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The environmental effects of urbanization and globalization are still subject to debate among scholars. South Africa is the most globalized, most urbanized and the most carbon-intensive economy in Sub Saharan Africa (SSA) region. Taking this into cognizance, this study examines the effects of urbanization and globalization on CO 2 emissions for South Africa using time series annual data for the period 1980–2017. Zivot and Andrews single and Bai and Perron multiple structural break unit root tests are employed to assess if all the series are stationary. This procedure follows ARDL cointegration test to check the presence of a long-run association among variables. Having been confirmed about such a cointegrating relation, ARDL short-run and long run coefficients indicate that urbanization induces CO 2 emissions while only long-run significant emissions effect of globalization was noted. Toda-Yamamoto non-causality test reports a bi-directional causal link between urbanization and CO 2 emissions. No causal link is observed between globalization and CO 2 emissions. Variance decomposition results do not rule out these effects in future. Policy implications are discussed.

Computational Intelligence (CI) holds the key to the development of smart grid to overcome the challenges of planning and optimization through accurate prediction of Renewable Energy Sources (RES). This paper presents an architectural framework for the construction of hybrid intelligent predictor for solar power. This research investigates the applicability of heterogeneous regression algorithms for 6 hour ahead solar power availability forecasting using historical data from Rockhampton, Australia. Real life solar radiation data is collected across six years with hourly resolution from 2005 to 2010. We observe that the hybrid prediction method is suitable for a reliable smart grid energy management. Prediction reliability of the proposed hybrid prediction method is carried out in terms of prediction error performance based on statistical and graphical methods. The experimental results show that the proposed hybrid method achieved acceptable prediction accuracy. This potential hybrid model is applicable as a local predictor for any proposed hybrid method in real life application for 6 hours in advance prediction to ensure constant solar power supply in the smart grid operation.

The use of autonomous recordings of animal sounds to detect species is a popular conservation tool, constantly improving in fidelity as audio hardware and software evolves. Current classification algorithms utilise sound features extracted from the recording rather than the sound itself, with varying degrees of success. Neural networks that learn directly from the raw sound waveforms have been implemented in human speech recognition but the requirements of detailed labelled data have limited their use in bioacoustics. Here we test SincNet, an efficient neural network architecture that learns from the raw waveform using sinc-based filters. Results using an off-the-shelf implementation of SincNet on a publicly available bird sound dataset (NIPS4Bplus) show that the neural network rapidly converged reaching accuracies of over 65% with limited data. Their performance is comparable with traditional methods after hyperparameter tuning but they are more efficient. Learning directly from the raw waveform allows the algorithm to select automatically those elements of the sound that are best suited for the task, bypassing the onerous task of selecting feature extraction techniques and reducing possible biases. We use publicly released code and datasets to encourage others to replicate our results and to apply SincNet to their own datasets; and we review possible enhancements in the hope that algorithms that learn from the raw waveform will become useful bioacoustic tools.

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