Opencast coal mine production and construction activities have a certain impact on the ecological environment, while the development and utilization of large coal bases distributed in semi-arid steppe regions may have a more direct and significant impact on the eco-environment. Therefore, in-depth studies of the ecological impacts of human activities and natural environmental elements in opencast coal mines in typical semi-arid steppe regions and analyses of their driving forces are of great significance for protecting and restoring regional fragile steppe ecosystems. In this paper, the mining area southwest of the Shengli coalfield, a typical ore concentration area in eastern Inner Mongolia, was selected as the research object. Its remote sensing ecological index (RSEI) was calculated using the Google Earth Engine (GEE) platform to analyze the eco-environmental quality in the mining area and its surrounding 2 km from 2005 to 2021. The geographically weighted artificial neural network model (GWANN) was combined with the actual situation of mining activity and ecological restoration to discuss the driving factors of eco-environmental quality change in the study area. The results showed that: (1) the proportion of the study area with excellent and good eco-environmental quality increased from 20.96% to 23.93% from 2005 to 2021, and the proportions of areas with other quality grades fluctuated strongly. (2) The change in eco-environmental quality in the interior of the mining area was closely related to the reclamation of dump sites and migration of the mining area. (3) The maximum contribution rate of the mining activity factor to the external eco-environmental quality of the mining area reached 43.33%, with an annual average contribution rate of 34.48%; as the distance from the mining area increased, its contribution gradually decreased. This quantitative analysis of the driving forces of RSEI change in the mining area will complement future work in ecological evaluations of mining areas while also improving the practicality of ecological evaluation at the mining scale, thereby further helping the ecological management of mining areas.

Coal is China’s main resource, with open-pit mining accounting for a significant portion of global production. However, this activity, including mining and ecological restoration, can have a definite impact on ecosystem carbon storage and its distribution; its associated factors are also unclear. In this paper, we quantify the carbon storage changes in Haerwusu coal mine, a typical large-scale coal mine in China, based on land use/land cover (LULC) characteristics, and analyze the impact factors of carbon density from 2007 to 2022 by integrating the InVEST model with the landscape ecological function contribution ratio and multiple regression model. The results are as follows. (1) Carbon storage decreased from 159.95 × 10⁴ to 147.51 × 10⁴ from 2007 to 2017 and then increased to 151.91 × 10⁴ to 2022. (2) The degree of coordination between carbon storage forest and grassland area landscape pattern coupling ranged from 0.887 to 0.867 from 2007 to 2022, with the lowest point at 0.720 in 2012. (3) Carbon storage was significantly related to vegetation indices, temperature, and elevation, and these factors can explain 37.5% of the carbon storage spatial variability; stepwise regression analysis showed that the integration of landscape patterns, such as Shannon’s diversity index (SHEI) and the aggregation index (AI), could improve the explanation by 1.4%. (4) Based on the analysis of the landscape ecological function contribution ratio, the carbon storage-sensitive areas can be classified into three levels: extremely sensitive areas ranging 0 to 4 km from the mine, sensitive areas ranging 4 to 8 km, and insensitive areas ranging beyond 8 km. This study proposes a strategy for analyzing changes of carbon storage in coal mines, highlights the important role of landscape patterns in influencing carbon storage, and provides a reliable reference support for the ecological management of coal mines.