experiments and by using the objective results of the crop-growthmodel based on biology. However, evaluating changes in the quantity and price of agricultural products requires an economic
model. Partial equilibrium models can measure such changes, but they assume that agricultural markets do not affect the restof the economy (i.e., they are treated as exogenous). The computable general equilibrium (CGE) model can depict
inter-market relations and trade flows for the economy as a whole, including the circular flow of income and expenditure.
Therefore, they are better suited to analyzing global effects on agricultural markets, as is the case with climate change (Palatnik
and Roson, 2011). Many previous studies have used the CGE model to analyze the effects of climate change in Europe, theUSA, and developing countries, as shown in the next section. However, few CGE studies have evaluated the impact ofclimate change on the Japanese rice sector.
The present study uses the CGE model to comprehensively evaluate the influence of future climate change on Japan’s
rice sector and regional economies. The features of this study are as follows: (i) the recursive-dynamic regional CGE model isused to capture regional differences in climate change; (ii) direct effects of climate conditions on rice productivity are estimatedusing the crop-growth model and the hydrological model, in addition to the global climate model (GCM), Model forInterdisciplinary Research on Climate (MIROC); (iii) farmland is introduced into the model to consider restrictions on natural
resources; and (iv) the rice sector is separated from the agricultural sector, which is usually one aggregated sector in theJapanese inter-regional input-output table, to enable us to specifically study the effects on the rice sector.

2. Method

2.1 Structure of the recursive-dynamic regional CGE model
The model used here is the recursive-dynamic CGE model, with multiple regions. The structure of our model isbased on the work of Bann (2007), which uses GAMS (GAMS Development Corporation) and MPSGE (a modeling tool
using the mixed complementary problem), as developed by Rutherford (1999). The major modification points of this model
are as follows.

The cost functions derived from the production functions are defined as nested-type CES (constant elasticity of
substitution) forms. The elasticity of substitution of farmland to other input factors, which was not used in Bann (2007), is
assumed to be 0.1 for agriculture. Egaitsu (1985) concluded that the substitutability of farmland for other input factors waslow, but the substitutability between capital and labor was high, according to empirical evidence on Japanese rice production
from several studies. Based on these findings, we assumed that farmland is a semi-fixed input for agricultural production andcannot really be substituted by other factors.
TFPr,t and TFP0 in the production functions refer to the total factor productivity in year t and the substantial level (TFP0= 1 in this study), respectively. With respect to climate change, TFP varies per year, and this factor is defined in previous
studies as follows (Kunimitsu et al., 2013):


TFPr ,t   0  MAr ,t 1  KK t 2  CHI r ,t 3  CQI r ,tDRr  4

 CFI r ,t 5

(1)Here, MA is the management area per farmer, representing economies of scale, and KK is knowledge capital stocks
accumulated through research and development (R&D) investments. CHI, CQI, and CFI are the crop-yield index,
crop-quality index, and flood index, respectively. The β’s represent the coefficients estimated from the panel data regression
analysis, with β0 = –2.7014, β1 = 0.3285, β2 = 0.0590, β3 = 0.1824, β4 = 0.0863, and β5 = –0.0277. DR is a dummy variable,
taking the value 1 for Hokkaido, and 0 otherwise. As explained in Kunimitsu et al. (2013), CHI, CQI, and CFI are also
defined by the crop-growth model, crop-quality model, and hydrological model with using climate conditions, such astemperature, solar radiation, and precipitation.
Consumption is defined by the CES function with a substitution elasticity of 0.5. The elasticity values of substitution
in the consumption, import, and export functions are set to be the same as those used by Bann (2007), which were based on the
GTAP database. The government consumption and government investment are Leontief type fixed share function.
To form the recursive dynamic path, the capital stock equation is defined by annual investment (I) and disposable rate
(δ= 0.04), as follows.

K r ,t  (1 δ) K r ,t 1  I r ,t(2)

In this model, K shows capital supply and is defined for every year from I, which is endogenously defined by the CGE
model.

2.2 Data and simulation method
To calibrate the parameters of the model, the social accounting matrix (SAM) was estimated on the basis of Japan’s 2005
inter-regional input-output table. To analyze rice production more precisely, the rice sector was separated from the aggregatedagriculture, forestry, and fishery sectors in the IO table, based on regional tables (404 × 350 sectors). Then, the sectors werereassembled into 14 sectors: rice; other agriculture, forestry, and fishery; mining and fuel; food processing; chemical products;
general machinery; electric equipment and machinery; other manufacturing; construction; electricity and gas; wholesale andretail sales; financial services; and other services. Regions were also reassembled into eight regions: Hokkaido; Tohoku;
Kanto, including Niigata Prefecture; Chubu; Kinki; Chugoku; Shikoku; and Kyushu, including Okinawa.
The factor input value of farmland, which was not shown in the Japanese I/O Table, was estimated using farmland
cultivation areas (Farmland statistics, Ministry of Agriculture, Forestry, and Fishery, and every year) and multiplying the areas
by farmland rents. Then, the farmland factor input value was subtracted from the operation surplus in the original IO table.
The value of capital input was then composed of the rest of operation surplus and the depreciation value of capital.

To simulate the macroeconomic impacts of climate change, we considered the following cases.
CASE 0: This case represents a situation of business as usual (BAU), and is used as a base line. In this case, farmland supply
and labor supply in each region were fixed at the present levels shown in the SAM data. The technological growth rate of theJapanese economy was assumed to be 0 so as to show only the effects of climate change. The TFP of rice production was
also set to 1, showing no progress in technology and no change in climate conditions.
CASE 1: This case represents future climate change that only affects rice production. The exogenous variables other than
TFP were set to the same values as in CASE 0. These TFPs include the influence of both socio-economic factors (MA andKK) and climate factors (CHI, CQI, and CFI). To simulate the pure effect of the climate factors, the TFP in each region was
divided by TFP′ = f (MA, KK), which shows TFP changes resulting only from socio-economic factors, with the same estimatedcoefficients as Eq. (1). Changes in climate factors were predicted using the crop-growth model, crop-quality model, andhydrological model, along with the projection results of MIROC, high-resolution version 3.0 (K-1 Model Developers, 2004).
The greenhouse gas emission scenario was A1B which shows balanced growth with rapid economic growth, low population
growth, and the rapid introduction of more efficient technology in the Special Report on Emission Scenario (SRES)(Nakicenovic and Swart, 2000).

3. Results
To ensure the stability of the simulation results, a sensitivity analysis was conducted by changing the substitution elasticitiesof demand and inter-regional material demand as intermediate inputs. The degree of impact changed, but the directions of thechanges were the same for all variables. Therefore, the results presented below are reasonably stable and common, as long as
there is no change to the economic structure.

3.1 Rice production
Figure 1 shows chronological changes in rice production, estimated using the CGE model with TFP changes. Although
the exogenous variables were set as the status quo, rice production in CASE 0 increased as a result of an increase in capitalstocks, which were endogenously accumulated by annual investment. Hence, the difference between CASE 1 and CASE 0
shows only the effects of changes in climate factors.
In accordance with the TFP changes shown in Figure 1, climate change increased rice production in the north and easternregions, such as Hokkaido, Tohoku, and Kanto (including Niigata Prefecture). However, rice production decreased in thewestern regions, such as Kinki, Chugoku, Shikoku, and Kyushu. Annual production fluctuated under climate change, but the
average growth rates in Tohoku and Kanto were higher than other regions, so the scale of vertical axis in these regions was
double that of the other regions. In these two regions, rice production amount was relatively large, so there were some
capacities for the economies to allocate production resources to other sectors. In contrast, the growth rates of rice productionin Kinki and Shikoku were low, because rice production in these two regions was small in comparison to other agricultural
sectors and other industries.
Even in the north and eastern regions, production levels fell below the CASE 0 level in some years, with the exception of
Hokkaido. Rice production in Hokkaido benefitted in all years, even under bad climate conditions. The western regions
experienced worse production than CASE 0 in many years, but the difference between CASE 1 and CASE 0 fluctuated overtime. This degree of fluctuation degree increased after 2050. Until then, climate conditions tended to increase the crop yield,
but decrease crop quality. However, from the 2050s onwards, when temperatures were frequently beyond the threshold level,

Hokkaido

120
CASE 0CASE 1

110
100
billion yen
580
570
560
550
540
530
520

Tohokubillion yen
640
630
620
610
600
590
580

Kanto

billion yen
200

Chubu

CASE 0CASE 1

CASE 0CASE 1190
CASE 0CASE 1180
170
billion yen
180

Kinki

170
CASE 0CASE 1

160
150
billion yen
150

Chugoku

140
CASE 0CASE 1130
120billion yen
80

Shikoku

70
CASE 0CASE 160
50
billion yen
240

Kyushu230
CASE 0CASE 1220
210
Whole country

CASE 0CASE 1

2005
2010
2015
2020
2025
2030
2035
2040
2045
2050
2055
2060
2065
2070
2075
2080
2085
2090
2095
2100

billion yen
2150
2100
2050
2000
1950

Fig.1 Quantities of paddy rice production

ratio
1.2
1.15
1.1
1.05
1
0.95

Hokkaido

ratio
1.2
1.15
1.1
1.05
1
0.95

Tohoku

ratio
1.2
1.15
1.1
1.05
1
0.95

Kanto

ratio
1.2
1.15
1.1
1.05
1
0.95

Chubu

ratio
1.2
1.15
1.1
1.05
1
0.95

Kinki

ratio
1.2
1.15
1.1
1.05
1
0.95

Chugoku

ratio
1.2
1.15
1.1
1.05
1
0.95

Shikoku

ratio
1.2
1.15
1.1
1.05
1
0.95

Kyushu

ratio
1.2
1.15
1.1
1.05
1
0.95

Whole country

Case 0Case 1Case 0Case 1Case 0Case 1Case 0Case 1Case 0Case 1Case 0Case 1Case 0Case 1Case 0Case 1Case 0Case 1

2005
2010
2015
2020
2025
2030
2035
2040
2045
2050
2055
2060
2065
2070
2075
2080
2085
2090
2095
2100

billion yen
130

Fig.2 Changes in rice priceclimate conditions decreased both crop yield and crop quality, showing agglomeration effects. Tohoku, Kanto, and Kyushu

showed wider fluctuations in production, because their rice production amounts were larger than other regions. In the westernregions, the decreases in crop yield and crop quality became serious during the latter period of the simulation, largely because ofnegative agglomeration effects of temperature to rice TFP via rice yield and quality.
Figure 2 shows price changes in the rice sector resulting only from climate change. In contrast to production quantity, theprice of rice decreased in the northern and eastern regions, but increased in the western region. Since rice consumption does
not really increase, even after a decrease in price, the price dropped in those regions where production increased, but rose in
those regions where production decreased. This is because price elasticity of rice demand is low, which is common in food,and an imbalance in supply and demand is reflected in a sharp change in price. Such situations may be similar in otheragricultural sectors, but no climate simulations were conducted for any other sectors.

3.2 Prices of input factors and farmers’ income
Table 1 shows sum of the rice farmers’ income (deflated by consumer price index), as affected by rental rate, wage, andcapital service price. The same trends were evident in both agricultural farmers’ income including other agricultural sectors,
and rice farmers’ income excluding capital depreciation value. However, these results were omitted here because of a limit inspace. Farmers’ income decreased in the northern and eastern regions, where rice production increased after the climatechange, but farmers’ income in the western regions (decreased rice production) increased. This happened as a result of rice
price changes, which changed in the opposite direction to the changes in rice production. In other words, the degree of pricechanges was larger than the degree of production changes. Farmers’ income across the whole country decreased as a result of
the climate change. This negative impact on overall income also became more serious in the latter period of the simulation.
Therefore, overall, climate change does not benefit farmers.
Table 1 Income of farmers
billion yen
Regions
Hokkaido
Tohoku
Kanto
Chubu
Kinki
Chugoku
Shikoku
KyushuWhole

2005-2050
CASE 0CASE 164,552
63,82979,082
77,714
255,728
255,156
72,831
72,79892,88992,98340,01940,111
26,73826,82187,012
87,131
718,851
716,544

Diff.
-723-1,368
-571
-33
94
92
83
119
-2,3072051-2100
CASE 0CASE 1
16,175
16,073
19,68919,44765,326
65,213
18,492
18,481
24,04624,056
10,191
10,2066,816
6,825
22,03322,019182,768
182,320

Diff.
-102
-242
-114
-11
11
14
8
-14
-449

Table 2. Sum of GRP from 2005 to 2100 by regions
billion yen
Regions
Hokkaido
Tohoku
Kanto
Chubu
Kinki
Chugoku
Shikoku
KyushuWhole

CASE 0954,220
1,552,921
9,700,488
2,839,848
3,702,870
1,375,809
687,691
2,529,625
23,343,472

2005-2050
CASE 1
954,255
1,552,768
9,701,415
2,839,870
3,703,007
1,375,783
687,689
2,529,597
23,344,383

Diff.
35
-153
927
22
137
-26
-2
-28
911

CASE 01,055,123
1,742,009
10,971,719
3,217,558
4,090,211
1,538,474
760,886
2,835,303
26,211,2842051-2100
CASE 1
1,055,075
1,741,648
10,973,710
3,217,607
4,090,353
1,538,398
760,9022,835,196
26,212,888

Diff.
-48
-362
1,991
49
142
-76
16
-107
1,604

3.3 Gross regional production and social welfare
Table 2 shows the sum of gross regional production (GRP) during the simulation periods. In contrast to farmers’ incomechanges, changes in GRP were positive in Kanto and Chubu. Kanto and Chubu have sizeable manufacturing industries, so
the demand for manufacturing and service goods, which could increase by a shift of production factors from rice sector after theincrease in rice TFP, became concentrated in these regions. Overall, the total GRP for the country increased after the climatechange.

However, Chugoku and Shikoku experienced minor losses in GRP after the climate change, because the

manufacturing sector is relatively weak in these regions.

4. Discussion and conclusion
This study used the CGE model to comprehensively evaluate the influence of future climate change on Japan’s rice sector
and regional economies. We built the recursive-dynamic CGE model using multiple regions associated with the crop-growthmodel, crop-quality model, and hydrological model. This CGE model was used to simulate the future impact of climatechange. Based on our simulation results, there are several policy implications.
Simulation results demonstrate that future climate change will increase overall Japanese rice production nationwide, but thatthe price of rice will decrease. As a result, agricultural income will decrease, despite increased production in northern andeastern Japan, such as Hokkaido, Tohoku, and Kanto (including Niigata prefecture). Climate change will not benefit rice
farmers in these regions. However, the western region will benefit, despite the decrease in production, and the consumer
surplus in most regions will increase. This happens because rice demand is inelastic and an increase in production results in aserious decline in price, which more than offsets the effects of climate change on production. As such, the impacts of climatechange are complicated and differ by region, so a CGE model can provide useful information to consider policy
countermeasures.
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