The world’s growing municipal solid waste: trends and impacts
Open accessAbstract
Global municipal waste production causes multiple environmental impacts, including greenhouse gas emissions, ocean plastic accumulation, and nitrogen pollution. However, estimates of both past and future development of waste and pollution are scarce. We apply compositional Bayesian regression to produce the first estimates of past and future (1965–2100) waste generation disaggregated by composition and treatment, along with resultant environmental impacts, for every country. We find that total wastes grow at declining speed with economic development, and that global waste generation has increased from 635 Mt in 1965 to 1999 Mt in 2015 and reaches 3539 Mt by 2050 (median values, middle-of-the-road scenario). From 2015 to 2050, the global share of organic waste declines from 47% to 39%, while all other waste type shares increase, especially paper. The share of waste treated in dumps declines from 28% to 18%, and more sustainable recycling, composting, and energy recovery treatments increase. Despite these increases, we estimate environmental loads to continue increasing in the future, although yearly plastic waste input into the oceans has reached a peak. Waste production does not appear to follow the environmental Kuznets curve, and current projections do not meet UN SDGs for waste reduction. Our study shows that a continuation of current trends and improvements is insufficient to reduce pressures on natural systems and achieve a circular economy. Relative to 2015, the amount of recycled waste would need to increase from 363 Mt to 740 Mt by 2030 to begin reducing unsustainable waste generation, compared to 519 Mt currently projected.
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Estimates from other studies for global MSW generation in 2015 vary from 1999 to 2010 Tg/yr.
“Estimates from other studies vary from 19993 to 20104 Tg/yr for the same year.”
From Potential for future reductions of global GHG and air pollutants from circular waste management systems (2022) · cites this paper as [3]
Ref. 3 estimated MSW generation of about 3500 Tg/yr in 2050 for SSP2.
“while ref. 3 estimated a MSW generation of about 3500 Tg/yr in 2050 for the same scenario.”
From Potential for future reductions of global GHG and air pollutants from circular waste management systems (2022) · cites this paper as [3]
Our estimate for SSP3 and SSP4 in 2050 is similar to the 3539 Tg/yr projected by Chen et al.
“Looking at MSW generation projections, our estimate for the SSP3 and SSP4 in 2050 are similar to the 3539 Tg/yr projected by Chen et al.3.”
From Potential for future reductions of global GHG and air pollutants from circular waste management systems (2022) · cites this paper as [3]
Uncertainties are relatively high in developing countries compared to developed countries because data availability is limited.
“These uncertainties are relatively high in developing countries compared to developed countries as in various cases data availability is quite limited in the former case3.”
From Potential for future reductions of global GHG and air pollutants from circular waste management systems (2022) · cites this paper as [3]
It is difficult to do a quantitative uncertainty estimation because several data inputs go into the estimations.
“Regarding uncertainty, several data inputs (activity data, emission factors, type of management) go into the estimations, and therefore is difficult to do a quantitative uncertainty estimation3,20.”
From Potential for future reductions of global GHG and air pollutants from circular waste management systems (2022) · cites this paper as [3]
Cites
Production, use, and fate of all plastics ever made
Geyer, R., Jambeck, J. R., & Law, K. L. (2017) · Science Advances · doi:10.1126/sciadv.1700782 · Cited by 18,606 · see citing works
<b>brms</b> : An <i>R</i> Package for Bayesian Multilevel Models Using <i>Stan</i>
Bürkner, P.-C. (2017) · Journal of Statistical Software · doi:10.18637/jss.v080.i01 · Cited by 9,476 · cited 1× · see citing works
“All regressions were undertaken through the brms package (Bürkner 2017) as an interface to the Bayesian inference engine Stan (Carpenter et al 2017).”
Method<i>Stan</i> : A Probabilistic Programming Language
Carpenter, B., Gelman, A., Hoffman, M. D., Lee, D., Goodrich, B., Betancourt, M., Brubaker, M., Guo, J., Li, P., & Riddell, A. (2017) · Journal of Statistical Software · doi:10.18637/jss.v076.i01 · Cited by 7,754 · cited 1× · see citing works
“All regressions were undertaken through the brms package (Bürkner 2017) as an interface to the Bayesian inference engine Stan (Carpenter et al 2017).”
MethodThe rise and fall of the Environmental Kuznets Curve
Stern, D. I. (2004) · World Development · doi:10.1016/j.worlddev.2004.03.004 · Cited by 4,013 · see citing works
O’Neill, B. C., Kriegler, E., Ebi, K. L., Kemp-Benedict, E., Riahi, K., Rothman, D. S., van Ruijven, B. J., van Vuuren, D. P., Birkmann, J., Kok, K., et al. (2017) · Global Environmental Change · doi:10.1016/j.gloenvcha.2015.01.004 · Cited by 3,359 · see citing works
Present and long-term composition of MSW landfill leachate: a review
Kjeldsen, P., Barlaz, M. A., Rooker, A. P., Baun, A., Ledin, A., & Christensen, T. H. (2002) · Critical Reviews in Environmental Science and Technology · doi:10.1080/10643380290813462 · Cited by 2,522 · see citing works
Compositional Data Analysis: Theory and Applications
(2011) · Wiley eBooks · doi:10.1002/9781119976462 · Cited by 704 · cited 1× · see citing works
“We thus face a compositional data modelling problem: given the total amount of waste produced, the increase of a unit share of one waste type implies a reduction in other shares—the values are described within a k-1 simplex, given k degrees of freedom (Pawlowsky-Glahn and Buccianti 2011).”
BackgroundEnvironmental impacts of solid waste landfilling
El-Fadel, M., Findikakis, A. N., & Leckie, J. O. (1997) · Journal of Environmental Management · doi:10.1006/jema.1995.0131 · Cited by 692 · see citing works
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Gómez-Sanabria, A., Kiesewetter, G., Klimont, Z., Schoepp, W., & Haberl, H. (2022) · Nature Communications · doi:10.1038/s41467-021-27624-7 · Cited by 306 · see citing works
“(3) Estimation of emissions draw on the methodologies presented in refs.”
Method
- Authors
- Chen, D. M.-C., Bodirsky, B. L., Krueger, T., Mishra, A., & Popp, A.
- Affiliations
- Leibniz Association (DE)
- Humboldt-Universität zu Berlin (DE)
- Corresponding author
- Chen, David Meng-Chuen
- Venue
- Environmental Research Letters
- Publisher
- IOP Publishing
- Published
- 2020-06-23
- Volume
- 15
- Issue
- 7
- Pages
- 074021
- ISSN
- 1748-9326 (ISSN-L)
- OpenAlex
- W3014501575
- Cited by
- 524
- References
- 8
- Citations by year
- 2026: 73 · 2025: 97 · 2024: 115 · 2023: 99 · 2022: 91 · 2021: 45 · 2020: 4
- FWCI
- 36.92
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Environmental Research Letters (publishedVersion, cc-by)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
- edoc Publication server (Humboldt University of Berlin) (submittedVersion, cc-by)
- RENATE (submittedVersion, cc-by-nc-nd)
- Publication Database PIK (Potsdam Institute for Climate Impact Research (PIK)) (submittedVersion, cc-by)
- edoc Publication server (Humboldt University of Berlin) (cc-by)
- RENATE (cc-by)
- Funders
- DAAD Study Scholarship for Graduates of All Disciplines (57381416)
- SHAPE Project (01LS1907A-B-C)
- Topics
- Physical Sciences › Environmental Science › Waste Management and Disposal › Municipal Solid Waste Management
- Physical Sciences › Environmental Science › Waste Management and Disposal › Waste Management and Recycling
- Physical Sciences › Environmental Science › Pollution › Microplastics and Plastic Pollution
- Keywords
- municipal solid waste, waste generation forecasting, waste composition, waste treatment, recycling, composting, energy recovery, landfilling, organic waste, plastic waste, marine plastic pollution, greenhouse gas emissions, nitrogen pollution, environmental Kuznets curve, Sustainable Development Goals, circular economy, economic development
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