Maturity evaluation in China’s low carbon energy industry

Sun, Xudong; Jia, Xufan; Rong, Yafei

doi:10.1016/j.egypro.2018.09.234

Cited by 4 publications

(2 citation statements)

References 3 publications

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“…Yu et al (2019) [4] used the DEA method to construct the carbon performance evaluation system for the green supply chain in automobile manufacturing. Sun et al (2018) [30] evaluated the maturity of the low-carbon energy industry based on the AHP and fuzzy comprehensive evaluation method. It is applied to many subdivided industries, such as the power electronic equipment industry, intelligent wind power generation industry.…”

Section: Evaluation Methodsmentioning

confidence: 99%

The Evaluation Prediction System for Urban Advanced Manufacturing Development

Dou,

Sun,

Zhu

et al. 2023

Systems

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With the rapid development of the economy, it is important to reasonably evaluate the development status of the regional manufacturing industry. Given this, this article expands the evaluation indicators of urban advanced manufacturing (UAM) from the perspective of the push–pull-mooring (PPM). Then, it uses a machine learning (ML) method to predict the evaluation results of other cities through a small amount of sample data. The results show that: (1) From the current development status of UAM in Guangdong Province (GD), cities in the Pearl River Delta region occupy a dominant position. However, cities in eastern, western, and mountainous regions have strong development potential and lead cities. Therefore, each region has cities with high levels of development and has a demonstrative role. (2) By comparison, it was found that the overall development level of UAM in GD is not significantly different from that of the Yangtze River Economic Belt. However, due to significant differences in their extreme values, the proportion of cities above the average in the overall population is relatively small. This indirectly proves that GD’s UAM not only has a phased nature, but also has a demonstrative role. (3) The prediction effect of the perceptron model is better than other methods. Although neural network models have better prediction performance than other machine learning models, they should not overly rely on complex network structure prediction data. By comparing the results, the reliability is verified. Finally, according to the life cycle theory, we propose a targeted development path for different UAM.

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Section: Evaluation Methodsmentioning

confidence: 99%

The Evaluation Prediction System for Urban Advanced Manufacturing Development

Dou,

Sun,

Zhu

et al. 2023

Systems

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“…Wang et al (2017) proposed to assess the vulnerability of industrial ecosystems based on an integral vulnerability index. Sun et al (2018) evaluated the maturity of China's low-carbon energy industry by distinguishing two levels of industrial maturity: product readiness and market maturity. Tolstykh et al (2020) developed an entropic model to assess the sustainability of industrial ecosystems by assessing the level of cooperation between the industrial ecosystem participants.…”

Section: Literature Reviewmentioning

confidence: 99%

Methodology for Assessing Industrial Ecosystem Maturity in the Framework of Digital Technology Implementation

Бабкин¹,

Глухов²,

Shkarupeta³

et al. 2021

IJTech

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The essence of the transformation processes currently taking place in all sectors of the economy, including industry, is increasingly described by the ecosystem approach. Industrial ecosystems have proven to be the models with the highest production efficiency. The effective management of industrial ecosystems is possible only if the mechanisms underlying their dynamics are understood. Achieving a high level of maturity for industrial ecosystems in the framework of digital technology implementation is of particular relevance, and this research aims to develop and test a methodology for assessing the maturity level of such industrial ecosystems by relying on principal component analysis and hierarchical agglomerative clustering. A mature industrial ecosystem gets rapidly integrated into the new technological paradigm and global value chains, is able to compete in global markets in the long term, and has increased potential for industrialization and digital transformation. The proposed methodology is based on the environmental, social, and corporate governance (ESG) methodology for determining the levels of interests in the area of sustainable development of an industrial ecosystem. The proposed approach singles out a fourth assessment projection in addition to ESG, namely the digital maturity. All four maturity assessment projections are proven to be positively and significantly correlated. An industrial ecosystem maturity assessment scale has been developed, including six levels (very high; advanced; basic; elementary; zero; and minus one). The methodology has been tested on the national industrial ecosystem and the metallurgy and mining industrial ecosystem of Russia. The results show that the maturity level of the Russian industrial ecosystem as of mid-2021 is "basic" with the prospect of transition to "advanced." The Russian metallurgy and mining industrial ecosystem maturity level is "advanced." The key directions for increasing the maturity levels of Russian industrial ecosystems based on innovations in various industrial aspects are proposed.

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