beta

You're viewing our new website - find out more

Publication - Report

Climate change: evidence review in Agriculture, Forestry, Land Use, Waste

Published: 19 Jan 2017
Part of:
Environment and climate change
ISBN:
9781786527530

Evidence review of potential climate change mitigation measures in Agriculture, Forestry, Land Use and Waste.

156 page PDF

1.7MB

156 page PDF

1.7MB

Contents
Climate change: evidence review in Agriculture, Forestry, Land Use, Waste
4 Potential wider impacts of GHG mitigation in the waste sector

156 page PDF

1.7MB

4 Potential wider impacts of GHG mitigation in the waste sector

4.1 Employment benefits of diversion from landfill to recycling

4.1.1 Potential employment benefits in Scotland

The estimation of waste industry employment impacts hinges on the derivation of figures for the rate of employment per tonne of waste managed in different operations (e.g. collection, landfilling, incineration, etc.). This is based on the assumption that the rate of employment per tonne of waste managed for different management operations differs. The relative differences in treatment destinations can then be used to calculate the change in the number of FTEs across a range of scenarios. Hence, the key inputs required to derive employment impacts are:

  1. The estimated mass flow of various waste materials in the modelled scenario;
  2. The change in tonnages managed under different waste management operations; and
  3. The employment rate i.e. number of FTEs per tonne of each type of waste managed under each operation.

Employment is then usually estimated in terms of number of FTE jobs per 10,000 tonnes of waste processed (also referred to as 'employment intensity'). Employment intensity factors can be scaled in order to derive:

  1. In the first instance, employment generated under a particular waste management scenario; and
  2. More importantly, the net employment impact from a waste management policy proposal scenario compared to the counterfactual or baseline case.

The graphical overview of a basic employment impact model is provided in Figure 1. This example is taken from European Commission and involved modelling employment factors in relation to a range of waste management processes across a range of scenarios (Eunomia 2014).

Figure 1 Example overview of employment modelling

Figure 1 Example overview of employment modelling

4.1.2 Summary of findings of literature review

The estimate of employment benefits relies on the derivation of employment intensities, which in turn depends on waste mass flow and management data. A review of the available information for such data was carried out, with evidence presented by both waste management operation and material in Sections 4.1.2.1 and 4.1.2.2 respectively. The range of employment intensity estimates the literature reviewed is summarised in Table 12. Although a reasonable number of known information sources have been studied, the review is not exhaustive.

Table 12 Employment intensities from various data sources (full time equivalents ( FTEs) per 10,000 tonnes per annum)

Study Landfill Incinerator MBT Composting Windrow In-vessel AD Residual Waste Collection Recycling Collection Recycling Collection/ Reprocessing
SWAP, 1997 ( UK) 3-67
Murray, 1999 ( UK) ≈1 ≈1 6 21-40 2
Gray et al. 2004 ( UK) 5 (biowaste) 4-19
Seldman, 2006 ( USA) 1 1 4 25
Urban Mines and Walker Resource Management, 2012 ( UK) 5 2 2
Eunomia, 2014 ( EU) 4 2
TBU and Eunomia, 2003 2 - 3
University of Glamorgan, 2007 ( AU) 5
Greenpeace, 2009
5







Cottica & Kaulard, 1995 ≈1 2-4







European Commission, 2006








12
Friends of the Earth, 2010







32 49
Selected figure for modelling 1 1 4 4 4 2 2 6 Material specific data for recycling and reprocessing is in
Section4.1.2.2)

Notes: Figures are rounded to nearest integer. It is important to note that whilst Seldman's study was published in 2006, the data was collected in 1997.

4.1.2.1 Employment by waste management operation

Research indicated that the level of conformity in employment estimates varies between the different waste management operations. The literature review for landfill for example, which is an established disposal route, found far greater conformity in results compared to reprocessing technologies. Variation in the levels of mechanisation and technology between reprocessing facilities may contribute to the large range in employment intensities presented in different studies.

4.1.2.1.1 Landfill

Despite the date of research and lack of methodological transparency, the conformity of the results from the Seldman (2006), Murray (1999) and Cottica & Kaurlard (1995) studies imply that 1 is an acceptable figure to use for modelling. Being typically large scale (high throughput) facilities with respectively low process technology (landfill) these figures appear reasonable compared to the results for other technologies.

4.1.2.1.2 Incineration

The most recent study by Greenpeace (2009) about incineration in Spain gives an estimate of 4.8 jobs per 10,000 tonne per annum (tpa) based on 10 incinerators operational at the time in Spain. However, the report does note that the figure varies significantly between plants, giving the example of the 280,000 tpa Zagalgabri facility operated by just eleven people (equivalent to 0.4 FTEs per 10,000). A lower employment intensity of 1 FTE/10,000t is found in both the Murray (1999) and Seldman (2006) studies, with the Cottica & Kaulard study (1995) presenting a range from 1.9-3.7 FTE/10,000t.

Based on the literature findings, and given that incineration in most instances is a large scale highly mechanised process, a figure of 1 FTE/10,000t is considered reasonable.

4.1.2.1.3 Mechanical biological treatment

A detailed report on MBT (TBU and Eunomia 2003) gives personnel requirements as reproduced in Table 13. This suggests that a basic minimum number of staff are required for an MBT facility. The data indicates that at smallest viable scale for such a facility (40,000 tpa as indicated in the source reference), staff numbers may total perhaps 12 FTEs, or 3 employees per 10,000 tpa of capacity.

Table 13 Personnel requirements of a mechanized MBT with fermentation (source: TBU and Eunomia, 2003)

Function Responsibility Number of Staff
Operating manager Whole plant 1
Deputy operating manager Fermentation 1
Electrician, electronics engineer EMSR (Electrical, measurement, control and regulation technology) 1-2
Fitter Maintenance, repair 1
Mobile equipment operator Wheel loader, grab excavator, container vehicles 3-4
Cleaning staff Daily cleaning and cleaning of the grounds, externally if necessary 2-3
Laboratory staff Process control, material analysis Proportional
Replacement Estimation: ~ 25-30% Proportional
Administration
Proportional
Weighbridge, workshop
Proportional
Data administration, marketing Proportional

A comprehensive survey of the UK organics industry by WRAP elicited data for 10 MBT plants (Urban Mines and Walker Resource Management, 2012). The data was subsequently upscaled to account for plants that did not partake in the survey. Whilst WRAP's figures for AD and composting are calculated from site's annual material input, the employment figure for MBT was based on the plant's annual capacity. Data given in the report's Appendix 5, reveals that the 10 MBT sites successfully surveyed average 74,600 tpa of material input for an average 83,000 tpa of total annual capacity, and with an average 35.6 employees per facility. As such, we can derive an employment intensity of 4.8 FTE per 10,000 tpa of throughput.

One further reference is available for a 100,000 tpa facility in Austria incorporating mechanical (and manual) sorting, percolation and AD, biodrying, mechanical material separation (heavy/light fraction separation for SRF production), exhaust gas treatment and onsite disposal to landfill. The report states that "ZAK Ringsheim has 50 employees in total, including many administrative staff". A more simple MBT facility (without the digestion element), and where landfill is considered as a separate activity may be expected therefore to employ less than this 5 FTE per 10,000 tpa figure. Based on these comparisons, a figure of 4 FTE per 10,000 tpa is recommended for modelling.

4.1.2.1.4 Windrow and in-vessel composting

WRAP's study surveyed 199 composting sites across the UK (Urban Mines and Walker Resource Management, 2012). Whilst these included windrow, in-vessel and also aerated pile composting facilities, aerated pile accounted for <1% of the surveyed input and thus did not significantly show in the results.

Note that the report did not go into details of individual sites. Eunomia's research demonstrated an inverse relationship between site size and employment intensity for windrow composting sites (as may be expected), albeit with very few data points (Eunomia, 2014). However, this does not fully explain the differences between Eunomia's and WRAP's results: the average input per site for WRAPs study was 19,186 tpa compared to an average of 18,000 for this study.

The lack of available data points give very little upon which to base our assumptions, but the Eunomia (2014) study suggests a figure of 4 FTEs per 10,000 tpa may be reasonable for windrow compositing. The lower figure of 2 FTEs per 10,000 tpa is selected for in-vessel composting in order both to be conservative and to match the figure for AD.

4.1.2.1.5 Anaerobiic Digestion

WRAP's study surveyed 19 out of the total 48 AD sites in the UK, indicating an average of 2 FTEs per 10,000 tpa of capacity. Neither WRAP's (Urban Mines and Walker Resource Management, 2012) nor Eunomia's micro study (2014) focused specifically on AD sites processing food waste. Both studies, however, discerned a similar mean employment intensity. The data is not sufficient to show any trends for employment intensity varying with facility throughput. The conformity of WRAP's value with Eunomia's supports its use in employment modelling.

4.1.2.1.6 Waste collection and reprocessing

Table 14 illustrates the results from a study for DEMOS on waste and recycling collection systems (Murray, 1999). They clearly demonstrate higher employment intensity for recycling than residual waste collection. The values for recycling in particular are inclined to change, however, as recycling systems and rates have changed dramatically since the time of publication.

Table 14 Employment intensity for waste collection ( FTEs per 10,000 tpa) (source: Murray,1999)

Number of Staff
Recycling collection ≈ 21 - 40
Residual waste collection ≈ 6

Where recycling is concerned, data in the literature often conflates employment in waste collection with that in sorting and in reprocessing. There is some sense in this approach, as studies often attempt to demonstrate in a straightforward manner the additional employment associated with additional recycling, and thus the factors used include collection, sorting and reprocessing combined. This also minimises issues where employment moves between collection and sorting operations depending on the degree of separation during the collection operation. However, where studies focus on the employment created by additional recycling, they tend to miss the potential loss of employment associated with residual waste collection.

4.1.2.2 Employment by Material

Data on employment for reprocessing further suggests that employment intensity varies considerably depending on the material which is being reprocessed. Table 15 shows employment intensity by material reprocessed, based on data from SWAP (1997), ranging from 3 FTE/ 10,000 tpa for glass reprocessing to 67 FTE/ 10,000 tpa for plastics reprocessing. However, note that this data is almost 2 decades old.

Table 15 Employment for reprocessing by material ( SWAP, 1997)

Material Employees/10,000 t (includes admin and reprocessors)
Paper and Card 19
Glass 3
Steel 5
Aluminium 11
Plastic 67

The Seldman (2006) study of the US reprocessing industry also found a high employment intensity for plastics reprocessing in comparison to other materials. The study found that 93 FTE were employed per 10,000 t of plastic reprocessed and paper was the least employment intensive material to reprocess (18 FTE/ 10,000t).

A further study undertaken by LEPU in 2004 refers to job gains by quantity of material reprocessed. But that 'job gains' is not the same as employment intensities and therefore are not directly comparable with the previous source. In this case, the data includes employment related to collection and sorting operations in addition to that associated with reprocessing.

A 2006 report by the European Commission includes an assessment of the impact of the packaging directive obligations on the direct and first round indirect employment rate in the packaging recovery and recycling industry. This gives a figure of 42,000 FTEs which may be associated with the stated 36 million tonnes recovered (in 2002) indicating around 12 FTEs per 10,000 tpa (European Commission, 2006). Again, however, this might not be a directly comparable figure as the other sources do not seem to include the first round indirect employment - i.e. employment up and down-stream resulting from new direct employment in the recycling sector.

A more recent study by Friends of the Earth (2010) reviews employment intensities from a number of sources. It identifies that employment in different studies is taken to include some of all of the following activities associated with recycling:

  • Collectors;
  • Brokers (purchasing recyclable commodities for resale);
  • Processors (businesses that bale, crush, pelletise, compost, demanufacture or otherwise change the form of the recyclable material for sale);
  • End users / recycling manufacturers (businesses that use recyclable materials as feedstock in the production of a new product);
  • Reusers or remanufactures (businesses that remanufacture or reuse recyclable material such as furniture, white goods, computers and electronic appliances, wood, as well as retailers that sell used merchandise);
  • Recycling equipment manufacturers.

Table 16 reproduces the sources reviewed and assumptions taken by Friends of the Earth (2010) for the key recyclable materials considered in that study, and adds additional materials of interest. This study also applied a multiplier of 1.5 for first round indirect employment, which was increased to 1.75 for the inclusion of induced employment from expenditure of the additionally employed individuals.

Table 16 Employment intensity for recycling by material ( FTEs/10,000 tpa)

Material Gray et al. 2004 Cascadia (2009) citing Seldman (2006) Friends of the Earth (2010) Value for 2020 Eunomia 2014
Glass 7.5 26 7.5 7.5
Paper 35 18 18 18
Plastic 156 93 93 93
Iron & Steel 54 - 54 54
Aluminium 110 - 110 110
Wood 7.5 - 7.5 7.5
Textiles 50 85 50 50
WEEE 400 (computer reuse) 296 - 400
Furniture 136 - - 136
Biowaste 5 collection
+ 8 processing
4 4 5 collection
MRFs - 10 - -
Average all recycling 62 50 49 -

4.1.3 Issues with the Quality of Data

Given the findings of the above review, several key shortcomings associated with the data come to light. The OECD has previously recognised these intrinsic difficulties in the analysis and interpretation of employment data in the waste management industry ( OECD 1996). The key issues highlighted in the evidence reviewed are outlined below:

4.1.3.1 Lack of recent data

Many of the studies reviewed were conducted over a decade ago. The literature search suggests that a limited number of primary research studies have been conducted, and these are repeatedly cited in more recent studies. This poses a particular problem for waste industry data due to the scale of development that has taken place since the 1990s. For example, in the case of sorting facilities (or material recycling facilities - MRFs) where facilities have grown in size (perhaps relating to increasing rates of recycling over time) economies of scale are likely to have been experienced, reducing the employment intensity. Reprocessing technology and changes in the design of products that end up in recycling schemes are also likely to have had significant effects on MRF employment over time.

4.1.3.2 Lack of methodological transparency

This was the case with many of the studies reviewed. A widely cited report by Gray et al. (2004), for example, fails to properly reference or provide additional information on its sources of information. One reference is simply labelled " EU report". A similar instance can be seen in a study by Murray (1999), where no reference is given to the methodology behind the employment figures. Without access to the methodology behind these figures, it is difficult to understand what they relate to and, in turn, their practical utility.

4.1.3.3 Employment metric

A number of reports refer to number of employees as opposed to FTEs. In these cases, number of employees may not be directly comparable to number of FTEs. There is also inconsistency and difficulty in identifying the operations that qualify within the scope of employees being estimated. For example, a facility will have operational staff, but there are also likely to be office staff involved in the operation of the facility, some of whom may be responsible for a number of facilities. It is difficult to identify if their time has been included and if time has been apportioned between facilities.

4.1.3.4 Inclusion of indirect/ induced/ displacement employment

Certain studies, particularly related to recycling collection and processing, sometimes include indirect employment (i.e. employment up and down-stream resulting from new direct employment in the recycling sector) and induced employment (i.e. that associated with expenditure of the directly employed individuals) within the estimation of employment factors. Others (e.g. Eunomia 2014) takes account of displacement factors within the estimation of employment intensity. This is an important consideration, since a shift to a new waste management system will inevitably displace some employment in other operations, either via direct labour, or due to shifting purchasing power away from certain technologies (indirect unemployment). Hence, net employment creation will always be less than gross estimates, and may even be zero or negative.

4.1.3.5 Distribution of impacts

The literature reviewed provides limited information on the distribution of the various estimated employment benefits arising from shifting waste management operations. This is true firstly in terms of geographical distribution. This is related to both waste operation type (for example, closed loop recycling plants tend to be located near manufacturing sites and supply chains, and hence increased recycling by this method will not have evenly distributed employment benefits across the UK) and also to regional variations in the labour market.

Further, the literature also tends to skip over the proportion of employment benefits that can be allocated across the range of labour skill levels. A literature review on the nature of employment created in the circular economy (including shifting waste management practices) was carried out by the Green Alliance (2015) and is summarised in Table 17. This research went on to estimate that net job creation in circular economy activity to 2030 at the current growth rate in Scotland would be 0.07% of the labour force. This is not comparable to earlier estimates as it estimates employment generated across several circular economy activities rather than simply landfill diversion to recycling.

Table 17 Literature on the nature of employment creation in circular economy activities (source: Green Alliance, 2015)

Sector Study Coverage Skill level of jobs created
Recycling
EEA (2011) EU Low skilled work in particular, but also medium and high skilled jobs, ranging from collection, materials handling and processing to manufacturing products.
ILO (2011) Germany 16% low skilled, 47% skilled, 11% technical, 25% university.
Waste collection ECOTEC (2002) EU Labour required for waste collection and transport, at relatively low wage rates.
Remanufacturing APPSRG (2014) UK Skilled, with substantial training needs
Beck (2011) USA Relatively high skill and training requirements.
Waste Management SITA (2012) UK A range of jobs, but particularly significant numbers of mid-level (supervisors/ operators) and low level (manual) occupations.

Contact

Email: Debbie Sagar