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Suggested Citation:"5 Identifying Directions for Partnership." National Academies of Sciences, Engineering, and Medicine. 2019. Enhancing Urban Sustainability with Data, Modeling, and Simulation: Proceedings of a Workshop. Washington, DC: The National Academies Press. doi: 10.17226/25480.
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5

Identifying Directions for Partnership

Workshop participants divided into three groups, based on their areas of interest and expertise, to discuss specific issues related to urban sustainability. The first group discussed air and water systems; the second group discussed transportation and the physical infrastructure; and the third group discussed sustainable inclusive communities. What follows are brief overviews of presentations given during those sessions, as well as summaries of each group’s report-out to the rest of the workshop participants.

THEME 1: AIR AND WATER SYSTEMS

Anu Ramaswami, University of Minnesota, Moderator
Elena Craft, Environmental Defense Fund, Moderator
Katherine Bennett Ensor, Rice University, Moderator

Anu Ramaswami, University of Minnesota, opened this discussion with a brief presentation about how to use both models and data to inform actions in multi-objective systems. She noted that although urban areas directly occupy only approximately 3 percent of land area, they are impacting the planet through large transboundary resource draws (e.g., 70 percent of global greenhouse gas emissions are associated with cities), economic interactions, and trade. Cities seek multiple sustainability outcomes, but there are benefits and trade-offs among these outcomes. This results in an urgent and historic opportunity to act by leveraging social

Suggested Citation:"5 Identifying Directions for Partnership." National Academies of Sciences, Engineering, and Medicine. 2019. Enhancing Urban Sustainability with Data, Modeling, and Simulation: Proceedings of a Workshop. Washington, DC: The National Academies Press. doi: 10.17226/25480.
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and technological innovation through partnerships as well as through the adoption of interdisciplinary, multiscale frameworks (see Figure 5.1) based on theories, models, and data (Ramaswami et al., 2018).

Ramaswami also provided a brief overview of the complexity of benefits and trade-offs associated with the food-energy-water nexus in India from an urban systems perspective. While process-based air quality models are well developed for larger-scale air pollution modeling, further finer-scale innovation is needed. She shared examples of cities, such as Denver, that are explicitly creating transboundary environmental and economic models to better reflect relationships between the city and its surrounding and connected areas. To move from science to action, Ramaswami suggested taking a deep dive with a few cities to co-produce knowledge. This could include developing a better understanding of underlying principles and exploring typologies of cities to simplify science and to gain insights. Partnerships between individual cities and broader policy makers could be helpful. She concluded by noting that more data does not necessarily imply more knowledge, insights, or capacity to act; higher-order systems thinking, new models, and innovative co-production skills are needed to address transboundary sustainability.

Elena Craft, Environmental Defense Fund (EDF), presented a case study of Hurricane Harvey as a means to think about improving urban sustainability. She emphasized the value of understanding how to better

Image
FIGURE 5.1 An interdisciplinary research framework including multiple scales, sectors, objectives, and actors. SOURCE: From A. Ramaswami, A.G. Russell, P.J. Culligan, K. Rahul, and E. Kumar, 2016, Meta-principles for developing smart, sustainable, and healthy cities, Science 352(6288):940–943. Reprinted with permission from the American Association for the Advancement of Science.
Suggested Citation:"5 Identifying Directions for Partnership." National Academies of Sciences, Engineering, and Medicine. 2019. Enhancing Urban Sustainability with Data, Modeling, and Simulation: Proceedings of a Workshop. Washington, DC: The National Academies Press. doi: 10.17226/25480.
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integrate information (using data, simulation, and models) in order to increase preparedness. Craft explained that in the days following Hurricane Harvey, the Valero refinery reported small amounts of excess benzene emissions, but the Texas Commission on Environmental Quality (TCEQ) did not take any measurements. After the city of Houston received complaints about odor, it partnered with EDF, which arranged for mobile monitoring to be deployed from California to Houston. The measurements of benzene emissions were substantially larger than those reported by Valero, prompting EDF to release an air quality health alert to neighborhoods at risk. Although the Environmental Protection Agency (EPA) took independent measurements, it did not release the information to the public; instead, it released a statement to announce Valero’s under-reporting. Valero refiled its report, noting that the actual emissions were approximately 300 times as much as initially reported. TCEQ eventually released a summary of EPA’s findings more than 1 month after the initial concern was raised by Houston residents.

On the one hand, EDF believed that these concentrations were “at least 10 times higher than health officials deemed safe,” Craft explained. On the other hand, TCEQ believed that they “would not expect any adverse effects to occur as a result of exposure to these concentrations,” she continued. This was an example of the state environmental agency not providing effective public health protections related to a disaster scenario because the agency did not have enough data to understand the problems. She emphasized the value of using information collected by monitors to protect public health. Better risk management, infrastructure, and sustainability are needed, especially during a disaster, to ensure more effective policies. The Hurricane Harvey Registry1 is an example of a well-used tool for collecting information on both the hurricane and its impact on Houston.

Based on this small group’s discussion of sustainable air and water systems, Ramaswami offered the following summary of suggestions. Focusing on modeling and data within city boundaries does not address opportunities and challenges external to the city. New science and models are needed to address the benefits and trade-offs of the United Nations’ Sustainable Development Goals. She added that modeling complex phenomena requires a hierarchical approach (i.e., linking parameter models, process models, and data models), and geolocated data are often insufficient for insight. Shorter time frame forecasting and pollution source apportionments are both important to address air and water systems. New simulation approaches that link movement of people with pollution

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1 The website for the Hurricane Harvey Registry is HarveyRegistry.rice.edu, accessed March 12, 2019.

Suggested Citation:"5 Identifying Directions for Partnership." National Academies of Sciences, Engineering, and Medicine. 2019. Enhancing Urban Sustainability with Data, Modeling, and Simulation: Proceedings of a Workshop. Washington, DC: The National Academies Press. doi: 10.17226/25480.
×

exposure can be leveraged. Another key component of this discussion was balancing model simplification and transparency with model accuracy.

A cohort model for co-production of data, models, and knowledge among researchers, communities, and practitioners could be useful in supporting smaller cities. Ramaswami said that co-production needs the development of long-term trusted partnerships that can be activated during extreme events. Partnering with larger networks enables the institutionalization and diffusion of knowledge. Data platforms are most effective if they are open, collaborative, nimble, curated, and long-lived, and taxpayer-supported institutional data should be public and incorporate new data sources, according to Ramaswami.

THEME 2: TRANSPORTATION AND PHYSICAL INFRASTRUCTURE

Kaan Ozbay, New York University, Moderator
Auroop R. Ganguly, Northeastern University, Moderator
Christine Ehlig-Economides, University of Houston, Moderator

This discussion began with a brief presentation by Auroop R. Ganguly, Northeastern University. He spoke about systematic ways to use data and modeling for disaster recovery efforts and cautioned about various financial disincentives that can lead to engineering stagnation and outdated best practices. He discussed a variety of novel approaches for harnessing complex data and designing policy, including the Next Generation Digital Earth. The rise of urban technology (e.g., Uber, Lyft, Jupiter,2 KatRisk,3 risQ,4 and One Concern5) is also provoking changes in this space. Ganguly encouraged private, public, government, and intergovernmental partnerships to better solve problems with data. Highlighting the importance of frugal innovation and technology transfer, he noted that the United States could learn from more resilient developed nations (e.g., the Netherlands’ management of natural-built urban coastal infrastructures and systems) as well as emerging nations (e.g., Brazil, Russia, India, and China, each of which performs tasks under constrained resources).

Christine Ehlig-Economides, University of Houston, later shared highlights of conversations that occurred during this session. She emphasized the value of open and accessible data, open source models, and benchmarking models (perhaps through competitions). Individuals from this

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2 The website for Jupiter is https://jupiterintel.com, accessed March 12, 2019.

3 The website for KatRisk is http://www.katrisk.com, accessed March 12, 2019.

4 The website for risQ is https://www.risq.io, accessed March 12, 2019.

5 The website for One Concern is https://www.oneconcern.com, accessed March 12, 2019.

Suggested Citation:"5 Identifying Directions for Partnership." National Academies of Sciences, Engineering, and Medicine. 2019. Enhancing Urban Sustainability with Data, Modeling, and Simulation: Proceedings of a Workshop. Washington, DC: The National Academies Press. doi: 10.17226/25480.
×

group discussed how models are used by talking about disasters, such as hurricanes, that can have a huge impact on transportation infrastructure. Models can also be used to address resilience, optimize everyday life, help to envision a more sustainable future, and inform policy. Participants also discussed the risk of policy yielding unintended consequences, no matter the strength of the models, and the importance of bringing together individuals with diverse expertise to develop strategies. Ehlig-Economides echoed the concerns shared earlier in the workshop about privacy and privatization as well as data ownership and the competitive advantage that that might imply. Partners may help fund or enable modeling by opening up otherwise private data. Partners also help modelers by setting priorities for and boundaries on the models, based on defining the intended audience.

THEME 3: SUSTAINABLE INCLUSIVE COMMUNITIES

Bill Fulton, Rice University, Moderator
Aniruddha Dasgupta, World Resources Institute, Moderator
Seth Schultz, Urban Breakthroughs, Moderator

Bill Fulton, Rice University; Aniruddha Dasgupta, World Resources Institute; and Seth Schultz, Urban Breakthroughs, invited participants to discuss topics related to equitable access to data, the use of data to promote equity, the collection of relevant data that can be measured, accountability for decision making, the process of knowledge generation and sharing, the building of interconnected models, and the balance of multiple goals related to job growth, healthy climate, and quality of life.

On behalf of the group, Fulton identified key takeaways from the discussion on sustainable inclusive communities:

  • Equitable access to data and the use of data to achieve equitable outcomes. Fulton said that there are many different players who are generating data and analysis (e.g., government, universities, nonprofits, and the private sector), but not all of these data and their analyses are accessible to everyone. He raised a question about the role of the private sector and whether it is possible to establish an agreement between the public sector or a university and the private sector to share data that are of mutual benefit.
  • Role of both quantitative and qualitative data in telling stories that convey the fullness of communities and their issues, especially those that are underserved. Communities are empowered to use data to tell stories about people and their neighborhoods. The National
Suggested Citation:"5 Identifying Directions for Partnership." National Academies of Sciences, Engineering, and Medicine. 2019. Enhancing Urban Sustainability with Data, Modeling, and Simulation: Proceedings of a Workshop. Washington, DC: The National Academies Press. doi: 10.17226/25480.
×
  • Neighborhood Indicators Partnership,6 for example, provides neighborhood-level data for nonprofit organizations and community organizations.
  • Alignment of incentives across all four sectors. Generally speaking, the government is trying to provide services, university researchers are trying to do work that is rewarded in academia, nonprofits are trying to serve their constituencies, and private-sector players are trying to maximize profits. Aligning goals across these sectors (to the extent possible) is important, although Fulton noted that university research often takes too long to benefit public policy.
  • Feedback loop. Research should lead to policy that creates action, Fulton explained. It is crucial to identify what will happen if action is not taken and to develop the ability to feed that information back into the research.
  • Assurance of the durability of cross-sector partnerships. Part of developing partnerships (with either universities or the private sector) means building in resiliency across varying scales of time and levels of engagement. Establishing this durability is especially important to increase city governments’ capacity to take advantage of opportunities related to data-driven decision making.

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6 For more information about the National Neighborhood Indicators Partnership, see https://www.neighborhoodindicators.org, accessed March 12, 2019.

Suggested Citation:"5 Identifying Directions for Partnership." National Academies of Sciences, Engineering, and Medicine. 2019. Enhancing Urban Sustainability with Data, Modeling, and Simulation: Proceedings of a Workshop. Washington, DC: The National Academies Press. doi: 10.17226/25480.
×
Page 49
Suggested Citation:"5 Identifying Directions for Partnership." National Academies of Sciences, Engineering, and Medicine. 2019. Enhancing Urban Sustainability with Data, Modeling, and Simulation: Proceedings of a Workshop. Washington, DC: The National Academies Press. doi: 10.17226/25480.
×
Page 50
Suggested Citation:"5 Identifying Directions for Partnership." National Academies of Sciences, Engineering, and Medicine. 2019. Enhancing Urban Sustainability with Data, Modeling, and Simulation: Proceedings of a Workshop. Washington, DC: The National Academies Press. doi: 10.17226/25480.
×
Page 51
Suggested Citation:"5 Identifying Directions for Partnership." National Academies of Sciences, Engineering, and Medicine. 2019. Enhancing Urban Sustainability with Data, Modeling, and Simulation: Proceedings of a Workshop. Washington, DC: The National Academies Press. doi: 10.17226/25480.
×
Page 52
Suggested Citation:"5 Identifying Directions for Partnership." National Academies of Sciences, Engineering, and Medicine. 2019. Enhancing Urban Sustainability with Data, Modeling, and Simulation: Proceedings of a Workshop. Washington, DC: The National Academies Press. doi: 10.17226/25480.
×
Page 53
Suggested Citation:"5 Identifying Directions for Partnership." National Academies of Sciences, Engineering, and Medicine. 2019. Enhancing Urban Sustainability with Data, Modeling, and Simulation: Proceedings of a Workshop. Washington, DC: The National Academies Press. doi: 10.17226/25480.
×
Page 54
Next: 6 Reflections and Next Steps »
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On January 30-31, 2019 the Board on Mathematical Sciences and Analytics, in collaboration with the Board on Energy and Environmental Systems and the Computer Science and Telecommunications Board, convened a workshop in Washington, D.C. to explore the frontiers of mathematics and data science needs for sustainable urban communities. The workshop strengthened the emerging interdisciplinary network of practitioners, business leaders, government officials, nonprofit stakeholders, academics, and policy makers using data, modeling, and simulation for urban and community sustainability, and addressed common challenges that the community faces. Presentations highlighted urban sustainability research efforts and programs under way, including research into air quality, water management, waste disposal, and social equity and discussed promising urban sustainability research questions that improved use of big data, modeling, and simulation can help address. This publication summarizes the presentation and discussion of the workshop.

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