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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 Wikipedia page on this topic.

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 (ex-ante policy analysis). 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.). When deciding between different policy options, it is necessary to understand how effectively each strategy will reduce emissions, as well as how society may be affected. ML can also help retroactively, by analyzing the text of existing policies and by performing causal inference on historical data (ex-post policy analysis).

Machine Learning Application Areas[edit | edit source]

Gathering decision-relevant data[edit | edit source]

Data management, cleaning and imputation: ML can help with cleaning, merging and completing datasets that are relevant for policy-making.
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[edit | edit source]

Decision science
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.

Modeling and planning[edit | edit source]

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 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[edit | edit source]

Causal inference with machine learning: To analyze whether a policy intervention has had the desired effect, causal inference is an important tool.

Background Readings[edit | edit source]

General[edit | edit source]

Resources for Effective Climate Decisions. (Ch. 4) Informing an Effective Response to Climate Change (2010)^[1]: 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. Available here.
Social, Economic, and Ethical Concepts and Methods. (Ch. 3). (2014)^[2] : a report by the Intergovernmental Panel on Climate Change (IPCC) regarding the social and economic aspects of climate change. Available here.

Climate Change Policies (2016)^[3]: A report by the European Environmental Agency on defining successful, impactful policies for climate change. Available here.

Online Courses and Course Materials[edit | edit source]

Educational resources for policy analysis[edit | edit source]

Theory and Practice in Policy Analysis: Including Applications in Science and Technology (Morgan, 2017)^[4]: A rich resource for teaching classes on policy analysis with a focus on science and technology. Available here.
Policy Analysis: Concepts and Practice (Weimer & Vining, 2017)^[5]: Essential primer on policy analysis. Available here.

Conferences, Journals, and Professional Organizations[edit | edit source]

Major conferences[edit | edit source]

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).
International Association for Energy Economics (IAEE) Conferences: Main venue for academic and professional energy analyst, with a strong policy focus (international and regional).
Association for Public Policy Analysis and Management (APPAM) Conferences: Conferences and events dedicated to improving public policy and management (international and regional).
International Conference on Public Policy (ICPP):

Major societies and organizations[edit | edit source]

International Association for Energy Economics (IAEE): A major society for academic and professional energy analyst, with a strong policy focus.
Association for Public Policy Analysis and Management (APPAM): Society dedicated to improving public policy and management by fostering excellence in research, analysis, and education.
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[edit | edit source]

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:
- Evolutionary Multi-Objective Optimization (EMOO)
- 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.
Matlab package for multi-objective optimization: Matlab package for multi-objective optimization, accompanied by tutorial videos and explanations.

Data[edit | edit source]

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[edit | edit source]

Climate Policy Radar: an open resource of climate laws, policies and action plans from every national government. The database offers full text search of documents via the website.

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.
Climate Policy Database of the New Climate Institute: covering policies from top 30 emitting countries which cover 82% of global GHG emission.
Database of State Incentives for Renewables & Efficiency (DSIRE): US subnational climate policy database.
ClimActor, harmonized transnational data on climate network participation by city and regional governments: includes more than 10,000 city and regional governments.
Environmental Treaty Status Data Set, 2012 Release (1940–2012): provides information on the status of country participation in international environmental agreements.

Climate change impacts and adaptation[edit | edit source]

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[edit | edit source]

CO2 “price” in European ETS: Data about the European Union Emissions Trading System (ETS), coming mainly from the EU Transaction Log.

Data by or relevant to international organizations[edit | edit source]

World Bank ClimateSmart data portal: focuses on the needs of practitioners working in developing countries on low-emission development and climate resilient projects.
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)
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.
OECD Data: The OECD publishes many economic and social indicators that are relevant for climate policy.

References[edit | edit source]

↑ Council, National Research (2010-07-21). Informing an Effective Response to Climate Change. ISBN 978-0-309-14594-7.
↑ Social, Economic, and Ethical Concepts and Methods, Cambridge University Press
↑ Del Río, Pablo. "Climate Change Policies and New Technologies". Climate Change Policies. doi:10.4337/9781781000885.00016.
↑ Morgan, Granger (2017). Theory and Practice in Policy Analysis: Including Applications in Science and Technology. Cambridge: Cambridge University Press. doi:10.1017/9781316882665. ISBN 978-1-316-88266-5.
↑ Weimer DL, Vining AR. Policy analysis: Concepts and practice. Taylor & Francis; 2017 Mar 31.

[1] Council, National Research (2010-07-21). Informing an Effective Response to Climate Change. ISBN 978-0-309-14594-7.

[2] Social, Economic, and Ethical Concepts and Methods, Cambridge University Press

[3] Del Río, Pablo. "Climate Change Policies and New Technologies". Climate Change Policies. doi:10.4337/9781781000885.00016.

[4] Morgan, Granger (2017). Theory and Practice in Policy Analysis: Including Applications in Science and Technology. Cambridge: Cambridge University Press. doi:10.1017/9781316882665. ISBN 978-1-316-88266-5.

[5] Weimer DL, Vining AR. Policy analysis: Concepts and practice. Taylor & Francis; 2017 Mar 31.

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@@ Line 1: / Line 1: @@
+{{Stub}}
 ''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.''
@@ Line 7: / Line 9: @@
 == Machine Learning Application Areas ==
-=== Gathering decision-relevant data  ===
+=== 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.
-=== Decision science ===
+*'''[[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.
-=== Agent-based models ===
+=== Decision science ===
-=== Decision analysis  ===
+* '''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.
-=== Energy system modeling  ===
+=== Modeling and planning ===
+* '''[[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.
-=== Multi-criteria decision-making ===
+* '''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 ==
@@ Line 26: / Line 37: @@
 *'''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.]
-*'''Climate Change and Society: Approaches and Responses. (2011)'''<ref>{{Cite journal|last=Dryzek|first=John S.|last2=Norgaard|first2=Richard B.|last3=Schlosberg|first3=David|date=2011-08-18|title=Climate Change and Society: Approaches and Responses|url=http://dx.doi.org/10.1093/oxfordhb/9780199566600.003.0001|journal=Oxford Handbooks Online|doi=10.1093/oxfordhb/9780199566600.003.0001}}</ref>: a chapter from the Oxford Handbook of Climate Change and Society. [https://www.oxfordhandbooks.com/view/10.1093/oxfordhb/9780199566600.001.0001/oxfordhb-9780199566600-e-1 Available here.]
-*'''Assessment and Decision-making for Climate Change: An Overview of Four Approaches (2012):''' The second report of the West Kootenay Climate Vulnerability and Resilience Project. [https://www2.gov.bc.ca/assets/gov/environment/natural-resource-stewardship/nrs-climate-change/applied-science/holtreport2assess4approaches.pdf Available here.]
-*'''Social Capital, Collective Action, and Adaptation to Climate Change (2003)'''<ref>{{Citation|title=Social Capital, Collective Action, and Adaptation to Climate Change|url=http://dx.doi.org/10.1007/978-3-531-92258-4_19|publisher=VS Verlag für Sozialwissenschaften}}</ref>''':''' a review of the emerging perspectives of collective action and social capital. [https://www.crcresearch.org/files-crcresearch/File/adger_03.pdf 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.]
-==== Policy Making ====
-*'''From Science to Policy: Developing Responses to Climate Change. (Ch. 12). Climate Change and Human Health - Risks and Responses. (1996)'''<ref>{{Citation|title=The Politics of Climate Science|url=http://dx.doi.org/10.1007/978-94-015-8925-3_8|publisher=Springer Netherlands}}</ref>: a report by the World Health Organization on the risks to human health of developing responses to climate change. [https://www.who.int/globalchange/climate/en/chapter12.pdf Available here.]
-*'''Decision making under uncertainty in climate change mitigation: introducing multiple actor motivations, agency and influence (2018)'''<ref>{{Cite journal|last=Roelich|first=Katy|last2=Giesekam|first2=Jannik|date=2018-06-04|title=Decision making under uncertainty in climate change mitigation: introducing multiple actor motivations, agency and influence|url=http://dx.doi.org/10.1080/14693062.2018.1479238|journal=Climate Policy|volume=19|issue=2|pages=175–188|doi=10.1080/14693062.2018.1479238|issn=1469-3062}}</ref>: an article that presents an analysis that includes the perspectives and agency of multiple actors in climate change mitigation, through a case study of the UK construction sector. [https://www.tandfonline.com/doi/full/10.1080/14693062.2018.1479238 Available here.]
-*'''Decision Making and Climate Change Uncertainty: Setting the Foundations for Informed and Consistent Strategic Decisions. (2019)'''<ref>{{Cite book|title=Adapting Infrastructure to Climate Change|url=http://dx.doi.org/10.4324/9781315638973|publisher=Routledge|accessdate=2020-08-28}}</ref>''':''' an accessible explanation of how public officials, especially in low income countries, can address today's short- term pressing needs while preparing for tomorrow's climate-related impacts, written by the Climate Change Advisor of the Government of Quito, Chile. [https://www.wri.org/our-work/project/world-resources-report/decision-making-and-climate-change-uncertainty-setting 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 ====
-*'''Market Strategies for Climate Change (2004)'''<ref>{{Cite journal|last=Kolk|first=Ans|last2=Pinkse|first2=Jonatan|date=2004-06-01|title=Market Strategies for Climate Change|url=http://www.sciencedirect.com/science/article/pii/S0263237304000453|journal=European Management Journal|language=en|volume=22|issue=3|pages=304–314|doi=10.1016/j.emj.2004.04.011|issn=0263-2373}}</ref>''':''' examines current market responses, focusing on the drivers (threats and opportunities) and the actions being taken by companies to address climate change. [https://www.sciencedirect.com/science/article/abs/pii/S0263237304000453 Available here.]
-*'''The Critical Role of Markets in Climate Change Adaptation (2018)'''<ref>{{Cite journal|last=Anderson|first=Sarah|last2=Anderson|first2=Terry|last3=Hill|first3=Alice|last4=Kahn|first4=Matthew|last5=Kunreuther|first5=Howard|last6=Libecap|first6=Gary|last7=Mantripragada|first7=Hari|last8=Mérel|first8=Pierre|last9=Plantinga|first9=Andrew|last10=Smith|first10=V. Kerry|date=2018-05|title=The Critical Role of Markets in Climate Change Adaptation|url=http://www.nber.org/papers/w24645.pdf|language=en|location=Cambridge, MA|pages=w24645|doi=10.3386/w24645}}</ref>''':''' this paper summarizes and synthesizes the role of markets in facilitating climate change adaptation. It explains how market signals encourage adaptation through land markets. [https://www.nber.org/papers/w24645 Available here.]
-*'''Market-based strategies. (2019)'''<ref>{{Cite web|title=Market-Based Strategies|url=https://www.c2es.org/content/market-based-strategies/|website=Center for Climate and Energy Solutions|date=2020-06-13}}</ref>: An overview prepared by the Center for Climate and Energy Solutions about market-based approaches, like a carbon tax or cap-and-trade program, that can help reduce emissions at the lowest possible cost. [https://www.c2es.org/content/market-based-strategies/ Available here.]
 == Online Courses and Course Materials ==
@@ Line 58: / Line 57: @@
 *'''[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 ===
@@ Line 63: / Line 63: @@
 *'''[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:
-*[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://projects.g-node.org/emoo/ Evolutionary Multi-Objective Optimization (EMOO)]
@@ Line 77: / Line 77: @@
 === Climate policy databases ===
+* [https://app.climatepolicyradar.org Climate Policy Radar]: an open resource of climate laws, policies and action plans from every national government. The database offers full text search of documents via the website.
 * [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.
@@ Line 97: / Line 99: @@
 *[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.
+*[https://data.oecd.org OECD Data]: The OECD publishes many economic and social indicators that are relevant for climate policy.
 == References ==