Analyzing New Quality Productive Forces in New Energy Vehicle Companies Based on a New Multi-Criteria Decision Analysis Model DOI Creative Commons

Guozhen Hua,

Fanlong Zeng,

Huaping Sun

и другие.

World Electric Vehicle Journal, Год журнала: 2024, Номер 15(11), С. 503 - 503

Опубликована: Ноя. 2, 2024

Assessing the new quality productive forces (NQPF) of energy vehicle (NEV) companies is crucial for promoting sustainable development NEV industry. This paper systematically evaluated and analyzed NQPF Chinese listed from 2018 to 2022 using a novel multi-criteria decision analysis (MCDA) model. To address limitations in traditional MCDA models, such as unbalanced weight distribution, insufficient ranking differentiation, incomplete identification key influencing factors, this study introduced model, IDOCRIW-PROBID (integrated determination objective criteria weights—preference on basis ideal-average distance). First, an evaluation index system tailored companies’ was developed. Then, IDOCRIW method used objectively assign weights indicators, enhancing scientific rigor distribution. The PROBID employed rank based their NQPF, identifying differences between them. Additionally, obstacle degree model analyze compensating model’s regard. results showed, first, that proposed has high consistency with classical Entropy-TOPSIS (technique order preference by similarity ideal solution) terms (correlation coefficient = 0.91), offers higher differentiation compared other validating its reliability superiority. Second, during period, levels varied significantly, most at low level showing downward trend, indicating face considerable challenges improving NQPF. Third, revealed R&D lease fees, depreciation amortization, direct investment were primary factors hindering growth. research provides theoretical support decision-making insights strategic optimization informs government policy formulation.

Язык: Английский

The role of digital financial inclusion in promoting common prosperity: Evidence from Inner Mongolia DOI
Wuyunzhaola Borjigin,

Sarula He,

Huixian Xu

и другие.

International Review of Financial Analysis, Год журнала: 2025, Номер unknown, С. 103996 - 103996

Опубликована: Фев. 1, 2025

Язык: Английский

Процитировано

2
An optimized machine learning framework for predicting and interpreting corporate ESG greenwashing behavior DOI Creative Commons

Fanlong Zeng,

Jintao Wang,

Chaoyan Zeng

и другие.

PLoS ONE, Год журнала: 2025, Номер 20(3), С. e0316287 - e0316287

Опубликована: Март 6, 2025

The accurate prediction and interpretation of corporate Environmental, Social, Governance (ESG) greenwashing behavior is crucial for enhancing information transparency improving regulatory effectiveness. This paper addresses the limitations in hyperparameter optimization interpretability existing models by introducing an optimized machine learning framework. framework integrates Improved Hunter-Prey Optimization (IHPO) algorithm, eXtreme Gradient Boosting (XGBoost) model, SHapley Additive exPlanations (SHAP) theory to predict interpret ESG behavior. Initially, a comprehensive dataset was developed through extensive literature review expert interviews. IHPO algorithm then employed optimize hyperparameters XGBoost forming IHPO-XGBoost ensemble model predicting Finally, SHAP used model's outcomes. results demonstrate that achieves outstanding performance greenwashing, with R², RMSE, MAE, adjusted R² values 0.9790, 0.1376, 0.1000, 0.9785, respectively. Compared traditional HPO-XGBoost combined other algorithms, exhibits superior overall performance. analysis using highlights key features influencing outcomes, revealing specific contributions feature interactions impacts individual sample features. findings provide valuable insights regulators investors more effectively identify assess potential behavior, thereby efficiency investment decision-making.

Язык: Английский

Процитировано

0
Quantifying Virtual Urban Commercial Linkages Using Spatial Phone Call Data—A Comparative Study Between Guangzhou and Shenzhen DOI Creative Commons
H. B. Jiang, Hongyu Sun, Zheng Cao

и другие.

Urban Science, Год журнала: 2025, Номер 9(5), С. 176 - 176

Опубликована: Май 20, 2025

The importance of cities hinges on how they connect with other globally, yet research has been lacking in the exploration virtual linkages. This study takes Guangzhou and Shenzhen as samples to measure their urban linkage China. First, it improves gravity model by considering impact distance call intentions context phone conversations. Second, uses detail record (CDR) data based enhanced model. Lastly, employs a more effective geodetector analyze driving factors. results indicate following: southeast exhibit significantly higher connectivity; Guangzhou’s is pronounced than Shenzhen’s; volume import export trade stronger indicator linkage. measured through CDRs offers new insights into

Язык: Английский

Процитировано

0
Analyzing New Quality Productive Forces in New Energy Vehicle Companies Based on a New Multi-Criteria Decision Analysis Model DOI Creative Commons

Guozhen Hua,

Fanlong Zeng,

Huaping Sun

и другие.

World Electric Vehicle Journal, Год журнала: 2024, Номер 15(11), С. 503 - 503

Опубликована: Ноя. 2, 2024

Assessing the new quality productive forces (NQPF) of energy vehicle (NEV) companies is crucial for promoting sustainable development NEV industry. This paper systematically evaluated and analyzed NQPF Chinese listed from 2018 to 2022 using a novel multi-criteria decision analysis (MCDA) model. To address limitations in traditional MCDA models, such as unbalanced weight distribution, insufficient ranking differentiation, incomplete identification key influencing factors, this study introduced model, IDOCRIW-PROBID (integrated determination objective criteria weights—preference on basis ideal-average distance). First, an evaluation index system tailored companies’ was developed. Then, IDOCRIW method used objectively assign weights indicators, enhancing scientific rigor distribution. The PROBID employed rank based their NQPF, identifying differences between them. Additionally, obstacle degree model analyze compensating model’s regard. results showed, first, that proposed has high consistency with classical Entropy-TOPSIS (technique order preference by similarity ideal solution) terms (correlation coefficient = 0.91), offers higher differentiation compared other validating its reliability superiority. Second, during period, levels varied significantly, most at low level showing downward trend, indicating face considerable challenges improving NQPF. Third, revealed R&D lease fees, depreciation amortization, direct investment were primary factors hindering growth. research provides theoretical support decision-making insights strategic optimization informs government policy formulation.

Язык: Английский

Процитировано

1