ORIGINAL ARTICLE

The future role of agriculture and land use changefor climate change mitigation in Bangladesh

Tahsin Jilani & Tomoko Hasegawa & Yuzuru Matsuoka

Received: 11 September 2013 /Accepted: 27 January 2014# Springer Science+Business Media Dordrecht 2014

Abstract In Bangladesh, 53 % of domestic greenhouse gas (GHG) emissions were generatedin the agriculture and other land use sectors in 2005. However, no specified measures forclimate change mitigation have thus far been designated nationally in these sectors. In thispaper, we quantified future greenhouse gas emissions and mitigation potentials through 2025by using the Agriculture Forestry and Other Land Use Bottom-up model to clarify cost-effective technological options under different mitigation cost scenarios. We found that (1)GHG emissions of 69.1 MtCO2eq (Million tons of carbon dioxide (CO2) equilivalent)/yearwill be generated from the agriculture and land use sectors in 2025 in a baseline scenario, (2) areduction of 32 MtCO2-eq/year (a 47 % reduction from baseline emissions) is possible at a costof as much as US$10/tCO2-eq in 2025, (3) in agriculture, an emissions reduction of 10MtCO2-eq/year could be achieved by implementing midseason drainage in rice cultivation,generating bioenergy from livestock manure, and replacing roughage with concentrated feed atmitigation cost of US$10/tCO2-eq in 2025, and (4) in the other land use sector, a mean annualmitigation potential of 6.5 MtCO2-eq/year can be achieved with a total mitigation cost of lessthan US$10 million (52 % of baseline land use emissions in 2025).

Keywords Agriculture . Bangladesh . Bottom-upmodel . Climate change . Land use change .

Mitigation potentials

1 Introduction

Non-energy sectors and greenhouse gas (GHG) other than carbon dioxide (CO2), includingmethane (CH4) and nitrous oxide (N2O), have attracted an increasing amount of publicattention in terms of climate change mitigation in the early 2000s, whereas energy-relatedemissions were a strong focus in the 1990s. The 21st meeting of the Energy Modeling Forum

Mitig Adapt Strateg Glob ChangeDOI 10.1007/s11027-014-9545-8

T. Jilani (*) :Y. MatsuokaDepartment of Environmental Engineering, Graduate School of Engineering, Kyoto University, C cluster,Kyoto-Daigaku-Katsura, Nishikyoku, Kyoto 615-8540, Japane-mail: tahsinku@yahoo.com

T. HasegawaCenter for Social & Environmental Systems Research, National Institute for Environmental Studies, 16-2Onogawa, Tsukuba, Ibaraki 305-8506, Japan

was held on the topic of multigas mitigation and climate policy. Weyant et al. (2006) provide acomprehensive report of a comparative set of analyses on multigas mitigation discussed at theforum. The analyses indicated that non-energy sectors and non-CO2 gases are expected to havegreat mitigation potential. These sectors can play an important role in future mitigation,although there is greater uncertainty in estimates of CO2 emissions from land use and CH4

and N2O emissions in general than in estimates of CO2 emissions from fossil fuels. UnitedNations Intergovernmental Panel on Climate Change, IPCC (2007) summarized severalstudies with a wide range of mitigation options and quantified the mitigation potential inagriculture, forestry, and other land use (AFOLU) sectors. The global technical mitigationpotential from agriculture (excluding fossil fuel offsets from biomass) by 2030 is estimated tobe about 5,500–6,000 MtCO2-eq/year (medium agreement, medium evidence). The economicpotentials are estimated to be 1,500–1,600, 2,500–2,700 and 4,000–4,300 MtCO2-eq/year atcarbon prices of as much as US$20, US$50 and US$100/tCO2-eq, respectively (IPCC 2007).For the land use sector, there is still a wide range of estimates of forestry mitigation potential;bottom-up regional studies show the economic potential at costs of as much as US$100/tCO2-eqto contribute 1.3–4.2 GtCO2-eq/year in emissions reduction in 2030, whereas global top-downmodels predict far higher mitigation potentials (13.8 GtCO2-eq/year in 2030) at the same cost(IPCC 2007).

Asia has been a major contributor of GHG emissions—36 % of global emissions werederived from Asian countries in 2008 (EDGAR 2011). Land-based activities such as ricecultivation, livestock management and deforestation generated 23 % of Asian emissions in2008. United States Environmental Protection Agency (USEPA 2006) forecasted large in-creases in GHG emissions from animal sources in East Asia. Since the per-capita consumptionof meat and milk is still low in these countries, increasing trends of emissions are expected tocontinue for a relatively long time. They projected an increase of 153 % and 86 % in emissionsfrom enteric fermentation and manure management, respectively, from 1990 to 2020 in theEast Asian countries. In South Asia, emissions are increasing primarily because of theincreased use of nitrogen (N) fertilizers and manure to meet the increasing demand for foodcaused by rapid population growth.

In Bangladesh, AFOLU sectors are significant contributors to GHG emissions, accountingfor 53 % of total domestic emissions in 2005 (MoEF 2012). In 2005, agricultural emissionsaccounted for 37 % (livestock management, 24 %; rice cultivation, 7 %; cropland including Nfertilizer, 6 %) of total domestic emissions, and the land use sector generated 16 % of totalemissions (MoEF 2012). Mitigation studies of Bangladesh are very limited whereas impactand adaptation studies are paid more and more attention (Ahmed et al. 1999; Mutton andHaque 2004; Brouwer et al. 2007; Karim and Mimura 2008). The Asia Least-cost GreenhouseGas Abatement Strategy (ALGAS) projects that GHG emissions from agriculture will increaseby 11 % from 1990 to 2020 and that other land use emissions will increase by 10 % from 1990to 2013 (Bangladesh Centre for Advanced Studies 2000). ALGAS also estimated a futuremitigation potential of 120 MtCO2-eq in the other land use sectors. They used a static methodand took into consideration future climatic mitigation and assumed future amounts of mitiga-tion based on various experts’ judgments. Currently, there are no studies on mitigationmeasures based on economical optimization in Bangladesh even though the AFOLU sectormight play a fundamental role in reducing GHG emissions in the country.

Every country is expected to contribute to emissions reduction, some voluntarily (devel-oping countries) and some on a mandatory basis (developed countries). Bangladesh’s SecondNational Communication (MoEF 2012), which includes ideas, policies, and actions onmitigation and associated challenges, has thus become critical as a stepping stone to furtherreporting on non-energy sectors but more importantly in terms of real action on the ground.

Mitig Adapt Strateg Glob Change

The reduction of emissions from non-energy processes (i.e., the AFOLU and waste sectors) isfraught with many difficulties. At the present state of knowledge and availability of informa-tion and data for specific processes, it may be quite difficult to motivate economic agents tochange. In addition, some mitigation measures may conflict with other core developmentobjectives such as food security. In the case of agriculture, mitigation and adaptation may beintertwined and thus the problems become more complex and there is greater potential forconflict. Non-energy processes therefore need careful examination and implementation ofmitigation measures thus may be an issue for the future. Nationally, no significantmitigation approaches have yet been explicitly described for AFOLU sectors even thoughMinistry of Environment and Forest (MoEF 2012) quantified mitigation potentials in theenergy sectors.

In this study, we asked two primary research questions: how much future mitigationpotential will be possible under limited mitigation costs in the AFOLU sectors inBangladesh, and which types of mitigation measures will be cost-effective in these sectors?

2 Methodology

2.1 Model framework

To answer our research questions, we used the AFOLU-B model, a bottom-up type model foremissions mitigation (Hasegawa and Matsuoka 2013). By using this model, we are able to (1)estimate mitigation potentials and costs based on detailed information about mitigationtechnologies and (2) incorporate socioeconomic conditions, agricultural projections, and landplans proposed by the Bangladesh government to make assumptions on future humanactivities such as agricultural production and land use changes. Assumptions about the futurewere developed based on data from several published studies and other sources and fed intothe AFOLU-B model. The model then calculates GHG emissions and mitigation costs undervarious technological assumptions. The period of estimation was from 2005 to 2025.

2.2 AFOLU-B model

The AFOLU-B model was used to calculate GHG mitigation potentials in AFOLU sectors atthe country level, based on detailed information about specific mitigation measures(technologies). The model illustrates mitigation potentials and costs by comparing combina-tions of mitigation measures selected by agricultural producers (i.e., farmers) under theassumption of economic rationality. The model solves the optimization calculation based onfuture assumptions of agricultural production and land use change. In the technology selection,farmers’ profits are maximized under the assumed emission tax in the agriculture sector, andmitigation potential is maximized under a limited total cost in the land use sector. We set anoptimization scheme with the two following features to describe a realistic situation intechnology selection in which countermeasures are selected for annual optimization underan annual constraint. First, we conducted a recursive calculation, in which means a technologyselection for each year for annual optimization. Second, we assumed that only the mitigationeffects and costs for the present year are taken into consideration, and future years are notconsidered in the evaluation of mitigation effects and costs for the other land use sectors eventhough the mitigation effects of some technologies such as a long-rotation plantation might lastfor longer than that time period. Mitigation costs exclude the initial forest plantation costsassumed in the baseline scenario because this activity is not being done for the purpose of

Mitig Adapt Strateg Glob Change

mitigation. In the countermeasure scenarios, forest plantation in addition to the baselineamount is considered to be a mitigation activity. Benefits from activities such as improvedland use and wood harvesting are taken into account in these countermeasure scenarios.

Figure 1 summarizes the inputs and outputs of the AFOLU-B model (Hasegawa andMatsuoka 2012). The following three types of information are given exogenously: (1) futureassumptions of production of agricultural commodities and area of land use change in abaseline case where emissions reduction is not strongly addressed; (2) information on mitiga-tion countermeasures (costs, mitigation effects, etc.); and (3) policy scenarios on GHGemission taxes, in which represent the willingness to induce GHG emissions reduction.Future production levels are fixed in the model, and it is assumed that agricultural producerssupply the given amount of agricultural commodities.

The AFOLU-B model consists of two separate modules: Agriculture Bottom-up (AG-B)and Land Use change Bottom-up (LU-B). The AG/Bottom-up module calculates the emissionsand mitigation potentials related to agricultural production and a combination of mitigationtechnologies under an assumed emission tax, and the LU-B module calculates land use–induced emissions and mitigation potentials under a limited total mitigation cost (seeHasegawa and Matsuoka (2013) for more details). Table 1 shows the emission sources.Emissions of CH4 and N2O from agricultural production (livestock enteric fermentation,manure management, rice cultivation, and use of N fertilizers on managed soils) are calculatedby the AG-B model, whereas CO2 emissions from land use, land use changes, and forestry arecalculated by the LU-B model. Forest fire is not taken into account in this study because forestfires are not common in this area, perhaps as a result of the moist and wet conditions.Mitigation effects are calculated by multiplying the land area or number of livestock for whichthe measure is applied and the coefficients of mitigation effect per unit area or head oflivestock. The emission coefficients are based on IPCC (2006) but were adjusted in the baseyear to meet the emissions reported in MoEF (2012).

AFOLU Bottom-up model

(1) Future scenarios on- Land use change- Agricultural production- Fertilizer input- Manure management

system etc.

(3) Policy scenarios- Emission tax- Subsidy etc.

(2) Mitigation countermeasures- Cost- Mitigation efficiency- Lifetime etc.

Agriculturalmodule

Land usemodule

- GHG mitigation potentials- Combination of mitigation

technologies

Fig. 1 Inputs and outputs of the AFOLU-B model

Mitig Adapt Strateg Glob Change

2.3 Scenario settings

We prepared two scenarios, one without and one with the use of mitigation measures, that is, abaseline and a countermeasure scenario. GHG mitigation potential is defined as the differenceof GHG emissions between the two scenarios. In the countermeasure scenario, countermea-sures are applied in the years from 2010 to 2025. For the agriculture sector, all options ofwhich the cost is less than the assumed emission tax (US$0, US$10, US$100 and more thanUS$100/tCO2eq) are available. For the land use sector, total mitigation cost for the entireperiod is assumed to range from US$0.1 million to US$500 million, and the total costs werethen averaged over the time period to determine annual costs.

2.4 Data

Several different mitigation measures were used in the study for the AFOLU sectors.Information on technology was primarily collected from published international and domesticreports; important features include cost, mitigation effects, and agricultural productivity.Therefore, technology selection depends not only on cost and mitigation potentialbut also on the effects on agricultural productivity. Tables 2 and 3 show detailedinformation on the costs and mitigation potentials of the measures considered for theAFOLU sectors, respectively. The costs of the mitigation technologies from thereporting country were converted into domestic costs by using an adjustment factorbased on the relative wages of the reporting country and Bangladesh (World Bank2012). Wages for agricultural labour in Bangladesh in 2005 were US$507/worker/year.The wages were assumed constant for the future.

2.5 Future assumptions

We referred to domestic data sources for country-specific information to the extent possible.International data sources were used if domestic data sources were unavailable.

Table 1 GHG emission sources in the AFOLU-B model

Emission sources Classification Gases IPCCCategory

Enteric fermentation Dairy cattle (Cabose ), other cattle (Cabose ), buffalo(Syncerus caffer ), sheep (Ovis aries ), goats (Caper ),camels (Camelus ), horses (Equus ), mules and asses(Odocoiles hemionus ), swine (Porcus )

CH4 3A1

Manure management Dairy cattle, other cattle, buffalo, sheep, goats, camels,horses, mules, asses, swine, chickens (Gallus ), ducks(Anas ), turkeys (Gallupavo )

CH4, N2O 3A2

Land use, land usechange and forestry

Forestland, cropland, grassland, settlements, other land CO2 3B

Managed soils Direct N2O emission from managed soils N2O 3C4

Indirect N2O emission from managed soils N2O 3C5

Indirect N2O emissions from manure management N2O 3C6

Rice cultivations CH4 3C7

a IPCC category represents emission categories of IPCC (2006). Source: Hasegawa and Matsuoka(2013)

Mitig Adapt Strateg Glob Change

2.5.1 Crop production, harvested area, yield and fertilizers

Figure 2 shows the assumed levels of harvested crop area through 2025. We classified thecrops into 7 categories: rice (Oryza sativa), wheat (Triticum aestivum), other coarse grain

Table 2 Costs and mitigation potentials of agriculture technologies

EmissionSource

Countermeasures Code Cost[USD/(activityyear)]*

Mitigation[tCO2-eq/(activity year)]*

Reference

Entericfermentation

Improvement of geneticmerit of dairy cows

HGM 0 0.005 Bates(1998)

Replacement ofroughage withconcentrated feed

RRC -23 0.005 Graus et al.(2004),Shibataand Fuminori (2010)

Manuremanagement

Daily spread of manure DSM 0 0.002 Bates(1998), IPCC(2006)

Bioenergy from manure BM −93 0.17 USEPA(2006)

Rice cultivation Replace urea withammonium sulphate

RAS 20 0.31 USEPA(2006), Graus et al.(2004)

Midseason drainage MD 0 0.22 USEPA(2006)

Off-seasonincorporation ofrice straw

OIR 20 0.28 USEPA (2006)

Managed soils High efficiency fertilizerapplication

HEF 2 0.24 USEPA (2006), Hendrikset al. (1998), Amannet al. (2008)

Tillage and residuemanagement

TRM 5 0.34 USEPA(2006), IPCC(2007),Smith et al.(2007)

Slow-release fertilizer SRF 700 0.23 USEPA(2006), Akiyamaet al.(2010)

*Activity is the area for crop cultivation and number of animal for livestock

Table 3 Costs and mitigation potentials of technologies applied in the land use sector

Mitigation technologies Code CostUS$/ha/yr

MitigationtCO2eq/ha/yr

Timeperiod (yr)

Reference

Long rotational artificialreforestation

LRR 2.9 10.6 40 Tata Energy ResearchInstitute 2000

Medium rotational artificialreforestation

MRR 5.5 16.9 20 Tata Energy ResearchInstitute 2000

Medium rotation sal plantation MRP 7.4 18.0 20 Tata Energy ResearchInstitute 2000

Short rotational artificialreforestation

SRR 15.0 12.5 10 Tata Energy ResearchInstitute 2000

Short rotational participatorywoodlot plantation

SRP 11.6 12.5 10 Bangladesh Centre forAdvanced Studies2000

Reduced Impact Logging RIL 0.1 5.1 35 Boer 2001

Enrichment planting, Enhancednatural regeneration

ENR 0.5 7.3 35 Boer 2001

Mitig Adapt Strateg Glob Change

including maize (Zea mays), oil crops, sugar crops, vegetables, and other crops. Cropproduction, harvested area, and yields were set by referring to several domestic governmentalreports and plans (Ministry of Agriculture 2007; Bangladesh Bureau of Statistics 2010a, b;Ministry of Planning 2010; MoEF 2012) and some international ones (e.g., United NationsFood and Agriculture Organization, FAO 2010). To prioritize domestic reports over interna-tional ones in developing our assumptions, the harvested area and crop yield for 2005 was setby referring to Ministry of Agriculture (2007) and Bangladesh Bureau of Statistics (2010a, b).Data on crop yield in 2005 were taken from the Ministry of Agriculture (2007). For 2010, theharvested area and yield were extrapolated by using the data from 2005 and the annual growthrate of FAO (2010). Crop production was then calculated by using these values of harvestedarea and yield. For the future projections, rice production by 2025 was estimated by using theprojected mean annual growth rate from 2015 to 2021 (MoEF 2012). Future yields for othercrops were calculated based on estimates of a global agricultural trade model (Somwaru andDirkse 2012). The harvested area of rice was estimated by dividing future crop production byprojected crop yield. For other crops, future harvested areas were estimated by using theprojected annual mean growth rate of the individual harvested crop area from 2015 to 2021(Ministry of Planning 2010). Future production was estimated by multiplying the harvestedarea by the projected yields. Based on the above assumptions, the harvested area of rice, one ofthe main staple crops, comprised 79 % of the total cropland area in 2005 and is expected toincrease 17 % by 2025. This increase might be caused by high cropland intensity resultingfrom multi-cropping. For example, rice paddy is currently cultivated three times a year underdifferent water regimes (MoEF 2012), and the numbers of cultivation is expected to increaseby 2025. The current irrigation ratio of rice paddy is 40 % (IRRI 2011), and this ratio was fixedthroughout the study period. The amount of N fertilizer per harvested area of each crop in thebase year was assumed to be the same as those reported in IFA (International FertilizerIndustry Association)/IFDC (International Fertilizer Development Center)/(United NationsFood and Agriculture Organization)FAO (1999; 2002). The use of fertilizer was assumed toincrease in proportion to crop yields through 2025.

2.5.2 Number of livestock

Figure 3 shows the projected numbers of livestock through 2025. Data on livestock numbersfrom 2005 to 2010 were collected from Ministry of Finance (2011) and Ministry of Planning(2011). The proportions of dairy (Cabose) and beef cattle (Cabose) (dairy, 40 %; beef, 60 %)

0

2,000

4,000

6,000

8,000

10,000

12,000

14,000

2005 2010 2015 2020 2025

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vest

ed a

rea

(tho

usan

d he

ctar

e)

Rice

Wheat

Vegetables

0

100

200

300

400

500

600

700

800

900

2005 2010 2015 2020 2025

)eratcehdn asuoht(

ae rad et sev ra

H

Other coarse grain

Oil crops

Sugar crops

Other crops

Fig. 2 Estimated harvested area for rice, wheat, vegetables, other coarse grain, oil crops, sugar crops, and othercrops, 2005–2025

Mitig Adapt Strateg Glob Change

were taken from FAO (2011) and were assumed to be constant throughout the study period.Future livestock population was estimated based on the projected annual mean growth rate forboth cattle types from 2015 to 2021 (Ministry of Planning 2010). The projected cattlepopulation increases from 23 million to 28 million from 2005 to 2025. During the sameperiod, the combined number of sheep (Ovis aries) and goats (Caper) increases from 23million to 32 million while the combined number of chickens (Gallus) and ducks (Anas)increases from 233 million to 322 million.

2.5.3 Land use changes

Figure 4 shows the assumed land use changes through 2025. Land use data from 2005 to 2010were collected from FAO 2010 and Food and Agriculture Organization Corporate StatisticalDatabase (FAO 2011). Forest plantation areas from 2010 to 2025 were extrapolated based onthe governmental projection that production forests will increase by 20 % from 2010 to 2020(Ministry of Planning 2010). Natural forest areas constitute almost 31 % and forest plantation13 % of total forest areas in 2001 (Bangladesh Centre for Advanced Studies 2000). Futurecropland areas were estimated by using the mean annual growth rate from 2005 to 2010.Difference between physical cropland area and harvested area is filled by assuming highercrop yields and increased cropland intensity which might be achieved by the expansion of

0

5

10

15

20

25

30

2005 2010 2015 2020 2025

)noillim(

rebmun

kcot seviL

Dairy Cattle

Beef Cattle

Buffallo

Sheep

Goat

0

50

100

150

200

250

300

2005 2010 2015 2020 2025

Pou

ltry

num

ber

(mill

ion)

Chicken

Duck

Fig. 3 Estimated numbers of livestock animals (dairy cattle, beef cattle, buffalo (Bubalos), sheep (Ovis), goats,horses, swine, ducks, and chickens), 2005–2025

0

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2005 2010 2015 2020 2025

)seratcahnoilli

m(aera

esudna

L

Others

Inland water

Crops

Grasses

Forests

Fig. 4 Estimated land use area for forests, grasses, crops, inland water and other land areas, 2005–2025

Mitig Adapt Strateg Glob Change

irrigation facilities and introduction of high-yielding crop varieties (Ministry of Planning2011). Pasture land area was fixed at the present level because we assumed a high increasein livestock land productivity based on historical trends (FAO 2011). The area of inland waterwas also assumed to be fixed. Based on the above assumptions, we calculated the area of otherland uses by factoring in the total land constraint. Other land area is defined as built up areas(urban and rural settlements, highways, and other). The other land area increases notably, from17 % to 19 % of the total area, from 2010 to 2025, presumably to meet the growing demandfor settlement areas. In this study, we assumed that wood production was managed inproduction forests and did not cause deforestation.

3 Results

3.1 Baseline emissions from AFOLU sectors

Total GHG emissions from the AFOLU sectors is expected to increase from 56.2MtCO2-eq/yearin 2005 to 69.1MtCO2-eq/year in the baseline scenario, an increase of about 23%. In 2025, totalGHG emissions from the agriculture sector are expected to increase by 24 % from those of 2005and emissions from the other land use sectors are expected to change into a sequestration sourceafter 2015 and represent a sink of 6.5 MtCO2-eq/year in 2025. Total emissions from this sectorwill decrease from 13.3 MtCO2-eq/year in 2005 to 12.6 MtCO2-eq/year in 2025.

Figures 5 and 6 show the breakdown of GHG emissions from the agriculture sector (CH4

and N2O expressed as CO2-eq) and land use sectors (CO2) from 2005 to 2025, respectively.Manure management generates the largest amount of emissions in the agriculture sector, 6.4MtCO2-eq/year of CH4 and 11.4 MtCO2-eq/year of N2O in 2005, increasing to 8.2 MtCO2-eq/year and 14.8 MtCO2-eq/year, respectively, in 2025. Enteric fermentation in livestock is thenext largest contributor with 29 % of total GHG emissions in this sector in 2005. Ricecultivation generated 7.2 MtCO2-eq/year which increased to 8.7 MtCO2-eq/year in 2025, orabout 15 % of the total emissions. Emissions generated from the application of N fertilizer inthe context of managed soils increases to 9.6 MtCO2-eq/year in 2025 from 6.1 MtCO2-eq/yearin 2005. Total GHG emissions gradually increase from 42.9 MtCO2-eq/year in 2005 to 56.5

areas, 2005–2025.

0

10

20

30

40

50

60

2005 2010 2015 2020 2025

OCt

M(noissi

meG

HG

2-eq

/yea

r)

Enteric fermentation (CH4)

Manure management(CH4)

Manure management(N2O)

Rice cultivation (CH4)

Managed soils (N2O)

(CH4)

(CH4)

(CH4)

(N2O)

(N2O)

Fig. 5 Projected GHG emissions from the agriculture sector, 2005–2025

Mitig Adapt Strateg Glob Change

MtCO2-eq/year in 2025. N2O emissions increase by 37 % over the study period, primarilybecause of increased dependency on the utilization of artificial fertilizers to manage soilfertility and crop yield.

The main source of emissions in the other land use sectors is emission and removal fromsoils, followed by forest and grassland conversion (Fig. 6). In 2005, net emissions from theother land use sectors was about 13.3 MtCO2-eq/year, mainly from emission and removal fromsoil (16.1 MtCO2-eq/year), followed by forest and grassland conversion (3.6 MtCO2-eq/year);carbon sequestration as forest and other woody biomass stocks accounted for −6.5 MtCO2-eq/year. The emissions are estimated to increase by 12 % to 15 MtCO2-eq/year in 2010 as a resultof a substantial decrease in forest area and a considerable increase in settlement areas for thegrowing population. The emissions then decrease to 11.6 and 12.6 MtCO2-eq/year by 2020and 2025, respectively, primarily as a result of government actions taken to reduce emissionsfrom forest and grassland conversion in this sector.

Figure 7 shows the estimated GHG emissions in 2005 for our study and those for MoEF(2012). Emissions from the agriculture sector were estimated to be about 43 MtCO2-eq/year inboth studies. Emissions were higher (18.2 MtCO2-eq/year) in the other land use sectors in

-10

-5

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25

OCt

M(noissi

meG

HG

2-eq

/yea

r)Changes in forest and other woody biomass stocks

Forest and grassland conversion (excl. peatland)

Emission and removals from soils

2005 2010 2015 2020 2025

Fig. 6 Projected GHG emissions from the other land use sectors, 2005–2025

-10

0

10

20

30

40

50

GH

G e

mis

sion

(MtC

O2-

eq/y

ear)

Enteric fermentation

Manure management

Manure management

Rice cultivation

Managed soils

Changes in forest and other woody biomass stocks

Forest and grassland conversion (excl. peatland)

Emission and removals from soils

This Study

MoEF 2012

Other land useAgriculture

This Study

MoEF 2012

Fig. 7 Estimated GHG emissions from the AFOLU sectors in 2005 in this study and MoEF (2012)

Mitig Adapt Strateg Glob Change

MoEF (2012) than in our study (13.3 MtCO2-eq/year). GHG emissions from changes in forestand other woody biomass stocks accounted for −4.3 MtCO2-eq/year in MoEF (2012), whereasthey were about −6.5 MtCO2-eq/year in our study. Different assumptions about emissioncoefficients and activity amounts may also have contributed to the differences in the estima-tions in the other land use sectors.

3.2 Mitigation potentials in the agriculture sector

The breakdown of GHG mitigation potentials in agriculture in 2025 by applied technologies atvarious emission tax rates is shown in Fig. 8. At an emission tax of US$0/tCO2-eq, 10 MtCO2-eq/year of mitigation potential can be achieved in 2025. There are three types of mitigationtechnologies at this zero-cost level (often called no-regret technologies): bioenergy frommanure (BM), midseason drainage (MD), and replacement of roughage with concentrates(RRC). These technologies contribute 4.1 MtCO2-eq/year, 3.2 MtCO2-eq/year, and 2.7MtCO2-eq/year of mitigation potential, respectively.

At an emission tax of US$10/tCO2-eq, the mitigation potential increases to 12.8 MtCO2-eq/year (about 23 % of the agricultural baseline emissions) in 2025. The three no-regrettechnologies still contribute the largest shares (74 %), but they are augmented by off-seasonincorporation of rice straw (OIR; 10 %), replacing urea with ammonium sulphate (RAS; 6 %),and high efficiency fertilizer application (HEF; 9 %).

Higher emission taxes will generate higher mitigation potentials. At emission taxes ofUS$100/tCO2-eq and>US$100/tCO2-eq, the mitigation potential increases to 16.6 MtCO2-eq/year and 20.4 MtCO2-eq/year (29 % and 36 % of the total agricultural baseline emissions),respectively, in 2025. At emission taxes of US$100/tCO2-eq, the biggest contributors are BMfor livestock manure and MD for rice cultivation, which together account for a reduction of 7.7MtCO2-eq/year. Because slow-release fertilizer (SRF), high genetic merit (HGM), and dailyspread of manure (DSM) are relatively expensive technologies, they are only used at the twohighest tax rates. BM is replaced by DSM at a rate of>US$100/tCO2-eq because the totaltechnology cost (including the emissions tax) of DSM becomes lower than that of BM at ahigh emission tax rate. At the highest rate, DSM is estimated to generate an emissionsreduction of 7.7 MtCO2eq/year in 2025.

0

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Mit

igat

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pote

ntia

l (M

tCO

2-eq

/yea

r)

Emission tax (US$/tCO2-eq)

High genetic merit (HGM)

Daily spread of manure (DSM)

Replace urea with ammonium sulphate (RAS)Off-season incorporation of rice straw (OIR)High efficiency fertilizer application (HEF)

Tillage and residue management (TRM)

Slow-release fertilizer application (SRF)

Replacement of roughage with concentrates (RRC)Midseason drainage (MD)

Bioenergy from manure (BM)

Fig. 8 Annual mitigation potential in the agriculture sector in 2025 under different emission tax rates

Mitig Adapt Strateg Glob Change

At the US$10/tCO2-eq emission tax rate, the total mitigation potential is about 30 % greaterthat at the US$0/tCO2-eq rate. The same rate of improvement is seen going from the US$10/tCO2-eq to the US$100/tCO2-eq rates, but the tax rate has increased dramatically.

An increasing trend in mitigation potential at a tax of US$10/tCO2-eq can be seen in Fig. 9.Mitigation potential is expected to increase from 10.9 MtCO2-eq/year to 12.8 MtCO2-eq/yearfrom 2010 to 2025. Technologies for rice cultivation Midseason drainage (MD), Replace ureawith ammonium sulphate (RAS), and Off-season incorporation of rice straw (OIR)) contributeonly 9 % of the increase (4.3 MtCO2-eq/year to 4.7MtCO2-eq/year) in mitigation potentialduring the same period because the harvested area of rice is expected to slightly increase in thefuture. In contrast, BM and RRC from the livestock sector generate 17 % of the increase inmitigation potential because of the great increase in the number of livestock in the countryduring this period.

3.3 Mitigation potentials in the other land use sectors

Figure 10 shows the mean annual mitigation potential under a wide range of total mitigationcosts (US$0.1 million to US$500 million) for the period from 2010 to 2025. Under amitigation cost of US$1.0 million, only reduced-impact logging (RIL) and enhanced naturalregeneration (ENR) can be applied as cost-effective technologies. In addition, long-rotationartificial reforestation (LRR) can be applied achieving annual mitigation potential of 6.5MtCO2-eq/year at a total emission cost of US$10 million because LRR is the most cost-effective of the several reforestation/plantation options. At a total cost of US$50 million,medium-rotation artificial reforestation (MRR) and medium-rotation Sal (Shorea robusta)plantation (MRP) generate a mitigation potential of 6.1 MtCO2-eq/year, which would accountfor 48 % of land use baseline emissions in 2025.

High mitigation costs will generate higher mitigation potentials. Although the total mitiga-tion potential at US$10 million is 3.9 times the size of that at US$1 million, the mitigationpotential at US$100 million is only 2.1 times the size of that at US$10 million. This is becausesome land use countermeasures are relatively low cost and do not become dramatically moreeffective at higher costs, and the remaining technologies are relatively expensive. At a totalmitigation cost of US$100 million, the mitigation potential reaches 20.5 MtCO2-eq/year. Atthis cost level, ENR has the highest mitigation potential (5.4 MtCO2eq/year), followed by

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Fig. 9 Mitigation potential in the agriculture sector under an emission tax of US$10/tCO2-eq, 2010–2025

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MRR (4.6 MtCO2eq/year) and MRP (4.4 MtCO2eq/year). Under a total mitigation cost ofUS$500 million, 39.6 MtCO2-eq/year of mitigation potential can be achieved. This amount isequal to 166 % of total emissions from land use in 2005 and 120 % of emissions from theenergy sector in 2005 (31.7 MtCO2-eq/year) in Bangladesh. At this relatively highcost level, reforestation and plantations (LRR, MRP, and MRR; and SRP and SRR)contribute a combined 30.5 MtCO2eq/year, and ENR and RIL together contribute 9.1MtCO2-eq/year.

4 Discussion and conclusions

This study quantified GHG emissions and mitigation potentials in the AFOLU sectors inBangladesh by using the AFOLU-B model. The main findings of this study are as follows:

– Bangladesh’s AFOLU sectors are expected to generate GHG emissions of 42.9 to 56.5MtCO2eq/year and 13.3 to 12.6 MtCO2-eq/year from 2005 to 2025, respectively, in thebaseline scenario, in which emissions mitigation measures are not implemented.

– In the agriculture sector, 10.1 MtCO2-eq/year of emissions could be eliminated at no costin 2025 through the use of the following no-regret technologies: bioenergy from manure(BM), midseason drainage (MD) and replacement of roughage with concentrates (RRC).At US$10/tCO2-eq, BM and MD are expected to be the most efficient emission reductiontechnologies, reducing emissions by about 4.1 MtCO2-eq/year and 2.6 MtCO2-eq/year(32 % and 21 % of total emission reduction in 2025), respectively.

– In the other land use sectors, reduced impact logging (RIL) and enhanced naturalregeneration (ENR) are the most cost-effective emission reduction technologies inBangladesh.

A long-term point of view is necessary when selecting mitigation technologies because theuse of shorter timeframes may lead to the implementation of economically inefficient tech-nologies. This study suggests that land-use mitigation technologies have a great potential to

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Fig. 10 Mean annual mitigation potential in the other land use sector under different levels of total mitigationcost for the period 2010–2025

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remove equivalent amounts of emissions generated from other sectors (i.e., the energy sector).Our results show that LRR would be more cost effective than SRP/SRR even though it wouldtake a longer time (40 years) to see the full effect. Short-, medium-, and long-termmitigation targets have been announced in some countries and some sectors, but inthe other land use sectors, our results suggest that a long-term target (35–40 years)and a long-term viewpoint in technology selection should play a key role in GHGemissions mitigation.

On the basis of the mitigation potentials estimated in this study for the AFOLU sectors andour adjusted estimates from the MoEF (2012) study for the energy sectors in Bangladesh. Weestimated the total mitigation potential to be 36 MtCO2-eq/year at an emissions tax rate ofUS$10/tCO2-eq in 2025. This includes 4 MtCO2-eq/year from the energy sector and 32MtCO2-eq/year from the AFOLU sectors. The mitigation potential for the energy sector for2025 was estimated from results of MoEF (2012) by assuming the mitigation ratio at a cost ofless than US$10/tCO2eq to baseline emissions of 2030 to 2025. Mitigation potentialin the AFOLU sectors will be much higher than that in energy sector. Baselineemissions in 2025 are projected to be 102 MtCO2eq/year in the energy sector(MoEF 2012) and 69 MtCO2-eq/year in the AFOLU sector (this study). At thebaseline basis, 4 % of energy-induced emissions and 38 % of AFOLU emissionscan be mitigated at an emissions tax rate of US$10/tCO2-eq or less. It is clear thatapplication of mitigation technologies in the AFOLU sectors has a greater potential toreduce emissions at this cost level in Bangladesh.

There are, however, limitations in the framework and methods of this study in addition toother uncertainties. Climate change impacts and adaptations were not considered in this studyeven though they are important factors influencing AFOLU change. Here, we addressed thepotential mitigation strategies in one Asian country as a first study, focusing on primarily onmitigation. We would like to expand our focus in future research to encompass the impacts ofclimate change.

In addition, the costs of countermeasures might be over- or underestimated becausetechnological improvements and wage changes were not considered. To include these issuesin our evaluation, additional analysis is required by combining our model with a computablegeneral equilibrium model. Moreover when considering barriers to application, (i.e. lack ofsocial acceptability and accessibility of mitigation measures, limited skills and budget, lack ofmarket incentives and knowledge, low extension services and management of knowledge),mitigation potential might become lower than our estimates. Political actions (i.e. nationalmitigation target and related regulations, carbon credit,) would be required to mitiga-tion measure implementation. Although future changes in crop yield were given in themodel, the irrigation ratio of rice paddy and the multi-cropping ratio were fixed at thecurrent level. Yields and these ratios, however, might change depending on techno-logical development, future prices of agricultural commodities and capital, fertilizercosts, and climate conditions. The effect of irrigation expansion and multi-cropping onfuture crop productions is expected to be limited for the period studied and thereforeshould not strongly influence our results. In addition, the LU-B module does notaccount for emissions from wood harvesting because we assumed that wood harvest-ing is not a large factor influencing changes in land use or emissions. Finally, adownscaling of the country into regions could help clarify the spatial distribution ofland use changes.

Our study results are limited owing to the above-stated reasons, but we hope to addressthese issues in future research. The current research findings are a first attempt to provideessential information on GHG emissions and mitigation potentials. This research is a

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preliminary step in the formulation and promotion of mitigation policies and strategies in theAFOLU sectors of Bangladesh.

Acknowledgement This research was supported by the Global Environment Research Fund (S-6-1) by theMinistry of the Environment of Japan and Research Fellowships of the Japan Society for the Promotion ofScience for Young Scientists (23–7066). The first author is grateful for the financial support by theMonbukagakusho Scholarship under the Ministry of Education, Culture, Sports, Science and Technology, Japan.

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