10.2 Status of the waste management sector
10.2.1 Waste generation
The availability and quality of annual data are major problems for the waste sector. Solid waste and wastewater data are lacking for many countries, data quality is variable, definitions are not uniform, and interannual variability is often not well quantified. There are three major approaches that have been used to estimate global waste generation: 1) data from national waste statistics or surveys, including IPCC methodologies (IPCC, 2006); 2) estimates based on population (e.g., SRES waste scenarios), and 3) the use of a proxy variable linked to demographic or economic indicators for which national data are annually collected. The SRES waste scenarios, using population as the major driver, projected continuous increases in waste and wastewater CH4 emissions to 2030 (A1B-AIM), 2050 (B1-AIM), or 2100 (A2-ASF; B2-MESSAGE), resulting in current and future emissions significantly higher than those derived from IPCC inventory procedures (Nakicenovic et al., 2000) (See also Section 10.3). A major reason is that waste generation rates are related to affluence as well as population – richer societies are characterized by higher rates of waste generation per capita, while less affluent societies generate less waste and practise informal recycling/re-use initiatives that reduce the waste per capita to be collected at the municipal level. The third strategy is to use proxy or surrogate variables based on statistically significant relationships between waste generation per capita and demographic variables, which encompass both population and affluence, including GDP per capita (Richards, 1989; Mertins et al., 1999) and energy consumption per capita (Bogner and Matthews, 2003). The use of proxy variables, validated using reliable datasets, can provide a cross-check on uncertain national data. Moreover, the use of a surrogate provides a reasonable methodology for a large number of countries where data do not exist, a consistent methodology for both developed and developing countries and a procedure that facilitates annual updates and trend analysis using readily available data (Bogner and Matthews, 2003). The box below illustrates 1971–2002 trends for regional solid-waste generation using the surrogate of energy consumption per capita. Using UNFCCC-reported values for percentage biodegradable organic carbon in waste for each country, this box also shows trends for landfill carbon storage based upon the reported data.
Box 10.1: 1971–2002 Regional trends for solid waste generation and landfill carbon storage using a proxy variable.
Solid-waste generation rates are a function of both population and prosperity, but data are lacking or questionable for many countries. This results in high uncertainties for GHG emissions estimates, especially from developing countries. One strategy is to use a proxy variable for which national statistics are available on an annual basis for all countries. For example, using national solid-waste data from 1975–1995 that were reliably referenced to a given base year, Bogner and Matthews (2003) developed simple linear regression models for waste generation per capita for developed and developing countries. These empirical models were based on energy consumption per capita as an indicator of affluence and a proxy for waste generation per capita; the surrogate relationship was applied to annual national data using either total population (developed countries) or urban population (developing countries). The methodology was validated using post-1995 data which had not been used to develop the original model relationships. The results by region for 1971–2002 (Figure 10.3a) indicate that approximately 900 Mt of waste were generated in 2002. Unlike projections based on population alone, this figure also shows regional waste-generation trends that decrease and increase in tandem with major economic trends. For comparison, recent waste-generation estimates by Monni et al. (2006) using 2006 inventory guidelines, indicated about 1250 Mt of waste generated in 2000. Figure 10.3b showing annual carbon storage in landfills was developed using the same base data as Figure 10.3a with the percentage of landfilled waste for each country (reported to UNFCCC) and a conservative assumption of 50% carbon storage (Bogner, 1992; Barlaz, 1998). This storage is long-term: under the anaerobic conditions in landfills, lignin does not degrade significantly (Chen et al., 2004), while some cellulosic fractions are also non-degraded. The annual totals for the mid-1980s and later (>30 MtC/yr) exceed estimates in the literature for the annual quantity of organic carbon partitioned to long-term geologic storage in marine environments as a precursor to future fossil fuels (Bogner, 1992). It should be noted that the anaerobic burial of waste in landfills (with resulting carbon storage) has been widely implemented in developed countries only since the 1960s and 1970s.
Figure 10.3a: Annual rates of post-consumer waste generation 1971–2002 (Tg) using energy consumption surrogate.
Figure 10.3b: Minimum annual rates of carbon storage in landfills from 1971–2002 (Tg C).
Solid waste generation rates range from <0.1 t/cap/yr in low-income countries to >0.8 t/cap/yr in high-income industrialized countries (Table 10.1). Even though labour costs are lower in developing countries, waste management can constitute a larger percentage of municipal income because of higher equipment and fuel costs (Cointreau-Levine, 1994). By 1990, many developed countries had initiated comprehensive recycling programmes. It is important to recognize that the percentages of waste recycled, composted, incinerated or landfilled differ greatly amongst municipalities due to multiple factors, including local economics, national policies, regulatory restrictions, public perceptions and infrastructure requirements.
Table 10.1: Municipal solid waste-generation rates and relative income levels
|Country ||Low income ||Middle income ||High income |
|825-3255 ||3256-10065 ||>10066 |
Municipal solid waste generation rate
|0.1-0.6 ||0.2-0.5 ||0.3 to >0.8 |