The Interplay Between Climate and Urban Expansion on Building Energy Demand in Morocco

Abstract

Understanding building energy demand is critical for addressing climate uncertainty challenges and ensuring sustainable urban growth. This study develops a building energy demand (BED) model to explore how climate variation and urban expansion affect residential and commercial space heating and cooling demands in Morocco for three scenarios, namely, 2005, 2018, and 2018 + 1.5 °C. The results show that coastal cities have lower heating and cooling needs due to the oceanic influence, while interior cities require significantly higher heating demand per-unit-floorspace. Between 2005 and 2018, urban growth increased total heating and cooling demand by 218.8 GWh, particularly in northern and coastal regions, despite per-unit-floorspace reductions in milder climates and improved building efficiency in 2018. Residential heating remains the dominant energy use, though commercial demand is significant in urban centers. Under the 2018 + 1.5 °C hypothetical scenario, heating demand across Morocco declines by 335.8 GWh compared to 2018, with urban areas amplifying this trend. Meanwhile, cooling demand increases slightly by 44.4 GWh, with major cities experiencing relative increases of up to 50%. These findings highlight a trade-off where reduced winter heating needs are partly offset by increased summer cooling demands in densely urbanized areas. The study identifies key urban hotspots for targeted interventions, emphasizing the need for energy-efficient building designs, climate-adaptive urban planning, and resilient energy management strategies to sustainably address shifting seasonal energy patterns.

1. Introduction

With rapid urbanization, Morocco’s building sector is expanding and now contributes to approximately 35% of the country’s total energy consumption [1,2]. A significant portion of investment in Morocco is directed toward housing development for both residential and commercial buildings. Additionally, buildings account for 24% of total CO₂ emissions among all end users [3], highlighting their important role in achieving Morocco’s Nationally Determined Contribution (NDC) goals to reduce greenhouse gas (GHG) emissions by 45.5% compared to the “business as usual” scenario by 2030 [4]. Existing low-carbon strategies, such as energy-efficient construction practices (e.g., thermal insulation), aim to reduce energy consumption and their associated emissions [5,6]. However, temperature variations have a direct effect on building heating and cooling demand, potentially increasing pressure on Morocco’s energy system as climate uncertainty can increase seasonal temperature variations or extreme events (e.g., heat and cold waves) in many places [7,8]. A comprehensive understanding of how urban expansion, building energy efficiency, and climate jointly impact energy demand in buildings is essential to guide strategic planning in the building sector, thereby supporting Morocco’s goals for energy transition to sustainable development.

Heating and cooling demands in residential and commercial buildings are influenced by various factors, including building characteristics (e.g., shell efficiency, internal gains), temperature variability, urbanization typology, socioeconomic conditions, etc. [9,10,11,12]. In the context of Morocco, multiple studies have analyzed the impact of building efficiency standards and codes on overall building energy demand at the local scale (e.g., single building) or large scale (e.g., climate zones) [13,14,15,16,17,18,19]. For example, Romani [13] used regression analysis and dynamic simulations to optimize heating and cooling energy needs for single family houses located in six Moroccan climatic zones. Lachir and Nia [17] studied the impact of urban design and local climate on building energy demand and urban heat island effect across six cities. However, these studies are often limited to specific cities or single buildings and lack of literatures studying the combined influence of climate, urbanization, and building energy efficiency on the distribution of building energy demand across the country [20].

Temperature variation is one of the most critical factors affecting heating and cooling demands [21,22,23,24]. Morocco has experienced varying levels of increase in average annual temperatures historically, depending on the geographical location [25], and is expected to face continued warming in the future [26,27,28,29]. Climate uncertainty also alters intra-annual temperature variation in both timing and intensity (e.g., milder winters or hotter summers). Labriet et al. (2015) [30] conducted a study of climate impact on heating and cooling energy demand across the globe. They found that the dominant role of heating or cooling changes varies by the temperature levels at the global scale, while heating and cooling demands vary significantly depending on the climate conditions and energy system characteristics at the regional scale [30]. Interestingly, although Morocco has diverse climates, the climate variation impact on heating and cooling services across the country (e.g., regions or cities) has not been holistically studied.

Moreover, urbanization, driven by population growth and migration, directly contributes to increasing the overall building energy demand [31] and its uneven spatial distribution results in certain regions facing significantly higher demands than others. Morocco historically has a low installation rate of air conditioning in residential constructions as most households rely on natural ventilation for cooling. However, a recent study shows a rapid increase in installed air conditioners from 2000 to 2013 and its penetration rate in the residential sector is projected to reach 49% by 2030 [32]. Meanwhile, most studies focus on single cities or regions, overlooking the tradeoffs in heating and cooling demand changes across regions. Identifying hotspots of changing building energy demand (increase or decrease) in Morocco’s uncertain climate and urbanization is necessary for effective and sustainable energy system planning.

Our study addresses these gaps by developing a building heating and cooling demand model to assess various impacts at different spatial scales within Morocco: (1) the impact of seasonal climate variation and urbanization; (2) the effect of warmer surface temperatures; and (3) the compounded influence of climate variation, warming, and urban expansion. By analyzing heating and cooling demands from city to regional to national levels, our study provides a novel and comprehensive view of Morocco’s heating and cooling energy portfolio for both commercial and residential buildings under evolving climatic and socioeconomic conditions. Specifically, we evaluated the tradeoffs between building heating and cooling demands under these intersecting conditions to identify energy demand hotspots. This study contributes to the existing literatures by offering insights into strategies for energy security and climate adaptation, while promoting collaborative solutions to sustain Morocco’s energy resilience.

2. Materials and Methods

2.1. Study Region

Morocco is a country in North-West Africa, bordering the Atlantic Ocean to the west and the western part of the Mediterranean Sea in the north (Figure 1). Morocco is a diverse country regarding geography, climate, and levels or urbanization. Large urban environments in a coastal “Mediterranean” climate give way to the sparsely populated Atlas Mountains with high elevation and cold temperatures, as well as hot, arid regions in the Sahara. Additionally, Morocco has been experiencing rapid development and population shift to its urban centers in recent decades, making its landscape an ever changing one.

The diversity and rapid evolution of Moroccan cities gives rise to modeling challenges. In particular, the differences in climate and urbanization throughout the country require finer-resolution modeling to answer pointed questions about specific regions and cities. Building energy demand, as discussed in this paper, is inherently linked to local climate, building construction, and economic factors. Models that operate on a country level scale (or coarser) may not be reasonable to answer questions about any specific Moroccan city, even considerably less are those outside of the populous coastal regions, which are less represented.

2.2. The Building Energy Demand (BED) Model

The goal of the BED model is to provide a grid-cell level spatial distribution of heating and cooling energy demand by modeling individual grid cells with high resolution data.

In this study, estimation of the heating and cooling demand per-unit-floorspace relies on a slightly modified expression as proposed by [33,34]. These expressions (Equations (1) and (2)) aim to capture the interaction between temperature, local building properties, and socio-economic factors on heating and cooling demand, which we will break down below. Figure 2 also provides a conceptual view of the BED model framework, as follows:

(1)$d_H=k_H\left(HDH·\eta ·R-IG\right)\left[1-\exp \left(-{\displaystyle \frac{ln2}{{\mu }_H}}·{\displaystyle \frac{i}{P_H}}\right)\right],$

(2)$d_C=k_C\left(CDH·\eta ·R-IG\right)\left[1-\exp \left(-{\displaystyle \frac{ln2}{{\mu }_C}}·{\displaystyle \frac{i}{P_C}}\right)\right],$

In this expression, $k_H$ and $k_C$ are unitless calibration terms. Calibration will be discussed further later. HDH and CDH are heating and cooling degree-hours, respectively. This is slightly different from reference [34], in which the authors use heating and cooling degree-days. Finer temporal resolution better captures short-term diurnal temperature variations between day and night, including peak energy demand driven by hourly extreme temperatures. In contrast, heating and cooling degree-days reflect only average daily conditions, potentially underestimating energy demand by failing to account for these fluctuations. However, this alteration in reference [34] was made due to data availability for this study and the BED model could be made to use either degree-days or degree-hours. η is the thermal conductance (or U-factor) of a building estimated in each grid cell. R is a ratio of a building’s exposed surface area, i.e., the combined area of all roofs and exposed walls, to the building’s floor space. IG is the internal gain, heat generated inside a building by people, appliances or other sources that are not dedicated heating equipment. The discussed parameters so far form the term inside the parentheses, which represent the consumption of services required to maintain a comfort indoor temperature year-round.

The next bracketed term represents the fraction of demand that is met, capturing the effects of purchasing power through the relationship between price and income. μ is another unitless calibration term which captures the different preferences and behavior present in different sectors and regions under the same economic and climatic conditions. $i$ is the per-capita income. $P_H$ and $P_C$ denote the average price for heating and cooling services weighted by the demand, $d_{H,j}$ and $d_{C,j}$:

(3)$P_H={\displaystyle \frac{{\sum }_j{P}_{H,j}·d_{H,j}}{d_H}},$

(4)$P_C={\displaystyle \frac{{\sum }_j{P}_{C,j}·d_{C,j}}{d_C}},$

where each $j$ is a different heating or cooling technology, and each individual technology price includes initial costs, operating costs, and efficiency and fuel use. $d_H$ and $d_C$ denote the total heating and cooling demand from all technologies considered, respectively.

2.3. Calibration

To estimate building heating and cooling demand, the model run requires two years of input: a calibration year, and a year for estimation. In this study, we used data from 2010 for calibration given the availability of the data and generated estimates (e.g., hindcast) for both 2005 and 2018 that are apart for 13 years, to better show the urban expansion effect. The model is calibrated against the observed heating and cooling service demand in 2010 to determine the parameters k and μ. These calibrated parameters, along with the other input data described in Table 1 (excluding $d_H \mathrm{a}\mathrm{n}\mathrm{d} d_C$), are then used to estimate the heating and cooling demand for 2005 and 2018.

To calibrate k, we consider a case when income is arbitrarily high, implying that society is sufficiently wealthy to meet all energy demands. This assumption leads to the final bracketed term, defined as the fraction of demand that is met, being equal to 1 as per-capita income i approaches the upper limit and represents a condition where all demand is satiated. Given the known demand in the base year and an assumption about the level of satiation in the base year, we can calibrate k as the only unknown parameter. The level of satiation is a hyper parameter, where in this study, we assumed that satiation levels were 10% higher than the observed demand in Morocco in 2010, following the assumption used in [35]. It is worth noting that the calibration parameter k represents the relationships between the satiation demand and the climate, and the future demand will depend on the future HDH and CDH values while keeping k constant. After calibrating k, we can simply use the base year demand again, with the remaining data filled in, and then solve for μ.

Table 1

Data sources used for each variable in the model.

Variable	Data Source	Reference	Resolution
$HDD \mathrm{a}\mathrm{n}\mathrm{d} CDD$	Temperature data from ERA5	Muñoz Sabater (2019) [36]	0.1° grid
$R$	Building area and building height data from GHSL	European Commission: Joint Research Centre (2023) [37]	100 m grid
$\eta$	Thermal conductance requirements set in the report by the climate zone	ADEREE (2010) [38]	Climate zone
$IG$	Estimated from the IEA energy report	IEA (2023) [39]	Country
$i$	GDP per capita	High Commission for Planning [40]	Province
$d_H \mathrm{a}\mathrm{n}\mathrm{d} d_C$	Estimated from the IEA energy report (only for model calibration)	IEA (2023) [39]	Country
$P_H \mathrm{a}\mathrm{n}\mathrm{d} P_C$	Demand from IEA and the individual service price from GCAM	IEA (2023) [39]; Joint Global Change Research Institute (2024) [41]	Country

It is important to note that direct model validation against the observation is not always possible. Furthermore, the spatially distributed energy demand data in Morocco were not available. Future research could benefit from access to finer-resolution heating and cooling demand datasets to further validate and refine the model.

2.4. Data Source

The BED model requires spatially distributed data for all the terms in the model. In this study, we use multi-scale data (Table 1), including gridded, by province, by climate zone, or by country where no other data were available. For each variable, data were collected for both residential and non-residential sectors. The data sources we use for each variable are given in Table 1.

2.5. Data Preparation

Pre-processing steps were necessary with the data sourced in this study to extract the final variables used in the model. All data, except temperature, were harmonized to a common grid of 0.1° × 0.1° spatial resolution over Morocco to ensure consistency in model implementation. Each variable is split into residential and non-residential sectors when applicable. This harmonization process involved aggregating higher-resolution data (e.g., building data) and downscaling coarser resolution data (e.g., GDP per capita) to a common grid with the available meteorological data. While this approach is necessary for aggregating different data to the same grid resolution, it is important to recall that outputs are only related to a fraction of the buildings in the grid.

The BED model takes gridded, hourly temperature data as input [36]. Additionally, we define a band of comfort temperature from 18 to 21 °C as is common in previous studies, to calculate the heating and cooling degree-hours each year. HDH and CDH are calculated using a trapezoid discrete integration approach on single year periods for temperatures that fell below or above the band, respectively. Note that HDH and CDH are clearly independent of the sector.

The Global Human Settlement Layer (GHSL) data package on buildings provides the built-up surface area in each grid cell at 100 m resolution for both residential and non-residential (i.e., commercial) buildings [37]. The average building height in each grid cell is also provided, though not defined by separate building sectors. To estimate $R$, the surface-to-floor ratio, we consider each grid cell as one large building occupying the total built area and having the average building height of the grid cell. The model is based on the fact that this building is a cube, with an average height of 3 m between the floor and ceiling. With this, we can estimate the total floor space (i.e., total area of all floors within a building) and the surface area exposed to the atmosphere, and thus calculate $R$ for each grid cell and sector. Given the absence of detailed building foundation dimensions in the dataset, the simplification of representing buildings as cubes to allow for the calculation of R reflects the complexity of the building structures at the scale of the study. While this assumption may introduce inaccuracies in estimating R, the model calibration process partially absorbs them.

Residential and commercial building shell thermal conductance regulations were proposed in 2010 by the Agence Nationale pour le développement des énergies renouvelables in Morocco and the Agence Marocaine pour l’Efficacité Energétique (AMEE) [38]. These regulations were proposed for defined different climate zones [42], with the goal of hitting targets by 2020. In each climate zone and for residential and commercial sectors separately, we set $\eta$ in 2020 to be the average thermal conductance rating of exterior walls for each listed style of building. We then adopted a 0.6% year-over-year improvement rate of average thermal conductance to estimate $\eta$ for the years in this study’s experiments [35].

Internal gain for each year is taken for all of Morocco for each residential and commercial building as energy use from non-heating or cooling services from the IEA energy statistics [39]. The total energy is then distributed by floor space in each grid cell.

The base year heating and cooling demand is similarly taken for each residential and commercial building as energy use for all heating and cooling services from the IEA energy statistics in 2010 [39]. The demand is then estimated by the product of floor space and HDH and CDH for heating and cooling demand, respectively.

The per-capita income data used are the per-capita GDP reported for each province by the High Commission for Planning [40]. These data points are kept constant between the sectors.

The Global Change Analysis Model (GCAM) [43] provides country-level service prices for different heating and cooling technologies that are characterized by operating costs, efficiencies, and fuel requirements in a common unit (USD 1975 per kWh, where USD 1975 indicates the US dollar value in the year 1975) for residential and commercial sectors [41]. To obtain the weighted average heating and cooling service prices, we combine these prices with the sector-specific energy consumption reported in the IEA energy statistics for each relevant year. These prices are assumed constant throughout Morocco in this experiment. It is worth noting that the country-level service prices can overestimate or underestimate the regional energy demand as practical prices differ across regions. However, since the model is calibrated to the observed energy demand data, the calibrated parameters act as scaling factors that ensure the country-level disparities are taken into account.

2.6. Experiment Design

The model was run with the data as described above for 2005 and 2018, using 2010 as the calibration year. In addition, we designed a hypothetical scenario that adds 1.5 °C on top of the 2018 gridded temperature across all grid cells. This is to simulate the impact of a warmer climate if the temperature were to increase by 1.5 °C over the 2018 baseline. Table 2 lists three scenarios and their descriptions. The primary output of the model is per-capita heating and cooling energy demand in each of the residential and non-residential sectors in 2005, 2018, and 2018 with +1.5 °C, estimated for every grid cell.

Many regions in Morocco are sparsely populated. This results in grid cells with a very small proportion or no built-up area. This can lead to unrealistic results, particularly in estimates relating to various building properties. Thus, for analysis, we will only consider grid cells with at least 0.5% coverage of built-up area.

3. Results and Discussion

This section consists of three subsections, each analyzing building energy demands at commune, city, and regional level in Morocco. Section 3.1 presents heating and cooling energy demand per-unit-floorspace (kWh/m²) across various climate settings to isolate the impact of climate variations on building energy demand. Section 3.2 examines the impact of urban expansion on heating and cooling energy demand (kWh) under each climate scenario, and Section 3.3 identifies potential hotspots where building energy demand could increase in response to both urban expansion and warmer climate conditions.

3.1. Impact of Temperature Variation on Building Energy Demand

3.1.1. Spatial Distribution

Temperature variation plays a key role in shaping annual heating and cooling energy demands in buildings. Figure 3 shows the observed mean temperature differences between 2005 and 2018 for the winter and summer seasons. In 2018, winter temperatures around the Atlas Mountains were noticeably warmer compared to 2005, whereas summer temperatures along the coastal areas were cooler. Across Morocco, the average winter temperature increased by 0.14 °C, while the average summer temperature decreased by 0.58 °C from 2005 to 2018.

The spatial distributions of commercial and residential building heating and cooling demand in 2005 at the commune level are shown in Figure 4a. In terms of building cooling energy demand in 2005, the commercial sector had a rather small demand along the coastal region, in the eastern regions, and in the southern provinces. In these regions, the demand for cooling did not exceed the annual rate of 2.2 kWh/m². In the interior regions away from the ocean and the mountains’ influence, the cooling energy demand varied between 1.4 and 6.0 kWh/m². To put these figures into context, a regular 60-watts light bulb consumes 0.06 kWh/m². A similar pattern, but with much less intensity, was simulated for the residential sector with values in interior regions, where cooling is most needed, in the range of 0.1–0.2 kWh/m². The total commercial and residential cooling demands are considered at the lower end of the annual cooling energy demand of 2.7–42.7 kWh/m² as reported in [44] for other Mediterranean regions such as Spain.

A slightly different pattern emerges for the heating energy demand where both the coastal and the interior areas show commercial heating demand reaching a maximum of around 15.7 kWh/m². The demand for commercial heating is relatively lower for the eastern semi-arid and southern arid provinces. The heating energy demand for the residential sector was significant on the Atlas and in the north on the Rif Mountain extending to the northern coastal areas, with a maximum around 6 kWh/m² on the heights of the Atlas. The western coastal areas have relatively lower residential heating demand, staying within 2 kWh/m². This difference is due to the moderating role of the Atlantic Ocean on the temperature in western Morocco, reducing both heating and cooling needs compared to elevated regions with more extreme temperatures. In addition, commercial heating is the dominant demand source on a per-unit-floorspace basis among four building sectors, indicating that the temperature changes in the winter could have more impact on Morocco’s energy portfolio.

Inter-annual temperature variations and seasonal temperature ranges have a significant impact on building energy demand; however, building energy efficiency is an important modulator of energy demand and pattern. Figure 4b shows that in 2018, most of Morocco experienced lower heating and cooling energy demand per-unit-floorspace compared to 2005 due to a cooler summer, especially along the oceanic coastline and a relatively warmer interior winter (Figure 3), as well as higher requirements in building shell thermal conductance. With residential heating being the largest per-unit-floorspace energy demand (~6.9 kWh/m²) among the four sectors, it also had a higher decline especially within inland administrative regions.

To examine the impact of warming on building energy demand, a hypothetical scenario is constructed that assumes an average temperature increase of 1.5 °C. The scenario labeled “2018 + 1.5 °C” added 1.5 °C to the 2018 temperature across all grid cells in the study area. As a result, Figure 4c shows a projected decrease in heating in the entire study region for both the commercial and residential sectors. While the commercial sector heating requirement decreased mostly between 1.0 and 7.4 kWh/m² with smallest values in the southern and eastern provinces, the residential heating requirement dropped almost everywhere in the interior and mountainous regions with a maximum reduction of about 1.4 kWh/m². On the other hand, the scenario resulted in a small increase in cooling demand across most administrative regions for both sectors, while few areas experienced a steep increase in commercial cooling by as high as 2.5 kWh/m². As expected, across the country, the 1.5 °C temperature increase scenario resulted in a gain in cooling energy demand and a loss in heating energy demand for an average of 0.03 and −0.94 kWh/m², respectively. Warming may reduce the total per-unit-floorspace energy demand, primarily due to decreased heating, and the dominant energy demand sector, in both commercial and residential sectors.

3.1.2. Regional Characteristics

To better understand the surface temperature variation on building energy demand within administrative boundaries, Figure 5 presents the per-unit-floorspace heating and cooling energy demand for residential and commercial buildings across 27 Moroccan cities in 2005, 2018, 2018 + 1.5 °C, along with scenario-based differences. Similarly to the spatially distributed results, heating dominates energy demand in both residential and commercial buildings at the city level in all three scenarios. Figure 5a shows that interior cities, such as Tinghir, Beni Mellal, Ouarzazate, and Meknès and Fes, have the highest per-unit-floorspace total energy demands in 2005, between 10 and 12 kWh/m². In contrast, coastal cities like Casablanca, Rabat, Tangier, and Agadir show only about half of this demand rate. The variation in energy demand across cities shows patterns similar to those reported by Hamdaoui et al. [14] on heating and cooling needs in six Moroccan cities.

In terms of residential heating, cities under the oceanic influence (e.g., last 12 cities from Rabat to Dakhla in Figure 5a–c) have the smallest per-unit-floorspace heating demand, followed by cities in a semi-arid climate (e.g., Es Semara, Guelmim, Zagora). Popular tourist cities, such as Meknès, Fes, and Marrakech, have the highest per-unit-floorspace residential heating demand. Residential cooling remains minimal due to a lack of air conditioning units in most constructions [32]; most households rely on traditional cooling methods, such as architectural designs that promote natural ventilation. However, the tendency during the last couple of years has been to use the split-windows unit attached to the dwellings, but this is not generalized to all units.

Commercial buildings, however, account for over 80% of per-unit-floorspace energy demand in most cities, with heating as the primary driver (Figure 5a–c). The lowest heating demand was found in semi-arid regions following a northern temperature gradient from Dakhla, Layoune, and Tan Tan to Inezgane and Agadir, which are also under oceanic influence, to the interior cities of Ouarzazate and Tinghir. The commercial cooling is small in the coastal cities (El Jadida, Kenitra, and Safi) and southern cities (Dakhla, Layoune, and Tan Tan), with the minimum value simulated in Tétouan located in the foothills of the Rif Mountain and about 200 m of altitude. A high cooling demand is recorded in interior cities, particularly Zagora and Es Semara.

Overall, the per-unit-floorspace building energy demand in 2005 is the largest in interior cities, with the maximum in Tinghir at 1450 m in the Atlas Mountains, with a year around temperature varying between 2 °C and 36 °C. Commercial heating dominates total demand in all cities except semi-arid locations (Zagora, Es Semara, Tan Tan, Layoune, and Dakhla), where commercial cooling is the main driver.

The year 2018 had a milder climate with warmer winter and cooler summer than 2005 (Figure 3), which translates to less energy demand across the four components assessed in this study. Figure 5d shows the difference in building energy demand between 2018 and 2005, where almost all cities experienced a decrease in energy demand. The eastern cities of Oujda, Tinghir, and Zagora experienced the largest reductions in residential heating due to enhanced warming in the eastern region during the winter of 2018. In all cities, the residential cooling was small to start with, and the temperature changes between 2018 and 2005 did not significantly affect the cooling energy demand. Similarly, commercial heating reductions were largest in interior cities such as Oujda, Tinghir and Zagora, and a maximum decrease was observed in Nador (−1.5 kWh/m²). The model simulation indicates that the largest relative reductions in commercial cooling occurred in semi-arid cities, led by Es Semara, Guelmim, Tan Tan, and Zagora.

With a 1.5 °C increase from the 2018 temperature level, heating and cooling demands are expected to shift in opposite directions (Figure 5e). Residential cooling demand, already low in 2018 due to limited cooling devices, is projected to decrease further. In contrast, residential heating demand (Figure 5e) shows a much larger decrease compared to the reduction observed between 2018 and 2005 (Figure 5d). Interior cities experienced the most significant drops, with a magnitude approximately three times greater than that simulated for the 2018–2005 comparison. For example, in Nador, residential heating demand dropped by approximately 0.58 kWh/m² between 2005 and the 2018 + 1.5 °C scenario, compared to a decrease of only 0.2 kWh/m² from 2005 to 2018. Commercial heating demand also declines across all cities, with Nador recording the largest decrease (2.5 kWh/m²). As expected, the commercial cooling demand generally increases with a 1.5 °C warming scenario for most cities, except for some cities in semi-arid climates like Guelmim, Es Semara, and Tan Tan, which experienced a decrease compared to 2005 (Figure 5e).

A comparison between 2018 and the 2018 + 1.5 °C scenario isolates the direct impact of a 1.5 °C increase in mean temperature on energy demand in selected cities across Morocco (Figure 5f). The impact is a generalized decrease in both residential and commercial heating energy demand, with the reduction being more pronounced in northern interior cities compared to semi-arid cities. On the contrary, commercial cooling demand increased for all cities, with semi-arid cities (Zagora, Es Semara, and Dakhla) experiencing the largest energy demand (0.72–0.92 kWh/m²). However, because residential cooling demand was minimal in the 2018 baseline, the 1.5 °C temperature increase resulted in an average decrease in total energy use of 0.2 kWh/m² across the country.

3.2. Compounding Effect of Urban Expansion and Climate Variation on Building Energy Demand

3.2.1. Urban Expansion in Morocco

Urban expansion in Morocco between 2005 and 2018 is most prominent in the northwestern and central regions, particularly around major cities like Casablanca, Rabat, and Marrakech (Figure 6). In 2005, these areas already showed relatively high levels of urbanization compared to the rest of the country, as seen in the lighter shades, and this trend intensified by 2018. The map of changes in built-up area fraction (Figure 6b) shows that the highest rates of urban expansion occurred in these densely populated regions, likely driven by economic opportunities and infrastructure investments. This spatial variation in urban growth could lead to regional disparities in energy needs and could influence future energy planning.

3.2.2. Spatial Variation of Building Energy Demand

Urban expansion played an important role in shaping the spatial distribution of total building energy demand (in kWh), as shown in Figure 7. Overall, heating and cooling demand were predominately concentrated in the middle to northern regions of Morocco, with minimal energy demand in the south. Residential heating dominated total energy demand across building sectors (Figure 7a), largely driven by the large share of residential buildings and their high per-unit-floorspace heating demand. Similarly, residential cooling demand followed a similar but smaller spatial pattern. In contrast, commercial heating and cooling demands were sparsely distributed, with higher concentrations in metropolitan areas.

The combined effects of climate conditions and urban expansion led to diverse changes in total building energy demand between 2005 and 2018 (Figure 7b). Despite a reduction in energy demand per-unit-floorspace (in kWh/m²) in 2018 due to a milder climate (Figure 4b), total building energy demand increased in northwestern regions compared to 2005 (Figure 7b), mainly from residential and commercial heating, implying concentrated population growth along the coastal regions and west of the Atlas Mountains. Regions east of the Atlas Mountains experienced a decrease in residential heating demand with warmer winters in 2018, while urban expansion remained similar to 2005 levels (Figure 6). Residential cooling demand rose slightly around the Atlas Mountains, corresponding to the observed spatial patterns of warmer summer temperatures in 2018 (Figure 3). In the commercial sector, heating and cooling demands increased in areas undergoing urban expansion (Figure 6) but declined elsewhere, where climate effects outweighed urban expansion effects in the absence of significant urban growth. Across Morocco, net heating demand increased by 205.6 GWh (1 GWh = 1 million kWh) and net cooling demand slightly increased by 13.2 GWh from 2005 to 2018 (Table 3).

In the 2018 + 1.5 °C scenario, rising temperatures became the dominant driver of energy demand patterns, even in areas experiencing substantial urban expansion (Figure 7c). Overall, heating demand decreased while cooling demand increased across the country for both commercial and residential buildings compared to the 2018 control. In few metropolitan areas along the coastal and central northern regions, heating demand continued to increase, although less markedly than under non-warming conditions. The results indicate a total drop of 335.8 GWh in heating demand and a total increase of 44.4 GWh in cooling demand across Morocco. Residential cooling demand undertook a larger share of total energy demand, suggesting a growing need for air conditioning, particularly if Morocco’s air conditioning market remains underdeveloped.

3.2.3. Building Energy Demand in Major Cities

Casablanca and Marrakech consistently show the highest total space heating and cooling energy demand, driven mostly by residential heating, while cities with less urbanization and milder climates, like Sale and El Jadida, exhibit relatively lower demand across all scenarios (Figure 8). This distribution of the demand is largely attributed to the growth of urban areas and, consequently, the rise in the number of buildings. As cities expand, both residential and commercial sectors contribute more to the total heating requirements, indicating the dominating effects of urbanization on increasing energy use even with a cooler climate in 2018 than 2005.

Although urban expansion generally leads to higher overall energy demand, some cities show more aggressive changes than others under the compounding impact of climate variation and urban expansion (Figure 8d). For instance, among metropolises, Rabat and Marrakech experience relatively smaller changes from 2005 to 2018, compared to other major cities. In the 2018 + 1.5 °C scenario (Figure 8e,f), most cities experience decreases in heating demand, yet the demand for cooling does not escalate as much. However, the increase in commercial cooling is particularly pronounced in Casablanca, Inezgane, and Agadir, indicating that warmer temperatures could lead to substantial growth in cooling energy needs in dense urban areas. For example, the per-unit-floorspace commercial cooling demand in Inezgane barely increases with warming (see the example in Figure 5e), yet its total commercial cooling demand increases the most across all cities by 9.6 GWh (Figure 8e). This shifting balance suggests that cities with less seasonal temperature extremes might experience both reduced heating needs in winter and increased cooling needs in summer.

In particular, cooling needs could become dominant, especially as urban expansion amplifies energy peaks. With the projected air conditioning adoption rate expected to reach 49% [32] and the population forecasted to grow to 40 million by 2030 [45,46], the resulting increased cooling demand can pose challenges to the regional energy system. Therefore, enhancing the energy efficiency of buildings and air conditioning systems and energy grid resilience will be critical to ensure the regional to national security in the future.

The scenario with a +1.5 °C increase in temperature over 2018 illustrates the potential impact of warmer temperatures on reducing the total building energy demand in Morocco (Figure 8f). In this scenario, cooling demand slightly rises, while heating demand significantly drops across both the commercial and residential sectors in all 12 administrative regions. Coastal cities like Casablanca, Agadir, and Mohammedia, which typically have moderate temperatures due to the Atlantic Ocean influence, show divergent changes in both heating and cooling energy demand. In these areas, the increase in cooling demand partially offsets the decrease in heating demand under warmer conditions. Urban expansion further amplifies the effects of warming on energy demand, particularly in coastal metropolises where cooling infrastructure is growing to meet comfort needs. This trend introduces potential risks of unexpected summer peaks in cooling demand, especially in northern and central Morocco under a warming climate.

Urban expansion, coupled with warming, could intensify challenges related to energy management in Moroccan cities. Although the total building energy demand can decrease across the country, the cooling demand increased by 52% from 2005 and 2018 + 1.5 °C (Table 3). Major cities also experienced considerable increases in cooling demand, including Casablanca (+3.7 GWh, 32.7% increase from 2005) and Inezgane (+10.4 GWh, 74% increase from 2005). As cities grow, energy infrastructure must adapt not only to increased demand, but also to shifting seasonal energy patterns due to warmer temperatures. Effective urban planning and climate adaptation strategies, such as incorporating energy-efficient building designs and promoting natural cooling methods, could help mitigate these challenges and reduce reliance on energy-intensive heating and cooling systems.

3.3. Hotspots Under a Warmer Climate

Figure 9 provides a spatial overview of building energy demand across Morocco for both regional and city-specific variations over three scenarios: 2005, 2018, and 2018 + 1.5 °C. In 2005 (Figure 9a), energy demand is relatively low in most regions, with the largest demand concentrated in urban centers such as Casablanca and Marrakech. These two cities have the highest total building energy demand, in response to their dense population and extensive building infrastructure. The spatial variations of regional demands indicate that the socioeconomic growth is the main driver in building energy demand between urban and rural areas.

As the urban area expands unevenly across Morocco from 2005 to 2018 (Figure 6), total building energy demand increased, particularly in the northern and coastal regions (Figure 9b). This change aligns with urban expansion and population growth in big cities like Rabat, Agadir, and Inezgane, where total energy demand rose by more than 11 GWh since 2005. Administrative regions with more metropolitan areas experienced substantial increases in energy demand mainly due to the growing need for residential and commercial heating.

Under the warming scenario of a +1.5 °C temperature increase in 2018 (Figure 9c), energy demand shifts become more complex. In this scenario, urban hotspots show a mixed response, with warming-induced decreases in heating demand partly offset by rising cooling needs. For example, Casablanca and Rabat show a net reduction in energy demand (gray circles), as reduced heating requirements overweigh increased cooling needs. However, certain inland cities and coastal areas, such as Meknès and Inezgane, experience a net cooling demand surplus because of a hotter summer and more urban built-up areas that exacerbate the urban heat island effect.

A warmer climate may mitigate heating needs in many Moroccan cities, especially along the coast. Yet, this benefit is increasingly offset by a growing cooling demand in densely urbanized regions. This shift in energy portfolio highlights a potential trade-off for urban planners and policymakers: while rising temperatures may reduce winter energy demand, growing cooling needs could drive summer energy peaks, especially in commercial areas. In some key cities like Agadir and Inezgane, cooling demand already exceeds reductions in heating demand, which implies a heightened risk of summer energy stress.

To address these emerging challenges under warming, urban planners and policy makers should develop hotspot-specific strategies that reflect energy transitions in different cities. In cooling-dominated hotspots such as Agadir and Inezgane, efforts should focus on enhancing building cooling efficiency through stricter energy codes for new construction, retrofitting existing buildings with low conductance materials, and promoting high-efficiency air conditioning technologies. In Casablanca and Rabat, where total energy demand is decreasing due to warming, efforts could center on maintaining efficiency gains by expanding green urban infrastructure and leveraging passive cooling designs to minimize new cooling loads. In rapidly urbanizing areas such as Meknès, proactive planning should prioritize resilient urban design, including green spaces and heat-mitigating zoning policies. Across all hotspot areas, public awareness campaigns on energy conservation and targeted infrastructure upgrades (such as smart grids and district cooling systems) will be critical to managing seasonal energy peaks and ensuring sustainable urban growth in a warming climate.

4. Concluding Remarks

This study highlights the complex interplay between climate variation, urban expansion, and building space heating and cooling demand in Morocco. The findings reveal significant spatial and temporal variations in heating and cooling energy needs, shaped by both climate and socioeconomic factors. Without considering urban expansion, while coastal cities benefit from the moderating effects of the Atlantic Ocean, reducing both heating and cooling demands, inland and semi-arid regions face higher energy requirements due to more extreme seasonal temperatures.

Under a +1.5 °C warming scenario, the impact on per-unit-floorspace heating and cooling demand shows a dual effect: reduced heating demand is offset by increased cooling demand. Residential heating, currently the dominant component of energy demand, is expected to decline significantly in interior cities such as Tinghir and Meknès. Conversely, the growing adoption of air conditioning technologies and increased cooling needs in warmer summers are likely to shift the energy portfolio towards higher summer energy peaks, especially in metropolitan regions like Casablanca and Marrakech.

Urban expansion between 2005 and 2018 has compounded these challenges in northwestern and central regions. Major cities, such as Casablanca and Inezgane, experienced significant increases in total building heating and cooling demand due to population growth and infrastructure development. The total heating and cooling demand in Morocco increased by 218.8 GWh (17.7% increase from 2005 to 2018), despite the moderate climate in 2018. If considering a warmer climate with 1.5 °C increase from the 2018 level, heating demand may reduce by 335.8 GWh (25.3%), while the cooling demand could increase by 44.4 GWh (35.9%). The dual impact of heating and cooling demand, especially a 50% increase in cooling demand for cities like Casablanca, suggests that investment needs should shift to cooling infrastructures.

These results point to the importance of strategic planning to address the impact of urbanization and climate variation on Morocco’s future energy landscape. In particular, the results can inform updates to building energy codes by encouraging the integration of natural cooling methods, higher insulation standards, and requirements for high-efficiency cooling systems, especially in cooling dominated cities like Agadir, Casablanca, and Inezgane. Urban planners could prioritize compact, climate-responsive urban forms, expand green spaces to mitigate urban heat islands, and require zoning regulations that promote natural ventilation. Additionally, energy system management strategies can focus on strengthening grid resilience to accommodate growing total and peak energy demands and investing in renewable energy sources to meet shifting seasonal energy demands. Future urban development strategies should also incorporate adaptive measures to ensure sustainable energy management, minimizing the environmental and economic costs associated with rising cooling demand.

While this study offers valuable insights, it is subject to several limitations. First, due to the lack of large-scale, high-resolution data, direct validation of the model against observed finer-scale energy demand was not feasible. This limits the ability to assess the model’s accuracy in real-world conditions. Second, to maintain consistency in the modeling process, building data had to be aggregated. This aggregation may introduce inaccuracies and reduce the precision of the estimated building characteristics. Future research could address these limitations by utilizing detailed, high-quality fine-resolution building characteristics and energy demand data, which would enhance the robustness and validity of the model results.

In conclusion, the study emphasizes the need for an integrative approach to adapt to energy system evolution due to urbanization and climate in Morocco. By integrating energy efficiency standards, climate-adaptive urban design, and resilient energy system, Morocco can better navigate the challenges of climate uncertainty and expanding urbanization, ensuring a sustainable energy future for its cities and regions.

Author Contributions

Conceptualization, M.Z. and Z.K.; methodology, M.Z., N.P., and Z.K.; software, M.Z. and N.P.; validation, M.Z. and N.P.; formal analysis, M.Z. and L.B.; investigation, M.Z. and L.B.; resources, M.Z., L.B., N.P., and H.B.; data curation, M.Z. and N.P.; writing—original draft preparation, M.Z., L.B., and N.P.; writing—review and editing, M.Z., L.B., N.P., H.B., and Z.K.; visualization, M.Z. and N.P.; supervision, L.B.; project administration, M.Z. and L.B.; funding acquisition, M.Z. and L.B. All authors have read and agreed to the published version of the manuscript.

Data Availability Statement

The original data presented in the study are openly available in Zenodo at https://doi.org/10.5281/zenodo.14885413.

Conflicts of Interest

The authors declare no conflicts of interest.

Footnotes

Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Figures and Tables

Figure 1 Morocco map showing 12 administrative regions and the locations of the major cities used in this study. The shading in the background represents major topographic features.

Figure 2 Conceptual framework of the BED model.

Figure 3 Mean observed temperature difference for (a) the winter season (Dec, Jan, Feb) and (b) the summer season (Jun, Jul, Aug) between 2018 and 2005. Across Morocco, the average spatial difference (2018–2005) is +0.14 °C in winter and −0.58 °C in summer.

Figure 4 Spatial distribution of commercial and residential heating and cooling demand per-unit-floorspace (kWh/m²) at the commune level for (a) 2005; (b) the change between 2005 and 2018; (c) the change from 2005 to 2018 + 1.5 °C.

Figure 5 Heating and cooling energy demand per-unit-floorspace (kWh/m²) for commercial and residential buildings across 27 Moroccan cities for (a) 2005; (b) 2018; and (c) 2018 assuming a +1.5 °C increase in 2018 temperatures; (d) the change between 2005 and 2018; (e) the change from 2005 to 2018 + 1.5 °C; (f) the change from 2018 to 2018 + 1.5 °C. The colors of the city labels are to distinguish between coastal (green) and interior (black) cities.

Figure 6 Fraction of total built-up areas (commercial + residential) relative to the commune’s area for (a) 2005 and (b) fractional changes from 2005 to 2018.

Figure 7 Spatial distribution of commercial and residential heating and cooling demand (in million kWh, also as GWh) at the commune level for (a) 2005; (b) the change between 2005 and 2018; (c) the change from 2005 to 2018 + 1.5 °C.

Figure 8 Heating and cooling energy demand for commercial and residential buildings (in million kWh, also as GWh) across 27 Moroccan cities for (a) 2005; (b) 2018; and (c) 2018 assuming a +1.5 °C increase in 2018 temperatures; (d) the change between 2005 and 2018; (e) the change from 2005 to 2018 + 1.5 °C; (f) the change from 2018 to 2018 + 1.5 °C. The colors of the city labels are to distinguish between coastal (green) and interior (black) cities.

Figure 9 Building energy demand for (a) 2005, (b) 2018, and (c) 2018 assuming a +1.5 °C increase in 2018 temperatures for both cities and regions. The background color indicates the total energy demand for the region. The circle shows the total energy demand for the city. The color of the circle indicates the changing direction of the total building energy demand compared to the 2005 level for the city.

Table 2

Description of the scenario design.

Scenario	Description
2005	Building heating and cooling demands under the 2005 climate
2018	Building heating and cooling demands under the 2018 climate
2018 + 1.5 °C	Building heating and cooling demands under the 2018 climate + 1.5 °C

Table 3

The heating and cooling demand for selected cities and Morocco in GWh (1 GWh = 1 million kWh). The percentage value is the share of the service demand for a city over service demand for Morocco.

Service	Location	2005		2018		2018 + 1.5 °C
		[GWh]	Share [%]	[GWh]	Share [%]	[GWh]	Share [%]
Heating	Casablanca	79.2	7.05	92.1	6.93	62.7	6.31
	Marrakech	68.1	6.06	76.9	5.79	59.8	6.02
	Inezgane	40.1	3.57	66.0	4.97	42.5	4.28
	Agadir	21.0	1.87	30.6	2.30	19.4	1.95
	Fes	42.8	3.81	50.6	3.81	39.5	3.98
	Oujda	42.7	3.80	42.0	3.16	33.7	3.39
	Morocco	1123.4	-	1329.0	-	993.2	-
Cooling	Casablanca	11.3	10.23	10.1	8.16	15.0	8.92
	Marrakech	6.0	5.43	6.2	5.01	7.7	4.58
	Inezgane	14.0	12.67	17.2	13.9	24.4	14.52
	Agadir	6.8	6.15	8.1	6.55	11.1	6.60
	Fes	3.3	2.99	3.2	2.59	4.0	2.38
	Oujda	1.7	1.54	1.7	1.37	2.2	1.31
	Morocco	110.5	-	123.7	-	168.1	-
Total	Morocco	1233.9	-	1452.7	-	1161.3	-

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