Evolving Narratives of Low-Carbon Futures in Transportation

Abstract

Scenarios of low-carbon transport demonstrate that a vast range of different outcomes is possible and contingent on policy, technology and cultural developments. But a closer look indicates that different schools of thought suggest possible pathways diverging in their fine structure. This perspective reveals how three different scientific communities — integrated assessment modelers, transport-sector modelers, and place-based modelers — emphasize distinct solution domains. While integrated assessment models focus on fuel composition, transport-sector models put slightly higher emphasis on efficiency measures; in turn place-based research specifies idiosyncratic behavioral and infrastructural mitigation options that are likely to be beneficial in realizing local co-benefits. These specific local approaches could mitigate urban transport emissions by 20–50%, higher than that revealed in aggregate global models. We discuss differences in approach, possibilities for reconciliation, and the implications of normative assumptions. Targeted three-directional interactions would foster comprehensive understanding of possible low-carbon transportation futures.

Keywords:

• climate change mitigation
• low-carbon transport futures
• assessment
• modeling
• IPCC

Notes

1. the so-called autonomous energy efficiency improvement.

2. These models typically assume some price of carbon, by this making gasoline and diesel comparatively unattractive independent of oil resource availability.

3. In the IEA scenario, emissions are growing by about 100% from 2005 until 2050 in the baseline scenario. A 57% reduction in emissions in 2050 from 2050 baseline hence corresponds to 14% reduction in emissions from 2005 emission levels.

4. Population density is only correlated but causally only weakly connected to transport GHG emissions. While Newman and Kenworthy (1989) see population density as a major factor explaining transport energy use, Mindali, Raveh, and Salomon (2004) reveal that urban population density loses explanatory power if other variables such as per capita car km are included. Specific metrics, such as job density, remain statistically important. Ewing and Cervero (2010) indicate that more specific urban design metrics explain the proxy effect of population density. Urban economics help to understand that the high correlation between higher population density, less car travel, and more public transport is jointly driven by higher relative fuel prices (Creutzig, 2014).

5. The economic downturn even led to a reduction in per capita car travel in countries like the USA. But the saturation started well before the economic downturn, possibly caused by saturating car ownerships in households and the natural limits of suburbanization and exurbanization: further distance for commuting becomes prohibited by travel time costs.

6. A good example of policy testing the 2050 Pathway calculator of the UK (https://www.gov.uk/2050-pathways-analysis) that enables an immediate visual check of outcomes by changing policy assumptions.