Timelines for mitigating the methane impacts of using natural gas for carbon dioxide abatement

We first estimate the contributions of CH₄ emissions from natural gas systems and coal mining to power sector CH₄ emissions for all scenarios (1–5), which each assume different consumption levels of coal and natural gas. To estimate CH₄ emissions from natural gas systems, we draw on review studies to define a baseline range of natural gas leakage rates. The low end is based on the EPA's mean estimate for CH₄ emissions from natural gas systems published in the US greenhouse gas (GHG) inventory [3]. This estimate is a 'bottom-up estimate', where emissions are measured in the vicinity of individual pieces of equipment and statistical sampling methods and device counts are used to infer emissions rates for entire facilities, regions, or countries (e.g. [8, 32]). The top end of our range is from a review study [5] that covers both bottom-up measurements, as well as studies that measure total atmospheric CH₄ enhancement in larger geographical areas ('top-down' studies, e.g. [10, 33]). The resulting range of leakage rates is 1.5%–4.9% (see supplementary material 1 available online at stacks.iop.org/ERL/14/124069/mmedia).

As indicated by this range, the results of bottom-up and top-down studies often disagree. Top-down estimates tend to exceed bottom-up estimates. Differences in measurement techniques partially explain the differences, but the reasons for the discrepancies are an open question and active area of research [16]. Another source of variation, whether in bottom-up or top-down estimates, is the choice of regions in which emissions are sampled. The low end of our range accounts for regional variability in the CH₄ content of natural gas across production basins through a production-weighted national average [34]. However, other possible drivers of regional variability may not be captured, including local production levels, device operating practices and malfunctions, as well as regulations [16, 35]. For the high end of our range we therefore rely on a higher, regional top-down estimate from [5], with an understanding that this is not a national estimate but allows the estimated range to span both high and low observations in the literature, and recognizes that the national average rate is a subject of ongoing debate. In supplementary material 1 we discuss regional leakage rates that fall outside our baseline range, as well as CH₄ emissions at power plants and attributional errors due to the co-production of natural gas and oil. These factors have a small effect relative to the range we already consider.

Estimated uncertainties in CH₄ emissions from coal are smaller [3], and thus we consider only a single emission factor in our model (3 g CH₄/kg coal, see supplementary material 1). We neglect the CH₄ contribution from petroleum systems due to the only 1% contribution of petroleum to the final energy used to generate power in the US [36].

We assume that the contributions from natural gas and coal to power sector CH₄ emissions (${{e}}_{{\rm{M}},\mathrm{NG}}(t^{\prime} )$, ${{e}}_{{\rm{M}},{\rm{C}}}(t^{\prime} )$) change in proportion to the ratios of future to present power sector natural gas and coal consumption (${{P}}_{{\rm{NG}}}(t^{\prime} )$/${{P}}_{{\rm{NG}}}({t}_{0})$, ${{P}}_{{\rm{C}}}(t^{\prime} )$/P_C(t₀)), respectively:

$$\begin{eqnarray}&&{e}_{{\rm{M}},\mathrm{NG}}(t^{\prime} )={e}_{{\rm{M}},\mathrm{NG}}({t}_{0})\cdot \displaystyle \frac{{P}_{\mathrm{NG}}(t^{\prime} )}{{P}_{\mathrm{NG}}({t}_{0})},\end{eqnarray} \tag{ 1 }$$

$$\begin{eqnarray}&&{e}_{{\rm{M}},{\rm{C}}}(t^{\prime} )={e}_{{\rm{M}},{\rm{C}}}({t}_{0})\cdot \displaystyle \frac{{P}_{{\rm{C}}}(t^{\prime} )}{{P}_{{\rm{C}}}({t}_{0})}.\end{eqnarray} \tag{ 2 }$$

Future coal and natural gas consumption in scenario 1 are based on the EPA's Clean Power Plan [37]. Retirements of older, less efficient coal and natural gas-fired power plants reflected in EPA's projection of primary energy use [37] contribute to CH₄ emissions reductions per unit of electricity.

In all scenarios we convert CH₄ emissions from natural gas and coal use for electricity (${{e}}_{{\rm{M}},\mathrm{NG}}$, ${{e}}_{{\rm{M}},{\rm{C}}}$) to CO₂-equivalent emissions $e(t^{\prime} )$:

$$\begin{eqnarray}&&e({t}^{\prime} )={e}_{{\rm{K}}}({t}^{\prime} )+[{e}_{{\rm{M}},\mathrm{NG}}({t}^{\prime} )+{e}_{{\rm{M}},{\rm{C}}}({t}^{\prime} )]\cdot \mu ({t}^{\prime} ),\end{eqnarray} \tag{ 3 }$$

where e_K refers to power sector CO₂ emissions and μ is an emissions equivalency metric. We use a set of metrics including the current default metric, the global warming potential with a 100 year time horizon (GWP(100)), and three alternative metrics proposed in the literature (see section 3.2 for metric values and a discussion of policy implications).

The GWP(100) is based on the ratio of the time-integrated radiative forcing per unit mass of CH₄ and CO₂:

$$\begin{eqnarray}&&\mathrm{GWP}(t^{\prime} ,{t}_{s})=\displaystyle \frac{{\int }_{0}^{t}{A}_{{\rm{M}}}{f}_{{\rm{M}}}(t^{\prime} ){\rm{d}}{t}^{\prime} }{{\int }_{0}^{t}{A}_{{\rm{K}}}{f}_{{\rm{K}}}(t^{\prime} ){\rm{d}}{t}^{\prime} },\end{eqnarray} \tag{ 4 }$$

where A_M and A_K are the radiative efficiencies of CH₄ and CO₂ [38], respectively, and ${f}_{{\rm{M}}}(t^{\prime} )$ and ${f}_{{\rm{K}}}(t^{\prime} )$ are removal functions. Accounting for the CO₂ contribution from the oxidation of fossil CH₄ increases the metric value [38, 39].

The GWP(100)'s fixed time horizon leads to a constant impact value assigned to CH₄ regardless of when emissions occur. In contrast, dynamic metrics $\mu (t^{\prime} )$ place a higher weight per unit mass on CH₄ emissions over time by reducing the time horizon over which impacts are evaluated as a threshold year is approached. This metric formulation can reduce overshoots of climate thresholds [40, 41]. For the instantaneous climate impact (ICI) metric (equation (5)), the threshold year is the intended radiative forcing stabilization year t_s [40]. For the calculation of metric values, small increments of time are taken to approximate a continuous function [40] (see supplementary material 2).

$$\begin{eqnarray}&&\mathrm{ICI}(t^{\prime} ,{t}_{s})=\displaystyle \frac{{A}_{{\rm{M}}}{f}_{{\rm{M}}}({t}_{{\rm{s}}},t^{\prime} )}{{A}_{{\rm{K}}}{f}_{{\rm{K}}}({t}_{{\rm{s}}},t^{\prime} )}.\end{eqnarray} \tag{ 5 }$$

The stabilization year 2050 was used for this analysis and falls within a 15 year range of t_s defined by a set of possible emissions scenarios corresponding to a radiative forcing stabilization target of 3 W m⁻² [40].

Removal functions $f(t,t^{\prime} )$ take the general form [38]:

$$\begin{eqnarray}&&f(t,t^{\prime} )={a}_{0}+\displaystyle \sum _{i=1}^{n}{a}_{i}\cdot {e}^{-\tfrac{t-t^{\prime} }{{\tau }_{i}}},\end{eqnarray} \tag{ 6 }$$

where $f(t,t^{\prime} )$ gives the fraction of a gas emitted at time $t^{\prime}$ remaining in the atmosphere at time t. For CH₄, n = 1 and a₀ = 0, thus removal follows an exponential decline. In the case of CO₂, n = 3 and ${a}_{0}\ne 0$ and therefore the rate of removal changes with time. Parameter values represent a multi-model mean used in the Fifth Assessment Report (AR5) by the Intergovernmental Panel on Climate Change (IPCC) report (a₁ = 0.224, a₂ = 0.282, a₃ = 0.282, τ₁ = 394.4, τ₂ = 36.54, τ₃ = 4.304) [38, 42].

We also examine another dynamic metric, the time-dependent global temperature change potential (GTP), which is based on temperature change instead of radiative forcing. As shown in equation (7), the time-dependent GTP gives the ratio of temperature change ${\rm{\Delta }}T$ in a future year t_e due to a pulse emission of gas in year t [43]:

$$\begin{eqnarray}&&\mathrm{GTP}({t}_{e})=\displaystyle \frac{{\rm{\Delta }}{T}_{{\rm{M}}}({t}_{e})}{{\rm{\Delta }}{T}_{{\rm{K}}}({t}_{e})}.\end{eqnarray} \tag{ 7 }$$

The year t_e is a parameter to be chosen by policymakers and their constituents. We consider two evaluation years, 2050 and 2080. Details are given in supplementary material 2.

We calculate the fossil variants of the dynamic GTP using the same temperature model used to estimate temperature impacts of power sector GHG emissions scenarios. This approach produces metric values that match those in the IPCC AR5 report to within ± 10%–15% [38] for the GTP with a 20 and 50 year time horizon. See supplementary material 2 for a sensitivity analysis.

In scenarios 1–5, we model the CH₄ emissions reductions needed to achieve a 32% reduction in power sector CO₂-equivalent emissions over the 2005–2030 period. Target year CO₂-equivalent emissions ${e}_{\mathrm{target}}(t^{\prime} )$ are set equal to a fraction 1-p(t, t') of base year emissions, with base year emissions e(t) (see equation (8)). We consider a target year of t' =2030, a base year of t = 2005, and an emissions reductions target of p = 0.32. The 32% goal considered in scenarios 1–5 is based on an expansion of the reduction target for CO₂ emissions under the EPA's Clean Power Plan [37] to CO₂-equivalent emissions, and is also chosen to be consistent with the US commitment in the Paris Agreement of a 26%–28% reduction in 2005 economy–wide CO₂-equivalent emissions by 2025. The effect of applying other CO₂-equivalent targets is discussed in supplementary material 6.

Base year CO₂-equivalent emissions are defined by the EPA's CO₂ and CH₄ emissions estimate published in 2016 [3]. We apply a scaling factor to the natural gas contribution to CH₄ emissions of approximately 3 (based on a review [5]), to capture leakage uncertainties, and use a metric value in the base year, μ(t), to convert CH₄ emissions to CO₂-equivalent emissions. The same approach is used for the year 2014, in which emissions reductions begin (see Methods 2.1 and supplementary material 1).

$$\begin{eqnarray}&&{e}_{\mathrm{target}}(t^{\prime} )=e(t)\cdot [1-p(t,t^{\prime} )].\end{eqnarray} \tag{ 8 }$$

For each equivalency metric μ, the CH₄ emissions allowed in year t' (${e}_{{\rm{M}},\mathrm{allowed}}(t^{\prime} )$, t' = 2030) were determined by subtracting target power sector CO₂ emissions in that year (${e}_{{\rm{K}}}(t^{\prime} )$) from ${e}_{\mathrm{target}}(t^{\prime} )$, and dividing the resulting CO₂-equivalent emissions by the metric value $\mu (t^{\prime} )$:

$$\begin{eqnarray}&&{e}_{{\rm{M}},\mathrm{allowed}}(t^{\prime} )=\displaystyle \frac{{e}_{\mathrm{target}}(t^{\prime} )-{e}_{{\rm{K}}}(t^{\prime} )}{\mu (t^{\prime} )}.\end{eqnarray} \tag{ 9 }$$

Natural gas leakage rates q_NG are expressed as the mass fractions of dry natural gas production for the power sector ${P}_{\mathrm{NG}}(t^{\prime} )$ that leaks along the supply chain:

$$\begin{eqnarray}&&{q}_{\mathrm{NG}}=100\cdot \displaystyle \frac{{e}_{{\rm{M}},\mathrm{NG},\mathrm{allowed}}(t^{\prime} )}{{P}_{{\rm{M}},\mathrm{NG}}(t^{\prime} )},\end{eqnarray} \tag{ 10 }$$

$$\begin{eqnarray}&&{P}_{{\rm{M}},\mathrm{NG}}(t^{\prime} )={P}_{\mathrm{NG}}(t^{\prime} )\cdot {f}_{{\rm{M}},\mathrm{NG}}\cdot {\rho }_{{\rm{M}}}.\end{eqnarray} \tag{ 11 }$$

We assume a CH₄ density ρ_M (mass per unit volume) at temperature T = 15 °C and pressure p = 1 atm and employ the ideal gas law. We further assume a 85%–95%... volumetric CH₄ content (fraction f_M,NG) [44, 45]. The target level of CH₄ emissions from natural gas e_M,NG,allowed in the numerator of equation (10) is based on applying equal percent reductions to 2014 CH₄ emissions from coal and natural gas. Natural gas leakage rate reductions can be interpreted as changes in the system–wide natural gas leakage rate (see supplementary material 1).

Note that although we consider a range of metrics, we do not include a version of the GWP with a shorter time horizon. Under any emissions target defined as a fraction of emissions in a historical year, constant value metrics will lead to the same percentage CH₄ reductions as the GWP(100). A higher metric value increases the CO₂-equivalent budget for CH₄ in the target year but this effect will be offset by using the same value ($\mu (t^{\prime} )=\mu (t)$) to reconvert CO₂-equivalent emissions to allowed CH₄ emissions in 2030 (${e}_{{\rm{M}},\mathrm{allowed}}(t^{\prime} )$, t' = 2030):

$$\begin{eqnarray}&&\begin{array}{l}{e}_{{\rm{M}},\mathrm{allowed}}(t^{\prime} )=[({e}_{{\rm{K}}}(t)+{e}_{{\rm{M}}}(t)\,\cdot \,\mu \,(t))\\ \quad \cdot \,(1-p(t,t^{\prime} ))-{e}_{{\rm{K}}}(t)(1-{p}_{{\rm{K}}}(t,t^{\prime} ))]\,\cdot \,\displaystyle \frac{1}{\mu (t^{\prime} )},\end{array}\end{eqnarray} \tag{ 12 }$$

and

$$\begin{eqnarray}&&{e}_{{\rm{M}},\mathrm{allowed}}(t^{\prime} )=[({e}_{{\rm{M}}}(t)\cdot (1-{p}_{{\rm{M}}}(t,t^{\prime} )].\end{eqnarray} \tag{ 13 }$$

Using equations (12) and (13) we compute the fractional change in power sector CH₄ emissions from the base year of the policy, ${p}_{{\rm{M}}}(t,t^{\prime} )$:

$$\begin{eqnarray}&&\begin{array}{l}{p}_{{\rm{M}}}(t,t^{\prime} )=1-\left[\displaystyle \frac{1}{\mu (t^{\prime} )}\cdot \displaystyle \frac{{e}_{{\rm{K}}}(t)}{{e}_{{\rm{M}}}(t)}\cdot \left({p}_{{\rm{K}}}(t,t^{\prime} )\right.\right.\\ \left.\left.-\,p(t,t^{\prime} )\right)+\displaystyle \frac{\mu (t)}{\mu (t^{\prime} )}\cdot (1-p(t,t^{\prime} ))\right].\end{array}\end{eqnarray} \tag{ 14 }$$

The metric that requires the largest emissions cut depends on the scenario. In scenario 1, where the target is met through natural gas leakage rate reductions, the ICI requires the largest reduction in power sector CH₄ emissions because the metric values show the greatest change over time. In the scenarios 2–5, where the target is met through deeper CO₂ reductions, the GTP(t_e = 2050) calls for the largest reduction in CO₂ emissions because it places the largest weight on CH₄ emissions in 2030 (see sections 3.3 and 3.4).

We model alternative scenarios that also meet the 2030 CO₂-equivalent emissions target, but without the reductions in the natural gas leakage rate assumed in scenario 1. Instead, CO₂ emissions are reduced by more than 32% over the 2005–2030 period. Power sector CO₂ emissions in 2030 therefore equal:

$$\begin{eqnarray}&&{e}_{{\rm{K}},\mathrm{allowed}}(t^{\prime} )={e}_{\mathrm{target}}(t^{\prime} )-{e}_{{\rm{M}},\mathrm{NG}}(t^{\prime} )\mu (t^{\prime} ),\end{eqnarray} \tag{ 15 }$$

where ${e}_{\mathrm{target}}(t^{\prime} )$ is computed as in scenario 1 (equation (8)). In scenarios 2–5, CO₂ emissions from coal electricity generation are reduced by replacing coal with other electricity sources, in order to meet the CO₂-equivalent target. We label these sources 'carbon–free' but their lifecycle emissions are not zero [46] and are accounted for in the analysis (see supplementary material 3). Additional scenarios (6–9) instead replace natural gas with other electricity sources and are discussed in supplementary material 3. The effect of varying the CO₂-equivalent target is discussed in supplementary material 6.

We model concentration and temperature responses to CO₂ and CH₄ emissions separately in all scenarios using exponential impulse response functions (IRFs) fitted to the results of atmosphere-ocean coupled general circulation models (see supplementary material 5). This simplified representation of the climate system has been shown to closely reproduce the results of more complex models such as fully coupled atmosphere-ocean general circulation models (AOGCMs) [47–49]. To isolate the warming impacts of US electricity sector emissions, we do not consider emissions by other sectors, and we compare warming impacts of US emissions to those under IPCC's Representative Concentration Pathway 2.6.

Equation (16) gives the linearized IRF for relating the change in global mean surface temperature (ΔT) to the change in radiative forcing ($\tfrac{{d}{\rm{R}}{\rm{F}}}{{d}{t}^{\prime} }$). This linearization is adequate for an RF range between 3 and 4 W m⁻², which corresponds to a doubling of atmospheric CO₂ concentration [47].

$$\begin{eqnarray}&&{\rm{\Delta }}T(t^{\prime} )={\int }_{\tau ={t}_{0}}^{t}R(t^{\prime} -\tau )\displaystyle \frac{{d}{\rm{R}}{\rm{F}}}{{d}{t}^{\prime} }{d}\tau .\end{eqnarray} \tag{ 16 }$$

R(t) is represented as a sum of three exponentials (equation (17)) with different amplitudes and time constants:

$$\begin{eqnarray}&&R(t^{\prime} )=\displaystyle \frac{1}{{r}_{0}}\displaystyle \sum _{j=1}^{3}{A}_{j}\cdot (1-{e}^{-\tfrac{-t^{\prime} }{{\tau }_{j}}}),\end{eqnarray} \tag{ 17 }$$

where r₀ is the height of the RF step function used to simulate the climate response to a doubling of CO₂ concentration. This functional form has been shown to provide the best fit to simulation results for an ensemble of 20 AOGCMs that participated in phase 5 of the Coupled Model Intercomparison Project (CMIP5) [48].

The amplitudes A_j and time constants τ_j taken from CMIP5 are A₁ = 0.60, τ₁ = 0.655, A₂ = 0.86, τ₂ = 9.46, A₃ = 1.04 and τ₃ = 257.1 [48]. We use an equilibrium climate sensitivity (ECS) of 2.5 °C (i.e. the long-term equilibrium temperature response to a doubling of atmospheric CO₂ concentration), which is within the ECS range considered as likely in IPCC's AR4 (2 °C–4.5 °C) and AR5 (1.5 °C–4.5 °C) and was supported by a study incorporating the full observational record from 1765 to 2011 [50]. We examine different ECS choices in supplementary material 5.

In many modeled scenarios, natural gas use continues to displace coal in the US power sector over the next two decades [37, 51]. In one scenario where power sector CO₂ emissions decline by 32% below 2005 levels by 2030, the Environmental Protection Agency (EPA) projects a 21% growth in natural gas electricity between 2014 and 2030, and a 28% decline in coal electricity (figure 1(a)) [37]. We first explore this example (scenario 1, see Methods 2.1), and then examine other scenarios. Scenario 1 is not a projection but rather represents one possible outcome under a policy that reduces CO₂ emissions while continuing to rely on CH₄-emitting fuels.

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Power sector CH₄ emissions in scenario 1 are shown in figures 1(c) and (d). The decline in CH₄ emissions from coal production is not substantial enough to offset increasing CH₄ emissions from natural gas. The resulting trend in power sector CH₄ emissions is nearly flat. The low estimate of the contribution of natural gas (63%) to power sector CH₄ emissions is shown in figure 1(c). It is based on estimates for CH₄ emissions from natural gas systems and coal mining from the US GHG inventory, scaled in proportion to the share of coal and natural gas used for electricity [3]. For the high estimate (figure 1(d)) we multiply the natural gas contribution by an adjustment factor that reflects reviews of measurements [5, 52, 53], many of which exceed estimates in the US GHG inventory [5–8, 10–12, 35, 52, 53] (Methods 2.1).

Having estimated the CH₄ emissions associated with scenario 1, we ask how they can be counted toward a CO₂-equivalent emissions target. Policy goals are often framed in terms of CO₂-equivalent emissions, as this allows for a single-unit comparison of commitments across locations and sectors emitting different ratios of GHGs such as CO₂ and CH₄. Measuring all emissions on a single CO₂-equivalent scale requires a metric for converting non-CO₂ emissions to CO₂-equivalent units. Selecting an emissions equivalency metric for this purpose requires the choice of a climate impact indicator (e.g. radiative forcing, temperature change, economic damages) and a time horizon [40, 54–57]. Many different metrics have been proposed in the literature [40, 43, 56, 58–64]. However, despite these many proposals, most policies use one metric, the global warming potential with a 100 year time horizon (GWP(100)) (see Methods 2.2, equation (4)). This metric was originally proposed as a placeholder [43, 65], but its use has nonetheless persisted over time. The GWP(100) was adopted in the Kyoto protocol, and it was used in most country pledges to the Paris Agreement [66, 67]. As shown in figure 2(a), the GWP(100)'s fixed time horizon leads to a constant impact value assigned to CH₄ regardless of when emissions occur. This formulation has been criticized for underemphasizing the decadal scale radiative forcing impacts of CH₄, which is shorter-lived but has a higher radiative efficiency than CO₂ (e.g. [40, 41, 60, 68, 69]).

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Another approach to selecting an emissions equivalency metric is to evaluate its performance against the climate change mitigation goal of a policy [40, 41]. This approach can allow a decision-maker to weigh the benefits and drawbacks of choosing different indicators and time horizons. For example, a policy maker may choose to use a metric with a time horizon that shortens as a pre-determined threshold in radiative forcing or temperature is approached, thereby capturing short-term impacts as the threshold is approached [40, 41].

Although current climate policies predominantly use the GWP(100), it is relevant for policymakers and other decision makers to understand the impacts of choosing this over other metrics. This importance is reflected in an active debate about which metrics to use in policy [70, 71]. Because of this we consider in our analysis not just one but a set of metrics. This set covers the range in conversion factors resulting from different emissions equivalency metric formulations proposed in the literature (see Methods 2.2). As shown in figure 2(a), the values of the metrics in our set range from high to low and static to time-dependent (see equations (4)–(7) in Methods).

When we use this set of metrics to evaluate scenario 1, where only CO₂ is mitigated, we find that CH₄ emissions add substantially to CO₂-equivalent emissions (figure 2(b)). While CO₂ emissions decline by 32% over the 2005–2030 period, the CO₂-equivalent changes are substantially less when applying the ICI and GTP metrics (figure 2(b)). The CO₂-equivalent emissions cuts under the GWP(100) come closest to achieving the 32% target. However, as shown in the section on 'Temperature impacts', the near-term warming impacts are higher when applying the GWP(100) to regulate CO₂-equivalent emissions than when using the other metrics. In the next section we explore the CH₄ mitigation that would be needed to reach a 32% cut in CO₂-equivalent emissions across a range of emissions equivalency metrics and technology transition pathways.

Having examined the CO₂-equivalent emissions in scenario 1, where CO₂ alone is regulated, we now simulate the effects of adding a policy that regulates CH₄. We first examine scenario 1, and determine the CH₄ cuts required to reach a reduction in CO₂-equivalent emissions of 32% below 2005 levels by 2030. In scenario 1, CO₂ is also reduced by 32% over this period [37]. We then consider other scenarios that achieve deeper CO₂ cuts to reach the same 32% cut in CO₂-equivalent emissions by 2030, without requiring a reduction in the natural gas leakage rate. Our modeling approach is described in Methods section 2.3. The results are presented both in terms of a reduction in CH₄ from electricity, including contributions from natural gas and coal, and a reduction in the natural gas leakage rate from today's (2014) levels (equation (10) in Methods).

The emissions target examined, a 32% reduction in 2005 CO₂-equivalent emissions by 2030, is meant to represent a power sector goal that would help enable the US to meet its economy-wide commitments to the Paris Agreement. In the US nationally determined contribution (NDC) to the Paris Agreement, the economy-wide CO₂-equivalent emissions goal is a 26%–28% reduction by 2025, relative to 2005 levels, and the NDC also references a more ambitious target to cut emissions by 80% by 2050 [24, 72]. Meeting these goals would likely require a decarbonization of electricity at rates comparable to those analyzed here [73, 74]. In supplementary material 6, we discuss the impact on our results of adjusting the target.

We find that scenario 1 would require natural gas leakage rate reductions of roughly 40%–90% over the 2014–2030 period to meet the 2030 CO₂-equivalent target (figure 6). The low end of this range is based on using the GWP(100), and the upper end is based on using the ICI. The target range for 2030 leakage rates is 0.2%–0.9% when assuming low natural gas leakage rates in 2005, which is the base year for the CO₂-equivalent target. The 2030 target range is 0.5%–2.6% for high natural gas leakage rates. See figure 6 for results based on low leakage rates and supplementary table 3 for the full set of results for all metrics. As shown in figure 4, the required changes in overall power sector CH₄ emissions range from 30% to 90% over the 2005–2030 period, up to three times greater than the 32% CO₂ cut. The decline in total power sector CH₄ emissions would also require a reduction in CH₄ from coal mining relative to 2014, both in absolute terms and per unit of electricity generated (supplementary material 3).

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An alternative to cleaning up the natural gas supply chain is to pursue CO₂ cuts that are deeper than 32%, in order to meet the 2030 CO₂-equivalent target despite CH₄ emissions from natural gas and coal. We explore this approach in scenarios 2–5 (figure 3), which are designed to require no reduction in the natural gas leakage rate. Natural gas electricity generation and resulting CH₄ emissions are held constant relative to scenario 1, and coal electricity is reduced until the CO₂-equivalent target is met (see Methods section 2.4). Generation from non-hydro renewables is increased to supply carbon-free energy. Expanding nuclear or hydro would lead to similar results, with small differences due to the variation in non-zero life-cycle emissions intensities of 'carbon-free' sources (supplementary material 3). Results are given in units of energy (TWh) to indicate that these results represent outcomes of energy transitions needed to meet climate policy goals, rather than power capacity changes needed to reach these outcomes. Examining possible implementation strategies is beyond the scope of this paper, and would require more detailed regional analyses of supply mix changes. Instead our analysis can inform the end goal of these strategies.

As shown in figure 4(b), the required CO₂ cuts can be substantially greater than the 32% assumed in scenario 1. The metric choice is the most important determinant of the amount of carbon-free energy needed to meet the 2030 CO₂-equivalent target, followed by the natural gas leakage rate. Applying the GTP(t_e = 2050) and the ICI requires up to 50% more carbon-free power in 2030 than using the GWP(100) (figure 3), and roughly twice as much as in scenario 1.

A wider set of scenarios are discussed in supplementary material 3 and 6, spanning a range of conditions that may be present in other policy and market contexts. We consider the effects of prioritizing coal over natural gas, adopting different emissions targets, and realizing different electricity demand levels. In scenarios 1–5 above, the CO₂ cuts are equal to or greater than the percent reduction in CO₂-equivalent emissions. In the supplementary information we also consider scenarios where the CO₂ cuts are less ambitious than the target reduction in CO₂-equivalent emissions. A similar decision emerges across these scenarios, between a strategy that emphasizes a CH₄ clean-up or deeper CO₂ cuts through faster expansion of carbon-free power.

Here we evaluate the temperature impacts of scenarios that meet the 2030 CO₂-equivalent target. We find that metric choices affect warming impacts under the policy considered here. Metrics that require the greatest CH₄ or CO₂ cuts—the ICI or the GTP(t_e = 2050) in the set of metrics examined—also achieve the greatest temperature reductions in 2030 relative to a no-policy scenario (figure 5). This no-policy scenario is based on EPA's projection of primary energy use without CO₂ and CH₄ regulations [37].

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As shown in figure 5, using the ICI or the GTP(t_e = 2050) to reach the 2030 CO₂-equivalent target can reduce warming in 2030 by twice as much as the GWP(100), as compared to the no-policy scenario. The absolute effects on global mean temperature are limited, since we are considering a near term (2030) emissions target for one country's electricity sector (see supplementary material 5). But relative to the impact of the policy, the choice of metric makes a significant difference.

Both strategies for achieving the 32% CO₂-equivalent emissions cut—emphasizing CH₄ or CO₂ reductions—result in similar temperature profiles between now and 2030, but diverge in later years. Due to the much longer atmospheric lifetimes of CO₂, the strategy that emphasizes reducing CO₂ is much more effective at mitigating temperature increases in the longer term. This effect is studied in supplementary material 5 by examining the temperature profiles resulting from the two strategies if all emissions cease in the year 2030.

Finally, we find that the uncertainties in CH₄ leakage estimates and metric choice have a similar magnitude of impact on the 2030 temperature change. In other words, changing the metric can have a similar impact on the estimated temperature profile to changing the CH₄ leakage rate from the high to the low values considered in this analysis.

How difficult will it be to achieve CH₄ cuts at the scale suggested by this analysis? Here we compare natural gas leakage rates that are consistent with the 2030 CO₂-equivalent target to past natural gas leakage rate estimates in order to gain historical perspective. We also briefly discuss different mitigation strategies and past examples in the transportation sector.

As shown in figure 6, the range of historical estimates is significantly above the natural gas leakage rates required to meet the 2030 CO₂-equivalent target. The target leakage rates are lowest for the dynamic metrics considered. The substantial scatter in historical estimates point to large and persistent measurement uncertainties, even when considering one data source alone [3]. Multiple data points are sometimes shown for the same year, indicating adjustments of estimates in the US EPA's GHG inventory as protocols changed.

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Whether these reductions in the natural gas leakage rate are realistic will depend on the costs of monitoring, mitigating, and preventing leaks [75, 76], particularly if aiming for the lowest leakage rates suggested by the dynamic metrics (figure 6). Recent literature suggests that targeting disproportionately high-emitting equipment, point sources, or regions, often referred to as 'super-emitters', may be an effective mitigation strategy [5, 8, 10, 32, 52, 75–80]. However, knowledge of why these disproportionate emissions rates occur is still limited. Studies of individual regions point to equipment malfunctions and routine high-emissions operations as causes of disproportionate emissions, but these studies have also suggested that major gaps remain in our understanding of both spatial and temporal variation in emissions rates from point sources [16, 77, 78, 81, 82]. These gaps add uncertainty as to whether the same CH₄ mitigation strategies will work across different sites and production regions, which will likely impact the cost reductions achievable.

One past example of success in monitoring and mitigating point source emissions, including from older, high-emitting equipment, is the reduction in carbon monoxide emissions from vehicles [83]. However, vehicles are mobile and thus eventually pass by even sparsely distributed sensors. In contrast, sensors in the stationary natural gas supply chain would need to travel to the source. To lower the costs of comprehensive monitoring, innovations will likely be needed both in the 'soft' technologies to enable effective CH₄ mitigation (e.g. knowledge embodied in firms and institutions, skills of technology inspectors, web-tools for sharing emissions data) and in hardware (e.g. sensors, cameras).