Download Barriers to Energy Efficiency

COMPATIBILITY OF THE SE4ALL ENERGY EFFICIENCY
OBJECTIVE WITH RENEWABLE ENERGY, ENERGY
ACCESS, AND CLIMATE MITIGATION TARGETS
Jay Gregg1, Olexandr Balyk1, Ola Solér1, Simone La Greca1, Cristian Hernán Cabrera Pérez1, Tom Kober2
1
Systems Analysis, Technical University of Denmark
2
Energy Research Centre of the Netherlands
ABSTRACT
The objectives of the Sustainable Energy for All (SE4ALL), a United Nations (UN) global initiative, are to achieve, by
2030: 1) universal access to modern energy services; 2) a doubling of the global rate of improvement in energy
efficiency; and 3) a doubling of the share of renewable energy in the global energy mix (United Nations, 2011;
SE4ALL, 2013a). The purpose of this study is to determine to what extent the energy efficiency objective supports
the other two objectives, and to what extent the SE4ALL objectives support the climate target of limiting the global
mean temperature increase to 2° C over pre-industrial times. To accomplish this, pathways are constructed for
each objective, which then form the basis for a scenario analysis using the Energy Technology System Analysis
Program TIMES Integrated Assessment Model (ETSAP-TIAM). We find that, in general, the energy efficiency
objective is reinforced by the renewable energy objective, but not by the universal access objective. Achieving the
energy efficiency objective is made cheaper (in terms of the net present value of investment costs) when the
renewable energy target is also achieved. However, achieving both the renewable energy and energy efficiency
targets require more investment than achieving the renewable energy objective alone. Furthermore, we find that
the universal access objective requires much more investment in the residential sectors of developing regions of
the world, and makes the meeting the other two objectives more expensive. Meeting any of the objectives also
requires increased investment in the transportation sector. While achieving the SE4ALL objectives does not limit
warming to 2° C on its own, it makes a substantial contribution toward that goal, particularly if the renewable
energy and energy efficiency objectives are met.
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INTRODUCTION
BACKGROUND
Energy savings through energy efficiency are viewed as a one of the primary avenues to address anthropogenic
climate change for many years. Investing in energy efficiency has long been viewed as “win-win”, as the same level
of end-use service is delivered at reduced cost and reduced emissions (Jaffe et al., 1999). Three of the fifteen
“stabilization wedges”, actions that can reduce emissions by 1 GtC/year (3.34 GtCO 2/year)1 by 20542 for solving
climate change identified by Pacala, et al. (2004) involve energy efficiency improvements. It is estimated that
energy efficiency improvements in vehicles, buildings, and baseload power generation could contribute
approximately a third of total reductions in emissions necessary to address climate change (Pacala et al., 2004).
Additionally, technologies for achieving these reductions in emissions are largely available (McNeil, et al. 2012),
thus presenting a particularly attractive mitigation option for the short- and medium-term.
While engineering studies estimate that carbon emissions could be inexpensively reduced by 20-25% by globally
switching to more efficient end-use technologies (e.g. lighting, appliances, insulation, etc.), economic studies
emphasize that consumers are primarily motivated to switch products when there is a price incentive to do so
(though other qualitative features can play a role); thus technological developments into more efficient products
are motivated by price differentials (Markandya, 2001). Moreover, the existence of a huge “energy efficiency gap”,
i.e. the gap between potential energy efficiency gains and realized ones, has recently been questioned by some
economists (Allcott & Greenstone, 2012). Likewise, though the Working Group III of the Intergovernmental Panel
on Climate Change (IPCC WG3) indicated that in order to remain below 2°C warming (equivalent to a stabilization
of 430-530 ppm CO2) by the end of the century, annual investments in energy efficiency for transport, buildings,
and power would need to be increased by USD 336 billion compared to reference scenarios, this conclusion had
only limited evidence, medium agreement and high uncertainty (USD 1 – 641 billion) (Gupta, et al., 2014).
MODELING OF ENERGY EFFICIENCY
Energy efficiency was one of the main topics addressed by the 27 th Energy Modeling Forum (EMF) in Stanford,
California, 2014, providing a detailed comparison of energy-economic models and integrated assessment models.
A suite of 18 different models ran multiple scenarios for energy efficiency, and included both simulated
technological improvements as well as simulated structural changes in the economy (Sugiyama, et al., 2014). Some
of the models accomplished this simulation by modifying end-use efficiencies while others adjusted the
“autonomous energy efficiency improvement” (AEEI) 3 rates to meet the specified energy demand reduction
targets defined in each scenario. The comparative study showed that energy efficiency improvements occurred
quicker under a climate policy, and this improvement rate was enhanced when technology was constrained (with
fewer technological options for reducing emissions, efficiency improvements become more essential to achieving a
climate target) (Sugiyama, et al., 2014). The study showed that the second objective of the SE4ALL initiative was
feasible, that without a climate policy, Energy Intensity Improvement Rates (EIIR) were around 2% per annum
(Sugiyama, et al., 2014). This result is corroborated by Kriegler, et al. (2014), who found that doubling the rate of
1
1 tC is equal to 3.67 tCO2, given the atomic mass ratio of 44/12.
The study by Pacala et al. considered a 50-year window from the present (then 2004) and made reference to
2054 as a target year, rather than the more commonly used 2050 benchmark.
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AEEI is discussed in more depth in the model input data assumptions.
2
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improvement in energy intensity of GDP significantly reduced global mitigation costs. Kriegler, et al. (2014) has
noted, however, that most models have a very crude representation of demand side investments and costs, and
that the models may therefore be underestimating the mitigation costs. Likewise, Sugiyama, et al. (2014) found
large variances across models in the efficiency improvement rates and potentials, particularly at the regional and
sectoral levels. General equilibrium models tended to reduce service demands, while in contrast, partial
equilibrium models preferred technological substitution to meet climate targets (Sugiyama, et al., 2014).
The Times Integrated Assessment Model at the Energy Research Centre of the Netherlands (TIAM-ECN) was
recently used to examine the question of energy efficiency at the specific technology level (Kober, 2014). The study
focused on the G20 countries, and primarily on the transportation sector, using energy efficiency parameters from
the International Energy Agency (IEA) (resulting in 46% and 42% reduction in energy use for cars and trucks,
respectively). Kober (2014) used four different climate policy scenarios: BAU and 3 carbon tax prices of $40, $70,
and $100 per tonne of CO2e; and found that energy efficiency measures are more effective with an increased
carbon price ($40/t, $70/t and $100/t). Emissions are reduced by 2-3 GtCO2e by 2030, representing 15-25% of
greenhouse gas (GHG) reductions in relation to the BAU baseline.
Rogelj, et al. (2013) conducted an analysis of the SE4ALL objectives using the MESSAGE IAM framework, which
included climate impacts addressed by using the climate model (MAGICC). MAGICC was used to limit the global
temperature increase to less than 2° C by the end of the century (Rogelj, et al. 2013). They found that the SE4ALL
objectives were compatible with climate goals- that sustainable energy and providing universal access to energy
were important steps to mitigating climate change and remaining below 2° C warming (Rogelj, et al. 2013). This
study emphasizes the importance of making a sensitivity analysis in both the renewable energy and the energy
efficiency scenarios because the GDP projections (and accordingly energy demand) will change in the future,
therewith affecting the quality of our results and robustness of our model. Second, the provision of universal
energy access has a limited impact on the achievement of the SE4ALL objectives and on climate protection.
Furthermore, this is unlikely to be achieved before the 2060s (Rogelj, et al. 2013). The universal access goal also
would in turn reduce the global renewables share of final energy by about 2%- this may be due to the replacement
of conventional biomass (use for cooking and heating) to electricity or LPG (Rogelj, et al. 2013). On the other hand,
this demand may be met by distributed renewable energy which would increase the share of renewables. Third,
the energy intensity indicator cannot be used as the sole yardstick to measure climate action since climate action
can only be measured and assessed in terms of the actual effectiveness of policies in limiting and reducing the
absolute amount of GHG emissions (Rogelj, et al. 2013). However, the scenario analyses by Rogelj, et al (2013) do
not include policy instruments such as feed-in tariffs or carbon tax that would trigger the implementation of
specific measures. Other current projects, including the Bottom-Up Energy Analysis (BUENAS) project model
appliance energy demand and efficiency improvements to determine the effect on greenhouse gas emission
reduction (IESG, 2015).
HISTORIC TRENDS IN ENERGY EFFICIENCY
The Global Tracking Framework (SE4ALL, 2015) highlights some success in attaining the SE4ALL objectives: over the
last 20 years, over 1 billion people gained access to electricity, global renewable energy share has increased from
16% to 18%, and energy intensity has dropped. Nevertheless, faster progress is necessary if the objectives are to
be achieved, summarized in Table 1.
Table 1. Progress in achieving the SE4ALL objectives (SE4ALL, 2015).
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Doubling global
rate of
Doubling share
improvement of renewable
Universal access to modern
of energy
energy in
energy services
efficiency
global mix
Energy
Renewable
Year
Electrification Cooking
Efficiency
Energy
1990
76
47
-1.3
16.6
2010
83
59
-1.3
17.8
2012
84.6
58.4
-1.7
18.1
2030 (projected)
89
72
-2.2
24
2030 (target)
100
100
-2.6
36
National level energy intensity (of GDP PPP) and primary energy consumption data were attained from Enerdata
(2015) for the years 1990-2013, representing 88% of global energy consumption. Countries not represented in the
Enerdata (2015) database were estimated by calculating the difference between the regional totals and the
reported national statistics that comprise the respective regions. The energy intensity statistics were divided by
the energy consumption statistics and inverted to produce internally consistent data for GDP PPP. In Figure 1, the
historic regional trends are depicted. China and the Former Soviet Union have the highest energy intensity (highest
levels of energy consumption per unit of economic output) whereas Europe has among the lowest energy
intensity. There is not a discernable relationship between the level of economic development and level of energy
intensity. The global rate of energy intensity has decreased rather steadily over the 23 years in the data.
30
Africa
Australia
Canada
Primary Energy Intensity (MJ/dollar PPP)
25
China
Central and South America
Eastern Europe
20
Former Soviet Union
India
Japan
15
Middle East
Mexico
10
Other Developing Asia
South Korea
United States
5
Western Europe
Europe
GLOBAL
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
2011
2012
2013
0
Figure 1. Historical Energy Intensity, by region based on 2005 GDP PPP.
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Moreover, the rate of change in energy intensity varies substantially year to year. In Figure 2, different rates of
change for global energy intensity are plotted together. The data indicate that the rate of improvement can vary
wildly from year to year (which depends on both the economy and the quality of the data). This is the case both for
the average rate of change and the compound annual growth rates (CAGR) calculated from the endpoints. In
general, there is an upward trend in these lines, suggesting that the energy intensity improvement rate (EIIR) is
diminishing as time goes on. These data align well with the Global Tracking Framework (GTF) estimates, plotted as
diamonds. The estimates from the GTF match well with the analysis from Enerdata (2015) data. Using CAGR, the
decadal global change in energy intensity is between -0.9 and -1.6%. The long term point average is about -1.3%, as
seen also in Table 1.
1.0%
0.5%
Rate of Change (%/year)
0.0%
-0.5%
-1.0%
-1.5%
Annual Change
5-year average
10-year average
20-year average
5-year CAGR
10-year CAGR
20-year CAGR
GTF 10-year CAGR
GTF 20-year CAGR
-2.0%
-2.5%
-3.0%
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
2011
2012
2013
-3.5%
Figure 2. Rate of change in global energy intensity of GDP PPP, using 2005 as a basis: 5-, 10- and 20- year average
smoothing has been applied, as well as calculation of the compound annual growth rate (CAGR) for the previous 5,
10, and 20 years. For reference, the CAGR estimates reported in the Global Tracking Framework (SE4ALL, 2015) are
also included here.
BARRIERS TO ENERGY EFFICIENCY
Engineering estimates of energy efficiency potentials are often not achieved in the real world due to these barriers
in adoption of energy efficiency (Allcott & Greenstone, 2012; DOE, 2015). Achieving energy savings through energy
efficiency measures is more than just a technical problem or a question of cost. Schleich, et al. (2008) surveyed
2848 German companies and public institutions, and developed a model for efficiency improvements for each
subsector of organization types. They found that not only do barriers to energy efficiency vary consistently across
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sub-sectors, but that there is no clear pattern of combination of barriers (Schleich, et al., 2008). At the firm level,
Decanio (1998) also examined barriers to energy-saving investments, using a multiple regression analysis of
economic and organizational favors on firm profitability resulting from lighting upgrades. Dacanio (1998) found
that economic factors alone were not enough to explain firm behavior, and in fact, when it came to energy-savings
investments, firms sometimes strayed from profit maximization, and thus did not act economically. Thus, potential
energy-saving investments are often not realized due to internal impediments of the organization (Decanio, 1998).
Nevertheless, the economic potential for cost savings is still the most important motivation in investing in energy
efficiency (De Groot, et al., 2001). While most firms accept government regulation, they prefer it at the
international level (e.g., the EU) and such that the policies maximize their freedom and flexibility for meeting the
regulation requirements (De Groot, et al., 2001).
However, Schliech, et al. (2008) noted that “organisations with rented buildings and office space also tend to know
less about energy consumption patterns” with regular consistency, indicating a lack of financial incentive to
improve building efficiency if the tenant is responsible for the energy costs. Additionally, more energy intensive
sub-sectors (which had higher economic incentive to improve energy performances) had significantly less barriers
to efficiency improvements; whereas sub-sectors with public or quasi-public ownership exhibited higher impact
from barriers to efficiency improvements (Schleich, et al., 2008). In the commercial, private, and public sectors,
lack of information about energy efficiency measures was a significant barrier (due to weak technical expertise)
(Schleich, et al., 2008). This corroborates a finding by De Groot, et al. (2001), from a survey of 135 Dutch firms,
where 30% of firms were not aware of existing new technologies and a further 20% had only limited knowledge on
technologies that were in use by other firms. They furthermore noted that competitive firms tended to delay the
adoption of new technology on account of uncertainty, particularly in regard to future price reductions (De Groot,
et al. 2001). Thus, information campaigns on energy savings technologies should concentrate on these sectors,
combined with an effective mix of policies to reduce investors' transaction costs for energy savings measures (De
Groot, et al., 2001; Schleich, et al., 2008). Employing Energy Services Companies (ESCOs) could potentially be an
effective way to overcome some barriers (e.g., risk, lack of capital, lack of time, and lack of staff for energy
monitoring and assessment); however, ESCOs are generally reluctant to do business in the commercial sector
because they have lower financial risk when operating in the public sector, and because ESCOs prefer large
projects (most of which are outside the commercial sector) where the savings can be found in minimizing
transaction costs (Schleich, et al., 2008).
A summary of many of the other potential political, economic, social, technological, legal, environmental, and
governmental (PESTLEG) barriers to energy efficiency improvements is presented in Table 2.
Table 2. Barriers to energy efficiency improvements
Barrier
Political
Financial priority
Value priority
Economic
High initial cost
Lack of funding possibilities
Description
Other investments or expenditures might take precedence over
energy efficiency measures in municipality budgets, even though
measures have a high return on investment (Ecorys, 2012).
Other values such as cultural and historical could be prioritized over
desired measures such as energy efficiency improvements (Meijer et.
al, 2009)
A high initial cost could stop the measure adoption (Anderson &
Newell, 2004; Yearwood Travesan, et al., 2013).
Access to capital is limited even though the investment is judged as
profitable. A bank might not be familiar with the measure or the
financial return of the measure is too far in the future, leading to
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Low rate of return
Long payback period
Incentive for owner(s)/user(s)
Uncertainty
Transparency
possibilities
of
funding
Split incentives
Low awareness
Limited resources
Social
Risk averse behavior
Opposition/resistance
Technological
Infrastructure lock-in
Lack of enabling infrastructure
High technology adaptation
Legal
Limited data access
Permitting procedure
restricted lending (Yearwood Travezan, et al., 2013).
A low financial rate of return could hinder measure adoption
(Ástmarsson, et al. 2013).
A long payback period could hinder measure adoption (Anderson &
Newell, 2004; Yearwood Travezan, et al., 2013).
Principal-agent dilemma could exist: e.g. the owner of a building has
no direct incentive to improve energy-efficiency, through retrofitting,
since the costs for energy (and water) are paid by the tenant
(Ástmarsson, et al., 2013). The building owner should also have an
incentive in those cases where some of the energy costs are included
in the rent. This incentive gets lowered if the rent is regulated since
the owner does not want to implement measures if the costs cannot
be transferred to the tenants (Lind, 2012).
The measure might get uneconomical due to cost increases, lower
improvements than expected, non-optimal operation, delayed
construction or energy cost changes (Yearwood Travezan, et al.,
2013).
Lack of transparency makes it hard to find funding from public sources
(Yearwood Travezan, et al., 2013).
The decision to invest in a measure is shared between several actors
and seeking agreement from all actors can be difficult (Meijer et al.,
2009).
Low awareness and knowledge of the potential and multiple benefits
of energy efficiency (energy savings, increased comfort, reduced costs,
etc.) could affect measure adoption (Schleich, 2009). Low knowledge
can also lead to rebound effects (Immendoerfer, et al., 2014).
Competent staff for the assessment of potentials and risks and for
leading the implementation of improvements could be limited.
Risk adverse behavior, especially for measures that are not well
known, could affect measure adoption (Farsi, 2010).
Social opposition could lower measure adoption, e.g. opposition
against renewable energy production with wind turbines, against
refurbishing certain buildings or against development of cycling paths.
Existing infrastructure, with associated business models and human
behavior, could hinder the implementation of a measure, e.g. the
operator of an existing thermal heat grid could oppose investments in
building insulation.
The lack of enabling infrastructure, e.g. smart meters, could hinder
other measures such as the adoption of smart household appliances.
The involved technologies could require a certain amount of
adaptation to fit with the intention of a measure. High need for
adaptation might lower measure adoption (Fleiter, et al., 2012).
Access to quality data, e.g. on electricity, heat, gas and water
consumption, is crucial to the identification, implementation and
monitoring of measures. Unavailability of data, data censoring
because of privacy issues or proprietary rights, limited data collection
or poor data structuring, could lower measure adoption (McKenna et
al., 2012).
Permitting procedures and support structures could be spread out
over several authorities (Ecorys, 2012).
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Environmental
Governance
Environmental side-effects
A measure that will result in environmental side-effects could face
challenges. An example could be energy efficiency improvements with
materials (e.g. refrigerants) that use environmentally harmful
substances.
Procurement process clarity
The procurement process of organizations, mostly public sector
agents, is not uniform for all departments, has conflicting objectives
or lacks clarity (Borg, et al., 2006; Yearwood Travezan, et al., 2013).
Monitoring of quality
Lacking quality control could decrease the success of a measure
(Immendoerfer, et al., 2014).
CURRENT POLICIES FOR ENERGY EFFICIENCY
Many countries have future targets for reducing energy intensity, with varying degrees of ambition. A selection of
major economies that have adopted targets to reduce energy intensity is given in Table 3. Individual national
targets within the EU are assumed to be subsumed by the EU Energy Efficiency Directive (2012/27/EU). The goal of
this directive is to reduce primary energy consumption in 2020 by over 15,000 PJ, relative to a reference scenario
provided within the policy. This is an ambitious target, nearly doubling the historic rate of energy intensity
improvement, and it covers countries that represent a large amount of energy consumption. Japan seeks to reduce
energy intensity of GDP by 30% by 2030, relative to 2003 (ABB, 2012a). This is also quite ambitious considering
historic rates of reduction, and also on account that Japan already has a low energy intensity of GDP. South Korea
seeks to reduce energy intensity by 46% between the years 2007 and 2030 (ABB, 2013b). Like Japan, this is quite
an ambitious target, given the historic trend in energy intensity. Russia and Kazakhstan has the goal to reduce
energy intensity by 40% by 2020 relative to 2007 (ABB, 2012b) and 2008 (Kazakhstan Energy Charter Secretariat &
Kazenergy, 2014), respectively. Turkey seeks to reduce energy intensity of GDP by 20% between 2008 and 2023
(ABB, 2013a). In Brazil, implementation of the National Policy for Energy Efficiency is expected to result in a
gradual energy savings up to 106 TWh/year to be reached in 2030 (ABB, 2013c). Brazil also has policies specifically
designed to reduce electricity consumption (ABB, 2013c), not included in Table 3. The New Zealand Energy Policy
promotes energy intensity improvement of 1.3 percent per annum for the years 2010-2030 (New Zealand Ministry
of Economic Development, 2011). As part of their 12th 5-year plan, China sought to reduce energy intensity of GDP
by 16% by 2015 (ABB, 2013d). This is now nearly a historic target, but the 2015 data are not yet available. The 13th
5-year plan will be released in early 2016. Finally, India seeks to reduce energy intensity 20% by 2020 from 2005
levels, as part of their 12th Five Year Plan (Planning Commission Government of India, 2013). Many other countries
have energy efficiency policies targeted at improving specific technologies or sectors, with various metrics for
assessment (e.g. the US CAFE standards for vehicle fuel economy). Those are not included in Table 3, as they are
not a policy directly targeting national energy intensity of GDP.
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Table 3. Current Energy Intensity reduction policies of major economies. Historic CAGR is calculated from Enerdata
(2015). The estimated annual energy savings at the target year is that reported by the specific policy or projected
from historic CAGR values versus the target value, calculated with per capita GPD PPP projections from OECD
(2014) and population projections from the World Bank (2014). *No per capita GDP projections were available for
Kazakhstan; therefore, it is assumed that the ratio of per capita GDP to Russia in 2010 is the same in 2020.
Country/ Region
Historic CAGR
(1990-2010)
Target
year
Target CAGR
(2010-target
year)
Estimated
Annual
Energy
Savings
at
Target Year (PJ)
EU
-1.6%
2020
-2.7%
15407
Japan
-0.3%
2030
-1.6%
5844
Russia
-1.5%
2020
-2.7%
4345
Turkey
-0.2%
2023
-1.9%
1604
South Korea
-0.1%
2030
-3.2%
1172
Brazil
0.2%
2030
0.1%
382
New Zealand
-0.8%
2030
-1.3%
154
Kazakhstan
-2.1%
2020
-2.4%
136*
India
-2.1%
2020
-1.2%
-4110
China
-4.7%
2015
-3.6%
-7787
India and China are interesting cases, as the targets for improvement in energy intensity are below the historic
rates of reduction. This leads to a negative energy savings, and can be interpreted as targets that are not
particularly ambitious. On the other hand, the historic rate of reduction was higher from 1990-2000 for China
(Figure 1), in particular. There is also a lot of uncertainty concerning both China’s GDP and China’s energy
consumption (Akimoto, et al., 2006; Gregg, et al., 2008; Sinton, 2001). Nevertheless, India and China are projected
to become a larger share of the global economy and also dramatically increase their energy consumption in the
future. How these countries develop will greatly influence the global energy intensity, and whether or not the
SE4ALL energy efficiency objective is ultimately met.
OUTLINE
This study provides background Energy Technology System Analysis Program TIMES Integrated Assessment Model
(ETSAP-TIAM), and then creates scenarios that constrain the technical pathways for meeting different
combinations of the SE4ALL objectives. From there, we identify regions and sectors where the most potential lies
for energy efficiency improvements from the criteria of cost effectiveness, and the extent to which the various
SE4ALL objectives support each other in terms of investment costs and greenhouse gas emissions.
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The main objective of this study is to determine how the global SE4ALL objectives on energy efficiency (in terms of
the rate of energy intensity reduction) can be achieved by 2030 given the simultaneous goals for renewable energy
and universal energy access.
METHOD
OVERVIEW
First, a base scenario is run which represents the default assumptions for technology improvement and cost
optimization with no policy incentives or other constraints on technology development. Next, reference scenarios
are created based on the historical rates of EIIR, a default energy system (described below), and current carbon
taxes. Alternative scenarios are created that represent different constraints (universal access as expressed in
residential electricity consumption and phase out of traditional biomass, renewable energy targets, and energy
efficiency for various regions). The scenarios are modeled using ETSAP-TIAM. When comparing the alternative
scenarios to the reference scenario(s), it is possible to determine the effect the SE4ALL targets have on the
technological and structural development within the energy system. The framework for the analysis is presented in
Figure 3.
Current:
-Carbon price
-Renewable Energy Profile
-Energy efficiency trends
-Traditional biomass use
-Technology profiles
ETSAP-TIAM
Reference
Scenario
ETSAP-TIAM
Alternative
Scenario
Changes in:
Renewable Energy Profile
Energy Consumption,
GHG Emissions
Costs
-Renewable Energy Targets for
2010-2030
-Energy Intensity Targets
for 2010-2030
-Increased Energy Access &
Phase-out Traditional Biomass
Assessment of Pathways
Regional Potentials
Sector & Subsector
Potentials
Policy Recommendations
Figure 3. Diagram of framework for analysis and work flow.
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SCENARIOS
The following six scenarios are constructed and input into ETSAP-TIAM. In all scenarios, ETSAP-TIAM optimizes the
energy systems based on resource availability, existing infrastructure stock, and prices given the exogenous
constraints. Thus, constraints on resources can define the technology choices because the process of switching
across energy carriers generally accompanies technological changes.
(i)
(ii)
(iii)
(iv)
(v)
Baseline (BASE): This scenario includes the basic model structure and available technologies, but no
policy constraints or targets for energy efficiency or renewable energy, no carbon price, and no
barriers to efficiency improvements. Thus it represents a cost optimal solution to meeting the energy
service demands.
Reference (REF): This scenario reflects the development of the global, regional and sectoral energy
demand if current trends are continued and current policies and pledges come to fruition. This
scenario takes into account current technological mixes, performance and cost data for conventional
technologies, and default assumptions for AEEI. It also takes into account the current carbon price,
holding it constant until 2030. It does not, however, take into consideration any major energy
efficiency improvements and policy interventions. Two alternate Reference scenarios are
constructed:
a. Regional historic trends (REFReg): For each ETSAP-TIAM region, improvements in energy
intensity were projected using OECD (2014) GDP PPP projections until 2030, and the historic
average annual reduction rate of energy intensity for the years 1990-2013, calculated from
Enerdata (2015) (Figure 1 and Table 5).
b. Global historic trend (REFGbl): Improvements in energy intensity were projected at the global
level using OECD (2014) GDP PPP projections until 2030, and the historic average annual
reduction rate of energy intensity for the years 1990-2013, calculated from Enerdata (2015)
(Figure 1 and Table 5). No regional constraints are applied, allowing ETSAP-TIAM to optimize the
regional allocation of energy efficiency improvements.
Renewable Energy Scenario (RE): This scenario sets region-specific targets for renewable energy
deployment based off the realistic potential outlined by International Renewable Energy Agency
(IRENA) global renewable roadmap (REMap2030). It uses the energy efficiency assumptions of the
reference scenario.
Energy Efficiency Scenario (EE): This scenario aims at achieving SE4ALL objective related to energy
efficiency by achieving a 2.6% reduction in global energy intensity in 2030, relative to 2010.
Energy Efficiency and Renewable Energy Scenario (EE&RE): This combines the constraints from both
the renewable energy and Energy Efficiency scenarios. Two alternative versions of this scenario are
created:
a. EE&RE: A renewable energy that does not phase out traditional biomass phase-out or electricity
access as part of the scenario.
b. EE&RE&EA Renewable energy that also phases out the use of traditional biomass phase-out, and
meets a minimum electricity demand, thus achieving the three SE4ALL objectives.
CARBON PRICE
The current carbon price is included in all scenarios except the base scenario. The World Bank (2014) released a
report that documented the current state of carbon taxes and carbon emission trading schemes (ETS) and their
price levels. Some changes and updates to these carbon pricing schemes have occurred: e.g., the carbon tax in
Australia was scrapped in July 2014 (Dayton, 2014) and an ETS started in the Republic of Korea, changing the
carbon price levels (World Bank, 2015a). Further information on ETS was taken from the International Carbon
11
Action Partnership (ICAP, 2015) and from other nation specific sources (China Carbon, 2015; Cho, 2015; OTC-X,
2015).
While ETSAP-TIAM is capable of simulating cap-and-trade carbon markets such as the ETS, for simplicity, carbon
markets were modeled as a tax by taking the current carbon price. Some nations have more than one pricing
mechanism operating simultaneously, e.g. a national tax and ETS. In such cases, the prices were summed into one
price applicable to the specific sector and region. Some regions have several carbon prices applying to different
sectors, and this was retained in the ETSAP-TIAM input. Mexico has a carbon tax applying to fossil fuels, where the
tax is the difference between the emissions from combustion of petroleum and coal versus the emissions that
would have occurred were natural gas used instead, in effect, creating a tax on emissions from petroleum and coal.
For Mexico, we applied a 25% ratio for petroleum, and a 40% ratio for coal, representative of the approximate
ratios in emissions per unit of energy relative to natural gas. In the case where a country has both an upper and
lower bound for carbon, then the upper bound was used.
The carbon prices were then aggregated to the ETSAP-TIAM regions. This aggregation was done by computing the
nation’s share of energy (and cement production) carbon emissions relative to the total emissions from its
corresponding ETSAP-TIAM region. The carbon price was then converted to 2005 US dollars 4 and scaled by this
amount. An analogous computation was performed for carbon prices applying only to specific states in the USA,
provinces in Canada, and cities in China and Japan. Data on greenhouse gas emissions and the share for different
nations, states and cities were taken from the Carbon Dioxide Information Analysis Center (CDIAC) (Boden, Andres,
& Marland, 2010), from the Global Carbon Atlas (Global Carbon Project, 2014), from Environment Canada
(Environment Canada, 2015), from United States Environmental Protection Agency (EPA, 2014), from Wang, Zhang,
Liu, & Bi (2012) and from the World Bank (2014). Carbon taxes are summarized in Table 4 and are applied in
ETSAP-TIAM for the periods 2015-2030 in the reference scenario.
Table 4. Current carbon prices in 2005 USD per Tonne CO2.
Region
Africa
Australia/ New
Zealand
Canada
China
Central
and
South America
Eastern Europe
Former Soviet
Union
India
4
Industry
Power
Heat
4.68
0.88
6.39
0.88
5.51
5.51
5.51
0.72
0.72
0.72
Buildings
Transport
(excluding
Aviation)
Agriculture
1.00
0.88
0.63
0.63
Exchange rates from:
https://www.ecb.europa.eu/stats/exchange/eurofxref/html/eurofxref-graph-usd.en.html
http://www.xe.com/
http://www.bankofcanada.ca/rates/exchange/daily-converter/
12
Oil
Coal
Japan
Middle East
Mexico
Other
Developing Asia
South Korea
USA
Western
Europe
1.16
1.16
1.16
1.16
1.16
0.62
4.93
0.06
4.93
0.14
4.93
7.02
11.35
7.02
4.93
4.93
1.00
4.93
5.43
ENERGY EFFICIENCY
The historic average rate of annual change in energy intensity is calculated from the Enerdata (2015) statistics for
the historic years 1990-2013 for each ETSAP-TIAM region, and for the world. In the REF-Regional scenario, the
historic regional EIIR were extended for the years 2010-2030 (Table 5). By multiplying these energy intensity
projections by the OECD (2014) GDP PPP projections, a total primary energy constraint was created for each
region. When summed, this produces a global reduction rate higher than the historic global reduction rate (Table
5). This is because developing countries, which have higher historic rates of reduction in energy intensity, are
projected to make up a larger proportion of the global GDP in the future. REF-World, the average reduction rate
for the years 1990-2030 was extrapolated to the years 2010-2030. This variant of the reference scenario leaves
more flexibility of ETSAP-TIAM to make regional improvements where they are most cost effective.
Similar to the process for establishing the bounds in the reference scenario, the SE4ALL objective of a 2.6% EIIR by
2030 (relative to 2010) was determined from the exogenous global GDP PPP projections from the OECD (2014) and
applying a 2.6% reduction in energy intensity for the years 2010-2030. From here, 2030 global total primary energy
constraints were established within ETSAP-TIAM (Table 5).
Table 5. EIIR for the reference and alternative energy efficiency scenarios. These are based off the historical
average change over the years 1990-2013, and applied to the years 2010-2030 within ETSAP-TIAM.
Region
AFR
AUS
CAN
CHI
CSA
EEU
FSU
IND
JPN
MEA
MEX
ODA
SKO
USA
WEU
Global
REFReg
(CAGR 2010-2030)
-0.8%
-1.4%
-1.4%
-4.3%
-0.4%
-3.5%
-1.4%
-2.1%
-0.3%
1.5%
-0.7%
-0.2%
0.0%
-1.7%
-1.2%
-1.4%
REFGbl
(CAGR 2010-2030)
EE Scenarios
(CAGR 2010-2030)
Optimized by ETSAP-TIAM
Optimized by ETSAP-TIAM
-1.2%
-2.6%
13
The calculations for EIIR do not distinguish between changes in energy intensity due to improvements in
technological efficiency and changes in energy intensity due to structural change in the economy. This is an active
area of research (see, for example, the adjusted EIIR analysis done by the Global Tracking Framework (SE4ALL,
2015)). Furthermore, there are barriers to both technological improvements and structural changes in the
economy, as discussed in the background of this report. ETSAP-TIAM does not have any other way to explicitly
model these various barriers. Nevertheless, the barriers are implicitly assumed in ETSAP-TIAM, because the
reference scenario is defined by the historic EIIR; given that energy service demands are a result of exogenous
drivers, this has the effect of limiting the EIIR and therefore representing barriers to energy efficiency
improvements. In other words, this constraint would limit the EIIR to not exceed historical rates of improvement
for the reference scenario. The alternative scenarios for energy efficiency are far more ambitious, and therefor
may result in a regional improving efficiency at a rate greater than what has historically transpired.
RENEWABLE ENERGY
RE targets were obtained for each of the ETSAP-TIAM regions from the IRENA REMap2030 study (IRENA, 2014). In
the REMap study, 26 countries were analyzed, and included the renewable energy share of final energy
consumption in the base year, 2010. Targets for a 27th country, Poland, have been obtained from IRENA,
representing the first country within the EEA (Eastern European Union) region in ETSAP-TIAM. Further adjustments
and alignments of the RE model scenario have been included after a workshop meeting with IRENA experts.
The REMap 2010 data and reference scenario 2030 projections were used to define the reference scenario in
ETSAP-TIAM. To do so, the IRENA countries’ renewable energy targets were weighted according to their share of
the Total Final Energy Consumption (TFEC) (IEA, 2014) within their respective regions. The IRENA REMap study also
created an optimistic scenario for renewable energy deployment based on what they determined to be realistic
potential (IRENA, 2014). Based on this, targets for renewable energy shares were also created for the ETSAP-TIAM
regions in 2030 for the RE scenarios. A summary of the IRENA renewable energy targets is given in Table 6.
Table 6. Regional renewable energy shares of final energy consumption for 2010 and 2030 reference and 2030 RE
scenarios (IRENA, 2014).
Region
AFR
AUS
CAN
CHI
CSA
EEU
FSU
IND
JPN
MEA
MEX
ODA
SKO
USA
WEU
REMap 2010
15%
7%
21%
7%
41%
4%
4%
17%
4%
5%
4%
5%
3%
8%
10%
REMap 2030 Reference Scenario
9%
12%
22%
16%
40%
6%
8%
12%
10%
10%
10%
6%
8%
10%
21%
14
2030 REMap Scenario
21%
23%
33%
25%
54%
9%
15%
25%
19%
15%
21%
21%
13%
27%
33%
UNIVERSAL ACCESS
The IEA estimates in their central scenario the number of people in 2030 without access to electricity to below 1
billion and without access to clean cooking facilities to just above 2.5 billion (IEA, 2014). The universal energy
access target for 2030 is defined as 100% access to electricity and 100% primary reliance to non-solid fuel (SE4ALL,
2013a). The SE4ALL initiative stresses that these binary targets fail to capture many aspects of energy access, such
as not considering energy applications outside of the household sector (SE4ALL, 2013b). An official target for
energy electricity consumption is lacking. The SE4ALL scenario in the Global Energy Assessment assumes a 100%
electrification rate and household electricity consumption of 420 kWh/year (SE4ALL, 2013b). This corresponds to
the use of lighting, air circulation, televisions and light appliances according to World Bank’s tiered electricity
consumption framework. The level can be traced back to a study in a Tanzanian village where the average
household electricity consumption was 35 kWh/month (Ilskog, Kjellström, Gullberg, Katyega, & Chambala, 2005).
Bazilian & Pielke (2013) criticize this level of energy access, pointing out that the per capita electricity consumption
in wealthy countries is at least ten times higher. They emphasize the importance of electricity for businesses,
industries and hospitals for economic development and want more focus on universal modern energy access that
alleviates poverty.
Many studies have looked into the correlation between energy or electricity consumption and economic
development (e.g. Asafu-Adjaye, 2000; Lee, 2006; Shiu & Lam, 2004; Wolde-Rufael, 2006). The direction of
causality cannot always be shown, but the fact that energy or electricity consumption has a positive connection
with economic development (measured in GDP) is clear (Ozturk, 2010). From this follows that one part of a crude
target for energy access could be to set a level of electricity consumption that is close to that of wealthy countries.
Statistics for per capita electricity consumption in 2011 and the share of the population that had access to
electricity in 2010 are shown in Table 7 for certain countries and regions (World Bank, 2015b).
Table 7. Per capita electricity consumption and electricity access for selected countries
Country
USA
European Union
South Africa
China
World
India
Least developed
classification5
Haiti
countries:
UN
Per capita electricity consumption
(kWh/capita/year)
13246
6115
4606
3298
3045
684
174
Electricity access
population)
100%
100%
82.7%
99.7%
83.1%
75%
31.5%
32
33.9%
(share
of
Other ways of measuring energy access have been proposed. For example, Nussbaumer, Bazilian, & Modi (2012)
review energy access (or poverty) metrics and suggest a multidimensional energy poverty index that focuses on
energy services; cooking, lighting, entertainment and education, communication and services from household
appliances.
5
The least developed countries consist of 48 countries with a total population of around 900 million people (World
Bank, 2015c).
15
Chakravarty & Tavoni (2013) calculate the additional energy consumption in 2030 resulting from eradicating
energy poverty. They first map the number of people on different levels of energy consumption by using a model
that builds on income distribution data. They then estimate the additional residential electricity consumption in
2030 that comes from raising energy poor people’s electricity use to at least 750 kWh/capita/year. This level is
called Productive uses by the UN and corresponds to the level in the IEA’s energy access model used to calculate
the investments needed to achieve the SE4All objectives (SE4ALL, 2013b). The level assumes electricity for lighting,
health, education, communication and use in the agricultural sector. It should be noted that the availability of
more efficient technologies will reduce the electricity use target over time, whereas electrification of transport and
boilers and heaters will act in the other direction. Chakravarty & Tavoni (2013) also assume that everyone uses at
least 150 kg oil/capita/year. The estimated additional residential consumption of electricity and oil in the ETSAPTIAM regions based on their data is shown in Table 8. Only the additional residential electricity consumption in
2030 is introduced in the universal energy access scenario. The additional oil consumption is left out since some
extra fossil fuel consumption will occur due to the phase out of traditional biomass as described below.
Table 8. Additional residential energy consumption in 2030 to eradicate energy poverty
Electricity (PJ)
Oil (PJ)
Africa
349
4501
India
122
1578
Other Developing Asia
140
1810
China
31
404
Central and South America
30
390
In order to represent the 100% non-primary reliance on solid fuel SE4ALL goal, traditional biomass is set to
decrease 7.5% per year, and is phased out entirely by 2030 for the alternative scenarios (Figure 4). Lacking any
detailed literature on pathways to phase out traditional biomass by 2030, this rate of decrease (7.5%/year) was
chosen to create a roughly linear decline to 2030. Additionally, the constraints (on the minimum amount of
biomass to be used in the residential water heating and space heating) were relaxed in ETSAP-TIAM for energy
sources to hot water and space heating, allowing a greater degree of fuel switching for these end use demands.
16
35000
Annual Consumption (PJ)
30000
25000
MEX
CSA
20000
ODA
15000
IND
CHI
10000
AFR
5000
0
2005
2010
2015
2020
2025
2030
Figure 4. Phase out of traditional biomass use for alternative scenarios.
ASSESSMENT
The scenarios are assessed ETSAP-TIAM, which optimizes the energy system to meet the energy service demands
on the basis of total system cost. ETSAP-TIAM is one of the most detailed IAMs in terms of its technology database.
Furthermore, there are many options for creating constraints, targets, or other policy incentives, allowing for the
creation of the scenarios described above. Each alternative scenario’s model output will be subtracted from output
in the reference scenario, yielding an estimate of the ‘saving’ effect of the alternative scenario.
ETSAP-TIAM
TIMES Architecture Background
The TIMES (The Integrated MARKAL-EFOM System) model generator, is an evolved version of MARKAL (MARket
Allocation model), developed under the IEA implementing agreement, ETSAP. TIMES is a model generating set of
optimization equations6 that computes an inter-temporal dynamic partial equilibrium on energy and emission
markets based on the maximization of total surplus (defined as the sum of supplier and consumer surpluses). In
essence, a model generated by TIMES finds the least-cost solution for the entire energy system with flexibility in
terms of time resolution and sectorial focus.
Model Structure
As ETSAP-TIAM is based on the TIMES equations, it is a perfect foresight, linear optimization model (ETSAP-TIAM
optimizes all time periods simultaneously). The objective function that is maximized is the discounted net present
6
A complete description of the TIMES equations appears in http://www.iea-etsap.org/web/Documentation.asp.
17
value7 of the total surplus8 for the entire world. The surplus maximization can be subject to many exogenouslydefined constraints on a regional, sectoral or global basis, such as supply bounds (in the form of detailed supply
curves that describe resource availability at different price points) for the primary resources, technical constraints
governing the creation, operation, and abandonment of each technology, balance constraints for all energy forms
and emissions, timing of investment payments and other cash flows, and the satisfaction of a set of demands for
energy services in all sectors of the economy.
As an integrated energy system model, ETSAP-TIAM is built to represent the total energy chain, including energy
extraction, conversion and demand (e.g., fossil and renewable resources), potentials of storage of CO2 (which
comes into play with a carbon price and can be adjusted via cost parameters) and region-specific demand
developments. The region and sector-specific demands for end-use energy and industrial products are driven by
socio-economic parameters which are described below. The model contains explicit detailed descriptions of
hundreds of technologies as well as hundreds of energy, emission and demand flows within each region (regionspecific parameters can be defined), logically interconnected to form a Base Energy System (Figure 5). Such
technological detail allows precise tracking of optimal capital turnover, and provides a precise description of
technology and fuel competition. The long-distance trade of energy between the regions of ETSAP-TIAM is
endogenously modeled for coal, natural gas (gaseous or liquefied), crude oil, various refined petroleum products,
and biofuels. Global and regional (partial agreement) GHG emission trading is also possible. ETSAP-TIAM is driven
by a set of demands for energy services in agriculture, residential buildings, commercial buildings, industry, and
transportation. Each technology has a hurdle rate that varies from 5% to 20%, depending on the sector. The hurdle
rate is used to convert the capital cost in an annual cash flow: discounted multi-year interest rate payments are
included when calculating an annual payment for an investment and payback time (a technology with a high
hurdle rate means a short payback rate is required, while a technology with a low hurdle rate allows a longer
payback time). Learning curves are exogenously assumed for each technology through the price inputs contained
in the ETSAP-TIAM database. Thus technologies generally become cheaper in future time periods.
The model's variables include the investments, capacities, and activity levels of all technologies at each period of
time, as well as the amounts of energy, material, and emission flows in and out of each technology, and the
quantities of traded energy between all pairs or regions. For sectors that use electricity and heat, the flow
variables are defined for each of six time-slices: three seasons (summer, winter, and autumn/spring) times two
diurnal (day and night) divisions. ETSAP-TIAM is a partial equilibrium model, and although it does not include
macroeconomic variables beyond the energy sector, there is evidence that accounting for price elasticity of
demands captures the majority of the feedback effects from the economy to the energy system (Bataille, 2005;
Labriet, et al., 2012; Scheper & Kram, 1994).
7
8
A discount rate of 5% is assumed. Net present value is calculated to 2005.
Total surplus is here defined as the sum of supplier and consumer surpluses.
18
Trade
Fossil Fuel
Reserves
(oil, coal, gas)
Trade
OI****
Extraction
GA****
CO****
OPEC/
NON-OPEC
regrouping
Secondary
Transformation
Upstream
Fuels
BIO***
OIL***
GAS***
COA***
ELC
Carbon
capture
CH4 options
Biomass
Potential
Climate
Module
Atm. Conc.
ΔForcing
ΔTemp
Used for
reporting &
setting
targets
Other
Renewables
CO2
Carbon
sequestration
BIO***
HYD
Power and
Heat Fuels
ELC***
ELC
ELC
Cogeneration
I***
HET
Heat
End Use
Fuels
BIO***
IND***
Industrial
Tech.
AGR***
INDELC
INDELC
IS**
Non-energy
sectors (CH4)
SYNH2
HET
NUC
I** (6)
Hydrogen production
and distribution
Electricity
WIN SOL
GEO TDL
Nuclear
Industrial
Service
Composition
Terrestrial
sequestration
Auto Production
COM***
RES***
TRA***
Agriculture
Tech.
Commercial
Tech.
Residential
Tech.
Transport
Tech.
A** (1)
C** (8)
R** (11)
T** (16)
Cogeneration
N2O options
CH4 options
CH4 options
Landfills
Manure
Demands
Bio burning, rice,
enteric ferm
Wastewater
Figure 5. Base energy system within ETSAP-TIAM. Technological efficiencies are included in the industrial,
agriculture, commercial, residential, and transport technology boxes. Other efficiency adjustments are possible
within the fuel production chains.
ETSAP-TIAM integrates a climate module permitting the computation and modeling of global changes related to
GHG concentrations, radiative forcing and global temperature increase. The climate module was originally inspired
by the Nordhaus and Boyer (1999) model, but now consists of three sets of equations, dynamically calculating the
atmospheric concentrations of the three main GHGs (CO 2, CH4, and N2O), the atmospheric radiative forcing of
these three gases, and the resultant change in mean global temperature. The climate module has been calibrated
and compared to other, more detailed climate modules, during several past multi-model experiments (Loulou, et
al., 2009). The CO2, CH4, and N2O emissions related to the energy sector are explicitly represented in the model at
the level of the individual technologies. The emissions from non-energy sectors (landfills, manure, rice paddies,
enteric fermentation, wastewater, agriculture, land-use) are also included in the model, but in a more rudimentary
way. The other GHGs (CFCs, HFCs, SF6, etc.) are not explicitly modeled, but their radiative forcing is represented in
an exogenous manner. Options for GHG emission reductions available in the model include: specific CH 4 and N2O
destruction, mitigation of emissions from agriculture, CO2 capture (upstream, power plants, biofuel refineries,
hydrogen generation) and sequestration (in geological sinks), biological sequestration via reforestation, and finally,
numerous fuel and technology switching options in each sector (which would simultaneously improve energy
efficiency and correspondingly induce a reduction in energy intensity). Thus, carbon price can be used as a simple
lever for policy intervention, and this can be applied globally or differentially across regions.
19
Regions and Time Frame
In ETSAP-TIAM, the world is divided into 15 regions (Figure 6).
ETSAP-TIAM Regions
AFR Africa
AUS Australia & NZ
CAN Canada
CHI China
CSA Central and South America
EEU Eastern Europe
FSU Former Soviet Union
IND India
JPN Japan
MEA Middle East
MEX Mexico
ODA Other Developing Asia
SKO South Korea
USA United States
WEU Western Europe
Figure 6. Fifteen regions of the Energy Technology System Analysis Program TIMES Integrated Assessment Model
(ETSAP-TIAM).
The model architecture of ETSAP-TIAM is built off of 2005 data. Primary energy consumption and demand driver
data (population, GDP, number of households, etc.) have been updated and constrained within ETSAP-TIAM to
match key 2010 historical data, thus making 2010 a de-facto base year. This is done in order to avoid optimization
of the past. 2010 also serves as a base year in energy efficiency improvement calculations. From 2010, ETSP-TIAM
is run on 5-year time steps to the SE4ALL target year of 2030.
20
RESULTS
Figure 7 displays pathways for energy intensity, by scenario. The EE&RE and EE&RE&EA scenarios follow the same
path (by design; they achieve the SE4ALL energy efficiency target of 2.6% compound annual reduction relative to
2010). The RE scenario has a pathway that closely follows the BASE scenario. The two reference scenarios have the
lowest rate of reduction.
Figure 7. Global primary energy intensity pathways, by scenario.
21
In Figure 8, total global net present value (NPV) of investment costs are shown, per sector. In the RE scenario, most
of the investment is needed in the transportation sector, while the residential sector can realize cost savings by
meeting renewable energy targets. In the EE scenario, the majority of the investment must also be directed toward
the transport sector. In comparing the EE&RE to the EE&RE&EA scenarios, it is clear that the later requires far
more investment in the residential sector.
Figure 8. Global sectoral investment (net present value), by scenario
22
In Figure 9, large savings in renewable energy can be found in Other Developing Asia. Western Europe and the
United States will require the most investment in order to meet the SE4ALL Energy Efficiency objectives. Meeting
the energy access objective will require substantially more investment in currently developing regions: Africa,
Other Developing Asia, India, and Central and South America.
Figure 9. Regional investment (net present value), by scenario.
23
The global CO2 emissions pathways for the energy system are presented in Figure 10. The Representative
Concentration Pathway that maintains 2.6 W/m2 radiative forcing (RCP 2.6), which would likely prevent Earth from
warming over 2° C, is the more ambitious than any of the scenarios modeled in this study. The EE scenario reduces
emissions more than the RE scenario, and taken together, both together (EE&RE) reduce emissions more than
either alone. The EE&RE&EA has slightly higher emissions than the EE&RE scenario through 2020 (fossil resources
can substitute for traditional biomass in this scenario), but then ends on a pathway with lower emissions by 2030.
Figure 10. Global greenhouse gas emission pathways, by scenario.
24
When comparing total NPV investment versus the mitigation (Figure 11), the reference scenarios have the highest
emissions. Locking in regional trends requires more total investment than a scenario with one global target. The
BASE scenario is the cheapest, and actually has lower emissions than the reference scenarios, and only slightly
higher emissions than either of the EE or RE scenarios. The EE scenario requires more investment than the EE&RE
scenario, yet has higher emissions. The EE&RE and EE&RE&EA scenarios have the lowest emissions, with the latter
requiring the most investment.
Figure 11. Total investment costs (net present value) versus total emissions (2010-2030), by scenario.
ANALYSIS
SUMMARY OF FINDINGS
The results above suggest that the SE4ALL renewable energy target is cost-effective (it saves cost over the
reference scenarios). Achieving the renewable energy objective also makes achieving the energy efficiency
objective cheaper, and together, they reduce greenhouse gas emissions more than either target alone. Moreover,
the EE scenario promotes slightly more renewable energy deployment relative to the reference scenarios (16%
versus 15%). Thus there is a synergistic relationship between these two SE4ALL objectives.
There is some likelihood that the SE4ALL Universal Energy Access objective makes the other two objectives more
difficult to achieve. It is very ambitious to phase out traditional biomass by 2030, and the most economic nearterm option to replace this fuel is likely to be fossil-based. Moreover, meeting the increased service demands
requires more energy consumption in the developing regions of the world. Thus meeting the universal access
25
objective tends to reduce the percentage of renewable energy, even when traditional biomass is not counted as a
renewable energy source. It also potentially has an effect on energy intensity, as the distribution and availability of
fossil fuels would likely increase fossil energy consumption. Much of the increased investment and energy
consumption would be in developing regions of the world, thus this is primarily a sustainable development
initiative.
Given OECD (2014) projections of GDP, the SE4ALL energy intensity objective of 2.6% reduction, if achieved, will
reduce global energy consumption by nearly 200 EJ/year in 2030 versus the historic 1.3% reduction rate. This will
still mean an increase in global energy consumption of nearly 90 EJ/year relative to 2010. As such, the SE4ALL
objectives are not sufficient in themselves to meet the target of remaining below 2° C global warming. Therefore,
additional climate policies will be necessary to achieve a path to a 2° target, such as a price on carbon or other
climate policy mechanisms.
BARRIERS TO ENERGY EFFICIENCY
The optimal BASE model solution is much cheaper than all the scenarios, including the reference scenarios, and yet
the energy system in the BASE scenario has emissions that are roughly equivalent to the EE and RE scenarios.
Moreover the energy efficiency pathway from the BASE scenario tracks closely with that of the RE scenario. This
suggests that energy efficiency through technology improvement is a cost effective means of reducing emissions.
Moreover, it shows that there are indeed real world barriers that prevent us from transitioning to an “optimal”
business-as-usual solution. These barriers are thus implicitly represented in the difference between the BASE and
reference scenarios. Nevertheless, it suggests the investment in energy efficiency and renewable energy makes
sense, in terms of cost, as an end in itself if these barriers can be overcome (through policies, for example). It also
shows there are limitations to applying results from cost optimization based modeling to the real world, thus
highlighting the importance of research to better understand the barriers to adopting energy efficiency.
LIMITATIONS AND UNCERTAINTIES
As a measure of energy efficiency, energy intensity is not the most robust of statistics. This is because it
incorporates uncertainties in both energy consumption and the economy. Economic uncertainty is compounded by
uncertainties in purchasing power parity, which depends on the relative buying power across economies.
Globalization trends dilute the interpretation of energy intensity, as production and consumption of goods are
geographically separated. Furthermore, given the vicissitude of the economy, energy intensity can vary widely year
to year without any discernable change in the energy technology or conservation. This is problematic when
establishing future targets for energy intensity. In effect, there are two degrees of freedom, both with high
degrees of uncertainty. The hidden variable in efficiency metrics is time; in the calculation of energy intensity
(GDP/year divided by primary energy consumption /year), the year cancels algebraically, but tacitly, it still
understood to be there. Efficiency ultimately concerns the rate of production over the rate of consumption, and
greater efficiency can be attained while increasing both, so longs as the rate of production increases at a greater
rate than the rate of consumption. SE4ALL objectives to promote development are obscured by potential drops in
consumption by employing the energy intensity metric (e.g. a global recession). Likewise, the SE4ALL objectives are
to address the climate challenge are also obscured by potential increases in production. In summary, though
widely used, energy intensity of GDP as a statistic and a target is highly uncertain, difficult to forecast, and does
not necessarily guarantee development or environmental goals are achieved. On the other hand, one principle
advantage of energy efficiency is that it is a “no regrets” option.
26
ETSAP-TIAM is a linear model, and the results presented are an algebraically optimal solution to a set of input data
and constraints. In this sense, they give a picture of how to most economically achieve an a priori energy intensity
or renewable energy pathway. However, ETSAP-TIAM does have its limitations. As a linear model it cannot handle
feedback effects, such as Jevons Paradox, structural changes to the economy, or economic development as a result
of technological development. Thus, ETSAP-TIAM, as with all integrated assessment models, should not be
considered a truth-machine and the results contained herein are not predictions or forecasts, but rather solutions
to preset scenarios. IAMs with large databases quickly become outdated; this is a particular challenge with working
with IAMS, as naturally, results are only as good as the input assumptions. While the demand drivers were updated
for this analysis, there are thousands of technology parameters that are based on 2005 technology development
assumptions. Therefore, there is some uncertainty stemming from outdated input data, particularly for technology
subsectors that are rapidly developing.
Moreover, with population and GDP as exogenous demand drivers, there is some additional uncertainty in
modeling the universal access objective. The phasing out of traditional biomass would likely coincide with rapid
economic development, thus affecting GDP. ETSAP-TIAM is unable to handle such non-linear feedbacks.
CONCLUSIONS
ETSAP-TIAM is useful in determining where the largest potentials for energy efficiency lie and where the most cost
effective investments in energy efficiency can be made, regionally, and technologically. This can aid in crafting
efficient policies to meet the SE4ALL objectives for energy efficiency, renewable energy, and energy access, as well
as provide a pathway for dramatically reducing global greenhouse gas emissions. This suggests that renewable
energy is an economically attractive means to meet energy efficiency targets, and that likewise, policies that
promote renewable energy make it easier to achieve energy efficiency targets. Achieving universal access will
require more investment globally, and this invites more research into the larger question of values surrounding
sustainable development.
27
REFERENCES
ABB (2013d). China energy efficiency report. http://new.abb.com/docs/librariesprovider46/EE-Document/chinareport-en.pdf?sfvrsn=2 (Accessed 25 Jun, 2015)
ABB (2013c). Brazil energy efficiency report.
https://library.e.abb.com/public/c6d0b52cc84505a2c1257be80052c5a7/Brazil.pdf (Accessed 25 Jun, 2015)
ABB (2013a). Turkey energy efficiency report.
https://library.e.abb.com/public/a2c92d1d4f7f2405c1257be9002c5060/Turkey.pdf (Accessed 25 Jun, 2015)
ABB (2013b). South Korea energy efficiency report.
https://library.e.abb.com/public/557d50223ed20a76c1257beb0044f3bc/South%20Korea.pdf?filename=South%20
Korea.pdf (Accessed 25 Jun, 2015)
ABB (2012b). Russia energy efficiency report.
https://library.e.abb.com/public/1112256e3dbeb710c12579e6003937a3/Russia%20Energy%20efficiency%20Repo
rt.pdf (Accessed 25 Jun, 2015)
ABB (2012a). Japan energy efficiency report.
https://library.e.abb.com/public/4a34976258d9db4bc12579e600391cf7/Japan%20Energy%20efficiency%20Report
.pdf (Accessed 25 Jun, 2015)
Akimoto, H., T. Ohara, J. Kurokawa, and N. Horii (2006), Verification of energy consumption in China during 1996–
2003 by using satellite observational data, Atmos. Environ., 40, 7663– 7667
Allcott, H., & Greenstone, M. (2012). Is there an “energy gap”? Journal of Economic Perspectives, 26(1), 3–28.
http://doi.org/10.1257/jep.26.1.3
Anderson, S. T., & R. G. Newell (2004). Information programs for technology adoption: the case of energyefficiency audits. Resource and Energy Economics 26(1): 27–50. doi:10.1016/j.reseneeco.2003.07.001
Asafu-Adjaye, J. (2000). The relationship between energy consumption, energy prices and economic growth: time
series evidence from Asian developing countries. Energy Economics 22: 615–625. http://doi.org/10.1016/S01409883(00)00050-5
Ástmarsson, B., P. A. Jensen & E. Maslesa (2013). Sustainable renovation of residential buildings and the
landlord/tenant dilemma. Energy Policy 63: 355–362. doi:10.1016/j.enpol.2013.08.046
Bataille, C. G. F. (2005). Design and application of a technologically explicit hybrid energy-economy policy model
with micro and macro-economic dynamics (Doctoral dissertation, School of Resource and Environmental
Management, Simon Fraser University).
Bazilian, M., & Pielke, R. (2013). Making energy access meaningful. Issues in Science and Technology, 29(4), 74–78.
Boden, T., Andres, B., & Marland, G. (2010). Ranking of the world’s countries by 2010 total CO2 emissions from
fossil-fuel burning, cement production, and gas flaring. Retrieved May 27, 2015, from
http://cdiac.ornl.gov/trends/emis/top2010.tot
28
Borg, N., Y. Blume, S. Thomas, W. Irrek, H. Faninger-Lund, P. Lund, & A. Pindar (2006). Release the power of the
public purse. Energy Policy 34(2): 238–250. doi:10.1016/j.enpol.2004.08.027
Chakravarty, S. & M. Tavoni (2013) Would Universal Energy Access Boost Climate Change? Review of Environment,
Energy and Economics (Re3), Forthcoming. Available at SSRN: http://ssrn.com/abstract=2255974
China Carbon. (2015). Trading data. Retrieved May 27, 2015, from http://chinacarbon.net.cn
Cho, M. (2015). Trading dries up in South Korea’s new carbon market. Retrieved May 27, 2015, from
http://www.reuters.com/article/2015/02/05/emission-southkorea-idUSL4N0VF1QN20150205
Dayton, L. (2014). Australia scraps carbon tax. Retrieved May 27, 2015, from
http://news.sciencemag.org/asiapacific/2014/07/australia-scraps-carbon-tax
DeCanio, S. J. (1998). The efficiency paradox: bureaucratic and organizational barriers to profitable energy-saving
investments. Energy Policy 26(5): 441-454.
De Groot, H. L., E. T. Verhoef, & P. Nijkamp (2001). Energy saving by firms: decision-making, barriers and policies.
Energy Economics 23(6): 717-740.
Ecorys. (2012). Local investments options in Energy Efficiency in the built environment - Identifying best practices
in the EU, (November).
Enerdata (2015). Global Energy Statistical Yearbook 2014. Retrieved May 20, 2015.
https://yearbook.enerdata.net/energy-primary-production.html
Environment Canada. (2015). Greenhouse gas emissions by province and territory, Canada, 1990, 2005 and 2013.
Retrieved May 27, 2015, from http://open.canada.ca/data/en/dataset/9a673fa1-22af-41c1-9ae5-de38de714c83
EPA (Environmental Protection Agency). (2014). State Energy CO2 Emissions. Retrieved May 27, 2015, from
http://epa.gov/statelocalclimate/resources/state_energyco2inv.html
Farsi, M. (2010). Risk aversion and willingness to pay for energy efficient systems in rental apartments. Energy
Policy 38(6): 3078–3088. doi:10.1016/j.enpol.2010.01.048
Fleiter, T., S. Hirzel & E. Worrell (2012). The characteristics of energy-efficiency measures – a neglected dimension.
Energy Policy 51: 502–513. doi:10.1016/j.enpol.2012.08.054
Gregg, J.S., R. Andres & G. Marland (2008). China: Emissions pattern of the world leader in CO2 emissions from
fossil fuel consumption and cement production. Geophysical Research Letters 35:L08806,
doi:10.1029/2007GL032887
Gupta S., J. Harnisch, D. C. Barua, L. Chingambo, P. Frankel, R. J. Garrido Vázquez, L. Gómez-Echeverri, E. Haites, Y.
Huang, R. Kopp, B. Lefèvre, H. Machado-Filho, & E. Massetti (2014). Cross-cutting Investment and Finance Issues.
In: Climate Change 2014: Mitigation of Climate Change. Contribution of Working Group III to the Fifth Assessment
Report of the Intergovernmental Panel on Climate Change [Edenhofer, O., R. Pichs-Madruga, Y. Sokona, E.
Farahani, S. Kadner, K. Seyboth, A. Adler, I. Baum, S. Brunner, P. Eickemeier, B. Kriemann, J. Savolainen, S.
Schlömer, C. von Stechow, T. Zwickel and J.C. Minx (eds.)]. Cambridge University Press, Cambridge, United
Kingdom and New York, NY, USA.
29
ICAP. (2015). ETS Map. Retrieved May 27, 2015, from https://icapcarbonaction.com/ets-map
Ilskog, E., B. Kjellström, M. Gullberg, M. Katyega, & W. Chambala (2005). Electrification co-operatives bring new
light to rural Tanzania, Energy Policy, 33:10, 1299-1307
Immendoerfer, A., M. Winkelmann & V. Stelzer (2014). Energy Solutions for Smart Cities and Communities Recommendations Policy Makers (p. 128).
IEA (International Energy Agency) (2014). World Energy Investment Outlook. Paris, France.
IESG (International Energy Study Group) (2015). Appliance Energy Efficiency. Lawrence Berkeley National
Laboratory, US Department of Energy. https://ies.lbl.gov/research-area/appliance-energy-efficiency Accessed
4/12/2015
IRENA (International Renewable Energy Agency) (2014). REMAP 2030 - A Renewable Energy Roadmap. Abu Dhabi.
http://irena.org/remap/REmap_Report_June_2014.pdf
Jaffe, A. B., Newell, R. G., & Stavins, R. N. (1999). Energy-efficient technologies and climate change policies: issues
and evidence.
Kazakhstan Energy Charter Secretariat & Kazenergy (2014). Review of the National Policy of The Republic of
Kazakhstan in the Area of Energy Saving and Energy Efficiency. Brussels, Belgium.
http://www.encharter.org/fileadmin/user_upload/Publications/Kazakhstan_EE_2014_ENG.pdf (Accessed 25 Jun,
2015)
Kober, T. (2014). Impact of Energy Efficiency Measures on Greenhouse Gas Emission Reduction, ECN (ECN-E–14038)
Kriegler, E., J. P. Weyant, G. J. Blanford, V. Krey, L. Clarke, J. Edmonds, A. Fawcett, G. Luderer, K. Riahi, R. Richels, S.
K. Rose, M. Tavoni, & D. P. van Vuuren (2014). The role of technology for achieving climate policy objectives:
overview of the EMF 27 study on global technology and climate policy strategies. Climatic Change 123(3-4): 353–
367. doi:10.1007/s10584-013-0953-7
Lee, C. C. (2006). The causality relationship between energy consumption and GDP in G-11 countries revisited.
Energy Policy, 34(9), 1086–1093. http://doi.org/10.1016/j.enpol.2005.04.023
Lind, H. (2012). Pricing principles and incentives for energy efficiency investments in multi-family rental housing:
The case of Sweden. Energy Policy 49: 528–530. doi:10.1016/j.enpol.2012.06.054
Loulou, R., M. Labriet, & A. Kanudia (2009). Deterministic and stochastic analysis of alternative climate targets
under differentiated cooperation regimes. Energy Economics 31: S131-S143.
Markandya, A., K. Halsnaes, A. Lanza, Y. Matsuoka , S. Maya, J. Pan, J. Shogren, R. S. de Motta, T. Zhang, & T. Taylor
(2001). In Climate Change 2001: Mitigation, Contribution of Working Group I to the Third Assessment Report of the
Intergovernmental Panel on Climate Change [Jochem, E. (ed.)] Cambridge University Press, Cambridge, United
Kingdom and New York, NY, USA.
McKenna, E., I. Richardson & M. Thomson (2012). Smart meter data: Balancing consumer privacy concerns with
legitimate applications. Energy Policy 41: 807–814. doi:10.1016/j.enpol.2011.11.049
30
McNeil, M.A., V. E. Letschert, S. de la Rue du Can, & J. Ke (2012). Bottom-Up Energy Analysis System –
Methodology and Results. Lawrence Berkeley National Laboratory.
http://www.superefficient.org/Resources/~/media/Files/BUENAS%20Methodology.pdf
Meijer, F., Itard, L., & Sunikka-Blank, M. (2009). Comparing European residential building stocks: performance,
renovation and policy opportunities. Building Research & Information, 37(5-6), 533–551.
doi:10.1080/09613210903189376
New Zealand Ministry of Economic Development (2011). New Zealand Energy Strategy 2011–2021: Developing our
energy potential. http://www.med.govt.nz/sectors-industries/energy/pdf-docs-library/energy-strategies/nzenergy-strategy-lr.pdf (Accessed 25 Jun, 2015)
Nordhaus W. D. & J. Boyer (1999). Roll the DICE Again: Economic Models of Global Warming. Yale University,
manuscript edition.
Nussbaumer, P., Bazilian, M., & Modi, V. (2012). Measuring energy poverty: Focusing on what matters. Renewable
and Sustainable Energy Reviews, 16(1), 231–243. http://doi.org/10.1016/j.rser.2011.07.150
OECD (2014). Economic Outlook No 95 - May 2014 - Long-term baseline projections.
http://stats.oecd.org/OECDStat_Metadata/ShowMetadata.ashx?Dataset=EO95_LTB&ShowOnWeb=true&Lang=en
OTC-X. (2015). CO2-Emissionsrecht CHU. Retrieved May 27, 2015, from https://www.otcx.ch/markt/instrument/valor/999999.html
Ozturk, I. (2010). A literature survey on energy-growth nexus. Energy Policy, 38(1), 340–349.
http://doi.org/10.1016/j.enpol.2009.09.024
Pacala, S. & R. Socolow (2004). Stabilization Wedges: Solving the Climate Problem for the Next 50 Years with
Current Technologies. Science 305: 968-972
Planning Commission Government of India (2013). Twelfth Five Year Plan (2012–2017) Economic Sectors Volume II.
http://planningcommission.gov.in/plans/planrel/12thplan/pdf/12fyp_vol2.pdf (Accessed 25 Jun, 2015)
Rogelj, J., D. L. McCollum & K. Riahi (2013). The UN’s ‘Sustainable Energy for All’ initiative is compatible with a
warming limit of 2 °C. Nature Climate Change 3: 545-551
Shiu, A., & Lam, P.-L. (2004). Electricity consumption and economic growth in China. Energy Policy, 32(1), 47–54.
http://doi.org/10.1016/S0301-4215(02)00250-1
Sinton, J. E. (2001), Accuracy and reliability of China’s energy statistics, China Econ. Rev., 12, 373– 383.
SE4ALL (Sustainable Energy for All). (2013a). Executive Summary. In Global Tracking Framework (pp. 9–16). Vienna,
Austria: United Nations.
SE4ALL (Sustainable Energy for All). (2013b). Universal Access to Modern Energy Services. In Global Tracking
Framework (pp. 74–132). Vienna, Austria: United Nations. Retrieved from
http://www.sustainableenergyforall.org/tracking-progress
31
SE4ALL. (2015). Progress Toward Sustainable Energy 2015. 09/10/2015. Retrieved from http://www.se4all.org/wpcontent/uploads/2013/09/GTF-2105-Full-Report.pdf
Scheper, E., & T. Kram (1994). Comparing MARKAL and MARKAL-MACRO for The Netherlands, ECN Policy Studies,
Draft, presented at the May 1994 meeting of ETSAP.
Schleich, J., & Gruber, E. (2008). Beyond case studies: Barriers to energy efficiency in commerce and the services
sector. Energy Economics 30(2): 449-464.
Sugiyama, M., O. Akashi, K. Wada, A. Kanudia, J. Li, & J. Weyant (2013). Energy efficiency potentials for global
climate change mitigation. Climatic Change 123(3-4): 397–411. doi:10.1007/s10584-013-0874-5
U.S. Department of Energy (DOE). (2015). Barriers to Industrial Energy Efficiency. Retrieved October 9, 2015, from
http://energy.gov/sites/prod/files/2015/06/f23/EXEC-2014-005846_5 Study__0.pdf
http://energy.gov/eere/amo/downloads/barriers-industrial-energy-efficiency-study-appendix-june-2015
Yearwood Travezan, J., R. Harmsen, & G. van Toledo (2013). Policy analysis for energy efficiency in the built
environment in Spain. Energy Policy 61: 317–326. doi:10.1016/j.enpol.2013.05.096
Wang, H., Zhang, R., Liu, M., & Bi, J. (2012). The carbon emissions of Chinese cities. Atmospheric Chemistry and
Physics, 12(14), 6197–6206. http://doi.org/10.5194/acp-12-6197-2012
Wolde-Rufael, Y. (2006). Electricity consumption and economic growth: A time series experience for 17 African
countries. Energy Policy, 34(10), 1106–1114. http://doi.org/10.1016/j.enpol.2004.10.008
World Bank (2014). Health Nutrition and Population Statistics: Population estimates and projections (Last:
09/26/2014). http://databank.worldbank.org/data/home.aspx
World Bank. (2014). State and Trends of Carbon Pricing 2014. Washington, DC.
World Bank. (2015a). Carbon Pricing Watch 2015. Washington, DC.
World Bank. (2015b). World Bank Open Data. Retrieved June 16, 2015, from http://data.worldbank.org/
World Bank. (2015c). Least developed countries: UN classification. Retrieved June 16, 2015, from
http://data.worldbank.org/region/LDC
32