Search Results (89)

Governance is often treated as a slow-moving background condition in energy transition research, even though institutional reform and implementation capacity shape outcomes over long horizons. This study adopts a time–frequency perspective to examine how institutional quality aligns with energy-system and carbon-efficiency transition dynamics using multivariate wavelet coherence. Unlike mean-based regression approaches, the multivariate design allows assessment of whether governance aligns with carbon efficiency through three distinct systems—external integration, energy transition with resource rents, and governance coherence—using carbon intensity of GDP (CIGDP) as a common anchor. Using annual data for a comparative sample of GCC economies and non-GCC emerging economies over the period 1996–2022, we examine the evolution of coherence among governance indicators, energy use, renewable energy consumption, external economic exposure, and carbon efficiency, with emissions-related measures explicitly incorporated into the wavelet systems. Environmental implications are therefore interpreted only for systems that directly include carbon-efficiency indicators. The results indicate that institutional quality is most strongly associated with transition dynamics at low frequencies, pointing to persistent long-run alignment rather than short-run adjustment. Across GCC economies, low-frequency coherence is stronger and more continuous, while medium-term weakening appears as time-specific episodes that do not disrupt the underlying long-run structure. In non-GCC emerging economies, long-run coherence remains evident but is less continuous, and medium-horizon fragmentation is more frequent and more prolonged. At high frequencies, coherence is generally weak across countries, suggesting that short-run variation appears more closely associated with external shocks and market conditions than with structural or institutional alignment. Overall, the findings position institutional quality as a stabilising and conditioning factor in energy and carbon-efficiency transitions, operating primarily through long-run coherence and resilience. Systematic differences across governance regimes reflect variation in the continuity and stability of alignment across time horizons, rather than differences in the relevance of governance itself. Full article

This study examines the dynamic relationship between economic growth, environmental sustainability, and other important factors related to energy usage and macroeconomic variables in Saudi Arabia from 1960 to 2024. This study aimed to analyze the relationship between energy consumption, renewable energy, trade openness, and oil rents, and their impact on economic performance and environmental results. This study is based on the annual data obtained by the World Bank in the World Development Indicators (WDI) database. The model considers that renewable energy consumption has a positive and significant effect on economic growth (ECGR) and environmental sustainability (ES). ADF unit root tests, ARDL bounds testing, ECM, and T-values were used to determine the significance of the statistics to determine the direction and strength of the relationships. The findings reveal that PEC has a significant positive impact on economic growth and environmental sustainability. Conversely, the effects of RE, TO, and OR are weak or negative, indicating that the dependence on traditional energy sources and oscillating oil revenues constrains their contribution to sustainable development. This study offers empirical data concerning the effect of the energy–environment–growth nexus in Saudi Arabia through a long historical context, which is valuable to policymakers, researchers, and practitioners interested in sustainable development and energy transition aspects. Full article

This paper examines how openness of international trade is dynamically related to environmental sustainability in sixteen member states of the Southern African Development Community (SADC) between 2000 and 2024, taking into consideration institutional quality factors, economic development, and structural factors. The study uses the Panel Fully Modified Ordinary Least Squares (FMOLS), Pedroni panel cointegration tests, and quantile regression to examine the determination of per capita CO₂ emissions by using trade openness, GDP per capita, government effectiveness, energy use, natural resource rents, and urbanisation. The findings of cointegration prove a long-run equilibrium stability. FMOLS estimates show that trade openness positively but insignificantly increases the typically pooled long-run specifications through urbanisation and natural resource rents and negatively through GDP per capita, which is in line with the phase upper-Environmental Kuznets Curve. The outcome of quantile regression reveals a large distributional heterogeneity with the trade openness decreasing emissions only among high-emitting economies at the seventy-fifth and at the ninetieth percentile which is the imperative effect of the quantile technique demonstrating the need for country-differentiated trade and environmental policy across the SADC. Full article

As network slicing gains traction in cloud computing environments, efficient management and orchestration systems are required to realize the benefits of this technology. These systems must enable dynamic provisioning and resource optimization of virtualized services spanning multiple network slices. Nevertheless, the common resource overprovisioning practice implemented by service providers leads to the inefficient use of resources, limiting the ability of Mobile Network Operators (MNOs) to rent new network slices to more vertical customers. Hence, efficient resource allocation mechanisms are essential to achieve optimal network performance and cost-effectiveness. This paper proposes a predictive model for network slice resource optimization based on resource sharing between Virtualized Network Functions (VNFs). The model employs deep learning models based on Long Short-Term Memory (LSTM) and Transformers for CPU resource usage prediction and a reactive algorithm for resource sharing between VNFs. The model is powered by a telemetry system proposed as an extension of the 3GPP network slice management architectural framework. The extended architectural framework enhances the automation and optimization of the network slice lifecycle management. The model is validated through a practical use case, demonstrating the effectiveness of the resource sharing algorithm in preventing VNF overload and predicting resource usage accurately. The findings demonstrate that the sharing mechanism enhances resource optimization and ensures compliance with service level agreements, mitigating service degradation. This work contributes to the efficient management and utilization of network resources in 5G networks and provides a basis for further research in network slice resource optimization. Full article

The COVID-19 pandemic, particularly during its declared Public Health Emergency of International Concern (PHEIC) period, has flattened the bid-rent curve and increased the rate of functional obsolescence of older office buildings. A critical question remains as to whether these trends have persisted or moderated following the official end of the PHEIC in May 2023. This study investigates the trajectory of office market dynamics in Hong Kong during and after the PHEIC period. Using secondary transaction data from Hong Kong, we find that the decline in the marginal value of proximity to the central business district (CBD), which was most pronounced during the PHEIC period, has subsequently moderated. In addition, this moderation is significantly stronger for high-end offices than for low-end ones. Furthermore, we find that the functional obsolescence of older office buildings not only accelerated during the PHEIC period but continued and further strengthened after the PHEIC period. Full article

Using the Autoregressive Distributed Lag (ARDL) model cointegration framework, this paper examines the long- and short-run impact of domestic financial investment and natural resource rents on economic growth in Niger within the period 1990–2021. The Bounds test confirms a long-run relationship among variables: F = 4.646 > 3.79 at 5%. Long-run results indicate that increasing domestic investment by 1% raises real Gross Domestic Product (GDP) per capita by approximately 0.30%, whereas 1% increase in natural resource rents leads to a reduction in growth by approximately 0.06%. At the same time, exports have a positive but very small effect, while imports and labor have negative long-run influences. Short-run dynamics further support a significant positive impact of domestic investment, at p = 0.0007, and a lagged effect of natural resources at p = 0.0308. The error-correction term is negative and significant, at −0.75, showing rapid adjustment toward equilibrium. Diagnostic tests confirm an absence of serial correlation and heteroskedasticity, while stability is confirmed by CUSUM and CUSUMSQ tests. The findings reveal a dualism in the growth path of Niger in that domestic financial investments favor sustainable expansion, whereas resource-based revenues undermine the growth process in the long run and call for financial market deepening and improved governance of resource revenues. Full article

Understanding the forces that shape farmland rental prices in major agricultural regions such as the U.S. Corn Belt is essential for evaluating the economic and environmental resilience of agricultural regions. This study develops an integrated framework that combines spatial modelling with uncertainty-aware spatial analysis to examine how macroeconomic conditions influence rental dynamics across the core Corn Belt. Using geographically weighted regression, the analysis captures spatial variation in the sensitivity of rental prices to oil prices, interest rates, and economic activity, revealing substantial geographic heterogeneity in macroeconomic exposure. The results reveal pronounced spatial heterogeneity in rental price responses, with geographically weighted models consistently outperforming global linear specifications. Despite strong spatial variation in rental sensitivities, neither prediction uncertainty nor maize yield volatility displays a clear regional pattern, indicating that production stability and model reliability are highly localised. By linking spatially varying rent sensitivities with indicators of economic pressure and production instability, this study provides new insights into agricultural sustainability risk. The findings highlight the importance of place-based policy and region-specific risk management under increasing macroeconomic volatility. Full article

In recent decades, Peru’s manufacturing sector has steadily declined in its share of gross domestic product, despite sustained economic growth and repeated improvements in the terms of trade. This study investigates whether this divergence between external bonanza and industrial stagnation reflects a manifestation of Dutch disease, with long-term implications for the structural sustainability of the country’s manufacturing base. A quantitative approach is applied through a multiple linear regression model estimated by Ordinary Least Squares, using quarterly data from 2012 to 2024. The analysis includes control variables such as real gross domestic product, private gross fixed investment, the real exchange rate, and a dummy for COVID-19. The results reveal a negative and statistically significant relationship between terms of trade and manufacturing performance, suggesting that favorable external shocks may undermine productive capacities by exacerbating structural vulnerabilities. Beyond quantifying this effect, the study offers a structural interpretation of how external shocks can erode industrial resilience in economies dependent on commodity exports. These findings underscore that structural sustainability depends not only on external conditions, but also on internal factors such as investment dynamics, institutional governance, and technological innovation capacity. In addressing a gap in the literature on Dutch disease and sectoral sustainability in the Peruvian context, the study concludes by calling for a strategic reorientation of industrial policy toward a more diversified, inclusive, and innovation-driven growth model, capable of absorbing external rents productively and ensuring the long-term resilience of the manufacturing sector amid persistent global volatility. Full article

This study examines the short- and medium-run linkages within Mexico’s energy–economy system under conditions of limited information. The analysis is motivated by the structural relevance of hydrocarbons for fiscal stability and by the growing need to understand how energy shocks propagate through economic and environmental subsystems. Using a vector autoregression (VAR) framework, nine interdependent macroeconomic and energy variables are jointly evaluated after harmonizing mixed-frequency data, standardizing series, and ensuring stationarity through ADF and KPSS tests. Dynamic responses are assessed through impulse response functions (IRFs), generalized IRFs (GIRFs), and forecast error variance decomposition (FEVD), complemented by Granger causality tests. Results show that oil rents exert a persistent and positive influence on GDP and public expenditure, while shocks to coal-fired generation and oil prices consistently reduce economic activity and increase emissions. Renewable capacity expands pro-cyclically but displays limited autonomous effects. Overall, the evidence reveals a fiscally and environmentally constrained system dominated by hydrocarbons, underscoring the importance of improving PEMEX’s operational efficiency, accelerating fiscal diversification, and strengthening institutional conditions for renewable investment. Full article

This paper investigates whether carbon emission intensity influences capital formation in rent-dependent economies, using the Gulf Cooperation Council (GCC) as a case study. In contrast to conventional growth models, the study tests carbon lock-in as a driver, rather than an outcome, of investment in rentier states and links it empirically to resource curse mechanisms. Using panel data for six GCC countries over 2000–2022, we estimate a fixed effects investment model and use System GMM as a robustness check. Results show that a one standard deviation increase in CO₂ intensity is associated with a 2.27 percentage point increase in gross capital formation (GCF) (p < 0.01), consistent with carbon lock-in theory, while oil rents have a significant negative relationship with investment (coefficient = −0.271, p < 0.01), in line with resource curse dynamics. The study contributes by embedding carbon lock-in theory in a standard macro panel investment function, treating emissions intensity as a structural regressor alongside oil rents in the specific context of rentier states. A behavioural interpretation is also offered: high-carbon strategies persist because they continue to yield relatively high short-term returns under existing incentives, so investment systems tend to reinforce carbon-intensive pathways. These insights have implications for both theory and practice, suggesting that screening public projects by emissions intensity, greening sovereign wealth portfolios, and phasing out fossil subsidies may help break carbon-intensive investment inertia. Full article

Market power remains a persistent challenge in liberalized electricity spot markets, where generators can manipulate bids to distort prices and extract rents. Traditional monitoring approaches—such as structural indices or simulation-based models—offer partial insights but fail to capture the nonlinear, spatially correlated propagation of strategic behavior across transmission-constrained networks. This paper develops a diffusion neural learning framework for market power risk assessment that integrates welfare optimization, nodal pricing dynamics, and graph-based deep learning. Specifically, a Graph Diffusion Network (GDN) is trained on simulated spot market scenarios to learn how localized strategic deviations spread through the network, distort locational marginal prices, and alter system welfare. The modeling framework combines a system-wide welfare maximization objective with multi-constraint market clearing, while the GDN embeds network topology into predictive learning. Results from a case study on an IEEE 118-bus system demonstrate that the proposed method achieves an R² of 0.91 in predicting market power indices, outperforming multilayer perceptrons, recurrent neural networks, and Transformer baselines. Welfare analysis reveals that distributionally robust optimization safeguards up to 3.3 million USD in adverse scenarios compared with baseline stochastic approaches. Further, congestion mapping highlights that strategic bidding concentrates distortions at specific nodes, amplifying rents by up to 40 percent. The proposed approach thus offers both predictive accuracy and interpretability, enabling regulators to detect emerging risks and design targeted mitigation strategies. Overall, this work establishes diffusion-based learning as a novel and effective paradigm for electricity market power assessment under high uncertainty and renewable penetration. Full article

This paper examines the impact of natural resource rents on the economic growth of Tunisia between 1990 and 2023, emphasizing the aspect of resource diversification. The annual time-series data extracted from the World Bank’s World Development Indicators were analyzed using the Autoregressive Distributed Lag model to outline both the short- and long-run dynamics. The results confirm the existence of a long-term relationship between economic growth and oil, natural gas, mineral, and forest rents. Among them, oil and forest rents have strong positive long-term impacts, whereas natural gas and mineral rents contribute relatively moderately due to the structural inefficiencies and absence of value-added activities in these sectors. It was also found that the labor force participation has been affecting growth adversely with continuous impacts, which are driven by skill mismatches, low productivity, and high unemployment, hence indicating structural labor market imbalance that weakens the growth effect of labor. On the other hand, capital formation is still one of the key drivers of long-term growth. The findings highlight the rationale for diversification of the economy, governance reforms, and sustainable management of resources. However, the study suffers from some limitations due to data availability and excluded institutional variables, apart from being narrowed to a single-country case study, which might affect the generalizability of the results. Future works could consider incorporating the indicators of governance, examining nonlinear effects, or expanding the analysis into a multi-country framework. Full article

This study investigates whether targeted progress on Sustainable Development Goals (SDG 7, 8, and 9) can cushion the impact of oil dependence on unemployment in Gulf Cooperation Council (GCC) economies. Using panel data for six countries from 2000 to 2021 and regression models with country fixed effects and system GMM, we incorporate interaction terms between oil rents and both disaggregated and composite SDG indicators. The results show that SDG 8 (Decent Work) exerts the strongest stabilizing effect, significantly reducing unemployment sensitivity to oil rents. SDG 7 (Clean Energy) exhibits transitional dynamics, with short-term adjustment costs during early stages of the energy transition. SDG 9 (Infrastructure) does not display consistent short-run effects. A composite SDG index also moderates the oil–unemployment link, though this effect is largely driven by SDG 8. Overall, the findings suggest that inclusive labour institutions and clean energy reforms enhance labour market resilience in resource-dependent economies, reducing vulnerability to external shocks and supporting more sustainable development pathways. Full article

The rapid accumulation of public debt raises global concern over its implications for financial markets. This study examines the effect of domestic public debt on financial development in Middle East and North Africa (MENA) countries, a region marked by sharp heterogeneity in institutions, debt dynamics, and oil dependence, using annual panel data for 16 countries over the period (2000–2020). Our analysis employs a fractional response model (FRM), which accounts for the bounded nature of the dependent variable, corrects for heteroskedasticity, and incorporates country fixed effects. The findings reveal a significant negative effect of domestic public debt on financial development, consistent with the lazy banks and crowding-out hypotheses. This adverse relationship persists across different income groups and debt percentiles, with modest attenuation at higher debt levels. Oil rents are also found to exert a robust negative effect, highlighting the structural vulnerabilities associated with oil dependence. These results emphasize the importance of debt management, fiscal frameworks that account for commodity cycles, and policies to reduce the sovereign–bank nexus in fostering sustainable financial development in the region. Full article

Carbon neutrality has emerged as a critical issue in the 21st century, particularly in the Middle East and North Africa (MENA) region. These nations have demonstrated significant commitment to investing in renewable energy and implementing initiatives aimed at achieving carbon neutrality. The global spotlight on environmental concerns, encompassing the responsibilities of all economic stakeholders, has prompted the convening of COP 27, a pivotal meeting dedicated to reducing carbon emissions on a global scale. However, research on carbon neutrality in the MENA region remains relatively limited, particularly in terms of in-depth analysis of green energy, green technology, oil revenues, and the efficacy of government interventions. This study seeks to address this gap in existing research by investigating the factors influencing the attainment of carbon neutrality in the MENA region from 2000 to 2022. Specifically, the research focuses on the roles of green energy, green technology, oil revenues, and government effectiveness in this context. Utilizing the Method of Moments’ Quantile Regression, this study aims to analyze the impact of location and scale on the conditional distribution of carbon emissions. The findings reveal that investments in green energy, adoption of green technology, increases in service-added value, and oil revenues are associated with decreased carbon emissions, while greater trade openness correlates with emission reductions. However, all governance metrics examined exhibit a positive correlation with carbon emissions. These results underscore the importance of prioritizing investments in green energy and enhancing the effectiveness of governmental initiatives to steer economic growth towards achieving carbon neutrality. Moving forward, policymakers in the MENA region are encouraged to place greater emphasis on sustainable energy solutions and to implement strategies that enhance the efficacy of government interventions to accelerate progress towards carbon neutrality. Full article