Tracking construction material over space and time: Prospective and geo-referenced modeling of building stocks and construction material flows

Construction material plays an increasingly important role in the environmental impacts of buildings. In order to investigate impacts of materials on a building level, we present a bottom-up building stock model that uses three dimensional and geo-referenced building data to determine volumetric information of material stocks in Swiss residential buildings. We used a probabilistic modeling approach to calculate future material flows for the individual buildings. We investigated six scenarios with different assumptions concerning per capita floor area, building stock turnover, and construction material. The Swiss building stock will undergo important structural changes by 2035. While this will lead to a reduced number in new constructions, material flows will increase. Total material inflow decreases by almost half while outflows double. In 2055 the total amount of material in- and outflows are almost equal, which represents an important opportunity to close construction material cycles. Total environmental impacts due to production and disposal of construction material remain relatively stable over time. The cumulated impact is slightly reduced for the wood-based scenario. The scenario with more insulation material leads to slightly higher material-related emissions. An increase per capita floor area or material turnover will lead to a considerable increase in impacts. The new modeling approach overcomes the limitations of previous bottom-up building models and allows for investigating building material flows and stocks in space and time. This supports the development of tailored strategies to reduce the material footprint and environmental impacts of buildings and settlements.ISSN:1088-1980ISSN:1530-929

The interannual change of atmospheric CO2: contribution of subtropical ecosystems?

The global terrestrial carbon cycle model CARAIB (CARbon Assimilation In the Biosphere) is used to study the response of the terrestrial ecosystems to the large scale climate variations over the period 1980-1993. The global net carbon exchange flux with the atmosphere is calculated and compared with the terrestrial contribution derived from the deconvolution of the atmospheric CO2 and delta(13)C measurements. A fairly large CO2 biospheric source is predicted during the strong El Nino events of 1982-83 and 1986-87 as a consequence of the induced global warming. The direct and indirect temperature controls of the primacy production and respiration dominate the CO2 anomaly. An analysis of the relative contribution by latitudinal bands and ecosystems shows that low-latitude vegetation dominates the variability at the El Nino time scale. In savannas, the model indicates that the interannual changes result, to a large extent, from the control of soil water content on gross primary production (GPP). In the tropical cain forests, both respiration and GPP contribute to the response of the net biospheric flux

The seasonality of the CO2 exchange between the atmosphere and the land biosphere: A study with a global mechanistic vegetation model

Two simulations of the seasonal variation of the global atmospheric CO2 distribution are obtained by combining an atmospheric transport model, two parameterizations of soil heterotrophic respiration (SHR), and a mechanistic model of carbon assimilation in the biosphere (CARAIB) that estimates the net primary production (NPP) of continental vegetation. The steady state hypothesis of the biosphere allows the spatial distribution and the global content of the soil carbon to be expressed as a function of the root fractions of soil respiration under forested and herbaceous vegetation covers. The sensitivity of the modeled CO2 signal to the wind field does not exceed the observed interannual variability. The influence of the various vegetation zones is quantified by the Fourier analysis of the modeled atmospheric signal. In the northern hemisphere, the temperate ecosystems dominate the seasonal atmospheric signal of the extratropical latitudes. The ecosystems of the tropical northern zone determine the local signal, while the southern tropical ecosystems influence largely the signal in the whole southern hemisphere. The results give credence to the mechanistic modeling of NPP since the simulated atmospheric signal is comparable with that obtained with normalized difference vegetation index (NDVI) based diagnostic models coupled with a parameterization of SHR fitted to optimize the atmospheric signal