Theil's Coefficient Research Articles

Genome‐Enabled Parameterization Enhances Model Simulation of CH4 Cycling in Four Natural Wetlands

AbstractMicrobial processes are crucial in producing and oxidizing biological methane (CH4) in natural wetlands. Therefore, modeling methanogenesis and methanotrophy is advantageous for accurately projecting CH4 cycling. Utilizing the CLM‐Microbe model, which explicitly represents the growth and death of methanogens and methanotrophs, we demonstrate that genome‐enabled model parameterization improves model performance in four natural wetlands. Compared to the default model parameterization against CH4 flux, genomic‐enabled model parameterization added another contain on microbial biomass, notably enhancing the precision of simulated CH4 flux. Specifically, the coefficient of determination (R2) increased from 0.45 to 0.74 for Sanjiang Plain, from 0.78 to 0.89 for Changbai Mountain, and from 0.35 to 0.54 for Sallie's Fen, respectively. A drop in R2 was observed for the Dajiuhu nature wetland, primarily caused by scatter data points. Theil's coefficient (U) and model efficiency (ME) confirmed the model performance from default parameterization to genome‐enabled model parameterization. Compared with the model solely calibrated to surface CH4 flux, additional constraints of functional gene data led to better CH4 seasonality; meanwhile, genome‐enabled model parameterization established more robust associations between simulated CH4 production rates and environmental factors. Sensitivity analysis underscored the pivotal role of microbial physiology in governing CH4 flux. This genome‐enabled model parameterization offers a valuable promise to integrate fast‐cumulating genomic data with CH4 models to better understand microbial roles in CH4 in the era of climate change.

Market Structure as a Vehicle for its Plasticity: an Introduction to the International Cachaça Market from the Perspective of the Industrial Organization Paradigm

Objective: The objective of this study was to present how market structure serves as a vehicle for market plasticity. To achieve this objective, the example of the international cachaça market was used. Theoretical Framework: To understand the structure, we utilized the theory of the Industrial Organizations paradigm. This paradigm specifically considers three areas of concentration for its study efforts: the firm, the markets, and the industries. Method: Using secondary data provided by the English beverage magazine Millionaires, a quantitative study of the distilled beverages market, and more specifically the Brazilian cachaça market, was developed. For the empirical study of the market, measures used included the Concentration Ratio (Cr), the Herfindahl-Hirschman Index (HHI), and the Theil Coefficient (ET). Results and Discussion: In the direct competition of the international distilled beverages market, the cachaça industry stands out with 55.71% of the total market. In terms of concentration in the international distilled beverages market, the concentration ratio (Cr) of the four largest companies was 59.76%, indicating a moderately competitive market. The HHI (Herfindahl-Hirschman Index) was 928.99, which suggests a competitive market according to the standards of the U.S. Department of Justice. The Theil Coefficient (T) was 0.92, indicating a low concentration market. Research Implications: Supported by the Structure-Conduct-Performance approach, Industrial Organization enabled the identification of some dynamics of the cachaça market in this study. Finally, we believe we have contributed to the understanding of the structure of the international cachaça market and some of the influences of this structure on market plasticity. Originality/Value: As presented in this study, the market structure denotes the potential to reshape the market, promoting responses to the actions of its players.

Assimilation of PSO and SVR into an improved ARIMA model for monthly precipitation forecasting

Precipitation due to its complex nature requires a comprehensive model for forecasting purposes and the efficiency of improved ARIMA (IARIMA) forecasts has been proved relative to the conventional models. This study used two procedures in the structure of IARIMA to obtain accurate monthly precipitation forecasts in four stations located in northern Iran; Bandar Anzali, Rasht, Ramsar, and Babolsar. The first procedure applied support vector regression (SVR) for modeling the statistical characteristics and monthly precipitation of each class, IARIMA-SVR, which improved the evaluation metrics so that the decrease of Theil's coefficient and average relative variance in all stations was 21.14% and 17.06%, respectively. Two approaches are defined in the second procedure which includes a forecast combination (C) scheme, IARIMA-C-particle swarm optimization (PSO), and artificial intelligence technique. Generally, most of the time, IARIMA-C-PSO relative to the other approach, exhibited acceptable results and the accuracy improvement was greater than zero at all stations. Comparing the two procedures, it is found that the capability of IARIMA-C-PSO is higher concerning the IARIMA-SVR, so the decrease in the normalized mean squared error value from IARIMA to IARIMA-SVR and IARIMA-C-PSO is 36.72% and 39.92%, respectively for all stations. The residual predictive deviation (RPD) of IARIMA-C-PSO for all stations is greater than 2, which indicates the high performance of the model. With a comprehensive investigation, the performance of Bandar Anzali station is better than the other stations. By developing an improved ARIMA model, one can achieve a high performance in structure identifying and forecasting of monthly time series which is one of the issues of interest and importance.

A combination prediction model based on Theil coefficient and induced continuous aggregation operator for the prediction of Shanghai composite index

This paper proposes an interval combination prediction model for Shanghai composite index, utilizing the Theil coefficient and the induced continuous generalized ordered weighted logarithmic harmonic averaging (ICGOWLHA) operator. The effectiveness of the proposed model under specific weight conditions and the existence of its analytical solution are demonstrated. Shanghai composite index's case analysis demonstrates that, in terms of interval root mean squared error (IRMSE), interval mean absolute error (IMAE), interval mean absolute percentage error (IMAPE), and interval mean squared percentage error (IMSPE), the proposed model's predictive performance improvements over the best-performing single prediction model are 29.33%, 25.72%, 26.10%, and 28.86%, respectively. At the same time, the theoretical properties of the model are verified in the results of the case analysis, and the model's convergence is reflected in sensitivity analysis. Through extensive model comparisons, it is observed that the model proposed in this paper exhibits strong generalization, without specific limitations on data size or feature count. It demonstrates good aggregation prediction performance for interval data. Moreover, it is applicable to various fields, including finance, environment, and others.

The parameter estimation of the COVID-19 death based on the Gumbel distribution through the multi-objective programming: Turkey case

Nearly all nations, including Turkey, were impacted by the 2019 new coronavirus (COVID-19) infections that Wuhan, China reported as the disease's first official case. Turkey is one of the most impacted nations in the globe because to the high number of infected patients. To comprehend the pattern of the virus's propagation and its impacts, it is crucial of examine the pandemic statistics of Turkey. The Gumbel distribution is used to model the maximum distribution of several samples with various distributions. Therefore, we used the Gumbel distribution to estimate the daily number of COVID-19-related deaths. In this study, based on the RMSE, R2, and Theil coefficient method's multi-objective programming approach, the parameter estimation of Gumbel distribution is proposed. A comprehensive Monte-Carlo simulation study was conducted to examine the performance of single-objective RMSE, R2, Theil’s coefficient and multi-objective RMSE-R2, RMSE-Theil, R2-Theil, RMSE-R2-Theil programming estimation methods. When the simulation results were analyzed, the case formed by the RMSE-R2-Theil estimator has the best Def value for all cases. The application of the real dataset containing COVID-19 death data is examined and it can be seen that Theil, RMSE-Theil, and R2-Theil were better estimators for winter data, while RMSE was a better estimator for autumn and autumn-winter data.

Multi-model ensemble successfully predicted atmospheric methane consumption in soils across the complex landscape

Multi-model ensemble successfully predicted atmospheric methane consumption in soils across the complex landscape

Income inequality and export‐oriented commercialization in colonial Africa: Evidence from six countries

AbstractLimited knowledge of African historical inequality trajectories hampers our understanding of inequality outcomes today and leads to a major omission in debates about global inequality. Economies in colonial Africa were characterized by a process of export‐oriented commercialization. We hypothesize that this process itself, the capital intensity of the commodities produced, and the relative importance of European and Asian expatriates and settlers in the economy shaped heterogeneous inequality outcomes. We evaluate these hypotheses using 33 social tables from six predominately agricultural countries between 1914 and 1969. Social tables capture income across the full distribution, aggregated in classes. We assess and improve the commensurability of the different social tables. We then apply different inequality metrics, and find that Gini and Theil coefficients and Inequality Extraction Ratios rose over time. Gini coefficients moved in conjunction with the real value of commodity exports per capita. Using Theil decompositions, we observe a trade‐off between inequality among African classes on the one hand, and among non‐Africans and between races on the other. Whenever present, non‐Africans captured a large share of the export profits. Inequality patterns towards the end of the period suggest that capital‐intensive commodities were associated with higher levels of inequality in the agricultural sector.

Spatial and Temporal Characteristics and Influencing Factors of G20 Box Office Revenues: A Film Geography Perspective

The geography of film is an interdisciplinary field of geography and communication. Understanding the spatial and temporal characteristics of the global box office scale represented by the G20 is important for expanding the research scope of film geography focusing on “space” and promoting the development of film industries around the world. This paper studies the spatial and temporal characteristics and influencing factors of G20 box office revenues 2003–2019 using the Theil coefficient, spatial analysis, and a panel vector auto-regressive model. According to the research: (1) the distribution of the top box office revenues within the G20 is obvious; the scale differences among these countries are gradually decreasing and the scale differences between China and the United States are the most significant; (2) the box office differences of the G20 are gradually decreasing; the Asian movie market represented by China and South Korea is developing rapidly; (3) from the perspective of the transfer of movie box office scale types within a Markov Chain, the number of countries in type II and III is the largest and the transfer among different types is mainly to high-level types; and (4) the factors influencing the box office of G20 movies are the number of screens, per capita gross national income, the working-age population, and GDP, respectively.

Spatio-Temporal Evolution and Influencing Factors of Open Economy Development in the Yangtze River Delta Area

Since economic globalization is unstable, it is difficult for the traditional open economic development model to meet the requirements of China’s development, and there is an urgent need for new ideas and models to be reoriented. Based on the analysis of the development mechanism of China’s open economy at this stage, we used the entropy method, Theil coefficient, Gini coefficient, and spatial Durbin model (SDM) to analyze the spatio-temporal pattern and influencing factors of the high-quality development level of the open economy in the Yangtze River Delta Area (YRDA). The results indicated that during the study period, the development level and development difference of open economy were on the rise, and the spatial difference in different regions was significant. The development of open economy was affected by many factors, and there was a spatial spillover effect. Based on the existing problems, at the stage of high-quality economic development, the YDRA should seize the opportunities brought by the new development pattern, improve government services, optimize innovation drive, and promote the development of open economy to a higher level. We believe that the results of this study can also provide relevant experience for the development of open economy in other regions of China.

Evaluation of Cropland System Resilience to Climate Change at Municipal Scale Through Robustness, Adaptability, and Transformability: A Case Study of Hubei Province, China

A cropland system is one of the most sensitive socio-ecological systems to climate change, such as drought and flood. Facing frequent extreme weather events worldwide, how to improve cropland system resilience to climate change (CSRCC) and thus ensure food production has been concerned. Although a small number of studies have attempted to evaluate CSRCC through single or multiple indicators, few studies have considered the perspective of the three basic capacities of resilience (i.e., robustness, adaptability, and transformability), which could ignore the dynamic characteristics of cropland system resilience against shocks within a certain period. Therefore, this study first constructs an evaluation index system from the three capacities of system resilience. Then, taking Hubei province, China, as a case and comprehensively using the methods of Delphi, AHP, and TOPSIS to assess the spatio-temporal characteristics of CSRCC at the municipal scale from 2011 to 2018. On this basis, the regional disparities of CSRCC are analyzed by using the Theil coefficient. The results show that the CSRCC of Hubei province fluctuates on a downward trend, with the lowest in 2017 and the highest in 2013. Most municipalities have witnessed a pattern of fluctuated decline, except for a few ones in the plains, such as Wuhan and Jingmen. Generally, municipalities in the plains have greater scores, while some municipalities in the southern and eastern hilly regions show higher adaptability and transformability. In addition, adaptability contributes the least to the CSRCC at the municipal scale. At last, indicator selection against different research objects, influencing mechanism of CSRCC, and policy implications are discussed. This study is expected to provide a reference for the practice in sustainable management and utilization of cropland systems.

동아시아 및 환태평양 국가의 1인당 이산화탄소 배출량 수렴에 관한 연구

Economic and environmental damage is increasing due to global warming. Carbon dioxide is the main cause of this global warming. The Paris Agreement was reached with international cooperation to reduce carbon emissions. The carbon dioxide emission is a byproduct of the economic development process and is closely related to the environmental destruction and the quality of life. Due to global warming, research on carbon dioxide emissions in East Asia and Pacific coastal countries, which are the regions around the Pacific Ocean, is significant. Stakeholders’ interest on global warming are needed. This study is conducted from 1961 to 2018 to investigate whether convergence of carbon dioxide emissions is present among 30 countries that make up the East Asian and Pacific regions, or not. As a result, absolute ß-convergence was observed in East Asia and the Pacific, and the the annual speed rate of convergence rate was estimated to be about 0.07%. Using coefficient of variation, Gini coefficient, Theil coefficient, Mld coefficient, σ-convergence was observed during the 1961-2018 period in East Asian and Pacific Countries. In terms of absolute ß-convergence, the average annual convergence rate was estimated to be 0.26% for the period 1961 to 2018, 6.00% for the period 1961 to 1969, and 0.07% for the period 1970 to 2018.

Use of computer simulation to assess measures of variation of deviation in budget control

A natural consequence of the budgeting process is budget control, the effectiveness of which depends largely on the quality of the deviation analysis. As part of this analysis, the expected quantities are most often compared with the observed/actual ones, and the reasons for the deviations are investigated. In budget control, it is important to deconstruct deviations into components and to determine the impact of factors affecting these deviations. Therefore, budgetary control should be extended to the analysis of budget deviations in terms of its subject and temporal nature. This generally applies to every planning and control process, while the authors in their research concentrate on budgeting revenues and operating costs in enterprises. In this study, three measures used in economics – the GINI, Hirschman-Herfindahl, and Theil coefficients – were evaluated as potential metrics for budget deviation differentiation. Simulated computer data was used for this study.The GINI coefficient was the most statistically significant metric tested, making it the selected measure for budget deviation differentiation. The GINI coefficient is a normalized variable (ratio) with a range between 0 to 1, making it a convenient metric to evaluate differences in deviations between individual budget items, between years, and between budgeted units.

Прогнозирование осадок оснований автомобильных дорог на высоких насыпях на основе машинного обучения по данным геодезических измерений

The paper describes the research on forecasting of road base settlements on high embankments by means of geodetic observations, and also considers the question of geodetic data representation in the form of a mathematical model which takes into account the laws of settlements development and also allows to reveal upward or downward trends of the observed deformations. The relevance and novelty of the research is that there are no requirements for making forecast models by the geodetic observation data. Whereas it is noted that building of such models is important for rational planning of repair works and more accurate determination of geodetic observations periodicity. The aim of the study is to develop a method of geodetic monitoring of foundation settlements on high embankments. The following machine learning methods were considered: linear regression, adaptive linear regression, exponential smoothing, support vectors, and random forest. After models were trained and their parameters were chosen, the forecast on a test set was carried out. The parameters that characterized the quality of the models were: root mean square error, mean absolute error percentage, mean error percentage, determination coefficient, and Theil's coefficient of variance. Minimal value of root mean squared error RMSE = 0,8 mm was obtained by the method of support vectors. The support vectors method showed the highest forecast accuracy when taking into account all indicators of model quality assessment.

A Theil coefficient-based combination prediction method with interval heterogeneous information for wind energy prediction

This paper constructs the continuous-Young optimal weighted arithmetic averaging (C-YOWA) operator and the continuous-Young optimal weighted geometric (C-YOWG) operator based on definite integral and Young inequality. A series of special cases and main properties of the proposed aggregation operators are also investigated. In order to integrate heterogeneous interval data and obtain more accurate prediction results, the heterogeneous interval combination prediction (HICP) model based on C-YOWA operator, C-YOWG operator and Theil coefficient is proposed. The HICP model consider not only the existence of both additive and multiplicative interval information, but also the preference information of experts. Finally, the model is applied to the empirical analysis of wind energy prediction. The comparison of results shows that the established model can effectively improve the accuracy of prediction.

Evaluation and difference analysis of regional energy efficiency in China under the carbon neutrality targets: Insights from DEA and Theil models

In the context of carbon neutrality, the paper uses Super Efficiency Data Envelopment Analysis (DEA) to evaluate the level of regional energy efficiency in China, then selects Theil index to analyze the difference and change of regional energy efficiency. The results are as follows: (1) The overall level of energy efficiency in China is relatively low, and the average efficiency level from 2007 to 2019 is 0.568. Among them, Beijing is the highest, at 1.261, and Xinjiang is the lowest, at only 0.205. The energy efficiency of the eastern region is the highest in China: the average is 0.812. The central region is lower than the national average, with the value of 0.534. The lowest energy efficiency is in the western region, which is only 0.349. It can be seen that there are significant differences in regional energy efficiency, and from west to east has increased. (2) The Theil coefficient increased before 2012 and began to show a downward trend in 2013. The difference in the eastern region maintained the smallest downward trend among the three regions. The difference in the central region fluctuated and increased before 2011, and dropped sharply after 2012. In the western region, the coefficient is relatively stable and has not changed significantly. (3) Differences within and between regions in China show an overall downward trend. From the perspective of contribution rate, the difference between regions contributes the most to the overall difference.

Spatial Equilibrium State and Its Time Evolution of the Multi-functionalization of Regional Land Use in the Eastern China

In order to revealed the spatial-temporal correlation characteristics and interaction relationship of multifunctional land use in the county of Jiangsu Province, which could provide theoretical support for the spatial planning of the county. This paper constructed multiple functional identification system from three dimensions of production-living-ecological, and quantitatively measures 55 counties’ spatial equilibrium state and space-time effect of “production-living-ecological” function in 1996, 2005 and 2015 by means of coupling coordination degree model, Theil coefficient and its decomposition, and LISA time path analysis. The results showed that spatial equilibrium level of multifunctional land use function in the county of Jiangsu Province was relatively low from 1996 to 2015, presenting the spatial characteristics of the overall dispersion and local agglomeration. The coordination degree of multifunctional land use had spatial differentiation characteristics,which were closely related to the stage of social economic development. The time-space transition of multiple functional coordination degree was generally consistent with the cold-hot spot pattern. The regions with relatively long LISA time path were scattered in southern Jiangsu (Nanjing, Suzhou, Wuxi, Changzhou and Yangzhou). The regions with relatively short LISA time path were scattered in northern Jiangsu, central Jiangsu. The multiple functional coordination degree was relatively low in these areas.The county with the largest LISA time path curvature were Zhenjiang, Xinghua, Yancheng, Guanyun etc. Nevertheless, the curvature of LISA time path in most parts of central Jiangsu and northern Jiangsu was generally small. It can be seen that the path locking effect of development of multifunctional land use coordination degree was strong.

Natural Resource Rents, Inequality, and Terrorism in Africa

ABSTRACT This study examines the impact of natural resource rents on terrorism via inequality channel in 34 African economies, straddling the period 1980–2012. This study employs a negative binomial regression, in which the following findings are established: first, the unconditional impact of natural resource rents on terrorism is found to be positive across the model specifications, particularly when Gini and Theil indices are controlled for. Second, inequality has no discernable first-order impact on terrorism across the board. Third, the marginal impacts of interactions between inequality measures, specifically Gini and Theil coefficients and total natural resource rents on terrorism are significantly negative. Four, the corresponding net effects of interactions between natural resource rents and inequality (Gini and Theil coefficients) on terrorism are positive, thus lending support to earlier submission of involving all constitutive variables in the specifications for the parameters to make economic sense. The results are robust to accounting for fixed and country effects using the Poisson Pseudo maximum likelihood high-dimension fixed effects estimator. On the policy front, maintaining fairness and equity in the distribution of rents from the ‘free gifts of nature’ remains a veritable policy menu, especially for the resource-rich economies, to counteracting terrorist activities.

Value-added interregional inequality assessment of border regions in Russia

The economic development of Russian border regions is accompanied by a high level of interregional disparities both within the group of border regions and across the country. The purpose of the article is to assess the degree of interregional inequality of border regions in comparison with inner ones. Based on the key indicator of regional economic development, Gross Regional Product (GRP) per capita, various indicators of interregional inequality (coefficients of variation, the ratio of the maximum value to the minimum value, the Gini and Theil coefficients) were calculated, and σ-convergence and absolute β-convergence were estimated. Estimations of all indicators show that sharp changes in inequality, as well as the level of interregional inequality itself, are characteristic of border regions. For the interior regions, the dynamics are smoother, and the degree of inequality is significantly lower. This allows concluding that the main reason for the formation of interregional inequalities in Russia is inter-regional inequality in the group of border regions.

Wybrane koncepcje pomiaru nierówności dochodowych

Determining the level of income inequality requires the adoption of a specific measurement methodology. The aim of the study was to review and discuss the methodologies used to measure income inequality. Four measures are presented, each based on different assumptions. These measures were the Gini coefficient, Theil coefficient, Kukuła coefficient and unevenness coefficient. The first three measures, and in particular the Gini coefficient, are commonly described in the literature, while the unevenness coefficient is the author’s proposal for measuring income inequality. The empirical material for the research consists of data on the distribution of disposable income by decile groups in households in Poland for the years 2005–2017. The most important issue in practice regarding the measurement of income inequality was the transfer principle. Depending on the methodology adopted, the transfer of income is treated differently. The Gini, Theil and Kukula coefficients respond to any change in the income distribution, while the unevenness coefficient only to changes above the average. In a situation where the Gini coefficient (Theil and Kukula) decreases (increases), the level of inequality decreases (increases), but it is not known which transfers led to such a result. The decreasing (growing) unevenness coefficient means that these were transfers from groups with shares in income above (below) the average for groups with shares below (above) the average.

ASSESSMENT OF REGIONAL MORTGAGE INEQUALITY: COMPARATIVE ANALYSIS OF METHODS AND RESULTS

The article focuses on the study of the problems of assessing regional mortgage inequality in the Russian Federation. Based on an analysis of the experience of Russian and foreign economists and regionalists in the field of spatial inequality, possible approaches to assessing the disproportions in the regional mortgage market development are investigated. Calculations were carried out using the differentiation coefficient for extreme values of regions, fund coefficient for extreme groups of regions, log-normal distribution according to the 90/10 method, variation coefficient, Lorentz concentration coefficient and Gini index, Theil coefficient, as well as a differential indicator (author's development) of the development level of the housing mortgage lending system (HML) in the region and some other indicators. An economic interpretation of the calculation results is given. The significant variability of the various methods of estimation results is shown. On the basis of a comparative analysis of the approaches and their results, a substantiated method for assessing regional mortgage inequality was carried out. Statistical methods, including multivariate analysis, as well as expert assessment, were used as the methodological base of scientific research. The theoretical and practical significance of the study lies in substantiating the expediency of using the differential indicator proposed by the authors, characterizing the development level of the HML system in specific regions, to solve the problems of assessing the mortgage inequality of Russian subjects.