Forecast Scenarios Research Articles

Clustering Technique Suitable for Eulerian Framework to Generate Multiple Scenarios from Ensemble Forecasts

Abstract In operational weather forecasting, it is effective to aggregate information on all members of an ensemble forecast through cluster analysis. The temporal coherence of ensemble members in each cluster is an important piece of information about the robustness of the forecast scenario given by clusters. This information is especially important for forecasts for which the target area is a city or prefecture, that is, an Eulerian framework, because the members that compose each cluster can change over time because of the small size of the target area. This study provided the temporal coherence of members in clusters by performing principal component analysis and cluster analysis on 3-hourly 500-hPa geopotential height forecasts and linking the clustering results in the time direction. The new method provided a consistently well-divided forecast scenario throughout the forecast period for Eulerian frame forecasts, as well as information on the temporal coherency of the members in the clusters, which was demonstrated to be effective through the experiment to preselect a cluster with small errors. The application of the new technique to improve precipitation forecasts was also discussed. Significance Statement Numerical weather forecasts always contain errors. Although the uncertainty of such forecasts cannot be obtained from the forecast itself, ensemble forecasts, which are aggregates of many forecasts, can be used to estimate the uncertainty of the forecast. In this study, a new method was developed to transfer the information contained in many ensemble forecasts into four forecasts by cluster analysis and to provide forecast information suitable for a small forecasting area such as a prefecture. The use of this method for improving precipitation forecasts was also examined.

Assessment of the Profitability Indicators of Armenian Commercial Banks and the Forecast of Possible Scenarios to Increase Profitability

Purpose: The main purpose of the study is the comparative analysis of total capital, total assets, net profit, return on assets (ROA), return on equity (ROE) and net interest margin (NIM) of 17 commercial banks operating in Armenia for several years are presented, the advantages and disadvantages of the banks with high and low results in the above-noted indicators have been briefly commented on. Theoretical framework: Currently, the banking system of Armenia is the most developed one in comparison with other sectors of the economy, as the processes of attracting deposits, providing loans and fulfilling the standards set for commercial banks are regulated in line with the world experience. The object of study is operational environment of the banks. Design/methodology/approach: The profitability indicators are considered to be the most basic tool for assessing the efficiency of operations carried out by commercial banks. Methodological tools of the study methods were interest income, interest expense and average annual return on profitable assets, which were analyzed separately. Findings: The relevance of this scientific problem decision is that the negative consequences the COVID-19 pandemic within the business environment of the Armenian banking system have been studied. Research, Practical & Social implications: In the context of new economic relations, the banking system holds an important part in ensuring the economic growth and economic development of any country, including the Republic of Armenia. Possible ways to increase the profitability of the Armenian banking system were suggested in this study, the existing challenges of the banking system were identified. Originality/value: Based on the results of the research of the profitability indicators of the RA banking system and the conclusions drawn, several ways of increasing the profitability of banks were proposed.

Forecasting the dynamics of indicators of economic development of Russian regions, taking into account the level of balance of innovation processes

Aim. Development of scenario forecasts of the dynamics of indicators of regional development, taking into account the level of balance of innovation processes. Tasks. To determine the factors of balanced economic and innovation development of the regions; to form a matrix of balanced innovation development; to work out a methodology for calculat­ing the integral index of balanced innovation development of the regions; to analyze the dynam­ics of the index of balanced innovation development of the signal regions of the Volga Federal District (VFD); to prepare scenario forecasts of the index of balanced innovation development of the signal regions of VFD. Methods. The author used general scientific and special research methods, taking into account their cognitive potential. The latter include correlation and regression, discriminant and clus­ter types of analysis, content analysis, graphic representation, as well as methods of individu­al and collective expert evaluations. SPSS Statistics software product was used during statisti­cal data processing. Results. The proposed methodology for calculating the index of balanced innovation development is based on the use of ideas about the internal factors of balance, which include the compliance of economic systems with the signs of stability and equilibrium, as well as the proportionality of changes in its elements. Conclusions. The methodology substantiated by the author complements the analytical tools for studying the patterns of innovation development by including the indicators of the level of balance and their impact on the dynamics of economic indicators. The obtained conclusions can be used to refine the forecasts of indicators of economic development of the regions, taking into account the state of innovation processes.

Hybrid Wars in the Macroeconomic Supersystem of the XXI Century

We do care about what the foreseeable future will be… The recent period has clearly shown that expectations of W estern experts regarding the planned collapse of Russian and Chinese economies have not materialized. The ar ticle analyzes the main pressure tools and strategies of the main global politics actors on peripheral countries in order to solve the accumulated problems associated with increased debts and the need for fur ther trade expansion. Aggregated forecast scenarios for the situation development in relation to the GDP growth/fall of the leading countries of the world are structured . The authors’ calculations proved that, despite the risks and threats of fur ther sanctions escalation and other restrictions, the implemented macroeconomic policy of Russia and China ensures preser vation of their key socio-economic indicators, providing national sovereignty.

INTERRELATION SYSTEM OF TRANSFORMATIONAL PROCESSES OF AGRICULTURAL ECONOMIC STRUCTURES AND PROBLEMS OF SUSTAINABLE FOOD SECURITY

The relevance of the problem under study is connected with the identification of the relationship between the transformation processes of agrarian structures and issues of sustainable food security; with the definition of contradictions and uncertainties of the harmonious interaction of different forms of management formed in the process of transformation; revealing their functional differences; typology and scenario forecasts of development. The dualistic nature of the results of the transformation of the main forms of management in the system of the national economy is substantiated.
 Purpose. Identification of key aspects of the specifics of the results of transformational processes of economic structures in the agrarian economy of the Russian Federation and substantiation of the importance of a harmonious relationship with each other in accordance with the tasks of food security in the face of modern challenges.
 Materials and methods. The methodology of an integrated approach was used based on the systematic approach of Western and Russian researchers, as well as on the methodology of small economic structures Chayanova A.V., Kondratieva N.D. Methods of comparative economic analysis using the data of official statistics of the Russian Federation and FAO, as well as methods of visualizing the results of the study are involved. The case method was used to study the specifics of various economic structures, and the scenario modeling method was used to determine the trends for further development.
 Results. Based on a comparative analysis of the results of the market transformation of economic structures and the economic effects of their activities, it was revealed that: firstly, the dynamics and rates of food inflation in the system of the Russian economy are lower than the global ones; secondly, large economic structures prevail in the structure of production and rates of output of food products; thirdly, the predominance of market power on the part of large economic structures leads to the deformation of market relations and the need to find a solution to the problem of creating equal opportunities for large and small economic structures.
 Conclusion. The importance of creating a model of harmonious relations between different forms of management is substantiated. Scenario forecasts of possible trends in the further development of small and large economic structures have been made.

Forecasting the demographic situation in single-industry towns of the Russian Federation

Subject. This article explores the problem of labor resources in single-industry towns of Russia. Objectives. The article aims to develop forecast scenarios for changes in the demographic situation in single-industry towns of Russia. Methods. For the study, we used statistical, regression, and scenario analyses. Results. The article finds that various factors contribute to the growth of the population of single-industry towns, such as the expansion of housing construction, increasing the availability of health services, the development of trade, etc. Conclusions. The development of Russia's single-industry towns requires long-term investment in production, and diversification of the economy is also necessary.

Application of Scenario Forecasting Methods and Fuzzy Multi-Criteria Modeling in Substantiation of Urban Area Development Strategies

The existing approaches to supporting the tasks of managing the urban areas development are aimed at choosing an alternative from a set of ready-made solutions. Little attention is paid to the procedure for the formation and analysis of acceptable options for the use of territories. The study's purpose is to understand how various factors affect the efficiency of using the city’s territory. In addition, we are trying to use this understanding to assess the possible consequences of the implementation of management decisions on the territory transformation. We use the method of structuring knowledge about the study area, taking into account the influence of the external environment. This method implements the significant factors list formation and assessment of their impact on development. Fuzzy cognitive modeling was used to build scenarios for identifying contradictions in achieving sustainable development goals. The scenario modeling results are necessary for the formation of the alternative. Alternatives are evaluated on the basis of fuzzy multi-criteria optimization. The integration of methods makes it possible to increase the objectivity of the analysis of strategies for urban areas development. The Belman-Zadeh method is used to analyze the selected options based on criteria that determine the feasibility and effectiveness of each project.

ПРОГНОЗУВАННЯ ОБСЯГІВ ВИДОБУТКУ ВУГІЛЛЯ В УКРАЇНІ

A software subsystem for the formation of forecasts of coal production in Ukraine has been developed under the conditions of instability of the structure of the country's fuel bases, which arise as a result of force majeure factors, in particular, martial law, temporary occupation of the territory, destruction of mining enterprises, etc. The program was created with the aim of providing opportunities for a quick assessment of the impact of emergency situations in the country on production volumes in the coal industry and their further consideration during developing forecasts of the fuel supply of the economy, and the energy balance of the country as a whole. The software is created on the basis of databases detailed by coal mining facilities, and professional forecasts of the development of the coal industry, which take into account the mining and geological, technological, economic, and social features of the functioning of mines, as well as possible scenarios of their technological conversion, optimization of production areas and allows to make such forecasts by changing the expected production state of the mine upon the occurrence of force majeure circumstances. The developed forecast adjustment subsystem enables change quickly the structure and composition of the country's coal base; calculate the aggregate volumes of coal production, differentiated by grades of coal and its technological purpose and according to several scenarios of the technological development of the coal industry, which will take place under selected changes in the structure of the mine fund; form general production forecasts, which contain calculated aggregate volumes for all terms of the forecasting period. With the loss of all mines in the Luhansk region and some mines in the Donetsk region as a result of the full-scale invasion of Russia into Ukraine, the forecast scenarios of the development of the coal industry of Ukraine for the period until 2040 were adjusted using the software subsystem for forecasting the volume of coal production. Keywords: coal industry, software subsystem, forecasting, mining, development scenarios.

Evaluation of the economic implications of varied pressure drawdown strategies generated using a real-time, rapid predictive, multi-fidelity model for unconventional oil and gas wells

Experience has suggested that pressure maintenance in hydraulically fractured reservoirs via lower, more sustained production drawdowns may offer improved cumulative recovery and overall resource extraction efficiency compared to more rapid drawdown approaches aimed at generating high initial production. However, given the inherent variability of oil and natural gas markets, operators pursue production strategies that maximize profitability over resource extraction efficiency. This study focuses on evaluating the implications of contrasting pressure drawdown strategies on the long-term production and resulting economics for a real, producing unconventional gas well in the Marcellus Shale of the Appalachian Basin using a techno-economic analysis approach. Our research combines elements of well-specific horizontal well design, production forecasting, equipment sizing and capital cost estimation, operating cost estimation, and revenue and tax calculations. Gas production forecast outlook scenarios were generated under varying pressure drawdowns using two approaches: 1) a novel physics-informed machine learning workflow and 2) traditional reservoir simulation. A discounted cash flow model was used to evaluate the resulting economic implications for each drawdown scenario—generating output for exploring the coupled effect of factors like the timing and volume of gas production, prevailing economic and market conditions for natural gas, and overall estimated ultimate recovery on profitability metrics such as internal rate of return and net present value. Results show that there is potential to maximize the cumulative gas produced in the specific case study well by employing a lower pressure drawdown. Conversely, the greatest profitability is achieved using rapid drawdown as signified by a small, specific subset of our outlook scenarios. On an averaging basis, we find that the combinations of highest cumulative producing and most profitable scenarios occur under lower drawdowns with long (>40 years) producing timeframes, but require higher relative gas price and lower discounting considerations. The machine learning predictive outlooking capability proved effective for enabling rapid generation of a multitude of scenario forecasts. As a result, a variety of prominent example cases could be generated to strike the balance of greater productivity and economic return given their associated producing features and economic conditions when compared to similar producing scenarios—critical insight that offers improved decision support for unconventional oil and gas operations.

The Use of Ensemble Clustering on a Multimodel Ensemble for Medium-Range Forecasting at the Weather Prediction Center

Abstract This article introduces an ensemble clustering tool developed at the Weather Prediction Center (WPC) to assist forecasters in the preparation of medium-range (3–7 day) forecasts. Effectively incorporating ensemble data into an operational forecasting process, like that used at WPC, can be challenging given time constraints and data infrastructure limitations. Often forecasters do not have time to view the large number of constituent members of an ensemble forecast, so they settle for viewing the ensemble’s mean and spread. This ignores the useful information about forecast uncertainty and the range of possible forecast outcomes that an ensemble forecast can provide. Ensemble clustering could be a solution to this problem as it can reduce a large ensemble forecast down to the most prevalent forecast scenarios. Forecasters can then quickly view these ensemble clusters to better understand and communicate forecast uncertainty and the range of possible forecast outcomes. The ensemble clustering tool developed at WPC is a variation of fuzzy clustering where operationally available ensemble members with similar 500-hPa geopotential height forecasts are grouped into four clusters. A representative case from 15 February 2021 is presented to demonstrate the clustering methodology and the overall utility of this new ensemble clustering tool. Cumulative verification statistics show that one of the four forecast scenarios identified by this ensemble clustering tool routinely outperforms all the available ensemble mean and deterministic forecasts. Significance Statement Ensemble forecasts could be used more effectively in medium-range (3–7 day) forecasting. Currently, the onus is put on forecasters to view and synthesize all of the data contained in an ensemble forecast. This is a task they often do not have time to adequately execute. This work proposes a solution to this problem. An automated tool was developed that would split the available ensemble members into four groups of broadly similar members. These groups were presented to forecasters as four potential forecast outcomes. Forecasters felt this tool helped them to better incorporate ensemble forecasts into their forecast process. Verification shows that presenting ensemble forecasts in this manner is an improvement on currently used ensemble forecast visualization techniques.

Organization of effective management of the stock market on the basis of researching the processes of formation of the value of shares of the issuer companies

The global economic crisis and the socio-economic consequences of the COVID-19 pandemic have a significant impact on the increase in volatility and risks of manipulation of stock asset quotes. The subject of the study is the organization of management of the investment fund process, taking into account non-market mechanisms for manipulating the value of shares of issuing companies. The purpose of the article is to improve the efficiency of managing the stock process based on the stock market model and procedures for fuzzy valuation of the value of shares of issuing companies within this model. The research methodology is based on the application of methods for analyzing economic phenomena and processes, a systematic approach to studying the development of issuing companies and stock markets. Modeling of fund processes is based on fuzzy logic theory and efficiency theory. The principles of stock market idealization and the principles of stock market management in a manipulated information environment are formulated. A model of the stock market is presented, which includes: an ideal model of the stock market, a model of fundamental disturbing factors and a model of stock market manipulation. Within the framework of the stock market model, an economic and mathematical model for estimating the value of shares of issuing companies has been developed, in which the uncertainty of parameters is described by fuzzy numbers. The novelty of the research lies in the formulated concept of factor psychodynamics (including a list of factors, functions of factors, the strength of factors and inertia of factors), which serves as the basis for the stock market model; as well as in the developed methodology for fuzzy valuation of shares of issuing companies. Participants of the stock market, potential investors, owners and acquirers of companies on the basis of the presented model of the stock market have the opportunity to obtain additional information about ongoing stock processes. Fuzzy procedures for estimating the value of shares of issuer companies make it possible to calculate the value of their shares in accordance with various forecast scenarios for the development of companies.

Assessment of the Impact of Technological Development and Scenario Forecasting of the Sustainable Development of the Fuel and Energy Complex

The beginning of the 21st century for the fuel and energy complexes of most countries is characterized as a period of active restructuring and a fundamental shift in developmental priorities. The basis of these changes is technological development. Industry 4.0 technologies have particular importance in achieving maximum optimization of production processes. In the same way, they are applicable in establishing effective interaction between the energy sector and other sectors of the economy. The authors outline an approach to assessing the country’s fuel and energy balance state through the selected properties: sustainability, accessibility, efficiency, adaptability and reliability. Hence, a model of the fuel and energy complex was created on the example of the Russian Federation, considering the country’s territorial and functional division. The methodology is based on scenario modeling of the influence level of external challenges in conjunction with the accompanying technological development. The mathematical model allowed forecasting changes in the properties of the energy system. The scientific significance of the work lies in the application of a consistent hybrid modeling approach to forecast the state of the fuel and energy balance. The results of the study are useful in compiling scenarios for the regional and entire development of the fuel and energy complex. Further model improvements should include an expansion of the number of counted industries and their relations.

Assessing the investment potential of issuing companies in conditions of the stock market dynamics

Subject. Based on the developed procedure for fuzzy valuation of shares of issuing companies, the article considers topical issues related to forecasting their investment potential in conditions of stock market dynamics. Objectives. The purpose is to assess the investment potential of issuing companies for various scenarios of their development. Methods. The study rests on methods of analysis of economic phenomena and processes, systems approach to reviewing the development of issuing companies, estimation and prediction of their economic activities. It applies the fuzzy logic theory and the efficiency theory, the method of analogies, statistical processing of factual material, the comparative and expert analysis. Results. The developed methodology includes the formation of scenarios for companies’ development based on their financial and economic projections in the form of fuzzy numbers. I constructed membership functions of the initial fuzzy financial and economic indicators. The paper provides projected fuzzy estimates of the value of shares of the issuing company and its investment potential for the selected fuzzy financial and economic indicator and forecast scenario. The methodology was tested on the case of model forecast scenarios for ExxonMobil development. Conclusions. The presented methodology enables a forecast assessment of investment potential based on the database of quotations and financial and economic indicators of issuing companies. The predictive assessment is made within the formed fuzzy forecast scenario of the company's evolution in accordance with trends in the development of internal and external risk factors.

Assessment and modelling the dynamics of building materials prices in the current conditions

Construction, which is the driver of economic development in the current crisis conditions, needs to determine the correct cost and sufficiency of material resources, as well as systematic forecasting of price dynamics in order to plan the implementation of investment projects. High material capacity of construction products, along with the specificity of its pricing, leads to the necessity of not only keeping sufficient accuracy of quantitative and cost calculations regarding basic construction materials but also predicting the dynamics of cost in order to determine the volume of capital costs and accounting for the cost of work. The unstable dynamics of the cost of construction materials in 2022, caused by foreign policy pressure, requires the systematization of the factor space in order to predict trends more reliably. The factor space of the price dynamics of construction materials has undergone fundamental changes, starting with the impact of the coronavirus pandemic, and further due to changes in the geopolitical situation, sanctions pressure from unfriendly countries and corresponding changes in the economic principles of globalization, the transformation of the global and domestic logistics system, as well as the actions of local country and industry scale predictors. The authors have proposed a system of factors affecting the cost of building materials, structures and products in modern conditions, on the basis of the factor analysis the prices of basic building materials (armature and concrete) have been corrected by the expert method, a scenario forecast of price dynamics has been made. The authors substantiate the necessity for calculation-expert forecasting in conditions of high uncertainty and variability of forecasting results. In general, in the short term, a moderate upward trend in the cost of basic building materials with a rather high value coefficient of variation, explained by the strong influence of non-economic factors on the price.

Conceptualization of the task of information support of strategic planning processes at the regional level

At present, improving the quality of strategic planning at the regional level is one of the top priority tasks for the government authorities in the Russian Federation. It is mainly connected with the necessity of improving information maintenance of the process as the key element of the management cycle (analysis, planning, execution) determining the target parameters of social and economic development of the region. The study involves analysis of the legal and regulatory framework of strategic management and the system of strategic planning instruments in the Russian Federation. The authors point out that their development includes both official procedures and content stages of determining parameters of strategic management of the regional development. Improvement of the effectiveness of their implementation may be achieved by applying formalized methods for describing the object of management and the process of strategic planning itself. The study highlights and describes its main stages: goal setting (determines the directions of the region’s development with the consideration of numerous levels of administration, goals of development and the criteria for their achievement), scenario forecasting (considers probable variants of changes in internal and external factors), creating an indicative plan (provides for the quantitative characteristics of the desired state of the regional system) and regulating (determines the vector of control actions directed towards correction of the indicator values observed). It is pointed out that in practice the above mentioned stages can overlap in time and be repeated many times in order to update the process. The stages are directly implemented through the set of functions described in the article with the key stages involving development of strategic decisions and making strategic choices. Thus, it is these functions that are very often the least formalized ones and that demand appropriate information support. Within the study, a number of groups of sources of structured and poorly structured information were identified (including the results of surveys, expert evaluation, etc.). As a whole, the formed information environment of the decision-maker involves both information sources and the interrelation between the stages of strategizing, as well as the strategic planning instruments.

Practical aspects of the task of modeling and scenario forecasting of the passenger port operation

This article presents a simulation model of scenario forecasting of a passenger terminal in order to obtain analytical information about operational activities for making decisions about the work of port services. The main goal is to provide an analysis tool for a passenger service system that depends on the time spent on various service channels. The paper examines the internal performance indicators of the port model created using AnyLogic software. The purpose of this model is to help in making strategic decisions using "what if" scenarios. The solutions include determining the average service time in the passenger terminal. The results revealed and suggested the solution of bottlenecks. The results also indicate that simulation modeling is a tool that helps to predict the stability of the system.

Occupancy states forecasting with a hidden Markov model for incomplete data, exploiting daily periodicity

To automatize HVAC energy savings in buildings, it is useful to forecast the occupants’ behaviour. This article deals with such a forecasting problem by exploiting the daily periodicity of the input variables and the ability of the proposed model to learn from missing data. We propose a case study of occupancy behaviour, for which only a history of indirect information is available: CO2 and decibel levels. The unobserved classes are modeled by a Hidden Markov Model (HMM) which emits sensors’ data and periodic variables. The fact that Baum-Welch’s algorithm for HMMs is an instance of the EM algorithm allows the model to learn from incomplete data. In this case, the missing data are the class labels at every instant (past and future) and the sensors’ data in the future, but the time periodic variables are known at every instant: this enables the model to classify both the past instants (history) and the future instants (forecasting). Our method is tested on simulated data sets with 5%, 15%, 25% and 50% missing data that provide respectively an average classification accuracy of 93%, 89%, 85% and 80%. Then, experiments on one-day and one-week forecasting scenarios achieved an average accuracy of 82% and 81%. The proposed method is also applied on a real-life data set which seems to provide promising results.

A Monte Carlo simulation and sensitivity analysis framework demonstrating the advantages of probabilistic forecasting over deterministic forecasting in terms of flood warning reliability

Despite the significant progress in probabilistic forecasting science in the last two decades, particularly in the quantification of predictive uncertainty (PU), most operational flood early warning systems (FEWSs) continue to be based on deterministic forecasts. Thereupon, additional work is needed to demonstrate the advantages of using PU over deterministic forecasting to enhance the uptake of probabilistic forecasts in flood warning decision-making. In this paper, a Monte-Carlo (MC)-based sensitivity analysis is done to explore how the outcomes of flood peak water level-based deterministic and -probabilistic warning strategies behave when factors controlling the forecast quality are perturbed. The flood warning reliability is evaluated through the probability of detection (POD) and false alarm ratio (FAR) based on two criteria: a flooding threshold-based criterion (FTC), and a new floodplain property-based criterion (FPC) based on inundation level forecasting. The results of this work show that the advantage of a probabilistic strategy over a deterministic one is greater when PU is relatively high. The probabilistic strategy is robust to biases in the mean and variance of forecasts by maintaining POD and FAR stability, while this is not the case for a deterministic warning strategy. Likewise, it was concluded that if inundation level forecasting is undertaken (FPC), improved forecasts would be needed to achieve the same reliability level as for FTC, reflecting the more demanding FPC criterion. Also, the levels of correlation needed to achieve acceptable operational values of POD and FAR are shown. These results provide new insight into the advantages of a probabilistic forecasting strategy under several forecast quality scenarios and guidance for the design of operational FEWSs.

Modelling and Forecast of Air Pollution Concentrations during COVID Pandemic Emergency with ARIMA Techniques: the Case Study of Two Italian Cities

An efficient and punctual monitoring of air pollutants is very useful to evaluate and prevent possible threats to human beings’ health. Especially in areas where such pollutants are highly concentrated, an accurate collection of data could suggest mitigation actions to be implemented. Moreover, a well-performed data collection could also permit the forecast of future scenarios, in relation to the seasonality of the phenomenon. With a particular focus on COVID pandemic period, several literature works demonstrated a decreasing of pollutant concentrations in air of urban areas, mainly for NOx, while CO and PM10, on the opposite, has been observed to remain still, mainly because of the intensive usage of heating systems by the people forced to stay home (on specific regions). With the present contribution the authors here present an application of Time Series analysis (TSA) approach to pollutants concentration data of two Italian cities during first lockdown (9 march – 18 may 2020), demonstrating the possibility to predict pollutants concentration over time.

Assessing environmental benefits of the transition from standard fossil fuels to liquefied natural gas: The Sardinia Region case study

The ever-increasing attention to environmental pollution and the greenhouse effect, together with the need to reduce harmful emissions into the atmosphere, has led to a general enforcement of stricter legislation worldwide which imposes the adoption of more efficient and sustainable solutions in many different fields. The use of traditional fossil fuels must be reduced, favoring both renewable energy sources and alternative fuels. This paper outlines a procedure for calculating the environmental advantages of using liquified natural gas (LNG) instead of traditional fossil fuels in terms of reduced emissions: it is based on the choice and application of emission factors (EFs), available through a dedicated and extensive database managed and updated by the European Environmental Agency (EEA). Whenever the EF database lacks data, additional literature studies and databases are used to integrate the missing information. The proposed methodology is then applied to the Sardinia Region: starting from data regarding regional energy needs, the emissions deriving from the current situation are calculated to define a reference point to be compared with two forecast scenarios. These assume that, over a 10-year horizon, certain shares of fossil fuels will be substituted by LNG. Comparisons are made between the emission levels of six different compounds (PM2.5, PM10, CO, CO2, NOX, SOX) showing that, overall, LNG adoption can be considered effective in reducing air pollution for the compounds considered. In particular, as an example, reductions of up to 70 % of PMs and 38 % of CO2 can be achieved for the thermoelectric sector, whereas for the transportation sector the reductions of the same compounds can be respectively 15 % and 10 %.