Global Carbon Budget 2018

Background

“Global Carbon Budget 2018” extends the global carbon dioxide (CO2) budget through 2017 and provides a 2018 emissions projection, documenting a renewed acceleration in fossil emissions after a brief slowdown. Atmospheric CO2 reached 405.0 ± 0.1 ppm in 2017, reflecting cumulative industrial era emissions and ongoing dependence on fossil fuels and land-use change. The paper places these trends in the context of mitigation targets that depend on the magnitude of remaining carbon budgets.

Goals and Methods

The authors aim to quantify five major budget components (fossil emissions, land-use change emissions (ELUC), atmospheric growth, land sink (SLAND), and ocean sink (SOCEAN)) for 1959–2017, with a decadal focus on 2008-2017 and an outlook for 2018. Fossil emissions are calculated from energy statistics and cement production data, and ELUC is derived from land-use and land-use change datasets and bookkeeping models. Atmospheric growth is based on direct CO2 measurements, while SOCEAN and SLAND come from global process models constrained by observations. The budget imbalance (BIM) measures mismatches between total emissions and combined atmospheric, ocean, and land changes.

Conclusions and Takeaways

For 2008-2017, average fossil emissions were 9.4 ± 0.5 GtC yr⁻¹ and land-use change emissions 1.5 ± 0.7 GtC yr⁻¹, with atmospheric growth of 4.7 ± 0.02 GtC yr⁻¹, ocean uptake of 2.4 ± 0.5 GtC yr⁻¹, and land uptake of 3.2 ± 0.8 GtC yr⁻¹. ABIM of 0.5 GtC yr⁻¹ suggests slightly overestimated sources or underestimated sinks. In 2017, fossil emissions grew by about 1.6% to 9.9 ± 0.5 GtC yr⁻¹, while preliminary 2018 data indicate a further ~2.7% increase, signaling that the earlier apparent plateau in emissions has not been sustained. The analysis underscores persistent disagreements on land-use change emissions, northern land sink strength, and ocean variability, guiding practitioners toward cautious interpretation of remaining carbon budgets and emphasizing the need for improved land-use data and observation-constrained sink estimates.