This paper investigates the role of the digital economy in reducing carbon emissions, with a particular focus on the moderating and threshold effects of green finance. An analysis of data from 30 Chinese provinces shows that the digital economy significantly reduces carbon emission intensity by restructuring energy consumption and promoting green technological innovation. Green finance plays a crucial moderating role by alleviating financial barriers to digital transformation and supporting the implementation of emission-reducing technologies. The study reveals a nonlinear relationship, with green finance exhibiting a “strong initial, weak subsequent” threshold effect. At the same time, the digital economy’s impact on carbon reduction strengthens over time as technological development progresses. These findings contribute to understanding how digitalisation and green finance can work synergistically to drive sustainable low-carbon development.

Since the start of the industrial revolution, the manufacturing industry has been essential for economic growth but has also contributed to environmental pollution problems. The United Nations declared the 2030 Sustainable Development Goals (SDG) agenda to make sure that the well-being of the global environment is taken care of alongside the expansion of the world economies. As the leading manufacturing country worldwide, studying the evolution of China’s green development policies in manufacturing has significant implications for pollution management in manufacturing in other countries. This research analyzes China’s legal and policy documents on green development in the manufacturing industry based on planning objectives and actual effects with qualitative content analysis. It divides them into four periods: the exploring period (1949–1977), the formal establishment period (1978–2001), the improvement and strengthening period (2002–2011), and the comprehensive improvement period (2012 to present). Although the Chinese government has made progress in implementing green development policies, it still faces many challenges: (1) compatibility between economic development and environmental protection needs to be strengthened; (2) primarily command-and-control based policy structure needs to be reformed; (3) collaboration of multi-departmental management system needs to be enhanced. These challenges are the primary obstacles to China’s manufacturing industry achieving its environmental goals. The future policies for the green development of the manufacturing industry should focus on three aspects: (1) aligning environmental and manufacturing policies in setting strategic objectives and benchmarks; (2) concentrating on the systemic nature of policies and the interdependence of policy tools; (3) enhancing processes for policy creation, implementation, monitoring, and evaluation.

Digital technologies hold significant potential for addressing environmental issues, such as air pollution and rising global temperatures. China is focusing on accelerating the dual transformation of industrial greening and digitization to accomplish the UN’s 2030 Agenda for Sustainable Development and sustainable economic growth. By combining a two-way fixed effect model, a mediated effect model, and a panel threshold model, this research endeavors to explore the effect that the expansion of the digital economy has on the level of carbon emission intensity that is produced by industry. The research yielded the following primary conclusions. (1) The digital economy effectively reduces the industrial carbon intensity via three distinct mechanisms: enhancements to the technological and innovative capacities of China, improvements in energy efficiency, and enhancements to the country’s overall industrial structure. (2) Regions where industrialization and digitization are highly integrated and developing, as well as the early pilot regions of the Comprehensive Big Data Pilot Zones, are particularly susceptible to this inhibitory effect. This research offers a theoretical backing for advancements in the digital economy; the achievement of energy-saving and carbon-reducing sustainable development objectives; and the establishment of green, ecologically friendly, and recycling development strategies.

Mariculture areas are an important non-renewable natural resource and continuously improving their efficiency is important for increasing mariculture output and adjusting its structure. The aim of this study was to measure the mariculture area production efficiency (MAPE) considering undesirable outputs, further analyze its spatiotemporal disparities, and analyze the reasons for the differences observed during the period from 2008 to 2019. The super-efficiency Engel–Blackwell–Miniard (S-EBM) model and global Malmquist–Luenberger (GML) index was selected to analyze the technical efficiency and productivity of MAPE from both the static and dynamic aspects, and the Theil index was used to decompose the regional differences. The results showed that the MAPE showed fluctuation and an increasing trend overall; the production efficiency and technical progress showed a fluctuating rising trend, and technical progress had a significant driving effect on the production efficiency; and intra-regional differences were the main factors that cause the differences in MAPE. The findings suggest the increase of scientific and technological investment in mariculture, changes in mariculture methods, the establishment of environmental monitoring centers in mariculture areas, and the sharing of information technology between regions to achieve sustainable development.

Integrated ocean management (IOM) aligns with the United Nations’ Sustainable Development Goals (SDGs) and serves as a crucial strategy for promoting the enduring health of marine ecosystems and the sustainable utilization of marine resources. An analysis of the evolution of China’s integrated ocean management policy (IOMP) is crucial for providing valuable guidance in achieving sustainable growth in marine management and the marine economy for both China and other coastal nations worldwide. This paper studies the evolution characteristics, challenges, and prospects of IOMP in China since 1978 using content analysis. The findings indicate that the evolution of IOMP can be categorized into four stages: the germination period (1978–1998), the construction period (1999–2009), the systematization period (2010–2017), and the strategization period (2018–present), based on the state of international marine management. The IOMP has transitioned over time from its initial emphasis on resource development during the germination period to a strategization period that prioritizes the full development of all parts of the system. However, the ultimate goal has consistently been to achieve harmonious coexistence between people and the sea. China’s IOMP has experienced a progressive development; nevertheless, it continues to encounter obstacles such as the pressing requirement to revise sea-related policies and the absence of policy alignment. The objective of this paper is to explore the evolution, challenges, and prospects of China’s IOMP to better improve the land–sea coordination policy, strengthen international judicial cooperation. and provide effective policy reference for other maritime countries.

The long-term development of the manufacturing industry relies on sustainable tax management, which plays a key role in optimizing production costs. While artificial intelligence models have been applied to tax-related predictions, research on their application for predicting tax management levels is quite limited, with no studies focused on the manufacturing industry in China. To enhance digital innovation in corporate management, this study applies interpretable artificial intelligence models to predict the tax management level, which helps decision-makers maintain it within a sustainable range. The ratio of total tax expense to total profits (ETR) is used to represent the tax management level, which is predicted using decision trees, random forests, linear regression, support vector regression, and artificial neural networks with eight input features. Comparisons among the developed models indicate that the random forest model exhibits the best performance in terms of prediction accuracy and generalization capability. Additionally, the Shapley additive explanations (SHAP) technique is integrated with the developed model to enhance the interpretability of its predictions. The SHAP results reveal the importance of the input features and also highlight the dominance of certain features. The results show that the ETR from the previous year holds the greatest importance, being more than twice as significant as the second most important factor, whereas the effect of board size is negligible. Moreover, benefiting from the local interpretations using SHAP values, this approach aids managers in making rational tax management decisions.