Paper prepared for the IEA Bioenergy Task 29 Workshop in Alberta, Canada, 28-31 May 2001 How to Maintain Competition and Diversity? A socio-ecologicaleconomic assessment of bioenergy options with a focus on CHP Reinhard Madlener CEPE – Centre for Energy Policy and Economics, Swiss Federal Institutes of Technology ETH-Zentrum WEC, CH-8092 Zurich, Switzerland Tel: +41 1 6320652 Fax: +41 1 6321050 E-mail: [email protected] Modern bioenergy options offer a great potential to provide sustainable energy services and strategies, and to alleviate a multitude of socio-economic and environmental problems, particularly in rural and/or remote areas (IEA Bioenergy 1998). At the same time and depending on the specific project design and implementation, however, they can also have important adverse impacts on society, the economy and the environment. Often, such positive and negative impacts are only treated as “secondary effects” to the planning and implementation of projects on economic grounds, although they can greatly influence a particular project’s appropriateness and sustainability in a local context (UNDP 2000). Hence focusing on (short-term) economic efficiency gains alone cannot assure that the market-dominating energy technologies are those with the least adverse ecological impact, highest social benefit, and most benign long-term economic impact. Neither are diversity of supply and distributional and gender aspects adequately addressed. The manyfold impacts of energy projects call for an assessment by means of a comprehensive set of appropriate criteria that properly takes into account the needs and wants of the various stakeholders involved. Assessments of energy projects should generally rely on the (case-specific) local conditions. Moreover, the issues considered crucial in energy planning decision-making are markedly different in less developing countries and developed countries. Whereas the people affected by a particular project in the less developed world often struggle several hours a day with the satisfaction of the very basic needs, and therefore need to care much more about the trade-offs attached to the project in order not to endanger the most vulnerable groups of the population (e.g., local poor, women, children), developed countries these days tend to be more preoccupied with issues linked to market liberalisation, and environmental and climate protection. Based on coevolutionary theory (Gowdy 1994; Norgaard 1994), and a theoretical concept introduced in Madlener and Stagl (2000) and elaborated further in Madlener and Stagl (2001), we suggest to cardinally differentiate energy technologies according to their socio-ecological-economic (SEE) impact, measured by a battery of indicators derived from life-cycle analyses along the entire fuel chain (Table 1). The evaluation itself is based both on expert advice and the social preferences articulated by the stakeholders involved (group decision process). The social preferences can be mapped by the use of a suitable multi-criteria decision aid (MCDA) tool (cf. De Montis, De Toro, et al. 2000, for a comparison of methods). Table 1. Socio-ecological-economic indicators for energy project evaluation in developing countries (example), Source: based on Madlener & Stagl (2000); UNDP (2000). Social dimension Ecological dimension: net employment impact provision of pump water provision of lighting communication land issues revegetation of barren land water pollution protection of watersheds and rainwater harvesting Economic dimension income generation food prices Paper prepared for the IEA Bioenergy Task 29 Workshop in Alberta, Canada, 28-31 May 2001 access to information gender implications (labour, power, resource access) pressure on fuelwood resources provision of local species habitat river banks and/or slopes stabilisation reclamation of waterlogged and salinated soils reduction of residue disposal pollution problems The empirical illustration is focused on small- and medium-sized heat and power, and combined-heatand-power (CHP), supply technologies in Germany with a particular focus on biogas-fuelled systems. By relying on data contained in the database of GEMIS1, the presentation depicts how such assessments can be used in practice for ranking, and choosing among, alternative regional energy planning options based on an multi-dimensional impact assessment and multiple stakeholder decisions. Among the numerous MCDA tools available, we have decided to employ Expert Choice 20002, which is based on the analytic hierarchy process (AHP) method developed mainly by Saaty (Saaty 1980, 1992; Saaty and Alexander 1989). AHP requires pairwise comparisons of alternatives and allows cardinal rankings. Some of the results are shown below. Figure 1 depicts the contribution of the various impact (sub-)categories to the overall impact assessment. Figure 2 shows the results of a sensitivity analysis for the impact category greenhouse gas (GHG) emissions. Table 2. Impact criteria considered for case study Air pollutants GHG emissions Energy use Material use Solid wastes Liquid waste pollutants Land use Economics 1 ozone precursor (eq.) SO2 (eq.) dust particles CO2 (eq.) non-renewables renewables other non-renewables renewables other ashes SO2 scrubber residuals sewage sludge production waste rubble nuclear waste (highly active) N AOX CSB BSB5 non-organic salts surface requirements total costs Global Emission Model for www.oeko.de/service/gemis/english/. 2 Cf. www.expertchoice.com . Integrated Systems; see Öko-Institut (1994; 1998) or Paper prepared for the IEA Bioenergy Task 29 Workshop in Alberta, Canada, 28-31 May 2001 Figure 1: Contribution of the various categorized impacts to the overall impact assessment Figure 2: Ranking sensitivity analysis: GHG emissions (criteria weight increased from 12.5% to 50%) The empirical results obtained from the technology assessment are preliminary only and serve mainly illustrative purposes. At least two extensions to our research project are necessary to reach some final conclusions: (i) we need to obtain some additional and consistent data in order to cover all major indicators (currently there is a lack of data particularly regarding the social indicators); and (ii) we need to confront real stakeholders with a description of the technology options and their impacts in order to find out about their particular social preferences (for our illustration we assumed equal weights for all indicators; if available a real energy planning problem could be used, or, alternatively, a laboratory study developed). References De Montis, A.; De Toro, P.; et al. (2000). Multicriteria decision aid and sustainable development – a comparison of methods. World Meeting ‘Man and City towards a human and sustainable development’, Univ. of Naples, Naples. Gowdy (1994). Coevolutionary economics: the economy, society, and the environment, Kluwer, Boston. IEA Bioenergy (1998). The Role of Bioenergy in Greenhouse Gas Mitigation. Position Paper prepared by IEA Bioenergy Task 25 “Greenhouse Gas Balances of Bioenergy Systems”, November. Paper prepared for the IEA Bioenergy Task 29 Workshop in Alberta, Canada, 28-31 May 2001 Madlener, R.; Stagl, S. (2000). Promoting Renewable Electricity Generation through Guaranteed Feedin Tariffs vs Tradable Certificates: An Ecological Economics Perspective, 3rd Biennial Conference of the European Society for Ecological Economics (ESEE), Vienna, 3-6 May 2000. Madlener, R.; Stagl, S. (2001). Quotenregelungen mit Zertifikathandel und garantierte Einspeisevergütungen für Ökostrom: Sozio-ökologisch-ökonomische Bewertung förderungswürdiger Technologien, Zeitschrift für Energiewirtschaft, 25(1): 53-66. Norgaard (1994). Development betrayed: the end of progress and a coevolutionary revisioning of the future, Routledge, London and New York. Öko-Institut (1994). Umweltanalyse von Energie-, Transport- und Stoffsystemen: Gesamt-EmissionsModell integrierter Systeme (GEMIS) Version 2.1 – erweiterter und aktualisierter Endbericht. Study on behalf of and published by Hessisches Ministerium für Umwelt, Energie und Bundesangelegenheiten, Wiesbaden, 1995. Öko-Institut (1998). Kurz-Information zu Gesamt-Emissions-Modell integrierter Systeme – eXtended version (GEMIS 3.x). Ein Computer-Instrument zur Umwelt- und Kostenanalyse von Energie-, Transport- und Stoffsystemen, Öko-Institut, Darmstadt/Freiburg/Berlin, December (www.oeko.de/service/ gemis). Saaty, T. L. (1980). The Analytic Hierarchy Process for Decision in a Complex World . RWS Publications, Pittsburgh, PA. Saaty, T. L. (1992). Multicriteria Decision Making – The Analytic Hierarchy Process. RWS Publications, Pittsburgh, PA. Saaty, T. L. and J. M. Alexander (1989). Conflict Resolution – The Analytic Hierarchy Process. Praeger, New York. UNDP (2000). Bioenergy Primer. Modernised Biomass Energy for Sustainable Development. United Nations Development Programme, New York.
© Copyright 2019