# Is there an empirical environmental Kuznets curve relationship between mismanaged plastic waste per capita and income per capita? A caveat

Takuro Uehara, Mateo Cordier

► To cite this version:

Takuro Uehara, Mateo Cordier. Is there an empirical environmental Kuznets curve relationship between mismanaged plastic waste per capita and income per capita? A caveat. 2019. hal-04566056

HAL Id: hal-04566056

https://hal.science/hal-04566056

Preprint submitted on 2 May 2024

HAL is a multi-disciplinary open access
archive for the deposit and dissemination of sci-
entific research documents, whether they are pub-
lished or not. The documents may come from
teaching and research institutions in France or
abroad, or from public or private research centers.

L'archive ouverte pluridisciplinaire HAL, est
destinée au dépôt et à la diffusion de documents
scientifiques de niveau recherche, publiés ou non,
émanant des établissements d'enseignement et de
recherche français ou étrangers, des laboratoires
publics ou privés.

<figure><img src="image_1.png" /></figure>

Distributed under a Creative Commons Attribution 4.0 International License
1 Is there an empirical environmental Kuznets curve
2 relationship between mismanaged plastic waste per capita
3 and income per capita? A caveat

4
5 Takuro Uehara ¹,* and Mateo Cordier ²

6
7 College of Policy Science, Ritsumeikan University, 2-150 Iwakura-Cho, Ibaraki City, 567-
8570 Osaka, Japan; takuro@fc.ritsumei.ac.jp

8
9
10 Research Centre Cultures-Environnements-Arctique-Représentations-Climat (CEARC), Université de Versailles-Saint-Quentin-en-Yvelines, UVSQ, 11 Boulevard d'Alembert, 78280 Guyancourt, France; mateo.cordier@uvsq.fr

11
12 * Correspondence: takuro@fc.ritsumei.ac.jp; Tel.: +81-754663347 (T.U.)

13 Abstract

14 A study by Barnes (2019) concluded that there exists an empirical environmental Kuznets curve
15 (EKC) relationship between mismanaged plastic waste per capita and income per capita.
16 However, this result needs careful interpretation. The study adopted data that used the World
17 Bank database to compute mismanaged plastic waste amounts. Because data to compute them
18 were not available for all countries, missing data were estimated by relating them to economic
19 classification (i.e., income level). In other words, the data used for the analysis by Barnes simply
20 assumed—without scientific validation—that mismanaged plastic waste amounts are related to
21 economic classification (i.e., income level).

22
23 **Keywords:** Environmental Kuznets curve; plastic waste; income level

24
25 A study by Barnes (2019) concluded that there exists an empirical environmental Kuznets curve
26 (EKC) relationship between mismanaged plastic waste per capita and income per capita by using
27 data created by Jambeck et al. (2015). However, the finding needs careful interpretation because
28 not all the data for mismanaged plastic waste were computed using raw data. A substantial
29 amount of data were estimated assuming that there exists a relationship between economic
30 classifications based on national income per capita (HIC = high income; UMI = upper middle
31 income; LMI = lower middle income; LI = low income) and variables computing mismanaged
32 plastic waste when the raw data were not available (Jambeck et al., 2015). In other words, the
33 data Barnes (2019) used were created assuming that income level explains the degree of
34 mismanaged plastic waste per capita.

35
36 Mismanaged plastic waste per capita for country i (i = 1, ..., 192) was computed by Jambeck et al. (2015) as follows:
1 Is there an empirical environmental Kuznets curve
2 relationship between mismanaged plastic waste per capita
3 and income per capita? A caveat

4
5 Takuro Uehara ¹,* and Mateo Cordier ²

6
7 College of Policy Science, Ritsumeikan University, 2-150 Iwakura-Cho, Ibaraki City, 567-
8570 Osaka, Japan; takuro@fc.ritsumei.ac.jp

8
9
10 Research Centre Cultures-Environnements-Arctique-Représentations-Climat (CEARC), Université de Versailles-Saint-Quentin-en-Yvelines, UVSQ, 11 Boulevard d'Alembert, 78280 Guyancourt, France; mateo.cordier@uvsq.fr

11
12 * Correspondence: takuro@fc.ritsumei.ac.jp; Tel.: +81-754663347 (T.U.)

13 Abstract

14 A study by Barnes (2019) concluded that there exists an empirical environmental Kuznets curve
15 (EKC) relationship between mismanaged plastic waste per capita and income per capita.
16 However, this result needs careful interpretation. The study adopted data that used the World
17 Bank database to compute mismanaged plastic waste amounts. Because data to compute them
18 were not available for all countries, missing data were estimated by relating them to economic
19 classification (i.e., income level). In other words, the data used for the analysis by Barnes simply
20 assumed—without scientific validation—that mismanaged plastic waste amounts are related to
21 economic classification (i.e., income level).

22
23 **Keywords:** Environmental Kuznets curve; plastic waste; income level

24
25 A study by Barnes (2019) concluded that there exists an empirical environmental Kuznets curve
26 (EKC) relationship between mismanaged plastic waste per capita and income per capita by using
27 data created by Jambeck et al. (2015). However, the finding needs careful interpretation because
28 not all the data for mismanaged plastic waste were computed using raw data. A substantial
29 amount of data were estimated assuming that there exists a relationship between economic
30 classifications based on national income per capita (HIC = high income; UMI = upper middle
31 income; LMI = lower middle income; LI = low income) and variables computing mismanaged
32 plastic waste when the raw data were not available (Jambeck et al., 2015). In other words, the
33 data Barnes (2019) used were created assuming that income level explains the degree of
34 mismanaged plastic waste per capita.

35
36 Mismanaged plastic waste per capita for country i (i = 1, ..., 192) was computed by Jambeck et al. (2015) as follows:
$$ 
\begin{array}{l}
37 \quad Mismanaged plastic waste_i/Coastal population_i = Waste generation rate_i \times \\
38 \quad percentage of plastic in the waste stream_i \times \\
39 \quad (percentage of inadequately managed waste_i + percentage of littered waste) \quad (1)
\end{array}
$$

40 For countries whose raw data were not available to compute equation (1), Jambeck et al. (2015) devised their own estimates based on the following four assumptions. First, for 84 of the 192 coastal countries, they applied average values of waste generation rate for each economic classification based upon gross national income per capita. Second, for 122 of the 192 coastal countries that lacked the data for percentage of plastic in the waste stream, they applied the same method, albeit based upon economic classifications. Third, for 111 of the 192 coastal countries, the percentage of inadequately managed waste was estimated by applying a logistic regression model. The best model, based on Akaike's Information Criterion (AIC) score, includes economic classifications and geographic information as explanatory variables. Fourth, although the model shows a statistically significant relationship between percentage of inadequately managed waste and economic classifications, the computations of the data for the dependent variable (i.e., the probability of inadequate waste management) were based on economic classifications. That is, the data for 81 coastal countries in the model estimation were processed using their respective economic classifications (e.g., landfills in low-income countries are considered to be inadequately managed).

55 We investigated how the relationship between the percentage of inadequately managed waste and income per capita varies depending on how the percentage of inadequately managed waste is computed. As the data used by Barnes (2019) were not publicly accessible, we used the data provided by Jambeck et al. (2015) and the World Bank (2018) instead. The percentage of inadequately managed waste is computed by combining multiple waste disposal methods, as shown in Table 1. To make the comparison, we created five different rules to compute the percentage of inadequately managed waste, as seen in Table 1. Rule 1 does not make any adjustment based upon the economic classification, whereas the remainder of the rules apply different assumptions regarding the relationship between waste disposal methods and economic classification. For the five different rules, we used the data provided by the World Bank (2018), which are more recent (mostly containing data from 2011–2017), while Barnes (2019) used data from 2010. It should be clearly noted that we do not claim which assumed rule best captures the actual percentage of inadequately managed waste. These rules are just examples to show how the rule chosen to build the data for the percentage of inadequately managed waste impacts the data's relationship with gross domestic product (GDP) per capita.

70 As shown in Table 1, the results of Barnes (2019) show the largest negative coefficient (−0.7275), indicating the strongest negative relationship between percentage of inadequately managed waste and GDP per capita. Rule 1, which does not use economic classifications, has the weakest negative correlation among alternatives (−0.3416). Moreover, it shows that the correlation varies from −0.3416 to −0.7275 depending on the assumption made.

75 Table 1. Barnes (2019) and five alternative aggregation rules to estimate the percentage of inadequately managed wastes, and Pearson's correlation coefficients for GDP per capita and various percentages of inadequately managed waste.
$$ 
\begin{array}{l}
37 \quad Mismanaged plastic waste_i/Coastal population_i = Waste generation rate_i \times \\
38 \quad percentage of plastic in the waste stream_i \times \\
39 \quad (percentage of inadequately managed waste_i + percentage of littered waste) \quad (1)
\end{array}
$$

40 For countries whose raw data were not available to compute equation (1), Jambeck et al. (2015) devised their own estimates based on the following four assumptions. First, for 84 of the 192 coastal countries, they applied average values of waste generation rate for each economic classification based upon gross national income per capita. Second, for 122 of the 192 coastal countries that lacked the data for percentage of plastic in the waste stream, they applied the same method, albeit based upon economic classifications. Third, for 111 of the 192 coastal countries, the percentage of inadequately managed waste was estimated by applying a logistic regression model. The best model, based on Akaike's Information Criterion (AIC) score, includes economic classifications and geographic information as explanatory variables. Fourth, although the model shows a statistically significant relationship between percentage of inadequately managed waste and economic classifications, the computations of the data for the dependent variable (i.e., the probability of inadequate waste management) were based on economic classifications. That is, the data for 81 coastal countries in the model estimation were processed using their respective economic classifications (e.g., landfills in low-income countries are considered to be inadequately managed).

55 We investigated how the relationship between the percentage of inadequately managed waste and income per capita varies depending on how the percentage of inadequately managed waste is computed. As the data used by Barnes (2019) were not publicly accessible, we used the data provided by Jambeck et al. (2015) and the World Bank (2018) instead. The percentage of inadequately managed waste is computed by combining multiple waste disposal methods, as shown in Table 1. To make the comparison, we created five different rules to compute the percentage of inadequately managed waste, as seen in Table 1. Rule 1 does not make any adjustment based upon the economic classification, whereas the remainder of the rules apply different assumptions regarding the relationship between waste disposal methods and economic classification. For the five different rules, we used the data provided by the World Bank (2018), which are more recent (mostly containing data from 2011–2017), while Barnes (2019) used data from 2010. It should be clearly noted that we do not claim which assumed rule best captures the actual percentage of inadequately managed waste. These rules are just examples to show how the rule chosen to build the data for the percentage of inadequately managed waste impacts the data’s relationship with gross domestic product (GDP) per capita.

70 As shown in Table 1, the results of Barnes (2019) show the largest negative coefficient (−0.7275), indicating the strongest negative relationship between percentage of inadequately managed waste and GDP per capita. Rule 1, which does not use economic classifications, has the weakest negative correlation among alternatives (−0.3416). Moreover, it shows that the correlation varies from −0.3416 to −0.7275 depending on the assumption made.

75 Table 1. Barnes (2019) and five alternative aggregation rules to estimate the percentage of inadequately managed wastes, and Pearson’s correlation coefficients for GDP per capita and various percentages of inadequately managed waste.
<table><thead><tr><th></th><th>Barnes<br>(2019), based<br>upon<br>Jambeck et al.<br>(2015)</th><th>Rule 1</th><th>Rule 2</th><th>Rule 3</th><th>Rule 4</th><th>Rule 5</th></tr></thead><tbody><tr><td>Anaerobic<br>digestion</td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Compost<br>(percent)</td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Controlled<br>landfill</td><td>X (LIC<br>countries)</td><td>X (LIC<br>countries)</td><td>X (LIC,<br>LMC, UMC<br>countries)</td><td>X (LIC,<br>LMC<br>countries)</td><td>X (LIC,<br>LMC<br>countries)</td><td>X (LIC<br>countries)</td></tr><tr><td>Incineration</td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Landfill<br>(unspecified)</td><td>X (LIC<br>countries)</td><td>X (LIC<br>countries)</td><td>X (LIC,<br>LMC, UMC<br>countries)</td><td>X (LIC,<br>LMC<br>countries)</td><td>X (LIC,<br>LMC<br>countries)</td><td>X (LIC<br>countries)</td></tr><tr><td>Open dump</td><td>X (All<br>countries)</td><td>X (All<br>countries)</td><td>X (All<br>countries)</td><td>X (All<br>countries)</td><td>X (All<br>countries)</td><td>X (All<br>countries)</td></tr><tr><td>Other</td><td>(Weighting<br>factor for all<br>countries) *</td><td>X (LIC,<br>LMC, UMC<br>countries)</td><td>X (LIC,<br>LMC, UMC<br>countries)</td><td>X (LIC,<br>LMC, UMC<br>countries)</td><td>X (LIC,<br>LMC, UMC<br>countries)</td><td>X (All<br>countries)</td></tr><tr><td>Recycling</td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Sanitary<br>landfill, landfill<br>gas system</td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Unaccounted<br>for</td><td>(Weighting<br>factor for all<br>countries) *</td><td>X (LIC,<br>LMC, UMC<br>countries)</td><td>X (LIC,<br>LMC, UMC<br>countries)</td><td>X (LIC,<br>LMC, UMC<br>countries)</td><td>X (LIC,<br>LMC, UMC<br>countries)</td><td>X (All<br>countries)</td></tr><tr><td>Discarded in<br>waterways and<br>at sea</td><td>X (All<br>countries)</td><td>X (All<br>countries)</td><td>X (All<br>countries)</td><td>X (All<br>countries)</td><td>X (All<br>countries)</td><td>X (All<br>countries)</td></tr><tr><td>Correlation coefficient</td><td>-0.7275</td><td>-0.3416</td><td>-0.5504</td><td>-0.6987</td><td>-0.5978</td><td>-0.5022</td></tr><tr><td>p-value</td><td>&lt; 0.001</td><td>&lt; 0.001</td><td>&lt; 0.001</td><td>&lt; 0.001</td><td>&lt; 0.001</td><td>&lt; 0.001</td></tr><tr><td>N</td><td>140</td><td>161</td><td>161</td><td>161</td><td>161</td><td>161</td></tr></tbody></table>

Note: HIC = high income; UMI = upper middle income; LMI = lower middle income; LI = low income

Empty cells correspond to adequately managed waste disposal methods.

* In some cases, the “Other” and “Unaccounted for” categories of waste disposal methods account for as much as 94% of the total reported fates, although the median share of the reported fates in the “Other” category was 0.015%. Jambeck et al. (2015) accounted for this by using the ratios of waste in the “Other” and “Unaccounted for” categories to the total waste as weights for the data in the regression, thus down-weighting data where there was significant uncertainty with respect to fate.

In conclusion, all three variables—excluding percentage of littered waste, which is assumed to be 2% for all countries—apply economic classifications as assumptions to estimate the missing data. As Jambeck et al. (2015) did not assert that these assumptions have been empirically tested,
<table><thead><tr><th></th><th>Barnes<br>(2019), based<br>upon<br>Jambeck et al.<br>(2015)</th><th>Rule 1</th><th>Rule 2</th><th>Rule 3</th><th>Rule 4</th><th>Rule 5</th></tr></thead><tbody><tr><td>Anaerobic<br>digestion</td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Compost<br>(percent)</td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Controlled<br>landfill</td><td>X (LIC<br>countries)</td><td>X (LIC<br>countries)</td><td>X (LIC,<br>LMC, UMC<br>countries)</td><td>X (LIC,<br>LMC<br>countries)</td><td>X (LIC,<br>LMC<br>countries)</td><td>X (LIC<br>countries)</td></tr><tr><td>Incineration</td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Landfill<br>(unspecified)</td><td>X (LIC<br>countries)</td><td>X (LIC<br>countries)</td><td>X (LIC,<br>LMC, UMC<br>countries)</td><td>X (LIC,<br>LMC<br>countries)</td><td>X (LIC,<br>LMC<br>countries)</td><td>X (LIC<br>countries)</td></tr><tr><td>Open dump</td><td>X (All<br>countries)</td><td>X (All<br>countries)</td><td>X (All<br>countries)</td><td>X (All<br>countries)</td><td>X (All<br>countries)</td><td>X (All<br>countries)</td></tr><tr><td>Other</td><td>(Weighting<br>factor for all<br>countries) *</td><td>X (LIC,<br>LMC, UMC<br>countries)</td><td>X (LIC,<br>LMC, UMC<br>countries)</td><td>X (LIC,<br>LMC, UMC<br>countries)</td><td>X (LIC,<br>LMC, UMC<br>countries)</td><td>X (All<br>countries)</td></tr><tr><td>Recycling</td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Sanitary<br>landfill, landfill<br>gas system</td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Unaccounted<br>for</td><td>(Weighting<br>factor for all<br>countries) *</td><td>X (LIC,<br>LMC, UMC<br>countries)</td><td>X (LIC,<br>LMC, UMC<br>countries)</td><td>X (LIC,<br>LMC, UMC<br>countries)</td><td>X (LIC,<br>LMC, UMC<br>countries)</td><td>X (All<br>countries)</td></tr><tr><td>Discarded in<br>waterways and<br>at sea</td><td>X (All<br>countries)</td><td>X (All<br>countries)</td><td>X (All<br>countries)</td><td>X (All<br>countries)</td><td>X (All<br>countries)</td><td>X (All<br>countries)</td></tr><tr><td>Correlation coefficient</td><td>-0.7275</td><td>-0.3416</td><td>-0.5504</td><td>-0.6987</td><td>-0.5978</td><td>-0.5022</td></tr><tr><td>p-value</td><td>&lt; 0.001</td><td>&lt; 0.001</td><td>&lt; 0.001</td><td>&lt; 0.001</td><td>&lt; 0.001</td><td>&lt; 0.001</td></tr><tr><td>N</td><td>140</td><td>161</td><td>161</td><td>161</td><td>161</td><td>161</td></tr></tbody></table>

Note: HIC = high income; UMI = upper middle income; LMI = lower middle income; LI = low income

Empty cells correspond to adequately managed waste disposal methods.

* In some cases, the “Other” and “Unaccounted for” categories of waste disposal methods account for as much as 94% of the total reported fates, although the median share of the reported fates in the “Other” category was 0.015%. Jambeck et al. (2015) accounted for this by using the ratios of waste in the “Other” and “Unaccounted for” categories to the total waste as weights for the data in the regression, thus down-weighting data where there was significant uncertainty with respect to fate.

In conclusion, all three variables—excluding percentage of littered waste, which is assumed to be 2% for all countries—apply economic classifications as assumptions to estimate the missing data. As Jambeck et al. (2015) did not assert that these assumptions have been empirically tested,
they remain just that—assumptions. In other words, the use of economic classifications to fill in
the missing data very likely does not serve the purpose at hand. We do not claim that these
assumptions are wrong, but we do suggest that they have not been tested robustly and
empirically. In addition, these assumptions can result in significant differences, as shown in
Table 1. Therefore, we believe that it is crucial to interpret the EKC created by Barnes (2019)
with caution as it is based on data derived by assuming relationships between economic
classifications and three variables on mismanaged plastic waste per capita. Thus, there is
adequate room to revisit and empirically test Barnes’ EKC hypothesis.

97

98

References

Barnes, S. J., 2019. Understanding plastics pollution: The role of economic development and technological research. Environ. Pollut. 249, 812–821.

Jambeck, J. R., Geyer, R., Wilcox, C., Siegler, T. R., Perryman, M., Andrady, A., Narayan, R.,
Law, K. L., 2015. Plastic waste inputs from land into the ocean. Science 347, 768–771.

World Bank, 2018. “What a Waste Global Database”. Available at:
https://datacatalog.worldbank.org/dataset/what-waste-global-database. Accessed on July
19, 2019.
they remain just that—assumptions. In other words, the use of economic classifications to fill in
the missing data very likely does not serve the purpose at hand. We do not claim that these
assumptions are wrong, but we do suggest that they have not been tested robustly and
empirically. In addition, these assumptions can result in significant differences, as shown in
Table 1. Therefore, we believe that it is crucial to interpret the EKC created by Barnes (2019)
with caution as it is based on data derived by assuming relationships between economic
classifications and three variables on mismanaged plastic waste per capita. Thus, there is
adequate room to revisit and empirically test Barnes’ EKC hypothesis.

97

98

References

Barnes, S. J., 2019. Understanding plastics pollution: The role of economic development and technological research. Environ. Pollut. 249, 812–821.

Jambeck, J. R., Geyer, R., Wilcox, C., Siegler, T. R., Perryman, M., Andrady, A., Narayan, R.,
Law, K. L., 2015. Plastic waste inputs from land into the ocean. Science 347, 768–771.

World Bank, 2018. “What a Waste Global Database”. Available at:
https://datacatalog.worldbank.org/dataset/what-waste-global-database. Accessed on July
19, 2019.