the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 04 Dec 2025
Spatial and Economic Prioritization for Distributed Wind
Abstract. This study investigates how distributed wind (DW) energy could be strategically deployed in areas with elevated energy burdens by analyzing spatial, economic, and demographic factors. We use a set of metrics that incorporate residential and macroeconomic variables, including algebraic transformations of energy burden to better capture affordability across different income levels. These metrics are correlated with demand-adjusted annual energy production, which reflects DW potential across residential, commercial, and industrial sectors. Using mixed-effects modeling and state-level fixed-effects regressions, we identify key covariates associated with high energy burden. Our results reveal significant geographic variability both across and within states, with stronger correlations between DW potential and residential energy burden in regions where burden is closely tied to poverty rates and agricultural activities. Based on these patterns, we group states into two categories and special cases reflecting correlation strength and DW potential, highlighting potential opportunities to improve energy affordability through targeted siting of distributed wind projects.
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Status: final response (author comments only)
- RC1: 'Comment on wes-2025-213', Anonymous Referee #1, 10 Dec 2025
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RC2: 'Comment on wes-2025-213', Anonymous Referee #2, 18 Dec 2025
This paper leverages existing data sets such as the Distributed Wind Energy Futures Study to evaluate the overlap of potential distributed wind energy generation with areas experiencing large energy burden. The study provides a novel level of spatial resolution and represents a useful step forward in understanding how distributed wind maybe deployed to benefit specific communities. The objectives, methods, results, and discussion are generally well written and easy to follow. However, I have found two major issues and a few minor issues that I would like to see revised ahead of publication.
Major Issue 1:
Given the dependence upon DWEFS AEP for many of the findings, I did not find much discussion about the methods of the DWEFS and what limitations those previous methods might impart on this study. The most significant concern I have is based on Table 7 in Lockshin et. al, 2025. In that table, Lockshin presents AEP results for two scenarios: one based on technical viability, and another based on cost viability. Which of these did you use? If you used AEP based on just technical viability, then this seems to directly conflict with the underlying focus on energy affordability. If you used AEP based on cost-viability, are these results dependent upon the status of the federal investment tax credit or incentives? If so, can you comment on the potential impact from recent changes to incentives such as the investment tax credit?
Another related concern that I have is ensuring that the DWEFS data and publications are being clearly referenced. In Line 36 and Table 1 you reference the “Distributed Wind Energy Futures Study (Lockshin et al., 2025).” However, this specific reference is “A parcel-level evaluation of distributed wind opportunity in the contiguous united states” which is not the DWEFS report. Can you please clarify in the text how this reference and the DWEFS report are related? Furthermore, when you mention the DWEFS in the text (line 42) you also reference the DOE WETO Wind Data Hub where the data can be accessed. When I follow the link in this reference (https://wdh.energy.gov/ project/dw), it does not take me to a specific data set. After some searching, I believe the link should be https://wdh.energy.gov/project/dw/data or https://wdh.energy.gov/ds/dw/btm.
Major Issue 2:
There are instances where the positive impact of DW on energy affordability is taken for granted or is perhaps just a bit overstated. For example, on line 119 you state “we generate a weighted ranking to identify states where distributed wind deployment could be most effective at improving residential energy affordability.” Another example is on line 349, where the conclusion states “Taken together, these findings enhance our understanding of how distributed wind could impact energy affordability and offer guidance on where such solutions are most likely to have meaningful impact.” In these instances, I am not convinced that you have actually evaluated how DW could positively impact affordability. To your credit, you have clearly shown the relationship of potential DW energy generation to regions where energy affordability is a challenge. However, you have not presented any metrics explicitly demonstrating that DW is more affordable than the existing energy supply. Although this is a major concern, I believe the concern could be easily remedied by removing any statements that overstate impact of DW’s affordability. Alternatively, you could provide support of DW’s affordability compared to existing energy sources, but this would require much more effort.
Minor issues:
Line 30 – You mention that access to renewable energy technologies could be explored as a potential avenue of relief for those affected by high EB. Could you add a sentence or two explaining why you have chosen to explore distributed wind, as opposed to other technologies such as solar PV? Or perhaps a similar study for PV is also warranted?
Multiple locations – DW acronym is used inconsistently. For example, see lines 44 and 46 where both “DW” and “distributed wind” are used.
Lines 120, 127, 205, and 279 – Equation numbers are not present. It seems there is likely an equation missing, or at least formatting issues with the equation references.
Figure 1 – The caption states “County-level ((e) 2025 – (f) 2035),” but county level results are (c)2025 – (d)2035.
Line 282 – Energy burden acronym is already defined earlier in document
Multiple locations – There are many paragraphs that appear as only one or two sentences. I would combine many of these smaller paragraphs together into larger, standard paragraph lengths.
Citation: https://doi.org/10.5194/wes-2025-213-RC2 -
RC3: 'Comment on wes-2025-213', Anonymous Referee #3, 03 Jan 2026
Overall Comment
The manuscript addresses an important and timely question at the intersection of energy burden and distributed wind deployment opportunity. However, because the grouping framework and regression analysis form the backbone of the prioritization argument, the reader needs additional clarification, justification, and methodological transparency before the results can be fully evaluated. The concerns listed below combine feedback from two co-reviewers.
Major Concerns from Reviewers- Missing Composite Scoring Equation
The state grouping framework is a central contribution of this manuscript; however, it is currently not possible to fully evaluate or reproduce this framework because the composite scoring equation is missing (Eq. ??). Further, because the composite score directly determines state rankings and group assignments, the absence of this equation prevents a thorough methodological review and raises concerns about reproducibility. Recommendation: Please insert the full composite scoring equation, clearly defining all terms, weights, normalization steps, and ranking logic. - Clarification and Justification of the State Grouping Logic
Closely related to the previous concern, the manuscript would benefit from clearer justification of the grouping logic used to classify states. How was grouping determined? Why two groups and special cases? The reasoning for this particular approach is missing in the methodology.
Currently, grouping appears to rely on a combination of composite ranking, correlation between EB and AEPDemand, and narrative “special cases,” which makes the framework difficult to interpret consistently. Recommendation: present a 2×2 quadrant categorization based on need (energy burden level), and Opportunity (AEPDemand level) with spatial correlation or overlap used as an additional diagnostic indicator rather than the primary grouping criterion. Such a framework could help clarify the logic of the composite score and make prioritization decisions more transparent. Or, add more justification for a correlation-driven grouping approach (The authors acknowledge limitations of this approach for states such as California and Texas). - Limited Interpretation and Integration of Regression Results
The regression analysis described in Section 2.5 is presented as an important analytical component of the study; however, its role in the overall framework is unclear. While the models are described as explanatory rather than causal, the manuscript does not clearly explain how the regression results inform the grouping framework or broader conclusions. Specifically, it is unclear whether the regression results are intended to justify the weighting or thresholds used in the composite score, whether they are meant to validate the grouping framework, whether they will be used for future prediction or scenario analysis, or whether they serve only as descriptive background analysis.
In addition, the manuscript provides limited interpretation of regression coefficients in substantive or policy-relevant terms. Key modeling choices are also insufficiently motivated, such as the use of a linear specification, the selection of covariates, and the appropriateness of state-level aggregation given acknowledged spatial heterogeneity within states. As a result, the regression results feel analytically disconnected from the grouping framework and do not clearly advance the decision-making narrative of the paper. Recommendation: Please expand the discussion of the regression models to clarify their analytical purpose, justify key modeling choices, and explain how (if at all) the results feed into the grouping framework or future applications. - Background and Contextualization
The authors find “stronger correlations between DW potential and residential energy burden in regions where burden is closely tied to poverty rates and agricultural activities.” The authors also note on Line 170 that to better understand energy affordability, future work must explore “broader economic and geographic factors.” The intersection between distributed wind potential and rural markets has been getting more and more attention, especially under DOE’s Rural and Agricultural Income and Savings from Renewable Energy (RAISE) Initiative. Contextualizing the landscape of current trends (rural electrification, transition away from natural gas) could better inform where there are particular regions or intrastate pockets that may be more suitable for DW. Is it possible to disaggregate AEPDemand further by agricultural sector?
Minor Concerns from Reviewers- Proofreading and Clarity / Grammatical Considerations
The manuscript would benefit from a careful proofreading pass once the major concerns are addressed.
Line 25: Numbers under 10 should be spelled out. - Technical Details
The authors note that “Distributed wind systems typically consist of a single wind turbine or several turbines at heights between 30 and 60 m” but BTM-project for industrial energy users are typically large – greater than 1 MW, featuring turbines at heights of 80+m. There are some mid-sized DW projects for industrial energy users closer to the 60m mark, but it may be helpful to delineate how DW technical specifications may change based on end-user (e.g., residential is likely to be small wind turbines [10kW or less, at 30m hub height] vs. industrial is more likely to be larger wind turbines).
Line 22: Stimulating local economic development is a strong claim for BTM systems, which are the focus of this analysis. This would be more common for a utility-scale FOM system where a larger workforce is needed to support development, and where there may be potential for local tax revenue generation or similar.
The analysis draws on outputs from the DWEFS, which utilizes threshold capital expenditure to show economic feasibility based on maximum viable system costs. Maximum system costs will likely not be feasible for high EB/low NER households. Do the authors account for this in the analysis? Also please define the NER metric earlier in the manuscript, as you do with energy burden in Line 23.
Citation: https://doi.org/10.5194/wes-2025-213-RC3 - Missing Composite Scoring Equation
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- 1
The manuscript Spatial and economic prioritization for distributed wind by Abril Guevara et al. explores locations that behind-the-meter distributed wind energy could be deployed to benefit customers experiencing high energy burden. One of the underlying datasets to the analysis, the Distributed Wind Energy Futures Study, has a valuable resolution to provide localized guidance as to where distributed wind projects could be advantageously deployed.
While this manuscript has potential to be helpful in distributed wind decision-making, I have two significant concerns and urge major revisions prior to publication.
Major concerns:
1. Categorizing states with low wind resource into Group 1 that represents High Economic Burden and High Distributed Wind Potential
Lines 204-218: Is it appropriate to include states like Georgia and Louisiana in Group 1: High-Need, High-Potential States? High-need, sure. But high-potential? You even state on Lines 214-216 that Louisiana’s AEP and AEPDemand are low and assign all the reasoning behind the placement into Group 1 to EB. The wind resource in states like Georgia and Louisiana is quite low. Take a look at the USGS Wind Turbine Database. If large utility-scale developers are cautious about building wind farms with higher hub heights in such states, is it acceptable to suggest that Louisiana and Georgia have high potential for lower hub height BTM DW? I recommend reevaluating this dual nature classification when some states are heavily skewed by one of the two considerations.
Lines 232-237: I hold the same concern for Alabama as I do for Louisiana and Georgia. I completely agree with your statement that the need is “urgent” from an EB standpoint, but what are you suggesting here for DW’s role? Again, this is a state with very low wind resource and I urge caution on drawing relationships concerning the potential of DW relative to high EB. Alabama is indeed noteworthy when it comes to EB. This does not imply that DW will be a successful solution to that problem.
Lines 278-270: Again, I am deeply concerned about the inclusion of states like Georgia and Louisiana, which represent some of the lowest wind resource in the continental United States, alongside Iowa, which has some of the highest wind resource, in Group 1 which you characterize as having high residential wind potential.
Lines 296-297: Particularly for Alabama, are there regions that you identified that have high poverty rates, agricultural industry, and viable wind resources? If so, please add them to the Special Cases discussion so that energy planners in these specific locations can benefit from your analysis. Also, how are you defining “viable wind resources”?
Lines 311-314: “It is important to clarify that while variables like unemployment and poverty frequently emerge as key covariates in explaining EB, this does not imply that distributed wind deployment will directly reduce those underlying socioeconomic conditions. However, by potentially lowering energy costs in areas where these conditions are prevalent, distributed wind may help ease energy-related hardship and indirectly contribute to improved quality of life.” This is a great takeaway and I think you should use it as a caution alongside your results much earlier in the text.
2. Concerns about the methodology, including weighting procedures and adding in counterbalancing factors to better align with the study aims. Additionally, some level of proofreading would have avoided the most significant equation being dropped, which made understanding the analysis difficult.
Lines 120, 126, 205, and 279: The reference to an equation is listed as Eq. ??
Line 120: Why does the ranking emphasize correlation strength so heavily, and how did you decide to go with half the total weight instead of, say 45% or 60%? There needs to be some scientific reasoning behind the weighting scheme that isn’t clear.
Line 125: Just the letter v is here.
Lines 130-133: Why not simply normalize the AEPdemand results by each state’s Total Electricity Demand? Maybe I’m missing something because of the Eq. ?? = v issue. Could you elaborate, scientifically, as to why you included residential demand as a counterbalancing factor, beyond just improving the alignment of the ranking with the study’s aim? The current phrasing in the transparency note could be easily misconstrued as cherry picking to achieve the results you wanted.
Additional comments:
Line 16: Specify that you are speaking of hub heights. Also, do you have a reference for the height information? Many FOM turbines, in particular, have larger hub heights than 30-60 m.
Lines 19-22: Do you have references or elaboration you can provide here? Help the reader understand how, for instance, BTM applications can stimulate local economic development when appropriately sited.
Line 63: Can you please explain the exclusion of Alaska and Hawaii?
Section 2.2: Offer context for each metric as to what it means when they are high or low.
Lines 126-129: The concept of the two groups and special cases is confusing. Why two groups? What are the special cases? States that don’t fall into either classification? For what reasons? How do you intend each of the multiple classifications to uniquely highlight potential priority areas? There needs to be some kind of contextual link between your classification methodologies and what each is expected to elaborate to the reader.
Line 136: “states modeled as random effects” – what does this mean? Can you provide some background understanding of mixed- and fixed-effects modeling to help your readers?
Lines 175-176: How significantly does the transformation alter the ranking?
Lines 177-181: Can you add some commentary as to why DW would be economically favorable in these states? Wind resource? Cost of electricity? Policies?
Lines 182-186 and Figure 5: Texas and Georgia are important enough to warrant discussion in the text and mention in Figure 5’s caption, but they’re not actually shown in Figure 5. Should the reader be looking elsewhere to visualize the takeaways for these states?
Line 218: “specially” -> “especially”?
Line 222: “thigh” -> “the high”?
Section 3.5: This section is two sentences long with no tables, graphics, analysis, or numeric metrics. It should probably be deleted if it’s receiving so little attention from the authors.
Conclusions: Most of these paragraphs are 1-2 sentences long and could easily be linked. Additionally, it would be helpful to include a paragraph on how a state-level energy decision maker will benefit from this work, particularly if you have identified their state in one of the highlighted groups. What are some next steps they could take?
References: Need to be organized according to last name instead of first name to make citations easily findable from references in the text.