Energy development in rural China toward a clean energy system: utilization status, co-benefit mechanism, and countermeasures

Yin, Shi; Zhao, Zheng

doi:10.3389/fenrg.2023.1283407

Cited by 41 publications

(15 citation statements)

References 39 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…They explored the relationships between different stakeholders, providing guidance for optimizing the construction of rural new energy systems. This research emphasized the significance of understanding and coordinating various stakeholders’ interests in advancing rural new energy development (Yin and Zhao, 2023, [ 9 ]). Yin et al (2022) combined symbiosis theory with six analytical methods to innovatively construct a new framework for selecting joint investment partners in digital green innovation projects.…”

Section: Literature Reviewmentioning

confidence: 99%

The impact of technological innovation on the green digital economy and development strategies

Liu,

Yang,

Zhang

et al. 2024

PLoS ONE

View full text Add to dashboard Cite

To investigate the interplay among technological innovation, industrial structure, production methodologies, economic growth, and environmental consequences within the paradigm of a green economy and to put forth strategies for sustainable development, this study scrutinizes the limitations inherent in conventional deep learning networks. Firstly, this study analyzes the limitations and optimization strategies of multi-layer perceptron (MLP) networks under the background of the green economy. Secondly, the MLP network model is optimized, and the dynamic analysis of the impact of technological innovation on the digital economy is discussed. Finally, the effectiveness of the optimization model is verified by experiments. Moreover, a sustainable development strategy based on dynamic analysis is also proposed. The experimental results reveal that, in comparison to traditional Linear Regression (LR), Decision Tree (DT), Random Forest (RF), Support Vector Machine (SVM), and Naive Bayes (NB) models, the optimized model in this study demonstrates improved performance across various metrics. With a sample size of 500, the optimized model achieves a prediction accuracy of 97.2% for forecasting future trends, representing an average increase of 14.6%. Precision reaches 95.4%, reflecting an average enhancement of 19.2%, while sensitivity attains 84.1%, with an average improvement of 11.8%. The mean absolute error is only 1.16, exhibiting a 1.4 reduction compared to traditional models and confirming the effectiveness of the optimized model in prediction. In the examination of changes in industrial structure using 2020 data to forecast the output value of traditional and green industries in 2030, it is observed that the output value of traditional industries is anticipated to decrease, with an average decline of 11.4 billion yuan. Conversely, propelled by the development of the digital economy, the output value of green industries is expected to increase, with an average growth of 23.4 billion yuan. This shift in industrial structure aligns with the principles and trends of the green economy, further promoting sustainable development. In the study of innovative production methods, the green industry has achieved an increase in output and significantly enhanced production efficiency, showing an average growth of 2.135 million tons compared to the average in 2020. Consequently, this study highlights the dynamic impact of technological innovation on the digital economy and its crucial role within the context of a green economy. It holds certain reference significance for research on the dynamic effects of the digital economy under technological innovation.

show abstract

Section: Literature Reviewmentioning

confidence: 99%

The impact of technological innovation on the green digital economy and development strategies

Liu,

Yang,

Zhang

et al. 2024

PLoS ONE

View full text Add to dashboard Cite

show abstract

“…Liang et al 19 pointed out that a sound farmland transfer mechanism can optimize the allocation of resources like land, loans, and labor, thereby facilitating the unified development of farmland and finance. In addition, increasing the construction of clean energy in rural areas and combining green finance with agriculture and rural areas can help promote the upgrading of rural industries 20 – 22 . The role of rural financial services is also crucial in this integration.…”

Section: Literature Reviewmentioning

confidence: 99%

Agricultural land management and rural financial development: coupling and coordinated relationship and temporal-spatial disparities in China

Gong,

Xi,

et al. 2024

Sci Rep

View full text Add to dashboard Cite

The integrated development of agricultural land and finance not only promotes rural financial innovation and breaks the bottleneck of agricultural financing but also facilitates agricultural land transfer and scaled operations. This leads to the advancement of the effective growth of contemporary agriculture. The reform of the 'separation of three rights' in agricultural land promotes land circulation, which, in turn, offers an institutional guarantee for the tandem development of rural finance and agricultural land management. This paper measures the comprehensive development index of agricultural land management and rural finance in 30 provinces of China from 2005 to 2020. In light of this, it calculates the degree of coupling and coordination between China's agricultural land management and rural financial development. The Dagum Gini coefficient, kernel density, and the Moran index were used to analyze regional differences and patterns of agglomeration. The study found that the degree of coupling coordination between China's agricultural land management and rural finance is increasing annually. However, there remains a significant gap in achieving high-quality coupling. Notably, the growth rate of rural financial development exceeds that of agricultural land management, and hypervariable density is a major source of regional variation. There is polarization in the coupled development of farmland management and rural finance. Provinces in the eastern and central regions tend to be located in the high–high agglomeration (H–H) in terms of the level of development of agricultural land and financial integration, while the western region tends to fall in low–low aggregation (L–L).

show abstract

“…Symbiosis theory provides a theoretical reference for the development and growth of innovation ecosystems [ 12 ]. Exploring the symbiotic evolution mechanism of digital innovation ecosystems can promote synergistic coexistence among various stakeholders to achieve stable development [ 13 ]. Nevertheless, the aforementioned research concentrates primarily on the evolutionary relationships and linkages of digital innovation ecosystems, while the mutual evolutionary mechanism of digital innovation ecosystems remains unexplored.…”

Section: Introductionmentioning

confidence: 99%

Multi-group symbiotic evolutionary mechanisms of a digital innovation ecosystem: Numerical simulation and case study

Li,

2024

PLoS ONE

View full text Add to dashboard Cite

In the digital innovation ecosystem, the symbiosis mode formed between ecosystem members not only relates to their survival and development but also affects the ecosystem’s symbiosis evolution mechanism. Based on symbiosis theory, this study first explores the evolutionary equilibrium strategy and its stability for three types of populations—core enterprises, digital platforms, and university research institutes—and then uses numerical simulation and a case study to explore the symbiotic evolution mechanism of the digital innovation ecosystem. The results show that: First, the digital innovation ecosystem is a complex adaptive system in which the three types of populations form different symbiotic relationships under different symbiotic modes and conduct symbiotic activities, such as value co-creation, to characterize the unique symbiotic evolutionary structure. Second, in this ecosystem, the symbiotic relationship formed by the combined values of different symbiotic coefficients between populations determines the outcome of symbiotic evolution. Third, the ideal direction of the evolution of the digital innovation ecosystem is a mutually beneficial symbiotic relationship. Thus, the symbiotic relationship between populations should be transformed into a mutually beneficial symbiotic relationships as much as possible. This study makes theoretical contributions by shedding light on the symbiotic evolution mechanism of the digital innovation ecosystem. It also offers countermeasures for the digital innovation cooperation of various stakeholders in China’s digital innovation ecosystem.

show abstract

Energy development in rural China toward a clean energy system: utilization status, co-benefit mechanism, and countermeasures

Cited by 41 publications

References 39 publications

The impact of technological innovation on the green digital economy and development strategies

The impact of technological innovation on the green digital economy and development strategies

Agricultural land management and rural financial development: coupling and coordinated relationship and temporal-spatial disparities in China

Multi-group symbiotic evolutionary mechanisms of a digital innovation ecosystem: Numerical simulation and case study

Contact Info

Product

Resources

About