Changes in energy efficiency in Australia: A decomposition of aggregate energy intensity using logarithmic mean Divisia approach

This paper provides an empirical estimation of energy efficiency and other proximate factors that explain energy intensity in Australia for the period 1978-2009. The analysis is performed by decomposing the changes in energy intensity by means of energy efficiency, fuel mix and structural changes using sectoral and sub-sectoral levels of data. The results show that the driving forces behind the decrease in energy intensity in Australia are efficiency effect and sectoral composition effect, where the former is found to be more prominent than the latter. Moreover, the favourable impact of the composition effect has slowed consistently in recent years. A perfect positive association characterizes the relationship between energy intensity and carbon intensity in Australia. The decomposition results indicate that Australia needs to improve energy efficiency further to reduce energy intensity and carbon emissions. © 2012 Elsevier Ltd.

Divisia indexes, money and welfare

We suggest the use of a particular Divisia index for measuring welfare losses due to interest rate wedges and in‡ation. Compared to the existing options in the literature: i) when the demands for the monetary assets are known, closed-form solutions for the welfare measures can be obtained at a relatively lower algebraic cost; ii) less demanding integrability conditions allow for the recovery of welfare measures from a larger class of demand systems and; iii) when the demand speci…cations are not known, using an index number entitles the researcher to rank di¤erent vectors of opportunity costs directly from market observations. We use two examples to illustrate the method.

The n-dimensional bailey-divisia measure as a general-equilibrium measure of the welfare costs of inflation

This paper shows that in economies with several monies the Bailey-Divisia multidimensional consumers surplus formula may emerge as an exact general-equilibrium measure of the welfare costs of in ation, provided that preferences are quasilinear.

Does Money Matter in the Euro area? Evidence from a new Divisia Index. Bruegel Working Paper 2014/12, 6 November 2014

The purpose of this paper is to examine the possible role of money shocks on output and prices in the euro area. Since no Divisia monetary aggregates are available for the euro area, we first create and make available a database on euro-area Divisia monetary aggregates. We plan to update the dataset in the future and keep it publicly available. Using different SVAR models, we find sensible and statistically significant responses to Divisia money shocks, while the responses to simple-sum measures of money and interest rates are not statistically significant, and sometimes even the point estimates are not sensible.

A comparison of linear forecasting models and neural networks:an application to Euro inflation and Euro Divisia

Linear models reach their limitations in applications with nonlinearities in the data. In this paper new empirical evidence is provided on the relative Euro inflation forecasting performance of linear and non-linear models. The well established and widely used univariate ARIMA and multivariate VAR models are used as linear forecasting models whereas neural networks (NN) are used as non-linear forecasting models. It is endeavoured to keep the level of subjectivity in the NN building process to a minimum in an attempt to exploit the full potentials of the NN. It is also investigated whether the historically poor performance of the theoretically superior measure of the monetary services flow, Divisia, relative to the traditional Simple Sum measure could be attributed to a certain extent to the evaluation of these indices within a linear framework. Results obtained suggest that non-linear models provide better within-sample and out-of-sample forecasts and linear models are simply a subset of them. The Divisia index also outperforms the Simple Sum index when evaluated in a non-linear framework. © 2005 Taylor & Francis Group Ltd.

Evolving weights for a new United Kingdom Divisia

Divisia money is a monetary aggregate that gives each component asset an assigned weight. We use an evolutionary neural network to calculate new Divisia weights for each component utilising the Bank of England monetary data for the U.K. We propose a new monetary aggregate using our newly derived weights to carry out quantitative inflation prediction. The results show that this new monetary aggregate has better inflation forecasting performance than the traditionally constructed Bank of England Divisa money. This result is important for monetary policymakers, as improved construction of monetary aggregates will yield tighter relationships between key macroeconomic variables and ultimately, greater macroeconomic control. Research is ongoing to establish the extent of the increased information content and parameter stability of this new monetary aggregate.

Forecasting the UK/US exchange rate with divisia monetary models and neural networks

This paper compares the UK/US exchange rate forecasting performance of linear and nonlinear models based on monetary fundamentals, to a random walk (RW) model. Structural breaks are identified and taken into account. The exchange rate forecasting framework is also used for assessing the relative merits of the official Simple Sum and the weighted Divisia measures of money. Overall, there are four main findings. First, the majority of the models with fundamentals are able to beat the RW model in forecasting the UK/US exchange rate. Second, the most accurate forecasts of the UK/US exchange rate are obtained with a nonlinear model. Third, taking into account structural breaks reveals that the Divisia aggregate performs better than its Simple Sum counterpart. Finally, Divisia-based models provide more accurate forecasts than Simple Sum-based models provided they are constructed within a nonlinear framework.

Decomposition analysis of air pollution abatement in China: empirical study for ten industrial sectors from 1998 to 2009

This study analyzes the management of air pollutant substance in Chinese industrial sectors from 1998 to 2009. Decomposition analysis applying the logarithmic mean divisia index is used to analyze changes in emissions of air pollutants with a focus on the following five factors: coal pollution intensity (CPI), end-of-pipe treatment (EOP), the energy mix (EM), productive efficiency change (EFF), and production scale changes (PSC). Three pollutants are the main focus of this study: sulfur dioxide (SO2), dust, and soot. The novelty of this paper is focusing on the impact of the elimination policy on air pollution management in China by type of industry using the scale merit effect for pollution abatement technology change. First, the increase in SO2 emissions from Chinese industrial sectors because of the increase in the production scale is demonstrated. However, the EOP equipment that induced this change and improvements in energy efficiency has prevented an increase in SO2 emissions that is commensurate with the increase in production. Second, soot emissions were successfully reduced and controlled in all industries except the steel industry between 1998 and 2009, even though the production scale expanded for these industries. This reduction was achieved through improvements in EOP technology and in energy efficiency. Dust emissions decreased by nearly 65% between 1998 and 2009 in the Chinese industrial sectors. This successful reduction in emissions was achieved by implementing EOP technology and pollution prevention activities during the production processes, especially in the cement industry. Finally, pollution prevention in the cement industry is shown to result from production technology development rather than scale merit. © 2013 Elsevier Ltd. All rights reserved.

Decomposition of toxic chemical substance management in three U.S. manufacturing sectors from 1991 to 2008

This study analyzes toxic chemical substance management in three U.S. manufacturing sectors from 1991 to 2008. Decomposition analysis applying the logarithmic mean Divisia index is used to analyze changes in toxic chemical substance emissions by the following five factors: cleaner production, end-of-pipe treatment, transfer for further management, mixing of intermediate materials, and production scale. Based on our results, the chemical manufacturing sector reduced toxic chemical substance emissions mainly via end-of-pipe treatment. In the meantime, transfer for further management contributed to the reduction of toxic chemical substance emissions in the metal fabrication industry. This occurred because the environmental business market expanded in the 1990s, and the infrastructure for the recycling of metal and other wastes became more efficient. Cleaner production is the main contributor to toxic chemical reduction in the electrical product industry. This implies that the electrical product industry is successful in developing a more environmentally friendly product design and production process.

Decomposition of energy-related CO2 emissions in Australia: Challenges and policy implications

Changes in energy-related CO2 emissions aggregate intensity, total CO2 emissions and per-capita CO2 emissions in Australia are decomposed by using a Logarithmic Mean Divisia Index (LMDI) method for the period 1978-2010. Results indicate improvements in energy efficiency played a dominant role in the measured 17% reduction in CO2 emissions aggregate intensity in Australia over the period. Structural changes in the economy, such as changes in the relative importance of the services sector vis-à-vis manufacturing, have also played a major role in achieving this outcome. Results also suggest that, without these mitigating factors, income per capita and population effects could well have produced an increase in total emissions of more than 50% higher than actually occurred over the period. Perhaps most starkly, the results indicate that, without these mitigating factors, the growth in CO2 emissions per capita could have been over 150% higher than actually observed. Notwithstanding this, the study suggests that, for Australia to meet its Copenhagen commitment, the relative average per annum effectiveness of these mitigating factors during 2010-2020 probably needs to be almost three times what it was in the 2005-2010 period-a very daunting challenge indeed for Australia's policymakers.

Mesure du Progres Technique: Theories et Methodes

Ce Texte Constitue un Survol des Differentes Approches Destines a Mesurer le Progres Technique. Nous Utilisons une Notation Uniforme Tout au Long des Demonstrations Mathematiques et Nous Faisons Ressortir les Hypotheses Qui Rendent L'application des Methodes Proposees Envisageable et Qui En Limitent la Portee. les Diverses Approches Sont Regroupees D'apres une Classification Suggeree Par Diewert (1981) Selon Laquelle Deux Groupes Sont a Distinguer. le Premier Groupe Contient Toutes les Methodes Definissant le Progres Technique Comme le Taux de Croissance D'un Indice des Outputs Divise Par un Indice des Inputs (Approche de Divisia). L'autre Groupe Inclut Toutes les Methodes Definissant le Progres Technique Comme Etant le Deplacement D'une Fonction Representant la Technologie (Production, Cout, Distance). Ce Second Groupe Est Subdivise Entre L'approche Econometrique,La Theorie des Nombres Indices et L 'Approche Non Parametrique. une Liste des Pricipaux Economistes a Qui L'on Doit les Diverses Approches Est Fournie. Cependant Ce Survol Est Suffisamment Detaille Pour Etre Lu Sans Se Referer aux Articles Originaux.

El capital humano como factor de crecimiento de la productividad regional

Resumen tomado de la publicaci??n

Measuring productivity growth when technological change is biased - a new index and an application to UK agriculture

Productivity growth is conventionally measured by indices representing discreet approximations of the Divisia TFP index under the assumption that technological change is Hicks-neutral. When this assumption is violated, these indices are no longer meaningful because they conflate the effects of factor accumulation and technological change. We propose a way of adjusting the conventional TFP index that solves this problem. The method adopts a latent variable approach to the measurement of technical change biases that provides a simple means of correcting product and factor shares in the standard Tornqvist-Theil TFP index. An application to UK agriculture over the period 1953-2000 demonstrates that technical progress is strongly biased. The implications of that bias for productivity measurement are shown to be very large, with the conventional TFP index severely underestimating productivity growth. The result is explained primarily by the fact that technological change has favoured the rapidly accumulating factors against labour, the factor leaving the sector. (C) 2004 Elsevier B.V. All rights reserved.

Regulatory reform and productivity change in Indian banking

This paper examines the impact of regulatory reform on productivity growth and its components for Indian banks in 1992-2009. We estimate parametric and non-parametric efficiency frontiers, followed by Divisia and Malmquist indexes of Total Factor Productivity respectively. To account for technology heterogeneity among ownership types we utilise a metafrontier approach. Results are consistent across methodologies and show sustained productivity growth, driven mainly by technological progress. Furthermore, results indicate that different ownership types react differently to changes in the operating environment. The position of foreign banks becomes increasingly dominant and their production technology becomes the best practice in the industry.

An ordering of measures of the welfare cost of inflation in economies with interest-bearing deposits

This paper builds on Lucas (2000) and on Cysne (2003) to derive and order six alternative measures of the welfare costs of inflation (five of which already existing in the literature) for any vector of opportunity costs. The ordering of the functions is carried out for economies with or without interestbearing deposits. We provide examples and closed-form solutions for the log-log money demand both in the unidimensional and in the multidimensional setting (when interest-bearing monies are present). An estimate of the maximum relative error a researcher can incur when using any particular measure is also provided.