Balance Methodology Research Articles

Impacts of Climate Change on Rainfall ‘Seasonality Index’ and Its Potential Implications on Water Savings and Reliability through Household Rainwater Tanks

This study depicts potential climate change impacts on annual rainwater savings from household rainwater harvesting using two different climate projection models; ACCESS 1.0 and CSIRO-Mk3.6. This paper also investigates potential changes in the relationships of ‘water saving efficiency’ and reliability with rainfall ‘seasonality index’ under the mentioned climate change scenarios. The annual water savings were calculated for three weather conditions: dry, average, and wet. Historical daily rainfall amounts provided by the Australian Bureau of Meteorology were used for three locations within the city of Brisbane (Australia). For the same locations, projected future daily rainfall amounts were collected from an online data portal facilitated by the Australian government. Potential annual water savings, water saving efficiency, and reliability values for the selected locations were calculated through a widely used tool, eTank, developed on water balance methodology at a daily scale. It was found that for the coastal location, Manly, the future water savings are not likely to change significantly. However, for the inland location, Sunnybank, the future water savings are expected to decrease under all the weather conditions through both the considered climate projections. For the far inner location, Oxley, the water savings are likely to decrease in the dry year, whereas in wet year, they are likely to increase. Also, it was found that the overall average relationship of SI–water saving efficiency is steeper for ACCESS 1.0 projected data compared to that produced through CSIRO-Mk3.6 data, and that significant differences exist among individual relationships for each location. The overall reliabilities calculated through the model projected data show lower values compared to the reliabilities calculated using historical data.

When time is of the essence: ethical reconsideration of XAI in time-sensitive environments

The objective of explainable artificial intelligence systems designed for clinical decision support (XAI-CDSS) is to enhance physicians’ diagnostic performance, confidence and trust through the implementation of interpretable methods, thus providing...

Econometric forecasting of production matrix by total output in the framework of interindustry balance: Evidence from European firms

The paper is devoted to testing the core prerequisite of inter-industry balance methodology which states, that intermediate consumption is strictly proportional to the total output of an industry, or, in other words, production matrix is constant over time. The paper uses standard econometric and statistical tools to test the hypothesis of proportionality of intermediate consumption to the total output. The statistical base of the study is the world input-output tables collected for the period from 2000 to 2022 from European firms. By conducting a rigorous empirical analysis, we find that tested prerequisite cannot be considered as valid. We also propose a methodology of point forecasting of production matrices by total output, which helps to make more accurate forecasts compared to the trivial model, when production matrix is assumed to stay unchanged. The proposed methodology is to extend the input-output methodology by introducing the coefficients of proportionality of changes in intermediate consumption and changes in the total output. Proportionality coefficients are proposed to be obtained by regression equations based on statistical data of input-output tables. The results of this work can be useful for executive authorities in planning large state economic projects, as well as in assessing the impact of external shocks on the country's economy.

Is monopoly truly effective for producers?

PurposeThe paper is devoted to modeling a pricing policy of competitive firms in a “closed” economy framework.Design/methodology/approachThe proposed model can be regarded as an analog to CGE model and is based on the intersectoral balance methodology incorporating linear demand functions for goods and services.FindingsBy performing different model experiments, we show that a certain degree of competition can bring more profit to all competing firms, than in case of complete absence of such competition, what is also supported by empirical investigation. This finding implies that monopolies may perform worse than competitive firms, what contradicts with the modern provisions of economic theory, stating that monopoly is the most lucrative type of market structure for a producer. The discovered effect occurs due to the aggressive pricing policy, adopted by monopolies, spurring up the inflation spiral, which is most obvious if monopolies are strongly interdependent in terms of production matrix. This inflation spiral drives prices too high, what negatively reflects on firms’ costs and, consequently, results in monopolies receiving less profit.Originality/valueThe proposed model can also be useful for understanding and assessing various economic consequences after different external or internal shocks, what is especially crucial when conducting monetary or fiscal policy.

Investigating the Relationship Between the Dynamics of the Structure of Intermediate Consumption and Output of Industries: Cross-Country Analysis

This paper examines one of the main prerequisites of the inter-industry balance methodology on the proportional dependence of the output of industries and their intermediate consumption. The statistical base of the study is the world input-output tables collected for the period from 2000 to 2014. According to the results of the work, it was revealed that the prerequisite of a proportional change in the vector of intermediate consumption with a change in output is not consistent. However, significant positive relationships were found between intermediate consumption and the output of industries. On the basis of the identified relationships, we conducted a clustering procedure on industries, where, in particular, clusters with strong, medium, and weak relationships between intermediate consumption and output are distinguished. Thus, the paper proposes to expand the input-output methodology by introducing proportionality coefficients for changes in the intermediate consumption of an industry with a change in its output. Proportionality coefficients are proposed to be obtained on the basis of regression equations built on statistical data from the input-output tables. It should be especially noted that in addition to obtaining point forecasts of intermediate consumption for each industry when its output changes, it is also possible to obtain probability distributions for the gross output of each industry, their value added, wages, taxes, and profits through Monte Carlo simulations based on the constructed regression equations, which will facilitate a scenario analysis of the development of the considered economic system. The results of this work can be useful to executive authorities in planning large state economic projects as well as in assessing the impact of external shocks on the countryʼs economy.

Different Approaches to Enhance Critical Thinking in Digital Education

. This paper explores the integration of critical thinking (CT) in English language teaching, emphasizing its vital role in both academic and professional settings. Although critical thinking is frequently highlighted in curriculum guidelines, many English language teachers find it challenging to effectively teach these skills. This study reviews various approaches to CT instruction, including general, infusion, immersion, and mixed methods, and evaluates their effectiveness in enhancing students' critical analysis and reasoning abilities. It highlights how CT is linked to language proficiency and cultural context, noting the potential resistance due to traditional teaching methods and lack of materials. The discussion includes practical strategies for fostering CT in the ESL classroom, such as Socratic questioning, genre awareness, corpus analysis, and collaborative work. Additionally, the study examines simulation/game-based approaches and content-based instruction as effective methods for embedding CT in language learning. Results indicate that the infusion and immersion approaches are particularly successful in subject-specific CT development, while the mixed approach provides a balanced methodology. The paper concludes by stressing the importance of teacher training in CT and the necessity of creating a supportive learning environment to reduce student anxiety. By fostering critical thinking, ESL educators can better prepare students to navigate complex academic and professional landscapes, ensuring their long-term success. This study underscores the need for a nuanced understanding of CT and the careful scaffolding of language to enhance students’ analytical skills.

Assessing Food Loss and Waste in Chile: Insights for Policy and Sustainable Development Goals

The Food and Agriculture Organization (FAO) reports that Latin America and the Caribbean experienced the most rapid rise in food insecurity, with approximately 47.7 million individuals in the region affected by hunger in 2022. In Chile, almost three million people, 15.6% of the country’s population, do not have regular access to sufficient nutritious food and suffer from some form of food insecurity. Moreover, Chile is particularly susceptible to the impacts of climate change and contends with the depletion of several critical natural resources, notably water, stemming from severe and prolonged drought conditions. This article aims to comprehensively evaluate food loss and waste (FLW) in Chile and analyzes the implications of FLW on the sustainable development goal (SDG). This will be achieved by utilizing a top-down mass balance methodology that integrates various data sources and an in-depth analysis of the main food categories at different stages of the food supply chain (FSC). In 2021, Chile generated 5.18 million tons of FLW, with fruit being the largest contributor at 2.5 million tons (48% of total FLW). Vegetables accounted for 0.8 million tons (16%). Other food groups each contributed 1% to 10% of the total FLW. Per capita FLW was 295 kg. FLW varied by food group and FSC stage. Fruits, vegetables, starchy roots, and pulses had the most FLW early in the FSC, while cereals had it later. Comparing FLW with the domestic supply quantity of food, it is observed that 68% of the fruit available for the population was discarded. Vegetables and starchy roots also showed significant volumes of discarded food, with 48% and 29% of the availability of these products in Chile. Furthermore, we explore the implications of FLW on realizing SDG 2—zero hunger, particularly emphasizing its correlation with target 12.3. The research underscores the potential of its findings to significantly shape public policies and strategies concerning FLW and their alignment with the associated SDGs, making a tangible impact on the lives of millions.

Miller Fisher's Rules and Digital Health: The Best of Both Worlds.

Professor Fisher's legacy, defined by meticulous observation, curiosity, and profound knowledge, has established a foundational cornerstone in medical practice. However, the advent of automated algorithms and artificial intelligence (AI) in medicine raises questions about the applicability of Fisher's principles in this era. Our objective was to propose adaptations to these enduring rules, addressing the challenges and leveraging the opportunities presented by digital health. The adapted rules we propose advocate for the harmonious integration of traditional bedside manners with contemporary technological advancements. The judicious use of advanced devices for patient examination, recording, and sharing, while upholding patient confidentiality, is pivotal in modern practice and academic research. Additionally, the strategic employment of AI tools at the bedside, to aid in diagnosis and hypothesis generation, underscores their role as valued complements to clinical reasoning. These adapted rules emphasize the importance of continual learning from experience, literature, and colleagues, and stress the necessity for a critical approach toward AI-derived information, which further consolidates clinical skills. These aspects underscore the perpetual relevance of Professor Fisher's rules, advocating not for their replacement but for their evolution. Thus, a balanced methodology that adeptly utilizes the strengths of AI and digital tools, while steadfastly maintaining the core humanistic values, arises as essential in the modern practice of medicine. A commitment between traditional medical wisdom and modern technological capabilities may enhance medical practice and patient care. This represents the future of medicine - a resolute commitment to progress and technology, while preserving the essence of medical humanities.

Firms' sustainable and responsible innovation in Nigeria: Does informality play a role?

AbstractAt the turn of the millennium, developing countries face a twofold challenge. First, for reasons related to both intra‐ and inter‐generational justice, these countries need to follow sustainable development pathways. Second, they need to understand the deep principles underpinning informality, which is by now recognized as a structuring phenomenon of their economies. This paper sheds light on the relationship between these two goals by investigating how a Nigerian firm being formal versus informal affects its sustainable and responsible innovation (S&RI) activity. Using the entropy balancing methodology to analyze a novel database extracted from the Nigerian Business Innovation Surveys, we find that registered Nigerian firms engage in S&RI much more than those that are not. This suggests that, from the perspective of sustainable development, there should be no hiatus between recognizing the important role of the informal sector in the economy and promoting policies that give firms incentives to exit from it. By encouraging the registration of firms operating informally, these policies should help Nigeria in its transition to a sustainable market economy.

Carbon budget of diversified cropping systems in southwestern China: Revealing key crop categories and influencing factors under different classifications

Cropping systems are considered the largest source of agricultural GHG emissions. Identifying key categories and factors affecting cropping systems is essential for reducing these emissions. Most studies have focused on the carbon budget of cropping systems from the perspective of a single crop or crop category. Comprehensive studies quantifying the carbon budget of diversified cropping systems, including farmland and garden crops, are still limited. This study aims to fill this gap by quantifying the carbon budget of diversified cropping systems, clarifying their carbon attributes, and identifying key crop categories and influencing factors within different classifications of the system. This study analyzed the carbon budget of a diversified cropping system consisting of 19 crops in Yunnan Province, southwestern China, using a crop-based net greenhouse gas balance methodology based on the “cradle-to-farm” life cycle idea. Crops were categorized into three levels of categories to assess the potential impact of categorization within the cropping system on its carbon balance. Results showed that Yunnan's diversified cropping system is a significant carbon sink, with net sequestration of 33.1 Mt CO2 eq, total emissions of 37.4 Mt CO2 eq, and total sequestration of 70.5 Mt CO2 eq. Cereals, vegetables, and hobby crops were the main contributors to carbon emissions, accounting for 41.61%, 21.87%, and 15.37%, respectively. Cereal crops also made the largest contribution to carbon sequestration at 53.18%. Bananas had the highest emissions per unit area (11.45 t CO2 eq ha−1), while walnuts had the highest sequestration (20.64 t CO2 eq ha−1). In addition, this study highlights effective strategies to reduce greenhouse gas emissions, such as reducing nitrogen fertilizer use, minimizing reactive nitrogen losses, and controlling methane emissions from rice fields. By elucidating the impact of carbon dynamics and crop categories, this study provides insights for sustainable agricultural practices and policies.

Estimation of Piston Surface Temperature During Engine Transient Operation for Emissions Reduction

Abstract Piston surface temperature is an important factor in the reduction of harmful emissions in modern gasoline direct injection (GDI) engines. In transient operation, the piston surface temperature can change rapidly, increasing the risk of fuel puddling. The prediction of the piston surface temperature can provide the means to significantly improve multiple-pulse fuel injection control strategies through the avoidance of fuel puddling. It could also be used to intelligently control the piston cooling jet (PCJ), which is common in modern engines. Considerable research has been undertaken to identify generalized engine heat transfer correlations and to predict piston and cylinder wall surface temperatures during operation. Most of these correlations require in-cylinder combustion pressure as an input, as well as the identification of numerous model parameters. These requirements render such an approach impractical. In this study, the authors have developed a thermodynamic model of piston surface temperature based on the global energy balance (GEB) methodology, which includes the effect of PCJ activation. The advantages are a simple structure and no requirement for in-cylinder pressure data, and only limited experimental tests are needed for model parameter identification. Moreover, the proposed model works well during engine transient operation, with maximum average error of 6.68% during rapid transients. A detailed identification procedure is given. This and the model performance have been demonstrated using experimental piston crown surface temperature data from a prototype 1-liter 3-cylinder turbocharged GDI engine, operated in both engine steady-state and transient conditions with an oil jet used for piston cooling turned both on and off.

Machine intelligence based early prediction methods for Cardiovascular Diseases (CVDs)

Exploring early indications of heart disease is challenging in today's society. It may lead to death if not detected early. Early identification of cardiac disease in remote, rural, and semi-urban locations in developing nations can be greatly aided by an accurate decision support system (DSS). To assist in heart-related disease detection at an early stage, this system offers a DSS which uses artificial learning techniques using the patient's clinical details. A hybridized class balancing methodology using the chi2 feature selection algorithm, random oversampling, and undersampling techniques were used to find the suitable features from the presented dataset. Standard scalar approaches have also been utilized for data preprocessing. In the last stage of developing the suggested Artificial Intelligence (AI) system, the system used support vector machines (SVM), naive Bayes, random forest classifiers, logistic regression, and improved Artificial Neural Network (ANN) classifiers. Tests were conducted on the Python-based simulation environment to evaluate the proposed system. Evaluation of the system is completed using the Cleveland heart disease dataset by the machine repository at UCI. A 96.74% accuracy rate was reached, which is better than some previously published models for heart-related disease prediction.

Machine intelligence based early prediction methods for Cardiovascular Diseases (CVDs)

Exploring early indications of heart disease is challenging in today's society. It may lead to death if not detected early. Early identification of cardiac disease in remote, rural, and semi-urban locations in developing nations can be greatly aided by an accurate decision support system (DSS). To assist in heart-related disease detection at an early stage, this system offers a DSS which uses artificial learning techniques using the patient's clinical details. A hybridized class balancing methodology using the chi2 feature selection algorithm, random oversampling, and undersampling techniques were used to find the suitable features from the presented dataset. Standard scalar approaches have also been utilized for data preprocessing. In the last stage of developing the suggested Artificial Intelligence (AI) system, the system used support vector machines (SVM), naive Bayes, random forest classifiers, logistic regression, and improved Artificial Neural Network (ANN) classifiers. Tests were conducted on the Python-based simulation environment to evaluate the proposed system. Evaluation of the system is completed using the Cleveland heart disease dataset by the machine repository at UCI. A 96.74% accuracy rate was reached, which is better than some previously published models for heart-related disease prediction.

Natural attenuation of dissolved petroleum fuel constituents in a fractured Chalk aquifer: contaminant mass balance with probabilistic analysis

A plume-scale mass balance is developed to assess the natural attenuation (NA) of dissolved organic contaminants in fractured, dual-porosity aquifers. This methodology can be used to evaluate contaminant distribution within the aquifer, plume source term, contaminant biodegradation and plume status. The approach is illustrated for a site on the UK Upper Chalk aquifer impacted by petroleum fuel containing methyl tert -butyl ether (MTBE) and tert -amyl methyl ether (TAME). Variability in site investigation data and uncertainty in the mass balance were assessed using probabilistic analysis. The analysis shows that benzene, toluene, m/p-xylene and o-xylene (BTEX) compounds are biodegraded primarily by denitrification and sulfate reduction in the aquifer, with an equivalent plume-scale first-order biodegradation rate of 0.49 a −1 . Other biodegradation processes are less important. Sorption contributes to hydrocarbon attenuation in the aquifer but is less important for MTBE and TAME. Uncertainty in the plume source term and site hydrogeological parameters had the greatest effect on the mass balance. The probabilistic analysis enabled the most likely long-term composition of the plume source term to be deduced and provided a site-specific estimate of contaminant mass flux for the prediction of plume development. The mass balance methodology provides a novel approach to improve NA assessments for petroleum hydrocarbons and other organic contaminants in these aquifer settings. Supplementary material: Plume organic chemistry, aquifer hydraulic conductivity and probability distribution functions used for mass balance inputs are available at https://doi.org/10.6084/m9.figshare.c.7016429 Thematic collection: This article is part of the Monitoring the aquifers collection available at: https://www.lyellcollection.org/topic/collections/monitoring-the-aquifers

Modeling Price Policy of Industries within the Frames of Inter-Sectoral Balance Methodology

The subject of inter-sectoral balance has been adequately developed in scientific community, however, we think that insufficient attention was paid to inserting price rates of industries in the model of inter-sectoral relations, as they produce a serious impact on making decisions concerning each industry production. The article deals with working-out a mathematic approach to modeling industry behavior in view of price policy development. The theoretical base of the research was formed by key provisions of the economic theory and the theory of inter-sectoral balance. The authors analyze dynamics of product prices set by industries with regard to inter-sectoral dependence and respective changes in output, added value, margin and volumes of sold products in each industry. As a result of the research the inter-sectoral model of shaping price policy of industries was built, which takes into account parameters of demand and urge of every industry for maximum profits. Different scenarios of changing demand parameters and production interactions were studied. Model experiments allowed authors to come to the conclusion that a rise in demand for one industry product cannot always increase production of related industries. As product price in the industry goes up, costs of other industries also rise, which can have an adverse effect on their product demand on the part of end customer. And on the contrary, a drop in demand for one industry product can support other related industries on constant demand factors at the expense of cost cutting. The proposed model could be useful for private and state sectors in planning investment policy at the same time it could reveal macro-economic trends, which is necessary to resolve problems of tardy or inadequate response of business entity on possible risks.

Optimization of systems of industrial ventilation – case study

Industrial ventilation involves a deep knowledge of fluid dynamics, in the modern computerized version, three-dimensional heat propagation, complex fluid flow, equilibrium state and transient conditions, operating problems, contamination inside or outside the installations, etc. Industrial buildings generally comprise large spaces with varied sources of damage. The type of sources and their location depend on the technological process in each section. In order to dilute the harms, to ensure the environmental conditions necessary for the protection of the work and the realization of the microclimate required by the production process, through the industrial ventilation installations large air flows are carried. The design of the ventilation systems in the industrial sections requires a thorough knowledge of the technological process, of the machines and their location, the nature and quantity of the damages, the environmental conditions required from the technological point of view and of the labor protection.. In order to optimize the industrial ventilation systems, an industrial ventilation installation for exhausting and neutralizing VOC emissions was chosen. The optimization of the ventilation installation was possible with the help of the pressure loss balancing methodology.

패션물류센터의 효율적인 B2C 작업 방식에 대한 연구

The fashion industry consists of textile industries that produce and sell textile raw materials clothing industries that manufacture and sell clothing distribution industries that sell fashion consumer goods fashion information fashion publishing and fashion consulting. As the fashion industry gradually moves from offline to online transactions at online shopping malls in the fashion county rose 3.3 percent year-on-year to 49.8158 trillion won in 2022, according to the Seoul Investment Administration. In other words through the pandemic due to COVID-19, the restructuring of fashion distribution channels has accelerated reducing offline distribution networks and rapidly growing online distribution networks. ssfshop, LF, Kolon FnC, and E-Land Group are strengthening their online distribution networks and marketing while Musinsa, W Concept, Zigzag, Able and Brandy which are called the online fashion platform “Big 5,” have “stormous growth.” In this way with the acceleration of online distribution in the fashion industry logistics centers are also establishing an online-only system. However unlike B2B fashion logistics B2C fashion logistics is not easy to achieve logistics operation optimization through automated facilities (Auto Sorter, Digital Picking System (DPS). The reason for this is that B2C fashion logistics manually performs a series of processes such as single/multiple picking of individual units invoice output packaging invoice attachment and delivery preparation according to individual orders of consumers. In addition B2C Fashion Logistics requires work speed and efficiency for rapid and accurate order processing coping with seasonal shipping demand packaging accuracy of skilled personnel and rapid return processing. A study on the efficient working method of B2C fashion logistics was published in 2022 through a study on how to streamline the B2C process of shoe logistics applying the Line Balance methodology. However there was a limit to the fact that the previous paper was not able to conduct comparative analysis with various companies by research and analysis centered on Company A. Therefore this study aims to expand line balancing research in 2022 to introduce the “Flow Shop and Job Shop Method,” which is a work process method and to compare and study work efficiency by applying the Flow Shop and Job Shop methods, focusing on the B2C tasks of Fashion Logistics Center A and B. In addition, this study differs from the meaning of mutual comparison and research on ways to increase work productivity by applying the Flow Shop and Job Shop methods of logistics centers to revitalize online channels in the fashion industry, focusing on the cases of Company A and B.

Water Balance, Climate Classification and Empirical Distribution of the Probability of Rain for the Region of Tangará da Serra – MT

Objective: The present work aimed to describe the Climatological Water Balance (BHC), carry out the Climate Classification (CC) and evaluate the Empirical Probability (EP) of rainfall occurrence for the region of Tangará da Serra – MT. Theoretical Frame: This study used the following main citations as a theoretical basis: National Water Agency (ANA) and National Institute of Meteorology (INMET), source of climate data; Thornthwaite & Mather (1955) for the climatological water balance methodology; Thornthwaite (1948) and Koppen (1900) for climate classification; and Thom (1966) for the methodology of probability of occurrence and return period of rain. Method: To carry out the study, a series of 31 years of climate data was used, covering the period from 1989 to 2019, obtained from the National Water Agency (ANA) and the National Institute of Meteorology (INMET). Filling gaps in the data was carried out using the Regional Weighting method, according to the methodology of Mello et al. (2017). The climatological water balance was estimated according to Thornthwaite & Mather (1955). The climate classification was obtained using the methodologies of Thornthwaite (1948) and Koppen (1900). The probability of occurrence and the period of return of rain were achieved using the methods of Thom (1966). Results and Conclusions: Through studies carried out, it was found that the average annual precipitation in Tangará is 1603 mm and the average annual air temperature is 24.5 °C. The months of May, June, July, August and September present a water deficit, while in the months of December, January, February, March and April there is a water surplus. According to Thornthwaite's methodology, the region has a humid climate, with moderate water deficiency in winter, megathermal and with potential evapotranspiration in the summer less than 48% of the annual potential evapotranspiration, whereas according to Koppen's methodology, the location was classified as megathermal or tropical with drought in the winter. The probability of rainfall less than the maximum average of 47.7 mm is 93.3% and its return time is approximately 15 years. Implications of the research: This research is classified as of great importance for the purposes of the Department of Agricultural Engineering of the State University of Mato Grosso, Campus of Nova Mutum and Tangará da Serra - MT, as well as for the entire academic and scientific community and interested rural producers. in climatic data, water balance and climate of the studied region. Originality and value: This study aimed to show the academic and scientific community and rural producers the behavior of climatic variables throughout the year in the municipality of Tangará da Serra – MT, where this information can be used for agricultural planning in the region and also serve as a basis for future studies.

Balance™ methodology – converting carbon finance to biodiversity creation

ABSTRACT This paper addresses two interlinked problems in sustainable development and suggests a methodology to resolve them. The first is the reduction of atmospheric greenhouse gas emissions, especially carbon dioxide. The second is the maintenance of biodiversity. Current carbon financing and environmental stewardship mechanisms underwhelm, often diluting intended positive effects. Most existing carbon credits do not have protection after 40 years, placing projects substantially beneath the C02 radiative forcing cycle. This paper presents the ‘Balance’ approach to sustainable development, including contractual principles ensuring C02 reduction, biodiversity enhancement and financial accountability. We describe two novel measures: a carbon calculator for commercial entities, and a new metric, the Balance Unit, combining biodiversity creation with carbon credits. A case study, spanning over 20 years at the Forest of Marston Vale, is then presented. It finds an increase in tree cover, CO2 sequestration, reduction in agricultural GHG emissions, sulphur dioxide and particulate matter absorption, and annual local economic benefits totalling £UK12.83 million. Expository detail regarding the ‘Planting Principles’ practised at Marston Vale is also provided. We argue that the Balance methodology, especially the Balance Unit, enables greater measurement reliability and long-term efficacy for maintaining biodiversity and reducing GHG emissions than current carbon financing approaches.

An estimation of reservoir pressure dynamics and recoverable gas reserves based on the data of production well surveys

In the development of gas fields, reservoir pressure can be used to estimate gas reserves by means of the material balance methodology and to calculate drained reserves. This provides an indication of the effectiveness of the development system. The authors of the article suggest a methodology for determining drained reserves in wells without reservoir pressure measurements. This approach is useful if the well stock is put into operation concurrently or within a brief period of time. Numerical modelling and analysis of field development data have led the authors to conclude that the current flow rate of wells, the hydrodynamic parameters of the productive formation and the interference of nearby wells are the most significant factors controlling the amount of drained reserves. The use of this technique will improve the understanding of the current processes occurring in the reservoir, clarify the assessment of the energy of the deposit and the value of the initial gas reserves, and also create an additional source of information for timely and more accurate decision-making in field development. Adopting this methodology can improve the understanding of ongoing processes in the reservoir, enhance evaluations of reservoir energy and initial gas reserve sizes, and provide additional information resources for making decisions on field development in a more timely and precise manner.