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

Digital financial inclusion and common prosperity are pivotal elements in promoting the sustainable socioeconomic development of China. This study introduces a novel Multi-Criteria Decision Analysis (MCDA) method to evaluate the Common Prosperity Index (CPI). Using this index, alongside the Digital Financial Inclusion Index (DFII) released by Peking University, it examines the evolution of the coupling coordination relationship between digital financial inclusion and common prosperity within the Yangtze River Delta (YRD) urban agglomeration from 2011 to 2021. By integrating gravity models and social network analysis, in this paper, we thoroughly investigate the spatiotemporal evolution characteristics of the spatial network of this coupling coordination relationship. The results indicate that both the DFII and CPI generally exhibit an upward trend, but the decline in the coupling degree reflects a weakened interaction strength between them. Specifically, Anhui significantly lags behind Jiangsu, Zhejiang, and Shanghai in the development of digital financial inclusion and common prosperity, indicating regional development imbalances. Furthermore, the strength of spatial connections in city coupling coordination has significantly increased, with Nanjing’s siphon effect on cities in Anhui becoming markedly stronger, and the number of core cities in the network increasing, which demonstrates a geographical proximity feature in network development. Additionally, the overall network characteristics are transitioning towards higher density and “small-world” properties, suggesting a trend toward network stabilization. The disparity in centrality among cities has decreased, with an overall enhancement in centrality, where the spatial spillover effects from core areas such as Hangzhou-Ningbo, Nanjing-Changzhou, and Shanghai-Suzhou-Wuxi significantly promote the development of peripheral cities. Based on these findings, this paper proposes policy recommendations for the sustainable development of digital financial inclusion and common prosperity in the YRD region.

As an effective approach to mitigating urban environmental issues, New Energy Vehicles (NEVs) have become a focal point of research regarding their current development status and future prospects in China. Addressing the significant disparities in the development of the NEVs industry across different cities, this study focuses on ten typical Chinese cities and develops a novel multi-attribute decision-making (MADM) framework to evaluate the prospects of NEVs promotion in these cities. The study first establishes a comprehensive indicator system that covers key dimensions such as economy, policy support, infrastructure, technological innovation, and environment, encompassing five different types of evaluation information. This system incorporates five different types of evaluation information: exact numbers, interval numbers, triangular fuzzy numbers, hesitant fuzzy numbers, and probabilistic linguistic term sets (PLTS), enhancing the framework’s ability to handle diverse data types. Subsequently, the improved entropy (IEntropy) weight method is employed to determine the objective weights of the evaluation indicators. These objective weights are then integrated with the Vlsekriterijumska Optimizacija I Kompromisno Resenje (VIKOR) method, facilitating a structured group decision-making approach that synthesizes hybrid evaluation information. Based on modular thinking, hybrid evaluation information is synthesized to evaluate and rank the NEVs development prospects of each city. Sensitivity analysis and comparative analysis further demonstrate the robustness and reliability of the proposed MADM framework. The ranking results indicate that Shanghai and Guangzhou lead in NEVs promotion, while cities like Harbin and Zhengzhou lag behind. Based on these findings, the study proposes targeted policy recommendations to promote the sustainable development of the NEVs industry in major Chinese cities.

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

Assessing the urban green transportation development level (UGTDL) is of great significance for addressing traffic issues in megacities and promoting urban sustainable development. An evaluation framework for the UGTDL is proposed based on Multi-Criteria Decision Analysis (MCDA) methods. Firstly, from both macro and micro perspectives, a comprehensive evaluation indicator system is constructed, covering multiple dimensions such as traffic spatial organization efficiency, green travel, new energy vehicle development, traffic safety, and the traffic environment. Secondly, to address the uncertainties and fuzziness in the evaluation process, the Probability Language Term Set (PLTS) is introduced to represent expert evaluation information, thereby reducing the information loss. Thirdly, the improved Step-wise Weight Assessment Ratio Analysis (SWARA) method is employed to calculate the weights of the indicators, improving the computational efficiency. Finally, the extended Combined Compromise Solution (CoCoSo) method is used to calculate the UGTDL, avoiding the compensatory issues in the traditional decision-making methods. The proposed approach is applied to assess the UGTDL in Guangzhou from 2020 to 2023. The results show that the UGTDL scores for Guangzhou from 2020 to 2023 are 1.6367, 2.2325, 2.1141, and 1.8575, respectively. Sensitivity analysis verifies the effectiveness and stability of the approach. Further obstacle analysis shows that the promotion of new energy vehicles (NEVs) has led to a marginal decrease in the utility of Guangzhou’s UGTDL. In the future, Guangzhou should take further measures to improve the traffic space organization efficiency and traffic safety.

Exploring the regional disparities in and spatiotemporal evolution of the new energy vehicle promotion level (NEVPL) is essential for facilitating low-carbon and environmentally sustainable development. This study employs a multidimensional index system to assess the NEVPL across 31 Chinese provinces from 2017 to 2023, utilizing data on NEV ownership, annual NEV sales, the number of public charging piles, and the vehicle-to-pile ratio. The NEVPL scores were estimated using the Entropy-TOPSIS method. Spatial patterns and the mechanisms of regional disparities were examined using a suite of spatial analytical techniques, including the standard deviation ellipse (SDE), gravity center analysis, Dagum Gini coefficient decomposition, and kernel density estimation. The results reveal three principal findings. First, NEVPL exhibited a sustained upward trend nationwide. The eastern region consistently maintained a leading position, the central and western regions demonstrated steady growth, and the northeastern region remained underdeveloped, leading to an expanding regional gap. Second, spatial distribution transitioned from an early dispersed pattern to a structure characterized by both agglomeration and differentiation. The promotional center gradually shifted toward the southeast, and high-value regions became increasingly isolated, forming island-like clusters. Third, spatial inequality was mainly driven by inter-regional differences, which contributed to over 70 percent of the total variance. The rising intra-regional disparity within the eastern region emerged as the predominant factor widening the national gap. These findings offer empirical evidence to support the refinement of new energy vehicle (NEV) policy frameworks and the promotion of balanced regional development.