The advancement of the big data industry is playing a pivotal role in urban land management refinement. Recently, China initiated a big data strategy, establishing national big data comprehensive pilot zones (NBDCPZs) across diverse regions. These initiatives present substantial opportunities for enhancing the urban land green use efficiency (ULGUE). Consequently, in this study, we utilized the super-efficiency slack-based measure (SBM) model with undesirable outputs to assess the ULGUEs across 281 prefecture-level cities in China from 2006 to 2021. Subsequently, leveraging the NBDCPZ establishment as a quasi-natural experiment, we employed the difference-in-differences (DID) method to empirically explore the impact of the NBDCPZ policy on the ULGUE for the first time. The findings revealed the following: (1) The implementation of the NBDCPZ policy significantly enhances the ULGUE; (2) the effects are mediated through mechanisms such as fostering technological innovation, mitigating resource misallocation, and promoting industrial agglomeration; (3) the heterogeneity analysis emphasizes the increased policy effectiveness in cities characterized by fewer natural resources, lower economic growth pressures, stable development stages, and moderate digital infrastructure and human capital levels; and (4) further analysis demonstrates the significant positive spillover effects of the NBDCPZ policy on the ULGUEs of neighboring non-pilot cities, with a diminishing impact as the proximity between pilot and non-pilot cities decreases. Overall, this study contributes to the literature on the relationship between the digital economy and land utilization, offering valuable insights for achieving sustainable urban development.

In the context of addressing climate change, the uncertainty of climate policies has intensified the environmental and regulatory risks faced by enterprises, forcing them to adjust their strategies for fulfilling ESG responsibilities in pursuit of sustainable development. This paper uses panel data from listed non-financial enterprises on China’s Shanghai and Shenzhen A-share markets from 2011 to 2022, employing a fixed-effects panel model to examine the impact of climate policy uncertainty on corporate ESG performance. The findings indicate that climate policy uncertainty significantly hampers the ESG performance of enterprises. The mechanism analysis reveals that climate policy uncertainty negatively affects ESG performance by deepening corporate financing constraints and increasing short-term financial performance. The heterogeneity analysis shows that in terms of ownership structure, the negative impact of climate policy uncertainty on the ESG performance of state-owned enterprises is relatively weaker. In terms of industry heterogeneity, climate policy uncertainty suppresses the ESG performance of enterprises in technology-intensive industries. From a regional perspective, climate policy uncertainty has a stronger inhibitory effect on the ESG performance of enterprises in eastern China. This study provides valuable insights for both national climate policy formulation and corporate efforts to enhance ESG performance.

As climate change intensifies, climate-related policy instruments have become essential tools for governments to mitigate climate risks. The formulation of such policies typically involves balancing economic growth and ecological goals while ensuring fairness and feasibility, often resulting in frequent policy adjustments. Drawing on panel data from A-share listed firms in China between 2008 and 2023, this study conducts a comprehensive investigation into how climate policy uncertainty affects corporate green transformation, with a particular focus on the underlying mechanisms. The empirical evidence shows that climate policy uncertainty serves as a significant catalyst in promoting firms’ green upgrading. Mechanism analysis indicates that this uncertainty has enhanced enterprises’ environmental awareness, encouraged their green investment, and promoted strategic planning oriented towards environmental sustainability. Further heterogeneity analysis reveals that the positive effect is especially pronounced in state-owned enterprises, labor-intensive industries, and firms exposed to high levels of climate risk. This study enriches the understanding of corporate behavioral responses under climate-related policy uncertainty and offers practical implications for optimizing policy design to support sustainable transformation in the corporate sector.

Background: Agriculture and food security are key areas for climate change adaptation efforts. Amid the interplay between climate change and coordinated regional development, revealing the spatiotemporal evolution and network dynamics of China’s food production resilience holds significant theoretical and policy implications for synergistically enhancing resilience and promoting regional balance. Methods: Based on statistical measurements of climate resilience in food production (CRFP), this paper comprehensively analyzes the evolutionary characteristics and driving factors of China’s CRFP from the perspectives of spatiotemporal dynamics, regional disparities, and spatial correlation networks. Results: The research results indicate that China’s CRFP showed an overall upward trend from 2000 to 2022, but exhibited fluctuations and regional disparities, along with strong spatial dependence and spillover effects. Since 2006, the eastern region has surpassed other areas in terms of CRFP, and the gap has gradually widened, while the resilience level in the northeastern region is relatively low and more volatile. Additionally, China’s CRFP demonstrates extensive spatial correlation network characteristics, with increasingly tighter and more complex. The main driving factors influencing the spatial correlation network of CRFP include spatial geographic location, disparities in economic development levels, differences in industrial structure, and variations in agricultural mechanization levels. Conclusion: The findings provide a quantitative basis and decision-making support for enhancing China’s CRFP and regional coordination, while also offering empirical evidence to advance interdisciplinary research at the intersection of climate change and food security.

Amid the ongoing transformation of global climate governance, climate policy uncertainty has emerged as an increasingly important factor influencing both energy and agricultural commodity markets, with direct implications for energy and food security. Using monthly data from 2008 to 2025, this study applies the TVP-VAR-DY and TVP-VAR-BK frameworks, together with complex network analysis, to investigate spillover dynamics among climate policy uncertainty, energy, and agricultural markets from both time-varying and frequency-based perspectives. The results show that spillover effects evolve substantially over time and become more pronounced during periods of major external shocks, particularly under the influence of short-run factors. Notably, the transmission effect of climate policy uncertainty is stronger for bioenergy-related agricultural commodities, especially soybeans and corn. While the agricultural market exhibits strong internal connectedness, cross-market risk transmission is heterogeneous across commodities, with corn remaining a relatively stable net transmitter of risk. By contrast, crude oil generally acts as a net receiver, whereas climate policy uncertainty behaves as a net receiver in the short run but gradually shifts into a net transmitter over the medium and long term, suggesting a lagged transmission pattern. Robustness checks based on alternative lag lengths, forecast horizons, and CPU proxies confirm that the main connectedness structure is stable and not driven by specific parameter choices. These findings provide useful evidence for policymakers seeking to improve the stability and transparency of climate policy and mitigate cross-market risk, while also offering practical guidance for investors in portfolio allocation and hedging against policy-induced volatility.

The sustainability of local implicit debt reflects its effect on promoting economic growth. By analyzing the sustainability of local implicit debt, valuable insights can be gained to support the high-quality economic development of relevant countries. This study, using provincial panel data from China spanning 2006 to 2020, constructs a measurement method for local implicit debt using the MIMIC model and investigates the sustainability of local implicit debt from an economic growth perspective. The results show that local implicit debt has a rising trend but strong economic tournament pressure; an imperfect financial system and stricter financial regulation will affect the scale of local implicit debt. The economic effects of small-scale local implicit debt are not significant; however, when the scale of local implicit debt exceeds CNY 123.88 billion, it can have a significant stimulating effect on regional economic growth. Local implicit debt has a significant sustainability and can significantly drive regional economic growth, with the driving effect being more pronounced in the western regions and at higher thresholds.