论文标题
多维经济复杂性和包容性绿色增长
Multidimensional Economic Complexity and Inclusive Green Growth
论文作者
论文摘要
为了实现包容性的绿色增长，各国需要考虑多种经济，社会和环境因素。这些通常是由贸易地理的经济复杂性指标所捕获的，因此缺少有关创新活动的关键信息。为了弥合这一差距，我们将贸易数据与专利申请和研究出版物的数据结合在一起，以建立模型，从而显着，稳健地提高经济复杂性指标来解释国际绿色增长的国际变化的能力。我们表明，基于贸易和专利数据的复杂性衡量标准结合了解释未来的经济增长和收入不平等，并且在所有三种指标中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，技术和研究的地理如何结合解释包容性绿色的增长。
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.
