Quantile connectedness between the climate policy and economic uncertainty: Evidence from the G7 countries

Green bond and green stock in China: The role of economic and climate policy uncertainty

Climate and economic policy uncertainties undermine carbon trading supply-demand dynamics, triggering price volatility. To explore how the carbon price will change, we develop a unified price forecasting model based on multi-frequency data. Based on this, carbon trading markets in Hubei and Shanghai are chosen as research cases to explore the different price volatility of markets across market activity under climate and economic policy uncertainties, respectively. This paper presents a novel study optimizing the accuracy of existing price forecasting models by considering dual policy uncertainties. Results revealed that in the short term, the Shanghai carbon trading pilot exhibits asymmetry in short-term asset price volatility, whereas such a characteristic is not observed in the Hubei carbon trading market. In the long term, climate policy uncertainties cause more significant price volatility under both markets, while economic policy uncertainties only have an impact on the Hubei carbon market. Additional study indicates that including macroeconomic factors, especially economic policy uncertainty, can enhance the predictive prowess of carbon trading price forecasting models.

Recent events, such as the financial crisis, oil price shocks or fluctuations, Brexit, the US–China trade war, the COVID-19 pandemic, the Russia–Ukraine conflict and the subsequent energy crisis, have surged global economic policy uncertainty. As climate change has recently been more pronounced around the globe, discussions about climate policies and related uncertainties have also become a major concern. This study investigates the role of economic policy uncertainty (EPU) and climate policy uncertainty (CPU) on climate change (environmental degradation) for selected emerging and developed economies, expanding the IPAT framework and merging it with the Environmental Kuznets Curve (EKC) hypothesis. The IPAT framework examines the impact (I) of population (P), affluence (A), and technology (T) on the environment, whereas the EKC hypothesis proposes an inverted U-shaped curve between affluence and environmental degradation. Two models were created and tested for emerging and developed countries, namely Model 1 with EPU and Model 2 with CPU. A Pooled Mean Group (PMG) estimator is employed to investigate the interrelation between carbon dioxide (CO2) emissions and selected variables; namely the real Gross Domestic Product (GDP) per capita, squared real GDP per capita, renewable share in consumption, the EPU, the CPU and population. Test results indicate that the EKC hypothesis is verified only in Model 1 and for emerging countries, whereas population escalates climate change in both country groups. Furthermore, in line with the consumption effect theorized earlier in the literature, EPU is negatively related to carbon emissions in emerging countries. Thus, the EPU leads to a decrease in the use of energy and pollution-intensive commodities and mitigates climate change in EMEs. Compatible with our ex-ante expectations, renewable energy consumption alleviates climate change in both country groups in the short term. In Model 2, with CPU, we find no evidence supporting the EKC hypothesis for any country groups. However, we reaffirm that renewable energy consumption decreases CO2 emissions in developed countries, which is in support of the argument that energy transition holds the key to tackling climate change. Finally, CPU is associated with a decrease in CO2 emissions in emerging countries in the short term, potentially leading to a reduction in overall economic activity and alleviating climate change. This might also be attributable to the fact that the decisions of economic agents substantially rely on current and future policy (both economic and climate) expectations. Overall, verifying the EKC hypothesis for emerging countries in Model 1, we might argue that there is good potential for emerging countries to save money and time on environmental costs via the adoption of clean technologies and related policies. Last but not least, on a global scale, energy transition with better utilization of renewable sources holds the key to tackling climate change and reducing emissions.

Does economic and climate policy uncertainty matter the oil market?

The severe challenges posed by the environmental crisis and climate change have stimulated the development of the green bonds (GB) market aimed at providing bridging financing for carbon reduction. This investigation employs the wavelet-based quantile-on-quantile method to probe the asymmetric impacts among economic policy uncertainty (EPU), climate policy uncertainty (CPU) and GB. The empirical findings demonstrate that EPU and CPU have time-varying impacts on GB across different time scales. In the short term, the negative effects of EPU and CPU predominantly influence the GB market. In the long term, EPU and CPU have positive effects on the GB market during bullish market conditions but negative effects during bearish market conditions. These outcomes indicate that GB cannot always be deemed a diversifier for EPU and CPU shocks. These outcomes deviate from the Intertemporal Capital Asset Pricing Model (ICAPM) model’s conclusions, which emphasize uncertainties’ positive influence on the GB market. Policymakers can incorporate these findings into policy design to mitigate risks stemming from uncertainty and promote the GB market growth. Furthermore, investors should consider the effects of uncertainty to optimize their investment strategy and obtain higher returns.

Energy consumption within policy uncertainty: Considering the climate and economic factors

Given the significance of fostering sustainable climate conditions for long-term economic stability and financial resilience, this study probes the connection between climate-related policy ambiguity and its implications for currency valuation. In doing so, the current study investigates the interconnected effects of climate policy on economic policy uncertainty and geopolitical risk with the currency valuation in ASEAN countries. Employing wavelet coherence analysis and partial wavelet coherence analysis, the paper highlights the complex relationships among these factors and their implications for exchange rate fluctuations. Using data from 2000 to 2022, the findings reveal that climate policy uncertainty is an important driver of exchange rate movements, amplifying the impact of economic policy uncertainty and geopolitical risk. Furthermore, the study identifies a vicious cycle between climate policy uncertainty and exchange rates, potentially impacting the region's macroeconomic stability and long-term economic growth. The study presents several policy recommendations to address economic and climate policy uncertainties comprehensively based on the findings. These recommendations include establishing national frameworks for climate risk management, enhancing policy credibility and macroeconomic stability, and promoting regional integration to mitigate the influence of geopolitical risk on exchange rates.

Policy uncertainties can directly affect the outcomes of policies to be implemented and other related policies. Therefore, it is important to reduce policy uncertainties. Identifying policy uncertainties and related factors is important in this regard. This study examines the impact of economic and monetary policy uncertainty on climate policy uncertainty in the United States. The relationship between the variables is examined asymmetrically using monthly data for 1988-2022. First, the “Augmented Dickey-Fuller Unit Root Test” and the “Fractional Frequency Fourier Augmented Dickey-Fuller Unit Root Test” are applied. The Asymmetric Wavelet Transform Coherence Test is also used to determine the direction and frequency of the relationship between the variables. Asymmetric time-varying causality analysis was used for the causality dimension. The significant relationship between “economic policy uncertainty”, “monetary policy uncertainty” and “climate policy uncertainty” varies at different time periods.

With rising climate policy uncertainty (CPU) and economic policy uncertainty (EPU), it is crucial to analyze the factors influencing the renewable energy stock market (RE) from a comprehensive perspective. Using data from January 2009 to May 2022, we use a time‐varying parameter vector autoregressive model with stochastic volatility (TVP‐VAR‐SV) to examine CPU, EPU, macroeconomic factors, and RE in a unified framework. We analyze the various responses of RE to CPU and EPU. Furthermore, we test for differences in the impact of the four classifications of EPU on RE. The findings are as follows. The time‐varying impact of CPU on RE is centered on the short term and is positive during non‐crisis periods. In contrast, the impact of EPU on RE is negative in the short term. In addition, causal identification at the micro level reveals that RE can increase by 0.932% on average after being affected by CPU. Further comparing the four classifications of EPU, we find that exchange rate policy uncertainty has the largest negative impact. Our study enriches the investment theory on RE. It avoids biased interpretations of various policy uncertainties and has a significant implication for policymakers, renewable energy firms, and investors.

Based on the economic and climate policy uncertainty index and the price data of major carbon emission trading markets from May 2014 to August 2023, this paper uses the generalized autoregressive conditional heteroskedasticity and mixing data sampling (GARCH-MIDAS) model to analyze the impact of policy uncertainty on carbon market price volatility. The results indicate the following: (1) The price volatility in the Hubei carbon market is influenced by both economic and climate policy uncertainties, while the Guangdong market is only affected by climate policy uncertainty, and the Shenzhen carbon market is only affected by economic policy uncertainty. (2) Before the establishment of the national carbon market, the carbon market prices in Hubei were impacted by both policy uncertainties, while Guangdong and Shenzhen carbon markets were only affected by climate policy uncertainties. (3) On the contrary, after the establishment of the national carbon market, only the Shenzhen carbon market was affected by both policy uncertainties, and the price volatility in the Guangdong and Hubei carbon markets was not affected by policy uncertainties. The above research conclusions are helpful for regulatory agencies and policymakers to assess the future direction of the pilot carbon market and provide an empirical basis for preventing and resolving policy risks. At the same time, the proposed GARCH-MIDAS model effectively solves the inconsistent frequency problem of policy uncertainty and carbon price volatility, providing a new perspective for the study of factors affecting carbon market volatility.

This study investigates how uncertainty in climate and global economic policies affects private investment in sub‐Saharan Africa (SSA). Using panel data from 41 countries over the period 2000–2022, the study employs a dynamic panel model to estimate the effects of these two types of uncertainty on the private investment‐to‐gross domestic product ratio. The study finds that both global economic policy uncertainty (EPU) and climate policy uncertainty have a negative and significant influence on private investment, implying that higher levels of uncertainty discourage private investors from undertaking long‐term projects in the sub‐region. The study also finds that the effect of uncertainty on climate policy is stronger than that of EPU, suggesting that private investors are more sensitive to the lack of clarity and coherence in the global climate policy framework. The findings are robust to different estimation techniques. The study concludes that reducing policy uncertainty, especially in the area of climate change, is crucial for enhancing private investment and promoting sustainable development in SSA.

Building resilient clean energy transitions: Does economic, fiscal, and climate policy uncertainties contribute to renewable energy consumption in United States.

Purpose The term environmental, social and governance (ESG) has gained momentum in recent years. Thus, understanding its underlying driving mechanisms has become increasingly intriguing. In this study, we examine the effects of economic policy uncertainty (EPU), climate policy uncertainty (CPU) and geopolitical risk (GPR) on firms’ ESG disclosure. Design/methodology/approach To achieve the paper’s goal, we use the mixed data sampling (MIDAS) regression model on a sample of 500 firms from the US stock market. Findings The results indicate that EPU and GPR have significant adverse effects on ESG performance, whereas the CPU index exhibits a positive impact. However, the significant associations of CPU and GPR with ESG score observed in the whole sample do not hold consistently across industries. This suggests that, while overarching trends might exist, individual sector characteristics and varying external influences can shape the relationship between uncertainty factors and ESG performance. This highlights the importance of context when analyzing ESG practices across different industries. Furthermore, we identified a range of influential financial parameters that drive ESG practices. Practical implications The findings of our study have significant implications for many parties including policymakers, managers, governments, investors and shareholders. Additionally, sector-specific policies may be needed to encourage sustainable practices, particularly in industries sensitive to environmental and geopolitical risks. Originality/value The paper’s originality lies in its examination of the combined impact of EPU, CPU, and GPR on ESG disclosure – a relationship rarely explored in existing literature. Using the MIDAS model, it provides a more refined analysis by integrating both high- and low-frequency data. The study also offers industry-specific insights, revealing how these external uncertainties affect ESG disclosure differently across sectors. By incorporating financial drivers and offering policy implications, this research presents a novel and comprehensive approach to understanding ESG disclosure under increasing global uncertainties.

ABSTRACTWe investigate the quantile effects of climate policy uncertainty (CPU) on real estate investment trusts (REITs) returns in the United States. We use the quantile autoregressive distributed lags (QARDL) method on the monthly economic policy uncertainty (EPU), the market volatility index (VIX) and interest rates (INT) from March 2006 to April 2023. The results show that the impact coefficients of CPU, EPU and interest rates on REIT returns are significant in the short and long term. In addition, CPU demonstrates unidirectional causality with REIT returns across all quantiles, whereas REITs only show unidirectional causality with CPU in lower quantiles. Furthermore, EPU and interest rates show bidirectional causality with REIT returns across most quantiles. Policymakers and REIT investors can utilise the relationships and causality between REITs and CPU to update REIT investments, hedge against CPU and REIT stocks, construct a diversified portfolio and make informed decisions about the price movements of REITs in climate crises.

PurposeThis paper aims to investigate the effect of the economic policy uncertainty (EPU), geopolitical risk (GPR) and climate policy uncertainty (CPU) of USA on Bitcoin volatility from August 2010 to August 2022.Design/methodology/approachIn this paper, the authors have adopted the empirical strategy of Yen and Cheng (2021), who modified volatility model of Wang and Yen (2019), and the authors use an OLS regression with Newey-West error term.FindingsThe results using OLS regression with Newey–West error term suggest that the cryptocurrency market could have hedge or safe-haven properties against EPU and geopolitical uncertainty. While the authors find that the CPU has a negative impact on the volatility of the bitcoin market. Hence, the authors expect climate and environmental changes, as well as indiscriminate energy consumption, to play a more important role in increasing Bitcoin price volatility, in the future.Originality/valueThis study has two implications. First, to the best of the authors’ knowledge, the study is the first to extend the discussion on the effect of dimensions of uncertainty on the volatility of Bitcoin. Second, in contrast to previous studies, this study can be considered as the first to examine the role of climate change in predicting the volatility of bitcoin. This paper contributes to the literature on volatility forecasting of cryptocurrency in two ways. First, the authors discuss volatility forecasting of Bitcoin using the effects of three dimensions of uncertainty of USA (EPU, GPR and CPU). Second, based on the empirical results, the authors show that cryptocurrency can be a good hedging tool against EPU and GPR risk. But the cryptocurrency cannot be a hedging tool against CPU risk, especially with the high risks and climatic changes that threaten the environment.