Can the Marketization of Data Elements Improve Forest Carbon Sink Efficiency? An Empirical Study from Hebei, China
Under the “dual carbon” goals and the Digital China strategy, can marketizing data as a production factor effectively enhance forest carbon sink efficiency? This question is critical because the forestry sector remains a latecomer in data-driven transformation. This study utilizes panel data from 52 mountainous counties in Hebei Province (2007–2023) to construct an indicator system using the entropy method and the super-efficiency SBM-ML model. We then employ a two-fixed-effects model to empirically examine the impact of data element marketization (DEM) on forest carbon sink efficiency (FCSE) and its underlying mechanisms. The findings provide robust evidence that DEM is positively associated with FCSE, with a coefficient of 0.9507; this association is supported by a series of stability and endogeneity tests. Mechanism analysis suggests that DEM enhances FCSE by promoting forestry technology innovation; however, this positive effect weakens after green finance crosses the double thresholds of 0.0473 and 0.0971. Heterogeneity analysis indicates that the promotional effect of DEM is more pronounced in the Yanshan region of northern Hebei, as well as in digitally less advanced or environmentally more aware areas. Our findings underscore that advancing data element marketization in the forestry sector is not a standalone solution. It requires coordination with green finance regulation and adaptation to local conditions.
Authors
- Shuai Zhong (ORCID: https://orcid.org/0000-0001-7529-454X)
- Xiaojing Ma
- Guofeng Zhang (ORCID: https://orcid.org/0009-0009-9473-7807)
- Jiaxin Liu (ORCID: https://orcid.org/0009-0009-1109-5096)
Institutions
- Institute of Geographic Sciences and Natural Resources Research (CN)
- Hebei GEO University (CN)
Publication Details
- Journal
- Forests
- Published
- 2026-10-15
- DOI
- https://doi.org/10.3390/f17101230
- Primary Topic
- Forest Management and Policy
- Type
- article
- Field-Weighted Citation Impact
- 0.00