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Valuing the Global Mortality Consequences of Climate Change Accounting for Adaptation Costs and Benefits
The Quarterly Journal of Economics
This paper estimates that the release of an additional ton of carbon dioxide today will cause mean damages to global mortality risk valued at $36.6 under a high emissions scenario and $17.1 under a moderate scenario, using a 2% discount rate that is justified by US Treasury rates over the last two decades. It is a core input to the Climate Impact Lab's Data-driven Spatial Climate Impact Model (DSCIM).

Published November 1, 2022

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Data-driven Spatial Climate Impact Model User Manual, Version 092023-EPA
The Climate Impact Lab (CIL) has developed the Data-driven Spatial Climate Impact Model (DSCIM), a robust, empirically-based model for estimating SCGHGs that is grounded in the best available science and economics and is consistent with recommendations set out by the National Academies of Sciences (NASEM). The theory, framework, and implementation of the CIL’s complete approach has been peer-reviewed and is published in Nature and The Quarterly Journal of Economics, with many technical elements, including the construction of empirical damage functions and valuation of uncertain and unequal local impacts, published in our earlier 2017 Science article. In this user manual, we provide an overview of the key components of an implementation of DSCIM, referred to as DSCIM-EPA, for the U.S. Environmental Protection Agency’s September 2022 draft technical report, "Report on the Social Cost of Greenhouse Gases: Estimates Incorporating Recent Scientific Advances."

Published October 2, 2023

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Mandatory disclosure would reveal corporate carbon damages
The US Securities and Exchange Commission recently proposed a rule that would mandate that public companies report their greenhouse gas (GHG) emissions. One rationale is that disclosure will provide information on material risks to investors, making it evident which firms are most exposed to future climate policies. In addition, some believe that reporting will galvanize pressure from companies’ key stakeholders (e.g., customers and employees), leading them to voluntarily reduce their emissions. But what might such disclosure reveal? We provide a first-cut preview of what we might learn about the climate damages caused by each company’s GHG emissions by drawing on one of the largest global datasets, which covers roughly 15,000 public companies.

Published August 24, 2023

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DSCIM-Coastal v1.1: An open-source modeling platform for global impacts of sea level rise
European Geosciences Union
Sea level rise may impose substantial economic costs to coastal communities worldwide, but characterizing its global impact remains challenging because SLR costs depend heavily on natural characteristics and human investments at each location – including topography, the spatial distribution of assets, and local adaptation decisions. To date, several impact models have been developed to estimate the global costs of SLR. Yet, the limited availability of open-source and modular platforms that easily ingest up-to-date socioeconomic and physical data sources restricts the ability of existing systems to incorporate new insights transparently. In this paper, we present a modular, open-source platform designed to address this need, providing end-to-end transparency from global input data to a scalable least-cost optimization framework that estimates adaptation and net SLR costs for nearly 10 000 global coastline segments and administrative regions.

Published July 31, 2023

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Communicating future sea-level rise uncertainty and ambiguity to assessment users
Nature Climate Change
Future sea-level change is characterized by both quantifiable and unquantifiable uncertainties. Effective communication of both types of uncertainty is a key challenge in translating sea-level science to inform long-term coastal planning. This article reviews how past IPCC and regional assessments have presented sea-level projection uncertainty, how IPCC presentations have been interpreted by regional assessments and how regional assessments and policy guidance simplify projections for practical use.

Published June 19, 2023

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Global downscaled projections for climate impacts research (GDPCIR): Preserving extremes for modeling future climate impacts
Global climate models are important tools for understanding the climate system and how it is projected to evolve under scenario-driven emissions pathways. Their output is widely used in climate impacts research for modeling the current and future effects of climate change. However, climate model output remains coarse in relation to the high-resolution climate data needed for climate impacts studies, and it also exhibits biases relative to observational data. Treatment of the distribution tails is a key challenge in existing downscaled climate datasets available at a global scale; many of these datasets used quantile mapping techniques that were known to dampen or amplify trends in the tails. In this study, we apply the trend-preserving Quantile Delta Mapping (QDM) bias-adjustment method (Cannon et al., 2015) and develop a new downscaling method called the Quantile-Preserving Localized-Analog Downscaling (QPLAD) method that also preserves trends in the distribution tails. The output dataset of this study is the Global Downscaled Projections for Climate Impacts Research (GDPCIR), a global, daily, 0.25° horizontal-resolution product which is publicly hosted on Microsoft AI for Earth’s Planetary Computer.

Published January 16, 2023

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