Global warming: Forecasts by scientists versus scientific forecasts

In 2007, the Intergovernmental Panel on Climate Change’s Working Group One, a panel of experts established by the World Meteorological Organization and the United Nations Environment Programme, issued its Fourth Assessment Report. The Report included predictions of dramatic increases in average world temperatures over the next 92 years and serious harm resulting from the predicted temperature increases. Using forecasting principles as our guide we asked: Are these forecasts a good basis for developing public policy? Our answer is “no.” To provide forecasts of climate change that are useful for policy-making, one would need to forecast (1) global temperature, (2) the effects of any temperature changes, (3) the effects of alternative policies, and (4) whether the best policy would be successfully implemented. Proper forecasts of all four are necessary for rational policy making. The IPCC Report was regarded as providing the most credible long-term forecasts of global average temperatures by 31 of the 51 scientists and others involved in forecasting climate change who responded to our survey. We found no references to the primary sources of information on forecasting methods despite the fact these are easily available in books, articles, and websites. We audited the forecasting processes described in Chapter 8 of the IPCC’s WG1 Report to assess the extent to which they complied with forecasting principles. We found enough information to make judgments on 89 out of a total of 140 forecasting principles. The forecasting procedures that were described violated 72 principles. Many of the violations were, by themselves, critical. The forecasts in the Report were not the outcome of scientific procedures. In effect, they were the opinions of scientists transformed by mathematics and obscured by complex writing. Research on forecasting has shown that experts’ predictions are not useful. We have been unable to identify any scientific forecasts of global warming. Claims that the Earth will get warmer have no more credence than saying that it will get colder.accuracy; audit; climate change; evaluation; expert judgment; mathematical models; public policy

Review of recent research towards power cable life cycle management

Power cables are integral to modern urban power transmission and distribution systems. For power cable asset managers worldwide, a major challenge is how to manage effectively the expensive and vast network of cables, many of which are approaching, or have past, their design life. This study provides an in-depth review of recent research and development in cable failure analysis, condition monitoring and diagnosis, life assessment methods, fault location, and optimisation of maintenance and replacement strategies. These topics are essential to cable life cycle management (LCM), which aims to maximise the operational value of cable assets and is now being implemented in many power utility companies. The review expands on material presented at the 2015 JiCable conference and incorporates other recent publications. The review concludes that the full potential of cable condition monitoring, condition and life assessment has not fully realised. It is proposed that a combination of physics-based life modelling and statistical approaches, giving consideration to practical condition monitoring results and insulation response to in-service stress factors and short term stresses, such as water ingress, mechanical damage and imperfections left from manufacturing and installation processes, will be key to success in improved LCM of the vast amount of cable assets around the world

How Will Energy Demand Develop in the Developing World?

Most of the medium-run growth in energy demand is forecast to come from the developing world, which consumed more total units of energy than the developed world in 2007. We argue that the main driver of the growth is likely to be increased incomes among the poor and near-poor. We document that as households come out of poverty and join the middle class, they acquire appliances, such as refrigerators, and vehicles for the first time. These new goods require energy to use and energy to manufacture. The current forecasts for energy demand in the developing world may be understated because they do not accurately capture the dramatic increase in demand associated with poverty reduction.

Approaching Economic Issues through Epidemiology–An Introduction to Business Epidemiology

In the tradition of transferring models and concepts from one science to another, our research explores the possibility of importing some concepts, definitions and approaches from human epidemiology to economic research, based on the extensive usage of medical terms and concepts in economy. The article explores some basic epidemiology concepts and their possible relevance to economic research, with the final goal to provide a new viewpoint over the economic phenomena, usable in economic crisis. The article introduces the concept of “business epidemiology” as a possible scientific approach to the economic crisis.epidemiology; business disease; company health; research methodology; financial contagion

Ensemble Committees for Stock Return Classification and Prediction

This paper considers a portfolio trading strategy formulated by algorithms in the field of machine learning. The profitability of the strategy is measured by the algorithm's capability to consistently and accurately identify stock indices with positive or negative returns, and to generate a preferred portfolio allocation on the basis of a learned model. Stocks are characterized by time series data sets consisting of technical variables that reflect market conditions in a previous time interval, which are utilized produce binary classification decisions in subsequent intervals. The learned model is constructed as a committee of random forest classifiers, a non-linear support vector machine classifier, a relevance vector machine classifier, and a constituent ensemble of k-nearest neighbors classifiers. The Global Industry Classification Standard (GICS) is used to explore the ensemble model's efficacy within the context of various fields of investment including Energy, Materials, Financials, and Information Technology. Data from 2006 to 2012, inclusive, are considered, which are chosen for providing a range of market circumstances for evaluating the model. The model is observed to achieve an accuracy of approximately 70% when predicting stock price returns three months in advance.Comment: 15 pages, 4 figures, Neukom Institute Computational Undergraduate Research prize - second plac

Shock to the system: dealing with falling electricity demand

This report argues that Australians are using less power but paying more for it, with potentially highly damaging consequences for the electricity system.OverviewElectricity use in Australia is falling. From the 1960s to the end of the 20th century, electricity consumption increased at an average annual rate of six per cent. Investment in power stations and electricity networks also rose steadily. Since 2009, however, eastern states’ electricity production has fallen and in Western Australia growth has plateaued since 2011.Yet this extraordinary fall in demand has not led to a fall in price, as would occur in a conventional market. Since 2006 the average household has reduced power use by more than seven per cent. But in that period the average household power bill has risen more than 85 per cent: from $890 to$1660 a year. One reason is that Australians are funding billions of dollars of infrastructure that falling consumption has made redundant. These price rises are unsustainable, but who will pay for the correction: power companies, governments or – once again – consumers?Falling consumption has several causes. Customers are responding to high prices by reducing use or switching to a new breed of more energy-efficient appliances. The cost of solar energy has fallen: a million households now have solar PV panels on their roofs. The economy has become less energy intensive as the manufacturing sector has declined.The nature of Australia’s energy market means that these changes are not leading to lower prices. Electricity generators operate in a free market: when consumption falls they must produce power at a lower price in order to sell it, or reduce production. But network businesses – which carry power from the generator to the business or home and which take about 45 per cent of a household’s electricity bill – are regulated monopolies not subject to market forces.For years, regulators have allowed these companies to earn excessive profits by setting tariffs that are too high given the low risk they face as monopolies. Some states have also allowed the companies to overinvest in infrastructure. This was less of a problem when demand was rising and higher costs were spread over a larger volume of sales. But when electricity use falls, the high cost of the network is spread over a smaller volume and customers pay more. Continually rising prices could induce them to disconnect from the network. Enough disconnections would trigger a crisis that insiders call the ‘death spiral’.To prevent this from happening governments must:Ensure that network companies make future investments that better match future power needs. Begin the hard task of reforming network tariffs so that prices companies charge reflect the costs they incur. Review the value of network assets to decide who should pay for any write-down of surplus infrastructure. These solutions are neither simple nor painless. But consumers deserve a better system. A future Grattan Institute report will produce recommendations for how that can be achieved

Exploring the trend of New Zealand housing prices to support sustainable development

The New Zealand housing sector is experiencing rapid growth that has a significant impact on society, the economy, and the environment. In line with the growth, the housing market for both residential and business purposes has been booming, as have house prices. To sustain the housing development, it is critical to accurately monitor and predict housing prices so as to support the decision-making process in the housing sector. This study is devoted to applying a mathematical method to predict housing prices. The forecasting performance of two types of models: autoregressive integrated moving average (ARIMA) and multiple linear regression (MLR) analysis are compared. The ARIMA and regression models are developed based on a training-validation sample method. The results show that the ARIMA model generally performs better than the regression model. However, the regression model explores, to some extent, the significant correlations between house prices in New Zealand and the macro-economic conditions

Forecasting the European carbon market

In an effort to meet its obligations under the Kyoto Protocol, in 2005 the European Union introduced a cap-and-trade scheme where mandated installations are allocated permits to emit CO2. Financial markets have developed that allow companies to trade these carbon permits. For the EU to achieve reductions in CO2 emissions at a minimum cost, it is necessary that companies make appropriate investments and policymakers design optimal policies. In an effort to clarify the workings of the carbon market, several recent papers have attempted to statistically model it. However, the European carbon market (EU ETS) has many institutional features that potentially impact on daily carbon prices (and associated …nancial futures). As a consequence, the carbon market has properties that are quite di¤erent from conventional financial assets traded in mature markets. In this paper, we use dynamic model averaging (DMA) in order to forecast in this newly-developing market. DMA is a recently-developed statistical method which has three advantages over conventional approaches. First, it allows the coefficients on the predictors in a forecasting model to change over time. Second, it allows for the entire forecasting model to change over time. Third, it surmounts statistical problems which arise from the large number of potential predictors that can explain carbon prices. Our empirical results indicate that there are both important policy and statistical benefits with our approach. Statistically, we present strong evidence that there is substantial turbulence and change in the EU ETS market, and that DMA can model these features and forecast accurately compared to conventional approaches. From a policy perspective, we discuss the relative and changing role of different price drivers in the EU ETS. Finally, we document the forecast performance of DMA and discuss how this relates to the efficiency and maturity of this market