M PRA
Munich Personal RePEc Archive
Multicriterial Assessment of RES- and
Energy-Efficiency Promoting Policy
Mixes for Russian Federation
Alexander Didenko
Financial University under the Government of the Russian
Federation
September 2013
Online at http://mpra.ub.uni-muenchen.de/59350/
MPRA Paper No. 59350, posted 21. October 2014 07:39 UTC
Review of Business and Economics Studies
Volume 1, Number 1, 2013
Multicriterial Assessment of RES- and
Energy-Efficiency Promoting Policy Mixes
for Russian Federation*
Alexander Didenko, Ph.D.
Deputy Dean, International Finance Faculty, Financial University, Moscow
alexander.didenko@gmail.com
Abstract. We focus on assessing RES- and energy-efficiency promoting policy mixes for Russia from multicriteria
perspective with emphasis on GHG emission reduction. We start from two surveys: the first one studies country’s
energy saving and RES potential to determine possible range of outcomes for policy mixes in question;
the second one reviews corpus of relevant official documents to formulate policy alternatives, which the
policymakers are facing. Our findings are then blended with forecasts of government and international agencies
to obtain three scenarios, describing possible joint paths of development for Russian energy sector in the
context of demographic, economic and climatic trends, as well as regulatory impact from three policy portfolios,
for period from 2010 (baseline year) till 2050. Scenarios are modeled in Long-Range Energy Alternatives
Planning (LEAP) environment, and the output in the form of GHG emissions projections for 2010–2050 is
obtained. We then assess three policy portfolios with multi-criteria climate change policies evaluation method
AMS. Our analysis suggests that optimistic scenario is most environmentally friendly, pessimistic one is easier
to implement, and business-as-usual balances interests of all stakeholders in charge. This might be interpreted
as an evidence of lack of governmental regulation and motivation to intervene in energy sector to make it
greener and more sustainable. Research was done with support of grant under European Union FP7 program
PROMITHEAS-4 “Knowledge transfer and research needs for preparing mitigation/adaptation policy portfolios”.
Аннотация. В данной статье методы многокритериального принятия решений применяются для оценки
эффективности государственной политики РФ в области развития возобновляемых источников энергии
(ВИЭ) и повышения энергоэффективности. Особый акцент при оценке политики делается на достигаемые ей
уровни сокращения выбросов парниковых газов. Для этого сначала предпринимается оценка потенциала
страны в области энергоэффективности и развития ВИЭ. Затем анализируется законодательство страны, как
уже принятое, так и планируемое, для определения спектра возможных альтернатив в области политики.
Выводы затем дополняются прогнозами, взятыми из официальных государственных и международных
источников, на основании чего строятся три сценария, описывающие возможные траектории развития
российской энергетики в контексте демографических, экономических и климатических трендов, а также
регуляторного воздействия государства на период до 2050 г. Моделирование сценариев осуществляется
в среде Long-Range Energy Alternatives Planning (LEAP), а результатом являются долгосрочные прогнозы
выбросов парниковых газов для российской экономики. Три портфеля политик, реализуемые в рамках
сценариев, оцениваются многокритериальным методом принятия решений AMS. Наш анализ свидетельствует,
что наилучшие показатели по сокращению выбросов имеет оптимистический сценарий, пессимистический —
проще в реализации, а базовый — балансирует интересы вовлеченных сторон, имеющих доступ к принятию
стратегических решений. Это можно рассматривать как свидетельство недостатка государственного
регулирования и мотивации к вмешательству в дела энергетического сектора в целях устойчивого развития
в России.
Key words: regulatory impact assessment, multi-criteria evaluation, MCDA, AMS, MAUT, SMART, long-range energy
alternatives planning (LEAP), climate policy, climate change, energy policy, mitigation/adaptation, RES promotion,
energy efficiency, GHG emissions.
* Многокритериальная оценка государственной политики Российской Федерации в области возобновляемых источников
энергии и энергоэффективности
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Electronic copy available at: http://ssrn.com/abstract=2494905
Review of Business and Economics Studies
I ntro d u ction
more than 8.5 billion kWh of electricity has been
produced annually with RES, which is less than 1
percent of total production of electricity in the
Russian Federation. The volume of technically
available renewable energy sources in the Russian Federation is higher than 3220 Mtoe. However,
due to the world energy market conditions and the
modern technology restrictions only a small part
of available renewable energy sources, excluding hydropower, is feasible without state subsidies. The feasible potential of renewable energy
sources in Russia is around 189 Mtoe, including:
geothermal sources 80 Mtoe, small hydro sources
45.6 Mtoe, biofuel sources 25.5 Mtoe, solar sources
8.75 Mtoe, wind sources 7 Mtoe, low temperature
energy applications 25.5 Mtoe.
In the past support for RES has been poor in
Russia. Only in November 2009, the national energy
policy included a mandate for increasing RES energy generation from less than 1% to 4.5% by the year
2020 leading to additional 22 GW (Government of
Russian Federation et al., 2009), estimated by EBRD
(2009). Russian experts in 2008 estimated that the
amount of economically recoverable renewable energy is more than 270 million tons of coal equivalent (Mtce) per year, including 115 Mtce/y of geothermal energy, 65 Mtce/y of small hydropower, 35
Mtce/y of biomass, 12.5 Mtce/y of solar, 10 Mtce/y
of wind and 31.5 Mtce/y of low potential heat (European Parliament, 2008). More recent estimates
refer to technical resource of about 4.5 billion Mtoe
with a major share attributed to solar and wind energy (EU-Russia Energy Dialogue, 2011). The corresponding economic potential is estimated at approximately 450 Mtoe (EU-Russia Energy Dialogue,
2011). These figures are mentioned also at “The
Main Directions of the State Policy in the Energy
Efficiency of RES Electricity for the Period up to
2020 (No.1-r)”. The large RES potential is utilized
to a small extent by large hydropower and wood
energy use. In 2009, electricity generation based
on RES (excluding large hydro power stations) was
6,75 TWh (less than 1% of total power generation)
and including large hydro power plants — approximately 170 billion kWh (or almost 16% of the total
energy mix) (EU-Russia Energy Dialogue, 2011).
Estimations refer to an increase of RES-based
power production and consumption volume ratio
(excluding hydro power stations with established
capacity over 25 MW) from 0.5% in 2008 to 2.5%
by 2015 and 4.5% by 2020 (EU-Russia Energy Dialogue, 2011).
One of the greatest Russian energy resources
accounting in year 2009 for approximately 21% of
The integration of renewable energy sources (RES)
into Russian energy system and improving the energy efficiency of Russian economy and further
transition to the low-carbon economy are among
the most important topics for Russian and international policy makers. Many social, economic and
technological factors have significant influence
on development and evolution to the low carbon
economy in Russia.
A comprehensive review of computer tools for
analyzing various national energy systems was
presented by Connoly et al. (2010). Authors considered 37 different computer packages that can
be used to generate scenario prediction for development of national energy systems and finally
concluded: “LEAP would be more suitable due to …
lengthy scenario timeframe”.
LEAP (Long-Range Energy Alternatives Planning) is an integrated modeling tool for analyzing
energy consumption, transformation and production in all sectors of national economy. The Stockholm Environmental Institute and its US office
in Boston developed LEAP in 1980 and now more
than 5000 institutions all over the world use LEAP
in their research. LEAP contains technological and
environmental database (TED), which allows to
input and process national economy and energy
system datasets.
To compare different scenarios for development
of national economy and energy system the efficient multi-criteria evaluation methods should be
selected. In analysis of possible scenarios we used
the multi-criteria climate change policies evaluation method AMS, combining MCDA procedures
AHP, MAUT and SMART, developed by Konidari et
al. (2007, 2008).
The rest of the paper is organised as follows. In
the next two chapters we briefly survey energyefficiency/RES potential and energy policy options
currently being in the centre of discourse among
Russian policy makers. Then we proceed with description of scenarios as were modeled in LEAP. Finally, we assess results of our simulation with AMS
climate policy multicriteria decision-making tool.
R E S potentia l an d energ y efficiency
RES potential. Today in Russia the total installed
capacity of electricity generation plants and power
plants using renewable energy (without the hydroelectric power plants with installed capacity
of more than 25 MW) do not exceed 2200 MW. No
Volume 1, Number 1, 2013
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Electronic copy available at: http://ssrn.com/abstract=2494905
Review of Business and Economics Studies
Volume 1, Number 1, 2013
the total generating capacity is water, although it
corresponds to about 16% of production. In 2009
the country was the world’s fifth largest producer
of hydropower with approximately 167 TWh/yr, but
only 18% of its hydropower potential was developed (EBRD, 2009).
Estimations of the total hydropower technical potential refer to about 2,400 billion kWh per
year, the majority of which is based on medium
and large rivers. The respective economic potential is 850 billion kWh per year (EBRD, 2009). Small
hydro is the most mature RES type in the country.
The potential of smaller rivers amounts to approximately 46% of total hydro energy potential (European Parliament, 2008).
Most of this potential is located in Central and
Eastern Siberia and in the Far East. The Far East
and Eastern Siberia combined account for more
than 80% of hydropower potential, and could produce about 450–600 billion kWh per year (EBRD,
2009). The North Caucasus and the western part
of the Urals also have good hydropower potential.
Installed capacity amounts to 1,000 MW (European
Parliament, 2008).
There is also rather high potential for wide and
effective use of biomass resources since Russia has
approximately 22% of the world’s forests located
on its territory (EBRD, 2009; European Parliament,
2008). The forest industry is an important Russian economic sector, a large potential supplier
and consumer of biomass (wood waste) products.
These products are only being minimally exploited.
The technical potential of biomass is estimated at
more than 50 Mtce.
Apart from the forestry sector, the agricultural sector is also an important source of biomass
resources, but the vast majority of Russia’s agricultural resources are not being used at all. An estimated 850 million liters of biofuel could be produced on this territory.
The majority of the energy produced from biomass has been used for heating purposes, and not
for power generation although it is considered as
most suitable solution for power production and
for cogeneration of heat and electricity (European
Parliament, 2008; EBRD, 2009). Approximately 40
thermal power stations use biomass (mostly waste
from the wood processing industry) along with
other fuels. Biomass is also used as solid fuel in
certain district heating boilers being a potential
niche market for biomass in the district heating systems. Installed capacity (until year 2008)
accounted for 1,270 MW (European Parliament,
2008).
The technical potential of solar energy was estimated as 18.7*10 6 GWh, with an economic potential around 1*10 5 GWh per year (EBRD, 2009).
Some areas receive more than 300 sunny days per
year, and the cold temperatures also improve the
efficiency of solar cells.
Russia possesses vast geothermal resources,
and over 3,000 wells have been drilled to take advantage of this renewable energy type. Geothermal
energy is used for heat supply and electricity production. In 2009 there were 92–129 MW of geothermal power plants operating, and about 55 MW
of planned additional capacity (EBRD, 2009).
Up to 2009, Russia had only over 20 MW of wind,
and new wind turbines had not been built since
2002. Estimated gross wind potential is 26,000
million tons of coal equivalent, technical potential
is 2,000 Mtce, and economic potential — 10 Mtce.
Approximately 30% of this economic potential is
concentrated in the Far East, 16% in West Siberia
and another 16% in East Siberia (EBRD, 2009).
Most of Russia’s tidal power is dissipated in the
Arctic regions, in particular the White Sea is considered to have a great potential. In the Mezen Bay,
the difference between low tide and high tide is
greater than 20 feet.
In 2007, a 1.5 MW tidal power plant by Gidro
OGK began operation as a pilot project in the same
bay. In case of success, the company plans 10 GW
of electricity generation, and potentially to build
several more tidal electro stations in other Russian
bays (EBRD, 2009).
Energy efficiency. According to MED, energy efficiency in Russia is significantly lower compared
to developed countries. According to information
of Ministry of Energy, total energy consumption in
Russia averages to about 990 millions of standard
fuel tons. If Russia would implement energy saving
to a scale common for European Union countries,
its energy consumption would fall by 35% to 650
millions of tons of standard fuel. Energy intensity
of GDP in Russia is 250% higher than world average and 250–350% higher than in developed countries (GPEE-2020). Bashmakov (2009) provides
sectoral estimates of energy saving potential for
Russia. The technical potential in the transportation sector is approximately 38.30 Mtoe. The potential in both heat and electricity generation will
be the outcome of efficiency improvements at the
generation facilities and reductions of power- and
heat end-use. In electricity generation, the potential is 93 Mtoe, and in the heat supply sector — 107
Mtoe, while the potential of fuel production and
transformation efficiency improvement is 41 Mtoe.
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Review of Business and Economics Studies
Estimations of the technical potential in electricity of the residential buildings refer to reductions
of energy use for the following applications: 25.5%
for space heating; 51.9% for hot water; 29.1% for
cooking; 78.8% for lighting; 23.5% for appliances
(refrigerators and freezers, washers, VT and video,
air conditioners and other appliances).
• Federal Program “Energy Efficient Economy”
for 2002–2005 and up to 2010 (Decree of the Russian Federation No.83-p, January 22, 2001);
• Draft Program of socio-economic development of the RF in the medium term (2005–2008);
• Federal Program “Modernization of Transport
System of Russia (2002–2010)” (Decree of the Russian Federation, No.232-p, February 16, 2001).
As for energy efficiency and RES usage it sets
the following targets:
• Energy consumption in the transport sector was expected to be restricted from 9.3 Mtce in
2004 to 10.3 Mtce in 2008 (goal was initially set in
Federal Program “Modernization of Russian Transport System (2002–2010)”);
• Reduction of specific fuel consumption for
electricity generation in power plants of RAO “UES
of Russia” was set at 8% for the period 2004–2008
(Energy Strategy of RF until 2020);
• Gas transmission and distribution losses from
upstream to distribution were expected to be reduced by 47 billion m 3 for the time interval 2006–
2010 (initially set by Federal Program “Energy Efficient Economy” for 2002–2005 and up to 2010);
• The share of renewable energy in total primary energy production was expected to be increased
from 0,1% to 0.22%-0.3% in 2010 (initially set by
Federal Program “Energy Efficient Economy” for
2002–2005 and up to 2010).
The Presidential Decree No. 889 “On some
measures to improve the energy and environmental
efficiency of RF economy” was approved on June 4,
2008. It is a brief document, containing only one
important quantitative goal for energy efficiency:
decrease of GDP energy intensity up to 2020 by
40% of 2007 level. It also contains several important president’s orders to the government, with
deadlines, aimed at achieving the mentioned goal.
The adoption of “The Main Directions of The
State Policy in the Energy Efficiency of RES Electricity for the Period up to 2020 (No.1-r)” on January 8, 2009, became the next step, which declared
the purposes and principles of RES use in RF, set
quantitative targets for the share of RES electricity
production/consumption in the total energy balance and defined the measures to achieve them.
The document deals explicitly with the supply
side of electricity balance; expands and refines
goals for the Action Plan about RES by setting the
following targets for RES-generated electricity
(except for electricity generated by hydro power
plants with power exceeding 25 MW): by 2010–
1.5%, by 2015–2.5%, by 2020–4.5% share in total
electricity generation.
Po l icy options for miti g ation
po l icies in R u ssia
Analysis of relevant government documents shows
that in Russia climate change mitigation and adaptation discourse almost is not reflected in official national climate strategy documents and
climate-related laws, especially in terms of measurable goals and actionable plans. However Russia has very developed and complex structure of
government-adopted and parliament-voted documents for RES promotion and energy efficiency,
from high-level strategic documents and laws to
low-level federal programs, bylaws, rules and regulations. As these policies could potentially impact
GHG emissions, we interpret it as climate change
policies.
Historically, first targets for increasing the
use of RES and energy-efficiency were set in the
following federal programmes: “Energy Efficient
Economy for 2002–2005 and Period until 2010”
(adopted by government on 17.11.2001); “South
of Russia” (adopted by government on 8.08.2001);
“Economic and Social Development of Far East and
Baikal Region” (adopted on 15.04.1996) (Helio International, 2006).
The “Energy Strategy of Russia up to 2020”
(Government decree No.1234-r issued on 28.08.03)
was the first strategic energy program in RF. It emphasized increasing energy efficiency and implementation of proper energy pricing policy to overcome country’s heavy dependence on natural gas.
Its share in energy balance was about 50% during
the 1990s. The “Energy Strategy 2020” proposed a
wider use of coal and nuclear energy with an anticipated share in year 2020 of 21–23% and 6% respectively (Helio International, 2006).
In 2005 the “Integrated Action Plan for Implementation of Kyoto Protocol in RF” was approved by the Interdepartmental Commission. It
was a detailed action plan for the period up to
2010 with quantifiable goals and workable plans
as follows:
• Energy Strategy of RF until 2020 (Decree
of the Russian Federation, No.1234-r, August 28,
2003);
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Review of Business and Economics Studies
The Climate Doctrine of RF (CD RF) (approved
by Presidential Decree No.864p on December 17,
2009) is a short framework paper, describing briefly
and in general terms the main notions of climate
policy in RF, declaring risks and positive outcomes
of global climate change for the country, wide categories of mitigation/adaptation instruments, etc.
It contains not quantitative, but qualitative goals.
The “Energy Strategy for the Period of 2030”,
adopted in 2009, is an updated version of the previously mentioned “Energy Strategy 2020”. It analyses the level of accomplishment of the previous
Strategy and contains further details and expanded
goals. Specifically, it points out that non-realized
potential for energy intensity for Russian economy could be equal to 40% of domestic energy consumption.
The “Energy Strategy 2030” breaks down this
potential into various components, namely:
• Residential buildings — 18–19%;
• Power generation, industry, transport — 13–
15% each;
• Heating, services, construction — 9–10%
each;
• Fuel production, gas flaring, energy government agencies — 5–6% each;
• Agriculture — 3–4%.
The “Energy Strategy 2030” sets a 56% energy
intensity reduction target for 2030 (compared with
year 2005). To reach this goal Russia plans to create a favourable economic environment, including
progressive liberalization of energy prices on the
domestic market; to promote more rational energy use, and to establish a market for energy services. New standards, tax incentives and penalties,
as well as energy audits need to be adopted. The
“Energy Strategy 2030” also aims to increase the
energy efficiency of buildings by 50% for the time
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interval 2008–2030 (+10% for the period 2008–
2015) by implementing new mandatory construction standards.
Finally, the state program “GPEE-2020” (“Energy saving and improving energy efficiency for a period up to 2020”) was approved by the Government
of Russian Federation on 27.12.2010. This program
aims to decrease GDP energy intensity by 13.5%,
and save up to 100 millions of standard fuel per
year by 2016 and 195 millions of standard fuel per
year by 2020. This goal has the following sectoral
subgoals (in terms of total energy savings).
S cenario ass u mptions
Scenarios reflecting various paths for energy and
economy development in Russia are modeled in
LEAP. Long-Range Energy Alternatives Plannning
(LEAP) is modeling environment, which allows
to create simulation models of energy economy
of certain region. It is a well established tool,
used many times both by practitioners and academicians (see, for example, Konidari & Mavrakis
(2007), Miranda-da-Cruz (2007), Cai, Huang, Lin,
Nie & Tan (2009), Kalashnikov, Gulidov & Ognev
(2011), Tao, Zhao & Changxin (2011), Zhang, Feng
& Chen (2011), Shan, Xu, Zhu & Zhang (2012), Ke,
Zheng, Fridley, Price & Zhou (2012)). Basic idea is
as follows: we populate historical energy balances
for Russia in LEAP with data from EIA; we set energy consumption structure in economy according
to historical data from Rosstat; we add historical
trends, reflecting changes in temperature, precipitation, country population and GDP.
We further define three scenarios: (1) businessas-usual (BAU), serving as baseline for (2) optimistic (OPT) and (3) pessimistic (PES) scenarios. Basic
assumptions about economic activity, energy sec-
Table 1. Sectoral targets for energy efficiency.
Sector
Goal for 2011–2015
Goal for 2011–2020
334 million tons of standard fuel
1124 million tons of standard fuel
108 billion m 3
330 billion m 3
Electricity
218 billion kWt/h
630 billion kWt/h
Heat
500 million Gcal
1550 million Gcal
5 million tons
17 million tons
Primary energy
Natural Gas
Oil and products
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Figure 1. Sectoral distribution of output, BAU scenario.
Figure 2. Total demand for energy 2011–2050 broken down to sectors (above) and sources of energy (below).
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Review of Business and Economics Studies
Volume 1, Number 1, 2013
tor development paths, demography and climate
for these scenarios are based on official estimates
of either government or various international
agencies and organisations (World Bank, IMF, UN).
We use historical trends as a kind of reality check
for plausibility of basic assumptions. BAU scenario
contains moderate estimates of basic assumptions
variables and reflects only regulations and national energy strategy, adopted and actually enacted
on December 31, 2010. As for basic assumptions
in OPT and PES scenarios, we used the most optimistic of all available options for OPT (the milder
path for warming, better demography and GDP, innovational scenario and forced speed of development for energy sector), and the most pessimistic
for PES (slower implementation of innovations,
low GDP growth rate, severe climate change, bad
demography). OPT and PES scenarios reflect augmented set of policies, based on what is actually
discussed by government, as if it was adopted in
2011–2013 and further applied to economy and energy sector. OPT assumes that policies are implemented faster with better results, and PES — that it
is implemented slower with worse results.
Using trends for economic activity detailed
assumptions about sectoral structure of energy
consumption (based on historical values), LEAP
projects sectoral energy consumption for period
2010–2050. Using built-in technology database
and energy intensity, LEAP defines GHG emissions
levels for period mentioned. GHG emissions forecast is main output of LEAP model. We further use
it as an input in AMS climate policy assessment
procedure.
Business-as-usual (BAU) scenario. BAU-scenario
is built on policy portfolio effective as of December
31, 2010, as well as scenario assumptions, grounding forecasts of government of RF and international organisations.
Population dynamics in BAU-scenario follows
dynamics from scenario оf “Long Term Forecast of
Social-Economic Development of Russian Federation for a Period of up to 2030”.
Forecast contains several scenarios for population. For BAU moderate rate forecast was selected. According to this scenario slight decrease in
population is expected in 2020–2025, with subsequent recovery to 2010 level in 2030. After 2030
we assume population stabilizes and remains unchanged till 2050.
In 2008 Roshydromet published “Report on
Climate Change and its Consequences in Russian
Federation”. Report notes beginning of a trend of
temperature rise since beginning of 21 century. According to Roshydromet estimates, average temperature rise till 2050 in Russian Federation could
be from 1 to 6 degrees Celsius, with probability of
standard deviation quite high.
Roshydromet estimates are confirmed by several research organisations in Russia and abroad.
Roshydromet/RAS Institute of Global Climate and
Ecology, with participation of Hydrometcentre and
other state-funded research organisations, published global scenario forecasts for climate change
up to 2020, 2050, and 2080. Average temperature is
estimated with ensemble of models, and deviation
of predicted values could be up to 3 degrees Celsius. In our research we average historical values
Figure 3. Historical levels and forecast for 2000–2050 of electricity generation: BAU-scenario, energy sources breakdown.
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Review of Business and Economics Studies
for temperature and precipitation for 1901–2009,
published by World Bank, and long-term forecasts
of Roshydromet and RAS. Average surface temperature for RF was about –5 degrees Celsius, according to World Bank.
Along with that, significant volatility of temperature around average level was observed, but
generally during 20th century trend was horizontal, and only in 1990s and in the beginning of 21th
century upward slope was observed. Taking average for 20th century as baseline, we build BAU-scenario with linear increase of average yearly temperature up to +3 degrees in 2050, which is in line
with moderate forecasts of Roshydromet and RAS.
According to World Bank, long-term average
level of precipitation was 460 mm. We take this
level as baseline, and use RAS assumptions to
model yearly change in precipitation.
Unlike scenarios for surface temperature, assuming significant changes, precipitation was
assumed not to change significantly. In BAU we
assume total decrease in average level of precipitation by 2 mm during all the period.
GDP as indicator of economic activity is key
factor for forecasting GHG emission. In Russia this interplay is even tighter, moderated by
low energy efficiency and significant role of energy sector in economy. GDP dynamics, with
e n e r g y- e f f i c i e n c y d y n a m i c s a n d s t r u c t u r a l
change in economy is thus key factors of energy demand and, accordingly — GHG emissions.
In BAU GDP change is modeled as follows. GDP
growth in 2011–2012 is assumed to be equal to
historical estimates according to state statistics
(in 2010–4.3%, in 2011–3.4%, in 2012–2.4%). After
2012 GDP growth rate is assumed to be equal to
constant rate of 3.1%, which is in line with conservative forecast of the government of RF. We assume in BAU that this rate will persist over period
of 2030–2050. Sectoral distribution of GDP will
follow this dynamics too (Figure 1).
Energy eff iciency. Basis for energy efficiency
modeling is historical data by EIA and forecasts of
state program for energy efficiency till 2020. Program has two scenarios: innovational and inertial.
For BAU scenario we used inertial scenario of the
program. After achieving goals of state program in
2030, energy efficiency is assumed to remain unchanged. Given that Russian economy is one of the
most energy inefficient in the world, in 2030 it will
still have huge potential for improving energy efficiency.
Oil and natural gas prices. Oil and gas prices are
modeled according to IEA World Energy Outlook
for 2010.
Energy consumption. For this section inertial
scenario of Federal Target Program “Energy saving and energy efficiency till 2020” was adopted.
It is assumed that after 2020 increase in energy
consumption intensity will continue with twice as
lower rate as during realisation of federal target
program. Accounting for increase in energy efficiency total demand for energy with sectoral and
energy source breakdown will look as follows (Figure 2).
Transformation: losses. According to “Energy
Strategy 2030”, if all measures of the strategy will
be rendered, losses in heat generation will be decreased by 50% by 2030, and in electricity generation — by 2% by 2030. Assumptions of the strategy
are put in BAU scenario.
Electricity generation. Historical data for primary fuel consumption for electricity generation
are taken from “Energy Strategy 2030”. This paper
assumes achievement of definite structure of electricity generation in 2020 and 2030. In particular,
it assumes increase of the share of non-fuel generation, and increase of natural gas and coal share
in fuel generation. “Strategy” has no details about
structure of all the other sources of electricity generation (nuclear, hydro, small RES, etc.) We model
shares of these types of energy as proportional to
historical structure of 2010. Change of shares toward numbers set by “Strategy 2030” is obtained
by linear interpolation of shares for non-fuel, natural gas, coal and heating oil from levels of 2010.
After 2030 structure of generation is assumed to
remain unchanged.
OPT scenario, apart from faster realisation, assumes further improvement of structure of generation (Figure 3).
Land management policy mix was considered
in the draft federal target program “Development
of the reclamation of agricultural land in Russia
until 2020”, developed in accordance with the decision of the board of the Ministry of Agriculture
of Russia No.7 on August 26, 2008, and on the basis of Article 8 of the federal law dated 29.12.2006
No.264-FZ “On the development of the agriculture
sector”.
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Volume 1, Number 1, 2013
Results of policies simulation and its assessment
The graph on Figure 4 displays greenhouse gas emissions by various sectors and types of fuel.
Figure 4. Historical levels and forecast for 2000–2050 of final energy demand: BAU-scenario, fuel type breakdown.
Figure 5. Historical levels and forecast for 2000–2050 of final energy demand: BAU-scenario, sectoral breakdown.
Figure 6. Historical levels and forecast for 2000–2050 of GHG emissions for households sector: BAU-scenario, fuel type breakdown.
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Volume 1, Number 1, 2013
Figure 7. Historical levels and forecast for 2000–2050 of GHG emissions for agriculture sector: BAU-scenario, fuel type breakdown.
Figure 8. Historical levels and forecast for 2000–2050 of GHG emissions for industry sectors: BAU-scenario, fuel type breakdown.
Figure 9. Historical levels and forecast for 2000–2050 of GHG emissions for industry sectors: BAU-scenario, sectoral breakdown.
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Figure 10. Historical levels and forecast for 2000–2050 of GHG emissions for services sector: BAU-, OPT-, and PES-scenario, all fuel.
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AMS -assessment of policy mixes
to GHG emission reductions and lessening the impacts
of climate change. In our case, all three scenarios — PES,
OPT and BAU — contain parts promoting green (or at
least “more green”) technologies: energy efficiency, energy saving, smart grid, shift in energy demand, RES, etc.
PES only assumes slower and less effective rendering of such policies compared to OPT. So, both OPT
and PES receive high grade for this criterion, 6 each.
And BAU receives 4, as it assumes less mentioned technologies.
• Competitiveness criterion is used to assess the
impact of certain policy portfolio implementation on
the ability of the national economy to compete with
other economies both via prices and products/services. Two common factors for economy, affecting all
three scenarios, will be the price for oil and climate
change. Russia is net exporter of oil, and one of minority of countries supposed to benefit from climate
change. Export of oil has generally negative impact
on national competitiveness when oil price is higher,
both in short and long term, as it keeps ruble high
and lowers motivation of industry for modernization.
So PES with lower price for oil will score higher and
OPT — lower given only oil factor. Climate change is
assumed to be more severe in PES case, but consequences are unclear: whether Russian economy will
be in position to leverage climate change challenges
or will be hurt is a separate research question. Country has no particular emission reduction goals, which
are regarded as lowering competitiveness, so no particular impact here. OPT scenario assumes forced
implementation of energy-saving technologies and
R&D support, which will contribute to higher score
According to procedure proposed in Konidari (2007,
2012), we use output of LEAP simulation as input in
AMS procedure to obtain final grades for various policy
mixes in question. Final performance of policy mixes
is assessed along following criteria: two subcriterions
for environmental efficiency, assessing direct and indirect effects; several sub-criterions for political acceptability — static and dynamic cost efficiency, and
competitiveness; equity; flexibility; stringency for
non-compliance; and several sub-criterions for feasibility — implementation of network capacity, administrative and financial feasibility. Subcriterions of environmental efficiency are handled as follows: (1) for
direct contribution to GHG emission reductions the
outcome of LEAP for the total expected GHG emissions
in year 2020 is used, and (2) for indirect environmental
effects, the total amount of the total environmental effects provided by LEAP is used. For political acceptability criterion, there are following sub-criterions:
• Cost efficiency measures capacity of policy portfolio to achieve target parameters under financial constraints both acceptable and affordable to stakeholder
entities. BAU includes the lowest volumes of regulations, many of which already have sources of financing
allocated. OPT and PES require more financing, and
given this, PES achieves even less reduction than BAU.
Consequently, BAU is assigned the highest grade: 6,
OPT: 4, PES: 2.
• Dynamic cost efficiency criterion captures opportunities, which certain policy portfolio creates to support R&D, various technologies and innovations leading
Volume 1, Number 1, 2013
Table 2. AMS results for BAU, OPT and PES scenarios.
Weight
BAU
OPT
PES
BAU
OPT
PES
Direct contribution to GHG emission
reductions
0.833
218.7458
137.9448
254.3982
262.6
165.6
305.4
Indirect environmental effects
0.167
0.8183
0.5344
0.9853
4.9
3.2
5.9
Environmental performance — A
219.5641
138.4792
255.3835
Cost efficiency
0.473
2.838
1.892
0.946
6
4
2
Dynamic cost efficiency
0.183
0.732
1.098
1.098
4
6
6
Competitiveness
0.085
0.34
0.51
0.425
4
6
5
Equity
0.175
0.875
1.05
0.35
5
6
2
Flexibility
0.05
0.3
0.15
0.15
6
3
3
Stringency for non-compliance
0.034
0.204
0.136
0.136
6
4
4
5.289
4.836
3.105
Political acceptability — B
Implementation network capacity
0.309
1.854
1.236
1.545
6
4
5
Administrative feasibility
0.581
3.486
2.324
2.905
6
4
5
Financial feasibility
0.11
0.77
0.44
0.55
7
4
5
4.256
2.764
3.455
Feasibility of implementation — C
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Volume 1, Number 1, 2013
of OPT. Summing up, in OPT scenario economy will
be more competitive due to higher energy efficiency,
lower ruble rate, bigger share of knowledge economy
in GDP, and (supposedly) effective use of climate
change. On the opposite, competitiveness in PES will
be oppressed by high prices for oil, but supported by
climate change, which could have positive impact
on agriculture competitiveness. The assigned grades
are: BAU: 4, OPT: 6, PES: 5.
Equity criterion measures “fairness” of scenario
in distributing costs and benefits associated with
scenario among entities and citizens of the country.
We measure intragenerational equity, social equity
and sector equity. Intragenerational equity is measured as total change of GDP per capita divided by
total change in emissions (MtCO2eq) per capita over
2010–2050, higher the change — lesser the score. Social equity is emission reduction per capita compared
to BAU in 2050. Sector equity is standard deviation of
sectoral emissions in each of three scenarios. As for
intragenerational equity, PES scenario assumes slight
increase in emissions per capita, so preliminary score
will be negative and high. OPT and BAU have slightly
different and positive change, so total score for social
equity will be: OPT — 6, BAU — 5, PES — 0. For social
equity, BAU will score 5, OPT — 6, and PES — 4. For
sector equity, the lower standard deviation is in OPT
scenario, it scores 6, with BAU slightly lower than
PES (4 and 3 accordingly). For total equity criterion
we will average all scores: BAU — 5, OPT — 6, PES — 2.
Flexibility criterion captures the ability of the policy instruments to offer a range of compliance options.
BAU imposes minimal obligation on stakeholders and
consequently offers higher flexibility. Due to the similarity of the introduced instruments in PES and OPT,
equal grades are given for both. The assigned grades
are: BAU — 6, OPT — 3, PES — 3.
Stringency for non-compliance and non-participation reflects the level of sanctions, imposed by regulations in each of the scenarios. Although in all scenarios
regulation is quite loose, OPT and PES contain more
policy instruments, and therefore should be graded
lower. The grades are: BAU — 6, OPT — 4, PES — 4.
Feasibility of implementation has the following
subcriterions:
• Implementation network capacity. OPT and PES
scenarios contain extra policies as compared to BAU,
which assume extra load for existing implementation
network. The assigned grades are: BAU — 6, OPT — 4,
PES — 5.
• Administrative feasibility is high for BAU, slightly
lower for PES and even more lower for OPT. BAU includes well-known instruments, many of which are already being implemented. OPT and PES include more
innovational instruments, with OPT including more
than PES. The assigned grades are: BAU — 6, OPT — 5,
PES — 4.
• For financial feasibility, only BAU has relatively
high performance (scored 6). It includes policy instruments associated with federal programs, which guarantees financial recourses pre-allocated. In addition,
BAU includes minimal set of policies possible. Financial requirements of OPT and PES are much higher
(with OPT being the most financial resource intensive),
and financial source is not defined yet. The assigned
grades are: BAU — 7, OPT — 4, PES — 5.
Discussion and conclusions
Based on the analysis of official documents and governmental programs, three scenarios of economic
development of Russia until 2050 were developed.
Mentioned scenarios accounted for greenhouse gas
emissions from various sectors of Russian economy.
As part of the research, an econometric model in
LEAP environment was built, encompassing fuel and
energy balances data, as well as historical and forecasted national GDP, industry and energy structure,
sectoral and total energy efficiency, and the demand
for energy from sectors of economy was forecasted for
up to 2050.
According to the BAU scenario, GHG emissions
will be reduced by 22% by 2020 and decrease by 36%
by 2050. OPT scenario will achieve reductions in GHG
emissions by 28% and 60% in 2020 and 2050, respectively. Analysis of GHG emissions by sectors shows a
non-monotonic behavior of the service sector GHG
emissions in all scenarios, an increase in GHG emissions in 2020 from 11% to 34% in OPT and PES scenarios respectively. Calculations showed a decrease
in energy intensity of GDP in 2020 to 38% for BAU
and OPT, and by 22% for the PES scenarios. Modeling
showed anticipatory reduction of GHG emissions by
households, which reaches in 2050 52%, 72% and 48%
for the BAU, OPT and PES respectively.
Final assessment according to AMS procedure could
be done as follows. For criterion of environmental performance, OPT offers better grade of all scenarios; PES
has the lowest, and BAU is in the middle. This could be
interpreted as lack of regulation (driven, perhaps, by
lack of motivation) of regulatory bodies to decrease
environmental impact of Russian economy. There is
definitely great leeway for improving environmental
performance of the economy through implementation
of new policies, many of which are currently discussed.
In line with above-mentioned considerations, and
as probable explanation to it, BAU has greatest score
for political acceptability, combining better cost ef-
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Review of Business and Economics Studies
Volume 1, Number 1, 2013
ficiency, better flexibility and lowest sanctions level
with moderate equity and competitiveness features.
BAU could be regarded as status quo, maximizing egoistic utility of stakeholders having access to political
power for reflecting their interest in policy. OPT scenario features more high-tech and green options, as
it offers less natural resources-heavy options at the
expense of more financial resources involved. Still
it could find some political support in Russia, and it
scores as the second. PES is less cost-effective both in
static and dynamic aspects, it offers much less equity
than OPT, and less competitiveness than BAU. Being
a kind of loose-loose outcome in political aspect, it
scores the third.
In addition to being the most politically acceptable,
BAU has also the greatest score for feasibility of implementation. PES involves less modernization and regulatory activity, therefore it is more feasible than OPT,
although less than BAU. OPT has less feasible policy
mix of all three scenarios. To sum up, OPT is the most
environmentally friendly, PES is easier to implement,
and BAU balances interests of all stakeholders in charge.
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