How to Maintain Competition and Diversity? A socio-ecological-

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
land issues
revegetation of barren land
water pollution
protection of watersheds and
rainwater harvesting
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
pressure on fuelwood
provision of local species
river banks and/or slopes
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
ozone precursor (eq.)
SO2 (eq.)
dust particles
CO2 (eq.)
SO2 scrubber residuals
sewage sludge
production waste
nuclear waste (highly active)
non-organic salts
surface requirements
total costs
Cf. .
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).
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Paper prepared for the IEA Bioenergy Task 29 Workshop in Alberta, Canada, 28-31 May 2001
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