**5. Conclusions**

In this paper, we employed a statistical approach to establish the baseline demand for future transport manufacturing to anticipate the electricity demand and CO2 emissions for that industry. To increase the forecast accuracy, we used the errors to train the causal model for the energy–carbon nexus. To accurately estimate the CO2 emissions for effective mitigation and reduction by climate change targets, it is essential to identify the flaws in previous data using the notion of demand intelligence. The model used here will aid in lowering CO2 emissions, which is necessary for ongoing technological advancement, for investing in cutting-edge energy and resource efficiency, for setting up programs to lower greenhouse gas emissions, and for contributing to climate science research. The contribution of this study is threefold. Firstly, we have demonstrated the energy-efficiency paradox in manufacturing, concerning whether the energy efficiency and carbon emissions signature of the manufactured products. Secondly, we have contributed to the literature by extending the understanding of the low carbon emission of products with an approach towards adoption in a production facility. Thirdly, we have carefully studied the energy performance by testing the moving average over the past year, i.e., the last 12 values were determined with specific functions for rolling statistics in Pandas. The energy efficiency of production industries and the carbon emissions signature of a manufactured product is bringing a revolution to manufacturing industries by using big data in industry 4.0 technologies. This study has evaluated research that has been conducted in future transport-manufacturing literature, establishing that minimal research has been conducted on the relationship between energy efficiency, energy savings, and carbon intensity for the future transport manufacturing industries. The interrelationship between the examined variables resulted in a 29% improvement in the total energy intensity in the vehicle body part products, 7.22% in the cumulative energy savings, and 16.25% in the energy efficiency. At a micro level, industries' adoption of energy efficiency in terms of fuel is

still limited in responses to climate change campaigns, while EE marginal abatement cost could provide an insight into incentives for industries to exploit investments in EE through this study. The study will encourage companies to optimize their profits through more cash in hand, due to the energy savings, the energy intensity reduction, and a low carbon footprint of products. As evidenced in the reviewed literature, the harmful effects of GHG pollution and local air pollution on the environment and human health is growing to pose a threat to development, if no changes are made to investment strategies and legislation to enforce cleaner environmental practices, due to a perceived threat to their profits. The scope that has been examined in this paper will be interesting to agencies of government, researchers, policymakers, business owners, and practicing engineers in future transport manufacturing, and it could serve as a fundamental guideline for future studies in these areas. Future research will concentrate on the incorporation of energy and carbon emission prediction efficiency into a data monitoring device for online and mobile applications for the manufacturing of future transportation systems.

**Author Contributions:** Conceptualization, R.K.M. and O.T.A.; methodology, R.K.M.; software, O.T.A.; validation, R.K.M., O.T.A. and K.M.; formal analysis, R.K.M. and O.T.A.; investigation, O.T.A.; resources, R.K.M. and O.T.A.; data, O.T.A.; editing, K.M.; visualization, O.T.A.; supervision, O.T.A. and K.M.; project administration, R.K.M.; funding acquisition, K.M. All authors have read and agreed to the published version of the manuscript.

**Funding:** This research was funded by the National Research Foundation (NRF), gran<sup>t</sup> number 123575, and the APC was funded by the Research Chair in Future Transport Manufacturing Technologies.

**Acknowledgments:** The researchers acknowledge the support and assistance of the Industrial Engineering Department of Tshwane University of Technology, Gibela Rail, and the National Research Foundation (123575) of South Africa for their financial and material assistance in executing this research project. The opinions that are presented in this paper are those of the authors and not the funders.

**Conflicts of Interest:** The authors declare no conflict of interest.
