Introduction to the Special Section on “Modeling Sustainable Risk Management”

ABSTRACT

In recent years, risk management has attracted a great deal of attention from both researchers and practitioners. Risk management practice has required new tools to achieve sustainability when dealing with loss exposure. Sustainable risk management is studied from different silo disciplinary perspectives.

Key Words:

• sustainable risk management
• model
• risk management

We are very pleased to present this special section of Human and Ecological Risk Assessment: “Modeling Sustainable Risk Management.” Over the past several decades, risk management has attracted much attention from both researchers and practitioners. Risk management for the purposes of this special section can be defined as the process of identification, analysis, and either acceptance or mitigation of uncertainty in investment decision-making. In a complex world, risk management practice has required new tools to achieve sustainability when dealing with loss exposure. The state-of-the-art research in enterprise risk management (ERM; Olson and Wu 2008) or emergency management (Baranoff 2004) aims to achieve sustainable risk management from different silo disciplinary perspectives beyond the traditional financial risk management. For example, game theory such as Global Games discovered by Carlsson and Van Damme (1993) has been introduced to explain bank run and financial crisis, where Nash equilibrium is found to achieve coordination and sustainability (Allen and Gale 2009).

Our call for articles cited substantial and important growth in the application of quantitative analysis to interdisciplinary problems arising in risk management, with special attention given to management of environmental hazards. We seek to provide the primary forum for both academic and industry researchers and practitioners to propose and foster discussion on state-of-the-art research and development of quantitative analysis in the areas of risk management.

This special section includes the broad coverage we were seeking, with theoretical modeling research using EGARCH in the European Union Emission Trading System (EU ETS); a case study of sustainable development and cleaner technology in Brazilian Energy CDM projects: consideration of risks; a methodology article for using data envelopment analysis to assess eco-efficiency in China; a comparative methodology article for environmental risk assessment; a modeling article in a micro-grid generation structure of a wind farm; a modeling article on an emergency response process with the case of a high-speed railway accident in China; and a comparative methodology article on safety risk assessment and improvement in a food production process.

The special section includes various optimization tools, to include GARCH for times series analysis, convex programming and multi-objective programming models in operation research, Dynamic Bayesian Networks, and heuristic algorithms.

In the first article, Chen et al. (2013) analyze the price mechanism of the EU ETS over its first two phases. After a detailed description of the European Union Allowance (EUA) price movements in the last several years, efforts are made to explore some methods to model returns of emission allowances and EGARCH is suggested for modeling. Then estimation and forecasting are made respectively based on two phases that the EU ETS identified. Results suggest that both price mechanism and volatility are dramatically different between Phase I and Phase II.

Choi et al. (2013) employ a two-stage slack-based undesirable-output DEA model to measure the eco-efficiency of China. Research shows that the level of industrialization does not contribute to eco-efficiency; however, promotion of the service industry, FDI, investment for the environment, and regional innovation have positive effects on eco-efficiency.

Costa Silva, Jr. et al. (2013) focus on evaluation of the contribution of energy Clean Development Mechanism (CDM) projects for the generation of cleaner technologies and the promotion of sustainable development in Brazil. The data collected were compared using a data triangulation technique and further analyzed in the light of an analysis model. The result shows that Brazilian energy CDM projects contribute to cleaner technology generation and to the promotion of triple bottom line sustainable development.

Gao et al. (2013) studied food safety risk in serial and parallel modes, and develop a heuristic algorithm of polynomial time to minimize safety risks in food production with a constant budget. This algorithm was validated by a numerical example of peanut milk production. The primary procedures in peanut milk production were found and the advice on investment allocation was given to improve production.

Luo and Wu (2013) examined how catastrophe events affect risk analysis from a financial perspective. Data from different industries such as Advanced Sustainable Performance Indices (ASPI), gold, energy, real estate, and insurance were collected and analyzed. The performance of these funds was compared by using various financial ratios. The ASPI Index gave the best diversification. From their analysis, Luo and Wu concluded that portfolio diversification is a good way to hedge against caatastophic risk.

Park et al. (2013) conducted a comparison analysis between the PM2.5 apportioned from the Chemical Mass Balance receptor model using organic tracers as molecular markers with those from the source-based Community Multiscale Air Quality (CMAQ) model. The results show that both models have strengths and limitations, and each model's strengths can be utilized to help overcome the other model's limitations.

This section concludes with Liu et al. (2013), in which the micro-grid generation structure of a wind farm was established, a processing flow chart of intelligent management system was drawn, and a risk management model of a micro-grid wind farm was built from the angle of cost risk, including construction of a cost risk model and operation cost risk model. The work provides a reference and mirror for solving grid-connected wind energy problems and an important basis for wind energy risk policy-making, and for avoiding the risks in the process of planning, design, and operation management of wind farms.

Maintaining a certain level of risk has become a key strategy to make profits in today's economy. Risk in enterprise can be quantified and managed using various models. Models also provide support to organizations seeking to control enterprise risk. We have discussed various risk modeling to optimize risk management.

ACKNOWLEDGMENTS

We thank all the referees for their valuable time and effort. We thank HERA's editor, Barry L. Johnson, for many valuable suggestions, his energy, and effort in bringing forth this special section.