Assessing the effects of extreme climate risk on urban ecological resilience in China.

The frequent occurrence of extreme weather events has imparted significant pressure on urban ecosystem management. Evaluating the relationship between extreme climate risk (ECR) and urban ecological resilience (UER) is a key issue in achieving the green and sustainable development objectives of cities. This study measures UER in China from 2005 to 2020 using the entropy weight method-TOPSIS method, investigates the relationship between ECR and UER using the dynamic GMM model, and further explores the influencing mechanism. The results suggest that ECR has an inhibiting influence on UER. Additionally, the moderating mechanism investigation demonstrates that environmental regulation can mitigate the threat of ECR to UER to a certain extent, and with the regulation effect based on the government's environmental concern being better than that of the market pollution fee payment. The group test outcomes demonstrate that the discrepancies in regions and marketization lead to certain differences in the relationship between ECR and UER. Additional investigation indicates that ECR has an asymmetric relationship with UER at distinct quantiles. Our findings reflect the subtle associations between ECR and UER as a whole, and will help relevant organizations in formulating more precise and scientific policies to enhance urban ecological resilience.

The nexus among CO2 emission, health expenditure and economic development in the OECD countries: New insights from a cross-sectional ARDL model.

To find a way to realize sustainable development, this paper applied a cross-sectional ARDL (CS-ARDL) method to explore the interaction between carbon emissions, economic development, and health care expenditure for OECD countries. Firstly, we conduct a cross-sectional test to check whether the data is confronted with this issue. Secondly, we conduct a panel unit root test and cointegration test to confirm whether the ARDL-based method is suitable for our data. Thirdly, we analyze the results and provide possible explanations. Lastly, we conduct a short-term causality test to detect the connection between different variables. The main conclusion of our study includes: 1) Health care is a necessity in OECD countries. 2) Environmental deterioration places a heavy burden on health care expenditure in OECD countries. 3) Health care expenditure of last year negatively affects health care expenditure. 4) There is a short-run causality relationship from CO2, economic development, and dependency rate of youth to health care expenditure in OECD countries. Related policy proposals are provided according to our analysis of the results.

Asymmetric effects of crude oil prices and USD exchange rate on precious metals returns：Evidence from pre and during COVID-19 outbreak.

Trading in commodities such as precious metals and crude oil is vital to the economy. Frequent exchange rate fluctuations have led to constant changes in commodity prices since 2000. Using quantile regression, this paper examines the impact of oil prices and the US dollar exchange rate on gold, silver, platinum, and palladium from January 1, 2013 to 5 May 2023. Oil prices positively affect precious metals returns, and positive and negative oil price shocks are asymmetric. Exchange rate movements negatively affect precious metal returns. In addition, gaps in the existing literature are filled by analyzing the effects of oil prices and the exchange rate on precious metals before and during COVID-19. This paper provides substantial evidence for revising the impact of the crisis.

Modelling extreme risks for green bond and clean energy.

Value at Risk and Expected Shortfall are two traditional tools used to measure extreme risk in financial markets. However, there is little research on measuring extreme risk in emerging markets such as green bonds and clean energy. This paper uses both semi-parametric models with simultaneous excitation functions and traditional models to estimate extreme risk in SP500 Green Bond (GB) and Global Clean Energy (GCE), selecting Expected Shortfall (ES) and Value at Risk (VaR) as the indices of extreme risk. Then, the paper uses a breakpoint scan of the predictions of the different types of models. The results find that the green bond market was relatively stable while the global clean energy market experienced sharp fluctuations after the COVID-19 pandemic outbreak. Representative models in GCE have at least two breakpoints, but those for GB have no breakpoints. The GARCH model with normal innovations performs the best among all target models, and the GARCH-FZ model fits the best among all semi-parametric candidates. Our research could help governments, companies, and investors with risk management and improve model accuracy and mechanisms for measuring extreme risks.

Facilitate or Inhibit: Corporate Environmental Performance and Financing Costs.

The proposed carbon peak and carbon neutralization goals have ushered China into an era of emissions reduction and a climate-oriented economy. With the proposed double carbon goal, China has formulated many environmental protection and green credit policies. This paper aims to assess the impact of corporate environmental performance (CEP) on financing costs, using a panel dataset of companies in China's heavily polluting industries from 2010 to 2019. We employed fixed-effect models, moderating-effect models, and panel quantile regression (PQR) to analyze the impact, underlying mechanisms, and asymmetric features of CEP on financing costs. Our results indicate that CEP has an inhibitory effect on financing costs, with political connections strengthening this effect and GEA weakening it. Moreover, the impact exhibits asymmetry at different levels of financing costs, wherein lower financing costs see a greater weakening effect from CEP. Improved CEP helps to optimize the financing performance of companies and reduce financing costs. Therefore, policy makers and regulatory authorities should work to unblock financing channels for companies, encourage environmental investment, and remain flexible in implementing environmental policies.

Does digital finance promote the "quantity" and "quality" of green innovation? A dynamic spatial Durbin econometric analysis.

Based on the panel data of 284 prefecture-level cities in China, this paper uses the dynamic spatial Durbin model to explore the impact of digital finance on green innovation from the dimensions of "quantity" and "quality." The results show that digital finance has a positive impact on both the quality and quantity of green innovation in local cities, but the development of digital finance in neighboring cities has a negative impact on the quantity and quality of green innovation in local cities, and the impact on the quality of green innovation is greater than that on the quantity of green innovation. And after a series of robustness tests, it was shown that the above conclusions are robust. In addition, digital finance can have a positive impact on green innovation mainly through industrial structure upgrading and informatization level. Heterogeneity analysis shows that the breadth of coverage and the degree of digitization are significantly related to green innovation, and digital finance has a more significant positive impact in eastern cities than in mid-western cities.

Climate risk and financial systems: A nonlinear network connectedness analysis.

To deeply analyze and understand the macro-financial impact of climate change, this paper investigates the effect of climate risk on systemic financial risks by employing a network approach. The results demonstrate that climate risk not only affects a single financial market but also induces risk co-movement, which aggravates potential systemic financial risks. Specifically, the system-wide connectedness across the financial system respectively increased by 2.52% and 1.76% after the withdrawal of the US from the Kyoto Protocol and the Copenhagen UN Climate Change Conference. The bond and stock markets are the primary transmitters of climate shocks, while the forex and commodity markets appear to be more sensitive to climate-related information. In addition, the vulnerability of financial asset price fluctuations to climate risk changes substantially over time. Quantile regressions reveal the positive impact of climate risk on total connectedness across the financial system. This study provides novel insight into how the financial system responds to climate-related information and how systemic risk dynamics materialize.

The role of the COVID-19 pandemic in time-frequency connectedness between oil market shocks and green bond markets: Evidence from the wavelet-based quantile approaches.

This study contributes to the existing literature on the relationship between oil market shocks and the green bond market by investigating the impact of the COVID-19 pandemic on their dynamic correlation. We first decompose the oil market shocks into components using a time-frequency framework. Then, we combine wavelet decomposition and quantile coherence and causality methods to discuss changes during the COVID-19 era. We observe positive effects of both supply-driven and demand-driven oil shocks on the green bond market at most quantile levels. However, supply-driven oil price changes play a major role. The results also indicate that long-term changes have a greater impact than short-term changes on the connection between oil and green bond markets. Nevertheless, the COVID-19 pandemic changed the nature of the causal relationship, as we observed no relationship under extreme market conditions during the pandemic era. We argue that the economic and social impacts of the COVID-19 pandemic have left investors focusing on the short-term substitution between oil and green bond markets.

Is hepatic resection always a better choice than radiofrequency ablation for solitary hepatocellular carcinoma regardless of age and tumor size?

In this study, we aimed to compare survival outcomes after receiving radiofrequency ablation (RFA) and hepatic resection (HR) for solitary hepatocellular carcinoma (HCC) with stratification by tumor size and age. A retrospective cohort was obtained from the Surveillance, Epidemiology, and End Results (SEER) database from 2004 to 2015. Patients were grouped by tumor size (0-2, 2-5, and > 5 cm) and age (>65 and ≤65). Overall survival (OS) and disease-specific survival (DSS) were assessed. For patients >65 with tumors measuring 0-2 and 2-5 cm, the HR group had better OS and DSS compared with the RFA group. For patients >65 with tumors > 5 cm, OS and DSS did not differ significantly between the RFA and HR groups (p = 0.262 and p = 0.129, respectively). For patients ≤65, the HR group had better OS and DSS compared with the RFA group regardless of tumor size. For patients with resectable solitary HCC, regardless of age, HR is the better choice not only for tumors ≤ 2 cm, but also for tumors 2-5 cm. For resectable solitary HCC with tumors ＞5 cm, HR is the better choice for patients ≤65 but for patients >65, the issue of treatment choice needs to be further studied.

Analysis of technological innovation on provincial green development levels of logistics industry in China.

The transition from traditional logistics to green and low-carbon logistics is crucial and inevitable due to the pressure of climate change and sustainable development in China. Meanwhile, technological innovation is perceived as an important factor affecting the development of the logistics industry. To explore the impacts of technological innovation and other factors and to propose proper policies based on the results, this study utilizes a generalized estimating equations (GEE) regression model to analyze panel data of 30 provinces during 2001-2019. Firstly, the entropy weight method is applied to calculate the green logistics development level based on an index system considering green factors. Secondly, a GEE model which considers the correlation among different observations is used to investigate the impacts of crucial factors on the green logistics development level. Moreover, regional heterogeneity is also analyzed in this paper by comparing the regression results of the Eastern region, Central region, and Western region. Based on the above analysis, several conclusions are drawn: (1) In terms of the average green logistics development levels, the Eastern region ranks 1st, the Central region ranks 2nd, and the Western region ranks 3rd. (2) GEE regression model is proved effective in our sample. (3) For the full sample, technological innovation, trade openness, and logistics infrastructure positively affect the green logistics development level; while, government regulation and energy intensity negatively influence the green logistics development level. (4) Regional heterogeneity is confirmed in our sample. Related policy recommendations are proposed based on our regional regression results. Take the Eastern region as an example, the local governments in the Eastern region should upgrade the manufacturing industry, reduce government financial investment in the transportation sector, and enhance environmental control expenditure in the transportation sector.

How does digital finance affect industrial structure upgrading? Evidence from Chinese prefecture-level cities.

Digital finance is playing an increasingly prominent role in economic development. This paper examines the impact of digital finance on industrial structure upgrading based on panel data from 289 Chinese prefecture-level cities from 2011 to 2020. The paper adopts fixed effects, mediating effects, and spatial econometric models and the findings are as follows. First, digital finance development significantly boosts industrial structure upgrading in Chinese cities. The evidence remains valid after various robustness tests. Second, digital finance and industrial structure upgrading exhibit positive spatial spillover effects. Third, digital finance indirectly affects industrial structure upgrading through innovation, entrepreneurship and the structure of household consumption channels. Fourth, the influence of digital finance is more significant in cities with more developed economies, less financialization and lower income inequality. Finally, among the sub-indicators of digital finance, the breadth of coverage plays the most significant role, inspiring policymakers and financial institutions to speed up the digitization infrastructure in backward areas.

Pollution prevention strategies of SMEs in a green supply chain finance under external government intervention.

Small- and medium-sized enterprises (SMEs) are an important force in the promotion of economic development. However, SMEs in the supply chain are often severely punished by the government for polluting the environment through their production processes. Therefore, it is crucial to solve the environmental pollution problem of SMEs. To compensate for the "common knowledge" assumption of participants in traditional game theory, this study constructs an evolutionary game model of a green supply chain composed of SMEs, core enterprises, and commercial banks. The strategic decisions of the supply chain members are analyzed in relation to external interventions of the government. Subsequently, the impact of government incentives and penalties on these strategic choices is considered. The results show that government incentives can promote SMEs' choice to use pollution control strategies, and government penalties can reduce the risk of an SME defaulting, thus promoting the healthy development of a green supply chain finance system. Furthermore, increasing the new income of SMEs, raising the guarantee rate of core enterprises, and reducing the cost of green loans reviewed by banks and the guarantee cost of core enterprises can effectively promote the equilibrium of the tripartite evolutionary game. These findings indicate that the game model introduced here can effectively solve the environmental pollution problems of SMEs and promote the healthy development of a green supply chain finance system.

The local-neighborhood effects of low-carbon city pilots program on PM2.5 in China: A spatial difference-in-differences analysis.

Using the balanced panel of 260 cities in China from 2005 to 2018, this study explores the local-neighborhood effects of the low-carbon city pilots (LCCPs) program on PM2.5 concentration by utilizing the spatial difference-in-differences (SDID) method. The results show that the LCCPs program can not only reduce the local PM2.5 concentration but also effectively alleviate the smog pollution in neighboring cities. The reduction effect of LCCPs on PM2.5 in local cities is more significant in central and western areas, second-tier and above cities and resource-based cities. Nevertheless, the spillover effect on neighboring cities is more significant in central and western areas, third-tier and below and non-resource-based cities. In addition, the impact of policy is mainly through green innovation, while the intermediary role of industrial structure upgrading is not significant. These findings can provide useful policy inspiration for scientifically implementing air pollution prevention and control actions and winning the battle to defend the blue sky.

Can China's carbon trading policy improve the profitability of polluting firms: a retest of Porter's hypothesis.

The future trends and development trajectory of China's carbon emissions trading scheme (ETS), one of the key policy instruments for curbing peak carbon emissions and achieving carbon neutrality, have drawn a lot of interest. However, the Porter hypothesis (PH) and its validity boundary have not been explored sufficiently. We use micro-firm data from 2010 to 2019 to investigate whether the triple-difference (DDD) method could reveal the weak PH on the policy viewing ETS as a quasi-natural experiment in this work. Meanwhile, we use the panel threshold model and the moderated mediation effect model to assess the scientific border of the PH on the ETS. The findings show that by verifying the weak PH, the ETS may greatly enhance investment and foster the inventiveness of heavy-polluting industries (HPE). In contrast, the strong PH on the ETS has unstable validity and has non-linear characteristics. In particular, the ETS shows a U-shaped link between innovation and profitability by first decreasing and then increasing HPE's profitability through R&D. The cost of R&D and compliance costs being combined negatively impacts HPE's profitability. Further analysis shows that ETS will have different effects on the profitability of HPE due to R&D level and the threshold change of the compliance cost. This paper will offer some insightful points of view for the implementation of carbon market mechanisms in developing nations like China.

Selection of outsourcing logistics providers in the context of low-carbon strategies.

As more attention is given to climate change and sustainable development in China, enterprises start to care about carbon emissions related to their supply chains. One important issue faced by an enterprise is to select a logistics provider who can provide a high-quality service with low carbon levels. To solve this issue, this paper proposes a selection criterion from the perspective of low carbon levels based on previous studies. The selection criterion consists of comprehensive strength, financial status, market competitiveness, development potential and low carbon level. Next, this paper applies a combined method of information entropy and grey correlation vlsekriterijumska optimizacija i kompromisno resenje (VIKOR) model to evaluate the proposed selection criterion. Subsequently, this paper took company S and its four logistics providers as a case study to check the applicability of our proposed selection method and to illustrate how to use it. Sensitivity analysis and comparative analysis are also conducted. Related managerial insights are also proposed based on the evaluation results. One finding of this paper is to establish a decision-making framework to evaluate logistics suppliers under the new background of 'emission peak and carbon neutrality'.

Gold or Bitcoin, which is the safe haven during the COVID-19 pandemic?

This study compares the dynamic spillover effects of gold and Bitcoin prices on the oil and stock market during the COVID-19 pandemic via time-varying parameter vector autoregression. Both time-varying and time-point results indicate that gold is a safe haven for oil and stock markets during the COVID-19 pandemic. However, unlike gold, Bitcoin's response is the opposite, rejecting the safe haven property. Further analysis shows that the safe-haven effects of gold on the stock market become stronger when the pandemic critically spreads.

TOP2A inhibition reverses drug resistance of hepatocellular carcinoma to regorafenib.

Hepatocellular carcinoma (HCC) is the third leading cause of cancer-related death attributed to high frequency of metastasis and multiple drug resistance. We aim to examine the underlying molecular mechanism and to seek potential strategies to reverse primary/acquired resistance to regorafenib. Topoisomerase IIα (TOP2A) is critical for tumorigenesis and carcinogenesis. Clinically, high-TOP2A expression was correlated to shorter overall survival (OS) of patients, but its role in drug resistance of HCC remains unknown. Here, we screened the expression profiling of TOP2A in HCC and identified TOP2A as an upregulated gene involved in the resistance to regorafenib. Sustained exposure of HCC cells to regorafenib could upregulate the expression of TOP2A. Silencing TOP2A enhanced HCC cells' sensitivity to regorafenib. TOP2A inhibition by doxorubicin or epirubicin synergized with regorafenib to suppress the growth of sorafenib-resistant HCC tumors that possessed the sorafenib-resistant features both in vitro and in vivo. Thus, targeting TOP2A may be a promising therapeutic strategy to alleviate resistance to regorafenib and thus improving the efficacy of HCC treatment.

Market implied volatility and natural disasters: international evidence.

Using daily data, we provide fresh international evidence that an occurrence of natural disaster is accompanied by an increase in the market implied volatility of the main equity index in the same country contemporaneously and in the future. The relation between market implied volatility and natural disaster is a short-run relationship, mainly driven by the subsample of countries with a low frequency of disaster or with developed financial markets. Our analysis shows that in contrast to ex post volatility measures, implied volatility can reflect more climate physical risk information, which provides insight for multinational equity investment in coping with environmental physical risk. Our findings provide insight for multinational equity investment in coping with climate physical risk.

Exploring the bidirectional causality between green markets and economic policy: evidence from the time-varying Granger test.

The vigorous development of green markets and the effective mitigation of economic policy fluctuations are current hotspots that intrigue our interest in exploring the causal relationships between green market returns and economic policy uncertainty (EPU). Green bonds, corporate environmental responsibility, green technology investment, and the carbon trading market are our research objects to comprehensively understand the interaction among them, from both macro and micro perspectives. Considering the importance of temporal heterogeneity and spillover direction in causation, we employ the time-varying Granger causality method to obtain bidirectional real-time identification. We find that green market returns exhibit a time-varying bidirectional causality with EPU over most of the sample period. In contrast, green markets are more a risk spillover than a recipient. Notably, this causality is vulnerable to exogenous financial risks, especially structural changes caused by the COVID-19 pandemic. Overall, this paper provides insights into the deep-seated causes of price fluctuations, volatile market uncertainty, and the interaction mechanism between them, as well as implications for market participants and policymakers.