Search Results (129)

This paper examines the changing role of emissions trading systems (ETSs) within the macro-financial framework of energy markets, emphasizing price dynamics and systemic spillovers. Utilizing monthly data from seven ETS jurisdictions spanning January 2021 to December 2024 (N = 287 observations after log transformation and first differencing), which includes four auction-based markets (United States, Canada, United Kingdom, South Korea), two secondary markets (China, New Zealand), and a government-set fixed-price scheme (Germany), this research estimates a panel vector autoregression (PVAR) employing a Common Correlated Effects (CCE) model and augments it with machine learning analysis utilizing XGBoost and explainable AI methodologies. The PVAR-CEE reveals numerous unexpected findings related to carbon markets: ETS returns exhibit persistence with an autoregressive coefficient of −0.137 after a four-month lag, while increasing inflation results in rising ETS after the same period. Furthermore, ETSs generate spillover effects in the real economy, as elevated ETSs today forecast a 0.125-point reduction in unemployment one month later and a 0.0173 increase in inflation after two months. Impulse response analysis indicates that exogenous shocks, including Brent oil prices, policy uncertainty, and financial volatility, are swiftly assimilated by ETS pricing, with effects dissipating completely within three to eight months. XGBoost models ascertain that policy uncertainty and Brent oil prices are the most significant predictors of one-month-ahead ETSs, whereas ESG factors are relevant only beyond certain thresholds and in conditions of low policy uncertainty. These findings establish ETS markets as dynamic transmitters of macroeconomic signals, influencing energy management, labor changes, and sustainable finance under carbon pricing frameworks. Full article

With the support of the dual-carbon strategy and related policies, China’s offshore wind power has experienced rapid development. However, constrained by the inherent intermittency and volatility of wind power, large-scale expansion poses significant challenges to grid integration and exacerbates government fiscal burdens. To address these critical issues, this paper proposes a multi-market coupling trading model integrating energy storage-equipped offshore wind power into electricity–carbon–green certificate markets for large-scale grid networks. Firstly, a day-ahead electricity market optimization model that incorporates energy storage is established to maximize power revenue by coordinating offshore wind power generation, thermal power dispatch, and energy storage charging/discharging strategies. Subsequently, carbon market and green certificate market optimization models are developed to quantify Chinese Certified Emission Reduction (CCER) volume, carbon quotas, carbon emissions, market revenues, green certificate quantities, pricing mechanisms, and associated economic benefits. To validate the model’s effectiveness, a gradient ascent-optimized game-theoretic model and a double auction mechanism are introduced as benchmark comparisons. The simulation results demonstrate that the proposed model increases market revenues by 17.13% and 36.18%, respectively, compared to the two benchmark models. It not only improves wind power penetration and comprehensive profitability but also effectively alleviates government subsidy pressures through coordinated carbon–green certificate trading mechanisms. Full article

This paper applies the Multivariate Stochastic Volatility (MSV) model, alongside its extended DGC-t-MSV model, and integrates Bayesian methods with MCMC techniques to develop the DGC-t-MSV-BN model. This model is specifically designed to analyze the volatility spillover effects between stock and futures markets. Key findings are as follows: (1) Significant volatility spillover effects exist from futures market to stock market. Notably, the spillover effects among the Chinese carbon futures market and both the Chinese and international stock markets are stronger than those within the Chinese carbon futures market, as well as the international gold and crude oil futures markets. (2) A notable negative volatility spillover effect is observed between Chinese carbon futures market and the international stock market. Conversely, a significant positive volatility spillover effect exists in the Chinese carbon futures market and the Chinese stock market. (3) The Chinese carbon futures market, as an emerging sector, displays high volatility and immaturity, yet it is developing at a rapid pace. Full article

U.S. gasoline and diesel prices are often volatile, driven by geopolitical risks and disruptions in the fossil fuel market. Forest biomass fuels, particularly renewable diesel derived from logging residues, offer a low-carbon alternative with the potential to stabilize fuel prices. This study evaluates whether biomass can moderate fuel price volatility using ANOVA, Tukey post hoc tests, and quadratic regression based on monthly data for biomass production, inventories, and retail fuel prices. Findings reveal the existence of a significant nonlinear relationship between forest biomass inventory levels and fossil fuel prices. Average gasoline prices peaked in the medium-inventory group (M = 0.837) and dropped in the high-inventory group (M = 0.684). Diesel prices followed a similar pattern, with the highest values in the medium-inventory group (M = 0.963) and the lowest in the high-inventory group (M = 0.759). One-way ANOVA results were statistically significant for both gasoline (F(2, 99) = 7.39, p = 0.001) and diesel (F(2, 99) = 7.22, p = 0.0012). Tukey tests confirmed that diesel prices fell significantly from both medium to high and low to high-inventory levels. This result remains robust when using the biomass index level and the biomass production level. These results indicate a threshold effect: only at higher biomass inventories do fossil fuel prices decline, suggesting a potential for substitution. However, current policies inadequately support biomass integration, highlighting the need for targeted reforms. Full article

The global shift to low-carbon energy systems has significantly increased demand for critical raw materials like lithium, cobalt, nickel, rare earth elements, and copper. These materials are essential for renewable technologies and energy storage. However, their extraction and processing produce significant carbon emissions and face challenges from supply chain vulnerabilities and price volatility. This review examines the complex relationship between carbon pricing mechanisms—such as carbon markets and taxes—and raw material markets. It explores the strategic importance of these materials, recent policy developments, and the transmission of carbon pricing impacts through supply chains. The review also analyzes the systemic risks created by carbon pricing, including regulatory uncertainty, market volatility, and geopolitical tensions. We then discuss financial tools and corporate strategies for managing these risks, such as carbon-linked derivatives and supply chain diversification. Finally, this review identifies key challenges and suggests future research to improve the resilience and sustainability of raw material supply chains. Here, resilience is defined as the capacity to adapt to carbon pricing volatility, geopolitical disruptions, and regulatory shocks, while maintaining operations. The paper concludes that coordinated policies and flexible risk management are urgently needed to support a reliable and sustainable energy transition. Full article

This paper examines the firm-level carbon intensity of 83 constituent stocks in the Hang Seng Index, constructs two distinct indexes from the 20 firms with the highest and lowest carbon intensities, and analyzes the connectedness of their annualized daily volatilities with four key external factors over the past 15 years. Our findings reveal that low-carbon stocks—often represented by high-tech and financial firms—tend to exhibit higher volatility, reflecting their more dynamic business environments and greater sensitivity to changes in revenue and profitability. In contrast, high-carbon companies, such as those in the utilities and energy sectors, display more stable demand patterns and are generally less exposed to abrupt market shocks. We also find that oil price shocks result in greater volatility spillovers for low-carbon stocks. Among external influences, the U.S. stock market and Treasury yield exert the most significant spillover effects, while crude oil prices and the U.S. dollar–Chinese yuan exchange rate act as net volatility recipients. Full article

In a dynamic global market, platinum-group metals (PGMs), particularly palladium, are in high demand across various industries due to their unique properties. Palladium plays a crucial role in environmentally friendly technologies, such as catalytic converters, which mitigate harmful automotive emissions. Additionally, it is essential for clean energy production, particularly in hydrogen generation, which makes palladium a critical resource for building a sustainable and secure supply chain. This study evaluates the prospects of the palladium market through strategic analysis, focusing on the Russian mining and metals company PJSC MMC Norilsk Nickel. The research employs strategic and industry analysis methods to examine palladium production, market dynamics, and technological advancements, as well as emerging applications in the context of a green economy. The article analyzes the economics of palladium production, including price volatility driven by stringent environmental regulations and the rising adoption of electric vehicles. The palladium market faces challenges such as a constrained resource base, supply disruptions due to sanctions, price instability, and growing demand from key sectors, particularly the automotive industry. Nevertheless, innovation-driven trends offer promising opportunities for market growth, aligning with sustainable development principles and the transition toward a green, low-carbon economy in both established and emerging markets. As a key scientific contribution, this study proposes a modified methodological approach to industry analysis, enabling the assessment of a mining and metals company’s competitive sustainability in the palladium market over the medium and long term. Furthermore, the research models the life cycle of palladium as a commodity, considering evolving market trends and the rapid development of new industries within the green economy. Full article

Carbon market price prediction is critical for stabilizing markets and advancing low-carbon transitions, where capturing multifractal dynamics is essential. Traditional models often neglect the inherent long-term memory and nonlinear dependencies of carbon price series. To tackle the issues of nonlinear dynamics, non-stationary characteristics, and inadequate suppression of modal aliasing in existing models, this study proposes an integrated prediction framework based on the coupling of gradient-sensitive time-series adversarial training and dynamic residual correction. A novel gradient significance-driven local adversarial training strategy enhances immunity to volatility through time step-specific perturbations while preserving structural integrity. The GSLAN-BiLSTM architecture dynamically recalibrates historical–current information fusion via memory-guided attention gating, mitigating prediction lag during abrupt price shifts. A “decomposition–prediction–correction” residual compensation system further decomposes base model errors via wavelet packet decomposition (WPD), with ARIMA-driven dynamic weighting enabling bias correction. Empirical validation using China’s carbon market high-frequency data demonstrates superior performance across key metrics. This framework extends beyond advancing carbon price forecasting by successfully generalizing its “multiscale decomposition, adversarial robustness enhancement, and residual dynamic compensation” paradigm to complex financial time-series prediction. Full article

The global energy transition is accelerating, bringing new challenges to power systems. A high penetration of renewable energy increases grid volatility. Virtual power plants (VPPs) address this by dynamically responding to market signals. They integrate renewables, energy storage, and flexible loads. Additionally, they participate in multi-tier markets, including energy, ancillary services, and capacity trading. This study proposes a load factor-based VPP pre-dispatch model for optimal resource allocation. It incorporates the coupling effects of electricity–carbon markets. A Nash negotiation strategy is developed for multi-VPP cooperation. The model uses an accelerated adaptive alternating-direction multiplier method (AA-ADMM) for efficient demand response. The approach balances computational efficiency with privacy protection. Revenue is allocated fairly based on individual contributions. The study uses data from a VPP dispatch center in Shanxi Province. Shanxi has abundant wind and solar resources, necessitating advanced scheduling methods. Cooperative operation boosts profits for three VPPs by CNY 1101, 260, and 823, respectively. The alliance’s total profit rises by CNY 2184. Carbon emissions drop by 31.3% to 8.113 tons, with a CNY 926 gain over independent operation. Post-cooperation, VPP1 and VPP2 see slight emission increases, while VPP3 achieves major reductions. This leads to significant low-carbon benefits. This method proves effective in cutting costs and emissions. It also balances economic and environmental gains while ensuring fair profit distribution. Full article

This study examines the price dynamics of the UK Emission Trading Scheme (UK ETS) by integrating advanced computational methods, including deep learning and statistical modelling, to analyze and simulate carbon market behaviour. By analyzing long-memory effects and price volatility, it assesses whether UK carbon prices align with theoretical expectations from carbon pricing mechanisms and market efficiency theories. Findings indicate that UK carbon prices exhibit persistent long-memory effects, contradicting the Efficient Market Hypothesis, which assumes price movements are random and fully reflect available information. Furthermore, regulatory interventions exert significant downward pressure on prices, suggesting that policy uncertainty disrupts price equilibrium in cap-and-trade markets. Deep learning models, such as Time-series Generative Adversarial Networks (TGANs) and adjusted fractional Brownian motion, outperform traditional approaches in capturing price dependencies but are prone to overfitting, highlighting trade-offs in AI-based forecasting for carbon markets. These results underscore the need for predictable regulatory frameworks, hybrid pricing mechanisms, and data-driven approaches to enhance market efficiency. By integrating empirical findings with economic theory, this study contributes to the carbon finance literature and provides insights for policymakers on improving the stability and effectiveness of emissions trading systems. Full article

Driven by the global “double carbon” goal, the volatility of renewable energy poses a challenge to the stability of power systems. Traditional methods have difficulty dealing with high-dimensional nonlinear data, and the single deep learning model has the limitations of spatiotemporal feature decoupling and being a “black box”. Aiming at the problem of insufficient accuracy and interpretability of power load forecasting in a renewable energy grid connected scenario, this study proposes an interpretable spatiotemporal feature fusion model based on an attention mechanism. Through CNN layered extraction of multi-dimensional space–time features such as meteorology and electricity price, BiLSTM bi-directional modeling time series rely on capturing the evolution rules of load series before and after, and the improved self-attention mechanism dynamically focuses on key features. Combined with the SHAP quantitative feature contribution and feature deletion experiment, a complete chain of “feature extraction time series modeling weight allocation interpretation and verification” is constructed. The experimental results show that the determination coefficient R² of the model on the Australian electricity market data set reaches 0.9935, which is 84.6% and 59.8% higher than that of the LSTM and GRU models, respectively. The prediction error (RMSE = 105.5079) is 9.7% lower than that of TCN-LSTM model and 52.1% compared to the GNN (220.6049). Cross scenario validation shows that the generalization performance is excellent (R² ≥ 0.9849). The interpretability analysis reveals that electricity price (average absolute value of SHAP 716.7761) is the core influencing factor, and its lack leads to a 0.76% decline in R². The research breaks through the limitation of time–space decoupling and the unexplainable bottleneck of traditional models, provides a transparent basis for power dispatching, and has an important reference value for the construction of new power systems. Full article

In the context of China’s strategic push toward high-quality development, the concept of new quality productive forces (NQPF)—which emphasizes technological innovation, green transformation, and digital upgrading—has received a lot of attention. However, the increasing volatility and ambiguity in climate-related policymaking present a serious institutional challenge. This study addresses the underexplored question of how climate policy uncertainty (CPU) affects the regional development of NQPF in China. Unlike traditional productivity, NQPF relies on long-term innovation and sustainable investment, which are highly sensitive to external policy signals. Drawing on panel data from 30 Chinese provinces between 2013 and 2021, this paper uses fixed-effects regressions to empirically assess the influence of CPU on NQPF. The findings reveal that CPU significantly suppresses the development of NQPF, but this effect is mitigated by financial inclusion, carbon market participation, and strong local government sustainability performance. This paper provides new insight into the risks posed by climate uncertainty to economic development and highlights institutional tools that can buffer its negative effects. Full article

In the context of the clean and low-carbon transformation of power systems, addressing the challenge of day-ahead electricity market price prediction issues triggered by the strong stochastic volatility of power supply output due to high-penetration renewable energy integration, as well as problems such as limited dataset scales and short market cycles in test sets associated with existing electricity price prediction methods, this paper introduced an innovative prediction approach based on a multi-modal feature fusion and BiGRUSA-ResSE-KAN deep learning model. In the data preprocessing stage, maximum–minimum normalization techniques are employed to process raw electricity price data and exogenous variable data; the complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) and variational mode decomposition (VMD) methods are utilized for multi-modal decomposition of electricity price data to construct a multi-scale electricity price component matrix; and a sliding window mechanism is applied to segment time-series data, forming a three-dimensional input structure for the model. In the feature extraction and prediction stage, the BiGRUSA-ResSE-KAN multi-branch integrated network leverages the synergistic effects of gated recurrent units combined with residual structures and attention mechanisms to achieve deep feature fusion of multi-source heterogeneous data and model complex nonlinear relationships, while further exploring complex coupling patterns in electricity price fluctuations through the knowledge-adaptive network (KAN) module, ultimately outputting 24 h day-ahead electricity price predictions. Finally, verification experiments conducted using test sets spanning two years from five major electricity markets demonstrate that the introduced method effectively enhances the accuracy of day-ahead electricity price prediction, exhibits good applicability across different national electricity markets, and provides robust support for electricity market decision making. Full article

Significant shortfalls in meeting the climate mitigation targets and volatile energy markets make evident the need for an urgent transition from fossil fuels to sustainable alternatives. However, the integration of zero-carbon fuels like green hydrogen and ammonia is an immense project and will take time and the construction of new infrastructure. It is during this transitional period that lower-carbon natural gas alternatives are essential. In this study, the industrial sectors of Lithuania are analysed based on their energy consumption. The industrial sectors that are the most energy-intensive are food, chemical, and wood-product manufacturing. Synthetic natural gas (SNG) has become a viable substitute, and biomethane has also become viable given a feedstock price of 21 EUR/MWh in the twelfth year of operation and 24 EUR/MWh in the eighth year, assuming an electricity price of 140 EUR/MWh and a natural gas price of 50 EUR/MWh. Nevertheless, the scale of investment in hydrogen production is comparable to the scale of investment in the production of other chemical elements; however, hydrogen production is constrained by its high electricity demand—about 3.8 to 4.4 kWh/Nm³—which makes it economically viable only at negative electricity prices. This analysis shows the techno-economic viability of biomethane and the SNG as transition pathways towards a low-carbon energy future. Full article

This paper examines how the slope of the merit-order curve and the share of non-zero-dollar dispatched energy affect photovoltaic (PV) price cannibalization and the declining market value of all generation types. Using historical merit-order data from Alberta, Canada—during its coal-to-gas transition—we simulated the introduction of zero-marginal-cost PV offers. The increased PV penetration rapidly suppresses midday electricity prices, forming a “duck curve” that challenges solar project economics. Emission reductions improve with rising carbon prices, indicating environmental benefits despite declining market revenues. Years with steeper merit-order slopes and lower non-zero-dollar dispatch shares show intensified price cannibalization and a reduced PV market value. The integration of battery storage alongside PV significantly flattened daily price profiles—raising the trough prices during charging and lowering the highest prices during discharging. While this reduces price volatility, it also diminishes the market value of all generation types, as batteries discharge at zero marginal cost during high-price hours. Battery arbitrage remains limited in low- and moderate-price regimes but becomes more profitable under high-price regimes. Overall, these dynamics underscore the challenges of integrating large-scale PV in energy-only markets, where price cannibalization erodes long-term investment signals for clean energy technologies. These insights inform sustainable energy policy design aimed at supporting decarbonization, and investment viability in liberalized electricity markets. Full article