Public Policy and Decision Science: Difference between revisions
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''This page is about the intersection of policy-making and machine learning. For an overview of policy-making and decision science, please see the [https://en.wikipedia.org/wiki/Policy Wikipedia page] on this topic.''▼
▲''This page is about the intersection of policy-making and machine learning. For an overview of policy-making and decision science, please see the [https://en.wikipedia.org/wiki/Policy Wikipedia page] on this topic.''
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== Machine Learning Application Areas ==
=== Gathering decision-relevant data ===
* [[Data management, cleaning and imputation|'''Data management, cleaning and imputation''']]: ML can help with cleaning, merging and completing datasets that are relevant for policy-making.
*'''[[Remote Sensing|Remote sensing]]''': Satellite data can provide a lot of valuable information to policy makers, for example by helping map infrastructure, land use and ecosystem health. The field of remote sensing has seen large improvements with ML.
* '''[[Computational text analysis]]:''' Text documents are an important source of information to inform climate policy. Natural language processing can help to analyze large corpora of text.
=== Decision science ===
* '''Decision science'''
* [[Multi-criteria decision-making|'''Multi-criteria decision-making''']]: Multi-criteria decision-making can also help policy-makers manage trade-offs between different policies. Computational approaches and machine learning can help with finding solutions to these optimization problems.
* '''[[Agent-based modeling]]:''' ABMs are used in simulating the actions and interactions of agents in their environment, and ML can help integrate data-driven insights into these models, for example by learning rules or models for agents based on observational data.
* '''Energy system modeling'''
* '''Urban planning:''' With new techniques such as [[surrogate modeling]], ML can help with complex planning tasks.
* '''[[Power System Planning]]:''' Algorithms for planning new low-carbon energy infrastructure are often large and slow. ML can help speed up or provide proxies for these algorithms.
* '''[[Integrated Assessment Models (IAMs)|Integrated assessment models]]:''' IAMs are large simulations used to explore future societal pathways that are consistent with climate goals, which can be improved with ML.
=== Ex-post policy analysis ===
* '''[[Causal inference with machine learning]]:''' To analyze whether a policy intervention has had the desired effect, causal inference is an important tool.
== Background Readings ==
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*'''Resources for Effective Climate Decisions. (Ch. 4) Informing an Effective Response to Climate Change (2010''')<ref>{{Cite book|url=https://www.nap.edu/catalog/12784/informing-an-effective-response-to-climate-change|title=Informing an Effective Response to Climate Change|last=Council|first=National Research|date=2010-07-21|isbn=978-0-309-14594-7|language=en}}</ref>: A chapter from a report published after a series of five coordinated activities convened by the National Research Council in response to a request from Congress. [https://www.nap.edu/read/12784/chapter/6#126 Available here.]
*'''Social, Economic, and Ethical Concepts and Methods. (Ch. 3). (2014)'''<ref>{{Citation|title=Social, Economic, and Ethical Concepts and Methods|url=http://dx.doi.org/10.1017/cbo9781107415416.009|publisher=Cambridge University Press}}</ref> : a report by the Intergovernmental Panel on Climate Change (IPCC) regarding the social and economic aspects of climate change. [https://www.ipcc.ch/site/assets/uploads/2018/02/ipcc_wg3_ar5_chapter3.pdf Available here.]
*'''Climate Change Policies (2016)'''<ref>{{Cite journal|last=Del Río|first=Pablo|title=Climate Change Policies and New Technologies|url=http://dx.doi.org/10.4337/9781781000885.00016|journal=Climate Change Policies|doi=10.4337/9781781000885.00016}}</ref>''':''' A report by the European Environmental Agency on defining successful, impactful policies for climate change. [https://www.eea.europa.eu/themes/climate/policy-context Available here.]
*
▲==== Markets and Pricing ====
== Online Courses and Course Materials ==
===
* '''Theory and Practice in Policy Analysis: Including Applications in Science and Technology (Morgan, 2017)'''<ref>{{Cite book|url=http://ebooks.cambridge.org/ref/id/CBO9781316882665|title=Theory and Practice in Policy Analysis: Including Applications in Science and Technology|last=Morgan|first=Granger|date=2017|publisher=Cambridge University Press|isbn=978-1-316-88266-5|location=Cambridge|doi=10.1017/9781316882665}}</ref>''':''' A rich resource for teaching classes on policy analysis with a focus on science and technology. [https://www.cambridge.org/core/books/theory-and-practice-in-policy-analysis/1EF075A251F55FFD9F1FA04A5887EF72 Available here.]
*'''Policy Analysis: Concepts and Practice (Weimer & Vining, 2017)<ref>Weimer DL, Vining AR. Policy analysis: Concepts and practice. Taylor & Francis; 2017 Mar 31.</ref>:''' Essential primer on policy analysis. [https://www.routledge.com/Policy-Analysis-Concepts-and-Practice/Weimer-Vining/p/book/9781138216518?gclid=CjwKCAiA4o79BRBvEiwAjteoYGkhl-kZ5nOz4RQwn5GEMDdU5JasYA2EVOLMgHpJDA1Yx07EJfvMUBoCT2IQAvD_BwE Available here.]
== Conferences, Journals, and Professional Organizations ==
=== Major conferences ===
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*'''[https://dataforpolicy.org Data for Policy]''': A global forum for interdisciplinary and cross-sector discussions around the impact and potentials of the digital revolution in the government sector (international conference, UK-based).
*'''[https://www.iaee.org/en/conferences/ International Association for Energy Economics (IAEE) Conferences:]''' Main venue for academic and professional energy analyst, with a strong policy focus (international and regional).
*'''[https://www.appam.org/events/ Association for Public Policy Analysis and Management (APPAM) Conferences]:''' Conferences and events dedicated to improving public policy and management (international and regional).
*[https://www.ippapublicpolicy.org '''International Conference on Public Policy (ICPP):''']
=== Major societies and organizations ===
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*'''[https://www.iaee.org International Association for Energy Economics (IAEE)]:''' A major society for academic and professional energy analyst, with a strong policy focus.
*'''[https://www.appam.org/about-appam/general-info/ Association for Public Policy Analysis and Management (APPAM)]:''' Society dedicated to improving public policy and management by fostering excellence in research, analysis, and education.
*'''[https://www.ippapublicpolicy.org International Public Policy Association (IPPA)]:''' A non-profit organization with the aim of promoting scientific research in the field of Public Policy, and to contribute to its international development.
== Libraries and Tools ==
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:
* Python packages for multi-objective optimization:
**[https://projects.g-node.org/emoo/ Evolutionary Multi-Objective Optimization (EMOO)]
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== 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
=== Climate policy databases ===
* [https://www.iea.org/policies Policies database of the International Energy Agency (IEA)]: one of the largest international climate and energy policy databases, integrating the IEA/IRENA Renewable Energy Policies and Measures Database, the IEA Energy Efficiency Database, the Addressing Climate Change database, and the Building Energy Efficiency Policies (BEEP) database.
* [https://newclimate.org/portfolio/climate-policy-database/ Climate Policy Database of the New Climate Institute]: covering policies from top 30 emitting countries which cover 82% of global GHG emission.
*[https://www.dsireusa.org Database of State Incentives for Renewables & Efficiency (DSIRE)]: US subnational climate policy database.
*[https://www.nature.com/articles/s41597-020-00682-0?utm_source=feedburner&utm_medium=feed&utm_campaign=Feed%3A+sdata%2Frss%2Fcurrent+%28Scientific+Data%29 ClimActor, harmonized transnational data on climate network participation by city and regional governments]: includes more than 10,000 city and regional governments.
*
=== Climate change impacts and adaptation ===
* [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).
=== Carbon price data ===
* [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): dataset for the evaluation of climate change impact curated by the Intergovernmental Panel on Climate Change (IPCC)▼
=== Data by or relevant to international organizations ===
* [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.▼
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*[https://data.oecd.org OECD Data]: The OECD publishes many economic and social indicators that are relevant for climate policy.
== References ==
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