T-MACC Explorer: Temperature-Based Marginal Abatement Cost Curves

Background

From dollars per tonne to dollars per degree

Four short notes for readers new to abatement cost curves. If you already know what a MACC is and why GWP₁₀₀ is contested, skip to the figures.

Abatement cost curves rank climate action by price
A marginal abatement cost curve (MACC) lines up mitigation measures from cheapest to most expensive, so policymakers can see how much can be done, and at what price. The traditional version measures everything in dollars per tonne of CO₂-equivalent avoided.
CO₂-equivalent hides when the warming happens
Converting every gas to CO₂-equivalent with a 100-year Global Warming Potential flattens time. Methane and black carbon warm intensely but fade within years to a decade; CO₂ lingers for centuries. One equivalence number cannot capture both the size and the timing of their effects.
The target is temperature, not tonnes
With warming likely to overshoot 1.5 °C before declining, what matters for policy is the temperature path itself: how high the peak goes and how fast it comes back down. Short-lived pollutants are the fastest lever on that path, yet a tonnes-based curve makes them look marginal.
T-MACC prices measures in degrees avoided
The Temperature-MACC re-expresses each measure in dollars per degree of warming avoided, using pollutant-specific atmospheric lifetimes and warming potentials. The same measures reorder, and short-lived-pollutant and integrated measures rise to the top where near-term temperature is concerned.

In short Scored in degrees rather than tonnes, roughly three-quarters of the needed action turns out to save money: the paper finds about 0.95 °C of cost-negative potential, and short-lived climate pollutants supply 58 percent of the near-term temperature benefit despite being a small share of emissions. Reframing the curve around temperature turns overshoot management from a cost to be borne into an opportunity to be captured.

The timing

Different pollutants warm on different clocks

The whole framework turns on one physical fact: a tonne cut today buys a different amount of near-term cooling depending on the gas. This is what a tonnes-based curve cannot see, and what pricing in degrees restores.

Fig. 1 · Interactive

Why timing changes the ranking: three pollutants, three clocks

Each curve is the share of a pollutant’s near-term(20-year) temperature benefit that has actually arrived by year t, for a sustained cut starting now. Black carbon (~7-day lifetime) delivers essentially all of it within weeks; methane (12.4 years) most of it within a decade; CO₂’s benefit keeps accumulating for centuries, so at 20 years only a fraction of its eventual effect is in. For minimizing an overshoot peak, the fast forcers do the work.

Time horizon 40 years

Black carbon (τ ~7 days)Methane (τ 12.4 yr)CO₂ (τ centuries)100% of 20-yr benefit

Computed from each pollutant’s atmospheric burden build-up (§2.4): black carbon reaches 100% within a year, methane 79% by its 12.4-year lifetime, while CO₂ passes 221%of its 20-year value only by year 50 and keeps climbing. Curves are normalized to each gas’s own 20-year value to isolate timing from magnitude; the T-MACC then weights each by its warming potential.

View as table

Share of 20-year temperature benefit realized by year
Year	Black carbon	Methane	CO₂
1.0	100%	10%	6%
5.0	100%	41%	29%
10.0	100%	69%	54%
12.4	100%	79%	66%
20.0	100%	100%	100%
30.0	100%	114%	143%
40.0	100%	120%	183%

The curve

Every measure, ordered by cost per degree

The signature T-MACC figure, built live and filterable. Order measures cheapest first, give each a width equal to the temperature it avoids, and the cost-negative territory below the line becomes impossible to miss.

Fig. 2 · The curve

1.232 °C of mitigation, ordered by cost-effectiveness

The selected measures, laid out cheapest first. Width is temperature avoided, height is marginal cost. Bars below the line are cost-negative. Hover or tap a bar for detail; muted bars are avoided-upstream methane co-benefits.

Built live from the paper’s Table 1 (31itemized measures). The paper’s aggregate tables report 1.296 °C across a fuller 38-measure set; the small gap to the itemized total is Table 1-vs-Table 2 rounding in the source, kept rather than papered over.

Temperature avoided

1.232 °C

31 measures selected

Cost-negative potential

0.747 °C

15 measures that pay for themselves

Portfolio-average cost

$2/°C

Net +$3 B across the set

Ranked measures

The same measures as a league table

#Measure°C$/°CTemperature avoided

1Beef → pork/chicken shift0.040−$35

2Agricultural burning prevention0.065−$30

3Landfill gas capture0.045−$25

4EVs, urban delivery0.025−$25

5Urban delivery EVs, avoided CH₄avoided0.008−$25

6Reducing deforestation0.120−$20

7EVs, buses0.020−$20

8Buses, avoided CH₄avoided0.006−$20

9Oil & gas methane (LDAR)0.100−$15

10Onshore wind0.095−$10

11EVs, light duty0.065−$10

12Onshore wind, avoided CH₄avoided0.025−$10

13Light-duty EVs, avoided CH₄avoided0.020−$10

14Utility-scale solar0.090−$5

15Solar, avoided CH₄avoided0.023−$5

16Coal mine methane capture0.035$5

17AWD rice cultivation0.030$10

18Clean cookstoves0.050$15

19Feed additives (30–80%)0.085$20

20Refrigeration / AC replacement0.045$25

21EVs, long-haul trucks0.015$25

22Long-haul EVs, avoided CH₄avoided0.005$25

23Improved livestock feeding0.020$30

24Offshore wind0.055$35

25Offshore wind, avoided CH₄avoided0.015$35

26Manure management0.025$40

27Organic waste diversion0.018$50

28Diesel particulate filters0.040$55

29Industrial HFC reduction0.020$65

30Wastewater treatment0.012$70

31Aerobic rice0.015$80

The breakdown

Where the degree comes from, and what it costs

The paper’s aggregate results: temperature reduction split by pollutant and by cost band. Together they make the central claim, that the majority of near-term cooling is both short-lived-pollutant driven and cheaper than inaction.

Total potential

1.296 °C

Across 38 measures, 2025–2045

Short-lived pollutants

58%

CH₄ + black carbon share of the total

Cost-negative

0.946 °C

22 measures that save money

Net savings

$950 B

From the cost-negative measures (§5.4)

Fig. 3 · By pollutant

Where the degree comes from

The paper’s Table 2. Short-lived forcers, methane and black carbon, supply the majority of near-term temperature reduction, far out of proportion to their share of emissions.

CH₄ (avoided) is the upstream methane leakage eliminated when clean energy displaces gas and oil, a co-benefit invisible to combustion-only accounting.

View as table

Temperature reduction by pollutant category
Pollutant	Direct (°C)	Avoided (°C)	Total (°C)	Share
CO₂	0.485	–	0.485	37%
CH₄ (direct)	0.460	–	0.460	35%
CH₄ (avoided)	0.000	0.102	0.102	8%
Black carbon	0.184	–	0.184	14%
HFCs	0.065	–	0.065	5%

Fig. 4 · By cost

How much is already cheaper than doing nothing

The paper’s Table 3. Bars: temperature reduction available in each cost band. Line: the cumulative curve. The first band alone, measures that save money, delivers 0.946 °C.

Temp. reduction in bandCumulative °C

22 of 38 measures are cost-negative, together worth 0.946 °C, most of the way to the 1.30 °C total before any net-cost measure is reached.

View as table

Temperature reduction by cost category
Cost range	Measures	Reduction (°C)	Cumulative (°C)
< $0	22	0.946	0.946
$0–25	8	0.249	1.195
$25–50	3	0.055	1.250
> $50	5	0.046	1.296

Applied case

A state methane portfolio, scored in temperature

The same temperature lens applied to real policy: state-level methane mitigation in Haryana, India, across six sectors and their alternative scenarios, using the AGTP method. Results are in micro-degrees Celsius, the global warming one state’s methane cuts avoid.

Data provenance · note for reviewEmissions data in this section comes from the published report “A Dual Strategy Sprint Towards Sustainability: Non-CO₂ Pathways for Haryana”(HSPCB, IGSD & TERI, 2025): the 2019 sectoral CH₄ inventory, BAU projections, and each ALT scenario’s quantified reductions at 2030 / 2040 / 2047. The report publishes emissions only, not temperature, so ΔT is computed here with the AGTP method stated by the IGSD dashboard; the kernel’s single scale constant is calibrated to the dashboard’s published 2047 total (47.26 µ°C). The 2040 total is left as an independent check, and the computation reproduces it within ~2% (20.75 µ°C published). Scenario values for interim years the report does not tabulate are interpolated from its figures.

Select state

Target year for analysis

Indirect effects multiplier for CH₄ 1.40×

Temperature avoided by 2040

21.21 µ°C

0.00002121 °C

Temperature avoided by 2047

47.26 µ°C

0.00004726 °C

Maximum Ambition Scenario

6 sectors

Best ALT per sector · Haryana

µ°C(micro-degrees Celsius) = 10⁻⁶ °C = 0.000001 °C. Values look small because they are the global temperature effect of one Indian state’s methane; they aggregate with every other source worldwide. Methane is potent but short-lived (~12-year lifetime), so cuts act fast.

Fig. 5 · By sector

Temperature reduction by sector (2047, MAS)

Contribution of each sector’s Maximum-Ambition scenario in 2047. Livestock dominates Haryana’s methane temperature impact.

View as table

Sector contributions
Sector	ΔT 2047 (µ°C)
Livestock	36.15
Waste	5.63
Agriculture	4.43
Transport	0.77
Residential	0.22
Industry	0.06

Fig. 6 · Over time

MAS impact timeline (2030–2047)

Avoided warming accumulates as reductions compound and the AGTP kernel integrates past cuts. Every point is a full AGTP convolution of the report’s emission series up to that year.

LivestockWasteAgricultureTransportResidentialIndustryTotal

Fig. 7 · Scenario comparison

Alternative scenarios within a sector (2047)

Each sector has several ALT mitigation strategies; the Maximum Ambition Scenario picks the most effective one (highlighted). Choose a sector to compare its options.

Livestock MAS uses ALT 3: Purna Gau Charan Bhumi (open grazing of all dairy cattle).

Method

How the temperature impact is calculated

The dashboard uses the AGTP (Absolute Global Temperature change Potential) approach: for each target year, it sums the temperature response of every past annual emission reduction, weighted by the pollutant’s decay, indirect feedbacks, and climate inertia.

ΔE(t) = E_BAU(t) − E_ALT(t)

ΔT(target) = Σ_t [ ΔE(t) × AGTP(lag) × f_ind × f_inertia ]

lag = target_year − emission_year
AGTP kernel: temperature response per unit emission, set by methane’s ~12-year lifetime and radiative forcing

f_inertia = 1 − e^−lag/τ, τ = 10 yr

f_ind (indirect effects): ozone formation, stratospheric water vapor, CO₂ from oxidation. Combined ≈ 1.75×; slider above.
Scenarios: BAU (business as usual), ALT 1–4 (alternatives), MAS (best ALT per sector)
Emissions data: HSPCB / IGSD / TERI report (2025), sectoral CH₄ BAU + ALT reductions, 2019–2047
Calibration: kernel scale set to the published 2047 total; 2040 reproduced within ~2% as an independent check

Scenarios

Mitigation scenario descriptions for Haryana

Each sector’s alternative (ALT) strategies. The scenario selected into the Maximum Ambition Scenario is highlighted.

LivestockMAS: ALT 3

Scenario	Mitigation strategy
ALT 1	Gausamvardhan (selective breed development)
ALT 2	Limited Gau Charan Bhumi (indigenous cattle pasture grazing)
ALT 3	Purna Gau Charan Bhumi (open grazing of all dairy cattle)

WasteMAS: ALT 4

Scenario	Mitigation strategy
ALT 1	32% diversion (waste-to-energy + MRFs)
ALT 2	40% diversion (compost, AD, RDF, recycling)
ALT 3	50% diversion (compost, AD, RDF, recycling)
ALT 4	60% diversion (compost, AD, RDF, recycling)

AgricultureMAS: ALT 3

Scenario	Mitigation strategy
ALT 1	System of Rice Intensification (SRI), 5% annual adoption
ALT 2	Natural Farming on SRI-converted land, 5% annual adoption
ALT 3	ALT 1 + 2 + crop diversification (rice to non-rice), 5% annual

TransportMAS: ALT 4

Scenario	Mitigation strategy
ALT 1	Electrification of bus fleet
ALT 2	Vehicle scrappage policy (30/60/80%)
ALT 3	Hydrogen blending in CNG (18% by 2047)
ALT 4	EV policy for all new vehicles (50/70/100%)

ResidentialMAS: ALT 3

Scenario	Mitigation strategy
ALT 1	Improved biomass cookstoves for non-LPG households
ALT 2	Improved cookstoves 30% + biogas 30% + solar cooking 40%
ALT 3	Phased transition to solar cooking (100% by 2047)

IndustryMAS: ALT 2

Scenario	Mitigation strategy
ALT 1	Coal to natural gas in industrial boilers (raises CH₄)
ALT 2	Community boilers, 30% fuel-consumption reduction
ALT 3	Green hydrogen expansion (5% by 2040, 8% by 2047)

The method

The four steps behind this page

T-MACC rests on established climate physics: absolute global temperature potentials, pollutant-specific decay, and technology learning curves. What changes is the output unit, temperature, which makes the metric answer the question policymakers actually ask.

Step 1

Emissions → temperature (AGTP)

Rather than integrate radiative forcing like GWP, the framework uses the Absolute Global Temperature Potential: the temperature change at a chosen time from a pulse emission (Aamaas et al. 2013; Fuglestvedt et al. 2010), summed over pollutants and years.

ΔT(t) = Σ_i Σ_t′ E_i(t′) · AGTP_i(t − t′)

AGTP_icarries each pollutant’s own atmospheric lifetime, so a tonne of black carbon and a tonne of CO₂ map to temperature on entirely different clocks.

Step 2

Pollutant-specific decay

CO₂ persists across four sink timescales; short-lived forcers decay exponentially with a single lifetime (§2.4.2).

C_CO₂(t) = 0.277 + 0.217e^−t/4.3 + 0.224e^−t/36.5 + 0.282e^−t/394

C_CH₄(t) = e^−t/12.4 · C_BC(t) = e^{−t/(7 d)}

GWP₂₀: CH₄ = 82, BC = 900, HFCs = 1000–4000 (AR6 Table 7.15)
Climate sensitivity: 0.45 °C per 1000 GtCO₂e

Step 3

Dynamic cost (Wright’s Law)

Costs fall predictably with cumulative production, so a static $/tonne understates fast- learning technologies. Each doubling cuts cost by the learning rate LR (§2.5.1).

Cost(n) = Cost₁ · n^−b, LR = 1 − 2^−b

Solar PV 20% · Batteries 19% · Wind 15% · LED 25%
Battery packs: $100/kWh (2024) → $25/kWh (2045) on the reference path

Step 4

The temperature cost metric

Total implementation cost (net of co-benefits) is divided by total temperature reduction, converting the whole curve from $/tonne into $/°C avoided (§2.8.1).

MAC_T =C_total − Σ co-benefitsΔT_total

The reordering: a measure with high $/tCO₂e can have low $/°C if it targets a short-lived pollutant with high near-term warming.
Co-benefits: health, agricultural and ecosystem values can exceed direct costs, turning positive-cost measures cost-negative.

T-MACC Explorer · Temperature-Based Marginal Abatement Cost Curves · an interactive companion, figures computed live from the paper
Companion to the working paper “Temperature-Based Marginal Abatement Cost Curves: A New Framework for Climate Mitigation Prioritization” (v4). Values reproduce the paper’s Tables 1 to 3. Built with React and Recharts.

Price climate action in degrees, not tonnes, and the ranking changes.

From dollars per tonne to dollars per degree

Abatement cost curves rank climate action by price

CO₂-equivalent hides when the warming happens

The target is temperature, not tonnes

T-MACC prices measures in degrees avoided

Different pollutants warm on different clocks

Why timing changes the ranking: three pollutants, three clocks

Every measure, ordered by cost per degree

1.232 °C of mitigation, ordered by cost-effectiveness

The same measures as a league table

Where the degree comes from, and what it costs

Where the degree comes from

How much is already cheaper than doing nothing

A state methane portfolio, scored in temperature

Temperature reduction by sector (2047, MAS)

MAS impact timeline (2030–2047)

Alternative scenarios within a sector (2047)

How the temperature impact is calculated

Mitigation scenario descriptions for Haryana

LivestockMAS: ALT 3

WasteMAS: ALT 4

AgricultureMAS: ALT 3

TransportMAS: ALT 4

ResidentialMAS: ALT 3

IndustryMAS: ALT 2

Build a temperature budget of your own

Assemble a temperature budget from the menu of measures

Why today’s costs mislead: the learning curve

The four steps behind this page

Emissions → temperature (AGTP)

Pollutant-specific decay

Dynamic cost (Wright’s Law)

The temperature cost metric