We develop a method that identifies the attention paid by earnings call participants to firms' climate change exposures. The method adapts a machine learning keyword discovery algorithm and captures exposures related to opportunity, physical, and regulatory shocks associated with climate change. The measures are available for more than 10,000 firms from 34 countries between 2002 and 2020. We show that the measures are useful in predicting important real outcomes related to the net-zero transition, in particular, job creation in disruptive green technologies and green patenting, and that they contain information that is priced in options and equity markets.

中文翻译：

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公司层面的气候变化风险

我们开发了一种方法来识别收益电话会议参与者对公司气候变化风险的关注。该方法采用机器学习关键字发现算法，并捕获与气候变化相关的机会、物理和监管冲击相关的风险敞口。2002 年至 2020 年间，来自 34 个国家/地区的 10,000 多家公司可以使用这些措施。我们表明，这些措施可用于预测与净零过渡相关的重要实际成果，特别是颠覆性绿色技术和绿色专利申请中的就业创造，并且它们包含在期权和股票市场中定价的信息。