Estimating Mitigation Costs ofGreenhouse Gas from Agricultural
Production: A Combined Top-down
and Bottom-up Model of Korea
August 2016
Oh Sang KWON*, Sung Won KANG**, Seongho LEE*
*Seoul National University** Korea Environment Institute1

Contents(1) Introduction(2) Top down module(3) Bottom up module(4) Hybridization(5) A Policy Simulation(6) Summary2(1) Introduction
 Top-down climate change economic model– Mostly based on CGE models– Single-country vs. Global, Static vs. Recursive Dynamic– Almost all variables are endogenously determined inside of the model Bottom-up climate change economic model– Mostly optimization models– Static vs. Dynamic– Prices are either endogenous or exogenous– Very detailed description of technology and resource endowments isallowed– Industry specific: many variables are exogenously given outside of themodel3

(1) Introduction
 Linking or combining two approaches may help
– Taking advantages of two approaches– Soft link: two models are built-up independently, and run recursively– Hard link: an integrated model containing both TD and BU models•Run one-shot or recursively until reaching a convergence4(1) Introduction Böhringer and Rutherford (2009)– A very well known linkage model used for an electricity sector– TD determines prices ⇒ BU determines micro-level production levels
given prices⇒ Supply sector of TD is replaced by the outputs provided by BU⇒ New prices are found by TD ⇒ BU finds new supplies, …

 We construct a BR linkage model with 2010 Korean Data
– Top-down model: a single country static CGE model– Bottom-up model: a multi-output quadratic agricultural sector model– Hybridization of micro and macro data5

(1) Introduction
 Why agricultural BU model?
- Agriculture occupies very small proportions of output and emission• The joint research team of KEI and several universities is constructing linkage
models for electricity and other industries as well
- It is uncertain whether the BR algorithm will work for a multi-commodity BU
model• There are many commodities with different prices in agricultural sector- A calibration issue of the BU model• There are many micro units of production (i.e., production unit of each productin each region)• Unless the BAU production of micro units are recovered by the BU model, thewhole linkage model converges to a “wrong solution”• BR is very unclear about calibration of the BU model• We combine the technique of PMP (positive mathematical programming) with
CGE and show that applying PMP is essential in finding the “exact solution”

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(2) Top down module7(2) Top down module
Intro Single country recursive dynamic CGE with trade 7 industry, 7 commodity, 1 household, 3 institutions– Calibrated share form of BR → level form

 Production: Böhringer and Rutherford (2009) industry definition andnesting structure Household consumption: Roy’s identity from log utility function– Household savings : fixed share of disposable income + depreciation8

(2) Top down module
Intro Three institutions : Government/ Savings-Investment/ Rest of the world– Government : Using tax revenue to finance government expenditureand transfer payment•Taxes : Production tax, tariff, income tax•Closure : Government saving adjusts to tax revenue– Rest of world : Using import revenue to finance export expenditure•Closure : Exchange rate adjusts to trade balance.
– Savings-Investment: Collect savings to finance investment expenditure•Closure : Investment adjusts to savings level.

 Data: 2010 basic price Input-Output table (Bank of Korea), 2010 Energy
Balance (KEEI)

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(2) Top down module
Production function nesting structure10(2) Top down module
Industry

Model entry

Coverage

ELECElectricity

GAS-Heat

City Gas, LNG, Heat

OIL

Refinery product, Crude Oil

COAL

Coal, Coal product

ENIT*NEINTAGRI

Iron and steal, Cement, Organic chemistry, Mineral.
Nonferrous metal, Transportation
Wood and Paper, Fiber, Machinery, Electronics, Automobile,
Shipbuilding, Food, Construction, Service
Agriculture, Forestry, Fishery* ENIT: energy cost exceeds 5% of total cost11

(2) Top down moduleTradeRest of World : Revenue = import sales revenue, cost = export purchaseexpenditure– Import- domestic demand: CES Armington composite– Export –domestic supply : CET transformation function– Foreign savings = Import sales – export purchase in domestic currency

Import-domestic demand

Export-domestic supply12

(3) Bottom up module13

(3) Bottom up moduleIntro Multi product Quadratic Programing model– Multi product : sum of consumer surpluses + producer surpluses of 13agriculture products.
– Regional choice : choice over output quantity of 13 products from 9regions.
•Each Ag output has 17 cost items•Each region has different unit costs

 Data Construction : Input Output table (National) + Agricultural micro
data (Regional)– National input- output data : 2010 basic price Input-output table– Regional share : Agriculture, Food and Rural Affairs Statistics Yearbook(MAFRA), Regional Agriculture Income Statistics (RDA). Miscellaneoussources14

(3) Bottom up module• DD : Top down demand
function (Q0, P0 : data)• 1st approximation of DD at(P0,Q0): bottom up demand
function• Q1 : Bottom up output– Q0: lowest mc production
option– Q1-Q0: Second lowest mc
production option

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(3) Bottom up module
Data and Equations
Data Construction

Quadratic Programming16

(3) Bottom up module
Output, Cost, Region

Model entry
Output (i)Rice, Barley, Bean, Potato, Vegi, Fruit, Flower, MissCrop, (Crops)
Dairy, Meat, Pork, Poultry, MissLstock (Livestocks)

Cost (m)SEED, FERT, PEST, FEED, CUB, MED, ENERGY, WATER, SMACH,LMACH, FACIL,OINPUT, OSERV, (Intermediate Inputs)
KCOST, LCOST, SUR, (Value Added)
TW(Taxes and Residue)

Region (r ) GG(Gyunggi), GW(Gangwon), CB (Chungbook), CN(Chungnam),JB(Junbook), JN(Junnam),KB(KyungBook), KN(Kyungnam) JJ(Jeju)

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(4) Hybridization① Industry definition② Top down module adjustment③ Bottom up module adjustment④ Linking module18

(4)-① Industry definition
 Top down module AGRI industry disaggregation:13 Agriculture industry– Single price for AGRI doesn’t make sense; Significant heterogeneity•Ex) Same price for Rice, Meat, Flower ?

 Match Top down Agri industry = Bottom up Agri output– Forestry and Fishery : relocate from Agri to EINT (energy intensiveindustry)– Threshing (Input Output entry): relocate from NIENT to rice (97.5%)
and Barley (2.5%)

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(4)-① Industry definition
Input-Output

Hybrid model

Input-Output table

Hybrid model

1RiceRice

14

Dairy

Dairy

2

Barley/other grain

Barley15

MeatMeat

3

Bean

Bean

16

Pork

Pork

4

PotatoPotato17

Poultry

Poultry5Vegetables

Vegi

18

Other Livestock

MissLstock

6

FruitFruit

19

Forestry

EINT

7

Medicinal crops

MissCrop

20

WoodEINT8

Other food crops

MissCrop

21

food forest product

EINT

9

Tabaco

MissCrop

22

Other forest product

EINT

10

FlowerFlower23

Fishing

EINT

11

Rubber

MissCrop24

marine farming

EINT

12

seed/ seedling

MissCrop

25

Agriculture service

NIENT

13

Other nonfood crops

MissCrop

42

Thrashing

Rice, Barley20

(4)-② Top down adjustment
Overview

 Production quantity of 13 ag. Sectors are provide by the BU model Intermediate input demand of those 13 sectors are also provided by theBU model Value-added demands of the 13 sectors are also provided by the BUmodel There are no zero-profit conditions for those 13 sectors– Profits are determined by the BU model– Profits given by the BU model is distributed to the household

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(4)- ③ Bottom up adjustment
Bottom up in Hybrid model• DD : Top down demand
function (Q0, P0 : data)• 1st approximation of DD at(P0,Q0): bottom up demand
function• Q1 : Bottom up output– Q0: lowest mc production
option– Q1-Q0: Second lowest mc
production option• In reality, each micro
production unit does notproduce up to its capacity
limit (intro. PMP)

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(4)- ③ Bottom up adjustment
As long as the MCs are constant, there is no way to recover
the observed choice of each production unit.

q01,q02 : data ≠ qM1, q12: Q. P. BAU solution

q01,q02 : data = Q. P. BAU solution23

(4)- ③ Bottom up adjustment
PMP in Action (1)24

(4)- ③ Bottom up adjustment
PMP in Action (2)25

(4)-④ Linking module
Exportmodule Adjusted CGE + PMP BU model BAU linking model achieves BAU solution at the 1st iteration
 Both TD and BU solutions converge mostly within 20 iterations when apolicy shock is introduced

 PMP procedure has another advantage of generating smoother responsesto policy shocks

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(5) A Policy Simulation27

(5) A Policy Simulation
Overview

 Investigates mitigation costs of specific technological measures inagricultural production in Korea (non-energy source GHGs)

 Considers producer’s response to the measure by using a BU agricultural
sector model Make prices endogenous by linking the TD model with the BU model GHGs emission sources in agriculture (IPCC, 1996)– Rice cultivation: flooded rice fields– Domestic livestock enteric fermentation– Domestic livestock manure management– Agricultural soils– Field burning of agricultural residues

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(5) A Policy Simulation
Overview

 Intermittently flooded irrigation– In rice production– Reduces the scaling factor for water management– Reduces the emission coefficient by converting continuously flooded
paddy fields (single aeration) into intermittently flooded faddy fields(multiple aeration), installing irrigation facilities•1 and 0.83 → 0.66 (IPCC, 1997)– An increases in irrigation cost is necessary and incorporated into 𝑐𝑖𝑟 inthe BU model•Irrigation facilities cost + labor and operating costs per ha (RDA, Cho2014), 630,000 Won/ha29

(5) A Policy Simulation
Overview

 Enteric Fermentation– Reduces methane emission from digestion process, improving thequality of roughage– Applied to dairy and meat cows
– Cost increase is calculated from the cost data
– Meat cow methane reduces by 9kg per head a year
– Dairy cow methane reduces by 24kg per head a year (RDA)

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(5) A Policy Simulation
Overview

 Manure Management– Reduces emission coefficients of N2O by constructing and operating
improved manure management facilities: purification, energyconversion, …, but the exact instruments are unknown yet.
– It is difficult to find construction cost data of manure management
facilities, which are mostly supported by the government– Liquefying has the smallest emission coefficient (0.02 or 0.005 vs.
0.001) with additional costs (IPCC, 1996)– Each animal may need different liquefying cost, but we applied per unit
cost of swine to all animals (cows, swine, and poultries) (2,000Won/ton)– The policy assumed is increasing the share of liquefying by 100%31

(5) A Policy Simulation
Overview

 Results
Change in Amount%-9.0 (B. Won)

-0.001

Total CO2 eq-2,078 (1,000 ton)

-0.374

Ag. CO2 eq-2,080 (1,000 ton)

-12.306

Irrigation-999 (1,000 ton)

-13.652

EntericFermentation-773 (1,000 ton)

-18.142

ManureManagement-309 (1,000 ton)

-5.795GDP32

(5) A Policy Simulation
Overview

 Results– Only with 3 mitigation measures, a substantial amount of agriculturalemission can be reduced– Marginal abatement cost is about 4,313 Won/ton, which is less than
the anticipated CO2 permit price– Non-energy mitigation costs occupy relatively small shares in total cost
– But there are some caveats because fixed facility costs often funded by
the government are not completely incorporated into the simulation– Marginal abatement costs are smaller than the estimates from a standalone BU model (Kwon et al., 2015)33

(6) Summary What we found– Decomposition technique can be applied to multiple output case
– Both TD and BU module needs significant modification for hybrid modelconstruction– PMP technique in BU module is quite helpful– Decomposition allows more production options in production– Marginal abatement costs of GHGs reduction in agricultural sector does
not seem to be large

34Thank you!

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