Commit 40c9669a authored by Ben Anderson's avatar Ben Anderson
Browse files

added first carbon tax scenario

parent 07719811
......@@ -594,15 +594,77 @@ ggplot2::ggplot(sotonMSOA_DT, aes(x = sumBEIS_tCO2,
\@ref(fig:energyMSOAPlot) shows that
# Carbon Tax Scenarios
## No change to carbon intensity
* no emissions allowances (unlike the ETS and also [current UK government proposals](
* [Carbon tax rate of £16/T]( (currently only proposed for [businesses](
Applying these rates enables us to calculate the Southampton and MSOA level Carbon Tax liability of households via the EPC and BEIS observed energy consumption methods.
```{r, msoaScenario1}
sotonMSOA_DT[, ct_BEIS := sumBEIS_tCO2 * 16]
sotonMSOA_DT[, ct_EPCs := sumEPC_tCO2 * 16]
t <- sotonMSOA_DT[, .(CarbonTaxBEIS_GBP = prettyNum(sum(ct_BEIS), big.mark = ","),
CarbonTaxEPCs_GBP = prettyNum(sum(ct_EPCs), big.mark = ",")),
keyby = .(LAName)]
kableExtra::kable(t, caption = "Estimated Carbon tax liability for Southampton households/properties under Scenario 1") %>%
As we would expect the values are relatively close due to the similar total emissions values estimated above.
If we look at the values by MSOA (\@ref(fig:carbonTaxMSOAPlot)), we find that values differ quite substantially between the methods depending on the levels of EPC records (or missing households - see above) that we are likely to have.
```{r, carbonTaxMSOAPlot, fig.cap="Energy demand comparison"}
ggplot2::ggplot(sotonMSOA_DT, aes(x = ct_BEIS/1000,
y = ct_EPCs/1000,
colour = round(ownerOcc_pc))) +
geom_abline(alpha = 0.2, slope=1, intercept=0) +
geom_point() +
scale_color_continuous(name = "% owner occupiers \n(Census 2011)", high = "red", low = "green") +
#theme(legend.position = "bottom") +
labs(x = "EPC 2020 derived total Carbon Tax £k/year",
y = "BEIS 2018 derived total Carbon Tax £k/year",
caption = "x = y line included for clarity")
#outlier <- t[sumEpcMWh > 70000]
Perhaps of more interest however is the relationship between estimated Carbon Tax £ and levels of deprivation. Figure \@ref(fig:carbonTaxMSOAPlotDep) shows the estimated total Carbon Tax (in £k per year) per MSOA against the proportion of households in the MSOA who do not suffer from any dimension of deprivation as defined by the English [Indices of Multiple Deprivation]( As we can see the higher the proportion of households with no deprivation, the higher the total MSOA Carbon Tax. This suggests that a Carbon Tax will be regressive - those who pay the most are likely to be those who use more energy and thus are likely to be those who can afford to do so.
But we need to be very careful. Some deprived households might well spend a high proportion of their income on energy in order to heat very energy efficient homes. For them, a Carbon Tax would be similar to VAT - an additional burden that might be relatively small in £ terms (compared to a well-off high energy-using household) but high in terms of the % of their income (or expenditure). This is a well known issue highlighted by recent [ONS data on family energy expenditures](
```{r, carbonTaxMSOAPlotDep, fig.cap="Energy demand comparison"}
t1 <- sotonMSOA_DT[, .(MSOACode, ctSum = ct_EPCs, dep0_pc)]
t1[, source := "BEIS 2018"]
t2 <- sotonMSOA_DT[, .(MSOACode, ctSum = ct_BEIS, dep0_pc)]
t2[, source := "EPC 2020"]
National Grid’s Future Energy Scenarios:
plotDT <- rbind(t1,t2)
ggplot2::ggplot(plotDT, aes(x = dep0_pc, y = ctSum, colour = source)) +
geom_point() +
geom_smooth() +
#theme(legend.position = "bottom") +
labs(x = "% with no deprivation dimensions \n(Census 2011)",
y = "Carbon Tax £k/year",
caption = "x = y line included for clarity")
#outlier <- t[sumEpcMWh > 70000]
## National Grid’s Future Energy Scenarios:
* 2030 emissions level for electricity of 0.102 kgCO2/kWh
* gas unchanged
# R packages used
* rmarkdown [@rmarkdown]
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