PLASTIC POLLUTION & ECONOMIC GROWTH:
INFLUENCE OF CORRUPTION AND LACK OF
EDUCATION

Mateo Cordier, Takuro Uehara, Juan Baztan, Bethany Jorgensen, Yan Huijie

► To cite this version:

Mateo Cordier, Takuro Uehara, Juan Baztan, Bethany Jorgensen, Yan Huijie. PLASTIC POLLUTION & ECONOMIC GROWTH: INFLUENCE OF CORRUPTION AND LACK OF EDUCATION. MICRO 2020 - Fate and impacts of microplastics: knowledge and responsibilities, Cabildo de Lanzarote; Spanish Biosphere Reserves Network; CEARC-OVSQ; UNESCO; University of Plymouth; etc., Nov 2020, Arrecife (Lanzarote), Spain. hal-04566765

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Distributed under a Creative Commons Attribution 4.0 International License
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PLASTIC POLLUTION & ECONOMIC GROWTH:
INFLUENCE OF CORRUPTION AND LACK OF EDUCATION

Mateo Cordier

Takuro Uehara

Juan Baztan

Bethany Jorgensen

Huijie Yan
<figure><img src="image_2.png" /></figure>

PLASTIC POLLUTION & ECONOMIC GROWTH:
INFLUENCE OF CORRUPTION AND LACK OF EDUCATION

Mateo Cordier

Takuro Uehara

Juan Baztan

Bethany Jorgensen

Huijie Yan
## CONTEXT

* **Green economic growth** led by **technological solutions** is often mentioned as a solution for mitigating plastic pollution.

* **However, economic growth (global GDP, Gross Domestic Product) appears to be in contradiction to planetary boundaries (Global ecological footprint).**

To maintain our current appetite for natural resources, we would need the equivalent of 1.7 Earths

### Global ecological footprint (Number of Earth)

<figure><img src="image_2.png" /><figcaption>Global GDP over 1961-2016
(billion US $, constant prices of 2010)</figcaption></figure>

<table><caption>Linear regression: Ecological footprint = f (LN (GDP))</caption><thead><tr><th>Source</th><th>SS</th><th>df</th><th>MS</th></tr></thead><tbody><tr><td>Model</td><td>3.88352381</td><td>1</td><td>3.88352381</td></tr><tr><td>Residual</td><td>.110282952</td><td>54</td><td>.002042277</td></tr><tr><td>Total</td><td>3.99380676</td><td>55</td><td>.072614668</td></tr></tbody></table>

Number of obs = 56
F( 1, 54) = 1901.57
Prob > F = 0.0000
R-squared = 0.9724
Adj R-squared = 0.9719
Root MSE = .04519

<table><thead><tr><th>Ecological footprint</th><th>Coef.</th><th>Std. Err.</th><th>t</th><th>P>|t|</th><th>[95% Conf. Interval]</th></tr></thead><tbody><tr><td>LN (GDP)</td><td>.5025289</td><td>.0115241</td><td>43.61</td><td>0.000</td><td>.4794245 .5256332</td></tr><tr><td>_cons</td><td>-3.984623</td><td>.1204577</td><td>-33.08</td><td>0.000</td><td>-4.226126 -3.74312</td></tr></tbody></table>

**Sources of data for the graph:**

- Global Footprint Network (2020). Country Trends. https://data.footprintnetwork.org/#/countryTrends

- World Bank (2020). PIB ($ US constants de 2010). https://donnees.banquemondiale.org/indicator/NY.GDP.MKTP.KD
## CONTEXT

* **Green economic growth** led by **technological solutions** is often mentioned as a solution for mitigating plastic pollution.

* However, economic growth (global GDP, *Gross Domestic Product*) appears to be in contradiction to **planetary boundaries** (*Global ecological footprint*).

To maintain our current appetite for natural resources, we would need the equivalent of 1.7 Earths

<figure><img src="image_3.png" /><figcaption>Global GDP over 1961-2016
(billion US $, constant prices of 2010)</figcaption></figure>

Linear regression: Ecological footprint = *f* (LN (GDP))

<table><thead><tr><th>Source</th><th>SS</th><th>df</th><th>MS</th></tr></thead><tbody><tr><td>Model</td><td>3.88352381</td><td>1</td><td>3.88352381</td></tr><tr><td>Residual</td><td>.110282952</td><td>54</td><td>.002042277</td></tr><tr><td>Total</td><td>3.99380676</td><td>55</td><td>.072614668</td></tr></tbody></table>

Number of obs = 56
F( 1, 54) = 1901.57
Prob > F = 0.0000
R-squared = 0.9724
Adj R-squared = 0.9719
Root MSE = .04519

<table><thead><tr><th>Ecological footprint</th><th>Coef.</th><th>Std. Err.</th><th>t</th><th>P>|t|</th><th>[95% Conf. Interval]</th></tr></thead><tbody><tr><td>LN (GDP)</td><td>.5025289</td><td>.0115241</td><td>43.61</td><td>0.000</td><td>.4794245 .5256332</td></tr><tr><td>_cons</td><td>-3.984623</td><td>.1204577</td><td>-33.08</td><td>0.000</td><td>-4.226126 -3.74312</td></tr></tbody></table>

**Sources of data for the graph:**

- Global Footprint Network (2020). Country Trends. https://data.footprintnetwork.org/#/countryTrends

- World Bank (2020). PIB ($ US constants de 2010). https://donnees.banquemondiale.org/indicator/NY.GDP.MKTP.KD
RESEARCH QUESTION

* Which **non-technological solutions** could reduce plastic contamination of the global ocean ?
RESEARCH QUESTION

* Which **non-technological solutions** could reduce plastic contamination of the global ocean ?
METHOD

* We answer that question by developing **two**
**worldwide socio-economic models**, for
forecasting inadequately managed plastic
waste up to the year 2050 across **217**
**countries and territories**.

Plastic pollution and economic growth: the influence of
corruption and the lack of education

Preprint - May 2020

DOI: 10.13140/RG.2.2.23198.97601

ResearchGate

READS

673

4 authors:

<figure><img src="image_3.png" /></figure>

Mateo Cordier

Université de Versailles Saint-Quentin

<figure><img src="image_4.png" /></figure>

Takuro Uehara

Ritsumeikan University

<figure><img src="image_5.png" /></figure>

Juan Baztan

Université de Versailles Saint-Quentin

<figure><img src="image_6.png" /></figure>

Bethany Jorgensen

Cornell University

More information available here

Cordier M., Uehara T., Baztan J., Jorgensen B., 2020. "Plastic pollution and economic growth: the influence of corruption and the lack of education".

Preprint ResearchGate, DOI: 10.13140/RG.2.2.23198.97601. License CC BY-NC-SA 4.0. Available at:

https://www.researchgate.net/publication/341258750_Plastic_pollution_and_economic_growth_the_influence_of_corruption_and_the_lack_of_education
METHOD

* We answer that question by developing **two**
**worldwide socio-economic models**, for
forecasting inadequately managed plastic
waste up to the year 2050 across **217**
**countries and territories**.

Plastic pollution and economic growth: the influence of
corruption and the lack of education

Preprint - May 2020

DOI: 10.13140/RG.2.2.23198.97601

ResearchGate

READS

673

4 authors:

<figure><img src="image_4.png" /></figure>

Mateo Cordier

Université de Versailles Saint-Quentin

<figure><img src="image_5.png" /></figure>

Takuro Uehara

Ritsumeikan University

<figure><img src="image_6.png" /></figure>

Juan Baztan

Université de Versailles Saint-Quentin

<figure><img src="image_7.png" /></figure>

Bethany Jorgensen

Cornell University

More information available here

Cordier M., Uehara T., Baztan J., Jorgensen B., 2020. "Plastic pollution and economic growth: the influence of corruption and the lack of education".

Preprint ResearchGate, DOI: 10.13140/RG.2.2.23198.97601. License CC BY-NC-SA 4.0. Available at:

https://www.researchgate.net/publication/341258750_Plastic_pollution_and_economic_growth_the_influence_of_corruption_and_the_lack_of_education
# METHOD: MODEL EQUATIONS

* **Equation 1**: general equation merging Eq. 2., Eq. 3 (or Eq. 3bis) and population size

$$ \textit{Inadequately managed plastic waste} = \textit{plastic waste per capita} \times \textit{inadequately managed waste \%} \times \textit{population} $$

* **Equation 2**: estimating plastic waste per capita

$$ \ln(\textit{Plastic waste per capita}) = \alpha + \beta \ln(Q_i) + \gamma \ln(Q_i)^2 + \delta X_i + \varepsilon_i $$

where the subscript i denotes countries, α is an intercept, Q_i is income per capita, X_i is a vector of exogenous variables explaining plastic waste per capita, and ε_i are the disturbance terms.

* **Equation 3**: estimating inadequately managed waste percentage (includes **corruption control policy**)

$$ \textit{Inadequately managed waste \%} = \frac{e^{\rho_1 + \sigma_1 \ln(Q_i) + \tau_1 C_i + \varphi_1 Z_{1i}}}{1 + e^{\rho_1 + \sigma_1 \ln(Q_i) + \tau_1 C_i + \varphi_1 Z_{1i}}} $$

where ρ_1 is the constant term, C_i is the degree of corruption control, and Z_{1i} is a vector of the exogenous variable explaining Inadequately managed waste %.

* **Equation 3bis**: estimating inadequately managed waste percentage (includes **education**)

$$ \textit{Inadequately managed waste \%} = \frac{e^{\rho_2 + \sigma_2 \ln(Q_i) + \tau_2 E_i + \varphi_2 Z_{2i}}}{1 + e^{\rho_2 + \sigma_2 \ln(Q_i) + \tau_2 E_i + \varphi_2 Z_{2i}}} $$

where ρ_2 is the constant term, E_i is the educational level, and Z_{2i} is a vector of the exogenous variable explaining Inadequately managed waste %.
# METHOD: MODEL EQUATIONS

* **Equation 1**: general equation merging Eq. 2., Eq. 3 (or Eq. 3bis) and population size

$$ \textit{Inadequately managed plastic waste} = \textit{plastic waste per capita} \times \textit{inadequately managed waste \%} \times \textit{population} $$

* **Equation 2**: estimating plastic waste per capita

$$ \ln(\textit{Plastic waste per capita}) = \alpha + \beta \ln(Q_i) + \gamma \ln(Q_i)^2 + \delta X_i + \varepsilon_i $$

where the subscript i denotes countries, α is an intercept, Q_i is income per capita, X_i is a vector of exogenous variables explaining plastic waste per capita, and ε_i are the disturbance terms.

* **Equation 3**: estimating inadequately managed waste percentage (includes **corruption control policy**)

$$ \textit{Inadequately managed waste \%} = \frac{e^{\rho_1 + \sigma_1 \ln(Q_i) + \tau_1 C_i + \varphi_1 Z_{1i}}}{1 + e^{\rho_1 + \sigma_1 \ln(Q_i) + \tau_1 C_i + \varphi_1 Z_{1i}}} $$

where ρ_1 is the constant term, C_i is the degree of corruption control, and Z_{1i} is a vector of the exogenous variable explaining Inadequately managed waste %.

* **Equation 3bis**: estimating inadequately managed waste percentage (includes **education**)

$$ \textit{Inadequately managed waste \%} = \frac{e^{\rho_2 + \sigma_2 \ln(Q_i) + \tau_2 E_i + \varphi_2 Z_{2i}}}{1 + e^{\rho_2 + \sigma_2 \ln(Q_i) + \tau_2 E_i + \varphi_2 Z_{2i}}} $$

where ρ_2 is the constant term, E_i is the educational level, and Z_{2i} is a vector of the exogenous variable explaining Inadequately managed waste %.
# METHOD: MODEL STRUCTURE

## In Equation 2 (plastic waste per capita)

- We included the influence of:

- Income per capita (GDP per capita in $/year)

- Urbanisation (% of total population)

- International tourism on small islands (dummy variable [0, 1])

- Market regulatory quality (percentile rank)

<table><caption>Table 2. Linear regression model estimate for Plastic waste per capita (natural logarithm) (Eq. 2).</caption><thead><tr><th>Variable</th><th>Coefficient</th><th>Std. Error</th><th>p-Value</th><th></th></tr></thead><tbody><tr><td>LN (GDP per capita)</td><td>1.573</td><td>0.708</td><td>0.028</td><td>**</td></tr><tr><td>(LN (GDP per capita))<sup>2</sup></td><td>-0.080</td><td>0.039</td><td>0.041</td><td>**</td></tr><tr><td>Small islands</td><td>0.562</td><td>0.139</td><td>0.000</td><td>***</td></tr><tr><td>Urban population</td><td>0.012</td><td>0.003</td><td>0.001</td><td>***</td></tr><tr><td>Market regulatory quality</td><td>0.008</td><td>0.004</td><td>0.065</td><td>*</td></tr><tr><td>Constant</td><td>-5.347</td><td>3.151</td><td>0.092</td><td>*</td></tr><tr><td>R<sup>2</sup></td><td colspan="4">0.4804</td></tr><tr><td>AIC</td><td colspan="4">326.3714</td></tr><tr><td>BIC</td><td colspan="4">344.3951</td></tr><tr><td>N</td><td colspan="4">149</td></tr></tbody></table>

<footnote>*** p < 0.01, ** p < 0.05, * p < 0.10.</footnote>
# METHOD: MODEL STRUCTURE

## In Equation 2 (plastic waste per capita)

- We included the influence of:

- Income per capita (GDP per capita in $/year)

- Urbanisation (% of total population)

- International tourism on small islands (dummy variable [0, 1])

- Market regulatory quality (percentile rank)

<table><caption>Table 2. Linear regression model estimate for Plastic waste per capita (natural logarithm) (Eq. 2).</caption><thead><tr><th>Variable</th><th>Coefficient</th><th>Std. Error</th><th>p-Value</th><th></th></tr></thead><tbody><tr><td>LN (GDP per capita)</td><td>1.573</td><td>0.708</td><td>0.028</td><td>**</td></tr><tr><td>(LN (GDP per capita))<sup>2</sup></td><td>-0.080</td><td>0.039</td><td>0.041</td><td>**</td></tr><tr><td>Small islands</td><td>0.562</td><td>0.139</td><td>0.000</td><td>***</td></tr><tr><td>Urban population</td><td>0.012</td><td>0.003</td><td>0.001</td><td>***</td></tr><tr><td>Market regulatory quality</td><td>0.008</td><td>0.004</td><td>0.065</td><td>*</td></tr><tr><td>Constant</td><td>-5.347</td><td>3.151</td><td>0.092</td><td>*</td></tr><tr><td>R<sup>2</sup></td><td colspan="4">0.4804</td></tr><tr><td>AIC</td><td colspan="4">326.3714</td></tr><tr><td>BIC</td><td colspan="4">344.3951</td></tr><tr><td>N</td><td colspan="4">149</td></tr></tbody></table>

<footnote>*** p < 0.01, ** p < 0.05, * p < 0.10.</footnote>
# METHOD: MODEL STRUCTURE

## In Equation 3 (inadequately managed waste %)

- We included [corruption control policies](estimate from -2.5 to +2.5) following Biswas et al. (2012) who demonstrated controlling corruption reduces polluting emissions (Candau and Dienesch, 2017; Biswas et al., 2012; Milfont and Markowitz, 2016; Damania et al., 2003).

— Corruption = when the public power is used for private gain, including petty and grand forms of corruption and the State is captured by elites and private interests (definition from the World Bank).
# METHOD: MODEL STRUCTURE

## In Equation 3 (inadequately managed waste %)

- We included [corruption control policies](estimate from -2.5 to +2.5) following Biswas et al. (2012) who demonstrated controlling corruption reduces polluting emissions (Candau and Dienesch, 2017; Biswas et al., 2012; Milfont and Markowitz, 2016; Damania et al., 2003).

— Corruption = when the public power is used for private gain, including petty and grand forms of corruption and the State is captured by elites and private interests (definition from the World Bank).
# METHOD: MODEL STRUCTURE

In Equation 3<sub>bis</sub> (inadequately managed waste %)

- We included education (number of years spend at school per inhabitant) because:

- making informed pro-environmental choices is difficult if one has no knowledge (Gifford and Nilsson, 2014; Vicente-Molina et al., 2013; Hidalgo-Ruz et al., 2018).

- A minimum level of education is required to develop the ability to manage contrasting information, a skill that is central to ecological behaviors (Otto and Pensini, 2017).
# METHOD: MODEL STRUCTURE

In Equation 3<sub>bis</sub> (inadequately managed waste %)

- We included education (number of years spent at school per inhabitant) because:

- making informed pro-environmental choices is difficult if one has no knowledge (Gifford and Nilsson, 2014; Vicente-Molina et al., 2013; Hidalgo-Ruz et al., 2018).

- A minimum level of education is required to develop the ability to manage contrasting information, a skill that is central to ecological behaviors (Otto and Pensini, 2017).
# METHOD: MODEL STRUCTURE

## In Equation 3 and 3<sub>bis</sub>

* We also included other explanatory variables :

- GDP per capita (natural logarithm)

- International tourism on small islands (dummy variable [0, 1])

- Geographical variables for Middle East and African countries, and Latin American countries (dummy variables [0, 1])

<table><caption>Table 3. Logistic model estimate for inadequately managed waste percentage with a focus on corruption control policies (Eq. 3).</caption><thead><tr><th>Variable</th><th>Coefficient</th><th>Std. Error</th><th>p-Value</th></tr></thead><tbody><tr><td>LN (GDP per capita)</td><td>-1.159</td><td>0.410</td><td>0.005 ***</td></tr><tr><td>Corruption control policies</td><td>-1.244</td><td>0.562</td><td>0.027 **</td></tr><tr><td>Middle East and African countries</td><td>2.215</td><td>0.926</td><td>0.017 **</td></tr><tr><td>Latin American countries</td><td>3.057</td><td>0.885</td><td>0.001 ***</td></tr><tr><td>Small islands</td><td>-1.684</td><td>0.809</td><td>0.037 **</td></tr><tr><td>Constant</td><td>9.926</td><td>3.683</td><td>0.007 ***</td></tr><tr><td>Pseudo R<sup>2</sup></td><td colspan="3">0.5411</td></tr><tr><td>Log likelihood</td><td colspan="3">-38.8019</td></tr><tr><td>AIC</td><td colspan="3">89.6037</td></tr><tr><td>BIC</td><td colspan="3">106.4278</td></tr><tr><td>N</td><td colspan="3">122</td></tr></tbody></table>

<table><caption>Table 4. Logistic model estimate for inadequately managed waste percentage with a focus on education policies (Eq. 3<sub>bis</sub>).</caption><thead><tr><th>Variable</th><th>Coefficient</th><th>Std. Error</th><th>p-Value</th></tr></thead><tbody><tr><td>Years of school</td><td>-0.437</td><td>0.174</td><td>0.012 **</td></tr><tr><td>LN (GDP per capita)</td><td>-1.385</td><td>0.387</td><td>0.000 ***</td></tr><tr><td>Latin American countries</td><td>3.287</td><td>1.120</td><td>0.003 ***</td></tr><tr><td>Constant</td><td>16.179</td><td>3.717</td><td>0.000 ***</td></tr><tr><td>Pseudo R<sup>2</sup></td><td colspan="3">0.6140</td></tr><tr><td>Log likelihood</td><td colspan="3">-26.7485</td></tr><tr><td>AIC</td><td colspan="3">61.4971</td></tr><tr><td>BIC</td><td colspan="3">71.9177</td></tr><tr><td>N</td><td colspan="3">100</td></tr></tbody></table>

<footnote>*** p < 0.01, ** p < 0.05, * p < 0.10.</footnote>

<footnote>*** p < 0.01, ** p < 0.05, * p < 0.10.</footnote>
# METHOD: MODEL STRUCTURE

## In Equation 3 and 3<sub>bis</sub>

- We also included other explanatory variables :

- GDP per capita (natural logarithm)

- International tourism on small islands (dummy variable [0, 1])

- Geographical variables for Middle East and African countries, and Latin American countries (dummy variables [0, 1])

<table><caption>Table 3. Logistic model estimate for inadequately managed waste percentage with a focus on corruption control policies (Eq. 3).</caption><thead><tr><th>Variable</th><th>Coefficient</th><th>Std. Error</th><th>p-Value</th></tr></thead><tbody><tr><td>LN (GDP per capita)</td><td>-1.159</td><td>0.410</td><td>0.005 ***</td></tr><tr><td>Corruption control policies</td><td>-1.244</td><td>0.562</td><td>0.027 **</td></tr><tr><td>Middle East and African countries</td><td>2.215</td><td>0.926</td><td>0.017 **</td></tr><tr><td>Latin American countries</td><td>3.057</td><td>0.885</td><td>0.001 ***</td></tr><tr><td>Small islands</td><td>-1.684</td><td>0.809</td><td>0.037 **</td></tr><tr><td>Constant</td><td>9.926</td><td>3.683</td><td>0.007 ***</td></tr><tr><td>Pseudo R<sup>2</sup></td><td colspan="3">0.5411</td></tr><tr><td>Log likelihood</td><td colspan="3">-38.8019</td></tr><tr><td>AIC</td><td colspan="3">89.6037</td></tr><tr><td>BIC</td><td colspan="3">106.4278</td></tr><tr><td>N</td><td colspan="3">122</td></tr></tbody></table>

<table><caption>Table 4. Logistic model estimate for inadequately managed waste percentage with a focus on education policies (Eq. 3<sub>bis</sub>).</caption><thead><tr><th>Variable</th><th>Coefficient</th><th>Std. Error</th><th>p-Value</th></tr></thead><tbody><tr><td>Years of school</td><td>-0.437</td><td>0.174</td><td>0.012 **</td></tr><tr><td>LN (GDP per capita)</td><td>-1.385</td><td>0.387</td><td>0.000 ***</td></tr><tr><td>Latin American countries</td><td>3.287</td><td>1.120</td><td>0.003 ***</td></tr><tr><td>Constant</td><td>16.179</td><td>3.717</td><td>0.000 ***</td></tr><tr><td>Pseudo R<sup>2</sup></td><td colspan="3">0.6140</td></tr><tr><td>Log likelihood</td><td colspan="3">-26.7485</td></tr><tr><td>AIC</td><td colspan="3">61.4971</td></tr><tr><td>BIC</td><td colspan="3">71.9177</td></tr><tr><td>N</td><td colspan="3">100</td></tr></tbody></table>

<footnote>*** p < 0.01, ** p < 0.05, * p < 0.10.</footnote>

<footnote>*** p < 0.01, ** p < 0.05, * p < 0.10.</footnote>
# RESULTS BUSINESS-AS-USUAL SCENARIO (COUNTRY BY COUNTRY)

* The top 43 countries generating the highest amounts of inadequately managed plastic waste in 2017

* Estimations from Models 1 and 2 and comparison with observed data in 141 pays in 2011-2017.

<table><thead><tr><th colspan="6">Observed data from World Bank (2018a, 2019)</th><th colspan="4">BAU scenario (results from Model 2 except in the ranges where results from Models 1 and 2 are shown)</th></tr><tr><th>Country</th><th>Income category</th><th>Corruption control policy estimate</th><th>Years of schooling</th><th>Population (million)</th><th>Plastic waste generation rate (kg/person/yr)</th><th>% Inadequately managed waste</th><th>Inadequately managed plastic waste (MMT/yr)</th><th>Inadequately managed plastic waste (MMT/yr)</th></tr><tr><th>(Years) →</th><th>1996</th><th>2017</th><th>1995</th><th>2010</th><th>2017</th><th>2017</th><th>2017</th><th>2017</th></tr></thead><tbody><tr><td>1 India</td><td>LMC</td><td>-0.38</td><td>-0.24</td><td>3.51</td><td>5.39</td><td>20.2</td><td>79.6%</td><td>13.85 - 21.57</td><td>4.50 - 8.98</td></tr><tr><td>2 China</td><td>UMC</td><td>-0.27</td><td>-0.27</td><td>5.69</td><td>7.12</td><td>31.0</td><td>32.2%</td><td>12.38 - 13.82</td><td>1.71 - 10.05</td></tr><tr><td>3 Brazil</td><td>UMC</td><td>-0.02</td><td>-0.53</td><td>4.84</td><td>7.66</td><td>44.8</td><td>94.0%</td><td>8.64 - 8.75</td><td>5.11 - 8.66</td></tr><tr><td>4 Mexico</td><td>UMC</td><td>-0.51</td><td>-0.93</td><td>6.48</td><td>8.33</td><td>45.0</td><td>85.1%</td><td>4.78 - 5.29</td><td>1.80 - 7.46</td></tr><tr><td>5 Indonesia</td><td>LMC</td><td>-0.86</td><td>-0.25</td><td>4.21</td><td>7.26</td><td>30.1</td><td>53.7%</td><td>2.89 - 4.28</td><td>0.78 - 0.80</td></tr><tr><td>6 Pakistan</td><td>LMC</td><td>-1.22</td><td>-0.78</td><td>2.77</td><td>4.45</td><td>17.8</td><td>90.8%</td><td>2.77 - 3.37</td><td>3.46 - 3.49</td></tr><tr><td>7 Nigeria</td><td>LMC</td><td>-1.19</td><td>-1.07</td><td>N.A.</td><td>N.A.</td><td>19.4</td><td>80.5%</td><td>2.91 - 2.98</td><td>7.76 - 7.99</td></tr><tr><td>8 Bangladesh</td><td>LMC</td><td>-0.97</td><td>-0.83</td><td>3.29</td><td>4.91</td><td>15.5</td><td>92.1%</td><td>2.01 - 2.29</td><td>1.57 - 1.69</td></tr><tr><td>9 Colombia</td><td>UMC</td><td>-0.51</td><td>-0.37</td><td>6.09</td><td>8.45</td><td>46.6</td><td>92.8%</td><td>2.10 - 2.12</td><td>1.98 - 3.20</td></tr><tr><td>10 Argentina</td><td>HIC</td><td>-0.10</td><td>-0.26</td><td>8.34</td><td>9.48</td><td>44.3</td><td>79.4%</td><td>1.70 - 1.55</td><td>0.56 - 1.57</td></tr><tr><td>11 Vietnam</td><td>LMC</td><td>-0.49</td><td>-0.58</td><td>4.60</td><td>7.45</td><td>19.6</td><td>69.9%</td><td>1.16 - 1.30</td><td>0.23 - 0.67</td></tr><tr><td>12 Philippines</td><td>LMC</td><td>-0.36</td><td>-0.48</td><td>7.12</td><td>8.18</td><td>27.0</td><td>44.3%</td><td>1.26 - 1.54</td><td>0.15 - 1.16</td></tr><tr><td>13 Peru</td><td>UMC</td><td>-0.40</td><td>-0.50</td><td>7.25</td><td>8.68</td><td>45.5</td><td>86.8%</td><td>1.24 - 1.34</td><td>0.32 - 1.75</td></tr><tr><td>14 Egypt</td><td>LMC</td><td>-0.47</td><td>-0.54</td><td>4.05</td><td>6.55</td><td>19.7</td><td>53.8%</td><td>1.02 - 1.68</td><td>0.13 - 2.29</td></tr><tr><td>15 Ethiopia</td><td>LIC</td><td>-0.93</td><td>-0.56</td><td>N.A.</td><td>N.A.</td><td>9.6</td><td>97.6%</td><td>0.90 - 1.00</td><td>0.92 - 1.20</td></tr><tr><td>16 Morocco</td><td>LMC</td><td>-0.11</td><td>-0.13</td><td>2.66</td><td>4.24</td><td>29.8</td><td>84.6%</td><td>0.90 - 0.93</td><td>0.53 - 1.45</td></tr><tr><td>17 Chile</td><td>HIC</td><td>1.45</td><td>1.04</td><td>8.40</td><td>9.71</td><td>61.2</td><td>72.3%</td><td>0.59 - 0.82</td><td>0.24 - 0.44</td></tr><tr><td>18 Venezuela</td><td>UMC</td><td>-0.86</td><td>-1.36</td><td>5.5</td><td>8.16</td><td>31.2</td><td>89.1%</td><td>0.82 - 0.88</td><td>0.38 - 1.09</td></tr><tr><td>19 Turkey</td><td>UMC</td><td>-0.15</td><td>-0.19</td><td>4.81</td><td>6.56</td><td>40.4</td><td>24.4%</td><td>0.56 - 0.80</td><td>0.08 - 0.17</td></tr><tr><td>20 Europe - 28<br/>[...]</td><td>HIC</td><td>1.18</td><td>1.09</td><td>9.13</td><td>11.23</td><td>49.7</td><td>3.1%</td><td>0.80 - 1.01</td><td>0.07 - 1.13</td></tr></tbody><tfoot><tr><td>Total 43 countries</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td>77.3 - 86.9</td><td>49.6 - 90.9</td></tr><tr><td>Total world (217 countries)</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td>91.0 - 95.4</td><td>61.2 - 110.2</td></tr></tfoot></table>

<figure><img src="image_7.png" /></figure>

<figure><img src="image_8.png" /></figure>

GDP per capita (PPP, constant 2011 international $/person) (natural log)
# RESULTS BUSINESS-AS-USUAL SCENARIO (COUNTRY BY COUNTRY)

* The top 43 countries generating the highest amounts of inadequately managed plastic waste in 2017

* Estimations from Models 1 and 2 and comparison with observed data in 141 pays in 2011-2017.

<table><thead><tr><th colspan="6">Observed data from World Bank (2018a, 2019)</th><th colspan="4">BAU scenario (results from Model 2 except in the ranges where results from Models 1 and 2 are shown)</th></tr><tr><th>Country</th><th>Income category</th><th>Corruption control policy estimate</th><th>Years of schooling</th><th>Population (million)</th><th>Plastic waste generation rate (kg/person/yr)</th><th>% Inadequately managed waste</th><th>Inadequately managed plastic waste (MMT/yr)</th><th>Inadequately managed plastic waste (MMT/yr)</th></tr><tr><th>(Years) →</th><th>1996</th><th>2017</th><th>1995</th><th>2010</th><th>2017</th><th>2017</th><th>2017</th><th>2050</th></tr></thead><tbody><tr><td>1 India</td><td>LMC</td><td>-0.38</td><td>-0.24</td><td>3.51</td><td>5.39</td><td>20.2</td><td>79.6%</td><td>13.85 - 21.57</td><td>4.50 - 8.98</td></tr><tr><td>2 China</td><td>UMC</td><td>-0.27</td><td>-0.27</td><td>5.69</td><td>7.12</td><td>31.0</td><td>32.2%</td><td>12.38 - 13.82</td><td>1.71 - 10.05</td></tr><tr><td>3 Brazil</td><td>UMC</td><td>-0.02</td><td>-0.53</td><td>4.84</td><td>7.66</td><td>44.8</td><td>94.0%</td><td>8.64 - 8.75</td><td>5.11 - 8.66</td></tr><tr><td>4 Mexico</td><td>UMC</td><td>-0.51</td><td>-0.93</td><td>6.48</td><td>8.33</td><td>45.0</td><td>85.1%</td><td>4.78 - 5.29</td><td>1.80 - 7.46</td></tr><tr><td>5 Indonesia</td><td>LMC</td><td>-0.86</td><td>-0.25</td><td>4.21</td><td>7.26</td><td>30.1</td><td>53.7%</td><td>2.89 - 4.28</td><td>0.78 - 0.80</td></tr><tr><td>6 Pakistan</td><td>LMC</td><td>-1.22</td><td>-0.78</td><td>2.77</td><td>4.45</td><td>17.8</td><td>90.8%</td><td>2.77 - 3.37</td><td>3.46 - 3.49</td></tr><tr><td>7 Nigeria</td><td>LMC</td><td>-1.19</td><td>-1.07</td><td>N.A.</td><td>N.A.</td><td>19.4</td><td>80.5%</td><td>2.91 - 2.98</td><td>7.76 - 7.99</td></tr><tr><td>8 Bangladesh</td><td>LMC</td><td>-0.97</td><td>-0.83</td><td>3.29</td><td>4.91</td><td>15.5</td><td>92.1%</td><td>2.01 - 2.29</td><td>1.57 - 1.69</td></tr><tr><td>9 Colombia</td><td>UMC</td><td>-0.51</td><td>-0.37</td><td>6.09</td><td>8.45</td><td>46.6</td><td>92.8%</td><td>2.10 - 2.12</td><td>1.98 - 3.20</td></tr><tr><td>10 Argentina</td><td>HIC</td><td>-0.10</td><td>-0.26</td><td>8.34</td><td>9.48</td><td>44.3</td><td>79.4%</td><td>1.70 - 1.55</td><td>0.56 - 1.57</td></tr><tr><td>11 Vietnam</td><td>LMC</td><td>-0.49</td><td>-0.58</td><td>4.60</td><td>7.45</td><td>19.6</td><td>69.9%</td><td>1.16 - 1.30</td><td>0.23 - 0.67</td></tr><tr><td>12 Philippines</td><td>LMC</td><td>-0.36</td><td>-0.48</td><td>7.12</td><td>8.18</td><td>27.0</td><td>44.3%</td><td>1.26 - 1.54</td><td>0.15 - 1.16</td></tr><tr><td>13 Peru</td><td>UMC</td><td>-0.40</td><td>-0.50</td><td>7.25</td><td>8.68</td><td>45.5</td><td>86.8%</td><td>1.24 - 1.34</td><td>0.32 - 1.75</td></tr><tr><td>14 Egypt</td><td>LMC</td><td>-0.47</td><td>-0.54</td><td>4.05</td><td>6.55</td><td>19.7</td><td>53.8%</td><td>1.02 - 1.68</td><td>0.13 - 2.29</td></tr><tr><td>15 Ethiopia</td><td>LIC</td><td>-0.93</td><td>-0.56</td><td>N.A.</td><td>N.A.</td><td>9.6</td><td>97.6%</td><td>0.90 - 1.00</td><td>0.92 - 1.20</td></tr><tr><td>16 Morocco</td><td>LMC</td><td>-0.11</td><td>-0.13</td><td>2.66</td><td>4.24</td><td>29.8</td><td>84.6%</td><td>0.90 - 0.93</td><td>0.53 - 1.45</td></tr><tr><td>17 Chile</td><td>HIC</td><td>1.45</td><td>1.04</td><td>8.40</td><td>9.71</td><td>61.2</td><td>72.3%</td><td>0.59 - 0.82</td><td>0.24 - 0.44</td></tr><tr><td>18 Venezuela</td><td>UMC</td><td>-0.86</td><td>-1.36</td><td>5.5</td><td>8.16</td><td>31.2</td><td>89.1%</td><td>0.82 - 0.88</td><td>0.38 - 1.09</td></tr><tr><td>19 Turkey</td><td>UMC</td><td>-0.15</td><td>-0.19</td><td>4.81</td><td>6.56</td><td>40.4</td><td>24.4%</td><td>0.56 - 0.80</td><td>0.08 - 0.17</td></tr><tr><td>20 Europe - 28<br/>[...]</td><td>HIC</td><td>1.18</td><td>1.09</td><td>9.13</td><td>11.23</td><td>49.7</td><td>3.1%</td><td>0.80 - 1.01</td><td>0.07 - 1.13</td></tr></tbody><tfoot><tr><td>Total 43 countries</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td>77.3 - 86.9</td><td>49.6 - 90.9</td></tr><tr><td>Total world (217 countries)</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td>91.0 - 95.4</td><td>61.2 - 110.2</td></tr></tfoot></table>

<figure><img src="image_8.png" /></figure>

<figure><img src="image_9.png" /><figcaption>GDP per capita (PPP, constant 2011 international $/person) (natural log)</figcaption></figure>
RESULTS: CONTROLLING CORRUPTION SCENARIO

- In the top 43 countries discarding the highest amounts of inadequately managed plastic waste, corruption & lobby control policies are raised closer to the level of France and Estonia in 2017

<figure><img src="image_9.png" /><figcaption>Annual plastic waste inadequately managed globally (MMT/year)</figcaption></figure>

<figure><img src="image_10.png" /></figure>

60% reduction
RESULTS: CONTROLLING CORRUPTION SCENARIO

- In the top 43 countries discarding the highest amounts of inadequately managed plastic waste, corruption & lobby control policies are raised closer to the level of France and Estonia in 2017

<figure><img src="image_10.png" /><figcaption>Annual plastic waste inadequately managed globally (MMT/year)</figcaption></figure>

<figure><img src="image_11.png" /></figure>

60% reduction
RESULTS: EDUCATION SOLUTION SCENARIO

- In the top 43 countries, the number of schooling years for individuals ≥ 25 years old is increased to 12 years

<figure><img src="image_11.png" /><figcaption>Annual plastic waste inadequately managed globally (MMT/year)</figcaption></figure>
RESULTS: EDUCATION SOLUTION SCENARIO

- In the top 43 countries, the number of schooling years for individuals ≥ 25 years old is increased to 12 years

<figure><img src="image_12.png" /><figcaption>Annual plastic waste inadequately managed globally (MMT/year)</figcaption></figure>
CONCLUSION

* Our model simulations show the **growth of GDP per capita** in the BAU scenario will not create sufficient investment capacity in waste treatment infrastructures to resolve plastic waste contamination of the ecosystems by 2050.

* Non-technological solutions such as **corruption & lobby control policies** as well as **education** are able to reduce inadequately managed plastic waste. They must be part of implemented interventions.

<figure><img src="image_12.png" /><figcaption>Base 100 = value of 1996</figcaption></figure>

<figure><img src="image_13.png" /><figcaption>World GDP over 1990-2050 (in PPP constant 2011 international 10¹² $)</figcaption></figure>
## CONCLUSION

* Our model simulations show the **growth of GDP per capita** in the BAU scenario will not create sufficient investment capacity in waste treatment infrastructures to resolve plastic waste contamination of the ecosystems by 2050.

* Non-technological solutions such as **corruption & lobby control policies** as well as **education** are able to reduce inadequately managed plastic waste. They must be part of implemented interventions.

<figure><img src="image_13.png" /><figcaption>Base 100 = value of 1996</figcaption></figure>

<figure><img src="image_14.png" /><figcaption>World GDP over 1990-2050 (in PPP constant 2011 international 10¹² $)</figcaption></figure>
Plastic pollution and economic growth: the influence of
corruption and the lack of education

Preprint · May 2020

DOI: 10.13140/RG.2.2.23198.97601

ResearchGate

READS

673

4 authors:

<figure><img src="image_14.png" /><figcaption>Mateo Cordier</figcaption></figure>

Université de Versailles Saint-Quentin

<figure><img src="image_15.png" /><figcaption>Juan Baztan</figcaption></figure>

Université de Versailles Saint-Quentin

<figure><img src="image_16.png" /><figcaption>Takuro Uehara</figcaption></figure>

Ritsumeikan University

<figure><img src="image_17.png" /><figcaption>Bethany Jorgensen</figcaption></figure>

Cornell University

THANK YOU FOR LISTENING

More information is available here:

https://www.researchgate.net/publication/341258750_Plastic_pollution_and_economic_growth_the_influence_of_corruption_and_the_lack_of_education
Plastic pollution and economic growth: the influence of
corruption and the lack of education

Preprint · May 2020

DOI: 10.13140/RG.2.2.23198.97601

ResearchGate

READS

673

4 authors:

<figure><img src="image_15.png" /><figcaption>Mateo Cordier</figcaption></figure>

Université de Versailles Saint-Quentin

<figure><img src="image_16.png" /><figcaption>Juan Baztan</figcaption></figure>

Université de Versailles Saint-Quentin

<figure><img src="image_17.png" /><figcaption>Takuro Uehara</figcaption></figure>

Ritsumeikan University

<figure><img src="image_18.png" /><figcaption>Bethany Jorgensen</figcaption></figure>

Cornell University

THANK YOU FOR LISTENING

More information is available here:

https://www.researchgate.net/publication/341258750_Plastic_pollution_and_economic_growth_the_influence_of_corruption_and_the_lack_of_education