TA-8111 PH - Revised Road Map_CLEANED

DRAFT UMRBPL ECOTOWN GREEN GROWTH ROAD MAP REPORT
ADB TA-8111 PHI: Climate Resilience and Green Growth in the Upper Marikina River Basin Protected Landscape:
Demonstrating the Eco-town Framework (46225-001) of the Climate Change Commission (CCC)
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TABLE OF CONTENTS
1.0 Introduction .................................................................................................... 1
2.0 Baseline Information, Vulnerability Assessment and GHG Inventory........ 2
2.1 Land Cover changes in UMRBPL (2004 and 2012) .................................... 2
2.2 Bio-physical, land use and natural resource conditions .............................. 5
2.3 Over-all household survey results............................................................... 7
2.4 UMRBPL demographics ............................................................................. 9
2.5 Poverty....................................................................................................... 9
2.6 Local community adaptive capacity and gender ......................................... 11
2.7 GHG Inventory ........................................................................................... 12
2.8 Natural Resource Accounting (NRA)/Economic Valuation of UMRBPL’s
Forest, Water and Agriculture Resources................................................... 17
3.0 UMRBPL Climate change scenarios (2020-2050) and potential impacts.... 19
3.1 Climate change projections and potential impacts:
worst case future scenario.......................................................................... 19
3.2 Vulnerability Assessment in the UMRBPL .................................................. 25
4.0 UMRBPL Ecotown Strategic Green Growth Road Map................................ 56
4.1 UMRBPL Ecotown Strategic ‘Green Growth’ Road Map Methodology........ 56
4.2 UMRBPL’s multi-criteria variate analysis (MCVA) framework for prioritizing
climate change adaptation and mitigation measures .................................. 58
4.3 Key Implementation Issues and Challenges ............................................... 61
5.0 Proposed UMRBPL Ecotown Green Growth Strategic Road Map
(Priority Mitigation and Adaptation Measures) and
Proposed Local Climate Change Action Plans (LCCAP)............................. 62
5.1 Proposed UMRBPL Ecotown Green Growth – Strategic Framework .......... 62
5.2 Vision, Mission and Goal ............................................................................ 63
5.3 Strategies For Green Growth and Climate Resilient Communities.............. 66
5.4 Proposed Green Growth Roadmap Implementation Period (10-year)......... 74
5.5 UMRBPL Priority Mitigation and Adaptation Measures............................... 75
5.6 Local climate change action (mitigation and adaptation) plans ................... 82
5.7 Key Implementation Arrangements............................................................. 82
6.0 Proposed Local Plan Mainstreaming and Implementation Arrangements. 94
6.1 Integration and Mainstreaming of Priority CC Mitigation and
Adaptation Measures in Local Plans and Programs.................................... 94
6.2 Specific Recommendations and Action Points............................................ 96
7.0 Monitoring and Evaluation ............................................................................ 99
8.0 Estimated Investment Cost/Timetable of UMRBPL Ecotown Green
Growth Road Map (Priority Mitigation and Adaptation Measures) ............. 99
8.1 Priority Adaptation and Mitigation Measures and
Estimated Period of Implementation........................................................... 100
8.2 Estimated Investment Cost of Priority Adaptation and Mitigation Measures 102

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LIST OF TABLES
Table 1. Land Cover changes in UMRBPL (2002 and 2012) ........................... 4
Table 2. LGUs and barangays coverage areas inside UMRBPL...................... 5
Table 3a. Land Use per Sub-river basin ............................................................ 6
Table 3b. Land Use by Municipality ................................................................... 6
Table 4a. Over-all Land Classification of UMRBPL............................................ 7
Table 4b. Land Classification by LGUs inside the UMRBPL .............................. 7
Table 5. Population, population density and land area..................................... 10
Table 6. Temperature changes under mid-range in 2050 ................................ 20
Table 7. Potential climate change-related impacts in the Municipality and
Barangay level INSIDE the UMRBPL................................................. 23
Table 8. Potential climate change-related impacts in the Municipality and
Barangay level OUTSIDE the UMRBPL............................................. 24
Table 9. Categorization of LGUs in the UMRBPL in terms of vulnerability ....... 25
Table 10. Project vulnerability types in terms of landslides/erosion and droughts,
forestry sector LGUs, UMRBPL, 2030 ............................................... 26
Table 11. Estimating exposure and sensitivity to climate change....................... 37
Table 12. Estimating vulnerability to climate change.......................................... 37
Table 13. Perceived Changes in Climate Attributes........................................... 42
Table 14. Summary of Key Mitigation and Adaptation Measures of
UMRBPL Green Growth Road Map ................................................... 77
Table 14a. UMRBPL MCA measures based on vulnerability ............................... 79
Table 14b. UMRBPL GHG measures emission reduction.................................... 81
Table 15. Key Vulnerabilities of UMRBPL LGUS in Key Sectoral Areas ............ 85
Table 16. Key Priority Measures in Proposed Local Climate Action Plan of
UMRBPL LGUs.................................................................................. 86
Table 17a. Local climate change action (mitigation and adaptation)
plan priority activities.......................................................................... 88
Table 17b. Proposed priority GHG emission reduction measures per LGU ......... 92
Table 18. Priority Adaptation Measures............................................................. 100
Table 19. Priority Mitigation Measures............................................................... 102
Table 20. Estimated cost of priority adaptation measures.................................. 103
Table 21. Cost Estimate of proposed Mitigation Measures ................................ 104
LIST OF FIGURES
Figure 1. UMRBPL Map.................................................................................... 1
Figure 2. Ecotown Green Growth Component .................................................. 2
Figure 3. New satellite imagery......................................................................... 2
Figure 4. Land Cover Map of UMRBPL (2002 and 2012).................................. 3
Figure 5. Population and Density Map .............................................................. 10
Figure 6. Income levels of UMRBPL barangays................................................ 10
Figure 7. Carbon emission due to electricity consumption in UMRBPL............. 14
Figure 8. Carbon emission of the LUCF sector, UMRBPL................................. 15
Figure 9. Methane emission from the agriculture sector in five municipalities ... 16
Figure 10. Estimated CO2 emission from wastes by UMRBPL LGUs ................. 17
Figure 11. Multi-hazards map ............................................................................. 22
Figure 12. Sensitivity level of LGUs in connection with CC impacts
in the forestry sector .......................................................................... 27
Figure 13. Exposure level of LGUs in connection with CC impacts
Figure 14. Adaptive capacity level of LGUs in connection with CC impacts
Figure 15. Vulnerability level of LGUs in connection with CC impacts

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in the agriculture sector...................................................................... 31
on the water sector ............................................................................ 34
Figure 24. Vulnerability level of roads and bridges.............................................. 38
Figure 25. Extent of transport facility over land use............................................. 39
Figure 26. Extent of transport network classified according to type land use ...... 40
Figure 27. Extent of transport facilities classified according to level of vulnerability to
climate change over land use ............................................................ 40
Figure 28. Satellite image of Marcos Highway segment in Cupang, Antipolo...... 41
Figure 29. Level of sensitivity and exposure to landslide based
on population to landslide .................................................................. 43
Figure 30. Level of sensitivity and exposure to landslide based
on population to flooding.................................................................... 44
Figure 31. Level of sensitivity and exposure based on poverty ........................... 45
Figure 32. Female population exposure and sensitivity....................................... 46
Figure 33. Sensitivity and exposure based on main source of income ................ 47
Figure 34. Adaptive Capacity Index based on education .................................... 48
Figure 35. Adaptive Capacity based on Participation in Community Action......... 49
Figure 36. Adaptive Capacity Based on Knowledge and Information
on Climate Change............................................................................ 50
Figure 37. Sensitivity and Exposure Index of the Study Area.............................. 51
Figure 38. Adaptive Capacity Index of the Study Area........................................ 52
Figure 39. Vulnerability Assessment to Climate Change..................................... 53
Figure 40. Total Projected Population in the Study Area under
Different Annual Growth Rates .......................................................... 54
Figure 41. CCC Ecotown Framework ................................................................. 56
Figure 42. Continuum of Development Responses from Development
to Climate Change............................................................................. 60
Figure 43. Strategic framework for climate resilience and
green growth in UMRBPL .................................................................. 67
Figure 44. Exposure and sensitivity of female population ................................... 73
Figure 45. Entry-points in Integrating CCA and DRRM at the CLUP Process ..... 95
Figure 46. Integrating CCA/DRRM into the Comprehensive
Development Planning (CDP) Process .............................................. 96
LIST OF ANNEXES
Annex 1 Stakeholders’ Identified List of Possible Mitigation and Adaptation Measures
Annex 2 UMRBPL MCVA 10-point evaluation criteria
Annex 3 Priority Adaptation and Mitigation Measures with MCA scores
Annex 4 Detailed Estimated Costs for UMRBPL Priority Mitigation and Adaptation Projects

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LIST OF ACRONYMS
ADB Asian Development Bank
CC Climate Change
CCC Climate Change Commission
CSO Civil Society Organization
DA Department of Agriculture
DAR Department of Agrarian Reform
DENR Department of Environment and Natural Resources
DILG Department of Interior and Local Government
DMF Design and Monitoring Framework
DOST Department of Science and Technology
DRR Disaster Risk Reduction
DRRM Disaster Risk Reduction and Management
FGD Focus Group Discussion
GHG Green House Gas
GIS Geographic Information System
IP Indigenous People
IPCC Intergovernmental Panel on Climate Change
KII Key Informant Interview
LDCs Least Developed Countries
LGU Local Government Unit
LLDA Laguna Lake Development Authority
LPG Liquefied Petroleum Gas
LRA Land Registration Authority
LUCF Land-Use Change and Forestry
MCVA Multi-Criteria Variate Analysis
MGB Mines and Geosciences Bureau
NAMRIA National Mapping and Resource Information Authority
NCIP National Commission for Indigenous People
NEDA National Economic Development Authority
NGAs National Government Agencies
NGO Non-Governmental Organization
NRM Natural Resources Management
PA Protected Area
PAGASA Philippine Atmospheric Geophysical and Astronomical Services Administration
PAMB Protected Area Management Board
PASU Protected Area Superintendent
PAWCZM Protected Areas Wildlife and Coastal Zone Management
PENRO Provincial Environment and Natural Resources Office
PPDO Provincial Planning and Development Office
PTWG Project Technical Working Group
RTD Regional Technical Director
SEARCA Southeast Asian Regional Center for Graduate Study and Research in Agriculture
TA Technical Assistance
TWG Technical Working Group
UMRBPL Upper Marikina River Basin Projected Landscape
UPLB University of the Philippines Los Baños
URS University of Rizal System
VA Vulnerability Assessment

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UMRBPL ECOTOWN GREEN GROWTH ROAD MAP
ADB TA-8111 PHI: CLIMATE RESILIENCE AND GREEN GROWTH IN THE UPPER
MARIKINA RIVER BASIN PROTECTED LANDSCAPE:
DEMONSTRATING THE ECO-TOWN FRAMEWORK (46225-001)
1.0 Introduction
Through Proclamation No. 296 issued by the President of the Philippines on 24 November
2011, the Upper Marikina River Basin was declared a protected area. The proclamation
enabled the creation of the Protected Area Management Board (PAMB) lead by the
Department of Environment and Natural Resources (DENR) to administer the Upper Marikina
River Basin Protected Landscape (UMRBPL).
The Asian Development Bank (ADB) granted the request of the Climate Change Commission
(CCC) for a Technical Assistance (TA) to demonstrate the Eco-town Framework in the
UMRBPL. The project focuses on the demonstration of the Eco-town Framework and
enhancing resilience and green growth in the UMRBPL.
One of the main outputs of the ADB TA grant
to CCC is the formulation of a strategic
UMRBPL Eco-town “green growth road map”
as part of the implementation of the country’s
National Climate Change Action Plan
(NCCAP) adopted in 2011.
As espoused in the NCCAP 2011–2028, the
implementation of climate change action plans
at the local level will be packaged using the
concept of ecologically stable and
economically resilient towns or Eco-town. As
defined in the NCCAP, an Eco-town is a
planning unit composed of municipalities or a
group of municipalities located within and in
the boundaries of critical key biodiversity areas
(forest, coastal/marine and fishery, or
watersheds), highly vulnerable to climate
change risks due to its geography, geographic
location, and poverty situation.
Under the CCC’s Eco-town Framework (see
Figure 2), the ‘green growth’ strategic road
map shall be composed of prioritized mitigation and adaptation measures which were decided
upon by local stakeholders and partners based on the results and findings of science-based
vulnerability assessment (VA) and greenhouse gas (GhG) emissions inventory and the use of
a multi-criteria variable analysis (MCVA).
The Eco-town ‘green growth’ road map shall also serve as basis for the implementation of
local climate change action plans (LCCAP) that would be integrated into the local land use
and development plans of the five UMRBPL local government units (LGUs) (Antipolo, Baras,
San Mateo, Rodriguez and Tanay).
Similarly, from these prioritized mitigation and adaptation measures, the Project shall ‘pilot’
selected green growth and resilience measures in the UMRBPL.
Figure 1. UMRBPL Map

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Figure 2. Ecotown Green Growth Component
Source: A. Lapiz, Climate Change Commission (CCC) Ecotown Powerpoint presentation, 2012.
2.0 Baseline Information, Vulnerability Assessment and GHG Inventory
2.1 Land Cover changes in UMRBPL (2004 and 2012)
The satellite imagery (December 2012 – False
Color Composite) obtained by the Project1
(see Figure 3) showed significant changes in
the current land use within the UMRBPL
areas. A comparative analysis of the land
use/cover maps from 2004 (DENR) and 2012
(ADB-Upper Marikina) showed noticeable
expansions in built-up and production (e.g.
cultivated and agricultural) areas and
reduction of open canopy and close canopy
forest areas (see Figure 4).
Table 1 shows the estimates in the land
cover/use changes in the UMRBPL. It shows
significant increases from double to more than
seven times in the built-up areas and drastic
decreases in open canopy forests from 70% -
100%. Approximately 686 hectares of built up
areas were detected and confirmed through
ground truthing at the Wawa sub-watershed
especially in the Barangay of Pinugay and
Cuyambay area. Open canopy forest in the
Montalban sub-watershed were gone and
converted into crop areas or grassland as a
consequence of slash and burn farming and
charcoal making as confirmed during the Natural Resource Survey of the area.
1
This is satellite imagery obtained by the Project through Rapid Eye.
Figure 3. New Satellite Imagery

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The largest gains in built-up areas are in San Mateo (1,750% or 2,500 hectares), Rodriguez
(1,007% or 2,805 hectares), Tanay (959% or 1,123 hectares) and Antipolo City (487% or 5,806
hectares). A total of 11,740 hectares of settlement/built-up areas were seen within the
UMRBPL LGUs. Likewise, around 12,840 hectares of agricultural/cultivate land uses were
converted into non-agricultural uses, presumably settlements/built-area. The largest
conversion of agricultural lands to non-agriculture use was in Antipolo City (6,029 hectares),
San Mateo (2,502 hectares) and Tanay (2,373 hectares).
On the other hand, the widest decline in open canopy forests by land area was in Rodriguez
with more than 9,000 hectares lost and Tanay with 1,390 hectares lost. Over the last 10 years,
UMRBPL lost a total 12,950 hectares of open and closed canopy forests or an annual
deforestation rate of 1,295 hectares which is about half the size of Marikina City. These were
either converted into cultivate lands or grasslands.
Among the key issues to the drastic changes in land uses/cover in UMRBPL were attributed
to increased urban population, resettlement, slash and burn agriculture/kaingin, charcoal-
making, and land conversion.
Figure 4. Land Cover Map of UMRBPL (2002 and 2012)
Source: DENR (2004) and ADB-UMRBPL Project (2013).

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Table 1. Land Cover changes in UMRBPL (2002 and 2012)
LGU Land Cover 2002 2012 Difference %
Antipolo
City
Arable land, crops mainly cereals and sugar 3,551.90 284.72 (3,267.18) -92
Built-up areas 1,193.21 6,999.27 5,806.06 487
Coconut plantations 505.75 367.25 (138.50) -27
Cultivated area mixed with brushland/grassland 24,518.39 21,893.92 (2,624.47) -11
Grassland, grass covering >70 percent 2,972.47 4,977.45 2,004.98 67
Open canopy, mature trees covering < 50 percent 2,527.22 746.32 (1,780.9)1 -70
Baras
Arable land, crops mainly cereals and sugar 2,151.11 257.35 (1,893.7)6 -88
Built-up areas 227.78 227.78 100
Cultivated area mixed with brushland/grassland 1,999.27 1,957.45 (41.82 ) -2
Grassland, grass covering >70 percent 91.20 91.49 (0.30) 0
Rodriguez
Built-up areas 207.12 2,292.14 2,085.02 1007
Closed canopy, mature trees covering > 50 percent 3,369.40 2,616.35 (753.05) -22
Cultivated area mixed with brushland/grassland 9,988.45 19,224.79 9,236.35 92
Grassland, grass covering >70 percent 234.91 372.81 137.89 59
Open canopy, mature trees covering < 50 percent 11,255.96 2,260.77 (8,995.20) -80
San
Mateo
Built-up areas 142.85 2,642.43 2,499.59 1750
Cultivated area mixed with brushland/grassland 3,198.75 2,896.89 (301.86) -9
Open canopy, mature trees covering < 50 percent 183.95 187.72 3.77 2
Arable land, crops mainly cereals and sugar 2,201.50 0.00 (2,201.50) -100
Tanay
Arable land, crops mainly cereals and sugar 1,119.27 373.82 (745.46) -67
Built-up areas 117.14 1,240.11 1,122.98 959
Coconut plantations 55.16 3.85 ( 51.31) -93
Cultivated area mixed with brushland/grassland 18,215.47 16,638.21 (1,577.26) -9
Grassland, grass covering >70 percent 4,491.87 7,131.86 2,639.99 59
Open canopy, mature trees covering < 50 percent 1,751.77 527.91 (1,223.86) -70
Closed canopy, mature trees covering > 50 percent 168.56 (168.56) -100
Total 96,212.64 96,212.64 (0.00) 0
Source: ADB-UMRBPL Ecotown Project, 2013.

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2.2 Bio-physical, land use and natural resource conditions
The main findings indicate that UMRBPL’s rugged physical features, coupled with the
uncontrolled and creeping spread of human settlements, are posing constant threats to its
natural resources. Physically, UMRBPL covers a total land area of 29,505.8 hectares and
encompasses the political boundaries of the City of Antipolo and Municipalities of Baras,
Rodriguez, San Mateo, and Tanay, all in the Province of Rizal (see Table 2).
Table 2. LGUs and barangays coverage areas inside UMRBPL.
LGU Barangay
UMRBPL* Total Area
(ha)**
Area in UMRBPL
(%)Area (ha) %
Antipolo City
Calawis 5,426.9
28.60
5,581.12 97.00
Inarawan 13.9 959.90 <1
San Jose 2,971.1 13,787.77 22.00
San Juan 27.9 3,327.69 <1
Total 8,439.7 23,656.48 35.67
Rodriguez
(Montalban)
Balite (no data)
42.77
53.88 (no data)
Mascap 410.9 7,576.41 5.42
Puray 16.1 15,437.70 <1
San Isidro 75.5 3,792.00 1.99
San Rafael 595.8 6,061.45 9.82
Total 12,620.9 32,921.44 38.34
San Mateo Pintong Bocawe 306.1 1.04 753.00 40.65
Baras Pinugay (no data)
(27.59)
6,059.10 (no data)
Tanay Cuyambay (no data) 5,923.00 (no data)
Total 29,505.8 100 69,313.02 42.57
Source: Comprehensive Upper Marikina River Basin Protected Landscape Management Plan (January 2012),
Volume 1 – River Basin Characterization; Volume 2 – The Plan.
The Upper Marikina River Basin straddles the headwaters of four major sub-river basins: the
sub-river basins of Boso-Boso, Montalban, Tayabasan all flowing westward to Wawa Sub-
river Basin.
In terms of land use (see Table 3a/b), UMRBPL is dominated by brush and shrub lands which
constitute 33.8% (9,965.5 hectares). Protection forest covers 27.9% (8,222.5 hectares) of the
area which are mostly located in Montalban Sub-river Basin (5,126.8 hectares) and
Tayabasan Sub-river Basin (2,604 hectares). Around 14% (4,216.7 hectares) are devoted to
agriculture located mainly in Boso-Boso Sub-river Basin (2,813.6 hectares). Production Forest
covers roughly 12% (3,521.2 hectares) with Boso-Boso, Tayabasan, and Wawa Sub-river
Basins having approximately 1,000 hectares each. About 12% of the area is grassland of
which 1,737.4 hectares are located in Boso-Boso Sub-river Basin and another 1,036 hectares
found in Tayabasan Sub-river Basin.
By municipality, most of the protection forest is located in the Municipality of Montalban
(7,291.8 hectares) with the remaining patches situated in Antipolo (359.1 hectares). However,
there are 1,685.3 hectares of production forest in Antipolo and 757.5 hectares in Montalban.
The former has 1,828.6 hectares of agricultural lands while the latter has 485.7 hectares.
Grassland areas in Antipolo cover a total land area of 1,516.3 hectares while those of

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Montalban cover 564.6 hectares land area. Only 7.6 hectares of built-up areas are located in
Antipolo while 75.6 hectares are located in Montalban.
Table 3a. Land Use per Sub-river basin.
Table 3b. Land Use by Municipality.
More than 90% (26,983.3 hectares) of the total land area of the Upper Marikina River Basin is
declared as river basin reservation (see Table 4a/b). The alienable and disposable land (A&D)
accounts for only 2.5% (746.5 hectares) of the total area while forest reserve covers 3.6%
(1,061.2 hectares). About 2.4% (714.8 hectares) are unclassified public forests.
Table 4a/b shows that only Boso-Boso Sub-river Basin (124.1 hectares) and Wawa-Sub-river
Basin (622.3 hectares) have A&D lands located inside the forest reservation. It implies that
those within the boundary of Montalban and Tayabasan Sub-river Basins are all classified as
forestland. A sizable area of unclassified public forest is also found within Wawa Sub-river
Basin. More than 40% (12,620.9 hectares) of the Upper Marikina River Basin is part of
Montalban while 28.6% (8,439.7 hectares) belongs to Antipolo. About 25% (7,259.9 hectares)
of the area is subject to conflicting claims between adjacent municipalities. About 4% (1,185.2
hectares) of the area needs to be validated on the ground.
Slash and burn farming system (kaingin) is the most prevalent agricultural practice in the
UMRBPL. The annual crops raised in the kaingin include corn, rice, vegetables and root crops.
Small-scale fruit production of mango (Mangifera indica) and cashew (Anacardium
occidentale) is practiced in some areas of UMRBPL. There are also small woodlots of exotic
fast growing species such as ipil-ipil (Leucaena leucocephala), mahogany (Swietenia spp.),
mangium (Acacia mangium), and yemane (Gmelina arborea). A large expanse of UMRBPL,

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from the western portion of the Montalban Sub-river Basin down to a greater extent in Antipolo,
including some sites at the slopes of the high and low plateau region of Boso-Boso Sub-river
Basin are blanketed with Imperata (cogon). Grass fires usually occur in summer months that
affect the grassland ecosystems.
Dense stands of an endemic, erect bamboo Schizostachyum lumampao locally known as
buho occur in pockets in UMRBPL. There are patches of young secondary forests in areas
that had been cleared by slash and burn but had been left as fallow and allowed to regenerate.
Secondary forest also arises after abandonment of plantations such as yemane (Gmelina
arborea). A considerable part of the northeast portion of the UMRBPL, particularly in Brgy.
Puray, Rodriguez, still harbors residual stands of lowland forests. In Antipolo City, the last
remaining fragments of residual forests are in Libis (496 masl) at the middle slopes of Mt.
Amaya and Tayabasan (479 masl) in Mt. Masola.
Table 4a. Over-all Land Classification of UMRBPL.
Land Classification
Sub-River Basin (ha)
Total Percent
Boso-Boso Montalban Tayabasan Wawa
Alienable/Disposable 124.1 377.5 680.8 1,061.2 2.5
Forest Reserve 2.9 6,782.9 7,361.7 2,886.9 26,983.3 3.6
Unclassified Public Forest 714.8 714.8 2.4
River Basin Reservation 9,951.8 6,782.9 7,361.7 2,886.9 26,983.3 91.5
Total 10,078.8 7,160.4 7,361.7 4,904.9 29,505.8
Percentage 34.2 24.3 24.9 16.6 100.0
Table 4b. Land Classification by LGUs inside the UMRBPL
Land Classification
Municipality
Total Percent
Antipolo Montalban Conflict No Data
Alienable/Disposable 114.1 590.3 42.1 746.5 2.5
Forest Reserve 2.9 606.2 452.1 1,061.2 3.6
Unclassified Public Forest 689.0 25.8 714.8 2.4
River Basin Reservation 8,322.8 10,735.4 6,739.9 1,185.2 26,983.3 91.5
Total 8,439.7 12,620.9 7,259.9 1,185.2 29,505.8 100.0
Percentage 28.6 42.8 24.6 4.0 100.0
2.3 Over-all household survey results
The major findings in the household survey are in the context of UMRBPL’s agricultural,
forestry and water resources concerns as well as on the perceptions of the respondents on
the significance of the UMRBPL as an ecosystem. Among the key household survey results
are:
Around 80% (173 respondents) relied on rain (rainfed) as a source of irrigation water
while 19% (40 respondents) mentioned that they irrigated their crops. Among the major
crops planted that rely on rainwater are root crops (20%), cassava (16%), fruit trees
(17.5%), and corn (8%) with rice (57.5%) as the most irrigated crop.
Crop plots are prepared mainly by manual means (96%) and only 2% did land
preparation mechanically and 2% used the combination of manual and mechanical
techniques.
Majority of the 71 respondents (76%) applied inorganic fertilizers to their crops such
as rice, corn and vegetables compared to around 14% that used organic fertilizer
mainly for ginger, cassava, sweet potato and bitter gourd. Almost the same percentage
use pesticide (75%) for pest protection.
Almost all of respondents (98%) surveyed manually use soil and water conservation
techniques for their crops.

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Around 179 respondents disposed their farm wastes by composting (38%), burning
(23%) and burying (39%).
In terms of the forest services that the UMRBPL forests provide to their communities,
the majority of the respondents mentioned water (73%), fresh air (64%), wood (55%)
and fuel wood (53%). The other forest services were income (35%), protection of soils
and slopes (28%), and habitats for wildlife (22%).
On the other hand, of the 71 respondents who identified where they source or buy their
forest products, 83% said they get the products direct from the forests, 8% bought from
the market, 7% from the vendors and 2% from the farm. Most of the products are
bamboo (buho), fern fruits and orchids.
In terms of resource management, reforestation is the most activity that the
respondents mentioned (44%) in support to the protection of the UMRBPL followed by
forest protection (7%) and information campaigns (7%). The other forms of support
are solid waste management (6%), trainings (6%), Bantay Gubat (6%), farm-to-market
roads (4%), and slope stabilization (2%).
Respondents who are willing to pay to protect UMRBPL per year gave an average
value of PhP243/year/respondent and the mode is PhP60/year/respondent. The
highest amount is PhP2,400/year and the lowest is PhP60/year.
Generally, due to its rugged terrain, UMRBPL has limited areas that are suitable for large scale
grain production. Thus, it is a deficit area in terms of production of basic agricultural products
such as rice, corn and vegetables.
Due to its burgeoning population, the UMRBPL’s demand for food far exceeds its production
capacity and, as such, the area is dependent on food supplies from other provinces. The
remaining forest resources are being exploited to meet the needs of the increasing demands
of the populace. For example, firewood extraction and charcoal-making are prevalent in the
rural barangays of UMRBPL.
Though mining operations of non-metallic minerals are in areas outside the UMRBPL, the
offsite impacts of quarrying are evident in downstream portions of UMRBPL. UMRBPL’s river
system is still providing the water source for the Marikina River, thus, maintaining its significant
role of regulating the water supply that eventually impacts the downstream areas of Laguna
de Bay and Metro Manila.
General trends on utilization and supply of natural resources have been established from the
updated CLUPs, comprehensive development plans and Eco profiles of Antipolo City and the
Municipalities of Rodriguez, Tanay, San Mateo and Baras. For example, Antipolo City is a
deficit area in terms of agricultural commodities (rice and vegetables) because it is mainly a
settlement place for Metro Manila workers. Rodriguez and Tanay have vast areas of
forestlands that are declared as protection zones
All in all, the natural resources management (NRM) baseline information points to the
increasing overexploitation of the UMRBPL’s natural resources and this must be addressed
in line with Eco-town’s tenets. Measures must be planned and implemented to arrest this
trend.

SEARCA-ERGONS 9
2.4 UMRBPL demographics
The 2010 estimated total population of
the 16 UMRBPL barangays is
345,236 with Brgy. Cupang and San
Jose in Antipolo City as the most
populated while Brgy. San Andres
having the least number of residents
(See Table 5). However, in terms of
population density, the barangays
within and in the buffer area of the
UMRBPL are distinct. Population
density in the 16 UMRBPL barangays
ranges from a low 0.47 to a high of
34.85.
The barangays with low population
density (less than 1) are from Tanay
(2 barangays), Rodriguez (Mascap)
and Antipolo (0.27). These barangays
are located in the mountainous and
upland areas of the protected area.
The high population density areas are
found in Antipolo City (Brgy. Bagong
Nayon - 29.88, Cupang - 17.47, San
Jose - 23.75). This is expected since
Antipolo City is already considered a
highly urbanizing city and the most
urbanized city in the whole province of
Rizal.
By 2030, the estimated population in the UMRBPL barangays will triple in size to 1.09 million
based on the average growth rate of 3.4% for Rizal province over the last 10 years. By 2050,
the UMRBPL barangay population will explode to 2.33 million people. The annual growth rate
(2000-2010) of Rizal province is nearly twice the national average of 1.98% and the highest
for the CALABARZON region. Together with Laguna province, Rizal has the highest rate of
population increase due to natural growth and in-migration. This is largely attributed to the
continuous economic growth of the two provinces over the last two decades due to industrial
and settlement expansion.
2.5 Poverty
Majority of the barangays reported to earn between PhP1,000 and PhP5,000 monthly income
(Income Class 4), way below the poverty threshold (set at PhP250/day - rural barangays)
which is followed by income class earning from PhP5,001 to PhP10,000 (Income Class 3), a
better figure compared with the former. Survey results also recorded households earning a
better figure than the previous income classes (PhP10,001-PhP15,000). (Figure 6)
The extreme level of income earning (less than PhP1,000) is recorded to many barangays,
ranging from 5% to 57% of sample population. San Andres in the Municipality of Tanay
recorded the highest occurrence of earnings in this income class (57% of sample population).
Income above PhP15,000 was only recorded in Brgy. Calawis of Antipolo City (4%).
Figure 5. Population and Density Map

SEARCA-ERGONS 10
Based on the household income profile from survey results, the poverty level may be
determined by looking at the percentage of household within income classes 3, 4 and 5.
Households in income classes 2 and 1 are those that can be considered above the poverty
threshold in the country.
Table 5. Population, population density and land area
No. LGU Barangay
Population
NSO (2010)
Total Land
Area (ha)
Population
Density
1 Antipolo City Bagong Nayon 45,152 1,510.89 29.88
2 Antipolo City Calawis 4,252 15,793.38 0.27
3 Antipolo City Cupang 84,187 4,818.99 17.47
4 Antipolo City Inarawan 18,026 3,357.80 5.37
5 Antipolo City San Jose 88,222 3,714.33 23.75
6 Antipolo City San Juan 8,488 3,032.84 2.80
7 Baras Pinugay 7,396 421.47 17.55
8 Rodriguez Burgos 38,554 1,106.38 34.85
9 Rodriguez Geronimo 5,417 1,992.75 2.72
10 Rodriguez Mascap 4,425 7,085.22 0.62
11 Rodriguez Puray 2,941 1,961.30 1.50
12 Rodriguez Rosario 5,881 2,717.16 2.16
13 Rodriguez San Rafael 24,710 6,759.85 3.66
14 San Mateo Pintong Bocawe 4,080 2,202.44 1.85
15 Tanay Cuyambay 2,442 5,300.33 0.46
16 Tanay San Andres 1,063 2,242.76 0.47
Total 345,236 64,017.89 5.39
Source: NSO 2010 and ADB-Ecotown Project, 2013.
Figure 6. Income levels of UMRBPL barangays.
Source: ADB-Ecotown Project, 2013.

SEARCA-ERGONS 11
2.6 Local community adaptive capacity and gender
The baseline structured survey on the adaptive capacity2
of households done by the Project
involving 300 respondents/residents as sampling population from the 16 UMRBPL-covered
barangays to serve as basis for vulnerability and capacity building assessment showed that
people living inside the protected area were predominantly poor to medium income group, with
majority having completed elementary education. Small percentage of the sample population
has reached college. In terms of gender issue, majority of the respondents are female,
although more females have had more years in school. Among the key findings in this area
are:
The mean age of the total respondents is about 43 years, with more female
respondents (60.3%) than males (39.7%). Most of the respondents are married (74%),
while only a few (10.3%) are living as common law husband/wife. Some are solo
parent (1.7%); widowed (5.7%) and separated (2%).
Literacy is relatively high with almost all of the respondents have spent some years in
school. High percentage have reached high school (20.3%) and graduated (21%) while
some have finished college (8.3%)
Majority of the respondents have also been in the area for many years, with mean
value of 25 years, even if originally they are from other places. Significant numbers are
originally from Rizal Province. Others are from Albay, Aklan, NCR, Quezon, Samar,
and Leyte. Due to family (33.7%) and work (39.0%) are the most common reasons for
migration in the area.
Sources of income for males and females differ slightly. Survey showed that more
males are in crops and livestock production while more females are drawn to labor and
in putting up sari-sari store. Merchandising, OFW remittance and market vending is
dominated by males, including practice of profession and government service.
Physical and financial capitals are those that pertain to infrastructures and services available
as well as financial services as major indicators. Some of the barangays at the buffer zones
have better access to roads, health and medical facilities. On the other hand, those who are
remote tend to be adversely affected. Though the interests are very high, credit sources are
always wanting and informal lending facilities are getting more popular.
Social capital is high in all of the covered areas based on their level of participation in
community actions ranging from cleaning canals, relief and rehabilitation and in many training
activities. This is a resource that the local leadership can tap to mainstream climate change
adaptation (CCA) and disaster risk reduction (DRR) in community development activities.
Among the key related survey results are:
Almost all of the respondents shared the view that the climate has changed over the
past 10 years compared with conditions 10 years ago. The most observed changes
are in terms of rainfall (57.7%) and temperature (19%).
2
Human capital such as education, gender, income and health were used as indicators for differential
susceptibility

SEARCA-ERGONS 12
Typhoons are still the most observed weather-related event and heavy rains are now
considered climate hazards and have only been observed in 2009 and 2011. In fact,
the respondents said that the duration of frightening heavy rains they have experienced
lasted for 4 days non-stop while 60 days for flooding. The sample population for the
survey has never experienced drought.
Damages to dwellings are the most apparent impacts (15.6%) especially in the upland
barangays where houses are made of light materials. Livelihood and work is also
disrupted, especially when the roads connecting the barangays to their work stations
are damaged.
Some noted occurrence of diseases as one impact that needs to be addressed,
particularly dengue, malaria and leptospirosis, and diarrhea.
In terms of duration to bounce back to a better state or state before the extreme events,
majority mentioned that it could take them 840 days or almost 3 years to recover
especially from destructive typhoons. In terms of flooding, it can take them 240 days
or up to a year to recover.
Local government at the barangay level serves as a primary support service to
communities in cases of extreme events like typhoons and flooding followed by
municipal offices, albeit still insufficient.
There is a prevalence of poor knowledge on climate change impacts and threats, climate
change adaptation options and skills needed to promote and integrate these practices in daily
lives despite increased national level communication programmes. Knowledge on programs
and policies of the LGU on CCA is not also well known among the sample population, including
budget allocation. This may be attributed to the fact that there are really no legal provisions
that mandate the LGU to integrate budget for CCA, when compared with DRR.
In all areas under study, while there is expressed willingness to adopt new approach or
technologies in order to enhance adaptation, there is an apparent low level of access to new
information that emanates or can emanate from the research stations. Although the local
governments have shown serious actions to mainstream disaster prepared and risk reduction
programs, it appears there is some promise in improving the mechanisms for information
dissemination and community engagement.
Any capacity and knowledge building programmes in the areas should focus on climate
change science simplified; climate change impacts and threats; climate change adaptation
options and skills; forest management and climate change adaptation, disaster risk reduction,
leadership training and value formation for CCA/DRR and health and sanitation including
prevention, early detection and emergency responses in cases of climate sensitive diseases.
2.7 GHG inventory
Of the six sectors assessed, waste is found to be the biggest source of emission in UMRBPL.
Total emission of the waste sector is 6.52 Mt which represents 81% of the total emissions in
the UMRBPL. Following the waste, is the energy sector which has a total CO2 emission of
1.15 Mt. This comprises 14% of the total emissions at UMRBPL. The remaining sectors have
very little share on the total CO2 emissions in the UMRBPL.
For instance, emission from the agriculture sector represents 1.97% only while that from
transportation is a mere 1.16%. The Land-Use Change and Forestry (LUCF) sector has total
emission of about 0.13 Mt which comprises 1.58% of the total emissions while the industry

SEARCA-ERGONS 13
sector is negligible as emission is estimated to be 1.5 tons only. This likely emission trend is
due to the following factors: (1) dense population of the five municipalities included in
UMRBPL; (2) proximity to Metro Manila; (3) presence of many manufacturers and
establishments; and (4) massive land conversion into settlement.
Results of the inventory in UMRBPL show different trend compared with the results of the
national GHG inventory. Based on the 2000 national GHG inventory, energy shares more
than half of the total Philippine emission while inventory in UMRBPL reveals that it is the waste
sector that gets the largest share in the total emission of the basin. At the national level,
emissions of the sectors have the following trend: energy > agriculture > waste > industry. At
UMRBPL, the emission trend observed is as follows: waste > energy > agriculture > LUCF >
transportation > industry. At the national level, LUCF sector is a huge sink of carbon
sequestering a total of 105.11 Mt while in UMRBPL, LUCF sector is a source of carbon emitting
about 0.13 Mt. The basic reason for this is the land use changes that occurred in the basin
during the last 10 years. Both inventories are however in agreement in terms of the share of
the industrial sector. At the national and basin levels the industry has the smallest emission.
In terms of percent share of the different municipalities in the total carbon emission of
UMRBPL, results show that Antipolo City’s emissions comprise more than 50% of the total
emissions of the basin. The remaining 48% of the total emissions of UMRBPL is shared by
the remaining four municipalities: San Mateo (24%); Rodriguez (12%); Tanay (8%); and Baras
(4%). Results of the GHG inventory indicate that of the five municipalities, Antipolo City is the
most urbanized and has the largest population hence has the largest emission. Rural towns
such as Tanay and Baras that host less number of people have less carbon emissions.
Energy
One of the sources of carbon emissions from the energy sector is the use of electricity by the
following: residential, commercial, industrial and streetlight. Results show that a total of 1.15
Mt CO2 were emitted in 2012 in UMRBPL due to use of electricity. Almost half of this total
CO2 emission was contributed by the residential sector. Around 321,110.4 tons of CO2 or
28% of the total was due to electricity consumption of the commercial sector while 278,453.5
tons or 24% was attributed to consumption by the industrial sector. A mere 9058.4 tons of
CO2 or 0.78% of the total emissions was contributed by the streetlights.
Figure 7 shows the carbon emissions from electricity consumption in 2012 at the
municipalities covered by UMRBPL by source. Comparing the five municipalities, results show
that Antipolo City exhibits the highest amount of carbon emission (808,987 tons) while Baras
has the least (15,745 tons). This is understandable as Antipolo City is the most urbanized and
highly populated municipality while Baras is the least populated and a rural municipality of the
province. Following Antipolo City in terms of amount of carbon emission due to electricity
consumption is Rodriguez which has a total emission of 143,438.1 tons of CO2. San Mateo,
on the other hand, ranks 3rd
in terms of carbon emission as it has emitted a total of 132,977
tons. Tanay occupies the 4th
place with an emission of 53,272.7 tons.

SEARCA-ERGONS 14
Figure 7. Carbon emission due to electricity consumption in UMRBPL.
LUCF
In the LUCF sector, results show that the UMRBPL is a net source of carbon. As mentioned
earlier, total carbon emitted by the sector amounts to 127,000 tons of CO2 (Figure 8). This is
largely contributed by the massive conversion of forested forestlands into other land uses that
contain low or no carbon. Based on the results of the land cover change analysis for 2004-
2012, significant decline (70% - 100%) on the area covered by the open canopy forest
occurred during the said period to favor the increase in the built-up areas by as much as seven
times and the grassland area as a result of slash and burn activities and charcoal making.
At the municipal level, results show that the LUCF sectors of Antipolo City and Tanay are net
sinks of carbon. Total CO2 sequestered by this sector of Antipolo City and Tanay amount to
39,000 tons and 52,000 tons, respectively. This is mainly due to the presence of brush and
open canopy forests in the area. Carbon sequestered by the LUCF sector due to change in
forest/woody biomass in Antipolo City is about 193,000 tons of CO2 while in Tanay said activity
resulted to sequestration of 133,000 tons. Carbon emission due to forest/land use change in
Antipolo City and Tanay amount to 154,000 tons and 81,000 tons, respectively. Thus, despite
the changes in land uses occurring in these two municipalities, emissions of the LUCF sector
are offset by the regrowth of trees in the brush and open canopy forests of the areas.
On the other hand, LUCF sectors of Baras, Rodriguez and San Mateo are all net sources of
carbon arising from land use conversion of open canopy forests and grass/shrub lands into
agriculture or cultivated lands and settlements/built-up areas. These land use conversions
are clearly shown in the 2004-2012 land use/cover change analysis UMRBPL. Largest net
carbon emission of the sector is observed in Rodriguez with a value of 203,000 tons. LUCF
sectors of San Mateo and Baras have net emissions of 11,000 tons and 2,000 tons,
respectively.
0
100000
200000
300000
400000
500000
600000
700000
800000
900000
Antipolo Baras Tanay Rodriguez San Mateo
Carbonemissionsintons
Municipality

SEARCA-ERGONS 15
Figure 8. Carbon emission of the LUCF sector, UMRBPL.
Transportation
In the transportation sector, total CO2 emissions in the UMRBPL amounts to 93,770 tons. Bulk
of this total emission is contributed by Antipolo City. This is understandable because Antipolo
City is highly urbanized and hosts large number of public utility vehicles that consume huge
quantities of fuel. Its proximity to Metro Manila allows locals who work and attend schools in
Metro Manila to commute from Antipolo City to their respective offices and schools everyday
resulting to large demand for public utility vehicles in the area. Also, a large number of the
town’s population belongs to middle and high income families who can afford to buy private
cars. CO2 emissions due to fuel consumption in Antipolo City amount to 57,480 tons.
The town of Rodriguez comes in second in terms of contribution to the total emission of the
transportation sector. Rodriguez has become the refuge for informal settlers in Metro Manila.
Large part of the town that was formerly devoted to agricultural production is currently used
as a resettlement/residential area. Commuters from Rodriguez create a big demand for
jeepneys and FX since the town is just 20 minutes away from Quezon City. Consequently,
such big demand for public utility vehicles results to huge consumption of fuel. Around 15,000
tons of CO2 is emitted by the transportation sector of Rodriguez.
San Mateo, the adjacent town of Rodriguez ranks third in terms of total CO2 emissions from
the transportation sector in the UMRBPL. A total of 13,820 tons of CO2 is estimated to be
emitted due to fuel use in San Mateo. Similar to Rodriguez, San Mateo’s proximity to Metro
Manila makes commuting daily of the residents of the municipality from the town to the
metropolis when going to offices and schools highly feasible. This has resulted to large
demand for public transportation and fuel.
The transportation sector of Tanay emits 6,670 tons of CO2 making it to rank 4th
among the
municipalities inside UMRBPL. Baras, on the other hand, ranks 5th
in terms of total emission
of the transportation sector in the UMRBPL. Calculation of the total CO2 emission due to fuel
use of vehicles in Baras reveals that emissions from this sector amounts to 320 tons only.
These results are understandable because Tanay and Baras are not urbanized and host
relatively small population hence fuel consumption due to vehicle use of the two municipalities
are relatively low.
-600
-400
-200
0
200
400
600
800
Change in
Forest/Woody
Biomass
Forest/Land Use
Change
TOTAL
Carbonemissionin000t
Component

SEARCA-ERGONS 16
Agriculture
Total methane emissions from the agriculture sector in UMRBPL amounts to 57560 tons.
Large percentage of this is contributed by livestock while a mere 7% is due to emission from
rice production.
At the municipal level, Rodriguez contributes the largest share in terms of the total emissions
of the agriculture sector in UMRBPL (Figure 9). Livestock and rice production in Rodriguez
emit a total of 30620 tons of methane or 53% of the total methane emission in the UMRBPL.
Around 99.58% of the methane emission in Rodriguez is due to livestock production while
0.42% is attributed to paddy fields. The fairly small percentage contribution of rice production
is due to the small area devoted to growing rice in the area.
Tanay ranks 2nd
in terms of total methane emission in the agriculture sector with 12780 tons
emission. This represents 22% of the total methane emission of the agriculture sector in
UMRBPL. About 11Gg or 91% of the total emission is due to livestock production while 1.14
Gg or 9% is contributed by rice production.
Baras is next with an estimated total emission of 8.67 Gg representing 15% of the total
emission. Around 7.42 Gg (85.66%) of methane was produced by the livestock industry while
1.24 Gg (14.24%) was due to rice production. Antipolo City ranks 4th
as its methane emission
of 4 Gg contributes only about 7% of the total methane emissions in UMRBPL. Around 59%
of Antipolo’s methane emission came from the livestock industry while 41% is due to rice
production in the area.
San Mateo has the least level of methane produced by its agriculture sector. Its total methane
emission is merely 3%. A total of 1.73 Gg of methane is produced by the agriculture sector of
San Mateo, most (around 87%) of which comes from livestock while around 13% or 0.22 Gg
is due to rice production.
Figure 9. Methane emission from the agriculture sector in five municipalities
0
5000
10000
15000
20000
25000
30000
35000
Antipolo Baras Rodriguez San Mateo Tanay
Methaneemissionintons
Municipality

SEARCA-ERGONS 17
Waste
Results show that, of the five municipalities, Antipolo City exhibits the highest emissions
followed by San Mateo. Tanay ranks 3rd
while Rodriguez occupies the 4th
place. Baras, being
the smallest town in terms of population, has the least amount of methane emission from the
waste sector. This trend is understandable as among the municipalities, Antipolo City is the
most urbanized while Baras is the most rural. Total methane emission from waste in UMRBPL
is 2.37 Mt.
Of these total methane emission, 51% is from waste of Antipolo City while around 27% comes
from San Mateo. Wastes from Tanay contribute about 9% of the total emissions while the
waste sectors from Rodriguez and Baras comprise 7.85% and 5%, respectively of the total.
Equivalent CO2 emissions from waste in the UMRBPL amount to 6.52 Mt. Antipolo City’s
wastes emit 3.33 Mt CO2 while that of Baras give off 0.33 Mt CO2 only. Waste sector of San
Mateo has emitted a total of 1.75 Mt CO2 while the same sector of Tanay and Rodriguez
discharge 0.60 Mt and 0.51 Mt, respectively (Figure 10).
Figure 10. Estimated CO2 emission from wastes by UMRBPL LGUs
Most of the wastes generated in UMRBPL are domestic waste coming from
residential/settlement areas and agricultural waste products (from livestock and
crops/vegetables). Most of these are largely biodegradable waste. Antipolo and San Mateo
generate most of the residual wastes, largely from packaging materials and
commercial/industrial wastes.
2.8 Natural Resource Accounting (NRA)/Economic Valuation of UMRBPL’s Forest,
Water and Agriculture Resources
UMRBPL provides important environmental and socioeconomic services to the population of
the five on-site LGUs and to off-site areas that include Metro-Manila. The landscape provides
livelihood sources to its inhabitants. It is a source of freshwater for various domestic,
agricultural, commercial and industrial uses. It provides various environmental functions such
as carbon sequestration, soil conservation, flood control, biodiversity, aesthetics and
recreation. However, these environmental goods and services do not have prices or their
prices are so low such that their real values are not reflected. The absence of price for these
resource functions lead to further exploitation. Along this line, the natural resource
0.00
0.50
1.00
1.50
2.00
2.50
3.00
3.50
AntipoloCity
Baras
Rodriguez
SanMateo
Tanay
CarbonemissioninMt
Municipality

SEARCA-ERGONS 18
accounting/economic valuation of UMRBPL’s water, forest and agriculture resources was
undertaken as component of the ecotown principle. The estimation was done by using the
total economic value (TEV) framework that determined their use values and non-use values.
The accounting/valuation work was anchored on the general trends on utilization and supply
of forest, water and agriculture resources in UMRBPL. The current conditions of these
resources served as the physical accounts. The figures were based on secondary data
(CLUPs, comprehensive development plans and ecoprofiles of the five LGUs) and primary
data from the household surveys and GIS figures. On the other hand, the economic accounts
(economic values) are mainly based on benefits-transfer, that is, results of similar studies were
used. The household survey conducted in June 2013 validated the secondary data that were
used in the estimation.
In terms of the sectoral accounts, the forests in UMRBPL are classified into closed canopy,
mature trees covering >50 percent and open canopy, mature trees covering <50 percent. The
direct-use value, expressed in timber accounts (at current prices), of the closed canopy forests
is P463 million and P132 million for the open canopy forests. The indirect-use values of the
carbon stock are P803 million for the closed canopy forests and P604 million for the open
canopy forests. The soil conservation value of the entire watershed is P303 million;
biodiversity value is P288 million; flood control is P1 billion; and aesthetics and recreational
value at P200 million. Summing all the figures, the total value of UMRBPL’s forests is P3.97
billion (at current prices).
UMRBPL’s water resources contribute significantly to the socioeconomic development of its
inhabitants as well as to those living in the off-site communities, particularly those in Metro
Manila, where domestic water supply and demand are a big issue. The water context is further
refocused when the impacts of climate change on UMRBPL’s hydrologic cycle is put into the
overall equation. Such impacts would be the changes in water quantity (high during the wet
season and low in summer months), water quality effects due to an intensification of rainfalls
(accelerating soil erosion and rapid infiltration towards groundwater), and shift of the main
recharge period of groundwater.
The household survey in June 2013 indicated that the domestic water needs include drinking
water, water for hygiene/sanitation, food preparation, and washing clothes. The value of raw
water from UMRBPL was estimated based on the resource charge formula from the National
Strategy and Action Plan for the Water Supply and Sanitation Sector (NEDA 2000). For the
entire UMRBPL river systems, the total surface water yield is 22.239 m3
/s/km2
and a resource
charge of P25/m3
, the total value of water is about P18 billion/year.
The agricultural resource accounting focused on two basic groups of questions:
(1) How are production activities in UMRBPL supporting local consumption? Is the
agricultural resource base able to sustain these consumption needs?
(2) Are the agricultural resource bases appreciating or depreciating, and how fast are
they depreciating as consumption increases?
Generally, due to its rugged terrain, UMRBPL has limited areas that are suitable for large scale
agricultural production. Thus, it is a deficit area in terms of production of basic agricultural
products such as rice, corn and vegetables. Due to its burgeoning population, the UMRBPL’s
demand for food far exceeds its production capacity and, as such, the area is dependent on
food supplies from other provinces.

SEARCA-ERGONS 19
Agriculture is not a major industry in UMRBPL (and in Rizal Province, in general). In terms of
income, the sector is lumped with forestry contributing only 6.2 percent of the provincial
income. Agriculture trails the area’s major sectors of manufacturing, transport, wholesale and
retail, construction and public administration. Agriculture has the lowest share of total (family)
income by major industry classification despite Rizal Province being categorized as an
agricultural province. Over the years, the area’s agricultural economy apparently slowed down
because the labor force is being absorbed by other sectors. This trend can be accounted for
by the increasing urbanization and industrialization. Being proximate to Metro Manila, Rizal
Province (including UMRBPL’s LGUs) has become a favorite choice as a residential area.
Due to its rugged topography, slash-and-burn farming system (kaingin), is the most common
agricultural practice in the UMRBPL. The annual crops grown include corn, upland rice,
vegetables and root crops. Fruit production (mango, banana, avocado) is practiced in some
areas. Backyard agricultural productions (home gardens) are also practiced by households
in most rural barangays. The values of the agricultural net benefits of farm households inside
UMRBPL are based on their net incomes from their crop and livestock farms. Net incomes
range from P10,000 to P20,000/ha/year for crop farms and for poultry and livestock farming,
a much higher income/unit can be realized due to the high demand of these commodities, with
primary markets in nearby Metro Manila.
All in all, the estimated values of forest, water and agriculture resources of UMRBPL point to
substantial monetary values of the direct and indirect attributes in both on-site and off-site
influence areas. However, with the current trends of resource utilization, the values of the
resources are depreciating at an increasing rate due to the inevitable population increases,
unplanned land use conversions and development activities. As a recommendation based
on the economic analysis (natural resource accounting and valuation), the rate of deforestation
should be arrested; water resource management should be enhanced; and the dwindling
agricultural resource base should be protected, in the face of climate change challenges. All
these measures are embodied in the UMRBPL’s management plans but a cohesive
implementation scheme is needed that is backed by sufficient financial support and strong
political will.
3.0 UMRBPL Climate change scenarios (2020-2050) and potential impacts
3.1 Climate change projections and potential impacts: worst case future
scenario
The Project adopted the three climate scenarios developed by the Intergovernmental Panel
on Climate Change (IPCC) in its Special Report on Emission Scenarios (IPCC SRES) namely,
A2 (high-range), A1B (mid-range), and B2 (low-range). The A2 scenario is at the so-called
higher end of the emission scenarios (although not the highest), and is preferred by most
countries because, from an impacts and adaptation point of view, if man can adapt to a larger
climate change, then the smaller climate changes of the lower end scenarios can also be
adapted. The B2 scenario representing the low-range emissions is, therefore, the most
unlikely, even if it represents the low end. It also used the Philippine Atmospheric Geophysical
and Astronomical Services Administration (PAGASA) future climate change projections for
2020 and 2050.3
3
This model was developed by the UK Met Hadley Centre (in the United Kingdom) using the PRECIS (Providing
Regional Climates for Impact Studies) model in two time frames; 2020 and 2050.

SEARCA-ERGONS 20
Based on these climate projections and scenarios, the Project used the A1B (mid-range)
scenario because the future climates in the next 30-40 years will be greatly influenced by past
emissions, principally due to the long lifetimes of carbon dioxide (CO2). The project only
focused on the 2050 (2036-2065) impacts since the country should prepare for the worst case
scenario and planning for CCA and DRR strategies on a long term basis.
All areas of the UMRBPL will get warmer, more so in the relatively warmer summer
months (Table 6);
Annual mean temperatures (average of maximum and minimum temperatures) in all
areas in the UMRBPL are expected to rise by 1.9 °C in 2050.
Likewise, all seasonal mean temperatures will also increase and these increases
during the four seasons (e.g., DJF, MAM, JJA and SON) are quite consistent.
In terms of seasonal rainfall change, generally, there is a substantial spatial difference
in the projected changes in rainfall in 2050 in the UMRBPL, with reduction in rainfall
during the summer season (MAM) making the usually dry season drier, while rainfall
increases are likely in during the southwest monsoon (JJA) and the SON seasons,
making these seasons still wetter, and thus with likelihood of both droughts and floods
in areas where these are projected;
The northeast monsoon (DJF) season rainfall is projected to increase;
During the southwest monsoon season (JJA), a larger increase in rainfall is expected,
thus the repeat of Typhoon Ondoy in the future is a reality.
What the projections clearly indicate are the likely increase in the performance of the
southwest and the northeast monsoons in the UMRBPL exposed to these climate controls
when they prevail over the area. Moreover, the usually wet seasons become wetter with the
usually dry seasons becoming also drier. These could lead to more occurrences of floods and
dry spells/droughts, respectively.
Table 6. Temperature changes under mid-range in 2050.
TEMPERATURE
2020 2050
DJF MAM JJA SON DJF MAM JJA SON
Baseline 21.1 22.9 23.5 22.9 21.1 22.9 23.5 22.9
Minimum 0.9 1.1 1.1 1 1.9 2.1 2.1 1.9
Maximum 1.1 1.3 0.8 1.1 2.1 2.4 1.6 2.1
Average 0.3 0.3 0.2 0.3 2.0 2.3 1.9 2.0
Biophysical Impacts
The biophysical impacts of climate change was calculated by integrating other thematic maps
in the map overlay in a Geographic Information System (GIS) environment such as the
projected impacts of climate change by taking the 2050 scenario. The rainfall accumulated
during typhoon Ondoy (1,000 mm) in 2009 which devastated Marikina City and affected almost
90% of Metro Manila was used for the extreme events on rainfall.

SEARCA-ERGONS 21
Inside UMRBPL
Results of the integrated geospatial analysis showed that the greatest climate-induced threat
to UMRBPL area would come from landslides. Approximately 73% (19,088.03 ha) of the
UMRBPL is susceptible to landslide (see Figure 11). Of the four sub-watersheds comprising
the UMRBPL, the sub watershed of Tayabasan has the largest landslide threat area of
7,295.83 hectares, followed by Boso-Boso with 5,404 hectares.
The second largest climate-induced threat in UMRBPL is erosion and drought. A total of 5,050
hectares or 19.33% are threatened. Almost three-fourths (3/4) of these are seen in the
Montalban covering 3,728 hectares. Only 1.92% of the total area is susceptible to flooding
mainly in the areas of the Boso-Boso sub-watershed.
At the municipality and barangay levels, the greatest threat to landslides among the UMRBPL
communities is in Brgy. Calawis in Antipolo City with 5,768.74 hectares or 30% of the total
19,000 hectares of threatened areas in the whole UMRBPL (see Table 7). In the case of
erosion and drought, Barangays Rodriquez – Mascap (1,716) and San Rafael (1, 470) has
more than 60% of all threatened areas in UMRBPL.
Outside UMRBPL
Similarly, landslides largely threaten the outlying areas of the UMRBPL. Of the more than
39,000 hectares outside the UMRBPL area, around 56% or 22,355 hectares are susceptible
to landslide (See Table 8). The six barangays of Antipolo City has the largest percentage
among UMRBPL LGUs with more than 13,000 hectares with Brgys. Calawis (4,894 hectares.),
Cupang (3,195 hectares) and San Jose (2,234 hectares) as the most heavily threatened.
In the case of erosion and drought, some 7,216 hectares are threatened with the largest
located in Brgy. Mascap, Rodriguez with a total of 2,415 hectares under threat. Landslides
and drought, on the other hand, affects some 6,691 hectares of outlying areas of the UMRBPL.
Both Brgy. Calawis in Antipolo City and Brgy. Mascap in Rodriguez are the most susceptible
to these combined threats with more 5,700 hectares directly threatened.

ADB TA-8111 PHI: Climate Resilience and Green Growth in the Upper Marikina River Basin Protected Landscape: Demonstrating the Eco-town Framework (46225-001) of the Climate Change
Commission (CCC)
SEARCA-ERGONS 22
Figure 11. Multi-hazards map
Source: ADB-Ecotown Project, 2013.

Commission (CCC)
SEARCA-ERGONS 23
Table 7. Potential climate change-related impacts in the Municipality and Barangay level INSIDE the UMRBPL.
No
Sub
Watershed
City/Municipality Barangay Landslide
Landslide
+ Flooding
Erosion
Erosion +
Drought
Erosion +
Flooding
+ Drought
Flooding Total
1 Boso-Boso Antipolo City Bagong Nayon 73.11 73.11
2 Boso-Boso Antipolo City Calawis 1,833.09 426.53 120.59 856.48 133.27 3,369.97
3 Boso-Boso Antipolo City Cupang 102.47 102.47
4 Boso-Boso Antipolo City Inarawan 1,946.41 36.83 1,983.24
5 Boso-Boso Antipolo City San Jose 102.34 35.42 2.71 5.86 146.32
6 Boso-Boso Tanay Cuyambay 1,119.36 236.30 206.62 282.32 54.04 361.74 2,260.37
7 Boso-Boso Tanay San Isidro 227.95 14.69 242.64
Sub Total Boso-Boso 5,404.73 698.24 327.21 1,193.03 54.04 500.87 8,178.12
8 Montalban Antipolo City San Juan 604.06 604.06
9 Montalban Rodriguez Burgos 518.91 518.91
10 Montalban Rodriguez Mascap 119.87 116.39 1,716.68 198.25 2,151.20
11 Montalban Rodriguez Puray 355.20 541.32 896.52
12 Montalban Rodriguez San Rafael 1,277.00 62.86 1,470.05 9.36 2,819.27
Sub Total Montalban 2,875.04 179.25 - 3,728.05 207.61 - 6,989.96
13 Tayabasan Antipolo City Calawis 3,811.32 1.65 3,812.98
14 Tayabasan Antipolo City Inarawan 553.41 553.41
15 Tayabasan Antipolo City San Juan 2,094.94 2,094.94
16 Tayabasan Rodriguez San Rafael 836.16 127.61 963.77
Sub Total Tayabasan 7,295.83 - - 129.27 - - 7,425.09
17 Wawa Antipolo City Bagong Nayon 1,372.41 5.32 1,377.73
18 Wawa Antipolo City Inarawan 376.36 376.36
19 Wawa Antipolo City San Juan 333.43 333.43
20 Wawa Rodriguez Geronimo 237.10 237.10
21 Wawa Rodriguez Burgos 586.84 586.84
22 Wawa Rodriguez Puray 8.98 8.98
23 Wawa Rodriguez Rosario 164.77 164.77
24 Wawa San Mateo Pintong Bocawe 432.54 13.59 446.14
Sub Total Wawa 3,512.42 18.91 - - - - 3,531.33
Grand Total UMRBPL 19,088.02 896.40 327.21 5,050.34 261.65 500.87 26,124.50
Source: ADB-Ecotown Project, 2013

Commission (CCC)
SEARCA-ERGONS 24
Table 8. Potential climate change-related impacts in the Municipality and Barangay level OUTSIDE the UMRBPL.
No
City/
Municipality
Barangay Landslide
Landslide +
Drought
Landslide +
Flooding
Erosion
Erosion +
Flooding
Erosion +
Drought
Erosion +
Flooding +
Drought
Flooding Total
1 Antipolo City Bagong Nayon 60.04 60.04
2 Antipolo City Calawis 4,894.90 3,197.27 72.84 337.04 108.43 8,610.48
3 Antipolo City Cupang 3,195.20 720.81 722.32 78.20 4,716.53
4 Antipolo City Inarawan 234.05 54.11 38.31 118.32 444.79
5 Antipolo City San Jose 2,234.98 159.30 757.79 415.94 3,568.00
6 Antipolo City San Juan 0.44 0.44
7 Baras Pinugay 58.76 268.98 56.43 37.29 421.46
8 Rodriguez Burgos 0.64 0.64
9 Rodriguez Geronimo 1,674.36 81.29 1,755.65
10 Rodriguez Mascap 2,518.04 2,415.78 4,933.81
11 Rodriguez Puray 608.17 20.59 427.05 1,055.80
12 Rodriguez Rosario 1,025.94 30.39 193.52 21.93 963.30 317.23 2,552.30
13 Rodriguez San Isidro 1,323.81 407.12 11.46 25.29 6.35 1,774.02
14 Rodriguez San Rafael 1,710.49 976.44 18.33 265.46 6.12 2,976.83
15 San Mateo
Pintong
Bocawe 1,460.62 10.61 285.07 1,756.30
16 Tanay Cuyambay 1,933.54 241.36 709.77 154.91 0.41 3,039.98
17 Tanay San Isidro 1,998.79 1,998.79
Total 22,355.97 6,691.74 1,366.49 702.51 33.39 7,216.15 541.04 758.58 39,665.87
Source: ADB-Ecotown Project, 2013

SEARCA-ERGONS 25
3.2 Vulnerability Assessment in the UMRBPL
In identifying climate change vulnerability we adapted the concept contained in the third IPCC
Assessment Report where vulnerability is defined as: “the degree to which a system is
susceptible to, or unable to cope with the adverse effects of climate change, including climate
variability and extremes. Vulnerability can thus be defined as a function of exposure (E),
sensitivity (S), and adaptive capacity (AC), express in the equation below:
V = ∑ (E+S +AC)
Typically, attempts are made to quantify each of these components, usually by identifying
appropriate indicators for each, and then combining them into indices, and subsequently
combining the components into an integrated index of vulnerability. Some of the indicators
(primarily of exposure and sensitivity) are from the biophysical realm; others (mainly
describing adaptive capacity) are drawn from socio-economic statistical sources
The vulnerability profile of the LGUs in UMRBPL is defined using a number of selected
indicators to represent the levels of exposure, sensitivity and adaptive capacity. These
indicators have been selected based on the availability of data and the analyst’s knowledge
and understanding on the connection between the data and level of exposure, sensitivity and
adaptive capacity and their connection with climate variability and climate change.
The vulnerability profile of the LGUs in the UMRBPL can be grouped into five types as shown
in Table 9. The two extreme types are the ones have low adaptive capacity and high sensitivity
and exposure level will be the most vulnerable (Type 5), while the ones with high capacity
index with low sensitivity and exposure index will be least vulnerable (Type 1). Another
approach for defining the vulnerability profile of villages is by applying cluster analysis to the
indicators and indices used for defining level of exposure, sensitivity, and adaptive capacity
(e.g. Lüdeke, et al. 2007;; O’brienet al. 2004).
Table 9. Categorization of LGUs in the UMRBPL in terms of vulnerability.
LGU Type
Vulnerability
Level
Sensitivity
Index
Exposure
Index
Adaptive Capacity
Index (ACI)
Type 5 Very vulnerable High High Low
Type 4 Vulnerable Low Low Low
Type 3 Medium Medium Medium Medium
Type 2 Quite Vulnerable High High High
Type 1 Less or not Vulnerable Low Low High
Source: Vulnerability and Climate Risk Assessment of Villages at the Citarum River Basin, Integrated Climate
Change Mitigation and Adaptation Strategy for the Citarum River Basin (Package E) by R.Boer, A.Rakhman, and
J. Pulhin (2013).

SEARCA-ERGONS 26
Projected VA results of CC impacts on UMRBPL’s forestry sector
One of the effects of climate change is the worsening heat stress or drought, which is defined
as a period of dry weather that can be extensive in time and injurious to plants. A measure of
drought and its various effects to the water sector is through its sensitivity/exposure and
adaptive capacity.
Drought: The sensitivity/exposure (SE) analysis indicates that two LGUs (Antipolo City and
Tanay) are categorized under Low SE while Baras, San Mateo and Rodriguez are under
Medium SE. Only Antipolo City has a high adaptive capacity and the rest are under medium
category.
Rain-induced landslides/erosion: The sensitivity/exposure (SE) analysis indicates that two
LGUs (Antipolo City and San Mateo) are categorized under Low SE while Baras and
Rodriguez are under Medium SE. The adaptive capacity of Antipolo City and Tanay are high
while Baras, San Mateo and Rodriguez are of medium category.
The projected 2030 VA indicates that Antipolo City and San Mateo showed a consistent Type
1 category (Less or Not Vulnerable). Tanay is a Type 2 LGU (Quite Vulnerable) while Baras
and Rodriguez are under Type 3 (Medium Vulnerable) (Table 10). Illustrated in Figures 12,
13, 14, and 15 are maps of vulnerability assessment in the forestry sectors.
Table 10. Projected vulnerability types in terms of landslides/erosion and droughts,
forestry sector LGUs, UMRBPL, 2030.
CC Impact
(Water
Sector)
Indicators/LGU Type
Local Government Unit (LGU)
Antipolo Baras Rodriguez San Mateo Tanay
Landslide
erosion
Sensitivity/ Exposure Low Medium Medium Low Low
Adaptive Capacity High Medium Medium Medium High
LGU Type* Type 1 Type 3 Type 3 Type 2 Type 1
Droughts
Sensitivity/ Exposure Low Medium Medium Medium Low
Adaptive Capacity High Medium Medium Medium Medium
LGU Type* Type 1 Type 3 Type 3 Type 1 Type 2
* Type 3 Medium Vulnerable, Type 2 Quite Vulnerable Type 1 Less or not Vulnerable

SEARCA-ERGONS 27
Figure 12. Sensitivity level of LGUs in connection with CC impacts in the forestry
sector, UMRBPL, 2014
Figure 13. Exposure level of LGUs in connection with CC impacts in the forestry
sector, UMRBPL, 2014.

SEARCA-ERGONS 28
Figure 14. Adaptive capacity level of LGUs in connection with CC impacts in the
forestry sector, UMRBPL, 2014.
Figure 15. Vulnerability level of LGUs in connection with CC impacts in the forestry

SEARCA-ERGONS 29
Projected VA of UMRBPL’s forestry sector, 2030
Maintaining a healthy and stable forest ecosystem is a necessity in the face of changing
climates. Forest ecosystems provide essential services for communities: food and water;
regulating functions on climate, floods and diseases; cultural and recreational benefits;
support functions such as nutrient cycling, water cycling, soil formation and retention, and
others. Climate change, coupled with destructive human activities, exerts tremendous
pressure on forest ecosystems’ resiliency to capacity to resist and adapt. UMRBPL’s objective
on the forestry sector is to enhance its resilience and stability by including the communities
that depend on the forestry sector. The focus would be on forest ecosystem protection and
rehabilitation of ecological services with the assumptions that the following activities are done:
CC mitigation and adaptation strategies developed and implemented;
Management and conservation of UMRBPL improved;
Environmental laws strictly implemented;
Capacity for integrated ecosystem-based management approach enhanced;
Natural resource accounting institutionalized;
Relevant environmental laws and regulations strictly implemented;
Support system established (financing, training and capacity building; and legal &
institutional concerns), and
Mapping is done for hazard, risk and vulnerability.
Several activities would lead to the attainment of this long-term outcome, including the
following:
Gendered ecosystem vulnerability and risk assessment conducted
Mitigation and adaptation strategies planned and implemented
The National REDD Plus Strategy (NRPS) implemented
Gender-fair innovative financing mechanisms and a bundle of CCA assistance
designed
Moratorium on pollutive and extractive industries in UMRBPL implemented
Increase knowledge and capacity for integrated ecosystem-based management
at the community levels
Training programs on wealth accounting or ENRA for key government agencies
implemented
Projected VA results of CC impacts on UMRBPL’s Agriculture Sector
UMRBPL’s agricultural sector vulnerability assessment identifies the most vulnerable
agricultural areas, how these areas are affected, the determination of adaptation strategies
and interventions that enhance their resilience. The effects of climate change on the
agricultural sector are the occurrence of floods due to intense rainfall, droughts (due to
worsening heat stress), and rain-induced landslides. Floods directly affect crops and livestock
production by physically submerging or uprooting the crops and for livestock, destroying the
facilities and causing diseases. Indirectly, floods would exacerbate the incidence of plants and
livestock pests and diseases that decrease productivity. Droughts are periods of prolonged
dry weather that adversely affect the water supply that can be detrimental to crops and
livestock production. Droughts cause reduced soil moisture that may lead to wilting of crops,
thus, decreasing productivity. Rain-induced landslides clog the waterways, cause damages
to infrastructure and facilities that affect the production and marketing of agricultural products.

SEARCA-ERGONS 30
URMBPL’s agriculture sector, as shown by the vulnerability assessment, reacts differently to
droughts, floods and rain-induced landslides. Based on the analysis at the municipal (LGU)
level, the agricultural sector of some LGUs are more resilient than those of the others. For
example, Antipolo City’s agricultural sector, despite its diminished role in the City’s economy,
can withstand droughts, floods and rain-induced landslides. This is a result of the many
interacting factors, both biophysical and socioeconomic support systems.
Generally, agriculture plays a minor role in the overall economy of UMRBPL. The sector lags
far behind the other sectors (commercial, service, transport, tourism) in terms of economic
contribution such as income and employment.
Although all agricultural areas are considered at low risk to flood hazards, droughts, and rain-
induced landslides, preventive measures should be undertaken to minimize the adverse
effects of these climate-change related occurrences to agricultural production and marketing.
The usual measures such as slope stabilization, provision of early warning device,
reforestation, putting up of demarcation lines/buffer zones, and information dissemination
campaign, should be strengthened in the protected landscape.
Specifically, San Mateo will be severely affected in a rare event of flooding and needs
immediate prioritization in terms of relevant and effective mitigation such as flood control
structures and drainage control, declogging of rivers and creeks, and the provision of
relocation sites of informal settlers that built their houses in riverbanks and flood-prone areas.
All municipalities will also be affected by rain induced landslide in all level of susceptibility.
Interventions should be slope stabilization, reforestation, and relocation of residents along the
hazard prone area, provision of housing facilities and strict implementation of the zoning
ordinance.
Climate change will likely affect food security in terms of water resource allocation, particularly
that of irrigation which can be regarded as an adaptation measure for prolonged droughts.
Over the years, the National Irrigation Administration (NIA) is mandated to address the
irrigation needs of the country and in so doing, NIA has been contributing unconsciously to
alleviating the adverse climate change impacts by storing and distributing water for irrigation.
Also, the LGUs with the UMRBPL have small scale irrigation programs to help the farmers
address insufficient water supply during periods of droughts. Working with the stakeholders,
the LGU and NIA can alleviate the impacts of the lack of water for agricultural production but
also contributing to food security’s intermediate outcome of ensured food availability, stability,
access, and safety amidst increasing climate change and disaster risks. Illustrated in Figures
16, 17, 18, and 19 are the output map of the VA assessment for agriculture.

SEARCA-ERGONS 31
Figure 16. Adaptive capacity level of LGUs in connection with CC impacts in the
agriculture sector, UMRBPL, 2014.
Figure 17. Exposure level of LGUs in connection with CC impacts in the agriculture

SEARCA-ERGONS 32
Figure 18. Sensitivity level of LGUs in connection with CC impacts in the agriculture
Figure 19. Vulnerability level of LGUs in connection with CC impacts in the agriculture

SEARCA-ERGONS 33
Projected VA of the agriculture sector by 2030
As a highly vulnerable area to climate change risks and natural hazards, UMRBPL’s LGUs
must address climate change risks to agriculture for long-term benefits of their stakeholders.
Actions towards building a food secure community amidst climate change must address
poverty and sustainable livelihood, human and institutional capacities, and advancement in
scientific knowledge on climate change risks and adaptation technologies in the food
production and distribution sector. The agricultural sector in UMRBPL shall focus on two
outcomes: enhanced resilience of agriculture production and distribution systems and
enhanced resilience of agricultural communities in the midst of climate change. The scenario
in 2030 assumes that having gone into the different stages of transformation of communities,
LGUs have finally acquired knowledge and capacity for self-protection against climate change
and have created political will for adopting climate-sensitive policy, plans and programs to suit
changing climate and environment. To achieve these planned outcomes by 2030, the LGUs
of UMRBPL are further assumed to have done the following interventions:
Enhancement of site-specific knowledge on the vulnerability of agriculture;
Establishment of gender-responsive, climate-smart policies, plans and budgets;
Building of adaptive capacity of farming communities taking into account the
differentiated impacts of climate change on women and men; and
Strengthening the resilience of men and women in agricultural communities
through the development of appropriate climate risk transfer and social protection
mechanisms.
It is further assumed that future activities shall be focused on updating scientific information
and database, reviewing the sector plans, scaling up the implementation of adaptation
measures and technologies, and evaluating progress towards resilience to climate change.
Recently, the country’s super- typhoons are increasing and have shown some properties such
as excessive wind speeds, expanding coverage and prolonged rainfall events. The climate
related events affecting local food security efforts are related to climate change permutation
where “there is too little water or there is too much water” and that while there are “losers” in
each extreme climate events, there are likewise “gainers.”
Projected VA results of CC impacts on UMRBPL’s Water Sector
The vulnerability of UMRBPL’s water sector, in connection with climate change, has been
assessed in terms of two indicators, namely, rain-induced erosion/landslides and droughts.
Rain-induced soil erosion/landslides occur in several areas of UMRBPL that can be attributed
to its rugged, mountainous terrain coupled with human activities that alter the ecosystems.
The impacts of rain-induced soil erosion/landslides affect the biophysical regime of the water
bodies of UMRBPL as well the socioeconomic conditions of both on-site and off-site
communities.
The sensitivity analysis indicates that two LGUs (Baras and San Mateo) are categorized under
Low Sensitivity while Antipolo City, Rodriguez and Tanay are under Medium Sensitivity. In
terms of exposure, Antipolo city, Rodriguez and Tanay are classified under the Medium
category while Baras and San Mateo are under Low category. The adaptive capacity of
Antipolo City and San Mateo are high while Baras, Rodriguez and Tanay are of medium
category. Illustrated in Figures 20, 21, 22, and 23 are the results of the VA analysis.

SEARCA-ERGONS 34
Figure 20. Exposure level of LGUs in connection with CC impacts on the water sector,
UMRBPL, 2014.
Figure 21. Sensitivity level of LGUs in connection with CC impacts on the water sector,
UMRBPL, 2014.

SEARCA-ERGONS 35
Figure 22. Adaptive capacity level of LGUs in connection with CC impacts on the water
Figure 23. Vulnerability level of LGUs in connection with CC impacts on the water

SEARCA-ERGONS 36
Projected vulnerability assessment of the water sector by 2030
UMRBPL’s priority on the water sector is to ensure that water resources be sustainably
managed and equitably accessed. In light of climate change, a comprehensive review and
subsequent restructuring of the entire water sector governance is required. It is also important
to assess the resilience of the area’s major water resources and infrastructure, to manage
supply and demand, to manage water quality, and to promote conservation. For the 2030
projections, the following interventions are assumed to have been done by the LGUs of
UMRBPL:
Restructuring water governance to be better responsive to climate change, such as ,
review of the Water Code and other water resources laws and regulations, streamlining
and structuring government institutions responsible for water and building the capacity
of relevant agencies;
Conducted vulnerability and risk assessment of water resources, infrastructure and
communities, including analysis of the differences in vulnerabilities of men and women
to the impacts of climate change;
Formulation of a roadmap for climate-proofing critical water infrastructure based on the
results of the vulnerability and risk assessments;
Rehabilitation of water distribution systems;
Completion of the characterization of the UMRBPL subwatersheds;
Conducted water supply and demand analysis under various hydrologic conditions and
scenarios;
Reviewed and modified the processes and supply/demand management of existing
and new water supply systems;
Establishment of flood plain zones and development of flood plain management and
hazard reduction operating plans as a modular or incremental adaptation measure
(through the coordinative efforts of DENR, DA, DOE and DPWH);
Updated water resources database and monitoring systems; and
Developed gendered knowledge products and materials and disseminate using media,
outreach and other means to target stakeholders.
The actions for achieving this long-term objective should focus on harmonization of policies,
programs and implementation plans along and consistent with the convergence creation for
the awareness, knowledge, and capacity building and governance of water-related institutions
and other stakeholders. Since water resources are not adequately documented and locally
quantified and that water has multiple roles to economic and social development, well-focused
and targeted research-based and action agenda for policy and programs are important
elements for sustainability and access to safe and sufficient water supply.

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