9.4 Mitigation and Adaptation

Addressing climate change requires both mitigation -- reducing greenhouse gas emissions to limit future warming -- and adaptation -- adjusting to the impacts that are already locked in. The Paris Agreement established goals to limit warming to well below 2 degrees C, with efforts toward 1.5 degrees C. Achieving these targets demands rapid transformation of energy systems, carbon removal, and systemic societal adaptation across all sectors.

Paris Agreement and Carbon Budget Framework

The 2015 Paris Agreement commits nearly all nations to limit global warming. The near-linear relationship between cumulative CO2 emissions and temperature (TCRE) enables a "carbon budget" approach: for any temperature target, there is a finite total amount of CO2 that can be emitted.

~400 GtCO2

Remaining carbon budget for 1.5 degC (50% chance, from 2023)

At ~40 GtCO2/yr, exhausted by ~2033

~1150 GtCO2

Remaining carbon budget for 2 degC (67% chance, from 2023)

At ~40 GtCO2/yr, exhausted by ~2052

The budget concept implies that temperature stabilization requires net-zero CO2 emissions. For 1.5 degC, global CO2 emissions must reach net-zero by approximately 2050, with deep reductions (45% below 2010 levels) by 2030. All greenhouse gases combined must reach net-zero by approximately 2070 for 1.5 degC.

Kaya Identity

The Kaya identity decomposes CO2 emissions into four driving factors, providing a framework for understanding emission trajectories and mitigation levers:

$$\text{CO}_2 = P \times \frac{\text{GDP}}{P} \times \frac{E}{\text{GDP}} \times \frac{\text{CO}_2}{E}$$

$P$ = population, $\text{GDP}/P$ = per-capita income, $E/\text{GDP}$ = energy intensity, $\text{CO}_2/E$ = carbon intensity of energy

Population (P)

Global population is projected to peak at ~10.4 billion around 2080 (UN medium variant). Population growth contributes ~1%/yr to emission growth. Per-capita emissions vary enormously: ~16 tCO2/person in the US vs ~2 tCO2/person in India.

Per-Capita GDP (GDP/P)

Economic growth historically drives emission growth (~2%/yr globally). Decoupling GDP growth from emissions is essential. Some developed nations have achieved absolute decoupling (GDP up, emissions down) through structural shifts and efficiency gains.

Energy Intensity (E/GDP)

Energy per unit GDP has declined ~1.5%/yr globally due to efficiency improvements and structural economic shifts (services vs manufacturing). Further reduction through building efficiency, industrial processes, and electrification of transport.

Carbon Intensity (CO2/E)

CO2 per unit energy depends on the fuel mix. Coal: ~95 kgCO2/GJ, oil: ~73, gas: ~56. Renewables and nuclear: ~0. This factor must decline rapidly through energy transition. Historical decline ~0.3%/yr; needs to accelerate to ~5-7%/yr for 1.5 degC.

Renewable Energy and Decarbonization Technologies

Solar PV Learning Curve (Swanson's Law)

Solar PV module prices follow a learning curve: costs drop ~20-24% for every doubling of cumulative installed capacity. This is known as Swanson's Law. From ~$76/W in 1977 to ~$0.20/W in 2023 -- a 99.7% cost reduction. The learning rate can be expressed as:

$$C(x) = C_0 \cdot x^{-\alpha} \quad \text{where} \quad \alpha = -\frac{\ln(1-LR)}{\ln 2}$$

$C$ = cost, $x$ = cumulative production, $LR$ = learning rate (~0.24 for solar PV), $\alpha$ = experience parameter

Energy Return on Investment (EROI)

EROI measures the ratio of energy delivered to society versus energy invested in obtaining it. A minimum EROI of ~5-7 is needed to sustain modern civilization:

$$\text{EROI} = \frac{E_{\text{out}}}{E_{\text{in}}}$$

Typical values: coal ~30, natural gas ~20, wind ~18, solar PV ~10-15 (and improving), nuclear ~10-75 (varies widely), oil sands ~3-5. Battery storage reduces effective EROI for variable renewables.

Wind Power Capacity Factor

Wind turbine power output scales as the cube of wind speed: P = 0.5 * rho * A * C_p * v^3, where C_p is the power coefficient (Betz limit = 16/27 = 0.593). Modern offshore turbines achieve capacity factors of 40-55%, onshore 25-45%. Capacity factors have improved steadily with taller towers and larger rotors accessing stronger, steadier winds aloft.

Carbon Capture, Storage, and Direct Air Capture

Carbon Capture and Storage (CCS)

CCS captures CO2 from point sources (power plants, cement kilns, steel mills) before it enters the atmosphere. The dominant technology is amine scrubbing using monoethanolamine (MEA):

CO2 + 2RNH2 ↔ RNHCOO- + RNH3+ (absorption at 40-60 degC, regeneration at 100-120 degC)

Energy penalty: 25-40% of plant output for capture + compression. Cost: $50-120/tCO2. Current global CCS capacity: ~40 MtCO2/yr (needs to scale 100x for climate targets).

Direct Air Capture (DAC)

DAC removes CO2 directly from ambient air (~420 ppm = 0.042%). The thermodynamic minimum work to separate CO2 from air is:

$$W_{\min} = RT \ln\left(\frac{1}{x_{\text{CO}_2}}\right) \approx 20 \, \text{kJ/mol}$$

At T = 300 K, x_CO2 = 0.00042. Actual energy ~250-500 kJ/mol (10-25x minimum).

Current cost: $250-600/tCO2. Targets: $100-150/tCO2 at scale. Technologies: solid sorbent (Climeworks) and liquid solvent (Carbon Engineering) approaches.

Solar Geoengineering

Solar radiation management (SRM) aims to reduce warming by reflecting a small fraction of incoming sunlight. The most studied approach is stratospheric aerosol injection (SAI), mimicking the cooling effect of large volcanic eruptions:

$$\Delta T_{\text{SRM}} \approx -\frac{\Delta F_{\text{SRM}}}{\lambda} \quad \text{where} \quad \Delta F_{\text{SRM}} = -\frac{S_0}{4} \Delta\alpha$$

lambda = climate feedback parameter (~1.2 W/m2/degC), Delta-alpha = change in planetary albedo

An albedo increase of ~0.01 would offset approximately 2 degC of greenhouse warming. This could potentially be achieved with ~5-10 Tg SO2/yr injected into the stratosphere (comparable to a large volcanic eruption every year). Estimated cost: $10-50 billion/yr -- cheap compared to mitigation costs.

Risks and Governance Challenges

- Does not address ocean acidification (CO2 remains elevated)
- Termination shock: rapid warming if injection stops abruptly
- Uneven regional effects: potential disruption of monsoon patterns
- Stratospheric ozone depletion from sulfate aerosols
- Moral hazard: may reduce motivation for emission reductions
- Governance: no international framework for unilateral deployment

Nature-Based Solutions and Integrated Assessment

Nature-Based Solutions

Afforestation and reforestation sequester carbon at rates of 3-10 tCO2/ha/yr depending on climate and species. Total potential: 3-12 GtCO2/yr. Other approaches include:

- Peatland restoration: preventing 2-3 GtCO2/yr of emissions
- Soil carbon sequestration: improved agricultural practices, biochar
- Blue carbon: mangroves, seagrass, salt marshes (high per-area rates)
- Avoided deforestation (REDD+): preventing ~4-5 GtCO2/yr

Limitations: permanence risks (fire, disease), saturation, land competition with food production.

Integrated Assessment Models (IAMs)

IAMs couple economic, energy, land-use, and climate models to explore mitigation pathways and compute costs. Key models include:

- DICE/RICE (Nordhaus): cost-benefit, social cost of carbon
- REMIND-MAgPIE: detailed energy system + land use
- MESSAGE-GLOBIOM: energy-land-water nexus
- GCAM: global change assessment model

Social cost of carbon (SCC) estimates: $50-200/tCO2 (central ~$185/tCO2 per US EPA 2023). Represents the total economic damage from one additional tonne of CO2 emitted.

Adaptation Strategies

Even with aggressive mitigation, significant climate impacts are locked in from past emissions. Adaptation reduces vulnerability and builds resilience:

Coastal Protection

Sea walls, storm surge barriers, managed retreat, beach nourishment, natural barriers (mangroves, reefs). The Netherlands' Delta Works protects against 10,000-year floods. Cost-benefit depends on assets at risk vs protection cost.

Agricultural Adaptation

Heat-tolerant crop varieties, shifting planting dates, irrigation efficiency, drought-resistant cultivars (CRISPR breeding), diversification, index-based crop insurance. Yields decline ~5% per degree of warming without adaptation.

Early Warning Systems

Heat action plans, flood early warning, hurricane tracking, drought monitoring. Every $1 invested in early warning yields $3-16 in avoided losses. UN target: universal early warning coverage by 2027.

Fortran: Carbon Budget Model -- Cumulative Emissions vs Warming

This Fortran program tracks cumulative CO2 emissions and computes warming using the TCRE framework, comparing different emission pathways against the 1.5 degC and 2 degC carbon budgets:

program carbon_budget_model
  ! ===========================================================
  ! Carbon budget model: track cumulative emissions vs warming
  ! for different emission pathways using TCRE relationship.
  ! Compile: gfortran -o carbon_budget carbon_budget_model.f90
  ! Run:     ./carbon_budget
  ! ===========================================================
  implicit none

  integer, parameter :: dp = selected_real_kind(15,307)
  integer, parameter :: nyears = 81     ! 2020-2100
  integer, parameter :: nscen  = 3      ! emission scenarios

  real(dp) :: emissions(nscen, nyears)  ! GtCO2/yr
  real(dp) :: cumulative(nscen, nyears) ! GtCO2 (from 2020)
  real(dp) :: warming(nscen, nyears)    ! deg C above pre-industrial
  real(dp) :: year

  ! TCRE parameters
  real(dp), parameter :: TCRE = 0.45_dp      ! degC per TtCO2
  real(dp), parameter :: T_2020 = 1.1_dp     ! warming already realized
  real(dp), parameter :: cumul_2020 = 2390.0_dp  ! historical cumul GtCO2

  ! Carbon budgets (total from pre-industrial)
  real(dp), parameter :: budget_15 = 2790.0_dp  ! GtCO2 for 1.5 C (50%)
  real(dp), parameter :: budget_20 = 3540.0_dp  ! GtCO2 for 2.0 C (67%)

  real(dp) :: remaining_15, remaining_20
  real(dp) :: t, rate
  integer  :: iy, is
  character(len=20) :: scen_names(nscen)

  scen_names(1) = 'Net-Zero-2050'
  scen_names(2) = 'Gradual-Decline'
  scen_names(3) = 'Current-Policies'

  ! === Define emission pathways ===
  do iy = 1, nyears
    year = 2020.0_dp + real(iy-1, dp)
    t = year - 2020.0_dp

    ! Scenario 1: Net-zero by 2050 (linear decline)
    if (t <= 30.0_dp) then
      emissions(1, iy) = 40.0_dp * (1.0_dp - t/30.0_dp)
    else
      emissions(1, iy) = -5.0_dp  ! net negative (DAC + BECCS)
    end if

    ! Scenario 2: Gradual decline (halve by 2050, net-zero by 2070)
    if (t <= 30.0_dp) then
      emissions(2, iy) = 40.0_dp * (1.0_dp - 0.5_dp*t/30.0_dp)
    else if (t <= 50.0_dp) then
      emissions(2, iy) = 20.0_dp * (1.0_dp - (t-30.0_dp)/20.0_dp)
    else
      emissions(2, iy) = -2.0_dp
    end if

    ! Scenario 3: Current policies (slight decline)
    emissions(3, iy) = 40.0_dp * (1.0_dp - 0.005_dp*t)
  end do

  ! === Integrate cumulative emissions and warming ===
  do is = 1, nscen
    cumulative(is, 1) = 0.0_dp
    do iy = 2, nyears
      cumulative(is, iy) = cumulative(is, iy-1) + emissions(is, iy)
    end do
    ! Warming = T_2020 + TCRE * additional cumulative / 1000
    do iy = 1, nyears
      warming(is, iy) = T_2020 + TCRE * cumulative(is, iy) / 1000.0_dp
    end do
  end do

  ! === Output results ===
  write(*,'(A)') '================================================================'
  write(*,'(A)') ' Carbon Budget Model: Cumulative Emissions vs Warming'
  write(*,'(A)') '================================================================'
  write(*,'(A)') ''

  ! Print header
  write(*,'(A6)', advance='no') 'Year'
  do is = 1, nscen
    write(*,'(A20, A12)', advance='no') &
         '  Emis(GtCO2/yr)', '  T(degC)'
  end do
  write(*,*)
  write(*,'(A)') '----------------------------------------------------------------'

  do iy = 1, nyears, 5
    year = 2020.0_dp + real(iy-1, dp)
    write(*,'(I6)', advance='no') nint(year)
    do is = 1, nscen
      write(*,'(F14.1, F12.2)', advance='no') &
           emissions(is,iy), warming(is,iy)
    end do
    write(*,*)
  end do

  ! === Budget analysis ===
  remaining_15 = budget_15 - cumul_2020
  remaining_20 = budget_20 - cumul_2020
  write(*,'(A)') '=== Carbon Budget Analysis ==='
  write(*,'(A,F8.0,A)') ' 1.5C budget remaining: ', remaining_15, ' GtCO2'
  write(*,'(A,F8.0,A)') ' 2.0C budget remaining: ', remaining_20, ' GtCO2'
  do is = 1, nscen
    write(*,'(A,A20)') ' Scenario: ', scen_names(is)
    write(*,'(A,F8.0,A)') '   Cumul emissions (2020-2100): ', &
         cumulative(is, nyears), ' GtCO2'
    write(*,'(A,F5.2,A)') '   Peak warming: ', &
         maxval(warming(is,:)), ' degC'

    if (cumulative(is, nyears) < remaining_15) then
      write(*,'(A)') '   Status: WITHIN 1.5C budget'
    else if (cumulative(is, nyears) < remaining_20) then
      write(*,'(A)') '   Status: Exceeds 1.5C, within 2.0C budget'
    else
      write(*,'(A)') '   Status: EXCEEDS 2.0C budget'
    end if
    write(*,*)
  end do

end program carbon_budget_model

Interactive Simulation: Carbon Budget Calculator

Python

Calculates remaining carbon budget for 1.5C and 2.0C targets. Shows cumulative emissions by sector, compares business-as-usual vs net-zero pathways, and includes wedge stabilization analysis showing how each sector must decline to reach net-zero.

carbon_budget_calculator.py94 lines

import numpy as np
import matplotlib.pyplot as plt

# Carbon Budget Calculator: Emissions pathways & remaining budget
years = np.arange(2000, 2101)
n = len(years)
TCRE = 0.45  # degC per TtCO2

# Historical cumulative emissions to 2023 (GtCO2)
cumul_hist = 2390.0
T_current = 1.1  # current warming

# Sector emissions (GtCO2/yr in 2023)
sectors = {'Energy': 15.0, 'Transport': 8.0, 'Industry': 9.0,
           'Buildings': 3.0, 'Agriculture': 5.5, 'Land Use': 3.5}
total_2023 = sum(sectors.values())

# Define pathways
pathways = {
    'Business-as-usual': {'rate': 0.005, 'color': '#ef4444', 'ls': '-'},
    'Net-Zero by 2050': {'rate': -0.075, 'color': '#22c55e', 'ls': '-'},
    'Net-Zero by 2070': {'rate': -0.042, 'color': '#f59e0b', 'ls': '--'},
}

fig, axes = plt.subplots(1, 3, figsize=(16, 5.5))
fig.patch.set_facecolor('#0f172a')
for ax in axes:
    ax.set_facecolor('#1e293b')
    ax.tick_params(colors='#cbd5e1')
    ax.xaxis.label.set_color('#cbd5e1')
    ax.yaxis.label.set_color('#cbd5e1')
    ax.title.set_color('#cbd5e1')
    for spine in ax.spines.values():
        spine.set_color('#334155')
    ax.grid(True, color='#334155', alpha=0.4)

# Panel 1: Annual emissions by pathway
for name, p in pathways.items():
    emissions = np.zeros(n)
    for i, yr in enumerate(years):
        if yr <= 2023:
            emissions[i] = total_2023 * (0.7 + 0.3 * (yr - 2000) / 23)
        else:
            prev = emissions[i-1] if i > 0 else total_2023
            emissions[i] = max(prev * (1 + p['rate']), -5)
    axes[0].plot(years, emissions, color=p['color'], lw=2, ls=p['ls'], label=name)
axes[0].axhline(0, color='#94a3b8', ls=':', alpha=0.5)
axes[0].set_xlabel('Year')
axes[0].set_ylabel('Annual Emissions (GtCO2/yr)')
axes[0].set_title('Emission Pathways')
axes[0].legend(facecolor='#1e293b', edgecolor='#334155', labelcolor='#cbd5e1', fontsize=8)

# Panel 2: Cumulative emissions & carbon budgets
budget_15 = 400   # remaining from 2023 for 1.5C (50%)
budget_20 = 1150  # remaining from 2023 for 2.0C (67%)
for name, p in pathways.items():
    emissions = np.zeros(n)
    for i, yr in enumerate(years):
        if yr <= 2023:
            emissions[i] = total_2023 * (0.7 + 0.3 * (yr - 2000) / 23)
        else:
            prev = emissions[i-1] if i > 0 else total_2023
            emissions[i] = max(prev * (1 + p['rate']), -5)
    cumul = np.cumsum(emissions) - np.cumsum(emissions)[years == 2023][0]
    axes[1].plot(years, cumul, color=p['color'], lw=2, ls=p['ls'], label=name)
axes[1].axhline(budget_15, color='#38bdf8', ls='--', alpha=0.7, label='1.5C budget')
axes[1].axhline(budget_20, color='#c084fc', ls='--', alpha=0.7, label='2.0C budget')
axes[1].set_xlabel('Year')
axes[1].set_ylabel('Cumulative Emissions from 2023 (GtCO2)')
axes[1].set_title('Carbon Budget Analysis')
axes[1].legend(facecolor='#1e293b', edgecolor='#334155', labelcolor='#cbd5e1', fontsize=7)

# Panel 3: Wedge stabilization (sector breakdown)
colors_s = ['#ef4444', '#f97316', '#f59e0b', '#22c55e', '#06b6d4', '#8b5cf6']
wedge_years = np.arange(2023, 2051)
bottom = np.zeros(len(wedge_years))
for (sec, val), col in zip(sectors.items(), colors_s):
    decline = val * np.maximum(1 - np.linspace(0, 1, len(wedge_years))**0.8, 0)
    axes[2].fill_between(wedge_years, bottom, bottom + decline, color=col, alpha=0.7, label=sec)
    bottom += decline
axes[2].set_xlabel('Year')
axes[2].set_ylabel('Emissions (GtCO2/yr)')
axes[2].set_title('Stabilization Wedges to Net-Zero')
axes[2].legend(facecolor='#1e293b', edgecolor='#334155', labelcolor='#cbd5e1', fontsize=7, loc='upper right')

plt.tight_layout()
plt.savefig('output.png', dpi=150, bbox_inches='tight', facecolor=fig.get_facecolor())

print("=== Carbon Budget Calculator ===")
print(f"Current warming: {T_current} C above pre-industrial")
print(f"Current annual emissions: {total_2023:.1f} GtCO2/yr")
print(f"Remaining budget for 1.5C (50%): ~{budget_15} GtCO2 ({budget_15/total_2023:.0f} years at current rate)")
print(f"Remaining budget for 2.0C (67%): ~{budget_20} GtCO2 ({budget_20/total_2023:.0f} years at current rate)")
print(f"TCRE: {TCRE} C per TtCO2")

Click Run to execute the Python code

Code will be executed with Python 3 on the server

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