Mid-term Horizon Research Articles

How do precious and industrial metals hedge oil in a multi-frequency semiparametric CVaR portfolio?

This paper tries to reduce extreme risk of Brent oil by constructing multivariate portfolios with precious and industrial metals in a multi-frequency framework. Extreme risk is measured by the parametric CVaR and more elaborate semiparametric CVaR measure, while the wavelet technique is used to build portfolios in different time-horizons. The results indicate that gold dominates in the precious metal portfolios, creating the lowest downside risk in most cases. The back-testing results reveal that the raw data CVaR portfolio with precious metals is the best, while the midterm mCVaR portfolio with industrial metals has upper hand. In terms of forecasting, CVaR portfolios with precious and industrial metals are the best in the short- and midterm horizons, respectively. In the long-term horizon, none of the portfolios is good in back-testing and forecasting, which means that CVaR and mCVaR models are not adequate risk functions for identifying realized risk in the long term. In the oil-dominated portfolios, the CVaR portfolios with gold are the best in terms of the lowest risk as well as back-testing and forecasting performances. When Brent is replaced by WTI oil, the share of WTI is somewhat smaller in the portfolios because WTI has higher risk. When the US 10Y bond is added to the portfolios, the portfolio risk is reduced because the bonds are very weakly correlated with the commodities.

Short- and Mid-Term Forecasting of Pan-Arctic Sea Ice Volume Using Variational Mode Decomposition and Bidirectional Long Short-Term Memory

The well-documented decrease in the annual minimum Arctic sea ice extent over the past few decades is an alarming indicator of current climate change. However, much less is known about the thickness of the Arctic sea ice. Developing accurate forecasting models is critical to better predict its changes and monitor the impacts of global warming on the total Arctic sea ice volume (SIV). Significant improvements in forecasting performance are possible with the advances in signal processing and deep learning. Accordingly, here, we set out to utilize the recent advances in machine learning to develop non-physics-based techniques for forecasting the sea ice volume with low computational costs. In particular, this paper aims to provide a step-wise decision process required to develop a more accurate forecasting model over short- and mid-term horizons. This work integrates variational mode decomposition (VMD) and bidirectional long short-term memory (BiLSTM) for multi-input multi-output pan-Arctic SIV forecasting. Different experiments are conducted to identify the impact of several aspects, including multivariate inputs, signal decomposition, and deep learning, on forecasting performance. The empirical results indicate that (i) the proposed hybrid model is consistently effective in time-series processing and forecasting, with average improvements of up to 60% compared with the case of no decomposition and over 40% compared with other deep learning models in both forecasting horizons and seasons; (ii) the optimization of the VMD level is essential for optimal performance; and (iii) the use of the proposed technique with a divide-and-conquer strategy demonstrates superior forecasting performance.

A Crowd-Sourcing Project to Understand, Prevent and Manage Incidences of Injury and Wounding to Astronauts and Off-Earth Colonists

Recent technical successes of the unmanned Mars 2020, Tianwen-1 and Hope Mars missions have further increased our hunger for space exploration and the possible colonisation of Mars is now firmly on the mid-term horizon. Furthermore, renewed commitment to settle on the Moon is anticipated within the next three years, led by NASA’s Artemis programme utilising the pan-space agency constructed lunar Gateway or Space X’s Starship system. These programmes will necessitate a larger number of astronauts spending longer periods in space and despite rigorous risk identification and mitigation procedures, injury is an inevitable consequence and management procedures will demand efficient and effective implementation. We have employed Cognitive Work Analysis to derive an abstraction hierarchy for reducing the potential for physical injury and managing the consequence of injury in space. We have used a crowd-sourcing approach to cluster factors and themes which may emanate from within or without habitat and consider solution management in the light of current and emerging technology. In addition, we also consider mental fitness as a confounder which may emerge during missions and propose methods for both measurement and management. Doi: 10.28991/HEF-2022-03-03-04 Full Text: PDF

Development of railway services and financial regulation in market conditions

The financial regulation of transportation services turns to be crucial for each economy. In a long–term perspective Mr. Adam Smith’s “invisible hand” ideally provides for this regulation. However, the analysis proves that on the mid–term horizon the basic supply and demand rules do not work properly and cyclically may lead either to a proficit or a deficit of the required amount of rolling stock. To smooth out these extremes, the state financial regulation of railway operators is required. This article considers one of the crucial elements of state financial regulation – tax incentives for active investors – and provides for the new ideas in fiscal stimulation for railway operators in Russia. These ideas can be widely used in various countries all over the world to reduce negative consequences of macroeconomic cyclical extremes.

Prospects for carbon-neutral maritime fuels production in Brazil

The International Maritime Organization (IMO) has compromised to reduce its greenhouse gas (GHG) emissions in the mid- and long-terms. Besides energy efficiency measures, the development of potentially carbon neutral fuels in the upcoming years is key to achieve the sector's goals. Brazil is a major commodity exporter whose unfavored geographical position increases the carbon intensity of its maritime transportation. In this context, this study presents a multicriteria methodology to compare possible alternative fuels for the Brazilian maritime trade. To this end, 14 fuel options are evaluated according to technical, economic, and environmental criteria to which different weights are assigned. The ranking of results indicates that drop-in fuels such as Fischer-Tropsch diesel, alcohol-based diesel, straight and hydrotreated vegetable oils and e-diesel stand out as promising mid-term alternatives. Biomass-based liquefied natural gas (Bio-LNG) performance in the evaluation is hampered mostly by the risk of methane slip. Green hydrogen and green ammonia, on the other hand, seem to be less competitive alternatives in the mid-term horizon for Brazil but may become alternatives for cabotage transport in the long-term.

Interpretable Forecasting of Energy Demand in the Residential Sector

Energy demand forecasting is practiced in several time frames; different explanatory variables are used in each case to serve different decision support mandates. For example, in the short, daily, term building level, forecasting may serve as a performance baseline. On the other end, we have long-term, policy-oriented forecasting exercises. TIMES (an acronym for The Integrated Markal Efom System) allows us to model supply and anticipated technology shifts over a long-term horizon, often extending as far away in time as 2100. Between these two time frames, we also have a mid-term forecasting time frame, that of a few years ahead. Investigations here are aimed at policy support, although in a more mid-term horizon, we address issues such as investment planning and pricing. In this paper, we develop and evaluate statistical and neural network approaches for this mid-term forecasting of final energy and electricity for the residential sector in six EU countries (Germany, the Netherlands, Sweden, Spain, Portugal and Greece). Various possible approaches to model the explanatory variables used are presented, discussed, and assessed as to their suitability. Our end goal extends beyond model accuracy; we also include interpretability and counterfactual concepts and analysis, aiming at the development of a modelling approach that can provide decision support for strategies aimed at influencing energy demand.

Improved Methods for Mid-Term Blood Glucose Level Prediction Using Dietary and Insulin Logs.

Background and Objectives: The daily lifestyle management of diabetes requires accurate predictions of the blood glucose level between meals. The objective of this study was to improve the accuracy achieved by previous work, especially on the mid-term, i.e., 120 to 180 min prediction horizons, for insulin-dependent patients. Materials and Methods: An absorption model-based method is proposed to train an artificial neural network with the bolus and basal insulin dosing and timing, the baseline blood glucose level, the maximal glucose infusion rate, and the total carbohydrate content as parameters. The approach was implemented in various algorithmic setups, and it was validated on data from a small-scale clinical trial with continuous glucose monitoring. Results: Root mean square error results for the mid-term horizons are 1.72 mmol/L (120 min) and 1.95 mmol/L (180 min). The accuracy of the proposed model measured on the clinical data is better than the accuracy reported by any other currently available and comparable models. Conclusions: A relatively short (ca. two weeks) training sample of a continuous glucose monitor and dietary/insulin log is sufficient to provide accurate predictions. For the outpatient application in practice, a hybrid model is proposed that combines the present mid-term method with the authors’ previous work for short-term predictions.

REVUB-Light: A parsimonious model to assess power system balancing and flexibility for optimal intermittent renewable energy integration – A study of Suriname

A new model, REVUB-Light, an extended modelling approach for the recently developed “Renewable Energy Variability Upscaling and Balancing” (REVUB) model, is highlighted in this study for its relatively simple and effective parameterization for power balance optimization simulations, considering high Intermittent Renewable Energy integration. We demonstrate the effective use of the REVUB-Light model, considering a study for Suriname. The results for different cases are compared to those of the REVUB model, previously used for similar assessments for Suriname. Following distinct optimization procedures, our results confirm the REVUB model findings, i.e., a highly complementary role for wind energy, with up to 20–30% feed-in potential, based on the assumed (currently most probable) hydrological and wind regimes. The REVUB-Light model is validated based on actual operational data, enabling assessments of the potential real world response, and the benefits of flexible (optimized) operational regimes, underlining its potential for renewable energy modelling. It is further shown that Suriname has potential for (near) 100% renewable energy supply, for the mid-term horizon (2030), if additional hydropower potential is developed in combination with hydro-supported wind energy. Overall, the parsimonious approach of REVUB-Light proves to be a reliable modelling tool, and facilitates general usability by “non-experts” such as policymakers.

Investigating Iran Housing Market Cycles and Possible Scenarios in Short-Term and Midterm Horizon Using the System Dynamics

Housing is one of the important sectors of any economy. Given the lack of risk of the value of assets decline and the possibility of great numbers investment, investment in housing is attractive for the majority of Iranians The prices' shock causes problems on the demand side, particularly for vulnerable groups and on the supply side fluctuates investors' market confidence and risk. Under current conditions, also housing market remains in a deep stagnation. The present paper attempts to investigate causes of the problem and the housing market's future status using the dynamic system tools. In this study, after literature review the problem of the cause and effect model of the housing market has been outlined and the important and effective cycles have been specified. Then, using this model rate and accumulation model has been addressed and housing cycles have been specified in the simulation. In presenting scenario, also the status of parameters of Iran macroeconomic and sanctions is investigated. The result of presenting scenario shows that no sharp jump is predicted in housing price in future decade and in most scenarios the price is developed proportional to the inflation or less than it.

ДВАДЦАТЬ ПЯТЬ ЛЕТ СООБЩЕСТВУ РОССИИ И БЕЛАРУСИ: ИТОГИ И ПЕРСПЕКТИВЫ

April 2,2021 marks the 25th anniversary of the signing of the Treaty on the Union between Belarus and Russia, which laid the foundation for integration of the two countries. The article analyses results of the twenty- five-year development of the Russia-Belarus relations and assesses their pros- pects with account taken of the events developing in Belarus in 2020–2021. Considering the activities of the Union State of Russia and Belarus, the author summarises two key historical periods in the association functioning and gives their analytical characteristics. The paper focuses on the main integration achievements of cooperation in spheres of the economy (interstate trade, mutual investments, joint ventures, industrial and high tech cooperation, cus- toms policy), defence (organising a common defence space, mutual armament supplies, manoeuvres), science and culture. The author also studies numerous issues that arise from time to time in the relations between the Russian Feder- ation and the Republic of Belarus (energy disputes, the Dairy and Meat Wars, etc.). Finally, the paper contains a detailed analysis of the development scena- rios for the Russia-Belarus relations on the immediate and mid-term horizons.

Leagile supply chain network design through a dynamic two-phase optimization in view of order penetration point

In the contemporary world, combining the concept of agile and lean manufacturing (LM) is one of the most strategic and appealing concerns in the industrial environments. In this paper, a new Leagile structure is proposed for a supply chain. This research covers long term and mid-term horizon by designing a supply chain network up to the order penetration point (OPP) and final assembly and sale planning respectively. The problem is programmed in two phases. First, a bi-objective optimization is developed to minimize the total cost related with LM. In the second phase, the total cost and the customer service level (CSL) are considered as the agile manufacturing (AM) architecture. In the proposed model, a utility function is applied to set balance between the price and customer satisfaction. In addition, a robust credibility-based fuzzy programming (RCFP) is developed to handle uncertainty of the first phase. The proposed model and the solution method are implemented for a real industrial case study to show the applicability and usefulness of this study. According to the results, improving the customer service level can enhance the total cost of the second phase meaning that customer responsiveness price is too high for the proposed system.

The effect of urbanization on agricultural water consumption and production: the extended positive mathematical programming approach.

One of the most important factors in the agricultural sector is the supply of water. There are numerous factors affecting the supply of water in the agricultural sector. One of these factors is urban development. After the agricultural and industrial revolution, urban revolution is one of the most important issues in the economic development of countries. Urban development has two implications for agricultural water use; this both increases the demand for agricultural crops and the consumption of water in the household sector, which both lead to a reduction in available water in the agricultural sector. Therefore, determination of the impact of urbanization on water consumption in agriculture is necessary. In this study, we have examined the extended positive mathematical programming method on the agricultural sector of Qazvin Province. The results of the study showed that 1% increase in urbanization would reduce water consumption in agriculture by 0.639mm3 compared to the base year. Also, household water consumption will reach 92.92-761.9mm3 in base year. On the other hand, the cultivated area and the production of some irrigated crops (wheat, barley, rapeseed, sunflower, other oilseeds, alfalfa) will decrease to 86.36 hectare and 346.6 tons. In the indirect effect of increasing urbanization, per each percentage urbanization, the household water consumption will decrease by 2.19mm3, and after a 20-year horizon, the total capacity of the Taleghan Dam will be allocated to Tehran's household water,. So that, 288mm3 of agricultural water will be reduced, and it will reduce the cultivated area for 28,250 (ha). Also, farmers' gross profits will be 10.5 percent lower than the base year which is a threat for Qazvin plain farming. Solutions were proposed to reduce the negative effects of the urban development phenomenon, including the use of modern irrigation systems in the agricultural sector will significantly increase irrigation efficiency. So that only 1% increase in irrigation efficiency can completely neutralize the direct and indirect negative impacts of urbanization on a 12-year mid-term horizon.

Time series forecasting on multivariate solar radiation data using deep learning (LSTM)

Energy management is an emerging problem nowadays and utilization of renewable energy sources is an efficient solution. Solar radiation is an important source for electricity generation. For effective utilization, it is important to know precisely the amount from different sources and at different horizons: minutes, hours, and days. Depending on the horizon, two main classes of methods can be used to forecast the solar radiation: statistical time series forecasting methods for short to midterm horizons and numerical weather prediction methods for medium- to long-term horizons. Although statistical time series forecasting methods are utilized in the literature, there are a limited number of studies that utilize deep artificial neural networks. In this study, we focus on statistical time series forecasting methods for short-term horizons (1 h). The aim of this study is to discover the effect of using multivariate data on solar radiation forecasting using a deep learning approach. In this context, we propose a multivariate forecast model that uses a combination of different meteorological variables, such as temperature, humidity, and nebulosity. In the proposed model, recurrent neural network (RNN) variation, namely a long short-term memory (LSTM) unit is used. With an experimental approach, the effect of each meteorological variable is investigated. By hyperparameter tuning, optimal parameters are found in order to construct the best models that fit the global solar radiation data. We compared the results with those of previous studies and we found that the multivariate approach performed better than the previous univariate models did. In further experiments, the effect of combining the most effective parameters was investigated and, as a result, we observed that temperature and nebulosity are the most effective parameters for predicting future solar radiance.

An efficient hybrid structure to solve economic-environmental energy scheduling integrated with demand side management programs

Nowadays, by increasing the amount of greenhouse gases (GHGs) emitted from electricity generation sector, substantial challenges will be forced to the power system scheduling problems. However, under the smart environment, the amount of GHGs can be declined by handling the consumers’ demand. In this regard, this paper presents a two-stage framework concentrating on demand side management including energy efficiency programs and demand response programs as significant aspects of smart power system to handle system expenditures as well as pollutants. Therefore, in the first stage, investment rate on energy efficiency is specified over the midterm horizon time, and in the second one, a formulation of cost-and-emission-based generation scheduling in the presence of demand side management programs has been performed. Finally, a novel index the so-called emission mitigation index is nominated to investigate the impacts of demand side management on evaluation of GHGs emissions’ level. The IEEE 10 unit standard test system is conducted to evaluate the capability of demand side management in reduction of GHGs emissions and financial burden. Results indicate that by efficient utilization of demand side management programs, significant improvement is obtained.

Economic and Mathematical Modelling of Food Exports’ Turnover in Russia on a Mid-Term Horizon

Economic and Mathematical Modelling of Food Exports’ Turnover in Russia on a Mid-Term Horizon

Mid-term bio-economic optimization of multi-species fisheries

• We analyze the dynamics of a multi-species fisheries system in the presence of harvesting. • We propose a rather general model for studying multi-species interactions which covers previous ones. • We solve a new problem: finding the optimal strategy for a mid-term horizon with a fixed final stock of each species. • The proposed method is not gravely influenced by the dimensionality of the problem or by the complexity of the model. In this paper, we analyze the dynamics of a multi-species fisheries system in the presence of harvesting. We solve the problem of finding the optimal harvesting strategy for a mid-term horizon with a fixed final stock of each species, while maximizing the expected present value of total revenues. The problem is formulated as an optimal control problem . For its solution, we combine techniques derived from Pontryagin’s Maximum Principle, cyclic coordinate descent and the shooting method. The algorithm we develop can solve problems both with inter-species competition and with predator–prey behaviors. Several numerical examples are presented to illustrate the different possibilities of the method and a study of the dependence of the behavior on some parameters is performed.

Co-movements in commodity markets and implications in diversification benefits

This study examines the co-movement and causality relationship between prices of crude oil, precious metals, and agricultural commodities. We use a novel approach called wavelet coherence analysis, which allows the measurement of co-movements in the time–frequency space based on the daily prices of commodities. We decompose data from September 1986 to September 2017 into 12 levels and 5 subperiods to find more generalized and convincing results. We confirm that commodity prices are in-phase and co-move. Particularly, the coherence is the largest in the long term and rises sharply in the mid-term during the crisis period. The heterogeneous directions of arrows provide strong evidence that the causality relationship between commodity prices varies over time for different frequencies. We find that the mixed commodities portfolio can provide diversification benefits in the mid-term horizons. The findings of this study can guide investors who want to benefit from diversification while investing in commodity markets.

Combining literature-based and data-driven fuzzy models to predict brown trout (Salmo trutta L.) spawning habitat degradation induced by climate change

A fuzzy rule-based system combining empirical data on hydraulic preferences and literature information on temperature requirements was used to foresee the brown trout (Salmo trutta L.) spawning habitat degradation induced by climate change. The climatic scenarios for the Cabriel River (Eastern Iberian Peninsula) corresponded to two Representative Concentration Pathways (4.5 and 8.5) for the short (2011–2040) and mid (2041–2070) term horizons. The hydraulic and hydrologic modelling were undertaken with process-based numerical models (i.e., River2D© and HBV-light) while the water temperature was modelled by assembling the predictions of three machine learning techniques (M5, Multi-Adaptive Regression Splines and Support Vector Regression). The predicted rise in the water temperature will not be compensated by the more benign lower flows. Consequently, the suitable spawning habitat will be reduced between 15.4–48.7%. The entire population shall suffer the effects of climate change and will probably be extirpated from the downstream segments of the river.

A novel two-stage structure for coordination of energy efficiency and demand response in the smart grid environment

In recent years, Energy Efficiency Programs (EEPs) have been greatly expanded in multifarious aspects. Here, EEPs is considered as a virtual resource affecting operating expenditures of the power system. On the other hand, under the smart environment, Demand Response (DR) programs are also contemplated as virtual power plants in energy policy decisions ranging from short term to long term. Therefore, in this paper, a novel integrated model of demand in presence of EEPs as well as DR programs has been nominated. The presented model is handled in a Two-Stage Structure to Coordinate the EEPs and DR programs, namely TSSCDREEP. In the first stage, the level of Energy Efficiency Investment (EEI) is determined over the midterm horizon time. In the next stage, regarding the rate of EEI, a unit commitment problem incorporating EEPs as well as DR programs is handled while the interactions between EEPs and DR have been scrutinized. The proposed problem is implemented in GAMS. To evaluate the capability of EEPs and DR programs coordination, several analysis are carried out on the IEEE 10-unit to confirm the capability of the proposed framework from economic aspect.

The Demand for Military Expenditure in Europe: The Role of Fiscal Space in the Context of a Resurgent Russia

A variant of established work on the demand for military expenditure is developed based on a practical concept of fiscal space from the perspective of short-term government choices concerning public expenditures. A new indicator, referred to as fiscal capacity, is defined and used as a candidate explanatory variable in an empirical model of European defence spending over the 2007–2016 period. Fiscal capacity is found to outperform simpler measurements of economic conditions, notably GDP growth forecasts, in explaining changes in defence spending efforts as a share of GDP. Regarding security environment variables, the results suggest that Russia has recently come to be seen as a potential military threat by European nations, leading to defence spending increases, the more so the shorter the distance to stationed or deployed Russian forces, and particularly so by those European nations that have a land border with Russia. A prospective exercise is then carried out in order to assess the capacity of EU member states that are also members of NATO to reach NATO’s 2% goal for defence spending over a mid-term horizon.