In the face of shocks, a region’s economic resilience decides whether it can quickly recover or slip into long-term economic stagnation. This study took 2801 counties in China as the research object and distinguished them into long-term and short-term economic resilience by taking 2007–2020 as the research time period, and used spatial autocorrelation, the semi-variance function, and the geodetector method to analyze the spatial evolution pattern and driving mechanism of economic resilience of China’s counties in different time periods. The research found that: (1) From a long-term perspective, the economic resilience of China’s counties was dominated by the moderate level of resilience, and although its characteristics varied slightly over time, the overall performance showed that the level of resilience was increasing. Over time, the number of counties with very high levels of resilience has been increasing, and the number of counties with very low levels has been gradually decreasing. (2) In terms of spatial layout, China’s county economic resilience exhibited spatial autocorrelation, with similar areas clustered and distributed spatially, with high-high concentration (H-H) and hot spot (99% confidence) areas distributed in the eastern coast and its hinterland, and low-low concentration (L-L) and cold spot (99% confidence) areas distributed in Inner Mongolia and the northeast. The evolution of its spatial pattern was influenced by both stochastic and structural factors, and the spatial divergence was mainly reflected in the northeast–southwest direction, while the northwest–southeast direction was more balanced. (3) Long-term economic resilience and short-term economic resilience had different influencing factors. The industrial structure diversification index, which characterized economic factors, could significantly improve the long-term economic resilience of cities, while the influencing factors of short-term economic resilience differed from period to period.

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The hidden economy, as an important source of environmental pollution, can have a significant impact on environmental regulation, but it has not received much attention as an indicator of institutional weakness. Therefore, this study took the Yangtze River Economic Belt (YREB) as the research object and used the MIMIC model and entropy method to measure the hidden economic scale and environmental pollution index of its 112 cities from 2011 to 2020, respectively. We applied the spatial Durbin model to analyze the effect of heterogeneous environmental regulation (formal and informal environmental regulation) and hidden economies on pollution. It is found that: (1) the average hidden economic scale of YREB from 2011 to 2020 was between 13.15% and 14.30% and showed a slow upward trend. (2) Environmental pollution in the YREB had obvious spatial clustering characteristics, with high pollution clustering areas mainly in Chongqing in the upper reaches and Hubei, Anhui, and Jiangsu in the middle and lower reaches, while low pollution clustering areas were mainly in Yunnan and Sichuan in the upper reaches. (3) Formal environmental regulation reduced pollution directly, and on the other hand exacerbated it through interaction with the hidden economy. Overall, an environmental regulation's net effect depended on the hidden economic scale. Informal environmental regulation effectively reduced local hidden economic activity and was an effective mean to govern the hidden economy and pollution. Accordingly, the government should formulate appropriate laws and regulations to guide the legal part of the hidden economy to gradually shift to the official economy, and at the same time adopt a diversified environmental protection strategy to jointly combat pollution in the YREB.