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The world has experienced climate-related issues, which increase the importance of ESG disclosures and corporate governance (CG) of companies, which take place at the heart of economies. Therefore, improving ESG disclosures and CG practices becomes significant to combat climate change at the company level. Considering that Türkiye restructured ESG disclosures in 2022, this study investigates the role of CG on the nexus between ESG scores of publicly traded companies (PTC) and ESG reports. So, the study analyzes 102 PTC (full sample), 51 PTC in Borsa Istanbul Corporate Governance Index (in-sample), and the remaining 51 PTC (out-sample) using ESG disclosures of 2022 and applying novel super learner (SL) algorithm. Our results show that (i) SL has a higher prediction performance reaching ∼94.3%; (ii) the environment (governance) layer has the highest (lowest) total relative importance (contribution) to ESG scores in all samples; (iii) C8, S6, and E5 are the most important ESG principles in the full sample, in-sample, and out-sample, respectively; (iv) the contribution of each ESG principles to the total ESG scores varies by sample; (v) CG plays a smoothing role for the relative importance of each ESG principle, while the relative importance in the out-sample shows much higher volatility. Overall, the study reveals the non-linear contributions of ESG principles on ESG scores and suggests that PTC should prioritize highly important ESG principles, consider the moderating role of CG on the link between ESG scores and ESG disclosures, and use ESG disclosures as a strategic tool to develop ESG scores and disclosures., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

Public interest in climate change-related problems has been developing with the contribution of the recent energy crisis. Accordingly, countries have been increasing their efforts to decarbonize economies. In this context, energy transition and energy-related research and development (R&D) investments can be important strategic tools to be helpful to countries in the decarbonization of economies. Among all, Nordic countries have come to the force because of their well-known position as green economies. Hence, this study examines Nordic countries to investigate the impact of energy transition, renewable energy R&D investments (RRD), energy efficiency R&D investments (EEF) on carbon dioxide (CO 2 ) emissions by performing wavelet local multiple correlation (WLMC) model and using data from 2000/1 to 2021/12. The outcomes reveal that (i) based on bi-variate cases, energy transition and RRD have a mixed impact on CO 2 emissions in all countries across all frequencies; EEF has a declining impact on CO 2 emissions in Norway (Sweden) at low and medium (very high) frequencies; (ii) according to four-variate cases, all variables have a combined increasing impact on CO 2 emissions; (iii) RRD is the most influential dominant factor in all countries excluding Norway, where EEF is the pioneering one. Thus, the reach proves the varying impacts of energy transition, RRD, and EEF investments on CO 2 emissions. In line with the outcomes of the novel WLMC model, various policy endeavors, such as focusing on displacement between sub-types of R&D investments, are argued to ensure the decarbonization of the economies., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

This study aims to analyze comprehensively the impact of different economic and demographic factors, which affect economic development, on environmental performance. In this context, the study considers the Environmental Performance Index as the response variable, uses GDP per capita, tariff rate, tax burden, government expenditure, inflation, unemployment, population, income tax rate, public debt, FDI inflow, and corporate tax rate as the explanatory variables, examines 181 countries, performs a novel Super Learner (SL) algorithm, which includes a total of six machine learning (ML) algorithms, and uses data for the years 2018, 2020, and 2022. The results demonstrate that (i) the SL algorithm has a superior capacity with regard to other ML algorithms; (ii) gross domestic product per capita is the most crucial factor in the environmental performance followed by tariff rates, tax burden, government expenditure, and inflation, in order; (iii) among all, the corporate tax rate has the lowest importance on the environmental performance followed by also foreign direct investment, public debt, income tax rate, population, and unemployment; (iv) there are some critical thresholds, which imply that the impact of the factors on the environmental performance change according to these barriers. Overall, the study reveals the nonlinear impact of the variables on environmental performance as well as their relative importance and critical threshold. Thus, the study provides policymakers valuable insights in re-formulating their environmental policies to increase environmental performance. Accordingly, various policy options are discussed., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

Consistent with the increasing environmental interest, the clean energy transition is highly critical to achieving decarbonization targets. Also, energy security has become an important topic under the shadow of the energy crisis,. Accordingly, countries have been trying to stimulate clean energy use to preserve the environment and ensure energy security. So, considering the leading role of economic size and volume of energy use, the study examines the USA to define whether energy transition helps decrease energy security risk (ESR) and curb CO 2 emissions. So, the study applies a disaggregated level analysis by performing quantile-based models for the period from 2001/Q1 through 2022/Q4. The results demonstrate that (i) the energy transition index decreases environmental ESR at higher quantiles and reliability ESR at lower and middle quantiles, whereas it is not beneficial in declining economic and geopolitical ESR; (ii) energy transition curbs CO 2 emissions in building and transport sectors at lower quantiles, whereas it does not help decrease CO 2 emissions in industrial and power sectors; (iii) energy transition is mostly ineffective on ESR, whereas it is highly effective in curbing CO 2 emissions in all sectors except for transport across various quantiles as time passes; (iv) the results differ according to the aggregated and disaggregated levels; (v) the results are consistent across main and alternative models. Hence, the study highlights the dominant effect of energy transition in curbing sectoral CO 2 emissions rather than easing ESR. Accordingly, the study discusses various policy implications for the USA., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

This study aims to examine the heterogeneous causality and impact of environmental taxes at both aggregated and disaggregated levels on environmental quality. In this context, the study focuses on Nordic countries as green economies; handles carbon dioxide (CO 2 ) emissions as an environmental quality indicator; includes aggregated and disaggregated levels of environmental taxes as explanatory variables; uses quarterly data for the period 1994/Q1-2020/Q4 as the most recent available data; applies novel nonparametric Granger causality-in-quantiles (GCQ) and quantile-on-quantile regression (QQR) approaches as the main models while using quantile regression (QR) for robustness check. The results present that (i) causal impacts of environmental taxes on CO 2 emissions exist in most quantiles at disaggregated levels excluding some lower, middle, and higher quantiles, whereas indicator-, country-, and quantile-based results vary; (ii) environmental tax on energy (ETE) has a mainly decreasing impact in Iceland, a mixed impact in Denmark, Finland, Norway, and Sweden based on quantiles; (iii) environmental tax on pollution (ETP) has the highest decreasing impact in most quantiles in Denmark, Iceland, and Norway; (iv) environmental tax in transport (ETT) has a decreasing impact in Norway and Sweden, whereas it has a reverse impact in Denmark, Finland, and Iceland; (v) impact of total environmental tax (TET) has a decreasing impact in Denmark and Norway at some quantiles, whereas an increasing impact in Finland, Iceland, and Sweden; (vi) the robustness of the QQR results are confirmed by the QR approach. Hence, the results underline the importance of country and quantile-based disaggregated analyses and Nordic countries should re-adjust environmental taxes to increase environmental quality., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2022 Elsevier Ltd. All rights reserved.)