Achieving Sustainable Land Use Allocation in High-Altitude Area by 2030: Insights from Circle Structure and Scenario Predictions for Production–Living–Ecological Land in Xining Marginal Area, China

Round 1

Reviewer 1 Report

Comments and Suggestions for Authors

1.     In section 2.2, the authors have selected only "Population," "GDP," "Primary roads," and "Seat of County government" as the socio-economic datasets. However, the authors did not provide an explanation for this specific selection. Additionally, the authors did not clarify how these socio-economic datasets were utilized in the study. If these datasets, excluding "Seat of County government," were used as driving factors for the PLUS model, it is important for the authors to explain their rationale for choosing these three elements over others.

2.     In section 2.2, the authors did not provide an explanation for the usage of the Elevation and Slope data. If these data were utilized as driving factors for the PLUS model, the authors should clarify why they specifically chose these two elements, rather than assuming that the readers would infer their purpose.

3.     In section 2.2, the authors solely mentioned the "Land dataset" and its sub-data "Land use." However, it would be beneficial for the authors to first introduce and elaborate on the specific land use types included in this dataset within section 2.2 itself, rather than solely listing them under the "Corresponding land use type" column in Table 2.

4.     In Table 7, the authors presented the “Core-edge mode of sub-land change”, however, the use of terms such as “Slightly increase” or “Increase” make it difficult to display the modes of sub-land change clearly. Considering the authors have the shapefile data “Seat of County government”, it might be better to add a map that emphasizes the sub-land change mode of “core” and “edge”. That might be a more comprehensive and easily understandable depiction of the change mode.

Author Response

To Reviewer 1:

1. In section 2.2, the authors have selected only "Population," "GDP," "Primary roads," and "Seat of County government" as the socio-economic datasets. However, the authors did not provide an explanation for this specific selection. Additionally, the authors did not clarify how these socio-economic datasets were utilized in the study. If these datasets, excluding "Seat of County government," were used as driving factors for the PLUS model, it is important for the authors to explain their rationale for choosing these three elements over others.
2. In section 2.2, the authors did not provide an explanation for the usage of the Elevation and Slope data. If these data were utilized as driving factors for the PLUS model, the authors should clarify why they specifically chose these two elements, rather than assuming that the readers would infer their purpose.

 

Response to 1 and 2:

The process of land cover change in Xining periphery has a fundamentally different driving mechanism compared to inland regions.

Firstly, there is an active adaptation of human activities and urban construction to the fragile ecological environment of the high-altitude, cold, and oxygen-deficient plateau. In this active adaptation process, elevation serves as the primary constraint on the expansion of human activities, with most of the development of plateau towns and agricultural activities limited to the river valleys. As an important evaluation criterion for urban development and cultivated land conservation, slope profoundly affects the evolution of built-up land and cultivated land. Therefore, among the natural driving factors, we have selected these two indicators. 

Secondly, there are progressive processes, as well as external driving processes. The progressive process mainly considers the inertia of development based on existing towns, as the scale of towns in the Xining periphery is generally small. New construction land is primarily concentrated around existing towns where population density and industrial activities are relatively vibrant. Moreover, the driving force for urbanization in the Qinghai-Tibet Plateau is mainly top-down government-led initiatives. Thus, we have chosen population density, GDP, and government locations as indicators for such processes. The external driving process primarily includes the impact of tourism and targeted assistance, both of which depend on transportation infrastructure conditions. Additionally, when operating the PLUS model, it is essential to consider the volume of data. After multiple attempts and assessments of the factors' driving force, we ultimately selected "distance to primary roads" as the indicator for transportation as external driving forces.

The above content has been added to the manuscript (line 159-179).

 

3. In section 2.2, the authors solely mentioned the "Land dataset" and its sub-data "Land use." However, it would be beneficial for the authors to first introduce and elaborate on the specific land use types included in this dataset within section 2.2 itself, rather than solely listing them under the "Corresponding land use type" column in Table 2.

 

Response to 3:

We have integrated the land use classification table and its description from section 2.3 into section 2.2. Before discussing the driving forces and listing datasets, we first explain the classification system of PLE land for better logical coherence in our presentation (line 158). However, since the PLE land classification system is quite complex, we have retained the PLE classification table (table 1) for clarity. Additionally, in the row named land use dataset in Table 2 (Data sources), we have changed the sub-data from “land use” to "PLE land classification in Table 1", in order to help readers connect Table 1 and Table 2.

The above content has been modified in the manuscript.

 

 

 

4. In Table 7, the authors presented the “Core-edge mode of sub-land change”, however, the use of terms such as “Slightly increase” or “Increase” make it difficult to display the modes of sub-land change clearly. Considering the authors have the shapefile data “Seat of County government”, it might be better to add a map that emphasizes the sub-land change mode of “core” and “edge”. That might be a more comprehensive and easily understandable depiction of the change mode.

 

Response to 4:

Firstly, "Seat of government" does not refer to changes of the land around the specific point where the government is located; rather, we consider the entire county where the government of an autonomous prefecture or city is based as the "Seat of government" or the “core area”.  This has been clarified in the article (line 389-390): "We investigate the government seat as the core area (Haiyan County, Gonghe County, and Tongren City), and the remaining counties as external areas." 

Secondly, the description of the core-edge mode in Table 7 using only text is not rigorous. We have replaced it with specific numbers (the rates of change of different land types in different areas) to provide a more intuitive representation of the core-edge pattern of land use. Additionally, we have revised certain discussions in Section 3.4 (line 391-399). 

The modified table 7 is shown as below:

	2030-A		2030-B		2030-C		2030-D		Conclusion
	Core	Edge	Core	Edge	Core	Edge	Core	Edge
b1	0.455	0.316	-0.122	-0.591	0.454	0.316	0.439	0.688	all slightly increase except Scenario B
b2	6.880	7.668	6.318	7.901	6.917	7.652	0.216	0.274	increase overall except Scenario D
b3	36.658	8.292	43.500	13.050	36.705	8.457	0.016	-1.682	more growth in the core area
b4	-0.310	-0.254	5.504	3.316	-0.307	-0.255	5.535	3.541	varying according to the Scenario
b5	2.745	1.352	1.808	0.123	2.715	1.348	1.272	2.258	all slightly increase
b6	-1.097	-0.120	-1.098	-0.532	-1.091	-0.121	-0.524	-0.385	all slightly decrease
b7	-1.109	0.082	-3.485	-0.663	-1.107	0.080	-2.878	-2.086	more decrease in the core area
b8	63.892	44.063	60.806	50.150	63.937	46.679	5.809	0.992	more growth in the core area

 

 

Author Response File: Author Response.docx

Reviewer 2 Report

Comments and Suggestions for Authors

The paper approaches the interesting question of the balance between ecology and urbanisation. For this it uses a case study. However, the method, well illustrated in Fig. 2, should prevail and draw conclusions for other, globally situated, similar situations. The location of the case study on the Tibet plateau is nevertheless interesting.

The paper is well structured.

The research questions are well posed for the case study. The discussion well approaches the limitations as well. The reference list shows the relevant discussion in the field, with emphasis on sustainability.

The figures are relevant to underline the thesis and proper mathematical background has been provided.

Author Response

The paper approaches the interesting question of the balance between ecology and urbanization. For this it uses a case study. However, the method, well illustrated in Fig. 2, should prevail and draw conclusions for other, globally situated, similar situations. The location of the case study on the Tibet plateau is nevertheless interesting.

The paper is well structured.

The research questions are well posed for the case study. The discussion well approaches the limitations as well. The reference list shows the relevant discussion in the field, with emphasis on sustainability.

The figures are relevant to underline the thesis and proper mathematical background has been provided.

 

Response:

Thank you for recognition. To further extend our research ideas to a broader scope, we have added a paragraph in the conclusion and discussion:

The human-environment relationship system is essentially a complex system, and many global challenges are interconnected, and addressing only one aspect may exacerbate another. The intricate human-environment relationship system in Xining periphery requires planners to reconcile the conflicts between traditional agricultural livelihoods and modern development, as well as the pressures of ecological environment protection and social development goals. This process involves multi-stakeholder models and cross-regional management, with the ultimate goal of enhancing human well-being. Similar issues exist in many regions of the Global South, particularly in areas that are impoverished, ecologically vulnerable, and in urgent need of social well-being enhancement, such as the Ganges River basin in India, the Amazon rainforest, and Southeast Africa.

This study provides ideas for sustainable development path design for these regions from three perspectives: land use prediction, land use interaction, and administrative unit interaction. It is essential to consider the future interactions between different spatial relationships and the competitive dynamics among different regional units. By adopting a zoning approach, strategically allocating advantageous land use types in favorable regions can promote regional sustainable development.

 

 

 

 

 

 

To Editor:

The manuscript has a somewhat high repetition rate. Please make revisions accordingly.

 

Response:

We have carefully reviewed the manuscript and made revisions to reduce redundancy. Unnecessary repetitions have been eliminated, and we have rephrased sections to enhance the flow and improve readability. We believe these changes have strengthened the overall quality of the manuscript. 

 

 

Other revisions:

The original title of the article is not smooth enough and does not clearly convey the core meaning. We have changed the title to: Achieving Sustainable Land Use Allocation in High-Altitude Area by 2030: Insights from Circle Structure and Scenario Predictions for Pro-duction-Living-Ecological Land in Xining Periphery, China