Curbing global solid waste emissions toward net-zero warming futures

A historical data inventory is developed based on a bottom-up approach for the 43 highest municipal solid waste (MSW)-generating countries, representing ~86% of the global MSW generation in 2016, based on World Bank. The selected countries comprise all four income groups: 14 high income, 13 upper-middle income, 13 lower-middle income, and 3 low income. The data inventory covers MSW generation, composition, and treatment facilities allocation data from 1990 to 2020. The data gaps for the MSW generation are filled using a panel data regression model with the gross domestic product (GDP) per capita as the MSW generation primarily grows with GDP and population growth. The greenhouse gas (GHG) emissions (i.e., carbon dioxide (CO2), methane (CH4), nitrous oxide (N2O)) from the MSW disposal and treatment facilities in 1990–2020 are calculated based on the 2006 Intergovernmental Panel on Climate Change (IPCC) Guidelines for National GHG Inventories with consideration of the 2019 Refinement. The disaggregated GHG emissions for the global MSW system is forecasted using a Bayesian-optimized artificial neural network in order to assess its potential in alleviating global warming and meeting climate goals (i.e., Paris Agreement goals and Global Methane Pledge). The potential in alleviating global warming is assessed through scenario analysis by adopting four types of mitigation strategies (i.e., Retrofit landfill, Compost organics, Digest organics, and Half waste).

With our estimated global MSW generation (2.56–3.33 Gt by 2050), implementing abrupt technical and behavioral changes can result in a net-zero warming solid waste system relative to 2020, permitting 11–27 Gt of carbon dioxide warming-equivalent (CO2-we) emissions under the temperature limits. These changes, however, require accelerated adoption within 9–17 years (by 2033–2041) to align with the Global Methane Pledge. Rapidly reducing methane, carbon dioxide and nitrous oxide emissions is necessary to maximize the short-term climate benefits and stop the ongoing temperature rise.

Description of the data and file structure

Data S1. (.xlsx)
This MS Excel spreadsheet contains the data to plot Figures 1 to 3 in the manuscript, as divided into their respective tabs.
(i) Data in "Fig. 1" shows the cumulative GHG emissions reduction potential of the MSW-related mitigation strategies from 2020 to 2050 compared to the business-as-usual (BAU) in CO2-we. Note that the "BAU" row contains "-" under "Mitigation strategy" because the BAU scenario is the baseline.
(ii) Data in "Fig. 2" shows the cumulative GHG emissions pathway of the MSW-related mitigation strategies from 2020 to 2050 with respect to the IPCC cumulative emissions budget to achieve the 1.5°C and 2°C targets in CO2-we.
(iii) Data in "Fig. 3A" shows the CH4 emissions pathway of each mitigation strategy with respect to the Global Methane Pledge at a linear increase in emissions reduction rate starting in 2023 until complete adoption in 2050, in carbon dioxide-equivalent (CO2-eq). Note that the empty cells in "Fig. 3A" is due to the mitigation strategies are only to be adopted starting in 2023.
(iv) Data in "Fig. 3B" shows the comparison of the resulting CH4 emissions in 2030 among scenarios of BAU, completely adopting the mitigation strategies by 2050, and accelerating the year of complete adoption to be on track with the Global Methane Pledge.

Data S2. (.xlsx)
This MS Excel spreadsheet contains the MSW raw data and results, GHG emissions calculation, and GHG emissions forecast, also divided into their respective tabs. The data are comprised of country-specific data from the 43 highest MSW-generating countries. The historical data are annual data from 1990 to 2020, and the forecasted data are annual data from 2021 to 2050.
(i) "MSWdata" tab shows the country-specific MSW generation (total, urban, rural, collected, uncollected), disposal and treatment of collected MSW, disposal of uncollected MSW, and MSW composition, from 1990 to 2020.
(ii) "GHGdata" shows the country-specific GHG emissions from MSW disposal and treatment, total CO2, CH4, and N2O emissions from 1990 to 2020. Note that the "Total CH4 emissions, GWP*" column contains "-" from 1990 to 2009 because the GWP* metric considers the difference in emissions across 20 years, hence the earliest calculated year is 2010 (i.e., the GWP* metric would consider the difference in emissions between 2010 and 1990).
(iii) "GHGforecast" tab shows the forecasted country-specific GHG emissions (CO2, CH4, N2O) from 2021 to 2050, based on the Shared Socioeconomic Pathways (SSP).
(iv) "GHGreduction" tab shows the country-specific emissions reduction potential for CO2, CH4, and N2O based on various mitigation strategies. Note that the "BAU" row contains "-" under the "reduction potential" columns because the BAU scenario is the baseline.
(v) "GHGpathway" tab shows the country-specific GHG emissions data (CO2, CH4, and N2O) under each mitigation scenario.

Data S3.
This zip file contains the MATLAB code of the Bayesian-optimized artificial neural network for forecasting the country-specific GHG emissions, and data inputs to run the code. Description for each section of the scripts are embedded within the code itself.

Sharing/Access information

Links to other publicly accessible locations of the data: The complete methodology is available under Supplementary Materials of the manuscript.