Assessing the Energy and Economic Performance of Green and Cool Roofs: A Life Cycle Approach

Abstract

Green and cool roofs have significant potential to reduce energy consumption in buildings, but high initial costs and the need for local adaptation limit their adoption. This study aims to compare the life cycle energy assessment (LCEA) and life cycle cost analysis (LCCA) of green, cool, and standard (fibre cement) roofs in three Brazilian cities with different climatic and economic contexts. Computer simulations were carried out on a multifamily residential building model to assess the energy performance of the roofs. The simulation results and literature data were used to estimate the roofs’ energy consumption and cost over the life cycle. Over a 40-year life cycle, green and cool roofs reduced energy consumption by 13% to 22% compared to standard roofs. Cool roofs showed the lowest life cycle costs, while green roofs faced cost-effectiveness challenges due to high initial and maintenance costs. However, in areas with high energy demands and electricity tariffs, the life cycle cost of green roofs may be decreased. The study highlights the crucial role of material selection in embodied energy and emphasises the dominant impact of the operational phase on energy consumption and life cycle costs. These findings underscore the need for customised design strategies and localised assessments to support decision-making.

1. Introduction

Urbanisation and climate change have intensified the need for resilient and energy-efficient solutions within the built environment. In this context, some roof typologies have been recognised for their role in protecting buildings and their ability to influence energy performance, urban microclimates, and environmental sustainability. Among the diverse alternatives, green and cool roofs have received increasing attention as promising strategies to enhance thermal comfort, reduce energy demand, and mitigate greenhouse gas emissions, offering environmental and socio-economic benefits [1,2].

Recent studies have shown a growing interest in the life cycle performance of green and cool roofs, though significant gaps remain. Economic assessments of green roofs are still concentrated in North America and parts of Asia, with limited attention to other climatic and socio-economic contexts [3]. Many analyses focus only on construction costs [4]. Findings on green roofs’ cost-effectiveness vary depending on whether environmental benefits are monetised [3]. In contrast, cool roofs are often seen as cost-effective alternatives due to their low cost and environmental impact [5]. However, comparative evaluations across different climates remain limited, and the influence of the local balance between heating and cooling demand on both environmental and economic performance warrants further attention [4,5]. Furthermore, their widespread implementation is frequently hindered by high initial investment requirements, particularly in the case of green roofs, due to the necessity for contextual adaptation to local variables, such as climate conditions, building envelope typologies, and regional electricity tariffs [6,7].

These challenges are especially pronounced in Brazil, where conventional fibre cement roofs dominate due to their affordability and broad availability [8]. In the Brazilian context, the adoption of green roofs is still limited, mainly due to high initial costs, the restricted availability of specialised professionals, and the predominant preference for standard roof solutions, which are often considered more reliable by builders and clients. Concerning cool roofs, although they are not yet widely adopted in Brazil, their integration into conventional buildings is relatively simple, as they can be achieved by painting standard roof tiles white or using factory-pigmented white tiles, despite their higher cost compared to traditional options.

Life cycle assessment (LCA) has established itself as a critical tool for assessing the environmental performance of construction systems throughout their entire life span, from the extraction of raw materials to final disposal. By offering a comprehensive view of environmental burdens across all life cycle stages, LCA enables informed decision-making regarding material selection and design strategies, supporting the development of more sustainable building practices [9,10]. However, comparative LCA studies encompassing green, cool, and standard roofs are relatively scarce, particularly those that adopt fibre cement as the reference scenario. This omission is likely due to the limited prevalence of fibre cement roofs in international contexts despite their extensive use and economic relevance within the Brazilian construction sector. In Brazil, this type of roof tile accounts for around half of the national roof tile market [8].

In this context, life cycle energy assessment (LCEA) is particularly suited for evaluating energy inputs and outputs across the life cycle of a building. This streamlined yet impactful approach prioritises energy consumption data, making it a cost-effective and time-efficient alternative to more comprehensive LCA frameworks. Life cycle cost analysis (LCCA) complements this perspective by assessing the economic implications over the life cycle, providing a basis for decision-making regarding the long-term viability of different roofing solutions [11,12].

This study contributes to the field by comparing the LCEA and LCCA of green, cool, and standard fibre cement roofs across three Brazilian cities with different climatic and socio-economic characteristics. Emphasis is placed on the contribution of each phase to the life cycle energy and life cycle cost. The analysis offers valuable insights into the trade-offs and synergies among roof typologies, ultimately supporting evidence-based policymaking and encouraging the integration of sustainable roofing technologies in diverse urban contexts. Furthermore, the study reinforces the importance of customised design strategies tailored to local climatic conditions and economic constraints, emphasising that the suitability of roof typology is highly context-dependent.

2. Materials and Methods

Computer simulations were carried out on a multifamily residential building model to investigate the energy performance of green and cool roofs compared to standard roofs, with and without thermal insulation, in three Brazilian climates. These results and data from the literature were used to estimate the energy consumption and cost over the life cycle of these roofs. Figure 1 shows the main steps of the methodology.

2.1. Computer Simulation

A multifamily residential building representing Brazilian social housing was modelled in EnergyPlus version 22.1. The model was simulated in an open field, i.e., without including urban elements in the surroundings. An energy analysis was carried out in a 38.6 m² floor area flat on the upper floor of the building. Figure S1 in the Supplementary Materials shows the building model and floor plan of the flat. The characteristics of the envelope (including standard roof), openings and internal loads relating to occupants, lighting, and equipment can be found in the Supplementary Materials. Air-conditioning was modelled for the living room and bedrooms during occupied hours, with thermostat settings of 21 °C for heating and 26 °C for cooling [13,14]. The bathroom window remained open, and the other openings were closed [15]. The annual energy consumption was obtained using the annual cooling and heating loads obtained in the simulation and the energy efficiency coefficients of 5.5 for cooling and 4.47 for heating [16].

The study compared the standard roofs in Brazilian multifamily buildings with green and cool roofs, all with and without thermal insulation (expanded polystyrene). The cool roof was a factory-pigmented fibre cement roof in the colour white. It was characterised by the same thermal transmittance and capacity as the standard roof but featured a solar absorptance equal to 0.42. An extensive green roof was considered, and the parameters of the green roof module used in the simulation are detailed in

Table 1. Thermophysical properties of the green roof.

Layer	Parameter	Value
Vegetation	Vegetation height (m)	0.10
	Leaf area index (LAI)	3.00
	Leaf reflectivity	0.19
	Leaf emissivity	0.97
	Minimum stomatal resistance (s/m)	120.00
Substrate	Thickness (m)	0.10
	Dry soil conductivity (W/m·K)	0.20
	Dry soil density (kg/m3)	1020.00
	Dry soil specific heat (J/kg·K)	1093.00
	Saturated volumetric moisture content	0.26
	Residual volumetric moisture content	0.01
	Initial volumetric moisture content	0.15

Source: Based on [7,20,21].

. The additional layers of the green roof were modelled individually. A geotextile mat was considered as the filter layer [17], high-density polyethylene (HDPE) as the drainage [18], low-density polyethylene (LDPE) as the root barrier membrane [18,19], and bituminous membrane as the waterproofing layer. When thermal insulation was included, expanded polystyrene was used.

The simulation duration was set at 1 year, and the climate files TMY 2007–2021 [22] of Florianópolis (Cfa), Curitiba (Cfb), and Brasília (Aw) [23] were considered. According to the Brazilian bioclimatic zone, Florianópolis is located in zone 3A (mixed and humid zone), Curitiba in zone 1M (very cold zone with moderate winter), and Brasília in zone 3B (mixed and dry zone). Florianópolis has annual temperatures ranging from 6.4 °C to 34.6 °C, with an average of 21.2 °C. Curitiba has colder winters, with temperatures ranging from 0.2 °C to 32.2 °C and an annual average of 18.1 °C. Brasília has annual temperatures between 8.6 °C and 33.3 °C, averaging 21.5 °C [22].

2.2. Life Cycle Assessment

The life cycle assessment focused on estimating total energy consumption and cost over the life cycle. We sought to fulfil the phases of the process defined in ISO Standards 14040 and 14044 [10,24]: definition of the goal and scope, life cycle inventory, assessment, and interpretation of the life cycle impacts.

2.2.1. Goal and Scope

The function selected for comparing the different roof typologies was ‘to provide coverage for a specific area over a defined period’. The functional unit was one m² of roof area over a 40-year lifespan, a usual parameter in LCA studies of green roofs [3]. For the LCEA, the metric used was energy consumption per square metre (kWh/m²) over 40 years. For the LCCA, the metric was cost, in Brazilian reais, per square metre (BRL/m²) (on 6 May 2025, USD 1.00 was equivalent to BRL 5.69) over the same period. Costs were also expressed as net present value (NPV) per square metre (BRL/m²). The lifespan of fibre cement roofs (standard and cool roofs) was set at 40 years, aligning with the range defined by [15] and life cycle studies [25].

Similarly, the lifespan of green roofs was assumed to be 40 years [2,3]. A cradle-to-grave system boundary was applied for the LCEA and LCCA, encompassing raw material extraction, manufacturing, assembly, operation, maintenance, and deconstruction. Due to data limitations, stages involving reuse, recovery, or recycling were not considered.

2.2.2. Inventory

The quantities of materials, equipment, transport, and labour used in the LCEA and LCCA were based on the National System for Research into Construction Costs and Indices (SINAPI) [26] for the three cities, supplemented by Price Composition Tables for Budgets (TCPO) [27] and market data. SINAPI is a Brazilian reference database for construction costs, while TCPO is a widely used tool for detailed project estimations. Initial embodied energy, embodied energy in maintenance, and deconstruction energy for the LCEA were obtained from national or international literature databases, depending on data availability. Life cycle costs were calculated using SINAPI tables and market price information.

Energy consumption for air-conditioning (during the LCEA and LCCA operational phase) was estimated using the annual thermal load (heating and cooling) obtained through computer simulations. The ratio between the thermal load and the energy efficiency coefficient of the air-conditioning determines the annual energy consumption. Energy consumption for air-conditioning was assumed to remain equal every year.

2.2.3. Life Cycle Energy Assessment

The analysis includes the initial embodied energy, operational energy, maintenance-embodied energy, and deconstruction energy, expressed in primary energy and calculated according to Equations (1)–(4).

Table 2. Energy components, assumptions, and parameters considered in each phase of LCEA.

Phase	Energy Components Considered	Assumptions and Parameters
Initial	- Embodied energy in material production - Energy for transport to the construction site - Energy for on-site assembly	- 80 km one-way transport distance (160 km round trip) - Tipper truck energy intensity: 0.00045 kWh/kg·km [28] - Electricity-to-primary energy conversion factor: 1.6 [29]
Operational	- Electricity consumption for air-conditioning over 40 years	- Constant annual consumption - Lighting and equipment excluded
Maintenance	- Standard and cool roofs: washing every 5 years - Green roof: fertilisation twice a year (8 g/m2 of NPK fertiliser), annual substrate replenishment (0.5% loss/year) 1 - Embodied energy in materials - Transport energy - Equipment use	- 80 km transport distance - Light commercial vehicle 2 energy intensity: 0.0012 kWh/kg·km [30] - Absorptance assumed constant after 3 years 3
Deconstruction	- Disassembly energy - Transport to landfill	- 50 km distance to landfill - Tipper truck energy intensity: 0.00045 kWh/kg·km [28]

¹ According to Vacek et al. [31]. ² This vehicle reflects a smaller material volume than the initial phase. ³ While effective in restoring reflectance, frequent washing was impractical due to the high frequency required [32].

outlines the energy components considered in each phase and the underlying assumptions and parameters adopted. While energy consumed by equipment and lighting was modelled, it was excluded from the LCEA and LCCA as it remained constant across all the simulated models. Irrigation was not considered in order to assess the influence of each climate.

$${\mathrm{E}\mathrm{E}}_{\mathrm{i}}={\sum }_{\mathrm{i}=1}^{\mathrm{n}}({\mathrm{E}\mathrm{E}}_{\mathrm{m}\mathrm{a}\mathrm{t}}·{\mathrm{m}}_{\mathrm{m}\mathrm{a}\mathrm{t}})+{\mathrm{E}}_{\mathrm{f}\mathrm{u}\mathrm{e}\mathrm{l}}· {\mathrm{m}}_{\mathrm{m}\mathrm{a}\mathrm{t}}· {\mathrm{d}}_{\mathrm{m}\mathrm{a}\mathrm{t}}+{\mathrm{P}}_{\mathrm{e}\mathrm{q}}· \mathrm{t}· {\mathrm{f}}_{\mathrm{c}}$$

(1)

where EE_i is the initial embodied energy required to build the roofs (kWh); i is the construction material under analysis; n is the number of construction materials; EE_mat is the embodied energy of the material (kWh/kg); m_mat is the mass of the material (kg); E_fuel is the energy consumption for transport (kWh/kg·km); d_mat is the distance between the material’s factory and the construction site (km); P_eq is the equipment’s power (kW); t is the equipment’s utilisation time (h); and f_c is the conversion factor for converting electricity into primary energy.

$${\mathrm{E}}_{\mathrm{o}}={\mathrm{E}}_{\mathrm{c}.\mathrm{a}\mathrm{n}}·\mathrm{H}· {\mathrm{f}}_{\mathrm{c}}$$

(2)

where E_o is the operational energy (kWh); E_c.an is the annual energy needed for air-conditioning (kWh/year); H is the time horizon (year); and f_c is the conversion factor for converting electricity into primary energy.

$${\mathrm{E}\mathrm{E}}_{\mathrm{m}}={\sum }_{\mathrm{j}=1}^{\mathrm{m}}\left\{\right[{\sum }_{\mathrm{i}=1}^{\mathrm{n}}({\mathrm{E}\mathrm{E}}_{\mathrm{m}\mathrm{a}\mathrm{t}} ·{\mathrm{m}}_{\mathrm{m}\mathrm{a}\mathrm{t}})+{\mathrm{E}}_{\mathrm{f}\mathrm{u}\mathrm{e}\mathrm{l}}· {\mathrm{m}}_{\mathrm{m}\mathrm{a}\mathrm{t}}· {\mathrm{d}}_{\mathrm{m}\mathrm{a}\mathrm{t}}+{\mathrm{P}}_{\mathrm{e}\mathrm{q}}· \mathrm{t}· {\mathrm{f}}_{\mathrm{c}}] · {\displaystyle \frac{\mathrm{H}}{{\mathrm{P}}_{\mathrm{m}}}}\}$$

(3)

where EE_m is the maintenance-embodied energy (kWh); j is the maintenance activity under analysis; m is the number of maintenance activities; i is the construction material under analysis; n is the number of construction materials; EE_mat is the embodied energy of the material (kWh/kg); m_mat is the mass of the material (kg); E_fuel is the energy consumption for transport (kWh/kg·km); d_mat is the distance between the material’s factory and the construction site (km); P_eq is the equipment’s power (kW); t is the equipment’s utilisation time (h); f_c is the conversion factor for converting electricity into primary energy; H is the time horizon (year); and P_m is the periodicity of the maintenance activity (year).

$${\mathrm{E}}_{\mathrm{d}}={\mathrm{P}}_{\mathrm{e}\mathrm{q}}· \mathrm{t}· {\mathrm{f}}_{\mathrm{c}}+{\mathrm{E}}_{\mathrm{f}\mathrm{u}\mathrm{e}\mathrm{l}}· {\mathrm{m}}_{\mathrm{d}\mathrm{e}\mathrm{m}}· {\mathrm{d}}_{\mathrm{d}\mathrm{e}\mathrm{m}}$$

(4)

where E_d is the deconstruction energy (kWh); P_eq is the equipment’s power (kW); t is the equipment’s utilisation time (h); f_c is the conversion factor for converting electricity into primary energy; E_fuel is the energy consumption for transport (kWh/kg·km); m_dem is the mass of the materials from disassembly (kg); and d_dem is the distance between the construction site and the landfill (km).

Table S1 in the Supplementary Material shows the material-embodied energy used in the life cycle energy assessment of the roof typologies. The materials selected for the cool and green roofs were those deemed most probable (mainly due to their availability in the market) within the Brazilian context, where adopting such roofing systems is not yet widespread. Alternative materials could be investigated in future studies and conceptualised within a framework aimed at incentivising the implementation of green roofs.

2.2.4. Life Cycle Cost Assessment

The analysis considered the initial, operational, maintenance, and deconstruction costs of the roof typologies. The specific components considered in each phase are outlined in

Table 3. Cost components, assumptions, and parameters considered in each phase of LCCA.

Phase	Cost Components Considered	Assumptions and Parameters
Initial	- Material purchase - Transportation to the construction site - On-site assembly	- 80 km transport distance (factory to site) - 10.5-tonne tipper truck [28] - Diesel consumption: 1 litre per 3 km - Transport cost based exclusively on fuel cost
Operational	- Electricity for heating and cooling over 40 years	- Electricity tariffs: 0.57302 BRL/kWh (Florianópolis), 0.63051 BRL/kWh (Curitiba), and 0.69871 BRL/kWh (Brasília) [33] - Annual tariff adjustments: 5.28% (Florianópolis), 7.09% (Curitiba), and 6.44% (Brasília) 1 [33] - Lighting and equipment costs were excluded
Maintenance	- Material replacement - Transport to the construction site - On-site assembly	- 80 km transport distance - Light commercial vehicle - Gasoline consumption: 1 L per 10 km - Inflation rate: 5.69%/year 2 - Wage adjustment: 6.74%/year 3
Deconstruction	- Disassembly - Transport to landfill - Landfill disposal	- 50 km distance to landfill - Transport was included in the disposal cost - Inflation and wage adjustments were applied

¹ These figures were derived from the average tariff adjustments implemented by local energy providers in the selected cities, considering historical data spanning the previous five years. ² The average inflation rate in Brazil over the past five years. ³ The average minimum wage adjustment over the past five years.

, and the costs were calculated using Equations (5)–(8). The maintenance activities over the 40-year time horizon were outlined in Section 2.2.3.

$${\mathrm{C}}_{\mathrm{i}}={\mathrm{C}}_{\mathrm{r}\mathrm{o}\mathrm{o}\mathrm{f}}+{\mathrm{C}}_{\mathrm{t}\mathrm{r}.\mathrm{m}\mathrm{a}\mathrm{t}}+{\mathrm{C}}_{\mathrm{l}\mathrm{a}\mathrm{b}}$$

(5)

where C_i is the initial cost (BRL); C_roof is the current cost of the materials and equipment needed to assemble the roof (BRL); C_tr.mat is the current cost of transporting the material to the site (BRL); and C_lab is the current cost of labour to assemble the roof (BRL).

$${\mathrm{C}}_{\mathrm{o}}={\sum }_{\mathrm{t}=1}^{\mathrm{p}}\left\{\right[{{\mathrm{T}}_{\mathrm{c}\mathrm{u}\mathrm{r}}·(1+{\mathrm{T}}_{\mathrm{a}\mathrm{d}\mathrm{j}})}^{\mathrm{t}}] · \mathrm{E}\}$$

(6)

where C_o is the operational cost (BRL); t is the year under analysis (year); p is the number of years assessed (year); T_cur is the current electricity tariff (BRL); T_adj is the average electricity tariff adjustment over the past five years (% per year); and E is the annual energy needed for air-conditioning (kWh/year).

$${\mathrm{C}}_{\mathrm{m}}={\sum }_{\mathrm{j}=1}^{\mathrm{m}}\left\{\left[{{\mathrm{C}}_{\mathrm{r}\mathrm{o}\mathrm{o}\mathrm{f}}·\left(1+\mathrm{I}\right)}^{\mathrm{t}}\right]+{{[\mathrm{C}}_{\mathrm{t}\mathrm{r}.\mathrm{m}\mathrm{a}\mathrm{t}}·\left(1+\mathrm{I}\right)}^{\mathrm{t}}\right]+\left[{{\mathrm{C}}_{\mathrm{l}\mathrm{a}\mathrm{b}}·(1+\mathrm{W})}^{\mathrm{t}}\right]\}$$

(7)

where C_m is the maintenance cost (BRL); j is the maintenance activity under analysis; m is the number of maintenance activities; C_roof is the current cost of the materials and equipment needed to assemble the roof (BRL); I is the average inflation rate over the last five years (% per year); t is the year under analysis (year); C_tr.mat is the current cost of transporting the material to the site (BRL); C_lab is the current cost of labour to assemble the roof (BRL); and W is the average minimum wage adjustment over the last five years (% per year).

$${\mathrm{C}}_{\mathrm{d}}=\{\left[{{\mathrm{C}}_{\mathrm{e}\mathrm{q}}·\left(1+\mathrm{I}\right)}^{\mathrm{t}}\right]+\left[{{\mathrm{C}}_{\mathrm{d}.\mathrm{l}\mathrm{a}\mathrm{b}}·(1+\mathrm{W})}^{\mathrm{t}}\right]+\left[{{\mathrm{C}}_{\mathrm{d}\mathrm{i}\mathrm{s}\mathrm{p}}·(1+\mathrm{I})}^{\mathrm{t}}\right]\}$$

(8)

where C_d is the cost of deconstruction (BRL); C_eq is the current cost of disassembly equipment (BRL); I is the average inflation over the last five years (%); t is the year under analysis (year); C_d.lab is the current cost of disassembly labour (BRL); W is the minimum wage adjustment over the last five years (% per year); and C_disp is the current cost of disposing of roofing materials in landfill (BRL).

The net present value (NPV) was also calculated for the analysis period, according to Equation (9). Operation, maintenance, and deconstruction costs were brought to the present value using a discount rate. The average SELIC rate (Brazilian benchmark interest rate of the economy) over the last five years (7.61% per year) [34] was used as the discount rate.

$$\mathrm{N}\mathrm{P}\mathrm{V}=-{\mathrm{C}}_i-{\sum }_{\mathrm{t}=1}^{\mathrm{p}}{\displaystyle \frac{[{\mathrm{C}}_{\mathrm{o},\mathrm{t}}+{\mathrm{C}}_{\mathrm{m},\mathrm{t}}+{\mathrm{C}}_{\mathrm{d},\mathrm{t}}]}{{(1+\mathrm{d})}^{\mathrm{t}}}}$$

(9)

where NPV is the net present value (BRL); C_i is the initial cost (BRL); C_o is the operational cost (BRL); C_m is the maintenance cost (BRL); C_d is the deconstruction cost (BRL); t is the year under analysis; p is the years evaluated (year); and d is the discount rate in decimal numbers.

The NPV of the roof typologies was calculated by varying the discount rate, electricity tariff adjustment, inflation, and minimum wage adjustment within a range of −50% to +50%, aiming to encompass realistic variations. The discount rate and electricity tariff adjustment were individually varied within their potential ranges to assess their specific impacts on the NPV, while other input variables were held constant. Conversely, the inputs for inflation and minimum wage adjustment were varied simultaneously, as both factors influence maintenance and deconstruction costs.

Table S2 in the Supplementary Material shows each roof component’s costs, including materials, equipment for assembly, and labour.

2.2.5. Interpretation

Interpretation focused on analysing the results and addressing potential errors to ensure robust conclusions. One sought to identify the materials and processes with the highest energy demand and cost and the life cycle phases with the greatest energy consumption and financial burden. This approach highlighted the components requiring careful consideration during selection and the phases with the highest potential for impact and cost reduction. To explore the relationship between operational energy and total life cycle energy, a simple linear regression analysis was performed for each climatic context, using operational energy as the independent variable and total life cycle energy as the dependent variable. This statistical analysis was conducted in Microsoft Excel and included the calculation of the coefficient of determination (R²) and significance level (p-value). Additionally, the performance of the roofs was compared to identify more sustainable alternatives tailored to climatic conditions. Lastly, the research sought to present benchmark values, addressing a notable gap in research within the Brazilian context.

3. Results and Discussion

3.1. Energy in the Life Cycle Phases

This section presents the energy consumption across the life cycle phases of a top-floor flat in a multifamily building (Figure 2). Based on the same dataset, the initial embodied, maintenance, and deconstruction energy remained equal across the three cities. Standard and cool roofs have the same embodied and deconstruction energy; they only have different roof tile colours. Operational energy was obtained through computational simulation and varied according to the climatic contexts. Maintenance and operational energy refer to the 40-year period.

The embodied energy of the materials was the most significant contributor in all cases, accounting for over 96% of the initial embodied energy. In the standard and cool roofs, the metal structure was the component with the most significant energy impact (52%), followed by the roof tiles (16%). In the green roof, the waterproofing layer accounted for 56% of the initial embodied energy, followed by the drainage layer (19%). In the drainage layer, HDPE modules have a high embodied energy per kilogram. Thermal insulation ranked as the third most energy-intensive component for all the roof typologies. Although expanded polystyrene sheets are lightweight and moderately represented in terms of mass, they show one of the highest embodied energies per kilogram among the construction materials.

The green roof had 7% less initial embodied energy than standard and cool roofs. Thermal insulation increased the embodied energy of the roofs by up to 15%. The embodied energy of roofs has been the subject of limited investigation in the literature. Higher values (747 kWh/m²) have been reported for green roofs, primarily attributed to using high-embodied-energy materials, such as slate aggregate, and including feedstock energy in the calculations for organic compound [35]. Feedstock energy, which refers to the retained energy within a material that may be partially recoverable at the end of its life cycle (e.g., through incineration) [36], plays a crucial role in determining embodied energy and the proportional contribution of individual components. Our study adopted a consistent approach to feedstock energy, incorporating it into embodied energy estimates when it represented an irreversible resource loss, as with fossil-based plastics, such as LDPE and HDPE, and bituminous materials [36]. Feedstock energy was reported separately for materials where energy recovery is possible, such as organic compost. This methodology ensures a transparent and standardised evaluation of embodied energy contributions.

The operational energy varied substantially according to roof typology and climate. The standard roof without insulation in all three cities resulted in the highest energy consumption. With the standard roof, insulation reduced energy use in Curitiba but showed negligible effects in Florianópolis and Brasília. Cool roofs achieved the greatest reductions in operational energy in warmer climates (Florianópolis and Brasília), while green roofs were most effective in Curitiba, a colder city. These findings are consistent with previous studies [37,38,39,40] indicating that cool roofs usually provide superior cooling performance compared to green roofs. However, the high reflectance can increase heating loads during colder periods, reducing solar heat gains.

The maintenance-embodied energy for standard and cool roofs was entirely attributed to the energy consumption of equipment, specifically a high-pressure washer. In the case of the green roof, 82% of the maintenance-embodied energy was associated with the materials used for fertilising and replacing the substrate, while the remaining 18% corresponded to the energy required for transporting these materials to the construction site. Notably, the maintenance-embodied energy remained equal for roofs with and without insulation, as maintenance activities were independent of the insulation layer.

The deconstruction energy for standard and cool roofs was determined by the energy consumed by hydraulic cranes during the removal of tiles and metal structures and the subsequent transportation of these materials to a landfill. For green roofs, it was considered the manual removal of roofing materials. Consequently, about half of the deconstruction energy for standard and cool roofs was attributed to transportation and the other half to equipment operation. In contrast, the deconstruction energy for green roofs was entirely associated with the energy consumed during material transportation.

3.2. Total Life Cycle Energy

Figure 3 shows the total energy used in the life cycle of the roof typologies in Florianópolis, Curitiba, and Brasília. Cool roofs achieved the lowest energy consumption over a 40-year analysis in warmer climates, such as Florianópolis and Brasília, while green roofs presented the lowest life cycle energy consumption in Curitiba. Thermal insulation only reduced life cycle energy for standard roofs in Curitiba, aligning with trends observed during the operational energy phase. In fact, operational energy accounted for the largest share of life cycle energy, ranging from 69% to 86% depending on roof typology and climate, while initial embodied energy represented 14% to 31%. A strong linear relationship was observed between operational energy and the total life cycle energy of the roofs in all climatic contexts, with R² equal to 0.98 (Florianópolis), 0.95 (Curitiba), and 0.99 (Brasília). All the regressions were statistically significant (p < 0.001), confirming the dominant influence of operational energy on total life cycle energy consumption. This finding aligns with a previous study on the energy life cycle of buildings, which also found a linear relationship between operational and total energy [41], reinforcing the significance of operational energy in overall life cycle energy assessments. Maintenance and deconstruction energies were negligible. The analysis assumed a roof lifespan equal to the study period, with no material replacement, and deconstruction excluded material reuse or recycling, considering only disassembly and landfill transport.

Transportation energy, calculated using fixed distances between factories, construction sites, and landfills, has minimal impact on life cycle energy results. Variations of ±50% in transport distances changed the contribution of transportation to total energy consumption by no more than 1.2 percentage points, indicating that, over the 40-year analysis period, total energy use is only marginally influenced by changes in transport distances.

3.3. Cost in the Life Cycle Phases

The costs obtained for the roof typologies in each life cycle phase are presented in Figure 4. The construction of the standard and cool roofs involved transporting and assembling a metallic structure, tiles, ridge caps, gutters, and flashings. For the green roof, the transportation and layer assembly were considered, including waterproofing, root barrier, drainage, filter, substrate, vegetation, and hydrosanitary systems for draining absorbed water. Roofs with thermal insulation included the transportation and expanded polystyrene board assembly. Across all the roof typologies and cities analysed, acquiring materials and equipment for assembly represented the most significant proportion of initial costs, accounting for approximately 90% of the total.

The green roof had the highest initial cost due to the larger quantity of materials. Vegetation, waterproofing, and drainage layers were the main contributors to the initial cost. In contrast, the metallic structure and tiles represented most initial costs for standard and cool roofs. Comparing roofs without insulation, the initial cost of green roofs was between 30% and 51% higher than standard roofs and between 21% and 40% higher than cool roofs, depending on the city. These findings are consistent with the existing literature, highlighting the higher initial costs associated with green roofs, irrespective of the cost variations and typological differences observed in conventional roofs. For instance, Sproul et al. [42] reported that the initial cost of a green roof is 7.8 times greater than that of flat black or white (cool) membrane roofs. Ulubeyli and Arslan [43] found that the initial cost of green roofs may be nearly twice that of wooden roofs. Similarly, Kim et al. [44] noted that green roofs are 5.6 times more expensive than flat roofs with waterproof membranes.

The operational costs were markedly higher in Brasília, ranging from 79% to 116% above those observed in the other cities, depending on the roof typology and city comparison. This increase is attributed to higher energy consumption for air-conditioning in Brasília’s hot and dry climate (tropical climate with dry winters, according to [23]) and higher electricity tariff than Florianópolis and Curitiba. In Brazil, electricity tariffs are defined by state-level companies and regulated by the National Electricity Agency (ANEEL), resulting in regional variations in pricing across the country [33]. Standard roofs without insulation consistently showed the highest costs in all the cities, particularly in Brasília, where operational expenses approached BRL 1900/m². Cool roofs significantly reduced operational costs in Florianópolis and Brasília (savings of 17% and 25% compared to standard roofs, respectively), reflecting their potential in mitigating cooling loads. In Curitiba, however, their economic advantage was less pronounced. In this city, the green roof is more effective, reducing the cost by 28% compared to the standard roof and by 8% compared to the cool roof. The inclusion of thermal insulation increased the operational costs by 2% to 21%, depending on the city, when using cool and green roofs. For the standard roof, thermal insulation resulted in notable operational cost reductions only in Curitiba, achieving a 10% decrease.

Labour costs were the predominant maintenance cost—over 87% in all roof typologies and cities assessed. Despite their operation savings, green roofs presented maintenance costs 879% higher than those associated with standard and cool roofs. This increase was primarily driven by the higher frequency of maintenance activities (defined based on the existing literature) and the effects of the Brazilian inflation rate over time. Inflation rates in Brazil have been historically volatile, and the value adopted in this study (5.69% per year, according to Section 2.2.4) may be considered relatively high for long-term cost projections. This raises the cumulative cost of labour-intensive services, such as those required for green roof maintenance, resulting in significant challenges to their long-term economic sustainability and overall financial viability.

The deconstruction cost is a future cost and was therefore calculated considering the average inflation rate and the increase in the minimum wage in year 40. The deconstruction cost of standard and cool roofs is equivalent, given that the same volume of material is considered for constructing these roofs. Despite the greater quantity of materials used in constructing the green roof (and therefore higher labour, transport, and landfill disposal costs), no equipment was considered for disassembling it, only manual services. Labour costs were the most significant component across all the roof typologies and cities, accounting for over 60% of the deconstruction costs.

3.4. Total Life Cycle Cost

The total life cycle cost of the roof typologies in Florianópolis, Curitiba, and Brasília is shown in Figure 5. For standard and cool roofs, the operational phase was the most significant contributor to the 40-year life cycle cost, representing between 42% and 67% of the total. The operational and maintenance phases for green roofs had comparable contributions in Florianópolis and Curitiba, each accounting for 27% to 48% of the total costs. In Brasília, characterised by high energy demands for air-conditioning and high electricity tariffs, the operational phase surpassed 45% of the total life cycle cost.

In all the climatic contexts, the cool roof without insulation had the lowest life cycle cost. Despite the higher initial cost of the cool roof compared to the standard roof (about 7% higher), the low operational cost (between 17% and 25% lower) results in a 40-year life cycle cost between 7% and 16% lower. Maintenance and deconstruction costs are equivalent for these roofs. Similar results were reported by Sproul et al. [42]. In addition to energy savings, the authors considered other economic benefits of cool roofs (reduction in emissions and the contribution to global cooling). The most significant reduction in life cycle costs was observed in Brasília, given the higher electricity consumption for air-conditioning and the greater representativeness of operational costs in this climatic context.

The benefits of the green roof in reducing the electricity cost for air-conditioning are overshadowed by the initial and maintenance costs (as observed by Sproul et al. [42] and Ziogou et al. [21]), especially in Florianópolis and Curitiba. Over the 40-year life cycle, the cost of the green roof without insulation is 36% higher than the standard roof in Florianópolis and 33% in Curitiba. In Brasília, the life cycle cost was 16% higher than the standard roof, a lower difference due to the representativeness of the operational cost concerning the total life cycle cost. Life cycle costs were highest in Brasília (23–49% higher than in Florianópolis and Curitiba), primarily due to high operational costs.

The economic feasibility of implementing cool and green roofs depends on location. In areas with higher energy demands and higher electricity tariffs, cool roofs tend to be more advantageous, and the costs associated with green roofs may not significantly exceed or could even offset their higher initial and maintenance expenses. It is worth noting that this study focused on the energy life cycle of roofs, prioritising benefits that could be translated into financial costs for property owners within the Brazilian context. Other advantages, such as habitat creation [6], emission reductions [7,21], property value appreciation, acoustic insulation [43], and stormwater management benefits [7,42], are challenging to quantify and are often based on values reported in other studies, which do not always translate into direct financial savings for the property owner.

Shi et al. [7] observed improved cost-effectiveness for cool and green roofs over standard ones in warmer climates. This study, which focused on tropical dry and temperate humid climates, did not corroborate this trend. Cool roofs decreased life cycle costs most significantly in Brasília (−16%), followed by Curitiba (−11%) and Florianópolis (−7%). Despite Florianópolis’s higher energy demand for cooling, Curitiba’s higher electricity tariffs and rate adjustments resulted in greater life cycle costs for standard roofs in that city. For green roofs, the smallest increase in life cycle costs relative to standard roofs was observed in Brasília (+16%), followed by Curitiba (+33%) and Florianópolis (+36%). These findings underscore the importance of evaluating the economic feasibility of these roofs based on specific local conditions, as electricity tariffs are as impactful as energy demand for climate control.

Figure 6 shows the net present value (NPV) of the roof typologies per floor-plan area in Florianópolis, Curitiba, and Brasília. In this analysis, only costs were considered over the 40-year service life of the roofs. Consequently, in all cases, the NPV was negative. The uninsulated cool roof was the most cost-effective solution (NPV ranging from −438.8 to −548.4 BRL/m²) due to lower electricity costs despite a higher initial investment. Conversely, green roofs had the poorest economic performance (NPV ranging from −723.0 to −874.3 BRL/m²), primarily due to higher initial and maintenance costs.

Figure 7 shows the effects of variations in the discount rate, electricity tariff adjustments, inflation, and minimum wage adjustments on the NPV of standard roofs without insulation in Florianópolis, as similar sensitivity patterns were observed across all the typologies and cities. A full summary is available in Table S3 in the Supplementary Materials.

The discount rate was identified as the most influential variable, with variations of −50% to +50% causing NPV changes between −82% and 29%, depending on the city. In contrast, inflation and minimum wage adjustments had a lesser impact, primarily reflecting maintenance and deconstruction costs. Electric tariff increases had a greater effect on the NPV of standard roofs due to higher energy consumption but were insufficient to make green roofs economically viable. The results revealed non-linear responses, particularly regarding discount rate variations. Such behaviour underscores the risk sensitivity of long-term economic projections in volatile markets. This analysis highlights the importance of thoroughly assessing economic variables in countries like Brazil, which are characterised by high volatility in interest rates and price adjustments [34,45,46].

4. Implications and Suggestions for Future Research

Given the absence of a comprehensive comparative study on the environmental and economic life cycle performance of green roofs relative to standard and cool roofs [2,3], the findings of this research provide a clearer and more nuanced understanding of their respective performances. This study offers significant insights into their applicability across diverse Brazilian climatic contexts by delivering a critical and detailed analysis of the energy performance and life cycle costs of green, cool, and standard roofs. The findings are particularly relevant in the context of sustainable building practices in Brazil, where climate-responsive design remains underexplored in economic terms.

The analysis reaffirms that material-embodied energy constitutes the largest share of initial energy consumption, underscoring the critical role of material selection in reducing environmental impacts. Considering life cycle energy performance, both cool and green roofs (without insulation) proved more efficient than the standard roof in all the cities analysed. The cool roof was particularly effective in Brasília and Florianópolis, where cooling demands are higher, while the green roof performed better in Curitiba’s cooler climate. However, only the cool roof remains a viable alternative when life cycle cost is considered, as the green roof presented significantly higher overall costs. Therefore, the choice between the roof typologies should be guided by climatic conditions and budget constraints. Future work could explore integrating the Theory of Inventive Problem Solving (TRIZ) methodologies [47] to address design contradictions arising from life cycle energy and cost assessments.

Despite these economic challenges, one underscores the broader benefits of green roofs. These include urban heat island mitigation, enhanced stormwater management, improved air quality, noise reduction, biodiversity support, and aesthetic enhancement [48]. Although these benefits are challenging to monetise—especially in the Brazilian context, where financial incentives for ecosystem services are limited—they contribute significantly to urban resilience. Future work should prioritise quantifying and valuing such benefits to support evidence-based policymaking.

The analysis also showed that cool roofs could be strongly recommended in such Brazilian urban contexts, making them an attractive option for policymakers seeking immediate energy savings and climate mitigation. From a public policy perspective, the promotion of cool roofs could be integrated into energy efficiency programmes and building codes with relatively low investment. In contrast, green roofs may require targeted financial incentives, regulatory frameworks, and technical capacity-building efforts to overcome economic and logistical barriers.

Future research should focus on improving the economic feasibility of green roofs by investigating alternative materials with reduced embodied energy and costs, alongside exploring diverse vegetation types that optimise their thermal and environmental performance. Given that vegetation parameters play a critical role in determining green roof efficiency [49], such analyses are essential for maximising their benefits and addressing current barriers. Further investigations should also explore the impact of climate change scenarios on the thermal and economic performance of roof typologies, considering shifting temperature profiles, precipitation patterns, and urban heat island effects. Furthermore, a holistic life cycle approach is essential, incorporating advanced waste management strategies beyond landfilling to reduce environmental burdens further. Additionally, developing multidisciplinary methodologies is critical for evaluating the complex impacts of green roofs at building, city, and societal scales, ensuring their integration into broader urban sustainability frameworks.

Finally, the study highlights the need for adaptive design strategies tailored to specific climate conditions, effectively balancing heating and cooling demands. Policymakers and urban planners are encouraged to consider targeted incentives and support mechanisms to address the financial barriers associated with green roofs, enabling widespread adoption and unlocking their full potential as a sustainable building solution. This comprehensive approach could drive the transition to energy-efficient, environmentally conscious, and socially beneficial roofing systems in urban areas.

5. Conclusions

This study conducted a comparative energy and economic life cycle analysis of green, cool, and standard roofs on a flat on the top floor of a building. This analysis provides a valuable reference for policymakers by comparing these roof typologies’ performances in Brazil’s different climate zones for the first time.

Cool and green roofs demonstrated life cycle energy reductions of 13% to 22% compared to standard roofs, highlighting their potential for energy savings. However, the life cycle cost analysis showed a contrasting trend: cool roofs offered cost reductions of 7% to 16%, whereas green roofs incurred increases of 16% to 33%. The results also underscore the critical role of the operational phase, which emerged as the dominant contributor to both energy consumption and life cycle costs. Thus, accurate assessments of specific building designs, climatic conditions, and local pricing dynamics are essential for decision-making rather than relying on generic assumptions. While cool roofs proved to be more economically and energy efficient in most scenarios, green roofs remain a compelling option for policymakers due to their broader societal and environmental benefits, such as improved air quality, stormwater management, and community benefits. Though not easily translated into monetary value, these advantages warrant consideration in urban planning strategies.

Despite its contributions, this study has some limitations. A key constraint is the lack of a comprehensive and current national database on the embodied energy of construction materials. Although initiatives like the Construction Environmental Performance Information System (Sidac) have progressed, their scope remains limited. Consequently, this study relied on older national data and, where unavailable, international benchmarks. Some simplifications have been applied regarding energy consumption, including the assumption of constant operational energy demand for air-conditioning and a fixed conversion factor from electricity to primary energy over time. Furthermore, the environmental assessment focused exclusively on energy indicators that align with life cycle cost analysis. In contrast, other environmental and social indicators, such as stormwater management, improved air quality, and urban biodiversity, were excluded due to the challenges associated with their monetisation in the Brazilian context, where financial incentives for such metrics remain absent.

Finally, while the findings directly apply to Brazilian cities—particularly those with climates classified as Cfa, Cfb, or Aw according to Köppen’s classification—they also offer broader insights for regions with similar climatic conditions. However, caution is warranted when extrapolating these results to other locations. Economic factors such as local electricity tariffs, inflation rates, and labour costs can significantly modify the life cycle costs of the roof typologies. The selection and availability of construction materials, which vary widely across countries and regions, also play a critical role in shaping both the environmental and economic outcomes. Therefore, while the study provides a robust comparative framework, its broader applicability depends on careful contextual adaptation to local climatic, economic, and material availability conditions.