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This paper contributes to mitigating the debate on whether carbon taxes have positive or negative effects on climate change by presenting an investigation on the relationship between carbon taxes and renewable energy consumption. Taking advantage of the panel CS-ARDL model, we distinctly inspect the dynamics of the CO2 tax–renewable energy consumption nexus in both short and long runs, based on a dataset of 39 economies covering 1991–2019. Our findings suggest that in the short run, a CO2 tax initially impedes renewable energy consumption but promotes it in the long run, with more significant effects observed in advanced economies. Additionally, the marginal effect of a CO2 tax on renewable energy consumption is not influenced by its initial level or change direction. In accordance with our findings, we provide several implications for decision-makers.

Abstract Rapidly increasing cryptocurrency prices have encouraged cryptocurrency miners to participate in cryptocurrency production, increasing network hashrates and electricity consumption. Growth in network hashrates has further crowded out small cryptocurrency investors owing to the heightened costs of mining hardware and electricity. These changes prompt cryptocurrency miners to become new investors, leading to cryptocurrency price increases. The potential bidirectional relationship between cryptocurrency price and electricity consumption remains unidentified. Hence, this research thus utilizes July 31 2015–July 12 2019 data from 13 cryptocurrencies to investigate the short- and long-run causal effects between cryptocurrency transaction and electricity consumption. Particularly, we consider structural breaks induced by external shocks through stationary analysis and comovement relationships. Over the examined time period, we found that the series of cryptocurrency transaction and electricity consumption gradually returns to mean convergence after undergoing daily shocks, with prices trending together with hashrates. Transaction fluctuations exert both a temporary effect and permanent influence on electricity consumption. Therefore, owing to the computational power deployed to wherever high profit is found, transactions are vital determinants of electricity consumption.

It is crucial for the advancement of political economics and innovation economics to examine the relationship between income inequality and green innovation (GI). Using the panel fixed effect model, this study investigates the influence of income inequality on GI across 97 countries from 1991 to 2018 and demonstrates a significant non-linear association between the two. The empirical data exhibit an inverted U-shape relationship, suggesting that there is an optimal degree of income inequality that optimizes GI output, and the inflection point of our overall sample is at a Gini coefficient of 0.366. Additionally, we choose a set of robustness tests to validate the results by substituting explained variables, adding omitted variables, and employing the difference and system generalized method of moments (GMM) estimations. Moreover, heterogeneity analysis reveals that the non-linear patterns vary among samples, with the U-shape relationship being more significant in countries with lower income, higher corruption, and weaker government effectiveness. Our findings provide government decision-makers with a crucial reference for maximizing the importance of income distribution in fostering GI and achieving sustainable development. First published online 24 August 2023

Epidemics and their resulting pandemics have become essential factors influencing economic development, financial stability, poverty, and ultimately a country’s innovation level, including green technology innovation. This research thus investigates epidemic events’ correlation to green innovation by operating with skewed panel data involving 134 countries from 1971 to 2018 and provides compelling proof that Epidemics have a detrimental effect on green innovation, not only for the current year but also for the next six years. We also show that the quality of institutions and financial development levels weaken epidemics’ detrimental effects on green innovation. Overall, the findings would draw particular attention from policymakers. First published online 17 April 2023

Much attention has been paid to the complex risk transmission between carbon and energy markets along with the increasing global financial market integration. This research thus uses the DCC-MVGARCH model and spillover index method to investigate volatility linkages between the European carbon emissions and energy markets. The results make it clear that there are indeed volatility correlations and bidirectional spillover effects for the above two markets. In particular, the volatility correlations and the spillover index dramatically change when global economic turbulence and political events occur. Apart from the strongest volatility correlation between coal and carbon markets, the volatility spillover effect from the renewable energy market to the carbon market is gradually increasing. The role of the carbon market is also changing: the net information recipient becomes a transmitter from phase II to phase IV. Therefore, our findings provide investors with flexible portfolio strategies by making full use of dynamic volatility linkages between the two markets. This research also gives a decision-making basis for market regulators to improve the carbon market's risk management mechanism. It means that regulators should pay close attention to potential market risk sources. Further policy recommendations are put forward in the conclusion section.

This research investigates and robustly verifies the impact of expansionary monetary policy actions on green innovation, conducted on a panel covering 133 countries from 1960 to 2018. Overall, we find that such actions have a significantly positive effect on green innovation performance, no matter in the static or dynamic model. A lower degree of central bank independence and poorer property rights protection in developing countries may hinder monetary policies’ effect to be transmitted to green innovation activities smoothly. Moreover, stringent environmental regulation contributes to magnifying the expansionary monetary policy’s positive effect on green innovation, but such a positive moderating effect should be supported by good national governance quality (including better control over corruption, higher efficiency of governments and a complete law system). Accordingly, several policy suggestions are provided.

This research examines the impact of economic policy uncertainty (EPU) on green innovation by using the panel fixed effects model from 2000 to 2017 for the samples of 31 provinces in China. The general conclusion is that there exists a positive link from EPU to green innovation, and that the impact of EPU varies significantly among different provinces that have diverse levels of marketization and trade openness. Specifically, provinces with higher marketization and degrees of trade openness have witnessed stronger positive effects from EPU on green innovation, whereas the correlation is rather weak in regions with low levels of those two factors. Our findings serve as a research reference for how governments may boost green innovation in the face of increasing EPU. First published online 25 November 2022

Since China is facing a complicated international situation and sustainable development requirement at the same time, this paper examines the effects of external uncertainty, international sanctions, on green innovations by adopting the system generalized method of moments (GMM) estimation for 30 provinces (autonomous region and municipalities) from 1997 to 2019. We employ green inventions as the dependent variable and 5 indicators of sanctions (including unilateral, plurilateral, multilateral, economic, and intensity) as the main explanatory variables alternately. For further robustness tests, we use substitution variable green utility models, adopt sub-samples in different regions, change the empirical methodology, and add omitted variables. We also examine the mechanism effects of three possible channels. The conclusion is that plurilateral and economic sanctions both present significant negative impacts on green innovations, whereas China was not affected by unilateral or multilateral sanctions during the sample period. GDP, interpersonal globalization, and environment are proved to be the possible channels through which sanctions affect green innovations. Our research findings should assist Chinese-listed companies suffering from sanctions to make better responses on their way to green innovations. First published online 24 November 2022

This research analyzes the impact of government effectiveness on innovation by using unbalanced panel data from the World Bank on 166 countries spanning 1996 to 2018. We analyze the impact of government efficiency on innovation through various panel fixed-effects models, while incorporating control variables such as GDP, education, and industrial structure into the analysis framework. The empirical results conclude that, in our selected countries, government efficiency has a significantly positive impact on innovation output and more importantly verify the positive impact from the improvement of bureaucracy quality on innovation. The evidence again shows the positive impacts of government efficiency on innovation output by addressing endogenous and robustness checking via the series of methods. Furthermore, the heterogeneity and mechanism of this relationship would be explored. Therefore, the research results provide an alternative method for national governments to promote innovation output by improving government effectiveness.

Using daily data from January 1, 2020 to March 31, 2021, this research explores COVID-19 shocks on the stock market of 15 representative oil exploration and production enterprises from 7 countries. We measure the COVID-19 epidemic from two levels, government response stringency index and number of confirmed cases, and employ stock prices and stock market returns to reflect the stock market. Our research results confirm that both the government response stringency index and the number of confirmed cases have a significantly negative influence on stock prices. We further find that the negative reaction of the stock market to the government response stringency index is greater than that from confirmed cases. Finally, we conclude that the government response stringency index have a significantly positive effect on stock market returns of oil exploration and production enterprises. Similar findings arise from analyzing specific enterprises. Overall, our conclusions provide some useful information for the decision-making of oil exploration and production enterprises’ investors and policy makers.

Tylophorine-based compounds and natural cardiotonic steroids (cardenolides and bufadienolides) are two classes of transmissible gastroenteritis coronavirus inhibitors, targeting viral RNA and host cell factors, respectively. We tested both types of compounds against two types of coronaviruses, to compare and contrast their antiviral properties, and with view to their further therapeutic development. Examples of both types of compounds potently inhibited the replication of both feline infectious peritonitis virus and human coronavirus OC43 with EC50 values of up to 8 and 16 nM, respectively. Strikingly, the tylophorine-based compounds tested inhibited viral yields of HCoV-OC43 to a much greater extent (7–8 log magnitudes of p.f.u./ml) than the cardiotonic steroids (about 2–3 log magnitudes of p.f.u./ml), as determined by end point assays. Based on these results, three tylophorine-based compounds were further examined for their anti-viral activities on two other human coronaviruses, HCoV-229E and SARS-CoV-2. These three tylophorine-based compounds inhibited HCoV-229E with EC50 values of up to 6.5 nM, inhibited viral yields of HCoV-229E by 6–7 log magnitudes of p.f.u./ml, and were also found to inhibit SARS-CoV-2 with EC50 values of up to 2.5–14 nM. In conclusion, tylophorine-based compounds are potent, broad-spectrum inhibitors of coronaviruses including SARS-CoV-2, and could be used for the treatment of COVID-19.

Research background: Cross-national sanctions are external factors with high uncertainty. The current situation of green innovation is not optimistic and the future of the uncertain development is worth exploring. Purpose of the article: This study intends to investigate how cross-national sanctions affect green innovations by examining data from 130 countries from 1990 to 2020. We use six different forms of sanctions (including those from the U.S., EU, UN, as well as unilateral, plurilateral, and economic sanctions) as the primary explanatory variables, with environmental management innovations serving as a proxy for green innovations. Methods: Difference-in-Difference (DID) technique; Logit model. Findings & value added: The number of environmental management inventions produced by a nation's inventors is negatively affected by plurilateral and economic sanctions, as well as by the US and the EU sanctions, while other types of sanctions have weaker effects. In terms of sub-indicators, plurilateral and the US sanctions have a serious negative impact on air pollution control and water pollution control inventions, while plurilateral, The EU and economic sanctions have a negative impact on waste management innovations. However, unilateral and the UN sanctions have no apparent effect on any type of environmental technology. Further logit model tests show that the US and economic sanctions lead to a decrease in green innovations. Moreover, the difference between Asian and African countries is that the negative effects of plurilateral, the U.S., the EU, and economic sanctions are more obvious in African countries, while cross-national sanctions have no significant impact on Asian countries. The results of our study should help countries under sanctions to respond more effectively to environmental progress. [ABSTRACT FROM AUTHOR]

It is crucial for the advancement of political economics and innovation economics to examine the relationship between income inequality and green innovation (GI). Using the panel fixed effect model, this study investigates the influence of income inequality on GI across 97 countries from 1991 to 2018 and demonstrates a significant non-linear association between the two. The empirical data exhibit an inverted U-shape relationship, suggesting that there is an optimal degree of income inequality that optimizes GI output, and the inflection point of our overall sample is at a Gini coefficient of 0.366. Additionally, we choose a set of robustness tests to validate the results by substituting explained variables, adding omitted variables, and employing the difference and system generalized method of moments (GMM) estimations. Moreover, heterogeneity analysis reveals that the non-linear patterns vary among samples, with the U-shape relationship being more significant in countries with lower income, higher corruption, and weaker government effectiveness. Our findings provide government decision-makers with a crucial reference for maximizing the importance of income distribution in fostering GI and achieving sustainable development. [ABSTRACT FROM AUTHOR]

Epidemics and their resulting pandemics have become essential factors influencing economic development, financial stability, poverty, and ultimately a country's innovation level, including green technology innovation. This research thus investigates epidemic events' correlation to green innovation by operating with skewed panel data involving 134 countries from 1971 to 2018 and provides compelling proof that Epidemics have a detrimental effect on green innovation, not only for the current year but also for the next six years. We also show that the quality of institutions and financial development levels weaken epidemics' detrimental effects on green innovation. Overall, the findings would draw particular attention from policymakers. [ABSTRACT FROM AUTHOR]

Research background: The twin pressures of economic downturn and climate change faced by countries around the world have become more pronounced over the past decade. A renewable energy transition is believed to play a central role in mitigating the economic-climate paradox. While the architectural and computational power of artificial intelligence is particularly well suited to address the challenges of massive data processing and demand forecasting during a renewable energy transition, there is very scant empirical assessment that takes a social science perspective and explores the effects of AI development on the energy transition. Purpose of the article: This paper aims to answer two key questions: One is, how does AI software development promote or inhibit the shift of energy consumption towards renewables? The other is, under what policy interventions does AI software development have a more positive effect on promoting renewable energy consumption? Methods: We employ a dataset of 62 economies covering the period 2011-2020 to analyze the impact of AI software development on the energy transition, where possible confounders, including political and economic characteristics and time-invariant elements, are controlled using fixed-effects estimation along with specified covariates. Findings & value added: AI software development can promote the energy transition towards renewables. There is suggestive evidence that the core mechanism linking such a positive relationship tends to lie in improving innovation performance in environmental monitoring rather than in green computing. Government support for R&D in renewable energy technologies is found to be significantly beneficial for harnessing the positive impact of AI software development on the energy transition. Compared to non-market-based environmental policies, market-based environmental policies have a more significant positive moderating effect on the relationship between AI software development and energy transition. [ABSTRACT FROM AUTHOR]