Data and methodologies used by the Waste Methane Assessment Platform, or WasteMAP, created by RMI and the Clean Air Task Force, with funding from the Global Methane Hub, to improve waste methane emissions transparency and to highlight mitigation opportunities and best practices to reduce solid waste methane emissions. 

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WasteMAP | Data & Methodology

Data & Methodology | WasteMAP

Data & Methodology

WasteMAP is powered by waste and methane emissions data collected from multiple sources:

<a href="https://di.unfccc.int/time_series?_gl=1*1q59xa6*_ga*MTE3MTg5NjkwOS4xNzAxMTg4NzI2*_ga_7ZZWT14N79*MTcwMTE4ODc0OS4xLjAuMTcwMTE4ODc0OS4wLjAuMA..">UNFCCC</a>
<a href="https://edgar.jrc.ec.europa.eu/index.php">EDGAR</a>
<a href="https://datacatalog.worldbank.org/search/dataset/0039597/What-a-Waste-Global-Database">World Bank What A Waste</a>
<a href="https://unhabitat.org/waste-wise-cities">UN Habitat Waste Wise Cities</a>
<a href="https://climatetrace.org/">Climate Trace</a>
<a href="https://carbonmapper.org/data/">Carbon Mapper Data Portal</a>
<a href="https://earth.sron.nl/data-and-instruments/">SRON</a>
<a href="https://hubresiduoscirculares.org/en/"> Inter-American Development Bank Solid Waste and Circular Economy Hub </a>

In addition to collecting public data, the WasteMAP team also models city level methane emissions through the Solid Waste Emissions Estimations Tool (SWEET). SWEET is originally an Excel model created by the US EPA for calculating greenhouse gas emissions from the waste disposal process. The WasteMAP team ported key components of the Excel version to Python to power the Decision Support Tool. You can find the repo for the SWEET Python model below. We welcome community contributions! 


Data and Code

### About the Waste Methane Assessment Platform (WasteMAP) 
Methane is a potent greenhouse gas that has been responsible for 30% of global warming since pre-industrial times and causes more than 80 times the warming impact of carbon dioxide over a 20-year time frame. The waste sector is the third largest methane emitter behind oil and gas and agriculture, accounting for 18% of total methane emissions, of which municipal solid waste is responsible for 11%. Reducing methane emissions from the waste sector is crucial not only for combating climate change but also for safeguarding human health and minimizing environmental nuisances. 

![A pie chart mapping anthropogenic methane emissions by source.](https://rmiwastemapprod.blob.core.windows.net/cms/assets/graph_1_b29172887e.png)
_Figure 1: Global anthropogenic methane emissions by source, 2020 [[GMI](https://www.globalmethane.org/documents/gmi-mitigation-factsheet.pdf)]_

Currently, there is a lack of consistent waste sector and related methane emissions data, especially at the city and asset levels, and data availability varies significantly by country and by region. At the national level, countries report their greenhouse gas emissions according to the United Nations Framework Convention on Climate Change (UNFCCC). At sub-national levels, different entities, including the United Nations, the World Bank, C40, and aid agencies have implemented initiatives to promote data and information collection and sharing.  

WasteMAP brings together reported and modeled data sources to paint a picture of the current methane emissions situation from municipal solid waste (MSW) globally, aiming to highlight the data and resource gaps that exist.

The information below focuses on the internals of WasteMAP, for information on how to use the user interface, please see [How to Use WasteMAP](/how-to-use-wastemap).

### Map Layers 
WasteMAP presents waste data and emissions data from MSW treatment and disposal in three different map layers: a country-level choropleth layer that shows national inventories normalized by population, and two point-of-interest (POI) layers that show cities and facility-level waste characteristics and emissions. Satellite and flyover observed methane plumes data are also attached to the facility POI layer where available.  
 
### Country-level layer 
WasteMAP shows country-level emissions from two different sources. One is [Emission Database for Global Atmospheric Research (EDGAR)](https://edgar.jrc.ec.europa.eu/gallery?release=v70ghg&substance=CH4&sector=SWD_LDF) and the other is the UNFCCC, which includes reporting from [Annex I countries](https://di.unfccc.int/ghg_profile_annex1?_gl=1*7xgmq5*_ga*MjEzNzE5MDc2Mi4xNzAxMzgwOTg3*_ga_7ZZWT14N79*MTcwMTQ1MzMwNS4yLjAuMTcwMTQ1MzMwNS4wLjAuMA..) and [non-Annex I countries](https://di.unfccc.int/ghg_profile_non_annex1?_gl=1*aduuz*_ga*MjEzNzE5MDc2Mi4xNzAxMzgwOTg3*_ga_7ZZWT14N79*MTcwMTQ1MzMwNS4yLjAuMTcwMTQ1MzMxMS4wLjAuMA..).

EDGAR provides modeled global sector-specific estimates of anthropogenic emissions of greenhouse gases — including methane — and air pollutants. It is maintained by the Joint Research Centre of the European Commission, under the EU. To calculate methane emissions from municipal solid waste, EDGAR uses a first-order decay model as specified by the 2006 IPCC report. Parameters include how much waste is generated per person, waste characterization, waste decay rates and degradable carbon contents of those waste types, landfill technology and characteristics, and what percentage of waste is diverted to compost, incineration, or other types of facilities instead of being deposited in a dump site or landfill. In most cases, EDGAR uses research-backed regional and country-specific parameters provided by the 2006 IPCC report, but some parameters for some countries are determined with data reported to the UNFCCC. 

The UNFCCC is a treaty to reduce “human interference with the climate system,” whose secretariat maintains a database of information, submitted by party countries, on anthropogenic emissions of greenhouse gases (including methane) and air pollutants. This emissions-related information takes a variety of forms. For waste methane emissions, countries reporting to the UNFCCC generally follow the 2006 IPCC first-order decay waste model.  However, depending on the quality of data collected by each country, a variety of procedures of varying complexity are available. Parameters and values used in the models can be IPCC regional and country-specific defaults like those used by EDGAR, but reporting countries are encouraged to collect and use detailed information from their waste management systems. Annex I Countries and non-Annex I countries have different reporting requirements. National inventory submissions for both Annex I and non-Annex I countries are included in WasteMAP. 

WasteMAP provides two country-level emission values: total emissions, and emissions per capita. Bulk emissions are reported in kilotons of methane per year, while emissions per capita are reported in kilograms per person per day.  
 
### City-level layer 
City-level emissions are estimated with the [Solid Waste Emissions Estimation Tool (SWEET)](https://globalmethane.org/resources/details.aspx?resourceid=5176) developed by the U.S. Environmental Protection Agency (EPA). SWEET uses information on waste characteristics and mass, as well as landfill design, waste management practices, and environmental factors to estimate greenhouse gas emissions and other short-lived climate pollutants. The WasteMAP team developed a [Python version](https://github.com/rmi/sweet_python) of the model by adapting the underlying model that estimates methane emissions from waste diversion and disposal. 

The model begins with per capita waste generation to determine total waste generation in a given city. The destination of generated waste is then determined: waste is either disposed of in landfills or dumpsites, or diverted to composting, anaerobic digestion, incineration, or recycling. Methane emissions are calculated from landfilled or dumped waste using a first-order decay model similar to the one deployed in the UNFCCC. Input values and parameters are determined with city-specific information where available; where data are not currently available, regional default values from SWEET, the 2006 IPCC report, and 2019 update to that report are used. The output of the SWEET model methane emissions in tons per year — is then normalized by the population of the city to generate per capita estimates.  

### Input Data for Python SWEET Model 
A city-level waste dataset was generated by combining existing waste data and processed by the code built off SWEET. The compiled waste dataset includes information from the [World Bank What a Waste (2018)](https://datacatalog.worldbank.org/search/dataset/0039597/What-a-Waste-Global-Database) and [UN Habitat Waste Wise Cities Tool](https://unh.rwm.global/Map), which includes waste generation, waste characterization, and waste destination (treatment and disposal).  As we continue to expand the user base and collaboration of WasteMAP, more data are being added to the database, including contributions from the Circulate Initiative, Clean Air Task Force, as well as the Lagos Waste Management Authority (LAWMA) through RMI's country engagement efforts. Raw data was processed to align variable names and extract relevant information for the Python SWEET model. 

In a situation where source data for total waste generation and total population of a city don't align, in current datasets for example, population growth rate was used to "increase the population" to the same year as the total waste generation data to allow an accurate calculation of per capita waste generation rate. This growth rate is calculated in the same way as we do for any other population growth rates that were used throughout the tool, as detailed below. In SWEET Python modeling, the per-capita waste generation rate is assumed to be constant across years and total waste generated increases over time because population grows.

In addition to waste generation, waste characterization and waste destination, the Python SWEET model also needs precipitation, and population growth rates data as model inputs. Population growth rates are derived from historical and projected population data from the UN Department of Economic and Social Affairs, Urban Agglomerations ([World Urbanization Prospects: 2018 Revision](https://population.un.org/wup/Publications/Files/WUP2018-Report.pdf)).  Population data was matched by city name while cities without a match were tied to the next nearest population center from the UN dataset.  Precipitation was retrieved from [WorldClim](https://worldclim.org) at 10-minute intervals, which is a spatial resolution of 340 km2.  The data is compiled using 264 rasters (TIF files).  Each raster represents the total precipitation for a single month in a single year from 2000-2021.  Precipitation is grouped and summed by year, and then averaged to get average precipitation from 2000-2021.  <a href="http://openstreetmap.org/copyright">OpenStreetMap's</a> nominatim package, &copy; OpenStreetMap contributors, was used to generate latitude and longitude coordinates for all the cities in WasteMAP.
 
### Site-level layer 
WasteMAP uses [Climate TRACE waste sector facility level emissions dataset](https://climatetrace.org/downloads) for the site-level layer. Climate TRACE uses the [IPCC first order decay model](https://unfccc.int/sites/default/files/resource/Session6.pdf) to estimate site-level methane emissions from inputs such as land area, waste-in-place and annual capacity (i.e., annual incoming waste). The site level methane absolute emissions from Climate TRACE are normalized by annual accepted waste (ton/yr) to get emissions intensity in the unit of kgCH4/ton waste accepted to make sites comparable.  

In addition to model-generated facilities emissions data, the site-level cards also include time series satellite and flyover observed methane emissions rates and the latest methane plume image for the site. The satellite data are obtained from SRON Netherlands Institute for Space Research through the [Targeting Waste emissions Observed from Space (TWOS)](https://earth.sron.nl/project/targeting-waste-emissions-observed-from-space/) project. Ten sites in ten different cities in the Global South were selected to monitor methane emissions from the waste sector. New satellite data will be added to the WateMAP webtool as they become available. Users of the WasteMAP site are also advised that the satellite data provided by SRON will be published in a scientific paper by SRON et al. If you would like to use parts of these data in a scientific paper please contact SRON at <a href="mailto:TWOS@sron.nl">TWOS@sron.nl</a>. GHGSat data will remain property of GHGSat, commercial use not permitted. 
Airborne survey and satellite methane plume data for a select set of landfill sites are obtained from [Carbon Mapper](https://data.carbonmapper.org/) Data Portal. The use of Carbon Mapper data for non-commercial purpose is subject to [Modified Creative Commons Attribution Share Alike 4.0](https://carbonmapper.org/wp-content/uploads/2021/12/Carbon-Mapper-Modified-Creative-Commons-License-21-11-09.pdf). Please contact Carbon Mapper if you plan to use their data for any purposes that are not in line with their [terms of use](https://carbonmapper.org/terms-of-use/). Additional satellite and airborne data from Carbon Mapper will be added to the Waste MAP site when they become available.   

### Decision Support Tool 
One unique feature of WasteMAP is the decision support tool (DST), which allows users to estimate baseline methane emissions from current waste management practices in a given city and project alternative methane emission scenarios with improved waste management practices. The DST is a step forward for emissions data transparency, because it enables users to uncover the material impact of certain waste management practices on methane emissions mitigation. This extra know-how makes it possible for users to translate emissions transparency into actionable policies and practices that lead to emissions mitigation. 
  
Underlying the DST is the Python SWEET model. API endpoints are developed for the SWEET Python model to enable the users of the WasteMAP frontend to interact with the model and explore methane emissions scenarios under different waste management practices.  

For the cities that we have modeled and published their baseline emissions in the city-level map layer, the additional data required to use the DST include diversion information (% composted, recycled, combusted, or anaerobically digested), waste information (% sent to landfill vs. dumpsite), availability of landfill gas capture, and diversion scenario start year.  Behind the scenes, the Python script uses defaults, available information, or calculations to calculate methane emissions from the alternative scenarios and present a comparison between baseline and alternatives.  

For cities that do not currently have a modeled time series in the city-level layer of WasteMAP, users need to provide additional information, including population, precipitation, and waste generation rates.  

For a comparison between the functionalities and assumptions between the original version of SWEET and the Python version, please see the documentation [here](https://github.com/RMI/SWEET_python/blob/main/python_excel_differences.txt). 

To access the original version of SWEET to conduct more detailed modeling of scenarios, please visit [here](https://globalmethane.org/resources/details.aspx?resourceid=5176).

### Note on Data and Modeling Accuracy 
Due to existing data gaps and significant variations in data availability in different countries, IPCC regional defaults (of per-capita waste generation and waste characterization) are used often during the estimation of city-level methane emissions in the current version of the platform, which may impact the accuracy of these estimates in the local context. The data and modeling results shown are reflective of best estimates given current availability, and as more local data becomes available, these estimates will be refined accordingly.  


Data & Methodology

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