Biodiversity is profoundly influenced by habitat quality, and Harbin, a provincial capital situated in a cold climate zone, stands out as one of China’s regions most susceptible to the repercussions of climate change. To ensure the city’s continued sustainable growth, a thorough assessment of habitat quality must be conducted. This study employs a comprehensive approach integrating the InVEST model, the PLUS model, a landscape pattern analysis, geographic detector, and a geographically weighted regression model. The goal is to assess how land use and habitat quality have changed in Harbin City, investigate factors contributing to spatial heterogeneity in habitat quality, thoroughly examine evolutionary patterns under the inertial development scenario from 2030 to 2050, and propose spatial optimization strategies. There are four key findings. First, from 2000 to 2020, agricultural land and forest were Harbin City’s two most prevalent land use types. The most notable transition occurred from forest to grassland, and the expansion of construction land primarily resulted from its encroachment into agricultural areas. Second, within the area of study, the landscape heterogeneity increased while simultaneously experiencing a decrease in connectivity, and the landscape had a tendency toward a more fragmented spatial distribution. Third, overall habitat quality rose between 2000 and 2020 but declined between 2030 and 2050. There was a “weak in the west and high in the east” distribution pattern in the spatial heterogeneity of habitat quality. Fourth, population density has the most impact on habitat quality, with the NDVI and GDP close behind. Conversely, precipitation and slope had comparatively smaller influences on habitat quality. Natural factors combined had a primarily favorable influence on habitat quality across the research region in terms of spatial distribution. Conversely, population density had a discernibly detrimental impact. Given these findings, this study suggests targeted strategies to optimize habitat quality. These recommendations are relevant not only for biodiversity conservation but also for the development of an ecologically sustainable community, particularly in a cold climate region.

Land use intensity (LUI) is an important indicator for assessing human activities, and quantitatively studying the impact of LUI on ESs can help to realize the scientific management of urban ecosystems as well as sustainable development. In this study, we quantified five important ecosystem service bundles in the study area with the aid of the R-language “kohonen” package and used bivariate spatial autocorrelation modeling to examine the effects of LUI on the ESs in Harbin City from 2000 to 2020. These ESs include food supply (FP), water conservation (WC), soil conservation (SC), carbon storage (CS), water purification (WP), and habitat quality (HQ). The results show the following: (1) The LUI in Harbin City had a trend from 2000 to 2020 of “decreasing and then growing”, with a spatial distribution pattern of “high in the west and low in the east.” (2) Except for FP, all other ESs exhibit a similar spatial pattern of “west-low-east-high”; WC and WP exhibit a trend of continuous increase, SC exhibits a trend of decreasing and then increasing, and CS and HQ are generally more stable, with less fluctuation. The built-up area is situated in the high-value area of LUI, and the area exhibits a significant expansion trend. (3) Ecological conservation bundles, FP–WP synergistic bundles, ecological transition bundles, CS–WP–HQ synergistic bundles, and FP bundles are the five ecosystem service bundles that were discovered in Harbin. (4) From 2000 to 2020, there is a predominately “low LUI-high ESs” and “high LUI-low ESs” aggregation type, with a substantial positive correlation between LUI and FP and a significant negative correlation between LUI and other ESs. Harbin City should strengthen the management of ESs in the western part of the city and, at the same time, maintain the favorable ecological conditions in the ecological barriers of Zhangguangcai Range and Xiaoxing’an Mountains.