import numpy as np,cdutil import scipy.interpolate as itp Hincome_list = ["ABW", "AND", "ARE", "ATG", "AUS", "AUT", "BEL", "BHR", "BHS", "BMU", "BRB", "BRN", "CAN", "CHE", "CHI", "CHL", "CUW", "CYM", "CYP", "CZE", "DEU", "DNK", "ESP", "EST", "FIN", "FRA", "FRO", "GBR", "GIB", "GRC", "GRL", "GUM", "HKG", "HRV", "HUN", "IMN", "IRL", "ISL", "ISR", "ITA", "JPN", "KNA", "KOR", "KWT", "LIE", "LTU", "LUX", "LVA", "MAC", "MAF", "MCO", "MLT", "MNP", "NCL", "NLD", "NOR", "NZL", "OMN", "PAN", "PLW", "POL", "PRI", "PRT", "PYF", "QAT", "SAU", "SGP", "SMR", "SVK", "SVN", "SWE", "SXM", "SYC", "TCA", "TTO", "TWN", "URY", "USA", "VGB", "VIR"] Mincome_list = ["ALB", "ARG", "ARM", "ASM", "AZE", "BGR", "BIH", "BLR", "BLZ", "BRA", "BWA", "CHN", "COL", "CRI", "CUB", "DMA", "DOM", "DZA", "ECU", "FJI", "GAB", "GEO", "GNQ", "GRD", "GTM", "GUY", "IRN", "IRQ", "JAM", "JOR", "KAZ", "LBN", "LBY", "LCA", "LKA", "MDV", "MEX", "MHL", "MKD", "MNE", "MUS", "MYS", "NAM", "NRU", "PER", "PRY", "ROU", "RUS", "SRB", "SUR", "THA", "TKM", "TON", "TUR", "TUV", "VCT", "VEN", "WSM", "XKX", "ZAF"] Lincome_list = ["AFG", "BDI", "BEN", "BFA", "CAF", "COD", "ERI", "ETH", "GIN", "GMB", "GNB", "HTI", "LBR", "MDG", "MLI", "MOZ", "MWI", "NER", "NPL", "PRK", "RWA", "SLE", "SOM", "SSD", "SYR", "TCD", "TGO", "TJK", "TZA", "UGA", "YEM", "AGO", "BGD", "BOL", "BTN", "CIV", "CMR", "COG", "COM", "CPV", "DJI", "EGY", "FSM", "GHA", "HND", "IDN", "IND", "KEN", "KGZ", "KHM", "KIR", "LAO", "LSO", "MAR", "MDA", "MMR", "MNG", "MRT", "NGA", "NIC", "PAK", "PHL", "PNG", "PSE", "SDN", "SEN", "SLB", "SLV", "STP", "SWZ", "TLS", "TUN", "UKR", "UZB", "VNM", "VUT", "ZMB", "ZWE"] def selectSSPs(var1, SSP_name, YearsSSP, Model_name, CountryList): tmp1 = np.zeros(len(YearsSSP)) for i in range(len(var1[:,0])): if var1[i,0] == Model_name and var1[i,1] == SSP_name and var1[i,2] in CountryList: tmp1 = np.c_[tmp1,var1[i,6:16]] tmp2 = tmp1[:,1:].T return tmp2 def interpolate(var_in, YearsSSP): yr_org = np.copy(YearsSSP) yr_aft = np.arange(2010,2101,1) tmp1 = np.zeros([177,91]) for i in range(177): tmp2 = np.array(var_in[i]) itpfun = itp.interp1d(yr_org,tmp2) tmp1[i] = itpfun(yr_aft) return tmp1 def future_emi_global(CountryList, year, tableL, tableM, tableH, gdp): emi = np.zeros([5,177,91]) ciL = tableL[1]/(1+tableL[2])**(year-tableL[0]) ciM = tableM[1]/(1+tableM[2])**(year-tableM[0]) ciH = tableH[1]/(1+tableH[2])**(year-tableH[0]) for ssp_idx in range(5): for cty_idx in range(177): if CountryList[cty_idx] in Lincome_list: emi[ssp_idx, cty_idx] = gdp[ssp_idx, cty_idx] * ciL if CountryList[cty_idx] in Mincome_list: emi[ssp_idx, cty_idx] = gdp[ssp_idx, cty_idx] * ciM if CountryList[cty_idx] in Hincome_list: emi[ssp_idx, cty_idx] = gdp[ssp_idx, cty_idx] * ciH return emi def cal_adjuated_emissions(shreshold, ramprate, co2EmiCtry, percapitagdpSSPs, interpolatedyearSSP, need_country): adjuated_AnnCO2Emit_ctyLevel = np.copy(co2EmiCtry) #(5,177,91) percapitagdp_tmp = np.copy(percapitagdpSSPs) #(5,177,91) interpolatedyear_tmp = np.copy(interpolatedyearSSP) #(91) # adjust annual CO2 emissions: for ssp_index in range(5): for i in range(177): for j in range(91): if percapitagdp_tmp[ssp_index,i,j] >= shreshold and interpolatedyear_tmp[j] >= 2020: idx = j for k in range(91 - idx): adjuated_AnnCO2Emit_ctyLevel[ssp_index,i,k+idx] = adjuated_AnnCO2Emit_ctyLevel[ssp_index,i,k+idx-1] * (1.-ramprate) break # calculate other emissions adjuated_AnnCO2Emit_gloLevel = cdutil.averager(adjuated_AnnCO2Emit_ctyLevel,axis=1,weights='equal',action='sum') adjuated_CumCO2Emit_gloLevel = np.zeros([5,91]) for ssp_index in range(5): for i in range(91): adjuated_CumCO2Emit_gloLevel[ssp_index,i] = cdutil.averager(adjuated_AnnCO2Emit_gloLevel[ssp_index,:i+1] ,axis=0,weights='equal',action='sum') if need_country == 1: adjuated_CumCO2Emit_ctyLevel = np.zeros([5,177,91]) for ssp_index in range(5): for i in range(177): for j in range(91): adjuated_CumCO2Emit_ctyLevel[ssp_index,i,j] = cdutil.averager(adjuated_AnnCO2Emit_ctyLevel[ssp_index,i,:j+1], axis=0,weights='equal',action='sum') else: adjuated_CumCO2Emit_ctyLevel = 0. return adjuated_AnnCO2Emit_ctyLevel, adjuated_AnnCO2Emit_gloLevel, adjuated_CumCO2Emit_ctyLevel, adjuated_CumCO2Emit_gloLevel ############################################################################################################ #### main function starts from here ######################################################################## ######################################################################################################## def future_projections_DiffRate(gdpRawSSP, popRawSSP, countryList, table_usedL, table_usedM, table_usedH, shreshold_list, ramprate_list, need_country): factor2010gdp2005 = 0.909585947164884 model_name = 'OECD Env-Growth' SSP_list = {0:'SSP1', 1:'SSP2', 2:'SSP3', 3:'SSP4', 4:'SSP5'} yearsSSP = np.array([2010,2020,2030,2040,2050,2060,2070,2080,2090,2100]) # step1, select data from input files gdpSSPs = np.zeros([5,177,10]) popSSPs = np.zeros([5,177,10]) for i in range(5): gdpSSPs[i] = selectSSPs(gdpRawSSP, SSP_list[i], yearsSSP, model_name, countryList) popSSPs[i] = selectSSPs(popRawSSP, SSP_list[i], yearsSSP, model_name, countryList) # step2, interpolate to yearly resolution interpolatedyearSSP = np.arange(2010,2101,1) interpolatedgdpSSPs = np.zeros([5,177,91]) interpolatedpopSSPs = np.zeros([5,177,91]) percapitagdpSSPs = np.zeros([5,177,91]) for i in range(5): interpolatedgdpSSPs[i] = interpolate(gdpSSPs[i], yearsSSP) * (10**-3) * factor2010gdp2005 interpolatedpopSSPs[i] = interpolate(popSSPs[i], yearsSSP) * (10**-3) percapitagdpSSPs[i] = interpolatedgdpSSPs[i] / interpolatedpopSSPs[i] # step 3, calculate original future emissions original_AnnCO2Emit_ctyLevel = future_emi_global(countryList, interpolatedyearSSP, table_usedL, table_usedM, table_usedH, interpolatedgdpSSPs).astype(np.float) # derived from original_AnnCO2Emit_ctyLevel original_AnnCO2Emit_gloLevel = cdutil.averager(original_AnnCO2Emit_ctyLevel,axis=1,weights='equal',action='sum') original_CumCO2Emit_ctyLevel = np.zeros([5,177,91]) for i in range(5): for j in range(177): for k in range(91): original_CumCO2Emit_ctyLevel[i,j,k] = cdutil.averager(original_AnnCO2Emit_ctyLevel[i,j,:k+1] ,axis=0,weights='equal',action='sum') original_CumCO2Emit_gloLevel = np.zeros([5,91]) for i in range(5): for j in range(91): original_CumCO2Emit_gloLevel[i,j]= cdutil.averager(original_AnnCO2Emit_gloLevel[i,:j+1] ,axis=0,weights='equal',action='sum') # step 4, calculate adjuated future emissions len1 = len(shreshold_list) len2 = len(ramprate_list) adjuated_AnnCO2Emit_gloLevel = np.zeros([len1,len2,5,91]) adjuated_AnnCO2Emit_ctyLevel = np.zeros([len1,len2,5,177,91]) adjuated_CumCO2Emit_gloLevel = np.zeros([len1,len2,5,91]) adjuated_CumCO2Emit_ctyLevel = np.zeros([len1,len2,5,177,91]) for ii in range(len1): for jj in range(len2): [adjuated_AnnCO2Emit_ctyLevel[ii,jj], adjuated_AnnCO2Emit_gloLevel[ii,jj], adjuated_CumCO2Emit_ctyLevel[ii,jj], adjuated_CumCO2Emit_gloLevel[ii,jj] ] = cal_adjuated_emissions(shreshold_list[ii], ramprate_list[jj], original_AnnCO2Emit_ctyLevel, percapitagdpSSPs, interpolatedyearSSP, need_country) return [original_AnnCO2Emit_ctyLevel, original_AnnCO2Emit_gloLevel, original_CumCO2Emit_ctyLevel, original_CumCO2Emit_gloLevel, adjuated_AnnCO2Emit_ctyLevel, adjuated_AnnCO2Emit_gloLevel, adjuated_CumCO2Emit_ctyLevel, adjuated_CumCO2Emit_gloLevel, percapitagdpSSPs, interpolatedyearSSP, interpolatedgdpSSPs, interpolatedpopSSPs]