The United States’s Nationally Determined Contribution (NDC) to the 2015 Paris climate accord committed to reducing greenhouse gas emissions to 26–28% below 2005 levels by 2025 and with a longer-term target of reducing emissions to 80% below 2005 levels by 2050 (United States of America 2015).^{1} This vignette will compare what these goals imply for rates of improving energy efficiency and transitioning from fossil fuels to clean energy sources. The methods will follow Roger Pielke, Jr.’s approach for both bottom-up and top-down analysis (Pielke 2009b, 2009a, 2010, 2011).

Pielke’s analysis rely on the Kaya identity: \[ F = P \times g \times e \times f, \] where

*F*is energy-related CO_{2}emissions (here, measured in billions of metric tons),*P*is the population in billions,*g*is per-capita GDP (here, measured in thousands of dollars),*e*is the energy intensity of the economy (here, measured in quads of primary energy consumed per trillion dollars of GDP^{2}),- and
*f*is the carbon intensity of the energy supply (here, measured in millions of metric tons of CO_{2}per quad).

Pielke begins his bottom-up analysis by examining projections of future population and per-capita GDP. In this vignette, I develop those projections not from demographic and economic models, but simply by extrapolating from recent trends.

Begin by loading historical values of the Kaya identity parameters for the United States:

```
suppressPackageStartupMessages({
library(magrittr)
library(dplyr)
library(stringr)
library(tidyr)
library(purrr)
library(broom)
library(knitr)
library(scales)
library(kayadata)
})#> Warning: package 'tidyr' was built under R version 4.0.4
#> Warning: package 'broom' was built under R version 4.0.4
<- get_kaya_data("United States") kaya
```

Let’s start by looking at trends in *P*, *g*, *e*, and *f* starting in 1990. I plot these with a log-scale on the y-axis because a constant growth rate produces exponential growth, so it should look linear on a semi-log plot.

```
plot_kaya(kaya, "P", log_scale = TRUE, start_year = 1990, trend_line = TRUE, points = FALSE) +
theme_bw()
#> `geom_smooth()` using formula 'y ~ x'
```

```
plot_kaya(kaya, "g", log_scale = TRUE, start_year = 1990, trend_line = TRUE, points = FALSE) +
theme_bw()
#> `geom_smooth()` using formula 'y ~ x'
```

```
plot_kaya(kaya, "e", log_scale = TRUE, start_year = 1990, trend_line = TRUE, points = FALSE) +
theme_bw()
#> `geom_smooth()` using formula 'y ~ x'
```

```
plot_kaya(kaya, "f", log_scale = TRUE, start_year = 1990, trend_line = TRUE, points = FALSE) +
theme_bw()
#> `geom_smooth()` using formula 'y ~ x'
```

All of these trends look reasonable, except for *f*. Let’s try *f* again, but fitting the trend only since 2005:

```
plot_kaya(kaya, "f", log_scale = TRUE, start_year = 2005, trend_line = TRUE, points = FALSE) +
theme_bw()
#> `geom_smooth()` using formula 'y ~ x'
```

That looks better. The abrupt changes in trend at 1990 and again at 2005 illustrate the difficulties of predicting future values by extrapolating from past trends, and indicates that the extrapolations we will use here should be taken with several grains of salt.

Now let’s calculate the historical trends:

```
<- c("P", "g", "e", "f")
vars <- map_dbl(vars,
historical_trends ~kaya %>%
gather(key = variable, value = value, -region, -year) %>%
filter(variable == .x,
>= ifelse(.x == "f", 2005, 1990)) %>%
year lm(log(value) ~ year, data = .) %>% tidy() %>%
filter(term == "year") %$% estimate
%>% set_names(vars)
)
tibble(Variable = names(historical_trends),
Rate = map_chr(historical_trends, ~percent(.x, 0.01))) %>%
kable(align = c("c", "r"))
```

Variable | Rate |
---|---|

P | 0.94% |

g | 1.48% |

e | -2.05% |

f | -1.08% |

Next, calculate the implied rate of change of *F* under the policy. This is not an extrapolation from history, but a pure implication of the policy goals: From the Kaya identity, \(F = G \times e \times f\), so the rates of change are \(r_F = r_G + r_e + r_f = r_G + r_{ef}\).

```
<- 2005
ref_year <- c(2025, 2050)
target_years <- c(0.26, 0.80)
target_reduction
<- kaya %>% filter(year == ref_year) %$% F
F_ref <- tibble(year = target_years, F = F_ref * (1 - target_reduction)) %>%
F_target mutate(implied_rate = log(F / F_ref) / (year - ref_year))
%>%
F_target mutate(implied_rate = map_chr(implied_rate, ~percent(.x, 0.01))) %>%
rename("Target F" = F, "Implied Rate" = implied_rate) %>%
kable(align = c("crr"), digits = 0)
```

year | Target F | Implied Rate |
---|---|---|

2025 | 4346 | -1.51% |

2050 | 1175 | -3.58% |

For the bottom-up analysis of decarbonization, I will use \(r_{ef}\), the implied rate of decarbonization of the economy, conditional on future economic growth following the historical trend. This is expressed in the equation \(r_{ef} = r_F - r_G\), where \(r_F\) is the rate of emissions-reduction implied by the policy (see above) and \(r_G\) is the historical growth rate of GDP:

```
<- F_target %>%
implied_decarb_rates transmute(year, impl_F = implied_rate,
hist_G = historical_trends['P'] + historical_trends['g'],
hist_ef = historical_trends['e'] + historical_trends['f'],
impl_ef = impl_F - hist_G)
%>%
implied_decarb_rates mutate_at(vars(starts_with("hist_"), starts_with("impl_")),
list(~map_chr(., ~percent(.x, 0.01)))) %>%
select(Year = year,
"implied F" = impl_F,
"historical G" = hist_G,
"implied ef" = impl_ef,
"historical ef" = hist_ef
%>%
) kable(align="rrrrr")
```

Year | implied F | historical G | implied ef | historical ef |
---|---|---|---|---|

2025 | -1.51% | 2.43% | -3.93% | -3.12% |

2050 | -3.58% | 2.43% | -6.00% | -3.12% |

To meet the goals for 2025 would require increasing the rate of reducing *ef* from -3.12% per year to -3.93% per year: 1.3 times faster.

To meet the goals for 2050 would require increasing the rate of reducing *ef* from -3.12% per year to -6.00% per year: 1.9 times faster.

The top-down analysis is very similar to the bottom-up analysis, but instead of looking at the elements of the Kaya identity individually, we use predictions from macroeconomic integrated assessment models that consider interactions between population, GDP, and energy use to predict future energy demand:

```
<- get_top_down_trends("United States")
top_down_trends
%>% select(P, G, E) %>%
top_down_trends mutate_all(list(~map_chr(., ~percent(.x, 0.01)))) %>%
rename("P trend" = P, "G trend" = G, "E trend" = E) %>%
kable(align="rrr")
```

P trend | G trend | E trend |
---|---|---|

0.60% | 2.10% | 0.30% |

In the bottom-up analysis, we calculated the implied rate of decarbonizing the economy by comparing the rate of emissions reduction implied by the policy (\(r_F\)) to the predicted rate of change of GDP (\(r_G\)). Here, in the top-down analysis, we calculate the implied rate of decarbonizing the energy supply (\(r_f\)) by comparing the rate of emissions-reduction implied by policy (\(r_F\)) to the predicted rate of growth of energy demand (\(r_E\)): \(F = E \times f\), so \(r_F = r_E + r_f\), which we rearrange to find that \(r_f = r_F - r_E\).

```
<- F_target %>%
implied_decarb_rates_top_down transmute(year, impl_F = implied_rate,
top_down_E = top_down_trends$E,
hist_f = historical_trends['f'],
impl_f = impl_F - top_down_E)
%>%
implied_decarb_rates_top_down mutate_at(vars(starts_with("hist_"), starts_with("impl_"),
starts_with("top_down")),
list(~map_chr(., ~percent(.x, 0.01)))) %>%
select(Year = year,
"implied F" = impl_F,
"top-down E" = top_down_E,
"implied f" = impl_f,
"historical f" = hist_f
%>%
) kable(align="rrrrr")
```

Year | implied F | top-down E | implied f | historical f |
---|---|---|---|---|

2025 | -1.51% | 0.30% | -1.81% | -1.08% |

2050 | -3.58% | 0.30% | -3.88% | -1.08% |

To meet the goals for 2025 would require increasing the rate of reducing *f* from -1.08% per year to -1.81% per year: 1.7 times faster.

To meet the goals for 2050 would require increasing the rate of reducing *f* from -1.08% per year to -3.88% per year: 3.6 times faster.

Pielke, Roger A., Jr. 2009a. “Mamizu Climate Policy: An Evaluation of Japanese Carbon Emissions Reduction Targets.” *Environmental Research Letters* 4(4): 044001.

———. 2009b. “The British Climate Change Act: A Critical Evaluation and Proposed Alternative Approach.” *Environmental Research Letters* 4(2): 024010.

———. 2010. *The Climate Fix: What Scientists and Politicians Won’t Tell You about Global Warming*. Basic Books.

———. 2011. “An Evaluation of the Targets and Timetables of Proposed Australian Emissions Reduction Policies.” *Environmental Science & Policy* 14(1): 20–27.

United States of America. 2015. *U.S. Cover Note, INDC, and Accompanying Information*. https://www4.unfccc.int/sites/submissions/INDC/Published%20Documents/United%20States%20of%20America/1/U.S.%20Cover%20Note%20INDC%20and%20Accompanying%20Information.pdf.