Search Results (9,057)

Environmental protection is one of the key challenges facing mankind today. Finding out what young people, referred to as Generation Z, think about this issue is extremely important, as they will be the first to experience the negative effects of environmental degradation. Research has shown that Generation Z has the greatest hope for solutions from the technological sphere. Thus, the economic and political spheres should support the development of technology in this area. The social sphere is rated lowest, which may reflect young people’s personal withdrawal and the delegation of responsibility for the environment’s future to engineers, entrepreneurs, and politicians. It is equally important to learn what constitutes an environmental imperative for Generation Z. It is based on new energy sources, energy producers, and the state’s pursuit of a policy of international cooperation in this area, supported by national legislative activity toward entrepreneurs and citizens. Research has demonstrated the need to raise awareness among young people, with a focus on individuals treated as subjects in their interaction with modern technology. Full article

With the continuous increase in the proportion of distributed energy output in the distribution network and the limited equipment on the management side of the active distribution network, it is very important to give full play to the regulating role of the dispatchable potential of large-scale electric vehicles for the economic operation of the distribution network. To deal with this issue, this paper proposes an optimal dispatching model of the distribution network considering the combination of the dispatchable potential of electric vehicle clusters and demand response. Firstly, the active distribution network dispatching model with the demand response is introduced, and the equipment involved in the active distribution network dispatching is modeled. Secondly, the bidirectional long short-term memory network algorithm is used to process the historical data of electric vehicles to reduce the uncertainty of the model. Then, the shared energy-storage characteristics based on the dispatchable potential of electric vehicle clusters are fully explored and the effect of peak shaving and valley filling after the demand response is fully explored. This approach significantly reduces the network loss and operating cost of the active distribution network. Finally, the modified IEEE-33 bus test system is utilized for test analysis in the case analysis, and the test results show that the established active distribution network model can reduce the early construction cost of the system’s energy-storage equipment, improve the energy-utilization efficiency, and realize the economic operation of the active distribution network. Full article

Evaluation of the effectiveness of state policy instruments is a permanent element of economic science. This paper addresses the issue of investment support under the Common Agricultural Policy (CAP). Using data on Polish farms from 2015–2023, a Propensity Score Matching–Difference in Differences (PSM-DiD) analysis was conducted to assess changes in the economic results of agricultural producers that invest using this support. The comparison of the economic results achieved by supported investors with both non-investing agricultural producers and unsupported investors is a distinguishing element of this study. The relatively rarely used Competitivness Index (CI), which measures the ratio of earned income to the sum of the alternative use of the owned means of production, was used. The positive change in the CI during the analyzed period was 0.14 higher for supported investors than non-investors. No statistically significant change was found were compared to unsupported investors. A clear increase in income, total fixed assets, liabilities, and the level of production in the population of producers using support in relation to non-investors and investing without CAP support was also observed. However, in relationships with investors using their own funds, these differences were mainly due to the difference in the level of investments and were not statistically significant when introducing a correction regarding the scale of the investment. The obtained results remain in line with the results of research shown by a significant part of economists undertaking a similar issue. Full article

Economic systems face critical challenges, including widening income inequality, unemployment driven by automation, mounting public debt, and environmental degradation. This study introduces Economocracy as a transformative framework aimed at addressing these systemic issues by integrating democratic principles into economic decision-making to achieve social equity, economic efficiency, and environmental sustainability. The research focuses on two core mechanisms: Economic Productive Resets (EPRs) and Economic Periodic Injections (EPIs). EPRs facilitate proportional redistribution of resources to reduce income disparities, while EPIs target investments to stimulate job creation, mitigate automion-related job displacement, and support sustainable development. The study employs a theoretical and analytical methodology, developing mathematical models to quantify the impact of EPRs and EPIs on key economic indicators, including the Gini coefficient for inequality, unemployment rates, average wages, and job displacement due to automation. Hypothetical scenarios simulate baseline conditions, EPR implementation, and the combined application of EPRs and EPIs. The methodology is threefold: (1) a mathematical–theoretical validation of the Cycle of Money framework, establishing internal consistency; (2) an econometric analysis using global historical data (2000–2023) to evaluate the correlation between GNI per capita, Gini coefficient, and average wages; and (3) scenario simulations and Difference-in-Differences (DiD) estimates to test the systemic impact of implementing EPR/EPI policies on inequality and labor outcomes. The models are further strengthened through tools such as OLS regression, and Impulse results to assess causality and dynamic interactions. Empirical results confirm that EPR/EPI can substantially reduce income inequality and unemployment, while increasing wage levels, findings supported by both the theoretical architecture and data-driven outcomes. Results demonstrate that Economocracy can significantly lower income inequality, reduce unemployment, increase wages, and mitigate automation’s effects on the labor market. These findings highlight Economocracy’s potential as a viable alternative to traditional economic systems, offering a sustainable pathway that harmonizes growth, social justice, and environmental stewardship in the global economy. Economocracy demonstrates potential to reduce debt per capita by increasing the efficiency of public resource allocation and enhancing average income levels. As EPIs stimulate employment and productivity while EPRs moderate inequality, the resulting economic growth expands the tax base and alleviates fiscal pressures. These dynamics lead to lower per capita debt burdens over time. The analysis is situated within the broader discourse of institutional economics to demonstrate that Economocracy is not merely a policy correction but a new economic system akin to democracy in political life. Full article

The rapid development of digital technology is reshaping the global economic landscape. However, its impact on firms’ capacity utilization rate (CUR), particularly through technological innovation, remains unclear. This study investigates this issue by developing an endogenous growth model that connects digital technology to CUR. The empirical analysis is based on data from Chinese A-share manufacturing firms. The methods employed include quantile regression, instrumental variable techniques, and various tests to explore underlying mechanisms. CUR is calculated using a special model that looks at random variations, and digital transformation is assessed using text analysis powered by machine learning. The findings indicate that digital transformation significantly enhances CUR, especially for firms with average capacity utilization levels, but has a limited effect on low- and high-end firms. Moreover, technological innovation mediates this relationship; however, factors like “double arbitrage” (involving policy and capital markets) and “herd effects” tend to prioritize quantity over quality, which constrains innovation potential. Improvements in CUR lead to enhanced firm performance and productivity, generating industry spillovers and demonstrating the broader economic externalities of digitalization. This study uniquely applies endogenous growth theory to examine the role of digital transformation in optimizing CUR. It introduces the “quantity-quality” technology innovation paradox as a crucial mechanism and highlights industry spillovers to address overcapacity while offering insights for fostering sustainable economic and social development in emerging markets. Full article

The construction industry is a cornerstone of New Zealand (NZ)’s economic growth, yet strategic infrastructure planning is constrained by fragmented and inconsistent pipeline data. Despite the increasing availability of construction pipeline datasets in NZ, their limited clarity, interoperability, and standardisation impede effective forecasting, policy development, and investment alignment. These challenges are compounded by disparate data structures, inconsistent reporting formats, and semantic discrepancies across sources, undermining cross-agency coordination and long-term infrastructure governance. To address this issue, the study begins by assessing the quality of four prominent pipeline datasets using Wang and Strong’s multidimensional data quality framework. This evaluation provides a necessary foundation for identifying the structural and semantic barriers that limit data integration and informed decision-making. The analysis examines four dimensions of data quality: accessibility, intrinsic quality, contextual relevance, and representational clarity. The findings reveal considerable inconsistencies in data fields, classification systems, and levels of detail across the datasets. Building on these insights, this study also develops a conceptual minimum dataset (MDS) framework comprising three core thematic categories: project identification, project characteristics, and project budget and timing. The proposed conceptual MDS includes unified data definitions, standardised reporting formats, and semantic alignment to enhance cross-platform usability and data confidence. This framework applies to the New Zealand context and is designed for replication in other jurisdictions, supporting the global push toward open, high-quality infrastructure data. The study contributes to the construction informatics and infrastructure planning by offering a practical solution to a critical data governance issue and introducing a transferable methodology for developing minimum data standards in the built environment to enable more informed, coordinated, and evidence-based decision-making. Full article

In the context of both domestic and international economic landscapes, regional policy has emerged as an increasingly influential factor shaping the developmental trajectories of Chinese enterprises. Despite its growing significance, the extant literature lacks a comprehensive and systematically visualized synthesis that encapsulates the scope and trends of research in this domain. This study addresses this critical gap by conducting an integrative bibliometric and qualitative review of the academic output related to regional policy and Chinese firm growth. Drawing on a final dataset comprising 3428 validated academic publications—selected from an initial pool of 3604 records retrieved from the Web of Science Core Collection between 1991 and 2022, the research employs a two-stage methodological framework. In the first phase, advanced bibliometric tools, and software applications, including RStudio, Bibliometrix, VOSviewer, and CitNetExplorer, are utilized to implement techniques such as keyword co-occurrence analysis, thematic clustering, and the tracing of thematic evolution over time. These methods facilitate rigorous data cleansing, breakpoint identification, and the visualization of intellectual structures and emerging research patterns. In the second phase, a targeted qualitative review is conducted to evaluate the influence of regional policies on Chinese firms across three critical stages of business development: start-up, expansion, and maturity. The findings reveal that regional policy interventions generally exert a positive influence on firm performance throughout all stages of development. Notably, a significant concentration of citation activity occurred prior to 2017; however, post-2017, the volume of scholarly publications, journal-level impact (as measured by h-index), and author-level influence experienced a marked increase. Among the 3428 analyzed publications, a substantial portion—2259 articles—originated from Chinese academic institutions, highlighting the strong domestic research interest in the subject. Furthermore, since 2015, there has been a discernible shift in keyword co-occurrence trends, with increasing scholarly attention directed towards sustainable development issues, particularly those related to carbon dioxide emissions and green innovation, reflecting evolving policy priorities and environmental imperatives. Full article

This study focuses on cultural and urban development policies under China’s 14th Five-Year Plan, exploring the content and semantic structure of discussions on the “East Asian Cultural Capital” project on the Weibo platform. It analyzes how national cultural development policies are reflected in the discourse system related to the “East Asian Cultural Capital” on social media and emphasizes the guiding role of policies in the dissemination of online culture. When China announced the 14th Five-Year Plan in 2021, the strategic direction and policy framework for cultural development over the five-year period from 2021 to 2025 were clearly outlined. This study employs text mining and semantic network analysis methods to analyze user-generated content on Weibo from 2023 to 2024, aiming to understand public perception and discourse trends. Word frequency and TF-IDF analyses identify key terms and issues, while centrality and CONCOR clustering analyses reveal the semantic structure and discourse communities. MR-QAP regression is employed to compare network changes across the two years. Findings highlight that urban cultural development, heritage preservation, and regional exchange are central themes, with digital media, cultural branding, trilateral cooperation, and cultural–economic integration emerging as key factors in regional collaboration. Full article

This study intends to provide insights and challenges for the shape of the B movement, an emerging paradigm that fosters cross-sectoral partnerships and encourages ethical business practices through so-called purpose-driven businesses. Focusing on Italy, the first European country to adopt this managerial model, the research investigates Italian Benefit Corporations, known as Società Benefit (SB), and their most appealing sustainability claims from a consumer perspective. The analysis intends to inform theory development by assuming the cultural and creative industry (CCI) as a field of interest, utilizing a within-subjects experimental design to analyze data from a diverse consumer sample across various contexts. The results indicate that messaging centered on economic sustainability emerged as the most effective in generating positive consumer responses, highlighting a prevailing inclination toward pragmatic factors such as affordability, economic accessibility, and tangible benefits rather than social issues. While sustainable behaviors are not yet widespread, latent ethical sensitivity for authentic, value-driven businesses suggests that economic and ethical dimensions can be strategically synthesized to enhance consumer engagement. This insight highlights the role of BCs in catalyzing a shift in consumption patterns within ethical-based and creative-driven sectors. Full article

In recent years, growing concerns over environmental degradation and deepening awareness of the necessity of sustainable development have propelled green and low-carbon energy transition into a focal issue for both academia and policymakers. By decomposing energy transition into the transformation of energy structure and the upgrading of energy efficiency, this study investigates the impact and mechanisms of green finance on energy transition across 30 provinces (municipalities and autonomous regions) in China, with the exception of Tibet. In addition, the impact of climate change is incorporated into the analytical framework. Empirical results demonstrate that green finance development significantly accelerates energy transition, a conclusion robust to rigorous validation. Analysis of the mechanism shows that green finance promotes energy transition through the facilitation of technological innovation and the upgrade of industrial structures. Moreover, empirical evidence reveals that climate change undermines the promotional influence of sustainable finance on energy system transformation. The magnitude of this suppression varies nonlinearly across provincial jurisdictions with differing energy transition progress. Regional heterogeneity analyses further uncover marked discrepancies in climate–finance interactions, demonstrating amplified effects in coastal economic hubs, underdeveloped western provinces, and regions with mature eco-financial markets. According to these findings, actionable policy suggestions are put forward to strengthen green finance and accelerate energy transition. Full article

Unemployment and weak economic productivity are significant global issues, particularly in West Africa. Recently, through diverse mechanisms, remittances and foreign direct investment (FDI) have been sources of foreign capital flow that have positively influenced many less developed economies, including ECOWAS (ECOWAS stands for Economic Community of West African States). Nevertheless, these financial flows have exhibited significant inconsistencies, primarily resulting from economic downturns in migrants’ destination countries, with remarkable implications for beneficiary economies. This study, therefore, examines the effect of remittances and FDI on employment in ECOWAS. Specifically, the study assesses the effects of the inflow of remittances and FDI on employment using panel dynamic ordinary least squares (PDOLS) and also investigates the shock effects of remittances and FDI by employing Panel Vector Error Correction (PVECM), which involves variance decomposition. The results show that foreign direct investment (FDI) positively and significantly affects employment. Other variables that show a significant relationship with employment are wage rate, education expenditure, and interest rate. The variance decomposition result revealed that external shocks on remittances and FDI have short- and long-term effects on employment. The above findings imply that foreign direct investment has a far-reaching positive impact on the economy-wide management of the West African sub-region and thus calls for relevant policy options. Full article

Under the rapid development of the digital economy, the interactive relationship between exports and the digital economy has become an important issue for promoting regional economic growth. Based on the panel data of 31 provinces and municipalities in China from 2012 to 2022, this paper systematically examines the impact of exports on economic growth and the moderating role of the digital economy, and it introduces research and development (R&D) investment to test its mediating mechanism. The research finds that exports significantly promote regional economic growth. The digital economy has a negative moderating effect on the export growth effect, and it is significant in the eastern region but not significant in the central and western regions, showing obvious regional heterogeneity. R&D investment has played a partial mediating role between exports and economic growth. This paper suggests that the government should focus on regional differences, promote the deep integration of the digital economy and exports, enhance technological innovation capabilities, formulate differentiated policies based on local conditions, strengthen the construction of digital infrastructure, optimize the export structure, support the development of R&D-driven enterprises, and build a digital export system that promotes regional coordination and high-quality growth, so as to achieve high-quality coordinated sustainable regional development. This paper also has certain reference value for other developing economies, in promoting the integration of the digital economy and trade. Full article

In the Italian intra-day electricity market (MI-XBID), where energy positions can be adjusted up to one hour before delivery, imbalance charges due to forecast errors from non-programmable renewable sources represent a critical issue. This work focuses on photovoltaic (PV) systems, whose production variability makes them particularly sensitive to forecast accuracy. To address these challenges, a comprehensive methodology for assessing and mitigating imbalance penalties by integrating a short-term PV forecasting model with a battery energy storage system is proposed. Unlike conventional approaches that focus exclusively on improving statistical accuracy, this study emphasizes the economic and regulatory impact of forecast errors under the current Italian imbalance settlement framework. A hybrid physical-artificial neural network is developed to forecast PV power one hour in advance, combining historical production data and clear-sky irradiance estimates. The resulting imbalances are analyzed using regulatory tolerance thresholds. Simulation results show that, by adopting a control strategy aimed at maintaining the battery’s state of charge around 50%, imbalance penalties can be completely eliminated using a storage system sized for just over 2 equivalent hours of storage capacity. The methodology provides a practical tool for market participants to quantify the benefits of storage integration and can be generalized to other electricity markets where tolerance bands for imbalances are applied. Full article

Forest volume is an important information for assessing the economic value and carbon sequestration capacity of forest resources and serves as a key indicator for energy flow and biodiversity. Although remote sensing technology is applied to estimate volume, optical remote sensing data have limitations in capturing forest vertical height information and may suffer from reflectance saturation. While LiDAR data can provide more detailed vertical structural information, they come with high processing costs and limited observation range. Therefore, improving the accuracy of volume estimation through multi-source data fusion has become a crucial challenge and research focus in the field of forest remote sensing. In this study, we integrated Sentinel-2 multispectral data, Resource-3 stereoscopic imagery, UAV-based LiDAR data, and field survey data to quantitatively estimate the forest volume in Saihanwula Nature Reserve, located in Inner Mongolia, China, on the southern part of Daxing’anling Mountains. The study evaluated the performance of multi-source remote sensing features by using recursive feature elimination (RFE) to select the most relevant factors and applied four machine learning models—multiple linear regression (MLR), k-nearest neighbors (kNN), random forest (RF), and gradient boosting regression tree (GBRT)—to develop volume estimation models. The evaluation metrics include the coefficient of determination (R²), root mean square error (RMSE), and relative root mean square error (rRMSE). The results show that (1) forest Canopy Height Model (CHM) data were strongly correlated with forest volume, helping to alleviate the reflectance saturation issues inherent in spectral texture data. The fusion of CHM and spectral data resulted in an improved volume estimation model with R² = 0.75 and RMSE = 8.16 m³/hm², highlighting the importance of integrating multi-source canopy height information for more accurate volume estimation. (2) Volume estimation accuracy varied across different tree species. For Betula platyphylla, we obtained R² = 0.71 and RMSE = 6.96 m³/hm²; for Quercus mongolica, R² = 0.74 and RMSE = 6.90 m³/hm²; and for Populus davidiana, R² = 0.51 and RMSE = 9.29 m³/hm². The total forest volume in the Saihanwula Reserve ranges from 50 to 110 m³/hm². (3) Among the four machine learning models, GBRT consistently outperformed others in all evaluation metrics, achieving the highest R² of 0.86, lowest RMSE of 9.69 m³/hm², and lowest rRMSE of 24.57%, suggesting its potential for forest biomass estimation. In conclusion, accurate estimation of forest volume is critical for evaluating forest management practices and timber resources. While this integrated approach shows promise, its operational application requires further external validation and uncertainty analysis to support policy-relevant decisions. The integration of multi-source remote sensing data provides valuable support for forest resource accounting, economic value assessment, and monitoring dynamic changes in forest ecosystems. Full article

The carbon dioxide spillover effects and influencing factors of the “Belt and Road Initiative” (BRI) in African countries must be assessed to evaluate the effectiveness, promote low-carbon transmissions in African countries, and provide recommendations for achieving the 2030 Sustainable Development Goals. This novel study employs carbon dioxide emission intensity (CEI) and per capita carbon dioxide emissions (PCE) as dual indicators to evaluate the spatial spillover effects of 54 BRI African countries on their neighboring countries’ carbon emissions from 2007 to 2023. It identifies the key factors and mechanisms affecting these spillover effects using the spatial differences-in-differences (SDID) model. Results indicate that since the launch of the BRI, the CEI and PCE of BRI African countries have significantly increased, largely due to trade patterns and industrialization structures. Greater trade openness has further boosted local economic development, thereby increasing carbon dioxide’s spatial spillover. Government management and corruption control levels show some heterogeneity in the spillover effects, which may be attributed to long-standing issues of weak institutional enforcement in Africa. Overall, this study reveals the complex relationship between BRI African economic development and environmental outcomes, highlighting the importance of developing sustainable development strategies and establishing strong differentiated regulatory regimes to effectively address environmental challenges. Full article