Using the fuzzy set theory to developing an environmental impact assessment index for a thermal power plant

Korean thermal power has had a prominent position in energy policies for the past decades. However, pollutants from the combustion of fossil fuels used in converting thermal energy into electrical energy have undesirable effect on the environment. This paper applies fuzzy set theory (FST) to identifying the significance of various environmental impacts using a specific case study of the electric power utilities in Korea. To this end, we consider nine environmental impacts: mortality, morbidity, forest, agricultural production, materials, visibility, thermal discharge water, landscape, and global warming. A survey of randomly sampled 1,000 households was administrated to obtain value judgments from FST about the environmental impacts of a thermal power plant. The overall results show that global warming is the most important attribute in environmental effect-related decision-making processes and followed by morbidity and mortality in order. The implications of the results for electric power utilities are also discussed. We found that the work and results can provide valuable insights for major decision making in energy and environmental planning of the electric power utilities.     

Suspending disbelief: Influencing engagement in scenarios of forest futures

Scenario analysis is an approach to long-term planning that informs decision-making in contexts of highly uncertain future conditions. Scenario-based studies are rapidly growing in popularity, yet many aspects of the method are not fully understood. Participants' willingness to suspend disbelief in possible futures is an integral component of scenario-based studies, essential for considering alternative future scenarios, yet little is known about the factors that affect it. Participation in a scenario project does not necessarily imply willingness or ability to suspend disbelief; participants may not suspend disbelief simply upon request. However, the scenario literature says little about the aspects of scenario-based studies that influence suspension of disbelief.This article explores the factors that influenced the suspension of disbelief in one scenario exercise about the future of Canada's forests. Participants differed in their abilities to suspend disbelief in the scenarios in part because of their past experiences and expertise in particular areas, and their assessments of probable and desired future outcomes. Elements of the project design and implementation influenced participants' abilities to engage the scenarios. The results have implications for those interested in using scenarios for helping people and communities envision possible - and sustainable - futures.     

The state of technological and social change: Impressions

As the long-wave theory has predicted, we are seeing a period of consolidation in which the pace of radical technological innovation seems exceeded by the pace of social change. Peter Drucker's dictum, that technology changes faster than society, appears now to have been reversed. The article offers research and anecdotal support for these assertions, linking them to specific trends and trend interactions, including patents and intellectual property litigation, new product development, and politics and revolution.     

An alternative to the weighted score card approach to measuring performance: An analysis of English and Welsh probation boards

The UK government published a weighted score card approach in 2003 to analyse the performance of Probation Boards in England and Wales. However, there has not been a thorough analysis of whether non-parametric methods could provide more advanced options for analysing performance than the standard Weighted Score Card approach – a variant of the Balanced Score Card. Our results show there is considerable divergence in the ranks of Probation Boards from that of the WSC when we include the input variable ‘resource expenditure’ within a DEA model, calling into question recent policy initiatives to increase efficiency in the national probation service.     

Competition and public high school performance

Increasing the level of school competition has been suggested as a way to improve school performance. This study examines one of the most extreme examples of such reform using data from New Zealand public high schools. In the 1990s school zoning was abolished in New Zealand and public schools competed for students, not just with private schools, but also with each other. A categorical Data Envelopment Analysis model using data on school resources and student academic performance, stratified using student socio-economic characteristics, is used to calculate efficiency scores for schools. A regression model is then used to analyse differences in these efficiency scores and their relationship to different levels of competition.     

Are VIX futures prices predictable? An empirical investigation

This paper investigates whether volatility futures prices per se can be forecasted by studying the fast-growing VIX futures market. To this end, alternative model specifications are employed. Point and interval out-of-sample forecasts are constructed and evaluated under various statistical metrics. Next, the economic significance of the forecasts obtained is also assessed by performing trading strategies. Only weak evidence of statistically predictable patterns in the evolution of volatility futures prices is found. No trading strategy yields economically significant profits. Hence, the hypothesis that the VIX volatility futures market is informationally efficient cannot be rejected.     

Advances in forecasting with neural networks? Empirical evidence from the NN3 competition on time series prediction

This paper reports the results of the NN3 competition, which is a replication of the M3 competition with an extension of the competition towards neural network (NN) and computational intelligence (CI) methods, in order to assess what progress has been made in the 10 years since the M3 competition. Two masked subsets of the M3 monthly industry data, containing 111 and 11 empirical time series respectively, were chosen, controlling for multiple data conditions of time series length (short/long), data patterns (seasonal/non-seasonal) and forecasting horizons (short/medium/long). The relative forecasting accuracy was assessed using the metrics from the M3, together with later extensions of scaled measures, and non-parametric statistical tests. The NN3 competition attracted 59 submissions from NN, CI and statistics, making it the largest CI competition on time series data. Its main findings include: (a) only one NN outperformed the damped trend using the sMAPE, but more contenders outperformed the AutomatANN of the M3; (b) ensembles of CI approaches performed very well, better than combinations of statistical methods; (c) a novel, complex statistical method outperformed all statistical and CI benchmarks; and (d) for the most difficult subset of short and seasonal series, a methodology employing echo state neural networks outperformed all others. The NN3 results highlight the ability of NN to handle complex data, including short and seasonal time series, beyond prior expectations, and thus identify multiple avenues for future research.