Several recent events in the U.S. have highlighted the criticality and vulnerability of infrastructure systems to sudden shocks such as natural disasters, terrorist attacks, and food shortages. Proper understanding of such disruptive scenarios and their impact using holistic and integrated systems modeling techniques is crucial for effective resource allocation and disaster management. For example, the disruptive ability of emerging technologies such as biofuels, nanotechnology, and newer materials has been anticipated by many. Development and in turn impact of such radically different technologies on the economy and the associated infrastructure systems will be fueled by environmental and economic concerns and changing human preferences. However, attempts to study their long term economic-environmental impact and future trajectories pose both methodological and empirical challenges for engineers and policy makers alike. This is because emerging technologies and the associated infrastructure systems are part of complex hierarchical dynamic industrial systems with highly interconnected processes and technologies that makes system characterization difficult. Understanding their dynamics and the associated economic-environmental implications require data at both the micro and the macro level which is not readily available. Another challenge is associated with the technological change over time and hence the associated uncertainties with respect to the resource consumption and its impact. These issues ranging from the lack of data for evaluating emerging technologies to the development of models that could provide a better understanding of complex hierarchical systems and its implications for long-term sustainability assessments of emerging technologies are the focus of this work.

The present work focuses on exploring the utility of Input-Output (I/O) models for studying the effect of sudden shocks and quantifying the associated risks. Economic Input-Output (EIO) models have been used for various purposes including exploring the effect of changes in final demand, taxes and other economic changes [1,2]. EIO model uses information contained in the detailed I/O tables of the economy. It uses a matrix representation of interindustry relations in a nation's (or a region's) economy. The matrix contains data about the monetary transactions between each pair of industrial sectors. Models based on the EIO model have also been developed for life cycle assessment by combining the EIO model with data about emissions and resource use. Economic Input-Output life cycle assessment (EIOLCA) mainly focuses on the impact of emissions and energy use by combining economic data with the emissions details for specific sectors and is mainly an output-side approach [3]. More recently, Ecologically based life cycle assessment (Eco-LCA) has been developed which is novel and unique in its ability to quantify the contribution of ecosystem goods and services. [4,5]

We are using the Eco-LCA model to understand the impact of changes in the availability of natural resources including natural capital. This includes understanding the potential impact of loss of services such as pollination, water scarcities, and soil fertility. Such information is used to determine sectors that are likely to face maximum risk due to sudden disruptions. Since the EIO model considers a static and linear state of the economy, it is not able to simulate long-term effects of such disruptions. However, it is appropriate for gaining insight into the short-term effects of disruptions before any adaptation due to market forces or policies. Such simulation is relevant to understanding the effect of environmental changes as well as human-induced changes such as terrorism and natural disasters.

The utility of the proposed approach will be illustrated using case studies involving energy system transitions. The case studies involve the use of I/O models coupled with scenarios in a prospective dynamic analysis and with explicit treatment of uncertainty to evaluate the broader impact of technology transitions. The implications of the present work are to enhance the fundamental understanding and modeling and provide insights for resilient and sustainable technology transitions. Methodologically, the goal is to enhance and extend presently available tools and develop new modeling approach that explains the dynamics of complex systems with specific focus on emerging technologies. In the long term, this work is expected to complement the traditional static biophysical models and methods by including the dynamic behavior of complex adaptive systems, various uncertainties, risks, and study their implications for decision-making and sustainability.

References

1. R. E. Miller and P. D. Blair. Input-Output analysis. Foundations and extensions. Prentice-Hall, Inc., Englewood Cliffs, New Jersey, 1985.

2. W. W. Leontief. Input-Output Economics. Oxford University Press, 1985.

3. EIOLCA. www.eiolca.net, accessed on May 10, 2008.

4. N. U. Ukidwe and B. R. Bakshi. Industrial and ecological cumulative exergy consumption of the united states via the 1997 input-output benchmark model. Energy, 32(9):1560–1592, 2007.

5. N.U. Ukidwe and B.R. Bakshi. Thermodynamic Accounting of Ecosystem Contribution to Economic Sectors with Application to 1992 U.S. Economy. Environmental Science and Technology, 38(18):4810–4827, 2004.