Spillover Effects of Green Finance on Attaining Sustainable Development: Spatial Durbin Model

Kwilinski, Aleksy; Lyulyov, Oleksii; Pimonenko, Tetyana

doi:10.3390/computation11100199

Cited by 53 publications

(10 citation statements)

References 68 publications

(103 reference statements)

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“…Green energy and associated The second cluster (green) merged the investigations of the ecological footprint, green investment, and green economy development (Figure 3). Green investment acts as a catalyst for the development and expansion of sustainable energy solutions [84][85][86]. Allocating financial resources to renewable energy projects, energy-efficient technologies, and ecofriendly initiatives enhances the capacity to transition toward a green economy [87][88][89].…”

Section: Resultsmentioning

confidence: 99%

Mapping Global Research on Green Energy and Green Investment: A Comprehensive Bibliometric Study

Kwilinski

2024

Energies

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The spillover effects of climate change require the exploration and implementation of appropriate ways to reduce ecological issues while simultaneously maintaining economic and social well-being. The expansion of green energy allows for a reduction in the negative anthropogenic impact on the environment without restricting economic growth or social welfare. However, the expansion of green energy necessitates additional green investment. This paper aims to provide a comprehensive bibliometric analysis of studies on the interactions between green energy and green investment. The study is based on Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines and employs Scopus Tools Analysis and VOSviewer version 1.6.20 software. The metadata for the investigation were compiled from scientific databases in Scopus. The findings allow for the identification of the most prolific countries and authors and their collaborative efforts, which contribute to the theoretical landscape of green energy and green investment. The study also illustrates the evolution of the investigation of the linkages between green energy and green investment. Furthermore, the results enable the identification of core scientific clusters in the analysis of green energy and green investment: the first cluster focuses on renewable energy and sustainable development; the second on government and green energy; and the third on green investment as the catalyst for green energy. The results of the meta-analysis facilitate the identification of new research areas related to the connection between green investment and green energy.

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Section: Resultsmentioning

confidence: 99%

Mapping Global Research on Green Energy and Green Investment: A Comprehensive Bibliometric Study

Kwilinski

2024

Energies

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“…Strong environmental regulations may influence the energy industry's structure and intensity by promoting cleaner practices. The percentage of GDP spent on environmental protection indicates the government's commitment to sustainability: green investments (GFDIs) [147][148][149]. Representing green foreign direct investments, this variable incorporates the impact of foreign capital specifically targeted toward environmentally friendly initiatives.…”

Section: Methodsmentioning

confidence: 99%

Digitalization and Energy in Attaining Sustainable Development: Impact on Energy Consumption, Energy Structure, and Energy Intensity

Dzwigol,

Kwilinski,

Lyulyov

et al. 2024

Energies

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The relentless advancement of digital technologies has the potential to significantly reshape the energy landscape as digital technologies become increasingly integrated into diverse sectors of the economy. This study explores the intricate relationship between digitalization and energy in EU countries, focusing on its direct and indirect impacts on energy consumption, structure, and intensity. Furthermore, this study explores the mediating mechanisms that facilitate these changes, including the role of technological innovation and government efficiency in the relationship between digitalization and energy outcomes. Focusing on EU countries and using the system-GMM method, this research accounts for the heterogeneity in the impact of digitalization on energy across various member states. It examines the varying effects in different countries, considering their income levels, environmental regulations, and green investments. The results demonstrate that the strategic deployment of digitalization in EU countries substantially benefits the energy sector. By optimizing energy consumption and enhancing the energy structure through the integration of renewable sources, the EU could move closer to its sustainability objectives.

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“…The more commonly used spatial econometric models include spatial lag model (SLM), spatial error model (SEM) and spatial Durbin model (SDM). Spatial Durbin model can be converted into spatial lag model and spatial error model under certain conditions [ 30 ]. Since the purpose of this paper is to analyze the spatial spillover effect of regional logistics capacity and its influencing factors, this paper mainly focuses on whether it is possible to choose the spatial Durbin model that contains endogenous and exogenous interaction effects.…”

Section: Methodsmentioning

confidence: 99%

Analysis of logistics capacity, influencing factors and spatial spillover effect in Yangtze River Economic Belt

Fanghu,

Yinnan,

Biao

2024

PLoS ONE

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The logistics industry plays a crucial role in facilitating regional economic development. Serving as a vital link connecting producers, consumers, and various components of the supply chain, it has a direct and profound impact on the prosperity and advancement of regional economies. Based on the panel data of 11 provinces in the Yangtze River Economic Belt from 2016 to 2020, this paper constructs the logistics capability evaluation index system from four aspects: regional economic base, logistics infrastructure, logistics development scale, information technology and talent support, and uses the entropy weight TOPSIS method to measure the logistics capability of each province. The adjacency space weight matrix, geographical distance weight matrix and economic distance weight matrix are selected to build a spatial econometric model to analyze the influencing factors and spatial spillover effects of regional logistics capability. The following conclusions can be drawn from the analysis. From 2016 to 2020, the regional logistics capacity of the Yangtze River Economic Belt shows a trend of increasing year by year, but the logistics capacity of different provinces within the region has a large room for improvement. From the perspective of spatial dimension, the logistics capacity of the Yangtze River Economic Belt is "high in the east and low in the west". The results of spatial econometric analysis based on the spatial Durbin model show that there are significant spatial spillover effects on the logistics capacity of provinces in the Yangtze River Economic Belt. Factors such as road network density, port throughput, water freight turnover, transportation, warehousing and postal employment will not only affect the logistics capacity of the region, but also have a spillover effect on the material capacity of neighboring provinces in the Yangtze River Economic Belt. This study improves the level of regional logistics capacity and promotes the regional economic development of the Yangtze River Economic Belt. It can be used as a reference for other regions or countries in terms of enhancing regional logistics capacity and promoting regional economic development.

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Spillover Effects of Green Finance on Attaining Sustainable Development: Spatial Durbin Model

Cited by 53 publications

References 68 publications

Mapping Global Research on Green Energy and Green Investment: A Comprehensive Bibliometric Study

Mapping Global Research on Green Energy and Green Investment: A Comprehensive Bibliometric Study

Digitalization and Energy in Attaining Sustainable Development: Impact on Energy Consumption, Energy Structure, and Energy Intensity

Analysis of logistics capacity, influencing factors and spatial spillover effect in Yangtze River Economic Belt

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