kpi 2: number of people and social institutions with …...for the purpose of kpi 2, social...

Climate Change Compass helps the UK Government

monitor, evaluate and learn from UK International Climate Finance

Join the conversation at climatechangecompass.org 1

Number of people and social institutions

with improved access to clean energy

as a result of ICF

KPI 2 Methodology Note

November 2018

http://www.climatechangecompass.org/




Acronyms ........................................................................................................................................ 3

Rationale ......................................................................................................................................... 4

Summary table ............................................................................................................................... 4

Technical Definition ....................................................................................................................... 5

Methodological Summary .............................................................................................................. 5

Methodology ................................................................................................................................... 6

Worked Example ........................................................................................................................... 9

Data Management ........................................................................................................................ 10

Data Disaggregation ..................................................................................................................... 11

Annex 1: Further worked example ............................................................................................. 13

Annex 2: Comparability and synergies with other external indicators ................................... 13

Annex 3: Definitions of key methodological terms used across Methodology Notes ............ 14

Annex 4: Low Carbon Conversion Table from Gold Standard Foundation ........................... 15

*Click on page numbers above to navigate to specific sections

About Climate Change Compass

The UK government has committed to provide at least £5.8 billion of International Climate Finance between 2016 and

2020 to help developing countries respond to the challenges and opportunities of climate change.

Visit www.gov.uk/guidance/international-climate-finance to learn more about UK International Climate Finance, its

results and read case studies. Visit www.climatechangecompass.org to learn more about how Climate Change

Compass is supporting the UK Government to monitor, evaluate, and learn from the UK International Climate Finance

portfolio.


https://www.gov.uk/guidance/international-climate-finance





Acronyms

DFID Department for International Development

gMJd Grams per megajoule delivered to the pot

g/min/L Grams per minute / litre

HH Household

HMG Her Majesty’s Government

ICF International Climate Finance

KPI Key Performance Indicator

KW Kilo Watt

M&E Monitoring & Evaluation

Mg/min Milligrams per minute

Mg/min/L Milligrams per minute / litre

MJ/min/L Megajoules per minute / litre

MtCO2 Metric Tonnes Carbon Dioxide

MW Mega Watt

PLF Plant Load Factor

SDGs Sustainable Development Goals

SE4All Sustainable Energy for All

SHS Solar Home System

SREP Scaling Up Renewable Energy Program in Low Income Countries

UNDP United Nations





Number of people and social institutions with improved

access to clean energy as a result of ICF

Rationale

Energy access is crucial to development and other services such as education, communication,

refrigeration. Better access to information are contingent on, or enhanced by energy access.

More-efficient cook stoves also have human health and time-saving co-benefits. This is particularly the

case for women and children who often suffer more from the negative impact of indoor air pollution and

may spend time collecting fuel wood instead of attending to other vital family needs or going to school or

playing.

Clean energy should also displace fossil fuels, thereby resulting in a reduced burden to the family

economy, a substantive reduction in smoke, and lower carbon emissions in the target community and

host country.

Summary table

Table 1: KPI 2 summary table

Units Absolute number of people; and absolute number of social institutions

Disaggregation

summary

(click for full

details)

Number of people with improved access to clean energy due to ICF projects (2.1)

disaggregated on a piloted basis by:

• Number of people with improved access to clean cooking (2.1.1)

• Number of people with improved access to clean electricity (2.1.2)

• Data should also be disaggregated1 by sex, disability, geography, age.

Number of social institutions with improved access to clean energy due to ICF

projects.

Headline data

to be reported

2.1) Number of people with improved access to clean energy due to the ICF

project (inclusive of 2.1.1 and 2.1.2)

• Number of people with improved access to clean cooking (2.1.1)

• Number of people with improved access to clean electricity (2.1.2)

2.2) Number of social institutions with improved access to clean energy due to

the ICF project

Latest revision September 2018.

The main revisions to this Methodology Note are:

• Disaggregated reporting for clean cooking and clean electricity;

• Additional reporting requirement for the number of social institutions

with improved access to clean energy due to the ICF project.

Timing issues When to report: ICF programmes will be required to report ICF results once each

year in March. Please bear in mind how much time is needed to collect data

required to report ICF results and plan accordingly.

Reporting lags: Your programme may have produced results estimates earlier in

the year, for example during your programme’s Annual Review. It is acceptable to

provide these results as long as they were produced in the 12 months preceding

the March results commission. In some cases, data required for producing results

1 As per DFID Action Plan for the Inclusive Data Charter; see also Washington Group on Disability Statistics





estimates will be available after the results were achieved – if because of this,

results estimates are only available more than a year away from when results are

delivered, this should be noted in the results return.

Links across

the KPI

portfolio

There is a strong link with KPI 9 (Low Carbon Technologies disseminated) and

KPI 2 as all LCTs commercialised in KPI 9, as currently defined, will be for the

energy access market. There is transformational potential within KPI 2,

particularly with regards to the possible effect on market systems and the wider

effects of improved energy access (such as education), although success will only

be evident with a time lag. In this context, and as noted above, KPI 15 is

particularly important as a complement to KPI 2. There is a weaker link to KPI 5

(employment), which may be reported as a co-benefit at outcome level in

programmes, as well as KPI 11 and 12 (public/private finance leveraged) and KPI

14 (institutional knowledge).

Technical Definition

Clean energy access refers to:

• New household connections to off-grid renewable energy sources such as micro or mini-grids,

or solar home systems2

• Households that use more efficient cookstoves that materially improve energy efficiency of

essential services daily (e.g. air quality, reduced fuel consumption, fuel savings or improved family

income)

• Solar lamps and/or solar lanterns with or without chargers

Clean energy is generated from both combustible and non-combustible renewables. Non-combustible

renewables include: geothermal, solar, wind, hydro, tide and wave energy. Combustible renewables

include: biofuels (biogas, bioethanol, biodiesel); biomass products (fuelwood, vegetal waste, pulp and

paper waste, animal waste, bagasse); municipal waste (waste produced by the residential, commercial and

public service sectors that is collected by local authorities for disposal); and industrial waste.

Methodological Summary

Please report a total number of people at the household level with improved access to clean energy. This

should be the sum of the two sub-indicators3:

• # people with improved access to clean cooking (2.1.1)

• # people with improved access to clean electricity (2.1.2)

Note that an individual or household can be serviced twice (i.e. serviced for clean cooking and separately

for clean electricity) and should therefore be head-counted twice4.

Separately, the number of social institutions receiving improved access to clean energy should be

reported (2.2). For the purpose of KPI 2, Social Institutions are defined as: schools, universities, hospitals,

2 Note that on-grid access is not included in these figures because once energy access is on-grid, it is often difficult to

determine the energy source. However, where the on-grid energy source is known to be clean, we can report on clean

energy access based on the on-grid renewable energy sources. 3 Separate reporting on people with improved access to clean cooking and clean electricity will be piloted for a minimum of

one year before publishing. 4 This double counting is allowable to make the indicator easier to calculate (i.e. to enable adding 2.1.1 and 2.1.2 to get

2.1). It is very unlikely there will be instances where people will be served twice, in terms of both cooking and electricity.





health centres, government institutions, state owned infrastructure (e.g. water pumps, state owned

business centres, etc.), and civil society organisations having physical infrastructure that benefits from

clean energy.

The diagram below summarises the KPI 2 Methodology, which is further expanded in the Methodology

section immediately below this, and in worked examples that follow:

Figure 1: KPI 2.1 methodological summary

Figure 2: KPI 2.2 methodological summary

Methodology

To calculate “Number of people with improved access to clean energy as a result of ICF projects” (2.1):

The following steps should be followed to calculate number of people with improved access to clean

energy due to ICF projects. These steps can be applied to both clean cooking and clean electricity, and

the results should be combined to give the total number of people with improved access to clean energy,

with clean cooking (2.1.1) and clean electricity (2.1.2) also reported separately.

1. Check that clean cooking and clean electricity meet minimum standards





Check that cookstoves are professionally designed. This will serve as a proxy measure that they meet

minimum safety and emissions standards for cookstoves. Locally manufactured (‘artisanal’) cookstoves

that do not have a standardised design sometimes offer little or no improvement over a traditional (3-

stone) fire.

For electricity, check that Tier 1 is reached in the Tier system for electrification, as shown in Table 2

below.

Table 2: Tier system for electrification

Tier Services Typical system

1 Medium bright light and, if possible, limited radio use and

telephone charging

Pico Photovoltaic (incl. solar

lanterns), battery charging station

2 Bright light, radio, telephone plus use of devices typically

requiring tens of watts like TV, video, fan

Solar Home System

3 Tier 2 services, plus use of devices typically requiring a

few hundred watts such as rice cookers and refrigerators

Mini Grid

4 Tier 3 services, plus use of devices typically requiring a

kilowatt such as water heaters and irons

Limited Grid

5 Tier 4 services plus use of devices typically requiring a few

kilowatts such as air conditioners

Grid

See example

2. Obtain data from programme level M&E

Programme-level M&E data should ideally provide what is required, including: number of households and

average household size, or number of individuals supported; type of support provided, i.e. whether clean

cooking or clean electricity or both. If household size data is not available, this should be determined

from the most recent national census data or from a nationally or sub-regionally representative

household survey. See section on data management below for more details.

See example

3. If using number of households (HHs) and average HH size, convert HH data into

number of people

If data is collected at the household level, the number of households needs to be converted into the

number of people by multiplying by the average household size (as per target country standards for

average family).

See example

4. Allow for HHs that would have received the product without the programme

(additionality)

To compare results to the counterfactual and account for additionality5, an allowance needs to be made

for HHs that would have received the product (e.g. solar lantern) without the programme. If available,

use an estimate based on household surveys through project/programme M&E. If you are not able to

estimate what the counterfactual it is suggested to use an ‘adjustment factor’, which should be high (e.g.

5 See Annex 3 for definition of counterfactual and additionality





95%) if you are confident your results are additional, and your data quality is good. A lower ‘adjustment

factor’ (e.g. 50%) should be used if you have a lot of uncertainty and there are other partners in the area

undertaking similar activities.

See example

5. Calculate pro-rata share where HMG only funded part of the programme

(attribution)

If HMG is the sole investor in a project or programme, it should assume all responsibility for any results

(where the results are assessed to be additional and where HMG has a causal role).

In many instances HMG may be acting alongside one or more other development partners or multilateral

bodies that also provide funding or support for projects or programmes – and where each partner has

played a role towards the results. In these cases, HMG should only claim responsibility for the portion of

results that can be attributed to its support.

If HMG is only funding part of a project/programme, reporters should calculate results as a pro-

rata attributable share based on the value of all public co-financing towards the project.

In instances where ICF programmes leverage (public or private) finance that helps to deliver programme

results, please contact your central ICF teams on how to address attribution of results delivered. See

methodology notes for KPI 11 and 12 for definitions (of public, private, and leveraged finance and co-

finance).

If HMG is contributing to a fund

‘First best’ approach: use project/programme level attribution (as above)

In this approach, reporters calculate results attributable to the UK for each project/programme

implemented by the fund using the project/programme level attribution approach, and then sum results

across all projects/programmes in the fund to reach total UK attributable results.

This approach allows for recognition of other co-finance contributions at the project/programme level.

However, this approach may be complicated or not always possible in practice as it relies on (i) full

information about project/programme level inputs, (ii) additional work to calculate results at the

project/programme level.

‘Second best’ approach: use fund-level attribution

Reporters apply fund-level attribution (i.e. at point of UK investment) for reporting results. I.e. results

should be shared across all donors that contribute to a fund. All results are attributable to the relevant

fund (e.g. CIFs, CP3, GAP) regardless of whether these funds blend with other sources of finance in

implementing projects at levels below the point of UK investment. This approach assumes that any

further finance towards the project is counted as leveraged. Where this is known to not be the case, a

more conservative approach to attribution may be appropriate, please contact your central ICF teams on

further guidance.

While this is the less preferred approach as it does not recognise additional contributions at the

project/programme level, it may be more practical to implement where full data on project/programme

level inputs is not available.

Note: The distinction between attribution at the project/programme level and at the fund level (or at

point of UK investment) is only an issue where the UK is investing in funds where there are multiple

investment levels.

See example





Alternative to above methodology: Converting KPI 9 into KPI 2

If only the absolute number of units of low carbon technologies installed are known and there is no

household data (i.e. just KPI 9 data available), then the Gold Standard Foundation’s Low Carbon

Conversion Table in Annex 4 can be used to estimate the number of beneficiaries for Micro/Pico Hydro,

Solar Home Systems, Solar Lanterns and Cookstoves. Steps 1, 4 and 5 referenced above still need to be

followed.

To calculate “Number of social institutions with improved access to clean energy as a result of ICF projects” (2.2):

The following steps should be used to calculate the number of social institutions with improved access to

clean energy as a result of ICF projects. These steps can be applied to both clean cooking and clean

electricity, and the results should be combined to give the total number of social institutions with

improved access to clean energy.

1. Check intervention meets minimum Tier 1 for electrification performance

See step 1 in “Number of people with improved access to clean energy as a result of ICF projects”

section above.

2. Obtain data from project level M&E


section above.

3. Allow for social institutions that would have received the product without the

programme


section above.



section above.

Worked Example

Worked example 1 (KPI 2.1)

Based on a fictitious programme where ICF co-financed a project to provide 1,000 households with solar

lanterns.

1. Check that clean cooking and clean electricity meet minimum standards

The solar lanterns fall within Tier 1 of electrification (see Table 2 above)

2. Obtain data from programme level M&E

Based on household surveys conducted through programme M&E, an estimate of 5% of these HHs would

have received solar lanterns anyway, so assume 95% of households were supported in addition to what

would have happened without the project/programme.





3. If using number of households (HHs) and average HH size, convert HH data into

number of people

The average HH size is taken from national census data for the region and is found to be 5.4 (*where this

data does not exist, the project should seek to survey a large enough sample to establish average HH size, and to

enable disaggregation by income level).

Therefore, HH size converted into people is 1,000HH * 5.4 = 5,400

4. Allow for HHs that would have received the product without the programme

(additionality)

The number of people that have improved access to clean energy is: Number of households supported x

factor for additionality adjustment x average household size = 1,000HH * 0.95 * 5.4 = 5,130 people that

have improved access to clean energy.


(attribution)

As DFID only funded 50% of the project, 2,565 beneficiaries can be attributed to the ICF.

*See Annex 1 for a worked example on the number of social institutions with improved access to clean energy as

a result of ICF projects

Data Management

Data sources

Some data will be available directly from programmes, for example from project-level M&E (e.g.

household surveys, project reporting). Data on household size should be determined from the most

recent national census data or from a nationally or sub-regionally representative household survey.

Ideally, the duty to collect data should be the responsibility of recipients of ICF funding, or a third-party

auditing entity. This information will need to be kept up to date by liaising with programme managers.

Most recent baseline

The reporting baseline should reflect pre-project status before ICF funding, and contain projections of

what would happen without the ICF. For long-running programmes, the baseline should be taken as 2015

unless otherwise stated. The baseline should align with the economic appraisal in the project design.

Quality assurance

All results estimates should be quality assured before they are submitted during the annual ICF results

return, ideally at each stage data is received or manipulated. For example, if data is provided by partners,

this data should be interrogated by the ICF programme team for accuracy, or at the very least data

should be sense checked for plausibility. When converting any provided data into KPI results data, quality

assurance should be undertaken by someone suitable and not directly involved in the reporting

programme. Suitable persons vary by department; this could be an analyst, a results / stats / climate and

environment adviser / economist.

Central ICF analysts will quality assure results that are submitted and this may lead to follow up requests

during this stage.





To avoid inherent reporting biases, it is strongly recommended that, where possible, data collection is

undertaken by a third party that is not directly involved with implementing the project. Where not

possible, consider using independent evaluations or alternative means to periodically check the validity of

results claims.

Any concerns about data quality or other concerns should be raised with your departmental ICF analysts

and recorded in documentation related to your results return.

Additional points to note

On-grid electricity: It is generally not possible to disaggregate grid electricity by source (clean vs. fossil).

Furthermore, providing additional energy to the grid does not necessarily translate into improved energy

access, unless new connections were simultaneously established in the target populations. This KPI 2

indicator therefore excludes an assessment of on-grid energy access. Any measurements of energy access

are likely to be conservative and be a subset of results as improved access to the grid cannot be

measured directly.

Data Disaggregation

HMG is strongly committed to the principle that “every person counts and should be counted.” It has

endorsed the United Nations Global Partnership for Development Data, and is now operationalising this

commitment. HMG has prioritised four disaggregation axes, which are required for all programmes going

forward. While HMG encourages all programmes to disaggregate data by these four axes to the extent

that it is feasible, the requirement is not retroactive to programmes with logframes finalized prior to

publication of the July 2018 DFID Inclusive Data Charter Action Plan. Disaggregation should only apply to

direct beneficiaries.

Sex

Programmes should disaggregate by males/females. Disaggregation should be based on actual data, not

models or estimates.

• Report:

o # male direct beneficiaries

o # female direct beneficiaries

o Disaggregated data by sex is not feasible to collect

Disability

Programmes should incorporate the Washington Group “short set” of six disability questions to their

population surveys6. These questions and further guidance for them can be found at:

http://www.washingtongroup-disability.com/washington-group-question-sets/short-set-of-

disability-questions/. While all HMG programmes are expected to apply and track all six survey

questions in beneficiary surveys, at the KPI level HMG expects reporting towards two categories: disabled

and not disabled. Anyone who answers “a lot of difficulty” or “cannot do it all” to one or more of the six

questions should be counted as disabled.

• Report:

o # disabled direct beneficiaries

6 The Washington Group was established under the United Nations Statistical Commission in 2001 to address the need

for cross-nationally comparable population-based measures on disability. It is composed of representatives of National

Statistical Offices around the world.


http://www.washingtongroup-disability.com/washington-group-question-sets/short-set-of-disability-questions/

http://www.washingtongroup-disability.com/washington-group-question-sets/short-set-of-disability-questions/




o # not disabled direct beneficiaries

o Disaggregated data by disability is not feasible to collect

o Programme is not required to report disaggregated data because logframe was finalised prior to

July 2018

Geography

At the KPI level, all programmes should report on whether beneficiaries are urban or rural.

• Report:

o # of urban direct beneficiaries

o # of rural direct beneficiaries

o Disaggregated data by geography is not feasible to collect

o Programme is not required to report disaggregated data because logframe finalised prior to July

2018

While the concepts of “urban” and “rural” are easily understood, in an era of migration and burgeoning

peri-urban populations, the distinctions may blur in many cases. Moreover, there are substantial

differences between how various countries approach this question, and there is no internationally-agreed

upon definition. For statistical purposes, HMG (and the United Nations) allows each country to define

urban and rural individually. You should follow the definitions set for your country by its National

Statistics Office (or equivalent agency).

Age

Whilst all programmes are expected to collect and analyse detailed data on the age of beneficiaries (see

DFID Guidance on Disaggregating Programme Data by Age), at the KPI level ICF-affiliated programmes

should report numbers consolidated into four categories: children, youth, adults, and elders.

• Report:

o # of child direct beneficiaries (age 0-14)

o # of youth direct beneficiaries (age 15-24)

o # of adult direct beneficiaries (age 25-64)

o # of elder direct beneficiaries (age 65+)

o Disaggregated data by age is not feasible to collect

o Programme is not required to report disaggregated data because logframe finalised prior to July 2018

Note: As of the August 2018 writing of this Disaggregation Guideline, HMG is prioritising these four data

disaggregation axes. However, it is committed to ultimately disaggregating data to the full suite of eight

axes (i.e., income, age, sex, race, ethnicity, migratory status, disability, and geography) as advocated by the

United Nations. As such, disaggregation guidance may be expanded in the coming years, probably

beginning with income.





Annex 1: Further worked example

Worked example 2 (KPI 2.2)

Based on a fictitious programme where ICF co-financed a programme to provide 20 health centres with

solar-powered refrigerators.

To estimate results:

1. Check intervention meets minimum Tier 1 for electrification performance

DFID co-financed a programme to provide 20 health centres with solar-powered refrigerators, which fall

within Tier 3 of electrification (see Table 2).

2. Obtain data from project level M&E

Based on surveys through project M&E, an estimate of 5% of these social institutions would have received

solar-powered refrigerators anyway, so assume 95% of health centres were supported in addition to

what would have happened without the project/programme.

3. Allow for social institutions that would have received the product without the

programme

Therefore, the number of social institutions that have improved access to clean energy is: Number of

social institutions supported x [factor for additionality adjustment] = 20 *0.95 = 18 social institutions that

have improved access to clean energy.


As DFID funded only 50% of the project, 9 social institutions can be attributed to the ICF.

Annex 2: Comparability and synergies with other external

indicators

KPI 2 directly relates to Sustainable Development Goal 7: “Ensure access to affordable, reliable,

sustainable and modern energy for all”. SDG 7 targets are:

• 7.1: By 2030, ensure universal access to affordable, reliable and modern energy services;

• 7.2: By 2030, increase substantially the share of renewable energy in the global energy mix;

• 7.3: By 2030, double the global rate of improvement in energy efficiency.

Note that SDG Target 7.1 does not include a ‘clean’ aspect, so while ICF initiatives (reporting to KPI 2)

clearly contribute to SDG Target 7.1, mitigation objectives have also been linked directly with the ICF

energy access agenda (i.e. ‘clean energy’, not just ‘energy’). By contrast, the SDG energy targets have

‘clean’ objectives falling separately under SDG 7.2 (Renewable Energy) and, to an indirect extent, 7.3

(Energy Efficiency).

ICF initiatives reporting against KPI 2 for the (clean, off-grid) subset of the proposed SDG Target 7.1 will

contribute directly to both 7.1 indicators:





• Indicator 7.1.1: Percentage of population with access to electricity

• Indicator 7.1.2: Proportion of population with primary reliance on clean fuels and technology

KPI 2’s explicit link to KPI 6 (mitigation) results in there being minor inconsistency with SDG Targets7,

but KPI 2 results can be used directly as contributing to SDG 7 Indicators 7.1.1 and 7.1.2. Therefore, KPI

2 is well-aligned with the SDGs, and is likely to remain pertinent to climate and development until 2030.

Annex 3: Definitions of key methodological terms used

across Methodology Notes

As different HMG departments may use the same terminology to refer to different concepts, this section

sets out definitions for key terms used across Methodology Notes for ICF KPIs. The terms used in these

notes refer to the concepts as defined below, rather than to alternative, department-specific usages of

these terms.

Counterfactual: The situation one might expect to have prevailed at the point in time in which a

programme is providing results, under different conditions. Commonly, this is used to refer to a ‘business

as usual’ (BAU) counterfactual case that would have been observed if the ICF-supported intervention had

not taken place.

Additionality: Impacts or results are additional if they are beyond the results that would have occurred

in the absence of the ICF-supported intervention. That is, results are additional if they go beyond what

would have been expected under a BAU counterfactual.

Causality: Causality refers to the assessment that one or more actors bear responsibility for additional

results or impacts, because of funding provided though the ICF or actions taken under an ICF

programme. Multiple development partners may be assessed to have played a causal role in delivering

results.

Attribution: Attribution refers to allocating responsibility for impacts or results among all actors that

have played a causal role in programmes that deliver additional results. Results are commonly attributed

to causal actors based on their financial contributions to programmes (though there may be cases where

greater nuance is needed, as with KPI 11 and KPI 12).

7 That is, ICF requires ‘clean’ energy access, while SDGs require an increase in renewable energy, but could include fossil-

fuel energy access as a subset of Goal 7.





Annex 4: Low Carbon Conversion Table from Gold Standard

Foundation

Table 3: Low Carbon Conversion Table from Gold Standard Foundation

A. Micro/Pico Hydro

Number Unit

PLF (Plant Load Factor) 55%

Available days 30 days

Month 12

Hours 24 hr

Per capita consumption 500 kWh/yr

Standard Family size 5

Installed capacity (KW) Beneficiaries

1 9

2 18

3 27

4 36

5 45

50 450

100 903

B. Solar Home System 1 SHS 1 HHs of 5 family members 6 hrs

(approximate)

C. Solar Lantern - Standard

Size

4 W 1 HHs of 5 family members 2 hrs

(approximate)

D. Cookstove - Standard

Size

1 stove 1 HHs of 5 family members 6 hrs

(approximate)





Photo credits

Photos used in this KPI guidance note series were sourced from two websites, Climate Visuals and Unsplash (except the

photo for KPI 14 which belongs to IMC Worldwide). They are available for use under a Creative Commons license, which

enables organisations provided that photographers are credited. Photographers for this KPI guidance note series are

credited below.

KPI 1, Kenya. Georgina Smith / CIAT

KPI 2, Bhutan. Asian Development Bank.

KPI 4, Indonesia. Sigit Deni Sasmito/CIFOR.

KPI 5, Afghanistan. Asian Development Bank.

KPI 6 Roshni Sidapara.

KPI 7, Sri Lanka. Sansoni / World Bank. Photo ID: DSA0020SLA World Bank.

KPI 9, Abbie Trayler-Smith / Panos Pictures / Department for International Development.

KPI 11, Sri Lanka. Dominic Sansoni / World Bank. Photo ID: DSA0233SLA World Bank

KPI 12, Samuel Zeller. Solar panels.

KPI 13, Bangladesh. Sajid Chowdhury / Big Blue Communications. 2013

KPI 14, Rawpixel

KPI 15, Ryan Searle.

KPI 16 Alessandro Bianchi


https://www.flickr.com/photos/asiandevelopmentbank/9386865583

https://www.flickr.com/photos/cifor/39961517291

https://www.flickr.com/photos/asiandevelopmentbank/19388181110

https://unsplash.com/photos/h5M6LhYIDKU

https://www.flickr.com/photos/worldbank/1988403964/

https://www.flickr.com/photos/dfid/4058700982/

https://unsplash.com/photos/A18TFMJxzcw

http://www.bigbluecomms.com/

https://pixabay.com/en/alternative-energy-business-people-3382791/

https://unsplash.com/photos/oeSxn9z_15I

https://unsplash.com/photos/_kdTyfnUFAc

kpi 2: number of people and social institutions with …...for the purpose of kpi 2, social...

Documents