Quantitative metrics of system resilience: assessing system flexibility to environmental constraints

Global environmental issues such as decreasing resource endowments and climate change threaten to disrupt our economic systems. As such, system resilience is an integral aspect of sustainable development, to the extent that... [ view full abstract ]

Global environmental issues such as decreasing resource endowments and climate change threaten to disrupt our economic systems. As such, system resilience is an integral aspect of sustainable development, to the extent that one can no longer be understood without the other. While resilience research is gaining momentum, there is a lack of quantitative metrics for measuring system resilience. This makes it challenging to assess the ability of economic systems to tolerate changes in natural resource stocks. Concepts and methods from industrial ecology (IE) could significantly contribute to the quantitative assessment of system resilience. In this study, we focus on developing quantitative metrics for flexibility, a key aspect of resilience. While resilience is understood as the capacity of a system to maintain its functionality under disruption (e.g. producing certain goods and services during a drought), flexibility is the ability of a system to meet that criteria through reconfiguring its structure (e.g., through technology substitution).

We approach system flexibility as the number of possible ‘optimal’ technology combinations when the system is disrupted. This reflects the number of substitution options a system has, and its ability to minimize the effects of sub-optimal technology configurations (e.g. power generation mix) on the allocation of resources (e.g. production costs). Under similar disruption levels, a system with more substitution options can be considered to be more flexible and thus more resilient. We use IE-based methods such as material flow analysis and environmental input-output analysis to characterize economic systems and their environmental implications. We then apply linear programming to calculate possible technology configurations under increasingly stringent resource and emission constraints (e.g. water use and GHG emissions). Based on these calculations we derive various quantitative metrics of system flexibility. We test this approach on two case studies pertaining to different scales: electricity generation in the 43 largest world economies, and electricity generation from 118 coal-fired plants in the Hebei Province in China.

 For the case of country-level electricity generation, we found economic systems to maintain high flexibility when subjected to constraints in metal use, and low flexibility when constrained to GHG emissions and water use. We also observed striking differences in flexibility between countries. This is mostly related to the share of renewable energies, but also due to varying production costs and other technological aspects (e.g. energy efficiency). Significant differences in flexibility were also found in the case of coal-fired plants in Hebei for various resources and air pollutants. This system keeps its flexibility when constrained in dust emissions, but not so much to when constrained in SO₂ and NO_x emissions, water use, and energy use. The production technologies are found to notably influence the flexibility metrics, especially regarding the effect of cooling technologies on total water use. Overall, the results show that the developed flexibility metrics are valuable to assess the resilience of the studied systems in the face of plausible environmental constraints, for instance the Paris Agreement Intended Nationally Determined Contributions (INDCs) to GHG emission reduction and local air pollution mitigation actions.