%0 Journal Article %1 Elbersen2014Framework %A Elbersen, B. S. %A Annevelink, E. %A Roos Klein-Lankhorst, J. %A Lesschen, J. P. %A Staritsky, I. %A Langeveld, J. W. A. %A Elbersen, H. W. %A Sanders, J. P. M. %B Regional Environmental Change %D 2014 %I Springer Berlin Heidelberg %K renewables bioenergy energy model landusechange %P 1--14 %R 10.1007/s10113-014-0584-1 %T A framework with an integrated computer support tool to assess regional biomass delivery chains %U http://dx.doi.org/10.1007/s10113-014-0584-1 %X In this paper, we first provide a brief overview of other decision support tools for bioenergy and assess to which extent the integrated tool central in this paper is different and novel. Next, a description is given of the tool, the different models used and the functionalities. The working of the tool is then illustrated with three case studies based in the northern part of The Netherlands. The computerised tool is meant to support the communication process between stakeholders to come to the implementation of regional biomass delivery chains. It helps to create a quick and common understanding of optimal biomass use in a region. Although the tool has been applied only to bioenergy chains, other biochemical and biomaterial chains are also suitable to be incorporated. The three case studies presented include a conventional sugar beet bioethanol production chain, an advanced Miscanthus bioethanol conversion chain and a straw-based electricity chain. The main conclusions are that optimal biomass use for non-food purposes from a sustainability and resource-efficient perspective depend on many different factors specific to the conversion chains. For example, the green house gas (GHG) emission and mitigation potential of a sugar beet-based bioethanol chain requires careful organisation particularly on the primary biomass production and transport, while in a straw-based electricity chain, the largest efficiency gains can be reached in the conversion part. Land use change (LUC) to sugar beet generally causes more negative environmental impacts than LUC to Miscanthus. This applies to both GHG efficiency, soil organic carbon content and emissions of nitrogen to surface waters. At the same time, it becomes clear that the different scenario assumptions can be very influential, particularly on the final economic performance of a chain. Overall, it is clear from the cases that the users understand much better under which circumstances and through which mechanisms the designed chains can become profitable and can become more environmentally sustainable.

@article{Elbersen2014Framework, abstract = {In this paper, we first provide a brief overview of other decision support tools for bioenergy and assess to which extent the integrated tool central in this paper is different and novel. Next, a description is given of the tool, the different models used and the functionalities. The working of the tool is then illustrated with three case studies based in the northern part of The Netherlands. The computerised tool is meant to support the communication process between stakeholders to come to the implementation of regional biomass delivery chains. It helps to create a quick and common understanding of optimal biomass use in a region. Although the tool has been applied only to bioenergy chains, other biochemical and biomaterial chains are also suitable to be incorporated. The three case studies presented include a conventional sugar beet bioethanol production chain, an advanced Miscanthus bioethanol conversion chain and a straw-based electricity chain. The main conclusions are that optimal biomass use for non-food purposes from a sustainability and resource-efficient perspective depend on many different factors specific to the conversion chains. For example, the green house gas (GHG) emission and mitigation potential of a sugar beet-based bioethanol chain requires careful organisation particularly on the primary biomass production and transport, while in a straw-based electricity chain, the largest efficiency gains can be reached in the conversion part. Land use change (LUC) to sugar beet generally causes more negative environmental impacts than LUC to Miscanthus. This applies to both GHG efficiency, soil organic carbon content and emissions of nitrogen to surface waters. At the same time, it becomes clear that the different scenario assumptions can be very influential, particularly on the final economic performance of a chain. Overall, it is clear from the cases that the users understand much better under which circumstances and through which mechanisms the designed chains can become profitable and can become more environmentally sustainable.}, added-at = {2018-06-18T21:23:34.000+0200}, author = {Elbersen, B. S. and Annevelink, E. and Roos Klein-Lankhorst, J. and Lesschen, J. P. and Staritsky, I. and Langeveld, J. W. A. and Elbersen, H. W. and Sanders, J. P. M.}, biburl = {https://www.bibsonomy.org/bibtex/261c16c3cd5b0a6ba9492e12e10c56fd1/pbett}, booktitle = {Regional Environmental Change}, citeulike-article-id = {12924836}, citeulike-linkout-0 = {http://dx.doi.org/10.1007/s10113-014-0584-1}, citeulike-linkout-1 = {http://link.springer.com/article/10.1007/s10113-014-0584-1}, doi = {10.1007/s10113-014-0584-1}, interhash = {6080116363100c1ef3491722cef1579d}, intrahash = {61c16c3cd5b0a6ba9492e12e10c56fd1}, keywords = {renewables bioenergy energy model landusechange}, pages = {1--14}, posted-at = {2014-01-16 16:49:08}, priority = {2}, publisher = {Springer Berlin Heidelberg}, timestamp = {2018-06-22T18:33:58.000+0200}, title = {A framework with an integrated computer support tool to assess regional biomass delivery chains}, url = {http://dx.doi.org/10.1007/s10113-014-0584-1}, year = 2014 }