Search results for: macroeconomic uncertainty

Authors: Khaled Ramadan Elbeydi

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This study is carried out to provide an insight into the analysis of the impact of selected macro-economic variables on gross fixed capital formation in Libya using annual data over the period (1970-2010). The importance of this study comes from the ability to show the relative important factors that impact the Libyan gross fixed capital formation. This understanding would give indications to decision makers on which policy they must focus to stimulate the economy. An Auto-Regressive Distributed Lag (ARDL) modeling process is employed to investigate the impact of the gross domestic product, monetary base, and trade openness on gross fixed capital formation in Libya. The results of this study reveal that there is an equilibrium relationship between capital formation and its determinants. The results also indicate that GDP and trade openness largely explain the pattern of capital formation in Libya. The findings and recommendations provide vital information relevant for policy formulation and implementation aimed to improve capital formation in Libya.

Keywords: ARDL, bounds test, capital formation, co-integration, Libya

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Authors: Ehsan Mehryaar, Seyed Armin Motahari Tabari

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Coarse-grained soils are known and commonly used in a wide range of geotechnical projects, including high earth dams or embankments for their high shear strength. The most important engineering property of these soils is friction angle which represents the interlocking between soil particles and can be applied widely in designing and constructing these earth structures. Friction angle and dilative behavior of coarse-grained soils can be estimated from empirical correlations with in-situ testing and physical properties of the soil or measured directly in the laboratory performing direct shear or triaxial tests. Unfortunately, large-scale testing is difficult, challenging, and expensive and is not possible in most soil mechanic laboratories. So, it is common to remove the large particles and do the tests, which cannot be counted as an exact estimation of the parameters and behavior of the original soil. This paper describes a new methodology to simulate particles grading distribution of a well-graded gravel sample to a smaller scale sample as it can be tested in an ordinary direct shear apparatus to estimate the stress-strain behavior, friction angle, and dilative behavior of the original coarse-grained soil considering its confining pressure, and relative density using a machine learning method. A total number of 72 direct shear tests are performed in 6 different sizes, 3 different confining pressures, and 4 different relative densities. Multivariate Adaptive Regression Spline (MARS) technique was used to develop an equation in order to predict shear strength and dilative behavior based on the size distribution of coarse-grained soil particles. Also, an uncertainty analysis was performed in order to examine the reliability of the proposed equation.

Keywords: MARS, coarse-grained soil, shear strength, uncertainty analysis

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Authors: Marcel Meinhardt, Manfred Keuser, Thomas Braml

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Cracking of reinforced concrete is a complex phenomenon induced by direct loads or restraints affecting reinforced concrete structures as soon as the tensile strength of the concrete is exceeded. Hence it is important to predict where cracks will be located and how they will propagate. The bond theory and the crack formulas in the actual design codes, for example, DIN EN 1992-1-1, are all based on the assumption that the reinforcement bars are embedded in homogeneous concrete without taking into account the influence of transverse reinforcement and the real stress situation. However, it can often be observed that real structures such as walls, slabs or beams show a crack spacing that is orientated to the transverse reinforcement bars or to the stirrups. In most Finite Element Analysis studies, the smeared crack approach is used for crack prediction. The disadvantage of this model is that the typical strain localization of a crack on element level can’t be seen. The crack propagation in concrete is a discontinuous process characterized by different factors such as the initial random distribution of defects or the scatter of material properties. Such behavior presupposes the elaboration of adequate models and methods of simulation because traditional mechanical approaches deal mainly with average material parameters. This paper concerned with the modelling of the initiation and the propagation of cracks in reinforced concrete structures considering the influence of transverse reinforcement and the real stress distribution in reinforced concrete (R/C) beams/plates in bending action. Therefore, a parameter study was carried out to investigate: (I) the influence of the transversal reinforcement to the stress distribution in concrete in bending mode and (II) the crack initiation in dependence of the diameter and distance of the transversal reinforcement to each other. The numerical investigations on the crack initiation and propagation were carried out with a 2D reinforced concrete structure subjected to quasi static loading and given boundary conditions. To model the uncertainty in the tensile strength of concrete in the Finite Element Analysis correlated normally and lognormally distributed random filed with different correlation lengths were generated. The paper also presents and discuss different methods to generate random fields, e.g. the Covariance Matrix Decomposition Method. For all computations, a plastic constitutive law with softening was used to model the crack initiation and the damage of the concrete in tension. It was found that the distributions of crack spacing and crack widths are highly dependent of the used random field. These distributions are validated to experimental studies on R/C panels which were carried out at the Laboratory for Structural Engineering at the University of the German Armed Forces in Munich. Also, a recommendation for parameters of the random field for realistic modelling the uncertainty of the tensile strength is given. The aim of this research was to show a method in which the localization of strains and cracks as well as the influence of transverse reinforcement on the crack initiation and propagation in Finite Element Analysis can be seen.

Keywords: crack initiation, crack modelling, crack propagation, cracks, numerical simulation, random fields, reinforced concrete, stochastic

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Authors: Muhamad Sahir Bin Ahmad Shatiry, Wan Normeza Wan Zakaria, Mohamad Zaki Hassan

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The pandemic Covid19 has significantly impacted the world; the spreading of the Covid19 virus initially from China has dramatically impacted the world's economy. Therefore, the world reacts with establishing the new way or norm in daily life. The rapid rise of the latest technology has been seen by introducing many technologies to ease human life to have a minor contract between humans and avoid spreading the virus Covid19. Digital twin technologies are one of the technologies created before the pandemic Covid19 but slow adoption in the industry. Throughout the Covid19, most of the companies in the world started to explore to use it. The digital twin technology provides the virtual platform to replicate the existing condition or setup for anything such as office, manufacturing line, factories' machine, building, and many more. This study investigates the effect on the economic perspective after the companies use the Digital Twin technology in the industry. To minimize the contact between humans and to have the ability to operate the system digitally remotely. In this study, the explanation of the digital twin technology impacts the world's microeconomic and macroeconomic.

Keywords: productivity, artificially intelligence, IoT, digital twin

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Authors: Ranjit Kumar Sahu, Ramakar Jha

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Urban Water Management is the practice of managing freshwater, waste water, and storm water as components of a basin-wide management plan. It builds on existing water supply and sanitation considerations within an urban settlement by incorporating urban water management within the scope of the entire river basin. The pervasive problems generated by urban development have prompted, in the present work, to study the spatial extent of urbanization in Golden Triangle of Odisha connecting the cities Bhubaneswar (20.2700° N, 85.8400° E), Puri (19.8106° N, 85.8314° E) and Konark (19.9000° N, 86.1200° E)., and patterns of periodic changes in urban development (systematic/random) in order to develop future plans for (i) urbanization promotion areas, and (ii) urbanization control areas. Remote Sensing, using USGS (U.S. Geological Survey) Landsat8 maps, supervised classification of the Urban Sprawl has been done for during 1980 - 2014, specifically after 2000. This Work presents the following: (i) Time series analysis of Hydrological data (ground water and rainfall), (ii) Application of SWMM (Storm Water Management Model) and other soft computing techniques for Urban Water Management, and (iii) Uncertainty analysis of model parameters (Urban Sprawl and correlation analysis). The outcome of the study shows drastic growth results in urbanization and depletion of ground water levels in the area that has been discussed briefly. Other relative outcomes like declining trend of rainfall and rise of sand mining in local vicinity has been also discussed. Research on this kind of work will (i) improve water supply and consumption efficiency (ii) Upgrade drinking water quality and waste water treatment (iii) Increase economic efficiency of services to sustain operations and investments for water, waste water, and storm water management, and (iv) engage communities to reflect their needs and knowledge for water management.

Keywords: Storm Water Management Model (SWMM), uncertainty analysis, urban sprawl, land use change

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Authors: Alexander Guzman Urbina, Atsushi Aoyama

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The sustainability of traditional technologies employed in energy and chemical infrastructure brings a big challenge for our society. Making decisions related with safety of industrial infrastructure, the values of accidental risk are becoming relevant points for discussion. However, the challenge is the reliability of the models employed to get the risk data. Such models usually involve large number of variables and with large amounts of uncertainty. The most efficient techniques to overcome those problems are built using Artificial Intelligence (AI), and more specifically using hybrid systems such as Neuro-Fuzzy algorithms. Therefore, this paper aims to introduce a hybrid algorithm for risk assessment trained using near-miss accident data. As mentioned above the sustainability of traditional technologies related with energy and chemical infrastructure constitutes one of the major challenges that today’s societies and firms are facing. Besides that, the adaptation of those technologies to the effects of the climate change in sensible environments represents a critical concern for safety and risk management. Regarding this issue argue that social consequences of catastrophic risks are increasing rapidly, due mainly to the concentration of people and energy infrastructure in hazard-prone areas, aggravated by the lack of knowledge about the risks. Additional to the social consequences described above, and considering the industrial sector as critical infrastructure due to its large impact to the economy in case of a failure the relevance of industrial safety has become a critical issue for the current society. Then, regarding the safety concern, pipeline operators and regulators have been performing risk assessments in attempts to evaluate accurately probabilities of failure of the infrastructure, and consequences associated with those failures. However, estimating accidental risks in critical infrastructure involves a substantial effort and costs due to number of variables involved, complexity and lack of information. Therefore, this paper aims to introduce a well trained algorithm for risk assessment using deep learning, which could be capable to deal efficiently with the complexity and uncertainty. The advantage point of the deep learning using near-miss accidents data is that it could be employed in risk assessment as an efficient engineering tool to treat the uncertainty of the risk values in complex environments. The basic idea of using a Near-Miss Deep Learning Approach for Neuro-Fuzzy Risk Assessment in Pipelines is focused in the objective of improve the validity of the risk values learning from near-miss accidents and imitating the human expertise scoring risks and setting tolerance levels. In summary, the method of Deep Learning for Neuro-Fuzzy Risk Assessment involves a regression analysis called group method of data handling (GMDH), which consists in the determination of the optimal configuration of the risk assessment model and its parameters employing polynomial theory.

Keywords: deep learning, risk assessment, neuro fuzzy, pipelines

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Authors: Azar Baghtaghi

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Financial risk management is critically important as it enables companies to maintain stability and profitability amidst market fluctuations and unexpected events. It involves the precise identification of risks that could impact investments, assets, and potential revenues. By implementing effective risk management strategies, companies can insure themselves against adverse market changes and prevent potential losses. In today's era, where markets are highly complex and influenced by various factors such as macroeconomic policies, exchange rate fluctuations, and natural disasters, the need for meticulous planning to cope with these uncertainties is more pronounced. Ultimately, financial risk management means being prepared for the future and the ability to sustain business in changing environments. A company capable of managing its risks not only achieves sustainable profitability but also gains the confidence of shareholders, investors, and business partners, enhancing its competitive position in the market. In this article, the types of financial risk and risk management strategies for improving financial performance were investigated. By identifying the risks stated in this article and their evaluation techniques, it is possible to improve the organization's financial performance.

Keywords: strategy, risk, risk management, financial performance

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Authors: Ashika Govender

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This study seeks to determine whether Okun’s Law is valid for the South African economy, using time series data for the period 2004 to 2014. The data were accessed from the South African Reserve Bank and Stats SA. The stationarity of the variables was analysed by applying unit root tests via the Augmented Dickey-Fuller test (ADF), the Phillips-Perron (PP) test, and the Kwiatkowski–Phillips–Schmidt–Shin test (KPSS) test. The study used an ordinary least square (OLS) model in analysing the dynamic version of Okun’s law. The Error Correction Model (ECM) was used to analyse the short-run impact of GDP growth on unemployment, as well as the speed of adjustment. The results indicate a short run and long run relationship between unemployment rate and GDP growth rate in period 2004q1-2014q4, suggesting that Okun’s law is valid for the South African economy. With a 1 percent increase in GDP, unemployment can decrease by 0.13 percent, ceteris paribus. The research culminates in important policy recommendations, highlighting the relationship between unemployment and economic growth in the spirit of the National Development Plan.

Keywords: unemployment, economic growth, Okun's law, South Africa

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Authors: A. Aulinger, A. M. Backes, J. Bieser, V. Matthias, M. Quante

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High concentrations of particles pose a threat to human health. Thus, legal maximum concentrations of PM10 and PM2.5 in ambient air have been steadily decreased over the years. In central Europe, the inorganic species ammonium sulphate and ammonium nitrate make up a large fraction of fine particles. Many studies investigate the influence of emission reductions of sulfur- and nitrogen oxides on aerosol concentration. Here, we focus on the influence of ammonia (NH3) emissions. While emissions of sulphate and nitrogen oxides are quite well known, ammonia emissions are subject to high uncertainty. This is due to the uncertainty of location, amount, time of fertilizer application in agriculture, and the storage and treatment of manure from animal husbandry. For this study, we implemented a crop growth model into the SMOKE emission model. Depending on temperature, local legislation, and crop type individual temporal profiles for fertilizer and manure application are calculated for each model grid cell. Additionally, the diffusion from soils and plants and the direct release from open and closed barns are determined. The emission data was used as input for the Community Multiscale Air Quality (CMAQ) model. Comparisons to observations from the EMEP measurement network indicate that the new ammonia emission module leads to a better agreement of model and observation (for both ammonia and ammonium). Finally, the ammonia emission model was used to create emission scenarios. This includes emissions based on future European legislation, as well as a dynamic evaluation of the influence of different agricultural sectors on particle formation. It was found that a reduction of ammonia emissions by 50% lead to a 24% reduction of total PM2.5 concentrations during winter time in the model domain. The observed reduction was mainly driven by reduced formation of ammonium nitrate. Moreover, emission reductions during winter had a larger impact than during the rest of the year.

Keywords: ammonia, ammonia abatement strategies, ctm, seasonal impact, secondary aerosol formation

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Authors: Lule Basha, Eralda Gjika

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The exchange rate is one of the most important economic variables, especially for a small, open economy such as Albania. Its effect is noticeable in one country's competitiveness, trade and current account, inflation, wages, domestic economic activity, and bank stability. This study investigates the fluctuation of Albania’s exchange rates using monthly average foreign currency, Euro (Eur) to Albanian Lek (ALL) exchange rate with a time span from January 2008 to June 2021, and the macroeconomic factors that have a significant effect on the exchange rate. Initially, the Random Forest Regression algorithm is constructed to understand the impact of economic variables on the behavior of monthly average foreign currencies exchange rates. Then the forecast of macro-economic indicators for 12 months was performed using time series models. The predicted values received are placed in the random forest model in order to obtain the average monthly forecast of the Euro to Albanian Lek (ALL) exchange rate for the period July 2021 to June 2022.

Keywords: exchange rate, random forest, time series, machine learning, prediction

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Authors: Bozena Leven

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The middle-income trap (MIT) describes a situation faced by countries at a relatively mature stage of development that often poses an obstacle to sustainable long-term growth. MIT is characterized by declining factor productivity from the exhaustion of labor intensive, import and Foreign Direct Investment (FDI) based strategies when middle-income status is achieved. In this paper, we focus on MIT and Poland. In the past two decades, Poland experienced steady growth based largely on imported technologies and low-cost labor. Recently, that economic growth has slowed, prompting economists to ask whether Poland is experiencing MIT. To answer this question, we analyze changes in investment in Poland; specifically- its growth and composition – as well as savings, FDI, educational attainments of the labor force, development of new technologies and products, the role of imports, diversification of exports, and product complexity. We also examine the development of modern infrastructure, institutions (including legal environment) and demographic changes in Poland that support growth. Our findings indicate that certain factors consistent with MIT are gaining importance in Poland, and represent a challenge to that country’s future growth rate.

Keywords: engines of growth, factor productivity, middle income trap, sustainable development

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Authors: Paul Ojeaga

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Can the level of innovation use drive un-validated business models across regions? To what extent does industrial sector attractiveness drive firm’s success across regions at the time of start-up? This study examines the role of innovation on start-up success in six regions of the world (namely Sub Saharan Africa, the Middle East and North Africa, Latin America, South East Asia Pacific, the European Union and the United States representing North America) using macroeconomic variables. While there have been studies using firm level data, results from such studies are not suitable for national policy decisions. The need to drive a regional innovation policy also begs for an answer, therefore providing room for this study. Results using dynamic panel estimation show that innovation counts in the early infancy stage of new business life cycle. The results are robust even after controlling for time fixed effects and the study present variance-covariance estimation robust standard errors.

Keywords: industrial economics, un-validated business models, scalable models, entrepreneurship

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Authors: Hasti Abbasi

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Creative writers have long followed the tradition of romantic exile, looking inward in an attempt to construct new viewpoints through the power of imagination. The writer, who attempts to resist uncertainty and locate her place in the new country through writing, resists creativity itself. For a writer, certain satisfaction can be achieved through producing a creative art away from the anxiety of the sense of dislocation. Dislocation, whether enforced or self-inflicted, could in many ways be a disaster but it could also cultivate a greater creative capacity and be a source of creative expression. This paper will investigate the idea of the creative writer as exiled self through reflections on the relationship between dislocation and writing.

Keywords: dislocation, creative writing, remaking identity, exile literature

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Authors: Oguzhan Cepni, Doruk Kucuksarac, M. Hasan Yilmaz

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Changes in the global risk appetite cause co-movement in emerging market risk premiums. However, the sensitivity of the changes in risk premium to the global risk appetite may vary across emerging markets. In this study, how the global risk appetite affects Credit Default Swap (CDS) premiums in emerging markets are analyzed using Principal Component Analysis (PCA) and rolling regressions. The PCA results indicate that the first common component derived by the PCA accounts for almost 76 percent of the common variation in CDS premiums. Additionally, the explanatory power of the first factor seems to be high over the sample period. However, the sensitivity to the global risk factor tends to change over time and across countries. In this regard, fixed effects panel regressions are used to identify the macroeconomic factors driving the heterogeneity across emerging markets. The panel regression results point to the significance of government debt to GDP and international reserves to GDP in explaining sensitivity. Accordingly, countries with lower government debt and higher reserves tend to be less subject to the variations in the global risk appetite.

Keywords: credit default swaps, emerging markets, principal components analysis, sovereign risk

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Authors: I. M. L. Nadeesha Jayaweera, Adao Alex Trindade

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In choosing a candidate model in likelihood-based modeling via an information criterion, the practitioner is often faced with the difficult task of deciding just how far up the ranked list to look. Motivated by this pragmatic necessity, we construct an uncertainty band for a generalized (model selection) information criterion (GIC), defined as a criterion for which the limit in probability is identical to that of the normalized log-likelihood. This includes common special cases such as AIC & BIC. The method starts from the asymptotic normality of the GIC for the joint distribution of the candidate models in an independent and identically distributed (IID) data framework and proceeds by deriving the (asymptotically) exact distribution of the minimum. The calculation of an upper quantile for its distribution then involves the computation of multivariate Gaussian integrals, which is amenable to efficient implementation via the R package "mvtnorm". The performance of the methodology is tested on simulated data by checking the coverage probability of nominal upper quantiles and compared to the bootstrap. Both methods give coverages close to nominal for large samples, but the bootstrap is two orders of magnitude slower. The methodology is subsequently extended to two other commonly used model structures: regression and time series. In the regression case, we derive the corresponding asymptotically exact distribution of the minimum GIC invoking Lindeberg-Feller type conditions for triangular arrays and are thus able to similarly calculate upper quantiles for its distribution via multivariate Gaussian integration. The bootstrap once again provides a default competing procedure, and we find that similar comparison performance metrics hold as for the IID case. The time series case is complicated by far more intricate asymptotic regime for the joint distribution of the model GIC statistics. Under a Gaussian likelihood, the default in most packages, one needs to derive the limiting distribution of a normalized quadratic form for a realization from a stationary series. Under conditions on the process satisfied by ARMA models, a multivariate normal limit is once again achieved. The bootstrap can, however, be employed for its computation, whence we are once again in the multivariate Gaussian integration paradigm for upper quantile evaluation. Comparisons of this bootstrap-aided semi-exact method with the full-blown bootstrap once again reveal a similar performance but faster computation speeds. One of the most difficult problems in contemporary statistical methodological research is to be able to account for the extra variability introduced by model selection uncertainty, the so-called post-model selection inference (PMSI). We explore ways in which the GIC uncertainty band can be inverted to make inferences on the parameters. This is being attempted in the IID case by pivoting the CDF of the asymptotically exact distribution of the minimum GIC. For inference one parameter at a time and a small number of candidate models, this works well, whence the attained PMSI confidence intervals are wider than the MLE-based Wald, as expected.

Keywords: model selection inference, generalized information criteria, post model selection, Asymptotic Theory

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Authors: Naser Kazemi Eilaki, Carolyn Ahmer, Ilona Heldal, Bjarne Christian Hagen

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Older people and people with disabilities are recognized as at-risk groups when it comes to egress and travel from hazard zone to safe places. One's disability can negatively influence her or his escape time, and this becomes even more important when people from this target group live alone. This research deals with the fire safety of mentioned people's buildings by means of probabilistic methods. For this purpose, fire safety is addressed by modeling the egress of our target group from a hazardous zone to a safe zone. A common type of detached house with a prevalent plan has been chosen for safety analysis, and a limit state function has been developed according to the time-line evacuation model, which is based on a two-zone and smoke development model. An analytical computer model (B-Risk) is used to consider smoke development. Since most of the involved parameters in the fire development model pose uncertainty, an appropriate probability distribution function has been considered for each one of the variables with indeterministic nature. To achieve safety and reliability for the at-risk groups, the fire safety index method has been chosen to define the probability of failure (causalities) and safety index (beta index). An improved harmony search meta-heuristic optimization algorithm has been used to define the beta index. Sensitivity analysis has been done to define the most important and effective parameters for the fire safety of the at-risk group. Results showed an area of openings and intervals to egress exits are more important in buildings, and the safety of people would improve with increasing dimensions of occupant space (building). Fire growth is more critical compared to other parameters in the home without a detector and fire distinguishing system, but in a home equipped with these facilities, it is less important. Type of disabilities has a great effect on the safety level of people who live in the same home layout, and people with visual impairment encounter more risk of capturing compared to visual and movement disabilities.

Keywords: fire safety, at-risk groups, zone model, egress time, uncertainty

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Authors: Parma Chakravartti

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The impact of government spending and taxation on economic growth has remained a central issue of fiscal policy analysis. There is a wide range of opinions over the strength of fiscal policy’s effect on macroeconomic variables. It can be argued that the impact of fiscal policy depends on the structure and economic condition of the economy. This study makes an attempt to examine the effect of fiscal policy shocks on growth in India using the structural vector autoregressive model (SVAR), considering data from 1950 to 2019. The study finds that government spending is an important instrument of growth in India, where the share of revenue expenditure to capital expenditure plays a key role. The optimum composition of total expenditure is important for growth and it is not necessarily true that capital expenditure multiplier is more than revenue expenditure multiplier. The study also finds that the impact of public economic activities on private economic activities for both consumption expenditure and gross capital formation of government crowds in private consumption expenditure and private gross capital formation, respectively, thus indicating that government expenditure complements private expenditure in India.

Keywords: government spending, fiscal policy, multiplier, growth

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Authors: A. Abbasi Moshaii, M. Soltan Rezaee, M. Mohammadi Moghaddam

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Control of some mechanisms is hard because of their complex dynamic equations. If part of the complexity is resulting from uncertainties, an efficient way for solving that is robust control. By this way, the control procedure could be simple and fast and finally, a simple controller can be designed. One kind of these mechanisms is 3-RRR which is a parallel mechanism and has three revolute joints. This paper aims to robust control a 3-RRR planner mechanism and it presents that this could be used for other mechanisms. So, a significant problem in mechanisms control could be solved. The relevant diagrams are drawn and they show the correctness of control process.

Keywords: 3-RRR, dynamic equations, mechanisms control, structural uncertainty

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Authors: Zhou Jianhong

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Model averaging is a desirable approach to deal with model uncertainty, which, however, has rarely been explored for Poisson regression. In this paper, we propose a model averaging procedure based on an unbiased estimator of the expected Kullback-Leibler distance for the Poisson regression. Simulation study shows that the proposed model average estimator outperforms some other commonly used model selection and model average estimators in some situations. Our proposed methods are further applied to a real data example and the advantage of this method is demonstrated again.

Keywords: model averaging, poission regression, Kullback-Leibler distance, statistics

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Authors: Safa’ Abdelkarim Hammad

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The electrical power system is one of the main parts of the nation's infrastructure, and the availability and cost of electricity are critical factors in industrial competitiveness and strategy. Restructuring of the electricity supply industries is a very complex exercise based on national energy strategies and policies, macroeconomic developments, and national conditions, and its application varies from country to country. Electricity regulation of natural monopolies is a challenging task. Regulators face the problem of providing appropriate incentives for improvement of efficiency. Incentive regulation is often considered as an efficient regulatory tool to handle the problem, and it is widely applied in several countries. However, the exact regulation methodologies differ from one country to another. Network quantitative reliability evaluation is an essential factor with regard to the quality of supply. The main factors used to judge the reliability of supply is measured by the number and duration of interruptions experienced by customers. Several indicators are used to evaluate reliability in distribution networks. This paper addresses the impact of incentive regulation and performance benchmarking in the field of electricity distribution in Jordan. The theory of efficiency measurement and the most common models; NCSQS and DEA models are presented.

Keywords: incentive regulations, reliability, restructuring, Tarrif

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Authors: Sofiya Rao, Sagnik Dey

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Aerosol-cloud-precipitation interaction continues to be one of the largest sources of uncertainty in quantifying the aerosol climate forcing. The uncertainty is increasing from global to regional scale. This problem remains unresolved due to the large discrepancy in the representation of cloud processes in the climate models. Most of the studies on aerosol-cloud-climate interaction and aerosol-cloud-precipitation over Indian Ocean (like INDOEX, CAIPEEX campaign etc.) are restricted to either particular to one season or particular to one region. Here we developed a theoretical framework to quantify aerosol indirect radiative forcing using Moderate Resolution Imaging Spectroradiometer (MODIS) aerosol and cloud products of 15 years (2000-2015) period over the Indian Ocean. This framework relies on the observationally constrained estimate of the aerosol-induced change in cloud albedo. We partitioned the change in cloud albedo into the change in Liquid Water Path (LWP) and Effective Radius of Clouds (Reff) in response to an aerosol optical depth (AOD). Cloud albedo response to an increase in AOD is most sensitive in the range of LWP between 120-300 gm/m² for a range of Reff varying from 8-24 micrometer, which means aerosols are most sensitive to this range of LWP and Reff. Using this framework, aerosol forcing during a transition from indirect to semi-direct effect is also calculated. The outcome of this analysis shows best results over the Arabian Sea in comparison with the Bay of Bengal and the South Indian Ocean because of heterogeneity in aerosol spices over the Arabian Sea. Over the Arabian Sea during Winter Season the more absorbing aerosols are dominating, during Pre-monsoon dust (coarse mode aerosol particles) are more dominating. In winter and pre-monsoon majorly the aerosol forcing is more dominating while during monsoon and post-monsoon season meteorological forcing is more dominating. Over the South Indian Ocean, more or less same types of aerosol (Sea salt) are present. Over the Arabian Sea the Aerosol Indirect Radiative forcing are varying from -5 ± 4.5 W/m² for winter season while in other seasons it is reducing. The results provide observationally constrained estimates of aerosol indirect forcing in the Indian Ocean which can be helpful in evaluating the climate model performance in the context of such complex interactions.

Keywords: aerosol-cloud-precipitation interaction, aerosol-cloud-climate interaction, indirect radiative forcing, climate model

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Authors: Dominique Arrouays, Songchao Chen, Anne C. Richer-De-Forges

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Soils are at the crossing of many issues such as food and water security, sustainable energy, climate change mitigation and adaptation, biodiversity protection, human health and well-being. They deliver many ecosystem services that are essential to life on Earth. Therefore, there is a growing demand for soil information on a national and global scale. Unfortunately, many countries do not have detailed soil maps, and, when existing, these maps are generally based on more or less complex and often non-harmonized soil classifications. An estimate of their uncertainty is also often missing. Thus, there are not easy to understand and often not properly used by end-users. Therefore, there is an urgent need to provide end-users with spatially exhaustive grids of essential soil properties, together with an estimate of their uncertainty. One way to achieve this is digital soil mapping (DSM). The concept of DSM relies on the hypothesis that soils and their properties are not randomly distributed, but that they depend on the main soil-forming factors that are climate, organisms, relief, parent material, time (age), and position in space. All these forming factors can be approximated using several exhaustive spatial products such as climatic grids, remote sensing products or vegetation maps, digital elevation models, geological or lithological maps, spatial coordinates of soil information, etc. Thus, DSM generally relies on models calibrated with existing observed soil data (point observations or maps) and so-called “ancillary co-variates” that come from other available spatial products. Then the model is generalized on grids where soil parameters are unknown in order to predict them, and the prediction performances are validated using various methods. With the growing demand for soil information at a national and global scale and the increase of available spatial co-variates national and continental DSM initiatives are continuously increasing. This short review illustrates the main national and continental advances in Europe, the diversity of the approaches and the databases that are used, the validation techniques and the main scientific and other issues. Examples from several countries illustrate the variety of products that were delivered during the last ten years. The scientific production on this topic is continuously increasing and new models and approaches are developed at an incredible speed. Most of the digital soil mapping (DSM) products rely mainly on machine learning (ML) prediction models and/or the use or pedotransfer functions (PTF) in which calibration data come from soil analyses performed in labs or for existing conventional maps. However, some scientific issues remain to be solved and also political and legal ones related, for instance, to data sharing and to different laws in different countries. Other issues related to communication to end-users and education, especially on the use of uncertainty. Overall, the progress is very important and the willingness of institutes and countries to join their efforts is increasing. Harmonization issues are still remaining, mainly due to differences in classifications or in laboratory standards between countries. However numerous initiatives are ongoing at the EU level and also at the global level. All these progress are scientifically stimulating and also promissing to provide tools to improve and monitor soil quality in countries, EU and at the global level.

Keywords: digital soil mapping, global soil mapping, national and European initiatives, global soil mapping products, mini-review

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Authors: Jayanth Abishek Subramanian, Ramin Dabirian, Ilias Gavrielatos, Ram Mohan, Ovadia Shoham

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Downcomers are important conduits for multiphase flow transfer from offshore platforms to the seabed. Uncertainty in the predictions of the pressure drop of multiphase flow between platforms is often dominated by the uncertainty associated with the prediction of holdup and pressure drop in the downcomer. The objectives of this study are to conduct experimental and theoretical scale-up study of the downcomer. A 4-in. diameter vertical test section was designed and constructed to study two-phase flow in downcomer. The facility is equipped with baffles for flow area restriction, enabling interchangeable annular slot openings between 30% and 61.7%. Also, state-of-the-art instrumentation, the capacitance Wire-Mesh Sensor (WMS) was utilized to acquire the experimental data. A total of 76 experimental data points were acquired, including falling film under 30% and 61.7% annular slot opening for air-water and air-Conosol C200 oil cases as well as gas carry-under for 30% and 61.7% opening utilizing air-Conosol C200 oil. For all experiments, the parameters such as falling film thickness and velocity, entrained liquid holdup in the core, gas void fraction profiles at the cross-sectional area of the liquid column, the void fraction and the gas carry under were measured. The experimental results indicated that the film thickness and film velocity increase as the flow area reduces. Also, the increase in film velocity increases the gas entrainment process. Furthermore, the results confirmed that the increase of gas entrainment for the same liquid flow rate leads to an increase in the gas carry-under. A power comparison method was developed to enable evaluation of the Lopez (2011) model, which was created for full bore downcomer, with the novel scale-up experiment data acquired from the downcomer with the restricted area for flow. Comparison between the experimental data and the model predictions shows a maximum absolute average discrepancy of 22.9% and 21.8% for the falling film thickness and velocity, respectively; and a maximum absolute average discrepancy of 22.2% for fraction of gas carried with the liquid (oil).

Keywords: two phase flow, falling film, downcomer, wire-mesh sensor

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Authors: Saima Ali, Pathamanathan Rajeev, Imteaz A. Monzur

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In this paper, a brief review of the corrosion mechanism in buried pipe and modes of failure is provided together with the available corrosion models. Moreover, the sensitivity analysis is performed to understand the influence of corrosion model parameters on the remaining life estimation. Further, the probabilistic analysis is performed to propagate the uncertainty in the corrosion model on the estimation of the renaming life of the pipe. Finally, the comparison among the corrosion models on the basis of the remaining life estimation will be provided to improve the renewal plan.

Keywords: corrosion, pit depth, sensitivity analysis, exposure period

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Authors: José M Martín-Moreno, Rafaela Pérez, Jesús Ruiz

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In this paper we use a small open economy Dynamic Stochastic General Equilibrium Model (DSGE) for the Spanish economy to search for a deeper characterization of the determinants of Spain’s macroeconomic fluctuations throughout the period 1970-2008. In order to do this, we distinguish between tradable and non-tradable goods to take into account the fact that the presence of non-tradable goods in this economy is one of the largest in the world. We estimate a DSGE model with supply and demand shocks (sectorial productivity, public spending, international real interest rate and preferences) using Kalman Filter techniques. We find the following results. First of all, our variance decomposition analysis suggests that 1) the preference shock basically accounts for private consumption volatility, 2) the idiosyncratic productivity shock accounts for non-tradable output volatility, and 3) the sectorial productivity shock along with the international interest rate both greatly account for tradable output. Secondly, the model closely replicates the time path observed in the data for the Spanish economy and finally, the model captures the main cyclical qualitative features of this economy reasonably well.

Keywords: business cycle, DSGE models, Kalman filter estimation, small open economy

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Authors: Camille Van Eupen, Dirk Maes, Marc Herremans, Kristijn R. R. Swinnen, Ben Somers, Stijn Luca

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In ecology, species distribution models are commonly implemented to study species-environment relationships. These models increasingly rely on opportunistic citizen science data when high-quality species records collected through standardized recording protocols are unavailable. While these opportunistic data are abundant, uncertainty is usually high, e.g., due to observer effects or a lack of metadata. Data quality filtering is often used to reduce these types of uncertainty in an attempt to increase the value of studies relying on opportunistic data. However, filtering should not be performed blindly. In this study, recommendations are built for data quality filtering of opportunistic species occurrence data that are used as input for species distribution models. Using an extensive database of 5.7 million citizen science records from 255 species in Flanders, the impact on model performance was quantified by applying three data quality filters, and these results were linked to species traits. More specifically, presence records were filtered based on record attributes that provide information on the observation process or post-entry data validation, and changes in the area under the receiver operating characteristic (AUC), sensitivity, and specificity were analyzed using the Maxent algorithm with and without filtering. Controlling for sample size enabled us to study the combined impact of data quality filtering, i.e., the simultaneous impact of an increase in data quality and a decrease in sample size. Further, the variation among species in their response to data quality filtering was explored by clustering species based on four traits often related to data quality: commonness, popularity, difficulty, and body size. Findings show that model performance is affected by i) the quality of the filtered data, ii) the proportional reduction in sample size caused by filtering and the remaining absolute sample size, and iii) a species ‘quality profile’, resulting from a species classification based on the four traits related to data quality. The findings resulted in recommendations on when and how to filter volunteer generated and opportunistically collected data. This study confirms that correctly processed citizen science data can make a valuable contribution to ecological research and species conservation.

Keywords: citizen science, data quality filtering, species distribution models, trait profiles

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Authors: Maria Amelia V. C. Araujo, Billy J. Araujo, Brian Greenwood

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The flow over a backward-facing step is characterized by the presence of flow separation, recirculation and reattachment, for a simple geometry. This type of fluid behaviour takes place in many practical engineering applications, hence the reason for being investigated. Historically, fluid flows over a backward-facing step have been examined in many experiments using a variety of measuring techniques such as laser Doppler velocimetry (LDV), hot-wire anemometry, particle image velocimetry or hot-film sensors. However, some of these techniques cannot conveniently be used in separated flows or are too complicated and expensive. In this work, the applicability of the acoustic Doppler velocimetry (ADV) technique is investigated to such type of flows, at various Reynolds numbers corresponding to different flow regimes. The use of this measuring technique in separated flows is very difficult to find in literature. Besides, most of the situations where the Reynolds number effect is evaluated in separated flows are in numerical modelling. The ADV technique has the advantage in providing nearly non-invasive measurements, which is important in resolving turbulence. The ADV Nortek Vectrino+ was used to characterize the flow, in a recirculating laboratory flume, at various Reynolds Numbers (Reh = 3738, 5452, 7908 and 17388) based on the step height (h), in order to capture different flow regimes, and the results compared to those obtained using other measuring techniques. To compare results with other researchers, the step height, expansion ratio and the positions upstream and downstream the step were reproduced. The post-processing of the AVD records was performed using a customized numerical code, which implements several filtering techniques. Subsequently, the Vectrino noise level was evaluated by computing the power spectral density for the stream-wise horizontal velocity component. The normalized mean stream-wise velocity profiles, skin-friction coefficients and reattachment lengths were obtained for each Reh. Turbulent kinetic energy, Reynolds shear stresses and normal Reynolds stresses were determined for Reh = 7908. An uncertainty analysis was carried out, for the measured variables, using the moving block bootstrap technique. Low noise levels were obtained after implementing the post-processing techniques, showing their effectiveness. Besides, the errors obtained in the uncertainty analysis were relatively low, in general. For Reh = 7908, the normalized mean stream-wise velocity and turbulence profiles were compared directly with those acquired by other researchers using the LDV technique and a good agreement was found. The ADV technique proved to be able to characterize the flow properly over a backward-facing step, although additional caution should be taken for measurements very close to the bottom. The ADV measurements showed reliable results regarding: a) the stream-wise velocity profiles; b) the turbulent shear stress; c) the reattachment length; d) the identification of the transition from transitional to turbulent flows. Despite being a relatively inexpensive technique, acoustic Doppler velocimetry can be used with confidence in separated flows and thus very useful for numerical model validation. However, it is very important to perform adequate post-processing of the acquired data, to obtain low noise levels, thus decreasing the uncertainty.

Keywords: ADV, experimental data, multiple Reynolds number, post-processing

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Authors: Naomi Grigoryan, Alexandros Loutsioli Daskalakis, Anna Elisse Uy, Yihe Huang, Aude Laurent (Webanck)

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In response to the building and construction sectors accounting for a third of all energy demand and emissions, the European Union has placed new laws and regulations in the construction sector that emphasize material circularity, energy efficiency, biodiversity, and social impact. Existing design tools assess sustainability in early-stage design for products or buildings; however, there is no standardized methodology for measuring the circularity performance of building components. Existing assessment methods for building components focus primarily on carbon footprint but lack the comprehensive analysis required to design for circularity. The research conducted in this paper covers the parameters needed to assess sustainability in the design process of architectural products such as doors, windows, and facades. It maps a framework for a tool that assists designers with real-time sustainability metrics. Considering the life cycle of building components such as façades, windows, and doors involves the life cycle stages applied to product design and many of the methods used in the life cycle analysis of buildings. The current industry standards of sustainability assessment for metal building components follow cradle-to-grave life cycle assessment (LCA), track Global Warming Potential (GWP), and document the parameters used for an Environmental Product Declaration (EPD). Developed by the Ellen Macarthur Foundation, the Material Circularity Indicator (MCI) is a methodology utilizing the data from LCA and EPDs to rate circularity, with a "value between 0 and 1 where higher values indicate a higher circularity+". Expanding on the MCI with additional indicators such as the Water Circularity Index (WCI), the Energy Circularity Index (ECI), the Social Circularity Index (SCI), Life Cycle Economic Value (EV), and calculating biodiversity risk and uncertainty, the assessment methodology of an architectural product's impact can be targeted more specifically based on product requirements, performance, and lifespan. Broadening the scope of LCA calculation for products to incorporate aspects of building design allows product designers to account for the disassembly of architectural components. For example, the Material Circularity Indicator for architectural products such as windows and facades is typically low due to the impact of glass, as 70% of glass ends up in landfills due to damage in the disassembly process. The low MCI can be combatted by expanding beyond cradle-to-grave assessment and focusing the design process on disassembly, recycling, and repurposing with the help of real-time assessment tools. Design for Disassembly and Urban Mining has been integrated within the construction field on small scales as project-based exercises, not addressing the entire supply chain of architectural products. By adopting more comprehensive sustainability metrics and incorporating uncertainty calculations, the sustainability assessment of building components can be more accurately assessed with decarbonization and disassembly in mind, addressing the large-scale commercial markets within construction, some of the most significant contributors to climate change.

Keywords: architectural products, early-stage design, life cycle assessment, material circularity indicator

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Authors: Ezeoke Callistus Obiora, Ezeoke Nneka Angela

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The study investigated the effect of deficit financing on macroeconomic variables in Nigeria. The specific objectives included to find out the relationship between deficit financing and GDP, interest rate, inflation rate, money supply, exchange rate and private investment respectively on a time series covering a period of 44 years (1970 – 2013). The Ordinary Least Square multiple regression produced statistics for the coefficient of determination (R2), F-test, t-test used for the interpretation of the study. The findings revealed that Deficit financing has significant positive effect on GDP and exchange rate. Again, deficit financing has a positive and insignificant relationship inflation, money supply and investment. Only interest rate recorded negative yet insignificant relationship with deficit financing. The implications of the findings are that deficit financing can be a veritable tool for boosting economic development in Nigeria, but the influential positively rising exchange rate implies that deficit financing devalues the Naira exchange rate to other currencies indicating that deficit financing can affect Nigerians competitive advantage at the world market. Thus, the study concludes that deficit financing has not encouraged economic growth in Nigeria.

Keywords: deficit financing, money supply, exchange rate, inflation, GDP, investment, Nigeria

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Authors: L. L. Ivy-Yap, H. A. Bekhet

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As the Malaysian residential electricity consumption continued to increase rapidly, effective energy policies, which address factors affecting residential electricity consumption, is urgently needed. This study attempts to investigate the relationship between residential electricity consumption (EC), real disposable income (Y), price of electricity (Pe) and population (Po) in Malaysia for 1978-2011 periods. Unlike previous studies on Malaysia, the current study focuses on the residential sector, a sector that is important for the contemplation of energy policy. The Phillips-Perron (P-P) unit root test is employed to infer the stationary of each variable while the bound test is executed to determine the existence of co-integration relationship among the variables, modeled in an Autoregressive Distributed Lag (ARDL) framework. The CUSUM and CUSUM of squares tests are applied to ensure the stability of the model. The results suggest the existence of long-run equilibrium relationship and bidirectional Granger causality between EC and the macroeconomic variables. The empirical findings will help policy makers of Malaysia in developing new monitoring standards of energy consumption. As it is the major contributing factor in economic growth and CO2 emission, there is a need for more proper planning in Malaysia to attain future targets in order to cut emissions.

Keywords: co-integration, elasticity, granger causality, Malaysia, residential electricity consumption

Procedia PDF Downloads 246