Gridlock in compromise, or is multi-objective optimisation possible in renewable energy planning? A stakeholder analysis using scenario-MCDA

Figures & data

Figure 1. Methodology for stakeholder analysis using an MCDA approach.

Table 1. Characteristics of survey groups.

Survey group no.	Characteristics
#1	Stakeholders involved in renewable energy planning were contacted through snowballing, and then randomly distributed to the survey group.
#2
#3	Stakeholders approached on an event with a flyer that contained a QR code linking to the survey. The event ‘Species Conservation and Wind Energy Planning – Quo Vadis?’, organised by the Berlin Institute of Technology in Berlin, Germany on March 21, 2023, included a variety of participants who were involved in wind energy planning (regional planning agencies, conservation organisations, ministries, research institutions, wind energy project developers, planners, and funding agencies).

Table 2. Number of respondents in the three survey groups for the first part survey. Stakeholders could also select more than one category. Therefore, the numbers do not add up to the total number of respondents. During the second part survey, participation changed slightly, but overall consultation remained.

	Survey group #1		Survey group #2		Survey group #3
	First part	Second part	First part	Second part	First part	Second part
Planning offices	2	3	3	2	3	2
Regional planning agencies	3	3	4	1	6	2
Cities and municipalities	0	0	2	2	0	0
Project developers, energy suppliers, citizen wind corporations	1	1	4	4	2	1
Environment associations	2	1	7	2	1	1
Energy associations	2	1	1	1	1	0
Forest associations	0	0	0	1	0	0
Landscape and tourism associations	0	0	1	0	0	0
Forestry authority and state forestry offices	1	1	0	0	0	0
Nature conservation agencies and state environmental agencies	4	5	1	1	3	1
Permitting agencies	0	0	0	0	0	1
Research	2	1	5	1	2	0
Funding agencies (foundations)	2	2	0	2	1	1
Centres of Competence	1	1	2	1	1	1
Other	0	1	4	2	0	0
Number of participated stakeholders	18 (100%)	20 (111%)	26 (100%)	20 (77%)	17 (100%)	10 (59%)
Sum of participated stakeholders (first-part survey)	61 (100%)
Sum of participated stakeholders (second-part survey)	50 (82%)

Figure 2. Case situation of th

e scenario framework taken from Weber, Steinkamp, and Reichenbach (2023). Identified areas for wind energy only embrace 1.67% of the region’s area, leaving a delta to meet the state-specific area targets of 2.2%, values according to Regionale Planungsgemeinschaft Havelland-Fläming (2020).

Table 3. Criteria used in this scenario-MCDA, which have been derived from the scenario framework by Weber, Steinkamp, and Reichenbach (2023). The cliparts were previously published in Weber, Steinkamp, and Reichenbach (2023).

Criterion	Explanation
Priority zones for bird species	Priority zones for bird species are designated areas that are likely to cluster frequent site-related occurrences of bird or bat species, e.g. for the red kite or the osprey (Kompetenzzentrum Naturschutz und Energiewende 2021). Priority zones may have been identified through habitat modelling, which extrapolates suitable areas for bird or bat species based on breeding sites and land use (Weber, Steinkamp, and Reichenbach 2023).
Coniferous forest	Forest areas in this assessment are coniferous stands, which are partly open to use for wind energy. Conflicting interests on developing wind energy in forests yet remain, e.g. with bird and bat conservation, and aesthetics (Bunzel et al. 2019; Enevoldsen 2016; Reers et al. 2017; van der Werf et al. 2009; Karakislak and Schneider 2023).
Landscape protection areas	Landscape protection areas are protected areas under nature conservation legislation. They are designed to preserve the general character of the landscape, including for recreation and tourism. Wind energy is not restricted until spatial energy targets are met.^5
Nature Park	Nature Parks are protected areas under nature conservation legislation, often covering large areas of landscape, which have been created through long-standing forms of landscape management. They usually combine several other categories of protection.^6 Wind energy is partly developed in nature parks (Deshaies and Herrero-Luque 2015).
Open space areas	Open space areas comprise the regionally valuable and interconnected parts of the open space (i.e. undeveloped, open areas). It is a category of planning that serves, for example, to preserve the landscape and prevent landscape fragmentation (cf. Regionale Planungsgemeinschaft Havelland-Fläming 2020).
Settlement buffers	Settlement buffers from wind turbines, which exceed the noise protection buffers, are often set by decision-makers to ensure the acceptance of wind energy by local residents (cf. Regionale Planungsgemeinschaft Havelland-Fläming 2020; Felber and Stoeglehner 2014; Langer et al. 2023).
Ground-mounted solar complements the wind energy delta	Ground-mounted photovoltaic systems are elevated modules for electricity generation that are placed in the landscape (cf. Awan et al. 2020; Beylot et al. 2014 on environmental impacts). They are often installed along motorways and railways, for example, as these sites are eligible for subsidies.^7 It is assumed that ground-mounted photovoltaic systems will be developed at these supported locations. In some federal states, regional planning also provides for ground-mounted PV systems only at these locations (Einig 2022).
Agri-PV complements the wind energy delta	Agri-PV (agricultural photovoltaics) are elevated modules installed on agricultural land that enable energy and agricultural production on the same area. For example, berries can be grown under the modules and synergy effects (shading, altered water balance) can be used (Efremov, Sanduleac, and Braga 2022; Amaducci, Yin, and Colauzzi 2018; Barron-Gafford et al. 2019).

Table 4. Criteria, criteria levels and combined scenarios to only close the gap (‘delta’) for spatial wind energy target achievement of 2.2% of the regional area, on the basis on multi-criteria scenarios from Weber, Steinkamp, and Reichenbach (2023). The arrow indicates how to read the table.

Criteria and levels for closing the ‘delta’ in wind energy planning Scenarios	Bird priority zones	Coniferous forests	Landscape protection	Nature parks	Open space areas	Settlement buffers	Ground-mounted solar complements the wind energy delta	Agri-PV complements the wind energy delta
Wind energy in bird priorities zones (for the red kite, osprey)	10%	8%	0%	0%	0%	1000 m	0.31% ^c	0.76% ^c
Wind energy in coniferous forests	0%	12%	0%	0%	0%	1000 m	0.31% ^c	0.76% ^c
Wind energy in landscape protection areas	0%	8%	3%	0%	0%	1000 m	0.31% ^c	0.76% ^c
Wind energy in nature parks and landscape protection areas	0%	8%	1.5%	1%	0%	1000 m	0.31% ^c	0.76% ^c
Wind energy in open space areas	0%	8%	0%	0%	4%	1000 m	0.31% ^c	0.76% ^c
Reduced buffer to settlements and wind energy in open space areas	0%	8%	0%	0%	1.2%	800 m	0.31% ^c	0.76% ^c
Complementing wind energy with ground-mounted solar energy partially	0%	8%	0%	0%	0%	1000 m	0.56% ^a	0%
Complementing wind energy with agri-PV partially	0%	8%	0%	0%	0%	1000 m	0%	1.11% ^b
Explanations	^a 0.31% of the area to meet solar energy targets + 0.25% of the area for solar energy to complement the wind energy delta = 0.56%
	^b 0.76% of the area to meet solar energy targets by agri-PV + 0.35% of the area for agri-PV to complement the wind energy delta = 1.11%
	^c In parallel, solar energy is being develpoped in line with the development targets of the Renewable Energy Act and translated into the area required, according to Weber, Steinkamp, and Reichenbach (2023)

Figure 3. Indirect weighing of two criteria each using the PAPRIKA method and the software 1000 minds. Percentages indicate the percentage of land (criterion) required for wind (or solar) energy per scenario to meet the spatial wind energy target of 2.2%.

Figure 4. Ranking of scenarios and preference values for scenarios for the three survey groups. The lower the preference value, the more is the preference for the scenario.

Figure 5. Preferred criteria for wind energy development across the three survey groups

Figure 6. Acceptance of stakeholders’ own ranking of the scenarios in each survey group using PAPRIKA (left). For stakeholders indicating dissatisfaction with criteria applied, the named criteria a visualised in a word cloud in total. The bigger the word, the more often this criterion is rejected across all survey groups (right).

Figure 7. Results and arguments regarding opinions on ranking and willingness to compromise. The bubbles show the percentage of agreement (green) or disagreement (orange) with the questions. The word clouds display the most frequently cited arguments. The larger the word, the more times it was mentioned.

Table 5. Cross-table of satisfaction and acceptance of ranking across stakeholder sectors. Red frames and shaking hands symbols indicate sectors that are willing to compromise despite dissatisfaction with the ranking (12.9%). Grey frames and lightning bolts indicate sectors that value own perceptions (35.49%).

Figure 8. Results and arguments regarding perception of the process, satisfaction and transparency. The bubbles show the percentage of agreement (green) or disagreement (orange) with the questions. The word clouds display the most frequently cited arguments. The larger the word, the more times it was mentioned.

Table 6. Cross-table on whether stakeholders imagine the scenario process as part of a real planning process. Green frames indicate a preference for scenario processes within sector groups. Orange frames indicate disapproval.

Figure 9. Results and arguments regarding external obstacles and practicability. The bubbles show the percentage of agreement (green) or disagreement (orange) with the questions. The word clouds display the most frequently cited arguments. The larger the word, the more times it was mentioned.

Table 7. SWOT analysis and patterns of argumentations, indicating four stakeholder groups toward compromise.

SWOT on scenario-MCDA
Internal analysis	Strength ⇒ Satisfied with overall ranking when own preferences are met ⇒ Compromise is accepted for the higher good of energy transition as long participants are involved ⇒ Compromise is accepted as a democratic decision (legitimacy of decisions) ⇒ Discussing concerns through personal engagement and participation ⇒ Knowledge support and transparency of the planning process ⇒ Awareness of the need for compromise solutions ⇒ Rethinking own opinions ⇒ Solution-oriented approach	Weaknesses ⇒ Not satisfied with overall ranking when own preferences are not met ⇒ Compromise is not accepted as long preferences are not included ⇒ Legitimacy of decision-making is unclear, democratic decisions are not best ⇒ Target scenarios must be defined by planning authorities ⇒ No new knowledge is derived ⇒ No will for engagement as discussions are not meaningful and preferences are not met ⇒ Compromises are unlikely, results are not comprehensible ⇒ Alternatives are only compromises, no good solutions
External analysis	Opportunities ⇒ Simplicity of the approach ⇒ Limited choice makes the subject tangible ⇒ Time is manageable ⇒ Alternative assessment and balancing trade-offs transparently ⇒ Decision-making support tool ⇒ All stakeholders need to participate ⇒ Allows to influence planning in terms of conservation	Threats ⇒ Complexity reduces applicability, knowledge is needed ⇒ Time restrictions ⇒ No ‘problem awareness’ as conservation is considered as non-compromising ⇒ Too little expertise in planning processes ⇒ Mix of methods needed ⇒ Minorities are given minor weight in ranking ⇒ Legitimacy of negotiators is needed ⇒ Current planning is sufficient ⇒ No room in practice for ranking ⇒ No clear majority emerges in alternatives, leaving a range of possibilities

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