Estimating Mitigation Costs of Greenhouse Gas from Agricultural. Production: A Korean Agricultural Sector Model Analysis

Transcription

1 Estimating Mitigation Costs of Greenhouse Gas from Agricultural Production: A Korean Agricultural Sector Model Analysis by Oh Sang KWON Hanbin LEE Hye-Jung KANG Kyung-Won PARK May 05 A paper prepared for the 5 th Congress of the East Asian Association of Environmental and Resource Economics (EAAERE), Academia Sinica, Taipei, Taiwan August 05.

2 Estimating Mitigation Costs of Greenhouse Gas from Agricultural Production: A Korean Agricultural Sector Model Analysis Oh Sang KWON ) Hanbin LEE ) Hye-Jung KANG 3) Kyung-Won PARK ) Abstract We construct a Korean agricultural sector model in order to analyze the impacts of adopting improved rice irrigation and livestock roughage to mitigate agricultural greenhouse gases. We extend the usual positive mathematical programming (PMP) model into the case where the market equilibrium conditions are incorporated. The model is an equilibrium model where only an imperfect substitution between domestic goods and imports are allowed. Our results show that adopting those two measures may almost attain the national targets of mitigation imposed on agricultural sector, although per unit reduction cost is quite high. The adoption may result in some substitution effects of resource use, and the shares of rice and beef in Korea agricultural production will decline slightly. Because of the substitution activities the mitigation costs may be saved substantially. Key Words: GHGs, Agricultural Sector, Mitigation Costs ) Professor, Department of Agricultural Economics and Rural Development, College of Agriculture and Life Sciences, Seoul National University, Gwanak-ro, Gwanak-gu, Seoul 5-9, Korea, Tel: , Fax: , kohsang@snu.ac.kr ) Graduate Student, Department of Agricultural Economics and Rural Development, College of Agriculture and Life Sciences, Seoul National University 3) Associate Professor, Department of Agricultural Economics, Chonnam National University, Gwangju, Korea This research is supported by the research projects of Korea Environment Institute: Development of an Integration Top-down and Bottom-up System for Greenhouse Gas Reduction in Korea funded by Korea Environmental Industry and Technology Institute

3 Introduction Although agriculture is one of the major sectors of emitting GHGs, the economic costs of reducing GHG emission from farming have not been analyzed rigorously in Asian countries. Most of the existing works applied the IPCC guidelines to reducing GHGs and estimated only the direct costs of applying the measures. This direct approach may over- or underestimate the real mitigation costs because it ignores producer's response to the adoption of mitigation measures. It is required to investigate how producers change their choice of inputs and production scales responding to the productivity change that will be caused by adopting mitigation measures. Our study investigates mitigation costs of agricultural greenhouse gases explicitly incorporating farmers' responses to the abatement measures. More specifically, our study focuses on reducing emission of methane from paddy rice farming by introducing multiple aerations, which are known as the most effective mitigation measure of rice farming, and reducing emission of methane from animal farming by improving feeding practices. We incorporate the productivity impacts of adopting those two measures, estimated by crop and animal scientists, and combine the information with an economic model. The economic model that our study constructs is a Korean agricultural sector model where production activities of 47 crops and 7 animal products of 9 regions are incorporated. Agricultural trade is also incorporated into the model. This is an optimization model that incorporates market equilibrium conditions. In the model, producers are allowed to adjust to the introduction of mitigating measures to maximize the net value of agricultural production. Our model resolves several shortcomings of optimization or programming models. One of the well-known shortcomings of the programming approach is that its solution is not 3

4 guaranteed to be equal to the observed production decision of the economy. We circumvent the problem using the positive mathematical programming (PMP) approach of Howitt (995) and Paris and Howitt (998), which has a built-in process of keeping the consistency between its model solutions with the observed data. Moreover, our study makes output prices endogenous by incorporating market equilibrium conditions into the programming model. Given Korean consumers attitudes of discriminating imported foods from domestic ones, we allow imperfect substitution between imported and domestic foods following the specification of Dixit (988). It would be our study s contribution combining the PMP approach with a nonlinear programming model where market equilibrium conditions are incorporated into the objective function. We derive the changes in crop and animal production in each region and derive regionspecific costs of GHGs mitigation. Our results show that per unit cost of mitigating agricultural CO emission is quite high. At the same time our result implies that the usual approach ignoring farmer's response to the mitigation measure may substantially overestimate mitigation costs. The Model One of the characteristics of food consumption is that domestic outputs are differentiated from imports. Differentiation is quite vivid in Korea where Country of Origin Labeling (COOL) is being implanted. Dixit (989) has introduced a way to incorporate an imperfect substitutability between domestic goods and imports. The method has been used by Lehtonen (00) for a Finnish agricultural (non PMP) sector model. Let and denote domestic rice consumption and imported rice consumption, respectively. Following Dixit (989), we employ a quasi-linear utility function, and the consumer s utility maximization problem is: 4

5 () max 0 + U(, ) = 0 + a + a (b + b + k ) 0,, s.t., p + p + 0 = m The inverse demand functions are derived as the following: () p = a b k p = a b k In the system those two goods are perfect substitutes if k = b = b, and are independent from each other if k=0. If k is greater than zero but is not equal to b or b, then there is an imperfect substitutability. Dixit (989) has shown that we can recover all 5 parameters a, a, b, b, and k using the information on equilibrium quantities, prices, demand elasticities, and substitution elasticity between and. A system of nonlinear equation is solved to recover the 5 parameters. The national welfare is represented by the benefits from consumption of and subtracted by the production and import costs: (3) where Z = WTP WTP * * * * * * + WTP c p = U(, ) p * * * * * (a b k )d = a b k 0 =, WTP * * * * * = (a b k)d = a b k 0 c = per unit production cost of, and p = import price of. Suppose there are J outputs, K inputs, and I regions. The st step of the PMP modeling is solving the following nonlinear programming model: J (4) max Z = ajj + a j j (bjj + b j j + k jj j ) {x j, j } I j= i= s.t., x = j for all j i= I c x p j j 5

6 J θ jk x w ik for all i and k (multiplier µ ik ) j= x 0 x ( + ε) for all i and j (multiplier λ ), 0 j ( + ) for all j (multiplier λ j ) j ε x 0 for all j j 0 for all j. x in (4) is the region i's activity level of j, and θ jk is the k th input requirement coefficient of the j th output. wi k is the region i s endowment of the k th resource. The 0 conditions x x ( + ε) and are imposed so that the solution production 0 j j activity levels and imports are not much different from the actual levels of those variables in the base case, and ε is a small positive number. Those two conditions are called calibration conditions while the usual resource use conditions θ J j= jk x w ik are called structural constraints. Calibration conditions represent all unobservable effects of policy interventions, technology restrictions and so on so that the solutions are equivalent to the actual outcomes that must be affected by those unobservable effects. The nd stage of PMP modeling is solving the following nonlinear programming model with quadratic cost functions: (5) max Z = {x, j } J j= a I j J j J i= j= j= + a d j j' x j x ' (b j J j J j= j= + b d jj' j j j + k j' j j j ) I s.t., x = j for all j i= 6

7 J j= θ jk x w ik for all k x 0 for all i and j j 0 for all j. The nd stage model, (5) does not include the calibration conditions of (4) any more. Instead, the model contains cost functions of domestic production and import transformed into quadratic forms from the linear forms used for (4). The idea is that if we can find the parameter matrices of { d j' } and { jj' } d that make the optimizing conditions of (4) and (5) are identical, then the model (5) recovers the observed choices at BAU, 0 x and 0 j even without the calibration conditions imposed on model (4). The FOCs of (4) and (5) are identical if the matrices { d j' } and { jj' } (6) c λ = dj' x ' for all i and j + J j' J p j + λ j = d jj' j' for all j. j' d satisfy the following conditions: Thus the quadratic cost function parameters depend on the parameters of the original linear cost function, and the multipliers of the calibration constraints in (4). For an empirical implementation of the method, several different ways of finding the conditions in (6) have been suggested (e.g., Paris and Howitt 998; Heckelei and Wolff, 003; Howitt 005; Paris 0). We employ the simplest approach that assumes all the off-diagonal elements of { d j' } and { d jj' } are zero. The Data In the model there are 47 plant crops, 7 animal products, two endowed inputs, land and labor, and 9 province-level regions (Table ). In addition to the data for those variables we 7

8 need data on each product's import, per unit production cost, technology coefficients of land and labor, and per unit price. The base year is 009. A greenhouse product is differentiated from its field product because the former's production period, cost, season is quite different from the latter's. Table. Commodities in the Model Level Level Product Rice Rice Barley Common Barley, Naked Barley, Beer Barley Food Crops Vegetable Potatoes Miscellaneous Pulse Fruit-bearing Vegetables Leafy and Stem Vegetables Root Vegetables White Potatoes, Sweet Potatoes Corn Soybeans Water Melons (field/greenhouse), Melons (field/greenhouse), Strawberries (field/greenhouse), Cucumber (field/greenhouse), Pumpkins (field/greenhouse), Tomatoes (field/greenhouse) Spinach (field/greenhouse), Lettuce (field/greenhouse), Chinese Cabbage (Spring field/fall field/greenhouse), Cabbage White Radish (spring field/fall field/greenhouse), Carrots Fruit Oilseed Crop Livestock Spice & Red Peppers (field/greenhouse), Culinary Green Onions, Ginger, Garlic Vegetables Apples, Asian Pears, Peaches, Grapes, Tangerines, Persimmons, Plums Sesame, Peanuts Korean Breeding Cow, Korean Beef Cattle, Beef Cattle, Dairy Cow, Hog, Layer, Broiler Table shows the data sources. 9 provinces, Gyeonggi, Gangwon, Chungbuk, Chungnam, Jeonbuk, Jeonnam, Gyeongbuk, Gyeongnam, and Jeju are producing crops and other 8

9 products. Each province s data on labor and land productivities, land and labor endowments, and unit production costs are obtained from the sources listed in Table. Table Data Sources Data Source Land productivity of each product in each region Ministry of Food, Agriculture, Forestry and Fisheries Annual Statistics of MFAFF (00) Output of each product in each region Ministry of Food, Agriculture, Forestry and Fisheries Annual Statistics of MFAFF (00) Statistics on Animal Production, KOSIS (00) Price of each product Rural Development Administration Regional Agricultural Income Data (009) Per unit production cost of each product in each region Rural Development Administration Regional Agricultural Income Data (009) Labor productivity of each product in each region Rural Development Administration Regional Agricultural Income Data (009) Land endowment of each region Annual Statistics of MFAFF (00) Import of each product Korea Agro-Fisheries Trade Corporation Agricultural Trade Information ( Price elasticity of each product s demand Korea Rural Economic Institute, Agricultural Outlook (007) Elasticity of substitution between domestic GTAP Database 7. product and import Information on elasticities is obtained from the agricultural outlook model of KREI (Korea Rural Economic Institute) and GTAP Database 7.. A GAMS program is used to solve a system of nonlinear equations shown by Dixit (989), and all the parameters in the demand functions, a j ' s, a j ' s, b j ' s, b j ' s and k j ' s are derived. Animal production is a dynamic activity. In order to incorporate the dynamic nature of animal production into our static model, we incorporate the Louhichi et al.'s (03) concept Some crops are produced only in a limited number of provinces. For those crops not produced in a certain province it is assumed that their productivities are lower than the national average by more than 0 percent. 9

10 of dressed animal. That is, a certain type of animal production activity is assumed to require a fixed gender and age composition of animals. The information required to construct the compositions was obtained from the production cost data of RDA (Rural Development Administration). The outcome of feeding is transformed into meat and dairy products to be incorporated into the commodity market equilibrium conditions based on the transformation coefficients. The Greenhouse Gas Mitigation Policy The Korean government has set its national goal of CO emission reduction, which is a 30 percent reduction from its BAU forecasted emission in 00. Agricultural, forestry and fisheries sector is being asked to mitigate.5 million COe ton by 00 (Office for Government Policy Coordination et al. 04). Our work investigates only the emission from crop and livestock production, not that from other agricultural production activities and forestry and fisheries. CO emission from agricultural activity comes from agricultural (crop) production and livestock production. Emission from agricultural production is again composed of methane emissions from rice cultivation, nitrous oxide emissions from agricultural soils and manure management. Emission from livestock sector is composed of enteric fermentation and manure management (Table 3; IPCC 007). Our model can incorporate the impacts of the measures of mitigating emissions from rice cultivation and livestock production. Table 3 shows the estimated COe emission from each source provided by GIR (Greenhouse Gas Inventory & Research Center of Korea) and our own calculation. Both calculations are based on IPCC's Tier method released in

11 Table 3. Greenhouse Gas Emission from Agriculture and Livestock Sector in Korea Agriculture Livestock Biomass Biomass Cropland Enteric Manure Manure Total Rice CH4 Burning Burning Use CH4 CH4 CH4 NO CH4 NO GIR (03) 6, , ,063.46,3.3 3,930.09,35.93 Our Model 5,90.40 n.a. n.a. n.a. 3,844.35,80. 3,9.54 4,7.40 Note. Base year is 009. Unit is Gg COe. The main instrument of reducing emission from rice cultivation is introducing an intermittently flooded irrigation in rice management. IPCC (997) reduces the scaling factor for water management from to 0.6 when the improvement in the irrigation is introduced. Because rice is the single most important crop in Korean agricultural production, the environmental and economic impacts of the mitigation instrument can be substantial. High quality roughage in cow and cattle management is the main instrument of reducing emission from livestock sector. Kim et al (0) estimated that the enteric methane emission factor reduces by 9 by the switch of roughage. Adopting those two mitigation activities will increase production costs of rice and cow and cattle production. Cho (04), based on RDA estimates, suggested that rice production cost will increase by 3,394 Won per ton. Most of the cost comes from the installment of new irrigation facility required for the improved irrigation method. RDA (00) estimated that switching to the high quality roughage will increase the cost of hay by 44 percent. Using the information on the share of irrigation and hay in the total production costs of rice and cow and cattle, the cost impacts of those two instruments are incorporated into the change in the corresponding { c } and { j' } d of model (4) and (5), respectively. One US dollar is approximately,070 Korean Won as of May, 05.

12 Simulation Results Table 4 summarizes the mitigation effects and costs. When producers are allowed to optimally respond to the increasing cost of rice and livestock production, the emission from rice production will decline by almost 0 percent. The emission from enteric fermentation declines by 6.64 percent and will become the largest source of agricultural CO emission mitigation. Because domestic size of livestock production reduces, the emissions from manure also decline although the rates are not so high. Table 4. Mitigation Effects and Costs BAU Optimal Response Passive Response SIM SIM-BAU RATE SIM SIM-BAU RATE Rice CH Gg COe Enteric CH Manure CH Manure N O Total (A) (A) -8.0 Million $US $US/t Irrigation in Rice Management High uality Roughage Total Unit Cost (B/A)* (B) (B) The last three columns of the upper part of the table shows the usual estimates which are derived under the assumption that producers maintain their production levels even after they adopt new irrigation methods and roughage. Hence, there is no mitigation reduction from manure management. Moreover, emissions from rice cultivation and enteric fermentation

13 are also higher than those of the optimal response because the changes in rice production area and cow and cattle herds are not incorporated. The lower part of the table shows mitigation costs. The costs are calculated by the change in total agricultural production cost. That is, the cost of optimal response incorporates changes in unit production costs of rice and livestock as well as changes in outputs in each region while that of passive response takes into account only changes in unit production costs. The mitigation cost is lager under passive response than under optimal response although its mitigation effect is smaller than the latter's 3. Per ton mitigation cost is $US 467 under the optimal response and $US 544 under the passive response. The latter is higher than the former by 6.4 percent. This cost estimates may be compared with the estimates of Michel (007) where the costs of 9 agricultural mitigation measures were estimated for European farms. Depending on the mitigation measures and types of farms, Michel (007) derived cost estimates distributing between -387 and 763 Euro per COe ton. Although per unit mitigation cost is not small even under an optimal production response to the mitigation regulations, the mitigation effect is also very substantial. Improving only rice irrigation system and roughage management will mitigate agricultural CO emission by.3 million ton while the mitigation target imposed on entire agricultural, forestry and fisheries sector is.5 million CO ton. We assumed that mitigation activities are imposed only on rice cultivation and livestock production, but those activities may affect production of other commodities as well because they change relative net-price (i.e., price minus unit cost) for each commodity. Because there are many commodities in our agricultural sector model, we aggregate commodities into groups as shown by Table 5. The Laspeyres quantity and the Laspeyres price indices are constructed to measure the impacts on each group of agricultural commodity production and price. The production of rice and livestock decline in all provinces but the declining rates 3 Alternatively, we may compare the change in the value of Z, the objective of the optimization problem in (5), that will be caused by the adoption of mitigation measures, under the two different scenarios. We found that the decline in the value of Z, caused by the adoption, is smaller under the optimal response scenario, and the difference between the two scenarios is very close to the difference of costs shown by Table 4. 3

14 are less than percent. Even if production costs of those two commodity groups increase by the mitigation activities, they are still preferred by producers because those two groups of commodities are under government programs and have relatively high net-prices. Domestic rice price is supported by a deficiency payment program in Korea, and its import is controlled by a minimum market access (MMA) policy. Producers of Korean beef have successfully differentiated their supplies from imported beef products in Korean market and are maintaining relatively high prices. Because of the commodity substitution effects, production of all other commodity groups will increase although the increasing rates are small. Table 5. Indices of Domestic Production and Price by Product Group Food Crops Vegetables Rice Barley Potatoes Miscella neous Pulse Fruit-bearing Vegetables Leafy and Stem Vegetables Root Vegetables Spice & Culinary Vegetables Fruit Oilseed Livestock Domestic Price Total Total Gyeonggi Gangwon Chungbuk Domestic Production Chungnam Jeonbuk Jeonnam Gyeongbuk Gyeongnam Jeju Note. Base is 00 Table 5 shows that there are some regional variations of the impacts as well. Two noteworthy regions are Gangwon and Chungbuk which are relatively hilly areas with higher production costs in Korea. The increase in non-rice food production lowers those commodities market prices, and the decline in market prices may negatively affect production 4

15 in those two regions. It is found that especially the slack of (=unused) labor increases in those two regions as a result of the introduction of mitigation measures. Finally, Table 6 summarizes mitigation activity s impacts on each individual commodity s domestic production, import, price, and land use at the national level. Although the declining rate of rice production is very small its impact on price is quite substantial (a.07 percent increase). Because the share of imported rice is small in Korean market due to the MMA policy, a small decline in domestic price results in a relatively large rate of import increase, which is 3.58 percent. Production of domestic beef products decline quite substantially due to the increase in feed costs. Almost 3 percent of declining in domestic production is anticipated, and almost the same rate of price increase may occur. Production and land use of all other agricultural commodities will increase because of the resource substitution occurring after the increase in rice and beef production costs. The land shares of corn and some field vegetables such as tomatoes, Chinese cabbage, spinach may increase especially. 5

16 Table 6. Impacts on each Crop s Production, Import, Price, and Land use (%) Domestic Production Import Domestic Domestic Land use Price Production Import Domestic Price Land use Rice Cabbage Common Barley White Radish (spring field) Naked Barley White Radish (fall field) Beer Barley White Radish White Potatoes Carrots Sweet Potatoes Corn Red Peppers (field) Red Peppers Soybean Green Onions Water Melons (field) Onions Water Melons Ginger Melons (field) Garlic Melons Apples Strawberries (field) Asian Pears Strawberries Peaches Cucumber (field) Grapes Cucumber Tangerines Pumpkins (field) Persimmons Pumpkins Plums Tomatoes (field) Sesame Tomatoes Peanuts Chinese Korean Cabbage (spring Breeding Cow field) n.a. Chinese Korean Beef Cabbage (fall Cattle field) n.a. Chinese Cabbage Beef Cattle n.a. Spinach (field) Dairy Cow n.a. Spinach Hog n.a. Lettuce (field) Layer n.a. Lettuce Broiler n.a. 6

17 Summary and Conclusion We construct a Korean agricultural sector model in order to analyze the impacts of adopting improved rice irrigation and livestock roughage to mitigate agricultural greenhouse gases. We extend the usual positive mathematical programming (PMP) model into the case where the market equilibrium conditions are incorporated. The model is an equilibrium model where only an imperfect substitution between domestic goods and imports are allowed. Our results show that adopting those two measures may almost attain the national targets of mitigation imposed on agricultural sector, although per unit reduction cost is quite substantial. The adoption may result in some substitution effects of resource use, and the shares of rice and beef in Korea agricultural production will decline slightly. Because of the substitution activities the mitigation costs may be saved substantially. 7

18 References Cho, E (04), Analyzing the Economic and Environmental Effects of Adopting Mitigation Measures in Agriculture Sector, Master dissertation in economics, Department of Agricultural Economics and Rural Development, Seoul National University. Dixit, A. (989), Optimal Trade and Industrial Policies for the US Automobile Industry, in R. C. Feenstra, ed., Empirical Methods for International Trade, MIT Press. Greenhouse Gas Inventory and Research Center of Korea (03), National Greenhouse Gas Inventory Report of Korea 0. Heckelei, T. and H. Wolff (003), Estimation of Constrained Optimisation Models for Agricultural Supply Analysis Based on Generalised Maximum Entropy, European Review of Agricultural Economics, Vol 30 (): 4. Howitt, R. (995), "Positive Mathematical Programming," American Journal of Agricultural Economics 77: Howitt, R.E. (005), Agricultural and Environmental Policy Models: Calibration, Estimation and Optimization. Davis, CA: Department of Agricultural Economics. IPCC (997), Revised 996 IPCC Guidelines for National Greenhouse Gas Inventories. Kim, C, H. Jeong, Y. Kim, T. Kim, D. Mun (0), Strategies for Agriculture, Food, Forestry and Fishery Industries against Climate Change, Korea Rural Economic Institute. Korea Agricultural Trade Information (Kati), Korea Rural Economic Institute (007), A Study on Modelling of the Korea Agricultural Simulation Model. Korea Rural Economic Institute (000), Presumed Supply and Demand Function of Major Vegetables and Fruits. Lehtonen, H (00), Principles, Structure and Application of Dynamic Regional Sector Model of Finnish Agriculture, Ph. D. dissertation, Department of Engineering Physics and Mathematics, Helsinki University of Technology. Louhichi, K., S. G. Paloma, H. Belhouchette, T. Allen, J. Fabre, M. B. Fonseca, R. Chenoune, S. Acs, and G. Flichman (03), Modelling Agri-Food Policy Impact at Farm-household Level in Developing Countries (FSSIM-Dev), JRC Scientific and Policy Reports. European Commission Joint Research Center. Michel, A. W. J. (007), Greenhouse Gas Emissions and Mitigation Costs of Selected 8

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