Abstract
The present research proposes a macroeconomic assessment of the role of waste incineration with energy recovery (WtE) and controlled landfill biogas to electricity generation and their potential contribution to a CO2 emission reduction policy, within a recursive-dynamic computable general equilibrium model. From the modeling viewpoint, introducing these energy sectors in such a framework required both the extension of the GTAP7 database and the improvement of the ICES production nested function. We focus our analysis on Italy as a signatory of the GHG reduction commitment of 20 % by 2020 with respect to 1990 levels proposed by the European Community; the rest of the world is represented by 21 geo-political countries/regions. It is shown that albeit in the near future WtE and landfill biogas will continue to represent a limited share of energy inputs in electricity sector (in Italy, around 2 % for WtE and 0.6 % for biogas in 2020), and they could play a role in a mitigation policy context. The GDP cost of the EU emission reduction target for the Italian economy can indeed be reduced by 1 % when the two energy generating options are available. In absolute terms, this translates into an annuitized value of 87–122 million €.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
Notes
In this report, we consider only waste incineration with energy recovery that indeed represents almost the total of the waste incineration (both with and without energy recovery).
See Bosello et al. (2010) for the extended version of the research.
This is an Italian institute devoted to the collection of environmental data on behalf of the main industrial and commerce Italian associations. Since 1996, the ECOCERVED database collects data about waste categories defined in EWC (European Waste Catalogue), keeping also track of waste management options.
GHG emissions imputable to the overall waste management are much higher: in 2006, they amounted to 18.7 million tons of CO2 equivalent.
It is important to notice that biogas collection and the resulting use in power generation can greatly contribute to climate change mitigation, reducing the methane (CH4) emissions in atmosphere from uncontrolled landfills; as known, methane has a much higher global warming potential than CO2 and represents the most serious environmental concern in waste management.
Detailed information on the model can also be found at the ICES web site: http://www.feem-web.it/ices.
The electricity sector in GTAP7 also includes heat and heat/electricity cogeneration.
A sensitivity analysis has been performed increasing by up to 5 times the substitution elasticities of both WtE and landfill biogas with other energy generation technologies in the ICES production nest. On the one hand, these are the key parameters driving the development of the two sources in the baseline and in the policy case; on the other hand, it is highly uncertain, basically lacking of estimates used as reference in the top-down literature. As long as the elasticities are doubled or tripled no detectable changes are shown. For higher values, some changes are indeed experienced, not in the baseline trends of the two energy generation technologies but on their potential to reduce policy costs. In general, higher substitution elasticity is associated with a reduced cost saving opportunity provided by the technology. This may appear counter intuitive, but is in fact perfectly understandable considering that the mitigation policy reduces energy use, including that of WtE and landfill biogas. The higher the elasticity, the less the use of WtE and landfill biogas decreases in the policy case and the closer it is to the baseline case. Therefore, the benefits offered by the two policies also decrease as we compute them contrasting policy costs when their use is free against policy cost when their use is fixed at the baseline levels (see Sect. 4).
Note that the model simulates the period 2004–2020. Nevertheless, we worked to replicate the historical trend (GDP, emission, fossil fuels’ prices, etc.) for the period 2004–2007.
A similar concept is that of “option value” applied by Leimback et al. (2010) which relates to the introduction of specific technologies in the energy mix. It is defined explicitly as the contribution provided by a non-traditional energy source to cost reduction in achieving a policy target. In our case, however, the situation is rather peculiar as total electricity use, and accordingly also that of energy from waste is reduced by the policy.
References
Bianchi D (2008) Il Riciclo Ecoefficiente—Performance e Scenari Economici, Ambientali ed Energetici. Edizioni Ambiente, Milano
Bohringer C, Rutherford T, Tol R (2009) The EU 20/20/20 targets: an overview of the EMF22 assessment. Energy Econ 31:268–273
Bosello F, Botteri M, Campagnolo L, Carraro C, Eboli F, Medoro M, Parrado R (2010) Energia da Rifiuti in Italia: Potenzialità di Generazione e Contributo alle Politiche di Mitigazione dei Cambiamenti Climatici. http://www.ecocerved.it/Download/2010-E=mc2_Relazione.pdf
Burniaux J-M, Truong TP (2002) GTAP-E: an energy environmental version of the GTAP model. GTAP technical paper no. 16
Burniaux JM, Nicoletti G, Oliveira-Martins J (1992) GREEN: a global model for quantifying the costs of policies to curb CO2 emissions. OECD Econ Stud 19
Consonni S, Giugliano M, Grosso M (2005) Alternative strategies for energy recovery from municipal solid waste. Part B: emission and cost estimates. Waste Manage (Oxf) 25:137–148
Delhotal C, de la Chesnaye F, Gardiner A, Bates J, Sankovski A (2006) Estimating potential reductions of methane and nitrous oxide emissions from waste, energy and industry. In: de la Chesnaye FC, Weyant J (eds) Energy J Spec Issue Multi Greenhouse Gas Mitig Clim Policy
Eboli F, Parrado R, Roson R (2010) Climate change feedback on economic growth: explorations with a dynamic general equilibrium model. Environ Dev Econ 15(5):515–533
EC-European Commission (2007) Limiting global climate change to 2 degrees Celsius. The way ahead for 2020 and beyond, COM/2007/2
ENEA (2006) Rapporto sul Recupero Energetico da Rifiuti Urbani in Italia, Roma
EP-European Parliament (2008) Directive 2008/98/EC
EUROBSERVER (2009) Interactive EUROBSERV database. http://www.eurobserv-er.org/
European IPPC Bureau (2006a) Waste management, best avalable techniques reference documents
European IPPC Bureau (2006b) Waste incineration, best available techniques reference documents
Gottinger HW (1998) Greenhouse gas economics and computable general equilibrium. J Policy Model 20(5):537–580
IEA-International Energy Agency (2009a) Energy balances of OECD countries—extended balances
IEA-International Energy Agency (2009b) Energy balances of non-OECD countries—extended balances
IPCC (2007) Waste management, chapter 10 of contribution of working group III to the fourth assessment report of the Intergovernmental Panel on Climate Change. http://www.ipcc.ch
ISPRA (2009) Italian Greenhouse Gas Inventory 1990–-National Inventory Report 2009. http://unfccc.int/national_reports/annex_i_ghg_inventories/national_inventories_submissions/items/4771.php
Leimback M, Bauer N, Baumstark L, Lüken M, Edenhofer O (2010) Technological change and international trade—insights from REMIND-R. Energy J 31(2):109–136
Monni S, Pipatti R, Lehtilä A, Savolainen I Syri S (2006) Global climate change mitigation scenarios for solid waste management. Technical Research Centre of Finland, vol 603. VTT Publications, Espoo, pp 51
Narayanan BG, Walmsley TL (2008) Global trade, assistance, and production: the GTAP 7 data base. Center for Global Trade Analysis, Purdue University
Sue Wing I (2008) The synthesis of bottom-up and top-down approaches to climate policy modeling: electric power technology detail in a social accounting framework. Energy Econ 30:547–573
UN (2002) International Standard Industrial Classification of All Economic Activities-ISIC Rev. 3.1. http://unstats.un.org/unsd/statcom/doc02/isic.pdf
UNFCCC (1997) Kyoto Protocol to the United Nations Framework Convention on Climate Change. http://unfccc.int/resource/docs/convkp/kpeng.pdf
US Energy Information Administration (2007) Annual energy outlook 2007—with projections to 2030, Office of Integrated Analysis and Forecasting, US Department of Energy, Washington
Wajsman N (1995) The use of computable general equilibrium models in evaluating environmental policy. J Environ Manage 44:127–143
Acknowledgments
Authors gratefully acknowledge Ecocerved and Unioncamere for the financial support of the present research, developed within the “E = mc2 − energy from waste: an assessment of the contribution to climate change mitigation policies in Italy” project (http://www.cmcc.it/research/research-projects/concluded-projects/e-mc2?set_language=en).
Author information
Authors and Affiliations
Corresponding author
Appendix: The ICES model
Appendix: The ICES model
As in all CGE models, ICES (Eboli et al. 2010) makes use of the Walrasian perfect competition paradigm to simulate market adjustment processes, although the inclusion of some elements of imperfect competition is also possible. Industries are modeled through a representative firm, minimizing costs while taking prices as given. In turn, output prices are given by average production costs. The production functions are specified via a series of nested CES functions. Domestic and foreign inputs are not perfect substitutes, according to the so-called “Armington” assumption (Fig. 12).
Nested tree structure for industrial production processes of the ICES model
A representative consumer in each region receives income, defined as the service value of national primary factors (natural resources, land, labor, capital). Capital and labor are perfectly mobile domestically, but immobile internationally. Land and natural resources, on the other hand, are industry-specific. This income is used to finance three classes of expenditure: aggregate household consumption, public consumption, and savings. The expenditure shares are generally fixed, which amounts to saying that the top-level utility function has a Cobb-Douglas specification.
Public consumption is split in a series of alternative consumption items, again according to a Cobb-Douglas specification. However, almost all expenditure is actually concentrated in one specific industry: non-market services.
Private consumption is analogously split in a series of alternative composite Armington aggregates. However, the functional specification used at this level is the constant difference in elasticities form: a non-homothetic function, which is used to account for possible differences in income elasticities for the various consumption goods (Fig. 13).
Nested tree structure for final demand of the ICES model
Investment is internationally mobile: savings from all regions are pooled and then investment is allocated to achieve equality of expected rates of return to capital. In this way, savings and investments are equalized at the world, but not at the regional level. Because of accounting identities, any financial imbalance mirrors a trade deficit or surplus in each region.
The recursive-dynamic engine for the model can replicate dynamic economic growths based on endogenous investment decisions. As standard in the CGE literature the dynamic is recursive. It consists of a sequence of static equilibria (one for each simulation period which in the present exercise is the year) linked by the process of capital accumulation. As investment decisions, which build regional capital stocks are taken 1 year to the other, i.e. not taking into account the whole simulation period, the planning procedure is “myopic”. Two factors endogenously drive investment and its international allocation—the equalization of the expected rate of return to capital and the international GDP differentials. In other words, a country can attract more investment and increase the rate of growth of its capital stock when its GDP and its rate of return to capital are relatively higher than those of its competitors.
About this article
Cite this article
Bosello, F., Campagnolo, L., Eboli, F. et al. Energy from waste: generation potential and mitigation opportunity. Environ Econ Policy Stud 14, 403–420 (2012). https://doi.org/10.1007/s10018-012-0043-5
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s10018-012-0043-5