Waste collection criticality index in African cities

Waste Management 103 (2020) 187–197

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Waste Management journal homepage: www.elsevier.com/locate/wasman

Waste collection criticality index in African cities Faten Loukil a,⇑, Lamia Rouached b a b

UAQUAP, ISG Tunis, Université de Tunis, Tunisia LEGI, Ecole Polytechnique, ISG Tunis, Université de Tunis, Tunisia

a r t i c l e

i n f o

Article history: Received 9 May 2019 Revised 16 December 2019 Accepted 17 December 2019

Keywords: Solid waste management Urbanization Waste indicators Cities typology

a b s t r a c t The paper proposes to define a waste collection criticality index that is based on a quantified census of the current solid waste management situations of 24 African cities. The proposed index allows to compare collection efforts, to draw up a typology of cities and to highlight those where the situation is alarming. Identifying direct and indirect factors that may accentuate the specific problem of uncollected waste provides an interesting context. The exploration of the current context in different African cities has shown a growing interest in the particular issues of uncollected waste. In addition, the specificities related to the composition of waste and the problem of rapid change in lifestyle, as well as inadequate infrastructures require revisions and new perspective into public policy responses. However, decisionmaking in waste management still needs a dashboard with reliable indicators as well as recent data that is essential for economic analysis and for choosing an appropriate governance method adapted to each city. In this context, the criticality index applied to African cities, has revealed the impossibility of enhancing waste management without improving urban infrastructure and reducing poverty. Ó 2019 Elsevier Ltd. All rights reserved.

1. Introduction

1.2. Review of waste management indicators in developing countries

1.1. Background

Guerrero et al. (2012) showed that socio-economic status, climate and demographics are determining factors for solid waste generation rates and composition. The authors associated waste production to consumption behaviors and economic organization. The composition of a family, the educational level and the monthly income have an impact on waste production (Sujauddin et al., 2008). Chen (2018) and Ho et al. (2017) have recently shed light on the effects of some indicators of urbanization on municipal solid waste management. More particularly, the studies considering OECD countries of Johnstone and Labonne (2004) show notably positive effects of population density on household waste generation. In fact, it has been proved that larger families tend to produce less packaging waste as they keep the packs of consumer items such as food and beverages for a longer period of time. Conversely, and even though Jenkins (1993) determined a negative effect of population density on the amount of generated waste, it is obvious that the waste collection service is insufficient, unfulfilled and often neglected in high-density and low-income habitats (Coffey and Coad, 2010). With overloaded capacity, local authorities are unable to provide adequate service in these undeveloped settlements (Henry et al., 2006). The lack of infrastructure and the narrow roads complicate the access for collection vehicles particularly during the rainy season. Unfortunately, in Africa and developing countries, urbanization is associated with unplanned

Waste management is a priority for African countries which are faced by an institutional and organizational inability to collect and recycle waste. According to Hoornweg and Bhada-Tata (2012), waste collection rate is very low and does not exceed 50% in most cities, so the uncollected fraction still very high. Indeed, uncontrolled landfills and informal sector recycling represent the dominant mode of waste disposal (Maccaglia and Tabarly, 2008) without precautionary measures that would preserve the health of residents (Wilson et al., 2015). Although awareness is widespread, the lack of reliable quantifiable data precludes meaningful analysis. In addition, whenever these single indicators are available, they are often treated separately and consequently their assessment risks lose importance and they end up deviating from their real meaning. For example, the average waste generation is only 0.65 kg/capita/day in subSaharan Africa and estimated to 2.2 kg/capita/day in the OECD countries (Hoornweg and Bhada-Tata, 2012). Hence, considering the generation indicator as acceptable in African countries does not mirror the actual importance of the problem. ⇑ Corresponding author. E-mail address: [email protected] (F. Loukil). https://doi.org/10.1016/j.wasman.2019.12.027 0956-053X/Ó 2019 Elsevier Ltd. All rights reserved.

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and unsustainable development which makes waste management more complex and inefficient (Nsokimieno et al., 2010, Cobbinah et al., 2015, Vij, 2012). According to Dasgupta et al. (2014), in many African cities, less than 10% of the population lives in formal housing areas. Furthermore, as highlighted by Lall et al. (2017), the low economic density makes African cities more vulnerable. Thus, urban areas dominated by crowded quarters are characterized by the accentuation of unemployment and poverty. For these cities, the importance of the informal sector is largely related to the development of slums (Muchadenyika and Waiswa, 2018) without decent sanitation services and facilities (Cohen, 2006). Moreover, the lack of storage space complexes the problem in slum areas which need a more frequent collection (Coffey and Coad, 2010). So, without the economic density that stimulates the efficient use of resources, African cities will remain fragmented and disconnected (Dave, 2010, Lall et al., 2017, Dodman et al., 2017). Facing this, Lall et al. (2017) highlight social, economic, environmental and urban challenges for African cities. As the degree of efficiency of the waste management sector is a key indicator of good governance in cities, benchmarking their waste management performance is interesting for policy makers to choose the best strategy and improve the cities sustainable development (Rodrigues et al., 2018; Mendes et al., 2013). On the other hand, waste management indicators are not usually enclosed in the sustainable indexes of cities as the Sdewes city sustainability index (Kılkısß 2015, 2016), or limited to treated wastewater (50%) and formal solid waste disposal (50%) in the city development index (UNDP/UNCHS, 1996). In Green city index (EIU, 2012), waste indicators are incorporated, but most of them are qualitative due to the unavailability of data related to African cities. Moreover, it is important to note the insufficient number of studies that investigate the performance indicators for developing cities as most research focuses on adapting indicators to the context of developed cities (Cifrian et al., 2010, Greene and Tonjes, 2014). For the waste management system, most reviews focused on specific steps or dimensions as waste material performance through the zero waste resources depletion index (Zaman, 2013), waste collection (Bertanza et al., 2018) and the recycling performance indicators (Wen et al., 2009). The original work of Un Habitat (2010) and that of Wilson et al. (2015) investigate waste management stakeholder awareness through waste aware indicators. Cervantes et al. (2018) analyze strengths and weaknesses of 40 sets of indicators in the field of waste management in assessing performance, sharing best practices, detecting problems or predicting future actions. However, in most studies, the effect of urbanization on waste collection performance is not well highlighted. As mentioned by Bertanza et al. (2018), waste collection has become costlier and the ineffective collection operations affect the following phases of reuse and recycling. 1.3. Steps for constructing a composite index Literature focusing on the process of building a composite index proposes an approach made of several stages: the definition of the object of study by reference to a theoretical context, the selection of suitable single indicators, the normalization of each indicator and the choice of an aggregation method (OECD, 2008, Cifrian et al., 2014, Mazziotta and Pareto, 2013). Although the articulation of the process is relatively common, there are some differences in the procedures to follow at each stage. Mazziotta and Pareto (2013) suggest a flow chart that indicates how the best solution will be adopted by following the appropriate path depending successively on the type of indicators, the compensatory versus non compensatory approach, the aggregation function, data normalization (to perform comparisons in absolute or relative terms) and

weight of singular indicators. Moreover, the authors review the literature on the most commonly used methods to build a composite indicator and highlight the choice of the aggregation method where the arithmetic average approach is often compared to the geometric one. Cifrian et al. (2014) pay special attention to the choice of indicators by subjecting them to different evaluation criteria. According to the degree of individual compliance with the criteria obtained, only the indicators that obtain more than 50% of the maximum score are qualified as viable and feasible. Hence, the selection allows access to the other steps of normalization, weighting and aggregation. The guide for constructing a composite indicator published by OECD (2008) relies on the multivariate analysis to identify related groups of indicators or countries. 1.4. Goal and structure of the paper The object of this research is to assess urban waste criticality system in African cities through the proposition of a waste collection criticality index that allows us to benchmark waste management collection system in municipalities, cities or countries. Based on the ISO 31000 standard and Knobloch et al. (2018), this study defines criticality as a measurement of failure modes related to urban waste collection management and their impacts on the increasing vulnerability of African cities. Here, the priority of the approach is to define appropriate indicators as well as to overcome the lack of data in many developing countries. After the introduction (Section 1), the analysis is organized as follows: Section 2 develops the methodology adopted to build the waste collection criticality index. It underlines factors such as waste generation (qualitative and quantitative), inefficient collection services and inadequate urban infrastructure that are affecting criticality and exposes the difficulties which are faced in the process of reliable data collection as well as and how these hardships could be overcome in African cities. Section 3 discusses the results of calculating the waste collection criticality index for 24 African cities. A classification of the urban criticality collection waste for cities is exposed through the principal component analysis. It highlights the alarming situation in cities like Nouakchott, Lusaka, N’djamena and Lome, and underscores the urbanization as a structural challenge for African cities. Section 4 formulates recommendations for policy makers. And finally, Section 5 summarizes the main results. 2. Methodology for developing the waste collection criticality index It is obvious that the construction of a composite index summarizing multidimensionality has to follow a rigorous methodology to identify, as exhaustively as possible, all the indicators relevant to the purpose of the research without disregarding the information provided by each individual indicator. In line with the literature review, this article proposes a multi-step approach that starts by stating and justifying the choice of indicators, then, it describes the adopted normalization process and thereafter, it highlights the weighting and the aggregation methods that have been used. 2.1. Factors affecting waste collection criticality index In a preliminary phase, it is essential to prepare the data for the studied cases and to cluster information in a two-entry table by country and singular indicators. For African cities, data collection requires special attention given the lack of regularly published, recent and reliable data at the city level or even sometimes at the country level. The exploitation of data for strategic or political purposes affects their availability and may even call into question

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their veracity, especially when the information has an economic impact or generates social sensitivity. The unavailability of information can lead to the absence of action or to a lack of analysis of the situation causing erroneous decisions at the expense of the city. Nevertheless, the weakness of the available information should not hinder the application of the methodology proposed in this study for the evaluation of African cities according to their level of criticality. To overcome these difficulties, the database is completed by crossing different sources of information. In the case of missing data, the use of approximations is also unavoidable. Given the unavailability of regular survey data in a number of countries, institutions such as the World Bank or Demographia rely on approximations to make estimates. To this end, several statistical techniques (means, headcount poverty rate, growing rate, linear interpolation. . .) are used to fill the gap caused by missing data. According to Roser and Ortiz-Ospina (2017), in spite of their imperfection, the estimates convey significant and useful information. By focusing on the problem of poverty, the authors consider that inaccurate estimators underestimate the real numbers and do not overestimate them. The waste criticality collection index makes it possible to overcome this constraint and position each city according to each component (or indicator) compared to the most successful cities on the one hand and the most critical cities on the other hand. It also offers the possibility to compare the effort over the duration and the trajectory of evolution of each city. In order to deal with these considerations, indicators are built with regard to the purposes of the research and the specificities of the studied cases following OECD recommendations (2008). The major waste management problems of African cities are at the beginning of the system and particularly concern the collection process and its sub-processes of generation, pre-collection and transport of waste (Loukil and Rouached, 2018). Factors influencing the waste collection can be classified according to the extent of their involvement in the collection process. Thus, the nested structure is illustrated in Fig. 1. As Marshall and Farahbakhsh (2013) pointed out, the situation of developing countries in terms of waste management is very different from the one that has been historically experienced by developed countries. The proliferation of waste volumes in developing countries is reminiscent of the situation that had been experienced by developed countries some decades ago and this may suggest applying the same solutions to the same issues. However,

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urbanization, socio-economic aspects, governance, and institutional issues are among the factors that have aggravated the current problem of developing countries. Firstly, this study focuses on the quantitative and qualitative factors related to the generation of waste. In fact, the amount of waste produced per inhabitant is a quantitative indicator of household consumption habits and their level of income. However, the composition of waste is an indicator of a qualitative waste generation. When the country income rises, the proportion of biodegradable waste decreases while plastic, paper and other synthetic materials increase. The environmental consequences of such qualitative change are harmful because of non-biodegradable waste. In Africa, the organic part is the largest, but expected to lessen with the movement of industrialization. Secondly, this study highlights the impact of urbanization on waste criticality and considers that urbanization increases criticality when it becomes an obstacle to collecting and reducing the costs of waste collection and recovery. As such, the inefficient collection service is an indicator of an urban planning problem and is evaluated through the rate of non-collection of waste. This uncollected waste poses an environmental problem and health risks. It is therefore an indicator of poor control over the growth of the city as well as the current and future inability to cope with increasing urbanization. Urban density also has an impact on increasing the criticality of waste collection and has two important components, particularly for developing countries. The first is the population density that can increase the concentration of waste production in cities and raise questions about the collecting ability of cities. The second is the economic density that reflects the economic concentration of cities and their ability to develop the formal structures and services associated with all citizens and in all areas. It’s possible to enrich the index with other indicators (Da silva et al., 2019), but the problem will be the availability of information. For this, the simplicity of the index allows its transposition to other cities, regions, countries. The accuracy of the calculated index will certainly benefit from the inclusion of updated data whenever available. 2.2. Method of normalization and aggregation of indicators All indicators are normalized and aggregated to have a single waste collection criticality index. For this, we use the min max method (OECD- JRC 2008) as given by the equation (1).

Fig. 1. Factors affecting cities waste criticality index in developing countries.

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 Iie C j ¼


 Die C j  minDic maxDic



minDic

8i ¼ 1;    :5; 8j ¼ 1;    :N

ð1Þ

The superscript i is reserved to indicators and can be from 1 to 5. The city is referred to as Cj so that the subscript j varies to represent the N cities considered. Moreover, two subscribers related to time are used: e which represents the estimated date and c which represents the current date.

 Iie C j and Die C j are respectively, the indicator aggregated and the data before aggregation associated to the indicator i for the City C j at the estimated date e. It is important to note that when the current date is recent, we can apply the available data at date c instead
 of the estimated data Die C j at date e. Also minDic and maxDic respectively represent the minimum and maximum values for each indicator i in the cities around the world for which data is available at the current date c (these extreme values can be found in Table 1). Using the min and max values among cities around the world, allows to have the same denominator for indicators and it therefore makes the methodology reproducible to cities in other continents. An alternative to this hypothesis is to select the min and max values from those of the African cities. In this case, the relative values taken by this indicator would change without changing the ranking among the cities in the sample. The two assumptions are consistent with our objective to benchmark cities. However, according to the hypothesis assumed in this study, other cities can be included from outside the sample. Table 1 describes the normalization results and summarizes values used from Hoornweg and Bhada-Tata (2012), Demographia (2017) and Un Habitat (2014). Regarding the aggregation method, we retain the simple arithmetic average for the four factors influencing the waste collection: Production, Composition, Inefficient collection service and Urbanization. The average of the factors is based on a weighting by the same coefficients to reflect an identical importance to all the main factors. However, we will refine the contribution of the last factor by applying a weighted average of the two indicators: slum rates and demographic density. In fact, given the specificity of African cities, it is considered that the weight of slum rates reflects the urban problem and the low economic density more than the demographic density. Geographical density can bring an economic advantage, but in African cities, the low investment in infrastructure such as transport and housing increases congestion and the cost of services (Lall et al., 2017). Under these conditions, the following formula performing the waste collection criticality index W C I e (C j) is proposed. 3 X 1 i 1 1 2 Ie ðCjÞ þ ½ I4e ðCjÞ þ I5e ðCjÞ WCIeðCjÞ ¼ 4 4 3 3 i¼1

8j ¼ 1; 2       N ð2Þ

The approach in this research aims to compare the criticality of waste collection of N African cities in order to detect the factors that contribute the most to this criticality. Implicitly, the key issue is also to analyze the interactions between all the indicators. As such, a scale of criticality that compares cities on each factor is defined. In statistics, for a grouping of the data in classes, one usually retains the square root of the total number of selected cities, i.e. N. To determine the extent of each class, the selection follows two considerations: ranking cities according to their level of criticality and the homogeneity of classes in terms of size. In order to analyze the interaction between the factors, we opt for the analysis of the main component that allows to organize the variables and to show the strong interactions. The analysis relies on the SPSS 18.1 software. The first step of analysis consists in determining the number of axes to be retained in order to examine the different correlations between the variables as well as between the modalities. As a rule, the axes are kept as long as the total eigenvalue is greater than 1. In the second step, the degree of correlation of the variables with each axis is examined. 2.3. African cities sample and data collection for analysis Africa includes 61 countries and over 86 cities with more than one million inhabitants (Data population, 2016). As shown in Table 2, the mobilized sample gives an account of the situation of 24 African cities whose population varies from 385,000 inhabitants in Windhoek to 16.225.000 inhabitants in Cairo (Demographia, 2017). The collection of reliable data for African cities was challenging. Much data is unavailable or not related to the same year. Table 3 summarizes the methodology used for collecting data. 2.3.1. Estimation of waste production I1 To deal with the great disparity of data collection dates, waste growth rates from Hoornweg and Bhada-Tata (2012) predictions are used. We note G1 ðK j Þ the average annual growth of waste in a country K j and G1 ðC j Þ the average annual growth of waste in the City C j so 1




G Kj ¼

sffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi D12025 ðK j Þ

2025c

D1c ðK j Þ

 18c 2 ½1993; 2012

ð3Þ

The average annual growth rate of waste in each country is supposed to be equivalent to the waste growth rate in cities. Keeping this assumption in mind and using (3), the amount of waste estimated in the city is as follows:



 ec D1e C j ¼ ½1 þ G1 C j  D1c ðC j Þfore ¼ 2017

ð4Þ

For Guinea, Conakry, the quantity of produced waste is not available, so the 2009 average annual growth of waste in Africa is used.

Table 1 Methodology of normalizing indicators. Indicator i
 D1e C j = Quantity of waste produced for each cityC j
 2 De C j ¼Non organic waste/ Total waste produced 3
 De C j ¼Quantity of waste not collected/ Total waste produced
 D4e C j = Population density** 5
 De C j = Population living in slums/ urban Population*** * ** ***

Unit

Maximum value maxDic

Minimum value minDic

Normalized indicator

Kg/ cap/ day

14 (Trinidad and Tobago)

0.09 (Ghana)

I1e ðC j Þ

%

96 (China*)

12 (Ethiopia)

I2e ðC j Þ

%

100

0

I3e ðC j Þ

Cap/ km2

45.700 (Bangladesh)

500 (United States)

I4e ðC j Þ

%

95.6 (South Sudan)

5.5(Costa Rica)

I5e ðC j Þ

In Macao (Hoornweg and Bhada-Tata 2012). Maximum value in the city of Dhaka and minimum value in the city of Birmingham (Demographia 2017). UN HABITAT (2014) from the United Nation’s Millennium Development Goals database. Data are available at: http://mdgs.un.

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F. Loukil, L. Rouached / Waste Management 103 (2020) 187–197 Table 2 Description of the African cities sample. Cities. countries

Country classification/region*

Country classification/income

Total population/city

Algeria. Algiers Burkina Faso. Ouagadougou Cameroun. Douala Cameroun. Yaoundé Chad. Ndjamena Côte D’Ivoire. Abidjan Egypt. Cairo Gambia. Banjul Ghana. Accra Guinea. Conakry Kenya. Nairobi Madagascar. Antananarivo Mauritania. Nouakchott Morocco. Rabat Namibia. Windhoek Nigeria. Ibadan Congo Republic. Brazzaville Rwanda. Kigali Senegal. Dakar Tanzania. Dar es Salaam Togo. Lomé Tunisia. Tunis Zambia. Lusaka Zimbabwe. Harare

MENA AFR/WEST AFR/CENT AFR/CENT AFR/CENT AFR/WEST MENA AFR/WEST AFR/WEST AFR/WEST AFR/EAST AFR/EAST AFR/WEST MENA AFR/SOUTH AFR/WEST AFR/CENT AFR/EAST AFR/WEST AFR/EAST AFR/WEST MENA AFR/SOUTH AFR/SOUTH

UPPER MIDDLE INCOME LOW INCOME MIDDLE INCOME (lower) MIDDLE INCOME (lower) LOW INCOME MIDDLE INCOME (lower) MIDDLE INCOME (lower) LOW INCOME MIDDLE INCOME (lower) LOW INCOME MIDDLE INCOME (lower) LOW INCOME MIDDLE INCOME (lower) MIDDLE INCOME (lower) UPPER MIDDLE INCOME MIDDLE INCOME (lower) MIDDLE INCOME (lower) LOW INCOME MIDDLE INCOME (lower) LOW INCOME LOW INCOME UPPER MIDDLE INCOME MIDDLE INCOME (lower) LOW INCOME

3.730.000 3.020.000 3.180.000 3.360.000 1.320.000 4.980.000 16.225.000 565.000 4.175.000 1.735.000 5.545.000 2.645.000 915.000 2.065.000 385.000 2.990.000 1.980.000 1.200.000 3.320.000 4.715.000 1.790.000 2.260.000 2.785.000 2.240.000

* MENA: Middle East and North Africa, AFR/WEST: Western Sub-Saharan Africa, AFR/EAST: Eastern Sub-Saharan Africa, AFR/CENT: Central Sub-Saharan Africa, AFR/SOUTH: Southern Sub-Saharan Africa.

Table 3 Methodology for collecting data. Indicators 1

I : Waste production

I2 : Waste composition

I3: Uncollected waste

I4: Population density I5: Living in slums

Sources  Hoornweg and Bhada-Tata (2012): collected data from subsequent sources as UNSD (2009), Achankeng (2003).  Sweepnet (2014), Mbiba (2014), Oecd (2015) and Senzige et al (2014) to complete missing data.  Hoornweg and Bhada-Tata (2012): data collected from Metap (2004), UNSD (2009), Asase et al (2009), Imam et al (2008).  Arcens (1997), Ngnikam and Tanawa (2006), Sané (2002), Mbiba (2014), Sweepnet (2014), Hasheela (2009) and UNEP (2010), to complete missing data.  Hoornweg and Bhada-Tata (2012), collected data from Metap (2004), UNSD (2009), Parrot et al. (2009)  Ymelé (2012), Obirih-Opareh and Post (2002), Hasheela (2009) to complete missing data.  Demographia world urban (2017).  Lall et al. (2017)

The following example explains the procedure to calculate the various indicators for a given city and to obtain its waste criticality index. Consider the city of Algiers (Algeria), in order to calculate the indicator aggregated on waste production designated by
 Iie C j , the data of the indicator i = 1 is used for Cj = Algiers at the estimated date e = 2017. This data is denoted by D12017 ðAlgiersÞ. The following values related to the country of Algeria are given by Hoornweg and Bhada-Tata (2012) and Metap (2004): D12025 ðAlgeriaÞ ¼ 1:45; D12002 ðAlgeriaÞ ¼ 1:21.

For

the

data

of

D1c ðAlgiersÞ at the current date c = 2012, Sweepnet (2014) data

Data availability

Data estimated

1993 to 2012

2017

1997 to 2012

2017

1995 to 2013

Current available data

2008 to 2014 2014

2017 Current available data

non-organic waste based on the classification of countries by income (low, lower middle, upper middle and high). An average annual growth rate of non-organic waste G2 ðxÞ is defined for each level of country income. This level income is designated as x which can be: low, lower middle, upper middle or high. Using the same methodology as in (3), the result is as follows:

2

G ðxÞ ¼

sffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi D22025 ðxÞ

2025c

D2c ðxÞ

 1 8c 2 ½1997; 2012

ð5Þ

are used to obtain D12012 ðAlgiersÞ ¼ 0:9. Using (3) and (4), it is easy

Accordingly, for the year e = 2017, the percentage of non-

to determine the value ofD12017 ðAlgiersÞ = 1.04*0.9 = 0.936. From Table 1, one can find the extreme values corresponding to

organic waste estimated D2e ðC j Þ in the city C j of the country being classified in level income x is expressed as:

the indicator i = 1 which are minD1c ¼0.09 and maxD1c ¼ 14. Now, using the Eq. (1), one can calculate the indicator aggregated on waste production I12017 ðAlgiersÞ ¼ 0:061. 2.3.2. Estimation of waste composition I2 The Hoornweg and Bhada-Tata (2012) prediction is adopted for the year 2025 in order to determine the average growth rate of



 ec D2e C j ¼ ½1 þ G2 C j  D2c ðC j Þ fore ¼ 2017

ð6Þ

When information about waste composition is not available for a city, waste composition for the country is used. For N’djamena and Nouakchott, sand represents more than 40% of the waste is not integrated in the analysis.

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2.3.3. Estimation of uncollected waste I3 The current available data is used. It is assumed that due to urbanization on the one hand, and the lack of structural reforms on the other hand, improvements in collection rates are not significant. For cities where information is not available, the average collection rate of the country is applied. 2.3.4. Urban density Urban density is estimated through two indicators: population density and living in slums. Estimation of Population density I4: Demographia estimated population density based in population projections. For the data concerning rural areas, Demographia uses National Census, where available, and mapping software. Living in slums I5: Population living in slums is an indicator of low economic density, lack of capital attraction and inefficient urbanization in African cities. 3. Results and discussion 3.1. Waste collection criticality index results Based on Table 4, the situation of waste collection is critical in African cities and criticality is higher in some cities such as Nouakchott (WCI = 0.486), Lusaka (WCI = 0.480), N’djamena (WCI = 0.437) and Lomé (WCI = 0.427). The situation is less critical in cities such as Algiers, Tunis, Rabat, Cairo and Harare. These cities have a common characteristic: the problem of urbanization is better controlled. Results also show a relationship between development and environmental degradation. So, cities with high waste collection criticality index are mostly classified as low income countries. This result confirms the hypothesis assumed by many studies (Dodman et al., 2017, Cobbinah et al., 2015) that underdevelopment and poverty accentuate the risks of environmental degradation in Africa. Table 4 also provides the value of each normalized indicator composing the waste criticality index. It is important to underline that the lack of mastering urbanization has a negative impact on waste collection. Hence, in cities like N’djamena (Chad), Abidjan (Côte d’Ivoire), Nairobi (Kenya), Nouakchott (Mauritania) which are affected by high urban density, the rate of uncollected waste remains very alarming. The cases of Douala (Cameroun) and Yaoundé (Cameroun) are interesting to benchmark. The situation seems more critical in Yaoundé which faces real difficulties to collect waste. Many works have shown the growth of the cost of collection in Yaoundé due to the increase in the road network and the distances of collection and transportation as consequences of the urban extension perimeter (Parrot et al., 2009). The proposed methodology allows the application of the index to other cities around the world. The comparison between cities can be extended to other continents. For example, with a criticality value of 0.17, Hanoi (Vietnam) is positioned between Douala and Harare (Data from Nguyen Leroy and Cong, 2015; Demographia, 2019). A more detailed analysis of the results of different indicators shows that Hanoi has a profile that is closer to Harare while having more difficulties in terms of waste production (0.089), but less ones in terms of slum population (0.06). In addition, the results suggest a ranking of the 24 African cities considered according to their level of criticality. The complication herein is that the indicators are estimated using different reference dates and are not usually specific to the city. An interesting question is whether the results are robust: would the ranking be the same if we introduce more recent data for different cities? Would the updating of missing data lead to radical changes in the benchmarking of cities?

In this research, the two indicators: population density (I4) and population living in slums (I5) are relatively recent. Thus, the problem of availability of information mainly concerns production, composition and coverage rate indicators. Most of the data is more recent at the national level than at the city one. For example, for the waste production indicator (I1), the main source of data is drawn from Hoornweg and Bhada-Tata (2012), which refers to 15 cities in Achankeng (2003) published statistics dating back to 1993. Nationally, the same source postpones several studies (METAP, 2004; USAID, 2009 . . .) and allows more recent data for most countries (this is the case for countries AFR / CENT or AFR / WEST, data for 2005 instead of 1993). Regarding indicators: waste composition (I2) and uncollected waste (I3), it is interesting to note that data for the two indicators are relatively newer than for the production indicator (I1). Research and specific studies have significantly contributed to improve the availability of information on these aspects. Indeed, among the 24 surveyed cities, only 09 have data referring to the period before 2005. The temporal proximity can give an idea about the degree of certainty of the information. The approximation method does not preclude the comparison of cities that are at the same level on the axis of temporal proximity. As indicated, and considering that the accuracy can be linked to the temporal proximity of the data and to the specificity level of the characteristic (city/country/ region), we can simultaneously consider the ranking of cities with accuracy constraints. According to the Table 4, four clusters can emerge (from the highest criticality to the lowest). The choice of criticality thresholds is often considered subjective. In line with the methodology, we retain 4 intervals1. It appears that within the clusters, cities seem to be globally homogeneous in terms of accuracy. However, the updating of information may affect more cities than others. The least sensitive to an updating of information are most likely to keep the same ranking. Examples like Lusaka (First cluster), Nairobi (second cluster) and Harare (fourth cluster) show that cities which do not have updated statistics have different criticality levels. This interesting relationship is in contrast with the general idea that the most critical cities are often those that lack the most accurate data. Our study on the 24 African cities reveals that the least critical group suffers from a weakness of specific information about the city, especially concerning the waste composition indicator.

3.2. Cities typology waste criticality The first results highlight the complexity of waste management in African cities. To deepen the analysis and highlight the interaction between different factors, the main component analysis is chosen. Table 5 shows that living in slums and waste collection variables contribute the most to the construction of axis 1, so the inability to ensure waste collection is closely linked to the persistence of slums in African cities and the low economic density. For sub-Saharan African cities, the pressures of urban population growth and poverty (Croese et al. 2016) are added to the pressure of uncollected waste and environmental degradation. The findings in this study suggest that informal settlements increase environmental vulnerability for African cities. As mentioned by Muchadenyika and Waiswa (2018), reducing and treating slums 1 Following the procedure announced in the methodology, the integer part of the square root of 24 recommends a grouping in 4 classes. Since the maximum value of WCI does not exceed 0.5, the lower bound of the last interval (containing values qualified by the most critical) must be less than this limit value. Finding that 50% of the sample has a criticality between [0.2; 0.4], a division of this intermediate interval in two is adopted to ensure intervals homogeneity.

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F. Loukil, L. Rouached / Waste Management 103 (2020) 187–197 Table 4 Indicators of waste collection criticality index.

(continued on next page)

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F. Loukil, L. Rouached / Waste Management 103 (2020) 187–197 Table 5 Component matrix. Component 1

Component 2

I1 : Waste production

1.879 37.585 37.585 0.078

1.336 26.713 64.298 0.711

I2 :Waste composition

0.490

0.295

I3 :Waste collection

0.914

0.169

I4 :Population density

0.115

0.843

I5 : Living in slums

0.886

0.059

Eigenvalues % of variance % cumulated

as well as informal settlements may provide a new opportunity to promote inclusive cities. In contrast, and on a second axis, waste production and population density variables are diametrically opposed. This interesting result shows that the population density is negatively correlated with waste production for African cities. Fig. 2 presents a classification of African cities waste collection criticality measured across two axes. Axis 1 reflects the structural inability of cities to organize waste collection. Cities on the right as Nouakchott, N’djamena and Lusaka have a great difficulty in mastering waste collection and urbanization. The cities on the left, such as Tunis, Algiers, Rabat and Cairo, have a better and appropriate waste collection infrastructure. Difficulties related to collection are not specific to the African continent. Research on urban waste management in Asian cities (Aleluia and Ferrao, 2016) also found that the collection rate is higher in large and rich cities with high economic density. However, in small towns, the collection rate is very low due to a lack of human and technical resources of their local authorities as well as to the absence of a formal collection system. It is rather the informal sector that provides the collection service. In Vietnam, for example, the collection rate in major cities like Hanoi or Ho Chi Minh is over 90%. While in small towns like Bac lieu or Tu San, it is less than 50%. Poverty and the strong presence of the informal sector increase the complexity of the urban governance of waste management. Axis 2 highlights the incapacity of cities to control the generation of waste and shows that cities such as Douala, Dakar, Brazzav-

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ille and Yaoundé have an increased capacity to control the production of waste despite a high geographical density. On the other hand, cities like Accra, Lomé and Windhoek have a high production of waste despite a low geographical density. This result confirms previous researches that have shown a link between urbanization indicators and waste management (Chen, 2018, Ho et al., 2017). While in the developed countries, several studies agree on the economic and environmental efficiency of the ‘‘compact cities” model, the debate is more controversial in the developing countries, where poor urban planning accentuates pollution and the degradation of the environment (Dave, 2010, Cobbinah et al., 2015). For African cities, the high density of the population has a positive impact on reducing waste generation (per capita) and can be an opportunity for African cities to achieve economies of scale and to reduce collection costs. So, in terms of waste management, the ‘‘compact city” model contributes to an economy of scale in the production of waste and thus contributes to a reduction of waste production in African cities. 4. Recommendations This research proposes a new index to measure waste collection criticality and its findings can support policy makers in defining funding priorities in order to improve the sustainable development of cities. Furthermore, by applying it to 24 African cities, the analysis shows that while most countries face the same problems, the situation appears more critical in some cities as Nouakchott, Lusaka, N’djamena and Lomé. The results highlight the urbanization issue for African cities and especially the low economic density, which appears as a real obstacle for sustainable development. It seems that the movement towards urbanization and industrialization is not sufficiently accompanied by the adequate measures to strengthen and restructure the collection and treatment of waste systems. Economic policies should give priority to the enhancement of the infrastructure, as a necessary condition to ensure the cleanliness of African cities. Then, public authorities must direct their efforts towards increasing the collection rate, by improving access to the collection service in slums. This may be possible through the integration of the infor-

Fig. 2. Cities typology waste collection criticality.

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mal sector, which is already very active in disadvantaged areas, or by enhancing the involvement of producers and households in the pre-collection of waste through incentive mechanisms and strengthening the regulatory framework. Information technologies should also be promoted in order to improve waste collection, particularly in poor neighborhoods. In order to reduce the gap between neighborhoods, it is important to adapt the equipment to the infrastructure of cities and make it accessible in slums. In this sense, it seems interesting to question the possible relationship between the availability of information and the national priority given to the waste treatment method. Whenever the collection of waste represents the major concern for municipalities, publications about the rate of coverage statistics are available. Secondly, official activities related to recycling, reuse and waste recovery are still insufficient to motivate ongoing collection of waste composition data at the city level. This is made more difficult and more complex by the strong presence of the informal sector in developing countries. For these reasons, the accuracy of information on the composition of waste (and in particular, the nonorganic component) is stained with uncertainty and the majority of studies are done at the national level and not at the decentralized level of cities. The unavailability of accurate data is a real handicap when making decisions. It reflects the current lack of will for improvement by the regulator and is an alarming signal of a situation that may worsen in the absence of appropriate action. 5. Conclusion This research has served to develop a composite index following a methodological approach and, above all, to adapt it to the specificities of cities in developing countries such as the majority of African cities. Despite the obstacles encountered in the process of collecting reliable information, which usually hinder any empirical study focused on Africa, this study identifies the factors that influence waste collection criticality index. Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. References Achankeng, E., 2003. Globalization, urbanization and municipal solid waste management in Africa. In Proceedings of the African Studies Association of Australasia and the Pacific 26th annual conference. African on a Global Stage. P. 22. (accessed September 2018). Aleluia, J., Ferrão, P., 2016. Characterization of urban waste management practices in developing Asian countries: a new analytical framework based on waste characteristics and urban dimension. Waste Manage. 58, 415–429. https://doi. org/10.1016/j.wasman.2016.05.008. Arcens, M.T., 1997. La Participation de la communauté à la gestion des déchets solides. Programme UWAP WASTE ENDA TIERS-MONDE. (accessed September 2018). Asase, M., Yanful, E.K., Mensah, M., Stanford, J., Amponsah, S., 2009. Comparison of municipal solid waste management systems in Canada and Ghana: A case study of the cities of London, Ontario, and Kumasi, Ghana. Waste Manage. 29 (10), 2779–2786. https://doi.org/10.1016/j.wasman.2009.06.019. Bertanza, G., Ziliani, E., Menoni, L., 2018. Techno-economic performance indicators of municipal solid waste collection strategies. Waste Manage. 74, 86–97. https://doi.org/10.1016/j.wasman.2018.01.009. Cervantes, D.E.T., Martínez, A.L., Hernández, M.C., de Cortázar, A.L.G., 2018. Using indicators as a tool to evaluate municipal solid waste management: a critical review. Waste Manage. 80, 51–63. https://doi.org/10.1016/j.wasman. 2018.08.046. Chen, Y.C., 2018. Effects of urbanization on municipal solid waste composition. Waste Manage. 79, 828–836. https://doi.org/10.1016/j.wasman.2018.04.017.

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