Balance Table Research Articles

Moderating role of big data usage in intellectual capital and innovation performance: evidence from Turkish banking sector

PurposeIn this study, the moderator effect of the use of big data by Turkish banks on the innovation performance of the intellectual capital components, human capital, structural capital, and relational capital is discussed.Design/methodology/approachIn the research, 618 survey data applied to bank employees and weighted according to population in seven regions were used. The data were analyzed through the structural equation model.FindingsAccording to the empirical results, intellectual capital components and big data usage explain 65% of the variance in innovation performance. It has been determined that the other two components of intellectual capital, except structural capital, have a statistically significant effect on innovation performance. According to the Standardized Regression Weights, one unit change in human capital affects innovation performance by 0.162, and one unit change in relational capital affects innovation performance by 0.244. In addition, a one-unit change in big data usage affects innovation performance by 0.480. It has been understood that the use of big data significantly affects the innovation performance of banks with a rate of 0.480.Research limitations/implicationsAlthough this study is important, it could have been done with senior managers instead of being based on a survey. Instead of a survey, it could have been done with a data set taken from banks' balance sheets and tables. Additionally, the use of big data has been considered as a moderator but can be reconsidered as a mediator or external construct. Moreover, this study was conducted on a sample of participants working in the developing Turkish commercial banking sector. Therefore, the results of the study can be done in different countries and at different development levels.Originality/valueThe study is one of the first studies to examine the moderating effect of intellectual capital by considering its subcomponents in a developing country. In addition, it is thought that the results will contribute to managers, policy makers and researchers who want to increase competition and market share in the sector, as well as filling the gap in the literature.

The Influence of Several Host Types on the Balance of Life Sitophilus Sp

Sitophilus sp is a warehouse pest that attacks grain. In its life cycle, Sitophilus sp requires hosts such as rice, corn and sorghum for its survival process. This warehouse pest can damage crops in the form of pods or seeds in the field or in storage. This research aims to determine the life cycle of Sitophilus sp on several different types of hosts. This research used a single factor Completely Randomized Design (CRD) consisting of six levels which were repeated 10 times. The results of the study showed that the fastest development of Sitophilus sp was in local white rice (35.00 days), and the longest development was in Lamuru corn (46.00 days). In the life balance table, local white corn produces the highest growth and development (proportion) of live Sitophilus sp. Meanwhile, the lowest live proportion is found in lamuru corn.

ANALYZING THE PROCESSES OF ANALYTICAL PROCESSING OF INVENTORY ACCOUNTING INFORMATION FOR THE PURPOSE OF ACCOUNTABILITY

Topicality. The importance of financial and management reporting in enterprises plays a greater role in the effective presentation and control of resources. These reports allow for more efficient organization and rapid adjustment of business management processes, taking into account the importance of backup data. Aim and tasks. This research will examine in detail the theoretical importance and analytical use of reserve data in financial and management reporting. This article provides information on inventory accounting and how it is important for an enterprise, threats that may arise as a result of improper accounting, and the theoretical importance of analytical accounting. Materials and methods. Data Collection, literature review, policy implications were used in the study. Analytical accounting offers analysis and evaluation of inventories by material types and prices. This includes analysis of material movements, evaluation of material types, diversification, identification of safety points and management of material operations. Analytical analysis results contribute to the determination and development of material management policy. Research results. The greatest advantage of the article is that it combines the theoretical and practical aspects of analysis and emphasizes the value of analysis, reflecting important steps and practical tips for material resource management and production process analysis. This helps to develop the material handling process in an effective and safe manner. Conclusion. The accounting of the quantity of materials is carried out by the persons responsible for the material in the warehouse accounting book according to the first documents. The balance of materials as of the 1st of each month is transferred from the warehouse accounting book to the balance table by the accounting staff or the warehouse manager. Then the balance sheet is reconciled with the data of the synthetic account. Thus, in the application of the balance method, accounting does not repeat the work that would be done by materially responsible persons for accounting of materials, and instead organizes control over their work.

No star is good news: A unified look at rerandomization based on [formula omitted]-values from covariate balance tests

Randomized experiments balance all covariates on average and are considered the gold standard for estimating treatment effects. Chance imbalances are nonetheless common in realized treatment allocations. To inform readers of the comparability of treatment groups at baseline, contemporary scientific publications often report covariate balance tables with not only covariate means by treatment group but also the associated p-values from significance tests of their differences. The practical need to avoid small p-values as indicators of poor balance motivates balance check and rerandomization based on these p-values from covariate balance tests (ReP) as an attractive tool for improving covariate balance in designing randomized experiments. Despite the intuitiveness of such strategy and its possibly already widespread use in practice, the literature lacks results about its implications on subsequent inference, subjecting many effectively rerandomized experiments to possibly inefficient analyses. To fill this gap, we examine a variety of potentially useful schemes for ReP and quantify their impact on subsequent inference. Specifically, we focus on three estimators of the average treatment effect from the unadjusted, additive, and interacted linear regressions of the outcome on treatment, respectively, and derive their asymptotic sampling properties under ReP. The main findings are threefold. First, the estimator from the interacted regression is asymptotically the most efficient under all ReP schemes examined, and permits convenient regression-assisted inference identical to that under complete randomization. Second, ReP, in contrast to complete randomization, improves the asymptotic efficiency of the estimators from the unadjusted and additive regressions. Standard regression analyses are accordingly still valid but in general overconservative. Third, ReP reduces the asymptotic conditional biases of the three estimators and improves their coherence in terms of mean squared difference. These results establish ReP as a convenient tool for improving covariate balance in designing randomized experiments, and we recommend using the interacted regression for analyzing data from ReP designs.

Characterization of the life cycle carbon footprint of the automobile industry in the Republic of Korea by environmentally extended input-output model

The automobile industry is a major economic driver in Republic of Korea (Korea). However, little is known about its climate change contribution. This study estimates the direct and indirect greenhouse gases (GHG) emissions of the Korean automobile industry for the first time, by using a 2017 environmentally extended input-output (EEIO) model integrated by energy balance, GHG inventory and input-output table of 2017. The results show that the final demand of Korean automobile industry led to 8.4% of national GHG emissions in 2017, mostly because of indirect emissions embodied in the supply chain. The study also found that the Scope 1, Scope 2 and Scope 3 emissions on average accounted for 3.0%, 2.9%, and 94.7%, respectively. This highlights the contribution of the upstream supply chain such as primary metals (40.9%) and electricity (32.5%) when assessing the GHG emissions. Finally, the study underscores that carbon taxes could have a significant impact on the competitiveness of automobile export. Overall, this study provides valuable insights on countermeasures by identifying the GHG emissions characteristics of the automobile industry. The results of the study could be used to develop policies and strategies to reduce the GHG emissions and promote sustainable practices in the Korean automobile industry.

Quantifying water loss in leaky micro-dam reservoir through water balance analysis and high-resolution water level data modeling

Abstract This study aimed to assess the runoff, recharge, and response of a shallow aquifer to leakage from the Arato micro-dam reservoir (MDR). The assessment was conducted using the Soil Conservation Service Curve Number (SCS-CN), soil moisture balance (SMB), and diver (automatic data logger) measurements in both the MDR and a shallow hand-dug well. Recharge was estimated using the chloride mass balance (CMB) and water table fluctuation (WTF) methods. The results revealed that the annual runoff from the catchment was 48.8 mm, which accounted for approximately 0.71 million m3. The yearly groundwater recharge was estimated to be 104, 92.8, and 100 mm using the SMB, CMB, and WTF methods, respectively. Furthermore, the water balance model of the Arato MDR indicated a leakage rate of 13.2 mm/day. It is noteworthy that the estimated leakage exceeded the seepage initially anticipated during the project's design phase (9,965 m3/year). This research project highlights the significance of utilizing local climatic and physical data from the specific watershed under investigation when planning reservoirs and other water resources. It also underscores the importance of conducting thorough site investigations to accurately quantify hydraulic conductivity for leakage estimation purposes.

Effects of various land utilization types on groundwater at different temporal scales: a case study of Huocheng plain, Xinjiang, China

Different land utilization types have unignorable impacts on adjacent aquifers, so studying the effects of varying land utilization types on groundwater balance and groundwater table in arid and semi-arid areas is crucial to facilitate the rational development of territorial space and groundwater resource management. This study investigated the relationship between land utilization type variations and groundwater dynamics from spatial and temporal scales in Huocheng plain, Xinjiang, China, via taking advantage of different land utilization maps provided by remote sensing techniques in 1990, 2000, and 2015. Based on the analysis of results, the findings can be summarized as follows. First, the cultivated and construction land has expanded significantly in the past 25 years. The migration of the regional center of cultivated land was insignificant, as it is still located almost in the center of the entire study area. However, the transfer span of the location center of construction land was more prominent, and its moving direction was mainly from south to northeast, directly affecting the groundwater balance state and groundwater table. Second, the total amount of groundwater storage exhibited a remarkable reduction, from a recharge in 1990 to a continuous discharge in 2000–2015, during which the total recharge and discharge had both continuously declined. Meanwhile, recharging and discharging elements were also changed profoundly as less groundwater recharges from ambient aqueous environments and more groundwater extraction. Thus, human activities were the fundamental driving terms causing the changes in groundwater sources. Third, the groundwater table gradually and continuously dropped from south to northeast in the study area over time. The drawdown of spatially averaged groundwater tables was positively correlated with construction and cultivated area extension. In addition, compared to the former literature, this study offers an alternative approach for groundwater protection and optimization of territorial space development and utilization in arid and semi-arid areas.

CBDT-Oglyc: Prediction of O-glycosylation sites using ChiMIC-based balanced decision table and feature selection.

O-glycosylation (Oglyc) plays an important role in various biological processes. The key to understanding the mechanisms of Oglyc is identifying the corresponding glycosylation sites. Two critical steps, feature selection and classifier design, greatly affect the accuracy of computational methods for predicting Oglyc sites. Based on an efficient feature selection algorithm and a classifier capable of handling imbalanced datasets, a new computational method, ChiMIC-based balanced decision table O-glycosylation (CBDT-Oglyc), is proposed. ChiMIC-based balanced decision table for O-glycosylation (CBDT-Oglyc), is proposed to predict Oglyc sites in proteins. Sequence characterization is performed by combining amino acid composition (AAC), undirected composition of [Formula: see text]-spaced amino acid pairs (undirected-CKSAAP) and pseudo-position-specific scoring matrix (PsePSSM). Chi-MIC-share algorithm is used for feature selection, which simplifies the model and improves predictive accuracy. For imbalanced classification, a backtracking method based on local chi-square test is designed, and then cost-sensitive learning is incorporated to construct a novel classifier named ChiMIC-based balanced decision table (CBDT). Based on a 1:49 (positives:negatives) training set, the CBDT classifier achieves significantly better prediction performance than traditional classifiers. Moreover, the independent test results on separate human and mouse glycoproteins show that CBDT-Oglyc outperforms previous methods in global accuracy. CBDT-Oglyc shows great promise in predicting Oglyc sites and is expected to facilitate further experimental studies on protein glycosylation.

Groundwater Recharge Assessment for Small Karstic Catchment Basins with Different Extents of Anthropogenic Development

Climate change and anthropogenic development considerably influence groundwater resource distribution and conditions. Catchment basin groundwater recharge—discharge computation reliability is needed for effective groundwater management policy formulation and implementation and also for resolving environmental challenges in such a watershed. This paper compares groundwater recharge patterns between urbanized and nearly natural small catchment basins of Israel’s Western Mountain Aquifer (WMA). The correlation between precipitation volumes and surface runoff shows that surface runoff volume constitutes 3–4% of the precipitation volume in the Natuf catchment and 1–2% in the Te’enim catchment. These assessments reflect the differences in the land use, outcrop lithology, topography and hydrodynamic properties of the WMA within the model basins. A groundwater recharge assessment based on water balance and water table fluctuation methods was performed for the mountainous karstic Te’enim and Natuf catchment basins for all the available data from 2000 to 2020. The water balance method provided reliable estimates. The groundwater recharge assessment considered land use classification and climate changes during this period. The average multiannual groundwater recharge values for the 2000–2021 period varied from 17.6 × 106–24.8 × 106 m3 to 24.5–29.2 × 106 m3 for the Te’enim and Natuf catchment basins, respectively. For the relatively dry period of the 2013/2014–2017/2018 hydrological years when detailed measurements of the surface runoff were available, the corresponding groundwater recharge volumes were 17.6 × 106 m3 and 24.5 × 106 m3. The corresponding local groundwater recharge coefficients constitute 0.46–0.57 for the mostly agricultural Te’enim basin and 0.29–0.32 for the urbanized Natuf basin. A significant difference in the groundwater recharge coefficients between the studied catchments is caused mostly by the differences in land use. It is suggested that applying such a groundwater recharge estimation for small hydrological sub-basins can improve one’s understanding of the groundwater recharge distribution within a major basin, enabling the application of an accurate regional hydrogeological model that may be extrapolated to other similar regions.

Decoupling Effect and Driving Factors of Land-Use Carbon Emissions in the Yellow River Basin Using Remote Sensing Data

Land-use change is a crucial element influencing the patterns of carbon sinks/sources in the Yellow River Basin (YRB). Therefore, studying land-use carbon emissions (LUCE) in the YRB and the decoupling from economic development can help formulate emission reduction strategies. In order to explore the spatiotemporal characteristics of LUCE in the YRB, we estimated the LUCE in 69 cities in the YRB using the downscale energy balance table estimation method and land-use remote sensing data for seven phases from 1990 to 2020. The spatial and temporal features of LUCE were researched from three different spatial scales: the whole spatial scale of the YRB, the sub-basin level, and the city level. Furthermore, the Tapio decoupling model was utilized to research the decoupling state between LUCE and economic development using a multi-scale approach. The Logarithmic Mean Divisia Index (LMDI) model was employed to explore the influencing factors of LUCE in the YRB. These results showed the following: (1) The LUCE in the YRB went through two stages: “stable growth” (1990–2000) and “rapid growth” (2000–2020). The LUCE increased from 165 million tons in 1990 to 1.414 billion tons in 2020, and the average annual growth rate was 25.12%. The spatial pattern of LUCE in the YRB exhibited significant variations, with the LUCE showing a geographic differentiation of midstream > downstream > upstream. (2) Except for the expansive coupling state during 2000–2005 (e: 0.952) and the expansive negative decoupling state during 2015–2020 (e: 2.151), the YRB was in the weak decoupling state for the majority of the time periods. (3) Economic development was the major positive driving factor for the rise of LUCE in this basin, while energy consumption intensity was the primary inhibiting factor. Through a discussion of the features and influencing factors of LUCE, these results can be utilized to provide carbon emission reduction recommendations tailored to the characteristics of cities’ resources and economic development, which will be helpful for achieving low-carbon and sustainable development in the YRB.

Determination of water availability and long growth period of maize plant in Boalemo Regency, Indonesia

The lack of water availability and the short period of maize growth has the potential to be the main obstacles to achieving optimal production. Therefore, this research aimed to determine the water availability and long growth period (LGP) of maize plants in Boalemo Regency, which was included in the agrokilmat zone E2. Rainfall and temperature data from Saritani, Tangkobu, and Bubaa climate stations in Boalemo Regency were used over ten years, from 2012 to 2022. The soil profile moisture content data used pF 2.5 and pF 4.2 while the tool used Cropwat8.0. Furthermore, the determination of available water and LGP was conducted using a water balance table with the Thornwaite method. The results showed that the highest and lowest water availabilities were 556 mm and 118 mm at the Saritani and Tangkobu climate stations, with a maximum LGP of maize at 242 days and 181 days, respectively. Based on climate stations, the availability of water and the amount of LGP follow the pattern of Saritani climate station > Bubaa climate station > Tangkobu climate station.

Hydrological modeling with physics-based models in the oueme basin: Issues and perspectives for simulation optimization

Study regionOueme Basin, threatened by climate change and upstream energy projects, is limited by the quality of the adapted meteorological data through the assessment of their impacts with some models. Study focusThis study focuses on the ParFlow-CLM model and describes its ability to simulate the water balances with good (observation data) and bad (observation transformed and reanalysis corrected and uncorrected data) meteorological forcing. New hydrological insights for the regionThe model correctly simulates all the terms of water balance, mainly evaporation and water table depth (WTD) with KGE always higher than 0.70. It is less sensitive to temperature, shortwave (SW), and longwave (LW) forcing. With those three variables, ParFlow-CLM simulates none significative difference in water balance terms using the reanalysis forcings on which we note an overestimation of temperature and an underestimation of LW. The same results are obtained with the other so-called bad meteorological forcings. On the other hand, the precipitation of the latter significantly influences the water balance. The annual water balance and especially evapotranspiration shows 683 ± 70 mm for observations versus 753 ± 63 mm and 662 ± 44 mm for ERA5 and MERRA2 respectively. The corrections applied on these variables seem to work better on MERRA2 (734 mm or 51 mm difference with observations) compared to ERA5 (760 mm or 77 mm difference with observations). The temperate, SW, LW variables of the reanalyses can be used for hydrological simulations with ParFlow-CLM and probably with other physics-based models over the Beninese basins but the precipitation of these must be corrected before use.

Development of flow model for partly and fully saturated soils using water balance and water table depth fluctuation analysis

Accurate estimation of soil water content and water table depth is important for making irrigation measures and rational groundwater use in arid agricultural areas with shallow water table depth. Soil water balance models are widely used due to their high computational efficiency, while they are less applicable in areas with shallow water table depths because they usually ignore the matric potential. In addition, the effect of capillary rise on soil water state is also ignored. To address these problems, a flow model for partly and fully saturated soil, referred to as modified UBMOD, was developed in this study based on UBMOD and the water table depth fluctuation analysis. UBMOD is a soil water balance model that differs from other water balance models by considering soil water movement driven by matric potential. In the modified UBMOD, soil water flow and groundwater recharge rate are calculated using the original UBMOD algorithm, and water table depth is calculated using the water table depth fluctuation analysis. The field capacity used in the modified UBMOD was considered as a variable related to the water table depth to reflect the impact of capillary rise, which helps to obtain accurate water flux across the saturated–unsaturated interface. Two synthetic cases, simulated with HYDRUS, were used to test the sensitivities of the model parameters and specifications. Two real-world cases were used to show the fidelity of the model to the observed data. The results demonstrated that the model could solve the unsaturated–saturated problems based on more readily available parameters, without stringent requirements on spatial and time steps, with a full guarantee of water balance and low computational cost. This study provides an effective tool for hydrodynamic studies in areas with shallow water table depths.

Intelligent control of gas separation during nitric acid production

The article considers the possibilities of modeling the characteristics of gas absorption and cooling processes in the production of nitric acid and controlling this process using the radial basis function and neural networks of direct connection. When training a neural network, the input and output results are obtained from the material balance tables and properties of the absorbing column model to train the network with values. The results obtained using neural network models are mainly compared with the results obtained from modeling books. The result obtained shows that relatively simple neural network models can be used to simulate the stationary state of an absorption column. Using modeling, we will see how the neural network type allows the use of modern management methods.

Characterization of Greenhouse Gases Emissions by Economic Sectors Using Environmentally Extended Input-Output Analysis (EEIOA)

Objectives : As climate change deepened, the concept of 2050 carbon neutrality was introduced. The COP 26 Glasgow Agreement strengthened the national greenhouse gases (GHGs) target of 2030. However, there are controversies over the feasibility of these GHGs reduction goals, considering the economic sectors energy policy and the attributes of GHGs emissions. Taking this into consideration, this study aimed to formulate the 2017 Environmentally Expanded Input-output Table to analyze the characteristics of GHGs emissions in Korea's economic sectors.Methods : The carbon dioxide emission was calculated by multiplying carbon dioxide emission factors to fuel consumption in the 2017 energy balance table, while other GHGs emissions are taken from the national GHGs inventory. All the GHGs emissions calculated and taken were allocated to 381 basic sectors of the Input-Output Table to represent each sector's characteristics of GHGs emissions. Then 381 sectors are combined into a large category of 35 sectors to formulate the 2017 Republic of Korea Environmentally Extended Input-Output Table (ROKEEIOT). Using this ROKEEIOT, the emission of Scope 1. 2, and 3 by economic sectors were estimated, and the GHGs emissions and GHGs intensity by economic sectors.Results and Discussion : The carbon dioxide emissions calculated by the 2017 ROKEEIOT prepared in this study showed a difference of 2% from the 2017 national GHGs emission statistics, confirming that the 2017 ROKEEIOT is very effective. The three economic sectors with the highest direct GHGs emissions were electricity, steam, chilled or hot water, air conditioning supply (262,280 kt CO2eq.), primary metal products (117.098 kt CO2eq.), and transportation equipment (58,332 kt CO2eq.). However, the total GHGs emissions were different in the order of construction (151,476 kt CO2eq.), transportation equipment (112,168 kt CO2eq.), computer, and electronic and optical instruments (107,868 kt CO2eq.). As a result of classifying the scope of GHGs emissions, 6 industries exceeded 50% in Scope 1, and 29 sectors exceeded 50% in Scope 3, indicating that GHGs reduction measures were necessary for the supply chain in consideration of the GHGs emissions characteristics by economic sectors. In particular, 41.68% of GHGs emissions induced by final demand were generated by exports confirming, confirming the urgent need to strengthen the carbon competitiveness of export industry in preparation for the introduction of the carbon border adjustment mechanism.Conclusion : The economic contribution, direct GHGs emissions of industries, and the amount of GHGs emissions induced by supply chains and value chains show very different patterns by economic sectors. Therefore, it was confirmed that scientific policies to reduce GHGs emissions, such as net-zero and climate change measures, should reflect the characteristics of Scope 1, which is direct emission by industry, Scope 2 emissions caused by electricity and steam consumption and scope 3 emissions generated along the supply chain or value chain.

Upscaling Hydrological Processes for Land Surface Models With a Two‐Hydrologic‐Variable Model: Application to the Little Washita Watershed

AbstractLand Surface Models (LSMs) are key components of Earth System Models, which the Intergovernmental Panel on Climate Change relies on in many of their studies. However, these models either neglect or oversimplify basin‐scale hydrological processes that produce the land surface water balance. Bringing the dominant physical processes from the local scale up to the LSMs presents a significant challenge for improving the models. This article presents an upscaling (or bottom‐up) approach to identify the basin‐scale driving variables that need to be exported into LSMs. The approach is developed on the Little Washita Watershed (OK, USA) using 20‐year hydrology (1993–2013). A physically based 3D model built with HydroGeoSphere first produces a reference simulation. An equivalent hillslope model is then able to capture both 3D simulated water balance and local water table dynamics with reasonable accuracy. The physical analysis of the water balance in the different hillslope compartments leads to the identification of two driving variables: seepage face extension and water table slope. The two variables are then implemented in a conceptual model. Results show a good capacity of this model to capture the water balance of hillslopes having different lengths and slopes. Moreover, the model is able to capture the water balance of the reference simulation with reasonable accuracy. The proposed approach thus reduces the 3D watershed model to a two‐variable conceptual model that constitutes a basis for developing an improved LSM hydrology.

Estimation of the input-output balance model for the South of the Tyumen Oblast

Subject. Monitoring and predicting the trajectories of the development of regions on the basis of the Leontief balance model. Objectives. Development of the balance tables estimating schemes for the south of the Tyumen Oblast according to statistical data of typical economic indicators. Methods. Two options for solving the problem of estimating balance tables are considered. In the first variant the reference matrix of direct costs is adjusted to the statistical data of the system of national accounts for the Tyumen Oblast. The second variant is to use the industry cost structure for Russia to customize the regional table. Results. In the first approximation, the structure of aggregation of industries into subsystems was chosen in accordance with the development strategy of the Tyumen Oblast until 2030. The obtained images of the tables correspond to the balance ratios and productivity conditions of the Leontief matrix. Conclusions. The results of adjusting the input-output model for the south of the Tyumen Oblast according to the available statistical data in general demonstrate the efficiency of the presented methods for assessing direct cost matrices. The choice of the structure of aggregation of indicators in accordance with the development strategy of the region allows to compare the predicted and model trajectories of economic development for characteristic groups of industries.

Estimation of city energy consumption in China based on downscaling energy balance tables

The accurate estimation of city-level energy consumption is of great significance for the formulation of energy conservation and emission reduction policies and for addressing global climate change. Previous studies can only provide the estimation of the total energy consumption rather than energy consumption by sector or energy type, and the estimation accuracy of these methods are rarely validated. Meanwhile, few studies on carbon emission accounting based on the energy balance table provide some enlightenment on energy consumption estimation by sector. However, these studies suffer from the problems of incomplete distribution indicator systems and discrepancies between the provincial total and city-level aggregation data, resulting in inaccuracy. To address these challenges, in this study, a systematic accounting method based on downscaling energy balance tables is developed to estimate the energy consumption of prefecture-level cities in China. With this method, we compiled the energy consumption inventory of 286 cities in 2003–2019, covering 18 energy activities and 30 fossil fuels. To completely evaluate the performance of the proposed method, we also provided a comparison of the estimation results with the inversion simulation method by nighttime light data and the previous top-bottom allocation method. The comparison results reveal that the proposed method outperforms all the other approaches with a small average error, better stability and higher accuracy. Overall, this method provides an effective way to obtain continuous, long-term and full-coverage energy consumption data in cities, which provides reliable data support for the formulation of rational energy conservation and emission reduction policies at the city level.

Assessing Water Losses in Drinking Water Distribution Systems Using the SCADA System: The Erzincan Case

Reasons such as rapid population growth, urbanization, unconscious water use, environmental pollution, and changes in climate conditions increase water consumption, and water is consumed before completing its cycle in nature. This situation has directed the water producers to search for new resources in the face of increasing water demand and decreasing resources, but due to the high cost of the resource search, the water producers have turned to the understanding of reducing the high amounts of lost water and using water resources in a more planned and efficient manner. Minimizing water losses in drinking water distribution networks is among these objectives.
 In this study, drinking water data between January 2014 and January 2020 in Erzincan was examined, and the SCADA (Supervising Control and Data Acquisition) system placed in the drinking water distribution network in March 2018 was evaluated by considering the pre and post-drinking water data in the system. First of all, the terms between January 2014 and March 2018 which means before the installation of the SCADA system were examined, the data of the amount of water produced and the data of water consumed by the subscribers was collected from the Municipal Waterworks Unit, these data were transferred to the Water Balance Table, the results was analyzed and the actual water loss rates in the system were estimated. As a result of this estimation, before the SCADA system was established, the total physical and administrative water loss rate was seen as 64%, while the physical water loss rate was 28%. After the establishment of the SCADA automation system after March 2018, the date of the amount of water produced received from the SCADA system and the amount of water consumed was transferred to the Water Balance Table and the total physical and administrative loss was seen as 37% while the physical water loss rate was 14%. According to these results, it was observed that the water loss rate approached the minimum level within a short period with the SCADA automation system.

Srbija 2030 - testiranje održivosti duga i performansi rasta

The aim of this paper is to examine the adequacy of the chosen growth model of the country through the lens of Serbia's external debt sustainability. For this purpose, an empirical model was developed for making long-term projections and testing the debt sustainability, which can be considered as an operational tool when compared to existing models developed by the International Monetary Fund and the World Bank. Unlike these models, the proposed model binds all macroeconomic balance tables so that the item of one table indicates the dynamics and levels of items from other tables, thus ensuring both internal and external validity of the model. The proposed model of macroeconomic projections, in fact, implies the development of simultaneous projections of individual macroeconomic aggregates and variables as well as their reconciliation. As a result, a complete system of interconnected long-term projections is obtained. On the basis of such macro-projections, it is possible to examine the movement of debt and in this regard to examine the sensitivity of projected debt values to various macroeconomic shocks. This is extremely important in the context of growing uncertainty in the world, both in terms of economic developments and security. The consequences of the COVID-19 pandemic are yet to be summed up, but it is quite obvious that due to increased market risks, investors will be increasingly cautious when investing in other countries, which can pose serious challenges for Serbia, as a small and open economy, to finance its chosen path of economic growth and development.