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This article studies the benefits of using spatially randomized experimental designs which partition the experimental area into distinct, non-overlapping units with treatments assigned randomly. Such designs offer improved policy evaluation in online experiments by providing more precise policy value estimators and more effective A/B testing algorithms than traditional global designs, which apply the same treatment across all units simultaneously. We examine both parametric and nonparametric methods for estimating and inferring policy values based on this randomized approach. Our analysis includes evaluating the mean squared error of the treatment effect estimator and the statistical power of the associated tests. Additionally, we extend our findings to experiments with spatio-temporal dependencies, where treatments are allocated sequentially over time, and account for potential temporal carryover effects. Our theoretical insights are supported by comprehensive numerical experiments.

With the booming of the unconventional oil and gas industry, its inevitable damage to the environment and human health has attracted public attention. We applied text mining on a total 6057 the type of Environmental Health and Safety compliance reports from 2008 to 2018 lunched by the Department of Environmental Protection in Pennsylvania, USA, to discover the intern mechanism of environmental violations., Comment: 23 pages

Forecasting electricity demand plays a fundamental role in the operation and planning procedures of power systems and the publications about electricity demand forecasting increasing year by year. In this paper, we use Scientometric analysis to analyze the current state and the emerging trends in the field of electricity demand forecasting form 831 publications of web of science core collection during 20 years: 1999-2018. Employing statistical description, cooperative network analysis, keyword co-occurrence analysis, co-citation analysis, cluster analysis, and emerging trend analysis techniques, this article gives the most critical countries, institutions, journals, authors and publications in this field, cooperative networks relationships, research hotspots and emerging trends. The results of this article can provide meaningful guidance and some insights for researchers to find out crucial research, emerging trends and new developments in this area.

With the booming of unconventional gas production in the world, how to balance environment pollution risk and economy of unconventional gas have become a common dilemma around the world. The aim of this study is to elucidate the research about environmental issue brought with development of unconventional oil and gas industry. To achieve this goal, we present a bibliometrics overview of this field from 1990 to 2018. Firstly, this study outlines a basic statistical analysis over journals, publications, authors, institutions and documents. Secondly, VOSviewer is employed to visualize the collaborative relationship to show the link between different author, institutions, regions and journals. Finally, document bibliographic coupling, cooccurrence and keyword burst detection are analyzed to reveal the emerging trend and hot topic. The results indicate that among all countries, America was the most productive country as well as cooperated the most with other countries, followed by China, while the China University of Petroleum is the most productive institution in the world, with 105 publications. Additionally, most articles were classified as energy fuels, environmental sciences and geosciences multidisciplinary. Furthermore, based on emerging trends analysis, it was concluded that hydraulic fracturing technology has become a hot topic, other popular research topics include: energy policy and regulation of unconventional gas development, greenhouse gas emissions, energy and water consumption of unconventional gas life cycle assessment.

The Asian-pacific region is the major international tourism demand market in the world, and its tourism demand is deeply affected by various factors. Previous studies have shown that different market factors influence the tourism market demand at different timescales. Accordingly, the decomposition ensemble learning approach is proposed to analyze the impact of different market factors on market demand, and the potential advantages of the proposed method on forecasting tourism demand in the Asia-pacific region are further explored. This study carefully explores the multi-scale relationship between tourist destinations and the major source countries, by decomposing the corresponding monthly tourist arrivals with noise-assisted multivariate empirical mode decomposition. With the China and Malaysia as case studies, their respective empirical results show that decomposition ensemble approach significantly better than the benchmarks which include statistical model, machine learning and deep learning model, in terms of the level forecasting accuracy and directional forecasting accuracy.

The accurate seasonal and trend forecasting of tourist arrivals is a very challenging task. In the view of the importance of seasonal and trend forecasting of tourist arrivals, and limited research work paid attention to these previously. In this study, a new adaptive multiscale ensemble (AME) learning approach incorporating variational mode decomposition (VMD) and least square support vector regression (LSSVR) is developed for short-, medium-, and long-term seasonal and trend forecasting of tourist arrivals. In the formulation of our developed AME learning approach, the original tourist arrivals series are first decomposed into the trend, seasonal and remainders volatility components. Then, the ARIMA is used to forecast the trend component, the SARIMA is used to forecast seasonal component with a 12-month cycle, while the LSSVR is used to forecast remainder volatility components. Finally, the forecasting results of the three components are aggregated to generate an ensemble forecasting of tourist arrivals by the LSSVR based nonlinear ensemble approach. Furthermore, a direct strategy is used to implement multi-step-ahead forecasting. Taking two accuracy measures and the Diebold-Mariano test, the empirical results demonstrate that our proposed AME learning approach can achieve higher level and directional forecasting accuracy compared with other benchmarks used in this study, indicating that our proposed approach is a promising model for forecasting tourist arrivals with high seasonality and volatility.

The availability of tourism-related big data increases the potential to improve the accuracy of tourism demand forecasting, but presents significant challenges for forecasting, including curse of dimensionality and high model complexity. A novel bagging-based multivariate ensemble deep learning approach integrating stacked autoencoders and kernel-based extreme learning machines (B-SAKE) is proposed to address these challenges in this study. By using historical tourist arrival data, economic variable data and search intensity index (SII) data, we forecast tourist arrivals in Beijing from four countries. The consistent results of multiple schemes suggest that our proposed B-SAKE approach outperforms benchmark models in terms of level accuracy, directional accuracy and even statistical significance. Both bagging and stacked autoencoder can effectively alleviate the challenges brought by tourism big data and improve the forecasting performance of the models. The ensemble deep learning model we propose contributes to tourism forecasting literature and benefits relevant government officials and tourism practitioners.

Accurate and timely metro passenger flow forecasting is critical for the successful deployment of intelligent transportation systems. However, it is quite challenging to propose an efficient and robust forecasting approach due to the inherent randomness and variations of metro passenger flow. In this study, we present a novel adaptive ensemble (AdaEnsemble) learning approach to accurately forecast the volume of metro passenger flows, and it combines the complementary advantages of variational mode decomposition (VMD), seasonal autoregressive integrated moving averaging (SARIMA), multilayer perceptron network (MLP) and long short-term memory (LSTM) network. The AdaEnsemble learning approach consists of three important stages. The first stage applies VMD to decompose the metro passenger flows data into periodic component, deterministic component and volatility component. Then we employ SARIMA model to forecast the periodic component, LSTM network to learn and forecast deterministic component and MLP network to forecast volatility component. In the last stage, the diverse forecasted components are reconstructed by another MLP network. The empirical results show that our proposed AdaEnsemble learning approach not only has the best forecasting performance compared with the state-of-the-art models but also appears to be the most promising and robust based on the historical passenger flow data in Shenzhen subway system and several standard evaluation measures.