Browsing by Author "Mukhtarov, Shahriyar"

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Citation - WoS: 2
Citation - Scopus: 1
Effect of AI-Related Patents, Energy Transition, Environmental Policy Stringency, Income, and Energy Consumption Sub-Types on the Environmental Sustainability: Evidence from China by KRLS Approach
(Academic Press Ltd- Elsevier Science Ltd, 2025) Taşkın, Dilvin; Kim, Eonsoo; Mukhtarov, Shahriyar; Kirikkaleli, Derviş; Kılıç Depren, Serpil; Park, Jinsu; Depren, Serpil Kilic; Kartal, Mustafa Tevfik
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.
Citation - Scopus: 1
Impact of disaggregated level clean electricity on CO2 emissions: Evidence from EU-5 countries by bivariate and multivariate QQ approaches
(SAGE PUBLICATIONS LTD, 2024) Tevfik Kartal; Ugur Korkut Pata; Dilvin Taskin; Shahriyar Mukhtarov; Taşkın, Dilvin; Mukhtarov, Shahriyar; Pata, Ugur Korkut; Kartal, Mustafa Tevfik
Considering the energy crisis in Europe and searching for alternatives this study investigates the impact of clean electricity generation (EG) types on the environment. So the study focuses on EU-5 countries (Germany-DEU Spain-ESP France-FRA United Kingdom-GBR and Italy-ITA) uses CO2 emissions as environmental indicator and considers clean EG types as explanatory variables by controlling geopolitical risk. Accordingly the study uses data from 2(nd) January 2019 to 29(th) February 2024 and applies bivariate and multivariate quantile-on-quantile regression (BQQ & MQQ) and Granger causality-in-quantiles (GCQ) as the fundamental approaches while quantile regression (QR) is performed for the consistency check. The outcomes reveal that (i) hydro EG increases CO2 emissions across countries excluding DEU at lower and middle quantiles, (ii) solar EG curbs CO2 emissions at middle quantiles in DEU higher quantiles in ESP and FRA and middle and higher quantiles in ITA, (iii) wind EG has an almost decreasing impact across quantiles excluding higher quantiles in DEU and FRA, (iv) clean EG types are almost causally impactful on CO2 emissions across quantiles, (v) geopolitical risk decreases the power of the impact of clean EG alternatives on CO2 emissions but does not change them in a reverse way. To sum up the impact of clean EG types on CO2 emissions in EU-5 countries varies across EG types quantiles and countries. Thus the study suggests that wind EG is highly beneficial for all EU-5 countries while there is also room for growth to benefit from hydro and solar EG for some countries.
Citation - WoS: 1
Citation - Scopus: 1
Relationship between CO2 Emissions and Energy Consumption Sub-Types under Impact of AI-Related Patents and Energy-Related R&D Investments: Evidence from the USA by Novel Quantile-Based Methods
(Elsevier Sci Ltd, 2026) Taşkın, Dilvin; Kim, Eonsoo; Mukhtarov, Shahriyar; Kirikkaleli, Derviş; Kılıç Depren, Serpil; Park, Jinsu; Depren, Serpil Kilic; Kartal, Mustafa Tevfik
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.