Socio-Economic and Environmental Trade-Offs of Sustainable Energy Transition in Kentucky

Round 1

Reviewer 1 Report

Comments and Suggestions for Authors

It is recommended that the author make improvements in the following aspects:

1. It is recommended that the author emphasize the contribution of this article relative to existing literature in the introduction.
2. The theoretical framework should be a theoretical analysis, exploring sustainable energy transformation from a theoretical perspective.
3. The theoretical framework of this article may be a research method. It is recommended that the author explain the theoretical basis for using this method in detail.
4. It is recommended that the author explain the process of data acquisition and the credibility of the data in detail.
5. It is recommended that the author discuss the differences between the following research conclusions of this article and existing research, so as to highlight the conclusions of this article.
6. It is recommended that the author propose the policy implications of this article.

Author Response

Reviewer 1

It is recommended that the author make improvements in the following aspects:

 

1.It is recommended that the author emphasize the contribution of this article relative to existing literature in the introduction.

 

Response to Reviewer:
 Thank you for the thoughtful comment. We appreciate the opportunity to clarify this point. We respectfully note that the literature review is already integrated into the review of studies section (Lines 144 to 176) where we explicitly compare our selected attributes to those used in prior studies on energy transition and choice experiments.

 

In addition, lines 101–110 frame the broader policy context and theoretical contributions of our study, highlighting how our work builds upon and extends the existing literature on just energy transitions. “Stakeholders increasingly recognize the need to manage this decline while promoting viable alternatives. Benefits of the transition include reduced air and water pollution and new revenues from renewable investments. However, these are accompanied by challenges such as job losses and expensive mine reclamation efforts [22]. In Kentucky, the shift from coal to renewables must be guided by a sustainable policy framework that reflects the political, cultural, and economic realities of coal-reliant communities [3,12,13]. Rising social inequalities due to transition-related disruptions highlight the need for equitable policies that create a level playing field for affected populations [23]. An integrated approach that incorporates political, cultural, and economic values will be critical to delivering a just and sustainable energy transition”.

Unlike many studies that examine isolated attributes, our contribution lies in examining how people weigh trade-offs across multiple, interrelated dimensions such as economic, environmental, social, and cultural within a coal-dependent context. We place special emphasis on cultural values and lived experiences, areas that are often underexplored in traditional energy policy research.  Line 113 to 124: A just energy transition must reduce existing inequalities in the current energy system while preventing the creation of new ones [12, 24]. Thus, energy policy must account for these dimensions to secure broad public support [23, 25]. A central concern of just transition framework is identifying how different populations perceive the benefits and burdens of energy transitions. These perceptions shape the social acceptability of new policies and can either bridge or widen the gap in public engagement. Bridging this gap requires policies that mitigate adverse economic effects while ensuring social equity. These complex challenges also create a unique opportunity to develop hybrid policies that integrate environmental, economic, and social priorities. Rather than simply replacing coal with renewables, the transition must also redefine how energy is provided through inclusive, community-benefiting programs. This underscores the need to center the voices of those most affected by the transition [25].

Revision in the Manuscript

We have added a clarifying statement in the revised manuscript to make this contribution more explicit and appreciate the reviewer’s feedback in helping us strengthen the framing of our work. We  outline the use of contextually tailored Discrete Choice Experiment (DCE) methods allowing us to capture nuanced preferences in ways that are both theoretically grounded and policy-relevant. Lines 127 to 136:  explains the unique methodology we used in our study and how this compares with previous energy transition papers. “ The scientific novelty of this article lies in its comprehensive integration of diverse attributes within a Discrete Choice Experiment (DCE) framework to evaluate the multi-faceted public preferences for energy transition programs in a historically coal-dependent region. Unlike previous studies that often focus on isolated components or single variables, this research specifically evaluates the trade-offs between social, economic, environmental, and cultural dimensions. Furthermore, it defines cultural values through a more diverse and context-specific set of attributes, directly reflecting the lived realities and priorities of Kentucky residents. This holistic approach provides a more robust understanding of public preferences and perceived fairness, offering insights crucial for developing socially acceptable and economically equitable policies”.

2.The theoretical framework should be a theoretical analysis, exploring sustainable energy transformation from a theoretical perspective.

 

Response to Reviewers

To reflect the reviewer's concern, our theoretical framework as explained in the line 207 to 215, Explicitly describes the hypothetical energy transition program attributes, and also includes the option of opting out from these programs. “The utility function of the model, without covariates apart from the error term (εjtq ) is expressed as a linear function of an attribute vector (X1, X2, X3, X4, X5, X6) which represents the following attributes: possible clean energy alternatives, post mining land use, new job creation, preserving cultural values, social support programs,  and cost.  The model also includes an alternative-specific constant (ASC) to capture the utility associated with selecting the status quo option, which accounts for factors not explicitly included in the attribute vector (Xjtqk ).”

Revision to manuscript.

To strengthen our theoretical framework and provide further clarity we  add the individual characteristics that are the socio-demographic variables applied in the study, as follows.

Line 214 to 215: “The RPL framework accounts for preference heterogeneity by incorporating individual characteristics such as the socio-demographic variables (gender, age, education, employment rate, and political party affiliation)  (Zq)”.

 

3. The theoretical framework of this article may be a research method. It is recommended that the author explain the theoretical basis for using this method in detail.

 

Response to the Reviewer

Overall the RPL model provides both the flexibility and theoretical grounding necessary for capturing the complex, context-specific preferences relevant to the energy transition. It ensures that estimates of WTP and other model outputs reflect the true diversity of public opinion, thereby enhancing the credibility and policy relevance of the findings.

 

Revision in the Manuscript.

 To further explain the theoretical basis for using the DCE method we add a line  194 to 195, that strengthens the theoretical framework explanation DCEs, “informed by RUT, allow researchers to estimate the trade-offs individuals are willing to make between different policy attributes by observing their selections among hypothetical alternatives”.

 

 To account for the explanation of the theoretical basis we add  four more lines explaining in detail why the RPL method was most suitable for our study as follows: Line 241 to 249. “This approach allows us to quantify preferences and examine how individuals prioritize the various dimensions of hypothetical energy transition programs. The RPL model was preferred for this study due to the nature of energy transition in regions like Kentucky, where opinions about clean energy, post-coal economic development and environmental values are often politically and culturally polarized. The attributes analyzed in this study are not likely to be valued uniformly across respondents, making it imperative to account for unobserved heterogeneity in preferences. Moreover, the RPL model aligns with theoretical approaches in environmental sociology and political economy, which emphasize how individual choices related to environmental policy are shaped by broader ideological , cultural, and institutional structures [17]”.

 

4. It is recommended that the author explain the process of data acquisition and the credibility of the data in detail.

 

Response to Reviewer:

We appreciate the reviewer’s insightful comment. We acquired our data using pre-stratification weighted Qualtrics survey answers to meaningfully incorporate coal miner perspectives, and this sample was run through a Pearson chi-square independence test for socio-demographic variables for balance with U.S. Census Bureau 2021 data. We also did a literature review to verify our methodology as explained by our choice of attributes in section 2.3: Identifying attributes.

 

Revision to Manuscript:

We discuss the data acquisition for the study in lines 398-411. Lines 413-423 addresses the prior statistical analysis of the survey responses for further analysis of results. References to literature review for this section is throughout Section 2. Materials and Methods.

 

To further strengthen our emphasis on credibility of the data, we bring the data acquisition protocol expressed in the  sampling approach from results section 3.1 to methods section. Additionally, we added a new line 413 to 416:  2.5. State Socio-Demographic Variables: The sampling approach was guided by key demographic characteristics including age, household size, education level, gender, proportion of coal miners, urban/rural residence, county classification (mining vs. non-mining), and political party affiliation, in order to ensure the sample was broadly representative of the target population and to minimize sampling bias. To assess the representativeness of the survey sample to the population and reduce potential sampling bias, we used the Pearson’s chi-square (χ²) independence test on the socio-demographic variables for balance with US census 2021 data for Kentucky (Table 2). The results showed no statistical difference in most socio-demographic variables, with the exception of the percentage of coal miners in Kentucky and the area of residence. This was due to pre-stratification weighting procedures to meaningfully incorporate the perspectives of coal miners [14].

Table 2. Socio-demographic characteristics of survey respondents’ vs the Census for Kentucky (2020). (US Census Data Bureau [76]).

Categories	Sample (n=675)	Kentucky	Pearson χ2  test
Median age (years)	42	39.2	***
Household size (people).	2.83	2.52	***
Education (bachelor’s degree or higher).	20.89%	19.1%	***
Female (%)	50.37%	50.58%	***
Employment rate (%)	62.2%	56.8%	***
Coal miners (%)	1.62%	0.078%	-
Area of residence (Rural)	58.51%	41.38	-
Political party affiliation (Republican)	40.00%	42.89%	***
Population from mining county	26.22%	19.55%	***

The data represents significance at 1% according to the Pearson χ² test. ***Indicates significance at 1% level, ** indicates significance at 5% level, * indicates significance at 10%.

 

5. It is recommended that the author discuss the differences between the following research conclusions of this article and existing research, so as to highlight the conclusions of this article.

 

Response to Reviewer:

Thank you for highlighting the importance of cross-referencing our conclusions with prior literature. We would like to clarify that while Sections 3.1 (Parameter Estimates) and 3.2 (WTP Estimates) present the core empirical findings, we have intentionally aligned our interpretation of the results with recently conducted studies on energy transition, particularly in coal-affected regions. We also note that relatively few existing studies focus as extensively on willingness-to-pay (WTP) estimates across multiple socio-political attributes, especially in coal-dependent contexts. Nonetheless, we have incorporated references to comparable studies where available and discussed both shared findings and the limitations of discrete choice experiments in the concluding section.

 

For example

Section 3.1

• Line 453 to 458: This mixed result was somewhat unexpected given Kentucky’s historical and cultural attachment to coal. However, it may reflect a growing recognition of the role renewables can play in a just energy transition, despite persistent political rhetoric favoring coal [77,78]. Several factors may explain this shift in public perception: (1) declining coal employment and increased awareness of renewable-related job opportunities  [79, 80];
• Line 471 to 472: Renewable energy and cultural attributes offer more tangible benefits like job and health improvements [83].

 

• Line 478 to 481: Oluoch et al. [38], Maxim et al. [40], and Amar et al. [41] found that job creation increases individuals’ willingness to pay for renewables. The "preserving cultural values" level had a positive and statistically significant mean and standard deviation at the 1% level

 

• Line 483 to 487: In the U.S., Lewin [16] and others note that Appalachian coal communities often feel alienated by federal climate policies and environmental groups. These findings underscore how cultural dimensions such as land stewardship, self-reliance, pride and heritage, memory, pride are not just symbolic, but foundational to support just energy transition.

 

• Line 491 to 492: Trandafir et al. [88] found that support for energy policies was higher when SSPs were perceived to directly benefit the community. Dell et al. [89] also note that there is widespread support for degrowth policies, such as universal healthcare, work-time reductions, fossil fuel production caps, and advertising restrictions. However, SSPs can be viewed as bureaucratic or inaccessible [90].

 

For section 3.2 WTP

• Lines 512 to 515: Furthermore, Kentucky’s political identity may respond differently to symbolic associations of solar energy, which are often associated with progressive environmentalism, whereas wind may be seen as more neutral or pragmatic [77-79].

 

• Line 519 to 521: This aligns with national trends, with Democrats favoring environmental restoration, while Republicans prioritizing fiscal restraint and local autonomy [93-95].

 

• Lines 527 to 529: Independents' strong support likely reflect a desire to preserve local identity and community heritage without aligning with partisan narratives [16,96,97].

 

Revision to Manuscript:

Limitations from literature review of discrete choice experiments was highlighted in lines 573-577:  Methodologically, the study acknowledges limitations common in choice experiments [100]. Respondents demonstrated stronger engagement with tangible concepts such as job creation or cultural identity, while abstract or unfamiliar attributes, like SSPs and PLU, generated weaker or inconsistent signals. These insights highlight the need for clearer framing and community education to improve salience and understanding.

 

6. It is recommended that the author propose the policy implications of this article.

 

 Response to Reviewer:

Thank you for the comment. We have proposed policy incentives such as RPS and solar land-leasing for farmers in the conclusion section of the paper. 

 

Revision to Manuscript:

We have also added the following line 554 to 556: These results underscore the significance of both economic opportunity and symbolic attachment to local identity in shaping preferences for energy transition policies.

 

Line 558 to 560: These findings suggest that policies perceived as abstract, redistributive, or disconnected from immediate economic benefit are less compelling, particularly to conservative respondents.

 

Lines 560-564 exemplifies these policy incentives, “Despite persistent barriers such as cultural attachments to coal, misinformation, limited internet access in rural areas, and the absence of state-level renewable mandates (RPS), public interest in renewables is growing. Initiatives like community solar projects and solar land-leasing for farmers offer pragmatic, income-generating pathways that complement broader policy efforts.”

 

Line 587 to 596:  Overall, these findings underscore the importance of successful energy transition policies that not only address economic and environmental concerns, but also cultural identity, political trust, and perceived fairness. Moreover, bipartisan messaging that frames clean energy around jobs, health, and resilience can help bridge ideological divides and enhance policy acceptance. Ultimately, this study reinforces that equitable energy transition cannot rely solely on market mechanisms. Achieving a sustainable energy transition requires policy reform, inclusive community engagement, and recognition of local social, cultural, and economic contexts. Integrating these dimensions will be critical to building durable support and avoiding the replication of historical injustices in the clean energy future.

Reviewer 2

The article clearly describes the research problem and consistently and thoroughly analyzes and describes the results.I only have a few minor comments.

1. In section: Review of studies, we can read: “In contrast, our DCE integrates a broader set of attributes that collectively reflect the social, economic, and environmental dimensions of the clean energy transition.” Please explain the meaning of the abbreviation DCE immediately after its first occurrence in the article.

 

Response to Reviewers

DCE explained in the first occurrence as Discrete Choice Experiments: Line 128 to 129.

 

2.In section Materials and methods, we can read: This study applies the DCE method, estimated using a RPL model to assess individual preferences for hypothetical energy transition programs in Kentucky.” Please explain the meaning of the abbreviation RPL immediately after its first occurrence in the article.

 

Response to Reviewers

RPL is first defined as Random Parameter Logit in line 186.

 

3.The abbreviations in Table 3 have been used earlier in the text (PCEA, PLU, NJC, PCV, SSPs) and should be given in full when first used in the text.

 

Response to reviewers

Abbreviations given in full for the first time in line 258 to 261: These attributes include possible clean energy alternatives (PCEA) [37-39], new job creation (NJC) [37,38,40,41], preserving cultural values (PCV) (16,44, 45,46], post-mining land uses (PLU) [47-51], social support programs (SSPs) [52-54], and cost [37,38,42,43].

 

4. Should the abbreviation PCEA be in bold in the sentence: “The WTP values for the attribute levels under PCEA reveal that support for solar energy was consistent across political affiliations.”?

 

Response to Reviewer:

 Corrected and removed the bold PCEA.

 

Reviewer  3

1.Abstract should include the results obtained.

Comments to reviewer

Clear results of WTP values for each attribute based on political affiliation was adopted in the abstract.

Revision in the manuscript:

 From line 7 to 25 has inputs with values for WTP as key results in the study. A just and sustainable energy transition in historically coal-dependent regions like Kentucky requires more than the adoption of new technologies and market-based solutions. This study uses a stated preferences approach to evaluate public support for various attributes of energy transition programs, revealing broad backing for moving away from coal, as indicated by negative willingness to pay (WTP) for the status quo (-$4.63). Key findings show strong bipartisan support for solar energy, with Democrats showing the highest WTP at $8.29, followed closely by independents/others at $8.22, and Republicans at $8.08. Wind energy also garnered support, particularly among Republicans ($4.04), who may view it as more industry-compatible and less ideologically polarizing. Job creation was a dominant priority across political affiliations, especially for Independents ($9.07), indicating a preference for tangible, near-term economic benefits. Similarly, preserving cultural values tied to coal received support among independents/others ($4.98), emphasizing the importance of place-based identity in shaping preferences. In contrast, social support programs (e.g., job retraining) and certain post-mining land uses (e.g., recreation and conservation) were less favored, possibly due to their abstract nature, delayed benefits, and political framing. Findings from Kentucky offer insights for other coal-reliant states like Wyoming, West Virginia, Pennsylvania,  Indiana, and Illinois. Ultimately, equitable transitions must integrate local voices, address cultural and economic realities, and ensure community-driven planning and investment.

2. Why are all options from Table 1 not included in the questionnaire?

Comments to Reviewers

The sample choice card, figure 2, is an example of the one of the energy transition program options presented, but we explain in detail the number of questions that were received that would have been inconceivable to present in the questionnaire.  For our study each respondent received a total of 6 choice cards with varying attributes and levels, for all the respondents had 6  as explained in line 372 to 381:  Participants were asked to evaluate six hypothetical energy transition programs, each defined by a unique combination of attributes and levels as outlined in Table 1. The full factorial design, based on the combination of six attributes with varying levels (3x3x3x2x3x4), would have produced 648 unique profiles. To ensure feasibility and minimize respondent fatigue, a D-efficient fractional factorial design was generated using the R statistical software. This design optimized orthogonality, level balance, and minimum attribute overlap, yielding 72 unique profiles. These profiles were randomly paired to form 36 choice sets, divided into six blocks, with each respondent randomly assigned one block consisting of six choice cards. All of these 72 unique profile covered the all the options in Table 1.

3. Are Authors using correlated or not correlated RPL models?

Comments to Reviewers

We estimated an uncorrelated Random Parameters Logit model, assuming independent distributions across the random coefficients. This simplifies estimation while still allowing for heterogeneity in preferences across individuals. In our paper we are using an uncorrelated RPL model stressed in our theoretical framework. Line line 214: Assuming the error terms are distributed independently and normally distributed according to a type I extreme value distribution, the choice probability becomes

4. It will be good to compare the simulated WTA values of correlated and uncorrelated RPL on graphs (for various effects).

Comments to Reviewers

Much as it would be good to compare both the correlated and uncorrelated RPL, given that our paper was more for delivering an insightful analysis of energy transitions perspective as opposed to a methodological study. Given that adding these tables may have added an additional dimension of looking and comparing the results from both correlated and uncorrelated tables, may have taken the discussion away from the core of our narrative.   Also, Correlated RPL models require estimating a full covariance matrix, which increases the number of parameters and requires more simulation draws. These are more computationally intensive and usually only chosen if the researchers expect correlation across preferences (e.g., someone who values clean energy might also strongly value job retraining).

5. It would be good to refer to specific data (in table, graph) in the description (text) of the results obtained.

Response to Reviewer:

Thank you for the critique, revision  can be found throughout sections 3.2 Parameter Estimation results and 3.3 The Marginal WTP measures by Political Affiliation.

6.The graphs in Fig. 3 should be more visible and clearer, the quality of the entire figure should be improved.

Response to Reviewer

The overall quality and visibility of figure 3 improved as suggested by reviewer in Line 501

7. What is the scientific novelty in the article that significantly affects the development of the field?

Response to Reviewer

To strengthen our narrative on scientific novelty in the development of the field of energy transition, we reedit the statement of purpose to incorporate application of the RPL model, in assessing key attributes, that unlike previous studies that do not integrate social, cultural, and political dimension. In addition, the use of preference heterogeneity and the link to political affiliation in the WTP, introduces a connection for further assessing how these attributes are perceived by Kentucky residents.  The table below assesses the novel elements of the paper.

Novel Element	Explanation	Why It Matters
Context-specific analysis (Kentucky)	Focuses on a coal-dependent U.S. state	Contributes to global literature on energy transitions in fossil-fuel regions
Inclusion of sociocultural attributes	Attributes like preserving cultural values and post-mining land use are rarely modeled	Broadens the scope beyond socio-economic factors
Use of RPL with political affiliation heterogeneity	Identifies variations in preferences by political identity	Enhances understanding of the role of ideology in energy transition acceptance
Policy-relevant modeling of job creation and social support	Quantifies public support for just transition programs	Directly relevant to RPS, IRA and DOE-funded policy initiatives
First-of-its-kind design in the region	Applies stated preference method in a previously under-studied location	Fills a geographic gap in the literature on clean energy preferences

Revision in manuscript:

 we add the Line 127 to 136:  “This study offers scientific novelty by applying a stated preference choice experiment, coupled with a Random Parameters Logit (RPL) model, to empirically examine how individuals in a historically coal-dependent region Kentucky prioritize key attributes of the clean energy transition (possible clean energy alternatives postmining land-use, new job creation, preserving cultural values, social support programs, cost). Unlike previous studies that treat energy preferences as homogeneous or purely economic, this research integrates social, cultural, and political dimensions such as the preservation of cultural values, post-mining land use, and social support programs. By explicitly modeling preference heterogeneity and linking it to political affiliation, the study advances the understanding of public acceptance and resistance to clean energy policies in fossil-fuel-reliant communities. This approach provides nuanced, locally grounded evidence that can directly inform place-based policy design for just energy transitions, a dimension that is currently underrepresented in both sustainability,  environmental economics, and policy implementation literature”.

8. Only survey research was conducted and analyzed using RPL modeling. What about further analysis? The authors should conduct a more in-depth analysis of the obtained data, including tests of significance of differences, such as Student's t test or analysis of variance (ANOVA).

Response to reviewer
 Thank you for your insightful question. We chose not to use ANOVA because our data structure and research design did not align with the assumptions and purposes of ANOVA. Specifically:

1. Nature of the Data: Our dataset consists of repeated discrete choice observations from individuals across multiple scenarios, which creates a panel data structure with correlated observations within respondents. ANOVA assumes independent observations, which is violated in this context.
   
   
2. Modeling Preference Heterogeneity: We employed a Random Parameters Logit (RPL) model to explicitly account for heterogeneity in preferences and repeated measures at the individual level. This model is more suitable for analyzing discrete choice experiment data where the dependent variable is choice probabilities rather than continuous outcomes, as required by ANOVA.
   
   
3. Appropriate for Choice Data: ANOVA is designed for continuous dependent variables and comparisons of means across groups. Our outcome variable is categorical (choice selections), making regression-based choice models like RPL a more appropriate analytical approach.
   
   
4. Testing Differences in Coefficients: Where relevant, we conducted significance testing on model parameters and used likelihood ratio tests to compare nested models. This approach aligns with best practices in choice modeling and provides more robust inference than traditional ANOVA.

Revisions in manuscript

Nonetheless other tests such as R2, loglikelihood and using 400 Halton draws that confirm that our RPL model had an acceptable goodness of fit, providing an in-depth analysis as follows: Line 432 to 435: Overall, the model demonstrated acceptable goodness-of-fit (pseudo-R² = 0.1861). The loglikelihood at convergence (-3621.233) indicates a reasonably good model fit, reflecting the model’s ability to explain observed choices. The RPL model was estimated using 400 Halton draws to simulate the integrals over the distributions of random parameters. Halton sequences provide more efficient and accurate numeral integration than conventional random draws, resulting in more stable and reliable estimates of preference heterogeneity.

5. Why did the authors only analyze the influence of political beliefs on the obtained results, and not other factors, such as gender, education, origin, etc.

Response to Reviewers

• Thank you for your valuable comment. We focused our analysis on political affiliation because our primary research objective was to understand how political identity shapes public preferences for energy transition policies in Kentucky, where political polarization significantly influences energy and environmental attitudes. Given the region’s historical reliance on coal and the politicization of climate and energy discourse, political ideology presents a particularly salient and policy-relevant dimension for understanding preference heterogeneity.
• While we recognize that other sociodemographic factors such as gender, education, and geographic origin can also play important roles in shaping public preferences, we prioritized political affiliation due to its strong theoretical and empirical link to energy policy attitudes in the existing literature.
• That said, we acknowledge the value of further examining these additional dimensions. As a next step, we plan to extend our analysis by exploring interaction effects with other sociodemographic variables to provide a more comprehensive understanding of heterogeneity in preferences in our future studies.

Revision in Manuscript

 We have added a note to this effect in the revised manuscript’s limitations and future research sections. Line 550 to 558: Further research could benefit from  assessing the interaction effects of other socio-demographic variables such as gender, age, and education to provide a more comprehensive understanding of heterogeneity in preferences. Furthermore, integration of comparative modelling tools, such as computable general equilibrium (CGE) models, to explore region-specific policy effects and identify equitable pathways for renewable energy adoption will be critical for achieving sustainable energy transition in the region.

6.Chapter 3.3 needs some summarization at the end.

Response to Reviewer:

We add a paragraph to sum the chapter 3.3.

Revision in manuscript.

Line 534 to 542: Sums the chapter 3.3 concisely: Overall, the WTP results from the PCEA reveal broad, cross-partisan support for solar energy, driven by its economic and health-related framing. Wind energy received less support overall, but surprisingly, Republicans showed the highest WTP, suggesting that wind may be viewed as more pragmatic and less politically charged. Restoration and recreation programs were generally unpopular, though Democrats showed relatively higher support, reflecting national environmental attitudes. Job creation (NJC) had the highest WTP overall, especially among Independents, highlighting a shared priority for economic development. Cultural preservation (PCV) was most valued by Independents, and job retraining programs (SSP) were least supported by Republicans, likely due to distrust in federally run programs.

7.There is a lack of obtained results in Conclusions.

Response to reviewer

Revise to include obtained results in conclusion as follows:

Response to reviewer

We have added a paragraph from line 548 to 561 to account for results in conclusions. “This study finds that Kentucky residents broadly support clean energy alternatives across all political affiliations, with solar energy receiving robust bipartisan backing (WTP: Democrats $8.29, Independents/others $8.22, Republicans $8.08) and wind energy garnering notable support, particularly among Republicans (WTP: $4.04). Job creation emerged as the strongest driver of public support, especially among Independents/others (WTP for 1280 jobs: $9.07), followed closely by cultural preservation  (Independents WTP: $4.98). These results underscore the significance of both economic opportunity and symbolic attachment to local identity in shaping preferences for energy transition policies. In contrast, Social Support Programs (SSPs) and some Post-mining Land Use (PLU) options, such as recreation and conservation, received negative WTP values (e.g., WTP for job retraining: -$2.42). These findings suggest that policies perceived as abstract, redistributive, or disconnected from immediate economic benefit are less compelling, particularly to conservative respondents.”

 

8. Author should compare the obtained results with other literature positions to prove that the selected option is better than usually used.

Response to Reviewer: This comment is similar to the concern raised by Reviewer 1 and will be addressed in the similar manner.

 

5. It is recommended that the author discuss the differences between the following research conclusions of this article and existing research, so as to highlight the conclusions of this article.

 

Response to Reviewer:

Thank you for highlighting the importance of cross-referencing our conclusions with prior literature. We would like to clarify that while Sections 3.1 (Parameter Estimates) and 3.2 (WTP Estimates) present the core empirical findings, we have intentionally aligned our interpretation of the results with recently conducted studies on energy transition, particularly in coal-affected regions. We also note that relatively few existing studies focus as extensively on willingness-to-pay (WTP) estimates across multiple socio-political attributes, especially in coal-dependent contexts. Nonetheless, we have incorporated references to comparable studies where available and discussed both shared findings and the limitations of discrete choice experiments in the concluding section.

 

For example

Section 3.1

• Line 456 to 460: This mixed result was somewhat unexpected given Kentucky’s historical and cultural attachment to coal. However, it may reflect a growing recognition of the role renewables can play in a just energy transition, despite persistent political rhetoric favoring coal [77,78]. Several factors may explain this shift in public perception: (1) declining coal employment and increased awareness of renewable-related job opportunities  [79, 80];
• Line 473 to 474: Renewable energy and cultural attributes offer more tangible benefits like job and health improvements [83].

 

• Line 480 to 483: Oluoch et al. [38], Maxim et al. [40], and Amar et al. [41] found that job creation increases individuals’ willingness to pay for renewables. The "preserving cultural values" level had a positive and statistically significant mean and standard deviation at the 1% level

 

• Line 485 to 489: In the U.S., Lewin [16] and others note that Appalachian coal communities often feel alienated by federal climate policies and environmental groups. These findings underscore how cultural dimensions such as land stewardship, self-reliance, pride and heritage, memory, pride are not just symbolic, but foundational to support just energy transition.

 

• Line 493 to 497: Trandafir et al. [88] found that support for energy policies was higher when SSPs were perceived to directly benefit the community. Dell et al. [89] also note that there is widespread support for degrowth policies, such as universal healthcare, work-time reductions, fossil fuel production caps, and advertising restrictions. However, SSPs can be viewed as bureaucratic or inaccessible [90].

 

For section 3.2 WTP

• Lines 514 to 517: Furthermore, Kentucky’s political identity may respond differently to symbolic associations of solar energy, which are often associated with progressive environmentalism, whereas wind may be seen as more neutral or pragmatic [77-79].

 

• Line 521 to 523: This aligns with national trends, with Democrats favoring environmental restoration, while Republicans prioritizing fiscal restraint and local autonomy [93-95].

 

• Lines 530 to 531: Independents' strong support likely reflect a desire to preserve local identity and community heritage without aligning with partisan narratives [16,96,97].

 

Reviewer 4

 

1. Figure 1 is provided in the article. Coal production in the United States. There is no reference to this figure and no analysis of it. It needs to be supplemented.

Response to reviewer:

Corrections made on Line 32 that describes Kentucky’s decline in coal production.

 

2. The first reference to Table 1 is given on line 154. It would be useful to give the table immediately after this reference.

 

Response to Reviewer:

Journal preferences on position, we can recommend the  changes to be made accordingly.

 

3. It would be advisable to clearly describe the six attributes for which the study is conducted. At the beginning of the paper, it is easy to list these attributes, because a full description of them follows.

 

Response to Reviewer:

Changing the format of our study to move the attributes to the beginning of the paper will alter the flow of our paper. In our literature review we talk about the attributes and refer to the section in which they are described in detail as follows

 

Line 177 to 183: Overall, our study incorporates a comprehensive and context-sensitive set of attributes that more accurately reflect the lived realities and priorities of Kentucky residents. Our studies also differ by evaluating not only these individual attributes but also the trade-offs between them. Hence, we provide a more holistic understanding of public preferences and perceived fairness of energy transition, thereby contributing to the broader discourse on just transitions. A detailed review of previous studies and their relation to our attributes is further described in section 2.3.

 

Also traditionally most DCEs, describe attributes in the methods section For example:

• Oluoch, S., Lal, P., Susaeta, A., & Wolde, B. (2021). Public preferences for renewable energy options: A choice experiment in Kenya. Energy Economics, 98, 105256.

 

• Ku, S. J., & Yoo, S. H. (2010). Willingness to pay for renewable energy investment in Korea: A choice experiment study. Renewable and Sustainable Energy Reviews, 14(8), 2196-2201.
•  
• Azarova, V., Cohen, J., Friedl, C., & Reichl, J. (2019). Designing local renewable energy communities to increase social acceptance: Evidence from a choice experiment in Austria, Germany, Italy, and Switzerland. Energy Policy, 132, 1176-1183.

 

4. The last attribute is a value variable designed as a payment instrument that reflects respondents' willingness to sell through additional monthly utility bills to support an energy transition programme (line 330-331). Do the authors take into account risks and uncertainty?

Response to Reviewers
 Thank you for this important question. In our choice experiment, the additional monthly utility bill served as the payment vehicle to estimate respondents’ willingness to pay (WTP) for different energy transition attributes. While the design captured trade-offs between cost and program benefits, we acknowledge that it did not explicitly incorporate risk or uncertainty components (e.g., variability in future energy prices, implementation uncertainty, or policy credibility) into the attribute levels or choice tasks.

That said, we recognize that perceptions of risk, particularly regarding the effectiveness, fairness, and durability of energy transition programs, can significantly influence WTP responses. These considerations were indirectly accounted for to some extent through random parameter specifications in the RPL model, which capture unobserved heterogeneity in preferences, potentially reflecting differing levels of trust, risk tolerance, and perceived uncertainty.

We have now clarified this point in the revised manuscript’s limitations section and suggest that future research incorporate explicit risk attributes or scenario-based uncertainty (e.g., probability of success, future rate fluctuations) to better understand how perceived risks shape public support for energy transition policies.

Revisions in the manuscript

We add line 566 to 575: While this study provides valuable insights into public preferences for energy transition programs in Kentucky, several limitations warrant attention. First, although the additional monthly utility bill was used as a realistic and policy-relevant payment vehicle to estimate willingness to pay (WTP), the design did not explicitly incorporate risk or uncertainty such as implementation delays, fluctuating energy costs, or policy failure into the choice tasks. These factors can influence how respondents perceive and value different programs. Although the Random Parameters Logit (RPL) model captures some unobserved heterogeneity, including preferences potentially shaped by trust, risk aversion, or political skepticism, we acknowledge that this does not fully substitute for explicit modeling of risk scenarios.

 

 

5. The article cites 100 references. The question arises whether all of them are included in the article? Source 98 and 99.

 

Response to reviewers:

Corrections on references made

 

6. Regarding state support programmes. It would be necessary to at least mention some of them

 

In the identification of attributes, we mention and define our SSP programs based on examples from literature from 54 to 55 as follows:
Line 344 to 350: The fifth attribute, SSPs are central to ensuring community resilience during energy transition, creating safety nets, and building adaptive capacity for impacted households. Informed by adaptive capacity frameworks [52-54], the SSP attribute includes three levels: energy efficiency programs, job retraining, and medical compensation. These support systems target vulnerable populations affected by mine closures, unemployment, or coal-related health issues. Medical assistance was selected over traditional weatherization programs to better reflect public concerns about long-term health impacts from mining.

 

Also Line 493 to 494 in the results and discussion sections further elaborates the role of SSPs in different studies: Dell et al. [89] also note that there is widespread support for degrowth policies, such as universal healthcare, work-time reductions, fossil fuel production caps, and advertising restrictions.

 

7. The conclusions of the study could be more generalised and presented in a more concise form in relation to the title of the article Socioeconomic and Environmental Trade-offs of the Transition to Sustainable Energy in Kentucky.

 

Response to reviewers:

The request to have a more generalised and concise conclusion is somewhat a Contradiction other reviewers who wanted a more detailed conclusion, Hence we adopted a middle ground approach that generalizes and made in more concise by adding WTP values, policy implications, future research perspectives and limitations (See above sections for other reviewers).

 

8: The quality of the figures could be improved.

 

Response to Reviewers

All figures were corrected.

 

 

Author Response File: Author Response.docx

Reviewer 2 Report

Comments and Suggestions for Authors

The article clearly describes the research problem and consistently and thoroughly analyzes and describes the results.

I only have a few minor comments.

In section Review of studies, we can read: “In contrast, our DCE integrates a broader set of attributes that collectively reflect the social, economic, and environmental dimensions of the clean energy transition.” Please explain the meaning of the abbreviation DCE immediately after its first occurrence in the article.

In section Materials and methods, we can read: This study applies the DCE method, estimated using a RPL model to assess individual preferences for hypothetical energy transition programs in Kentucky.” Please explain the meaning of the abbreviation RPL immediately after its first occurrence in the article.

The abbreviations in Table 3 have been used earlier in the text (PCEA, PLU, NJC, PCV, SSPs) and should be given in full when first used in the text.

Should the abbreviation PCEA be in bold in the sentence: “The WTP values for the attribute levels under PCEA reveal that support for solar energy was consistent across political affiliations.”?

 

Author Response

Reviewer 2

The article clearly describes the research problem and consistently and thoroughly analyzes and describes the results.I only have a few minor comments.

1. In section: Review of studies, we can read: “In contrast, our DCE integrates a broader set of attributes that collectively reflect the social, economic, and environmental dimensions of the clean energy transition.” Please explain the meaning of the abbreviation DCE immediately after its first occurrence in the article.

 

Response to Reviewers

DCE explained in the first occurrence as Discrete Choice Experiments: Line 128 to 129.

 

2.In section Materials and methods, we can read: This study applies the DCE method, estimated using a RPL model to assess individual preferences for hypothetical energy transition programs in Kentucky.” Please explain the meaning of the abbreviation RPL immediately after its first occurrence in the article.

 

Response to Reviewers

RPL is first defined as Random Parameter Logit in line 186.

 

3.The abbreviations in Table 3 have been used earlier in the text (PCEA, PLU, NJC, PCV, SSPs) and should be given in full when first used in the text.

 

Response to reviewers

Abbreviations given in full for the first time in line 258 to 261: These attributes include possible clean energy alternatives (PCEA) [37-39], new job creation (NJC) [37,38,40,41], preserving cultural values (PCV) (16,44, 45,46], post-mining land uses (PLU) [47-51], social support programs (SSPs) [52-54], and cost [37,38,42,43].

 

4. Should the abbreviation PCEA be in bold in the sentence: “The WTP values for the attribute levels under PCEA reveal that support for solar energy was consistent across political affiliations.”?

 

Response to Reviewer:

 Corrected and removed the bold PCEA.

Author Response File: Author Response.docx

Reviewer 3 Report

Comments and Suggestions for Authors

Review

Manuscript Number: materials-3725412

Socio-economic and Environmental Tradeoffs of Sustainable Energy Transition in Kentucky

 

There are some questions and remarks to be answered:

 

1. Abstract should include the results obtained.
2. Why are all options from Table 1 not included in the questionnaire?
3. Are Authors using correlated or not correlated RPL model?
4. It will be good to compare the simulated WTA values of correlated and uncorrelated RPL on graphs (for various effects).
5. It would be good to refer to specific data (in table, graph) in the description (text) of the results obtained.
6. The graphs in Fig. 3 should be more visible and clearer, the quality of the entire figure should be improved.
7. What is the scientific novelty in the article that significantly affects the development of the field? Only survey research was conducted and analyzed using RPL modeling. What about further analysis? The authors should conduct a more in-depth analysis of the obtained data, including tests of significance of differences, such as Student's t test or analysis of variance (ANOVA).
8. Why did the authors only analyze the influence of political beliefs on the obtained results, and not other factors, such as gender, education, origin, etc.
9. Chapter 3.3 needs some summarization at the end.
10. There is a lack of obtained results in Conclusions.
11. Author should compare the obtained results with other literature positions to prove that the selected option is better than usually used.

Author Response

Reviewer  3

1.Abstract should include the results obtained.

Comments to reviewer

Clear results of WTP values for each attribute based on political affiliation was adopted in the abstract.

Revision in the manuscript:

 From line 7 to 25 has inputs with values for WTP as key results in the study. A just and sustainable energy transition in historically coal-dependent regions like Kentucky requires more than the adoption of new technologies and market-based solutions. This study uses a stated preferences approach to evaluate public support for various attributes of energy transition programs, revealing broad backing for moving away from coal, as indicated by negative willingness to pay (WTP) for the status quo (-$4.63). Key findings show strong bipartisan support for solar energy, with Democrats showing the highest WTP at $8.29, followed closely by independents/others at $8.22, and Republicans at $8.08. Wind energy also garnered support, particularly among Republicans ($4.04), who may view it as more industry-compatible and less ideologically polarizing. Job creation was a dominant priority across political affiliations, especially for Independents ($9.07), indicating a preference for tangible, near-term economic benefits. Similarly, preserving cultural values tied to coal received support among independents/others ($4.98), emphasizing the importance of place-based identity in shaping preferences. In contrast, social support programs (e.g., job retraining) and certain post-mining land uses (e.g., recreation and conservation) were less favored, possibly due to their abstract nature, delayed benefits, and political framing. Findings from Kentucky offer insights for other coal-reliant states like Wyoming, West Virginia, Pennsylvania,  Indiana, and Illinois. Ultimately, equitable transitions must integrate local voices, address cultural and economic realities, and ensure community-driven planning and investment.

2. Why are all options from Table 1 not included in the questionnaire?

Comments to Reviewers

The sample choice card, figure 2, is an example of the one of the energy transition program options presented, but we explain in detail the number of questions that were received that would have been inconceivable to present in the questionnaire.  For our study each respondent received a total of 6 choice cards with varying attributes and levels, for all the respondents had 6  as explained in line 372 to 381:  Participants were asked to evaluate six hypothetical energy transition programs, each defined by a unique combination of attributes and levels as outlined in Table 1. The full factorial design, based on the combination of six attributes with varying levels (3x3x3x2x3x4), would have produced 648 unique profiles. To ensure feasibility and minimize respondent fatigue, a D-efficient fractional factorial design was generated using the R statistical software. This design optimized orthogonality, level balance, and minimum attribute overlap, yielding 72 unique profiles. These profiles were randomly paired to form 36 choice sets, divided into six blocks, with each respondent randomly assigned one block consisting of six choice cards. All of these 72 unique profile covered the all the options in Table 1.

3. Are Authors using correlated or not correlated RPL models?

Comments to Reviewers

We estimated an uncorrelated Random Parameters Logit model, assuming independent distributions across the random coefficients. This simplifies estimation while still allowing for heterogeneity in preferences across individuals. In our paper we are using an uncorrelated RPL model stressed in our theoretical framework. Line line 214: Assuming the error terms are distributed independently and normally distributed according to a type I extreme value distribution, the choice probability becomes

4. It will be good to compare the simulated WTA values of correlated and uncorrelated RPL on graphs (for various effects).

Comments to Reviewers

Much as it would be good to compare both the correlated and uncorrelated RPL, given that our paper was more for delivering an insightful analysis of energy transitions perspective as opposed to a methodological study. Given that adding these tables may have added an additional dimension of looking and comparing the results from both correlated and uncorrelated tables, may have taken the discussion away from the core of our narrative.   Also, Correlated RPL models require estimating a full covariance matrix, which increases the number of parameters and requires more simulation draws. These are more computationally intensive and usually only chosen if the researchers expect correlation across preferences (e.g., someone who values clean energy might also strongly value job retraining).

5. It would be good to refer to specific data (in table, graph) in the description (text) of the results obtained.

Response to Reviewer:

Thank you for the critique, revision  can be found throughout sections 3.2 Parameter Estimation results and 3.3 The Marginal WTP measures by Political Affiliation.

6.The graphs in Fig. 3 should be more visible and clearer, the quality of the entire figure should be improved.

Response to Reviewer

The overall quality and visibility of figure 3 improved as suggested by reviewer in Line 501

7. What is the scientific novelty in the article that significantly affects the development of the field?

Response to Reviewer

To strengthen our narrative on scientific novelty in the development of the field of energy transition, we reedit the statement of purpose to incorporate application of the RPL model, in assessing key attributes, that unlike previous studies that do not integrate social, cultural, and political dimension. In addition, the use of preference heterogeneity and the link to political affiliation in the WTP, introduces a connection for further assessing how these attributes are perceived by Kentucky residents.  The table below assesses the novel elements of the paper.

Novel Element	Explanation	Why It Matters
Context-specific analysis (Kentucky)	Focuses on a coal-dependent U.S. state	Contributes to global literature on energy transitions in fossil-fuel regions
Inclusion of sociocultural attributes	Attributes like preserving cultural values and post-mining land use are rarely modeled	Broadens the scope beyond socio-economic factors
Use of RPL with political affiliation heterogeneity	Identifies variations in preferences by political identity	Enhances understanding of the role of ideology in energy transition acceptance
Policy-relevant modeling of job creation and social support	Quantifies public support for just transition programs	Directly relevant to RPS, IRA and DOE-funded policy initiatives
First-of-its-kind design in the region	Applies stated preference method in a previously under-studied location	Fills a geographic gap in the literature on clean energy preferences

Revision in manuscript:

 we add the Line 127 to 136:  “This study offers scientific novelty by applying a stated preference choice experiment, coupled with a Random Parameters Logit (RPL) model, to empirically examine how individuals in a historically coal-dependent region Kentucky prioritize key attributes of the clean energy transition (possible clean energy alternatives postmining land-use, new job creation, preserving cultural values, social support programs, cost). Unlike previous studies that treat energy preferences as homogeneous or purely economic, this research integrates social, cultural, and political dimensions such as the preservation of cultural values, post-mining land use, and social support programs. By explicitly modeling preference heterogeneity and linking it to political affiliation, the study advances the understanding of public acceptance and resistance to clean energy policies in fossil-fuel-reliant communities. This approach provides nuanced, locally grounded evidence that can directly inform place-based policy design for just energy transitions, a dimension that is currently underrepresented in both sustainability,  environmental economics, and policy implementation literature”.

8. Only survey research was conducted and analyzed using RPL modeling. What about further analysis? The authors should conduct a more in-depth analysis of the obtained data, including tests of significance of differences, such as Student's t test or analysis of variance (ANOVA).

Response to reviewer
 Thank you for your insightful question. We chose not to use ANOVA because our data structure and research design did not align with the assumptions and purposes of ANOVA. Specifically:

1. Nature of the Data: Our dataset consists of repeated discrete choice observations from individuals across multiple scenarios, which creates a panel data structure with correlated observations within respondents. ANOVA assumes independent observations, which is violated in this context.
   
   
2. Modeling Preference Heterogeneity: We employed a Random Parameters Logit (RPL) model to explicitly account for heterogeneity in preferences and repeated measures at the individual level. This model is more suitable for analyzing discrete choice experiment data where the dependent variable is choice probabilities rather than continuous outcomes, as required by ANOVA.
   
   
3. Appropriate for Choice Data: ANOVA is designed for continuous dependent variables and comparisons of means across groups. Our outcome variable is categorical (choice selections), making regression-based choice models like RPL a more appropriate analytical approach.
   
   
4. Testing Differences in Coefficients: Where relevant, we conducted significance testing on model parameters and used likelihood ratio tests to compare nested models. This approach aligns with best practices in choice modeling and provides more robust inference than traditional ANOVA.

Revisions in manuscript

Nonetheless other tests such as R2, loglikelihood and using 400 Halton draws that confirm that our RPL model had an acceptable goodness of fit, providing an in-depth analysis as follows: Line 432 to 435: Overall, the model demonstrated acceptable goodness-of-fit (pseudo-R² = 0.1861). The loglikelihood at convergence (-3621.233) indicates a reasonably good model fit, reflecting the model’s ability to explain observed choices. The RPL model was estimated using 400 Halton draws to simulate the integrals over the distributions of random parameters. Halton sequences provide more efficient and accurate numeral integration than conventional random draws, resulting in more stable and reliable estimates of preference heterogeneity.

5. Why did the authors only analyze the influence of political beliefs on the obtained results, and not other factors, such as gender, education, origin, etc.

Response to Reviewers

• Thank you for your valuable comment. We focused our analysis on political affiliation because our primary research objective was to understand how political identity shapes public preferences for energy transition policies in Kentucky, where political polarization significantly influences energy and environmental attitudes. Given the region’s historical reliance on coal and the politicization of climate and energy discourse, political ideology presents a particularly salient and policy-relevant dimension for understanding preference heterogeneity.
• While we recognize that other sociodemographic factors such as gender, education, and geographic origin can also play important roles in shaping public preferences, we prioritized political affiliation due to its strong theoretical and empirical link to energy policy attitudes in the existing literature.
• That said, we acknowledge the value of further examining these additional dimensions. As a next step, we plan to extend our analysis by exploring interaction effects with other sociodemographic variables to provide a more comprehensive understanding of heterogeneity in preferences in our future studies.

Revision in Manuscript

 We have added a note to this effect in the revised manuscript’s limitations and future research sections. Line 550 to 558: Further research could benefit from  assessing the interaction effects of other socio-demographic variables such as gender, age, and education to provide a more comprehensive understanding of heterogeneity in preferences. Furthermore, integration of comparative modelling tools, such as computable general equilibrium (CGE) models, to explore region-specific policy effects and identify equitable pathways for renewable energy adoption will be critical for achieving sustainable energy transition in the region.

6.Chapter 3.3 needs some summarization at the end.

Response to Reviewer:

We add a paragraph to sum the chapter 3.3.

Revision in manuscript.

Line 534 to 542: Sums the chapter 3.3 concisely: Overall, the WTP results from the PCEA reveal broad, cross-partisan support for solar energy, driven by its economic and health-related framing. Wind energy received less support overall, but surprisingly, Republicans showed the highest WTP, suggesting that wind may be viewed as more pragmatic and less politically charged. Restoration and recreation programs were generally unpopular, though Democrats showed relatively higher support, reflecting national environmental attitudes. Job creation (NJC) had the highest WTP overall, especially among Independents, highlighting a shared priority for economic development. Cultural preservation (PCV) was most valued by Independents, and job retraining programs (SSP) were least supported by Republicans, likely due to distrust in federally run programs.

7.There is a lack of obtained results in Conclusions.

Response to reviewer

Revise to include obtained results in conclusion as follows:

Response to reviewer

We have added a paragraph from line 548 to 561 to account for results in conclusions. “This study finds that Kentucky residents broadly support clean energy alternatives across all political affiliations, with solar energy receiving robust bipartisan backing (WTP: Democrats $8.29, Independents/others $8.22, Republicans $8.08) and wind energy garnering notable support, particularly among Republicans (WTP: $4.04). Job creation emerged as the strongest driver of public support, especially among Independents/others (WTP for 1280 jobs: $9.07), followed closely by cultural preservation  (Independents WTP: $4.98). These results underscore the significance of both economic opportunity and symbolic attachment to local identity in shaping preferences for energy transition policies. In contrast, Social Support Programs (SSPs) and some Post-mining Land Use (PLU) options, such as recreation and conservation, received negative WTP values (e.g., WTP for job retraining: -$2.42). These findings suggest that policies perceived as abstract, redistributive, or disconnected from immediate economic benefit are less compelling, particularly to conservative respondents.”

 

8. Author should compare the obtained results with other literature positions to prove that the selected option is better than usually used.

Response to Reviewer: This comment is similar to the concern raised by Reviewer 1 and will be addressed in the similar manner.

 

5. It is recommended that the author discuss the differences between the following research conclusions of this article and existing research, so as to highlight the conclusions of this article.

 

Response to Reviewer:

Thank you for highlighting the importance of cross-referencing our conclusions with prior literature. We would like to clarify that while Sections 3.1 (Parameter Estimates) and 3.2 (WTP Estimates) present the core empirical findings, we have intentionally aligned our interpretation of the results with recently conducted studies on energy transition, particularly in coal-affected regions. We also note that relatively few existing studies focus as extensively on willingness-to-pay (WTP) estimates across multiple socio-political attributes, especially in coal-dependent contexts. Nonetheless, we have incorporated references to comparable studies where available and discussed both shared findings and the limitations of discrete choice experiments in the concluding section.

 

For example

Section 3.1

• Line 456 to 460: This mixed result was somewhat unexpected given Kentucky’s historical and cultural attachment to coal. However, it may reflect a growing recognition of the role renewables can play in a just energy transition, despite persistent political rhetoric favoring coal [77,78]. Several factors may explain this shift in public perception: (1) declining coal employment and increased awareness of renewable-related job opportunities  [79, 80];
• Line 473 to 474: Renewable energy and cultural attributes offer more tangible benefits like job and health improvements [83].

 

• Line 480 to 483: Oluoch et al. [38], Maxim et al. [40], and Amar et al. [41] found that job creation increases individuals’ willingness to pay for renewables. The "preserving cultural values" level had a positive and statistically significant mean and standard deviation at the 1% level

 

• Line 485 to 489: In the U.S., Lewin [16] and others note that Appalachian coal communities often feel alienated by federal climate policies and environmental groups. These findings underscore how cultural dimensions such as land stewardship, self-reliance, pride and heritage, memory, pride are not just symbolic, but foundational to support just energy transition.

 

• Line 493 to 497: Trandafir et al. [88] found that support for energy policies was higher when SSPs were perceived to directly benefit the community. Dell et al. [89] also note that there is widespread support for degrowth policies, such as universal healthcare, work-time reductions, fossil fuel production caps, and advertising restrictions. However, SSPs can be viewed as bureaucratic or inaccessible [90].

 

For section 3.2 WTP

• Lines 514 to 517: Furthermore, Kentucky’s political identity may respond differently to symbolic associations of solar energy, which are often associated with progressive environmentalism, whereas wind may be seen as more neutral or pragmatic [77-79].

 

• Line 521 to 523: This aligns with national trends, with Democrats favoring environmental restoration, while Republicans prioritizing fiscal restraint and local autonomy [93-95].

 

• Lines 530 to 531: Independents' strong support likely reflect a desire to preserve local identity and community heritage without aligning with partisan narratives [16,96,97].

Author Response File: Author Response.docx

Reviewer 4 Report

Comments and Suggestions for Authors

Figure 1 is provided in the article. Coal production in the United States. There is no reference to this figure and no analysis of it. It needs to be supplemented.

The first reference to Table 1 is given on line 154. It would be useful to give the table immediately after this reference.

It would be advisable to clearly describe the six attributes for which the study is conducted. At the beginning of the paper, it is easy to list these attributes, because a full description of them follows.

The last attribute is a value variable designed as a payment instrument that reflects respondents' willingness to sell through additional monthly utility bills to support an energy transition programme (line 330-331).  Do the authors take into account risks and uncertainty?

The article cites 100 references. The question arises whether all of them are included in the article? Source 98 and 99.

Regarding state support programmes. It would be necessary to at least mention some of them.

The conclusions of the study could be more generalised and presented in a more concise form in relation to the title of the article Socioeconomic and Environmental Trade-offs of the Transition to Sustainable Energy in Kentucky.

The quality of the figures could be improved.

Author Response

Reviewer 4

 

1. Figure 1 is provided in the article. Coal production in the United States. There is no reference to this figure and no analysis of it. It needs to be supplemented.

Response to reviewer:

Corrections made on Line 32 that describes Kentucky’s decline in coal production.

 

2. The first reference to Table 1 is given on line 154. It would be useful to give the table immediately after this reference.

 

Response to Reviewer:

Journal preferences on position, we can recommend the  changes to be made accordingly.

 

3. It would be advisable to clearly describe the six attributes for which the study is conducted. At the beginning of the paper, it is easy to list these attributes, because a full description of them follows.

 

Response to Reviewer:

Changing the format of our study to move the attributes to the beginning of the paper will alter the flow of our paper. In our literature review we talk about the attributes and refer to the section in which they are described in detail as follows

 

Line 177 to 183: Overall, our study incorporates a comprehensive and context-sensitive set of attributes that more accurately reflect the lived realities and priorities of Kentucky residents. Our studies also differ by evaluating not only these individual attributes but also the trade-offs between them. Hence, we provide a more holistic understanding of public preferences and perceived fairness of energy transition, thereby contributing to the broader discourse on just transitions. A detailed review of previous studies and their relation to our attributes is further described in section 2.3.

 

Also traditionally most DCEs, describe attributes in the methods section For example:

• Oluoch, S., Lal, P., Susaeta, A., & Wolde, B. (2021). Public preferences for renewable energy options: A choice experiment in Kenya. Energy Economics, 98, 105256.

 

• Ku, S. J., & Yoo, S. H. (2010). Willingness to pay for renewable energy investment in Korea: A choice experiment study. Renewable and Sustainable Energy Reviews, 14(8), 2196-2201.
•  
• Azarova, V., Cohen, J., Friedl, C., & Reichl, J. (2019). Designing local renewable energy communities to increase social acceptance: Evidence from a choice experiment in Austria, Germany, Italy, and Switzerland. Energy Policy, 132, 1176-1183.

 

4. The last attribute is a value variable designed as a payment instrument that reflects respondents' willingness to sell through additional monthly utility bills to support an energy transition programme (line 330-331). Do the authors take into account risks and uncertainty?

Response to Reviewers
 Thank you for this important question. In our choice experiment, the additional monthly utility bill served as the payment vehicle to estimate respondents’ willingness to pay (WTP) for different energy transition attributes. While the design captured trade-offs between cost and program benefits, we acknowledge that it did not explicitly incorporate risk or uncertainty components (e.g., variability in future energy prices, implementation uncertainty, or policy credibility) into the attribute levels or choice tasks.

That said, we recognize that perceptions of risk, particularly regarding the effectiveness, fairness, and durability of energy transition programs, can significantly influence WTP responses. These considerations were indirectly accounted for to some extent through random parameter specifications in the RPL model, which capture unobserved heterogeneity in preferences, potentially reflecting differing levels of trust, risk tolerance, and perceived uncertainty.

We have now clarified this point in the revised manuscript’s limitations section and suggest that future research incorporate explicit risk attributes or scenario-based uncertainty (e.g., probability of success, future rate fluctuations) to better understand how perceived risks shape public support for energy transition policies.

Revisions in the manuscript

We add line 566 to 575: While this study provides valuable insights into public preferences for energy transition programs in Kentucky, several limitations warrant attention. First, although the additional monthly utility bill was used as a realistic and policy-relevant payment vehicle to estimate willingness to pay (WTP), the design did not explicitly incorporate risk or uncertainty such as implementation delays, fluctuating energy costs, or policy failure into the choice tasks. These factors can influence how respondents perceive and value different programs. Although the Random Parameters Logit (RPL) model captures some unobserved heterogeneity, including preferences potentially shaped by trust, risk aversion, or political skepticism, we acknowledge that this does not fully substitute for explicit modeling of risk scenarios.

 

 

5. The article cites 100 references. The question arises whether all of them are included in the article? Source 98 and 99.

 

Response to reviewers:

Corrections on references made

 

6. Regarding state support programmes. It would be necessary to at least mention some of them

 

In the identification of attributes, we mention and define our SSP programs based on examples from literature from 54 to 55 as follows:
Line 344 to 350: The fifth attribute, SSPs are central to ensuring community resilience during energy transition, creating safety nets, and building adaptive capacity for impacted households. Informed by adaptive capacity frameworks [52-54], the SSP attribute includes three levels: energy efficiency programs, job retraining, and medical compensation. These support systems target vulnerable populations affected by mine closures, unemployment, or coal-related health issues. Medical assistance was selected over traditional weatherization programs to better reflect public concerns about long-term health impacts from mining.

 

Also Line 493 to 494 in the results and discussion sections further elaborates the role of SSPs in different studies: Dell et al. [89] also note that there is widespread support for degrowth policies, such as universal healthcare, work-time reductions, fossil fuel production caps, and advertising restrictions.

 

7. The conclusions of the study could be more generalised and presented in a more concise form in relation to the title of the article Socioeconomic and Environmental Trade-offs of the Transition to Sustainable Energy in Kentucky.

 

Response to reviewers:

The request to have a more generalised and concise conclusion is somewhat a Contradiction other reviewers who wanted a more detailed conclusion, Hence we adopted a middle ground approach that generalizes and made in more concise by adding WTP values, policy implications, future research perspectives and limitations (See above sections for other reviewers).

 

8: The quality of the figures could be improved.

 

Response to Reviewers

All figures were corrected.

 

 

Author Response File: Author Response.docx

Round 2

Reviewer 1 Report

Comments and Suggestions for Authors

All issues have been addressed by the authors.

Reviewer 2 Report

Comments and Suggestions for Authors

Thank you 

Reviewer 3 Report

Comments and Suggestions for Authors

The authors responded appropriately to all comments.

Reviewer 4 Report

Comments and Suggestions for Authors

The authors' answers and corrections are satisfactory.