“…4 B). Individuals living in wealthier regions in China may have greater access like using air conditioning 46 , to coping mechanisms for dealing with extremely high temperature than those residing in poorer cities do. Figure 4 ( A ) The coefficient of extreme temperatures of cities in northern China or southern China.…”
The rising sentiment challenges of the metropolitan residents may be attributed to the extreme temperatures. However, nationwide real-time empirical studies that examine this claim are rare. In this research, we construct a daily extreme temperature index and sentiment metric using geotagged posts on one of China's largest social media sites, Weibo, to verify this hypothesis. We find that extreme temperatures causally decrease individuals' sentiment, and extremely low temperature may decrease more than extremely high temperature. Heterogeneity analyses reveal that individuals living in high levels of PM2.5, existing new COVID-19 diagnoses and low-disposable income cities on workdays are more vulnerable to the impact of extreme temperatures on sentiment. More importantly, the results also demonstrate that the adverse effects of extremely low temperatures on sentiment are more minor for people living in northern cities with breezes. Finally, we estimate that with a one-standard increase of extremely high (low) temperature, the sentiment decreases by approximately 0.161 (0.272) units. Employing social media to monitor public sentiment can assist policymakers in developing data-driven and evidence-based policies to alleviate the adverse impacts of extreme temperatures.
“…4 B). Individuals living in wealthier regions in China may have greater access like using air conditioning 46 , to coping mechanisms for dealing with extremely high temperature than those residing in poorer cities do. Figure 4 ( A ) The coefficient of extreme temperatures of cities in northern China or southern China.…”
The rising sentiment challenges of the metropolitan residents may be attributed to the extreme temperatures. However, nationwide real-time empirical studies that examine this claim are rare. In this research, we construct a daily extreme temperature index and sentiment metric using geotagged posts on one of China's largest social media sites, Weibo, to verify this hypothesis. We find that extreme temperatures causally decrease individuals' sentiment, and extremely low temperature may decrease more than extremely high temperature. Heterogeneity analyses reveal that individuals living in high levels of PM2.5, existing new COVID-19 diagnoses and low-disposable income cities on workdays are more vulnerable to the impact of extreme temperatures on sentiment. More importantly, the results also demonstrate that the adverse effects of extremely low temperatures on sentiment are more minor for people living in northern cities with breezes. Finally, we estimate that with a one-standard increase of extremely high (low) temperature, the sentiment decreases by approximately 0.161 (0.272) units. Employing social media to monitor public sentiment can assist policymakers in developing data-driven and evidence-based policies to alleviate the adverse impacts of extreme temperatures.
“…As the world's most complex environmental problem resulting in the largest externality, climate change has a broad and profound impacts (Tang Y. et al, 2023). It leads to a greater frequency of extreme weather events and an increasingly pronounced negative impact on the economic system which are manifested in a wide range of areas such as agriculture, industry, manufacturing, ecosystems and human health (World Bank, 2016;Bank, 2017;Duan et al, 2022;Tang W. et al, 2023;Duan et al, 2023). In addition, climate change-induced natural disasters have led to fluctuations in asset pricing, impacting the profitability and solvency of companies, increasing the credit risk, liquidity risk, and operational risk of the financial system and even posing a contagious risk, jeopardizing financial stability (Dietz et al, 2016;Lamperti et al, 2019;.…”
Climate transition risks pose growing financial stability concerns, but research on quantifying climate policy impacts remains underexplored. This paper helps address this gap by evaluating how carbon tax (CT) and green supporting factor (GSF) influence China’s financial stability. An innovative dynamic stochastic general equilibrium (DSGE) model incorporating the banking sector is developed to quantify transmission channels, improving on conceptual studies. It reveals that more intense climate policies heighten impacts on financial stability, with CT improving it but GSF hampering it in the long run. However, both policies negatively affect stability initially, albeit insignificantly. These diagnostics underscore calibrating policy intensities and sequencing to balance climate and economic objectives. Furthermore, this study reveals asymmetric effects on polluting and non-polluting enterprises, with the former seeing reduced output and lending but the latter gaining. The differentiated approach proposed, tailored to firm emissions levels, provides key insights for unlocking smooth green transitions while maintaining financial system resilience. The paper makes important contributions by bringing together climate policies, adaptation, and financial stability. The findings offer insights into achieving a smooth climate transition while maintaining financial stability. Specific implications include starting with low CT on the heaviest emitters, gradually lowering risk weights for green lending, and using public incentives and investment to aid polluting firms’ transition. This study offers valuable quantitative insights for developing country-specific climate financial risk policies.
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