Scholarlink

<p>Climate damage functions (DFs) are vital for understanding the economic impacts of climate change and shaping policy responses. This study introduces a systematic, model-independent methodology for estimating gross domestic product (GDP) losses due to climate change, using a production frontier approach combined with outlier removal techniques. Global GDP is modeled as a function of average global temperature from 1960 to 2024, with a box-and-whisker plot applied to remove outliers and enhance the reliability of DF parameter estimates. Parameters are estimated using the least squares method for two models—with and without an intercept. The performance of these models is evaluated against established DFs using R-squared (R²) and mean squared error (MSE) metrics. The proposed models demonstrate strong performance, achieving R² values above 0.85 and MSE values below 0.18%, outperforming existing models in capturing the economic risks posed by rising temperatures. The analysis shows that GDP losses escalate significantly with higher temperatures. At a 3&#xa0;°C global temperature increase, projected economic damages reach $7.97 trillion (with existence of intercept in the model) and $7.15 trillion (without intercept). These projections emphasize the non-linear nature of climate-related economic damages and highlight the need for effective mitigation and adaptation strategies. Bootstrap bias-correction of production frontier efficiency estimates confirms the robustness of the results, while threshold effects highlight critical points where GDP losses accelerate disproportionately. By avoiding reliance on specific integrated assessment models, this approach provides a more transparent and adaptable framework for estimating long-term economic impacts. The findings offer policymakers a robust tool for assessing climate risks and underscore the urgency of coordinated global action to address climate change.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Estimating Climate Damage with the Production Frontier and Least Squares Method: A New Approach to Economics Losses

  • 期刊论文

  • 发表日期：

    2026年5月11日

查看全文
  • Reza Nadimi,
  • Koji Tokimatsu

摘要

Climate damage functions (DFs) are vital for understanding the economic impacts of climate change and shaping policy responses. This study introduces a systematic, model-independent methodology for estimating gross domestic product (GDP) losses due to climate change, using a production frontier approach combined with outlier removal techniques. Global GDP is modeled as a function of average global temperature from 1960 to 2024, with a box-and-whisker plot applied to remove outliers and enhance the reliability of DF parameter estimates. Parameters are estimated using the least squares method for two models—with and without an intercept. The performance of these models is evaluated against established DFs using R-squared (R²) and mean squared error (MSE) metrics. The proposed models demonstrate strong performance, achieving R² values above 0.85 and MSE values below 0.18%, outperforming existing models in capturing the economic risks posed by rising temperatures. The analysis shows that GDP losses escalate significantly with higher temperatures. At a 3 °C global temperature increase, projected economic damages reach $7.97 trillion (with existence of intercept in the model) and $7.15 trillion (without intercept). These projections emphasize the non-linear nature of climate-related economic damages and highlight the need for effective mitigation and adaptation strategies. Bootstrap bias-correction of production frontier efficiency estimates confirms the robustness of the results, while threshold effects highlight critical points where GDP losses accelerate disproportionately. By avoiding reliance on specific integrated assessment models, this approach provides a more transparent and adaptable framework for estimating long-term economic impacts. The findings offer policymakers a robust tool for assessing climate risks and underscore the urgency of coordinated global action to address climate change.