Introduction: South Africa is ranked among the 20 highest greenhouse gas (GHG) emitters worldwide, mainly due to its heavy reliance on coal for energy. The residential building industry can play a significant role towards the minimization of both GHG emissions and energy consumption. This research assesses the beneficial impacts of utilizing wall envelope insulation measures using five insulation materials, for a detached residential building, within the seven energy zones of South Africa in accordance with the South African National code for Building energy efficiency (SANS10400-XA).Methods: Lifecycle analysis (for 50 years), regression methods and sequential search techniques of building energy modelling were used to determine both the energy saving implications of adopting the insulation materials (at various levels of thickness), and their corresponding energy payback periods. The study area consisted of eight selected locations representing all the seven energy zones in South Africa (Welkom, Witbank, Thohoyandou, Cape Town, Pretoria, Ixopo, Sutherland, and Fraserburg).Results: Sutherland (zone 6), Cape Town (zone 4) and Fraserburg (zone 7) benefitted most from energy savings due to application of insulation measures. Witbank (Zone 2), Thohoyandou (zone 3) and Pretoria (zone 5) never benefitted from application of wall envelope insulation (in terms of energy savings). Cellulose and straw yielded the lowest payback periods. Generally, polyurethane yielded the highest net energy savings at lower insulation thickness levels, while cellulose was preferable at higher insulation thickness levels in Welkom (zone1) Cape Town (zone 4), Ixopo (zone 5H), Sutherland (zone 6) and Fraserburg (zone 7).Discussion: Lower optimal wall envelope insulation thicknesses (that maximized energy savings) appeared to correspond to locations with higher annual temperature ranges. Higher temperature ranges (hence, higher degree days) would ensure that more energy is saved in order to keep the indoor temperatures within the acceptable comfort limits, due to application of insulation. However, the selection of insulation material is also influenced by other factors such as resistance to insect and mould attack (which easily affect bio-insulation materials), fire retardancy, durability (which affects cellulose), sound proofing, structural strength, and resistance to water vapour.

This article aims to assess the benefits of floor slab insulation measures using extruded polystyrene (XPS) and polyisocyanurate (also referred to as polyiso or PIR) insulation materials at various levels of insulation thicknesses for a detached residential building. An EnergyPlus simulation analysis was carried out within the seven energy zones (represented by eight locations) of South Africa in accordance with the South African national code for building energy efficiency (SANS10400-XA). The energy savings and payback periods related to the use of the insulation over a lifecycle period of 50 years were assessed. Cape Town (zone 4) behaved differently from other locations and hardly benefited from the application of floor slab insulation measures. Generally, polyiso (PIR) insulation performed better than XPS for vertical gap insulation, and lower insulation thicknesses required higher insulation depths to maximize energy savings. Similarly, lower insulation thicknesses (25 mm and 50 mm) required higher perimeter insulation widths to maximize energy savings for horizontal perimeter insulation, especially in Sutherland (zone 6) and Cape Town. The maximization of energy savings was also achieved at low insulation thickness for the full floor slab insulation method, except for Sutherland and Fraserburg (zone 7). The locations that benefitted most from the floor slab insulation methods were Pretoria (zone 5), Thohoyandou (zone 3), Sutherland (zone 6), Fraserburg (zone 7), Welkom (zone 1), Ixopo (zone 5H), Witbank (zone 2), and Cape Town (zone 4), in that order. Generally, higher net energy savings are achieved in areas with lower humidity levels and areas with greater annual sums of both cooling and heating degree days.

The introduction of the carbon tax by South Africa was primarily aimed at reducing pollution, and possibly improving the welfare of South African households. One of the ways of reducing pollution in the construction industry is to discourage the use of building materials that are high carbon emitters or have high energy intensities. This article used the Input-Output (IO) method and sensitivity analysis to study the effects of carbon tax on welfare distribution of South African households, using the 2014-2015 Living Conditions Survey (LCS). The study also set out to determine the relative sensitivity of price changes of some building materials after application of the 2019 carbon tax. Results showed that non-ferrous, ferrous, and prefabricated-based building materials had higher relative price sensitivities to carbon tax compared to other materials that were predominantly based on glass, cement, and treated metals. Increases in carbon tax may not discourage usage of relatively higher emissions-intensity materials like cement compared to wood. Operational building costs were dominated by electricity costs, with the burden being highest for lower income households. Any revenue-recycling efforts of the 2019 carbon tax for welfare purposes were marginal. However, the tax can be used to subsidize energy for lower income households.

The adoption of innovative building technologies (IBTs) and social welfare policies in South Africa has facilitated an increase in decent homeownership among low-income groups, thus improving their quality of life. However, due to the escalating costs of building materials, the capital and lifecycle costs of implementing these technologies may no longer be affordable. This research aims to provide a comparative evaluation of the affordability of some readily available IBTs in the South African construction industry, relative to existing homeownership subsidy grants. The method used involved the use of secondary data for these IBTs and the income constraint methods. The results showed that, apart from the technologies suitable for the provision of temporary structures, most of the other technologies were not affordable for the complete subsidisation of the top structure when both capital and lifecycle costs were used, except the Moladi and Robust structure IBTs under some low-income homeownership programmes. Further analysis using credit-linked subsidies revealed that the minimum household income required to achieve affordable homeownership (and their rankings) depends both on the evaluation technique (lifecycle or capital costs) and technology used. To improve affordability, any implementing government can either raise the amount of the top structure subsidy grant, promote the use of cheaper but durable IBTs, or promote the use in incremental building methods, such as the Enhanced People’s Housing Process (EPHP) for the case of South Africa.

The tenure statuses made by households (renter-occupancy or owner-occupancy) are influenced by a multitude of factors, some of which cannot be directly measured. However, economists are still interested in knowing the relative severity and patterns of influence these factors have on housing tenure status. The purpose of this research was to determine the effect of assuming joint dependency between housing tenure and affordability on the model results. The effect of model-mis-specification on severity and relative importance of the explanatory variables was also assessed. Joint bivariate binary regression was applied to multi-year cross-sectional General Household Survey (GHS) data from Statistics South Africa (STATSA). An assumption of a univariate model when modeling both housing affordability and tenure led to model mis-specification, because most of the coefficients between the univariate and bivariate joint models were significantly different. Model mis-specification also led to significant differences in rankings of the levels of influence of the explanatory variables. Bivariate joint modeling with appropriate error-model copulas improved the model results. Older households that were above 49 years were consistently more likely to be owner-occupiers, and the household head age variable for older households was the most influential factor for housing owner-occupancy and affordability.