The reporting quality and environment, social, and governance (ESG) scores have become critical in investment and trading decisions. Accordingly, this study finds out the association between Integrated Reporting Quality (IRQ), its pillars, and ESG risk (ESGR) scores of 33 international banks, having Sustainalytics’ ESGR scores and publishing integrated reports between 2020 and 2023. IRQ scores are calculated by considering “Fundamental Concepts”, “Guiding Principles”, and “Content Elements” pillars of IR framework. The findings show that IRQ and its pillars are associated with lower ESGR scores. Also, the robustness of the results is confirmed through green bond issuance and carbon dioxide (CO2) emissions. Therefore, the critical contribution of this study is to empirically reveal the role of IRQ in each pillar and to relate them to ESGR. It introduces a multi-pillar scoring approach that provides a replicable framework for assessing IRQ and ESGR scores. By focusing on ESGR, this study suggests a risk-based perspective on IR and presents how reporting quality functions as a governance mechanism for mitigating sustainability-related risks. The results enable a better theoretical understanding of IR as both a risk mitigation and stakeholder communication mechanism and provides insights for regulators, investors, and institutions.

Climate-related issues have increasingly attracted the attention of policymakers and governments due to their severe environmental, economic, and social consequences. Among these issues, physical climate risk (PCR), including extreme temperature, rainfall, and drought events, have posed substantial challenges to environmental sustainability and economic resilience. In this context, green finance instruments, particularly green bond (GB), have emerged as strategic tools for mitigating the adverse impacts of climate-related risks and supporting sustainable development. Accordingly, this study investigates the relationship between disaggregated-level PCR indicators and GBs in the USA using monthly data from 2014/8 to 2023/12. To capture heterogeneous and tail-dependent dynamics that conventional methods may overlook, the study employs novel quantile-based methods. The empirical findings reveal that: (i) GBs exert a consistently negative impact on the extreme high-temperature index across all quantiles; (ii) GBs have mixed (positive and negative) impacts on the extreme rainfall index, extreme drought index, and climate physical risk index across quantiles; (iii) causal relationships generally run from GBs to PCR subtypes across quantiles, except at several specific quantiles; (iv) the impacts of GBs on aggregated and disaggregated level PCR measures differ mainly at lower quantiles, whereas similar patterns emerge across remaining quantiles; (v) the results remain mostly robust in case of employing alternative GB indicators and econometric model as well as considering income as controlling macroeconomic factor. Providing disaggregated-level evidence and applying novel quantile-based methods, this study contributes to the climate finance literature through offering deeper insights into the heterogeneous role of GBs in managing PCRs. The findings provide policy options for the USA policymakers, regulators, and investors seeking to strengthen sustainable finance strategies and enhance climate resilience.

Environmental problems have been attracting the interest of all relevant parties because of the increasing negative effects on humanity. At this point, further clean, especially nuclear, energy consumption (EC) is seen as a strategic option to combat environmental deterioration (ED). Because clean energy, nuclear energy-related R&D investments (NRD), energy security risk (ESR), as well as increasing economic policy uncertainty (EPU) and trade policy uncertainty (TPU) in recent times have the potential to affect clean EC, this research uncovers the contribution of nuclear EC (NEC) in combating ED by considering also gross domestic product (GDP) and renewable EC (REC) along with the interaction terms of NEC with NRD, ESR, EPU, and TPU. In this vein, the study focuses on the USA case as the biggest economy and leading country in NEC, applies the kernel regularized least squares (KRLS) approach on data from 1974 through 2022, and uses carbon dioxide (CO2) emissions in the main analysis and ecological footprint (EFP) in checking robustness as an ED indicator. The empirical results show that (i) NEC (REC & EPU) is completely ineffective (beneficial) to reduce CO2 emissions; (ii) GDP, ESR, and TPU is almost completely unhelpful to decline CO2 emissions; (iii) the interaction of NRD and EPU with NEC provide a decrease in CO2 emissions; (iv) KRLS approach successfully estimates variations in CO2 emissions around 95%; (v) some variables (e.g., GDP & TPU) have a varying effect across percentiles, whereas others don’t. Thus, the study reveals the efficiency of certain factors (e.g., REC, EPU, interaction of NEC with NRD & EPU) on CO2 emissions, whereas GDP, NEC, ESR, & TPU can’t be helpful to protect the environment. Accordingly, the study argues policy implications (e.g., allocating free/low cost land, ensuring low cost financing support, removing customs-related barriers to import relevant components to install new clean EC capacity in short term, trying to nationally produce clean EC components in long term, ensuring long-term security of rare earth minerals, as well as preventing the displacement between REC and NEC through simultaneously supporting both REC and NEC to appropriately allocating incentives) for USA policymakers.

Countries have been facing environmental problems in recent years, especially due to increasing energy demand. Therefore, resolving this challenge of carbon dioxide (CO2) emissions and moving to eco-friendly, sustainable, and cleaner energy sources has emerged as a major global concern. Accordingly, this research examines the nexus hip between clean electricity generation (EG) subcomponents and power sector CO2 (PCO2) emissions under the moderating role of critical minerals (CMs) prices and geopolitical risk (GPR) in China, which is the biggest economy in the world. In this vein, the study novel quantile-based approaches on data from 2nd January 2019 to 30th June 2025 uncover the daily varying effect. The empirical outcomes demonstrate that (i) EG from clean sources (i.e., hydro, solar, and wind) generally do not reduce PCO2 emissions on a daily basis in bivariate cases; (ii) price changes of CMs for each EG subtype present moderating effects, where there are reducing effects on PCO2 emissions across various quantiles; (iii) there are causal effects from EG subtypes and price changes of CMs to PCO2 emissions across almost all levels, with a few exceptions at lower and middle quantiles; (iv) the moderating role of price changes of CMs causes a weakening effect on the nexus between each EG subtype and PCO2 emissions; (v) These outcomes are robust based on an alternative approach. Thus, the study reveals that the nexus varies across EG subtypes and quantiles under the moderating effect of CM price changes. Accordingly, the study discusses policy options (e.g., ensuring a displacement in favor of renewable energy sources, focusing on hydro EG to decarbonize PCO2, providing various financial and fiscal incentives to stimulate further installation of renewable energy capacity, trying to ensure a price stability in CMs’ market, prioritizing domestic market first) to decarbonize the Chinese power sector.

Countries’ and societies’ interest in becoming green has been developing in almost every area. Accordingly, the recent focus point has been becoming green economics. Therefore, dealing with green economics is important because it implicitly includes many issues. Therefore, to be compatible with the developing interest and importance of green economics, this study empirically examines green economics. In doing so, the study analyzes the global condition; uses the Nasdaq Green Economy Index (GREC) as the proxy of green economics; considers the Factset Supply Chain Logistics Index (SCLI) and Nasdaq Green Transportation Index (GTRI) as explanatory variables, which represent the critical factors in becoming green economics; controls the S P Green Bond Index (SPGB), Brent Crude Oil Price (OIL), and geopolitical risk (GPR) index; uses data between 2nd January 2017 and 31st May 2024; and applies novel nonlinear quantile methods. The study shows that (i) SCLI and GTRI have a strong and increasing impact on GREC, where the power of increasing impact varies across quantiles; (ii) SCLI has a greater increasing impact than GTRI does on GREC; (iii) the impact of SCLI and GTRI on GREC continues to increase under the moderating impact of SPGB, OIL, and GPR, whereas these factors cause slight weakening; (iv) there is strong bidirectional causality; and (v) the robustness of the outcomes is verified by the alternative method. In this way, the empirical outcomes highlight the key role of supply chain logistics and green transportation in ensuring green economics even under the impact of green finance, oil prices, and geopolitical risk. Thus, in terms of outcomes, this study discusses policy endeavors (e.g., digitalization of supply chain logistics and electrification of transportation) to benefit from these factors to ensure further greening of the global economy.

Countries have been facing environmental problems, especially due to increasing energy demand. Therefore, addressing the challenge of carbon dioxide (CO2) emissions by transitioning to cleaner energy sources is a major global concern. Accordingly, this research examines the relationship between clean electricity utilization (EU) subcomponents and power sector CO2 (PCO2) emissions, considering the moderating roles of critical mineral (CM) prices and geopolitical risk (GPR) in the USA, the world's largest economy. In this vein, the study applies a novel kernel-regularized least squares (KRLS) model from 1st January 2019 to 28th November 2025, using the most recent accessible data, to uncover daily-varying impact. The results demonstrate that (i) hydro (wind) EU is associated with an insignificant (reducing) relationship with PCO2 emissions, whereas solar EU and oil price are associated with higher PCO2 emissions at higher percentiles; (ii) CMs' price changes moderate impact of clean EU subcomponents on PCO2 emissions while some CMs' prices are much more important than others for each clean EU subcomponents (i.e., steel for hydro EU; silicon for solar; neodymium for wind); (iii) GPR strengthens the impact of solar EU on PCO2 emissions, whereas it is not effective for the impact of other EU subcomponents' impact on PCO2 emissions; (iv) KRLS explains similar to 66 % of variations in PCO2 emissions; (v) the results are generally consistent based on the use of alternative CMs' prices and GPR series (i.e., 7 days average). Thus, this research highlights percentile-based varying relationship of some (e.g., hydro & solar) EU subcomponents and oil price, whereas some factors (e.g., wind EU) exhibit a stable negative relationship with PCO2 emissions across percentiles, as well as the moderating role of CMs' prices and GPR on the relationship between clean EU subcomponents and PCO2 emissions. Accordingly, the study discusses policy implications to ensure a reduction in PCO2 emissions in the USA.

The importance of AI and R&D investments has become increasingly salient in the context of rising carbon dioxide (CO2) emissions. So, this study examines how CO2 emissions relate to energy consumption (EC) sub-types and whether AI-related patents (AIP) and energy-related R&D investments (ERD) moderate the relationship. In this vein, the study focuses on the USA, uses EC sub-types as explanatory variables, considers the moderating role of AIP and ERD, and applies novel quantile-based methods on data from 1981/Q2 to 2020/Q4. The results indicate that (i) oil and coal EC are associated with higher CO2 emissions across quantiles in both bivariate and multivariate models; (ii) while gas EC increases CO2 emissions across all quantiles in bivariate and multivariate cases, there is a decreasing impact at lower quantiles with ERD moderation; (iii) nuclear EC increases CO2 emissions across all quantiles in bivariate case, whereas the impact changes under the moderating impacts of AIP and ERD; (iv) renewable EC decreases CO2 emissions across all quantiles in bivariate case, while the reducing impact is almost same under the moderating impacts of AIP and ERD; (v) AIP has a much stronger moderating impact than ERD on relationship between CO2 emissions and EC sub-types; (vi) there are generally causal impacts across quantiles, except for some lower, middle, and higher ones, where the causal impact varies across the variables pairs. Accordingly, the study outlines policy options consistent with the distributional patterns observed.

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.

In response to the climate change problem, ensuring a transformation to green economies is highly critical. Therefore, countries have been trying to make their economies decarbonized by taking various measures. Accordingly, this study examines the USA case by using carbon dioxide emissions (load capacity factor) as the main (robustness) environmental proxy, considers energy-related research and development (R&D) investment types as main explanatory variables, controls income and energy utilization sub-types, and performs a novel kernel-based least squares (KRLS) model on data from 1974 to 2022 to apply a marginal effect analysis. The results show that (i) R&D investment sub-types have an insignificant effect on carbon dioxide (CO2) emissions; (ii) income structure does not contribute in greening economy; (iii) among energy utilization sub-types, only renewable energy has a decreasing effect on CO2 emissions, whereas nuclear and fossil energy sub-types have a reverse ones; (iv) the effects of the factors on CO2 emissions differentiate across percentiles and estimation models; (v) the robustness of the empirical results are verified based on alternative indicator; (vi) the KRLS model has a high estimation capability around 99.7%. Hence, the empirical results reveal the critical role of renewable energy use, while the current R&D investment structure, energy utilization, and income are not supportive of a green economy in the USA because these do not provide a decrease in CO2 emissions.

This study explores links between uncertainty metrics and fossil and green energy sectors, applying an innovative quantile-on-quantile connectedness method to analyze spillovers across quantiles from August 2004 to December 2023. Our sample comprises the clean and fossil energy market indices and key uncertainty measures, including climate, economic, geopolitical, and infectious diseases uncertainty indices. All total connectedness indices were found to peak at extremely reversely related quantiles, except for climate policy uncertainty. The strongest connectedness is between high economic policy uncertainty and low clean energy returns. The economic policy uncertainty index was dynamically reversely related to energy markets in all quantiles. However, after 2016, the connectedness between climate policy uncertainty and energy market indices converted to positive, possibly due to the impact of the Paris Agreement. Compared to climate-related uncertainty, geopolitical and economic uncertainties have a notably more substantial influence on energy markets, particularly in the green energy sector. Other findings reveal that energy market performance significantly influences climate policy uncertainty, and that infectious disease uncertainty is transmitted across various quantiles. Given the findings, we propose policy implications for investors and policymakers, emphasizing the critical need for considering different quantiles in measuring the dynamic connectedness between various uncertainties and energy markets.

Due to the increasing negative effects on humanity, searching for potential solutions to combat environmental problems has been developing. Accordingly, the study examines the effect of a set of critical factors on environmental sustainability (ES) proxied by ecological footprint (EFP) and load capacity factor (LCF) in China. In this context, the study considers AI-related patents, energy transition, environmental policy stringency (EPS), income, and energy consumption (EC) sub-types and applies the Kernel Regularized Least Squares (KRLS) approach on data from 2000 to 2020 within the context of marginal effect analysis. The outcomes show that (i) AI-related patents and energy transition are completely ineffective to ensure ES; (ii) EPS are marginally effective only at 0.25th and 0.75th percentiles to support ES; (iii) economic growth as well as oil, gas, and coal EC are not good for ES across all percentiles; (iv) nuclear EC is only helpful at 0.25th percentiles, whereas renewable EC is completely unbeneficial; (v) KRLS approach presents successful prediction outcomes around 99.7 % (vi) some variables (i.e., nuclear and renewable EC as well as EPS); have marginal and varying effects across percentiles, whereas some others have not. Thus, the study empirically demonstrates the inefficiency of AI-related patents and energy transition on the ES, whereas EPS and nuclear EC can be helpful to develop ES in the Chinese case.

Given the effects of Environmental, Social, and Governance (ESG) scores on financial performance and stock returns, the prediction of future ESG scores is highly crucial. ESG scores are calculated using an enormous number of variables related to the sustainability practices of firms; thus, it is impractical for investors to come up with predictions of ESG performance. This paper aims to fill this gap by using only the past score-based and rating-based ESG performance as the determinant of future ESG performance using four machine learning-based algorithms; decision tree (DT), random-forest (RF), k-nearest neighbor (KNN), and logistic regression (LR). The proposed model is validated in BIST sustainability index companies. The results suggest that past ESG grade-based and numerical scores can be used as a determinant of future ESG performance. The results prove that a simple indicator could serve to predict future ESG scores rather than complex data alternatives. Using data from BIST sustainability index companies in Turkey, the findings demonstrate that past ESG grades and scores are reliable predictors of future ESG performance, offering a simple yet effective alternative to complex data-driven methods. This study not only contributes to advancing sustainable finance practices but also provides practical tools for emerging markets like Turkey to align corporate strategies with global sustainability standards. The methodological contributions also have broader relevance for international financial markets.

Environmental degradation (ED) has emerged as a significant challenge against the increasing demands of modern civilization. Therefore, transforming the economic structure into an eco-friendly structure is highly critical. So, this study focuses on the impacts of productive capacity shifts in key areas on ED in leading six developed economies by considering carbon dioxide (CO2) emissions as a dependent variable; using the productive capacity index (PCI) for human capital (PCI-HCA), transport (PCI-TRA), institutions (PCI-INS), energy-related public R&D investments, economic growth, nuclear energy, and renewable energy as independent variables; and applies a kernel-based regularized least squares (KRLS) method on data from 2000 to 2022. The results show that (i) PCI-HCA curbs CO2 emissions in all countries except the United Kingdom; (ii) PCI-TRA and PCI-INS are ineffective in declining CO2 emissions in all countries); (iii) R&D investments are helpful in all countries except Canada and Japan; (iv) economic growth structure is not eco-friendly in all countries; (v) nuclear (renewable) energy use is beneficial in Japan (all countries except Canada & France; (vi) KRLS method provides high estimation results similar to 99.2 %. Accordingly, the study discusses policy implications to prevent the ED by benefitting from productive capacity shifts, clean energy, and R&D investments in transforming economic structure.

This paper aims to examine the impacts of selected stress variables, such as FSI (Financial Stress Index), VIX (Volatility Index), and EPU (Economic Policy Uncertainty), on dynamic connectedness between green markets (stocks and bonds) and fossil energy commodities. We employ the TVP-VAR model to measure connectedness and the Fourier Cumulative Granger Causality test to investigate the impacts of these stress variables on this connectedness from November 1, 2012, to November 15, 2022. The results indicate moderate return connectedness, mainly from short-term dynamics, suggesting that diversification may be more beneficial for long-term investments. We observe high connectedness during the COVID-19 pandemic. The connectedness is high among fossil energy commodities but low among green stock and bond markets, except for water company stocks. Water stocks have a significant impact on markets, followed by oil. Our causality test results indicate that the FSI and VIX impact the connectedness between them.

This study analyzes the impact of critical factors (i.e., energy consumption (EC), income (GDP), geopolitical risk (GPR), energy transition, and energy prices). In doing this, the study focuses on Brazil, Russia, India, China, and South Africa (BRICS) countries, which are the leading emerging countries, considers carbon dioxide (CO2) emissions industry, power, and transport sectors, uses yearly data from 2000 to 2022, and performs a kernel-based regularized least squares (KRLS) approach to uncover the marginal impact of the factors. The outcomes demonstrate that (a) the impacts of the factors on sectoral CO2 emissions vary marginally across economic sectors, factors used, and levels of the variables; (b) the statistical significance of the factors considered differentiate, which implies that some factors are much more critical than others across countries and sectors; (c) for industry sector CO2 emissions, Brazil can benefit from the marginal decreasing impact of gas and renewable EC, GDP, and GPR, whereas it is valid in Russia (South Africa) for gas (GPR and energy prices) impact; (d) for power sector CO2 emissions, Brazil can use nuclear EC, energy transition, and energy prices, whereas nuclear and renewable EC, as well as GDP and GPR (renewable EC and GPR), is beneficial for Russia (South Africa); (e) for transport sector CO2 emissions, GPR (renewable EC) can be relied on in Brazil (Russia); and (f) the KRLS approach has a superior prediction capacity reaching 99.8%. Overall, the study empirically shows the varying marginal impacts of the factors on the decarbonization of the sectors.

The increasing global emphasis on environmental sustainability has amplified the strategic relevance of green finance and clean energy within decarbonization initiatives. In alignment with this paradigm, the present study empirically investigates the impact of green bonds (GBs) and various subcategories of clean energy production (EP), while accounting for oil price dynamics and geopolitical risk (GPR), across the period from January 1, 2019, to July 31, 2024. The analysis employs the Kernel-Based Regularized Least Squares (KRLS) methodology to capture the marginal effects of these variables on sector-specific decarbonization outcomes. The empirical findings reveal several key insights: (i) neither GBs nor nuclear EP effectively contribute to sectoral decarbonization; (ii) hydro EP enhances decarbonization in the residential sector; (iii) solar EP significantly supports decarbonization in both the power generation and residential sectors; (iv) wind EP facilitates decarbonization particularly in the transportation and power sectors; (v) elevated oil prices and heightened geopolitical risk are associated with improved decarbonization outcomes in the industrial and power sectors; (vi) the KRLS model demonstrates a robust predictive capability, achieving an accuracy rate of approximately 97 %; and (vii) the marginal effects of the independent variables are heterogeneous across sectors, determinants, and distributional percentiles. These results substantiate the argument that while GBs currently fall short in delivering effective decarbonization, specific clean EP modalities, alongside market and geopolitical conditions, exert varied and sector-dependent influences. Thus, the study offers critical empirical evidence to inform policymakers and investors regarding the nuanced role of green finance and clean EP in advancing global decarbonization agendas.

Countries have been concerned about the energy transition and related research and development (R&D) investments in energy to combat environmental degradation due to their beneficial effects on energy use as a critical factor in environmental change. Accordingly, the study uncovers the marginal effects of the energy transition, renewable and nuclear energy R&D investments, income, and energy use on carbon dioxide emissions (ecological footprint for robustness) in six advanced countries by using data from 2000 to 2022 and performing the Kernel-based Least Squares method, which provides marginal effect across percentiles. The results show that energy transition only develops the environment in France. Moreover, R&D investments in renewable (nuclear) energy are beneficial for the environment in France and the UK (the US and the UK). Moreover, income is helpful in most countries, except Japan and the UK. On the other hand, energy use is harmful in all countries. Thus, the results present the beneficial effect of R&D investments in energy and the less helpful effect of the energy transition. Thus, France, the UK, and the US have a better position to benefit from the energy transition and energy-related R&D investments.

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.

Because the role of stringent environmental policies, energy use, and eco-friendly economic growth is highly critical in combating climate-related problems and preserving environmental quality, this study uncovers the incremental impact of aforementioned factors on load capacity factor (LCF) in G7 countries between 2000 and 2020 by performing a kernel-based regularized least squares (KRLS) model. The outcomes show that (i) gross domestic product (GDP) has only a supporting impact on LCF in the USA; (ii) market-based environmental policies are beneficial in Canada, France, Japan, and the USA; (iii) nonmarket-based environmental policies are helpful in France and USA; (iv) renewable energy use has positive support in Germany, Italy, Great Britain, and USA; (v) fossil energy use is harmful in all countries; (vi) the KRLS model has a high prediction performance; (vii) with regarding to G7 countries, the USA has the most positive condition. Thus, the study empirically highlights the average and pointwise incremental impact of the factors considered on LCF across countries and percentiles. Accordingly, the study discusses various policy options, such as mainly focusing on market-based environmental policies through making required regulations, considering also nonmarket-based environmental policies as a supportive mechanism, relying on further use of renewable energy through support packages and incentives, which should be taken into account in case of any additional measures application in the environmental area.