Green Roofs Research Articles

Effectiveness of Cool and Green Roofs Inside and Outside Buildings in the Brazilian Context

Several studies have assessed the thermal performance of green and cool roofs. However, few have comprehensively addressed Brazilian buildings and climates, considering indoor and outdoor environments. Considering three Brazilian cities, this study aims to assess the performance of green and cool roofs compared with traditional fibre cement roofs in a typical multifamily residential building. Energy consumption, thermal comfort, and outside surface temperature were assessed using computer simulation. The results show that the cool roofs performed better in cities with warmer climates (e.g., Cfa and Aw), reducing electricity consumption by up to 24.8% compared with traditional roofs. Green roofs are better suited for colder climates (e.g., Cfb), with up to 28.2% energy savings. Green roofs provided the highest percentage of thermal comfort hours in all climates. Cool and green roofs provided hourly reductions in outside roof surface temperature of up to 16.5 °C and 28.4 °C, respectively, compared with the traditional roof. This work reinforces that the choice between these two roof types for each city depends on the parameter used for comparison. Based on the relevant information applied to Brazilian buildings and representative climates presented, this work provided recommendations for urban planning policies and building regulations in Brazil.

Enhancing Urban Climate Resistance Through the Application of Selected Strategies and Technologies

Adapting cities for climate resilience is crucial as climate change increases the frequency and severity of extreme weather events. This study outlines a comprehensive set of resilience strategies aimed at enhancing urban resilience across four key domains: water, food, shelter, and energy. These strategies, applicable to both new and existing neighborhoods, range from simple, short-term measures to complex, long-term initiatives. A three-pronged evaluation framework, consisting of three platforms, is introduced to assess these strategies where criteria are initially selected based on their impact on strategy adoption and implementation. This framework employs hypothetical scores and weights that can be adjusted for specific urban contexts through detailed studies. Key outcomes of the evaluation conducted in the first platform include a systematic method to rank strategies based on six criteria: cost, infrastructure impact, scalability, regulatory and zoning challenges, community acceptance, and maintenance needs. For example, community gardens and rainwater harvesting systems are highly scalable and accepted, whereas green roofs require more investment and maintenance. The second and third platform of the framework facilitate the identification of strategies that enhance resilience across each of the resilience domains, as well as across several domains. The results highlight the top-performing strategies under different weighted scenarios. Strategies like green roofs strategy scores high in domains like water management, due to its capacity to absorb and manage stormwater, and energy, by providing natural insulation that reduces heating and cooling demands. Additionally, green roofs contribute to food production when utilized for urban agriculture and enhance shelter by improving building durability and increasing biodiversity This data-driven framework supports the strategic prioritization of resilience strategies, enhancing urban planning and investment decisions globally. Its modularity ensures adaptability to diverse urban settings and climatic issues.

Evaluating Urban Heat Island Mitigation Strategies in Rajshahi, Using ENVI-Met: A Remote Sensing Approach

The Urban Heat Island (UHI) effect is a critical environmental challenge in the 21st century, intensified by rapid urbanization and industrialization. This study focuses on Rajshahi, a rapidly urbanizing city in Bangladesh, where the UHI effect has already begun to manifest significantly. Utilizing ENVI-met software, a comprehensive analysis was conducted to evaluate the effectiveness of urban vegetation strategies, such as green roofs and street planting, in mitigating local temperatures and improving outdoor thermal comfort in Rajshahi's Central Business District. The findings reveal that these mitigation strategies can reduce air temperatures by up to 10 Kelvin, providing substantial cooling benefits. This research highlights the importance of integrating green infrastructure into urban planning to combat the UHI effect, enhance sustainability, and improve the overall livability of urban environments. The study offers valuable insights and practical recommendations for urban planners and policymakers, aiming to foster resilient and sustainable urban development in rapidly growing cities like Rajshahi.

Evaluation of Mediterranean perennials for extensive green roofs in water-limited regions: A two-year experiment

Extensive green roofs (EGRs) play a crucial role in urban environments, offering numerous environmental, economic, and social benefits. However, their performance largely depends on plant selection and adaptation to local climatic conditions. This study investigates the suitability of six perennial Euro-Mediterranean species for EGRs in Mediterranean and semi-arid regions, under different water regimes. A two-year experimental analysis was conducted in Rome (Italy) assessing flowering and mortality rates. Results revealed species-specific responses to irrigation levels, with notable performances observed in Thymus serpyllum, Saponaria ocymoides, and Teucrium chamaedrys, showcasing resilience to water stress. Conversely, Lavandula stoechas and Cerastium tomentosum exhibited sensitivity to water availability, emphasizing the importance of species selection for EGRs. No species completely adhered to the expected flowering period, but showed a general tendency of anticipation, and sometimes an extended flowering period, with some differences between the species. The study underscores the complexity of plant-environment interactions and highlights the need for diversified species composition to enhance EGR functionality and resilience.

Seasonal dynamics of carbon, nitrogen, and phosphorus stoichiometric traits in an extensive green roof in Nanjing, China

Green roofs (GRs) are vital for shaping the material cycles of urban ecosystems as a form of distributed green infrastructure. However, current studies have predominantly focused on the material exchange between GRs and the urban environment, neglecting the internal distribution and equilibrium of constituent elements. By monitoring carbon (C), nitrogen (N), and phosphorus (P) in Sedum lineare Thunb and four substrates throughout the seasons, this study analyzes the ecological stoichiometric characteristics of an extensive GR in Nanjing, China. Intra-annual ratios of C:N (46.02, 13.38, 15.40), C:P (252.41, 57.85, 47.22), and N:P (5.75, 4.23, 3.84) were identified in the plant, substrate, and substrate microbial biomass, respectively. The intra-annual ratios of plant to substrate C, N, and P were roughly 9:1, 7:3, and 6:4, respectively. The use of different substrates resulted in significant variations in plant C, N, and P levels and their quantitative ratios, leading to distribution differences of these elements. Furthermore, substrate C, N, and P levels exhibit a generalized threshold effect on microbial biomass and plant C, N, and P concentrations. Notably, the substrate demonstrates an organic C sink potential of 7.11 g/kg/season, surpassing that of plants in unit mass. These findings contribute to understanding the distribution and dynamics of C, N, and P elements within extensive GRs.

SPATIAL DISTRIBUTION AND ASSESSMENT OF PUBLIC GREEN SPACES IN AN URBAN SETTLEMENT

The article examines the current land use of green spaces as an integral part of the urban economy through the example of a city in central Ukraine, Kropyvnytskyi. It analyses the regulatory framework for the functioning of green spaces. The study also includes an analysis of the geospatial distribution of public green spaces and their structure by number and area in the regional centre. The authors estimate the green areas covered with tree plantations based on vegetative indicators using remote sensing data. The Normalised Difference Vegetation Index (NDVI) allowed for clarifying the boundaries of green areas, their area, and the density of green mass of vegetation. This index helps to identify the spatial-temporal trends of vegetation, which are necessary to support scientific-grounded management decisions for city governance. The results of determining the NDVI are presented in the example of the park-monument of landscape art ‘Dendropark’. For vegetation in good condition, its value exceeds 0.7, corresponding to 58 % of the area. The Shannon Diversity Index has been used to assess the distribution of land use types in the green areas of general use. We identified six types of green area land use in Kropyvnytskyi city and performed the index calculations according to these types of land use. The calculation of the provision of public green spaces for city residents confirmed its low value compared to the standards. The research’s results determined that Kropyvnytskyi city needs to create new green zones, preserve existing ones, inventory, and develop a database of all green areas. Planting areas of new residential buildings and applying the experience of other countries to increase green areas due to alternative landscaping types (vertical landscaping, green roofs, and others) will allow for an expansion in the green areas of settlements, especially in large cities. The practical importance of the research results is their potential use for creating a strategy to develop and preserve green plantings in the city of Kropyvnytskyi for future years. Keywords: vegetation index, land use, diversity index, green areas, parks.

Exploring maladaptive patterns of small-scale green roofs through evaluation in a capacity of heat mitigation: A case study in seoul

Climate change leads to more frequent and intense heat waves, exposing urban populations to extreme heat conditions and significant health risks. Many cities are adopting Nature-based Solutions (NbS) to mitigate urban heat, with green roofs emerging as a widely recognized NbS. Although numerous green roof projects are implemented on a small scale, research on the effectiveness of small-scale green roofs on an urban scale is limited, making it challenging to identify the critical factors for both maladaptation and success. Therefore, we assessed the cooling potential of 18 small-scale green roofs in Seoul, Republic of Korea. This comparative study that adopted a multi-site approach examined the cooling capacity of green roofs to reduce surface temperature and identified characteristics of effective and ineffective green roofs. We utilized a developed difference-in-differences method to improve causal inference, effectively isolating the effects of individual green roofs from background climate change. The multi-site comparative approach and more robust causal inference methods improved our understanding of the effects of small-scale green roofs. The findings of this study indicate that three out of 18 green roofs were statistically significant maladaptation cases with an increase in LST. This evidence can help an urban planner reduce ineffectiveness and enhance effective adaptation practices. Our proposed method is expected to support government projects, especially those with limited budgets, in efficiently managing urban heat and reducing trial and error.

The impact of green roofs’ composition on its overall life cycle

Green roof systems have been developed to improve the environmental, economic, and social aspects of sustainability. Selecting the appropriate version of the green roof composition plays an important role in the life cycle assessment of a green roof. In this study, 10 compositions of an intensive green roof for moderate zone and 4 green roof compositions for different climatic conditions were designed and comprehensively assessed in terms of their environmental and economic impacts within the “Cradle-to-Cradle” system boundary. The assessment was carried out over a 50-year period for a moderate climate zone. The results showed that asphalt strips and concrete slab produced the highest total emissions. It was found that most greenhouse gases emissions were released in the operational energy consumption phase and in the production phase. The energy consumption phase (48.78%) for automatic irrigation and maintenance caused the highest Global Warming Potential (GWP) value (758.39 kg CO2e) in the worst variant, which also caused the highest life cycle cost (878.47€). On the contrary, in the best variant, planting more vegetation and lower maintenance and irrigation requirements led to a reduction in GWP (445.0 kg CO2e), but in terms of cost (506.6€) this composition didn't represent the best variant. The Global Warming Potential Biogenic (GWP-bio) compared to the Global Warming Potential Total (GWP-total) represents a proportion ranging from 0.8% to 78% depending on the proposed vegetation. Overall higher biogenic carbon values (up to 1525 kg CO2e) were observed for the proposed tall vegetation of Magnolia, Red Mulberry, Hawthorne, Cherry, and Crab-apple Tree. Based on the results of the multicriteria analysis, which included core environmental & economic parameters, biogenic carbon emission levels, the outcome of this paper proposed optimal green roof composition. Optimal intensive green roof composition was subjected to a sensitivity analysis to determine the impact of changing climatic conditions on CO2 emissions and life cycle costs. The results of the sensitivity analysis show that the optimal variant of the green roof can be implemented in the cold and subtropical zone with regard to CO2 emissions, but not with regard to life cycle costs.

Sustainable Construction in post-industrial Ursus district in Warsaw: Biologically Active Areas on Roofs and Underground Garages

Urbanization leads to the expansion of urban areas and increased population density, which has a negative impact on natural resources, including green spaces. To address these environmental challenges, various green measures and sustainable development practices offer environmental, economic and societal benefits. This article provides an overview of different types of green roofs, discusses their advantages and disadvantages, and outlines current legislation on biologically active areas in new residential developments. A residential project in the Ursus district of Warsaw was used as a case study to compare intensive and extensive green roofs. The project involved the installation of vegetation on the roof of an underground garage and later on the roof of the building, with detailed descriptions of the roof layers and vegetation used. One experiment examined seasonal variations in water runoff from green roofs. Three test sites simulated different seasons, with substrates in containers and vegetation watered twice daily. Results showed that spring, with temperatures around 15°C, provided optimal conditions for green roof establishment, while winter posed challenges due to frost. Both intensive and extensive green roofs have positive environmental, social and economic impacts, supporting the pillars of sustainable development. Examples of global green infrastructure further illustrate these benefits.

Innovative Sustainable Design Approaches in Urban Architecture: Balancing Aesthetics and Environmental Impact

Innovative sustainable design approaches in urban architecture play a crucial role in addressing the growing challenges of urbanization, environmental degradation, and the need for aesthetically pleasing living spaces. This study explores various sustainable design strategies, such as biophilic design, Net-Zero Energy Buildings (NZEBs), green roofs, vertical gardens, adaptive reuse, and circular economy principles, highlighting their ability to balance environmental impact with aesthetic appeal. Biophilic design integrates natural elements into urban environments, enhancing mental and physical well-being, while NZEBs focus on energy efficiency and reducing carbon footprints through renewable energy sources. Green roofs and vertical gardens provide ecological benefits, including air purification and biodiversity, while also creating visually engaging urban landscapes. Adaptive reuse and the circular economy emphasize the repurposing of existing structures and materials, promoting sustainability while preserving cultural heritage. Through a qualitative literature review, this research identifies the benefits and challenges associated with these design approaches and discusses their potential to transform urban architecture into more sustainable and livable spaces. The findings suggest that a holistic approach, integrating aesthetics and sustainability, is essential for creating urban environments that are resilient, environmentally responsible, and socially inclusive. This study provides valuable insights for architects, urban planners, and policymakers seeking to implement sustainable design principles in contemporary urban settings.

Building Information Modeling and AI Algorithms for Optimizing Energy Performance in Hot Climates: A Comparative Study of Riyadh and Dubai

In response to increasing global temperatures and energy demands, optimizing buildings’ energy efficiency, particularly in hot climates, is an urgent challenge. While current research often relies on conventional energy estimation methods, there has been a decrease in the efforts dedicated to leveraging AI-based methodologies as technology advances. This implies a dearth of multiparameter examinations in AI-driven extreme case studies. For this reason, this study aimed to enhance the energy performance of residential buildings in the hot climates of Dubai and Riyadh by integrating Building Information Modeling (BIM) and Machine Learning (ML). Detailed BIM models of a typical residential villa in these regions were created using Revit, incorporating conventional, modern, and green building envelopes (BEs). These models served as the basis for energy simulations conducted with Green Building Studio (GBS) and Insight, focusing on crucial building features such as floor area, external and internal walls, windows, flooring, roofing, building orientation, infiltration, daylighting, and more. To predict Energy Use Intensity (EUI), four ML algorithms, namely, Gradient Boosting Machine (GBM), Random Forest (RF), Support Vector Machine (SVM), and Lasso Regression (LR), were employed. GBM consistently outperformed the others, demonstrating superior prediction accuracy with an R2 of 0.989. This indicates that the model explains 99% of the variance in EUI, highlighting its effectiveness in capturing the relationships between building features and energy consumption. Feature importance analysis (FIA) revealed that roofs (29% in Dubai scenarios (DS) and 40% in Riyadh scenarios (RS)), external walls (19% in DS and 29% in RS), and windows (15% in DS and 9% in RS) have the most impact on energy consumption. Additionally, the study explored the potential for energy optimization, such as cavity green walls and green roofs in RS and double brick walls with VIP insulation and green roofs in DS. The findings of the paper should be interpreted in light of certain limitations but they underscore the effectiveness of combining BIM and ML for sustainable building design, offering actionable insights for enhancing energy efficiency in hot climates.

Assessment of low impact development (LID) strategies under different land uses in an urban sub-catchment in the Philippines

Lack of studies in developing countries with tropical climate such as the Philippines limit local LID adoption. This study compared the performance of different LID scenarios across different urban land use types at the sub-catchment level using peak flow, runoff volume and flood reductions as performance criteria. Results showed that the most effective strategies for each land use are: 1) combined green roof and bioretention for low-density residential (reduction up to 10% peak flow, 11% runoff volume and 33% flood volume); 2) green roof for high-density residential (8% peak flow, 6% runoff volume and 18% flood volume); 3) combined rain barrel, bioretention and permeable pavement for industrial (23% peak flow, 41% runoff volume and 56% flood volume), and 4) combined vegetative swale and detention pond for urban open spaces (81% peak flow, 8% runoff volume and 84% flood volume). While effective for most low intensity storms, the observed sharp decrease in LID performance with increased rainfall intensity poses a major challenge, especially in the context of the Philippines frequented by high intensity storms. This study also examined how differences in land use characteristics influence LID performance, unlike most studies that focused on LID type comparisons. It showed that low urban density setting positively affected peak flow and flood reduction performance of rain barrels and green roofs, while good drainage infrastructure quality positively affected peak flow and flood reduction performance of rain barrels and bioretention. Decision-makers may use these findings to conduct rapid assessments on LID selection and siting, provided similarities between land use characteristics described in this study and those at their localities are justified. This can lead to increased LID adoption towards building water resilient, and sustainable cities.

Building energy consumption analysis and measures: a case study from an administration building in Chengdu, China

With the peak of carbon dioxide emissions and carbon neutrality, China is placing greater emphasis on energy expenditure. Office buildings occupy a prominent position in building energy consumption, which is one of the main energy consumption areas. Taking an administration building in Chengdu as an example, this article simulates the building energy consumption based on Design Builder software, examines the variables influencing energy consumption, and suggests energy-saving strategies combined with fresh ideas for sustainable architectural design. The results showed that the modeling building was a high-energy-consuming building, with an energy consumption of 724,857.59 kWh, and a unit area energy consumption of 288.17 kWh/m2 in Chengdu. For energy conservation and emission reduction, this article proposes the following three energy-saving measures. The first is to apply heat recovery technology for air conditioning systems. The second is photovoltaic glass, which provides partial electricity demand for buildings and reduces dependence on traditional energy sources. The third is roof greening, which utilizes the plants to purify the air and beautify the environment. The results showed that the heat recovery technology in air conditioning systems reduced the total energy consumption of buildings from 642144.04 kWh/m2 to 502937.83 kWh/m2, photovoltaic glass reduced 552243.87 kWh/m2, and roof greening reduced to 635947.35 kWh/m2. All of these have good energy-saving and emission reduction effects. The above three strategies not only help reduce building energy consumption, but also provide substantial support for China to achieve carbon neutrality.

The edible green roof: sustainable urban landscape design through biophilic concept for food security in Semarang Metropolitan Region, Indonesia

The edible green roof: sustainable urban landscape design through biophilic concept for food security in Semarang Metropolitan Region, Indonesia

BP neural network multi-module green roof thermal performance prediction model optimized based on sparrow search algorithm

Green roofs, as a passive building energy-saving technology, offer a range of benefits including reduced energy consumption and mitigation of the urban heat island effect. To analyze the thermal performance of green roofs more accurately and efficiently, this study introduces a novel predictive model that utilizes a Sparrow Search Algorithm (SSA) optimized Backpropagation Neural Network (BPNN) for forecasting the thermal performance of green roofs in subtropical regions. Compared to traditional BPNN, the SSA-BPNN model enhances the network's convergence and generalization capabilities by optimizing initial weights and thresholds. The model, established using meteorological data from Guangzhou, China, and simulated green roof temperature data, underwent rigorous training and validation. The results indicate that predictive accuracy has significantly improved across all seasons, with substantial reductions in all 12 evaluation metrics, and the model GR3 (SSA-BP) emerging as the optimal model. Particularly in summer, the SSA-BPNN model exhibits the highest accuracy, underscoring its effectiveness for subtropical climates. The findings suggest that the SSA-BPNN model is a powerful tool for sustainable urban planning and green roof design in subtropical regions, marking a significant advancement in the integration of artificial intelligence in environmental engineering.

Advanced thermal prediction for green roofs: CNN-LSTM model with SSA optimization

Green roofs serve as an innovative strategy for urban architecture, playing a pivotal role in mitigating urban heat island effects and enhancing building energy efficiency. This study proposes a hybrid model based on the Sparrow Search Algorithm (SSA) optimized Convolutional Neural Network − Long Short-Term Memory Network (CNN-LSTM) for rapid and accurate prediction of the thermal performance of green roofs. The model integrates the spatial feature extraction of CNN with the temporal analysis capability of LSTM, enhancing the predictive accuracy of multi-feature time series through global optimization by SSA. Taking the green roof in Guangzhou China as a case study and utilizing hourly meteorological and thermal property data, this model has demonstrated superior predictive performance: the Mean Absolute Error (MAE), Root Mean Square Error (RMSE), R-squared (R2), and Mean Absolute Percentage Error (MAPE) for the prediction of inner surface temperature are 0.718, 0.908 %, 0.922, and 0.025 %, respectively; for the outer surface temperature prediction, the corresponding values are 0.618, 0.886 %, 0.983, and 0.028 %. Comparative analysis with existing AI models and ablation experiments further confirm the superiority of this model. Additionally, through Spearman correlation analysis, this study reveals the correlation between key input parameters and surface temperatures, providing data support for the effective design and management of green roofs. The model presented in this study not only provides an efficient tool for predicting the thermal performance of green roofs but also holds significant importance for promoting green building and sustainable urban development.

The role of Nature-Based Solutions for the water flow management in a Mediterranean urban area

Due to climate change and rapid urban sprawl, central Mediterranean regions are subjected to short but intensive storm events resulting in a significant increase of flow rates and runoff volumes in low-lying coastal urban areas. Within the framework of green urban infrastructures (GUIs), a suitable combination of Nature-Based Solutions (NBS) with traditional grey infrastructures should be adopted to mitigate flood risk effects in urban and sub-urban areas contributing at the same time to achieve multiple benefits for both the environment and the community. The aim of this study is (i) to evaluate flood hazard maps for identifying the areas within a Sicilian hydrological river basin (Toscano catchment) with a high risk of flooding by implementing the HEC-RAS model at catchment scale; (ii) to quantify the peak flow and floodplain area reduction; (iii) to assess the effectiveness of small-scale NBS (green roofs, porous pavements, rain gardens and rain barrel), in terms of peak flow and runoff volume reductions, at urban block scale by using the EPA SWMM model Model simulations are performed integrating the hydraulic (HEC-RAS) and hydrological (EPA SWMM) models for return periods of 10, 50 and 200 years. The results showed that the estimated peak flow (m3 s−1) obtained from the simulations performed at catchment scale for each T in the current scenario (without NBS) using HEC-RAS and EPA SWMM models are very close to each other (R2 = 0.99). In addition, the utopian scenario simulation showed the HEC-RAS model sensitivity to CN calibration achieving a peak flow reduction up to about 60% with the floodplain area decreasing up to about 17%. Finally, the model EPA SWMM shows its sensitivity to NBS implementation at urban block scale with a peak flow reduction up to about 16% and a runoff volume (mm) reduction up to about 24%. These reductions decrease as T increases with a higher NBS mitigation effects for the lowest T. The results highlighted that the integration of NBS in urban areas could have hydrological and hydraulic positive effects, particularly in terms of peak flow and runoff volume reduction. Furthermore, the results suggest that small-scale NBS have a potential to be effective to smaller rainfall events, but a combination with large-scale NBS is necessary to cope with extreme events.

Optimizing Building Orientation for Passive Cooling

This research paper explores the application of passive design strategies in three museums in Lagos, Nigeria: The National Museum, Jaekel House, and the Heritage Museum in Badagry. The study focuses on building orientation as a key passive design strategy and evaluates its effectiveness in enhancing energy efficiency and thermal comfort in tropical climates. With the use of data collection from Google Earth Pro, a comparative analysis examines the architectural features and design elements of each museum, highlighting their use of building orientation, shading devices, and landscaping to optimize passive cooling and natural lighting. The innovative layout and strategic orientation of the National Museum demonstrate a successful integration of passive design strategies, while Jaekel House's use of deep overhangs and vegetation showcases a deliberate commitment to sustainable architecture. The Heritage Museum in Badagry, with its historical significance, presents an intriguing case study for passive design. While limited information is available, satellite imagery suggests an optimal orientation for passive solar design, complemented by deep overhangs and a reflective roof. The findings suggest that the three museums can benefit from further enhancements to their passive design strategies, including the integration of renewable energy sources, smart building systems, green roof systems, and water harvesting and management systems. In addition, educational initiatives can raise awareness and promote sustainable practices among visitors and the local community.

The impact of roof systems on cooling and building energy efficiency

The excessive warming in the built environment, due to urbanization and anthropogenic heat emissions, has adverse effects on building energy consumption. Diverse technology using, e.g., vegetated roofs or innovative roof materials, have been proposed and implemented to ameliorate both indoor and outdoor thermal environments and reduce energy consumption. In this study, we apply a state-of-the-art urban canopy model to simulate the thermal performance of multiple roof technology, viz. the white, green, and hybrid roofs, in the contrasting urban environments of Princeton, NJ and Phoenix, AZ, USA. In addition, we estimate the combined energy-water saving potential for green roofs with five different irrigation schemes. It is found that green roofs can achieve a combined energy-water saving of $9.68 m−2 roof area in Phoenix with moisture-controlled irrigation, and $5.23 m−2 in Princeton without irrigation. These results can help to promote building energy efficiency by adapting to flexible and sustainable roof technology for heat mitigation.

Selection of tropical plants for an extensive green roof with abilities of thermal performance, energy conservation, and greenhouse gas mitigation

The development of green roofs to mitigate urban heat island effect is critical for conserving energy and reducing carbon footprint. This study evaluated the thermal performance of an extensive green roof design compared with a conventional roof and assessed the suitability of thirty-two plants for low-maintenance green roofs in a tropical area. Experiments were carried out in four phases, covering the tropical climatic conditions from June 2016 to December 2017. The results indicate that a green roof temperature was significantly lower than that of a conventional roof by 12 °C. This led to an 84 % reduction in heat transfer through the building, potentially leading to a decrease in electricity consumption by 0.20 kWh/m2/8 h and saving electricity costs by 0.019 USD/m2/8 h, achieved through a reduction in the cooling load on air conditioners. The highest carbon dioxide removal achieved in this study was 3.01 kgCO2/m2, with an effective mitigation of greenhouse gases by 28.46 kgCO2eq/m2/year. The top three plant species recommended for a low-maintenance green roof are Dracaena cochinchinensis, Santisukia kerrii, and Dracaena kaweesakii. The plant characteristics for low-maintenance green roofs in tropical climates include drought and extreme weather tolerance, disease and insect resistance, short and spreading roots, succulent leaves with ability to store water, low water requirements, slow growth rate, easy availability locally and affordability, thriving in low-nutrient conditions, and a high evapotranspiration rate. In applicable contexts, green roofs designed with suitable tropical plants could potentially enhance urban environments and contribute to achieving low-carbon and environmental sustainability.