This paper explores the impact of economic complexity and financial development on energy efficiency through two indicators: energy intensity and carbon intensity, within the top 10 complex economies. Other control variables, such as economic growth, urbanization, and human capital, are also included in the models. Random and fixed-effects estimators with heteroskedasticity-consistent standard errors were used. Clustered random and fixed-effects estimators were considered to handle within-group (cluster) dependence. The study utilized the Driscoll-Kraay (DK) method to address non-spherical disturbances and ensure consistent standard errors and robustness against dependence. Feasible Generalized Least Squares (FGLS) considered entity-specific autocorrelation, and System Generalized Method of Moments (Sys-GMM) accommodated unobserved panel-level effects, were also employed. Our findings reveal that economic complexity is crucial in reducing carbon and energy intensity, highlighting the significance of fostering technological sectors to diversify and sophisticate the productive structure. However, the influence of financial development on these intensity measures remains ambiguous and necessitates further exploration. We propose policy implications aimed at boosting technological sectors, facilitating a green transition in the economy, and advancing the adoption of renewable energy technologies.

Import product diversification is a major parameter in international trade. Import diversification contributes to economic growth and affects the environment due to its impact on energy consumption. In this article, we investigate the impact of import product diversification along with income, oil prices, natural resources, population, and foreign direct investment on energy demand, covering a composite sample of 102 developing and 36 developed economies over the period from 1995 to 2020. We also assess the impact of import diversification on energy demand considering all sub-samples. We find a significant long-run cointegration between total energy demand and import diversification for both developed and developing countries, confirmed by Pedroni cointegration tests. We further denote that import diversification together with other independent variables is stationary after the first differences in LLC unit root tests. Contrary to traditional methods, we apply panel quantile regression and conclude that import diversification, GDP, oil prices, foreign direct investment, natural resources, and population share long-run integration with total energy consumption for both developed and developing countries. The Dumitrescu and Hurlin short-run causality test confirms the existence of pair-wise bidirectional causality between all independent variables including import diversification, GDP, oil prices, natural resources, foreign investment, and population with energy demand. Our empirics conclude with important policy implications for sustainability.

In recent years, the escalating trade activities (imports and exports) have spurred a growing body of research on their linkages with economic progress and sustainability. However, the impacts of imports and export diversification on sustainable economic growth have been rarely explored. This paper aims to fill this gap by exploring the role of import product diversification in the sustainable economic performance of South Asian countries. More precisely, this paper introduces the export-led growth hypothesis and explores the role of import product diversification, natural resources, and human capital for sustainable economic growth. Empirically, this paper uses data from 1995 to 2020 with 9 South Asian economies and runs advanced panel cointegration techniques along with long-run estimation techniques robust to endogeneity, cross-sectional dependence, autocorrelation, and other panel data problems. The outcomes display a significant correlation between import product diversification, natural resources, and sustainable development. The findings can provide insights to formulate efficient import diversification strategies for sustainable growth, especially in developing countries.

Economic complexity is considered key a driver of social change, structural change, and economic development. Economic complexity is mostly used to capture issues apropos product diversification of exports, trade, technological innovation, human knowledge, and skills. The current study has conducted a detailed bibliometric review of economic complexity, export quality, and trade diversification. In doing so, the authors used the literature up to 2021 to unveil economic complexity’s contextual information that witnessed structural change, social change, and trade indicators. The current study is the first integrative review to report the theoretical contribution, future research agendas, and thematic analysis of economic complexity, export quality, and export diversification. Our study, on the subject of economic complexity, export diversification, and import diversification in the period from 1966 to 2021, was carried out by systematically scanning 386 documents, and it is one of the pioneering studies in this field. In addition, economic diversity, development, and economic complexity; export diversification, import diversification, trade openness, and economic growth; energy, environmental Kuznets curve, and economic complexity; and sustainability and economic diversification are the four main research topics of the study. The findings are discussed apropos of economic complexity and exports, methodological aspects of economic complexity, and environmental issues nexus with economic complexity. The current study reports novel findings toward a path for achieving SDG-9 (industry and innovation) and SDG-13 (climate action). The biometric review enables researchers and policymakers to understand export quality, economic complexity, and the trade nexus and report future research directions for achieving sustainable growth in industries and innovation.

The prime objective of this article is to examine the policy-making role of metal markets, gold resources, and clean energy markets in the post-COVID-19 era and the Russia-Ukrainian military conflict. In doing so, we analyze the role of fossil fuels, clean energy, and metals markets, considering the military conflict in Ukraine in 2022. The study employs event study methodology (ESM), Total connectedness index (TCI), and network analyses. The results indicate that natural gas and clean energy prices are less affected by conflict in the aftermath of an invasion than traditional energy and metals markets. In addition, we observe an increase in the TCI in the energy markets during announcement days. The TCI of the metals market is greater than that of the energy market. According to network connectivity, the key asset class transmitters of the shock in Europe are the Geopolitical index (GPR), gold, and the clean energy stock index (ERIX). The U.S. markets are less affected by the situation in Ukraine. The average hedge suggests that the optimal hedge differs from one market to the next, with fossil fuels and renewable energy, respectively, being more hedge effective and reducing risk by an average of around 0.80 and 0.59, given their ability to function as a hedging instrument.