Utilising farm‐level panel data to estimate climate change impacts and adaptation potentials
结合农场会计与高分辨率气象数据，采用面板数据模型估计气候变化对农场利润的影响及适应潜力，发现到2040年利润平均下降4.4%至10%，适应措施可显著缓解不利影响。
Abstract We combine farm accounting data with high‐resolution meteorological data, and climate scenarios to estimate climate change impacts and adaptation potentials at the farm level. To do so, we adapt the seminal model of Moore and Lobell (2014) who applied panel data econometrics to data aggregated from the farm to the regional (subnational) level. We discuss and empirically investigate the advantages and challenges of applying such models to farm‐level data, including issues of endogeneity of explanatory variables, heterogeneity of farm responses to weather shocks, measurement errors in meteorological variables, and aggregation bias. Empirical investigations into these issues reveal that endogeneity due to measurement errors in temperature and precipitation variables, as well as heterogeneous responses of farms toward climate change may be problematic. Moreover, depending on how data are aggregated, results differ substantially compared to farm‐level analysis. Based on data from Austria and two climate scenarios (Effective Measures and High Emission) for 2040, we estimate that the profits of farms will decline, on average, by 4.4% (Effective Measures) and 10% (High Emission). Adaptation options help to considerably ameliorate the adverse situation under both scenarios. Our results reinforce the need for mitigation and adaptation to climate change.