Chi Truong

It’s not now or never: Implications of investment timing and risk aversion on climate adaptation to extreme events

Public investment into risk reduction infrastructure plays an important role in facilitating adaptation to climate impacted hazards and natural disasters. In this paper, we provide an economic framework to incorporate investment timing and insurance market risk preferences when evaluating projects related to reducing climate impacted risks. The model is applied to a case study of bushfire risk management. We find that optimal timing of the investment may increase the net present value (NPV) of an adaptation project for various levels of risk aversion. Assuming risk neutrality, while the market is risk averse, is found to result in an unnecessary delay of the investment into risk reduction projects. The optimal waiting time is shorter when the insurance market is more risk averse or when a more serious scenario for climatic change is assumed. A higher investment cost or a higher discount rate will increase the optimal waiting time. We also find that a stochastic discount rate results in higher NPVs of the project than a discount rate that is assumed fixed at the long run average level.

Managing risks from climate impacted hazards – The value of investment flexibility under uncertainty

Incomplete knowledge about climate change and the related uncertainty in climate prediction makes adaptation inherently difficult. We introduce a real options framework to determine the optimal adaptation to catastrophic risk that takes into account climate change uncertainty. The framework can be used to select the optimal adaptation project from a number of alternative projects or to determine the optimal investment sequence of the considered projects. In applying the model to the management of bushfire risk at a local government area, we find that the framework can significantly increase the value of adaptation investment, above the current net present value, and also improve upon deterministic dynamic models. We also find that it is important to consider sequential investment to preserve investment flexibility under the uncertainty of climate change. When decision makers can afford multiple investment projects, the loss associated with the use of a simple net present value rule can be substantial, and it is important to use a deterministic dynamic model or a real options model.

Determinants of illegal logging in Indonesia: An empirical analysis for the period 1996–2010

ABSTRACT Illegal logging is a significant problem in Indonesia, which is one of the few countries with a large forest area. In this study, we investigate the factors that affect harvesting and supply of illegal timber from Indonesia to China and Japan. Moreover, we investigate the factors that lead to the demand of Indonesian illegal timber from China and Japan. A simultaneous-equation econometric model of illegally logged timber demand and supply is developed and tested using the annual data over the period 1996–2010. We find that corruption and decentralization in Indonesia have significant and positive impacts on the illegally logged timber supply while excess demand in Japanese construction and furniture industries as well as Japan’s housing starts are the significant factors that affect the illegal logging in Indonesia. The law enforcement or policies aimed at reducing illegal harvesting in Indonesia are found to be more effective than the policies targeting the import of illegally logged timber into Japan and China.

Discounting the distant future—Data on Australian discount rates estimated by a stochastic interest rate model

Data on certainty equivalent discount factors and discount rates for stochastic interest rates in Australia are provided in this paper. The data has been used for the analysis of investments into climate adaptation projects in ׳It׳s not now or never: Implications of investment timing and risk aversion on climate adaptation to extreme events׳ (Truong and Trück, 2016) [3] and can be used for other cost-benefit analysis studies in Australia. The data is of particular interest for the discounting of projects that create monetary costs and benefits in the distant future.

Systemic Financial Risk Inference in a Global Setting

We propose a new top-down approach to measure systemic risk in the financial system. Our framework uses a combination of macroeconomic, financial and rating factors in representative regions of the world. We formulate a mixed-frequency state-space model to estimate macroeconomic factors. To derive financial risk factors, we use Moody’s/KMV expected default frequencies after accounting for ratings of major financial institutions in the considered regions. The estimated factors are combined to derive probabilities for systemically relevant defaults in the financial industry. Regional macroeconomic factors are significant predictors of the existence and number of systemically important defaults, while regional financial risk and ratings factors are relevant for the existence only. For major events, global credit risk also matters.