Difference between revisions of "Public Policy and Decision Science"

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(First draft of this page, some TODOs remain)
(Updated contextualization and explanations)
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'''''TODO: add context regarding contribution to emissions, connection to ML, and selected readings (summarize)'''''
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=== Policy-making and decision science ===
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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 source and provide new techniques for working with models commonly used by decision-makers (e.g. integrated assessment models, multi-objective optimization, etc.) ML can also help retroactively, by analyzing the text of existing policies and by performing causal inference on historical data.
  
'''Decision + Policy'''
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=== '''Carbon Markets''' ===
 
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Generally speaking, carbon markets aim to reduce GHG emissions by setting limits on emissions and enabling the trading of emission units, which represent emission reductions. ML can help predict prices in carbon markets and analyze the main drivers of these prices to improve their efficiency. In terms of the design of market-based strategies, such as carbon tax or cap-and-trade programs, 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.
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).
 
 
 
Decision-makers often construct mathematical models to help them assess or trade off between different policy alternatives. ML can help provide new techniques for working with integrated assessment models, multi-objective optimization, and other models commonly used by decision-makers.
 
 
 
When creating new policies, decision-makers may wish to understand previous policies and analyze how these policies performed. ML can help on both fronts by analyzing the text of existing policies and by performing causal inference on historical data.
 
 
 
'''Markets'''
 
 
 
Carbon pricing and other market-based measures can incentivize the reduction of greenhouse gas emissions. ML can help predict prices in carbon markets and analyze the main drivers of these prices.
 
 
 
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 ==
 
== Data ==
'''''TODO: Intro Paragraph'''''
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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]
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* [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)]
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* [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]
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* [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] (European Union Emissions Trading System (EU ETS) data from EUTL)
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* [http://datahub.io/core/eu-emissions-trading-system CO2 “price” in European ETS]: Data about the European Union Emissions Trading System (ETS), coming mainly from the EU Transaction Log.
* [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)
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* [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)
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* [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 ==
 
== Methods and Software ==
'''''TODO: Intro par'''''
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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]
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* [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:
 
* Python packages for multi-objective optimization:
** [https://projects.g-node.org/emoo/ Evolutionary Multi-Objective Optimization (EMOO)]  
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**[https://projects.g-node.org/emoo/ Evolutionary Multi-Objective Optimization (EMOO)]
** [https://platypus.readthedocs.io/ Platypus - Multiobjective Optimization in Python]: Platypus is a framework for evolutionary computing in Python with a focus on multiobjective evolutionary algorithms (MOEAs), providing optimization algorithms and analysis tools for multiobjective optimization.
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** [https://platypus.readthedocs.io/ Platypus - Multi-objective Optimization in Python]: Platypus is a framework for evolutionary computing in Python with a focus on multiobjective evolutionary algorithms (MOEAs), providing optimization algorithms and analysis tools for multiobjective optimization.
* [https://www.mathworks.com/discovery/multiobjective-optimization.html Matlab package for multi-objective optimization]
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* [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.
  
<br />
 
 
== Recommended Readings ==
 
== Recommended Readings ==
  

Revision as of 15:28, 25 August 2020

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 source and provide new techniques for working with models commonly used by decision-makers (e.g. integrated assessment models, multi-objective optimization, etc.) ML can also help retroactively, by analyzing the text of existing policies and by performing causal inference on historical data.

Carbon Markets

Generally speaking, carbon markets aim to reduce GHG emissions by setting limits on emissions and enabling the trading of emission units, which represent emission reductions. ML can help predict prices in carbon markets and analyze the main drivers of these prices to improve their efficiency. In terms of the design of market-based strategies, such as carbon tax or cap-and-trade programs, 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

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:

Recommended Readings

General

Policy Design

Markets and Pricing

Community

Journals and conferences

Societies and organizations

Past and upcoming events

Important considerations

Next steps

References