This study conducts a literature review coupled with case-study calculations comparing the thermal contribution of semi-intensive and intensive (deeper) green roofs to non-insulated and insulated building roofs, and enhancing comprehension by validating applied scenarios with published literature-based data. Mitigation of the urban heat island is crucial for reducing energy consumption and enhancing urban sustainability, especially through natural solutions such as green (i.e., planted) roofs. The energy and environmental benefits of green roofs include energy conservation, thermal comfort, noise reduction, and aesthetic improvement. Legal mandates, innovative business models, financial subsidies and incentives, regulations, etc. are all components of green roof policies. Conflicts between private property owners and the public, regulatory gaps, and high installation costs are among the challenges. Green roofs are layered and incorporate interacting thermal processes. Green roof models are either based on the calculation of thermal transmittance (U-values), an experimental energy balance, or data-driven (primarily neural network) approaches. U-values were calculated for eight hypothetical scenarios consisting of four non-insulated and four insulated roofs, with or without semi-intensive and intensive green roofs of various materials and layer thicknesses. While the non-planted, non-insulated roof had the highest U-value, planted roofs were particularly effective for non-insulated roofs. Three of these scenarios were in reasonable accord with experimental and theoretical thermal transmittance literature values. Finally, a non-insulated planted roof, particularly one with rockwool, was found to provide a certain degree of thermal insulation in comparison to a non-planted insulated roof.

Urbanization and climate change are two major issues that humanity faces in the 21st century. Megacities are large urban agglomerations with more than 10 million inhabitants that emerged in the 20th century. The world’s top 100 economies include many North and South American megacities, such as New York, Los Angeles, Mexico City, Sao Paulo and Buenos Aires; European cities such as London and Paris; and Asian cities such as Tokyo, Osaka, Seoul, Beijing and Mumbai. This paper addresses a dearth of megacity energy metabolism models in the literature. Cross-sectional data for 36 global megacities were collected from many literature and Internet sources. Variables included megacity name, country and region; population; area; population density; (per capita) GDP; income inequality measures; (per capita) energy consumption; household electricity prices; (per capita) carbon and ecological footprint; degree days; average urban heat island intensity; and temperature and precipitation. A descriptive comparison of the characteristics of megacities was followed by ordinary least squares with heteroskedasticity-robust standard errors that were used to estimate four alternative multiple regression models. The per-capita carbon footprint of megacities was positively associated with the megacity GDP per capita, and the megacity ecological footprint; and negatively associated with country income inequality, a low-income country dummy, the country household electricity price, and the megacity annual precipitation. Targeted policies are needed, but more policy autonomy should be left to megacities. Collecting longitudinal data for megacities is very challenging but should be a next step to overcome misspecification and bias issues that plague cross-sectional approaches.