Narrow Results

Because ocean acidification has only recently been recognized as a problem caused by CO₂ emissions, impact studies are still rare and estimates of the economic impact are absent. This paper estimates the economic impact of ocean acidification on coral reefs which are generally considered to be economically as well as ecologically important ecosystems. First, we conduct an impact assessment in which atmospheric concentration of CO₂ is linked to ocean acidity causing coral reef area loss. Next, a meta-analytic value transfer is applied to determine the economic value of coral reefs around the world. Finally, these two analyses are combined to estimate the economic impact of ocean acidification on coral reefs for the four IPCC marker scenarios. We find that the annual economic impact rapidly escalates over time, because the scenarios have rapid economic growth in the relevant countries and coral reefs are a luxury good. Nonetheless, the annual value in 2100 in still only a fraction of total income, one order of magnitude smaller than the previously estimated impact of climate change. Although the estimated impact is uncertain, the estimated confidence interval spans one order of magnitude only. Future research should seek to extend the estimates presented here to other impacts of ocean acidification and investigate the implications of our findings for climate policy.

I provide evidence on the existence of unspanned macro risk. I investigate the usefulness of unspanned macro information for forecasting bond risk premia in a macro-finance term structure model from the perspective of a bond investor. I account for model uncertainty by combining forecasts with and without unspanned output and inflation risks optimally from the forecaster’s objective. Incorporating macro information generates significant gains in forecasting bond risk premia relative to yield curve information at long forecast horizons, especially when allowing for time-varying combination weight. These gains in predictive accuracy significantly improve investor utility.

Understanding the economic value of irrigation water is essential for supporting policies relating to the irrigation sector, irrigation water allocation decisions, water pricing and to compare the variable impacts of water reform within and across sectors of the economy. In this paper, we apply the residual method as a complement to other methods for determining the value of the water used over a wide range of irrigated crops in different seasons and regions of Australia’s Murray–Darling Basin. Using Monte Carlo simulation and probability theory, we estimated the combined impacts of biophysical and economic factors on the economic productivity of irrigation water use by individual activities. The estimated residual values vary across regions and in response to water availability as we would expect and warrant consideration of these factors in making any future water policy and investment decisions in different regions. As anticipated perennial (fruits and nuts, grapes) and high capital annual activities (cotton) represent the highest value water uses. Water trading from low to high value activities results in economic losses that are much lower than the proportional decline in water availability during periods of drought.

Two statistical post-processing methods, ensemble Model Output Statistics (EMOS) and Ensemble Kernel Density MOS (EKDMOS), are applied in 20-year reforecasts of the National Centers for Environmental Prediction (NCEP) global ensemble forecast system version 12 (GEFS v12) to produce calibrated and downscaled 1-14-day probabilistic forecasts of cold extremes at specific stations over Taiwan. To generate an EMOS forecast, the MOS equation is built using the ensemble mean, and applied to each ensemble member. The EKDMOS uses a kernel density estimation (KDE) to create a probability density function (PDF) from the EMOS forecasts.

Calibration is performed using a leave-one-out cross-validation procedure, where one winter is used for validation, and the remaining 19 winters are used for training. Forecast evaluation shows that the EMOS is under-dispersive, just like the raw ensemble (RawEns) forecasts, with some bias removed. In contrast, the EKDMOS well represents the forecast uncertainty with most of the bias removed. Compared to the RawEns or EMOS, the EKDMOS obviously improves the reliability and discrimination of probabilistic forecasts. The EKDMOS increases the Brier skill score (BrSS) of the RawEns and EMOS by decreasing its reliability, and increasing its resolution components. For any threshold and any lead time, users with a wider spectrum of cost/loss ratio can obtain more benefit from the EKDMOS as compared to the RawEns or EMOS. The EKDMOS distribution, as expected from a reliable forecast system, necessarily approaches the climatology of the training sample when forecast informativeness is lost beyond 10 days.

Demand for probabilistic forecasts of consecutive days without measurable rainfall has grown significantly by users in different sectors of society, especially in agriculture, livestock, and water resource management. The purpose of this study is to provide users with reliable and skillful forecasts, which help users obtain more economic benefits in decision making. In this study, Analog post-processing (AP) is applied in 20-year reforecasts of the National Centers for Environmental Prediction (NCEP) Global Ensemble Forecast System version 12 (GEFS v12) to produce calibrated and downscaled probabilistic forecasts of consecutive days without measurable rainfall over Taiwan land area. Long-term forecast evaluation indicates that: (1) the problem of under-dispersion of the raw forecasts is effectively mitigated through the AP. (2) The probabilistic forecasts of consecutive days without measurable rainfall have good reliability and discrimination (potential usefulness) within next four weeks. (3) The calibrated forecasts provide higher economic benefits for users with a much wider spectrum of cost-to-loss ratio compared to the raw forecasts.

This chapter considers national parks and other protected areas as tourism attractions, with examples from Australia. The focus in this chapter is on measures of economic value and impact based on expenditure by tourists. National parks are important tourist attractions in Australia — a recent national level study found that over one-fifth of all tourism expenditure was incurred by people who visited national parks as part of their trip. This chapter places the expenditure approach into the context of economic valuation methods and discusses the most appropriate ways to separate out the attraction effect of the national park from all tourism expenditure. A number of case studies conducted in Australia at regional and state levels are included to illustrate methodology and results. Managers of national parks and protected areas, government funding agencies, the tourist industry and regional communities are all interested in gaining an appreciation of the economic value of these areas in order to inform decisions on appropriate funding for management and presenting and promoting these areas for sustainable tourism.