An efficient monitoring of forest fire risk and its dynamic changes is an important way to prevent and reduce forest fire hazards. This study integrated the ignition effect of fire sources and the impact of landform into the calculation of forest fire risk using digital-terrain-slope factor, and developed an optimized forest fire risk model (fire-potential-index slope, FPIS). Combined with Landsat 8 satellite images, the study retrieved and analyzed the variations of forest fire risk in Zhaoqing City, Guangdong province, for four consecutive periods in the dry season, 2019. It was found that the high forest fire risk area was mainly distributed in the valley plains of Huaiji district, Fengkai district and Guangning district, the depressions of the Sihui district, and mountain-edge areas of Dinghu district and Gaoyao district, and accounted for 8.9% on 20 October but expanded to 19.89% on 7 December 2019. However, the further trend analysis indicated that the forest fire risk with significant increasing trend only accounted for 6.42% in Zhaoqing. Compared to the single high forest fire risk results, the changing trend results effectively narrowed the key areas for forest fire prevention (2.48%–12.47%) given the actual forest fires in the city. For the four forest fire events (Lingshan mountain, Hukeng industrial area, Xiangang county and Huangniuling ridge forest fires), it was found that the forest fire risk with significant increasing trend in these regions accounted for 26.63%, 35.84%, 54.6% and 73.47%, respectively, which further proved that the forest fire risk changing trend had a better indicated significance for real forest fire events than the high forest fire risk results itself (1.89%–71.69%). This study suggested that the forest fire risk increasing trend could be well used to reduce the probability of misjudgment and improve the accuracy of the early-warning areas when predicting forest fires.

China’s economy has shifted from high-speed development to high-quality development as the marginal benefits of the traditional growth model decline. This study explores how new quality productive forces (NQPFs) drive industrial structure upgrading via factor endowment upgrading and factor synergy, with the goal of addressing development bottlenecks. Using 2013–2024 provincial panel data, NQPFs are measured through the entropy weight-TOPSIS method (factor endowment upgrading) and a coupled coordination model (factor synergy), integrated into a comprehensive index, and analyzed via two-stage least squares (2SLS) regression. The empirical results show that China’s NQPFs are in their initial stage, with factors still in the process of accumulation and adaptation. NQPFs significantly promote industrial structure advancement and rationalization, with factor synergy outperforming factor endowment upgrading. Its promotion of advancement is universal, but its impact on rationalization is regionally heterogeneous, with inadequate transmission in underdeveloped and high-environmental-regulation provinces and ineffective dual mechanisms in manufacturing-led provinces. Notably, the driving effect of NQPFs on manufacturing upgrading is stronger than that on services. This study provides guidance for NQPF cultivation and contributes to the theory and Chinese practice of industrial structure upgrading from a dual-factor perspective.