Annex 1: Ex ante estimation of the project net emissions
Title of the JCM project: Prey Lang Wildlife Sanctuary - Stung Treng REDD+ projectIn this document, equation numberings correspond to those described in the applied methodology, i.e.,
JCM_KH_AM004 ver01.1.
I.

Selection of the Option

Option 1 provided in the applied methodology, which uses the National FRL transition probabilities
from forest to non-forest classes only, was selected for the establishment of the project reference level
in JCM Project Design Document Form for REDD-plus (PDD) and therefore was used for ex anteestimation of the project net emissions.
II. Ex ante carbon stock change in the project area in year y (∆CSpj y)
In ex post calculation, the actual areas converted from forest to non-forest, CApj i y, will be determinedusing Cambodia’s official forest map. However, because Cambodia’s official forest map is only createdevery two years and was not available for ex ante estimation, we used the result of own remote sensing
analysis using Sentinel 2A imagery in ex ante calculation. This remote sensing analysis was performed
mainly for the implementation of effective patrolling, and its result will not be used in ex post calculation.
Visual delineation of land entities (digitization) is listed in Chapter 2.1 MONITORING CHANGES INFOREST AREA under Step 5.2: analysis methods of the GOFC-GOLD Source book COP 22 version
11. This method was selected because (a) data already being collected for law enforcement monitoringof the forest area, (b) it is possible to complete the manual digitization in monthly intervals due to therelatively small size of the project area, (c) does not require advanced levels of technical expertise, and(d) the high degree of familiarity of the forest area by the digitizers. Note that the project reference levelhas been established and available in PDD.
Sentinel 2A imagery was downloaded from http://glovis.usgs.gov and used to detect and estimate
deforestation in the project site. The Ministry of Environment’s official forest cover map for 2016 wasused as the base map, and non-forested areas in the map was excluded from the analysis. Every month
where cloud cover was low enough for analysis, a set of two satellite images (Table A-1) were comparedusing ArcGIS 10.8. Visually detected areas of deforestation were manually digitized by drawing
polygons using editor tool, image analysis tool, composite band tool and drawing tool in ArcGIS 10.8.
Monthly, twenty point locations were opportunistically selected for field-checking by ranger teams.
Additionally, polygons with unclear classification, (e.g. deforestation and older agricultural fields) wereground-truthed by project teams using GPS units and Drones (DJI Phantom and Mavic).

1

http://www.gofcgold.wur.nl/redd/sourcebook/GOFC-GOLD_Sourcebook.pdf

Table A- 1 Sets of remote sensing images used to detect areas of deforestation during each monitoring
periodIDSatelliteStart dateEnd date

1Sentinel 2

01 Jan 201825 Feb 2018

2Sentinel 2

25 Feb 201822 December 2018

3Sentinel 2

22 December 201821 Jan 2019

4Sentinel 2

21 Jan 201915 Feb 2019

5Sentinel 2

15 Feb 201922 Mar 2019

6Sentinel 2

22 Mar 201916 Apr 2019

7Sentinel 2

16 Apr 201921 Oct 20198Sentinel 2

21 Oct 201902 Dec 2019

9Sentinel 2

02 Dec 201927 Dec 201910Sentinel 227 Dec 201919 Jan 2020

The project was started in March 2018, and the remote sensing analysis was performed to the project
area for 2018 and 2019. Table A-2 shows the deforestation areas estimated from the analysis, and theseresults were used as ex ante areas converted from forest to non-forest in the project area, CApj i y, in2018 and 2019. From 2020 to 2024 and then from 2025 onwards, we applied a reduction of 10% and20%, respectively, each year from the deforestation area in the previous year, based on expectation thatdeforestation will decrease as a result of project activities. The percentages were determined based on
professional judgment. Table A- 3 shows the ex ante estimations of carbon stock change in the project
area in year y, ∆CSpj y, which were calculated with CApj i y, emission factors in Table 2 in the applied
methodology and Equation 11.
Table A- 2 Ex ante estimation of areas converted from forest i to non-forest, CApj i y
Estimation

Area converted from forest class i to non-forest in the project area,

methodhaYear

Evergreen

Semi-evergreen

Deciduousforest

forestforestBambooForestregrowth

Remote2018

43185

324sensing2019

149

380

20

128

10% reduction 2020

134

342

18

125from120308

161232022

108

277

14

1212023

98

249

13

119

2024

88

225

12

1

17

20% reduction 2025

70

180

9

113

analysisthe 2021

previous year

fromthe 2026

previous year

56

144

70

112027

45

115

60

92028

36

92

50

72029

29

74

40

5

Table A- 3 Ex ante carbon stock change in the project area in year y, ∆CSpj yYear
Ex ante estimation of ∆CSpj y, tC
2018
29,243
2019

67,1142020

60,4022021

54,3622022

48,9262023

44,0332024

39,6302025

31,7042026

25,3632027

20,2902028

16,2322029

12,986

Total450,285

III. CO2 emissions from fossil fuel combustion in year y (Efuel y)
The only planned activity which results in fossil fuel combustion is for patrolling and for community
supports with motorbikes, and the ex ante Efuel y was calculated based on the actual fossil fuel use, which
was recorded as the purchase receipts.
In 2018, 2019 and 2020, the project used 4,602, 5,846 and 6,957 liter of motor gasoline respectively,
and these values were applied for the 1st, 2nd and 3rd year estimation. For the ex ante estimation fromthe 4th year onward, we used the amount of fuel consumption in the 3rd year. The amount in volume wasconservatively converted to that in mass by applying 0.783 g/cm3, which is the highest density allowed
under Japanese Industrial Standard2.
For ex ante estimation, we assume that similar level of activities and the same quantity of fuelconsumption continue for the entire monitoring period. Efuel y was calculated with the direct method and2006 IPCC default numbers 0.0443 and 0.0693 for net calorific value (NCV) and CO2 emission factor(EFfuel) respectively (2006 IPCC Guidelines Volume 2, Table 1.2 of Chapter 1 and Table 3.2.1 of Chapter3) by applying Equation 14. Table A- 4 shows the results.
2

https://www.jisc.go.jp/app/jis/general/GnrJISUseWordSearchList?toGnrJISStandardDetailList

Table A- 4 Quantity of fuel type f consumed in year y, FCf y, and ex ante estimation for CO2 emissions
from fossil fuel combustion in year y, Efuel y
YearFCf y, kg
2018
3,603
2019
4,577

Ex ante estimation for Efuel y, tCO2
11.1
14.1

2020

5,447

16.7

2021

5,447

16.7

2022

5,447

16.7

2023

5,447

16.7

20245,447

16.7

2025

5,447

16.7

2026

5,447

16.7

2027

5,447

16.7

2028

5,447

16.7

2029

5,447
62,654

16.7
192.3

Total

IV. GHG emissions from fertilizer application within the activity area as a part of the project activities
in year y (Efertilizer y)
The project plans to utilize green manure in local villagers’ farms to improve agricultural production,and candidate species for green manure are listed below:‒

Canavalia ensiformes‒

Crotalaria juncea L.

‒

Panicum purpurascens Mez.

‒Brachiaria brizantha‒

Eleusine coracana Gaern‒Cajanus cajan‒

Stylosanthes guianensis

Based on the project management record, Crotalaria juncea L., which is a legume, was applied in 2 haof cashew plantation and 0.2 ha of rice field in 2020 as a demonstration, but no green manure wasutilized in 2018 and 2019. In all cases, Crotalaria juncea L. was harvested twice a year, making theannual harvested area double, i.e., 4 ha in cashew plantation and 0.4 ha in a rice field in 2020. This
information was used for the 1st, 2nd and 3rd year estimation. For the ex ante estimation from the 4th year
onward, we assume that beans would be used as N-fixing crop throughout the area under the project’s
support, though there would be some area where no green manure, in particular early stage of the project,
or other types of green manure is utilized, or non-paddy area where green manure is utilized. We assume

that the area under the project’s support will be increased 200 ha every 2 years until 600 ha and be allrice field. Same as for 2020, the areas were doubled to calculate the annual harvested areas.
We calculated 1) direct N2O emissions as a result of nitrogen application within the project area and theactivity area for the implementation of the project activities in year y, Edirect-N y, 2) indirect N2O emissions
as a result of nitrogen application within the project area and the activity area for implementation of theproject activities in year y, Eindirect-N y, and 3) GHG emissions from fertilizer application within theproject area and the activity area for implementation of the project activities in year y, Efertilizer y byapplying the parameters described in Table A- 5, Equations 16, 17, 20, 21 and 24. The results aresummarized in Table A- 6.
In addition to green manure, the project also plans to use organic fertilizer. According to themethodology, organic fertilizer which is made from organic materials sourced from inside the project
area and the activity area are NOT accounted, and this is the case for the project. Therefore, green
manure is the only source of N2O emissions from fertilizer application.
Table A- 5 Parameters applied to calculate emissions as a result of fertilizer application
Parameter

ValueSource

Harvested annual dry matter yield for N- 1.1579

FAOSTAT, Bean yieldfixingof Cambodia in 2019

cropT,introduced

duringtheimplementation of the project activities incropland type c in the activity area in year y,
Cropc T y, t d.m./ha
Total annual area harvested of N-fixing crop In rice paddy, 0 ha in the ProjectstmanagementndT, introduced during the implementation of 1 and 2 year, 0.4 ha in record
the project activities in cropland type c in the the 3rd year, 400 ha in 4th
activity area in year y, Area c T y, haand 5th year, 800 ha in 6thand 7th year, and 1200 hain the following years.
In general (non paddy), 0
ha in the 1st and 2nd year,
4 ha in the 3rd year, and 0
ha in the following years.

Fraction of total area under N-fixing crop T 1 dimentionlessInterviewthat is renewed annually, FracRenew T

agriculture(availableforlocalexpertinProject

management record)
Ratio of above-ground residues to harvested 1.864 t d.m. (t d.m.)-1 (*) Cropc T y and values for“Beans & pulses” inyield for N-fixing crop T, RAG T

Table 11.2 of Ch. 11,
Vol, 4 of 2006 IPCC
Guidelines
N content of above-ground residues for N- 0.008 t N (t d.m.)-1

Value for “Beans &fixing crop T, NAG Tpulses” in Table 11.2 ofCh. 11, Vol, 4 of 2006
IPCC Guidelines

Ratio of below-ground residues to harvested 0.544 t d.m. (t d.m.)-1 Cropc T y and values foryield for crop T, RBG T“Beans & pulses” in(**)

Table 11.2 of Ch. 11,Vol, 4 of 2006 IPCC
Guidelines
N content of below-ground residues for crop 0.008 t N (t d.m.)-1

Value for “Beans &T, NBG Tpulses” in Table 11.2 ofCh. 11, Vol, 4 of 2006
IPCC Guidelines

Global Warming Potential for N2O, GWPN2O

298 tCO2 tN2O-1

Table 2.14 in Ch.2 ofWorking

GroupI

contribution to the IPCC
ForthAssessment

ReportFraction of N that area lost through leaching 0.3 dimensionless

Table 11.3 of Ch. 11,and runoff, FracleachVol, 4 of 2006 IPCC
Guidelines

Emission factor for N2O emissions from N 0.0075
leaching and runoff, EFleach-N

tN2O-N(t Table 11.3 of Ch. 11,

leaching and runoff)-1

Vol, 4 of 2006 IPCC
Guidelines

Emission factor for N2O emission from N 0.003inputs for Rice paddy (flooded rice field), input)

tN2O-N-1(tN- Table 11.1 of Ch. 11,
Vol, 4 of 2006 IPCC

EFdirect-N (paddy)

Guidelines

Fraction that volatilized as NH3 and NOx for 0.20 dimentionless

Table 11.3 of Ch. 11,

organic fertilizers, FracON

Vol, 4 of 2006 IPCC
Guidelines

Emission factor for N2O emissions from 0.010 tN2O-N (tNH3-N Table 11.3 of Ch. 11,
atmospheric deposition of N on soils and and NOX-N volatilized)-1 Vol, 4 of 2006 IPCC
water surfaces,
EFindirect-N

Guidelines

Fraction of N that is lost through leaching and 0.30 dimentionless

Table 11.3 of Ch. 11,runoff, FracleachVol, 4 of 2006 IPCCGuidelines(*) RAG T was calculated as ratio of above-ground residues dry matter for crop T, AGDM T, to harvested
yield for crop T, Cropc T y by following the method described under equation 11.6 of Ch. 11, Vol. 4 of2006 IPCC Guidelines. AGDM T was calculated by applying the coefficients for “Beans & pulses”, 1.13
and 0.85, in Table 11.2 of Ch. 11, Vol. 4 of 2006 IPCC Guidelines and Cropc T y, 1.1579, in Table A-5above. RAG T = AGDM T / 1.1579; AGDM T = 1.1579 * 1.13 + 0.85.
(**) RBG T was calculated by following a method described under equation 11.6 of Ch. 11, Vol. 4 of2006 IPCC Guidelines, i.e., multiplying RBG-BIO, 0.19, in Table 11.2 of Ch. 11, Vol. 4 of 2006 IPCC
Guidelines by the ratio of total above-ground biomass to Cropc T y.,1.1579. RBG T = 0.19 * (AGDM T +1.1570)/ 1.1579; AGDM T = 1.1579 * 1.13 + 0.85.

Year2018

Table A- 6 Emissions as a result of fertilizer application
Edirect-N y,
Eindirect-N y,
Eliming y,
Eurea y,tCO2-eqtCO2-eqtCO2-eqtCO2-eq
0.0
0.0
0.0
0.0

Efertilizer y,
tCO2-eq
0.0

2019

0.0

0.0

0.0

0.0

0.0

2020

0.4

0.1

0.0

0.0

0.5

2021

12.5

9.4

0.0

0.0

21.9

2022

12.5

9.4

0.0

0.0

21.9

2023

25.1

18.8

0.0

0.0

43.9

2024

25.1

18.8

0.0

0.0

43.9

2025

37.6

28.2

0.0

0.0

65.8

2026

37.6

28.2

0.0

0.0

65.8

2027

37.6

28.2

0.0

0.0

65.8

2028

37.628.2

0.0

0.0

65.8

2029

37.6
263.7

28.2
197.5

0.0

0.0

0.0

0.0

65.8
461.2

Total

V. Estimation of displaced emissions
Displaced emissions will be estimated as increases of emissions from reference emissions in thedisplacement belt. Please refer PDD for the reference emissions in the displacement belt. In ex post
calculation, the actual areas converted from forest to non-forest in the displacement belt, CAd pj i y, will
be determined using Cambodia’s official forest map. However, because Cambodia’s official forest mapis only created every two years and was not available for ex ante estimation, we used the result of own
remote sensing analysis using Sentinel 2A imagery in ex ante calculation. As described above, thisremote sensing analysis was performed mainly for the implementation of effective patrolling and its

result will not be used in ex post calculation.
As with the project area, the remote sensing analysis was performed on the displacement belt for 2018and 2019. Table A- 7 shows the deforestation areas estimated from the analysis, and these results wereused as ex ante areas converted from forest to non-forest in the displacement belt, CAd pj i y, in 2018 and2019. From 2020 to 2024 and then from 2025 onwards, we applied a 10% and 20% reduction each year
from the deforestation area in the previous year. The ex ante estimations of carbon stock change in thedisplacement belt in year y, ∆CSd pj y were calculated with CAd pj i y, emission factors in Table 2 in theapplied methodology and Equation 34. Table A- 8 shows the results.
Table A- 7 Ex ante estimation of areas converted from forest i to non-forest in the displacement belt,
CAd pj i y
Estimation

Area converted from forest class i to non-forest in the displacement

methodbelt, haYear

Evergreen

Semi-evergreen

Deciduous

forestforestforestBambooForestregrowth

Remote

2018

62

318

54

0

28

sensing

2019

287

710

71

3

60

10% reduction 2020

258

639

64

3

54

from

232

575

58

2

49

2022

209

517

52

2

442023

188

466

47

2

40

2024

169

419

42

2

36

20% reduction 2025

135

335

34

1

29

from

108

268

27

1

23

2027

87

215

22

1182028

69

172

17

1

152029

55

137

14

112

analysisthe 2021

previous year

the 2026previous year

Ex ante project emissions from the displacement belt in year y, DPy, were then calculated with Equation
29. DPy was compared with reference emissions from the displacement belt in year y, DRy (Table F inPDD), and ex ante displaced emissions to the displacement belt in year y, DEy, was estimated withEquation 27. Table A- 8 shows the results.

Year
2018

Table A- 8 Ex ante displaced emissions to the displacement belt
∆CSd pj y, tC
DPy, tCO2
DEy, tCO2
52,394
192,111.7
56,919.8

2019

128,081469,629.5

306,867.2

2020115,273

422,666.5

265,298.2

2021

103,745

380,399.9

228,207.1

2022

93,371

342,359.9

195,136.5

2023

84,034308,123.9

165,674.9

2024

75,630277,311.5

139,452.2

2025

60,504

221,849.2

88,404.7

2026

48,403

177,479.4

48,283.6

2027

38,723141,983.5

16,878.72028

30,978113,586.8

0.0

2029

24,783855,919

90,869.4
3,138,371.2

0.0
1,511,122.8

Total

VI. Ex ante estimation of the project net emissions in year y, PEy
Based on ex ante estimation of ∆CSpj y, Efuel y, Efertilizer y and DEy, project net emissions in year y, PEy,
was estimated by applying Equation 10. Table A- 9 summarizes the results.

Year
2018

Table A- 9 Ex ante estimation of project net emissions in year y∆CSpj y * 44/12, tCO2
Efuel y, tCO2
Efertilizer y, tCO2-eq
DEy, tCO2
107,224
11.1
0.0
56,919.8

PEy, tCO2
164,154.5

2019

246,08314.1

0.0

306,867.2

552,964.1

2020

221,475

16.7

0.5

265,298.2

486,790.0

2021

199,327

16.7

21.9

228,207.1

427,572.9

2022

179,394

16.7

21.9

195,136.5

374,569.6

2023

161,455

16.7

43.9

165,674.9

327,190.4

2024

145,309

16.7

43.9

139,452.2

284,822.3

2025

116,248

16.7

65.8

88,404.7

204,734.8

2026

92,99816.7

65.8

48,283.6

141,364.2

2027

74,39816.7

65.8

16,878.7

91,359.7

2028

59,519

16.7

65.8

0.0

59,601.3

2029

47,615

16.7

Total

1,651,045

192.3

65.8
461.2

0.0
1,511,122.8

47,697.6
3,162,821.5