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=== Policy-making and decision science ===
When creating policies, decision-makers must often negotiate fundamental uncertainties in the underlying data and construct mathematical models to help them assess or trade off between different policy alternatives. ML can help alleviate some of this uncertainty by extracting information from satellite imagery, sensors, social media posts, policy documents, and other
=== '''
▲When creating policies, decision-makers must often negotiate fundamental uncertainties in the underlying data. ML can help alleviate some of this uncertainty by extracting information from satellite imagery, sensors, social media posts, policy documents, and other sources (as detailed elsewhere in the paper).
▲When designing market-based strategies, it is necessary to understand how effectively each strategy will reduce emissions, as well as how the underlying socio-technical system may be affected. ML can help assess the outcomes of market-based strategies to ensure they are effective and equitable.
== Data ==
There are several sources of data at various global, regional, and national levels, all of which are useful for modeling the impact of policies, as well as markets
* [https://www.climatesmartplanning.org/data.html World Bank ClimateSmart data portal]: focuses on the needs of practitioners working in developing countries on low-emission development and climate resilient projects.
* [https://sedac.ciesin.columbia.edu/data/set/entri-treaty-status-2012 Environmental Treaty Status Data Set, 2012 Release (1940–2012)]: provides information on the status of country participation in international environmental agreements.
* [https://www.cgdev.org/publication/dataset-vulnerability-climate-change Vulnerability to Climate Change Dataset]: quantifies the vulnerability of 233 countries to three major effects of climate change (weather-related disasters, sea-level rise, and reduced agricultural productivity).
* [http://datahub.io/core/eu-emissions-trading-system CO2 “price” in European ETS]: Data about the
* [https://sedac.ciesin.columbia.edu/data/set/ipcc-socio-economic-baseline IPCC Socio-Economic Baseline Data, v1] (1980, 1990, 1991, 1992, 1993, 1994, 1995, 2025): dataset for the evaluation of climate change impact curated by the Intergovernmental Panel on Climate Change (IPCC)
* [https://sedac.ciesin.columbia.edu/data/set/ipcc-ar4-observed-climate-impacts IPCC Fourth Assessment Report (AR4) Observed Climate Change Impacts, v1 (]1970–2004): database with observed responses to climate change for multidisciplinary studies curated by the IPCC.
== Methods and Software ==
Given the importance of representing the impacts of decision-making and market-based strategies, interactive simulation tools and packages for multi-objective optimization are particularly useful in this application. Some of these are listed below:
* [https://www.carbonpricingleadership.org/calendar/2019/1/18/carbonsim-edfs-carbon-market-simulation-tool Carbon market simulation tool]: demystifies how to develop and implement a carbon portfolio management strategy, and demonstrates that results are driven by design.
* Python packages for multi-objective optimization:
**
** [https://platypus.readthedocs.io/ Platypus -
* [https://www.mathworks.com/discovery/multiobjective-optimization.html Matlab package for multi-objective optimization]: Matlab package for multi-objective optimization, accompanied by tutorial videos and explanations.
== Recommended Readings ==
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