Avoided economic impacts of climate change on agriculture: integrating a land surface model (CLM) with a global economic model (iPETS)

Crop yields are vulnerable to climate change. We assess the global impacts of climate change on agricultural systems under two climate projections (RCP8.5 and RCP4.5) to quantify the difference in impacts if climate change were reduced. We also employ two different socioeconomic pathways (SSP3 and SSP5) to assess the sensitivity of results to the underlying socioeconomic conditions. The integrated-Population-Economy-Technology-Science (iPETS) model, a global integrated assessment model for projecting future energy use, land use and emissions, is used in conjunction with the Community Earth System Model (CESM), and particularly its land surface component, the Community Land Model (CLM), to evaluate climate change impacts on agriculture. iPETS results are produced at the level of nine world regions for the period 2005–2100. We employ climate impacts on crop yield derived from CLM, driven by CESM simulations of the two RCPs. These yield effects are applied within iPETS, imposed on baseline and mitigation scenarios for SSP3 and SSP5 that are consistent with the RCPs. We find that the reduced level of warming in RCP4.5 (relative to RCP8.5) can have either positive or negative effects on the economy since crop yield either increases or decreases with climate change depending on assumptions about CO2 fertilization. Yields are up to 12 % lower, and crop prices are up to 15 % higher, in RCP4.5 relative to RCP8.5 if CO2 fertilization is included, whereas yields are up to 22 % higher, and crop prices up to 22 % lower, if it is not. We also find that in the mitigation scenarios (RCP4.5), crop prices are substantially affected by mitigation actions as well as by climate impacts. For the scenarios we evaluated, the development pathway (SSP3 vs SSP5) has a larger impact on outcomes than climate (RCP4.5 vs RCP8.5), by a factor of 3 for crop prices, 11 for total cropland use, and 35 for GDP on global average.

[1]  James R. McFarland,et al.  Climate change impacts on US agriculture and forestry: benefits of global climate stabilization , 2015 .

[2]  Atul K. Jain,et al.  Spatial modeling of agricultural land use change at global scale , 2013 .

[3]  P. Kyle,et al.  The SSP4: A world of deepening inequality , 2017 .

[4]  C. Tebaldi,et al.  Estimated impacts of emission reductions on wheat and maize crops , 2015, Climatic Change.

[5]  P. Kyle,et al.  Land‐use change trajectories up to 2050: insights from a global agro‐economic model comparison , 2014 .

[6]  N. Ramankutty,et al.  Characterizing the Spatial Patterns of Global Fertilizer Application and Manure Production , 2010 .

[7]  P. Kyle,et al.  Why do global long‐term scenarios for agriculture differ? An overview of the AgMIP Global Economic Model Intercomparison , 2014 .

[8]  Benjamin Leon Bodirsky,et al.  Climate Change Impacts on Agriculture and Food Security in 2050 under a Range of Plausible Socioeconomic and Emissions Scenarios , 2014 .

[9]  Atul K. Jain,et al.  An introduction to simple climate models used in the IPCC second assessment report , 1997 .

[10]  James W. Jones,et al.  Assessing agricultural risks of climate change in the 21st century in a global gridded crop model intercomparison , 2013, Proceedings of the National Academy of Sciences.

[11]  Steven J. Smith,et al.  Model Documentation for the MiniCAM , 2003 .

[12]  D. Lobell,et al.  The Influence of Climate Change on Global Crop Productivity1 , 2012, Plant Physiology.

[13]  Atul K. Jain,et al.  A globally aggregated reconstruction of cycles of carbon and its isotopes , 1996 .

[14]  C. Brooks Climatic Change , 1913, Nature.

[15]  J. Whalley,et al.  The Armington Assumption and the Size of Optimal Tariffs , 2015 .

[16]  W. Schlenker,et al.  Nonlinear temperature effects indicate severe damages to U.S. crop yields under climate change , 2009, Proceedings of the National Academy of Sciences.

[17]  A. Thomson,et al.  The representative concentration pathways: an overview , 2011 .

[18]  J. Bruinsma,et al.  World agriculture towards 2030/2050: the 2012 revision , 2012 .

[19]  Bruce A. Kimball Lessons from FACE: CO2 Effects and Interactions with Water, Nitrogen and Temperature , 2010 .

[20]  F. Tubiello,et al.  Global food security under climate change , 2007, Proceedings of the National Academy of Sciences.

[21]  D. Lobell,et al.  Regional disparities in the CO2 fertilization effect and implications for crop yields , 2013 .

[22]  Zong-Liang Yang,et al.  Technical description of version 4.5 of the Community Land Model (CLM) , 2013 .

[23]  Keywan Riahi,et al.  A new scenario framework for climate change research: the concept of shared socioeconomic pathways , 2013, Climatic Change.

[24]  Peter E. Thornton,et al.  Simulating the Biogeochemical and Biogeophysical Impacts of Transient Land Cover Change and Wood Harvest in the Community Climate System Model (CCSM4) from 1850 to 2100 , 2012 .

[25]  Atul K. Jain,et al.  Evaluation of 11 terrestrial carbon–nitrogen cycle models against observations from two temperate Free-Air CO2 Enrichment studies , 2014, The New phytologist.

[26]  Kiyoshi Takahashi,et al.  Climate change impact and adaptation assessment on food consumption utilizing a new scenario framework. , 2014, Environmental science & technology.

[27]  K. Riahi,et al.  The roads ahead: Narratives for shared socioeconomic pathways describing world futures in the 21st century , 2017 .

[28]  M. Dalton,et al.  Global demographic trends and future carbon emissions , 2010, Proceedings of the National Academy of Sciences.

[29]  Brian C. O'Neill,et al.  Population aging and future carbon emissions in the United States , 2008 .

[30]  Tomoko Hasegawa,et al.  Consequence of climate mitigation on the risk of hunger. , 2015, Environmental science & technology.

[31]  G. Fischer,et al.  Socio-economic and climate change impacts on agriculture: an integrated assessment, 1990–2080 , 2005, Philosophical Transactions of the Royal Society B: Biological Sciences.

[32]  Jean Chateau,et al.  Long-term economic growth projections in the Shared Socioeconomic Pathways , 2017 .

[33]  J. Edmonds,et al.  RCP4.5: a pathway for stabilization of radiative forcing by 2100 , 2011 .

[34]  N. Ramankutty,et al.  Farming the planet: 2. Geographic distribution of crop areas, yields, physiological types, and net primary production in the year 2000 , 2008 .

[35]  K.,et al.  Carbon–Concentration and Carbon–Climate Feedbacks in CMIP5 Earth System Models , 2012 .

[36]  Paul S. Armington A Theory of Demand for Products Distinguished by Place of Production (Une théorie de la demande de produits différenciés d'après leur origine) (Una teoría de la demanda de productos distinguiéndolos según el lugar de producción) , 1969 .

[37]  S. Levis,et al.  CLMcrop yields and water requirements: avoided impacts by choosing RCP 4.5 over 8.5 , 2018, Climatic Change.

[38]  Christoph Schmitz,et al.  Agriculture and climate change in global scenarios: why don't the models agree , 2014 .

[39]  Brian C. O'Neill,et al.  The effect of urbanization on energy use in India and China in the iPETS model , 2012 .