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RESEARCH
CORRESPONDING AUTHOR:
Chibuikem Chrysogonus
Nwagwu
Department of Energy &
Process Engineering, Norwegian
University of Science and
Technology, Høgskoleringen 1,
7034 Trondheim, NO
chibuikem.nwagwu@sintef.no
KEYWORDS:
archetype; building stock;
energy modelling; life-cycle
assessment; residential
buildings; Nigeria
TO CITE THIS ARTICLE:
Nwagwu, C. C., Akin, S.,
& Hertwich, E. G. (2024).
Modelling Nigerian residential
dwellings: bottom-up approach
and scenario analysis. Buildings
and Cities, 5(1), pp. 521–539.
DOI: https://doi.org/10.5334/
bc.452
Modelling Nigerian
residential dwellings:
bottom-up approach and
scenario analysis
CHIBUIKEM CHRYSOGONUS NWAGWU
SAHIN AKIN
EDGAR G. HERTWICH
*Author affiliations can be found in the back matter of this article
ABSTRACT
Nigeria’s residential buildings consume a substantial amount of the country’s energy, so
achieving a net-zero building sector with a rapidly growing population is a key challenge.
To bridge the gap in research at a national level and support Nigeria’s commitment to
an unconditional 20% reduction in emissions by 2030, this study develops bottom-up
archetype models of different residential building typologies to estimate the energy and
material use of Nigerian residential buildings. This creates an overview of the residential
stock and how different archetypes perform. The study calculates a baseline energy and
material use of Nigeria’s residential building stock using the BuildME tool and converts
these data into CO2 emissions using a life-cycle assessment. Scenarios are modelled for
2020. Nigeria’s residential dwellings use approximately 0.3 kt of material per dwelling
over a lifetime of 50 years and 2404 kWh/yr of energy per dwelling. Annualised, dwellings
emit 2500 kgCO2-eq per dwelling due to material and energy use. Scenarios proposed for
meeting Nigeria’s emissions targets will require improved energy efficiency, decarbonising
the building envelope through a shift in construction materials and decarbonising grid
electricity.
POLICY RELEVANCE
This study provides a comprehensive analysis of the energy and material use of residential
buildings in Nigeria, focusing on achieving the country’s commitment to a 20% reduction
in emissions by 2030. It employs a bottom-up approach to model energy and material
use, revealing the peculiarities of the dwelling stock across Nigeria’s four climatic zones.
The study explores the implications of different policy scenarios on sustainable housing.
The research suggests that meeting Nigeria’s emissions targets requires improved energy
efficiency, a shift in construction materials and decarbonisation of grid electricity. It also
highlights the potential benefits of a policy switch to materials such as timber, earthen
blocks, adobe bricks and clay, which could significantly reduce construction-related
emissions. These changes could improve the quality of life of households in Nigeria,
combat climate change on a global scale and bring economic advantages to Nigeria.
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DOI: 10.5334/bc.452
1. INTRODUCTION
1.1 ENVIRONMENTAL IMPACTS AND ASPECTS OF BUILDINGS
Shelter provided by buildings is an essential human need (Zhong et al. 2021). This might explain in part
why the building sector alone accounts for one-third of the final energy consumed and nearly 40%
of the energy-related greenhouse gases (GHG) emissions worldwide (UNEP 2023). This trend places
buildings at the centre of climate change mitigation and adaptation efforts as they provide low-cost
and short-term opportunities for achieving set goals. They offer many opportunities for material and
energy efficiency, improved health and wellbeing of citizens, and economic growth (Müller et al. 2013;
WRI 2016). Several global goals emphasise reducing energy consumption, promoting sustainable
construction and moving towards net-zero carbon operations, all while ensuring affordable, safe
and inclusive housing (UN DESA n.d.; Pauw et al. 2020; UNEP 2022; WGBC 2019). Delay in planning
concerning the built environment is dangerous as this delay can lead to a possible lock-in because of
the long lifetime of buildings and their persistent energy use (Davis et al. 2010).
Estimates by the Global Alliance for Buildings and Construction show that the global building floor
area will double by 2060. According to these estimates, most of the floor area to be added to
the present dwelling stock will be in Africa, as an estimated 70% of the African building stock
in 2040 is still yet to be built. The United Nations Environment Programme (UNEP) opines that
Africa stands a chance at sustainable construction given the established correlation of urban
growth with material use and emissions. This thought is due to the abundance of low-carbon
local construction techniques and materials such as timber and sand (UNEP 2022). Currently, in
Africa, only South Africa, Ghana, Nigeria, Tunisia, Morocco and Egypt have mandatory or voluntary
building codes, with seven other countries in the process of developing the same (UNEP 2021).
Even for these countries with building codes, the level of compliance is lower than what is required
to make significant changes. Some African countries have started making deliberate efforts to
change this using the bottom-up and other building modelling approaches at subregional and
regional levels (Attia et al. 2012; Deru et al. 2011; Geissler et al. 2018). However, the authors found
no recent national residential building stock models for African countries.
While it is a developing method in Africa, the use of bottom-up modelling is not new as several studies
have used similar approaches to represent building stocks in Canada (Farahbakhsh et al. 1998),
Finland (Snäkin 2000), the United States (Huang & Brodrick 2000), Belgium (Hens et al. 2001) and
the UK (Shorrock & Dunster 1997). The bottom-up modelling approach has its flaws related to the
sparsity of relevant detailed input and output model data, which leads to the use of domain-heavy
assumptions, making reproducibility difficult (Kavgic et al. 2010). However, compared with other
approaches to building modelling such as the top-down (econometric and technological) or hybrid
approach, this method’s strength lies in its ability to obtain estimated results at a disaggregated level
(Ugwoke et al. 2024). Such a method is very useful for policymakers and construction managers to
identify improvement hotspots at (or closer to) the source and propose tailored solutions.
1.2 NIGERIAN CONTEXT
Nigeria is a Sub-Saharan African country located close to the equator with an estimated population
in 2020 of over 200 million (UN DESA 2024). Although Nigeria is fully within the tropical zone, there
are significant climatic variations because of two principal wind currents, resulting in four climate
zones. Nigeria boasts of having one of the strongest and largest economies in Africa, largely
dependent on crude oil and natural gas exports (Federal Ministry of Environment 2021b). This fact,
in turn, means that it is economically vulnerable to efforts to reduce emissions (Federal Ministry of
Environment 2021b) as 80% of grid electricity generation capacity is gas based and the remainder
is hydropower (Geissler et al. 2018). Because of grid instability, Nigeria imports back-up generators,
with 80% diesel generator ownership in 2014 (Elinwa et al. 2021; Geissler et al. 2018).
The 2017 climate change vulnerability index (CCVI) classifies Nigeria as a high-risk region, being
among the top 10 most vulnerable countries globally (Federal Ministry of Environment 2021b).
In 2017, Nigeria signed the Paris Agreement, committing to cut its carbon emissions and reach
net zero by 2060 with a five-year emission budget. The country through its nationally determined
contributions (NDCs), pledges an unconditional 20% reduction in business-as-usual emissions by
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2030 (Federal Ministry of Environment 2021b). The National Renewable Energy Master Plan of
2014 projects that over 26% of the country’s energy use by 2025 will be from renewable sources
(Federal Ministry of Environment 2021b) to cut emissions by 38%. The Building Energy Efficiency
Code signed in August 2017 is a first step to implementing its NDCs and the Nigerian government
projected energy savings of up to 40% by 2030 compared with the baseline (Federal Ministry of
Power, Works, and Housing 2017). This target is particularly relevant considering that residential
buildings consume over 80% of energy in Nigeria (IEA 2017), a higher share than in other countries,
with the world average at 30% (IEA 2023). However, to make tailored interventions rather than
one-size-fits-all policies, modelling the Nigerian residential stock is critical.
1.3 EXISTING RESEARCH ON NIGERIAN RESIDENTIAL BUILDINGS
In exploring sustainable energy transitions, Dioha & Kumar (2020) showed that integrated
assessment models (IAMs), which have formed the major aspect of quantitative top-down
modelling in Nigeria, do not describe the diversity of energy use sufficiently. The researchers
developed a TIMES-Nigeria-Residential model that provides the least-cost energy system based
on technological and non-technological scenarios at a disaggregated level. Their results reveal
the value of affordable and clean energy in reducing building final energy demand. However, the
model places a high premium on cost as the main indicator and the CO2 calculations appear not
to follow standardised methodology.
Oluwole et al. (2020) developed a bottom-up weather-sensitive residential demand model to
estimate uninterrupted electricity demand in Nigeria using socio-economic data and household
surveys. Their results show a monthly consumption of 63 kWh. However, as they are limited to
households connected to the grid, the results must be read with caution considering the low on-
grid access in Nigeria (World Bank 2023).
Ochedi & Taki (2019, 2022) conducted bottom-up building models for Lokoja, Nigeria, to estimate
thermal comfort and energy use. They developed a multicriteria framework for energy-efficient
residential buildings in Lokoja. However, the results are specific to Lokoja only, and the study does
not consider material use in reaching energy efficiency.
Ugwoke et al. (2024) employed the bottom-up modelling approach using reference building
models for Nigerian buildings in different geopolitical and climate zones to analyse the energy
performance of low-cost buildings. They identified and created reference buildings using the
characteristics of real buildings based on statistical data coupled with international/national
standards. The study found the average energy use in residential dwellings to be 113 kWh/m2/yr
(cooking excluded) and proposed energy efficiency measures. The results are hardly generalisable
beyond the specific real buildings analysed, signifying the need for more comprehensive models.
Investigating the energy use and CO2 emissions of Lagos State public houses, Ezema & Okorigba
(2022) found that over the building life-cycle, operational energy use was three times higher than
the energy used to produce building materials. The same trend was observed for CO2. The authors
found that for every 1 m2 of housing added to the existing stock, over 117 kWh/yr of energy
would be needed for the operation phase alone (cooking included). Hence, they caution that due
to the reliance on non-renewable energy, an increase in energy use would negatively impact
resource sustainability and climate change. The study only focuses on public housing in one of
Nigeria’s 36 states and assumes a grid electricity supply of 25%. Like other researchers, they
equally recommend future research on alternative envelope materials such as timber and clay
for their adaptive, energy-serving thermal properties, their low cost and their availability (Ezema &
Okorigba 2022; Nwalusi et al. 2019; Onyenokporo & Ochedi 2018).
1.4 RESEARCH GAP, AIM AND OBJECTIVES
The dwelling stock in Nigeria is classified differently from that of European dwellings, and from
statistics of different years, the classification keeps changing. This change makes it quite challenging
to develop building typologies without first understanding the differences and similarities. The high
number of new buildings expected to be created within the next 40 years compared with what
exists means stakeholders must avoid past mistakes by using robust models that are realistic in the
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Nigerian context. The authors find that there is a gap in research offering a consistent whole-scale
comparison of typologies for the entire stock across the different climate zones in the country.
To bridge this gap, this study develops bottom-up archetype models of different residential
building typologies in Nigeria to estimate the energy and material use of residential buildings in
2020. It presents five distinct archetypes: apartments, bungalows, compounds, mud houses and
improvised settlements, along with their resource use and associated GHG emissions. The study
is attentive to the different climate, architectural and economic characteristics, contributing to a
holistic understanding of the diverse built environment in Nigeria.
The study’s objectives are to provide an overview of residential dwellings in Nigeria, assess how
the different archetypes differ in terms of energy and material use and emissions, and propose
scenarios for sustainable residential buildings. It begins by identifying the main residential
building typologies. Thereafter it calculates the baseline energy and material use in Nigerian
residential buildings and translates these results to carbon emissions. The implications of different
policy scenarios on sustainable housing given the huge emission impact and existing informal
settlements’ environmental burdens are further discussed.
2. METHODS
The study formulates a bottom-up model with model drivers focusing on the characteristics that
best describe energy use in the 2020 building stock (Figure 1). This work adopts the methodological
framework for archetype development as described by Ali et al. (2019) and refined by Akin et al.
(2023) due to similarity in research objectives. The developed archetypes are further investigated
in scenario analysis and life-cycle assessment (LCA). Further details of each stage and descriptions
of the archetypes are given in the supplemental data online.
2.1 DATA COLLECTION
As a result of the paucity of data, the comprehensive dwelling stock data used in this study
are a product of data collection and aggregation from multiple sources and using relevant
ratios from statistics and literature (Table 1). One of the authors employed a local perspective in
addition to three-month-long fieldwork conducted in Nigeria to gather relevant on-site knowledge
and data from local architects and households. Data on the different model parameters influencing
energy use such as set points, airtightness, shading, lighting density, equipment load, metabolic
rate and oven energy were obtained from different literature sources and statistics as provided
in the extended datasets (Nwagwu et al. 2024). The study identified five residential building
typologies: apartments, bungalows, compounds, mud houses and improvised settlements.
Apartments, bungalows and compound houses are typically surrounded by a fence made
of sandcrete blocks and joined with a gate at the front (thatch for mud houses and none for
improvised settlements). Walls are mostly constructed from uninsulated hollow sandcrete blocks
(clay for mud houses and wood for improvised settlements). Roofs are often uninsulated, pitched
with corrugated metal sheets and wooden/asbestos ceilings (straws or tree branches for mud
houses). Windows are single-glazed glass with an aluminium frame and a window-to-wall ratio
of 25% (10% is assumed for mud houses and improvised settlements based on available building
drawings). Floors are reinforced concrete slabs of 150 mm thickness (none/clay for mud houses
and wood for improvised settlements). Interior doors are wooden, while exterior doors may be
aluminium, steel or wood depending on wealth and personal preference (always wood for mud
houses). Shading effects from fences and burglary-proof steel beams are considered in this study
(overhangs and fences for mud houses and none for improvised settlements). The materials used
for fences are not considered in this study.
2.2 BUILDING MODEL AND ENERGY MODEL DEVELOPMENT
The study created a single representative geometry per typology across the climate zones, and the
resulting archetype geometries and their shares in the building stock are presented in Figure 2. The
study used DesignBuilder software, integrated with EnergyPlus v.9.2.0 for geometry creation and
energy modelling (DesignBuilder Software 2022). Household energy was modelled based on the
lighting, cooling and equipment needs in this study. Energy use related to cooking is not within the
study’s scope. This study relies on ideal loads as the main heating, ventilation and air-conditioning
system with mixed-mode ventilation settings activated. To estimate the cooling needs of the
building, weather files generated via Meteonorm (AG, Meteotest 2023) were used for each climate
zone. The study exported the files from DesignBuilder as input data files (IDFs) to the EnergyPlus-
supported BuildME Python model (Heeren 2019) and computed the material and energy use of the
different archetype energy models.
Due to some limitations in the structural framework of the building archetypes using DesignBuilder,
additional surrogate elements such as beams, columns and shear walls were specified in line with
Akin et al.’s (2023) approach. Although these elements have little impact on energy use, they
are important for material use. Energy demand (kWh/m2/yr) was scaled up to the country’s total
residential building floor area to obtain the total energy use of Nigeria’s residential building stock.
The results, representing 2020, were validated using the data on the use of oil products, electricity
and biomass by Nigerian households obtained from the United Nations 2020 Energy Balances (UN
2023). It should be emphasised that this study did not consider vacancy rates but rather assumed
that all dwellings were fully occupied all year round.
PARAMETER NOTES AND DESCRIPTION SOURCES
Dwellings 41 million
75% built before 2017 and 25% built 2017–20
79% informal and 21% formal
Estimated based on household size in the NLSS of
2018/19 (National Bureau of Statistics 2020)
Typology Apartment: flat/apartment, living quarters, boy’s quarters,
duplex, single-room apartment, storied building
National Bureau of Statistics (2011) and NLSS of
2018/19 (National Bureau of Statistics 2020)
Bungalow: separate house, semi-detached, two rooms
Compound: compound (colloquially called ‘Face Me I Face You’
or ‘Face Me I Slap You’ for row dwellings that face each other)
Mud house: hut, mud, thatch
Improvised settlement: timber houses (obtained from mass
balance since most statistics have it as ‘others’)
Climate zones Tropical monsoon (zone 1), tropical wet savannah (zone 2), hot
semi-arid (zone 3) and hot deserts (zone 4)
British Geological Survey (MediaWiki 2019)
Settlement type Rural and urban Based on the Exposure database (Paul et al. 2022) using
the same ratio for 2016 and 2020
Cohort type Pre-2017 (buildings completed before 2017)
Post-2017 (buildings completed 2017–20)
Based on the Exposure database (Paul et al. 2022) and
applying simple computation using the other parameters
Electricity access On-grid only: rural: 25%; urban: 84%
Off-grid inclusive: rural: 35%; urban: 92%
World Bank Global Electrification Database (World Bank
2020) and NLSS (National Bureau of Statistics 2020)
Population 2016: 194 million
2020: 208 million
2016 (National Bureau of Statistics 2016)
2020 (UN 2022)
People per dwelling Zone 1: SFH (6), MFH (5) Estimated based on data from 2015 statistics
(National Bureau of Statistics 2016)
Zone 2: SFH (5.7), MFH (4.5)
Zone 3: SFH (7.2), MFH (6)
Zone 4: SFH (7.5), MFH (6)
Useful floor area (UFA; m2)
per dwelling
Apartment: formal, 122; informal, 89 Exposure database; floor plans from local experts
and the existing literature available in Zenodo
(Nwagwu et al. 2024)
Bungalow: formal, 300; informal, 183
Compound: formal, 177; informal, 110
Mud house: 75
Improvised settlement: 91
Table 1: Key parameters for the
representation of the Nigerian
building stock.
Note: NLSS = National Living
Standards Survey; Single-family
house (SFH) = bungalow, mud,
improvised; multi-family house
(MFH) = apartment, compound.
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2.3 LIFE-CYCLE ASSESSMENT (LCA) METHODOLOGY
The study used calculated national energy and material use results for the LCA, following
ISO 14040 and 14044 (ISO 2006; Standard Norge 2006). It also followed the European Standard
(EN 15978) guidelines for the environmental performance of buildings using the LCA methodology
(CEN 2012).
2.3.1 Goal and scope
The goal of the LCA is to estimate the climate change impacts of the Nigerian residential dwelling
stock in 2020 using global warming potential (GWP) for a time horizon of 100 years, denoted as
GWP100, as given by the Intergovernmental Panel on Climate Change (IPCC) in 2021. The scope
covers only the material-related and operational emissions, and the end-of-life (EOL) indirectly
through the cut-off method as given in the Ecoinvent database. A simplified flowchart presented
in Figure 1 shows the system design, flows and boundaries. The two main foreground systems
are the construction and operation/use phases. From European Standard (EN 15978), this work
investigates stage A (product stage plus transport to site) and stage B6 (operational energy use).
Modules B1 (use), B2 (maintenance), B3 (repair), B5 (refurbishment) and B7 (operational water
use) were excluded because they generally have low environmental impacts (Frischknecht et al.
2019; Huang 2018; Krych et al. 2021). Module A5 (construction and installation) is excluded due
to a lack of quality data on the installation and assembly of residential buildings, while module
B4 (replacement) is excluded due to a lack of quality data on repair frequency or patterns by
households. As in many other studies, stages C and D, related to reuse, recycling and energy
recovery, are outside the system boundary because there are very limited demolition data for
Nigeria. The functional unit is one dwelling unit of a residential building within one year of operation.
The results are also given per m2 of usable floor area to aid comparison across typologies. The
assumed lifetime of a typical Nigerian building is 50 years, in line with the Nigerian Institute of
Building (Oghifo 2021).
2.3.2 Inventory analysis
Data collection and inventory modelling for the LCA uses data collected and processed as
described above and detailed in the supplemental data online. The energy and material use of
the operational and material-related phases from BuildME are matched with Ecoinvent v.3.9 to
calculate the life-cycle impact of each archetype. Considering the substantial utilisation of direct
fuel for standby generators, as previously mentioned, this study examines the proportion of
residences employing such generators and the duration of their operation per annum. The ratios
and fuel use metrics are presented in the supplemental data online.
Figure 2: Nigerian residential
building typologies in this study.
Source: Authors.
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Computations involving direct fuel use were performed in line with relevant international energy
and emissions protocols to estimate energy use at the operational and construction-related
phases. Given the volume of petrol imported from the Netherlands, this work uses data on Dutch
petrol from the Ecoinvent database. Subsequently, the life-cycle inventory results of the respective
typologies are computed.
2.4 SCENARIO DEVELOPMENT
2.4.1 Target and scenarios
In line with Nigeria’s NDCs, the baseline for the targets is 2015. In 2015, Nigeria recorded about
1134 TWh of residential energy use (UN 2017), which increased by 10% to 1239 TWh in 2020
(UN 2023) according to the UN Energy Balances. Direct operational emissions from residential
dwellings were about 36 MtCO2-eq in 2015 according to a government report (Federal Ministry
of Environment 2021a). Four different scenarios are proposed in comparison with the baseline
in terms of energy use and carbon emissions with 2020 as the target year and the operational
emissions reduction target in all scenarios as 33 MtCO2-eq/yr based on the country’s yearly
reduction target of 1.5%. No targets have been set for material emissions due to unavailable
references. The study does not account for new constructions in the scenarios but applies stated
assumptions to the national stock representation as in the baseline. These different measures
are tested individually and as a combination for the dwelling stock in 2020 (Table 2) (for a more
detailed explanation of the scenarios, see the supplemental data online).
• Scenario 1 (WIRE): assumes the swift implementation of various energy efficiency measures.
Key focus areas include lighting energy loads with the replacement of incandescent bulbs
with compact fluorescent lamps (CFLs).
• Scenario 2 (UHURU): as the name implies in Swahili, it assumes total freedom and is used
to describe a scenario where everything is as good as possible (high gross domestic product
(GDP) per capita and electricity access). This scenario does not assume that power blackouts
are eliminated, but that every household has access to back-up generators.
• Scenario 3 (SLIM): given the huge share of total emissions from materials production, this
SLIM scenario is one in which residential building construction changes. From lightweighting
to the use of thatched roofs, this scenario focuses on reducing material-related emissions.
• Scenario 4 (EXPO): explores the possibility of meeting Nigeria’s commitments to climate
change mitigation while achieving rapid growth in GDP per capita up to the level of the
average European country. Given Nigeria’s abundant access to similar renewable energy
sources as in Sweden, the Swedish electricity grid is chosen as a reference. It does not
assume behaviour pattern changes, therefore all lighting, cooling and equipment schedules
remain unchanged.
Table 2: The four different
scenarios.
Note: CFL = compact
fluorescent lamp.
SCENARIO/
INTERVENTION
PATHWAYS
CFL BULBS RE-
PLACE 70% OF
INCANDESCENT
BULBS
100% ELECTRI-
CITY ACCESS
(ON- AND OFF-
GRID)
50% COMPLY
WITH EXIST-
ING REGULA-
TIONS
100%
ON-GRID
ELECTRI-
CITY
REPLACE CON-
CRETE BLOCKS
WITH CLAY
THATCH
ROOFS
ON-GRID
ELECTRICITY
HAS 31% RE-
NEWABLES
ON-GRID ELEC-
TRICITY HAS
SWEDISH-LEVEL
OF RENEWABLES
WIRE-A ×
WIRE-B × ×
UHURU-A ×
UHURU-B × ×
UHURU-C × × ×
SLIM-A × × ×
SLIM-B × × × ×
EXPO-A × × × × ×
EXPO-B × × × × ×
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3. RESULTS
3.1 ARCHETYPE ENERGY AND MATERIAL-USE ESTIMATIONS
The results from the archetype energy and material-use modelling show that the Nigerian
residential dwellings hold 11 Gt of material, 0.3 kt per dwelling (Table 3). Apartments and
bungalows use more material by mass than other typologies per dwelling. The findings (Figure 3)
Figure 3: Material-use split for
Nigerian residential building
types and climate zones.
Note: APT = apartment; BUN =
bungalow; COM = compound;
INF = informal settlements;
MUD = mud houses.
Source: Authors, using Flourish
(n.d.).
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prove that concrete is the primary construction material used, accompanied by supplementary or
related materials such as sandcrete block, cement, sand and gravel. Concrete in this study refers
solely to load-bearing materials (shears, beams, columns), thus the authors measured them
mutually exclusively.
ANNUAL EUI
(kWh/m2/yr)
TOTAL
ENERGY USE
(TWh/yr)
ANNUAL EUI
(kWh/yr per
dwelling)
TOTAL
MATERIAL
USE (Mt)
SPECIFIC MUI
(kt/unit)
SPECIFIC MUI
(t/m2/50yrs)
Typologies
Apartment 26 12 2,557 1,161 0.25 2.6
Bungalow 16 44 3,333 5,989 0.45 2.1
Compound 18 37 2,249 3,422 0.21 1.6
Mud house 9 4 688 254 0.04 0.6
Improvised 26 0.4 2,389 10 0.06 0.6
Wealth
Formal 21 40 4,621 3,732 0.43 2.0
Informal 15 58 1,809 7,105 0.22 1.8
Time cohort
Pre-2017 15 67 2,171 7,849 0.26 1.8
Post-2017 22 32 3,098 2,987 0.29 2.0
Climate zone
Zone 1 18 33 2,613 3,547 0.28 1.9
Zone 2 17 46 2,400 5,013 0.26 1.8
Zone 3 15 17 2,130 1,919 0.24 1.8
Zone 4 16 3 2,158 357 0.25 1.8
Settlement
Rural 9 23 1,240 4,685 0.26 1.8
Urban 23 76 3,343 6,152 0.27 1.9
National
Totals 17 98 2,404 10,836 0.26 1.9
Table 3: Material and energy-
use results at different
segmentation levels.
Note: EUI = energy-use
intensity; MUI = material-use
intensity.
Figure 4: Energy uses and
energy supply in the Nigerian
residential building stock, 2020.
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Annual energy use amounted to 98 TWh in 2020, which is 17 kWh/m2/yr of useful floor area
(UFA), or 2404 kWh per dwelling. At the typology level, apartments and bungalows use more
energy than other dwelling types (Table 3), while mud houses use the least energy per dwelling.
Figure 4 shows that most of the energy used in Nigerian residential dwellings is for lighting. Cooling
and equipment cumulatively account for only 49% of the average national energy use. The most
important energy carrier is on-grid electricity (Figure 4) based on a mix of 74.5% natural gas and
25.5% hydro (Ecoinvent 2023; IEA 2024), while the off-grid electricity is diesel-based.
3.2 LIFE-CYCLE ASSESSMENT (LCA)
The LCA results show that around 6 GtCO2-eq will be emitted from Nigeria’s 2020 residential
dwellings stock over its entire lifetime. On average, material-related emissions are 1.4 tCO2-eq/yr
per dwelling and operational emissions are 1.3 tCO2-eq/yr per dwelling.
Table 4 shows that bungalows contribute most to Nigeria’s residential emissions, followed by
compound houses. However, per dwelling, apartments have higher emissions than compound
houses because of the high material use in apartments (mostly concrete and steel for beams,
shears and structural elements). The results also show that while compound houses have
much higher material-related emissions than improvised dwellings, they have almost the same
operational emissions. Table 4 also shows that informal dwellings contribute most to total
emissions. Newly constructed dwellings have higher emissions per dwelling unit than pre-2017
dwellings, although, overall, pre-2017 dwellings contribute > 70% of residential emissions. The top
five materials contributing to material-related emissions are concrete, cement, sandcrete block,
aluminium and steel (Figure 5).
Table 4: Emissions at different
segmentation levels.
TOTAL
OPERATIONAL
EMISSIONS
(MtCO2-eq/yr)
TOTAL
MATERIAL
EMISSIONS
(MtCO2-eq/yr)
SPECIFIC
OPERATIONAL
EMISSIONS
(kgCO2-eq/yr
per dwelling)
SPECIFIC
MATERIAL
EMISSIONS
(kgCO2-eq/yr
per dwelling)
SPECIFIC
EMISSIONS
(kgCO2-eq/yr
per dwelling)
Typologies
Apartment 6 9 1,376 1,903 3,279
Bungalow 23 30 1,763 2,237 4,000
Compound 20 16 1,185 987 2,172
Improvised 0.2 0.1 1,240 462 1,702
Mud 2 0.8 359 129 488
Wealth
Formal 21 19 2,453 2,166 4,618
Informal 31 37 955 1,153 2,108
Time cohort
Pre-2017 35 40 1,150 1,307 2,458
Post-2017 17 16 1,634 1,547 3,181
Climate zone
Zone 1 17 18 1,382 1,478 2,860
Zone 2 24 26 1,270 1,363 2,632
Zone 3 9 10 1,127 1,220 2,347
Zone 4 2 2 1,142 1,283 2,425
Settlement
Rural 10 24 552 1,319 1,872
Urban 42 32 1,852 1,406 3,258
National
Totals 52 56 1,272 1,367 2,639
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3.3 SCENARIOS
The results of the four scenarios show how different intervention measures might have influenced
2020’s energy use and carbon emissions. Figure 6 shows clearly that most interventions have
higher energy use compared with the baseline, especially in scenarios where all dwellings have
100% access to electricity. This is despite a higher share of CFLs in use. The same trend is observed
in UHURU scenarios where 50% of dwellings comply with the 2016 regulations without additional
intervention measures. Energy savings of 10% from baseline are seen in WIRE-A, which is a
scenario where 70% of the dwellings switch to CFL, but low electricity access and substandard
housing persist. Energy savings of 9% from baseline are also seen in UHURU-B when the switch to
CFLs is matched with 50% of dwellings complying with existing regulations while electricity access
remains low. The SLIM-A and SLIM-B scenarios, which build on the WIRE-B scenario, but with the
additional switch from sandcrete block walls and metal roofs to clay walls and thatched roofs,
use 24% more energy than the baseline. The high increase in operational energy use for EXPO
scenarios is because no blackouts are experienced as on-grid electricity access is assumed for 24
h all year round, unlike in other scenarios.
The results given in Figure 6 show the same trend for operational emissions across all scenarios,
except for the EXPO-B scenario where on-grid electricity comes mostly from renewables. This
scenario yields a reduction of 48% from the baseline (operational and material-related) emissions
Figure 6: Energy use and
total emissions compared
with the baseline for different
interventions.
Figure 5: Annualised
greenhouse gas (GHG)
emissions for the materials
used in Nigeria’s residential
building stock, 2020.
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results for 2020 and surpasses the target of 33 MtCO2-eq/yr of operational emissions. Although the
SLIM and EXPO-A scenarios led to a reduction in material-related emissions, their high operational
emissions due to 100% electricity access minimised the overall impacts.
4. DISCUSSION
Limited attention to buildings in Nigeria and the fact that both construction and fuel provision are
often part of the informal economy has affected this work due to a shortage of public statistics.
This is not unique to this work but a common limitation of bottom-up models, as explained by
Kavgic et al. (2010). Here the present paper compares the results with other literature values to
check their plausibility and give some overview of the range of values.
4.1 MATERIAL USE
At the national level, the results show that the 41 million residential dwellings in Nigeria in 2020
used about 11 Gt of material, translating to about 0.3 kt per dwelling (or 1.9 t/m2) material use.
According to Heeren & Fishman (2019), material intensity typically fluctuates around a magnitude
of 1 t/m2. Another study on residential dwellings in Kuwait showed that material quantities were
in the range of 0.5 and nearly 3.0 t/m2 (De Wolf 2014). Krause and Hafner’s work on residential
dwellings in Germany put the material intensities between 0.61 and 1.96 t/m2, depending on the
typology (Krause & Hafner 2022), further supporting the fact that multifamily dwellings (such as
apartments and compounds in this study) use the most material by mass/m2, but that bungalows
use more material by mass overall because they are often bigger. Krause and Hafner also show
that wooden dwellings (such as improvised dwellings in this work) use around 0.6 t/m2. The above
literature sources place the result of this study (1.9 t/m2) at the higher end of the range. UNEP
(2022) also conforms with this work’s results in Figure 3 as it identifies cement (used in concrete
and sandcrete block) as the major material contributing to urban material use. Unlike the UNEP
estimate and similar to the results of De Wolf (2014) and Akin et al. (2023), the use of steel is low
for Nigerian residential dwellings since most of the dwellings are not made to have load-bearing
properties, except for apartments.
4.2 ENERGY USE
The UN Energy Balance puts Nigerian residential energy use at about 200 kWh/m2 in 2020 (UN
2023), while this work’s model results give 17 kWh/m2 in 2020. The variation in the result may
be due to the study’s exclusion of cooking loads, which represent nearly 97% of the UN’s (2023)
reported value. For operational energy use by an average residential dwelling of 120 m2, Ezema
& Okorigba (2022) reported 14,379 kWh/yr with 24% on-grid supply. This work’s results show
around 2404 kWh/yr per dwelling. This difference is perhaps due to this study’s assumption of only
about 35% electricity access, 70% on-grid supply and blackouts for 12 h/day. Using consumption
surveys in Khulna, the third largest city of Bangladesh, which has a relatively similar GDP to Nigeria,
Rahman et al. (2017) found that the monthly electrical energy use in residential dwellings in 2017
was between 234 and 340 kWh depending on the number of rooms. This is equivalent to between
2808 and 4080 kWh/yr per dwelling, placing the results of this work within a plausible range.
4.3 ENVIRONMENTAL ASSESSMENT
The LCA by Ezema & Okorigba (2022) showed the high contribution of cement and steel in material-
related energy and emissions. From Figure 5, the same trend is observed because concrete and
sandcrete blocks are cement products. Steel is also a high contributor after aluminium, only
because aluminium has a 10 times higher emission factor in the Ecoinvent database (from Figure
3, 18 Mt of aluminium are used in the entire stock compared with 90 Mt for steel). Unlike in
Ezema & Okorigba’s work, where operational emissions were three times more than the material-
related emissions, operational emissions were nearly equal to the material-related emissions in
this study. De Wolf (2014) equally found that, on average, residential buildings’ materials typically
emit between 250 and 700 kgCO2/m2 for the lifetime of the building. This validates this work’s
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results of 477 kgCO2-eq/m2 material-related emissions over the 50-year lifetime. Additionally,
since post-2017 dwellings emit more than those built before the energy standards, this means
that if households continue to build as they do (based on wealth and aesthetics), the emissions
are bound to be much higher in future.
4.4 SCENARIOS
The WIRE-A scenario, for instance, clearly shows how great an impact switching from
incandescent to CFL bulbs is on energy savings and carbon emissions as it leads to 10% and
5% reductions, respectively, without the switch to renewable (on- or off-grid) power sources. All
UHURU combinations (high GDP per capita and electricity access) cause higher emissions than the
baseline. This shows that current low residential operational emissions in Nigeria are the result
of low electricity access and energy poverty seen from high unmet energy needs. Hence, since
Nigeria has a high energy use per unit of GDP compared with other countries (Kwag et al. 2019),
combining the UHURU scenario of higher formal dwelling types with energy efficiency measures in
lighting (UHURU-B) yields lower emissions than having a higher share of formal dwellings without
these energy efficiency measures (UHURU-A).
The SLIM and EXPO scenarios, which involve replacing sandcrete walls with clay and metal roofs
with thatch, perform better than the other scenarios in terms of material emissions. However, due
to the increase in electricity access in these scenarios, the operational emissions are higher than
the baseline. This confirms the World Bank’s and UNEP’s claims that Africa stands an excellent
chance at sustainable construction using low-carbon local materials such as clay. Indeed, the
results prove that erecting buildings using mineral materials such as concrete is the primary cause
of the high material-related emissions in Nigeria. Nnimmo Bassey’s theory (Bassey 1996/97, as
cited in Nwalusi et al. 2019) that thatched roofs help to increase thermal comfort was inconclusive
as the inclusion of thatched roofs in the SLIM scenario had very little visible effect on energy use,
but more on emissions. This disparity might be because additional layers of wooden struts were
added to the model’s thatched roofs in keeping with recent building styles. On the other hand,
the statement by Onyenokporo & Ochedi (2018) that the building’s envelope design can reduce
energy use was proven to be correct. This is because replacing sandcrete blocks with clay led
to a reduction in energy use and operational emissions as well compared with the WIRE-B and
UHURU-C scenarios which share similar interventions.
The final scenario, EXPO, being the only scenario where the country’s emissions reduction targets
are reached, proves that there is no single solution to the energy and emissions crises, but that
combining different strategies is more effective. The difference between EXPO-A and EXPO-B
further shows that although Nigeria’s target of 26% on-grid renewable electricity is helpful in
some ways, if all households have access to on-grid electricity without blackouts, then emissions
will stay high. Therefore, given the country’s abundant access to renewable energy (hydropower,
solar, wind), achieving EXPO-B with about 58% renewables in the electricity grid as in Sweden
(Swedish Energy Agency 2019) should be a reference guide sheet. Overall, the results show that an
increase in the on-grid transmission capacity, 26% renewables share and 100% electricity access,
without changing the existing energy use patterns, will worsen the emissions compared with the
baseline unless significantly high amount of renewables are deployed.
4.5 LIMITATIONS AND FUTURE WORK
The following uncertainties posed a limitation to this study. Due to poor or unavailable government
data, the study relied upon tables, texts and graphs from literature sources. The module B2–B5
phases of the LCA were excluded from this study, but despite these omissions, the authors have high
confidence in the LCA results based on the other phases included. The authors noted the limitations
of assessing only one impact category on trade-offs, but continued with this to remain within the
scope of the study. Future research can extend this to more impact categories and to the endpoint
level of aggregation. The study excluded cooking from the operational energy calculations, which
means that most of the residential energy and emissions from that activity are omitted. However,
to reduce the uncertainty in the model results, the analysis proceeded without them.
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Future studies can extend the WIRE scenario to see how great an impact turning off lights when
not in use might have on energy emissions. The research community would also benefit from
a cost-effectiveness analysis of all outlined scenarios and the baseline to test the feasibility of
individual households making these switches given the current minimum wage. This is in line
with previous studies that stated that the climate and energy transition would be faster at the
individual household level (Emodi et al. 2017; Kwag et al. 2019).
5. CONCLUSIONS
Nigeria’s residential dwellings stock is large, heterogeneous and growing, but many housing demands
are not met considering the overall population. Comprising different typologies with features that
are unique to the country, the residential stock is also mostly informal with high data sparsity and
uncertainties. This influences national-scale modelling and requires the use of ratios, databases,
assumptions and local context. Based on the validation using different previous studies, the models
defined here correctly estimate the energy and material use of the average Nigerian residential
dwelling. Considering the country’s overall high energy use relative to its gross domestic product
(GDP), residential energy use is low in this study because of low access to electricity and frequent
blackouts. Excluding cooking in this study revealed the high impact lighting loads have on energy use
in Nigeria’s residential dwellings. Material use is high as cement and cement-based materials are the
main contributors to material use and emissions in the stock. Energy efficiency by households and
the switch to renewables were found to be very effective for energy and emissions savings.
To meet Nigeria’s nationally determined contribution (NDC) of a 20% reduction in CO2 emissions,
it is recommended that a policy switch is made to low-carbon materials such as timber, earthen
blocks, adobe bricks and clay. A huge reduction in construction-related emissions was shown in
the switch from sandcrete block to clay.
The scenarios developed in this study have far-reaching benefits beyond energy and climate
change as they can improve the quality of life of households in Nigeria. The potential for lock-in is
also seen as newly constructed dwellings emit more than the older ones, meaning stakeholders
have to act quickly and smartly as they build more to match prevailing housing needs.
AUTHOR AFFILIATIONS
Chibuikem Chrysogonus Nwagwu orcid.org/0000-0003-1587-0140
Department of Energy & Process Engineering, Norwegian University of Science and Technology, Trondheim, NO
Sahin Akin orcid.org/0000-0003-3926-3476
Department of Energy & Process Engineering, Norwegian University of Science and Technology, Trondheim, NO
Edgar G. Hertwich orcid.org/0000-0002-4934-3421
Department of Energy & Process Engineering, Norwegian University of Science and Technology, Trondheim, NO
AUTHOR CONTRIBUTIONS
CCN, SA and EGH: conception and design. CCN: data collection, analysis and interpretation. SA:
supported the analysis and interpretation. EGH: supported the results interpretation and discussion
and critically revised the article.
COMPETING INTERESTS
The authors declare they have no known competing financial interests or personal relationships
that could have appeared to influence the work reported in this paper.
DATA ACCESSIBILITY
Additional data can be accessed on Zenodo (Nwagwu et al. 2024). Data sheets for emissions
calculations are not included in the supplementary data due to non-disclosure agreements around
database values. However, these data sheets are available upon request.
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ETHICAL APPROVAL
Ethical review and approval were not required for this study following the national legislation and
the institutional requirements.
FUNDING
CCN was supported by Chair funds from the Norwegian University of Science and Technology
(NTNU). SA was supported by a PhD stipend from the Faculty of Engineering, NTNU. EGH was
funded by the European Union (grant number 101056868, Horizon Europe Project).
SUPPLEMENTAL DATA
Supplemental data for this paper can be found at: https://doi.org/10.5334/bc.452.s1
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539Nwagwu et al.
Buildings and Cities
DOI: 10.5334/bc.452
TO CITE THIS ARTICLE:
Nwagwu, C. C., Akin, S.,
& Hertwich, E. G. (2024).
Modelling Nigerian residential
dwellings: bottom-up approach
and scenario analysis. Buildings
and Cities, 5(1), pp. 521–539.
DOI: https://doi.org/10.5334/
bc.452
Submitted: 23 April 2024
Accepted: 08 October 2024
Published: 22 October 2024
COPYRIGHT:
© 2024 The Author(s). This is an
open-access article distributed
under the terms of the Creative
Commons Attribution 4.0
International License (CC-BY
4.0), which permits unrestricted
use, distribution, and
reproduction in any medium,
provided the original author
and source are credited. See
http://creativecommons.org/
licenses/by/4.0/.
Buildings and Cities is a peer-
reviewed open access journal
published by Ubiquity Press.
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