Energy Transition

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.

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.

Abstract: This paper proposes a methodology for quantifying the climate-related transition impacts on energy-intensive companies. In this study, we use a publicly available dataset created by the Bank of Canada that combines the scenarios developed by the MIT Economic Projection and Policy Analysis (EPPA) model with the results from two macroeconomic models (ToTEM and BoC-GEM-Fin) to illustrate price and production patterns for 10 emission-intensive sectors across 8 aggregated regions. Our focus lies on mapping the trajectories of future sectoral revenues and operating expenditures (direct and indirect costs) to company-level impacts. We align these indicators with the top-down approach used by the European Central Bank to measure issuer-specific exposure to transition risk. By incorporating company-level data, such as revenues in sub-activities and direct emissions, we are able to compute issuer-level financial statements that are particularly relevant to define scenario- based equity valuation ratio and corporate credit risk. By examining the narrative established by the Network for Greening the Financial System (NGFS) – which includes current policies, nationally determined contributions, net-zero targets, staying below 2°C, and delayed transition – we assess the added value of employing such models for asset allocation. The conclusions drawn from our case study analysis suggest a significant heterogeneity within sectors and demonstrate that the diversification of corporate revenues in sub-activities leads to distinct valuation patterns.

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