The use of supplementary cementitious materials (SCMs) to replace part of the clinker in cement is the most successful strategy to reduce CO2 emissions in the global cement industry. However, limited ...supplies of conventional SCMs make it difficult to take this strategy further unless new types of SCMs become available. The only type of material available in the quantities needed to meet demand is clay containing kaolinite, which can be calcined to produce an effective SCM. Such clays are widely available in countries where most growth in demand for cement is forecast.
Calcined clays have previously been used as pozzolans, but calcination makes the economics of substitution marginal in a conventional pozzolanic blend. The major innovation presented here is the possibility to make a coupled substitution of cement with calcined clay and limestone. This allows much higher levels of substitution. Blends where calcined clay is used as a pozzolan, typically have clinker contents around 65–70%. Combination of calcined clay with limestone allows higher levels of substitution down to clinker contents of around 50% with similar mechanical properties and improvement in some aspects of durability. The replacement of clinker with limestone in these blends lowers both the cost and the environmental impact.
Energy consumption is an important issue of global concern. Accurate energy consumption forecasting can help balance energy demand and energy production. Although there are various energy consumption ...forecasting methods, the forecasting accuracy still needs to be improved. This study applied a long short-term memory-based model in energy consumption forecasting to achieve a better prediction performance and the more critical influencing factors are emphasized. Results of one comparative example and two extended applications show the proposed model achieves better prediction accuracy compared with basic long short-term memory and other existing popular models. Mean absolute percentage errors of the proposed model for three real-life cases are 4.01 %, 5.37 %, and 1.60 %, respectively. Therefore, the proposed model is a satisfactory method for energy consumption forecasting due to its high accuracy. The high-precision forecasting technology is important for the energy systems.
•The study focuses on multifactor-influenced energy consumption forecasting problem.•Impacts of influencing factors are analyzed by attention-based method.•A long short-term memory-based model is used to achieve better forecasting accuracy.•Mean absolute percentage errors for three real-life cases are below 6 %.•Role of producer price index or crude oil imports is significant.
We forecast quarterly US inflation based on the generalized Phillips curve using econometric methods that incorporate dynamic model averaging. These methods not only allow for coefficients to change ...over time, but also allow for the entire forecasting model to change over time. We find that dynamic model averaging leads to substantial forecasting improvements over simple benchmark regressions and more sophisticated approaches such as those using time varying coefficient models. We also provide evidence on which sets of predictors are relevant for forecasting in each period.
The booming of floating PV Cazzaniga, Raniero; Rosa-Clot, Marco
Solar energy,
05/2021, Volume:
219
Journal Article
Peer reviewed
•The floating PV plants (FPV) are an emerging technology and the FPV installed power doubles every year.•This is due to their efficiency and low cost but mainly to the fact that no land is occupied ...by these plants.•This booming will be limited in the next few years and the paper discuss what will be the growth rate.•The quick increase of this sector will further promote the renewable energy sources.•Forecast to 2030 is discussed in detail.
The global trend of electric energy production is analysed with a forecast up to 2030. The current status of the Floating PV is discussed, taking into account data up to 2019. The growth rate for the main renewable energy sectors is analysed and on this basis a naïve exponential forecast up to 2030 is given. Corrections to this forecast are discussed and the value for the installed FPV plants in 2030 is suggested.
In the last decades, the world's energy consumption has increased rapidly due to fundamental changes in the industry and economy. In such terms, accurate demand forecasts are imperative for decision ...makers to develop an optimal strategy that includes not only risk reduction, but also the betterment of the economy and society as a whole. This paper expands the fields of application of combined Bootstrap aggregating (Bagging) and forecasting methods to the electric energy sector, a novelty in literature, in order to obtain more accurate demand forecasts. A comparative out-of-sample analysis is conducted using monthly electric energy consumption time series from different countries. The results show that the proposed methodologies substantially improve the forecast accuracy of the demand for energy end-use services in both developed and developing countries. Findings and policy implications are further discussed.
•Electricity demand across different countries is forecasted 24 months in advance.•The potential gains of using bagging techniques to enhance forecasts are explored.•A new variation of a bagging procedure is proposed.•The proposed techniques provided consistently accurate forecasts in most cases.
Impacts of the COVID-19 pandemic on rural America Mueller, J Tom; McConnell, Kathryn; Burow, Paul Berne ...
Proceedings of the National Academy of Sciences - PNAS,
01/2021, Volume:
118, Issue:
1
Journal Article
Peer reviewed
Open access
Despite considerable social scientific attention to the impacts of the COVID-19 pandemic on urbanized areas, very little research has examined its impact on rural populations. Yet rural ...communities-which make up tens of millions of people from diverse backgrounds in the United States-are among the nation's most vulnerable populations and may be less resilient to the effects of such a large-scale exogenous shock. We address this critical knowledge gap with data from a new survey designed to assess the impacts of the pandemic on health-related and economic dimensions of rural well-being in the North American West. Notably, we find that the effects of the COVID-19 pandemic on rural populations have been severe, with significant negative impacts on unemployment, overall life satisfaction, mental health, and economic outlook. Further, we find that these impacts have been generally consistent across age, ethnicity, education, and sex. We discuss how these findings constitute the beginning of a much larger interdisciplinary COVID-19 research effort that integrates rural areas and pushes beyond the predominant focus on cities and nation-states.
Wavelet transform (WT), as a data preprocessing algorithm, has been widely applied in electricity price forecasting. However, this deterministic-based algorithm does not present stable performance ...owing to the experiential selection of its orders and layers. For determining the selection of WT’s orders and layers in U.S. electricity prices forecasting, this paper designs a crossover experiment with 240 schemes of WT parameter selection and forecasts each scheme with stacked autoencoder (SAE) and long short-term memory (LSTM), generating a novel hybrid model WT-SAE-LSTM. The results show that the proposed model outperforms other AI models, such as back propagation neural network et al., in forecasting accuracy. The best performance of WT-SAE-LSTM in residential, commercial, and industrial electricity price cases obtained by five order four layers, five order four layers, and four order seven layers, where the MAPE is 0.8606%, 0.4719%, and 0.4956%, respectively. Additionally, the difference between the proposed forecasting model and the forecasting result of Energy Information Administration (U.S.) is small. This paper determines the optimal orders and layers of WT in U.S. electricity prices forecasting, which provides an effective reference for the application of WT in other forecasting scenarios and for electricity market participants.
•WT-SAE-LSTM is proposed to forecast electricity prices of U.S.•SAE-LSTM has better forecasting accuracy.•The optimal wavelet’s orders and layers are determined.•WT-SAE-LSTM has practical application value.
Increasingly, researchers and policy makers across the globe explore the transformative role of entrepreneurshlp In the development process. What remains relatively under Interrogated In this process ...Is the Issue of entrepreneurial Intentions within the Caribbean region. Where entrepreneurial Intentions play a pivotal role In future entrepreneurial activity, this area of research can provide useful Insights for development policy and practice. Considering the above, three main objectives guide this paper. Firstly, we comparatively examine the entrepreneurial Intentions drawn from adult populations across Barbados, Jamaica and Trinidad and Tobago. Secondly, we assess the relative Importance of entrepreneurial skills, knowledge, and opportunity to entrepreneurial Intentions. Thirdly, we also explore for possible soclodemographlc variations (specifically based on sex, age, level of educational attainment, and type of current profession or career) In the levels of entrepreneurial Intentions. To do this, we utilize available raw data from the Global Entrepreneurshlp Monitor (GEM) survey for the Caribbean countries. We use this data set to test for the relative significance of key antecedent variables for understanding entrepreneurial Intentions. Point to variability In the relationship between attltudlnal factors, soclo-demographlc backgrounds, and entrepreneurial Intentions between countries In the study. Implications for a more contextuallzed theorlzatlons of entrepreneurial Intentions are discussed.
Summarising the complexity of a country's economy in a single number is the holy grail for scholars engaging in data-based economics. In a field where the Gross Domestic Product remains the preferred ...indicator for many, economic complexity measures, aiming at uncovering the productive knowledge of countries, have been stirring the pot in the past few years. The commonly used methodologies to measure economic complexity produce contrasting results, undermining their acceptance and applications. Here we show that these methodologies - apparently conflicting on fundamental aspects - can be reconciled by adopting a neat mathematical perspective based on linear-algebra tools within a bipartite-networks framework. The obtained results shed new light on the potential of economic complexity to trace and forecast countries' innovation potential and to interpret the temporal dynamics of economic growth, possibly paving the way to a micro-foundation of the field.
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