Traded plastic, traded impacts? Designing counterfactual scenarios to assess environmental impacts of global plastic waste trade

The corresponding paper has been published in Environmental Science & Technology (https://pubs.acs.org/doi/10.1021/acs.est.4c02149)

Please cite the code via this link: 

 Li, K.; Ward, H.; Lin, H. X.; Tukker, A. Traded Plastic, Traded Impacts? Designing Counterfactual Scenarios to Assess Environmental Impacts of Global Plastic Waste Trade. Environmental Science & Technology 2024, 58 (20), 8631-8642. DOI: 10.1021/acs.est.4c02149..

code_md_z.py: The Python script for calculating results and plotting. 

1. Import packages
2. Functions and lists defined
3. Retrieve bilateral trade of 2013-2022 from the UN Comtrade database
4. Read original data 2013-2022
5. Drop "TOTAL MOT" in the reporter country if other MOT exist
6. Replace 0 or nan netweight if other weight metrics(altQty or qty) exist
7. Replace zero and empty netweight values from similar trade
8. Average only: drop "TOTAL MOT" and repeated trade from the partner
9. Transfer X into M (exchange the position of reporter and partner)
10. Research country selection and trade share
11. Build common transport modes for 26 research countries
12. Use common transport modes to replace unspecified modes
13. Life cycle inventory of transport
14. Treatment environmental impacts
15. Plots

All the supporting pickle files and Excel files:

df10.pkl: The dataframe of original trade data retrieved from the UN COMTRADE database spanning 2013 to 2022.

lst2_model.pkl:  The list of dataframes including trade data after dropping redundant transport modes. 

lst5_model.pkl: The list of dataframes including trade data after replacing trade with 0 or nan net weight and averaging from the repeated trade. 

df_3.pkl: The dataframe of selected research countries after ranking them during 2020-2022. 

df_td: The dataframe of the common transport modes among research countries aiming to replace unspecified transport modes in trade data. 

lst9.pkl: The list of dataframes of trade data after replacing the unspecified transport modes. 

CERDI-seadistance.xlsx: The Excel file of the CERDI-seadistance database sourced from https://ferdi.fr/en/indicators/the-cerdi-seadistance-database.

df_ship.pkl: The dataframe of sea, air and road distance between research countries (sea distance from CERDI-seadistance database; road distance from calling API of Bing map; Air distance by measuring the great-circle distance given capital coordinates). 

sce_trans.xlsx: The Excel file of the environmental impact intensity of transports. 

df_EItra.pkl: The dataframe of the environmental impact of transport per kg after multiplying the environmental impact intensity of transports and distance across research countries and transport modes. 

sce_rt4.xlsx: The dataframe of the environmental impact intensity of plastic waste treatments across countries. 

df_lp2.xlsx: The required recycling rate (RRR) across research countries and plastic waste types. 

df3_4.xlsx: The Excel file of the environmental impact results under different scenarios.