This study aims to understand the long-term (2020–2050) urban water–energy–food (WEF) resources access and sustainability in Addis Ababa city through a nexus modeling approach. Several feasible scenarios in line with improving WEF resources supply and access through conservation, system rehabilitation and technology input are explored. The water system scenarios include rehabilitation and conservation scenario, water supply enhancement scenario, technology input scenario and integrated water improvement scenario. The energy scenario includes energy conservation scenario and new renewable supply enhancement scenarios and integration of both scenarios as integrated energy scenario. The food system scenarios include crop yield productivity and irrigation water use efficiency scenarios of urban agricultural system. The integrated WEF nexus scenario is the integration of all scenarios under one nexus framework. The results are evaluated against baseline scenario. At a system level, the integrated water scenario result provides a water saving potential of 26 and 52% from the baseline scenario by 2030 and 2050, respectively, whereas the integrated energy use scenario saves energy by as much as 22 and 48%. For respective years, under the integrated WEF nexus scenario, the integrated water use scenario for low energy intensity reduces the energy use for urban water system by 23 and 72% from the baseline scenario. Similarly, urban food production have also shown enhancement. Urban food production system in Addis Ababa city is relatively small and does not significantly affect the food import from other parts of the country. Overall, the results WEF nexus modeling approach revealed the importance of exploring integrated nexus approach to sustainable urban water energy and food development and management as a first attempt at the urban scale.

  • Water, energy and food are interdependent.

  • Water–energy–food nexus studies help policymakers to make decision to efficiently use these utilities.

Graphical Abstract

Graphical Abstract
Graphical Abstract

Cities are the center of WEF system discussions due to the growing resource demands, supply and nexus (Zhang et al., 2019). Meeting WEF demands in cities has become the most serious challenge for attaining sustainable development (Schlor et al., 2018). Being a consumer of WEF resources, cities mostly depends on non-urban areas outside of their physical boundaries. The geographically expanding city boundaries and rapid urbanization trends play a vital role on growing of WEF resources. Apart from that, the fast-growing cities are also faced with a range of developmental challenges particularly severe in developing countries, due to factors like high population density, poverty, lack of basic infrastructure services, etc. (Gupta & Hall, 2020). The rapid growth of urbanization in Addis Ababa city is characterized by a large-scale population that has put a great pressure on the WEF resources. To overcome the constraints and meet the challenges of urban development in the long-term, the integrated WEF nexus has greater importance (Hoff, 2011). Integrated WEF system nexus models are required to quantify future challenges and to design least-cost policies that leverage responses in the connections between resource supply and demand (Howells et al., 2013). Concerning the existing developmental challenges, efficient use of WEF resources has become a critical solution to enhance the provision of urban WEF service. Addressing the WEF nexus at the city level has therefore become central to achieving sustainable urban development, as it affects the extent to which the security of WEF resources can be simultaneously achieved (Biggs et al., 2015). The WEF nexus approach is a central to respond to the sustainability challenge for an effective management of WEF resources. This approach has a significant role as a method for understanding and modeling the connections between the WEF resource systems for managing efficiently WEF systems and reinforcing their integration in order to guarantee a secure and sustainable use of resources (Albrecht et al., 2018). As part of the integrated WEF nexus exploration, sustainable development should include decreasing water, energy, and wastewater or waste output, as well as providing renewable, clean and efficient resources (Gurdak et al., 2017). In recent years, soaring research interests have been directed toward enabling technology integration for urban development (Guo et al., 2019). While the scientific literature on city's efficient resources continues to be dominated by technology aspects, limited studies have focused on enhancing the efficiency of WEF resources from an interconnected and nexus perspective. However, the resource inputs in cities are still not methodically characterized and WEF nexus remains to be a challenge at the local level (Liang et al., 2019; Sukhwani et al., 2019). To bridge the gaps for enhancing WEF resource efficiency through advanced innovative technologies development, this study contributes to the development of a framework for linked WEF resource to service systems, focusing on the efficient resources use technology or resource management scenarios to meet or improve urban WEF demands for Addis Ababa city. This can be addressed through WEF nexus model using a Model Management Infrastructure (MoManI) tool. This tool is used for WEF system nexus model by combining individual system and nexus perspective as integrated system, which helps to optimize resources supply and demands. The nexus model helps to inform decision-makers toward the sustainable long-term development. This study uses a WEF nexus model based on possible scenarios to understand urban WEF resource management over a long-term (2020–2050) horizon. In addition to the baseline scenario, WEF system scenarios are explored based on resources management. Water system scenarios are developed based on water conservation and demand management (WCDM) measures. Energy system scenarios also developed depending on energy conservation and demand management (ECDM) measures, whereas food production scenarios are based on production intensification measures. Scenarios for increasing WEF resource through conservation, system rehabilitation, technological input, supply enhancement and food production enhancement are investigated. The WEF system scenario-based decisions over the long term for individual and nexus system perspective of Addis Ababa city are given as identification of the WEF system scenarios that represent the demand and identification of WEF system scenarios that can be expanded over a time horizon. The optimization problem can be solved to find a minimum cost value of the scenarios that represent demand and optimal expansion capacities of scenarios in order to meet future demand.

Overview of Addis Ababa city

Addis Ababa city (38°44′E and 9°1′N) is home to 25% of the urban population in Ethiopia and one of the highly growing in Africa (CSA, 2013). Addis Ababa's gross domestic product (GDP) is growing annually by 14% and contributes about 50% toward the national GDP (World Bank, 2015). The city water scarcity is become significant due to the high growth of urbanization and individual water demand. The total water sourced from groundwater and surface water is about 0.45 million m3/day and 36.5% of the water is lost due to leakage (World Bank, 2015). The energy supply is deprived due to the aging of the distribution system network and outage, which are less to provide efficient and reliable service to end users.

MoManI

An interface that was developed for capacity building; it offers an easy way to build a simple model, explore existing models and create different scenarios. The model calculates the optimal flows of WEF carriers, services or their proxies that are produced and managed to meet the demands. MoManI is an optimization model typically consists of an objective function, decision variables (or plainly said: ‘variables’), model parameters (‘parameters’), constraints (‘equations’), index sets (‘sets’) and the input dataset. Sets define the physical structure of a model, usually independent from the specific scenarios that will be run. Parameters are the numerical inputs to the model. While usually the structure of a model, therefore the sets, remains fixed across scenarios, it is common practice to change the values of some parameters when running different scenarios and/or sensitivity analyses. Variables are the unknown variables of the problem that need to be determined to solve the problem is decision variable. The variables are the outputs computed by the code. As much as the parameters, also the variables are functions of the elements in one or more sets. The domain of several variables has been constrained to be positive, in order to decrease the size of the solution space and computational effort. The equation of the MoManI interface is divided into blocks of equations that comprise one objective function and several constraints. The blocks of code allow for a modular structure in different functionalities that can be added or removed based on the user's needs. The objective of the model is to minimize the total system cost, over the entire model period subjected to the constraint. The objective in the MoManI calculates the lowest cost of WEF system to meet given demand. To achieve this, the interface distinguishes between fuels and technologies. WEF carriers and services are called fuels in the model and hence are referred to like this from this point on. Each fuel represents a specific WEF carrier, a group of similar ones, or their proxies. Furthermore, fuels are produced, transformed and used by technologies. Additionally, technologies represent all kinds of WEF using, producing or transforming techniques. The technologies can run in different modes of operation if applicable. When converting certain fuels into another type, technologies have a defined InputActivityRatio and OutputActivityRatio. Technologies with only one of these ratios defined are either supply or demand nodes. The objective function equation of optimization model is given as follows.

  1. Objective Function: The objective function minimizes the cost of WEF system to meet the given demands over a given time period (2016–2050) subject to a set of constraints (e.g., demand requirements, resource limits). This is done by summing up the total discounted costs of each WEF technology (t) in each year (y) and region (r) using Equation (1).
    formula
    (1)
  2. Costs: Costs incur when building new capacities of technologies (DCI), maintaining capacities (DOC) is given using Equation (2).
    formula
    (2)
    where TDC is the total discounted cost, DOC is the discounted operating cost and DCI is the discounted capital investment.

Structure of the WEF system model development

The energy system model includes the electric systems from source, to transmission, distribution and final consumption. The water system model also includes source, transmission, distribution and final demand. However, existing and planned supply (transmission substation and distribution) and final demand for electric energy is considered, whereas for water, supply source and demand are reflected in this study, which are combined with resource management measures such as technology interventions. In the model, dividing the total water–energy demand into different sectors such as industry, residential and commercial represents the demand coming from the supply. In addition, an important feature of the food system of the model is a detailed crop yield and land-use representation with water sectors, which are combined with production enhancement option. Furthermore, the WEF system such as existing, planned supply, import, land and resource management option to improve or meet the demand with their representation structure in the model are frame-worked as shown in Figure 1.

Fig. 1

WEF system model representation in the MoManI tool.

Fig. 1

WEF system model representation in the MoManI tool.

Close modal

Data used to develop model

WEF demand disaggregation

MoManI tool uses two different kinds of input demands. It uses accumulated annual demand to represent a demand that is not time slice dependent and can be satisfied at any time of the year, as long as the total is met. It is used for water, food and petroleum energy demand, however not be used for modeling an electric energy demand, as electricity has to be supplied instantly when demand occurs. Specified annual demand is used to represent demands that are time slice dependent. The distribution of the electric energy demand in each time slice is entered through the specified demand profile. In the model, the units used for the water, energy and food demands are BCM, PJ and MT, respectively, which are given in Table 1.

Table 1

Water, energy and food demands (Bedassa et al., 2021a).

WEFAbbreviationDescriptionYear
2016202520302035204020452050
Water RESWAT Residential 0.17 0.27 0.36 0.47 0.61 0.79 1.02 
COMWAT Commercial 0.04 0.07 0.09 0.12 0.15 0.20 0.26 
INDWAT Industrial 0.07 0.10 0.12 0.16 0.20 0.26 0.33 
Energy COMELC Commercial 3.2 9.7 14.7 21.3 29.7 40.6 54.7 
INDELC Industrial 5.2 14.2 20.8 29.4 40.5 54.8 73.3 
RESELC Residential 4.54 13.2 19.7 28.1 39 53.1 71.3 
KER Kerosene 3.83 5.6 7.1 9.1 11.6 14.8 18.9 
DSL Diesel 9.88 21.5 30.5 41.9 56.2 73.9 95.7 
GSL Gasoline 7.9 14.2 19.3 25.9 34.4 45.4 59.6 
Food CRP001 Potato 0.06 0.15 0.25 0.43 0.72 1.20 2.01 
CRP002 Onion 0.10 0.25 0.42 0.71 1.19 2.00 3.35 
CRP003 Tomato 0.03 0.08 0.14 0.24 0.40 0.67 1.12 
CRP004 Cabbage 0.02 0.04 0.07 0.12 0.20 0.33 0.56 
CRP005 Teff 0.30 0.58 0.85 1.27 1.92 2.96 4.61 
CRP006 Maize 0.00 0.01 0.02 0.04 0.06 0.11 0.18 
CRP007 Wheat 0.01 0.01 0.02 0.02 0.02 0.02 0.02 
CRP008 Barley 0.01 0.02 0.03 0.04 0.07 0.12 0.20 
WEFAbbreviationDescriptionYear
2016202520302035204020452050
Water RESWAT Residential 0.17 0.27 0.36 0.47 0.61 0.79 1.02 
COMWAT Commercial 0.04 0.07 0.09 0.12 0.15 0.20 0.26 
INDWAT Industrial 0.07 0.10 0.12 0.16 0.20 0.26 0.33 
Energy COMELC Commercial 3.2 9.7 14.7 21.3 29.7 40.6 54.7 
INDELC Industrial 5.2 14.2 20.8 29.4 40.5 54.8 73.3 
RESELC Residential 4.54 13.2 19.7 28.1 39 53.1 71.3 
KER Kerosene 3.83 5.6 7.1 9.1 11.6 14.8 18.9 
DSL Diesel 9.88 21.5 30.5 41.9 56.2 73.9 95.7 
GSL Gasoline 7.9 14.2 19.3 25.9 34.4 45.4 59.6 
Food CRP001 Potato 0.06 0.15 0.25 0.43 0.72 1.20 2.01 
CRP002 Onion 0.10 0.25 0.42 0.71 1.19 2.00 3.35 
CRP003 Tomato 0.03 0.08 0.14 0.24 0.40 0.67 1.12 
CRP004 Cabbage 0.02 0.04 0.07 0.12 0.20 0.33 0.56 
CRP005 Teff 0.30 0.58 0.85 1.27 1.92 2.96 4.61 
CRP006 Maize 0.00 0.01 0.02 0.04 0.06 0.11 0.18 
CRP007 Wheat 0.01 0.01 0.02 0.02 0.02 0.02 0.02 
CRP008 Barley 0.01 0.02 0.03 0.04 0.07 0.12 0.20 

Time disaggregation of the WEF nexus model

To carry out a model, the tool offers a much more disaggregated approach concerning to times and time-dependent data. This is accomplished by dividing the year into several time slices, which can be defined by the model user to suit the needs of the application. For this model specification, two seasons are chosen (summer and winter), one day type, and three daily time brackets (Base, intermediate, peak). The base time bracket ‘base’ is set to 8 h (1/3 of one day), while ‘peak’ is 6 h long (1/4 of one day) and ‘intermediate’ is 10 h long (5/12 of one day). Summer seasons include from June to September and winter seasons from October to May season. The summer has 122 days, whereas winter has 243 days. (Calculation example: Summer base = 122 day × (8 h for base)/8,760 day = 0.11.) Table 2 presents the fraction per year for all the time slices used (given in % of one year).

Table 2

Time disaggregation for WEF demand by percent or fraction of one year.

YearSummer
Winter
BasePeakIntermediateBasePeakIntermediate
Split (%) 11 14 22 17 28 
Split (fraction) 0.11 0.08 0.14 0.22 0.17 0.28 
YearSummer
Winter
BasePeakIntermediateBasePeakIntermediate
Split (%) 11 14 22 17 28 
Split (fraction) 0.11 0.08 0.14 0.22 0.17 0.28 

WEF system production capacity

The WEF supply or production, fuel import, food import from out of urban area, urban food production, ECDM and WCDM technologies measures are considered as a set of technology in each time step in the proposed model.

  • 1.

    Water

The model uses the total annual activity lower and upper limit parameters to define the minimum and maximum amount of water that should be supplied during the modeling period, respectively. The total annual existing and planned water supply from surface water and groundwater for non-agricultural use is considered to represent these parameters; the values are given in Figure 2. The water constraint ensures that the total annual minimum available supplied water quantity has to be equal to the maximum available supplied water in each time period.
formula
(3)
where represent the total technology annual activity minimum and maximum limit of water supply, respectively, whereas represents the total technology annual activity or level of water supply in each time step.
  • 2.

    Energy

Fig. 2

Annual water supply capacity of Addis Ababa city (Seifemicheal, 2018).

Fig. 2

Annual water supply capacity of Addis Ababa city (Seifemicheal, 2018).

Close modal
Electric energy supply is considered in the model for transmission substation in terms of activity parameters, which is given in Figure 3. The existing and planned annual minimum supplied electric energy must equal to the maximum available supplied energy in each time period is ensured as a constraint input in the model.
formula
(4)
where represent the total technology annual activity minimum and maximum limit of electric energy supply (EES), respectively, whereas represents the total technology annual activity or level of EES in each time step.
  • 3.

    Land availability

Fig. 3

Total annual energy supply of substations.

Fig. 3

Total annual energy supply of substations.

Close modal
The constraint represents that the total land use must be equal to its maximum permissible available land in each period, which is around 54 kha. The future land-use of Addis Ababa city has estimated based on existing land-use trends by extrapolating, which are fitted by polynomial equation (Figure 4). The estimated land-use is used to develop future urban agricultural land availability scenarios for urban food crop (potato, onion, tomato and cabbage) production in the domain of the integrated WEF system nexus model. In the case of urban food production in Addis Ababa, land resources indicate a total of urban agricultural land. Urban crop production area should not exceed the available total urban agricultural land.
formula
(5)
Fig. 4

Trend of land use (extrapolated from Nahimi et al. (2019)).

Fig. 4

Trend of land use (extrapolated from Nahimi et al. (2019)).

Close modal

where represents the total area (TA) of urban land for crop production and represents the upper limit area (ULA) of the total urban land available for food production.

For setting forests, built-up, pastures and meadows in the model, the total annual activity lower limit parameter represents the minimum amount of each land-use area for baseline development.

  • 4.

    Food production

The food crop such as potato, tomato, onion and cabbage are considered for this study, which are mostly grown along with the upper and middle parts of the Akaki River. Defining the areas for cultivation, the residual capacity parameter helps to introduce the areas under the cultivation for each of the four crops in the model. This parameter defines how much land is currently used for the cultivation of the respective crops. The food production is calculated by land area multiplied by food yield. Therefore, the yields of these four crops per unit area of land, as well as their land are used in the model, given in Figure 5.

Fig. 5

Baseline yields of crops with their land area (Data from Addis Ababa agricultural office).

Fig. 5

Baseline yields of crops with their land area (Data from Addis Ababa agricultural office).

Close modal

Cost of WEF supplies

All WEF technologies sets are modeled using an average unit capital, fixed and variable cost parameters, which are mostly depicted through a literature review since the model identifies the least-cost technologies that balance supply and demand. The parameters are expressed in the model in the unit of million-dollar per billion cubic meters (M$/BCM) for water, million-dollar per million ton (M$/MT) for food, million-dollar per kilo hectare (M$/kha) for crop production and million-dollar per peta joule (M$/PJ) for energy. Tables 35 show cost-related parameters in the integrated WEF system optimization model, including average electric energy, water supply, urban food production and import cost. The costs related to electric energy, water supply, food production and import are assumed to be constant over the long-term period. The different sets of technology costs considered in the model are as follows:

  • 1.

    Water and energy supply

Table 3

Unit cost for water and energy provision service (Helena & Dale, 2019).

Water systemCost (M$/BCM)
Fixed cost 190 
Capital cost 700 
Total cost 890 
Energy systemCapital cost (M$/PJ)
Transmission substation 3.89 
Distribution 6.39 
Total 10.28 
Water systemCost (M$/BCM)
Fixed cost 190 
Capital cost 700 
Total cost 890 
Energy systemCapital cost (M$/PJ)
Transmission substation 3.89 
Distribution 6.39 
Total 10.28 
Table 4

Economic land productivity (M$/kha) of food crop production.

FoodFixed costVariable costCapital costSources
Tomato 0.5–0.6 2.4–2.9 2.9–3.5 Frank et al. (2015), MoFED (2006)  
Onion 0.2–0.6 0.8–2.9 1.0 Addisu et al. (2017), Frank et al. (2015)  
Vegetable 0.3 1.4 1.6 Fitsum et al. (2006)  
FoodFixed costVariable costCapital costSources
Tomato 0.5–0.6 2.4–2.9 2.9–3.5 Frank et al. (2015), MoFED (2006)  
Onion 0.2–0.6 0.8–2.9 1.0 Addisu et al. (2017), Frank et al. (2015)  
Vegetable 0.3 1.4 1.6 Fitsum et al. (2006)  
Table 5

Average unit cost of food crop import (Minot et al., 2015; Brenna et al., 2019; USDA, 2019).

Crop nameAbbreviationVariable cost (M$/MT)
Potato IMPCRP1 336 
Onion IMPCRP2 540 
Tomato IMPCRP3 483 
Cabbage IMPCRP4 141 
Barley IMPCRP5 588 
Maize IMPCRP6 370 
Teff IMPCRP7 664 
Wheat IMPCRP8 505 
Crop nameAbbreviationVariable cost (M$/MT)
Potato IMPCRP1 336 
Onion IMPCRP2 540 
Tomato IMPCRP3 483 
Cabbage IMPCRP4 141 
Barley IMPCRP5 588 
Maize IMPCRP6 370 
Teff IMPCRP7 664 
Wheat IMPCRP8 505 

The average annual unit costs of water and energy services are based on utility financial, customer billing, capital and operation and maintenance costs, production levels, and losses during production and distribution (Helena & Dale, 2019), as given in Table 3.

The cost of petroleum import for gasoline, diesel and kerosene are predicted up to 2050 based on the new policies scenario for Ethiopia, as indicated in Figure 6.

Fig. 6

Petroleum prices projection until 2050 (IEA, 2016).

Fig. 6

Petroleum prices projection until 2050 (IEA, 2016).

Close modal

The set of technology representing imports in the model is set up to have a single output and no input. Setting output to activity ratio of 1 for each, leaves the variable costs to represent the price of imported fuel (other costs are left as zero).

  • 2.

    Food production

The economic land productivity represents the economic value of production output per area of land, which is given as indicated in Table 4.

From Table 5, the average unit cost for fixed and variable costs are around 0.4 and 2 M$/kha, respectively, which are assumed to be considered for all four food crop (potato, onion, tomato and cabbage) in the WEF system nexus model.

  • 3.

    Food supply or import

In terms of domestic food crop production, Addis Ababa city is insecure to meet the demand. Due to this, the model incorporates additional food crop imports from out of urban areas connected with the cost to satisfy demand. Therefore, the import crop (IMPCRP) should be included in the model, along with the annual average cost of import data, as shown in Table 5.

Input and output activity of the WEF system

WEF system components are linked together using parameters such as the input activity ratio and the output activity ratio; these represent the rate at which technology is doing something. For instance, crop yield is considered as the output activity ratio for irrigated land, whereas cropland, irrigation and precipitation water are considered as input activity ratios. Consequently, crop import from out of the urban area and petroleum import is taken as the output activity ratio. In addition, electric and petroleum energy are taken as input and output for the energy demand set of technology. The input for irrigation and non-irrigation water can be surface, groundwater or both, with the output of water. This parameter with its values is indicated in Table 6.

Table 6

WEF system nexus model technology input and output activity parameter.

TechnologyParameter
InputInput activity ratioOutputOutput activity ratio
Water demand Water Water 0.66 
Non-agricultural water Surface water 
Non-agricultural water Groundwater 
Irrigation water Surface water 
Kerosene demand Kerosene Kerosene 
Diesel demand Diesel Diesel 
Gasoline demand Gasoline Gasoline 
Electricity demand Electricity Electricity 0.8 
Transmission substation Electricity Electricity 0.97 
Crop1 irrigated Land Crop1 (yield) 0.017 
Water 0.0091 
Crop2 irrigated Land Crop2 (yield) 0.0076 
Water 0.0062 
Crop3 irrigated Land Crop3 (yield) 0.013 
Water 0.0093 
Crop4 irrigated Land Crop4 (yield) 0.026 
Water 0.0082 
Import crop 1  Crop1 
Import crop 2  Crop2 
Import crop 3  Crop3 
Import crop 4  Crop4 
Land resources  Land 
IMPDSL  Diesel 
IMPGSL  Gasoline 
IMPKER  Kerosene 
TechnologyParameter
InputInput activity ratioOutputOutput activity ratio
Water demand Water Water 0.66 
Non-agricultural water Surface water 
Non-agricultural water Groundwater 
Irrigation water Surface water 
Kerosene demand Kerosene Kerosene 
Diesel demand Diesel Diesel 
Gasoline demand Gasoline Gasoline 
Electricity demand Electricity Electricity 0.8 
Transmission substation Electricity Electricity 0.97 
Crop1 irrigated Land Crop1 (yield) 0.017 
Water 0.0091 
Crop2 irrigated Land Crop2 (yield) 0.0076 
Water 0.0062 
Crop3 irrigated Land Crop3 (yield) 0.013 
Water 0.0093 
Crop4 irrigated Land Crop4 (yield) 0.026 
Water 0.0082 
Import crop 1  Crop1 
Import crop 2  Crop2 
Import crop 3  Crop3 
Import crop 4  Crop4 
Land resources  Land 
IMPDSL  Diesel 
IMPGSL  Gasoline 
IMPKER  Kerosene 

Note: Water–energy output activity ratios are described in a fraction, whereas others have a unit of BCM/kha, except crop yield (MT/kha).

As indicated in Table 6, the non-zero value of the output activity ratio for the component of WEF commodity and technology pair indicates that the technology is producing at least some amount of that commodity or technology requires at least some amount of that commodity to produce an output. Generally, for a technology that produces a WEF carrier, a non-zero output to activity ratio is added (specifically relating one to the other). Similarly, for a technology that uses a WEF carrier a non-zero input to activity ratio is added.

Scenario used in the model

Meeting long-term WEF demands by existing supply in the city is the most critical for Addis Ababa city due to its rapidly increasing urbanization, which may cause a non-feasibility solution of the model. To avoid the non-feasibility of the model, feasible scenarios in line with improving WEF resources through conservation, system rehabilitation and technology input in the domain of integrated WEF system have to be implemented for the solution of the model. Five water system scenario and four basic energy system scenarios are given in Table 7.

Table 7

Model input scenarios for individual resources intervention technologies.

ScenarioWater scenario typeDescriptionAbbreviation
Scenario 1 Baseline scenario Business as usual continues WCDM0 
Scenario 2 Rehabilitation and water conservation Replacing old fixtures, etc. WCDM1, WCDM3 
Scenario 3 Technology scenario for selected high consumers For major government offices, businesses and institutions (20% uses 80%) WCDM4, WCDM5, WCDM6 
Scenario 4 Supply enhancement scenario Water loss reduction, rain and runoff harvesting WCDM2, WCDM7 
Scenario 5 Integrated scenario All scenarios (conservation, demand and supply improvement and technology application) All the above 
ScenarioEnergy scenario typeDescriptionAbbreviation
Scenario 1 Baseline scenario Current use ECDM0 
Scenario 2 Demand-side energy conservation Implementing various energy saving appliances and methods (efficient lightening, industrial motor machine, baking plate, stoves) ECDM1, ECDM2, ECDM3, ECDM5 
Scenario 3 Solar energy, loss reduction Around 8.3% of the available rooftop area (5,310 ha) is suitable for PV (∼442 ha) ECDM4, ECDM6 
Scenario 4 Integrated scenario All scenarios (energy conservation, demand and supply improvement and technology application) All the above 
ScenarioWater scenario typeDescriptionAbbreviation
Scenario 1 Baseline scenario Business as usual continues WCDM0 
Scenario 2 Rehabilitation and water conservation Replacing old fixtures, etc. WCDM1, WCDM3 
Scenario 3 Technology scenario for selected high consumers For major government offices, businesses and institutions (20% uses 80%) WCDM4, WCDM5, WCDM6 
Scenario 4 Supply enhancement scenario Water loss reduction, rain and runoff harvesting WCDM2, WCDM7 
Scenario 5 Integrated scenario All scenarios (conservation, demand and supply improvement and technology application) All the above 
ScenarioEnergy scenario typeDescriptionAbbreviation
Scenario 1 Baseline scenario Current use ECDM0 
Scenario 2 Demand-side energy conservation Implementing various energy saving appliances and methods (efficient lightening, industrial motor machine, baking plate, stoves) ECDM1, ECDM2, ECDM3, ECDM5 
Scenario 3 Solar energy, loss reduction Around 8.3% of the available rooftop area (5,310 ha) is suitable for PV (∼442 ha) ECDM4, ECDM6 
Scenario 4 Integrated scenario All scenarios (energy conservation, demand and supply improvement and technology application) All the above 

Note: WCDM1, Replacing older inefficient toilet fixtures; WCDM2, Water loss reduction; WCDM3, Improving billing system; WCDM4, Distributing low flow showerhead and faucets; WCDM5, Installing low flow toilets and urinals; WCDM6, Cloth washer rebate; WCDM7, Stormwater harvesting; ECDM1, Efficient lighting; ECDM2, efficient energy machine; ECDM3, Efficient baking plate; ECDM4, Energy loss reduction; ECDM5, Electric stove standards; ECDM6, Solar energy.

Resource management assumptions of the model

  • a.

    Potential capacity of water–energy management measures

The potential capacity limits of water–energy management are a model constraint to predict the quantitative values of water–energy resource management for different considered actions. The water–energy resource management potential was defined by Bedassa (Bedassa et al., 2021b), which is used to build a quantitative integrated WEF nexus model. The model determines the least cost of resource management measures such as technology improvement and supply enhancement subjected to their constraint to improve the supply security. The quantitative level of each water–energy management measures has to be between the minimum and maximum limits for each time step.
formula
(6)
formula
(7)
where and are the total technology annual activity lower and upper limits for water management (WM) technologies, respectively, and is the total technology activity or level of each water management measure in each time step, whereas , and represent for electric energy management (EEM) technologies.

As presented by Bedassa (Bedassa et al., 2021b), the model parameters related to potential constraints of resource management measures are implemented for the total technology annual activity upper and lower limits parameters, which are indicated in Figures 7 and 8 for water and energy, respectively.

  • b.

    Techno-economic parameter of management measures

Fig. 7

Water constraints (upper graphs is the high saving assumption, whereas the bottom is low saving).

Fig. 7

Water constraints (upper graphs is the high saving assumption, whereas the bottom is low saving).

Close modal
Fig. 8

Energy constraints (upper graphs is the high saving assumption, whereas the bottom is low saving).

Fig. 8

Energy constraints (upper graphs is the high saving assumption, whereas the bottom is low saving).

Close modal

Water and energy resource management measures techno-economic parameters (average unit cost and life span) input of the model are presented in Table 8.

Table 8

Techno-economic parameters assumptions of management measures.

MeasureCost (M$/BCM)Life (year)Source
WCDM1 217 20 Mengistu et al. (2010), Edward et al. (2020)  
WCDM2 18 20 Mengistu et al. (2010), David (2008)  
WCDM3  
WCDM4 129  
WCDM5 226 20 Mengistu et al. (2010), Edward et al. (2020)  
WCDM6 221 12  
WCDM7 925 50 Antonaropoulos (2011)  
MeasureCost (M$/PJ)Life (year)Source
ECDM1 4.72 12 ECOFYS (2015), Edward et al. (2020)  
ECDM2 8.33 20 Gómez-Sarduy et al. (2020)  
ECDM3 7.78 World Bank (2010), ECOFYS (2015)  
ECDM4 8.89 40 World Bank (2010)  
ECDM5 11.67 15 Marc & Subhrendu (2012)  
MeasureCost (M$/BCM)Life (year)Source
WCDM1 217 20 Mengistu et al. (2010), Edward et al. (2020)  
WCDM2 18 20 Mengistu et al. (2010), David (2008)  
WCDM3  
WCDM4 129  
WCDM5 226 20 Mengistu et al. (2010), Edward et al. (2020)  
WCDM6 221 12  
WCDM7 925 50 Antonaropoulos (2011)  
MeasureCost (M$/PJ)Life (year)Source
ECDM1 4.72 12 ECOFYS (2015), Edward et al. (2020)  
ECDM2 8.33 20 Gómez-Sarduy et al. (2020)  
ECDM3 7.78 World Bank (2010), ECOFYS (2015)  
ECDM4 8.89 40 World Bank (2010)  
ECDM5 11.67 15 Marc & Subhrendu (2012)  

In addition, renewable energy (solar PV) which is considered as ECDM6 measure also has techno-economic assumptions. The techno-economic data for solar PV are cost (Figure 9) and operational life (25 years).

Fig. 9

Projections of unit costs of solar PV until 2050 (IEA, 2016).

Fig. 9

Projections of unit costs of solar PV until 2050 (IEA, 2016).

Close modal

Nexus scenarios used in the model

  • 1.

    Water–energy

As energy is needed for the urban water cycle, energy intensity was understood to analyze their future nexus perspective. The baseline energy intensity presented by Bedassa (Bedassa et al., 2020) serves as a baseline for the development of an integrated urban WEF system nexus model. For urban water cycle, when considering the integration of water supply and wastewater energy intensity, it is important to distinguish energy demand between water use of integrated and baseline scenarios. In addition to the baseline scenario, other scenarios of energy intensity have to implement in the model to analyze the various scenarios of energy use in the urban water system (extraction, conveyance, treatment, distribution and wastewater treatment). Based on the baseline energy intensity scenario, the other feasible alternative scenario for water supply and wastewater treatment are assumed in the range of global level (Pabi, 2013), as given in Table 9.

  • 2.

    Water–food

Table 9

Model input feasible WEF nexus scenarios.

ScenariosDescriptionKey assumptions
Scenario 1 Baseline energy intensity scenario Baseline year (2017) average annual energy intensity values of wastewater treatment (WWT) and potable water supply (PWS) system is around 0.6 and 1.9 kWh/m3, respectively (Bedassa et al., 2020), which continues as usual. 
Scenario 2 High energy intensity scenario In this case, energy intensity for WWT and PWS will increase annually from the baseline year of EI by 1.5%. This can be based on the type and quality of raw water entering a treatment system (low quality), high concentration of the total suspended solids and the nature of the filters in the water treatment system and greater elevation change in water distribution and transmission systems. 
Scenario 3 Low energy intensity scenario In this scenario, energy intensity will decrease annually by 1.5% from the baseline year. Improving the energy efficiency of the water system, equipment upgrades exist in WWT and water distribution by gravity system due to smaller geographical differences can reduce the energy intensity. 
ScenariosDescriptionsKey assumptions
Scenario 1 Baseline Base year cropland, yield and water efficiency continue as usual 
Scenario 2 Low improve crop yield, land and water use efficiency 1.7% (potato), 2.5% (onion), 1.7% (tomato) and 0.5% (cabbage) annual improvement of crop yield. Total cropland increases to 2 kha from the base year. 5% improvement of urban irrigation water use efficiency 
Scenario 3 High improve crop yield, land and water use efficiency 3.5% (potato), 5% (onion), 3.4% (tomato) and 1.1% (cabbage) annual improvement of crop yield. Total cropland increases to 3 kha from the base year. 10% improvement of urban irrigation water use efficiency from the base year 
ScenariosDescriptionKey assumptions
Scenario 1 Baseline energy intensity scenario Baseline year (2017) average annual energy intensity values of wastewater treatment (WWT) and potable water supply (PWS) system is around 0.6 and 1.9 kWh/m3, respectively (Bedassa et al., 2020), which continues as usual. 
Scenario 2 High energy intensity scenario In this case, energy intensity for WWT and PWS will increase annually from the baseline year of EI by 1.5%. This can be based on the type and quality of raw water entering a treatment system (low quality), high concentration of the total suspended solids and the nature of the filters in the water treatment system and greater elevation change in water distribution and transmission systems. 
Scenario 3 Low energy intensity scenario In this scenario, energy intensity will decrease annually by 1.5% from the baseline year. Improving the energy efficiency of the water system, equipment upgrades exist in WWT and water distribution by gravity system due to smaller geographical differences can reduce the energy intensity. 
ScenariosDescriptionsKey assumptions
Scenario 1 Baseline Base year cropland, yield and water efficiency continue as usual 
Scenario 2 Low improve crop yield, land and water use efficiency 1.7% (potato), 2.5% (onion), 1.7% (tomato) and 0.5% (cabbage) annual improvement of crop yield. Total cropland increases to 2 kha from the base year. 5% improvement of urban irrigation water use efficiency 
Scenario 3 High improve crop yield, land and water use efficiency 3.5% (potato), 5% (onion), 3.4% (tomato) and 1.1% (cabbage) annual improvement of crop yield. Total cropland increases to 3 kha from the base year. 10% improvement of urban irrigation water use efficiency from the base year 

Management practices opportunities do exist to increase food production through more efficient use of available land and increasing crop yield. To reduce water footprint in urban food production system and increase food production, different food production system scenarios related to crop yield productivity, cropland, and irrigation water use efficiency (focusing on water demand per unit area of land) for urban agricultural system are considered in this study. Setting food crop (potato, onion, tomato and cabbage) productivity at different scenarios can boost future crop production. The water demand per unit area of land was determined for the base year in chapter of quantifying water–food nexus in urban food production system in Addis Ababa city. Therefore, based on baseline crop yield and estimated baseline water demand per unit area of land, other alternative feasible scenarios are implemented in the model to optimize the WEF resources. To develop other feasible urban crop yield scenarios in the model, the annual yield output ratio for land is assumed in the range of research settings standards (spreading best management practices, intensive use of pesticides, fertilizers and water quality) (Desalegn & Akililu, 2012). Overall, energy intensity and food productions scenarios from nexus perspective are considered in the model as given in Table 9.

Food crop yield and energy intensity are considered the output activity ratio with the input activity ratio of zero value, whereas irrigation water is taken as the input activity parameter to model urban WEF system nexus.

Water system analysis for different scenarios

The WEF nexus model provides insights on water systems over the modeled time horizon. The main results for water system are given under this subsection. Due to the rapidly increasing urbanization in perceptions of population and economic development, the water consumption of Addis Ababa will greatly increase with affecting the supply. An integrated WEF system optimization model was implemented to balance the water supply-demand gap by adopting different water system scenarios. The water system scenario includes baseline scenario (BLS), rehabilitation and water conservation scenario (RWCS), technology scenario (TS), supply enhancement scenario (SES) and integrated water scenario (IS).

Water demand under different scenarios

The regression model was used to estimate the water demand for the baseline water scenario using basic urbanization drivers such as socio-economic. This scenario shows due to the rapidly increasing socio-economic factor, Addis Ababa city's water demand will greatly increase in 2050 by 1.03 BCM from 2030. However, the demand can reduce through water saving by adopting water management measures such as innovative water technologies. The water saving from conservation and demand management measure is estimated from the nexus model approach based on their saving potential constraint. The model showed water demand under a rapid urbanization of a baseline scenario (BLS) is growing faster, while in the demand-side management (RWCS and TS) water saving is slower, as given in Figure 10.

Fig. 10

Water demand in Addis Ababa city for different scenarios.

Fig. 10

Water demand in Addis Ababa city for different scenarios.

Close modal

Under the baseline water scenario (BLS), the water demand of Addis Ababa city by 2030 and 2050 will be 0.6 and 1.6 BCM, respectively, whereas 0.46 and 1.04 BCM for the combined scenario (RWCS and TS). For respective years, the water demand of combined scenario (RWCS and TS) is about 23 and 35% lower than the baseline scenario. This is due to the future increases of implementing water efficiency, conservation and technology input.

Water saving of water system scenarios

Depending on water conservation and demand management measures, different water system scenarios were developed to generate future available water. However, saving potential constraints and costs of measures affect water saving target of different water system scenarios throughout the planning horizon considered. Therefore, due to these constraints and costs, the long-term temporal water saving trends from the RWCS, TS, SES and IS will be different. The RWCS will provide water saving of about 15.3 and 28.8% of baseline water demand in 2035 and 2050, respectively. For respective years, SES will save about 8.2 and 17.3% of baseline demand, whereas IS will result to save around 27.1 and 52.1% of baseline demand, which are given in Figure 11. In the case of SES, from 0.32 BCM of expected stormwater runoff available, the model provides that about 60% (0.2 BCM) will be optimal to harvest in 2050, whereas it is 50% for the studies in other urban (Hunt & Lombardi, 2012). The optimal percentage of water demand that will be met by stormwater harvesting will be about 5% (2030) and 12% (2050). The result also showed, about 8–22% (2030–2050) of residential water demand will save by rainfall-runoff water harvesting, this is ratified with the other urban as up to 50% is possible (Hunt & Lombardi, 2012).

Fig. 11

Optimal water saving potential under different water system scenarios.

Fig. 11

Optimal water saving potential under different water system scenarios.

Close modal

The integrated water scenario will have better water saving effect than the RWCS, SES, TS and have the potential to replace annually about 0.83 BCM of dependency on conventional water supply by 2050. Additionally, the existing and planned water supply will greatly improve the baseline water demand from 2030 to 2050 when 28 to 52% of water is save; this will achieve under integrated water scenario, which is the alternative path for future sustainable development.

Water scarcity and implications

At the system level, the estimates show that there is a shortfall of water supply for all the evaluated water system scenarios from 2030 to 2050 except for the integrated water scenario. Under integrated water scenario, the shortage of water access will occur from 2045 to 2050 (Figure 12). For the baseline water scenario, water supply shortages will occur due to insufficient existing and planned water supplies (such as Sibilu and Gerbi) as well as rapid urbanization of Addis Ababa city will increases water resources demand. These delays in water shortages can be improved by prioritizing all WCDM measures, which is integrated water scenario. The supply shortfall under baseline scenario and integrated water scenario will reach 65 and 27%, respectively, by 2050 for existing and planned water supply. This indicates about 38% of the shortfall under the baseline scenario is covered by integrated water scenario by 2050. This also shows that, existing and planned water supply of Addis Ababa city will greatly improve the water demand when integrated water scenario is considered. Figure 12 shows the surplus and deficit of water supply for different water system scenarios. The negative and positive sign shows the water deficit and surplus respectively in each scenario.

Fig. 12

Water supply shortages and surplus under different scenarios.

Fig. 12

Water supply shortages and surplus under different scenarios.

Close modal

The water scenarios under integrated scenario, rehabilitation and water conservation scenario, supply enhancement scenario and technology input scenario show that unmet water demand will reach 0.21, 0.58, 0.77 and 0.95 BCM in 2050, which also represents about 80, 44, 26 and 9% decrease in unmet water demand, respectively, against baseline scenario. From the foregoing results, it is clear that the integrated water scenario approach will meet the demand or greatly reduce the unmet water demand of the city throughout the modeling period.

Supply capacity for water system scenarios

The supply alternatives can be the use of additional new water supply from local water resources and trans-basin water diversion in a water shortage under the water use of baseline scenario. However, there may be substantial infrastructure and operation costs for the large-scale use of these two water sources (local and diversion water sources), which are limited to cost. From all considered water system scenarios, the best way to avoid future water shortages is through combining the addition new water supply with the integrated water scenario (all water system scenarios). The highest water savings under application of integrated water scenario through technical and structural water savings will permit local water resources to meet the city's future demands.

At a system level, due to an increase in water demands, there is the optimal need to have a plan of a new additional water supply from 2030 to 2050 for baseline water scenario and from 2045 to 2050 for integrated water scenario, which almost fulfills the water demands. By 2050, the optimal new water capacity plan will be 1.04 and 0.21 BCM for baseline and integrated water scenarios, respectively. This may indicate integrated water scenario will make enough of existing and planned water supply when combined with developing a new small water supply and infrastructure for meeting the future shortfall supply (2045–2050). Consequently, if all potential WCDM technology measures are implemented, they will greatly reduce the water supply shortage, or reduces the gap between supply and demand. Therefore, it is necessary to carry out all considered water management or WCDM measures in Addis Ababa city for sustainable water management.

Energy system analysis for different scenarios

The rapidly increasing urbanization of Addis Ababa city will increase its energy demand, which will highly affect the supply. However, the WEF nexus model was implemented to balance the energy supply-demand gap through understanding different feasible energy system scenarios within the city. The energy system scenario includes baseline energy scenario, energy conservation scenario, energy supply enhancement scenario and integrated energy scenario.

Energy demand alternatives

The baseline energy demand was estimated considering the impact of the urban socio-economic parameter without considering energy resources management through innovative efficient technologies that will reduce energy demand and save energy. For this reason, energy demand under the baseline will greatly increase. However, the demand will reduce through energy saving by adopting resources management within the city. The target energy saving from management measures is estimated by the model based on their saving potential constraint. The result showed that energy demand for the baseline scenario is growing faster, while in the energy conservation scenario is slower due to efficient energy technologies that will implement. From Figure 13, the energy conservation scenario would result in slower growth of energy demand with an annual average of 4.6% between 2030 and 2050, whereas the baseline scenario results in 6.6%.

Fig. 13

Energy demand of Addis Ababa city under two water system scenarios.

Fig. 13

Energy demand of Addis Ababa city under two water system scenarios.

Close modal

Under energy conservation scenario, the energy demand will reduce in 2050 by 44% from the baseline scenario, whereas other study states by as much as 15–40% (IPCC, 2014). This indicates the value of this study has an insignificant variation with other estimations. In addition, the significant reduction potential of energy demand will achieve if all energy management measures of this present study are implemented such as energy supply enhancement, demand management and energy efficiency improvements (e.g., changing light efficient bulb, industrial motor, stove, baking plate).

Energy saving of energy system scenarios

With reliance on ECDM technologies measure, energy system scenarios were developed to estimate the upcoming of energy saving. Conversely, saving potential constraints and costs of ECDM measures affect the saving target of various energy system scenarios considered through the modeling period. Due to these saving potential constraints and costs, the energy saving target of energy supply enhancement scenario (ESES), energy conservation scenario (ECS) and integrated energy scenario (IS) will be diverse. Energy resources management through ECDM measure provides about 4, 43 and 47% of baseline energy demand will save under energy system scenarios of supply enhancement scenario, conservation scenario and integrated scenario in 2050, respectively (Figure 14). These indicate that the integrated energy scenario will have a better energy saving against other scenarios and have the potential to replace annually around 95.7 PJ of reliance on grid EES by 2050. Correspondingly, the integrated energy scenario is the alternative path for future sustainable development of Addis Ababa city.

Fig. 14

Optimal energy saving potential under the three scenarios.

Fig. 14

Optimal energy saving potential under the three scenarios.

Close modal

Furthermore, under energy conservation scenario, gradual implementation of efficient electric light labeling, baking plate, stove and industrial motor, starting at a small scale and scaling the impact up to about 95% by 2050 will save 43% of baseline energy demand. Whereas 13% will save by 2030, that is less than 53% saving of Ethiopia (EEA, 2019).

Energy shortages under different scenarios

The estimates show that there will be an energy shortage between 2025 and 2050 under all assessed energy system scenarios due to the increment of energy demand and the limitations of the existing and planned energy supply, as indicated in Figure 15.

Fig. 15

Energy supply surplus and deficit under energy system scenarios.

Fig. 15

Energy supply surplus and deficit under energy system scenarios.

Close modal

The energy shortage can be enhanced by selecting all ECDM technologies measure considered in this study. The current and planned energy supply will prominently improve the energy demand when all ECDM technologies or integrated energy scenario is deliberated. The energy supply shortfall under integrated energy scenario will reach 68.8 PJ by 2050, which is a minimum against other energy system scenarios.

Supply capacity for energy system scenarios

Increasing energy distribution and transmission lines to meet future energy demands will be necessary for all considered energy system scenarios of this study. Accompanying new energy capacity will provide enough energy supply for the demand for all feasible energy system scenarios. However, execution of the integrated energy scenario is the greatest energy savings that will permit existing and planned energy capacity to improve the city's future demands. Therefore, the paramount alternatives that will elude energy shortage are through the combination of new supply capacity with integrated energy scenario (all ECDM technologies measures or all energy system scenarios).

Due to rising electric energy demands, a strategy for new extra energy supply from 2025 to 2050 is required for both the baseline and energy scenarios to meet the energy demands. In 2050, the additional new EES development required to meet the demand for integrated energy scenario will be very less against the baseline energy scenario, which is about 68 PJ, whereas for the baseline is about 164 PJ. This indicates that prompting of integrated energy scenario will be more viable to reduce the enactment of new additional energy supply development. Therefore, to provide less new energy supply by 2050, the utility has to plan the enlargement of transmission and distribution lines in and around the city with engaging all ECDM measures as integrated energy scenario.

Scenario-based water–energy nexus

The water system relies on energy (main electricity as source). As water–energy nexus is approached from the energy intensity or energy embeddedness of water flows in the urban water cycle, this study used an alternative energy intensity scenario based on baseline average annual energy intensity of water provision and wastewater treatment, which are calculated by the energy intensity method. Therefore, ensuring scenarios of energy intensity inputs for water provision and wastewater treatment, the model has estimated the long-term energy demand of the urban water system. The model output of energy demand for water is evaluated by conjoining energy intensity scenarios with water system scenarios for water supply and wastewater treatment system. However, to show the significance of energy demand for water system, only the baseline and integrated water scenario are considered for analysis.

Water–energy nexus scenario for baseline water scenario

  • 1.

    Water and energy

The WEF nexus model estimates the energy demand of the water supply system using inputs of baseline water scenario and energy intensity scenarios of water supply such as baseline energy intensity scenario (BEIS), high energy intensity scenario (HEIS) and low energy intensity scenario (LEIS). Throughout the modeling period, the total energy demand of water will increase with an increase in water system of baseline scenario for BEIS, HEIS and LEIS, which are given in Figure 16. Under the baseline water scenario, the energy demand of water supply will drop significantly in 2030 and 2050 for LEIS by 19% (1.56 kWh/m3) and 40% (1.14 kWh/m3), whereas for HEIS will increase by 23% (2.34 kWh/m3) and 66% (3.15 kWh/m3), respectively, from the BEIS (1.9 kWh/m3).

Fig. 16

Energy use for water supply system under the baseline water scenario.

Fig. 16

Energy use for water supply system under the baseline water scenario.

Close modal

For the baseline water scenario, water supply is a challenge to meet the future demand because most of the focus can be on conventional water systems. These systems can be energy-intensive if water is pumped over long distances. Non-conventional (decentralized) water sources such as stormwater and others may fulfill water demand. Accordingly, the baseline water scenario of HEIS, the future energy demand of water supply system is high against other considered energy intensity scenarios, which are indicated in Figure 17. This can be due to a gap for water management integrated with energy-inefficient water infrastructure. Therefore, in the case of the baseline water scenario for considered energy intensity scenarios of water supply, the future energy demand of water supply can be reduced when energy-efficient water technologies are combined with poor water management.

  • 2.

    Wastewater and energy

Fig. 17

Energy demand for WWT under a water system of baseline scenario.

Fig. 17

Energy demand for WWT under a water system of baseline scenario.

Close modal

The nexus perspective of energy use for wastewater treatment is determined by the energy intensity of wastewater treatment and wastewater potential. The energy use of wastewater treatment (WWT) are estimated from the model, which provides that the annual energy demand of WWT in 2050 will be 2.8, 4.6 and 1.7 PJ for BEIS, HEIS and LEIS, respectively, for wastewater potential of 1280 MCM. These indicate that the energy demand of WWT under the water system of the baseline scenario is greater for HEIS against the BEIS and LEIS (Figure 17). The energy demand of WWT by 2050 for HEIS of this study is within range of regional projection of WWT energy demand, which is from 4.3 to 20.8 PJ (Goldstein & Smith, 2002).

Furthermore, about 28% of the wastewater generated was treated using existing WWTPs. A substantial increase will expect in the generating wastewater volume due to increases in water demand. There are 14 WWTPs, which exist and planned with the annual total treatment capacity of 41 MCM. These 14 WWTPs are not enough to treat the annual volume of 1280 MCM wastewater generated without urban water management. This indicates about 3.2% of the wastewater generated by 2050 will treat using 14 WWTPs. Even if existing plans for the expansion of WWT are realized, there will not be enough capacity for full treatment. Therefore, plans for the new expansion of wastewater treatment plants in addition to 14 WWTPs are realized to achieve enough capacity for the full treatment of future wastewater potential. However, energy recovery from wastewater systems is a basic alternative to treat wastewater, rather than expansion of WWTP. WWTPs can be used for energy recovery through micro hydropower based on their respective head and flow (Akelile & Geremew, 2019) and estimated potential energy capacity of 129 to 4,635 GJ (cover from 1.7 to 58.8% of the energy demand of WWTPs).

Water–energy nexus scenario under integrated water scenario

  • 1.

    Water and energy

The model estimated energy demand for the water supply system from the inputs of integrated water scenario and feasible energy intensity scenarios of potable water supply. The water–energy nexus from supply-driven perspective result showed that integrated water scenario for LEIS will provide the best optimal energy use for water supply against other considered feasible energy intensity scenarios. Under integrated water scenario, energy use will drop significantly in 2050 for LEIS and HEIS by 71 and 20%, respectively, from baseline water scenario of BEIS. The energy demand will drop significantly under integrated water scenario of LEIS by 18% (2030) and 40% (2050), whereas HEIS will increase by 23% (2030) and 65% (2050) from the BEIS. Overall, the minimum energy will achieve under integrated water scenario of LEIS, indicating that energy use for water supply will be 3.10 and 3.15 PJ in 2030 and 2050, respectively, which are indicated in Figure 18.

Fig. 18

Energy use for water supply under the integrated water scenario.

Fig. 18

Energy use for water supply under the integrated water scenario.

Close modal

In the case of integrated water scenario, it is expected that there will be less total energy demand for the water supply against the baseline water scenario. Hence, as potable water is reduced in all water use strategies, it is expected that there will be less total energy use for the water supply system. Moreover, the WCDM technologies measure and energy-efficient water technologies measure will attain integrated water scenario of LEIS, which are used to significantly reduce and save energy use of the water supply system. The LEIS can reduce the energy demand through energy efficiency (installation of high-efficiency pumps in all of the water infrastructure and other efficient appliances). In addition, integrated water scenario leads to lower energy across the water supply chain because less water needs to be treated, distributed, transported and less wastewater needs to be collected and released, resulting in energy savings for water service utilities. For example, stormwater harvesting as one component of integrated water scenario can be used for toilets, clothes washing, cleaning the floor and bathing, selection decreases the pressure for high-quality potable water requirements, leading to a reduction in related energy consumption. Similarly, water loss reduction as a component of integrated water scenario will reduce from the current of 37% to about 20% by 2050 during water supply with energy saving almost 17% of energy consumption. The analysis of water–energy nexus consent that establishing all feasible WCDM program as a water system of integrated scenario and installing energy-efficient pump to attain LEIS will significantly reduce the energy use of water supply system (extraction, conveyance, distribution and treatment) in Addis Ababa city.

  • 2.

    Wastewater and energy

The application of various water scenarios has effects on the energy required for wastewater treatment. For instance, wastewater potential will decrease under a water system of integrated scenario against the baseline scenario, which is 615 MCM by 2050. More specifically, all water use strategies would lead to saving potable water although it has relatively similar proportions in wastewater reuse strategies. The reduction in water demand will yield a reduction in WWT and its energy requirements. A water reduction in the case of IS will provide significant energy savings from WWT for different energy intensity scenarios of WWT. The energy demand to treat generated wastewater for integrated water scenario and energy intensity scenarios is shown in Figure 19.

Fig. 19

Energy demand of WWT for integrated water scenario.

Fig. 19

Energy demand of WWT for integrated water scenario.

Close modal

In wastewater treatment (WWT), under integrated water scenario of BEIS, HEIS and LEIS, the potential of energy savings is expected to be 1.4, 2.4 and 0.9 PJ, respectively, from baseline water scenario by 2050. Integrated water scenario of LEIS, BEIS and HEIS will save the energy of 2.0, 1.5 and 0.6 PJ, respectively, from the baseline water scenario of BEIS in 2050. In addition, integrated water scenario of LEIS will reduce energy needs of WWT by 14 and 38% from BEIS by 2030 and 2050, respectively, which will expect widely contribute to reducing energy demand. Integrated water use scenario will cover 52% of baseline water demand in 2050. For respective year, integrated water scenario of LEIS, HEIS and BEIS will reduce energy demand for WWT by 71, 50 and 20%, respectively, from the baseline water scenario. These values indicate that the energy demand for WWT will significantly decrease under integrated water scenario of LEIS against baseline water scenario of BEIS, HEIS and LEIS. In addition, water efficiency initiatives will offer opportunities for delivering significant energy saving for WWT.

Scenario-based water–food nexus

Addis Ababa supplies more of the food it consumes from its surrounding. It is also important to increase urban production through a comprehensive understanding of the interconnected urban water–food/land systems. To understand this interconnection, three scenarios of urban food production system are considered in the model to enhance crop production through the urban water–food/land nexus approach, and model output for each scenario is interpreted in this subsection.

Production and land

Growth in urban food (potato, onion, tomato and cabbage) production to improve Addis Ababa city's food security demand for a projected population of about 18.8 million by 2050 can come from enhancing inputs of production. The enhancing inputs may include increasing productivity and expanding land for production. However, some of the inputs can have a constraint. For instance, the amount of suitable urban land of Addis Ababa city to bring production is quite a constraint. Considering constraints, three scenarios of urban production system are considered in the model. The model gives the optimal land and production by minimizing the objective function subject to the constraint. The three scenarios are the baseline scenario; scenario 1 and scenario 2, which are representing reference, low and high enhancement of these input values of cropland, water use efficiency and crop yield, respectively. For baseline scenario, the yield and land input parameter is minimal, which affords a smaller quantity of product with a greater quantity of imports from out of the urban area. Whereas a high enhancement of crop yield and land under scenario 2 will achieve a greater production in 2050.

To minimize the total system cost over the modeled time horizon, it can be observed that areas under cultivation for all crops show the existing trends, except for tomato under scenario 1 and scenario 2. The cropland allocated to tomato cultivation is further increased. This is likely due to competition with other crops such as potato, onion and cabbage. The optimal urban land required for production is given in Figure 20.

Fig. 20

Optimal land capacity for scenario 1 (left) and scenario 2 (right).

Fig. 20

Optimal land capacity for scenario 1 (left) and scenario 2 (right).

Close modal

From Figure 20, the total cropland will expand from the baseline to increase the production that will improve a rapidly growing crop demand. Total cropland increases from 90 ha in 2016 to 200 ha for scenario 1 and increases to 300 ha for scenario 2 in 2030 and 2050. However, there will be urban crop production deficit for these scenarios of crop production by 2030 and 2050 with bringing additional land starting from the base year of 2016. The feasible amount of total urban food production for scenario 2 is 77,300 tons (1.4% potato, 0.2% onion, 97% tomato and 1.2% cabbage) and 203,000 tons (1.1% potato, 0.1% onion, 98% tomato and 0.6% cabbage) in 2030 and 2050, respectively, which fails to satisfy the socio-economic demands for food efficiently. However, this scenario provides a better optimal urban food production (Table 10).

Table 10

Optimal production (103 tons) capacity output of the model.

ScenarioFood cropYear
20162020202520302035204020452050
Scenario 1 Potato 0.69 0.74 0.80 0.87 0.95 1.03 1.12 1.22 
Onion 0.08 0.08 0.09 0.11 0.12 0.14 0.16 0.18 
Tomato 0.13 0.14 29.05 31.60 34.38 37.41 40.70 44.27 
Cabbage 0.77 0.79 0.81 0.83 0.85 0.87 0.89 0.92 
Total 1.67 1.75 30.75 33.41 36.30 39.45 42.87 46.59 
Scenario 2 Potato 0.69 0.79 0.94 1.11 1.32 1.57 1.87 2.22 
Onion 0.08 0.09 0.10 0.12 0.14 0.17 0.20 0.24 
Tomato 0.13 0.16 58.89 75.16 95.92 122.42 156.25 199.42 
Cabbage 0.77 0.81 0.85 0.90 0.95 1.01 1.06 1.12 
Total 1.67 1.84 60.78 77.30 98.34 125.17 159.38 202.99 
ScenarioFood cropYear
20162020202520302035204020452050
Scenario 1 Potato 0.69 0.74 0.80 0.87 0.95 1.03 1.12 1.22 
Onion 0.08 0.08 0.09 0.11 0.12 0.14 0.16 0.18 
Tomato 0.13 0.14 29.05 31.60 34.38 37.41 40.70 44.27 
Cabbage 0.77 0.79 0.81 0.83 0.85 0.87 0.89 0.92 
Total 1.67 1.75 30.75 33.41 36.30 39.45 42.87 46.59 
Scenario 2 Potato 0.69 0.79 0.94 1.11 1.32 1.57 1.87 2.22 
Onion 0.08 0.09 0.10 0.12 0.14 0.17 0.20 0.24 
Tomato 0.13 0.16 58.89 75.16 95.92 122.42 156.25 199.42 
Cabbage 0.77 0.81 0.85 0.90 0.95 1.01 1.06 1.12 
Total 1.67 1.84 60.78 77.30 98.34 125.17 159.38 202.99 

In 2030 and 2050, it is determined that the production of total vegetable food within urban boundaries will meet only 0.18 and 0.024% of total food demand for baseline scenario, respectively. Correspondingly, the production will meet 3.8 and 0.7%, and 8.8 and 2.9% of total food demand for scenario 1 and scenario 2, respectively.

Water and water footprint

Water plays an important role in food crop production. Quantifying the water–food nexus requires due to consideration of the embedded water content of urban agriculture products. Accordingly, the outcomes of this study explained the situation of cropland use under different scenarios, which has effect on the total annual volume of water demand and water footprint. As indicated in Figure 21(b), the total annual water demand for total crop production under scenario 2 and scenario 1 will be about 31 and 22 times higher than the baseline water demand in 2050, respectively. Furthermore, the increase in agricultural land results in growing water demands, which increases from 0.8 MCM in 2016 to 17 BCM in 2050 for scenario 1, whereas it increases to 24 BCM for scenario 2. These values of water demand difference in each scenario are due to the variation of cultivation land. These values of water demand may represent a challenge to Addis Ababa city with limited water resources. However, treated wastewater is an alternative resource for urban irrigation. For instance, the wastewater potential of Addis Ababa city in 2030 will be around 450 and 330 MCM for baseline and integrated water scenarios, respectively, and these values will cover the total annual water demand of crop production.

Fig. 21

(a) Yield of crops, (b) total water demand of crops, (c) total water demand per total area of land and (d) water footprint of crops.

Fig. 21

(a) Yield of crops, (b) total water demand of crops, (c) total water demand per total area of land and (d) water footprint of crops.

Close modal

The water footprint is combined with crop production to solve constraints of urban water, land and food crop security. Crop yield and water required for crop production are considered as a factor affecting crop water footprint (m3/ton). The increase of yield will often improve water footprint because as yields increase the water consumption can remain equal. The temporal distribution of the total water footprint per unit mass of four crops varies for scenario 1 and scenario 2. Moreover, scenarios output such as total volume of water, yield, water per unit area and water footprint of four crops is given in Figure 21.

The high value of temporal total water footprint is obtained in scenario 1, while the low value is obtained in scenario 2. A comprehensive comparison shows that the total water footprint of all four crops in scenario 2 is low, indicating high water use and high yield. The scenarios difference of total water footprint of four crops is caused by the differences of water use and yield. The average water footprint per ton of crop (potato, onion, cabbage and tomato) will decreases from 398 m3/ton (2025) to 117 m3/ton (2050) for scenario 2, whereas for scenario 1 decrease from 573 m3/ton to 374 m3/ton (Figure 21(d)). For scenario 2, the results also show that the average yield of all food crop will increase by 3.5 times of the base year in 2050, which results in the average water footprint will decrease by 0.25 times of the base year. From Figure 21(d), the minimum water footprint is the foremost to optimize the crop production and water, which is achieved by scenario 2. Therefore, scenario 2 (raising crop yield, improving water efficiency and increasing cropland) is an efficient approach to reduce water footprint in urban irrigation agricultural systems.

Energy–food import/supply

Food import

The output of urban food crops (potato, onion, tomato and cabbage) will enable to meet the growing urban demand, which will alleviate by expanding food imports out of urban areas, as well as help to reduce the pressure from urban agricultural sectors on water and energy (for wastewater treatment). High levels of Addis Ababa city food demand are exacerbated due to rapid urbanization, leading to a high diversion of resources from food production in rural areas. The model optimizes the supply by considering production within urban areas and import from out of urban areas for four food crops. The amount of each food crop import for each scenario of crop production is shown in Table 11.

Table 11

Estimated food crop (103 tons) import from out of the urban area.

ScenarioImportFoodYear
202520302035204020452050
Baseline IMPCRP1 Potato 147.9 251.2 424.4 714.4 1,199.2 2,009.3 
IMPCRP2 Onion 247.6 419.7 708.4 1,191.7 1,999.7 3,349.9 
IMPCRP3 Tomato 82.4 139.8 236.0 397.1 666.5 1,116.5 
IMPCRP4 Cabbage 40.6 69.3 117.5 198.1 333.0 558.3 
Total 518.6 880.0 1,486.2 2,501.3 4,198.4 7,034.0  
Scenario 1 IMPCRP1 Potato 147.8 251.0 424.1 714.0 1,198.8 2,008.7 
IMPCRP2 Onion 247.6 419.7 708.3 1,191.6 1,999.7 3,349.8 
IMPCRP3 Tomato 53.5 108.3 201.8 359.9 625.9 1,072.4 
IMPCRP4 Cabbage 40.5 69.2 117.4 198.0 332.8 558.2 
Total 489.5 848.2 1,451.6 2,463.5 4,157.2 6,989.1  
Scenario 2 IMPCRP1 Potato 147.7 250.8 423.7 713.5 1,198.0 2,007.7 
IMPCRP2 Onion 247.6 419.7 708.3 1,191.6 1,999.6 3,349.7 
IMPCRP3 Tomato 23.7 64.8 140.2 274.8 510.4 917.2 
IMPCRP4 Cabbage 40.5 69.2 117.3 197.9 332.7 558.0 
Total 459.5 804.4 1,389.5 2,377.8 4,040.7 6,832.7  
ScenarioImportFoodYear
202520302035204020452050
Baseline IMPCRP1 Potato 147.9 251.2 424.4 714.4 1,199.2 2,009.3 
IMPCRP2 Onion 247.6 419.7 708.4 1,191.7 1,999.7 3,349.9 
IMPCRP3 Tomato 82.4 139.8 236.0 397.1 666.5 1,116.5 
IMPCRP4 Cabbage 40.6 69.3 117.5 198.1 333.0 558.3 
Total 518.6 880.0 1,486.2 2,501.3 4,198.4 7,034.0  
Scenario 1 IMPCRP1 Potato 147.8 251.0 424.1 714.0 1,198.8 2,008.7 
IMPCRP2 Onion 247.6 419.7 708.3 1,191.6 1,999.7 3,349.8 
IMPCRP3 Tomato 53.5 108.3 201.8 359.9 625.9 1,072.4 
IMPCRP4 Cabbage 40.5 69.2 117.4 198.0 332.8 558.2 
Total 489.5 848.2 1,451.6 2,463.5 4,157.2 6,989.1  
Scenario 2 IMPCRP1 Potato 147.7 250.8 423.7 713.5 1,198.0 2,007.7 
IMPCRP2 Onion 247.6 419.7 708.3 1,191.6 1,999.6 3,349.7 
IMPCRP3 Tomato 23.7 64.8 140.2 274.8 510.4 917.2 
IMPCRP4 Cabbage 40.5 69.2 117.3 197.9 332.7 558.0 
Total 459.5 804.4 1,389.5 2,377.8 4,040.7 6,832.7  

Throughout the modeling period, a high shortfall of total vegetable urban food production will occur for all considered food production system scenarios indicating that external import from out of the urban area will be required to meet the future demand. To meet the urban food demand of Addis Ababa city by 2050, almost 97% of total vegetable/root crop will need to supply from out of city boundary. The paramount optimal import of total crop will achieve by scenario 2. This indicates that scenario 2 (high improved yield, expansion of land and efficient use of water) will decrease the import of food crop from outside of the city boundary against other scenarios used. To satisfy demand an annual average of 2.65 million tons has to import from 2025 to 2050 in addition to total internal food crop production under scenario 2. This result indicates that the annual production of 2.65 million tons will require an average annual land area of about 70 kha. However, producing 2.65 million tons within Addis Ababa city on a land of 70 kha may be difficult due to urbanization continues. Therefore, Addis Ababa city has to put a great emphasis on onion, tomato, cabbage and potato import. Furthermore, cereal food demand of Addis Ababa city is relying on food imports from nearby or neighboring suppliers to meet the demand. The amount of cereal food import out of city boundary was estimated from the model. The result shows the total cereal crop import that includes teff (IMPCRP5), wheat (IMPCRP6), maize (IMPCRP7) and barley (IMPCRP8) from out of urban will be about 900 and 5,000 × 103 tons by 2030 and 2050, respectively, which are equal to the demand for respective years.

Energy import

Most of the fuel energy in the country comes from imports from neighboring countries and it is important to recognize the amount of import needed for the demand. The amount of petroleum import required to meet the long-term demand is found from the optimization model, which is represented as total import diesel (industrial and transport sector), gasoline (transport sector) and kerosene (residential sector). The total energy (kerosene, gasoline and diesel) import to meet the demand by 2030 and 2050 is about 56.93 and 174.25 PJ, respectively, which are equal to the total demand for respective years.

Integrated WEF system capacity option

The rapid development of urbanization in Addis Ababa city has caused a string of problems such as population congestion and WEF resource shortages. Therefore, the city needs to seek a sustainable development pattern to save resources for its future development. As the basic components for urban development, the supply and demand of WEF becomes the most significant, and they have interdependences in production, consumption and management that could be termed as WEF nexus. The main motive behind developing the WEF nexus model is to highlight external and internal WEF resources management, efficiency, technology deployment and enhancing crop productivity within the city by identifying and determining the optimal of WEF resources, which result in sufficient resources and sustainable development of the city. The proposed WEF nexus model tool is decent enough to address individual and interconnected WEF system within the city from a realistic point of view. Correspondingly, it evaluates the interdependencies and management of the WEF resource as an integrated process. Techno-economic parameters, resource management measures constraints and their optimality are efficiently quantified over the planning periods. Since the tool proposed for the WEF nexus model is designed for multi-period and multi-dimensional scenarios, has a high tendency to grasp the temporary characteristic features of the resources management measures and capabilities to create the cost-effective policies and opportunities for optimizing individual and interconnected WEF system.

This study considered resource management of urban WEF resources based on water–energy conservation and demand management measures, or technology input, enhancing crop productivity and energy-efficient water use technologies as an integrated process. The expansion of the integrated WEF nexus shows the contribution of resource conservation and demand management to deliver clean WEF demand satisfaction. In addition, the amount of WEF resource management has to represent closely the Addis Ababa city government's long-term target.

It is also worth mentioning the energy requirements for urban water production and water requirement for urban food production system in Addis Ababa city. In 2050 for baseline energy intensity scenarios, the overall energy requirements for urban water system (e.g., extraction, water distribution, treatment, transmission and wastewater treatment) is around 13.8 and 6.6 PJ for the BLS and IS, respectively. At a system level, under integrated water scenario, the annual water savings for 2050 will be nearly 0.83 BCM; and when this water savings convert to energy savings using the three energy intensity scenario estimates, the total energy savings will be 2.9 PJ (LEIS), 7.2 PJ (BEIS) and 11.9 PJ (HEIS). In other words, when the demand for water decreases due to integrated water scenario, it will put great pressure to decrease the demand of energy for urban water systems.

Another observation is related to the expansion of the water and energy resources to meet the required water–energy demand. The additional new water supply capacity of 1.04 and 0.21 BCM will be needed for BLS and IS by 2050, respectively. Consequently, the expansion of new EES of 164.5 and 68.8 PJ are needed by 2050 for BLS and IS, respectively. The integrated energy scenario causes EES expansion to double, whereas a baseline scenario is four times by 2050. The optimal sum of food crop (potato, onion, tomato and cabbage) production and import out of the urban area is 0.88 and 7.04 MT by 2030 and 2050, respectively, for each scenario of production, which are equivalent to food demands. However, the total cost of four crops production and import out of the urban area for each scenario is different, whereas for each scenario, the sum of production and import out of the urban area are the same. As cereal food crop (barley, maize, wheat and teff) demands increase, the optimized food supply out of the urban area during the 2030 and 2050 planning periods is 0.9 and 5 MT, respectively, which slightly meets food demands. For respective years, the total petroleum import is 56.9 and 174.3 PJ, which also encounters the demand. Moreover, Figure 22 indicates the capacity expansion option of the WEF system for the 2030 and 2050 planning horizon.

Fig. 22

Water, energy and food/land system model output for the baseline and integrated WEF nexus scenarios.

Fig. 22

Water, energy and food/land system model output for the baseline and integrated WEF nexus scenarios.

Close modal

Integrated WEF system nexus such as integrated water scenario, integrated energy scenario, water–energy nexus for LEIS and urban food production system under scenario 2 (improving crop yield productivity, irrigation water use efficiency and cropland) collectively plays a critical role in reducing the limitation of WEF supply and improving sustainable urban WEF utilization.

Model evaluation

Overall performance

The user is able to create different WEF system scenario depending on urban resource management measures such as efficient technology input, conservation and rehabilitation, water supply enhancement, renewable solar PV energy and enhancement of urban food production. Figure 22 presents water–energy system for baseline and integrated scenario, three energy intensity scenarios as indicators of water–energy nexus and three water–food/land nexus scenarios for demonstration and further discussion. These scenarios were created with the aid of WEF nexus model. Figure 22 shows the model results for different proposed WEF system scenarios. For instance, food crop imports (vegetables or roots) are linearly affected by the change in cultivation land and production, which are decreasing as production and land increase. In addition, the energy demand for water system (wastewater treatment and water supply) are linearly affected by change energy intensity and water system scenarios, which is decreasing as energy intensity and water use decrease (e.g., for the integrated water scenario, water supply and demand will decrease against the baseline). However, energy requirements are more sensitive to water system scenarios than the energy intensity scenario.

Similarly, the relationship exists concerning cost assessment, accounting for the cost of internal food crop production and import out of the urban area, water–energy supply, as well as demand and supply management. As urban food production system decreases, the total costs of WEF system increase. This comes because the cost of imports, in almost every case, is higher than the cost of local production for the specific products. Insufficient internal food production capacity will lead to high import costs from out of urban area of Addis Ababa city, which affects the total cost of WEF system. The cost is also considered as directly affected by the change in food production values. Consequently, as water and energy system decreases under an integrated scenario, the total cost of the integrated WEF nexus scenario also decreases against the baseline scenario. This comes because the cost of developing new additional water and energy supply to meet the demand, in almost every case, is lower under integrated water scenario and integrated energy scenario against the baseline scenario.

Sensitivity analysis

Further evaluation of the WEF nexus model performance is accomplished through sensitivity analysis of input parameters. The analyses of the implemented WEF system nexus model and the obtained outcomes are performed based on the various aspects. However, to identify the most sensitive parameters in the WEF system nexus model, a standardized regression coefficient can be employed as a sensitivity analysis technique, which is used as a form of nominal range sensitivity. The choice of water–energy conservation and demand management option, energy intensity, water footprint, urban cropland and yield determines how much land, water and cost are needed for urban food production system, energy is needed for water, and integrated WEF nexus capacity and cost are needed to improve resources access and sustainability. After determining how much WEF is needed for the different scenarios, the WEF supply capacity has to be chosen. Based on the selected capacity of water input, the energy needed for providing that water be quantified.

The change in energy intensity, water footprint, energy system, water system, cropland, crop yield and the unit cost of the WEF system (supply, production and import) affect the quantity of model output (e.g., water, energy, land, food import and production, cost). For the baseline WEF nexus scenario, the cost of water supply, energy supply and food crop production are the most vulnerable parameters in the model, which greatly increase against the integrated WEF nexus scenario. In the case of domestic food crop production, there is an inverse relationship between cultivation land and import illustrated through negative sensitivity values that exist among them. When crop cultivation land increases, the cumulative costs of imports decrease. This shows that the change in land affects crop production and import costs, thus validating the performance of the tool in that regard. As the cultivation land (cropland) increases, there is a linear trend in resource requirements due to the linearity of the model relations. When food crop production decreases, the total costs to meet the demand increase. This comes because the cost of imports, in almost every case, is higher than the cost of production for the specific products. The model also identifies the least cost balance associated with developing new water–energy supply capacity and efficient water–energy use technologies. Moreover, integrated water scenario and integrated energy scenarios that are developed based on conservation and demand management technologies measure are the least cost relative to constructing a new water–energy supply, thus agreeing with the performance of the model. Figure 23 shows sensitivity variables of WEF system model output.

Fig. 23

Effect of sensitivity variable on the WEF model.

Fig. 23

Effect of sensitivity variable on the WEF model.

Close modal

The resource management are explained as both demand-side and supply-side management for water and energy. From Figure 23, among the water and energy system scenarios, rehabilitation and water conservation scenarios (RWCS) and demand-side energy conservation scenarios (ECS) are the most influential of integrated water system and integrated energy system output, respectively. In addition, the total vegetable yield will greatly affect the total production. Moreover, these scenarios have more effect on the output of capacity of integrated WEF nexus.

Policy perspective of integrated WEF system

Based on this study, several policies of recommendations can be made aimed at promoting long-term WEF security for sustainable development in Addis Ababa city. This section aims at recommending policy strategies for the urban integrated WEF nexus scenario, based on resource management measure within Addis Ababa city and import (or supply) used in modeling, and results. In this study, it is established that high improvement of crop yield, cropland and urban irrigation water use efficiency for food production under scenario 2, integrated water scenario and integrated energy scenarios combined with LEIS should go together as most prominent of urban integrated WEF system nexus for sustainable development in Addis Ababa city. Furthermore, the future policies and measures of integrated WEF nexus scenario and import/supply out of the city to meet the future demand are discussed in the following:

  • Resource management from insight of water and energy saving has to be strengthened through conservation and demand management practices in the different end-use sectors. Acquisition of conservation and efficiency has to be indispensable to improve supply shortages.

  • The study suggests that promoting universal efficient water and energy use technologies has to promote (e.g., efficient water retrofitting and energy appliances) to minimize the need for water and energy supply and minimize the cost of integrated WEF system nexus.

  • Alongside the energy supply expansion, more attention has to be given to energy conservation and demand management measure such as efficient compact lighting, diversified rooftop solar PV energy, induction-baking plate and stove, efficient industrial motor.

  • The study suggests that supply enhancement measures focus on stormwater harvesting, as a means to reduce the reliance on mains water supply and energy use for water supply. In addition, the government has to set stringent criteria that ensure the most efficient pump that is suitable for the application is installed.

  • The study suggests that water systems offer a better solution to supplying water in an energy-efficient manner, compared to energy-intensive large-scale centralized systems.

  • Water and energy utilities have a significant role to play in reducing their energy and water consumption, respectively. As identified in this study, water and energy efficiency offer significant advantages and can readily offset the requirement for new water supply capacity in the future. It is important, however, that the measures target those sectors that exhibit the highest water and energy intensities. The measures would also assist in reducing the embedded water and energy content of other sectors that rely heavily on outputs from these water and energy-intensive sectors.

  • Under integrated water scenario, small boost of new water supply expansion has to be implemented by AAWSA in order to meet the increasing demand against the baseline scenario, whereas for integrated energy scenario, the small new supply expansion has to be considered by EEU.

  • The city government has to implement policies and technical support to address the enhancement of urban food production system and has to invest in infrastructure.

  • The reuse of wastewater in urban and peri-urban agriculture has to be given attention to reducing pressing water stress that exists in the city since freshwater is saved when residual water is reused. As well as wastewater has to treat with energy demanding to improve vegetable healthy risk and increase the yield.

  • To meet future food demand, great emphasis is being put on encouraging domestic crop production and import. Domestic crop production is based on enhancing crop productivity, efficient use of irrigation water, improvement of cropland and use of treated wastewater for irrigation. Noticeably, the keywords of rural areas, food supply chains, etc., also recognized on food systems.

The WEF resource management pursuit advances integrated WEF system nexus modeling to provide important strategies for urban sustainable development. The increasing pressure on WEF resources illustrates the urgent need to maximize the benefits for all users as part of the appropriate efficient use of technologies for resources management at the city scale within individual WEF system and nexus perceptions. This study understands a scenario-based long-term possible means of resource access and sustainability in terms of meeting the WEF needs of Addis Ababa city through the nexus approach. In this study, each WEF element describes the resource and demand projections until the year 2050. The WEF nexus model combines these resources and provides a dynamic representation of resources activities from management, import, production and supply to final services. The WEF system nexus model considers strategic decisions of WEF resources through different variables embedded in the model. A dynamic and versatile WEF system nexus model is developed under the MoManI interface linear programming model. The WEF nexus model provides a robust decision analysis support system for an optimal WEF resources supply (production) and management of resources. The MoManI (open-source modeling) is a linear programming model that is used to support the development of more comprehensive and transparent long-term WEF resources scenarios planning. In this paper, different feasible WEF system scenarios across nexus, individual system and integrated WEF viewpoints were developed and explored, taking into account different strategies from 2020 to 2050. The analysis was carried out to minimize the total costs to examine WEF cost impacts associated with the constraints and give a decision on the best optimal urban WEF system scenarios for future sustainable development. The results of the observed or base period year serve as verification of the functionality of the WEF nexus model. This study reviewed the city's current WEF status, existing, planned WEF supply capacity, and found out that the existing WEF supply or production settings are short-term perspectives, fragmented and internally inconsistent. Therefore, these are incapable of addressing the long-term WEF supply challenges facing Addis Ababa city. To address this shortcoming and to provide meaningful insight, this study has developed and filled the gap by analytical integrated WEF nexus framework as a scenario-based approach and used a single modeling tool that integrates all WEF system components. In addition, tactical decisions involve intra-system of enhancing food production, all water conservation and demand management measure, all energy conservation and demand management measures within the city and supply or import of WEF out of the city to ensure the WEF demand of Addis Ababa city over the long-term. The case study is solved for the WEF demand. The results showed that the model is sensitive concerning cost change and capacity constraints. In addition, the model showed that the WEF should be analyzed simultaneously to spot the WEF system dependence on each other. The optimal cost objectives are determined using the discussed approaches and the proposed MoManI tool. Strategies and policies for the optimal finding of WEF supplies are addressed for fulfilling the expected demand of Addis Ababa city. The overall result of WEF nexus modeling indicates based on several feasible scenarios, the integrated WEF nexus scenario that includes intra-system integrated water scenario, integrated energy scenario, LEIS and urban food production system under scenario 2 is alternative pathways for a long-term urban WEF sustainable development against baseline scenario. Therefore, this study understands the importance of WEF nexus modeling approach to explore integrated WEF nexus scenario for sustainable WEF development at the urban level.

This study received no external funding.

The authors declare no conflict of interest.

The authors would like to thank Addis Ababa Water and Sewerage Authority (AAWSA), Central Statistical Agency (CSA), Ethiopian Electric Utility (EEU) and Ethiopian Petroleum Enterprise (EPE) for providing data.

All relevant data are included in the paper or its Supplementary Information.

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