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- ArticleAçık Erişim2026Fighting against energy consumption risk by diversifying energy sources
Kuziboev, Bekhzod; Radulescu, Magdalena; Çatık, A. Nazif; Makhmudov, Samariddin; Balli, Esra; Allayarov, Sardor
Currently economic turbulence and geopolitical fluctuations are causing high prices and supply disruptions in energy markets. As a result, energy consumption risks are becoming more severe. Diversifying energy sources is one way to address these challenges. This study examines how energy source diversification influences energy consumption risk in 36 developed countries from 2000 to 2018, using Method of Moments Quantile Regression (MMQR) and Panel Threshold Regression (PTR) Models. The MMQR captures heterogeneous effects across various levels of energy consumption risk, offering insights into how diversification strategies differ in effectiveness across the risk distribution. The PTR Model identifies potential nonlinear relationships and regime-dependent effects based on economic development levels, enabling an analysis of how the diversification-risk relationship changes across different stages of economic advancement. These methods are well-suited for the non-normal distribution of variables and the potential structural breaks in the relationship between energy diversification and consumption risk. The findings align with theory, revealing that the effects of energy source diversification on energy consumption risk are negative across all quantiles, from 10 % to 90 %. Furthermore, PTR results reveal that energy source diversification reduces energy consumption risk in both the lower and upper regimes of economic development. The study's results provide valuable guidelines for policymakers.
RDL Kalıcı Kaydıİncele - ArticleAçık Erişim2026Nonlinear effects of financial development on energy infrastructure: Insights from nonlinear panel models
Helmi, Mohamad Husam; Balli, Esra; Çatik, Abdurrahman Nazif; Kuziboev, Bekhzod; Ruzmetova, Maxbuba; Makhmudov, Samariddin
This study examines the impact of financial development on energy infrastructure needs in a sample of 85 countries over the period 2000–2023. The analysis combines linear panel estimators with advanced nonlinear methods, including Method of Moments Quantile Regression (MMQR), the Instrumental-Variable Partially Linear Functional-Coefficient model (IVPLFC), and the dose–response function. These methods allow us to examine heterogeneity, dynamic persistence, and nonlinear marginal effects. A key contribution of the study is the use of the Energy Infrastructure Needs index, which captures deficiencies across electricity generation, transmission, distribution, and storage, rather than relying on narrower traditional proxies. The results show that stronger financial development significantly reduces energy infrastructure needs. This effect is strongest at earlier stages of financial development, while diminishing marginal effects emerge as financial systems mature. The MMQR results reveal distributional differences across levels of energy infrastructure needs, while the IVPLFC and dose–response results confirm clear nonlinear effects. Overall, the findings show that financial development plays an important but uneven role in reducing energy infrastructure needs. Financially underdeveloped economies benefit most from expanding access to credit, strengthening banking intermediation, and improving liquidity, whereas more financially developed economies require complementary institutional and regulatory reforms to achieve further progress.
RDL Kalıcı Kaydıİncele - ArticleAçık Erişim2025Impact of surface temperature change on food production: Evidence from PLFC and MMQR models
Catık, Abdurrahman Nazif; Bucak, Çağla; Akdeniz, Coşkun; Ballı, Esra; Kuziboev, Bekhzod; Makhmudov, Samariddin; Ashurova, Nasiba
The analysis of the mechanisms through which temperature variations affect food production has become a paramount concern for sustainable development and policy formulation, as global food security faces unprecedented challenges from accelerating climate change. Temperature anomalies are threatening agricultural systems that sustain billions of people worldwide. Using panel data from 40 countries from 1980 to 2021, this study investigates the influence of annual surface temperature fluctuations on food production. The Cross-Sectional Autoregressive Distributed Lag (CS-ARDL) model is estimated to analyze this correlation in the mixed evidence on the integration levels of the variables. The results corroborate the cointegration among the variables. Temperature change has a significant negative effect on food production in both the short and long run. Food production is positively influenced by economic growth and renewable energy consumption. The study also considers potential nonlinearity by utilizing the Partially Linear Functional Coefficient (PLFC) and the Method of Moments Quantile Regression (MMQR) model. The PLFC estimates imply that economies with lower GDP levels are more adversely influenced by temperature change, emphasizing the crucial role of economic growth in mitigating climate change. Significant negative effects of temperature change are also corroborated by the MMQR estimates in all quantiles, with the largest effects obtained at the higher quantiles. The variation in the impact of renewable energy consumption over quantiles implies that energy policies should be modified according to the developmental phases of countries. The empirical findings have significant implications for formulating sustainable agricultural policies and climate adaptation strategies. This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International license, which permits unrestricted re-use, distribution, and reproduction in any medium, provided the original work is properly cited.
RDL Kalıcı Kaydıİncele - ArticleAçık Erişim2025Time-varying dynamics of energy and sustainability uncertainty
Kuziboev, Bekhzod; Çatık, A.Nazif; Pícha, Kamil; Makhmudov, Samariddin; Matyakubova, Aziza; Balli, Esra
This article examines the link between energy-related uncertainty and sustainability uncertainty by utilizing monthly data from November 2002 to October 2022. To this aim, wavelet coherence and time-varying parameter vector autoregression (TVP-VAR) methodologies are utilized. Wavelet coherence results uncover statistically significant long-term correlation between energy and sustainability uncertainties, particularly during periods of global economic crises. The time-varying responses reveal that the impact of energy-related uncertainty shocks is positive over the analysis period. The impacts of energy-related uncertainty are found to be more pronounced during the turbulent periods, such as the 2008 global financial crisis and the COVID-19 pandemic. The time-varying impact of global economic activity on the sustainability uncertainty does not follow a stable trajectory, and it is not statistically significant. The time-varying impact of financial stress on the sustainability uncertainty is not stable, including both positive and negative effects over the analysis period. The results generally reveal the complex interconnections between energy markets, economic conditions, and sustainability issues. This evidence highlights the importance of policymakers taking these connections into account when formulating strategies for energy security and sustainable development, particularly during periods of global economic uncertainty.
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