The methodological features of managing the value of companies introducing "green" innovations

Although it is a common assumption that innovations are one of the most important factors of economic development, there is a need to review some provisions of innovation methodology so that new fundamental values are taken into account more fully. Most recent business models are based on the depletion of natural environment, whose potential has been almost exhausted. It is necessary to introduce new ideas that are of use for society and create values for companies. One way of achieving this goal is “green” (environmental) innovations. The next decade is expected to see a rapid growth in environmental innovations. Their organization and management will require modern — and adequate to the objectives set — technologies. One of those is the quest for value methodology. To date, the quest for value methodology has given rise to several conceptual approaches, which can be used to evaluate the effectiveness of environmental innovations. This article discusses the advantages and disadvantages of major approaches. The author comes to a conclusion that that the modern theory and practice of corporate finance still lacks a generally accepted approach to assessing the value of companies that explicitly takes into account the impact of environmental factors on the cost. The article outlines the basic theoretical frameworks for the formation of such approach

An empirical model for probabilistic decadal prediction: global attribution and regional hindcasts

Empirical models, designed to predict surface variables over seasons to decades ahead, provide useful benchmarks for comparison against the performance of dynamical forecast systems; they may also be employable as predictive tools for use by climate services in their own right. A new global empirical decadal prediction system is presented, based on a multiple linear regression approach designed to produce probabilistic output for comparison against dynamical models. A global attribution is performed initially to identify the important forcing and predictor components of the model . Ensemble hindcasts of surface air temperature anomaly fields are then generated, based on the forcings and predictors identified as important, under a series of different prediction ‘modes’ and their performance is evaluated. The modes include a real-time setting, a scenario in which future volcanic forcings are prescribed during the hindcasts, and an approach which exploits knowledge of the forced trend. A two-tier prediction system, which uses knowledge of future sea surface temperatures in the Pacific and Atlantic Oceans, is also tested, but within a perfect knowledge framework. Each mode is designed to identify sources of predictability and uncertainty, as well as investigate different approaches to the design of decadal prediction systems for operational use. It is found that the empirical model shows skill above that of persistence hindcasts for annual means at lead times of up to 10 years ahead in all of the prediction modes investigated. It is suggested that hindcasts which exploit full knowledge of the forced trend due to increasing greenhouse gases throughout the hindcast period can provide more robust estimates of model bias for the calibration of the empirical model in an operational setting. The two-tier system shows potential for improved real-time prediction, given the assumption that skilful predictions of large-scale modes of variability are available. The empirical model framework has been designed with enough flexibility to facilitate further developments, including the prediction of other surface variables and the ability to incorporate additional predictors within the model that are shown to contribute significantly to variability at the local scale. It is also semi-operational in the sense that forecasts have been produced for the coming decade and can be updated when additional data becomes available

Effect of Silicate Additive on Structural and Electrical Properties of Germanium Nanowires Formed by Electrochemical Reduction from Aqueous Solutions

Layers of germanium (Ge) nanowires (NWs) on titanium foils were grown by metal-assisted electrochemical reduction of germanium oxide in aqueous electrolytes based on germanium oxide without and with addition of sodium silicate. Structural properties and composition of Ge NWs were studied by means of the scanning and transmission electron microscopy, X-ray photoelectron spectroscopy, X-ray diffraction, and Raman spectroscopy. When sodium silicate was added to the electrolyte, Ge NWs consisted of 1–2 at.% of silicon (Si) and exhibited smaller mean diameter and improved crystallinity. Additionally, samples of Ge NW films were prepared by ultrasonic removal of Ge NWs from titanium foils followed with redeposition on corundum substrates with platinum electrodes. The electrical conductivity of Ge NW films was studied at different temperatures from 25 to 300 °C and an effect of the silicon impurity on the thermally activated electrical conductivity was revealed. Furthermore, the electrical conductivity of Ge NW films on corundum substrates exhibited a strong sensor response on the presence of saturated vapors of different liquids (water, acetone, ethanol, and isopropanol) in air and the response was dependent on the presence of Si impurities in the nanowires. The results obtained indicate the possibility of controlling the structure and electrical properties of Ge NWs by introducing silicate additives during their formation, which is of interest for applications in printed electronics and molecular sensorics