Parameter Space of Waste Management

Official statistics form an important source of insights into the workings of the municipal solid waste sector. The official statistical institute of the EU, Eurostat, is among the most cited sources of well curated indicators in quantitative research published in journals such as ‘Journal of Cleaner Production’, ‘Waste Management’, ‘Journal of Environmental Management’ to name a vew. Eurostat holds a vast collection of various datasets on the contextuality of a country (CC, a country which is also a EU member state) and the municipal solid waste management (MSWM). The majority of publications in the field of waste management seem to make use of those indicators without further elaborating, e.g., the choice of unit (per capita, total, €, kg, …), their uncertainty, their applicability, etc. To gain a better understanding of the most commonly used indicators and how they link to each other and the sustainable development goals (SDG), I had a closer look at them.

I want to start by noting that while the EU and it’s various institutes and policy areas have been explored exhaustively from various angles, Eurostat and its statistics compilation, how these figures are forged and how they work, have seen little attention [1]. It is however clear, that when relying on Eurostat data for transnational comparison, one has to be conscious about the differences in national statistics traditions [2] [3], that Eurostat is not a fully autonomous institution but develops statistical indicators relevant to the European policy-makers [4], and that governments have misused and manipulated statistical information handled by their statistical authorities that report to Eurostat in the past\footnote{For example the case of unemployment statistics during Margaret Thatcher’s government in the UK or the crises that emerged in 2009 over Greek economic statistics on budget deficit and public debt that were too low.} [1]. As we introduce and motivate our chosen indicators below, we accentuate their ambiguity, uncertainty, or indeterminacy to highlight their political sensitivity.

We start by introducing 8 indicators of a countries contextuality (CC), which are the primary parameters available to describe the constraints in which waste management has to operate on a national level. First, contextual indicators most often accounted for in literature are population count and density [5] and [6]; examples include [7] [8] [9] [10]. It would be shortsighted to think, that population count and waste generation have a positive causal relation across countries, as becomes apparent when comparing most Global South and North countries. Although this is an extreme example, it holds within the European context. Instead, we use population count as a pointer to the number of seats, and therefore the influence on policy making, a member state has in the EU parliament. Comparably counter-intuitive is the relation between population density and waste management. On could think that shorter distances for curbside collection in urban relative to rural settings reduces the time to collect the same amount of waste. In reality urban areas are less efficient due to, e.g., congestion, less composting of putrescible waste, and higher consumption of pre-packaged food [11] [12] [13].

Second, we account for the root cause of an absent causal link between population count and waste generation in the cross-country context by including individual consumption [14]. It is furthermore linked to the SDG indicator 12.2.2 on domestic material consumption. Although domestic material consumption is included in similar studies (such as, [15] [16] [17]), we deliberately focus on individual instead of domestic consumption, as the latter includes many non-household related waste streams.

Third, individual consumption and other indicators are expressed in ‘per capita’ terms. Stating indicators only as averages while neglecting their spread “erases inequities in wealth that impact how waste flows through household and regions” \cite{liboiron_pollution_2021}. To counter the shift of responsibility from the dominant to marginalised classes, we include income inequality \cite{eurostat_income_2024}. Just as relevant is its positive affect on waste generation \cite{vieira_impact_2018} and negative affect on collection rates and quality of collected waste \cite{pehlken_formal_2019,nikou_bridging_2023}.

Fourth, with the agreement on the SDGs came a significant normative move towards the establishment of tax and total government revenue as the primary means to finance sustainable development (see SDG 17.1.1 \cite{eurostat_government_2023} and 17.1.2 \cite{eurostat_main_2023}). As the SDGs cover the waste hierarchy, achieving waste targets depends on the financial support by governments.

Fifth, since the 1960s solid waste is thought of and dealt with in the framework of contemporary environmentalism in Western countries [11] \cite{macbride_recycling_2011}. Therefore we include general government expenditure on environmental protection \cite{eurostat_general_2023}. As it covers indicators unrelated to waste management (e.g., protection of biodiversity and landscape; indicators GF0504,GF0505,GF0506) as well as related indicators (e.g., waste and pollution management; GF0501,GF0502,GF0503), we associate the former to a countries contextuality (CC) and the latter to MSW management (MSWM) panel data. Additionally, as many member states see waste management as a public service we also include those responsible for …

Finally, we consider the Gross Domestic Product (GDP, at market prices per capita; [14]) as indicator of economic performance whose growth is enshrined in the SDG (target SDG-8.1) and the New Circular Economy Action Plan (CEAP, \cite{european_parliament_new_2020}). As economic growth requires material extraction and accumulation that results in waste, no matter how labour and the means of production are arranged and embedded in a economic systems \cite{gille_cult_2007,kao_city_2013}, it is positively related to MSW generation (via individual consumption).

Moving on to the 9 indicators of municipal solid waste management (MSWM), which are direct measures of the waste stream from its generation to its treatment and the required labour and financial expenditure. Eurostat defines it to encompass paper, paperboard and paper products, food and garden waste, plastics, glass, metals, and textiles generated by households, commerce and trade, office buildings and institutions, small businesses, and selected municipal services \cite{eurostat_municipal_2023}.a These material streams are processed through different treatment methods of which we include the following. First, we use recycling to stand for the combined rate of material recycling, composting and anaerobic digestion. Second, incineration is thermal treatment in an incineration plant irrespective of energy recovery or not. Last, landfilling into and onto land in internal and external sites excluding the release to sea. It is clear that the comparison of reported quantities conflate true differences in volume with differences in interpretations, organisation, weighting, and calculation \cite{hogg_impact_2014,hestin_blueprint_2017,eurostat_municipal_2023}. While their impact are unknown but assumed to be small, a bigger issue is the fact that member states tend to report recycling rates based on what is collected for recycling instead of the actual amounts that have been recycled \cite{hestin_blueprint_2017}. This can extremely distort the image of reality as some waste is wrongly send to recycling facilities, where it has to be sorted out and send elsewhere, oftentimes to landfills \cite{hestin_blueprint_2017}.

The required financial expenditure to run the wast stream is estimated through the general government expenditures on waste and pollution management which covers: “administration, supervision, inspection, operation or support of waste collection, treatment and disposal systems; grants, loans or subsidies to support the operation, construction, maintenance or upgrading of such systems” \cite{eurostat_manual_2019}. It therefore accounts for contractual or public-private partnership modes which have been increasing in developed economies since the 1970s [11] \cite{macbride_recycling_2011}. As these activities are carried out by workers, we partially account for them through the percentage employed in sectors contributing to the circular economy. Eurostat defines these sectors to be focused on reuse, repair, and recycling while it excludes sectors that focus on waste prevention as their impact is too diffuse and difficult to isolate \cite{eurostat_persons_2022}. A unrecorded and understudied facet of the labour driving the rise of recovery-for-recycling activities are its profound inequalities and divisions as it is carried out predominantly by migrants working for long hours and low wages within repellent conditions \cite{michie_waste_2015,weghmann_waste_2018}.

Another form of labour left out by Eurostat, but crucial for current waste management operations and posited as pivotal in achieving EUs Circular Economy, is conducting the considerable amount of intra-European trade and shipment in waste \cite{european_parliament_new_2020}. Visible are the dependencies by Denmark, Sweden, and The Netherlands on waste imports to feed their waste-to-energy facilities as shown in Sec.~\ref{sec:app_b} and by \cite{weghmann_waste_2023}. More hidden are illicit shipments from the West to illegal waste dumps in East Europe, travelling unnoticed as border checks were removed with eastern enlargement, especially from Germany to Czech Republic and Poland \cite{vail_illegal_2007,gille_cult_2007,klenovsek_international_2011,weghmann_waste_2023}.

In comparative analysis studies on WM and CE numerous other parameters have been used that we consciously avoid for the following reasons. First, a measure of resource productivity has been used from Eurostat (\cite{eurostat_resource_2024}, e.g., \cite{tantau_models_2018,banacu_entrepreneurial_2019,robaina_determinants_2020,lopez-portillo_waste_2021}). However, this indicator is by definition not comparable between countries and years simultaneously, as the metadata at Eurostat mentions. Second, the governments general expenditure on R\&D has been used (\cite{eurostat_gerd_2023}, e.g., \cite{marin_catching-up_2018,gardiner_municipal_2020,lopez-portillo_waste_2021}), as general technological advancements is seen as important to meet ambitious recycling rates. As we cover expenditure on R\&D more targeted on public services and environmental protection, we do not include \cite{eurostat_gerd_2023} as its definition is too broad.

[1] heiskala_eurostat_2018

[2] wagner_how_1991

[3] hantrais_statistical_1996

[4] egeberg_administering_2006

[5] eurostat_population_2023-1

[6] eurostat_population_2023

[7] de_jaeger_wasteful_2011

[8] halkos_assessing_2019

[9] amaral_performance_2022

[10] struk_factors_2022

[11] gandy_recycling_1994

[12] worthington_measuring_2001

[13] melosi_garbage_2005

[14] eurostat_main_2024

[15] crociata_output-orientated_2016

[16] marino_comparing_2020

[17] marques_assessment_2022