The next generation of the PRIMAP climate policy analysis suite
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Updated
Mar 30, 2026 - Python
The next generation of the PRIMAP climate policy analysis suite
An agent-based model to evaluate and compare the performance of an emission tax and permit market under heuristic behavior, heterogeneity, and dynamic markets.
Tutorial notebook of the pyam package
Open Course on Energy and Climate Policy.
Deep Policy Lab (Deep Energy and Climate Policy Lab): Data-driven, Evidence-based, Energy and Climate, Policy Research
Report of industry and agriculture greenhouse gas emissions from energy use (fuel combustion or electricity use) in a user-selected region of New York State (economic development regions), or for the state as a whole. Data are for years 2010-2016; primary data source is the NREL Industrial Energy Data Book (IEDB).
An agent-based model to investigate how differing climate policies amongst countries influence firm-level microeconomic behaviour.
Multi-sector climate pathway calculation engine for the Transition Compass platform
Interactive Streamlit dashboard for Mexico's GHG decarbonization scenario analysis (2020–2050). Four scenarios × 8 sectors with Monte Carlo uncertainty bands and DMDU robustness table.
UC Berkeley EEP 147
Work-in-progress repository for the Climate Policy Design Dataset: domestic consumer climate subsidies & regulations (1990–2022)
This project studies how European banks adjust their balance sheets and capital ratios in response to climate transition policies. Rather than estimating causal effects, the focus is on mechanisms and dynamics that is capital buffers, asset growth, leverage, and profitability.
This repository collects my work for the summative assessment of the "Comparative Political Economy of Advanced Democracies" module, as taught in the academic year 2023/2024. In the paper, I examine the effect of corporatism on climate policy.
At Climate Policy Radar, we’re building an open-source knowledge graph for climate policy. Using an ontology defined by climate policy experts, we create a set of machine learning models to highlight where each concept is mentioned in a comprehensive dataset of the world's climate laws, policies, and related documents.
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