Stéphanie Dos Santos
email: stephanie.dossantos@ird.fr

Jean-Paul Peumi

Abdramane Soura

Dr Dos Stéphanie

Santos
email: dossantos@ird.fr

345x

Dr Stephanie Dos

Risk Factors of Becoming a Disaster Victim. The flood of September 1st, 2009, in Ouagadougou (Burkina Faso)

Introduction

The frequency and intensity of natural disasters are increasing in urban areas. Ongoing climate change will likely further increase these risks. The United Nations University Institute for Environment and Human Security [START_REF] Bogardi | Two billion will be in flood path by 2050[END_REF] estimates that two billion people will be vulnerable to extreme weather events by 2050, unless preventive measures are stepped up. Some regions, such as the Sahel, are particularly sensitive, with increasing drought and rainfall fluctuations due to climate change (IPCC, 2007).

Urban areas can play a role in amplifying natural hazards: as more and more land is cleared to make room for urban development, erosion increases and runoff intensifies. Cities are particularly susceptible to floods, especially in Africa [START_REF] Douglas | Unjust waters: climate change, flooding and the urban poor in Africa[END_REF]. Paved ground, impermeable to water, limits drainage in some places and concentrates it in others, increasing runoff and peak flood flows. These increased risks are centered just where major social and economic issues are clustered. The situation in Africa is exacerbated by factors such as intense tropical weather patterns, where as much as several hundred millimeters of precipitation can fall in a 24-hour period. Costly drainage infrastructure, however necessary, is often not available due to severely limited financial resources. The severity of the situation has been illustrated time and time again, with the flooding that occurred in Niamey (Niger) in 1998 [START_REF] Issaka | Les inondations à Niamey, enjeux autour d'un phénomène complexe[END_REF], in Addis Ababa (Ethiopia) in 2006 [START_REF] Douglas | Unjust waters: climate change, flooding and the urban poor in Africa[END_REF], in Abeokuta (Nigeria) in 2007 [START_REF] Adelekan | Vulnerability assessment of an urban flood in Nigeria: Abeokuta flood 2007[END_REF], in Ouagadougou (Burkina Faso) (PNUD and World Bank, 2010), or this year in Abidjan (Gouvernement de Côte d 'Ivoire, 2018).

Yet, urbanization in Africa will be the most rapid in the world between 2020 and 2050, with a threefold increase of the urban African population during this period (United Nations, 2014). Nevertheless, the urban growth of African cities, especially in capital cities, is leading to the development of informal settlements that do not have access to basic services and sustainable settlements and housing ( UN-Habitat, 2014). UN-Habitat estimates that one quarter of the world urban population lives in an informal settlement; one third of the developing urban population and almost two thirds of the Sub-Saharan urban population ( UN-Habitat, 2014). Hence, in many urban areas in developing countries, the consequences of flooding are becoming increasingly severe, partly due to the spread of unregulated shantytowns.

In light of the expected growing natural hazards and the continued growth of urban populations, there is concern that the vulnerability of a significant portion of the urban African population will increase even further Therefore, there is a need for better understanding of the social dimension of climate change (ICSU, 2012), and especially for indepth research on the main features of the populations most vulnerable to flood risks. According to the definition given by [START_REF] Turner | A framework for vulnerability analysis in sustainability science[END_REF], a comprehensive vulnerability analysis should considerer three main components: exposure, sensitivity and resilience to the risk. The first refers to physical and geographical exposure. The second indicator is related to the environmental and human conditions associated with hazards (e.g. social capital, demographic and economic characteristics, institutions, economic structures, etc.). Finally, the third, resilience, consists of coping responses, impacts of hazards and adjustments postdisaster. The second indicator of vulnerability is specifically addressed in the present article, including the concept of social vulnerability.

Until recently, there were few studies that examine social factors related to natural disaster differentials [START_REF] Pelling | au Burkina Faso. Evaluation des dommages, pertes et besoins de construction, de reconstruction et de relèvement[END_REF]. Even though previous research has highlighted evidence of a relationship between exposure to natural disasters and poverty [START_REF] Bern | Risk factors for mortality in the Bangladesh cyclone of 1991[END_REF][START_REF] Li | Spatio-temporal distribution of flood disasters and analysis of influencing factors in Africa[END_REF], the complex processes underlying this association remain poorly studied. A recent review [START_REF] Fatemi | Social vulnerability indicators in disasters: Findings from a systematic review[END_REF] identified 4 main individual or household factors that defined the social vulnerability to natural disaster : gender, demographic characteristics, socio-economic status and disability. By studying risk factors for mortality in the Bangladesh cyclone of 1991, [START_REF] Bern | Risk factors for mortality in the Bangladesh cyclone of 1991[END_REF] highlighted that the poor, women, migrants, the young and the elderly are more often vulnerable to natural disasters. More particularly, [START_REF] Cutter | Social vulnerability to environmental hazards[END_REF] showed that the households which have a woman for head of the household have a greater risk of social vulnerability, because of gender status in most countries in the world. The proportion of women in the household is equally positively associated with higher social vulnerability to disaster. Age groups, and specifically children of less than 5 and people 65 years and older, were highlighted as more vulnerable to disaster. The effect of education of the household head is more mixed [START_REF] Fatemi | Social vulnerability indicators in disasters: Findings from a systematic review[END_REF]. Few studies have taken into account all these factors in the same explicative model, all things being equal.

On the other hand, many studies are based on a qualitative approach [START_REF] Douglas | Unjust waters: climate change, flooding and the urban poor in Africa[END_REF], which, although highly relevant for focusing on local perceptions, is less likely to draw specific conclusions regarding factors of vulnerability to disasters than a quantitative method [START_REF] Adelekan | Flood risk perception in flood-affected communities in Lagos, Nigeria[END_REF].

Observation biases also occur. The social sciences usually construct their research questions and consider generally the issue of vulnerability to risk with a focus on the area delimited by the physical processes, as taken as conceived by the earth sciences [START_REF] Metzger | Les risques en milieu urbain : éléments de réflexion[END_REF]. This implies that studies are rarely performed by analyzing social differentials between households having directly undergone damage, and those who have not -because the zone of study concerns only with the site of the disaster. And yet, this fine level of analysis is essential because factors of social vulnerability are thought of as contextdependent [START_REF] Wisner | At Risk: Natural Hazards, People's Vulnerability and Disasters[END_REF]. In addition, many quantitative studies have focused on the vulnerability characteristics of populations living in areas exposed to floods [START_REF] Adelekan | Vulnerability assessment of an urban flood in Nigeria: Abeokuta flood 2007[END_REF], or even studies on vulnerability to natural disasters are focused on resilience, i.e on strategies and resources to cope with the disaster, based on data coming generally from post-disaster field-surveys involving questioning some victims ex-post.

Finally, research is still very often focused on developed countries, and rarely concern developing countries [START_REF] Fatemi | Social vulnerability indicators in disasters: Findings from a systematic review[END_REF]. This issue is still insufficiently studied in African urban areas [START_REF] Campion | Rainfall variability, floods and adaptations of the urban poor to flooding in Kumasi[END_REF] : few studies have examined what specific individual and household factors are linked to floods at a fine scale, e.g. the household level [START_REF] Douglas | Flooding in African cities, scales of causes, teleconnections, risks, vulnerability and impacts[END_REF], all things being equal. More specifically, and to our knowledge, very few studies in sub-Saharan Africa have undertaken a soundly-based quantitative study of social vulnerability by comparing individual and household characteristics of the victims who have lost their houses and subsequently left their plot, with those of non-victims of the floods, given the complexity of finding those who have left their plot. The aim of the present article is precisely to attempt to make good this gap by contributing to a better understanding of the way socio-demographic and environmental living conditions influence vulnerability to a major flood impact (loss of housing) of social groups in west-African cities.

This study focuses on the city of Ouagadougou, the capital of Burkina Faso. On September 1 st , 2009, the city experienced torrential rainfall, leading to water runoff and floods. Over 180,000 people were severely affected by this natural disaster and 35,000 of them completely lost their homes (PNUD and World Bank, 2010). We will first present the general background of Ouagadougou, by focusing on the rainfall regime and highlighting how exceptionally high the level of the precipitation was during September 1 st , 2009. After describing the methodology and the originality of the study design, we will present the results, and discuss them. Finally, we will call for a more holistic approach to urban planning, with the aim of introducing an early warning system for flood risk in vulnerable areas such as informal settlements.

Background

Study area

Ouagadougou (latitude 12°21'N and longitude 01°31'E), the capital city of Burkina Faso, is part of the Sudano-Sahelian area. The town lies at an altitude of about 306 m above sea level and occupies an area of 518 km 2 . Burkina Faso is one of the poorest countries in the world. With a per capita gross national income of US $1141, the country is ranked 181th out of 186 countries on the UNDP's Human Development Index (UNDP, 2013). With a population of 1.5 million (according to the 2006 census), the city of Ouagadougou is home to 46% of the country's urban population. The population growth of Ouagadougou is one of the most rapid worldwide. In almost 50 years, from 1960 to 2006, the date of the last population census, the population of Ouagadougou has increased 25-fold, from 59,126 to 1475,223 inhabitants [START_REF] Boyer | Ouaga 2009 -Peuplement de Ouagadougou et Développement urbain. Rapport provisoire[END_REF]. The current population is estimated at 2800,000 inhabitants and the annual population growth rate at 5.9%, due both to natural growth and to rural-urban migration [START_REF] Insd | Monographie de la commune urbaine de Ouagadougou[END_REF]. According to median growth population projections, the city will reach almost 6 million residents by 2030 (UN/DESA, 2015).

With population growth, and the increasing prevalence of individual houses, the city has experienced rapid geographical expansion, growing in size from 14 square kilometers in 1960 to 520 square kilometers in 2009 [START_REF] Insd | Monographie de la commune urbaine de Ouagadougou[END_REF]. This mostly unplanned and uncontrolled spread of the urban population has reinforced its social and spatial segregation [START_REF] Boyer | Ouaga 2009 -Peuplement de Ouagadougou et Développement urbain. Rapport provisoire[END_REF]. Alongside the official sector, a customary sector enabled many of the less wealthy to find the means of accessing a plot. The unzoned neighborhoodsspontaneous and informal settlements where land has not been officially apportioned and deeded by the government -make up a third of the total area of the city, and approximately 35% of Ouagadougou's households live in those neighborhoods [START_REF] Boyer | Ouaga 2009 -Peuplement de Ouagadougou et Développement urbain. Rapport provisoire[END_REF], where the people tend to be younger, poorer, less well-educated and more often migrants than those living in formal settlements [START_REF] Rossier | The Ouagadougou Health and Demographic Surveillance System[END_REF]. These peri-urban informal neighborhoods are traditional residential areas, characterized by single-family homes built from mainly local building materials. For example, the great majority of house walls in these areas are built of traditional adobe bricks, made with sun-dried mud blocks [START_REF] Fournet | Une urbanisation différenciée[END_REF]. Informal areas also lack service from paved roads and drainage systems. Streets of any kind are largely absent, save for the sandy tracks created by the inhabitants. There are few trees, with very scarce and dry ground vegetation, few irrigated home gardens and a great deal of livestock grazing [START_REF] Lindén | Nocturnal Cool Island in the Sahelian city of Ouagadougou, Burkina Faso[END_REF]. This precariousness leaves these residents extremely vulnerable to natural disasters.

Rainfall

Ouagadougou is part of the Sudano-Sahelian area and features a tropical savanna climate, with two very distinct seasons: the rainy season (which lasts approximately four months, from May/June to September), and the dry season, during which blows the Harmattan, a hot dry wind from the Sahara.

Figure 1. Annual rainfall in Ouagadougou (1993Ouagadougou ( -2010) ) The region is also experiencing a long-term downward trend in rainfall [START_REF] Lebel | From GCM scales to hydrological scales: Rainfall variability in West Africa[END_REF], which has led to increasing cycles of drought and famine [START_REF] Haile | Weather patterns, food security and humanitarian response in sub-Saharan Africa[END_REF]. For the city of Ouagadougou in particular, annual rainfall pattern for the period 1993-2010 shows a longterm downward trend in precipitation (Figure 1, thin line). In addition, Ouagadougou has experienced wide year-to-year variations in total annual precipitation, ranging from 588 mm in 1997 to 896 mm in 2009 (Figure 1, bold line). The mean annual rainfall for this period measured 722 mm (Figure 1, dashed line).

The magnitude of rainfall in September 1 st , 2009

During the year 2009, 890 millimeters of water fell on the city of Ouagadougou, a record for the last twenty years. The distribution followed a relatively normal trend, with a rising level of precipitation during the period between June and September (Figure 2). The month of September, however, was particularly wet, with 34% of the total rainfall of the year 2009. The total rainfall during the month of September was largely due to a single event. In the early hours of September 1 st , 2009, torrential rainfall began in Ouagadougou. The rain fell relentlessly for 12 hours, with 261 mm accumulating in that short time, representing 36% of the area's annual mean precipitation (1993-2010 annual mean), and 86% of the total monthly precipitation in September 2009.

Figure 2. Monthly Rainfall in Ouagadougou in 2009

This extreme rainfall led to significant urban runoff and flooding that destroyed paths, felled trees and washed away poor quality houses. In terms of damage, it accounted for 41 deaths, 62 casualties, 180 386 people became homeless and 33 172 houses were destroyed; the damage has been estimated at almost $3 billion US (PNUD and World Bank, 2010). This toll is truly staggering for a developing country like Burkina Faso, especially by reference to the classification scheme proposed by [START_REF] Brooks | The determinants of vulnerability and adaptive capacity at the national level and the implications for adaptation[END_REF], in which Burkina Faso is one of the most vulnerable countries in terms of its capacity to adapt to climate variability and, by extension, climate change.

Methods

Data collection

Data come from the Ouagadougou Health and Demographic Surveillance System (Ouaga HDSS). This is a research and intervention platform established in 2008 in five neighborhoods on the periphery of the city (Figure 3); two formal areas in which the land is deeded and government services are provided (Kilwin and Tanghin) and three informal settlements, essentially illegal settlements around the periphery of the city, without access to electricity, piped water and other public services (Nioko 2, Nonghin and Polesgo). In 2010, the population of the study area was estimated at 79,240 residents distributed among 17,975 households (Table 1).

Household characteristics

The Ouaga HDSS monitors an urban population in which poor individuals are overrepresented, but which is also socioeconomically diverse [START_REF] Rossier | The Ouagadougou Health and Demographic Surveillance System[END_REF]). On the basis of household wealth, measured as a function of the durable goods present in the home (television, refrigerator, motorbike, car), households in the informal neighborhoods are more often poor (with 66% of homes classified as poor) than in formal neighborhoods (where only 27% are poor). The latter figure is comparable to the situation for the city as a whole. The informal neighborhoods of the Ouaga HDSS are nevertheless wealthier than the rural areas of Burkina Faso, where 73% of households are classified as poor.

Concerning tenure, 62% of households fully own their accommodation in the formal neighborhoods. In informal settlements, 81% of households own their accommodation purchased through the traditional authorities, pending future zoning and formal housing developments during which plots will be allocated to local landowners. Associated with tenure and poverty, almost all households (99%) had a house made with mud in informal settlements, comparing with 74% of households living in a house constructed with cement bricks in the formal neighborhoods of the Ouaga HDSS.

An "unexpected" study design

During the first round of the Ouaga HDSS, in the first six months of 2009, a household survey collected data on household goods, economic activities, level of education, home ownership and housing characteristics. This survey was completed just before the disastrous flood. After the flood, in the course of a regular round, fieldworkers registered the households that had completely lost their houses and then disappeared from the database. This unique, "unexpected", design provides a rare and original dataset, especially in the context of West Africa, where national vital events data are poor and census data are not regularly updated. This gives us a unique opportunity to study the characteristics of the households which were victim of this natural disaster before the disaster, that is to say, households that completely lost their homes.

Data analysis

The aim is to estimate the effect of a set of independent variables, mainly examining head of household characteristics as related to the status of "disaster victim".

Since our dependent variable is binary (a household was either declared a disaster victim or not), we used a logistic regression. The model can be expressed as:

ln(q i /1-q i )=β 0 + β i x i
where q is the probability of being declared as a disaster victim for the ith household, β 0 is the baseline constant, β i is a series of unknown coefficients and x i is an array of independent variables. The estimated coefficients (β i ), when exponentiated, are interpreted as the odds of being a disaster victim (q i /1-q i ), with certain characteristics relative to the odds of being a disaster victim in a reference (or baseline) group of households: that is, relative odds or odds ratios (OR).

To better understand the effects of the household factors net of other key covariates, variables were introduced separately into the models, allowing for a comparison of coefficients across the different equations. The status of disaster victim is then modeled step by step according to certain characteristics of the head of household or of the household itself. A modeling process in four stages was performed. Model 1 tests these to see if their effect is as expected. Consequently, this model included only socio-demographic and economic characteristics of the head of household (sex, age, education and wealth index). Model 2 incorporated housing characteristics (sanitation availability, source of lighting, age of the house and occupation status of the head of household). Model 3 adds the migrant status of the head of household (place of birth and duration of residence in Ouagadougou). Finally, Model 4 tested all the variables mentioned earlier, including the composition of the household (marital status of the head of household, the number of usual members in the household and the sex ratio in the household).

Before estimating these models, we analyzed some descriptive results to sketch the urban heterogeneity in terms of socio-economic and environmental conditions. Geographical mapping analysis was performed by using MapInfo Version 9.0 for Windows, and statistical models were performed using Stata Version 11.0.

Results

Descriptive results

Though there was only one neighborhood that completely escaped large-scale damage, in the areas monitored by the Ouaga HDSS, the large majority of disaster victims were concentrated in a single zone, the informal neighborhood of Nioko 2. This concentration begged the question of what distinguishing characteristics of Nioko 2 led to such destruction, while other neighborhoods (even informal ones) were relatively spared.

At first glance, Nioko 2 presents the typical characteristics of informal settlements: unsanitary living conditions, very poor quality housing, mainly single-family homes, no paved roads and no drains; sandy tracks, few trees and very sparse and dry ground vegetation, etc. From a demographic and socio-economic perspective, the vast majority of heads of household declare themselves as owner of their plot of land, even if this ownership is not legal and remains traditional. They are generally younger, living in a nuclear family, and tend to be poorer than those in formal settlements.

The biggest difference between Nioko 2 and the other informal settlements measured by the Ouaga HDSS concerns certain notable physical characteristics. In particular, this area is completely surrounded by a water system (Figure 4). Of the 3,580 households followed by the Ouaga HDSS, 311 (8.7%) of them are classified as disaster victims, completely losing their homes to the flood. All of these households were situated at the neighborhood's lowestlying point, at the border of a gully, just in front of a canal (Figure 4). A glance at the maps extracted from Google Earth before (31st March 2009) and after the disaster (5 th December 2012) is illustrative of the extent of the disaster in this area. The red lines encircle the disaster area monitored by the Ouaga HDSS. After the storm, no houses remained in this zone or around the water system in general (Figure 5). Hence, a key predictive variable is the proximity to the gully, as the correlation between the latter variable and the status of disaster victim in this study is equal to one.

Given the precise location of exposure, it is interesting to explore, all things being equal, the other characteristics of the heads of the households that had established their homes in such a risky zone.

Multivariate results

Table 2 presents the results of the multivariate analysis, including characteristics of the head of household. Our results are generally consistent with the literature (scientific and grey) on individual and household variables associated with vulnerability to extreme events, in the sense that it is the result of the combination of several socioeconomic factors of social inequalities (e.g. [START_REF] Campion | Rainfall variability, floods and adaptations of the urban poor to flooding in Kumasi[END_REF][START_REF] Cannon | Gender and Climate Hazards in Bangladesh[END_REF][START_REF] Cutter | Social vulnerability to environmental hazards[END_REF][START_REF] Rufat | Social vulnerability to floods: Review of case studies and implications for measurement[END_REF]. The effect of the age of the head of household is particularly noteworthy: younger heads of household had lower risk of becoming a disaster victim than older heads of household. This was even more marked when comparing heads of household between 35-49 years old with the reference category (under 35 years old). Both education level and wealth were significantly associated with the status of disaster victim, as expected. Households where the head had no formal education, and poorer households, had higher risk of completely losing their home during the storm. A recent review highlights how [START_REF] Rufat | Social vulnerability to floods: Review of case studies and implications for measurement[END_REF] this set of socio-demographic variables are key factors in explaining social vulnerability to floods.

There was one result, however, that went against the expectations of the literature. Households where the head was a woman had a lower risk of becoming a disaster victim, nearly 50% less than households where the head was a man. Hence, before discussing this unexpected but intriguing result of the gender effect observed here, we have to make sure that this result is robust and persistent when other household characteristics are included in the model.

The introduction of some more refined socioeconomic factors had noticeable effects on variables included in Model 1, especially on the variable used to measure levels of wealth/poverty. The effect of this proxy wealth index, based on the possession of functional durable goods, was no longer significant when the availability of sanitation and the source of lighting were included in the model. In contrast, the odds ratios associated with those variables were very high and significant, highlighting the extreme economic vulnerability of the households that were victims of the disaster (no sanitation and cheap source of lighting).

A look at how long a household had been present on the plot also gives interesting findings. Households in place there for three to nine years were more at risk than newcomers (who had been there less than three years), or households installed for ten years or more. Thus, it seems that the oldest households (in terms of duration of residence in the neighborhood) settled in the least risky areas, the most suitable in terms of topography, because they were the first to arrive. For their part, the most recently arrived households were probably better informed about the risks of living in low-lying areas, at the edge of a gully. Better informed of the risks (catastrophic events more widely broadcast, etc.), these newcomers have settled less in those areas at risk, mostly already occupied by households that arrived in the preceding years.

Though the effect of the occupation status is not significant, the direction of the odds ratios follows the expected gradient. Those who owned the home in which they live seemed to be at greater risk of losing the house during the storm than those who rent. This result is not surprising, given the literature on urban residential integration on the outskirts of Bamako [START_REF] Marcoux | L'insertion résidentielle à Bamako[END_REF][START_REF] Morin | Habiter à Bamako : conditions de logement et réponses des ménages dans une ville sahélienne[END_REF], the capital of Mali (a neighboring country), where households who rent were better equipped than those who own their homes. One possible explanation for this is that people are willing to make some trade-offs (such as foregoing urban services and infrastructure) in exchange for owning their own homes. In Ouagadougou, too, home ownership is a top priority for many, ranked above housing quality or access to water (Dos [START_REF] Santos | Is the tap locked ? An event history analysis of piped water access in Ouagadougou -Burkina Faso[END_REF]. Furthermore, since those living in informal areas often do not legally own their plots of land (which have yet to be officially allocated by the municipality), they are reluctant to invest in high quality permanent structures. Rather, they prefer to construct provisional housing on the plots to "save their place," so that when the land is formally allotted, they will have staked the first claim. Another hypothesis would be that tenants are more concerned about the location of their rented housing, while some households agree to become homeowners on more risky plots (e.g. at the border of a gully).

As expected, the place of birth seems to play a role in the risk of becoming a disaster victim. Those of rural origin were the most at risk, which was expected in the light of the literature on the process of the urban in-migration carried out in several major cities in West Africa [START_REF] Campion | Rainfall variability, floods and adaptations of the urban poor to flooding in Kumasi[END_REF], and on residential trajectories [START_REF] Antoine | Trois générations de citadins au Sahel[END_REF]. Length of time lived in Ouagadougou impacts results in the same direction as length of time lived on that given plot of land. The intermediate duration (10-29 years) in Ouagadougou was significantly associated with a much greater risk than a shorter duration in Ouagadougou (less than 10 years) or a longer duration (30 years and over). A similar explanation can be found as for the duration of residence on the plot, but a deeper understanding of the complex processes underlying this association still needs to be sought. We can note that the effect of the demographic and socioeconomic characteristics of the head of household was unchanged by the introduction of migration status (stable odds ratios between Models 2 and 3).

Finally, three variables were intended to serve as proxies for the composition of the household. First, marital status played a role with regard to the phenomenon under study, as married heads of household were much more likely to be disaster victims than those who were never married. The number of usual household members, however, did not seem to be associated with the risk of losing the house during the storm. The sex ratio in the household, though, did follow a meaningful gradient, even if the odds ratios were not significant. The more men there were in the household, the less the household was at risk. We have no hypothesis for possible explanations for this result, which remains, moreover, not statistically significant. We can only note that the introduction of this variable strengthened the effect of the sex of the head of household in Model 4 (as compared to Model 3). Many studies have highlighted the importance of the gender variable as an important factor of social vulnerability to floods [START_REF] Mukuna | Vulnerability Analysis of the Gender-Differentiate Impact of Flooding in Budalangi Flood Plains, Kenya[END_REF], but this is especially important when it is interacted with other social factors, such as family structure [START_REF] Ajibade | Urban flooding in Lagos, Nigeria: Patterns of vulnerability and resilience among women[END_REF].

Discussion

Global environmental change has led to a burgeoning literature on climate change vulnerability and adaptation. One part of this literature focuses on developing higherresolution climate models to better characterize uncertainty in the regional climate projections offered to decision-makers -the "top-down" methods. The other part deals with past and present climate variability for reducing vulnerability, in a "bottom-up" approach [START_REF] Wilby | Robust adaptation to climate change[END_REF]. Climate impact on populations will largely depend upon a large set of socioeconomic determinants pertaining to individual and household levels [START_REF] Rufat | Social vulnerability to floods: Review of case studies and implications for measurement[END_REF], as well as local and national levels [START_REF] Brooks | The determinants of vulnerability and adaptive capacity at the national level and the implications for adaptation[END_REF], and the role of institutions as adaptive capacity drivers [START_REF] Berman | The role of institutions in the transformation of coping capacity to sustainable adaptive capacity[END_REF]. In this sense, and as social scientists, we wanted to contribute to the second part of this literature, as we believe, along with Washington and colleagues [START_REF] Washington | African climate change: taking the shorter route[END_REF], that coping with present climate variability is enough of a challenge, particularly in growing and partly uncontrolled urban areas.

This paper helps to document how natural hazards can amplify urban inequities [START_REF] Reckien | Climate change, equity and the Sustainable Development Goals: an urban perspective[END_REF], by highlighting the low resilience of some households compared to others (in another context, see also [START_REF] Linnekampa | Household vulnerability to climate change: Examining perceptions of households of flood risks in Georgetown and Paramaribo[END_REF]. In Ouagadougou, almost 180,000 people have seen their housing totally destroyed by the waters during this extreme event, or nearly 10% of the total population of the city. The results presented here confirm that they were among the poorest. Generally, living standards indexes based on habitat characteristics (nature of the roof, walls, soil) and durable consumer goods are barely adequate to differentiate the heterogeneity of poverty situations [START_REF] Kobiané | Essai de construction d'un profil de pauvreté des ménages à Ouagadougou à partir des caractéristiques de l'habitat[END_REF]. Certain socio-economic factors are more discriminating markers of vulnerability than others. Results show how the variables related to the environmental conditions of life (sanitation, source of lighting) are much more discriminating than those of the wealth index (habitat and goods). In this case-study, the evidence still confirms that it is especially the most precarious of them who have been affected, those who do not have the minimum necessary to live for a decent life, that is to say, a private place to meet their natural needs (no latrines), and a rudimentary lighting mode (candle or flashlight). In informal areas in OPO, only 10% of households do not have sanitation. These households are among the most vulnerable in terms of health, for example, but also, as we have just seen, of vulnerability to flood risk. These are the ones that already had the lowest resilience to withstand such a shock. Disasters, even if less severe, and more generally accidental events (illness, loss of employment, etc.) are accelerators of precariousness, because they erode the fragile resilience of some households.

In terms of response capabilities, and in the face of this extreme event, which had not been anticipated by any early warning system, society as a whole was relatively resilient. Thanks to the support of the Government and its partners, essentially the European Commission, two shelters, named Yagma and Basséko, were set up in the far outskirts of Ouagadougou to welcome flood victims from 2009 in Ouagadougou. In particular, in the Yagma welcome network, located about 20 km north-west of Ouagadougou, more than 24,000 households are sharing this site of about 900 hectares. In addition to a plot, households received materials to build a house, namely bags of cement and 20 sheets of corrugated iron. The government has put in place certain infrastructure such as schools. Six years after the first installations, a health center was constructed. However, locals believe that this is insufficient, in particular in terms of access to water and road infrastructure. While many households had to abandon their professional activities, mainly in the informal sector, having lost their raw materials during the flood, the remoteness and isolation of this site did not favor the resumption of an activity. In addition, a number of households have left the site, preferring to sell the plot and return to the center of the city in order to find a job.

Consequently, and for some years, the inhabitants have been returning to the sites which have been devastated. This is particularly true for the area studied in this article, the neighborhood of Nioko 2 (Figure 6). Field surveys show that households are gradually reoccupying their plots. It is particularly remarkable to the north of the disaster zone under study in this article, delimited in red. The same plots of land, of which they are not officially owners, have nevertheless been bought from the chief of the neighborhood. Knowing the risks involved, having already suffered in 2009, the inhabitants make the reasoned choice of a return, and explain this with a common expression: 'it is always better to sleep on your own mat than that of someone else". Thus, as [START_REF] Issaka | Les inondations à Niamey, enjeux autour d'un phénomène complexe[END_REF] point out in the case of Niamey, having a plot in a high-risk area can also be seen as a survival strategy, in cases of increased vulnerability. In a context where access to land to build a house, however precarious, is perceived as the priority of priorities, settling on this type of plot can be perceived as a guarantee of land tenure. This risk exposure ensures that there is no risk of "expropriation" by the municipal authorities, because the plot has no market value. This perceived risk does not therefore outweigh a day-to-day empiricism which, in a geographical context where it only rains a few weeks a year, dictates that the balance of costs and benefits tends to make it a rational preference to accept a share of risk .

Conclusion

The aim of this paper was to examine the socio-demographic and economic risk factors of "disaster victim" status based on the case study of the storm of the September 1 st 2009 in Ouagadougou. The availability of data from a Demographic Surveillance Systemthe characteristics of the population as well as that of their dwellings before the flood of September 1st 2009 -offered a unique opportunity to address the impact of such an event among the different social groups. Exploring these original data, we aimed to document risks factors of this extreme event in order to reduce vulnerability in preparation for future disaster.

The neighborhood of Nioko 2 is one of the vulnerable areas that the Ouaga HDSS monitors. Preliminary results suggested that the 9% of the households who fell victims to the natural disaster were also the most vulnerable from a socioeconomic standpoint as well. These results suggest that natural disaster exposure is influenced by a range of socioeconomic and demographic factors. Results reinforce the idea that the main cause of disaster is not hazards, but even more the vulnerability of the population given the role of variables related to extreme poverty (no sanitation, no sustainable light) as determinant factors.

This study provides evidence on the necessity to integrate socioeconomic and demographic factors in effective mitigation and adaptation strategies. Measures must be taken to enhance the quality of life of the poor, through improvements in housing conditions and the development of strong disaster preparedness campaigns. We believe, along with Terri Cannon, that "hazards are natural, but that in general disasters are not, and should not be seen as the inevitable outcome of a hazard's impact" (Cannon, 1994, p. 13). The results presented here show how environmental living conditions allow a reinterpretation of markers of social and economic vulnerability to the risks of natural disasters. These type of variable are better able to capture extreme vulnerability rather than more traditional economic indicators, such as a poverty index using the possession of durable goods. Thus, the possession of toilets and the type of lighting better capture the risk of becoming a disaster victim than the index of standard of living. This result thus questions the importance of the temporalities of vulnerability: at certain moments in life, vulnerability can be accentuated, even if certain material goods are acquired. This result thus completes the analytical framework of Turner [START_REF] Turner | A framework for vulnerability analysis in sustainability science[END_REF] in integrating environmental living conditions, complementing data from other social sciences and from earth sciences, to help in developing effective policies.

The manifestations of climate change and environmental change, coupled with the increase in food, water and energy insecurity that the large cities of sub-Saharan Africa are already facing, are beginning to provide a good understanding of the extreme urgency of rethinking urban management priorities in order to prepare for the growth of the cities of the 21st century. These floods have also revealed dysfunctions in governance, urban planning, infrastructure maintenance and land management, as well as the lack of an early warning system. There is a need for future research that will combine the delineation of flood-prone areas [START_REF] De Risi | Probabilistic delineation of flood-prone areas based on a digital elevation model and the extent of historical flooding: the case of Ouagadougou[END_REF] and urban dynamics in certain hot-spots in Sub-Saharan Africa, in particular by making greater use of the opportunities offered by spatial planning [START_REF] Biesbroek | The mitigation-adaptation dichotomy and the role of spatial planning[END_REF]. In particular, recent studies have attempted to assess the limit state of safety where the life of inhabitants of informal settlements is at risk of flooding [START_REF] De Risi | Flood risk assessment for informal settlements[END_REF]. In Sub-Saharan Africa, early warning systems should be seen as essential in urban settings, not only for drought, but also for floods. There is a real opportunity to think of new, more holistic paradigms, including all scientific areas (pluri-disciplinarity) and all stakeholders, that will be better able to meet the particular needs of the vulnerable urban populations of Africa today, and especially on the basis of a longer-term vision.

Figure 3 .

 3 Figure 3. Location of settlements monitored by the Ouaga HDSS[START_REF] Rossier | The Ouagadougou Health and Demographic Surveillance System[END_REF]

Figure 4 .

 4 Figure 4. Location of the disaster areas in Nioko 2 monitored by the Ouaga HDSS

Figure 6 .

 6 Figure 6. Map from Google Earth: location of the disaster areas (red line) monitored by the Ouaga HDSS (dated 12th October 2017)

Table 1 .

 1 Number of households, usual residents and household size in each settlement monitored by the Ouaga HDSS, 2009

		Number of households Number of usual residents	Household size
	Formal	7,266	42,192	5.8
	Kilwin	3,854	21,408	5.5
	Tanghin	3;412	20,784	6.1
	Informal	10,709	37,048	3.5
	Nonghin	5,649	18,789	3.3
	Polesgo	1,480	4,849	3.3
	Nioko 2	3,580	13,410	3.7
	TOTAL	17,975	79,240	4.4

Table 2 .

 2 Factors influencing the "disaster victim" status -Ouagadougou HDSS (preliminary results)

	Variables of the head of household	Model 1	Model 2	Model 3	Model 4
	Sex (male)
	Female	0.56**	0.58*	0.61+	0.47 *
	Age (< 35 years)
	35-49 years	0.66**	0.77+	0.74*	0.71*
	50 years and over	0.73+	1.09	1.12	1.09
	Education (none)
	Primary	0.46***	0.45***	0.48***	0.50***
	Secondary and higher	0.39***	0.37***	0.39***	0.40***
	Wealth index (low)
	Medium and high	0.68**	0.88	0.86	0.84
	Sanitation (no facility/bush/field)
	Latrine		0.26***	0.23***	0.23***
	Source of lighting (lamp with kerosene/modern)
	Flashlight		3.35***	3.44***	3.51***
	Other (candle, none, etc.)		1.51	1.56	1.58+
	Duration of residence on the plot (less than 3
	3-9 years		1.73***	1.69***	1.77***
	10 years and over		0.15***	0.24***	0.26***
	Occupation status (owner)
	Tenant		0.68	0.66	0.60
	Hosted		1.28	1.25	1.26
	Place of birth (rural area in Burkina Faso)
	Ouagadougou			0.42***	0.46***
	Other towns of Burkina Faso			0.86	0.85
	Abroad			0.14*	0.13*

Duration of residence in Ouagadougou (less than

	10-29 years	1.72***	1.68***
	30 years and over	1.00	0.97
	Marital Status (never married)
	Married		1.73*
	Divorced, separated or widowed		1.44
	Number of usual members (1)
	2-3		0.98
	4-7		0.80
	8 and over		0.49 +
	Sex ratio in the household (1)
	Household without men		1.72
	Sex ratio < 1		1.27
	Sex ratio > 1		0.88
	Household without women		0.86
	Reference category in parenthesis.
	Significant levels : *** p<1% ; ** p<5% ; * p<10% ; + p<15%

Acknowledgements

The project leading to this work has received funding from the European Union's Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement No 690984, project DEMOSTAF. The authors would like thank Dr. Aude Nikiema (INSS/CNRST) for her contribution to the realization of some maps and for her comments on an early draft of this paper.


Mateo Cordier
email: mateo.cordier@uvsq.fr

Takuro Uehara

How much innovation is needed to protect the ocean from plastic contamination?

Keywords: marine ecosystem, waste management, ocean cleanup, system dynamics, decoupling GDP, marine litter

Introduction

Plastics have become increasingly dominant in the consumer marketplace since their commercial development in the 1930s and 1940s (Jambeck et al., 2015). Global plastic production -that is, global polymer resin and fiber production -reached 381 million tons per year in 2015, an 8-fold increase from 1975 (data from Geyer et al., 2017, supplementary material). (All ton units used hereinafter are metric tons). In 1960, plastics made up less than 1% of municipal solid waste by mass in the United States (the United States Environmental Protection Agency, 2011). By 2000, this proportion increased by one order of magnitude (Jambeck et al., 2015).

The release of plastics into the marine environment occurs from land through a variety of sources, including atmospheric transport, littering, and illegal dumping, as well as discharge from rivers, storm drains, and sewage outflows. Plastic waste is sometimes also directly discarded at sea by fishing, aquaculture, and shipping activities [START_REF] Lebreton | River plastic emissions to the world's oceans[END_REF][START_REF] Horton | Microplastics in freshwater and terrestrial environments: evaluating the current understanding to identify the knowledge gaps and future research priorities[END_REF]. Since 2014, scientists have succeeded in providing gross estimates of the ecological, social and economic impacts of plastic pollution (UNEP, 2014;[START_REF] Jaacks | The ecological cost of continued use of endocrinedisrupting chemicals[END_REF][START_REF] Mcilgorm | The economic cost and control of marine debris damage in the Asia-Pacific region[END_REF]. Plastics in the marine ecosystem are of increasing concern because of their persistence in the environment [START_REF] Li | Plastic waste in the marine environment: A review of sources, occurrence and effects[END_REF] and their effects on the oceans and wildlife [START_REF] Barnes | Accumulation and fragmentation of plastic debris in global environments[END_REF][START_REF] Baztan | [END_REF][START_REF] Da Costa | Nano)plastics in the environment -Sources, fates and effects[END_REF], and, potentially, on human health [START_REF] Trasande | Estimating Burden and Disease Costs of Exposure to Endocrine-Disrupting Chemicals in the European Union[END_REF]Thompson et al., 2009;Shea and Committee on Environmental Health, 2003).

Plastic debris input volumes from land to the global ocean are closely correlated with the world economy as shown in Figure 1. Several authors have studied the possibilities of decoupling contaminant generation from economic growth [START_REF] Jackson | Prosperity without growth: Economics for a finite planet[END_REF][START_REF] Sjöström | Decoupling waste generation from economic growth -A CGE analysis of the Swedish case[END_REF]. However, the close relationship between plastic contaminants and global Gross Domestic Product (GDP) in Figure 1 suggests that decoupling is a challenge. A report from the MacArthur Foundation (World Economic Forum et al., 2016) claimed that technological innovation can solve the plastic problem. However, the report does not say how much innovation is needed and does not analyze the feasibility of such interventions. Moreover, "waste management has not proven to be a 'one size fits all' solution. If we look all over the world we see a diversity of strategies to reduce, reuse, recycle and clean up our waste" [START_REF] Rochman | Strategies for reducing ocean plastic debris should be diverse and guided by science[END_REF]. In addition, historical studies show that technological innovations alone cannot solve ecological issues in an ever growing economy. To date, improvements in terms of reduction in contaminant and waste generation through cleaner technology innovation are often offset by increases in consumption and production levels driven by economic growth [START_REF] Haberl | Ecological Embeddedness of the Economy: A Socioecological Perspective on Humanity's Economic Activities 1700-2000[END_REF][START_REF] Jackson | Prosperity without growth: Economics for a finite planet[END_REF][START_REF] Victor | Managing without growth: slower by design, not disaster[END_REF][START_REF] Krausmann | Growth in global materials use, GDP and population during the 20th century[END_REF]. This means that although cleaner technology innovations have succeeded in reducing contaminant and waste generation per unit of GDP, the continuous growth of GDP has overtaken improvements made in the ways in which each unit of GDP is produced. In this paper, we develop a world model that simulates plastic waste emission by human activities, transport from land to the ocean and accumulation in the marine ecosystem. The model is designed in Powersim following the system dynamics method [START_REF] Sterman | Business Dynamics: Systems Thinking And Modeling For A Complex World[END_REF].

To date, there are no estimations of the overall global technological and economic effort required to reduce the annual flow of plastics entering the global ocean (estimated to 4.8-12.7 million tons/year by Jambeck et al., 2015) as well as the total stock accumulated in the ocean. And yet, this is quite important, since without waste management infrastructure improvements, the cumulative quantity of plastic waste entering the ocean from land (i.e., mismanaged waste) is predicted to double 2010 levels by 2025 (Jambeck et al., 2015). Jambeck et al. (2015) use their estimations to evaluate potential global mitigation strategies in terms of mismanaged waste. They propose mitigation strategies in the 20 top countries ranked by the mass of mismanaged plastic waste. The top 20 countries' mismanaged plastic waste encompasses 83% of the total in 2010. They define mismanaged waste as material that is either littered or inadequately disposed of.

Inadequately disposed waste is not formally managed and includes disposal in dumps or open, uncontrolled landfills, where it is not suitably contained. Mismanaged waste could eventually enter the ocean via inland waterways, wastewater outflows, and transport by wind or tides. With the world model developed in this paper, we assess the impact of three mitigation strategies proposed by Jambeck et al. (2015) on the plastic stock in the global ocean. Our aim is to verify, using the model, whether the simultaneous implementation of a portfolio of strategies (Table 1) could better achieve environmental targets than individual solutions.

The remainder of the paper is organized as follows. Section 2 outlines the methodology.

In that section, we develop the model used to simulate scenarios of various environmental measures. We also describe each scenario. Section 3 is devoted to the simulation results

showing the potential impact of each scenario. Section 4 is reserved for discussion of the results, and Section 5 for the conclusions.

[Insert Figure 1 here] [Insert Table 1 here]

Method

All scenarios presented in Section 3 include a world population annual growth rate that varies between 1.0% and 1.2% depending on the year considered, based on forecasts from the United Nations (2017). The simulation model is based on System dynamics (SD) [START_REF] Sterman | Business Dynamics: Systems Thinking And Modeling For A Complex World[END_REF] to capture the dynamics of marine plastic debris from their origin (their generation on land) to their fate (when they enter the ocean). System dynamics is a computer-aided approach to a system of coupled, nonlinear, first-order differential (or integral) equations [START_REF] Richardson | Feedback Thought in Social Science and Systems Theory[END_REF]. System dynamics is suitable because it describes the complex dynamics of a system with a specific emphasis on flows and stocks. Marine plastic debris involves complex, dynamic social-ecological systems where stock is a key variable. Marine plastic debris flows from the land to the ocean where it accumulates, generating a stock of floating plastics in the water and depositing plastic on the seabed.

System dynamics model

Figure 2 shows the stock and flow diagram of the system dynamics model for marine plastic wastes. We used Powersim Studio 10 (http://www.powersim.com) to build the model.

[Insert Figure 2 here]

There are two critical stocks in the model: coastal population and plastic waste in the ocean (the full model in Powersim format can be provided upon request). The dynamics of the coastal population is defined as follows (more equations can be found in supplementary material online):

= ℎ + (1)
The model focuses on the dynamics of the coastal population. Changes in the coastal population are assumed to be the same as changes in the world population using global population growth rate forecasts of the United Nations (2017).

Waste generation is proportional to the coastal population and is defined as follows:

= × × 365 (2)
Plastic waste in the ocean is defined as:

! ℎ = " # - + ! ℎ (3)
The model assumes that the volume of plastic waste in the ocean accumulates in the ocean. It does not decline unless ocean cleanup operations are implemented. The entering rate is determined by mismanaged plastic waste and fractional entering rate (Figure 2).

In addition to the cleanup rate, there are primarily three variables related to environmental policies that can influence the entering rate: Waste generation rate, % Plastic waste in stream, and % Inadequately managed waste (Figure 2). The model allows environmental targets to be set for each of these three variables. The time taken to achieve the environmental targets can be modified by changing the variable adjustment time in the model. The equations for each of the variables have the same structure. For example, Waste generation rate is modelled as:

= " ℎ ! + ℎ ! (4) ℎ ! = %&'() *&+ ) (),)'& -., '& ) / 01&+ ) (),)'& -., '& ) / 2345+ 6), -6) 7 / (5)
Therefore, Waste generation rate is equalized to Target waste generation rate in the next period when Adjustment time 3 = 1. The greater an adjustment time, the longer a variable takes to achieve the target level.

Scenario simulation

Using Target plastic waste level in the ocean and Plastic waste in the ocean in the model.

Technical details of all the scenario calculations can be found in supplementary material online as well as the values of the model parameters used in the scenarios (Supplementary Table S2). Table 2 below summarizes the values for several key variables.

[Insert Table 2 here]

Scenario 1 -Business As Usual

The Business As Usual scenario (BAU) is a reference scenario against which all the other scenarios can be compared. This scenario assumes that no additional environmental measures are implemented beyond those implemented in 2010, the reference year of the model. We assume the ocean cleanup effort to be very low. We arbitrarily set the cleanup effort at an annual removal percentage of 0.10 % of the total stock of plastics in the oceans worldwide (plastics floating at the water surface, in the water column, and deposited on the seabed).

Scenario 2.1 -"Ocean cleanup for 2020 stabilization"

The second set of scenarios are aimed at stabilizing the total plastic stock in the oceans at 2020's level by 2030. The model parameters described below in scenarios 2.1-2.4 have been estimated using an optimization technique, the evolutionary search algorithm Powersim Solver 2.5, to achieve stabilization. In this scenario, 4.22% of the stock of plastic debris in the ocean is removed every year between 2020 and 2030. This percentage is the Fractional cleanup rate whole term, i.e., one of the variables of the model from Figure 2. This is a curative "end-of-pipe" solution (i.e., solutions 18-20 in Table 1). Such technological solutions are currently in the testing phase in the ocean, for example the Ocean Cleanup Project [START_REF] Slat | How The Oceans Can Clean Themselves. A Feasibility Study[END_REF].

Scenario 2.2 -"Zero inadequately managed waste"

This scenario simulates improvements made to waste collection infrastructure, a preventive "middle" solution (i.e., solutions 13-17 in Table 1) such as developing landfill sealing and improving collection of wastes, installing public plastic recycling bins, etc. in a way that 100% of waste collection infrastructure is managed properly. This strategy would require substantial infrastructure investment primarily in low-and middle-income countries, where it is lacking. In the absence of additional contributions by high-income countries (e.g., implementation of plastic solutions in high-income countries such as in scenario 2.3 or 4.2), there will be low social and political acceptability at the international level, which might reduce the likeliness of implementation.

Scenario 2.3 -"Waste management & ocean cleanup"

This scenario simulates the same preventive "middle" solutions as in scenario 2.2 (i.e., solutions 13-17 in Table 1) except that the effort has been halved (only 50% of inadequately managed waste collection infrastructure is improved) and combined with a curative "end-of-pipe" solution: plastic removal from the ocean (i.e., solutions 18 to 20 in Table 1). In this scenario, 2.33% of the stock of plastic debris in the ocean is removed every year between 2020 and 2030 in order to stabilize the level of plastic in the global ocean to the2020 levels.

Scenario 2.4 -"Zero plastic litter"

This scenario simulates preventive "middle" solutions to reduce the number of people who litter plastic wastes to zero -i.e., solution 14 in Table 1 -that could be achieved through awareness raising campaigns and/or encouraging collaborative strategies, based on the approaches proposed by [START_REF] Benkler | The Penguin And The Leviathan: How Cooperation Triumphs Over Self-Interest[END_REF] and [START_REF] Ostrom | A multi-scale approach to coping with climate change and other collective action problems[END_REF].

Scenario 2.5 -"Combined strategy I"

This scenario combines several solutions: ocean cleanup as in scenario 2.1, waste management as in scenario 2.3 and zero plastic litter as in scenario 2.4. This scenario assumes that 4.22% of the stock of plastic debris in the ocean is removed every year between 2020 and 2030 (scenario 2.1); 50% of inadequately managed waste are improved and turned into properly managed waste (scenario 2.3), and the percentage of plastic waste litter is reduced to zero (scenario 2.4).

Scenario 3.1 -"Ocean cleanup for 25% reduction"

This scenario is designed as scenario 2.1 except that the optimization process achieves a 25% reduction in the level of plastic debris in the ocean below 2010 levels in 2030. The optimization results from the model show that to achieve this target, 15.16% of plastic debris in the ocean must be cleaned up every year between 2020 and 2030.

Scenario 3.2 -"Ocean cleanup for 50% reduction"

This scenario is similar to the previous one except that the optimization process achieves a 50% reduction in the level of plastic debris in the ocean below 2010 levels in 2030. The optimization results from the model show that to achieve this target, 21.14% of plastic debris in the ocean must be cleaned up every year between 2020 and 2030.

Scenario 4.1 -"Waste management in 20 countries"

The fourth set of scenarios simulate some of the environmental measures proposed by Jambeck et al. (2015). Scenario 4.1 simulates a preventive "middle" solution similar to scenario 2.2 with only two differences: inadequately managed wastes are reduced only by 50% and exclusively in the 20 top countries ranked by mass of mismanaged plastic waste. Selecting a limited amount of countries should ease the implementation of such an ambitious target.

Scenario 4.2 -"Capping wastes"

This scenario simulates preventive "at-the-source" solutions based on the first 12 solutions (in Table 1) from the following three categories: i) avoid and reduce waste production, ii) reuse or repair old products, iii) recycle in closed-cycles and extend producer responsibility. Individual waste generation is capped at 1.2 kg/day/capita (the world average in 2010 for all kinds of wastes, plastic and non-plastic ones). Such an environmental measure would target higher-income countries and might require smaller global investments. Most of the low-and middle-income countries generate less waste per capita than high-income countries.

The percentage of plastics in the waste stream are capped at 11.1% (the world average in 2010). Such an environmental policy would universally target low-, middle-and highincome countries which exceed this world average. Scenario 4.2 could be achieved through awareness raising campaigns and encouraging collaborative strategies based on approaches proposed by [START_REF] Benkler | The Penguin And The Leviathan: How Cooperation Triumphs Over Self-Interest[END_REF][START_REF] Ostrom | A multi-scale approach to coping with climate change and other collective action problems[END_REF]. Such approaches might help to incite people to mitigate overconsumption behaviors in general (plastic products included). Installing public recycling bins and developing returnable bottle systems also helps in achieving the target in this scenario.

Scenario 4.3 -"Zero inadequately managed waste in 10 countries & capping plastic wastes"

In this scenario, full waste management is achieved, as in scenario 2.2, except that the requirement for reducing the volume of mismanaged plastic waste is limited to the top-10 ranked countries in Jambeck et al. (2015). These top 10 countries are all low-and middle-income countries. Such plastic waste management is a preventive "middle" solution (solutions 13-17 in Table 1). Simultaneously, plastic waste generation is capped at 11% in all countries, as described in scenario 4.2, which universally targets low-, middle-and high-income countries. Such a cap is a preventive "at-the-source" solution based on changing consumer and producer behaviors (solutions 1 to 12 in Table 1).

Scenario 4.4 -"Combined strategy II"

This scenario combines waste management in the 20 top countries (as in scenario 4.1), capping wastes (as in scenario 4.2), and ocean cleanup for 2020 stabilization (as in scenario 2.1). This means it simulates a preventive "middle" solution, a preventive "atthe-source" solution and a curative "end-of-pipe" solution.

Estimating plastic flows entering the ocean

The first estimations of the quantity of plastic entering the ocean from waste generated on land were calculated in 1975 (National Research Council, 1975 [START_REF] Lebreton | River plastic emissions to the world's oceans[END_REF][START_REF] Schmidt | Export of plastic debris by rivers into the sea[END_REF]GESAMP, 2016). It is also important to note that the river models from [START_REF] Schmidt | Export of plastic debris by rivers into the sea[END_REF] and [START_REF] Lebreton | River plastic emissions to the world's oceans[END_REF] are limited to buoyant plastics found on river surface waters, whereas Jambeck et al. (2015) consider all types of plastics found in municipal waste. Finally, river models only consider a limited range of the full spectrum of plastic debris, as particles smaller than the mesh size of the sampling nets are not accounted for and debris larger than the aperture size of the trawl devices are underrepresented [START_REF] Lebreton | River plastic emissions to the world's oceans[END_REF]. For these reasons, the estimates of Lebreton et al.

(2017) and [START_REF] Schmidt | Export of plastic debris by rivers into the sea[END_REF] should be considered conservative.

Moreover, Jambeck et al. (2015) limited their assessment to plastic wastes generated by populations living within 50 km of a coast (assuming that they are those likely to generate most of the waste becoming marine debris). Although coastal populations are a key factor directly affecting the input of plastic into the ocean, populations living along large rivers, travelers to the beaches, etc. may also contribute to marine plastic debris.

Some researchers estimate that terrestrial waterways are the main transport processes by which debris is transported from the land to the coast [START_REF] Willis | Differentiating littering, urban runoff and marine transport as sources of marine debris in coastal and estuarine environments[END_REF]. A very rough estimation predicts that 70% to 80% of marine litter, most of it plastics, originates from inland sources and are transported by rivers to the oceans (Bowmer and Kershaw, 2010;[START_REF] Wagner | Microplastics in freshwater ecosystems: what we know and what we need to know[END_REF]Jambeck et al., 2015). This means that the higher range estimated by 5700 million tons [START_REF] Geyer | use, and fate of all plastics ever made[END_REF], from which we subtracted the flows from 2011-2015 to obtain the stock of plastic debris cumulated from 1950-2010. We then applied the parameters from Jambeck et al. (2015) to account for the percentage of the world population living within 50 km of the coast (29%), the share of plastic wastes that is inadequately managed (30%) and littered (2%) as well as the share of mismanaged waste (i.e., inadequately managed waste and littered waste) that enters the ocean (15%).

Calculation details are available in supplementary materials in Eq. (S1).

Our estimation of the total stock of plastics accumulated in the oceans from 1950-2010 correlate with the annual flow calculated in other studies for the year 2010. We extrapolated this annual flow for the year 2010 into an accumulated flow over 1950-2010.

To do so, we recalculated the total stock of plastic in the ocean using an alternative method. First, we estimated annual flows of land-based plastic entering the ocean every year (8 -1 in Eq. ( 6)) over the period 1950-2010 assuming the growth rate is proportional to the growth rate in the world plastic industrial production ( in Eq. ( 6), data provided by [START_REF] Geyer | use, and fate of all plastics ever made[END_REF], supplemental materials). This translates into the following equation:

8 -1 = 8 -1 (6)
Using Eq. ( 6) to calculate the ocean plastic input, for example in 2009, we enter in the equation Plastic productiont-1=2009 = 288 million tons, Plastic productiont=2010 = 313 million tons [START_REF] Geyer | use, and fate of all plastics ever made[END_REF], in supplemental materials) and Ocean plastic inputt=2010

The result gives 8 0:;<==> = 4.42 million tons/year in 2009. Second, applying Eq. ( 6) for each year back to the year 1950 and summing the annual results from 1950 to 2010 gives a total stock of plastic debris in the ocean of 92.9 million tons. This is quite close to our estimation based on Eq. (S1) (79.24 million tons).

Figure 3 also shows that the result from Eq. ( S1) is quite close to the estimation obtained when entering in Eq. ( 6) for 2010, after correction, the higher range from Lebreton et al.

( [Insert Figure 3 here]

Results

We simulated four sets of scenarios that describe the evolution of plastic stock in the world's ocean from 2010 to 2030. All environmental scenarios (scenarios 2 to 4) simulate the implementation of plastic solutions over the period 2020-2030.

The first set of scenarios displayed in Figures 456is Scenario 1 Business As Usual (BAU). Figure 4 and Table 3 suggest that, in the absence of environmental measures, the stock of plastic debris accumulated in the world ocean might reach 183 million tons in 2030 (plastics floating at the surface, in the water column and deposited on the seabed).

The second set of scenarios displayed in Figure 4

Discussion

Inciting citizens to stop plastic littering could theoretically help to reduce plastic flows from land to the ocean. However, simulation results for the scenario Zero plastic litter (scenario 2.4) suggest that even if all inhabitants in the world stop littering waste, plastic flows entering the world ocean would continue to increase. This suggests that focusing all efforts on reducing plastic littering will contribute little to solving the plastic problem.

The final consumer's environmental responsibility is quite limited. Solutions should be focused on collective efforts for a significant positive impact to be observed in terms of abatement of plastic ocean contamination. In Boyan Slat's feasibility study (Slat, 2014, pp. 30-32 and 241), they plan to remove 70320 tons of plastics from the oceans over a period of 10 years, which might cost 3639 €/ton (base case), 4511 €/ton (best case) or 5236 €/ton (worst case). These costs include:

(i) the required investment in capital expenditures; (ii) the estimated operating expenses over ten years, (iii) the replacement cost of equipment that has a useful life of 5 years and

(iv) decommissioning costs after 10 years (Slat, 2014, pp. 32 and 435).

In the scenario Ocean cleanup for 2020 stabilization (scenario 2. 3) could drastically reduce the cost estimations as well as the total amount of plastics to be cleaned up in scenarios 2.1 and 2.3. This should be investigated in further research. Moreover, calculating allocation rules [START_REF] Cordier | The shared environmental responsibility principle: new developments applied to the case of marine ecosystems[END_REF] for these costs to be spread over time as well as across economic sectors and countries could also significantly reduce the GDP percentages estimated above.

For more stringent environmental targets, such as aiming to achieve the mid-2000s levels, additional measures must be undertaken. Simulation results for the Combined strategy I (scenario 2.5 displayed in Figure 4) show that a combination of strategies is more likely to achieve that goal. Scenario 2.5 combines ocean cleanup for 2020 stabilization (scenario 2.1), improved waste management to reduce inadequately managed waste systems by 50% in all countries (part of scenario 2.3), and individual behavior changes to incite people to stop littering plastic products (scenario 2.4). This suggests that combining different kinds of environmental measures downstream and upstream across socialecological systems ("end-of-pipe" and "at-the-source" solutions -Table 1) as well as downstream and upstream across plastic contamination causal chains ("curative" and "preventive" solutions) is more successful than scenarios where only one type of environmental measure is undertaken.

The scenarios Waste management in 20 countries (scenario 4.1), Capping wastes (scenario 4.2) and Zero inadequately managed waste in 10 countries & capping plastic wastes (scenario 4.3) are intended to be more realistic and achievable. Scenario 4.1

proposes to improve only 50% of the inadequately managed waste -not 100% -in only 20 countries -not the entire world. The idea is to obtain a marine ecological improvement with the minimum effort required in order to make plastic solutions more feasible. However, scenario 4.1 shows poor results in terms of ecological impact. That is, there is not much of an improvement in the level of plastic stock in the ocean compared to the BAU level. Scenario 4.2 performs better and succeeds in stabilizing the stock of plastic debris in the ocean by 2022 via two strategies: capping individual waste generation at 1.2 kg/day/capita (the world average in 2010) in all countries and capping plastics in the waste stream at 11.1% (the world average in 2010). Scenarios 4.3 also succeeds in stabilizing the stock of plastic debris in the ocean by 2022 via two strategies:

improving waste management systems in 10 countries to achieve zero inadequately managed waste in those countries and capping plastics in the waste stream at 11.1% in all countries.

The combined strategy implemented in Scenario 4.3 combines a 100% reduction in inadequately managed waste in the 10 top countries with the capping of plastics in the waste stream at 11.1%. This combined strategy could be regarded as more equitable and socially and politically acceptable because low-, middle-and high-income countries would all participate in plastic solutions on the basis of the principle of their common but differentiated responsibilities. That is, they would all bear a common environmental responsibility but their contribution to plastic solutions would be differentiated according to their level of responsibility and to their affordability (i.e. their ability to pay for plastic solutions). This makes the implementation of this scenario more likely. However, capping plastics in waste stream at 11% would require changes in consumer behavior. This is not easy to achieve unless awareness raising campaigns are designed appropriately to encourage consumers/citizens to behave collectively towards a common target, a plastic-free ocean. Specific approaches are required to change individual mentality and switch from individualistic to collaborative behaviors [START_REF] Benkler | The Penguin And The Leviathan: How Cooperation Triumphs Over Self-Interest[END_REF][START_REF] Ostrom | A multi-scale approach to coping with climate change and other collective action problems[END_REF].

The scenario Combined strategy II (scenario 4.4) is another combined strategy since it merges scenarios Waste management in 20 countries (scenario 4.1), Capping wastes (scenario 4.2) and Ocean cleanup for 2020 stabilization (scenario 2.1). Scenario 4.4 involves three categories of solutions: a preventive "middle" solution (scenario 4.1), a preventive "at-the-source" solution (scenario 4.2) and a curative "end-of-pipe" solution (scenario 2.1). Implementation of avoid, reduce, reuse strategies, improvements in waste collection infrastructure, and ocean cleanup encourage changes in behavior in addition to technological solutions. Scenario 4.4 follows a similar approach as scenario 2.5 but it performs better and succeeds in achieving 2013 levels. This strengthens our assumption from scenario 2.5 that combining different kinds of environmental measures downstream and upstream ( (Jambeck et al., 2015).

The combined upstream and downstream solutions simulated in Combined strategy I (scenario 2.5) and Combined strategy II (scenario 4.4) could either come from the top (political and economic decision makers) or the bottom of the society (citizens, environmental associations, small size enterprises). These solutions will require a change in mentality to encourage individuals to act collectively. [START_REF] Ostrom | A multi-scale approach to coping with climate change and other collective action problems[END_REF] and [START_REF] Benkler | The Penguin And The Leviathan: How Cooperation Triumphs Over Self-Interest[END_REF] have identified about 10 conditions required to create a context in which people are willing to self-organize at multiple levels and collaborate to find a solution to a common problem, such as achieving a plastic-free ocean.

Given the current marine biodiversity crisis in the world [START_REF] Pauly | Comments on FAOs State of World Fisheries and Aquaculture (SOFIA 2016)[END_REF][START_REF] Halpern | A Global Map of Human Impact on Marine Ecosystems[END_REF][START_REF] Worm | Impacts of biodiversity loss on ocean ecosystem services[END_REF], it might be interesting to test scenarios that allow for greater plastic abatement. Figure 5 shows that cleanup effort alone -scenario 3. Slat (2014, pp. 30-32 and 241). This would represent respectively 0.79%, 0.98% and 1.13% of the world GDP in 2017 (World Bank, 2018). In the scenario Ocean cleanup for 25% reduction (scenario 3.1), these percentages drop to respectively 0.69%, 0.85% and 0.99% of the 2017 world GDP.

In further research, scenarios simulated in Figure 5 could be improved by taking into account other cleanup strategies, such as addressing plastic waste directly dumped or lost in the ocean through aquaculture and fishing activities, and implementing cleanup activities on beaches.

Conclusions

Our paper demonstrates that combining multiple strategies to solve ocean plastic contamination provides better results than individual solutions. Focusing all efforts on a single solution, such as innovation in ocean cleanup technologies, would require huge volumes of plastic debris to be removed from the global ocean. The economic feasibility of small scale cleanup projects is being demonstrated by The Ocean Cleanup Project [START_REF] Slat | How The Oceans Can Clean Themselves. A Feasibility Study[END_REF]. However, extending this project to the global ocean would require investing in 1924 similar projects to achieve a 25% reduction in the total ocean plastic stock below 2010 levels by 2030. The cost of achieving such a target is estimated at between 0.7% and 1.0% of the world GDP in 2017.

In addition, focusing exclusively on ocean cleanup technologies does not reduce the plastic waste inputs from land to the ocean, which would necessitate continuous ocean cleanup effort. Can we afford a never-ending cleanup, and would this effort be economically feasible, as well as socially and politically acceptable? Is it sustainable given that cleanup effort will probably have to be increased in order to offset the exponential growth of plastic product consumption based on current trends? What will be the impact in terms of plastic debris ultimately reaching the ocean? Moreover, technology cannot solve all the problems also because plastics will degrade into microplastics and even nanoplastics. Cleanup technologies are currently unable to remove such small particles from the ocean.

The most effective mitigation strategies focus on source reduction, not on ocean cleanup technologies [START_REF] Rochman | Strategies for reducing ocean plastic debris should be diverse and guided by science[END_REF][START_REF] Sherman | Modeling marine surface microplastic transport to assess optimal removal locations[END_REF]. This could be achieved through improving waste management infrastructure (Jambeck et al., 2015), preventing microfibers from clothing and small plastic fragments and beads from entering wastewater by putting filters on washing machines (Browne, 2015), removing plastic microbeads from personal care products [START_REF] Rochman | Scientific evidence supports a ban on microbeads[END_REF], etc. Sherman and van

Sebille (2016) argue that marine plastic pollution will persist unless we stop plastic input altogether.

Further research is required to assess the economic feasibility of the proposed solutions to plastic contamination in the global oceans. Economic principles must be designed or adapted to overcome financial, social and political difficulties, for example, the polluters pays principle (OECD, 1972;OECD, 1974), the extended producer responsibility (OECD, 2004), and the shared environmental responsibility principle [START_REF] Cordier | The shared environmental responsibility principle: new developments applied to the case of marine ecosystems[END_REF]. The shared environmental responsibility principle might be a promising strategy in contrast to the conventional polluter pays principle, which might be less affordable. The long term ecological impacts of each scenario (beyond 2030) should be assessed to ensure that short term plastic solutions are feasible on the mid-to long term.

In further work, the quantification of flows and stocks of plastics from land to the global ocean in our model could be improved, through incorporating values derived from other models, such as [START_REF] Lebreton | River plastic emissions to the world's oceans[END_REF] and [START_REF] Schmidt | Export of plastic debris by rivers into the sea[END_REF].

Preventive measures

Avoid and reduce waste production 1. Awareness raising campaigns and collaborative strategies [START_REF] Benkler | The Penguin And The Leviathan: How Cooperation Triumphs Over Self-Interest[END_REF][START_REF] Ostrom | A multi-scale approach to coping with climate change and other collective action problems[END_REF] to encourage households to reduce waste generation and overconsumption behaviors in general. 2. Inciting industries to substitute plastic materials with aluminum, glass, bio-plastics, etc. 3. Nurdle spillage prevention in plastic factories (nurdles are pre-production microplastic pellets). 4. Fiscal instruments and compulsory legislation to reduce the amount of packaging. 5. Compulsory legislation to ban micro beads in cosmetics or in toothpastes, or to ban single-use plastic products, etc.

Reuse or repair old products 6. Returnable glass or PET bottle systems. 7. Moving from an economy of ownership to one of service functionality. 8. Online systems designed to help to share, trade, exchange, lend or rent second-hand products between neighbors, including plastic products. 9. Legislation forbidding planned obsolescence of products. 10. Fiscal rules that favor longer lasting products, etc. Recycling 11. Recycling in closed cycles (e.g., recycling of plastic bottles, plastic bags, etc.). 12. Legislation for compulsory extended producer responsibility in the plastic industry.

Palliative measures

Averting behaviors to avoid exposure to plastic chemicals 22. Final consumers purchasing glass bottles instead of plastic ones, switching from plastic bottles of mineral water to public tap water, etc.

Upstream

Solutions at the source of the problem

End-of-pipe solutions

Middle solutions

Downstream

  [START_REF] Schmidt | Export of plastic debris by rivers into the sea[END_REF] (4.0 million tons/year) represents about 75% (the middle of the range 70-80% mentioned above) of the total annual mass of plastic debris reaching the ocean from land. A simple rule of thumb allows us to estimate the total flow of plastics entering the ocean at 5.3 million tons/year: 4.0/75 * 100 = 5.3. In this way, we corrected the annual flows from[START_REF] Schmidt | Export of plastic debris by rivers into the sea[END_REF] and also those from[START_REF] Lebreton | River plastic emissions to the world's oceans[END_REF], which are mentioned in the legend of Figure3, before entering them into Eq. (6). The higher estimate given by[START_REF] Schmidt | Export of plastic debris by rivers into the sea[END_REF], after correction, is close to 4.8 million tons/year, which is the value we chose as a starting parameter for the reference year in our model (i.e., the lower range calculated byJambeck et al. (2015)).Using the parameters fromJambeck et al. (2015) corresponding to their lower range and applying them to the estimations of[START_REF] Geyer | use, and fate of all plastics ever made[END_REF], we obtained a total stock of ocean plastic debris of 79.24 million tons in 2010 (Figure3). Our calculation is based on the total volume of plastic wastes generated in the world over the 1950-2015 period, i.e.

Figure 2 .

 2 Figure 2. Stock and flow diagram of the system dynamics model for marine plastic wastes (designed in Powersim). Stocks and flows are represented by boxes and double arrows, respectively. Circles and diamonds denote auxiliary variables and constants. Clouds indicate infinity and mark the model boundaries.

Figure 3 .

 3 Figure 3. Total stock of plastic debris in the world ocean computed using different estimations of annual plastic flows entering the ocean from land. The 6 curves are cumulated values over 1950-2010 computed by summing the annual results of Eq. (6).

Figure 4 .

 4 Figure 4. Impact of the second set of scenarios on total stock of plastic debris in the global ocean.

  Scenarios 2.1 to 2.4 have been designed with the aim of stabilizing the amount of plastic debris in the ocean to 2020 levels by 2030. In Figures 4 to 6, the stock of plastics in the world oceans includes plastics originating from land that are floating at the water surface, in the water column and deposited on the seabed.

Figure 5 .

 5 Figure 5. Impact of the third set of scenarios on total stock of plastic debris in the global ocean. Scenarios 3.1 and 3.2 simulate ocean cleanup efforts designed to reduce the amount of ocean plastic debris below 2010 levels.

Figure 6 .

 6 Figure 6. Impact of the fourth set of scenarios on total stock of plastic debris in the global ocean.

  Scenarios 4.1 to 4.3 are adapted from Jambeck et al. (2015) and are assumed to be more realistic and feasible scenarios.

  Since then, other studies have quantified the amount of plastic entering the ocean from land via rivers.[START_REF] Schmidt | Export of plastic debris by rivers into the sea[END_REF] estimate for the period 2004-2016 that 0.41 to 4.00 million tons of plastics were probably entering the global ocean every year from rivers.[START_REF] Lebreton | River plastic emissions to the world's oceans[END_REF] estimate this range to be between 1.15 and 2.41 million tons a year for the period 2005-2015. If rivers are the main pathway for plastic debris to the ocean from land, this would mean that the range proposed byJambeck et al. (2015) might be overestimated (at least for their higher margin, 12.7 million tons/year). However, plastic

	). Jambeck et al. (2015)
	proposed new estimations in 2015 by linking worldwide data on solid waste, population
	density, and economic status to estimate the mass of land-based plastic waste entering the
	ocean. They calculated for the year 2010 that 275 million tons of plastic waste was
	generated by coastal populations (i.e., populations living within 50 km of a coast) in 192
	coastal countries, with a total of 4.8 to 12.7 million tons entering the ocean annually at
	the global scale.

wastes within coastal areas do not only enter the oceans through rivers. They can also reach oceans by other processes, such as direct littering near beaches by tourists or at sea by fishermen and aquaculture activities, storm water runoff, tidal or wind transport, illegal dumping, and sewage outflows during heavy rains

Table 1 )

 1 is more successful than scenarios where only one type of environmental measure is undertaken. This scenario also combines environmental

	measures in different categories of countries. Improving waste management infrastructure
	in developing countries (Scenario 4.1 Waste management in 20 countries) is paramount
	and will require substantial resources and time. While such improvements in
	infrastructure are being implemented, industrialized countries can take immediate action
	by reducing waste and curbing the use of single-use plastics (scenarios 4.2 Capping
	wastes and 4.3 Zero inadequately managed waste in 10 countries & capping plastic
	wastes) as well cleaning-up the ocean (scenario 2.1)

2

  Oceancleanup for 50% reduction -could reduce the amount of plastic stock in the global ocean to 50% of 2010 levels in 2030. However, this would require removing 21.14% of the plastic stock from the ocean every year over the period 2020-2030. Scenario 3.1 Ocean

	cleanup for 25% reduction shows that reducing this target by half (achieve a 25%
	reduction in the level of plastic stock in the ocean below 2010 levels in 2030) would
	require removing 15.16% of plastic stock from the ocean every year over the same
	period.
	In the scenario Ocean cleanup for 50% reduction (scenario 3.2), removing 21.14% of
	ocean plastic stock every year over 10 years results in removal of a total of 154.45
	million tons of plastics. The total cost of this scenario might amount to € 562.04 billion
	(base case), € 696.72 billion (best case) or € 808.70 billion (worst case) -own calculation
	based on unit costs from

Table 1 . Categorization of environmental solutions to solve plastic issues

 1

	Location across the social-system ecological	Location causal chain across the problem	Solution categories	Examples of solutions

  Collection of plastic debris in oceans, for example the Ocean Cleanup Project (Slat, 2014). 19. Beach cleanups. 20. River interception techniques before plastic wastes enter the ocean, or filters in water treatment plants, etc.

			13. Invest in landfill sealing to avoid plastic waste leakages
		Improvement in waste collection infrastructure	through rain, waterways or wind, from dumps or open, uncontrolled landfills, where waste is not suitably contained. 14. Invest in public garbage cans, waste collection system,
			awareness raising to reduce littering, etc.
		Incineration	15. Plastic waste incineration.
		Energy recovery	16. Plastic waste incineration with energy cogeneration.
		Composting biodegradable plastic bottles	17. Biodegradable (compostable) plastics made of starch that meet standards for biodegradability and compostability.
	Curative measures	Restore, e.g., remove plastics from ecosystems 18. Health measures 21. Medical services to cure health impacts due to consumption of chemicals in plastic (e.g., Bisphenol-A
			and other endocrine disruptors).

Table 2 . Main assumptions for key variables entered in the model.

 2 Further details are available in the supplementary material online.

		Scenario 1.	Scenario 2.1.	Scenario 2.2.	Scenario 2.3.	Scenario	Scenario	Scenario	Scenario	Scenario 4.1.	Scenario	Scenario 4.3. Zero	Scenario
		Business	Ocean cleanup	Zero	Waste	2.4. Zero	2.5.	3.1. Ocean	3.2. Ocean	Waste	4.2.	inadequately	4.4.
		As Usual	for 2020	inadequately	management &	plastic	Combined	cleanup for	cleanup for	management in	Capping	managed waste in	Combined
			stabilization	managed waste	ocean cleanup	litter	strategy I	25%	50%	20 countries	wastes	10 countries &	strategy II
								reduction	reduction			capping plastic
												wastes
	Percentage of
	littered waste (%) 1	2.0	2.0	2.0	2.0	0.0	0.0	2.0	2.0	2.0	2.0	2.0	2.0
	Percentage of
	plastic waste in stream (%) 2	11.1	11.1	11.1	11.1	11.1	11.1	11.1	11.1	11.1	9.9	9.9	9.9
	Percentage of
	inadequately managed waste	30.0	30.0	0.0	15.0	30.0	15.0	30.0	30.0	17.3	30.0	8.6	17.3
	(%) 2
	Ocean cleanup rate (%) 3	0.1	4.2	0.1	2.3	0.1	4.2	15.2	21.1	0.1	0.1	0.1	4.2
	Individual waste
	generation rate (kg/	1.2	1.2	1.2	1.2	1.2	1.2	1.2	1.2	1.2	0.9	1.2	0.9
	person/day) 2

Acknowledgements

We would like to thank Juan Baztan from the Université de Versailles-Saint-Quentin-en-Yvelines (France), Ana Carrasco Martin from the Biosphere Reserve of Lanzarote (Spain), Aquilino Miguélez López from the Biosphere Observatory of Lanzarote (Spain) and Bethany Jorgensen from Cornell University (United States) who helped us improve our understanding of plastic issues at local, medium and global scales.

Funding

This research was supported by the ACE-ICSEN project -Adaptation aux Changements Environnementaux: une approche multi-échelle et transdisciplinaire -Institut des Changements Socio-Environnementaux) -a project funded by the Université Paris-Saclay (grant number ANR-11-IDEX-0003-02).

Data availability statement

The datasets and materials generated and analyzed during the current study are available from the corresponding author on request.

Author Contributions Statement

Competing interests

The authors declare no competing interests.

Table 3. Summary of simulation results: total stock of plastic debris in the global ocean (million tons).

The data in this table includes plastics originating from land that are floating at the water surface, in the water column and deposited on the seabed.