Environment & Climate Change

Steven E. Miller provides an analysis of what the Russian invasion of Ukraine means for the security dynamics of the Arctic and Northern Europe.

We used the US-TIMES energy-system model in conjunction with integrated assessment models for air pollution (AP3, EASIUR, InMAP) to estimate the consequences of local air pollutant (LAP) and carbon dioxide (CO₂) policy on technology-choice, energy-system costs, emissions, and pollution damages in the United States. We report substantial policy spillover: Both LAP and CO₂ taxes cause similar levels of decarbonization. Under LAP taxes, decarbonization was a result of an increase in natural gas generation and a near-complete phaseout of coal generation in the electric sector. Under a CO₂ tax, the majority of simulated decarbonization was a result of increased electric generation from wind and solar. We also found that the timing of the CO₂ and LAP taxes was important. When we simulated a LAP tax beginning in 2015 and waited until 2025 to introduce a CO₂ tax, the electric sector was locked into higher levels of natural gas generation and cumulative 2010–2035 energy system CO₂ emissions were 8.8 billion tons higher than when the taxes were implemented simultaneously. A scenario taxing CO₂ and LAPs simultaneously beginning in 2015 produced the highest net benefits, as opposed to scenarios that target either CO₂ or LAPs, or scenarios that delayed either LAP or CO₂ taxes until 2025. Lastly, we found that net benefits compared to business as usual are higher under a regional versus a national LAP-tax regime, but that efficiency gains under the regional tax are not substantially higher than those under the national LAP-tax policy.

While China is building nuclear reactors faster than any other country in the world, major constraints may limit nuclear energy’s ability to grow to the scale of hundreds of gigawatts that would be required for it to play a major part in decarbonizing China’s energy system. This chapter explores the major constraints on, and risks of, large-scale nuclear energy growth in China, and how both new policies and new technologies might address them. It focuses particularly on the two biggest constraints – economics and siting. Substantial government policies to support nuclear power and advanced reactor systems designed to address some of the key constraints are both likely to be needed for nuclear to have a chance of playing a major role in decarbonizing China’s energy system; nuclear energy’s role may be bigger in the second half of this century than in the first half.

Cop26 — the 26th Conference of the Parties — is a UN meeting that will bring together representatives of more than 190 countries with only one aim: tackling climate change.

In the World Nuclear Industry Status Report 2021, Mariana Budjeryn writes about the Chernobyl disaster 35 years later, while Ali Ahmad writes about nuclear power and climate change resilience.

Mr. Biden said he would name Mr. Kerry to a cabinet-level climate post, a role laden with political and diplomatic challenges in the post-Trump era. 

Ambassador Nicholas Burns speaks about the "devastating portrait" of President Trump's leadership during the COVID-19 pandemic. 

Director of the Project on Europe and the Transatlantic Relationship and the Future of Diplomacy Project, Cathryn Clüver Ashbrook speaks to WDR 5 on micro-multilateralism and the impact of urban diplomacy global diplomacy, particularly on climate change.

The study of existential risk — the risk of human extinction or the collapse of human civilization — has only recently emerged as an integrated field of research, and yet an overwhelming volume of relevant research has already been published. To provide an evidence base for policy and risk analysis, this research should be systematically reviewed. In a systematic review, one of many time-consuming tasks is to read the titles and abstracts of research publications, to see if they meet the inclusion criteria. The authors show how this task can be shared between multiple people (using crowdsourcing) and partially automated (using machine learning), as methods of handling an overwhelming volume of research.