Transport Sector GHG Mitigation Measures: Abatement Costs Application Review

Abstract

The transport sector is a major contributor to global greenhouse gas emissions, making its decarbonization critical for climate change mitigation efforts. The Marginal Abatement Cost (MAC) curve is a vital tool that evaluates the cost-effectiveness of mitigation measures by comparing their emission reduction potential against their implementation costs. This paper conducts a literature review to analyze the application of the MAC curve in the transport sector, identifying common mitigation measures, comparing abatement costs, and assessing the tool’s role in shaping decarbonization policies. The findings reveal a predominance of technology-focused, bottom-up methodologies, with a significant research gap in the freight sector, which is largely overlooked compared with passenger transport. The results show that the abatement costs for similar measures vary considerably across geographical contexts, influenced by local factors such as fuel prices and gross domestic product (GDP). The analysis suggests that combining technological solutions with behavioral and structural changes creates synergistic effects, yielding greater benefits than isolated actions. The strong alignment observed between measures analyzed in the literature and subsequent national climate policies confirms the MAC curve’s strategic importance as an evidence-based instrument for policymakers to construct economically rational decarbonization pathways.

1. Introduction

As climate change and global warming advance, measures must be taken to ensure the sustainable development of society. This has led to a global effort to mitigate the impacts caused by the emission of Greenhouse Gases (GHGs), with one of the main goals being reducing the share of fossil fuels in the world’s energy mix [1]. The goal is to lower energy-related CO₂ emissions and help limit the global temperature increase to 1.5 °C above pre-industrial levels [2].

The transport sector plays a key role in the global race for decarbonization, as mobility accounts for approximately 21.4% of the energy sector’s global GHG emissions [3]. This number could rise to 40% by 2030 if no action is taken [4]. In the Brazilian context, transport is even more representative, contributing around 53% of the energy sector’s GHG emissions [5]. To align the sector with global Net Zero emissions by 2050, a 3% annual reduction in emissions is necessary until 2030 [6].

Within the transport sector, the most prominent mode for both passenger and freight transport is road transport. It was responsible for 70% of the sector’s direct emissions worldwide in 2019 [2], which highlights the need for transformative changes in the sector to meet GHG mitigation goals. Achieving this requires identifying optimal implementation measures; however, a crucial factor in this decision-making process is balancing the implementation cost against the emission reduction potential of these mitigating measures.

The Marginal Abatement Cost curve (MAC) is one of the methods used to determine the cost-effectiveness of selected mitigating measures. It enables the calculation of the cost of avoided emissions and the feasibility of applied strategies [7,8], providing clarity for decision-makers proposing policies.

While the existing literature features many individual case studies using the MAC curve, it lacks a systematic review that compiles and quantitatively contrasts abatement costs and reduction potentials within the transport sector across different regional contexts. Furthermore, a consolidated examination of how these academic works translate into subsequent climate policies is absent. This review aims to bridge this gap by providing an evidence-based summary of the trends, costs, and real-world policy outcomes of applying the MAC curve.

Following this introduction, Section 2 presents a brief introduction of the main concepts and context pertaining to the MAC curve. Section 3 presents the article selection methodology for the literature review and mitigation name standardization. Section 4 provides an overview of the selected articles and presents the results on emission reduction potential and cost. Section 5 discuss the findings and their role in policy development. Section 6 consolidates the conclusions obtained in this study.

2. Background Information

There are three methods for developing a MAC curve: top-down, bottom-up, or hybrid. The top-down method focuses on macroeconomic models that consider energy supply and demand, as well as market and scenario changes [9]. The bottom-up method, on the other hand, focuses on technological development and existing policies [9]. The hybrid method combines the technological aspects of the bottom-up method with the policies and future scenarios of the top-down approach; however due to the high volume of data and complexity required, it is still rarely used [9,10].

Graphically represented by a step graph, the MAC curve is built by applying N GHG mitigation measures, with each step corresponding to a specific measure. The curve illustrates the Marginal Abatement Cost in $/tCO₂(e) (Y-axis) versus the potential for emission reduction in MtCO₂(e)/year (X-axis). Figure 1 illustrates a graphical representation of the MAC curve.

The MAC curve can show measures with both negative and positive abatement costs. For measures with negative costs, there are net savings over time, which makes them more financially attractive. However, this does not mean that decision-makers will necessarily choose these options, as it depends on their chosen strategy for prioritizing the implementation of measures. This can also be reflected in the choice based on abatement potential, where the measure with the highest mitigation potential is not always the most utilized [12], highlighting the need for progress in created policies and guidelines for the application of the MAC curve as a tool supporting the decision-making process.

Equation (1) provides the basic structure for calculating the MAC curve. It is composed of the cost difference (ΔC) from implementing a mitigation measure (C_mit) compared to the business-as-usual (BAU) scenario (C_BAU), divided by the emissions difference (ΔE) between the business-as-usual scenario (E_BAU) and the scenario with the mitigation measure (E_mit).

$$\mathrm{MAC}={\displaystyle \frac{\mathrm{A}\mathrm{n}\mathrm{n}\mathrm{u}\mathrm{a}\mathrm{l}\mathrm{i}\mathrm{z}\mathrm{e}\mathrm{d} \mathrm{n}\mathrm{e}\mathrm{t} \mathrm{c}\mathrm{o}\mathrm{s}\mathrm{t}\mathrm{s} \mathrm{o}\mathrm{f} \mathrm{t}\mathrm{h}\mathrm{e} \mathrm{m}\mathrm{i}\mathrm{t}\mathrm{i}\mathrm{g}\mathrm{a}\mathrm{t}\mathrm{i}\mathrm{o}\mathrm{n} \mathrm{m}\mathrm{e}\mathrm{a}\mathrm{s}\mathrm{u}\mathrm{r}\mathrm{e}}{\mathrm{A}\mathrm{n}\mathrm{n}\mathrm{u}\mathrm{a}\mathrm{l} \mathrm{e}\mathrm{m}\mathrm{i}\mathrm{s}\mathrm{s}\mathrm{i}\mathrm{o}\mathrm{n} \mathrm{r}\mathrm{e}\mathrm{d}\mathrm{u}\mathrm{c}\mathrm{t}\mathrm{i}\mathrm{o}\mathrm{n}}}={\displaystyle \frac{∆\mathrm{C}}{∆\mathrm{E}}}={\displaystyle \frac{{\mathrm{C}}_{mit}-C_{BAU}}{{\mathrm{E}}_{BAU}-{\mathrm{E}}_{mit}}},$$

(1)

It is important to note that the evaluated cost is the net cost, meaning it includes both the Capital Expenditure (Capex) and Operational Expenditure (Opex) for implementing the measure [9]. Additionally, to account for projections over time, the discount rate (r) variable must be added to the equation [13,14], leading to Equation (2). Equations (1) and (2) provide the basic parameters for calculating the MAC.

$$\mathrm{C}i=\sum _{i=1;\mathrm{t}=t_0}^{\mathrm{N};\mathrm{T}}{\displaystyle \frac{{\mathrm{C}\mathrm{a}\mathrm{p}\mathrm{e}\mathrm{x}}_i+{\mathrm{O}\mathrm{p}\mathrm{e}\mathrm{x}}_i}{{(1+\mathrm{r})}^{\mathrm{t}-{\mathrm{t}}_0}}}$$

(2)

• Ci: Marginal Abatement Cost for Measure N.
• Capex_i: Capital Expenditure for measure N.
• Opex_i: Operational Expenditure for measure N.
• r: discount rate.
• t: projected year.
• t₀: BAU year.

Factors such as gross domestic product (GDP), energy consumption, emissions, energy price, and technical changes can impact MAC calculations [10]. Specifically, the transport sector is significantly affected by fleet type, fleet age, fuel used, transport structure, and travel behavior [15].

The MAC curve can be a crucial tool for decision-making because it balances environmental benefits with economic viability. Its essence lies in demonstrating that decarbonization is not only an environmental issue but also an economic imperative. This is especially relevant given the political instability that affects global climate commitments. For example, the United States’ withdrawal from and subsequent rejoining of the Paris Agreement [16] illustrates how sustainability can be vulnerable to changes in government. By providing a solid economic foundation, the MAC curve offers a pragmatic argument for action, making mitigation efforts more resilient to political volatility and more attractive to nations with limited capital.

3. Methodology

To select articles for review, a bibliographic search was conducted in April 2025 using the Scopus and Web of Science scientific databases, which were chosen for their rigorous selection criteria, extensive coverage and well-established metrics. The terms “transport”, “abatement cost”, and “mitigation” were used to delimit the search, resulting in 219 articles, published between January of 2010 and April 2025.

Table 1. Search information.

Database	Query	Filter
Web of Science	“abatement cost” and “transport” and “mitigation” (Title) OR “abatement cost” and “transport” and “mitigation” (Author Keywords) OR “abatement cost” and “transport” and “mitigation” (Abstract)	Years: 2010 to 2025
Scopus	TITLE-ABS-KEY (abatement cost AND transport AND mitigation) AND PUBYEAR > 2009 AND PUBYEAR < 2026	Years: 2010 to 2025 Document type: Article

presents the query and filters applied for the search.

From the 219 resulting articles, a primary screening step removed duplicate articles, yielding 208 unique articles. A second screening step then selected articles exclusively focusing on applications in the transport sector; their titles and abstracts were analyzed, removing 164 articles and narrowing the results to 44. From these, the full articles, that were accessible via download, were analyzed to determine whether they provided values for the abatement potential and cost for each mitigation measure. This eliminated articles that only graphically expressed the MAC curve, resulting in 10 articles selected for further analysis that featured a quantitative calculation of the MAC curve, allowing for a comparative analysis of abatement costs and reduction potential, which is a central objective of this study. Figure 2 provides a workflow of the article selection process.

In order to allow for comparison between the studies, which use varying terminology, the names of the mitigation measures were standardized into categories based on their technical purpose. For the standardization process, six groups of mitigation measures were created, named as follows: alternative fuels; alternative vehicles; energy efficiency; fleet modernization; modal shift; and travel behavior. The resulting standardization is presented in

Table A1. Standardization of the mitigation measures.

Paper	Mitigation Measures	Standardized Mitigation Measures
[17]	Improvement of driver behavior	Energy efficiency
	Improvement of travel behavior	Travel behavior
	Advancement of vehicle equipment	Fleet modernization
	Introduction of low-carbon fuels	Alternative fuels
	Improvement of vehicle fleet	Fleet modernization
[18]	Improved fuel efficiency and green driving	Energy efficiency
	Electric mobility	Alternative vehicle
	Reduction in freight fleet oversupply and renewal	Fleet modernization
	Fuel substitution	Alternative fuel
	Modal change	Modal shift
[19]	Urban form, walking, cycling, and public transport	Modal shift
	Car-sharing and ride-sharing	Travel behavior
	Cars with reduced energy consumption	Energy efficiency
	Cars exploiting alternative energy	Alternative fuels
	Alternative fuels	Alternative fuels
	Energy efficiency in road freight vehicles	Energy efficiency
	Alternative energy in freight transport	Alternative fuels
[14]	Improved fuel economy fully implemented	Energy efficiency
	Improved fuel economy partially implemented	Energy efficiency
	EV	Alternative vehicle
	Natural gas vehicle	Alternative fuel
[20]	HEV	Alternative vehicle
	BEV	Alternative vehicle
[21]	New vehicles	Fleet modernization
	HEV	Alternative vehicle
	PHEV	Alternative vehicle
	BEV	Alternative vehicle
	TDM	Travel behavior
	Sedan modal shift	Modal shift
	Taxi modal shift	Modal shift
	Motorcycle modal shift	Modal shift
[22]	BRT	Modal shift
	Urban toll	Modal shift
	Tramway	Modal shift
	EV-HEV	Alternative vehicle
	Car-sharing	Travel behavior
[23]	Efficient vehicles	Energy efficiency
	HEV	Alternative vehicle
	PHEV	Alternative vehicle
	BEV	Alternative vehicle
	Travel demand management	Travel behavior
	Modal shift bus	Modal shift
	Modal shift non-motorized	Modal shift
	Modal shift train	Modal shift
[12]	Urban public transport enhancements and expansion of the electric and hybrid buses fleet	Alternative vehicle
	Expansion of the electric and hybrid urban freight fleet	Alternative vehicle
	Changes in freight transport patterns and infrastructure—rail	Modal shift
	Changes in freight transport patterns and infrastructure—water	Modal shift
	Expansion of active transport and tele-activities	Modal shift
	Logistics optimization	Energy efficiency
	Increased use of hydrous ethanol	Alternative fuel
	Increased use of biodiesel	Alternative fuel
	Expansion of the electric and hybrid light-duty passenger fleet	Alternative vehicle
	Expansion of mass transportation systems Increased use of biokerosene	Modal shift Alternative fuel
[24]	Improved Fuel Economy	Energy efficiency
	EV	Alternative vehicle
	HEV	Alternative vehicle

of Appendix A.

4. Results

This section presents and summarizes the findings obtained in this study.

4.1. Articles Selected and Characterization

Table 2. Articles selected.

Author (Year)	Title	Country	Transport Mode	Transport Focus	Type of Intervention
Dedinec et al. (2013) [17]	Assessment of climate change mitigation potential of the Macedonian transport sector	Macedonia	Road	Passenger	Energy efficiency and alternative fuels
Espinosa Valderrama et al. (2019) [18]	Challenges in greenhouse gas mitigation in developing countries: A case study of the Colombian transport sector	Colombia	Road, rail maritime, and air	Passenger and Freight	Alternative vehicles, alternative fuels, modal shift, public transport, and energy efficiency
Liimatainen et al. (2018) [19]	CO2 reduction costs and benefits in transport: socio-technical scenarios	Finland	Road	Passenger and Freight	Alternative vehicles, alternative fuels, and energy efficiency
Zeng et al. (2021) [14]	Cost-effectiveness analysis on improving fuel economy and promoting alternative fuel vehicles: A case study of Chongqing, China	China	Road	Passenger	Alternative vehicles, alternative fuels, and energy efficiency
Gopal et al. (2018) [20]	Hybrid- and battery-electric vehicles offer low-cost climate benefits in China	China	Road	Passenger	Alternative vehicles
Selvakkumaran and Limmeechokchai (2015) [21]	Low carbon society scenario analysis of transport sector of an emerging economy-The AIM/End use modelling approach	Thailand	Road and rail	Passenger and Freight	Alternative vehicles, alternative fuels, and modal shift
Saujot and Lefèvre (2016) [22]	The next generation of urban MACCs. Reassessing the cost-effectiveness of urban mitigation options by integrating a systemic approach and social costs	France	Road and rail	Passenger	Alternative vehicles and public transport
Jayatilaka and Limmeechokchai (2015) [23]	Scenario Based Assessment of CO2 Mitigation Pathways: A Case Study in Thai Transport Sector	Thailand	Road and rail	Passenger	Alternative vehicles and modal shift
Goes et al. (2020) [12]	Transport-energy-environment modeling and investment requirements from Brazilian commitments	Brazil	Road, rail, and maritime	Passenger and Freight	Alternative vehicles, alternative fuels, modal shift, and public transport
Valenzuela et al. (2017) [24]	Uncertainty of greenhouse gas emission models: A case in Colombia’s transport sector	Colombia	Road	Passenger	Alternative vehicles and fuel efficiency

presents the 10 selected articles that met the inclusion criteria described in Section 3, listing the articles’ titles and their respective authors and also identifying the country where the study was conducted, the transport mode, the transport focus, and the type of intervention addressed in the study.

All of the studies address passenger transport, relegating freight transport to the background (Figure 3), with freight being studied alongside passenger transport. None of the studies focus solely on freight transport.

A modal shift intervention is often associated with freight transport, as seen in Table 2, where three [12,18,21] of the four studies that addressed freight transport had this type of intervention. Most of the interventions focused on the implementation of alternative vehicles and alternative fuels, as shown in Figure 4, which presents graphically the results from Table 2.

4.2. Assumptions and Methodological Approach

Table 3. Overview of methods and assumptions.

Paper	Methodology	Base Year	Projected Year	Alternative Scenarios	Discount Rate
[17]	Bottom-up	2010	2020	1	No data
[18]	Bottom-up	2010	2030/2050	2	10%
[19]	Not available	2010	2030/2050	2	No data
[14]	Bottom-up	2015	2035	4	5% and 8%
[20]	Bottom-up	2015	2030	1	7%
[21]	Bottom-up	2010	2050	7	No data
[22]	Bottom-up	2010	2030	4	4% and 20%
[23]	Bottom-up	2010	2050	4	No data
[12]	Hybrid	2020	2030	3	8%
[24]	Not available	2010	2040	2	No data

presents an overview of the method used to calculate the MAC curve and the assumptions that influence the curve variables.

Regarding the assumptions for projecting the MAC curve (Table 3), the period considered generally follows a pattern of 2010/2015 to 2030/2050. This aligns with the scenarios from the United Nations’ 2030 Agenda for Sustainable Development [25]. The year 2050 also aligns with the IEA [6] and IPCC scenarios for achieving Net Zero and limiting the temperature increase to 1.5 °C.

For the implemented scenarios, the variations include different implementation timelines for the measures [23], measures implemented in isolation and simultaneously [14], variations in the discount rates considered in the MAC curve calculation [14,22], the definition of emissions taxes [21], different levels of penetration and ambition for the goals to be achieved with the implementation of mitigating measures [12,21], and different final years for the curve projection [18,19].

4.3. Calculating the Marginal Abatement Cost (MAC)

Among the selected studies, only two [19,23] did not present the formula for calculating the MAC curve. Of the remaining works, three [12,17,20] presented the annualized cost calculation, and the remaining five presented the calculation of the cumulative cost for the reference year(s), as seen in

Table 4. General results.

Paper	CO2	CO2e	Base Emissions for the Projected Year (Mt)	Cumulative Potential of Reduced Emissions (Mt)	Average Abatement Cost (USD 2020) e
[17]	X		2	0.45	90 USD/tCO2
[18]		X	48.6 a	58.4/449 b	−13/638 c USD/tCO2e
[19]	X		No data	68	81.6 USD/tCO2
[14]		X	No data	193	No data
[20]	X		No data	2.12	No data
[21]	X		No data	1230	125.8 USD/tCO2
[22]	X		No data	30% d	373.7 USD/tCO2
[23]	X		120	840	No data
[12]		X	245	398	−123.2 USD/tCO2e
[24]	X		No data	59	No data

^a Only the baseline emissions for the year 2030 were specified. ^b On the left is the cumulative potential for 2030 and on the right for 2050. ^c On the left is the average abatement cost for 2030 and on the right for 2050. ^d The study by Saujout and Lefèvre (2016) [22] was the only one that only presented the potential in percentage. ^e Conversion formula: USD (2010) = EUR (2010) × 1.325; USD (2020) = USD (2010) × 1.185.

.

Although various GHGs contribute to environmental pollution, the studies tend to focus on calculating emissions and MAC based only on CO₂. As seen in Table 4, seven studies only considered CO₂ for their calculations. Three studies [12,14,18] considered CO₂e.

Table 4 also presents the average abatement cost, where it is possible to observe how the average cost for a set of simultaneously applied measures can behave differently from the individual cost. Of the six studies that presented the average abatement cost, only two had a negative average cost [12,18]. The study by Espinosa Valderrama et al. [18] showed a negative cost in only one of the projected years (2030), with the implementation cost of these measures increasing over time. This could indicate that the measures may become less cost-effective over time. Another impact observed in the Jayatilaka and Limmeechokchai [23] study was that the initial year of measure implementation impacted cost and effectiveness, being negatively affected by delays in implementation.

4.4. Mitigation Measures Applied

Table 5. Applied mitigating measures, potential emission reduction, and abatement cost.

Paper	Mitigation Measures	Potential CO2 or CO2e Reduction per Measure (Mt)	Abatement Cost per Measure (USD 2020) c
[17]	Improvement of driver behavior	0.02 (CO2)	−625 (USD/tCO2)
	Improvement of travel behavior	0.01	−560
	Advancement of vehicle equipment	0.03	−91
	Introduction of low-carbon fuels	0.26	91
	Improvement of vehicle fleet	0.12	98
[18]	Improved fuel efficiency and green driving	23.3/152.5 a (CO2e)	No data
	Electric mobility	11.2/118.2
	Reduction in freight fleet oversupply and renewal	12.2/72.6
	Fuel substitution	10.8/69.3
	Modal change	0.9/36.7
[19]	Urban form, walking, cycling, and public transport	18.6 (CO2)	−270.1 (USD/tCO2)
	Car-sharing and ride-sharing	8.7	−1497.9
	Cars with reduced energy consumption	7.1	530.7
	Cars exploiting alternative energy	4.5	279.5
	Alternative fuels	7.1	87.9
	Energy efficiency in road freight vehicles	9.3	252.8
	Alternative energy in freight transport	12.2	245
[14]	Improved fuel economy fully implemented	71.8 (CO2e)	−66.7 (USD/tCO2e)
	Improved fuel economy partially implemented	43.6	−61.9
	EV	66.2	110.2
	Natural gas vehicle	12	−108.4
[20]	HEV	1.38 (CO2)	−138 (USD/tCO2)
	BEV	0.74	−515
[21]	New vehicles	26.6 (CO2)	113.7 (USD/tCO2)
	HEV	19.3	605.1
	PHEV	17.8	47.5
	BEV	8.5	754.9
	TDM	2.5	−366.3
	Sedan modal shift	8.4	−1173.6
	Taxi modal shift	0.16	−328
	Motorcycle modal shift	0.43	−938.1
[22]	BRT	No data	1203.8/474.3 b (USD/tCO2)
	Urban toll		665.5/329.7
	Tramway		1220.9/646.7
	EV-HEV		1320.1/2046
	Car-sharing		−1361.7/−2030.6
[23]	Efficient vehicles	11.3 (CO2)	−47 (USD/tCO2)
	HEV	9.9	76
	PHEV	8.1	508
	BEV	19.7	559
	Travel demand management	3.5	−438
	Modal shift bus	3.1	−438
	Modal shift non-motorized	1.3	−999
	Modal shift train	8.9	−2065
[12]	Urban public transport enhancements and expansion of the electric and hybrid buses fleet	71.6 (CO2e)	−419.6 (USD/tCO2e)
	Expansion of the electric and hybrid urban freight fleet	1.7	−401.2
	Changes in freight transport patterns and infrastructure—rail	44.6	−220.7
	Changes in freight transport patterns and infrastructure—water	43.9	−149.2
	Expansion of active transport and tele-activities	10.5	−101.4
	Logistics optimization	26.9	−42.6
	Increased use of hydrous ethanol	93	−15.4
	Increased use of biodiesel	85.9	0.9
	Expansion of the electric and hybrid light-duty passenger fleet	17.9	63
	Expansion of mass transportation systems	1.1	254.1
	Increased use of biokerosene	0.6	456.9
[24]	Improved fuel economy	32 (CO2)	18 (USD/tCO2)
	EV	19	47
	HEV	8	114

^a Potential reduction for the year 2030 on the left and 2050 on the right. ^b Abatement cost for a discount rate of 4% on the left and 20% on the right. ^c Conversion formula: USD (2010) = EUR (2010) × 1.325; USD (2020) = USD (2010) × 1.185; USD (2015) = JPY (2015) × 0.158; USD (2020) = USD (2015) × 1.088. Abbreviations: EV—Electric Vehicle. BEV—Battery Electric Vehicle. PHEV—Plug—in Hybrid Electric Vehicle. HEV—Hybrid Electric Vehicle. TDM—Travel Demand Management.

lists the mitigating measures applied in each study, along with their respective emission reduction potentials (except for the Saujot & Lefèvre [22] study) and the abatement cost of each measure (except for the Espinosa Valderrama et al. [18] study).

The Marginal Abatement Costs presented a significant variation between the studies, as seen in Table 5. The values ranged from negative costs (−2065 USD/tCO₂ [23]) to high positive costs (2046 USD/tCO₂ [22]).

The standardization of the mitigation measures names presented in Table 5, previously described in Section 3 and presented on Table A1 in Appendix A, made it possible to verify the range of abatement potential for the mitigation measures shown in Figure 5 and the most sought-after measures (Figure 6).

4.5. Impact of the Mitigation Measures

Table 6. Classification of the mitigation measures according to the ASI and ASIF methodologies.

Mitigation Measure	ASI	ASIF
Modal shift	S	S
Alternative vehicles	I	I
Energy efficiency	I	I
Alternative fuels	S	F
Fleet modernization	I	I
Travel behavior	A	A

ranks the applied measures and indicates which dimension of the Avoid–Shift–Improve (ASI) methodology they impact, with the ASI methodology being a hierarchical policy and planning structure for sustainable transport developed by the GIZ [26], as well as their impact on the Activity–Structure–Intensity–Fuel (ASIF) methodology, which is an analytical tool for breaking down the causes of emissions [27]. These methodologies are consistent with the MAC curve application because they incentivize emission reduction (ASI) and diagnose what impacts the emission changes (ASIF).

Most measures (alternative vehicles, energy efficiency, and fleet modernization) are related to the optimization and improvement of resources use (ASI). A direct connection is observed with the reduction in the intensity of their use (ASIF). These measures require technological advancements for implementation, aligning with the bottom-up methodology used in the studies.

For measures that focus on shifting the transport activity, where a modal shift affects existing infrastructure, even though it was the second most applied measure (Figure 6), a change in the infrastructure may require a longer time for implementation, mostly when dealing with the construction phase. Meanwhile, the use of alternative fuels impacts the shift to energy sources with lower emissions, directly affecting GHG emissions. Finally, the behavior of transport system users affects the level of transport activity; the desired scenario is to avoid an increase in activity, which reduces energy demand.

4.6. Policy Implications Based on the Analyzed Studies

Based on the mitigation measures used in the MAC curves developed in the analyzed studies, a verification of whether there were policies made or actions taken pertaining to the said measures after the date of publication of the studies resulted in

Table 7. Policies alignment of the studies.

Country (Year)	Measures for MAC	Policies/Plans/Strategies Developed
Macedonia (2013)	Energy efficiency Travel behavior Fleet modernization Alternative fuels	Update of the NDC a in 2021. Target for transport (2030): 10% in final energy consumption [28]. Long-term Strategy on Climate Action and Action Plan; Measures: Biofuels introduction (10% of energy consumption), penetration of HEV and BEV by 2030, introduction of hydrogen and greater penetration of CNG, expansion of HEV for freight transport by 2040, energy efficiency, fleet modernization, modal shift—railway, travel behavior (walking, cycling, and electric scooters) [29].
Colombia (2017; 2019)	Energy efficiency Alternative vehicle Fleet modernization Alternative fuel Modal shift	Update of the NDC in 2025 (version 3). Measures: establishment of the National Adaptation Plan on Climate Action, development of national policies and strategies that promote the use of electric vehicles (600.000 vehicles by 2030), fleet modernization, changes in travel behavior (active mobility: a 5.5%increase in the modal share by 2030), intermodality for freight transport, modal shift (waterway and railway), alternative fuels for public transport [30].
Finland (2018)	Modal shift Travel behavior Energy efficiency Alternative fuels	Follow the Europe Union (EU) NDC, last updated in 2023. Carbon-neutral Finland 2035—national climate and energy strategy. Measures: follow the EU binding CO2 limitations values on cars, vans, and trucks; promote energy efficiency, electric vehicles, biofuels, fleet modernization, and modal shift [31].
China (2018; 2021)	Energy efficiency Alternative vehicle Alternative fuel	NDC published in 2021, progress update in 2022. Measures: promotion of multimodality, energy efficiency, alternative vehicles, alternative fuels, and travel behavior (active mobility and public transportation) [32].
Thailand (2015)	Energy efficiency Alternative vehicle Travel behavior Modal shift Fleet modernization	Updated NDC in 2022. Measures: modal shift, energy efficiency, travel behavior, and alternative vehicles [33,34]. Thailand’s Long-Term Low Greenhouse Gas Emission Development Strategy (Thailand’s LT-LEDS). Measures: alternative vehicles (estimated 30% by 2030), energy efficiency, travel behavior, and alternative fuels [3].
France (2016)	Modal shift Alternative vehicle Travel behavior	Follow the Europe Union (EU) NDC, last updated in 2023. The Law on Mobility Orientation published in 2019. Measures: modal shift, travel behavior, alternative fuels, and energy efficiency [35]. France’s climate action strategy. Measures: alternative vehicles (66% of new cars sales by 2030), travel behavior (increase public transport by 25% by 2030), modal shift (rail transport), and alternative fuels [36].
Brazil (2020)	Travel behavior Alternative vehicle Modal shift Energy efficiency Alternative fuel	Updated NDC in 2024. Measures: alternative fuels (expansion of biofuels share by 50%, Law Fuel of the Future), alternative vehicles, energy efficiency, travel behavior, and modal shift [37]. Law Fuel of the Future [38]: subsidizes the Brazilian strategy for sustainable low-carbon mobility, integrating consolidated public policies such as RenovaBio, the Mover Program, the Brazilian Vehicle Labeling Program (PBEV), and the Vehicle Emissions Control Program (Proconve).

^a NDC—Nationally Determined Contribution.

. The results indicate a strong alignment, suggesting that the measures analyzed in the academic studies can be incorporated into climate policy planning.

5. Discussion

This section interprets the results presented in Section 4, exploring their implications, the reasons behind the observed trends, and the research gaps identified.

5.1. The Gap in Freight Transport Application

A standout result is the negligence of freight transport research in the literature on MAC curve applications, as seen in Table 2 and Figure 3. This may result from policies developed with an urban context in mind, which prioritize passenger transport. Other factors that could impact research into freight transport are as follows:

• Data complexity: The freight sector is highly fragmented, involving multiple private actors, complex logistics, and heterogeneous fleets, making the collection of cost and activity data significantly more difficult than in passenger transport, which is usually centered on public institutions [39].
• Transnational nature: Freight (road, sea, air) often operates in international corridors, which complicates national scope analyses that are common in MAC curve studies aligned with NDCs [40].
• Investments periods: Freight infrastructure (railways, ports) has very long investment cycles, making cost–benefit analysis more complex than replacing passenger vehicles [41].

This gap can lead to an underestimation of both the costs and the abatement potential reductions in the transport sector as a whole.

5.2. Implications of Methodological Approach

The strong predominance of bottom-up methodologies (Table 3) suggests that the literature has focused on the technical feasibility and implementation cost of specific technologies; 70% of the studies used the bottom-up approach, which results in interventions that focus on measures such as changing the technology of vehicles and fuels, which are technological in nature.

While useful, this approach fails to capture the macroeconomic impacts (effects on GDP, employment) that a top-down approach would analyze. The absence of hybrid models, which integrate both aspects, is a notable limitation, likely due to the high complexity and data requirements. Only the study by Goes et al. [12] used the hybrid method, which combines the bottom-up, top-down, and Activity–Structure–Intensity–Fuel (ASIF) methods for its model development.

5.3. Mitigation Measures and Their Cost and Abatement Variation

Upon analyzing the mitigating measures that have been applied, it is clear that research focuses on alternative technologies (which is consistent with the use of the bottom-up approach). This includes measures that change the type of vehicle or the type of fuel used, as well as incentives for greater use of higher-capacity transport modes for both freight and passengers (Figure 5). The studies also cover the practice of multimodality and improvements in logistics processes.

The research trend for using vehicles with alternative technology tends to focus on the application of electric vehicles. Six of the ten studies present this as a mitigating measure, with the study by Gopal et al. [20] focusing exclusively on the application of electric vehicles. Regarding the abatement cost of this measure, only two of the six studies had a negative abatement cost, which may reflect the country where the technology is being applied.

Another frequently mentioned measure (50% of the studies) was fuel substitution, either with more efficient or less polluting fuels. In this case, two of the five studies had a positive abatement cost, another two had a negative abatement cost, and one did not specify the abatement cost.

The variation in the abatement cost reflects the variables that influence the calculation of the MAC curve (as noted in Section 2), which considers aspects that are susceptible to local aspects, such as GDP, fuel prices, etc. Thus, it is expected that there is a range of abatement for the same measure, which means that the curve must be constructed using the local context. On the other hand, one way to address the lack of comparability of measures in absolute terms due to the variation in local context would be to express the potential for emissions abatement as a percentage. Doing so would make it possible to compare results and potentially establish a range for the abatement potential.

Based on Figure 5, the implementation of alternative fuels was the measure with the greatest range of abatement, followed by alternative vehicles, demonstrating how the technological advancement associated with the use of renewable energy could have a greater abatement potential.

From Figure 6, we can see that the use of alternative vehicles and modal shift are the most sought-after measures, with the use of alternative vehicles being supported by using electric vehicles for passenger transport, even though it resulted in a negative abatement cost in only two studies. In addition, for measures focused on passenger modal shift, which involves replacing individual transport with public transport or active transport (walking and cycling) for short distances, a negative abatement cost was reported in three studies and a positive one in a single study.

For example, the study by Gopal et al. [20] found negative costs for HEVs (−USD138/tCO2) and BEVs (−USD515/tCO2), whereas other studies on the same measures show higher values. This variation suggests that costs depend on the technology, market conditions, and regional contexts. This heterogeneity highlights the need for measures that are adaptable to each region’s conditions, advocating for a decentralization of global policies in favor of measures that can be tailored to each specific region or market.

An interesting finding from the Saujot and Lefèvre [22] study is that a change in the discount rate from 4% to 20% led to a decrease of approximately half of the abatement cost for four of the five measures applied. The variation in the articles’ discount rate ranged from 4% to 20% as previously seen in Table 3.

Overall, 70% of the studies analyze measures and technologies in isolation within the regional contexts, which creates an opportunity to develop integrated packages. In the studies by Selvakkumaran and Limmeechokchai [21] and Jayatilaka and Limmeechokchai [23], modal shift measures—such as promoting public transport or non-motorized modes—yield strongly negative abatement costs. When combined with higher-level technological actions (like improving vehicle efficiency or using alternative fuels in the energy mix for the transport sector), these measures have a synergistic effect that can surpass the economic and environmental benefits of isolated measures.

5.4. Political Relevance

The close correlation between academic research and Nationally Determined Contributions (NDCs) (as shown in Table 7) is beneficial, demonstrating that academic work genuinely contributes to evidence-based public policy formulation. The MAC curve analyses offer a consistent metric (cost per ton of CO₂) that assists policymakers in prioritizing investments.

It is evident that all reviewed studies propose measures consistent with their respective countries’ national strategies and policies, as reflected in their NDCs. Consequently, MAC curves can be a valuable instrument for policymakers, enabling them to choose from an evidence-based selection of measures. This allows for the combination of affordable, quickly implementable solutions (like efficiency improvements and fleet modernization) with impactful, long-term investments (such as electrification and modal shifts), thereby ensuring that NDC goals are both ambitious and economically sound.

Three measures are highly recommended by the studies and seen as priorities by the NDCs: Modal shift to public transport, since it led to negative abatement cost [12,19,23]. The implementation of electric mobility, for their abatement potential and quality of air improvement in the urban context, even though it can lead to either positive or negative abatement cost [14,20,24]. Finally, the use of biofuels, since they have a greater abatement potential [12,17].

5.5. Future Research

This review suggests potential pathways for future research aimed at refining the implementation and policy development that relies on the MAC curve tool:

• Develop robust MAC curves for the freight transport sector.
• Employ hybrid methodologies to capture both technical feasibility and macroeconomic impacts.
• Explore the standardization of abatement potentials (such as a percentage reduction) to allow for better comparability between studies.

6. Conclusions

Driven by the need to limit global warming and slow down the progress of climate change, measures must be implemented to mitigate the impact of economic activities that emit atmospheric pollutants. The transport sector is one of the biggest contributors to these emissions.

To quantitatively understand trends in transport sector mitigation measures and identify unaddressed research areas and policy implications, this study conducted a literature review of previously published works. It specifically focused on studies that assessed mitigation measures through a cost-effective lens, utilizing the Marginal Abatement Cost curve, which led to the analysis of 10 articles.

The analysis of the selected studies reveals a trend for applying bottom-up, technology-focused methodologies, which consequently prioritize interventions such as the adoption of alternative vehicles and fuels, followed by a modal shift. A predominant focus on passenger transport was identified across the literature, creating a significant research gap concerning the decarbonization of freight transport, a major contributor to the sector’s emissions.

The findings demonstrate considerable variability in the abatement costs for similar measures across different studies, underscoring the critical influence of local contexts, including economic conditions, fuel prices, and existing infrastructure. This high sensitivity to local parameters suggests a limitation in using the MAC for formulating standardized national policies, a challenge further compounded by a lack of methodological transparency in some studies which hinders direct comparability

Notably, while the introduction of electric vehicles is a frequently assessed measure, its cost-effectiveness varies, with some studies reporting negative costs and others positive. Conversely, measures promoting a modal shift towards public and active transport consistently showed negative abatement costs, highlighting their potential for yielding net economic savings. The analysis also suggests that integrating technological solutions with behavioral and structural changes, such as combining vehicle efficiency improvements with modal shift incentives, can create synergistic effects, leading to greater economic and environmental benefits than isolated actions.

The strong alignment observed between the measures analyzed in the reviewed papers and the subsequent climate policies and NDCs of the respective countries affirms the MAC curve’s strategic importance for policymakers. It serves as an evidence-based tool to construct economically rational decarbonization pathways by balancing high-impact, long-term investments with low-cost, readily implementable options.

Future studies should aim to critically address the identified gap in freight transport research. While urban passenger transport is an important target, the decarbonization of logistics chains and long-haul road transport is fundamental to achieving Net Zero targets. Applying the MAC curve to assess measures like truck electrification and the shift in goods to rail and waterways is therefore essential, as the current absence of cost-effective studies for this segment makes it difficult to create effective policies for one of the sector’s largest emitters.

Another gap identified in this research is that only one study considered varying the discount rate. While this study showed a desirable result—a reduction in abatement costs—further research with other parameters is needed to verify if there is a consistent pattern in the curve’s behavior.

Furthermore, a greater emphasis on hybrid methodologies could provide a more holistic analysis by integrating technological detail with macroeconomic factors. To enhance the comparability of findings across different regions, future studies could benefit from standardizing the expression of abatement potential, for instance, as a percentage, since the costs of transport decarbonization are highly context-dependent. Ultimately, this review reaffirms the MAC curve’s role as a crucial and pragmatic instrument that grounds climate action in local economic reality, thereby making mitigation efforts more resilient and achievable.