Search Results (26)

For the net-zero emission goal by 2050, the government of Taiwan has mandated large electricity consumers to utilize 10% green electricity to mitigate carbon emissions. Major enterprises face challenges in selecting appropriate green power options and integrating the benefits of carbon reduction into corporate governance decision-making. This study aims to optimize the combination of various green power options through a system dynamics approach, incorporating existing power purchase conditions and electricity consumption data from enterprises. In addition, by utilizing financial estimations with the monetization of environmental benefits, we constructed a more complete evaluation model for enterprises transitioning to green power. The results indicate low investment returns in various green energy portfolios. However, if power storage equipment is utilized to participate in auxiliary services, the investment return of green energy can be significantly enhanced. This evaluation model is also available online for business professionals across various sectors to explore and reference. Full article

To ensure a smooth transition towards peak carbon emissions and carbon neutrality, one key strategy is to promote a low-carbon transition in the energy sector by facilitating the coordinated development of the electricity market, carbon market, and other markets. Currently, China’s national carbon market primarily focuses on the power generation industry. High-energy-consuming industries such as the steel industry not only participate in the electricity market but also play a significant role in China’s future carbon market. Despite existing research on market mechanisms, there remains a significant research gap in understanding how steel enterprises adjust their trading behaviors to optimize costs in multi-market coupling contexts. This study employs a system dynamics approach to model the trading interconnection between electricity trading (ET), carbon emission trading (CET), and tradable green certificates (TGC). Within this multi-market system, thermal power enterprises and renewable generators serve as suppliers of carbon allowances and green certificates, respectively, while steel companies must meet both carbon emission constraints and renewable energy consumption obligations. The results show that companies can reduce future market transaction costs by increasing the proportion of medium to long-term electricity contracts and the purchase ratio of green electricity. Additionally, a lower proportion of free quotas leads to increased costs in the carbon market transactions in later stages. Therefore, it is beneficial for steel companies to conduct cost analyses of their participation in multivariate market transactions in the long run and adapt to market changes in advance and formulate rational market trading strategies. Full article

This review proposes a novel integration of game-theoretical methods—specifically Evolutionary Game Theory (EGT), Stackelberg games, and Bayesian games—with deep reinforcement learning (DRL) to optimize electricity markets. Our approach uniquely addresses the dynamic interactions among power purchasing and generation enterprises, highlighting both theoretical underpinnings and practical applications. We demonstrate how this integrated framework enhances market resilience, informs evidence-based policy-making, and supports renewable energy expansion. By explicitly connecting our findings to regulatory strategies and real-world market scenarios, we underscore the political implications and applicability of our results in diverse global electricity systems. By integrating EGT with advanced methodologies such as DRL, this study develops a comprehensive framework that addresses both the dynamic nature of electricity markets and the strategic adaptability of market participants. This hybrid approach allows for the simulation of complex market scenarios, capturing the nuanced decision-making processes of enterprises under varying conditions of uncertainty and competition. The review systematically evaluates the effectiveness and cost-efficiency of various control policies implemented within electricity markets, including pricing mechanisms, capacity incentives, renewable integration incentives, and regulatory measures aimed at enhancing market competition and transparency. Our analysis underscores the potential of EGT to significantly enhance market resilience, enabling electricity markets to better withstand shocks such as sudden demand fluctuations, supply disruptions, and regulatory changes. Moreover, the integration of EGT with DRL facilitates the promotion of sustainable energy integration by modeling the strategic adoption of renewable energy technologies and optimizing resource allocation. This leads to improved overall market performance, characterized by increased efficiency, reduced costs, and greater sustainability. The findings contribute to the development of robust regulatory frameworks that support competitive and efficient electricity markets in an evolving energy landscape. By leveraging the dynamic and adaptive capabilities of EGT and DRL, policymakers can design regulations that not only address current market challenges but also anticipate and adapt to future developments. This proactive approach is essential for fostering a resilient energy infrastructure capable of accommodating rapid advancements in renewable technologies and shifting consumer demands. Additionally, the review identifies key areas for future research, including the exploration of multi-agent reinforcement learning techniques and the need for empirical studies to validate the theoretical models and simulations discussed. This study provides a comprehensive roadmap for optimizing electricity markets through strategic and policy-driven interventions, bridging the gap between theoretical game-theoretic models and practical market applications. Full article

The simplified tax system is the most common special tax regime in the Russian Federation in terms of the number of taxpayers. Tax revenues from the simplified tax system account for 6% of the structure of tax revenues of the consolidated budgets of the constituent entities of the Russian Federation and more than 93% of the structure of tax revenues from special tax regimes. The purpose of this study is to identify and assess the factors influencing tax revenues from the tax levied in connection with applying the simplified system of taxation (taxable object—income reduced by the amount of expenses). The objective of this study is to determine a set of factors used by economists to model the level of tax revenues and to conduct a corresponding econometric analysis of the influence of the selected factors on the dependent variable to identify characteristics of the simplified taxation system functioning in the Russian Federation. The object of this study is the per capita tax revenue from the tax levied in connection with applying the simplified system of taxation (the object of taxation is income reduced by expenses) in the Russian Federation. The subject of the research is a set of economic relations, which arise because of tax-legal relations between tax authorities and taxpayers in relation to the calculation of the tax levied in connection with the application of the simplified taxation system. This study’s hypothesis is that the amount of tax revenues is influenced by factors characterizing the economic situation and development of small and medium businesses in the constituent territories of the Russian Federation. This study was conducted in 83 constituent territories of the Russian Federation in 2020–2022. The research methods are statistical analysis and econometric modeling on panel data. During this study, six econometric models were constructed. Based on the results of specification tests, the least squares dummy variables model was selected. The results of the modeling show that the tax rate, the number of taxpayers, and the real average per capita monetary income of the population have a statistically significant impact on the per capita tax revenue under the simplified tax system (the object of taxation is income reduced by the number of expenses). As a result, the focus of economic policy at both macro and meso levels should be on the support of small and medium-sized enterprises in the early stages of their life cycle, as well as on the increase of the purchasing power of the population. Based on the results obtained, it is possible to forecast the revenue side of the budgets of the constituent entities of the Russian Federation. Full article

The consumption decision-making in educational tourism exhibits dual-agent characteristics, requiring alignment of consumption intentions between both agents to generate actual purchasing behavior. However, research on this characteristic is still relatively scarce. Understanding the psychological mechanisms and heterogeneity of consumption decision-making among students and parents in forest educational tourism is crucial for implementing precise consumer incentive strategies in related tourist attractions. This study constructs a theoretical model of the consumer psychological mechanism of dual decision-making agents in forest educational tourism, incorporating perceived value and perceived risk based on the Theory of Planned Behavior. A structural equation model is employed to validate the explanatory power and heterogeneity of this theoretical model, as well as to explore the moderating effect of family decision-making empowerment. The results indicate that the formation of the consumer psychological mechanism of dual decision-making agents in forest educational tourism is heterogeneous: the negative impact of perceived risk on perceived behavioral control and the positive impact of perceived behavioral control on consumption intention are only valid in the student group, not in the parent group; perceived behavioral control serves as a mediator only in the relationship between perceived value, perceived risk, and consumption intention for the student group, without any mediating effect for the parent group; family decision-making empowerment moderates certain paths in the consumer psychological influence mechanism of forest educational tourism decision-making agents. This study expands the Theory of Planned Behavior, enriching the research perspective on factors influencing consumption psychology, exploring the heterogeneity of dual decision-making agents in educational tourism, and examining the impact of family decision-making empowerment on consumer psychology. The findings provide relevant tourism enterprises and forest attractions with a deeper understanding of the consumption psychology of dual decision-making agents in forest educational tourism, offering a scientific basis for tourism enterprises and forest attractions to optimize marketing strategies, while also enhancing the consumption experience on the demand side. Full article

New energy vehicle (NEV) power batteries are experiencing a significant “retirement wave”, making second-life utilization (SLU) a crucial strategy to extend their lifespan and maximize their inherent value. This study focuses on prominent enterprises in China’s SLU sector, including BAIC Group, BYD, China Tower, and Zhongtian Hongli. Employing a multi-case study approach, a variety of business models and applicable scenarios developed through the cooperation between NEV manufacturers and SLU enterprises are effectively identified, including “co-constructing and purchase”, “co-constructing and leasing”, “self-constructing and purchase”, and “self-constructing and leasing”. The choice of collaborative business model is closely linked to the developmental stage of the NEV manufacturers and SLU enterprises. Additionally, this paper finds that the achievement of collaboration is influenced by the interplay between market dynamics and government policies. The theoretical framework developed from this study offers valuable insights for NEV manufacturers and SLU enterprises to establish stable and effective collaborative business models. Full article

The pandemic crisis has created significant challenges for small farms, leading to increased energy costs, higher prices for feed and nutrients, unreliable supplies of chemical fertilizers, and disruptions in product sales markets. These factors have collectively compromised the operational viability and economic sustainability of small-scale agricultural enterprises. To address these challenges, this paper explores the concept of a self-sufficient farming system, focusing on locally producing most of the resources needed to sustain operations and reduce dependence on external sources. A self-sufficient integrated pisciculture farming system is proposed and evaluated, promoting an autonomous circular model that prioritizes environmental sustainability. This system incorporates the integration of local livestock into fish diets, production of renewable energy sources, and efficient water and sludge management to reduce reliance on external resources. The detailed methodology used to evaluate sustainability indicators objectively demonstrates that the proposed system can be self-sustainable and autonomous; however, it requires considerable initial investments that can be recovered within at least six years. Optimizing the energy management plan can reduce daily power consumption by up to 25%. However, local conditions may challenge the efficiency of photovoltaic–hybrid energy production, requiring slight oversizing of the system. The research indicated that rearing carp with cereal-based feed mixtures produces growth results comparable to those achieved with commercially purchased feed. The indicators of resource efficiency, reliability, flexibility, productivity, environmental impact, and social impact were met as expected. The weakest indicator was the technology’s potential for scalability, due to its strong dependence on various regional factors. Full article

Enhancing residents’ purchasing intention of prepared dishes is crucial for the sustainable development of the prepared dishes industry. Understanding how residents’ cognition influences their continuous purchasing intention can provide valuable insight for developing and refining company strategies, thereby reducing industry development obstacles. Based on the theory of distributed cognition, this study utilizes questionnaire data from urban residents in Beijing and Shanghai, and employs Structural Equation Modeling to explore the influence of cognition on the continuous purchasing intention of Chinese urban residents towards prepared dishes. The study results reveal that: (1) Individual power and geographical power have a significant positive effect on residents’ continuous purchasing intention for prepared dishes, while cultural power does not have a significant effect. (2) Risk perception partially mediates the effect of individual power and geographical power on continuous purchasing intention and fully mediates the effect of cultural power on continuous purchasing intention. Recommendations include: (1) The government should enhance standardization and supervision to create a favorable consumption environment; (2) Enterprises should provide more objective and transparent information to improve residents’ knowledge of prepared dishes and establish a good reputation. Full article

Power consumers can obtain authoritative green environmental value certification through green electricity trading, which plays an important role in improving the production competitiveness of enterprises, especially for international product trade affairs. However, the credibility of green electricity transactions faces serious challenges in the enterprise green authentication affairs, especially the user’s identity authentication, the traceability of green electricity transactions, and the standardization of green electricity transactions. Aiming to solve the certification and traceability problem of tradable green certificates, this paper proposes an integrated green certificate trading protocol, which solves its double-trading problem and helps to improve the credibility of renewable energy use. The main contribution is providing a solution based on the consortium blockchain technology to solve the main challenges mentioned above. The main solved scheme designs a series of protocols, which includes a purchase protocol, payment protocol, and non-transferable protocol. The whole process ensures the credibility, traceability, and non-transferability of green certificate trading. Multiple verification measures are adopted to address security and privacy challenges in green certificate management. Through security analysis, the protocol effectively defends against attacks such as double payments, transaction rollback, and transaction replays while ensuring users’ privacy. Full article

This article analyzes the dynamics of the changes in indicators of socio-economic development under conditions of financial and economic crises and their negative consequences. The study proves that financial crises are associated with severe and prolonged downturns in economic activity. The socio-economic development of European countries in times of crises was analyzed. The cyclical nature of the onset of crises was confirmed via the study of the dynamics of socio-economic development indicators. The main emphasis was on the financial crisis of 2008–2009 and the COVID-19 crisis (2020–2021). The main indicators characterizing the crises were identified based on an analysis of literary sources. Their classification was developed according to the following groups: leading indicators, lagging indicators, and client leading indicators of expansion. Based on the correlation analysis, indicators that have a significant impact on socio-economic development and are predictors of crisis onset were identified. The authors suggest considering such leading indicators as increases in the private credit in the GDP, budget deficit, balance of payment deficit, and real interest rate. The major lagging indicators that have strong correlations with the GDP, such as the employment rate, general government debt, stock price volatility, and investment, were identified. Client leading indicators of expansion include unemployment, an increase in the number of new enterprises, an increase in purchasing power, etc. Some indicators, such as unemployment, can be both lagging indicators and client leading indicators of expansion. The negative consequences of the crisis are caused by the crisis itself as well as by the imbalances preceding the crisis. Therefore, the study of the predictors of crisis onset is relevant for timely decision making in order to prevent the negative consequences of the crisis. Based on the identified lagging indicators, the 2008–2009 crisis and the COVID-19 crisis were studied. To study the development processes of these crises, the authors analyzed by quarters the dynamics of the development of the following macroeconomic indicators: the GDP, employment, and investment levels. The similarities and discrepancies were identified in the natures of the emergences and courses of the 2008–2009 crisis and the COVID-19 crisis using the comparison method. The case study of the Eurozone and individual EU countries (Germany, France, Italy, and Spain) was used. Considering the similar courses of the crises, the forecast of the socio-economic development was made using the analyzed indicators during the COVID-19 crisis based on the 2008–2009 crisis data. The forecast approximation indicators were calculated, and a method for constructing further forecasts was selected. Based on retrospective data, the GDP forecast was developed via the use of the extrapolation method for 2023–2024. It is necessary to consider that while forecasting crises caused by unforeseen events and external influences, it is advisable to use qualitative analysis along with quantitative analysis. This article will be useful to researchers, political elites, experts, and financial analysts when developing programs for the socio-economic development of countries. Full article

The automobile is an important part of transportation systems. Accurate prediction of sales prospects of different power vehicles can provide an important reference for national scientific decision making, flexible operation of enterprises and rational purchases of consumers. Considering that China has achieved the goal of 20% sales of new energy vehicles ahead of schedule in 2025, in order to accurately judge the competition pattern of new and old kinetic energy vehicles in the future, the automobile market is divided into three types according to power types: traditional fuel vehicles, new energy vehicles and plug-in hybrid vehicles. Based on the monthly sales data of automobiles from March 2016 to March 2023, the prediction effects of multiple models are compared from the perspective of univariate prediction. Secondly, based on the perspective of multivariate prediction, combined with the data of economic, social and technical factors, a multivariate prediction model with high prediction accuracy is selected. On this basis, the sales volume of various power vehicles from April 2023 to December 2025 is predicted. Univariate prediction results show that in 2025, the penetration rates of three types of vehicles will reach 43.8%, 44.4% and 11.8%, respectively, and multivariate prediction results show that the penetration rates will reach 51.0%, 37.9% and 11.1%, respectively. Full article

In recent decades, green entrepreneurship has been at the center of attention as an effective strategy to maintain sustainability and create a competitive advantage for organizations in a circular economy. However, the successful implementation of this strategy requires organizations to have internal enablers. This study endeavored to identify and evaluate organizational enablers for green entrepreneurship in manufacturing Small and Medium Enterprises (SMEs) in Iran. Identifying organizational enablers can help SMEs in facilitating the conditions for adopting green entrepreneurship. To these ends, organizational enablers were extracted by reviewing the literature and then, using the viewpoints of 17 active experts in different industries in SMEs, they were classified. In the next step, the “Best Worst Method” was employed to prioritize the identified enablers (5 factors) and sub-enablers (20 factors). The contextual hierarchical relationships between these factors were identified through the “Interpretive Structural Modeling” method. Using the Matrix of Cross-Impact Multiplications Applied to Classification (MICMAC) analysis, the driving and dependence powers of organizational enablers were computed and the enablers were clustered. Based on the results, among the five enablers, three including total quality management, circular supply chain management, and corporate social responsibility were the most important from the point of view of the experts. Moreover, among the sub-enablers, strategic planning, green purchasing, and corporate social responsibility motivation were more important than other sub-enablers. The results of ISM analysis provided a seven-level hierarchical model and the relationships between them. The results of the MICMAC analysis led to the clustering of 20 organizational enablers in three main clusters: driving (nine factors), linkage (four factors), and dependent (seven factors). The results of this study provide practical suggestions for active senior managers to implement green entrepreneurship in SMEs. Full article

The paper introduces a framework of operation of maritime-related enterprises like port authorities and ship-owning or operating companies along with electric energy providers in the electric energy market as a consequence of the global decarbonization effort and, in particular, due to the implementation of ship electrification at berth. Within this context, the main rules of this energy market framework will consist of a proper combination of power purchase agreements along with contracts for difference in an attempt to obtain transactions that are mutually beneficial at least on a mid-term basis. The methodology, which is fully compatible with the electric energy market rules of the European Union, is enriched by a variety of alternative scenarios on the selling prices of electricity, showing that even when monthly or annual periods are used for reference, it is highly possible that all parties engaged have benefits. Full article

A gasification plant may partially replace an industrial thermal plant and hydrogen production plant by polygenerating valuable products (hydrogen, power, steam) from low-value materials. Carbon energy analysis is one way of conceptually evaluating such processes. In this paper, the integration of a heavy residue (HR) gasification plant into a mid-size oil refinery (5 million t per year crude processing rate) is conceptually assessed via the comparison of electricity, natural gas and heavy residue consumption, and CO₂ emissions. The main purpose of the integration is to reduce the consumption of natural gas currently used for hydrogen production at the expense of increased HR consumption and to achieve a reduction in CO₂ emissions. Two case studies with different modes of operation were compared to base case showing that annual reduction of 2280 GWh in natural gas consumption with constant heat and hydrogen production is possible, accompanied with a slight increase in electricity purchase by 28 GWh per year. HR processing in the refinery increases by over 2800 GWh per year. The refinery’s CO₂ emissions increase by more than 20% (up to 350 kt per year) as a result, while, after incorporating external emissions into the balance, a decrease of more than 460 kt CO₂ per year can be achieved. This confirms that the integration of gasification plants within industrial enterprises and clusters has a positive environmental and energy impact and supports the idea of converting low-value material to more valuable products in polygeneration plants. The economics of HR gasifier integration in varying operations under real refinery conditions remain to be explored. Full article

With the continuous development of business intelligence technology, the application research of decision support systems (DSSs) is deepening. In China, the work in this area started relatively late, and there are few DSS research cases to assist in marketing decision-making. Currently, marketing decision support systems have shortcomings in data integration, historical data, query functions, and data analysis. This article analyzes the characteristics of marketing decision-making, discusses the application of data warehouse, OLAP, and data mining technology in marketing decision support systems, and designs a marketing decision support system based on data mining technology. The system uses a BP neural network to conduct data mining marketing forecasting. A three-layer network model for marketing prediction is established, with sales time, product price, and customer purchasing power as network inputs and output as the sales volume of a certain type of product in different locations. The test results show that the average absolute percentage error of this method is 15.13%, and the prediction accuracy is high. Research shows that with the continuous development of data mining technology, the system cannot only help users conduct scientific and reasonable marketing decision-making analyses, making the marketing decision-making process more scientific and reasonable, but also can bring new ideas to enterprise decision-makers, promoting the continuous improvement and progress of the system. Full article