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Targeted Environmental Peacebuilding

Mission and Vision

Our Mission and Vision

Our vision is to contribute to the creation of a sustainable world in which the goals of human security, the well-being of populations, and climate change mitigation are all tackled together. We aim to hope to break and prevent cycles of climate change, environmental destruction, and conflict by ensuring that those communities who are most vulnerable receive the most support. We recognize we are working among a global network of institutions, organizations, and individuals with similar goals. Our contribution to this shared mission is in identifying locations where climate impacts will most likely lead to escalating conflict—in time to prevent it. To do so, we plan to

  • Identify precise subnational locations that are most vulnerable,
  • Communicate these locations and their needs broadly to stakeholders, and
  • Engage in and promote the most cost-effective and empowering environmental peacebuilding interventions.

We believe this approach will bridge current critical gaps in environmental conflict prediction and assessment of effective interventions, helping the international community further target existing resources and leverage additional funds for this important work.

Next Steps

Next Steps

We have divided our work into two distinct phases: Research and Implementation. During the first phase, we will construct a model to help us predict where environmental impacts are most likely to lead to sociopolitical instability and conflict globally, as well as build relationships with individual domain experts and organizations working in environmental conflict prediction and prevention. Once we have created a predictive model, we plan to use it as an action plan for the next stage of our work, in conjunction with the strategic advice we have received during the process.

Research

Our goal over the next few months is to create a global model predicting future sociopolitical instability and related violence at the city level, as well as in which locations environmental peacebuilding work is likely to make the greatest difference in preventing this conflict. To do so, we plan to improve upon our existing approach in the following ways.

Scale and Scope of Data

We will expand from an analysis of Syria to a global scale. For this reason, all satellite and events data we use will be global in context. Our next analysis will include our original data sources on rainfall, drought severity, NDVI, cropland percentage, population density and population growth rates.1 To this, we will add the following:

  • National and subnational governance data on regime type and stability, as well as indicators of well-being including infant mortality, GDP and GDP growth;16
  • Environmental data including temperature, soil moisture content, and predicted climate impacts such as rainfall variability and flood and drought occurrence;
  • Known conflict risk factors including ethic composition and fractionalization, presence of petroleum and high value mineral resources, and past incidence of conflict;16 and
  • Events and media reports from the GDELT Project indicating occurrence of violent events, media tone, and keyword mentions of political and environmental concerns.17

Algorithms and Approach

Because standard regression models are often poor at predicting conflict compared to more recently developed machine learning algorithms,18 we plan to improve our forecasting ability through application and comparative testing of three to four machine learning models. To do so, we will select the random forest and similar supervised learning models, train them on a subset of available data related to past conflicts, and then cross-validate them using the remaining available past conflict data. After fine-tuning each model, we will select the one with the highest predictive accuracy. This approach has advantages over typical regression models in that, with sufficient data, it allows for very high accuracy.18 We believe decision tree based approaches like the random forest model will be especially helpful because they can provide some indication of the potential causal role each of our variables played in spurring conflict, and can be trained using a smaller set of data points.19,20 These last two characteristics are particularly helpful to our study both because conflicts are relatively rare events21 and because we plan to investigate what role environmental factors played in each conflict.

Domain Expertise

In order to construct the best possible model and ensure thoroughness in providing input data, we plan to consult practitioners with expertise in the following domains:

  • Conflict prediction and prevention,
  • Disaster and conflict early warning systems,
  • Climate change impacts and adaptation in developing nations,
  • Environmental peacebuilding methods and impact evaluation,
  • International governance and state stability, and
  • Machine learning in social science contexts.

These experts will be able to provide practical insights from their past experience, help us refine the questions we are posing, guide our analytics in the right direction, and help evaluate the effectiveness of our results.22 With the help of our team members and advisers, we expect to have this process completed within 4-6 months. Once our algorithm has been validated, we plan to begin the application phase of our work while iterating our research process to further improve our model.

We plan to engage in a cyclical research process (illustrated above) beginning with data gathering and process validation through interviews and discussions with practitioners. We will use this step to refine our questions and ensure we have acquired all available related data. After feeding this data into a multiple test models, we will select and refine our top performing model, interpret the results, and review them in collaboration with practitioners. A few iterations of this research process over the course of 4-6 months will provide a solid foundation for the application phase of our work and continue to support our intervention-focused work thereafter.

We plan to engage in a cyclical research process (illustrated above) beginning with data gathering and process validation through interviews and discussions with practitioners. We will use this step to refine our questions and ensure we have acquired all available related data. After feeding this data into a multiple test models, we will select and refine our top performing model, interpret the results, and review them in collaboration with practitioners. A few iterations of this research process over the course of 4-6 months will provide a solid foundation for the application phase of our work and continue to support our intervention-focused work thereafter.

Application

As a delay often exists between advances in research and their application, we hope to speed along this process by developing organizational partnerships that will allow us to ensure our research benefits local communities as soon as possible. We are looking at options ranging from advocacy within larger organizations, to launching a pilot study to better determine the impact of this work in climate and conflict vulnerable locations, to raising funding and support for local advocates and organizers in vulnerable locations. Because the UNEP has identified impact assessment as a major gap to be filled for targeted environmental peacebuilding work to gain widespread institutional support, we believe launching a pilot study in partnership with a larger organization and assessing its impact will be our most important next step.11 This pilot study would likely include a subset of the intervention strategies proposed above, tailored to the local context and centered around empowerment of local community organizers. We expect our long-term strategy, however, to involve a multi-faceted approach shaped by the relationships we build and the advice we are able to obtain during our research-focused phase over the next few months. Our ultimate goal is to help individuals and organizations working to address climate change and conflict focus their efforts as effectively as possible, and to ensure that those in vulnerable locations are able to receive the funding and resources they need.

Support Peace Rising

How you can support Peace Rising

Thank you for your interest in the Peace Rising project. We need your help to achieve our mission. There are currently a variety of ways you can get involved or support the project’s work.

Organizations

We are interested in collaborating with organizations and institutions doing similar research and are excited to learn more about your team’s work. We are also seeking a fiscal sponsor capable of accepting tax deductible donations until we have successfully obtained 501(c)(3) status.

Domain Experts and Practitioners

We are concerned about how best to address barriers to targeting, funding, implementing, and assessing environmental peacebuilding work and would be excited to talk with you about your past experience. We are currently seeking information and recommendations in the following domains:

  • Conflict prediction and prevention,
  • Disaster and conflict early warning systems,
  • Climate change impacts and adaptation in developing nations,
  • Environmental peacebuilding methods and impact evaluation,
  • International governance and state stability, and
  • Machine learning in social science contexts.

Join the Team

Over the next few months, we will be engaged in grant writing, research, and outreach. New team members will have the opportunity to build research skills and experience, connect with related organizations and experts in peacebuilding and conflict, and achieve lasting global impact while working as part of a close-knit team. While our ability to hire additional paid researchers is currently limited, we can provide significant assistance applying for institutional funding and fellowships.

Please reach out if you would like to join our team by emailing Peace Rising project director Amber Houghstow at amber@peace-rising.org. We look forward to hearing from you.

Team

Team

Amber Houghstow

Amber Houghstow directs the Peace Rising project, which was inspired by her graduate research. Peace Rising’s methodology builds on thesis research conducted during her master’s in international relations at Harvard. Previously, Amber organized a program for tech entrepreneurship in Sri Lanka, conducted design research for the MIT D-Lab, advised the Ray and Tye Noorda Foundation in its development of a climate grants portfolio, and taught junior high math through Teach for America. Amber earned her bachelor’s degree in mechanical engineering from MIT.

Sheila Baber

 As Peace Rising’s newest member, Sheila Baber heads the group’s efforts in data aggregation and analysis. Drawing from her experience of living abroad in multiple countries, Sheila also acts as the outreach director in the Middle East and has previously worked for the Public Affairs Office at the United States Consulate General in Jerusalem. Sheila is currently in the process of earning her undergraduate degree in Earth, Atmospheric and Planetary Science from MIT.

Thomas Delgado

Thomas Delgado brings technical expertise and critical analysis to Peace Rising. Thomas studied computer science and philosophy at MIT and has used his coding knowledge to consult for social media based advertising strategy and build an affordable community organizing platform. He has advised the Peace Rising project from its earliest stages.

 

Daniel Tatar

Dan Tatar earned his bachelor’s degree from Brown, where he studied history, economics, and religion. His professional experience as a researcher and writer, combined with his background on the relationship between economics, history, and conflict have been valuable in guiding the Peace Rising project’s ongoing work.

 

Advisers

Farhana Khan

Farhana Khan formerly directed Peace Rising’s data science research. She now serves as an adviser to the group, contributing expertise from her background in computer science and anthropology. Previously, Farhana worked as a software engineer at Conduit, with prior roles at the Yunus Centre and Grameen Bank. Farhana earned her bachelor’s from MIT, where she studied anthropology and computer science, and also holds a master’s in CS.