Low Carbon City
Shenzhen Center for Design. ALCCA parallel research Grant. China. Team: Ming Tang, Chris Auffrey, Xinhao Wang, Mingming Lu, Zhou Yan. Students: Desai Sagar, Reinersman Michael, Davis Seth, Block Olga. 2015
Low Carbon City. Shenzhen Center for Design. ALCCA parallel research Grant. China. RMB 50,000 ($8,069) PI: Tang. Co-PI: Auffrey, C., Wang, X., Lu, M. 2015
This academic research project is organized by the Shenzhen Center for Design and conducted in parallel to the Alternatives for Low Carbon City and Architecture (ALCCA) planning and design competition. This research project brings together professors, researchers, and students from multiple international universities from the region and around the world: Shenzhen University, University of Hong Kong, Columbia University, University of Cincinnati, the University of Syracuse, and Harvard University. Each research team is tasked to produce one ‘User’s Manual’ about specific topics involved in the planning, design, and implementation of low-carbon urban development. These ‘Manuals’ aim to provide substantiated knowledge and innovative ideas for the discussion of the environmental, economic, social, and cultural issues surrounding low carbon projects in Shenzhen and the rest of the world.
Web Applications
- Energy summary
- Transportation CO2 Emission
- Building C02 Emission
The goal of this research is to construct a relational model allowing developers to better understand the complex relationships among various urban parameters such as population, density, carbon emission, car usage, development intensity, zoning, and energy consumption. The use of dynamic/parametric modeling has allowed us to compare the advantages and disadvantages of underground, surface, and vertical development, as well as different transportation and building densities and coverages, and to propose an optimal strategy for new infrastructure development and land use. We believe the great challenge for the PINGDI1.1 project is to create evaluation systems that can quantify various parameters of the urban built environment, and ensure a low carbon lifestyle for all residents through various scenarios including iterative proposals on urban infrastructure, land use, building programs, waste management, renewable energy and transportation systems.
Step 1: Construct measurable Low carbon indicators
Low carbon indicators from various aspects were proposed. These indicators will be very helpful in establishing an eco-city performance monitoring system for the low-carbon city. Step 2: Construct Assumptions
Quantifiable Relationships were established based on the following assumptions of the PINGDI low carbon city starting zone.
- Population density
- Industrial space requirement
- Carbon emission per employee by industry (ton/person)
- Energy consumption rates per area by industry sector (J/sq.m.)
- Commercial/office space requirement (square meters per employee):
- Residential
- Energy consumption rate per residential area (J/sq.m.)
- Carbon emission rate per residential area (ton/sq.m)
- Water consumption
- Wastewater generation
- Municipal waste generation
- Stormwater runoff
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- Proportion impervious area
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- Automobile carbon emission rate (ton/km)
- Assumption of surface parking
- Transit carbon emission rate (ton/km)
- Percentage by travel modes
- Total distance traveled per person (km)
- Carbon sequestration rates (ton/sq.m)
Step 3: Construct site database
A digital model of the PINGDI site is constructed using advanced parametric modeling tools, which include block and building. Street network, Land use type by block, FAR, Building height, Building use type, and other parameters will be coded into the database allowing further computing. Three scenarios named high-density development, mid-density development, and low-density development were constructed.
4. Scenario-based analysis
We offer a brief discussion of each concept below along with example illustrations of their application. The parametric modeling results are analyzed based on low-carbon city criteria related to various services including school, healthcare, recreation, commercial, and parks.
Conclusion
The conclusions are made based on the analysis of various scenarios based on the GIS scenario 360 program in the relation to the low carbon planning methods. Final Report
The project is also featured in my book chapter.
Tang, M. Innovative Tools. Data-Driven Landscapes. Edited by Jonathon Anderson, Daniel Ortega. Innovations in Landscape Architecture. Routledge. ISBN: 1072954 UK. 2016.
