COVID-19 has significantly influenced global financial markets, including Bitcoin. Recent studies have focused on investigating the first wave of the COVID-19 outbreak and accounting for market changes, which were mostly due to the pandemic. This research not only analyzes the contagion effects of COVID-19 but also considers aftermath events beyond the first pandemic wave to examine spillovers of Bitcoin. The study employs Diebold and Yilmaz’s method to explore the static and dynamic spillovers of the selected variables and identifies several major global events, including crypto-specific affairs, macroeconomic policies, and geopolitical conflicts, to explain the new market dynamics of Bitcoin using network analysis. The findings identify a few high-contagion periods related to Bitcoin. The paper also found that Bitcoin is more likely to produce extreme returns and is more connected to other markets. Contagion effects “from” and “to” other markets are asymmetrical in terms of arrival time and market response. Bitcoin is more likely to be affected by other markets in extreme situations and receives spillovers from them sooner than it transmits spillovers to others. In the context of various global events, impacts arising from developed countries are stronger. China still has some impact on cryptocurrency markets, but they are waning. Bitcoin is thus not a safe haven from the shocks of global events, but can sometimes work as a hedge or diversifier. The results offer alternative explanations for Bitcoin’s different market dynamics and enrich our understanding of Bitcoin’s safe haven, hedge, and diversifier properties within a diversified portfolio.

Against the backdrop of increasingly binding land constraints and the transition toward high-quality development, improving total factor productivity through more economical and intensive land use has become an important policy concern. This study treats the designation of National Model Counties for Economical and Intensive Use of Land Resources as a quasi-natural experiment and uses panel data for 1965 Chinese counties from 2004 to 2021 to examine its impact on county-level total factor productivity. A multi-period DID model shows that the policy significantly raises the county-level annual TFP growth index, which increases by an average of 0.0249 index points. The result remains robust after a series of robustness tests. Further analysis provides evidence on several potential channels. The policy significantly promotes invention patent grants, while its effects on design and utility model patents are statistically insignificant. It also reduces firm entry and the number of incumbent firms, indicating an adjustment in local firm dynamics, and significantly increases industrial agglomeration as measured by the location quotient. The heterogeneity analysis further suggests that the policy effect is weaker in areas with greater administrative fragmentation and more pronounced in counties facing higher fiscal pressure. These findings provide new evidence on how land-use constraints may promote county-level productivity improvement and offer policy implications for strengthening intensive land use, improving land-resource allocation, and supporting high-quality county-level development.