Content uploaded by Daniel Violette
Author content
All content in this area was uploaded by Daniel Violette on Jul 04, 2015
Content may be subject to copyright.
1
An Initial View on Methodologies for Emission Baselines:
Case Study on Energy Efficiency
IEA and OECD Information Paper
Prepared by:
Hagler Bailly Services, Inc.
Daniel Violette, Principal Advisor
Christina Mudd, Senior Associate
Marshall Keneipp, Summit Blue Ventures, LLC
Paris, June 2000
The ideas expressed in this paper are those of the authors and do not necessarily represent
views of the OECD or its member countries.
2
FOREWORD
This document was prepared by Hagler Bailly Services Inc. for the IEA and OECD Secretariats at the
request of the Annex I Expert Group on the United Nations Framework Convention on Climate
Change. The Annex I Expert Group oversees development of analytical papers for the purpose of
providing useful and timely input to the climate change negotiations. These papers may also be useful
to national policy makers and other decision-makers. In a collaborative effort, authors work with the
Annex I Expert Group to develop these papers. However, the papers do not necessarily represent the
views of the OECD or the IEA, nor are they intended to prejudge the views of countries participating
in the Annex I Expert Group. Rather, they are Secretariat information papers intended to inform
Member countries, as well as the UNFCCC audience.
The Annex I Parties or countries referred to in this document refer to those listed in Annex I to the
UNFCCC (as amended at the 3rd Conference of the Parties in December 1997): Australia, Austria,
Belarus, Belgium, Bulgaria, Canada, Croatia, Czech Republic, Denmark, the European Community,
Estonia, Finland, France, Germany, Greece, Hungary, Iceland, Ireland, Italy, Japan, Latvia,
Liechtenstein, Lithuania, Luxembourg, Monaco, Netherlands, New Zealand, Norway, Poland,
Portugal, Romania, Russian Federation, Slovakia, Slovenia, Spain, Sweden, Switzerland, Turkey,
Ukraine, United Kingdom of Great Britain and Northern Ireland, and United States of America.
Where this document refers to “countries” or “governments” it is also intended to include “regional
economic organisations”, if appropriate.
This case study is part of a larger analytical project undertaken by the Annex I Expert Group to
evaluate emissions baseline issues for project-
based mechanisms in a variety of sectors. Additional
work
in this and other sectors will seek to address further the issues raised in this and other case
studies.
ACKNOWLEDGEMENTS
This paper was prepared by Daniel Violette, Christina Mudd (Hagler Bailly Services Inc.) and
Marshall Keneipp (Summit Blue Ventures) for the the IEA and OECD Secretariats. The authors thank
Martina Bosi (IEA) for her comments and oversight of this project. The authors are also grateful for
useful comments and suggestions received from Jonathan Pershing and Benoît Lebot (IEA), Jan
Corfee-Morlot, Jane Ellis, Thomas Martinsen and Stéphane Willems (OECD), as well as from the
delegations to the Annex I Expert Group.
The IEA and OECD remain responsible for any remaining errors and omissions.
Questions and comments should be sent to:
Martina Bosi
Administrator
Energy & Environment Division
International Energy Agency
9, Rue de la Fédération
75015 Paris, FRANCE
Fax: 33 (0)1 40 57 67 39
E-mail: martina.bosi@iea.org
OECD and IEA information papers for the Annex I Expert Group on the UNFCCC can be
downloaded from the following site: http://www.oecd.org/env/cc.
See also http://www.iea.org
3
TABLE OF CONTENTS
Executive Summary ........................................................................................................................................ 5
Glossary and Acronyms .................................................................................................................................. 9
1. Introduction ............................................................................................................................................ 11
2. Sector Overview .................................................................................................................................... 12
2.1 Energy Efficiency Sector Trends .................................................................................................... 12
2.2 Energy Efficiency Market Trends .................................................................................................. 14
2.3 Barriers to Investments in Energy Efficiency ................................................................................. 15
2.4 Trends in Energy Efficiency Projects and Baseline Implications ................................................... 16
3. Baseline Construction ............................................................................................................................ 17
3.1 Baseline Calculation: Data Needs, Quality, and Availability ......................................................... 17
3.1.1 Simplified Application – A Lighting Equipment Example ................................................. 18
3.1.2 Simplified Application — Estimating the Energy Use Baseline for a Lighting Project ..... 20
3.1.3 Simplified Application — Converting Energy Use to GHG Emissions ............................. 21
3.2 Essential Steps in Baseline Construction ........................................................................................ 21
3.3 Energy Use Baseline Construction — Application to Energy Efficiency Projects and
Programmes Implemented in Developing Countries ...................................................................... 24
3.3.1 Audit-Based Programmes as a Means of Constructing Baselines ....................................... 24
4. Standardising Baseline Assumptions ..................................................................................................... 26
4.1 Determining Standard Baseline Performance ................................................................................. 28
4.2 Standard Calculation and Data Protocols – Lighting and Motors .................................................. 29
4.2.1 Standard Calculation and Data Collection Protocols for Lighting ...................................... 29
4.2.2 Standard Calculation and Data Collection Protocols for Motors ........................................ 30
4.3 Standardising Operating and Performance Parameters – Lighting and Motors ............................. 30
4.3.1 Standardised Operating and Performance Parameters for Lighting .................................... 31
4.3.2 Standardised Operating and Performance Parameters for Motors ...................................... 33
4.4 Energy Use Indices – Lighting and Motors .................................................................................... 35
4.4.1 Standardised Energy Use Indices (EUI) for Lighting ......................................................... 35
4.4.2 Standardise Energy Use Indices for Motors ........................................................................ 36
4.5 Data Issues ...................................................................................................................................... 37
5. Issues in Constructing Baselines ............................................................................................................ 38
5.1 Baseline Construction and the Potential Volume of Energy Efficiency Projects ........................... 38
5.1.1 Balancing Risks in Energy Efficiency Projects ................................................................... 38
5.2 Potential Biases in Baseline Construction: Free Riders and Spillover Effects ............................... 40
5.3 Baseline Stringency ........................................................................................................................ 41
5.3.1 In-Field Efficiency Levels in the Business-as-Usual Case.................................................. 41
5.3.2 Assumptions about Business-as-Usual Investments in Energy Efficiency ......................... 42
6. Insights and Conclusions ....................................................................................................................... 43
References ..................................................................................................................................................... 46
ANNEX A: Examples Of Baseline Development For Energy Efficiency Projects And Energy
Efficiency Market Assessment
1. Fluorescent Lamp and Compact Fluorescent Lamp Market Transformation Project — Thailand ... 49
2. Ilumex Compact Fluorescent Lamp Replacement Project — Mexico (AIJ Pilot Phase) .................. 50
3. Demand-Side Management Assessment — Morocco ....................................................................... 52
4. High-Efficiency Motors Replacement Project — Mexico ................................................................ 55
5. ENERCON Technical Energy Efficiency Assistance Programme — Pakistan ................................ 58
6. Tubewell Audit and Retrofit Project, — Pakistan ............................................................................ 60
7. Boiler/Furnace Tune-up Energy Efficiency Assessment Programme — Pakistan ........................... 61
4
TABLES
Table 1. Vietnam’s Changing Sectoral and End-Use Electrical Energy Shares .................................. 14
Table 2. Summary of Implications for Standardisation from Energy Efficiency Case Studies ........... 27
Table 3. Lamp Performance Data (Mexico Ilumex project) ................................................................ 32
Table 4. Summary of Lighting Characteristics by Urban and Rural Areas ......................................... 32
Table 5. Summary of Commercial Indoor Lighting (Morocco DSM Research Study) ....................... 33
Table 6. Framework Example of Standardised Baseline Parameters for Lighting Efficiency
Projects .................................................................................................................................. 33
Table 7. Distribution of Electric Motors by Application and Demand (Pakistan Energy
Efficiency Assistance Programme) ....................................................................................... 35
Table 8. Framework Example for Standardised Energy Use Baseline Motor Efficiencies for
Energy Efficiency Projects .................................................................................................... 35
Table 9. Framework Example for Lighting Energy Use Indices ......................................................... 36
Table 10. Framework Example for Electric Motor Energy Use Intensities .......................................... 37
Table A-1. Lamp Performance Data .................................................................................................... 51
Table A-2. AIJ Component Baseline and Estimated GHG Reductions ............................................... 52
Table A-3. Incandescent and CFL Wattages ....................................................................................... 53
Table A-4. Summary of Lighting Characteristics by Urban and Rural Areas ..................................... 54
Table A-5. Distribution of Indoor Incandescent Lamps by Wattage ................................................... 54
Table A-6. Summary of Commercial Indoor Lighting ........................................................................ 54
Table A-7. Distribution of Electric Motors by Application and Demand ............................................ 59
Table A-8. Distribution of Lighting Fixtures by Wattage and Type .................................................... 59
FIGURES
Figure 1. Illustration of an Energy Use Baseline Estimation ............................................................... 23
Figure 2. Distribution of Motors by Horsepower (Mexico Motors Replacement Project) .................. 34
Figure A-1. Distribution of Motors by Brand ...................................................................................... 56
Figure A-2. Distribution of Motors by Horsepower ............................................................................ 56
Figure A-3. Distribution of Motors by Percentage of Load ................................................................. 57
5
Executive Summary
Extending previous OECD and IEA work on emission baselines (Ellis and Bosi, 1999), this case study
provides an initial view on the potential for standardising the construction methods for greenhouse gas
emissions baselines for JI and CDM energy efficiency projects. The focus is on baselines in the
lighting and motors sectors.
This analysis is largely based on the experience gained through energy efficiency projects and
programmes in industrialised countries – which may provide valuable lessons for the construction of
baselines for JI and CDM projects in developing countries, as well as in economies in transitions. It is
also based on the examination of seven examples of energy efficiency projects and programmes
undertaken in four developing countries1 (i.e. Mexico, Morocco, Pakistan, and Thailand).
Three key factors point towards a possibly large potential for energy efficiency projects in the context
of the Clean Development Mechanism:
• High growth in energy demand is forecast for developing countries, with electricity use
expected to increase significantly by 2015.
• The most cost-effective energy efficiency projects tend to be those implemented as part of new
construction or major facility modification efforts, and these types of projects are projected to
be significant in developing countries.
• Most developing countries did not participate in the wave of energy efficiency investment that
occurred (mostly in OECD countries) after the oil price shocks of the 1970s and 1980s.
Consequently, there are still numerous opportunities to increase energy efficiency in developing
countries (as well as in countries with economies in transition).
Energy efficiency projects tend to have particularities that need to be taken into account when
developing baselines. In contrast to other sector projects, energy efficiency projects often comprise
bundles of smaller projects. For example, one AIJ pilot phase energy efficiency project in Mexico (see
Annex A) involved the replacement of existing lights with higher-efficiency compact fluorescent
lamps. While there are some single-site, large-scale energy efficiency projects that have been
implemented (e.g., a large district heating system or a single large industrial facility), energy
efficiency projects are more likely to be characterised by two factors:
• They span a large number of sites or locations.
• There is a specified target market area, although multiple sites may be targeted (e.g., the AIJ
lighting project in Mexico spanned many households in a target market covering two cities).
The development of GHG emission baselines for energy efficiency projects can be divided into two
main steps: (i) the development of the energy use baseline; and (2) the translation of this baseline into
GHG emissions.
There are essentially three options, or levels, for the standardisation of energy use baselines for energy
efficiency projects. These are (a) standardising baseline calculation methods, (b) standardising
operating and performance parameters, and (c) standardising energy use indices; each is discussed
below:
1 This case study focuses on energy efficiency projects in the context of developing countries, and thus the
Clean Development Mechanism. However, many issues and insights from this study are likely to be
applicable in the context of JI energy efficiency projects, although this may merit further examination.
6
a) Standardising baseline calculation methods and data collection protocols (i.e., the algorithms
and models used to compute energy use and the data that provide inputs to the algorithms)
In traditional energy efficiency projects and programmes undertaken to date, relatively little attention
has been paid to the development of baselines (or reference scenarios). JI and CDM energy efficiency
projects will clearly demand a greater focus on these.
Standardising calculation algorithms, data requirements supporting those algorithms, and data
collection protocols would promote the application of appropriate procedures and reduce project
developer’s uncertainty. Such standardisation could be reasonably done for a variety of energy
efficiency projects, including equipment replacement (e.g., lighting, motors, and appliances).
Simplified calculation algorithms for the construction of baselines for lighting and motors energy
efficiency projects are developed in this case study.
It is important to keep in mind that data are key in the development of baseline. In fact, most debates
over the quality of the baseline revolve around concerns about whether the sample selected for the
baseline development is indeed representative of the project participants and their energy use. As a
result, the selection of a sample is often a crucial determinant of “good” baseline”. The use of
sampling is important in that it keeps the costs of establishing a baseline using project-specific
information manageable.
In the particular context of energy efficiency projects in the lighting sector, baseline calculation
methodology (algorithms) and data collection protocols appear suitable to standardisation. A review
of lighting sector projects across three countries indicated that differences in the technologies,
targeted participants and in-field operating conditions in each country make it inappropriate to share
baseline data (i.e. use identical baselines across countries). However, a common approach to
collecting the required data using state-of-the-practice techniques could be shared across countries.
Similarly, in the case of the development of baselines in electric motors, the calculation methodology
used for estimating the energy use for a population could be standardised across countries. Data on
the number of motors categorised by horsepower can be collected at either the site or the population
level. The data for the efficiency and operating hours can be obtained through estimation from
technical data, engineering methods or field observations.
However, not all energy efficiency projects are amenable to the same level of standardisation; some
require project-specific data to establish baselines. Some simplification is still possible; methods exist
whereby national or regional sector baselines can be used as starting points and then adjusted
according to in-field data collected from participants in the project. These methods use statistical
procedures (e.g., energy-use realisation rates, ratio estimation methods) and can significantly lower
the cost of baseline construction below that incurred using methods where each project developer has
to start de novo, i.e., without a “prior” estimate of the baseline from aggregate data. Such a method
essentially combines standardisation with project-specific elements to produce a cost-effective hybrid
approach.
b) Standardising operating (e.g. number of hours) and performance (e.g. motor efficiency)
parameters necessary for the baseline calculation (i.e., the values that describe the energy use
characteristics of a given technology or end-use)
The standardisation of baseline operating and performance parameters brings greater uniformity and
consistency to the CDM/JI baseline development process. It would also reduce the time and cost of
estimating the energy use baselines for project developers – although it does not, of itself, establish
baseline energy use for energy efficiency projects.
In the lighting sector, it is likely that the standardisation of operating and performance parameters
would be possible for most common types of lighting devices. This seems to be particularly
7
appropriate in the residential and commercial sectors, where lighting operating hours tend to be
relatively consistent. The operating hours parameters would need to be differentiated according to
market sector/segment, and developed and standardised on a country-by-country basis (or on a
regional basis if circumstances are sufficiently similar), in order to take into account differences in
domestic markets and the mix of technologies. Similarly, performance parameters such as input
wattage for the most common lighting fixture types could be standardised. For example, it would be
possible to establish baseline data on wattages for common types of incandescent and fluorescent
residential and commercial fixtures.
In the motors sector, it would be possible to standardise motor efficiency parameters, as equipment
performance tends to be more uniform across market segments than other operating characteristics.
Such standardisation seems particularly applicable for certain motor types and size ranges that are
most common in the commercial sector and industrial application. In fact, it would seem useful to
further examine, in the context of developing countries, the possibility of standardising motor
efficiencies for the most common types, sizes, classes and applications, based on manufacturers’ data.
In addition to motor efficiencies, energy use baselines in the motors sector requires data on operating
hours, load factors and “diversity” factors. These latter parameters lend themselves to standardisation
(albeit with certain limitations). As operating hours tend to be relatively consistent within specific
market segments, particularly in the commercial sector, this parameter could be conservatively
standardised by market sector/segment. These baseline values would need to be derived from end-use
load information on a country-by country, or possibly regional, basis.
c) Standardising energy use indices (EUI) by sector, market segment and/or end-use (i.e., indices
that are representative of the energy use of a population of technologies or segment of the
population, such as lighting kWh per square meter for certain commercial building types).
With respect to lighting projects, it seems possible to standardise indoor lighting EUIs (e.g. lighting
kWh/square meter) for certain market segments of the commercial sector (e.g. offices, schools, and
hospitals). In the residential sector, it may be possible to standardise EUIs for certain appliances (e.g.
refrigerators). Standardised lighting EUIs are probably less applicable in the industrial sector, where
a hybrid approach combining standardised and project specific elements is likely more appropriate.
Standardisation of motor energy use indices (e.g. kWh/square meter) for the commercial sector does
not seem appropriate, as motor energy use is often tabulated or subsumed in other end-uses,
particularly space heating and cooling. However, in the industrial sector, where motors are often the
primary energy-consuming devices, it may be possible to develop baseline motor energy use indices
related to the unit of production (motor kWh/unit of production) for selected industries.
Other Energy Efficiency Baseline Issues
The environmental risks associated with accepting an “incorrect” baseline varies significantly by type
of energy efficiency project. The potential negative environmental consequences of using an
“incorrect” baseline are probably highest if an energy efficiency project includes only one or two very
large facilities (e.g., district heating systems, large industrial applications). Projects that embody a
portfolio concept where several energy efficiency measures are installed across a large number sites
may pose less environmental risk (as the baseline would probably be “correct” for the project as a
whole, even though some individual components may not be “additional” on their own).
The methodologies examined to estimate energy use baselines for energy efficiency projects normally
only consider direct energy use. However, energy efficiency projects may lead to two indirect energy
use (and GHG) effects: free riders and spillover effects. These two indirect effects work in opposite
directions and both are difficult to quantify. Until better information is available, it may be practical
to assume (as have some regulatory jurisdictions in the case of traditional energy efficiency projects
and programmes) that these two effects cancel each other out.
8
Project developers need a framework that allows them to assess the economics of a project. Several
actions can be taken that help ensure environmental integrity and help project developers better and
more efficiently, evaluate potential JI/CDM projects, including:
• Setting an emissions rate per kWh reduced for a pre-determined period of time. This is likely
to be the most important standardisation action that can be taken, as it would apply equally to
all projects across all sectors in a given country/region and thereby would help encourage all
energy efficiency projects.
• Setting the crediting lifetime associated with an energy use baseline. This paper proposes a
five-year crediting lifetime, arguing it provides project developers with enough time to recover
costs and earn a return on a wide range of energy efficiency projects and stimulate investments
in JI/CDM energy efficiency projects. (Such a baseline could be set in such a way that energy
efficiency would be required to increase at a given rate over the five-year period; project
confidence would be possible only if such a dynamic rate were agreed at the outset of the
project).
In terms of the level of stringency of the energy efficiency baseline level, the key criteria should be
what most reasonably reflects the likely “business-as-usual” scenario.
Setting the baseline level based on what investments should theoretically take place using traditional
financial assessment criteria (e.g. pay-back period), is likely not a good proxy for “business-as-usual”,
as such theoretical financial criteria do not take into account the various (e.g., non-monetary) barriers
to energy efficiency investments (e.g. information cost, attention cost, market distortion cost, public
policy costs, cultural barrier costs, etc.).
The other two main approaches of determining an appropriate baseline stringency level are based on:
(i) existing stock of equipment in the field; and (ii) “best practice”, using either highest rated
equipment found in the field or equipment for sale. The most appropriate choice would depend on
what is reasonable to assume under a business-as-usual scenario. In a case where all new sales are for
equipment that has a higher efficiency level than older equipment, then the new equipment efficiency
level should be used for developing the baseline. On the other hand, if the technology is entirely new
and only a small fraction (e.g. less than 30 per cent) of new sales represent this technology, then the
average efficiency level (or potentially a reasonable “better-than-average” efficiency level) of the
stock of equipment in the field may be more appropriate.
Finally, it is important to recognise that some energy efficiency JI or CDM projects will probably
“beat the system” and receive more emissions credits than they deserve. No process will be perfect,
and any energy efficiency baseline construction process likely will have defects. However, as search
for perfection will likely result in no process being judged as acceptable, the goal instead should be to
strike both a reasonable balance among various risks: among environmental objectives; among the
interests of project developers, and among those of potential host countries.
9
Glossary and Acronyms
AIXG: Annex I Experts Group on the United Nations Framework Convention on Climate Change
(UNFCC).
Audit-based programmes: Programmes that rely on the systematic collection of data on building and
energy system performance characteristics at the customer site. The goal of these programmes is
typically to identify and quantify energy efficiency improvement opportunities in combination with an
implementation plan.
Bench tests: Tests of equipment performance characteristics conducted in a controlled environment
such as a laboratory or manufacturer’s test facility.
CDM: Clean Development Mechanism.
CFL: Compact fluorescent lamp
Demand-side management (DSM): Utility programmes designed to control, limit or alter energy
consumption by the end user. DSM objectives may include energy conservation, load management,
fuel substitution and load building.
Diversity factor: The ratio of the peak demand of a population of energy consuming equipment to the
sum of the non-coincident peak demands of the individual equipment.
End-use indices (EUI): The ratio of the energy use of a building, system or end-use over a given time
period to a commonly recognised index of size or capacity. Examples include lighting energy use per
square foot of floor area and motor energy use per unit of production output.
Fluorescent lamps: A discharge lamp in that a phosphor coating transforms ultraviolet light into
visible light. Fluorescent lamps require a ballast that controls the starting and operation of the lamp.
GHG: Greenhouse gas
Hp: Horsepower
HPS: High-pressure sodium lamps
HVAC: Mechanical heating, ventilating and air-conditioning of buildings.
IEA: International Energy Agency
Incandescent lamps: A lamp that produces visible light by heating a filament to incandescence by an
electric current.
JI: Joint Implementation
Load curve: A plot of the demand placed on an energy system during an hour, day, year or other
specified time period.
Market segment: A segment of a customer or end-user market identified by common demographic,
firmographic or energy use characteristics. Examples include the single-family detached home
segment in the residential sector and the office building segment in the commercial sector.
10
Nameplate data: Data provided by equipment manufacturers that identify the make, model and
performance characteristics of the equipment. These data are published in the manufacturer’s product
literature and key data elements are affixed to the equipment on the nameplate. Often the equipment
nameplate itself does not provide sufficient information for energy analysis.
OECD: Organisation for Economic Co-operation and Development
Off-peak load: The demand that occurs during the time period when the load is not at or near the
maximum demand.
Peak load: The maximum demand or load over a stated period of time. The peak load may be stated
by category or period such as annual system peak, customer class peak, or daily peak.
Run-time monitoring: Recording equipment or system runtime over a specific monitoring period.
Often conducted with devices specifically designed for recording operating hours.
SAE: Statistically adjusted engineering analysis
Spot-watt measurements: One-time or instantaneous measurements of input wattage to a system or
piece of equipment.
UNFCCC: United Nations Framework Convention on Climate Change
USAID: US Agency for International Development
USEA: US Energy Association
11
1. Introduction
This paper extends previous IEA and OECD work on greenhouse gas emissions baselines for the
Kyoto Protocol’s project based mechanisms: Joint Implementation (JI) and the Clean Development
Mechanism (CDM). Specifically, Ellis and Bosi (1999) examine issues in developing greenhouse gas
(GHG) emission baselines, including the possibility of standardising them (i.e., multi-project
baselines). This paper examines issues surrounding the potential standardisation of baselines for JI
and CDM energy efficiency projects, focusing on approaches that have been used to establish
baselines in conjunction with planning, implementing and evaluating energy efficiency projects in
selected developing countries.2 Energy efficiency projects from Thailand, Mexico, Morocco and
Pakistan are used as examples.
Establishing emission baselines for energy efficiency projects is a two-step process. First, an energy
use baseline must be established for the energy efficiency project. Second, this baseline must be
translated into a GHG emissions baseline. This paper focuses on the first step – establishing baselines
for energy use, i.e., what would the energy consumption have been if the demand-side energy
efficiency project had not been installed. The second step – translating the change in energy use into a
change in GHG emissions, which requires emission values associated with electricity use – is being
addressed in a separate case study undertaken at the request of the Annex I Expert Group (AIXG) of
UNFCCC (i.e., Bosi, 2000).
The accomplishments of energy efficiency programmes to date imply that using energy efficiency as a
way to reduce GHG emissions within both Annex I and non-Annex I countries has the potential to
greatly reduce the costs of GHG mitigation. Further, these benefits extend beyond GHG emissions
reductions by providing host countries with other environmental benefits associated with reduced
energy use (local air, water and land use impacts), the installation of current technology in important
sectors, and the development of a sustainable infrastructure. In addition, there are likely to be
spillover economic and environmental benefits for all parties.
Given the magnitude of the environmental and economic benefits that can be expected, the challenge
is how to set up a reasonable process for constructing baselines. A number of suggestions regarding
the baseline-setting process are offered below. Issues addressed include: areas were the process might
be standardised, trade-offs in baseline complexity, balancing risks, baseline stringency, and potential
biases in the baselines and their impact on the selection of potential energy efficiency projects.
2 This report focuses on baselines for energy efficiency projects in the context of developing countries, and
thus the Clean Development Mechanism. However, many issues and insights from this report are also likely
applicable in the context of JI energy efficiency projects, although this may warrant further examination.
12
2. Sector Overview
Energy efficiency projects may be found in a wide variety of initiatives in the residential, commercial,
and industrial sectors. This diversity makes it difficult to determine the exact size of the market and
opportunities for GHG mitigation through energy efficiency projects. However, a handbook on
climate change mitigation options for developing countries prepared by the USEA/USAID estimated
that current world-wide energy demand could be by reduced 3-7 per cent by year 2010, with
corresponding reductions in GHG emissions through Demand-side management (DSM).3
Three factors make developing countries strong candidates for energy efficiency projects:
• High growth in energy demand is forecast for developing countries, with electricity use
expected to increase nearly eight-fold by 2015.
• The most cost-effective energy efficiency projects tend to be those implemented as part of new
construction or major facility modifications,4 and these types of projects are projected to be
significant in developing countries.
• Most developing countries did not participate in the wave of energy efficiency investment that
occurred (mostly in OECD countries) after the OPEC oil embargo and the resulting high energy
prices of the 1970s and 1980s. Consequently, there are still numerous opportunities to increase
energy efficiency in developing countries, as well as in countries with economies in transition.
As a result of these factors, a significant fraction of the GHG emissions reductions achieved via the
Kyoto Protocol’s project-based mechanisms could potentially result from successful energy efficiency
projects implemented in developing countries.
2.1 Energy Efficiency Sector Trends
On average, the residential sector typically accounts for 20 to 35 per cent of a country’s energy use.5
Candidate residential sector projects can be directed at: 1) improving the energy efficiency of
residential lighting and appliances; 2) improving the energy efficiency of new and existing
construction; and 3) improving the energy efficiency of space heating and cooling systems. Efficient
residential construction and high-efficiency appliances can reduce household energy use by 33 per
cent using available technologies.
Commercial sector energy use typically accounts for 10 to 30 per cent of a country’s energy use.
Candidate commercial sector projects are likely to be designed to address: 1) building envelopes; 2)
efficient equipment (e.g., lighting, motors, variable speed drives, heating, ventilation and air
conditioning (HVAC) equipment); and 3) community energy systems such as district heating in
3 USEA/USAID Handbook, 1999, pp. 7-3.
4 Many energy efficiency advocates argue that it is of critical importance to implement projects in new
construction, since once a facility is built, it will not be cost-effective to go back and retrofit it for some time.
These missed or “lost opportunities” can reduce the overall potential for energy savings in a country.
5 According to the IEA’s Energy Statistics of OECD Countries (1999a), the residential sector, on average,
makes up 30 per cent of total electricity consumption (kWh) in a country. Mexico’s residential electricity
consumption accounts for 22 per cent of that nation’s total. Developing countries tend to have a wider range
in the share of energy devoted to electricity in the residential sector with, for example, Thailand at 22 per
cent and Pakistan at 41 per cent (Energy Statistics of Non-OECD Countries (1999b); statistics for
non-electric energy use are not available).
13
commercial areas. Some energy efficiency programmes have lead to reductions in energy use of up to
50 per cent with the installation of efficient lighting, space conditioning, and building controls.6
The industrial sector is typically the largest energy using sector, often accounting for more than 40
per cent of a country’s electricity use.7 The industrial sector accounts for almost one half of global
energy-related CO2 emissions. With industry-specific energy intensities in developing countries often
being two to four times greater than the average in OECD countries, energy efficiency and process
improvements in the industrial sectors can produce substantial reductions in GHG emissions. These
energy efficiency projects can be focused (e.g., they might address a single industrial process such as
aluminium smelting) or diffuse (e.g., an industrial sector motors replacement project spanning
hundreds of facilities).
Large-scale energy efficiency projects can produce substantial GHG emissions reductions in all three
sectors. In addition, these projects are likely to provide various economic spin-off benefits through,
for example, the education and training of regional workers, operational improvements, enhanced
technology transfer, localised environmental improvements, enhanced competitiveness of regional
industries, and an overall improvement in regional economies.
Only a few developing countries have undergone the end-use profiling of energy demand that allows
for the successful planning of energy efficiency projects. Independent of the value this information
might have for establishing baselines, national end-use energy analyses will be critical to the
identification of cost-effective, high-impact energy efficiency projects that might be implemented in
developing countries.
As discussed later in this case study, it is likely that the estimated baselines for most JI and CDM
energy efficiency projects will rely on some data that are unique to that project, rather than relying
entirely on national or standardised data. However, the project developer’s screening of and planning
for candidate energy efficiency projects will have to be based in large part on end-use data for major
sectors that are collected on a national basis. National data are critical for these planning applications
since, in the planning phases, there are no identified project participants. The quality of these national
data will affect the realisation rates for baseline estimation, i.e., the ratio of planned or expected
baseline energy use to the in-field8 estimated baseline energy use for the actual project participants
once the project is rolled out. In addition, countries with high-quality national data (e.g. on sectoral
and end-use energy consumption) that allow for good project planning would likely attract more
JI/CDM energy efficiency projects.
6 Energy Statistics of OECD Countries (IEA, 1999a) shows that in OECD countries, the commercial sector
makes up 27 per cent of electricity consumed (kWh) on average (Note: Mexico has a commercial sector
share of 18 per cent). Again the range is broader for non-OECD countries but, in general, the share of
electricity use in the commercial sector is lower in non-OECD countries than in OECD countries (e.g.,
Thailand at a 10 per cent share and Pakistan at a 14 per cent share for commercial sector electricity use).
7 Energy Statistics of OECD Countries (1999a) shows, on average, the industrial sector comprising 40 per
cent of electricity consumed (kWh) and 32 per cent of heat energy (TJ) consumed. Average shares for
industrial energy use in the countries analysed in the examples included in the Annex to this paper are as
follows: Mexico — 60 per cent of total electricity consumed, Thailand – 42 per cent of electricity consumed,
and Pakistan – 28 per cent of electricity consumed (statistics for non-electric/heat energy use are not
available).
8 In-field estimates are based on measurements taken at specific facilities. In-field estimates for a specific set
of facilities are used to verify and modify energy use baselines from more aggregate national databases
constructed using sector averages to make them more representative of the actual sites that are participating
in a specific energy efficiency project. A realisation rate of 1.1 would indicate that the baseline obtained
from in-field data from project participants is 10 per cent greater than a baseline estimated using aggregated
national data. The in-field estimate is assumed to be more accurate since it takes into account information
specific to that subset or sector of facilities that are participating in a given energy efficiency project.
14
As an example of the current status of one developing country, Table 1 illustrates end-use and sector
projections of electricity use for Vietnam. These data indicate that motor drives will account for 76
per cent of the electricity use in the industrial sector in 2010 and that the industrial sector as a whole
will increase its share of national electricity use from 42 to 62 per cent. Lighting makes up a large
fraction of commercial and residential sector energy use, but HVAC (i.e. mechanical heating,
ventilating and air conditioning of buildings) shows the largest growth and will surpass lighting in
electric use in residential and commercial buildings by 2010. These general end-use trends are not
uncommon for developing countries located in warm climate zones.
Table 1. Vietnam’s Changing Sectoral and End-Use Electrical Energy Shares
(Based on GWh Sales Projections)
Sector/End-Use
1994
End-Use
Share
1994
Sectoral Share of
Sales
2010
End-Use Share
2010
Sectoral Share of
Sales
Industrial
42%
62%
Motors
76%
76%
Lighting
4%
4%
Process
20%
20%
Commercial
9%
12%
Lighting
56%
34%
HVAC
23%
49%
Other
21%
17%
Residential
34%
22%
Lighting
45%
28%
Refrigeration
5%
7%
Cooking
20%
9%
Other
30%
56%
Other
15%
4%
Sectoral Total
100%
100%
Source: Demand-Side Management Assessment for Vietnam: Phase I Final Report, prepared for the World Bank
by Hagler Bailly Consulting, Inc., August 1996.
2.2 Energy Efficiency Market Trends
Nearly all OECD countries have seen substantial improvements in the efficiency of their energy using
equipment in the past two decades. As a result, they have established markets for energy-efficient
products and services with personnel trained in the installation and maintenance of high-efficiency
equipment. The oil price shocks of the 1970s highlighted the economic benefits of energy efficiency,
and developed countries had the capital resources required to make energy efficiency investments. In
contrast, the energy efficiency wave of the 1970s largely bypassed developing countries, where
national governments lacked the institutional capabilities to implement and promote energy efficiency
policies. Today (in the foreseeable future), new market drivers are expanding the energy efficiency
sector in developing countries. Some key trends in the energy efficiency sector include:
• Subsidy removal. In recent years, many developing countries have begun to decrease or
remove energy subsidies. This makes the true cost of energy more apparent to end-users and
increases the incentives for efficiency;
• Restructuring and privatisation. Restructuring of the electricity sector is typically undertaken
to open the power sector to competition and encourage outside investment. In the course of
restructuring, many countries are privatising their state-owned utilities and major industries,
which generally increases the pressure on companies to cut costs and increase efficiency;
• Demand-side management (DSM). Governments struggling with power supply problems,
brown outs, black outs, and increasing electricity demand often encourage energy efficiency
15
through DSM. DSM is viewed as a means of implementing load management and energy
conservation initiatives to mitigate these problems;
• Construction boom. Economic growth in developing countries has led to a construction boom,
expanding the demand for greenfield energy efficiency projects, specifically those related to
building envelope and control technologies;
• Environmental concerns. A growing interest in energy efficiency is coming from the threat
local and global environmental problems, including global climate change and concerns for
resource scarcity.
2.3 Barriers to Investments in Energy Efficiency
Traditional benefit-cost assessments of energy efficiency investments typically show many projects to
be very cost-effective. It is not uncommon to see study-based benefit-cost ratios exceed 10 to 1. Still,
large-scale investment in these projects has not generally been undertaken by developing countries.
Various barriers to implementation are typically cited as the reason why these potentially
cost-effective investments are not undertaken. Another view is that traditional benefit-cost analyses do
not fully account for all the costs involved in implementing energy efficiency projects in developing
countries. To the extent that barriers exist and represent costs of implementing energy efficiency
projects, they need to be addressed as part of the baseline (i.e., they are part of the business-as-usual
case). A list of barriers might include the following:
• An information cost — a lack of awareness and general misinformation about the benefits of
energy efficiency projects;
• An attention cost — managers and households have limited time and attention to devote to the
manifold aspects of their business and lives. Energy efficiency projects may have a high rate
of return, but still be too small and too complicated to justify the expenditure of attention;
• A technical cost — lack of technical specifications required to select the most appropriate
technology;
• A market distortion cost — pricing policies that under-price the true value of the resources
being consumed makes conservation less economic for participants;
• Capital allocation costs — the capital pool in the country may not be adequate for
incremental/discretionary investments in energy-efficient technologies, which drives up the
cost of capital and allocates it to the highest risk-adjusted return projects;
• Public policy costs — taxes and tariffs that discourage the import of foreign-manufactured
energy-efficient equipment;
• Cost of cultural barriers — local customs and inertial behaviour can work to maintain the
status quo in the design, selection, and operation of energy-using equipment.
This partial list of factors might explain different propensities to invest in what may be viewed, in
some circumstances, as “economic” energy efficiency projects. Traditional financial analyses may not
appropriately address the costs of these barriers. Some of these costs can be overcome by JI/CDM
investments (e.g., the availability of capital and technical specifications). Other costs (e.g., those
related to cultural barriers) may remain for JI/CDM project developers.
16
2.4 Trends in Energy Efficiency Projects and Baseline Implications
In contrast to other sector projects, energy efficiency projects often comprise bundles of smaller
projects. For example, one AIJ pilot phase energy efficiency project in Mexico (see Annex A)
involved the replacement of existing lights with higher-efficiency compact fluorescent lamps. This
project targeted residential energy use and in two geographic areas – the cities of Guadalajara and
Monterrey. While there are some single-site, large-scale energy efficiency projects that have been
implemented (e.g., a large district heating system or a single large industrial facility), energy
efficiency projects are more likely to be characterised by two factors:
• They will span a large number of sites or locations;
• While targeting multiple sites, there still is a specified target market area (e.g., the AIJ Pilot
Phase lighting project in Mexico spanned many households in a target market covering two
cities).
For example, a candidate CDM energy efficiency project might involve retrofitting lighting fixtures in
existing commercial buildings larger than 1,000 square meters in that country’s four largest cities, i.e.,
the specified target market. The logistics of project implementation have a significant impact on
project design. Commercial buildings larger than 1,000 square meters might be targeted since the
project developer will want to ensure that, if an engineering team is sent to a site, there will be enough
savings at that site to justify the set-up costs of the installation. The target market is limited to the four
largest cities in a country due to the costs of shipping and warehousing the lamps and ballasts.
Logistics related to the timing of a lighting replacement at a specified site often determines whether
an energy efficiency project turns out to be cost-effective in practice as opposed to theory.
The fact that many energy efficiency projects are “targeted” due to implementation challenges can
pose problems for establishing baselines. Unadjusted national and even sector-specific energy-use
data may not be appropriate for baselines where energy efficiency project developers target a narrow
set of facility types in specific regions. Highly tailored energy efficiency projects that focus on only
certain types of facilities, with pre-specified energy consumption characteristics, in select markets
may not lend themselves to the general application of national or even regional baseline data. The risk
that actual project participants will be substantively different in their baseline energy use than those
used to create aggregate sector data may be unacceptably high for many energy efficiency projects.
As mentioned earlier, project planning, the screening of energy efficiency measures, and final project
design will require national and regional aggregate data. However, for project baselines, some
information specific to the facilities and energy users that choose to participate will need to be
collected to augment or adjust more aggregate sector baselines. Thus, standardised baseline data for
project types and sectors are critical for project planning. However, project baselines likely will need
to be more precise and require some information specific to the energy efficiency project participants
that can augment or adjust aggregate sector baselines.
Adjustments to standardised energy use baselines to better reflect the particular participants and
markets addressed by a specific energy efficiency project can be one component of a standard
baseline setting process using project-specific data (see Section 4). A starting-point energy use
baseline subject to adjustment using in-field data on project participants is an established approach
and is expected to be much less expensive than having each project develop its own baseline de novo.
The types of building block information for use in baseline construction that can be developed at a
national or regional level are discussed in Section 4.
17
3. Baseline Construction
This section presents a simplified example of the typical construction of a baseline for an energy
efficiency project. All of the examples of energy efficiency projects and programmes in Annex used
variants of this simple case. This approach begins with an algorithm that calculates energy use; then,
data are gathered as inputs to this algorithm. In most cases, cost-effective data collection requires the
use of a sample from the target population. This sample is used to provide “average value” baseline
inputs to the standard algorithm (or calculation).
3.1 Baseline Calculation: Data Needs, Quality, and Availability
Energy efficiency baseline construction requires information on the energy consumption
characteristics of the energy end use/application targeted by the project. The following equation
provides a simple generic formulation of energy use for a population of electrical technologies:
Equation 1: Baseline Energy Use
Energy Use = Quantity x Power x Operating Hours x Diversity Factor
Where:
Quantity is the number of devices in each type and size category.
Power is the electrical9 input to the device. This is typically reported as Watts for lighting fixtures.
For other technologies, this value is often estimated from other performance parameters. For
example, motor power can be estimated from horsepower, efficiency and load factor.
Operating hours is the annual hours of during which the device operates.
Diversity factor is a measure that account for the fact that in a population of devices, some fraction
of the units will either be off or out of service at any point in time due to burnout, modernisation or
repairs/maintenance; this factor is related to the quantity.
This formula is important because it defines which data need to be collected, which factors have to be
estimated, and which factors lend themselves to standardisation. This generic formula is adaptable to
most end-use and technology categories. Energy efficiency projects are typically directed at
influencing some combination of power and/or operating hours, through equipment control, repair,
retrofit or replacement.
The diversity factor accounts for the real-world operating conditions of a population of devices and
has the net effect of discounting estimates that are based only on gross equipment counts and/or rated
conditions. This is an important factor in assuring that the energy use and savings attributable to
energy efficiency projects are not overestimated.
While energy-use analyses typically begin with an algorithm, these algorithms can be part of a model
that incorporates many energy uses and therefore many algorithms (e.g., energy audit software for
buildings or industrial processes). They can also be made more complex by incorporating multiple
pieces of equipment and interactions across end-uses (e.g., installing energy-efficient lights reduces
the amount of heat given off by the light fixtures and thereby reduces energy required for space
cooling during hot weather). However, these algorithms use the same basic parameters regardless of
the equipment or end-use they are meant to address. Issues in the construction of the baseline often
stem from the way in which data are collected for use in an algorithm, rather than the algorithm itself.
9 “Power” could also represent other fuels.
18
3.1.1 Simplified Application – A Lighting Equipment Example
Energy efficiency projects in lighting are a good example of potential JI/CDM projects since they are
relatively simple, and lighting is an energy application where large gains in efficiency often can be
obtained at relatively low cost.10
The basic algorithm used to calculate baseline energy use (kWh) for a lighting fixture may be
constructed as follows:
Equation 2: Calculation for Baseline Energy Use for a Lighting Fixture
Lighting Fixture Energy Use (kWh) = Power (kW) x Hours of Operation
For a specific existing lighting fixture in a building, the baseline annual energy use (kWh) is the
measured (or estimated) kW, multiplied times the hours of operation. Issues in determining the
baseline energy use for this fixture involve different ways of obtaining the data that are input to the
algorithm and used to calculate annual energy consumption.
The first term in the equation (power) is measured in kW and can be obtained using several different
methods.
Methods for Estimating Power (kW) for Lighting Fixtures
1. Nameplate ratings can be used where the manufacturer of the lamp and the ballast is identified.
The manufacturer’s ratings can be used as the estimate of the in-field power draw (kW).
2. Bench tests can be made using different lamp and ballast11 combinations to determine the actual
kW draw for each combination. Bench test results are used rather than the manufacturers’
nameplate ratings.
3. In-field spot-watt measurements can be taken. An energy efficiency analyst can take a
measurement from a specific fixture, or set of fixtures, on a lighting electric circuit. The kW draw
for that circuit or fixture is measured using a watt meter that provides a kW measurement for a
single point in time. Watt meters are inexpensive and often used in lighting applications since the
kW draw for lighting fixtures is not expected to vary much across time, i.e., the light is either on
or off. Other equipment such as motors and air conditioners can run at “part loads” and the kW
draw may vary over time.
4. Interval metering of lighting equipment can be performed. The three options listed above
provide only instantaneous kW measurements. Operating hours to determine energy use (kWh)
10 Energy-efficient lighting is one of the largest market segments in the energy efficiency market. Lighting
accounts for over 25 per cent of carbon dioxide emissions in the commercial and residential sectors and
offers, perhaps, the largest and most cost-effective opportunity for reducing energy use in these sectors.
Fluorescent (tubes and compact fluorescent lamps/CFLs) and incandescent lamps are widely used in the
commercial and residential sectors. Fluorescent lamps generate less heat than incandescent lamps and are
much more energy efficient, reducing energy by as much as 75 per cent through a simple replacement
programme. Since incandescent lamps dominate the residential market in both industrialised and developing
countries a lighting replacement project represents a significant opportunity for energy savings and potential
emissions reductions.
11 Fluorescent lighting fixtures are comprised of different lamp/ballast combinations– one to four lamps, one or
two ballasts. Energy efficiency improvements in lighting can be made by replacing existing lamps with
high-efficiency lamps, replacing existing ballasts with high-efficiency ballasts, or by installing a
combination of these actions. In general, different lamp and ballast combinations have different power
draws.
19
must be obtained from some other source. Interval metering provides both kW and hours of
operation. A meter is installed at a lighting fixture or on a lighting circuit and left in place for a
period of time. This provides a measure of kW on 5-minute or 15-minute intervals, and it also
provides data on operating hours. Thus, it can provide an estimate of kWh for the time period in
which the meter is installed, including any variations in kW from hour to hour, should such
variations occur. Short-term metering refers to a meter installation that lasts for a period of weeks.
Long-term metering refers to periods of nine months or more, and could be for periods of up to
several years. Long-term metering can capture changes in operating hours that occur seasonally.12
There is a significant cost trade-off between short- and long-term metering. If the meter interval is
only two weeks, then the meters can be re-used at different sites. In long term metering, a larger
inventory of meters is needed, resulting in higher costs for baseline data collection.
5. Most efficient replacement equipment is also used in some instances as the appropriate baseline
value for kW. The argument is that the average kW power draw of existing equipment does not
represent what would have been installed in a facility if the energy efficiency project did not exist.
In this case, the baseline assumption is that, instead of replacing the existing lamp with another
lamp that has the same level of efficiency, a more efficient lamp would have been installed. In this
case, lamps in stock at suppliers would be examined and the nameplate rating of the most often
sold equipment would be used as the baseline data input.
Nameplate ratings are the simplest and least-costly data source, and are reasonably accurate for most
lighting applications. Bench tests and spot-watt measurements also are cost-effective methods of
establishing baseline power and performance characteristics, particularly for technologies with
relatively constant performance characteristics such as most lighting applications. Interval metering is
typically not necessary or justifiable on a cost basis for lighting, whereas it can be a valuable tool for
technologies that exhibit variable performance characteristics.
The three lighting project examples shown in Annex A used the kW of existing in-field equipment as
the basis for constructing the baseline. The use of the “most-efficient replacement” kW value is not a
common method for determining energy use baselines in developing countries. A more efficient piece
of equipment may have a lower lifetime cost, but capital constraints in developing countries make it
likely that the lowest initial cost equipment will be selected. There is also an educational component
to this decision.13
For lighting baselines, it has been common even in developed countries to use the average in-field
efficiency of lighting fixtures for projects aimed at replacing existing equipment, as was done in the
three developing country case studies. In contrast, most developed countries use “most efficient
replacement equipment” for other end uses such as air conditioning and refrigeration, which have
shown steady efficiency improvements over time and where replacements typically only occur at the
time of equipment failure or a major renovation project. The justification for using different
12 The appropriate interval for metering is an issue of debate among energy efficiency engineers and
statisticians. In general, the state-of-the-practice has been the use of short-term metering for lighting
projects, with an adjustment factor estimated to account for seasonal effects (e.g., less hours of daylight in
the winter). Often, the compromise has been to do mixed metering. A small number of installations have
been metered for nine months to capture both the summer and winter seasons. A larger number of
installations have short-term interval metering (e.g., two weeks). A ratio that shows the relationship between
hours of operation in the summer, between summer and the shoulder seasons (spring and fall), and between
summer and winter. This ratio is then applied as an adjustment factor to energy use estimates from the
sample that only has short-term metering available. In some regions, there has been extensive metering on
lighting use from earlier studies where results from previous projects are used to calculate adjustment factors
for new energy efficiency projects. This is one example of how field data can be used across projects.
13 Studies have shown that many purchasers are sceptical about the energy efficiency claims made by
manufacturers, and consumers may select the lower initial-cost equipment because they are uncertain about
whether the savings in terms of higher equipment efficiency will, in fact, be realised.
20
approaches is that lighting has generally represented a “change in technology” and the new
technology would not have been available without the programme. Appliances such as refrigerators
have not seen the step-change in technology but, instead, have shown steady improvement over time
and, therefore, would have been available in the business-as-usual case as replacement technology.
After estimating the power (kW) for a lighting fixture, it is necessary to estimate the operating hours
to determine electricity use. Just as there are several options for estimating power for lighting fixtures,
there are several methods for estimating baseline operating hours.
Methods for Estimating Operating Hours for a Lighting Fixture
1. Occupant estimated hours of use can be obtained. This approach simply surveys the building
occupants to obtain their estimates of the hours a lighting fixture is on.
2. Runtime meters can be installed. Runtime meters measure the number of hours a piece of
equipment is on or off. These meters are relatively inexpensive and easy to use, and they can
measure the number of on/off hours in more than one time period, e.g., they can measure the
number of hours of operation during a peak period and the number of hours of operation in an
off-peak period.
3. Interval metering can also be used, as discussed above. An interval meter is installed for a period
of time and measures both kW and operating hours, as well as the load curve. A load curve
provides the kW loads for each 5- to 15-minute interval during a day. These data can be used to
calculate contributions to peak demand (i.e., peak coincident factors) and diversity factors (i.e.,
what fraction of the participating lighting fixtures are on at any point in time). If information on
peak demand and peak period energy use is important for calculating reductions in emissions, then
interval metering provides the information necessary to make these calculations.
The most common approach, in the context of traditional energy efficiency programmes, calculates
energy use baselines for lighting fixtures using either occupant surveys or runtime meters to obtain
estimates of operating hours. Interval metering has generally been viewed as cost prohibitive14 for
most lighting projects in developing countries. For kW estimates, the most common approaches are to
obtain estimates from nameplate ratings, bench tests or watt meters. Occasionally, short-term interval
metering has been used for lighting equipment. While the appropriate estimation approach may
depend upon the specifics of the lighting project, it is generally becoming recognised that run-time
meters combined with spot-watt measurements provide accurate information at a relatively low cost.
The lighting example discussed above generally applies to other energy using equipment as well. All
the methods for estimating kW and operating hours discussed above were used in one or more of the
project examples presented in the Annex A, with the exception that no example used the kW of “most
efficient replacement equipment” for either lighting or motor baseline.
3.1.2 Simplified Application — Estimating the Energy Use Baseline for a Lighting Project
The discussion above focused on how an energy efficiency baseline can be established for a specific
piece of energy using equipment. Energy efficiency projects typically involve large numbers of
equipment at many different sites. For example, the Mexico AIJ energy efficiency project, Ilumex,
targeted lighting across many residences. Most energy efficiency projects target a specific sector and
14 While interval metering has not been common for use in energy use baseline development in developing
countries, it has been used after the installation of the energy efficiency equipment to monitor how well the
equipment is working. Extending this post-installation interval metering to pre-/post-metering that also
provides baseline estimates is not difficult, and the costs of metering equipment are rapidly declining with
more variants of equipment available.
21
end-use (e.g., residential lighting, industrial sector motor drive, commercial sector lighting, and
refrigeration in food service applications). As a result, the baseline must address energy consumption
across many pieces of equipment in a selected sector.
While the details of energy use baseline assessment vary, the general approach in most applications is
composed of a common set of elements. The basic steps in developing a baseline for an energy
efficiency project again start with the standard engineering algorithm. Continuing to use lighting as
the example, the algorithm for the project energy use baseline becomes:
Equation 3: Calculation of Energy Use Baseline for Energy Efficiency Project
Baseline for Lighting Energy Use (kWh) = Average Power (kW) x Average Hours of
Operation x Number of Sites
The energy use baseline needs to represent the average15 equipment energy use as determined in the
data collection. This is done by selecting a sample of project participants, collecting data on kW and
hours of operation for the sample (as discussed above), and using the mean values of these sample
data as estimates for average use for all project participants.16 Note that there is no need to establish
the total energy used for lighting since the role of the energy use baseline is to assist in estimating the
change in energy use due to the sites participating in the project. This change equation is shown
below:
Equation 4: Calculation of Energy Savings from Energy Efficiency CDM Projects
Energy Savings at Site (i) = [(Baseline Lighting Use) – (New More Efficient Lighting
Use)] x [No. of Fixtures at Site (i)]
Total project energy savings is the sum of the site energy savings across all project participants.
3.1.3 Simplified Application — Converting Energy Use to GHG Emissions
Once baseline electricity use has been established, it still is necessary to translate the electricity use
into GHG emissions. The conversion of electricity use into GHG emissions is the subject of a separate
case study on methodologies for emission baselines in the electricity sector (see Bosi, 2000).
3.2 Essential Steps in Baseline Construction
This section summarises the basic steps constructing baselines for energy efficiency projects. The
baseline setting approach comprises six steps, namely:
1. Define project participation criteria. One of the most important steps is to develop criteria for
participation in the energy efficiency project. This sets the initial project boundaries and defines
the population whose energy use is to be represented by the estimated baseline. For example, the
15 It may also be useful to consider developing energy use baselines based on a “better than average”
equipment energy use. This may warrant further examination.
16 As discussed earlier, instead of using the average efficiency level of existing equipment in the field, another
option is to use the average or even the highest efficiency level of equipment being sold as replacements.
Compromises can also be made where replacement equipment is estimated to have an efficiency between
that of the in-field equipment and highest available efficiency. This assumes that a fraction of the
participants would have purchased the most-efficient equipment. In such cases, “better than average”
efficiency might be appropriate.
22
population of potential project participants might include all residences in a geographic area, or all
commercial buildings over 1,000 square meters in five major cities.
2. Determine sample size. The sample size is determined using appropriate techniques; however, for
lighting projects, sample sizes of between 60 and 100 participants have been adequate.
3. Draw baseline sample. Once criteria are established for participation and the sample size is
determined, the next step is to draw the sample from the population of eligible participants. Data
are collected on these sample participants and used to develop the energy use baseline.
4. Determine method for estimating power and operational and performance factors for the
baseline sample. For the sample, power (kW) and operating hours are estimated using the
methods presented in Section 3.1.
5. Establish energy use baseline. Using the operational and performance data collected in Step 4,
the project energy use baseline is estimated from the baseline energy use calculated for the sample.
6. Calculate corresponding GHG emissions baseline. Here, the baseline energy use is translated
into GHG emissions.
The data collection and analysis for estimating the energy use for a lighting fixture or piece of
equipment can be approached in a number of ways, depending on time and cost constraints. In
developing countries, the survey- and field-intensive data collection methods are often preferable to
installing measurement equipment since the cost of capital and technology is often well above the cost
of labour. In countries without privatised electricity markets, there can be high amounts of unmetered
electricity use both from direct theft and equipment failure. Information in developing countries may
also be more difficult to find, for example, public records listing building size and use may not always
be available. This poses as many problems for project planning and implementation as it does for
baseline construction. Countries with better information and more accurate energy billing will likely
be more attractive to host JI and CDM energy efficiency projects.
The use of sampling is important in that it keeps the costs of establishing a baseline using
project-specific information manageable. Now, information is required on only a sample of project
participants. Since the baseline is based on energy use for the sample, the baseline for a specific site
may be, in some cases, inaccurate individually. However, if a large number of sites participate in the
energy efficiency project, the baseline aggregate energy savings will be accurate in general. This
approach assumes that the sample selected to calculate average consumption is representative of the
sites that actually participate in the programme.
Most debates over the quality of the baseline revolve around concerns about whether the sample
selected is indeed representative of the project participants and their energy use going forward. As a
result, the selection of a sample is often a crucial determinant of a “good” baseline.
As a summary, Figure 1 illustrates the baseline energy use estimation challenge.
23
Figure 1. Illustration of an Energy Use (Per Unit of Energy Service) Baseline Estimation
A
T
Participation
B
C
C'
B'
B
Natural Change
Program Impacts
Gross Change
(Year 1- Pre Period) (Year 2 - Post Period)
(B - C)
(C - C')
(B - C')
Overall
Net
Energy
Use
Time
kWh
The line segment AB shows the business-as-usual trend in average energy consumption (per unit of
energy service) for a group of customers prior to their participation in an energy efficiency project.
The segment B’ — C’ shows the new trend in energy consumption after participating in the project.
The line segment BC shows the business-as-usual energy consumption trend that the participating
customers would have been on had they not participated in the project. Project impacts, in terms of
reduced energy use, occur after the measures are installed, i.e., at time T = Tpart, and are equal to the
difference between C and C’, i.e., the difference between the necessarily estimated baseline
consumption C and C’, current energy use which can be measured through monitoring the energy
efficiency project.
Segment AC represents the energy use per unit of energy service baseline and it needs to account for
projected increases in efficiency, technological change, economic growth and other exogenous factors
that affect the level of energy use (or emissions) at a facility or region targeted by the CDM project.
The basic approach outlined in this section uses the in-field experience of representative project
participants, and compares that experience to the energy use of project participants after participating
in the project. In certain cases, it will be important to review the baseline over time via a participant
or comparison group to obtain an estimate of the time trend in baseline energy use. This is represented
by line segments AB and BC in Figure 1. Additional information on energy use baseline estimation
issues — such as free riders, free drivers and self-selection, in the context of energy efficiency
programmes, can be found in IEA (1996).
24
3.3 Energy Use Baseline Construction — Application to Energy Efficiency Projects and
Programmes Implemented in Developing Countries
All of the examples of energy efficiency project baselines presented in Annex A used the basic
approach discussed above. Two general types of energy efficiency activities are addressed in Annex
A: (1) targeted equipment replacement projects (e.g., lighting, motors), and (2) audit-based evaluation
and installation programmes.
Examples of data collection protocols, which are necessary for the construction of energy use
baselines, can be found in the energy efficiency projects and programmes in Annex A. In addition, a
number of industrialised countries have created national data sets that are used both in project
planning and energy use baseline development. Such data sets could also be created in developing
countries and countries with economies in transition. It may be difficult, however, to standardise all of
the data inputs required by the algorithms used to set baselines for energy efficiency projects due to
project and site diversity. Nonetheless, the baseline-setting process for energy efficiency projects
does appear to lend itself to a high degree of standardisation in the general method used as well as
guidelines and standards for project-specific data collection (this is further discussed below).
3.3.1 Audit-Based Programmes as a Means of Constructing Baselines
Several of the energy efficiency examples presented in Annex A involve audit-based programmes
where participating facilities underwent an energy audit to determine which energy efficiency
measures were the most cost-effective, i.e., which achieve the greatest reduction in energy use per
dollar expenditure. Significant energy savings can occur when a project combines audits with the
implementation of identified energy efficiency measures. This has resulted in a number of combined
“audit/implementation” programmes being undertaken in developed countries, and several of the
Annex A examples represent programmes of this type.
Audits typically examine a comprehensive set of measures spanning a wide range of end-use
applications. One advantage of these programmes is their comprehensiveness. Since audits are
designed to examine all major energy end-uses, there are few lost opportunities at a site (i.e., few
cost-effective measures not identified). In addition to equipment replacement options, audits will
generally examine thermal shell measures such as wall and ceiling insulation, high-efficiency
windows, and using light-coloured roofing materials to reflect heat from the building. A second
advantage is that audits are more likely to examine interactions among installed energy efficiency
measures. For example, reducing lighting wattage will also reduce the cooling requirements of a
commercial space.
The audit process uses the same algorithms discussed above and the process can be standardised, even
to the extent of having common software packages developed for specific types of assessments. One
difference is that the population of participants undergoes an audit. In some cases, a sample of
participants will be given a more detailed audit, supported by run-time and kWh metering, to calibrate
less rigorous audits that may borrow data from other similar audits (e.g., an audit of one commercial
building may borrow data on lighting intensity per square meter from an earlier audit of a similar
building). In this instance, all the basic steps for baseline construction discussed above, including
sampling, still apply. However, the baseline estimation problem is simplified because all participants
undergo an energy audit, which establishes the baseline energy use at that site. The energy use
baseline for the project is then the sum of each project participant’s audit baseline.
25
One concern might be that the auditor would have an incentive to overstate current energy
consumption, thereby inflating the estimated energy savings from measures installed at that site.
However, there are a number of controls that can be implemented to reduce the likelihood that this
will occur. Three such controls are:
• Audit standards and protocols can be established. For example, several professional
associations offer training and certify energy professionals as “energy auditors.” Several
universities also have programmes that provide similar credentials.
• Audits are designed such that the sum of energy used in all end-uses equals the total measured
consumption as shown on bills or as meter reads. This helps prevent egregious errors in energy
baseline assessment.
• The auditors themselves can be audited. A number of audit-based programmes have had
provisions where outside professionals would re-audit a sample of facilities to assess the quality
of the work.
26
4. Standardising Baseline Assumptions
This section employs the energy-use baseline construction methods from the Annex A examples to
develop several options for preparing energy use baselines and standardised protocols. Lighting and
motor replacement programmes are the focus of this section to allow specific options to be developed,
but the general conclusions can be applied to any equipment replacement project or audit programme.
Several of the examples in Annex A estimate a reference case or baseline for energy use as part of a
larger impact evaluation or market assessment study. These examples did not focus on baseline
construction per se, but instead concentrated on estimating and monitoring the post-installation energy
use of the energy-efficient technologies promoted by the project. For many energy efficiency projects
in developing countries (and even in developed countries), it is generally assumed that the project is
cost-effective. After all, older less-efficient equipment is being replaced by more efficient equipment.
As a result, the primary concern in many studies of energy efficiency projects in developing countries
was not energy use baseline estimation but, instead, the focus was on the appropriate installation,
operation and maintenance of the new energy efficient equipment. In many cases, there was limited
experience with the new equipment in developing countries and there was concern over how to install
and operate the equipment to obtain maximum benefits. Consequently, the methods of constructing
energy use baselines for a number of the energy efficiency projects in Annex A were generally not
well documented and were supported by relatively little data.
In the case of JI and CDM energy efficiency projects, there will clearly need to be a greater focus on
the baselines. CDM or JI project developers (and the international community) will be concerned
about the actual magnitude of energy saved (not just whether the programme exceeds a
cost-effectiveness threshold) and the corresponding GHG emission reductions. For this reason,
historically-applied energy use baseline methods in developing countries may not be an appropriate
roadmap for future JI and CDM baselines.
Table 2 presents an overview and summary of seven examples of energy efficiency projects and
programmes in developing countries. A full discussion of the examples is found in Annex A. For the
reasons cited above, these examples were generally weak in their documentation of energy use
baseline assumptions and reporting of statistical information. This may indicate a need to develop
reporting guidelines for JI and CDM energy efficiency projects explicitly requesting this type of
information.
27
Table 2. Summary of Implications for Standardisation from Energy Efficiency Case Studies
(See Annex A for more detail)
Country
Sector/End
Use
Energy Use Baseline Development
Approach
Implications for Baseline
Standardisation for Energy Efficiency
Projects
Thailand
Residential
and
non-residenti
al lighting
A sampling protocol was developed
to collect on-site data for estimating
typical operating parameters for all
project participants.
Calibrated engineering algorithms
were used to compute baseline
energy usage.
Performance parameters were
verified via on-
site spot
measurements and data logging.
Develop standardised calculation
procedures using algorithms similar to
those used in this project.
Build “efficiency timeline”
considerations (e.g.
, persistence) into
algorithms.
Develop standardised performance
parameters (e.g., operating hours) in the
residential and commercial sectors.
Develop standardised sampling and data
collection protocols in order to allow data
collected from a sample of sit
es to be
representative of all project sites.
Mexico
Residential
lighting
A sampling protocol was developed
to leverage information across sites.
Surveys were used to estimate the
number and wattage of lamps
installed.
Actual watt savings and operating
hours were verified via on-site spot
measurements and data logging.
Develop standardised performance
parameters (e.g., operating hours) in the
residential sector.
Develop standardised sampling and data
collection protocols in order to allow data
collected
from a sample of sites to be
representative of all project sites.
Morocco
All major end
uses in
residential,
commercial
and industrial
sectors for
the national
assessment,
and
residential
lighting for
the pilot
study.
A sampling protocol was developed
to collect on-site data for estimating
typical operating parameters for all
programme participants.
Surveys and interviews with samples
of end-
users to collect detailed
information on all major end uses.
Baseline energy used initially
estimated with engineering
algorithms.
Energy savings estimated via a
statistically-
adjusted engineering
approach using billing data and
samples of end-use metered sites.
Develop standardised calculation
procedures using algorithms similar to
those used in this project.
Build
“efficiency timeline”
considerations (e.g., persistence) into
algorithms.
Develop standardised performance
parameters (e.g., operating hours) in
residential and commercial sectors.
Develop standardised sampling and data
collection protocols in order to allow data
collected from a sample of sites to be
representative of all project sites.
Mexico
Commercial
and industrial
electric
motors
A sampling protocol was developed
to facilitate data collection.
Detailed on-
site audits were
conducted for a sample of s
ites to
collect data on technical
performance and operating
characteristics.
Develop standardised sampling and data
collection protocols. Sampling and
in-field data collection protocols could be
developed to follow standardised
guidelines for use in developing baselines
using project-specific data.
Pakistan
Residential
and non-
residential
lighting (the
project
covered all
major end
uses, the
example
included
herein
focused only
on lighting).
A sampling protocol was developed
to collect on-site data for compiling
building and energy system features
that characterise the entire
population.
Detailed on-
site audits were
conducted for a sample of buildings
for all major end uses.
Develop standardised sampling and data
collection protocols in order to allow data
collected from a sample of sites to be
representative of all potential project
sites.
28
Country
Sector/End
Use
Energy Use Baseline Development
Approach
Implications for Baseline
Standardisation for Energy Efficiency
Projects
Pakistan
Agricultural
tubewell
water
pumping
Detailed on-site audits were
conducted to collect data on
technical performance and operating
characteristics.
Baseline pre-
retrofit operating data
and energy usage were determined
from the on-site data.
This project indicates that sampling and
in-
field data collection protocols could be
developed that follow standardised
guidelines.
In general, operational and performance
parameters were determined from
site-specific audits.
Pakistan
Commercial
and industrial
boilers
A sampling plan and protocol were
developed.
Detailed on-
site audits were
conducted for a sample of sites to
collect data on technical
performance and operating
characteristics.
This project indicates that sampling and
in-
field data collection protocols that
follow standardised guidelines could be
developed.
Site-
specific operational and
performance parameters were determined
by energy audits.
4.1 Determining Standard Baseline Performance
This section assesses the different options for constructing energy baselines in the lighting and electric
motors sectors, with emphasis on how standard approaches might be used to simplify energy use
baseline construction. In each instance, examples are drawn from Annex A to show how these options
have already been, and are currently being, adapted to a certain extent in the examination of energy
efficiency projects in developing countries.
Factors that tend to support or limit standardisation possibilities include:
• Energy use characteristics of the market segment. Constructing energy use baselines
requires information on the particular consumption patterns of market segments. Energy
consumption patterns may be more uniform in one segment than another, and this supports the
standardisation of consumption values and indices. In addition, equipment performance (e.g.,
efficiency) for certain technologies may be relatively uniform across segments, but operating
characteristics (e.g., operating hours) may vary, and each may have characteristics that are
unique to, or typical of, that sector/segment.
• Homogeneity of markets. Residential applications tend to be more homogenous than
industrial, for example. Thus, more opportunities exist to standardise energy use characteristics
in the residential sector. As a general rule, moving across the market segment spectrum from
residential to industrial, energy markets are increasingly heterogeneous and less subject to
overall standardisation.
• Technology performance variability. End uses that tend to have more constant performance
characteristics lend themselves to greater standardisation of baseline performance. For example,
residential refrigerator energy use tends to be relatively uniform within certain categories, and
lighting systems tend to show more constant performance characteristics (they are either on or
off and may have well defined operating hours). Space heating and cooling, on the other hand,
are very weather dependent and have variable output and efficiency characteristics. Energy use
baseline construction needs to account for this variability.
29
There are essentially three options available for energy use baseline standardisation for energy
efficiency projects. They are:
• Standardising baseline calculation methods and data collection protocols. The algorithms and
models used to compute energy use and the data that provide inputs to the algorithms.
• Standardising operating and performance parameters. The values that describe the energy use
characteristics of a given technology or end use, such as lamp wattage for lighting and motor
efficiency for electric motors.
• Standardising energy use indices. Indices that are representative of the energy use of a
population of technologies or segment of the population, such as lighting kWh per square meter
for certain commercial building types.
Each of these options is discussed below along with insights from the examples presented in Annex A.
4.2 Standard Calculation and Data Protocols – Lighting and Motors
One option is to standardise baseline calculation methods (i.e., algorithms and models), baseline data
requirements and collection methods for different energy efficiency applications. To a certain extent,
this has already been done through and EPRI impact analysis literature17 and the IPMVP protocols
(US DOE, 1997). This body of work has universal applications for energy use assessment and could
provide a sound theoretical and methodological basis for the development of energy use baselines for
CDM projects in developing countries.
4.2.1 Standard Calculation and Data Collection Protocols for Lighting
As noted in Equation 3, the annual energy consumption for a population of lighting devices is a
function of power draw (input wattage to the fixture) and operating hours. Data for each of the
parameters need to be collected or estimated.
Standardisation of the calculation algorithms, data requirements to support those algorithms, and
data collection protocols would help promote the application of appropriate procedures and reduce
the uncertainty in baseline construction faced by JI or CDM project developers. Wattage and
operating hours could be estimated from technical data tables, engineering methods, or a sample of
field observations. Standardised guidelines could be prepared to lend uniformity and consistency to
these data collection tasks for energy efficiency projects. Three of the energy efficiency examples in
Annex A provide some insights on the application of this approach:
• The Thailand CFL lighting replacement project (in progress) has planned to employ a uniform
calculation methodology similar in format to Equation 3 for estimating programme impacts,
and systematic data collection methods supplemented by in-field and bench test spot-watt
measurements. Calculation algorithms and spot-watt measurements for estimating input
wattage similar to those used in Thailand could be standardised for similar lighting projects.
• The Mexico Ilumex project used the wattage of the incandescent lamps replaced by the
programme as the baseline. Compact fluorescent lamp wattages were determined from
spot-watt measurements, and operating hours were estimated from a sample of on-site, run-time
measurements. Spot-watt measurements and run-time data collection protocols and sampling
techniques similar to those used in this project could be standardised to estimate operating hour
17 Electric Power Research Institute, publications in references.
30
assumptions for other residential sector lighting projects. In the commercial sector, a similar
process could be used to standardise lighting operating hours, although it would be necessary to
disaggregate the results by market segment.
• The Morocco residential Lighting Pilot Project employed standard engineering algorithms with
well-developed and fundamentally sound data collection techniques. Engineering estimates of
savings were developed using the following algorithm:
Energy Savings = (Wattsincandescent – WattsCFL) x Operating Hours x (1 + Take Back Factor)
The approach used in this project provides an example of methods that could be standardised for
cross-sector baseline development that accounts for factors such as urban and rural energy use
variation.
All three examples of lighting projects employed a similar approach to baseline energy use
construction using a general energy use algorithm similar to Equation 1. Differences in data collection
methods were found in the examples examined; however, no information is available from these
examples on how the different data collection techniques might have influenced the baselines. In
general, it is believed that it would not have been appropriate to use a standardised set of data across
countries. Data on the technologies deployed and their operating conditions unique to each country
would most likely be required to develop appropriate baselines. However, a common (standardised)
approach to collecting the data, using state-of-the-practice techniques, could be shared across all
three countries.
4.2.2 Standard Calculation and Data Collection Protocols for Motors
Annual energy consumption for a population of motors can also be characterised by an algorithm
similar to Equation 1. Energy use is then characterised by motor size, efficiency and operating hours.
While the basic energy algorithm is simple, energy use baseline development depends on values for
each of these parameters. Data for each of the parameters need to be collected or estimated. As a
general guide, the number of motors by horsepower category is collected and tabulated at either the
site or population level (population of motors within a market or market segment to which the
efficiency project will apply). Efficiency and operating hours, on the other hand, must be estimated
from technical data, engineering methods or a limited sample of field observations.
For example, the Mexico industrial motors efficiency project discussed in Annex A used a standard
engineering algorithm to compute energy use for both standard and energy-efficient motors. This is a
typical approach for motor applications and is well suited to a standardised methodology.
4.3 Standardising Operating and Performance Parameters – Lighting and Motors
Equation 1 identified the generic types of operating and performance parameters that need to be
known or estimated in order to develop baseline energy use for an end-use or sector. For many
common energy efficiency applications, it is possible to define typical baseline values for operating
and performance parameters. Parameters such as operating hours, efficiency and power draw lend
themselves to standardisation within certain sectors and applications. For example, experience with
demand-side management programmes in developed countries has shown that it is possible to tabulate
baseline efficiencies for common types and sizes of electric motors. This would be a reasonable
option to undertake for a country or for selected regions. While the authors are not aware of a study
that has been conducted to validate any specific regional delineation, it is likely that areas such as
Central America could share a common data set, and possibly portions of Asia and Africa. However,
the homogeneity of any region can only be known after the data are collected and analysed in a
variance/co-variance analysis.
31
The standardisation of baseline operating and performance parameters does not, of itself, establish the
baseline energy use for energy efficiency projects. However, it reduces the time and cost of estimating
the energy use baseline for project developers and brings greater uniformity and consistency to the
CDM/JI baseline development process. Developing parameter values that err on the conservative side
could minimise uncertainty and opportunities for gaming the system, while maintaining reasonable
incentives for investors. Examples of “conservative” parameter values are presented below for
lighting and motors projects.
4.3.1 Standardised Operating and Performance Parameters for Lighting
Performance parameters such as fixture input wattages could be standardised for typical fixture types.
The average input wattage for common types of fixtures, lamps and ballasts can be estimated from
manufacturers’ data and verified with a sample of in-situ spot measurements or bench tests. Variations
in manufacturers’ products in different countries could complicate this process, although there are
typically only a few dominant manufacturers in each country. The standardisation of operating and
performance parameters would be applicable to the most common types of devices, particularly in
residential and commercial applications. For example, baseline data on wattages for common types
of incandescent and fluorescent residential and commercial lighting fixtures could be established,
whereas large industrial projects such as stadium lighting would most likely be based on specific site
data. Even though it would be difficult to standardise operating and performance parameters for
these types of industrial projects, analytic and data collection methodologies could still be
standardised.
Operating hours and the diversity factor are examples of parameters needed to compute energy use for
a population of lighting devices, and lend themselves to standardisation within certain limitations.
Operating hours tend to be relatively consistent within specific market segments (particularly in the
residential and commercial sectors) and could be conservatively standardised. Operating hours,
however, are variable by market sector/segment. Establishing these baseline values would require
end-use load research on a country-by-country, or possibly regional, basis. For example, residential
lighting operating hours are typically 1000-1100 annually, and commercial office lighting
applications are in the 3500-4000 range. With a reasonable sample of observations, these could be
conservatively estimated across a sector or segment by selecting the lower end of this range. This
would still provide a reasonable incentive to potential project developers, while assuring that energy
savings are not overestimated. The same reasoning applies to other performance factors. For example,
fluorescent ballast input wattages for different lamp/ballast combinations vary by manufacturer. By
selecting a set of input wattages for the most common/standard fixture types that tend toward the
lower end of the range, a conservative baseline condition is established. This approach has been
applied in North American DSM projects. Baseline development for a lighting energy efficiency
project applied across a broad market in a country may require an estimate of the quantity of lighting
devices by type and input wattage.
Examples from Annex A showing how operating and performance parameters can be estimated
include:
• The Thailand CFL lighting replacement project has planned to estimate average input wattages
for each lamp type promoted by the programme and average operating hours for the participant
population. A similar approach could be taken for common lamp, ballast and fixture
configurations, and for lighting operating hours. In order to support realistic incentives to
project sponsors, it would be desirable and possible to differentiate operational parameters such
as operating hours by market segments.
• The Mexico Ilumex project estimated input wattages from a sample of spot-watt measurements
and operating hours from a sample of on-site, run-time tests. Wattages and operating hours are
summarised in Table 3. Average run-time hours were applied to all project participants in this
32
project. This was a residential sector project, and since operating hours tend to be relatively
uniform across the residential sector, this represents a reasonable approach to standardising a
key performance parameter. Again, in the commercial sector (e.g., office buildings,
convenience stores, and schools) lighting operating hours could be standardised by market
segment.
Table 3. Lamp Performance Data (Mexico Ilumex project)
Baseline Incandescent
Watts
CFL Watts
Daily Hours of Operation
Nominal
Measured
100
23
21.1
3
75
20
17.8
3
60
15
16.1
3
• The Morocco DSM market research study and the residential lighting pilot produced a valuable
dataset of energy use characteristics. This project provides an excellent example of an approach
that could be employed to develop standardised performance and operating parameters in
selected market sectors. Examples of operating and performance parameters from this study
that could potentially be standardised include:
Residential sector: average number of lamps per household, average baseline lamp wattages, and
average lamp operating hours for both urban and rural customers. Table 4 summarises the lighting
characteristics determined by the market research study.
Table 4. Summary of Lighting Characteristics by Urban and Rural Areas
(Morocco DSM Market Research Study)
Urban
Rural
Average Number of Lamps per Household
9
6
Average Wattage of Lamps
93
87
Average Daily Hours of Use
2
2
Commercial sector: input wattages by lighting type (incandescent, fluorescent, halogen and compact
fluorescent), and average daily operating hours by facility type. Table 5 summarises commercial
indoor lighting characteristics found by the market research study.
33
Table 5. Summary of Commercial Indoor Lighting (Morocco DSM Research Study)
Type of Lighting
Average Number of
Units Per Facility
Average Wattage
Average Daily
Hours of Use
Fluorescent Lamps
263
53
10
Incandescent Lamps
269
93
6.5
Halogen Lamps
39
262
9.2
Compact Fluorescent Lamps
47
22
6.6
These lighting examples and the wide range of experience in developed countries show that it would
be possible to develop a set of standardised operating and performance parameters for energy
efficiency projects in the lighting sector. Table 6 gives an example of a framework for organising
lighting performance data that has been successfully employed in North American DSM projects.
These parameters would be used in Equation 1 to build up an estimate of the energy use baseline for
the given market or market segment to which the project may apply. It is important to note that the
actual datasets would most likely need to be developed on a country-by-country (or at least regional)
basis to account for the particularities of the local market and the mix of technologies deployed in
each market segment.
Table 6. Framework Example of Standardised Baseline Parameters for Lighting
Efficiency Projects
Efficiency Project
Lamp/Ballast/
Fixture Type
Baseline Equipment
Performance Parameters
Baseline Operating
Parameters
Lighting Type
Input Watts
per Lamp/
Ballast/Fixture
Sector/
Segment
Operating
Hours
High-Efficiency
Lamp/Ballast/Fixtu
re Replacements
Type 1
Standard
fluorescent
lamps/ballasts
w1
Sector 1
h1
Type 2
w2
Sector 2
h2
…
…
…
…
Type n
wn
Sector n
hn
Compact
Fluorescent Lamp
Replacements
Type 1
Incandescent
lamps
w1
Sector 1
h1
Type 2
w2
Sector 2
h2
…
…
…
…
Type n
wn
Sector n
hn
4.3.2 Standardised Operating and Performance Parameters for Motors
Performance parameters such as motor efficiencies could be standardised. This is particularly true for
certain types of motors. The average efficiency for each horsepower can be estimated from
manufacturers’ data. Standardisation would be most applicable for certain motor types and size ranges
that are most common in the commercial sector and certain industrial applications. For example, DSM
applications in North America have typically developed baseline efficiencies for motors in two frame
types (open drip-proof and totally enclosed fan-cooled), four speeds (900, 1200, 1800 and 3600
RPM), and horsepowers ranging from 1 to 200. Similarly, it may be possible to standardise
assumptions for these types, sizes, classes and applications of motors in developing countries.
34
Operating hours, load factor, and diversity factor are parameters needed to compute energy use for a
population of motors, and lend themselves to standardisation within certain limitations. Operating
hours tend to be relatively consistent within specific market segments, particularly in the commercial
sector, and could be conservatively standardised. Operating hours, however, are variable by market
sector/segment. Establishing these baseline values would require end-use load research on a
country-by-country, or possibly regional, basis. For example, operating hours for 3-shift industrial
plants may well be over 8000 per year, whereas commercial ventilation fans might be only 3500 –
4000. A reasonable sample of these could be analysed and a conservative estimate selected from the
lower end of this range. For example, motor efficiencies for different types and sizes of
standard-efficiency motors vary by manufacturer. By selecting a set of efficiencies that tend toward
the upper end of the range in each category, a conservative baseline condition is established.
Insights on the potential standardisation of parameters in motors projects from the examples presented
in Annex A include:
• The Mexico motor efficiency project examined motors that ranged in size from 15 to 600 hp
with 70 per cent of the motors in the range of 1-20 hp. Figure 2 presents a distribution of
motors by horsepower. This type of information and data organisation is necessary to quantify
baseline energy use for an end use such as electric motors. Typically, certain types of common
motors under 200 hp fall into fairly consistent efficiency ranges, whereas larger motors and
motors of custom or specialised construction are evaluated on a unit-by-unit basis. The largest
fraction of the motors in this study would then be likely candidates for the standardisation of at
least baseline efficiencies.
Figure 2. Distribution of Motors by Horsepower (Mexico Motors Replacement Project)
0
30
60
90
120
150
180
210
240
1
1.5
2
3
5
7.5
10
15
20
25
30
40
50
60
75
100
125
150
200
250
300
350
400
500
600
Power (hp)
Quantity #
0.00%
1.85%
3.69%
5.54%
7.39%
9.24%
11.08%
12.93%
14.78%
Dist ributio n (%)
35
• The Pakistan ENERCON project tabulated data on over 350 electric motors in buildings. Data
were recorded on maximum rated demand (kW) and operating hours. It does not appear that
efficiency information was either available or recorded for the project. However, for a sample
of this size, it would be possible with a carefully constructed study to develop standardised
values for certain operating parameters such as operating hours for certain motor applications
(e.g., HVAC fan motors) in selected market segments. Table 7 presents a distribution of motors
by application and maximum rated demand determined by the project.
Table 7. Distribution of Electric Motors by Application and Demand
(Pakistan Energy Efficiency Assistance Programme)
Motor Application
Number of Motors by Maximum Rated Demand (kW)
<1
1-2
2-5
5-10
10-20
>20
Air Compressors
2
-
-
-
-
-
Air Movement
12
-
3
24
24
5
Boiler Systems
-
2
12
-
-
-
Chiller Systems
-
-
25
-
-
5
Cooling Towers
-
6
-
12
19
2
Lifts
-
-
5
9
6
6
Water Pumping
9
7
18
4
19
10
Other
-
-
43
22
31
2
An example of a framework for organising baseline efficiency data for motor efficiency projects that
has been used in North American DSM projects is shown in Table 8.
Table 8. Framework Example for Standardised Energy Use Baseline Motor
Efficiencies for Energy Efficiency Projects
Motor
HP
Open Drip-Proof
Totally Enclosed Fan-Cooled
RPM
RPM
900
1200
1800
3600
900
1200
1800
3600
1
e1
e1
E1
e1
e1
e1
e1
e1
2
e2
e2
E2
e2
e2
e2
e2
e2
…
…
…
…
…
…
…
…
…
200
e200
e200
e200
e200
e200
e200
e200
e200
4.4 Energy Use Indices – Lighting and Motors
Another level of standardisation for energy use baselines, in the context of energy efficiency projects,
involves the development of unit energy use indices by sector, segment and/or end use. Energy use
indices such as kWh/square meter can be useful for characterising energy use within a market
segment or end use, and in some cases could serve as baseline values. For example, commercial
lighting end-use intensities could be defined by market segment and used as baseline values,
particularly for new building construction projects. In the residential sector, it would be possible to
construct energy use baseline consumption values such as annual kWh/appliance. Similarly, in the
industrial sector, it may be possible to develop annual kWh/unit of production values within a country
for selected industries.
4.4.1 Standardised Energy Use Indices (EUI) for Lighting
For the commercial sector, indoor lighting EUIs (e.g., lighting kWh/square meter) could be developed
by market segment. While EUIs are useful for general comparison purposes, they could also serve as
commercial sector baselines in certain market segments such as offices, schools and hospitals. For
example, energy efficiency efforts following the energy crisis of the 1970s were successful in
reducing lighting power densities in U.S. commercial office buildings from 38-43 watts/square meter
36
to 27-32 watts/square meter, and progressive standards and DSM efforts were successful in further
reducing this value (particularly in new construction) to 15-22 watts/square meter by the 1990s. EUIs
are probably less useful in the industrial sector, where a hybrid baseline approach (i.e., combining
standardisation with project-specific elements) would be more generally applicable. In this case,
performance parameters such as input wattages for each common fixture type could be standardised
and operating parameters would be site/project-specific. The examples from Annex A include:
• The ENERCON project in Pakistan collected detailed information from on-site audits for a
sample of 50 buildings. These data could be used to support the development of baseline values
for a wide range of energy indices for both the residential and non-residential sectors. In
lighting, it appears that the necessary data were collected to develop baseline lighting power
densities (i.e., watts/square meter) for a sample of building types. This is useful in
characterising baseline energy use.
• The EGAT lighting replacement project in Thailand has planned to develop average load shape
profiles for both residential and non-residential lighting from the on-site runtime data collection
effort.
• The Moroccan DSM potential and market assessment study produced a valuable set of
information on energy use characteristics within all major customer sectors. Among the results
of the study were end-use energy breakdowns and load shapes. These data form useful
benchmarks for understanding energy use within a county and market sector, and while not
explicitly included in this study, these types of data could be used to produce useful indices
such as annual lighting energy use per household in the residential sector and lighting energy
use per square meter for key segments of the commercial sector.
These examples from the case studies demonstrate the potential for standardising selected energy use
indices and per-unit values. As a further example, baseline office lighting energy use for new
construction could be defined for a given country as follows:
Baseline lighting intensity = 27 watts/square meter
Standardised operating hours for office buildings = 3500 hours per year
Baseline energy use intensity = 27 watts/sq. meter. x 3500 hours per year = 94.5 kWh/sq. meter
Table 9 provides another framework example of EUI development for the commercial sector.
Table 9. Framework Example for Lighting Energy Use Indices
Segment
Lighting
Energy Use
(MWh/yr)
Floor
Stock
(Sq.)meter
Lighting
EUI
(kWh/Sq. meter
Office
MWh
o
sf
o
eui
o
Retail
MWh
r
sf
r
eui
r
…
…
…
…
Misc.
MWh
m
sf
m
eui
m
4.4.2 Standardise Energy Use Indices for Motors
For the commercial sector, motor energy use indices such as kWh/square meter tend to be less useful
because motor energy use is often tabulated or subsumed in other end uses, primarily space heating
and cooling. In the industrial sector however, since motors are often the primary energy-consuming
devices, it may be useful to develop baseline motor energy consumption indices related to unit of
37
production (motor kWh/unit of production) for selected industries. Table 10 presents a framework
example of how this type of index could be formulated.
Table 10. Framework Example for Electric Motor Energy Use Intensities
Sector
Segment
Motor
Energy Use
(kWh/yr)
Production
Units
Motor
EUI
(kWh/Unit)
Segment 1
KWh
1
u
1
eui
1
Segment 2
KWh
2
u
2
eui
2
…
…
…
…
Segment n
KWh
n
u
n
eui
n
4.5 Data Issues
The challenges to implementing standardisation opportunities should not be understated. Given that
much of the methodology, approach and data structure has already been established in the energy
efficiency industries in industrialised nations and that this infrastructure could be transferred to
developing nations, the primary challenge lies in the data themselves. From the perspective of setting
baselines for common energy efficiency measures, key data challenges include:
• Cost and time of collection. Data collection can be expensive and time consuming. Skilled
labour in the form of trained energy analysts and auditors is also required.
• Management and maintenance. End-use data systems require management and maintenance.
That is, data need to be systematically organised so that relevant data can be accessed and
manipulated for the needs of different projects.
• The need for supplemental sources. The data sets available for the assessment of efficiency
opportunities are rarely a perfect fit for a given project. Each project invariably has somewhat
unique data needs. This typically requires that the analyst developing the baseline supplement
the data on hand from other sources. This is not to diminish the value of the initial data,
however, because a foundation dataset, often at the country level, invariably reduces the time
and cost of developing a unique dataset for the project at hand, by allowing existing data to be
leveraged with new data specifically designed to make the existing data set more representative
of project participants.
• The need to periodically refresh the data. End-use data sets age over time with advances in
energy technology and changes in the energy consuming market. As such, these data need to be
periodically refreshed with additional data collection and analysis. The actual time periods at
which these data need to be revisited depend on technology advancements, market dynamics
and the degree to which energy efficiency initiatives are stimulating the entrance of
more-efficient products into the market. However, North American DSM experience suggests
that it would be reasonable to refresh baseline energy use datasets every three to five years. The
existence of a foundation dataset and protocols that are established from the first phases of
developing the data infrastructure make this a much less daunting and costly task.
Several of the examples examined in this case study demonstrate the type of sampling and data
collection approach necessary to develop foundation data for energy efficiency analysis and project
assessment. Most notably, the Morocco and Pakistan projects provide examples of how this type of
information infrastructure project can be executed. In this regard, it is clear that the foundation has
38
been laid (at least in part) for establishing data collection methods to support standardised baseline
development for CDM and JI projects in developing countries.
5. Issues in Constructing Baselines
This section discusses a number of issues surrounding baseline estimation. These issues may be of
particular importance given the magnitude of the potential benefits that can be expected from the use
of energy efficiency projects as an approach for mitigating GHG emissions. Practical baseline
estimation methods should seek to balance the interests of the various parties. The issues discussed
below address balancing risks in baseline construction, assumptions affecting baseline stringency, and
potential biases in baseline construction.
5.1 Baseline Construction and the Potential Volume of Energy Efficiency Projects
The volume of projects undertaken under JI/CDM is a crucial factor in determining the environmental
effectiveness of the project-based mechanisms. Several factors could influence potential JI/CDM
project developers’ willingness to undertake energy efficiency projects. Two important factors are: 1)
the balance of risks in the construction of the baseline, and 2) how the business deal is determined for
project sponsors, i.e., is it framed in a manner that will allow project developers to assess the
economics of the project?
5.1.1 Balancing Risks in Energy Efficiency Projects
For JI and CDM to be successful overall, a large number of projects will have to be implemented.
This means that the risks of under-stating baselines (i.e., being overly conservative) and thereby
understating project benefits should not be so great as to overly discourage potential project
developers. At the same time, there must be some assurances that the expected environmental
improvements are, in fact, occurring. Different methods or measures could be used to balance these
risks. Each is discussed below.18
• Establish the burden of proof that has to be met. For example, a burden of proof could be set
based on a one-tailed 75 per cent confidence interval. As long as the baseline is estimated so
that there is only a 75 per cent probability that the “true”19 baseline would be equal to or lower
than the estimated baseline, the baseline is judged to be estimated with the necessary degree of
confidence. This is a reasonably high burden of proof. It means that there can be no more than a
25 per cent likelihood that the actual baseline is higher that the baseline estimated for the
project. This burden of proof results in the discounting of impacts down to a level that might be
judged as a reasonable assurance against over-estimating environmental improvements. The
highest discount rates will likely be for projects that focus on only one large site, because
reasonable sensitivity analyses around the selected energy use baseline will have large impacts
on the estimated emissions reductions at a single site. The process of constructing the 75 per
cent confidence interval around a given baseline can be statistical where sampling approaches
18 The U.S. EPA has employed these general methods of balancing risks in awarding SO2 emission credits to
electric utilities as part of its Acid Rain programme and emissions trading process. This is documented in
U.S. EPA (1995).
19 While the “true” baseline will never be known, statistical inference can be used to develop interval estimates
around values that cannot be observed. The application of statistical methods requires an accurate definition
of the participant population and the use of appropriate sampling methods. In general, statistical methods
focus on estimating the pre-project baseline energy use of energy efficiency project participants.
Assumptions are required to determine how this initial baseline might change over time in the
business-as-usual case. However, establishing a sound estimate of pre-project baseline energy use ensures
that benefits in the initial years of the project (e.g., five years) will be estimated quite accurately, since it is
likely that there would have been little change in energy use patterns over the short term.
39
are used; it can be based on simulation analyses with judgementally assigned probabilities to
alternative scenarios; or it can be determined with Bayesian approaches using subjective
probability assessment techniques.20 Regardless of whether the confidence interval is based on
sampling and statistics, sensitivity analyses, or expert judgements; the final confidence interval
will have to be assessed judgementally, i.e., does it span a reasonable range of possible
outcomes?
• Less rigorous baseline estimation approaches can be allowed, and the resulting baseline
would be discounted. This provides the project developers with a choice. On the one hand,
they could use a simpler estimation method and have a lower energy use baseline, resulting in
lower GHG reductions and thus fewer GHG emission credits. On the other, they could use a
more sophisticated estimation approach with larger number of in-field measurements and
potentially obtain a greater number of emissions reduction credits (i.e., less or no discounting of
emissions would be done).
• The rate at which energy reductions are translated into GHG emissions could be fixed. A
key uncertainty for JI and CDM energy efficiency project developers is the rate at which energy
reductions are translated into GHG emissions. This rate can be set in advance to remove this
uncertainty for project sponsors. It is important that the project developers have some certainty
over time with respect to the emissions reduction credits they receive for each kWh of
electricity conserved. Setting an emissions rate awarded per kWh for a period of time through
electricity multi-project baselines (which would be fixed for given period of time) is probably
the most significant standardisation action that could be taken, since it would encourage all
forms of energy efficiency projects across all sectors. It is possible that the difference in
electrical system emissions rates per kWh could vary across peak and off-peak periods.
• A fixed baseline crediting lifetime could be set for energy efficiency projects. The length of
time during which a baseline is considered valid for calculating a particular energy efficiency
CDM project’s GHG emission reductions (and thus emission credits) could be limited. After
that period of time, it would be equivalent to assuming that the baseline is no longer valid. The
authors recommend a five-year crediting lifetime. This is based on a subjective assessment, but
one factor influencing the choice of this term is the payback periods seen for most energy
efficiency projects. Holding the baseline set for five years would provide project sponsors with
a planning period long enough to recover their costs and earn a return on a wide variety of
energy efficiency projects, i.e., it is a timeframe that would not unduly reduce the number of
economically viable projects available to developers.21 While the recommendation is that the
baseline be set for five years, that is not the same as holding the baseline constant. The baseline
could be set such that energy efficiency is assumed to increase at a given rate over the five-year
period. However, once the baseline terms are set, they should be kept in place so that they
provide project sponsors with a five-year planning horizon.
These methods22 have been undertaken in North America in the case of traditional energy efficiency
projects and programmes, but may require further examination with respect to their application in the
context of CDM/JI.
20 There are formal methods for dimensioning the uncertainty around judgements. These are discussed in EPRI
(1991).
21 No specific decision is likely to be appropriate in all circumstances. Where a fast moving energy-efficient
technology can be identified that is expected to change the market in less than five years, then it may be
appropriate to hold the baseline constant for a period of less than five years. The five-year recommendation
for holding the baseline constant is a subjective decision.
22 These types of risk balancing measures have been taken by agencies in North America responsible for
overseeing energy efficiency projects of $100 million or more, with potential for monetary incentives to be
40
5.2 Potential Biases in Baseline Construction: Free Riders and Spillover Effects
Two main issues that arise in estimating an energy efficiency project’s contribution to GHG emissions
reductions based on a baseline are the potential for free riders and project spillover:
Free riders. Free riders are defined as those that would obtain emission credits for whole projects that
would have gone ahead in the absence of CDM/JI projects23. The concern is that a large number of
free riders could inflate the number of projects and resulting emission credits. The free-rider issue is a
baseline estimation problem that stems from a systematic bias in the construction of the baseline.
There are many types of free riders. A full free rider is an entity that would have installed the same set
of energy efficiency measures at the same point in time as they did under the offered energy
efficiency project. A partial free rider is an entity that would have installed some, but not all of the
energy efficiency measures offered by the project or would have installed the measures, but at a later
time. In most instances, there are likely to be more partial free riders than full free riders.
Spillover effects24. These are additional energy efficiency impacts that result from the project, but are
viewed as indirect rather than direct impacts. These can occur through a variety of channels including
1) an energy-using facility hearing about an energy efficiency project-sponsored measure from a
participant and deciding to pursue it on his or her own (the so-called free-driver effect); 2) project
participants who undertake additional, but unaided (e.g. without CDM emission credits), energy
efficiency actions based on positive experience with the project; 3) equipment manufacturers
changing the efficiency of their products, and/or retailers and wholesalers changing the composition
of their inventories to reflect the demand for more efficient goods created by the project; and 4)
governments adopting new building codes or appliance standards because of improvements to
equipment resulting from energy efficiency projects (e.g., the U.S. DOE’s Energy Star Programme).
Together, these effects can transform the market for energy-related equipment in a positive manner
and they are a consequence of a project developer’s energy efficiency project offerings.
Theoretically, spillover impacts should be identified and measured as benefits to energy efficiency
projects. Practically, they are difficult to identify and measure. However, some of the attempts to
measure spillover in areas that have had an energy efficiency project in place for a period of time has
shown that these impacts can be large.
When spillover and free riders are taken together, the end result is that there are two
difficult-to-quantify baseline estimation biases that work in opposite directions. Some regulatory
jurisdictions have decided that, in the absence of better information, they will assume these two
effects cancel each other out for projects that reach a large number of facilities, unless substantive
evidence is produced to indicate otherwise.25
paid to project sponsors that are in the tens of millions of dollars. In this context, a survey of twelve U.S.
states viewed to be leaders in the promotion of energy efficiency found that each state believed it was able to
design oversight procedures that meet the baseline estimation challenges, i.e., provide assurances that
impacts were accurately estimated and that any financial incentives paid were warranted. See NARUC
(1994).
23 See Ellis and Bosi (1999) for more information on this topic.
24 Good discussions of spillover and market transformation can be found in Violette (1996) and EPRI (1995A,
Chapter 6).
25 See NARUC (1994) for a discussion of how free riders and spillover have been addressed in North America.
41
5.3 Baseline Stringency
It is important that baseline assumptions provide reasonable assurances that the expected
environmental benefits from the energy efficiency projects are, in fact, occurring. Baseline stringency
is influenced by the assumptions used to define the “business-as-usual” case. Depending upon the
assumptions made, the baseline can be set at a low energy use level, leaving little room for
incremental contributions to emissions reductions from potential energy efficiency projects; or they
can be set to produce a high energy use baseline that will result in higher estimated emissions
reductions, all else being equal. Key factors that influence the stringency of the business-as-usual
case, which include the assumptions about the energy efficiency of energy-using equipment, the
assumptions about what energy efficiency investments would have occurred in the business-as-usual
case, and the variability across projects, are discussed below:
5.3.1 In-Field Efficiency Levels in the Business-as-Usual Case
There are two basic methods for setting the baseline efficiency levels of energy-using equipment. The
first involves examining the existing stock of equipment in the field. The average efficiency of
in-place equipment would be used as the baseline level. This value can be ascertained by selecting a
sample of facilities and determining the efficiency of the equipment present in that sample using the
methods discussed earlier. The second method takes a “best practice” approach and either uses the
highest rated equipment found in the field, or looks at the equipment for sale that could replace
existing equipment. New equipment may have higher efficiency levels that the average for equipment
currently installed at facilities. Further, it could be argued that energy efficiency projects that install
new equipment should use the efficiency levels of the likely replacement equipment, had their project
not been offered.
One approach to determining which method to use is to examine the technology involved and the
trend over time in the efficiency levels of that equipment. In general, it would seem appropriate to use
the efficiency levels of new equipment rather than the average in-field level when there has been a
steady improvement in efficiency over time. This has occurred in refrigerators, air conditioners, and
other types of equipment. However, it may be appropriate to use the average in-field efficiency levels
for equipment that represent new technology breakthroughs. The move toward T-8 lamps and
electronic ballasts represents a new technology. In some developing countries, there is virtually no
penetration of these efficient lighting technologies. As a result, it may be appropriate to use the
average in-field efficiency for the lighting baseline when such projects span a large number of
participants.
In summary, the selection of “best practice” efficiency levels or the use of average efficiency levels of
in-field equipment should be made on the basis of which will more accurately represent the
business-as-usual baseline. If all new sales are for equipment that has a higher efficiency level than
older equipment, then the new equipment efficiency level should be used. However, if the technology
is new and only a small fraction of new sales represent that technology, then the average efficiency
level (or potentially a reasonable “better-than-average” efficiency level) of the stock of equipment in
the field may be more appropriate. In this case, 70 per cent of participants in a project might have
purchased the lower efficiency level equipment and only 30 per cent would have purchased the new,
more efficient technology. Even today, the penetration of highly efficient compact fluorescent lamps
(CFLs) in OECD countries is only a small fraction of conventional 60 watt and 100 watt incandescent
light bulbs. It would be inaccurate to assume as the baseline that all lighting purchases are for the
most efficient CFL. Simply stated, that is not the current baseline and it would penalise project
developers for JI and CDM projects with the potential to greatly discourage the design of
cost-effective energy efficiency projects.
42
5.3.2 Assumptions about Business-as-Usual Investments in Energy Efficiency
One baseline issue commonly raised concerns what types of energy efficiency investments should be
assumed to take place in the business-as-usual case and, therefore, be included in the baseline. Some
have proposed that energy efficiency investments currently judged as economic should automatically
be included in the baseline. For example, should all projects with an estimated payback of less than
two years be considered projects that would have been undertaken anyway? The answer to this
question revolves around what is appropriate to assume for the baseline. It is important to remember
that the baseline is supposed to be representative of energy efficiency project participants. If none of
the participants are currently implementing these energy-efficient measures (even though they are, in
theory, viewed as very cost-effective), what is going to change in the future? If a specific factor
cannot be identified that will eliminate a barrier to implementation, the past is probably the best
predictor of the future.
Potential barriers to energy efficiency investments were presented in Section 2.3. These barriers can,
in many cases, be viewed as costs that are frequently omitted from traditional economic analyses of
projects. It would thus be inappropriate to ignore them in setting baselines for energy efficiency in
developing countries. In summary, if certain theoretically economically energy efficiency actions are
not currently being undertaken, it would seem to be inappropriate to assume that, under a BAU
scenario, these investments would be made in the absence of an identified factor that would change
this behavior, e.g., remove the barriers to investment in energy efficiency.
43
6. Insights and Conclusions
Significant energy efficiency opportunities are generally believed to exist in developing countries (as
well as in economies in transition), particularly as these countries did not experience the wave of
energy efficiency improvements experienced in industrialised countries after the oil price shocks of
the 1970s. Although many of these potential opportunities appear “economic” according to
traditional cost-benefit assessments, there are barriers (e.g. in the form of information costs, technical
costs, market distortion costs, public policy costs, etc.) that impede their implementation. The Kyoto
Protocol’s project-based mechanisms (i.e. CDM and JI) could help overcome some of these barriers,
particularly if the development and use of baselines is made transparent and consistent.
The development of GHG emission baselines for energy efficiency projects can be divided into two
main steps: (1) the development of the energy use baseline; and (2) the translation of this baselines
into GHG emissions.
There are essentially three options, or levels, for the standardisation of energy use baselines for energy
efficiency projects. Extensive experience with energy efficiency projects and programmes in
industrialised countries, as well as some developing country experience in energy efficiency projects
and programmes (in the lighting and motors sector) examined in the context of this case study, allow
to draw some initial insights on the different baseline standardisation possibilities:
a) Standardising baseline calculation methods and data collection protocols
There is likely significant scope for the standardisation of baseline calculation methods and data
collection protocols.
Insights can be drawn from the lighting and motors project examples in developing countries
examined in this case study on the potential for standardisation. Standardisation of baseline
calculation methodologies could contribute to consistency, rigor and reproducibility of analytic
methods and data systems for future JI and CDM energy efficiency projects.
The baseline calculation methodology (algorithms) and data collection protocols necessary for the
construction of baselines for energy efficiency projects in the lighting sector appears suitable to
standardisation. Such standardisation could apply across countries.
Similarly, in the case of the development of energy use baselines in the motors sector, the calculation
methodology used for estimating the energy use for a population of motors could be standardised
across countries. The data on the number of motors by horsepower category can be collected at either
the site or the population level. The data for the efficiency and operating hours would need to be
obtained through estimation from technical data, engineering methods or field observations.
b) Standardising operating (e.g. number of hours) and performance (e.g. motor efficiency)
parameters necessary for the baseline calculation
The standardisation of baseline operating and performance parameters would bring greater uniformity
and consistency to the CDM/JI baseline development process.
In the lighting sector, it is likely that the standardisation of operating and performance parameters that
are necessary for the development of energy use baselines for energy efficiency projects would be
possible for the most common types of lighting devices. This seems to be particularly appropriate for
in the residential and commercial sectors, where lighting operating hours tend to be relatively
consistent. For example, it would be possible to establish baseline data on wattages for common
types of incandescent and fluorescent residential and commercial fixtures. The operating hours
parameters would need to be differentiated according to market sector/segment. In addition, it would
be necessary to develop and standardise these baseline values on a country-by-country basis (or on a
44
regional basis if circumstances are sufficiently similar), in order to take into account differences in
domestic markets and the mix of technologies. The standardisation of operating hours could be done
through conservative estimates based on a reasonable sample of observations. Similarly, performance
parameters such as input wattage for the most common lighting fixture types could be standardised.
In the case of large industrial lighting projects, site-specific data would be more appropriate.
In the motors sector, it would be possible to standardise motor efficiency parameters, as equipment
performance tends to be more uniform across market segments than operating characteristics. Such
standardisation seems applicable particularly for certain motor types and size ranges that are most
common in the commercial sector and industrial application. In fact, it would seem useful to further
examine the possibility of standardising motor efficiencies for the most common types, sizes, classes
and applications, based on manufacturers’ data, in developing countries.
In addition to parameter values for motor efficiencies, the calculation of energy use baselines for
energy efficiency projects in the motors sector requires parameter values for operating hours, load
factors and “diversity” factors. These latter parameters lend themselves to standardisation with
certain limitations. As operating hours tend to be relatively consistent within specific market
segments, particularly in the commercial sector, this parameter could be conservatively standardised
by market sector/segment. These baseline values would need to be based on end-use load information
on a country-by country, or possibly regional, basis
c) Standardising energy use indices by sector, market segment and/or end-use (e.g. lighting kWh
per square meter for certain commercial building types)
With respect to the potential for standardising energy use indices (EUI) for lighting projects, it would
seem possible to standardise indoor lighting EUIs (e.g. lighting kWh/square meter) for certain market
segments of the commercial sector (e.g. offices, schools, and hospitals). Such EUIs could be used as
the baseline values for energy use related to lighting in those commercial sector market segments.
In the residential sector, it may be useful to consider the potential standardisation of EUIs for certain
appliances (e.g. refrigerators).
Standardised lighting EUIs are probably less applicable in the industrial sector, where a hybrid
approach combining standardised and project specific elements is likely more appropriate.
Motor energy use indices (e.g. kWh/square meter) for the commercial sector do not seem appropriate,
as motor energy use is often tabulated or subsumed in other end-uses, particularly space heating and
cooling. However, in the industrial sector, where motors are often the primary energy-consuming
devices, it may be possible to develop baseline motor energy use indices related to the unit of
production (motor kWh/unit of production) for selected industries.
Other Baseline Issues
Translating energy saved to GHGs: The rate at which reductions in energy use are translated into
GHG emissions is one of the key elements of developing an emission baseline for energy efficiency
projects. Setting an emission rate per kWh (which could be differentiated for peaking and baseload
electricity use, for example) for a fixed period of time through electricity multi-project baselines is
probably one of the most significant baseline standardisation elements for CDM/JI energy efficiency
projects.
Crediting lifetime: Another important baseline standardisation element is the crediting lifetime
associated with a particular baseline. The authors recommend fixing, at the start of an energy
efficiency CDM/JI project, the length of time during which a baseline is considered valid for
calculating that project’s GHG emission reductions (and thus emission credits). A baseline crediting
lifetime of about five years would seem adequate to balance environmental and project developers’
45
interests. This would not preclude the possibility that the baseline be set such that energy efficiency is
assumed to increase at a given rate over the five-year period. However, this would need to be
determined at the outset.
Free riders and spillover effects: The methodologies examined to estimate energy use baselines for
energy efficiency projects normally only consider direct energy use. However, energy efficiency
projects may lead to two indirect energy use, and GHG, effects: free riders and spillover effects.
These two indirect effects work in opposite directions and both are difficult to quantify. Until better
information is available, it may be practical to assume, as have assumed some regulatory jurisdictions
in the case of traditional energy efficiency projects and programmes, that these two effects cancel
each other out.
Determining stringency: In terms of the appropriate stringency level for energy efficiency projects,
it is important that the level provide a reasonable reflection of the “business-as-usual” case. Basing
the baseline level on what investments should, theoretically, take place, such as through a traditional
economic assessment criteria (e.g. pay-back period), is likely not a good proxy for “business-as-
usual”, as they do not take into account the various (non-purely economic) barriers to energy
efficiency investments.
The other two main approaches of determining an appropriate baseline stringency level are based on:
(i) existing stock of equipment in the field; and (ii) “best practice”, using either highest rated
equipment found in the field or equipment for sale. The most appropriate choice would depend on
what is reasonable to assume under a business-as-usual scenario. In a case where all new sales are for
equipment that has a higher efficiency level than older equipment, then the new equipment efficiency
level should be used for developing the baseline. On the other hand, if the technology is brand new
and only a small fraction (e.g. less than 30 per cent) of new sales represent this technology, then the
average efficiency level (or potentially a reasonable “better-than-average” efficiency level) of the
stock of equipment in the field may be more appropriate.
Finally, it is important to recognise that some of energy efficiency JI or CDM projects will probably
“beat the system” and receive more emissions credits than they deserve. No process will be perfect,
and any energy efficiency baseline construction process will likely have defects. A search for
perfection will likely result in no process being judged as acceptable. The goal is to strike both a
reasonable balance among various risks including among the interests of project developers, those of
potential host countries, and the environmental objectives of the project-based mechanisms.
46
References
AGRA Monenco Inc., 1998. DSM Program Evaluation Plan – Conservation Program Impact
Evaluation, prepared for Electricity Generating Authority of Thailand, prepared by AGRA
Monenco, Inc., Oakville, Ontario.
Bosi, Martina, 2000, “An Initial View on Methodologies for Emissions Baselines: Electricity
Generation Case Study”, IEA Information Paper, Paris, June.
Begg, Katie et al., 1999. Overall Issues for Accounting for Emissions Reductions of JI Projects,
Centre for Environmental Strategy, University of Surry, UK.
Chomitz, K., 1998. Baselines for Greenhouse Gas Reductions: Problems, Precedents and Solutions,
World Bank.
EGAT, 1997a. High Efficiency Fluorescent Tube Program — Program Plan Evaluation Plan,
Demand-Side Management Office, Planning and Evaluation Department, January.
EGAT, 1997b. Compact Fluorescent Lamp Program — Program Plan Evaluation Plan,
Demand-Side Management Office, Planning and Evaluation Department, January.
Ellis, Jane, 1999. “Experience with Emission Baselines Under the AIJ Pilot Phase,” OECD
Information Paper, ENV/EPOC(99)23/FINAL, May.
Ellis, Jane and Martina Bosi, 1999, “Options for Project Emission Baselines,” OECD and IEA
Information Paper, Paris, October.
EPRI, 1991, Impact Evaluation of Demand-Side Management Programs, Volume 1: A Guide to
Current Practice, EPRI Research Project 2548-11, CU-7179. Prepared by RCG/Hagler, Bailly.
Palo Alto, California: Electric Power Research Institute.
EPRI, 1995a, Performance Impacts: Evaluation Methods for the Nonresidential Sector. EPRI
Research Project 3269, TR-105845. Edited by D. Violette, M. Keneipp, and I. Obstfeld.
Prepared by Xenergy, Inc. Palo Alto, California: Electric Power Research Institute.
EPRI, 1995b, Evaluation of Commercial-Sector Lighting Retrofit Programs, EPRI RP 3823-02. Palo
Alto, California: Electric Power Research Institute.
EPRI. 1996. End-Use Performance Monitoring Handbook, Research Project 3269-19, TR-106960.
Edited by D. Violette and M. Keneipp, Hagler Bailly. Palo Alto, California: Electric Power
Research Institute.
Hirst, E. and Reed, J. eds., 1991, Handbook of Evaluation of DSM Programs, Oak Ridge National
Laboratories, ORNL/Con-336, December.
Hagler Bailly Consulting, Inc., 1996 Demand-Side Management Assessment for Viet Nam, Phase 1
Final Report, prepared for the World Bank, August.
Hagler Bailly, Inc., 1990, Pakistan ENERCON Technical Assistance Project Summary Report,
prepared for US Agency for International Development, Washington, DC.
Hagler Bailly, Inc., 1987, Preliminary Energy Surveys in Buildings, Summary Report, ENERCON,
prepared for US Agency for International Development, Washington, DC.
47
Hagler Bailly, Inc., 1988, Tubewell Pre-Audit Survey Report, Technical Assistance Project Summary
Report, ENERCON, prepared for US Agency for International Development, Washington, DC.
Hagler Bailly Services, Inc., 1997, Demand-Side Management in Morocco, Volumes 1, 2 and 3,
prepared for US Agency for International Development, Washington, DC.
Hagler Bailly Services, Inc., 1998, Pilot Project for the Substitution of Standard Motors with
High-Efficiency Motors, prepared for US Agency for International Development, Washington,
DC.
Hagler Bailly Services, Inc., 2000, Impacto de Los Evaluaciones (Mexico Motors Project), prepared
for US Agency for International Development, Arlington, VA.
Hagler Bailly Services, Inc., 2000, Análisis de la Base de Datos del Proyecto Piloto para la
Substitución de Motores Estándar por Motores de Alta Eficiencia (Mexico Motors Project),
prepared for US Agency for International Development, Arlington, VA.
International Energy Agency (IEA), 1996, Evaluation, Verification and Performance Measurement of
Energy Efficiency Programs, prepared by D. Violette, Hagler Bailly Consulting, Inc., 1996.
IEA, 1999a, Energy Statistics of OECD Countries: 1996-1997. Paris.
IEA, 1999b, Energy Statistics of non-OECD Countries: 1996-1997. Paris.
NARUC, 1994, Regulating DSM Program Evaluation: Policy and Administrative Issues for Public
Utility Commissions, ORNL/Sub/95X-SH985C. Co-authors: J. Raab and D. Violette.
Washington, D.C.: National Association of Regulatory Utility Commissioners.
Oak Ridge National Laboratories, 1996, A DSM Manual for the APEC Economies, prepared for
Douglas Bauer, ORNL, prepared by Ahmad Faruqui and Kathleen McElroy, Hagler Bailly
Services, Inc.
OECD, 1999, “Experience with Emission Baselines Under the AIJ Pilot Phase,” OECD Information
Paper, ENV/EPOC(99)23/FINAL.
U.S. Agency for International Development, 1997. Demand-Side Management in Morocco: Volume II
– National Market Research, Final Report,” prepared for USAID Office of Energy,
Environment, and Technology, Washington, DC, prepared by Hagler Bailly Consulting,
Arlington, VA, March.
U.S. Agency for International Development/Hagler Bailly Services, Inc., 1997, The Energy Efficiency
Market in Developing Countries: Trends and Policy Implications.
U.S. Department of Energy, 1997, International Performance Measurement and Verification
Protocols.
U.S. Energy Association/U.S. Agency for International Development, 1999, Handbook of Climate
Change Mitigation Options for Developing Country Utilities and Regulatory Agencies.
U.S. Environmental Protection Agency, 1995, U.S. EPA Conservation Verification Protocols: Version
2.0, EPA Publication 430/B-95-012, Governmental Printing Office.
48
U.S. Environmental Protection Agency, 1999, Clean Development Mechanism Baselines: An
Evaluation of the Benchmarking Approach, prepared for U.S. EPA Contract No. 68-W6-0055
(Contact: Shari Friedman), prepared by Stratus Consulting, Boulder, CO and Tellus Institute,
Boston, January, 1999.
Vine, E. and J. Sathaye, J., 1997, The Monitoring, Evaluation, Reporting and Verification of Climate
Change Mitigation Projects, prepared for U.S. Environmental Protection Agency.
Vine, E. et al., 1999, “Project Monitoring, Reporting and Verification,” Chapter 13 in The U.N.
Framework Convention on Climate Change Activities Implemented Jointly (AIJ) Pilot:
Experiences and Lessons Learned, R. Dixon (Ed.). Kluwer Academic Publishers.
Violette, D. et al., 1991, Handbook of Evaluation of Utility DSM Programs, Volumes I and II: A
Guide to Current Practice, Electric Power Research Institute.
Violette, D., 1993, “Statistically Adjusted Engineering Estimates,” in Proceedings of the 1993
International Energy Program Evaluation Conference, IEA.
Violette, D., 1995, Performance Impacts: Evaluation Methods for the Non-Residential Sectors,
Electric Power Research Institute, EPRI RP-3269, TR-105845.
Violette, D., 1996, Evaluation, Verification, and Performance Measurement of Energy Efficiency
Programmes, prepared for the IEA by Hagler Bailly Consulting, Inc.
Violette, D. et al., 1998, Evaluating Greenhouse Gas Mitigation through DSM Projects: Lessons
Learned from DSM Evaluation in the United States, World Bank.
World Bank, 1996. Demand-Side Management Assessment for Viet Nam: Phase I Final Report,
prepared by Hagler Bailly Consulting, Inc., August.
World Bank, 1997, Mexico High Efficiency Lighting Pilot Project, AIJ Pilot Phase.
World Bank, 1998. Evaluating Greenhouse Gas Mitigation Through DSM Programs, Prepared for
Carbon Offsets Unit, Environment Department (Contact: Kenneth Chomitz), prepared by D.
Violette et al., Hagler Bailly Services, Inc.
49
ANNEX A: EXAMPLES OF BASELINE DEVELOPMENT FOR ENERGY EFFICIENCY
PROJECTS AND ENERGY EFFICIENCY MARKET ASSESSMENT
This section presents selected examples where baseline energy use and/or market characteristics were
developed as part of an energy efficiency project evaluation or market assessment to determine the
cost-effective potential for an energy efficiency project. None of the examples focused on stand-alone
energy use baseline assessments, nor did they deal with the issue of forecasting baseline conditions
into the future. For the project examples that developed a reference case (or baseline) for energy use,
the baseline was part of a larger study or project to estimate actual energy savings or the market
potential for savings. In those cases where estimates of energy savings were the objective, the studies
were less focused on baseline construction than on estimating the energy use of the newly installed or
proposed energy-efficient technologies.
Although the purpose of these examples was generally not to help evaluate GHG emission reductions
resulting from energy efficiency projects (with the exception of the Mexican Ilumex project), they
nonetheless may provide useful insights for the development of baselines for energy efficiency
projects undertaken in the context of JI and CDM.
Seven case studies of lighting, motors and audit-based energy efficiency projects in developing
countries follow.
1. Fluorescent Lamp and Compact Fluorescent Lamp Market Transformation Project26 —
Thailand
This project is a national DSM programme intended to transform the indoor lighting market. The
existing standard fluorescent lamps installed in commercial buildings are 40 watt and 20 watt “fat
tube” (T12) fluorescent lamps. An agreement was reached between the national government and
manufacturers of lamps sold in Thailand. This agreement called for manufacturers to stop making the
40 and 20 watt fat tube T12 lamps and replace them with 36 and 18-watt thin tube (T8) lamps. The
project also encouraged the replacement of incandescent lamps with more efficient compact
fluorescent lamps (CFL) in both residential and non-residential applications.
The energy use baselines required by these two projects focused on the typical performance and
operational factors (i.e., wattage and operating hours) of 40 and 20 watt tubular fluorescent and the
older incandescent lamps (to be replaced by the CFLs), maximum peak coincident demand, and
baseline annual energy consumption.
The data used to construct the energy baselines were based on the following sources:
• Spot-watt measurements of a sample of fixtures using fat tubes to estimate the average kW per
lamp.
• Calibration of these spot-watt measurements by bench tests of different lamp and ballast
combinations for fluorescent lamps.
• Estimation of operating hours from customer self-reports for all participants, as well as
run-time data from a sample of installations. The customer estimates of operating hours are
compared to the run-time logger data and an adjustment factor is calculated using the ratio
between the two numbers. This ratio is then used to “calibrate” estimated operating hours as
reported by building or residence occupants.
26 EGAT reports (1997a and 1997b) contain information on the lighting programme plans and evaluation plans.
50
Baseline energy use is to be estimated by taking the product of estimated lamp wattage (kW)
multiplied by estimates of operating hours to obtain kWh. The actual data used in the analysis, the
results of the bench tests and field monitoring, and the final evaluation findings are not public
information at this time and thus cannot be reported here. The evaluation will utilise a calibrated
engineering algorithm27 approach that accounts for the number of units installed lamp wattage and
average annual lamp operating hours for both the fat tube and CFL projects.
There are several notable aspects of the baseline constructed for this application. First, a sample of
older lamps had to be identified and spot-watt measurements taken on these lamps, along with the use
of run-time data loggers to obtain accurate estimates of operating hours for the baseline technologies.
Occupant self-reports of operating hours are also obtained from customer surveys. These self-reported
estimates are adjusted by the estimates from the more accurate run-time loggers, resulting in an
adjustment factor or ratio. This approach of using a ratio comprising a more accurate estimate divided
by a less accurate estimate and then applying that ratio to the larger number of self-reported operating
hours is an example of data leveraging. Data leveraging procedures are increasing in use and can be
expected to become standard in baseline development in the context of energy efficiency projects.28
2. Ilumex Compact Fluorescent Lamp Replacement Project29 — Mexico (AIJ Pilot Phase)
This AIJ pilot phase energy efficiency project provided rebates for the purchase of compact
fluorescent lamps (CFLs) sold through retail outlets. The project was intended to encourage the
widespread use of CFLs and was restricted to residential applications in two metropolitan areas,
Guadalajara and Monterrey.
The energy use baseline developed in this project is a performance and operational baseline (i.e.,
wattage and operating hours) of existing incandescent lamps resulting in a baseline annual energy
consumption of a population of lamp replacement projects. The data used to construct the baseline
included vendor surveys of lamp sales and stocks used to estimate the number and type of units sold,
and participant surveys used to estimate the number and type of CFLs installed and characteristics of
the incandescent lamps they replaced. The project baseline assumes that participants would have
continued to use ordinary incandescent lamps and that the replacements would not have occurred in
the absence of the project.
Initial planning assumptions specified that each wattage of CFL would replace an equivalent
incandescent lamp (e.g., a 15-watt CFL would replace a 60-watt incandescent bulb). The wattage
values used for the baseline incandescent lamps and the CFLs are presented in Table A-1. The
preliminary results of the spot-watt measurements 30 showed that the average energy savings per lamp
were 50 watts compared to the planned 54 watts. While these data allow the quantification of
baselines for typical programme participants, the project report clearly states that “… data do not exist
for Mexico which would allow us to define a meaningful detailed national baseline projection.”
27 The report describes this approach as a “calibrated engineering approach.” This approach is described in
Section 4.2 in Violette (1996).
28 A data leveraging approach uses two methods of data collection. A small sample is selected for which
extremely high-quality data are obtained. A larger sample is also selected and a less expensive, less accurate
estimation is approach is applied to this larger sample. A ratio estimate is used to leverage the small amount
of highly accurate data within the larger sample of data. This is described in Violette (1991).
29 World Bank (1997) and related updates and descriptions.
30 The detailed evaluation of the programme by CFE is not public information. World Bank and FCCC reports
provide only aggregate data.
51
The baseline operating hours were assumed to be 4 hours per day. However, the monitoring and
evaluation plan called for baselines for impact assessment to be developed from post-implementation
surveys and follow-up runtime hour data logging on a sample of lamps. The results of the runtime
metering at a sample of sites (as of 1997) showed that the average operating hours for the lamps was
about 3 hours per day compared to the 4 hours in the planning assumptions.
Table A-1. Lamp Performance Data
Baseline
Incandescent
Watts
CFL Watts
Watt Savings
Nominal
Measured
100
23
21.1
78.9
75
20
17.8
57.2
60
15
16.1
43.9
While the study indicates the need to adjust the baseline over time to account for natural change and
market transformation effects and the challenges associated with doing so, it does not provide a
specific forecast of the baseline into the future. It was assumed that the energy savings benefits of the
project would extend for eight years; the anticipated life of a CFL.
The GHG emissions reductions of the project were estimated as follows:
1. The kilowatt hours of electricity use avoided from replacing 1.7 million incandescent bulbs
with the more efficient CFLs. These estimations were based on the following parameter values:
• number and type of bulbs installed by month;
• an average bulb use of three hours per day (based on preliminary results of on-site metering of
bulb use in participants’ homes) and 30 days per month;
• a bulb lifetime of 8,760 hours (12.4 per cent less than the technical specifications of the CFLs);
• an average savings of 50 watts per bulb (taken from the difference between the average
incandescent bulb wattage and the average wattage of the CFLs used as replacements);
• assuming transmission and other losses of 18 per cent on the CFE system.
2. The kilowatt hours not generated are converted to emissions saved using:
• standard emissions factors for each fuel type, expressed in tons per Tera Joule;
• fuel mix actually used at the Monterrey and Manzanillo plants in 1995 and 1996;
• heat rate, or efficiency, of the plants.
Table A-2 summarises the results of the GHG reduction estimates.
52
Table A-2. AIJ Component Baseline and Estimated GHG Reductions
Units
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
A) Baseline
Scenario
MWh 1,748 7,016
12,918
15,763
15,763 15,763
15,763
15,763
14,346 9,257 3,272 36
B) Project
Scenario
MWh 499 2,004
3,694 4,504 4,504 4,504 4,504 4,504 4,099 2,645 935 10
C)Effect (B
-A)
MWh not
Consumed
MWh 1,249 5,011
9,224 11,259
11,259 11,259
11,259
11,259
10,247 6,612 2,337 26
D) MWh not
Generated
MWh 1,523 6,111
11,261
13,731
13,731 13,731
13,731
13,731
12,496 8,064 2,850 31
GHG
Reductions
Metric
tonnes
E) Effect of (D)
CO2 1,176 4,721
8,700 10,608
10,608 10,608
10,608
10,608
9,654 6,230 2,202 24
CH4
0.03
0.12
0.22
0.27
0.27
0.27
0.27
0.27
0.25
0.16
0.06
0.00
F) Cumulative
CO2
1,176
5,897
14,597
25,205
35,813
46,421
57,029
67,637
77,291
83,521
85,723
85,748
Effect of (D)
CH4
0.03
0.15
0.37
0.64
0.91
1.18
1.45
1.72
1.97
2.13
2.19
2.19
Source: World Bank (1997).
3. Demand-Side Management Assessment31 — Morocco
The Morocco DSM study included three components: 1) a national assessment of DSM potential, 2) a
national market research study of energy use patterns, equipment distribution channels, and customer
attitudes and preferences, and 3) a pilot residential lighting demonstration project.
Growth in lighting energy use constitutes a large fraction of growth in residential electricity demand
in Morocco. Most residential lighting is incandescent, with the majority of lamps being either 75 watt
or 100 watt. The residential lighting pilot project installed 2,147 compact fluorescent lamps in 1,412
households.
All major end uses in the residential, commercial and industrial sectors were examined for the
National Assessment of DSM Potential and the National Market Research Study. The data used to
construct the energy use baseline came from a national market research study. This study included of
surveys of 2000 residential customers, 61 commercial sector interviews, 52 industrial sector
interviews and 50 interviews with trade allies (e.g., architects, engineers, and installers). These data
were compiled and analysed to characterise energy use and energy use parameters in each market
sector, and to produce the estimate of DSM potential.
For the residential lighting pilot project, baseline consumption was estimated using a standard
engineering algorithm that accounted for lamp wattage and operating hours. It was found from the
field research that 50 per cent of incandescent lamps were 100 watt and 28 per cent were 75 watt.
Operating hours were determined from runtime metering at a sample of sites. For the hours of
operation, only one time period was taken into account.32 The average usage per lamp across the
retrofit and control groups was 3.5 hours/day. Hours of operation did not show any significant
changes after the retrofit, demonstrating that there is no short-term take back. The actual operating
hours averaged only 75 per cent of the operating hours estimated by the resident.
31 Hagler Bailly Services, Inc. (19997).
32 The second time period would have conflicted with Ramadan.
53
For the residential lighting pilot project, the following categories of data and data collection activities
were used for the energy analysis:
• Customer survey data, including both the participant group and a control group.
• Programme tracking data for the lighting programme.
• Utility billing data for both the participant group and the control group.
• On-site metered data for a sample of sites.
• Post-installation survey data for a sample of the customers receiving the CFL retrofits.
The baseline was used to produce estimates of energy savings using an engineering estimation
procedure with final estimates calibrated through a statistically adjusted engineering analysis (SAE)33
of utility billing records. The “realisation rate” of the engineering estimate based on the SAE analysis
was 75 per cent.34 A number of other useful and informative factors influencing energy savings (and
energy use) were also identified in this study including:
• Estimated incandescent lamp wattages for each equivalent CFL (Table A-3).
• Typical seasonal weekday and weekend lamp operating hours.
• Operating hour estimates were also adjusted to account for the Ramadan holiday (this is an
instructive example of the need to examine the in-country particularities that might effect a
standardised baseline).
• Load shapes were developed and coincident diversity factors were estimated.
• The “takeback” effect (taking back savings in terms of greater lighting use) was estimated.
Table A-3. Incandescent and CFL Wattages
Existing Incandescent Wattage
CFL Wattage
100 23
75 20
60 15
The baseline stemming from this national assessment of DSM potential produced a useful
characterisation of electricity use in Morocco, including breakdowns by market sector and subsector,
end-use breakdowns for major markets, sector and end-use load shape profiles, and average wattage
ratings and operating hours for a variety of lighting uses. These data form an excellent foundation for
baseline construction for energy efficiency projects. The national market research took the analysis a
step further to develop a detailed baseline profile of energy use by major end use. The study employed
surveys of and interviews with customers that examined all aspects of energy use.
33 Descriptions of statistically adjusted engineering estimates are presented in Violette (1993 and 1996).
34 “Realisation rate” is defined as the percentage of the engineering estimate of energy savings that is realised,
on average, according to an analysis of actual consumption records.
54
For residential lighting, the market research survey produced energy use and performance and
operating data for both rural and urban customers, including:
• A profile of the number of lamps and average number of lamps per household (Table A-4).
• Average lamp wattages and a breakdown of wattages by wattage category (Table A-5).
• Average lamp operating hours.
Table A-4. Summary of Lighting Characteristics by Urban and Rural Areas
Urban
Rural
Average Number of Lamps per Household
9
6
Average Wattage of Lamps
93
87
Average Daily Hours of Use
2
2
Table A-5. Distribution of Indoor Incandescent Lamps by Wattage
<60 W
60 W
75 W
100 W
>100 W
2%
14%
37%
42%
4%
For commercial lighting, the study produced energy use and performance and operating parameters
for both indoor and outdoor lighting, including:
• A breakdown of lighting by type (incandescent, fluorescent, halogen and compact fluorescent).
• Average number of lamps per facility by lamp type.
• Average daily operating hours by lighting type.
A summary of commercial indoor lighting characteristics is presented in Table A-6.
Table A-6. Summary of Commercial Indoor Lighting
Type of Lighting
Average Number of
Units Per Facility Average Wattage
Average Daily Hours of
Use
Fluorescent Lamps 263 53 10
Incandescent Lamps 269 93 6.5
Halogen Lamps 39 262 9.2
Compact Fluorescent
Lamps 47 22 6.6
55
The industrial lighting assessment produced a similar dataset to the commercial sector data.
4. High-Efficiency Motors Replacement Project35 — Mexico
In this pilot project, 1,624 standard efficiency motors at 20 customer sites were analysed for
replacement with high-efficiency motors. The participating customers included industries in the
manufacturing, food processing, chemical, rubber processing, steel production, mining,
pharmaceutical, and paper industries. The programme included a detailed audit of the existing motor
systems with recommendations for replacement motors. To encourage implementation, the audit was
free for companies that implemented the recommended measures, including motor replacement.
The motor system audit included the following information/data for each motor in the facility:
• Information on the existing motor: brand, body-type (open, closed, etc.), capacity/power in
horsepower, speed (revolutions per minute), voltage, current, efficiency (if available), power