Spatial Association and Quantitative Attribution of Regional Ecological Risk: A Case Study of Guangxi, China

Abstract

Regional ecological risk assessment (RERA) is a valuable tool for analyzing ecological risks at a broad-scale whose potential needs to be further exploited, especially when it comes to the in-depth mining of the final risk. Thus, in this research, based on RERA results acquired through land use function valuation and the ecological risk source-receptor-vulnerability framework, spatial autocorrelation analysis and geographical detector methods were employed to explore the spatial association features of regional ecological risk and its significant influencing factors in Guangxi, China. Next, a bivariate local spatial autocorrelation analysis tool was used to manifest the spatial impact directions of the important affecting factors on the final risk. The results of the study indicate that: (1) the north and west parts of Guangxi had a higher final ecological risk than that of the southeast; (2) from a percentage viewpoint, the low, medium, high, and very high levels of ecological risk accounted for 41.85%, 28.31%, 21.86%, and 7.98% of the total area, respectively; (3) the final regional ecological risk exhibited significant positive spatial correlation (Moran’s I = 0.466, p = 0.000) and the high-high association type was concentrated in the north and west parts of Guangxi while there was a low-low type in the southeast; (4) the most significant influencing factors for final risk consisted of lithology, land use ecology and production functions, slope, and soil; (5) compared with ecology and production functions, lithology, slope, and soil exhibited stronger positive influences on the final risk. Spatial association and quantitative attribution studies can increase the deepness of RERA and undoubtedly advance this field in the future. Moreover, based on the findings from the spatial quantitative attribution analysis, more explicit sustainable development countermeasures could be determined for the region.

1. Introduction

With the focus on the possible damage of ecosystems, ecological risk research has gained a lot of concerns and has become more popular in the eco-environmental protection fields since the 1980s [1,2,3,4]. The assessment scale has shifted step by step from a single point where a simple cause–effect link is considered [5] to regional/landscape scales where the nonlinear complex relationship between risk sources and receptors needs to be taken into account [3,6,7,8,9]. Among them, regional ecological risk assessment (RERA) is an important comprehensive characterization method for ecological risk on the broad-scale [5,10,11,12]. As it can deal simultaneously with a series of ecological risk sources and receptors through the indicator normalization procedure [10,13], this method has been applied successfully for the comprehensive assessment of environmental issues and management purposes all over the world [9,13,14,15,16,17,18].

However, within the previous pertinent studies, the depiction and explanation of the RERA results were almost all based on qualitative approaches [3,4,19], and the in-depth mining of results with quantitative approaches has rarely been carried out. Specifically, apart from area proportion analysis, almost no other methods have been adopted for the quantitative expression of RERA results [3,12,19]. Thus, the combination of RERA with potential spatial quantitative analysis tools for the further exploration of results is urgently needed. In this aspect, spatial autocorrelation analysis and geographical detector tools are promising research methods. For example, spatial autocorrelation analysis can exhibit the spatial association features of landscape ecological risk [7,8,20,21], whereas a geographical detector can manifest its dominant influencing factors quantitatively [22,23,24]. However, as another important broad-scale ecological risk assessment framework, the RERA has not achieved the same incorporation level of these two quantitative analysis tools for the depiction and explanation of its results [3,4,9,11,12,19]. Up to now, these two kinds of methods have been widely used in a series of eco-environmental evaluation research fields [7,21,22,23,24,25,26,27] and achieved great success on the whole. Therefore, these two helpful spatial analysis tools are needed for RERA study to deepen the quantitative exploration levels of the final regional ecological risk.

In order to resolve this problem, spatial autocorrelation analysis and geographical detector tools were employed in this RERA study to characterize the spatial association features of final regional ecological risk and ascertain the significant affecting factors. To realize the intended purpose, the final regional ecological risk needed to be acquired first. Therefore, a karst concentrated area (i.e., the Guangxi Zhuang Autonomous Region [28,29,30,31], China) was first selected as the case study area for the proposed research. Then, the final ecological risk of Guangxi was obtained by using the production-living-ecology land use function valuation method [32,33,34,35] and the ecological risk source-receptor-vulnerability framework [9], an extensively adopted framework for RERA research. Finally, based on the RERA results, the spatial association features and significant influencing factors of final regional ecological risk were analyzed quantitatively with the help of the two kinds of methods. This study can enhance the depth of RERA research and hopefully advance this field in the future. Furthermore, this study can also help promote sustainable development of the region by explicitly linking management strategies with the findings of the spatial quantitative attribution analysis.

2. Materials and Methods

2.1. Study Area

The Guangxi Zhuang Autonomous Region (104°29′–112°04′ E, 20°54′–26°23′ N) is located in the southwestern of China. It has a total area of 23.8 × 10⁴ km² consisting of 14 prefectures (Figure 1) and a total population of about 5.6 × 10⁷ persons comprising Han and other nationalities (e.g., the Zhuang minority) [36]. In terms of terrain, from north and northwest to the southeast, this region has been transformed step by step from the extension of the Yunnan-Guizhou Plateau into a series of basins and plains and hills. In particular, the highest point of Guangxi, Maoer Hill, is located in Guilin City with an elevation of 2141 m above sea level [37]. Moreover, because of the frequent appearance of karst stratum, a series of peak-cluster depressions are widely distributed in the southwest and northwest parts of Guangxi [38]. With regard to climate, a sub-tropical monsoon climate is the main climate type of the region with an annual average temperature of 22.3 °C and annual average precipitation of 1500 mm [36]. Due to the fragile eco-environment and irrational human disturbances, soil erosion, and subsequent karst rocky desertification (KRD) hazards, referring to land degradation featured by serious soil erosion, extensive bedrock exposure and the appearance of a desert-like landscape [28,29,30,31] are severe sustainable development issues faced by the region [28,39].

2.2. Data Source

There were mainly six categories of data used in this study, referring to land use/land cover (2020) with second-level classification information, slope, annual normalized difference vegetation index (NDVI) (2020), soil type, annual aridity (2020), and karst distribution rectification (lithology) datasets. Specifically, the land use/land cover, NDVI, soil, aridity, and karst distribution datasets were obtained from the Resource and Environment Data Cloud Platform, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences (https://www.resdc.cn/). In contrast, the slope dataset was acquired from China’s Geospatial Data Cloud platform (http://www.gscloud.cn/). Because the land use/land cover, NDVI, and aridity data were all from the same year (2020), the temporal consistency of datasets within this work is guaranteed. Moreover, the basic geographical information data used in this research were mostly obtained from the 1:1,000,000 basic geographical information database of the National Catalog Service for Geographic Information Systems (https://www.webmap.cn). Using the datasets, all the maps were produced in the ArcGIS and GeoDa platforms (the Albers Conic Equal Area Projection System was selected in the mapping process) whereas bar graphs and the heatmap were produced with the help of Origin 2024. All the raw data were transferred to 1 × 1 km grids before using them for RERA calculation and further spatial autocorrelation and geographical detection analyses.

2.3. RERA Indicator System Establishment

Through employing the production-living-ecology land use function manifestation scheme [32,33,34,35] and ecological risk source-receptor-vulnerability RERA framework [3,9], the RERA index system of Guangxi, China was finally established. Among them, the production function indicates material supplies for human society (i.e., provision function of products from the agricultural and secondary and tertiary industries); living function includes residing, employing, educating, transporting, security maintaining, and leisure provision for human beings; and the ecology function constructs the support foundation for humans, referring to a series of natural ecosystem services (e.g., soil protection, waste treatment, biodiversity conservation and climate regulation) [12,19,32,33,34,35]. Correspondingly, land use production and living functions constituted regional human-related ecological risk sources whereas ecology function was used as the ecological risk receptor within the RERA context [12,19]. Furthermore, the integrated eco-environmental vulnerability of the region was acquired by weighted stacking of the slope, soil, NDVI, aridity, and lithology vulnerability traits of the study area.

The whole RERA index system for this research is shown in Figure 2.

2.4. Regional Ecological Risk Calculation

2.4.1. Production-Living-Ecology Land Use Function Valuation

The land use data in 2020 with a second-level classification system (Figure 3) were used for the valuation purpose of regional production-living-ecology land use functions. Using the artificial valuation method and the references’ expertise [32,33,34,35], the adapted land use functions with values 1–4 [12] were derived as follows (

Table 1. The valuation standards for land use production-living-ecology functions in Guangxi.

First-Level Land Use Type		Second-Level Land Use Type		Production Function	Living Function	Ecology Function
Code	Land Use Type	Code	Land Use Type
1	Cropland	11	Paddy field	3	0	2
		12	Dry cropland	3	0	1
2	Forestland	21	Woodland	1	0	3
		22	Shrubbery	1	0	2
		23	Sparse woodland	0	0	1
		24	Other woodland	2	0	1
3	Grassland	31	High coverage grassland	1	0	3
		32	Medium coverage grassland	1	0	2
		33	Low coverage grassland	0	0	1
4	Waterbody	41	River/canal	2	0	4
		42	Lake	2	0	4
		43	Reservoir/pond	2	0	4
		45	Mudflat	0	0	4
		46	Bench land	0	0	4
		99	Sea waterbody	2	0	4
5	Construction land	51	City and town area	4	4	1
		52	Rural settlement	3	4	1
		53	Industrial, mining and transportation land	4	1	1
6	Unused land	61	Sandy land	0	0	1
		63	Saline-alkali land	0	0	1
		64	Swamp land	0	0	4
		65	Bare land	0	0	1
		66	Bare rock-texture land	0	0	1

).

2.4.2. Risk Source Intensity Calculation

The formula employed for calculating human-related ecological risk source intensity is as follows:

$$H=F_{production}\times w_1+F_{living}\times w_2$$

(1)

where $H$ is the final human-related ecological risk source intensity, $F_{production}$ and $F_{living}$ are the final ranking values of production and living function indices of the region, respectively, whereas $w_1$ and $w_2$ are the weights of the two indicators. In this study, the value 0.5 was assigned to both $w_1$ and $w_2$ [12,19] which means that the importance of the two indicators was considered equal for the expression of human-caused ecological risk source intensity. Likewise, the final value of H was then partitioned into four grades (namely values 1, 2, 3 and 4) for the subsequent calculation procedure.

2.4.3. Eco-Environmental Vulnerability Calculation

First, the comprehensive eco-environmental vulnerability of Guangxi was acquired by the following formula [9,12,19]:

$$V={\sum }_{k=1}^s{w_{k}f}_k$$

(2)

where V refers to the comprehensive eco-environmental vulnerability of Guangxi, w_k refers to the weight of the kth specific vulnerability index, f_k refers to the normalized grading value (i.e., 1, 2, 3, and 4) of the concrete vulnerability index, s refers to the number of eco-environmental vulnerability factors used in this formula, and 0 < k < s + 1.

Based on pertinent karst studies [28,29,40] and previous RERA research [3,41], four eco-environmental vulnerability indices were taken into account in Formula (2) (i.e., slope, NDVI, soil, and aridity indices). The normalized value of f_k (slope, NDVI and aridity) was mainly acquired through ArcGIS’s “natural breaking point” algorithm [3,7,8,9,11,12], a widely used ranking method in related studies that helps minimize within-class variance and maximize between-class variance, thus revealing inherent breakpoints as well as classification boundaries for continuous datasets [42]. On the contrary, the w_k value was obtained with the help of the artificial valuation method [3,12]. Furthermore, the soil vulnerability grading values were produced according to every soil type’s capacity in terms of carbon sequestration, organic matter maintaining, and anti-erosion [43,44,45]. In this procedure, because the soil erosion degree normally decreases with the severity rising of KRD hazard where the outcropping bedrocks’ interception and gathering effect for rainwater can reduce the actual soil erosion within karst fissures and pores [25,46,47,48], the bare rocks of Guangxi were assigned the soil vulnerability grade value of 1 in this study. As for w_k, the weights assigned for slope, NDVI, soil, and aridity were 0.3, 0.3, 0.2, and 0.2, respectively, in line with their significance for the comprehensive eco-environmental vulnerability manifestation purpose [3,41] of a region. In other words, theoretically, the slope and NDVI factors have stronger impacts on the regional comprehensive eco-environmental vulnerability than those of soil and aridity. Empirically, this weighting scheme was derived from a series of previous related RERA studies [3,9,41]. Then, considering the natural condition of Guangxi [28,29,30,40], the karst distribution rectification factor referring to the bedrocks’ lithological trait (containing carbonate strata or not) was employed for the final integrated eco-environmental vulnerability expression of the study area. Based on a previous eco-environmental vulnerability rectification scheme for RERA [3,41], the final vulnerability of karst distribution areas was added as a value of 1, whereas the non-karst area’s value remained unchanged. Finally, the final integrated eco-environmental vulnerability of the region was obtained and then grouped into four grades before further processing using the natural breaking point method of ArcGIS 10.2. The integrated eco-environmental vulnerability manifestation scheme of Guangxi is shown in

Table 2. Indices used for the karst eco-environmental vulnerability assessment in Guangxi.

Factors	Grades Assigned				Weight
	1	2	3	4
Slope (°) Vegetation coverage (%)	<7 >87.58	7–16 78.79–87.58	16–26 60.99–78.79	>26 <60.99	0.3 0.3
Soil vulnerability	Purple soil, skeleton soil, red clay, mountain meadow soil, Shajiang black soil, bare rock	Red soil, yellow soil, yellow-brown soil, laterite, lateritic red soil	Paddy soil, moisture soil	Limestone soil, volcanic ash soil	0.2
Aridity	<740	740–838	838–939	>939	0.2
Revised	Karst distribution area (Yes, No), Yes = 1, No = 0

.

2.4.4. Final Regional Ecological Risk Calculation

The final ecological risk levels of the region were acquired through the following formula [3,9]:

R = f(H) × g(V) × h(E)

(3)

where f, g, h are the employed calculation functions for H, V, and E, respectively. As above-mentioned, H, V, and E are the human-related ecological risk source intensity, risk receptor capital (i.e., ecology function in this research), and integrated eco-environmental vulnerability, respectively, and all three indicators were first normalized into ranks 1, 2, 3, and 4 before calculating the final regional ecological risk index R. Then, after the calculation, the R value was partitioned into four levels (1, 2, 3 and 4) with the help of the natural breaking point method, and the RERA purpose was realized eventually.

2.5. Spatial Autocorrelation Analysis

Spatial association feature of the final ecological risk of the region was analyzed through the spatial autocorrelation analysis method [7,20,21,49] including global and local Moran’s I indices employed within this research.

The global Moran’s I index was calculated based on the following formula [7,8,22,50]:

$$Global Moran’s I={\displaystyle \frac{n{\sum }_{i=1}^n{\sum }_{j=1}^n{w}_{ij}({\mathrm{x}}_i-\overline{x})({\mathrm{x}}_j-\overline{x})}{{\sum }_{i=1}^n{\sum }_{j=1}^n{w}_{ij}\sum _{i=1}^n{({\mathrm{x}}_i-\overline{x})}^2}}$$

(4)

where n refers to the number of final ecological risk grid cells, x_i and x_j refer to the calculated values of ecological risk in adjacent paired spatial grid cells unit i and unit j (i ≠ j), $\overline{x}$ refers to the average value of x_i, w_ij refers to the adjacent relationship between grid cells i and j [7,8]. If grid cells i and j are adjacent, w_ij equals 1, but if grid cells i and j are not adjacent, w_ij equals 0 [7]. Moreover, the range of global Moran’s I value is [−1, 1]: I < 0 means a negative correlation and that dissimilar risk values cluster together; likewise, I = 0 means no correlation, and I > 0 means a positive correlation and that similar risk values cluster together in the study area [7,8,50].

On the other hand, the local Moran’s I index was calculated based on the following formula [22,50,51]:

$$Local Moran’s I={\displaystyle \frac{n({\mathrm{x}}_i-\overline{x})\sum _{j=1}^n{w}_{ij}({\mathrm{x}}_j-\overline{x})}{\sum _{i=1}^n{({\mathrm{x}}_i-\overline{x})}^2}}$$

(5)

where n is the number of samples, x_i and x_j are the attribute values of grid cells i and j, respectively, $\overline{x}$ is the mean attribute value of all data, and w_ij is the spatial weight matrix [22,51]. The local Moran’s I index can identify the local spatial association types of high and low ecological risk values [8]. In this regard, a high positive value means that a unit area has a similar high or low value as its neighbors, whereas a high negative value indicates that the corresponding spatial location is different compared with its surrounding areas [7]. A high-high (H-H) association represents a significant spatial cluster of high ecological risk values, and the low-low (L-L) association represents a significant cluster of low ecological risk values; on the contrary, high-low (H-L) and low-high (L-H) cluster types show that the high ecological risk unit is surrounded by low ecological risk values and the low ecological risk unit is surrounded by high ecological risk values, respectively [7,8].

Moreover, to further explore the spatial affecting directions (positive, negative, or not significant) between the influencing factors and final regional ecological risk and clustering patterns [27], the bivariate local spatial autocorrelation analysis tool [27,52,53,54,55] was also employed in this research. Using this tool, the H-H, L-L, H-L, and L-H spatial association features [26,55] between the final risk and a number of significant influencing factors chosen by geographical detector were analyzed. Within this process, the final risk index was selected as the dependent variable y whereas those significant affecting factors were chosen as the independent variable x [27].

The formula for the bivariate local spatial autocorrelation analysis [53,54,56] is as follows:

$$Bivariate Local Moran’s I={\displaystyle \frac{x_i^k-\overline{x_k}}{{\mathsf{\sigma }}^k}}{\sum }_{j=1}^n(w_{ij}{\displaystyle \frac{x_j^I-\overline{x_I}}{{\mathsf{\sigma }}^I}})$$

(6)

where $x_i^k$ and $x_j^I$ are the k risk factor index’s value of the i-th unit and the I final risk index’s value of the j-th unit, respectively, $\overline{x_k}$ is the average value of the risk factor index k, $\overline{x_I}$ is the average value of the final risk index I, ${\mathsf{\sigma }}^k$ and ${\mathsf{\sigma }}^I$ are variances of the risk factor index k and final risk index I, respectively, n is the number of analysis units, and $w_{ij}$ is the spatial weight between units i and j [53,54,55,56].

In particular, all of the above-mentioned spatial autocorrelation analyses were completed within the GeoDa and ArcGIS platforms.

2.6. Geographical Detection Analysis

Geographical detector is a useful tool for the quantitative detection of significant influencing factors within landscape ecological risk assessment research [22,23,24]. Therefore, it was adopted in this quantitative attribution analysis study for RERA, another important broad-scale ecological risk assessment framework. In this regard, the factor detector calculation formula [25,57] is as follows:

$$q=1-{\displaystyle \frac{\sum _{h=1}^L{N}_h{{\sigma }_h}^2}{N{\sigma }^2}}$$

(7)

where q represents the explanatory power of independent variable X for the dependent variable Y, namely the explanatory power of a number of risk source-receptor-vulnerability indicators for the final regional ecological risk within this study, h is the stratification of variable Y or variable X, N_h and N are the total number of grid units in strata h and the whole region, and ${{\sigma }_h}^2$ and σ² are the variances of the Y factors for layer h and the whole region, respectively [58]. The range of the q value is [0, 1] [25,57]. The greater the q value, the larger the explanatory power of the independent variable X for the dependent variable Y, also meaning that the explanatory power of the certain risk source-receptor-vulnerability factor for spatial differences in regional ecological risk is greater [25,26,57].

The whole flowchart of this research is shown in Figure 4.

3. Results

3.1. Regional Ecological Risk Calculation Result

3.1.1. Ecological Risk Source Intensity

The regional human-caused ecological risk source intensities are shown in Figure 5. Basically, the very high-degree risk source intensity mainly denotes the scope of the construction lands and the high degree indicates the distribution of croplands, waterbodies, and other types of economic forests (e.g., tea/fruit gardens). Furthermore, the low and medium levels as shown in Figure 5 mainly manifested the scopes of natural forestlands, grasslands, and unused lands. From a proportion point, the low, medium, high, and very high levels of human-caused ecological risk source intensity accounted for 12.21%, 60.47%, 24.63%, and 2.69%, respectively, of the whole region.

3.1.2. Ecological Risk Receptor Capital

The ecological risk receptor capitals of Guangxi are shown in Figure 6. From Figure 6, the high-level ecological capital was mainly distributed in the four corners’ marginal areas of Guangxi whereas low/medium levels were distributed in the central part. In particular, the low ecological capital level included most of the concentrated areas of human activity in Guangxi such as urban/town/village construction lands, dry croplands, and tea/fruit gardens. From a proportion point, the low, medium, high, and very high levels of ecological capital accounted for 27.47%, 26.94%, 43.99%, and 1.61% of Guangxi, respectively.

3.1.3. Eco-Environmental Vulnerability

The comprehensive karst eco-environmental vulnerabilities of Guangxi are shown in Figure 7. From Figure 7, in general, the north and west parts of Guangxi had high and very high degrees of final integrated eco-environmental vulnerability whereas the non-karst areas within the southeast part had lower levels of final vulnerability. This result was in line with the higher slope and aridity degrees and vast distributions of karst carbonate rocks and limestone soil within north and southwest Guangxi. From a proportion point, the low, medium, high, and very high integrated eco-environmental vulnerability grades accounted for 22.12%, 30.29%, 28.48%, and 19.11% of the whole region, respectively.

3.1.4. Final Regional Ecological Risk

The final regional ecological risk distributions of Guangxi are shown in Figure 8. From Figure 8, it can be seen that the high and very high risk levels were concentrated in the north and west parts of Guangxi, whereas the southeast part was mainly covered by low/medium risk ranks. From a proportion point, the low, medium, high, and very high risk grades accounted for 41.85%, 28.31, 21.86, and 7.98% of the whole region, respectively.

The area proportions of the final regional ecological risk for Guangxi and its 14 prefectures are shown in Figure 9a,b. From Figure 9a, the ecological risk source and final risk had larger area proportions within low and medium grades whereas eco-environmental vulnerability had almost the same area percentages within the four ranks. After integrating the calculations, the area proportions of final risk declined gradually from low to medium and then to high/very high grades, meaning that the severe very high-level risk occupied only a limited percentage of the region. From Figure 9b, the north and west parts of Guangxi (e.g., Chongzuo, Baise, Guilin, Hechi, and Liuzhou) had higher final risk levels than that of southeast part (e.g., Beihai, Qinzhou, Yulin, Fangchenggang, and Wuzhou). Furthermore, the central part of Guangxi (e.g., Nanning and Guigang) had middle-level area proportions of high and very high final risk grades.

3.2. Spatial Autocorrelation Feature

After calculation, the value of the global Moran’s I index of the final regional ecological risk was 0.466 (I > 0, p = 0.000), and thus there was a significant positive spatial correlation for the final risk. In other words, similar risk values (high-high or low-low values) clustered together significantly in the study area. In order to further explore the spatial association features of the clustering, the local Moran’s I index was then calculated, and the results are shown in Figure 10 and

Table 3. Area proportions of spatial association types of the final risk in Guangxi.

Association Type	High-High Association	Low-Low Association	High-Low Association	Low-High Association	Not Significant
	18.09%	19.55%	0.91%	1.46%	60.00%

.

From Figure 10, the H-H association of final risk was concentrated in the north and west parts of Guangxi, while the L-L association was in the southeast. From the area proportion viewpoint (Table 3), the H-H association type occupied 18.09% of the total area of Guangxi whereas the L-L type occupied 19.55%. In contrast, the H-L (0.91%) and L-H (1.46%) association types had very small occupations in the final risk distribution, also indicating an obvious positive spatial correlation for the final risk.

3.3. Geographical Detection Result

A geographical detector was used for ascertaining the significant influencing factors for final regional ecological risk in Guangxi. The results are shown in

Table 4. Factor detection results for the final risk in Guangxi.

Risk Factor	Production	Living	Ecology	NDVI	Aridity	Slope	Soil	Lithology
q statistic	0.15479	0.00267	0.29222	0.00991	0.04922	0.12431	0.12184	0.29846
p value	0.000	0.000	0.000	0.000	0.000	0.000	0.000	0.000
Order of q	3	8	2	7	6	4	5	1

Note: The significant impact factors detected for the final risk are in bold font.

and

Table 5. Interaction detection results for the final risk in Guangxi.

	Production	Living	Ecology	NDVI	Aridity	Slope	Soil	Lithology
Production	0.15479
Living	0.15608	0.00267
Ecology	0.30896	0.30060	0.29222
NDVI	0.18999	0.01566	0.32417	0.00991
Aridity	0.22227	0.05262	0.36200	0.06008	0.04922
Slope	0.27721	0.12471	0.39863	0.18024	0.17160	0.12431
Soil	0.27244	0.12426	0.40253	0.13540	0.16718	0.22007	0.12184
Lithology	0.49272	0.29998	0.64668	0.30715	0.32409	0.38764	0.33334	0.29846

Note: All the interaction q values larger than 0.3 are in bold font.

and Figure 11.

As shown in Table 4 and Figure 11a, the most significant affecting factors for final risk consisted of lithology, land use ecology function, land use production function, slope, and soil, with q values of 0.29846, 0.29222, 0.15479, 0.12431, and 0.12184, respectively, in descending order. The remaining three factors (living function, aridity, and NDVI), on the other hand, had relatively much smaller influences on the final risk distribution. Moreover, the arbitrary combinations between any two of the eight affecting factors all had a larger explanatory power than the random single one within the pair (Table 5 and Figure 11b). Thereinto, the most significant bivariate combinations included lithology ∩ ecology (0.64668), lithology ∩ production (0.49272), soil ∩ ecology (0.40253), slope ∩ ecology (0.39863), lithology ∩ slope (0.38764), aridity ∩ ecology (0.36200), lithology ∩ soil (0.33334), ecology ∩ NDVI (0.32417), and lithology ∩ aridity (0.32409), etc., with q values all larger than 0.3 (bold font within Table 5).

3.4. Bivariate Local Spatial Autocorrelation Analysis

Through using a geographical detector, the important affecting factors for the final risk were ascertained. Based on this result, the spatial impact directions and association types between these significant influencing factors and final risk were further explored using the bivariate local spatial autocorrelation analysis tool within the GeoDa platform. The results are shown in Figure 12 and

Table 6. Area proportions of the spatial association types between the final risk and its important influencing factors in Guangxi.

Risk Factor	Lithology	Ecology Function	Production Function	Slope	Soil
HH	19.81%	9.57%	4.64%	15.26%	11.89%
LL	19.97%	13.43%	15.36%	12.71%	18.22%
HL	3.71%	12.54%	9.25%	12.99%	5.40%
LH	2.38%	13.97%	19.49%	7.38%	11.79%
NS	54.14%	50.50%	51.27%	51.67%	52.70%

Note: HH, LL, HL, LH and NS indicate the high-high, low-low, high-low, low-high, and not significant spatial association types, respectively.

.

From the corresponding figure and table, we can see that compared with the land use production and ecology functions, the eco-environmental vulnerability factors (i.e., lithology, slope, and soil) had a higher and more obvious positive effect on the final risk distribution. Specifically, the combinations lithology ∩ risk, slope ∩ risk, and soil ∩ risk had larger area proportions in terms of H-H and L-L associations and smaller area proportions in the H-L and L-H associations. And thereinto, the lithology ∩ risk combination had the highest area proportion with regard to the H-H and L-L associations together (39.78%) and the lowest area proportion in H-L and L-H associations (6.09% totally), indicating that the lithology factor exhibited the strongest positive effect on the final risk distribution. In this sense, regional ecological risk management (RERM) emphasis should be given to the west and north parts of Guangxi, where karst areas, high slopes, and vulnerable limestone soil are widely distributed and a highly positive association (H-H clustering) exists between these vulnerability factors and the final regional ecological risk. In comparison, the southeast part of Guangxi exhibited a lower final risk level because of its non-karst lithology, gentle slopes, and non-limestone soils (obvious L-L clustering between the lithology, slope, soil factors, and the final risk). Therefore, the RERM concern for southeast Guangxi is secondary to west and north Guangxi according to this research.

4. Discussion

4.1. The Advancement of the Research and Further Improvements Needed in the Future

This research verifies that the current spatial statistical analysis technics (e.g., spatial autocorrelation indices and geographical detector tool) can also be utilized for the study of RERA. Specifically, as in previous RERA research, the linguistic description of final risk and then its qualitative attribution analysis based on spatial similarities between the final risk and those affecting factors were the main streams [3,12,19]. On the contrary, the in-depth mining of the final RERA result through employing quantitative analysis technics and tools is still scarce. In this sense, the proposed research adopting spatial autocorrelation analysis and geographical detector methods for the spatial association and quantitative attribution analyses of final risk represents an important improvement for RERA study and can undoubtedly advance this field in the future.

With respect to further progress in the future, adopting more suitable quantitative analysis tools in terms of spatial trait depiction and attribution analysis for the final risk, such as the obstacle degree [59,60,61,62] and geographically weighted regression [26,63,64,65] models, might be very helpful. All of these potential practices can eventually enrich the RERA domains.

4.2. Key Points and Effective Countermeasures for KRD Prevention and Cure

As manifested above through bivariate local spatial autocorrelation analysis, eco-environmental vulnerability indices, such as lithology, slope, and soil, were more significant positive affecting factors for the final risk. Therefore, the prime RERM concern should be focused on the north and west parts of Guangxi, where karst terrains, steep slopes, and limestone soil are widely distributed. First, special RERM attention should be given to those vast distribution areas of carbonate rocks to better mitigate the influences of the chemical weathering of limestone/dolomite, which can normally result in a shallow discontinuous regolith followed by soil erosion and KRD issues [66,67]. Particularly, limestones have a higher dissolution speed than dolomites [40]. Thus, the higher the proportion of limestones, the more serious the KRD issue generally will be. Second, for agricultural production, dry croplands within karst hillsides, especially those with higher slope degrees (e.g., 15–25° range) should be terraced, and soil and water conservation works added [68,69]. On the contrary, when the slope of karst croplands is larger than 25°, the grain for green projects is needed. Third, karst limestone soil is normally thin and discontinuous [30,31]. After destruction, it generally needs a long time to form a new soil layer with enough thickness [70,71]. Therefore, effective soil and water conservation works are urgently needed to conserve these vulnerable karst soils in the future.

Moreover, the geographical detection results tell us that the land use ecology and production functions also had significant influences on the final risk. However, because of the tradeoff effect [53,56] between these two kinds of land functions and the aggregation method of multiplication (R = f(H) × g(V) × h(E)) for the final risk, the influences of the two functions cancel each other out considerably, and thus their impact directions on the final risk distribution are not as much positive as those eco-environmental vulnerability factors. In this sense, the effectiveness of another aggregation choice (e.g., addition method) should be verified for future RERA research. Nevertheless, as manifested by the geographical detector, these two functions still have important impacts on the final risk. Therefore, corresponding countermeasures in terms of optimizing human production activities and protecting regional ecology function are of great importance, especially for those vulnerable karst areas. For example, first, agricultural production structure adjustment, such as replacing maize cultivation with herbivorous animal husbandry, is conducive to soil protection, and then KRD prevention and cure [68,71]. Second, fostering alternative non-agricultural livelihood for karst farmers, such as ecotourism [72] and other ecological product value realization approach (e.g., eco-compensation) [73,74], is necessary. Third, for damaged natural forests, close-up and afforestation measures are very helpful and can eventually improve the ecological health status/ecosystem services provision [29,70,75,76,77,78]. Fourth, ecological restoration projects, such as the Natural Forest Conservation Program or Rocky Desertification Control Project [79,80], can play an important role for karst ecological restoration on a broader scale [79].

Ultimately, all of the above-mentioned key points and effective countermeasures derived from RERA quantitative attribution analysis will promote sustainable development of the region.

5. Conclusions

Through using spatial autocorrelation analysis (including three indices, i.e., global Moran’s I, local Moran’s I, and bivariate local Moran’s I) and geographical detector methods, the in-depth mining of RERA results in terms of spatial association and quantitative attribution analyses was realized in this research. The results of this study showed that: (1) the north and west parts of Guangxi had a higher final ecological risk than that of the southeast part; (2) in terms of proportion, the low, medium, high, and very high final risk grades accounted for 41.85%, 28.31%, 21.86%, and 7.98% of the whole region, respectively; (3) final risk exhibited an obvious positive spatial correlation trait and the H-H association was mainly distributed within the north and west parts of Guangxi whereas the L-L association was mainly located in the southeast; (4) through geographical detection, the important influencing factors for final risk were ascertained including lithology, land use ecology and production functions, slope, and soil; (5) subsequently, the bivariate local spatial autocorrelation analysis tool was used to explore the spatial affecting directions between those significant influencing factors and final risk. The corresponding results indicated that compared with land use production and ecology functions, eco-environmental vulnerability factors (i.e., lithology, slope, and soil) had a more obvious positive effect on the final risk distribution. Undoubtedly, this research can promote the deepening of RERA in the future, especially when it comes to the in-depth mining of the final results of this kind of study. Furthermore, this research can also help ascertain the key effective RERM countermeasures based on the findings of a spatial quantitative attribution analysis for the study area.