Open AccessModel collaboration for the improved assessment of biomass supply, demand, and impacts
Author(s) -
Wicke Birka,
Hilst Floor,
Daioglou Vassilis,
Banse Martin,
Beringer Tim,
GerssenGondelach Sarah,
Heijnen Sanne,
Karssenberg Derek,
Laborde David,
Lippe Melvin,
Meijl Hans,
Nassar André,
Powell Jeff,
Prins Anne Gerdien,
Rose Steve N. K.,
Smeets Edward M. W.,
Stehfest Elke,
Tyner Wallace E.,
Verstegen Judith A.,
Valin Hugo,
Vuuren Detlef P.,
Yeh Sonia,
Faaij André P. C.
Publication year - 2015
Publication title -
gcb bioenergy
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.378
H-Index - 63
eISSN - 1757-1707
pISSN - 1757-1693
DOI - 10.1111/gcbb.12176
Subject(s) - computer science , supply and demand , consistency (knowledge bases) , greenhouse gas , biomass (ecology) , linkage (software) , supply chain , key (lock) , representation (politics) , production (economics) , environmental economics , environmental resource management , environmental science , business , economics , ecology , politics , biochemistry , chemistry , computer security , macroeconomics , marketing , artificial intelligence , gene , political science , law , biology , microeconomics
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.