A wide range of climate change mitigation policies have been developed around the world and these policies have become one of the major concerns, however there is still debate among scientists about what are the main external benefits and how to account for them and prepare effective climate change mitigation policies that might be widely accepted by society in general. One of the main ways to assess external benefit of climate change mitigation in energy sector is to conduct Willingness to Pay (WTP) assessments for climate change mitigation options by households. There are many studies on WTP assessment for climate stability conducted in recent years. The paper surveys the existing literature on WTP for climate change mitigation policy in the energy sector. The aim of the paper is to identify the common variables across a varied set of WTP studies in order to establish a basis for comparison. The key variables selected for analysis of WTP studies for climate change mitigation in energy sector addressed in the paper are: the WTP assessment methods; the main attributes used for comparing alternatives in WTP studies, targeted climate change mitigation policies in energy sector, mathematical model used to estimate WTP, the main socio-demographic factors having impact on WTP for climate change mitigation policies. The analysis of WTP studies for climate change mitigation is grouped in two areas: renewables and energy efficiency measures in households. The paper provides analytical structure for future studies to evaluate the effects of variation in key comparative elements upon WTP.

Various socio-economic systems (countries, regions, or cities) and their economies suffer different kinds of economic shocks. If the system is not resilient, its economy can incur losses. Only the systems whose economies are less vulnerable and/or are able to recover from the economic shock quite quickly are able to ensure economic sustainability, competitiveness, and welfare both now and in the future. The concepts of socio-economic systems’ resilience to economic shocks, vulnerability, and recovery, as well as the resilience assessment peculiarities, are all analyzed in this article. The methodology introduced for the assessment of a socio-economic system’s resilience to economic shocks consists of two parts: a model of a system’s resilience to the economic shock’s capacity-related factors (Resilio) and an index of a socio-economic system’s resilience to the economic shocks (Resindicis). The Resilio model could be used as a universal methodological framework for analyzing the resilience of socio-economic systems of different levels (countries, regions, or cities). The set of quantitative indicators compiling Resindicis should be adjusted to the specifics of each socio-economic system and the availability of statistical data. Empirically, the methodology was validated on the example of 10 Lithuanian regions (counties). The methodological principles for the assessment of a socio-economic system’s resilience are also provided. The main advantages and drawbacks of the methodology are discussed in order for further development and an increase in its practical application.

The impact of technological advances in all industry sectors is being felt and, thus, there is no doubt that digital transformation will have significantly affect Lithuanian manufacturing sector. In order to assess the extent to which Lithuanian processing industry companies are digitalized, an in-depth descriptive analysis of installed digital technologies in these companies was executed. The goal of this analysis – to determine whether Lithuanian companies of processing industry has been sufficiently digitalized and are ready to completely adopt the principles of Industry 4.0 in the installation of digital solutions within all segments of the value-chain creation. The research on already-applied digital tools and technologies in those companies was made during the phase of analysis. There was also an attempt to define the most digitalized processes of operation and the least or non-digitalized processes of operations in those companies. After the assessment of a current digitalization level in the company was made, there was an attempt to clarify the strengths and problematic challenges as well as the underlying reasons for its challenges. After the above-mentioned data was collected, recommendations on which additional tools and means to apply in order to encourage the process of digitalization in the companies were formulated and passed on to the companies.

Ukraine’s reconstruction, estimated at $524 billion over the next decade, presents an unprecedented opportunity to embed circular economy principles into industrial rebuilding, but the financial architecture currently deployed for reconstruction is structurally blind to circular outcomes. This paper examines how digital tools and innovative financing mechanisms can channel investment toward circular industrial reconstruction in Ukraine, drawing on Germany’s National Circular Economy Strategy (NCES, adopted December 2024) as a reference model. A comparative institutional analysis combines a documentary review of Ukrainian reconstruction policy frameworks (Ukraine Plan 2024–2027, RDNA4, Ukraine Facility) and German NCES instruments with the construction of a financing−technology pathway typology. Five pathways are proposed: circular bond issuance with Digital Product Passport integration; blended finance with blockchain impact verification; EU Facility conditionality with AI-driven resource management; war risk insurance with circular construction standards; and SME digitalisation credit with circular economy competency building. Each pathway is assessed against five criteria: investment scale, risk mitigation, circular measurement, digital readiness, and institutional feasibility, and applied to four industrial corridors (Dnipro region, Zaporizhzhia region, Kharkiv region, and Donetsk region). The analysis reveals that no single pathway is sufficient; a layered strategy differentiating by region is required. Digital tools, particularly the Digital Product Passport and blockchain traceability, serve as partial substitutes for institutional trust in post-conflict settings, reducing information asymmetry between investors and project operators. The paper contributes a practically oriented framework at the under-theorised intersection of post-conflict reconstruction finance and circular economy scholarship.

The article describes the construction of a predictive model of corporate sustainability, the DACSI Index, for measuring sustainability. The aim of the paper is to propose a predictive model DACSI Index based on economic IEcoi and non-financial indicators IESGi and appropriately selected predictive models DAEco and DAESG for manufacturing companies according to CZ-NACE classification. Predictive models were developed with the use of Multiple Discriminant Analysis (MDA). MDA results showed that the inclusion of non-financial indicators did not result in any significant changes in the classification of companies into individual groups compared to classification on the basis of economic indicators only. From MDA results it also follows that the statistical significance of non-financial indicators is low, but they signal a causal relationship between individual economic and non-financial indicators of sustainability. The results also showed that the predictive model DACSI Index, composed of economic indicators, environmental indicators, social indicators and corporate governance indicators has a much higher accuracy than the predictive model composed of economic indicators only. The essential conclusion of our research into corporate sustainability measurement is that the traditional performance assessment using economic indicators no longer suffices and does not reflect current performance of the company from the long-term perspective, and it is therefore necessary to include both economic and non-financial indicators into the predictive model DACSI Index. And the predictive model DACSI Index is just the type of model that will provide relevant information about the company’s sustainability status to both the owners and investors.DOI: http://dx.doi.org/10.5755/j01.ee.26.4.11480