Employment and Output Effects of Climate Policies

Recently academic work has been put forward that argues for a great urgency to implement effective climate policies to stop global warming. Concrete policy proposals for reducing CO2 emissions have been developed by the IPCC. One of the major instruments proposed is a carbon tax. A main obstacle for its implementation, however, are concerns about the short-term effects on employment and output. In order to miti-gate possible negative effects of enviromental taxes on output and employment, several European countries have introduced so-called environmental tax reforms (ETR) which are designed in a budget neutral manner: Revenues from the tax can be used to reduce existing distortionary taxes or to subsidize less polluting activities. We apply this idea to a carbon tax scheme by performing a vector autoregression (VAR) with output and employment data of nine industrialized countries. We impose a simultaneous policy shock on the economy whereby a carbon tax is levied on high-carbon intensive industries and the resulting tax revenue is redistributed to low-carbon intensive industries. Impulse response analysis shows that such a policy allows for net gains in terms of output and employment

Application of Stationary Wavelet Support Vector Machines for the Prediction of Economic Recessions

This paper examines the efficiency of various approaches on the classification and prediction of economic expansion and recession periods in United Kingdom. Four approaches are applied. The first is discrete choice models using Logit and Probit regressions, while the second approach is a Markov Switching Regime (MSR) Model with Time-Varying Transition Probabilities. The third approach refers on Support Vector Machines (SVM), while the fourth approach proposed in this study is a Stationary Wavelet SVM modelling. The findings show that SW-SVM and MSR present the best forecasting performance, in the out-of sample period. In addition, the forecasts for period 2012-2015 are provided using all approaches

Betting and Belief: Prediction Markets and Attribution of Climate Change

Despite much scientific evidence, a large fraction of the American public doubts that greenhouse gases are causing global warming. We present a simulation model as a computational test-bed for climate prediction markets. Traders adapt their beliefs about future temperatures based on the profits of other traders in their social network. We simulate two alternative climate futures, in which global temperatures are primarily driven either by carbon dioxide or by solar irradiance. These represent, respectively, the scientific consensus and a hypothesis advanced by prominent skeptics. We conduct sensitivity analyses to determine how a variety of factors describing both the market and the physical climate may affect traders' beliefs about the cause of global climate change. Market participation causes most traders to converge quickly toward believing the "true" climate model, suggesting that a climate market could be useful for building public consensus.Comment: All code and data for the model is available at http://johnjnay.com/predMarket/. Forthcoming in Proceedings of the 2016 Winter Simulation Conference. IEEE Pres

Local Short Term Electricity Load Forecasting: Automatic Approaches

Short-Term Load Forecasting (STLF) is a fundamental component in the efficient management of power systems, which has been studied intensively over the past 50 years. The emerging development of smart grid technologies is posing new challenges as well as opportunities to STLF. Load data, collected at higher geographical granularity and frequency through thousands of smart meters, allows us to build a more accurate local load forecasting model, which is essential for local optimization of power load through demand side management. With this paper, we show how several existing approaches for STLF are not applicable on local load forecasting, either because of long training time, unstable optimization process, or sensitivity to hyper-parameters. Accordingly, we select five models suitable for local STFL, which can be trained on different time-series with limited intervention from the user. The experiment, which consists of 40 time-series collected at different locations and aggregation levels, revealed that yearly pattern and temperature information are only useful for high aggregation level STLF. On local STLF task, the modified version of double seasonal Holt-Winter proposed in this paper performs relatively well with only 3 months of training data, compared to more complex methods

Volatility Cluster and Herding

Stock markets can be characterized by fat tails in the volatility distribution, clustering of volatilities and slow decay of their time correlations. For an explanation models with several mechanisms and consequently many parameters as the Lux-Marchesi model have been used. We show that a simple herding model with only four parameters leads to a quantitative description of the data. As a new type of data we describe the volatility cluster by the waiting time distribution, which can be used successfully to distinguish between different models.Comment: 15 pages TeX, 6 figures PostScrip

Disaggregated Approaches to Freight Analysis: A Feasibility Study.

Forecasting the demand for freight transport is notoriously difficult. Although ever more advanced modelling techniques are becoming available, there is little data available for calibration. Compared to passenger travel, there are many fewer decision makers in freight, especially for the main bulk commodities, so the decisions of a relatively small number of principal players greatly influence the outcome. Moreover, freight comes in various shapes, sizes and physical states, which require different handling methods and suit the various modes (and sub-modes) of transport differently. In the face of these difficulties, present DTp practice is to forecast Britain's freight traffic using a very simple aggregate approach which assumes that tonne kilometres will rise in proportion to GDP. Although this simple model fits historical data quite well, there is a clear danger that this relationship will not hold good in the future. The relationship between tonne kilometres and GDP depends on the mix of products produced, their value to weight ratios, number of times lifted and lengths of haul. In the past, a declining ratio of tonnes to GDP has been offset by increasing lengths of haul. This has come about through a complicated set of changes in product mix, industrial structure and distribution systems. A more disaggregate approach which studies changes in all these factors by industrial sector seems likely to provide a better understanding of the relationship between tonne kilometres and GDP. However, there are also problems with disaggregation. As we disaggregate we get more understanding of what might change in the future, but are less able to project trends forward. This can be seen if we consider the future amounts of coal movements. Theoretically there is clearly scope for better forecasting by allowing for past trends to be overturned by a movement towards gas powered electricity generation and more imports of coal direct to coastal power stations. However, making such a sectoral forecast is extremely difficult, and inaccuracy here may more than offset the theoretical gain referred to earlier. This is because it is usually easier to forecast to a given percentage accuracy an aggregate rather than its components. For example, the percentage error on sales forecasts of Hotpoint washing machines will be greater than that for the sales of all washing machines taken together. This occurs because different makes of washing machines are substitutes for each other, so forecasts for Hotpoint washing machines must take into account uncertainty over Hotpoint's market share as well as uncertainty over the future total sales of washing machines. Nevertheless, a disaggregate investigation of the market could spot trends which were `buried' in the aggregate figures. For example, rapidly declining sales for one manufacturer might indicate their leaving the market, which with less competition would then price up and so reduce the total future sales. We have assumed above that the use of the term disaggregate in the brief refers to disaggregation by industrial sector. An alternative usage of the word disaggregate in this context is when referring to modelling at the level of the individual decision making unit. Disaggregate freight modelling in this sense would involve analysing decisions in order to determine the utility weight attached to different attributes of available transport options. Because data on suitable decisions is not readily available in this country, due to commercial confidentiality, we have recently undertaken research in which we have presented decision makers with hypothetical choices, and obtained the necessary utility weights from their responses. Whilst initial scepticism is understandable, this method has produced results acceptable for use in major projects. ITS itself has provided algorithms (known as Leeds Adaptive Stated Preference) which have been used to derive utility weights for use by British Rail in forecasting cross-channel freight, by DTp in evaluating the reaction of commercial vehicles to toll roads, and by the Dutch Ministry of Transport in modelling freight in the Netherlands. In the light of the above, the following objectives were set for the feasibility study: (1)To determine if a forecasting approach disaggregated by industrial sectors, as under the first definition above, can be used to explain recent trends in freight transport; (2)To test the feasibility of the disaggregated approach for improving the understanding of likely future developments in freight markets, this being informed by current best understanding of the disaggregate decision-making process as under the second definition above