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This study examines the effect of digitalization on poverty and income inequality in developing countries. The study answers the question of whether digitalization is a way for developing countries to get out of poverty and income inequality.

The study uses data from 17 developing countries with data from 2005 to 2021. The study employs fully modified ordinary least squares (FMOLS) and dynamic ordinary least squares (DOLS), with an augmented mean group (AMG) for robustness. Digitalization, as the variable of interest, is proxied by the digitalization index (DI), constructed using principal component analysis (PCA). The dependent variables are poverty and income inequality, which are used in different models.

The evidence indicates that digitalization decreases poverty and income inequality in developing countries. These findings are justified when we use the AMG estimator, but the strength of the coefficients and significance levels are higher in the FMOLS and DOLS estimators. The results of the control variables also show that human development (LHDI), CO2 emissions and foreign direct investment (FDI) have decreasing effects on poverty and income inequality. Thus, digitalization is a good option for developing countries to get out of poverty and income inequality to achieve sustainable development goals (1&10).

This study provides rigorous empirical evidence on the effect of digitalization on poverty and income inequality in developing countries. Unlike the previous studies on developing countries, this study used a DI to proxy digitalization. In addition, the authors use FMOLS and DOLS estimators, with an AMG estimator for robustness, to provide long-run coefficients.

The peer review history for this article is available at: https://publons.com/publon/10.1108/IJSE-08-2023-0586

Considering the evolving importance of green finance, this study uses climate-related development mitigation finance as a proxy of green finance and investigates the impact of green finance on ecological footprint as an indicator of environmental quality along with the influence of economic growth, renewable energy, greenhouse gas emissions, trade openness and urbanization across 47 developing countries over the period 2000–2018.

After finding the presence of cross-sectional dependency among variables, the second-generation panel unit root test was employed to detect the order of integration among the variables. Since all the variables were found to be stationary, Westerlund cointegration technique was employed to detect the long-run relationship among the variables. Then, the long-run elasticity among the dependent and independent variables was tested using fully modified ordinary least squares (FMOLS), dynamic ordinary least squares (DOLS) and pooled mean group–autoregressive distributed lag (PMG–ARDL) approaches.

The empirical findings suggest the presence of long-run relationship among all the variables, namely, ecological footprint, green finance, economic growth, renewable energy consumption, greenhouse gas emissions, trade openness and urbanization for the selected developing countries in the sample. Furthermore, economic growth, greenhouse gas emissions, trade openness and urbanization, all have a positive and significant impact on the ecological footprint, whereas renewable energy consumption and green finance have a significant and negative impact on the ecological footprint, which supports the view that environmental quality is improved with the greater use of renewable energy technologies and allocation of greater amounts of more green finance.

The empirical results of this study offer policymakers and regulators some implications for environmental policy for protecting the countries from ecological issues.

This study aims to examine the relationship between information and communication technology (ICT) and economic growth in all organization for economic co-operation and development (OECD) countries.

This paper employs annual panel data together with fixed-effects (FE), random effects (RE), fully modified ordinary least squares (FMOLS), dynamic ordinary least squares (DOLS) and generalized method of moments (GMM) estimators for production function estimation.

The results indicate that ICTs, non-ICT (NICT) capital services and employment significantly and positively affect economic growth.

Information is an important driving force behind economic growth and productivity, and communication technologies have made it more accessable. Also, many countries aimed to invest in ICT to improve their economic growth and productivity. However, these investments failed to produce the expected outcome for some years and countries.

To our knowledge, no study examines the ICT and growth relation in all OECD countries for 2000–2018 period. We intend to fill this gap by examining whether or not the expected returns from ICT investment are achieved in all OECD countries between 2000 and 2018.

Today, many industries are implementing creative approaches in response to increasing environmental awareness. It is of great importance to answer the question of whether the military sector, one of the most important sectors, can support renewable energy (RE) adaptation. This study aims to examine how military spending affects the supply of RE in 27 Organization for Economic Cooperation and Development (OECD) nations as well as the regulatory function of factors such as innovation, international trade and oil prices between 1990 and 2021.

The study examines the effects of military spending, income, green innovation, international trade, oil prices and the human development index on the supply of RE using various econometric approaches, which are the cointegration test, moments quantile regression and robustness test.

The findings demonstrate that all factors, excluding military spending, quite likely affect the expansion of the renewable supply. Military spending negatively influences the RE supply; specifically, a 1% increase in military spending results in a 0.88 reduction in the renewable supply. In addition, whereas income elasticity, trade and human development index in OECD nations are higher in the last quantiles of the regression than in the first quantiles, the influence of military spending and innovation on renewable supply is about the same in all quantiles.

OECD nations must consider the practical implications, which are essential to assess and update the military spending of OECD countries from a green energy perspective to transition to clean energy. Based on the study’s overall findings, the OECD countries should incorporate the advantages of innovation, economic growth and international trade into their clean energy transition strategies to lessen the impact of military spending on renewables.

The study aims to fill a gap in the literature regarding the role of military expenditures in the RE development of an OECD country. In addition, the results of the methodological analysis can be used to guide policymakers on how military spending should be in the field of RE.

The underdevelopment of the financial sector could be one of the barriers to the deployment of renewable energies in developing countries. The purpose of this paper is therefore to analyse the effect of financial development in the deployment of renewable energies in sub-Saharan African countries.

The empirical analysis is based on a production approach and a cross-sectionally augmented autoregressive distributive lag error correction model estimate for 25 sub-Saharan African countries over the period 1990–2018. The augmented mean group (AMG) and common correlated effects mean group (CCEMG) estimators were used for the robustness analysis.

Two results emerge: financial development contributes positively to renewable energy deployment in sub-Saharan African countries in the short and long run; and fossil fuel dependence impedes significantly renewable energy deployment in the short and long run. The robustness analyses using the AMG and CCEMG methods confirm these results.

These results suggest the need for policies to support and strengthen the development of the financial sector to improve its ability to effectively finance investments in renewable energy technologies.

The originality of this paper lies in the fact that the analysis is based on a renewable energy production approach. Indeed, the level of renewable energy deployment is measured by the production and not the consumption of renewable energy, unlike other previous work. In addition, this research uses recent econometric estimation techniques that overcome the problems of cross-sectional dependence and slope heterogeneity.

This article investigates the long-run impact of remittance inflows on agricultural productivity (AGP) in emerging Asian economies (Bangladesh, Sri Lanka, Malaysia, India, Nepal, Philippines, Pakistan, and Vietnam), employing a panel dataset from 2000 to 2018.

This study initially applies cross-sectional dependence (CSD), second-generation unit root, Pedroni, and Westerlund panel co-integration techniques. Next, it uses the augmented mean group (AMG) and common correlated effect mean group (CCEMG) methods to investigate the long-term impact of remittance inflows on AGP while controlling for several other important determinants of agricultural growth, such as cultivated area, fertilizers, temperature change, credit, and labor force.

The empirical findings are as follows: The results first revealed the existence of CSD and long-term co-integration between AGP and its determinants. Second, remittance inflows significantly boosted AGP, indicating that remittance inflows played a crucial role in improving AGP. Third, global warming (changes in temperature) negatively impacts AGP. Finally, additional critical elements, for instance, cultivated area, fertilizers, credit, and labor force, positively affect AGP.

This study suggests that policymakers of emerging Asian economies should develop an exclusive remittance-receiving system and introduce remittance investment products to utilize foreign funds and mitigate agricultural production risks effectively.

This is the first empirical examination of the long-term impact of remittance flows on agricultural output in emerging Asian economies. This study utilized robust estimation methods for panel data sets, such as the Pedroni, Westerlund, AMG, and CCEMG tests.