|
|
Scollan, N., Bannink, A., Kipling, R., Saetnan, E., & Van Middelkoop, J. (2015). Livestock and feed production, especially dairy and beef. In FACCE MACSUR Reports (Vol. 6, pp. Sp6–3). Brussels.
Abstract: Improving health and welfare is an important adaptation and mitigation strategyDeveloping process based modelling, responsive to adaptationLinks to climate and land use change modelling are essential Livestock systems likely to be hit hardest by climate changeNeed to develop animal health models that respond to adaptation by farmersBringing together direct and indirect impacts of climate change vitalAdaptation and mitigation need to be considered and modelled togetherLinking models across scales is important to support policy decisionsLearning between sectors carries potential for novel solutions and methodological advancesEffective communication of outcomes to stakeholders (how?) No Label
|
|
|
|
Kipling, R. (2015). LiveM and the knowledge hub concept: Grassland and livestock modelling in MACSUR Phase 2 (Vol. 4).
|
|
|
|
Ruiz-Ramos, M. (2015). Les modèles de culture face au changement climatique : les enjeux des projets nationaux, européens et internationaux..
|
|
|
|
Humpenöder, F., Popp, A., Stevanovic, M., Müller, C., Bodirsky, B. L., Bonsch, M., et al. (2015). Land-use and carbon cycle responses to moderate climate change: implications for land-based mitigation. Environ Sci Technol, 49(11), 6731–6739.
Abstract: Climate change has impacts on agricultural yields, which could alter cropland requirements and hence deforestation rates. Thus, land-use responses to climate change might influence terrestrial carbon stocks. Moreover, climate change could alter the carbon storage capacity of the terrestrial biosphere and hence the land-based mitigation potential. We use a global spatially explicit economic land-use optimization model to (a) estimate the mitigation potential of a climate policy that provides economic incentives for carbon stock conservation and enhancement, (b) simulate land-use and carbon cycle responses to moderate climate change (RCP2.6), and (c) investigate the combined effects throughout the 21st century. The climate policy immediately stops deforestation and strongly increases afforestation, resulting in a global mitigation potential of 191 GtC in 2100. Climate change increases terrestrial carbon stocks not only directly through enhanced carbon sequestration (62 GtC by 2100) but also indirectly through less deforestation due to higher crop yields (16 GtC by 2100). However, such beneficial climate impacts increase the potential of the climate policy only marginally, as the potential is already large under static climatic conditions. In the broader picture, this study highlights the importance of land-use dynamics for modeling carbon cycle responses to climate change in integrated assessment modeling.
|
|
|
|
Camacho, C., & Pérez-Barahona, A. (2015). Land use dynamics and the environment. Journal of Economic Dynamics and Control, 52, 96–118.
Abstract: This paper builds a benchmark framework to study optimal land use, encompassing land use activities and environmental degradation. We focus on the spatial externalities of land use as drivers of spatial patterns: land is immobile by nature, but local actions affect the whole space since pollution flows across locations resulting in both local and global damages. We prove that the decision maker problem has a solution, and characterize the corresponding social optimum trajectories by means of the Pontryagin conditions. We also show that the existence and uniqueness of time-invariant solutions are not in general guaranteed. Finally, a global dynamic algorithm is proposed in order to illustrate the spatial-dynamic richness of the model. We find that our simple set-up already reproduces a great variety of spatial patterns related to the interaction between land use activities and the environment. In particular, abatement technology turns out to play a central role as pollution stabilizer, allowing the economy to reach a time-invariant equilibrium that can be spatially heterogeneous. (C) 2014 Elsevier B.V. All rights reserved.
|
|
