Based on the theories of structure–function correlation in Geography, and landscape pattern-ecological function correlation in Landscape Ecology, the correlation between land use fragmentation and vegetation activity was quantified. Effective mesh size (m_eff) was calculated to represent landscape fragmentation for land use, and the normalized difference vegetation index (NDVI) was used to reflect vegetation activity. The geographically weighted regression (GWR) model was applied to explore the spatial non-stationary relationship between m_eff and NDVI in a karst basin of the southwestern China, where environmental factors (i.e., climate, topography, and vegetation) are spatially heterogeneous. The spatial variation and scale dependence of landscape fragmentation and its relationship with vegetation activity, as well as the influence of lithology types and landforms relief, were considered. Firstly, the optimal ‘slide window’ size for landscape fragmentation was determined to be 500 m, and spatial pattern of m_eff displayed clear heterogeneity with a serious degree of fragmentation. Landscape fragmentation was more severe in carbonate areas than non-carbonate areas, reflecting the influence of landforms relief. More serious fragmentation in dolomite areas meant that the impact of human activities on the landscape morphological characteristics was much more significant than that in the limestone areas with steeper slope. Multi-scale analysis was used to verify a neighborhood size of 7 km for GWR in the study area. Negative effects on vegetation activity from landscape structural changes were more significant in limestone areas, which may be due to the more vulnerable ecosystems there. This research can provide scientific guidance for landscape management in karst regions as it considers the multi-scaled and spatially heterogeneous effects of lithology, geomorphology, and human factors on landscape structure and its correlation with vegetation activity.

In the context of the increasing frequency of natural disasters caused by climate change in recent years, rational territorial spatial planning must pay attention to production–living–ecological (PLE) risks under climate change scenarios. In this study, a method synthesizing the Box–Cox transformation and area weighted averaging is established for characterizing the PLE risks in China’s provinces, which are divided into three zones to cope with PLE risks. Further, targeted strategies from the perspective of the disaster-induced factors and disaster-affected objects are explored for the regions within the different zones. The results show that the regions with a high production risk are mainly distributed in Guangdong, Henan, and Shandong, with an index between 0.80 and 1.00; the regions with a high living risk are concentrated in Jiangsu, Anhui, Guangdong, and Hainan, with an index exceeding 0.72; and the regions with a high ecological risk are concentrated in Guangxi, Ningxia, and Yunnan, with an index exceeding 0.50. The overall PLE risk is high along the southeastern coast, intermediate in central and western China, and low on the Tibetan Plateau. From the A to C zones, the number of risk types and intensity of risks requiring attention gradually decrease. For the category A zone, recommended measures include the construction of disaster risk monitoring and early warning systems for coastal cities and major grain-producing regions, the development of urban ecological protection zones, and the adjustment of economic and energy structures, etc. Production and living risks are central to the category B zone, while ecological and production risks are central to the category C zone. This study can provide theoretical support for China’s scientific development of land planning and the realization of a beautiful China.

Northwest China has become a base for wind and solar energy development due to its rich wind and solar resources and large area of desert and unutilized land. However, whether the scarce water resources in the arid regions there can meet the requirements for renewable energy development is currently a pressing, critical problem. Therefore, the life cycle water footprint (WF) of the renewable energy industry—its wind energy, photovoltaic (PV), and concentrated solar power (CSP) electricity generation enterprises—in the basin area of Golmud, Qinghai, a typical arid region in Northwest China, has been investigated in this study. Water consumption by local renewable energy enterprises was estimated under current (2020) scenarios [i.e., different (local/vs. nonlocal) origins of equipment and raw materials used, and employees hired by these enterprises] and three future scenarios (i.e., different ratios between installed capacities of wind energy, PV, and CSP at a fixed total renewable energy electricity generation capacity assuming China’s carbon emission will peak then). The results revealed that water consumption by local renewable energy enterprises in 2020 was 1.62 × 10⁶–1.31 × 10⁷ m³, accounting for 0.07–0.6% of the current total water resources in the basin area of Golmud. Water consumption by the local salt chemistry industry, a pillar industry in Golmud whose water consumption is high, accounted for 2.69% of the total water resources being 4.24–34.37 times that of the local renewable energy industry. To reach the goal of carbon emissions peaking by 2030 requires an increase of 6.17 × 10⁶ kW in the installed capacity for wind and solar power generation in Golmud, would translate into an increase of 1.57 × 10⁷–6.46 × 10⁷ m³ in water consumption, this accounting for 7.15–19.35% of the remaining available water resources in the basin area of Golmud. Our results indicate that the expansion of the local renewable energy industry has exerted significant pressure on the already scarce water resources in Golmud. Therefore, future increases in the installed capacity for renewable energy electricity generation should be planned scientifically, by considering the availability of water resources as a constraint.

The unique geological conditions of karst regions create highly heterogeneous habitat characteristics, and the addition of human disturbance results in rocky desertification. Water and soil loss are the core questions, and moreover, runoff is the key factor in this process. To further investigate these problems, a typical karst peak cluster depression in southwestern China was selected for this study. Based on the optimal simulation of the runoff yield and flow in this area, the factor detectors and interaction detectors in the geographical detector method were used to quantitatively analyze the factors influencing runoff and their interactions for different geomorphic types. The results show that: (1) the three main factors influencing the total river runoff, surface runoff, and groundwater are landscape fragmentation, land use type, and precipitation, but the ranking of these main influencing factors in each geomorphic type region exists different; (2) the dominant factor in the relatively higher elevation regions is precipitation; (3) the interaction detector results reveal that the interactions between factors enhance the overall influence of a single factor on the runoff generation in all of the geomorphic type regions, including two interaction types of nonlinear enhancement and bifactor enhancement; and (4) the interactions between the factors in the middle elevation plain, middle elevation terrace, and middle relief mountain regions are stronger than those in the middle elevation hill and small relief mountain regions. Quantitative analysis of the factors influencing runoff in karst areas cannot only promote optimization of the water and soil services, but it also provides a scientific basis for improving the comprehensive treatment of rocky desertification.