Horizon CDT Research Highlights

Research Highlights

Ecological Applications on Maximising the Efficiency of Urban Metabolism

  Dan Wang (2015 cohort)   www.nottingham.ac.uk/~psxdw1

Although 2% of land surface covered by cities, they house over 50 % of global population and consume over 75% of global resources. Continually growing urban dwellers in China pose great burden on material and energy supply and waste disposal within cities and their surrounding areas. Moreover, Chinese cities are extending dramatically with extremely low metabolism efficiency (Zhang et al. 2009). Chengdu, the capital city of Sichuan Province, is becoming the economic, transportation and culture center of Southwestern China and undergoing urbanization. It is imperative to understand how sustainable this developing city can be. I am interested in how efficiency and the pathways of the urban metabolism of this city and what efforts we can make to maximizing the efficiency of the urban metabolism of Chengdu. Urban metabolism (UM) studies are aiming to quantify and assess the process of energy and materials flowing through cities and its consequences to aid the urban planning by adding more circles within the metabolic process and the design of cities as more closure and sustainable systems (Holmes and Pincetl 2012, Morowitz et al. 2005). The analysis of UM is based on two main methods which are energy flow analysis (EFA) and material flow analysis (MFA) (Zhang 2013).

My research question is how efficiency the metabolism of Chengdu can be? In attempting to answer this broad research question, it can be broken down and conducted into the following points:

1) To collect data on the net energy, and material flows into and out of Chengdu. This will need to be broken down into classifications of how this energy is sourced, and its end use, and a classification of the materials consumed and the waste products produced

2) A Mass flow analysis within the case study cities indicating the material pathways and uses. Ideally this would be broken down by a classification of the material description and its use both spatially and temporally (this will be data dependent though). The analysis should also indicate what happens to the waste flows, i.e. landfill, recycling etc.

3) Perform a Life Cycle analysis on the above material flows.

4) Perform a similar process for the energy flows in the case study cities.

5) Link these flows to environmental consequences.

6) Build a framework which incorporates all the above flows. However this would also incorporate alternative pathways. i.e. recycling pathways.

7) Indicate the energy consequences of these alternative pathways. Link this up to the environmental consequences of the energy consumption (i.e. CO2).

8) Also link the alternative material flow pathways to additional environmental consequences.

9) Link approximate economic cost to the various material and energy pathways

10) The environmental consequences listed above should be linked up to environmental indicators. These environmental indicators need to be developed in my research by referring ecological concepts.

11) Explore concepts of ecological sustainability (for example closed ecological systems) and ecological indicators of sustainability to aid with coming up with urban sustainability definitions and indicators of sustainability. Link with the above.

References

1) Holmes, T. & S. Pincetl (2012) Urban metabolism literature review. Los Angeles, California Centre for Sustainable Communities at UCLA.

2) Morowitz, H., J. Allen, M. Nelson & A. Alling (2005) Closure as a scientific concept and its application to ecosystem ecology and the science of the biosphere. Advances in Space Research, 36, 1305-1311.

3) Zhang, Y. (2013) Urban metabolism: a review of research methodologies. Environmental pollution, 178, 463-473.

4) Zhang, Y., Y. Zhao, Z. Yang, B. Chen & G. Chen (2009) Measurement and evaluation of the metabolic capacity of an urban ecosystem. Communications in Nonlinear Science and Numerical Simulation, 14, 1758-1765.

This work was carried out at the International Doctoral Innovation Centre (IDIC). The authors acknowledge the financial support from Ningbo Education Bureau, Ningbo Science and Technology Bureau, China's MOST, and the University of Nottingham. The work is also partially supported by EPSRC grant no EP/L015463/1.