The International Energy Agency (IEA) and Intergovernmental Panel on Climate Change (IPCC) [1
] data and estimates report that the transport sector accounts for a significant share of worldwide energy consumption and greenhouse gas (GHG) emissions. Scenarios for later years indicate the sector as still strategically important in global GHG emissions. Several global initiatives seek to decarbonize the sector. However, according to the Paris Process on Mobility and Climate (PPMC) report during the Marrakech Partnership for Global Climate Action (MPGCA) initiative, many factors make green transport difficult, including lack of will and political awareness, tax incentives for fossil fuels, lack of access to financing, municipal governments with low autonomy, lack of financial and human resources, lack of prioritization of urban mobility in public investments, and lack of data from the sector.
Following the strong urban growth of this region, the transport sector in Latin America contributes 35% of GHG emissions, which is higher than other regions worldwide, with the highest emission rate growth among all of the sectors of the economy and with the greatest difficulties in implementing GHG emission reductions [3
]. GHG emissions from this sector are mostly from the road segment, reaching up to 92% of contributions [3
]. Comparing, for example, with South Asia, another developing region that is climatically vulnerable and experiencing a strong urbanization process [7
], the transport sector, which accounted for 27% of total emissions in 2005, should reduce its share in 2030 to 19%, due to the intense increase in emissions from electricity generation, from 30% to 46.6% of total emissions [9
]. The GHG Emissions Inventory, which reveals the current state of emission levels and their sources, is the first step in analyzing city emissions, using a predetermined systematic method [10
In order to contribute to an adequate direction of urban development, the Brazilian Federal Government promulgated Law 12587/12, which establishes the National Urban Mobility Policy (PNMU), and is in compliance with the constitutional determination that the Union must establish guidelines for urban development. The PNMU also establishes that Brazilian municipalities with over 20,000 inhabitants should develop Urban Mobility Plans (PlanMob). The Urban Mobility Plan is a technical document that defines the goals and guides the actions that should be adopted by the municipality in the following years, aiming at urban mobility improvement. It is also the instrument for implementing the National Urban Mobility Policy, aiming at integrating the different modes of transport and improving the accessibility and mobility of both people and cargo in the municipality territory. It should present an analysis of the urban mobility system in accordance with the principles of universal accessibility, fair access to public transport, the use of public spaces for circulation, and the sustainable development of cities, as well as planning for short, medium, and long-term actions. This document should include collective public transport services, road traffic, urban mobility system infrastructures, accessibility for people with disabilities and mobility restrictions, among other mobility aspects, aiming at quality of life gains [11
Despite the important role of cities in controlling and mitigating GHG emissions [13
], urban mobility planning may depart from national commitments of several countries, especially in cities located in developing countries, due to investment and staff constraints [14
], hindering the quantification and consequent mitigation of their emissions. The mid-sized Brazilian city of Natal, which is the capital of the state of Rio Grande do Norte, has an estimated population of 885,180 inhabitants distributed throughout a 167,264 km2
area. Natal is the largest city in a metropolitan region in Brazil, comprising 14 municipalities and 1,577,072 inhabitants [15
]. The service sector accounts for 63.6% of its gross domestic product (GDP), followed by the industrial sector, with 16.5%, and agricultural activities contributing with less than 0.1% [15
]. Natal’s occupation and development characteristics are similar to other medium-sized cities in Brazil and Latin America, suffering a strong economic and disordered population growth in the last decades of the 20th century [16
Concerning the transport sector, this growth was reflected in an increase in motorization rates, especially those of the private vehicle fleet, causing congestions, pollution, and traffic accidents. This motorization rate was further aggravated by the poor quality of the local public transport, the dispersed occupation pattern of suburban regions [17
], public incentives for the automotive industry, and subsidies for fossil fuels [19
The present study analyzes emissions from Natal’s urban transport sector that do not yet have a GHG Emissions Inventory.
Even though this is a case study, not many studies on emissions have been carried out in Brazil and, especially in cities. This is particularly relevant since, as previously reported, a federal law requires cities with over 20,000 inhabitants to prepare an urban mobility plan. The significance of this study is to contribute to the literature on GHG transport and emissions, aiming to understand the main emitters in the urban sector, and providing data for public policy makers and committee members to elaborate Urban Mobility Plans concerning the impacts of not prioritizing non-motorized public and modal modes of transport in their planning.
2. Literature Review
The relationships between urban planning, public policies, urban mobility planning, GHG emissions, and emission scenarios have been addressed by several authors, but not simultaneously and in an applied way as in the present study. The work by Silva [23
] analyzed the traffic-oriented development applied to a small city and the consequent reduction of GHG emissions. Jain and Tiwari [24
] analyzed mobility emission scenarios with bus and cycleway insertions. Lee and Lim [25
] analyzed compact cities with lower demands for mobility. Franzitta et al. [26
] analyzed the insertion of hydrogen-driven public transport in emissions in a case study. Hukkalainen et al. [27
] analyzed a methodology to evaluate the impact of urban planning in a case of small urban agglomerations, in terms of energy and emissions, including transportation planning. Madlener and Sunak [28
] developed the relationships between urbanization and energy consumption, citing learned lessons that can compose sustainable public policies. Shekarrizfard et al. [29
] established a model for the quantification of emissions linked to transport in an urban area case. Fenton [30
] carried out an analysis of reasons that led to the success of a city considered a reference in sustainable transport. Kwan and Hashim [31
] carried out a review on the co-benefits generated by public transportation. Muneer et al. [32
] conducted a case study of GHG emissions in the transportation of a small city, including a scenario of electric vehicle insertion. Dhakap and Schipper [17
] carried out extensive and important descriptive analyses of transport and emissions in Asian developing cities, relating the co-benefits and synergies that can be proven and quantified in several aspects in the present study, reinforcing their conclusions. This study also proposes, within the reported mitigation scenarios, a more efficient and less energy-wasteful public transport, mitigating traffic and promoting equity access to urban mobility, as proposed by Pupim de Oliveira et al. [33
] and Doll and Puppim de Oliveira [34
], focusing on meeting urgent demands and generating environmental and social co-benefits that can also be measured [35
]. Tan et al. [36
], after an in-depth analysis of urban transport systems and their energy sources, created public policy proposals that display potential for the effective mitigation of GHG emissions. The case studies by Wang et al. [37
], Kim et al. [38
], and Alonso et al. [39
] related changes in land use with impacts on travel generation, consequently impacting emissions.
The Global Protocol for Community-Scale Greenhouse Gas Emission Inventories (GPC) methodology was created by the International Council for Local Environmental Initiatives (ICLEI) in partnership with the World Resources Institute (WRI) and the Climate Leadership Group (C40) in 2014, based on the 2006 National GHG Inventory Guidelines published by the IPCC. It aims to be a robust and clear methodology, allowing for the generation of reliable and comparable inventories. The adoption of this methodology by several cities globally will allow the correct direction of sustainable development and emission mitigation action planning. The emissions of all of the GHGs are converted to total of equivalent carbon dioxide (CO2eq
) emissions from the Global Warming Potential (GWP) values that are specific to each gas. The GWPs show the global warming potential of each gas measured in values established from IPCC studies, and are essential for comparisons between gases and CO2
The baseline approach for applying the GPC/IPCC methodology is the top–down approach, which uses fossil fuel supply (consumption) data to calculate CO2
emissions from its combustion. However, in order to analyze a specific sector with data availability, it is possible to carry out the bottom–up, or sectorial, approach that considers—in the case of mobile combustion—the mileage and fuel consumption data of vehicles [41
This methodology seeks to standardize city emission quantification, subsidizing the reach of the Paris Agreement targets (2015). The study carried out by Mengel et al. [42
] pointed out that maintaining the temperature below 2 °C, which is the goal of the Paris Agreement, is insufficient to prevent a 1.5 m rise in sea level by 2300, underscoring the importance of more intense short-term actions to reduce emissions. Ari and Sari [43
] developed indicators to facilitate the fair accountability of countries regarding GHG emissions, considering environmental, social, economic, and technical factors, suggesting a revision of the current responsibility criteria of the Paris Agreement. Obersteiter et al. [44
] concluded on the risk of the Paris Agreement assigning carbon dioxide removal (CDR) technologies to the main mitigations to be achieved in the second half of this century, given their uncertainty regarding implementation feasibility.
The latest IPCC Special Report, Global Warming of 1.5 °C, highlights the mitigation of environmental, social, and economic damage by setting a target of 1.5 °C against the 2 °C target established in the Paris Agreement. This report also points out that heating impacts on natural and human systems are already being observed due to an increase of about 1 °C compared to pre-industrial temperatures. The propensity of occurrence of extreme climate phenomena is greater when comparing the 2 °C heating with that of 1.5 °C. Similarly, impacts on sea level rises and coastal communities, impacts on biodiversity and ecosystems, risks to human health, food security, water supply, and economic growth are highest. However, to reach the target of 1.5 °C, more intensive decarbonization measures are required, including in the transport sector [45
This section details how GHG emissions in CO2eq
, were calculated using data from the various urban transport sectors. Although all of the data were subjected to the same GPC/IPCC methodology explained in Section 3
, this section allows for the subsequent replication of the calculations for other cities, according to locally available data.
4.1. Inventory of Urban Transport Emissions and Urban Mobility Plan
In this study, the emissions of GHG of Scope 1 of Natal’s transportation sector were analyzed, adopting the Tier 2, and applying the national emission factors [54
] of this category.
Total transportation fuel sales are displayed in Table 1
, according to the obtained data. These data allow for the calculation of total greenhouse gas emissions in CO2eq
of Natal’s urban transport sector, according to the aforementioned GCP/IPCC methodology.
The calculation bases of the public urban road transport granted to the private initiative, as displayed in Table 2
, came from PlanMob, which indicates the traveled kilometers and the existing typology of the vehicles. According to PlanMob, there was no change in the lines and kilometers of this sub-sector between 2012–2015.
The data used to calculate emissions from intercity transport are presented in Annex 1, which lists the nomenclature of intercity lines with origin or destination in the municipality of reference. According to the Origin–Destination (O–D) matrix reported in PlanMob, the municipality of Natal concentrated 80% of the motivations of metropolitan interurban journeys. This percentage was adopted to adjust the emissions of this sector, accounting for the municipality that generates travel.
In addition to the road systems, a 56.2-km system exists in the rail mode for passengers only, operated in a simple way, with diesel-powered equipment. This system makes internal connections in the municipality, as well as intermunicipal connections in the metropolitan region of Natal. No Origin–Destination search data was found for this system, but it shows little participation in the city’s transport network, and it is the municipality of Natal that concentrates the displacement demands and the billing. The emissions for this system were calculated conservatively in the reference municipality (Table 3
There is little current relevant data for municipal freight transport in PlanMob. The data available are from 2007, which is from an Origin–Destination survey carried out in the municipality [55
]. No updates to this data in PlanMob 2017 were carried out. According to the 2007 O–D survey, 35,226 truck trips are carried out daily, of which 13,073 were internal, 6901 originated in the municipality, and 6601 had the municipality as their destination. This 2007 data was used for a bottom–up estimation of fuel consumption [40
], considering a mean fleet age of five years [48
], heavy diesel trucks for intermunicipal transportation, medium-sized diesel trucks for internal transport, and a transport distance corresponding to the approximate extension of the municipality, at its largest dimension of 25 km for internal transportation, and half of this value, 13 km, for intermunicipal transportation. This estimate was compared to diesel sales in the municipality in 2007 in order to quantify the weight of this sector for the same year (Table 4
). Due to the precariousness of the data update, the cargo sector was joined to other urban public service sectors that use diesel vehicles, such as private bus transportation and leased minivans (transfers), public maintenance services, garbage collection, and school transportation.
4.2. GHG Emission Scenarios
PlanMob presents urban mobility scenarios for 2020 and 2025, according to a specific methodology to this planning that takes into account GDP evolution, population growth, and increases in the current fleet. The analysis of the methodology applied by the municipal government in the elaboration of PlanMob is not within the scope of this study. However, according to the cited bibliography [46
], primary data were obtained through Classified Volumetric Counting (CVC), documentary, and secondary data and filming cameras, and technical meetings, and public hearings were held. We also developed models for population income growth and used the Aimsum software to simulate traffic and elaborate our own scenarios. In these scenarios, a percentage increase of fuel consumption is observed due to the degradation of the level of traffic. The business as usual, or reference, scenario does not indicate works or changes in the road system. In addition to the reference scenario, PlanMob presents a list of 41 non-structural works that, if implemented, would generate a slight benefit in relation to the initial scenario. This scenario predicts an increase in fuel consumption that is lower than the baseline scenario. The impacts on fuel consumption are displayed in Table 5
. Increases in individual motorized trips for 2020 and 2025 are listed in Table 6
. The emissions were calculated for 2020 and 2025 following the business-as-usual projection, generating the GHG emission scenario termed CPMob1. GHG emissions were then calculated for the Urban Mobility Planning interventions, generating scenario CPMob2. Emissions were calculated by the aforementioned top–down methodology [40
Since the CPMob1 and CPMob2 scenarios were constructed considering a linear increase in fuel consumption, the emission estimates of each sub-sector were the same as in the reference year, 2015. In addition to the emission scenarios based on the PlanMob data, mitigations focusing on reducing GHG emissions, primarily acting in the sub-sectors with the highest emissions participation, were calculated according to the inventory for the base year. The first mitigation scenario, CM1, presents an effort to direct urban mobility public policies to emission reductions, which is an aspect that is not addressed in PlanMob. The additional mitigation scenario—CM2—is more ambitious, considering actions that are more impacting or more intense than those adopted in CM1. The mitigation scenarios are not only aimed at reducing GHG emissions, they are also aimed at improving city mobility.
The literature review allowed us to direct the mitigation scenarios of this study to solutions that are applicable to the reference city. Pereira Jr et al. and La Rovere et al. [56
], in their national study concerning Brazil, addressed the following actions in the initial mitigation scenario: investments in Bus Rapid Transit (BRT), the expansion of cycle paths, traffic optimization, improvement in the energy efficiency of light and heavy vehicles, and expansion of ethanol and biodiesel use. In these studies, the additional mitigation scenario also included the insertion of electric buses, additional light vehicle energy efficiency, subway and light rail train (VLT) expansion, and investment in waterways and railways for cargo transportation. The impact of improving combustion vehicle energy efficiency is further reinforced by several authors [2
]. The focus on public transportation is decisive in all of the literature related to urban transport decarbonization [64
]. Gouvello et al. [67
] also cited the direction that the urban transport sector must take for its decarbonization: the development of high-capacity bus systems and rail transportation in high-traffic locations, urban mobility management to reduce congestion—a 20 km/h to 25 km/h increase in hourly traffic speed can generate up to a 5% decrease in emissions, the establishment of strategies to limit individual transportation, integrating different transport modes, integrating land use with transport—reducing travel distances, encouraging non-motorized transportation, and strengthening the use of biofuels, especially ethanol. The Transport and Urban Mobility Sector Plan for Mitigation and Adaptation to Climate Change [65
] also directs actions, focusing on public transportation, urban planning with densification and the mixed use of regions, the creation of infrastructure for mobility by collective public transport, and the replacement of fossil fuels in urban buses. The adoption of electric vehicles is also considered an inevitable path [63
] in the reduction of total emissions.
4.2.1. Medium-Sized Cities
The scope of this study is to privilege mitigation scenarios that can be effectively developed by the public policies of the municipalities [5
]. However, medium cities, as the object of this study, present limitations in their mitigation actions when compared to larger cities:
The subway system—transport on high-capacity electrified rails—is unviable at the medium-sized city scale [65
In spite of the great importance of the type of fuel used in the generated emissions, municipalities in general display limited incentive or even disincentive capacity for one fuel or another: the tributes are mostly state and/or nationwide. The municipality may only restrict the schedules or traffic zones of certain vehicles;
Cities also display little capacity for actions aimed at improving the energy efficiency of vehicles, as these are normally regulated by national standards. They can encourage less polluting vehicles and modes, giving them greater freedom of movement or exclusive parking areas.
These particularities are considered in scenarios CM1 and CM2, which privilege the actions that can be taken in a municipal scope. The descriptions of the considerations adopted in each mitigation scenario will be presented during the analysis of the results, since they depend on the previous stage of the GHG emissions inventory.
4.2.2. Based on Official Data, Official Planning, and Official Problems
The construction of the scenarios followed the forecasts of increases in motorized individual trips (Table 6
) estimated by the PlanMob [46
]. It also followed the series of growth in the number of urban train passengers, according to the increase in capacity that the network has been suffering since the 2014 World Cup event. However, this has been limited by the difficulty that the CBTU has been undergoing to reach its daily passenger targets due to delays in the delivery schedule of their compositions. Of the 12 diesel compositions acquired in 2014, only five were in operation by the end of 2017. Due to the lack of Origin–Destination studies for this transport mode, there will be no mode substitution when its growth occurs; it is also modal, with a subsidized rate of about USD $
1.00 quoted at R $
3.35 as of 25 December 2017) against the USD $
1.00 fare on urban buses and up to USD $
2.27 on intercity buses, possibly replacing non-motorized modes within its activity. For intercity transportation, the same growth rate was adopted for the number of individual trips, with no modal migration; there are no plans to change its structure according to the state body [51
], nor plans for its integration into urban transport in the PlanMob. The diesel freight and urban service sector will expect emissions growth as defined by the planned top–down quantification for all traffic.
PlanMob suggests the deployment of an extensive cycling network, from the existing 31 km to 210 km, but no quantification for budgets or sources of resources for this action, as well as an execution schedule, are available. This implementation will be included in the CM1 and CM2 mitigation scenarios, to be completed in 2025, but distant from the basic scenarios by the simple absence of a government plan for its implementation. The 2007 data [55
] estimate that 4% of daily trips were carried out by bicycle, even with no adequate bicycle structure. In the projections reported by Gouvello et al. [67
], an adequate cycle network deployment allows for a decrease of up to 1.6% in total emissions, which would migrate from individual transport. The evaluated city has the potential to reach higher levels of mitigation, but, due to a lack of updated O–D data, the same estimate reported by Gouvello et al. [67
] for the fully installed network will be applied.
PlanMob 2017 also mentions:
The inefficiency of the transport network of urban buses with point-to-point lines, overlapping roads, and concurrent routes, but does not present proposals for change; instead, it only mentions that a shift to a hierarchical structural network should be carried out, within the main transportation corridors
The generalized deficiency in pedestrian mobility, but also without presenting any plan of action for correction. The only planned intervention is the rehabilitation of a historic neighborhood, which is currently not very representative of urban mobility as a pole that generates travel;
The possible implementation of a bicycle-sharing network, but without any quantification of its impact or viability.
For these reasons, these actions were not included in the CPMob1 and CPMob2 scenarios, and the lack of data on their impact on urban mobility and, consequently, on GHG emissions, were not included in the mitigation actions. The literature review also identified studies regarding new modes of transport [70
], analyzing the implementation of a tramway (VLT) and trolleybus system, respectively, in Natal. The deployment of a 10-km trolleybus line was considered in the CM1 scenario for 2025. Additionally, the CM2 scenario will also include the implementation of a total of 28 km of tramway lines, in another road stretch in the municipality. The analyzed literature [70
], although presenting initial indicators of cost estimates, does not present the analysis of the investment return for the possible operator, which prevents an effective analysis of the actual cost to implement these modalities. The 10-km trolleybus system has an estimated investment of R $
17.3 million for infrastructure and R $
22.77 million for the acquisition of 18 articulated trolleybuses. The 28-km tramway system, including infrastructure, expropriation, station construction, maneuvering yards, parking lots, and vehicle acquisition, totals an estimated R $
2 billion, but lacks a revenue analysis of the system and parking operations throughout the tramway shelf-life.
The mitigation scenarios also included replacing part of the diesel bus fleet with electric vehicles. Pereira Jr et al. [56
], in the mitigation scenario for Brazil, estimated an 8% dissemination of this type of vehicle until 2030, but in the reference city, passengers were concentrated in only a few lines, so there is the possibility of replacing a higher percentage of traveled mileage maintaining the existing network. Percentages of 10% for CM1 and 15% for CM2, for 2025, and 3% and 5% for 2020, respectively, will be adopted.
After the scenario elaborations, public policy proposals for the urban transport sector that stimulate sustainable development, and consequently better quality of life for the population, were constructed [72
This work was developed with available public data or data requested from the governing bodies responsible for the mobility sectors. Data such as the O–D matrix and the vehicle count that are not kept up to date by the responsible agencies can generate distortions, especially in the scenario projections.
This study delves into detailing the sub-sectors of urban mobility, but did not present an exploratory scope, exploring research paths beyond existing official documents. There is vast literature that deals with the impact of land use on GHG emissions. In addition to the bibliographies cited above, Bart (2010), Deal et al. (2017), Kennedy et al. (2011), Hankey et al. (2010), and Pan et al. (2018) [77
] deepened studies on the environmental impacts of urban sprawl, including GHG emissions, as well as mitigation policy proposals. However, the main urban mobility planning document—PlanMob—has not yet been integrated with Urban Land-Use Planning, or the Urban Master Plan.
This study also displays limitations because it does not comprise comparison parameters with Brazilian cities or of similar developing countries, being one of the first studies directed toward medium cities; this presents deficiencies already mentioned in the previous topics in keeping their data updated regarding municipality planning.
The results obtained by this study allow for the characterization of the urban transport sector, defining the sub-sectors that exhibit the greatest impact. Individual motorized transport accounts for 59.43% of greenhouse gas emissions from urban transport, followed by the freight and urban services sector, at 28.3%, for the base year of 2015. The public bus transport sector, with a fleet of only 0.23% of the total of vehicles in the city, contributes 10.43% of the CO2eq emissions. The freight and public utilities sector use diesel engines in their operations and include, in its total, the use of diesel vehicles such as pick-ups and diesel SUVs in private transportation, as there is no data available separating diesel vehicles from the city’s fleet. The public bus transport sector comprises exclusively diesel vehicles that have a high average age and high average monthly mileage, which explains their proportionally high CO2eq emissions.
The sub-sector detailing of the GHG emissions inventory of urban transport reinforces the need to direct public policies for collective transportation, which would improve energy efficiency and attractiveness, make urban transport comfortable and safe, and discourage the displacement of individual motorized vehicles.
The planning of urban mobility actions presented by the PlanMob distanced the national incentive policies for public and non-motorized individual transport, with negative consequences in the resulting GHG emission scenarios. The scenario planned by the city in its PlanMob will result in a 20.45% increase in CO2eq emissions in 2025. In turn, the mitigation scenarios include actions to encourage collective transportation, the replacement of fossil fuels by electric motors, and incentives toward non-motorized transportation Mitigation scenarios would result in an initial mitigation of up to 44,800 tCO2eq by 2025, and can achieve mitigation values of up to one-third of the total GHG emissions from urban transport if high-capacity and electrified public transport such as tramways were to be integrated into the transport network.
The development of the scenarios also indicated that the execution of a bicycle system would lead to the greatest direct emission decreases, maintaining the urban road transport system in a similar way to the existing system. Electromobility modes—the trolleybus and the tramway—enable additional mitigations with increased operational urban transport capacity. The tramway alone, which was calculated as a simple substitution to the road mode, has the potential to mitigate at least 71,000 tCO2eq, which is about 9% of the total city emissions. Its implementation must be associated with a reorganization of the daily commuting flow of collective transportation and incentives regarding individual transport migration.
These results should support municipal public policy makers in reviewing the PlanMob adopted criteria, approaching national emission reduction commitments, directing investments to less emitting sectors, such as non-motorized and electrified public transport, and taking mitigation action on the most emitting sectors.