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R. Stöllinger
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The Effects of Wind Turbines on Housing Prices in the Netherlands
Assessing Onshore, Offshore, and Heterogeneous Effects
The Netherlands is rapidly expanding its wind energy capacity both onshore and offshore as part of its broader energy transition. Although wind capacity has more than doubled from 2019 to 2024, residents experience localised externalities from wind turbines, including visual impact, noise, and shadow flicker. These costs are often capitalised into housing prices. Therefore, the placement of turbines involves balancing the progress of the energy transition with the burdens imposed on local communities, emphasising the importance of reliable evidence on the extent and distribution of these effects to inform spatial planning strategies.
Among the many externalities of wind turbines, ranging from the effects on landscape to impacts on nature, housing prices are particularly relevant. They can be directly observed in housing transaction records and combine various perceived local disamenities into a single, quantifiable measure. Furthermore, house prices are linked to Dutch compensation policy: the Omgevingswet, or the Dutch Environment and Planning Act, sets a societal risk threshold of four per cent of the property’s value. If the depreciation falls below this threshold, it is not eligible for any compensation. Therefore, analysing house prices provides a tangible, policy-relevant insight into who bears the local costs of the energy transition. This aligns with the Engineering and Policy Analysis (EPA) programme’s goal of employing quantitative methods to inform decision-making regarding grand societal challenges.
While prior studies have examined how turbines impact housing prices in the Netherlands, two important gaps still persist. First, the impact of offshore turbines on housing prices has not been examined in the Dutch context. Second, existing studies capture spatial variation through geographic categories but leave the role of socioeconomic factors almost entirely unexplored. Additionally, the Dutch estimates are based on methods that could introduce bias as turbine adoption is staggered. This raises questions about the current magnitude of these effects when staggered adoption is taken into account, and how these effects are distributed among communities exposed to turbines.
This thesis estimates the effects of onshore wind turbines using a Difference-in-Differences (DiD) design, based on a sample of 1:83 million housing transactions from 2010 to 2023 from the Nederlandse Vereniging van Makelaars (NVM), merged with WindStats turbine data and neighbourhood-level Socioeconomic Status (SES) data. To extend current literature and to account for the staggered nature of Dutch wind turbine construction, the analysis uses the Sun & Abraham (2021) estimator and systematically controls for socioeconomic composition, which prior Dutch studies did not. A new addition to current literature is the offshore component, which is deliberately descriptive: It documents coastal price trends associated with offshore wind farm development and clarifies why the Dutch data do not support credible causal identification.
The results show that the effects of onshore wind turbines are small, not robust across different control group definitions, highly localised, and statistically indistinguishable from zero under most alternatives. In the preferred specification, they are found to slightly lower nearby property prices by about 0:8% within two kilometres. This is roughly a fifth of the four per cent compensation threshold of the Omgevingswet, so most affected homeowners do not qualify for any compensation. The estimated price effect is smaller than previous Dutch estimates, primarily because the sample from 2010 to 2023 limits the identifying variation needed to estimate causal effects. The causal interpretation is further limited by imperfect pre-trends, though robustness checks suggest these do not drive the estimated effect. These effects are unlikely to be lower due to changes in perceptions of wind energy, as Dröes & Koster (2019) have shown that willingness to pay to live next to turbines is generally stable over time.
The most robustly tested finding in the heterogeneity analysis is that additional turbines do not produce larger effects, meaning they do not lead to higher price effects, while also reducing the number of properties exposed to turbines. Subgroup analyses indicate effects are strongest near turbines and in densely populated regions, although these differences between subgroups are not statistically significant. Additionally, while turbines are disproportionately sited in neighbourhoods with lower transaction prices, associated with lower SES, whether the price effect itself differs by SES could not be established. In all these analyses, none of the estimated effects approaches the four per cent compensation threshold set by the Omgevingswet. The offshore wind analysis remains descriptive rather than causal due to the small number of offshore parks, their recent clustered construction, a limited coastal sample, and the lack of visibility data, all of which prevent reliable causal identification.
These findings inform a siting strategy focused on reducing housing-market costs of turbines. The only well-supported principle is that clustering turbines concentrates price effects on fewer properties, without increasing their magnitude relative to single-turbine installations. The weaker, non-significant patterns suggest, cautiously, that turbines should be placed away from urbanised areas. Since onshore turbines are already mostly located in communities with lower prices, associated with lower SES, an efficiency-based siting strategy that ignores distributional risks will exacerbate existing inequalities in turbine placement. Where turbines are sited is therefore ultimately a political choice that should weigh minimising housing market impacts against the fair distribution of turbine placements; this thesis explicitly highlights this trade-off by analysing both the magnitude of effects and the populations most affected. More broadly, the small estimated effects imply that turbines have a limited impact on the Dutch housing market, inviting a shift in the debate toward more difficult-to-measure externalities, such as landscape and health concerns. ...
Among the many externalities of wind turbines, ranging from the effects on landscape to impacts on nature, housing prices are particularly relevant. They can be directly observed in housing transaction records and combine various perceived local disamenities into a single, quantifiable measure. Furthermore, house prices are linked to Dutch compensation policy: the Omgevingswet, or the Dutch Environment and Planning Act, sets a societal risk threshold of four per cent of the property’s value. If the depreciation falls below this threshold, it is not eligible for any compensation. Therefore, analysing house prices provides a tangible, policy-relevant insight into who bears the local costs of the energy transition. This aligns with the Engineering and Policy Analysis (EPA) programme’s goal of employing quantitative methods to inform decision-making regarding grand societal challenges.
While prior studies have examined how turbines impact housing prices in the Netherlands, two important gaps still persist. First, the impact of offshore turbines on housing prices has not been examined in the Dutch context. Second, existing studies capture spatial variation through geographic categories but leave the role of socioeconomic factors almost entirely unexplored. Additionally, the Dutch estimates are based on methods that could introduce bias as turbine adoption is staggered. This raises questions about the current magnitude of these effects when staggered adoption is taken into account, and how these effects are distributed among communities exposed to turbines.
This thesis estimates the effects of onshore wind turbines using a Difference-in-Differences (DiD) design, based on a sample of 1:83 million housing transactions from 2010 to 2023 from the Nederlandse Vereniging van Makelaars (NVM), merged with WindStats turbine data and neighbourhood-level Socioeconomic Status (SES) data. To extend current literature and to account for the staggered nature of Dutch wind turbine construction, the analysis uses the Sun & Abraham (2021) estimator and systematically controls for socioeconomic composition, which prior Dutch studies did not. A new addition to current literature is the offshore component, which is deliberately descriptive: It documents coastal price trends associated with offshore wind farm development and clarifies why the Dutch data do not support credible causal identification.
The results show that the effects of onshore wind turbines are small, not robust across different control group definitions, highly localised, and statistically indistinguishable from zero under most alternatives. In the preferred specification, they are found to slightly lower nearby property prices by about 0:8% within two kilometres. This is roughly a fifth of the four per cent compensation threshold of the Omgevingswet, so most affected homeowners do not qualify for any compensation. The estimated price effect is smaller than previous Dutch estimates, primarily because the sample from 2010 to 2023 limits the identifying variation needed to estimate causal effects. The causal interpretation is further limited by imperfect pre-trends, though robustness checks suggest these do not drive the estimated effect. These effects are unlikely to be lower due to changes in perceptions of wind energy, as Dröes & Koster (2019) have shown that willingness to pay to live next to turbines is generally stable over time.
The most robustly tested finding in the heterogeneity analysis is that additional turbines do not produce larger effects, meaning they do not lead to higher price effects, while also reducing the number of properties exposed to turbines. Subgroup analyses indicate effects are strongest near turbines and in densely populated regions, although these differences between subgroups are not statistically significant. Additionally, while turbines are disproportionately sited in neighbourhoods with lower transaction prices, associated with lower SES, whether the price effect itself differs by SES could not be established. In all these analyses, none of the estimated effects approaches the four per cent compensation threshold set by the Omgevingswet. The offshore wind analysis remains descriptive rather than causal due to the small number of offshore parks, their recent clustered construction, a limited coastal sample, and the lack of visibility data, all of which prevent reliable causal identification.
These findings inform a siting strategy focused on reducing housing-market costs of turbines. The only well-supported principle is that clustering turbines concentrates price effects on fewer properties, without increasing their magnitude relative to single-turbine installations. The weaker, non-significant patterns suggest, cautiously, that turbines should be placed away from urbanised areas. Since onshore turbines are already mostly located in communities with lower prices, associated with lower SES, an efficiency-based siting strategy that ignores distributional risks will exacerbate existing inequalities in turbine placement. Where turbines are sited is therefore ultimately a political choice that should weigh minimising housing market impacts against the fair distribution of turbine placements; this thesis explicitly highlights this trade-off by analysing both the magnitude of effects and the populations most affected. More broadly, the small estimated effects imply that turbines have a limited impact on the Dutch housing market, inviting a shift in the debate toward more difficult-to-measure externalities, such as landscape and health concerns. ...
The Netherlands is rapidly expanding its wind energy capacity both onshore and offshore as part of its broader energy transition. Although wind capacity has more than doubled from 2019 to 2024, residents experience localised externalities from wind turbines, including visual impact, noise, and shadow flicker. These costs are often capitalised into housing prices. Therefore, the placement of turbines involves balancing the progress of the energy transition with the burdens imposed on local communities, emphasising the importance of reliable evidence on the extent and distribution of these effects to inform spatial planning strategies.
Among the many externalities of wind turbines, ranging from the effects on landscape to impacts on nature, housing prices are particularly relevant. They can be directly observed in housing transaction records and combine various perceived local disamenities into a single, quantifiable measure. Furthermore, house prices are linked to Dutch compensation policy: the Omgevingswet, or the Dutch Environment and Planning Act, sets a societal risk threshold of four per cent of the property’s value. If the depreciation falls below this threshold, it is not eligible for any compensation. Therefore, analysing house prices provides a tangible, policy-relevant insight into who bears the local costs of the energy transition. This aligns with the Engineering and Policy Analysis (EPA) programme’s goal of employing quantitative methods to inform decision-making regarding grand societal challenges.
While prior studies have examined how turbines impact housing prices in the Netherlands, two important gaps still persist. First, the impact of offshore turbines on housing prices has not been examined in the Dutch context. Second, existing studies capture spatial variation through geographic categories but leave the role of socioeconomic factors almost entirely unexplored. Additionally, the Dutch estimates are based on methods that could introduce bias as turbine adoption is staggered. This raises questions about the current magnitude of these effects when staggered adoption is taken into account, and how these effects are distributed among communities exposed to turbines.
This thesis estimates the effects of onshore wind turbines using a Difference-in-Differences (DiD) design, based on a sample of 1:83 million housing transactions from 2010 to 2023 from the Nederlandse Vereniging van Makelaars (NVM), merged with WindStats turbine data and neighbourhood-level Socioeconomic Status (SES) data. To extend current literature and to account for the staggered nature of Dutch wind turbine construction, the analysis uses the Sun & Abraham (2021) estimator and systematically controls for socioeconomic composition, which prior Dutch studies did not. A new addition to current literature is the offshore component, which is deliberately descriptive: It documents coastal price trends associated with offshore wind farm development and clarifies why the Dutch data do not support credible causal identification.
The results show that the effects of onshore wind turbines are small, not robust across different control group definitions, highly localised, and statistically indistinguishable from zero under most alternatives. In the preferred specification, they are found to slightly lower nearby property prices by about 0:8% within two kilometres. This is roughly a fifth of the four per cent compensation threshold of the Omgevingswet, so most affected homeowners do not qualify for any compensation. The estimated price effect is smaller than previous Dutch estimates, primarily because the sample from 2010 to 2023 limits the identifying variation needed to estimate causal effects. The causal interpretation is further limited by imperfect pre-trends, though robustness checks suggest these do not drive the estimated effect. These effects are unlikely to be lower due to changes in perceptions of wind energy, as Dröes & Koster (2019) have shown that willingness to pay to live next to turbines is generally stable over time.
The most robustly tested finding in the heterogeneity analysis is that additional turbines do not produce larger effects, meaning they do not lead to higher price effects, while also reducing the number of properties exposed to turbines. Subgroup analyses indicate effects are strongest near turbines and in densely populated regions, although these differences between subgroups are not statistically significant. Additionally, while turbines are disproportionately sited in neighbourhoods with lower transaction prices, associated with lower SES, whether the price effect itself differs by SES could not be established. In all these analyses, none of the estimated effects approaches the four per cent compensation threshold set by the Omgevingswet. The offshore wind analysis remains descriptive rather than causal due to the small number of offshore parks, their recent clustered construction, a limited coastal sample, and the lack of visibility data, all of which prevent reliable causal identification.
These findings inform a siting strategy focused on reducing housing-market costs of turbines. The only well-supported principle is that clustering turbines concentrates price effects on fewer properties, without increasing their magnitude relative to single-turbine installations. The weaker, non-significant patterns suggest, cautiously, that turbines should be placed away from urbanised areas. Since onshore turbines are already mostly located in communities with lower prices, associated with lower SES, an efficiency-based siting strategy that ignores distributional risks will exacerbate existing inequalities in turbine placement. Where turbines are sited is therefore ultimately a political choice that should weigh minimising housing market impacts against the fair distribution of turbine placements; this thesis explicitly highlights this trade-off by analysing both the magnitude of effects and the populations most affected. More broadly, the small estimated effects imply that turbines have a limited impact on the Dutch housing market, inviting a shift in the debate toward more difficult-to-measure externalities, such as landscape and health concerns.
Among the many externalities of wind turbines, ranging from the effects on landscape to impacts on nature, housing prices are particularly relevant. They can be directly observed in housing transaction records and combine various perceived local disamenities into a single, quantifiable measure. Furthermore, house prices are linked to Dutch compensation policy: the Omgevingswet, or the Dutch Environment and Planning Act, sets a societal risk threshold of four per cent of the property’s value. If the depreciation falls below this threshold, it is not eligible for any compensation. Therefore, analysing house prices provides a tangible, policy-relevant insight into who bears the local costs of the energy transition. This aligns with the Engineering and Policy Analysis (EPA) programme’s goal of employing quantitative methods to inform decision-making regarding grand societal challenges.
While prior studies have examined how turbines impact housing prices in the Netherlands, two important gaps still persist. First, the impact of offshore turbines on housing prices has not been examined in the Dutch context. Second, existing studies capture spatial variation through geographic categories but leave the role of socioeconomic factors almost entirely unexplored. Additionally, the Dutch estimates are based on methods that could introduce bias as turbine adoption is staggered. This raises questions about the current magnitude of these effects when staggered adoption is taken into account, and how these effects are distributed among communities exposed to turbines.
This thesis estimates the effects of onshore wind turbines using a Difference-in-Differences (DiD) design, based on a sample of 1:83 million housing transactions from 2010 to 2023 from the Nederlandse Vereniging van Makelaars (NVM), merged with WindStats turbine data and neighbourhood-level Socioeconomic Status (SES) data. To extend current literature and to account for the staggered nature of Dutch wind turbine construction, the analysis uses the Sun & Abraham (2021) estimator and systematically controls for socioeconomic composition, which prior Dutch studies did not. A new addition to current literature is the offshore component, which is deliberately descriptive: It documents coastal price trends associated with offshore wind farm development and clarifies why the Dutch data do not support credible causal identification.
The results show that the effects of onshore wind turbines are small, not robust across different control group definitions, highly localised, and statistically indistinguishable from zero under most alternatives. In the preferred specification, they are found to slightly lower nearby property prices by about 0:8% within two kilometres. This is roughly a fifth of the four per cent compensation threshold of the Omgevingswet, so most affected homeowners do not qualify for any compensation. The estimated price effect is smaller than previous Dutch estimates, primarily because the sample from 2010 to 2023 limits the identifying variation needed to estimate causal effects. The causal interpretation is further limited by imperfect pre-trends, though robustness checks suggest these do not drive the estimated effect. These effects are unlikely to be lower due to changes in perceptions of wind energy, as Dröes & Koster (2019) have shown that willingness to pay to live next to turbines is generally stable over time.
The most robustly tested finding in the heterogeneity analysis is that additional turbines do not produce larger effects, meaning they do not lead to higher price effects, while also reducing the number of properties exposed to turbines. Subgroup analyses indicate effects are strongest near turbines and in densely populated regions, although these differences between subgroups are not statistically significant. Additionally, while turbines are disproportionately sited in neighbourhoods with lower transaction prices, associated with lower SES, whether the price effect itself differs by SES could not be established. In all these analyses, none of the estimated effects approaches the four per cent compensation threshold set by the Omgevingswet. The offshore wind analysis remains descriptive rather than causal due to the small number of offshore parks, their recent clustered construction, a limited coastal sample, and the lack of visibility data, all of which prevent reliable causal identification.
These findings inform a siting strategy focused on reducing housing-market costs of turbines. The only well-supported principle is that clustering turbines concentrates price effects on fewer properties, without increasing their magnitude relative to single-turbine installations. The weaker, non-significant patterns suggest, cautiously, that turbines should be placed away from urbanised areas. Since onshore turbines are already mostly located in communities with lower prices, associated with lower SES, an efficiency-based siting strategy that ignores distributional risks will exacerbate existing inequalities in turbine placement. Where turbines are sited is therefore ultimately a political choice that should weigh minimising housing market impacts against the fair distribution of turbine placements; this thesis explicitly highlights this trade-off by analysing both the magnitude of effects and the populations most affected. More broadly, the small estimated effects imply that turbines have a limited impact on the Dutch housing market, inviting a shift in the debate toward more difficult-to-measure externalities, such as landscape and health concerns.
Inter-firm Network Resilience under Flood Shocks
Evidence from a Dynamic Network
Floods are among the climate change effects most likely to intensify, and they account for a disproportionate share of climate-related economic losses in the EU. Economic production is organized around complex, interconnected supply chains where sectors continuously buy from one another. Through buyer-supplier relationships, disruptions propagate to firms that were never directly exposed. Ignoring these inter-firm and inter-sectoral dependencies, therefore, risks underestimating the true economic burden of floods. This makes it important to understand not just how individual firms adapt, but how the network of interdependent firms as a whole regains its resilience after a shock.
While flood adaptation research has examined post-shock firm behaviors, these studies did not focus on how the network structure evolves because of them. The literature examines the changing network structure following a shock and the firm-level behaviors that influence it. However, it emphasizes the opportunity of researching what the restructuring of the network does to its long-term resilience. From this knowledge gap, the following research question is addressed: How do firm-level rewiring behaviors, including those exhibiting path dependency through firms’ memory of past shocks, reshape the inter-firm network structure and affect its resilience following a flood shock?
To address this gap, the EMERGO model developed within the NWO VIDI project “Social Tipping Points for Transformational Climate Change Adaptation” (grant number 191.015) was extended in three steps. First, a new sectoral interdependency structure was introduced between the capital goods, consumer goods, and consumer services sectors, capturing intermediate goods and services dependencies that were previously only implicit in the model (SQ1). Second, this was used to structure an explicit, dynamic inter-firm network in which supplier relationships evolve over time as firms enter, exit, and switch suppliers (SQ2). Third, several rewiring behaviors were implemented and tested: probabilistic rewiring based on competitiveness, rewiring toward market leaders, risk-averse rewiring based on suppliers’ past disruption history, and combinations of these. Each scenario was run 30 times and subjected to two sequential flood shocks, after which network-structure and resilience metrics were calculated and compared (SQ3, SQ4).
The key findings show two distinct patterns of network structure evolution: scenarios driven by competitiveness-based rewiring centralized gradually, while scenarios including market leader-based rewiring centralized rapidly early on. These structural differences mattered for resilience: the gradually centralizing scenarios showed better absorptive, adaptive, and restorative capacity after the first shock, while continuous rewiring improved resilience across all scenarios by the second shock. Firms’ memory of past shocks did not produce a visible difference in network structure, but it did improve restorative capacity under repeated shocks. This suggests resilience operates through two separate dimensions: a structural one, where the speed and pattern of network centralization shape resilience to the first shock, and a behavioral one, where memory of past disruptions improves recovery without changing the network structure itself.
These findings carry several policy implications. Excessive concentration around dominant suppliers, as seen in the pure Market Leader scenario, removes network redundancy and increases vulnerability to cascading failures, whereas performance-based rewiring, applied without such concentration, gradually strengthens resilience. Risk-averse, memory-based switching improves recovery but not initial shock absorption, indicating it should complement rather than replace other resilience measures. Because resilience effects differ by sector, policy should be sector-specific rather than uniform, and given the volatility observed in capital goods, sufficient inventory buffers appear especially important for capital-intensive sectors.
The study has several limitations. Attempts at sensitivity analysis revealed extreme sensitivity to the capital goods and intermediates utilization parameters. Moreover, the model showed no sensitivity to switching weight parameters and moderate sensitivity to supplier range parameters. Resilience could only reliably be assessed through inventory levels, meaning the analysis effectively measures inventory resilience rather than broader economic resilience. The strength of inter-sectoral dependencies modeled is also constrained to a relatively low, stable value, meaning the results reflect weaker interdependencies than may exist in practice. The capital goods sub-network’s resilience metrics could not be meaningfully interpreted due to high volatility.
Based on these findings, policymakers could discourage rapid, excessive supplier concentration around market leaders and instead encourage performance-based supplier selection, which produces a more distributed and resilient structure. Improving the availability of flood-risk information at the industry level could also support better-informed supplier decisions. Since resilience benefits differ across sectors, a uniform resilience policy is unlikely to be effective.
Future research should explore longer sequences of repeated shocks, the role of memory at the individual firm level, and less radical implementations of market leader preferences. Combining risk-averse rewiring with explicit firm-level adaptation actions would offer a more realistic representation of path dependency. Applying the full set of resilience metrics proposed by Bruckler et al. (2024) would give a more complete picture of network resilience. Finally, future research could include switching behavior from the supplier’s perspective and examine whether the model’s shock mechanism generalizes to other climate-related hazards beyond flooding. ...
While flood adaptation research has examined post-shock firm behaviors, these studies did not focus on how the network structure evolves because of them. The literature examines the changing network structure following a shock and the firm-level behaviors that influence it. However, it emphasizes the opportunity of researching what the restructuring of the network does to its long-term resilience. From this knowledge gap, the following research question is addressed: How do firm-level rewiring behaviors, including those exhibiting path dependency through firms’ memory of past shocks, reshape the inter-firm network structure and affect its resilience following a flood shock?
To address this gap, the EMERGO model developed within the NWO VIDI project “Social Tipping Points for Transformational Climate Change Adaptation” (grant number 191.015) was extended in three steps. First, a new sectoral interdependency structure was introduced between the capital goods, consumer goods, and consumer services sectors, capturing intermediate goods and services dependencies that were previously only implicit in the model (SQ1). Second, this was used to structure an explicit, dynamic inter-firm network in which supplier relationships evolve over time as firms enter, exit, and switch suppliers (SQ2). Third, several rewiring behaviors were implemented and tested: probabilistic rewiring based on competitiveness, rewiring toward market leaders, risk-averse rewiring based on suppliers’ past disruption history, and combinations of these. Each scenario was run 30 times and subjected to two sequential flood shocks, after which network-structure and resilience metrics were calculated and compared (SQ3, SQ4).
The key findings show two distinct patterns of network structure evolution: scenarios driven by competitiveness-based rewiring centralized gradually, while scenarios including market leader-based rewiring centralized rapidly early on. These structural differences mattered for resilience: the gradually centralizing scenarios showed better absorptive, adaptive, and restorative capacity after the first shock, while continuous rewiring improved resilience across all scenarios by the second shock. Firms’ memory of past shocks did not produce a visible difference in network structure, but it did improve restorative capacity under repeated shocks. This suggests resilience operates through two separate dimensions: a structural one, where the speed and pattern of network centralization shape resilience to the first shock, and a behavioral one, where memory of past disruptions improves recovery without changing the network structure itself.
These findings carry several policy implications. Excessive concentration around dominant suppliers, as seen in the pure Market Leader scenario, removes network redundancy and increases vulnerability to cascading failures, whereas performance-based rewiring, applied without such concentration, gradually strengthens resilience. Risk-averse, memory-based switching improves recovery but not initial shock absorption, indicating it should complement rather than replace other resilience measures. Because resilience effects differ by sector, policy should be sector-specific rather than uniform, and given the volatility observed in capital goods, sufficient inventory buffers appear especially important for capital-intensive sectors.
The study has several limitations. Attempts at sensitivity analysis revealed extreme sensitivity to the capital goods and intermediates utilization parameters. Moreover, the model showed no sensitivity to switching weight parameters and moderate sensitivity to supplier range parameters. Resilience could only reliably be assessed through inventory levels, meaning the analysis effectively measures inventory resilience rather than broader economic resilience. The strength of inter-sectoral dependencies modeled is also constrained to a relatively low, stable value, meaning the results reflect weaker interdependencies than may exist in practice. The capital goods sub-network’s resilience metrics could not be meaningfully interpreted due to high volatility.
Based on these findings, policymakers could discourage rapid, excessive supplier concentration around market leaders and instead encourage performance-based supplier selection, which produces a more distributed and resilient structure. Improving the availability of flood-risk information at the industry level could also support better-informed supplier decisions. Since resilience benefits differ across sectors, a uniform resilience policy is unlikely to be effective.
Future research should explore longer sequences of repeated shocks, the role of memory at the individual firm level, and less radical implementations of market leader preferences. Combining risk-averse rewiring with explicit firm-level adaptation actions would offer a more realistic representation of path dependency. Applying the full set of resilience metrics proposed by Bruckler et al. (2024) would give a more complete picture of network resilience. Finally, future research could include switching behavior from the supplier’s perspective and examine whether the model’s shock mechanism generalizes to other climate-related hazards beyond flooding. ...
Floods are among the climate change effects most likely to intensify, and they account for a disproportionate share of climate-related economic losses in the EU. Economic production is organized around complex, interconnected supply chains where sectors continuously buy from one another. Through buyer-supplier relationships, disruptions propagate to firms that were never directly exposed. Ignoring these inter-firm and inter-sectoral dependencies, therefore, risks underestimating the true economic burden of floods. This makes it important to understand not just how individual firms adapt, but how the network of interdependent firms as a whole regains its resilience after a shock.
While flood adaptation research has examined post-shock firm behaviors, these studies did not focus on how the network structure evolves because of them. The literature examines the changing network structure following a shock and the firm-level behaviors that influence it. However, it emphasizes the opportunity of researching what the restructuring of the network does to its long-term resilience. From this knowledge gap, the following research question is addressed: How do firm-level rewiring behaviors, including those exhibiting path dependency through firms’ memory of past shocks, reshape the inter-firm network structure and affect its resilience following a flood shock?
To address this gap, the EMERGO model developed within the NWO VIDI project “Social Tipping Points for Transformational Climate Change Adaptation” (grant number 191.015) was extended in three steps. First, a new sectoral interdependency structure was introduced between the capital goods, consumer goods, and consumer services sectors, capturing intermediate goods and services dependencies that were previously only implicit in the model (SQ1). Second, this was used to structure an explicit, dynamic inter-firm network in which supplier relationships evolve over time as firms enter, exit, and switch suppliers (SQ2). Third, several rewiring behaviors were implemented and tested: probabilistic rewiring based on competitiveness, rewiring toward market leaders, risk-averse rewiring based on suppliers’ past disruption history, and combinations of these. Each scenario was run 30 times and subjected to two sequential flood shocks, after which network-structure and resilience metrics were calculated and compared (SQ3, SQ4).
The key findings show two distinct patterns of network structure evolution: scenarios driven by competitiveness-based rewiring centralized gradually, while scenarios including market leader-based rewiring centralized rapidly early on. These structural differences mattered for resilience: the gradually centralizing scenarios showed better absorptive, adaptive, and restorative capacity after the first shock, while continuous rewiring improved resilience across all scenarios by the second shock. Firms’ memory of past shocks did not produce a visible difference in network structure, but it did improve restorative capacity under repeated shocks. This suggests resilience operates through two separate dimensions: a structural one, where the speed and pattern of network centralization shape resilience to the first shock, and a behavioral one, where memory of past disruptions improves recovery without changing the network structure itself.
These findings carry several policy implications. Excessive concentration around dominant suppliers, as seen in the pure Market Leader scenario, removes network redundancy and increases vulnerability to cascading failures, whereas performance-based rewiring, applied without such concentration, gradually strengthens resilience. Risk-averse, memory-based switching improves recovery but not initial shock absorption, indicating it should complement rather than replace other resilience measures. Because resilience effects differ by sector, policy should be sector-specific rather than uniform, and given the volatility observed in capital goods, sufficient inventory buffers appear especially important for capital-intensive sectors.
The study has several limitations. Attempts at sensitivity analysis revealed extreme sensitivity to the capital goods and intermediates utilization parameters. Moreover, the model showed no sensitivity to switching weight parameters and moderate sensitivity to supplier range parameters. Resilience could only reliably be assessed through inventory levels, meaning the analysis effectively measures inventory resilience rather than broader economic resilience. The strength of inter-sectoral dependencies modeled is also constrained to a relatively low, stable value, meaning the results reflect weaker interdependencies than may exist in practice. The capital goods sub-network’s resilience metrics could not be meaningfully interpreted due to high volatility.
Based on these findings, policymakers could discourage rapid, excessive supplier concentration around market leaders and instead encourage performance-based supplier selection, which produces a more distributed and resilient structure. Improving the availability of flood-risk information at the industry level could also support better-informed supplier decisions. Since resilience benefits differ across sectors, a uniform resilience policy is unlikely to be effective.
Future research should explore longer sequences of repeated shocks, the role of memory at the individual firm level, and less radical implementations of market leader preferences. Combining risk-averse rewiring with explicit firm-level adaptation actions would offer a more realistic representation of path dependency. Applying the full set of resilience metrics proposed by Bruckler et al. (2024) would give a more complete picture of network resilience. Finally, future research could include switching behavior from the supplier’s perspective and examine whether the model’s shock mechanism generalizes to other climate-related hazards beyond flooding.
While flood adaptation research has examined post-shock firm behaviors, these studies did not focus on how the network structure evolves because of them. The literature examines the changing network structure following a shock and the firm-level behaviors that influence it. However, it emphasizes the opportunity of researching what the restructuring of the network does to its long-term resilience. From this knowledge gap, the following research question is addressed: How do firm-level rewiring behaviors, including those exhibiting path dependency through firms’ memory of past shocks, reshape the inter-firm network structure and affect its resilience following a flood shock?
To address this gap, the EMERGO model developed within the NWO VIDI project “Social Tipping Points for Transformational Climate Change Adaptation” (grant number 191.015) was extended in three steps. First, a new sectoral interdependency structure was introduced between the capital goods, consumer goods, and consumer services sectors, capturing intermediate goods and services dependencies that were previously only implicit in the model (SQ1). Second, this was used to structure an explicit, dynamic inter-firm network in which supplier relationships evolve over time as firms enter, exit, and switch suppliers (SQ2). Third, several rewiring behaviors were implemented and tested: probabilistic rewiring based on competitiveness, rewiring toward market leaders, risk-averse rewiring based on suppliers’ past disruption history, and combinations of these. Each scenario was run 30 times and subjected to two sequential flood shocks, after which network-structure and resilience metrics were calculated and compared (SQ3, SQ4).
The key findings show two distinct patterns of network structure evolution: scenarios driven by competitiveness-based rewiring centralized gradually, while scenarios including market leader-based rewiring centralized rapidly early on. These structural differences mattered for resilience: the gradually centralizing scenarios showed better absorptive, adaptive, and restorative capacity after the first shock, while continuous rewiring improved resilience across all scenarios by the second shock. Firms’ memory of past shocks did not produce a visible difference in network structure, but it did improve restorative capacity under repeated shocks. This suggests resilience operates through two separate dimensions: a structural one, where the speed and pattern of network centralization shape resilience to the first shock, and a behavioral one, where memory of past disruptions improves recovery without changing the network structure itself.
These findings carry several policy implications. Excessive concentration around dominant suppliers, as seen in the pure Market Leader scenario, removes network redundancy and increases vulnerability to cascading failures, whereas performance-based rewiring, applied without such concentration, gradually strengthens resilience. Risk-averse, memory-based switching improves recovery but not initial shock absorption, indicating it should complement rather than replace other resilience measures. Because resilience effects differ by sector, policy should be sector-specific rather than uniform, and given the volatility observed in capital goods, sufficient inventory buffers appear especially important for capital-intensive sectors.
The study has several limitations. Attempts at sensitivity analysis revealed extreme sensitivity to the capital goods and intermediates utilization parameters. Moreover, the model showed no sensitivity to switching weight parameters and moderate sensitivity to supplier range parameters. Resilience could only reliably be assessed through inventory levels, meaning the analysis effectively measures inventory resilience rather than broader economic resilience. The strength of inter-sectoral dependencies modeled is also constrained to a relatively low, stable value, meaning the results reflect weaker interdependencies than may exist in practice. The capital goods sub-network’s resilience metrics could not be meaningfully interpreted due to high volatility.
Based on these findings, policymakers could discourage rapid, excessive supplier concentration around market leaders and instead encourage performance-based supplier selection, which produces a more distributed and resilient structure. Improving the availability of flood-risk information at the industry level could also support better-informed supplier decisions. Since resilience benefits differ across sectors, a uniform resilience policy is unlikely to be effective.
Future research should explore longer sequences of repeated shocks, the role of memory at the individual firm level, and less radical implementations of market leader preferences. Combining risk-averse rewiring with explicit firm-level adaptation actions would offer a more realistic representation of path dependency. Applying the full set of resilience metrics proposed by Bruckler et al. (2024) would give a more complete picture of network resilience. Finally, future research could include switching behavior from the supplier’s perspective and examine whether the model’s shock mechanism generalizes to other climate-related hazards beyond flooding.
Designing robust global value chains under trade-policy uncertainty
A regret-based network design framework
Trade measures change within months, while the production networks they tax take years to build, so a firm commits its network before it knows which measures it will face. Duty falls on the full customs value at every border a good crosses, so where each production step sits decides how much a measure can tax.
Trade economics prices duty on the full value of goods at each border they cross, but takes the production network as given. Network design chooses locations, but treats tariffs as a known cost. This thesis asks how a firm should design a multi-stage global value chain so that it remains cost-efficient under normal trading conditions and limits its exposure to trade policy it cannot foresee.
Its main contribution is a two-stage robust optimisation model that needs no probabilities for political decisions. The firm first commits the locations where each production step can run, and once a measure is known it adjusts production and shipments within those locations. Six regimes represent future policy. Each is a documented class of measure, such as trade defence, safeguards or export controls. It has a rule for which shipments it reaches and a cap, the increment it adds in full to their tariff. The model accepts a design only if, under every regime, its worst-case cost stays within a chosen percentage of the best the firm could have secured had it known that regime. Among the accepted designs, it commits the one that is cheapest in normal times. This adapts the $p$-robustness of Snyder and Daskin to trade policy, and tightening the percentage traces what each level of protection costs. To the author's knowledge, no earlier model combines production across successive stages, uncertainty specific to each trade measure and a tariff base that follows the design.
Applied to three constructed chain architectures and to a solar photovoltaic chain serving the United States, the model yields three findings. First, a firm can protect itself without forecasting tariff rates, because what a measure reaches matters more than its rate. Defending against bilateral escalation costs up to 12 percent more than the cheapest network under normal tariffs. Defending against anti-circumvention, at the same cap, costs nothing extra. Second, a few alternative locations deliver most of the protection. A design's worst shock cost is the most that any regime adds to its cost. A second location per step achieves 91 to 94 percent of the cut in worst shock cost that allowing every location achieves. The most protected designs cost about 4 to 10 percent more in normal times, 6.6 percent in solar, and each unit of that extra cost removes about ten units of worst shock cost. Third, protection stops where the remaining exposure is cheaper to keep than to remove. The most protected solar design cuts the worst shock cost from 189.3 to 5.5 MUSD per GW. It keeps wafer and cell production in China, because leaving China would raise its extra cost in normal times from 6.6 to 9.2 percent.
A firm should hold its alternatives where its bill of materials forces goods across a border, at merges that join separately made components, at the closing step and where supplied inputs arrive. These steps account for 155 of the 164 changes the model makes as protection tightens. Because value jumps at each merge, moving these steps towards the market leaves the crossing on cheaper goods upstream. This lowers the value a tariff can reach and protects against regimes on movements into a market. Conditions outside the production network, where the firm sells and where it buys, decide how much protection it needs. ...
Trade economics prices duty on the full value of goods at each border they cross, but takes the production network as given. Network design chooses locations, but treats tariffs as a known cost. This thesis asks how a firm should design a multi-stage global value chain so that it remains cost-efficient under normal trading conditions and limits its exposure to trade policy it cannot foresee.
Its main contribution is a two-stage robust optimisation model that needs no probabilities for political decisions. The firm first commits the locations where each production step can run, and once a measure is known it adjusts production and shipments within those locations. Six regimes represent future policy. Each is a documented class of measure, such as trade defence, safeguards or export controls. It has a rule for which shipments it reaches and a cap, the increment it adds in full to their tariff. The model accepts a design only if, under every regime, its worst-case cost stays within a chosen percentage of the best the firm could have secured had it known that regime. Among the accepted designs, it commits the one that is cheapest in normal times. This adapts the $p$-robustness of Snyder and Daskin to trade policy, and tightening the percentage traces what each level of protection costs. To the author's knowledge, no earlier model combines production across successive stages, uncertainty specific to each trade measure and a tariff base that follows the design.
Applied to three constructed chain architectures and to a solar photovoltaic chain serving the United States, the model yields three findings. First, a firm can protect itself without forecasting tariff rates, because what a measure reaches matters more than its rate. Defending against bilateral escalation costs up to 12 percent more than the cheapest network under normal tariffs. Defending against anti-circumvention, at the same cap, costs nothing extra. Second, a few alternative locations deliver most of the protection. A design's worst shock cost is the most that any regime adds to its cost. A second location per step achieves 91 to 94 percent of the cut in worst shock cost that allowing every location achieves. The most protected designs cost about 4 to 10 percent more in normal times, 6.6 percent in solar, and each unit of that extra cost removes about ten units of worst shock cost. Third, protection stops where the remaining exposure is cheaper to keep than to remove. The most protected solar design cuts the worst shock cost from 189.3 to 5.5 MUSD per GW. It keeps wafer and cell production in China, because leaving China would raise its extra cost in normal times from 6.6 to 9.2 percent.
A firm should hold its alternatives where its bill of materials forces goods across a border, at merges that join separately made components, at the closing step and where supplied inputs arrive. These steps account for 155 of the 164 changes the model makes as protection tightens. Because value jumps at each merge, moving these steps towards the market leaves the crossing on cheaper goods upstream. This lowers the value a tariff can reach and protects against regimes on movements into a market. Conditions outside the production network, where the firm sells and where it buys, decide how much protection it needs. ...
Trade measures change within months, while the production networks they tax take years to build, so a firm commits its network before it knows which measures it will face. Duty falls on the full customs value at every border a good crosses, so where each production step sits decides how much a measure can tax.
Trade economics prices duty on the full value of goods at each border they cross, but takes the production network as given. Network design chooses locations, but treats tariffs as a known cost. This thesis asks how a firm should design a multi-stage global value chain so that it remains cost-efficient under normal trading conditions and limits its exposure to trade policy it cannot foresee.
Its main contribution is a two-stage robust optimisation model that needs no probabilities for political decisions. The firm first commits the locations where each production step can run, and once a measure is known it adjusts production and shipments within those locations. Six regimes represent future policy. Each is a documented class of measure, such as trade defence, safeguards or export controls. It has a rule for which shipments it reaches and a cap, the increment it adds in full to their tariff. The model accepts a design only if, under every regime, its worst-case cost stays within a chosen percentage of the best the firm could have secured had it known that regime. Among the accepted designs, it commits the one that is cheapest in normal times. This adapts the $p$-robustness of Snyder and Daskin to trade policy, and tightening the percentage traces what each level of protection costs. To the author's knowledge, no earlier model combines production across successive stages, uncertainty specific to each trade measure and a tariff base that follows the design.
Applied to three constructed chain architectures and to a solar photovoltaic chain serving the United States, the model yields three findings. First, a firm can protect itself without forecasting tariff rates, because what a measure reaches matters more than its rate. Defending against bilateral escalation costs up to 12 percent more than the cheapest network under normal tariffs. Defending against anti-circumvention, at the same cap, costs nothing extra. Second, a few alternative locations deliver most of the protection. A design's worst shock cost is the most that any regime adds to its cost. A second location per step achieves 91 to 94 percent of the cut in worst shock cost that allowing every location achieves. The most protected designs cost about 4 to 10 percent more in normal times, 6.6 percent in solar, and each unit of that extra cost removes about ten units of worst shock cost. Third, protection stops where the remaining exposure is cheaper to keep than to remove. The most protected solar design cuts the worst shock cost from 189.3 to 5.5 MUSD per GW. It keeps wafer and cell production in China, because leaving China would raise its extra cost in normal times from 6.6 to 9.2 percent.
A firm should hold its alternatives where its bill of materials forces goods across a border, at merges that join separately made components, at the closing step and where supplied inputs arrive. These steps account for 155 of the 164 changes the model makes as protection tightens. Because value jumps at each merge, moving these steps towards the market leaves the crossing on cheaper goods upstream. This lowers the value a tariff can reach and protects against regimes on movements into a market. Conditions outside the production network, where the firm sells and where it buys, decide how much protection it needs.
Trade economics prices duty on the full value of goods at each border they cross, but takes the production network as given. Network design chooses locations, but treats tariffs as a known cost. This thesis asks how a firm should design a multi-stage global value chain so that it remains cost-efficient under normal trading conditions and limits its exposure to trade policy it cannot foresee.
Its main contribution is a two-stage robust optimisation model that needs no probabilities for political decisions. The firm first commits the locations where each production step can run, and once a measure is known it adjusts production and shipments within those locations. Six regimes represent future policy. Each is a documented class of measure, such as trade defence, safeguards or export controls. It has a rule for which shipments it reaches and a cap, the increment it adds in full to their tariff. The model accepts a design only if, under every regime, its worst-case cost stays within a chosen percentage of the best the firm could have secured had it known that regime. Among the accepted designs, it commits the one that is cheapest in normal times. This adapts the $p$-robustness of Snyder and Daskin to trade policy, and tightening the percentage traces what each level of protection costs. To the author's knowledge, no earlier model combines production across successive stages, uncertainty specific to each trade measure and a tariff base that follows the design.
Applied to three constructed chain architectures and to a solar photovoltaic chain serving the United States, the model yields three findings. First, a firm can protect itself without forecasting tariff rates, because what a measure reaches matters more than its rate. Defending against bilateral escalation costs up to 12 percent more than the cheapest network under normal tariffs. Defending against anti-circumvention, at the same cap, costs nothing extra. Second, a few alternative locations deliver most of the protection. A design's worst shock cost is the most that any regime adds to its cost. A second location per step achieves 91 to 94 percent of the cut in worst shock cost that allowing every location achieves. The most protected designs cost about 4 to 10 percent more in normal times, 6.6 percent in solar, and each unit of that extra cost removes about ten units of worst shock cost. Third, protection stops where the remaining exposure is cheaper to keep than to remove. The most protected solar design cuts the worst shock cost from 189.3 to 5.5 MUSD per GW. It keeps wafer and cell production in China, because leaving China would raise its extra cost in normal times from 6.6 to 9.2 percent.
A firm should hold its alternatives where its bill of materials forces goods across a border, at merges that join separately made components, at the closing step and where supplied inputs arrive. These steps account for 155 of the 164 changes the model makes as protection tightens. Because value jumps at each merge, moving these steps towards the market leaves the crossing on cheaper goods upstream. This lowers the value a tariff can reach and protects against regimes on movements into a market. Conditions outside the production network, where the firm sells and where it buys, decide how much protection it needs.
Offshore wind projects require high upfront capital investment and have long payback periods. This makes revenue uncertainty a key issue for financing and investment decisions. Support mechanisms such as Contracts for Difference are widely used to reduce exposure to electricity price risk. However, conventional production based designs do not fully remove project level revenue variability or protect against downside financial risk, especially when electricity prices are volatile.
This thesis evaluates the financial risk mitigation performance of capability based Contracts for Difference for offshore wind projects. The analysis uses a project level cash flow perspective and compares capability based settlement with conventional support mechanisms and full merchant market exposure. A reference offshore wind project based on the IEA 740 MW offshore wind farm is applied consistently to the Dutch and Danish electricity markets under multiple electricity price scenarios.
The evaluation focuses on revenue stability, downside risk, bankability indicators, consumer outcomes, and price based basis risk. Monthly cash flow modelling is used to assess revenue volatility and tail risk. Alternative reference price constructions are also tested to examine how reference price design affects settlement outcomes and basis risk.
The results show that capability based settlement can significantly improve revenue stability and reduce downside risk relative to production based mechanisms. These effects are strongest in scenarios with lower average electricity prices and higher price volatility, where negative price periods and curtailment incentives occur more often. However, improved stability alone does not ensure project profitability or bankability. These outcomes remain mainly driven by the strike price level. Reference price construction plays a key role in shaping basis risk, while consumer outcomes depend strongly on strike price assumptions and market conditions.
Overall, this thesis provides quantitative evidence on the risk mitigation properties of capability based Contracts for Difference and clarifies the trade off involved. It shows why revenue stability, profitability, and bankability should be assessed separately when evaluating offshore wind support mechanisms. The findings highlight that settlement design, strike price calibration, and market context are critical for the performance of future support schemes. ...
This thesis evaluates the financial risk mitigation performance of capability based Contracts for Difference for offshore wind projects. The analysis uses a project level cash flow perspective and compares capability based settlement with conventional support mechanisms and full merchant market exposure. A reference offshore wind project based on the IEA 740 MW offshore wind farm is applied consistently to the Dutch and Danish electricity markets under multiple electricity price scenarios.
The evaluation focuses on revenue stability, downside risk, bankability indicators, consumer outcomes, and price based basis risk. Monthly cash flow modelling is used to assess revenue volatility and tail risk. Alternative reference price constructions are also tested to examine how reference price design affects settlement outcomes and basis risk.
The results show that capability based settlement can significantly improve revenue stability and reduce downside risk relative to production based mechanisms. These effects are strongest in scenarios with lower average electricity prices and higher price volatility, where negative price periods and curtailment incentives occur more often. However, improved stability alone does not ensure project profitability or bankability. These outcomes remain mainly driven by the strike price level. Reference price construction plays a key role in shaping basis risk, while consumer outcomes depend strongly on strike price assumptions and market conditions.
Overall, this thesis provides quantitative evidence on the risk mitigation properties of capability based Contracts for Difference and clarifies the trade off involved. It shows why revenue stability, profitability, and bankability should be assessed separately when evaluating offshore wind support mechanisms. The findings highlight that settlement design, strike price calibration, and market context are critical for the performance of future support schemes. ...
Offshore wind projects require high upfront capital investment and have long payback periods. This makes revenue uncertainty a key issue for financing and investment decisions. Support mechanisms such as Contracts for Difference are widely used to reduce exposure to electricity price risk. However, conventional production based designs do not fully remove project level revenue variability or protect against downside financial risk, especially when electricity prices are volatile.
This thesis evaluates the financial risk mitigation performance of capability based Contracts for Difference for offshore wind projects. The analysis uses a project level cash flow perspective and compares capability based settlement with conventional support mechanisms and full merchant market exposure. A reference offshore wind project based on the IEA 740 MW offshore wind farm is applied consistently to the Dutch and Danish electricity markets under multiple electricity price scenarios.
The evaluation focuses on revenue stability, downside risk, bankability indicators, consumer outcomes, and price based basis risk. Monthly cash flow modelling is used to assess revenue volatility and tail risk. Alternative reference price constructions are also tested to examine how reference price design affects settlement outcomes and basis risk.
The results show that capability based settlement can significantly improve revenue stability and reduce downside risk relative to production based mechanisms. These effects are strongest in scenarios with lower average electricity prices and higher price volatility, where negative price periods and curtailment incentives occur more often. However, improved stability alone does not ensure project profitability or bankability. These outcomes remain mainly driven by the strike price level. Reference price construction plays a key role in shaping basis risk, while consumer outcomes depend strongly on strike price assumptions and market conditions.
Overall, this thesis provides quantitative evidence on the risk mitigation properties of capability based Contracts for Difference and clarifies the trade off involved. It shows why revenue stability, profitability, and bankability should be assessed separately when evaluating offshore wind support mechanisms. The findings highlight that settlement design, strike price calibration, and market context are critical for the performance of future support schemes.
This thesis evaluates the financial risk mitigation performance of capability based Contracts for Difference for offshore wind projects. The analysis uses a project level cash flow perspective and compares capability based settlement with conventional support mechanisms and full merchant market exposure. A reference offshore wind project based on the IEA 740 MW offshore wind farm is applied consistently to the Dutch and Danish electricity markets under multiple electricity price scenarios.
The evaluation focuses on revenue stability, downside risk, bankability indicators, consumer outcomes, and price based basis risk. Monthly cash flow modelling is used to assess revenue volatility and tail risk. Alternative reference price constructions are also tested to examine how reference price design affects settlement outcomes and basis risk.
The results show that capability based settlement can significantly improve revenue stability and reduce downside risk relative to production based mechanisms. These effects are strongest in scenarios with lower average electricity prices and higher price volatility, where negative price periods and curtailment incentives occur more often. However, improved stability alone does not ensure project profitability or bankability. These outcomes remain mainly driven by the strike price level. Reference price construction plays a key role in shaping basis risk, while consumer outcomes depend strongly on strike price assumptions and market conditions.
Overall, this thesis provides quantitative evidence on the risk mitigation properties of capability based Contracts for Difference and clarifies the trade off involved. It shows why revenue stability, profitability, and bankability should be assessed separately when evaluating offshore wind support mechanisms. The findings highlight that settlement design, strike price calibration, and market context are critical for the performance of future support schemes.