Mind the gap: personal carbon trading for road transport in Kenya

Fahd Mohamed Omar Al-Guthmy and Wanglin Yan

Climate Policy, 2020, vol. 20, issue 9, 1141-1160

Abstract: Developing countries have generally been overlooked in downstream carbon trading-based policy research. This can be attributed to the greater responsibility of developed economies for climate change, and the greater socio-demographic, political and technological challenges faced by their less-developed counterparts. This paper attempts to address this gap by examining the practicality of implementing Personal Carbon Trading (PCT) for personal road transport in Kenya. PCT is investigated with a focus on political considerations, potential system operation and the distributional impact of quota allocation to motorists. Three quota allocation methods are modelled using data from a survey of 500 motor vehicle owners in Nairobi and Mombasa counties. Equal per-capita, equal per-vehicle and needs-based allocation methods are modelled and assessed to determine the distributional impact on various groups of interest. Emissions were found to be highly correlated to vehicle engine size and the number of dependents per vehicle, but not to area of residence or income levels. None of the three allocation methods disproportionately imposed burdens on vulnerable groups, and all exhibited progressive tendencies. The proposed system is intended to add new insight into the possibility of PCT becoming a globally inclusive policy option.Key Policy Insights Despite PCT being a bold policy proposal even for developed countries, less developed counterparts can meet the political, institutional and technological requirements to implement it.The wealth of functionality available through SIM card features on cheap and abundant mobile phones may unlock a simple and cost-effective, globally-applicable PCT system.Public resistance to fuel price increases stems from the lower income demographic which is, conversely, rewarded with surpluses under PCT.Equal-per capita allocation remains the most progressive method.Needs-based allocation is highly progressive and may be worth considering when more stringent targets are deemed necessary.

Date: 2020
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DOI: 10.1080/14693062.2020.1785380

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