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Humpenöder, F.; Popp, A.; Dietrich, J.P.; Klein, D.; Lotze-Campen, H.; Bonsch, M.; Bodirsky, B.L.; Weindl, I.; Stevanovic, M.; Müller, C. |
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Title |
Investigating afforestation and bioenergy CCS as climate change mitigation strategies |
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Journal Article |
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Year |
2014 |
Publication |
Environmental Research Letters |
Abbreviated Journal |
Environ. Res. Lett. |
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Volume |
9 |
Issue |
6 |
Pages |
064029 |
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Keywords |
climate change mitigation; afforestation; bioenergy; carbon capture and storage; land-use modeling; land-based mitigation; carbon sequestration; land-use change; crop productivity; carbon capture; energy; storage; model; food; conservation; agriculture; scenarios |
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Abstract |
The land-use sector can contribute to climate change mitigation not only by reducing greenhouse gas (GHG) emissions, but also by increasing carbon uptake from the atmosphere and thereby creating negative CO2 emissions. In this paper, we investigate two land-based climate change mitigation strategies for carbon removal: (1) afforestation and (2) bioenergy in combination with carbon capture and storage technology (bioenergy CCS). In our approach, a global tax on GHG emissions aimed at ambitious climate change mitigation incentivizes land-based mitigation by penalizing positive and rewarding negative CO2 emissions from the land-use system. We analyze afforestation and bioenergy CCS as standalone and combined mitigation strategies. We find that afforestation is a cost-efficient strategy for carbon removal at relatively low carbon prices, while bioenergy CCS becomes competitive only at higher prices. According to our results, cumulative carbon removal due to afforestation and bioenergy CCS is similar at the end of 21st century (600-700 GtCO(2)), while land-demand for afforestation is much higher compared to bioenergy CCS. In the combined setting, we identify competition for land, but the impact on the mitigation potential (1000 GtCO(2)) is partially alleviated by productivity increases in the agricultural sector. Moreover, our results indicate that early-century afforestation presumably will not negatively impact carbon removal due to bioenergy CCS in the second half of the 21st century. A sensitivity analysis shows that land-based mitigation is very sensitive to different levels of GHG taxes. Besides that, the mitigation potential of bioenergy CCS highly depends on the development of future bioenergy yields and the availability of geological carbon storage, while for afforestation projects the length of the crediting period is crucial. |
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English |
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1748-9326 |
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CropM, TradeM |
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MA @ admin @ |
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4627 |
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Author |
Hoveid, Ø. |
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Title |
An economist’ wish list for crop modelling |
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Conference Article |
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Year |
2014 |
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TradeM |
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MACSUR CropM International Symposium and Workshop: Modelling climate change impacts on crop production for food security, Oslo, Norway, 2014-02-10 to 2014-02-12 |
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no |
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MA @ admin @ |
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2493 |
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Author |
Hoveid, Ø. |
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Title |
Linking models of climate, weather, crops and economic behavior by Bayesian calibration.” |
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Conference Article |
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2014 |
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TradeM |
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MACSUR TradeM workshop: Exploring new ideas for trade and agriculture model integration for assessing the impacts of climate change on food security, The Natural Resource and Environmental Research Center (NRERC), University of Haifa, Israel, 2013-03-03 t |
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no |
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MA @ admin @ |
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2494 |
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Author |
Hoveid, Ø. |
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Title |
Prototype stochastic general equilibrium model of a global food system |
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Conference Article |
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2014 |
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A model of a global food system need at least two points in time per year and two locations with different growing seasons so that planting and harvesting have different timing across locations. Moreover, planting decisions reflect soil states affected by stochastic weather since previous point in time, while harvest reflect the planting decisions and the stochastic weather through the growing season up to next point. Decisions on trade, storage and consumption are taken at every point in time. Despite stochastic influence, deterministic stationary general equilibrium is applicable. The world then runs in circles through a likely sequence of N given weather scenarios, while the decision-makers do not know the next scenario. The model will provide a setting in which the consequences of climate change can be assessed both with respect to expectations and variances. It will by construction be an integrated assessment model (IAM) in the sense that outcomes follow from agent choices in a world of biophysical processes. In this case the biophysical world is stochastic. At the prototype stage neither existing behavioral nor bio-physical models will be applied. |
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FACCE MACSUR Mid-term Scientific Conference |
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3(S) Sassari, Italy |
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FACCE MACSUR Mid-term Scientific Conference, 2014-04-01 to 2014-04-04, Sassari, Italy |
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no |
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MA @ admin @ |
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5117 |
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Horváth, F.; Ittzés, P.; Ittzés, D.; Barcza, Z.; Dobor, L.; Hidy, D.; Marosi, A.; Hardisty, A. |
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Supporting environmental modelling with Taverna workflows, web services and desktop grid technology. In iEMSs 2014 proceedings edited by DP Ames, NWT Quinn and AE Rizzoli (in press) |
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2014 |
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LiveM |
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7th International Congress on Environmental Modelling and Software, San Diego, California, USA, 2014-06-15 to 2014-06-19 |
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no |
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MA @ admin @ |
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2491 |
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