The coordinated development of digitalization and greening is essential for economic transformation and upgrading, especially given the pressing global carbon emission challenges. China’s commitment to achieving “dual carbon” goals highlights the need for sustainable solutions, particularly in the manufacturing sector, which is a significant source of energy consumption and emissions; carbon emissions account for more than 30%. Integrating advanced digital technologies with manufacturing is critical for reducing carbon and sustainable growth. According to the research results, more than 70% of scholars believe that digital transformation boosts green innovation and low-carbon development, but the mechanisms still need to be clarified, slowing transformation efforts and reducing efficiency. Taking the intellectualization and green low-carbon development of manufacturing enterprises as latent variables, and taking the nine paths obtained by scholars’ research results and investigation interviews to promote green low-carbon performance as observation variables, this paper constructs a structural equation model and deeply explores the mechanism and paths of the intellectualization transformation of manufacturing enterprises affecting carbon reduction, emission reduction and sustainable development of enterprises. The research results show that the digital intelligent transformation of manufacturing enterprises affects the green and low-carbon performance improvement and sustainable development of enterprises through technological innovation, industrial structure transformation and upgrading, and reshaping resource allocation. These strategies lower energy use and emissions, strengthen sustainability, and improve green performance. The findings offer theoretical and practical insights, providing a roadmap for efficient digital transformation in manufacturing to achieve the “dual carbon” goals and support sustainable development.

The Fourth Industrial Revolution, driven by advancements in information technology, has ushered humanity into the age of intelligence. As digital technologies like artificial intelligence and large-scale models continue to evolve and gain traction, the convergence of digital innovation and green development within manufacturing enterprises has emerged as a pivotal trend. This integration not only fosters high-quality, sustainable growth, but also increasingly validates the impact of digital intelligence on advancing low-carbon performance. This study delves into how manufacturing enterprises can attain sustainable and low-carbon growth via digital transformation, employing the entropy TOPSIS evaluation model to assess the effectiveness of various empowerment strategies. Based on the findings, the paper offers actionable recommendations for enhancing sustainable practices in manufacturing during this digital shift. Beyond enriching the theoretical framework on the synergy between digital intelligence and sustainability in manufacturing, this research provides practical insights and guidance for enterprises leveraging next-generation digital technologies to drive their green and low-carbon initiatives more effectively.

(1) Background: with the emergence and continuous development of more multinational corporations, capital and resources flow rapidly in the form of global supply chains around the world. Furthermore, government subsidies for R&D are one of the key factors that affect foreign-funded R&D activities and their innovation output and performance in global supply chains. (2) Methods: in this paper, firstly, based on two sets of time series and dynamic panel data, we propose a distribution time lag model to test the effect of R&D subsidy policies from the macro perspective. Secondly, we employ the propensity score matching method to test the micro effect of R&D subsidy policies. (3) Results: our empirical results show that there are significant differences in the impacts of R&D subsidy policies on foreign capital funded innovation and domestic innovation. The main effect of government subsidy on foreign capital R&D is to improve the innovation output. However, regarding domestic R&D, it is to promote innovation performance. (4) Conclusions: Government subsidy is the main cause of the individual differences among the foreign funded R&D institutions in terms of innovation output and innovation performance. From the perspective of global supply chains, our analysis and results provide managerial and policy insights on subsidizing foreign investment in R&D in China.

This study aims to explore the role of China’s Ecological Civilization Pilot Policies in carbon emissions reduction within the urban green energy industry. It further investigates how these policies influence carbon emissions. To achieve this, a unique incentive–constraint model is established considering China’s distinctive political system. The DID model was used in this study, employing Chinese city data spanning from 2009 to 2020 and analyzing urban panel data with the use of two specific policies as quasi-natural experiments. The analysis reveals the following key findings: (i) Ecological Civilization Pilot Policies in the energy industry substantially contribute to carbon emission reduction through the effects of technological progress and industrial structure optimization; (ii) the unique incentive–restraint mechanism within these policies enhances their effectiveness, with short-term incentives and carefully designed assessment criteria playing a pivotal role in their successful implementation. These findings carry substantial implications for shaping environmental policies within the energy industry, emphasizing the importance of such policies in the ongoing global effort to reduce carbon emissions and promote sustainability.

The development of human society and the production and operation activities of enterprises have brought about global warming, resulting in frequent natural disasters. It has become the consensus of all countries in the world to develop a green and low-carbon economy. Under this background, enterprises, as the main body of economic activities, especially energy industry enterprises, should optimize and upgrade the traditional production and operation mode with high pollution, high consumption, and low output to a high-efficiency and low-pollution mode, and pay attention to the co-ordinated development of economic benefits, social benefits, and ecological benefits. Financial performance evaluation indicators have become the main basis for senior leaders of energy industry enterprises to make decisions and evaluate the low-carbon economic benefits of enterprises. This paper constructs a set of financial evaluation index systems of energy industry enterprises under the background of a low-carbon economy from four dimensions: profitability, asset quality, debt risk, and business growth. The analytic hierarchy process (AHP) is used to measure the comprehensive contribution of financial indicators of low-carbon production and operation. The purpose of this study is to provide scientific financial management decisions for energy enterprises to reduce costs and increase the efficiency and low-carbon operation under the background of a low-carbon economy. The research results show that the comprehensive evaluation index system after the traditional financial evaluation index of energy industry enterprises is integrated with the evaluation index of a low-carbon economy can help enterprises make more correct and effective financial decisions in the process of survival, development, and growth, and, at the same time, the financial evaluation index of a low-carbon economy should pay more attention to the indicators with a higher comprehensive contribution, so as to effectively promote the low-carbon operation efficiency of enterprise production, management, and sales. Compared with other research results, this paper innovatively constructs a financial management decision-making index system for measuring the low-carbon operation of energy enterprises in theory, which has important value in guiding energy enterprises to reduce costs and increase the efficiency and low-carbon operation in practice.