地方层面天气日波动与经济增长：来自泰国的证据
Daily Fluctuations in Weather and Economic Growth at the Subnational Level: Evidence from Thailand
AI总结：
本研究考察泰国地方层面日气温波动对经济增长的影响，通过固定效应面板回归发现气温与人均GPP年增长呈倒U型关系，农业受不利影响，结合气候预测评估到2090年的经济冲击，强调模型不确定性和气温动态在地方气候影响评估中的重要性。
摘要：
本文研究了日气温波动对泰国次国家级经济增长的影响。利用1982年至2022年的人均年度省级生产总值（GPP）数据和高分辨率再分析气象数据，我估计了固定效应面板回归，以分离出气温中看似外生的省内逐年变化。结果表明，气温与人均GPP年增长之间存在统计上显著的倒U型关系，且不利影响集中在农业部门。工业和服务业产出对短期天气变化不敏感。分布滞后模型表明，气温冲击对增长轨迹具有持续影响，尤其是在平均气温较高的低收入省份。我将这些估计值与RCP4.5和RCP8.5排放情景下的气候预测相结合，以评估到2090年的省级经济影响。在不调整气候预测偏差或滞后气温效应的情况下，预计气候变化将使63-86%的泰国人口人均产出下降，其中RCP4.5情景下人均GDP影响中位数介于-4%至+56%之间，RCP8.5情景下介于-52%至-15%之间。在纠正预计的变暖偏差（但忽略滞后动态）后，中位数损失分别增至57-63%（RCP4.5）和80-86%（RCP8.5）。考虑气温的延迟效应会进一步将上限估计值推高至接近全部损失。这些结果突显了在次国家级气候影响评估中考虑模型不确定性和气温动态的重要性。所有预测都应谨慎解读。
英文摘要：
This paper examines the effects of daily temperature fluctuations on subnational economic growth in Thailand. Using annual gross provincial product (GPP) per capita data from 1982 to 2022 and high-resolution reanalysis weather data, I estimate fixed-effects panel regressions that isolate plausibly exogenous within-province year-to-year variation in temperature. The results indicate a statistically significant inverted-U relationship between temperature and annual growth in GPP per capita, with adverse effects concentrated in the agricultural sector. Industrial and service outputs appear insensitive to short-term weather variation. Distributed lag models suggest that temperature shocks have persistent effects on growth trajectories, particularly in lower-income provinces with higher average temperatures. I combine these estimates with climate projections under RCP4.5 and RCP8.5 emission scenarios to evaluate province-level economic impacts through 2090. Without adjustments for biases in climate projections or lagged temperature effects, climate change is projected to reduce per capita output for 63-86% of Thai population, with median GDP per capita impacts ranging from -4% to +56% for RCP4.5 and from -52% to -15% for RCP8.5. When correcting for projected warming biases - but omitting lagged dynamics - median losses increase to 57-63% (RCP4.5) and 80-86% (RCP8.5). Accounting for delayed temperature effects further raises the upper-bound estimates to near-total loss. These results highlight the importance of accounting for model uncertainty and temperature dynamics in subnational climate impact assessments. All projections should be interpreted with appropriate caution.
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