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- W2937403208 abstract "The aim of this study is to investigate the most important drivers of environmental impacts and identify the influence of parameters on the uncertainty of the environmental impacts in various climate zones and future climate scenarios. We couple a combined greenhouse energy demand-yield simulation tool with a life cycle assessment to identify the drivers for greenhouse energy, water and CO2 demand as well as yield production. Environmental impacts are evaluated using the methods of IPPC for assessing climate change and available water remaining (AWARE) for water scarcity impacts. Furthermore, we compare the results for all five main climate world regions. With a global sensitivity analysis, we identify the parameters with the highest influence on life-cycle impact for each region. Crop growth features (e.g. node development rate and plant density), energy systems (e.g. heating and cooling supply systems), cover materials and inside temperature are the most influential input parameters for climate change impacts, but the ranking between these parameters depends on the location and climatic conditions of the greenhouse. In cold climates and higher latitudes, heating and electricity (mostly for lighting) processes are on average responsible for 85 to 90% of total climate change impacts. In hot climates, active cooling, in addition to natural ventilation, as well as electricity processes rank the highest (in the range of 60 to 75%) and in moderate climates, heating and cooling systems account for 60 to 70% of climate change impacts. Also for the AWARE results, crop growth related parameters are most influential. Among different processes in greenhouse, irrigation is responsible for 90% of water impacts in all regions, but the absolute magnitude of impact varies greatly among the different greenhouse locations." @default.
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- W2937403208 date "2019-07-01" @default.
- W2937403208 modified "2023-09-27" @default.
- W2937403208 title "A comparative study on the environmental impact of greenhouses: A probabilistic approach" @default.
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- W2937403208 doi "https://doi.org/10.1016/j.scitotenv.2019.04.092" @default.
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