Net primary production (NPP) is an important ecological metric for studying forest ecosystems and their carbon sequestration, for assessing the potential supply of food or timber and quantifying the impacts of climate change on ecosystems. The global MODIS NPP dataset using the MOD17 algorithm provides valuable information for monitoring NPP at 1-km resolution. Since coarse-resolution global climate data are used, the global dataset may contain uncertainties for Europe. We used a 1-km daily gridded European climate data set with the MOD17 algorithm to create the regional NPP dataset MODIS EURO. For evaluation of this new dataset, we compare MODIS EURO with terrestrial driven NPP from analyzing and harmonizing forest inventory data (NFI) from 196,434 plots in 12 European countries as well as the global MODIS NPP dataset for the years 2000 to 2012. Comparing these three NPP datasets, we found that the global MODIS NPP dataset differs from NFI NPP by 26%, while MODIS EURO only differs by 7%. MODIS EURO also agrees with NFI NPP across scales (from continental, regional to country) and gradients (elevation, location, tree age, dominant species, etc.). The agreement is particularly good for elevation, dominant species or tree height. This suggests that using improved climate data allows the MOD17 algorithm to provide realistic NPP estimates for Europe. Local discrepancies between MODIS EURO and NFI NPP can be related to differences in stand density due to forest management and the national carbon estimation methods. With this study, we provide a consistent, temporally continuous and spatially explicit productivity dataset for the years 2000 to 2012 on a 1-km resolution, which can be used to assess climate change impacts on ecosystems or the potential biomass supply of the European forests for an increasing bio-based economy. MODIS EURO data are made freely available at ftp://palantir.boku.ac.at/Public/MODIS_EURO.

The potential environmental impacts of producing and using future electric vehicles (EVs) are important given their expected role in mitigating global climate change and local air pollutants. Recently, studies have begun assessing the effect of potential future changes in EVs supply chains on overall environmental performance. This study contributes by integrating expected changes in future energy, iron, and steel production in the life cycle assessment (LCA) of EVs. In this light, the study examines the impacts of changes in these parameters on producing and charging future EVs. Future battery electric vehicles (BEV) could have a 36–53% lower global warming potential (GWP) compared to current BEV. The change in source of electricity generation accounts for 89% of GWP reductions over the BEV’s life cycle. Thus, it presents the highest GWP reduction potential of 35–48%. The use of hydrogen for direct reduction of iron in steelmaking (HDR-I) is expected to reduce vehicle production GWP by 17% compared to current technology. By accounting for 9% of the life cycle GWP reductions, HDR-I has the second-highest reduction potential (1.3–4.8%). The results also show that the potential for energy efficiency improvement measures for GWP reduction in vehicle and battery manufacture would be more beneficial when applied now than in the distant future (2050), when the CO₂ intensity of the EU electricity is expected to be lower. Interestingly, under the same conditions, the high share of renewable energy in vehicle supply chains contributed to a decrease in all air pollution-related impact categories, but an increase in toxicity-related categories, as well as land use and water consumption.