Paying attention to human activities in terms of land grazing infrastructure, crops, forest products, and carbon impact, the so-called ecological impact (EF) is one of the most important economic issues in the world. For the present study, global database data were used. The ability of the penalized regression (RR) approaches (including Ridge, Lasso and Elastic Net) and artificial neural network (ANN) to predict EF indices in the G-20 countries over the past two decades (1999–2018) was illustrated and compared. For this purpose, 10-fold cross-validation was used to evaluate the predictive performance and determine the penalty parameter for PR models. According to the results, the predictive performance compared to linear regression improved somewhat using the penalized methods. Using the elastic net model, more global macro indices were selected than Lasso. Although Lasso selected only a few indicators, it had better predictive performance among PR ns models. In addition to relative improvement in the predictive performance of PR methods, their interest in selecting a subset of indicators by shrinking coefficients and creating a parsimonious model was evident and significant. As a result, PR methods would be preferred, using variable selection and interpretive considerations to predictive performance alone. On the other hand, ANN models with higher determination coefficients (R²) and lower RMSE values performed significantly better than PR and OLS and showed that they were more accurate in predicting EF. Therefore, ANN could provide considerable and appropriate predictions for EF indicators in the G-20 countries.

关注人类活动在土地放牧基础设施、农作物、林产品和碳影响方面的所谓生态影响（EF）是世界上最重要的经济问题之一。对于本研究，使用了全球数据库数据。说明并比较了过去二十年（1999-2018）中惩罚回归（RR）方法（包括 Ridge、Lasso 和 Elastic Net）和人工神经网络（ANN）预测 20 国集团国家 EF 指数的能力. 为此，使用 10 折交叉验证来评估预测性能并确定 PR 模型的惩罚参数。根据结果​​，与线性回归相比，使用惩罚方法的预测性能有所提高。使用弹性网模型，比 Lasso 选择了更多的全球宏观指数。虽然 Lasso 只选择了几个指标，但它在 PR ns 模型中具有更好的预测性能。除了 PR 方法的预测性能相对提高外，他们对通过收缩系数和创建简约模型来选择指标子集的兴趣是显而易见且重要的。因此，PR 方法将是首选，它仅使用变量选择和解释性考虑来预测性能。另一方面，具有较高决定系数的 ANN 模型 (R 他们对通过缩小系数和创建简约模型来选择指标子集的兴趣是显而易见和重要的。因此，PR 方法将是首选，它仅使用变量选择和解释性考虑来预测性能。另一方面，具有较高决定系数的 ANN 模型 (R 他们对通过缩小系数和创建简约模型来选择指标子集的兴趣是显而易见和重要的。因此，PR 方法将是首选，它仅使用变量选择和解释性考虑来预测性能。另一方面，具有较高决定系数的 ANN 模型 (R² ) 和较低的 RMSE 值明显优于 PR 和 OLS，表明它们在预测 EF 方面更准确。因此，ANN 可以为 G-20 国家的 EF 指标提供大量和适当的预测。