Environment Factors of Energy Companies and Their Effect on Value: Analysis Model and Applied Method

Abstract

Practical tasks related to property management and disposal in energy companies can be solved only if the actual property value of these companies is known. Traditional practical property valuation methods are based on the analysis of incurred property development costs or financial indicators of activities and are inadequate to account for the influence of environment factors on corporate activities and, in turn, on the value of companies. An analysis model for environment factors affecting energy companies was developed to improve objectiveness of valuation; through analysis of the model's components it is possible to analyse external macro, meso and microenviron‐ment of a particular company and to assess efficiency of such environment, as well as the impact of separate factors on value. A criteria system developed using the Analysis Model for Environment Factors and the Decision Support System for Measurement of Effect of Environment Factors on Value of Energy Companies, which was developed by the authors, were used to solve a practical task, which helped to evaluate the effectiveness of the Model of Environment Factors. The practical task included measurement of the utility degree and market value of the selected electricity companies and assessment of the impact of the criteria, which affect the environment, on the value of the selected energy companies. Santrauka Energetikos įmonių turto valdymo, disponavimo turtu praktiniams uždaviniams spręsti reikalinga tikroji šių įmonių turto vertė. Tradiciniai praktikoje taikomi turto vertinimo metodai pagrįsti turto sukūrimo sąnaudų ar veiklos finansinių rodiklių analize, jie neįvertina daugelio vertę veikiančių aplinkos veiksnių įtakos. Siekiant padidinti vertės nustatymo objektyvumą, pasiūlytas taikyti energetikos įmonių aplinkos veiksnių tyrimo modelis, kurio elementų analizė leidžia įvairiais aspektais pažvelgti į šių įmonių turto vertę ir įvertinti vertės kitimo priežastis. Sukurtas modelis leidžia analizuoti įmonės aplinką, vertinti jos efektyvumą, aplinką ir suinteresuotas grupes veikiančią išorinę makro-, mezo- ir mikroaplinką kaip visumą. Naudojantis rodiklių, apibūdinančių analizuojamo sektoriaus aplinką, sistema, pasiūlyta infrastruktūros įmonėms vertinti, taikyti daugiakriterine analize paremtus metodus. Siekiant nustatyti aplinkos veiksnių tyrimo modelio veiksmingumą, išspręstas praktinis uždavinys – taikant modelį ir remiantis daugiakriterinės analizės metodais nustatytas pasirinktų elektros energetikos įmonių naudingumo laipsnis ir rinkos vertė.

Sprendimų priėmimas energetikos sektoriuje

“…DSP instrumento parinkimas priklauso nuo uždavinio ir kriterijų, pagal kuriuos priimamas spren dimas, skaičiaus [30].…”

Decision Support System for the Assessment and ranking of Energy Generation Technologies

“…The development and use of MCDM tools in energy-sector decision-making were primarily determined by the liberalisation of energy markets, implementation of the principles of sustainable development, involvement of stakeholder groups in energy-sector decisionmaking, and the attempts to make sure the general public was represented in policy making. An analysis of literature revealed that, in the energy industry, MCDM tools are most often used to address such issues as energy policies and relevant environmental aspects, but rarer to analyse the choice of energy generation technologies [7], [8], [9].…”

Selection of the Climate Parameters for a Building Envelopes and Indoor Climate Systems Design

“…This could be demonstrated by calculating the average external air temperature of the coldest five-day period t e5 as one of the most important temperatures, which is used for thermal protection of the enclosing structures and calculation of thermal power of the heating and ventilation systems. Due to the fact, that a five-day period is related to discrete events, the direct application of the analytical expression for the probability density function of a normally distributed random value with parameters that characterize a year in tote would be difficult because such expression would describe the temperature behaviour of a continuous time interval (Šliogerienė et al 2009;Wang et al 2011). Consequently, numerical simulation using software that generates daily temperatures with sensors of pseudorandom values is required.…”