This study utilizes bibliometric analysis to examine historical and present research patterns in the area of energy transition and green finance and to forecast potential future domains. Using the bibliometric method, 328 scholarly articles from the Web of Science database were evaluated. This paper identifies influential publications, maps the research landscape, and forecasts emerging tendencies through co-citation and co-word analyses. Co-citation analysis found three main clusters, while co-word analysis revealed four main clusters. Despite the growing significance of research on energy transition and green finance research, further in-depth investigation is necessary to offer a thorough depiction of the research domain. This research represents a pioneering endeavour in the utilization of bibliometric analysis to investigate the interrelationship between two items. It offers valuable insights into the rapidly expanding field of energy transition and green finance, effectively highlighting its contours and indicating potential future developments.

The digital era is reshaping agricultural practices, opening new avenues for sustainable growth, and proving indispensable in global challenges like food security and environmental conservation. However, a comprehensive understanding of this evolving landscape remains paramount. This research evaluates 344 papers from the Web of Science database to delve into sustainable agriculture's historical and current patterns in the digital era through bibliometric analysis and project future domains. Specifically, citation analysis identified influential papers, journals, institutions, and countries, while co-authorship analysis verified the interactions between authors, affiliations, and countries. Co-citation analysis found four hotspot clusters: prosperity and challenges in agricultural sustainability, digital information and agricultural development, innovations for sustainable agriculture, and geospatial analysis in environmental studies. The co-occurrence of keywords analysis revealed four main clusters for future studies: smart agriculture and biodiversity conservation, digitalization and sustainable agriculture, technologies and agricultural challenge management, and digital intelligence and farmer adoption. The study pioneers the use of bibliometric analysis to explore sustainable agriculture in the digital era. It presents invaluable insights into the evolving landscape of this field, summarizing its hotspots and suggesting future trajectories.

China has placed significant importance on the development of a circular economy and achievement of sustainable prosperity. It employs multiple fiscal and tax policies to facilitate clean production and improve resource efficiency by fostering corporate green innovation. Policy signalling boosts companies’ external funding, including debt and equity. As such, this research focuses on how government subsidies and tax incentives influence corporate green innovation efficiency, accounting for the mediating roles of debt financing and equity financing. Under the SBM model, we utilise the Luenberger index to quantify green innovation efficiency. In addition, the fixed-effect regression with 19,228 firm-year observations from 3549 firms between 2015 and 2021 is used. Based on empirical findings, government subsidies reduce green innovation efficiency, while tax incentives increase it. In addition, debt financing mediates the association between tax incentives and corporate green innovation efficiency. Furthermore, government subsidies and tax incentives play more significant roles in non-state-owned enterprises (non-SOEs) and for businesses in growing and mature stages than other listed firms. To improve access to external financing and green innovation efficiency, it is suggested that the government implement various government subsidies or tax incentives according to business characteristics, with each company applying policies customised to its specific circumstances.

The application of ecological techniques by farmers is important for ensuring the environmentally sustainable advancement of the grain sector. Based on micro-level survey data from 921 Chinese wheat growers in the Hebei and Henan provinces, this study employed an endogenous switching probit model and counterfactual analysis to investigate the impact and mechanisms of digital information utilization on ecological production technology adoption. The results indicated that 43.87% of sample wheat farmers had a low level of adoption of ecological techniques. The utilization of digital information significantly promoted farmers’ adoption. If farmers who currently used digital information were to opt-out, the probability of their high adoption would decrease by 11.26%. The utilization of digital information significantly enhanced the adoption of ecological technologies through three mediating factors: technological cognition, production monitoring, and market channels. Therefore, it is imperative to encourage farmers to broaden their social networks and enhance their perception of the importance of digital information. Additionally, it is essential to promote the industrialization and scale operation of wheat production, direct policy subsidies towards new types of management entities, and ensure the accuracy of the supply of digital information for green production through multiple channels. Therefore, it is imperative to expand farmers’ social networks and leverage rural communities to increase their perceived importance of digital information. Governments should increase subsidies and promote the scale and industrialization of wheat production. Moreover, the accuracy of digital information supply for sustainable production should be promoted through digital learning platforms, production monitoring systems, and e-commerce networks.

Despite a large number of government subsidies, Chinese listed companies still face numerous challenges. This requires research into the effects of government subsidies on corporate investment efficiency. The paper provides empirical evidence to investigate investment efficiency and enriches the study on the interactions between government intervention, rent-seeking, and ownership structure. Generalized least square (GLS) models with fixed effects were constructed using 2012–2020 data from 869 Chinese listed A-share non-financial firms. Results show that government subsidies received by listed companies significantly damage investment efficiency (β = .138, p < .01). This can be attributed to their rent-seeking behaviors to obtain subsidies, which also significantly harms investment efficiency (β = .915, p < .05). Government subsidies are also found to significantly mediate the impact of rent-seeking on investment efficiency. In three-step regression for testing mediating effect, coefficients are 0.475, 0.915, and 0.131 at the level of 1%, 5%, and 5%, respectively. Furthermore, ownership structure shows a moderating effect in the relationship between subsidies and investment efficiency. The management shareholding ratio significantly reinforces the negative impact (β = 1.369, p < .01), while the institutional shareholding ratio shows no significant moderating effect (β = 0.0571, p = n.s). Non-state-owned enterprises show a more significant negative impact (β = 0.17, p < .05) than state-owned enterprises (β = 0.148, p < .1). Finally, the study tests the above relationships for companies in the manufacturing industry that receive the most percentage of government subsidies in China, and the results are robust.