Abstract
Decarbonization of economies is seen as a high priority for many countries to reduce the impact of climate change. As such, policy makers need robust and reliable decoupling indicators to assess decarbonization progress. This paper utilizes a regression-based approach to estimate decoupling indicators and identify decoupling states in the transport sector. The proposed method is easy to use, data driven, providing robust and reliable estimates of decoupling elasticities and fits within existing policy frameworks. Analyzing the transport sector for 59 countries from across the world beginning 1970, only 16 are weakly decoupling. Most of these have been identified as being part of a group with sustained economy-wide CO2 reductions. Stronger and localized green transport policy action is needed to meet emissions targets for most countries.
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Notes
When we refer to the decoupling elasticity or elasticity henceforth, we refer to the GDP elasticity of CO2 (decarbonization). Tapio also used other elasticities in his framework which relate to dematerialization (underlying activity to CO2 emissions) and immaterialization (underlying activity to GDP) (Tapio et al. 2007).
Economists may note that the type of change point regression we use corresponds to structural break time series modelling. Occasionally we refer to either change point or structural change, but they mean the same thing in the context of this paper.
Many of the non-OECD papers listed take this approach.
Five-year blocks to 2015, then a three-year block. The elasticity is computed as the ratio of the percentage changes in negative externality to percentage changes in national income (GNI).
See Table 1 next section for a description of the states in the Tapio framework.
Unfortunately, individual country breakdown over time was not provided.
The province was the decision-making unit.
As far as the author can tell.
Tapio (2005) used 10-year periods in his presentation.
Calculations were done in the R software environment with the package ‘regsc’ written by Qian and Su.
Inference can proceed for each regime assuming the breaks are correctly estimated, see Theorem 3.6 in Qian and Su (2016).
All changes in GDP were positive for each period, while the same was the case for CO2 emissions except for the second periods for Germany and Switzerland.
See for Haque et al. (2013) for a discussion of Singaporean transport policy development.
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Appendix
Appendix
1.1 Appendix A
See Tables 7, 8 and Figs. 2 and 3.
Visual summary of Table 5
Visual summary of Table 6
1.2 Appendix B
1.2.1 A note about the CO2 data
The basis of the data is the IEA’s emissions from fuel combustion for transport computed from the IEA’s World Energy Balance. The basis of the emissions are fuels combusted for domestic aviation, domestic navigation, road rail and pipeline. The energy data do not include military consumption and are collected from national statistical agencies and harmonized within the IEA’s framework and computed using the IPCC guidelines. The key equation used by the IEA is \({CO}_{2}=AD\times NCV\times CC\times COF\), where CO2 are the emissions from fuel combustion, AD is the activity data, NCV is the net calorific value, carbon the content of the fuel, and COF is the carbon oxidation factor (IEA 2022). The emissions are summed appropriately to form the required flows data. Table 9 provides a description of the underlying categories of IPCC standard report code 1.A.3 for which we only use fossil fuel (i.e., ignoring biofuels).
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Parker, S. Assessing progress in decoupling transport CO2 emissions from GDP growth since 1970. Empir Econ 66, 27–51 (2024). https://doi.org/10.1007/s00181-023-02459-x
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DOI: https://doi.org/10.1007/s00181-023-02459-x