JP

Estimation of the atmospheric hydroxyl radical oxidative capacity using multiple hydrofluorocarbons (HFCs)

Abstract: The hydroxyl radical (OH) largely determines the atmosphere’s oxidative capacity and, thus, the lifetimes of numerous trace gases, including methane (CH4). Hitherto, observation-based approaches for estimating the atmospheric oxidative capacity have primarily relied on using methyl chloroform (MCF), but as the atmospheric abundance of MCF has declined, the uncertainties associated with this method have increased. In this study, we examine the use of five hydrofluorocarbons (HFCs) (HFC-134a, HFC-152a, HFC-365mfc, HFC-245fa and HFC-32) in multi-species inversions, which assimilate three HFCs simultaneously, as an alternative method to estimate atmospheric OH.

We find robust estimates of OH regardless of which combination of three HFCs are used in the inversions. Our results show that OH has remained fairly stable during our study period from 2004 to 2021, with variations of <2 % and no significant trend. Inversions including HFC-32 and HFC-152a (the shortest-lived species) indicate a small reduction in OH in 2020 (1.6 % ± 0.9 % relative to the mean over 2004–2021 and 0.6 ± 0.9 % lower than in 2019), but considering all inversions, the reduction was only 0.5 ± 1.1 % and OH was at a similar level to that in 2019.

A systems analysis of sustainability impacts of agricultural policies in India

Abstract

We apply a systems framework for analyzing the overall sustainability impacts of interventions to a case of the rice-wheat cropping system of Punjab (India), where agricultural practices lead to air pollution-related health impacts, over-exploitation of groundwater, over-use of fertilizers and reduced local crop diversity. We use this case to quantify how varying degrees of change in interventions result in sustainability impacts using an inclusive wealth-based approach.

We show that either improving the existing cropping system or inducing fundamental changes in the cropping system, can lead to substantial and wide-ranging sustainability benefits. We also show that interventions that improve human health show the largest quantitative benefit due to the assumed high marginal value of human life. Accurate localized estimates of marginal values of stocks are needed for estimating overall sustainability impacts.  

Key Points

  1. We apply a systems framework for analyzing policy interventions to the rice-wheat cropping system of Punjab (India). 
  2. We quantify the sustainability impacts of interventions involving varying degrees of change in the system using an inclusive weath-based approach.
  3. We show how policy-induced changes can lead to substantial and wide-ranging sustainability benefits.

Plain Language Summary

We use a systems-based approach for studying air pollution as a challenge embedded in a broader network of sustainability issues, and analyze the cross-sectoral impacts of policy interventions. We use the rice-wheat cropping system in Punjab, India, as a case study, since agricultural practices in this system are associated with a number of inter-linked sustainability challenges such as air pollution-related health impacts, over-exploitation of groundwater, over-use of fertilizers and reduced local crop diversity. We analyze the sustainability impacts of varying degrees of policy-induced change in this system and show that both incremental and fundamental changes can lead to wide-ranging sustainability benefits.

Phytoplankton thermal trait parameterization alters community structure and biogeochemical processes in a modeled ocean

Abstract: Phytoplankton exhibit diverse physiological responses to temperature which influence their fitness in the environment and consequently alter their community structure. Here, we explored the sensitivity of phytoplankton community structure to thermal response parameterization in a modelled marine phytoplankton community. Using published empirical data, we evaluated the maximum thermal growth rates (µmax) and temperature coefficients (Q10; the rate at which growth scales with temperature) of six key Phytoplankton Functional Types (PFTs): coccolithophores, cyanobacteria, diatoms, diazotrophs, dinoflagellates, and green algae. Following three well-documented methods, PFTs were either assumed to have (1) the same µmax and the same Q10 (as in to Eppley, 1972) (2) a unique µmax but the same Q10 (similar to Kremer et al. 2017) or (3) a unique µmax and a unique Q10 (following Anderson et al. 2021). These trait values were then implemented within the MIT biogeochemistry and ecosystem model (called Darwin) for each PFT under a control and climate change scenario.

Our results suggest that applying a µmax and Q10 universally across PFTs (as in Eppley, 1972) leads to unrealistic phytoplankton communities, which lack diatoms globally. Additionally, we find that accounting for differences in the Q10 between PFTs can significantly impact each PFT’s competitive ability, especially at high latitudes, leading to altered modeled phytoplankton community structures in our control and climate change simulations. This then impacts estimates of biogeochemical processes, with, for example, estimates of export production varying by ~10% in the Southern Ocean depending on the parameterization.

Our results indicate that the diversity of thermal response traits in phytoplankton not only shape community composition in the contemporary and future, warmer ocean, but that these traits have significant feedbacks on global biogeochemical cycles.
 

Deploying direct air capture at scale: how close to reality?

Abstract: The role of negative emissions in achieving deep decarbonization targets has been demonstrated through Integrated Assessment Models (IAMs). While many studies have focused on bioenergy with carbon capture and storage (BECCS), relatively little attention has been given to direct air capture (DAC) in IAMs beyond assessing the role of low-cost DAC with carbon storage (DACCS). In this study, we employ an economywide model to more fully explore the potential role of DAC, considering the full range of cost estimates ($180-$1,000/tCO2), DAC units supplied by either dedicated renewables or grid electricity, and both the storage of captured CO2 (DACCS) or its utilization (DACCU) to produce fuels.

Our results show that the deployment of DAC is driven by its cost and is dominated by DACCS, with little deployment of DACCU. We analyze the technical and policy conditions making DACCS compete with BECCS, investigating scenarios in which BECCS is limited and there is no emissions trading across countries. With an international emissions trading system (ETS), we find that Africa takes advantage of its large and cheap renewable potential to export emissions permits and contributes more than half of total global negative emissions through DAC. However, DAC also proves essential when no ETS is available, particularly in Asian countries due to scarce and expensive access to land and bioenergy.

Our analysis provides a comprehensive evaluation of the impact of DAC on the power system, economy, and land use.

Highlights

  • The deployment of DAC should be discussed relative to its cost.

  • DAC is deployed at scale at a cost lower than $400/tCO2 in our baseline.

  • Limiting BECCS and international emissions trading increases DAC deployment.

  • DAC stresses the power sector and land use locally but provides economic benefits.

     

Power sector impacts of the Inflation Reduction Act of 2022

Abstract: The Inflation Reduction Act (IRA) is regarded as the most prominent piece of federal climate legislation in the U.S. thus far. This paper investigates potential impacts of IRA on the power sector, which is the focus of many core IRA provisions. We summarize a multi-model comparison of IRA to identify robust findings and variation in power sector investments, emissions, and costs across 11 models of the U.S. energy system and electricity sector.

Our results project that IRA incentives accelerate the deployment of low-emitting capacity, increasing average annual additions by up to 3.2 times current levels through 2035. CO2 emissions reductions from electricity generation across models range from 47%–83% below 2005 in 2030 (68% average) and 66%–87% in 2035 (78% average).

Our higher clean electricity deployment and lower emissions under IRA, compared with earlier U.S. modeling, change the baseline for future policymaking and analysis. IRA helps to bring projected U.S. power sector and economy-wide emissions closer to near-term climate targets; however, no models indicate that these targets will be met with IRA alone, which suggests that additional policies, incentives, and private sector actions are needed.

KEY INSIGHTS
• Power sector CO2 emissions could drop 66-87% by 2035 with IRA from 2005 (compared with 39-68% without IRA).
• IRA could accelerate clean electricity deployment, including 1.4-6.2 times current installed wind and solar capacity by 2035.
• Low-emitting generation shares—including renewables, nuclear, and carbon capture—in 2035 range from 59-89% with IRA, compared with 46-74% without IRA.
• Total fiscal costs of IRA's power sector provisions could range from $240-960 billion through 2035. Energy costs could be $73-370 per household per year lower by 2035 with IRA.

Compounding uncertainties in economic and population growth 1 increase tail risks for relevant outcomes across sectors

Abstract: Understanding the long-term effects of population and GDP changes requires a multisectoral and regional understanding of the coupled human-Earth system, as the long-term evolution of this coupled system is influenced by human decisions and the Earth system. This study investigates the impact of compounding economic and population growth uncertainties on long-term multisectoral outcomes. We use the Global Change Analysis Model (GCAM) to explore the influence of compounding and feedback between future GDP and population growth on four key sectors: final energy consumption, water withdrawal, staple food prices, and CO2 emissions.

The results show that uncertainties in GDP and population compound, resulting in a magnification of tail risks for outcomes across sectors and regions. Compounding uncertainties significantly impact metrics such as CO2 emissions and final energy consumption, particularly at the upper tail at both global and regional levels. However, the impact of staple food prices and water withdrawal depends on regional factors. Additionally, an alternative low-carbon transition scenario could compound uncertainties and increase tail risk, particularly in staple food prices, highlighting the influence of emergent constraints on land availability and food-energy competition for land use.

The findings underscore the importance of considering and adequately accounting for compounding uncertainties in key drivers of multisectoral systems to enhance our comprehensive understanding of the complex nature of multisectoral systems. The paper provides valuable insights into the potential implications of compounding uncertainties.

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