# U.S. Nationally Defined Contribution to Paris Accord

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).

# Bottom-Up Analysis

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

• F is energy-related CO2 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 GDP2),
• and f is the carbon intensity of the energy supply (here, measured in millions of metric tons of CO2 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.

## Implied Decarbonization Rates

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 $$r_F = r_G + r_e + r_f = r_G + r_{ef}$$.

ref_year <- 2005
target_years <- c(2025, 2050)
target_reduction <- c(0.26, 0.80)

F_ref <- kaya %>% filter(year == ref_year) %% F F_target <- tibble(year = target_years, F = F_ref * (1 - target_reduction)) %>% 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 4339 -1.51% 2050 1173 -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: implied_decarb_rates <- F_target %>% 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% 2.42% -3.93% -2.88% 2050 -3.58% 2.42% -6.00% -2.88% ## Results To meet the goals for 2025 would require increasing the rate of reducing ef from -2.88% per year to -3.93% per year: 1.4 times faster. To meet the goals for 2050 would require increasing the rate of reducing ef from -2.88% per year to -6.00% per year: 2.1 times faster. # Top-Down Analysis 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: top_down_trends <- get_top_down_trends("United States") top_down_trends %>% select(P, G, E) %>% 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% ## Implied Decarbonization 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 $$r_f = r_F - r_E$$. implied_decarb_rates_top_down <- F_target %>% transmute(year, impl_F = implied_rate, top_down_E = top_down_trendsE,
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% -0.94%
2050 -3.58% 0.30% -3.88% -0.94%

## Results

To meet the goals for 2025 would require increasing the rate of reducing f from -0.94% per year to -1.81% per year: 1.9 times faster.

To meet the goals for 2050 would require increasing the rate of reducing f from -0.94% per year to -3.88% per year: 4.1 times faster.

1. The Trump administration has announced its intention to withdraw from the Paris accord. The analysis in this vignette does not address that development and simply presents the implications of the pledged contribution.