This paper focused on the environmental performance of a nearly zero-energy wood-based educational building (NZEB-W) via the life cycle impact assessment (LCIA). It identifies the environmental impacts of construction materials and operational energy demands of the NZEB-W and compares them using the SimaPro 8 software with the IMPACT 2002+ method. The LCIA results from NZEB-W show that the overall environmental impact of construction materials (98.9 Pt) and 45 years operational energy demands (98.6 Pt) will be at the same level. Its overall environmental impact 197.75 Pt for 45 years is relatively small. NZEB-W has the greatest impact on the environment in the category of damage respiratory inorganics (34.5%), 419 kg PM2.5 eq from construction materials, and 271 kg PM2.5 eq from operational energy for 45 years; follows global warming (31.7%), 1.98 × 10⁵ kg CO₂ eq from construction materials, and 4.23 × 10⁵ kg CO₂ eq from operational energy for 45 years; and non-renewable energy (21.8%), 2.82 × 10⁶ MJ primary from construction materials, and 3.73 × 10⁶ MJ primary from operational energy for 45 years. As this environmental assessment shows, the material composition of construction materials compared to the energy consumption in the use phase is an essential element for understanding the life cycle impact of buildings.

Through its anthropogenic activities in construction, human society is increasingly burdening the environment with a predominantly adverse impact. It is essential to try to use building materials that allow us to build environmentally friendly buildings. Therefore, this article deals with the determination of the environmental performance of a cross-prestressed timber-reinforced concrete bridge using life cycle assessment (LCA) compared with a reinforced concrete road bridge with a similar span and load. The positive environmental performance of the wooden concrete bridge was proved, with a relatively small (22.9 Pt) total environmental damage. The most significant impact on the environment is made by the wood–concrete bridge materials in three categories of impacts: Respiratory inorganics (7.89 Pt, 79.94 kg PM2.5 eq), Global warming (7.35 Pt, 7.28 × 10⁴ kg CO₂ eq), and Non-renewable energy (3.96 Pt, 6.01 × 10⁵ MJ primary). When comparing the wood–concrete and steel concrete road bridge, a higher environmental performance of 28% per m² for the wood–concrete bridge was demonstrated. Based on this environmental assessment, it can be stated that knowledge of all phases of the life cycle of building materials and structures is a necessary step for obtaining objective findings of environmental damage or environmental benefits of building materials or structures.

Transparent wood is a modern bio-renewable material with great potential for both science and industrial applications. However, the fire hazard of transparent wood is still almost unexplored. This study aims to investigate the impact of pristine basswood modification to epoxy-based transparent wood on the fire hazard and to train neural networks for the prediction of heat release rate from mass loss rate of pristine basswood, epoxy-based transparent wood, and epoxy resin. Transparent wood was prepared by lignin modification in pristine small-leaved basswood (Tilia cordata Mill.) and subsequent vacuum infiltration by epoxy resin. The fire hazard of the samples was determined by the cone calorimeter at four heat fluxes of 20–50 kW m⁻². The fire hazard of investigated materials was compared based on the critical heat flux, ignition temperature, heat release rate, effective heat of combustion and time to flashover. Transparent wood showed higher resistance to ignition (higher critical heat flux and ignition temperature) than pristine wood. However, other parameters (heat release rate and effective heat of combustion) were higher (worse) and the time to flashover was lower (worse) for transparent wood than for pristine wood. Trained neural networks for predicting heat release rate from the mass loss rate of wood (both pristine and transparent) and epoxy resin showed coefficients of determination from 0.70 to 0.92. Trained neural networks with a coefficient of determinations above 0.90 are usable for low-cost heat release rate measurements in both science and industrial applications.