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Urban Crediting Methodology

Application in the city of Amman

24 June 2020

2© Ricardo-AEA Ltd Ricardo Energy & Environment in Confidence

Agenda


• With a rapidly growing urban population, cities are

crucial to achieve the Paris Agreement

• Article 6 of the Paris Agreement encourages

international collaboration through Internationally

Transferred Mitigation Outcomes (ITMOs), which

allow emission reductions achieved in one country to

meet the NDC target of another

• Crediting is one of the main tools to implement this

principle

• To date, the application of project-based crediting in

the urban environment has been limited

Introduction to urban crediting methodology

A scaled-up crediting approach for cities can address these barriers and deliver the

emission reductions and climate finance that cities need


Urban actions for meeting NDC targets: current situation and

challenges

Identified Barriers

Alignment of city target with NDC targets

Target

alignment

Target

ambition

Defining cities'

expected

contribution

NDC targets were

largely developed

without

consideration of

the city level

mitigation

potential and do

not include city

targets

Cities’ targets

are generally

more ambitious

than those set

by the

respective

national

governments in

NDCs

Separating

emission

responsibilities and

cross-cutting nature

of sectors present

in cities, can make

it challenging to

align city targets

with NDC


Urban actions for meeting NDC targets: current situation and

challenges

Identified Barriers

Cities' capacity to contribute to NDC

MRV Finance

Power,

authority and

autonomy

Low-quality systems for

monitoring, reporting and

verifying emissions and

climate actions in cities,

which are not aligned

with activities at the

national level

Cities with the greatest GHG

emissions and the greatest need to

access climate finance often have

the lowest capacity to access such

funds. International finance is often

channelled through national

entities, and city budgets allocated

either to specific projects or routine

service delivery

Many climate

actions that have

the biggest

mitigation impact lie

outside the scope of

control of cities.

Capacity and

resources

Many cities experience a lack of

capacity and resource to

address climate issues.


Methodological recommendations for urban carbon crediting

programmes


Steps 1-3: ensuring NDC alignment

Step 1: NDC target metric alignment

Step 2: Establishing a NDC pathway

Step 3: Defining a city NDC pathway as a minimum requirement for the urban crediting programme baseline

If a NDC target is not expressed in quantified GHG emission reduction, it should be translated into

quantified emission reductions through the necessary calculations and/or modelling.

If a NDC target is not expressed as a series of annual emission reduction targets, the annual targets

should be defined through calculation and/or modelling, potentially through evaluation of the

sectoral emission abatement potential

In order to define the contribution expected from a given city, the overall national NDC targeted

emission reduction should be broken down by sectors and distributed based on the share of each

sector represented by each city.


Step 3: Defining city’s BAU emissions for non-NDC aligned crediting



programmes


Step 4: Setting a crediting baseline



programmes


Step 5: Developing crediting baseline dynamics

• Static ex ante baseline: the baseline is calculated once for the crediting programme duration and all reductions

against this baseline will become creditable units

• Dynamic ex ante baseline: assumes projection of the baseline emissions for the whole duration of the crediting

programme and regular updates of this baseline through:

• Routinely updating the baseline prior to the beginning of each crediting period, or

• By updating the baseline based on certain triggers.

• Dynamic ex post baseline: the initial baseline is developed, however, following the end of each crediting period,

the baseline is readjusted retrospectively and then carbon credits are issued against the revised baseline.


Step 6: Developing discounting approach (MRV data related)


Data scoring matrix


Non-methodological barriers


City inventory review and alignment steps


City inventory for measurement of mitigation progress

High quality

data

Data

availability

QA/QC

Timeseries/

recalculations

The biggest

challenge for

cities is the ability

to source high

quality data.

Data availability is

a serious challenge

for cities in less-

developed

countries and often

a combination of

these data-sources

is required to get a

reasonably

accurate inventory.

Many cities

implement their own

internal checks and

controls on data, but

routine checking

and review is not

widely undertaken.

To improve the

ability to track the

impact of mitigation

actions in the

inventory requires

either a dramatic

improvement in the

policy-sensitivity of

the inventory or

through a set of

indicators for

tracking the impact

of actions alongside

higher-level

inventory data

There is currently no

explicit requirement

on cities to either

report timeseries or

recalculate unless

the inventory and/or

city has undergone

changes that might

trigger a

recalculation

Identified Barriers

Ability to track

the impact


• This is highly important as it defines the activities for which the city

will include within the crediting boundary and will measure progress

against. These include:

• Geographic boundary

• Temporal boundary

• Emission sources and sectors

• Emission scopes included and reporting framework(s)

• Base year for the inventory and timeseries

• Boundary-alignment solutions are possible

Step A: define the boundary of the GHG inventory and align with

crediting approach

The first step is to define the boundary of the GHG inventory to be used as the tool to measure urban

mitigation progress, and the sectors reported relevant to a crediting approach and assessment of any

limitations


Step A: define the boundary - sources


Step B: assess the quality of the available data and methods

employed in calculating the inventory

Step C i: Establish inventory crediting

boundary

The inventory should be assessed for quality to determine whether there are any grounds for exclusion

of certain sectors or sources, and to inform the use of discounting (Step 6)

Based on the assessment of the

boundary conditions, relevant

sources and sinks, and prioritisation

criteria, define the sub-sectors

included within the crediting

boundary.


Amman’s context

Jordan’s NDC target:

Unconditional: outcome target as 1.5% GHG emission reductions below business-as-usual (BAU)

scenario by 2030

Conditional: 12.5% emission reduction below the BAU scenario by the same year

• Applicability for carbon crediting:

– Overall applicability: positive - measured in GHG reductions

– Gaps: Not detailed and not elaborated as a pathway

– Updates: Jordan has updated its mitigation analysis in the BUR1,

submitted in November 2017. This provides an update to the

baseline assumptions of the NDC

• Observation:

It might take time for Jordan to update and elaborate its NDC target into a

pathway suitable for carbon crediting, therefore Amman might decide to

consider a non-NDC aligned carbon crediting programme with potential

alignment


• Stationary Energy Quality: low (scaled national data, grid factor calculated from old IEA data)

• Transport Quality: medium-low (estimated/scaled traffic model)

• Waste Quality: medium (accurate tonnages, old compositional analysis, unclear

treatment assumptions)

Amman’s GHG inventory

Geographic scope of Amman's Inventory

Overview

Inventory

data quality

• 22 of 27 districts included

• Two inventories: 2014 and 2016

• Following BASIC reporting level


Emissions by sector and scope

2014 emissions by scope

2016 emissions by scope


• Step A: Define the boundary of the GHG inventory and align with crediting approach:

Development of urban crediting programme in Amman


• Step B: assess the quality of available data and methods employed in calculating the inventory –

– Bi initial review of inventory quality assessment – update as needed and screen out any major data quality

issues to determine quality issues for crediting boundary

– Bii detailed scoring under Step 6 for application of Discounting

Data quality assessment


• Step C: Establish inventory crediting boundary

– Define and screen our sources and sectors that meet criteria and establish base year inventory that fulfils

criteria

Crediting boundary


Carbon crediting approach for Amman

Not applicable

• Historical population and GDP/capita annual growth rate (5.5%)

• GAM supplied population (1.8%) plus GDP/capita (2%) annual

growth rate

• GAM supplied population and GDP/capita growth rates applied

differently by sub-sector

• Sectoral growth rates as specified in the BUR1 baseline

scenario assumptions (and derived from the projected energy

balance)

• Historical population growth only (2.3%)

• GAM supplied population growth only (1.8%)

• Recommend to balance conservativeness with accuracy

• Growth rates by sector, from GAM or BUR1, are recommended for the

BAU

Step 1, 2: Establish a NDC pathway

Step 3: Defining Amman’s BAU emissions, from highest growth to lowest growth scenario


Defining Amman’s BAU scenario using CURB

Amman's

identified

mitigation

actions

Range of

possible

business

as usual

scenarios

Most

conservative

Lowest risk

Most

challenging

Least

conservative

Highest risk

Least

challenging


Further steps for Amman

Recommended to use dynamic ex ante baseline dynamics, to

– Build capacity of city government

– Build trust of investors in scheme

Step 4: Setting a crediting baseline

Step 5: Defining the crediting baseline dynamics


• Matrix process

Defining discounting approach to address uncertainty

Step 6: Defining discounting approach to address uncertainty

Each dataset used as activity data in

the calculation of sectoral/sub-sectoral

emission estimates is scored against

the 8 data quality indicators

The quality of emission factors is

assessed more simply involving a

more subjective assessment of quality

Scores are multiplied together to give

a total quality score, per sector/sub-

sector

If there is more than 1 activity data

dataset used to calculate emissions for

a sector/sub-sector, they are

assessed separately and the

uncertainty is combined

Sectoral/sub-sectoral emissions are

multiplied by the data quality score

and summed, then divided by total

emissions, to give a total inventory

weighted discount value

There is also an overarching

inventory quality score assessing

how well the inventory as been quality

assured which applies to the whole

inventory – it applies to all sectors


Data scoring matrix


Data scoring matrix (2)

Emission factors


Amman data quality scoring

Inconsistent year = biggest overall

issue affecting inventory quality

Stationary Energy sector

= lowest overall quality


Recommendations for urban crediting application in Amman

✓ Work towards including additional sources and increase

the geographic coverage to include all of ‘Amman’

(particularly where national alignment is sought)

✓ Improve inventory data quality in key sectors (particularly

energy) and where easy updates can be made to enhance

accuracy and improve the discount applied

✓ Report an annual inventory where possible, but as a

minimum a biennial inventory

✓ Use 2-year dynamic ex-ante baseline approach

✓ Apply discounting to account for data limitations and

incrementally improve these

✓ Consider adjusting the crediting baseline ex post,

based on actual emission factors only, where there is a

considerable shift in conditions.

✓ Move towards national alignment

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