Do natural resources impact economic growth:An investigation of P5+1 countries under sustainable management

Natural resources represent the base of our living and the entire economic activity.Their depletion is a major challenge for the economic development of both developed and developing economies.Their effi-cient use is an indispensable requirement and must be the aim of the public policies designed by the authorities worldwide.In this research,we have investigated the impact of the natural resources rent on the economic growth in some major wealthy economies of the world(P5+1 countries namely:US,UK,France,China,Russia,and Germany).We have applied a quantile-on-quantile regression to analyse this impact on different quantiles and a cross-sectional autoregressive distributed lag(CS-ARDL)approach for the panel of these six countries.The Dumitrescu-Hurlin panel causality test was also used to check the causality between natural resource rents and economic growth in these countries.Results show a negative relationship between natural resources rent and economic growth for the panel but a different impact on quantiles in each country.Only for China and the US,a positive effect can be noticed for both lower and higher quantiles of natural resources and economic growth.The Dumitrescu-Hurlin causality test shows that natural resources can predict economic growth only in China,the U.S.,and the panel.In contrast,no causality was found for the other four countries included in the panel.We suggest that nations invest in wind and solar projects,use biofuels and nuclear energy,introduce a temporary profit tax to protect consumers from escalating energy prices,and increase energy efficiency in buildings and industry.Businesses would benefit from a regulatory framework that is uniform and exhaustive,as well as easier to traverse and more receptive to innovation and creativity.Public-private partnership investments in innovation,innovation incentives,and environmental sector opportunities may foster long-term economic growth。

Review of Dimension Reduction Methods

Purpose: This study sought to review the characteristics, strengths, weaknesses variants, applications areas and data types applied on the various Dimension Reduction techniques. Methodology: The most commonly used databases employed to search for the papers were ScienceDirect, Scopus, Google Scholar, IEEE Xplore and Mendeley. An integrative review was used for the study where 341 papers were reviewed. Results: The linear techniques considered were Principal Component Analysis (PCA), Linear Discriminant Analysis (LDA), Singular Value Decomposition (SVD), Latent Semantic Analysis (LSA), Locality Preserving Projections (LPP), Independent Component Analysis (ICA) and Project Pursuit (PP). The non-linear techniques which were developed to work with applications that have complex non-linear structures considered were Kernel Principal Component Analysis (KPCA), Multi-dimensional Scaling (MDS), Isomap, Locally Linear Embedding (LLE), Self-Organizing Map (SOM), Latent Vector Quantization (LVQ), t-Stochastic neighbor embedding (t-SNE) and Uniform Manifold Approximation and Projection (UMAP). DR techniques can further be categorized into supervised, unsupervised and more recently semi-supervised learning methods. The supervised versions are the LDA and LVQ. All the other techniques are unsupervised. Supervised variants of PCA, LPP, KPCA and MDS have been developed. Supervised and semi-supervised variants of PP and t-SNE have also been developed and a semi supervised version of the LDA has been developed. Conclusion: The various application areas, strengths, weaknesses and variants of the DR techniques were explored. The different data types that have been applied on the various DR techniques were also explored.

Energy-growth nexus for‘Renewable Energy Country Attractiveness Index’countries:Evidence from new econometric methods

This study explores the connections between renewable energy consumption(REC),non-renewable energy consumption(NREC),gross fixed capital formation(GFCF),the labor force(LF),and economic growth(GDP)in Renewable Energy Country Attractiveness Index(RECAI)countries for 1991-2016.We quantify the nexus between REC,NREC,and GDP while utilizing a production model framework and including the measures of labor and capital,for suggesting a phase-wise strategy to attain the sustainable development goals.We use robust methodologies including Lagrange Multiplier(LM)panel unit root tests with trend shifts,Westerlund cointegration test,LM bootstrap technique for cointegration with breaks,continuously updated fully modified(CUP-FM)and continuously updated bias-corrected(CUPBC)estimators,Augmented Mean Group(AMG)approach,fully modified ordinary least squares,dynamic ordinary least squares,Canonical Cointegrating Regression(CCR),and panel causality test proposed by Canning&Pedroni.We compute non-parametric time-varying coefficients with fixed effects for seeing the impact of GFCF,LF,REC,and NREC on GDP.Our results press upon policymakers to shift toward clean energy and REC for attaining the environmental goals(SDGs 6,7,13,and 15)and the economic goals(SDGs 1,2,8,and 10).While this shift would help developed economies,which have already attained the economic goals,to progress on the front of environmental goals,it would enable developing countries to progress on both fronts in a balanced manner.