This study develops a theoretical framework to quantify the impacts of technological progress, renewable energy consumption and international trade on carbon emissions (CO₂), unlike many other studies that consider variables of interest in an ad hoc manner. The developed framework is then applied to the data from the BRICS countries for 1990–2017 period. The study also takes into consideration the integration, co‐integration, as well as cross‐country interdependence and heterogeneity properties of the panel data, and hence, the obtained results are robust and policy insights are well‐grounded. We estimate that technological progress, renewable energy consumption, and export size contribute to the reduction of the CO₂ emissions, while gross domestic product (GDP) and import size increase the pollution both in the long‐ and short‐run. Our main policy recommendations would be the implementations of the measures, regulations, and establishment of the legislative frameworks that foster the technological enhancements and transition toward sustainable energy.

This paper investigates the CO₂ emissions–economic growth relationship in Kazakhstan for the period 1992–2013. Johansen, ARDLBT, DOLS, FMOLS, and CCR cointegration methods are used for robustness purpose. We start with the cubic functional form to rule out any misleading results that can be caused by misspecification. Although the estimation results suggest “U”-shaped relationship, the turning point of income is out of the period. It means that the impact of economic growth on CO₂ is monotonically increasing in the long run indicating the Environmental Kuznets Curve (EKC) hypothesis does not hold for Kazakhstan. Moreover, we calculate that the income elasticity of CO₂ is about unity. The paper concludes that the Kazakhstani policymakers should focus on less energy-intensive sectors as well as using more renewable energy in order to avoid higher pollution effects of economic growth. They may also set new policy regulations for CO₂ reduction.

This study investigated bank-specific and macroeconomic determinants of bank profitability in Azerbaijan, an oil-dependent economy in transition. A huge drop in oil prices, a significant devaluation of the national currency, and the financial distress were the main motivations of the study. We applied Panel Generalized Method of Moments to the data in the framework of dynamic model of the bank profitability. It was found that bank size, capital, and loans, as well as economic cycle, inflation expectation, and oil prices were positively related to the profitability, whereas deposits, liquidity risk, and exchange rate devaluation were negatively associated with it. We further found that the bank profitability demonstrated moderate persistence and ignoring the country-specific features could lead to bias and poor performance in estimations. The conclusions of this research would aid in setting banking policies towards increasing profitability. This may be supplemented by ensuring strong research departments within the banks tasked with analyzing and forecasting the main macroeconomic indicators. The novel features of the study include utilizing recent economic trends, accounting for country-specific features, and for the first time, examining the effects of the economic cycle on the bank profitability in Azerbaijan. In addition, the study featured proper addressing time series properties of the panel data, and performances of robustness checks for consistency of results.

On the one hand, economies, particularly developing ones, need to grow. On the other hand, climate change is the most pressing issue globally, and nations should take the necessary measures. Such a complex task requires new theoretical and empirical models to capture this complexity and provide new insights. Our study uses a newly developed theoretical framework that involves renewable energy consumption (REC) and total factor productivity (TFP) alongside traditional factors of CO2 emissions. It provides policymakers with border information compared to traditional models, such as the Environmental Kuznets Curve (EKC), being limited to income and population. Advanced panel time series methods are also employed, addressing panel data issues while producing not only pooled but also country-specific results. 20 Renewable Energy Country Attractiveness Index (RECAI) nations are considered in this study. The results show that REC, TFP, and exports reduce CO2 emissions with elasticities of 0.3, 0.4, and 0.3, respectively. Oppositely, income and imports increase emissions with elasticities of 0.8 and 0.3. Additionally, we show that RECAI countries are commonly affected by global and regional factors. Moreover, we find that shocks can create permanent changes in the levels of the factors but only temporary changes in their growth rates. The main policy implication of the findings is that authorities should implement measures boosting TFP and REC. These factors are driven mainly by technological progress, innovation, and efficiency gains. Thus, they can simultaneously reduce emissions while promoting long-run green economic growth, which addresses the complexity mentioned above to some extent.

Policymakers in developing and transitional economies require sound models to: (i) understand the drivers of rapidly growing energy consumption and (ii) produce forecasts of future energy demand. This paper attempts to model electricity demand in Azerbaijan and provide future forecast scenarios—as far as we are aware this is the first such attempt for Azerbaijan using a comprehensive modelling framework. Electricity consumption increased and decreased considerably in Azerbaijan from 1995 to 2013 (the period used for the empirical analysis)—it increased on average by about 4% per annum from 1995 to 2006 but decreased by about 4½% per annum from 2006 to 2010 and increased thereafter. It is therefore vital that Azerbaijani planners and policymakers understand what drives electricity demand and be able to forecast how it will grow in order to plan for future power production. However, modeling electricity demand for such a country has many challenges. Azerbaijan is rich in energy resources, consequently GDP is heavily influenced by oil prices; hence, real non-oil GDP is employed as the activity driver in this research (unlike almost all previous aggregate energy demand studies). Moreover, electricity prices are administered rather than market driven. Therefore, different cointegration and error correction techniques are employed to estimate a number of per capita electricity demand models for Azerbaijan, which are used to produce forecast scenarios for up to 2025. The resulting estimated models (in terms of coefficients, etc.) and forecasts of electricity demand for Azerbaijan in 2025 prove to be very similar; with the Business as Usual forecast ranging from about of 19½ to 21 TWh.