Environmental Assessment of the Potential Effects and ... GHG envirnmnt analysis...BP British Petroleum CCGT Combined cycle gas turbine CGE Computable General Equilibrium ... LPG Liquid

Environmental Assessment of the Potential Effects and Impacts of

Removal of Fossil Fuel Subsidies and of Fuel Taxes

Nguyen Minh Bao and John Sawdon

September 2011 (with minor edits December 2011)

Final Report of Package 3 on Environmental Impacts, under the United Nations

Development Programme Project Research on Fossil Fuel Prices And Taxes, And

Their Effects On Economic Development And Income Distribution In Viet Nam

Supported by UNDP Viet Nam (Policy Advisory Team); and the EU-Viet Nam

(Multilateral Trade Assistance Project III, MUTRAP III;

EuropeAid/126313/C/SER/VN)

The opinions, analyses and recommendations contained in this document do not

necessarily reflect the opinions of the United Nations Development Programme

in Viet Nam or the EU in Viet Nam. The Report is an independent publication

commissioned by UNDP and MUTRAP III.

Environmental Assessment of Removal of Fossil Fuel Subsidies and of Fuel Taxes

i

Table of Contents

List of Acronyms ......................................................................................................................... ii

1. Introduction ............................................................................................................................. 1

1.1 Background........................................................................................................................... 1

1.2 Objectives and scope .......................................................................................................... 1

1.3 Methodology ........................................................................................................................ 2

1.4 Structure of this report ....................................................................................................... 2

2. Air pollution in Viet Nam: trends and drivers ..................................................................... 3

2.1 Air pollution ......................................................................................................................... 3

2.2. Trends in GHG emissions ................................................................................................ 4

2.2 Viet Nam’s policy and strategic context for GHG mitigation ...................................... 8

3. Estimating emissions reductions from increased fossil fuel prices ................................. 11

3.1 Methodologies for estimating the emissions impact of changes in relative prices ... 11

3.2 LEAP emissions model for Viet Nam ............................................................................ 11

3.3 BAU projections of emissions to 2030 ........................................................................... 12

3.4 Subsidy reduction and tax imposition modeling scenarios .......................................... 14

3.4.1 Measuring the responsiveness of energy demand to price changes ................................. 15

3.4.2 Estimating the price elasticity of demand in Viet Nam ..................................................... 17

3.4.3 Subsidy reduction and tax imposition ................................................................................... 19

3.6 Emissions modeling results .............................................................................................. 19

3.6.1 Aggregate emissions ................................................................................................................ 19

3.6.2 Emissions from the power sector ......................................................................................... 20

3.6.3 Demand-side emissions .......................................................................................................... 22

4. Conclusion ............................................................................................................................. 23

Bibliography ............................................................................................................................... 24

Annex 1: Overview of Coal Demand Elasticities in the Literature ..................................... 28


ii

List of Acronyms

AAGR Average annual growth rate

BAU Business as usual

bbl Barrel

BP British Petroleum

CCGT Combined cycle gas turbine

CGE Computable General Equilibrium

EVN Electricity Vietnam

GDP Gross Domestic Product

GHG Greenhouse Gas

GoV Government of Vietnam

GSO General Statistical Office

IE Institute of Energy

IEA International Energy Agency

IMF International Monetary fund

Kgoe Kilogramme of oil equivalent

KWh Kilo-watt hours

LEAP Long-range energy alternatives planning

LPG Liquid petroleum gas

MoF Ministry of Finance

Mote Million tons of oil equivalent

MtCO2e Mega-tons of carbon dioxide equivalent

MUTRAP III Viet Nam Multilateral Trade Assistance Project III

MW Mega watts

NGL Natural Gas Liquids

OCGT Open cycle gas turbine

OECD Organisation for Economic Co-operation and Development

PDP Power Development Plan

R&D Research and Development

RD&D Research, development and dissemination

SOE State owned enterprise

TFEC Total final energy consumption

TWh Tetra-watt hours

UNDP United Nations Development Programme

UNFCCC United Nations Framework Convention on Climate Change

USc United States Cents

USD United States Dollars

VAT Value added tax

VND Vietnamese Dong


1

1. Introduction

1.1 Background

Increasing energy consumption has been an essential constituent of Viet Nam‟s rapid growth

and development. With limited additional potential from other energy sources, an increasing

proportion of Viet Nam‟s growing energy needs are being met by fossil fuels.1 Increased

consumption of fossil fuels has brought with it obvious and immediate economic benefits, but

it has also raised significant environmental concerns relating to be generation of air pollutants

of both local and global significance.2 Moreover, higher global energy prices and volatility in

global energy markets, combined with Viet Nam‟s increasing dependence on energy imports

mean that issues of energy supply and security are becoming more prominent in domestic

energy policy.3

Ensuring access to energy for households and industry through fossil fuel price subsidies has

long been an important part of social and industrial policy. Despite the laudable motives

behind fossil fuel subsidies, they are a blunt policy instrument. Subsidies are often regressive

with the relatively well off, who tend to consume larger quantities of energy, benefiting

disproportionately from them. By making fossil fuels relatively cheap subsidies discourage

energy efficiency investments and investment in alternative energy sources. Fossil fuel

subsidization therefore leads to higher levels of consumption and pollution than would

otherwise be the case. With rising energy consumption, energy prices and increased import

dependency, they also represent an increasingly significant drain on public financial

resources (The Economist 2009, Del Granado et al. 2010, IEA et al. 2010) (PeaPROs 2011).

Given the immediate fiscal pressures for policy reform and the long-term strategic

considerations relating to energy security and climate change, the imperative to reform fossil

fuel pricing policy is now extremely pressing. It is in this context that this project was

commissioned to examine the implications of changes in fossil fuel pricing policy in Viet

Nam.

1.2 Objectives and scope

This report is the third and final part of a three-part study looking at the fossil fuel sector in

Viet Nam and the economic, social and environmental implications of reducing subsidies and

increasing taxation of fossil fuels. Package 1 of this study reviewed the value chain and

1 In 2007 an estimated 44% of Viet Nam‟s energy needs were met through non-commercial energy, usually in

the residential combustion of biomass. Hydropower has also constituted a significant portion of power supply,

but economically feasible potential is quickly being used up. 2Securing the supply of fossil fuels also implies a number of significant local environmental risks. Perhaps most

visible in the coal producing areas of Quang Ninh province, and in the well recorded environmental degradation

of Ha Long Bay. But also in terms of risks related to off-shore oil extraction - highlighted by recent high-profile

oil pollution incidents. 3While the focus of this paper is on emissions from fossil fuel combustion, there are a number of highly

contentious domestic and regional environmental policy issues in the energy sector relating to hydropower in

particular.


2

policies related to the fossil fuel sector with a special emphasis on subsidies and taxation in

the sector. Package 2 of the study built on this through using outputs from the package one

study to develop a computable general equilibrium (CGE) model of the Vietnamese economy

and model the fiscal and social impact of price changes due to tax imposition and subsidy

removal. This part of the study, package 3, looks specifically at the environmental impacts of

fossil fuel subsidies and taxes.

The objective of package 3 is to establish the range of possible environmental impacts of

increases in fossil fuel prices due to subsidy removal and tax imposition. Environmental

impacts occur all along the fossil fuel value chain, from the activities of the extractive

industries (particularly for coal and oil), through any intermediate processes (transportation,

refining, power generation) to final the consumption of energy. But as the price changes

implied by the different policy scenarios under consideration are most likely to have their

effects felt though levels of consumption, the focus of this study is upon the environmental

effects of fossil fuel combustion in intermediate processes and with end users.4 Thus this

report focuses on air pollution with and in particular GHG emissions and global air pollution,

although some aspects of local air pollution will also be considered.

1.3 Methodology

The approach adopted in this paper relies upon a bottom-up emissions accounting model,

using the Long-range Energy Alternatives Planning system (LEAP) software developed by

the Stockholm Environment Institute (SEI). This model was initially developed for the

emissions modelling included in Viet Nam‟s Second National Communication to the

UNFCCC, published in 2010 (MoNRE 2010). This report also draws on the analysis

performed in package 1, and from secondary sources on local air pollution in Viet Nam.

1.4 Structure of this report

The first section of this report looks briefly at the situation as regards air pollution in Viet

Nam, specifically GHG emissions, the factors behind its rapid growth and broader policy and

strategic context. The second section of this report focuses upon estimating future GHG

emissions and modelling using the LEAP model developed for the second communication to

the UNFCCC. The final section of this report concludes and offers recommendations for

policy and additional research.

4The intermediate processes referred to here are the transformation of energy from one form to another, i.e. in

electrical power generation. Refining fossil fuels also results in fossil fuel emissions, although it is not

considered on the projections used in this report as besides other used emissions from fossil fuel refining are not

expected to be significant.


3

2. Air pollution in Viet Nam: trends and drivers

2.1 Air pollution

Air pollutants from fossil fuels consist of a range of gasses and particulate matter that are

harmful to human health and the environment. Most significant impacts on human health

from localized fossil fuel air pollution result as a consequence of their combustion-

particularly from transportation. Common pollutants, which can cause significant health

problems, include sulphur-dioxide (SO2), nitrogen dioxide (NO2), ozone (O3), carbon

monoxide (CO) and particulate matter (PM). These pollutants can have serious impacts on

human health in high enough concentrations. Short-term exposure is associated with a

number of respiratory problems. Long-term exposure is linked to a wide range of health

problems including chronic respiratory diseases, cardiovascular disease and cancer (WHO

2005). Local air pollution attributable to fossil fuel combustion is at high levels in some

locations in Viet Nam, with high concentrations of PM a particular concern. National and

international air quality standards are frequently exceeded in some urban locations. In

particular, high levels of air pollution causing respiratory illness are an acute problem in both

Hanoi and HCMC (CAI-Asia 2010).5

Table 1: Estimated emissions from major sources in Viet Nam 2005

Source CO NO2 SO2

Thermal power plants 4,562 57,263 123,665

Industry, service and domestic

activities

54,004 151,031 272,497

Transport 301,779 92,728 18,928

Total 360,345 301,022 415,090 Source: (CAI-Asia 2010)

In addition to local pollution problems, broader scale regional pollution from sulphur and

nitrogen dioxide is also an important pollution problem associated with the combustion of

coal in particular. In sufficient concentrations this causes acid rain and the acidification of

land and surface water. These environmental consequences have received less attention in

recent years (ICEM 2007), but local and regional air pollution requires serious attention.

Increased efficiency in fossil fuel use and decreased consumption of fossil fuels would also

imply a total reduction in production of these regional and local pollutants. But pollution

abatement measures in these cases do not necessitate reduction in fossil fuel consumption.

Frequently better end-of pipe or combustion technologies can realize dramatic reductions in

5A 2007 study with a sample of 1,000 households in HCMC found that 90% of children under five were

suffering respiratory illnesses (CAI-Asia 2010).


4

these pollutants, without reducing fossil fuel consumption.6This is because their creation is

generally an undesirable side-effect of the combustion process. This differs fundamentally

from GHG emissions, which are released during fossil fuel combustion as an intrinsic part of

the process of energy release. Reduction in GHG emissions therefore can only be achieved

through a reduction in use of fossil fuels as an energy source.

The focus of this analysis of the environmental impacts are the impacts of fossil fuel pricing

policy is on GHG emissions resulting from fossil fuel combustion, which is predominantly

carbon dioxide. While the production of fossil fuels frequently involves the release of

methane (CH4) (coal-bed methane and methane emissions associated with oil extraction),

which is a considerably stronger GHG than carbon dioxide (CO2) though has a shorter half-

life in the atmosphere.7 However, as these supply side emissions are unlikely to be influenced

by changes in fossil fuel pricing policy they have not been considered further in this analysis.

2.2. Trends in GHG emissions8

Viet Nam is not a major contributor to global GHG emissions.9 Per capita emissions at an

estimated 1.9 tCO2e per capita in 2000 ranked Viet Nam with one of the lowest per capita

emissions in the world. Growth in emissions has however been rapid and is accelerating

(table 2). Emissions growth between 1994 and 1998 was relatively slow relative to GDP

growth (running at between 5.5% and 9.5%), probably as a result of one-off efficiency gains

in the economy as the country moved away from inefficient allocations of resources under

the centrally planned economy. In contrast, emissions growth jumped between 1998 and

2000, despite lower GDP growth in the period, suggesting an increase in the emissions

intensity of the economy.

Table 2: Key emissions indicators 1990-2005

Indicator 1994 1998 2000

Total emissions (MtCO2e) 103.9 121.2 150.9

Growth rate (%) - 3.97 11.49

Emissions per capita tCO2e 1.5 1.6 1.9

Source: (UN Viet Nam 2011)

6For example, catalytic converters fitted to automobiles.

7Carbon dioxide is also frequently associated with oil extraction.

8While available emissions estimates do tell a similar story in terms of emissions growth trends, the sectoral

source of emissions (and related to this the composition of emissions in terms of different GHGs), figures can

vary widely. For example, WRI figures for emissions in 2005 are around 180MtCO2e, compared to MoNRE's

estimate of 2010 emissions as 169MtCO2e (MoNRE 2010a, WRI 2011). MoNRE estimates of the reporting

error for emissions figures in 1995 were ± 20%, at ± 15% for 2000 figures this is considerably lower but still

means emissions could have been around 22 MtCO2e higher. 9 WRI and World Bank figures have been used, which allow comparison between countries.


5

In 1990 methane emissions from agriculture were the largest source of emissions accounting

for over 50% of the total. Emissions from agriculture grew between 1994 and 2000 but the

relative share of agriculture in total emissions had fallen to around 43% by 2000. Energy and

industrial sectors have shown rapid and accelerating emissions growth over the period.

Industry has increased its share from 3.7% in 1994 to 6.6% in 2000, and the energy sector has

increased its share of emissions from 24.7% in 1994 to 35% in 2000 (Figure 1).

Figure 1: Emissions by sector 1994 - 2008 (MtCO2e)

Source: (UN Viet Nam 2011)

Table 3 gives total greenhouse gas emissions in 1994 and 2000 as reported in the national

inventory.10

Inventory figures show an increase in emissions of approximately 50% between

1994 and 2000, driven largely by increases in emissions from energy, which doubled over the

period. Emissions from agriculture grew more slowly than other sectors and those from

LULUCF declined as large reforestation programs got underway.

This rapid change in Viet Nam‟s emissions profile has been driven by economic growth and

industrialization. Viet Nam‟s economy grew at an average annual rate of 7.5% between 1991

and 2010, considerably outstripping declining population growth rates. As a consequence

nominal value added per capita has risen from USD 142 in 1991 to USD 1,172 in 2010

(World Bank 2010). This has been matched by growth in energy consumption, from one of

the lowest levels of energy consumption per capita in the world in 1991 energy consumption

has risen on average by 5.1% per year between 1991 and 2008, a rate which doubles per

capita energy demand roughly every 15 years (England and Kammen 1993, APERC 2009,

World Bank 2010). Viet Nam still lags behind in terms of per capita energy consumption at a

10

The National greenhouse gas inventory for the year 2000 was conducted in accordance with the Revised

Guidelines of Intergovernmental Panel on Climate Change (IPCC) for energy, industrial processes, agriculture,

land use, land-use change and forestry (LULUCF), and waste sectors, with respect to the most important

greenhouse gases: CO2, CH4 and N2O.


6

level of around 689 Kgoe/capita in 2008 or approximately 55% of middle-income average

consumption of 1,255 Kgoe/capita. While some of this difference reflects climatic conditions,

population distribution, and economic structure amongst other things, nevertheless the figure

is low. This also implies that energy consumption in Viet Nam will need to grow

significantly if it is to meet its economic aspirations.

Table 3: GHG emissions by sector 1994 and 2000 (MtCO2e)

Sector 1994 2000

MtCO2e Percentage MtCO2e Percentage

Energy 25.6 24.7 65.1 43.1

Industrial processes 3.8 3.7 10.0 6.6

Agriculture 52.5 50.5 52.8 35.0

LULUCF 19.4 18.6 15.1 10.0

Waste 2.6 2.5 7.9 5.3

Total 103.8 100 150.9 100

Source: (MoNRE 2010)

The efficiency of energy use in the Vietnamese economy also lags other middle-income

countries. Viet Nam has a considerably higher energy intensity than other middle-income

countries and has as yet, been unable to close the energy productivity gap between itself and

other middle-income countries.11

Figure 2 shows Viet Nam‟s energy intensity has declined by

about 35%, from around 400 Kgoe/1,000 USD value-added in 1991 to around 260

Kgoe/1,000 USD in 2008, around 13% higher than the middle income country average in

2008. The aggregate energy use data suggests that Viet Nam uses less energy per-person than

most middle-income countries, and that which it does use it uses less efficiently.

Growth in Viet Nam‟s energy demand has been accompanied by increases in the portion of

this demand that is satisfied by fossil fuels, and, as a direct consequence, the emissions

intensity of GDP. Between 1991 and 2008 the portion of final energy demand satisfied by

fossil fuels grew from around 20% to almost 54%, at an average rate of around 11% per year.

Increased availability and use of commercial energy in the residential and industrial sectors

leading to a switch away from biomass has been an important driver of this trend. Otherwise,

this shift towards fossil fuel consumption is also explained by growth in the transportation

sector, industry and increased natural gas and coal fired power generation as commercially

viable hydropower resources are fully utilized (Figure 3 and 4). This has resulted in a rise in

the emissions intensity of GDP, which has grown by about 2.7% per year over between 1991

and 2008.

11

As the figures show, estimated energy productivity lagged middle income countries by 18% in 2008 down by

less than 0.2% from 1991. When compared to low income countries grouping Viet Nam has faired better.


7

Figure 2: Energy consumption and energy intensity in Viet Nam and middle-income

countries 1991-2008

Source: (World Bank 2010b)

Figure 3: Share of fossil fuels in Viet Nam’s energy consumption and carbon intensity of

value added 1991 – 2008

Source: (World Bank 2010)

0"

50"

100"

150"

200"

250"

300"

350"

400"

450"

0"

200"

400"

600"

800"

1000"

1200"

1400"

1991" 1996" 2001" 2006"

Kgo

e/&1,000&&2005&PPP&adjusted&&U

SD&

Kgoe/cap

ita&

Vietnam"Energy"use/capita"(le="hand"axis)" Middle"income"Energy"use/capita"

Vietnam"Energy"use/unit"value"added"(right"hand"axis)" Middle"income"Energy"use/unit"value"added"


8

Figure 4: Power generation by technology 1971 - 2008

Source: (World Bank 2010)

2.2 Viet Nam’s policy and strategic context for GHG mitigation

The government has recognized the need to address environmental concerns related to energy

production and consumption. The 2005 Law on Environmental Protection includes a

provision for the government to encourage GHG emissions reductions and the National

Target Program to Respond to Climate Change (NTP-RCC), includes mentions the

promotion of low carbon development (National Assembly 2005, MoNRE 2008)12

. But,

compared to countries in the region the policy and institutional context for emissions

reductions in Viet Nam remains weak (Olz and Beerepoot 2010, Sawdon 2011).13

12

In both cases the provision pays lip-service to emissions reduction and implies little or no commitment to

regulatory or investment measures to promote emissions reduction. For example, the law on environmental

protection states, “The State shall encourage production, business and service establishments to reduce

greenhouse gas emissions” (clause 3, Article 84), the NTP-CC states as one of its general objectives, “Strategic

objectives of the NTP are to ......to ensure sustainable development of Viet Nam, take over opportunities to

develop towards a low-carbon economy, and joint international community‟s effort to mitigate climate change

impacts and protect global climatic system.” (page 28). 13

PRC has adopted strong support policies for renewables and energy efficiency over both the 11th and 12th

Five Year Plans, adopting a target for the reduction in the energy intensity of the economy by 40-45% of 2005

levels by 2020. India has some strong renewables policies such as the National Solar Mission as well as other

measures adopted in the 11th Five Year Plan – also including a target to improve energy efficiency by 20%

between 2007 and 2017. Smaller countries in the region such as Malaysia and Thailand have also adopted

significant support policies for renewables such as generous feed-in-tariffs and tax breaks for the manufacture of

renewable energy technologies to name a few.

TW

h

Oil

Natural Gas

Hydro electric

Coal


9

Modest renewable energy targets have been adopted in the Sixth and Seventh Power

Development Plans, the National Energy Strategy and in the Renewable Energy Strategy.

Biofuels mixing targets have not been adopted production targets for meeting 1% of oil and

gasoline demand by 2015 and 5% by 2025 have been adopted. An avoided cost tariff was

also adopted for small (<30MW) off-grid renewables capacity in 2008, in general renewable

electricity generation is seen as primarily an option for remote communities, where the

extension of on-grid power is prohibitively expensive (Baumuller 2010).14

By-and-large these

renewables initiative have yet to be backed up by more concrete and effective support

mechanisms such as renewable energy obligations, feed-in-tariffs, or tax breaks for

renewable and energy efficiency investment projects.

Similarly, energy efficiency measures have been limited. Energy efficiency legislation was

adopted in 2003, legal provisions have subsequently been strengthened through a number of

supporting pieces of legislation including the Electricity Law of 2005. The National Energy

Efficiency Program, including a wide variety of energy efficiency measures was instituted in

2006 and will run until 2015. As with renewables, weak institutional capacity, poor

coordination and limited financial resources have resulted in slow progress (Institute of

Energy Economics 2006, APERC 2010, Crampé 2010). Price controls and complex cross

subsidies in the energy supply chain have maintained relatively cheap energy prices by

regional and global standards. Low energy prices, have been an important contributing

factor, retarding energy efficiency efforts in Viet Nam. Currently, relatively low energy

prices mean that end-users (and large consumers in industry in particular) have limited

incentive to economize on their use of energy resources (Institute of Energy Economics

2006) (Crampe 2010)(PeaPROs 2011).

Nevertheless, over the last 15 years, a confluence of domestic and international pressures has

been forcing the government to restructure and liberalize the energy sector. Low energy

tariffs have not only discouraged investments in energy efficiency, they have also

discouraged would-be private sector investors and acted as a significant barrier to much

needed capital investment in power generation (Conaty 2010, Crampé 2010). What is more,

Viet Nam‟s rapid transition from net energy exporter to a net energy importer, means the

country is increasingly exposed to rising prices and volatility on international energy markets

which might undermine the feasibility of subsidies (APERC 2009, Omoteyama

2009)(PeaPROs 2011).

To these current reform pressures we may add a number of long-term strategic considerations

that could influence the reform of energy policy, favouring a focus on cleaner energy

systems. First, globally the low carbon technology sector is growing rapidly, by estimates

suggest that the market for low carbon technologies could be worth $500 billion by 2050, this

presents an important opportunity for Viet Nam‟s manufacturing sector(Carbon Trust 2011).

This is widely appreciated in the region with PRC, South Korea, India and Malaysia in

particular adopting significant industrial support policies in these sectors (Crampé 2010, Olz

14

Although there is an „avoided cost‟ tariff for renewables generation projects, this provides support for off-grid

renewables where they can compete in terms of cost with conventional power.


10

and Beerepoot 2010, Sawdon 2011). Second, the energy intensity of Viet Nam‟s economy is

high and falling relatively slowly, rising energy prices could erode productivity gains hitherto

dependent upon increasing levels of energy inputs. Linked to this increasing fossil fuel and

energy import dependency will also expose Viet Nam‟s energy sector to increasingly volatile

global energy markets, raising energy security concerns in the long-term(APERC 2009).

Third, Viet Nam has yet to satisfy a significant portion of its infrastructure requirements, it

therefore has the opportunity to avoid locking-in dependence on fossil fuels through the

construction of carbon intensive infrastructure. For example, power generation and

distribution networks and transportation systems are key areas in which the choice of

relatively long-lived infrastructure will influence the carbon intensity in the medium to long

term (Unruh 2000, Unruh and Carrillo-Hermosilla 2006).15 Fourth, there are a number of co-

benefits from GHG mitigation such as fuel efficiency savings and reduction in air pollution in

urban areas, which would bring economic and health benefits (Chandler et al. 2002, Jochem

and Madlener 2003, Sims et al. 2007).16Finally, there are a number of global political and

political economy reasons that mean it is likely to be in Viet Nam‟s immediate interests to

undertake significant GHG emissions mitigation in the near term. These include the prospect

of significant transfers of technology, know-how and resources from developed countries, the

threat of tariffs on carbon embodied in trade with developed countries if mitigation measures

are not undertaken, and as a bargaining chip to gain concessions in other international

negotiations (Carraro and Siniscalco 1998, Stern 2008, Rose and Spiegel 2009).

Given the need to maintain rapid economic growth and poverty reduction, Viet Nam has been

understandably cautious in adopting climate change mitigation policies. Trade-offs between

growth and clean development seem inevitable (Hudson and Sawdon 2010). There has also

been reluctance to address the thorny political and macro-economic issues surrounding fossil

fuel pricing. Nevertheless, there are a number of good reasons why policy makers should be

considering climate change mitigation more seriously in their long to medium term strategy.

15

For example, the Stern review gives the following typical life times for capital stock, hydro station 75 years

plus, buildings 45 years plus, coal station 45 years plus, nuclear station 30-60 years, gas turbine 25 years,

aircraft 25-35 years and motor vehicles 12-20 years. (Stern 2007) 16

A recent study of coal fired thermal power generation in the US has found that on reasonable assumptions the

costs of coal fired thermal generation in terms of pollution and impact on public health in particular, are likely to

outweigh the value-added by the sector. This is without considering the costs implied by climate change. This

may not be analogous to the situation in Viet Nam where the costs associated with poor health are lower, but

given the lower level of environmental technology in Viet Nam and higher population densities it is likely that

actual health effects per unit power generated are larger (Muller et al. 2011)


11

3. Estimating emissions reductions from increased fossil fuel

prices

3.1 Methodologies for estimating the emissions impact of changes in

relative prices

The analysis conducted for this study made use of two independent modelling approaches.

Package 2 used a CGE model of the Vietnamese economy to analyze the overall impact of

change in fossil fuel price (though subsidy and tax changes) on economic growth, sectoral

performance and the distribution of impacts between different groups under different

assumptions relating to fossil fuel price and the use of additional revenue derived from

subsidy reduction and tax imposition. Most of this section looks at modelling that was

performed using a bottom-up energy accounting approach using LEAP software. This

approach uses the model that was developed for the Second National Communication to the

UNFCCC (MoNRE 2010). This model has been used as a basis for estimating the effect of

fossil fuel price changes on expected emissions. In particular, the business-as-usual (BAU)

emissions projections to 2030 reported in the second communication will form the baseline

against which the impact of price changes is assessed.

3.2 LEAP emissions model for Viet Nam

The LEAP model is a scenario-based energy-environment modelling tool. LEAP scenarios

are based on comprehensive accounting of how energy is consumed, converted and produced

in a given region or economy. Model scenarios can incorporate a range of alternative

assumptions including those on population, economic activity, technology and price.17

Through the specification of the type and quantity of energy technologies across different

emissions sectors LEAP is able to calculate detailed sectoral energy use estimates and from

this using the relevant emissions factors, emissions estimates. The effect of changing key

variables on energy and emissions can therefore be assessed through this software.18

LEAP allows the user to forecast energy demand based on the key causal variables such as

population, income, GDP and energy consumption. In particular, LEAP can be used to model

the price response of energy consumption where the estimated relationship between price and

energy consumption is available, although this is determined by empirical analysis exogenous

to the model (see section 3.4.1). In the case of the LEAP model for Viet Nam developed by

the Institute of Energy, energy demand to 2030 was estimated based upon a function linking

energy demand to aggregate GDP, industrial GDP and population.19

17

While LEAP is not an optimization model. Technology choices are user defined. 18

It also contains a database of technologies, which can help in the definition of the relationship between the

quantity and composition of primary energy supply, the quantity and composition of final energy consumption. 19

These were calculated based upon energy consumption data from IEA Energy Balances for Non- OECD

Countries, GDP data from the World Bank‟s World Development Indicators Database and population data from

1986 to 2005.


12

Future energy-emissions scenarios were based on a number of crucial assumptions derived

from official planning documents including future technology choice, GDP growth rates,

population growth rates and changes in the (real) crude oil price. Table 4 gives the key

assumptions made for these projections.

Table 4: Basic assumptions for the BAU emissions modelling

Variable Values Source

GDP growth

rate

7.2% from 2011 - 2020 7.0% from

2021 - 2030

Socio-economic development plan 2006-2010 Socio-

economic development scenarios up to 2030, Ministry of

Planning and Investment (MPI)

Population

growth

1.0% from 2011 - 2020

0.7% from 2021 - 2030

Socio-economic development plan 2006-2010

Socio-economic development scenarios up to

2030, Ministry of Planning and Investment (MPI)

Power

generation

technology

Generation mix and fuel

requirements 2006-2025*

Power development plan VI Ministry of Industry and Trade

Crude oil

price

USD 112.6 /bbl in 2030 Institute of Energy Economics, Japan

*Estimates for the 2025-2030 period have assumed the same composition of power generation technologies as

those in 2025.

3.3 BAU projections of emissions to 2030

BAU projections from the Second National Communication to the UNFCCC for the 2010 -

2030 period are given by source and sector in tables 5 and 6 (MoNRE 2010). Table 4

describes emissions for the most important emissions sectors. Emissions from fossil fuels do

not make up a significant portion of emissions from either the agricultural or LULUCF

sectors. Emissions from these sectors are not the result of fossil fuel combustion. They are

not analyzed in greater detail here, save to note that compared to the energy sector emissions

from these sectors are expected to grow slowly, or in the case of forestation, decrease

significantly.


13

Table 5: GHG emission projections by source for principle emitting sectors 2010 - 2030

(MtCO2e)

Sector 2010 2020 2030

Energy 113.1 251.0 470.8

Agriculture 65.8 69.5 72.9

LULUCF -9.7 -20.1 -27.9

Total 169.2 300.4 515.8


Table 6 shows a more detailed breakdown for the energy sector, which by 2030 is expected to

account for over 90% of emissions. By 2030 power generation is expected to be by far the

largest source of emissions in the energy sector accounting for over 50% of energy emissions.

The power sector also shows the highest level of emissions growth. This is explained by the

expectation that power generation will rely increasingly on fossil fuels, and particularly coal

fired thermal generation. This is also reflected in table 7, which gives energy sector GHG

emissions by fuel. All fossil fuel use is expected to grow quickly, while the use of biomass

falls. Coal and natural gas consumption are expected to grow quickly, in particular coal,

which will come to dominate GHG emissions, as it has in other countries such as China.

Transport and industrial sectors are also expected to constitute a significant portion of energy

emissions in 2030, contributing 18% and 16% respectively. Growth in transportation is

expected to account for the lion‟s share of oil consumption.

Table 6: GHG emissions from energy 2010 - 2030 (MtCO2e)

Source 2010 2020 2030 AAGR 2010-

2030 (%)

Power generation 31.8 110.9 238.0 10.58

Energy use 81.3 140.1 232.1 5.40

Of which:

Industry 31.5 53.0 76.5 4.57

Transportation 28.2 48.6 86.0 5.73

Agriculture 2.1 2.4 2.9 1.71

Residential 14.0 25.3 49.4 6.51

Commercial/institutional 5.6 10.7 17.9 5.94

Total 113.1 251.0 470.8 7.39



14

Table 7: GHG emissions from energy by fuel 2010 - 2030 (MtCO2e)

Fuel 2010 2020 2030 AAGR 2010-2030 (%)

Biomass 4.0 3.8 3.7 -0.36%

Natural Gas 13.5 31.1 61.3 7.86%

Oil Products 50.9 89.1 159.4 5.87%

Coal 44.7 127.0 246.5 8.91%

Total 113.1 251.0 470.8 7.39%


3.4 Subsidy reduction and tax imposition modelling scenarios

Taking the BAU emissions projections as a base, the emissions modelling seeks to

investigate the effect of fossil fuel price changes (from subsidy removal and tax imposition)

on GHG emissions over approximately the next two decades. Two scenarios have been

developed for comparison to the BAU model. To enable the modelling of the impacts of price

changes, this model is supplemented with assumptions on how the relationship between fossil

fuel prices and level of consumption is characterized and on what price changes will take

place. These are discussed in more detail in section 3.4.2 and 3.4.3.

An extremely important caveat is the interpretation of these modelling results. We have

already noted that historical data on emissions, upon which future projections are based, have

an acknowledged margin of error of between 15% and 20%. Moreover, the model must make

assumptions (outlined above) about population growth, GDP growth, income growth,

available technologies, and how energy use responds to these, as well as price changes and

the way in which demand responds to changes in price. These assumptions themselves are

based upon independent expert analysis (such as national power development plans,

empirical work on the price elasticity of energy demand etc.).

Given the dependence upon assumptions external to the model, this raises questions about

how the modelling results should be interpreted. Modelling exercises allow simplified,

internally consistent characterizations of complex systems. They allow the characterization of

causal interactions between key variables, the way in which this relationship is characterized

is typically dependent upon empirical evidence.20As such models can illustrate how changes

in one variable/set of variables can affect the values of other related variables in the system.

Ideally models also give a guide to the likely magnitude of that effect relative to changes in

the other modelled variables. In this case, how changes in the price of energy effect energy

related GHG emissions.

20

For example, see the discussion on price elasticity of demand, one of the crucial variables in this analysis in

section 3.4.2


15

However, complex systems can be influenced though a range of effects, which will not be

captured within the model. Even if the relationship between key variables is correctly

specified, the veracity of their results depends upon the veracity of the assumptions upon

which they are based. As a result different models, which characterize the relationship

between variables in different ways and adopt different assumptions give different results.

The kind of modelling exercise conducted here is therefore best regarded as illustrative of

how key relationships between core variables work, and if adequate empirical data is

available, order of magnitude estimates of their future value. 21

3.4.1 Measuring the responsiveness of energy demand to price changes

Removal of subsidies for fossil fuels and the imposition of taxes on fossil fuels will increase

the price of fossil fuels to consumers. The relationship between changes in price and changes

in demand is known as the „price elasticity of demand‟, which measures the responsiveness

of demand to changes in price. It is defined as the proportionate change in the quantity of

demand divided by the proportionate change in price, holding all other factors, which may

affect demand constant. As demand usually declines in response to increases in price, price

elasticities are usually negative.22

The degree to which demand for a good is responsive to price will depend mainly upon the

existence of alternative goods than can act as a substitute, in this case other forms of energy

supply.23

In the case of fossil fuels, alternatives include nuclear energy, hydroelectricity and

renewables (as well as some degree of substitutability between fossil fuels).24Related to this,

more fundamentally, price elasticity will also be affected by the physical necessity of the

good in question for the creation of value. In the case of energy, in one form or another, it is

essential for all productive activity. Given that energy is essential and given that there are

limited substitutes for energy from fossil fuels, a low level of responsiveness to price

changes, in other words, a low elasticity, should not be surprising. Most empirical work

points to relatively low price elasticities. For example, recent research conducted by the IMF

for both OECD and developing countries found that the price elasticity for oil in the short

term was -0.02, meaning an increase of the oil price by 10% would result in a decrease in

demand of only 0.2% (IMF 2011).25

While energy is essential, to some extent it can be substituted for investment in capital stock

in technologies, which use less energy. Investment in capital stock can also allow substitution

between fuel sources (e.g. from coal fired generation to gas or wind). Investment in capital

stock does not happen instantaneously in response to price changes. As mentioned earlier,

energy systems can be quite long lived as a result capital stocks can be expected to turn over

21

For a fuller discussion of the limits of economic modeling see Peace and Weyant 2008. 22

Although for oil producing countries they can sometimes be positive due to the wealth effect caused by

increasing oil price (IMF 2011). 23

In general extreme caution needs to be exercised when interpreting elasticities. There are numerous empirical

difficulties related to estimating elasticities. 24

For example, coal and gas or gasoline and gas. 25

An important implication of this is that cost increases in energy are likely to be passed through in to more

generalized price increases and inflation and may result in only modest declines in GHG emissions.


16

relatively slowly. Moreover, even in the dynamic economic context of Viet Nam, where new

capital stock is continually being invested in energy systems, there can be a number of

technological, institutional and political barriers that give incumbent technologies inertia and

result in slow adjustment to prices.26

Demand will tend to reduce with the roll out of more

efficient and alternative energy technologies and the surmounting of the barriers they face.27

Therefore, in the short-run, elasticities tend to be lower than in the long-run, as it takes time

for capital stock to turnover and the economy to make the relevant adjustments to higher

prices. The same IMF study found that long-run elasticities (over 20 years) were over three

times as high as short-run elasticities at -0.07(IMF 2011).

The notion of price elasticities is an extremely powerful one, and elasticities are commonly

used in projecting future energy demand. However, an important caveat should be added here

on the interpretation of price elasticities. As we have seen elasticities are not constant over

time due to changes in technology (or other structural changes in the economy). Elasticities

may not be constant for different changes in price. In particular, demand for fossil fuel energy

is likely to be subject to threshold effects. If fossil fuel prices rise beyond a certain point then

other technologies may become commercially viable, for example where gas thermal power

generation becomes competitive with coal, or nuclear with gas. At this point on the demand

curve price demand may drop precipitously (albeit over a number of years).28

In the case of Viet Nam, on one hand, we can expect the short-run price elasticity for fossil

fuels to be relatively low in as available substitutes are limited and as fossil fuels represent a

high and rising proportion of the primary energy supply on which all productive activities in

the economy (to a greater or lesser extent) rely.29

On the other hand, Viet Nam is a rapidly

industrializing country, which means a considerable proportion of its capital stock has yet to

be constructed. The long-run opportunity for avoiding the construction of fossil fuel intensive

capital stock is also considerable, if commercially viable alternative low carbon technologies

and feasible low carbon development strategies are available. This may suggest that long-run

elasticities should be high relative to elasticities which are estimated based upon the

empirical record. This also serves to emphasize the importance of ensuring price levels

reflect long-term cost.

26

For example, urban transportation systems include not only cars and motorbikes but also roads, railways and

ports. Urban transport is so influential in shaping the layout and zoning of urban areas. Given these

characteristics, it is becomes clearer why these systems have so much inertia. 27

Typically, the switch between types of technologies over time is characterized as an S-shaped (sinusoidal)

curve, as technologies diffuse slowly at first due to institutional and other constraints, once these are overcome

technologies diffuse at a much more rapid rate, this rate tails off as late adopting sectors which show more

technological inertia are converted relatively slowly. For a recent example of state of the art technological

modeling see Mercure (Mecure 2011) 28

Similarly, if energy costs mean it is not longer economical to undertake production, demand for energy may

decline rapidly. 29

This may also have implications for inflation in that any price rises are not likely to be accommodated by

economizing in the use of fossil fuels and price rises are likely to be passed on to end consumers.


17

3.4.2 Estimating the price elasticity of demand in Viet Nam

Elasticities are difficult to estimate and require substantial datasets and sophisticated

statistical techniques. In the case of Viet Nam, as with many developing countries, sufficient

data is not available to allow the calculation of price elasticities. For data that is available,

price controls mean that there is not enough variation in price to allow the identification of its

effect on the level of demand. For these reasons to enable a projection of the impacts of price

increases on fossil fuel demand and emissions, the emissions modelling had to rely on

estimates of price elasticities derived from other studies. Elasticities were selected for

different fossil fuel sectors and sub-sectors, which were deemed sufficiently similar in their

characteristics. Table 8 gives the short-run and long-run values for elasticities adopted for the

modelling exercise, and the rationale for their adoption. More information on the selected

studies and the literature reviewed for this study is given in annex A.

Table 8: Estimated price elasticities of demand for sectors and sub-sectors in Viet Nam

Fuel Sector/sub-sector Estimated value for Viet

Nam Range from available studies

Short run

Elasticity

(<5yrs)

Long run

Elasticity

(>5yrs

Short run

Elasticity

Long run

Elasticity

Coal Power generation -0.20 -0.30 -0.12 to -0.43 -0.3

Other sectors -0.20 -0.50 -0.1 to -0.34 -0.5

Gasoline Transport -0.21 -0.44 -0.21 to -0.23 -0.43 to -0.44

Petrol

Products Power generation -0.26 -0.45 -0.26 to -0.44 -0.45 to -0.64

Others -0.15 -0.27 -0.019 to -

0.25

-0.072 to -

0.53

Natural Gas Power generation -0.42 -0.61 -0.42 to -0.43 -0.61 to -0.73

Industry -0.17 -0.67 -0.17 to -0.6 -0.67 to -2.39

Residential/

commercial

-0.1 -0.36 -0.1 to -0.18 -0.36 to -0.96

Electricity Residential -0.16 -0.61 -0.16 to -0.24 -0.32 to -0.7

Industry -0.36 -0.99 -0.36 -0.99

Commercial -0.21 -0.97 -0.21 -0.97

Source: (Ball and Loncar 1991, Espey 1998, Bernstein and Griffin 2005, Perkins 2007, Athukorala and Wilson

2010, Iimi 2010, Sultan 2010, IMF 2011, Truby and Paulus 2011)

Estimates of elasticities vary greatly between available studies. Most of these studies have

been conducted for OECD countries, due to data difficulties related to developing countries.

Studies span different time periods, include different sectors, sub-sectors and countries in

their samples, and use different statistical methods to estimate price elasticities.


18

Nevertheless, there are a few general observations that can be drawn from the literature. First,

the very range of elasticities resulting from the studies is indicative of different country and

historical contexts. For example, as a recent IMF study points out recent price elasticities of

demand for oil have been low as prior to the start of the time series data they considered

(before 1990), most OECD countries had already switched power generation capacity away

from oil to cheaper coal fired generation, demand therefore had less latitude to diversify away

from oil in the post 1990 period (IMF 2011).

Second, some sectors tend to be more responsive in the short run to changes in price than

others. This is in part due to the ease with which substitution can be made between different

energy sources. For example, natural gas fired power generation is generally more responsive

to gas price. This is because power plants using natural gas have tended be a more expensive

source of power (compared to coal fired thermal plants and some hydropower plants). But the

technical characteristics of gas power plants mean they can be turned on and off relatively

quickly when compared to typical base-load power plants such as coal or nuclear. Therefore,

gas is frequently used to supply peak capacity at the time of the day when demand is high.

This means that, in general, gas fired plants are running for fewer hours in the day than base

load coal plants. If gas becomes cheaper, then the spare capacity these plants represent can be

dispatched, if it becomes more expensive then these plants can be dispatched less(out-side

peak times). Therefore, power from natural gas tends to be more responsive changes in the

fuel price, and hence has a high short run elasticity relative to coal (World Bank 2006,

2010a). Conversely, in a recent study in South-Eastern Europe low power price elasticities in

the residential sector relative to industry were indicative of a limited choice of these

technologies available and already low levels of consumption in the sector (Iimi 2010).

Similar, considerations are applicable to other fuels in other sectors.

Third, short-run price elasticities can be indicative of the efficiency of energy use in the

sector in question. For energy sources that do not have a ready substitute, such as electricity,

low elasticities suggest that further energy efficiency savings are likely to be difficult in the

short-run, higher elasticities are frequently indicative of efficiency savings to the made. In

the case of the studies considered above, a relatively high short-term elasticity of -0.36 in the

industrial sector, is in contrast to lower figures in residential and commercial sectors where

energy efficiency savings are likely to be more difficult.30

One final caveat is also warranted before proceeding to consider the results of the modelling.

The extent to which elasticities based upon past conditions can inform us about likely future

conditions. Given global climate change and energy security concerns increasing amounts of

investment in RD&D for energy technologies are being undertaken. Commercially available

clean technologies will increasingly be able to compete with fossil fuels, as these become

cheaper it will be easier for users in some sectors and sub-sectors to utilize these

technologies, and therefore increase the price elasticity of demand, meaning elasticity figures

30

A key part of this may also be the behavioral response to increased power prices. In the residential sector the

quantity of power used is quite small, in contrast to the energy intensive industrial sector where large quantities

of electricity are used. This means that even price increases may have a larger effect in an industry for which it

is a significant input, than in the residential sector where it is relatively small.


19

based upon the portfolio of energy technologies available in the past will tend to

underestimate elasticity in the future as better technologies become available. Moreover,

threshold effects are likely to be important both in terms of the price of fossil fuels (in that

beyond a certain fossil fuel price level key clean technologies will become cost competitive

in some sectors), and in terms of the availability of alternatives (for example, natural gas

infrastructure for transport).

3.4.3 Subsidy reduction and tax imposition

Based on the available data, reported in package 1 and package 2 reports, subsidies in Viet

Nam are estimated to amount to approximately 11.3% of the full cost of supply (IEA 2010).

For the purposes of the scenario development in package 2 and this report user price

subsidies are assumed to be 20% for coal, 5% for gasoline and other petrol fuel products, and

10% for electricity (Omoteyama 2009, Baumuller 2010, Willenbockel and Hoa 2011).

Similarly, the scenario developed here assumes the gradual phasing out of the subsidies over

a period of three years, from 2013 to 2015. The imposition of taxes is also assumed to be the

same as the scenarios developed for package 2, with the tax rate for coal levied at a rate of

30%, for petroleum products at 3.6% and for natural gas at a rate of 10%. As with subsidies

these are assumed to be gradually introduced over a period three years between 2013 and

2015 (Willenbockel and Hoa 2011). Table 9 gives the detailed schedule for subsidy reduction

and tax imposition assumptions for the emissions modelling.

Table 9: Scenario energy price increases due to fossil fuel subsidy removal and

environmental tax imposition (% change)

Scenario Fuel 2013 2014 2015

Subsidy removal

Coal 6 13 20

Petrol products 1.6 3.3 5

Electricity 3.3 6.7 10

Environmental tax

Coal 10 20 30

Petrol products 3.6 3.6 3.6

Natural Gas 3 6 10

Source: (Willenbockel and Hoa 2011)

3.6 Emissions modelling results

3.6.1 Aggregate emissions

The results of the Package 3 modelling suggest that, given the assumptions we have made

above, both the reduction in fuel subsidies and the imposition of an environmental tax on


20

fossil fuels could result in significant reduction in emissions (Figures 5 and 6). Under the

subsidy reduction scenario, decreases in the demand for fossil fuels results in emissions

reductions of around 3% of BAU emission by 2015, rising to over 9% by 2020 and remaining

at that level to 2030. In absolute levels of emissions reductions this is equivalent to a

reduction of around 5.7 MtCO2e by 2015, 24.8 MtCO2e by 2020, and 44.2 MtCO2e by 2030.

Under the second scenario which includes both subsidy reduction and the imposition of an

environmental tax on fossil fuels larger reductions in emissions reflect the greater price rises

for fossil fuels, for example, by 2030 in the second scenario coal prices would be 50% and

price of petroleum would be 8.6% above the baseline level, compared to 20% and 5%

respectively under the first scenario. Projected aggregate emissions reductions for the second

scenario reach 5.4% of BAU emissions by 2015, 13.5% by 2020 and around 12.9% by 2030.

In absolute emissions reductions this is equivalent to around 9.4 MtCO2e by 2015, 35.8

MtCO2e by 2020, and 63.9 MtCO2e by 2030, a third higher than the level of emissions

reductions realized in the first scenario.

Figure 5: Aggregate emissions under

different fossil fuel price scenarios 2012-2030

Figure 6: Emissions reductions from BAU

scenarios 2012-2030

Source: LEAP model

3.6.2 Emissions from the power sector

The power sector is the largest single consumer of fossil fuels and single largest emissions

source. It also accounts for the largest decline in decline in emissions due to the price

changes. Emissions in each of the modelling scenarios are given in Figures 6 and 7. Under

the subsidy reduction scenario, emissions reductions attributable to the power sector

constitute 68% of total emissions reductions by 2015, this increase to 77% by 2020 and 79%

by 2030. Whereas in the subsidy reduction and tax imposition scenario the sector constitutes


21

a share of emissions reductions, 54% by 2015, 62% by 2020 and 67% by 2030 of total

emissions reductions.

Figure 7: Power sector emissions under


Figure 8: Power sector emissions

reductions from BAU scenarios 2012-2030

Source: LEAP model

Figure 9: Power sector emissions from coal under

different fossil fuel price scenarios 2012 - 2030

Figure 10: Power sector emissions from natural gas

under different fossil fuel price scenarios 2012 - 2030

Source: LEAP model

Emissions reductions result mainly from the increased price of coal and other fossil fuels,

which drives generators to switch to cheaper energy sources. Coal emissions see large

declines reflecting a large increase in coal price in both scenarios, resulting emissions by


22

2030 are 12% below BAU for the subsidy removal scenario and 17% below BAU in the

subsidy removal and tax imposition scenario (Figure 9). Emissions from natural gas are 9%

less by 2030 relative to BAU in the subsidy removal and tax imposition scenario, which is

less than in the subsidy removal only scenario which is 23% less than the BAU scenario by

2030 (Figure 10). This reflects the switch of generators away from relatively expensive coal

towards gas. It is important to bear in mind that the extent to which this switching can occur

is constrained by the technological characteristics of the sector (which are given in the 6th

Power Development Plan).

3.6.3 Demand-side emissions

Emissions attributable to end-use energy consumption, or demand side energy emissions

include those from transport, industry, commercial and residential sectors. Compared to the

power sector emissions reductions in these sectors are expected to be modest. Emissions

reductions under the subsidy removal scenario are around 2% below BAU by 2015, and

about 4% from 2020 onwards. Emissions reductions in the subsidy removal and tax

imposition scenario are about 4% below BAU in 2015 and around 10% below BAU from

2020 onwards. Unlike the power sector the main energy sources used in these sectors will see

relatively modest price increases in each of the policy change scenarios. Moreover, reported

long run and short run elasticities for gasoline and petroleum products which make up a large

proportion of the fuels in this sector are relatively low, meaning that price increases do not

result in much demand reduction.

Figure 11: Demand-side emissions under


Figure 12: Demand-side emissions

reductions from BAU scenarios 2012-2030

Source: LEAP model


23

4. Conclusion

The results of the emissions modelling suggests that changes in the price of fossil fuels

brought about by changes in pricing policy (including a decrease in subsidy levels and

increases in the levels of taxation) is likely to lead to a decrease in levels of demand, and a

result a significant decline in emissions from the energy sector. The LEAP modelling for the

two scenarios here suggests that emissions reductions realized through fossil fuels pricing

policy changes could average between 26 and 37 MtCO2e per year between 2013 and 2030

(see table 10). As a proportion, the emissions reductions due to the subsidy reduction only

scenario are estimated to be 8.6% of total emissions and under the subsidy reduction and tax

imposition scenario around 12.3% of total emissions.

Table 10: Emissions reductions from fossil fuel price policy scenarios 2012 - 2030

Scenario Scenario MtCO2e Proportion of total

emissions

Subsidy removal

only

Average Annual emissions reductions (2013 - 2030) 26.1

8.6%

Cumulative emissions reductions (2013-2030) 469.0

Subsidy removal

and tax

imposition

Average Annual emissions reductions (2013 - 2030) 37.1

12.3%

Cumulative emissions reductions (2013-2030) 666.9

While not under-estimating the short term political and inflationary difficulties rising energy

prices may cause, aside from emissions reductions there are a number of important co-

benefits which should be considered. First, the increased incentive to invest in energy

efficiency and alternative energy technologies, including enhanced prospects for the

development of a domestic clean energy technology sector in the medium to long term.

Second, a lowered dependence on imported fossil fuels and less vulnerability to volatile fossil

fuel prices. Finally, by introducing unilateral legislation that will significantly reduce future

emissions, Viet Nam is would be taking a lead in the global effort to address climate change.

This would put Viet Nam in a more powerful position in future negotiations and place it well

for benefiting support mechanisms when they become available (for example the nascent

Technology Mechanism).

It should be noted that, as the CGE modelling shows, many of the outcomes are contingent

upon how the additional revenue that becomes available from subsidy reduction and tax

imposition are spent. If government invested the additional revenue on low carbon capital

stock, elasticities could be increased and inflation lessened. Given the environmental, social,

economic and political imperatives there is an increasingly strong case for allowing the fossil

fuel prices to rise.


24

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28

Annex 1: Overview of Coal Demand Elasticities in the Literature

Article Methodology Time

period Sector Region

Short run

Elasticity

Long run

Elasticity

Coal

Katrina Ball and Tomislar Loncar (1991) Time series analysis 1978-1988 Power generation Europe, OECD -0.20 -0.30

Johannes Truby, Morits Paulus (2011) Time series analysis 2006-2008 Power generation Europe -0.12 ÷ -0.43

Chan and Lee (1997) Time series analysis 1953-1994 All sectors China -0.26 ÷ -0.32

Kulshreshta and Parik (2000) Time series analysis 1970 to1995 Power generation India -0.34

John L Perkins Based on experience All sectors World- wide -0.1 -0.5

For Viet Nam case, based on above information, we assume that demand for coal will be elastic at small value

in the short run, and higher in the long run, these should be -0.2 and -0.3 respectively for power generation

and -02 and -0.5 respectively for other sectors.

Power generation Viet Nam -0.20 -0.30

Other sectors Viet Nam -0.20 -0.50

Gasoline

Molly Espey (1998) Meta-analyse 1966-1997 Transport World- wide -0.23 -0.43

R Sultan (2010) Autoregressive Distributed

Lag 1976-2009 Transport Mauritius -0.21 -0.44

For Viet Nam case, based on above information, we assume that demand for gasoline will be elastic at -0.21

in the short run, and -0.44 in the long run. Transport Viet Nam -0.21 -0.44

Petrol Products

Katrina Ball and Tomislar Loncar (1991)

Time series analysis 1978-1988 Power generation Europe -0.44 -0.64

Time series analysis 1978-1988 Power generation OECD -0.26 -0.45

Ibrahim B. Ibrahim and Christopher Hurst (1990) Time series analysis 1970-Mid-

1980s All Developing countries -0.11 ÷-0.25

-0.15 ÷ -

0.53

International Monetary Fund (2011) Time series analysis 1990-2009 All OECD and Non-OECD -0.019 -0.072


29

For Viet Nam case, based on above information, we assume that the short run, and the long run price elasticity

for oil products power generation should be smaller value due to limitation of budget and technology

availability with short run price elasticity of -0.26, and long run of -0.45 and for other uses should be - 0.15

for short run and -0.27 for the long-run (similar to Brazil).


Others Viet Nam -0.15 -0.27

Natural Gas

Katrina Ball and Tomislar Loncar (1991)

Time series analysis 1978-1988 Power generation Europe -0.42 -0.61

Time series analysis 1978-1988 Power generation OECD -0.43 -0.73

Philip J.Romero (2007)

Survey and estimation Industry Pacific North-west -0.17÷-0.6 -0.67÷-2.39

Residential/ commercial Pacific North-west -0.1÷-0.18 -0.36÷-0.96

For Viet Nam case, based on above information, we assume that the price elasticities should be -0.42 for short

run and -0.61 for long run for natural gas power generation and the price elasticities for industry should be

smaller due to limitation of budget and technology availability with short run price elasticity of -0.17, and

long run of -0.67. For residential and commercial these also are smaller with - 0.1 for short –run and -0.36 for

the long-run.


Industry Viet Nam -0.17 -0.67

Residential/ commercial Viet Nam -0.1 -0.36

Electricity

Wasantha Athukorala & Clevo Wilson (2010) Time series analysis 1960 - 2003 Residential Sri Lanka -0.16 -0.61

MarkA.Bernstein,JamesGriffin (2005) Survey and estimation Residential -0.2 -0.7

World Bank (2010)

Survey and estimation Residential US -0.24 -0.32

Survey and estimation Industry US -0.36 -0.99

Survey and estimation Commercial US -0.21 -0.97

For Viet Nam case, based on above information, we assume that the price elasticities should be -0.16 for short

run and -0.61 for long run for residential and the price elasticities for industry with short run price elasticity of

-0.36, and long run of -0.99, and for commercial these should be - 0.21 for short run and -0.97 for the long

run.

Residential VN -0.16 -0.61

Industry VN -0.36 -0.99

Commercial VN -0.21 -0.97

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