Senior management in tertiary institutions desires an efficient system that could help them assess and evaluate learning outcomes so that effective policies can be implemented to enhance teaching and learning. This gets intensified as broader issues arise and higher expectations are put on tertiary education—build a creative workforce and adapt to new technologies to analyze the large volume of teaching and learning data. Government and higher education policymakers have to rapidly adjust relevant policies to surmount the challenges from the pandemic and also to keep up with technological advancement. This demands a novel and efficient way for policymakers and senior management to see and gain insights from a large volume of data (e.g., student course and teacher evaluation). In this study, the researchers present such a system through various examples. The findings generated from this study contribute to the scholarship, and they provide a solution to senior management in tertiary institutions wanting to implement effective policies efficiently. The use of online analytical processing, virtual campus, online, and machine learning in education is growing. However, the use of these technology-enhanced approaches is rare in performing arts education. There has been no in-depth study, especially on technology-enhanced learning that leads to the improvement of teaching. This study utilizes a multi-dimensional analysis approach on the course student evaluation, a key aspect of the teaching and learning quality assurance for higher education. A novel analytical framework is developed and implemented at a leading performing arts university in Asia. It analyzes the course evaluation data of all courses (669 courses and 2664 responses) in the academic year 2018/2019 to make evidence-based recommendations. Such a framework provides an easy and effective visualization for senior management to identify courses that need closer scrutiny to ascertain whether and what areas of course enhancement measures are warranted.

As digital technology and green development become more integrated, exploring how the digital economy affects energy consumption is vital for coordinating economic growth with energy conservation and emission reduction. This study uses panel data from 30 Chinese provinces covering the years 2011 to 2023, constructing a digital economy development index system using the entropy weighting method. Additionally, it employs panel threshold models and spatial Durbin models to empirically examine the nonlinear effects and spatial spillover effects of the digital economy on energy consumption. The research reveals that: First, the digital economy exhibits a pronounced threshold effect on energy consumption. When the level of digital economic development falls below the threshold value of 0.024, it effectively curbs energy consumption. Once this threshold is exceeded, it paradoxically promotes energy consumption exhibiting a nonlinear pattern of “initial suppression followed by promotion”. This confirms the existence of the demand-side green paradox of the digital economy effect—where efficiency gains from technological progress are offset by consumption growth, unexpectedly stimulating higher energy consumption. Second, the digital economy exhibits a complex spatial effect on energy consumption characterized by “local promotion and neighboring suppression”. Although it demonstrates negative spillovers in surrounding areas, the overall regional effect remains positive, revealing the spatial complexity of this “green paradox”. These findings challenge traditional linear assumptions and offer valuable insights into regional digital economic planning and the promotion of green, low-carbon development.

Enhancing the allocation efficiency of hydropower resources is critical to achieving China’s carbon peaking and carbon neutrality goals. Based on panel data of 29 Chinese regions from 2001 to 2020, this study first measures the degree of hydropower resource misallocation via a counterfactual decomposition approach, and then adopts a threshold model to explore the impact of green finance on hydropower resource misallocation. The results show that: (1) Western and southwestern regions with abundant hydropower resources yet insufficient economic absorption capacity suffer from hydropower oversupply, while eastern coastal and central regions with advanced economies and intensive industries face hydropower undersupply. The inverse spatial distribution pattern of hydropower supply and demand cores reflects prominent hydropower resource misallocation. (2) Green finance has an inverted U-shaped nonlinear relationship with hydropower resource misallocation. Moderate development of green finance can improve hydropower resource allocation efficiency, yet exceeding the optimal threshold may trigger problems including greenwashing and thus exacerbate hydropower resource misallocation.

Geopolitical conflicts continue to disrupt global energy supply chains, causing sharp changes in energy prices. These changes reshape the cost structure and efficiency levels of grain circulation. Based on panel data from 30 Chinese provinces covering 2011–2022, this study constructs a proxy for grain circulation efficiency using a resource misallocation framework and counterfactual decomposition. We employ panel threshold regression and double machine learning methods to systematically examine the nonlinear impact of energy price levels on grain circulation efficiency and to reveal regional differences. The findings are as follows: (1) Energy price levels exhibit a significant single-threshold effect. When energy prices remain within a low range, rising prices exert a positive technology push effect; beyond the threshold, a cost-squeeze suppression dominates. (2) The eastern region shows the highest tolerance for price increases, whereas the western region has the lowest tolerance, with the central region falling in between. (3) Double machine learning feature importance analysis reveals that research and experimental development investment and economic development are the dominant factors affecting agricultural product circulation efficiency in the eastern region; water consumption and capital stock are the key factors in the central region; and energy prices, foreign trade dependence, and infrastructure are the most sensitive factors in the western region. This study provides empirical evidence for designing differentiated regional circulation policies and enhancing the resilience of the grain circulation system.

Achieving a secure and stable energy supply while steadily advancing the dual-carbon goals constitutes a dual strategic task for China’s sustainable development. Capacity governance policies serve as an effective tool for the government to regulate industrial capacity, facilitating a balance between supply and demand through interventions on both the production and consumption sides. As a fundamental energy source in China, coal capacity governance policies involve complexity and span multiple domains, with their effectiveness relying on synergy across different levels and departments. Using a hierarchical linear model, this study examines the specific impacts of policy synergy in capacity governance—from both the central and local government perspectives—on local economic, environmental, and social outcomes. The findings indicate that policy synergy at the central level yields significant economic and social benefits, while its environmental benefits remain less evident. In contrast, policy synergy at the local level demonstrates significant positive effects across all three dimensions: economic, environmental, and social. By quantitatively assessing the tiered differences in the multidimensional benefits of policy synergy, this study provides a scientific basis and theoretical support for both central and local governments to pursue maximized economic, environmental, and social benefits.

Whether the market-oriented allocation of data factors can simultaneously improve electricity resource allocation efficiency and environmental performance is a key question for the synergy between the digital economy and the green low-carbon transition. Using the entropy weight method to construct a data factor marketization index and a Cobb–Douglas production function to measure electricity resource misallocation, this paper examines the impact of data factor marketization on electricity resource misallocation and its carbon emission consequences, using panel data of 233 prefecture-level cities in China from 2008 to 2023. We find that data factor marketization significantly reduces electricity resource misallocation, and this conclusion remains robust across a series of robustness checks. Mechanism analysis shows that data factor marketization alleviates misallocation through two channels: compressing inappropriate government intervention in electricity allocation and improving capital-output efficiency; the mitigating effect is stronger in cities with higher industrialization intensity and human capital levels. Heterogeneity analysis indicates that the effect is more pronounced in eastern and central-western regions but not significant in the old industrial base of Northeast China. Further environmental consequence analysis shows that electricity resource misallocation significantly raises per capita CO₂ emissions, while data factor marketization not only directly suppresses emissions but also indirectly reduces emissions by lowering misallocation, with the emission-reduction effect being stronger in cities with larger population sizes. It should be emphasized that the data factor marketization index measures the enabling conditions for the market-oriented allocation of data factors (infrastructure foundation, development capacity and overall scale) rather than the realized volume of data transactions; the results therefore speak to the effects of the foundations of data factor marketization. Our findings provide empirical evidence for coordinating data factor market construction, electricity system reform, and carbon reduction policies.