Does environmental policy stringency foster organic agriculture? Evidence from OECD members, accession candidates, and key partner economies

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Type of the article: Research Article

Abstract
Sustainable agrifood transformation requires clearer evidence on whether stricter environmental policy frameworks are associated with measurable changes in organic agricultural land use. This study examines whether environmental policy stringency is associated with the development of organic agriculture across OECD member countries, accession candidates, and key partner economies during 2004–2023. The analysis uses three unbalanced panel datasets covering total organic agricultural land, organic cropland, and organic permanent meadows and pastures, with 951 observations for 49 countries, 461 observations for 35 countries, and 457 observations for 35 countries, respectively; the methodology applies a set of panel-data specifications with robustness checks based on alternative specifications and individual policy-component models. The results show that international policy stringency has the strongest association with total organic agricultural land. In the preferred two-way fixed-effects model, a one-point increase in lagged international policy stringency is associated with a 0.721 percentage-point increase in the organic agricultural land share. In comparison, cross-sectoral policy stringency is associated with a 0.155 percentage-point increase. For organic cropland, both policy dimensions are positive and statistically significant, with coefficients of 0.216 and 0.354, respectively. For organic permanent meadows and pastures, policy coefficients indicate weaker responsiveness to broad environmental policy frameworks. Supplementary checks provide more cautious evidence: the first-difference result for international policy stringency is positive but only weakly significant, while the quadratic specification indicates a U-shaped pattern with an estimated turning point near 2.00 on the 0–10 scale. The findings should be interpreted as conditional associations rather than causal effects.

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    • Figure B1. Average organic agriculture share: Total organic agricultural land, 2004–2023
    • Figure B2. Average organic agriculture share: Organic cropland, 2004–2023
    • Figure B3. Average organic agriculture share: Organic permanent meadows and pastures, 2004–2023
    • Table 1. Correlation matrix of environmental policy components and multicollinearity diagnostics
    • Table 2. Panel diagnostics for cross-sectional dependence and serial correlation
    • Table 3. First-difference robustness results for total organic agricultural land share
    • Table A1. Descriptive statistics for the sample based on the total agricultural area under organic agriculture
    • Table A2. Descriptive statistics for the sample based on cropland area under organic agriculture
    • Table A3. Descriptive statistics for the sample based on the permanent meadows and pastures area under organic agriculture
    • Table C1. Main panel regression results for the total organic agricultural land share
    • Table C2. Robustness panel regression results for the organic cropland as a share of total agricultural land
    • Table C3. Robustness panel regression results for organic permanent meadows and pastures as a share of total agricultural land
    • Table D1. Individual environmental policy component models for the total organic agricultural land share
    • Table D2. Individual environmental policy component models for organic cropland as a share of total agricultural land
    • Table D3. Individual environmental policy component models for organic permanent meadows and pastures as a share of total agricultural land
    • Table E1. Turning point estimates from quadratic models
    • Table F1. Country coverage by sample
    • Conceptualization
      Aleksandra Kuzior, Tatjana Tambovceva, Iryna Kychko, Tetiana Vasylieva
    • Project administration
      Aleksandra Kuzior, Tatjana Tambovceva, Iryna Kychko, Tetiana Vasylieva
    • Supervision
      Aleksandra Kuzior, Tatjana Tambovceva, Tetiana Vasylieva
    • Visualization
      Aleksandra Kuzior, Iryna Kychko, Ivan Hroma
    • Writing – original draft
      Aleksandra Kuzior, Tatjana Tambovceva, Iryna Kychko, Ivan Hroma, Tetiana Vasylieva
    • Writing – review & editing
      Aleksandra Kuzior, Tatjana Tambovceva, Iryna Kychko, Ivan Hroma, Tetiana Vasylieva
    • Funding acquisition
      Tatjana Tambovceva
    • Resources
      Tatjana Tambovceva, Iryna Kychko
    • Validation
      Tatjana Tambovceva, Iryna Kychko, Ivan Hroma, Tetiana Vasylieva
    • Data curation
      Iryna Kychko, Ivan Hroma
    • Formal Analysis
      Ivan Hroma, Tetiana Vasylieva
    • Investigation
      Ivan Hroma
    • Methodology
      Ivan Hroma
    • Software
      Ivan Hroma