When deep recessions hit, some governments spend to rescue and recover their economies. Key economic objectives of such countercyclical spending include protecting and creating jobs while reinvigorating economic growth—but governments can also use this spending to achieve long-term social and environmental goals. During the coronavirus disease 2019 (COVID-19) pandemic, claims have been made that green recovery investments can meet both economic and environmental objectives. Here, we investigate the evidence behind these claims. We create a bespoke supervised machine learning algorithm to identify a comprehensive literature set. We analyze this literature using both structured qualitative assessment and machine learning models. We find evidence that green investments can indeed create more jobs and deliver higher fiscal multipliers than non-green investments. For policymakers, we suggest strong prioritization of green spending in recovery. For researchers, we highlight many research gaps and unalignment of research patterns with spending patterns.

中文翻译：

---

绿色刺激有多刺激？绿色财政支出的经济属性

当严重衰退袭来时，一些政府会花钱拯救和恢复经济。这种反周期支出的主要经济目标包括保护和创造就业机会，同时重振经济增长，但政府也可以利用这些支出来实现长期的社会和环境目标。在 2019 年冠状病毒病 (COVID-19) 大流行期间，有人声称绿色复苏投资可以实现经济和环境目标。在这里，我们调查这些说法背后的证据。我们创建了一个定制的监督机器学习算法来识别全面的文献集。我们使用结构化定性评估和机器学习模型来分析这些文献。我们发现有证据表明，与非绿色投资相比，绿色投资确实可以创造更多就业机会并带来更高的财政乘数。对于政策制定者来说，我们建议在复苏过程中优先考虑绿色支出。对于研究人员来说，我们强调了许多研究差距以及研究模式与支出模式的不一致。