DOI: 10.5061/dryad.bvq83bkfv
title: Data for: Curbing global solid waste emissions toward net-zero warming futures

description: No global analysis has considered the warming that could be averted
 through improved solid waste management and how much that could contribute
 to meeting the Paris Agreement’s 1.5° and 2°C pathway goals or the terms
 of the Global Methane Pledge. With our estimated global solid waste
 generation of 2.56 to 3.33 billion tonnes by 2050, implementing abrupt
 technical and behavioral changes could result in a net-zero warming solid
 waste system relative to 2020, leading to 11 to 27 billion tonnes of
 carbon dioxide warming–equivalent emissions under the temperature limits.
 These changes, however, require accelerated adoption within 9 to 17 years
 (by 2033 to 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: ## 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.