Azerbaijan State University of Economics

This study investigates the interconnectedness between the European Emissions Trading System (ETS) and key sources of uncertainty, Geopolitical Risk (GPR), Physical Climate Risk (PCI), and Transitional Climate Risk (TCI), using a novel Quantile-on-Quantile Connectedness Index (QQCI) for EU countries from 2005 to 2022. Our results reveal that geopolitical and climate risks exert significant influences on ETS dynamics, with the strongest spillovers occurring at inverse quantile combinations, where low risk levels coincide with high ETS quantiles. Specifically, the connectedness from TCI to ETS reaches 66.5 at extreme conditions, where low levels of risk coincide with high ETS values, indicating pronounced sensitivity of carbon prices to transition-related shocks. Correlation analysis shows that ETS maintains weak but statistically significant ties with GPR about 0.021 and GPRACTS by 0.009, while PCI − 0.009 and TCI − 0.007 display small negative associations. Furthermore, temporal analysis highlights sharp spikes in connectedness during major events such as the COVID-19 pandemic and the Russia–Ukraine war. These findings underscore the need for adaptive, data-driven risk-management strategies within the ETS framework. Incorporating real-time geopolitical and climate-risk indicators into carbon-market design, combined with the expansion of green finance initiatives, can enhance market stability and support long-term carbon-neutrality objectives.

This study develops a stochastic network-constrained unit commitment framework integrating hydrogen energy storage trains, solar photovoltaic generation uncertainty, and demand response programs to optimize operational costs and enhance grid reliability. A Vector Autoregressive Moving Average model coupled with Kantorovich Distance scenario reduction captures solar variability while preserving critical tail events and spatial-temporal correlations. The framework employs Generalized Benders Decomposition to solve the two-stage stochastic mixed-integer linear program, where first-stage decisions determine unit commitment and train routing. At the same time, second-stage subproblems evaluate operational feasibility across solar scenarios. Hydrogen trains are explicitly modeled as mobile energy storage assets through a vehicle routing formulation that incorporates delivery time windows, capacity constraints, and multi-station scheduling. Case studies on the IEEE 24-bus system demonstrate 10.01% total cost reduction compared to deterministic baselines, with 12.88% savings from advanced scenario reduction versus conventional methods. The Kantorovich Distance approach reduces solar curtailment penalties by 56.9% and fuel costs by 14.3% through coordinated dispatch of renewable and hydrogen sources. Sensitivity analyses reveal diminishing returns beyond threshold capacities for hydrogen production and storage, while variations in transportation costs significantly impact optimal routing strategies and delivery frequencies.

The increasing urgency to address climate change has intensified the need for a deeper understanding of how renewable energy adoption, environmental pollution, and economic growth interact, particularly within spatially interconnected regions where cross-border externalities and policy spillovers fundamentally shape environmental outcomes. This study investigates the dynamic and spatially distributed relationships among renewable energy consumption, carbon dioxide emissions, and economic development across 31 Provincial administrative Units(Including provinces, autonomous regions, municipalities, and SAR hereinafter refered to as provinces) over the period 2000 to 2023, employing advanced spatial econometric techniques that explicitly account for geographical interdependencies often overlooked in conventional panel data analyses. Utilizing both static and dynamic Spatial Durbin Models alongside comprehensive robustness checks including alternative spatial weight matrices and model specifications, the analysis captures direct within-country effects, cross-border spillover effects, and feedback mechanisms that traditional non-spatial approaches fail to identify. The empirical findings reveal that renewable energy adoption generates substantial emissions reductions both domestically and regionally, with a one percent increase in renewable energy share reducing a country's own CO2 emissions by 0.259 percent while simultaneously decreasing emissions in neighboring countries by 0.047 percent through mechanisms including technology diffusion, cross-border electricity trade, and policy emulation. Economic growth exhibits a nonlinear inverted U-shaped relationship with pollution consistent with the Environmental Kuznets Curve hypothesis, with high-income Provinces having surpassed the turning point at approximately $8,000 to $12,000 GDP per capita, beyond which additional economic development contributes to emissions reduction rather than environmental degradation. Industrial activities demonstrate a negative association with emissions when integrated with decarbonization technologies and cleaner production processes, challenging conventional assumptions about the inevitable environmental costs of industrialization. Cross-border trade, contrary to the Pollution Haven Hypothesis, exerts a negative total effect on emissions of 0.493 percent, reflecting China's structural shift toward high-value, low-emission goods and services alongside harmonized environmental standards that prevent the outsourcing of pollution-intensive activities. Spatial diagnostic tests including Moran's I statistics and LISA cluster maps confirm persistent positive spatial autocorrelation in emissions throughout the study period, validating the necessity of spatial modeling approaches, while the spatially lagged dependent variable coefficient of 0.074 demonstrates that emissions levels in neighboring countries directly influence domestic environmental outcomes. Effect decomposition analysis reveals that 72 percent of renewable energy's total impact operates through direct domestic channels while 28 percent manifests as cross-border spillovers, highlighting the substantial regional externalities generated by national-level clean energy investments. Dynamic specifications incorporating temporal lags confirm the persistence and amplification of these relationships over time, with renewable energy effects strengthening in recent years due to technological improvements, falling costs, and policy reinforcement. These results underscore the critical importance of coordinated regional policies that promote renewable energy diffusion through targeted investment mechanisms s, facilitate cross-border grid integration to enable efficient redistribution of clean electricity, harmonize environmental regulations to internalize spatial externalities, and provide differentiated support for lagging economies to achieve sustainable and spatially inclusive environmental outcomes across China. The study contributes methodologically by demonstrating the superiority of spatial econometric approaches over traditional panel methods in capturing the complex interdependencies characterizing integrated regional systems, and empirically by providing robust quantitative evidence on the magnitude and pathways of renewable energy spillovers that can inform evidence-based climate policy design. Policy implications emphasize that isolated national strategies are insufficient in spatially interconnected contexts, and that maximizing collective environmental benefits while ensuring equitable transitions requires supranational coordination, strategic infrastructure investments, technology transfer mechanisms, and institution-building support for regions with limited renewable energy capacity or weaker governance structures.

Accurate assessment of embedded carbon emissions (ECE) is critical for designing effective climate governance strategies and promoting the low-carbon transformation of export-oriented industries. Unlike traditional multi-regional input-output approaches that focus on sectoral trade linkages, this study adopts a firm-level perspective to analyze the drivers of ECE in China's light industry. Using panel data for Chinese A-share listed light-industry firms from 2010 to 2024, a two-way fixed-effects econometric model is employed to estimate the impacts of export intensity, energy structure, green innovation, firm size, profitability, and capital intensity on embedded carbon emissions. The empirical results show that export intensity significantly increases firms' ECE (beta = 0.171, t = 4.55, p < 0.01), indicating that expansion of export-oriented production remains associated with higher carbon burdens. Conversely, green innovation significantly reduces ECE (beta = -0.065, t = -3.17, p < 0.01), while improvements in clean energy utilization also exhibit a significant carbon-mitigation effect (beta = -0.118, t = -4.07, p < 0.01). Firm size and capital intensity are positively associated with emissions (beta = 0.054 and 0.039, respectively; p < 0.05), reflecting the scale effect of industrial production, whereas profitability shows a modest negative relationship with ECE (beta = -0.021, p < 0.10), suggesting that more efficient firms tend to achieve better carbon performance. Economically, the results imply that a 1% increase in export intensity leads to a 0.17% rise in embedded carbon emissions, whereas a 1% increase in green innovation reduces ECE by approximately 0.065%. Heterogeneity analysis further reveals that the export-driven carbon effect is stronger in heavy-polluting subsectors and non-high-tech firms, while the carbon-reduction effect of green innovation is more pronounced in large firms and state-owned enterprises. Moderating-effect estimations indicate that environmental regulation (beta = -0.041, p < 0.05) and digital transformation (beta = -0.036, p < 0.05) significantly weaken the positive relationship between exports and ECE. Robustness checks using alternative emission measures, lagged variables, and instrumental-variable estimation confirm the stability of the findings. Overall, this study provides new firm-level evidence on the carbon consequences of export-oriented growth in China's light industry and offers important policy implications for improving carbon efficiency, encouraging green innovation, and advancing sustainable export development under China's dual-carbon goals.

This study examines the economic implications of energy efficiency improvements in industrial cooling systems, analyzing their effects on economic growth, energy production costs, and financial market performance through a multi-objective optimization framework. The research employs econometric methods including the Generalized Method of Moments (GMM), Data Envelopment Analysis (DEA), and panel regression models to assess how technological investments in energy-intensive cooling infrastructure influence firm productivity, energy market dynamics, and financial sector valuations. Using panel data from manufacturing sectors across China's provinces (2010-2023), we quantify the economic returns from optimized energy configurations through three transmission channels: operational cost reduction, total factor productivity enhancement, and energy price stabilization. The optimized systems demonstrate cost reductions of 31.20% and productivity gains of 4.25% compared to conventional technologies, while simultaneously reducing energy market volatility. Furthermore, we develop an economic framework linking microeconomic efficiency improvements to macroeconomic outcomes, incorporating energy derivative pricing, green finance mechanisms, and regional economic multiplier effects. Fixed-effects regression analysis reveals that optimized energy infrastructure generates spillover effects on GDP growth, with each 10% improvement in industrial energy efficiency correlating with 1.2% regional economic growth in energy-intensive provinces. The study introduces entropy generation as a novel measure of economic inefficiency in energy markets, demonstrating that optimized systems reduce economic waste by 67% while enhancing energy security and improving current account balances through reduced energy imports. We analyze the financial sector response through energy commodity markets, green bond issuance, and carbon pricing mechanisms, finding significant positive effects on sustainable investment flows and energy stock valuations. The application of economic optimization models substantiates the theoretical framework linking energy efficiency to economic performance, providing empirical evidence for policymakers designing energy subsidy programs, carbon taxation schemes, and green finance regulations. This research offers comprehensive insights for understanding energy-economy-finance nexus and supports evidence-based policy formulation for sustainable economic development.