Moral Hazard and Risk Management in Agri‐environmental Policy

This paper develops the key finding of Ozanne, Hogan and Colman (2001) that risk aversion among farmers ameliorates the moral hazard problem in relation to agrienvironmental policy compliance. It is shown that risk averse farmers who face uncertainty in their production income are more likely to comply with such a policy as a means of risk management. In addition, it is shown that a principal who has control over both the level of monitoring and the size of penalty, if detected, can reduce non‐compliance by adjustments to these instruments which increase the variance of farmers' income but leave the expected penalty unchanged. It is concluded that risk management by both principals and agents has the potential to diminish the moral hazard problem, especially given proposed developments in agri‐environmental policy in the European Union.     

Dynamic modelling of pollution abatement in a CGE framework

This paper deals with the specification of pollution abatement in dynamic computable general equilibrium (CGE) models and analyses the dynamic feedback mechanisms between economy and abatement in the context of environmental policy. A Ramsey-type economic model is presented, in which bottom-up technical and economic information on abatement techniques is integrated in a top-down dynamic CGE context. The practical suitability of the specification is illustrated by an empirical application for climate change and acidification in the Netherlands. The results show that a mixture of some slowdown of economic growth, a substantial restructuring of the economy and implementation of most technical abatement measures is optimal.     

Seasonality and Markov switching in an unobserved component time series model

It is generally acknowledged that the growth rate of output, the seasonal pattern, and the business cycle are best estimated simultaneously. To achieve this, we develop an unobserved component time series model for seasonally unadjusted US GDP. Our model incorporates a Markov switching regime to produce periods of expansion and recession, both of which are characterized by different underlying growth rates. Although both growth rates are time-varying, they are assumed to be cointegrated. The analysis is Bayesian, which fully accounts for all sources of uncertainty. Comparison with results from a similar model for seasonally adjusted data indicates that the seasonal adjustment of the data significantly alters several aspects of the full model. First Version Received: January 2001/Final Version Received: February 2002 Send offprint requests to: Rob Luginbuhl?Correspondence to: Rob Luginbuhl     

The trend in female labour force participation: what can be expected for the future?

This study investigates the increase in the labour force participation rate of women. We estimate a binary age–period–cohort model for a sample of Dutch women born between 1925 and 1986. The results indicate that the increasing level of education and the diminishing negative effect of children have played an important role. Moreover, we find important unobserved cohort effects for pre-1955 generations, which is in line with results of studies on social norms. It is shown that the growth in female participation is likely to slow down in the near future, as such cohort effects are not relevant for younger generations.     

The multiple effects of carbon values on optimal rotation

Non-consumptive benefits which increase with crop age, like keeping carbon sequestered, lengthen optimal rotation compared with rotation for timber alone. High proposed carbon prices may extend rotation indefinitely. Carbon storage in wood products reduces this tendency. Biomass as an energy source displacing fossil fuels favours rotations near those of maximum biomass productivity. Use of sawn timber to displace structural materials with high embodied carbon favours somewhat longer rotations. Effects of rotation on soil carbon, and fossil carbon volatilised in harvesting operations, are further complications. Including all carbon effects results in optimal rotations somewhat longer than those based only on timber value, but shorter than those based on timber plus forest carbon. To include all factors intuitively is not possible: balanced appraisal needs economic calculations.     

A contrast analysis of online hotel web service purchasers and browsers

Ever since the introduction of Internet technology to general business, websites have played an important role in the business and promotional efforts of hotels. However, the existing tourism and hospitality literature contains only a limited number of articles that examine the perceived importance of the different attributes of a hotel website. This paper reports a study that analyzed the different perceptions of specific hotel website attributes between online purchasers and browsers and between Western and Asian users. The empirical findings reveal a set of attributes that are the most important in developing effective hotel websites. The findings also indicate the necessity of designing English- and Chinese-language hotel websites differently, based on the significant differences in the responses obtained from these two groups of online users.     

mLearning as a softer visitor management approach for sustainable tourism

With increased visitation to protected natural areas over the last four decades, there is a need for implementation of effective visitor management strategies at these sites to mitigate visitor impacts. This study explores the application of mobile learning (mLearning) in environmental interpretation and visitor education within the context of conservation and sustainable tourism. Specifically, it proposes a conceptual framework for mLearning as a visitor management tool for sustainable tourism. Current developments and innovations in mobile broadband networks, smartphone technology, and mobile software applications present opportunities for the utilization of such mobile-driven applications in interpretive programs to encourage free-choice learning and mindful visitor experiences. If effectively implemented, such interpretive programs and mLearning applications can affect visitor perceptions, attitudes, and future intentions toward conservation and environmental protection.     

Crude oil price forecasting based on internet concern using an extreme learning machine

The growing internet concern (IC) over the crude oil market and related events influences market trading, thus creating further instability within the oil market itself. We propose a modeling framework for analyzing the effects of IC on the oil market and for predicting the price volatility of crude oil’s futures market. This novel approach decomposes the original time series into intrinsic modes at different time scales using bivariate empirical mode decomposition (BEMD). The relationship between the oil price volatility and IC at an individual frequency is investigated. By utilizing decomposed intrinsic modes as specified characteristics, we also construct extreme learning machine (ELM) models with variant forecasting schemes. The experimental results illustrate that ELM models that incorporate intrinsic modes and IC outperform the baseline ELM and other benchmarks at distinct horizons. Having the power to improve the accuracy of baseline models, internet searching is a practical way of quantifying investor attention, which can help to predict short-run price fluctuations in the oil market.