Can environmental sustainability be decoupled from economic growth? Empirical evidence from Eastern Europe using the common correlated effect mean group test

The European Union(EU) and Organisation for Economic Co-operation and Development(OECD) aim to develop long-term policies for their respective member countries. Having observed increasing dangers to the environment posed by rising economic growth, they are seeking pathways to enable policy action on economic growth and environmental sustainability. Given the facts in theoretical and empirical studies, this study assessed the validity of the decoupling hypothesis by investigating asymmetricity in the relationship between environmental sustainability and economic growth in nine Eastern European countries from 1998 to 2017 using the cross-section augmented Dickey-Fuller(CADF) unit root, panel corrected standard error(PCSE), common correlated effect mean group(CCEMG), and Dumitrescu Hurlin causality approaches. Both population growth and drinking water are used as controlled variables. The outcomes establish strong cointegration among all the variables of interest. According to the results of CCEMG test, economic growth exerts short-term environmental degradation but has long-term environmental benefits in Eastern Europe;and population growth and drinking water exert a positive effect on environmental sustainability in both the short-and long-run. The results of Dumitrescu Hurlin causality test indicate that environmental sustainability is unidirectionally affected by economic growth. Based on these outcomes, we suggest the following policies:(1) the EU and OECD should implement member-targeted policies on economic growth and fossil-fuel use towards regulating industrial pollution, water use, and population control;and(2) the EU and OECD member countries should invest in environmental technologies through green research and development(R&D) to transform their dirty industrial processes and ensure productive energy use.

Blockchain technology‑based FinTech banking sector involvement using adaptive neuro‑fuzzy‑based K‑nearest neighbors algorithm

The study aims to investigate the financial technology(FinTech)factors influencing Chinese banking performance.Financial expectations and global realities may be changed by FinTech’s multidimensional scope,which is lacking in the traditional financial sector.The use of technology to automate financial services is becoming more important for economic organizations and industries because the digital age has seen a period of transition in terms of consumers and personalization.The future of FinTech will be shaped by technologies like the Internet of Things,blockchain,and artificial intelligence.The involvement of these platforms in financial services is a major concern for global business growth.FinTech is becoming more popular with customers because of such benefits.FinTech has driven a fundamental change within the financial services industry,placing the client at the center of everything.Protection has become a primary focus since data are a component of FinTech transactions.The task of consolidating research reports for consensus is very manual,as there is no standardized format.Although existing research has proposed certain methods,they have certain drawbacks in FinTech payment systems(including cryptocurrencies),credit markets(including peer-to-peer lending),and insurance systems.This paper implements blockchainbased financial technology for the banking sector to overcome these transition issues.In this study,we have proposed an adaptive neuro-fuzzy-based K-nearest neighbors’algorithm.The chaotic improved foraging optimization algorithm is used to optimize the proposed method.The rolling window autoregressive lag modeling approach analyzes FinTech growth.The proposed algorithm is compared with existing approaches to demonstrate its efficiency.The findings showed that it achieved 91%accuracy,90%privacy,96%robustness,and 25%cyber-risk performance.Compared with traditional approaches,the recommended strategy will be more convenient,safe,and effective in the transition period.

Economic complexity and environmental sustainability in eastern European economies:Evidence from novel Fourier approach

Globally,economies have become complex and new technologies have transformed and facilitated the modernization of economies.In the previous literature,economic complexity approach has become one of the popular tools in the development and innovation studies of economic geography.Researchers have found that green technology and eco-innovation approaches should be used to decisively reduce the effects of carbon emissions on the environment.However,debates about the impact of economic complexity on environment remain unsettled since some emerging production technologies have far-reaching pollution effects.This study explored the impacts of economic complexity on environmental sustainability in Turkey using the novel Fourier-based approaches,namely:Fourier Augmented Dickey-Fuller(FADF)and Fourier Autoregressive-Distributed Lag(FARDL)models.The Fourier-based approaches indicated that all variables(economic complexity index(ECI),GDP,energy consumption,and CO_(2)emission(CO_(2)E))are cointegrated in the long run.Additionally,the FARDL model implied that(i)in the long run,the effect of ECI(as a proxy for economic complexity),GDP(as a proxy for economic growth),and energy consumption on CO_(2)E(as a proxy for environmental quality)are important;(ii)economic complexity decreases environmental degradation in Turkey;and(iii)economic growth and energy consumption negatively affect environmental quality.The results also showed that economic complexity could be used as a policy tool to tackle environmental degradation.The findings also revealed that the fossil fuelbased economy will continue to expand and undermine Turkey’s efforts to meet its net zero emission target by 2053.Therefore,policy-makers should take actions and establish diversified economic,environmental,and energy strategies.For policy insights,the Turkish governments can use the combination of tax exemptions and technical support systems to support knowledge creation and the diffusion of environmentally friendly technologies The governments can also impose strict environmental regulations on the knowledge development phases.

Co‑movement of commodity price indexes and energy price index:a wavelet coherence approach

This research sheds light on the causal link between commodity price indexes,i.e.,the Agricultural Raw Materials Price Index,Industry Input Price Index,Metal Price Index,and Energy Price Index,in the global market,using wavelet coherence,Toda–Yamamoto causality,and gradual shift causality tests over the period 1992M1 to 2019M12.Findings from the wavelet power spectrum and partial wavelet coherence reveal that:(1)there was significant volatility in the Agricultural Raw Materials Price Index,Industry Input Price Index,Metal Price Index,and Energy Price Index between 2004 and 2014 at different frequencies;and(2)commodity price indexes significantly caused the energy price index at different time periods and frequencies.It is noteworthy that the outcomes of the Toda–Yamamoto causality and gradual-shift causality tests are in line with the results of wavelet coherence.

Relevance of hybrid artificial intelligence for improving the forecasting accuracy of natural resource prices

The prediction performance of traditional forecasting methods is low due to the high level of complexity in a series of energy prices.The present study attempts to compare the traditional regression,machine learning tools and hybrid models to conclude the outperforming model.The first step is to propose the effective denoising technique for Tadawul energy index,which has confirmed the superiority of CSD based denoising.However,we use the CSD-ARIMA,CSD-ANN,and CSD-RNN as hybrid models.As a result,CSD-RNN outperforms both other models in terms of MSE,MAPE,RMSE and Dstat.The findings are useful for policy makers,investors and portfolio managers to forecast the energy trends,and hedge the portfolio risk accordingly.

Does patents on environmental technologies matter for the ecological footprint in the USA? Evidence from the novel Fourier ARDL approach

The outcome of patents on environmental(POET)technologies on the EF in the USA has not been com-prehensively explored.Therefore,to close this breach in the literature,the present study discovers how patents on ecological technologies affect ecological footprint(EF)in the USA while regulatory for GDP and EC using the Fourier-based approaches.The conclusions of the present study reveal that POET are an important predictor of EF in the USA and cause a reduction in ecological deprivation in the long run;as expected,economic growth negatively affects environmental sustainability.The outcomes suggest that it is possible to resolve conflicts between the economy and the environment by using technological inno-vation.The USA government must reconsider its policy focus,particularly on coal energy sourcing and industrial energy,while continuing with heavy investments in its ambitious renewable energy technol-ogy development plan.Moreover,the government should continue to promote investments in environ-mental technologies.