PROJECT PIPELINE
5.2. CDM IN INDONESIA: MODALI- TIES OF IMPLEMENTATION
5.2.1. Baseline
Estimation of baseline CO2 emissions or removal is critical to the success of AJI and CDM projects.
Projects will result in CO2 emission credits if baseline emissions are higher than actual project emissions, or if the baseline removal is lower than the actual CO2 removal by the project.
Baseline emissions can be estimated using two approaches: the static and the dynamic ap- proaches (Ellis and Bosi, 1999). The static ap- proach involves establishing a static or fixed emission rate at the start of the project, which will be used for the lifetime of the project (static baseline). The dynamic approach requires that emissions should be re-estimated at certain intervals during the project lifetime (dynamic baseline). From the investor’s point of view the static baseline would be preferable, as it would
give a greater level of certainty; the amount of emission credits could be estimate more pre- cisely before the project started. However, a static baseline means that emission credits may not reflect the actual reduction in CO2 emissions.
The type of data used to establish the baseline will also affect emission credit projections. Data can be generated in various way: from a model;
historical data; data based on pre-existing planning for an area. The origin of the data affects project projections would need to be studied thoroughly.
5.2.2. Leakage
Leakage refers to the indirect effect of emission reduction policies or activities, leading to a rise in emissions elsewhere (as defined in Draft Working Paper: An annotated glossary of commonly used climate change terms. CSDA/FIELD/WRI. 1998) . In paragraph 89 (c) FCCC/CP/2000/CRP.2/Add.1 it is referred to as: ‘Potential sources of increased greenhouse gases emissions outside project boundary that are significant and reasonably attributable to the project activity.
Leakage occurs if the system boundary does not capture all the emissions resulting from the project. According to Andrasko et al. (1996) the magnitude of leakage is determined by many factors; these include changes in relevant regula- tions and laws, the trend in autonomous effi- ciency improvements, and changes in other basic variables such as development of markets for products of the project, etc. It is necessary to define the system boundary for a CDM project from the outset. However, there is no interna- tional guidance on where or how exactly the boundary should be set (Ellis, 1999). Further- more, project system boundaries cannot be generalized, and case studies only provide sector-specific guidance. For example, it is
generally held that both direct and indirect emissions from electricity generation should be included in the project system boundary; other indirect emissions, such as transport-related emissions, might be either included or excluded (Willems, 2000). Sink projects can be treated either as isolated projects or as an integral part of a larger system (Chomitz, 2000).
Leakage can also occur as a result of unexpected circumstances, such as an improperly defined baseline, improperly defined project lifespan, or inappropriate project design. Examples of leakage in energy projects include:
Fossil fuel substitution may lead to a decline in fossil fuel prices causing customers elsewhere to increase their usage of – the now cheaper - fossil fuel.
After conventional boilers are replaced with higher efficiency models in project plants, they may be used in other plants.
Transnational boundary leakages: for ex- ample, switching to renewable fuel sources may lead to the export of fossil fuel, which will create CO2 emissions in other countries.
The type of leakage described under point one above also occurs as a result of Annex B domes- tic measures; consequently it may not be appro- priate to burden CDM projects with the task of reducing this type of leakage.
5.2.3. Project and baseline lifetimes The amount of CO2 saved by projects will be heavily dependent on the length of time over which emission credits are allowed to accrue. A longer timeline will result in more emission credits. The timeline for AIJ projects varied consid- erably, even for projects of a similar nature (Ellis, 1999). Creating a standard methodology to calculate an emission timeline would have many
advantages (Ellis and Bosi, 1999). It would increase both the transparency of emission baselines and comparability between projects. It would also reduce the time and costs involved in setting up an emission baseline. The use of static baselines may be acceptable for projects with a short lifetime. Projects with long lifetimes would use dynamic baselines based on the emissions and operating data of the project but also taking policy and macro-economic changes into ac- count to adjust the baseline (Andrasko et al., 1996).
Timelines can be calculated in a variety of man- ners:
Using numerical limits (e.g. five years for energy-sector JI-type projects; NEFCO, quoted in Puhl, 1998, and 99 years for biotic projects; the Dutch FACE Foundation).
Using project-category formulae (e.g. 10 years for replacing an old power plant with one based on renewable energy sources, or the commercial life of a new power plant;
Michaelowa, 1998).
Using the technical or economic lifetime of the project.
If the project baseline will not be constant
throughout the lifetime of the project, the lifetime of the baseline would also need to be defined.
Setting a baseline lifetime would allow baseline revision to anticipate changes in economic direction that might challenge the validity of the baseline over time. This is particularly vital for countries such as Indonesia, where the eco- nomic situation is subject to rapid change.
5.2.4. Additionality
In the version of the negotiation text FCCC/CP/
2000/CRP.2/Add.1 dated 24 November 2000, paragraph 63, an option for determining the
‘additionality’ of a CDM project is stipulated. The paragraph describes three kinds of additionality against which a CDM project shall be measured:
environmental, financial, and investment additionality. It should be noted that final agree- ment on this paragraph has not been reached.
Environmental additionality
If GHG emissions are reduced by more than could have occurred in the absence of the project activity (i.e., if the are ‘additional’) a CDM project is said to exhibit environmental additionality. Conse- quently it is vital to determine baseline emissions, with which actual project emissions can be compared. Various methods for determining baselines are discussed in a greater detail in chapter 6. Environmental additionality serves as a significant criterion for establishing the environ- mental credibility of CDM projects.
Financial Additionality
A CDM project is financially additional if funds for the acquisition of emission credits (the emission credits generated by CDM projects are called certified emission reductions or CERs) do not fall within financial obligations of Annex B to the UNFCCC, or within the framework of the financial mechanism, or originate from official develop- ment assistance funds.
Unlike environmental additionality, which is relatively uncontroversial, the concept of financial additionality has involved lengthy debate since its inception. From the project point of view, tracing the source of funding would be complex and time consuming.
Investment additionality
The text stipulates that investment additionality is fulfilled if the risk-adjusted internal rate of return of the CDM project activity is below a certain
percentage, which should be determined via further negotiation.
Investment additionality ensures that the CDM will not just provide funding for business-as-usual projects, i.e., projects that would have been commercially viable without incentives from CDM funding.
Assessments of additionality may be challenged. A common example is renewable energy projects;
some hold that additionality is only achieved if the opportunity for fossil fuel replacement exists (Michaelowa 1999, Chomitz 2000). Once fossil fuel is phased out, either by market forces or govern- ment regulation, many renewable energy projects would be profitable, and micro-economically they are no longer ‘additional’ (Michaelowa 1999, Chomitz 2000). Therefore, the additionality of a project should be carefully assessed, particularly when dealing with macro and micro economic issues, with a view to achieving a balance between environmental integrity and economic efficiency.
AIJ experience has shown that baseline determi- nation can be a source of uncertainty and high transaction costs. Thus, baseline setting that does not incur high transaction costs and is not overly laborious will be more attractive to investors.
Transparency in baseline determination is also important. The methodology used should be made available for evaluation, replication and verification.
Rules for establishing additionality should be carefully considered. Overly complex rules may result in high administrative costs and discourage investment; overly simple rules could expose the system to corruption.
In order to ensure that real, measurable and long- term environmental benefits are achieved,
baseline methodology, and the measurability and verifiability of CO2 emission reductions, are of
prime importance. Specific guidance on these issues is requisite, and needs to be agreed at international level to ensure consistent quality of the emission credits (CERs in the case of the CDM) produced worldwide. A UNFCCC Baseline Refer- ence Manual was discussed during the COP negotiations in November 2000. Once a decision has been made on the CDM, either the IPCC or the Subsidiary Body for Scientific and Technological Advice (SBSTA) will develop this manual. In the absence of such rules, it is expected that baseline studies for CDM projects will include items as listed in Table 5.3.
5.2.5. Methodologies for determining baselines
Various methods can be used to determine baselines, but these methods are subject to approval by the CDM executive board. Limited experience of developing CDM project baselines and the diversity of project types may lead project proponents to propose their own baseline methodology. Ideally, national baselines will be set up prior to credit trading to prevent leakage and ensure additionality. However, political and technical considerations in developing countries may not allow this to be achieved easily.
Baselines can be established at the national, sectoral, project or technology level. National level baselines are often referred to as top-down baselines (Puhl, 1998). Top-down baselines are usually highly aggregated, and reflect national government objectives and policies. They can be used to assess emission reductions resulting from policy initiatives (e.g. Puhl et al., 1998).
Sectoral level baselines are developed from a set of activities in one sector. In the energy and industry sectors, these baselines would probably be based on emission rate (tons carbon - tC - per unit outputs, e.g. tC/GWh). At project level, the baselines are developed to evaluate emission
reductions resulting from one particular project. A comparison of the different baseline types is presented in table 5.4.
Michaelowa (2000) suggested that the type of baseline used would depend on both the specific situation and the project type.
Project Type: Forestry, infrastructure, policies, large number of projects in all sectors.
Baseline Type: highly aggregated, bench- marks.
Projects Type: Large projects, many projects in a specific sector, fuel substitution. Baseline Type: sector-specific, technology or default
matrix, project-related standardization,
Projects Type: Small projects such as renewables, retrofits, and small number of projects. Baseline Type: project-related standardization to technology or default matrix.
Four baseline types that may be applied in Indone- sia will be discussed here:
1. Sectoral baseline (model simulation) 2. Multi-project baseline
3. Project specific
4. Simplified baseline for small projects.
Table 5.3. Content of baseline study Item
Definition of system boundary
Type of baseline and baseline methodology used
Data on additionality factors
Project lifetime (duration)
Baseline lifetime
Abatement cost
Estimates of possible leakage
Compliance with national environmental standards and related regulations
Purpose of inclusion
To determine the approach used and the scope of measur- able GHG emission reductions.
To ensure transparency and credibility in estimating emission reductions and to enable validation/verifiability.
To indicate if baseline revision will be carried out.
To show real and measurable emission reductions will take place.
To indicate that financing of project is additional to any overseas development assistance funding.
To demonstrate that the project would not proceed without revenues from CERs.
To indicate the length of time over which the project can generate emission credits.
Baseline lifetime will generally be shorter than project lifetime due to revisions during the project lifetime.
To suggest the cost of the mitigation activity.
To address possible leakages resulting from the project activity.
To show that relevant national sustainable development objectives are met.
To ensure that the project contributes to improving environment.
5.2.5.1. Forecast based on model simulation Description
A model called MARKAL has been used to predict possible least-cost scenarios in developing the power sector in Indonesia. This model makes use of actual data from power generators and energy- related industries, together with their emission factors (Chomitz, 1998).
Information on the future supply of and demand for energy is fed into the MARKAL model. The output is usually the optimal cost, but the model can look at other considerations such as GHG emissions. The output of generation scenario Gbu is called E-business-as-usual (Ebu), and the corresponding CO2 emission is Cbu, while the optimized cost is Rbu.
Assume the same output Ebu is used, but with reduced CO2 emission (CCDM), for example, 10 percent reduction, and the corresponding
scenario is GCDM and optimized cost RCDM, then: Gbu would look like this: F1,…,Fm, Geo1,…,Geon, H1,…,Hp, PVbu ; GCDM would look like this: F1‘,…,Fq, Geo1‘,…,Geor, H1‘,…,Hs, PVCDM, CDM1,…,CDMt. where
F = Fossil Fuel Geo = Geothermal
H = Hydro
PV = Photo Voltaics CDM = CDM Project
(PVCDM – PVbu), CDM1,…,CDMt stand for CDM projects that would really offset carbon release by 10 percent.
Attention has to be given to the fact that RCDM >
Rbu; the difference is expected to be made up by the sale of emission credits to industrialized countries. If the emission credits are not sold, or only partially sold, then government has to invest more in the power sector to maintain sufficient power supply.
Constant emissions based Very low Very poor Much too low
on historical levels
Linear extrapolation Low Relatively good a) Too high if growth rate decreases b) Too low if growth rate increases
Based on economic develop- Very high Good a) Correct if assumptions realistic
ment and population growth b) Too high if assumptions over-optimistic
Country level
Sector level
Project-related scenarios with Low Good Correct if project typologies cover indirect
sectoral project typologies effects
Individual project-related Low-high Relatively good Correct, if indirect effects compensate for
scenarios (depending each other
on detail)
Project level
Sector-specific Rather high Relatively good Too low
Baseline definition Cost to develop
the scenario Depiction of reality Emission reduction indicated Table 5.4. Possible methods for baseline determination
Source: Michaelowa and Fages, 1999
Once a CDM scenario is generated, identification of projects or activities to match the output scenario, can begin. The selection of projects can be refined by bringing in other criteria, such as leakage, credibility, simplicity, and verifiability, which can be determined at a later stage.
Advantages and Disadvantages
The advantage of using this model is that the leakage issue is addressed on a national level, as the emissions from sources throughout the country will be taken into account. Such modeling encourages the identification and screening of technology that is genuinely needed by the host country.
The main weakness of this method is the high cost associated with the collection of the data required to run the model; the data are usually complex and the quantity large. To date MARKAL has only been used for simulation in specific sectors, i.e.
energy and industry. It is unlikely that MARKAL can be used in other sectors.
Applicability
In Indonesia, MARKAL has been used in the last few years by several institutions to predict the future energy mix scenario. Consequently the data are fairly complete. Projects beginning in the near future could take advantage of this, thus minimiz- ing data collection costs.
Although the output of the model is an energy mix scenario, it is quite possible to derive typical values (i.e., benchmark) for emissions by different technologies. The values can then be used to approximate the business-as-usual emissions for each technology. It is also possible to re-run MARKAL to test the sensitivity of any given sce- nario when new conditions and factors are introduced.
5.2.5.2. Project Specific Description
This type of baseline involves establishing what would happen if the CDM activity did not take place. Project-specific baselines are limited; they consider only the direct effects of the CDM activity, and do not consider emissions and emission reductions from other sources. The methodology used to establish a project-specific baseline and calculate emission reductions can vary according to the type and characteristics of the project. It is also possible to use the invest- ment analysis model to establish a project- specific baseline (Chomitz, 1998).
1. For a private sector project, the analysis would involve an assessment of the project’s profit- ability, assuming the private company would seek to maximize its profits. Thus, the baseline would reflect the least-cost solution or the scenario with the highest financial rate of return in the absence of CDM emission credits.
2. For a public sector project, full cost-benefit analysis would be performed. The objective of the exercise would be to maximize social benefits for the community. Therefore, the baseline would reflect the scenario with the highest social reimbursements in the ab- sence of CDM emission credits.
Advantages and disadvantages
Project-specific baselines provide more accurate data at the project management level. Project- specific baselines, particularly those derived from investment analysis, can be applied to any sector to assess the project’s feasibility. From the investor’s point of view, project-specific baseline studies are relatively simple to carry out and provide more certainty in calculation of emission
reductions, and therefore emission credits, that will result from project activity.
However, certain disadvantages are inherent in project-specific baselines. They may fail to recognize indirect effects (leakage) of the project activities. Project proponents may try to influence the baseline to their advantage. Furthermore, sustainable development objectives may be understated, since sustainability and social indicators are more difficult to assess and quantify.
Applicability
The type of baseline and the data used to gener- ate it would depend to some extent on the physical characteristics and socio-economy profile of the project area of operation. If an interconnection network comprising a mix of power generation types serves the area (as is the case on Java and Sumatra), average emission (carbon intensity) of the grid would be used. If the area were not linked (and unlikely to be linked) to the grid, assuming the continued use of diesel would be appropriate for the baseline, unless other energy systems were being used.
With the project-specific approach it would be possible to derive baseline data from a ‘source of emission’, provided emission from that source in the project area is capped. For example, the baseline for a project involving replacement of a coal-fired power plant by a gas combined cycle plant would use data on emissions from the coal- fired plant. However, if several gas turbine plants are already operating in the area, one might argue that the baseline should be the average of emis- sions from neighboring gas-fired plants. Thus, the baseline for a given project type (gas-fired to replace coal-fired, in this case) could be site specific. See (2000) recommended that the source of emission (in this case, coal-fired plant) could be treated as the baseline if a particular ‘superior’
technology (such as gas turbine plants) consti- tuted less than 50 percent of existing technologies in the region at the time of project investment.
A project-specific baseline would be appropriate for Indonesia’s AIJ solid sludge recycling project.
Because recycling and generating power from solid wastes is not a common practice in the pulp and paper industry a project-specific baseline would be the best means of generating and handling data.
5.2.5.3. Multi-project baseline Description
A multi-project baseline (MPB) is based on data derived from a representative number and mix of projects. Consequently multi-project emission factors (MPEF) are needed to establish the overall baseline emission.
Multi-project baselines seek to avoid the costly and time-consuming process of establishing and validating project-specific baselines. MPEF can be calculated based on data from selected individual factories or plants. Data from a num- ber of sources can be weighted and used in aggregate form, or specific representative data subsets might be used. In the power sector, for example, MPEF can be compared to the follow- ing reference:
fuel-specific (plant using same fuel)
all fossil (all fossil-fuel plants)
sector wide (whole electricity sector) Multi-project baselines have been used and studied in India, South Africa, and China. The main findings from these studies are:
Different baselines may need to be selected for different CDM projects.