Assessment of the Ecological Niche of Photovoltaic Agriculture in China

Wang, Lingjun; Ying, Wang; Chen, Jian

doi:10.3390/su11082268

Cited by 18 publications

(14 citation statements)

References 37 publications

(33 reference statements)

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“…ese two methods determined the entropy value of the graded normal cloud model from different angles, which may lead to conflicting state determination conclusions [21,43]. erefore, a cloud entropy optimization algorithm is used to solve the entropy problem in this study, which guarantees the clarity of the classification and takes into account the principle of fuzziness.…”

Section: Cloud Entropy Optimization Algorithmsmentioning

confidence: 99%

Research on the Risk Assessment of New Energy Automobile Industry Based on Entropy Weight-Cloud Model in China’s Jiangsu Province

Cao

Bian

Wang

et al. 2021

Mathematical Problems in Engineering

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The development of the new energy vehicle industry is necessary for its advantages of saving energy and reducing greenhouse-gas emissions. However, the industry is currently facing risks with regard to, for example, technology, market, and the policy. Most existing studies of industry risk focused on analyzing and evaluating risk factors and summarizing and interpreting risk phenomena. In this study, systematic classification and quantitative analysis for the risk of the new energy vehicle industry were investigated, in which the entropy weight method and cloud model were combined to evaluate and quantify the industry risk. The formation mechanism of the industry risk from endogenous and exogenous perspectives was analyzed to screen out risk-evaluation factors. Combining the expert-investigation and fuzzy-statistics methods, a risk-evaluation index system with six primary indicators and twenty-four secondary indicators was constructed. On the basis of the entropy weight-cloud model, the risk of the new energy automobile industry of Jiangsu province in July 2019 was evaluated. Results indicated that the impact of exogenous risk on the industry was greater than that of endogenous risk, and industry risk was higher than medium risk, which was close to a higher medium level. A series of suggestions are given for preventing industry risk, such as improving the industry’s own ability to resist risk and building the industry’s soft environment.

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Section: Cloud Entropy Optimization Algorithmsmentioning

confidence: 99%

Research on the Risk Assessment of New Energy Automobile Industry Based on Entropy Weight-Cloud Model in China’s Jiangsu Province

Cao

Bian

Wang

et al. 2021

Mathematical Problems in Engineering

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“…en, financial institutions should change the single evaluation criteria of performance as the king, include serving small-and medium-sized microenterprise entities, serve the development of the local real economy into the evaluation system, and reasonably guide the flow of financial capital [44]. Finally, we should step up our efforts to crack down on illegal behavior in the financial market, establish and improve local financial supervision systems, and reasonably protect the real economy.…”

Section: 2mentioning

confidence: 99%

Does Financial Development Hamper or Improve the Resource Curse? Analysis Based on the Panel Threshold Effect Model

Sun

Cai

2020

Mathematical Problems in Engineering

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This paper analyzes the “resource curse” and “financial threshold effect” that may exist in China and then uses the data from 30 provinces from 2004 to 2018 as research samples. We used linear regression and nondynamic panel threshold models to analyze the financial threshold effects of the “resource curse” hypothesis and the “resource curse” phenomenon. At the same time, we divided the level of financial development to verify the robustness of the research conclusions in this paper. The study found the following: (1) There is a certain correlation between the abundance of resources and economic growth. Whether this can be seen as a “curse” or a “blessing” of resources is significantly related to the degree of financial development. (2) Whether financial development can alleviate the “resource curse” depends on the degree of financial development. In the extremely scarce stage of financial resources, the resource endowment effect is obvious, and the level of economic development in resource-based regions will be higher than in other regions; when the level of financial development is low (financial resources are not scarce and have not reached a reasonable level), the phenomenon of the “resource curse” appears; when the level of financial development is highly developed, economic development benefits more from financial development, and the effects of resource endowment decline. Only when financial development is at a reasonable level can resource endowments effectively raise the level of economic development.

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“…Therefore, modern agriculture needs technology and new energy sources as the driving force to advance agriculture. As a new type of land use, photovoltaic agriculture is a product of the development of a sophisticated combination of modern agriculture and clean energy, which can give full play to the advantages of zero pollution in photovoltaic power generation [1]. It enables the conversion of solar photovoltaic power to agricultural production activities: supporting greenhouse irrigation systems, supplementing light to crops, meeting the heating needs of greenhouse crops, etc.…”

Section: Introductionmentioning

confidence: 99%

“…For the above reasons, this study uses Sentinel-1 in a rice phenology monitoring method based on growth curve simulation. Combining rice growth morphology and key phenological periods, this study divided the Sentinel-1 time-series data to establish a simulated growth curve of rice, so that there is a good distinction between the phenological stages, and use it as prior knowledge to divide and extract the phenological interval and data, as follows: (1) This study analyzed and sorted the collected data in the experimental area and established the relationship between the phenological scale and the day of the year. Combined with the key phenological period of rice and the transit time of satellite photography, the Sentinel-1 time-series data in 2018 were divided into intervals of rice phenology.…”

Section: Introductionmentioning

confidence: 99%

Rice Phenology Retrieval Based on Growth Curve Simulation and Multi-Temporal Sentinel-1 Data

et al. 2022

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The accurate estimation and monitoring of phenology is necessary for modern agricultural industries. For crops with short phenology occurrence times, such as rice, Sentinel-1 can be used to effectively monitor the growth status in different phenology periods within a short time interval. Therefore, this study proposes a method to monitor rice phenology based on growth curve simulation by constructing a polarized growth index (PGI) and obtaining a polarized growth curve. A recursive neural network is used to realize the classification of phenology and use it as prior knowledge of rice phenology to divide and extract the phenological interval and date of rice in 2021. The experimental results show that the average accuracy of neural network phenological interval division reaches 93.5%, and the average error between the extracted and measured phenological date is 3.08 days, which proves the application potential of the method. This study will contribute to the technical development of planning, management and maintenance of renewable energy infrastructure related to phenology.

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Assessment of the Ecological Niche of Photovoltaic Agriculture in China

Cited by 18 publications

References 37 publications

Research on the Risk Assessment of New Energy Automobile Industry Based on Entropy Weight-Cloud Model in China’s Jiangsu Province

Research on the Risk Assessment of New Energy Automobile Industry Based on Entropy Weight-Cloud Model in China’s Jiangsu Province

Does Financial Development Hamper or Improve the Resource Curse? Analysis Based on the Panel Threshold Effect Model

Rice Phenology Retrieval Based on Growth Curve Simulation and Multi-Temporal Sentinel-1 Data

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