Electricity consumption–real GDP causality nexus: Evidence from a bootstrapped causality test for 30 OECD countries

The goal of this paper is to examine any causal effects between electricity consumption and real GDP for 30 OECD countries. We use a bootstrapped causality testing approach and unravel evidence in favour of electricity consumption causing real GDP in Australia, Iceland, Italy, the Slovak Republic, the Czech Republic, Korea, Portugal, and the UK. The implication is that electricity conservation policies will negatively impact real GDP in these countries. However, for the rest of the 22 countries our findings suggest that electricity conversation policies will not affect real GDP.

The energy consumption - real GDP nexus revisited : empirical evidence from 93 countries

In this paper, we analyse the long-run relationship between energy consumption and real GDP for 93 countries. We find mixed results on the impact of energy consumption on real GDP, with greater evidence at the country level supporting energy consumption having a negative causal effect on real GDP. For the G6 panel of countries, we find significant evidence that energy consumption negatively Granger causes real GDP. This means that for countries where energy consumption has a negative long-run causal effect on real GDP, energy conversation policies should not retard economic growth. We identify these countries and regional panels. We argue that these countries/regions should play a greater role in reducing carbon dioxide emissions.

Workplace "bargaining" and the wages/working time nexus: counting the cost of decentralisation

The impact of deregulation on dispersion of earnings in Victoria has been
acknowledged in the findings of the recent task force enquiry into industrial relations in Victoria. This paper argues that the link between hours worked and rates of pay has played a significant role in this increased dispersion. Drawing upon detailed analysis of hours and wages in Victorian agreements, data is presented on declining take-home pay flowing from the loss of penalty rates. This, we argue, is attributable to
the lack of substantive and procedural protections available to Victorian workers under schedule 1A of the Workplace Relations Act, and formerly under the Victorian Employee Relations Act, 1992. We contrast these findings with collective agreements trading off penalty rates certified by the Australian Industrial Relations Commission, and Australian Workplace Agreements approved by the Office of the Employment
Advocate. We conclude by suggesting there is a scale of fair outcomes attached to the wages/hours trade-off, directly attributable to the various institutional mechanisms now influencing Australian wage determination.

What will affect software reuse: a causal model analysis

Software reuse is an important topic due to its potential benefits in increasing product quality and decreasing cost. Although more and more people are aware that not only technical issues, but also nontechnical issues are important to the success of software reuse, people are still not certain which factors will have direct effect on the success of reuse. In this paper, we applied a causal discovery algorithm to the software reuse survey data [2]. Ensemble strategy is incorporated to locate a probable causal model structure for software reuse, and find all those factors which have direct effect on the success of reuse. Our discovery results reinforced some conclusions of Morisio et al. and found some new conclusions which might significantly improve the odds of a reuse project succeeding.

Accelerating linear causal model discovery using hoeffding bounds

Efficiently inducing precise causal models accurately reflecting given data sets is the ultimate goal of causal discovery. The algorithms proposed by Dai et al. has demonstrated the ability of the Minimum Message Length (MML) principle in discovering Linear Causal Models from training data. In order to further explore ways to improve efficiency, this paper incorporates the Hoeffding Bounds into the learning process. At each step of causal discovery, if a small number of data items is enough to distinguish the better model from the rest, the computation cost will be reduced by ignoring the other data items. Experiments with data set from related benchmark models indicate that the new algorithm achieves speedup over previous work in terms of learning efficiency while preserving the discovery accuracy.

Ensembling MML causal discovery

This paper presents an ensemble MML approach for the discovery of causal models. The component learners are formed based on the MML causal induction methods. Six different ensemble causal induction algorithms are proposed. Our experiential results reveal that (1) the ensemble MML causal induction approach has achieved an improved result compared with any single learner in terms of learning accuracy and correctness; (2) Among all the ensemble causal induction algorithms examined, the weighted voting without seeding algorithm outperforms all the rest; (3) It seems that the ensembled CI algorithms could alleviate the local minimum problem. The only drawback of this method is that the time complexity is increased by δ times, where δ is the ensemble size.

Body mass index and parent-reported self-estem in elementary school children: evidence for a causal relationship.

OBJECTIVE: To clarify relationships between body mass index (BMI) and self-esteem in young children at a population level. To assess whether low self-esteem precedes or follows development of overweight/obesity in children. DESIGN: Prospective cohort study in elementary schools throughout Victoria, Australia. Child BMI and self-esteem were measured in 1997 and 2000. SUBJECTS: Random sample of 1,157 children who were in the first 4 y of elementary school (aged 5-10 y) at baseline. MEASURES: BMI was calculated from measured height and weight, then transformed to z-scores. Children were classified as nonoverweight, overweight or obese based on international cut-points. Low child self-esteem was defined as a score below the 15th percentile on the self-esteem subscale of the parent-reported Child Health Questionnaire. RESULTS: Overweight/obese children had lower median self-esteem scores than nonoverweight children at both timepoints, especially at follow-up. After accounting for baseline self-esteem, higher baseline BMI z-score predicted poorer self-esteem at follow-up (P=0.008). After accounting for baseline BMI z-score, poorer baseline self-esteem did not predict higher BMI z-score at follow-up. While nonoverweight children with low baseline self-esteem were more likely to develop overweight/obesity (OR=2.1, 95% CI=1.2, 3.6), this accounted for only a small proportion of the incidence of overweight. CONCLUSIONS: Our data show an increasingly strong association between lower self-esteem and higher body mass across the elementary school years. Overweight/obesity precedes low self-esteem in many children, suggesting a causal relationship. This indicates that prevention and management strategies for childhood overweight/obesity need to begin early to minimise the impact on self-esteem.

The climate change and development nexus

The rapid economic success achieved by the developing countries in general, and India and China in particular, has brought the issue of climate change, which is a spin-off of development, to the fore. Economic growth is essential for the eradication of poverty and generation of wealth. However, it drives energy consumption and demand for energy which, in turn, produces toxic gases like carbon dioxide (CO2 ). Thus, the price of economic growth is climate change. The paradox lies in the fact that when economic growth is the only solution to poverty, the resultant climate change (characterized by emission of greenhouse gases) also affects the poor greatly. In this context, it is observed that while traditionally the developed countries were charged with polluting the environment globally, now the developing countries have overtaken their counterparts as polluters. The developing countries have emerged, over the years, as the agents responsible for growing pollution in the world, though they are also the victims, as most of the poor people belong to the developing countries. The author explores the nexus between climate change and development in the context of the economic growth of the developing countries and its impact on them.

Health impact assessment : environmental management vs healthy public policy perspective - exploring the nexus between the two

This paper will explore the links between the traditional role of HIA in an environmental management context and the new and emerging trend internationally to subject government policy to prospective HIA.  The goal of this new iteration of HIA is to develop healthy public policy across all sectors of government creating a more inclusive and evidence-based approach to public policy formation.  The risk-based, health protection approach is more widely understood, as it draws on existing health protection experience and is allied with risk assessment theory.  The new model is based on the health promotion perspective, and emphasizes social determinants of public health.  This latter approach draws on the foundations of the former.  It is vital that the links between the two are therefore considered especially from the perspective of transfer of knowledge between the two.  The paper will explore the similarities, the differences, the tensions and the lessons that can be learned.  It will report on the progress of a national study being conducted by Mary Mahoney and Gillian Durham that is looking at what is happening (or has happened) in other countires including Canada, New Zealand, Sweden, Netherlands, Germany and the United Kingdom

Linear causal model discovery using the MML criterion

Determining the causal structure of a domain is a key task in the area of Data Mining and Knowledge Discovery.The algorithm proposed by Wallace et al. [15] has demonstrated its strong ability in discovering Linear Causal Models from given data sets. However, some experiments showed that this algorithm experienced difficulty in discovering linear relations with small deviation, and it occasionally gives a negative message length, which should not be allowed. In this paper, a more efficient and precise MML encoding scheme is proposed to describe the model structure and the nodes in a Linear Causal Model. The estimation of different parameters is also derived. Empirical results show that the new algorithm outperformed the previous MML-based algorithm in terms of both speed and precision.

An improved approach for the discovery of causal models via MML

Discovering a precise causal structure accurately reflecting the given data is one of the most essential tasks in the area of data mining and machine learning. One of the successful causal discovery approaches is the information-theoretic approach using the Minimum Message Length Principle[19]. This paper presents an improved and further experimental results of the MML discovery algorithm. We introduced a new encoding scheme for measuring the cost of describing the causal structure. Stiring function is also applied to further simplify the computational complexity and thus works more efficiently. The experimental results of the current version of the discovery system show that: (1) the current version is capable of discovering what discovered by previous system; (2) current system is capable of discovering more complicated causal models with large number of variables; (3) the new version works more efficiently compared with the previous version in terms of time complexity.

An empirical study of encoding schemes and search strategies in discovering causal networks

Efficiently inducing precise causal models accurately reflecting given data sets is the ultimate goal of causal discovery. The algorithm proposed by Wallace et al. [10] has demonstrated its ability in discovering Linear Causal Models from data. To explore the ways to improve efficiency, this research examines three different encoding schemes and four searching strategies. The experimental results reveal that (1) specifying parents encoding method is the best among three encoding methods we examined; (2) In the discovery of linear causal models, local Hill climbing works very well compared to other more sophisticated methods, like Markov Chain Monte Carto (MCMC), Genetic Algorithm (GA) and Parallel MCMC searching.

Mining sequential causal patterns with user-specified skeletons in multi-sequence of event data

The approaches proposed in the past for discovering sequential patterns mainly focused on single sequential data. In the real world, however, some sequential patterns hide their essences among multi-sequential event data. It has been noted that knowledge discovery with either user-specified constraints, or templates, or skeletons is receiving wide attention because it is more efficient and avoids the tedious selection of useful patterns from the mass-produced results. In this paper, a novel pattern in multi-sequential event data that are correlated and its mining approach are presented. We call this pattern sequential causal pattern. A group of skeletons of sequential causal patterns, which may be specified by the user or generated by the program, are verified or mined by embedding them into the mining engine. Experiments show that this method, when applied to discovering the occurring regularities of a crop pest in a region, is successful in mining sequential causal patterns with user-specified skeletons in multi-sequential event data.

An examination on the performance of MML causal induction

This paper presents an examination report on the performance of the improved MML based causal model discovery algorithm. In this paper, We firstly describe our improvement to the causal discovery algorithm which introduces a new encoding scheme for measuring the cost of describing the causal structure. Stiring function is also applied to further simplify the computational complexity and thus works more efficiently. It is followed by a detailed examination report on the performance of our improved discovery algorithm. The experimental results of the current version of the discovery system show that: (l) the current version is capable of discovering what discovered by previous system; (2) current system is capable of discovering more complicated causal networks with large number of variables; (3) the new version works more efficiently compared with the previous version in terms of time complexity.