Mokslo publikacijos

This paper investigates whether green taxation can yield a household double dividend—greener consumption and higher household welfare—via production-side mechanisms. Using a sectorally disaggregated computable general equilibrium (CGE) model calibrated to China’s 2020 Social Accounting Matrix, we distinguish between traditional and green products within each industry to capture production—consumption linkages.

Renewable energy investment evaluation continues to rely predominantly on technoeconomic and environmental criteria, while equity-related considerations remain weakly embedded within formal decision-support frameworks. Although recent research increasingly acknowledges social impacts, spatial constraints, policy uncertainty, and financing structures, these dimensions are rarely integrated in a systematic and operational manner into investment appraisal.

Despite the practical relevance of many tourism research studies, organizations and policymakers often struggle to integrate them due to time constraints, language barriers, limited resources, and interaction challenges. Generative artificial intelligence (GenAI) offers new capabilities to overcome these barriers. We propose a GenAIenabled knowledge translation process with three stages: (i) research curation to identify and translate relevant literature; (ii) content creation to produce materials; and (iii) market research using synthetic guests to pre-test their effectiveness.

Agriculture, as a foundational industry with both economic and ecological attributes, plays a crucial role in ensuring the stable supply of ecological products, which is essential for sustainable development. As a key institutional mechanism for optimizing land resource allocation, the Rural Collective Property Rights System Reform (RCPRSR) has not been systematically examined in terms of its impact on the supply of agricultural ecological products.

Benefit of the doubt (BoD) models are widely used for multicriteria analysis. A BoD model allows the weights of criteria to vary across observations based on the observed data; however, this can reduce the discriminatory power and induce rank reversal. The common weight BoD was developed to mitigate these issues at the expense of the “benefit of the doubt” itself because a degree of weight flexibility is lost.

The study addresses the critical challenge of uneven digital technology adoption and its varied impact on sustainability in the agricultural sector globally. It investigates how digital technologies can be leveraged to enhance key sustainability metrics, including renewable energy integration and resource efficiency, within the bioeconomy framework.

Reducing energy consumption and improving energy efficiency are particularly important for mitigating climate change. The main motives for saving energy and improving energy efficiency are economic and environmental. It is also necessary to implement the environmental policy. Thus, this paper aims to analyse attitudes towards ecological policy and the impact of economic and environmental factors on energy consumption and energy efficiency behaviours in European Union (EU) countries in 2022.

This study examines family business performance through three theoretical lenses: dynamic capabilities, entrepreneurial orientation, and socioemotional wealth. Data were gathered from 156 Spanish family businesses and interviews with eight family business CEOs. Partial least squares structural equation modeling (PLS-SEM) was used to analyze the data. The results reveal valuable findings. Dynamic capabilities, particularly sensing, seizing, and resource reconfiguration, are critical for navigating dynamic environments and achieving sustainable competitive advantage.

Purpose – This study investigates the effects of institutional pressures on the adoption of supply chain analytics (SCA) and its subsequent impact on supply chain (SC) innovation and frugal innovation. It also examines the correlation between SC innovation and frugal innovation.

Forecasting hotel occupancy during external shocks is particularly challenging due to their disruptive effects. This study develops a forecasting framework that integrates multisource data using a time-varying parameter state-space model (TVP-SSM). In this framework, search engine data (SED) are used to construct exogenous variables, intervention variables are used to reflect the severity of external shocks, and holiday and weekend dummy variables are used to capture the seasonal effect.