This study investigates the relationship between ecological footprint, GDP per capita, population density, and water productivity in three South Asian countries—Bangladesh, India, and Pakistan—over the period 1990 to 2022. Using a system of linear equations, the analysis incorporates the Common Correlated Effect Mean Group (CCEMG) model and Panel-Corrected Standard Errors (PCSE) to capture long-run dynamics among the variables. The results indicate that ecological footprints exert a positive and statistically significant effect on environmental outcomes, suggesting that environmental pressures intensify over time. In contrast, GDP per capita is found to reduce environmental degradation in the long run, providing evidence of a potential turning point consistent with the Environmental Kuznets Curve hypothesis. Water productivity emerges as an important determinant of ecological outcomes, underscoring the role of efficient resource management in reducing environmental stress. Overall, the findings enhance understanding of the interactions between economic, demographic, and environmental factors in South Asia and offer policy insights for mitigating ecological impacts while advancing sustainable development goals.
We examine two approaches to the monetization of natural gas and the generation of revenues for resource-rich countries. The cash flow taxation model, adopted in Norway, is the closest to a rent tax and generates higher revenues for the host country over the long term while still attracting investors. Its drawback, however, is that the government acts as a silent partner in the project, providing “loss refunds” when cash flow is negative—a fiscal commitment that low-income and lower-middle-income resource-rich countries often cannot afford. By contrast, under a production sharing agreement (PSA), revenues flow to the government from the first year, while the company bears high front-end investment costs and substantial downside risk. This misalignment of incentives and risk perceptions can lead to the stranding of otherwise profitable projects, thereby hampering the production of natural gas as a lower-carbon fuel during the energy transition. International financial institutions (IFIs) engaged in climate change mitigation can help bridge the gap between the cash-flow regime expectations of investors and the PSA regime expectations of governments. For governments, this would translate into higher revenues for development. IFI intermediation can also help alleviate broader political and country risks that hinder credible intertemporal commitments between investors and host states. Such mechanisms are critical not only to unlock supply in the gas sector but also to support investment in green minerals—either directly or indirectly, by using natural gas as the least carbon-intensive fossil fuel to provide the energy required for the extraction and processing of these minerals.
Artificial intelligence (AI) has become an increasingly important tool across multiple industries, with machine learning (ML) in particular offering new opportunities for optimizing oilfield operations. In petroleum reservoir management, traditional approaches to evaluating inter-well connectivity rely heavily on physical and numerical simulations, which can be time-consuming, computationally intensive, and constrained by model assumptions. In contrast, machine learning techniques establish data-driven correlations through iterative training, enabling more accurate and efficient characterization of reservoir connectivity. This study applies machine learning to evaluate inter-well connectivity in the Mesdar Field, Algeria. Two ensemble learning algorithms—Extreme Gradient Boosting (XGBoost) and Random Forest—were implemented due to their demonstrated robustness in handling complex, sequential datasets. Model interpretability was incorporated through agnostic interpretation methods, with training performed using a comprehensive reservoir database. Empirical results from field data reveal that both algorithms perform strongly in predicting production rates, achieving accuracy levels exceeding 97%. Moreover, the connectivity patterns inferred through “Permutation Feature Importance” and “Feature Importance” align closely with those obtained using conventional static methods. These findings suggest that ML-based approaches not only enhance predictive reliability but also provide a cost-effective and scalable alternative for estimating inter-well connectivity in petroleum reservoirs. The integration of such techniques has the potential to improve reservoir management strategies, reduce operational risks, and contribute to more efficient hydrocarbon recovery.
At the household level, numerous studies in developing countries have demonstrated that the energy ladder hypothesis does not hold, with energy stacking remaining the prevailing pattern of fuel choice. This paper extends the debate to the macroeconomic level, examining whether national energy transitions in developed and developing economies align more closely with the energy ladder or energy stacking framework. Using data from the World Development Indicators (2022) and the World Energy and Climate Statistics Yearbook (2022), and applying an ARDL model, we assess the dynamics of income growth and energy use. Our results indicate that, with the exception of Canada, the energy ladder hypothesis is supported in developed economies, where rising incomes are associated with a shift toward cleaner energy sources. By contrast, in developing countries, energy stacking persists as the dominant pattern. These findings suggest that, apart from Canada, developed economies can achieve environmental sustainability alongside economic growth. However, in African economies and in Canada, growth alone does not guarantee sustainable energy use. For these cases, targeted environmental regulations and policy interventions are essential to advance sustainable economic development.
The Russia–Ukraine war, beginning in February 2022, has profoundly disrupted global commodity markets, with particularly severe impacts on the energy sector. Sanctions and trade restrictions on Russian oil exports intensified supply chain disruptions and contributed to heightened crude oil price volatility. This study examines the effects of the conflict on Brent crude oil price dynamics, develops forecasting models suitable under conditions of geopolitical tension, and evaluates associated market risks. Using daily Brent crude oil prices from July 1, 2020, to April 3, 2025, we compare pre-war and wartime return series. The wartime period is characterized by higher volatility, reflected in greater standard deviations and more extreme fluctuations. Return distributions exhibit negative skewness and high kurtosis, indicating fat tails and frequent extreme values. To capture these dynamics, we estimate several ARMA–GARCH family models. The ARMA(1,1)–EGARCH(1,1) model with Student’s t residuals best fits the data by effectively capturing asymmetric and persistent volatility. For short-term forecasting, however, the ARMA(1,1)–GJR(1,1) model provides superior predictive performance over a 29-day horizon. Market risk assessment, conducted via Value at Risk (VaR) and Expected Shortfall (ES), reveals that risk models assuming normality consistently underestimate the likelihood of extreme losses. In contrast, historical simulation and Student’s t-based approaches yield more accurate estimations, underscoring the importance of robust, fat-tailed models. Overall, the findings demonstrate that the Russia–Ukraine war has substantially intensified volatility in the crude oil market. The study highlights the need for advanced econometric modeling and risk measures that incorporate asymmetry and fat tails, thereby supporting more informed decision-making by investors and policymakers.
