Climate adaptation as mitigation: the case of agricultural investments

By David B. Lobell, Uris Lantz C. Baldos, Thomas W. Hertel. Environmental Research Letters, 2013

134 citations recorded by the source corpus.

Successful adaptation of agriculture to ongoing climate changes would help to maintain productivity growth and thereby reduce pressure to bring new lands into agriculture. In this paper we investigate the potential co-benefits of adaptation in terms of the avoided emissions from land use change. A model of global agricultural trade and land use, called SIMPLE, is utilized to link adaptation investments, yield growth rates, land conversion rates, and land use emissions. A scenario of global adaptation to offset negative yield impacts of temperature and precipitation changes to 2050, which requires a cumulative 225 billion USD of additional investment, results in 61 Mha less conversion of cropland and 15 Gt carbon dioxide equivalent (CO2e) fewer emissions by 2050. Thus our estimates imply an annual mitigation co-benefit of 0.35 GtCO2e yr−1 while spending $15 per tonne CO2e of avoided emissions. Uncertainty analysis is used to estimate a 5–95% confidence interval around these numbers of 0.25–0.43 Gt and $11–$22 per tonne CO2e. A scenario of adaptation focused only on Sub-Saharan Africa and Latin America, while less costly in aggregate, results in much smaller mitigation potentials and higher per tonne costs. These results indicate that although investing in the least developed areas may be most desirable for the main objectives of adaptation, it has little net effect on mitigation because production gains are offset by greater rates of land clearing in the benefited regions, which are relatively low yielding and land abundant. Adaptation investments in high yielding, land scarce regions such as Asia and North America are more effective for mitigation. To identify data needs, we conduct a sensitivity analysis using the Morris method (Morris 1991 Technometrics 33 161–74). The three most critical parameters for improving estimates of mitigation potential are (in descending order) the emissions factors for converting land to agriculture, the price elasticity of land supply with respect to land rents, and the elasticity of substitution between land and non-land inputs. For assessing the mitigation costs, the elasticity of productivity with respect to investments in research and development is also very important. Overall, this study finds that broad-based efforts to adapt agriculture to climate change have mitigation co-benefits that, even when forced to shoulder the entire expense of adaptation, are inexpensive relative to many activities whose main purpose is mitigation. These results therefore challenge the current approach of most climate financing portfolios, which support adaptation from funds completely separate from—and often much smaller than—mitigation ones.

Subfields: Ecology, Evolution, Behavior and Systematics, Economics and Econometrics, Soil Science

View Paper: https://www.semanticscholar.org/paper/b2b04ec87913ce1a065e7d183fcd342af0263451

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Citations in the corpus

The role of international trade in managing food security risks from climate change (2015, methodology): “SIMPLE has also been used to analyze climate change mitigation and adaptation in the agricultural sector (Lobell et al. 2013), the environmental implications of future agricultural productivity growth (Hertel et al. 2014) and the prospects for food security by 2050 (Baldos and Hertel 2014).”
Global food security in 2050: the role of agricultural productivity and climate change (2014, methodology): “The model has been used in studies focusing on climate change mitigation and adaptation (Lobell et al. 2013) as well as model validation and evaluation (Baldos and Hertel 2013).”
Does intensification slow crop land expansion or encourage deforestation (2014, methodology): “Indeed, agricultural R&D to save land may be one of the most cost-effective ways to mitigate climate change from deforestation (Lobell et al., 2013).”
Agriculture and climate change in global scenarios: why don't the models agree (2014, background): “See Lobell and Burke (2008) and Lobell et al. (2013) for more recent statistical approaches to the effects of climate change on agriculture.”