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This paper aims to comprehensively analyze the relationship between energy price and green energy innovation in China, and first studies the impact of energy price on China’s green energy innovation, then further investigates the moderating role of energy price distortion in the price–innovation relationship, especially in the context of lagging energy marketization level in the process of China’s transition from planned economy to the market economy. Based on the data of 30 provinces in China from 2003 to 2017, this paper provides a measurement of green energy innovation capacity through the number of “alternative energy production” and “energy conservation” patents. Our results show that energy price has a significantly positive impact on China’s green energy innovation, no matter the number of green energy patent applications or the number of green energy patent grants is used as the proxy of green energy innovation capacity. However, there exists heterogeneity related to the influence of energy price on green energy innovation. Specifically, energy price has a noticeable role in promoting green energy innovation in central and western China, but not in eastern China. Further research results show that energy price distortion significantly reduces the inducing effect of energy price on green energy innovation. Meanwhile, the distortion degrees of energy price in the central and western regions of China are significantly lower than that in the eastern region, which explains to a large extent why the inducing effect of energy price on innovation is more prominent in the central and western regions.

This study compares the dynamic characteristic of Chinese and American energy prices from the perspectives of learning expectation, volatility, persistence, and so on. First, the most suitable learning speeds for energy prices are determined and the energy price expectations are calculated by the learning models. Second, volatility characteristics and Granger-spillover effects among different energy prices and expectations are examined using the stochastic models based on the coefficient significance and DIC criteria. Third, the dynamic correlation coefficients are obtained by the selected stochastic models that have the lower DIC values. Fourth, expectation, volatility, and foreign energy price are introduced into the persistence model, and the persistence characteristics and reasons behind Chinese and American energy prices are empirically tested and compared. Finally, conclusions and suggestions are given based on the theoretical analysis and empirical results.

PurposeThe study's objective is to measure the response of the food prices to the aggregate and disaggregate geopolitical risk events, Russia's geopolitical risks and global energy prices in the context of two European regions, i.e. Eastern and Western Europe covering the monthly data from January 2001 to March 2022.Design/methodology/approachThe authors apply a novel and sophisticated econometric method, the cross-quantilogram (CQ) approach, to analyse the authors’ monthly data properties. This method detects the causal relationship between the variables under the bi-variate modelling approach. More importantly, the CQ procedure divulges the bearish and bullish states of the causal association between the variables under short, medium and long memories.FindingsThe authors find that aggregate measures of geopolitical risk reduce food prices in the short term in the Eastern Europe but increases food prices in the Western Europe. Besides, the decomposed measures of geopolitical risk “threats” and “acts” have heterogeneous effects on the food prices. More importantly, Russia's geopolitical risk events and global energy prices enhance the food inflation under long memory.Research limitations/implicationsThe authors provide diverse policy implications for Eastern and Western Europe based on the authors’ findings. First, the European policymakers should take concrete and joint policy measures to tackle the detrimental effects of geopolitical risks to bring stability to the food markets. Second, this region should emphasize utilizing their unused agricultural lands to grow more crops to avoid external dependence on food. Third, the European Union and its partners should begin global initiatives to help smallholder farmers because of their contribution to the resilience of disadvantaged, predominantly rural communities. Fourth, geopolitically affected European countries like Ukraine should deal with a crippled supply chain to safeguard their production infrastructure. Fifth, fuel (oil) scarcity in the European region due to the Russia-Ukraine war should be mitigated by searching for alternative sources (countries) for smooth food transportation for trade. Finally, as Europe and its Allies impose new sanctions in response to the Russia-Ukraine war, it can have immediate and long-run disastrous consequences on the European and the global total food systems. In this case, all European blocks mandate cultivating stratagems to safeguard food security and evade a long-run cataclysm with multitudinous geopolitical magnitudes for European countries and the rest of the world.Originality/valueThis is the maiden study that considers the aggregated and disaggregated measures of the geopolitical risk events, Russia's geopolitical risks and global energy prices and delves into these dynamics' effects on food prices. Notably, linking the context of the Russia-Ukraine war is a significant value addition to the existing piece of food literature.

The Russia–Ukraine war and new sanctions against Russia have created economic losers and winners. Supply chain shocks are made by two factors: the market’s extraordinary swings and the breadth of commodities exported by Russia and Ukraine including energy and raw material. This paper adopts the cross-quantilogram approach to visualize the effects of energy price shocks on the exchange rate movements during this war. Our findings indicate that energy price hikes are associated with the appreciation of the Canadian dollar against the Euro and Japanese yen. Considering the ongoing war in Ukraine, the best feasible policy responses are discussed.

This paper uses data on the life satisfaction of more than 100,000 individuals in 21 European countries from 2002 to 2011, to study the relationship between subjective well-being and the affordability for households of electricity, heating oil and natural gas. We find that energy prices have statistically and economically significant effects on subjective well-being. The effect sizes are smaller than but comparable to the effects of important personal factors of well-being. Effects above average are found in individuals from the lowest income quartile. In addition, effects are strongest at times when required energy expenditures can be expected to be high. The empirical results are consistent with the prediction that greater fuel poverty implies a greater effect of energy prices on well-being.

We build a Brown Energy Price Index (BEPI) for EU economies and sectors. The index is then incorporated in PVAR models to analyse the reaction to price shock, the impact on economic activity and the change in the energy mix. The panel dimension comes from the thirteen EU economies and it is declined across five economic sectors. At the macroeconomic level, a positive shock to BEPI triggers some substitution between brown and green energy. Since the substitution falls short of the needs, activity contracts; nonetheless, part of the energy substitution is maintained after the shock is absorbed. At the sector level, our results confirm the substitution effect and the adverse impact on activity. Moreover, the magnitude of the responses are shown to be correlated with the sectors brown energy intensity of production. JEL Codes: C30, E30, Q40

Oil has historically been the most significant primary energy source for our daily lives and business activities. However, recent skyrocketing oil prices have been one of the greatest concerns among policymakers, business executives, and the general public due to their impacts on daily necessities, including food, clothing, and automobile transportation. As a result, fast-rising inflation on the global scale is attributed to mounting oil prices. Even though many countries have made a conscious effort to tame oil prices and the subsequent inflation, their efforts are often in vain due to some uncontrollable situations. These situations include the ongoing war between Ukraine and Russia, where Russia began weaponizing its oil resources and limiting oil supplies to its neighboring European countries. Faced with the current energy crisis, a growing number of policymakers and business executives have attempted to develop energy-induced risk mitigation strategies. With this in mind, the primary purpose of this paper is to investigate what may have caused oil price hikes and to determine how significantly oil prices influence commodity prices. This paper then proposes ways to mitigate energy-induced supply chain risks by analyzing four decades of secondary data obtained from multiple sources.

Sustainable economies require effective energy planning that goes beyond relying on functioning forecasting models to comprehend energy dynamics, and also provides well-defined decision-making (DM) models that can address risk, ambiguity, and conflicting eco-economic objectives. This type of strategic planning requires an integrated assessment approach that can evaluate forecasting choices in an uncertain and dynamic environment. This paper presents a new and modified methodology for ranking energy forecasting models within a Pythagorean Fuzzy Set (PFS) system by integrating the CRITIC (Criteria Importance Through Inter-Criteria Correlation) weighting framework and the MAIRCA (Multi-Attributive Ideal-Real Comparative Analysis) ranking scheme. In the suggested framework, expert uncertainty and vagueness are represented by the PFS environment. In contrast, some of the leading eco-economic indicators are objectively weighted using CRITIC, and forecasting model alternatives are prioritized based on MAIRCA. A comparative study is conducted on a hypothetical data set that represents realistic energy system capabilities, including adaptability, carbon policy integration, and computing efficiency. The findings suggest that the framework contributes to consistent, interpretable, and uncertainty-aware rankings, and the Deep Q-Network (DQN) model was ranked to be the most effective alternative. The study contributes to the development of more sophisticated decision-support mechanisms to facilitate sustainable energy planning, enabling informed and balanced decisions as the eco-economic climate evolves rapidly.

This paper empirically examines the nexus between fossil energy prices and carbon emissions using a balanced panel of 119 economies spanning the period from 1990 to 2023. The baseline regression results indicate that a 1% rise in fossil energy prices results in a 0.009% reduction in CO2 emissions, equivalent to approximately 3.1 million tons of CO2. Further analysis reveals two key mechanisms. First, energy efficiency partially mediates the price–emission relationship: higher prices significantly improve efficiency, which in turn reduces CO2 emissions, although a rebound effect of 13.6% offsets part of the expected savings. Second, renewable energy penetration serves as an additional pathway, with higher prices accelerating renewable adoption and thereby contributing to carbon mitigation. Overall, the findings confirm the direct and indirect impacts of fossil energy prices on emissions, underscoring their role as an effective lever for achieving global sustainability targets. Policy implications include the need to align fossil energy prices with true economic and environmental costs, while complementing price mechanisms with efficiency standards and renewable incentives to counterbalance hirebound effects.

We investigate the direct role of technological innovation and other factors influencing industrial energy intensity across 17 EU countries over 1995–2009. We develop an innovative industry-level patent dataset and find compelling evidence that patent stock negatively influences industrial energy intensity. In particular, we find a much stronger effect of patent stock on energy-intensive industries with an estimated coefficient of –0.138 which almost double that of less energy-intensive industries (estimated at –0.085). While our results show that energy price remains the major determinant of energy intensity, the chemicals industry, which is not covered by the EU Emissions Trading Scheme (ETS) during the sample period, appears more susceptible to energy prices relative to other energy-intensive industries that are covered by the EU ETS. Exploring regional differences in carbon taxation, we find a significant decline in energy intensity in Northern Europe owing to the carbon tax policy implemented in the early 1990s across the Nordic countries.