Input Variables for Green Economic Growth Modelling

This dataset provides the full set of variables used to model green economic growth through the Malmquist–Luenberger Productivity Index (TFPCH) for 27 EU member states and Ukraine. It follows the OECD Green Growth Measurement Framework, complemented by a production-theoretic operationalization of green growth through dynamic productivity analysis. All variables are compiled from open-access international statistical sources, including the World Bank, OECD AI Venture Capital data, UN SDG indicators, and Our World in Data. Gross domestic product (GDP) is used as the desirable output, representing economic performance in line with productivity and growth literature. Environmental degradation is incorporated through five undesirable outputs: CO₂ emissions, PM2.5 air pollution, water stress, fertilizer consumption, and forest loss (operationalized as the inverse of forest area). These indicators capture long-term climate impacts, human health externalities, resource overexploitation, agricultural pressure, and biodiversity loss. The undesirable indicators are aggregated into a composite environmental burden index using the entropy weighting method, which assigns objective weights based on statistical dispersion across countries and time, avoiding subjective parameterization. The dataset enables the computation of the Malmquist–Luenberger Productivity Index (TFPCH), defined through the directional distance function and decomposed into technological change (TECH) and efficiency change (TECCH). These measures allow the dynamic assessment of green economic growth under explicit environmental constraints. All variables are presented in their original measurement units and, where appropriate, in natural logarithm form to support the log-linear implementation of the directional distance function. The dataset is suitable for replication of all empirical procedures in the study, including the construction of the directional distance function, estimation of the Malmquist–Luenberger index, calculation of entropy-weighted undesirable outputs. Researchers may use the data for efficiency analysis, green productivity measurement, environmental performance modelling, and cross-country sustainability assessments consistent with OECD and EU methodological frameworks.