A new study evaluates the potential of two trade restriction strategies to compel non-compliant countries to meet their Paris Agreement climate targets.

The first strategy is for compliant countries to impose border carbon adjustments (BCAs)—tariffs on embodied carbon emissions—on non-compliant countries. A commercial product’s embodied emissions are those produced in its manufacture, assembly and transport. Such BCAs would reduce the competitiveness of exports from non-compliant countries, thereby giving them an incentive to meet their Paris commitments (and avoid BCAs). The second strategy is for compliant countries to impose “strategic tariffs” on non-compliant countries. Strategic tariffs aim to improve the terms-of-trade—the ratio between a country’s export prices and import prices—of the country imposing them, thus boosting national economic growth while penalizing other countries.

Applying a numerical economy-wide model with energy-sector detail that’s derived from the Joint Program’s Economic Projection & Policy Analysis (EPPA) model, the study assessed the potential of each trade restriction strategy for moving the U.S. from non-compliance (at the national level, its current position is to allow unrestricted greenhouse gas emissions) to compliance with its Paris climate pledge.

The study found that when BCAs were imposed on U.S. exports, the nation’s welfare losses (measured as the overall change in income due the policy that’s in effect) were significantly lower than what they would be if the U.S. complied with its Paris pledge. So the imposition of BCAs on its exports would offer the U.S. no economic incentive to shift from non-compliance to compliance.

A simulation of the impact of strategic tariffs—which are much higher than BCA rates—on a non-compliant U.S. showed that a trade war would result, leading to larger domestic welfare losses than what they would be if the U.S. complied with its Paris pledge. At the same time, Paris-compliant countries imposing strategic tariffs on the U.S. would also suffer considerable welfare losses. The study concluded that strategic tariffs could be used to enforce Paris Agreement commitments as long as compliant countries are willing to absorb substantial economic losses on the home front.

Sustainable energy transitions involve the shift of resources between competing industrial sectors and political constituencies. Stakeholders in this process have varying degrees of political and economic power, and understanding how political economic factors influence clean energy transitions is crucial to effective policy formulation and facilitating transitions to sustainable energy systems. In partnership with the Joint Institute for Strategic Energy Analysis (JISEA), UNU-WIDER gathered together a substantial group of experts from around the world—from both developed and developing countries—to launch a multidisciplinary research project seeking to contribute to our enhanced understanding of these factors. The project sought to facilitate an energy transition that will generate very large environmental and economic benefits, particularly over the long run. The beneficiaries of clean energy transitions are highly diffuse and include future generations not yet born. This book is the distilled essence of the cross-cutting academic project. I express my sincere and professional appreciation to the large group of expert authors for their dedication to the project, and to my fellow editors in helping bring together the book for readers to enjoy and absorb along with the findings and policy implications.

A coordinated set of Arctic modelling experiments, which explore how the Arctic responds to changes in external forcing, is proposed. Our goal is to compute and compare climate response functions (CRFs) – the transient response of key observable indicators such as sea-ice extent, freshwater content of the Beaufort Gyre, etc. – to abrupt step changes in forcing fields across a number of Arctic models. Changes in wind, freshwater sources, and inflows to the Arctic basin are considered. Convolutions of known or postulated time series of these forcing fields with their respective CRFs then yield the (linear) response of these observables. This allows the project to inform, and interface directly with, Arctic observations and observers and the climate change community. Here we outline the rationale behind such experiments and illustrate our approach in the context of a coarse-resolution model of the Arctic based on the MITgcm. We conclude by summarizing the expected benefits of such an activity and encourage other modelling groups to compute CRFs with their own models so that we might begin to document their robustness to model formulation, resolution, and parameterization.

Atmospheric greenhouse gas (GHG) concentrations are at unprecedented, record-high levels compared to the last 800 000 years. Those elevated GHG concentrations warm the planet and – partially offset by net cooling effects by aerosols – are largely responsible for the observed warming over the past 150 years. An accurate representation of GHG concentrations is hence important to understand and model recent climate change. So far, community efforts to create composite datasets of GHG concentrations with seasonal and latitudinal information have focused on marine boundary layer conditions and recent trends since the 1980s. Here, we provide consolidated datasets of historical atmospheric concentrations (mole fractions) of 43 GHGs to be used in the Climate Model Intercomparison Project – Phase 6 (CMIP6) experiments. The presented datasets are based on AGAGE and NOAA networks, firn and ice core data, and archived air data, and a large set of published studies. In contrast to previous intercomparisons, the new datasets are latitudinally resolved and include seasonality. We focus on the period 1850–2014 for historical CMIP6 runs, but data are also provided for the last 2000 years. We provide consolidated datasets in various spatiotemporal resolutions for carbon dioxide (CO₂), methane (CH₄) and nitrous oxide (N₂O), as well as 40 other GHGs, namely 17 ozone-depleting substances, 11 hydrofluorocarbons (HFCs), 9 perfluorocarbons (PFCs), sulfur hexafluoride (SF₆), nitrogen trifluoride (NF₃) and sulfuryl fluoride (SO₂F₂). In addition, we provide three equivalence species that aggregate concentrations of GHGs other than CO₂, CH₄ and N₂O, weighted by their radiative forcing efficiencies. For the year 1850, which is used for pre-industrial control runs, we estimate annual global-mean surface concentrations of CO₂ at 284.3 ppm, CH₄ at 808.2 ppb and N₂O at 273.0 ppb. The data are available at https://esgf-node.llnl.gov/search/input4mips/ and http://www.climatecollege.unimelb.edu.au/cmip6. While the minimum CMIP6 recommendation is to use the global- and annual-mean time series, modelling groups can also choose our monthly and latitudinally resolved concentrations, which imply a stronger radiative forcing in the Northern Hemisphere winter (due to the latitudinal gradient and seasonality).