Bioenergy with carbon capture and storage (BECSS): Life cycle environmental and economic assessment of electricity generated from palm oil wastes

Full text

Applied Energy 349 (2023) 121506
Available online 1 August 2023
0306-2619/© 2023 The Author(s). Published by Elsevier Ltd. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
Bioenergy with carbon capture and storage (BECSS): Life cycle
environmental and economic assessment of electricity generated from palm
oil wastes
Djasmine Mastisya Saharudin, Harish Kumar Jeswani , Adisa Azapagic
*
Department of Chemical Engineering, The University of Manchester, UK
HIGHLIGHTS GRAPHICAL ABSTRACT
•BECCS with palm oil wastes achieves
−840 to −1729 kg CO
2
eq./t CO
2
removed.
•However, all other impacts are higher
by 13–217% compared to systems
without CCS.
•Levelised costs of electricity of BECCS
are 3.6–4.1 times higher than without
CCS.
•CCS emits additional 0.175–0.183 t CO
2
eq. and costs US$50–53 per t CO
2
removed.
•BECCS could reduce emissions from the
Malaysian electricity sector by 10%.
ARTICLE INFO
Keywords:
Bioenergy with carbon capture and storage
Carbon dioxide removal
Life cycle assessment
Life cycle costing
Negative emissions technologies
Renewable energy
ABSTRACT
Rapid deployment of negative emissions technologies (NETs) will be needed to help mitigate climate change.
Among various NETs, bioenergy with carbon capture and storage (BECCS) is seen as an option with multiple
environmental benets, including increasing the share of renewable energy while capturing carbon and
providing an effective solution for waste management if waste biomass is utilised. This research presents the rst
environmental and economic sustainability assessments of BECCS utilising palm oil waste abundant in palm oil
producing countries, such as Malaysia. The following ve types of waste are considered: fronds, trunks, empty
fruit bunches (EFBs), shells and bres. Process simulation, techno-economic analysis and life cycle assessment
(LCA) are combined to determine the life cycle impacts and costs of BECCS from cradle to grave. Two units of
analysis are considered for both the impacts and costs: ‘generation of 1 MWh of electricity’ and ‘capture of 1
tonne of CO
2
’. The global warming potential (GWP) is net-negative for both functional units, ranging from
−1270 to −1410 kg CO
2
eq./MWh and −840 to −1729 kg CO
2
eq./t CO
2
removed, the latter depending on the
credits for electricity generation. However, all other 17 impacts increase by 13–217% with the addition of CCS.
The systems without CCS have net-positive GWP of 59–126 kg CO
2
eq./MWh. Per MWh electricity, the system
* Corresponding author.
E-mail address: adisa.azapagic@manchester.ac.uk (A. Azapagic).
Contents lists available at ScienceDirect
Applied Energy
journal homepage: www.elsevier.com/locate/apenergy
https://doi.org/10.1016/j.apenergy.2023.121506
Received 9 March 2023; Received in revised form 2 June 2023; Accepted 20 June 2023

Applied Energy 349 (2023) 121506
2
with bres has the lowest and palm fronds the highest impacts for most of the categories considered; per tonne of
CO
2
removed, there is no clear feedstock preference. The levelised costs of electricity (LCOE) of BECSS plants are
US$98–119/MWh, with bres being the best and fronds the worst feedstock. Compared to the systems without
CCS, the LCOE of BECCS are 3.6–4.1 times higher. The costs of BECSS per tonne of CO
2
removed are US$66–74,
with CCS contributing US$50–53/t CO
2
. Based on the current availability of palm oil wastes in Malaysia, the
system could generate 7730 GWh/yr, boosting the national share of bioenergy by 7.6 times, while removing 12
Mt CO
2
/yr, equivalent to 10% of annual emissions from the electricity sector.
1. Introduction
Extreme weather events, such as heatwaves, ooding, droughts and
hurricanes, are increasing in many parts of the world as a result of
climate change. A signicant effort to combat climate change was made
in 2015 through the Paris Agreement, with the objective to limit the
global temperature increase to 2 ◦C while continuing efforts to restrict it
to 1.5 ◦C [1]. A number of scenarios have been considered in Integrated
Assessment Models to help identify the best pathways for achieving this
objective and most of them include negative emissions technologies
(NETs) [2–4]. NETs can provide the solution for climate change through
the removal of CO
2
from the atmosphere, since current emission-
reduction efforts are insufcient in controlling the rate of global
warming. Among many, the technology that received the most attention
is bioenergy with carbon capture and storage (BECCS), due to its po-
tential for large scale applications anywhere in the world with sufcient
feedstock availability and suitable storage locations [2,5]. One of the
reasons for the focus on BECCS is that CO
2
emitted from energy-related
sectors is the main contributor to climate change, with 36.3 bn t out of
the total 40.8 bn t emitted globally from these sectors [6,7].
Negative emissions can be achieved in BECCS through CO
2
removal
via two mechanisms: i) its sequestration from the atmosphere by
biomass during its growth; and ii) capture and permanent storage of the
CO
2
released during the conversion of biomass into energy [5,8]. A
variety of feedstocks can be used in BECCS, including oilseed crops (e.g.
rapeseed, soybeans), sugar and starch crops (e.g. beetroot, sugarcane),
lignocellulosic biomass (e.g. forestry and agricultural residues) and wet
biomass (e.g. municipal solid waste and animal waste). Equally, a
number of conversion technologies can be used in BECCS, including
combustion, gasication, anaerobic digestion and fermentation,
depending on the nature of feedstock and the intended energy products,
such as heat and power, biodiesel, bioethanol, hydrogen and other
biofuels [5,9,10].
CO
2
capture technologies are divided into three main categories:
post-conversion, pre-conversion and oxy-fuel combustion. The selection
of suitable technology depends on many factors, including the type of
industry, the concentration of CO
2
and the energy or fuel conversion
technology [11]. Some of the examples of post-conversion capture
technologies include absorption in amine-based and alkaline solvents
and ionic liquids. Pre-conversion technologies include absorption on
Selexol or rectisol and absorption in monoethanolamine (MEA) [11].
Among these, post-combustion capture with MEA is the most mature
option, with MEA being widely available at a low cost [8,12].
Over the years, improved methods for CO
2
capture have been
developed, which include adsorption on activated carbons, metals and
polymers networks, membrane separation with inorganic, polymeric
and hybrid membranes, cryogenic separation and chemical looping
[11–13]. However, these technologies are fairly new and yet to be tested
on a large scale. A study by Bhave and colleagues [13], who reviewed
real applications of BECCS in different conversion and capture
technologies, showed that the techno-economic analysis of newer
technologies was associated with higher uncertainties compared to
technologies with higher technology-readiness levels, such as amine
scrubbing. Hence, for commercial deployment, it may be better to use
the proven and tested MEA scrubbing to ensure the plant’s viability,
despite a high energy penalty of up to 48.6% [14].
After the capture, CO
2
is compressed at high pressure, typically
>100 bar [15,16], and transported via pipelines to storage locations.
Suitable storage locations include geological formations, such as
depleted oil and gas wells and saline aquifers, with the latter having
larger storage capacities, reported to be as high as 700–900 bn t CO
2
[11].
While BECCS has a potential to reduce greenhouse gas emissions
from the energy-related sectors, it is also important to consider other
environmental impacts, as well as the associated costs that will arise
over its life cycle. Life cycle assessment (LCA) and life cycle costing
(LCC) can be used for these purposes. This paper sets out to evaluate the
life cycle environmental and economic sustainability of BECCS for
electricity generation via LCA and LCC, considering various palm oil
wastes abundant in palm oil producing countries, such as Malaysia.
While numerous LCA and LCC studies of BECCS for electricity genera-
tion have been carried out so far, as discussed in the next section, this
study is the rst to consider these types of feedstock and to focus on
Malaysia. Other main contributions of this work include:
1) exploring the potential of energy generation from waste and negative
GHGs emissions in the context of a developing country;
2) a comprehensive LCA combined with process simulation, consid-
ering two different functions of the system (electricity generation
and removal of CO
2
) and a full suite of environmental impacts, in
addition to global warming potential; and
3) an economic assessment with a feasibility evaluation of implement-
ing BECCS for electricity generation in a developing country.
The paper is structured as follows. Section 2 provides an overview of
the LCA and LCC studies of BECCS available in the literature for other
feedstocks and regions. The methods used in this research are presented
in Section 3, followed in Section 4 by the results of the environmental
and economic sustainability assessments, and the potential of BECCS
deployment at the national level. Finally, the study conclusions are
summarised in Section 5.
2. Literature review
Numerous LCA studies of BECCS have been conducted, but as the
focus of this study is electricity generation, the literature review con-
siders only those studies. As indicated in Table 1, various feedstocks
have been evaluated for electricity BECCS, including woody biomass,
energy crops, agricultural waste and municipal solid waste. As can be
seen, no studies have considered palm oil wastes to date.
D.M. Saharudin et al.

Applied Energy 349 (2023) 121506
3
Regarding the CCS technologies, the focus is mainly on post-
combustion capture in MEA, with some considering pre-combustion.
The system boundaries vary across the studies, with most being from
cradle to gate. The majority of the studies exclude electricity distribution
and use, except for two [15,17]; plant decommissioning is also consid-
ered in only two studies [18,19]. Most studies have dened the func-
tional unit as 1 kWh or 1 MWh of electricity generated [15,17,20–26].
Regarding the impacts, most papers have focused on GWP, with a
handful including some other impacts (Table 1), but none reported a full
suite of categories normally considered in LCA studies. In the few studies
which considered other impacts in addition to GWP, the life cycle impact
assessment methods include CML 2001, ReCiPe and Eco-indicator 95.
The average GWP of BECCS reported in the literature is net-negative,
with the values ranging from −852 to −551 kg CO
2
eq./MWh. The
average net removals vary from 622 to 1092 kg CO
2
eq./MWh, while the
systems without CCS have an impact of 67–241 kg CO
2
eq./MWh.
However, the total impact of BECSS varies widely, from −1810 to 100 kg
CO
2
eq./MWh. The latter (net-positive value) is due to the inclusion of
land use change (LUC) in the study by Mac Dowell and Fajardy [27],
with indirect LUC accounting for >26% of the additional carbon emis-
sions. Hence, if LUC is involved in the feedstock production, BECCS may
no longer be a NET. This could be relevant to palm oil wastes if used as
BECCS feedstock since the expansion of palm oil plantations can lead to
a LUC of 3453 m
2
/ha [28] and annual carbon emissions from the soil of
0.25 Mt C [29]. However, if the waste is otherwise unutilised and/or
abundant, the LUC impact is attributed to palm oil, the main product of
the plantation, and does not affect BECCS. If, on the other hand, the
amount of waste is constrained, then its use for BECCS should not drive
the expansion of plantation areas as the resulting LUC impacts may
counteract its environmental benets.
Regarding the other impacts, Gibon et al. [24] have found that
adding CSS to a biomass electricity plant increases the impacts by
8–34%. Yang et al. [30] reported an even higher increase in BECCS
impacts, ranging from up to 116% for marine aquatic ecotoxicity,
photochemical oxidant formation and terrestrial ecotoxicity, to 770%
for ozone depletion. This is related to a decrease in the plant efciency
due to CCS and the impacts from MEA production and degradation
during its use. According to Oreggioni et al. [23], post-combustion CCS
Table 1
Summary of LCA studies of bioenergy with carbon capture and storage.
Study Biomass feedstock Capture technology Goal and scope Functional
unit
LCA method and impacts
a
Yang et al. [15] Switchgrass Post-combustion capture in
MEA
b
Comparison of BECCS with different coal co-ring
ratios, from cradle to grave
1 MWh
electricity
CML 2001: GWP, ODP,
HTP, POCP, AP, EP, TETP,
FETP and METP
Pour et al. [17] Bagasse and
forestry residues
Post-combustion capture in
MEA
b
GHG emissions from different BECCS
congurations, from cradle to grave
1 kWh
electricity
ALCAS
c
: GWP, ODP, HTP,
POCP, PM, AP, EP, FETP
and WD
Carpentieri et al.
[18]
Dry poplar Post-combustion capture in
DEA and MDEA
d
Impacts of IBGCC
e
with CCS, from cradle to gate
f
1 MJ energy Eco-indicator 95: GWP,
HTP, PM, AP and FDP
Corti & Lombardi
[19]
Dry poplar Post-combustion capture in
DEA and MDEA
d
Comparison of IBGCC
e
with CCS with coal and
natural gas combined cycle, from cradle to gate
f
1 MJ energy GWP only
Lu et al. [20] Crop residues Pre-combustion capture via
WGS
g
and acid gas removal
Impacts of coal and biomass gasication for
electricity production, with and without CCS, from
cradle to gate
f
1 kWh
electricity
GWP only
Takeda et al. [21] Woodchips (dry) Oxy-combustion Comparison of solar hybrid and conventional
BECCS with other electricity options, from cradle to
gate
f
1 kWh
electricity
GWP only
Cumicheo et al. [22] Woody biomass Post-combustion capture in
MEA
b
Comparison of three BECCS systems with various
biomass-natural gas ratios, from cradle to gate
f
1 MWh
electricity
GWP only
Oreggioni et al. [23] Round wood chips Pre-combustion and post-
combustion capture in MEA
b
Comparison of CHP plant with pre-combustion
pressure swing adsorption and post-combustion
capture options, from cradle to gate
f
1 kWh
electricity
ReCiPe 1.08: GWP, HTP,
PM, AP and FDP
Gibon et al. [24] Woody crops and
forest residues
Not specied Comparison of low carbon electricity generation
options, from cradle to gate
f
1 kWh
electricity
ReCiPe 1.08: GWP, ODP,
HTP, PM, AP, EP, FETP and
ADP
Spath & Mann [25] Agricultural
wastes
Post-combustion in MEA
b
Comparison of GHG emissions from BECCS and
fossil-fuel electricity options, from cradle to gate
f
1 kWh
electricity
GWP only
Zang et al. [26] Pinewood Pre-combustion via WGS
g
and post-combustion
capture in MEA
b
Comparison of IBGCC
e
power plants with pre- and
post-combustion capture, with and without CCS,
from cradle to gate
f
1 MWh
electricity
CML 2001: GWP, ODP,
HTP, AP and EP
a
ADP: Abiotic depletion potential; AP: Acidication potential; EP: Eutrophication potential; FDP: Fossil depletion potential; FETP: Freshwater aquatic ecotoxicity
potential; GWP: Global warming potential; HTP: Human toxicity potential; ODP: Ozone depletion potential; POCP: Photochemical oxidants formation/creation po-
tential; METP: Marine ecotoxicity potential; PM: Particulate matter formation potential; TETP: Terrestrial ecotoxicity potential; WD: Water depletion.
b
MEA: Monoethanolamine.
c
ALCAS: Australian LCA Society. The impacts are estimated using either the CML 2001, ReCiPe, ILCD and/or TRACI methods.
d
DEA: Diethanolamine; MDEA: Methyldiethanolamine.
e
IBGCC: Integrated biomass gasication combined cycle.
f
Refers to the system boundary of electricity generation, excluding electricity distribution and use. Plant construction is excluded in Yang et al. [15], Cumicheo et al.
[22] and Gibon et al. [24]. Plant decommissioning is excluded in all studies, except for Carpentieri et al. [18] and Corti & Lombardi [19].
g
WGS: Water gas shift.
D.M. Saharudin et al.

Applied Energy 349 (2023) 121506
4
increases fossil depletion by 57%, human toxicity by 95%, terrestrial
acidication by 20% and particulate matter formation by 31%. On the
other hand, there is only a small (7%) decrease in the acidication in
pre-combustion CCS, while the other impacts increase by 6–24%. Much
higher impacts for post-combustion CCS are also reported by Yang et al.
[30], with eutrophication, human toxicity, ozone depletion and terres-
trial acidication 2–3 times higher.
Similar to the LCA studies, the economic assessments of BECCS have
also covered generation of electricity [10,13,31–35] and combined heat
and power [10,36]. Various feedstocks have been considered (Table 2),
but the most studied is woody biomass. In terms of the biomass con-
version route, most authors have focused on combustion [10,31,33,34],
while some have compared it with gasication [13,17,35] and hydro-
thermal treatment [35]. The choice of capture technology is also similar
to the LCA studies with most papers focusing on post-combustion cap-
ture in MEA. Overall, the costs range between US$89–366/MWh and US
$80–319/t CO
2
removed; the costs of the CO
2
capture alone are US
$40–153/t CO
2
.
In one of the costs studies, Al-Qayim et al. [34] have compared the
costs of oxy-fuel combustion and post-combustion MEA capture
assuming combustion of wood pellets in the UK. The authors have
concluded that the BECCS system with oxy-fuel CO
2
capture, which has
lower plant efciency, has lower marginal CO
2
abatement costs
compared to post-combustion. Similar ndings have been reported by
Mac Dowell and Fajardy [27], where higher-efciency plants costed
more than the lower-efciency ones due to higher capital expenditure
for the former.
Studies on agricultural residues agree that BECCS plants are not
economically viable without nancial incentives, such as carbon credit,
premium feed-in tariffs for renewable electricity, renewable energy
certicates, government subsidies and others [10,31,35]. For instance,
comparing the CO
2
removal costs in electricity-only and combined heat
and power plants in the UK, Bui et al. [10] have found that the former
would require much higher incentives (US$126/t CO
2
) than the latter
(US$45/t CO
2
). In a similar study based in the US, Cheng et al. [35] have
found that electricity from BECCS would require nancial incentives of
US$82–137/t CO
2
to breakeven.
The costs of BECCS, in terms of electricity generation via the com-
bustion route, can vary widely depending not only on the country, but
also on various assumptions. Among the studies, an Australian-based
study by Pour et al. [17] reported the lowest cost range of US
$174–230/MWh compared to US$191–266/MWh in other developed
countries [13,33,34]. However, these values include the credits for
avoiding electricity from coal and assuming a 0% ination rate, with the
latter being highly unlikely, especially over a long term. Yang et al. [31]
have also included revenue from government incentives and the sale of
electricity. Hence, comparison of the costs among the studies is difcult
due to different assumptions.
Looking at a wider perspective, the vast majority of the BECSS
studies are focused on developed countries. The only developing nation
studied for BECCS is China, with only three papers found in the litera-
ture to date [15,20,31]. However, deploying NETs such as BECCS in
developing countries will also be needed to combat the climate crisis.
Moreover, BECCS in developing countries can have many advantages,
such as lower material and labour costs, as well as creating job oppor-
tunities. Therefore, this paper sets out to address this and other above-
mentioned knowledge gaps by focusing on a developing country such
as Malaysia and providing a comprehensive environmental and eco-
nomic assessment of BECCS to help inform relevant stakeholders on the
implications of future deployment of this NET.
Table 2
Summary of cost studies of bioenergy with carbon capture and storage.
Study Method Country Biomass feedstock Conversion technology Capture technology Cost (2022 US$)
a
US
$/MWh
US$/t CO
2 b
Bui et al. [10] LCC
c
UK Waste wood, forest and
crop residues
Combustion Post-combustion using
MEA
d
/ MDEA
e
– 165–204
Bhave et al. [13] TEA
f
UK Wood chips Combustion and gasication Pre-, post- and oxy-
combustion
209–366 253–319
(108–153)
Pour et al. [17] Not
specied
Australia MSW, bagasse and LFG
g
Combustion and gasication Post-combustion using
MEA
d
174–231 129–246
Lu et al. [20] Not
specied
China Wheat straw Gasication Post-combustion using
MEA
d
106 –
Yang et al. [31] LCC
c
China Straw Combustion Post-combustion using
MEA
d
169 159 (89)
Rhodes and Keith
[32]
Not
specied
US Not specied Gasication Post-combustion using
glycol/membrane
89–140 (42–55)
Negri et al. [33] LCC
c
Europe Energy crops Combustion Post-combustion using
MEA
d
191–277 160–225
Al-Qayim et al.
[34]
LCC
c
UK White wood pellets Combustion Post-combustion using
ecoamine
317–334 104–206
(109–111)
Cheng et al. [35] Not
specied
US Crop and woody residues,
and biosolids
Hydrothermal treatment,
gasication and combustion
Post-combustion – 80–314
Mollersten et al.
[36]
Not
specied
Sweden Woody biomass Pulp mill (integrated) Absorption (Selexol
process)
– (40–79)
a
All costs are converted to the 2022 US$ from the original costs and currency using the EPPI-Centre Cost Converter [37].
b
The cost ranges not in brackets are the total cost of BECCS per t CO
2
removed, while those in brackets are the cost of CO
2
capture only.
c
LCC: Life cycle costing.
d
MEA: Monoethanolamine.
e
MDEA: Methyldiethanolamine.
f
TEA: Techno-economic assessment.
g
MSW: Municipal solid waste; LFG: Landll gas.
D.M. Saharudin et al.

Applied Energy 349 (2023) 121506
5
3. Methods
This section details the methods used to carry out the environmental
and economic assessments of BECCS. Prior to that, the next section
provides an overview of the potential for BECCS deployment in
Malaysia.
3.1. Potential for BECCS in Malaysia
Malaysia currently does not have any BECCS power plants or any
plans to deploy them, probably because the country is relying heavily on
coal and natural gas for power generation, which provided respectively
47% and 36% of the total electricity generated in 2020 [38]. The current
share of biomass power is only 0.56% [38,39], which indicates signi-
cant underutilisation of the biomass available in the country, as dis-
cussed below.
The agricultural sector is the largest source of biomass waste in
Malaysia, contributing 91% of the biomass generated annually [40].
This is dominated by palm oil plantations, with a 70% share of the total
mass of agricultural waste [41]. Other agricultural activities include
livestock production, forestry logging and shing, along with rubber,
pineapple, rice and many other plantations [42]. However, the amount
of waste generated by these activities is insignicant compared to palm
oil plantations.
As the second largest palm oil producer in the world (after
Indonesia), Malaysia has 5.7 million hectares of land covered in palm oil
plantations, 45% of which are scattered across Peninsular Malaysia and
the remaining across East Malaysia (Sabah and Sarawak) [41]. The
wastes generated at palm oil plantations are fronds, trunks, empty fruit
bunches (EFBs), kernel shells and mesocarp bres, with a total of
approximately 65 Mt (dry weight) produced annually [41,43,44].
Trunks are available during replanting which occurs every 20–25 years,
while other wastes are available throughout the year [43].
A fraction of these wastes are converted into energy and other
products. For example, fronds are used in the pulp and paper industry, as
animal feed and can also be compressed into wood-based construction
materials [45]. Trunks are also used in construction, as well as for
furniture. Palm bres can be utilised as llers in thermoplastics and
thermosets composites, and in mattress production. A fraction of kernel
shells, bres and EFBs are combusted for electricity generation in palm
oil mills [44]. However, current utilisations are still very limited and
unutilised wastes are either landlled or left to decompose in the
plantation elds. As shown in Table 3, our estimates show that around
22.6 Mt of palm oil wastes would be available in Malaysia for potential
utilisation in BECSS.
Apart from feedstock availability, Malaysia also has a number of
geological storage locations which are suitable for CO
2
storage. An
offshore site in the Central Luconia Province has the potential to store
56–75 bn t CO
2
[47]. The largest and extensively studied storage loca-
tion is the Malay Basin sandstone aquifer, which has four sites – Jerneh,
Dulang, Tangga and Semangkok – with a collective storage potential of
114 bn t CO
2
[48,49].
It is clear from the above discussion that BECCS deployment is
suitable for Malaysia due to the large availability of waste palm oil
feedstocks and storage capacity. In addition to generating renewable
electricity and removing CO
2
, BECCS can also provide management
solutions for palm oil wastes, hence reducing the amount being land-
lled. In the context of a developing country, BECCS deployment can
also increase the energy security and provide job opportunities. In
addition, BECCS would help Malaysia reduce GHGs emissions by 45% by
2030 on 2005 levels, pledged as part of the Nationally Determined
Contribution [50]. It would also contribute towards the target of
increasing the renewable energy share to 31% of the total installed ca-
pacity by 2030 [51]. Furthermore, Malaysia has also pledged to stop
building new coal power plants at the UN Climate Change Conference
(COP26) in 2021 [52]. This will lead to 7 GW of coal power plants being
retired by 2033 [52], providing an opportunity for retrotting BECSS.
3.2. Life cycle assessment
The LCA of BECCS has been conducted in accordance with ISO
14040/44 [53,54], following the attributional approach. The next sec-
tion describes the goal and scope of the study, followed by inventory
data in Section 3.2.2 and in the subsequent section by the method used
to estimate the impacts.
3.2.1. Goal and scope denition
The goals of this study are as follows:
i) to estimate and compare the environmental impacts of BECCS for
electricity generation from different types of palm oil waste
considering different functional units;
ii) to identify environmental hotspots; and
iii) to compare the environmental impacts of BECCS to the system
without CCS to determine the additional impacts associated with
CO
2
removal.
As mentioned earlier, ve types of palm oil waste are considered:
fronds, trunks, EFBs, shells and bres. Fronds and trunks are sourced
directly from plantations, while the others are sourced from palm oil
mills. Fig. 1 shows the system boundaries for the BECCS system, which
comprise biomass acquisition, its transport and pre-treatment, power
plant operation to generate electricity and CO
2
capture, transport and
storage. Construction and the plant infrastructure are considered, but
the end of life (i.e. decommissioning and recycling of construction ma-
terials) is not considered due to a lack of data.
As mentioned earlier, BECSS can serve different functions, i.e. gen-
eration of electricity, CO
2
removal and treatment of waste. The rst two
functions are the primary drivers for BECCS deployment, with the third
considered a useful addition. Therefore, two functional units are
considered here to determine if and how the outcomes may change
depending on different perspectives for the primary driver for BECCS:
i) generation of 1 MWh of electricity; and
ii) sequestration of 1 t of CO
2
.
Table 3
Estimated availability of palm oil wastes in Malaysia, excluding current
utilisation.
Availability (Mt/year)
a
Total (Mt/
year)
Region Fronds Trunks EFBs
b
Shells Fibres
Peninsular
Malaysia
5.71 0.66 1.25 0.98 1.57 10.17
East Malaysia 6.98 0.81 1.52 1.20 1.92 12.43
Total 12.69 1.47 2.77 2.18 3.49 22.60
a
Calculated as part of this work for the year 2021, based on the plantation
areas in MPOB [41], waste yields in Hamzah et al. [43] and factors of accessi-
bility and current utilisation in Grifn et al. [46].
b
EFBs: Empty fruit bunches.
D.M. Saharudin et al.

Applied Energy 349 (2023) 121506
6
For the rst functional unit, the main function of the BECCS system is
electricity generation, with CO
2
removal being an additional feature.
This functional unit can assist policy makers in their efforts to increase
the renewable energy share in Malaysia to 31% of total installed ca-
pacity by 2025 as targeted by the government [51]. This functional unit
has also been assumed in all other LCA studies of BECCS (as discussed in
Section 2). For the second functional unit, CO
2
removal is considered the
main function of the system, with electricity being a useful by-product
for which the system can be credited. This functional unit is also help-
ful for policy makers and other stakeholders in identifying BECCS op-
tions that can contribute the most to net-zero targets while also
minimising other impacts. As far as the authors are aware, this is the rst
time such an approach has been applied in BECCS LCA studies. Different
credits have been applied to gauge the inuence on the results using
system expansion, assuming in turn the credits for electricity from the
Malaysian grid, coal, natural gas and biomass.
3.2.2. Inventory data and assumptions
The foreground data have been collected from different sources,
including ofcial government publications and annual reports from
energy and palm oil companies in Malaysia, data from process modelling
carried out as part of this work and from literature. The background data
are from Ecoinvent V3.8 [28], adapted for Malaysian conditions as far as
possible. The inventory data are detailed below for each life cycle stage
in turn, following the stages in Fig. 1.
3.2.2.1. Biomass acquisition. Depending on the type of palm oil waste,
biomass acquisition can include harvesting, pre-processing and collec-
tion from the point of source. The steps involved in the biomass acqui-
sition stage for different types of feedstock, which reect the common
practice in Malaysia [44,45,55–57], are detailed in Fig. S1 in the Sup-
plementary Information (SI).
Fronds are cut from palm trees during fresh-fruit bunches harvesting
and left in the eld to dry naturally [58]. After that, the fronds are
collected, chopped into small pieces (3–5 cm) and transported by trucks
to the power plant. Trunks are cut down after 25 years during replan-
tation [57] and also left in the eld to dry. They are then chipped and
trucked to the power plant. The parameters and assumptions for the
acquisition of fronds and trunks are detailed in Table S1 in the SI. For
EFBs, shells and bres, only collection is considered as they are sourced
directly from oil palm mills. They are sun-dried at the mills before
collection, with 83% moisture reduction from the initial moisture con-
tent [58].
3.2.2.2. Biomass transport. All feedstocks are assumed to be transported
by 26 t trucks, to a distance of 200 km for the plantations and 100 km for
the mills wastes. Other distances are explored in a sensitivity analysis
(10–400 km). A biomass loss during transport of 0.5% (wt.) is assumed.
3.2.2.3. Biomass pre-treatment. At the power plant, biomass feedstock is
screened to lter out impurities, such as stones and metals. The feed-
stock is then ground into 3-mm particles using a hammer mill before
being fed into the boiler. A summary of the data for the pre-treatment
stage can be found in Table S2 in the SI.
3.2.2.4. Power plant operation. The bioenergy plant with a capacity of
200 MW and a 30-year lifetime is considered. The latter is common for
BECCS systems and has been used in previous studies
[10,13,17,35,59,60]. The plant size is selected based on the average size
of the large-scale power plants in Malaysia, with the consideration of
land and feedstock availability [38,41]. Plant construction and sur-
rounding roadworks are also considered in the assessment, with the
inventory data sourced from Ecoinvent [28]. The power plant operation
has been modelled using the process simulation software Aspen Plus
V8.8 [61] to obtain the inventory data as they are not available in
literature for electricity generation from palm oil wastes.
As shown in Fig. 2 for the example of palm fronds, the Aspen model
covers biomass combustion, power generation through the steam
Rankine cycle and air emissions control units, which include the
removal of SOx, NOx and particulates from the ue gas. Separate models
Biomass
acquisition
Biomass
transport
Biomass
pre-
treatment
Power plant
operation
CO
2
capture &
transport
CO
2
storage
Emissions and waste
Materials and energy
Electricity
Without CCS
With CCS
Plant
construction
Fig. 1. System boundary for BECCS from palm oil wastes.
D.M. Saharudin et al.

Applied Energy 349 (2023) 121506
7
Fig. 2. Process owsheet for power generation.
The owsheet refers to palm fronds but it is the same as for the other feedstocks, except that their different compositions are considered in the simulation. For feedstock composition and the list of unit operations and
inputs, see Tables S3 and S4 in the SI, respectively.
D.M. Saharudin et al.

Applied Energy 349 (2023) 121506
8
have been created for each feedstock using their respective composi-
tions, as specied in Table S3 in the SI. The key parameters and as-
sumptions for each component can be found in Table S4 in the SI.
Biomass combustion has been modelled following Aspen Plus
guidelines for solid fuel combustion [62]. The combustion is assumed to
take place in a circulating uidised bed (CFB) boiler at 850 ◦C and 1 bar.
This boiler type has been selected as it is a preferred option for the
country’s power sector [63], with the rst installation commissioned in
2020. The boiler is suitable for large scale applications and can cater for
a variety of feedstocks with up to 75% moisture content [64,65]. Its
relatively low furnace temperature also reduces NOx emissions as it
inhibits the formation of thermal NOx [66]. The bed material commonly
used for CFB boilers is silica sand, with 5 kg of fresh sand added for every
1000 kg of feedstock combusted [65] to make up for the sand being
discharged together with the bottom ash. The life cycle impacts of sand
production are considered for the make-up sand but excluded for the
initial amount of sand which is small compared to the make-up.
Additionally, the CFB boiler requires only 10–30% excess air [64,65]
to achieve complete combustion, as compared to 80–95% in a conven-
tional boiler [67]. The average value of 20% excess air has been assumed
here for complete combustion, with the reactions inside the boiler set to
reach the equilibrium. The theoretical amount of air needed is calcu-
lated using Eqs. (1) and (2) [68] based on the elemental composition of
the feedstocks in Table S3:
˙
mO2=˙
mfeed × (2.66C +8H +S–O)(1)
˙
mair =˙
mO2/0.23 (2)
where
˙
mO2 oxygen owrate (kg/h); ˙
mfeed feedstock owrate (kg/h); ˙
mair
theoretical air owrate (kg/h); C,H,S,O carbon, hydrogen sulphur and
oxygen in the fuel (%).
The air owrates calculated via Eq. (2) have been validated using the
industrial combustion-air calculation tool Thermodyne [69] with the
values deviating from those in the tool by 0.3%.
Ground lime (CaO) is injected into the boiler to remove SOx and HCl.
The spent lime is collected at the bottom of the boiler and removed
together with the bottom ash and landlled. Selective catalytic reduc-
tion (SCR) with anhydrous ammonia is used to remove NOx. Solids and
particulates are separated from the ue gas using a cyclone, bag lter
and electrostatic precipitator.
The removal of SOx, HCl and NOx has each been modelled using a
stoichiometric reactor in Aspen Plus (for details, see Table S4 in the SI),
set to achieve the emissions of 15% below the Malaysian limits (Table
S5). This is equivalent to the removal rates of 21–46% for SOx, 61–86%
for HCl and 25–49% for NOx [61,74]. The removal ranges refer to the
different composition of the feedstocks. The reason for assuming the
emissions of 15% below the limits is to minimise the operating costs
related to the materials needed for the removals, especially ammonia
due to its high cost. The latter would be three times higher if the usual
NOx removal rate of 90% [66,75,79] were assumed (for details, see
Section S2 in the SI).
Exiting the air emissions control units, the ue gas is cooled from
160 ◦C to 50 ◦C in a heat exchanger to recover low-grade heat of
165–185 GJ for use within the system (i.e. for air and water pre-
heating). After this point, the ue gas is either released into the atmo-
sphere in the systems without CCS, or sent to the CO
2
capture unit in the
BECCS systems.
The overall plant electrical efciencies estimated through process
simulation range from 32.5% to 39.8% (without CCS; Table S6 in the SI)
across the feedstocks, based on their respective lower heating values
(LHVs). Detailed information on this can be found in Section S3 in the SI.
The estimated efciencies are within the range of existing biomass
plants and those published in literature (25–45%) [80–82].
The key outputs from the Aspen Plus simulations are detailed in
Table S8 in the SI. These values have been converted to correspond to
each of the two functional units (Table 5) and Table 6) and used as
Table 4
Key parameters and assumptions in process simulation of power generation.
Stage Input data Unit Values Source(s)
Biomass combustion in CFB Temperature ◦C 850 [64,65]
Pressure bar 1 [64,65]
Excess air % 20 [64,65]
Air inlet pressure bar 1.3 [61]
Flue gas temperature at boiler exit ◦C 180 [70]
Pressure drop bar 0.1 Assumption
Electricity generation (the Rankine cycle) Steam temperature ◦C 565 [71]
Steam pressure bar 166 [71]
Turbine type Isentropic
Turbine efciency % 90 [72]
Turbine outlet pressure bar 0.1 Assumption
Condenser setting Vapour fraction =0 [61]
Condenser pressure bar 0.1 [61]
Pump efciency % 92 [73]
Pump discharge pressure bar 166 [71]
Air emissions control SOx and HCl removal (lime)
Ca/S ratio 3:1
a
[64,65]
SO
2
removal rate % 21–46
a
[61,74]
SO
3
removal rate % 21–46
a
[61,74]
HCl removal rate % 61–86
a
[61,74]
NOx removal (selective catalytic reduction)
Ammonia-to-NOx mass ratio 0.3:1
a
[28]
Ammonia slip ppm 2 [66,75]
NOx removal rate % 25–49
a
[61,74]
Type of catalyst Titanium dioxide [76]
Amount of catalyst m
3
/MW 0.75 [77]
Lifetime of catalyst hours 100,000 Assumption (from [75])
Particulates removal
Cyclone efciency % 80 [78]
Bag lter efciency % 98 [78]
Electrostatic precipitator efciency % 99.5 [78]
a
These reect the assumption of the emissions being 15% below the Malaysian limits.
D.M. Saharudin et al.

Applied Energy 349 (2023) 121506
9
inventory data in the LCA modelling.
3.2.2.5. Post-combustion CO
2
capture, transport and storage. The type of
CO
2
capture selected in this study is absorption in MEA. Despite a high
energy demand, especially for MEA regeneration, this option is chosen
due to its maturity and use in industry [5,83–85]. Moreover, MEA is
available commercially and can be purchased at a low price in Malaysia
[86].
The main steps involved in this stage are summarised in Fig. S2 in the
SI and the key parameters used in the modelling can be found in Table 7.
The ue gas is passed through the absorber where CO
2
is absorbed into
MEA and the remaining gas is discharged into the atmosphere. The CO
2
-
rich MEA is sent to a stripping column to separate CO
2
from the mixture
and regenerate the MEA. The amount of heat required for the regener-
ation varies from 2.2 to 4.2 GJ/t CO
2
removed [84,85,87–89],
depending on the CO
2
source and its concentration in the inlet ue gas
[90]. This high heat demand is due to the high stripping temperature
and volume in the MEA regeneration column (Table 7), which can
consume ~30% of the gross energy produced by the power plant [91]. In
this study, a lower value in the range is assumed (2.5 GJ/t CO
2
) as CFB
requires much less excess air, leading to signicantly higher CO
2
con-
centration in the ue gas (18.42–20.14 wt%; Table S8) and, hence,
consuming signicantly less energy for CO
2
capture [90].
MEA degradation due to oxidation and its emissions to air and
wastewater are also considered in this study [90,92]. Oxidative degra-
dation occurs when MEA reacts with the oxygen in the ue gas in the
absorber, forming heat-stable salts and resulting in the reduction of the
absorption capacity [92]. MEA degradation also occurs when it reacts
with impurities in the ue gas, such as NOx, SOx, ash and other con-
taminants [93].
Besides the MEA, other materials used in the CO
2
capture system are
activated carbon, used to adsorb the degraded MEA, and sodium hy-
droxide to remove the salts from MEA during the regeneration process
[87]. After being separated from the MEA, the CO
2
is compressed to 150
bar. The compression has been modelled in Aspen Plus and the resulting
power consumption of 64.5 kWh/t CO
2
is within the values published in
the literature (24–111 kWh/t CO
2
) [91,94,95].
Finally, the compressed CO
2
is transported via a 400 km under-
ground pipeline (200 km onshore; 200 km offshore) and stored in an
offshore geological deposit. The transportation pipeline is assumed to
have the capacity of 10 Mt/yr and a lifetime of 40 years [96]. It is
assumed to be shared with other CCS plants, with the amount of CO
2
considered in this study occupying 15% of the total pipeline capacity, i.
e. 1.47–1.59 Mt CO
2
/yr. Therefore, the impacts of pipeline construction
are allocated in proportion to this amount of CO
2
, taking into account
the pipeline lifetime. For storage, electricity consumption of 7 kWh/t
CO
2
for injection into geological deposit is considered [15].
The breakdown of energy requirements for CCS for different palm oil
wastes is shown in Table 8, along with the energy used by the power
plant and the net power generated in the system with and without CCS
(for details, see Section S3 and Table S7 in the SI). The values for energy
consumption by the fan for combustion air, the Rankine cycle pump and
compressor for CO
2
compression are from the Aspen Plus simulation,
Table 5
Key parameters for power plant operation per 1 MWh of net electricity produced.
Item Without CCS With CCS
Unit Fronds Trunks EFBs
a
Shells Fibre Fronds Trunks EFBs Shells Fibre
Feedstock ow (wet basis) kg/MWh 890.61 848.43 811.33 643.29 680.70 1532.76 1417.35 1365.81 1053.90 1103.61
Combustion air ow t/MWh 5.18 5.19 5.12 4.48 4.75 8.92 8.66 8.62 7.35 7.70
Lime for SOx removal kg/MWh 4.27 1.11 12.28 8.41 8.76 7.35 1.86 20.67 13.78 14.20
Ammonia for NOx removal kg/MWh 0.44 0.36 0.55 1.19 0.93 0.76 0.61 0.92 1.95 1.51
Air emissions prole
b
Ammonia g/MWh 11.74 12.03 11.80 9.99 10.80 20.21 20.09 19.86 16.36 17.51
Carbon dioxide t/MWh 1.22 1.17 1.19 1.14 1.12 0.211 0.196 0.200 0.186 0.181
Chlorine mg/MWh 79.48 638.64 311.89 256.22 243.29 136.78 1066.88 525.04 419.77 394.45
Fine particulates g/MWh 0.53 0.69 1.26 0.52 1.56 0.91 1.16 2.11 0.85 2.53
Hydrochloric acid kg/MWh 0.61 0.65 0.64 0.53 0.58 1.06 1.09 1.07 0.88 0.94
Nitrogen dioxide g/MWh 20.26 21.89 20.06 13.99 16.19 10.46 10.97 10.13 6.88 7.87
Nitrogen oxide kg/MWh 3.05 3.24 3.16 2.66 2.88 5.25 5.41 5.31 4.35 4.66
Sulphur dioxide kg/MWh 3.01 3.19 3.12 2.63 2.85 5.18 5.33 5.25 4.32 4.62
Sulphur trioxide g/MWh 63.21 68.96 60.70 37.46 45.25 108.78 115.21 102.18 61.37 73.36
Solid waste composition
Bottom ash kg/MWh 4.63 7.08 10.06 7.39 7.63 7.96 11.83 16.93 12.11 12.37
Fly ash kg/MWh 0.94 3.38 4.56 3.32 3.52 1.62 5.65 7.68 5.44 5.71
a
EFBs: Empty fruit bunches.
b
After the emissions control.
Table 6
Key parameters for power plant operation per 1 t CO
2
removed.
BECCS system Unit Fronds Trunks EFBs
a
Shells Fibres
Feedstock ow (wet
basis)
kg/t
CO
2
809 804 760 628 676
Combustion air t/t CO
2
4.71 4.92 4.80 4.38 4.72
Lime for SOx removal kg/t
CO
2
3.88 1.06 11.51 8.22 8.70
Ammonia for NOx
removal
g/t
CO
2
401 345 512 1164 923
Air emissions prole
b
Ammonia g/t
CO
2
10.67 11.40 11.06 9.75 10.72
Carbon dioxide kg/t
CO
2
111 111 111 111 111
Chlorine mg/t
CO
2
72 605 292 250 242
Fine particulates g/t
CO
2
0.48 0.66 1.18 0.51 1.55
Hydrochloric acid kg/t
CO
2
0.56 0.62 0.60 0.52 0.57
Nitrogen dioxide g/t
CO
2
18.40 20.75 18.81 13.66 16.07
Nitrogen oxide kg/t
CO
2
2.77 3.07 2.96 2.60 2.86
Sulphur dioxide kg/t
CO
2
2.73 3.03 2.92 2.57 2.83
Sulphur trioxide g/t
CO
2
57.42 65.38 56.89 36.58 44.93
Solid waste
composition
Bottom ash kg/t
CO
2
4.20 6.71 9.43 7.22 7.58
Fly ash kg/t
CO
2
0.86 3.20 4.27 3.24 3.50
a
EFBs: Empty fruit bunches.
b
After the emissions control unit.
D.M. Saharudin et al.

Applied Energy 349 (2023) 121506
10
while the others are calculated from literature.
3.2.2.6. Waste management. Waste streams generated from the system
are the bottom and y ash from biomass combustion (including spent
lime in the former), spent catalyst from SCR and spent MEA (including
NaOH) from CO
2
capture. All wastes are landlled, except for the spent
MEA which is incinerated with no energy recovery, consistent with
waste management in Malaysia.
3.2.3. Life cycle impact assessment
The LCA modelling has been carried out in GaBi V10.6 [104] and the
impacts determined according to the ReCiPe 2016 V1.1 Midpoint (H)
[105] method. All 18 environmental impacts in this method are
considered as follows: global warming potential (GWP), acidication
potential (AP), freshwater eutrophication potential (FEP), marine
eutrophication potential (MEP), human toxicity potential, cancer (HTP-
C), human toxicity potential, non-cancer (HTP-NC), freshwater
ecotoxicity potential (FETP), marine ecotoxicity potential (METP),
terrestrial ecotoxicity potential (TETP), particulate matter formation
potential (PMFP), ozone depletion potential (ODP), fossil depletion
potential (FDP), metal depletion potential (MDP), ionising radiation
potential (IRP), photochemical ozone formation potential, ecosystems
(POFP-E), photochemical ozone formation potential, human (POFP-H),
land use potential (LUP) and freshwater consumption potential (FCP).
The carbon in the feedstock is treated as biogenic carbon in this
study, while that emitted from fossil-based sources is considered as fossil
carbon. The biogenic carbon originally sequestered by the feedstock and
subsequently captured and stored after combustion in the power plant is
considered to be a ‘negative’ emission with the characterisation factor of
−1 kg CO
2
eq./kg CO
2
, while the release of fossil carbon is a ‘positive’
emission with the characterisation factor of 1 kg CO
2
eq./kg CO
2
[106,107].
Table 7
Key parameters and assumptions for CO
2
capture, transport and storage.
Key parameters Unit Value Source(s)
Absorber temperature ◦C 50 [85]
Absorber pressure bar 0.7 [85]
Stripper temperature ◦C 120 [85,97]
Stripper pressure bar 2 [97]
MEA concentration wt% 30 [93,97,98]
MEA absorption capacity g CO
2
/kg MEA 448 [98]
CO
2
capture rate % 90 [31,87]
NO
2
absorbed into MEA % of inlet concentration 70 [99]
MEA degradation kg/t CO
2
1.45 [85,89,100]
Material requirements
MEA g/t CO
2
60 [87]
Water kg/t CO
2
18.1 [87]
Activated carbon g/t CO
2
70 [87]
Sodium hydroxide g/t CO
2
30 [85]
Energy consumption
Heat MJ/t CO
2
removed 2.5 [84,85]
Electricity for MEA pumping kWh/t CO
2
removed 33.8 [87]
Electricity for CO
2
compression kWh/t CO
2
removed 64.5 [87]
CO
2
transport and storage
Distance km 400 Assumption
Fugitive emissions of CO
2
% 2 [101,102]
Electricity for transport (compressor drive) Wh/tkm 72.5
a
[28]
Electricity for storage (injection) kWh/t CO
2
7 [15]
a
Equal to 29 kWh/t CO
2
removed for the 400 km pipeline and amount of CO
2
of 1.47–1.59 Mt/yr.
Table 8
Breakdown of energy consumption for the power plant and CCS.
Unit Fronds Trunks EFBs Shells Fibres
Power plant energy requirement
Screen
a
Wh 147 142 135 108 114
Grinder
b
MWh 9.71 9.39 8.89 7.12 7.52
Fan power (combustion air) MWh 3.20 3.23 3.20 3.09 3.13
Rankine cycle pump MWh 2.70 0.25 2.70 2.70 2.70
Other equipment
c
MWh 3.38 3.38 3.38 3.38 3.38
Net power output (without CCS) MWh 180.86 183.61 181.70 183.60 183.16
Power plant efciency
d
% 32.53 33.63 36.71 39.62 37.85
CCS energy requirement
Heat
e
MWh 55.45 53.93 53.98 52.35 51.36
MEA pump MWh 7.48 7.27 7.28 7.06 6.93
CO
2
compression MWh 12.84 12.49 12.50 12.12 11.90
Net power output (with CCS) MWh 105.09 109.91 107.93 112.07 112.97
Power plant efciency
d
% 17.10 18.48 19.81 22.20 22.49
CCS energy penalty (relative to power output without CCS) % 41.90 40.14 40.60 38.96 38.32
a
Calculated by multiplying the feedstock owrate with the energy consumption of screening unit as specied in Table S1.
b
Calculated by multiplying the feedstock owrate with the energy consumption of the grinder as specied in Table S1.
c
Calculated based on the auxiliary energy requirements detailed in [103].
d
Calculated based on the LHVs of the feedstocks. For more details, see Section S3 in the SI.
e
Calculated by multiplying the CO
2
capture rate with the heat requirements specied in Table 7.
D.M. Saharudin et al.

Applied Energy 349 (2023) 121506
11
3.3. Life cycle costing
The LCC method is based on the methodology proposed by Hunkeler
et al. [108]. For the power plant, the LCC estimation is also inspired by a
guide published by RICS [109], which adopted the ISO 15686-5:2017
[110] guidelines for LCC of buildings and constructed assets.
The goals of the LCC are equivalent to the LCA goals, namely:
i) to quantify and compare the costs of BECCS for the different
feedstocks considered;
ii) to identify the main contributor(s) to the costs of BECCS; and
iii) to compare the costs of BECCS with the equivalent systems
without CCS to determine the marginal CO
2
abatement costs.
The system boundary and the functional units for the LCC are the
same as for the LCA as outlined in Section 3.2. The costs considered in
the study are summarised in Fig. 3. Unless specied otherwise, all the
costs used in the study represent the average values in the period from
2013 to 2022. They have been converted to 2022 US dollars (US$)
taking into account the ination rate and the exchange rate for different
currencies where relevant [37].
In the power generation sector, the life cycle costs are often
expressed as levelised costs of electricity (LCOE) [111–115]. Therefore,
LCOE are used to estimate the LCC for the functional unit of ‘generating
1 MWh electricity’. They are dened as the present value of the dis-
counted costs of the whole system, expressed per unit of electricity
produced over the plant’s lifetime [111,115]. The LCOE of the BECCS
systems are calculated according to the equation below; the LCOE of the
systems without CCS are estimated in the same way, minus the costs of
CCS:
LCOEBECCS =
CI +∑n
t=1
(BFACt+BTCt+O&Mt+CCSt) (1+i)t
(1+r)t+EOLD
∑n
t=1Et
(3)
where
LCOE
BECCS
levelised costs of BECCS systems (US$/MWh); CI over-
night capital costs (US$); n power plant lifetime (yr); BFAC
t
biomass
feedstock acquisition costs in year t (US$/yr); BTC
t
biomass transport
costs in year t (US$/yr); O&M
t
power plant operation and maintenance
costs in year t (US$/yr); CCS
t
CO
2
capture, transport and storage costs in
year t (US$/yr); i ination rate (%); r discount rate (%); EOL
D
end-of-life
costs of power plant decommissioning (US$); E
t
amount of electricity
produced in year t (MWh/yr); t time (yr).
For the functional unit of ‘1 t CO
2
removed’, the costs of the BECCS
systems are calculated as:
CBECCS =LCOEBECCS ×∑n
t=1Et
∑n
t=1mCO2t
(4)
where
C
BECCS
costs of BECCS (US$/t CO
2
removed); mCO2t amount of CO
2
removed in year t (t CO
2
removed/yr).
The costs of CCS, or marginal CO
2
abatement costs, can be calculated
using the following equation [91,96,116–118]:
MCCO2=LCOEBECCS −LCOEBE
mCO2
(5)
where
MCCO2 marginal CO
2
abatement costs (US$/t CO
2
removed); LCOE
BE
LCOE of the bioenergy system without CSS (US$/MWh); mCO2 amount of
CO
2
removed (t CO
2
/MWh).
3.3.1. Biomass feedstock acquisition costs
The biomass costs used in the study are detailed in Table 9.
Depending on the feedstock, they comprise both harvesting and
collection (fronds and trunks), or collection costs alone (EFBs, shells and
bres). The harvesting costs refer to the various activities mentioned in
Section 3.2.2.1, including fronds and trunks cutting and chipping.
Collection costs are related to the expenses incurred for loading the
biomass onto trucks for transport to the bioenergy plant.
3.3.2. Biomass transportation costs
These costs are calculated based on the amount of each feedstock
required for the power plant. They include transport-fuel costs, capital
expenditure for trucks, truck maintenance, insurance, road tax and truck
drivers’ salary, as follows:
BTC =TFC + (ntruck × (DW +CAPEXtruck +O&Mtruck) ) (6)
Fig. 3. Costs considered for life cycle costing.
Blue boxes represent costs in the systems without CCS and green boxes the additional costs of CCS considered in the BECCS systems. O&M costs: Operating and
maintenance costs. (For interpretation of the references to colour in this gure legend, the reader is referred to the web version of this article.)
D.M. Saharudin et al.

Applied Energy 349 (2023) 121506
12
where
BTC biomass transportation costs (US$/yr); TFC annual transport-
fuel costs (US$/yr); ntruck number of trucks; DW annual salary of truck
drivers (US$/yr); CAPEX
truck
capital expenditure for trucks (US$/yr);
O&M
truck
operating and maintenance costs of trucks (US$/yr).
The annual transport-fuel costs are estimated as:
TFC =FP ×FC ×D×˙
mfeed ×OH
TC (7)
where
TFC transort-fuel costs (US$/yr); FP fuel (diesel) costs (US$/l); FC
fuel consumption by trucks (l/km); D transport distance (km); ˙
mfeed
biomass feedstock owrate (t/h); OH annual power plant operating
hours (h/yr); TC truck capacity (t).
Based on the assumed distances from the eld (200 km) and the mill
(100 km) to the power plant, the daily number of trips is four for eld
wastes (fronds and trunks) and six for the mill wastes (EFBs, shells and
bres). This is used to determine the number of trucks needed to meet
the daily feedstock requirements of the power plant, which then used to
calculate the annual drivers’ wage (DW) and the annual capital
(CAPEXtruck) and operating costs (O&Mtruck) of the trucks (Eq. (6)). The
average costs of biomass transport are summarised in Table 9, covering
the period 2016–2022.
3.3.3. Power plant costs
As can be seen in Table 9, power plant costs comprise three main cost
elements: capital, operating and maintenance (O&M) and end-of-life
(EOL) costs of decommissioning. For capital costs, two options are
considered: building a new BECCS plant and retrotting an existing coal
power plant. As mentioned earlier, the latter has a higher implementa-
tion potential in Malaysia and could also lead to cost and environmental
savings.
For the new plant, capital costs include the cost of equipment, pre-
construction (planning and designing), installation and grid connec-
tion [121]. They are calculated from the data provided by IRENA [81],
which reect the actual average capital costs of a bioenergy plant in
countries other than China, Europe, the United States and India. The
costs are adjusted to match the plant capacity in this study using the six-
tenth rule [122], as follows:
CCA
CCB
=(ICA
ICB)0.6
(8)
Table 9
Key parameters and assumptions for economic assessment.
Costs Unit Value Source(s)
Biomass feedstock acquisition costs
Fronds US$/t 11.16 [120]
Trunks US$/t 13.05 [120]
Empty fruit bunches US$/t 6.32 [135]
Shells US$/t 6.32 [135]
Fibres US$/t 6.32 [135]
Biomass transport costs
Fuel price (diesel) US$/l 0.497 [136]
Fuel consumption – full load l/km 0.225 [137,138]
Fuel consumption – empty l/km 0.365 [137]
Truck lifetime years 10 [28,139]
Capital costs of truck US$/truck 51,820 Averaged from [140]
Truck maintenance costs % of truck capital 5.5 [141]
Insurance and road tax % of truck capital 1 Assumption
Driver salary US$/month 1253 Calculated from [142,143]
Power plant costs
Capital costs (new plant) M US$/MW 1.184 Calculated from [81,123]
Capital costs (retrot) M US$/MW 0.417 Calculated from [124,126]
Annual xed O&M
a
costs % of capital 5 [121]
Variable O&M
a
costs:
Sand US$/t 30 [144,145]
Ammonia US$/t 546 [146]
Lime US$/t 110 [147,148]
Metal catalyst US$/m
3
5500 [75]
Lifetime of metal catalyst Hours 100,000 Estimated form [75]
Water US$/t 0.34 [149]
Bottom ash waste management US$/t 3.16 Calculated
Fly ash waste management US$/t 36.80 [150]
Spent catalyst waste management US$/t 36.80 [150]
Wastewater treatment US$/m
3
0.27 [151]
End of life cost (decommissioning) M US$/MW 0.345 Calculated from [128,129]
CO
2
capture costs
Capital costs M US$/MW 1.207 Calculated from [10,20]
Fixed O&M costs M US$/MW
.
yr 0.188 Calculated from [20]
Variable O&M costs:
MEA
b
US$/t 1430 [86]
Activated carbon US$/t 1240 [152]
NaOH US$/t 340 [153]
Spent solvent waste management US$/t 47 [154]
CO
2
transport and storage costs
CO
2
pipeline transport US$/t CO
2
4.2 [96,131]
CO
2
storage and monitoring US$/t CO
2
13 [131]
Other considerations
Discount rate % 3.5 Averaged from [132]
Ination rate % 3.0 Averaged from [155,156]
Currency conversion rate (July 2022) 1 US$ =RM 4.39 [157]
a
O&M: Operating and maintenance.
b
MEA: Monoethanolamine.
D.M. Saharudin et al.

Applied Energy 349 (2023) 121506
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Fig. 4. Environmental impacts per MWh of net electricity produced for the systems with and without CCS.
x-axis: FR: Fronds; TR: Trunks; EFB: Empty fruit bunches; SH: Shells; FB: Fibres (all without CCS); CCS: Systems with carbon capture and storage. y-axis: GWP: Global
warming potential; AP: Acidication potential; FEP: Freshwater eutrophication potential; MEP: Marine eutrophication potential; HTP-C: Human toxicity potential
(cancer); HTP-NC: Human toxicity potential (non-cancer); FETP: Freshwater ecotoxicity potential; METP: Marine ecotoxicity potential; TETP: Terrestrial ecotoxicity
potential; PMFP: Particulate matter formation potential; IRP: Ionising radiation potential; ODP: Ozone depletion potential; POFP-E: Photochemical ozone formation
potential (ecosystem); POFP-H: Photochemical ozone formation potential (human); FDP: Fossil depletion potential; MDP: Metal depletion potential; FCP: Freshwater
consumption potential; LUP: Land use potential.
D.M. Saharudin et al.

Applied Energy 349 (2023) 121506
14
Fig. 4. (continued).
D.M. Saharudin et al.

Applied Energy 349 (2023) 121506
15
where
CC
A
capital costs of plant A (considered in this study) (US$/MW);
CC
B
capital costs of plant B (from [81]) (US$/MW); IC
A
installed ca-
pacity of plant A (200 MW (this study)); IC
B
installed capacity of plant B
(50 MW [81]).
The estimated value of 1.18 million (M) US$/MW (Table 9) falls
within the range of the capital costs of power plants in Malaysia
1.04–2.85 M US$/MW installed capacity [121,123].
For the retrot option, the capital costs are estimated using data from
the Drax plant in the UK which has been converted from coal to biomass
[124,125]. These costs have also been adjusted for the difference in the
installed capacity using Eq. (8). To account for the cost differential be-
tween Malaysia and the UK, the Drax retrot capital costs have been
multiplied by the ratio of the costs of the same size power plants in the
two respective countries (0.48) [124,126]. The resulting retrot capital
costs are given in Table 9; as can be seen, they are nearly three times
lower than the costs of a new plant.
The O&M costs are divided into xed and variable costs, with the
latter being the same for the new and retrotted plants. The xed costs
are estimated from the capital costs and comprise power plant mainte-
nance, equipment replacement, labour, insurance, taxes and royalties
[81,112,139]. The variable costs take into account the material re-
quirements and waste management at the power plant. The sum of the
xed and variable costs is escalated by a factor of 1.25 [112,139] to
obtain the total O&M costs.
The EOL costs comprise the costs of plant decommissioning,
excluding any recovery of the building materials and plant equipment
due to the lack of data. As the decommissioning costs are not available
for Malaysia, they have been sourced from India, as another developing
country. The same approach has been used as for the retrot capital
costs to account for the price difference between the two countries
[128,129].
3.3.4. CO
2
capture costs
CO
2
capture costs include additional capital and xed and variable
O&M costs for the capture system. The additional capital costs are due to
the additional equipment for the CO
2
capture and compression system,
including the absorption and stripping columns, heat exchangers and
compressor. Capital and xed O&M costs are calculated using CCS cost
estimation ratios (1.02 and 1.18, respectively) with respect to bioenergy
plants without CCS [20]. The variable O&M costs are the costs of ma-
terials used in the capture system, calculated from the material balance.
The total O&M costs are scaled up using the scaling factor (1.25) rec-
ommended by Sadhukhan et al. [112] and Ng et al. [127]. The CO
2
capture costs are detailed in Table 9.
3.3.5. CO
2
transport and storage costs
CO
2
transport and storage costs are estimated from data published by
the US National Energy Technology Laboratory (NREL) [130] and the
Global CCS Institute [131], selected based on storage location and the
amount of CO
2
removed annually. The costs of CO
2
transport include
pipeline construction, maintenance and pipeline operation (including
compression), while the costs of CO
2
storage comprise CO
2
injection and
monitoring [130,131]. The plant is assumed to share the pipeline
transportation with other CO
2
capture plants, so the costs of CO
2
transport, storage and monitoring are also shared among them. To
reect this, lower cost values are selected from the range of costs in
literature; for details, see Table 9.
3.3.6. Other considerations
The discount rate of 3.5% is chosen based on the lending rates in
Malaysia over the past two years [132], which is the lowest in ten years
and is hence a conservative value. The ination rate of 3% is selected
based on the average rates over the past decade [133] considering the
long-term project duration; for comparison, a recent ination rate is
3.8% (December 2022) [134]. Both the discount and ination rates are
volatile and uctuate depending on the economic performance of a
country and, hence, unpredictable over the plant’s lifetime. To account
for these variations, other discount rates are explored in a sensitivity
analysis (Section 4.2.4).
4. Results and discussion
The LCA impacts are discussed in Section 4.1 and the life cycle costs
in Section 4.2. The impacts are rst presented per 1 MWh of electricity,
followed by the impacts per 1 t CO
2
removed. The LCC assessment starts
with the capital and operating costs of the systems with and without CCS
for new and retrotted plants, followed by their respective LCOEs, costs
of BECCS and the marginal CO
2
abatement costs.
4.1. Life cycle assessment
4.1.1. Environmental impacts per MWh of electricity
These results are presented in Fig. 4 for the ve feedstocks with and
without CCS, showing the contribution of different life cycle stages to
the total impacts (for the actual values, see Tables S9 and S10 in the SI).
Overall, the BECCS system with palm fronds has the highest impacts in
11 out of the 18 categories, while the system with bres has the lowest
values in ten impact categories. The difference between the best and the
worst BECCS systems for most of the impacts is <35%. As can be seen in
Fig. 4, GWP is net negative for all the feedstocks if CCS is used, but all
other impacts increase compared to the systems without CCS. If the
Fig. 4. (continued).
D.M. Saharudin et al.

Applied Energy 349 (2023) 121506
16
Fig. 5. Environmental impacts per t CO
2
removed for BECSS with palm fronds.
For the impact acronyms, see Fig. 4. NC: No electricity credit; SE-EM: System expansion with credit for electricity from the Malaysian grid; SE-C: System expansion
with credit for electricity from coal; SE-NG: System expansion with credit for electricity from natural gas; SE-B: System expansion with credit for electricity
from biomass.
D.M. Saharudin et al.

Applied Energy 349 (2023) 121506
17
Fig. 5. (continued).
D.M. Saharudin et al.

Applied Energy 349 (2023) 121506
18
latter is not used, GWP is net-positive. The results for GWP are discussed
in more detail below, followed by all the other impacts in the subsequent
section.
4.1.1.1. Global warming potential. As mentioned above, the GWP of the
BECCS systems is net-negative, ranging from −1270 to −1410 kg CO
2
eq./MWh. Note that biogenic carbon shown in the gure for the systems
without CCS is included for transparency of the results, but not
accounted in the total GWP. The BECCS system with palm fronds has the
lowest impact due to its lowest plant efciency (17.1%; Table 8). Lower-
efciency plants require more feedstock to generate the same amount of
electricity than the higher-efciency plants and thus emit more CO
2
to
be captured. This in turn leads to a higher CO
2
removal per MWh than in
the higher-efciency plants. Conversely, the system with bres, which
has the highest plant efciency (22.5%; Table 8), has the highest GWP.
As a result, the system with fronds has the highest CO
2
removal rate
(1.67 t CO
2
/MWh), while that with bres has the lowest (1.44 t CO
2
/
MWh); see Table S11 for the other feedstocks. These trends are in
agreement with those of Mac Dowell and Fajardy [42] and Fuss et al.
[163]. Although these ndings suggests that lower-efciency plants are
a better option for GWP, it should be borne in mind that higher-
efciency plants are preferred from the thermodynamic and economic
point of view and, hence, there is a trade-off between these competing
criteria.
In terms of the contribution to the total GWP, excluding the biogenic
carbon, the CCS system accounts for 37–47% of the total across different
feedstocks, mainly due to the electricity used for compressors along the
CO
2
transport pipeline. This is followed by biomass transport (29–37%)
and biomass acquisition (16–21%) for the systems with fronds and
trunks, due to the emissions from diesel used in trucks. Since the rest of
the feedstocks are collected directly from the palm oil mill plants (i.e. no
harvesting required), power plant operation for those is the third largest
contributor (21–22%). Of this, half is related to the combustion emis-
sions and the other half to the GWP of the materials used in the power
plant, particularly lime and ammonia.
Compared to BECCS, the systems without CCS have net-positive GWP
of 59–126 kg CO
2
eq./MWh. Using shells as a feedstock is the best option
for these systems and fronds the worst, as they require respectively the
lowest and highest amount of feedstock to generate the same amount of
electricity (Table 5). The GWP values for the power plant operation in
the BECCS systems are higher (23–48%) than for the systems without
CCS across the feedstocks. This is due to the energy penalty in the BECCS
systems which require more feedstock to generate the same amount of
net electricity (Table 8). The CCS system alone has a GWP of 77–90 kg
CO
2
eq./MWh (Fig. S3 in the SI). The impacts of the individual com-
ponents in the CCS system for all ve feedstocks can be found in Table
S12 and Fig. S3 in the SI.
4.1.1.2. Other impacts. As opposed to GWP, the BECCS system with
fronds has the highest impacts for most of the categories, while the
system with bres is the best option for ten impacts (Fig. 4). This is
because the BECCS plant with fronds has the lowest efciency and re-
quires the largest amount of feedstock and energy to generate the same
amount of electricity; the opposite is true for the system with bres. This
has an opposite effect on the impacts than on GWP as it is better for the
latter to generate and hence remove more CO
2
, which happens in the
systems with less efcient feedstocks.
Plant operation is the main hotspot in six categories, contributing
60–95% to the impacts across the feedstocks. The highest contribution is
seen in POFP-H and POFP-E (85–95%), followed by PMFP (84–91%) and
ODP (78–91%), where the impacts are heavily inuenced by the emis-
sions of NOx from combustion. This is related to the relatively high
assumption on the NOx emissions (Section 3.2.2.4); this assumption is
tested later in a sensitivity analysis (Section 4.1.4.2). TETP is also
affected by combustion due to the emissions of heavy metals. Water
requirements in the operation stage make up around 80% of FCP across
the feedstocks. For FDP, biomass transport (diesel use) and the CCS
system (electricity use) are the major contributors. Plant construction
inuences signicantly (34–64%) IRP, LUP, HTP-C, METP and FETP.
Mixed trends are observed in AP, FEP, MEP, HTP-C, HTP-NC and
MDP for different feedstocks. For example, the BECCS system with EFBs
has the highest FEP, MEP and HTP-NC as it generates more bottom and
y ash compared to the other feedstocks (Table 5). The CCS system is
also a major contributor to eutrophication, related to the electricity used
for compressors, and to HTP-C for the same reason and also the emis-
sions from the CO
2
pipeline construction.
The system utilising trunks has the highest AP, HTP-C and MDP due
to the additional impacts from wood chipping. For HTP-C, the impact of
trunks acquisition is double that of fronds, contributing 30% to the total,
mainly caused by the emissions of heavy metals to freshwater from
diesel burning during the chipping operation. However, plant operation
is the major contributor to AP (90–94%) due to the NOx emissions, while
plant construction is the main hotspot for HTP-C and MDP across all the
feedstocks.
As also shown in Fig. 4, all the impacts other than GWP are 13–217%
higher in the BECCS systems compared to their respective counterparts
without CCS. FEP, MEP, HTP-C and FDP increase by >100%, with FEP
showing the highest increment when using fronds. This is due to the
energy penalty for CCS, as discussed in the previous section. Overall,
using shells is the best option for the systems without CCS since the
system requires the lowest amount of biomass and has the lowest im-
pacts in 15 categories.
4.1.2. Environmental impacts per tonne of CO
2
removed
The environmental impacts per tonne of CO
2
removed are inuenced
by multiple factors, such as plant efciency, net electricity produced and
amount of CO
2
captured. Since the differences in these values are small
between the feedstocks (see Table 8), there are no clear trends indicating
the best and worst feedstocks in terms of their impacts. Hence, the re-
sults for one feedstock are discussed here, focusing on palm fronds as the
most abundant palm waste. The impacts for the other feedstocks and for
different credits for electricity generation can be found in Tables
S13–S17 in the SI. Regarding the credits, as mentioned in Section 3.2.1,
for this functional unit the system is credited for generating electricity
and different scenarios have been considered to determine the effect on
the impacts. The systems have been credited for generating 599–694
kWh of net electricity generated for every tonne of CO
2
removed across
the different feedstocks. As in the previous section, the results for GWP
Fig. 6. Global warming potential (GWP) of BECCS for generating electricity
with and without CCS.
See Table 1 for data sources. IBGCC: Integrated biomass gasication combined
cycle; CHP: Combined heat and power.
D.M. Saharudin et al.

Applied Energy 349 (2023) 121506
19
are presented rst, followed by the other impacts.
4.1.2.1. Global warming potential. Considering different feedstocks, the
GWP ranges from −840 to −899 kg CO
2
eq./t CO
2
removed without the
electricity credits, and from −863 to −1729 kg CO
2
eq./t CO
2
removed
with different electricity credits (Fig. 5 and Tables S13–S17 in the SI).
The impacts of CCS can be found in Fig. S4, which shows that for each
tonne of CO
2
removed, 0.175–0.183 t of CO
2
eq. are emitted, in addition
to the other impacts.
As shown in Fig. 5, crediting the system for displacing coal electricity
(SE-C) leads to the lowest GWP due to the highest credit (−716 kg CO
2
eq./t CO
2
removed) compared to the other electricity sources. Since
Malaysian grid relies heavily on fossil fuels, with 47% coal and 36%
natural gas [39], the GWP when the credits for the grid are assumed (SE-
EM) is between those for coal (SE-C) and natural gas (SE-NG). The
highest GWP (−840 kg CO
2
eq./t CO
2
removed) is seen in the case with
no electricity credits.
4.1.2.2. Other impacts. Due to the high emission savings from the
avoided electricity from coal, SE-C has the lowest values in 12 impact
categories, whereas SE–B performs the best in four categories (Fig. 5).
The system without electricity credits is the worst in all impact
categories.
Assuming credits for the grid (SE-EM) and coal (SE-C) lead to net-
negative impacts for some categories, including FDP, FEP, MEP, HTP-
C, FETP, METP, PMFP and LUP. This is because these impacts are
much higher for electricity generation from coal which has a high share
in the Malaysian grid (47% [39]). Furthermore, crediting the system for
biomass electricity (SE-B) results in a net-negative LUP. This is due to the
assumption that the biomass is wood, which requires a large area of land
for production [28], hence leading to high system credits for its
displacement by palm fronds.
4.1.3. Comparison of results with literature
As there are no other studies of BECCS with palm oil wastes, direct
comparison with literature is not possible. Instead, the impacts esti-
mated here are compared with those reported for BECSS using various
other biomass feedstocks. Furthermore, comparison is only possible per
MWh as no other studies considered impacts per tonne of CO
2
removed.
The GWP values reported in the literature and estimated here are
summarised in Fig. 6, comparing the impact with and without CCS. The
summary for the other impacts can be found in Fig. S5 in the SI. How-
ever, it should be noted that direct comparison of the results for the
other impacts is not possible, as only two studies have used the same
impact assessment method as this study (ReCiPe Midpoint) [23,24],
with the others using CML 2001, ALCAS and Eco-indicator 95, as indi-
cated in Table 1. Nevertheless, these results are included in Fig. S5 in the
SI (as long as the units for the impacts were the same as in this study) to
indicate the range of values reported in the literature.
As shown in Fig. 6, the GWP values estimated in this study (−1270 to
−1410 kg CO
2
eq./MWh) are within the range of 100 to −1810 kg CO
2
eq./MWh reported in previous BECCS studies related to electricity
generation in BECCS systems [15,17–26]. The GWP for the systems
without CCS of 59–126 kg CO
2
eq./MWh is also within the literature
range of 64–460 kg CO
2
eq./MWh [15,17,21,24–26,158].
However, the average GWP of BECCS in this study (−1342 kg CO
2
eq./MWh) is lower than the literature average (−551 to −852 kg CO
2
eq./MWh). This could be due to many factors, including different
feedstocks, conversion technologies, regions, assumptions and data.
Nevertheless, the average GWP is comparable to that reported by Spath
and Mann [25] (−1368 kg CO
2
eq./MWh) and Cumicheo et al. [22]
(−1150 kg CO
2
eq./MWh) who considered similar feedstocks (agricul-
tural wastes) and bioenergy conversion route (combustion). The average
impacts without CCS are comparable (85 kg CO
2
eq./MWh vs 67–241 kg
CO
2
eq./MWh in literature). In terms of the average net removals, the
result here (1427 kg CO
2
eq./MWh) is higher than in the literature
(622–1092 kg CO
2
eq./MWh). This could be due to many factors as
mentioned previously, but most likely due to many studies considering
energy crops (with the additional impacts from cultivation), bringing
down the average.
The values for AP are much higher in this study (6.35–7.91 kg SO
2
eq./MWh) than in the literature (−0.7 to 1.8 kg SO
2
eq./MWh)
[15,17,23,24,26]; see Fig. S5. Apart from the different methods used to
estimate the AP, another key reason for this difference is the assumed
removal of SOx and NOx from the ue gas: respectively 21–46% and
25–49% here (Table 4 vs >90% in the literature. The above-mentioned
negative AP reported by Pour et al. [17] is due to the credits for the
avoided electricity from coal, which has not been considered in any
other studies.
On the other hand, FEP, HTP, FETP, FCP and ODP are all within the
range of literature values [15,17,18,24,26] (Fig. S5). It is not possible to
compare the METP and TETP results due to different impact assessment
methods used by Yang et al. [15], the only other study that considered
these two impacts of BECCS.
Although no BECCS studies have originally reported the impacts per
tonne of CO
2
removed, Jeswani et al. [159] have estimated these im-
pacts in their literature review of NETs based on the results reported in
the literature per MWh of electricity generated. This has resulted in the
GWP range from −651 to −1106 kg CO
2
eq./t CO
2
removed. The GWP
estimated in this study (without the electricity credit) falls within this
range (−840 to −899 kg CO
2
eq./t CO
2
removed).
4.1.4. Sensitivity analysis
The sensitivity analysis explores the inuence on the results of the
following four parameters: biomass transport distance, NOx emissions
limits, heat consumption for MEA regeneration and electricity needed
for CO
2
transport. The reasons for selecting these parameters are
explained in the following sections. The analysis focuses again on the
system with palm fronds, since the trends would be the same across the
feedstocks. The results are shown in percentage change on the base case
with respect to 1 MWh of electricity generated. Only this functional unit
is considered here as the effects of these parameters on the impacts
expressed per tonne of CO
2
removed would follow a similar trend since
they are derived from the impacts per MWh of electricity.
4.1.4.1. Biomass feedstock transport distance. Assuming the feedstock
transport distance of 200 km in the base case, transport is a hotspot for
TETP, contributing 27–49%, also causing 12–25% of FDP, LUP, IRP and
METP. Since palm oil plantations are scattered across Peninsular
Malaysia, the distance between the biomass source and the power plant
can be as short as 10 km or twice as long as in the base case. Therefore,
the range between 10 and 400 km is considered in the sensitivity anal-
ysis. The results in Figure 7a show that the impacts change linearly with
the distance, in proportion to the amount of fuel used. Most impact
categories, including GWP, change by <±20%. TETP has the largest
variation of ±50%, followed by LUP (±40%), FDP (±30) and METP
(±25%). Therefore, the effect of feedstock transport distance on the
impacts is signicant.
4.1.4.2. NOx emissions limits. The NOx emissions contribute signi-
cantly to POFP-E and POFP-H (75%), AP (53–62%) and PMFP
(43–53%), assuming the post-treatment concentration of 425 mg/m
3
in
the base case. The effect of this assumption is evaluated in the sensitivity
analysis to account for the possibility of more stringent limits in the
future. For example, new power plants in Europe are expected to have
NOx emissions level at 50–85 mg/m
3
[160]. Hence, lower limits of
50–350 mg/m
3
are considered and the results are presented in Fig. 7b.
The largest reduction is seen in POFP-E and POFP-H (up to 75%), fol-
lowed by AP and PMFP (up to 20%) as NOx emissions are the main
contributor for these impacts, while the other categories are hardly
D.M. Saharudin et al.

Applied Energy 349 (2023) 121506
20
affected (<3%).
4.1.4.3. Heat consumption for MEA regeneration. As mentioned earlier,
the MEA regeneration consumes almost 30% of the gross energy pro-
duced by the plant, resulting in a major reduction in the net power
output and increasing the impacts. In the base case, the heat consump-
tion of 2.5 GJ/t CO
2
removed has been assumed (Table 7). In the
sensitivity analysis, the values range from 2 GJ/t CO
2
(assuming future
efciency improvements [9]) to 4.2 GJ/t CO
2
(the highest value re-
ported in the literature for pilot plants [84,89]).
As can be seen in Fig. 7c, GWP changes signicantly with heat
consumption. An increase of up to 55% can be expected for the increase
in heat consumption of 40% (from 2.5 to 4.2 GJ/ t CO
2
) relative to the
base case. This is because the higher heat requirement reduces the net
electricity produced, requiring more biomass to meet the electricity
generation target, in turn leading to more CO
2
being emitted and sub-
sequently captured. On the other hand, a 20% decrease in the heat de-
mand (from 2.5 to 2 GJ/ t CO
2
) results in a 10% reduction in the GWP.
The rest of the impact categories show an opposite trend of a similar
magnitude since burning more biomass to make up for the additional
heat needed leads to higher consumption of materials and energy and,
hence, the higher impacts.
4.1.4.4. Electricity for CO
2
transport as a function of transport distance.
This parameter is selected as one of the major contributors to GWP, as
discussed in Section 4.1.1.1. The value assumed in the base case is 29
kWh/t CO
2
for a 400 km pipeline [28] (Table 7). Varying this distance
from 100 km to 500 km results in the electricity use of 7.25–36.25 kWh/
t CO
2
removed. The results in Fig. 7d indicate a linear trend in all im-
pacts, with most varying by ±5% for every 7.25 kWh/t CO
2
(100 km).
The highest variation is seen in FEP with a reduction of 27%, followed by
MEP with 19%, when the pipeline distance is the quarter of the original
value (100 km; 50 km onshore and 50 km offshore).
4.2. Life cycle costing
Following the same structure as for the LCA results, this section
discusses rst the costs per MWh electricity generated (Section 4.2.1),
followed by the costs per tonne of CO
2
removed (Section 4.2.2). Com-
parison with the costs reported in the literature is provided in Section
4.2.3 and a sensitivity analysis in Section 4.2.4.
4.2.1. Costs per MWh of electricity
The next section presents the capital and operating costs and the
subsequent one the LCOE.
4.2.1.1. Capital and operating costs. Based on the data in Table 9, the
total capital costs estimated for the 200 MW bioenergy plant (excluding
CCS) are US$236.7 M for new and US$83.4 M for retrotted in-
stallations. Therefore, the latter are 65% cheaper. The capital costs of
the CCS system are US$241.5 M, bringing the total capital cost of the
new and retrotted BECCS plants to US$478.2 M and US$324.9 M,
Fig. 7. The effect of different parameters on the environmental impacts of BECCS with fronds.
For the impact acronyms, see Fig. 4. MEA: Monoethanolamine.
D.M. Saharudin et al.

Applied Energy 349 (2023) 121506
21
respectively. The capital costs per MWh of electricity spread over the
plant’s lifetime are US$13.09–20.38/MWh for the BECCS and US
$2.17–5.86/MWh for the plants without CCS. Using palm fronds leads to
the highest capital costs per MWh due to the lowest electricity output
(Table 8). As can be inferred from Fig. 8, the difference in the capital
costs between the new and retrotted plants without CCS is US
$3.60–3.66/MWh and US$5.86–6.30/MWh for the BECCS plants across
the feedstocks. The difference is almost twice as high for the BECCS
systems as the overall efciencies are reduced by around a half due to
the CCS energy penalty (Table 8).
The operating costs are the same for the new and retrotted plants
and range from US$114.68 to US$131.29/MWh for the BECCS systems
and from US$35.71–41.33/MWh for the plants without CCS across the
feedstocks. For the BECCS plants, CCS accounts for 46–49% of the total
operating costs, followed closely by power plant operation (40–45%).
Fixed O&M costs make up most of the total operating costs (71–82% for
the power plant and 84–90% for the CCS system). In terms of the
feedstocks, it can be seen from Fig. 9 that using fronds results in the
highest operating costs due to the system having the lowest electricity
output, as discussed earlier, and also because of the additional har-
vesting costs (Table 9). Conversely, the BECCS option with bres is the
least expensive as the system has the highest electricity output (Table 8).
4.2.1.2. Levelised costs of electricity. As seen in Fig. 10, The LCOE of
BECCS range from US$103.74–119.26/MWh for new plants to US
$97.89–112.96/MWh for retrotted installations. Therefore, retrotting
existing installations instead of building new plants would save only
around 5–5.5% as capital costs of bioenergy plants contribute only
11–12% to the total BECSS costs of new plants. In terms of the feedstock
performance, the trend follows that of the capital and O&M costs dis-
cussed earlier, where the systems with fronds have the highest LCOE due
to the lowest electricity output and additional harvesting costs. In
contrast, the option with bres is the least costly as it generates the
highest net electricity output compared with the other feedstocks (see
Fig. 8. Capital costs per MWh of net electricity generated for different feedstocks and installations.
Calculated by dividing the overnight capital cost with the plant’s lifetime and annual net electricity generated by the respective systems. EFBs: Empty fruit bunches.
131.29
40.99
127.10
41.33
121.90
37.29
115.43
35.71
114.68
35.91
0
20
40
60
80
100
120
140
BECCS no CCS BECCS no CCS BECCS no CCS BECCS no CCS BECCS no CCS
Fronds Trunks EFBs Shells Fibres
)hWM/$SU(stsocecnanetniamdna
g
nita
r
epO
Feedstock Power plant CCS system
Fig. 9. Operating and maintenance costs per MWh of net electricity generated for different feedstocks.
EFBs: Empty fruit bunches.
D.M. Saharudin et al.

Applied Energy 349 (2023) 121506
22
Table 8).
The CO
2
capture process incurs the highest costs, contributing 37–42%
to the total LCOE across the feedstocks (Fig. 10). Around 77% of this is due
to the O&M costs, which are inuenced by MEA consumption and treat-
ment. The costs of power plant operation follow closely with 31–37% of
the LCOEs. Conversely, biomass transport contributes the least across the
scenarios, despite the relatively high transport distance of 200 km. This is
due to the low diesel price and low drivers’ wages in Malaysia.
The LCOE values for the systems without CCS are US$27.74–31.96/
MWh for the new and US$24.14–28.35/MWh for the retrotted plants.
Therefore, CCS adds 72–76% to the total LCOE of BECSS, largely due to
the energy penalty associated with the CCS system and the use of MEA.
As a result, LCOE of BECCS are 3.6–4.1 times higher than for their
counterparts without CCS. The highest LCOEs for the installations
without CCS are seen for trunks and fronds (due to the highest feedstock
acquisition costs; Table 9) and the lowest for shells and bres (due to the
higher electricity output and the lowest acquisition costs; Table 8 and
Table 9).
4.2.2. Costs per tonne of CO
2
removed
The costs of the BECCS systems per tonne of CO
2
removed, estimated
from the LCOE (Eq. (4)), are presented in Fig. 11. Inuenced by the
higher feedstock acquisition costs, the BECCS plants using trunks have
the highest cost of US$73.78/t CO
2
for new and US$69.89/t CO
2
for
119.26
112.96
31.94 28.28
114.36
108.34
31.96 28.35
110.76
104.63
28.91 25.26
104.73
98.83
27.74 24.14
103.74
97.89
27.91 24.30
0
20
40
60
80
100
120
140
BECCS - new
BECCS - retrofit
no CCS - new
no CCS - retrofit
BECCS - new
BECCS - retrofit
no CCS - new
no CCS - retrofit
BECCS - new
BECCS - retrofit
no CCS - new
no CCS - retrofit
BECCS - new
BECCS - retrofit
no CCS - new
no CCS - retrofit
BECCS - new
BECCS - retrofit
no CCS - new
no CCS - retrofit
Fronds Trunks EFBs Shells Fibres
)hWM/$
S
U(yticirtcelef
o
tsocd
es
i
l
ev
e
L
Feedstock Feedstock transport Power plant capital cost Power plant operation CO₂capture capital cost CO₂capture CO₂transport CO₂storage
Fig. 10. Levelised costs of electricity for different feedstocks and installations.
EFBs: Empty fruit bunches.
Fig. 11. Costs of BECCS per tonne of CO
2
removed for different feedstocks and installations.
EFBs: Empty fruit bunches.
D.M. Saharudin et al.

Applied Energy 349 (2023) 121506
23
retrotted plants. Utilising EFBs would lead to the lowest costs for both
types of installation (US$69.66/t CO
2
for new and US$65.81/t CO
2
for
retrotted plants) as they have low LCOE and remove a high amount of
CO
2
per MWh electricity (1.59 t CO
2
/MWh; Fig. 4). Although the sys-
tems with fronds remove slightly more CO
2
(1.67 t CO
2
/MWh), their
LCOE are higher than those of EFBs (Fig. 10), which overall works in
favour of EFBs. Similar applies to bres which have the lowest LCOE but
also remove the lowest amount of CO
2
. The cost savings for the retro-
tted relative to new plants are 5.3–5.6%.
The marginal costs of CO
2
removal, estimated via Eq. (5), are be-
tween US$51.48–53.16/t CO
2
for new and US$49.92–51.60/t CO
2
for
retrotted installations (Fig. 12). In both cases, the same trends are
noticed as for the costs of the BECCS systems discussed above, with
trunks incurring the highest and EFBs the lowest CO
2
removal costs.
However, the difference in the removal costs between the new and
retrotted plants is small across the feedstocks (US$1.55–1.58/t CO
2
).
This indicates that the capital cost has a small inuence, adding only
around 4% to the total cost of BECCS in Malaysia.
4.2.3. Comparison of results with literature
The LCOE for BECCS calculated in this study (US$98–119/MWh) are
within the range of US$89–366/MWh reported in the literature
[13,17,31–34]. However, the studies based in developed countries, such
as in Europe [33], Australia [17] and the UK [13,34], reported 77–224%
higher LCOE. This is due to the higher costs of feedstocks, labour, energy
and raw materials. The only study based in a developing country (China)
[31] also reported higher LCOE (42–72%) than here. This could be due
to a much higher price of biomass used in that study (US$95/t straw),
compared to US$6–13/t here (Table 9).
Similarly, the feedstock transport costs (US$1.73–3.17/t biomass)
are at the lower end of the range in the literature (US$0–80/t biomass)
[120,130,161] due to low diesel price and low drivers’ wages in
Malaysia. The other costs are also lower than those reported in the
literature. For example, xed O&M costs estimated by the NREL [162]
for electricity generation (without CCS) from biomass range from US
$70–126/kW
.
yr while in this study they are US$59/kW
.
yr, 15% lower
than the NREL’s lower range.
For the same reasons as above, the BECCS costs per tonne CO
2
removed (US$66–74) are lower in the current work than most of the
previously published values (US$80–319) as they are largely based in
developed countries and have assumed the use of more expensive
feedstocks, such as wood chips, biomass pellets and energy crops
[10,13,17,31–36]. For example, the cost of US$160–225/t CO
2
removed
reported in Negri et al. [33] includes the cost of cultivation of energy
crops and the study is based in Europe. On the other hand, the average
value of US$95/t CO
2
reported by Cheng et al. [35] for crop and forest
residues in the US is close to the upper values found in this study, as both
studies considered waste feedstocks.
The marginal CO
2
abatement costs in this work (US$50–53/t CO
2
)
are at the lower end of the range of US$40–120/t CO
2
(for diluted CO
2
streams, e.g. in ue gas) published by the IEA [163]. They are also lower
than the values in other literature. For example, for biomass combustion,
Al-Qayim et al. [34] reported the marginal cost of US$109–111/t CO
2
removed, which is higher than here due to at least two reasons: wood
pellets are more expensive than the palm oil waste and their study is
based in the UK. Pour et al. [17] reported 37–88% higher CO
2
removal
costs for various biomass sources (e.g. municipal solid waste, bagasse,
forest residues, landll gas) in Australia, despite considering carbon
credits, renewable energy certicate benets and a higher discount rate
(10% vs 3.5% here).
4.2.4. Sensitivity analysis
The following six parameters have been selected for the sensitivity
analysis: feedstock costs, capital costs, discount rate, plant lifetime, CO
2
transport costs and CO
2
storage and monitoring costs. As the trend is
projected to be the same across the feedstocks, as in the LCA sensitivity
analysis, the discussion focuses on the BECCS system with palm fronds.
The ndings are also discussed only for the costs per MWh of electricity,
focusing on the LCOE, as the costs per tonne of CO
2
are derived from
these. The results are presented in Fig. 13, showing the percentage
change of the LCOE compared to the base case for both new and retro-
tted plants.
4.2.4.1. Feedstock costs. Feedstock costs may increase in the future
beyond the acquisition costs (i.e. harvesting and collection costs)
considered currently if there is enough market demand. For example,
the market price of palm kernel shells has seen a three-fold increase in
the past ve years due to the increase in demand from the bioenergy
sector in Japan [119]. Hence, the effect of the feedstock costs on the
LCOE is explored and the results are presented in Fig. 13a. It can be seen
that the LCOE increase by 9–35% if the feedstock costs are increased by
2–5 times relative to the base case. Similar trends are found for both the
new and retrotted plants, with the latter affected slightly more as the
feedstock costs have a greater effect on the LCOE due to their lower
capital costs compared to the new plants.
4.2.4.2. Capital costs. As described in Section 3.3.3, the capital cost of
1.18 million US$/MW (Table 9) has been used in the base case. This
value has been derived from the average capital costs of bioenergy
plants in countries other than China, Europe, the United States and India
52.29 53.16 51.48 52.02 52.66
50.71 51.60 49.92 50.46 51.10
0
10
20
30
40
50
60
Fronds Trunks EFBs Shells Fibres
OCf
o
t
s
oC
2
l
avo
m
er
OCt/$SU(
2
)d
e
vom
e
r
New plant Retrofit
Fig. 12. Marginal costs of CO
2
removal for different feedstocks and installations.
EFBs: Empty fruit bunches.
D.M. Saharudin et al.

Applied Energy 349 (2023) 121506
24
[81], resulting in the estimated BECCS capital costs of US$478 M.
However, when considering the whole range of reported values,
including the above countries, the total capital costs of BECCS here
could be in the range of US$230–612 M. Therefore, these values are
considered in the sensitivity analysis. As no other values were found for
the retrot capital costs, the analysis considers only the costs of new
plants. The results in Fig. 13b show that the LCOE would reduce by 9%
and increase by 5% when using the lower and upper capital cost range,
respectively.
4.2.4.3. Discount rate. Discount rate is country-specic and in this study
the rate of 3.5% has been selected. However, this rate is prone to change
with the country’s economic performance. Therefore, its inuence on
the overall costs is explored using the discount rate of 10%, the value
used most commonly in literature for the economic analysis of BECCS
systems [13,17,34,35,59]. Additionally, the intermediate rate of 5% is
also considered. As seen in Fig. 13c, the LCOE decreases by around 40%
and 60% as the discount rate increases to 5% and 10%, respectively. The
trend is very similar for the new and retrotted BECCS plant, with the
inuence of the discount rate slightly greater for the latter. This is
because the costs affected by the discount rate in the retrotted plant
have a greater contribution of the overall costs due to its lower capital
costs.
4.2.4.4. Plant lifetime. A plant lifetime of 30 years has been assumed in
this study based on the average lifetime in most BECCS studies in the
literature. However, the Global CCS Institute [131] suggests that the
optimal BECCS plant lifetime can vary between 25 and 40 years. Thus,
these values are considered in the sensitivity analysis. The results in
Fig. 13d reveal that the LCOE reduce by 23% for the lifetime of 40 years
and increase by 16% for 25 years for both new and retrotted plants.
4.2.4.5. CO
2
transport costs. The costs of CO
2
transport contribute
signicantly to the LCOE of BECCS (Fig. 10) and are hence explored
further here. The value of US$4.2/t CO
2
removed assumed in the base
case is tested against the range of US$3–22.5/t CO
2
reported in the
literature [131]. The results in Fig. 13e show a 1% reduction in the LCOE
Fig. 13. The effect of different parameters on the levelised costs of electricity from BECCS with fronds.
D.M. Saharudin et al.

Applied Energy 349 (2023) 121506
25
when using the lower CO
2
transport cost and a 17% increment when
using the upper value in the above range.
4.2.4.6. CO
2
storage and monitoring costs. Similar to the CO
2
transport
cost, a wide range of CO
2
storage and monitoring costs has been reported
in the literature (US$3–31/t CO
2
removed), depending on the size, type
and conditions of the storage [96,131]. This range is explored here
against the base value of US$13/t CO
2
removed. The results in Fig. 13f
show that the LCOE decrease by 10% if the costs are reduced to US$3/t
CO
2
and increases by 17% for the costs of US$31/t CO
2
.
4.3. BECCS at the national level
This section explores the potential of BECCS in Malaysia based on the
ndings of the sustainability assessment in this study. After taking into
consideration the current utilisation and accessibility of the palm oil
wastes [41,43,46], it is estimated here that approximately 4 and 5 Mt of
these wastes (dry wt) are available annually from palm oil plantations in
Peninsular and East Malaysia, respectively. These are derived from the
amount of available feedstocks in Table 3 (Section 3.1), excluding the
moisture content. Based on the availability and the other assumptions on
the conversion technologies, a total of 1783 MW installed capacity could
potentially be realised.
When coupled with CCS, a total of 7730 GWh of net electricity could
be generated annually from all available palm oil wastes in Peninsular
(3478 GWh) and East Malaysia (4252 GWh). This would increase the
share of biomass energy in the Malaysian energy mix from 0.56% to
4.23% (7.6 times higher), possibly displacing the fossil-fuel based en-
ergy. In addition, these plants could potentially remove 11.98 Mt CO
2
/
year (5.39 Mt CO
2
/year in Peninsular and 6.59 Mt CO
2
/year in East
Malaysia), equivalent to 10% of national emissions from the electricity
sector [164,165].
The Malaysian government plans to retire around 7000 MW of coal
power plants by 2039 [52,166]. Assuming the palm oil wastes can serve
half of this capacity due to their lower heating value than coal, around
17 BECCS plants with capacity of 200 MW could be retrotted to the old
coal plants across Malaysia, saving US$2513 M in capital cost (based on
the difference in the costs of new and retrot of US$148 M given in
Section 4.2.1.1).
As mentioned earlier, there are currently two large-capacity
geological sites in Malaysia: one in Malay Basin (Peninsular Malaysia)
with up to 114 bn t CO
2
storage capacity and another in Central Luconia
Province (East Malaysia) with a capacity of up to 75 bn t CO
2
[47–49]. If
all available palm oil wastes (22.6 Mt/yr; Table 3) were to be utilised for
BECCS, these sites could store the CO
2
produced from these plants for up
to 20,000 and 10,000 years in Peninsular and East Malaysia, respec-
tively, based on their above-mentioned storage capacities and the
amount of CO
2
removed annually.
Therefore, with plenty of readily available feedstocks, the planned
retirement of coal plants and suitable storage locations, BECCS
deployment could be considered suitable for Malaysia. However,
without any incentives (e.g. carbon credit, government subsidies and
premium feed-in tariff), this CO
2
removal pathway is not economically
viable due to net-positive life cycle cost values, as also found by previous
studies of BECCS in other countries [10,31,35]. If the generated elec-
tricity is sold at the current tariff alone (US$50.57/MWh [38]), this
would only reduce the cost of BECCS by US$30–35/t CO
2
removed,
bringing down the net cost from US$66–74 to US$33–41/t CO
2
removed. Hence, the plants need other forms of nancial support or
revenue stream of at least US$33/t CO
2
to breakeven and become
economically sustainable.
4.4. Study limitations
The main limitation of the study is related to the lack of primary data
for the LCA and LCC. Real applications of BECCS do not exist in Malaysia
yet and the existing BECCS plants around the world do not use palm oil
wastes as feedstock, so no primary data are available in terms of the
emissions proles, plant efciencies and other information related to
bioenergy from palm oil wastes. Moreover, the existing biomass power
plants in Malaysia are relatively small, with the largest being 14 MW,
powered by EFBs, bres and shells [38,167]. Getting the inventory data
for bioenergy plants is challenging since most plants are producing en-
ergy for own use (e.g. powering palm oil processing mills) and no ofcial
reports are available for these plants.
Another limitation is related to the combustion simulations in Aspen,
as it is not possible to specify the type of boiler. This may lead to the
emissions prole which may not reect accurately that of the CFB
assumed in this work, particularly with respect to NOx emissions. To
mitigate against this uncertainty, the effect of this parameter on the
impacts has been tested in the sensitivity analysis.
The same limitations related to data apply to the LCC study as no
primary data are available for Malaysian bioenergy systems. The capital
costs for new and retrotted plants have been estimated based on in-
stallations in developed countries, such as the US and the UK. Although
the estimates take into account the difference in the plant size, the
impact of ination and discount rates, as well as the price difference
between the countries, the calculated costs still carry some uncertainty.
A further limitation is the assumption that the ination and discount
rates remain unchanged over the 30 years of the plant’s lifetime, which
is unlikely since they uctuate depending on the economic performance
of a country at a given time. The same also applies to the price of the
materials, also assumed to be constant over time. In reality, the price of
materials depends largely on the market demand and supply. For
instance, ammonia prices in the Asia Pacic region have seen around a
50% increase over the past two years due to seasonal demand for crops
and restricted supply across the region [146]. Since predicting the
market’s demand and supply involves signicant uncertainties, eco-
nomic assessments should be conducted on a regular basis using the
most recent economic datasets to improve the accuracy of the analysis
before actual deployment.
5. Conclusions
This study has evaluated the environmental and economic sustain-
ability of BECSS systems using different types of palm oil waste in
Malaysia. The LCA results show that the environmental impacts per
MWh are 13–217% higher in systems with CCS than without it, except
for GWP. With CCS, the latter ranges from −1270 to −1410 kg CO
2
eq./
MWh. The systems without CCS have net-positive GWP of 59–126 kg
CO
2
eq./MWh. For most categories, the lowest BECCS impacts are ach-
ieved with bres and the highest with fronds, with the opposite trend for
GWP. The main environmental hotspots are the power plant operation
and MEA regeneration.
Per tonne of CO
2
removed, the GWP without the credits for elec-
tricity generation is −840 to −899 kg CO
2
eq./t CO
2
removed and, with
the credits, it ranges from −863 to −1729 kg CO
2
eq./t CO
2
removed,
depending on the feedstock and system credits. For each tonne of CO
2
removed, 0.175–0.183 t of CO
2
eq. are emitted, in addition to the other
impacts. Some of the impacts are net-negative, specically FDP, FEP,
MEP, HTP-C, FETP, METP, PMFP and LUP. Overall, there is no clear
winner for the best feedstock per tonne of CO
2
removed.
The LCOE of new BECSS plants are US$103.74–119.26/MWh and US
$97.89–112.96/MWh for retrotted installations. Compared to the
systems without CCS, the LCOE of BECCS are 3.6–4.1 times higher. The
systems with fronds have the highest LCOE and those with bres are the
least costly. The costs of BECSS per tonne of CO
2
removed are in the
range of US$66–74/t CO
2
, while those of CCS (marginal abatement
costs) are US$50–53/t CO
2
removed. The costs per tonne of CO
2
removed are the highest for trunks and lowest for shells.
If all available palm oil wastes were utilised for BECCS, a total of
D.M. Saharudin et al.

Applied Energy 349 (2023) 121506
26
7730 GWh of energy could be generated annually, which would increase
the share of biomass energy from the current 0.56% to 4.36% of the total
electricity generated in the country. At the same time, 11.98 Mt CO
2
could be removed annually from the utilisation of all available palm oil
wastes, equivalent to 10% of national emissions from electricity gener-
ation. Furthermore, the government plans to retire coal power plants
provide opportunities for their retrot with BECCS, with total potential
savings of US$2680 M in capital costs. Combined with its potential to
contribute towards negative GHGs emissions, BECCS should be consid-
ered as a viable option for Malaysia.
CRediT authorship contribution statement
Djasmine Mastisya Saharudin: Conceptualization, Formal anal-
ysis, Investigation, Methodology, Visualization, Writing – original draft.
Harish Kumar Jeswani: Conceptualization, Methodology, Supervision,
Writing – review & editing. Adisa Azapagic: Conceptualization, Fund-
ing acquisition, Methodology, Supervision, Writing – review & editing.
Declaration of Competing Interest
The authors declare that they have no known competing nancial
interests or personal relationships that could have appeared to inuence
the work reported in this paper.
Data availability
All the data are included in the paper.
Acknowledgements
The authors acknowledge the nancial support from the UK Engi-
neering and Physical Sciences Research Council (EP/K011820/1) and
Majlis Amanah Rakyat (MARA), Malaysia.
Appendix A. Supplementary data
Supplementary data to this article can be found online at https://doi.
org/10.1016/j.apenergy.2023.121506.
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