The Global Financial Crisis (2007-2008), which began due to the collapse of the U.S. housing market, had a negative effect in many regions across the globe. The global recession which followed the crisis in 2008 and 2009 showed how interdependent and synchronized many of the world's economies had become, with the largest advanced economies showing very similar patterns of negative GDP growth during the crisis. Among the largest emerging economies (commonly referred to as the 'E7'), however, a different pattern emerged, with some countries avoiding a recession altogether. Some commentators have particularly pointed to 2008-2009 as the moment in which China emerged on the world stage as an economic superpower and a key driver of global economic growth. The Great Recession in the developing world While some countries, such as Russia, Mexico, and Turkey, experienced severe recessions due to their connections to the United States and Europe, others such as China, India, and Indonesia managed to record significant economic growth during the period. This can be partly explained by the decoupling from western financial systems which these countries undertook following the Asian financial crises of 1997, making many Asian nations more wary of opening their countries to 'hot money' from other countries. Other likely explanations of this trend are that these countries have large domestic economies which are not entirely reliant on the advanced economies, that their export sectors produce goods which are inelastic (meaning they are still bought during recessions), and that the Chinese economic stimulus worth almost 600 billion U.S. dollars in 2008/2009 increased growth in the region.

According to a median projection in April 2025, China's GDP was expected to grow by *** percent in 2025. In the first quarter of 2020, the second-largest economy recorded the first contraction in decades due to the epidemic.  A root-to-branch shutdown of factories To curb the spread of the virus, the Chinese government imposed a lockdown in Wuhan, the epicenter, and other cities in Hubei province on January 23, 2020. A strict nationwide lockdown soon followed. Many factories remained closed in February, resulting in a plunge in manufacturing Purchasing Managers' Index (PMI). The shutdown of the “world’s factory” had severely disrupted global supply chains, especially automobile production. In March 2020, very few industrial sectors reported positive production growth.  The pharmaceuticals sector recorded a production increase, which was mainly driven by the global demand for vital medical supplies. China had exported over seven billion yuan worth of face masks. Ripple effects on global tourism Apart from the manufacturing industry, the prolonged closures of business had caused significant losses in various sectors in China. The travel and tourism sector was massively affected by a drastic decline in flight ticket sales  and hotel occupancy rates. The domestic tourism market expects a loss of 20 percent in revenues for 2020. Industry experts predicted that the global travel and tourism industry could lose about *** trillion U.S. dollars in that year. 

From the Summer of 2007 until the end of 2009 (at least), the world was gripped by a series of economic crises commonly known as the Global Financial Crisis (2007-2008) and the Great Recession (2008-2009). The financial crisis was triggered by the collapse of the U.S. housing market, which caused panic on Wall Street, the center of global finance in New York. Due to the outsized nature of the U.S. economy compared to other countries and particularly the centrality of U.S. finance for the world economy, the crisis spread quickly to other countries, affecting most regions across the globe. By 2009, global GDP growth was in negative territory, with international credit markets frozen, international trade contracting, and tens of millions of workers being made unemployed.

Global similarities, global differences

Since the 1980s, the world economy had entered a period of integration and globalization. This process particularly accelerated after the collapse of the Soviet Union ended the Cold War (1947-1991). This was the period of the 'Washington Consensus', whereby the U.S. and international institutions such as the World Bank and IMF promoted policies of economic liberalization across the globe. This increasing interdependence and openness to the global economy meant that when the crisis hit in 2007, many countries experienced the same issues. This is particularly evident in the synchronization of the recessions in the most advanced economies of the G7. Nevertheless, the aggregate global GDP number masks the important regional differences which occurred during the recession. While the more advanced economies of North America, Western Europe, and Japan were all hit hard, along with countries who are reliant on them for trade or finance, large emerging economies such as India and China bucked this trend. In particular, China's huge fiscal stimulus in 2008-2009 likely did much to prevent the global economy from sliding further into a depression. In 2009, while the United States' GDP sank to -2.6 percent, China's GDP, as reported by national authorities, was almost 10 percent.

In recent years, China ‘s major cities set off a climax of subway construction, but also brought an endless stream of safety accidents. In order to analyze the impact of the evolution process of urban subway construction collapse disaster on residents ‘ life and social economy, by collecting typical cases of subway construction collapse disaster, combined with disaster chain and complex network theory, the network model of subway construction collapse disaster chain is constructed, and the key node events and key propagation paths are analyzed. Based on this, targeted chain-breaking disaster reduction measures are proposed. The results show: the collapse disaster chain of urban subway construction can be divided into early, middle and late stages of disaster evolution. Through the destruction of collapse, underground pipeline rupture, road damage, affecting the lives of residents and building damage and other key nodes or cut off the collapse →  underground pipeline rupture, road damage →  traffic paralysis, collapse →  building damage, construction technology is not standardized →  collapse, construction equipment failure →  collapse and other key effects are significant. The relevant research results can provide a knowledge map for effectively coping with the collapse disaster chain of urban subway construction, identify key nodes and propagation paths, and establish strategies for emergency response and chain-breaking disaster reduction.

In 2024, the rate of surveyed unemployment in urban areas of China amounted to approximately 5.1 percent. The unemployment rate is expected to remain at 5.1 percent in 2025 and the following years. Monthly unemployment ranged at a level of around 5.3 percent in the first quarter of 2025. Unemployment rate in China In 2017, the National Statistics Bureau of China introduced surveyed unemployment as a new indicator of unemployment in the country. It is based on monthly surveys among the labor force in urban areas of China. Surveyed unemployment replaced registered unemployment figures, which were often criticized for missing out large parts of the urban labor force and thereby not presenting a true picture of urban unemployment levels. However, current unemployment figures still do not include rural areas.A main concern in China’s current state of employment lies within the large regional differences. As of 2021, the unemployment rate in northeastern regions of China was notably higher than in China’s southern parts. In Beijing, China’s political and cultural center, registered unemployment ranged at around 3.2 percent for 2021. Indicators of economic activities Apart from the unemployment rate, most commonly used indicators to measure economic activities of a country are GDP growth and inflation rate. According to an IMF forecast, GDP growth in China will decrease to about four percent in 2025, after five percent in 2023, depicting a decrease of more than six percentage points from 10.6 percent in 2010. Quarterly growth data published by the National Bureau of Statistics indicated 5.4 percent GDP growth for the first quarter of 2025.

In recent years, China ‘s major cities set off a climax of subway construction, but also brought an endless stream of safety accidents. In order to analyze the impact of the evolution process of urban subway construction collapse disaster on residents ‘ life and social economy, by collecting typical cases of subway construction collapse disaster, combined with disaster chain and complex network theory, the network model of subway construction collapse disaster chain is constructed, and the key node events and key propagation paths are analyzed. Based on this, targeted chain-breaking disaster reduction measures are proposed. The results show: the collapse disaster chain of urban subway construction can be divided into early, middle and late stages of disaster evolution. Through the destruction of collapse, underground pipeline rupture, road damage, affecting the lives of residents and building damage and other key nodes or cut off the collapse →  underground pipeline rupture, road damage →  traffic paralysis, collapse →  building damage, construction technology is not standardized →  collapse, construction equipment failure →  collapse and other key effects are significant. The relevant research results can provide a knowledge map for effectively coping with the collapse disaster chain of urban subway construction, identify key nodes and propagation paths, and establish strategies for emergency response and chain-breaking disaster reduction.

Here is a concise and professional Zenodo dataset description based on your paper, suitable for use as the metadata summary:

Title:
Uncertainty Is Not What It Used to Be: EPU and the Collapse of Classical Risk Logic

Description:
This dataset accompanies the study "Regime-Contingent Uncertainty Pricing: Strategic Risk, Liquidity, and Political Shocks," which develops a theory of regime-dependent pricing of economic policy uncertainty (EPU) in U.S. equity markets. Using monthly data from 2009 to 2025, the analysis identifies nonlinear shifts in the EPU-return relationship during two major political-economic shocks: the COVID-19 pandemic and the 2025 U.S.–China Trade War. The study demonstrates that EPU effects on asset prices are not time-invariant but depend on macro-regime context, investor behavior, and liquidity conditions.

The repository includes:

• Monthly return data for SPDR S&P 500 ETF (SPY)

• U.S. Economic Policy Uncertainty Index (EPU) data

• Python scripts for data processing, OLS estimation, and Markov-switching modeling

• Figures and tables illustrating regime dynamics

• A complete README with replication instructions

Key Contributions:

• Demonstrates that financial market responses to EPU invert during structural crises (e.g., COVID-19) and revert during politically driven uncertainty (e.g., Trade War)

• Advances dynamic capabilities and institutional theory by modeling uncertainty sensitivity as regime-contingent

• Introduces the concept of "reactivated uncertainty sensitivity," emphasizing the return of classical risk pricing under renewed political stress

Keywords:
Economic Policy Uncertainty (EPU), regime switching, COVID-19, U.S.–China Trade War, Markov switching model, strategic foresight, uncertainty pricing, institutional theory

License:
CC BY 4.0 – Openly available for reuse and replication

Weight assignment of edges in the complex network of urban subway construction collapse disaster.

This statistic depicts the direct economic loss caused by geological disasters in China from 2009 to 2019. In 2019, the economic loss caused by landslides, structural collapses and mudslides in China amounted to about 2.77 billion yuan.

Node numbers in the complex network of urban subway construction collapse disaster.

Edge vulnerability of complex network model of urban subway construction collapse disaster.

The statistic shows the gross domestic product (GDP) of the United States from 1987 to 2024, with projections up until 2030. The gross domestic product of the United States in 2024 amounted to around 29.18 trillion U.S. dollars. The United States and the economy The United States’ economy is by far the largest in the world; a status which can be determined by several key factors, one being gross domestic product: A look at the GDP of the main industrialized and emerging countries shows a significant difference between US GDP and the GDP of China, the runner-up in the ranking, as well as the followers Japan, Germany and France. Interestingly, it is assumed that China will have surpassed the States in terms of GDP by 2030, but for now, the United States is among the leading countries in almost all other relevant rankings and statistics, trade and employment for example. See the U.S. GDP growth rate here. Just like in other countries, the American economy suffered a severe setback when the economic crisis occurred in 2008. The American economy entered a recession caused by the collapsing real estate market and increasing unemployment. Despite this, the standard of living is considered quite high; life expectancy in the United States has been continually increasing slightly over the past decade, the unemployment rate in the United States has been steadily recovering and decreasing since the crisis, and the Big Mac Index, which represents the global prices for a Big Mac, a popular indicator for the purchasing power of an economy, shows that the United States’ purchasing power in particular is only slightly lower than that of the euro area.

Housing markets are often characterized by price bubbles, and governments have instituted policies to stabilize them. Under this circumstance, this study addresses the following questions. (1) Does policy tightening change expectations in housing prices, revealing a regime change? (2) If so, what determines the housing market’s reaction to policy tightening? To answer these questions, we examine the effects of policy tightening that occurred in 2016 on the Chinese housing market where a price boom persisted in the post-2000 period. Using a log-periodic power law model and employing a modified multi-population genetic algorithm for parameter estimation, we find that tightening policy in China did not cause a market crash; instead, shifting the Chinese housing market from faster-than-exponential growth to a soft landing. We attribute this regime shift to low sensitivity in the Chinese housing market to global perturbations. Our findings suggest that government policies can help stabilize housing prices and improve market conditions when implemented expediently. Moreover, policymakers should consider preparedness for the possibility of an economic crisis and other social needs (e.g., housing affordability) for overall social welfare when managing housing price bubbles.

Housing markets are often characterized by price bubbles, and governments have instituted policies to stabilize them. Under this circumstance, this study addresses the following questions. (1) Does policy tightening change expectations in housing prices, revealing a regime change? (2) If so, what determines the housing market’s reaction to policy tightening? To answer these questions, we examine the effects of policy tightening that occurred in 2016 on the Chinese housing market where a price boom persisted in the post-2000 period. Using a log-periodic power law model and employing a modified multi-population genetic algorithm for parameter estimation, we find that tightening policy in China did not cause a market crash; instead, shifting the Chinese housing market from faster-than-exponential growth to a soft landing. We attribute this regime shift to low sensitivity in the Chinese housing market to global perturbations. Our findings suggest that government policies can help stabilize housing prices and improve market conditions when implemented expediently. Moreover, policymakers should consider preparedness for the possibility of an economic crisis and other social needs (e.g., housing affordability) for overall social welfare when managing housing price bubbles.

This paper analyzes the causes of equity pledge, external conduction mechanisms, and economic consequences from the perspective of insurance participation by integrating insurance participation, equity pledge, and stock price crash risk into a unified framework. An empirical analysis of sample data from listed companies in Shanghai and Shenzhen between 2007–2021, indicates that equity pledge reduces the risk of collapse as companies hedge the risk induced by the equity pledge. Further research has revealed that insurance participation can mitigate stock price crash risk brought by equity pledge through a regulatory effect, which is more pronounced for private companies and those with a high shareholding ratio, and companies in manufacturing industry. This is because private companies have a higher demand for capital as their property rights are not state-owned, the degree of separation of powers and agency conflicts is greater in companies held by large shareholders, manufacturing companies usually have stable earnings and cash flow performance, and the financial support provided by insurers for equity pledges at risk can effectively reduce the risk of their collapse.

This paper analyzes the causes of equity pledge, external conduction mechanisms, and economic consequences from the perspective of insurance participation by integrating insurance participation, equity pledge, and stock price crash risk into a unified framework. An empirical analysis of sample data from listed companies in Shanghai and Shenzhen between 2007–2021, indicates that equity pledge reduces the risk of collapse as companies hedge the risk induced by the equity pledge. Further research has revealed that insurance participation can mitigate stock price crash risk brought by equity pledge through a regulatory effect, which is more pronounced for private companies and those with a high shareholding ratio, and companies in manufacturing industry. This is because private companies have a higher demand for capital as their property rights are not state-owned, the degree of separation of powers and agency conflicts is greater in companies held by large shareholders, manufacturing companies usually have stable earnings and cash flow performance, and the financial support provided by insurers for equity pledges at risk can effectively reduce the risk of their collapse.