Data needs for benchmarking the TBIF method to represent energy and materials use.

Buildings energy use and industrial process emissions . Total residential floor area (smr) . Total commercial floor area (million smc) . Total floor area per capita (sm/capita) . Residential electricity, natural gas, cooking fuels, and heating fuels use . Commercial-industrial- government electricity, natural gas, other fuel use . Total waste generated in city

Transportation energy use . Allocated daily VKT (VKT/capita/day) . Fleet fuel efficiency . Volume of gasoline, diesel, and CNG used in road transport . Number of enplaned passengers at regional airport (domestic, international) . Jet fuel liters loaded into airplanes . Percentage of planes fueling at airport . Tonnes of long-distance freight and liters of fuel per ton moved . Energy used in rail transport

Materials use Volume of water used (i.e., pumped) . Energy used in pumping water . Volume of wastewater treated . Energy used in wastewater treatment . Percentage of water used for residential, commercial, and industrial uses . Food consumed/used in the community . Cement use in the community

data <- read.csv(file = "tbif.csv", header=TRUE);
library(datasets); require(stats); require(graphics);
data <-data[1:3,2:18];
summary(data);
##    kWh.HH.mo    L.LPG.HH.mo   L.kerosene.HH.mo Total.MJ.HH.mo..end.use.
##  Min.   :253   Min.   :18.8   Min.   : 9.18    Min.   :1438            
##  1st Qu.:281   1st Qu.:30.7   1st Qu.: 9.72    1st Qu.:1464            
##  Median :310   Median :42.5   Median :10.26    Median :1489            
##  Mean   :341   Mean   :47.0   Mean   :10.29    Mean   :1684            
##  3rd Qu.:386   3rd Qu.:61.1   3rd Qu.:10.84    3rd Qu.:1807            
##  Max.   :462   Max.   :79.8   Max.   :11.42    Max.   :2125            
##  Total.Commercial.industrial.intensity.MJ.GDP.yr..end.use.
##  Min.   :0.480                                            
##  1st Qu.:0.545                                            
##  Median :0.610                                            
##  Mean   :0.653                                            
##  3rd Qu.:0.740                                            
##  Max.   :0.870                                            
##  Total.Commercial.industrial.intensity.MJ.capita.yr..end.use.
##  Min.   : 2064                                               
##  1st Qu.: 3297                                               
##  Median : 4530                                               
##  Mean   : 5598                                               
##  3rd Qu.: 7365                                               
##  Max.   :10200                                               
##  Industrial.process..t.waste.capita.yr. Electricity.EF..kg.CO2.eq.kWh.
##  Min.   :1.30                           Min.   :0.820                 
##  1st Qu.:1.45                           1st Qu.:0.895                 
##  Median :1.60                           Median :0.970                 
##  Mean   :1.63                           Mean   :0.940                 
##  3rd Qu.:1.80                           3rd Qu.:1.000                 
##  Max.   :2.00                           Max.   :1.030                 
##  Surface.travel.intensity..VKT.capita.day.
##  Min.   :4.10                             
##  1st Qu.:5.29                             
##  Median :6.47                             
##  Mean   :6.46                             
##  3rd Qu.:7.63                             
##  Max.   :8.80                             
##  Air.Travel..L.jet.fuel.enplaned.passenger
##  Min.   : 26.5                            
##  1st Qu.: 30.9                            
##  Median : 35.2                            
##  Mean   : 71.0                            
##  3rd Qu.: 93.2                            
##  Max.   :151.2                            
##  Water..treated.water.WW..1000.liters.capita.yr. GDP.capita....capita.
##  Min.   :14.3                                    Min.   :6037         
##  1st Qu.:15.3                                    1st Qu.:6544         
##  Median :16.4                                    Median :7050         
##  Mean   :21.5                                    Mean   :7477         
##  3rd Qu.:25.1                                    3rd Qu.:8198         
##  Max.   :33.9                                    Max.   :9345         
##  Total.local.population..capita. Population.density..capita.km2.
##  Min.   : 1055450                Min.   : 9252                  
##  1st Qu.: 6777725                1st Qu.: 9296                  
##  Median :12500000                Median : 9340                  
##  Mean   :10385483                Mean   :17640                  
##  3rd Qu.:15050500                3rd Qu.:21834                  
##  Max.   :17601000                Max.   :34328                  
##  Total.homes..HH.  Total.commercial.floor.area..million.smc.
##  Min.   : 277000   Min.   : 1.44                            
##  1st Qu.:2046052   1st Qu.:12.76                            
##  Median :3815104   Median :24.08                            
##  Mean   :3030701   Mean   :17.07                            
##  3rd Qu.:4407552   3rd Qu.:24.89                            
##  Max.   :5000000   Max.   :25.70                            
##  Total.floor.area.per.capita..sm.cap.
##  Min.   : 6.03                       
##  1st Qu.: 8.06                       
##  Median :10.10                       
##  Mean   : 9.47                       
##  3rd Qu.:11.19                       
##  Max.   :12.28

Plots are as follows:

pairs(data,panel = panel.smooth, main = "
      TBIF,
      figure 4",pch=21,bg = c("red", "green", "white"));

plot of chunk unnamed-chunk-2

Manipulate function

library(manipulate);

plotting two variables

Regression modelling

y<-data$Total.Commercial.industrial.intensity.MJ.GDP.yr..end.use.;
plot(data$Electricity.EF..kg.CO2.eq.kWh.,data$Total.Commercial.industrial.intensity.MJ.GDP.yr..end.use.,bg =  c("red", "green", "white"),type="p",main = "figure 5", 
     col = "black", cex = 1.1, pch = 21,frame = FALSE)
abline(lm(Total.Commercial.industrial.intensity.MJ.GDP.yr..end.use. ~ Electricity.EF..kg.CO2.eq.kWh., data = data), col="red")
legend("right", c("Delhi","Mumbai","Chandigarh"), pch = 21,pt.bg=c("red","green","white"),bg="white")

plot of chunk unnamed-chunk-4

cor(data$Electricity.EF..kg.CO2.eq.kWh.,data$Total.Commercial.industrial.intensity.MJ.GDP.yr..end.use.)
## [1] -0.9986
fit <- lm(Electricity.EF..kg.CO2.eq.kWh. ~ Total.Commercial.industrial.intensity.MJ.GDP.yr..end.use., data = data)
coef(fit)
##                                               (Intercept) 
##                                                     1.295 
## Total.Commercial.industrial.intensity.MJ.GDP.yr..end.use. 
##                                                    -0.544
e <- resid(fit)
yhat <- predict(fit)
max(abs(e - (y  - yhat)))
## [1] 0.55

Regression modelling b

y<-data$L.kerosene.HH.mo;
plot(data$Electricity.EF..kg.CO2.eq.kWh.,data$L.kerosene.HH.mo,bg =  c("red", "green", "white"),type="p",main = "figure 6", 
     col = "black", cex = 1.1, pch = 21,frame = FALSE)
abline(lm(L.kerosene.HH.mo ~ Electricity.EF..kg.CO2.eq.kWh., data = data), col="red")
legend("right", c("Delhi","Mumbai","Chandigarh"), pch = 21,pt.bg=c("red","green","white"),bg="white")

plot of chunk unnamed-chunk-5

cor(data$Electricity.EF..kg.CO2.eq.kWh.,data$L.kerosene.HH.mo)
## [1] 0.9656
fit <- lm(Electricity.EF..kg.CO2.eq.kWh. ~ L.kerosene.HH.mo, data = data)
coef(fit)
##      (Intercept) L.kerosene.HH.mo 
##         -0.01905          0.09323
e <- resid(fit)
yhat <- predict(fit)
max(abs(e - (y  - yhat)))
## [1] 10.39

Regression modelling c

y<-data$L.kerosene.HH.mo;
plot(data$Surface.travel.intensity..VKT.capita.day.,data$L.kerosene.HH.mo,bg =  c("red", "green", "white"),type="p",main = "figure 7", 
     col = "black", cex = 1.1, pch = 21,frame = FALSE)
abline(lm(L.kerosene.HH.mo ~ Surface.travel.intensity..VKT.capita.day., data = data ), col="red")
legend("right", c("Delhi","Mumbai","Chandigarh"), pch = 21,pt.bg=c("red","green","white"),bg="white")

plot of chunk unnamed-chunk-6

cor(data$Water..treated.water.WW..1000.liters.capita.yr. , data$L.LPG.HH.mo)
## [1] 0.9556
fit <- lm(Surface.travel.intensity..VKT.capita.day. ~ L.kerosene.HH.mo, data = data)
summary(fit)
## 
## Call:
## lm(formula = Surface.travel.intensity..VKT.capita.day. ~ L.kerosene.HH.mo, 
##     data = data)
## 
## Residuals:
##       1       2       3 
##  0.0221 -0.0426  0.0205 
## 
## Coefficients:
##                  Estimate Std. Error t value Pr(>|t|)   
## (Intercept)       28.0333     0.3402    82.4   0.0077 **
## L.kerosene.HH.mo  -2.0975     0.0329   -63.7   0.0100 **
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.0522 on 1 degrees of freedom
## Multiple R-squared:     1,   Adjusted R-squared:     1 
## F-statistic: 4.05e+03 on 1 and 1 DF,  p-value: 0.01
coef(fit)
##      (Intercept) L.kerosene.HH.mo 
##           28.033           -2.098
e <- resid(fit)
yhat <- predict(fit)
max(abs(e - (y  - yhat)))
## [1] 7.32

Regression modelling d

y<-data$L.kerosene.HH.mo;
plot(data$Air.Travel..L.jet.fuel.enplaned.passenger,data$L.kerosene.HH.mo,bg =  c("red", "green", "white"),type="p", 
 main = "figure 8",    col = "black", cex = 1.1, pch = 21,frame = FALSE)
abline(lm(L.kerosene.HH.mo ~ Air.Travel..L.jet.fuel.enplaned.passenger , data = data), col="red")
legend("right", c("Delhi","Mumbai","Chandigarh"), pch = 21,pt.bg=c("red","green","white"),bg="white")

plot of chunk unnamed-chunk-7

cor(data$Air.Travel..L.jet.fuel.enplaned.passenger,data$L.kerosene.HH.mo)
## [1] -0.8862
fit <- lm(Air.Travel..L.jet.fuel.enplaned.passenger ~ L.kerosene.HH.mo, data = data)
coef(fit)
##      (Intercept) L.kerosene.HH.mo 
##           637.39           -55.06
e <- resid(fit)
yhat <- predict(fit)
max(abs(e - (y  - yhat)))
## [1] 142

Regression modelling e

y<-data$L.kerosene.HH.mo;
plot(data$GDP.capita....capita.,data$L.kerosene.HH.mo,bg =  c("red", "green", "white"),type="p", 
     main = "figure 9", col = "black", cex = 1.1, pch = 21,frame = FALSE)
abline(lm(L.kerosene.HH.mo ~ GDP.capita....capita., data = data), col="red")
legend("right", c("Delhi","Mumbai","Chandigarh"), pch = 21,pt.bg=c("red","green","white"),bg="white")

plot of chunk unnamed-chunk-8

cor(data$GDP.capita....capita.,data$L.kerosene.HH.mo)
## [1] 0.9802
fit <- lm(GDP.capita....capita. ~ L.kerosene.HH.mo, data = data)
coef(fit)
##      (Intercept) L.kerosene.HH.mo 
##            -7777             1483
e <- resid(fit)
yhat <- predict(fit)
max(abs(e - (y  - yhat)))
## [1] 9334

Regression modelling f

y<-data$Total.Commercial.industrial.intensity.MJ.GDP.yr..end.use.;
plot(data$Electricity.EF..kg.CO2.eq.kWh.,data$Total.Commercial.industrial.intensity.MJ.GDP.yr..end.use.,bg =  c("red", "green", "white"),type="p",main = "figure 10", 
     col = "black", cex = 1.1, pch = 21,frame = FALSE)
abline(lm(Total.Commercial.industrial.intensity.MJ.GDP.yr..end.use. ~ Electricity.EF..kg.CO2.eq.kWh., data = data), col="red")
legend("right", c("Delhi","Mumbai","Chandigarh"), pch = 21,pt.bg=c("red","green","white"),bg="white")

plot of chunk unnamed-chunk-9

cor(data$Electricity.EF..kg.CO2.eq.kWh.,data$Total.Commercial.industrial.intensity.MJ.GDP.yr..end.use.)
## [1] -0.9986
fit <- lm(Electricity.EF..kg.CO2.eq.kWh.~ Total.Commercial.industrial.intensity.MJ.GDP.yr..end.use., data = data)
coef(fit)
##                                               (Intercept) 
##                                                     1.295 
## Total.Commercial.industrial.intensity.MJ.GDP.yr..end.use. 
##                                                    -0.544
e <- resid(fit)
yhat <- predict(fit)
max(abs(e - (y  - yhat)))
## [1] 0.55

Regression modelling g

y<-data$Total.Commercial.industrial.intensity.MJ.GDP.yr..end.use.;
plot(data$Surface.travel.intensity..VKT.capita.day.,data$Total.Commercial.industrial.intensity.MJ.GDP.yr..end.use.,bg =  c("red", "green", "white"),type="p", 
  main = "figure 11",   col = "black", cex = 1.1, pch = 21,frame = FALSE)
abline(lm(Total.Commercial.industrial.intensity.MJ.GDP.yr..end.use.~ Surface.travel.intensity..VKT.capita.day. , data = data), col="red")
legend("right", c("Delhi","Mumbai","Chandigarh"), pch = 21,pt.bg=c("red","green","white"),bg="white")

plot of chunk unnamed-chunk-10

cor(data$Surface.travel.intensity..VKT.capita.day.,data$Total.Commercial.industrial.intensity.MJ.GDP.yr..end.use.)
## [1] 0.981
fit <- lm(Surface.travel.intensity..VKT.capita.day.~ Total.Commercial.industrial.intensity.MJ.GDP.yr..end.use., data = data)
coef(fit)
##                                               (Intercept) 
##                                                    -1.128 
## Total.Commercial.industrial.intensity.MJ.GDP.yr..end.use. 
##                                                    11.610
e <- resid(fit)
yhat <- predict(fit)
max(abs(e - (y  - yhat)))
## [1] 7.93

Regression modelling h

y<-data$GDP.capita....capita.;
plot(data$Total.local.population..capita.,data$GDP.capita....capita.,bg =  c("red", "green", "white"),type="p", main = "figure 12",
     col = "black", cex = 1.1, pch = 21,frame = FALSE)
abline(lm(GDP.capita....capita. ~ Total.local.population..capita., data = data), col="red")
legend("right", c("Delhi","Mumbai","Chandigarh"), pch = 21,pt.bg=c("red","green","white"),bg="white")

plot of chunk unnamed-chunk-11

cor(data$Total.local.population..capita.,data$GDP.capita....capita.)
## [1] -1
fit <- lm(Total.local.population..capita.~ GDP.capita....capita., data = data)
coef(fit)
##           (Intercept) GDP.capita....capita. 
##              47765619                 -4999
e <- resid(fit)
yhat <- predict(fit)
max(abs(e - (y  - yhat)))
## [1] 17594963

Regression modelling i

y<-data$Total.local.population..capita.;
plot(data$Total.commercial.floor.area..million.smc.,data$Total.local.population..capita.,bg =  c("red", "green", "white"),type="p",main = "figure 13", 
     col = "black", cex = 1.1, pch = 21,frame = FALSE)
abline(lm(Total.local.population..capita. ~ Total.commercial.floor.area..million.smc., data = data), col="red")
legend("right", c("Delhi","Mumbai","Chandigarh"), pch = 21,pt.bg=c("red","green","white"),bg="white")

plot of chunk unnamed-chunk-12

cor(data$Total.commercial.floor.area..million.smc.,data$Total.local.population..capita.)
## [1] 0.9699
fit <- lm(Total.commercial.floor.area..million.smc.~ Total.local.population..capita., data = data)
coef(fit)
##                     (Intercept) Total.local.population..capita. 
##                       9.494e-01                       1.553e-06
e <- resid(fit)
yhat <- predict(fit)
max(abs(e - (y  - yhat)))
## [1] 17600974

Regression modelling j

y<-data$Surface.travel.intensity..VKT.capita.day.;
plot(data$GDP.capita....capita.,data$Surface.travel.intensity..VKT.capita.day.,bg =  c("red", "green", "white"),type="p",main = "figure 14", 
     col = "black", cex = 1.1, pch = 21,frame = FALSE)
abline(lm(Surface.travel.intensity..VKT.capita.day. ~ GDP.capita....capita., data = data), col="red")
legend("right", c("Delhi","Mumbai","Chandigarh"), pch = 21,pt.bg=c("red","green","white"),bg="white")

plot of chunk unnamed-chunk-13

cor(data$GDP.capita....capita.,data$Surface.travel.intensity..VKT.capita.day.)
## [1] -0.9769
fit <- lm(GDP.capita....capita.~ Surface.travel.intensity..VKT.capita.day., data = data)
coef(fit)
##                               (Intercept) 
##                                   12026.6 
## Surface.travel.intensity..VKT.capita.day. 
##                                    -704.6
e <- resid(fit)
yhat <- predict(fit)
max(abs(e - (y  - yhat)))
## [1] 9341

Regression modelling k

y<-data$Surface.travel.intensity..VKT.capita.day.;
plot(data$Total.local.population..capita.,data$Surface.travel.intensity..VKT.capita.day.,bg =  c("red", "green", "white"),type="p", main = "figure 15",
     col = "black", cex = 1.1, pch = 21,frame = FALSE)
abline(lm(Surface.travel.intensity..VKT.capita.day. ~ Total.local.population..capita., data = data), col="red")
legend("right", c("Delhi","Mumbai","Chandigarh"), pch = 21,pt.bg=c("red","green","white"),bg="white")

plot of chunk unnamed-chunk-14

cor(data$Total.local.population..capita.,data$Surface.travel.intensity..VKT.capita.day.)
## [1] 0.9774
fit <- lm(Total.local.population..capita.~ Surface.travel.intensity..VKT.capita.day., data = data)
coef(fit)
##                               (Intercept) 
##                                 -12368285 
## Surface.travel.intensity..VKT.capita.day. 
##                                   3524074
e <- resid(fit)
yhat <- predict(fit)
max(abs(e - (y  - yhat)))
## [1] 17600991

Regression modelling l

y<-data$Total.Commercial.industrial.intensity.MJ.GDP.yr..end.use.;
plot(data$Total.local.population..capita.,data$Total.Commercial.industrial.intensity.MJ.GDP.yr..end.use.,bg =  c("red", "green", "white"),type="p", main = "figure 16",
     col = "black", cex = 1.1, pch = 21,frame = FALSE)
abline(lm(Total.Commercial.industrial.intensity.MJ.GDP.yr..end.use. ~ Total.local.population..capita., data = data), col="red")
legend("right", c("Delhi","Mumbai","Chandigarh"), pch = 21,pt.bg=c("red","green","white"),bg="white")

plot of chunk unnamed-chunk-15

cor(data$Total.local.population..capita.,data$Total.Commercial.industrial.intensity.MJ.GDP.yr..end.use.)
## [1] 0.9179
fit <- lm(Total.local.population..capita.~ Total.Commercial.industrial.intensity.MJ.GDP.yr..end.use., data = data)
coef(fit)
##                                               (Intercept) 
##                                                 -15203358 
## Total.Commercial.industrial.intensity.MJ.GDP.yr..end.use. 
##                                                  39166593
e <- resid(fit)
yhat <- predict(fit)
max(abs(e - (y  - yhat)))
## [1] 17600999

Regression modelling m

y<-data$Total.Commercial.industrial.intensity.MJ.GDP.yr..end.use.;
plot(data$GDP.capita....capita.,data$Total.Commercial.industrial.intensity.MJ.GDP.yr..end.use.,bg =  c("red", "green", "white"),type="p",main = "figure 17", 
     col = "black", cex = 1.1, pch = 21,frame = FALSE)
abline(lm(Total.Commercial.industrial.intensity.MJ.GDP.yr..end.use. ~ GDP.capita....capita., data = data), col="red")
legend("right", c("Delhi","Mumbai","Chandigarh"), pch = 21,pt.bg=c("red","green","white"),bg="white")

plot of chunk unnamed-chunk-16

cor(data$GDP.capita....capita., data$Total.Commercial.industrial.intensity.MJ.GDP.yr..end.use.)
## [1] -0.917
fit <- lm(GDP.capita....capita.~ Total.Commercial.industrial.intensity.MJ.GDP.yr..end.use., data = data)
coef(fit)
##                                               (Intercept) 
##                                                     12591 
## Total.Commercial.industrial.intensity.MJ.GDP.yr..end.use. 
##                                                     -7827
e <- resid(fit)
yhat <- predict(fit)
max(abs(e - (y  - yhat)))
## [1] 9345

Regression modelling n

y<-data$L.LPG.HH.mo;
plot(data$Total.MJ.HH.mo..end.use.,data$L.LPG.HH.mo,bg =  c("red", "green", "white"),type="p",main = "figure 18", 
     col = c("red", "green", "white"), cex = 1.1, pch = 21,frame = FALSE)
abline(lm(L.LPG.HH.mo ~ Total.MJ.HH.mo..end.use., data = data), col="red")
legend("right", c("Delhi","Mumbai","Chandigarh"), pch = 21,pt.bg=c("red","green","white"),bg="white")

plot of chunk unnamed-chunk-17

cor(data$L.LPG.HH.mo , data$Total.MJ.HH.mo..end.use.)
## [1] -0.8336
fit <- lm(Total.MJ.HH.mo..end.use.~ L.LPG.HH.mo, data = data)
coef(fit)
## (Intercept) L.LPG.HH.mo 
##     2172.24      -10.38
e <- resid(fit)
yhat <- predict(fit)
max(abs(e - (y  - yhat)))
## [1] 2106

As, correclation is >80%, it is highly correaltred and thus can be used with suitable accuracy

Regression modelling o

y<-data$L.LPG.HH.mo;
plot(data$Industrial.process..t.waste.capita.yr.,data$L.LPG.HH.mo,bg =  c("red", "green", "white"),type="p",main = "figure 19", 
     col = "black", cex = 1.1, pch = 21,frame = FALSE)
abline(lm(L.LPG.HH.mo ~ Industrial.process..t.waste.capita.yr., data = data), col="red")
legend("right", c("Delhi","Mumbai","Chandigarh"), pch = 21,pt.bg=c("red","green","white"),bg="white")

plot of chunk unnamed-chunk-18

cor(data$Industrial.process..t.waste.capita.yr., data$L.LPG.HH.mo)
## [1] -0.9779
fit <- lm(Industrial.process..t.waste.capita.yr.~ L.LPG.HH.mo, data = data)
coef(fit)
## (Intercept) L.LPG.HH.mo 
##     2.15883    -0.01117
e <- resid(fit)
yhat <- predict(fit)
max(abs(e - (y  - yhat)))
## [1] 78.5

As, correclation is >80%, it is highly correaltred and thus can be used with suitable accuracy

Regression modelling p

y<-data$L.LPG.HH.mo;
plot(data$Water..treated.water.WW..1000.liters.capita.yr.,data$L.LPG.HH.mo,bg =  c("red", "green", "white"),type="p", main = "figure 20",
     col = "black", cex = 1.1, pch = 21,frame = FALSE)
abline(lm(L.LPG.HH.mo ~ Water..treated.water.WW..1000.liters.capita.yr. , data = data), col="red")
legend("right", c("Delhi","Mumbai","Chandigarh"), pch = 21,pt.bg=c("red","green","white"),bg="white")

plot of chunk unnamed-chunk-19

cor(data$Water..treated.water.WW..1000.liters.capita.yr. , data$L.LPG.HH.mo)
## [1] 0.9556
fit <- lm(Water..treated.water.WW..1000.liters.capita.yr.~ L.LPG.HH.mo, data = data)
coef(fit)
## (Intercept) L.LPG.HH.mo 
##      5.7534      0.3352
e <- resid(fit)
yhat <- predict(fit)
max(abs(e - (y  - yhat)))
## [1] 45.88

As, correclation is >80%, it is highly correaltred and thus can be used with suitable accuracy

data<-read.csv(file = "multiplied_by_ef.csv", header=TRUE);
data <-  data[1:3,2:8];
fit <- lm(Total.GHGs ~ . , data = data)
fit1 <- aov(Total.GHGs~kWh.HH.mo,data=data)

fit2 <- aov(Total.GHGs~kWh.HH.mo+L.LPG.HH.mo,data=data)

fit2 <- update(fit, Total.GHGs~kWh.HH.mo+L.LPG.HH.mo)
fit3 <- update(fit, Total.GHGs~kWh.HH.mo+L.LPG.HH.mo+L.kerosene.HH.mo)
fit4 <- update(fit, Total.GHGs~kWh.HH.mo+L.LPG.HH.mo+L.kerosene.HH.mo+Industrial.process..t.waste.capita.yr.)
fit5 <- update(fit, Total.GHGs~kWh.HH.mo+L.LPG.HH.mo+L.kerosene.HH.mo+Industrial.process..t.waste.capita.yr.+Air.Travel..L.jet.fuel.enplaned.passenger)
fit6 <- update(fit, Total.GHGs~kWh.HH.mo+L.LPG.HH.mo+L.kerosene.HH.mo+Industrial.process..t.waste.capita.yr.+Air.Travel..L.jet.fuel.enplaned.passenger+Water..treated.water.WW..1000.liters.capita.yr.)

anova(fit,fit1,fit2,fit3,fit4,fit5,fit6)
## Analysis of Variance Table
## 
## Model 1: Total.GHGs ~ kWh.HH.mo + L.LPG.HH.mo + L.kerosene.HH.mo + Industrial.process..t.waste.capita.yr. + 
##     Air.Travel..L.jet.fuel.enplaned.passenger + Water..treated.water.WW..1000.liters.capita.yr.
## Model 2: Total.GHGs ~ kWh.HH.mo
## Model 3: Total.GHGs ~ kWh.HH.mo + L.LPG.HH.mo
## Model 4: Total.GHGs ~ kWh.HH.mo + L.LPG.HH.mo + L.kerosene.HH.mo
## Model 5: Total.GHGs ~ kWh.HH.mo + L.LPG.HH.mo + L.kerosene.HH.mo + Industrial.process..t.waste.capita.yr.
## Model 6: Total.GHGs ~ kWh.HH.mo + L.LPG.HH.mo + L.kerosene.HH.mo + Industrial.process..t.waste.capita.yr. + 
##     Air.Travel..L.jet.fuel.enplaned.passenger
## Model 7: Total.GHGs ~ kWh.HH.mo + L.LPG.HH.mo + L.kerosene.HH.mo + Industrial.process..t.waste.capita.yr. + 
##     Air.Travel..L.jet.fuel.enplaned.passenger + Water..treated.water.WW..1000.liters.capita.yr.
##   Res.Df RSS Df Sum of Sq F Pr(>F)
## 1      0   0                      
## 2      1 736 -1      -736         
## 3      0   0  1       736         
## 4      0   0  0         0         
## 5      0   0  0         0         
## 6      0   0  0         0         
## 7      0   0  0         0

Summary of multi-variate Regression

summary(fit2)
## 
## Call:
## lm(formula = Total.GHGs ~ kWh.HH.mo + L.LPG.HH.mo, data = data)
## 
## Residuals:
## ALL 3 residuals are 0: no residual degrees of freedom!
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)
## (Intercept)   -83.49         NA      NA       NA
## kWh.HH.mo       1.60         NA      NA       NA
## L.LPG.HH.mo     0.63         NA      NA       NA
## 
## Residual standard error: NaN on 0 degrees of freedom
## Multiple R-squared:     1,   Adjusted R-squared:   NaN 
## F-statistic:  NaN on 2 and 0 DF,  p-value: NA

Scope and Uses of the Project

Two-variable Regression - To find the amount of GHGs contribution of an unknown variable when its relationship with another parameter is known

Multi-Variate Regression - To find the overall GHG production of a city or state when various contributors to GHGs are known

Scope of Improvement - Many More variables in the model - Using data of various cities and state to make a much better model - Also, using better regression by going into higher order regression modelling

Acknowledgement

We would like to thank Dr. B.R. Gurjar to give us and guide us in this project.

Thanks to Ajay Nagapure sir for his constatnt guidance by quick email replies

Road Ahead Working on this project to make a better and much more reliable model. Also, publishing research papers along the way.

Thanks