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：为实现包容性绿色增长，各国需综合考虑经济、社会及环境等多重因素。现有指标通常基于贸易地理格局衡量经济复杂性，因而缺失创新活动的关键信息。为弥补这一不足，我们融合贸易数据、专利申请数据与研究出版物数据，构建了显著且稳健提升经济复杂性指标解释包容性绿色增长国际差异能力的模型。研究表明，基于贸易与专利数据构建的复杂性指标共同解释了未来经济增长与收入不平等现象，且在三项指标均表现优异的国家往往具有更低的排放强度。这一发现揭示了贸易、技术与研究的地理格局如何共同解释包容性绿色增长的内在机理。