To achieve inclusive green growth, countries need to consider a multiplicity of economic, social, and environmental factors. These are often captured by metrics of economic complexity derived from the geography of trade, thus missing key information on innovative activities. To bridge this gap, we combine trade data with data on patent applications and research publications to build models that significantly and robustly improve the ability of economic complexity metrics to explain international variations in inclusive green growth. We show that measures of complexity built on trade and patent data combine to explain future economic growth and income inequality and that countries that score high in all three metrics tend to exhibit lower emission intensities. These findings illustrate how the geography of trade, technology, and research combine to explain inclusive green growth.
翻译:要实现包容性绿色增长,各国需要考虑多种经济、社会和环境因素。这些因素通常由基于贸易地理的经济复杂度指标捕获,因此缺少关于创新活动的关键信息。为了弥补这一差距,我们将贸易数据与专利申请和研究出版物数据相结合,建立了显著而稳健的模型,提高了经济复杂度指标解释包容性绿色增长的能力。我们表明,基于贸易和专利数据的复杂度度量相结合,能够解释未来的经济增长和收入不平等,并且在三个度量指标得分高的国家往往表现出较低的排放强度。这些发现说明了贸易、技术和研究地理的结合能够解释包容性绿色增长。