**1. Introduction**

Today's fast-paced manufacturing industry is increasingly characterized by technology. The manufacturing sector accounts for about 33% of the primary energy use and 38% of the CO2 emissions globally [1]. Concerning transport manufacturing, energy purchases have a major impact on the production costs and, ultimately, on the industry's competitiveness [2]. Energy efficiency becomes a driver for the manufacturing industry since it is historically one of the greatest energy consumers and carbon emitters in the world [3]. Author [4] argues that promoting efficiency without any curbs on the consumption will not tackle the problem of reducing CO2 emissions. The targets for the reduction of CO2 emissions have a grea<sup>t</sup> effect on the manufacturing industry. South African sectoral electricity, specifically for the industry sector, sits at 49% vs. a supply of 43.7% [5]. South Africa is committed to reducing emissions through the introduction of smart manufacturing as an essential route to meet the greenhouse gas (GHG) emissions target that was out in the agreements on climate change by the International Panel on Climate Change [6]. However, it is not only the introduction of smart manufacturing that will aid the achievement of the CO2 emission target, but the main enabler of the commission of these technologies, namely the policies that recognize the energy efficiency and carbon emission reduction system with the benefits of digital technologies to overcome the barriers to the implementation and the accelerated technology deployment in South Africa. Du Plessis 2015 [7] presented a study that explored the nature and the extent of the various policy instruments and legislation that relate to energy efficiency.

**Citation:** Adenuga, O.T.; Mpofu, K.; Modise, R.K. Energy–Carbon Emissions Nexus Causal Model towards Low-Carbon Products in Future Transport-Manufacturing Industries. *Energies* **2022**, *15*, 6322. https://doi.org/10.3390/en15176322

Academic Editors: Ershun Pan, Rongxi Wang, Yupeng Li, Xi Gu and Tangbin Xia

Received: 10 August 2022 Accepted: 28 August 2022 Published: 30 August 2022

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**Copyright:** © 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).

Several publications have focused on the application of energy efficiency in the different sector's policies [8–10], general trading, decision making [11,12], ICT [13], and the building sector [7]. Other energy efficiency-related research includes the industry application in the supply chain [14], the impact on the South African energy crisis on emissions [15], carbon accounting [16], and predicting carbon emissions [17,18]. A review of the literature that is related to carbon emissions in most of the applications, in general, accounts for carbon emission [19], integrated energy efficiency, and the carbon emissions in the industry, focusing on the implication of improvement for emissions [20], conducted an experiential study and modeling of energy efficiency by [21], while the reviewed literature focused on a causal relationship of energy consumption, price, and emissions. The manufacturing sector consumes a high amount of energy and emits even more [22], and extensive research has been carried out on the energy efficiency of manufacturing, focusing on improvement [23], low-cost energy efficiency measures [24], energy efficiency development in manufacturing [25], and energy managemen<sup>t</sup> that includes energy efficiency [26]. Further studies have focused on the energy efficiency of manufacturing systems and processes [3,27,28]. The research has observed that the literature that has been presented on energy efficiency has a high interest in modeling energy efficiency in manufacturing, including rail manufacturing [29–32] and carbon dioxide prediction [33]. Energy efficiency is recognized globally as a critical solution toward the reduction of energy consumption, while the managemen<sup>t</sup> of global carbon dioxide emissions complements climate change policies, abates the costs of reducing carbon emissions, and the improves economic competitiveness. Built on the existing reviews on energy efficiency and carbon emissions, this paper considers the causal relationship between energy consumption, energy intensity, and carbon emission in future transport manufacturing. The research considers an investigative study that explores the implications of energy efficiency improvement for CO2 emissions in the energy-intensive industries and includes the element of prediction, to advise the decision makers on lowcarbon products. The outline of the paper is structured as follows: Section 2 reviews the systematic pieces of the literary techniques in transport manufacturing and the future transport sector regarding energy resource consumption and carbon dioxide emission. The model for the asymmetric energy–carbon emission and energy and carbon emissions efficiency regression-based approach are formulated in Section 3. Section 4 expounds on the results and the discussion. Section 5 presents a conclusion.

#### **2. Literature Review**

The transport-manufacturing sector is one of the most important sectors in the industry and is considered to shape the economic growth and job creation that supports policies that are related to energy consumption [34], however, the policies that support energy systems through digital technologies are rare. A new framework for driving analytical data to reduce energy consumption and carbon emission in energy-intensive manufacturing has been suggested [35]. Authors [36] investigated the causal relationship between energy resource consumption, energy prices, and carbon dioxide emission in the building sector to determine the effects of energy sources and prices on carbon emissions, thus, further research is required in the industrial and transportation sectors. A structured literature review technique was used in the collection of empirical evidence in a particular field for the study to assert the evidence of the co-benefits between energy consumption and carbon emission to determine the current state of knowledge. It required a critical assessment of the evidence and the identification of both the potential for energy and carbon efficiency, with direct economic savings, and the ability to summarize the findings. To achieve the objective, we have adopted the following three-step approach to identify the relevant research literature: search term, filtering approach, and information removal.

#### *2.1. Future Transport Manufacturing*

The report 'European Commission' has emphasized the importance of the transport sector for economic growth and has widely acknowledged that targeted innovations and

targeted research activities are key factors for fostering competitiveness in the future transport sector [37]. Research that was conducted in United States has highlighted that transport manufacturing is the eighth largest industrial energy consumer; the energy expenditure increased by 20%, and the purchases of electricity went up by nearly 10% [2]. The modern/smart manufacturing industry is investing in new technologies, such as the Internet of Things (IoT), big data analytics, cloud computing, and cybersecurity, to cope with system complexity, to increase information visibility, to improve production performance, and to gain competitive advantages in the global market [8,38].

#### *2.2. Energy Efficiency and Carbon Emission in Future Transport Manufacturing*

The impact of the energy efficiency of emissions in the transport-manufacturing sector in South Africa can be seen from the recent technological improvements. Giampieri et al., 2019 [39] suggested that automotive manufacturers are facing economic and environmental pressure for the realization of a sustainable low carbon process, therefore, improved energy efficiency is necessary to decrease greenhouse gas emissions, and the carbon risks are mainly related to the emissions from the purchased electricity in Korean automobile manufactures [40]. The application of a conceptual model of the wind turbine into the transport sector to produce energy for powering the car has also been suggested [41]. However, there have been studies on a causal relationship between energy consumption and CO2 emission in building sectors [36].

#### *2.3. Recent Studies on Energy and Carbon Emission*

Botts et al., 2021 [42] developed a decision tool for the energy efficiency of a blower heater on a normalized basis, in terms of the performance and the cost. The energy consumption estimation in Indian refineries based on empirical-analytical-based panel data econometrics was postulated by [43], which concluded on the formulation of a policy to reduce the energy consumption. An energy planning and carbon dioxide estimation system dynamic model was presented for Nigeria's power sector by [44]. The model investigated ways to bridge the demand gaps and the electricity supply through the simulation of variables, real socio-economic factors, and the estimation of CO2, in various performance scenarios. Sunde, 2020 [45] investigated the economic growth and the energy consumption of SADC countries using causality analysis to model the growth variables with the implied notion of an increased level of energy consumption leading to an economic output increase. Brahmana and Ono [46] justified the need for energy efficiency as a significant part of company performance in Japanese listed companies, which affects the market-based performance with significant impacts on the return on assets (accountingbook performance), thereby debunking the energy-efficiency paradox. Olanrewaju et al. developed a forecast model that was dependent on an artificial neural network to model the energy consumption between 2002 and 2009, which was based on the gross domestic product and the population [47]. It was discovered that an artificial neural network is a better modeling technique compared to regression analysis. The link between an energyrestricted environment and the emissions in South Africa was evaluated by [15], with findings of undeniable facts on the negative impact of the emissions that are caused by energy production. Gamede et al., [48] proposed a business model for intergrading the energy efficiency performance in the manufacturing industry by using a rail car case study that was recommended for energy service companies. Authors [49] proposed the energy efficiency analysis modeling system (EEAMS) in transport manufacturing, focusing on rail. The tool provides an estimate of the energy costs by using the rail car manufacturing plan load profiles as a case study to provide a consumer-oriented analysis to produce a first-cut energy-efficient program baseline cost.

This paper examined the use of the casual relationship between energy consumption, energy intensity, and carbon dioxide emission in the future transport-manufacturing sectors, respectively.
