Towards Clean Energy Transition: An Exploratory Case Study from Rural Egypt

Abstract

Rural areas are ideal for renewable energy facilities, supporting sustainable development and energy transition. Egypt aims to reduce greenhouse gas emissions in the electricity sector by 37% and energy consumption by 17% by 2030. Rural Egypt, hosting two-thirds of the population and building stock, consumes one-third of the total electricity. Thus, this paper provides an exploratory study to diagnose and benchmark the energy-use intensity of rural buildings and quantify the correlation between residential electricity consumption, built environment elements, and socio-economic factors, in addition to promoting techno-economic assessments of renewable energy from photovoltaic panels in rural Egypt, supporting national policies amid rapid rural development. The study utilized different analytical and field methods and statistical analyses. A typical agriculture-based rural village in the Delta region, northern Egypt, was selected; the built environment, building types, and socio-economic factors were examined. The results revealed a significant correlation between lifestyle, built-up area, household size, and floor numbers with residential buildings’ electricity consumption. The average annual electricity use intensity was benchmarked at 2.5–92.3 kWh/m² for six non-residential building typologies and at 22 kWh/m² and 6.67 kWh/dwelling for residential buildings. Under current regulations, rooftop solar panels can generate electricity significantly, but are not profitable. Eventually, insights for policymakers to inform energy transition policies and national initiatives for rural regeneration were provided. The research focused on a local context, but the methodology can be applied to rural settlements in similar contexts.

1. Introduction

1.1. Research Background

The effects of climate change in rural areas will be complex and difficult to predict. They will impact infrastructure, cause loss of life, and change agriculture and ecosystems [1]. The global energy scene is experiencing a rapid transformation, driven by the imperative to mitigate climate change and secure renewable energy supplies [2] in response to the raised global mitigation targets in the Paris Climate Accord, the shift to clean energy has gained attention for improving energy efficiency and contributing to climate change mitigation [3]. In 2022, The greenhouse (GHG) emissions were 53.85 billion tons; by sector, energy use in buildings represents 17.5% (residential buildings are 10.9%) of energy-related emissions from electricity generation [4].

Renewable energy (RE) is a vital component of the clean energy transition; the latter refers to the shift from fossil fuels to cleaner energy sources, which increases the use of cleaner alternatives and improves energy efficiency as it has been explicitly recognized as a promising means of advancing rural development as an essential aspect of revitalizing rural areas in the clean-energy transition implementation [5]. RE benefits include improving air quality, reducing household chore time, allowing for more study time, and improving safety and comfort. They also boost productivity, support local businesses [6], and positively influence local communities’ financial well-being and employment [7].

In this light, efforts to mitigate climate change have increased globally, focusing on energy in rural areas. Thus, in recent years, many key strategies have been recognized for advancing sustainable development goals through employing renewable energies, such as agrivoltaics and renewable energy cooperatives [8]. Each country set targets to increase renewable energy shares in the energy mix. The global target is to reach 42% renewable energy sources for electricity generation; 68 countries will reach 100%. Europe set an increased goal of increasing the share of RE in final energy consumption from 32% to 42.5% by 2030, aiming to accelerate decarbonization and reinforce energy security [9]. However, rural policies face challenges in coordinating different policies and fitting their applications to target micro-scale rural areas because rural areas still account for almost half the world’s population and have embedded historical significance that varies in various contexts, either in intangible aspects, like lifestyle and economic activities, or the built environments.

Therefore, combining efforts from a bottom-up perspective with top-down perspectives is inevitable to boost sustainable rural development practices [10], and bottom-up initiatives are apt to be more manifest in the energy transition [11]. It is crucial to take a bottom-up approach driven by scholars and researchers to empower rural communities to take charge of the energy transition, including promoting local initiatives, supporting community projects, and promoting decentralized energy systems, e.g., rural renewable energy communities.

Similarly, it is essential to benchmark the consumption pattern of different building typologies and explain the influences between the socio-economic aspects and rural built environment (as the building sector is one of the most predominant factors) on energy consumption, to obtain reliable data that can aid decision-making in managing an efficient strategic energy transition [12]. The debate on investigating the correlation has occurred for decades. [13], and it is still gaining fast-growing interest in academic research, which endeavors to reach an overall conclusion in the field [14]. The following section discusses these aspects.

1.1.1. RE: Towards a Clean-Energy Transition

From a top-down perspective, for instance, Europe was at the forefront of investments in renewable energy communities for three decades, in many forms, like cooperatives, until the release of the EU Clean Energy Package in 2016, where the European Commission defined energy communities as collective actions where citizens take ownership of their vitality consumption. Energy communities can take three forms: individual self-consumption, which involves a renewable energy production plant where energy is self-consumed, promoting efficiency and sustainability; collective self-consumption, which involves multiple consumers in a building using a RE source; and energy communities, which include members collectively producing energy to meet consumption needs with renewable sources. RE communities are formed by self-consumption that focuses on maximizing the role of renewables among self-consumers; in other words, they enable individual users to meet self-consumption and to sell energy that exceeds the generated energy, in order to be prosumers. In this light, the European Commission initiated the Rural Energy Community Advisory Hub, to accelerate the development of sustainable energy community projects in European rural areas [15], to drive them to enhance economic activity and create jobs to reduce energy poverty and accelerate the energy transition in the EU [15]. In this context, She and Pezzulo [16] summarized the academic research in the past 6 years by reviewing 86 articles to explore the transition from localized renewable energy to advanced, integrated systems; they found the RE systems and technologies crucial, and they summarized the implementations that accelerate the energy transition, like smart grids and energy storage systems, biogas, and PV [16].

Using a bottom-up approach, Furmankiewicz et al. [17] investigated how bottom-up rural development initiatives fulfill the goals of climate and energy policies in rural Poland, and found a lack of awareness of struggling policy implementations. They mentioned that attention should be paid to implementing a clean-energy transition to provide a significant prospect of increasing RE capacity, driving innovation, and filling the “information gap” of societies. In Germany, Akizu et al. [18] discussed how bottom-up initiatives can complement government-led efforts to achieve sustainability goals, including reducing primary energy consumption, lowering residential electricity and heat usage, and increasing renewable energy generation for self-sufficiency. Similarly, in rural areas in Latin America and Africa, a hybrid energy system to meet electrical energy was proposed [19,20] and rooftop photovoltaics for decarbonization and a clean energy transition in Europe, India, China, and America was provided [21]. In Saudi Arabia, Mustafa et al. [22] simulated the application of a decentralized hybrid energy system consisting of solar photovoltaics (PV) on a target-case building to support national green energy and economy.

In Egypt, Salah et al. [23] reviewed Egypt’s energy landscape, including rural areas, examining its potential for sustainable energy, comparing it to global averages, and addressing challenges in developing the renewable energy sector. The hybrid RE and PV in rural areas are countable sources for remote areas because of high solar irradiation in Egypt, as presented in the References [24,25,26]. The authors developed a pilot zero-energy building using a stand-alone PV system in a social building in a rural village (the same target case as in this study) to pave the way to further implications and support the momentum of the clean-energy transition [27]. The First Author investigated national efforts in renewable energy-aligned sustainable rural development. In addition to investigating bottom-up practices for employing different RE sources at buildings and farms in rural Egypt as accelerators for energy transition and a decarbonized future, the study found solar energy applications predominated as RE sources in residential buildings; however, farms rely mainly on solar, then wind, and then biomass energy [28]. Kassem [29] emphasized the fact that integrating PV and biogas is the most effective RE solution to afford energy prices for rural dwellings in southern Egypt.

1.1.2. Diagnosing the Built Environment and Building Consumption for Energy Transition

Diagnosing the correlation between the built environment and building energy consumption is essential for several domains. Firstly, it can support policies and inform practice. For example, Torabi Moghadam et al. [30] relied on existing census and district heating energy consumption data. They used GIS as a methodology to characterize the impact of building morphology on energy planning to understand better residential building energy usage and the implications of energy performance policies. Juan et al. [31] conducted field investigations and interviews, followed by analyzing the residential conditions and energy use classifications using both bottom-up and top-down approaches, as they found this way is efficient for diagnosing the rural buildings and regional level in China; their findings supported climate-related-building policies that, in turn, accelerate the energy transition [31]. Sena et al. [32] mentioned that efforts on this issue in developing countries (like Egypt) still need to be improved, and that more investigation is needed. So, to fill this gap, they inspected building characteristics and socio-demographic factors (e.g., education level and employment status) by blending field and questionnaire surveys with statistical methods.

Secondly, it supports sustainable rural development practices; researchers such as Wang et al. [33,34] accentuated the fact that the land cover and road network, the spatial pattern of the surrounding environment, and building physics characteristics can significantly influence building energy consumption and GHG emissions, using simulation and analytical methods. Osorio et al. [35] stated that operational energy consumption patterns could control energy consumption during rapid urban growth and tailoring the implementation of mitigation strategies, as they estimated energy use for building operations using energy metrics to explore the link between energy consumption and chosen built-environment characteristics. The correlation can also promote effective energy conservation measures and techniques, as concluded by Woo and Cho, who diagnosed the built environment’s impact on the energy use of Korean buildings [36], but it can also decrease the connected GHG emissions and help to rationalize residential energy use and consequently reduce energy expenditures, as presented by Antonopoulos et al., who used a statistical model to explore the correlation between building features and demographics with building energy consumption in the USA [37].

Thirdly, it can help in the early planning stage for energy performance, as in Wang et al.’s approach [38], which assessed and quantified the influences of urban morphology on the energy performance of building stocks, blended field surveys to examine the status quo of the built environment and building characteristics, and then stimulated and modeled the energy demand. Likewise, Zhu et al. [39] recently characterized the impact of the urban morphological parameters on decisions to improve energy efficiency and outdoor thermal comforts via field methods and simulation tools, to quantify the effects of the built-environment morphology of dormitory blocks on energy consumption and renewable energy production [40].

Despite these broad benefits, numerous studies presented associated challenges. To give an example, Tam et al. [41] conducted a chronological review for six decades to diagnose energy-related occupant behavior and impacts on domestic consumption and its implications; their results found a substantial deviation between actual and predicted energy-consumption patterns in the European contexts; the measured energy consumption is larger by one-half than the predicted ones, in compliance with Heinonen and Junnila’s study [42], which conducted holistic on-site and analytical methods to examine different housing types and energy use in urban and rural Finnish areas [42]. Another study by Geng et al. [43] presented a simplified method to assess building energy performance (diagnoses of buildings) and collect information on energy bills and simulations in rural China to diagnose energy consumption; they reported that the actual measurements indicated less energy consumption values than the simulations by 12%. On the other hand, household energy consumption varies significantly by lifestyle. Lifestyle is influenced by family composition, age, housing type, appliance ownership and usage, and habits during leisure time. Social activities significantly affect energy consumption, making it hard to identify the most impactful ones, as discussed by Nishida et al., in a model for annual power use from a lifestyle questionnaire for estimating household seasonal energy use [44].

1.1.3. Diagnosing the Correlation for Energy Transition in Egypt

In the Egyptian context, the issue has been discussed by scholars; a secondary study by Alsaadani, entitled “A statistical review of a decade of residential energy research in Egypt” [45], provided a systematic literature review to discuss building or urban-scale elements’ correlation with domestic electricity consumption in Egypt between 2010 and 2020. Among 124 eligible articles focusing on the local realm, she found the research tendencies concentrated on assessing the architectural and material parameters of the building scale with buildings’ energy consumption, and few articles considered urban pattern factors. She recommended focusing on the impact of understudied areas, such as building systems, on whole-building performance to support future research and broaden the scope of Egyptian residential energy research. It is conspicuous that the contextualizing was not explicit (either urban or rural).

Nafeaa et al. [46] evaluated the influence of residential building morphology in urban areas in Egypt, utilizing simulation tools to quantify domestic consumption, either within their experiment or, as they reported, another study in a similar context that indicated a similar rate, as they highlighted the consumption affected by many factors like building systems, appliances, and behavior aspects. While Attia et al. [47] model the correlation between the building typology and domestic electricity consumption in different climatic zones, they do not explicitly state whether the correlation is urban or rural.

In the rural areas in the Delta region (similar to the target case’s context in this study), rural commons, Hegazy et al. [48] examined the impact of the urban form in rural areas in this Delta region in Egypt, simulating different building typology scenarios (attached, semi-detached, and detached) with various floors and different orientations; they found that attached typologies consumed the lowest average, while detached type consumed the highest average as the building envelope was exposed to thermal load. Also, the more floors there were, the more consumption; however, they did not determine the current consumption pattern of specific case studies (e.g., electricity bill collection).

Salama and Hassan [49] examined the statistical correlation between socio-economic and demographic aspects and energy-saving behavior in rural settlements in Ismalia Governorate, the Canal Suez region, and found a positive correlation between age, education level, and family member education. They spotted a negative correlation between income level, room number, family size, and electricity-saving behavior; on the contrary, Abdelhay [50] studied the impact of electricity accessibility on socio-economic factors and urban construction in a rural settlement in Fayoum Governorate, South Egypt. Finally, Elsabbagh et al. [51] mentioned that changes in economic activity affect consumption patterns, as discussed in a case study in the Al-Sharkya Governorate in the Delta region.

1.2. State of the Art

By taking a look at the primary and secondary studies that globally and in Egypt discussed and developed scenarios to quantify the correlation between the built environment and energy consumption building, which go hand in hand with promoting harvesting renewable energy towards clean-energy transition in Egypt, we found, as far as we know, that no study has been conducted from a bottom-up perspective to collect data on electricity consumption patterns, quantifying the correlation using statistical methods, and focusing on rural contexts to examine the correlation in rural villages in Egypt. This is the first, even though some studies investigate different approaches in individual case studies in rural Egypt, simultaneously to proposing rooftop PV on-grid systems towards clean-energy transition. Beyond these bounds, with respect to the confirmed facts and arguments mentioned in the previous studies, the academic progress of the issue is slow in developing countries, as per the claims of Sena et al. [32] and Quan and Li [14], in addition to Alsaadani’s research [45] in the Egyptian context and other insights regarding no clear benchmark for rural buildings, as per remarks from the studies of Attia et al. [47] and Nafeaa et al. [46]; their results significantly varied by about 80% for the same building typology in the same country. We would say that the bottom-up approach, coterminously exploring various perspectives, methodologies, and case studies, is inevitable in reducing the associated uncertainties, as we have presented. As well as the exploratory studies, this approach can also be set as the pillar of a beneficial research foundation that aims to yield a first inroad into an area that currently needs to be investigated and to “call for a few afterthoughts” [52], which is what our study provides.

1.3. Objective

This study promotes a pilot exploratory experiment to support the clean-energy transition in Egypt by determining the electricity consumption patterns of different building types and describing and observing the statistical correlation between residential electricity consumption and the rural built environment, focusing on urban characteristics, architectural typologies, socio-economic activities, and dwellers’ lifestyles. Then, it carries out a techno-economic assessment of rural renewable-energy communities within the current regulatory frameworks, to be integrated with the arguments and discussions presented for the rural Egyptian context in Reference [28].

1.4. Hypothesis

The study’s hypothesis in this exploratory study proposes that the current approach can be a game-changer tool in the Egyptian context, in line with the current development scene to meet the national goals to improve the electricity efficiency from buildings in rural areas and meet national energy and GHG emissions targets, as presented in (Section 3.1.1). This is in addition to the regional context, with the intention of being a regional hub for energy transition [53], and despite the associated uncertainties related to geopolitical tensions that increase year by year, where energy is at the core of this equation. Moreover, it will pave the way for scholars to develop different approaches, build reliable data drives for retrofitting practices, and guide energy-efficient buildings in early design stages to increase awareness of the issue, as confirmed by Tam et al. [41] and Furmankiewicz et al. [17].

1.5. Research Questions

The objective can be achieved by addressing each research question (RQ), as follows:

• RQ1. What is the current energy-use intensity (EUI) of rural buildings in Egypt, and what factors of built-environment elements and socio-economics influence the domestic energy consumption, mostly?
• RQ2. What is the techno-economic assessment of installing PV panels to promote renewable rural energy communities in Egypt within the current regulatory framework?

2. Methodology

Study Structure and Methodology

Qualitative analysis and quantitative field data were combined to address the objective and to tackle research questions, namely:

• Part 1—Theoretical and Analytical Methods

The aim is to define the context of the case study, which takes place by highlighting the main characteristics of the case study, energy profile, and buildings in a rural Egypt (Section 3.1.1) macro-context: the Delta region in northern Egypt (Section 3.1.2), diagnosing the target case study, Lasaifar Albalad village (Section 3.1.3), determining the built environment’s parameters, and providing criteria as input for the questionnaire employed.

• Part 2—Field Method and Statistical Analysis

The main aim is to benchmark the EUI, explore the influences on electricity consumption patterns, and investigate the correlation between these factors and electricity consumption. Hence, different tools have been utilized, namely on-site investigations, interviews, and a questionnaire, to carry out the following:

• Diagnose the current consumption patterns of residential and non-residential buildings (Section 3.2.1 and Section 3.2.2).
• Designate the relatedness between dependents and independent variables resulting from the questionnaire, using two statistical models: the Pearson Correlation Coefficient to characterize the correlation between numerical building characteristics and electricity consumption, and the nominal one-way Analysis of Variance (ANOVA) to investigate electricity consumption, socio-economic activities, and urban settings (Section 3.2.3).

• Part 3—Techno-economic assessment of installing PV panels

Defining the requirements to apply PV panels according to the regulatory framework and quantifying their economic and technical impacts (Section 3.2.4). Figure 1 visualizes the research design and the methodology.

3. Results

3.1. Defining the Case Study

3.1.1. Energy Profile and Building in Rural Egypt, at a Glance

In Egypt, the GHG emissions account for 319.69 metric tons of carbon dioxide equivalent (Mt CO₂eq) in 2022, representing 0.7% of global emissions [54]; despite the low emission share compared to the worldwide average, Egypt is vulnerable due to climate change impacts, and mainly, northern rural areas are among the most fragile regions due to sea-level rise [55]. According to the Egypt’s second Updated Nationally Determined Contribution report in June 2023 [56] and the Voluntary National Review in 2021 [57], Egypt set vital targets for 2030 compared to the business-as-usual scenarios, to reduce GHG by about one-third by 80–100 Mt CO₂eq, where the energy is a key enabler, in line with the sustainable development goal (SDG) 13—Climate Action, and to increase the share of RE in energy production to 30–32.5% (which will account for 42% by 2035), and in line with SDG 7—Clean Energy [58]. Concurrently, the second National Energy Efficiency Action Plan (NEEAP) 2018–2022 aims to reduce Egypt’s energy consumption by 18%, by 2030 [59].

Along the same lines, energy policies in Egypt are directed to addressing energy transition as well as climate action, especially in the aftermath of hosting the 2022 United Nations Climate Change Conference—Conference of the Parties—COP 27, and with these actions are reflected to the sustainable development of rural areas, represented in two mainstreams: firstly, by doubling the agriculture lands and inhabited areas, and secondly, by revitalizing and regenerating the existing rural built environments [28], such as the national initiative Decent Life (the Arabic expression is Haya Karima), which focuses on several aspects such as accelerating poverty eradication, developing the quality of housing, and providing adequate building, and infrastructure, interacting with seven of the sustainable development goals (SDGs) [58].

The building sector is responsible for 5%, and the electricity and heat sector is responsible for 32% of the total GHG emissions [54]. The total final energy consumption is 2,582,513 terajoules in 2022 from different energy sources; behind the transportation and industry sectors, the residential consumption came in third place, at 20%, that is, electricity representing one-half of energy sources among oil products, natural gas, and biofuel and waste, as visualized in Figure 2a. The distributed electricity in 2020 accounted for 122,443 GWh (30% for rural areas), divided into eight sectors; the uppermost consumer sector is the household with 61,542 GWh (36% in rural areas), and the total share of RE in electricity generation is 12%, as depicted in Figure 2b.

According to the Population, Housing, and Establishments Census, Egypt has 16,185,063 buildings; the residential buildings are 13,467,333, representing 83% of the total; rural areas host 70% of the residential buildings [62]. These numbers are increasing yearly, due to rapid development rates and the establishment of new urban communities and, consequently, national housing projects; for example, 1.7 million public housing units and 0.45 million social housing units were constructed between 2017 and 2023 [63,64]. In the same vein, the rural settlement patterns in Egypt have been altered due to various aspects, resulting in anthropological activities and contemporary built-environment typologies leading to the consumption of further energy and the generation of more GHG emissions. This increases the uncertainties associated with the future of consumption patterns, which this study can explore, contributing towards achieving the national energy and climate targets, supporting electricity sector and the national rural-regeneration processes.

3.1.2. Macro-Context: Delta Region, Egypt

Egypt’s regions are grouped into seven physical planning regions, and every region comprises three hierarchies: governorates, districts, and villages/satellite villages. The Delta, one of these regions, comprises five governorates, 106 districts and cities, and 1404 villages, and is one of the most important agricultural and fisheries source in Egypt; the agricultural land covers 77% of the total area, and is characterized by several aspects, predominately rural areas. It hosts around one-fourth of the population in Egypt (more than 22 million) within a total area of 12,357 km², representing 14% of the total inhabited area and 1.2% of the total; the population density exceeds 1773 km²/inhabitants. The Delta’s rural area also has about 2.5 million buildings, representing 78% of the Delta region’s buildings, 15% of the total national building stock, and 11% of the total land-use area [62].

The non-residential buildings in the region comprise public universities and many private higher-education buildings, in addition to numerous schools. There are about 1004, including all types, such as the Islamic religious sciences “Azhar”, public, commercial, more than 300 healthcare buildings, and more than 500 cultural buildings, besides the social and recreational buildings such as youth centers, stadiums, and services, like post offices [65,66]. Residential buildings predominate in the region, and represent 95% of the total. By floor number, the average predominant typologies are the one-story and two-story buildings, representing the highest number, with 70% (915,788 and 918,040), then the three-story, with 21%, then the four- and five-floor buildings, with 7% and 2%, respectively [62].

The rural areas in the Delta region consume one-third of the distributed electricity (the primary energy source for domestic use); residential lighting uses the highest amount of energy, with 55% [67], similar to the national average. Regarding household electricity usage, 89% of Egyptian families in rural areas own refrigerators, 33% use electric water heaters (along with 15% who have gas water heaters), 19% possess automatic washing machines, 5% have microwaves and ovens, and 4% depend on air conditioners. The urban fabric of all Egyptian villages consists of three major patterns: the linear pattern, which aligns with agricultural land boundaries; the scattered urban pattern of the expansion areas; and the compacted pattern in the center of the villages, which is a typical pattern in the Delta region [68].

3.1.3. Macro-Context: Lasiafar Albalad Village

Lasaifr Albalad is located between 31.18° N and 30.72° E, with an elevation map of 6 m. It is a typical rural agriculture-based village, representing the region’s villages, following the Konaieset Alsaradoosy local unit in Desouq District, Kafr El-Shiekh governorate. According to our survey, the population was nearly 14,000 in 2021, and in 2006, the population was 8589 inhabitants [69], which increased by about 40% in 15 years. The inhabited area inside the urban boundary is about 0.35 km². The settlement encompasses the urban fabric typical of villages in the Delta region (scattered, compacted, and some linear). The local road network includes four width gradients, mostly from 8, 6, 4, and 2 m; we notice that that the width of some roads varies, as per the unclear borders. It has few public and many private in-between spaces (e.g., backyards and gardens).

The village features around 700 buildings, with about 4% non-residential and 96% residential. The non-residential structures include six religious buildings (mosques), four preparatory and primary (general and Azhar) schools, and governmental buildings such as the mayor’s house and the agriculture association. Additionally, there are public service facilities like the post office and health unit, along with a youth sports center. In addition, some storage and commercial buildings are scattered along the water body.

These numbers have been verified in two ways; firstly, the First Author had an official layout from the agricultural association for the reference year 2006. He also used Open Street Maps to count building numbers; an example is available in the Reference [70], and he conducted a spatial analysis of the village to strengthen the collection of data. Finally, he provided the first open-source master layout and land use of the village in 2022 in AutoCAD format, version 2010, available as a reference [71].

The inhabitants’ lifestyles can be grouped into four groups. First, the youth meet in the youth center, another private playground, coffee shops, or a walk. Second, older people and adults have similar lifestyles. The differences are fewer sports activities and more family visits. Third, women have a more enclosed life, characterized by home visits and shopping in the temporary public market on a weekly basis. Fourthly, playing outside is one of the preferable activities for children. The settlement is a main point for primary education schools in the surrounding area. Contrariwise, the older students move to the Desouk district for secondary schools and other centers for higher education, like Kafr Elshiekh University. The primary means of transportation are small buses that lead to nearby districts. The small three-wheel car is the leading means of transportation, especially inside the settlement. Figure 3 depicts examples of the built environment of the Lasaifar Albalad rural settlement.

3.2. Field Study Results

The field study was conducted between April and May 2021 in two stages. The first stage was for interviews and personal meetings (Section 3.2.1); the selection criteria aimed to include volunteer influencers like the manager of the agricultural association, to represent the local authority, the locals, an older citizen to act as a cultural expert who is fully aware of the village, and other locals who facilitated the conducting of the experiment, collecting monthly electricity bills and exploring their standpoints on the influences on electricity consumption. In the second stage, an online questionnaire was developed (Section 3.2.2); the aim was to diagnose the residential buildings (the predominate ones), namely, building characteristics, demographic data, socio-economic activities, and average electrical energy consumption in summer and winter residential-building characteristics, noting that the questionnaire was primarily developed based on the non-published Ph.D. thesis of the First Author, supervised by the Second Author [72].

3.2.1. Phase 1: Interviews

The interviewed locals were asked open-ended questions to inspect the correlation between small-dimension agricultural economies and domestic electricity consumption, as follows:

What are the predominant agricultural activities? In this light, do you think there is a correlation between agricultural activities and monthly electricity bills? Finally, do you want to say anything related to this issue?

A farmer described the predominant local farming cycles: “After harvesting the rice and wheat crops, they prepared them in the relative industry, namely, wheat grinding and rice bleaching in a factory in the village or nearby village/district for domestic use and to sell them”. Also, the sugar beet crop is connected to the district’s governmental sugar-production factories; nearly all the workers in this area are from the villages. Both indicated no impact of these activities on the monthly electricity consumption.

Another dweller reported that he relies on a micro-scale poultry farm inside the building in a vacant dwelling in an extended family house, Figure 3e. Also, he mentioned that some families are carrying out similar activity in light structures on the roof of the building, external yards, or vacant rooms, like those shown in Figure 3d. Poultry farming requires substantial lighting for eight months each year to enhance productivity (the annual cycle), doubling monthly electricity consumption. The larger poultry farming generally demands air conditioning (no farms exist inside the village’s inhabited center). Figure 4 visualizes some of the non-residential buildings in the village.

Furthermore, the locals reported their monthly electricity bill expenditures, which are equivalent to an average of 200 kWh monthly, determined based on some collected electricity bills and the representatives’ reports.

Table 1. The characteristics and EUI of the non-residential buildings, developed by the Authors, after [27,72].

Building Typology	Building Characteristics			Electricity Consumption			Description
	Footprint	Floor	Built-Up Area	Monthly	Annual	EUI
	m2	Number	m2	kWh/ Month	kWh/Year	kWh/m2/ Year
The grand mosque	300	1	300	220	2400	8	The grand mosque is located in the village’s core; its maximum capacity is nearly 250 worshippers, as shown in Figure 4a.
Preparatory school	220	3	660	500	6000	9	There are eight classrooms, each equipped with a computer and a smart display board, along with two computer labs, a science lab, a library, and executive rooms, as seen in Figure 4b.
Primary school	210	3	630	250	3000	4.8	It is positioned near the center of the village and comprises ten classes.
Health-unit building	65	2	130	1000	12,000	92.3	The center provides all basic healthcare services, like vaccinations, as depicted in Figure 4c. Consumption is higher than the norm because of the equipment used, like the medical fridges.
The agriculture association	75	1	75	110	1320	17.6	This government building assists farmers by offering finance and marketing processes while lowering production costs, see Figure 4d.
Social	120	2	250	120	1440	6	The social development facility houses an official unit for social affairs and a kindergarten. It operates officially for 35 h each week and is essential to serving the local community, see Figure 4e.
Social (NGO) [27]	150	1	150	100	1200	8	The building belongs to Albakyat Alsalihat, a non-governmental organization (NGO), see Figure 4f. It accommodates a residential building providing social activities for children, orphans, and widows.
Independent shops	16–25	1	16–25	80–120	1200	2.5	It encompasses various types of businesses, including restaurants, vehicle services, mechanics’ workshops, and bakeries. These establishments are primarily located along the main road to serve commercial needs, operating for about 70 h weekly.

summarizes the investigated buildings’ average consumption patterns and energy-use intensity.

3.2.2. Phase 2: Questionnaire and Data Collection

Questionnaire Design

A close-ended structured questionnaire has been designed to investigate three mainstream factors: building characteristics, urban settings (built environment), and socio-economic factors, based on the criteria presented in Section 3.1.2 and Section 3.1.3, to tackle the purpose of this study (Section 1.3). In addition, an optional open-ended question was designed to enable locals to express opinions about environmental issues like improving energy and buildings, or any other issue. The rationale behind this is to engage locals by collecting their preferences and insights, if any. The three groups and their sub-factors are summarized as the following:

• Building characteristics: floor area, floor number, building form, orientation, building type (residential or mixed-use and, if mixed-use, what kind), and monthly average in summer and winter for the dwelling and for the entire building.
• Socio-economic activities: household size in dwellings, household size in the buildings, if any, household breadwinner’s work type, working as a farmer or not, mobility to work, breadwinner’s work location, lifestyle, and finally, wife’s work status.
• Urban characteristics: location by urban fabric and location by road network

Additionally, the scientific terms in the questionnaire have been simplified and written in Egyptian dialectic, and each question has a rationale behind it. These questions are summarized as the following:

Diagnosing building location by the urban fabric

• Is the building surrounded by numerous buildings, like those near the grand mosque? This indicates that the compacted (traditional) fabrics, like the mosque in the center of the village, are characterized by high building density, e.g., Figure 3d and Figure 4a.
• Is the building by the main road? This indicates that the linear urban fabric and the main road in the village, which is usually typical for most Egyptian villages, are aligned with the water body, as seen in Figure 4c.
• Is it near the village’s boundaries, like those near the healthcare buildings or cemeteries? This signifies the scattered urban fabric, e.g., Figure 4b.

Diagnosing building location by road network

• What is the road width in front of your building: 2, 4, or 6 meters? The replies to this question indicated that the buildings located on 2 m roads are entirely located on the compacted urban fabric, e.g., Figure 3a; likewise, the 4 m roads complied with the position of the scattered, e.g., Figure 3b, and the 6 m road with the linear, e.g., Figure 3c,f.

Diagnosing building type (all residential or mixed-use)

• Is there any activity on the ground floor, such as a shop, pharmacy, studio, or dairy factory? If yes (like the one shown in Figure 3c), indicate the activity type.

Building Form

• Is the building regular (square form), elongated and regular (rectangular form), or is the house unusually elongated and irregular (with projections and recesses in the facade)? This answer relies on the landlord’s perception and description.

Thermal Comfort

• The respondents were asked whether they felt thermal comfort. Hence, the question was whether your building is “Bahary”, meaning orientation towards the north seaside, and meaning good conditions, or “Kibli”, meaning the building is oriented towards the south, expressing uncomfortable conditions.

It is worth mentioning that, regarding the query about the mean electricity consumption in winter and summer, the respondents were asked to report the consumption of the other units in the building; this is because most buildings typically house extended families or relatives, which are common characteristics of Egyptian rural settlements. Google Forms created the questionnaire; it clearly indicates that the proposal aims to improve energy efficiency in the village, and will be used in academic research, and no personal data, such as telephone number and name, are requested. Also, it is clear that the participants are village residents and that each family should answer only once. The questionnaires were distributed to three Facebook groups in the village in May 2021, one of which is as in Reference [73]. The original version of the questionnare is available online, at Reference [74].

Questionnaire Sample and Results

A total of 37 responses were collected. After excluding the invalid inputs, two respondents were not residents of the village, and three did not report energy consumption; 32 eligible replies remained. Analyzing the data collected (Supplementary Materials S1 and S2), the sample represents 83 families, with a total of 458 individuals. Figure 5a illustrates the attributes of buildings and urban settings. Figure 5b illustrates the socio-economic activity attributes of the households.

To detect the positioning of buildings, 12 buildings (38% of the total) are situated within the organic fabric, evenly spread across various road widths. Then, the linear fabric comprises 13 buildings (41% of the total), most facing the 6 m road. Finally, the scattered fabric bordered by agricultural areas contains seven buildings positioned along the 4 m and 6 m roads.

The respondents were asked to describe their feelings toward thermal comfort, and 59% of the respondents stated good thermal comfort, “Bahary”; the residents of the buildings located on linear and scattered fabric expressed good thermal comfort; on the contrary, the buildings’ residents situated on the traditional fabric expressed non-comfortability. That indicated such a correlation between the urban fabric and thermal comfort.

Concerning monthly energy usage, the household’s consumption varies from 50 to 495 kWh in summer and from 50 to 560 kWh in winter, with an average of about 200 kWh, supporting the consumptions reported by the local people within the interviews (Section 3.2.1). The study classified the buildings by electricity consumption; the average annual energy consumption varies from 1140 to 14,964 kWh, with a mean of 5678 kWh. The consumption can be grouped as the following: the 3001 to 4000 kWh range accounts for 19%, followed closely by the 1000 to 2000 kWh range, at 16%. The less common range of 8001 to 9000 kWh is attributed to a single building, while the remaining categories are distributed fairly evenly. The findings indicate that the minimum annual energy intensity for rural dwellings is 7.9 kWh/m²/year, and the maximum is 43.3 kWh/m²/year, with an average of 22 kWh/m²/year.

By work type, 41% are employees, 22% of respondents are freelancers (working depending on the task), like construction laborers, 13% are not working (unemployed and retired), and 9% are self-employed or own a business. Regarding work location, 37% work in the village, and 22% work either inside or outside the region. Finally, 16% of the population work outside Egypt (in the Arabic Gulf states). Regarding mobility to work, the majority, one-half, rely on public transportation (the so-called microbus), 23% walk to work, and 13% use their cars. The data indicates that workers in Lasifar go to work on foot and by public transportation.

Two-thirds of wives prefer to stay at home. The prevailing pattern of the respondents’ recreational activities were family visits, 53%, which came behind other activities, including spending time in coffee shops and the youth center. Small families implement these activities. Walking and going to Disouk City, 12% for each, were in third place.

Notwithstanding, the responses to the optional question were irrelevant to electricity consumption and were not significant, as only three respondents (9%) answered and provided three recommendations: firstly, the essential need for eco-friendly waste management to preserve the environment; secondly, because of the insufficient garbage removal process, it was recommended that there should be a collaborative approach by the local people to make sure the garbage was collected sufficiently; thirdly, it is necessary to cover the sewage drains located in the center of the village, which have a negative environmental impact.

3.2.3. Statistical Analysis

Statistical analysis is crucial for organizing and processing data scientifically and mathematically [75], and explains the relations between variables with four main measurement scales: ordinal, nominal (for qualitative data), ratio, and interval (for quantitative data) [76]. These can be summarized as the following:

Qualitative data type

• Ordinal scale measurements are data that have some orders and ranks.
• Nominal scale measurements refer to names of tags that do not have numerical values for categorizing such an object. In our study, they are represented by socio-economic variables (e.g., lifestyle, economic activity, and mobility to work) and built-environment variables (e.g., building position by road width or urban tissue and building form).

Quantitative data types

• Ratio (continuous) scale measurements with interval data qualities, like the natural zero point; in this study, they are represented by electricity consumption and building characteristics (e.g., footprint, floor number, household size, and built-up type).
• Interval scale measurements: the measured data on a scale showing the distance from one to another.

Therefore, according to the data types and variables, two statistical models were exploited to explain the relations between the dependent and independent investigated variables. Firstly, the Pearson Correlation Coefficient, revealed by Equation (1), is a commonly used technique for assessing the linear strength and direction of correlation between two continuous variables: x represents the independent variable value, and y represents the dependent variable value (electricity consumption). The r value is the correlation coefficient that lies between (−1 to +1), where (−1) designates a perfect weak correlation, while (+1) means a strong linear correlation, and (0) indicates no existing relation [77,78]. Many open-source resource calculators can facilitate the conducting of Pearson correlation formulas, such as Reference [79].

$$r={\displaystyle \frac{n\sum xy-\left(\sum x\right)\left(\sum y\right)}{\sqrt{n\sum x^2-(\sum x{)}^2 }\sqrt{n\sum y^2-(\sum y{)}^2 }}}$$

(1)

Secondly, the one-way ANOVA was exploited to observe the statistical correlation between the data and to enable the interpretation of one ratio variable (electricity consumption) with one nominal variable (e.g., urban fabric). It is among the most commonly utilized statistical methods for hypothesis examination [80], as it allows the descriptor analysis that poses significant differences between groups of one nominal independent variable (x), the so-called explanatory variable, in correlation with the ratio response (dependent) variable (y) [76,81]. In other words, the one-way ANOVA assesses the average variation within a group against the variation derived from group means. It also requires a minimum sample size of 30 [82] (the number of study responses is 32). The answers to each question are ranked by number, to set numerical values of the variable; for example, in the question on Lifestyle, the answers are ordered as (1) Family Visits, (2) Going to Desouk, (3) Walking, and (4) Other, and likewise, for each group of variables, where each group under investigated variables, is coded and ordered separately, as shown further on between Tables 3–8. The steps taken in the one-way ANOVA analysis have been described, simplified, and summarized in Appendix A.

The analysis took place using IBM SPSS Statistics software to predict, plan, and analyze data to confirm assumptions and accurate conclusions to resolve problems in business and research [83]. The IBM SPSS Statistics software (version 30.0.0) was developed by the International Business Machines Corporation (IBM); this tool facilitates steering simple and complex statistical and mathematical models, which proved accurate in steering multiple simple and complex statistical and mathematical analyses valid in academic research [84,85], and is commonly used to diagnose the correlation between EUI and buildings, similar to those in our study, such as Reference [86]. We confirm its effectiveness during the analysis.

The data utilized in performing the statistical analysis in this study are extracted from SPSS and made available in the Supplementary Materials (S1). The same file is converted to an Excel format and available at Supplementary Materials (S2).

Numerical Data: Pearson Correlation Coefficient

The analysis of the Pearson Correlation Coefficient between annual electricity usage and building characteristics revealed a very strong positive relationship between the number of floors and built-up area, with correlation values of +0.887 and +0.836, respectively. Additionally, the number of households and total buildings demonstrated strong correlations, of +0.604 and +0.6843. Conversely, the footprint of buildings exhibited a weak statistical correlation with electricity consumption, showing only a weak positive correlation of +0.34, with a probability value (p-value) equal to 0.055 (more than 0.05), which shows no statistical significance (more elaboration on the p-value is presented in Appendix A).

Table 2. Correlation coefficient between numerical data and annual building electricity consumption, developed by the Authors, after [72].

Aspect	r Value	p -Value	Statistical Significance
Footprint	+0.342	0.055	No
Built-Up Area	+0.836	0.000	Yes
Total Members	+0.604	0.000	Yes
Household Number	+0.6843	0.000016	Yes
Floor Number	+0.887	0.000	Yes

shows the correlation coefficient between residential building characteristics and electricity consumption.

Socio-Economic Activities

For investigating the correlation between lifestyle and electricity consumption, the results seen in Table 3, which indicated that the f-Value was 4.432, at a probability value (p-value) with a significance level equivalent to 0.011 (less than 0.05), indicating statistical significance variations among variables. Thus, post hoc testing to enable multiple comparisons is conducted, where the table shows the increase in the mean consumption for the group who spent their recreational activities going to the Disouk district, with an annual mean of 8397 kWh, and in family visits, 6654.35 kWh out of the total sample’s mean (5678.25 kWh). Therefore, post hoc testing is requested to decide which particular groups differed.

Table 4 shows the variance between lifestyle group variables, where the building residents perform family visits as a predominant social activity (Group 1), compared to Group 4, where the residents tend to carry out other activities, like spending time in the youth center and coffee shops, has a significant mean difference, of 4614.355 kWh, at a p-value of 0.018. Likewise, the difference between Group 2, where the residents prefer to spend recreational time in Disuk City, compared to Group 4, was 6357 kWh, at a p-value of 0.020. In comparison, there was no statistical significance between the other groups.

Table 3. Variation analysis between lifestyle and annual electricity consumption, developed by the Authors, after [72].

Group Code	Group Description	Mean (kWh)	Standard Deviation (SD)	f-Value	p-Value	Statistically Significant
1	Family Visits	6654.35	3577.10
2	Going to the Disouk	8397	4390.266	4.432	0.011	Yes
3	Walking	5178	2834.736
4	Other	2040	1074.430

Table 3. Variation analysis between lifestyle and annual electricity consumption, developed by the Authors, after [72].

Group Code	Group Description	Mean (kWh)	Standard Deviation (SD)	f-Value	p-Value	Statistically Significant
1	Family Visits	6654.35	3577.10
2	Going to the Disouk	8397	4390.266	4.432	0.011	Yes
3	Walking	5178	2834.736
4	Other	2040	1074.430

Table 4. Multiple comparisons between lifestyle variables and annual electricity consumption, developed by the Authors, after [72].

Lifestyle Groups with Electricity Consumption	Mean Differences Between Groups (kWh)	p-Value	Statistically Significant
(2) Going to the Disouk, (4) Other	6357	0.020	Yes
(1) Family Visits, (4) Other	4614.355	0.018

Table 4. Multiple comparisons between lifestyle variables and annual electricity consumption, developed by the Authors, after [72].

Lifestyle Groups with Electricity Consumption	Mean Differences Between Groups (kWh)	p-Value	Statistically Significant
(2) Going to the Disouk, (4) Other	6357	0.020	Yes
(1) Family Visits, (4) Other	4614.355	0.018

Table 5 shows the variation analysis between building form and the annual electricity consumption; the p-value is 0.783 for the educational level and electricity consumption variables, which means there is no observed effect between them.

Table 5. Variation analysis relating building form to yearly electricity consumption, developed by the Authors, after [72].

Group Code	Group Description	Mean (kWh)	SD	f-Value	p-Value	Statistically Significant
1	University Education	5030.57	2448.254
2	Above Intermediate	6516	3734.526	0.358	0.783	No
3	Secondary	6540	5021.819
4	Preparatory	5284	5685.529

Table 5. Variation analysis relating building form to yearly electricity consumption, developed by the Authors, after [72].

Group Code	Group Description	Mean (kWh)	SD	f-Value	p-Value	Statistically Significant
1	University Education	5030.57	2448.254
2	Above Intermediate	6516	3734.526	0.358	0.783	No
3	Secondary	6540	5021.819
4	Preparatory	5284	5685.529

Table 6 shows the variation analysis between work factor variables (work status, work location, mobility mean to work, wives’ work status) and electricity consumption.

• The buildings where the people work as farmers have the highest annual electricity consumption mean, of 10,084 kWh, while the lowest mean in the buildings where their residents are not working is 3030 kWh. However, the statistical analysis results indicated that the p-value equals 0.235 (more than 0.05), which means there is no statistical significance between work type and electricity-consumption variables. In other words, no observed effect and no multiple comparisons are required.
• By work location, the buildings of the people working inside the village have the highest mean, of 7230 kWh, and the lowest occurred in the buildings where their residents work abroad, in Egypt, of 3096 kWh. However, the statistical analysis revealed that the p-value equals 0.087; therefore, there is no observed effect between the work location and electricity consumption.
• With respect to mobility means to work, the lowest electricity-consumption average happened in the buildings where residents rely on public transportation, at 5214.35 kWh, and the highest was for the buildings where their residents tend to walk to work, while the other means have a similar average. However, the statistical analysis revealed that the p-value equals 0.911; therefore, there is no effect between mobility to work and electricity consumption.
• Based on the wives’ work status, the buildings where the wives work consume an average annual of 6133 kWh more energy than those where the wives do not work, which is 5440 kWh. However, the statistical analysis revealed that the p-value equals 0.626; therefore, there is no observed effect between the wife’s work status and energy consumption.

Table 6. Variation analysis between work characteristics and annual electricity consumption, developed by the Authors, after [72].

Group Code	Group Description	Mean (kWh)	SD	f-Value	p-Value	Statistically Significant
Work Type
1	Employee	5592.92	2533.883
2	Self-employed	6800	1705.872
3	Farmer	10,084	7706.227	1.465	0.235	No
4	Freelance	5412	4318.061
5	Retired	4170	466.690
6	No Work	3030	2833.161
Work Location
1	Lasaifar Albalad	7230	4827.620
2	Kafr Elshiekh Gov.	7145.14	2480.721
3	Outside Delta	3658.29	1808.054	2.277	0.087	No
4	Outside Egypt	3096	1648.417
5	No Work	3840	0
Mobility to Work
1	Public Transportation	5214.35	3101.115
2	Private Car	6132	3639.398	0.177	0.911	No
3	Walk	6316.50	5490.798
4	Other	6000	3186.785
Wife’s Work Status
1	Working Wife	6133.09	3074.036
2	Housewife	5440	4092.959	0.242	0.626	No

Table 6. Variation analysis between work characteristics and annual electricity consumption, developed by the Authors, after [72].

Group Code	Group Description	Mean (kWh)	SD	f-Value	p-Value	Statistically Significant
Work Type
1	Employee	5592.92	2533.883
2	Self-employed	6800	1705.872
3	Farmer	10,084	7706.227	1.465	0.235	No
4	Freelance	5412	4318.061
5	Retired	4170	466.690
6	No Work	3030	2833.161
Work Location
1	Lasaifar Albalad	7230	4827.620
2	Kafr Elshiekh Gov.	7145.14	2480.721
3	Outside Delta	3658.29	1808.054	2.277	0.087	No
4	Outside Egypt	3096	1648.417
5	No Work	3840	0
Mobility to Work
1	Public Transportation	5214.35	3101.115
2	Private Car	6132	3639.398	0.177	0.911	No
3	Walk	6316.50	5490.798
4	Other	6000	3186.785
Wife’s Work Status
1	Working Wife	6133.09	3074.036
2	Housewife	5440	4092.959	0.242	0.626	No

Urban Fabric and Building Characteristics

Table 7 shows the variation analysis between the variables of urban fabric (urban fabric and road network) and building characteristics (building form and floor numbers) and electricity consumption

• With respect to building form, the results revealed that irregular buildings consumed the highest annual electricity consumption, a mean of 11,352 kWh, nearly double that of each different form. However, the statistical analysis results indicated that the p-value equals 0.078; hence, there is no observed effect between building form and electricity consumption.
• With respect to floor number, the results confirmed a positive link between the floor number and energy consumption; the five floors had the highest consumption of 14,072, while the one-floor buildings consumed an annual average of 1560 kWh. Although it makes sense that the higher the floor number, the more consumption, likewise, for the footprint variable, the results revealed that the p-value is less than 0.001 (a very high significance level). This means there is a statistical significance for between-group variables, so post hoc testing to enable multiple comparisons is conducted, to determine the mean differences. Table 8 shows the variance between the floor-number group variables, confirming this strong positive correlation.
• With respect to urban fabric, the buildings on the scattered fabric consume the highest annual average, of 6378.86 kWh, while the buildings at the linear fabric consume the lowest yearly average, of 4572.92 kWh. However, the statistical analysis results indicated that the p-value equals 0.626; hence, there is no observed effect between building location by urban fabric and electricity consumption.
• With respect to building position on the road network, buildings on roads with a width of 6 m consume the highest annual average, of 6870.40 kWh, followed by 4 m and 2 m width roads; this indicates a positive correlation between the width of the road and electricity consumption. However, the results showed that the p-value equals 2.218, meaning that no statistical effect was observed between location by urban fabric and electricity consumption.

Finally, Figure 6 depicts a visual summary of the mean electricity consumption of the factors examined in the questionnaire.

Table 7. Variation analysis between building and urban characteristic variables and annual electricity consumption, developed by the Authors, after [72].

Group Code	Group Description	Mean	(SD)	f-Value	p-Value	Statistically Significant
			Building Form
1	Rectangular Form	5081.45	3253.906
2	Oblong Form	5467.06	3788.301	2.785	0.078	No
3	Irregular Form	11,352	2562.555
			Floor Number
1	One Floor	1560	5321.165
2	Two Floors	4128.86	1983.013
3	Three Floors	5893.50	1799.388	30.937	<0.001	Yes
4	Four Floors	9432	806.146
5	Five Floors	14,072	900.107
			Urban Fabric
1	Traditional Fabric	5492	3659.470
2	Linear Fabric	4572.92	3823.805	0.242	0.626	No
3	Scattered Fabric	6378.86	4200.079
			Road Network
1	Road 2 m	3420	1535.187
2	Road 4 m	5470.80	3089.142	2.218	0.127	No
3	Road 6 m	6870.40	4436.363

Table 7. Variation analysis between building and urban characteristic variables and annual electricity consumption, developed by the Authors, after [72].

Group Code	Group Description	Mean	(SD)	f-Value	p-Value	Statistically Significant
			Building Form
1	Rectangular Form	5081.45	3253.906
2	Oblong Form	5467.06	3788.301	2.785	0.078	No
3	Irregular Form	11,352	2562.555
			Floor Number
1	One Floor	1560	5321.165
2	Two Floors	4128.86	1983.013
3	Three Floors	5893.50	1799.388	30.937	<0.001	Yes
4	Four Floors	9432	806.146
5	Five Floors	14,072	900.107
			Urban Fabric
1	Traditional Fabric	5492	3659.470
2	Linear Fabric	4572.92	3823.805	0.242	0.626	No
3	Scattered Fabric	6378.86	4200.079
			Road Network
1	Road 2 m	3420	1535.187
2	Road 4 m	5470.80	3089.142	2.218	0.127	No
3	Road 6 m	6870.40	4436.363

Table 8. Multiple comparisons between floor-number variables and annual electricity consumption (kWh).

Floor Number with Electricity Consumption	Mean Differences Between Groups	p-Value	Statistically Significant
(5) Five Floors: (1) One Floor	12,512	>0.001
(5) Five Floors: (2) Two Floors	9943.143	>0.001
(5) Five Floors: (2) Three Floors	8178.500	>0.001	Yes
(5) Five Floors: (2) Four Floors	4640	0.002

Table 8. Multiple comparisons between floor-number variables and annual electricity consumption (kWh).

Floor Number with Electricity Consumption	Mean Differences Between Groups	p-Value	Statistically Significant
(5) Five Floors: (1) One Floor	12,512	>0.001
(5) Five Floors: (2) Two Floors	9943.143	>0.001
(5) Five Floors: (2) Three Floors	8178.500	>0.001	Yes
(5) Five Floors: (2) Four Floors	4640	0.002

3.2.4. Techno-Economic Assessment of Installing PV

In the current regulatory framework, the net-metering scheme, first adopted in 2013, allows for an energy purchasing agreement between three parties, the Egyptian Electric Utility, and the Consumer Protection Regulatory Agency, responsible for implementing policy decisions and administering licenses, and the consumer. This enables an energy-purchasing agreement to be made between a qualified third-party company (operating and managing the plant) and a client who benefits from the purchased energy and from making a contract with the electricity company.

In a nutshell, the Electricity Distribution Company has to be contacted for supervision and to check if the roof is suitable for PV. Then, the necessary permit is obtained from the local authority. The qualified third party (accredited by the Ministry of Electricity) is responsible for reviewing and approving plans to ensure compliance with the PV plant installation regulations. Eventually, the third party will notify the distribution company to conduct a final inspection and approve the installation. An approved certificate for the implementing company is issued by the New and Renewable Energy Authority, and the contract between the implementing company and the project owner is launched to implement the project within 6 months of approval; all required data and procedures are available on the official solar energy platform [87], which it also provides estimators for PV size capacity and all fees [88].

Technically, designing and optimizing PV systems for Egyptian rural communities is promising, as Egypt lies in the Sunbelt region, which ensures very high monthly mean sunshine hours [88]. The studied area (North of Egypt’s Nile Delta) has about 180 kWh/m² monthly, and a 5 kW solar-power generation unit is suitable to be placed above an average house [24]; as well as this, it is the minimum PV system size accepted under a net-metering scheme. The technicalities of designing and installing PV systems for residential buildings in rural Egypt are discussed in depth in Reference [26], and for all buildings (residential and non-residential) in Reference [89], and have a lifetime of 25 years. Six scenarios have been developed to evaluate the technical performance based on the electricity-consumption tariff, employing a PV unit with 5 KWp, assuming that each segment’s group consumes the maximum load.

To determine the financial aspects, the First Author contacted some qualified PV local suppliers available on the official website [90], to collect financial quotations to update the pricing and explore their insights on the issue. Three local suppliers have been engaged; a manager of one requested to contribute anonymously, and the other two qualified companies provided the prices of USD 3655 and 3750, including all equipment (PV cells, inverter, wires, fixation steel structure, and transportation to the village), including the two-way meter from the electricity company, in the same context. Gabr et al. [91] estimated the cost to be USD 3125 (in 2020), which is in accordance with the PV Hub’s estimated price of USD 3045 [92]. The selling price of the kWh generated from RE is about USD 0.016. The residential electricity tariff is divided into seven segments, as shown in Table 9. Public buildings also belong to the residential tariff.

Two economic models were utilized to investigate the profitability of PV systems under six scenarios. First, the simple payback period (SPP) analysis, Equation (2), is the projected years required to regain the initial investment that occurs when the cumulative net cash flow equals zero [93]. The net present value (NPV), Equation (3), represents the present value of cash inflows minus cash outflows, determined by the expected net cash flow (CF) in a particular time (t). This is related to the project at a given discount rate, which is 27%, according to Central Bank of Egypt, which provides accurate profitability, considering different aspects like inflation and so on. Profitability is determined when the value becomes positive in a particular year, and vice versa; the project becomes unprofitable if the values become negative [94]. Table 10 shows the economic assessment of the PV units.

$$SPP={\displaystyle \frac{Initial Investement }{Net cash flows per period }}$$

(2)

$${\sum }_{t=0}^n{\displaystyle \frac{CFt}{{\left(1+r\right)}^t}}$$

(3)

Table 9. The residential electricity tariff for the fiscal year 2024/2025, the equivalent expenditure of the maximum consumption per segment, and the selling price of electricity generated (to be read in conjunction with Table 10).

Segment	1st	2nd	3rd	4th	5th	6th	7th
Consumption range (kWh) *	0–50	50–100	101–200	201–350	351–650	651–1000	>1000	Selling price of kWh/RE
Price (EGP) *	0.68	0.78	0.95	1.55	1.95	2.10	2.23	0.016
Price (USD) **	0.014	0.016	0.019	0.031	0.040	0.043	0.045
Maximum expenditure according to consumption
Monthly (kWh)	50	100	200	350	650	1000	N/A
Annually (kWh)	600	1200	2400	4200	7800	12,000
Monthly (USD)	0.7	1.6	3.8	10.85	26	43
Annually (USD)	8.4	19.2	45.6	130.2	312	516

* According to the new Electricity Tariff on 9/2024 [95]. ** The exchange rate of 1 USD is about 49.26 EGP, according to the Central Bank of Egypt in November 2024 [96].

Table 9. The residential electricity tariff for the fiscal year 2024/2025, the equivalent expenditure of the maximum consumption per segment, and the selling price of electricity generated (to be read in conjunction with Table 10).

Segment	1st	2nd	3rd	4th	5th	6th	7th
Consumption range (kWh) *	0–50	50–100	101–200	201–350	351–650	651–1000	>1000	Selling price of kWh/RE
Price (EGP) *	0.68	0.78	0.95	1.55	1.95	2.10	2.23	0.016
Price (USD) **	0.014	0.016	0.019	0.031	0.040	0.043	0.045
Maximum expenditure according to consumption
Monthly (kWh)	50	100	200	350	650	1000	N/A
Annually (kWh)	600	1200	2400	4200	7800	12,000
Monthly (USD)	0.7	1.6	3.8	10.85	26	43
Annually (USD)	8.4	19.2	45.6	130.2	312	516

* According to the new Electricity Tariff on 9/2024 [95]. ** The exchange rate of 1 USD is about 49.26 EGP, according to the Central Bank of Egypt in November 2024 [96].

Table 10. The economic assessment of assumed scenarios for residential and non-residential buildings (to be read in conjunction with Table 9).

Segment Group	Annual Consumption (kWh)	Annual Expenditure in Electricity (USD)	PV Unit Generation (kWh)	Surplus in Electricity Generation (kWh)	Selling Price (0.016 USD/kWh)	Economic Benefit (Saving + Generation) (USD)	Initial Cost (USD)	Economic Profitability
								SPP	NPV at 25 Years (USD)
First	600	8.4		8500	136	144.4		24.9	−3066
Second	1200	19.2		7900	126.4	145.6		24.7	−3062
Third	2400	45.6	9100	6700	107.2	152.8	3600	23.6	−3035
Fourth	4200	130.2		4900	78.4	208.6		17.3	−2829
Fifth	7800	312		1300	20.8	332.8		10.8	−2370
Sixth	12,000	516		−2900	−46.4	469.6		7.7	−1865

Table 10. The economic assessment of assumed scenarios for residential and non-residential buildings (to be read in conjunction with Table 9).

Segment Group	Annual Consumption (kWh)	Annual Expenditure in Electricity (USD)	PV Unit Generation (kWh)	Surplus in Electricity Generation (kWh)	Selling Price (0.016 USD/kWh)	Economic Benefit (Saving + Generation) (USD)	Initial Cost (USD)	Economic Profitability
								SPP	NPV at 25 Years (USD)
First	600	8.4		8500	136	144.4		24.9	−3066
Second	1200	19.2		7900	126.4	145.6		24.7	−3062
Third	2400	45.6	9100	6700	107.2	152.8	3600	23.6	−3035
Fourth	4200	130.2		4900	78.4	208.6		17.3	−2829
Fifth	7800	312		1300	20.8	332.8		10.8	−2370
Sixth	12,000	516		−2900	−46.4	469.6		7.7	−1865

The results indicated that the installed PV system led to positive energy consumption, with lower consumption and higher generation for PV units. Groups 1 and 2 achieved significant RE production, and the third and fourth achieved 279% and 117%, respectively. In the fifth group, the generated electricity represented 16% of the total, and the sixth indicated that the proposed system generated 75% of the annual consumption. The required size to achieve zero energy is 7 KWp. The SPP analysis revealed that in all scenarios within the lifetime of PV system (25-year period), the higher the RE generation, the longer the period. However, the NPV indicated that not all systems are profitable.

4. Discussion

In 2022, Egypt’s GHG emissions totaled 319.69 mt CO₂eq. The electricity and building sectors are responsible for one-third of these emissions, showing how much energy-efficiency intervention in both sectors significantly contributes to reducing emissions and supporting the state’s energy and climate policies related to building. This is in addition to reaching the targets and climate-action commitment by 2030, especially in rural areas that host three-fourths of the building sector, about 65% of the population in Egypt, and consume one-third of the distributed electricity. This study aimed to characterize the electricity consumption trends in rural buildings, and found its association with the built environment and socio-economic patterns by fostering a novel approach not conducted before in the local context. The Lasiafr Albalad rural settlement has been selected to represent typical agriculture-based settlements and rural–urban tissue, representing 339 main rural settlements in the Delta region that are predominately dedicated to agricultural activities and land areas.

The study provided a holistic exploratory approach, combining different methods: the analytical method identified the main characteristics of the ultimate context, using national census and official reports, which better understood the built environment in the Delta region, including tangible and intangible aspects, like urban setting, building types, socio-economic factors, and energy profile. Then, the field method, including an in situ survey, one-to-one interview, and questionnaire, diagnosed the intimate context of the village, including locals’ lifestyle and building energy consumption, in addition to spatial analysis, leading to the production of the first online, open-source editable map. Then, the statistical analysis examined and quantified the relationship between all these factors, utilizing the Pearson Correlation Coefficient for numerical variables and one-way ANOVA to examine one qualitative variable with one numerical one. After that, technical and economic assessments were conducted to explore the rooftop PV under the net-metering scheme. This thoroughly and effectively addressed the research questions presented in the sections below.

4.1. Addressing RQ1

An intensive on-site investigation lasted two months; the urban characteristics and architectural typologies were diagnosed within this. A sample of the local community, who volunteered and were enthusiastic about contributing to improving the village, was informed about the intervention to explore socio-economic activities and understand the possible benefits of the research. They shared their thoughts about electricity consumption, and the interviews revealed that the small-dimension economic activity dramatically affects electricity consumption, requiring lighting to provide a suitable environment for raising birds and poultry. These findings are contrary to another study conducted by the First Author [97], to diagnose the correlation between small-dimension fishing activities in a rural coastal settlement in the Delta region, which indicated no relationship between small-dimension fishery economies and residential electricity consumption, as well as the fact that changing economic activities lead to different electricity consumption patterns, as has been discussed by Elsabbagh et al. [51], and Abdelhay [50].

The electricity-use intensity of the non-residential buildings is between 2.5 and 18 kWh; the health-unit building alone has the highest consumption. It was noticed that the lighting demand is not affected, due to daily operating hours being in the morning, unlike the dwellings, in which the lighting represents about one-half of the consumption, as per the official census. This also showed the tendencies of household consumption patterns; these tendencies have confirmed the finding of a bottom-up intervention conducted by the First Author to examine the local social acceptance of proposed retrofitting solutions [69]; likewise, our study contributes another knowledge dimension to this issue.

The data collected from the questionnaire showed that the sample’s data were homogeneous and distributed normally. Based on the analysis, the annual electricity use intensity of the dwellings ranges between 7.9 and 43.3 kWh/m², with a mean of 21 kWh/m², where the population sample revealed that the yearly residential electricity is 534 kWh/capita, with a daily average of 6.67 kWh/dwelling.

These results complied with the study of Salama and Hassan, which reported that the monthly expenditure for a household is 232 EGP [49]. We converted this expenditure, which belongs to the fourth segment (according to 2020 tariff prices), into about a monthly 282 kWh and daily 8.5 kWh, which is above the average indicated in rural areas in different regions in Egypt. These are 5.075 kWh/day/dwelling [26] and from 1 to 2.2 kWh/day, with an average of 1.6 kWh/day [98] in the Asyut region, 3 kWh/day in the Greater Cairo region [24], and 2.936 kWh/day in the South Upper region [88].

For similar contexts, our results came below the average in rural Morocco, which has 11.26 kWh [99], and slightly above the studies of Ghafoor et al. [100] in Pakistan and those of Almutairi et al. [101] in Iran, who indicated 5.9 and 5.625 kWh/day/dwelling, respectively; they were significantly above the averages indicated by Mahmoud and Ibrik in Palestine, at 1.75 kWh [102], and those of Sukarno et al. in Indonesia, at 1 kWh/day/dwelling (the annual consumption is 300 kWh) [103]. Figure 7 depicts the energy-consumption benchmark of rural houses in Egypt and similar contexts.

These presented values can give a significant benchmark to the Egyptian rural units, considering that some of these contexts are remote villages with inadequate electrification, unlike the Delta’s high-density and 100%-electrified villages. This indicates a correlation between economic activities and energy consumption, as found in our study and by Salama and Hassan in similar agriculture-based settlements in the Suez Canal region.

The EUI in rural areas in Egypt remains too low compared to the urban contexts, at nearly less than one-fifth, compared to Nafeaa et al.’s findings [46], which indicated an EUI of 110 kWh/m². The average we found is within the range stated by Attia et al. [47]. However, again, neither study explicitly states whether the context is rural or urban. These variations also occur globally; for example, the EUI of residential, rural buildings in China is higher than that of urban areas by one-fourth [104]. Therefore, this study explored a case study to fill this gap and create momentum to classify and benchmark rural electricity consumption.

Another insight was collected from the study of Mustafa et al. [22] in Saudi Arabia, who benchmark the EUI of rural dwellings in the southwest region at 134 kWh/m², with a range of 125.6–182.4 kWh/m² for other regions. Thus, we advocate for scholars to fully define the consumption patterns of their case studies and data collection to support academic efforts in diagnosing rural building energy consumption, and which, in turn, fills significant gaps, as discussed within this article.

The study investigated the correlation between residential electricity consumption in numerous groups of variables, including five for numerical demographic and building features, presented in Table 2. There were also 10 nominal groups of variables, with a total of 41 sub-variables, as visualized in Figure 6, representing the socio-economic aspects and built environment. The diagnosed correlation can help pave the way to understanding the broader factors influencing energy behaviors and transition acceptability [105]. The study revealed an observed statistical correlation between social activities, namely home visits and going to nearby districts, with electricity consumption.

Meanwhile, a robust positive correlation has been confirmed between electricity consumption, household size, occupancy rate, floor numbers, and total built-up area. For the latter, our results are, notably, contrary to the findings of Hegazy et al. [48], whose results indicated a negative correlation for rural buildings in the same region; the higher built-up area has the lowest consumption. This variation might have occurred because their study was limited to simulation predictions with fixed operational activities for all scenarios.

Also, our findings supported Salama and Hassan’s findings that the more members, the more the electricity consumption. Finally, some insights were observed, like a positive relationship between the buildings in which the residents expressed good thermal conditions and those located on linear and scattered fabric; vice versa, for the people who described the worst thermal conditions, most buildings were situated in traditional urban fabrics. This requires deep investigation at a regional scale to draw, in a better way, the tendencies of the most influencing aspects of domestic electricity consumption.

4.2. Addressing RQ2

PV is a game-changer in the clean-energy transition for different buildings: residential (the majority) and public buildings. Egypt’s conducive climate, with high monthly sunshine hours, supports the installation of 5 kW solar units, suitable for average homes. The minimum PV system size aligns with net-metering regulations. The financial aspects were assessed using quotations from local suppliers; two economic models, the SPP and NPV, were used to evaluate the profitability of PV systems. The results revealed that the SPP could be feasible for the sixth-segment (651–1000 kWh) dwellings in year 11 and the seventh-segment (more than 1000 kWh) in year 8, as the SPP does not consider the discount rates and inflation. However, the NPV confirmed that PV rooftops on residential buildings are not economically-feasible, and the NPV of the projected life cycle is USD −1865 in the optimistic scenario, namely for the highest segment consumption, above 1000 kWh monthly.

Our finding supports Tsuchiya et al.’s conclusion that investments do not adequately cover current operational expenses costs of PV for rural villages in Tanzania [106], and are similar to those in Aref et al.’s study [107], which indicated that the SPP obtained in year 7.2 and the net present cost was, throughout the 25-year prediction period, under a discount rate of up to 15% (the year thet the profitability achieved was not explicit). However, their study differs from ours, as they conducted off-grid solutions for all the dwellings in a small, remote, rural Pakistani village, populated by 800.

Nafeh [108] opposes our finding that off-grid and on-grid PV is advantageous and ideal for long-term investments in rural, remote areas in Egypt. This is similar to Çamur et al. [109], who indicated that the prices of the energy generated from on-grid rooftop PV in Lebanese dwellings are competitive. Despite that, we would emphasize that the economic assessment of both studies estimated the discount rate (10% and zero) in Egypt and Lebanon, respectively.

In Poland (one of the economies most reliant on non-RE energy in Europe) Rej-Witt and Dębska simulated installing PV on a rural dwelling with the same power size as that used in our study (5 kW); they found the payback period at 4.3 years, because they conducted the economic assessment based on 20% of the total investment amount, assuming that the relevant financing scheme of the European Commission can co-finance the remaining 80% [110]. This shows the essential role of top-down support.

The long period for a return on investment raises crucial questions: who should initiate it? We would say that the government can lead the integration of PV solutions into national rural-development projects. Also, the private sector can invest in carbon offset and carbon credits in the field of carbon transformations, and work in this way to benefit from selling the carbon credits. The latter, fortunately, became a fact: in August 2024, the first voluntary market of carbon credit was established to promote green energy, which might encourage the private sector in investing in installing PV.

Although the potential benefits from PV toward clean-energy transition, especially in rural areas in Egypt and in similar climatic conditions, as well as the consumption patterns, was benchmarked, and its correlation with built environments and socio-economic factors diagnosed, many risks should be considered while conducting similar approaches. For example, Abdulhady and Metwally [111] classified the risks into the appropriate designs to adapt to the effects of climate conditions on PV system performance; dust accumulation significantly affects PV output. Measurements show a 10% reduction from rain and about 17% due to storms and dust. The impact is more significant in desert areas and less severe in regions with frequent rainfall (like the Delta region), and the shading is the most influential parameter on the PV module efficiency. Degradation manufacturers of solar PV systems typically guarantee a performance lifespan of 25 years. They assure that panels will produce at least 90% of their rated capacity in the first 10 years and around 80% in the following 10 to 15 years. This requires developing operational and technical manuals and building capacities for regular maintenance, besides designing PV systems to fit each climatic zone. On the other hand, we recommend that the qualified companies provide risk mitigation plans, case by case.

A recent policy paper was released by the IDSC Public Policy Forum, the Information and Decision Support Center—the Egyptian Cabinet has stated that Egypt struggles to attract foreign direct investment, and investors face difficulties due to unstable exchange rates and the significant disparity between the EGP official and black market rates, which inflate costs for components and raw materials. This is especially the case, in that regulatory barriers and intense market competition also hinder investment. The cost of solar panels and import and customs fees have risen significantly, impacting the overall affordability of home solar systems [112].

In this realm, a representative of a local PV supplier (who did not authorize us to mention his company’s name in the study) emphasized the fact that the issue is too challenging, based on three concerns: the readability of infrastructure in rural Egypt, building construction conditions, and the very high initial cost versus the extended return on the investment period. Consequently, this barrier should be considered in terms of the holistic economic situation. The discount rate is too high, meaning that the return on initial investment takes a long time, requiring that scholars in the financial domain review this issue. For example, if the discount rate dropped to 10%, as it was for many years before the current economic conditions, a higher profitability of greater than 1000 kWh/month would be achieved in the 16th year, which is also high.

Not only are the economic aspects a barrier, but we also advocate that considering the social aspects, namely, raising awareness of the local community, is crucial. An apparent example, fortunately, is that the authors conducted a pilot experiment to install a roof-top PV unit, off-grid in 2022, in a social building; the building was zero-energy consumption for nearly two years, the project financed by the private sector through a participatory approach [27]. Unfortunately, while writing this article, the landlady informed us that someone had broken the PV panel, as seen in Figure 8.

5. Conclusions and Implications

To conclude, this study has investigated a typical built-environment pattern in rural settlements in the Delta region of Egypt. The pilot experiment succeeded in quantifying domestic electricity consumption and finding novel statistical observations and correlations between the built environment and domestic electricity consumption. It provided a better understanding that can pave the way to achieve the ambitious goal of retrofitting the rurally built environment in Egypt, as an initial step from a bottom-up perspective, which supports the growing research tendencies at local levels and global ones [14]. These results led to a better understanding of domestic energy consumption, as a readiness step to improve energy efficiency, as emphasized in a similar context [113]. In addition to the technical efficiency of buildings and related activities, it is vital to fulfill global sustainability goals [114] and to inform practices like retrofitting and energy planning.

The novelty of this study is characterized by its holistic nature, and it defined the current status quo of electricity consumption and its role in associated GHG emissions in rural Egypt; it also diagnosed the built-environment patterns of rural settlements, as the EUI benchmarked, in an exploratory study. As far as we know, this is the first time this has been carried out in Egypt, engaging stakeholders (local community and the private sector), as it fostered different data-collecting and processing methodologies, and benefits energy transition [115].

The low electricity consumption, indicated from the results, could be a solid potential for reaching the goal of zero-energy buildings and positive clean-energy production. This can act as a tool for revitalizing rural commons and serving urban areas, especially by generating clean energy from rooftop PV panels, under the net-metering scheme. This proved the significant surplus in the generated clean energy, as the study promoted rooftop PV on-grid electricity in 100%-electrified rural settlements, unlike the prevailing literature in Egypt, which focused on off-grid solutions for remote areas. Moreover, the study filled an essential gap, by determining the most-influencing factors and correlations to accelerate the clean-energy transition, besides setting a benchmark consumption to support tailoring energy-efficiency strategies and improve energy efficiency, in compliance with the arguments presented in Section 1.1.2 and Section 1.1.3. The analysis was performed for rooftop PV with an installed capacity of 5 KW under the regulatory framework of the net-metering scheme. It estimated six electricity-generation scenarios for residential and non-residential buildings, following the maximum consumption of each consumption segment.

Manifestly, financial aspects burden the local people; in other words, the local people may prefer to invest in banks with a high interest rate (exceeding 20%) rather than in PV, apart from the already low monthly expenditure on electricity bills. Accordingly, the economists’ inputs are beneficial in addressing this issue; likewise, anthropologists and all relevant domains regarding clean-energy transition prepare the local people (for example, the possibility of breaking the PV panel) and consider the local people’s preferences. The role of scholars from the architecture and urban-planning disciplines who work in energy efficiency and retrofitting rural buildings is crucial to integrating innovative RE solutions for such interventions. In fact, we would emphasize the fact that fostering transdisciplinary approaches is inescapable for the clean-energy transition.

What is more, the private sector can make a significant contribution by providing business plans to help accommodate RE solutions or energy-efficient technologies, such as long-term payment plans, and by grasping the opportunities of numerous banks providing green loans to apply for PV panels for residential buildings.

The Authors would accentuate that no roadmap exists without exploratory studies and narration. Also, the scholars’ role as “knowledge brokers” and the bottom-up practices remain essential to support the momentum of creating a self-sufficient dogma that has become a demand not only for Egypt, but for the entire world, amid all the unprecedented extreme climate and geopolitical fluctuations. Into the bargain, this period, particularly, is a golden opportunity, because the state is already developing the national project Decent Life; the First Author intends to present this study to the decision-makers.

An apparent example that many scholars were advocating to localize the PV industry, is summarized by the First Author in reference [28]; fortunately, in 2024, the Prime Minister’s Resolution No. 2732 has recently established a national council called the “National Council for Localizing the Technology of Manufacturing Electronic Chips and Solar Cells”. The council aims to review legislation regulations, follow up on the implementation of governmental commitments, and identify investment opportunities and the necessary solutions to overcome the obstacles to investment in this field. This reflects the tendencies toward clean-energy transition and liberalizing and decentralizing the electricity sector.

5.1. Policy Implications

Diagnosing electricity consumption patterns and their correlation with socioeconomic activities can provide valuable insights for policymakers. This information can inform the development of effective energy policies that promote sustainable energy use, reduce energy poverty, and support economic growth, supporting the argument of Gouveia et al., which addressed energy policy gaps associated with non-residential buildings within the Mediterranean climate [116], and policy- and design-oriented notions of energy planning [117], as well as rural areas. These are essential for thriving energy transitions, and vice versa; they can boost sustainable rural development [118]. Our findings support filling the gap between predicted and actual consumption patterns, similar to Tam et al.’s approach.

Although the landscape in Egypt is transiting towards a clean energy future and a liberalized electricity sector, it is quite evident that, under the current net-metering schemes, the minimum size required is 5 kW, which is not feasible at all for individuals. Therefore, the policies can be tailored to enable the use of energy cooperatives in agricultural practices (e.g., farms and structures) or buildings. Maybe the agriculture association could lead this role, particularly as it already plays a significant role in agriculture-based communities. Also, affording different RE sources, like biomass, can be beneficial, through updating the NEEAP and relevant policies with a specific target, such as raising awareness, training, incentives, facilitating retrofitting buildings and farm structures, and so on, or as implemented with solar energy (the most used RE source), in addition to collaboration with the private sector, like incentives. Actually, top-down development and government support are inevitable to afford clean energy, as discussed by Rej-Witt and Dębska [110], and these factors can increase the mix, reaching 42% RE sources.

This intervention can provide guidelines for developing infrastructure and rural built environments linked with national projects, such as Decent Life, or within planning new agricultural communities, a core strategy of the Egyptian rural development policies to expand the inhabited areas. Also, policymakers can design targeted energy-efficiency programs that address the needs of different socio-economic groups. Also, to tailor buildings, the energy-efficiency codes that provide minimum energy-efficacy requirements for new and existing buildings, according to the national macro-climatic classification [119], might be insufficient, due to the variety of micro-climates as crucial influencers of energy consumption [120].

Our study quantifies the domestic electricity-consumption patterns, making rural commons qualified to be energy communities, and can act as energy baskets for serving urban areas. Eventually, understanding the factors driving electricity consumption can help policymakers develop effective strategies to reduce GHG emissions associated with energy use and to downscale these practices into local administrative-unit levels, which have to incubate more bottom-up initiatives and engage more stakeholders who, in turn, can support the overall policies, and will meet the targets of NEEAP and climate action in 2030, and the RE share in 2035, and beyond.

5.2. Limitation and Future Direction

Despite the study’s holistic nature, the authors declare it has limitations related to the context, as different target cases can lead to different results. Also, the examined non-residential buildings vary notably in terms of orientation, size, village population, occupancy, and operational activities; consequently, they might lead to a different EUI. Furthermore, examining different factors and widening the population sample and case studies are encouraged, to support the reliability of the results. On the other hand, using more advanced statistical models like two-way ANOVA and multi-criteria decision analysis can discover more indicators of more rural settlement patterns, like the remote ones [121].

Also, an important indicator was observed from the open-ended question in the questionnaire; despite it being based on a few respondents’ standpoints, it can give an indicator that the locals may prefer to mitigate the negative environmental impacts of the waste and drainage before improve energy efficiency, which refers to the importance of widening the in situ investigations by scholars from environmental science domains to be integrated with our study. More elaboration to examine the thermal comfort factor, hand-in-hand with defining the equipment and appliances in further studies, would be beneficial to classifying domestic consumption. The study focused on solar energy; hence, considering different RE sources like biomass and wind energy, can be helpful.

Finally, the research discussed a specific local context; the methodology can be replicated in all settlement patterns in the Egyptian governorates, which account for 4741 villages and 30,888 satellite ones. It could also provide indicators for similar contexts in the built environment within the Mediterranean climate, either for the authors or interested scholars, accelerating the solving of a real-world problem and implementing an on-the-ground practice for leading energy development practices to mitigate climate change and the high environmental challenges in rural settlements.