China Forestry Carbon-Inclusive Productivity Panel Data for 30 Provinces, 2000-2020

Panel data for 30 Chinese provinces from 2000 to 2020, used to measure carbon-inclusive total factor productivity (CTFP) in the forestry sector. The dataset, authored by Xia Jiang and shared under a CC-BY-4.0 license, incorporates forest carbon stock as a desirable output and carbon emissions as an undesirable output. It supports analysis of productivity trends, regional heterogeneity, and the drivers of sustainable forestry development.

Use Cases

• Decomposing productivity growth into efficiency and technological change based on the Luenberger productivity index framework described.
• Analyzing regional heterogeneity in forestry productivity between eastern, central, and western China as mentioned in the description.
• Modeling the contribution of carbon-related factors (stock and emissions) to economic-ecological productivity as outlined in the CTFP framework.
• Benchmarking provincial forestry performance against a meta-frontier to capture technological gaps described in the study.

Strengths

• Data spans 21 years (2000-2020), providing a long-term panel for trend analysis.
• Covers 30 provinces, enabling regional comparative studies as highlighted in the findings.
• Explicitly incorporates ecological outputs (carbon stock) and undesirable outputs (carbon emissions) into productivity measurement.

Limitations

• Column-level documentation is absent; field semantics must be inferred after download.
• Row count is unknown, which may limit suitability assessment.
• The primary file is a 689.3 KB DOCX document, which is a small file that likely contains summary results rather than the raw underlying data.

Provenance

Source

figshare, author Xia Jiang.

Collection Method

Likely compiled from official Chinese forestry and economic statistics, as the study uses panel data for provincial analysis.

Time Range

2000 to 2020

Freshness

Last updated 2026-05-29 04:48:27; freshness should be verified.

Geography

China, covering 30 provinces.