Recent years have seen a spike in the number of academics utilizing the ecological footprint as a stand-in for environmental depletion because of its extensive nature and its ability to capture the worsening of the ecosystem. Thus, this article brings a new effort to analyze the effect of Bangladesh's economic complexity and natural resources on its ecological footprint over a long period, from 1995 to 2018. Using a nonlinear autoregressive distributed lag (NARDL) model, this paper suggests that a more complex economy has a significantly positive effect on ecological footprint over the long term. If the economy is simplified, it has less impact on the environment. For Bangladesh, an increase in economic complexity of 1 unit leads to an ecological footprint increase of 0.13 units, while a drop in economic complexity of 1% causes an ecological footprint decrease of 0.41%. Results also demonstrate that both positive and negative changes in natural resources contribute to rises in environmental quality in Bangladesh, which negatively influences the country's ecological footprint. Quantitatively, a 1% increase in natural resources reduces the ecological footprint by 0.14%, whereas a 1% decrease in resources has the opposite effect, reducing it by 0.59%. In addition, an asymmetric Granger causality test confirms the existence of a unidirectional causal link from ecological footprint to a positive partial sum of natural resources and from a negative partial sum of natural resources to ecological footprint. Finally, the findings point to a two-way causal relationship between the size of an economy's ecological footprint and the complexity of its economy. Policymakers should boost technological advances and lessen operational costs by adopting an innovative Research and development framework and devoting more cash to natural resource policies that promote an adaptable ecological footprint.

This study fills a gap created by previous environmental investigations by including the impact of agricultural employment and technology on the load capacity factor (LCF) rather than solely focusing on CO₂ or greenhouse gas (GHG) emissions, which only measure from the demand side; LCF provides a complete picture of environmental degradation by evaluating both the demand side and supply side. This connection is moderated further by considering the renewable energy and natural resource rent impacts. In this analysis, panel econometric methods are incorporated, including the cross-sectional dependence test. This study uses the Pooled Mean Group–Autoregressive Distributed Lag (PMG-ARDL) model to evaluate the level of effect independent factors had on the LCF of the BRICS (Brazil, Russia, India, China, and South Africa) nations from 1992 to 2020 in the presence of a heterogeneous integration order. The estimations recognize heterogeneity in the effects of agricultural employment, renewable energy, natural resource rent, and technology on LCF, in the short run and long run. According to the empirical results, agricultural employment significantly enhances the LCF both in the short run and long run, implying that employment in agriculture improves environmental sustainability. However, increasing the use of renewable energy protects the environment from degradation only in the long run; it has no significant impact in the short run. On the contrary, natural resource rent and technology lead to a decline in environmental sustainability in the long run. Hence, this study recommends prioritizing renewable energy intake over other energies, providing proper incentives to motivate agricultural entrepreneurship to ensure a sustainable environment.