Search

This paper aims to investigate the impact of the macroeconomic uncertainty index (MU) on the futures returns of energy, including crude oil, heating oil, and natural gas. The quantile regression is used in this paper for examining the heterogeneity across market conditions. The empirical results show that changes in MU negatively affect futures returns of crude oil and heating oil in downward market conditions, and positive changes in MU dominate this impact. The MU changes also have a negative effect on natural gas futures returns in an extremely downward period, but this impact is not asymmetric. Further analysis indicates that speculation mitigates the negative impact of positive MU changes on crude oil futures returns during downward periods, while the association of the MU changes with the futures returns of heating oil and natural gas is not affected by speculation.

This paper discusses the heterogeneous responses of fuel, electric and plug-in hybrid electric vehicle sales to different time frequencies of oil prices using wavelet multiscale decomposition and quantile-on-quantile methods. The empirical results show that the link between oil prices and vehicle sales in each subsector of the automobile market varies, and the coefficients change and even present opposite directions at different quantiles and frequencies of the oil price. The major contribution of this paper is that the heterogeneities of oil price time frequencies, subdivided automobile markets and policy implications are fully considered, which can provide more accurate and valuable conclusions. Policy implications are further provided to alleviate external shocks from oil prices. Government plans for clean energies should be considered a cornerstone in establishing automobile industry policies. Subsidies, especially green technologies, are also necessary for fuel vehicle manufacturers to develop highly efficient engines and consequently reduce oil consumption. In addition, China's energy pricing mechanism needs to be reformed to prevent disconnection between domestic oil products and international oil markets.

Data complexity has a great impact on daily natural gas consumption forecasting. However, due to the existence of irregular data, complex periodic change, and volatility data, the conventional methods, such as Lyapunov exponent and sample entropy, are failed to assess the complexity of the consumption data. Thus, this paper proposes a hybrid method of complexity measure, named CMLS. The novel method combined correlation coefficient analysis, missing data detect, Lyapunov exponent, and skewness analysis. Compared with Lyapunov exponent and sample entropy, CMLS is more stable and insensitive to the length of data in complexity measures. Additionally, for revealing the relationship between data complexity and forecasting performance, we design three case studies including 56 sets of daily natural gas consumption, and forecast with three advanced models. The results show that the forecasting performance various a lot in different complexity level. Particularly in very hard level, the daily natural gas consumption data is very hard to be forecasted and the R2 of forecasts are all negative. This paper serves as an initial study seeks to reveal the impact of data complexity on forecasting performance. The findings can help forecasters to evaluate the performance and difficulty of natural gas consumption forecasting.

This paper explores the dynamic evolutionary characteristics of the multivariate price movement in the carbon-fuel energy markets system by analyzing the topological features of our constructed multivariate price movement evolution network. Firstly, we find that the evolution pattern of the multivariate price movement modes of the carbon-fuel energy markets system has been generally almost fixed after a while, which suggests there exist the essential evolution laws of multivariate price movement modes. Secondly, we extract the essential evolution laws by motif analysis, showing that the transmission probability from the favorable community promoting the greenhouse gases emission reduction to the unfavorable community hindering the greenhouse gases emission reduction is not low, which could result in the occurrence of the “short-term effectiveness traps” in greenhouse gases emission reduction. Finally, we also find that when the evolution occurs within the modes with slight fluctuation of the carbon and coal price, its evolution process is favorable to greenhouse gases emission reduction. The results we obtained are totally new and open up a new perspective for market regulators in the carbon trading market.

This paper estimates the price and income elasticity coefficients of domestic electricity demand for the period 1980 to 2018 in South Africa. South African electricity prices were falling in real terms between 1983 and 2005. It then increased sharply in response to substantial tariff increases between 2008 and 2011. A time-varying parameter model with the Kalman filter is applied to estimate the evolution of the elasticities over time. This allows the analysis to distinguish between the two regimes of decreasing and increasing real electricity prices and evaluate the evolution of demand elasticities accordingly. The main result, consistent with existing South African literature, is that electricity consumption was unresponsive to price changes in the period of falling real electricity prices up to 2005. However, when real prices started increasing, the price elasticity coefficient increased markedly in absolute terms. The key result that policymakers should take note of is that aggregate price sensitivity is notably higher when real prices are increasing. While price elasticity estimates remain lower than unity (i.e. relatively inelastic), further price increases may incentivise consumers to substitute into alternative sources of energy. This result is echoed by findings from the comparable international literature.

This paper studies the economic value of high-frequency data in the equity-oil hedge. Specifically, we use oil to perform a minimum variance hedge for equity indices. We find that intraday information helps generate more accurate hedge ratio forecasts and achieve more effective variance reduction. However, it does not always lead to larger tail risk reduction or utility gains, as the minimum variance hedging strategy aims for variance reduction without considering the first or high-order moments. Overall, the findings reveal the benefits and limitations of using intraday information in cross-asset hedges, which would be of interest to financial market participants, corporations, and regulators.

This paper proposes alternative methodologies for oil price forecasting using mixed-frequency data and a textual sentiment indicator. The latter variable was extracted from oil market reports issued by the Energy Information Administration. We used the root mean square error (RMSE) to evaluate the forecasting accuracy of the econometric models. Compared with other econometric models, the mixed data sampling (MIDAS) model with high-frequency financial indicators and the sentiment index as explanatory variables performs better for forecasting crude oil prices.

China is committed to seeing its peak carbon dioxide emissions before 2030 and then its carbon neutrality before 2060, which further highlights the importance of China's energy transition. On the basis of clarifying the logic of analysis, this paper clearly defines the objectives of both the low-carbon pathway and the green pathway, scientifically identifies the incentives when choosing the green & low-carbon pathway in China's energy transition, tentatively constructs an “Incentive-Driven Analysis Model for Green & Low-carbon Transition”, and quantitatively studies the driving effects of five-dimensional incentives. The econometric methodology is applied for empirical analysis using data from 2000 to 2019 for some reason, and the variable values are measured. The results show that: (1) In terms of structure, the institutional incentives are stronger than others, while the “Dual Circulation” incentives are becoming more and more important; (2) In terms of mode, judged by the vector characteristics, obvious differences in “process continuity” and “functional consistency” can be seen among different incentives; (3) In terms of effect, even the same driving force will have different functional attributes in different stages, especially in the “gear shift period” and the “gear shift acceleration period”. The main contributions can be summarized as follows: Theoretically, the “low carbon transition” and the “green transition” are clearly distinguished, while a framework with five dimensions for energy transition analysis is introduced for the first time. Practically, the effectiveness of different incentives for given periods is clearly explained from the perspectives of direction, intensity and timeliness, which provides reference for government decision-making.

From the perspective of low-carbon economy (LCE), this paper explores the optimal structure adjustment strategy, emission reduction potential and utilization efficiency of fossil energies (coal, petroleum, and natural gas) for 30 provinces in China from 2010 to 2019. A mixed integer data envelopment analysis model is proposed to identify the optimal structure adjustment and the potential emission reduction. Then, a new total-factor energy efficiency (TFEE) index based on energy structure adjustment is constructed to statically examine the utilization efficiencies of the three fossil energies. Finally, a novel calculation method of global Malmquist index is presented to dynamically analyze the energy productivity change. The results demonstrate that more than half of the provinces have the potential to reduce coal consumption, while for petroleum and natural gas, the optimal adjustment of different provinces is diverse. The CO2 emission reduction brought about by the energy structure adjustment accounts for about 11% of the emission, and the emission reduction potential of central and western regions is significantly larger than that of eastern region. Technological progress is the main driving of productivity improvement for both LCE and three fossil energies. However, significant differences can be found in the management level of different fossil energies among provinces.

The importance of manufacturing as a main determinant of economic growth has been explored extensively in the literature considering manufacturing as the engine of economic growth. In our study, a new database covering 117 countries for 1995–2017 containing four indicators, namely manufacturing value added share in GDP, growth of GDP, manufacturing energy intensity, and UNIDO's Competitive Industrial Performance score is constructed. Relying on this dataset and the Granger causality tests the paper investigates the effect of economic growth, energy intensity and the competitiveness index on manufacturing value added. Applying adequate econometric methods the effect of this set of variables on the relative importance of manufacturing in the economy as a whole measured by the share of MVA in GDP (MVAsh) is analyzed. An inverted U shape is verified with different turning points between static and dynamic analyses in the full sample, as well as in industrialized countries and emerging and developing countries separately, with turning points all within samples. Energy intensity has negative effect in most of the cases while CIP has positive effect with high magnitudes. The rate of adjustment is high.