In recent decades, scholars have increasingly focused on the effects of trade openness on economic performance worldwide, particularly in emerging nations. This results from globalization and a rise in regional, plurilateral, and multilateral trade agreements. The establishment of the World Trade Organization (WTO) in 1995 signified the most significant international trade reform since the conclusion of the Second World War, as these reforms facilitated integration deemed essential for the transition from autarky to an open economy (World Trade Organization, 2025; Zahonogo, 2016). In theory, more trade openness in an economy promotes technical transfer, innovation, and economic performance. This rationale has prompted developing nations to embrace a more liberalized trade framework due to the poor performance of trade policy strategies (Udeagha and Ngepah, 2021). Nonetheless, despite the theoretical connection, prior studies exhibit varied outcomes indicating that trade openness may either bolster or impede on economic performance. The correlation between trade openness and economic performance is significantly affected by the factor endowments of various countries, with effects differing among nations, although economic integration generally promotes global economic growth (Wani et al., 2023). Akinlo and Okunlola (2021) confirmed that trade openness has a detrimental influence on growth

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Advanced economies continue to adopt and embed digitalization into their everyday activities. One may ponder; how does a significant digitalization upgrade affect developing economies? To answer this question and highlight the economic & environmental effects of digitalization in a developing economy, this study adopts the singly-country dynamic Energy and Environment Integrated computable general equilibrium model (EEICGE) with a 5-year gradual digitalization policy plan design in Nigeria, a developing economy. 

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Reliance on fossil fuels for energy is a major contributor to climate change. Climate change has wide-ranging effects on various sectors of the economy, as well as on animal and human populations. A recent counterfactual analysis conducted by Kahn et al. [1] revealed that even a moderate increase in global temperature by 0.04OC could result in a substantial 7 % contraction of the global economy by the year 2100. In addition, Sattar et al. [2] conducted a comprehensive review of the effects of climate change on wildlife animals. They highlighted that climate change significantly disrupts the dynamic conditions of biomass production, trophic interactions, ecosystem balance and hydrological equilibrium. 

Authors: Isiaka Akande Raifu, Fidelis Ademola Obaniyi, Great Nnamani, Abdulkhalid Anda Salihu

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It is rather a norm for researchers to directly use the log difference of an asset price to compute returns. Just like using ln(X + 1) to avoid taking the natural logarithm of zero(s). However, this log returns is but a conditional approximation of the actual returns. Nonetheless, can log difference approximations and the ln(X + 1) common practices produce BLUE estimates? Using the log return as an example, this study discusses the approximation nature and conditions for using the log difference approximation both for the interest regressor and control variables. These conditions are; that both the sample average and variance of the original series tend to zero. When these conditions are not met, the log difference approximation is, in fact, not a good approximation and biases OLS causal estimators. When the conditions are met, it produces unbiased, consistent but less efficient estimators. Thereby making the estimates less precise and less accurate. Nonetheless, this is true for a log dif ferenced interest regressor(s) and control variables, when it correlates with the interest variable(s) and explains, in part, the dependent variable, even in large samples. Similarly, the common use of ln(X +1) biases the esti mation of the true causal effect, even the intercept term, except when X tends to infinity. A robust solution of using non-zero subsamples, against ln(X + 1), produces unbiased and consistent estimators for the true causal effects under the causal assumptions. These biasedness, inconsistencies, and inefficiencies do not disappear in large samples. Finally, both ex-ante and ex-post test statistics are discussed, however, the ex-post estimation test statistic is recommended to confirm both the choice of using log difference approximation and that of using ln(X + 1), in an empirical data causal regression analysis. Ideally, researchers should ensure the conditions for using the log difference approximation are met. Otherwise, these approximations and practices produce biased, inconsistent, and inefficient results, even in large samples, leading to misinformed policy implications.

Should Africa rather delay investments in renewable energy given their trivial contributions to global greenhouse gas emissions? This is strongly discouraged given the existing benefits of increased renewable energy investments in an African economy. Nigeria, the leading African economy is adopted as a representative to illustrate the prospects of improved (renewable) energy security in Africa. This study develops a dynamic recursive general equilibrium model to evaluate the prospects of renewable energy investment paths for Africa towards improving its energy security levels. Unlike other competing models, this model allows businesses to dynamically substitute between intermediate renewable energy and fossil fuel products, thus, taking active steps towards achieving a green economy. The results show that present economic welfare will be sacrificed for future welfare benefits and improved energy security. This confirms the transitioning of an economy from a lower steady state to a higher steady state path as postulated by the Solow model. However, a sustained gradual investment in the renewable energy sector yields the least welfare loss as the economy transitions through its energy security path. The one-off policy design produces relatively higher results in the immediate future while the sustained gradual incremental path smoothens these results into the far future. The results confirm that Africa’s demand for renewable energies substantially outweighs its supply, thereby suggesting a potential and non-trivial market for renewable energies, nonetheless. Results-based policies that are geared towards improving energy security are formulated for the African economies.