PCA results.

The Yellow River Economic Belt, where the degree of digital economy development is uneven, is the first research object used in this study. It then suggests a way to measure the degree of digital economy development and carbon emissions in order to address the problem of effectively controlling carbon emissions in the rapidly developing digital economy. Finally, a genetic method is presented to further enhance the backpropagation neural network model’s update process, which was improved utilizing the particle swarm optimization technique. According to the findings, this research identified three primary elements: digital industrialization, digital finance, and digital ecological environment. According to the findings, this research identified three primary elements: digital industrialization, digital finance, and digital ecological environment. With the use of digital technology, the digital ecological environment fosters a peaceful coexistence between people and the natural world. In addition to encouraging the advancement of digital technology, it may also help to integrate digital transformation and green development. The use of digital technology in ecological environment governance can assist accomplish sustainable development goals, improve resource allocation, and encourage intelligent and green production and life. In order to change conventional financial service models, the financial sector known as “digital finance” makes use of digital technologies and data components. It has the potential to be very important in encouraging industrial upgrading and propelling the growth of new industries. Additionally, the whole credit structure of the industrial chain may be improved by digital credit and risk management, which will support the economic structure’s optimization. The use of digital technology to a variety of sectors, encouraging their digital transformation and modernization, is known as digital industrialization. It is a key component of a contemporary industrial system that may drive new industries and formats, support the intelligent and information-based transformation of established industries, and improve the economic structure. At the same time, the associated carbon emissions dropped by 0.0439 units for every unit rise in the study area’s digital economy’s degree of growth. The region’s overall population, energy consumption, sophisticated industrial structure, and industrial structure rationalization all positively promote carbon emissions, whilst other variables have the opposite impact. The final study approach had the highest predictive performance, with a high goodness of fit of 0.9936 and an average absolute error of 16.971. The aforementioned study results demonstrate that the methodology can effectively evaluate the level of carbon emissions and the development of the digital economy across different regions and provide targeted solutions to lower carbon emissions in line with local conditions, thus fostering the vibrancy of the digital economy.

本研究以数字经济发展水平不均衡的黄河流域经济带为首要研究对象。为解决快速发展的数字经济领域碳排放有效管控难题，本研究提出了一套数字经济发展水平与碳排放的测度方法。最后，本研究提出一种遗传方法，以进一步优化经粒子群优化（particle swarm optimization）技术改进的反向传播神经网络（backpropagation neural network）模型的更新流程。研究结果表明，本研究识别出三大核心要素：数字产业化、数字金融与数字生态环境。研究结果表明，本研究识别出三大核心要素：数字产业化、数字金融与数字生态环境。数字生态环境依托数字技术，实现人与自然的和谐共生。其不仅能够推动数字技术迭代升级，还可助力数字转型与绿色发展深度融合。将数字技术应用于生态环境治理，有助于实现可持续发展目标、优化资源配置，并推动智能绿色生产生活方式的形成。“数字金融”作为依托数字技术与数据要素的金融业态，在推动产业升级、催生新兴产业发展方面具有重要作用。此外，数字信贷与风险管理能够优化全产业链信用结构，助力经济结构调整优化。数字产业化是指将数字技术应用于各产业领域，推动其数字化转型与现代化发展。其是现代产业体系的核心组成部分，能够催生新兴产业与业态，推动传统产业智能化、信息化转型，优化经济结构。与此同时，研究区域内数字经济发展水平每提升1个单位，相关碳排放将下降0.0439个单位。该区域总人口、能源消耗、产业结构高级化水平以及产业结构合理化水平均对碳排放产生正向促进作用，其余变量则呈现负向影响。本研究最终采用的模型方法具备最优的预测性能，拟合优度高达0.9936，平均绝对误差仅为16.971。上述研究结果表明，本研究提出的方法能够有效评估不同区域的碳排放水平与数字经济发展程度，并因地制宜地提出碳排放管控方案，从而激发数字经济发展活力。