Overview of ODS substitute issues

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LEVELS OF DATA AGGREGATION

Each application discussed above can be divided into sub-applications. When selecting a method for estimating emissions, it is good practice to consider the number and relevance of sub-applications, the data availability, and the emission patterns. Applications with a high number of sub-applications (refrigeration has six major subapplications; foam has even more) will generally benefit from a higher level of disaggregation in their data sets, owing to the differences between the sub-applications. Accordingly, for rigorous emissions estimates, inventory compilers are likely to favour estimating emissions for each sub-application separately. In this chapter, such an

3 Refer to http://hq.unep.org/ozone/ for the phaseout schedules dictated under the Montreal Protocol.

approach defines a Tier 2 method, whereas methods based on datasets aggregated at the application level are all classified as Tier 1. Even if few sub-applications exist, estimating emissions by sub-application may still be most appropriate owing to the differences in emission patterns, chemical use, data gathering methodologies, and/or data availability. Fire protection, for example, has only two major sub-applications, but each has unique emission characteristics and a disaggregated (Tier 2) method will produce better emission estimates. On the other hand, if emission patterns of sub-applications are similar and if data are difficult to collect in disaggregated form, estimating emissions at an aggregated application level (Tier 1) can be an appropriate approach to produce reliable emission estimates. For example, although several sub-applications exist within the aerosol propellants application, because the emission patterns and chemicals used are similar, estimating emissions at an application level may be sufficient to yield good results.

Figure 7.1 Disaggregation of chemical data across an application

Average charge by chemical or blend type for each sub-application functional unit

Quantities of blends and chemicals consumed in year

Chemicals imported/exported in products

Domestic production + Imports - Exports by product or equipment type (sub-application)

* Both required as a time-series

MATRIX

Consumption by chemical type by subapplication

* Both required as a time-series

Chemical Consumption, Bank, or Emissions

Product Data for Application

Sub-Application 1

Sub-Application 2

Etc.

Domestic Production

+ Imports

- Exports

Domestic Production

+ Imports

- Exports

Chemical Data for Application

Chemical 1

Chemical 2

Complete each cell (some may be

Chemical 3

zero) using chemical and product data as available. For blends,

Blend A

separate data into individual chemical constituents. Determine if

Blend B

chemical data includes that chemical used in blends or not.

Etc.

It is important early on in the estimation process to decide about how and from where data is to be collected. Data on chemical sales (sometimes referred to as top-down data) typically comes on a substance-by-substance basis, although even this can be complicated by the use of blends. Data on markets (sometimes referred to as bottom-up data) will tend to come in the form of equipment or product sales at the sub-application level, although this data will typically be influenced by the existence of imports and exports of such equipment or products. This data often need to be accompanied by an estimate of the share of the market that uses a particular technology. For example, different chemicals (including some not subject to reporting) may be used in the same sub-application. Additionally, the average amount of chemical used by each product type within the subapplication may vary. The two routes (chemicals and products) represent the two axes of a matrix and a disaggregated approach requires completion (or near completion) of that matrix (Figure 7.1). Completing this matrix is typically accomplished by using combinations of both types of data (i.e., both top-down and bottom-up data), comparing the results, and adjusting as appropriate.

DATA AVAILABILITY

There are often difficulties in collecting data for both Tier 1 and Tier 2 methods if chemical suppliers at the national level believe that there are confidentiality implications arising from disclosure of information. In practice, this has been one of the major barriers to reliable emissions estimates at the national level.

In order to overcome some of these constraints, there has, in recent years, been an effort to develop global and regional databases which provide information on historic and current activity (chemical consumption) data at the country level for specific applications and sub-applications. The value of this approach is that these data can be validated against chemical sales at regional, or even global, level and thereby avoids breeching confidentiality restrictions required by the suppliers. As these databases have developed, (for example, those developed under the oversight of the relevant UNEP Technical Options Committees under the Montreal Protocol) they have become increasingly sophisticated in their analyses of use patterns which are often well-understood at the subapplication level (see Box 7.1). This means that the two axes of the matrix described earlier can be addressed from these datasets and Tier 2 methods can be facilitated at a country level without a massive investment of resource. This activity data can then be combined with default emission factors or with country-specific emission factor data, if this is available, to derive appropriate emissions estimates. Of course, it is important to exercise care in making use of such databases and it is important to choose reputable well-documented sources. Nonetheless, the use of globally or regionally derived data of this type can deliver reliable estimates. An alternative strategy could be to use information generated from such a database to benchmark information collected nationally.

In either case, it is important that data is generated in a form that will fit with relevant reporting requirements (e.g., the Common Reporting Format of the United Nations Framework Convention on Climate Change (UNFCCC)). These requirements may vary with time during the lifetime of these Guidelines. Accordingly, the structuring of activity datasets should be sufficiently flexible to deal with such changes.

In some instances the complexity of the chemical and equipment supply chain can create additional challenges regarding data availability. As highlighted in Section 7.5, there are a range of containers that can be used to supply the mobile air conditioning market, from semi-bulk containers for OEMs; to intermediate containers for the average vehicle servicing centre (10-15kg); to small 300-500g cans for the do-it-yourself market. Since wastage levels will vary substantially between these differing supply-chain approaches, inventory compilers need to consider how to assess these losses in practice. The use of containers is not only limited to mobile air conditioning, but is often prevalent in other sectors of the refrigerant market, aerosols and in fire suppression. Inventory compilers could consider treating the supply of ODS substitutes as a separate element of the inventory. However, even if this route is taken, it will require detailed knowledge of the sub-applications to understand the range of sizes used and proportion of each. Accordingly, it is viewed as most appropriate to evaluate container losses (often termed heels) within each application and sub-application, although it would be good practice to compare estimated losses within different applications and sub-applications using the similar sized containers to ensure some uniformity of approach.

Box 7.1

Global and regional databases for ODS substitutes

Global and regional databases are typically developed for specific applications by experts in the field. These experts often have good professional contacts with industry sources, and are familiar with access to relevant market studies and other reports that shed light onto the consumption patterns of regions and countries. From this knowledge base it is possible to cross-reference product data, either at regional level or even at global level, with chemical consumption data. It is common for such databases to predict future consumption as well as to assess current consumption. This makes them valuable also as a policy development tool. However, it is important that such databases are properly maintained and are regularly cross-checked with actual chemical consumption data whenever it becomes available in order to be assured that any new trends or other sources of discrepancy are accounted for and fully reconciled wherever possible.

For example, individual members of the UNEP Technical Options Committees (TOCs) under the Montreal Protocol have prepared a number of global activity datasets that can assist countries in preparing estimates of ODS substitute emissions. Particularly relevant are the databases used to support the development of the IPCC/TEAP Special Report on Safeguarding the Ozone Layer and the Global Climate System: Issues Related to Hydrofluorocarbons and Perfluorocarbons (IPCC/TEAP, 2005), because information on the phase-out of ozone-depleting substances is directly relevant for estimating the phase-in of substitutes. The assumptions behind these datasets have been documented in a number of summary reports which can be found at http://epa.gov/ozone/snap/emissions/index.html (e.g., Clodic D., Palandre, L., McCulloch, A., Ashford, P. and Kuijpers, L.. 'Determination of comparative HCFC and HFC emission profiles for the Foam and Refrigeration sectors until 2015.' Report for ADEME and US EPA., 2004). These existing datasets have been regularly peer-reviewed by other experts from within the relevant TOCs and have been used by the Parties to the Montreal Protocol to assess transitions in chemical markets and chemical use patterns.

If national data are difficult to obtain, countries can search the IPCC Emissions Factor Database (EFDB) for datasets such as those discussed above. All such databases should be structured to facilitate their use in inventory reporting. The EFDB is likely to become the home for a number of such global/regional databases in due course, either as additional sources for applications already covered or as new sources for applications not previously covered. Although inclusion of databases in the EFDB provides general assurance of due process, it is good practice for countries to ensure that all data taken from the EFDB are appropriate for their national circumstances and that peer review is sufficient for this complex area of activity.

TYPES OF EMISSION ESTIMATES

In contrast with the earlier Guidelines, both Tier 1 and Tier 2 methods proposed in this chapter result in estimates of actual emissions rather than potential emissions. This reflects the fact that they take into account the time lag between consumption of ODS substitutes and emission, which, as noted previously, may be considerable in application areas such as closed cell foams, refrigeration and fire extinguishing equipment. A time lag results from the fact that a chemical placed in a new product may only slowly leak out over time, often not being released until end-of-life. A household refrigerator, for example, emits little or no refrigerant through leakage during its lifetime and most of its charge is not released until its disposal, many years after production. Even then, disposal may not entail significant emissions if the refrigerant and the blowing agent in the refrigerator are both captured for recycling or destruction.

The potential emission method, in which emissions are assumed to equal the amount of virgin chemical consumed annually in the country minus the amount of chemical destroyed or exported in the year of consideration, is now presented only as a reference scenario in the QA/QC section. As noted above, the potential method does not take into account accumulation or possible delayed release4 of chemicals in various products and equipment, which means that, over the short term (e.g., 10-15 years), estimates may become very inaccurate. Therefore, it is not considered good practice to use the potential method for national estimates.5

4 Sometimes from types of equipment and products which have since converted out of halocarbon technologies.

5 The Conference of the Parties to the UNFCCC, at its third session, affirmed '... that the actual emissions of hydrofluorocarbons, perfluorocarbons and sulphur hexafluoride should be estimated, where data are available, and used for

TIMING OF EMISSIONS AND THE SIGNIFICANCE OF BANKS

In many applications ODS substitutes such as HFCs and PFCs serve their purpose only if they are contained (e.g., refrigeration and air conditioning), while in other applications, they are meant to be emitted (e.g., as an aerosol propellant). These differences are important to understand, so that the year in which emissions occur can be accurately assessed, and hence actual emissions can be accurately estimated.

Where emissions occur within the first two years, they are usually referred to as prompt emissions. Examples of applications and sub-applications exhibiting prompt emissions include aerosols, aerosol solvents, open-cell foams and in some cases non-aerosol solvents. In general, emissions from applications or sub-applications exhibiting prompt emissions can be estimated by determining annual chemical consumption and then assuming all emissions occur within the first year or two of consumption. Thus, if chemical consumption is unknown prior to a certain date, emission estimates a year or two after that date will nonetheless be accurate and relatively little accuracy will be gained by searching for or estimating chemical consumption from prior years.

Where delays in emission occur, the cumulative difference between the chemical that has been consumed in an application or sub-application and that which has already been released is known as a bank. Applications in which banks typically occur include refrigeration and air conditioning, fire protection, closed-cell foams, and often non-aerosol solvents. The definition of bank encompasses the presence of the chemical at all parts of the lifecycle and may even include waste streams. By way of example, blowing agent still present in foamed products which may have already been land-filled is still part of the bank, since it is chemical which has been consumed and still remains to be released. In practice, most equipment-related sub-applications (e.g. in refrigeration and fire protection) are unlikely to carry their charges into the waste stream and the total of the chemical contained in the equipment currently in use becomes a close approximation to the actual bank.

Estimating the size of a bank in an application or sub-application is typically carried out by evaluating the historic consumption of a chemical and applying appropriate emission factors. Where more than one subapplication exists, but a Tier 1 method is being followed, a composite emission factor needs to be applied. However, in view of the uncertainties surrounding such composite emission factors, Tier 2 methods will always be preferred for applications with multiple sub-applications, particularly where these are dissimilar in nature.

It is also sometimes possible to estimate the size of bank from a detailed knowledge of the current stock of equipment or products. A good example is in mobile air conditioning, where automobile statistics may be available providing information on car populations by type, age and even the presence of air conditioning. With knowledge of average charges, an estimate of the bank can be derived without a detailed knowledge of the historic chemical consumption, although this is still usually useful as a cross-check.

APPROACHES FOR EMISSION ESTIMATES

Even among those applications which retain the chemicals over time, there are some significant distinctions. In some instances (e.g., refrigeration) the quantity of HFC or PFC is typically topped-up during routine servicing. If equipment were topped-up annually and the market was otherwise static (i.e., no growth in the equipment stock), the actual emissions would be consistent with consumption for that year. Under such circumstances, it is not necessary to know the precise equipment stock as long as the consumption of HFC or PFC is known by type at the sub-application level. This is the basis of the mass-balance approach which is referred to throughout this chapter as Approach B. More discussion on the mass-balance approach is found in Chapter 1, Section 1.5 of this volume. However, a mass-balance approach is not appropriate for other situations or for other products (e.g., foams) where consumption occurs only at the point of manufacture, while emissions may continue to a limited extent throughout the lifetime of the product. In such instances, it is usually better to revert to an emission-factor approach (i.e., methods based on activity (consumption) data and emission factors). Such methods can be operated at both aggregated (Tier 1) and disaggregated (Tier 2) levels and are referred to throughout this chapter as Approach A. Accordingly, a Tier 1a method will be an emission-factor approach with a low level of disaggregation, while a Tier 2b method will be a mass-balance approach with a relatively high degree of disaggregation (at least to the sub-application level). Further information on the choice between using a massbalance approach and an emission-factor approach is found in Chapter 1, Section 1.5. In general, mass-balance approaches are only considered for ODS substitutes stored or used in pressurised containers and so many applications do not consider Approach B at all. Where Approach B is considered (e.g., refrigeration and fire protection) the choice of method is discussed under that part of Chapter 7 addressing the application in question.

Some methods described for these specific applications can have characteristics of both approaches, and the mass-balance approach can be used to cross-check and validate the results of an activity (consumption) data/emission factor approach. Accordingly, whilst the labelling conventions will remain unchanged throughout the reporting of emissions. Parties should make every effort to develop the necessary sources of data;'. (Decision 2/CP.3, Methodological issues related to the Kyoto Protocol)

to avoid confusion, it may be that some methods are labelled Tier 1a/b or Tier 2a/b because they are seen to contain elements of both approaches. This is most common in the case of Tier 1 methods where data is limited and one approach can be usefully used to cross-check the other.

Table 7.2 below summarises what kind of data are required to implement different tiers and approaches.

Table 7.2

Overview of data requirements for different tiers and approaches

Approach A (emission-factor approach)

Approach B (mass-balance approach)

Tier 2 (emission estimation at a disaggregated level)

• Data on chemical sales and usage pattern by sub-application [country-specific or globally/regionally derived]

• Emission factors by sub-application [country-specific or default]

• Data on chemical sales by sub-application [country-specific or globally/regionally derived]

• Data on historic and current equipment sales adjusted for import/export by subapplication [country-specific or globally/regionally derived]

Tier 1 (emission estimation at an aggregated level)

• Data on chemical sales by application [country-specific or globally/regionally derived]

• Emission factors by application [country-specific or (composite) default]

• Data on chemical sales by application [country-specific or globally/regionally derived]

• Data on historic and current equipment sales adjusted for import/export by application [country-specific or globally/regionally derived]

In the six sections that follow (Sections 7.2 to 7.7), decision trees are included for each application to assist in the identification of data needs and the selection of approach for individual sub-applications, where these exist.

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