By constructing a two‐country, three‐commodity, three‐sector theoretical model, we show that trade liberalisation has double effects, i.e., ‘factor price effect’ and ‘technology progress effect’, on labour income share. China’s deviation from the Stolper–Samuelson Theorem is mainly due to the negative effect of technology progress, which weakens the positive pulling effect of trade liberalisation on labour income share. Based on the panel data of 29 provinces and cities in China dated from 1987 to 2006, we build up an empirical model. We find that because of the offsetting cancellation of opposite effects, the overall effect of trade liberalisation on labour income share is insignificant. However, when eliminating the negative effect of technology progress on labour income share, the effect of trade liberalisation becomes significantly positive. Moreover, the positive effect of trade liberalisation has become smaller in recent years. This is because of the transformation of export structure, which has led to a decrease in the positive effect of export on labour income share.

The research measures the driving force of innovation in economic structure transition. In order to change the pattern of economic development, China is implementing a strategy of innovation-driven development. China’s capacity of innovation has been increasing, especially since 2012, and China’s innovations have taken a leap-forward development. Nowadays, innovation has become a main driving force in China’s economic development and hi-tech industries particularly make a great contribution. Although China’s tertiary industry has been dominant and its share in three industrial sectors has been exceeding 50% since 2015, a problem still exists in China’s economy that the proportions of primary and secondary industries are relatively higher compared with developed countries. In this paper we use PLSR model to measure the impact of innovation on China’s economic structure transition. It is found that innovation can expand the tertiary industry through shrinking the proportions of primary and secondary industries, transforming China’s economic structure into a more advanced pattern. Additionally, China is also devoting itself to the “Belt and Road Initiative”, which should be combined with China’s domestic innovation-driven development and realize sustainable development of economy worldwide.

PurposeThe implementation of the innovation-driven development strategy is of practical significance for improving the quality and efficiency of economic growth and accelerating the transformation of economic development mode. The purpose of this paper is to study the impact of innovation-driven strategies on marine industry agglomeration and industrial transformation.Design/methodology/approachIn traditional grey correlation analysis, when the positive and negative areas cancel each other out during the integration process, the calculation result of the correlation degree is often inconsistent with the qualitative analysis. For this reason, from the perspective of curve similarity, this paper constructs two response curves through the relative change area of the two curves and the relative area change ratio of similar degree, thus constructing an improved grey relational model.FindingsThe authors find that the innovation investment has a better correlation with marine industrial agglomeration. It also found that Guangdong Province has the highest degree of correlation between innovation indicators and marine industrial agglomeration. Much beyond the authors’ expectation, in the areas where marine industrial agglomeration is high, the synergistic effect is not obvious by using the location entropy method.Originality/valueThe improved grey correlation analysis method can effectively overcome the phenomenon that the positive and negative areas cancel each other in the integration process of the original algorithm, and it can also effectively measure the negative correlation between variables. This paper explores the impact of innovation drive on the agglomeration of marine industries, which is of great significance to the sustainable development of marine economy.

The development of digital inclusive finance has alleviated the problem that traditional finance cannot fully cover rural areas, provided convenient services for Chinese farmers, and solved the problem of “difficult and expensive loans” in agricultural development. This paper used the panel data of Beijing University’s Digital Inclusive Finance Index and 31 provinces and cities in China from 2011 to 2020, and adopted the double-fixed-effect and panel threshold model to study the impact of the development of digital inclusive finance on the level of agricultural output and its internal mechanism. The study found that digital inclusive finance can significantly improve the level of agricultural output, and there is a double threshold for the impact of digital inclusive finance on the level of agricultural output. The heterogeneity analysis showed that the coverage and depth of digital inclusive finance can significantly improve the level of agricultural output, and the depth of use plays a greater role. Digital inclusive finance has significantly improved the level of agricultural output in the midwest regions and major agricultural provinces, but its impact on the eastern regions and non-agricultural provinces is not significant. Finally, the mechanism analysis found that digital inclusive finance can improve the level of agricultural output by promoting the level of agricultural mechanization and improving farmers’ willingness to participate in insurance. Therefore, we should continue to promote the development of digital inclusive finance according to local conditions.

As one of the environmental governance tools used to achieve green and low-carbon development in China, the ability of carbon emission trading schemes (CETS) to promote the green transition of enterprises is key to assessing the effectiveness of their implementation. Therefore, this paper used the panel data of China A-share listed heavy-polluting enterprises from 2010 to 2019, adopted the super-SBM model and GML index to measure the green total factor productivity (GTFP) of enterprises as an indicator of green transition, and further employed a staggered difference-in-difference model (DID) based on propensity score matching (PSM) to investigate the impact and mechanism of CETS on the green transition of enterprises. The results revealed that CETS significantly improved the green development efficiency of heavy-polluting enterprises and promoted green transition. In addition, the promotion was more pronounced among enterprises with weak cost transfer abilities, low levels of financing constraints, and high-quality internal control systems as well as in areas with high environmental enforcement intensity. More importantly, the mechanism analysis showed that heavy-polluting enterprises mainly chose to increase green technological innovation, especially substantive green technological innovation, and accelerated productive capital renewal to achieve their green transition targets. This study provides empirical evidence for improving the construction of the national carbon emission trading market and promoting the green transition and low-carbon development of heavy-polluting enterprises.