Countries have been dealing with environmental issues and searching for solutions. By considering China's leading position among the highest carbon dioxide (CO2)-emitting countries, as well as the lack of sectoral examination, this study uncovers the interrelationships among green bonds (GBs), credit default swap (CDS) spreads, geopolitical risk (GPR), and CO2 emissions in China. In doing this, the study considers main sectors (i.e., transport, industry, and power) and total CO2 emissions to consider sectoral heterogeneity, uses monthly data from 2019/1 to 2026/2, and applies the Wavelet Local Multiple Correlation (WLMC) method. The study empirically reveals that (i) in bi-variate analysis, GB, CDS, and GPR have mixed association with sectoral CO2 emissions across terms; (ii) in multivariate analysis, GB, CDS, and GPR have a positive association with sectoral and total CO2 emissions; (iii) at the sectoral level, GPR and CDS have a negative association with power sector and total CO2 emissions across short-term and medium-term. Also, GB has a mixed association with sectoral and total CO2 emissions across the medium-term; (iv) the dominant variable varies across sectors and terms, where CDS and GB are leading at short-term and long-term, while CDS and GPR are pioneering in medium-term; (v) the outcomes, where dominance of effective factors is searched, do not differ across aggregated and disaggregated level CO2 emissions. Thus, empirical outcomes highlight the differentiating association of the factors with sectoral and total CO2 emissions, whereas the dominance of the factors is stable across sectoral and total CO2 emissions. Accordingly, relying on the empirical findings, the study discusses various policy options for Chinese policymakers in decarbonizing economic sectors.

ABSTRACT The adverse effects of climate change on humanity have been escalating due to environmental degradation. Consequently, nations have been compelled to implement measures to address climate‐related challenges. Within this framework, traditional and recently acknowledged factors play a pivotal role in achieving SDGs, particularly SDG‐13. This study empirically examines the influence of newly recognized factors, such as the energy transition index (ETI) and environmental policy stringency (EPS), alongside traditional factors like gross domestic product (GDP), renewable energy use (REU), and foreign direct investments (FDI), on the environment, measured through ecological footprint and load capacity factor. Focusing on leading emerging economies—excluding Indonesia and Mexico due to data limitations—the study utilizes data from 2000 to 2020 and applies the kernel‐based regularized least squares (KRLS) approach under the marginal effect framework to explore this nexus. The findings indicate that (i) GDP and FDI do not exhibit environmentally friendly characteristics across the examined countries; (ii) REU contributes to environmental preservation only in Brazil; (iii) ETI and EPS do not significantly enhance environmental quality in any of the countries studied; (iv) the KRLS approach demonstrates high predictive accuracy, achieving a 99.6% success rate across various models. Overall, the research highlights the differential marginal effects of these factors on the environment, which vary by factor, percentile, and country. Based on the empirical evidence, the study discusses policy implications for the five leading emerging economies to effectively pursue SDG‐13 by leveraging the identified factors.

Countries take measures to protect the environment as compatible with the increasing interest in climate change-related issues. In this context, this research analyzes the impact of environmental policy stringency (EPS), energy transition index (ETI), and income (GDP) on environmental quality. In doing this, the study examines BRICS countries, which are the leading developing countries that cause higher emissions, consume higher amounts of energy, and have higher economic growth, uses data from 2000/Q1 to 2020/Q4, and performs wavelet local multiple correlation (WLMC) model to investigate the relationship under time and frequency-based diverging scheme. In bivariate cases, EPS, ETI, and GDP have a differentiating impact on load capacity factor (LCF) across countries, where there are some exclusions. These factors have a generally increasing impact on LCF. In contrast, EPS has a decreasing impact at low (medium) frequency in Russia (South Africa), ETI has a declining impact at high frequency in Brazil and China, GDP has a decreasing impact at lower frequency in Russia, medium and high frequency in China, and higher frequency Brazil and South Africa. In four-variate cases, the factors have a fully increasing impact on LCF in the countries for all times and frequencies, while the impact is stronger at higher frequencies. GDP is the dominant factor in India and China, while ETI (EPS) is leading in Brazil and Russia (South Africa). Thus, the study reveals the dynamic impacts of EPS, ETI, and GDP on LCF, which diverge across times, frequencies, and countries. Accordingly, policy options for BRICS are argued.

Considering the recent restructuration of environmental, social, and governance (ESG) reports in Türkiye, this study uncovers effectiveness of ESG reports in ESG score estimation across diverse liquidity levels. Accordingly, the study examines four different samples as the full sample, Borsa Istanbul 100 (XU100) index, Borsa Istanbul 50 (XU050) index, and Borsa Istanbul 30 (XU030) index, where 102, 60, 43, and 26 companies exist, respectively. The study considers restructured ESG reports for 2022 and 2023 and performs five different machine learning (ML) algorithms. The findings demonstrate that (i) among all segments, environment segment includes principles that have the highest importance, while social, common, and governance segments follow, respectively; (iii) absolute and relative variable importance of ESG principles differentiate; (iii) super learner (SL) is the best ML algorithm, where its estimative power (R2) is around 95\% for the best estimation. Thus, the results demonstrate that the estimative power of restructured ESG reports in the estimation of ESG scores is quite high. Hence, the study highlights a varying contribution of ESG segments and principles to the ESG scores of the companies and reveals a nonlinear need by companies to focus on highly important ESG principles so that companies can stimulate their ESG scores.

This study develops and applies the Financial Risk Meter (FRM) for Kuwait, a novel measure of systemic risk tailored for a commodity-dependent emerging economy. Using Lasso quantile regression, the FRM captures tail-event co-movements among key financial institutions, providing a robust indicator of systemic stress. This paper makes three primary contributions. First, it provides the first application of the FRM framework to an oil-exporting economy, identifying the distinct channels through which global financial shocks and commodity price volatility create systemic risk. Second, it quantitatively demonstrates the FRM's superior performance in tracking financial stress compared to the benchmark Conditional Value-at-Risk (CoVaR) model. Third, it identifies the specific drivers of systemic risk in Kuwait, offering actionable insights for policymakers. Our findings show that the FRM effectively pinpoints periods of high financial distress, aligns with global risk indicators, and can enhance recession forecasting. By providing a clear and timely measure of systemic risk, this study offers a valuable tool for regulators to bolster financial stability and advance sustainable economic development in Kuwait and other resource-dependent nations.

This paper examines whether corporate governance plays a moderating role in the impact of financial development on economic growth. The dataset consists of 39 advanced and developing countries for the 2006-2020 period. The empirical results show that the credit-to-GDP ratio is negatively associated with economic growth, and this finding is consistent with the literature, showing the relevance of "too much finance". The main findings indicate that the negative growth impact of credits is attenuated by corporate governance as measured by minority investor protection and disclosure extent. This moderating effect is economically significant and holds for different country groups and horizons. Hence, the paper argues that corporate governance measures the quality of financial markets, while the credit ratio measures its quantitative dimension. Therefore, it shows that both quality and quantity dimensions need to be taken into account to understand the finance-growth nexus properly.

The present paper examines the changing landscape of the finance-growth nexus using detailed industry-level data for 40 countries and 20 industries spanning 1980–2020. Regarding the long-term relationship between finance and growth, the findings indicate that industries more dependent on external finance experienced stronger growth in financially more developed markets during the 1980s and 1990s, but weaker growth in the 2000s and 2010s. Moreover, private credit changes are associated with lower industrial growth rates over the medium term. In terms of credit types, corporate credits generally have positive effects, while household credits display negative medium-term growth effects. These results highlight significant changes in the finance-growth relationship over the past four decades.

The importance of non-economic factors for the environment is increasing day by day. ICT, globalization, and political stability (PS) are becoming more and more important in environmental policymaking. However, their impact on the environment has not yet been addressed in the literature for the GCC countries. Therefore, the study focuses on the analysis of the impact of mobile cellular subscriptions and internet usage, two different ICT indicators, as well as income, globalization, and PS on environmental quality (EQ). The study considers a total of six GCC countries from 2000 to 2019, applies the augmented mean group approach at both panel and country levels, and tests the validity of the load capacity curve (LCC) hypothesis. The results show that (i) ICT contributes to the development of EQ; (ii) globalization and PS reduce EQ; (iii) income has no significant impact; (iv) the LCC hypothesis is not valid for the GCC panel, while it is valid in Qatar; (v) the results vary at the country level with regard to the panel level. The study recommends that GCC countries should make more use of the positive aspects of ICT in addressing environmental issues and promoting green digitalization to develop EQ; and work to transform their high-income into an eco-friendly structure so that the progress of EQ can be supported by the income; benefit from globalization to import green products and technologies that can help increase EQ; and use the PS to make eco-friendly decisions.

The studies have focused on changes in CO2 emissions over different periods, including the COVID-19 pandemic. Even if CO2 emissions are temporarily reduced during the pandemic according to annual figures, this may be misleading. Considering annual figures is important to understand the overall trend, but using data with much higher frequency (e.g., daily) is much better suited to investigate dynamic relationships and external effects. Therefore, this study comprehensively analyzes the association between CO2 emissions and disaggregated electricity generation (EG) sources across the globe by employing the novel wavelet local multiple correlation (WLMC) approach on daily data from 1st January 2020 to 31st March 2023. The results demonstrate that (1) based on the main statistics, daily CO2 emissions range between 69 MtCO2 and 116 MtCO2, indicating that there is an oscillation, but no sharp changes over the analyzed period. (2) based on the baseline regression using the dynamic ordinary least squares (DOLS) approach, the constructed estimation models have a high predictive ability of CO2 emissions, reaching ~ 94%; (3) in the further analysis employing the WLMC approach, there are significant externalities between EG resources, which affect CO2 emissions. The results present novel insights about time- and frequency-varying effects as well as a disaggregated analysis of the effect of EG on CO2 emissions, demonstrating the significance of the energy transition towards clean sources around the world.

This paper investigates the relationship between credits and sector-level output dynamics in a sample of 41 advanced and developing countries. The existing literature shows that household credits are associated with boom-bust cycles in aggregate output, whereas business loans do not cause such output dynamics. The present paper expands these findings in terms of advanced versus developing countries and manufacturing versus services sectors. The new findings indicate that the resulting boom-bust cycles in the aggregate output in response to household credits are generally observed in the sample of developing countries, with no similar dynamics in the advanced countries. Another significant conclusion is that the boom-bust cycles are generated mostly in the services sector. No equivalent boom-bust dynamics are observed in the manufacturing sector, but the negative medium-run effects of household credits are larger in this sector. These findings indicate that credit mechanisms and consequences can significantly vary across countries and sectors.

This study analyzes the effect of monetary policy, which are proxied by weighted average cost of funding (WACF) and Borsa Istanbul repurchase interest rate (REPO), on the returns of the main financial assets of monetary policy in Türkiye. Using daily data between January 4, 2011 and August 31, 2023, the study applies novel nonlinear time-series methods, such as wavelet coherence (WC) and quantile-on-quantile regression (QQ) as baseline methods and quantile regression (QR) for robustness. The findings demonstrate that (i) monetary policy has a stronger effect on financial asset returns at middle and higher frequencies across different periods; (ii) monetary policy has mainly declines (increases) effect on financial asset returns at lower and middle (higher) quantiles; (iii) the robustness of the outcomes is confirmed. Thus, the outcomes show that monetary policy has a significant effect on financial asset returns, and the effects vary across times, across frequencies, quantiles, and financial assets.

This paper examines the tail dependence structure between energy commodities (Brent oil, natural gas and gasoline) and agricultural commodities (wheat, soybean, corn, cotton, sugar, rice, oat, coffee and cocoa) from 01.06.2017 to 09.06.2023, spanning periods before, during and after Covid-19 pandemic. We employ the tail-restricted integrated regression function (IRF), a novel approach for analyzing nonlinear tail dependence, as it offers further insights into tail events by considering a continuum of quantiles, rather than focusing on a single quantile. The results reveal significant and persistent lower and upper tail dependence across all commodity pairs throughout each period, indicating asymmetric risk transmissions from energy commodities to agricultural commodities. Additionally, the findings are corroborated using cross-quantilogram analysis and nonparametric tests for Granger causality in distribution.

Given the increasing negative impact of environmental issues, mainly caused by high energy use, it is becoming increasingly important to focus on achieving the Sustainable Development Goals (SDGs). In light of these critical issues, this study comprehensively reviews the contemporary environment and energy-related literature from the point of view of SDGs so that the progress of the SDGs can be viewed from both environment and energy perspectives. Accordingly, the study undertakes a global scale by conducting a systematic review approach, identifying 2826 and 1917 articles in environment- SDGs and energy- SDGs areas in SCOPUS database. Through examination of these studies, the outcomes show that: (i) the number of SDGs-related studies has been increasing since 2015 and the number of environment-SDGs studies has exceeded the number of energy-SDGs studies; (ii) in the environment-SDGs studies, sustainable goals, renewable energy, and climate change are the basic themes, but it has changed from 2015 through 2022; (iii) in the energy-SDGs studies, renewable energy, sustainable development, and economic development terms have become motor themes after 2020; (iv) although climate action topic is in the niche theme before 2018, it is categorized as an "emerging or declining" theme after 2020; (v) most of the studies have been published by journals from Springer, MDPI, and Elsevier; (vi) the most prolific authors are from Tsinghua and Cyprus International Universities in environment-SDGs and energy-SDGs studies, in that order. This review assesses the emerging trends in the SDG-related environment and energy literature. Accordingly, policy implications and future research to support the achievement of the SDGs are discussed.

The growing societal concern regarding environmental matters has led to the implementation of many environmental measures intended to protect the environment and address global warming by lessening emissions and mitigating climate change. In line with this movement, this study scrutinizes the impact of these environmental measures on greenhouse gas (GHG) emissions to analyze the cases of Finland and Sweden. More specifically, the study employs the Environmental Policy Stringency (EPS) index as a proxy for environmental measures, explores sector-specific GHG emissions by employing nonlinear quantile-based methodologies (including quantile-on-quantile regression and Granger causality-in-quantiles methods as the primary model and quantile regression for robustness checking) spanning the period from 1991/Q1 to 2020/Q4. The findings show that: (i) EPS lessens GHG emissions from fuel exploitation, industrial combustion, and the power industry sector at lower and middle quantiles in Finland and Sweden; (ii) EPS decreases GHG emissions from processes, transportation, and waste sectors in Finland but increases them in Sweden at higher quantiles; (iii) EPS leads to an increase in GHG emissions from the agriculture and construction sectors at higher quantiles; (iv) EPS has a causal effect on sector-specific GHG emissions across different quantiles; (v) the robustness of the findings is largely confirmed. Hence, the study underscores the varying impacts of EPS on sectoral GHG emissions based on quantiles, sectors, and countries, emphasizing the need for policymakers to adopt environmental policies to comprise these differences and adjust the policy framework accordingly.

The world has witnessed serious climate-related problems. Even though there are various effective factors in this point, sustainable resource consumption takes place among critical factors that have been deeply affecting climate change. Accordingly, Sustainable Development Goals (SDGs) 8-12 have come to the fore for all related parties due to the directly affecting resource use. Among all, Gulf Cooperation Council (GCC) countries have a special position in terms of global climate change-related goals because they have high oil and resource consumption. Hence, this study firstly attempts to investigate the validity of the environmental Kuznets curve (EKC) hypothesis for material footprint (MAF) in GCC countries by analyzing the impact of income, energy consumption, financial institution development, and technological development, performing augmented mean group model, and using data for the period 2000-2019. The findings show that (i) the EKC hypothesis is valid for the GCC panel, whereas it is the case for only Oman on a country basis; (ii) energy consumption increases (decreases) MAF at GCC panel (Qatar); (iii) financial institution development does not affect MAF in the GCC panel, while it causes an increase in Saudi Arabia; (iv) technological development reduces MAF at GCC panel and in Bahrain, Kuwait, and Saudi Arabia on a country basis. Thus, the results highlight the need for GCC countries to focus on both income level and technological development to achieve climate-related targets and SDGs by decreasing resource-based consumption.

The study analyzes the effects of nuclear energy and political stability (PS) on environmental degradation. For this aim, the study uses carbon dioxide (CO2) emissions as the environmental degradation indicator, considers nuclear energy consumption (NEC) and political risk index (PRI) as explanatory variables, uses data between 1991/Q1 and 2021/Q4, and investigates eight highly politically stable countries in this way. Also, the study performs novel quantile-on-quantile regression and Granger causality-in-quantiles models as the fundamental models and applies the quantile regression model for robustness. The results reveal that (i) NEC has a mainly curbing effect on CO2 emissions at higher levels of NEC and is beneficial for Finland, Switzerland, Canada, Netherlands, and United Kingdom; (ii) PS has a generally decreasing effect on CO2 emissions at higher levels of PS and is effective in Finland, Canada, and Germany; (iii) NEC and PS have a causal mainly effects on CO2 emissions in the countries; (iv) the robustness of the results is verified through alternative approach. Overall, there are dependencies from NEC and PS to CO2 emissions and the effects of both NEC and PS on CO2 emissions vary across countries and quantiles. Hence, the results highlight the heterogeneous effects of NEC and PS on CO2 emissions and underline the significance of quantile and country-based analyses for better empirical examination. Various policy caveats are discussed based on the fact that Finland and Canada can benefit from both NEC and PS in decreasing CO2 emissions, whereas Sweden and the USA cannot, and the remaining countries have mixed results.

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 (CO 2 ) 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 CO 2 emissions, and compares the estimation performance of the models. The empirical findings reveal that (i) the ML algorithms outperform the TS models based on R 2 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 CO 2 emissions; (v) electric power and transportation sectors are the most important sectors in the estimation of the CO 2 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 CO 2 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 CO 2 emissions.

This research investigates the effects of income, total energy consumption (TEC), energy price index (EPI), crude oil price (COP), political risk index (PRI), and geopolitical risk (GPR) on environmental degradation. In this context, the study includes five Gulf Cooperation Council (GCC) countries, which are mainly oil-rich and have high fossil fuel energy consumption with increasing environmental degradation; considers monthly data from 2000/1 to 2021/12, and deploys novel quantile-based methods. The outcomes demonstrate that (i) an increase in income, TEC, and EPI stimulates environmental degradation in all GCC countries; (ii) PRI, COP, and GPR have mixed effects on environmental degradation; (iii) a causal effect from the regressors to CO 2 emissions exists in all quantiles except for some middle (0.45–0.55) and higher quantiles (0.95); (iv) the power of effect and causal effect vary according to quantiles and countries; (v) the consistency of the results is validated based on robust model. The findings reveal that an increase in income, TEC, and EPI is generally harmful to the environment in the GCC countries; but, PRI, COP, and GPR have mixed effects. The results of novel quantile-based methods underline the significance of political stability and geopolitical risk effect as non-economic and non-energy factors on environment degradation by demonstrating quantile-based varying effects of the regressors on the environment in GCC countries. Accordingly, various policies, such as focusing on increasing political stability, benefitting from geopolitical risk as leverage, and enabling the transition to clean energy, are discussed.

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.

In this study, dynamic links between central bank reserves (CBR), credit default swap (CDS) spreads, and foreign exchange (FX) rates are investigated. So, Turkey, which is a negative outlier country among other peer emerging countries, is examined by considering recent developments on these indicators. In doing so, the study covers relatively high frequency (i.e., weekly) data from January 2, 2004 to November 12, 2021, performs various econometric approaches as Wavelet Coherence (WC), Quantile-on-Quantile Regression (QQR), and Granger Causality in Quantiles (GCQ) as main models, and applies Toda-Yamamoto (TY) causality and Quantile Regression (QR) for the robustness. The results show that (i) there is a time-frequency dependency between the CBR, CDS spreads, and FX rates; (ii) a bidirectional link exists between the CBR and FX rates; between the FX rates and CDS spreads; and between the CDS spreads and CBR; (iii) the link exists in most quantiles except for some lower and middle quantiles for some indicators; (iv) explanatory effect of the indicators on each other varies based on quantiles; (v) the robustness of the results are validated by the TY causality test for the WC model and by the QR approach for the QQR model. The results suggest the significance of the CBR for the FX rates, the FX rates for the CDS spreads, and the CDS spreads for the CBR.