Research on the Impact of Large-Scale Photovoltaic Development on Regional Economic Growth—A Case Study of Qinghai Province

Round 1

Reviewer 1 Report

Comments and Suggestions for Authors Dear Authors, The topic of the article is:  "Research on the Impact of Large-Scale Photovoltaic Development on Regional Economic Growth-a Case Study of Qinghai Province". After reading the article, I have some doubts as to its accuracy. Based on the panel data of eight prefecture-level cities in Qinghai Province from 2018  to 2022 It is found that PV power generation has a significant positive effect on the GDP of each region in Qinghai Province. Please think about and propose a topic reflecting the most important conclusions from the research.  

In paper takes Qinghai Province, which has the highest large-scale photovoltaic power generation in China, as the research object, and adopts various analysis methods such as static panel data regression analysis, intermediary test and threshold effect test. 

  For the empirical studies accepted, please explain "Annual number of women employed in non-private sectors in each area" why the authors differentiate between genders? Are men deliberately omitted? Please explain.   "Through detailed regression analysis, the model shows that after including all control variables, the F-test value reaches 21.42 and the corresponding p-value is 0, which is much lower than the standard significance threshold of 0.05, which strongly indicates that the selected explanatory variables in the model have a significant statistical impact 233 on regional GDP."
Please explain - see lines 230-233. What does it mean according to the authors ... significant statistical impact?   The article was written in correct language, but I recommend that the most important conclusions be listed in bullet points. This form will be more legible to the recipients of the article.   Comments on the Quality of English Language

 The English could be improved to more clearly express the research.

Author Response

Dear reviewers, thank you for your questions and comments. After full consideration, we have extended the research period from 5 years to 10 years（2014-2023）, which covers all important stages of photovoltaic development in this region, so as to meet the accuracy.

To address the reviewer’s concern regarding gender-specific employment variables: we include the "annual number of women employed in non-private sectors" as a proxy for inclusive and stable employment trends, especially in less developed regions where female labor participation plays a unique role in regional economic resilience and income stability.  The selection of female employment (rather than total employment or male-specific employment) reflects two considerations: (1) non-private female employment in China’s western regions is often linked with relatively stable public-sector positions, and (2) the availability and consistency of gender-disaggregated data in regional statistical yearbooks for Qinghai during the study period.  There was no intentional omission of male employment;  rather, this variable showed greater representativeness and consistency as a control factor in the regression model.

Please explain - see lines 230-233. What does it mean according to the authors ... significant statistical impact?   The article was written in correct language, but I recommend that the most important conclusions be listed in bullet points. This form will be more legible to the recipients of the article. 

Response: 

"The F-test value of 68.82 (p < 0.01) indicates that the regression model as a whole is statistically significant, meaning that the group of explanatory variables jointly explain variations in regional GDP at a high level of confidence. This does not imply individual causality but suggests that the model captures meaningful associations between the independent variables and regional economic outcomes. As shown in Tables 8 below. In addition, the pseudo-judgment coefficient (R²) of the model is 0.8622, which means that the variables included in the model can explain 86.22% of the variation of regional GDP, while the remaining 13.78% of the variation has not been explained by the model. This indicates that there may be other important variables or external factors not considered in the model, which may also have an impact on regional GDP. Despite this unexplained variation, the overall significance of the model ensures the validity and applicability of the chosen set of variables in explaining regional GDP.

Author Response File: Author Response.pdf

Reviewer 2 Report

Comments and Suggestions for Authors

First of all, I think that this is study/research area that needs to be explored and published. However, there are to my opinion, some issues that the author(s) need to deal with. A regression analysis will not do. This is q matter that needs to be explored using causality tests (unit root tests, cointegration test, ECM/VAR test) - either ARDL approach or bootstrap causality...or whatever method for small sample(s). And now we come to the sample size. Why so small sample (i.e., 40 observations)? Was there a problem with the data availability, do you have a specific reason why you've chosen the period between 2018-2022? 

I'm not saying that the overall idea (and the paper in general) is bad...I'm just concerned that the methodological approach you took is to simple. The variables X4 and X5 seem to be interconnected. What's your explanation behind variable X6, how that fits/explains the model? Does the literature you've previously consulted uses the same/similar variables?

Conclusion part is also slim and short. Policy recommendations (based on your research results) are key and are missing in this part of the paper.

Author Response

First of all, I think that this is study/research area that needs to be explored and published. However, there are to my opinion, some issues that the author(s) need to deal with. A regression analysis will not do. This is q matter that needs to be explored using causality tests (unit root tests, cointegration test, ECM/VAR test) - either ARDL approach or bootstrap causality...or whatever method for small sample(s). And now we come to the sample size. Why so small sample (i.e., 40 observations)? Was there a problem with the data availability, do you have a specific reason why you've chosen the period between 2018-2022? 

Response: Thank you for your thoughtful feedback. We appreciate your suggestion to incorporate causality-based econometric methods, and we fully agree that understanding the causal relationship between photovoltaic (PV) development and economic growth is essential for advancing this research field. In response to your concerns, we have made the following major revisions in the updated manuscript: 1.   Expanded Sample Size and Time Frame:

We have extended the study period from 2018–2022 to 2014–2023, thereby increasing the total number of observations from 40 to 80. This allows us to capture the entire critical phase of large-scale PV development in Qinghai Province, from early deployment to the stage of industrial and grid-scale integration.  The full panel now includes data from eight prefecture-level cities over a ten-year period, providing broader temporal coverage and improved robustness in the analysis. 2. Model Selection and Diagnostic Enhancements:

We have strengthened the empirical strategy by systematically incorporating a comprehensive set of diagnostic tests. This includes: Multicollinearity checks using Variance Inflation Factors (VIF); Model selection via F-test, LM test, and Hausman test, confirming the appropriateness of the fixed-effects panel model;  Durbin-Wu-Hausman (DWH) test to examine potential endogeneity issues; White test to detect heteroskedasticity; Robust standard errors employed throughout to ensure statistical validity. These methodological additions, now reflected in Sections 3.3 of the revised manuscript, enhance the internal consistency of the model and address statistical concerns that often accompany small regional datasets. 3. On Causality Modeling:

While our current analysis emphasizes correlational findings, we acknowledge the potential of advanced causal inference techniques (e.g., ARDL, ECM, VAR, or bootstrap causality tests) and plan to implement them in follow-up studies. These approaches are indeed more appropriate once a longer time series becomes available and certain stationarity assumptions are met. We have now explicitly acknowledged this limitation in the manuscript’s discussion section to guide future research directions. We believe that the expanded dataset and improved model design significantly enhance the analytical robustness and reliability of our findings. Thank you again for encouraging us to refine our empirical foundation.

I'm not saying that the overall idea (and the paper in general) is bad... I'm just concerned that the methodological approach you took is to simple.  The variables X4 and X5 seem to be interconnected.  What's your explanation behind variable X6, how that fits/explains the model?  Does the literature you've previously consulted uses the same/similar variables?

Response: Thank you for your valuable comments. In response, we have enhanced the model specification and clarified variable justifications. First, although X4 (total fiscal revenue) and X5 (local public budgetary revenue) are related, they capture different dimensions of local fiscal capacity—centralized transfers versus local autonomous income—and our VIF analysis confirms that multicollinearity remains within acceptable levels. Second, variable X6, representing the number of women employed in non-private sectors, is included to reflect stable, formal employment patterns, particularly in less developed areas of Qinghai Province where female participation in public employment plays a key role in economic inclusion and household income stability. This approach is supported by prior literature on gendered employment and regional development. Additionally, the revised manuscript incorporates robustness checks, endogeneity testing, and model selection diagnostics to improve empirical rigor. These updates are reflected in Sections 3.3 and the revised manuscript.

Conclusion part is also slim and short. Policy recommendations (based on your research results) are key and are missing in this part of the paper.

response: We add policy recommendations at the end of the article.

Based on the empirical findings, the following policy recommendations are proposed: Regional Differentiation of PV Development Strategies and Optimization of PV Scale According to Regional Capacity. Firstly, It is recommended that differentiated PV development targets and fiscal policies be adopted according to local economic conditions. For less developed regions, governments should prioritize infrastructure investment and technical support, and offer sustained incentives such as tax reductions, direct subsidies, or low-interest loans to lower initial investment barriers. Programs like “PV for poverty alleviation” can be promoted to create employment opportunities, integrate PV with local agricultural or pastoral systems, and boost household income. Additionally, leveraging underutilized land in desert and barren areas for large-scale PV bases can enhance land-use efficiency, support local employment, and contribute to the sustainable energy supply. Environmental protection, technological innovation, and compatibility with existing energy systems should be considered to ensure both economic and ecological benefits. These policies can help minimize crowding-out effects in economically disadvantaged regions and enhance the inclusivity of renewable energy development. Secondly, the scale of PV development should be aligned with local economic maturity, energy needs, and market demand. In less developed high-altitude regions, while PV expansion can stimulate growth, more attention should be paid to complementary investments in infrastructure and human capital to avoid inefficient resource allocation. In developed areas, the focus should shift toward technological innovation—enhancing PV conversion efficiency, developing advanced storage systems, and building competitive energy markets. Market-based mechanisms such as green certificates and carbon trading should be introduced to promote efficiency and price transparency. Where large-scale PV plants are not feasible, decentralized rooftop systems can also serve as viable alternatives to support local economic development.

Author Response File: Author Response.pdf

Reviewer 3 Report

Comments and Suggestions for Authors

The repetition rate of the paper is too high, and the originality has yet to be verified.

Comments on the Quality of English Language

English can be improved to express research more clearly.

Author Response

We sincerely appreciate the reviewer’s comment regarding the similarity rate and the concern about originality.  We would like to clarify that this manuscript is entirely original and has not been published in any journal.  The elevated similarity rate was due to an earlier submission of a preliminary version of this manuscript to Frontiers in Energy Research.  During that process, we formally requested withdrawal of the manuscript after a prolonged period without progress.  We did not receive a timely confirmation and assumed the withdrawal had been processed.  Subsequently, we submitted the revised and improved version of the manuscript to your esteemed journal.  However, it appears that the previous journal may not have immediately removed the submission from its system, resulting in residual content being indexed and contributing to the high similarity score.  We have now received an official withdrawal confirmation from Frontiers in Energy Research, and we confirm that the manuscript has not been published or accepted elsewhere in any form.  We remain committed to upholding academic integrity and transparency and kindly request that the manuscript be evaluated based on its current content and originality. We have also made a lot of changes to the original text in the revised version to ensure that it meets the requirements of your journal

Author Response File: Author Response.pdf

Reviewer 4 Report

Comments and Suggestions for Authors

The manuscript is devoted to a topical issue. The material of the manuscript is quite well structured, presented logically, with sufficient explanations of the calculations presented and discussion of the results. 
The references to the manuscript can be considered sufficient, although they could be somewhat expanded by modern research on the analysed issues.
It would also be advisable to indicate in the conclusions to the manuscript what conclusions and recommendations can be formulated for policy makers in terms of supporting the development of photovoltaic generation, taking into account regional characteristics, to achieve sustainable economic growth and reduce the differentiation of regional development. 
In general, the manuscript is recommended for publication with minor corrections.

Author Response

We sincerely thank the reviewer for the positive evaluation and constructive suggestions. In response to your valuable comment regarding the need for clearer policy-oriented conclusions, we have expanded the conclusion section to include detailed policy recommendations aimed at supporting the development of photovoltaic (PV) generation in alignment with regional characteristics. Specifically, the revised manuscript now discusses how policymakers can adopt differentiated PV development strategies tailored to the economic maturity of each region. For less developed areas, we suggest measures such as infrastructure investment, fiscal incentives, and employment-oriented PV programs to promote inclusive growth and reduce regional disparities.  For more developed regions, we emphasize the importance of supporting technological innovation, market-based mechanisms (e.g., green certificates, carbon trading), and energy market liberalization to enhance the economic efficiency of PV integration. These additions aim to provide actionable insights for achieving sustainable economic growth while narrowing regional development gaps, consistent with the goals of ecological transition and national energy security.  The updated policy implications can be found in the revised Conclusion and Policy Implications section of the manuscript. We also acknowledge your suggestion on strengthening the references.  Accordingly, we have updated and expanded the literature review with several recent studies from the past ten years (2014-2023), ensuring that the theoretical foundation reflects current academic discourse on the socio-economic impacts of PV deployment. Thank you again for your encouraging feedback and helpful recommendations.

Author Response File: Author Response.pdf

Reviewer 5 Report

Comments and Suggestions for Authors

Comments 1: Please add figures where appropriate to enrich your article.

 

Comments 2: There is a problem with the subscript of formula (1) in the paper, which is inconsistent with the content of the paper and the specification of the paper. Please ensure the consistency and accuracy of the format.

 

Comments 3: Some of the references cited in this paper are relatively old, and more references in the recent five years can be cited.

 

Comments 4: In order to improve the overall logic of the article, it is suggested to transfer the content of the current situation of photovoltaic application in the study area in the Section 1 of the article to the Section 2.

 

Comments 5: Please add a follow-up research plan or outlook to the conclusion of the article.

Comments on the Quality of English Language

Can be improved

Author Response

response1: We sincerely thank the reviewer for the positive evaluation and constructive suggestions.   In response to your valuable To comment regarding the need for clearer policy-oriented conclusions, we have added Figure 1. 

 response2: Where Y_it represents explained variable: regional GDP; t Represents time (2014-2023) and X_it sequentially represents explanatory variables and control variables: X_1it represents photovoltaic power generation, X_2it represents total import and export trade, X_3it represents fixed asset investment growth rate, X_4it represents total fiscal revenue, X_5it represents local public fiscal budget revenue, and X_6it represents female employment in non-private units; accordingly, is the regression coefficient of explanatory variables; andare the constant term and the random disturbance term, respectively.

 response3: 

Thank you for your helpful suggestion. In response, we have reviewed and updated the literature cited in the manuscript to include more recent and relevant research from the past five years (2019–2024). These additions strengthen the theoretical foundation and improve the contextual relevance of the study. Notable updates include recent findings on the socio-economic impacts of solar PV deployment, spatial and dynamic modeling of renewable energy effects, and policy evaluations of PV expansion in developing regions. All new references have been integrated into the revised manuscript, and the reference list has been updated accordingly.

Chen H, Chen W. Status, trend, economic and environmental impacts of household solar photovoltaic development in China: Modelling from subnational perspective[J]. Applied Energy, 2021, 303: 117616.(in Chinese)

Semelane S, Nwulu N, Kambule N, et al. Evaluating economic impacts of utility scale solar photovoltaic localisation in South Africa[J]. Energy Sources, Part B: Economics, Planning, and Policy, 2021, 16(4): 324-344.

Qu Z., Yang S. & Xiao J. Double carbon in northwest China under the background of the present situation in the construction of photovoltaic power station and the potential analysis [J]. Journal of arid zone resources and environment, 2024, 38 (02) : 20 to 26,  (in Chinese)

Hu D. Research on the development path of regional renewable energy in China from the perspective of green economy [D]. China university of petroleum (Beijing), 2022. (in Chinese)

Zou M. Research on the integrated development of photovoltaic new energy system construction and rural economy under the background of "dual carbon" [J]. Shanxi agricultural economy, 2022 (21) : 125-128. (in Chinese)

Li N, Zhang G L, Zhou Y H, et al. Poverty reduction practice in China: evaluation of social and economic benefits of "photovoltaic poverty alleviation" policy in rural areas [J]. Journal of China Agricultural University, 2022,27(02):294-310. (in Chinese)

Song C, Guo Z, Liu Z, et al. Application of photovoltaics on different types of land in China: Opportunities, status and challenges[J]. Renewable and Sustainable Energy Reviews, 2024, 191: 114146.

 response4: 

This has been adjusted in the revised draft.

Qinghai Province is located in the inland northwest of China, adjacent to Gansu Province in the north and east, Xinjiang Uygur Autonomous Region in the northwest, Tibet Autonomous Region in the south and southwest, and Sichuan Province in the southeast. With a total area of 722,300 square kilometers, the province has jurisdiction over two prefecture-level cities and six autonomous prefectures. By the end of 2023, the province's permanent population is 5.94 million. The terrain of Qinghai Province is generally high in the west and low in the east, high in the north and south, low in the middle. The elevation in the west is high and steep, and it slopes to the east, descending in a ladder type. The eastern region is the transition zone from the Qinghai-Tibet Plateau to the Loess Plateau, with complex topography and diverse geomorphology. The mountains constitute the basic skeleton of the province's geomorphology. The total annual solar radiation of the province is second only to that of the Tibetan Plateau, with an average annual total radiation of 5860 ~ 7400 megajoules/m² and sunshine hours of 2336 ~ 3341 hours. Qinghai Province has carried out large-scale PV development over the years benefiting from the abundant solar energy resources. This long-term project has not only achieved remarkable economic and industrial benefits, but also promoted the sustainable development of regional economy. By 2023, the province's installed capacity of new energy will reach 37.5429 million kW, with a new installed capacity of 9.3971 million kW that year. Compared with 2015, the total installed capacity of new energy increased by more than six times, while the increase of new installed capacity was nearly 10 times. The overall trend of installed capacity shows rapid growth and increases year by year. Among them, the cumulative installed capacity of solar power generation has reached 25.612,100 kW, making it the largest type of power supply in Qinghai Province. In addition, the proportion of solar installed capacity in new energy remains above 65% all the year round and reaches 68.2% in 2023, but the proportion shows a trend of decline year by year, mainly due to the rapid rise of wind power in the new energy structure, increasing from 7.6% in 2015 to 31.5% in 2023. In terms of power generation, according to the National Energy Administration, in 2023, the total power generation of Qinghai province will be 100.82 billion KWH, showing an annual growth rate of 1.6%. Specifically, hydropower generation was 39.83 billion kWh, down 6.7 percent from last year; Thermal power generation reached 15.88 billion kWh, representing a year-on-year increase of 3%; Solar power generation increased significantly to 29 billion kWh, up 13.3% year on year; Wind power generated 16.1 billion kilowatt-hours, up 3.3 percent year on year. These data reflect that solar energy in Qinghai Province occupies a dominant position in the local new energy structure and continues to promote the optimization and development of the province's new energy structure. Figure 1 illustrates the annual trends in photovoltaic (PV) power generation (in billion kWh) and total regional GDP (in billion CNY) across all prefecture-level cities in Qinghai Province over the period 2014–2023. The figure reveals a steady and significant increase in both PV output and economic activity during the study period. The synchronized upward trends suggest a potential linkage between the growth of renewable energy infrastructure and regional economic development. These observed patterns provide preliminary empirical support for the hypothesis that large-scale PV deployment contributes positively to regional economic performance, warranting further econometric investigation.

Response 5: 

To deepen the understanding of the relationship between renewable energy deployment and regional development, future research will explore several key directions. These include the application of dynamic panel models such as System GMM or ARDL to capture potential lagged and causal effects, and the use of spatial econometric models to analyze spatial spillovers of PV development across neighboring regions. Additionally, micro-level investigations—such as household or firm-level surveys—will be conducted to assess the social and distributive impacts of PV projects. Further research will also examine the interaction between PV development and ecological restoration, particularly in fragile high-altitude ecosystems. Lastly, expanding both the temporal and geographical scope of analysis will allow comparisons between Qinghai Province and other renewable energy frontiers in western China. Together, these efforts aim to build a more comprehensive understanding of the economic, social, and environmental consequences of large-scale energy transitions in underdeveloped regions.

Author Response File: Author Response.pdf

Round 2

Reviewer 2 Report

Comments and Suggestions for Authors

The revised version of the manuscript shows significant improvement compared to the original submission. The authors have carefully addressed the comments provided by the reviewer and have made substantial enhancements to the clarity, structure, and academic rigor of the paper.

Author Response

We greatly appreciate the reviewers’ constructive feedback, which has been invaluable in guiding our revisions and strengthening the overall quality and clarity of the study.

Author Response File: Author Response.pdf

Reviewer 5 Report

Comments and Suggestions for Authors

Accept

Author Response

We greatly appreciate the reviewers’ constructive feedback, which has been invaluable in guiding our revisions and strengthening the overall quality and clarity of the study.

Author Response File: Author Response.pdf