District Selection Report - MCA Indonesia · District Selection Report Task 2 District ... RIMBA...

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District Selection Report Task 2 District Screening and Prioritization Report (consolidated) Support Services for Land Use Planning, District Readiness, Strategic Environmental Assessment, and Related Preparatory Activities in Indonesia for the Green Prosperity Project in Indonesia Contract # GS10F0086K Draft for MCC Review and Comment June 3, 2013 Prepared for: Millennium Challenge Corporation 875 15 th St., NW Washington, D.C. 20005 Submitted by: Abt Associates Inc. 4550 Montgomery Avenue Suite 800 North Bethesda, MD 20814

Transcript of District Selection Report - MCA Indonesia · District Selection Report Task 2 District ... RIMBA...

District Selection Report

Task 2 District Screening and

Prioritization Report

(consolidated)

Support Services for Land Use

Planning, District Readiness,

Strategic Environmental

Assessment, and Related

Preparatory Activities in

Indonesia for the Green Prosperity

Project in Indonesia

Contract #

GS10F0086K

Draft for MCC Review and

Comment

June 3, 2013

Prepared for: Millennium Challenge

Corporation 875 15th St., NW

Washington, D.C. 20005

Submitted by: Abt Associates

Inc. 4550 Montgomery

Avenue Suite 800 North

Bethesda, MD 20814

District Selection Report

In Partnership with:

ICRAF, Indonesia URDI, Indonesia

T

District Selection Report

Abt Associates Inc.

Table of Contents

Table of Contents ............................................................................................................................ 1

List of Exhibits ................................................................................................................................ 3

List of Abbreviations ....................................................................................................................... 4

1. Introduction .......................................................................................................................... 1

2. Key Indicators for District Selection Tool ............................................................................ 3

2.1 Conservation Corridors, Poverty Priority Areas and Indicators ...................................... 9

2.1.1 Buffer Zone Districts that Border High Conservation Value (HCV) Areas or National Strategic Areas of Conservation Value (NSA) ................................................. 9

2.1.2 Priority Districts under the Master Plan to Accelerate Poverty Alleviation (MP3KI) ..................................................................................................................... 15

2.2 District Ranking Indicators .......................................................................................... 18

2.2.1 Poverty Indicators .......................................................................................... 18

2.2.2 Renewable Energy Indicators: Supply Side .................................................... 18

2.2.3 Renewable Energy: Demand Factors .............................................................. 19

2.2.4 Natural Resource Management (NRM) and Carbon Capture........................... 20

2.2.5 Economic Factors .......................................................................................... 22

2.2.6 District Governance ....................................................................................... 22

3. District Ranking .................................................................................................................. 24

3.1 First-Stage Ranking Method ........................................................................................ 24

3.2 Using the Tool ............................................................................................................ 25

3.3 Results ........................................................................................................................ 28

3.3.1 Key Indicators ............................................................................................... 28

3.3.2 District Selection Scenarios............................................................................ 29

3.3.3 District Selection Process ............................................................................... 29

4. Conclusion and Next Steps .................................................................................................. 32

District Selection Report

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District Selection Report

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List of Exhibits

Exhibit 1: Green Prosperity Project Goal, Intermediate Outcomes and Indicators ............................... 4

Exhibit 2: District Selection Tool Variables by Category ................................................................... 6

Exhibit 3: RIMBA Integrated Ecosystem and Buffer Zone Potential Districts .................................. 10

Exhibit 4: Heart of Borneo Initiative and Buffer Zone Potential Districts ......................................... 12

Exhibit 5: Strategic Conservation Areas and Buffer Zone Potential Districts in Sulawesi Island ....... 13

Exhibit 6: Potential “Buffer Zone” Districts in Eastern Indonesia Islands ......................................... 14

Exhibit 7: Buffer Zone Districts (Corridor Districts) ........................................................................ 15

Exhibit 8: Priority Districts based on MP3KI ................................................................................... 17

Exhibit 9: Top 10 Districts in Each Island Based on 3 Indicatorsa, among Conservation Corridor Districts ................................................................................................................................... 29

Exhibit 10: Recommended Districts ................................................................................................ 31

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List of Abbreviations

AF Agro-Forestry

BPS Central Bureau of Statistics (Biro Pusat Statistika)

BZD Buffer Zone District

DRA District Readiness Assessment

FSC Forest Stewardship Council

GDP Gross Domestic Product

GIS Geographical Information System

GOI Government of Indonesia

GP Green Prosperity

HCV High Conservation Value

HCVA High Conservation Value Area

HLP High-Level Panel

HoB Heart of Borneo

IMIDAP Integrated Micro-Hydro Development and Application Project (UNDP)

IPP Independent Power Producer

LEG Local Economic Governance

MCA-I Millennium Challenge Account – Indonesia

MCC Millennium Challenge Corporation

MOF Ministry of Forestry

MP3EI Master Plan for the Acceleration and Expansion of Indonesian Economic Development (Master Plan Percepatan dan Perluasan Pembangunan Ekonomi Indonesia)

MP3KI Master Plan to Accelerate the Alleviation of Poverty in Indonesia (Masterplan Percepatan dan Perluasan Pengurangan Kemiskinan Indonesia)

NRM Natural Resource Management

NSA National Strategic Area

PLN State electric company (Perusahaan Listrik Negara)

PLTMH Micro-Hydro Power Plant (Pembangkit Listrik Tenaga Mikrohidro)

RE Renewable Energy

RIMBA Riau, Jambi, West Sumatra regions

SDG Sustainable Development Goal

SME Small and Medium Enterprise

TDP Business registration license (Tanda Daftar Perusahaan)

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1. Introduction

The Millennium Challenge Corporation (MCC) funded the Green Prosperity Project (GP Project) in an effort to increase the incomes of Indonesia’s poor in targeted districts. The $332.5 million project has four core activities: an investment facility, participatory land-use planning, technical assistance and oversight, and green knowledge capacity building. Together, these activities aim to achieve poverty reduction through two primary objectives:

1. increase productivity and reduce reliance on fossil fuels by expanding renewable energy; and

2. increase productivity and reduce land-based greenhouse gas emissions by improving land use practices and management of natural resources.

Given these objectives, the GP Project is focusing on provinces and districts with high potential for achieving poverty alleviation and environmental objectives. Twelve provinces were selected by MCC and the Government of Indonesia (GOI) as potential participants – Riau, Jambi, West Sumatra, South Sumatra, Bengkulu, West Sulawesi, South Sulawesi, Southeast Sulawesi, West Kalimantan, East Kalimantan, West Nusa Tenggara, and East Nusa Tenggara. These provinces were selected based on a range of social, economic, environmental, and institutional indicators, including poverty levels, renewable energy potential, economic growth potential, governance, conservation areas, forest cover, and peatlands under threat of degradation or destruction.

The process described above represents the first stage in implementing technical assistance and investments, based on broad agreement between MCC and GOI to initiate the GP Project in four districts in the provinces of Jambi and West Sulawesi – Muaro Jambi, Merangin, Mamuju and Mamasa. The four starter districts will be included in the next phase of roll-out with other locations incorporated following mutual agreement between MCC and the GOI, based on the results of a District Readiness Assessment (DRA) incorporating the criteria and indicators mentioned above, among others. The DRA consists of a transparent, well-documented assessment of candidate districts with respect to their capacity and investment opportunities to support environmentally sustainable, low-carbon economic growth consistent with GP Project objectives. This District Screening Report is the last formal written deliverable under Task 2 of the DRA, and documents the District Selection Tool, which uses key quantitative indicators similar to those used in the selection of the 12 provinces to help users identify the highest priority districts. The districts identified as a high priority will then be subject to a collaborative second-level screening effort by carefully selected members of the Abt Associates team and MCA-I colleagues, emphasizing more qualitative indicators. This second screening process will help MCA-I finalize an initial shortlist of 10-12 candidate districts for further follow-up, investment, and assistance. Abt Associates team will provide its support in this process to provide an objective assessment of the qualitative indicators and potential linkages to other donor funded program which fit GP’s vision, with the final decision of districts resting with MCA-I.

In the first-level district selection, the criteria are primarily intended to identify which districts are best suited for GP Project investments, based on their potential for productive GP investments and for carbon capture. This analysis uses a methodology for determining the selection screening and entry

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readiness criteria that incorporates two key considerations:

• That the approach taken will “permit a rapid and objective screening of potential provinces and districts based on their capacity and commitment to sustainable, low carbon development consistent with GP Project objectives.”

• That the identified criteria will best predict district readiness or economic potential based on the availability and quality of existing quantitative data, ability to replicate the analysis across a broad range of districts, level of effort required in gathering and analyzing data, and adherence to international best practice for enabling frameworks for low carbon development.1

Before applying the first- and second-level entry screening processes, it has become clear that the study needs to accommodate not only the two considerations above but also major existing policies and priorities of the GOI that have significant impact on funding for district development budgets that have been prioritized. These are described as overarching and given principles for district selection.

In the District Selection Tool, the user selects all indicators that he or she wants to consider for prioritizing districts. Each indicator is converted to a unit-free index so that the indicators are comparable. The user also can also decide to give a higher weight to one indicator than the other.2 Using the indicators selected by the users and their corresponding weight chosen by the user, a weighted average of indices is calculated to rank the districts. Alternatively, the user can select one indicator to rank the districts, repeat the process with other indicators, and select districts that have a high value based on all indicators. Another important point to keep in mind is that districts with high renewable energy potential may not necessarily have the highest potential for natural resource management projects that reclaim degraded land or increase carbon capture. Therefore, the user may consider two different rankings when identifying potential districts. For example, they may first rank the districts based on the poverty and renewable energy indicators in order to identify districts with the highest renewable energy potential, then conduct another run based on the poverty indicators and indicators for percentage of critical land (forest cover) to identify districts that have the highest potential for improving critical land (carbon capture).

The District Selection Tool will be delivered to MCA-I for utilization, along with accompany software licenses and related training from the Abt team. It can be modified and updated to incorporate additional data as the GP Project rolls out to additional locations. The scalable nature of the tool and the initial data embedded in the version delivered to MCA-I should make it an asset for the institution to use in further analysis, presentations, and future work under the Compact. In the sections that follow, chapter 2 presents the list of quantitative indicators currently included in the tool, chapter 3 presents the method used by the tool to rank districts and chapter 4 presents some basic results as an example to illustrate how results are presented in the tool.

1 Request for Quotations (RFQ), p.8

2 The weights are normalized so they add up to one.

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2. Key Indicators for District Selection Tool

As described earlier, MCC and the GOI selected 12 target provinces based on nine initial indicators, while also agreeing to the four starter districts to accelerate the GP process. The district selection tool includes data for these indicators and 14 additional indicators to help evaluate the readiness of the 137 districts located within these 12 provinces. The overarching principles applied to first-level district selection concern whether the candidate district has the following attributes:

• ‘Green or Sustainable Development’ potential under a single and coherent GP strategy that integrates economic growth, social inclusion and environmental sustainability. The Sustainable Development Goal (SDG) of the GP investment will be to reduce poverty by raising economic productivity and potential through GP investments. The overarching question here is whether the district candidate has the potential to generate local economic growth (through improvements in local economic efficiency or technology-enabled governance) while engaging in social inclusion and sustainability practices.

• Economic potential to develop new sources of energy through renewable energy (RE) projects so as to reduce dependency on fossil fuel, with commiserate demand for electrification.

• Economic potential to reduce carbon emissions through improved natural resource management (NRM) and agro-forestry (AF) projects.

• Being a “Buffer Zone” District (BZD) that is located near a National Strategic Area (NSA) having a conservation function.

These considerations are based on the GP Project’s overall goal and intermediate objectives presented in the MCC Compact with Indonesia, as well as in the Project’s Scope of WorkError! Reference source not found.. The appropriate indicators for district selection are those that help identify districts with the highest potential for achieving the overall goals GP. Accordingly, it is helpful to review the GP logical framework that sets out the ultimate goal, intermediate objectives and indicators for achieving these objectives, as illustrated in Exhibit 1 belowError! Reference source not found..3

3 Based on GP logic version dated March 27, 2013. Does not include indicators labeled as “Further details required to finalize and tailor the indicator.”

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Exhibit 1: Green Prosperity Project Goal, Intermedi ate Outcomes and Indicators

Since poverty reduction through economic growth is the ultimate objective, the district tool includes the poverty rate and the number of poor for each district. GP aims to reduce poverty in two ways.

First, GP will reduce poverty by improving access to renewable energy to enhance productivity and economic activity in target districts. The ideal districts to achieve these objectives are those that have a high potential for renewable energy and low availability of electricity. Therefore, one set of indicators relates to renewable energy potential and potential demand for renewable energy. The indicators for renewable energy potential include hydropower potential, biomass energy potential and solar potential (see Exhibit 2 for more details). As described in more detail later, the overall confidence in the quality of data for hydropower potential is higher than that for biomass energy and solar potential. To measure demand for energy and the potential to improve access to electricity, the tool includes existing electrification rates (both grid and off-grid) and the number of households without electrification. Population density and gross domestic product in agriculture are also included when measuring the potential demand for electricity from the agriculture sector and the potential for enhancing productivity in this sector.

Second, GP will reduce poverty by improving land use and natural resource management to increase productivity while reducing greenhouse gas emissions. The districts with the greatest potential for these objectives are those with a larger area set aside for forests, a larger area of critical watersheds,

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and protected high conservation land under threat. Indicators that measure areas of forest, areas of protected or conservation forest, and areas of critical watershed are included to identify districts with the highest potential for achieving this intermediate outcome.

Another factor that might affect GP effectiveness is the extent to which the GP program is aligned with other key government programs with similar objectives. Having worked closely with the MCC and MCA-I teams to review priority issues for selecting districts in the 12 provinces mentioned above, it was agreed that better alignment with these programs can help leverage funds and activities to create positive synergy with the GP investments. The tool allows the user to rank among districts considered a priority for economic development under the Master Plan to Accelerate the Alleviation of Poverty (MP3KI) or among corridor districts located inside 'critical' or significant important natural landscapes using a “corridor” approach, such as a) the 'RIMBA Corridor' in Sumatra; b) the 'critical' corridor connecting the highland area of Mt Kerinci Seblat to the lowland peatland swamp forest of Berbak; c) the 'Heart of Borneo’ (HoB) region in Kalimantan; d) the highland area of central Kalimantan where pristine forests and watersheds are located; e) the highland area of central Sulawesi (cutting through three provinces in Sulawesi – West, South and Southeastern Sulawesi); and f) the areas where the dry savannah lands in the islands of Nusa Tenggara meet rich marine biodiversity on the coast.

Another factor that can affect the success of the program is the quality of local governance. Three indicators that measure local governance – including the quality of local infrastructure, land tenure and security, and transactions costs – are also included in the tool. These and other indicators are described in more detail below. All first stage indicators – which are all quantitative – are also included in the district tool. A full list of the indicators included in the tool can be found in Exhibit 2.

In addition to these indicators, based on the meeting with MCA-I on May 27th, and May 31st, 2013 and meeting with MCC on June 3, 2012 the following changes will be made to the indicator list in the subsequent version of the tool:

• Review additional data sources for the forest cover, particularly review data from CIFOR, and Forest Alliance, Indonesian REDD+ Taskforce.

• Add information on deforestation rates using the best source for forest cover data.

• Add solar radiation index data as a measure of solar potential.

• Add data on total area and yield under cocoa, coffee, rubber and palm as measure of potential to increase productivity of land in these uses.

• Add gender development index.

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Exhibit 2: District Selection Tool Variables by Cat egory

Indicator Name Indicator Details Data Source

Categorical Variables

Conservation Strategic Corridor

Categorical indicator that allows users to

identify districts that are near high

conservation areas

Presidential Decree

Nos. 3/2012 and

13/2012, plus

Conservation Area

(Kawasan Konservasi)

and Protected Forest

(Hutan Lindung) maps

Prioritized MP3KI Districts

Districts Prioritized by Masterplan for the

Acceleration and Widening of Poverty

Alleviation (MP3KI)

SMERU

No. Indicator

1. Poverty Rate Percentage of households below the

poverty line

Central Bureau of

Statistics (BPS), 2011

2. Number of Poor Households Total number of poor households in the

district BPS, 2011

Renewable Energy : Supply Factors

3. Hydropower Potential Estimated Capacity of Micro-Mini

Hydropower (Kwh)

Integrated Micro-Hydro

Development and

Application Project

(IMIDAP – UNDP

Project, 2010) and Studi

Kelayakan Pembangkit

Listrik Tenaga

Mikrohidro (PLTMH)

4. Biomass Potential Production of Palm Oil (tons)

Palm Oil Production of

Estates by Type and

Regency/City –

Provinces in Figures

(Propinsi Dalam Angka,

2011)

5. Solar Potential Existing solar capacity of PLN as predictor

of future solar capacity (peak MW)

Thousand Islands PV

Development Program,

"Solar PV Based IPP."

Solar Energy

Conference. Bandung,

Indonesia. December

12, 2012

Renewable Energy: Demand Factors

6. Lack of Electrification (on grid)

Households Lacking Connection to PLN-

Electric Company (number of households

and percentage of total households)

PODES 2011, BPS, 2010

data

7. Lack of Electrification (on grid

or off-grid)

Households Lacking Connection to PLN-

Electric Company or off-grid (number of

households and percentage of total

households)

PODES 2011, BPS, 2010

data

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Indicator Name Indicator Details Data Source

8. Population Density Population (number per square

kilometer) BPS, 2010 Census

9. GDP Per Capita Gross domestic product (million rupiah)

Regional GDP data

(Tinjauan Berdasarkan

PRDB Kabupaten, Buku

1-5 – BPS, 2010)

Natural Resource Management (NRM) & Carbon Capture Indicators

10. Somewhat Critical Land Somewhat critical watershed area

(hectares)

Ministry of Forestry

(MOF) Directorate for

Planning and Evaluation

of Watershed Areas

(KemHut, Direktorat

Perencanaan dan

Evaluasi Penglolaan

DAS, DJ-BP-DAS &

PerhutNas, 2011)

11. Critical Land Critical area (hectares)

12. Very Critical Land Very critical watershed area (hectares)

13. Land in above three Critical

States

Very critical, critical, or somewhat critical

watershed area (hectares)

14. Potentially Critical Land

Potentially critical watershed area

15. Forest Cover

Forest area under legal status of

"Protected Forest", "Production Forest",

"Limited Production Forest", "Production

Conversion Forest", and "Conservation

Forest" (hectares)

MOF (KemHut/Badan

Planologi – Tutupan

Lahan/Tata Guna Lahan

Per Kabupaten, 2011)

16. Forest Cover Percentage

Forest area as percentage of district area

under "Protected Forest", "Production

Forest", "Limited Production Forest",

"Production Conversion Forest", and

"Conservation Forest" (percentage)

17. Forest Cover – Conservation or

Protected

Forest area under legal status of

"Protected Forest" or "Conservation

Forest" (hectares)

18. Forest Cover – Conservation or

Protected (Percentage)

Forest area as percentage of district area

under legal status of "Protected Forest"

or "Conservation Forest" (percentage)

19. Forest Cover by Actual Use

(Forest Use)

Forest area by use under " Primary Dry

forest ", " Secondary Dry forest ", "

Primary Mangrove ", " Primary Swamp

forest ", "Plantation Forest", " Secondary

Mangrove ", and " Secondary Swamp

forest " (hectares) MOF (KemHut/Badan

Planologi – Tutupan

Lahan/Tata Guna Lahan

Per Kabupaten, 2011)

20. Forest Use Percentage

Forest area by use under "Primary Dry

forest ", " Secondary Dry forest ", "

Primary Mangrove ", " Primary Swamp

forest ", "Plantation Forest", " Secondary

Mangrove ", and " Secondary Swamp

forest ", as percentage of total district

area (percentage)

Economic Factors

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Indicator Name Indicator Details Data Source

Acreage

Total land areas under cocoa, coffee,

rubber, and palm plantation –including

productive and unproductive acreage

(hectares). Acreage under palm includes

acreage under large, small estates and

with smallholders.

BPS (2012) Statistics

Indonesia for Bengkulu,

Jambi, Kalimantan

Barat, Kalimantan

Timur, Nusa Tenggara

Barat, Nusa Tenggara

Timur, Riau, Sulawesi

Barat, Sulawesi Selatan,

Sulawesi Tenggara,

Sumatera Selatan, and

Sumatera Barat

Average yield Yields in productive cocoa, coffee, rubber,

and palm acreage. (Tons/hectare)

District Governance Indicators4

21. Land Access Land Access and Security of Tenure (Sub-

Index of LEG Index)

Local Economic

Governance in

Indonesia (2007) for

Riau, South Sumatra,

East Kalimantan, South

Sulawesi, and Local

Economic Governance

in Indonesia (2011) for

West Sumatra, Jambi,

Bengkulu, West Nusa

Tenggara (NTB), East

Nusa Tenggara (NTT),

West Kalimantan,

Southeast Sulawesi,

West Sulawesi

22. Local Infrastructure Local Infrastructure (Sub-Index of LEG

Index)

23. Transaction Costs

Transaction Costs (Sub-Index of LEG

Index)

In the second stage, a qualitative assessment can be conducted on the following aspects:

• potential for investment and private-public partnerships,

• district commitment to low-carbon economic growth and sustainable development policies,

• development of spatial plans,

• government transparency,

4 Some districts were not found in 2011 or 2007 for the following reasons: Mentawai Islands were excluded from 2011 because of a natural disaster; Meranti Islands are missing for unknown reasons; Empat Lawang is missing for unknown reasons; Central Sumba was excluded from 2011 because it could not be analyzed; Sabu Raijua was excluded from 2011 because it could not be analyzed; Tana Tidung is missing for unknown reasons; North Toraja is missing for unknown reasons. Ogan Komering Ulu was duplicated twice in the 2007 dataset to provide a value for South Ogan Komering Ulu and East Ogan Komering Ulu.

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• potential land use and land tenure conflicts,

• district institutional and regulatory frameworks, and

• social and gender analysis.

Since qualitative assessments are likely to be more resource-intensive and will involve more engagement with the stakeholders of the candidate district, it is preferable for these assessments to be conducted on a manageable number of districts that have been prioritized from the first stage.

Detailed information on the quantitative indicators and two key categorical variables can be found in Sections 2.1 and 2.2.

2.1 Conservation Corridors, Poverty Priority Areas and Indicators

This section describes the two categorical variables that indicate whether a district falls in a prioritized group. As mentioned above, working with MCC and MCA-I, two criteria were identified as key variables for categorizing districts. The first criterion is whether the district falls within an NSA corridor, and the second is whether the district has been identified as a priority by the GOI’s poverty alleviation master plan (MP3KI). These two categories are described in more detail below, followed by a detailed description of each indicator that can be used to rank the districts. Users of the tool can sort the results by these categories.

2.1.1 Buffer Zone Districts that Border High Conser vation Value (HCV) Areas or National Strategic Areas of Conservation Value (NSA)

Buffer Zone Districts (BZDs) are defined as being contiguous to or neighboring National Strategic Areas of Conservation Value (NSAs), and generally have a broader meaning than “high-conservation value (HCV) corridors”5. This categorical variable indicates whether a district is part of a strategic cluster of districts adjacent to or surrounding either a High Conservation Value (HCV) area or an NSA, such as forests or peat land. Of the 137 districts, 68 districts were identified to be in this conservation corridor: 10 in Kalimantan, 35 in Sumatra, 13 in Sulawesi, and 10 in Nusa Tenggara. Between now and mid-June, MCA-I and the Project will work collaboratively using the District Selection Tool to select a further shortlist of 15-20 Districts, with a parallel qualitative analysis underway as well. This latter analysis will then be used to develop a final shortlist of 10-12 Districts for presentation to the MWA Board at the end of June 2013. The areas in the first two provinces were established by Presidential Decree (No. 3/2012 and No. 13/2012, respectively), while the districts in Sulawesi and Nusa Tenggara were designated as HCV priorities if they bordered or were situated within a Conservation Area (Kawasan Konservasi) or Protected Forest (Hutan Lindung).

Certain districts in Sumatra and Kalimantan have been prioritized as being part of a national strategic area through the issuance of two presidential decrees. Another presidential decree identifying priority districts in Sulawesi has also been drafted.

5 A High conservation value area (HCVA) is a natural area with environmental, socioeconomic, biodiversity or landscape value. This term is strictly used within forestry management certification systems developed by the Forest Stewardship Council (FSC). http://en.wikipedia.org/wiki/High_Conservation_Value

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In Sumatra, several ‘corridors’ have been declared priority zones. Three of these corridors concern districts located in the five GP priority provinces on that island – Riau, Jambi, South Sumatra, West Sumatra and Bengkulu. There have been several other legal precedents for their prioritization. A key outcome of the prioritization process is that priority districts receive additional central government funds and technical assistance for forest conservation, increasing the likelihood that GP investments will be successful there.6

The first such corridor is the “RIMBA” corridor, which was stipulated by Presidential Decree No. 13/2012. The RIMBA corridor passes through Riau, West Sumatra and Jambi provinces, where 19 potential districts are included in the pool of districts for the first-level district selection analysis: Dharmarya, Swahlunto, Limpuluhkoto, Pesisir Selatan, Solok, Solok Selatan, Tanahdata in Sumatera Barat province; Indragiri Hilir, Indragiri Hulu, Kampar, Kuantan Singingi in Riau province; Bungo, Kerinci, Merangin, Muaro Jambi, Sarolangun, Tanjung Jabung Barat, Tanjung Jabung Timur and Tebo in Jambi province. The map in Exhibit 3 shows the location of these districts.

Exhibit 3: RIMBA Integrated Ecosystem and Buffer Zo ne Potential Districts

Source: Agreement to Save Sumatra’s Ecosystem; http://www.wwf.or.id/?8580/Kesepakatan-Lanjutan-Untuk-

Menyelamatkan-Ekosistem-Sumatera

6 The relevant regulatory/legal documents include: 1) Presidential Decree on Spatial Planning in Sumatra (PerPres No. 13 Tahun 2012 tentang Rencana Tata Ruang Pulau Sumatera); 2) the Regional Government Law (UU No. 32 Tahun 2004 Pemerintah Daerah); 3) the Spatial Planning Law (UU No. 26 Tahun 2007 tentang Penataan Ruang); 4) the Law on Environmental Protection and Development (UU No. 32 Tahun 2009 tentang Perlindungan dan Pengelolaan Lingkungan Hidup); and 5) the ten Sumatra Governors’ Agreement and Ecosystem Salvation Road Map, Sumatra 2020 Vision (Kesepakatan 10 Gubernur Se-Sumatera tanggal 18 September 2008, yang dituangkan dalam Peta Jalan Menuju Penyelamatan Ekosistem Sumatera, Visi Sumatera 2020, which was launched on May 10, 2010).

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The Presidential Decree on Spatial Planning in Sumatra also determined several additional priority districts as part of other ‘strategic corridors’ in Bengkulu Province (North Bengkulu, South Bengkulu, Kaur, Kepahiang, Lebong, Muko-Muko, Rejanglebong, and Seluma) and South Sumatra Province (Musi Rawas, Lahat, Banyuasin, Musi Banyuasin, Ogan Komering Ilir, Ogan Komering Ulu, and South Ogan Komering Ulu).

Presidential Decree No. 3 of 2012 on Kalimantan Island, which focuses on the Heart of Borneo (HoB) conservation zone, is intended to ensure that 45 percent of Kalimantan will be preserved as “the lungs of the world”. This decree was issued in response to concern over the current high rate of deforestation in Kalimantan. The territories of three countries are covered by HoB – Indonesia, Malaysia, and Brunei Darussalam (see Exhibit 4). A basis has been established for further cooperation among these countries, known as the HoB Initiative.

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The HoB initiative has led to the selection of 10 districts for further analysis in East Kalimantan Province (West Kutai, East Kutai, Kutai Kartanegara, Berau, Bulungan, Malinau and Nunukan) and West Kalimantan Province (Kapuas Hulu, Sintang and Melawai). Exhibit 4: Heart of Borneo Initiative and Buffer Zo ne Potential Districts

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The Spatial Plan for Sulawesi Island, Perpres (Presidential Regulation) No. 8 Year 2011, covers a number of conservation areas, one of which – Wakatobi Marine Park – is located in Southeast Sulawesi, a GP priority province.

The decree recognizes 13 potential districts on Sulawesi Island. Five of them are in South Sulawesi (Luwu, Tana Toraja, North Luwu, East Luwu and North Toraja), five are in Southeast Sulawesi (Konawe, Kolaka, Wakatobi, North Kolaka and North Konawe), and the other three are in West Sulawesi (Polewali Mandar, Mamasa, Mamuju). These 13 districts are shown in Exhibit 5.

Exhibit 5: Strategic Conservation Areas and Buffer Zone Potential Districts in Sulawesi Island

Source: MCA-I, Green Prosperity Project, April 2013

The situation in East and West Nusa Tenggara (NTT and NTB) is different. There is neither a presidential decree nor a draft decree to ensure forest conservation. In these circumstances, Buffer Zone Districts (BZDs) are being selected based on the combined size of areas classified as a Conservation Area (Kawasan Konservasi) or Protected Forest (Hutan Lindung).

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Applying the criteria discussed above, 13 potential districts will undergo further analysis in the Eastern Indonesia island chain of Nusa Tenggara – West Lombok, Central Lombok, East Lombok, Dompu and North Lombok in NTB, and West Sumba, East Sumba, Ende, Manggarai and West Manggarai in NTT. These districts are illustrated in Exhibit 6. Exhibit 6: Potential “Buffer Zone” Districts in Eas tern Indonesia Islands

Source: MCA-I, Green Prosperity Project, April 2013

As mentioned above, the review of Buffer-Zone Districts located near NSAs based on Presidential Decrees produced an initial subset of 68 candidate districts (see Exhibit 7).

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Exhibit 7: Buffer Zone Districts (Corridor District s)

Island

NSA districts based on

Presidential Decree

# of Districts District Names by Province

Kalimantan 10

East Kalimantan Province (West Kutai, East Kutai, Kutai

Kartanegara, Berau, Bulungan, Malinau and Nunukan)

and West Kalimantan Province (Kapuas Hulu, Sintang

and Melawai)

Nusa Tenggara 10

West Nusa Tenggara (NTB) (West Lombok, Central

Lombok, East Lombok, Dompu, North Lombok) and East

Nusa Tenggara (NTT) (West Sumba, East Sumba, Ende,

Manggarai, West Manggarai)

Sulawesi 13

South Sulawesi (Luwu, Tana Toraja, North Luwu, East

Luwu, North Toraja), Southeast Sulawesi (Konawe,

Kolaka, Wakatobi, North Kolaka and North Konawe) and

West Sulawesi (Polewali Mandar, Mamasa, Mamuju)

Sumatra 35

West Sumatra (South Pesisir, Solok, Sijunjung/Sawah

Lunto, Tanah Datar, Lima Puluh Kota, Solok, Selatan,

Dharmasraya), Riau (Kuantan Singingi, Indragiri Hulu,

Indragiri Hilir, Kampar), Jambi (Batang, Hari, Kerinci,

Merangin, Sarolangun, Muaro Jambi, East Tanjung

Jabung, West Tanjung Jabung, Tebo, Bungo), Bengkulu

(North Bengkulu, South Bengkulu, Kaur, Kepahiang,

Lebong, Muko-Muko, Rejanglebong, Seluma), South

Sumatra (Musi Rawas, Lahat, Banyuasin, Musi Banyuasin,

Ogan Komering Ilir, Ogan Komering Ulu and South Ogan

Komering Ulu)

2.1.2 Priority Districts under the Master Plan to A ccelerate Poverty Alleviation (MP3KI)

In its efforts to reduce poverty, in 2009 the GOI designed (MP3KI. The program aims to directly target people experiencing extreme poverty in Indonesia. As a flagship program, MP3KI also aims to provide social welfare compensation to complement and be integrated with the Master Plan for the Acceleration and Expansion of Indonesian Economic Development (MP3EI).7

7 MP3EI and MP3KI are integrated in the National Government Work Plan (RKP 2013, June 12, 2012)

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The MP3KI program has had a significant impact on the budgets of candidate districts in the 12 GP priority provinces.8 Clearly, the presence of this program could enhance the success of GP investment projects.

This categorical variable identifies districts that are a priority for poverty reduction. The MP3KI categorizes districts into four categories, the first three of which are defined by the selection tool as economic priority districts:

(1) districts with a high poverty rate and high absolute number of people living in poverty;

(2) districts with a high poverty rate and low absolute number of people living in poverty;

(3) districts with a low poverty rate but high absolute number of people living in poverty; and

(4) districts with a low poverty rate and low absolute number of people living in poverty.

The MP3KI districts are presented in Exhibit 8.

http://www.bappenas.go.id/node/116/3562/mp3ei-dan-mp3ki-terintegrasi-dalam-rkp-2013/ The MP3KI program, launched on April 2, 2013, is managed by the Directorate for Poverty Alleviation at the National Planning Agency (Bappenas).

8 Under the MP3KI program, the GOI identified and provided assistance to the poor or target households (RTS) through

School Operational Assistance (BOS), which was budgeted at 27.67 trillion rupiah in 2012. The GOI also provides direct grants to extremely poor households (RTSM), free medical treatment at clinics and hospitals, and manages the National Program for Community Empowerment (PNPM Mandiri), setting aside a budget of 9.94 trillion rupiah to support this program. Under the program, each subdistrict receives assistance of up to 3 billion rupiah. In 2012, PNPM targeted 6,680 districts and 495 regencies and cities in 33 provinces.

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Exhibit 8: Priority Districts based on MP3KI

Island Number of

Districts

District Names

Categorical Variables

Kalimantan 10

Sambas, Landak, Ketapang, Melawi, North Kayong ,

Kutai Kartanegara, East Kutai, Malinau, Bulungan,

Tana Tidung

Lesser Sunda Islands 11

Central Lombok, East Lombok, North Lombok, West

Sumba, East Sumba, South Central Timor, Manggarai,

Rote Ndao, Central Sumba, Southwest Sumba, Sabu

Raijua

Sulawesi 14

Jeneponto, Gowa, Pangkajene & Islands, Bone,

Enrekang, North Luwu, North Toraja, Buton, Muna,

Kolaka, North Kolaka, North Buton , Majene, Polewali

Mandar

Sumatra 23

Mentawai Islands, South Pesisir , Solok, Padang

Pariaman

Agam, South Solok, Kuantan Singingi ,Indragiri Hilir

Pelalawan

Kampar

Rokan Hulu

Meranti Islands

Merangin

Batang Hari

East Tanjung Jabung

West Tanjung Jabung

Ogan Komering Ilir

Muara Enim

Lahat

Musi Rawas

South Bengkulu

Rejang Lebong

Kaur

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2.2 District Ranking Indicators

The subsections below describe the quantitative indicators included in the tool.

2.2.1 Poverty Indicators

The two poverty indicators are important variables for prioritizing districts. The poverty rate measures the pervasiveness of poverty, and the number of poor helps identify the total number of poor in a district. A district recording the highest number of poor people may not necessarily have the highest poverty rate. Although the district tool has already identified economic priority districts based on these poverty indicators, their inclusion as indicator values for ranking purposes allows the user to see the relative ranking of districts by poverty rate and by number of poor households.

Poverty Rate

This indicator provides the percentage of the district’s population with an income below their province’s poverty line based on Central Bureau of Statistics (BPS) statistics for 2011. The provinces’ poverty lines are adjusted by up to 100% to account for differences in the cost of living across provinces. This variable, however, does not capture the severity of poverty or the extent to which people’s incomes are below the poverty line.

Number of Poor Households

This variable complements the previous indicator by providing the absolute count (in thousands) of district households with incomes below their province’s poverty line. As in the Poverty Rate indicator, provinces’ poverty lines are adjusted by up to 100% to account for cost of living differences, so comparisons between districts in different provinces may be more valid than comparisons using a national poverty line. However, this variable does not capture the intensity of poverty, which is revealed by how far people’s incomes are below the poverty line.

2.2.2 Renewable Energy Indicators: Supply Side

The second group of indicators can be used to prioritize districts that have a high potential for renewable energy growth – hydropower potential, bioenergy potential, and solar energy potential

Hydropower Potential

Hydropower potential is approximated using spatially linked estimates of expected micro- and mini-hydropower capacity (in kWh) in the districts. Detailed information on the approach can be found in the IMIDAP report.9

Bioenergy Potential

The 2011 production of palm oil (in tons) is used as a proxy for this variable since palm waste matter from palm oil production is the most likely biomass available for energy generation in Indonesia.10

9 Integrated Micro-Hydro Development and Application Project (IMIDAP – UNDP PROJECT, 2010) and Studi Kelayakan Pembangkit Listrik Tenaga Mikrohidro (PLTMH)

10 Palm Oil Production by Estates, by Type and by Regency/City - Provinces in Figures (Propinsi Dalam Angka) (BPS, 2011)

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Palm oil production from large estates would have been a better measure because the biomass production is more concentrated. However, since not all provinces separate palm production values by type of ownership, total production is included. Based on discussion with MCA-I, additional indicators may be included such as the livestock population. As such the bioenergy potential indicator has a shortcoming in that it only considers palm production as a proxy. There can be other sources of biomass energy. However, there was not adequate data to include other sources. Therefore, this indicator is not as reliable as the hydropower potential indicator.

Solar Potential

2.2.3 The solar potential based on existing solar c apacity of PLN as predictor of future solar capacity (peak MW). Again, this is a limited indica tor in that there may be other areas where solar potential is high but has not been tapp ed. Renewable Energy: Demand Factors

For the GP investments to result in increased productivity, the user may need to consider the existing availability of electricity. Therefore, indicators on the current rate of electrification are included for both on-grid PLN connections and off-grid connections.

Lack of Electrification

The tool includes four indicators to measure lack of electrification. The first indicator is the number of households without a connection to the electricity grid operated by PLN, the state-owned utility. The second measure is the number of households as a percentage of total households in the district. The third indicator considers the number of households without electrification, whether from PLN or off-grid. The fourth indicator measures this number as a percentage of total households in the district. These indicators measure the potential demand for electricity and are also a proxy for electricity demand from businesses in the district. Considering that one GP indicator of success is “households with first-time access to electricity”, the number of households lacking any electricity connection is an important indicator to include in the district selection. The percentage of families with access to the grid was taken from the 2011 BPS Survey of Village Potential (PODES-BPS, 2011). The tool takes the inverse of these values so that a higher value implies greater potential for benefiting from GP investments. It is important to note that this statistic may over-report lack of electrification since there may be instances of households using illegal connections to the grid or receiving electricity from small-scale off-grid sources such as solar energy panels. Additionally, the unmet demand could all be residential if businesses are receiving priority grid access.

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GDP per Capita

The gross domestic product (GDP) per capita (in bil lions of rupiah) is measured on a regional basis without accounting for oil and gas income. 11 This measure can be included as a measure for potential demand for electricity and as a measure of the potential impact on the growth indicators. However, insofar as GP is workin g to improve economic growth, it may want to target areas with low GDP per capita. For t his reason, there is ambiguity in how the districts should be ranked by this indicator. Popul ation Density

Population density is a good measure for economies of scale of investment in general. It can be combined with the variables measuring the lack of electrification to identify areas where there is more concentrated demand for electricity. However, it is likely that the density information may be more relevant at a greater level of resolution. This indicator was taken from BPS’s 2010 census. The user may choose to include this variable in order to select for districts that are more urbanized. However, this indicator may misrepresent the level of urbanization if a rural district is heavily populated or if a district encompasses an urban area as well as large, sparsely populated areas.

2.2.4 Natural Resource Management (NRM) and Carbon Capture

This group of indicators can be used to prioritize the GP Project’s second intermediate objective of improving NRM and decreasing carbon emissions. Two indicators are the percentage of land in the district that is considered critical, and the percentage of the area that is under forest cover. These two variables are adjusted for the district’s land area so that they do not over-represent large districts. If the intention is to address larger areas under critical land or forest land, then the total areas under these types of land classifications can also be used as separate indicators. More details on the specific indicators can be found below.

Somewhat Critical Land

This indicator presents the first level of land sensitivity – land classified as being in a “somewhat critical state,” which is the first category officially recognized as “critical.” The user can include this variable if he or she wants to prioritize districts that have this type of land regardless of how much total land area they have. The data (in hectares) come from the MOF Directorate for Planning and Evaluation of Watershed Areas (KemHut, Direktorat Perencanaan dan Evaluasi Pengelolaan DAS, DJ-BP-DAS & PerhutNas, 2011). The indicator can be considered with the other NRM variables to account for various degrees of environmentally sensitive land in districts.

Critical Land

The Critical Land variable indicates the next level of land sensitivity – land classified as being in a “critical state” of use. The user can include this variable if he or she wants to prioritize districts that have this type of land regardless of how much total land area they have. The data (in hectares) come from the MOF Directorate for Planning and Evaluation of Watershed Areas (DJ-BP-DAS & PerhutNas, 2011). Again, this indicator can be considered with the other NRM variables to account for various degrees of environmentally sensitive land in districts.

11 Tinjauan Berdasarkan PRDB Kabupaten, Buku 1-5, BPS, 2011

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Very Critical Land

This indicator gives the area (in hectares) for the final level of land sensitivity – land classified as being in a “very critical state” of use. The user can include this variable if he or she wants to prioritize districts that have this type of land regardless of how much total land area they have. The data come from the MOF Directorate for Planning and Evaluation of Watershed Areas (DJ-BP-DAS & PerhutNas, 2011). Once again, this indicator can be considered with the other NRM variables to account for various degrees of environmentally sensitive land in districts.

Percentage of Land in All Three Critical States

The composite NRM indicator is the sum of Somewhat Critical Land, Critical Land, and Very Critical Land divided by Land Area for each district. This variable allows for a normalized comparison between districts so that those with more land area but a lower proportion of critical land are not ranked higher than smaller districts with a higher proportion of critical land. If the user wants to consider the total land area in these three critical states, any one of the three indicators can be selected as the basis for prioritization.

Potentially Critical Land

This indicator gives the amount of land (in hectares) that is potentially in a critical state of use. The user can include this variable if he or she wants to prioritize districts that have this type of land regardless of how much total land area they have. The data come from the MOF Directorate for Planning and Evaluation of Watershed Areas (DJ-BP-DAS & PerhutNas, 2011). Typically, this indicator would be used by users who want to include all other types of critical land when prioritizing districts based on various degrees of environmentally sensitive land in districts.

Forest Cover

This indicator measures the total area of land (in hectares) legally classified as any of the following forest types: conservation forest (Kawasan Konservasi), protected forest (Hutan Lindung), permanent production forest (Hutan Produksi Tetap), limited production forest (Hutan Produksi Terbatas), or converted production forest (Hutan Produski yang dapat dikonversi). Thus, the total forest use area is all land area in the district minus the land used for other uses or “other use areas” (APL), which are also outside the control of the MOF. These data come from the MOF GIS files (KemHut/Badan Planologi - Tutupan Lahan/Tata Guna Lahan Per Kabupaten, 2011). This geographic classification may not completely align with actual forest areas since deforestation has occurred on land that is still legally classified as forests. Therefore, another indicator is provided to measure actual forest cover.

Forest Cover Percentage

This indicator provides the total forest cover by legal status as a percentage of the district’s total land area. This indicator can be used instead of the total forest cover in cases where there is interest in prioritizing districts that have a larger percentage of their land area legally classified as forest.

Forest Use

This indicator provides the total area (hectares) in the district that is actually under forest cover as determined by satellite data. The total area under various forests is aggregated to obtain the total forest area, including primary dry forest, secondary dry forest, primary mangrove forest, secondary

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mangrove forest, primary swamp forest, secondary swamp forest and forest plantation. The area not categorized as forest covers all other land uses, which include plantations, settlements, mining and farming, swamp, savannah and fields. If peat lands are of particular interest to GP, then the area under peat land can be added as another indicator for considering districts. In addition, if there is interest in focusing on forest areas with steeper gradients, and therefore greater potential for micro-mini hydropower, then this variable can also be estimated.

Forest Use Percentage

This indicator provides the total forest cover by actual status as a percentage of the district’s total land area. This indicator can be used instead of the total area under forest in case there is interest in prioritizing districts that have a larger percentage of their land area under forest.

2.2.5 Economic Factors

The total area and yield under cocoa, coffee, rubber, and palm can serve as measure of the potential to increase productivity of land in each of these agricultural uses. These variables were taken from figures for each province from BPS’s Statistics Indonesia (2012).12 Total land areas under each crop include productive and unproductive acreage (in hectares). Acreage under palm includes acreage under large and small estates and with smallholders. Yields are measured in tons/hectare.

Note that some provinces reported district data in sub-groups (e.g., large estate/small estate, privately owned/state owned, Arabica/Robusta coffee), which were summed to obtain total acreage and yield. In addition, since a few districts only had data for either the area or yield of a certain crop, productivity could not be calculated in those districts.

2.2.6 District Governance

The variables in this section were taken from Local Economic Governance (LEG) in Indonesia (2007) for East Kalimantan, Riau, South Sulawesi, and South Sumatra provinces, and from Local Economic Governance in Indonesia (2011) for all other provinces (see list of LEG indicators in Exhibit 2 for more details). The 2011 LEG study notes that the 2011 and 2007 studies both use the same methodology, making the results across the two studies directly comparable.13 There are several sub-indicators for measuring district governance. Of these, only three indicators that were expected to be meaningful to compare across 2007 and 2011 were included in the tool. These include land access and security of tenure, local infrastructure, and transaction costs. The authors created these indices by calculating a district-level mean for each variable that was used to measure the indices, normalizing the average values by calculating a t-score (from 0 to 100) for the variables, calculating the inverse of adverse scores (100 minus the score) so that a higher index value always referred to better performance, and then averaging the t-scores. These indicators generally have the drawback that they can be skewed since they use the unweighted averages of their component survey variables, whereas some aspects may be more significant than others. Users can select any of these indicators to

12 Data for West Nusa Tenggara are for the year 2010, not 2011. 13 Page 3, Local Economic Governance - A Survey of Business Operators in 245 Districts/Municipalities in Indonesia, The

Regional Autonomy Watch (KPPOD)-The Asia Foundation, 2011.

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prioritize districts by the selected LEG sub-indicator. These variables are described in more detail below.

Land Access and Security of Tenure

The Land Access indicator represents firms’ access to land and security of tenure, which affect ability to invest in land, and investor confidence in doing so. The indicator was created by Local Economic Governance in Indonesia (2007; 2011) as an index of four survey variables:

1. time taken to obtain a land certificate;

2. perceived ease of obtaining land;

3. frequency of evictions in the region; and

4. overall assessment of the significance of land problems.

Local Infrastructure

The Local Infrastructure indicator represents the quality of local roads, electricity supply, street lighting, water supply, and telecommunications, all of which are essential for enabling business functionality and reducing the costs of business. The indicator was created by Local Economic Governance in Indonesia (2007; 2011) as an index of five survey variables:

1. the average perceived quality of five types of infrastructure (district roads, street lighting, water from the local water authority, electricity, telephone);

2. the average time (in days) to fix problems with these types of infrastructure;

3. the percentage of firms that have a generator;

4. the number of times in a week that the electricity is cut off; and

5. the overall assessment of how large a constraint is posed by problems with the infrastructure supplied by the local government.

Transaction Costs

This indicator assesses the degree to which local taxes, user charges, and other transaction costs are burdensome to local firms. The indicator was created by Local Economic Governance in Indonesia (2007; 2011) as an index of six survey variables:

1. the extent to which firms say they are bothered by user charges;

2. the percentage of firms that say that there are official user charges for transporting goods across district borders, and the log of user charges for distributing goods across district borders per firm employee;

3. the percentage of firms that say that they have had to pay donations or contributions to the local government in the last year, and the extent to which these payments bothered the firms;

4. overall assessment by firms of how much issues associated with licensing constrain their business activities;

5. the share of firms that say that they have to make additional payments to the police; and

6. overall assessment by firms of the degree to which issues associated with such transaction costs constrain their business activities.

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3. District Ranking

The overall district selection proceeds through two stages, where the first stage uses quantitative indicators and the second uses qualitative indicators. The district selection tool has been developed for the first stage in order to help select an additional estimated sixteen districts, supplementing the four starter districts previously agreed to. In this stage, the districts can be ranked using all of the quantitative indicators (poverty rate, forest cover, etc.) selected by the user. The districts can also be ranked individually by each indicator in turn to assess which districts rank high in all scenarios. The results of the ranking can be presented for only those districts that are a part of the conservation corridor or those that are a priority under MP3KI.

In the second stage, the districts selected from the first stage can be further prioritized using qualitative assessment of indicators by key stakeholders best suited to make a professional judgment. Secondary assessment of the qualitative indicators allows these difficult-to-measure factors to influence district selection in a tailored manner that is deemed to be appropriate.

3.1 First-Stage Ranking Method

In the first step, each indicator is converted into an index to allow comparison across indicators that are measured in different units (e.g., hectares, percentages, kilowatt hours).

Therefore, an index, ���, is created for each indicator, k, for each district, n, as follows:

��� = ��� −�������� −����

where:

��� is the value of indicator k for district n;

���� is the lowest value of the indicator k across all districts; and

���� is the maximum value of the indicator k across all districts.

This index measures the deviation of the indicator’s value from the lowest value as a proportion of the largest deviation.14 The highest value that this index can have is 1 and the lowest is 0, with higher values indicating districts that have a higher value relative to other districts for the selected indicator.15 All indicators included in the tool are presented in such a way that a higher value implies greater potential for GP investment.16

14 This formula is used for the Human Development Index (Malik, 2013).

15 To ensure that higher values imply higher priority for receiving GP investments, all indicators are presented in such a way that this becomes the case. For example, the electrification variable is entered as lack of an electricity connection.

16 For example, electrification data from PODES (2011) was provided as households with access to electricity. This was converted to number of households that do not have electricity connections, as GP investments are more suitable for districts where fewer households have electricity connections.

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The user can select any number of the available indicators to rank districts. At this stage, the user can also assign different priority weights for different indicators within a range of 1-5. The indicators’ indices are then aggregated into a composite index for each district and used to rank them.

The composite index, ��, is the average of the indices weighted by their priority weights as follows:

�� = ���� × ������������ℎ��∑ ������������ℎ���

��

where:

������������ℎ�� is the user-inputted value that determines whether the indicator should be included in the composite index, and how it should be weighted relative to the other indicators. It is set to 0 if the indicator is not included, 1 if it should be included with a standard weight, and greater than 1 if it should have a greater weight than others. Again, the highest value that this index can have is 1, and the lowest is 0. The district selection tool allows the user to see the relative weight of each index based on the selection.

Once the user decides the indicators and their weights for ranking the districts, the districts can be ranked by their conservation and poverty priority using the HCV and MP3KI categorical variables.

3.2 Using the Tool

The district selection tool houses all the data for the indicators and the unit-free indices for each data element. The steps for using the tool are outlined below:

1. Install Tableau reader, which is available free at http://www.tableausoftware.com/products/reader. The application can be installed by simply downloading and running the file, or directly running the file. The application does not work on Mac systems. The system requirements for Tableaux readers are:

• Microsoft® Windows® 8, 7, Vista, XP, Server 2012, Server 2008, Server 2003

• 56 megabytes minimum free disk space

• 32-bit color depth recommended

• 32-bit or 64-bit versions of Windows*

2. Open “DRA_District_Selection_<latest version>.TWBX” in the Tableau reader.

3. Select indicators from the “Indicator Selection” tab by selecting a value from 1-5. To give equal weight to all indicators, select the same value for all indicators.

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4. View results in the “Map View” tab. To see district rankings within province(s) or island(s), or specific districts, select the specific province or island under the “Geography” label in the “District Selection Report” tab. Note that if the user selects a specific island, the Map View shows the map for only the province selected. The legend colors the districts in five shades, from grey to blue, with the bluest districts corresponding to the highest value for the composite index.

5. View district results by Economic Priority or Forest Priority by specifying the selection under the “Economic Priority” and “Forest Priority” labels in the “District Selection Report”. To rank districts only among those classified as economic priority and forest priority, select “Conservation Corridor” under Forest Priority and “Economic Priority Strategic” under Economic Priority.

6. Export the results to an MS Excel sheet by going to the “Worksheet” menu in the District Selection Report banner and selecting Export �Crosstab to Excel. The user can also export the results to an MS Access database. Note that the user must hover the mouse cursor over the Interactive Table to make this feature active. If the mouse is in another area, this option will not be available.

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7. Export the map as an image file by going to the “Dashboard” menu in the Map View banner, and selecting Export. Users can also copy the image.

8. Export the weights used for the analysis by clicking on “Indicator Weights” and exporting the worksheet (refer to step 6).

9. View and extract the underlying data from the “Data” menu, and the data definitions from the “Data Definitions” menu.

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3.3 Results

As a result of the District Readiness Assessment process under Task 2, an extensive data set has thus been built and incorporated into an interactive tool for use in final district selection, as well as for the longer term by MCA-I and MCC. This data set incorporates information from public, private, and non-governmental resources and has also been subject to additional testing/verification by the Project’s experts to assure its integrity/quality. MCA-I continues to work with the Project team accordingly to test various scenarios and generate shortlists of Districts taking into account GP’s development hypothesis and the broader goals of the Indonesia compact. This process is ongoing, with multi-disciplinary teams established to further test and add data points, carry out the next phase of qualitative assessments, and review potential shortlists of districts with MCA-I leadership.

Several resulting shortlists have been prepared and discussed with MCA-I based on this interactive and collaborative methodology, with webinars and presentations for MCA-I staff and the GP Technical Lead completed on May 27th and May 29th in Jakarta. The data runs completed during these in-house expert sessions focused on clusters of districts in key economic corridors across GP’s target provinces, High Conversation Value (HCV) areas, and locations targeted as part of NREL’s analysis to promote cross-linkages with the investment process. The DRA process and tool will thus not only facilitate the requisite shortlisting, but also result in MCA-I having increased institutional capacity to test scenarios at the provincial and island level, generate GIS maps and overlays based on the indicators tested, and hone in on particular districts for further testing or analysis as GP rolls out. next section presents the results of the district selection using key quantitative indicators.

3.3.1 Key Indicators

The indicators used to rank the districts were based on the GP goal and its intermediate outcomes (see Exhibit 1). The district ranking was done from among the 68 conservation corridor districts since GP intends to focus its efforts in those districts. The following indicators were included to rank the districts:

• Poverty reduction through economic growth: The number of households in poverty;

• Increased productivity and economic activity: The number of households without any electricity;

• Improved access to electricity through renewable energy: Hydropower potential;17

• Improved natural resource management: Area under conservation and protected forest; and

• Decreased carbon emissions: Area under conservation and protected forest.

It is important to mention a few considerations in using the three indicators. First, numbers are used, rather than rates. Since the indicators are district-level, indicators that measure rate (e.g., poverty rate) can mask variation within districts (e.g., in a district that has pockets of poverty alongside rich areas).

17 Hydropower, biomass, and solar potential are all important considerations for renewable energy. However, data on biomass and solar potential were not as reliable as that for hydropower, which comes from UNDP’s IMIDAP report on micro-hydro potential.

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Therefore, it is more useful to use the following indicators that measure numbers: households below poverty, households without electrification, and acres of forested area. Second, the choice of indicators is based on data reliability and sufficient variation across districts (e.g., the governance and gender development indices do not vary significantly across districts).

3.3.2 District Selection Scenarios

Three scenarios for selecting districts were tried:

• Scenario 1 ranks districts by number of households in poverty, and electrification rate. This scenario focuses on renewable energy generation.

• Scenario 2 ranks districts by number of households in poverty, and area under conservation and protected forests. This scenario focuses on natural resource management and carbon emissions.

• Scenario 3 ranks districts by number of households in poverty, number of households without electrification, and area under conservation and protected forests. This scenario combines renewable energy and natural resource management.

The resulting district choices were robust across the three scenarios. Therefore, the remainder of this section focuses on results from scenario 3, which includes all key indicators.

The specific steps in the district selection process under Scenario 3 were as follows. Top districts among the 68 conservation corridor districts were determined based on three indicators (number of households in poverty, area under conservation and protected forests, and number of households without electrification) . Among the top districts, districts were filtered by those that had positive hydropower potential. Solar potential was also considered for the Lesser Sunda Islands. In addition, data on total area and yield under cocoa, coffee, and rubber were used as a measure of the potential to increase productivity of land.

Governance indicators (i.e., land access and tenure security, local infrastructure and transaction costs) and a gender development index were also considered. However, districts were not dropped based on these indicators because the indicators did not vary significantly across districts.

3.3.3 District Selection Results

Exhibit 9 presents the top 10 districts from each island, and asterisks indicate the top 4 districts in each island with positive hydropower potential.

Exhibit 9: Top 10 Districts in Each Island Based on 3 Indicators a, among Conservation Corridor Districts

Island District Names by Province

Kalimantanb

East Kalimantan Province: West Kutai*, East Kutai, Kutai Kartanegara,

North Kalimantan: Berau, Bulungan, Malinau* and Nunukan

West Kalimantan Province: Kapuas Hulu*, Sintang* and Melawai

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Island District Names by Province

Sulawesic

South Sulawesi: Luwu*, North Luwu*, East Luwu, North Toraja

Southeast Sulawesi: Konawe, Kolaka*, North Kolaka and North Konawe

West Sulawesi: Polewali Mandar*

Sumatra

West Sumatra: South Pesisir*, Solok

Riau: Indragiri Hilir, Kampar*

Jambi: East Tanjung Jabung

South Sumatra: Musi Rawas, Lahat, Banyuasin*, Ogan Komering Ilir, and

South Ogan Komering Ulu*

Lesser Sunda Islandsd

West Nusa Tenggara (NTB): West Lombok, Central Lombok, East Lombok,

Dompu, North Lombok

East Nusa Tenggara (NTT): West Sumba, East Sumba, Southwest Sumba,

Manggarai, West Manggarai

a The three indicators are: number of households in poverty, area under conservation and protected forests,

and number of households without electrification.

b Kalimantan only has 10 corridor districts.

c Sulawesi only has 9 corridor districts.

d For Lesser Sunda Islands, districts were ranked across all districts in the islands, not only among conservation

corridor districts.

Selecting districts based only on the rankings presented above would lead to spatially scattered districts, except in Kalimantan. Therefore, spatial and strategic considerations were also used. Specifically, spatial considerations included spatial contiguity of districts, proximity to the four starter districts, and contiguity of conservation corridors. These considerations led to the selection of 17 districts: 3 from Kalimantan and Sulawesi, 6 from Sumatra, and 5 from the Lesser Sunda Islands. Note that, while solar potential was considered for the Lesser Sunda Islands (in addition to hydropower), there was not a lot of variation in solar potential for conservation corridor districts in the Lesser Sunda Islands. Therefore, solar potential was not a differentiator for selecting districts.

A review of the yield and acreage under cocoa, coffee, and rubber crops suggests that the recommended selections will allow for improving productivity and returns in these crops. With the exception of West Lombok (where cocoa productivity is greater than 2 tons/ha), productivity is less than 1 ton/ha.

The 17 recommended districts are presented in Exhibit 10. Additional considerations for selecting districts in each island are described in the subsections below, including considerations for agricultural productivity.

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Exhibit 10: Recommended Districts

Island District Names by Province

Kalimantan

East Kalimantan Province: West Kutai, Malinau

West Kalimantan Province: Kapuas Hulu

Sulawesi

South Sulawesi: Luwu, North Luwu

West Sulawesi: Polewali Mandar

Sumatra

West Sumatra: South Pesisir, Solok, South Solok

Jambi: East Tanjung Jabung, Bungo, Kerinci

Lesser Sunda Islands

West Nusa Tenggara (NTB): West Lombok, Central Lombok, East Lombok,

North Lombok

East Nusa Tenggara (NTT): Southwest Sumba

Additional Considerations for Kalimantan

Other factors that may lead to the inclusion of other districts include synergy with other programs, logistical considerations, and political buy-in. Further qualitative assessments needs to be conducted to assess these opportunities, including the assessment of synergy with the Berau Forest Carbon Program (CFCP) to assess the inclusion of Berau. It is important to note that Berau has 10,000 poor households, 4,000 households without any electricity connection, and more than 350,000 hectares in conservation areas and protected forest. Berau does not rank high in the selection due to small numbers of poor households and households without electricity. Additional qualitative assessment will consider how the poor households are distributed across the districts and whether GP can make an impact by joining with BFCP to address poverty through either renewable energy or natural resource management interventions.

Regarding agricultural productivity, each selected district in Kalimantan (West Kutai, Malinau, and Kupuas Hulu) have cocoa productivity below 1 ton/ha. Specific acreages are:

• West Kutai has 1,202 hectares under cocoa and 1,714 hectares under coffee.

• Malinau has 4,379 hectares under cocoa and 1,932 hectares under coffee.

• Kupuas Hulu has 400 hectares under cocoa and 60 hectares under coffee, with low productivity.

Additional Considerations for Sulawesi

Sulawesi has the largest land area under cacao in Indonesia, but with very low yields. Insofar as improvement of cacao productivity is a focus of GP’s investment in the area, coordination with private entities such as Mars or Nestle could be beneficial. The final list of districts can therefore be refined based on these considerations and after further conversations with other entities with knowledge on cocoa foundation including USAID’s Amarta II project, the World Cocoa Foundation and the Indonesia Coffee and Cocoa Research Institute (ICCRI). he three selected districts (Polewali

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Mandar, North Luwu, and Luwu) have the highest hectares under cocoa (among the top 7 in Sulawesi island, with more than 35,000 hectares under cocoa). In all three districts, the productivity of cocoa is less than 1 ton/ha. Of the three districts, only Polewali Mandar has acreage under coffee (third highest in Sulawesi with 1,905 hectares); productivity is one of the lowest.

Additional Considerations for Sumatra

Given the selection of Muaro Jambi, Tanjung Jabung Timur is a natural choice because Berbak National Park and its underlying hydrology extend to this district. In addition, several districts (Solok, Solok Selatan, Pesisir Selatan, Bungo and Kerinci ) are part of the Rimba corridor. Kerinci and Bungo are contiguous to Merangin, with Kerinci forming the upper catchment for Merangin. The list of districts can be further narrowed by considering the investments within the Rimba corridor by other donors.

For agricultural productivity, East Tanjung Jabung has more than 3,000 hectares in coffee, 340 hectares in cocoa, and 7,700 hectares in rubber; all with very low productivity (approximately 0.3 ton/ha).

Additional Considerations for Lesser Sunda Islands

The list of districts for the Lesser Sunda Islands can be further narrowed depending on synergies with investments of other donors, the Sumba Iconic Island Project, and the Sustainable Tourism Development project. Since the Sumba Iconic Island project is a renewable energy project, GP will need to assess its incremental impact in coordinating with this project.

All four selected districts (North Lombok, East Lombok, Central Lombok, and West Lombok) have coffee and cocoa cultivation, but no rubber plantations. In all four districts, the productivity of coffee production is low. Since West Lombok has cocoa yield greater than 2 ton/hectare, it could potentially be dropped. East Lombok has very low cocoa productivity.

Overall, it is important to emphasize that when selecting the final districts several considerations other than the ranking on quantitative indicators need to be considered: logistical ease, coordination with other donors, political buy-in. As more is learnt about these factors for the proposed districts, some districts may be dropped, and others can be considered from among the 68 districts. The final decision on the districts will rest with MCA-I keeping in mind the various factors, along with a detailed description for the reasons to select the districts.

4. Conclusion and Next Steps

The district selection process implemented by the Abt Associates team and the associated District Selection Tool provide a data-driven, transparent and analytical approach to prioritize districts for further GP roll out. Currently it includes over 20 quantitative indicators that correspond to GP’s goals and intermediate outcomes. Additional indicators can be added as and when new and critical data are made available. The tool can also be used to conduct sensitivity analysis to see if the choice of districts is robust to selecting different indicators, raising the confidence on the first-stage selection.

Based on the initial analysis conducted and critical considerations outlined above, the MCA-I has worked with the team to arrive at a shortlist of 15-20 districts in addition to the four starter districts,

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with a further reduced shortlist to be developed incorporating qualitative analysis in advance of upcoming Board of Directors meetings which will address District Selection. During these meetings, the MCA-I’s leadership will determine the best means to present the final shortlist for discussion, with a request for Board approval then made by MCA-I to facilitate the next steps in the GP roll out process. This smaller set of districts may then be subject to additional qualitative assessments focused on districts with the highest potential to benefit from GP investments, as well as possible leverage with other donor funded programs in relevant areas. This assessment will include stakeholder outreach, additional data collection, and testing of various scenarios, as well as linkages to the District Selection Tool to promote long term sustainability and transparency of results.

The MCA-I team and Project experts have thus established a collaborative approach to completing the District Selection process, building on the unassailable data set collected behind the Tool, while also incorporating additional qualitative considerations. This strategy will enable the MCA-I to complete the requisite process of selecting a final shortlist of districts in a timely manner, present these for Board approval, and institutionalize the skills behind the DRA process into the core MCA-I team. As part of this effort, the Project team will also provide training to MCA-I on use, updating, and roll-out of the Tool, link it to other Tasks so that additional data can be incorporated, and oversee transfer of knowledge accordingly.