This paper adopts the slacks-based measure-directional distance function (SBM-DDF, 2009) method for deriving the “Green Innovation Growth” rates of 28 manufacturing industries in China. The results indicate that the overall level of green innovation growth in China’s manufacturing is relatively low, with a declining trend. The tradeoffs among energy, environment and economy are rather sharp, and the “Porter Effect (1995)” (environmental regulation will promote green technology innovation) is not currently realized quickly in manufacturing. These evaluations imply an unsustainable development model in China, with significant differences among industries. By using a dynamic panel threshold model and employing an industry-level panel dataset for 2008–2014, we show that external knowledge sourcing has a significant negative impact on green innovation growth but with different constraints on R&D levels among industries. With the strengthening of R&D levels, gradually surpassing “critical mass”, the negative role of external knowledge sourcing in driving this mechanism becomes smaller and smaller; it has a non-linear relationship with the “threshold effect”. Consequently, we provide insights into the relationship among energy consumption, environmental pollution and technology innovation, and show how the heterogeneity of the R&D threshold affects differences in external knowledge sourcing and green innovation growth. These insights lead to a better understanding of the driving force, realizing path and policy design for green innovation growth.

The negative effects of global warming are becoming more and more serious. The fundamental way to prevent global warming is by reducing carbon dioxide emissions. Achieving this has become a key concern for all countries. The logarithmic mean divisia index model was constructed to decompose the total carbon emission increment. Carbon finance effect was divided into green credit effect and carbon trading effect to analyze the impact of carbon finance on carbon emissions. The results showed that the total carbon emission reduction value caused by green credit effect from 2010 to 2016 in the Beijing-Tianjin-Hebei region was 66193.96 million tons, and the added value of carbon emission caused by carbon trading effect was 80266.68 million tons. There are regional differences in the effects of carbon finance on carbon emissions in these regions. It can be concluded that to a certain extent, green credit can reduce carbon emissions, and carbon trading can increase carbon emissions. Using the gradual expansion of carbon finance trading and market mechanism of carbon finance to solve the problem of carbon emission can improve the efficiency of carbon emission reduction.

As the world’s biggest emitter of carbon, China’s agricultural carbon emissions account for 16 to 17%, with agriculture being the second largest contributor to carbon emissions. The development of the digital economy has brought profound changes to agricultural ecology. Therefore, we utilize China’s data experience to construct an agricultural ecological transformation system by setting up a Super-SBM model. Then, based on a dynamic panel threshold model, we explore the nonlinear impact mechanism of the digital economy on agricultural ecological transformation from a low-carbon innovation perspective. Our results reveal that the overall level of China’s agricultural ecological transformation is not high, but in the long-term posture, it represents a relatively steady upward trend, with a significant “wealth gap” between different regions. It is worth noting that there is a significant threshold effect of low-carbon innovation heterogeneity in the impact mechanism of digital economy on agricultural ecological transformation: under lower low-carbon innovation levels, the development of digital economy is unable to effectively promote agricultural ecological transformation. However, as the level of low-carbon technological innovation increases and exceeds the critical value, it stimulates the driving effect of digital economy to some extent, thereby promoting the improvement of agricultural ecological transformation, presenting a “U” shaped relationship. The paper has clarified the differential “new phenomena” in the process of promoting agricultural ecological transformation, providing new insights for achieving “carbon reduction and economic promotion” in developing countries.

In the process of transitioning from closed to open innovation, regions in developing countries need to understand how to choose the most effective path within the complex innovation system while considering their own innovation factors. Based on provincial panel data from China’s high-tech industry and the improved dynamic threshold model, we introduce the threshold of knowledge accumulation (KLA) into the non-linear mechanism between innovation paths and innovation performance to compare the dynamic threshold effect and its heterogeneity. This research provides interesting insights into innovation paths, showing that the relationship between the innovation path and innovation performance is significantly influenced by the threshold effect of KLA. As the level of KLA strengthens, its effects on each innovation path change. Overall, this article shows how KLA affects the relationship between the innovation path and innovation performance. The article concludes with a discussion of the implications of these insights for innovation management and policy.

Under the Chinese government’s carbon neutral target, digital transformation enabling low-carbon technology innovation has become an innovative starting point to promote the new development concept. We use data from a sample of 270 manufacturing companies in 30 provinces and cities in China, this paper constructs an analysis framework of low-carbon technology innovation in manufacturing driven by digital transformation with knowledge reconstruction as the intermediary variable and knowledge sharing as the regulating variable, and empirically explores the impact of digital transformation on lowcarbon technology innovation. It is found that the digital transformation of the manufacturing industry has a significant positive impact on low-carbon technology innovation, and is an important power source to promote low-carbon technology innovation. Knowledge reconstruction plays a part of intermediary role between digital transformation and low-carbon technology innovation, and plays a key role as a bridge. Knowledge sharing plays a positive role in digital transformation, knowledge reconstruction and low-carbon technology innovation. The research clarified the differences and suitability in the process of low-carbon technology innovation in the digitally enabled manufacturing industry and answered the question of how to effectively use knowledge management to realize digitalization and help the industrial low-carbon innovation and upgrading in the digital economy era, which has important reference significance for guiding the green and high-quality development of the industry.