Climate Policy
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Achieving the Paris Agreement’s 2 degree target in
Nepal: the potential role of a carbon tax
Bijay B. Pradhan, Ram M. Shrestha & Bundit Limmeechokchai
To cite this article: Bijay B. Pradhan, Ram M. Shrestha & Bundit Limmeechokchai (2020)
Achieving the Paris Agreement’s 2 degree target in Nepal: the potential role of a carbon tax,
Climate Policy, 20:3, 387-404, DOI: 10.1080/14693062.2020.1740149
To link to this article: https://doi.org/10.1080/14693062.2020.1740149
Published online: 18 Mar 2020.
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CLIMATE POLICY
2020, VOL. 20, NO. 3, 387–404
https://doi.org/10.1080/14693062.2020.1740149
RESEARCH ARTICLE
Achieving the Paris Agreement’s 2 degree target in Nepal: the potential
role of a carbon tax
Bijay B. Pradhana, Ram M. Shresthab and Bundit Limmeechokchai
a
a
Sirindhorn International Institute of Technology, Thammasat University, Pathumthani, Thailand; bAsian Institute of Technology,
Pathumthani, Thailand
ABSTRACT
ARTICLE HISTORY
The 2015 Paris Agreement aims to limit global temperature rise in this century to well
below 2°C above the pre-industrial level, and pursue efforts to limit further the
temperature rise below 1.5°C. All parties ratifying the Paris Agreement have
submitted Nationally Determined Contributions (NDCs), many stating emission
reduction targets. The international climate research community has designed five
different Shared Socioeconomic pathways (SSPs) to characterize various possibilities
in demographic and economic changes over the next century. These SSPs were
implemented by six integrated assessment model (IAM) teams, which also
determined the carbon price trajectory required to limit the temperature rise below
2°C in respective SSPs. In SSP5, also termed the fossil-fueled development scenario,
only three IAMs identified that the 2°C target would be feasible, with carbon prices
ranging from 220 to 518 US$/tCO2e in 2050. This study aims to analyse the effects
of these carbon prices on energy and emissions during 2015–2050 in Nepal. It does
so using a long-term energy system model using the framework of the Asia-Pacific
Integrated Model/Enduse (AIM/Enduse) modelling tool. The base case scenario and
three carbon price scenarios are developed. Primary energy supply, energy security,
energy technology-mix (especially renewable energy usage and hydropower
development), emissions of greenhouse gases (GHGs) and local pollutants, and
local/regional environmental co-benefits are compared between the base case
scenario and carbon price scenarios. The study finds that the implementation of a
carbon tax would promote domestic hydropower, improve energy-efficiency and
reduce imports of fossil fuels when compared to the base case. Hydropower-based
electricity would have a major role in reducing emissions.
Received 19 July 2019
Accepted 5 March 2020
KEYWORDS
GHG mitigation; 2°C target;
carbon tax; AIM/Enduse;
Shared Socioeconomic
Pathways; Nepal
Key policy insights
. The carbon prices in SSP5 determined by IAMs to achieve the 2°C target would be
sufficient to achieve Nepal’s targets under its NDC in the energy sector.
. The industry and transport sectors would offer the highest GHG emission
reduction.
. Hydropower and biomass would have major roles in decarbonizing the energy
system.
Introduction
The Paris Agreement aims to limit global temperature rise to well below 2°C above pre-industrial levels and to
pursue efforts to limit it further to 1.5°C. The participating countries pledged to contribute voluntarily to the
reduction of greenhouse gases (GHG) emissions. As part of the fifth assessment report of the Intergovernmental
Panel on Climate Change (IPCC), four Representative Concentration Pathways (RCPs), describing the climate
forcing level in 2100, were developed to reach four different climate forcings, namely 2.6, 4.5, 6.0 and 8.5
W/m2 (IPCC, 2014). The concept of Shared Socioeconomic Pathways (SSPs) was proposed by Kriegler et al.
CONTACT Bundit Limmeechokchai
bundit@siit.tu.ac.th
© 2020 Informa UK Limited, trading as Taylor & Francis Group
388
B. B. PRADHAN ET AL.
(2012) and O’Neill et al. (2014) to develop an alternative set of scenarios of future societal development for
climate change research. The SSPs are scenario frameworks that integrate future climate impacts, vulnerabilities,
adaptation, and mitigation (O’Neill et al., 2017; Riahi et al., 2017). A total of six Integrated Assessment Model
(IAM) teams1; participated in the SSP development process (Riahi et al., 2017). A new framework combines
the SSPs and RCPs in a Scenario Matrix Architecture. Five SSPs have been developed to represent five
different pathways and provide the most up-to-date dataset on Integrated Assessment Modelling. The five scenarios derived from the SSPs are: Sustainability-Taking the Green Road for SSP1; Middle of the Road for SSP2;
Regional Rivalry-A Rocky Road for SSP3; Inequality-A Road Divided for SSP4; and Fossil-fueled DevelopmentTaking the Highway for SSP5. For more details on SSPs, see Riahi et al. (2017). The carbon prices needed to
achieve specific climate targets, i.e. RCP2.6, RCP4.5, RCP6.0 and RCP8.5 in different SSP scenarios have also
been assessed by IAM teams.
The report of the High-level Commission on Carbon Prices (HLCCP) emphasized that achieving the 2°C temperature target requires a major transformation in economic activities, especially in the energy sector (HLCCP,
2017). With proper climate policies and implementation, the 2°C target, which is in line with the 2.6 W/m2
RCP target, is achievable. Carbon pricing, if well designed, can be an effective way of efficiently reducing emissions. Carbon pricing offers several advantages. It alters the relative prices of goods and services, so that consumers and firms will not only consider the direct costs, but also the embedded direct and indirect
emissions associated with the product. The higher input cost of manufacturing the product will tend to lead
to a shift to lower emitting options. Carbon pricing also contributes to dynamic-efficiency because it encourages
the continuous development of less carbon-intensive technologies. Improvements in energy efficiency can
sometimes lead to rises in energy consumption (Sorrell, 2007). This happens when the cost-saving due to
energy efficiency makes a product more affordable; therefore, the use of energy and associated GHG emissions
increases. Carbon pricing is an effective mechanism to prevent such an energy/carbon rebound effect. Moreover, carbon pricing automatically affects the consumer’s behaviour while buying products and pushes the consumer to go for environmentally friendly products without the need to act pro-environmentally (Gsottbauer and
van den Bergh, 2011; Carattini et al., 2017).
In Nepal, biomass has traditionally been the major source of energy, and the development of modern energy
has been quite slow. The economy is mainly based on agriculture and the service sector, while economic growth
and industrialization have been hampered due to political disturbances and instability in the country. Since the
opening of the first hydropower plant in 1911, as of 2019, Nepal has harnessed less than 1.2 GW of its total
hydropower potential (Nepal Electricity Authority [NEA], 2019). In the past few decades, however, the situation
has started to change. The share of biomass has been decreasing while that of electricity and fossil fuels are
increasing. Infrastructure development, and income and population growth, has also led to a rise in oil consumption in the country. Liquefied petroleum gas (LPG) is now widely used as a modern fuel for cooking in the residential, as well as the commercial, sector. Past trends show that Nepal is currently following a fossil-fuelled
development path and, without new policies, is most likely to remain highly dependent on fossil fuel.
This study aims to analyse the potential effects of carbon pricing on energy and GHG emissions in Nepal. It
does so by assessing the implications for energy and emissions over the period 2015–2050 of the base case
scenario (i.e. without any climate policies) and three carbon tax scenarios. Primary energy mix, final energy consumption, technology mix, hydropower development, CO2 intensity, energy security, GHG emissions and local
pollutant (SO2 and NOx) emissions are assessed and compared. The carbon price scenarios associated with the
SSP5 scenario for RCP2.6 have been considered in the carbon tax scenarios. SSP5, which is also termed the fossilfueled development scenario, assumes higher dependency on fossil fuels compared to other SSP scenarios. Out
of the five SSP scenarios, SSP5 can be considered to be the least environmentally friendly, as it assumes high
energy demand growth coupled with economic development. In addition, development is directed towards
high fossil fuel use and alternative sources are not actively pursued. SSP5 also assumes that carbon intensity
will increase and there will not be any restriction on the use of fossil fuels. These assumptions mean that a
higher carbon price under the SSP5 scenario would be required compared to other SSPs in order to attain
the 2°C target.
Under the Paris Agreement, the participating countries have submitted nationally determined contributions (NDCs) to help achieve the 2 and 1.5°C targets. As part of its NDC, Nepal has set targets to
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achieve fossil fuel reduction by 50% by 2050 as well as reducing the use of fossil fuels in the transport sector
by 50% by 2050. This paper also analyses if these NDC targets of Nepal would be attainable with the carbon
price profiles under SSP5. However, there is ambiguity in Nepal’s NDC, as it does not quantify the basis for
the reduction targets. In our study, we have developed a base case or business as usual (BAU) scenario for
2015–2050 and considered the 2050 level as the reference. The reduction target is based on the BAU level in
2050.
There are several studies on low carbon development strategies based on long-term planning perspectives in
Nepal. Shrestha and Shakya (2012) studied the effects of CO2 emission reduction targets on energy use, local
pollutant emissions, energy security and energy system costs. Shakya and Shrestha (2011) studied the cobenefits of transport sector electrification on GHG and local pollutant emissions, energy security improvement
and employment generation. Shakya et al. (2012) did a similar study on the effects of a carbon tax in which three
different carbon tax trajectories under stabilization targets of 650 parts per million by volume (ppmv), 550 and
450 ppmv were considered. A study by Pradhan et al. (2018) analysed the effect of various carbon prices in GHG
emission reduction during 2010–2050 and also suggested carbon offset options for Nepal to become carbon
neutral. The carbon prices in different scenarios in their study vary from $10 to $800 per tCO2e and are
assumed to be fixed during 2020-2050. The carbon tax profiles considered in the aforementioned studies are
found not to be in line with the recent 2°C target.
This paper is divided into six sections. Section 2 gives an overview of the energy sector in Nepal. Section 3
presents the methodology used in the study. Section 4 gives a detailed description of the scenarios considered.
The two subsequent sections discuss the energy and emission implications of the BAU and carbon tax scenarios
respectively. The last section presents the concluding remarks.
Overview of the energy sector in Nepal
Eenergy supply and consumption trends in Nepal are presented in Table 1. Biomass in the form of firewood,
agricultural residue and animal dung remains the dominant primary fuel supply in Nepal. Besides biomass,
other major sources of primary energy include coal, oil products, hydropower and imported electricity. Other
renewables (solar, wind) have recently contributed to primary energy supply, although to a very limited
extent. The reason for the high dominance of biomass fuels in total primary energy supply (TPES) is the lack
of development of alternative sources and poor economic conditions in the country (Asian Development
Bank [ADB], 2017). During 2000-2015, TPES increased at a compounded annual growth rate (CAGR) of 2.5%.
The CAGR of coal, oil products and hydro were 5.3%, 3.3% and 5.3%, whereas the CAGR of biomass was
2.1%. The CAGR of imported electricity during the same period was 20.7%. In 2015, biomass still contributed
over 80% of TPES. It can be seen from Table 1 that the residential sector was the largest energy-consuming
sector over the period 2000-2015. The industrial sector used to be the second largest energy consumer, but
since 2010, the transport sector has dominated. Energy consumption by the commercial and agricultural
sectors energy consumption is lower in relative terms.
Table 1. Total primary energy supply and final energy consumption trend, toe.
Item
2000
2005
2010
2015
Total primary energy supply
Coal
Oil products
Hydro
Biomass
Imported electricity
Other renewables
Total final energy consumption
Industry
Transport
Residential
Commercial and public services
Agriculture/forestry
8108
258
713
140
6988
9
9131
248
724
216
7928
15
10,211
303
983
276
8592
57
8030
379
270
7,199
107
75
9029
388
275
8,128
166
72
10,097
449
637
8,718
175
118
11,692
557
1154
301
9528
151
1
11,551
731
746
9,716
224
134
390
B. B. PRADHAN ET AL.
Nepal is rich in renewable energy resources, mainly hydropower. The theoretical and economic potential of
hydropower is estimated to be 83 and 44 GW, respectively. Nepal has solar insolation ranging from 3.9-5.1 kWh/
m2 and average sunshine of 6.8 hours per day. The World Bank ([WB] 2017) reports that there is high feasible
potential for solar photovoltaic (PV) power generation in Nepal. The true potential of wind power for the
case of Nepal has not been evaluated yet.
The increase in fossil fuel use has also led to rises in CO2 emissions, with CO2 emissions from fossil fuel combustion increasing five-fold from 0.9 MtCO2 in 1990–5.6 MtCO2 in 2015. CO2 emissions per capita increased from
0.05 tCO2 in 1990–0.2 tCO2 in 2015 (IEA, 2017a).
Methodology
This section describes the energy system modelling framework used, which is followed by a discussion of the
AIM/Enduse model of Nepal as well as service demand projections and data requirements.
Model framework
This study uses the AIM/Enduse modelling framework to develop a national energy system model of Nepal for
the analysis. The AIM/Enduse modelling framework is presented in Figure 1. The model selects technologies and
energy options based on a linear optimisation framework where the total system cost is minimised under
several constraints, such as energy availability, material supplies and fulfilment of service demands. The AIM/
Enduse model consists of four modules: primary energy supply, conversion processes, energy service, and
environmental emissions. Primary energy supply comes from either domestic or indigenous energy sources
or imports. Energy conversion processes include secondary energy generation processes such as electricity generation from coal power plants, diesel generators, hydropower and others. The energy service module deals with
the estimation of either the end-use energy service demand or final services demand. For example, in the case of
Figure 1. Structure of AIM/Enduse model (Kainuma et al. 2003).
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cooking, the end-use energy service is the useful heat (i.e. useful heat produced for cooking from energy conversion technologies like stoves). Similarly, in the case of the cement industry, the energy service demand is
input as final service of the industries expressed as tons of cement. Energy and services can be represented
in two ways in the model. Energy or material that is supplied externally is called external energy or material,
and services that are supplied externally are called external services. An energy/material or service that is consumed and produced internally within the model is termed internal energy/material or service. For example,
steam is used in the paper and pulp industry. Steam is a service produced from the boiler and consumed as
energy in the industry. Therefore, steam is an internal energy/service. The environmental emission module
deals with estimation of both GHG and local pollutant emissions.
AIM/enduse model of Nepal
Nepal’s AIM/Enduse model comprises six economic sectors: residential, commercial, industrial, transport, agricultural and power. The socio-economic assumptions in this paper were updated using the most recently
available data. The AIM/Enduse model used in this study is the updated and improved version of the
AIM-Nepal model described in Pradhan et al. (2018). The base year was updated from 2010 to 2015. The residential sector is further categorized into urban and rural to capture the differences in technologies and
energy usage patterns. The end-use services in the residential sector are classified as cooking, lighting,
space heating, water heating, animal feed preparation, agro-processing, TV, refrigerator, space cooling, and
other electrical appliances. Similarly, the end-use services in the commercial sector include cooking, lighting,
space heating, water heating, TV, refrigeration, space cooling, and other electrical appliances. In the industrial
sector, end-use energy or final services include cement, bricks, paper, iron and steel products, motive power,
process heat and lighting. In the agriculture sector, the end-use services are tilling, irrigation and threshing.
Likewise, in the transport sector, the final services considered are passenger travel demand and freight transport. Passenger travel demand is classified into land and air passenger travel demand. Land travel demand is
provided by different modes of transport such as bus, car, three-wheelers, two-wheelers, rail and ropeway
technologies.
Service demand projection
The energy service demand projections in the model use GDP, population and income elasticity as the basis for
estimation. However, there are other socio-cultural factors that influence demand for energy services. Due to
data limitations, this study uses simple techniques that have also been used by other studies, rather than
more complex methods of estimation. Besides, the income elasticity values from other developing countries
have been employed in this study since they are not available for the case of Nepal. In this study, end-use
service demand projections are estimated using the econometric methods following Shrestha and Rajbhandari
(2010) and Shakya and Shrestha (2011). The end-use service demands in the residential sector and for passenger
transport demand are estimated as:
GDPt per capita a1
SDi,t = SDi,0 per capita ×
×Popt
(1)
GDP0 per capita
where SDi,0 is the service demand of service type i in the base year; SDi,t is the service demand of service type i
in year t; Popt is the population in year t; GDP0 is the GDP in the base year; GDPt is the GDP in year t; α1 is the
income elasticity of service demand for service type i.
GDP is one of the key drivers of freight transport demand (van de Riet et al., 2007). The freight service
demand is estimated as
GDPt a2
SDi,t = SDi,0 ×
(2)
GDP0
where α2 is the GDP elasticity of service demand for service type i.
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B. B. PRADHAN ET AL.
The end-use service demand in commercial, industry and agricultural sectors are estimated using the following equation:
VAt a3
SDi,t = SDi,0 ×
(3)
VA0
where VA0 is the sectoral value added in the base year; VAt is the sectoral value added in year t; α3 is the sectoral
value-added elasticity of demand for service type i.
The data used for the estimation of service demand for end-use services in each sector in the base year has
been obtained from different government organizations such as Central Bureau of Statistics (CBS, 2016), Department of Transport Management (DoTM, 2014), Ministry of Finance (MoF, 2014), Nepal Energy Efficiency Programme (NEEP/GIZ, 2012) and Water and Energy Commission Secretariat (WECS, 2014; WECS, 2010). The
elasticity values in different end-use service projections are based on Shakya and Shrestha (2011).
It should be noted that the level of electricity consumption is calculated endogenously by the model.
Data
Socio-economic data
Socio-economic drivers for the projection of future service demand are population and gross domestic product
(GDP). Population and GDP in the base year are based on the Economic Survey report prepared by the Ministry
of Finance, Government of Nepal (MoF, 2016). Population growth forecast is based on population growth rates
given by United Nations Population Division (UN, 2017). The Nepalese government assumes a GDP growth rate
in the range of 4.5% to 10% in low to high economic growth scenarios during 2015–2040 (WECS, 2017). This
study assumes a GDP growth rate of 6% to 8.5% during 2015-2050, the mid-point of the government scenarios.
The projected economic and demographic indicators are shown in Table 2.
Energy data
Oil prices for the base year are based on the average import price of the Nepal Oil Corporation (NOC) in 2015.
The coal price is based on the average export price of India for the year 2015, at the Raxaul border between
Nepal and India. Future oil and coal prices are based on the growth rates of global oil and coal prices given
in the New Policies Scenario of the World Energy Outlook 2017 (IEA, 2017c). Oil and coal prices are projected
until 2040. For the period 2040-2050, growth rates are assumed to be the same as those during the period
2030-2040.
Technology data
Technology data for the base year are taken from various national and international sources. Biomass-based
cooking has the major share in total final energy consumption of the country. Biomass-based cooking
options in this study are based on (ADB, 2018).
Other technology cost and efficiency data considered in this study are taken from the International Energy
Agency (IEA ETSAP, 2010; IEA, 2011; 2012a, 2012b, 2013, 2014, 2017b), MinErgy Nepal (MinErgy, 2012), the Nepal
Energy Efficiency Programme (NEEP/GIZ, 2012) and (Kainuma et al., 2003). The discount rate considered is 10%,
which is similar to that used for other infrastructure development projects in Nepal (Basnyat and Watkiss, 2017).
All the prices considered in this study are based on 2015 prices.
Carbon Capture and Storage (CCS) technology can be deployed in power generation and industries such as
cement and steel. Much of the literature emphasizes that deployment of CCS is necessary to achieve the 2°C
Table 2. Socio-economic parameters considered during 2015–2050.
Parameters
GDP (2015 USD)
GDP/capita (2015 USD/cap.)
Population (million)
2015
21.4
747
28.7
2020
27.4
905
30.3
2030
55.2
1663
33.2
2040
121.9
3476
35.1
2050
266.2
7374
36.1
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target (Riahi et al., 2012; Edmonds et al., 2013; van Vuuren et al., 2013; Rogelj, McCollum, Reisinger, et al., 2013;
Rogelj, McCollum, O’Neill, et al., 2013). The IPCC’s Fifth Assessment report, for example, states ‘Many models
could not limit likely warming to below 2°C if bioenergy, CCS and their combination (also known as Bioenergy
with CCS abbreviated as BECCS) are limited’ (IPCC, 2014). The IEA reports that CCS could be cost-competitive
with other dispatchable low carbon technologies by 2030 (IEA, 2015). Since the potential of CCS has not
been explored in Nepal, this study does not consider CCS as a viable option. CCS also requires geological
storage facilities for CO2. In the case of Nepal, there is no evidence of any geological storage sites for CO2
storage. Therefore, CCS is so far not suitable in Nepal.
Emission factors
In the energy system model, biomass (which includes fuelwood, agricultural-residues and animal dung) is
considered to be carbon neutral; however, CH4 and N2O emissions are also taken into consideration. It
should be noted that fuelwood consumption in the energy sector needs to be produced through sustainable
forestry in the land-use sector in order for it to be carbon neutral. Therefore, there is a constraint on the
biomass energy given to the model. The emission factors in this study are based on IPCC 2006 guidelines
(IPCC, 2006).
Scenario description
Four different scenarios are considered in this study: one base case and three carbon tax (CT) scenarios.
Base case
In the base case scenario (hereafter also referred to as the BAU scenario), technology and fuel use follow historical patterns. Socio-economic considerations for service demand projections are the same as discussed in the
preceding section. The changes in technology shares are assumed to follow past trends. For example, the
share of LPG in cooking is assumed to increase in future years. Likewise, the share of biomass-based cookstoves
is assumed to decrease. In the urban residential sector, constraints specifying the minimum permissible shares
of modern cooking technology are specified. For instance, in the case of urban residential sectors, the minimum
share of modern cooking (electric and LPG) would reach 75% in 2050. Similarly, the minimum share of less
efficient traditional cook stoves (TCS) is also constrained in this scenario in spite of their higher cost compared
to their efficient counterparts. For example, biomass-based improved cook stoves (ICS) are more cost-effective
compared to the TCS. However, it is assumed that a complete shift from TCS to ICS will not occur in practice. In
rural areas, the share of biomass-based cooking is assumed to decrease to 65% in 2050, but it has been assumed
that TCS would still be prevalent in 2050. Similarly, the minimum share of incandescent bulbs for lighting will be
constrained; however, the share is assumed to gradually decrease and would become zero by 2050. Selections of
the technologies depend on their cost competitiveness.
In the transport sector, the minimum share of two-wheelers and private four-wheelers is constrained to
prevent a complete shift to public modes of transport. Likewise, in the case of the agricultural sector, the
minimum share of diesel pumps is constrained till 2050 based on the assumption that the electricity grid will
not reach every part of the country.
For the present analysis, cleaner fuel options in the residential and commercial sectors include electricity,
solar and biogas. The energy-efficient technology options considered include LED bulbs for lighting, ICS
(biomass) and biogas cook stoves for cooking, energy-efficient appliances like efficient electric motors for
motive power in industries, efficient fans, air conditioners and refrigerators, as well as solar and electric water
heaters.
In the industrial sector, energy-efficient technologies such as energy-efficient motors and energy-efficient
boilers are considered in each end-use service. Technology switching options based on fuel types are also considered, such as electric motors for motive power instead of diesel motors. In the brick industry, Vertical shaft
brick kilns and improved moving chimney brick kilns are considered. Fuel switching from coal to biomass is
also included in coal intensive industries like cement and bricks.
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B. B. PRADHAN ET AL.
The transport sector includes options like biofuel vehicles, electric vehicles, hybrid vehicles, electric railways,
electric ropeways, intra-city metro rail and biofuel vehicles. Biofuel options in passenger transport include 100%
bioethanol used in passenger cars. In the case of freight transport, electric trucks, electric pick-ups and other
electric options like freight trains and electric ropeways are also considered. Blending diesel with a biofuel
share of up to 20%, commonly known as B20 fuel, has been considered in the transport sector.
Carbon tax scenarios
The carbon tax scenarios (hereafter referred to as CT scenarios) are similar to the BAU scenario except that these
scenarios also consider carbon taxation. In the CT scenarios, global carbon price trajectories obtained from three
different IAMs under the SSP5 scenario for RCP2.6 are considered. One global price that covers all countries and
sectors would curtail possible emissions leakage between countries. Since the 2°C target is global, it is important
to ensure that there is no emission leakage or spillover, i.e. an increase in GHG emissions in one country as a
result of a decrease in emissions in some other country. In the absence of one global price, carbon intensive
industry is likely to shift from a country with stringent climate regulation to one with less strict or no climate
policy, which would fail to result in an overall reduction in GHG emissions. One uniform global price all over
the world is therefore required to reduce overall emissions (MacKay et al., 2017; Baranzini et al., 2017). For
the purposes of this study, carbon price profiles are obtained from the SSP database (SSP Database, 2018).
The three IAMs are AIM-CGE (Asia-Pacific Integrated Model/Computable General Equilibrium), GCAM (Global
Change Assessment Model) and REMIND-MAgPIE (Regionalized Model of Investments and DevelopmentModel of Agricultural Production and its Impact on the Environment). The corresponding carbon tax scenarios
are hereafter referred to as ‘AIMC’, ‘GCAM’ and ‘RMDM’. The carbon price trajectories for the three models are
shown in Figure 2.
Energy and emissions in the BAU scenario
Primary energy supply
Under the BAU scenario, TPES in Nepal would increase from 11.7 Mtoe in 2015–49.8 Mtoe in 2050 (see Figure 3).
Biomass use in TPES would increase by 80% between 2015 and 2050. Use of petroleum products would increase
from 1.2 Mtoe in 2015–10.3 Mtoe in 2050, whereas coal use would increase from 0.6 Mtoe in 2015–7.7 Mtoe in
2050. Hydroelectricity, which accounted for only 0.3 Mtoe in 2015, would increase to 12.3 Mtoe in 2050.
Although biomass would remain the major source of energy during the period, its share in TPES would decrease
from 81% in 2015–34% in 2050. The shares of hydropower, coal and petroleum would increase significantly; i.e.
they would attain 24.7%, 15.5% and 24.8% in 2050 respectively from 2.6%, 4.8% and 10.3% in 2015. Electricity
imports, which contributed 1.3% to TPES in 2015 would have a negligible share in 2050, whereas other renewables (solar, wind), which accounted for a negligible share in 2015, would increase to 1.0% in 2050.
Figure 2. Carbon price trajectories considered in CT scenarios.
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Figure 3. Total Primary Energy Supply during 2015–2050 in base case.
GHG emissions
Changes in GHG emissions, including CO2, CH4 and N2O, in the BAU scenario are shown in Figure 4. Total GHG
emissions would increase from 8.6 MtCO2e in 2015–71.1 Mtoe in 2050. It should be noted here that CO2 emissions from biomass burning are not counted, as this form of energy is considered to be carbon neutral, whereas
CH4 and N2O emissions have been considered. The residential sector was the major source of GHG emissions in
2015, followed by industry (33.3%), transport (21.6%), the commercial sector (4.5%) and agriculture (4.0%). In
2050, however, the industrial sector would become the highest GHG emitter with a share of 42.5%. The transport
sector would have a share of 38.7%, followed by the agricultural sector (2.6%), the residential sector (7.8%) and
the commercial sector (2.2%).
Carbon tax scenarios: energy and emissions
This section discusses changes in TPES, GHG emissions, fossil fuel consumption, electricity generation, technology, CO2 intensity, energy security and local pollutant emissions in various carbon tax (CT) scenarios.
Total primary energy supply
The changes in TPES in the various CT scenarios in 2030 and 2050 are shown in Figure 5. TPES in 2030 would
decrease in the AIMC, GCAM and RMDM scenarios by 15.1%, 7.3% and 7.8%, respectively. There would be less
Figure 4. GHG emission during 2015–2050 in base case.
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B. B. PRADHAN ET AL.
Figure 5. TPES in various scenarios in 2030 and 2050.
use of coal and petroleum under the CT scenarios than that in the BAU scenario, whereas there would be higher
use of biomass, hydro and other renewables. TPES would be lower than in the base case by 21.6% in the AIMC,
19.7% in the GCAM and 20.0% in the RMDM scenarios. Similarly as in 2030, the use of coal and petroleum would
decrease while that of biomass, hydro and other renewables would increase in the subsequent years. In all CT
scenarios, there would be an increased use of liquid biofuels. There would be no electricity imports in either
2030 or 2050.
Final energy consumption
In 2030, final energy consumption (FEC) in AIMC, GCAM and RMDM would be 14.9, 15.9 and 15.8 Mtoe, respectively. The FEC in corresponding scenarios in 2050 would be 38.2, 39.2 and 39.0 Mtoe, respectively. In all CT scenarios, FEC is less than in the BAU scenario (see Figure 6). Electricity and biomass would both have a major role in
all CT scenarios. Electricity and biomass consumption would increase, while that of coal and petroleum would
decrease. Electricity consumption in 2050 would increase in the AIMC, GCAM and RMDM scenarios by 29.0%,
26.1% and 28.4% respectively, compared to BAU. Biomass use would increase by 17.3%, 19.8% and 19.7% in
the AIMC, GCAM and RMDM scenarios. Petroleum use in 2050 would be lower than in the BAU scenario by
71.6%, 67.7% and 70.7% in the AIMC, GCAM and RMDM scenarios respectively. In the same year, coal use
Figure 6. FEC in various scenarios in 2030 and 2050.
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would decrease by 85.3% in the AIMC and 80.9% in both the GCAM and RMDM scenarios. Biofuel consumption
would increase to 17.8, 20.2 and 13.4 million litres in the AIMC, GCAM and RMDM scenarios in 2050, whereas in
the BAU scenario, there would be no biofuel consumption. The decrease in total FEC in the CT scenarios is due to
fuel switching and to more energy-efficient technologies.
Fossil fuel consumption
In the BAU scenario, total fossil fuel consumption would increase by 160% over the 2015–2030 period and by
336% over the 2030–2050 period (see Figure 7). In the CT scenarios, the use of fossil fuels would be significantly
less than in BAU. In 2030, fossil fuel (coal and petroleum combined) consumption would decrease by 65.5% in
the AIMC, 36.0% in the RMDM and 42.1% in the GCAM scenarios. Similarly, fossil fuel consumption would
decrease by 76.9% in the AIMC, 72.7% in the RMDM and 74.6% in the GCAM scenarios when compared to
that in the BAU in 2050. In 2050, fossil fuel use would be more than 70% lower under all CT scenarios than
in the base case.
Electricity generation
Electricity generation under the base case and CT scenarios is presented in Figure 8. Almost all electricity generation would come from hydropower over the period 2015-2050. Electricity would substitute most of the fossil
fuel-based technologies in the CT scenarios. Electricity consumption (hence its generation) would be higher
under all the CT scenarios than in BAU. In 2050, electricity generation in the AIMC scenario would increase
by 19.6%, in the GCAM by 16.3% and in the RMDM by 17.6%. By 2050, installed capacity would be 33.4 GW
in the BAU scenario, while it would be 39.9 GW in the AIMC, 38.8 GW in the GCAM and 39.2 GW in the
RMDM scenarios. The cumulative generation of electricity during 2015–2050 would increase in the AIMC, the
GCAM and the RMDM scenarios by 17.9%, 9.8% and 11.4%, respectively.
Technology
In the residential and commercial sectors, ICS are found to be more cost-effective than TCS under the base case
scenario. Similarly, light-emitting diode (LED) bulbs are also the most cost-effective option for lighting services in
the base case. Compared to cooking based on LPG, biogas-based cooking is found to be more cost-effective for
rural households, whereas electric cooking is found to be more cost-effective for urban households under all
three CT scenarios. Solar water heating is found to be more economical than electric water heating in BAU.
Figure 7. Fossil fuel consumption in various scenarios during 2015-2050.
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Figure 8. Electricity generation requirement during 2015–2050 in various scenarios.
The electric space and water heating are found to be cheaper than LPG under all the CT scenarios. Energyefficient electric appliances are more cost-effective than conventional ones in the base case.
In the agricultural sector, efficient tractors and electric threshers are found to be economical in the base case,
while electric pumps are found to be more cost-effective than gasoline/diesel-based pumps. However, in areas
with no electricity access, energy-efficient gasoline/diesel pumps are more economical than conventional ones
in BAU. In the AIMC scenario, solar pumps are found to be more cost-effective than diesel and gasoline in areas
without access to grid electricity supply.
In the transport sector, diesel hybrid vehicles (cars and trucks) are found to be the most cost-effective option
in the BAU scenario. Also, electric cars would become cost-effective by 2035 due to their declining cost. In the CT
scenarios, electric vehicles (two-wheelers, cars, buses) become cost-effective option by 2023 in the AIMC scenario, by 2028 in the RMDM scenario and by 2030 in the GCAM scenario. It should be noted here that the study
assumes that electric vehicles would not replace all internal combustion engine vehicles. Biofuel blend vehicles
would also be cost-effective over pure gasoline/diesel vehicles in all CT scenarios. Flex-fuel vehicles (i.e. vehicles
that can operate on 100% pure ethanol) are found to be economical only in the AIMC scenario. Electric buses
and trucks are found to be cost-effective in all CT scenarios, while electric freight trains would be cost-effective in
the BAU, as well as all CT, scenarios.
In the industrial sector, energy-efficient coal boilers would substitute conventional coal boilers in the base
case. Heat pump boilers would also be cost-effective in the base case. In the case of motive power, energyefficient electric motors are more cost-effective than conventional electric motors in the base case. The use
of biomass (replacing coal) would be cost-effective in boilers (for process heat) and brick kilns in the CT scenarios. Improved moving chimney brick kiln technology is found to be cost-effective in the BAU scenario, as well
as the GCAM and RMDM scenarios. Vertical shaft brink kiln technology would be cost-effective in the AIMC scenario. In the case of the cement industry, vertical mills would be cost-effective over ball mills in clinker grinding to
make cement. In the clinker production process in cement manufacturing, a 4-stage cyclone suspension preheater plus calciner plus high-efficiency cooler technology would be cost-effective over conventional long dry kiln
and wet kiln technology in all CT scenarios.
GHG emissions
GHG emissions in the base case and CT scenarios over the period 2015–2050 are presented in Figure 9. Emissions in the CT scenarios would decrease significantly when compared to those in the BAU by 2030 and they
would decrease by an even higher percentage by 2050. There would be the highest reductions of 58.0% in
2030 in the AIMC scenario, while the reduction under the GCAM and RMDM scenarios would be 30.8% and
35.2%, respectively. In 2050, the reduction in the AIMC, GCAM and RMDM scenarios would be 73.9%, 69.8%
and 71.4%, respectively.
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Figure 9. GHG emissions in 2030 and 2050.
Cumulative GHG emissions over the period 2015–2050 in the base case would be 969.7 MtCO2e. In the AIMC
scenario, emissions would be lower by 62.8%. Emissions in the GCAM and the RMDM scenarios would be lower
by 53.6% and 55.3%, respectively.
Figure 10 shows sector-wise cumulative GHG emissions over the period 2015–2050 in the BAU and CT scenarios. The reduction in the industry sector varies from 67.9% in GCAM to 76.3% in the AIMC. In the transport
sector, emission reduction would be 55.7% in the AIMC and RMDM scenarios, and 58.7% in the GCAM scenario.
Likewise, sectoral emission reductions across the CT scenarios would be in the range of 24.7% to 30.5% in the
residential sector, 58.3% to 65.5% in the commercial sector, and 23.7% in the agricultural sector.
CO2 intensity
CO2 intensity over the period 2015–2050 in the BAU scenario would increase from 0.7 kgCO2e in 2015–
1.1 kgCO2e in 2030 and 1.5 kgCO2e in 2050 (see Figure 11). The decrease in CO2 intensity in the CT scenarios
is noteworthy. By 2030, it would decrease to 0.5 kgCO2e in the AIMC scenario, and 0.8 kgCO2e in the RMDM
and GCAM scenarios. By 2050, CO2 intensity would decrease to 0.5 kgCO2e in the AIMC scenario, a fall of
66.7%. Similarly, in the GCAM and RMDM scenarios, the decrease would be 62.4% and 64.3% respectively.
Energy security
As a measure of energy security, net energy import dependency (NEID) is calculated as the ratio of net energy
imports to TPES. Nepal has no reserves of coal, oil and gas and therefore all fossil fuels are imported. Table 3
Figure 10. Cumulative GHG emissions by sector in various scenarios.
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Figure 11. Changes in CO2 intensity during 2015–2050 in various scenarios.
Table 3. Net energy import dependency, (%).
Year
2015
2030
2050
BAU
AIMC
RMDM
GCAM
19.0
14.2
17.3
13.3
16.4
27.5
41.9
11.2
12.3
shows the NEID in 2015, 2030 and 2050 in the scenarios. It is seen that the NEID in the BAU scenario would
increase, from 16.4% in 2015–27.5% in 2030 and 41.9% in 2050. However, in the CT scenarios, the NEID
would be reduced significantly, for example to only 11.2% by 2030 and 12.3% by 2050 in the AIMC scenario.
Emission of local air pollutants
In addition to GHG emissions, this study has also estimated the reduction in emissions of two local/regional pollutants, namely, SO2 and NOx (see Figures 12 and 13). In the BAU scenario, NOx emissions would increase almost
eight-fold over the period 2015–2050. In the CT scenarios, NOx emissions would be reduced by 14.6% in the
AIMC scenario, 9.6% in the GCAM scenario and 12.9% in the RMDM scenario in 2030. Similarly, in 2050, emissions
would be reduced in the range of 61.2% in the AIMC scenario to 56.2% in the GCAM scenario.
SO2 emissions in the BAU scenario would increase nearly seven-fold over the period 2015–2050. In the AIMC
scenario, SO2 emissions would be reduced by nearly 49.8% in 2030 and by 63.9% in 2050. Similarly, in the RMDM
Figure 12. NOx emissions in 2030 and 2050.
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Figure 13. SO2 emissions in 2030 and 2050.
scenario, there would be an emission reduction of 26.5% in 2030 and 60.7% in 2050. The lowest percentage
reduction in emissions would occur in the GCAM scenario, that is, 24.1% in 2030 and 58.3% in 2050.
Conclusion
The study has assessed levels of energy use and GHG emissions under the base case, or BAU, scenario in Nepal
over the period 2015–2050. Furthermore, the study has analysed the implications of different levels of carbon
taxation on energy use and GHG emissions, and compared these with the BAU scenario. Specifically, it has considered the carbon tax trajectories required to limit global temperature rise below 2°C under the SSP5 scenario
generated by three IAMs (AIM/CGE, REMIND MagPie and GCAM5). Much of the literature suggests that BECCS
has a crucial role in achieving the 2°C target. However, Nepal has no geological storage for CCS technology.
Therefore, this study aimed to explore the utilization of abundant hydro resources in the country in order to
displace the use of fossil fuels under the aforementioned CT scenarios. This study has found that a reduction
in GHG emissions by more than 50% would be achievable with the three carbon tax scenarios considered. In
addition, results have been compared with the NDC targets of Nepal.
In the BAU scenario, TPES would increase more than three-fold over the period 2015–2050, while there would
be a 12-fold increase in the use of fossil fuels (coal and petroleum combined). In all CT scenarios, however, the
use of fossil fuels by 2050 would decrease by more than 70%. It is also worth noting that the government of
Nepal in its NDC has targeted to reduce fossil fuel consumption by 50% by 2050. However, the NDC document
has not mentioned any levels of fossil fuel consumption in 2050. The estimate of fossil fuel consumption in the
base case in this study highlights the level of reduction that would be required under the NDC target. This study
finds that the NDC target of Nepal would be achievable under all three CT scenarios.
The study has estimated that GHG emissions in the base case would increase more than eight-fold over the
period 2015–2050. This is higher than emission levels (and energy consumption) found in the previous study by
Pradhan et al (2018). It is mostly due to higher assumed GDP growth levels, based on socio-economic parameters from recent government sources. By 2050, emissions compared to the BAU would be reduced by
around 70% in all CT scenarios. Cumulative GHG emissions would be reduced by 53.6% in the GCAM scenario,
55.3% in the RMDM scenario and 62.8% in the AIMC scenario. In addition, the study has assessed the reduction
in SO2 and NOx emissions as co-benefits of introducing carbon taxes in energy system, finding that both SO2 and
NOx decrease notably under all three CT scenarios.
Hydropower-based electricity emerges as the preferred choice when carbon taxation is introduced, with
biomass also playing an important role in substituting coal for thermal application in industries. Substitution
of fossil fuels (petroleum and coal) by electricity and biomass also contributes to the reduction of fossil fuel
usage and corresponding GHG emissions. Electricity and a switch to electric vehicles would have a prime
role in the transport sector. In industry, advanced low carbon technologies and fuel switching (coal and
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petroleum to biomass and electricity) would contribute the most to emission reduction. Electricity would also
substitute for LPG and biomass use in the residential and commercial sectors in all CT scenarios. In total, in
all three CT scenarios, additional hydropower requirements in 2050 would be in the range of 23.8 TWh to
28.7 TWh.
As stated in Section 2, Nepal has enormous potential for hydropower development. The installed capacity
required in 2050 in all CT scenarios would still be lower than the economic potential of the country, which is
estimated to be 44 GW. However, the development of hydro power has been very slow. One of the major barriers is the high investment required. Domestic investment would not be enough to develop large hydro projects, and alternative financing modes need to be considered. In addition, strong market demand and market
reliability are key factors to be considered in such projects. Diversion of investment to hydropower development
might mean less investment in other sectors, such as health, roads and education. Besides, there are social and
environmental issues that hinder the development of hydropower projects, such as earthquakes, landslides and
heavy rainfall (Bhatt, 2017; Sharma and Awal, 2013). Storage-based hydropower also requires large areas of land
to be submerged under water, with associated potential negative impacts. All these issues would need to be
taken into consideration by new government policies aimed at increasing hydropower development.
Biofuel production required would be up to 20.2 million litres by 2050, corresponding to up to 32.3 thousand
hectares of land respectively for biofuel production (based on cultivation of the Jatropha Curcas plant) (Adhikari
and Wegstein, 2011). The question of food versus fuel was one of the early controversies regarding biofuel. Loss
of forest, conversion of agricultural land for biofuel production and food riots have discouraged the adoption of
biofuel as a potential substitute for fossil fuels globally (Tomei and Helliwell, 2016). The Government of Nepal
has, however, implemented a National Biofuel Programme since 2008/2009 to promote biofuel production in
the country with a special focus on the Jatropha Curcas plant. According to the Alternative Energy Promotion
Centre (AEPC), a government institution that promotes renewable energy resource and technology, Jatropha
cultivation as a source of biodiesel offers several benefits for Nepal, such as job opportunities and ensuring
that both arable and degraded lands are utilized optimally (AEPC, 2019). The Forest Resource Assessment
(FRA) Nepal project (2010–2014) conducted by The Department of Forest Research and Survey (DFRS) during
2010–2014 estimates that Nepal has 115 thousand hectares of degraded land (DFRS, 2015). Therefore, the
potential of biodiesel production in Nepal seems feasible without damaging the existing forests and agricultural
lands.
The transition to a low carbon development path is a challenging task and will require the implementation of
additional policies to promote low carbon technologies by the Nepalese government. Carbon pricing and a low
carbon technology policy are complementary to each other. In other words, carbon pricing encourages the
development and adoption of low carbon-emitting technologies. The provision of financial incentives in the
form of subsidies and tax exemptions for low carbon technologies could promote the adoption of such technologies. In principle, a single global carbon price could be introduced worldwide to achieve the 2°C target,
either through the same carbon tax or by an emission trading system (Baranzini et al., 2017), thereby avoiding
carbon leakage. In reality, the implementation of carbon taxation even at the national level has been challenging, due to concerns over increased prices and the burden on consumers. However, the impacts on consumers
can be minimized by proper recycling of the tax revenue, for example, to provide compensation or incentives to
low income households (Harrison, 2013; Flues and Thomas, 2015; Bowen, 2015). Several countries have
managed to successfully implement carbon taxation through effective policies (World Bank and Ecofys, 2018;
Organisation for Economic Co-operation and Development (OECD), 2016). One of the ways to tackle implementation challenges would be by learning from successful case studies and good practices in other developed as
well as developing countries (WB, 2019).
Note
1. From National Institute for Environmental Studies (NIES), Japan; International Institute for Applied Systems Analysis (IIASA),
Austria; Fondazione Eni Enrico Mattei (FEEM), Italy; Netherlands Environmental Assessment Agency (PBL), Netherlands;
Postdam Institute for Climate Impact Research (PIK), Germany; and Pacific Northwest National Laboratory (PNNL), United
States.
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Acknowledgement
The authors would like to thank the National Institute for Environmental Studies (NIES) Japan for supporting with AIM/Enduse model
to carry out this research. The authors express their deep gratitude to the anonymous reviews and the editors for their invaluable
insights and suggestions to improve this paper.
Disclosure statement
No potential conflict of interest was reported by the authors.
ORCID
Bundit Limmeechokchai
http://orcid.org/0000-0002-2177-2961
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