UC Berkeley

Evaluation of the potential economic impacts of climate change are crucial for informed policy responses. Several decades of rigorous research devoted to this topic have advanced the field enormously, and still there remain areas of active debate and research seeking to address open questions and shortcomings of existing work. I seek to address critical limitations and lines of inquiry through three primary projects. Through these three projects I tackle aspects of two challenges. First, a lack of usable domain-specific computational tools can slow climate research, discourage collaboration, and pose barriers to transparency. Second, policymakers must design climate policy in the face of substantial, sometimes unresolvable uncertainty along several dimensions. A fourth, concurrent effort, the development of the Mimi computational platform for integrated assessment modeling, is a vital contribution to the computational infrastructure that enables my own research as well as that of the broader research and policymaking communities.

Computational modeling and analysis are at the core of the research posed in both Chapters 2 and 3, and calls for new open-source, easy-to-use computational tools to promote research, transparency, and collaboration echo those across modern scientific and interdisciplinary domains. As a core developer of the Mimi platform , I produce high-quality open-source software aimed at supporting collaboration and making it possible for researchers, including myself, to investigate new research questions and more effectively support policy makers. A multitude of academic groups across several universities use the platform, and it also directly supports federal and state-level regulatory analysis. Mimi is the subject of the project described Chapter 1.

In Chapter 1 we employ both observation and semi-structured interviews to learn about designing software in climate change economics domain and gather generalizable insights about the design of embedded domain specific languages. Programming tools are increasingly integral to research and analysis in myriad domains, including specialized areas with no formal relation to computer science. Embedded domain-specific languages (eDSLs) have the potential to serve these programmers while placing relatively light implementation burdens on language designers. However, barriers to eDSL use reduce their practical value and adoption. In this project, we aim to deepen our understanding of how programmers use eDSLs and identify user needs to inform future eDSL designs. We performed a contextual inquiry (9 participants) with domain experts using Mimi, an eDSL for climate change economics modeling. A thematic analysis identified five key themes, including: the interaction between the eDSL and the host language has significant and sometimes unexpected impacts on eDSL user experience, and users preferentially engage with domain-specific communities and code templates rather than host language resources. The needs uncovered in our study offer design considerations for future eDSLs and suggest directions for future DSL usability research.

The social cost of carbon dioxide (SC-CO2) measures the monetized value of the damages to society caused by an incremental metric tonne of CO2 emissions and is a key metric informing climate policy. Used by governments and other decision-makers in benefit-cost analysis for over a decade, SC-CO2 estimates draw on climate science, economics, demography, and other disciplines. However, a 2017 report by the US National Academies of Sciences, Engineering, and Medicine (NASEM) highlighted that current SC-CO2 estimates no longer reflect the latest research. The report provided a series of recommendations for improving the scientific basis, transparency, and uncertainty characterization of SC-CO2 estimates. In Chapter 2 we show that improved probabilistic socioeconomic projections, climate models, damage functions, and discounting methods that collectively reflect theoretically consistent valuation of risk, substantially increase estimates of the SC-CO2. Our preferred mean SC-CO2 estimate is $185 per tonne of CO2 ($44-413/t-CO2: 5-95% range, 2020 US dollars) at a near-term risk-free discount rate of 2 percent, a value 3.6-times higher than the US government’s current value of $51/t-CO2. Our estimates incorporate updated scientific understanding throughout all components of SC-CO2 estimation in the new open-source GIVE model, in a manner fully responsive to the near-term NASEM recommendations. Our higher SC-CO2 values, compared to estimates currently used in policy evaluation, substantially increase the estimated benefits of greenhouse gas mitigation and thereby increase the expected net benefits of more stringent climate policies.

Evaluating the economic impacts of climate change is crucial to inform climate policy. One typical approach to assessing mitigation policy options uses integrated climate-economy models to analyze tradeoffs between the costs of reducing greenhouse gas emissions and the benefits of avoiding climate damages. However, the economic impacts of climate change are both expansive and deeply uncertain--adding a hurdle to designing tractable climate policy. Analysis using integrated climate-economy models thus often fails to fully represent the downside risk--what if the chosen model(s) are wrong? The deep uncertainty characterizing these models poses challenges for policymakers. In Chapter 3 we address this challenge using a robust decision-making framework to evaluate mitigation policy. We show that a shift from a decision framework that maximizes expected outcomes to one that is averse to regret supports precaution in the face of uncertainty and faster emissions cuts than currently implemented. Uncertainties about socioeconomic trajectories and the magnitude and functional form of climate damages create asymmetric consequences from delayed or weak mitigation policy.