The economic and environmental aspects of energy production have become important due to the increasing complexity energy sector and envoirnmental pollution, warranting to test the connection between financial imbalances, energy prices and carbon emission. The study aims to test the impact of vertical fiscal imbalances (VFI) on energy prices and carbon emission trends by considering the dual-perspectives of environmental regulation and industrial structure. The empirical outcomes indicated that vertical fiscal imbalances limited the environmental quality of Pakistan. Furthermore, VFI also caused environmental degradation by affecting industrial structure. VFI inhibits the intensity of environmental regulation, promotes the upgrade of industrial structures, both of which cause additional carbon emissions. The study suggest to energy ministries and energy regulation offices to revisit the machinism of energy prices determination and revised machanisim should provide a user-friendly assessment to understand the actual costs associated with the rising concern of environmental pollution. By this, envoirnmental protection maximization and optimal energy conservation is expacted to increase. Based on empirical findings, the study extends the suggestion that vertical fiscal imbalances should be considered an active indicator by the key policy makers and other stakeholders for energy prices determination and environmental quality upgradation.

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The evaluation of machine learning (ML) pipelines is essential during automatic ML pipeline composition and optimisation. The previous methods such as Bayesian-based and genetic-based optimisation, which are implemented in Auto-Weka, Auto-sklearn and TPOT, evaluate pipelines by executing them. Therefore, the pipeline composition and optimisation of these methods requires a tremendous amount of time that prevents them from exploring complex pipelines to find better predictive models. To further explore this research challenge, we have conducted experiments showing that many of the generated pipelines are invalid, and it is unnecessary to execute them to find out whether they are good pipelines. To address this issue, we propose a novel method to evaluate the validity of ML pipelines using a surrogate model (AVATAR). The AVATAR enables to accelerate automatic ML pipeline composition and optimisation by quickly ignoring invalid pipelines. Our experiments show that the AVATAR is more efficient in evaluating complex pipelines in comparison with the traditional evaluation approaches requiring their execution.