Firm-level correlates of low-carbon innovation in Chinese agribusiness firms
中国農業関連企業における低炭素イノベーションの企業レベルの相関要因 (AI 翻訳)
Alexandr A. Tarasyev, Weijun Zhu
🤖 gxceed AI 要約
日本語
中国の農業関連上場企業を対象に、低炭素特許出願の企業レベルの相関要因を分析。1998～2021年の316社・3920観測値のパネルデータを用い、低炭素特許を希少なアウトカムとして扱い、ポアソン疑似最尤法や線形確率モデル等で推定。企業規模と事前のグリーン特許ストックが最も安定した相関要因であることを発見。デジタル特許ストックやネットワーク変数は限定的な説明力しか持たない。
English
This study analyzes firm-level correlates of low-carbon patenting among Chinese agribusiness listed firms using a panel of 316 firms and 3,920 observations from 1998 to 2021. Treating low-carbon patents as a sparse outcome, it employs pooled Poisson pseudo-maximum likelihood and other models. Firm size and prior green patent stock are the most stable correlates, while digital patent stock and network proxies add limited explanatory power.
Unofficial AI-generated summary based on the public title and abstract. Not an official translation.
📝 gxceed 編集解説 — Why this matters
日本のGX文脈において
中国農業セクターの低炭素イノベーションの要因を明らかにした研究。日本の農業関連企業が低炭素特許戦略を考える際の参考になるが、直接的な政策連動性は低い。
In the global GX context
This paper provides descriptive evidence on low-carbon innovation correlates in an emerging economy's agribusiness sector. It complements global research on green patenting by focusing on a specific industry and using exact patent-level overlap checks.
👥 読者別の含意
🔬研究者:Useful for scholars studying determinants of low-carbon innovation, especially in agribusiness and using patent data.
🏢実務担当者:Highlights that low-carbon patenting is concentrated in larger, more innovative firms; may inform R&D strategy.
🏛政策担当者:Suggests policies to enhance innovation capacity in agribusiness firms to promote low-carbon patenting.
📄 Abstract（原文）
This study examines firm-level correlates of low-carbon innovation among Chinese agribusiness-related listed firms. Using a pooled firm-year panel of 316 firms and 3,920 observations from 1998 to 2021, it treats low-carbon patenting as a sparse innovation outcome rather than as a routinely observed continuous variable: 93.1% of firm-year observations record no low-carbon patent application. The empirical design separates two descriptive margins: whether a firm files at least one low-carbon patent in a given year and how the unconditional annual count varies across the full firm-year sample. The count outcome is estimated with pooled Poisson pseudo-maximum likelihood, while the occurrence margin is examined with pooled linear probability, logit, and complementary log-log specifications. The explanatory variables are organized into resource-capability conditions, adjacent innovation stocks, and supplementary linkage proxies. Across specifications, firm size and selected balance-sheet structure variables are the most stable correlates of low-carbon patenting. Pre-period green patent stock is the strongest adjacent innovation correlate. Because green and low-carbon patent categories may overlap, the study uses exact patent-level overlap exclusion as the main measurement check, and the positive green-stock association remains. Pre-period digital patent stock is more sensitive to model design and is more visible in occurrence and onset evidence than in full-sample count models. Branch-based geographic-reach proxies and static equity-link proxies add only limited incremental explanatory value after firm characteristics and lagged innovation stocks are controlled for. The findings are descriptive rather than causal and mainly reflect between-firm differences in the pooled panel. They suggest that low-carbon patenting in this sector is concentrated among larger and more innovation-capable firms rather than being a general outcome of network expansion.
🔗 Provenance — このレコードを発見したソース
- openalex https://doi.org/10.5281/zenodo.20970723first seen 2026-07-05 04:59:42
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