Review Special Issues

Activity and efficiency of the building sector in Morocco: A review of status and measures in Ifrane

  • One-third of all greenhouse gas emissions come from the world's building stock while accounting for 40% of global energy use. There is no way to combat global warming or attain energy independence without addressing the inefficiency of the building sector. This sector is the second consumer of electricity after the industrial sector in Morocco and is ranked third emitter after the energy sector and transportation sector. Using Ifrane as a case study, this paper examines and reviews the city's energy use and the initiatives taken to improve building efficiency. The findings showed that, during the analyzed period, i.e., from 2014 to 2022, Ifrane's annual electricity consumption climbed steadily from 35 to 43 GWh. The government of Morocco has implemented effective laws, guidelines and regulations, as well as publicized ways to reduce energy consumption and increase energy efficiency. However, gathered data and survey results revealed opportunities and challenges for enhancing Ifrane's efficient energy use.

    The study also evaluates government programs, codes/standards and related actions for the improvement of household energy efficiency. As part of the review, the available literature was analyzed to assess the effectiveness of energy behavior and awareness, the impact of an economical and sustainable building envelope, the impact of building retrofitting programs, the importance of energy-performing devices and appliances, the adoption of smart home energy management systems, the integration of renewable energies for on-site clean energy generation and the role of policies and governance in the building sector in Ifrane. A benchmark evaluation and potential ideas are offered to guide energy policies and improve energy efficiency in Ifrane and other cities within the same climate zone.

    Citation: Hamza El Hafdaoui, Ahmed Khallaayoun, Kamar Ouazzani. Activity and efficiency of the building sector in Morocco: A review of status and measures in Ifrane[J]. AIMS Energy, 2023, 11(3): 454-485. doi: 10.3934/energy.2023024

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  • One-third of all greenhouse gas emissions come from the world's building stock while accounting for 40% of global energy use. There is no way to combat global warming or attain energy independence without addressing the inefficiency of the building sector. This sector is the second consumer of electricity after the industrial sector in Morocco and is ranked third emitter after the energy sector and transportation sector. Using Ifrane as a case study, this paper examines and reviews the city's energy use and the initiatives taken to improve building efficiency. The findings showed that, during the analyzed period, i.e., from 2014 to 2022, Ifrane's annual electricity consumption climbed steadily from 35 to 43 GWh. The government of Morocco has implemented effective laws, guidelines and regulations, as well as publicized ways to reduce energy consumption and increase energy efficiency. However, gathered data and survey results revealed opportunities and challenges for enhancing Ifrane's efficient energy use.

    The study also evaluates government programs, codes/standards and related actions for the improvement of household energy efficiency. As part of the review, the available literature was analyzed to assess the effectiveness of energy behavior and awareness, the impact of an economical and sustainable building envelope, the impact of building retrofitting programs, the importance of energy-performing devices and appliances, the adoption of smart home energy management systems, the integration of renewable energies for on-site clean energy generation and the role of policies and governance in the building sector in Ifrane. A benchmark evaluation and potential ideas are offered to guide energy policies and improve energy efficiency in Ifrane and other cities within the same climate zone.



    Coronavirus disease 2019 (COVID-19) was first identified in Wuhan, China on December 2019 [1]. The World Health Organization (WHO) characterized COVID-19 as a pandemic on 11 March, 2020 [1], and it has caused enormous and serious damages to the health, medical, social and economic systems in many countries worldwide. As of 1 July, 2020, 10,357,662 people have been reported to be infected by COVID-19, and 508,055 people passed away due to COVID-19 [1].

    In Japan, the first case of COVID-19 was identified on 15 January, 2020 [1]. As of 1 July, 2020, the number of total reported cases of COVID-19 in Japan has reached to 18,723 and that of total deaths is 974 [1]. In [2], the author estimated the epidemic parameters for COVID-19 in Japan by using the data from 15 January to 29 February, 2020 [1]. The estimated epidemic curves seem to fit the actual data before 20 April (see Figure 1).

    Figure 1.  Daily number of newly reported cases in Japan from 15 January to 31 May, 2020. Black circles imply the data from 15 January to 29 February, 2020, which were used for estimating the epidemic curves (see also [2]). Red circles imply the data after 1 March, 2020. R0 is the basic reproduction number [3].

    The state of emergency (SOE) in Japan was first declared on 7 April, 2020 for 7 prefectures (Tokyo, Kanagawa, Sitama, Chiba, Osaka, Hyogo and Fukuoka), and it was then expanded to all 47 prefectures on 16 April, 2020. From Figure 1, we see that the daily number of newly reported cases of COVID-19 in Japan has tended to decrease since about 2 weeks passed from the first state of emergency.

    In Italy, the rapid exponential growth of the daily number of newly reported cases of COVID-19 was observed in late February, 2020, and the lockdown has started from 9 March, 2020. After about 2 weeks passed from the lockdown, the daily number of newly reported cases of COVID-19 in Italy has tended to decrease as of 31 May, 2020 (see Figure 2).

    Figure 2.  Daily number of newly reported cases in Italy from 31 January to 31 May, 2020. Black circles imply the data from 31 January to 8 March, 2020, which were used for estimating the epidemic curves. Red circles imply the data after lockdown on 9 March, 2020.

    As is reported in many references (see, e.g., [4][8]), not a few individuals infected by COVID-19 are asymptomatic. Therefore, the social distancing such as lockdown would be one of the most effective ways to control COVID-19 because it would contribute to reduce the number of contacts among undiagonosed individuals. However, the prolongation of the period of such a restrictive intervention could hugely affect the social and economic systems, its financial and psychological cost is too high.

    In South Korea, COVID-19 has been successfully controlled without lockdown as of 31 May, 2020 (see Figure 3).

    Figure 3.  Daily number of newly reported cases in South Korea from 20 January to 31 May, 2020. Black circles imply the data from 20 January to 29 February, 2020, which were used for estimating the epidemic curves. Red circles imply the data after 1 March, 2020.

    One of the remarkable differences between South Korea and Japan in the early stage was the proactivity for testing. As of 20 April, 2020, the total number of reported cases for COVID-19 in Japan (10,751) is almost the same as that in South Korea (10,674) [1], however, the total number of COVID-19 tests per 1,000 people in South Korea (10.98) is about 7 times larger than that in Japan (1.58) [9]. Since COVID-19 has high infectivity before symptom onset [10], testing would be one of the most effective ways to reduce the number of contacts among individuals with no symptoms. In particular, we can expect that massive testing would be less likely to affect the social and economic systems because it does not require any strong restrictions on the personal behavior. The efficacy of testing has been proved also in Taiwan, Vietnam and Hong Kong [9].

    In this paper, we discuss the possible effects of social distancing and massive testing with quarantine by constructing a mathematical model, which is based on the classical SEIR epidemic model (see, e.g., [11], [12] for previous studies on the control effect for SEIR epidemic models). The organization of this paper is as follows. In section 2, we formulate the basic asymptomatic transmission model to derive the control reproduction number Rc and the state-reproduction number. In section 3, we estimate the baseline parameters and examine the effects of social distancing and massive testing accompanied with quarantine by numerical simulation. In section 4, we briefly review the outcome of the early control strategy for COVID-19 and discuss the feasibility of the massive testing.

    Our basic model is a well-known SEIR epidemic model [3] with standard incidence, though it is extended to recognize the asymptomatic transmission. In order to focus on the effect of comtrol measures, for simplicity, we neglect the additional death due to the epidemic. If the total size of host population is so large, this assumption would be irrelevant to our conclusions. Let S be the susceptibles, E the asymptomatic infecteds, I the symptomatic infecteds, R the recovereds. Then the basic dynamics without intervention is formulated as follows (see also Figure 4):

    dSdt=(β1E+β2I)SNdEdt=(β1E+β2I)SNϵEdIdt=ϵEγIdRdt=γI
    where β1 denotes the asymptomatic transmission rate, β2 the symptomatic transmission rate, ε the onset rate, γ the recovery rate for infecteds. Under this assumption, the total population size N:= S + E + I + R is constant. Therefore, we can interpret each population size as the prevalence of each status if we set N = 1. Note that if β1 = 0 and β2 > 0, then (1) is the usual SEIR model without asymptomatic transmission. The asymptomatic transmission is taken into account only if β1 > 0.

    Figure 4.  Transfer diagram for model (1).

    The linearized system at the disease free steady state for (1) is

    dx(t)dt=(B+C)x(t)
    where x(t):= (E(t), I(t))T* and
    B:=(β1β200),C:=(ϵ0ϵγ)
    Therefore the next generation matrix with large domain K is calculated as
    K=B(C)1=(β1ϵ+β2γβ2γ00)
    Then the basic reproduction number is given by
    R0=β1ϵ+β2γ
    Let
    R01:=β1ϵ  and  R02:=β2γ
    be the reproduction numbers for the asymptomatic and symptomatic infection, respectively. Note that R0 = R01 + R02.

    Here it should be noted that the disease can not be eradicated by quarantine of symptomatic cases if R01 > 1. In case that R01 < 1, we can define the state-reproduction number for symptomatic infectives, denoted by T, ([3], [13]) as

    T:=R021R01
    Then the subcritical condition R0 < 1 is satisfied if and only if T < 1. The state-reproduction number can be interpreted as the average number of secondary cases of symptomatic infecteds produced by a primary symptomatic case during its entire course of infection without intermediate symptomatic case.

    Suppose that R01 < 1 and we can reduce the reproductivity of symptomatic individuals by quarantine and social distancing. Let ˆq be the reduction ratio of reproductivity of symptomatic cases. Then the number of secondary cases produced by a symptomatic infected individual becomes (1ˆq)R02, so the state-reproduction number becomes (1ˆq)T. Then the critical reduction ratio for symptomatic cases is calculated as

    q*=11T
    and ˆq>q* is sufficient to guarantee the subcritical condition
    R01+(1ˆq)R02<1
    where the left-hand side is called the control reproduction number under the prevention policy.

    Even when R01 > 1, the above control strategy can work if R01 becomes less than unity by using general social distancing policy. If the social distancing policy reduces the basic reproduction number R0 to (1 −r) R0 and the reproductivity of asymptomatic case become subcritical; (1 − r) R01 < 1, the control state-reproduction number for symptomatic infectives associated with the reduction proportion r ∈ (0,1) is

    Tr:=(1r)R021(1r)R01
    Then the critical reduction ratio for reproductivity of symptomatic cases under the social distancing policy is given by
    qr:=11Tr

    Next here we consider a situation that susceptibles and infecteds are all assumed to be exposed to massive testing (PCR test) with testing rate k followed by case isolation. Let p ∈ (0,1) be the sensitivity of the test, and q ∈ (0,1) be the specificity of the test. Suppose that if the test reaction is positive, individuales are quarantined, no matter whether the reaction is pseudo or not. Let Q be the quaratined population. We assume that the quarantined individuals are excluded from the contact process. Then under the massive testing and quarantine strategy, the total dynamics is described as follows (see also Figure 5):

    dSdt=(β1E+β2I)SNk(1q)SdEdt=(β1E+β2I)SN(ϵ+kp)EdIdt=ϵE(γ+kp)IdRdt=γI+ηQdQdt=k(1q)S+kp(E+I)ηQ
    where η denotes the recovery rate for the quarantined population. Now we can assume that N + Q = 1. Using the next generation matrix method again, it is easy to see that the control reproduction number under the massive testing and quarantine policy is given by
    Rc=β1ϵ+kp+β2ϵ(ϵ+kp)(γ+kp)
    Let
    Rc1:=β1ϵ+kp  and  Rc2:=β2ϵ(ϵ+kp)(γ+kp)
    be the control reproduction numbers for the asymptomatic and symptomatic infection, respectively. Note that Rc = Rc1 + Rc2.

    Figure 5.  Transfer diagram for model (12).

    Note that S is monotone decreasing and E, I → 0 as t → +∞8 in both of models (1) and (12). That is, similar to the classical Kermack-McKendrick model without demography [3], there is no endemic steady state and the solution always converges to the disease-free steady state.

    Let the unit time be 1 day. We set the average incubation period 1/ε to be 5 days [14][16], the average infectious period 1/γ to be 10 days [14], [17] and the average quarantine period 1/η to be 14 days [18]. That is, ε = 1/5 = 0.2, γ = 1/10 = 0.1 and η = 1/14 ≈ 0.07. We set the sensitivity p and the specificity q for testing to be 0.7 [19], [20] and 0.99 [20], respectively. Based on [2], we assume that the basic reproduction number in Japan is R0 = 2.6 (95%CI, 2.4–2.8), where CI denotes the credible interval.

    In [10], it was estimated that 44% of secondary cases were infected during the presymptomatic state. Based on this estimation, we assume that R01 = 0.44R0 and R02 = 0.56R0. From (6), we have

    β1=0.23(95%CI,0.210.25)ϵR01,β2=0.15(95%CI,0.130.16)γR02.
    Note that the reason why β1 > β2 in spite of R01 < R02 is that ε = 0.2 is twice larger than γ = 0.1. Consequently, we obtain the baseline parameters as shown in Table 1.

    Table 1.  Description of each symbol in our simulation.
    Symbol Description Value Reference
    β1 Asymptomatic infection rate 0.23 (95%CI, 0.21–0.25) [15]
    β2 Symptomatic infection rate 0.15 (95%CI, 0.13–0.16) [15]
    p Sensitivity 0.7 [19], [20]
    q Specificity 0.99 [20]
    1/ε Average incubation period 5 [14][16]
    1/γ Average infectious period 10 [14], [17]
    1/η Average quarantine period 14 [18]
    R0 Basic reproduction number 2.6 (95%CI, 2.4–2.8) [2]
    R01 Reproduction number for asymptomatic infection 0.44 R0 [10]
    R02 Reproduction number for symptomatic infection 0.56 R0 [10]
    r, u Reduction proportion 0–1 -
    Tr Control state-reproduction number (see Figure 6) [10]
    qr Critical reduction ratio (see Figure 6) [11]
    Rc Control reproduction number (see Figure 7) [13]
    h(t) Positive predictive value (see Figure 11) [17]

     | Show Table
    DownLoad: CSV

    We first consider the basic asymptomatic transmission model (1). From Table 1, we see that R01 = 0.44 R0 ≈ 1.14 (95%CI, 1.06–1.23) > 1. Hence, as stated in Section 2.2, we can not eradicate the disease only by quarantining symptomatic individuals. The control state-reproduction number Tr and the critical reduction ratio qr under the social distancing policy that reduces R0 to (1 – r)R0, for 0 ≤ r ≤ 1 are plotted in Figure 6.

    Figure 6.  The dependence on r of the control state-reproduction number Tr and the critical reduction ratio qr (which are given by (10) and (11), respectively).

    Figure 6 suggests us that if the social distancing leads to about 60% reduction of the contact rates (r = 0.6), then the disease can be eradicated without extra quarantine measure for symptomatic individuals. Moreover, it also suggests that even if the social distancing leads to relatively mild reduction of the contact rates, the disease can be eradicated with sufficient quarantine of symptomatic individuals. For instance, if r = 0.3 (30% reduction of the contact rates by social distancing), then qr = 0.80 (95%CI, 0.73–0.87), which implies that 80% reduction of the symptomatic individuals' contact rate by massive testing and quarantine could result in the eradication of the disease. Note that detection and quarantine of symptomatic individuals would be much easier than that of asymptomatic individuals. Thus, qr = 0.80 might not be an unrealistic goal.

    We next change our focus from the asymptomatic transmission model (1) to the testing and quarantine model (12). We investigate the effect of each intervention strategy by observing the sensitivity of Rc, which is defined by (13). First, we consider a quarantine of symptomatic individuals that results in reducing the symptomatic infection rate β2 to (1 − u)β2, where 0 ≤ u ≤ 1. In this case, the control reproduction number Rc decreases linearly with increasing u, however, Rc < 1 is not attained even if all symptomatic individuals are successfully quarantined (see Figure 7 (a)).

    Figure 7.  Sensitivity of the control reproduction number Rc for parameters u, r and k.

    Next, we consider a social-distancing that results in reducing the infection rates βi to (1 − r)βi, where i = 1, 2 and 0 ≤ r ≤ 1. In this case, the control reproduction number Rc decreases linearly with increasing r, and Rc < 1 is attained for about 60% (r = 0.6) reduction of the infection rates (see Figure 7 (b)). This result is consistent with the result in Section 3.2, Figure 6 (a).

    Finally, we consider a massive testing with quarantine that results in increasing the testing rate k. In this case, Rc is monotone decreasing and convex downward for 0 ≤ k ≤ 1, and Rc < 1 is attained for about k = 0.2 (see Figure 7 (c)). Note that Rc is highly sensitive for small k since it is convex downward. This implies that the increasement of the testing rate would be an effective strategy to control the disease especially in countries with an originally low level of testing rate such as Japan.

    We next observe the epidemic curves of model (12) under each intervention. We assume that one infective individual is confirmed in Japan at t = 0 (15 January, 2020) and, for simplicity, there was no exposed, removed and quarantined individuals at t = 0. That is,

    S(0)=111.26×108,E(0)=0,I(0)=11.26×108,R(0)=Q(0)=0
    where 1.26×108 implies the total population in Japan [21]. The epidemic curves of model (12) under the continued social distancing are displayed in Figure 8.

    Figure 8.  Time variation of the infective population I with the baseline parameters in Table 1 and the social distancing that reduces βi to (1 – r)βi, i = 1,2, where 0 ≤ r ≤ 1 (k = 0).

    From Figure 8, we see that 40% reduction of the infection rates (r = 0.4) results in the drastic reduction of the epidemic size. However, we have to keep such a social distancing during the full period and it could largely affect the social and economic systems. Moreover, the recurrence of the epidemic could possibly occur if we stop the intervention on the way (see also Section 3.5).

    The epidemic curves under the massive testing and quarantine are displayed in Figure 9.

    Figure 9.  Time variation of the infective population I with the baseline parameters in Table 1 and the massive testing and quarantine.

    We see from Figure 9 that increasing k up to 0.1 is sufficient for drastically reducing the infective population within this year. Since massive testing and quarantine would less affect the social and economic systems, to keep them for a long term could be one of the effective and realistic strategies.

    We next consider the possibility of the recurrence of the epidemic in Japan after the social distancing, which started when the state of emerngency (SOE) was declared on 7 April, 2020. As in the previous subsection, we regard t = 0 as 15 January, 2020 and the initial condition is given by (16). We assume that the infection rates βi (i = 1,2) are reduced to (1 – r)βi (i = 1,2) with r = 0.8 (80% reduction of the contact rate, which was recommended by the Japanese government at April) during the period of social distancing, which starts from t = 83 (7 April, 2020) to some planned date t* > 83.

    First, we assume that the SOE is lifted on the originally planned date 6 May, 2020, that is, t* = 112. In this case, the exponential growth of the infective population I starts again after the lifting of SOE (see Figure 10 (a)).

    Figure 10.  Time variation of the infective population I with the social distancing that leads to the 80% reduction of the contact rate during the period from t = 83 (7 April, 2020) to some planned date t* > 83 (k = 0).

    Next, we assume that the SOE is lifted on the extended date 25 May, 2020, that is, t* = 131. Similar to the previous case, the exponential growth of the infected population I starts again after the lifting of SOE (see Figure 10 (b)).

    From Figure 10, we can conjecture that the recurrence of the COVID-19 epidemic after the lifting of SOE is fully possible in Japan if the infection rates return to the original level after the lifting. To avoid this bad scenario, we should keep appropriate reduction of the contact rates even after the lifting of SOE and infected individuals must be tested and quarantined effectively, otherwise the second epidemic wave might cause a long-term damage to the social and economic systems.

    As is usually pointed out as a weak point of testing, the positive predictive value (probability that tested positive individuals are really infected) for testing is very small as long as the prevalence is low, and so a lot of tested positive individuals are in fact not infected. If we calculate the positive predictive value by using our modelling, it is given as

    h(t):=p(I+E)(1q)S+pI+E=(Testedpositive)(Testedpseudo-positive)+(Testedpositive)
    from which we know that it is very small in the early stage of epidemic (see Figure 11), hence many uninfected individuals will be quarantined because their test reaction is pseudo-positive, while many infecteds will be escaped from quarantine because of pseudo-negativeness.

    Figure 11.  Time variation of the positive predictive value h(t) in our model.

    This fact has been used to support the early control strategy such that instead of widespread testing, symptomatic individuals and asymptomatic individuals linked to infected local groups should be mainly targeted for testing, because their prior probability of positive is high and the positive predictive value is also high, so we can avoid quarantine of pseudo-positive individuals. However, as confirmed case numbers are rising, contact tracing is more difficult, many transmission routes that don't involve observed infection clusters appear, and so community spread in the big cities will be missed. If we have entered into such explosive phase of the epidemic, as is shown above, the massive testing could be a strong tool to prevent the disease as long as the positively reacted individuals will be effectively quarantined, no matter whether the positive reaction is pseudo or not.

    In our simulation above, the control reproduction number is less than unity when the testing rate k is about 0.2 (20 percent per day), which seems to be too high in realistic situation if the host population is assumed to be national level. However, if once the number of daily produced symptomatic individuals is lowered by comprehensive social distancing policy, and the risk group can be visualized, the target community size is not so large, the masssive testing would be very effective. As is shown in Figure 9, the epidemic is largely mitigated even if k is 5 percent par day, because the control reproduction number is very sensitive with respect to small k. Since total population could be seen as a superposition of smaller communities, we could understand how testing and quarantine policy might be powerful to control the infectious disease.

    Finally, we would like to point out a possible extension of our model. In the asymptomatic transmission model (1), we implicitly assume that all exposed individuals will finally become symptomatic, so they are becoming observable. On the other hand, it is reported that for covid-19 virus, there are many infecteds without symptom, who are unobservable infecteds as long as they are not tested. It is our future challenge to extend the basic model to examine the effect of existence of never symptomatic individuals.



    [1] UN-Habitat, The Value of Sustainable Urbanization. World Cities Report, Nairobi, 2020. Available from: https://unhabitat.org/sites/default/files/2020/10/wcr_2020_report.pdf.
    [2] Agence Française de Développement, Sustainable Cities. Focus, Paris, 2019. Available from: https://upfi-med.eib.org/wp-content/uploads/2020/04/AFD_CIS_FOCUS-VILLES_DURABLES_ENG_WEB-VF-BAT-1.pdf.
    [3] Dixon T, Connaughton J, Green S (2018) Sustainable Futures in the Built Environment to 2050: A Foresight Approach to Construction and Development, John Wiley & Sons. https://doi.org/10.1002/9781119063834
    [4] IEA (International Energy Agency), Energy technology perspectives pathways to a clean energy system, 2012. Available from: https://iea.blob.core.windows.net/assets/7136f3eb-4394-47fd-9106-c478283fcf7f/ETP2012_free.pdf.
    [5] UNEP (United Nation for Environment Programme), Transport. Investing in energy and resource efficiency, UNEP, 2011. Available from: https://wedocs.unep.org/bitstream/handle/20.500.11822/22013/10.0_transport.pdf?sequence=1&%3BisAllowed.
    [6] Bulkeley H, Broto VC, Maassen A (2013) Low-carbon transitions and the reconfiguration of urban infrastructure. Urban Studies 51: 1471–1486. https://doi.org/10.1177/0042098013500089 doi: 10.1177/0042098013500089
    [7] Dowling R, McGuirk P, Bulkeley H (2014) Retrofitting cities: Local governance in Sydney, Australia. Cities 38: 18–24. https://doi.org/10.1016/j.cities.2013.12.004 doi: 10.1016/j.cities.2013.12.004
    [8] Rutherford J, Jaglin S (2015) Introduction to the special issue—Urban energy governance: Local actions, capacities and politics. Energy Policy 78: 173–178. https://doi.org/10.1016/j.enpol.2014.11.033 doi: 10.1016/j.enpol.2014.11.033
    [9] UN-Habitat, Sustainable Urban Energy Planning—A handbook for cities and towns in developing countries. UNEP, Nairobi, 2009. Available from: https://seors.unfccc.int/applications/seors/attachments/get_attachment?code=LUZ4E1JJHTISK0JLBY55WLV36ICQR6WT.
    [10] Webb J, Hawkey D, Tingey M (2016) Governing cities for sustainable energy: The UK case. Cities 54: 28–35. https://doi.org/10.1016/j.cities.2015.10.014 doi: 10.1016/j.cities.2015.10.014
    [11] Ji C, Choi M, Hong T, et al. (2021) Evaluation of the effect of a building energy efficiency certificate in reducing energy consumption in Korean apartments. Energy Build 248: 111168. https://doi.org/10.1016/j.enbuild.2021.111168 doi: 10.1016/j.enbuild.2021.111168
    [12] El Hafdaoui H, Jelti F, Khallaayoun A, et al. (2023) Energy and environmental national assessment of alternative fuel buses in Morocco. World Electr Veh J 14: 105. https://doi.org/10.3390/wevj14040105 doi: 10.3390/wevj14040105
    [13] Prafitasiwi AG, Rohman MA, Ongkowijoyo CS (2022) The occupant's awareness to achieve energy efficiency in campus building. Results Eng 14: 10039. https://doi.org/10.1016/j.rineng.2022.100397 doi: 10.1016/j.rineng.2022.100397
    [14] Cô té-Roy L, Moser S (2022) A kingdom of new cities: Morocco's national Villes Nouvelles strategy. Geoforum 131: 27–38. https://doi.org/10.1016/j.geoforum.2022.02.005 doi: 10.1016/j.geoforum.2022.02.005
    [15] Delmastro, Chiara; De Bienassis, Tanguy; Goodson, Timothy; Lane, Kevin; Le Marois, Jean-Baptiste; Martinez-Gordon, Rafael; Husek, Martin, "Buildings, " IEA, 2021. Available from: https://www.iea.org/reports/buildings.
    [16] Ministère de la Transition Energétique et du Développement Durable, Stratégie Bas Carbone à Long Terme—Maroc 2050, Rabat, 2021. Available from: https://unfccc.int/sites/default/files/resource/MAR_LTS_Dec2021.pdf.
    [17] IEA (International Energy Agency), Transport—Improving the sustainability of passenger and freight transport, 2021. Available from: https://www.iea.org/topics/transport.
    [18] Ministère de la Transition Energétique et du Développement Durable (MTEDD), Consommation Energetique par l'Administration—Fès et Meknès, SIREDD, Rabat, Morocco, 2012. Available from: https://siredd.environnement.gov.ma/fes-meknes/indicateur/DetailIndicateurPartial?idIndicateur=2988.
    [19] Chegari B, Tabaa M, Moutaouakkil F, et al. (2020) Local energy self-sufficiency for passive buildings: Case study of a typical Moroccan building. J Build Eng 29: 101164. https://doi.org/10.1016/j.jobe.2019.101164 doi: 10.1016/j.jobe.2019.101164
    [20] Oubourhim A, El-Hami K (2020) Efficiency energy standards and labelling for residential appliances in Morocco. In Advanced Intelligent Systems for Sustainable Development, Marrakesh, Morocco, Springer, 97–109. https://doi.org/10.1007/978-3-030-36475-5_10
    [21] El Majaty S, Touzani A, Kasseh Y (2023) Results and perspectives of the application of an energy management system based on ISO 50001 in administrative buildings—case of Morocco. Mater Today: Proc 72: 3233–323. https://doi.org/10.1016/j.matpr.2022.07.094 doi: 10.1016/j.matpr.2022.07.094
    [22] Merini I, Molina-García A, García-Cascales MS, et al. (2020) Analysis and comparison of energy efficiency code requirements for buildings: A Morocco—Spain case study. Energies 13: 5979. https://doi.org/10.3390/en13225979 doi: 10.3390/en13225979
    [23] Sghiouri H, Mezrhab A, Karkri M, et al. (2018) Shading devices optimization to enhance thermal comfort and energy performance of a residential building in Morocco. J Build Eng 18: 292–302. https://doi.org/10.1016/j.jobe.2018.03.018 doi: 10.1016/j.jobe.2018.03.018
    [24] Jihad AS, Tahiri M (2018) Forecasting the heating and cooling load of residential buildings by using a learning algorithm "gradient descent", Morocco. Case Studies Therm Eng 12: 85–93. https://doi.org/10.1016/j.csite.2018.03.006 doi: 10.1016/j.csite.2018.03.006
    [25] Romani Z, Draoui A, Allard F (2015) Metamodeling the heating and cooling energy needs and simultaneous building envelope optimization for low energy building design in Morocco. Energy Build 102: 139–148. https://doi.org/10.1016/j.enbuild.2015.04.014 doi: 10.1016/j.enbuild.2015.04.014
    [26] Sghiouri H, Charai M, Mezrhab A, et al. (2020) Comparison of passive cooling techniques in reducing overheating of clay-straw building in semi-arid climate. Build Simul 13: 65–88. https://doi.org/10.1007/s12273-019-0562-0 doi: 10.1007/s12273-019-0562-0
    [27] Bendara S, Bekkouche MA, Benouaz T, et al. (2019) Energy efficiency and insulation thickness according to the compactness index case of a studio apartment under saharan weather conditions. J Sol Energy Eng 141: 04101. https://doi.org/10.1115/1.4042455 doi: 10.1115/1.4042455
    [28] Rochd A, Benazzouz A, Ait Abdelmoula I, et al. (2021) Design and implementation of an AI-based & IoT-enabled home energy management system: A case study in Benguerir—Morocco. Energy Rep 7: 699–719. https://doi.org/10.1016/j.egyr.2021.07.084 doi: 10.1016/j.egyr.2021.07.084
    [29] Lebied M, Sick F, Choulli Z, et al. (2018) Improving the passive building energy efficiency through numerical simulation—A case study for Tetouan climate in northern of Morocco. Case Studies Therm Eng 11: 125–134. https://doi.org/10.1016/j.csite.2018.01.007 doi: 10.1016/j.csite.2018.01.007
    [30] Bouhal T, Fertahi S e.-D, Agrouaz Y, et al. (2018) Technical assessment, economic viability and investment risk analysis of solar heating/cooling systems in residential buildings in Morocco. Sol Energy 170: 1043–1062. https://doi.org/10.1016/j.solener.2018.06.032 doi: 10.1016/j.solener.2018.06.032
    [31] Swan LG, Ugursal VI (2009) Modeling of end-use energy consumption in the residential sector: A review of modeling techniques. Renewable Sustainable Energy Rev 13: 1819–1835. https://doi.org/10.1016/j.rser.2008.09.033 doi: 10.1016/j.rser.2008.09.033
    [32] Martos A, Pacheco-Torres R, Ordóñ ez J, et al. (2016) Towards successful environmental performance of sustainable cities: Intervening sectors—A review. Renewable Sustainable Energy Rev 57: 479–495. https://doi.org/10.1016/j.rser.2015.12.095 doi: 10.1016/j.rser.2015.12.095
    [33] Howard B, Parshall L, Thompson J, et al. (2012) Spatial distribution of urban building energy consumption by end use. Energy Build 45: 141–151. https://doi.org/10.1016/j.enbuild.2011.10.061 doi: 10.1016/j.enbuild.2011.10.061
    [34] Pereira IM, Sad de Assis E (2013) Urban energy consumption mapping for energy management. Energy Policy 59: 257–269. https://doi.org/10.1016/j.enpol.2013.03.024 doi: 10.1016/j.enpol.2013.03.024
    [35] Mutani G, Todeschi V (2021) GIS-based urban energy modelling and energy efficiency scenarios using the energy performance certificate database. Energy Efficiency 14: 1–28. https://doi.org/10.1007/s12053-021-09962-z doi: 10.1007/s12053-021-09962-z
    [36] Todeschi V, Boghetti R, Kämpf JH, et al. (2021) Evaluation of urban-scale building energy-use models and tools—Application for the city of Fribourg, Switzerland. Sustainability 13: 1595. https://doi.org/10.3390/su13041595 doi: 10.3390/su13041595
    [37] Population of Ifrane 2023, AZNations, 2023. Available from: https://www.aznations.com/population/ma/cities/ifrane-1.[Accessed 27 March 2023].
    [38] Ministère de l'Intérieur, Monographie Générale. La Région de Fès-Meknès, 2015. Available from: https://knowledge-uclga.org/IMG/pdf/regiondefesmeknes-2.pdf.
    [39] Sick F, Schade S, Mourtada A, et al. (2014) Dynamic building simulations for the establishment of a Moroccan thermal regulation for buildings. J Green Build 9: 145–165. https://doi.org/10.3992/1943-4618-9.1.145 doi: 10.3992/1943-4618-9.1.145
    [40] Boujnah M, Jraida K, Farchi A, et al. (2016) Comparison of the calculation methods of heating and cooling. Int J Current Trends Eng Technol 2. Available from: http://ijctet.org/assets/upload/7371IJCTET2016120301.pdf.
    [41] Kharbouch Y, Ameur M (2021) Prediction of the impact of climate change on the thermal performance of walls and roof in Morocco. Int Rev Appl Sci Eng 13: 174–184. https://doi.org/10.1556/1848.2021.00330 doi: 10.1556/1848.2021.00330
    [42] Morocco sets regulations for energy efficiency. Oxford Business Group, 2015.[Online]. Available: https://oxfordbusinessgroup.com/analysis/morocco-sets-regulations-energy-efficiency.[Accessed 11 November 2022].
    [43] AMEE (Agence Marocaine pour l'Efficacité Energétique), Règlement Thermique de Construction au Maroc. Rabat, 2018. Available from: https://www.amee.ma/sites/default/files/inline-files/Lereglementthermique.pdf.
    [44] Bouroubat K, La construction durable: étude juridique comparative. HAL Open Science, Paris, 2017. Available from: https://theses.hal.science/tel-01617586/document.
    [45] M'Gbra N, Touzani A (2013) Energy efficiency codes in residential buildings and energy efficiency improvement in commercial and hospital buildings in Morocco. Mid-Term Evaluation Report on the UNDP/GEP Project, 5–34. Available from: https://procurement-notices.undp.org/view_file.cfm?doc_id=35481.
    [46] El Wardi FZ, Khabbazi A, Bencheikh C, et al. (2017) Insulation material for a model house in Zaouiat Sidi Abdessalam. In International Renewable and Sustainable Energy Conference (IRSEC), Tangier. https://doi.org/10.1109/IRSEC.2017.8477582
    [47] Gounni A, Ouhaibi S, Belouaggadia N, et al. (2022) Impact of COVID-19 restrictions on building energy consumption using Phase Change Materials (PCM) and insulation: A case study in six climatic zones of Morocco. J Energy Storage 55: 105374. https://doi.org/10.1016/j.est.2022.105374 doi: 10.1016/j.est.2022.105374
    [48] MHPV (Ministère de l'Habitat et de la Politique de la Ville), Guide des Bonnes Pratiques pour la Maitrise de l'Energie à l'Echelle de la Ville et de l'Habitat. Rabat, 2014. Available from: www.mhpv.gov.ma/wp-content/uploads/2021/11/Guide-de-bonnes-pratiques-pour-la-maitrise-de-l-energie.pdf.
    [49] PEEB (Programme for Energy Efficiency in Buildings), Building Sector Brief: Morocco. Agence Française de Développement, Paris, 2019. Available from: https://www.peeb.build/imglib/downloads/PEEB_Morocco_Country Brief_Mar 2019.pdf.
    [50] HCP (Haut-Commissariat au Plan), Les Indicateurs Sociaux du Maroc. Rabat, 2022. Available from: https://www.hcp.ma/Les-Indicateurs-sociaux-du-Maroc-Edition-2022_a3192.html#: ~: text=L'objectif%20de%20cette%20publication, une%20%C3%A9valuation%20des%20politiques%20publiques.
    [51] Lahlimi Alami A, Prospective Maroc—Energie 2030. HCP (Haut-Commissariat au Plan), Rabat, 2022. Available from: https://www.hcp.ma/downloads/?tag=Prospective+Maroc+2030.
    [52] Energy Efficiency in Buildings. AMEE, 2016. Available from: https://www.amee.ma/en/node/118.[Accessed 8 September 2022].
    [53] MTEDD (Ministère de la Transition Energétique et du Développement Durable), Campagne de Sensibilisation sur l'Economie d'Energie. 29 June 2022. Available from: https://www.mem.gov.ma/Pages/actualite.aspx?act=333.[Accessed 11 November 2022].
    [54] Ferreira D, Dey AK, Kostakos V (2011) Understanding human-smartphone concerns: A study of battery life. In International Conference of Pervasive Computing. https://doi.org/10.1007/978-3-642-21726-5_2
    [55] Karunarathna WKS, Jayaratne W, Dasanayaka S, et al. (2023) Factors affecting household's use of energy-saving appliances in Sri Lanka: An empirical study using a conceptualized technology acceptance model. Energy Effic, 16. https://doi.org/10.1007/s12053-023-10096-7
    [56] Waris I, Hameed I (2020) Promoting environmentally sustainable consumption behavior: an empirical evaluation of purchase intention of energy-efficient appliances. Energy Effic 13: 1653–1664. https://doi.org/10.1007/s12053-020-09901-4 doi: 10.1007/s12053-020-09901-4
    [57] HCP (Haut-Commissariat au Plan), Le secteur de l'emploi au Maroc. World Bank, Washington DC, 2021. Available from: https://www.hcp.ma/region-oriental/docs/Paysage%20de%20l%27%27emploi%20au%20Maroc%20_%20Recenser%20les%20obstacles%20a%20un%20marche%20du%20travail%20inclusif.pdf.
    [58] Gustafson S, Hartman W, Sellers B, et al. (2015) Energy sustainability in Morocco. Worcester Polytechnic Institute, Worcester. Available from: https://web.wpi.edu/Pubs/E-project/Available/E-project-101615-143625/unrestricted/energy-iqp_report-final2.pdf.
    [59] Hu Q, Qian X, Shen X, et al. (2022) Investigations on vapor cloud explosion hazards and critical safe reserves of LPG tanks. J Loss Prev Process Ind 80: 104904. https://doi.org/10.1016/j.jlp.2022.104904 doi: 10.1016/j.jlp.2022.104904
    [60] Zinecker A, Gagnon-Lebrun F, Touchette Y, et al. (2018) Swap: Reforming support for butane gas to invest in solar in Morocco. Int Inst Sustainable Dev. Available from: https://www.iisd.org/system/files/publications/swap-morocco-fr.pdf.
    [61] MEME (Ministère de l'Energie, des Mines et de l'Environnement), Feuille de Route Nationale pour la Valorisation Energétique de la Biomasse. Rabat, 2021. Available from: https://www.mem.gov.ma/Lists/Lst_rapports/Attachments/32/Feuille de Route Nationale pour la Valorisation Energétique de la Biomasse à l'horizon 2030.pdf.
    [62] Loutia M (2016) The applicability of geothermal energy for heating purposes in the region of Ifrane. Al Akhawayn University, Ifrane, 2016. Available from: www.aui.ma/sse-capstone-repository/pdf/spring2016/The Applicability Of Geothermal Energy For Heating Purposes In The Region of Ifrane.pdf.
    [63] Krarouch M, Lamghari S, Hamdi H, et al. (2020) Simulation and experimental investigation of a combined solar thermal and biomass heating system in Morocco. Energy Rep 6: 188–194. https://doi.org/10.1016/j.egyr.2020.11.270 doi: 10.1016/j.egyr.2020.11.270
    [64] HCP (Haut-Commissariat au Plan), Caractéristiques Démographiques et Socio-Economiques—Province Ifrane. Rabat, 2022. Available from: https://www.hcp.ma/region-meknes/attachment/1605477/.
    [65] HCP (Haut-Commissariat au Plan), Recensement Général de la Population et de l'Habitat 2014. HCP, Rabat, 2015. Available from: www.mhpv.gov.ma/wp-content/uploads/2019/12/RGPH-HABITAT.pdf.
    [66] Driouchi A, Zouag N (2006) Eléments pour le Renforcement de l'Insertion du Maroc dans l'Economie de Croissance. Haut-Commissariat au Plan, Ifrane, 2006. Available from: https://www.hcp.ma/downloads/?tag=Prospective+Maroc+2030.
    [67] MTEDD (Ministère de la Transition Energétique et du Développement Durable), Consommation énergétique par l'administration, 2019. Available from: https://siredd.environnement.gov.ma/fes-meknes/indicateur/DetailIndicateurPartial?idIndicateur=2988.[Accessed 3 September 2022].
    [68] Bami R (2022) Ifrane: L'énergie solaire remplace le bois. Yabiladi, 2022. Available from: https://www.yabiladi.com/article-societe-1636.html.[Accessed 18 November 2022].
    [69] MEMEE (Ministère de l'Energie, des Mines, de l'Eau et de l'Environnement), Stratégie Energétique Nationale—Horizon 2030. Rabat, 2021. Available from: https://www.mem.gov.ma/Lists/Lst_rapports/Attachments/33/Strat%C3%A9gue%20Nationale%20de%20l'Efficacit%C3%A9%20%C3%A9nerg%C3%A9tique%20%C3%A0%20l'horizon%202030.pdf.
    [70] Laroussi I (2017) Cost Study and Analysis of PV Installation per Watt Capacity in Ifrane. Al Akhawayn University, Ifrane, 2017. Available from: http://www.aui.ma/sse-capstone-repository/pdf/fall2017/PV%20INSTALLATION%20COST%20IN%20MOROCCO.%20ILIAS%20LAROUSSI.pdf.
    [71] Arechkik A, Sekkat A, Loudiyi K, et al. (2019) Performance evaluation of different photovoltaic technologies in the region of Ifrane, Morocco. Energy Sustainable Dev 52: 96–103. https://doi.org/10.1016/j.esd.2019.07.007 doi: 10.1016/j.esd.2019.07.007
    [72] Biodiesel Produced at AUI. Al Akhawayn University, Ifrane, 28 April 2016. Available from: http://www.aui.ma/en/media-room/news/al-akhawayn-news/3201-biodiesel-produced-at-aui.html.[Accessed 9 November 2022].
    [73] Derj A, Clean Energies Based Refurbishment of the Heating System of Al Akhawayn University Swimming Pool. Al Akhawayn University, Ifrane, 2015. Available from: www.aui.ma/sse-capstone-repository/pdf/Clean Energies Based Refurbishment of the Heating System of Al Akhawayn University Swimming Pool.pdf.
    [74] Farissi A, Driouach L, Zarbane K, et al. (2021) Covid-19 impact on moroccan small and medium-sized enterprises: Can lean practices be an effective solution for getting out of crisis? Manage Syst Prod Eng 29: 83–90. https://doi.org/10.2478/mspe-2021-0011
    [75] Yoo S-H (2005) Electricity consumption and economic growth: evidence from Korea. Energy Policy 33: 1627–1632. https://doi.org/10.2478/mspe-2021-0011 doi: 10.2478/mspe-2021-0011
    [76] Fatmi A (2022) Student Handbook & Planner. Al Akhawayn University, Ifrane. Available from: www.aui.ma/Student-handbook_2021-2022.pdf.
    [77] World Bank, The Social and Economic Impact of the Covid-19 Crisis in Morocco. Haut-Commissariat au Plan, Rabat, 2021. Available from: https://thedocs.worldbank.org/en/doc/852971598449488981-0280022020/original/ENGTheSocialandEconomicImpactoftheCovid19CrisisinMorocco.pdf.
    [78] Kharbouch Y, Mimet A, El Ganaoui M, et al. (2018) Thermal energy and economic analysis of a PCM-enhanced household envelope considering different climate zones in Morocco. Int J Sustainable Energy 37: 515–532. https://doi.org/10.1080/14786451.2017.1365076 doi: 10.1080/14786451.2017.1365076
    [79] Lachheb A, Allouhi A, Saadani R, et al. (2021) Thermal and economic analyses of different glazing systems for a commercial building in various Moroccan climates. Int J Energy Clean Environ 22: 15–41. https://doi.org/10.1615/InterJEnerCleanEnv.2020034790 doi: 10.1615/InterJEnerCleanEnv.2020034790
    [80] Nacer H, Radoine H, Mastouri H, et al. (2021) Sustainability assessment of an existing school building in Ifrane Morocco using LEED and WELL certification and environmental approach. In 9th International Renewable and Sustainable Energy Conference (IRSEC). https://doi.org/10.1109/IRSEC53969.2021.9741142
    [81] Houzir M, Plan Sectoriel—Eco Construction et Bâ timent Durable. UNEP, Rabat, 2016. Available from: https://switchmed.eu/wp-content/uploads/2020/04/02.-Sectoral-plan-construction-Morocco-in-french.pdf.
    [82] Beccali M, Finocchiaro P, Gentile V et al. (2017) Monitoring and energy performance assessment of an advanced DEC HVAC system in Morocco. In ISES Solar World Conference. https://doi.org/10.18086/swc.2017.28.01
    [83] Taimouri O, Souissi A (2019) Validation of a cooling loads calculation of an office building in Rabat Morocco based on manuel heat balance (Carrier Method). Int J Sci Technol Res 8: 2478–2484. Available from: https://www.ijstr.org/final-print/dec2019/Validation-Of-A-Cooling-Loads-Calculation-Of-An-Office-Building-In-Rabat-Morocco-Based-On-Manuel-Heat-Balance-carrier-Method.pdf.
    [84] IEA (International Energy Agency), Decree n. 2-17-746 on Mandatory energy audits and energy audit organizations, 2019. Available from: https://www.iea.org/policies/8571-decree-n-2-17-746-on-mandatory-energy-audits-and-energy-audit-organisations.[Accessed 26 October 2022].
    [85] Chramate I, Assadiki R, Zerrouq F, et al. (2018) Energy audit in Moroccan industries. Asial Life Sciences. Available from: https://www.researchgate.net/publication/330553987_Energy_audit_in_Moroccan_industries.
    [86] Lillemo SC (2014) Measuring the effect of procrastination and environmental awareness on households' energy-saving behaviours: An empirical approach. Energy Policy 66: 249–256. https://doi.org/10.1016/j.enpol.2013.10.077 doi: 10.1016/j.enpol.2013.10.077
    [87] Kang NN, Cho SH, Kim JT (2012) The energy-saving effects of apartment residents' awareness and behavior. Energy Build 46: 112–122. https://doi.org/10.1016/j.enbuild.2011.10.039 doi: 10.1016/j.enbuild.2011.10.039
    [88] Biresselioglu ME, Nilsen M, Demir MH, et al. (2018) Examining the barriers and motivators affecting European decision-makers in the development of smart and green energy technologies. J Cleaner Prod 198: 417–429. https://doi.org/10.1016/j.jclepro.2018.06.308 doi: 10.1016/j.jclepro.2018.06.308
    [89] Hartwig J, Kockat J (2016) Macroeconomic effects of energetic building retrofit: input-output sensitivity analyses. Constr Manage Econ 34: 79–97. https://doi.org/10.1080/01446193.2016.1144928 doi: 10.1080/01446193.2016.1144928
    [90] Pikas E, Kurnitski J, Liias R, et al. (2015) Quantification of economic benefits of renovation of apartment buildings as a basis for cost optimal 2030 energy efficiency strategies. Energy Build 86: 151–160. https://doi.org/10.1016/j.enbuild.2014.10.004 doi: 10.1016/j.enbuild.2014.10.004
    [91] Ferreira M, Almeida M (2015) Benefits from energy related building renovation beyond costs, energy and emissions. Energy Procedia 78: 2397–2402. https://doi.org/10.1016/j.egypro.2015.11.199 doi: 10.1016/j.egypro.2015.11.199
    [92] Song X, Ye C, Li H, et al. (2016) Field study on energy economic assessment of office buildings envelope retrofitting in southern China. Sustainable Cities Soc 28: 154–161. https://doi.org/10.1016/j.scs.2016.08.029 doi: 10.1016/j.scs.2016.08.029
    [93] Kaynakli O (2012) A review of the economical and optimum thermal insulation thickness for building applications. Renewable Sustainable Energy Rev 16,415–425. https://doi.org/10.1016/j.rser.2011.08.006
    [94] Bambara J, Athienitis AK (2018) Energy and economic analysis for greenhouse envelope design. Trans ASABE 61: 1795–1810. https://doi.org/10.13031/trans.13025 doi: 10.13031/trans.13025
    [95] Struhala K, Ostrý M (2022) Life-Cycle Assessment of phase-change materials in buildings: A review. J Cleaner Prod 336: 130359. https://doi.org/10.1016/j.jclepro.2022.130359 doi: 10.1016/j.jclepro.2022.130359
    [96] Arumugam P, Ramalingam V, Vellaichamy P (2022) Effective PCM, insulation, natural and/or night ventilation techniques to enhance the thermal performance of buildings located in various climates—A review. Energy Build 258: 111840. https://doi.org/10.1016/j.enbuild.2022.111840 doi: 10.1016/j.enbuild.2022.111840
    [97] Jaffe AB, Stavins RN (1994) The energy-efficiency gap—What does it mean? Energy Policy 22: 804–810. https://doi.org/10.1016/0301-4215(94)90138-4 doi: 10.1016/0301-4215(94)90138-4
    [98] Backlund S, Thollander P, Palm J, et al. (2012) Extending the energy efficiency gap. Energy Policy 51: 392–396. https://doi.org/10.1016/j.enpol.2012.08.042 doi: 10.1016/j.enpol.2012.08.042
    [99] Gerarden TD, Newell RG, Stavins RN (2017) Assessing the energy-efficiency gap. J Econ Lit 55: 1486–1525. https://doi.org/10.1257/jel.20161360 doi: 10.1257/jel.20161360
    [100] Chai K-H, Yeo C (2012) Overcoming energy efficiency barriers through systems approach—A conceptual framework. Energy Policy 46: 460–472. https://doi.org/10.1016/j.enpol.2012.04.012 doi: 10.1016/j.enpol.2012.04.012
    [101] Allcott H (2011) Consumers' perceptions and misperceptions of energy costs. Am Econ Rev 101: 98–104. https://doi.org/10.1257/aer.101.3.98 doi: 10.1257/aer.101.3.98
    [102] Davis LW, Metcalf GE (2016) Does better information lead to better choices? Evidence from energy-efficiency labels. J Assoc Environ Resour Econ 3: 589–625. https://doi.org/10.1086/686252 doi: 10.1086/686252
    [103] Shen J (2012) Understanding the Determinants of Consumers' Willingness to Pay for Eco-Labeled Products: An Empirical Analysis of the China Environmental Label. J Serv Sci Manage 5: 87–94. https://doi.org/10.4236/jssm.2012.51011 doi: 10.4236/jssm.2012.51011
    [104] Poortinga W, Steg L, Vlek C, et al. (2003) Household preferences for energy-saving measures: A conjoint analysis. J Econ Psychol 24: 49–64. https://doi.org/10.1016/S0167-4870(02)00154-X doi: 10.1016/S0167-4870(02)00154-X
    [105] Banerjee A, Solomon BD (2003) Eco-labeling for energy efficiency and sustainability: a meta-evaluation of US programs. Energy Policy 31: 109–123. https://doi.org/10.1016/S0301-4215(02)00012-5 doi: 10.1016/S0301-4215(02)00012-5
    [106] Sammer K, Wüstenhagen R (2006) The influence of eco-labelling on consumer behaviour—results of a discrete choice analysis for washing machines. Bus Strategy Environ 15: 185–199. https://doi.org/10.1002/bse.522 doi: 10.1002/bse.522
    [107] Shen L, Sun Y (2016) Performance comparisons of two system sizing approaches for net zero energy building clusters under uncertainties. Energy Build 127: 10–21. https://doi.org/10.1016/j.enbuild.2016.05.072 doi: 10.1016/j.enbuild.2016.05.072
    [108] Good C, Andresen I, Hestnes AG (2015) Solar energy for net zero energy buildings—A comparison between solar thermal, PV and photovoltaic–thermal (PV/T) systems. Sol Energy 123: 986–996. https://doi.org/10.1016/j.solener.2015.10.013 doi: 10.1016/j.solener.2015.10.013
    [109] Harkouss F, Fardoun F, Biwole PH (2018) Passive design optimization of low energy buildings in different climates. Energy 165: 591–613, 2018. https://doi.org/10.1016/j.energy.2018.09.019 doi: 10.1016/j.energy.2018.09.019
    [110] Penna P, Prada A, Cappelletti F, et al. (2015) Multi-objectives optimization of energy efficiency measures in existing buildings. Energy Build 95: 57–69. https://doi.org/10.1016/j.enbuild.2014.11.003 doi: 10.1016/j.enbuild.2014.11.003
    [111] Serbouti A, Rattal M, Boulal A, et al. (2018) Multi-Objective optimization of a family house performance and forecast of its energy needs by 2100. Int J Eng Technol 7: 7–10. Available from: https://www.sciencepubco.com/index.php/ijet/article/view/23235.
    [112] ONEEP (Office National de l'Electricité et de l'Eau Potable), Tarif Général (MT). ONEE, 1 January 2017. Available from: http://www.one.org.ma/FR/pages/interne.asp?esp=1&id1=2&id2=35&id3=10&t2=1&t3=1.[Accessed 24 November 2022].
    [113] ONEEP (Office National de l'Electricité et de l'Eau Potable), Nos tarifs. ONEEP, 1 January 2017. Available from: http://www.one.org.ma/FR/pages/interne.asp?esp=1&id1=3&id2=113&t2=1.[Accessed 24 November 2022].
    [114] Abdou N, EL Mghouchi Y, Hamdaoui S, et al. (2021) Multi-objective optimization of passive energy efficiency measures for net-zero energy building in Morocco. Build Environ 204: 108141. https://doi.org/10.1016/j.buildenv.2021.108141 doi: 10.1016/j.buildenv.2021.108141
    [115] Srinivas M (2011) Domestic solar hot water systems: Developments, evaluations and essentials for viability with a special reference to India. Renewable Sustainable Energy Rev 15: 3850–3861. https://doi.org/10.1016/j.rser.2011.07.006 doi: 10.1016/j.rser.2011.07.006
    [116] Hudon K (2014) Chapter 20—Solar Energy—Water Heating. In Future Energy: Improved, Sustainable and Clean Options for our Planet. Elsevier Science, 433–451. https://doi.org/10.1016/B978-0-08-099424-6.00020-X
    [117] Bertoldi P (2022) Policies for energy conservation and sufficiency: Review of existing. Energy Build 26: 112075. https://doi.org/10.1016/j.enbuild.2022.112075 doi: 10.1016/j.enbuild.2022.112075
    [118] Bertoldi P (2020) Chapter 4.3—Overview of the European Union policies to promote more sustainable behaviours in energy end-users, Energy and Behaviour: Towards a Low Carbon Future. Academic Press: 451–477. https://doi.org/10.1016/B978-0-12-818567-4.00018-1
    [119] Herring H (2006) Energy efficiency—A critical view. Energy 31: 10–20. https://doi.org/10.1016/j.energy.2004.04.055 doi: 10.1016/j.energy.2004.04.055
    [120] Sorrell S, Gatersleben B, Druckman A (2020) The limits of energy sufficiency: A review of the evidence for rebound effects and negative spillovers from behavioural change. Energy Res Soc Sci 64: 101439. https://doi.org/10.1016/j.erss.2020.101439 doi: 10.1016/j.erss.2020.101439
    [121] Sachs W (1999) The Power of Limits: An Inquiry into New Models of Wealth, in Planet Dialectics. Explorations in Environment and Development, London, ZED-BOOKS. Available from: https://www.researchgate.net/publication/310580761_The_power_of_limits.
    [122] Brischke LA, Lehmann F, Leuser L, et al. (2015) Energy sufficiency in private households enabled by adequate appliances. In ECEEE Summer Study proceedings. Available from: https://epub.wupperinst.org/frontdoor/deliver/index/docId/5932/file/5932_Brischke.pdf.
    [123] Spangenberg JH, Lorek S (2019) Sufficiency and consumer behaviour: From theory to policy. Energy Policy 129: 1070–1079. https://doi.org/10.1016/j.enpol.2019.03.013 doi: 10.1016/j.enpol.2019.03.013
    [124] Heindl P, Kanschik P (2016) Ecological sufficiency, individual liberties, and distributive justice: Implications for policy making. Ecol Econ 126: 42–50. https://doi.org/10.1016/j.ecolecon.2016.03.019 doi: 10.1016/j.ecolecon.2016.03.019
    [125] IEA (International Energy Agency), Energy Policies beyond IEA Countries: Morocco 2019. IEA, 2019. Available from: https://www.iea.org/reports/energy-policies-beyond-iea-countries-morocco-2019.
    [126] Palermo V, Bertoldi P, Apostolou M, et al. (2020) Assessment of climate change mitigation policies in 315 cities in the Covenant of Mayors initiative. Sustainable Cities Soc 60: 102258. https://doi.org/10.1016/j.scs.2020.102258 doi: 10.1016/j.scs.2020.102258
    [127] Kona A, Bertoldi P, Kilkis S (2019) Covenant of mayors: Local energy generation, methodology, policies and good practice examples. Energies 12: 985. https://doi.org/10.3390/en12060985 doi: 10.3390/en12060985
    [128] Tsemekidi Tzeiranaki S, Bertoldi P, Diluiso F, et al. (2019) Analysis of the EU residential energy consumption: Trends and determinants. Energies 12: 1065. https://doi.org/10.3390/en12061065 doi: 10.3390/en12061065
    [129] Köppen W (1900) Klassification der Klimate nach Temperatur, Niederschlag and Jahreslauf. Petermanns Geographische Mitteilungen 6: 593–611. Available from: koeppen-geiger.vu-wien.ac.at/pdf/Koppen_1918.pdf.
    [130] Chen D, Chen HW (2013) Using the Köppen classification to quantify climate variation and change: An example for 1901–2010. Environ Dev 6: 69–79. https://doi.org/10.1016/j.envdev.2013.03.007 doi: 10.1016/j.envdev.2013.03.007
    [131] Perez-Garcia A, Guardiola AP, Gómez-Martínez F, et al. (2018) Energy-saving potential of large housing stocks of listed buildings, case study: l'Eixample of Valencia. Sustainable Cities Soc 42: 59–81. https://doi.org/10.1016/j.scs.2018.06.018 doi: 10.1016/j.scs.2018.06.018
    [132] Wang X, Ding C, Zhou M, et al. (2023) Assessment of space heating consumption efficiency based on a household survey in the hot summer and cold winter climate zone in China. Energy 274: 127381. https://doi.org/10.1016/j.energy.2023.127381 doi: 10.1016/j.energy.2023.127381
    [133] Cao X, Yao R, Ding C, et al. (2021) Energy-quota-based integrated solutions for heating and cooling of residential buildings in the Hot Summer and Cold Winter zone in China. Energy Build 236: 110767. https://doi.org/10.1016/j.enbuild.2021.110767 doi: 10.1016/j.enbuild.2021.110767
    [134] Deng Y, Gou Z, Gui X, et al. (2021) Energy consumption characteristics and influential use behaviors in university dormitory buildings in China's hot summer-cold winter climate region. J Build Eng 33: 101870. https://doi.org/10.1016/j.jobe.2020.101870 doi: 10.1016/j.jobe.2020.101870
    [135] Liu H, Kojima S (2017) Evaluation on the energy consumption and thermal performance in different residential building types during mid-season in hot-summer and cold-winter zone in China. Proc Eng 180: 282–291. https://doi.org/10.1016/j.proeng.2017.04.187 doi: 10.1016/j.proeng.2017.04.187
    [136] Geraldi MS, Melo AP, Lamberts R, et al. (2022) Assessment of the energy consumption in non-residential building sector in Brazil. Energy Build 273: 112371. https://doi.org/10.1016/j.enbuild.2022.112371 doi: 10.1016/j.enbuild.2022.112371
    [137] El Hafdaoui H, El Alaoui H, Mahidat S, et al. (2023) Impact of hot arid climate on optimal placement of electric vehicle charging stations. Energies 16: 753. https://doi.org/10.3390/en16020753 doi: 10.3390/en16020753
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