Big data is a pivotal factor in propelling the digital economy forward and emerges as a novel driver in realizing the goals of carbon peaking and carbon neutrality. This study focuses on a quasi-natural experiment, namely national big data comprehensive pilot zones (NBD-CPZs), and employs a multi-period difference-in-differences (DID) model to identify the influence of big data on carbon emissions. The findings of this study are as follows. Overall, big data significantly reduces carbon emissions within the pilot zones. Mechanism analysis shows that big data reduces urban carbon emissions by promoting green innovation, optimizing energy structure, mitigating capital mismatch and improving public awareness of environmental protection. Heterogeneity analysis shows that the carbon reduction effect of big data are more pronounced in cities with high levels of digital economy, non-resource-based cities, cities with strong intellectual property rights protection and the Guizhou Province. Spatial effect analysis indicates that within a radius of 400–500 km, the NBD-CPZ increases urban carbon emissions, signifying a significant siphoning effect; within a radius of 500–900 km, the NBD-CPZ reduces urban carbon emissions, signifying a significant spillover effect, and beyond a distance of 900 km, the spatial effect of the NBD-CPZ is not significant. Based on the above conclusions, this study puts forward several policy recommendations to effectively exert the carbon emission reduction effect of big data.

Economic development often results in significant greenhouse gas emissions, contributing to global climate change, which demands immediate attention. Despite implementing various low-carbon policies to promote sustainable economic and environmental progress, current evaluations reveal limitations and deficiencies. Therefore, this study utilizes a dataset detailing policy intensity at a prefecture-level city in China to investigate the impacts of these policies on carbon emission reduction from 2007 to 2022 in 334 prefecture-level cities, employing a fixed-effects model. Additionally, it assesses the policies’ efficacy. The findings indicate a significant negative correlation between China’s low-carbon policies and carbon emissions, supported robustly by multiple tests. Specifically, a one-unit increase in China’s policy intensity correlates with a 0.53-unit reduction in carbon emissions. Furthermore, the heterogeneity analysis shows that variations in urban agglomerations, environmental resource endowments, pollution levels, and low-carbon policy intensities influence the effectiveness of these policies in reducing carbon emissions. This analysis underscores that policy intensity achieves emission reductions through technological innovation, industrial transformation, welfare crowding out, and pollution transfer, with varying impacts across different environmental contexts, pollution levels, and policy intensities. Based on this analysis, we recommend several policies: formulating low-carbon strategies tailored to local conditions, enhancing regional low-carbon policies, establishing cross-regional coordination mechanisms, and so on. These recommendations not only offer valuable policy insights for China but also serve as useful references for the green and sustainable development of other developing countries.

As global economies seek solutions to slowing productivity growth and sustainability challenges, China has advanced the concept of “New Quality Productive Forces” (NQPF) to guide innovation-led, green development. This study explores how corporate digital transformation (DT) contributes to regional NQPF, and how this relationship is moderated by two institutional forces: internal corporate ESG performance and external regional intellectual property protection (IPP). Using a matched panel dataset of 31,598 firm-year observations from Chinese A-share listed companies (2013–2022) and their corresponding regional data, we apply fixed-effect models and threshold regressions to empirically test the micro-to-macro productivity link. Results reveal that DT significantly enhances NQPF at the regional level, but this effect is conditional. Higher ESG performance amplifies the positive impact of DT, exhibiting an increasing marginal return. Meanwhile, IPP shows a non-linear moderating effect: only when protection surpasses a critical threshold does it unlock the productivity gains of corporate digitalization. These findings offer theoretical advances by clarifying the institutional boundaries of technology spillovers and contribute to the emerging NQPF literature by establishing a cross-level framework that links firm strategy to macroeconomic outcomes. Policy implications suggest that sustainable digitalization requires both strong internal ESG governance and robust external IP institutions to fully translate private innovation into public economic value.