Study of Mercury-containing lamp waste management in Sub-Saharan Africa

2.2SSA CFL market projection

2.2.1Factors driving CFL market development

Given that there is no reliable system to monitor the lighting market in Africa, whether lamp production in SSA or lamp imports, collecting reliable and representative data on the national and regional markets would be very complicated and time-consuming. Instead, we propose an estimated projection of the CFL market and CFL waste production using a relatively coherent and simplified model based on only few factors: rate of access to electricity, lighting consumption per household and the lifespan of the technology. Basically, the estimated quantity of CFLs discarded every year can be broken down as follows:

“How many lamps are installed?” i.e.:

How many households have access to electricity?

How many CFLs are installed per household?

“How long does a lamp last?”

The CFL waste flow in SSA is therefore simplified by the equation below. This equation applies with a time-lag equal to the lifespan: if a lamp lasts 6,000 hours and is used 4 hours a day on average, it is discarded 4 years after it is first installed.

Waste flow (measured in number of CFLs) =	Number of households x Electrification rate x Number of CFLs per household (measured in number of CFLs)
	Lifespan (measured in time)

Future waste flow trends were modeled with the aim of providing long-term data that can be used in phase 3 of the study, when defining possible treatment solutions and assessing their feasibility. Using long-term data is consistent with the timescale for the creation or improvement of a waste management scheme. The values used for the modeling exercise are realistic data from different official sources and interviews¹³.

Current (2009) data were estimated as follows. World Bank surveys provided the number of households per country with access to electricity and the number of CFLs per household. Electrification data were cross-referenced with WDI (World Development Indicators) and AICD data on population and electrification rates, either to complete missing data or to check consistency. The model ran with a low estimate and a high estimate for the electricity access rate (depending on the source: WB surveys or WDI and AICD database). Two values were therefore recorded in the modeling tool. For simplification purposes, a single average lifespan of 4.5 years was considered for 2009¹⁴.

A similar approach was used for future data. Change in the number of households with access to electricity (i.e. Number of households x Electrification rate) was based on AICD 10-year projections, which provide low and high estimates. Change in the number of CFLs per household, with low and high estimates, was assessed through interviews with World Bank representatives and according to national surveys¹⁵. Change in lifespan was assessed from interviews with World Bank representatives, so as to take into account an improved lamp lifespan as demanded by IFIs for promotional programs and possible stagnation of the lifespan of lamps provided by retailers.

The following table summarizes the parameters used for modeling, the values considered for these parameters in 2009 and 2020 (figures are given only if a total average is used for SSA), and the sources used to set these values.

Parameter	Year	Value	Sources
Number of households with access to electricity	2009	Different sources produce different values used to set low and high values country by country	AICD WDI WB surveys (domestic and utilities)^16
Change in the number of households with access to electricity	2020	Set country by country Low value = Growth of electricity demand^17 (no increase in the electrification rate) High value = Growth of electricity demand + Increase in electrification rate as deemed optimistic yet reasonable by AICD^18	AICD^19
Number of CFLs per household^20	2009	Low value = 1 High value = 3	Estimation based on WB surveys and interviews
	2020	Low value = 3 High value = 6	Estimation set through interviews with WB
Lifespan of CFLs	2009	Average value = 4.5 years	USAID WB surveys and interviews
	2020	Low value = 6 years High value = 9 years	Estimation set through interviews with WB

Table : Parameters used for market projection modeling

2.2.2CFL waste flows for 2009-2020

According to our calculations, SSA EoL CFL waste flows are estimated to reach 10m/y to 100m/y in 2020. The large variation of ranges for possible waste flows in 2020 is due to uncertainties regarding lifespan (which ranges from 6 to 9 years), market penetration (from 3 to 6 CFLs per household), and the growth rate for electricity demand (from 0% to 70%, the highest rate is that deemed optimistic yet reasonable by AICD). The average scenario would be that potential CFL waste flows in Sub Saharan Africa could reach 60 million in 2020, taking into account various growth dynamics, changes in consumer awareness, the economy, infrastructure networks, etc.

The following charts show the results for Sub-Saharan Africa and a selection of countries (average trend shown by the yellow arrow with indication of the 2020 average value). Some countries have been selected to illustrate the variety of market sizes and similar trends, as shown in the figure below.

Figure (Source: Ernst & Young): Diversity of End-of-Life CFL flows in SSA –2009 - 2020





Figure (Source: Ernst & Young): Estimated End-of-Life CFL flows in SSA –2009 - 2020

Significant differences exist between countries. Nigeria (18m/y)²¹ and South Africa (9m/y) are by far the two biggest potential markets in Africa, due to their population and high electrification rate. The third potential market is Ghana with 3.5m/y, closely followed by Sudan and Ethiopia (3.2m/y and 2.9m/y). On the lower side, countries like Rwanda have small markets (250,000 EoL CFLs/year only). The average market by country is 1.2m/y with a standard deviation of more than 200%.

The risk analysis presented in Section 3 is based on the high-range values to take the worst-case scenario into account. The national EoL CFL market size is an important factor for waste management solutions. The analysis in section 5, which is provided for guidance, is based on this model, but an in-depth feasibility study should be conducted based on more exact values.