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

The dark side of climate policy uncertainty: Hindering energy transition by shaping environmental taxes effectiveness

Climate policy uncertainty (CPU) may have an adverse impact on the environment by interfering with the effectiveness of environmental policies, but there is currently little evidence to support this indirect effect. By incorporating CPU into the transition function, this paper utilizes the panel smooth transition regression (PSTR) to dynamically analyze how CPU affects the relationship between environmental taxes (ETR) and energy transition. When CPU exceeds the threshold, the promoting effect of ETR on energy transition weakens or reverses. The robustness of the main conclusions is demonstrated by establishing a PSTR estimator with the instrumental variable. This paper also constructs a counterfactual scenario, showing that CPU reduces the positive impact of ETR on renewable energy consumption and generation by 7.6% and 3.5%, respectively. Further analysis indicates that this negative effect arises because CPU likely increases investment risk, particularly for long-term green projects, thereby inhibiting the clean energy market and energy-related green technological innovation. Heterogeneity analysis find that the weakening effect of CPU on the effectiveness of ETR is stronger in countries with low energy resource endowment, high energy intensity, and lower economic development levels, underscoring the need for tailored policy approaches. This research emphasizes that for countries with ambitious energy transition goals, climate policy stability is crucial for ensuring the healthy development of environmental taxes policy and renewable energy markets.

Accelerating decarbonized economic growth through net-zero entrepreneurship: Low-carbon economic development perspective

Net-zero entrepreneurship is a novel concept introduced in the context of carbon neutrality, and exploring whether it can catalyze decarbonized economic growth is a worthy pursuit. This study constructs a comprehensive, low-carbon endogenous economic growth model to scrutinize the intricate nexus between net-zero entrepreneurship and decarbonized economic growth. Empirical validation employs a series of multiple regression models to rigorously test the hypotheses derived from the theoretical framework using an extensive dataset spanning Chinese provinces. The results reveal a nuanced landscape. (i) Net-zero entrepreneurship plays a remarkable role in promoting decarbonization growth, with considerable regional heterogeneity. (ii) Green technology progress exhibits a notable mediating effect. (iii) Environmental regulation and industrial structure optimization have positive moderating effects. (iv) The results passed alternative dependent variable and one-phase lag regression robustness tests. In a distinct contribution to entrepreneurship literature, this study augments the discourse on strategies to steer low-carbon transitions. The research findings indicate that net-zero entrepreneurship can accelerate the global decarbonization process, and green technology progress is a significant driving mechanism in this process. Additionally, it is essential to strengthen environmental agencies' regulatory oversight and optimize industrial structures to pave the way for transformative sustainable growth. In the future, more entrepreneurs should be encouraged to engage in green technology and business model innovation to contribute to global decarbonization efforts.

The influence of digital intelligence transformation on carbon emission reduction in manufacturing firms

Severe global climate change has resulted in the focus of social attention shifting to the manufacturing industry's low-carbon transformation. Digital intelligent transformation (DIT) in enterprises is identified as a crucial driver in mitigating carbon emissions. An estimation of DIT's impact on manufacturing industries' carbon emission intensity (CEI) and its underlying mechanisms was conducted using data from Chinese A-share listed companies. Research findings indicate that DIT significantly reduces corporate CEI. Robustness tests, such as the instrumental variable method and variable substitution method, confirm this conclusion. By enhancing labor productivity and accelerating capital renewal, DIT indirectly lowers CEI. Furthermore, non-state-owned enterprises with superior market competitiveness have been observed to be markedly adept at harnessing DIT for CEI. The heterogeneity test found that DIT has a considerably significant effect on reducing CEI in enterprises that are not low-carbon pilots, non-broadband pilots, smart pilots, non-provincial capitals, and eastern cities. This study provides new evidence supporting the promotion of enterprise DIT for achieving green development, offering insights for corporate policy making.

Assessing the environmental impacts of clean energy investment in Pakistan using a dynamic autoregressive distributed lag model

In this study, the authors projected the impacts of clean energy investment on environmental degradation by applying a novel and dynamic Autoregressive Distributed Lag (DARDL) model for Pakistan from 1990 to 2022. Most researchers have used ecological footprint or CO2 emissions indicators to look at how clean energy investment affects environmental degradation, which primarily represents contamination induced by humans' consumption patterns and does not consider the impact of the supply side. Against this background, the study scrutinized the dynamic interaction between clean energy investment and environmental sustainability using the load capacity factor (LCF) as an ecological indicator in Pakistan, including economic growth, population density, trade openness, urbanization, and industrialization in the analysis. The long-run estimates from DARDL indicate that a 1 percent upsurge in clean energy investment mitigates environmental degradation by approximately 0.42 percent on average, controlling for other factors. Further, the study also revealed that a 1 percent increase in clean energy investment diminishes dirty energy consumption by approximately 0.45 percent. The validity of the findings is confirmed using alternate methods, i.e., KRLS. The study recommends that Pakistan prioritize investment in clean energy projects to promote environmental sustainability and enforce environmental regulations to reduce the adverse externalities associated with dirty energy activities.

Modeling green energy and innovation for ecological risk management using second generation dynamic quantile panel data model

Ecological risk management has emerged as a critical research and policy development area in energy and environmental economics. Sustained ecology is crucial for the standard of living and food security. As the adverse impacts of environmental degradation and climate change become increasingly apparent it is imperative to understand ecological risk and its interconnectedness with environmental pressure, clean energy, economic activity, globalization, and green technology. Ecological risk is assessed using the environmental performance index which is a holistic indicator of climate change, environmental pressures and human actions in which most of these indicators have spatial effects. This paper explores the multifaceted relationship between identified anthropogenic critical factors and their role in effectively managing ecological risk globally. This study has developed the second-generation dynamic panel quantile regression considering spatial effects of economic activities on ecology across borders of 55 countries between 1995 and 2022. This innovative hybrid estimation scheme that integrated theoretical and econometric aspects makes the model robust to major regression issues. Several implications ranked in decreasing order of its effectiveness are reducing environmental pressure, expediting energy transition, and embracing economic integration while there is a need to work on rejuvenating green technology and green growth.

The role of green growth and institutional quality on environmental sustainability: A comparison of CO2 emissions, ecological footprint and inverted load capacity factor for OECD countries

Green growth is of great importance in terms of solving environmental problems and achieving sustainable development goals. However, the existing literature has not investigated how green growth affects environmental degradation and environmental sustainability variables. In light of this gap, this study aims to analyse the impact of green growth and institutional quality on CO2 emissions, ecological footprint and inverse load capacity factor in OECD countries by constructing three different models. The results of the analysis indicate that (i) green growth exerts a significant mitigating and differentiating effect on CO2, ecological footprint and inverted load capacity factor in the long run. This is evidenced by a 1% increase in green growth reducing CO2, ecological footprint and inverted load capacity factor by 0.563%, 0.373% and 0.198%, respectively. (i) The impact of green growth on CO2 and inverted load capacity factor in the long run is negative and statistically significant; (ii) the impact of green growth on CO2 and inverted load capacity factor in the short run is negative and statistically significant; (iii) the impact of institutional quality on deterioration is positive and significant in the long run; (iv) the impact of population on deterioration and sustainability is significant and mixed. The findings indicate that decision-makers in OECD countries should review green energy policies when setting the sustainable development goals, as environmental sustainability is more challenging than reducing pollution.

Sustainable development through green transition in EU countries: New evidence from panel quantile regression

Sustainable development addresses global challenges by promoting practices that balance economic, social, and environmental considerations. Key factors include the shifting to green energy and the integrating of green technology in the sustainable development process. This study investigates the heterogenous effects of green technology development, green energy, R&D expenditures, FDI, economic growth, and urbanization on CO2 emissions in 25 European Union (EU) countries using panel quantile regression over the period 2000-2021. The results, based on panel quantile regression, indicate that green energy decreases CO2 emissions from the 10th to the 90th quantiles, while green technology development increases CO2 emissions at the lower quantiles (10th to 60th) and then turns negative. The robustness of the fixed effect model also confirms the findings of the study. Additionally, panel causality tests indicate no causal link between green technology development and CO2 emissions, but there is bidirectional causality between green energy and CO2 emissions. Therefore, the findings highlight that policymakers should thoroughly evaluate measures and strategies to encourage the development of green technologies and green energy sources to reduce high levels of CO2 emissions. One strategy is to provide financial aid and support technological advances to produce green energy at reduced costs.

Exploring the mediating role of financial inclusion in the relationship between economic policy uncertainty and CO2 emissions: A global perspective

This study examines the connection between economic policy uncertainty (EPU), CO2 emissions, and financial inclusion in developed and developing countries. Using the data from 2004 to 2021, advanced statistical techniques are employed, including Sobel test, to explore the mediating effect of financial inclusion on the relationship between economic policy uncertainty (EPU) and CO2 emissions. There is a dearth of research examining these three variables together in a single study. Similarly, using financial inclusion as a mediator in the relation of EPU and CO2 emissions is a novel concept. This article employs a multi-indicator approach to measure key variables like CO2 emissions and financial inclusion. The results indicate that uncertainties in economic policies contribute in practices that lead to higher CO2 emissions in overall panel data of 44 countries. In addition, when considering the relationship between EPU and FI, the results indicate a significant and negative relationship between EPU and FI. If there is uncertainty in economic policies, it may lead toward challenges and hurdles in financial inclusion. When the mediating affect was checked, it was found financial inclusion acts as a significant mediator in the relationship between EPU and CO2 emissions, depicting that financial inclusion fosters the environmental quality and mitigates the potential harmful effects of environmental aspects of economic policy uncertainty. Therefore, policies that promote financial inclusion should be given top priority by governments, particularly in emerging nations. Financial literacy and bank service accessibility should be promoted. These measures would lessen the impact of staggering economic policies on CO2 emissions. It is necessary for policymakers to include environmental factors, specifically those relating to carbon emissions, into economic strategies. This requires encouraging industries to adopt eco-friendly practices and coordinating economic strategies with sustainability objectives.

Assessing the impact of geopolitical, economic, and institutional factors on China's environmental management in the Russian-Ukraine conflicting era

In contemporary times, geopolitical risk, and natural resources prices are susceptible due to the Russian-Ukraine conflict. In the meantime, emerging economies are struggling to explore the factors that could reduce ecological challenges and enhance environmental management. This research aims to analyze several economic, environmental, political, and institutional variables to ascertain their influence on greenhouse gas emissions in China. Covering the latest period from 1990 to 2022, various time series tests, including normality, stationarity, and cointegration tests. The results confirm that the variables studied have a stable pattern over time and are connected in the long run. The non-normal distribution of variables leads to opt novel moment quantile regression, where the results are tested for robustness via parametric approaches. The empirical results asserted that economic growth, natural resource prices, and trade significantly enhance ecological challenges (emissions). However, globalization, geopolitical risk, and institutional quality significantly reduce such environmental challenges. The results are robust, and both unidirectional and bidirectional causal associations confirm the importance of these variables in environmental management. Based on the results, this study recommends engagement in environmentally-friendly trading, investment in clean and green energy, and strengthening institutional quality for the region's environmental recovery.

Do renewable energy and human capital facilitate the improvement of environmental quality in the United States? A new perspective on environmental issues with the load capacity factor

Recently, countries have been making intensive efforts to alleviate the burden on the environment and to make environmental conditions sustainable. In this context, our study aims to investigate the long-term impact of renewable energy consumption (REC) and human capital (HC) by considering the load capacity factor (LCF). We also investigate the long-term impact of economic growth (Y) and non-renewable energy consumption (NREC) on the LCF. In this context, we analyze annual data for the U.S. for the period 1965-2018 using the newly developed augmented ARDL (AARDL) approach. The long-term empirical results show the following. i) Increases in Y negatively affect LCF and deteriorate environmental quality. ii) Increases in NREC negatively affect LCF and accelerate the deterioration of environmental quality. iii) REC has no significant impact on environmental quality. iv) Increases in HC support the improvement of environmental quality. The empirical results show that contrary to expectations, renewable energy consumption does not have a significant impact on environmental quality in the U.S., whereas human capital is an important factor in improving environmental quality. In this context, US policymakers should pave the way for more investment in eco-friendly renewable energy investments and human capital to establish sustainable environmental quality. Policymakers should also take steps to reduce the use of fossil fuels.

The mechanism of China's renewable energy utilization impact on carbon emission intensity: Evidence from the perspective of intermediary transmission

Renewable energy (RE) plays a crucial role in global energy transformation, and a thorough study of the potential impact of RE on regional carbon emissions is of great significance. This is particularly relevant to China, which needs to clarify its path to carbon reduction. Using the sample data of 30 provinces in China from 2000 to 2021, this paper uses the Granger causality test to verify the causal relationship between carbon emission intensity (CEI) and other factors. It builds a mediation effect model on this basis to explore the direct impact effect and indirect transmission path of renewable energy utilization (REU) on CEI. The results show that REU has a one-way causal relationship with CEI. REU can directly and indirectly reduce CEI by improving social wealth and changing the direction of energy investment. In addition, REU indirectly increases CEI through the transmission paths of investment in the energy industry - social affluence and industrial level-social affluence. The CEI is indirectly reduced through the conduction paths of (social affluence-Urbanization rate), (Investment in the energy industry-Urbanization rate), (Industrial level-Urbanization rate), and (Industrial level-Investment in the energy industry). These conclusions will assist policymakers in exploring targeted pathways for low-carbon power development, providing a reference for strategic and sustainable carbon reduction policies.

Role of renewable and non-renewable energy consumption in environmental quality and their subsequent effects on average temperature: an assessment of sustainable development goals in South Korea

Environmental quality is a global concern, and an accurate assessment of environmental progress has emerged as a key element in achieving sustainable development goals. The study offers a two-pronged contribution by analyzing the interplay between renewable energy consumption, traditional energy consumption, and urbanization on environmental quality while simultaneously testing the impact of environmental quality on average temperature. Distinguishing itself from prior research, the study employs both carbon emissions and ecological footprint to ensure an intricate and reliable analysis. Current study analyzes data from 1970 to 2022 from South Korea to indicate that energy-environment association is influenced by energy consumption, urbanization, and economic growth, while renewable energy consumption is key in long-term environmental sustainability. Our extensive econometric analysis allows us to suggest that South Korea need to prioritize renewable energy, and green growth is prioritized so that economic growth and green agenda are synchronized to overcome environmental challenges. Addictingly, there is need to devise long-term environmental policies to strengthen the role of environmental innovation in energy productivity and boost renewable energy consumption.

Do life expectancy and hydropower consumption affect ecological footprint? Evidence from novel augmented and dynamic ARDL approaches

Human activities threaten the future of the ecosystem by emitting pollution to the air, water, and soil. Considering the increasing ecological footprint (EF), the study focuses on investigating the role of life expectancy and hydropower consumption by controlling also income, trade openness, and globalization on the environment under the environmental Kuznets curve (EKC) hypothesis for Turkey during 1971-2018. In this context, the study performs recently developed augmented autoregressive distributed lag (AARDL) and dynamic ARDL (DARDL) methods. The results show that (i) life expectancy increases the environmental pressure; (ii) hydropower consumption has no effect on the EF; (iii) globalization and trade openness reduce the EF; (iv) the EKC hypothesis is valid, but the estimated turning point lies between USD 19,914 and USD 20,571, which is far from the sample period in Turkey. From the overall results, it can be concluded that Turkey cannot solve environmental problems with insufficient income levels, an increasing elderly population, and ineffective use of hydropower. Hence, Turkey should rely on income much more, use hydropower much more efficiently, and benefit from the spillover effect of technological innovations related to globalization and foreign trade to significantly reduce the EF.

Natural gas supply cuts and searching alternatives in Germany: A disaggregated level energy consumption analysis for environmental quality by time series approaches

By considering the search for alternatives against Russia's natural gas supply cuts, this study explores the impact and causality of disaggregated level energy consumption indicators on environmental quality. Hence, the study investigates Germany, which is the leading economy in Europe and highly dependent on Russia's natural gas supply, by using carbon dioxide (CO2) emissions as the environment indicator, including annual data from 1970 to 2021, and applying novel time series approaches. In the empirical examination, Granger causality-in-quantiles (GCiQ), quantile-on-quantile regression (QoQR), and multivariate adaptive regression splines (MARS) are applied as base models while quantile regression (QR) and dynamic ordinary least squares (DOLS) are used for robustness. The empirical findings show that (i) there are causal impacts of disaggregated level energy consumption indicators on CO2 emissions; (ii) renewable energy and hydroelectricity consumption have a decreasing impact, whereas natural gas, coal, and oil energy consumption have an increasing impact on CO2 emissions; (iii) although nuclear energy has been discussed as a potential alternative, nuclear energy does not have a significant impact in decreasing CO2 emissions; (iv) natural gas consumption has an interaction with renewable energy, hydroelectricity, and coal energy consumption; (v) the power of disaggregated level energy consumption indicators on CO2 emissions vary according to quantiles, thresholds, and interactions between energy consumption indicators; (iv) alternative models validate robustness of the results obtained. Thus, the results imply that the most appropriate alternative is coal energy consumption in the short-term and renewable energy consumption in the long-term to compensate for Russia's natural gas supply cuts, whereas nuclear energy consumption is not a real alternative for Germany.

Assessing the connection between competitive industrial performance on load capacity factor within the LCC framework: Implications for sustainable policy in BRICS economies

Industrialization plays a crucial role in socio-economic development as it holds significant potential for creating new jobs, tightening the income gap, and promoting the use of advanced technology. As global competition intensifies, emerging economies emulate industrialized economies in accelerating manufacturing activity to improve national welfare and join the new global order. However, policymakers' understanding of how competitiveness in the industrial sector helps developing countries accomplish their sustainable development goals must be deepened. This paper aims to analyze the connections among competitive industrial performance, renewable energy consumption, urbanization, and load capacity factor (LCF) in the BRICS economies for the period between 1990 and 2018. Robust evidence from the continuously updated fully modified (CUP-FM) and continuously updated bias-corrected (CUP-BC) models shows that greater industrial competitiveness enhances environmental quality. The findings also reveal that income growth ultimately evolves as an ecologically friendly factor, confirming the validity of the load capacity curve (LCC) hypothesis. Another outcome of the econometric analysis indicates that renewable energy consumption contributes to the LCF, whereas urbanization damages the environment. Therefore, BRICS policymakers should concentrate on maintaining their competitiveness, implementing resilient urban planning, and promoting the usage of renewable energy to safeguard the environment while simultaneously achieving rapid economic growth.

The role of foreign direct investments, urbanization, productivity, and energy consumption in Finland's carbon emissions: an ARDL approach

This study investigates the effects of productivity, energy consumption, foreign direct investments, and urbanization on carbon dioxide emissions (CO2) in Finland during 2000-2020 using an autoregressive distributed lag (ARDL) model. The results show that (i) there is evidence of cointegration among variables; (ii) energy consumption has a positive effect on CO2 emissions in the long run; (iii) labor productivity and urbanization have a negative effect on CO2 emissions in the long run; (iv) foreign direct investments are not a significant explainer of CO2 emissions. The results are discussed with some policy implications and suggested future research.

Carbon efficiency and sustainable environment in India: impacts of structural change, renewable energy consumption, fossil fuel efficiency, urbanization, and technological innovation

Minimizing carbon pollution and fossil fuels is among the most crucial issues in the sustainable development goals (SDGs). However, global environmental concerns have increased since India did not sign the global coal pledge at COP 26. It is therefore a question mark how India will achieve the 2070 carbon-free target with the increasing use of coal and oil. In this contenxt, this work examines the impact of fossil fuel efficiency, structural change, renewable energy consumption, technological innovation, and urbanization on carbon efficiency in India from 1980 to 2019. Employing the dynamic autoregressive distributed lag approach; the study reveals that fossil fuel efficiency, structural change, renewable energy, and technological innovation improve carbon efficiency, while urbanization worsens environmental quality. Based on the study's findings, the Indian government should invest more and incentivize technological innovation that supports fossil fuel efficiency and renewable energy deployment to achieve the SDGs.

Digitalized world and carbon footprints: does digitalization really matter for sustainable environment?

There is a growing demand for energy to support economic and social development. There will be many shifts in the energy sector as a result of digitization. Hence, we aim analyzing the linkage between digitalization and environment sustainability by incorporating energy consumption as a moderating factor using data of UK from 1990 to 2020. Different dimensions of digitalization are used as explanatory variables, ecological and carbon footprints are used as outcomes and energy consumption is used as moderator. The findings of autoregressive distributed lag model show that internet users and technological advancement (fixed telephone subscription and mobile cellular) are negatively (positively) linked with ecological and carbon footprints. Energy consumption causes to enhance ecological and carbon footprints and plays an antagonistic role in the nexus of internet users, technological advancement, and ecological and carbon footprints. The effects of mobile cellular and fixed telephone subscription have increased in the presence of energy consumption as moderator which exhibits that energy consumption plays an enhancing role in the links between mobile cellular, fixed telephone subscription and ecological and carbon footprints. The results underscore the importance of taking a holistic approach to addressing the environmental impact of digital technologies. By promoting sustainable communication practices and investing in the development of more energy-efficient technologies, practitioners, managers, and society as a whole can work together to reduce the carbon and ecological footprints of digital technologies and create a more sustainable future for all.

The trilemma among CO2 emissions, energy use, and economic growth in Russia

This paper examines the relationship among CO₂ emissions, energy use, and GDP in Russia using annual data ranging from 1990 to 2020. We first conduct time-series analyses (stationarity, structural breaks, cointegration, and causality tests). Then, we performed some Machine Learning experiments as robustness checks. Both approaches underline a bidirectional causal flow between energy use and CO₂ emissions; a unidirectional link running from CO₂ emissions to real GDP; and the predominance of the "neutrality hypothesis" for energy use-GDP nexus. Therefore, energy conservation measures should not adversely affect the economic growth path of the country. In the current geopolitical scenario, relevant policy implications may be derived.

Financial innovation and environmental quality: Fresh empirical evidence from the EU Countries

Evidence shows that the European Union (EU) is a leader in using financial innovation to overcome environmental challenges and ensure sustainable development. This study explores the heterogeneous effects of financial innovations on carbon dioxide emissions (CO2) due to their destructive effects in the context of the EU, by employing the novel Method of Moments Quantile Regression (MM-QR). The study also evaluates the environmental Kuznets hypothesis during the period of 2000-2020. We used four proxies for financial innovation; the ratio of the aggregate money supply to narrow money (M3/M1), the ratio of broad to narrow money (M2/M1), the percentage change in domestic credit to the private sector (% of GDP) and a composite index of these indices using the Principal Component Analysis technique. The findings indicate that raising three financial innovation proxies can effectively raise environmental quality. It should be noted that while M3/M1 has a stronger and negative effect on CO2 emissions in low quantiles, M2/M1 has a stronger and negative effect on CO2 emissions in high quantiles. Therefore, it is recommended that a larger amount of M2 and M3 resources be directed towards green projects for financing in countries with both high and low levels of CO2 emissions, respectively.

Environmental reverberations of geopolitical risk and economic policy uncertainty resulting from the Russia-Ukraine conflict: A wavelet based approach for sectoral CO2 emissions

After the COVID-19 pandemic, Russia invaded Ukraine in February 2022, and a natural gas crisis between the European Union (EU) and Russia has begun. These events have negatively affected humanity and resulted in economic and environmental consequences. Against this background, this study examines the impact of geopolitical risk (GPR) and economic policy uncertainty (EPU) caused by the Russia-Ukraine conflict, on sectoral carbon dioxide (CO₂) emissions. To this end, the study analyzes data from January 1997 to October 2022 by using wavelet transform coherence (WTC) and time-varying wavelet causality test (TVWCT) approaches. The WTC results show that GPR and EPU reduce CO₂ emissions in the residential, commercial, industrial, and electricity sectors, while GPR increases CO₂ emissions in the transportation sector during the period from January 2019 to October 2022, which includes Russia-Ukraine conflict. The WTC analysis also indicates that the reduction in CO₂ emissions provided by the EPU is higher than that of the GPR for several periods. According to the TVWCT, there are causal impacts of the GPR and the EPU on sectoral CO₂ emissions, but the timing of the causal impacts differs between the raw and decomposed data. The results suggest that the EPU has a larger impact on reducing sectoral CO₂ emissions during the Ukraine-Russia crisis and that production disruptions due to uncertainty have the greatest impact on reducing CO₂ emissions in the electric power and transportation sectors.

Asymmetric effect of economic policy uncertainty, political stability, energy consumption, and economic growth on CO2 emissions: evidence from G-7 countries

This study deals with the asymmetric effect of economic policy uncertainty and political stability on carbon dioxide (CO₂) emissions considering also energy consumption and economic growth. In this context, the study investigates G-7 countries, which make up an important part of the world economy. Also, the study uses yearly data between 1997 and 2021 as the most available intersection data for all countries included. Besides, this study applies a novel nonlinear approach as quantile-on-quantile regression (QQR) as the base model, and quantile regression (QR) is used for robustness. The empirical results present that (i) economic policy uncertainty has a decreasing effect on CO₂ emissions in Italy, Japan, and the United States of America (USA), whereas it has a mixed effect in Canada, France, Germany, and the United Kingdom (UK); (ii) political stability also has a mixed effect on CO₂ emissions; (iii) energy consumption has an accelerating effect on CO₂ emissions while the power of effect changes at quantiles; (iv) economic growth has generally an increasing effect on CO₂ emissions, whereas it has a decreasing effect at lower quantiles in Japan, at middle quantiles in France and Germany, and at higher quantiles in Italy; and (v) the QR results support the robustness of QQR findings. Thus, the empirical results highlight that G-7 countries should consider the asymmetric and quantile-based varying effects of the economic policy uncertainty, political stability, and economic growth to reach their carbon neutrality targets.

Estimation performance comparison of machine learning approaches and time series econometric models: evidence from the effect of sector-based energy consumption on CO2 emissions in the USA

By considering the existence of two separate analysis families and the usage of different data frequencies, this study aims to examine the effect of method choice, data frequency, and sector-based energy consumption on carbon dioxide (CO₂) emissions by performing machine learning (ML) algorithms and time series econometric (TS) models simultaneously. In this situation, the study examines the United States (USA), considers sector-based energy consumption indicators as explanatory variables, uses monthly and yearly data between January 1973 and December 2021, estimates CO₂ emissions, and compares the estimation performance of the models. The empirical findings reveal that (i) the ML algorithms outperform the TS models based on R² and goodness of fit criteria; (ii) the estimation performance of the models increases with the high-frequency (i.e., monthly) data; (iii) the ML algorithms perform much better in case of high-frequency usage; (iv) some thresholds identify the effects of the sector-based energy consumption indicators on the CO₂ emissions; (v) electric power and transportation sectors are the most important sectors in the estimation of the CO₂ emissions for monthly and yearly data, respectively. Hence, the study provides to help the understanding role of method choice, data frequency, and sector-based energy consumption for the estimation of CO₂ emissions. Based on the results, this study proposes that US policymakers should consider the ML algorithms, use higher-frequency data, and include sector-based energy consumption indicators to have a better estimation of CO₂ emissions.

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.

Estimation of Impacts of Global Factors on World Food Prices: A Comparison of Machine Learning Algorithms and Time Series Econometric Models

It is a well-felt recent phenomenal fact that global food prices have dramatically increased and attracted attention from practitioners and researchers. In line with this attraction, this study uncovers the impact of global factors on predicting food prices in an empirical comparison by using machine learning algorithms and time series econometric models. Covering eight global explanatory variables and monthly data from January 1991 to May 2021, the results show that machine learning algorithms reveal a better performance than time series econometric models while Multi-layer Perceptron is defined as the best machine learning algorithm among alternatives. Furthermore, the one-month lagged global food prices are found to be the most significant factor on the global food prices followed by raw material prices, fertilizer prices, and oil prices, respectively. Thus, the results highlight the effects of fluctuations in the global variables on global food prices. Additionally, policy implications are discussed.

Role of hydroelectricity and natural gas consumption on environmental sustainability in the United States: Evidence from novel time-frequency approaches

This study analyzes time- and frequency-varying impacts of hydroelectricity energy consumption, natural gas energy consumption, and economic growth on environmental sustainability proxied by carbon dioxide (CO₂) emissions in the United States of America (the US) for the period 1965/Q1 to 2020/Q4. This study is the first of its kind to contribute to the current literature by analyzing dynamic relationships among these variables in the short-, medium-, and long-term at different time frequencies in the framework of a multivariate correlation, hence providing a more comprehensive picture about the impacts of these effective factors on CO₂ emissions. To meet the objectives of the study, Wavelet local multiple local (WLMC), which is a recent novel methodology developed by Polanco-Martínez et al. (2020), is applied. Moreover, the Wavelet coherence (WTC) approach is used for robustness check. The outcomes provide fresh insights into the long-term dynamic correlations among hydroelectricity energy consumption, natural gas energy consumption, economic growth, and CO₂ emissions. The study discovers a robust positive co-movement between natural gas energy consumption and CO₂ emissions and a negative correlation between hydro energy consumption and CO₂ emissions that is the most intense on the long-term frequencies. Furthermore, economic growth causes CO₂ emissions, which is evidenced by a positive relationship between both factors at short- and long-term time-frequencies. Supported by the outcomes of the study, the authors urge to suggest crucial insights and policy points for the US policymakers to shift from fossil energy to renewable energy sources to meet Sustainable Development Goals (SDGs), especially SDG-7 and SDG-13, since they induce lower emissions.