Climate change remains one of the biggest threats to life on Earth as we know it. In the next 10 to 15 years, we face the very real scenario of average global temperature increases exceeding the 1.5 degree warming threshold set out in Paris in 2015. The need for coordinated and decisive action to decarbonize our economy and society, and live more sustainably has never been more urgent.
A particularly important part of addressing global warming and improving sustainability is our focus on city ecosystems and the need to examine them holistically to ensure they are right for their residents and the larger environment. By 2050, nearly 2.5 billion more people will live in urban areas. Researchers in design and architecture will be at the forefront of studying these shifts — ensuring that our cities become more sustainable, equitable and enjoyable for generations to come.
Peng Du, PhD, assistant professor in the College of Architecture and the Built Environment, is director of Jefferson’s Urban Design and Geodesign programs, and co-editor of the new “Routledge Handbook on Greening High-Density Cities: Climate, Society and Health.” He and his colleagues are exploring how to use data-driven and simulation-based tools to address climate change, urban population, public health and resource depletion. In parallel, he is a program leader for the Council on Tall Buildings and Urban Habitat, a global research and education organization dedicated to helping create smart, sustainable cities.
Find out more about Dr. Du’s research and questions he’s trying to answer.
A: I have worked at Jefferson for a-year-and-a-half. I joined Jefferson to lead the two graduate programs (Master of Urban Design and MS in Urban Analytics and Geodesign) and I am very excited to work with our excellent faculty, students and industry leaders to explore sustainable, smart and healthy cities.
A: My expertise is in net-zero carbon buildings and cities, computational urban design, urban energy modeling and urban data analytics, incorporating interdisciplinary approaches. I am specifically exploring how to use data-driven and simulation-based tools to examine the relationships between urban form and environmental/social performance parameters, such as building energy-use intensity, daylight availability, urban mobility (all aspects of movement in cities, including travel behaviors), outdoor thermal comfort, visual interest or aesthetics, green space and solar energy potential. Against the background of climate change and rapid urbanization, the cities we build today urgently need to minimize carbon emissions wherever possible. This needs to occur at both the building and urban scales, and both operational carbon – the amount emitted when a building is in use — and embodied carbon – the amount emitted during the making of a building.
A: I was raised in urban areas and have lived in and loved big cities most of my life. From a young age, I viewed architects as great contributors to culture and human wellbeing. So it was natural that I became an architect focusing on creating urban environments that make people’s lives better.
With the development of data and digital technology, understanding “smart city” concepts and developing the capacity to analyze the impacts of smart technologies on urban life are increasingly important for architects, urban planners and policymakers. However, can the smart city be more than technology simply grafted onto a traditional urban design? What new tools for architects and urban designers are required to design a truly sustainable and smart city? I am interested in developing sustainable and smart cities by using various advanced computational and analytical tools. The biggest challenge to teaching this concept to my students has been balancing the roles of the computer and human throughout the design process. The data-driven computational design tools we use are extremely powerful, but we, as urban designers, should always be able to drive the design process based on our real and lived experience of cities.
A: More than half of the world’s population now lives in cities, and it is projected that another 2.5 billion new residents will be moving to cities by 2050. Cities face increasing social and environmental challenges, and growing demands from residents to deliver a better quality of life.
The biggest challenge to teaching this concept to my students has been balancing the roles of the computer and human throughout the design process. – Dr. Peng Du.
A: I have also implemented mixed reality (MR) technologies — which combines elements of both augmented reality and virtual reality – with urban design and analytics. Using this approach, I can explore the different urban design options and their environmental/social impacts through an iterative and analytical platform in real-time. We are currently working on a research project on urban mobility and health, partnering with the Philadelphia City Planning Commission. This research aims to develop a fast, data-driven urban mobility simulation platform that can predict carbon emissions and evaluate a variety of population-health impacts for a given built environment. This platform will be accessible through the website and be facilitated by MR technologies, and it will be able to present the urban model along with the simulation results in real-time as users change the design scenario for the future built environment.
A: I firmly believe technology-driven urban design can help build more livable, equitable, resilient and efficient cities for our futures. We need to learn and use the technologies that can support the process of designing and making cities, e.g., big data, modeling and simulation. Particularly, the use of artificial intelligence (AI) has become increasingly important to urban design, as it can help us design responsive urban environments that are driven by strategies backed by real time data. For example, AI can help urban designers identify routes that help with safety and health measurement, larger traffic management, equitable access to amenities, and smarter utilities with better efficiency.
