TY - JOUR
T1 - Model collaboration for the improved assessment of biomass supply, demand, and impacts
AU - Wicke, Birka
AU - van der Hilst, Floortje
AU - Daioglou, Vasileios
AU - Banse, Martin
AU - Beringer, Tim
AU - Gerssen - Gondelach, Sarah
AU - Heijnen, Sanne
AU - Karssenberg, Derek
AU - Laborde, David
AU - Lippe, Melvin
AU - van Meijl, Hans
AU - Nassar, André
AU - Powell, Jeff
AU - Prins, Anne Gerdien
AU - Rose, Steve N K
AU - Smeets, Edward M W
AU - Stehfest, Elke
AU - Tyner, Wallace E.
AU - Verstegen, Judith A.
AU - Valin, Hugo
AU - van Vuuren, Detlef P.
AU - Yeh, Sonia
AU - Faaij, André P C
PY - 2015/5
Y1 - 2015/5
N2 - Existing assessments of biomass supply and demand and their impacts face various types of limitations and uncertainties, partly due to the type of tools and methods applied (e.g., partial representation of sectors, lack of geographical details, and aggregated representation of technologies involved). Improved collaboration between existing modeling approaches may provide new, more comprehensive insights, especially into issues that involve multiple economic sectors, different temporal and spatial scales, or various impact categories. Model collaboration consists of aligning and harmonizing input data and scenarios, model comparison and/or model linkage. Improved collaboration between existing modeling approaches can help assess (i) the causes of differences and similarities in model output, which is important for interpreting the results for policy-making and (ii) the linkages, feedbacks, and trade-offs between different systems and impacts (e.g., economic and natural), which is key to a more comprehensive understanding of the impacts of biomass supply and demand. But, full consistency or integration in assumptions, structure, solution algorithms, dynamics and feedbacks can be difficult to achieve. And, if it is done, it frequently implies a trade-off in terms of resolution (spatial, temporal, and structural) and/or computation. Three key research areas are selected to illustrate how model collaboration can provide additional ways for tackling some of the shortcomings and uncertainties in the assessment of biomass supply and demand and their impacts. These research areas are livestock production, agricultural residues, and greenhouse gas emissions from land-use change. Describing how model collaboration might look like in these examples, we show how improved model collaboration can strengthen our ability to project biomass supply, demand, and impacts. This in turn can aid in improving the information for policy-makers and in taking better-informed decisions.
AB - Existing assessments of biomass supply and demand and their impacts face various types of limitations and uncertainties, partly due to the type of tools and methods applied (e.g., partial representation of sectors, lack of geographical details, and aggregated representation of technologies involved). Improved collaboration between existing modeling approaches may provide new, more comprehensive insights, especially into issues that involve multiple economic sectors, different temporal and spatial scales, or various impact categories. Model collaboration consists of aligning and harmonizing input data and scenarios, model comparison and/or model linkage. Improved collaboration between existing modeling approaches can help assess (i) the causes of differences and similarities in model output, which is important for interpreting the results for policy-making and (ii) the linkages, feedbacks, and trade-offs between different systems and impacts (e.g., economic and natural), which is key to a more comprehensive understanding of the impacts of biomass supply and demand. But, full consistency or integration in assumptions, structure, solution algorithms, dynamics and feedbacks can be difficult to achieve. And, if it is done, it frequently implies a trade-off in terms of resolution (spatial, temporal, and structural) and/or computation. Three key research areas are selected to illustrate how model collaboration can provide additional ways for tackling some of the shortcomings and uncertainties in the assessment of biomass supply and demand and their impacts. These research areas are livestock production, agricultural residues, and greenhouse gas emissions from land-use change. Describing how model collaboration might look like in these examples, we show how improved model collaboration can strengthen our ability to project biomass supply, demand, and impacts. This in turn can aid in improving the information for policy-makers and in taking better-informed decisions.
KW - Biomass supply and demand
KW - Bottom-up modeling
KW - Impacts
KW - Integrated assessment
KW - Model collaboration
KW - Top-down modeling
KW - valorisation
UR - http://www.scopus.com/inward/record.url?scp=84898766834&partnerID=8YFLogxK
U2 - 10.1111/gcbb.12176
DO - 10.1111/gcbb.12176
M3 - Article
SN - 1757-1693
VL - 7
SP - 422
EP - 437
JO - GCB Bioenergy [E]
JF - GCB Bioenergy [E]
IS - 3
ER -