Multiple Paths of Rural Transformation and Its Driving Mechanisms Under the Perspective of Rural–Urban Continuum: Taking Suzhou, China as an Example
Round 1
Reviewer 1 Report
Comments and Suggestions for AuthorsThe research produced has a well-developed conceptual structure, promoting relevant analysis and producing interesting results. Care has been taken to promote graphical structures, diagrams and methodologically supported analysis.
It is suggested that the discussion and conclusions, which appear together, be separated.
They should provide greater detail on the methodologies and analytical tools used, allowing us to understand the logic of their application and their respective rationale.
It is suggested that the results be more systematized and interpreted in such a way as to enable the assessments identified to be extracted more objectively.
They should check bibliographical references and citations in accordance with the journal's editorial and formatting rules.
Author Response
Point 1: The research produced has a well-developed conceptual structure, promoting relevant analysis and producing interesting results. Care has been taken to promote graphical structures, diagrams and methodologically supported analysis. It is suggested that the discussion and conclusions, which appear together, be separated.
Response 1: Thank you for your comments and suggestions. We readjusted the framework of the paper, taking the discussion as the fourth part of the research and the conclusion as the fifth part, making the research logic clearer.
Point 2: They should provide greater detail on the methodologies and analytical tools used, allowing us to understand the logic of their application and their respective rationale.
Response 2: We are very sorry that our current introduction to the research methods and tools is not detailed enough. On the existing basis, we have made supplementary explanations to facilitate readers' better understanding. It mainly includes two parts:
(1) In the Theoretical Framework Construction section of 2.2.1, we streamlined the background introduction of the rural-urban continuum and supplemented the methodological introduction of the dominant model, thereby highlighting how to summarize the rural transformation model based on rurality and urbanity.
(2) In the Evaluation Indice System section of 2.2.2, we supplemented the calculation formulas of Z-score standardization and exponential weighted summation, provided references on the entropy weight method, and deleted the repetitive contents with the previous text.
Point 3: It is suggested that the results be more systematized and interpreted in such a way as to enable the assessments identified to be extracted more objectively.
Response 3: Thank you for your valuable suggestions. There are some uncertainties in the current research results. We have made deletions and integrations to the existing analyses to better respond to the research topic of this article.
Point 4: They should check bibliographical references and citations in accordance with the journal's editorial and formatting rules.
Response 4: We have carefully checked the accuracy of the content and the standardization of the format of the references and citations in accordance with the requirements of the journal. We have corrected each error one by one. We apologize for this.
Reviewer 2 Report
Comments and Suggestions for AuthorsDear Authors,
Congratulations on your very good and highly interesting article.
I am more than satisfied with everything I have read.
First of all, I want to say that the time period you have covered is fascinating because in 30 years, the whole world has changed as well as China. The value of the research is also in the number of municipalities included – as many as 46! And during all that time, agriculture has changed, and both cities and villages have transformed as well.
The English is also very good; the article is easy to understand, and the conclusions are clearly presented.
I would suggest adding one or two references related to tourism and rural transformation. This could improve the clarity of your article, especially towards the end, when you discuss tourism. The sentences in that section could be improved for better understanding of rural transformation in sense of tourism.
Could you please explain what exactly you meant by 'live streaming & rural tourism'?
Thank you and one more time thank you for an interesting article.
Author Response
Point 1: I would suggest adding one or two references related to tourism and rural transformation. This could improve the clarity of your article, especially towards the end, when you discuss tourism. The sentences in that section could be improved for better understanding of rural transformation in sense of tourism.
Response 1: Your suggestions are of great help to the revision of our article. The discussion of rural tourism in this article mainly focuses on the end of the discussion. We admit that the analysis in this part is not comprehensive and clear enough. In view of this, we have made the following adjustments:
- We have supplemented two references on rural tourism and rural transformation. The first one analyzes the interaction effect among the integration of agriculture and tourism, tourism development and rural revitalization, highlighting the driving role of tourism development in the transformation and development of rural areas. The second article explores the content and mechanism by which the development of rural tourism in China drives the reconstruction of rural space, which corroborates the viewpoints in the previous text.
- Improvements have been made to the content section of rural tourism, which is now lines 674 to 680, to facilitate readers' better understanding of the driving role of rural tourism in rural development.
Point 2: Could you please explain what exactly you meant by 'live streaming & rural tourism'?
Response 2: Thank you very much for your question. In fact, the live streaming mentioned in this article refers to live-streaming sales, which is a new retail model that integrates real-time video live streaming technology with e-commerce. Its core lies in having hosts showcase products, explain functions, interact and answer questions to the audience on the live streaming platform, and guide consumers to make immediate purchases. This form shifts the "experiential marketing" of traditional offline shopping to the online platform. Through real-time visual and auditory transmission, it reduces the information gap between consumers and products. At the same time, it utilizes the personal influence of the live-streamer (such as character setting and fan trust) to stimulate purchasing behavior. At present, an increasing number of villages in China have established their own media platform accounts, inviting professional hosts or local villagers to shoot videos introducing the village's characteristic agricultural products or handicrafts, etc., in order to increase the sales of related products, which is a major way for villages to increase their income.
The rural tourism mentioned in this article refers to the tourism activities that take place in rural areas. It is a form of tourism that takes the rural geographical space as the carrier and takes the rural natural landscape, agricultural production activities, local culture (such as folk customs, architecture, and cuisine), and rural lifestyle as the core attractions, meeting tourists' demands for natural ecological experience, cultural appreciation, and leisure vacation. Its essence is the two-way interaction of the flow of factors between urban and rural areas - urban population outputs consumption power to rural areas, and rural areas achieve value transformation through the commercialization of resources. At present, against the backdrop of China's promotion of the rural revitalization and integrated urban-rural development strategy, rural tourism has become an important measure to boost the prosperity of rural industries.
Reviewer 3 Report
Comments and Suggestions for AuthorsThe authors provide an intriguing article that leads to interesting and insightful results. However, aside from some minor issues I perceive the need to rework the analytical part. I am going to detail these issues below.
- There are some grammatical and formating errors in the text and I would advise the authors to check it again. E.g., In Sections 2.2.2/2.2.3: add some space before the title; in line 193 it should read "analyze", in line 241 a space is missing; lines 288 and 289 there is a repetition.
- Why are the indices in Table 1 selected? Add some justification for the selection, i.e., in particular for using the GDP per capita as dependent variable, since the official objective of the article I understood as the level of urbanity-rurality.
Concerning the empirical approach multiple issues persist:
- Which distance measure is selected?
- Why is a Durbin model implemented, i.e., only the dependent variables are spatially lagged? Why is no spatial lag of the dependent variable considered? While I would not see the structure of a spatial error model here, it might be discussed as well.
- Since the implemented data has a panel format, why is this not utilized in the estimation?
- If Figure 9 is supposed to display the regression results, please indicate how it is constructed and to be interpreted. Even if the regression is conducted separately per year, there should be no coefficients for each of the observations.
- Ideally provide as well the regression results in standard form.
- What are "significance factors"? Independent variables, significance levels?
See above.
Author Response
Point 1: There are some grammatical and formating errors in the text and I would advise the authors to check it again. E.g., In Sections 2.2.2/2.2.3: add some space before the title; in line 193 it should read "analyze", in line 241 a space is missing; lines 288 and 289 there is a repetition.
Response 1: Thank you for your correction. We are so sorry that there were these mistakes in the article. In response to the questions you raised, this article has been revised one by one. In addition, we also found that there were errors in the capitalization of the subheadings of each chapter, and all of them have been adjusted. Thank you again for your careful review!
Point 2: Why are the indices in Table 1 selected? Add some justification for the selection, i.e., in particular for using the GDP per capita as dependent variable, since the official objective of the article I understood as the level of urbanity-rurality.
Response 2: Thank you for your comments and suggestions. First of all, we would like to explain again the meanings of rurality and urbanality mentioned in this article. Rural characteristics refer to the features of rural areas, while urban characteristics refer to the urban features of rural regions.
Secondly, the rural indicators reflect the natural resource endowment, agricultural modernization level and agricultural production characteristics of towns and townships, while urban indicators reflect the industrialization and urbanization levels of towns and townships and the living standards of villagers. The reasons for our selection of each indicator in Table 1 are as follows:
- Among the indicators of rurality, agricultural production is the main function of rural areas. It can be considered that the higher the proportion of primary industry in a place, the stronger its rurality. Similarly, per capita arable land area, per capita grain output, and per capita output of aquatic products are usually also positively correlated with rurality. The share of non-grain crops sown represents the degree of diversification of local agricultural production and reflects the regional agricultural characteristics and production advantages. The power of agricultural machinery indicates the level of intensification and mechanization of agricultural production, reflecting the agricultural production capacity.
- Among the urbanity indicators, social progress has promoted the transformation and upgrading of the industrial structure, specifically manifested as the transformation from the primary industry to the secondary and tertiary industries, which can be measured by the proportion of the output value of the two major industries. The transformation of the industrial structure not only releases the vitality of regional economic development, but also creates more job opportunities, absorbs surplus agricultural labor force, and thereby increases the proportion of non-agricultural employed population. The per capita investment in fixed assets and the per capita township enterprise profit tax have also increased simultaneously. Social progress and policy reform have provided more welfare subsidies for rural areas and farmers, thereby significantly improving the net income of farmers.
The above reasons have been supplemented in the original text.
Finally, regarding the issue you mentioned about the use of per capita GDP, the explanation is as follows:
(1) As mentioned in lines 498-500 of this article, per capita GDP is a comprehensive indicator of regional economic development, which can reflect the degree of penetration of industrialization and urbanization into rural space. In the southern Jiangsu region, the transformation of the economic development model from the "Southern Jiangsu Model" to the "New Southern Jiangsu Model" is directly reflected through the growth of per capita GDP and drives the rural areas to transform from traditional agriculture to multi-functionality (such as "agricultural modernization, eco-tourism, and industrial integration" mentioned in the paper).
(2) The essence of rural transformation is the result of the two-way flow of urban and rural factors, and economic factors are the core force driving the flow of factors.
Point 3: Which distance measure is selected?
Response 3:
I hope I have understood your question correctly. The GWR model defaults to using the Euclidean distance as the basic distance measure. In this paper, the calculation is based on the geographical coordinates (longitude and latitude) of the township unit. There are mainly three reasons for adopting the Euclidean distance: (1) The Euclidean distance conforms to the default calculation method of the spatial weight matrix in ArcGIS; (2) The research unit of the thesis is a township, with a relatively small spatial scale. The Euclidean distance is sufficient to reflect the spatial proximity relationship among townships. (3) The geometric characteristics of Euclidean distance (invariance of translation and rotation) can better satisfy the assumption of spatial smoothness.
Point 4: Why is a Durbin model implemented, i.e., only the dependent variables are spatially lagged? Why is no spatial lag of the dependent variable considered? While I would not see the structure of a spatial error model here, it might be discussed as well.
Response 4: Thank you for your comments and suggestions. We are very sorry for we don't have a good understanding of the question you raised. The spatial Durbin model was not used in this article.
Point 5: Since the implemented data has a panel format, why is this not utilized in the estimation?
Response 5: Thank you for your careful review of the research methods and contents. Regarding why the panel data characteristics were not used for estimation, the following explanations are provided in combination with the research objectives and data characteristics of this paper:
(1) The advantage of panel data lies in capturing the dynamic change patterns of individuals in the time dimension, which is suitable for analyzing the continuous changes of the same research unit over time. The key point of this paper's research lies in the spatial differences among different towns within the same period, rather than the dynamic evolution of a single town across time periods.
(2) The prominent manifestation of rural transformation in the southern Jiangsu region lies in the imbalance of spatial development rather than the stability of the time series. By using the local regression of the GWR model, the factor-driven spatial differentiation can be captured more accurately.
(3) It should be noted that this paper does not completely ignore the time effect, but achieves the combination of time and space through phased analysis. In the descriptive analysis (Sections 3.1-3.2), we sorted out the temporal changes of the rurality and urbanality indices according to three time periods, revealing the overall trend of "differentiation - reinforcement - stability"; In the analysis of the driving mechanism (Section 3.3), models are modeled respectively for each time point to identify its significant driving factors. This combination of time segmentation and spatial regression not only retains the temporal information of the panel data but also highlights the theme of spatial heterogeneity.
(4) If panel models such as spatial Durbin are adopted, it is necessary to assume that the spatial effects of the driving factors remain stable over time, which may mask the mechanism differences at different stages.
Point 6: If Figure 9 is supposed to display the regression results, please indicate how it is constructed and to be interpreted. Even if the regression is conducted separately per year, there should be no coefficients for each of the observations. Ideally provide as well the regression results in standard form.
Response 6: Thank you for your detailed suggestions on the presentation of Figure 9. The following is a detailed response regarding the construction logic, interpretation methods, etc. of this figure:
- Figure 9 shows the spatial distribution of local coefficients of the GWR model, which is used to present the intensity and direction of the influence of various driving factors on rural transformation in different years. There are mainly three construction steps: â‘ Sort out the independent and dependent variable results of the 46 towns studied in this paper year by year, as well as the longitude and latitude of the center of each administrative unit. â‘¡ Import the data into the MGWR2.2 software (this software can choose to run the GWR mode), select the parameters in sequence, and obtain the regression results of each influencing factor of the 46 research units. â‘¢ Load the regression results into ArcGIS for spatial display. Under normal circumstances, the larger the regression coefficient is, the stronger the driving force is. This paper classifies the driving forces into five categories from strong to weak according to the size of the regression coefficients.
(2) Unlike the globally unified coefficients of the common least square method (OLS), GWR allows each spatial position to have an independent coefficient, reflecting the spatial non-stationarity of the driving factors.
(3) The figure does not show the specific results of each observation, but rather a spatially continuous coefficient distribution pattern. For instance, the coefficient of per capita cultivated land area (x12) is positive and has a large absolute value in the traditional grain-producing areas in the north (such as Zhangjiagang), while it is extremely low in the densely water-netted areas in the south (such as Wujiang District), intuitively demonstrating the spatial pattern that agricultural drivers are concentrated in the northern part of Suzhou City.
(4) In order to convey spatial heterogeneity more efficiently and retain the intuitiveness of spatial patterns, the discrete point coefficients are not presented in this paper.
Point 7: What are "significance factors"? Independent variables, significance levels?
Response 7: Thank you for your attention to the research methods of this article. The following is a detailed explanation of these three.
(1) Significant factors refer to the driving factors that have a significant impact on the dependent variable in a statistical model, that is, the indicators tested by P < 0.1.
(2) Independent variables refer to the exogenous variables in the model used to explain the changes in the dependent variable. In this paper, the potential driving factors corresponding to rural transformation include the 12 indicators in Table 1.
(3) The significance level is the probability threshold for determining whether the relationship between the independent variable and the dependent variable is "statistically significant". P < 0.1 indicates a statistical difference, P < 0.05 indicates a significant statistical difference, and P < 0.001 shows an extremely significant statistical difference.
Round 2
Reviewer 3 Report
Comments and Suggestions for AuthorsThe authors have addressed the most relevant points of the first round of review.
Regarding the fourth point: The spatial weight matrix is applied only to the independent variables and not to lagged dependent variables. This is often referred to as a Durbin model of spatial effects. The spatial lag model would refer to a model where the spatial weight matrix is applied to the dependent (lagged) variables. In a spatial error model the spatial weight matrix is applied to the error terms. Since the main focus of the article lies in the spatial effects, this aspect might be picked up in the outlook or it might provide some impetus for future research.
