Comprehensive evidence implies a higher social cost of CO2.

The social cost of carbon dioxide (SC-CO2) measures the monetized value of the damages to society caused by an incremental metric tonne of CO2 emissions and is a key metric informing climate policy. Used by governments and other decision-makers in benefit-cost analysis for over a decade, SC-CO2 estimates draw on climate science, economics, demography and other disciplines. However, a 2017 report by the US National Academies of Sciences, Engineering, and Medicine1 (NASEM) highlighted that current SC-CO2 estimates no longer reflect the latest research. The report provided a series of recommendations for improving the scientific basis, transparency and uncertainty characterization of SC-CO2 estimates. Here we show that improved probabilistic socioeconomic projections, climate models, damage functions, and discounting methods that collectively reflect theoretically consistent valuation of risk, substantially increase estimates of the SC-CO2. Our preferred mean SC-CO2 estimate is $185 per tonne of CO2 ($44-$413 per tCO2: 5%-95% range, 2020 US dollars) at a near-term risk-free discount rate of 2%, a value 3.6 times higher than the US government's current value of $51 per tCO2. Our estimates incorporate updated scientific understanding throughout all components of SC-CO2 estimation in the new open-source Greenhouse Gas Impact Value Estimator (GIVE) model, in a manner fully responsive to the near-term NASEM recommendations. Our higher SC-CO2 values, compared with estimates currently used in policy evaluation, substantially increase the estimated benefits of greenhouse gas mitigation and thereby increase the expected net benefits of more stringent climate policies.

Choices in land representation materially affect modeled biofuel carbon intensity estimates.

Estimates of biofuel carbon intensity are uncertain and depend on modeled land use change (LUC) emissions. While analysts have focused on economic and agronomic assumptions affecting the quantity of land converted, researchers have paid less attention to how models classify land into broad categories and designate some categories as ineligible for LUC. To explore the effect of these land representation attributes, we use three versions of a global human and Earth systems model, GCAM, and compute the "carbon intensity of land-use change" (CI-LUC) from increased U.S. corn ethanol production. We consider uncertainty in model parameters along with the choice of land representation and find the latter is one of the most influential parameters on estimated CI-LUC. A version of the model that protects 90% of non-commercial land reduced estimated CI-LUC by an average of 32% across Monte Carlo trials compared to our baseline model. Another version that mimics the GTAP-BIO-ADV land representation, which protects all non-commercial land, reduced CI-LUC by an average of 19%. The results of this experiment demonstrate that land representation in biofuel LUC models is an important determinant of CI-LUC.

Carbon accounting and economic model uncertainty of emissions from biofuels-induced land use change.

Few of the numerous published studies of the emissions from biofuels-induced "indirect" land use change (ILUC) attempt to propagate and quantify uncertainty, and those that have done so have restricted their analysis to a portion of the modeling systems used. In this study, we pair a global, computable general equilibrium model with a model of greenhouse gas emissions from land-use change to quantify the parametric uncertainty in the paired modeling system's estimates of greenhouse gas emissions from ILUC induced by expanded production of three biofuels. We find that for the three fuel systems examined--US corn ethanol, Brazilian sugar cane ethanol, and US soybean biodiesel--95% of the results occurred within ±20 g CO2e MJ(-1) of the mean (coefficient of variation of 20-45%), with economic model parameters related to crop yield and the productivity of newly converted cropland (from forestry and pasture) contributing most of the variance in estimated ILUC emissions intensity. Although the experiments performed here allow us to characterize parametric uncertainty, changes to the model structure have the potential to shift the mean by tens of grams of CO2e per megajoule and further broaden distributions for ILUC emission intensities.

Greenhouse gas emissions from biofuels' indirect land use change are uncertain but may be much greater than previously estimated.

The life cycle greenhouse gas (GHG) emissions induced by increased biofuel consumption are highly uncertain: individual estimates vary from each other and each has a wide intrinsic error band. Using a reduced-form model, we estimated that the bounding range for emissions from indirect land-use change (ILUC) from US corn ethanol expansion was 10 to 340 g CO(2) MJ(-1). Considering various probability distributions to model parameters, the broadest 95% central interval, i.e., between the 2.5 and 97.5%ile values, ranged from 21 to 142 g CO(2)e MJ(-1). ILUC emissions from US corn ethanol expansion thus range from small, but not negligible, to several times greater than the life cycle emissions of gasoline. The ILUC emissions estimates of 30 g CO(2) MJ(-1) for the California Air Resources Board and 34 g CO(2)e MJ(-1) by USEPA (for 2022) are at the low end of the plausible range. The lack of data and understanding (epistemic uncertainty) prevents convergence of judgment on a central value for ILUC emissions. The complexity of the global system being modeled suggests that this range is unlikely to narrow substantially in the near future. Fuel policies that require narrow bounds around point estimates of life cycle GHG emissions are thus incompatible with current and anticipated modeling capabilities. Alternative policies that address the risks associated with uncertainty are more likely to achieve GHG reductions.

The climate impacts of bioenergy systems depend on market and regulatory policy contexts.

Biomass can help reduce greenhouse gas (GHG) emissions by displacing petroleum in the transportation sector, by displacing fossil-based electricity, and by sequestering atmospheric carbon. Which use mitigates the most emissions depends on market and regulatory contexts outside the scope of attributional life cycle assessments. We show that bioelectricity's advantage over liquid biofuels depends on the GHG intensity of the electricity displaced. Bioelectricity that displaces coal-fired electricity could reduce GHG emissions, but bioelectricity that displaces wind electricity could increase GHG emissions. The electricity displaced depends upon existing infrastructure and policies affecting the electric grid. These findings demonstrate how model assumptions about whether the vehicle fleet and bioenergy use are fixed or free parameters constrain the policy questions an analysis can inform. Our bioenergy life cycle assessment can inform questions about a bioenergy mandate's optimal allocation between liquid fuels and electricity generation, but questions about the optimal level of bioenergy use require analyses with different assumptions about fixed and free parameters.

Ethanol can contribute to energy and environmental goals.

To study the potential effects of increased biofuel use, we evaluated six representative analyses of fuel ethanol. Studies that reported negative net energy incorrectly ignored coproducts and used some obsolete data. All studies indicated that current corn ethanol technologies are much less petroleum-intensive than gasoline but have greenhouse gas emissions similar to those of gasoline. However, many important environmental effects of biofuel production are poorly understood. New metrics that measure specific resource inputs are developed, but further research into environmental metrics is needed. Nonetheless, it is already clear that large-scale use of ethanol for fuel will almost certainly require cellulosic technology.