A broad range of attempts have been made to quantify the macroeconomic impacts of climate change, such as those of intensifying weather extremes and of yield losses of major crops, which have been synthesized by the efforts to estimate the Social Cost of Carbon (e.g., the US Interagency Working Group, 2016). However, up to the present, few insights have been fed into these debates as to the economic impacts associated with climatic responses of aerosol emissions from wildfires. In this study, we shed light on the potential scale of macroeconomic impacts of Siberian wildfires’ climatic effects by drawing on results of sensitivity experiments on enhanced biomass burning (BB) emissions over the defined Siberian domain using a global aerosol climate model, MIROC-SPRINTARS, in which the model was coupled with the ocean model (i.e., Atmosphere-Ocean coupled Global Climate Model: AOGCM) – the scientific results of these simulations are also discussed in detail by our companion paper, Yasunari and Takemura (2019), in the same session at this AGU Fall Meeting 2019. We used sets of simulation results differing in the conditions of BB emissions and climate, in which three different reference levels of BB emissions over the defined Siberian domain were used under the present (RCP scenario in 2005) or future climate (RCP2.6 and RCP8.5 in 2030) conditions. Differentials of annual average temperatures estimated by the simulations were used to compute monetary-equivalent economic impacts attributable to climatic effects of BB by applying the functions of the RICE-2010 model (the 2010 version of the regional integrated model of climate and the economy model: Nordhaus, PNAS, 2010), which is a regionally disaggregated version of the most widely used climate-economy model, the DICE model. The economic impacts were estimated for the most affected countries and regions by Siberian wildfires, such as Russia, China and Europe.

As climate change adaptation is becoming a recognized policy issue, the needs are growing about quantitative economic evaluation of adaptation-related public investments, particularly in the context of climate finance. Irrigation, which enhances and stabilizes water supply for farming, is a potential means of climate change adaptation, but attempts of economic evaluation of its effectiveness as an adaptation measure are few in part because such assessment requires an integration of various types of simulation analyses. Against this background, we conduct a case study of a Kenyan irrigation development project (Mwea Irrigation Development Project), which is undertaken by the Kenyan government with a loan from the Japan International Cooperation Agency (JICA), to evaluate the effectiveness of the project for climate change adaptation by using a combination of simulation models. Specifically, we conduct a simple downscaling of CMIP5 climate simulation data, whose outputs are fed into a hydrological model (SHER model) and a yield forecasting model (DSSAT model). With these simulation data, together with data of socioeconomic parameters drawn from existing and original surveys, we compute economic variables such as farmers’ average income. Climate and other uncertainties are incorporated into analysis without probabilistic weights (conforming to the Robust Decision Making approach, e.g., Lempert et al., 2013) to highlight vulnerabilities for local farming. The results show that despite uncertainties of precipitation trends, increased temperatures due to climate change have a general tendency to reduce rice yields, and that irrigation development will mitigate that income impacts from the yield loss, i.e., it will likely be effective as a means for climate change adaptation.