Yaliu Yang

Artificial intelligence (AI) is reshaping insurance services, yet it introduces significant consumer risks such as privacy erosion, service exclusion, and trust deterioration. This systematic review clarifies how specific AI features—algorithmic opacity, hyper‐personalization, and data‐driven bias—trigger psychological responses and shape consumer decisions, ultimately producing negative outcomes. Drawing from 33 empirical studies, the review organizes fragmented findings using the TCCM (Theory–Context–Characteristic–Method) framework, revealing theoretical fragmentation, geographical concentration, and methodological imbalance. To move beyond static categorizations, the study proposes a novel Trigger–Psychology–Decision–Outcome (TPDO) framework that maps sequential pathways of consumer harm. Findings show that adverse consumer outcomes emerge primarily through fairness concerns, anxiety, and perceived loss of control, influencing behaviors such as disengagement and resistance to AI‐enabled insurance systems. This mechanism‐based synthesis provides theoretical clarity, outlines targeted avenues for future research, and informs consumer‐centric governance of algorithmic insurance.

Green innovation is an important starting point for implementing new developmental concepts and promoting high-quality development in the modern era. We construct an integrated analysis framework based on the environment–resource–subject configuration. Using 30 provinces and regions in China as an example, we employ the dynamic qualitative comparative analysis method to study the configuration path of multi-factor interactions affecting green innovation in an innovation ecosystem. The results indicate that (1) single factors are not necessary conditions for high regional green innovation. (2) The consistency level of the green financial environment exhibits a significant upward trend, demonstrating a clear temporal effect. (3) From the time dimension, three types of configuration paths exist for the interactive combination of environment, resources, and subjects to promote regional green innovation, and the consistency level of configuration between 2018 and 2022 fluctuates above 0.94. (4) From the spatial dimension perspective, clear differences exist in the paths driving the improvement of the green innovation level between the eastern, central, and western regions. This study broadens the scope of correlational research on green innovation, examining them across both temporal and spatial dimensions. It also provides enlightenment for regions seeking to enhance their green innovation.

With the proposal of the goal of “carbon peak, carbon neutrality”, the concept of environmental protection has become increasingly popular. To explore the characteristics and influencing factors of consumers’ green consumption behavior against a dual-carbon background, this study proposed research variables and hypotheses about green consumption behavior and influencing factors. With the research purpose, research variables, and hypotheses, a questionnaire was designed. Consumers in Anhui Province were chosen as research samples. SPSS26.0 was employed to conduct reliability and validity analysis, correlation analysis, and stepwise linear regression analysis of the questionnaire data to evaluate the factors affecting consumers’ green consumption behavior; through these measures, it was verified that there existed a significant relationship between green consumption behavior and the influencing factors of purchase motivation, channel factor, consumer innovation, and price factor. And it was also verified that the influence of incentive factors and psychological factors was not significant. Based on this, the current study proposes countermeasures and suggestions for setting up environmental awareness, reducing the price of green products, expanding green consumption channels, and stimulating consumer innovation.

Regional synergy innovation capability is an important driving force in promoting the sustainable and high-quality development of the regional economy. Taking the regional innovation development panel data of the Yangtze River Delta integration region from 2010 to 2019 as a sample, this study constructs an evaluation index system of regional synergy innovation capability, weights the index using the entropy weight method, and measures the capability of the Yangtze River Delta integration region (three provinces and one city) using the composite system synergy degree model. The empirical results show that the synergy of regional synergy innovation in the Yangtze River Delta integration has increased steadily, but there is still much room for improvement. Anhui has great potential for synergy innovation with Jiangsu, Zhejiang, and Shanghai. Therefore, this study proposes countermeasures and suggestions for the high-quality development of Anhui’s synergy innovation capability under the integration of the Yangtze River Delta. This study provides theoretical and methodological support for enhancing regional synergy innovation capability and provides decision support for the sustainable and high-quality development of the regional economy.

Coordinating regional innovation–economy–ecology (IEE) systems is an important prerequisite for overall continuous regional development. To fully understand the coordination relationship among the three, this study builds a data-driven multimodel decision approach to calculate, assess, diagnose, and improve the regional IEE system. First, the assessment indicator system of the regional IEE system is established. Secondly, the range method, entropy weight method, and weighted summation method are employed to calculate the synthetic developmental level. Thirdly, a multimodel decision approach including the coupling degree model, the coordination degree model, and the obstacle degree model is constructed to assess the spatiotemporal evolution characteristics of the regional IEE system coupling coordination and diagnose the main obstacles hindering its development. Finally, the approach is tested using Anhui Province as a case study. The results show that the coupling coordination degree of the Anhui IEE system presents a stable growth trend, but the coupling degree is always higher than the coordination degree. The main obstacle affecting its development has changed from the original innovation subsystem to the current ecology subsystem. Based on this, some countermeasures are put forward. This study, therefore, offers decision support methods to aid in evaluating and improving the regional IEE system.

Evaluating the degree of coordination among regional carbon emission systems is key to achieving an earlier carbon peak and carbon neutrality. However, quantifying the co-evolution of carbon emissions among regions is challenging. Therefore, we propose a data-driven method for evaluating the synergetic development of the regional carbon emission composite system. First, the proposed method employs relevant data to calculate the carbon emissions and carbon emission intensity of each subsystem within the region to describe the temporal trends. The inverse entropy weight method is then used to assign weight to each order parameter of the subsystem for data processing. Then, we perform synergetic development assessment of the composite system to measure the order degree of each subsystem, the degree of synergy among subsystems, and the overall synergetic degree of the temporal evolution of carbon emissions between regions. Finally, the evaluation results can be used to suggest measures for the regional coordinated reduction of carbon emissions. In this study, we used data from the Yangtze River Delta (YRD) region from 2010 to 2019 to demonstrate the feasibility and effectiveness of the method. The results show that there is still a long way to go to reduce carbon emissions in the Yangtze River Delta region. Economic development still relies heavily on fossil energy consumption, and the regional carbon emission reduction synergy is not high. This study provides theoretical and methodological support for regional carbon emission reduction. Moreover, the proposed method can be applied to other regions to explore low-carbon and sustainable development options.

Green development is an important connotation of high-quality development and is one of the goals of scientific and technological innovation. This study constructs a data-driven measurement model of the green development efficiency of regional sci-tech finance, measures the green development efficiency of sci-tech finance by using the super-slack-based measure model, and deeply analyses and evaluates the changes in green development efficiency of regional sci-tech finance by calculating Malmquist index. This study calculates the green development efficiency of sci-tech finance in the Yangtze River Delta. Results show that the green development efficiency of sci-tech finance in the Yangtze River Delta is on the rise as a whole and maintains an efficient state, but differences are observed between provinces and cities. This study provides theoretical and methodological support for the evaluation of the green development efficiency of regional sci-tech finance and serves as reference for policy makers and researchers of sci-tech finance.

This study develops a data-driven, comprehensive evaluation method to improve the science and technology innovative efficiency of industrial enterprises above designated size (hereinafter “industrial enterprises”). Based on an innovation value chain perspective, a two-stage evaluation index system is constructed. Thereafter, the Pearson correlation coefficient method was used to analyze correlations in the constructed index system. A two-stage network data envelopment analysis model with additional intermediate input was constructed to measure and evaluate industrial enterprises’ science and technology innovative efficiency from three aspects—research and development (R&D), commercialization, and comprehensive efficiencies—to reveal the temporal and spatial evolution. The feasibility and effectiveness of the method was verified using the statistical data of industrial enterprises in 16 cities in Anhui Province, China, from 2011 to 2020. The results show that the comprehensive efficiency of the scientific and technological innovation of industrial enterprises in these cities is at a medium level, and the efficiency development of the two stages is uncoordinated; the two-stage efficiency distribution tends to be “high R&D–high commercialization” and “low R&D–low commercialization”, and targeted countermeasures and suggestions are proffered. This study provides a reference for the sustainable development of industrial enterprises in relevant regions.

To promote the sustainability of mechanical production process, this study proposes an emergy-based sustainability evaluation method to evaluate the resource utilization and environmental pollution of the mechanical production process. Firstly, this study constructs the sustainability evaluation index system of the mechanical production process from the perspectives of economy and environment. Secondly, an emergy analysis method-based sustainable evaluation of the mechanical production process is constructed. Finally, taking the gear manufacturing process as an example, the method is tested. The results show that this method can effectively quantify and identify the sustainability of the gear manufacturing process and provide effective feedback to improve the process. The resulting improvements can aid in reconsidering the economic and environmental factors, improving the production efficiency and the sustainability of the production process, and reducing the difficulty of operating the lathe in the production process. This study provides method support for the sustainability evaluation of mechanical production process and decision support for its improvement.