Asset pricing and energy consumption risk

This paper proposes energy consumption in the US as a new measure for the consumption capital asset pricing model. We find that (i) industrial energy growth produces reasonable values for the relative risk aversion coefficient and the implied risk-free rate; (ii) compared to alternative consumption measures, industrial energy performs well in explaining the cross-sectional variation in stock returns with the lowest implied risk aversion and pricing errors; (iii) the industrial energy consumption risk model performs equally well as the Fama–French three-factor model in the cross-sectional asset pricing tests; and (iv) total energy consumption risk is priced in the presence of the Fama–French factor risks.     

Global energy forecasting competition 2017: Hierarchical probabilistic load forecasting

The Global Energy Forecasting Competition 2017 (GEFCom2017) attracted more than 300 students and professionals from over 30 countries for solving hierarchical probabilistic load forecasting problems. Of the series of global energy forecasting competitions that have been held, GEFCom2017 is the most challenging one to date: the first one to have a qualifying match, the first one to use hierarchical data with more than two levels, the first one to allow the usage of external data sources, the first one to ask for real-time ex-ante forecasts, and the longest one. This paper introduces the qualifying and final matches of GEFCom2017, summarizes the top-ranked methods, publishes the data used in the competition, and presents several reflections on the competition series and a vision for future energy forecasting competitions.     

A hybrid machine learning model for forecasting a billing period’s peak electric load days

Many models have been studied for forecasting the peak electric load, but studies focusing on forecasting peak electric load days for a billing period are scarce. This focus is highly relevant to consumers, as their electricity costs are determined based not only on total consumption, but also on the peak load required during a period. Forecasting these peak days accurately allows demand response actions to be planned and executed efficiently in order to mitigate these peaks and their associated costs. We propose a hybrid model based on ARIMA, logistic regression and artificial neural networks models. This hybrid model evaluates the individual results of these statistical and machine learning models in order to forecast whether a given day will be a peak load day for the billing period. The proposed model predicted 70% (40/57) of actual peak load days accurately and revealed potential savings of approximately USD $80,000 for an American university during a one-year testing period.     

Financing energy efficiency retrofits in Chilean households: The role of financial instruments,savings and uncertainty in energy transition

High firewood consumption for heating produces high levels of pollution in cities in central and southern Chile, with serious consequences for health and quality of life. Energy efficiency measures (EEMs) have been identified as the best strategy to reduce air pollution and maximize social benefits. However, their adoption has been slow. The objective of this article is to investigate household preferences for financial incentives needed to promote private investments in EEMs in Central-Southern Chilean households and study the role of energy savings and the uncertainty about potential savings in the investment decision, with the aim of finding solutions to increase the adoption of these technologies. We use a choice experiment to explore the trade-off between the investment costs, financial instruments, energy savings, and the uncertainty about achieving the theoretical savings provided by engineering and architectural models. The results show that financial instruments play the most important role in this decision, followed by the savings achieved by the retrofit. Householders prefer to finance their investments with a mix of their personal resources and medium-term credits, trying to avoid long-term commitments. Although uncertainty was found to be a significant variable, it seems to play a small role in the investment decision.     

Time-varying pricing may increase total electricity consumption: Evidence from Costa Rica

We study the implementation of a time-varying pricing (TVP) program by a major electricity utility in Costa Rica. Because of particular features of the data, we use recently developed understanding of the two-way fixed effects differences-in-differences estimator along with event-study specifications to interpret our results. Similar to previous research, we find that the program reduces consumption during peak-hours. However, in contrast with previous research, we find that the program increases total consumption. With a stylized economic model, we show how these seemingly conflicted results may not be at odds. The key element of the model is that previous research used data from rich countries, in which the use of heating and cooling devices drives electricity consumption, but we use data from a tropical middle-income country, where very few households have heating or cooling devices. Since there is not much room for technological changes (which might reduce consumption at all times), behavioral changes to reduce consumption during peak hours are not enough to offset the increased consumption during off-peak hours (when electricity is cheaper). Our results serve as a cautionary piece of evidence for policy makers interested in reducing consumption during peak hours—the goal can potentially be achieved with TVP, but the cost is increased total consumption     

International sanctions’ impact on energy efficiency in target states

This paper investigates the impact of international sanctions on energy efficiency by employing the panel fixed effect as well as average marginal effect from the Tobit model via data on 30 sanctioned states over the period 1996–2015 with international sanctions including unilateral, plurilateral, U.S., EU, UN, economic, and non-economic cases. Overall, we find that the imposition of unilateral sanctions leads to a 0.067% decrease in energy efficiency, but that of plurilateral sanctions positively contribute to energy efficiency in the case of the full sample of countries. Moreover, the imposition of UN sanctions has a greater decrease on energy efficiency in the target states than the 0.042% reduction of energy efficiency when the sanctionist is the U.S. For robustness, empirical results indicate that the imposition of plurilateral sanctions results in a drop of energy efficiency in Islamic countries, but an increase in non-Islamic countries, while there are also negative shocks induced by the imposition of EU sanctions on energy efficiency in Asian countries, but not for non-Asian countries. We also consider endogenous problems and dynamic specification by using indicator variables and System GMM. In summary, our empirical findings provide policy suggestions for those sanctioned countries about how to maintain energy efficiency when facing international sanctions.     

A multivariate distance nonlinear causality test based on partial distance correlation: a machine learning application to energy futures

This paper proposes a multivariate distance nonlinear causality test (MDNC) using the partial distance correlation in a time series framework. Partial distance correlation as an extension of the Brownian distance correlation calculates the distance correlation between random vectors X and Y controlling for a random vector Z. Our test can detect nonlinear lagged relationships between time series, and when integrated with machine learning methods it can improve the forecasting power. We apply our method as a feature selection procedure and combine it with the support vector machine and random forests algorithms to study the forecast of the main energy financial time series (oil, coal, and natural gas futures). It shows substantial improvement in forecasting the fuel energy time series in comparison to the classical Granger causality method in time series.     

地理邻近与知识产权——基于中国1 331项新能源技术发明专利的实证研究

虽然远程交流技术快速发展，但现有研究表明专利发明在很大程度上仍依赖于信息的地理邻近。以中国1992-2009年的1 331个新能源发明专利为研究对象，运用Jung Wonn,Jaffe等的实验设计方法，研究了专利引证的地理邻近是否会随时间递进而增大。结果表明，个人、大学、公司及其它种类发明专利引证的地理邻近会随着时间变化而增大，而政府受到的影响较小。另外，由于区域城市分属不同省份，因此并不能证明区域专利引证的地理邻近也会随着时间变化而增大。     

能效改善与能源节约：助力还是阻力

研究目标：测算中国分行业的能源回弹效应与综合能耗系数。研究方法：通过编制含能源实物流量的价值型能源投入产出可比价序列表，测算出能源回弹效应与综合能耗系数，结合行业综合能耗情况分析了能源回弹效应的行业异质性。研究发现：各行业在能耗方面表现迥异，能源回弹效应也存在典型的行业异质性；中国整体经济能源回弹程度并不高，能效改善可以有效节约能源，但整体经济低能源回弹的表象也掩盖了部分行业能源效率无改善和高能耗行业高能源回弹的事实。研究创新：从系统与结构的视角审视了行业的综合能耗情况，并给出了一种基于能源投入产出表的能源回弹效应计算方法。研究价值：有助于提出整体经济结构优化与节能减排的行业政策。     

能源回弹效应下高碳产业低碳转型过程中的技术突变性

考虑能源回弹效应下，分析高碳产业低碳技术突变技术门槛，发现技术碳减排存在由量变到质变的门槛效应，另外还发现能源回弹对门槛效应具有显著影响。运用Hansen门限面板模型、结合CD生产函数的能源回弹模型发现：①不考虑能源回弹效应，高碳产业低碳化过程中的技术突变存在两个门槛；②考虑能源回弹作用，将减少为一个门槛，并且将延长高碳产业完成低碳技术突变所需时间。因此，为加快高碳产业低碳技术突变，有效减少碳排放，一方面应缓解能源回弹效应的影响，对能源回弹效应较大的高碳产业采取实时监控，对能源回弹较小的高碳产业采取定期检查和不定期抽检方式监控；货币政策、财政政策和产业政策相互配合，在高碳产业拉动经济增长的同时，最大限度控制能源回弹效应。另一方面，市场手段和政府职能双管齐下，协助企业低碳技术升级和设备改造，优化税收结构，弥补低碳技术升级导致的负外部性，完善高碳产业转移或市场退出机制。