Wavelet analysis of impact of renewable energy consumption and technological innovation on CO2 emissions: evidence from Portugal

Decoding worldwide efficiency: Linking government spending, corruption, gender inequality, and renewable energy practices

Countries have become increasingly concerned about the impact of their activities and their alignment with sustainable development goals. Consequently, it is necessary to examine their performance efficiency in a unified manner, accounting for economic, environmental, and social variables. A country's performance efficiency is defined as the ratio of observed output to frontier output, given the country's productive resources. In this paper, we use the frontier technique of Data envelopment analysis (DEA) to estimate the frontier output. The objective of this study is to assess the performance efficiency of 111 worldwide countries and to examine whether gender inequality, the share of renewable energy consumption, the government control of corruption and government expenditure on education could explain differences in performance. We employed the non-parametric DEA model using three inputs (capital, labor, and primary energy consumption) and one output (GDP). We also considered an additional output (CO2 emissions); however, the results were identical to the one output model because CO2 emissions and primary energy consumption are highly correlated (0.96). We find that there is a negative impact of gender inequality, a positive impact of the share of renewable energy consumption and government control of corruption on performance efficiency. Our results also indicate that reducing CO2 emissions required transitioning towards renewable energy and using the energy efficiently. However, we did not find a positive relationship between government expenditures on education and country efficiency.

The road to decarbonization in Australia. A Morlet wavelet approach

The path toward sustainable development is closely related to the intensification of renewable energy sources and the continual innovation of technologies. To evaluate the role of renewable energy consumption and technological innovations on carbon emissions in Australia, this study uses the Morlet wavelet approach. This study identified temporal and frequency variations by applying wavelet correlation, continuous wavelet transforms, and partial and multiple wavelet coherence methods on data from 2000 to 2021. The wavelet correlation revealed that non-renewable energy, globalization, and economic growth are positively correlated with carbon emissions at all scales. In contrast, carbon emissions are negatively correlated with renewable energy and technological innovation at all scales. Meanwhile, the wavelet coherence analysis shows that non-renewable energy contributes to increased CO2 emissions from the short to long term, whereas renewable energy usage negatively affects CO2 emissions across all frequency scales. The study findings indicate that increasing the proportion of renewable energy usage in the total energy mix will curb CO2 emissions over the long run. Accordingly, the way to achieve sustainable development is shifting to a low-carbon economy centered on renewable energy sources, enhancing energy efficiency, and using carbon storage and capture technologies.

Role of natural resources, renewable energy sources, eco-innovation and carbon taxes in carbon neutrality: Evidence from G7 economies

Global warming has created problems for human life, and it has been increasing for a few years. All the developing and developed countries are establishing policies to attain zero carbon status. This study extends the ongoing debate on carbon emissions. It examines the effect of natural resources and RE (Biofuel and other renewable sources) on greenhouse gas (CO2 emission and PM2.5) emissions while using data over 22 years (1999-2021) from G7 countries. In addition, this study has investigated the effect of carbon taxes, financial development, and environmental policies on carbon neutrality. The cross-sectional-ARDL, the Common correlated effect means group (CCEMG), and the Augmented mean group (AMG) cutting-edge model have been employed. Quantile regression has been employed for robustness. The study results demonstrate that biofuel and other renewable energy (RE) sources, carbon taxes, environmental policy, and eco-innovation decrease greenhouse gas emissions (CO2 emissions). Meanwhile, financial development, and natural resource dependence positively impact carbon neutrality. The robustness result also verifies the findings from CS-ARDL, AMG, and CCEMG methods. The empirical findings are used to infer policy implications for G7 economies.

Time-varying causality relationships between trade openness, technological innovation, industrialization, financial development, and carbon emissions in Thailand

Over the last twenty years, there has been swift growth in industrialization and technological advancements, driving economic progress. Nevertheless, it is inevitable that these sectors will bring about environmental shifts. Thus far, endeavors have been undertaken to assess the influence of industrialization and technological advancements on environmental deterioration. Additionally, the extensive discussion surrounding the impact of financial development, trade openness, and technological innovation on the environment has not yielded conclusive empirical findings. Studies often operate under the assumption of symmetric relationships, potentially leading to biased results. Adding to the discussion on the drivers of carbon neutrality, the time-dependent effects of critical aspects such as financial development and technological innovation should inform meaningful policies for environmental management. This article explores the time-varying causal association between trade openness, industrialization, financial development, technological innovation, and CO2 emissions in Thailand using novel time-varying Granger causality tests. The time-varying causality outcomes demonstrate that the associations change significantly over time, in contrast to the results of Toda-Yamamoto causality. Overall, there exists a bidirectional relationship between industrialization, financial development, trade openness, technological innovation, and CO2 emissions over different time sequences. These outcomes have implications for both policy and research.

Assessing the spillover effects of various forms of energy on CO2 emissions - An empirical study based on dynamic spatial Durbin model

Previous studies ignored the geospatial dynamics spillover effects of energy consumption on CO2 emissions while assessing such impacts in developed and developing countries. Moreover, most studies wrongfully assess spillover effects in its aggregated format rather than decomposing by its components. This is important as not all energy sources share the same characteristics. We fill these gaps in the literature by investigating the spillover effects of various forms of energy, including fossil fuels, renewable energy, and nuclear power, on CO2 emissions in 135 developed and developing countries from 2000 to 2019. We used the Dynamic Spatial Durbin Model (DSDM) to better understand the results. A series of indicative tests confirmed using the DSDM model and including spatial interaction of CO2 emissions in the analysis. Our findings show evidence of indirect spillover effects of the various energy sources on CO2 emissions. Further considering the spillover effects of the energy sources of neighbouring countries, the paper finds that the driving increase in CO2 emissions mainly came from the energy consumption of the country itself and neighbouring countries' energy consumption. Nevertheless, the results indicate that the direct effects of energy consumption often exceed its indirect effects. The results also confirm that total and fossil energy consumption harms the environment, whereas adopting renewable and nuclear energy sources reduces CO2 emissions. Lastly, we find nuclear energy is the most environmentally sustainable energy source. The study concludes that the Dynamic Spatial Durbin Model is paramount in estimating the environmental impact of energy consumption in our sample. The practical policy implications drawn from this study could be used to promote increased collaboration to hasten the energy transition process and address global warming and climate change.

The impact of green innovation and renewable energy on CO2 emissions in G7 nations

This study aims to explore the effect of eco-innovation and renewable energy on carbon dioxide emissions (CDE) for G7 countries. Using regression models, the results reveal that eco-innovation and renewable energy lead to reducing CDE in the presence of governance variables. Additional analysis is conducted to examine whether Hofstede national culture dimensions moderate the nexus of "eco-innovation- carbon emission" and "renewable energy-carbon emission". The results show that individualism, long-term orientation, and indulgence dimensions moderate positively the eco-innovation-carbon emission relationship. Moreover, power distance and uncertainty avoidance dimensions moderate the relationship between renewable energy and CDE and help reduce carbon emissions. The outcomes of this study provide new insights and directives for policymakers and regulators. In fact, increased investment in eco-innovation and renewable energy will support the environmental agenda of G7 countries. National cultural dimensions should be taken into consideration to improve awareness of environmental quality. Moreover, the combination of governance indicators plays a key role in environmental sustainability.

The role of renewable energy and total factor productivity in reducing carbon emissions: A case of top-ranked nations in the renewable energy country attractiveness index

On the one hand, economies, particularly developing ones, need to grow. On the other hand, climate change is the most pressing issue globally, and nations should take the necessary measures. Such a complex task requires new theoretical and empirical models to capture this complexity and provide new insights. Our study uses a newly developed theoretical framework that involves renewable energy consumption (REC) and total factor productivity (TFP) alongside traditional factors of CO2 emissions. It provides policymakers with border information compared to traditional models, such as the Environmental Kuznets Curve (EKC), being limited to income and population. Advanced panel time series methods are also employed, addressing panel data issues while producing not only pooled but also country-specific results. 20 Renewable Energy Country Attractiveness Index (RECAI) nations are considered in this study. The results show that REC, TFP, and exports reduce CO2 emissions with elasticities of 0.3, 0.4, and 0.3, respectively. Oppositely, income and imports increase emissions with elasticities of 0.8 and 0.3. Additionally, we show that RECAI countries are commonly affected by global and regional factors. Moreover, we find that shocks can create permanent changes in the levels of the factors but only temporary changes in their growth rates. The main policy implication of the findings is that authorities should implement measures boosting TFP and REC. These factors are driven mainly by technological progress, innovation, and efficiency gains. Thus, they can simultaneously reduce emissions while promoting long-run green economic growth, which addresses the complexity mentioned above to some extent.

Do industrial solid waste recycling and technological innovation promote low-carbon development in China? New insights from NARDL approach

Recycling waste is crucial for consolidating resources and promoting sustainable development, serving a pivotal role in achieving the objectives of carbon peak and carbon neutrality. Nonetheless, most existing research has primarily focused on municipal solid waste (MSW) recycling, often neglecting the significant volume of industrial solid waste (ISW). This study aims to explore the asymmetric effects of industrial solid waste recycling and technological innovation on the low-carbon development. To this end, this study selects GDP and carbon intensity as indicators representing economic growth and environmental quality. A variable that can enhance GDP growth while reducing carbon intensity signifies its contribution to low-carbon development. By collecting data from China over the period of 1985-2020, non-linear autoregressive distributed lag (NARDL) models of GDP and carbon intensity are established to discover whether the low-carbon development can be achieved by enacting ISW recycling and technological innovation. The results show the asymmetric shocks of ISW recycling and technological innovation on economic growth and environmental quality. In the long run, both ISW recycling and technological innovation promote low-carbon development. In the short run, technological innovation proved to be detrimental to economic growth and environmental quality. This paper also highlights the inhibitory effect of the labor force on economic growth. The "pollution haven hypothesis" is supported by the finding that foreign direct investment reduces carbon intensity. Additionally, the Granger test revealed the direction of the variables' causality. Based on empirical findings, policymakers can protect the environment and create economic value simultaneously through waste recycling and technological innovation, thereby realizing low-carbon development.

A wavelet analysis of dynamic connectedness between geopolitical risk and renewable energy volatility during the COVID-19 pandemic and Ukraine-Russia conflicts

The study explores inter-relations between the geopolitical risk index and renewable energy volatility index at frequency dimensions from April 4, 2019, to June 13, 2022, using novel multivariate wavelet analysis approaches, such as partial wavelet coherency and partial wavelet gain. Our method allows us to study these interlinkages at various time frequencies. We also consider the influences of uncertain events like the COVID-19 pandemic and Ukraine-Russia conflicts on their interconnectedness. The multiple coherencies between the geopolitical risk index and the green energy sector suggest four cycles in the low-frequency range (50-130 days) from March 2020 to October 2021 and from February 2022 to June 2022. The partial coherency between the geopolitical risk index and renewable energy volatility index suggests connectedness between renewable energy dynamics and geopolitical risks during the COVID-19 duration and the Russia-Ukraine conflict. The partial wavelet coherency of the volatility of green bonds and geopolitical risks suggests that alterations in green bonds caused alterations in geopolitical risks, and the association is negative from February 2021 to April 2021. Both indicators are in-phase with geopolitical risks pushing from February 2020 to April 2020 and from October 2021 to the end of the sample. The partial coherence between clean energy and geopolitical risk suggests geopolitical risks pushing anti-phase connectedness from September 2020 to September 2022. Our findings help policymakers design the most effective policies to lessen the vulnerabilities of these indicators and reduce the spread of risk or uncertainty across them by having insightful knowledge about the primary antecedents of the contagions among these indicators.

Determining the green technology innovation accelator and natural resources towards decarbonization for the EU countries: evidence from MMQR

Gearing up for green technology innovation (GTI) and natural resources has become even more important in the transition to a zero-emission life, a green economy, and sustainable development goals. This attempt has become a situation that needs to be overpowered much sooner by the European countries, which have encountered challenges in many ways, especially regarding natural resources, energy supply, and the climate crisis. In this vein, the current study follows the novel, robust Method of Moment Quantile-Regression (MM-QR), which successfully yields heterogeneous information structure across quantiles, to examine the determinants of GTI for 15 EU countries over the period of 2003-2018. MM-QR estimation results indicate that the determinants of green technology innovation are heterogeneous across the EU countries. While green growth (GG) has an adverse impact on GTI in middle- and high-GTI countries, the effect of ecological footprint on GTI is positive for countries in the highest-GTI countries. The positive effects of financial development (FD) on GTI are revealed for all countries. Remarkably, environmental taxes have an adverse and positive influence on GTI in the lowest and highest quantile countries, respectively. Finally, renewable energy and greenfield FDI have no effect on GTI. Governments can promote GTI by providing financial resources, in the most immaculate way, to firms that engage in green technology projects, as well as by encouraging these through environmental taxes.

The role of environmental technologies, institutional quality, and globalization on environmental sustainability in European Union countries: new evidence from advanced panel data estimations

Factors such as investments in environmentally clean technologies, globalization, and institutional quality significantly increase environmental quality. The study aims to provide light on how environmental technologies, institutional quality, globalization, and economic growth affect a sustainable environment. In addition, this study evaluates the European Union's carbon zero target by 2050 and the results of achieving carbon neutrality by 2030, which was put on the agenda at the UN Climate Change Conference of the Parties (COP-26). For this purpose, ten countries (Germany, Austria, Denmark, Finland, France, Netherlands, Spain, Italy, Sweden, and Switzerland) that invest in the highest environmental technology in the European Union were selected in the study. The data range of the study is from 1990 to 2019. Also, the validity of the load capacity curve (LCC) hypothesis was investigated in these countries. The CCEMG and DCCE estimators were used to estimate long-run coefficients. When the panel was assessed as a whole, the LCC hypothesis was determined to be valid by both estimators. According to country-based results, it has been determined that the LCC hypothesis is valid only for Spain. The study also includes the following observations. (i) Environmental technologies increase LCF for Austria, improving environmental quality. (ii) Globalization reduces LCF for Austria. (iii) Institutional quality variable decreases LCF for Austria and increases LCF for Germany and France. These findings suggest that to attain a sustainable environment in the future, policymakers should raise research and development budgets for environmental technology, enhance the standards of institutions, and take globalization into account.

Long run renewable energy productivity, carbon capture patents and air quality in Taiwan

This study investigates the connection between nonrenewable energy productivity, renewable energy productivity, and air quality degradation in Taiwan from 2002 to 2019. We specifically emphasize the novel contribution of analyzing the productivity of renewable energy consumption. Robust estimation models, namely Nonlinear Autoregressive Distributed Lag (NARDL) and Robust Standard Estimation, are employed for comprehensive analyses. Our findings reveal a strong correlation between nonrenewable energy productivity and increased air pollutants, highlighting the significant impact of fossil fuels on air quality deterioration. Although renewable energy productivity demonstrates a negative association with air degradation, its effect is not statistically significant. This can be attributed to Taiwan's continued reliance on non-renewable energy sources within the overall energy mix. Hence, reducing dependence on fossil fuels is crucial for improving air quality. Importantly, the identified relationships have long-term implications, underscoring the necessity of persistent policy measures that promote renewable energy transition and emissions reduction over time. Our research emphasizes the urgency of addressing fossil fuel dependency to mitigate air pollution and highlights the potential benefits of enhancing renewable energy efficiency to achieve cleaner and healthier environments.

Pollution and renewable energy consumption in the V4 countries

Considering quite high levels of pollution in V4 countries, the aim of this paper is to assess if renewable energy consumption reduces CO2 emissions in the period 1996-2022. The time series approach based on autoregressive distributed lag models (ARDL models) for each state in the sample indicated that renewable energy consumption reduced CO2 emissions, while GDP enhanced pollution. Foreign direct investment had no effect on environmental quality. Political stability had no impact on pollution in Poland and Hungary, while it reduced CO2 emissions in the Czech Republic and enhanced them in Slovakia. The results are subject to policy proposals.

Can technological progress, renewable and nuclear energy consumption be the remedy for global climate crises? An examination of leading OECD countries

Energy is the most critical input for production and consumption. The inputs of energy cause irreversible damage to the environment. The studies carried out to reduce the environmental impact of the methods used in energy production are extremely valuable. This study aims to reveal the effects of technological development, nuclear energy consumption, and renewable energy use on environmental degradation. The patent numbers, technological development, GDP, renewable energy, and nuclear energy consumption data of 16 OECD countries covering the years 1996-2019 were used in the empirical analysis. The findings of panel FMOLS and DOLS methods reveal that technological progress, nuclear, and renewable energy consumption significantly reduce CO2 emissions. In line with these findings, critical policy implications have been suggested.

Inflation targeting: A time-frequency causal investigation

The enduring discourse regarding the effectiveness of interest rate policy in mitigating inflation within developing economies is characterized by the interplay of structural and supply-side determinants. Moreover, extant academic literature fails to resolve the direction of causality between inflation and interest rates. Nevertheless, the prevalent adoption of interest rate-based monetary policies in numerous developing economies raises a fundamental inquiry: What motivates central banks in these nations to consistently espouse this strategy? To address this inquiry, our study leverages wavelet transformation to dissect interest rate and inflation data across a spectrum of frequency scales. This innovative methodology paves the way for a meticulous exploration of the intricate causal interplay between these pivotal macroeconomic variables for twenty-two developing economies using monthly data from 1992 to 2022. Traditional literature on causality tends to focus on short- and long-run timescales, yet our study posits that numerous uncharted time and frequency scales exist between these extremes. These intermediate scales may wield substantial influence over the causal relationship and its direction. Our research thus extends the boundaries of existing causality literature and presents fresh insights into the complexities of monetary policy in developing economies. Traditional wisdom suggests that central banks should raise interest rates to combat inflation. However, our study uncovers a contrasting reality in developing economies. It demonstrates a positive causal link between the policy rate and inflation, where an increase in the central bank's interest rates leads to an upsurge in price levels. Paradoxically, in response to escalating prices, the central bank continues to heighten the policy rate, thereby perpetuating this cyclical pattern. Given this observed positive causal relationship in developing economies, central banks must explore structural and supply-side factors to break this cycle and regain control over inflation.

Do the effects of aggregate and disaggregate energy consumption on different environmental quality indicators change in the transition to sustainable development? Evidence from wavelet coherence analysis

In the 2030 Agenda for Sustainable Development, adopted by the United Nations (UN) member states in 2015, half of the target period has been exceeded. However, China, whose energy consumption relies heavily on fossil resources, remains at the top of the list of global polluters. Therefore, investigating the environmental impacts of energy types is essential to China's path towards Sustainable Development Goals (SDG)-7 and SDG-13. Based on this motivation, the paper offers new insights into the energy-environment literature for China with wavelet coherence analysis (WCA). This approach can investigate the relationship between variables in a periodic manner based on the frequency behavior of the models. The paper separately analyzes the effects of primary energy consumption (PEC), fossil energy consumption (FEC), renewable energy consumption (REC), nuclear energy consumption (NEC), GDP, and population (POP) on three different environmental indicators in China. Using two environmental pollution indicators (carbon emission (CO2) and ecological footprint (EF)) and one environmental quality indicator (load capacity factor (LCF)), the paper allows for comparison and robustness checks on the environmental impacts of energy indicators. Empirical findings reveal the following: (i) Except for REC and POP in the CO2 model, the variables in all three models largely move together during the period under observation; (ii) variables other than POP have consistent coefficient signs; (iii) PEC, FEC, NEC, and GDP increase CO2 and EF while decreasing LCF; (iv) the effect of NEC on LCF is more obvious until 2000; (v) unlike the others, REC affects CO2 and EF negatively and LCF positively; (vi) there is bidirectional causality between PEC and environmental indicators but not for REC; (vii) the causality relations of other variables with environmental indicators differ in terms of model, time, and direction of causality. In light of the findings, the paper highlights that only the REC improves environmental quality in China. Other energy indicators contribute to environmental degradation. China, whose ecological deficit has increased dramatically in recent years, urgently needs to reduce its dependence on fossil energy sources by accelerating investments in REC. Governments should also review nuclear energy policies, which are expected to help achieve carbon neutrality.

Clarifying the relationship between green investment, technological innovation, financial openness, and renewable energy consumption in MINT

The importance of Renewable energy has been well documented in the literature, especially in the nexus of renewable energy-led environmental sustainability. The purpose of the study is to gauge the effects of green investment (GI), technological innovation (TI), and financial Openness (FO) on Renewable Energy Consumption (REC) in MINT for the period 1996-2019. Several econometric tools have been considered in documenting the target nexus, including the panel unit root test following CADF and CIPS, Error correction Cointegration test, CS-ARDL, nonlinear ARDL, and directional causality test by employing the D-H Causality test. The panel unit root test revealed that all the variables become stationary after the first difference. The long-run association in the target model is unveiled with the panel cointegration test. A positive and statistically significant connection regarding FO, TI, and GI coefficients on REC has been exposed. It suggests that the progress in RE development and inclusion in economic activities could be amplified through FO, TI, and GI. Inferring the results of asymmetric valuation, the test statistics of a standard Wald test document asymmetric association in the long run and short run. Furthermore, the coefficients of positive and negative innovation in explanatory variables, i.e., TI, GI, & FO, divulge a positive statistically significant tie to REC, which is valid in long-run and short-run assessment.

The relationship between energy and non-energy factors and CO2 emissions in New Zealand

The use of renewable energy as a fuel source and expansion of forest areas are the best ways for reducing CO2 emissions. This study aims to examine the effects of forest plantation area, renewable energies, real gross domestic product (GDP), and technological innovation on CO2 emissions in 9 regions of New Zealand between 2006 and 2019. For this purpose, it employs a pooled mean group methodology. Investigating the regional impacts of various variables, especially the forest area, on CO2 emissions is the main contribution of this study. The results suggest that planted forest areas can reduce CO2 emissions in the long run, but its impact in the short run is not significant. Non-renewable energy consumption is the major contributor to CO2 emissions in both the short and long run. While technological innovation and renewable energy consumption appear effective in reducing carbon emissions in the short term, they still contribute to increased CO2 emissions in the long term. At the regional level, we found that the forest plantation areas in Manawatu-Whanganui and Gisborne are important regions for reducing CO2 emissions. By taking account of these results, New Zealand should take swift action to properly manage and increase the current level of forest areas and if applicable expand them. It also needs to improve the current level of use of renewable energy to achieve its abatement goals.

Examining the trade-offs in clean energy provision: Focusing on the relationship between technology transfer, renewable energy, industrial growth, and carbon footprint reduction

Sustainable energy mitigates climate change by reducing reliance on coal and oil for power generation, curbing global warming. It addresses environmental concerns and yields economic benefits-reduced fossil fuel dependence, financial inclusion, productive employment, and economic development. This research examines the impact of regional economic integration on environmental sustainability in 39 high-income European and Central Asian (ECA) nations from 2017 to 2021. Specifically, the study analyzes the influence of green energy demand, technological transfers, and trade openness on carbon emissions. The study employed various estimators, namely, a two-step Generalized Method of Moments (GMM) estimation, quantile regression, and the cointegration panel approach. These estimators were utilized to capture different aspects and dynamics of the research variables. The study finds that regional green programs and trade agreements effectively reduce carbon emissions, while technological advances and industrial output tend to raise them. Granger causality analyses reveal that emissions-led regional development, technical innovation, and trade openness are interconnected factors, and the deployment of renewable energy contributes to carbon emissions. The inter-temporal analysis suggests that regional economic integration factors will likely impact carbon emissions in the following decade. These findings support neoclassical growth theory, new institutional economics, and ecological modernization theory. Developing renewable energy sources in the region can minimize energy price fluctuations, strengthen energy security, and align with the carbon neutrality agenda. This research emphasizes the need for sustainable energy strategies and regional cooperation to foster a greener and more sustainable future.

How do logistics and financial ındicators contribute to carbon emissions in Turkiye?

Increasing carbon emissions (CO2) due to factors such as energy consumption (enco), industrialization, increase in world population, and decrease in green areas with the industrial revolution is one of the main causes of both climate change and global warming. In this context, due to the increasing commercial activities in Turkiye, the rapid growth of energy consumption and greenhouse gas (GHG) emissions in the logistics sector alert the government. However, there is a lack of standard measures for evaluating GHG emissions generated from freight transport operations. To improve this situation, Turkiye's policymakers need to evaluate GHG emissions for energy saving and pollution reduction. This background leads us to examine the GHG emission trajectories and features of Turkiye's freight transport patterns in the last three decades. In this context, it is aimed to determine the impacts of financial development (findev), GDP per capita, energy consumption, and amount of freight carried by rail and road on CO2 emissions within the framework of 1990-2021 time-series data for Turkiye. By doing so, the ARDL bound testing cointegration test is employed and observes that independent variables have similar and different effects on CO2 emissions. Energy consumption, findev, and per capita income variables have a positive effect on CO2 emissions in Turkiye. According to these results, it is seen that the environmental Kuznets curve (EKC) is valid in Turkiye. However, the effect of rail and road freight transport (FT) on CO2 emissions is negative. The unexpected finding is related to road FT. The amount of freight transported by road has a decreasing effect on CO2 emissions in Turkiye. This paradoxical situation in Turkiye may be due to the developments in the transportation infrastructure, which has enabled the convergence of space and time in recent years, young and modern vehicle fleets, and the efficiency provided through logistics companies. The findings will assist in formulating specific and effective policies for Turkiye's transport sector.

Ecological footprint-technological innovations nexus: new empirical evidence from panel data estimations

The aim of this approach is to establish a relation between ecological footprint and technological innovations by using panel data techniques and taking Union for the Mediterranean member countries as the case. On the one hand, the Mediterranean region is an alarming region when it comes to environmental degradation; on the other hand, existing intergovernmental organizations such as the Union for the Mediterranean may provide a sound ground to take common action among all members against such a degradation. The approach takes eleven member countries from the Union for the Mediterranean (Bosnia and Herzegovina, France, Greece, Israel, Italy, Jordan, Morocco, Portugal, Spain, Tunisia, Türkiye) and observes them for the time frame 1992-2020. By using panel unit root tests and cross-sectional dependency tests, the data is being analyzed for further evaluation using mean group, common correlated effect mean group, and augmented mean group analysis. Regarding the observed variables, we could not identify a common trend for these eleven countries. For the sample of eleven countries, the impact of technological innovations was not significant, but country-level augmented mean group analysis results indicated that for Portugal, this impact was significant and positive.

How natural resources depletion, technological innovation, and globalization impact the environmental degradation in East and South Asian regions

Rapid economic expansion has caused resource depletion, globalization issues, and environmental deterioration. Globalization has highlighted East and South Asian mineral richness. This article investigates the effects of technological innovation (TI), natural resources, globalization, and renewable energy consumption (REC) on environmental deterioration in the East and South Asian region from 1990 to 2021. The cross-sectional autoregressive distributed lag (CS-ARDL) estimator is used to estimate short- and long-run slope parameters and dependencies across countries. The results demonstrate that many natural resources significantly enhance environmental degradation, while globalization, TI, and REC reduce emission levels in East and South Asian economies and that economic growth significantly degrades ecological quality. This research suggests that governments in the East and South Asian region develop suitable policies that promote the efficient use of natural resources via technological advancements. Furthermore, future policies regarding energy consumption, globalization, and economic development should be aligned with the aims of sustainable environmental development.

Revisiting the environmental Kuznetz curve and pollution haven hypothesis in N-11 economies: Fresh evidence from panel quantile regression

Human activities in recent decades have severely affected environmental quality, and CO₂ emissions have irreparable consequences on human health and the survival of the earth. Moreover, achieving sustainable development goals requires the expansion of environmental literature to accelerate the performing of critical actions. With this in mind, this study evaluates the impact of foreign direct investment, economic complexity, and the utilization of renewable energy on CO₂ emission in N-11 countries from 1995 to 2019 by Panel Quantile Regression. As a novelty, the interaction between economic complexity and foreign direct investment is considered to get a better comprehension. Given the results, Environmental Kuznetz Curve is validated in N-11 countries through economic complexity. Notably, the impact of economic complexity is more substantial and robust in the incipient stages of industrialization. Furthermore, foreign direct investment is a destructive factor for environmental quality, and Pollution Haven Hypothesis is not rejected. Interestingly, the interaction of economic complexity and foreign direct investment mitigates the trend of CO₂ emissions. Eventually, the utilization of renewable energy reduces CO₂ emissions. Thereby, applying more strict environmental regulations and standards, developing green energy infrastructure and technologies, improving institutional quality, and supporting knowledge-based and technology-intensive exports are the main policy recommendations of this study.

Dynamic connectedness between green energy and carbon risk during Russia-Ukraine conflict: new evidence from a wavelet analysis

The study explores the inter-relations between green and renewable energy and carbon risk. Key market participants with varying time horizons include traders, authorities, and other financial entities. This research examines these relationships and frequency dimensions from February 7, 2017, to June 13, 2022, using novel multivariate wavelet analysis approaches, such as partial wavelet coherency and partial wavelet gain. The multiple coherencies between green bond, clean energy, and carbon emission futures imply that these regions were situated at low frequencies (relating to approximately 124-day frequency) and run from the beginning of 2017 to the beginning of 2018, in the first half of 2020, and from the beginning of 2022 to the end of the sample. The relationship between the solar energy index, envitec biogas, biofuels, geothermal energy, and carbon emission futures, is significant in the low-frequency band starting from early 2020 to middle 2022 and in the high-frequency band starting from early 2022 to middle 2022. Our research demonstrates the partial coherencies between these indicators during the Russia-Ukraine conflict. The partial coherency between the S&P green bond index and carbon risk suggests that carbon risk pushes anti-phase connectedness. The partial phase difference S&P global clean energy index and carbon emission futures (from early April 2022 to the end of April 2022) recommend that indicators are in-phase with carbon risk pushing and the phase (from early May 2022 to middle June 2022), suggesting that carbon emission futures are in-phase with S&P global clean energy index pushing.

The effects of trade, renewable energy, and financial development on consumption-based carbon emissions (comparative policy analysis for the G20 and European Union countries)

Recently, there has been a lot of focus on global trade and consumption-based carbon (CCO2) emissions. More research, however, has examined how financial development (FD) and international trade in renewable energy affect CO2 emissions. Furthermore, there are no distinct trends in the research about how globalization affects environmental quality. Our research analyzes and empirically investigates the relationship between CCO2 emissions and renewable energy, FD, and trade. A large panel of data from 41 G20 and European Union (EU) countries is assembled for empirical analysis from 1990 to 2019. The practical outcomes of panel quantile regression and feasible generalized least square (FGLS) approaches display that renewable energy and FD positively relate to CCO2 emissions; furthermore, trade to GDP hurts CCO2 emissions; market classification has been taken as a control variable which shows that the developed countries released more carbon than non-developed countries. These results suggest that the financial sector focuses more on supporting companies that use ecologically friendly techniques and pushing them to use other energy well-organized technologies in their production processes. As a result, CCO2 emissions will be reduced, preventing environmental damage at the non-renewable energy plant.

Sustainable environment quality: moderating role of renewable energy consumption in service sector for selected HDR listed countries

Considering environmental deterioration, an emerging global problem, this study is aimed at determining the impact of the service sector economic activity on environmental quality from the environmental Kuznets curve (EKC) perspective and finding ways to reduce the carbon impact of service sector within the EKC relationship. This study proposes that renewable energy intensity in the economy plays an important role in reducing carbon print of service sector. This study is based on secondary data from 1995 to 2021 for different development-wise categorized country groups leading to 115 countries, according to the Human Development Report (HDR) on the Human Development Index (HDI). Estimated results using panel feasible generalized least squares (FGLS) have confirmed inverted U-shaped for very high HDI and medium HDI and U-shaped EKC for low HDI countries. This study is instrumental in confirming the moderating role of renewable energy in the service sector EKC. Policymakers can plan a gradual reduction of carbon footprint in the service sector by transitioning toward renewable energy.

Environmental sustainability in Vietnam: evaluating the criticality of economic globalisation, renewable energy, and natural resources

Vietnam's goal of achieving a certain level of decarbonisation by 2030 is difficult despite its awareness of the threat posed by climate change. However, the country is endowed with natural resources and the increasing dependence on the global economy coupled with greater investment in alternative energy sources are some of the factors responsible for economic expansion in recent years. Hence, the question arises "what are the environmental impacts of economic globalisation, economic growth, natural resources, and renewable energy in Vietnam?", which constitutes a major policy problem. In this study, a time series dataset stretching from 1984 to 2019 is employed to scrutinise the impact of economic globalisation, economic growth, natural resources, and renewable energy on Vietnam's CO₂ emissions. This goal is achieved by employing the ARDL bounds testing procedure, dynamic ARDL, and spectral Granger-causality test. Moreover, the outcomes from the dynamic ARDL showed that economic globalisation and economic growth lead to environmental deterioration, whereas it is mitigated by renewable energy. Lastly, the outcomes from the spectral Granger-causality test indicate that a feedback causality association exists between CO₂ emissions and the regressors, namely economic globalisation, renewable energy, and economic growth, while no causality connection exists between CO₂ emissions and natural resources. Hence, we suggest that actions for reducing emissions should involve the implementation of energy-efficient techniques and renewable technologies within the energy value chain.

Enhancing environmental quality in Portugal: can CO2 intensity of GDP and renewable energy consumption be the solution?

Despite Portugal's effort to reduce carbon emissions, the country still emits about 1.6% of the European Union's CO₂ emissions. Meanwhile, limited empirical studies have been undertaken in the context of Portugal. As a result, this study examines the asymmetric and long-term impact of CO₂ intensity of GDP, energy consumption, renewable energy and economic growth on CO₂ emissions in Portugal from 1990 to 2019. The nonlinear autoregressive distributed lag (NARDL) is employed to uncover the asymmetric connection. The findings show that there is nonlinear cointegration among the variables. The long-run estimation reveals a positive change in energy consumption positively affects CO₂ emissions, while a negative shock in energy consumption has a neutral effect on CO₂ emissions. Furthermore, positive shocks of economic growth and CO₂ intensity of GDP enhance environmental deterioration by increasing CO₂ emissions. Meanwhile, the negative shock of these regressors positively impacts CO₂ emissions. In addition, positive shocks in renewable energy enhance environmental quality, while negative shocks in renewable energy increase environmental deterioration in Portugal. Policymakers should focus on reducing per-unit energy usage and CO₂ efficiency gains, which would necessitate a significant reduction in CO₂ intensity and energy density of GDP.

China's energy policy and sustainable energy transition for sustainable development: green investment in renewable technological paradigm

It is generally accepted that China is a significant cause of global warming and other climate change consequences. This paper applies panel cointegration tests and autoregressive distributed lag (ARDL) techniques to investigate the interactions among energy policy, technological innovation, economic development, trade openness, and sustainable development using panel data from China from 1990 to 2020. Results explain that renewable energy policy and technology innovation are negatively associated with sustainable development. However, research shows that energy use significantly increases both short-term and long-term environmental damage. The findings show that economic growth has a lasting impact on the environment by distorting it. The findings recommend that politicians and government officials hold the key to attaining a green and clean environment by focusing on developing the proper energy policy mix, urban planning, and pollution prevention without compromising economic growth.

Insights from BRICS-T economies on the impact of human capital and renewable electricity consumption on environmental quality

This paper evaluates the impact of electricity consumption from renewable and nonrenewable sources on the load capacity factor for BRICS-T nations using data from 1990 to 2018. The paper used linear and nonlinear autoregressive distributed lag (ARDL) approaches to explore these associations. The results of the Westerlund co-integration show long-run co-integration between load capacity factor and the independent variables. The results show that renewable electricity energy and human capital contribute to the sustainability of the environment, while electricity consumption, economic growth, and industrialization impede environmental sustainability. Similarly, the nonlinear effect of renewable electricity energy on LCF shows interesting findings. The positive (negative) shift in renewable electricity energy increases ecological sustainability in the BRICS-T nations. Furthermore, the Dumitrescu Hurlin panel causality gives credence to both linear and nonlinear ARDL results. The study suggests policy recommendations based on these results.

Effect of green bonds, oil prices, and COVID-19 on industrial CO2 emissions in the USA: Evidence from novel wavelet local multiple correlation approach

This study explores the effect of green bonds, oil prices, and the coronavirus disease 2019 (COVID-19) pandemic on industrial carbon dioxide (CO2) emissions. In this context, this study examines the United States of America (USA), which is the biggest economy in the world, uses weekly data between March 6, 2020 and September 30, 2022, and applies a novel wavelet local multiple correlation (WLMC) approach under time-varying and frequency-varying perspective. The novel empirical findings shows that (i) there is a strong negative (positive) co-movement between industrial CO2 emissions and green bonds in the short-run (long-run); (ii) there is a strong positive (negative) co-movement between industrial CO2 emissions and oil price in the medium-run (long-run); (iii) there is a strong negative (positive) co-movement between industrial CO2 emissions and the COVID-19 pandemic in the medium-run (long-run); (iv) the oil price is the dominant factor, whereas there are changing effect of the variables on each other at different times and frequencies; and (vi) overall, there are long-run asymmetric and dynamic correlations between industrial CO2 emissions and variables. Hence, the empirical results highlight the asymmetric, time-varying, and frequency-varying effects of green bonds, oil prices, and the COVID-19 pandemic on industrial CO2 emissions by presenting fresh and novel evidence. Moreover, the study proposes policy implications for the USA government.

Interrelationship among environmental policy stringency, financial globalization in OECD countries, and CO2 emission with the role of technological innovation and financial development

The study examines the nexus between financial globalization (FG), environmental policy stringency (EPS), financial development (FD), and technological innovation (INV) on CO2 emission with moderating effect of technological innovation on financial development and environmental degradation in 36 OECD countries with an updated dataset from the period of 1990 to 2020 using PMG (Pooled mean group) panel ARDL method. The results of stationarity tests; (Levin, Lin, and Chu test; ADF Fisher test) demonstrate that selected variables are stationary at level I(0) and first difference I(I); this confirms that PMG estimator can be employed. Cointegration tests indicate that cointegration exist among the variables. The empirical findings of the PMG estimator indicate that financial globalization and CO2 are negatively associated with each other. While financial development, environmental policy stringency, and technological innovation have positive impact on environmental degradation in OECD countries. Furthermore, technological innovation strengthens the association between financial development (FD) and environmental degradation (CO2 emission). In order to accelerate economic growth, the study recommends that policymakers should implement environmental policies to achieve low-carbon mechanisms, such as green infrastructure and renewable energy systems, which reduce energy consumption and greenhouse gas emissions. Therefore, it is crucial that the selected OECD countries should develop programs that increase awareness of the risks of carbon emissions.

Evaluating the role of renewable energy and technology innovations in lowering CO2 emission: a wavelet coherence approach

Environmental sustainability is one of the most critical issues that require efficient environmental and economic policies in modern times. Advancements in renewables and green technologies contribute significantly to sustained long-term development without affecting environmental quality. Several studies focus on the association of carbon dioxide emissions (CO₂e) with economic variables. However, they ignored the impact of technological innovations and renewable energy consumption on CO₂e in developed countries. Therefore, this study examines the relationship between CO₂e, energy consumption, gross domestic product (GDP), renewable energy consumption, and technology innovations in G-7 countries by employing cross-sectionally augmented autoregressive distributed (CS-ARDL) lag and wavelet coherence techniques during 1990-2020. The results depict that GDP and renewable energy consumption are inversely related to CO₂e. A 1% increase in CO₂e will decrease GDP and renewable energy consumption by 0.459 and 0.172% in the long run and by 0.471 and 0.183% in the short run in G7 countries. Technology innovations negatively impact CO₂e in the short run while positively influencing it in the long run. Considering the advancements in green technologies in different energy-dependent and manufacturing sectors is crucial for a sustainable environment in the long run. Such initiatives ensure the effective use of energy sources by limiting CO₂e in the atmosphere. Moreover, the dynamic common correlated effects mean group model confirms the reliability and effectiveness of the CS-ARDL. The wavelet coherence approach revealed a causality relation between CO2e and technology innovation in Italy, Japan, the UK, and the USA during the study period.

Analyzing the co-movement between CO2 emissions and disaggregated nonrenewable and renewable energy consumption in BRICS: evidence through the lens of wavelet coherence

This study investigates the time-frequency nexus of carbon dioxide (CO₂) emissions with economic growth, nonrenewable (i.e., coal, natural gas, and oil), and renewable (i.e., hydro and geothermal) energy consumption. In this context, BRICS countries (namely, Brazil, Russian Federation, India, China, and South Africa), which are leading emerging countries, are included, and quarterly data from 1990/Q1 to 2019/Q4 is used. The study employs the wavelet coherence (WC) approach to explore the co-movement between the variables at different frequencies. The empirical results show that (i) there is a strong and positive co-movement between CO₂ emission and economic growth; however, it is weak for Russia and South Africa in the medium and long-term; (ii) coal energy consumption is strongly and positively co-moved with CO₂ emission for all BRICS countries; (iii) natural gas energy consumption is strongly and positively co-moved with CO₂ emissions in Brazil, India, and China; however, it is weakly and positively co-moved in Russia and South Africa; (iv) oil energy consumption is strongly and positively co-moved with CO₂ emissions in Brazil, India, and China; however, it changes a bit for Russia and South Africa; (v) hydro energy consumption is weakly and positively co-moved with CO₂ emissions in general, whereas country-based results vary; (vi) geothermal energy consumption is also similar to hydro energy consumption. Thus, the WC results highlight the strong co-movement of economic growth and nonrenewable energy consumption with CO₂ emissions, whereas renewable energy consumption has a relatively lower co-movement. Based on the results, policy implications are also discussed for BRICS countries.

Is Moderating effect of Uncertain Economic Policies helpful for a Sustainable Environment in Emerging Economies?

The basic priority to neutralize carbon emissions (CE) is to achieve the sustainable development goal of climate action. In this regard, the role of renewable energy (RE) is widely debated. Transition to RE and environment-related innovation in technologies (ERIT) is a need of the hour. However, the challenge of uncertain economic policies in the transition process is interesting to study. Therefore, the present study intends to add a value to the literature by re-examining the interactive effect of economic policy uncertainty (EPU) in RE and ERIT and the transition process towards carbon neutrality. The second-generation econometric methodology is applied to empirically test the proposed interactive linkage of EPU, RE, ERIT, and CE. Findings reported the negative role of EPU in adopting RE and ERIT in seven emerging economies: Brazil, China, India, Indonesia, Mexico, Russia, and Turkey. However, the ERIT and RE are found to be supportive of neutralizing the CE. Emerging seven (E7) countries are suggested to be consistent in their economic policies to nurture the transition process toward a sustainable environment.

Asymmetric effect of political stability on production-based CO2 emissions in the UK: long-run evidence from nonlinear ARDL and frequency domain causality

The study deals with the effect of political stability on environmental degradation in the long run for the United Kingdom (UK). For this aim, the political stability effect on production-based carbon dioxide (CO₂) emissions is examined by considering trade openness, renewable energy, and economic growth and using quarterly data between 1995/Q1 and 2018/Q4. Nonlinear autoregressive distributed lag (NARDL), which allows the researcher to measure the asymmetric impact of explanatory indicators positively or negatively, is performed as the empirical approach. Also, Breitung & Candelon (BC) frequency domain causality test is applied to measure the causality effect of explanatory variables on CO₂ emissions. The results reveal that (i) political stability has a statistically significant effect on production-based CO₂ emissions and positive shocks have a higher power than negative shocks; (ii) economic growth has an increasing effect, whereas renewable energy has a decreasing effect on production-based CO₂ emissions; and (iii) there is frequency domain causality from political risk, economic growth, renewable energy consumption, and trade openness to production-based CO₂ emissions. Hence, empirical results highlight the asymmetric effect of political stability on environmental degradation in the long run for UK. Thus, UK policymakers should consider political stability in policy development and implementation process for limiting CO₂ emissions in the long run.

Endorsing sustainable development in BRICS: The role of technological innovation, renewable energy consumption, and natural resources in limiting carbon emission

This research aimed to examine the complex interaction between technological innovation, renewable energy consumption, natural resources, and carbon dioxide (CO₂) emissions of BRICS (i.e., Brazil, Russia, India, China, and South Africa) countries from 1990 to 2019, to accomplish the Paris Climate Conference (COP-21) objective of reducing CO₂ emissions to promote environmental sustainability. The long-run empirical estimations derived from the CS-ARDL technique, which considered other estimation issues like cross-sectional dependency and slope heterogeneity, indicated that technological innovation, renewable energy consumption, and natural resources increase environmental sustainability by limiting CO₂ emissions, in the short-run and long-run. The technological innovation-related activities have a CO₂ mitigating effect as shown by the negative coefficients which ranges between -0.05 and -0.14. This shows that they increase environmental sustainability and aid in achieving Sustainable Development Goals (SDGs) 13. Similarly, renewable energy and natural resources decrease CO₂ emissions as shown by the coefficient of renewable energy (-0.31 to -0.81) and natural resources (-0.01 to 0.95); thereby increasing ecological quality by limiting CO₂ emissions. Furthermore, the interaction of technological innovation with natural resource rent and renewable energy consumption also aids in mitigating CO₂ emissions and increases environmental health. Finally, panel causality analysis revealed a significant causality from all explanatory variables to CO₂ emissions. Based on the results, significant policy suggestions are provided, such as improving energy effectiveness, investing in energy technologies, and increasing renewable energy consumption to stimulate technological innovation to achieve the target of a net-zero‑carbon economy.

Do natural resource volatilities and renewable energy contribute to the environment and economic performance? Empirical evidence from E7 economies

The economies of the emerging seven (E7) are not insulated from the climate change challenges, which is a key concern for most countries. The E7 nations have undertaken part in initiatives to combat climate change, particularly in terms of reducing CO₂ emissions from the trajectory of productivity expansion in their countries. It is for this reason that this study examines the impact of resource volatility, renewable energy, and fossil fuel on both economic performance and CO₂ emission from 1990 to 2018. The present study used panel quantile regression and Driscoll-Kraay fixed effect-OLS estimators to examine these associations. From model I, the outcome shows that economic performance, natural gas rent, coal rent, and fossil fuel impact CO₂ emission positively. Moreover, oil rent, renewable energy, investment in energy, and the interaction between investment in energy and renewable energy also negatively and significantly impact CO₂ emission. On the other hand, model II which has economic performance as a dependent variable shows that all the understudy variables have significant positive relations with economic performance. Based on the empirical outcome, policy ramifications are provided.

Assessing the environmental sustainability corridor: linking oil consumption, hydro energy consumption, and ecological footprint in Turkey

Climate change has been a topic of significant discourse and debate among scholars and policy makers for several decades. In recent decades, it has become a major problem for the entire human race. Therefore, the present research evaluates the impact of oil consumption, hydro energy use, population density, and economic growth on ecological footprint in Turkey for the period from 1965QI to 2018Q4. This paper uses the BDS test to assess the nonlinearity of the variables in the pre-estimation analysis. The results of the test reveal that non-linearity occurs in all of the variables used in this study. As a consequence, using traditional linear methodologies would produce erroneous results. Our research uses the quantile techniques (quantile cointegration, quantile causality, quantile-on-quantile regression), which are recently developed nonlinear estimate methodologies to assess these associations. The results from the study reveal that oil consumption, hydro energy use, population density, and economic growth contribute to environmental degradation in Turkey in majority of the quantiles. The Granger Causality in Quantiles result also gives credence to the results. The study proposes policy recommendation based on these results.

Energy mix with technological innovation to abate carbon emission: fresh evidence from Mexico applying wavelet tools and spectral causality

The global warming issue arises from climate change, which draws scientists' attention toward cleaner energy sources. Among clean sources, renewables and nuclear energy are getting immense attention among policymakers. However, the significance of nuclear energy in reducing CO₂ emissions has remained ambiguous, necessitating further research. Therefore, the present study draws impetuous attention to the United Nations Sustainable Development Goals-7 (affordable clean energy) & 13 (climate change mitigation) by looking at the relationship between energy mix (fossil fuels, renewables, and nuclear), economic growth, technological innovation, and CO₂ emissions in Mexico from 1980 to 2019 using the autoregressive distributed lag (ARDL) model. In addition, to assess the direction of causality, this study applied wavelet techniques and spectral causality. The findings affirm that renewable and nuclear energy use and technological innovation tend to curb CO₂ emissions, whereas fossil fuel consumption and economic expansion trigger CO₂ emissions. The study lends support to the environmental Kuznets curve (EKC) phenomenon in Mexico. The FMOLS and DOLS tests show that our long-run estimates are reliable. In different time scales, the wavelet coherence result is also consistent. Finally, the results of the spectral causality approach demonstrate a significant causal association between the variables tested at various frequencies. As a result, in order to achieve SDGs 7 and 13 and support an environmentally friendly ecosystem, Mexico's energy mix must be changed to renewables and nuclear.

Production-based disaggregated analysis of energy consumption and CO2 emission nexus: evidence from the USA by novel dynamic ARDL simulation approach

The study investigates the effects of energy consumption on carbon dioxide (CO₂) emissions by focusing on production sources. In this context, the study focuses on the USA as the leading economy, includes monthly data between January 1973 and April 2022, and performs dynamic autoregressive distributed lag (ARDL) (DYNARDL) simulations. Besides, kernel-based regularized least squares (KRLS) and cointegrating regression approaches are applied for robustness checks. The results reveal that (i) there is cointegration between sub-components of the energy production and CO₂ emissions in the long run; (ii) fossil energy and nuclear energy production have an increasing effect on the CO₂ emissions in the both short and long run; (iii) renewable energy production has an increasing effect on the CO₂ emissions in the short run, but has a decreasing effect in the long run; (iv) negative (positive) shocks in the fossil energy production have a decreasing (increasing) effect on the CO₂ emissions, whereas negative (positive) shocks in the renewable energy production have an increasing (decreasing) effect on the CO₂ emissions in case of counterfactual shocks; (v) there is a casual-effect nexus between energy production sources and CO₂ emissions; and (vi) KRLS and cointegrating regression results validate the robustness of the DYNARDL simulation outcomes. Moreover, policy implications are discussed.

The effect of mandatory environmental regulation on green development efficiency: evidence from China

The existing literature finds that mandatory environmental regulation (MER) can significantly reduce environmental pollution. However, much less is known about how the implementation of MER affects green development efficiency (GDE). Based on the Air Pollution Control Action Plan which was enforced in 2013 in China's most developed regions as an exogenous shock, we find that first, MER has a significant negative effect on the improvement of GDE by reducing regional scale efficiency. Second, MER mainly reduces the GDE of cities with stronger regulation intensities and with larger economic volumes. Third, MER also has a negative impact on regional green total factor productivity by changing technical progress. We suggest that when implementing MER, governments should enhance regional and global cooperation, promote green technology, and use comprehensive policy tools to stimulate firms' green innovation.

Heterogeneous impact of eco-innovation on premature deaths resulting from indoor and outdoor air pollution: empirical evidence from EU29 countries

Environmental innovations play a vital role in reducing air pollution and the number of pollution-related mortality. Most of the previous studies have examined the role of eco-innovations in environmental quality. However, to our knowledge, no study has evaluated the effects of eco-innovation on air pollution as a cause of mortality. For this purpose, this research examines the effect of eco-innovations on premature deaths from indoor and outdoor air pollution in twenty-nine European countries from 1995 to 2019. The Method of Moments Quantile Regression (MM-QR) is used to assess the impacts. The results confirm the heterogeneous effects of the main variables in both models. Both models indicate that eco-innovations reduce premature deaths from outdoor and indoor air pollution, and these effects are more significant in high quantities (75th and 90th). Also, the effect of eco-innovations on reducing mortality due to indoor pollution is more significant than that related to outdoor pollution. Eco-innovation, economic growth, renewable energy consumption, and urbanization reduce premature mortality indoors and outdoors, but CO₂ emissions increase this mortality. The results of the Dumitrescu-Hurlin causality test also support that all variables, including eco-innovation and CO₂ emissions, have a bidirectional causal relationship with indoor (LIND) and outdoor (LOUT) mortality due to air pollution. Governments and politicians can help mitigate this problem by providing more environmental innovations by increasing support packages and reducing taxes.

The effect of financial globalization and natural resource rent on load capacity factor in India: an analysis using the dual adjustment approach

Currently, the most crucial economic and ecological issues are related to environmental degradation and sustainability. On this backdrop, this paper examines the impact of financial globalization and natural resource rent on load capacity factor, using the novel dual adjustment approach and time-frequency domain causality approaches, in the case of India. This study contributes to the extant body of knowledge in the area of environmental economics. First, it is the first attempt to analyze the factors responsible for load capacity factor, specifically for India. As such, studies on environmental concerns on both the supply and demand sides are put into consideration. Empirical results show that only renewable energy consumption lessens the load capacity factor, while economic growth and financial globalization are positively correlated with the load capacity factor, and natural resource rent is insignificant in the short run. In the long run, only economic growth is negatively correlated with load capacity factor, while the other series positively influence load capacity factor. To reap greater ecological merits, policymakers should focus on transitioning from conventional non-renewable energy sources that contribute to rising carbon emissions to more cost-effective and dependable renewable sources of energy that support sustainable growth and a healthy environment.

Asymmetric and long-run impact of political stability on consumption-based carbon dioxide emissions in Finland: Evidence from nonlinear and Fourier-based approaches

The study investigates the asymmetric and long-run impact of political stability on consumption-based carbon dioxide (CCO₂) emissions in Finland. In this context, the study examines the impact of political stability, economic growth, renewable energy consumption, and trade openness; includes quarterly data between 1990/Q1 and 2019/Q4, and applies nonlinear and Fourier-based approaches. The empirical outcomes reveal that (i) there is a long-run cointegration between CCO₂ emissions and political stability as well as other controlling variables; (ii) positive changes in political stability have statistically significant impacts on CCO₂ emissions, whereas negative shocks in political stability are not statistically significant. Also, positive shocks are more powerful than negative shocks; (iii) positive shocks in economic growth have significantly increasing impacts; (iv) positive and negative shocks in renewable energy have decreasing impacts on CCO₂ emissions, while positive shocks are more powerful; (v) positive (negative) shocks in trade openness have decreasing (increasing) impacts on CCO₂ emissions. Overall, the empirical results highlight the role of political stability on CCO₂ emissions. Thus, consideration of political stability by policymakers of Finland in the policy development and implementation processes is highly recommended to achieve a carbon-neutrality target by 2035.

The association of COVID-19 nexus on China's economy: A financial crisis or a health crisis?

This paper analyses the interaction between the novel coronavirus pandemic (COVID-19), unemployment rate, stock market, consumer confidence index (CCI), and economic policy uncertainty (EPU) index in China within a time-frequency framework. We compare the changes in economic indicators during the global financial crisis (GFC) and study the different impacts of the two events on China's economy. An unprecedented impact of COVID-19 shocks on the unemployment rate, CCI, EPU index, and stock market volatility over the low frequency bands is uncovered by applying the coherence wavelet method to China monthly data. The COVID-19 effect on the stock market volatility and the EPU index is substantially higher than on the unemployment rate and the CCI. On the contrary, the GFC's impact on the unemployment rate is much greater than that on the EPU index and CCI. Additionally, the impact of the GFC on the economy is more cyclical in the long-term, while the COVID-19 pandemic is a short-term shock with a relatively short oscillation cycle. This study concludes that the economic impact of COVID-19 will not spread into a financial crisis for China and believe that the COVID-19 pandemic is more of a health event than an economic crisis for Chinese economy.

Economic growth, technology, and CO2 emissions in BRICS: Investigating the non-linear impacts of economic complexity

Upgrading economic structures and producing less pollution-intensive goods are indispensable for achieving Sustainable Development Goals (SDGs) in BRICS (Brazil, Russia, India, China, and South Africa) that produce 41% of global CO₂ emissions. Economic complexity (ECC), which measures the sophistication of productivity and economic structure, has important environmental repercussions. Theoretically, the environmental impacts of economic complexity at higher levels and lower levels of complexity vary from each other. However, the majority of previous studies have overlooked these theoretical underpinnings while assessing the environmental repercussions of economic complexity. In addition, technological competencies are necessary to boost the economic complexity levels. Accordingly, this study uncovers the non-linear effects of economic complexity on CO₂ emissions including technology, population density, and economic growth in a STIRPAT model. To this end, the panel data from 1992 to 2018 is analyzed using the Continuously Updated Fully Modified method (CuP-FM) in the context of BRICS. The long-run results uncovered that CO₂ emissions intensify at a lower level of economic complexity. On the flip side, a higher level of economic complexity is beneficial in mitigating CO₂ in BRICS. Hence, the economic complexity and CO₂ connections follow an inverted U-shaped curve. The results also disclosed that expanding the level of technology lessens CO₂ and stimulates the quality of the environment. Further, population density and economic growth are evidenced to intensify CO₂. Moreover, economic complexity and technology Granger cause CO₂. Lastly, strategies are directed in the context of Sustainable Development Goals 9 and 13 to control CO₂ emissions by upgrading technology and products complexity.