中国家庭股市资产价值变动对居民消费的影响——来自CHFS的微观证据

近年来针对股市资产价值变动与消费关系的研究多采用宏观数据，难以揭示股市资产价值变动对居民消费影响的细节。为克服以上缺陷，文章利用中国家庭金融调查（CHFS）数据分析股市资产价值变动对中国居民消费的影响。实证结果表明：（1）股市资产价值变动对居民消费的影响总体较弱，其中市值变动的财富效应主要体现在改善性消费上，而对饮食等日常必须消费和奢侈品消费影响较小，这可能与我国居民的收入结构和投资方式有关；（2）随着年龄增长，居民的收入分布和资产配置倾向发生改变，股市资产价值变动对不同年龄居民消费的影响呈现先减小后增大的U型分布。研究结论揭示了股市资产价值变动对居民消费影响的分布特征，为今后股市改革方向提供了建议。     

新冠肺炎疫情对居民消费的影响及对策

2020年春节期间,湖北省武汉市爆发的由新型冠状病毒感染引起的肺炎疫情正在对中国经济和消费带来负向冲击。从逻辑上看,除了直接限制消费外,疫情还会通过收入效应、财富效应、实际购买力效应对消费形成不利影响。从历史上看,十七年前的"非典"疫情对经济和消费的影响是短期的,但此次疫情影响更大,持续期存在不确定性。稳定消费的关键是稳定居民收入,要稳定居民收入就需要稳定非农产业就业。基于分析,本文提出了一揽子政策建议。     

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.     

Tourism,tourist learning and sustainability: an exploratory discussion of complexities,problems and opportunities

Learning is often a central element of tourism. Tourists can learn actively, i.e. with a specific purpose, as well as passively through the comparison of values, norms and customs. It has been argued that travel supports active learning that has positive outcomes for sustainability, for instance, in the context of conservation. Yet, the complexity of active and passive learning processes and their outcomes for environmental sustainability and sustainable lifestyles remain insufficiently understood. Against this background, the paper discusses selected learning outcomes for transportation (air travel), accommodation (hotels) and activities (theme park visits). Findings suggest that “desirable” learning (defined as pro-sustainable development learning) in tourism may be very limited, while in particular, passive learning processes which redefine social norms frequently have outcomes that are largely detrimental to sustainable lifestyles. They include forms of moral licensing, the diffusion of responsibilities as well as the attenuation of the negative consequences of travel. Given the economic, social and cultural importance of tourism vís-a-vís its global implications for environmental sustainability, learning outcomes in tourism deserve to be studied in greater detail, while strategies need to be devised to enhance sustainable learning effects.