152 research outputs found
Comparison among Hamiltonian light-front formalisms at q+ = 0 and q+ <> 0: space-like elastic form factors of pseudoscalar and vector mesons
The electromagnetic elastic form factors of pseudoscalar and vector mesons
are analyzed for space-like momentum transfers in terms of relativistic quark
models based on the Hamiltonian light-front formalism elaborated in different
reference frames (q+ 0 and q+ 0). As far as the one-body approximation for
the electromagnetic current operator is concerned, it is shown that the
predictions of the light-front approach at q+=0 should be preferred,
particularly in case of light hadrons, because of: i) the relevant role played
by the Z-graph at q+ 0, and ii) the appropriate elimination of spurious
effects, related to the orientation of the null hyperplane where the
light-front wave function is defined.Comment: version to appear in Phys. Rev. C. No change in the results and in
the conclusion
Decay constants of heavy pseudoscalar mesons from QCD sum rules
We revisit the sum-rule extraction of the decay constants of the D, Ds, B,
and Bs mesons from the two-point correlator of heavy-light pseudoscalar
currents. We use the operator product expansion for this correlator expressed
in terms of the MSbar heavy-quark mass, for which the perturbative expansion
exhibits a reasonable convergence. Our main emphasis is laid on the control
over the uncertainties in the decay constants, related both to the input QCD
parameters and to the limited accuracy of the method of sum-rules. This becomes
possible due to the application of our procedure of extracting hadron
observables that involves as novel feature dual thresholds depending on the
Borel parameter. For charmed mesons, we find the decay constants f_D=206.2\pm
7.3(OPE)\pm 5.1(syst) MeV and f_Ds=245.3\pm 15.7(OPE)\pm 4.5(syst) MeV. For
beauty mesons, the decay constants turn out to be extremely sensitive to the
precise value of mb(mb). By requiring our sum-rule estimate to match the
average of the lattice results for f_B, a very accurate value mb(mb)=4.245\pm
0.025 GeV is extracted, leading to f_B=193.4\pm 12.3(OPE)\pm 4.3(syst) MeV and
f_Bs=232.5\pm 18.6(OPE)\pm 2.4(syst) MeV.Comment: 12 page
Good covers are algorithmically unrecognizable
A good cover in R^d is a collection of open contractible sets in R^d such
that the intersection of any subcollection is either contractible or empty.
Motivated by an analogy with convex sets, intersection patterns of good covers
were studied intensively. Our main result is that intersection patterns of good
covers are algorithmically unrecognizable.
More precisely, the intersection pattern of a good cover can be stored in a
simplicial complex called nerve which records which subfamilies of the good
cover intersect. A simplicial complex is topologically d-representable if it is
isomorphic to the nerve of a good cover in R^d. We prove that it is
algorithmically undecidable whether a given simplicial complex is topologically
d-representable for any fixed d \geq 5. The result remains also valid if we
replace good covers with acyclic covers or with covers by open d-balls.
As an auxiliary result we prove that if a simplicial complex is PL embeddable
into R^d, then it is topologically d-representable. We also supply this result
with showing that if a "sufficiently fine" subdivision of a k-dimensional
complex is d-representable and k \leq (2d-3)/3, then the complex is PL
embeddable into R^d.Comment: 22 pages, 5 figures; result extended also to acyclic covers in
version
ΠΡΡΠΌΠΎΠ΅ Π½Π΅ΡΠ΅ΡΠΊΠΎΠ΅ ΠΎΡΠ΅Π½ΠΈΠ²Π°Π½ΠΈΠ΅ Β«ΡΠ΅ΠΏΠΎΡΠ΅ΠΊΒ» ΡΠΈΠ½Π°Π½ΡΠΎΠ²ΡΡ ΡΠΈΡΠΊΠΎΠ² ΠΎΡΠ³Π°Π½ΠΈΠ·Π°ΡΠΈΠΈ
The object of the research is the diagnosis and evaluation of financial risks in order to create an effective risk management policy. The subject of the research is the methodology of direct fuzzy evaluation of financial risk βchainsβ of an organisation. The relevance of the problem is due, on the one hand, to the dynamic and chaotic macro-environment and the business environment of organisations, on the other hand, to the drawback of the analytical and expert methods used to assess financial risks. The former, moreover, imply statistical data processing and operate with quantitative measures. For the latter, the difficulty is the impossibility of their application in a short time interval. From the perspective of operational risk management, financial risks deserve special attention since the effective operation of the entire organisation depends on them. The purpose of the research is to form a methodology for direct fuzzy evaluation of financial risk βchainsβ of an organisation. The authors apply the methods of mathematical forecasting, fuzzy modelling, calculation of financial and economic indicators, and expert risk assessment. The proposed methodology consists of 12 stages, beginning with the analysis of business processes and the identification of financial risks of the organisation. The main stage is the construction of a fuzzy evaluation model and the calculation of indicators: the probability of occurrence and realization of risks and risky situations of the financial risk βchainsβ, and the degree of confidence of the calculations conducted. The final stage of the methodology is an analysis of the results obtained to adjust the selected development strategy of the organisation, and the choice of methods for managing identified financial risks bearing the most significant financial and economic losses. The authors conclude the developed methodology allows to accurately assess the threat of a certain risk βchainβ and losses from the implementation of specific risk situations for any organisation in the conditions of dynamic changes in internal and external elements of the business environment. The advantage of the methodology should be considered in the comparability of the accuracy of the evaluation and the low cost of modelling.ΠΠ±ΡΠ΅ΠΊΡΠΎΠΌ ΠΈΡΡΠ»Π΅Π΄ΠΎΠ²Π°Π½ΠΈΡ Π²ΡΡΡΡΠΏΠ°Π΅Ρ Π΄ΠΈΠ°Π³Π½ΠΎΡΡΠΈΠΊΠ° ΠΈ ΠΎΡΠ΅Π½ΠΊΠ° ΡΠΈΠ½Π°Π½ΡΠΎΠ²ΡΡ
ΡΠΈΡΠΊΠΎΠ² Ρ ΡΠ΅Π»ΡΡ ΡΠΎΠ·Π΄Π°Π½ΠΈΡ ΡΡΡΠ΅ΠΊΡΠΈΠ²Π½ΠΎΠ³ΠΎ ΡΠΈΡΠΊ-ΠΌΠ΅Π½Π΅Π΄ΠΆΠΌΠ΅Π½ΡΠ°. ΠΡΠ΅Π΄ΠΌΠ΅ΡΠΎΠΌ ΠΈΡΡΠ»Π΅Π΄ΠΎΠ²Π°Π½ΠΈΡ ΡΠ²Π»ΡΠ΅ΡΡΡ ΠΌΠ΅ΡΠΎΠ΄ΠΈΠΊΠ° Π½Π΅ΡΠ΅ΡΠΊΠΎΠ³ΠΎ ΠΏΡΡΠΌΠΎΠ³ΠΎ ΠΎΡΠ΅Π½ΠΈΠ²Π°Π½ΠΈΡ Β«ΡΠ΅ΠΏΠΎΡΠ΅ΠΊΒ» ΡΠΈΠ½Π°Π½ΡΠΎΠ²ΡΡ
ΡΠΈΡΠΊΠΎΠ² ΠΎΡΠ³Π°Π½ΠΈΠ·Π°ΡΠΈΠΉ. ΠΠΊΡΡΠ°Π»ΡΠ½ΠΎΡΡΡ ΠΏΡΠΎΠ±Π»Π΅ΠΌΠ°ΡΠΈΠΊΠΈ ΠΎΠ±ΡΡΠ»ΠΎΠ²Π»Π΅Π½Π°, Ρ ΠΎΠ΄Π½ΠΎΠΉ ΡΡΠΎΡΠΎΠ½Ρ, Π΄ΠΈΠ½Π°ΠΌΠΈΡΠ½ΠΎΠΉ ΠΈ Ρ
Π°ΠΎΡΠΈΡΠ½ΠΎΠΉ ΠΊΠ°ΠΊ ΠΌΠ°ΠΊΡΠΎΡΡΠ΅Π΄ΠΎΠΉ, ΡΠ°ΠΊ ΠΈ Π±ΠΈΠ·Π½Π΅Ρ-ΡΡΠ΅Π΄ΠΎΠΉ ΠΎΡΠ³Π°Π½ΠΈΠ·Π°ΡΠΈΠΉ, Ρ Π΄ΡΡΠ³ΠΎΠΉ β Π½Π΅Π΄ΠΎΡΡΠ°ΡΠΊΠ°ΠΌΠΈ ΠΏΡΠΈΠΌΠ΅Π½ΡΠ΅ΠΌΡΡ
Π°Π½Π°Π»ΠΈΡΠΈΡΠ΅ΡΠΊΠΈΡ
ΠΈ ΡΠΊΡΠΏΠ΅ΡΡΠ½ΡΡ
ΠΌΠ΅ΡΠΎΠ΄ΠΎΠ² ΠΎΡΠ΅Π½ΠΊΠΈ ΡΠΈΠ½Π°Π½ΡΠΎΠ²ΡΡ
ΡΠΈΡΠΊΠΎΠ². ΠΠ΅ΡΠ²ΡΠ΅ ΠΏΡΠΈ ΡΡΠΎΠΌ ΠΏΠΎΠ΄ΡΠ°Π·ΡΠΌΠ΅Π²Π°ΡΡ ΡΡΠ°ΡΠΈΡΡΠΈΡΠ΅ΡΠΊΡΡ ΠΎΠ±ΡΠ°Π±ΠΎΡΠΊΡ Π΄Π°Π½Π½ΡΡ
ΠΈ ΠΎΠΏΠ΅ΡΠΈΡΡΡΡ ΠΊΠΎΠ»ΠΈΡΠ΅ΡΡΠ²Π΅Π½Π½ΡΠΌΠΈ ΠΌΠ΅ΡΡΠΈΠΊΠ°ΠΌΠΈ. ΠΠ»Ρ Π²ΡΠΎΡΡΡ
ΡΡΡΠ΄Π½ΠΎΡΡΡ Π·Π°ΠΊΠ»ΡΡΠ°Π΅ΡΡΡ Π² Π½Π΅Π²ΠΎΠ·ΠΌΠΎΠΆΠ½ΠΎΡΡΠΈ ΠΈΡ
ΠΏΡΠΈΠΌΠ΅Π½Π΅Π½ΠΈΡ Π½Π° ΠΊΠΎΡΠΎΡΠΊΠΎΠΌ Π²ΡΠ΅ΠΌΠ΅Π½Π½ΠΎΠΌ ΠΈΠ½ΡΠ΅ΡΠ²Π°Π»Π΅. Π‘ ΠΏΠΎΠ·ΠΈΡΠΈΠΈ ΠΎΠΏΠ΅ΡΠ°ΡΠΈΠ²Π½ΠΎΠ³ΠΎ ΡΠΈΡΠΊ-ΠΌΠ΅Π½Π΅Π΄ΠΆΠΌΠ΅Π½ΡΠ° Π·Π°ΡΠ»ΡΠΆΠΈΠ²Π°ΡΡ ΠΎΡΠΎΠ±ΠΎΠ³ΠΎ Π²Π½ΠΈΠΌΠ°Π½ΠΈΡ ΡΠΈΠ½Π°Π½ΡΠΎΠ²ΡΠ΅ ΡΠΈΡΠΊΠΈ, ΠΏΠΎΡΠΊΠΎΠ»ΡΠΊΡ ΠΎΡ Π½ΠΈΡ
Π·Π°Π²ΠΈΡΠΈΡ ΡΡΡΠ΅ΠΊΡΠΈΠ²Π½ΠΎΠ΅ ΡΡΠ½ΠΊΡΠΈΠΎΠ½ΠΈΡΠΎΠ²Π°Π½ΠΈΠ΅ Π²ΡΠ΅ΠΉ ΠΎΡΠ³Π°Π½ΠΈΠ·Π°ΡΠΈΠΈ. Π¦Π΅Π»Ρ ΠΈΡΡΠ»Π΅Π΄ΠΎΠ²Π°Π½ΠΈΡ Π·Π°ΠΊΠ»ΡΡΠ°Π΅ΡΡΡ Π² ΡΠΎΡΠΌΠΈΡΠΎΠ²Π°Π½ΠΈΠΈ ΠΌΠ΅ΡΠΎΠ΄ΠΈΠΊΠΈ Π½Π΅ΡΠ΅ΡΠΊΠΎΠ³ΠΎ ΠΏΡΡΠΌΠΎΠ³ΠΎ ΠΎΡΠ΅Π½ΠΈΠ²Π°Π½ΠΈΡ Β«ΡΠ΅ΠΏΠΎΡΠ΅ΠΊΒ» ΡΠΈΠ½Π°Π½ΡΠΎΠ²ΡΡ
ΡΠΈΡΠΊΠΎΠ² ΠΎΡΠ³Π°Π½ΠΈΠ·Π°ΡΠΈΠΉ. ΠΡΠΏΠΎΠ»ΡΠ·ΠΎΠ²Π°Π½Ρ ΠΌΠ΅ΡΠΎΠ΄Ρ ΠΌΠ°ΡΠ΅ΠΌΠ°ΡΠΈΡΠ΅ΡΠΊΠΎΠ³ΠΎ ΠΏΡΠΎΠ³Π½ΠΎΠ·ΠΈΡΠΎΠ²Π°Π½ΠΈΡ, Π½Π΅ΡΠ΅ΡΠΊΠΎΠ³ΠΎ ΠΌΠΎΠ΄Π΅Π»ΠΈΡΠΎΠ²Π°Π½ΠΈΡ, ΡΠ°ΡΡΠ΅ΡΠ° ΡΠΈΠ½Π°Π½ΡΠΎΠ²ΠΎ-ΡΠΊΠΎΠ½ΠΎΠΌΠΈΡΠ΅ΡΠΊΠΈΡ
ΠΏΠΎΠΊΠ°Π·Π°ΡΠ΅Π»Π΅ΠΉ, ΡΠΊΡΠΏΠ΅ΡΡΠ½ΠΎΠΉ ΠΎΡΠ΅Π½ΠΊΠΈ ΡΠΈΡΠΊΠΎΠ². ΠΡΠ΅Π΄Π»Π°Π³Π°Π΅ΠΌΠ°Ρ ΠΌΠ΅ΡΠΎΠ΄ΠΈΠΊΠ° ΡΠΎΡΡΠΎΠΈΡ ΠΈΠ· 12 ΡΡΠ°ΠΏΠΎΠ², Π½Π°ΡΠΈΠ½Π°Π΅ΡΡΡ Ρ Π°Π½Π°Π»ΠΈΠ·Π° Π±ΠΈΠ·Π½Π΅Ρ-ΠΏΡΠΎΡΠ΅ΡΡΠΎΠ² ΠΈ ΠΈΠ΄Π΅Π½ΡΠΈΡΠΈΠΊΠ°ΡΠΈΠΈ ΡΠΈΠ½Π°Π½ΡΠΎΠ²ΡΡ
ΡΠΈΡΠΊΠΎΠ² ΠΎΡΠ³Π°Π½ΠΈΠ·Π°ΡΠΈΠΈ. ΠΡΠ½ΠΎΠ²Π½ΡΠΌ Π΅Π΅ ΡΡΠ°ΠΏΠΎΠΌ ΡΠ²Π»ΡΠ΅ΡΡΡ ΠΏΠΎΡΡΡΠΎΠ΅Π½ΠΈΠ΅ Π½Π΅ΡΠ΅ΡΠΊΠΎΠΉ ΠΎΡΠ΅Π½ΠΎΡΠ½ΠΎΠΉ ΠΌΠΎΠ΄Π΅Π»ΠΈ ΠΈ ΡΠ°ΡΡΠ΅Ρ ΠΏΠΎΠΊΠ°Π·Π°ΡΠ΅Π»Π΅ΠΉ: Π²Π΅ΡΠΎΡΡΠ½ΠΎΡΡΡ Π²ΠΎΠ·Π½ΠΈΠΊΠ½ΠΎΠ²Π΅Π½ΠΈΡ ΠΈ ΡΠ΅Π°Π»ΠΈΠ·Π°ΡΠΈΠΈ ΡΠΈΡΠΊΠΎΠ² ΠΈ ΡΠΈΡΠΊΠΎΠ²ΡΡ
ΡΠΈΡΡΠ°ΡΠΈΠΉ Β«ΡΠ΅ΠΏΠΎΡΠΊΠΈΒ» ΡΠΈΠ½Π°Π½ΡΠΎΠ²ΡΡ
ΡΠΈΡΠΊΠΎΠ², ΡΡΠ΅ΠΏΠ΅Π½Ρ ΡΠ²Π΅ΡΠ΅Π½Π½ΠΎΡΡΠΈ ΠΏΡΠΎΠ²ΠΎΠ΄ΠΈΠΌΡΡ
ΡΠ°ΡΡΠ΅ΡΠΎΠ². ΠΠΎΠ½Π΅ΡΠ½ΡΠΉ ΡΡΠ°ΠΏ ΠΌΠ΅ΡΠΎΠ΄ΠΈΠΊΠΈ ΡΠ²Π»ΡΠ΅Ρ ΡΠΎΠ±ΠΎΠΉ Π°Π½Π°Π»ΠΈΠ· ΠΏΠΎΠ»ΡΡΠ΅Π½Π½ΡΡ
ΡΠ΅Π·ΡΠ»ΡΡΠ°ΡΠΎΠ² Ρ ΡΠ΅Π»ΡΡ ΠΊΠΎΡΡΠ΅ΠΊΡΠΈΡΠΎΠ²ΠΊΠΈ Π²ΡΠ±ΡΠ°Π½Π½ΠΎΠΉ ΡΡΡΠ°ΡΠ΅Π³ΠΈΠΈ ΡΠ°Π·Π²ΠΈΡΠΈΡ ΠΎΡΠ³Π°Π½ΠΈΠ·Π°ΡΠΈΠΈ, Π²ΡΠ±ΠΎΡΠ° ΠΌΠ΅ΡΠΎΠ΄ΠΎΠ² ΡΠΏΡΠ°Π²Π»Π΅Π½ΠΈΡ Π²ΡΡΠ²Π»Π΅Π½Π½ΡΠΌΠΈ ΡΠΈΠ½Π°Π½ΡΠΎΠ²ΡΠΌΠΈ ΡΠΈΡΠΊΠ°ΠΌΠΈ, Π½Π΅ΡΡΡΠΈΠΌΠΈ Π½Π°ΠΈΠ±ΠΎΠ»Π΅Π΅ ΡΡΡΠ΅ΡΡΠ²Π΅Π½Π½ΡΠ΅ ΡΠΈΠ½Π°Π½ΡΠΎΠ²ΠΎ-ΡΠΊΠΎΠ½ΠΎΠΌΠΈΡΠ΅ΡΠΊΠΈΠ΅ ΠΏΠΎΡΠ΅ΡΠΈ. Π‘Π΄Π΅Π»Π°Π½ Π²ΡΠ²ΠΎΠ΄ ΠΎ ΡΠΎΠΌ, ΡΡΠΎ ΡΠ°Π·ΡΠ°Π±ΠΎΡΠ°Π½Π½Π°Ρ ΠΌΠ΅ΡΠΎΠ΄ΠΈΠΊΠ° ΠΏΠΎΠ·Π²ΠΎΠ»ΡΠ΅Ρ Ρ Π²ΡΡΠΎΠΊΠΎΠΉ ΡΠΎΡΠ½ΠΎΡΡΡΡ ΠΎΡΠ΅Π½ΠΈΡΡ ΡΠ³ΡΠΎΠ·Ρ Π²ΠΎΠ·Π½ΠΈΠΊΠ½ΠΎΠ²Π΅Π½ΠΈΡ ΠΎΠΏΡΠ΅Π΄Π΅Π»Π΅Π½Π½ΠΎΠΉ Β«ΡΠ΅ΠΏΠΎΡΠΊΠΈΒ» ΡΠΈΡΠΊΠΎΠ² ΠΈ ΠΏΠΎΡΠ΅ΡΠΈ ΠΎΡ ΡΠ΅Π°Π»ΠΈΠ·Π°ΡΠΈΠΈ ΠΊΠΎΠ½ΠΊΡΠ΅ΡΠ½ΡΡ
ΡΠΈΡΠΊΠΎΠ²ΡΡ
ΡΠΈΡΡΠ°ΡΠΈΠΉ Π΄Π»Ρ Π»ΡΠ±ΠΎΠΉ ΠΎΡΠ³Π°Π½ΠΈΠ·Π°ΡΠΈΠΈ Π² ΡΡΠ»ΠΎΠ²ΠΈΡΡ
Π΄ΠΈΠ½Π°ΠΌΠΈΡΠ½ΡΡ
ΠΈΠ·ΠΌΠ΅Π½Π΅Π½ΠΈΠΉ Π²Π½ΡΡΡΠ΅Π½Π½ΠΈΡ
ΠΈ Π²Π½Π΅ΡΠ½ΠΈΡ
ΡΠ»Π΅ΠΌΠ΅Π½ΡΠΎΠ² Π±ΠΈΠ·Π½Π΅Ρ-ΡΡΠ΅Π΄Ρ. ΠΠ΅ ΠΏΡΠ΅ΠΈΠΌΡΡΠ΅ΡΡΠ²ΠΎΠΌ ΡΠ»Π΅Π΄ΡΠ΅Ρ ΡΡΠΈΡΠ°ΡΡ ΡΠΎΠΏΠΎΡΡΠ°Π²ΠΈΠΌΠΎΡΡΡ ΡΠΎΡΠ½ΠΎΡΡΠΈ ΠΏΡΠΎΠ²ΠΎΠ΄ΠΈΠΌΠΎΠΉ ΠΎΡΠ΅Π½ΠΊΠΈ ΠΈ Π½Π΅Π±ΠΎΠ»ΡΡΠΈΡ
Π·Π°ΡΡΠ°Ρ Π½Π° ΠΌΠΎΠ΄Π΅Π»ΠΈΡΠΎΠ²Π°Π½ΠΈΠ΅
Constraints on R-parity violating supersymmetry from leptonic and semileptonic tau, B_d and B_s decays
We put constraints on several products of R-parity violating lambda lambda'
and lambda' lambda' type couplings from leptonic and semileptonic tau, B_d and
B_s decays. Most of them are one to two orders of magnitude better than the
existing bounds, and almost free from theoretical uncertainties. A significant
improvement of these bounds can be made in high luminosity tau-charm or B
factories.Comment: 14 pages, latex. A few references added, two typos corrected. Version
to be published in Physical Review
On Some rare weak decays of vector mesons
Some semileptonic weak decays of vector mesons are considered in the
framework of the most popular quark models. The predicted branching ratios are
unfortunately too small to make a study of these decays realistic at meson
factories under construction.Comment: 14 pages, LaTeX. Some typos correcte
Measuring the Photon Helicity in Radiative B Decays
We propose a way of measuring the photon polarization in radiative B decays
into K resonance states decaying to K\pi\pi, which can test the Standard Model
and probe new physics. The photon polarization is shown to be measured by the
up-down asymmetry of the photon direction relative to the K\pi\pi decay plane
in the K resonance rest frame. The integrated asymmetry in K_1(1400)\to
K\pi\pi, calculated to be 0.34\pm 0.05 in the Standard Model, is measurable at
currently operating B factories.Comment: 4 pages, final version to appear in Physical Review Letter
Evidence for Factorization in Three-body B --> D(*) K- K0 Decays
Motivated by recent experimental results, we use a factorization approach to
study the three-body B --> D(*) K- K0 decay modes. Two mechanisms are proposed
for kaon pair production: current-produced (from vacuum) and transition (from B
meson). The Bbar0 --> D(*)+ K- K0 decay is governed solely by the
current-produced mechanism. As the kaon pair can be produced only by the vector
current, the matrix element can be extracted from e+ e- --> K Kbar processes
via isospin relations. The decay rates obtained this way are in good agreement
with experiment. Both current-produced and transition processes contribute to
B- --> D(*)0 K- K0 decays. By using QCD counting rules and the measured B- -->
D(*)0 K- K0 decay rates, the measured decay spectra can be understood.Comment: 17 pages, 6 figure
ΠΠ»ΠΈΡΠ½ΠΈΠ΅ ΠΈΠΌΠΏΠΎΡΡΠΎΠ·Π°ΠΌΠ΅ΡΠ΅Π½ΠΈΡ Π½Π° ΡΠΎΡΡ ΠΏΡΠΎΠΈΠ·Π²ΠΎΠ΄ΡΡΠ²Π° ΠΌΠΈΠ½Π΅ΡΠ°Π»ΡΠ½ΡΡ ΠΏΡΠΎΠ΄ΡΠΊΡΠΎΠ² ΠΈ ΠΌΠ΅ΡΠ°Π»Π»ΡΡΠ³ΠΈΠΈ: ΠΊΡΠ°ΡΠΊΠΎΡΡΠΎΡΠ½ΠΎΠ΅ ΠΈ Π΄ΠΎΠ»Π³ΠΎΡΡΠΎΡΠ½ΠΎΠ΅ ΠΏΡΠΎΠ³Π½ΠΎΠ·ΠΈΡΠΎΠ²Π°Π½ΠΈΠ΅ Π±Π°Π·ΠΎΠ²ΡΡ ΠΎΡΡΠ°ΡΠ»Π΅ΠΉ Π½Π°ΡΠΈΠΎΠ½Π°Π»ΡΠ½ΠΎΠ³ΠΎ Ρ ΠΎΠ·ΡΠΉΡΡΠ²Π°
The purpose of the study is to identify ways of short- and medium-term development of mineral production and metallurgy in the Russian Federation in the context of the policy of sanctions based on economic and mathematical modeling. The impact of sanctions on production in the basic sectors of the Russian economy, as well as the impact of import substitution on production in the short- and long-term is investigated. The research methodology includes panel regression with fixed effects and Bayesian vector autoregression (BVAR model). The sanctions index is calculated based on a sentimental analysis of the texts of news publications. This index is based on the results of computer analysis of a set of thematic texts (evaluation of the frequency of words and phrases, correlation analysis, case analysis based on the BERT neural network). The paper demonstrates the importance of an industry-specific approach to the implementation of import substitution policy in view of its time horizon. For example, for the mineral products industry, the current import substitution policy can be considered effective in terms of the production index forecast, and for the metallurgical industry, the import substitution policy needs to be revised, since a sharp decline is expected in the short-term when the baseline scenario is implemented, and in the long-term production stabilizes without showing growth. As a result, the efficiency of the import substitution policy is considered to be completely dependent on the industry in which it is implemented. Fund intensity and other factors affecting industry cycles must be considered in order to forecast policy results. Import substitution also has a long-term positive impact.Π¦Π΅Π»Ρ ΠΈΡΡΠ»Π΅Π΄ΠΎΠ²Π°Π½ΠΈΡ Π·Π°ΠΊΠ»ΡΡΠ°Π΅ΡΡΡ Π² Π²ΡΡΠ²Π»Π΅Π½ΠΈΠΈ Π½Π° ΠΎΡΠ½ΠΎΠ²Π΅ ΡΠΊΠΎΠ½ΠΎΠΌΠΈΠΊΠΎ-ΠΌΠ°ΡΠ΅ΠΌΠ°ΡΠΈΡΠ΅ΡΠΊΠΎΠ³ΠΎ ΠΌΠΎΠ΄Π΅Π»ΠΈΡΠΎΠ²Π°Π½ΠΈΡ ΠΏΡΡΠ΅ΠΉ ΠΊΡΠ°ΡΠΊΠΎΡΡΠΎΡΠ½ΠΎΠ³ΠΎ ΠΈ ΡΡΠ΅Π΄Π½Π΅ΡΡΠΎΡΠ½ΠΎΠ³ΠΎ ΡΠ°Π·Π²ΠΈΡΠΈΡ ΠΌΠΈΠ½Π΅ΡΠ°Π»ΡΠ½ΠΎΠ³ΠΎ ΠΏΡΠΎΠΈΠ·Π²ΠΎΠ΄ΡΡΠ²Π° ΠΈ ΠΌΠ΅ΡΠ°Π»Π»ΡΡΠ³ΠΈΠΈ Π Π€ Π² ΡΡΠ»ΠΎΠ²ΠΈΡΡ
ΠΏΠΎΠ»ΠΈΡΠΈΠΊΠΈ ΡΠ°Π½ΠΊΡΠΈΠΎΠ½Π½ΠΎΠ³ΠΎ Π²ΠΎΠ·Π΄Π΅ΠΉΡΡΠ²ΠΈΡ. ΠΡΡΠ»Π΅Π΄ΡΠ΅ΡΡΡ Π²Π»ΠΈΡΠ½ΠΈΠ΅ ΡΠ°Π½ΠΊΡΠΈΠΉ Π½Π° ΠΏΡΠΎΠΈΠ·Π²ΠΎΠ΄ΡΡΠ²ΠΎ Π² Π±Π°Π·ΠΎΠ²ΡΡ
ΠΎΡΡΠ°ΡΠ»ΡΡ
ΡΠΊΠΎΠ½ΠΎΠΌΠΈΠΊΠΈ Π Π€, Π° ΡΠ°ΠΊΠΆΠ΅ ΠΈΠΌΠΏΠΎΡΡΠΎΠ·Π°ΠΌΠ΅ΡΠ΅Π½ΠΈΡ Π½Π° ΠΏΡΠΎΠΈΠ·Π²ΠΎΠ΄ΡΡΠ²ΠΎ Π² ΠΊΡΠ°ΡΠΊΠΎΡΡΠΎΡΠ½ΠΎΠΌ ΠΈ Π΄ΠΎΠ»Π³ΠΎΡΡΠΎΡΠ½ΠΎΠΌ ΠΏΠ΅ΡΠΈΠΎΠ΄Π°Ρ
. ΠΠ΅ΡΠΎΠ΄ΠΎΠ»ΠΎΠ³ΠΈΡ ΠΈΡΡΠ»Π΅Π΄ΠΎΠ²Π°Π½ΠΈΡ Π²ΠΊΠ»ΡΡΠ°Π΅Ρ Π² ΡΠ΅Π±Ρ ΠΏΠ°Π½Π΅Π»ΡΠ½ΡΡ ΡΠ΅Π³ΡΠ΅ΡΡΠΈΡ Ρ ΡΠΈΠΊΡΠΈΡΠΎΠ²Π°Π½Π½ΡΠΌΠΈ ΡΡΡΠ΅ΠΊΡΠ°ΠΌΠΈ ΠΈ Π±Π°ΠΉΠ΅ΡΠΎΠ²ΡΠΊΡΡ Π²Π΅ΠΊΡΠΎΡΠ½ΡΡ Π°Π²ΡΠΎΡΠ΅Π³ΡΠ΅ΡΡΠΈΡ (BVAR ΠΌΠΎΠ΄Π΅Π»Ρ). Π‘Π°Π½ΠΊΡΠΈΠΎΠ½Π½ΡΠΉ ΠΈΠ½Π΄Π΅ΠΊΡ ΡΠ°ΡΡΡΠΈΡΡΠ²Π°Π΅ΡΡΡ Π½Π° ΠΎΡΠ½ΠΎΠ²Π΅ ΡΠ΅Π½ΡΠΈΠΌΠ΅Π½Ρ-Π°Π½Π°Π»ΠΈΠ·Π° ΡΠ΅ΠΊΡΡΠΎΠ² Π½ΠΎΠ²ΠΎΡΡΠ½ΡΡ
ΠΏΡΠ±Π»ΠΈΠΊΠ°ΡΠΈΠΉ. ΠΠΎΡΡΡΠΎΠ΅Π½Π½ΡΠΉ ΠΈΠ½Π΄Π΅ΠΊΡ ΠΎΡΠ½ΠΎΠ²Π°Π½ Π½Π° ΡΠ΅Π·ΡΠ»ΡΡΠ°ΡΠ°Ρ
ΠΊΠΎΠΌΠΏΡΡΡΠ΅ΡΠ½ΠΎΠ³ΠΎ Π°Π½Π°Π»ΠΈΠ·Π° ΠΌΠ°ΡΡΠΈΠ²Π° ΡΠ΅ΠΌΠ°ΡΠΈΡΠ΅ΡΠΊΠΈΡ
ΡΠ΅ΠΊΡΡΠΎΠ² (ΠΎΡΠ΅Π½ΠΊΡ ΡΠ°ΡΡΠΎΡΠ½ΠΎΡΡΠΈ ΡΠ»ΠΎΠ² ΠΈ ΡΠ»ΠΎΠ²ΠΎΡΠΎΡΠ΅ΡΠ°Π½ΠΈΠΉ, Π°Π½Π°Π»ΠΈΠ· ΠΊΠΎΡΡΠ΅Π»ΡΡΠΈΠΉ, ΡΠ΅ΠΌΠ°ΡΠΈΡΠ΅ΡΠΊΠΈΠΉ Π°Π½Π°Π»ΠΈΠ· Π½Π° ΠΎΡΠ½ΠΎΠ²Π΅ Π½Π΅ΠΉΡΠΎΠ½Π½ΠΎΠΉ ΡΠ΅ΡΠΈ BERT). Π Π°Π±ΠΎΡΠ° Π΄Π΅ΠΌΠΎΠ½ΡΡΡΠΈΡΡΠ΅Ρ Π·Π½Π°ΡΠΈΠΌΠΎΡΡΡ ΠΈΠ½Π΄ΠΈΠ²ΠΈΠ΄ΡΠ°Π»ΡΠ½ΠΎΠ³ΠΎ ΠΎΡΡΠ°ΡΠ»Π΅Π²ΠΎΠ³ΠΎ ΠΏΠΎΠ΄Ρ
ΠΎΠ΄Π° ΠΊ ΠΏΡΠΎΠ²Π΅Π΄Π΅Π½ΠΈΡ ΠΏΠΎΠ»ΠΈΡΠΈΠΊΠΈ ΠΈΠΌΠΏΠΎΡΡΠΎΠ·Π°ΠΌΠ΅ΡΠ΅Π½ΠΈΡ Ρ ΡΡΠ΅ΡΠΎΠΌ Π΅Π΅ Π²ΡΠ΅ΠΌΠ΅Π½Π½ΠΎΠ³ΠΎ Π³ΠΎΡΠΈΠ·ΠΎΠ½ΡΠ°. Π’Π°ΠΊ, Π½Π°ΠΏΡΠΈΠΌΠ΅Ρ, Π΄Π»Ρ ΠΎΡΡΠ°ΡΠ»ΠΈ ΠΏΡΠΎΠΈΠ·Π²ΠΎΠ΄ΡΡΠ²Π° ΠΌΠΈΠ½Π΅ΡΠ°Π»ΡΠ½ΡΡ
ΠΏΡΠΎΠ΄ΡΠΊΡΠΎΠ² ΡΠ΅ΠΊΡΡΠ°Ρ ΠΏΠΎΠ»ΠΈΡΠΈΠΊΠ° ΠΈΠΌΠΏΠΎΡΡΠΎΠ·Π°ΠΌΠ΅ΡΠ΅Π½ΠΈΡ ΠΌΠΎΠΆΠ΅Ρ ΡΡΠΈΡΠ°ΡΡΡΡ ΡΡΡΠ΅ΠΊΡΠΈΠ²Π½ΠΎΠΉ Ρ ΡΠΎΡΠΊΠΈ Π·ΡΠ΅Π½ΠΈΡ ΠΏΡΠΎΠ³Π½ΠΎΠ·Π° ΠΈΠ½Π΄Π΅ΠΊΡΠ° ΠΏΡΠΎΠΈΠ·Π²ΠΎΠ΄ΡΡΠ²Π°, Π° Π΄Π»Ρ ΠΌΠ΅ΡΠ°Π»Π»ΡΡΠ³ΠΈΡΠ΅ΡΠΊΠΎΠΉ ΠΎΡΡΠ°ΡΠ»ΠΈ ΠΏΠΎΠ»ΠΈΡΠΈΠΊΠ° ΠΈΠΌΠΏΠΎΡΡΠΎΠ·Π°ΠΌΠ΅ΡΠ΅Π½ΠΈΡ Π½ΡΠΆΠ΄Π°Π΅ΡΡΡ Π² ΠΏΠ΅ΡΠ΅ΡΠΌΠΎΡΡΠ΅, ΠΏΠΎΡΠΊΠΎΠ»ΡΠΊΡ ΠΏΡΠΈ ΡΠ΅Π°Π»ΠΈΠ·Π°ΡΠΈΠΈ Π±Π°Π·ΠΎΠ²ΠΎΠ³ΠΎ ΡΡΠ΅Π½Π°ΡΠΈΡ Π½Π° ΠΊΡΠ°ΡΠΊΠΎΡΡΠΎΡΠ½ΠΎΠΌ ΠΏΠ΅ΡΠΈΠΎΠ΄Π΅ ΠΎΠΆΠΈΠ΄Π°Π΅ΡΡΡ ΡΠ΅Π·ΠΊΠΈΠΉ ΡΠΏΠ°Π΄, Π° Π² Π΄ΠΎΠ»Π³ΠΎΡΡΠΎΡΠ½ΠΎΠΌ ΠΏΡΠΎΠΈΠ·Π²ΠΎΠ΄ΡΡΠ²Π΅ ΡΡΠ°Π±ΠΈΠ»ΠΈΠ·ΡΠ΅ΡΡΡ, Π½Π΅ ΠΏΠΎΠΊΠ°Π·ΡΠ²Π°Ρ ΡΠΎΡΡ. Π’Π°ΠΊΠΈΠΌ ΠΎΠ±ΡΠ°Π·ΠΎΠΌ ΡΡΠ²Π΅ΡΠΆΠ΄Π°Π΅ΡΡΡ, ΡΡΠΎ ΡΡΡΠ΅ΠΊΡΠΈΠ²Π½ΠΎΡΡΡ ΠΏΠΎΠ»ΠΈΡΠΈΠΊΠΈ ΠΈΠΌΠΏΠΎΡΡΠΎΠ·Π°ΠΌΠ΅ΡΠ΅Π½ΠΈΡ Π²ΠΎ ΠΌΠ½ΠΎΠ³ΠΎΠΌ Π·Π°Π²ΠΈΡΠΈΡ ΠΎΡ ΠΎΡΡΠ°ΡΠ»ΠΈ, Π³Π΄Π΅ ΡΠ°ΠΊΠ°Ρ ΠΏΠΎΠ»ΠΈΡΠΈΠΊΠ° ΠΏΡΠΎΠ²ΠΎΠ΄ΠΈΡΡΡ. ΠΠ΅ΠΎΠ±Ρ
ΠΎΠ΄ΠΈΠΌΠΎ ΡΡΠΈΡΡΠ²Π°ΡΡ ΡΠΎΠ½Π΄ΠΎΠ΅ΠΌΠΊΠΎΡΡΡ ΠΈ Π΄ΡΡΠ³ΠΈΠ΅ ΡΠ°ΠΊΡΠΎΡΡ, Π²Π»ΠΈΡΡΡΠΈΠ΅ Π½Π° ΡΠΈΠΊΠ»ΠΈΡΠ½ΠΎΡΡΡ Π² ΠΎΡΡΠ°ΡΠ»ΠΈ, ΡΡΠΎ ΠΏΠΎΠ·Π²ΠΎΠ»ΡΠ΅Ρ ΠΏΡΠΎΠ³Π½ΠΎΠ·ΠΈΡΠΎΠ²Π°ΡΡ ΡΠ΅Π·ΡΠ»ΡΡΠ°ΡΡ ΠΏΡΠΎΠ²ΠΎΠ΄ΠΈΠΌΠΎΠΉ ΠΏΠΎΠ»ΠΈΡΠΈΠΊΠΈ. Π’Π°ΠΊΠΆΠ΅ ΠΏΠΎΠ΄ΡΠ²Π΅ΡΠΆΠ΄Π°Π΅ΡΡΡ ΠΏΠΎΠ»ΠΎΠΆΠΈΡΠ΅Π»ΡΠ½ΠΎΠ΅ Π²Π»ΠΈΡΠ½ΠΈΠ΅ ΠΈΠΌΠΏΠΎΡΡΠΎΠ·Π°ΠΌΠ΅ΡΠ΅Π½ΠΈΡ Π² Π΄ΠΎΠ»Π³ΠΎΡΡΠΎΡΠ½ΠΎΠΌ ΠΏΠ΅ΡΠΈΠΎΠ΄Π΅
ΠΠ·Π°ΠΈΠΌΠΎΠ·Π°Π²ΠΈΡΠΈΠΌΠΎΡΡΡ ΠΏΡΠΈΡΠΎΠ΄ΠΎΠΎΡ ΡΠ°Π½Π½ΠΎΠΉ Π΄Π΅ΡΡΠ΅Π»ΡΠ½ΠΎΡΡΠΈ ΠΈ ΠΈΠ½Π²Π΅ΡΡΠΈΡΠΈΠΎΠ½Π½ΠΎΠΉ ΠΏΡΠΈΠ²Π»Π΅ΠΊΠ°ΡΠ΅Π»ΡΠ½ΠΎΡΡΠΈ: ΡΠΈΠ½Π°Π½ΡΡ ΡΠΎΡΡΠΈΠΉΡΠΊΠΎΠΉ ΠΌΠ΅ΡΠ°Π»Π»ΡΡΠ³ΠΈΠΈ
All countries now share a long-term vision of the importance of implementing technology development and transfer to improve climate resilience and reduce greenhouse gas emissions. Metallurgical enterprises play a significant role in achieving this goal, since they produce a large amount of carbon dioxide emissions into the atmosphere. In connection with the changing operating conditions and changing markets of presence, the issues of ensuring their investment attractiveness are acquiring obvious importance in the framework of the finances of Russian metallurgical companies. The object of the study is an assessment of the investment attractiveness of Russian metallurgical companies. The subject of the study is the relationship between the investment attractiveness of metallurgical companies and the results of their environmental protection activities. The purpose of this study is to identify the interdependence of environmental metrics and the investment attractiveness of steel companies. The methodological basis is a regression analysis of the impact of environmental metrics on the investment attractiveness of metallurgical companies. The authors chose the following indicators of environmental performance: CO2 emissions, energy consumption, water recycling, waste. To assess the investment attractiveness of metallurgical companies, the following indicators were used: revenue, EBITDA, investment in R&D. The authors concluded that the environmental activities of companies have a significant impact on their investment attractiveness. The scientific novelty of the study lies in identifying the interdependence of environmental protection activities and the investment attractiveness of Russian metallurgy companies. The results of the study can be used by both Russian steel companies and institutional investors as part of the development of an investment strategy.Π Π½Π°ΡΡΠΎΡΡΠ΅Π΅ Π²ΡΠ΅ΠΌΡ Π²ΡΠ΅ ΡΡΡΠ°Π½Ρ ΡΠ°Π·Π΄Π΅Π»ΡΡΡ Π΄ΠΎΠ»Π³ΠΎΡΡΠΎΡΠ½ΠΎΠ΅ Π²ΠΈΠ΄Π΅Π½ΠΈΠ΅ Π²Π°ΠΆΠ½ΠΎΡΡΠΈ ΡΠ΅Π°Π»ΠΈΠ·Π°ΡΠΈΠΈ ΡΠ°Π·ΡΠ°Π±ΠΎΡΠΊΠΈ ΠΈ ΠΏΠ΅ΡΠ΅Π΄Π°ΡΠΈ ΡΠ΅Ρ
Π½ΠΎΠ»ΠΎΠ³ΠΈΠΉ Π΄Π»Ρ ΠΏΠΎΠ²ΡΡΠ΅Π½ΠΈΡ ΡΡΡΠΎΠΉΡΠΈΠ²ΠΎΡΡΠΈ ΠΊ ΠΈΠ·ΠΌΠ΅Π½Π΅Π½ΠΈΡ ΠΊΠ»ΠΈΠΌΠ°ΡΠ° ΠΈ ΡΠΎΠΊΡΠ°ΡΠ΅Π½ΠΈΡ Π²ΡΠ±ΡΠΎΡΠΎΠ² ΠΏΠ°ΡΠ½ΠΈΠΊΠΎΠ²ΡΡ
Π³Π°Π·ΠΎΠ². ΠΠ½Π°ΡΠΈΡΠ΅Π»ΡΠ½ΡΡ ΡΠΎΠ»Ρ Π² Π΄ΠΎΡΡΠΈΠΆΠ΅Π½ΠΈΠΈ ΡΡΠΎΠΉ ΡΠ΅Π»ΠΈ ΠΈΠ³ΡΠ°ΡΡ ΠΏΡΠ΅Π΄ΠΏΡΠΈΡΡΠΈΡ ΠΌΠ΅ΡΠ°Π»Π»ΡΡΠ³ΠΈΡΠ΅ΡΠΊΠΎΠΉ ΠΎΡΡΠ°ΡΠ»ΠΈ, ΠΏΠΎΡΠΊΠΎΠ»ΡΠΊΡ ΠΎΠ½ΠΈ ΡΠ²Π»ΡΡΡΡΡ ΠΏΡΠΎΠΈΠ·Π²ΠΎΠ΄ΠΈΡΠ΅Π»ΡΠΌΠΈ Π±ΠΎΠ»ΡΡΠΎΠ³ΠΎ ΠΊΠΎΠ»ΠΈΡΠ΅ΡΡΠ²Π° Π²ΡΠ±ΡΠΎΡΠΎΠ² ΡΠ³Π»Π΅ΠΊΠΈΡΠ»ΠΎΠ³ΠΎ Π³Π°Π·Π° Π² Π°ΡΠΌΠΎΡΡΠ΅ΡΡ. Π ΡΠ²ΡΠ·ΠΈ Ρ ΠΌΠ΅Π½ΡΡΡΠΈΠΌΠΈΡΡ ΡΡΠ»ΠΎΠ²ΠΈΡΠΌΠΈ ΡΡΠ½ΠΊΡΠΈΠΎΠ½ΠΈΡΠΎΠ²Π°Π½ΠΈΡ ΠΈ ΡΠΌΠ΅Π½ΠΎΠΉ ΡΡΠ½ΠΊΠΎΠ² ΠΏΡΠΈΡΡΡΡΡΠ²ΠΈΡ ΠΎΡΠ΅Π²ΠΈΠ΄Π½ΡΡ Π²Π°ΠΆΠ½ΠΎΡΡΡ Π² ΡΠ°ΠΌΠΊΠ°Ρ
ΡΠΈΠ½Π°Π½ΡΠΎΠ² ΡΠΎΡΡΠΈΠΉΡΠΊΠΈΡ
ΠΌΠ΅ΡΠ°Π»Π»ΡΡΠ³ΠΈΡΠ΅ΡΠΊΠΈΡ
ΠΊΠΎΠΌΠΏΠ°Π½ΠΈΠΉ ΠΏΡΠΈΠΎΠ±ΡΠ΅ΡΠ°ΡΡ Π²ΠΎΠΏΡΠΎΡΡ ΠΎΠ±Π΅ΡΠΏΠ΅ΡΠ΅Π½ΠΈΡ ΠΈΡ
ΠΈΠ½Π²Π΅ΡΡΠΈΡΠΈΠΎΠ½Π½ΠΎΠΉ ΠΏΡΠΈΠ²Π»Π΅ΠΊΠ°ΡΠ΅Π»ΡΠ½ΠΎΡΡΠΈ. ΠΠ±ΡΠ΅ΠΊΡΠΎΠΌ ΠΈΡΡΠ»Π΅Π΄ΠΎΠ²Π°Π½ΠΈΡ ΡΠ²Π»ΡΠ΅ΡΡΡ ΠΎΡΠ΅Π½ΠΊΠ° ΠΈΠ½Π²Π΅ΡΡΠΈΡΠΈΠΎΠ½Π½ΠΎΠΉ ΠΏΡΠΈΠ²Π»Π΅ΠΊΠ°ΡΠ΅Π»ΡΠ½ΠΎΡΡΠΈ ΡΠΎΡΡΠΈΠΉΡΠΊΠΈΡ
ΠΌΠ΅ΡΠ°Π»Π»ΡΡΠ³ΠΈΡΠ΅ΡΠΊΠΈΡ
ΠΊΠΎΠΌΠΏΠ°Π½ΠΈΠΉ. ΠΡΠ΅Π΄ΠΌΠ΅Ρ ΠΈΠ·ΡΡΠ΅Π½ΠΈΡ β ΡΠ²ΡΠ·Ρ ΠΌΠ΅ΠΆΠ΄Ρ ΠΈΠ½Π²Π΅ΡΡΠΈΡΠΈΠΎΠ½Π½ΠΎΠΉ ΠΏΡΠΈΠ²Π»Π΅ΠΊΠ°ΡΠ΅Π»ΡΠ½ΠΎΡΡΡΡ ΠΌΠ΅ΡΠ°Π»Π»ΡΡΠ³ΠΈΡΠ΅ΡΠΊΠΈΡ
ΠΊΠΎΠΌΠΏΠ°Π½ΠΈΠΉ ΠΈ ΡΠ΅Π·ΡΠ»ΡΡΠ°ΡΠ°ΠΌΠΈ ΠΏΡΠΎΠ²ΠΎΠ΄ΠΈΠΌΠΎΠΉ ΠΈΠΌΠΈ ΠΏΡΠΈΡΠΎΠ΄ΠΎΠΎΡ
ΡΠ°Π½Π½ΠΎΠΉ Π΄Π΅ΡΡΠ΅Π»ΡΠ½ΠΎΡΡΠΈ. Π¦Π΅Π»ΡΡ Π΄Π°Π½Π½ΠΎΠ³ΠΎ ΠΈΡΡΠ»Π΅Π΄ΠΎΠ²Π°Π½ΠΈΡ ΡΠ²Π»ΡΠ΅ΡΡΡ Π²ΡΡΠ²Π»Π΅Π½ΠΈΠ΅ Π²Π·Π°ΠΈΠΌΠΎΠ·Π°Π²ΠΈΡΠΈΠΌΠΎΡΡΠΈ ΡΠΊΠΎΠ»ΠΎΠ³ΠΈΡΠ΅ΡΠΊΠΈΡ
ΠΌΠ΅ΡΡΠΈΠΊ ΠΈ ΠΈΠ½Π²Π΅ΡΡΠΈΡΠΈΠΎΠ½Π½ΠΎΠΉ ΠΏΡΠΈΠ²Π»Π΅ΠΊΠ°ΡΠ΅Π»ΡΠ½ΠΎΡΡΠΈ ΠΌΠ΅ΡΠ°Π»Π»ΡΡΠ³ΠΈΡΠ΅ΡΠΊΠΈΡ
ΠΊΠΎΠΌΠΏΠ°Π½ΠΈΠΉ. ΠΠ΅ΡΠΎΠ΄ΠΎΠ»ΠΎΠ³ΠΈΡΠ΅ΡΠΊΡΡ ΠΎΡΠ½ΠΎΠ²Ρ ΡΠΎΡΡΠ°Π²Π»ΡΠ΅Ρ ΡΠ΅Π³ΡΠ΅ΡΡΠΈΠΎΠ½Π½ΡΠΉ Π°Π½Π°Π»ΠΈΠ· Π²Π»ΠΈΡΠ½ΠΈΡ ΡΠΊΠΎΠ»ΠΎΠ³ΠΈΡΠ΅ΡΠΊΠΈΡ
ΠΌΠ΅ΡΡΠΈΠΊ Π½Π° ΠΈΠ½Π²Π΅ΡΡΠΈΡΠΈΠΎΠ½Π½ΡΡ ΠΏΡΠΈΠ²Π»Π΅ΠΊΠ°ΡΠ΅Π»ΡΠ½ΠΎΡΡΡ ΠΌΠ΅ΡΠ°Π»Π»ΡΡΠ³ΠΈΡΠ΅ΡΠΊΠΈΡ
ΠΊΠΎΠΌΠΏΠ°Π½ΠΈΠΉ. ΠΠ²ΡΠΎΡΡ Π²ΡΠ±ΡΠ°Π»ΠΈ ΡΠ»Π΅Π΄ΡΡΡΠΈΠ΅ ΠΏΠΎΠΊΠ°Π·Π°ΡΠ΅Π»ΠΈ ΡΠΊΠΎΠ»ΠΎΠ³ΠΈΡΠ΅ΡΠΊΠΎΠΉ Π΄Π΅ΡΡΠ΅Π»ΡΠ½ΠΎΡΡΠΈ: Π²ΡΠ±ΡΠΎΡΡ CO2 , ΠΏΠΎΡΡΠ΅Π±Π»Π΅Π½ΠΈΠ΅ ΡΠ½Π΅ΡΠ³ΠΈΠΈ, Π²ΡΠΎΡΠΈΡΠ½ΠΎΠ΅ ΠΈΡΠΏΠΎΠ»ΡΠ·ΠΎΠ²Π°Π½ΠΈΠ΅ Π²ΠΎΠ΄Ρ, ΠΎΡΡ
ΠΎΠ΄Ρ. ΠΠ»Ρ ΠΎΡΠ΅Π½ΠΊΠΈ ΠΈΠ½Π²Π΅ΡΡΠΈΡΠΈΠΎΠ½Π½ΠΎΠΉ ΠΏΡΠΈΠ²Π»Π΅ΠΊΠ°ΡΠ΅Π»ΡΠ½ΠΎΡΡΠΈ ΠΌΠ΅ΡΠ°Π»Π»ΡΡΠ³ΠΈΡΠ΅ΡΠΊΠΈΡ
ΠΊΠΎΠΌΠΏΠ°Π½ΠΈΠΉ ΠΈΡΠΏΠΎΠ»ΡΠ·ΠΎΠ²Π°Π»ΠΈΡΡ ΡΠ»Π΅Π΄ΡΡΡΠΈΠ΅ ΠΏΠΎΠΊΠ°Π·Π°ΡΠ΅Π»ΠΈ: Π²ΡΡΡΡΠΊΠ°, EBITDA, ΠΈΠ½Π²Π΅ΡΡΠΈΡΠΈΠΈ Π² ΠΠΠΠΠ . ΠΠ²ΡΠΎΡΠ°ΠΌΠΈ ΡΠ΄Π΅Π»Π°Π½ Π²ΡΠ²ΠΎΠ΄ ΠΎ ΡΡΡΠ΅ΡΡΠ²Π΅Π½Π½ΠΎΠΌ Π²Π»ΠΈΡΠ½ΠΈΠΈ ΡΠΊΠΎΠ»ΠΎΠ³ΠΈΡΠ΅ΡΠΊΠΎΠΉ Π΄Π΅ΡΡΠ΅Π»ΡΠ½ΠΎΡΡΠΈ ΠΊΠΎΠΌΠΏΠ°Π½ΠΈΠΉ Π½Π° ΠΈΡ
ΠΈΠ½Π²Π΅ΡΡΠΈΡΠΈΠΎΠ½Π½ΡΡ ΠΏΡΠΈΠ²Π»Π΅ΠΊΠ°ΡΠ΅Π»ΡΠ½ΠΎΡΡΡ. ΠΠ°ΡΡΠ½Π°Ρ Π½ΠΎΠ²ΠΈΠ·Π½Π° ΠΈΡΡΠ»Π΅Π΄ΠΎΠ²Π°Π½ΠΈΡ Π·Π°ΠΊΠ»ΡΡΠ°Π΅ΡΡΡ Π² Π²ΡΡΠ²Π»Π΅Π½ΠΈΠΈ Π²Π·Π°ΠΈΠΌΠΎΠ·Π°Π²ΠΈΡΠΈΠΌΠΎΡΡΠΈ ΠΏΡΠΈΡΠΎΠ΄ΠΎΠΎΡ
ΡΠ°Π½Π½ΠΎΠΉ Π΄Π΅ΡΡΠ΅Π»ΡΠ½ΠΎΡΡΠΈ ΠΈ ΠΈΠ½Π²Π΅ΡΡΠΈΡΠΈΠΎΠ½Π½ΠΎΠΉ ΠΏΡΠΈΠ²Π»Π΅ΠΊΠ°ΡΠ΅Π»ΡΠ½ΠΎΡΡΠΈ ΠΊΠΎΠΌΠΏΠ°Π½ΠΈΠΉ ΡΠΎΡΡΠΈΠΉΡΠΊΠΎΠΉ ΠΌΠ΅ΡΠ°Π»Π»ΡΡΠ³ΠΈΠΈ, ΡΠ΅Π·ΡΠ»ΡΡΠ°ΡΡ ΠΈΡΡΠ»Π΅Π΄ΠΎΠ²Π°Π½ΠΈΡ ΠΌΠΎΠ³ΡΡ Π±ΡΡΡ ΠΈΡΠΏΠΎΠ»ΡΠ·ΠΎΠ²Π°Π½Ρ ΠΊΠ°ΠΊ ΡΠΎΡΡΠΈΠΉΡΠΊΠΈΠΌΠΈ ΠΌΠ΅ΡΠ°Π»Π»ΡΡΠ³ΠΈΡΠ΅ΡΠΊΠΈΠΌΠΈ ΠΊΠΎΠΌΠΏΠ°Π½ΠΈΡΠΌΠΈ, ΡΠ°ΠΊ ΠΈ ΠΈΠ½ΡΡΠΈΡΡΡΠΈΠΎΠ½Π°Π»ΡΠ½ΡΠΌΠΈ ΠΈΠ½Π²Π΅ΡΡΠΎΡΠ°ΠΌΠΈ Π² ΡΠ°ΠΌΠΊΠ°Ρ
ΡΠ°Π·ΡΠ°Π±ΠΎΡΠΊΠΈ ΠΈΠ½Π²Π΅ΡΡΠΈΡΠΈΠΎΠ½Π½ΠΎΠΉ ΡΡΡΠ°ΡΠ΅Π³ΠΈΠΈ
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