Passenger transport decarbonization in emerging economies: policy lessons from modelling long-term deep decarbonization pathways

Figures & data

Figure 1. Passenger mobility projections in pkm per capita, 2020–2050.

Note: Metropolitan areas defined as: the eight largest metropolitan areas in Brazil: São Paulo, Rio de Janeiro, Brasília, Salvador, Fortaleza, Belo Horizonte, Recife, and Porto Alegre with more than 1.5 million inhabitants (16% of the population); the six largest in Indonesia: greater Jakarta, Bandung Raya, Surabaya, Medan, Semarang and Palembang with more than 1.5 million inhabitants (18% of the population). In India, metropolitan areas cover all cities having more than 1 million inhabitants, which includes 9 megacities with more than 6 million and 70 cities with more than 1 million inhabitants (30% of the population). The South African team has not been able to advance on such disaggregation between metropolitan versus non-metropolitan areas.

Figure 2. Share of pkm generated by constrained activities in Metropolitan (Solid line) / non-Metropolitan (Dashed line) areas, 2020–2050.

Note: ‘Constrained’ refers to a specific type of travel activity that is deemed essential. For example, this includes getting to a workplace or place of education, going home or buying food. ‘Non-constrained’ mostly refers to ‘leisure’ type of activity.

Figure 3. Share of private motorized mobility (car and motorized 2-wheelers) in total mobility (%Gpkm), 2020–2050.

Figure 4. Share of private motorized mobility (car and 2-wheelers) for constrained and non-constrained trips in the two geographical areas, 2020–2050.

Note: In India, initial analyses have highlighted that the use of private mobility to fulfil constrained activities was more important in metropolitan than in non-metropolitan areas. This could be mostly explained by the fact that households in metropolitan areas have a better spending power and can afford a private vehicle.

Figure 5. Share of passenger transport final energy consumption from non-fossil fuel sources (biofuels and electricity), %EJ, 2020–2050.

Figure 6. (a) Share of full-electric vehicles in total car stock, and (b) share of liquid biofuels and biogas in total liquid fuels and pipe gas consumed by passenger transport, 2020–2050.

Note: Plug-and-Hybrid Electric Cars do represent an important part of the car stock in Brazil and Indonesia by 2050, around one-third.

Figure A1. DDP Design framework for passenger transport (Lefevre et al., 2020).

Table A1. Overview of the modelling and quantification architecture.

Research teams	Macro-economic modelling tools	Bottom-up, sectoral modelling tools
UFRJ	IMACLIM-BR is a hybrid multi-sectoral computable general equilibrium model with multiple economic sectors based on a standard Social Accounting Matrix (SAM) and a square Input-Output matrix.	Transport-Energy-Emissions Multi-Tier Analysis (TEMA) is a sectoral model covering passenger and freight transport services across all modes of transport. TEMA calculates carbon and pollutant emissions through three different approaches: bottom-up (using disaggregated data from mobile sources), top-down (using aggregated energy data) and ASIF (using disaggregated energy intensity data). The applicability of each approach varies depending on data availability and local circumstances.
IIMA/IIML	IMACLIM-IND is a hybrid multi-sector computable general equilibrium model. AIM outputs going into IMACLIM include the energy intensities of 22 economic sectors, households’ energy consumption, and capital intensities of 8 energy supplies and 5 non-energy supplies that is iron & steel, chemicals and petrochemicals, aluminium, cement and textile. IMACLIM output in terms of sectoral output share of total value added for non-energy sectors is fed into the bottom-up model. The data between the two models is exchanged till there is convergence of the shared variables. The convergence is reached in 2–3 iterations of exchanging the model outputs and running the two models.	AIM/Enduse is a bottom-up techno-economic energy-water systems model linking passenger transport sector with other sectors (industry, freight transport, residential, commercial, agriculture) and power systems.
UCT	South Africa General Equilibrium model (SAGE) is a dynamic recursive CGE model calibrated to a national SAM. SAGE is hard-linked to the SATIM energy model iterating to converge on growth rate. Changes in production functions in terms of energy intermediate inputs from SATIM capturing fuel efficiency and fuel switching.	South Africa TIMES (SATIM) is a TIMES least-cost linear optimization model calibrated to a national energy balance.
ITB	n/a	The Deep Decarbonization Pathways Building Tool proposes to calculate transport passenger emissions based on a detailed ASIF disaggregation. The tool also includes a calculation of the evolution of household budget and time dedicated to transport to assess the impact of the transformation on these two main socio-economic indicators.

Figure A2. Per capita and per passenger kilometre CO2eq emissions from passenger transport.

Lefevre, J., Briand, Y., Pye, S., Tovilla, J., Li, F., Oshiro, K., Waisman, H., Cayla, J.-M., & Zhang, R. (2020). A pathway design framework for sectoral deep decarbonization: The case of passenger transportation. Climate Policy, 21:1, 93-106. https://doi.org/10.1080/14693062.2020.1804817

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