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- Høgevold, Nils M., et al.
(författare)
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A triple bottom line construct and reasons for implementing sustainable business practices in companies and their business networks
- 2015
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Ingår i: Corporate Governance. - Bingley : Emerald Group Publishing Limited. - 1472-0701 .- 1758-6054. ; 15:4, s. 427-443
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Tidskriftsartikel (refereegranskat)abstract
- Purpose – The purpose of this study is to test a Triple Bottom Line (TBL)-construct as well as to describe the TBL-reasons for implementing sustainable business practices in companies and their business networks. This study explores how linking these seemingly disparate pillars of sustainability may be facilitated through a TBL construct. The notion of sustainable business practices has been evolving and is increasingly understood to encompass considerations of economic viability, as well as environmental sustainability and social responsibility.Design/methodology/approach – The research is quantitative in nature, exploring and analysing how companies in different Norwegian industries implement and manage sustainable business practices based on TBL. The survey results are reported here.Findings – The relevance of TBL to different aspects of sustainable business practices is outlined. The study generally supports the view that a heightened propensity for sustainable business practices ensures that organisations are better equipped for meeting the challenge of integrating TBL in companies and their business networks.Research limitations/implications – The study tested a construct of TBL in the context of sustainable business practices. It may be incorporated in further research in relation to other constructs. Suggestions for further research are proposed.Practical implications – Useful for practitioners to get insights into TBL-reasons for implementing business-sustainable practices in companies and their business networks. It may also be valuable to assess the general status of business-sustainable practices in a company and their business networks.Originality/value – Linking two traditionally separate and encapsulated areas of research, namely, the area of business sustainable practices and the area of TBL. The current study has contributed to a TBL-construct in relation to other constructs in measurement and structural models. It has also contributed to provide insights of priority into the main reasons to implement the elements of TBL within companies and their business networks. © Emerald Group Publishing Limited.
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| 3. |
- Sosa, Carmen, et al.
(författare)
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Effect of weather data aggregation on regional crop simulation for different crops, production conditions, and response variables
- 2015
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Ingår i: Climate Research. - : Inter-Research Science Center. - 0936-577X .- 1616-1572. ; 65, s. 141-157
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Tidskriftsartikel (refereegranskat)abstract
- We assessed the weather data aggregation effect (DAE) on the simulation of cropping systems for different crops, response variables, and production conditions. Using 13 process-based crop models and the ensemble mean, we simulated 30 yr continuous cropping systems for 2 crops (winter wheat and silage maize) under 3 production conditions for the state of North Rhine-Westphalia, Germany. The DAE was evaluated for 5 weather data resolutions (i.e. 1, 10, 25, 50, and 100 km) for 3 response variables including yield, growing season evapotranspiration, and water use efficiency. Five metrics, viz. the spatial bias (Delta), average absolute deviation (AAD), relative AAD, root mean squared error (RMSE), and relative RMSE, were used to evaluate the DAE on both the input weather data and simulated results. For weather data, we found that data aggregation narrowed the spatial variability but widened the., especially across mountainous areas. The DAE on loss of spatial heterogeneity and hotspots was stronger than on the average changes over the region. The DAE increased when coarsening the spatial resolution of the input weather data. The DAE varied considerably across different models, but changed only slightly for different production conditions and crops. We conclude that if spatially detailed information is essential for local management decision, higher resolution is desirable to adequately capture the spatial variability for heterogeneous regions. The required resolution depends on the choice of the model as well as the environmental condition of the study area.
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| 4. |
- Sosa, Carmen, et al.
(författare)
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Variability of effects of spatial climate data aggregation on regional yield simulation by crop models
- 2015
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Ingår i: Climate Research. - : Inter-Research Science Center. - 0936-577X .- 1616-1572. ; 65, s. 53-69
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Tidskriftsartikel (refereegranskat)abstract
- Field-scale crop models are often applied at spatial resolutions coarser than that of the arable field. However, little is known about the response of the models to spatially aggregated climate input data and why these responses can differ across models. Depending on the model, regional yield estimates from large-scale simulations may be biased, compared to simulations with high-resolution input data. We evaluated this so-called aggregation effect for 13 crop models for the region of North Rhine-Westphalia in Germany. The models were supplied with climate data of 1 km resolution and spatial aggregates of up to 100 km resolution raster. The models were used with 2 crops (winter wheat and silage maize) and 3 production situations (potential, water-limited and nitrogen-water-limited growth) to improve the understanding of errors in model simulations related to data aggregation and possible interactions with the model structure. The most important climate variables identified in determining the model-specific input data aggregation on simulated yields were mainly related to changes in radiation (wheat) and temperature (maize). Additionally, aggregation effects were systematic, regardless of the extent of the effect. Climate input data aggregation changed the mean simulated regional yield by up to 0.2 t ha(-1), whereas simulated yields from single years and models differed considerably, depending on the data aggregation. This implies that large-scale crop yield simulations are robust against climate data aggregation. However, large-scale simulations can be systematically biased when being evaluated at higher temporal or spatial resolution depending on the model and its parameterization.
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