Carbon Debt Explorer IIASA · ECE program

About this tool

The Carbon Debt Explorer distributes a remaining global carbon budget across the ten IAMC R10 regions using population (with optional GDP adjustment) and compares each region's fair share against projected cumulative emissions under NDCs or current policies. Developed in the Energy, Climate, and Environment (ECE) program at IIASA, originally to support stakeholder engagement within the ELEVATE project. The accounting framework is described in Pelz et al. (2025, PNAS). For deeper analysis or your own allocation work, the underlying fair-shares library exposes the full API.

What “carbon debt” means here

Carbon debt is an accounting identity:

debt = cumulative emissions − fair-share allocation

A positive value (debt) means a region has emitted — or is projected to emit — more than its fair share of the chosen budget. A negative value (credit) means it has emitted less. The debt number depends on the inputs you pick: the allocation principle (equal per capita, with or without capability adjustment), the first year of accounting, and the future pathway assumed. Change any of these and the numbers shift — sometimes substantially. The usefulness of the measure is in comparing those variants transparently.

Inputs

  • Temperature goal. Sets the remaining carbon budget from a 2023 baseline, sourced from Lamboll et al. (2023), Nature Climate Change: 250 GtCO₂ for 1.5 °C (50% probability), 500 GtCO₂ for 2 °C (90%), 940 GtCO₂ for 2 °C (66%). The same values are used by the fair-shares library, which is where this tool reads them from at build time.
  • First year of accounting. How far back history counts. Earlier years include more cumulative emissions in the budget pool; 2023 onwards treats the post-2022 budget as the only accounting window.
  • Future emissions scenario. REMIND-MAgPIE pathways for R10 under NDCs & pledges (case E) or current policies (case A). Range reflects variation across scenario permutations (Max / Median / Min aggregates).
  • Capability adjustment. Optional weighting: dividing the per-capita share by GDP (in 2017 PPP USD). This implements a simple “ability to pay” correction; richer regions receive a smaller share.

Method

Allocation math is sourced from fair-shares, a BSD-3-Clause Python library maintained by IIASA and collaborators. For the no-capability case this tool calls equal_per_capita_budget; for the 1/GDP case it calls per_capita_adjusted_budget with capability_functional_form="power", capability_exponent=1.0, and capability_reference_year pinned to the user's first year (clamped to 1990–2019 to match GDP data availability). Population, GDP (Penn World Tables 10), and historical CO₂ (FFI, Global Carbon Budget 2023) are regional aggregates inherited from the 2024 R Shiny version of this tool.

Limitations

  • R10 regional resolution only — no country-level detail.
  • LULUCF emissions are excluded; only fossil fuels and industry.
  • Historical data starts at 1900. Pre-1900 emissions are not assigned to regions here.
  • All non-CO₂ forcers are folded into the temperature-goal budget via AR6 WG I assumptions. The tool does not expose them directly.
  • Results should be read as illustrative of equity principles, not as prescriptive quotas.

Further reading

  • Pelz et al. (2025). Using net-zero carbon debt to track climate overshoot responsibility. PNAS 10.1073/pnas.2409316122 — the underlying framework for the debt / credit accounting shown here.
  • Pelz et al. (2025). Entry points for assessing ‘fair shares’ in national mitigation contributions. ERL 10.1088/1748-9326/ada45f — how to read equity principles into numeric allocations.
  • fair-shares documentation: From principle to code — guide to mapping equity positions onto allocation parameters.

Source and citation

Code at github.com/setupelz/fair-shares. Methodology documented at setupelz.github.io/fair-shares. Questions: pelz@iiasa.ac.at.