Available via license: CC BY 4.0
Content may be subject to copyright.
Citation: Arfiansyah, D.; Han, H.;
Zlatanova, S. Land Suitability
Analysis for Residential Development
in an Ecologically Sensitive Area: A
Case Study of Nusantara, the New
Indonesian Capital. Sustainability
2024,16, 5767. https://doi.org/
10.3390/su16135767
Academic Editor: Pere Serra
Received: 19 May 2024
Revised: 27 June 2024
Accepted: 2 July 2024
Published: 6 July 2024
Copyright: © 2024 by the authors.
Licensee MDPI, Basel, Switzerland.
This article is an open access article
distributed under the terms and
conditions of the Creative Commons
Attribution (CC BY) license (https://
creativecommons.org/licenses/by/
4.0/).
sustainability
Article
Land Suitability Analysis for Residential Development in an
Ecologically Sensitive Area: A Case Study of Nusantara, the New
Indonesian Capital
Dody Arfiansyah , Hoon Han * and Sisi Zlatanova
School of Built Environment, University of New South Wales, Sydney, NSW 2052, Australia;
d.arfiansyah@unsw.edu.au (D.A.); s.zlatanova@unsw.edu.au (S.Z.)
*Correspondence: h.han@unsw.edu.au
Abstract: Land suitability analysis is a process of evaluating various criteria to assess the appro-
priateness of land for specific purposes, such as agriculture, urban development, conservation, or
infrastructure projects. This paper integrates multi-criteria analysis (MCA) and geographic infor-
mation systems (GIS) to assess potential residential development suitability in Nusantara—the new
Indonesian Capital. This study used two models to evaluate residential development suitability—a
simple suitability model with equal criteria weight and a weighted suitability model using the ana-
lytic hierarchy process (AHP) approach with two scenarios (waterfront city and biodiversity-positive
city). Various criteria, including physical attributes, natural preservation and protection, blue ameni-
ties, transport accessibility, and natural disaster risks, were analysed. Integrating MCA with the
AHP approach and GIS can be considered an advanced methodology. The simple suitability model
is relatively more straightforward than the weighted suitability model since it does not require a
weighting process. However, the weighted suitability model produced more nuanced results for the
case study as the approach more accurately models real-world conditions. The weighted suitability
analysis showed that most of the western and eastern parts of the new capital are highly suitable for
future residential development. Comparing the Indonesian government’s planned residential areas
with the result of the weighted suitability model for the biodiversity-positive city scenario showed
that most planned residential areas are in highly suitable areas. The methodologies in the paper can
be extended to similar contexts in different geographical areas.
Keywords: AHP; GIS; land suitability analysis; MCA; new Indonesian capital
1. Introduction
Capital cities are significant as the symbols and identities of countries [
1
]. Most capital
cities are also the largest cities in their nations, with the capital cities of Australia, Canada,
and the USA being some remarkable exceptions. Countries often consider relocating and
re-establishing their capital cities to represent a new political ideology or after gaining
independence. Countries that have changed capitals after independence include Brazil,
Kazakhstan, Malawi, Nigeria, Pakistan, and Tanzania. The relocation aimed to reflect
independence, social equity, and a national-centric symbolic location [2].
However, new capital cities often experience non-natural spatial designs, unequal
social representations, urban sprawl, or minimal expansion [
2
]. It leads to further justifi-
cation for a capital city relocation to create a green and sustainable city development as a
symbol for new environmentally friendly policies, for example, Putrajaya in Malaysia [
3
]
and Sejong in South Korea [
4
]. Those development paradigms inspired Indonesia as it
built its new capital city, Nusantara. The construction of the new capital began in 2022
and is expected to be finished by 2045. The government is constructing the core area of
the government centre and various other basic infrastructures to initiate the development
stage. The city is in the tropical rainforest on eastern Borneo Island and is proposed to
Sustainability 2024,16, 5767. https://doi.org/10.3390/su16135767 https://www.mdpi.com/journal/sustainability
Sustainability 2024,16, 5767 2 of 34
become a smart, sustainable capital city. The Indonesian government intends to strengthen
human interaction with nature in the new capital by applying the forest city concept.
The president of Indonesia conveyed the urgency of relocating the national capital at
the Annual Meeting of the People’s Consultative Assembly of the Republic of Indonesia
in August 2019. The relocation was due to the concentration of economic activities in the
existing capital city of Jakarta and Java Island, resulting in economic disparities between
Java and outside Java Island. Moreover, some studies concluded that Jakarta could no
longer carry out its role as the national capital due to uncontrolled population growth,
declining environmental conditions and functions, and decreasing quality of life [
5
–
7
].
Therefore, relocating the national capital outside Java Island is expected to reduce inequal-
ity and increase regional economic growth outside of Java Island, especially in eastern
Indonesia [8].
A technical feasibility study in 2018–2019 was the basis for selecting the new location.
The relocation of the capital to Borneo Island was based on several considerations of
regional advantages. First, it is a very strategic central geographical location of Indonesia’s
territory. It is on the Indonesian Archipelagic Sea Lane (Alur Laut Kepulauan Indonesia/ALKI)
II in the Makassar Strait—the leading national and regional sea route. Second, the location
has a moderately complete infrastructure. Third, it is close to two developing cities,
Balikpapan City and Samarinda City. Fourth, there is adequate availability of government-
controlled land. Fifth, there is minimal risk of natural disasters. The relocation of the
capital city to Borneo Island aligns with the vision of the birth of a new economic “centre of
gravity” in the middle of the archipelago [9].
The Indonesian government aims to design the new capital according to the natural
conditions. The first key performance indicator (KPI) for the new capital development
is design with nature. There are two main challenges to implementing this KPI. First,
Nusantara is in an area with numerous river networks. Thus, water supply and water
regulation of ecosystem services are two main concerns. Second, the new capital is in
an ecologically sensitive area. Borneo’s natural tropical forest cover is rich in flora and
fauna diversity. It also has key biodiversity areas (KBAs)—sites contributing to the global
persistence of biological diversity (biodiversity). Unfortunately, human activities influence
forest degradation. The main threats to the lowland tropical forest ecosystem in the new
capital are bushfires, illegal logging, occupancy of communities and corporations both for
small-scale plantations and oil palm plantations, and mining permits [10].
The new capital’s population is 488,409 in 2024, projected to reach 1.9 million in 2045.
Building such a megaproject in a relatively short time requires extra effort to house the
fast-increasing population. Hence, a study is needed to identify suitable locations as a
planning support tool to accommodate future urban growth and meet the UN Sustainable
Development Goals.
A land suitability analysis focuses on a single type of land development at a time,
such as commercial, industrial, or residential [
11
]. In land suitability analysis, it is crucial
to examine many suitability criteria. The criteria will differ based on the intended objective.
Currently, there is no comprehensive global framework for determining a set of criteria
for urban development. Thus, most studies rely on the literature review, local context, the
study’s objective, and data availability to define the criteria. Ustaoglu and Aydınoglu [
12
]
selected physical attributes, land use accessibility, built-up area and infrastructure, veg-
etation and geology, and green and blue amenities to evaluate urban construction land
in Pendik, Istanbul, Türkiye. In their study, Karim et al. [
13
] employed criteria such as
geological formation, soil, streams/valleys, slopes, agriculture areas, urban areas, road
networks, railways, wells, power lines, crevasses/faults, and environmental areas to assess
the suitability of land in the rural-urban continuum between Ar Riyadh and Al Kharj Cities
in Saudi Arabia. Al-Ghorayeb et al. [
14
] employed data on slope, elevation, road network,
urban agglomeration, and land cover to identify areas in Nabatiyeh, Lebanon, that are
appropriate for sustainable urban development. Ullah and Mansourian [
15
] performed a
land suitability analysis to aid urban land use planning in Dhaka, Bangladesh. They consid-
Sustainability 2024,16, 5767 3 of 34
ered criteria such as land cover, access to utilities/infrastructure, physical and geological
characteristics, and access to community utilities. Bathrellos et al. [
16
] conducted land
suitability analysis for urban planning and industry development using natural hazard
maps and geological-geomorphological parameters such as physical attributes, distance
from streams, lithology, landslides, seismic intensity, distance from flood events, distance
from the main road network, and distance from main towns. This study introduces a
set of criteria for residential development suitability for a city built from scratch. It pays
more attention to the local conditions of the new Indonesian capital, located within an
ecologically sensitive area with numerous river networks, as well as the risk of natural
disasters. Thus, this study contributes to the emerging literature on land suitability analysis
by considering sustainable and resilient residential development criteria.
Furthermore, many studies used geographic information systems-multi-criteria anal-
ysis (GIS-MCA) with AHP weighting for land suitability analysis. The AHP approach
typically gathers information from the relevant literature and expert opinions for the
pairwise comparison matrix. Nevertheless, due to time constraints and the case study
dynamics, it can be challenging to effectively communicate with the experts to gain their
views. Hence, this study proposes a scenario-based AHP method for the weighting process
based on the literature review, local context, and relevant government documents. A study
in Diffun, the Philippines, by Sia et al. [
17
] used a relatively comparable process with no
scenario building.
This study aims to identify land suitable for residential development in the new
Indonesian capital based on the GIS-MCA method. ArcGIS Pro’s (version 3.2.1) simple
suitability model with equal weight for the criteria and weighted suitability models using
a scenario-based AHP approach are used. Several essential criteria to support sustainable
and resilient residential development are considered in five categories: physical attributes,
natural preservation and protection, blue amenities, transport accessibility, and natural
disaster risks.
2. Literature Review
Rapid urbanisation and uncontrolled urban development, occurring in many develop-
ing countries, put more pressure on the environment and urban ecosystem services [
18
,
19
].
Urban sprawl causes air pollution, green space deprivation and fragmentation, overuse of
natural resources, and biodiversity loss [
20
]. Developing management priorities and using
decision-support tools can help identify the most appropriate spatial pattern for future land
uses and form the right land use policies to promote sustainable urban development [
21
,
22
].
Thus, determining the best geographical locations for sustainable urban development and
preserving ecologically important areas has become increasingly important [
23
]. Among
numerous developed methods, land suitability analysis emerges as the most effective,
efficient, and commonly used method [
24
]. Land suitability analysis is an essential tool for
planners and policymakers to find optimal locations and reduce the adverse impacts of
urbanisation on the environment and corresponding ecosystem services [12,25–28].
Multi-criteria analysis (MCA) is a commonly used land suitability analysis method.
MCA is a set of systematic procedures used to design, evaluate, and choose decision
alternatives when faced with competing and incomparable criteria [
29
]. In the literature, it
is also referred to as multiple-criteria decision-making (MCDM), multiple-criteria decision
analysis (MCDA), or multi-dimensional decision-making (MDDM) [
30
]. This method
is frequently integrated with GIS to improve evaluation efficiency and accuracy and is
suitable for enhancing land use policy and planning [
16
,
26
,
31
]. Integration advances land
suitability analysis, improving traditional methods, and providing a high-performance
tool [22,32].
MCA can be traced back to Borda and Condorcet’s work on ranked preferential voting
systems in the eighteenth century. However, Edgeworth and Pareto created the essential
notions of MCA in the second half of the nineteenth century. They suggested a method for
merging conflicting criteria into a single evaluation index. Pareto also proposed one of the
Sustainability 2024,16, 5767 4 of 34
foundational concepts of modern MCA theory: the concept of efficiency (also known as
Pareto optimality) [
33
]. GIS-MCA has a second, separate history originating in landscape
architecture and planning. This viewpoint originated with hand-drawn map overlay
techniques by American landscape architects in the late nineteenth and early twentieth
centuries [
26
,
34
]. Landscape architects such as C. Eliot and W. Manning described the
overlay processes in detail, but neither explicitly explained the underlying philosophical
rationales [
34
]. McHarg [
35
] advanced the overlay techniques by proposing a procedure
for mapping data on the natural and human-made attributes of the environment within a
study area, presenting this information on individual, transparent maps using light-to-dark
shading (high suitability to low suitability), and superimposing the individual transparent
maps to construct the overall suitability maps for each land use. The overlay method was
probably the most significant antecedent to subsequent iterations of intricate GIS-MCA
techniques [33].
GIS-MCA techniques have been used in various decisions and management sce-
narios. The primary application areas of GIS-MCA are environmental planning and
management [
36
], transportation planning and management [
37
], urban and regional
planning [
12
–
15
,
22
,
38
], waste management [
39
], hydrology, and water resources manage-
ment [
40
], natural hazards [
41
,
42
], agriculture [
43
], and forestry [
44
]. More than 70% of
all GIS-MCA applications fall within these sectors [
45
]. Furthermore, the GIS-MCA tech-
niques have been applied in a wide range of fields, including real estate and housing [46],
industrial facility management [
47
], recreation and tourism management [
48
], geology
and geomorphology [
49
,
50
], and cartography [
51
,
52
]. In the real estate and housing sector,
specifically, GIS-MCA is crucial as it enables informed decision-making, risk management,
and the implementation of sustainable development practices. Through meticulously eval-
uating residential suitability, stakeholders can create sustainable and resilient residential
developments that effectively cater to the needs of residents.
In a simple suitability analysis, there is no weighting of criteria. Conversely, in
weighted suitability analysis, it is critical to identify the weight of each criterion that
influences land suitability and determines overlay techniques [53,54].
There are several weighting methods in MCA: weighted linear combination (WLC),
ordered weighted averaging (OWA), analytic hierarchy process (AHP), analytic network
process (ANP), and ELECTRE (ÉLimination Et Choix Traduisant la REalité/Elimination
and Choice Translating Reality). The WLC method is based on linear and additive assump-
tions. It can be implemented in a GIS environment using the map algebra technique. The
approach is also naturally appealing to decision-makers. As a result, GIS-WLC has been
used to analyse decision and management scenarios across a wide range of application
disciplines. OWA is a generalisation and extension of the WLC model. OWA is a map
combination process that assigns two weights to the maps: criteria and order weights.
AHP is one of the most thorough MCA techniques [
55
]. The three guiding concepts of
the approach are decomposition, comparative judgement, and priority synthesis. The
fundamental measurement mode used in AHP is pairwise comparison. The AHP model is
a type of WLC. The main benefit of adopting AHP over WLC is that AHP gives decision-
makers a tool to concentrate on creating a formal structure that captures all the crucial
components of a decision circumstance. Saaty [
56
] proposed ANP, a method for dealing
with choice issues in the presence of dependencies between decision situation variables.
The approach is an extension and generalisation of AHP. ANP, like AHP, is founded on
the principles of decomposition, comparative judgement, and synthesis of priorities. The
ELECTRE method evaluates each pair of options using the concepts of concordance and
discordance to uncover outranking relationships and generate a dominance matrix. Out-
ranking approaches have the advantage of considering both quantitative and qualitative
parameters [
33
]. Furthermore, the procedures require only a small quantity of informa-
tion from the decision-makers [
33
]. Among those methods, AHP is considered one of
MCA’s most outstanding approaches [
55
]. It is widely used because of its adaptability and
simplicity of use, as well as the availability of software packages and GIS integration.
Sustainability 2024,16, 5767 5 of 34
3. Materials and Methods
3.1. Study Area and Data Sources
Nusantara is the new planned capital city of Indonesia, which has been designated
to replace the current capital, Jakarta. The new capital is located between 0
◦
38
′
41.0856
′′
to 1
◦
8
′
26.3508
′′
S latitude and 116
◦
31
′
33.2976
′′
to 117
◦
16
′
15.6144
′′
E longitude (Figure 1).
This city is in East Kalimantan Province on the Island of Borneo and covers a land area of
approximately 252,660 ha and a marine area of approximately 69,769 ha. Nusantara Capital
City (Ibu Kota Nusantara/IKN) is located between two existing cities—Balikpapan City and
Samarinda City. It also borders Balikpapan Bay to the south and Makassar Strait to the
east. The population of this new capital city is projected to reach 1.9 million inhabitants
in 2045 [
57
]. The study area is planned as a city in the forest and is characterised by
predominantly existing vegetation at a large scale and several other land uses, including
built-up areas, waterbodies, croplands, and wetlands that will be restored and conserved.
Based on the Oldeman climate classification, the new capital and its surroundings have
climate types C1 and B1. Climate type B1 is wetter than C1. Climate type C1 is characterised
by a dry season of less than or equal to one month and a wet season of five to six months.
Climate type B1 is characterised by a dry season of less than or equal to one month and a
wet season of seven to nine months [10,58].
Sustainability 2024, 16, 5767 5 of 34
parameters [33]. Furthermore, the procedures require only a small quantity of information
from the decision-makers [33]. Among those methods, AHP is considered one of MCA’s
most outstanding approaches [55]. It is widely used because of its adaptability and sim-
plicity of use, as well as the availability of software packages and GIS integration.
3. Materials and Methods
3.1. Study Area and Data Sources
Nusantara is the new planned capital city of Indonesia, which has been designated
to replace the current capital, Jakarta. The new capital is located between 0°38′41.0856″ to
1°8′26.3508″ S latitude and 116°31′33.2976″ to 117°16′15.6144″ E longitude (Figure 1). This
city is in East Kalimantan Province on the Island of Borneo and covers a land area of ap-
proximately 252,660 ha and a marine area of approximately 69,769 ha. Nusantara Capital
City (Ibu Kota Nusantara/IKN) is located between two existing cities—Balikpapan City and
Samarinda City. It also borders Balikpapan Bay to the south and Makassar Strait to the
east. The population of this new capital city is projected to reach 1.9 million inhabitants in
2045 [57]. The study area is planned as a city in the forest and is characterised by predom-
inantly existing vegetation at a large scale and several other land uses, including built-up
areas, waterbodies, croplands, and wetlands that will be restored and conserved. Based
on the Oldeman climate classification, the new capital and its surroundings have climate
types C1 and B1. Climate type B1 is weer than C1. Climate type C1 is characterised by a
dry season of less than or equal to one month and a wet season of five to six months.
Climate type B1 is characterised by a dry season of less than or equal to one month and a
wet season of seven to nine months [10,58].
Figure 1. Geospatial and land cover of the study area.
The data for each suitability criterion considered in the analysis were collected from
relevant sources, as listed in Table 1.
Table 1. Data sources.
No. Criteria Data Format Source
1 Land cover Land cover Raster Copernicus Global Land Service
(CGLS) [59]
2 Slope Digital elevation
model (DEM) Raster United States Geological Survey
(USGS) [60]
3 Key biodiversity area Key biodiversity area Vector BirdLife International (BLI) [61]
4 Forest cover Forest cover Vector Global Forest Watch (GFW) [62]
Figure 1. Geospatial and land cover of the study area.
The data for each suitability criterion considered in the analysis were collected from
relevant sources, as listed in Table 1.
Table 1. Data sources.
No. Criteria Data Format Source
1 Land cover Land cover Raster Copernicus Global Land Service
(CGLS) [59]
2 Slope Digital elevation model
(DEM) Raster United States Geological Survey
(USGS) [60]
3 Key biodiversity area Key biodiversity area Vector BirdLife International (BLI) [61]
4 Forest cover Forest cover Vector Global Forest Watch (GFW) [62]
5Blue amenities (distance from
streams, lakes, and coastlines) Topographic map Vector Indonesia Geospatial
Information Agency (IGIA) [63]
6
Transport accessibility (distance
from roads, major airports, major
seaports, and major bus stations)
Topographic map Vector Indonesia Geospatial
Information Agency (IGIA) [63]
Sustainability 2024,16, 5767 6 of 34
Table 1. Cont.
No. Criteria Data Format Source
7New Indonesian Capital Spatial
Planning
New Indonesian Capital
Spatial Planning Vector
Indonesia Ministry of Agrarian
Affairs and Spatial Planning
(IMAASP)
8
Natural disaster risks (tsunami,
bushfire, extreme waves and
abrasion (EWA), extreme
weather, and flood risks)
Natural disaster risk maps Raster
Indonesia National Disaster
Management Agency (INDMA)
[64]
3.2. Methodology
This study used two models to assess residential development suitability: simple
suitability and weighted suitability. In the first model, all criteria were given equal weight,
so there was no process for weighting the criteria. The second model used the AHP method
to weight the criteria using two scenarios—the waterfront city and the biodiversity-positive
city. The models are summarised in Table 2.
Table 2. Summary of the models evaluated in the study.
Model 1 Model 2
Model type Simple suitability model Weighted suitability model—
waterfront city scenario
Weighted suitability
model—biodiversity-positive
city scenario
Development category Residential Residential Residential
Suitability scale
Based on a binary value.
1 is a suitable area, and 0 is an
unsuitable area
Based on a 1–3 scale.
1 is a low suitable area, 2 is a
moderate suitable area, and 3
is a high suitable area
Based on a 1–3 scale.
1 is a low suitable area, 2 is a
moderate suitable area, and 3
is a high suitable area
Weighting of the criteria Equal weight AHP AHP
The suitability modelling process involves creating rasters representing each model
criterion and then combining or overlaying the rasters into a single suitability surface that
meets the model goals [
65
]. Typically, the source dataset will not fully meet the input data
requirements for a suitability model, and it needs to be derived using geoprocessing tools or
raster functions of ArcGIS Pro 3.2.1 software. It is also important to note that each raster’s
cell size and projected coordinate system must be the same for the combining process.
The resample tool can be utilised to carry out this procedure. Cell size is defined on the
parameters tab, while the projected coordinate system is specified in the environments tab.
In this analysis, the cell size was defined as 30 m
×
30 m, and the projected coordinate
system was WGS 1984 UTM zone 50S, where the new Indonesian capital is located. The
AHP weighting was carried out using the AHP priority calculator [66].
The simple suitability model in this study used the model builder in ArcGIS Pro 3.2.1
software. The adopted technical approaches include (a) defining the goal and criteria,
(b) deriving data that represents the model variables that are defined by the criteria,
(c) transforming and reclassifying the values in each derived dataset to a common suitability
scale by assigning each cell a binary value, and (d) combining the transformed data, which
represent the suitability criteria, into a single suitability surface that meets the analysis goal.
The following diagram (Figure 2) presents the process of generating the simple suitability
model for the residential development map.
Sustainability 2024,16, 5767 7 of 34
Sustainability 2024, 16, 5767 7 of 34
Figure 2. Flowchart of simple suitability model using ArcGIS Pro’s model builder.
Then, the weighted suitability model used the suitability modeller in ArcGIS Pro 3.2.1
software. The technical approaches include (a) defining the goal and criteria, (b) deriving
data that represents the model variables that are defined by the criteria, (c) transforming
and reclassifying the values in each derived dataset to a suitability scale of 1 to 3 (low to
high suitability), (d) weighting the criteria using the AHP, (e) combining the transformed
data, which represent the suitability criteria, into a single suitability surface that meets the
analysis goal, and (f) performing sensitivity analysis to determine the effect of the model
assumptions on the results. The differences between simple and weighted suitability mod-
els are primarily in the transformation and weighting process. Figure 3 illustrates the pro-
cedure for creating the weighted suitability for the residential development maps.
Figure 2. Flowchart of simple suitability model using ArcGIS Pro’s model builder.
Then, the weighted suitability model used the suitability modeller in ArcGIS Pro 3.2.1
software. The technical approaches include (a) defining the goal and criteria, (b) deriving
data that represents the model variables that are defined by the criteria, (c) transforming
and reclassifying the values in each derived dataset to a suitability scale of 1 to 3 (low to
high suitability), (d) weighting the criteria using the AHP, (e) combining the transformed
data, which represent the suitability criteria, into a single suitability surface that meets
the analysis goal, and (f) performing sensitivity analysis to determine the effect of the
model assumptions on the results. The differences between simple and weighted suitability
models are primarily in the transformation and weighting process. Figure 3illustrates the
procedure for creating the weighted suitability for the residential development maps.
Sustainability 2024,16, 5767 8 of 34
Sustainability 2024, 16, 5767 8 of 34
Figure 3. Flowchart of weighted suitability model using ArcGIS Pro’s suitability modeller.
3.2.1. Defining Land Suitability Criteria
This study focused on residential development suitability, and the land suitability
criteria were mainly based on that urban development category. The suitability criteria
used in this study were classified into five categories: physical aributes, natural preser-
vation and protection, blue amenities, transport accessibility, and natural disaster risks.
Slope was used for the physical aributes; forest cover, land cover, and key biodiversity
areas were used for natural preservation and protection; distances from streams, lakes,
and coastlines were used for blue amenities; distances from roads, major airports, major
seaports, and major bus stations were used for transport accessibility; and tsunami, bush-
fire, extreme waves and abrasion, extreme weather, and flood risks were used for natural
disaster risks. The classification of criteria for simple suitability and weighted suitability
models differs as the simple suitability model uses binary values and the weighted suita-
bility model uses a suitability scale for the criteria.
Physical Aributes
Flatness is the most valued physical aribute since flat land is highly suitable for res-
idential development [16] (Figures 4a and 5a). Building on steep or extreme slopes in-
creases construction costs. Slope also influences site stability, increasing soil erosion risks,
and landslide hazards [67].
Natural Preservation and Protection
Figure 3. Flowchart of weighted suitability model using ArcGIS Pro’s suitability modeller.
3.2.1. Defining Land Suitability Criteria
This study focused on residential development suitability, and the land suitability
criteria were mainly based on that urban development category. The suitability criteria used
in this study were classified into five categories: physical attributes, natural preservation
and protection, blue amenities, transport accessibility, and natural disaster risks. Slope was
used for the physical attributes; forest cover, land cover, and key biodiversity areas were
used for natural preservation and protection; distances from streams, lakes, and coastlines
were used for blue amenities; distances from roads, major airports, major seaports, and
major bus stations were used for transport accessibility; and tsunami, bushfire, extreme
waves and abrasion, extreme weather, and flood risks were used for natural disaster risks.
The classification of criteria for simple suitability and weighted suitability models differs
as the simple suitability model uses binary values and the weighted suitability model uses
a suitability scale for the criteria.
Physical Attributes
Flatness is the most valued physical attribute since flat land is highly suitable for
residential development [
16
] (Figures 4a and 5a). Building on steep or extreme slopes
increases construction costs. Slope also influences site stability, increasing soil erosion risks,
and landslide hazards [67].
Sustainability 2024,16, 5767 9 of 34
Natural Preservation and Protection
Preserving and protecting ecosystems and natural resources are crucial for sustain-
able urban development. Uncontrolled urbanisation and urban sprawl have depleted
natural resources. Thus, forests and green and natural areas should be preserved and
protected
[68,69]
. The United Nations Sustainable Development Goals (UN SDGs) include
Goal 15—Protect, restore, and promote sustainable use of terrestrial ecosystems, sustain-
ably manage forests, combat desertification, and halt and reverse land degradation and
biodiversity loss [
70
]. In addition, new urban development must also side-step existing
urban and built-up areas to avoid social conflicts and evictions and reduce uncontrolled
urban sprawl. Locating suitable locations for residential development following the new
capital’s KPI principle—design with nature and the forest city concept—it is critical to
consider more detailed land cover, forest cover, and key biodiversity area data as the new
capital is in a forest area with high biodiversity (Figures 4b–d and 5b–d).
Blue Amenities
Areas close to water resources, particularly streams, lakes, and coastlines, are the
most valuable for residential development and the development potential of urban green
spaces (Figures 4e–g and 5e–g) because of their significance for recreation characteristics
and urban ecosystem services. Streams, lakes, and coastlines reflect the water resources’
condition [
16
,
17
,
22
,
71
,
72
]. Thus, it is essential to consider the distance from streams, lakes,
and coastlines to identify suitable locations for residential development aligned with the
new capital’s KPI principle—design with nature—and for aesthetic purposes.
Transport Accessibility
Transport accessibility is a crucial factor that promotes urban expansion and plays
a vital role in achieving sustainable urban development [
73
–
77
]. The growing focus on
sustainable urban development has highlighted the significance of accessibility in two
aspects. Firstly, it plays a crucial role in facilitating economic growth by enabling the
movement of people and goods, which is essential for the smooth functioning of the econ-
omy [
78
]. Secondly, it contributes to environmental goals by helping to reduce greenhouse
gas emissions and pollutants associated with different modes of transportation and their
usage [79].
The study utilised the existing road and major transportation networks, including air-
ports, seaports, and bus stations, in two cities adjacent to the new capital as criteria, recognising
the significance of transport accessibility. The existing road network data were obtained from
the IGIA [
63
]. The point data of major transportation networks were generated using a geopro-
cessing tool in ArcGIS Pro based on the exact coordinates of the transportation nodes. Then,
the distance from the road and major transportation networks was determined according to
the literature review and the Indonesian government regulations and was generated using
multiple ring buffers on ArcGIS Pro (Figures 4h–k and 5h–k).
Natural Disaster Risks
According to the Indonesia National Disaster Management Agency, the natural disaster
risk index of an area is calculated using Equation (1) [80]:
Risk =H azard ×Vulnerability
Ca pacity (1)
where Hazard is calculated based on a natural disaster’s spatial probability, frequency, and
magnitude. Vulnerability is computed based on socio-cultural, economic, physical, and
environmental parameters. Capacity is assessed using a regional resilience level approach
based on seven priorities: (1) strengthening policies and institutions; (2) risk assessment
and integrated planning; (3) development of information systems, training, and logistics;
(4) thematic handling of disaster-prone areas; (5) increasing the effectiveness of disaster
prevention and mitigation; (6) strengthening disaster preparedness and emergency man-
Sustainability 2024,16, 5767 10 of 34
agement; and (7) development of a disaster recovery system. According to the Head of
the National Disaster Management Agency Regulation Number 2 Year 2012 on General
Guidelines for Disaster Risk Assessment [
81
], the natural disaster risk index is grouped
into three classifications as a national standard: low risk (0–0.3), moderate risk (>0.3–0.6),
and high risk (>0.6–1) (Figures 4l–p and 5l–p). The details of each natural disaster risk
index used in this study are presented in Table 3.
Table 3. Natural disaster risk index [81].
Natural Disaster Risk Parameter Index Classification
Low (0–0.3) Moderate (>0.3–0.6) High (>0.6–1)
Tsunami Puddle depth <1 m 1–3 m >3 m
Bushfire
Forest type
Annual precipitation
Soil type
Forest
Wet season
Non-organic/non-peat
Plantation
Wet and dry season
Semi organic
Dry meadow
Dry season
Organic/peat
Flood Depth <0.76 m 0.76–1.5 m >1.5 m
Extreme weather Number of events
Impact of events
<2 times
<5 casualties
2–3 times
5–10 casualties
>3 times
>10 casualties
Extreme waves and
abrasion
Wave height
Current
Vegetation cover
Coastline shape
Beach typology
<1 m
<0.2
>80%
Bay
Coral rocky
1–2.5 m
0.2–0.4
40–80%
Bay—straight
Sandy
>2.5 m
>0.4
<40%
Straight
Muddy
3.2.2. Suitability Classification of the Criteria
The suitability scales of the selected criteria were transformed into common suitability
scales to evaluate the suitability of land uses [
17
,
22
]. There is no standard for making
scales for the criteria. Consequently, the literature and government documents were
reviewed. The thresholds are specific to the study area and criteria. Other research in a
similar context with different study areas and criteria may propose different values for the
suitability classification. For the simple suitability model, a binary value of 1 was assigned
for suitable and 0 for unsuitable (Table 4). A suitability scale of 1 to 3 was used for the
weighted suitability model, where 1 is low suitability, 2 is moderate suitability, and 3 is
high suitability (Table 5). Figures 4and 5visualise the results of the reclassification process
from Tables 4and 5. Figure 4shows the reclassified maps for the simple suitability criteria
in two colours—green for suitable and red for unsuitable. Then, the reclassified maps for
the weighted suitability criteria are illustrated in Figure 5in three colours: green for high
suitability, yellow for moderate suitability, and red for low suitability.
Table 4. Suitability classification for the simple suitability model.
Suitability Criteria Suitable Unsuitable References
SL (degree) <15 >15 [14,16,67,82]
KBA Non-KBA KBA [14,17]
FC Production forest, limited production
forest, and non-forest Protected areas and water bodies [14,17]
LC
Shrubs; sparse vegetation; herbaceous
vegetation; open forest, evergreen,
broadleaf; open forest, unknown; and
closed forest, unknown
Cropland; closed forest, evergreen,
broadleaf; herbaceous wetland;
urban/built up; permanent water
bodies; and open sea
[12,72]
DS (meter) >100 <100 [12,72,83]
DL (meter) >50 <50 [12,72,83]
Sustainability 2024,16, 5767 11 of 34
Table 4. Cont.
Suitability Criteria Suitable Unsuitable References
DC (meter) >100 <100 [12,84]
DR (meter) <2500 >2500 [12,72]
DMA (meter) <50,000 >50,000 [8]
DMS (meter) <50,000 >50,000 [8]
DMB (meter) <50,000 >50,000 [8]
FR <0.6 >0.6 [80]
EWR <0.6 >0.6 [80]
EWAR <0.6 >0.6 [80]
BR <0.6 >0.6 [80]
TR <0.6 >0.6 [80]
SL, slope; KBA, KBA; FC, forest cover; LC, land cover; DS, distance from streams; DL, distance from lakes; DC,
distance from coastlines; DR, distance from roads; DMA, distance from major airports; DMS, distance from major
seaports; DMB, distance from major bus stations; FR, flood risk; EWR, extreme weather risk; EWAR, extreme
waves and abrasion risk; BR, bushfire risk; TR, tsunami risk.
Table 5. Suitability classification for the weighted suitability model.
Suitability Criteria Criteria Classes Level of Suitability Score Value References
SL (degree)
0–10 High 3
[14,16,67,82]
>10–15 Moderate 2
>15 Low 1
KBA Non-KBA High 3 [14,17]
KBA Low 1
FC
Production forest and non-forest High 3
[14,17]
Limited production forest Moderate 2
Protected areas and water bodies Low 1
LC
Shrubs; bare/sparse vegetation;
herbaceous vegetation; open forest,
evergreen, broad leaf; open forest,
unknown; and closed forest, unknown
High 3
[12,22,85,86]
Cropland Moderate 2
Closed forest, evergreen, broad leaf;
herbaceous wetland; urban/built-up;
permanent water bodies; and open sea
Low 1
DS (meter)
>100–1500 High 3
[12,72,83]
>1500 Moderate 2
0–100 Low 1
DL (meter)
>50–1500 High 3
[12,72,83]
>1500 Moderate 2
0–50 Low 1
DC (meter)
>100–1500 High 3
[12,84]
>1500 Moderate 2
0–100 Low 1
DR (meter)
0–1500 High 3
[12,72]
>1500–2500 Moderate 2
>2500 low 1
DMA (meter)
0–25,000 High 3
[8]
>25,000–50,000 Moderate 2
>50,000 Low 1
Sustainability 2024,16, 5767 12 of 34
Table 5. Cont.
Suitability Criteria Criteria Classes Level of Suitability Score Value References
DMS (meter)
0–25,000 High 3
[8]
>25,000–50,000 Moderate 2
>50,000 Low 1
DMB (meter)
0–25,000 High 3
[8]
>25,000–50,000 Moderate 2
>50,000 Low 1
FR
0–0.3 high 3
[80]
>0.3–0.6 Moderate 2
>0.6–1 Low 1
EWR
0–0.3 High 3
[80]
>0.3–0.6 Moderate 2
>0.6–1 Low 1
EWAR
0–0.3 High 3
[80]
>0.3–0.6 Moderate 2
>0.6–1 Low 1
BR
0–0.3 High 3
[80]
>0.3–0.6 Moderate 2
>0.6–1 Low 1
TR
0–0.3 High 3
[80]
>0.3–0.6 Moderate 2
>0.6–1 Low 1
SL, slope; KBA, KBA; FC, forest cover; LC, land cover; DS, distance from streams; DL, distance from lakes; DC,
distance from coastlines; DR, distance from roads; DMA, distance from major airports; DMS, distance from major
seaports; DMB, distance from major bus stations; FR, flood risk; EWR, extreme weather risk; EWAR, extreme
waves and abrasion risk; BR, bushfire risk; TR, tsunami risk.
3.2.3. Weighting of the Criteria
The criteria identified to meet the suitability model goal are not equally important in
various cases. Thus, weights can be assigned to individual criteria to model real-world
conditions more accurately. The inclusion of weights in the weighted suitability modelling
process is unique to the weighted model type. The AHP technique was used to weigh
each criterion considered in the analysis. AHP is a multi-criteria decision-making process
initially developed by Saaty [
55
]. The technique emphasises the pairwise comparison
matrix to develop scales of preferences among a set of alternatives according to their
relative importance based on the Likert scale from 1 to 9. It also computes their eigenvalues
and final vector of weight coefficients for alternatives.
Lastly, the consistency ratio (CR) index was computed using Equation (2) [55]:
CR =λmax −n
(n−1)×RI (2)
where
λmax
is the principal eigenvalue of the matrix, nis the matrix’s order, and RI (random
index) represents the average of the resulting consistency index depending on order n.
In Saaty’s [
55
] explanation, a CR equivalent to 0.10 or less indicates a reasonable level
of consistency.
Sustainability 2024,16, 5767 13 of 34
Sustainability 2024, 16, 5767 13 of 34
Figure 4. Reclassified maps used for the simple suitability analysis for residential development in
the new Indonesian capital (green = suitable land, red = unsuitable land).
Figure 4. Reclassified maps used for the simple suitability analysis for residential development in the
new Indonesian capital (green = suitable land, red = unsuitable land).
Sustainability 2024,16, 5767 14 of 34
Sustainability 2024, 16, 5767 14 of 34
Figure 5. Reclassified maps used for the weighted suitability analysis for residential development
in the new Indonesian capital (green = high suitability land, yellow = moderate suitability land, and
red = low suitability land).
Figure 5. Reclassified maps used for the weighted suitability analysis for residential development in
the new Indonesian capital (green = high suitability land, yellow = moderate suitability land, and
red = low suitability land).
Sustainability 2024,16, 5767 15 of 34
3.2.4. Sensitivity Analysis
The One-At-a-Time (OAT) method is a frequently used approach in sensitivity anal-
ysis. It involved altering input criteria One-At-a-Time to see the resulting impact on the
output [
87
]. By altering one criterion at a time, the values of the other criteria can be fixed to
their baseline values. According to Chen et al. [
88
], sensitivity analysis improves the consis-
tency of the results. Three commonly employed approaches for analysing criteria sensitivity
are altering criteria values, altering the relative importance of criteria, and altering criteria
weights [
88
,
89
]. The objective of sensitivity analysis in this study was to determine the
impact of varying criteria weights on the spatial distribution of the suitability classification.
Sensitivity analysis is crucial for assessing the relative importance of each criterion, making
it a valuable tool for minimising the subjective nature of weight assignments [48,90].
To assess criteria sensitivity using the OAT method, it is necessary to define a feasible
range of variations in criteria weights. The range is a limited set of discrete percentage
changes from the original weight value of criteria [
88
,
89
]. One option is to use a uniform
range for all criteria, such as plus or minus 25%. Alternatively, distinct ranges might be
used for each criterion. A sensitivity analysis was performed to assess the feasibility of the
land suitability analysis for both scenarios. Following the approach by Romano et al. [91],
sensitivity testing was applied to the highest and lowest weights for residential develop-
ment, and the findings are given accordingly. In line with the procedure employed by
Chen et al. and Perpiña et al. [
88
,
89
,
92
], a set of suitability assessments was conducted by
modifying each criterion weight by a quarter percent (i.e., the highest or lowest weight)
within a range of
±
25%,
±
50%, and
±
75%, as defined by its feasible range (Equation (3)).
The weights of the other criteria were correspondingly changed in order to satisfy the
additive constraint stated in Equation (4), where the sum of all criteria weights equals one
(Equation (5)) (see [88,89] for the equations).
Runs =∑n
i=1ri(3)
∑n
i=1wi=1 (4)
W(pc)=∑n
i=1W(ci,pc)=1; where RPCmin ≤pc ≤RPCmax (5)
where nis the total number of criteria;
ri
is the number of increments of percentage change
(IPC) within a range of percent change (RPC) for criteria i;
wi
is the weight value for each
criterion;
W(ci,pc)
is the weight of the ith criterion at a certain percentage change (pc) level;
RPCmin
and
RPCmax
are the minimum and maximum values of the RPC, respectively.
When varying the weight of the main criterion,
cm
, its weight
W(cm,pc)
at a certain pc level
can be calculated as:
W(cm,pc)=W(cm, 0)+W(cm, 0)×pc,where 1≤m≤n(6)
where
W(cm, 0)
is the initial value of the weight of the main changing criterion. In order to
meet the condition in Equation (5), the weights of the other criteria
W(cm,pc)
should be
adjusted proportionally following W(cm,pc)(Equation (7)):
W(ci,pc)=(1−W(cm,pc)) ×W(ci, 0)/(1−W(cm, 0);where i =m1≤i≤n(7)
where
W(ci, 0)
is the initial value of the weight of the ith criterion
ci
, when the weight of
the main criterion is changed based on the conditions given in Equations (3)–(7), different
suitability criterion maps are generated for each run, and a summary table is created to
quantify the changes in criteria and suitability evaluation results. The sensitivity analysis
applied in this study is summarised in Table 6.
Sustainability 2024,16, 5767 16 of 34
Table 6. Criteria that are subjected to sensitivity analysis.
Criteria Subjected to
Sensitivity Analysis Sensitivity Test Explanation
Waterfront city scenario
Highest: distance from streams
Lowest: bushfire risk
±25%, ±50%, ±75% change in the value
of the subject criteria
The highest and lowest weights were
subject to sensitivity analysis (e.g.,
distance from streams and bushfire risk).
The criteria weights were changed in
percentage increments (from ±25% to
±
75%), and the weights of the remaining
criteria were adjusted proportionally to
satisfy the constraint where all criteria
weights are required to sum to one.
Biodiversity–positive city scenario
Highest: forest cover
Lowest: distance from major bus station
±25%, ±50%, ±75% change in the value
of the subject criteria
The highest and the lowest weights were
subject to sensitivity analysis (e.g., forest
cover and distance from major bus
station). The criteria weights were
changed in percentage increments (from
±25% to ±75%), and the weights of the
remaining criteria were adjusted
proportionally to satisfy the constraint
where all criteria weights are required to
sum to one.
4. Results
4.1. Weights from AHP Using Scenarios
The AHP weighting in this study was prepared using the AHP priority calculator by
Business Performance Management Singapore [
66
]. The weighting process was based on
the literature review, local context, and relevant government documents by formulating
two scenarios—the waterfront city and the biodiversity-positive city. The scenarios were
developed according to the review of the Strategic Environmental Study for the New
Indonesian Capital [
10
]. The weights are specific to the study area, criteria, and scenarios.
Other researchers may propose different values for the weights.
4.1.1. The Waterfront City Scenario
A waterfront city is a complex area that combines ecological, economic, and social
elements. It serves as a transitional and dispersal zone, physically situated between land
and water [
93
]. Urban waterfronts are integral to the interface between the natural and
human-made environment in cities, connecting the city and its residents with water. These
spaces are essential components of the green and public space networks within high-
density urban environments. They can accommodate various functions, such as residential,
commercial, leisure, recreation, cultural heritage, and art. Consequently, they provide
numerous economic, social, and environmental benefits [94].
The waterfront city scenario was selected due to the presence of extensive river
networks in the new capital, which offer significant opportunities for creating a city where
water plays a central role. Nevertheless, developing a waterfront city in the new capital
faces challenges in water supply and water regulation ecosystem services. BAPPENAS [
10
]
analysed the water supply ecosystem services as part of the Strategic Environmental Study
for the New Indonesian Capital, considering geographical conditions, plant types, and the
impact of human activities, including land use. The area exhibits three distinct geographical
formations: plains, hills, and mountains, as determined by the topography. The three main
vegetation types are forest, mangrove, and herbaceous vegetation. The land encompasses
many uses, including forests, plantations, arid agriculture, rice fields, settlements, and
mining. According to the findings, the water supply ecosystem services in the new capital
were determined to be low (12.42%), moderate (85%), and high (1%).
Sustainability 2024,16, 5767 17 of 34
Besides functioning as a water supply, the ecosystem also regulates water flow. The
new capital has high rainfall, significantly affecting each watershed’s hydrological system
or river flow. These streams can be used for daily needs, irrigation, transport, and recreation.
Ecosystem services that regulate water flow will also impact flood control so that there are
naturally formed floodplains. According to the data from the INDMA [
64
], some areas in
the new capital have moderate and high flood risks, especially those close to the main river
networks. Significant land use changes in the new capital will affect most of the existing
water flow system. Thus, it is vital to consider environmental management aspects and
ensure minimal changes to the water flow system after construction. The water regulation
was analysed using the same water supply ecosystem service parameters. The result
showed that the new capital has moderate water regulation and ecosystem services [10].
Almost all cities worldwide are built along waterways, whether rivers, lakes, or
seas. The multifaceted relationships between urban planning and water have structured
and influenced urban development throughout history and will continue to do so [
95
].
Living close to water, especially rivers, in an urban area is not just about scenic views;
it is a blend of environmental, social, and urban advantages to foster a sustainable and
vibrant community [
96
]. Rivers are biodiversity hotspots, supporting various flora and
fauna [
83
]. Proximity to water promotes health and well-being [
97
]. Riverfronts also offer
recreational spaces, fostering community interactions and physical activities. Waterfronts
attract tourists, boosting local economies. The development of river cruises, waterfront
cafes, and cultural events adds to this economic uplift [
98
]. However, waterfront cities may
face several challenges, such as prolonged drought, flooding, storm surges, and sea level
rise. It is essential to protect infrastructure and communities from any water-related risks.
Comprehensively planned and designed waterfronts can transform the urban landscape,
with sustainable transport opportunities like walking and cycling paths helping to decrease
traffic congestion and pollution and reduce disaster risks [98,99].
Table 7below shows the pairwise comparison matrix of the criteria based on the
descriptions of the waterfront city scenario.
Table 7. Pairwise comparison matrix for waterfront city scenario.
SL KBA FC LC DS DL DC DR
DMA DMS DMB
FR
EWR EWAR
BR TR
SL 1 0.50 0.50 1.00 0.11 1.00 0.50 1.00 5.00 5.00 5.00 0.20 3.00 3.00 3.00 3.00
KBA 2.00 1 1.00 7.00 0.50 1.00 1.00 1.00 5.00 5.00 5.00 1.00 3.00 3.00 3.00 3.00
FC 2.00 1.00 1 2.00 0.50 1.00 1.00 3.00 3.00 3.00 3.00 1.00 3.00 3.00 3.00 3.00
LC 1.00 0.14 0.50 1 0.11 0.33 0.33 3.00 3.00 3.00 3.00 0.33 3.00 3.00 3.00 3.00
DS 9.00 2.00 2.00 9.00 1 1.00 1.00 3.00 3.00 3.00 3.00 1.00 3.00 3.00 3.00 3.00
DL 1.00 1.00 1.00 3.00 1.00 1 1.00 3.00 3.00 3.00 3.00 0.33 3.00 3.00 3.00 3.00
DC 2.00 1.00 1.00 3.00 1.00 1.00 1 3.00 3.00 3.00 3.00 1.00 3.00 3.00 3.00 3.00
DR 1.00 1.00 0.33 0.33 0.33 0.33 0.33 1 1.00 1.00 1.00 0.33 3.00 3.00 3.00 3.00
DMA
0.20 0.20 0.33 0.33 0.33 0.33 0.33 1.00 1 1.00 1.00 0.33 3.00 3.00 3.00 3.00
DMS
0.20 0.20 0.33 0.33 0.33 0.33 0.33 1.00 1.00 1 1.00 0.33 3.00 3.00 3.00 3.00
DBS 0.20 0.20 0.33 0.33 0.33 0.33 0.33 1.00 1.00 1.00 1 0.33 3.00 3.00 3.00 3.00
FR 5.00 1.00 1.00 3.00 1.00 3.00 1.00 3.00 3.00 3.00 3.00 1 3.00 3.00 3.00 3.00
EWR
0.33 0.33 0.33 0.33 0.33 0.33 0.33 0.33 0.33 0.33 0.33 0.33 1 1.00 1.00 1.00
EWAR
0.33 0.33 0.33 0.33 0.33 0.33 0.33 0.33 0.33 0.33 0.33 0.33 1.00 1 1.00 1.00
BR 0.33 0.33 0.33 0.33 0.33 0.33 0.33 0.33 0.33 0.33 0.33 0.33 1.00 1.00 1 1.00
TR 0.33 0.33 0.33 0.33 0.33 0.33 0.33 0.33 0.33 0.33 0.33 0.33 1.00 1.00 1.00 1
SL, slope; KBA, KBA; FC, forest cover; LC, land cover; DS, distance from streams; DL, distance from lakes; DC,
distance from coastlines; DR, distance from roads; DMA, distance from major airports; DMS, distance from major
seaports; DMB, distance from major bus stations; FR, flood risk; EWR, extreme weather risk; EWAR, extreme
waves and abrasion risk; BR, bushfire risk; TR, tsunami risk.
The pairwise comparison was conducted by assessing the relative importance of each
criterion in a pair using a one-to-nine scale. Once finished, the AHP priority calculator
Sustainability 2024,16, 5767 18 of 34
developed by BPMSG [
66
] computed and verified the consistency of comparison in order to
determine the priorities/rankings. The method is mathematically grounded in the solution
of an eigenvalue problem. The results of the pairwise comparisons are organised in a
matrix. The first (dominant) normalised right eigen vector of the matrix provides the
ratio scale or weighting, whereas the eigenvalue determines the consistency ratio [
100
]. If
there is an inconsistency, the calculator will assist in determining which comparison needs
modification. The final weights for the waterfront city scenario are displayed in Table 8,
following the outlined procedure.
Table 8. Weighted criteria for waterfront city scenario.
Criteria
SL KBA FC LC DS DL DC DR
DMA DMS DMB
FR EWR EWAR BR TR
Weight
0.069 0.108 0.085
0.057
0.15 0.085 0.093 0.044 0.036 0.036 0.036 0.114 0.022 0.022 0.022 0.022
SL, slope; KBA, KBA; FC, forest cover; LC, land cover; DS, distance from streams; DL, distance from lakes; DC,
distance from coastlines; DR, distance from roads; DMA, distance from major airports; DMS, distance from major
seaports; DMB, distance from major bus stations; FR, flood risk; EWR, extreme weather risk; EWAR, extreme
waves and abrasion risk; BR, bushfire risk; TR, tsunami risk.
According to the weighting process for the first waterfront city scenario, the criteria
related to water mostly had high weights. Distance from streams had the highest weight,
and flood risk had the second highest. The following weight rankings were other blue
amenities (distance from coastlines and lakes), natural preservation and protection (key
biodiversity areas, forest cover, and land cover), physical attributes (slope), transport
accessibility (distance from roads, major airports, major seaports, and major bus stations),
and other natural disaster risks (extreme weather, EWA, bushfire, and tsunami risks). The
CR for the pairwise comparison was 0.08, indicating a reasonable level of consistency
according to Saaty’s [
55
] explanation. Therefore, this weighting result was appropriate for
the waterfront city scenario.
4.1.2. The Biodiversity-Positive City Scenario
The second scenario was a biodiversity-positive city. Borneo’s natural tropical forest
cover includes lowland tropical forests, swamps, karsts, and coastal forests. Lowland
tropical forest has the richest diversity of plant species among the various ecosystems that
make up this tropical forest. Unfortunately, human activities influence forest degradation.
The main threats to the lowland tropical forest ecosystem in the new capital are bushfires,
illegal logging, occupancy of communities and corporations both for small-scale plantations
and oil palm plantations, and mining permits [10].
The new capital also has key biodiversity areas, which are sites contributing to the
global persistence of biological diversity (biodiversity). “Biological diversity means the
variability among living organisms from all sources, including, inter alia, terrestrial, marine,
and other aquatic ecosystems and the ecological complexes of which they are part; this
includes diversity within species, between species, and of ecosystems” [101].
A global standard for identifying KBA [
102
] published by the International Union for
Conservation of Nature (IUCN) in 2016 establishes a consultative, science-based process
for identifying globally important sites for biodiversity worldwide. The KBA criteria have
quantitative thresholds and can be applied to species and ecosystems in terrestrial, inland
water, and marine environments. Sites qualify as global KBAs if they meet one or more of
the 11 criteria in five categories: (i) threatened biodiversity (threatened species and threat-
ened ecosystem types), (ii) geographically restricted biodiversity (individual geographically
restricted species, co-occurring geographically restricted species, geographically restricted
assemblages, and geographically restricted ecosystem types), (iii) ecological integrity, (iv) bi-
ological processes (demographic aggregations, ecological refugia, and recruitment sources),
and (v) irreplaceability [103].
At least 1967 tree species in 140 families have been identified in the new capital.
Wildlife species include mammals, aves (birds), and herpetofauna (amphibians and reptiles).
Endemic animals found in Balikpapan City include 32 species of aves and 18 species
Sustainability 2024,16, 5767 19 of 34
of mammals. There are 23 species of animals with high conservation status/threatened
(critically endangered, endangered, and vulnerable) in the new capital and its surroundings.
According to the Minister of Environment and Forestry of the Republic of Indonesia
Regulation Number P.106/MENLHK/SETJEN/KUM.1/12/2018 on Types of Protected
Plants and Animals, at least 33 protected wild animal species have been identified in the
new capital and its surroundings. These types occur in several forest areas [
10
]. According
to the explanations of the biodiversity-positive city scenario, the pairwise comparison
matrix of the criteria is shown in Table 9below.
Table 9. Pairwise comparison matrix for biodiversity-positive city scenario.
SL KBA FC LC DS DL DC DR
DMA DMS DMB
FR
EWR EWAR
BR TR
SL 1 0.50 0.50 0.33 2.00 2.00 7.00 7.00 7.00 7.00 7.00 1.00 4.00 5.00 6.00 7.00
KBA 2.00 1 0.50 4.00 2.00 2.00 7.00 7.00 9.00 7.00 9.00 2.00 4.00 5.00 6.00 7.00
FC 2.00 2.00 1 3.00 2.00 2.00 7.00 7.00 9.00 9.00 9.00 2.00 7.00 7.00 7.00 7.00
LC 3.00 0.25 0.33 1 2.00 2.00 7.00 7.00 7.00 7.00 7.00 2.00 7.00 7.00 7.00 7.00
DS 0.50 0.50 0.50 0.50 1 3.00 4.00 7.00 7.00 7.00 7.00 1.00 7.00 7.00 7.00 7.00
DL 0.50 0.50 0.50 0.50 0.33 1 3.00 7.00 7.00 7.00 7.00 0.50 3.00 3.00 2.00 3.00
DC 0.14 0.14 0.14 0.14 0.25 0.33 1 5.00 5.00 5.00 5.00 0.14 0.50 0.50 0.50 0.50
DR 0.14 0.14 0.14 0.14 0.14 0.14 0.20 1 2.00 1.00 2.00 0.11 0.14 0.14 0.14 0.14
DMA
0.14 0.11 0.11 0.14 0.14 0.14 0.20 0.50 1 1.00 1.00 0.11 0.14 0.14 0.14 0.14
DMS
0.14 0.14 0.11 0.14 0.14 0.14 0.20 1.00 1.00 1 1.00 0.11 0.14 0.14 0.14 0.14
DMB
0.14 0.11 0.11 0.14 0.14 0.14 0.20 0.50 1.00 1.00 1 0.11 0.14 0.14 0.14 0.14
FR 1.00 0.50 0.50 0.50 1.00 2.00 7.00 9.00 9.00 9.00 9.00 1 9.00 9.00 9.00 9.00
EWR
0.25 0.25 0.14 0.14 0.14 0.33 2.00 7.00 7.00 7.00 7.00 0.11 1 1.00 1.00 1.00
EWAR
0.20 0.20 0.14 0.14 0.14 0.33 2.00 7.00 7.00 7.00 7.00 0.11 1.00 1 1.00 1.00
BR 0.17 0.17 0.14 0.14 0.14 0.50 2.00 7.00 7.00 7.00 7.00 0.11 1.00 1.00 1 1.00
TR 0.14 0.14 0.14 0.14 0.14 0.33 2.00 7.00 7.00 7.00 7.00 0.11 1.00 1.00 1.00 1
SL, slope; KBA, KBA; FC, forest cover; LC, land cover; DS, distance from streams; DL, distance from lakes; DC,
distance from coastlines; DR, distance from roads; DMA, distance from major airports; DMS, distance from major
seaports; DMB, distance from major bus stations; FR, flood risk; EWR, extreme weather risk; EWAR, extreme
waves and abrasion risk; BR, bushfire risk; TR, tsunami risk.
The final criteria weights are indicated in Table 10.
Table 10. Weighted criteria for biodiversity-positive city scenario.
Criteria
SL KBA FC LC DS DL DC DR
DMA DMS DMB
FR EWR EWAR BR TR
Weight
0.10 0.144
0.16 0.129
0.103 0.059 0.022 0.01 0.009 0.009 0.009 0.123 0.031 0.031 0.031 0.030
SL, slope; KBA, KBA; FC, forest cover; LC, land cover; DS, distance from streams; DL, distance from lakes; DC,
distance from coastlines; DR, distance from roads; DMA, distance from major airports; DMS, distance from major
seaports; DMB, distance from major bus stations; FR, flood risk; EWR, extreme weather risk; EWAR, extreme
waves and abrasion risk; BR, bushfire risk; TR, tsunami risk.
In the second biodiversity-positive city scenario, natural preservation and protection
criteria had high rankings. Forest cover was the highest criterion in the final weights,
followed by KBA, land cover, flood risk, distance from streams, and slope, with each
weight greater than or equal to 0.10. Criteria with weights of less than 0.10 include other
blue amenities (distance from lakes and coastlines), other natural disaster risks (extreme
weather, EWA, bushfire, and tsunami risks), and transport accessibility (distance from
roads, major airports, major seaports, and major bus stations). The pairwise comparison
matrix’s consistency ratio (CR) of 0.095 was less than 0.10. Thus, based on the characteristics
of the biodiversity-positive city and the AHP weighting, the result was appropriate for
the scenario.
Sustainability 2024,16, 5767 20 of 34
4.2. Residential Suitability Areas in the New Indonesian Capital
The residential development land suitability analysis results from models 1 and 2
are given in Figures 6–8. Figure 6presents the result of the simple suitability model. The
suitable areas are indicated in green colour, and the unsuitable areas are indicated in red.
Figures 7and 8present the weighted suitability model’s results with the high suitability
areas shown in green colour, the moderate suitability areas in yellow, and the low suitability
areas in red.
The simple suitability map is limited in identifying suitable areas for residential
development (Figure 6). The suitable areas are mainly located in the southwest, and some
other areas are distributed in the east and northeast of the new capital. The unsuitable
areas are in the central, west, northwest, northeast, and southeast areas.
Sustainability 2024, 16, 5767 20 of 34
Weight 0.10 0.144 0.16 0.129 0.103 0.059 0.022 0.01 0.009 0.009 0.009 0.123 0.031 0.031 0.031 0.030
SL, slope; KBA, KBA; FC, forest cover; LC, land cover; DS, distance from streams; DL, distance from
lakes; DC, distance from coastlines; DR, distance from roads; DMA, distance from major airports;
DMS, distance from major seaports; DMB, distance from major bus stations; FR, flood risk; EWR,
extreme weather risk; EWAR, extreme waves and abrasion risk; BR, bushfire risk; TR, tsunami risk.
In the second biodiversity-positive city scenario, natural preservation and protection
criteria had high rankings. Forest cover was the highest criterion in the final weights, fol-
lowed by KBA, land cover, flood risk, distance from streams, and slope, with each weight
greater than or equal to 0.10. Criteria with weights of less than 0.10 include other blue
amenities (distance from lakes and coastlines), other natural disaster risks (extreme
weather, EWA, bushfire, and tsunami risks), and transport accessibility (distance from
roads, major airports, major seaports, and major bus stations). The pairwise comparison
matrix’s consistency ratio (CR) of 0.095 was less than 0.10. Thus, based on the characteris-
tics of the biodiversity-positive city and the AHP weighting, the result was appropriate
for the scenario.
4.2. Residential Suitability Areas in the New Indonesian Capital
The residential development land suitability analysis results from models 1 and 2 are
given in Figures 6–8. Figure 6 presents the result of the simple suitability model. The suit-
able areas are indicated in green colour, and the unsuitable areas are indicated in red.
Figures 7 and 8 present the weighted suitability model’s results with the high suitability
areas shown in green colour, the moderate suitability areas in yellow, and the low suita-
bility areas in red.
The simple suitability map is limited in identifying suitable areas for residential de-
velopment (Figure 6). The suitable areas are mainly located in the southwest, and some
other areas are distributed in the east and northeast of the new capital. The unsuitable
areas are in the central, west, northwest, northeast, and southeast areas.
Figure 6. The result of the simple suitability analysis (green = suitable land, red = unsuitable land).
Figure 6. The result of the simple suitability analysis (green = suitable land, red = unsuitable land).
In contrast, the weighted suitability models in both scenarios show better results with
more suitable locations for residential development throughout the new capital. The highly
suitable locations are spread evenly, except for the central part. In the waterfront city
scenario, the suitability values range from 2.88 (high suitability) to 1.47 (low suitability)
(Figure 7). Most of the western part of the new capital lies in moderate-to-high suitability
areas. Many low-suitability areas in this western part are within the river buffer zones.
The suitability level in the central part is dominated by moderate to low suitability since
it is located within protected forests and key biodiversity areas. In the eastern part, the
suitability level ranges from low to high. Many areas are identified as highly suitable for
residential development. Moderate- and low-suitability areas are primarily within the key
biodiversity areas and river buffer zones.
In the biodiversity-positive city, the suitability values are higher than in the waterfront
city scenario—from 2.98 (high suitability) to 1.32 (low suitability) (Figure 8). In the western
part of the new capital, the areas are highly suitable for residential development with
slightly low suitability, especially those at the river buffer zones. The central area, covered
mainly by protected forests and key biodiversity areas, is obviously within a low suitability
level for residential development. Many areas in the eastern part are highly suitable
Sustainability 2024,16, 5767 21 of 34
for residential development. The southeast and northeast areas close to the coastlines
are primarily moderate and low suitability since these areas are within key biodiversity
areas. The findings from the weighted suitability analysis indicated that slope, road and
transport networks, distance from water, not being in protected areas, key biodiversity
areas, and natural disaster risks are substantial aspects influencing future residential
development potential.
Sustainability 2024, 16, 5767 21 of 34
In contrast, the weighted suitability models in both scenarios show beer results with
more suitable locations for residential development throughout the new capital. The
highly suitable locations are spread evenly, except for the central part. In the waterfront
city scenario, the suitability values range from 2.88 (high suitability) to 1.47 (low suitabil-
ity) (Figure 7). Most of the western part of the new capital lies in moderate-to-high suita-
bility areas. Many low-suitability areas in this western part are within the river buffer
zones. The suitability level in the central part is dominated by moderate to low suitability
since it is located within protected forests and key biodiversity areas. In the eastern part,
the suitability level ranges from low to high. Many areas are identified as highly suitable
for residential development. Moderate- and low-suitability areas are primarily within the
key biodiversity areas and river buffer zones.
In the biodiversity-positive city, the suitability values are higher than in the water-
front city scenario—from 2.98 (high suitability) to 1.32 (low suitability) (Figure 8). In the
western part of the new capital, the areas are highly suitable for residential development
with slightly low suitability, especially those at the river buffer zones. The central area,
covered mainly by protected forests and key biodiversity areas, is obviously within a low
suitability level for residential development. Many areas in the eastern part are highly
suitable for residential development. The southeast and northeast areas close to the coast-
lines are primarily moderate and low suitability since these areas are within key biodiver-
sity areas. The findings from the weighted suitability analysis indicated that slope, road
and transport networks, distance from water, not being in protected areas, key biodiver-
sity areas, and natural disaster risks are substantial aspects influencing future residential
development potential.
Figure 7. The result of the weighted suitability analysis—waterfront city scenario (green = high suit-
ability, yellow = moderate suitability, and red = low suitability land).
Figure 7. The result of the weighted suitability analysis—waterfront city scenario (green = high
suitability, yellow = moderate suitability, and red = low suitability land).
As shown in Figure 8, the western and eastern parts of the new capital have a high
potential for residential development. Existing built-up areas are also observed in those
locations, which is one of several factors supporting residential development. In addition,
many rivers in the new capital also increase the potential since rivers provide many benefits
for urban areas, especially for their ecosystem services and recreational and water transport
functions. However, cities near water may face water-related shocks and stresses due to
changing climate and rapid urbanisation. One of the solutions to address water-related
risks is the City Water Resilience Approach (CWRA), developed by the Stockholm Interna-
tional Water Institute (SIWI) together with the Arup and Resilient Cities Network (RCN),
aiming to help cities take a holistic view of their water systems and build urban water
resilience [
104
–
107
]. The CWRA has five key stages: understanding the system, assessing
urban water resilience, developing an action plan, implementing the action plan, and
evaluating, learning, and adapting. It encourages stakeholders to evaluate the hydrological
context, including the basins, physical infrastructure, sociopolitical and economic contexts,
and their water system’s risks and resilience goals. In addition to ‘hard engineering’ in-
frastructure projects, the CWRA promotes nature-based solutions to water challenges and
supports a variety of ‘soft’ solutions, such as improved governance (planning, policy and
strategies, coordination, finance, regulation, monitoring, etc.), communication, and de-
mand management. Urban water resilience is critical for addressing many water challenges
at a local level. It will assist in achieving multiple UN SDGs, including SDG 6 (ensure
Sustainability 2024,16, 5767 22 of 34
availability and sustainable management of water and sanitation for all), SDG 11 (make
cities and human settlements inclusive, safe, resilient, and sustainable), and SDG 13 (take
urgent action to combat climate change and its impacts) [
108
]. This approach is also aligned
with Nusantara’s sponge city concept.
Sustainability 2024, 16, 5767 22 of 34
Figure 8. The result of the weighted suitability analysis—biodiversity-positive city scenario (green
= high suitability, yellow = moderate suitability, and red = low suitability land).
As shown in Figure 8, the western and eastern parts of the new capital have a high
potential for residential development. Existing built-up areas are also observed in those
locations, which is one of several factors supporting residential development. In addition,
many rivers in the new capital also increase the potential since rivers provide many ben-
efits for urban areas, especially for their ecosystem services and recreational and water
transport functions. However, cities near water may face water-related shocks and stresses
due to changing climate and rapid urbanisation. One of the solutions to address water-
related risks is the City Water Resilience Approach (CWRA), developed by the Stockholm
International Water Institute (SIWI) together with the Arup and Resilient Cities Network
(RCN), aiming to help cities take a holistic view of their water systems and build urban
water resilience [104–107]. The CWRA has five key stages: understanding the system, as-
sessing urban water resilience, developing an action plan, implementing the action plan,
and evaluating, learning, and adapting. It encourages stakeholders to evaluate the hydro-
logical context, including the basins, physical infrastructure, sociopolitical and economic
contexts, and their water system’s risks and resilience goals. In addition to ‘hard engineer-
ing’ infrastructure projects, the CWRA promotes nature-based solutions to water chal-
lenges and supports a variety of ‘soft’ solutions, such as improved governance (planning,
policy and strategies, coordination, finance, regulation, monitoring, etc.), communication,
and demand management. Urban water resilience is critical for addressing many water
challenges at a local level. It will assist in achieving multiple UN SDGs, including SDG 6
(ensure availability and sustainable management of water and sanitation for all), SDG 11
(make cities and human selements inclusive, safe, resilient, and sustainable), and SDG
13 (take urgent action to combat climate change and its impacts) [108]. This approach is
also aligned with Nusantara’s sponge city concept.
Given the outcomes of the suitability analysis, further development recommenda-
tions can be proposed. First, protected areas such as protected forests and key biodiversity
areas, mainly located in the central part of the new capital, should be strictly restricted for
development. Green belts [109–112] and wildlife corridors [113–115] can be implemented
Figure 8. The result of the weighted suitability analysis—biodiversity-positive city scenario
(green = high suitability, yellow = moderate suitability, and red = low suitability land).
Given the outcomes of the suitability analysis, further development recommendations
can be proposed. First, protected areas such as protected forests and key biodiversity
areas, mainly located in the central part of the new capital, should be strictly restricted for
development. Green belts [
109
–
112
] and wildlife corridors [
113
–
115
] can be implemented
to protect and conserve these areas. These efforts align with Nusantara’s forest city concept
and UN SDG 15 to protect, restore, and promote sustainable use of terrestrial ecosystems,
sustainably manage forests, combat desertification, and halt and reverse land degradation
and biodiversity loss. Second, moderately suitable areas for residential development have
the potential for future residential development. However, it needs to be firmly regulated
case-by-case by considering the local area conditions. Lastly, areas with high suitability
for residential development should be prioritised. Those areas also have the possibility of
being developed as green spaces and other urban development categories.
4.3. Results of Sensitivity Analysis
A sensitivity analysis was performed on the waterfront city and biodiversity-positive
city scenarios. Table 11 shows the results of sensitivity analysis for the waterfront city
scenario obtained for the highest criterion (distance from streams) and the lowest criterion
(bushfire risk) and the biodiversity-positive city scenario acquired for the highest criterion
(forest cover) and the lowest criterion (distance from major bus station).