3.1.2. Weight of Ecological Environment Index

In the hierarchical structure chart of eco-environmental bearing capacity evaluation (Figure 7), eco-environmental bearing capacity is the target layer (Layer A) of hierarchical structure, water resource elements, land resource elements, and other elements are the criteria layer (Layer B). Seven indicators, such as annual rainfall, water conservation, and cultivated land conditions are the indicator layer (Layer C) of hierarchical structure.

**Figure 7.** Hierarchical structure of ecological environment bearing capacity evaluation.

The consistency check ratio of the hierarchy total ranking *CR* was 0.0232, that is, it was less than 0.1, and so the consistency check was passed. The weight of the evaluation system of ecological environment bearing capacity is shown in Table 9.

#### 3.1.3. Weight of Social and Economic Index

In the hierarchical structure chart of social and economic bearing capacity evaluation (Figure 8), social and economic bearing capacity is the target layer of hierarchical structure (Layer A). Population factor, economic factor, and infrastructure support factor form the criteria layer (Layer B); five indicators, such as residential area distribution, GDP per capita, and transportation facility support are the indicator layers (Layer C).

**Figure 8.** Hierarchical structure of social and economic bearing capacity evaluation.

The consistency check ratio of the hierarchy total ranking *CR* was 0, that is, it was less than 0.1, and so the consistency check was passed. Thus, the weight of social and economic bearing capacity evaluation system is shown in Table 12.

#### *3.2. Comprehensive Quality Evaluation of Bearing Capacity*

First, the distance between each index layer grid unit and the optimal unit and the worst unit is calculated by using the grid calculator. The positive and negative ideal solutions of geological environment subsystem, ecological environment subsystem, social economy subsystem, and comprehensive bearing capacity are obtained by weighted superposition. Secondly, the grid calculator is used to calculate the corresponding closeness degree, and the natural breakpoint method is used to divide the load-bearing status closeness degree score into five grades I, II, III, IV, and V, which are converted into vector data for spatial distribution clustering research. The closeness degree score is shown in Figure 9.

**Figure 9.** Closeness degree score of comprehensive assessment of geological and ecological environment.

Among them, the areas of Grade I, II, III, IV, and V are 61.93 km2, 233.74 km2, 345.82 km2, 222.82 km2, and 61.3 km2 respectively, accounting for 6.69%, 25.25%, 37.36%, 24.07%, and 6.62% of the total area each.

#### *3.3. Local Clustering Result*

The hot spot analysis is used to calculate the closeness scores of the geological environment, ecological environment, socioeconomic, geological, and ecological environment comprehensive bearing capacity evaluation. The results of how high-valued and low-valued areas clustered in the calculation are divided into 7 levels: 99% confidence hotspot clustering (high and high neighboring), 95% confidence hotspot clustering (high and middle neighboring), 90% confidence hotspot clustering (middle and middle neighboring), which is not significant, 99% confidence cold spot clustering area (low and low neighboring, 95% confidence cold spot clustering zone (low and middle neighboring), 90% confidence cold spot clustering zone (high and low neighboring). Red zone represents high value clustering zone and blue zone represents the low. The value aggregation area is shown in Figure 10.

**Figure 10.** (**a**) Results of hotspot analysis: for hot spot analysis of geological environment subsystem; (**b**) hot spot analysis of ecological environment subsystem; (**c**) hotspot analysis of social economic subsystem.

As for the subsystem of geological environment, the regional geological bearing capacity of Xuankou Town, Zipingpu Town and the main stream of Minjiang River in the study area are clustered in high value. In the northwest direction of Xuankou Town, the debris flow channel and its outburst areas, such as Qipan Ditch and Taoguan Ditch, as well as the geological environment bearing capacity near Maowen fault zone (Longmen Mountain back the mountain fault) and Yingxiu fault zone, are low-value aggregated. The geological hazards in the region include debris flow, landslides, and earthquakes. The hot spot area is 430.55 km2 and the cold point area is 234.95 km2, as shown in Figure 10a.

As for the subsystem of ecological environment, the triangle area composed of Dujiangyan City, Yingxiu and Xuankou towns in the southern part of the study area shows high value aggregation, while the high mountain and forest areas in the northern part of the study area also show local high value aggregation. The high-altitude snow cover area in the northwest corner of Miansi Town, the high-drop vegetation-free growth area in the southwest corner of Ginkgo Township, and the area around Wenchuan County are low-value aggregation areas. The hot spot area is 317.16 km2 and the cold point area is 373.02 km2, as shown in Figure 10b.

As for social and economic subsystems, because of the relatively concentrated population in cities and towns, and the relatively complete construction of transportation, energy, and infrastructure, public service departments, such as hospitals and schools, are basically concentrated in the town center. The social and economic bearing capacity in space takes the town as the center, and tends to weaken outward, in accordance with the principle of attenuation. The central area of the town and the area along Du-Wen Road show high value aggregation, while the areas far from the central area of the town and the mountainous areas with higher elevation show low value aggregation. The hot area is 260.17 km2, and the cold point area is 531.95 km2, as shown in Figure 10c.

#### *3.4. Graded Result*

According to the calculation results of hot spot analysis, the low-low neighboring and low-middle neighboring clustering regions are defined as unsuitable construction areas according to their spatial distribution characteristics of low-value aggregation. The high-low neighboring and middle-middle neighboring clustering regions and random distribution regions are designated as backup reserve areas. The high-middle neighboring and high-high neighboring regions are designated as suitable construction areas, for their spatial distribution characteristics of high-value aggregation, as shown in Figure 11.

**Figure 11.** Comprehensive partitioning.

The bearing capacity of geological and ecological environment along Du-Wen Road has distinctive spatial clustering characteristics. In general, the bearing capacity of Xuankou Town and Yingxiu Town along the main stream of Minjiang River in the south is better than that of Weizhou Town and Miansi Town in the north. The bearing capacity is obviously low in the vicinity of faults and where debris flow, landslide, and other geological disasters have happened. Among them: the total area suitable for construction is 288.38 km2, accounting for 31.12% of the total area of the region; the total area of the reserve development area is 296.35 km2, accounting for 31.98% of the total area of the region; the total area unsuitable for construction area is 340.87 km2, accounting for 36.79% of the total area of the region.

#### **4. Conclusions**

This paper selects the area along the mountain road with characteristics of geological disasters as the research area, and combines the research results of existing bearing capacity with the first national geographical survey of Sichuan Province, and adds geological environmental factors to construct a reflection research area. The evaluation index system of different aspects of geological and ecological environment bearing capacity is determined by the AHP method. The data is pre-processed by ArcGIS software. The TOPSIS method and superimposed analysis tool are used to quantitatively evaluate the geological and ecological environment bearing capacity of the study area. The following conclusions are obtained:

(1) Based on the comprehensive quality evaluation of the bearing capacity of the study area and thinking from a spatial perspective, the priority scores are calculated by hot spot analysis tools. According to the calculation results, the study area is divided into suitable construction area, backup reserve area, and unsuitable construction area. From the perspective of the spatial distribution of the construction area, the bearing capacity of Xuankou Town and Yingxiu Town along the southern Minjiang River is superior to that of Weizhou Town and Miansi Town in the north. The research provides a new idea for the zoning planning of the comprehensive evaluation of regional bearing capacity.

(2) The zoning results consider the bearing capacity relationship among the quality-scale-space distribution. The evaluation results not only consider the development suitability from the perspective of comprehensive quality, but also consider the spatial stability of sustainable development from the perspective of spatial layout. It is of great significance for optimizing the development pattern and resource allocation of land space.

(3) After the Wenchuan Earthquake in 2008 and Lushan Earthquake in 2013, the mountain environment, especially the geological environment, has become more fragile in Sichuan. Therefore, to investigate the current situation and characteristics of the geological ecological environment and analyze its bearing capacity, has become an important premise for highway management departments to formulate safe operation and management strategies, and for township construction planning departments to reasonably develop and utilize the limited environmental resources along the mountain highway, which is a work of great practical significance.

**Author Contributions:** Author Contributions: Conceptualization, Zhoufeng Wang and Chen Zhang; methodology, Zhoufeng Wang and Chen Zhang; software, Zhoufeng Wang; validation, Zhoufeng Wang and Jianwei Xu; formal analysis, Zhoufeng Wang and Xiangqi He; investigation, Yujun Wang; resources, Zhoufeng Wang; data curation, Zhoufeng Wang and Chen Zhang; writing—original draft preparation, Zhoufeng Wang, Chen Zhang; writing—review and editing, Zhoufeng Wang, Chen Zhang, Yujun Wang, Jianwei Xu, and Xiangqi He; visualization, Zhoufeng Wang and supervision, Zhoufeng Wang, Chen Zhang, and Xiangqi He. All authors have read and agreed to the published version of the manuscript.

**Funding:** The work was supported by General Program of Chongqing science and Technology Bureau (Grant No.cstc2019jscx-msxmX0311), funding Plan for Young Teachers of Southwest Petroleum University (201131010020).

**Acknowledgments:** We sincerely appreciate the Editor's encouragement and the anonymous reviewer's valuable support.

**Conflicts of Interest:** The authors declare no conflicts of interest.

#### **References**


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