The Climate Instruments for the Transport Sector (CITS) study, commissioned by the Asian Development Bank (ADB) and the Inter-American Development Bank (IDB), gives an assessment of the current state of affairs with regard to the impact on the transport sector in developing countries by clean development mechanism (CDM), global environment facility (GEF) and clean technology fund (CTF). Based on desk analysis and case studies in Asian and Latin American cities, this study also provides recommendations for the successful scale-up of climate finance and capacity building, particularly by the use of nationally appropriate mitigation actions (NAMAs) for the transport sector.
Transport is responsible for an important and growing part of global greenhouse gas (GHG) emissions with most of the future increase in emissions coming from developing countries. In the light of overall global GHG emission reduction targets of over 50 per cent below 1990 levels, it can be expected that significant emission reductions are required in the transport sector in developing countries over the period 2020-2050 compared to a business-as-usual scenario. The manner in which developing countries develop their transport systems in the period up to 2020 will greatly determine the extent to which such larger emission reductions in the period 2020-2050 can be achieved.
There is a growing number of scenario analyses for the transport sector which indicate that such emission reductions are feasible in the transport sector, in particular as in the transport sector many co-benefits exist with air quality, congestion policy and energy security of supply. There is a shift in thinking on how to best mitigate climate change in the transport sector away from a focus on purely technological fixes, to include measures aimed at modal shift and avoidance of travel. The avoid-shift-improve (ASI) approach with its more broad understanding of mitigation is resulting in transport policies and programmes that can enable developing countries and cities in limiting the growth in GHG emissions from both passenger and freight transport.
External assistance for developing countries could help to more quickly replicate and scale up GHG emission reduction activities in the transport sector. Such external assistance is required for capacity building and policy development but also for additional demonstration projects and infrastructure funding to leverage domestic funding. Such assistance to adopt a more low-carbon growth trajectory for the transport sector can come from existing special climate funds or mechanisms: CDM, GEF or CTF, or from development agencies especially the multilateral development banks (MDBs). In addition, there are new climate instruments under development, which include nationally appropriate mitigation actions, sectoral approaches, low-emission development plans and a technology mechanism. Of these, the discussion on NAMAs is the most advanced, but each instrument could play a role in climate change mitigation, including the transport sector.
Climate change is becoming a specific strategic priority for the MDBs and they are increasingly embracing the ASI approach as the conceptual basis for their internal policies on climate action in the transport sector. The general increase in funding for MDBs and a realignment of funding priorities in transport sector away from road construction make it likely that MDBs can play a substantial role in assisting developing countries to replicate and scale up sustainable, low-carbon transport policies, programmes and projects. There appears to be a shared awareness that comprehensive approaches covering large parts of the transport sector will be required to best realize the mitigation potential in the transport sector.
Understanding the co-benefits of mitigation actions in transport
Transport policies and programmes usually target several policy objectives including improving mobility, reducing congestion, improving air quality, security of supply and climate change mitigation. Benefits of sustainable transport policies and projects can be distinguished into (Leather, 2009):
- Benefits – the primary intentional goal of policies and project, usually a reduction in transport operating costs or reduced traffic congestion
- Primary co-benefits – other benefits that directly result from transport policies or projects (e.g., GHG and air pollution reduction)
- Secondary co-benefits – benefits that indirectly result from transport policies or project (e.g., reduced health impact and costs from air pollution)
“The ASI approach will bring about different co-benefits, and these co-benefits may be different between developing and developed countries. Developing cities are dominated by large numbers of old high polluting vehicles and the policies focusing on ‘improve’ will have relatively high co-benefits. With many cities in developing countries yet to develop a strong planning capacity, planning instruments such as efficient mix of land use-transport-environment can bring about higher co-benefits compared to cities in developed countries. Similarly, in developing countries, regulatory and planning instruments targeting the freight sector can bring relatively large and immediate co-benefits compared to developed countries’ (Leather, 2009).
Some specific studies show the large size of the co-benefits of sustainable transport projects and policies. For instance, at the programme level, Woodcock et al. (2009) estimate the health effects of alternative urban land transport scenarios for London, UK, and Delhi, India. The authors noted that “reduction in carbon dioxide emissions through an increase in active travel and less use of motor vehicles had larger health benefits per million population (7332 disability-adjusted life-years [DALYs] in London, and 12 516 in Delhi in 1 year) than from the increased use of lower-emission motor vehicles (160 DALYs in London, and 1696 in Delhi).
However, combination of active travel and lower-emission motor vehicles would give the largest benefits (7439 DALYs in London, 12 995 in Delhi), notably from a reduction in the number of years of life lost from ischaemic heart disease (10-19% in London, 11-25% in Delhi).’ The authors conclude that “policies to increase the acceptability, appeal and safety of active urban travel, and discourage travel in private motor vehicles would provide larger health benefits than would policies that focus solely on lower-emission motor vehicles.’
Quantification of co-benefits remains challenging, and often subjective, with no widely accepted approach present as yet. Even on the level of individual co-benefits, e.g., health benefits of improved air quality, different methodologies are being used, let alone for other areas such as improvement in energy security or reduced congestion. In addition to the methodological difficulties, lack of activity data which hampers GHG emissions reduction is a barrier towards co-benefit quantification.
Case study: Jakarta, transport demand management (TDM) NAMA
To provide a working example of how a local-level NAMA in the transport sector may contribute to the mitigation of transport emissions, this study examined TDM in Jakarta, Indonesia.
Indonesia is proactively taking steps to address climate change mitigation at both national and local level. The government of Indonesia is committed to a voluntary 26 per cent reduction below the baseline by the year 2020 unilaterally, and a further 15 per cent (total 41 per cent reduction) with international support (Indonesian Ministry of Finance 2009).
Furthermore in Jakarta, a 30 per cent reduction target by 2030 (compared with BAU) has been set. Indonesia has also associated itself to the Copenhagen Accord, and has made a submission of its proposed NAMAs, which include “shifting to low-emission transportation mode’.
Indonesia faces a particular challenge in taking mitigation actions in the transport sector. The number of vehicles in Indonesia is predicted to grow by more than twofold between 2010 and 2035, with the growth expected to be largest in two-wheelers and light duty vehicles (ADB, 2006). Transport made up 23 per cent of the total CO2 emissions of the energy sector in 2005, with emission levels expected to increase roughly threefold over the next 20 years (Triastuti, 2010). The rapid growth of car ownership is also leading to chronic congestion and increasing levels of air pollution, noise/vibration and road safety issues.
Reflecting existing local priorities, and noting their inclusion in the Jakarta transport masterplan, three specific elements of TDM were examined, namely electronic road pricing (ERP), parking restraint and BRT. The TDM NAMA was studied in light of the three potential types of NAMAs – unilateral, supported and credited.
In assessing and quantifying CO2 and other co-benefits of TDM, the study suggested an approach that combines a transport demand model (driven by data from household surveys and traffic counts) with information on the vehicle fleet (e.g., emission factors). The results could be crosschecked using top-down methods utilizing (regional) fuel sales data, to improve the robustness.
The model was shown to provide a well-established list of output variables to express changes in CO2 and key co-benefits, namely:
- Traffic volumes in terms of passenger and tonne kilometres (which can be translated into carbon emissions by multiplying them with emission factors derived from a set of assumptions on the vehicle fleet.)
- Congestion levels, expressed as average speeds on the network
- Air quality pollutant emissions, expressed as e.g. average level of pollution within a designated zone
Scenario work using the TDM model has demonstrated that a typical combination of the three TDM policies leads to a sustained reduction of total transport demand (in vehicle kilometres, within the wider capital region of Jakarta, and below the baseline 40) by approximately 4-5 per cent, but up to 40 per cent when focussing on the central business district (CBD) where ERP would be targeted at. This demonstrates the highly location-specific impacts of TDM policies.
Expected CO2 reductions (expressed as changes to fuel consumption – a direct proxy) were calculated by combining specific data provided by the modelling, including km-travelled, with vehicle characteristics. A sustained reduction of between 20 per cent and 30 per cent compared to business as usual (BAU) was shown for an area within the Jakarta outer ring road, and even larger levels for the CBD. Such levels of reduction in transport emissions would translate into approximately 4-7 per cent saving of the entire city’s carbon profile, relative to the baseline in both 2010 and 2020.
The approach also allows key co-benefits to be modelled, including:
- Congestion levels, expressed as e.g. average speeds on the network
- Air quality pollutant emissions, expressed as e.g. average level of pollution within a designated zone
The results need to be treated with a degree of caution, due to limitations in the quality of input data and the large number of assumptions that dictate the final outcome. Capacity building in the area of data collection, database development and management is seen as a key priority in ensuring measuring, reporting and verification (MRV) of mitigation actions in the future, particularly in allowing TDM to be implemented as a tradable NAMA. Such efforts would also ensure that co-benefits could be better monitored.
Based on the analysis of the current situation, a roadmap for the future was developed, which suggests that in the short term, TDM would be most appropriate as a supported NAMA, whereby upfront support could be provided to reduce several ‘bottlenecks’ to implementation, including the transfer of key technologies (e.g., for ERP), infrastructure for BRT, technical assistance (in e.g. ERP design, BRT routing/ticketing, optimization of parking charges), and capacity building on MRV.
The complete report can be accessed here.