The methodology of quantitative assess economic output of climate change

CHOU JieMing ) 1 DONG WenJie 1 FENG GuoLin 0 0 National Climate Center , China Meteorological Administration, Beijing 100081, China 1 College of Global Change and Earth System Science, Beijing Normal University , Beijing 100857, China A method is introduced in this paper to study the effect of future climatic change on the economy. The researchers determine the economic output of climate change from historical data, and provide a method to quantitatively predict economic output of climate change by an economic-climatic model. A historical reciprocating examination is used to analyze output data for various crops in eight agricultural areas in China and meteorological data from 160 observatories in China from 1980 to 2000. The results show that the methods used are reasonable to a certain extent and good in application. - Enormous efforts have been made to simulate and predict regional responses to future climatic changes [13]. It is also necessary to assess quantitatively the impact of the changing information in these predictions on the social economy [47]. The Yield Impact of Climate Change (YICC) concept was introduced in the quantitative assessment of grain yield under the effects of climate change [8]. However, the YICC provides only a single index, which cannot show all the changes in economic output, and methods or indices to evaluate the impact of climate change on economic yield are scarce. This paper presents an economic modeling method to evaluate and predict the Economic Output Impact of Climate Change (EOICC) by making use of a derived economic-climatic model [9]. We validate the method using meteorological data from 160 observatories in China from 1981 to 2000, the total yield of grain, and the yield of other various crops across eight agricultural regions, and the results show that the predictions of the model are reasonable and effective in application. The significance of this research is the introduction of indices for evaluating the impact of climate change on economic output, which probes a new research area by incorporating economic factors into research on global climate change. Supposing that the economic output to be evaluated complies with the Cobb-Douglas production function [1012], great progress has been made on establishing and applying the economic-climatic model [8,9,13]. Taking grain output as an example, the following equation predicts the output by introducing climatic factors into the model: Y = x11 x22 x33. C = NcC , (1) where Y is grain output, Nc=x11 is the contribution of non-climatic factors per unit area [9], C is a climatic factor and is the production elasticity of C [9]. To predict EOICC for n years in the future, we may assume that the means of Y, Nc and C in the past n years are Y1, Nc1 and C1, and the means in the next n years are Y2, Nc2 and C2 respectively, Then we have Y1 = Nc1C1 , (2) The Author(s) 2011. This article is published with open access at Springerlink.com where Y* is the variation of economic output when Nc is changed and C is unchanged. The predicted EOICC, Y, is defined to be Equation (5) can be derived from where Y is the impact ratio of climate change on pre Y2 dicted economic output. This parameter can be used as an index to measure the impact of future climate change on economic output, and reflects the proportion of the future real output caused by climate change and the sensitivity of output to climate change. As time progresses, the non-climatic factors in the model vary in accordance with the real situation, while grain yield per unit area is assumed to be constant when the climate is unchanged and no other real information is available. Little work has been done on extracting real-world information, and hence it is hard to verify the predicted EOICC value. In the following, we present a method that may overcome these difficulties. Suppose C is the impact factor of the climate change, and C is the predicted change in C at a given point in time in the future. We will continue with the example of grain yield. Assume that mean grain yield, the effects of non-climatic factors, and the effects of climatic factors are given by Y1, k1 and C1, respectively, during years 1 to n. Similarly the mean grain yield and the effects of non-climatic and climatic factors are characterized by Y2, k2 and C2 during years n+1 to 2n. When C is very small, the higher order polynomial terms can be ignored. That is, F F F (C1, k2 ) + C = A + C, (7) C C where F is a function of k and C, and B is the grain yield for years n+1 to 2n when social and climatic factors are considered. Note that A=F(C1,k2), so A is the grain yield when the social factors are quantified by k2 and the climatic factor is unchanged. According to the definition of YICC, F D = B A = C, (8) C where D is the real EOICC to be estimated. Obviously, B and C can be obtained from historical data, so the EOICC F calculation is simplified to estimating only . However, C when there is a sudden climatic change, the value of C might be too large relative to the time scale to apply eq. (7). Grain yield is a function of a social factor k and a climatic factor C, and there is no correlation between k and C. As we know, the inter-annual variation in the social factor is much less than the chronological variation. However, the inter-annual variation in the climatic factor is much larger than the chronological variation. Taking these two points into consideration, the method of composed analysis may be used to calculate F , where F is the variability of C C various climate factors while the social factor k is considered as unchanged. Suppose that, as we would expect, the number of years in which the climatic factor is lower than its mean value of C1 is equal to the number of years in which it is higher than the mean value. Assume that for the n years with C<C1, the mean value of the climatic factor is Cl, and for the n years with C>C1, the mean of the climatic factor is Ch. Statistically, the mean value of the social factor is just the mean over the 2n years under consideration. Assuming the mean yield in years with C<C1 is Yl, and that the mean yield in the other years is Yh. Then The non-climatic factors Al and Ah are implicitly included when the time interval is divided into two groups in this way, and can be considered equal statistically. Be applying the aforementioned method, the real climatic change impacted amount can be measured from existing information, which can be considered as the real situation for the purposes of validating the evaluation forecast. Define D as the impact ratio of climatic change, which Y reflects the real climatic change impacted amount as a proportion of the overall real yield. With this definition, the applicability of the proposed method can be validated by comparing the results of a simulation. Eight agricultural regions (divided according to the division principle of Chinese agricultural regions) are sel (...truncated)