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Since its introduction in South Africa during 2009, the ability of vehicle emissions tax to reduce CO₂ emissions has been questioned, but not yet assessed. The purpose of this paper is to attempt such an assessment by considering tax designs to reduce passenger vehicle CO₂ emissions.

In this exploratory study, the authors reviewed literature on tax designs to reduce CO₂ emissions, and compared the design of current taxes on passenger vehicles in South Africa to the tax designs most advocated in the literature to evaluate the effectiveness of the current South African design for this purpose.

Tax designs refer to the stage when taxes are levied (purchase/ownership/usage taxes) – levying taxes at one stage may more effectively reduce emissions than levying them at another. The current tax focus on consumers may indeed affect taxes' ability to reduce emissions, and in the current tax mix, taxes on passenger vehicles may not be the most effective way of reducing emissions. The investigation of a “feebate” policy as an alternative initiative to address increased passenger vehicle CO₂ emissions is recommended.

Only anecdotal evidence questions the ability of the vehicle emissions tax to reduce CO₂ emissions. This study is intended to elicit further discussions on other fiscal reform initiatives aimed at reducing CO₂ emissions by passenger vehicles in South Africa.

This article examines the major modifications made to the national revenue system since the initial reform and opening up of China in the late 1970s. A detailed analysis of revenue structures at the central and local levels of government is presented; several emerging issues are also discussed. The authors suggest that past reforms were largely successful in terms of rationalization of intergovernmental fiscal relations and have profoundly impacted the current revenue system to meet certain policy goals and administrative needs. A brief comparison illustrates that although central and local revenue systems share basic similarities such as a general reliance on tax revenue and an overall dependence on turnover and income taxes, striking differences are also in evidence which include extra-budgetary revenues and a high concentration of land-related revenue in local governments as well as a highly diversified local revenue structure. Key emergent issues identified include the readjustment of the roles played by the different levels of government in revenue administration and expenditure assignment, the increased importance of intergovernmental transfer and accountability, and possibilities for the adoption of new taxes such as a real property tax, a social security tax, and a volume-based vehicle emission tax.

The purpose of this paper is to develop a greenhouse gas (GHG) management model for mitigating GHG emission. GHG emission by way of human activities is causing catastrophic effects on the natural environment in the form of climate change and global warming. GHG management of different products, bodies and processes is going on worldwide, expressed through carbon footprints by using product life cycle assessment (LCA). LCA is a useful approach, but it only looks at the micro level of cause-effect scenarios rather than the macro level cause-effect scenarios of GHG emission. Therefore, a system to scrutinize underlined assumptions and values of such policies/strategies is an urgent necessity.

This paper uses the double-loop learning concept, which was proposed by Argyris in 1976, to develop a triple cause-effect model for the management of GHG emission. The proposed model has a knowledge system that introduces the learning loop of GHG emission and environmental impact management.

A case study is conducted to demonstrate how the proposed triple cause-effect model is operationalized. The ideas and benefits of the proposed model are further discussed.

A triple cause-effect model for the measurement and analysis of GHG emission is proposed in this paper to complement GHG management by using only product LCA. This paper seeks to show that GHG management should look at not only a single tree (product LCA approach) but also the whole forest (the proposed model).

This study aimed two objectives: The first objective was to explore carriers' intentions to use cargo electric vehicles (EVs) and the factors influencing these intentions in last-mile delivery (LMD). The other objective was to provide recommendations for policymakers and manufacturers to promote and customize cargo EVs to meet the requirements of carriers in the LMD sector.

In this study the authors constructed a research framework that adjusted and extended the original technology acceptance model (TAM). The proposed model combines eight psychological factors, including attitude, perceived usefulness, perceived ease of use, perceived risk, public engagement, face consciousness, financial incentive policy and carrier intention, in which four factors, namely attitude, perceived usefulness, perceived ease of use and intention, were obtained from the original TAM and the four remaining factors, namely perceived risk, public engagement, face consciousness and financial incentive policy, were added.

The results showed that psychological factors such as attitude, perceived ease of use, perceived risk, public engagement and face consciousness might affect carriers' intentions to use electric cargo vehicles in LMD service in Vietnamese cities. These results agree with the previous studies and confirm that attitude, perceived ease of use, perceived risk, public engagement and face consciousness might be important for shaping intention to use electric cargo vehicles in emerging markets.

This study has several limitations, first, the data were collected in Vietnam, a country with weak academic contributions. Therefore, these findings might not be generalizable to other areas. The authors expect to apply the same research framework to other countries to explore the similarities and differences across the countries. Second, the authors conducted the surveys in three cities; except for Hanoi, the other two cities are not really large markets in the LMD service sector. Third, the authors ignored the relationship between the demographic characteristics and electric cargo vehicles. Further studies should address this gap.

Based on the findings, manufacturers should ensure the high-quality performance of electric cargo vehicles in terms of extending driving range and shortening recharging time. Policymakers should develop the roadmaps for electric cargo vehicles, starting from switching from conventional cargo motorcycles to electric motorcycles. In addition, developing public charging infrastructure should be prioritized, which is the fundamental basis for operating electric cargo vehicles. Finally, manufacturers should research and develop a product that would improve the reputation of carriers because carriers with higher face consciousness are more likely to pursue brand-name and high-priced products in order to enhance their reputation.

This study contributes to the literature in two aspects: First, the authors investigated intentions to use electric cargo vehicles in LMD service, which is rare from other studies, and they further identified the psychological determinants of carriers' intentions. Second, the findings increase the knowledge of carriers' intentions and suggest implications for policymakers and manufacturers to promote the adoption of electric cargo vehicles in last-mile deliver service.

In order to reduce logistics transportation costs and respond to low-carbon economy, the purpose of this paper is to study the more practical and common simultaneous pickup and delivery vehicle routing problem, which considers the carbon tax policy. A low-carbon simultaneous pickup and delivery vehicle routing problem model is constructed with the minimum total costs as the objective function.

This study develops a mathematical optimization model with the minimum total costs, including the carbon emissions costs as the objective function. An adaptive genetic hill-climbing algorithm is designed to solve the model.

First, the effectiveness of the algorithm is verified by numerical experiments. Second, the research results prove that carbon tax mechanism can effectively reduce carbon emissions within effective carbon tax interval. Finally, the research results also show that, under the carbon tax mechanism, the effect of vehicle speed on total costs will become more obvious with the increase of carbon tax.

This paper only considers the weight of the cargo, but it does not consider the volume of the cargo.

Few studies focus on environmental issues in the simultaneous pickup and delivery problem. Thus, this paper constructs a green path optimization model, combining the carbon tax mechanism for the problem. This paper further analyzes the impact of carbon tax value on total costs and carbon emission; at the same time, the effect of vehicle speed on total cost is also analyzed.

The purpose of this study is to investigate the potential of implementing a toxicity tax for heavily polluting vehicles in United Arab Emirates. A fast-changing world needs responsive policies to cope with the crisis human beings are currently facing on earth. Implementing a new policy represents a design for a change. Policies come in the form of regulations that have been officially agreed and chosen by either a political party, business or any other type of organization. This paper adopts the policy development cycle in the Australian policy handbook as a practical framework. A focus group comprising 35 participants and two decision makers thoroughly discussed each stage of the “policy development cycle”. A qualitative analysis of the data showed that there is potential for implementing a new tax to be imposed on highly polluting vehicles in United Arab Emirates (UAE) the most appropriate policy instrument in UAE is a policy enacted through restricted law; it might take more than 3 years to work; the expected tax would range between $280 and $1,100 per year; and there is concern that this new tax would slow Dubai’s preparation for EXPO 2020. These findings will help countries implement this new tax. Following the stages of the policy development cycle and their procedures will, therefore, inspire decision makers to formulate new environmental policies not only in UAE but around the world.

This research adopts the use of “focus groups” as its primary approach to data collection. A focus group is a common qualitative research technique and involves a small number of participants. The main reason for using qualitative research is because participants are asked for open-ended responses that convey their thoughts or feelings. The author of this paper served as the moderator who posed a series of questions intended to gain insight into the group’s views on the implementation of a toxicity tax for heavy polluting vehicles in the UAE. Time and attention were given to what it is that you expect to get out of focus-group research. The group was invited for one full day to a big hall and was seated around one table. The group was sometimes divided to obtain more focused views. Overall, four primary groups participated in this study. A fifth group, consisting of three senior members of a governmental agency who acted as decision makers, took part in one-to-one interviews at their place of work. Table I provides details on the backgrounds of the participant.

Research highlights the policy development cycle to investigate a new transport policy in UAE. A focus group comprising 35 participants and two interviews with two decision makers. There is potential for a tax to be imposed on highly polluting vehicles in UAE. The expected tax would range between $280 and $1,100 per year. The best policy instrument in the region is policy by law. The truck industry will be affected, and there will be a public refusal.

A new policy of implementing tax for high polluting vehicles was, therefore, discussed in this paper. Participants thought the tax would work, but an in-depth feasibility study on both microeconomic and macroeconomic aspects will be required; providing governmental incentives will help support the shift to less-polluting vehicles.

The policy development cycle was utilized as a practical framework for this research.

People were engaged into this study to make the policy viable. People will react positively to this policy.

Due to the lack of such research in this area, this paper will fill a gap in the knowledge. Moreover, the policy will be real implementation based on this publication. The findings will not only help UAE but also many countries that share same environmental and social contexts.

This paper aims to learn a decision-maker’s (DM’s) decision model that is characterized in terms of the attitudinal character and the attributes weight vector, both of which are specific to the DM. The authors take the learning information in the form of the exemplary preferences, given by a DM. The learning approach is formalized by bringing together the recent research in the choice models and machine learning. The study is validated on a set of 12 benchmark data sets.

The study includes emerging preference learning algorithms.

Learning of a DM’s attitudinal choice model.

Preferences-based learning of a DM’s attitudinal decision model.

The purpose of this paper is to investigate the diffusion dynamics of electric and hybrid commercial vans and its enabling factors in the city logistics (CL) contexts. The case of parcel delivery in Torino, Italy, is considered. Attention is paid to the influence on the choice of low impact vehicles of not only public strategies but also operational aspects characterizing urban freight distribution systems.

A System Dynamics model based on the Bass diffusion theory computes the number of adopters of low-emission vehicles together with the quantity of vans required and the associated economic savings. The model includes variables about freight demand, delivery frequency, van carrying capacity, routes, stops, distances traveled, and vehicle charging stations. A sensitivity analysis has been completed to identify the main diffusion levers. The focus is on advertising and other drivers, such as public contributions, taxes traditional polluting vehicles are subjected to, as well as on routing optimization strategies.

Advertising programs, green image, and word-of-mouth drive market saturation, although in a long time period. In fact, low-impact vehicles do not offer any economic advantage over traditional ones requiring higher investment and operating costs. Public incentives to purchase both green vehicles and charging stations, together with carbon taxes and a congestion charge affecting polluting vehicles, are able to shorten the adoption time. In particular, public intervention reveals to be effective only when it unfolds through a number of measures that both facilitate the use of environmentally friendly vehicles and discourage the adoption of traditional commercial vans. Route optimization also hastens the complete market saturation.

This work fosters research about the mutual relationships between the diffusion of low-emission commercial vehicles and the operational and contextual CL factors. It provides a structured approach for investigating the feasibility of innovative good vehicles that might be part of assessments of CL measures and requirements. Finally, the model supports studies about the cooperation among stakeholders to identify effective commercial vehicle fleets.

This study fosters collaboration among CL players by providing a roadmap to identify the key factors for the diffusion of environmentally friendly freight vehicles. It also enables freight carriers to assess the operational and economic feasibility of adopting low-impact vehicles. Finally, it might assist public authorities in capturing the effects of new urban transportation policies prior to their implementation.

Most of the current CL literature defines policies and analyzes their effects. Also, there are several contributions on the diffusion of low emission cars. The present study is one of the first works on the diffusion of low-impact commercial vehicles in urban areas by considering the associated key operational factors. A further value is that the proposed model combines operational variables with economic and environmental issues.

This paper studies a supply chain consisting of multiple suppliers and a single buyer. It considers the case where a set of heterogeneous trucks is used for transporting products, and develops a mathematical model that coordinates the supply chain. The purpose of this paper is to minimise the costs of producing and delivering a product as well as carbon emissions resulting from transportation. In addition, the authors analyse how imposing a tax on carbon emissions impacts the delivery of products from the suppliers to the buyer.

It is assumed that heterogeneous vehicles are used for transporting products, which have different performance and cost attributes. A mathematical model that considers both operating costs and carbon emissions from transportation is developed. The impact of vehicle attributes on lot sizing and routing decisions is studied with the help of numerical examples and a sensitivity analysis.

The analysis shows that considering carbon emissions in coordinating a supply chain leads to changes in the routing of vehicles. It is further shown that if carbon emissions lead to costs, routes are changed in such a way that vehicles travel long distances empty or with a low vehicle load to reduce fuel consumption and therewith emissions.

Several areas for future work are highlighted. The study of alternative supply chain structures, for example structures which include logistics service providers, or the investigation of different functional relationships between vehicle load and emission generation offer possibilities for extending the model.

The paper is one of the first to study the use of heterogeneous vehicles in an inventory model of a supply chain, and one of the few supply chain inventory models that consider ecological aspects.

This research explores mitigating carbon emissions and integrating sustainability in e-commerce logistics by optimizing the multi-depot pollution routing problem with time windows (MDPRPTW). A proposed model contrasts non-collaborative and collaborative decision-making for order assignment among logistics service providers (LSPs), incorporating low-carbon considerations.

The model is substantiated using improved adaptive large neighborhood search (IALNS), tabu search (TS) and oriented ant colony algorithm (OACA) within the context of e-commerce logistics. For model validation, a normal distribution is employed to generate random demand and inputs, derived from the location and requirements files of LSPs.

This research validates the efficacy of e-commerce logistics optimization and IALNS, TS and OACA algorithms, especially when demand follows a normal distribution. It establishes that cooperation among LSPs can substantially reduce carbon emissions and costs, emphasizing the importance of integrating sustainability in e-commerce logistics optimization.

This paper proposes a meta-heuristic algorithm to solve the NP-hard problem. Methodologies such as reinforcement learning can be investigated in future work.

This research can help logistics managers understand the status of sustainable and cost-effective logistics operations and provide a basis for optimal decision-making.

This paper describes the complexity of the MDPRPTW model, which addresses both carbon emissions and cost reduction. Detailed information about the algorithm, methodology and computational studies is investigated. The research problem encompasses various practical aspects related to routing optimization in e-commerce logistics, aiming for sustainable development.