How do China's petrochemical markets react to oil price jumps? A comparative analysis of stocks and commodities

The profound impacts of oil price jumps have caught the attention of scholars. Because the 2008 global financial crisis has seemingly already passed, the existence of oil price jumps is in doubt. In this paper, we provide evidence that the threat of dynamic jumps still exists in the global oil market in the post-crisis period, while the stocks and commodities of China's petrochemical markets are both affected by those jumps. To the best of our knowledge, this is the first study of the reaction of petrochemical markets to oil price jumps in the post-crisis period. In addition, a comparative analysis of petrochemical stocks and petrochemical commodity market is provided. In particular, we analyze the reactions of the returns and volatility of these markets to oil price jumps. We obtained the following findings. First, the returns of petrochemical stocks and petrochemical commodities are both negatively affected by current oil price jumps, while the effects of lagged jumps on these returns are opposite. Theoretically, the former is a reflection of panic induced by extreme risk information, while the latter is a reflection of rationality in speculators. Second, the volatilities of petrochemical stocks and petrochemical commodities respond differently to oil price jumps. The former is not affected, whereas the latter is positively and negatively affected by current and last oil price jumps, respectively. Finally, all the above conclusions still hold when considering the effects of normal oil price volatility, even after the co-movement between oil prices and petrochemical markets is eliminated.     

How does Germany's green energy policy affect electricity market volatility? An application of conditional autoregressive range models

Based on a dynamic model for the high/low range of electricity prices, this article analyses the effects of Germany's green energy policy on the volatility of the electricity market. Using European Energy Exchange data from 2000 to 2015, we find rather high volatility in the years 2000–2009 but also that the weekly price range has significantly declined in the period following the year 2009. This period is characterised by active regulation under the Energy Industry Law (EnWG), the EU Emissions Trading Directive (ETD) and the Renewable Energy Law (EEG). In contrast to the preceding period, price jumps are smaller and less frequent (especially for day-time hours), implying that current policy measures are effective in promoting renewable energies while simultaneously upholding electricity market stability. This is because the regulations strive towards a more and more flexible and market-oriented structure which allows better integration of renewable energies and supports an efficient alignment of renewable electricity supply with demand.     

How does daylight saving time affect electricity demand? An answer using aggregate data from a natural experiment in Western Australia

Daylight saving time (DST) affects the lives of more than 1.6 billion people worldwide, with energy saving being the original rationale for its implementation. This study takes advantage of natural experiment data from September 2006 to March 2013 in Western Australia in which DST was observed from December 2006 to March 2009, to estimate the effect of DST on electricity demand. Using the difference-in-differences (DD) approach, we find that DST has little effect on overall electricity demand and electricity generation costs. However, it has a strong redistributional effect by reducing electricity demand substantially in the late afternoon and early evening. This redistributional effect of DST may be of particular interest for policymakers who are interested in controlling high demand and the short term energy market price.     

How do daily changes in oil prices affect US monthly industrial output?

Detecting asymmetry has become increasingly difficult using single frequency data. This paper goes beyond the prevailing use of aggregate/averaged data in order to provide a more in-depth treatment of the dynamic effects of the price of crude oil on industrial output growth. To do so, we propose an Asymmetric Mixed Data Sampling (AMIDAS) model to examine if there is any concealed evidence of asymmetry arising from daily effects of the price of crude oil on monthly changes in industrial output in the United States (US). We find that this model is able to detect dynamic asymmetric impacts of a high frequency independent variable on a low frequency dependent variable more effectively than when the high frequency variable is aggregated up at the time interval of the low frequency variable. We find that, in comparison with the marginal lagged effects of a rise in the daily price of crude oil, the effects of a fall in the daily price of crude oil are more sluggish as it takes longer for the effects of the oil price drop to die off over time. This finding implies that a fall in the price of crude oil shifts the supply curve rightward less and at a much slower pace than an equivalent price rise shifts it to the left.     

How do oil producers respond to giant oil field discoveries?

The classical Hotelling model predicts that the optimal extraction level rises immediately after an unexpected resource discovery, whereas, in reality, there are substantial adjustment costs in petroleum production and an average lag of several years between a discovery and the start of production. Using a large panel of country-level production data and a difference-in-differences identification approach, I show that domestic production levels respond before a newly found oil field comes on line and that this increase is driven by non-OPEC producers, consistent with different responses of OPEC and non-OPEC drilling activity. Offshore fields and exceptionally large “super-” or “mega-giant” fields are also more likely to raise country-level production. Given that domestic petroleum consumption rises by less in response to a discovery, at least part of the increase in production seems to go into (net) oil exports.     

How do normalization schemes affect net spillovers? A replication of the Diebold and Yilmaz (2012) study

This paper replicates the Diebold and Yilmaz (2012) study on the connectedness of the commodity market and three other financial markets: the stock market, the bond market, and the FX market, based on the Generalized Forecast Error Variance Decomposition, GEFVD. We show that the net spillover indices (of directional connectedness), used to assess the net contribution of one market to overall risk in the system, are sensitive to the normalization scheme applied to the GEFVD. We show that, considering data generating processes characterized by different degrees of persistence and covariance, a scalar-based normalization of the Generalized Forecast Error Variance Decomposition is preferable to the row normalization suggested by Diebold and Yilmaz since it yields net spillovers free of sign and ranking errors.     

Watts your usage? A field study of householders’ literacy for residential electricity data

Smart metering studies typically focus on quantifying behavior change. However, little is known about how users understand energy information and analyze and interpret feedback from energy data visualizations. To investigate this, we gave 13 participants from nine UK households an electricity power clamp meter. Prior to installing and using the device, we conducted interviews with participants to gauge their understanding of their home electricity consumption and found that participants varied considerably from limited to substantial energy literacy. Two weeks after the clamp meter had been installed, we conducted a contextual inquiry in which we asked participants to explain the web-based time series visualization of their recorded electricity data. We found that the visualization proved unfit: participants relied on memories and suggested likely routines, while widely being unable to reliably identify specific events in the data visualization. In follow-up interviews 3 months later, we found that participants’ understanding of their home electricity consumption had hardly changed. Finally, we invited participants to generate ideas how smart electricity feedback could be optimized. They named different forms of disaggregation, higher temporal resolution, and interactivity as design requirements. In summary, these results suggest that people find home energy data very difficult to understand and link to everyday actions and behaviors.     

Investment in energy efficiency: do the characteristics of investments matter?

??Investment in energy efficiency: do the characteristics of firms matter??? In their famous 1998 paper, DeCanio and Watkins raised the question and answered it affirmatively. Our paper addresses a parallel question: ??Investment in energy efficiency: do the characteristics of investments matter??? To answer this question, we first describe our new investment decision-making model, applicable to all investment types. We then discuss our research results, based on questionnaires submitted to finance managers of 35 major electricity consumers in various commercial and industrial sectors. We show how characteristics other than profitability play an important role in investment choices. The investment category influences profitability evaluation, profitability requirement, and, ultimately, the decision made. For half of the firms in our study, energy-efficiency investments did not exist as a category. However, wide diversity regarding investment behavior is observed between firms. Our findings lead to a different explanation of the energy-efficiency gap and open the way for a new approach to promoting energy-efficiency investments, which is briefly discussed in the conclusion.     

How does tax system on energy industries affect energy demand,CO2 emissions,and economy in China?

Energy savings and CO₂ emission reduction have become a major issue in recent years. Taxes on energy production sectors may be an effective way to save energy, reduce CO₂ emissions, and improve environmental quality. This paper constructs a dynamic recursive Computable General Equilibrium (CGE) model to analyze the impact of the energy tax on energy, economy, and environment from the perspective of tax rates and tax forms (specific tax and ad valorem tax). The results show that adjusting the tax system and the tax rate has important implications for energy conservation while having minor impacts on the output of other industries. The impact of an increasing energy tax on the energy demand is greater than the impact on sectoral output, indicating that energy efficiency will be increased to some extent. The CO₂ reduction will increase over time when an ad valorem tax is implemented on enterprises. We found that ad valorem tax has greater elasticity of economic output, energy demand, and CO₂ emission reduction. The results support the direction of China's resource tax reform. However, we argue that it is better to increase the tax rate relatively and relax the control on energy prices so that energy efficiency will increase.     

How does the new-type urbanisation affect CO2 emissions in China? An empirical analysis from the perspective of technological progress

The development of traditional urbanisation has generated environmental problems, so the Chinese Government has proposed a new-type of urbanisation path with uniquely Chinese characteristics. How does this new-type of urbanisation affect CO₂ emissions? Based on panel data from 29 provinces in China (2005 to 2016), we apply an exploratory spatial data analysis model, a spatial econometric model, and a threshold model to analyse the spatial autocorrelation of CO₂ emissions, the direct and indirect effects of new-type urbanisation on CO₂ emissions, and the threshold characteristics produced by technological progress, respectively. The key results are: (1) CO₂ emissions show significant positive autocorrelation in China, and the spatial distribution of CO₂ emissions is HH (High-High) or LL (Low-Low) clustered in most provinces; (2) new-type urbanisation has a paradoxical effect on CO₂ emissions. Energy-saving technology has a rebound effect on CO₂ emissions, but environmental technology inhibits CO₂ emissions; (3) by eliminating the rebound effect of energy-saving technology on CO₂ emissions and promoting environmental technology, new-type urbanisation indirectly inhibits CO₂ emissions; (4) new-type urbanisation exhibits a threshold effect on CO₂ emissions due to the different levels of energy-saving technology and environmental technology. Finally, policy recommendations for CO₂ emissions reduction are proposed from the perspective of new-type urbanisation, energy-saving technology, and environmental technology.     

Does feedback usage lead to electricity savings? Analysis of goals for usage,feedback seeking,and consumption behavior

Findings from smart metering trials throughout the European Union in the last 5 years suggest that feedback on electricity consumption does not result in the expected increase in electricity savings of 10%. Instead, a smaller reduction in electricity consumption between 1.5 and 4% is typically recorded. In this paper, we aim to analyze knowledge and motivation gained from feedback usage and the resulting effects on behavior. For this purpose, we introduce a theoretical framework describing the process of feedback use in the Rubicon model (Heckhausen and Gollwitzer in Motivation and Emotion, 11, 101–120, 1987) and the transition from gained knowledge into motivation, and subsequent adaptation of consumption behavior, by applying the Feedback Intervention Theory (FIT, Kluger and DeNisi in Psychological Bulletin, 119(2), 254–284, 1996). Using log-files on usage behavior of a web-based feedback system, survey data and energy consumption data from a 1-year trial of smart metering systems and feedback with more than 600 consumers, we identify six patterns distinguishing how users interact with feedback systems (i.e., measured by the quantity of log-ins and clicks for different features) and are able to make inferences on their knowledge gains. Moreover, we examine whether any feedback usage pattern could be related to a stronger agreement with any goals. Finally, we compare the impact of these different usage patterns onto energy consumption in order to uncover which knowledge gains could be associated with energy conservation. Results show that feedback usage can be differentiated along several specific seeking strategies which are associated with individual goals. The only strategy of feedback usage found to be successful for saving electricity is actually the one showing the least evidence for long-term knowledge being gained. Possible explanations for these findings are discussed within the context of assumptions for the FIT.     

How sensitive to time period sampling is the asymmetric price response specification in energy demand modelling?

The purpose of this paper is to investigate the criticism that energy demand estimates based on a specific price decomposition are sensitive to the chosen time period used for the estimation. To analyse this in a systematic way, different time series sample periods are constructed from annual data for 17 OECD countries covering the overall period 1960 to 2008. The specific price decomposition under consideration, often used to estimate asymmetric price response models of energy demand, separates the impact of prices above the previous maximum, of a price recovery below the previous maximum and of a price cut. Therefore, the analysis does not just involve using different time periods; instead, for each time period investigated, a new dataset is constructed and for each dataset, the price variable is decomposed in this way. An energy demand relationship allowing for asymmetric price responses is therefore estimated for each different sample period and the results suggest that recalculation of the decomposed price variables for each different period does affect the stability of the estimated energy demand responses. In contrast, a similarly estimated energy demand relationship with symmetric price responses for each different sample period is found to have less instability.     

Social demand for electricity from forest biomass in Spain: Does payment periodicity affect the willingness to pay?

In this article, we analyze social preferences for a partial substitution programme of electricity generated by conventional energy sources, for energy generated from a local renewable energy source, such as forest biomass. This analysis sets arguments in favour of accelerating the introduction of this renewable technology in the Spanish Electricity System. Simultaneously, two methodological goals concerning the contingent valuation method are discussed. In the first one, we analyze if there are statistical differences in the willingness to pay (WTP) when a single- or a double-bounded format is employed to ask the valuation question. Results show that WTP estimates from single- and double-bounded significantly differ. In the second one, we analyze the effect of the periodicity of the payment vehicle on the estimates of welfare change. The timeframe specification of the payment vehicle has been scarcely studied, and this fact constitutes the main contribution of this paper to the specialized literature. Results show that periodicity influences upon the probability to favour the proposed change. The periodicity does not affect to the mean WTP obtained in the single-bounded format, but there are statistical differences in the double-bounded format. These results might be explained by the presence of yea saying and payment scale bias.     

Which factors affect the willingness of consumers to adopt renewable energies?

This study presents insights into the determinants of consumers' willingness to adopt renewable energies in the residential sector. The empirical analysis is based on the estimation of binary probit regression models. Empirical results suggest that middle-aged and highly educated people are probably more willing to adopt renewable energy sources in their home. In general, income positively affects consumers' acceptance of renewable energy projects in the residential sector. However, the results suggest that marital status and gender are not statistically significant factors in the willingness to adopt renewable energies. A tax deduction is estimated to be the most effective financial policy measure to promote consumers' acceptance of renewable energies in the residential sector, more so than an energy subsidy. Our analysis is focused on intention because we expect that those people willing to adopt renewable energy sources in their residence are a potentially relevant market segment for the application of renewable energies.     

How connected is the carbon market to energy and financial markets? A systematic analysis of spillovers and dynamics

Carbon allowances are a new class of financial instrument which aim to assist in limiting the extent and impact of global warming and climate change. The feedback mechanism in the “Carbon-Energy-Finance” system makes the information connectedness dynamics more complex since we add equity, bond and non-energy commodity assets into the system. Using modified error variance decomposition and network diagrams, we quantify and systematically analyze how the European carbon market connects with information from a wide range of other markets. Our results indicate: (i) the nature of information spillover changes over time, with system-wide return connectedness being higher and more variable than the volatility interdependence; (ii) both the oil and carbon markets closely connect with equity and non-energy commodity markets rather than bond markets; (iii) we identify three structural breaks in carbon volatility and their implication for carbon-finance linkages; (iv) financial risk-type macroeconomic factors make greater contributions to system-wide connectedness than commodity factors. These findings have economic implications for investors, portfolio managers and policymakers.     

How do oil price shocks affect the output volatility of the U.S. energy mining industry? The roles of structural oil price shocks

This paper focuses on how explicit structural shocks that characterize the endogenous character of international oil price change affect the output volatility of the U.S. crude oil and natural gas mining industries. To this end, we employ a modified structural vector autoregressive model (SVAR) to decompose real oil-price changes into four components: U.S. supply shocks, non-U.S. supply shocks, aggregate demand shocks, and oil-specific demand shocks mainly driven by precautionary demand. The results indicate that output volatility of the U.S. crude oil and natural gas mining industry has significantly negative responses to U.S. supply shocks, aggregate demand shocks, and oil-specific demand shocks, while lacks significant response to non-U.S. supply shocks. Variance decomposition and historical decomposition confirm that U.S. supply shocks occupy most explaining variations in output volatility among the four structural oil shocks. Moreover, the oil-specific demand shocks explain more variation than that of aggregate demand shocks for the crude oil mining industry, but the opposite is true for the natural gas mining industry.     

How do correlations of crude oil prices co-move? A grey correlation-based wavelet perspective

Previous research on the oil market has focused mainly on the static relationship between bivariate oil prices, ignoring the dynamic correlation of bivariate or multivariate oil prices. This study provides a novel perspective on multivariate dynamic correlations for studying the oil market by using an optimal wavelet analysis on the basis of grey correlation. We used China-Daqing and its three reference benchmark oil prices (Brent, Dubai and Minas) as empirical data. Our main findings are as follows. First, the time–frequency phenomena of the analysis results from one-to-one and many-to-one correlation time series support the hypothesis of the regional and global characteristics of the oil market, respectively. Second, the U-shaped wavelet variance plot indicates that the fluctuation intensity of the shortest and longest time–frequency domains plays a leading role in the dynamic process of oil price correlation. For the Chinese government, the oil price adjustment strategy in the short term should reduce the reference weights of Brent, and the long-term strategy should reduce the reference weights of Minas to avoid the risk of a single reference. The investor's portfolio management should pay more attention to the leading oil price of the corresponding period to make clear market timing. Third, the significant lead–lag relationships of oil price correlations showed a time-varying spread phenomenon of benchmark oil prices' relative influence on Daqing, which provides a useful time reference when crafting an oil price adjustment strategy and intertemporal arbitrage.     

Coal lumps vs. electrons: How do Chinese bulk energy transport decisions affect the global steam coal market?

This paper demonstrates the ways in which different Chinese bulk energy transport strategies affect the future steam coal market in China and in the rest of the world. An increase in Chinese demand for steam coal will lead to a growing need for additional domestic infrastructure as production hubs and demand centers are spatially separated, and domestic transport costs could influence the future Chinese steam coal supply mix. If domestic transport capacity is available only at elevated costs, Chinese power generators could turn to the global trade markets and further increase steam coal imports. Increased Chinese imports could then yield significant changes in steam coal market economics on a global scale. This effect is analyzed in China, where coal is mainly transported by railway, and in another setting where coal energy is transported as electricity. For this purpose, a spatial equilibrium model for the global steam coal market has been developed. One major finding is that if coal is converted into electricity early in the supply chain, worldwide marginal costs of supply are lower than if coal is transported via railway. Furthermore, China's dependence on international imports is significantly reduced in this context. Allocation of welfare changes particularly in favor of Chinese consumers while rents of international producers decrease.     

How fast can businesses in the new energy sector grow? An analysis of critical factors

For the new energy technology markets to grow, demand, prices, and business conditions need to be in balance. It is not just declining prices and increasing volumes that are important, but the business in the new energy sector also needs to be healthy, which is not always the case at present. We have analyzed the ability of businesses in the new energy sector to invest in new production capacity, which influences the total volume growth. Using the self-financeable growth rate (SGR) as an indicator, a declining trend was found among PV and wind power manufacturers. The prospects of initiating new investments through returns from operations are poor or negligible at present, which is explained by tougher competition, shrinking public support, and new entrants, among others. Reducing the cost of sales would be the most effective way to improve the growth prospects, though increasing revenues, e.g., through higher product prices, comes close to achieving the same result. Market measures such as consolidation, rationalization, better asset use, improving efficiency, etc. are equally important. The analysis results imply a growth limit of ca. 15–25% per year with present market conditions, which may also be a more permanent level, supported by findings from technology diffusion and growth model studies. The results suggest that it is not self-evident that the new energy technologies will meet the future goals set for these in the climate and energy policy strategies, unless policymakers and decision makers properly address the issue of restoring and securing sound business conditions.     

To drill or not to drill? An econometric analysis of US public opinion

Offshore drilling in the United States (US) has been the subject of public and political discourse due to multiple reasons which include economic impact, energy security, and environmental hazard. Consequently, several polls have been conducted over time to gauge public attitude towards offshore drilling. Nevertheless, the economic literature on this issue is sparse. This paper contributes to the literature and analyzes support for offshore drilling based on demographic, economic, social, belief, and shock (e.g. spill) factors. The data is taken from ten nationwide surveys conducted before, during and after the British Petroleum (BP) oil spill and analyzed within the framework of discrete choice model. The results from an ordinal probit model demonstrate that age, annual household income, affiliation to Republican Party, and residence in oil-rich states positively affect the probability of strong support and reduce the probability of strong opposition for offshore drilling. In contrast, the female gender, higher education, association to Democratic Party, and environmental concern affect opinion in opposite direction. Marginal effects show that belief about environmental consequences of drilling has the highest impact on opinion. Binary probit model also yields a similar result and suggests that BP oil disaster resulted in a transient decrease in support for offshore drilling.