The question of how to gradually transform the economic growth mode from extensive growth to intensive economic growth, and steadily improve the efficiency of green economic growth (GEGE), has become the focus of society and scholars. The present study uses the SBM-DDF directional distance function to measure GEGE from 2008 to 2021 in China, and then selects the bootstrap regression method to test the influencing factors of China’s GEGE. The following conclusions are obtained: (1) the GEGE level is still low, and its average value in the fourteen-year period from 2008 to 2021 is 0.484; areas with low levels of GEGE account for approximately 66.7% of the country, and those with relatively high levels account for approximately 20% of the country; high-level green development areas account for 13.3% of the country; (2) environmental regulation’s impact on GEGE has a U-shaped relationship, which means that when the regulation intensity increases, GEGE will first decrease and then increase, and once it crosses the inflection point, the effect of improving GEGE becomes obvious; the elasticity coefficient of educational human capital is significantly negative, which has a hindering effect on GEGE; (3) FDI is significantly negatively correlated with GEGE; there is a U-shaped relationship between GEGE and urbanization, and the industrial structure has a negative effect on GEGE, while trade dependence has the same effect.

As an essential element of higher education, course planning at the program level is a complicated multi-criteria decision making (MCDM) problem. In addition, a course planning process tailored to sustainable development is exceptionally important to sustaining the quality of academic programs. However, there is a scarcity of research on the program course planning problem at the operational level due to a diverse set of stakeholder requirements in practice. Motivated by the challenge, this study proposes an innovative MCDM model for sustainable course planning based on He-Xie management theory. In the introduced framework, the best worst method (BWM) can obtain the optimal weights of sustainability competencies, which are then embedded into the fuzzy filter ranking (FFR) method to generate the ranking of candidate courses by each course module, considering the connectivity between courses and the development of sustainability competencies. Finally, multi-choice goal programming (MCGP) is adopted to allocate each selected course to a semester, aiming to balance total credits and average difficulty level among semesters as much as possible. The practicability and reliability of the proposed course planning model is validated through a case study of an undergraduate accounting program. Results show that the proposed framework is a feasible tool for course planning. This research extends the existing literature on course planning by explicitly capturing the fuzzy nature of human decision making and avoids underestimation of the decision. The implications of the paper are not restricted to developing a sustainable course plan for an accounting program.

In the context of global warming, low-carbon economy has attracted more and more countries’ attention. Using second-generation unit root CIPS test and mean group (MG) estimation, we examined the impact of CO2 emissions and economic loss on forest productivity with panel data on 30 Chinese provinces from 2004 to 2020.The results showed that CO2 emissions and economic loss had significant negative impacts on it, technical innovation had positive impacts on it, and the share of secondary industry had negative impacts on it. Regarding regional heterogeneity, CO2 emissions in west China had the greatest impact on forest productivity, followed by east China. The coefficients of economic loss in west, east and middle China were all negative, but those in east and middle China did not pass significance test. The coefficients of technical innovation in east and west China were positive, with the coefficient in east China not passing significance test. In order to improve forest productivity and achieve carbon neutrality, it is necessary to reduce CO2 emissions, curb economic losses caused by factors like environmental pollution, natural disasters, geological disasters and forest fires, improve technological innovation, promote industrial upgrade, and implement differentiated policies based on local conditions.