Develop Forest Reference Emission Levels/Forest Reference Level and National Forest Monitoring System, Measurement, Reporting and Verification System for Redd++

Ministry of Climate Change Government of Pakistan

DEVELOP FOREST REFERENCE EMISSION LEVELS/FOREST REFERENCE LEVEL AND NATIONAL FOREST MONITORING SYSTEM, MEASUREMENT, REPORTING AND VERIFICATION SYSTEM FOR REDD++

National Forest Reference Emission Level National Forest Reference Emission Level for Pakistan Draft Final 3.0 February 2019

DISCLAIMER Arbonaut and WWF Pakistan makes its best effort to provide accurate and complete information while developing this document.

Copyright © 2019 Ministry of Climate Change, Government of Pakistan All rights reserved. No part of this publication may be reproduced or transmitted in any form or by any means, electronic or mechanical, including, but not limited to, photocopying, recording or otherwise.

National Forest Reference Emission Level / Forest Reference Level for Pakistan DRAFT FINAL 3.0

TABLE OF CONTENTS ABBREVIATIONS 5 EXECUTIVE SUMMARY 8 National and Sub-National FREL/FRL 2 1. INTRODUCTION 7 1.1 Forest sector and REDD+ context in Pakistan 7 2. REFERENCE TO UNFCCC MODALITIES 13 2.1 UNFCCC modalities 13 2.2 Consistency with national greenhouse gas reporting 15 3. INFORMATION USED FOR DEVELOPMENT OF FREL/FRL 18 3.1 Scale 18 3.2 Scope of activities 18 3.3 Forest definition and stratification 18 3.3.1 Forest definition 18 3.3.2 Stratification 18 3.4 Carbon pools and gases 21 3.5 Historic time period 22 3.6 Rationale for assessing adjustment needs based on the changed national circumstances 22 3.7 Approach and methodology for FREL/FRL establishment 24 3.8 Activity Data 25 3.9 Emission and Removal Factors 28 3.9.1 Forest inventory data for carbon stock modelling 28 3.9.2 Emission and removal factors 30 4. HISTORICAL FOREST COVER, TRENDS, EMISSIONS AND REMOVALS (1996-2016) 34 4.1 National forest cover and trends in 1996-2016 34 4.2 Sub-national forest cover and trends in 1996-2016 36 4.2.1 Azad Jammu and Kashmir (autonomous territory) 36 4.2.2 Balochistan 37 4.2.3 Federally Administered Tribal Area and Khyber Pakhtunkhwa 37 4.2.4 Gilgit-Baltistan 38 4.2.5 Punjab 38 4.2.6 Sindh 39 4.3 National and Sub-National FREL/FRL 40 4.4 Analysis of the national activity trends 41 5. FREL/FRL PROJECTIONS AND UNCERTAINTY ASSESSMENT 43 5.1 Projections (2016-2026) 43 5.2 Uncertainty assessment 44

6. TRANSPARENCY, COMPLETENESS, CONSISTENCY AND UNCERTAINTY OF INFORMATION 46 6.1 Transparency 46 6.2 Completeness 46 6.3 Consistency 46 6.4 Accuracy 46 7. PLAN FOR STEPWISE FREL/FRL IMPROVEMENT 47 8. REFERENCES 49 9. GLOSSARY OF RELEVANT TERMS 51

ANNEXES 56 ANNEX 1. ADMINISTRATIVE BOUNDARY MAP (SOP, 2012) 57 ANNEX 2. SATELLITE IMAGERY USED FOR LULC/AD MAPPING 58 ANNEX 3. REFERENCE WOOD DENSITITES BY SPECIES 64 ANNEX 4. FOREST CARBON DENSITY (ABOVE- AND BELOWGROUND) AND ANNUAL GROWTH RATES APPLIED BY SUB-NATIONAL AREAS 65 ANNEX 5. HARVESTS AND DISTURBANCES 66 ANNEX 6. NATIONAL CIRCUMSTANCES 68 ANNEX 7. SUB-NATIONAL ACTIVITY TRENDS (1992-2016) 79 ANNEX 8. JUSTIFICATION OF COMMISSION AND OMISSION OF ACTIVITIES, POOLS AND GASES 82

LIST OF FIGURES Figure 1 Land use and cover map 2016. 8 Figure 2 Stratification by the forest types. 19 Figure 3 The SLMS process to estimate the land use and forest cover in Pakistan. 27 Figure 4 National forest cover estimates with 95-% confidence intervals (1996-2016). 34 Figure 5 Annual deforestation rates in Pakistan (2004-2016). 35 Figure 6 Annual forest restoration rates in Pakistan (2004-2016). 35 Figure 7 Net forest land change in Pakistan (2004-2016). 36 Figure 8 Forest cover estimates with 95-% confidence intervals in AJK (1996-2016). 36 Figure 9 Forest cover area with 95-% confidence intervals in Balochistan (1996-2016). 37 Figure 10 Forest cover area with 95-% confidence intervals in FATA-KP (1996-2016). 38 Figure 11 Forest cover estimates with 95-% confidence intervals in GB (1996-2016). 38 Figure 12 Forest cover estimates with 95-% confidence intervals in Punjab (1996-2016). 39

Figure 13 Forest cover estimates with 95-% confidence intervals in Sindh (1996-2016). 39 Figure 14 Historical accounts in national forest emissions and removals (Mt CO₂-e). 40 Figure 15. National gross and net projections 2016-2026. 43 Figure 16 Share of Forestry in GDP of Pakistan (1996-97 to 2015-16). Data Source: Ministry of Finance’s Economic Survey Reports 72 Figure 17 Historical trends in Population dynamics and per capita availability of forests (1996-2019). Data Source: Ministry of Finance’s Economic Survey Reports 73

LIST OF TABLES Table 1 Official area statistics (SoP, 2012) 7 Table 2. Linear plantations controlled by the Forest Departments in 2012- 2013. 9 Table 3. Drivers of deforestation and forest degradation (draft REDD+ Strategy, May 2018). 12 Table 4. Pakistan’s FREL/FRL compliance concerning the relevant UNFCCC COP decisions. 14 Table 5. The forest land tables (3.B.1.a and 3.B.1.b) AFOLU land use 17 Table 6. Activities and carbon pools included in the national FREL/FRL. 22 Table 7 The number of sample plots interpreted visually for the LULC map accuracy assessment by provinces and territories. 26 Table 8 The height-diameter model types, precision (RMSE) and accuracy (bias) by species and species groups. 28 Table 9. The adopted allometric equations for the tree-level aboveground biomass / carbon stock calculation. D refers to diameter at breast height, H to height and ρ to wood density. 29 Table 10. The applied IPCC root-shoot ratios adapted from Table 3A.1.8 of the IPCC Good Practice Guidance (2006). 30 Table 11 Emission and removal factor calculation formulas. 32 Table 12 Carbon stock, emission and emission removal statistics by the sub- national areas and national totals. 41 Table 13 Pakistan’s circumstances against the FREL/FRL upward adjustment criteria. 44 Table 14. The plan for FREL/FRL improvement in the short- and long-term. 47 Table 15 National and Provincial Level Emissions (million tCO₂ eq) due to Wood Consumption (projected from 2003 to 2018). 75 Table 16 Average Annual Timber and Fuelwood Consumption and Associated Emissions (tons of CO₂ eq) for Pakistan 77 Table 17. Province Wise Fuelwood Consumption and Associated Emissions by House Hold Sector 78

ABBREVIATIONS AD Activity data AFOLU Agriculture, Forestry and Land Use AGB Aboveground Biomass AGC Aboveground Carbon AJK Azad Jammu & Kashmir (autonomous territory) ALGAS Asia Least-cost Greenhouse Gas Abatement Strategy ASAD Applied System Analysis Division BAU Business as Usual BEF Biomass Expansion Factor BGB Belowground Biomass BGC Belowground Carbon BN Balochistan (province) cm Centimetre

CO2-e Carbon dioxide equivalent CoP Conference of Parties DBH Diameter at breast height (at 1.3 m or 1.37 m height from the ground level) DWC Deadwood Carbon EF Emission Factor FAO Food and Agriculture Organization of the United Nations FATA Federally Administered Tribal Areas FCPF Forest Carbon Partnership Facility FD Forest Department (provincial) FREL Forest Reference Emissions Levels GB Gilgit-Baltistan (autonomous territory) GCISC Global Change Impact Studies Centre GEF Global Environment Facility GFRA Global Forest Resource Assessment GHG Greenhouse Gas GHG-I Greenhouse Gas Inventory GoP Government of Pakistan ha Hectare (1 ha = 10,000 m2)

ICIMOD International Centre for Integrated Mountain Development ICT Islamabad Capital Territory (federal capital territory) IPCC Intergovernmental Panel on Climate Change km2 Square kilometre (1 km2 = 1,000,000 m2) KP Khyber Pakhtunkhwa (province) LOC Line of Control LULUCF Land Use, Land Use Change and Forestry MOCC Ministry of Climate Change MRV Measurement, Reporting and Verification Mt Megaton (1 Mt = 1,000,000 tons) NDMA National Disaster Management Authority NFMS National Forest Monitoring System NFRRAS National Forest and Range Resources Assessment Study NTFP Non-Timber Forest Product PAEC Pakistan Atomic Energy Commission PAK-EPA Pakistan Environmental Protection Agency PAK-INDC Pakistan’s Intended Nationally Determined Contribution PB Punjab (province) PFI Pakistan Forest Institute REDD Reducing Emissions from Deforestation and Forest Degradation REDD+ Reducing emissions from deforestation and forest degradation and the role of conservation, sustainable management of forests and enhancement of forest carbon stocks in developing countries RF Removal Factor RMSE Root Mean Squared Error R-PP Readiness Preparation Proposal SD Sindh (province) SLMS Satellite Land Monitoring System SUPARCO Pakistan Space and Upper Atmosphere Research Commission THBS Timber Harvesting Ban Study UNFCCC The United Nations Framework Convention on Climate Change UNDP The United Nations Development Programme VHR Very High Resolution

WWF World Wildlife Fund ZSD Zoological Survey Department of Pakistan

EXECUTIVE SUMMARY

Forest sector and REDD+ context in Pakistan Pakistan has the official total area of 796,096 square kilometres (km2) with a current population of 207.8 million (GoP, 2018) placing it at the sixth most populous country of the world. Pakistan is mainly a dry land country, with 80 percent of its land in arid and semi-arid areas. The country is highly exposed to the future climatic threats. The forestry sector, commonly considered as bearing a high natural capital value for the society and a safeguard against climatic threats, has suffered heavily during the past two decades (R-PP MTR 2017).

At an average economic growth rate of 4.9 percent from 1952 to 2015 (GoP, 2016), which increased to 5.4 percent on average from 2016 to 2018 (GoP, 2018), Pakistan is classified as lower middle-income and primarily an agrarian country (GoP, 2016). The population directly and indirectly associated with the agriculture sector is estimated to be 42.3 percent (GoP, 2017) with a contribution of 18.9 percent to the overall GDP of the country. The forestry sector having current share of 0.39 percent in overall national GDP posted a growth of 7.17 percent in 2018.

In July 2013, Pakistan also became a member of the Forest Carbon Partnership Facility (FCPF) and submitted a REDD+ Readiness Preparation Proposal (R-PP) to the FCPF. This was approved, and the Islamic Republic of Pakistan was selected as a REDD+ Country participant. The Participant Committee (PC) of FCPF through its Resolution PC/16/2013/8 decided to allocate USD 3.8 million grant funding to Pakistan to enable it to move ahead with preparation for readiness. Under the FCPF grant, the REDD+ Readiness Preparation Activities are broadly categorized into four components. These include REDD+ Policy Analysis, REDD+ Technical Preparation (including development of forest reference emission levels/forests reference levels for Pakistan), REDD+ Readiness Management, and Designing and Testing of REDD+ Payment for Environmental Services.

UNFCCC modalities Within the context of the United Nations Framework Convention on Climate Change (UNFCCC or Convention), REDD+ FREL/FRLs serve two main purposes. Firstly, FREL/FRLs provide a business-as-usual (BAU) baseline against which actual emissions are compared to, whereby emission reductions are estimated as the difference between FRLs and actual emissions. FRLs depict what the emissions scenario would be in the absence of REDD+ implementation, and thus provides the basis for measuring its success. Secondly, FREL/FRLs are needed to determine the eligibility of UNFCCC Parties for international, results-based support for REDD+, and to calculate that support on the basis of measured, reported, and verified emission reductions. The creation of REDD+ FREL/FRLs as benchmarks for assessing performance are guided by modalities contained in UNFCCC Conference of Parties (CoP) decisions, most notably decision 12/CP.17 and its Annex. These modalities state that when establishing FREL/FRLs, Parties should do so transparently taking into account historic data and adjust them for national circumstances. A step-wise approach is

allowed that enables Parties to improve the FREL/FRL by incorporating better data, improved methodologies and, where appropriate, additional pools. FRLs are expressed in units of tons of CO2-e per year and must maintain consistency with a country’s greenhouse gas inventory (according to 12/CP.17, Paragraph 8). The UNFCCC invites Parties to submit information and rationale on the development of their forest reference emission levels and/or forest reference levels, including details of national circumstances and if adjusted include details on how the national circumstances were considered in accordance with the guidelines contained in annex to decision 12/CP.17. Para (b) of the annex to decision 12/CP.17 requires parties to provide transparent, complete, consistent and accurate information, including methodological information, used at the time of construction of forest reference emission levels and/or forest reference levels, including, inter alia, as appropriate, a description of data sets, approaches, methods, models, if applicable and assumptions used, descriptions of relevant policies and plans, and descriptions of changes from previously submitted information. Further guidance was provided in Decision 13/CP.19 which require country parties to provide description of relevant policies and plans (para d & e) to include assumptions about future changes to domestic policies in the construction of the forest reference emission level and/or forest reference (para h) Consistency with national greenhouse gas reporting Until the latest national greenhouse gas inventory (GHG-I) report the Revised 1996 IPCC Guidelines for National Greenhouse Gas Inventories have been mostly applied as the methodological guidance document. 2006 IPCC Guidelines for National Greenhouse Gas Inventories: Agriculture, Forestry and Other Land Use has been used to provide the FREL/FRL methodological framework.

FREL refers to gross annual emissions as CO2-equivalent tons per hectare and is calculated with the following equation using activity data and emission factors for each forest stratum separately.

FREL (tons CO2-e per year) = Activity data (hectares per year) X Emission factor (tons CO2-e per hectare) FRL accounts for both average annual emissions and removals. It is calculated with the following equation using activity data for enhancement with removal factors for each stratum separately:

FRL (tons CO2 per year) = Activity data (hectares per year) x Emission/Removal factors (tons CO2-e per hectare) The forest definition, harmonized forest classification system, forest area changes, carbon inventory and emission/removal factor data analysed are to be used as the Tier 2 and 3 level parameter inputs in scope of the future GHG-I inventories. Definitions for developing FREL/FRL Scale. The proposed FREL/FRL scale is national and it has been produced by mapping and compiling the available sub-national input datasets. The national rules and principles are used to harmonize sub-national datasets to ensure consistency. The sub-national datasets are harmonized by the province and territory boundaries.

Scope of activities. Carbon stock enhancement has been included besides deforestation and degradation being the key activity category for REDD+. Carbon stock enhancement takes place through forest restoration, afforestation, reforestation and natural regeneration. Forest degradation is included in the emission factors for the forest remaining as forests, even though there are some uncertainties involved. Forest definition. The national definition of forest complies with the following definition: “A minimum area of land of 0.5 ha with a tree crown cover of more than 10 % comprising trees with the potential to reach a minimum height of 2 meters. This will also include existing irrigated plantations as well as areas that have already been defined as forests in respective legal documents and expected to meet the required thresholds as defined in the national definition for Pakistan.” Stratification. The emission and removal factors are developed for the main forest types following the Champion et al (1965) classification system including natural forests and plantations. Carbon Pools and gases. The included terrestrial carbon pools are aboveground and belowground biomass for deforestation, forest degradation and carbon stock enhancement. The greenhouse gases accounted from the selected forest pools cover CO2 only. Historic time period. The approach based on historical average of net emission from deforestation, forest degradation and removals from enhancement of forest carbon stocks in 2004-2016 with 4 reference years in-line with the UNFCCC and FCPF/World Bank methodological framework guidance1. Three periods for change assessments are considered enough to create a regression line for predicting the business-as-usual trends of the forest cover development. Activity Data. Mapping is based on land use and cover classification using Landsat satellite imagery for each of the reference years. Very High-Resolution (VHR) satellite imagery from Google Earth are used as the historical mapping reference and verifying the map accuracies based on visually interpreted plots using Open Foris Collect. The error-adjusted forest cover estimated are according to Olofsson et al (2014) methodological guidance. Time-series analysis is conducted to generate activity data concerning deforestation and carbon stock enhancement through forest restoration activities. There are forestland remaining as forestland while their carbon stock changes or they get degraded due to growth, harvesting activities and disturbances. Deforestation refers to the areas where forest land is converted to other land. When other land areas are converted to forests, forestland is restored through afforestation, reforestation and natural regeneration Emission and Removal Factors represent emissions and net removals per hectare of land which is either remaining as forest or when forest has been converted to other land or land to forest land. The local emission and removal factors have been developed for each province by the main forest types using the existing and collected carbon measurement data.

1 minimum 3 historical points of time over the past 10-15 years considered as the most relevant reference

National and sub-national forest cover (1996-2016) The national forest cover has been assessed for 6 points of time including 1996, 2000, 2004, 2008, 2012 and 2016. Based on the time-series assessment with the 95-% confidence interval there has not been any significant difference identified between the individual years. The same conclusion applies to the provincial and territorial forest cover statistics following the national level figures. The national forest cover estimates vary from 5.9 to 6.4 percent of total surface between the years with ± 0.9 percentage points2 in average. There is limited reference data coverage available for accuracy assessment of the land use and cover maps 1996, 2000 and 2004.

Year Forest Area (Ha) % Area 1996 5,556,325 ± 809,137 6.3 ± 0.9 2000 5,674,499 ± 848,539 6.4 ± 1.0 2004 5,654,928 ± 849,924 6.4 ± 1.0 2008 5,528,031 ± 798,302 6.3 ± 0.9 2012 5,557,765 ± 797,919 6.3 ± 0.9 2016 5,239,103 ± 782,054 5.9 ± 0.9

2About ±15 percents interval in relation to the mean value with 95-% confidence.

National Forest Reference Emission Level / Forest Reference Level for Pakistan

The forest cover change trends have been assessed at national and sub- national levels with satellite time-series based activity data derived with land use and cover maps. The error-adjustment methodology published by Olofsson et al (2014) is implemented to produced unbiased change statistics. The change analysis has been conducted starting from 2004 onwards as a sufficient geographic and temporal coverage of Very High Resolution (VHR) satellite image data is available since then for visual verification of the occurred changes. There is an increasing trend from about 8,700 hectares to 17,000 hectares in terms of annual deforestation rates at national-level between 2004 and 2016. The mean annual forest restoration rate is at about 40,150 hectares per year over the same period. This forest restoration activities accounts for successful afforestation, reforestation and natural regeneration events. The trend analysis yields an approximate forest cover gain of 333,000 hectares from 2004 to 2016.

Annual deforestation rates in 2004-2016

18 000 AJK BN FATA-KP GB ICT PB SD 16 000 14 000 12 000 10 000 8 000 6 000

Hectares Hectares per year 4 000 2 000 0 2004-2008 2008-2012 2012-2016

Annual forest restoration rates 60 000 AJK BN FATA-KP GB ICT PB SD 50 000

40 000

30 000

20 000 Hectares Hectares per year 10 000

0 2004-2008 2008-2012 2012-2016

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Net forest land change 2004-2016 350 000 AJK BN FATA-KP GB ICT PB SD 300 000

250 000

200 000

150 000 hectares

100 000

50 000

0 2004-2008 2012 2016

National and Sub-National FREL/FRL The forests sequestrate carbon in growth with -16.8 Mt CO₂ - equivalent annually ( Mean Annual Emissions and Removals 2004-2016 10

0 2004-2008 2008-2012 2012-2016 -5

-10 Milliontons CO2 -15

-20

Emissions from deforestation Gross Annual Emissions Gross Annual Removals Net Annual emissions

Figure 14). The average annual emissions based on the historical deforestation are 0.9 Mt CO₂-e. The average annual emissions from wood removals and disturbances are 4.2 Mt CO₂-e, even though a part of timber used in wood construction storing carbon for a longer term. Total 5.1 Mt CO₂-e is accounted as the average annual emissions (FREL). There is an increasing trend in the forest sector emissions. The net annual emissions are -12.2 Mt CO2 (FRL) so the forests sequestrate more carbon dioxide in growth than they emit annually through deforestation, wood removals and disturbances.

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Historical Projections 2016-2026 10

0 2004-2008 2008-2012 2012-2016 2016-2020 2020-2024 2024-2026

-5

Milliontons CO2 -10

-15

-20

Emissions from deforestation Gross Annual Emissions Gross Annual Removals Net Annual emissions Linear (Emissions from deforestation) Linear (Gross Annual Emissions) Linear (Gross Annual Removals) Linear (Net Annual emissions)

Mean annual forest emissions and removals (Mt CO₂-e) and national business-as-usual trends.

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Average carbon stock, emission and emission removal statistics by the sub-national areas and national totals.

FATA- AJK BN KP GB PB SD TOTAL Total carbon stock (Million tons C) 20 11 144 27 24 5 231 Forest carbon density (ton C /ha) 51 10 73 73 30 10 45 Annual Emissions and Removals by Activities Deforestation (Mton CO2-e/year) 0.11 0.00 0.32 0.09 0.18 0.22 0.93 Wood removal and disturbance (Mton CO2-e/year) 0.31 0.02 3.54 0.20 0.07 0.00 4.15 Carbon stock enhancement - (Mton CO2-e/year) -1.41 -1.24 -7.72 -1.29 -3.56 1.25 -16.47 Forest restoration - (Mton CO2-e/year) -0.01 -0.01 -0.05 -0.01 -0.19 0.07 -0.35 FREL (Mton CO2-e/year) 0.42 0.02 3.39 0.30 0.25 0.22 5.08 FRL - (Mton CO2-e/year) -1.00 -1.23 -3.91 -1.00 -3.50 1.10 -11.74

National circumstances and FREL adjustment need for the future projections (2016-2026) The aim of involving national circumstances in projecting the rate of forest carbon emissions and removals is to assess the need for adjustment that allows projecting a difference of the expected emissions/removals for the period from 2016 to 2026, as compared to the historical average forest carbon emissions from 2004 to 2016. The adjustment needs were assessed by comparing and harmonizing the observed past trends with the projected future changes in national circumstances and based on social, demographic, environmental and economic factors such as projections of human population, Gross Domestic Product, and specific regional development plans.). Pakistan’s circumstances were compared against the FREL upward adjustment criteria defined, but it was concluded that the effects of future changes are well captured by the projected FREL (i.e. 2016-2026) without requiring an upward adjustment. Transparency, completeness, consistency and uncertainty assessment

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Comprehensive and clear documentation of methods and data have been included in this report and referred technical documents. All the significant emission/removal sources, pools, and gases are estimated and reported to ensure completeness. Emissions and removal over the historical period have been estimated in a manner that is consistent and shall remain functionally consistent during the REDD+ program implementation. The methodologies and data are also consistent with guidance by the UNFCCC decisions and 2006 IPCC Good Practice Guidelines. The key sources of uncertainty have been identified and are accounted for both activity data and emission and removal factors. The forest map classifications have been assessed through visual interpretation of systematically placed random points for each reference year. The deforestation and forest restoration magnitude are based on time-series analysis of the consecutive reference years and visually interpreted plots. Both the forest cover and forest change statistics are estimated using the methodology referring to Olofsson et al (2014) with 95- -% confidence intervals. Stepwise FREL/FRL improvement plan Pakistan’s REDD+ Readiness Preparation Proposal (2013) provides a roadmap for the projected features and capacity for major NFMS elements, such as the National FREL/FRLs. The following plan reflects the current stage and outlines the short- and long-term objectives foreseen in R-PP. Current Status Short-term objective Long-term objective (Phase 1/2) (Phase 3) Emission/removal factors Key category Key category have been developed assessment for assessment for carbon based on pilot NFI carbon pools pools completed for all inventory by forest types completed for major forest types. and provinces/states forest types. Emission factors for use Emission factors for outside of state-owned use outside of state- forests developed for all owned forests forest types. developed for major forest types. Carbon stock inventory Carbon stock Carbon stock estimation data is available for the estimation where data in all forests and main forest types from all supports it. assessment against the provinces and applicable FREL or FRL territories. to establish performance. Activity data derived for Activity data reported Activity data reported deforestation and forest on the basis of land on the basis of land use restoration data. The use categorization. categorization

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estimated distribution by Emission factors Emission factors the forest types is derived for major derived for all forest available. forest types. types. The forest sector NFMS will generate full emission and removal report on reduced factor data are stored in emissions and NFMS to be used in the enhanced removals on next GHG-I reports. Forest Land.

The current plan is to develop the FREL/FRL updates every 4 years, so accordingly next time the forest mapping will be completed for the period 2016- 2020. However, it is recommended that at forest cover time-series are produced for at least two points of time (e.g. year 2018, 2020, 2022) to allow performing robust forest cover permanence and consistency checks using the one additional time step ahead. The systematic NFMS data collection efforts are required to get timber, fuelwood harvesting and disturbance records to the forest compartment and REDD+ projects in the future. There should be organised more systematic collection of permanent NFI sample plot data for growth data and conducting biomass yield model research to cover the most prevailing forest types. There is a good selection of allometric biomass models available developed for temperate forests, but it is recommended to develop biomass models for the most common tree species in other forest types.

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1. INTRODUCTION

1.1 Forest sector and REDD+ context in Pakistan The official country territory is as reported and published by the Survey of Pakistan (SOP) is 796,096 km2 including Islamabad Capital Territory (ICT), Balochistan (BN), Punjab (PB), Sindh (SD) and Federally Administered Tribal Areas (FATA) which has been lately merged administratively with Khyber Pakhtunkhwa (KP). Gilgit-Baltistan and Azad Jammu and Kashmir are referred as “Disputed Territory” without demarcating the international border (SoP, 2012). Therefore, the land areas of these two provinces i.e. GB and AJK are not officially published.

Table 1 Official area statistics (SoP, 2012) Province/Territory Area (km2) Islamabad 906 Balochistan 347,190 Khyber Pakhtunkhwa 74,521 Punjab 205,345 Sindh 140,914 FATA 27,220 Total 796,096

The current population of 207.8 million (GoP, 2018) places the country at the sixth most populous country of the world. At an average economic growth rate of 4.9 percent from 1952 to 2015 (GoP, 2016), which increased to 5.4 percent on average from 2016 to 2018 (GoP, 2018), Pakistan is classified as lower middle-income and primarily an agrarian country (GoP, 2016). The population directly and indirectly associated with the agriculture sector is estimated to be 42.3 percent (GoP, 2017) with a contribution of 18.9 percent to the overall GDP of the country. The forestry sector having current share of 2.09 percent in agriculture and 0.39 percent in overall national GDP posted a growth of 7.17 percent in 2018 (versus -2.37 percent in 2017) due to higher timber production reported by Khyber Pakhtunkhwa province (GoP, 2018).

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Figure 1 Land use and cover map 2016 of Pakistan.

Pakistan is mainly a dry land country with 80 percent of its land in arid and semi- arid areas. According to the forest definition the total forest has been assessed as about 5.24 million hectares, which is approximately 5.9 percent3 of the country territory in 2016. Besides the tree vegetation meeting the forest definition thresholds, there are also about 57,912 km of linear plantations reported under control of the Provincial and State Forest Departments (FD) (Table 2).

3 with the error margin of ±0.9 %-percentage points with the 95-% confidence interval.

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Table 2. Linear plantations controlled by the Forest Departments in 2012- 2013. Province/State Road side Rail side Canal side Total Azad Jammu & Kashmir - - - - Balochistan 2,000 - - 2,000 Gilgit-Baltistan 13 - - 13 FATA - - - - Khyber Pakhtunkhwa 3,332 60 5,273 8,665 Punjab 10,665 2,580 33,796 47,041 Sindh - - - 193 Total 16,010 2,640 39,262 57,912

Source: Office Record of Chief Conservator of Forests Gilgit-Baltistan, 2012-2013 in State of Forestry in Pakistan 2016.

The country is highly exposed to the future climatic threats. The forestry sector, commonly considered as bearing a high natural capital value for the society and a safeguard against climatic threats, has suffered heavily during the past two decades (R-PP MTR, 2017).The increasing trend in projected emissions from forest sector has been attributed to the threat of accelerated deforestation and forest degradation in many parts of the country in the wake of rising population and associated wood demands, weak governance of tenure, encroachments and land cover changes superimposed by adverse impacts of climate change (Table 3).

Several socio-economic factors have been reported to accelerate the deforestation trends in the country (GoP, 2016), such as prominent among these being poverty, population pressure and lack of fiscal space for strong policy initiatives in protecting forests. Considerable efforts are being taken by the Government of Pakistan for the revival of forestry in the country. These include expanding the forest cover through mega tree plantation programmes4 strengthening the regulatory and forest protection policy mechanism, and implementation of international mechanisms under the United Nations Framework Convention on Climate Change (UNFCCC), such as, Reducing

4 National Green Pakistan Programme, Billion Tree Tsunami Project in Khyber Pakhtunkhwa, Sustainable Forest Management Project funded by UNDP/GEF

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Emissions from Deforestation and Forest Degradation with conservation, sustainable management of forests and enhancement of forest carbon stocks in developing countries (REDD+).

In July 2013, Pakistan became a member of the Forest Carbon Partnership Facility (FCPF) and submitted a REDD+ Readiness Preparation Proposal (R- PP) to the FCPF. Once that was approved the Islamic Republic of Pakistan was selected as a REDD+ Country participant. The Participant Committee (PC) of FCPF through its Resolution PC/16/2013/8 decided to allocate USD 3.8 million grant funding to Pakistan to enable it to move ahead with preparation for REDD+ readiness. Under the FCPF grant, the REDD+ Readiness Preparation Activities are broadly categorized into four components. These include REDD+ policy analysis, REDD+ technical preparation (including development of forest reference emission levels/forests reference levels for Pakistan), Readiness Management, and Designing and Testing of REDD+ Payment for Environmental Services. In January 2018, Pakistan also secured additional grant funding of USD 4.014 million from Readiness Fund of FCPF of the World Bank. The additional grant will be used for extending the sub-national readiness activities. These activities will strengthen the capacity of Pakistan to monitor deforestation and reduce forest and land use change related greenhouse gas emissions through a socially, environmentally, and technically sound national REDD+ strategy. The deliverables would make Pakistan compliant to the UNFCCC decisions the Cancun Agreement on REDD+ and Article 5 of the Paris Agreement.

The Ministry of Climate Change is responsible for supervising and controlling several attached departments and implementation agencies, such as Global Change Impact Studies Centre (GCISC), National Disaster Management Authority (NDMA), Pakistan Environmental Protection Agency (PAK-EPA) and Zoological Survey Department of Pakistan (ZSD). It has also specialized wings to deal with matters relating to Environment and Forestry. A solid foundation for REDD+ was laid by the Ministry of Climate Change (MOCC) in 2010 with the notification of the National Focal Point (NFP) for REDD+ followed by notification of provincial focal points in all provinces and territories of Pakistan. The REDD+ initiatives received the full governmental ownership with the inclusion of REDD+ in the Climate Change Policy 2012.

Pakistan’s National Forest Policy 2015 approved by the Council of Common Interest under the chairmanship of the Prime Minister of Pakistan in November 2016 also gives provision for mainstreaming REDD+ as a tool to curb deforestation and enhance forest cover and forest carbon stocks. In 2013, four working groups were formed on governance and management of REDD+; stakeholder engagement and safeguards; national forest monitoring system

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(NFMS) and Measurement, Reporting and Verification (MRV); and drivers of deforestation and forest degradation. The National Thematic Working Groups (WGs) serve as national platforms that engage stakeholders in scientific discussions, plan and organize research, collect data and serve as platform for providing the National Steering Committee (NSC) on REDD+ with validated data and information for its decisions.

The National REDD+ Office (NRO) is responsible to lead the design and coordinate technical implementation of the national REDD+ programme. The Provincial REDD+ Management Units are responsible for operational coordination in provinces. They coordinate with NRO when developing their provincial forest monitoring systems and draft provincial standards in conformity to the national standards. The coordination functions of the National REDD+ Office will be taken over by the Pakistan Climate Change Authority established under Section 5 of the Climate Change Act, 2017.

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Table 3. Drivers of deforestation and forest degradation (draft REDD+ Strategy, May 2018).

Drivers of Deforestation Drivers of Degradation • Unsustainable fuel wood extraction. • Unsustainable fuel wood extraction • Unsustainable timber extraction. • Unsustainable timber extraction • Free and uncontrolled livestock grazing • Agriculture expansion on marginal scale for subsistence • Infrastructure development (roads, dams, transmission • Livestock grazing, Over-grazing and browsing lines,) • Changes in water availability, mainly scarcity by water • Urban and rural expansion / habitation diversions upstream, drought, climate change). (important in • Agriculture expansion for subsistence mangrove and riverine) • Commercial agriculture expansion (Fish ponds, cash • Salinity and water logging crops, and subsistence crops) • Drought- changes in rainfall and climate patterns • Encroachment of by communities and other government • Infrastructure (pathways, stocking decks etc) agencies. Mainly for agriculture, lower degree for • Mining infrastructure, housing

• Mining • Forest fires (natural and anthropogenic) • Extreme weather events (landslides, floods, snowfall) • Oceanic intrusion • Forest clearing for security in conflict prone borders/areas (FATA, AJK)

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2. REFERENCE TO UNFCCC MODALITIES

2.1 UNFCCC modalities Within the context of the United Nations Framework Convention on Climate Change (UNFCCC or Convention), REDD+ FREL/FRLs serve two main purposes. Firstly, FREL/FRLs provide a business-as-usual (BAU) baseline against which actual emissions are compared to, whereby emission reductions are estimated as the difference between FRLs and actual emissions. FRLs depict what the emissions scenario would be in the absence of REDD+ implementation, and thus provides the basis for measuring its success. Secondly, FREL/FRLs are needed to determine the eligibility of UNFCCC Parties for international, results-based support for REDD+, and to calculate that support on the basis of measured, reported, and verified emission reductions. The creation of REDD+ FREL/FRLs as benchmarks for assessing performance are guided by the modalities contained in the UNFCCC Conference of Parties (CoP) decisions, most notably decision 12/CP.17 and its Annex. These modalities state that when establishing FREL/FRLs, Parties should do them transparently taking into account historic data and adjust them for national circumstances. A step-wise approach is allowed that enables Parties to improve FREL/FRLs by incorporating better data, improved methodologies and, where appropriate, additional pools. FREL/FRLs are expressed in units of tons of CO2- e per year and must maintain consistency with a country’s greenhouse gas inventory (according to 12/CP.17, Paragraph 8). A summary of Pakistan’s compliance with these UNFCCC decision modalities is given in Table 4. The UNFCCC invites Parties to submit information and rationale on the development of their forest reference emission levels and/or forest reference levels, including details of national circumstances and if adjusted include details on how the national circumstances were considered in accordance with the guidelines contained in annex to decision 12/CP.17. Para (b) of the annex to decision 12/CP.17 requires parties to provide transparent, complete, consistent and accurate information, including methodological information, used at the time of construction of forest reference emission levels and/or forest reference levels, including, inter alia, as appropriate, a description of data sets, approaches, methods, models, if applicable and assumptions used, descriptions of relevant policies and plans, and descriptions of changes from previously submitted information. Further guidance was provided in Decision 13/CP.19 which require country parties to provide description of relevant policies and plans (para d & e) to include assumptions about future changes to domestic policies in the construction of the forest reference emission level and/or forest reference (para h)

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Table 4. Pakistan’s FREL/FRL compliance concerning the relevant UNFCCC COP decisions. UNFCCC reference Description Pakistan’s FREL/FRL Decision 12/CP. 17 II. Submission of information - The methodological Paragraph 9 and rationale on the details included in the development of forest FREL/FRL document and

FRL/FRELs, about the in its annexes. details of national circumstances and their consideration Decision 12/CP.17 II Stepwise approach - Developing FREL/FRLs Paragraph 10 following a gain-loss approach for emission (EF) and removal factors (RF) at Tier 2 and 3 using sub-national datasets. - The first submission with the most available data and its subsequent analysis supporting selection of relevant activities and pools. Decision 12/CP. 17 The information contents - Besides the UNFCCC Annex II. Paragraph 9 guided by the most recent decisions the 2006 IPCC IPCC guidance and Guidelines have been

guidelines adopted to guide the development Submission of information The methodological and rationale on the - details included in the development of forest proposed FREL/FRL FRL/FRELs, about the document and in its details of national annexes. circumstances and their consideration Decision 12/CP.17 Activities - Inclusion of deforestation Annex, paragraph (c) - Including the impact of forest degradation in EF/RF - Inclusion of carbon stock enhancement including forest restoration aka natural regeneration, afforestation and reforestation activities Decision 12/CP.17 Pools and gases - Above-ground (AGB) Annex, paragraph (c) biomass for significance including CO2 emissions and removals.

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- Below-ground biomass (BGB) for significance including CO2 emissions. Decision 12/CP.17 Forest definition applied in - Forest definition notified in Annex, paragraph (d) the GHG inventories 2017: “A minimum area of land of 0.5 ha with a tree crown cover of more than 10 % comprising trees with the potential to reach a minimum height of 2 meters. This will also include existing irrigated plantations as well as areas that have already been defined as forests in respective legal documents and expected to meet the required thresholds as defined in the national definition for Pakistan.” - The definition to be adopted in the context of future greenhouse gas inventory (GHG-I) reporting requirements.

2.2 Consistency with national greenhouse gas reporting

The UNFCCC coordinates the global efforts for monitoring greenhouse gas (GHG) concentrations and to encourage climate change mitigation. Monitoring involves a systematic collection of the GHG emission records and trends for their regular country submissions to the UNFCCC as per the Good Practice Guidelines of Intergovernmental Panel on Climate Change (IPCC). Pakistan submitted its first GHG emission Inventory for 1993-94 with its Initial National Communication in 2003. The methodology covers three land use management practices that may result in net emissions, as well as changes in soil carbon. The practices considered include changes in forest and other woody biomass stocks, forest and grassland conversion and abandonment of managed lands. Of these, only the emissions from changes in forest and other woody biomass stocks have been estimated in this inventory, as other emission source categories were judged not to be applicable to Pakistan. The Second GHG inventory of Pakistan was prepared voluntarily by the Applied System Analysis Division (ASAD), Pakistan Atomic Energy Commission (PAEC) in 2009 on the request of the Pakistan Planning Commission’s Task Force on Climate Change (GoP, 2010). This GHG inventory was completed for the year 2007-08 using 2006 IPCC guidelines.

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The GHG inventory for the year 2011-12 was first developed and published officially by GCISC in 2016. The inventory was prepared based on the data sets of 2011-12. The main data sources used in the inventory were Pakistan Energy Year Book, Agricultural Statistics of Pakistan and Pakistan Economic Survey. Emissions from forestry sector were estimated with the projected NFRRAS data from 2004. The inventory was prepared using UNFCCC Non-Annex-I National Greenhouse Gas Inventory Software, Version 1.3.2 in accordance with Revised 1996 IPCC Guidelines. A Tier I approach relying on default emission factors was adopted. The most recent draft (dated in February 2017) report provides updated information on Pakistan’s GHG emissions for the year 2014-15 serving as baseline for the Pakistan’s Intended Nationally Determined Contribution submitted in November 2016 (Mir et al, 2017). This latest inventory has been prepared for Pakistan’s Intended Nationally Determined Contribution (PAK- INDC) and Second National Communication (SNC). The main data sources used in the inventory preparation include Pakistan Energy Year Book, Agricultural Statistics of Pakistan and Pakistan Economic Survey. The inventory was prepared using UNFCCC Non-Annex-I National Greenhouse Gas Inventory Software (Version 1.3.2) in accordance with Revised 1996 IPCC Guidelines. In these estimates, the Tier-I approach (which includes default emission factors) was used. Until the latest national greenhouse gas inventory (GHG-I) report the Revised 1996 IPCC Guidelines for National Greenhouse Gas Inventories have been mostly applied as the methodological guidance document. There are on-going development efforts to use 2006 IPCC Guidelines for National Greenhouse Gas Inventories (2006GL) for the following GHG-I reporting at GCISC. The forest definition, harmonized forest classification system, forest area changes, carbon inventory and emission factor data for developing FRL/FRELs and stored in the NFMS database will contribute as some potential Tier 2 and 3 level parameter inputs. The national FREL/FRL and NFMS development processes and products are to serve as key inputs of the AFOLU Forest Land Table 3.B.1. (Table 3) and AFOLU land use types data (Table 4).

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Table 5. The forest land tables (3.B.1.a and 3.B.1.b) AFOLU land use types data compliance with the FREL/FRL process inputs.

3.B.1.a Forest Land remaining National FREL/FRL data sources as Forest Land / 3.B.1.b Land converted to Forest Land Area Entry Table Forest land and other land area mapped, and statistical estimates produced in scope of the FREL/FRL development. Land use conversion matrix Data for land use conversion matrices for the periods 2004-2008, 2008-2012 and 2012-2016. Annual increase in carbon stocks Forest growth rates calculated with the in biomass mean carbon stock and rotation time by the forest types (Annex 4). Loss of carbon from wood Biomass harvest estimates produced removals with the NFI 2017/2018 data; Forest Department (FD) and other government reports (Annex 5). Loss of carbon from fuel wood Biomass harvest estimates with the NFI removals 2017/2018 data; Forest Department (FD) and other government reports (Annex 5). Loss of carbon from disturbance Carbon losses from aboveground and belowground carbon pools due to fire, insect outbreaks etc. leading to deadwood. Deadwood carbon density measured from the NFI plots used as an indicator (Annex 5). AFOLU land use types Land use subcategory Forest land is sub-classified according to the harmonized national forest stratification scheme (Chapter 3.3). Land management status All the forest Land in Pakistan is (managed/unmanaged) considered as managed land and therefore covered by FREL/FRL Carbon fraction of above-ground IPCC default value (0.47) biomass Ratio of belowground biomass to IPCC default values by (IPCC 2006)5 above-ground biomass

5https://www.ipcc-nggip.iges.or.jp/public/gpglulucf/gpglulucf_files/Chp3/Anx_3A_1_Data_Tables.pdf ~ 17 ~

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3. INFORMATION USED FOR DEVELOPMENT OF FREL/FRL

3.1 Scale The proposed FREL/FRL scale is national and it has been produced by mapping and compiling the available sub-national input datasets. The national rules and principles are used to harmonize sub-national datasets to ensure consistency. The sub-national datasets are harmonized by the province and territory boundaries. The national REDD+ programme includes the territories of GB (72,496 km2) and AJK (13,297 km2). Besides the mangrove forest patches and estuaries with vegetation in the southern coastal regions in BN and SD have been included. The total country territory area sums up to 881,889 km2 including the provinces and states as the sub-national entities.

3.2 Scope of activities Carbon stock enhancement has been included besides deforestation and degradation being the key activity category for REDD+. Carbon stock enhancement takes place through forest restoration (afforestation, reforestation and natural regeneration) and forest growth. Forest degradation is integrated in the emission factors for the forest remaining as forests. The sustainable forest management and conservation activity inclusion would require forest management inventory data and harvesting statistics consistently over these designated forest management areas throughout the country. Data is not currently available thus cannot be accounted for in the present FREL/FRL.

3.3 Forest definition and stratification 3.3.1 Forest definition The national definition of forest complies with the following definition (notification dated in 14th September 2017): “A minimum area of land of 0.5 ha with a tree crown cover of more than 10 % comprising trees with the potential to reach a minimum height of 2 meters. This will also include existing irrigated plantations as well as areas that have already been defined as forests in respective legal documents and expected to meet the required thresholds as defined in the national definition for Pakistan.” 3.3.2 Stratification The forest type classification has been adapted from the classification published and revised by Champion et al (1965). The forest type classification relies primarily on altitude range, slope orientation, weather data (average annual temperature/rainfall) and geographic location description. It has been complemented with land use classification and indicator tree species information (Figure 1). The irrigated plantations are an integral part of the cropland landscapes, so the cropland boundaries have been utilised for augmenting plantation classification in PB, SD and BN besides the map reference data received from the REDD+ focal points. The NFI plot data

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(2017/2018) has been used to validate forest type map classification. Their ecological characteristics and species composition are summarised for each forest types in the following paragraphs (adapted from Champion et al, 1965 and Sheikh, 1993).

Figure 2 Stratification by the forest types.

Natural Forests Sub-alpine forests Sub-alpine forests are the upper-most tree formation in the Himalayas located at an elevation from 3,353 to 3,810 meters (m) above sea level. Abies pindrow and Pinus wallichiana stand singly or in groups with a under storey of broadleaved trees in which Betula is typically prominent with Pyrus and Salix. The conifers are stunted, attaining heights of up to 8 meters. The broad-leaved trees attain heights of about 7 meters. Moist Temperate Forests Moist Temperate forests are characterized by the extensive growth of conifers. The forest formations extend along the whole length of the outer ranges of the Himalayas between the sub-tropical pine and the sub-alpine forests, at an approximate elevation of 1,372 to 3,048 meters. The main coniferous species are Pinus wallichiana, Cedrus deodara, Picea smithiana and Abies pindrow. The canopy formed by these species with 24-36 -meter height while some

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individual tree diameters may be up to 1.5 meters. Taxus also occurs locally in the lower canopy. Among the broad-leaved trees and Quercus spp. are prominent in the outer margins of the zone as their most common associate.

Dry Temperate Forests Dry temperate forests are distributed throughout the dry inner mountain ranges, beyond the effective monsoon reach, in the Northern areas, Chitral, Niilam and Kaghan Valleys, and Takht-e-Sulaiman, Shinghar and Ziarat in Balochistan. They occur at elevations of 1,524 to 3,353 meters and often even at higher altitudes. Free standing low branchy trees of Cedrus deodara, Pinus gerardiana, Juniperus excelsa, Pinus wallichiana, Picea smithiana and Quercus incana predominate as pure stands, often along with Fraxinus and Acer spp. Dry temperate forests can be divided further by their species composition into Montane Coniferous, Juniper and Chilghoza, Broad-leaved and Northern Dry Scrub Forest sub-types. Pinus gerardiana (Chilghoza) forms dominant stands in Suleiman range and elsewhere it grows as mixed stands with other species. Pine Forests Pinus roxburghii (Chir pine) forms the top canopy of the forest found from 914 up to 1,676-meter altitudes. Quercus incana is the key broad-leaved associate mixed with occasional Lyonia ovalifolia, Rhododendron arboreum, Pistacia intecerima, Syzygium cumini, Mallotus philippinensis and Ficus spp.

Broad-leaved evergreen forests Broad-leaved forests are scrub forests found in the foothills of Himalayas between the altitudes of 457 and 1,524 meters. These are low forests of branchy trees, mostly thorny and evergreen. Olea ferruginea, Acacia modesta and Dodonaea viscosa are the most prevailing species.

Dry deciduous forests Dry deciduous forests of low or moderate height are consisted almost entirely of deciduous species. This type does not occur extensively in Pakistan but mainly in the limited areas in the Rawalpindi foothills between approximate altitudes of 253 and 510 meters. The main tree species are Lannea, Bombax ceiba, Sterculia, Flacourtia, Mallotus and Acacia catechu.

Thorn forests Dry tropical thorn forests are mainly present in the arid areas of Indus Plain up to the altitude of 385 meters. Trees which are usually thorny, stunted, and dominated by Acacia spp., Salvadora oleoides, Tamarix aphylla, Prosopis cineraria, Zizyphus spp. and Capparis decidua, among others. These forests have the capacity to grow in areas where high temperatures. Riverine forests Riverine forests occur on the flood plains and banks of the major rivers of the Indus Basin. Flooding for about 6 weeks per year appears to be necessary to sustain the growth of the Riverine forests. The main species are Acacia nilotica,

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Tamarix dioica, Prosopis cineraria, Dalbergia sissoo and to some extent Populus euphratica. Littoral and swamp forests (Mangroves) Mangroves occur mainly in the Indus delta swamps. The native species are extremely slow-growing and there is very little natural regeneration. The main species are Avicennia marina with some occurrence of Rhizophora mucronata and Ceriops tagal.

Plantations Irrigated Plantations Irrigated plantations are mostly farm forest blocks around homes and in fields. The most common species planted by the farmers are Dalbergia sissoo, Acacia nilotica, Eucalyptus camaldulensis, Populus spp., Bombax cieba, and Melia azedarach. 3.4 Carbon pools and gases

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The included terrestrial carbon pools are aboveground and belowground biomass for deforestation, forest degradation and carbon stock enhancement. The greenhouse gases accounted from the selected forest pools cover carbon dioxide (CO2) only. The mode detailed justification for the commission and omission of the pools and gases is found in Annex 9.

Table 6. Activities and carbon pools included in the national FREL/FRL. Terrestrial Carbon Pool

Above Below Deadwoo Activities Litter SOC ground ground d Deforestation Included Included Excluded Excluded Excluded Forest Included6 Included Excluded Excluded Excluded Degradation Carbon stock Included Included Excluded Excluded Excluded enhancement Sustainable Excluded Excluded Excluded Excluded Excluded Forest Management Conservation Excluded Excluded Excluded Excluded Excluded

3.5 Historic time period The national FREL/FRLs for Pakistan are developed by producing land use maps for 2004-2016 and activity data for 3 corresponding epochs due to the limited reference data quantity available for the accuracy assessment and error- adjustment before the year 2004. 3.6 Rationale for assessing adjustment needs based on the changed national circumstances The UNFCCC requires parties to submit information and rationale on the development of their FREL/FRLs, including the details of national circumstances and if adjusted include details on how the national circumstances have been considered. The adjustments are usually carried out based on the assumptions linked to different socio-economic growth factors and the government future policies and programs that might put extra pressure on forest resources resulting in increased emissions as compared to historical trends. The aim of involving national circumstances in projecting the rate of forest carbon emissions and removals is to assess the adjustment needs that allows projecting a difference of the expected emissions/removals for the period from 2016 to 2026, as compared to the historical average forest carbon emissions The adjustment needs have been assessed by comparing and harmonizing the observed past trends with the projected future changes in national and sub-

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national circumstances and based on social, demographic, environmental and economic factors such as projections of human population, Gross Domestic Product, and specific regional development plans (e.g. resettlement plans, infrastructure and urban development). Likelihood of implementation of various plans was also considered if there was enough evidence and a statistically significant relationship between the implementation of such plans and consequent patterns of GHG emissions and removals. The initial outline of historical anthropogenic and non-anthropogenic changes in sub-national circumstances was developed during the group work conducted in the kick off workshop on development of the national FREL/FRLs for Pakistan in February 2017. The members of national WGs, provincial and other stakeholders also predicted the major trends over the 5-year epochs since 1992 until 2016 (Annex 7). Other secondary evidence documentation was discovered, compiled and linked to the outlined direct and underlying causes and trends changing the state of the forests by accelerating or halting deforestation, forest degradation as well as afforestation and reforestation processes in each epoch. The secondary evidences are comprised of both qualitative and quantitative data which were collected from various sources including the government’s official documents and reports, research papers, other global, regional and national level studies conducted by various organizations7. The secondary evidences were also comparatively analysed with the findings of current FREL/ FRLs study and other parallel assignments, such as the National REDD+ Strategy for Pakistan. Based on data availability and accessibility, the important variables considered for assessment of historical national circumstances are as follows: • Geographical regions and areas related to REDD+ activity identified through spatial analysis in different epochs from since 1996; • The most prominent national level drivers related to deforestation, degradation and enhancement of forest carbon stocks during the specific reference periods identified by national working group members, the draft National REDD+ Strategy (May 2018), and other secondary data sources8. The evidences related to direct drivers include data on increased / reduced logging practices, timber and firewood harvests reported under management plans, illegal firewood and timber harvests reported under government’s disposal policies, agricultural intensification, afforestation, reforestation, plantations etc. • The quantitative and qualitative evidences on socio-economic factors i.e. (infrastructure development, population growth, economic growth, wood supply and demands gaps, per capita availability of forests and local timber and fuel wood consumption patterns) The historical data was used to model the potential impact of the expected changes in the national and sub-national circumstances. In addition to the

7Food and Agriculture Organization of the United Nations - FAO, International Centre for Integrated Mountain Development - ICIMOD, Pakistan Space and Upper Atmosphere Research Commission - SUPARCO, Pakistan Forest Institute - PFI, and World Wildlife Fund. 8 Forestry Sector Master Plan 1992; GoP, 2003; NFRRAS, 2004; FAO GFRA, 2005; FAO, 2009; ICIMOD, 2010; WWF, 2010; PFI, 2012; FAO GFRA, 2015; National annual economic survey reports 1996-2016 ~ 23 ~

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historical analysis of forest emissions related to deforestation and forest degradation as well as removals due to enhancement of forest carbon stocks in this national FREL/ FRL, it is necessary to assess the possible future pathways regarding the country’s economic, social and cultural circumstances, which might modify the dynamics of forest transformation and which are not reflected on historical deforestation data. Pakistan considers essential to include China–Pakistan Economic Corridor (CPEC) as the major future potential economic growth. CPEC is expected to generate new dynamics of social and cultural occupation and land use, where deforestation patterns may be altered and differ from historical averages observed so far. The future development scenarios are assessed based on the following: • Projection of historical emissions into the future using statistical regression trend model; • The future related development plans, programmes and policies of the government; • The overall emission reduction targets (through both the conditional and unconditional support) under PAK-INDC document; • The expected increase in economic growth rate due to the impact of CPEC as stated by Government of Pakistan in national economic survey reports of 2016- 2017 and 2017-2018; • The future fuel wood and timber harvests under sustainable management plans; • The Government’s expected future spending in forestry sector for strategic direction under REDD+ Strategy; • The expected population growth in next ten years and status of per capita availability of forests. 3.7 Approach and methodology for FREL/FRL establishment Pakistan follows an approach based on the historical average of emissions from deforestation, forest degradation and removals from enhancement of forest carbon stocks between 2004 and 2016. The reason for the choice of this approach is due to the variable factors for the national circumstance assessment outlined. The three periods for temporal change assessments are considered as a reliable basis to create a regression line for the predication of the future trends. The past trends in gross and net deforestation, and carbon enhancement stock are acquired through activity data mapping. Modelling the historic emissions and removals relies on the activity data produced with the documented Satellite Land Monitoring System (SLMS) methodology and time-series analysis. The provincial emission and removal factors have been developed with a reference to the previous inventory data of the terrestrial carbon stocks. Forest inventory measurements have been conducted in October 2017–April 2018 covering all the main forest types in AJK, PB, SD, BN and FATA to be able to develop sub-national emission and removal factors. There has been forest carbon inventory conducted in 2013-2016 providing aboveground and

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belowground carbon data coverage in KP (Ali, 2017c). The provincial carbon inventory has been also conducted in Gilgit-Baltistan including above-ground and belowground carbon pools in 2015-2016 (Ali et al, 2017).

FREL refers to gross annual emissions as CO2-equivalent tons per hectare and is calculated with the following equation using activity data and emission factors for each forest stratum separately.

FREL (tons CO2 per year) = Annual deforestation (hectares per year) X EF/RF (tons CO2-e per hectare) FRL accounts for both average annual emissions and removals. It is calculated with the following equation using activity data for enhancement with removal factors for each stratum separately: 9 FRL (tons CO2 per year) = Annual Activities (hectares per year) x EF/RF (tons CO2-e per hectare) All the emission and removal calculations are conducted by the main forest types with the reference data for each sub-national area.

3.8 Activity Data

Deforestation refers to the areas where forest land is converted to other land. When other land areas are converted to forest land, tree vegetation restored through afforestation, reforestation and natural regeneration activities. It will take some time until the regeneration site becomes visible on the satellite images. The third activity type occurs when forest land is remaining forest land, even though there may be some changes in canopy cover and carbon stocks over time. Activity data mapping is based on the land use and land cover (LULC) classification using Landsat satellite imagery for each of the reference years. The list of source imagery is listed in Annex 2 (a-f). The complete Satellite Land Monitoring System (SLMS) workflow is illustrated in Figure 3Table 4Error! Reference source not found.. The SLMS manual provides the detailed process how the one-time maps have been produced for each point of time under interest based on the Random Forest machine learning technique. Very High-Resolution (VHR) satellite imagery available in Google Earth are used as reference data when producing LULC maps and verifying their accuracies with visually interpreted and multi-temporal systematic plots using Open Foris Collect (Table 7). Total 11944 plots with 50 x 50-meter dimensions have been sampled and interpreted as subsets of 10”, 5”, 2.5” and 1.25” systematic grid. The error-adjusted forest cover estimates are calculated following methodological guidance by Olofsson et al (2014).

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Table 7 The intensity of sample plots interpreted visually and used for the LULC map accuracy assessment by provinces and territories.

2016 2012 2008 2004 AJK 777 777 777 777 Balochistan 1,956 1,956 1,956 1,956 FATA 1,830 1,830 1,830 1,830 GB 1,718 1,718 1,718 1,718 ICT 213 213 213 213 KP 1,189 1,189 1,189 1,189 Punjab 2,373 2,373 2,373 2,373 Sindh 1,888 1,888 1888 1,888 TOTAL 11,944 11,944 11,944 11,944

The time-series analysis has been conducted to detect potential land use change areas between two consecutive points of time. The final activity data statistics concerning deforestation and carbon stock enhancement through forest restoration activities has been acquired by applying the error adjustment with the same systematic grid and additional randomly sampled visual plots (2300) over the mapped activity data areas (Olofsson et al 2014).

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Figure 3 The SLMS process to estimate the land use and forest cover in Pakistan.

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3.9 Emission and Removal Factors

3.9.1 Forest inventory data

The forest inventory calculation process produces carbon density value (C ton/ha) at sample plot-level by aggregating biomass estimates relying on bio- physical tree measurements. Tree height has usually very high prediction power in allometric equations but measuring heights for every sample tree may be time-consuming to measure accurately in field. Every 5th tree has been measured for its height during the NFI pilot campaign. Allometry is utilized to develop a height-diameter regression model and estimate missing tree heights. The height-diameter models by species and species groups have been assigned with plot wise model parameters (Table 8). Tree-level aboveground biomass has been estimated using allometric models presented in Table 9.

Table 8 The height-diameter model types, precision (RMSE) and accuracy (bias) by species and species groups. Tree species Model type Model (d=diameter) RMSE RMSE % Bias Bias% Bias p-value Pinus wallichiana Korf a*exp(-b*d^(-1)) 4.751 0.232 -0.003 0.000 0.994 Juniperus excelsa Naslund d^2/(a+b*d)^2 1.065 0.212 -0.082 -0.016 0.364 Quercus incana Naslund d^2/(a+b*d)^2 3.253 0.323 0.028 0.003 0.950 Eucalyptus spp. Naslund d^2/(a+b*d)^2 2.131 0.214 -0.123 -0.012 0.377 Acacia spp. Naslund d^2/(a+b*d)^2 1.731 0.272 -0.162 -0.025 0.118 Mangrove (Avicennia marina, Rhizophora mucronata, Ceriops Korf a*exp(-b*d^(-1)) 1.007 0.286 -0.017 -0.005 0.802 Dalbergia sissoo Naslund d^2/(a+b*d)^2 1.625 0.185 -0.035 -0.004 0.881 Pinus gerardiana Naslund d^2/(a+b*d)^2 1.733 0.215 -0.077 -0.010 0.770 Capparis decidua Meyer a*(1-exp(-b*d)) 1.841 0.303 -0.041 -0.007 0.933 Salvadora oleoides Korf a*exp(-b*d^(-1)) 1.149 0.277 -0.006 -0.001 0.964 Tamarix aphylla Power a*d^b 1.601 0.210 0.024 0.003 0.848 Monotheca buxifolia Naslund d^2/(a+b*d)^2 1.204 0.296 -0.031 -0.008 0.871 Olea ferruginea Korf a*exp(-b*d^(-1)) 1.354 0.246 -0.031 -0.006 0.780 Abies pindrow Meyer a*(1-exp(-b*d)) 5.505 0.335 -0.358 -0.022 0.541 Prosopis cineraria & juliflora Korf a*exp(-b*d^(-1)) 1.127 0.149 -0.011 -0.002 0.902 Cedrus deodara Naslund d^2/(a+b*d)^2 1.034 0.052 -0.027 -0.001 0.919 Pinus roxburghii Naslund d^2/(a+b*d)^2 2.191 0.134 -0.228 -0.014 0.405 Conifer spp. Korf a*exp(-b*d^(-1)) 3.530 0.251 -0.059 -0.004 0.702 Deciduous spp. Naslund d^2/(a+b*d)^2 1.948 0.270 -0.138 -0.019 0.009

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Table 9. The adopted allometric equations for the tree-level aboveground biomass / carbon stock calculation. D refers to diameter at breast height, H to height and ρ to wood density. Species Vernacular Allometric equation Reference scientific name name Cedrus deodara Deodar 퐴퐺퐵 Ali 2015 = 0.1779(퐷2퐻)0.8103 Pinus Kail 퐴퐺퐵 Ali 2015 wallichiana = 0.0631(퐷2퐻)0.8798 Pinus Chilghoza 퐴퐺퐵 = 0.0253퐷2.6077 Ali 2015 gerardiana Abies pindrow Fir 퐴퐺퐵 Ali 2015 = 0.0954(퐷2퐻)0.8114 Picea smithiana Spruce 퐴퐺퐵 Ali 2015 = 0.0843(퐷2퐻)0.8472 Avicennia White mangrove 퐴퐺퐵 Prasanna et marina (Timar) al 2017 = 0.827퐷 + 0.275

Rhizophora Kamo AGB = 0.8069D2.5154 Kirue et al mucronata 2007

General Coniferous 퐴퐺퐵 Ali 2015 species = 0.1645(휌퐷2퐻)0.8586 General All species AGB Chave et al = 0.112(ρD2H)0.976 2014

The above-ground biomass density is calculated using the sample plot data to represent biomass density per hectare. The belowground biomass for plot is calculated using the default IPCC root-shoot ratios (Table 10). The default IPCC fraction (0.47) is applied to convert biomass to carbon.

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Table 10. The applied IPCC root-shoot ratios adapted from Table 3A.1.810 of the IPCC Good Practice Guidance (2006). Vegetation type Aboveground Mean value biomass (t/ha) (BGB:AGB) Tropical/sub-tropical NS 0.27 dry forest Conifer <50 0.46 forest/plantation Conifer 50-150 0.32 forest/plantation Conifer >150 0.23 forest/plantation Oak forest >70 0.35 Eucalypt plantation <50 0.45 Eucalypt plantation 50-150 0.35 Eucalypt >150 0.20 forest/plantation Other broadleaf <75 0.43 forest Other broadleaf 75-150 0.26 forest Other broadleaf >150 0.24 forest

3.9.2 Emission and removal factors

The emission and removal factors have been developed according to the gain- loss approach described in the IPCC Guidelines (2006). The factors represent emissions and net removals per hectare of land which is either remaining as forest or has been converted to other land or land to forest land. The local emission and removal factors have been developed for each province and forest type using the existing and collected NFI data.

10 https://www.ipcc-nggip.iges.or.jp/public/gpglulucf/gpglulucf_files/Chp3/Anx_3A_1_Data_Tables.pdf

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Table 11 indicates the formulas that have been used to derive the emission and removal factor. The forest carbon density and annual net growth rate parameters are found in Annex 4. The wood removal and disturbance parameters derived with the NFI data are found in Annex 5. Forest land remaining as forest land The forests are sequestrating carbon in biomass through their photosynthetic growth every year, but this value is to be discounted with the estimated impact of timber, wood fuel removal and disturbances calculated form the NFI pilot field plots. Annex 5. Harvests and disturbance describes the data used as reference for calculating the annual growth, harvests and disturbances. To calculate the annual biomass growth of different forest types the Von Mantel’s Method has been applied. Thus, the annual increment is calculated as twice the growing stock per ha divided by the average rotation in years, assuming equal representation of all age classes and constant growth rate over the forest managed as even-aged stands. The Von Mantel’s formula is expressed as following:

where, ACC = Annual Allowable Cut (= annual growth), R = the rotation (cutting cycle) for the major tree species comprising the growing stock, V = the average total carbon volume (tons of carbon per ha) by forest stratum. The measured stump diameters have been used as proxy values for breast height diameter to calculate height and biomass of the harvested trees. Plot- level deadwood estimates are used to quantify the impact of disturbances. Both the observed harvests and disturbances have assumed to have taken place during the past 20 years. The annual harvest and disturbance rates are aggregated first at plot and then at stratum levels. In case of GB the provincial forest department statistics over the period of 1996-2016 have been used to calculate the annual rates of biomass extraction per hectare. In case of KP forest degradation due to harvests and disturbances have been accounted as the annual stock change (0.408 tCO2 per hectare per year) between the most recent and inventories. The estimation method and parameter values are found in Ali 2017b. The sub-alpine zone is assumed to be without harvest impacts due to accessibility.

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Forest land converted to other land The mean AG and BG carbon density has been aggregated the data collected during 2017-2018 inventory including plots in the main forest types by provinces. Sub-alpine forest reference data, as well as carbon density data for the remaining provinces, is sourced from the KP and GB carbon stock inventories (Ali, 2017c; Ali et al, 2017). The mean carbon densities are also calculated for other land use classes and aggregated by provinces. Other land converted to forest land Once a land area has been restored with forest (reforested, afforested or naturally regenerated), the change becomes visible on the satellite image derived LULC maps. When looking at the 4-year mapping period, the restoration process can be assumed to have started in the middle of the period. The regenerated forest has had time for two growing seasons in average to produce biomass by the end of the period, but the change can be observed only at the period end year. Based on this assumption carbon equaling to two-year biomass increment has been sequestrated in the tree biomass growth. The same annual biomass increment rates are applied as in case of the forests remaining as forests.

Table 11 Emission and removal factor calculation formulas. Term Variable definition / Formula A Annual net growth rate (ton C/ha) B Annual harvest and removal (timber + fuelwood) Forest remaining as forest (ton C/ha) C Annual disturbance (ton C/ha)

44/12 CO2-e conversion factor (ton CO2/C)

RFF A x 44/12

EFF (B + C) x 44/12

EFF/RFF Emission/Removal factor (ton CO2 -e/ha) D Forest carbon density, mean AGC+BGC, (ton C/ha) Forest converted to other land E Other land carbon density, mean AGC+BGC (ton C/ha)

EFN (D – E) x 44/12

EFN Emission factor (ton CO2-e/ha) F Annual forest growth (ton C/ha) x 2

RNF F x 44/12

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Other land converted to RNF Removal factor (ton CO2-e/ha) forest

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4. HISTORICAL FOREST COVER, TRENDS, EMISSIONS AND REMOVALS (1996-2016)

4.1 National forest cover and trends in 1996-2016 The national forest cover has been assessed for 6 points of time including 1996, 2000, 2004, 2008, 2012 and 2016. Based on the time-series assessment with the 95-% confidence interval there has not been any significant difference identified between the single years. The mean national forest cover estimates vary from 5.9 to 6.4 percent of total surface between the years with ± 0.9 percent units between 1996-2016 (

Figure 4 National forest cover estimates with 95-% confidence intervals (1996-2016).

The average annual deforestation rate has been 12,375 hectares per year since 2004. Deforestation rates have been gradually increasing from 2004 to 2016 (Figure 5). The forest restoration rate is estimated to be 40,154 hectares per year for the same period (Figure 6). The net forest cover change has a positive trend with cumulative 333,341 hectares over the same period (Figure 7).

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Annual deforestation rates in 2004-2016 18 000 AJK BN FATA-KP GB ICT PB SD 16 000 14 000 12 000 10 000 8 000

6 000 Hectares Hectares per year 4 000 2 000 0 2004-2008 2008-2012 2012-2016

Figure 5 Annual deforestation rates in Pakistan (2004-2016).

Annual forest restoration rates 60 000 AJK BN FATA-KP GB ICT PB SD 50 000

40 000

30 000

20 000 Hectares Hectares per year

10 000

0 2004-2008 2008-2012 2012-2016

Figure 6 Annual forest restoration rates in Pakistan (2004-2016).

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Net forest land change 2004-2016 350 000 AJK BN FATA-KP GB ICT PB SD 300 000

250 000

200 000

hectares 150 000

100 000

50 000

0 2004-2008 2012 2016

Figure 7 Net forest land change in Pakistan (2004-2016).

4.2 Sub-national forest cover and trends in 1996-2016

4.2.1 Azad Jammu and Kashmir (autonomous territory)

The mean forest cover estimates in AJK vary from 28.7 to 30.3 percent of the total surface with the 95-% confidence interval ± 3.1-3.2 percentage points between 1996 and 2016 (Figure 8). The observed annual deforestation and forest restoration rates have been remaining respectively at 963 and 1,699 hectares in average, while reaching 2,054 hectares in 2012-2016. The cumulative forest land accumulation is 8,838 hectares between 2004 and 2016.

Years Forest Area (HA) % Area 1996 381,054 ± 41,818 28.7 ± 3.1 2000 393,016 ± 41,372 29.6 ± 3.1 2004 382,900 ± 41,745 28.8 ± 3.1 2008 392,881 ± 42,209 29.5 ± 3.2 2012 399,988 ± 42,501 30.1 ± 3.2 2016 403,155 ± 42,730 30.3 ± 3.2

Figure 8 Forest cover estimates with 95-% confidence intervals in AJK (1996-2016).

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4.2.2 Balochistan

The mean forest cover estimates in BN vary from 3.3 to 3.7 percent of the total surface with the 95-% confidence interval of ± 0.7-0.8 percentage points between 1996 and 2016 (Figure 9). There is no significant deforestation observed. The forest land restoration rates have been remaining at 3,690 hectares in average. The cumulative forest land accumulation is 44,277 hectares between 2004 and 2016.

Years Forest Area (HA) % Area 1996 1,151,096 ± 227,250 3.3 ± 0.7

2000 1,290,480 ± 259,091 3.7 ± 0.7

2004 1,284 077 ± 255,358 3.7 ± 0.7

2008 1,211,888 ± 221,505 3.5 ± 0.6

2012 1,213,188 ± 220,247 3.5 ± 0.6

2016 1,113,985 ± 211,644 3.2 ± 0.6

Figure 9 Forest cover area with 95-% confidence intervals in Balochistan (1996-2016).

4.2.3 Federally Administered Tribal Area and Khyber Pakhtunkhwa The mean forest cover estimates in FATA-KP vary from 18.6 to 19.6 percent of the total surface with the 95-% confidence interval of ±1.7-1.9 percentage points between 1996 and 2016 (Figure 10). The average annual deforestation rate has been 2433 hectares. Deforestation was at the highest level between 2012 and 2016 when 18,506 ha has been deforested. The average net forest cover change has been positive with the total forest land increment of 75,013 hectares for 2004-2016.

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Years Forest Area (Ha) % Area 1996 1,979,490 ± 190,827 19.5 ± 1.9

2000 1,935,327 ± 189,971 19.0 ± 1.9

2004 1,895,773 ± 185,196 18.6 ± 1.8

2008 1,929,589 ± 178,000 19.0 ± 1.7

2012 1,952,081 ± 180,128 19.2 ± 1.8

2016 1,997,395 ± 193924 19.6 ± 1.9

Figure 10 Forest cover area with 95-% confidence intervals in FATA-KP (1996-2016).

4.2.4 Gilgit-Baltistan The mean forest cover estimates in GB vary from 4.8 to 5.4 percent of the total surface with the 95-% confidence interval of ± 0.8-0.9 percentage points between 1996 and 2016 (Figure 11). The average annual deforestation rate has been 289 hectares in 2004-2016. The average net forest cover change has been positive with the average annual increment of 1,735 hectares for 2004- 2016.

Figure 11 Forest cover estimates with 95-% confidence intervals in GB (1996-2016).

4.2.5 Punjab The mean forest cover estimates in PB vary from 4.1 to 5.2 percent of the total surface with the 95-% confidence interval of ± 0.8 percentage points between 1996 and 2016 (Figure 12). The average annual deforestation rate has been 3,998 hectares in 2004-2016, while it peaked with 5,718 hectares in 2008-2012. The average net forest cover change has been positive with the average annual

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increment of 16,834 hectares. The net forest land has accumulated with 154,035 hectares for 2004-2016.

Years Forest Area (HA) % Area 1996 986,724 ± 163,443 4.8 ± 0.8

2000 1,053,545 ± 172,947 5.1 ± 0.8

2004 1,056,732 ± 173,076 5.1 ± 0.8

2008 1,061,774 ± 171,757 5.2 ± 0.8

2012 1,061,007 ± 173,131 5.2 ± 0.8

2016 847,534 ± 154,810 4.1 ± 0.8

Figure 12 Forest cover estimates with 95-% confidence intervals in Punjab (1996-2016).

4.2.6 Sindh The mean forest cover estimates in PB vary from 3.5 to 4.8 percent of the total surface with the 95-% confidence interval of ± 0.8-0.9 percentage points between 1996 and 2016 (Figure 12). The average annual deforestation rate has been 4,643 hectares in 2004-2016, while it peaked with 6,400 hectares in 2012- 2016. The annual forest land restoration rate has remained at the same range with the average of 7,432 hectares. The net forest land has accumulated with 33,466 hectares for 2004-2016.

Years Forest Area (HA) % Area 1996 676,104 ± 127,023 4.8 ± 0.9

2000 644,908 ± 125,290 4.6 ± 0.9

2004 652,893 ± 127,005 4.6 ± 0.9

2008 564,061 ± 117,180 4.0 ± 0.8

2012 538,399 ± 113,908 3.8 ± 0.8

2016 491,269 ± 110,612 3.5 ± 0.8

Figure 13 Forest cover estimates with 95-% confidence intervals in Sindh (1996-2016).

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4.3 National and Sub-National FREL/FRL The forests sequestrate carbon in growth with -16.8 Mt CO₂ - equivalent annually ( Mean Annual Emissions and Removals 2004-2016 10

0 2004-2008 2008-2012 2012-2016 -5

-10 Milliontons CO2 -15

-20

Emissions from deforestation Gross Annual Emissions Gross Annual Removals Net Annual emissions

Mean Annual Emissions and Removals 2004-2016 10

0 2004-2008 2008-2012 2012-2016 -5

-10 Milliontons CO2 -15

-20

Emissions from deforestation Gross Annual Emissions Gross Annual Removals Net Annual emissions

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Figure 14 Mean annual forest emissions and removals (Mt CO₂-e) at national level.

Table 12 Average carbon stock, emission and emission removal statistics by the sub- national areas and as national totals. FATA- NATIO AJK BN KP GB PB SD NAL Total carbon stock (Million tons C) 20 11 144 27 24 5 231 Forest carbon density (ton C /ha) 51 10 73 73 30 10 45 Annual Emissions and Removals by Activities Deforestation (Mton CO2-e/year) 0.11 0.00 0.32 0.09 0.18 0.22 0.93 Wood removal and disturbance (Mton CO2-e/year) 0.31 0.02 3.54 0.20 0.07 0.00 4.15 Carbon stock enhancement - (Mton CO2-e/year) -1.41 -1.24 -7.72 -1.29 -3.56 1.25 -16.47 Forest restoration - (Mton CO2-e/year) -0.01 -0.01 -0.05 -0.01 -0.19 0.07 -0.35 FREL (Mton CO2-e/year) 0.42 0.02 3.39 0.30 0.25 0.22 5.08 FRL - (Mton CO2-e/year) -1.00 -1.23 -3.91 -1.00 -3.50 1.10 -11.74

4.4 Analysis of the national activity trends

In 1993 the provisional ban was imposed on commercial forest cutting and harvesting throughout the country which had visible impacts on the state of forests. However, the imposed ban did not abate deforestation and degradation

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of the forests in provinces. This could be due to persistence of illegal logging and fuel wood extraction at local level. Between 1997 and 2002 reforestation and afforestation programmes were implemented in KP, FATA, PB and BN. AJK faced conflicts and forest fire attributing to the degradation and affecting its regeneration programmes. During the same period, the Afghan refugee crisis was reported resulting in deforestation and forest degradation mostly in GB and BN, and SD coupled with poor governance in forestry sector. AJK reported an earthquake incident in 2005 in its northern areas, attributing to forest degradation and severe impacts on regeneration due extraction of timber for reconstruction of buildings and usage for fuel wood. In Punjab, government land was transferred to army personnel during 2005-2007 resulting in deforestation throughout the province. During 2007-2012, military operations in KP and FATA limited the access to the forest but deforestation in certain areas remained both in KP and FATA. Regeneration and afforestation were reported in Balochistan due to afforestation programs. Introduction of lease policy and poor forest governance attributed to the deforestation and forest degradation in Sindh despite imposed ban on felling and harvesting during 2002 - 2012 During the period of 2012-2016, KP and FATA reported regeneration as a result of Billion Tree Afforestation Program (BTAP) initiated by GoP. AJK reported degradation and impacts on regeneration due to energy shortages and price hikes. The rehabilitation program continued in Punjab attributing to regeneration of forests. Wildlife Act and Regulation was enacted in BN in 2014/15 contributing to regeneration and afforestation. Ban on harvest continued in GB during this period, however forest degradation persisted. Reforestation was also reported in GB due to enhancement of social forestry. More background information about the perceived sub-national activity trends is found in Annex 7.

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5. FREL/FRL PROJECTIONS AND UNCERTAINTY ASSESSMENT

5.1 Projections (2016-2026)

The proposed FREL/FRL is based on the assessment of national circumstances and development scenarios that can potentially impact the future forest carbon emissions or removals. There are clear trends with both increases and decreases over historical time in forest emissions due to deforestation and removals attributed to restoration within the historical reference period. The effects of future changes in national circumstances are well captured by the projections for 2016-2026 without an adjustment.

Historical Projections 2016-2026 10

0 2004-2008 2008-2012 2012-2016 2016-2020 2020-2024 2024-2026

-5

Milliontons CO2 -10

-15

-20

Figure 15. National gross and net business-as-usual emission/removal projections 2016-2026.

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Table 13 Pakistan’s circumstances against the FREL/FRL upward adjustment criteria. Adjustment justification criteria Pakistan circumstance Long-term historical deforestation has There has been significant been minimal across the country. deforestation occurring in the past and there is an increasing trend. Country has high forest cover Pakistan’s forest cover is low. National circumstances have changed The emission from the forest sector such that rates of deforestation and are expected to gradually grow due to forest degradation during the historical population and economic growth (i.e. reference period likely to increased timber/firewood demand, underestimate future rates of agricultural production and energy deforestation and forest degradation sector investments). Documented changes in national There are large afforestation and circumstances, evident before the reforestation projects which have end-date of the reference period started recently and increase removals. The evident national circumstances are documented in Annex 6 and sub-national historical trends in Annex 7.

5.2 Uncertainty assessment The key sources of uncertainty have been identified and are accounted for both activity data and emission and removal factors. There is some uncertainty which originates from the fact that there is no consistent time-series data available to reflect the variation in emission and removal factor values during the reference periods. The future projections always involve uncertainties, but it is given less weight as no FREL adjustments have been proposed. Both the mapping process and accuracy assessment benefit from availability of the VHR imagery through Google Earth. The reference data availability is very limited regarding to the years before 2004, which evidently increases the uncertainty for the total forest cover estimates and does not allow generating the error-adjusted values for the activity data. Total 95 forest inventory clusters with 1-5 forest sub-plots over AJK, PB, SD, BN and FATA areas contribute to the estimation of forest growth, harvests and disturbances. Some of the specific forest types in provinces rely on 1 cluster only. The GB and KP carbon stock assessments provide a wider sample base and the plot measurement protocol is compatible.

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The initial deforestation and reforestation values are based on the time-series analysis of two consecutive reference years. The change permanence verification procedure reduces the possibilities for false detections due to the different radiometric properties between two sets of satellite image mosaics. There are national biomass growth models covering the most important forest species and strata. The forest carbon stock assessment process involves a modelling chain with three critical steps: 1) Field measurements and data entries 2) Height modelling for individual trees 3) Allometric biomass modelling of AGB for individual trees 4) Applying defaults root-shoot ratios to estimate BGB at plot-level 5) Carbon and carbon dioxide conversions In this chain the relatively highest inaccuracy is vested with selection and application of the most suitable allometric biomass models, if no models developed in the context of Pakistan are available. In case of Pakistan, many recently developed allometric biomass models are available for the conifer species growing in the temperate zone. As explained earlier the uncertainties reside from the similar factors as when assessing the deforestation activity data. The better annual increment and biomass growth models are available for the young forest the more accurate results can be achieved.

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6. TRANSPARENCY, COMPLETENESS, CONSISTENCY AND UNCERTAINTY OF INFORMATION 6.1 Transparency Comprehensive and clear documentation of methods and data have been included in this report and referred technical documents. 6.2 Completeness All the significant emission sources, pools, and gases are estimated and reported. 6.3 Consistency Emissions during the historical period have been estimated in a manner that is consistent and shall remain functionally consistent during the REDD+ program implementation. The methodologies and data are also consistent with guidance by the UNFCCC decisions and IPCC Good Practice Guidelines 2006. 6.4 Accuracy Estimates of emissions and removals are unbiased due to the followed error- adjustment for the activity data and the emission/removal factors are from recent carbon inventory data collected with sound statistical sampling designs.

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7. PLAN FOR STEPWISE FREL/FRL IMPROVEMENT

Pakistan’s REDD+ Readiness Preparation Proposal (2013) provides a roadmap for the projected features and capacity for major NFMS elements, such as the National FREL/FRLs. Table 14 reflects the current stage and outlines the short- and long-term objectives foreseen in R-PP.

Table 14. The plan for FREL/FRL improvement in the short- and long- term. Current Status Short-term objective Long-term objective (Phase 1/2) (Phase 3) Emission/removal factors Key category Key category assessment have been developed assessment for carbon for carbon pools based on pilot NFI inventory pools completed for completed for all forest by forest types and major forest types. types. provinces/states Emission factors for use Emission factors for use outside of state-owned outside of state-owned forests developed for forests developed for all major forest types. forest types. Carbon stock inventory data Carbon stock Carbon stock estimation in is available for the main estimation where data all forests and forest types from all the supports it. assessment against provinces and territories. applicable FREL or FRL to establish performance. Activity data derived for Activity data reported Activity data reported on deforestation and forest on the basis of land use the basis of land use restoration data. The categorization. categorization estimated distribution by the Emission factors Emission factors derived forest types is available. derived for major forest for all forest types. The forest sector emission types. NFMS will generate full and removal factor data are report on reduced stored in NFMS to be used emissions and enhanced in the next GHG-I reports. removals on Forest Land.

The systematic NFMS data collection efforts are required to get timber, fuelwood harvesting and disturbance records to the forest compartment and REDD+ projects in the future. There should be organised more systematic collection of permanent NFI sample plot data for growth data and conducting biomass yield model research to cover the most prevailing forest types. There is a good selection of allometric biomass models available developed for the temperate zone forest, but it is recommended to develop biomass models for the most common tropical and sub-tropical tree species.

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8. REFERENCES

Akbar, M.; Ahmed, M.; Hussain, A.; Zafar, M.U.; Khan, M., 2011, Quantitative forests description from Skardu, Gilgit and Astore districts of Gilgit-Baltistan, Pakistan, FUUAST Journal of Biology, Vol. 1 No. 2, p. 149.

Ali, A., 2017a. Forest Cover Mapping of Khyber Pakhtunkhwa. Pakistan Forest Institute, Peshawar 2017

Ali, A., 2017b. Forest Reference Emission Level of Khyber Pakhtunkhwa. Pakistan Forest Institute, Peshawar 2017

Ali, A., 2017c. Carbon Stock Assessment of Khyber Pakhtunkhwa. Pakistan Forest Institute, Peshawar, 2017.

Ali, A, 2015. Local Volume Tables of Coniferous Species in Gilgit Baltistan. Forest, Wildlife and Environment Department, Gilgit Baltistan, Gilgit; Pakistan Forest Institute, Peshawar, 2015.

Ali, A.; Hussain, K.; Ismail; Hussain, K., 2017. Forest Carbon Inventory of Gilgit Baltistan. Forests, Wildlife and Environment Department, Gilgit Baltistan. Chave J.; Réjou-Méchain M.; Búrquez A.; Chidumayo E.; Colgan M.S.; Delitti W.B.C.;Duque A.; Eid T.; Fearnside P.M.; Goodman R.C.; Henry M.; Martínez- Yrízar A.; Mugasha W.A.; Muller-Landau H.C.; Mencuccini M.; Nelson B.W.; Ngomanda A.; Nogueira E.M.; Ortiz-Malavassi E.; Pélissier R.; Ploton P.; Ryan C.M.; Saldarriaga J.G.; Vieilledent G. (2014). Improved allometric models to estimate the aboveground biomass of tropical trees. Global Change Biology 20: 3177–3190. http://dx.doi.org/10.1111/gcb.12629.

Government of Pakistan, 2018. Pakistan’s Economic Survey Reports (2005- 2018), Ministry of Finance, Government of Pakistan. Available at http://www.finance.gov.pk/survey_1617.html

Government of Pakistan, 2017. Provisional summary results of 6th population and housing census-2017. Pakistan Bureau of Statistics. Retrieved from http://www.pbs.gov.pk/content/provisional-summary-results-6th-population- and-housing-census-2017-0

Government of Pakistan, 2016. Pakistan’s Intended Nationally Determined Contribution (PAK-INDC). Available at http://www4.unfccc.int/ndcregistry

Government of Pakistan, 2015. Pakistan’s National Forest Policy, Ministry of Climate Change, Government of Pakistan. Available at http://www.mocc.gov.pk/moclc/userfiles1/file/National%20Forest%20Policy%2 02015%20(9-1-17).pdf

Government of Pakistan 2012. Pakistan’s National Climate Change Policy, Ministry of Climate Change, Government of Pakistan. Available at http://www.gcisc.org.pk/National_Climate_Change_Policy_2012.pdf

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ICRAF, 2018. Tree Functional Attributes and Ecological Database. http://db.worldagroforestry.org/

Indufor; CHIP Consulting, 2018. Pakistan’s Draft National REDD+ Strategy, May 2018.

Intergovernmental Panel on Climate Change, 2006. 2006 IPCC Guidelines for National Greenhouse Gas Inventories: Agriculture, Forestry and Other Land Use.

Kirui, B.; Kairo, J.G; Karachi, M., 2006. Allometric Equations for Estimating Above Ground Biomass of Rhizophora mucronate Lamk. (Rhizophoraceae) Mangroves at Gazi Bay, Kenya Western Indian Ocean J. Mar. Sci. Vol. 5, No. 1, pp. 27–34, 2006 Maanics Int., 2004. Supply & Demand of Fuel Wood & Timber for Household & Industrial Sectors & Consumption Pattern of Wood & Wood Products in Pakistan (2003-2004). IGF Office, Ministry of Environment.Mahmood, K.; Chughtai, M.I.; Awan, A.R.; Waheed, R. A., 2016. Biomass Production of Some Salt Tolerant Tree Species Grown in Different Ecological Zones of Pakistan. African Journal of Biotechnology Vol. 10(9), pp. 1586-1592, 28 February 2011. Available online at http://www.academicjournals.org/AJB

Olofsson, P.; Foody, G.M.; Herold, M.; Stehman; S.V; Woodcock, C.E.; Wulder, M.A., 2014. Good practices for estimating area and assessing accuracy of land change. Remote Sensing of Environment, 148, 42-57.

Pakistan’s Mid-Term Review Report, 2017. REDD+ Readiness Preparation Project of Forest Carbon Partnership Facility of the World Bank. Available at https://www.forestcarbonpartnership.org/sites/fcp/files/2017/Sep/MTR_Pakista n%20FCPF_GRANT_REVISED_2017.pdf

Pakistan’s REDD+ Readiness Preparation Proposal, 2013. Final version with incorporation of PC-16 decision: July 25, 2014. Available at https://www.forestcarbonpartnership.org/sites/fcp/files/2014/october/Pakistan %27s%20Revised%20R-PP%20-%20September%2010%202014.pdf

Prasanna, J.; Anand, M.; Vijayasekaran, D.; Kumaraguru, A.K., 2017. Indian Journal of Geo Marine Sciences, Vol. 46 (08), August 2017, pp. 1682-1692.

R-PP, 2014. Pakistan’s Readiness Preparation Proposal. Final version with incorporation of PC-16 decision: July 25, 2014. Forest Carbon Partnership Facility (FCPF) The United Nations Collaborative Programme on Reducing Emissions from Deforestation and Forest Degradation in Developing Countries (UN-REDD). 112 p. + annexes. Sheikh, M.I., 1993.Trees of Pakistan. Pakistan Forest Institute, Peshawar. Zanne, A.E., Lopez-Gonzalez, G.*, Coomes, D.A., Ilic, J., Jansen, S., Lewis, S.L., Miller, R.B., Swenson, N.G., Wiemann, M.C., and Chave, J. 2009. Global wood density database. Dryad. Identifier: http://hdl.handle.net/10255/dryad.235

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9. GLOSSARY OF RELEVANT TERMS

Aboveground Biomass (AGB) Living vegetation above the soil, including stem, stump, branches, bark, seeds, and foliage.

Activity data (AD) Data on the extent of human activity causing emissions and removals.

Allometry In forestry, generally the relationship between tree diameter, height, crown size and biomass.

Belowground Biomass (BGB) The living biomass of roots greater than 2 mm diameter

Biomass The total mass of living organisms in a given area or volume; dead plant material can be included as dead biomass.

Carbon Dioxide (CO2) A naturally occurring gas, also a by-product of burning fossil fuels from fossil carbon deposits, such as oil, gas and coal, of burning biomass and of land use changes and other industrial processes. It is the principal anthropogenic greenhouse gas that affects the Earth’s radiative balance. It is the reference gas against which other greenhouse gases are measured and therefore has a Global Warming Potential of 1.

Conference of the Parties (COP) The supreme body of the Convention. It currently meets once a year to review the Convention's progress. The word "conference" is not used here in the sense of "meeting" but rather of "association". The "Conference" meets in sessional periods, for example, the "fourth session of the Conference of the Parties".

Crown Cover The percentage of the surface of an ecosystem that is under the tree canopy. Also referred to as ‘canopy cover’ or just ‘tree cover’.

Dead Wood The term used to describe all non‐living woody biomass not contained in the litter, either standing, lying on the round, or in the soil. Dead wood includes wood lying on the surface, dead roots, and stumps larger than or equal to 10 cm in diameter or any other diameter used by the host country

Deforestation Conversion of forest to other land.

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Degradation (forest degradation) The term used to describe the condition of a forest that has been reduced below its natural capacity, but not below the minimum (i.e. 10 percent crown cover) thresholds that qualifies as deforestation.

Disturbance An event reducing above-ground and belowground carbon pools such as fire, insect outbreak, flooding.

Drivers Refers to both direct and indirect causes of deforestation and forest degradation

Emission or removal factors GHG emissions or removals per unit of activity data.

Forest A vegetation type dominated by trees. Many definitions of the term forest are in use throughout the world, reflecting wide differences in biogeophysical conditions, social structure, and economics. Particular criteria apply under the Kyoto Protocol. For a discussion of the term forest and related terms such as afforestation, reforestation, and deforestation see the IPCC Special Report on Land Use, Land-Use Change, and Forestry (IPCC, 2000). See also the Report on Definitions and Methodological Options to Inventory Emissions from Direct Human-induced Degradation of Forests and Devegetation of Other Vegetation Types (IPCC, 2003)

Forest Carbon Forest carbon generally refers to the carbon stored in forests; usually in reference to climate change mitigation projects which aim to increase carbon sequestration in or decrease carbon dioxide emissions from forests.

Forest Monitoring Functions of a national forest monitoring system to assist a country to meet measuring, reporting and verification requirements, or other goals.

Green House Gases (GHGs) The atmospheric gases responsible for causing global warming and climate change. The major GHGs are carbon dioxide (CO2), methane (CH4) and nitrous oxide (N20). Less prevalent - but very powerful - greenhouse gases are hydro fluorocarbons (HFCs), perfluorocarbons (PFCs) and sulphur hexafluoride (SF6).

Green House Gas Inventory (GHGI) Anthropogenic greenhouse gas estimates with national territorial coverage produced using IPCC methods in accordance with decisions taken at the UNFCCC Conference of the Parties (COP).

Intergovernmental Panel on Climate Change (IPCC) Established in 1988 by the World Meteorological Organization and the UN Environment Programme, the IPCC surveys world-wide scientific and technical

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literature and publishes assessment reports that are widely recognized as the most credible existing sources of information on climate change. The IPCC also works on methodologies and responds to specific requests from the Convention's subsidiary bodies. The IPCC is independent of the Convention.

Kyoto Protocol An international agreement standing on its own, and requiring separate ratification by governments, but linked to the UNFCCC. The Kyoto Protocol, among other things, sets binding targets for the reduction of greenhouse gas emissions by industrialized countries.

Land Use, Land-Use Change and Forestry (LULUCF) A greenhouse gas inventory sector that covers emissions and removals of greenhouse gases resulting from direct human-induced land use, land-use change and forestry activities.

Land Use The total of arrangements, activities and inputs undertaken in a certain land- cover type (a set of human actions). The social and economic purposes for which land is managed (e.g. grazing, timber extraction, and conservation). Land-use change occurs when, e.g. forest is converted to agricultural land or to urban areas.

Measurement, Reporting and Verification (MRV) Measuring is estimating the effect of the action, reporting is communication to the international community, and verifying is checking the estimation; procedures for all three are to be agreed by the UNFCCC.

National Forest Inventory (NFI) A periodically updated sample-based system to provide information on the state of a country’s forest resources.

National Forest Monitoring/ Management System The institutional arrangements in a country to monitor forests. NFMS will presumably include representation from responsible Ministries, indigenous peoples and local communities, forest industry representatives, and other stakeholders. In the REDD+ context, a system for monitoring and reporting on REDD+ activities, in accordance with guidance from the COP. The COP has established that a NFMS should use a combination of remote-sensing and ground- based data, provide estimates that are transparent, consistent, as far as possible accurate, and that reduce uncertainties, taking into account national capabilities and capacities; and their results are available and suitable for review as agreed by the COP. NFMS may provide information on safeguards.

REDD+ Reducing emissions from deforestation; Reducing emissions from forest degradation; Conservation of forest carbon stocks; Sustainable management of forests; Enhancement of forest carbon stocks.

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Reference Emission Levels/ Reference Levels Are means to establish reference emission levels, based on historical data, taking into account, inter alia, trends, starting dates and the length of the reference period, availability and reliability of historical data, and other specific national circumstances

Reforestation Replanting of forests on lands that have previously contained forests but that have been converted to some other use.

Remote Sensing A method of measuring deforestation and/or forest degradation by a recording device that is not in physical contact with the forest, such as a satellite or aircraft.

Removals This is the opposite of an emission of greenhouse gas and occurs when greenhouse gases are removed from the atmosphere, for example, by trees during the process of photosynthesis.

Safeguards Undertakings to protect and develop social and environmental sustainability. Covers consistency with national forest programmes and relevant international conventions and agreements; transparency and effectiveness of national forest governance; respect for the knowledge and rights of indigenous peoples and members of local communities; participation of relevant stakeholders, in particular indigenous peoples and local communities. Soil Organic Carbon (SOC) The carbon pool that includes all organic material in soil but excluding the coarse roots of the belowground biomass pool. Source Source mostly refers to any process, activity or mechanism that releases a greenhouse gas, aerosol or a precursor of a greenhouse gas or aerosol into the atmosphere. Source can also refer to, e.g. an energy source. Subnational An administrative division, administrative unit, administrative entity or country subdivision (or, sometimes, geopolitical division or subnational entity) is a portion of a country or other region delineated for the purpose of administration. Administrative divisions are each granted a certain degree of autonomy and are usually required to manage themselves through their own local governments. Transparency Mean that decisions taken, and their enforcement are done in a manner that follows rules and regulations. It also means that information is freely available and directly accessible to those who will be affected by such decisions and their enforcement. It also means that enough information is provided and that it is provided in easily understandable forms and media.

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Uncertainty An expression of the degree to which a value (e.g., the future state of the climate system) is unknown. Uncertainty can result from lack of information or from disagreement about what is known or even knowable. It may have many types of sources, from quantifiable errors in the data to ambiguously defined concepts or terminology, or uncertain projections of human behaviour. Uncertainty can therefore be represented by quantitative measures, for example, a range of values calculated by various models, or by qualitative statements, for example, reflecting the judgment of a team of experts. United Nations Framework Convention on Climate Change (UNFCCC) The Convention was adopted on 9 May 1992 in New York and signed at the 1992 Earth Summit in Rio de Janeiro by more than 150 countries and the European Community. Its ultimate objective is the “stabilisation of greenhouse gas concentrations in the (UNFCCC) atmosphere at a level that would prevent dangerous anthropogenic interference with the climate system”. It contains commitments for all Parties. Under the Convention, Parties included in Annex I (all OECD member countries in the year 1990 and countries with economies in transition) aim to return greenhouse gas emissions not controlled by the Montreal Protocol to 1990 levels by the year 2000. The Convention entered in force in March 1994. Validation A process by which an independent third-party organization, which has been certified to evaluate projects according to a specific standard, thoroughly reviews the design, methodologies, calculations and strategies employed in a project, ensuring the project follows the rules of the chosen standard.

Verification The periodic independent review and ex-post determination of the monitored reductions in anthropogenic emissions by sources of greenhouse gases or increases in carbon stocks (carbon benefits) that have occurred as a result of a project activity during the verification period

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ANNEXES

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Forest Reference Emission Level / Forest Reference Level for Pakistan – Draft Final 3.0

ANNEX 1. ADMINISTRATIVE BOUNDARY MAP (SOP, 2012)

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ANNEX 2. SATELLITE IMAGERY USED FOR LULC/AD MAPPING

Annex 4a. List of satellite imagery (Landsat) used for forest cover mapping (2016)

SCENE_ID PATH_1 ROW_1 DATE CC CC_LAND SUN_AZIMUT SUN_ELEVAT LC81500342016314LGN00 150 34 9.11.2016 8.96 8.96 161.04842 33.377244 LC81500352016314LGN00 150 35 9.11.2016 9.44 9.44 160.372757 34.642624 LC81500362016298LGN00 150 36 24.10.2016 4.58 4.58 157.582609 40.707526 LC81500372016298LGN00 150 37 24.10.2016 9.71 9.71 156.724122 41.932105 LC81500382016314LGN01 150 38 9.11.2016 1.77 1.77 158.225069 38.402645 LC81500392015279LGN00 150 39 6.10.2015 0.04 0.04 149.284793 50.216337 LC81500402016282LGN01 150 40 8.10.2016 0 0 149.120767 50.503007 LC81500422016282LGN01 150 42 8.10.2016 0 0 146.445318 52.688509 LC81500432016314LGN01 150 43 9.11.2016 0.29 0.29 154.071103 44.506558 LC81570392016283LGN01 157 39 9.10.2016 0 0 150.715884 49.058144 LC81570402016283LGN01 157 40 9.10.2016 0 0 149.496147 50.185143 LC81480352016332LGN00 148 35 27.11.2016 26.07 26.07 160.764388 30.34148 LC81480362016332LGN00 148 36 27.11.2016 19.45 19.45 160.153881 31.607145 LC81480382016300LGN01 148 38 26.10.2016 0.06 0.06 156.231693 42.52697 LC81480392016252LGN01 148 39 8.9.2016 0.62 0.62 136.586349 58.043937 LC81550402016333LGN01 155 40 28.11.2016 0.16 0.16 157.516551 36.41245 LC81550412016317LGN00 155 41 12.11.2016 0.01 0.01 156.163901 41.291946 LC81550422016333LGN01 155 42 28.11.2016 0.06 0.06 156.069058 38.873118 LC81530382016303LGN00 153 38 29.10.2016 0 0 156.780984 41.598677 LC81530392016287LGN01 153 39 13.10.2016 0 0 152.005134 47.801988 LC81530402016287LGN01 153 40 13.10.2016 0 0 150.861486 48.950252 LC81530412016303LGN00 153 41 29.10.2016 0.04 0.04 154.063095 45.222763 LC81530422016335LGN01 153 42 30.11.2016 0.08 0.08 156.03968 38.503232 LC81510342016289LGN00 151 34 15.10.2016 3.5 3.5 157.489916 41.195751 LC81510352016289LGN00 151 35 15.10.2016 4.08 4.08 156.585795 42.41257 LC81510362016289LGN00 151 36 15.10.2016 0.31 0.31 155.648705 43.620075 LC81510372016289LGN00 151 37 15.10.2016 0.05 0.05 154.675339 44.816558 LC81510382016289LGN00 151 38 15.10.2016 0.04 0.04 153.661447 46.001156 LC81510392016289LGN01 151 39 15.10.2016 0 0 152.602558 47.173089 LC81510402016289LGN01 151 40 15.10.2016 0 0 151.494577 48.330689 LC81510412016289LGN00 151 41 15.10.2016 0.08 0.08 150.332202 49.47341 LC81510422016289LGN01 151 42 15.10.2016 0 0 149.111126 50.598989 LC81510432016289LGN01 151 43 15.10.2016 0 0 147.825679 51.706393 LC81490352016275LGN00 149 35 1.10.2016 3.96 3.96 152.703994 47.068213 LC81490362016291LGN00 149 36 17.10.2016 0.75 0.75 156.124793 42.968658 LC81490372016291LGN00 149 37 17.10.2016 0.18 0.18 155.179106 44.172358 LC81490382016291LGN01 149 38 17.10.2016 0.02 0.02 154.194899 45.364627 LC81490392016291LGN00 149 39 17.10.2016 0.05 0.05 153.167684 46.544806 LC81560402016324LGN01 156 40 19.11.2016 0.57 0.57 157.413403 38.32732 LC81560422016324LGN01 156 42 19.11.2016 0.08 0.06 155.87552 40.783461 LC81540392016310LGN01 154 39 5.11.2016 0.01 0.01 156.999538 40.755293 LC81540402016310LGN01 154 40 5.11.2016 0.01 0.01 156.169498 41.979271 LC81540412016294LGN00 154 41 20.10.2016 0.01 0.01 151.863437 47.938832 LC81540422016326LGN01 154 42 21.11.2016 0.22 0.25 155.965895 40.328428 LC81520372016312LGN00 152 37 7.11.2016 0.01 0.01 158.778466 37.711876 LC81520382016296LGN00 152 38 22.10.2016 0.04 0.04 155.404406 43.772515 LC81520392016296LGN01 152 39 22.10.2016 0 0 154.449969 44.971024 LC81520402016296LGN01 152 40 22.10.2016 0.01 0.01 153.453274 46.15763 LC81520412016296LGN00 152 41 22.10.2016 0.15 0.15 152.410064 47.331181 LC81520422016280LGN01 152 42 6.10.2016 0 0 145.604727 53.273947 LC08_L1TP_150041_20161125_20170317_01_T10 0 0 0 0 0 0 0 0 0 0 0 LC08_L1TP_155043_20161128_20170317_01_T10 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 LC08_L1TP_149034_20161102_20170318_01_T10 0 0 0 0 0 LC08_L1TP_149040_20161017_20170319_01_T10 0 0 0 0 0 0 0 0 0 0 0 LC08_L1TP_156041_20161119_20170318_01_T10 0 0 0 0 0 LC08_L1TP_156043_20161119_20170318_01_T10 0 0 0 0 0 0 0 0 0 0 0 LC08_L1TP_147036_20160917_20170321_01_T10 0 0 0 0 0 LC08_L1TP_154038_20161105_20170318_01_T10 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 LC08_L1TP_152036_20161107_20170318_01_T10 0 0 0 0 0 LC08_L1TP_152043_20161123_20170318_01_T10 0 0 0 0 0 0 0 0 0 0 0 ~ 58 ~

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Annex 4b. List of satellite imagery used for forest cover mapping (2012).

SCENE_ID PATH_1 ROW_1 DATE CC CC_LAND SUN_AZIMUT SUN_ELEVAT LT51500342011172KHC00 150 34 21.6.2011 3 3 117.928903 65.324053 LT51500352010345KHC00 150 35 11.12.2010 2 2 157.401974 27.497984 LT51500362010313KHC00 150 36 9.11.2010 4 4 156.668314 35.230889 LT51500372011268KHC00 150 37 25.9.2011 0 0 143.548379 50.060435 LT51500382011268KHC00 150 38 25.9.2011 2 2 142.107802 51.054018 LT51500392011268KHC00 150 39 25.9.2011 0 0 140.583008 52.034089 LT51500402011268KHC00 150 40 25.9.2011 0 0 139.007465 52.975776 LT51500412011268KHC00 150 41 25.9.2011 0 0 137.355845 53.887133 LT51500422011028KHC00 150 42 28.1.2011 0 0 144.907898 37.709867 LT51500432011156KHC00 150 43 5.6.2011 0 0 89.781108 66.643718 LT51570392011109KHC00 157 39 19.4.2011 0 0 124.983605 60.217835 LT51570402011109KHC00 157 40 19.4.2011 0 0 122.50827 60.818376 LT51480352010331KHC00 148 35 27.11.2010 4 4 158.071043 29.696691 LT51480362010123KHC00 148 36 3.5.2010 4 4 127.163717 62.117377 LT51480382011318KHC00 148 38 14.11.2011 9 9 155.016316 36.209906 LT51550402011159KHC05 155 40 8.6.2011 0 0 99.46926 66.939848 LT51550412011159KHC05 155 41 8.6.2011 0 0 95.936861 66.901505 LT51550422011159KHC05 155 42 8.6.2011 0 0 92.437151 66.777113 LT51550432011159KHC05 155 43 8.6.2011 3 0 88.954191 66.564159 LT51530382011273KHC00 153 38 30.9.2011 0 0 144.211237 49.58407 LT51530392011273KHC00 153 39 30.9.2011 0 0 142.801275 50.604534 LT51530402011273KHC00 153 40 30.9.2011 0 0 141.346085 51.590148 LT51530412011273KHC00 153 41 30.9.2011 0 0 139.818184 52.547866 LT51530422011081KHC00 153 42 22.3.2011 0 0 129.871284 53.436955 LT51510342011275KHC00 151 34 2.10.2011 1 1 149.884542 44.693397 LT51510352011275KHC00 151 35 2.10.2011 1 1 148.737919 45.790834 LT51510362011275KHC00 151 36 2.10.2011 0 0 147.537749 46.877414 LT51510372011275KHC00 151 37 2.10.2011 0 0 146.292505 47.938871 LT51510382011275KHC00 151 38 2.10.2011 0 0 144.992191 48.989239 LT51510392011275KHC00 151 39 2.10.2011 0 0 143.625699 50.024497 LT51510402011275KHC00 151 40 2.10.2011 0 0 142.215063 51.025628 LT51510412011275KHC00 151 41 2.10.2011 0 0 140.73929 52.002581 LT51510422011115KHC00 151 42 25.4.2011 0 0 114.019181 63.207479 LT51510432011115KHC00 151 43 25.4.2011 0 0 111.054269 63.55151 LT51490342011117KHC00 149 34 27.4.2011 2 2 133.710152 58.866352 LT51490352011117KHC00 149 35 27.4.2011 4 4 131.452673 59.64219 LT51490362010306KHC00 149 36 2.11.2010 5 5 155.810709 37.224474 LT51490372011165KHC03 149 37 14.6.2011 2 2 108.764243 66.516665 LT51490382011165KHC03 149 38 14.6.2011 0 0 105.281872 66.703479 LT51490392011117KHC00 149 39 27.4.2011 0 0 121.39485 62.272719 LT51490402011133KHC00 149 40 13.5.2011 0 0 110.313491 65.596594 LT51560402011230KHC00 156 40 18.8.2011 0 0 117.325357 61.085495 LT51560412011230KHC00 156 41 18.8.2011 0 0 114.658851 61.505289 LT51560422011262KHC00 156 42 19.9.2011 0 0 132.305305 56.212611 LT51560432011070KHC00 156 43 11.3.2011 0 0 132.077555 50.521213 LT51470362011215KHC00 147 36 3.8.2011 4 4 120.231887 61.558798 LT51540382011152KHC00 154 38 1.6.2011 0 0 108.722977 66.624238 LT51540392011152KHC00 154 39 1.6.2011 0 0 105.192827 66.816588 LT51540402011008KHC00 154 40 8.1.2011 0 0 150.552987 32.82791 LT51540412011072KHC00 154 41 13.3.2011 0 0 134.529457 49.554466 LT51540422011072KHC00 154 42 13.3.2011 0 0 132.991195 50.381062 LT51520362010359KHC00 152 36 25.12.2010 2 2 155.330536 27.768352 LT51520372011138KHC00 152 37 18.5.2011 0 0 117.638376 65.147163 LT51520382011186KHC00 152 38 5.7.2011 0 0 105.061869 65.58007 LT51520392011138KHC00 152 39 18.5.2011 0 0 111.12691 65.894849 LT51520402011138KHC00 152 40 18.5.2011 0 0 107.776761 66.149811 LT51520412011122KHC00 152 41 2.5.2011 0 0 113.064597 64.222406 LT51520422011026KHC00 152 42 26.1.2011 0 0 145.359662 37.334466 LT51520432011058KHC00 152 43 27.2.2011 0 0 135.947629 46.607515 LT05_L1TP_148039_20110607_20161010_01_T10 0 0 0 0 0 LT05_L1TP_155039_20110608_20161010_01_T10 0 0 0 0 0 LT05_L1TP_153043_20110202_20161010_01_T10 0 0 0 0 0 LT05_L1TP_151044_20110425_20161209_01_T10 0 0 0 0 0 LT05_L1TP_156039_20110919_20161006_01_T10 0 0 0 0 0 LT05_L1TP_147035_20111107_20161005_01_T10 0 0 0 0 0 LT05_L1TP_154043_20110313_20161209_01_T10 0 0 0 0 0 LT05_L1TP_152035_20110822_20161007_01_T10 0 0 0 0 0 LT05_L1GS_152044_20110211_20161010_01_T20 0 0 0 0 0

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Annex 4c. List of satellite imagery used for forest cover mapping (2008).

SCENE_ID PATH_1 ROW_1 DATE CC CC_LAND SUN_AZIMUT SUN_ELEVAT LT51500342008276KHC01 150 34 2.10.2008 2 2 148.843379 44.037996 LT51500352008276KHC01 150 35 2.10.2008 1 1 147.691314 45.126398 LT51500362008276KHC01 150 36 2.10.2008 1 1 146.504034 46.191897 LT51500372008276KHC01 150 37 2.10.2008 0 0 145.270116 47.239984 LT51500382008324KHC01 150 38 19.11.2008 0 0 154.345108 34.459843 LT51500392008324KHC01 150 39 19.11.2008 0 0 153.601088 35.655652 LT51500402008276KHC01 150 40 2.10.2008 0 0 141.224 50.270296 LT51500412008276KHC01 150 41 2.10.2008 0 0 139.763256 51.231099 LT51500422008324KHC01 150 42 19.11.2008 0 0 151.21704 39.139133 LT51500432008324KHC01 150 43 19.11.2008 7 7 150.355425 40.286358 LT51570392008325KHC01 157 39 20.11.2008 0 0 153.659214 35.421465 LT51570402008357KHC01 157 40 22.12.2008 0 0 151.783362 32.078231 LT51480352008278KHC01 148 35 4.10.2008 2 2 148.33178 44.484958 LT51480362008278KHC01 148 36 4.10.2008 1 1 147.170931 45.568027 LT51480382008294KHC01 148 38 20.10.2008 1 1 149.711589 42.757035 LT51550402008359KHC01 155 40 24.12.2008 2 2 151.5406 32.001414 LT51550412008359KHC01 155 41 24.12.2008 1 1 150.84234 33.144096 LT51550422008359KHC01 155 42 24.12.2008 1 1 150.120037 34.277676 LT51550432008359KHC01 155 43 24.12.2008 0 1 149.36576 35.408072 LT51530382008345KHC01 153 38 10.12.2008 0 0 154.210278 30.70972 LT51530392008361KHC01 153 39 26.12.2008 0 0 151.965879 30.804844 LT51530402008361KHC01 153 40 26.12.2008 0 0 151.288112 31.952147 LT51530412008361KHC01 153 41 26.12.2008 0 0 150.587751 33.090908 LT51530422008361KHC01 153 42 26.12.2008 0 0 149.86312 34.22048 LT51510342008187KHC01 151 34 5.7.2008 1 1 117.356594 63.945563 LT51510352008139KHC01 151 35 18.5.2008 12 12 122.99609 64.010107 LT51510362008347KHC01 151 36 12.12.2008 2 2 155.337462 28.102685 LT51510372008299KHC01 151 37 25.10.2008 0 0 151.813399 40.083576 LT51510382008347KHC01 151 38 12.12.2008 0 0 154.0783 30.476597 LT51510392008347KHC01 151 39 12.12.2008 0 0 153.407575 31.673704 LT51510402008347KHC01 151 40 12.12.2008 0 0 152.729078 32.842684 LT51510412008347KHC01 151 41 12.12.2008 0 0 152.028065 34.003567 LT51510422008347KHC01 151 42 12.12.2008 0 0 151.301759 35.155603 LT51510432008347KHC01 151 43 12.12.2008 0 0 150.543212 36.304515 LT51490342008269KHC01 149 34 25.9.2008 2 2 146.533021 46.282216 LT51490352008269KHC01 149 35 25.9.2008 4 4 145.256735 47.32869 LT51490362008269KHC01 149 36 25.9.2008 4 4 143.939456 48.349821 LT51490372008269KHC01 149 37 25.9.2008 0 0 142.569346 49.350231 LT51490382008285KHC01 149 38 11.10.2008 0 0 147.139445 45.532213 LT51490392008333KHC01 149 39 28.11.2008 0 0 153.897788 33.769202 LT51490402008269KHC01 149 40 25.9.2008 0 0 138.078172 52.217164 LT51560402008302KHC01 156 40 28.10.2008 0 0 149.611929 42.59243 LT51560412008366KHC01 156 41 31.12.2008 2 2 149.866176 33.089177 LT51560422008318KHC01 156 42 13.11.2008 1 1 150.585998 40.548452 LT51560432008366KHC01 156 43 31.12.2008 9 3 148.362353 35.319639 LT51470362008287KHC01 147 36 13.10.2008 12 12 149.906148 42.714516 LT51540382008336KHC01 154 38 1.12.2008 0 0 154.580574 32.019856 LT51540392008336KHC01 154 39 1.12.2008 0 0 153.887545 33.216926 LT51540402008336KHC01 154 40 1.12.2008 0 0 153.182598 34.393045 LT51540412008336KHC01 154 41 1.12.2008 0 0 152.452778 35.560456 LT51540422008336KHC01 154 42 1.12.2008 1 1 151.691004 36.725552 LT51520362008274KHC01 152 36 30.9.2008 1 1 145.806053 46.809778 LT51520372008274KHC01 152 37 30.9.2008 0 0 144.526415 47.850692 LT51520382008338KHC01 152 38 3.12.2008 0 0 154.532735 31.68935 LT51520392008338KHC01 152 39 3.12.2008 0 0 153.847252 32.886577 LT51520402008306KHC01 152 40 1.11.2008 2 2 150.411773 41.437555 LT51520412008306KHC01 152 41 1.11.2008 0 0 149.46709 42.557435 LT51520422008306KHC01 152 42 1.11.2008 0 0 148.47582 43.670244 LT51520432008306KHC01 152 43 1.11.2008 0 0 147.427412 44.780991 LT05_L1TP_148039_20081020_20161029_01_T10 0 0 0 0 0 LT05_L1TP_155039_20081005_20161029_01_T10 0 0 0 0 0 LT05_L1TP_153043_20081108_20161028_01_T10 0 0 0 0 0 LT05_L1TP_151044_20081025_20161029_01_T10 0 0 0 0 0 LT05_L1TP_156039_20081012_20161029_01_T10 0 0 0 0 0 LT05_L1TP_147035_20081013_20161029_01_T10 0 13.10.2008 0 0 0 0 LT05_L1TP_154043_20080523_20161031_01_T10 0 0 0 0 0 LT05_L1TP_152035_20080626_20161030_01_T10 0 0 0 0 0 LT05_L1GS_152044_20080423_20161101_01_T20 0 0 0 0 0

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Forest Reference Emission Level / Forest Reference Level for Pakistan – Draft Final 3.0

Annex 4d. List of satellite imagery used for forest cover mapping (2004).

SCENE_ID PATH_1 ROW_1 DATE CC CC_LAND SUN_AZIMUTSUN_ELEVAT LE71500342003126EDC00 150 34 6.5.2003 8 8 130.3401 60.99914 LE71500352003126EDC00 150 35 6.5.2003 16 16 127.8202 61.70115 LE71500362003126EDC00 150 36 6.5.2003 4 4 125.1858 62.35234 LE71500372003078ASN00 150 37 19.3.2003 0 0 138.3779 47.98692 LE71500382003126EDC00 150 38 6.5.2003 0 0 119.6071 63.47528 LE71500392003126EDC00 150 39 6.5.2003 0 0 116.674 63.94061 LE71500402003110ASN00 150 40 20.4.2003 0 0 121.6744 60.84469 LE71500412003110ASN00 150 41 20.4.2003 0 0 119.0821 61.37106 LE71500422003110ASN00 150 42 20.4.2003 0 0 116.415 61.83594 LE71500432003126ASN00 150 43 6.5.2003 0 0 104.1614 65.07858 LE71570392003063SGS00 157 39 4.3.2003 1 1 139.3305 44.41542 LE71570402003111EDC00 157 40 21.4.2003 1 1 121.1894 61.11315 LE71480352003064SGS00 148 35 5.3.2003 1 1 143.7784 40.87492 LE71480362003128ASN00 148 36 8.5.2003 3 3 124.3927 62.76528 LE71480382003064SGS00 148 38 5.3.2003 1 1 140.3256 43.80951 LE71550432003113EDC00 155 43 23.4.2003 1 0 111.9193 62.89408 LE71530382003147EDC00 153 38 27.5.2003 0 0 110.2484 66.172 LE71530392003131EDC00 153 39 11.5.2003 0 0 114.1982 64.77692 LE71530402003131EDC00 153 40 11.5.2003 0 0 111.0304 65.10717 LE71530412003147EDC00 153 41 27.5.2003 0 0 99.87342 66.60763 LE71530422003147EDC00 153 42 27.5.2003 0 0 96.39546 66.58293 LE71510342003117EDC00 151 34 27.4.2003 3 3 133.2604 58.70181 LE71510352003117EDC01 151 35 27.4.2003 13 13 131.0042 59.46843 LE71510362003005SGS00 151 36 5.1.2003 2 2 153.3448 27.83961 LE71510372003133EDC00 151 37 13.5.2003 0 0 119.4101 64.22227 LE71510382003117EDC00 151 38 27.4.2003 0 0 123.6573 61.48705 LE71510392003117EDC00 151 39 27.4.2003 0 0 121.0084 62.05345 LE71510402003069SGS00 151 40 10.3.2003 0 0 136.4685 47.40873 LE71510412003117EDC00 151 41 27.4.2003 0 0 115.4543 62.99868 LE71510422003149ASN00 151 42 29.5.2003 0 0 95.55818 66.61437 LE71510432003149ASN00 151 43 29.5.2003 1 1 92.10881 66.48386 LE71490342003151EDC00 149 34 31.5.2003 2 2 121.8525 64.83183 LE71490352003151EDC00 149 35 31.5.2003 2 2 118.7189 65.32872 LE71490362003151EDC00 149 36 31.5.2003 1 1 115.477 65.7578 LE71490372003103ASN00 149 37 13.4.2003 1 1 131.3562 56.79671 LE71490382003119EDC00 149 38 29.4.2003 0 0 122.7841 61.9686 LE71490392003151EDC00 149 39 31.5.2003 0 0 105.2851 66.5735 LE71490402003135ASN00 149 40 15.5.2003 0 0 108.984 65.61264 LE71560402003120EDC00 156 40 30.4.2003 0 0 116.7423 63.21118 LE71560412003120EDC00 156 41 30.4.2003 0 0 113.8232 63.61344 LE71560422003120EDC00 156 42 30.4.2003 0 0 110.8163 63.94331 LE71560432003088ASN00 156 43 29.3.2003 54 2 124.738 56.17475 LE71470362003041SGS00 147 36 10.2.2003 1 1 147.0075 34.30885 LE71540382003138EDC00 154 38 18.5.2003 0 0 114.0641 65.35279 LE71540392003138EDC00 154 39 18.5.2003 0 0 110.806 65.67762 LE71540402003138EDC00 154 40 18.5.2003 0 0 107.4711 65.92519 LE71540412003138EDC00 154 41 18.5.2003 0 0 104.0991 66.09194 LE71540422003138EDC00 154 42 18.5.2003 2 0 100.6989 66.17526 LE71520362003124EDC00 152 36 4.5.2003 1 1 125.9776 61.91405 LE71520372003124EDC00 152 37 4.5.2003 0 0 123.3067 62.52438 LE71520382003140EDC00 152 38 20.5.2003 0 0 113.1801 65.5785 LE71520392003140EDC00 152 39 20.5.2003 1 1 109.8818 65.88201 LE71520402003076SGS00 152 40 17.3.2003 0 0 134.4594 49.89039 LE71520412003076SGS00 152 41 17.3.2003 1 1 132.8839 50.71 LE71520422003076SGS00 152 42 17.3.2003 1 1 131.2492 51.50343 LE71520432003060SGS00 152 43 1.3.2003 0 0 134.9601 46.98815 LE07_L1TP_148039_20030305_20170126_01_T10 0 0 0 0 0 LE07_L1TP_155039_20030525_20170125_01_T10 0 0 0 0 0 LE07_L1TP_155040_20030509_20170125_01_T10 0 0 0 0 0 No good imagery available 0 0 0 0 0 0 LE07_L1TP_155042_20030306_20170126_01_T10 0 0 0 0 0 LE07_L1TP_153043_20031002_20170123_01_T10 0 0 0 0 0 LE07_L1TP_151044_20030326_20170125_01_T10 0 0 0 0 0 LE07_L1TP_156039_20030209_20170126_01_T10 0 0 0 0 0 LE07_L1TP_147035_20030210_20170126_01_T10 0 0 0 0 0 LE07_L1TP_154043_20030518_20170125_01_T10 0 0 0 0 0 LE07_L1TP_152035_20030520_20170125_01_T10 0 0 0 0 0 LE07_L1TP_152044_20030301_20170126_01_T20 0 0 0 0 0

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Annex 4e. List of satellite imagery used for forest cover mapping (2000).

SCENE_ID PATH_1 ROW_1 DATE CC CC_LAND SUN_AZIMUTSUN_ELEVAT LE71500342001280SGS00 150 34 7.10.2001 3 3 151.4371 42.90215 LE71500352001280SGS00 150 35 7.10.2001 2 2 150.3712 44.03065 LE71500362001280SGS00 150 36 7.10.2001 2 2 149.2704 45.14157 LE71500372000310EDC00 150 37 5.11.2000 1 1 155.644 37.4931 LE71500382000294SGS00 150 38 20.10.2000 0 0 151.7011 43.47189 LE71500392000294SGS00 150 39 20.10.2000 0 0 150.6829 44.61436 LE71500402000342SGS00 150 40 7.12.2000 0 0 154.5648 34.04572 LE71500412000118SGS00 150 41 27.4.2000 0 0 116.2169 63.86689 LE71500422000262SGS00 150 42 18.9.2000 1 1 132.7593 56.63674 LE71500432000150SGS00 150 43 29.5.2000 1 1 92.27781 67.25908 LE71570392000023EDC00 157 39 23.1.2000 1 1 149.1998 33.81422 LE71570402000263SGS00 157 40 19.9.2000 0 0 137.0344 54.67899 LE71480352000168SGS01 148 35 16.6.2000 3 3 115.9258 66.28044 LE71480362000168SGS01 148 36 16.6.2000 5 5 112.5113 66.6385 LE71550402000281SGS00 155 40 7.10.2000 0 0 145.2093 49.62007 LE71550412000281SGS00 155 41 7.10.2000 0 0 143.8576 50.65628 LE71550422000281SGS00 155 42 7.10.2000 0 0 142.4357 51.66751 LE71550432000329EDC00 155 43 24.11.2000 2 1 152.4617 39.90832 LE71530382000283SGS00 153 38 9.10.2000 0 0 148.4137 46.86903 LE71530392000283SGS00 153 39 9.10.2000 0 0 147.2166 47.95718 LE71530402000299SGS00 153 40 25.10.2000 0 0 150.9487 44.24417 LE71530412000299SGS00 153 41 25.10.2000 0 0 149.9161 45.37926 LE71530422000299SGS00 153 42 25.10.2000 0 0 148.8364 46.49956 LE71510342000301SGS00 151 34 27.10.2000 2 2 156.706 36.55947 LE71510352000301SGS00 151 35 27.10.2000 2 2 155.8984 37.76692 LE71510362000045SGS00 151 36 14.2.2000 3 3 147.4075 35.86885 LE71510372000173SGS00 151 37 21.6.2000 0 0 108.4994 66.72914 LE71510382000285EDC00 151 38 11.10.2000 0 0 149.0548 46.25026 LE71510392000285EDC00 151 39 11.10.2000 0 0 147.893 47.3493 LE71510402000333SGS00 151 40 28.11.2000 1 1 154.8371 35.5466 LE71510412000333SGS00 151 41 28.11.2000 2 2 154.1042 36.74331 LE71510422000333SGS00 151 42 28.11.2000 0 0 153.3432 37.93145 LE71510432000333SGS00 151 43 28.11.2000 1 1 152.5521 39.10983 LE71490342000303SGS00 149 34 29.10.2000 2 2 157.0114 35.94138 LE71490352000303SGS00 149 35 29.10.2000 2 2 156.2215 37.15286 LE71490362001273EDC01 149 36 30.9.2001 2 2 146.8948 47.34753 LE71490372000287SGS00 149 37 13.10.2000 2 2 150.8048 44.51969 LT51490382000103AAA02 149 38 12.4.2000 0 0 124.9041 55.10797 LT51490392000103AAA02 149 39 12.4.2000 0 0 122.8171 55.71537 LT51490402000151XXX02 149 40 30.5.2000 0 0 99.31852 63.97598 LE71560402000288SGS00 156 40 14.10.2000 0 0 147.7654 47.53654 LE71560412000288SGS00 156 41 14.10.2000 0 0 146.5521 48.61753 LE71560422000288SGS00 156 42 14.10.2000 0 0 145.281 49.67945 LE71560432000288SGS00 156 43 14.10.2000 0 0 143.9489 50.72077 LE71470362000305SGS00 147 36 31.10.2000 1 1 155.7303 37.75799 LE71540382000290SGS00 154 38 16.10.2000 0 0 150.6261 44.70645 LE71540392000290SGS00 154 39 16.10.2000 0 0 149.5457 45.83033 LE71540402000290SGS00 154 40 16.10.2000 0 0 148.4198 46.93934 LE71540412000290SGS00 154 41 16.10.2000 0 0 147.2432 48.03191 LE71540422000290SGS00 154 42 16.10.2000 0 0 146.0109 49.10623 LE71520362000292SGS00 152 36 18.10.2000 1 1 153.153 41.78349 LE71520372000292SGS00 152 37 18.10.2000 0 0 152.1838 42.9434 LE71520382000148SGS00 152 38 27.5.2000 0 0 111.0341 66.89117 LE71520392000292SGS00 152 39 18.10.2000 0 0 150.1291 45.22421 LE71520402000324EDC00 152 40 19.11.2000 0 0 154.6168 37.46064 LE71520412000324EDC00 152 41 19.11.2000 0 0 153.8363 38.65392 LE71520422000324EDC00 152 42 19.11.2000 0 0 153.0249 39.83728 LE71520432000308SGS00 152 43 3.11.2000 0 0 149.9404 45.08162 LE07_L1TP_148038_20001006_20170209_01_T10 0 0 0 0 0 LE07_L1TP_148039_20001225_20170208_01_T10 0 0 0 0 0 LE07_L1TP_155039_20000804_20170210_01_T10 0 0 0 0 0 LE07_L1TP_153043_20000923_20170209_01_T10 0 0 0 0 0 LE07_L1TP_151044_20001027_20170209_01_T10 0 0 0 0 0 LE07_L1TP_156039_20001115_20170209_01_T10 0 0 0 0 0 LE07_L1TP_147035_20000929_20170209_01_T10 0 0 0 0 0 LE07_L1TP_154043_20001203_20170208_01_T10 0 0 0 0 0 LE07_L1TP_152035_20000831_20170210_01_T10 0 0 0 0 0 LE07_L1TP_152044_20001221_20170208_01_T20 0 0 0 0 0

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Annex 4f. List of satellite imagery used for forest cover mapping (1996).

SCENE_ID PATH_1 ROW_1 DATE CC CC_LAND SUN_AZIMUTSUN_ELEVAT LT05_L1TP_157039_19960916_20170102_01_T1 0 0 0 0 0 0 LT05_L1TP_148039_19961206_20170102_01_T1 0 0 0 0 0 0 LT05_L1TP_155039_19961207_20170717_01_T1 0 0 0 0 0 0 LT05_L1TP_155042_19961207_20170717_01_T1 0 0 0 0 0 0 LT05_L1TP_153043_19961107_20170102_01_T1 0 0 0 0 0 0 LT05_L1TP_151044_19960922_20170102_01_T1 0 0 0 0 0 0 LT05_L1TP_156039_19961112_20170103_01_T1 0 0 0 0 0 0 LT05_L1TP_147035_19961113_20170103_01_T1 0 0 0 0 0 0 LT05_L1TP_154043_19961216_20170102_01_T1 0 0 0 0 0 0 LT05_L1TP_152035_19961015_20170102_01_T1 0 0 0 0 0 0 LT05_L1GS_152044_19961015_20170103_01_T2 0 0 0 0 0 0 LT51470361996318ISP00 147 36 13.11.1996 2 2 149.561 31.4265 LT51480352010331KHC00 148 35 27.11.2010 4 4 158.071 29.69669 LT51480361996309ISP00 148 36 4.11.1996 2 2 148.2762 33.77095 LT51480381996341ISP00 148 38 6.12.1996 1 1 149.1305 29.03305 LT51490341996284ISP00 149 34 10.10.1996 5 5 143.9179 38.88953 LT51490352011117KHC00 149 35 27.4.2011 4 4 131.4527 59.64219 LT51490361996284ISP00 149 36 10.10.1996 5 5 141.7536 40.85904 LT51490371996188ISP00 149 37 6.7.1996 5 5 100.4029 58.20269 LT51490381996028ISP00 149 38 28.1.1996 0 0 137.7629 27.51904 LT51490391996284ISP00 149 39 10.10.1996 0 0 138.2316 43.68909 LT51490401996284ISP00 149 40 10.10.1996 0 0 136.9838 44.5879 LT51500342011172KHC00 150 34 21.6.2011 3 3 117.9289 65.32405 LT51500351996275SGI00 150 35 1.10.1996 1 1 139.6268 42.46851 LT51500361996003ISP00 150 36 3.1.1996 23 23 143.4495 22.84703 LT51500371997037SGI00 150 37 6.2.1997 0 0 141.0242 31.49968 LT51500381997181SGI00 150 38 30.6.1997 0 0 100.2371 61.99542 LT51500391997053SGI00 150 39 22.2.1997 0 0 135.595 37.9185 LT51500401996003ISP00 150 40 3.1.1996 0 0 140.8391 26.77469 LT51500411996291ISP00 150 41 17.10.1996 0 0 138.475 43.69278 LT51500421996291ISP00 150 42 17.10.1996 0 0 137.2608 44.59738 LT51500431997165SGI00 150 43 14.6.1997 0 0 86.16163 61.86568 LT51510341996250ISP00 151 34 6.9.1996 1 1 130.3444 48.16554 LT51510351997188SGI00 151 35 7.7.1997 2 2 109.6202 61.11351 LT51510361997188SGI00 151 36 7.7.1997 3 3 106.8115 61.3187 LT51510371996266ISP00 151 37 22.9.1996 0 0 133.159 46.57657 LT51510381996122ISP00 151 38 1.5.1996 0 0 110.6569 55.28421 LT51510391996122ISP00 151 39 1.5.1996 0 0 108.4156 55.53503 LT51510401996122ISP00 151 40 1.5.1996 0 0 106.1687 55.73225 LT51510411996250ISP00 151 41 6.9.1996 0 0 117.9925 52.38817 LT51510421997124SGI00 151 42 4.5.1997 0 0 103.6725 60.2951 LT51510431997028ISP00 151 43 28.1.1997 0 0 137.8305 35.16401 LT51520361996289ISP00 152 36 15.10.1996 2 2 143.3971 39.42595 LT51520371996289ISP00 152 37 15.10.1996 0 0 142.3364 40.41681 LT51520381996289ISP00 152 38 15.10.1996 0 0 141.2421 41.39085 LT51520391996289ISP00 152 39 15.10.1996 0 0 140.1036 42.35213 LT51520401996289ISP00 152 40 15.10.1996 0 0 138.9339 43.28868 LT51520411996289ISP00 152 41 15.10.1996 0 0 137.7271 44.19715 LT51520421996113ISP00 152 42 22.4.1996 0 0 105.7971 54.27676 LT51520431996289ISP00 152 43 15.10.1996 0 0 135.1654 45.96702 LT51530381996280ISP00 153 38 6.10.1996 0 0 137.9017 43.83127 LT51530391996280ISP00 153 39 6.10.1996 0 0 136.6226 44.72482 LT51530401996344SGI00 153 40 9.12.1996 0 0 147.6476 30.82144 LT51530411996280ISP00 153 41 6.10.1996 0 0 133.9505 46.43113 LT51530421996312XXX01 153 42 7.11.1996 0 0 143.5194 39.30588 LT51540381996239ISP00 154 38 26.8.1996 0 0 117.7215 52.73509 LT51540391996351ISP00 154 39 16.12.1996 0 0 147.9369 28.96867 LT51540401996319ISP00 154 40 14.11.1996 0 0 146.509 35.53105 LT51540411996351ISP00 154 41 16.12.1996 0 0 146.5465 31.12282 LT51540421996287ISP00 154 42 13.10.1996 0 0 135.6593 45.58086 LT51550401996198ISP00 155 40 16.7.1996 0 0 95.19908 57.35983 LT51550411996230ISP00 155 41 17.8.1996 0 0 106.6073 54.8557 LT51550431997024ISP00 155 43 24.1.1997 0 1 138.7665 34.48944 LT51560401996333ISP00 156 40 28.11.1996 1 1 147.6738 32.55799 LT51560411996333ISP00 156 41 28.11.1996 0 0 146.9101 33.64027 LT51560421996365RSA01 156 42 30.12.1996 0 0 144.2922 31.52822 LT51560431996333ISP00 156 43 28.11.1996 0 1 145.2997 35.76939 LT51570401996340RSA00 157 40 5.12.1996 0 0 147.7441 31.38217

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Forest Reference Emission Level / Forest Reference Level for Pakistan – Draft Final 3.0

ANNEX 3. REFERENCE WOOD DENSITITES BY SPECIES Sources: Zanne et al. 2009, ICRAF 2018. Wood Wood density density Species (ton/m3) Species (ton/m3) Abies pindrow 0.420 Juniperus excelsa 0.504 Acacia catechu 0.801 Leucaena leucocephala 0.450 Acacia modesta 0.835 Mallotus philippinensis 0.676 Acacia nilotica 0.689 Malus domestica 0.610 Aesculus indica 0.465 Melia azedarach 0.451 Ailanthus altissima 0.536 Millingtonia hortensis 0.640 Albizia lebbeck 0.596 Monotheca buxifolia 0.851 Albizia procera 0.587 Morus alba 0.578 Alnus nitida 0.370 Olea ferruginea 0.887 Armenian plum 0.675 Picea smithiana 0.430 Avicennia marina 0.650 Pinus gerardiana 0.500 Azadirachta indica 0.620 Pinus roxburghii 0.327 Betula utilis 0.500 Pinus wallichiana 0.430 Bombax cieba 0.350 Pongamia pinnata 0.640 Capparis decidua 0.691 Populus caspica 0.370 Cedrela serrata 0.390 Populus deltoides 0.417 Cedrus deodara 0.430 Prosopis cineraria 0.663 Celtis australis 0.550 Prosopis juliflora 0.800 Celtis eriocarpa 0.549 Prunus bokharensis 0.548 Ceriops tagal 0.758 Prunus spp. 0.606 Cordia myxa 0.330 Punica granatum 0.771 Dalbergia sissoo 0.760 Pyrus pashia 0.643 Diospyros lotus 0.706 Quercus incana 0.635 Dodonaea viscosa 0.840 Rhizophora mucronata 0.820 Ehretia acuminata 0.526 Robinia robesta 0.610 Ehretia spp. 0.526 Salix acmophylla 0.424 Eucalyptus camaldulensis 0.570 Salix tetrasperma 0.340 Eucalyptus citriodora 0.830 Salvadora oleoides 0.594 Ficus religiosa 0.443 Schinus molle 0.525 Ficus sp. 0.443 Syzygium cumini 0.760 Gmelina arborea 0.560 Tamarix aphylla 0.640 Grewia optiva 0.646 Tecomella undulata 0.500 Juglans regia 0.533 Ulmus wallichiana 0.440 Zizyphus mauritiana 0.583

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ANNEX 4. FOREST CARBON DENSITY (ABOVE- AND BELOWGROUND) AND ANNUAL GROWTH RATES APPLIED BY SUB-NATIONAL AREAS Azad Jammu and Kashmir N:o Mean Carbon Rotation Net C forest Density time Growth Forest type clusters (C ton/ha) 11(years) (ton/ha/year) Broad-Leaved 5 14.59 20 1.46 Pine 3 20.45 80 0.51 Moist Temperate 5 77.57 150 1.03 Dry Temperate 2 122.07 150 1.63 Sub-Alpine12 - 20.14 150 0.27 Balochistan Littoral and swamp forest (Mangroves) 1 4.29 60 0.14 Thorn 3 6.20 20 0.62 Broad-Leaved 4 5.80 20 0.60 Dry temperate 7 11.70 150 0.16 Federally Administered Tribal Areas Broad-Leaved 4 8.30 20 0.80 Dry Temperate 2 62.90 150 0.84 Gilgit-Baltistan13 Dry Temperate 439 90.63 150 1.21 Sub-Alpine 98 20.14 150 0.27 Khyber Pakhtunkhwa Thorn - 5.69 20 0.57 Broad-leaved 22 5.74 20 0.57 Pine 99 32.70 80 0.82 Moist Temperate 146 104.60 150 1.39 Dry Temperate 90 122.27 150 1.63 Sub-Alpine - 45.24 150 0.60 Punjab Thorn 4 12.41 20 1.24 Riverine 2 16.80 30 1.12 Broad-leaved 1 10.90 20 1.09 Pine 1 63.45 80 1.59 Moist Temperate 1 87.80 150 1.17 Plantation 24 19.43 20 1.94 Sindh Littoral and swamp forest (Mangroves) 2 5.57 60 0.19 Thorn 7 11.46 20 1.15 Riverine 1 16.28 30 1.12 Plantation14 - 19.43 20 1.94

11 ALGAS (1998) 12 GB reference value 13 Measurements on single plots 14 PB reference value ~ 65 ~

Forest Reference Emission Level / Forest Reference Level for Pakistan – Draft Final 3.0

ANNEX 5. HARVESTS AND DISTURBANCES

Average annual carbon loss 1996-2016 due to harvests and disturbances: The pilot NFI campaign was conducted in 5 provinces/states (AJK, PB, SD, BN, FATA) between October 2017 and April 2018. Timber and fuel removal impact is calculated based on the measured stumps. Disturbances refer to the processes transferring biomass from aboveground and belowground carbon pools to deadwood pool. The removal and disturbance rates are averaged over 20 years, which takes for stems and stumps to decay in forest conditions. Number of Number Estimated Estimated Average Weight plots of Disturbance Wood annual carbon of total forest (ton C/ha) Removal reduction (ton forest plots (ton C/ha) C/ha/year) area over 20 years AJK Broad-leaved 14 0.37 0.18 0.03 19.0 % Pine 9 0.56 0.67 0.06 27.1 % Moist Temperate 21 2.69 4.29 0.35 47.7 % Dry Temperate 8 6.01 7.35 0.67 4.4 % Sub-Alpine - - - 0.00 1.8 % 100.0 % BN Littoral and swamp forest (Mangroves) 4 0.00 0.00 0.00 0.0 % Thorn 10 0.00 0.00 0.00 1.6 % Broad-Leaved 18 0.00 0.00 0.00 33.0 % Dry Temperate 22 0.17 0.03 0.01 65.3 % 100.0 % FATA Broad-Leaved 15 0.00 0.00 0.00 14.4 % Pine - - - 0.00 3.6 % Moist Temperate - - - 0.59 6.9 % Dry Temperate 8 2.92 8.85 0.59 74.4 % Sub-Alpine - - - - 0.6 % 100.0 % PB Thorn 20 0.01 0.00 0.00 13.3 % Riverine 9 0.87 0.11 0.05 0.1 % Broad-leaved 3 0.94 0.00 0.05 46.4 % Pine 3 0.00 0.00 0.00 34.0 % Moist Temperate 4 1.29 4.35 0.28 0.7 % Plantation 43 0.99 0.05 0.05 5.5 % 100.0 % SD

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Littoral and swamp forest (Mangroves) 10 0.01 0.00 0.00 40.1 % Thorn 22 0.01 0.00 0.00 47.8 % Riverine 3 0.00 0.00 0.00 8.2 % Broad-leaved - - - - 0.3 % Plantation - - - 0.05 3.7 % 100.0 %

The official statistics for annual historical harvests due to illegal timber harvesting and disturbances has been available from GB (Source: REDD+ focal point in GB) to estimate the annual harvest and disturbance rates. The following table has been constructed:

Period Million m3 AGB AGC (C Total Carbon cubic ft (tons)15 tons)16 emissions AGC+BGC ton (C tons)17 CO2/ha/year 1996 - 1.979 183855 140455 33007 41,259 0.11 2000 2000 - 3.861 358699 274025 64396 80,495 0.21 2004 2004 - 3.575 332128 253727 59626 74,532 0.19 2008 2008 - 3.222 299334 228674 53738 67,173 0.16 2012 2012 - 1.935 179767 137332 32273 40,341 0.10 2016

15 Conversion factor i.e. 1 ton = 1.309 mᶟ (GoP, 2003) 16 Average wood density 0.5 ton/m3; dry mass to carbon conversion factor i.e. 0.47 (IPCC, 2006) 17 Average root-shoot threshold 0.25 for GB (Ali et al, 2017) ~ 67 ~

Forest Reference Emission Level / Forest Reference Level for Pakistan – Draft Final 3.0

ANNEX 6. NATIONAL CIRCUMSTANCES

1. Government Policies and Programs Pakistan has taken various actions to mainstream climate change in its policies, programs and planning to safeguard sustainable development progress with due respect to international commitments and national development priorities.

The Government of Pakistan (GoP) is a party to various international conventions, agreements and treaties related to climate change (UNFCCC, UNFF, UNCCD, CBD, CMS etc) demonstrating country’s commitments to accomplish the responsibilities under these conventions. Pakistan, to date, has prepared five GHG inventories. The first National Communication (NC) document was submitted to UNFCCC in 2003 while the 2nd NC is still under the process of development. Pakistan has also succumbed six Nationally Appropriate Mitigation Actions (NAMAs) to the climate change’s NAMA’s registry of UNFCCC requesting support for preparation. One of the NAMAs is “Development and Installation of Carbon Dioxide Sequestration Technologies in Pakistan”.

The Constitution of Pakistan give full powers to the provincial governments to formulate their own strategies and action plans regarding forestry maters. The federal government has the directive to facilitate and coordinate the provinces through national projects, programmes and policies. The UNFCCC and its REDD+ mechanism, including other relevant international agreements, fall in the domain of national government’s functions. Therefore, in 2010, the Office of the Inspector General of Forests (IGF) under auspices of the Federal Ministry of Climate Change was designated as the National REDD+ Focal Point for Pakistan.

The Federal Ministry of Climate Change (MoCC) was developed in 2012 shadowed by approval of the National Climate Change Policy (NCCP) in 2012. The NCCP provides an inclusive framework for policy vision, goals, targets and actions to mainstream climate change, particularly, in those sectors of the economy which are socially and economically vulnerable including forestry sector. The NCCP identifies the need to restore and enhance Pakistan’s forest cover to withstand present and possible future impacts of climate change. It also recognizes the importance of retrieving climate finance through international windows to reduce emissions and build resilience by setting afforestation and reforestation targets and curbing illegal deforestation. However, these actions are contingent upon provision of international climate finance, affordability, capacity building and transfer of technology. There is a provision in NCCP “to prepare the framework for a national REDD+ strategy on priority basis and ensure its implementation in accordance with international

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Forest Reference Emission Level / Forest Reference Level for Pakistan – Draft Final 3.0 conventions / processes,” and “to develop the legal and institutional framework for improved forest management, investment clearly specifying rights to REDD+ credits”.

The Government of Khyber Pakhtunkhwa commenced the “Green Growth Initiative (GGI)” in 2012 with the aim of (i) bringing 30,000 hectares, on annual basis, of additional land under forests cover and set target to increase overall forest area by two percent, (ii) enhance density of the 7% degraded forest areas to enhance its canopy cover above 50% through establishing closures against fire and grazing and (iii) develop standard rules to assign carbon value to forests through comprehensive REDD+ strategy and institutionalize REDD+ as a tool to encourage conservation. Through GGI, the Government of Khyber Pakhtunkhwa started the “Billion Tree Afforestation Project” and successfully planted, through participatory approach, one billion trees in the province by end of 2017. Pakistan also became party to Paris Agreement and submitted its Intended Nationally Determined Contribution (INDC) to the UNFCCC in 2015. Emission reduction targets are not clearly specified in INDC document.

In November 2016, the Council of Common Interest, chaired by the Prime Minister of Pakistan, approved the first ever National Forest Policy of Pakistan. The goal of the policy is “expansion of national coverage of forests, protected areas, natural habitats and green areas for restoration of ecological functions and maximizing economic benefits while meeting Pakistan’s obligations to international agreements related to forests.” The forest policy gives provision for mainstreaming REDD+ as a tool to enhance forest cover, preserve forest carbon stocks and control deforestation. The policy also outlines that in Phase-I of REDD+, Pakistan will prepare a REDD+ national strategy, national forest reference emission level, national forest monitoring system, and a national safeguard information system.

The Green Pakistan program was launched in 2017. The main objective of the Programme is “to facilitate transition towards environmentally resilient Pakistan by mainstreaming notions of adaptation and mitigation through ecologically targeted initiatives covering afforestation, biodiversity conservation and enabling policy environment.” The programme aims to plant 100 million indigenous plants (including 20 % fruit plants) all over the country in five years i.e. 2017 - 2022. The programme also targets to recover and reorganize the functions of wildlife departments at provincial and territorial levels.

In 2016, Global Environmental Facility (GEF) allocated a funding of 9.338 million USD to Pakistan to promote Sustainable Forest Management (SFM) in the country.

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The aim of the project is to “secure multiple benefits in high conservation value forests by addressing problems of forest decline, biodiversity loss and greenhouse gas emissions”. The project also aims to promote enhancement and conservation of carbon stocks through sustainable land use management, land use change and forestry as well as enhance carbon sequestration through restoration of 10,772 ha and reforestation of 5,732 ha of land area.

The above-mentioned policy developments have led to the creation of new governing bodies and institutions to address climate change. These include new national and provincial REDD+ governing AND management arrangements to support development and implementation of Pakistan’s National REDD+ Strategy. One important development is the formulation of the Pakistan Climate Change Act (2016). The proposed Act envisages an over-arching Pakistan Climate Change Council headed by the Prime Minister of Pakistan, with representation of the sub-national governments at the Chief Ministerial level. It also envisages establishment of a high-powered Pakistan Climate Change Authority and Pakistan Climate Change Fund. The fund will mobilize resources from both domestic and international sources to support mitigation and adaptation initiatives in the country.

Pakistan’s Vision 2025, a major policy document which provides a roadmap for national development until the year 2025. The above-mentioned policies and programs are in sync with Pakistan’s Vision 2025. Vision 2025 rests on seven pillars for driving growth and development to transform Pakistan into a vibrant and prosperous nation. Pak-INDC (Pakistan’s Intended Nationally Determined Contribution) is engrained in the state’s strategic plan ‘Vision 2025’. It is lined up with the respective policies, plans and sectoral growth targets planned by various ministries and other government entities. Contributions to economic growth partly due to China-Pakistan Economic Corridor (CPEC) have been taken into consideration. Through, considerable efforts are in hand for the revival of forestry, aiming to expand the forest cover through mega tree plantation programmes and strengthening the regulatory & forest protection policy mechanism, but still, the future emissions are expected to increase. This is due to the ambitious plans of the present government to spark economic activity through large-scale investments in energy, communication and industrial infrastructure through China Pakistan Economic Corridor (CPEC). The forecasted economic growth is considered to be historically unprecedented and unmatched. Accordingly, the future emissions of the country will increase manifold. Consistent with historical trends, significant increase in emissions, due to increase pressure, is also expected from forestry sector like other sectors such as energy, agriculture, industrial processes and wastes.

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2. Socio-economic context 2.1 Economic changes Pakistan’s overall developing economy has shown a significant rise since last six to seven years. Major economic changes are also expected in macro-economic outline of the country due to the current and future development targets with due consideration to global economic growth models. Government aims a significant reduce in poverty by the year 2025 and eliminating extreme poverty before 2030 under its vision 2025 and SDGs target. Keeping in view these fundamentals, GDP growth rate of 7 % is forecast in pre-2020 period with further 1.5% rise by 2025 (Economic Survey Report, 2017-2018). GDP continued to grow above 5 % in each of the last 2 years reaching 5.79 % highest in 13 years in the outgoing fiscal year 2018 and 4 percent in each of the three preceding years. As a major development, Pakistan has ranked No. 1 in South Asia in private infrastructure investment, thus becoming one of the world's top five private participation in infrastructure investment destinations (Economic Survey Report, 2017-2018).

Forest sector’s share in national GDP is fractional as shown in Figure 16. Rather the contribution of forestry sector in GDP has been decreasing. These figures are, however, not illustrative of factual contribution of forestry sector in the national economy since many forest products and almost all forest services e.g. agriculture, water, tourism, soil conservation, carbon sequestration, and biodiversity etc. remain unaccounted for. Instead, only the marketed goods from state-managed forests are accounted for. About 95% of timber and 99% of fuel wood demands are met from farmlands (Supply and Demand Survey, 2003). The forest products of farmlands are either sold in the local market or used at farm level. Mechanisms to value these products for GDP calculations do not exist.

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30000 0,8 0,7 25000 0,6 20000 0,5 15000 0,4 0,3 10000

Pakistani Rs. inBillion 0,2 5000 0,1

0 0 Forestry Sharein Percentage 1996-2000 2000-2004 2004-2008 2008-2012 2012-2016

GDP (Rs. Billion) Forestry share (%) in GDP

Figure 16 Share of Forestry in GDP of Pakistan (1996-97 to 2015-16). Data Source: Ministry of Finance’s Economic Survey Reports

If long term sustainable forest management plans for commercial harvesting, including timber logging, are developed, continued and implemented, the share of forestry sector in GDP is likely to increase. It is also expected that the revenues from carbon credits under REDD+ would also increase by avoiding carbon emissions due to illegal and unsustainable forest cuttings. Serious efforts, if made, to explore and utilize non-conventional NTFP, in particular medicinal, aromatic and other economic plants, the share of forest sector would increase in GDP in the coming decades.

2.2 Population Dynamics and per Capita Availability of Forests The latest census (2017) has estimated population of about 207.7 million in 2017 representing a 57 percent increase in 19 years with average annual growth rate of 2.4 %. Out of this 63.6 % are in rural areas and 36.4 % in urban centers. Population is expected to reach the figure of 211 to 213 Million in 2020 with annual population growth rate of 2.4 %. National Population Vision, 2030 predicts that Pakistan will become the fifth country from top by 2030, with a population ranging between 230 and 260 million people, 60 percent of whom will live in urban areas. Further addition of 50 million people by 2020 will certainly put further pressure on the forest resources. Currently, with only 0.0186 ha of forest per capita against the World average of 1.0 ha, Pakistan is comparatively forest-poor. The high population growth rate is pushing the figure further down and, at present, despite the government efforts, it is not possible to enlarge public forest area at a high enough rate to keep up with demand for forest products. Due to limited availability of resources, rural population (63.6 %) is experiencing higher level of poverty than the

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Forest Reference Emission Level / Forest Reference Level for Pakistan – Draft Final 3.0 urban population both on economic and social indicators. The rural population has high dependence on forestry sector resources for their livelihoods. There is a big gap between the supply and demand due to mismatched forest resource base and continuously increasing population growth. The supply has only steadily increased from the farm trees whereas pressure on natural forests has continued to increase significantly. Forest cover per capita is declining due to increase in human population as well as deforestation (Figure 17).

Population v/s Per Capita Availability of Forests in Pakistan 0,030 250 0,025 200 0,020 150 0,015 100 0,010

0,005 50 Forest Forest in area ha

0,000 0

Populationinmillion

2001-2002 2017-2018 1997-1998 1998-1999 1999-2000 2000-2001 2002-2003 2003-2004 2004-2005 2005-2006 2006-2007 2007-2008 2008-2009 2009-2010 2010-2011 2011-2012 2012-2013 2013-2014 2014-2015 2015-2016 2016-2017 2018-2019 1996-1997 Per Capita Availability of Forests Population

Figure 17 Historical trends in Population dynamics and per capita availability of forests (1996-2019). Data Source: Ministry of Finance’s Economic Survey Reports

2.3 Agriculture Intensification Agriculture is the major land use in Pakistan. The cultivable area is 23.67 million ha i.e. 27.7 % of total area (GoP’s Economic Survey Report, 2018). The population directly and indirectly associated with the agriculture sector currently stands at 42.3 %, whereas contribution of the sector to the overall GDP is around 18.9 % (Economic Survey Report, 2018). According to INDC 2016, the forecasted economic growth rate, duly adjusted, shows a faster agriculture sector growth as compared to its average historical trend of about 3 % per annum. The minimum agriculture growth rate of 4 % has been determined by the government to improve food security and ensure minimum nutritional value for the growing population. With expected healthy rise in GDP and sizeable impacts of the CPEC interventions, this growth is likely to be well over 4% per annum (INDC, 2016).

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2.4 Poverty Pakistan is facing huge energy crises including power and fuel. About 63.6 %18 of total population in Pakistan is still living in rural areas and depend on firewood for domestic needs of energy. Many households in urban areas also use firewood due to lack of alternative sources of energy. The connection between forestry systems and integrated family health in the rural areas of KP and GB shows that poverty is one of the major factors of deforestation. Studies submit that the prevailing poverty situation in rural areas compelling the people to resort to cutting of trees as a source of income as well as consume natural resources for subsistence. Due to lack of the provision of electricity, gas, access and availability of renewable energy sources at reasonable rates, wood becomes only choice as energy source, for communities in hilly areas. High dependency on forests, due to poverty and lack of alternative livelihoods further aggravates the forest devastation. The National REDD+ strategy of Pakistan (2018) has also highlighted the poverty nexus with forestry resources. According to REDD+ strategy, one of the major drivers of deforestation and forest degradation in rural areas, in particular the hilly areas, is the unsustainable harvesting of wood due to lack of compensating mechanisms for forest communities by the Government for not using their legal or traditional rights of harvesting beyond sustainable limits. Notwithstanding additional supply of fuel wood from farmland trees, the forests and other sources of fuel woods failed to meet the continuously increasing demand for fuel wood on a sustainable basis. Subsequently, the demand is encountered through over utilization of natural forest resources to the degree of being the foremost factor for deforestation and degradation of forests. This tendency, if remains unimpeded, will have radical insinuations in the form of irreparable loss of forest resources at exceptional pace.

2.5 Emissions from Consumption of Timber and Fuel Wood Based on per capita consumption of wood and population growth at national level in 2003-04, the supply and demand survey by the government of Pakistan 2003, compiled and projected wood consumption by provinces for 15 years i.e. 2003-2018 (Maanics, 2004). Using these statistics, the national and provincial emissions in terms of million tons of carbon dioxide equivalents (Mt CO₂-e) with average annual change rates for different time periods are calculated and given in Table 14.

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Table 15 National and Provincial Level Emissions (million tCO₂ eq) due to Wood Consumption (projected from 2003 to 2018).

Emissions (million tons of CO₂ Annual Change Rates (million tons of CO₂ equivalent) equivalent per year) 2003-2008 2008-2013 2013-2018 Province / Sector Mt Mt Mt 2003 2008 2013 2018 CO₂ - CO₂ - CO₂ - % % % e/ e/ e/ year year year KP Industrial timber 1.25 1.38 1.52 1.67 0.03 2.01 0.03 2.02 0.03 2.02 Industrial fuelwood 0.48 0.53 0.58 0.64 0.01 2.03 0.01 2.01 0.01 2.01 Commercial fuelwood 0.20 0.22 0.24 0.27 0.00 2.05 0.00 1.98 0.00 2.02 Domestic fuelwood 4.53 4.99 5.49 6.04 0.09 2.02 0.10 2.02 0.11 2.02 Total volume 6.46 7.11 7.83 8.62 0.13 2.02 0.14 2.02 0.16 2.02 Punjab Industrial timber 4.39 4.83 5.32 5.86 0.09 2.02 0.10 2.02 0.11 2.02 Industrial fuelwood 1.69 1.86 2.05 2.25 0.03 2.01 0.04 2.02 0.04 2.02 Commercial fuelwood 0.27 0.29 0.32 0.36 0.01 2.02 0.01 2.01 0.01 2.03 Domestic fuelwood 7.82 8.61 9.48 10.43 0.16 2.02 0.17 2.02 0.19 2.02 Total volume 14.17 15.60 17.17 18.90 0.29 2.02 0.31 2.02 0.35 2.02 Sindh Industrial timber 1.80 1.98 2.18 2.39 0.04 2.02 0.04 2.01 0.04 2.02 Industrial fuelwood 0.69 0.76 0.84 0.92 0.01 2.01 0.02 2.03 0.02 2.00 Commercial fuelwood 0.12 0.13 0.15 0.16 0.00 2.05 0.00 1.96 0.00 2.05 Domestic fuelwood 2.23 2.45 2.70 2.97 0.04 2.02 0.05 2.02 0.05 2.02 Total volume 4.84 5.32 5.86 6.45 0.10 2.02 0.11 2.02 0.12 2.01 Balochistan Industrial timber 0.39 0.43 0.47 0.52 0.01 2.02 0.01 2.02 0.01 2.00 Industrial fuelwood 0.15 0.17 0.18 0.20 0.00 2.11 0.00 1.90 0.00 2.03 Commercial fuelwood 0.04 0.05 0.05 0.06 0.00 1.88 0.00 2.29 0.00 1.79 Domestic fuelwood 1.27 1.40 1.54 1.69 0.03 2.01 0.03 2.02 0.03 2.02 Total volume 1.85 2.04 2.25 2.47 0.04 2.02 0.04 2.02 0.05 2.01 AJK Industrial timber 0.17 0.19 0.21 0.23 0.00 2.05 0.00 1.99 0.00 2.00 Industrial fuelwood 0.07 0.07 0.08 0.09 0.00 1.96 0.00 1.96 0.00 2.11 Commercial fuelwood 0.02 0.02 0.02 0.02 0.00 2.22 0.00 2.00 0.00 1.82 Domestic fuelwood 0.57 0.63 0.69 0.76 0.01 2.02 0.01 2.02 0.01 2.01 Total volume 0.83 0.91 1.00 1.10 0.02 2.02 0.02 2.01 0.02 2.01 GB Industrial timber 0.06 0.06 0.07 0.08 0.00 2.05 0.00 2.06 0.00 1.87

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Industrial fuelwood 0.02 0.02 0.03 0.03 0.00 1.76 0.00 2.16 0.00 1.95 Commercial fuelwood 0.04 0.04 0.05 0.05 0.00 1.97 0.00 2.09 0.00 2.16 Domestic fuelwood 0.57 0.62 0.69 0.76 0.01 2.03 0.01 2.01 0.01 2.02 Total volume 0.69 0.76 0.83 0.92 0.01 2.02 0.02 2.02 0.02 2.01 National Total Industrial timber 8.06 8.88 9.77 10.76 0.16 2.02 0.18 2.02 0.20 2.02 Industrial fuelwood 3.10 3.41 3.75 4.13 0.06 2.02 0.07 2.02 0.08 2.02 Commercial fuelwood 0.69 0.76 0.84 0.92 0.01 2.03 0.02 2.01 0.02 2.00 Domestic fuelwood 16.98 18.69 20.58 22.65 0.34 2.02 0.38 2.02 0.42 2.02 Total volume 28.83 31.74 34.94 38.44 0.58 2.02 0.64 2.02 0.70 2.00

The average per capita emissions due to timber and fuel wood consumption in 2003 were 0.188 tCO₂-e per capita and projected as 0.185 tCO₂ eq per capita for a population of 207.7 million in 2018. The per capita emissions varies from province to province; highest in GB (0.484 tCO₂ eq per capita for a current population of 1.9 million) followed by KP (0.282 tCO₂ eq per capita for a current population of 30.52 million), AJK (0.272 tCO₂ eq per capita for a current population of 4.04 million), Balochistan (0.200 tCO₂ eq per capita for a current population of 12.34 million), Punjab (0.171 tCO₂ eq per capita for a current population of 110 million) and Sindh (0.135 tCO₂ eq per capita for a current population of 47.89 million) in a descending order. The average annual increase in emissions from 2003 to 2018 due to wood consumption is 2.02 %.

The above figures are also compared with average annual timber and fuel wood consumption per capita (m3/capita) calculated in 2002-03 for the current population of 207.7 million (Population Census 2017). The results show almost similar statistics regarding over all emissions (million tCO₂ eq) due to timber and fuelwood consumption for the current year i.e. 2018 (Table 16).

The study of consumption by urban and rural split at domestic scale (Table 16Table 17) has reinforced the common belief that the use of biomass as fuel is highest in rural areas as compared to urban areas. The consumption at provincial level revealed that the domestic sector in the province of Balochistan have typically consumed fuelwood due to extreme cold in winters and lack of alternative fuels. Punjab mostly consumes crop residues due to larger farm area. High level of urbanization is attributed to the use of electricity as energy source in Sindh. In KP, FATA, GB and AJK, where majority of the forest resources exists, most of the fuelwood is being consumed at household level attributed to lack of alternate biomass fuels in these areas.

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Table 16 Average Annual Timber and Fuelwood Consumption and Associated Emissions (tons of CO₂ eq) for Pakistan

Average Percent Share⁵ Total Per Total (million tCO2-e) Consumption Total Country Cons Total Cons. Total Cons. Capita Emission Wood Population (Million (Mt) (MtC) Cons. (Mt CO -e) State Farm- mᶟ) 2 Imports (mᶟ)⁶ Forest lands

T = 3.34 T = 91.44 T = 5.22 C = D = A B A*B D*3.67⁴ F.W. = F.W. = F.W.= (A*B)/1.309ᶦ C*0.5²*0.47ᶟ 0.20 91.8 0.00 Industrial 0.0796 207.7 16.53292 12.6301 2.9680 10.8929 0.3638 9.9604 0.0048 Wood Timber Fuelwood (domestic + 0.205 207.7 42.5785 32.5270 7.6439 28.0533 0.0561 25.7529 0.0000 commercial + industrial) Total 0.2846 59.1114 45.1577 10.6120 38.9462 0.4199 35.7134 0.0048

ᶦ Conversion factor i.e. 1 ton = 1.309 mᶟ (GoP, 2003) ² Conversion factor from wet weight to dry weight (IPCC, 2006) ᶟ Dry mass to carbon conversion factor (IPCC, 2006). ⁴ 1 ton of carbon equals 3.67 tons of CO₂ Eq. (IPCC, 2006) ⁵ Assuming percent shares of 2003 (GoP, 2003) for current year ⁶ Source: Supply and Demand Survey, Government of Pakistan, 2003

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Table 17. Province Wise Fuelwood Consumption and Associated Emissions by House Hold Sector

Per Capita Total Population Sources ⁶ Consumption Consumption Total Total (million)² Total Total (million tCO₂-e) (mounds)/ yearᶦ (Million maunds) Cons- Cons. Province (million (million (mill. (mill. tons C)⁴ tCO₂-e)⁵ maunds) tons)ᶟ Own Markets Others Rural Urban Rural Urban Rural Urban Lands (34%) (5%) ⁷ (61%) H = E = F = G = G*40 I = J = A B C D 61%*J 34%*J 5%*J (A*C) (B*D) (E + F) kg)/10 H*0.5*0.47 I*3.67 00

KP 1.126 0.748 25.13 5.39 28.30 4.03 32.33 1.29 0.304 1.115 0.680 0.379 0.056

PB 0.614 0.11 69.62 40.38 42.75 4.44 47.19 1.89 0.444 1.628 0.993 0.553 0.081 SD 0.591 0.028 22.99 24.9 13.59 0.70 14.28 0.57 0.134 0.493 0.301 0.168 0.025 BN 0.924 0.526 8.94 3.4 8.26 1.79 10.05 0.40 0.094 0.347 0.211 0.118 0.017 AJK 0.907 0.268 3.54 0.5 3.21 0.13 3.34 0.13 0.031 0.115 0.070 0.039 0.005 GB 2.503 1.2 1.57 0.33 3.92 0.40 4.32 0.17 0.040 0.149 0.090 0.051 0.007 Total 131.79 74.90 100.02 11.49 111.52 4.46 1.048 3.847 2.347 1.308 0.192

ᶦ Source: Supply and Demand Survey, Government of Pakistan, 2003 ² Source: Pakistan Bureau of Statistics, Government of Pakistan, 2017 ᶟ Conversion Factors i.e. 1 maund = 40 kg; 1 ton = 1000 kgs (Supply and Demand Survey, Government of Pakistan, 2003) ⁴ Conversion factor (0.5) from wet weight to dry weight; Dry mass to carbon conversion factor i.e. 0.47 (IPCC, 2006). ⁵ 1 ton of carbon equals 3.67 tons of CO₂ Eq. (IPCC, 2006) ⁶ Source of Percent Shares: Supply and Demand Survey, Government of Pakistan, 2003 ⁷ Assuming this as contribution from state forests

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ANNEX 7. SUB-NATIONAL ACTIVITY TRENDS (1992-2016) The following table provides a summary for the perceived REDD+ activity as a result of the group work conducted in presence of the Technical REDD+ Working Group members in February 2017. Area 1992-97 1997-2002 2002-2007 2007-2012 2012-2016 AJK Degradation Degradation Degradation Degradation Degradation caused due due to ban on due to caused due caused due to to Floods sustainable earthquake in to floods energy harvest, 2005 shortages and conflict over high demands ownership as well as rights and high market forest fire price for fuel wood/ imber Balochistan Deforestation Degradation Deforestation Deforestation Regeneration and due to and and as Wildlife Act degradation Drought. Also degradation degradation and due to BNRMP due to poor due to poor Regulation Drought, afforestation security security enacted in refugee, low programs situation/ law situation/ law 2014/15 regeneration and order. and order in Juniper. Some areas and floods. However, afforested Some areas regeneration/ under afforested afforestation BNRMP under also done programs BNRMP under programs BNRMP19 FATA-KP Deforestation Deforestation Deforestation Deforestation Regeneration and and and and and Degradation degradation degradation degradation afforestation due to Ban due to ban on due to ban on due to due to on sustainable sustainable military BTAP205, sustainable harvest harvest operations in Timber import, harvest resulting in resulting in border area alternative illegal cutting. illegal cutting. and ban on energy However, However, harvest and sources reforestation reforestation collection of projects also projects also wind fall trees introduced to introduced to resulting in meet the meet the illegal cutting demands and demands and

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restore the restore the forests forests GB Degradation Deforestation Degradation Degradation Afforestation due to ban occurred due due due and on to reintroduction continued regeneration sustainable Working Plan of ban on ban on due to harvest implemented harvest harvest continuation for Private of ban on forest harvest, enhancement of social forestry Punjab Deforestation Reforestation Deforestation Restoration Restoration and due to mass occurred and and Degradation afforestation mostly due to regeneration regeneration due to Ban program transfer of due to due to on Land to army rehabilitation rehabilitation sustainable (personnel) in Program, program- harvest 2005-07 Amendment of Forest Act, ban on felling Sindh Degradation Deforestation Deforestation Deforestation Deforestation due to ban and and and and on degradation degradation degradation degradation sustainable due to due to due to due to harvest poor introduction of introduction introduction of governance in Lease Policy, of Lease Lease Policy, forestry sector ban on Policy, ban ban on harvest, Poor on harvest, harvest, Poor governance in Poor governance in forestry sector governance forestry sector in forestry sector Overall Deforestation Deforestation, Deforestation, Deforestation Regeneration, Trend Degradation degradation degradation and Reforestation due to ban due to ban on due to ban degradation and on sustainable (KP, FATA & due to Afforestation sustainable harvest (KP & GB), military due to harvest, AJK), earthquake operations BTAP5, drought and Conflicts 2005 (AJK), (KP & FATA) Timber import, refugees (AJK), Transfer of and ban on alternative problems Droughts Land to Army harvest (KP energy (BN) (BN), Personals & GB and sources (KP & Unsustainable (PB), Poor collection of FATA), Timber Security and wind fall trees rehabilitation Harvest (GB) Law and resulting in programs and poor Order (BN) illegal cutting (PB), effective and lease (GB & KP), law

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governance in policies on floods (AJK), enforcements forestry sector forest lands poor security (BN). (SD) and law and However, Reforestation order Sindh and due to mass Reforestation situation (BN) AJK with awareness due to and lease deforestation program (PB) afforestation policies on and and programs in forest lands degradation in afforestation KP, FATA (SD) SD and AJK (BN). and BN Reforestation in PB and BN

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ANNEX 8. JUSTIFICATION OF COMMISSION AND OMISSION OF ACTIVITIES, POOLS AND GASES

Decision 12/CP.17 provides guidance on systems for providing information on how safeguards are addressed and respected and modalities relating to forest reference emission levels and forest reference levels as referred to in decision 1/CP.16. The Conference of the Parties agrees that a step-wise approach to national forest reference emission level and/or forest reference level development may be useful, enabling Parties to improve the forest reference emission level and/or forest reference level by incorporating better data, improved methodologies and, where appropriate, additional pools, noting the importance of adequate and predictable support as referenced by decision 1/CP.16, paragraph 71.

Decision 12/CP.17 Annex, paragraph (c) states that the information provided should be guided by the most recent Intergovernmental Panel on Climate Change guidance and guidelines, as adopted or encouraged by the COP, as appropriate, and include pools and gases, and activities listed in decision 1/CP.16, paragraph 70, which have been included in forest reference emission levels and/or forest reference levels and the reasons for omitting a pool and/or activity from the construction of forest reference emission levels and/or forest reference levels, noting that significant pools and/or activities should not be excluded.

The IPCC (2006) guidelines provide guidance for identifying the key categories in scope of the greenhouse gas inventories. A key category has a significant influence on a country’s total inventory of greenhouse gases in terms of the absolute level and the trend following the approach 1. The key categories cover both source and sink categories. The key categories are those that when summed together in descending order of magnitude, add up to 95 percent of the total level. The AFOLU sector related to land use included as the key category in the context of greenhouse gas inventories in Pakistan. A subcategory is significant if it accounts for 25-30% of emissions/removals for the overall category. Trend assessment is to identify categories that may not be large enough to be identified by the level assessment, but they should receive attention due to their different trend.

REDD+ activities

From the REDD+ perspective the considered AFOLU sub-categories are forest remaining as forest degradation, sustainable forest management and conservation), forest land converted to other land use (deforestation) and other land use converted to forest land (carbon stock enhancement through forest restoration).

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The included activities are deforestation, degradation and carbon stock enhancement. Degradation is considered in assessment of wood removal and harvest rates. There are no records available about the sustainable forest and conservation activities until the systematic collection is introduced though NFMS.

Carbon pools

The revised Readiness Preparation Proposal (R-PP, 2014) declares that carbon pools included in the FRELs and FRLs will initially be limited to above-ground biomass. As more information becomes available, for instance from field assessments stored in the NFMS, other carbon pools may be incorporated, possibly stratified by forest type, as FRELs and FRLs are periodically updated and as suggested in Decision 12/CP.17 paragraph 10.

The NFI pilot inventory indicates that the aboveground, belowground and soil carbon pools form 93-99 % of the total carbon stock. There is an immediate change in above-ground and belowground carbon stocks after a forest is harvested, as a significant part of them are converted to harvested wood and deadwood pools. The SOC change dynamics is very much subject to land management applied and the gradual change is accounted over a period of 20 years in scope of the GHG-I accounting (IPCC, 2006). It was found in some forest areas that the SOC contents was higher outside the forestland due to the applied land management. As the core aim of REDD+ is to provide incentives for reducing emissions from deforestation and forest degradation, the inclusion of the SOC pool would promote that objective effectively. Gases

In the context of national greenhouse gas inventories, it is mandatory for Non-Annex countries to report the CO2, CH4 and N2O emissions. Carbon dioxide must be always included in REDD+ accounting. The CH4 emissions are normally emitted from the forests growing in wet organic soils. Conversion of these forests through drainage is not an acceptable practice in scope of REDD+. Nitrous oxide emissions take place when biomass is burned, fertilizer is applied or nitrogen fixing trees are planted in forest, but those activities are taking place rarely in Pakistan. There are no certain prospects that these activities would get more widely promoted in the forestry sector, but if their importance increases as a emission source, they can be included following a stepwise approach.

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