Frederick S. Pardee Center for International Futures, Joseph Korbel School of International Studies, University of Denver, Denver, Colorado, United States of America.
Pacific Northwest National Laboratory, Joint Global Change Research Institute, College Park, Maryland, United States of America.
PLoS One. 2021 Feb 25;16(2):e0246797. doi: 10.1371/journal.pone.0246797. eCollection 2021.
Analysis with integrated assessment models (IAMs) and multisector dynamics models (MSDs) of global and national challenges and opportunities, including pursuit of Sustainable Development Goals (SDGs), requires projections of economic growth. In turn, the pursuit of multiple interacting goals affects economic productivity and growth, generating complex feedback loops among actions and objectives. Yet, most analysis uses either exogenous projections of productivity and growth or specifications endogenously enriched with a very small set of drivers. Extending endogenous treatment of productivity to represent two-way interactions with a significant set of goal-related variables can considerably enhance analysis. Among such variables incorporated in this project are aspects of human development (e.g., education, health, poverty reduction), socio-political change (e.g., governance capacity and quality), and infrastructure (e.g. water and sanitation and modern energy access), all in conditional interaction with underlying technological advance and economic convergence among countries. Using extensive datasets across countries and time, this project broadly endogenizes total factor productivity (TFP) within a large-scale, multi-issue IAM, the International Futures (IFs) model system. We demonstrate the utility of the resultant open system via comparison of new TFP projections with those produced for Shared Socioeconomic Pathways (SSP) scenarios, via integrated analysis of economic growth potential, and via multi-scenario analysis of progress toward the SDGs. We find that the integrated system can reproduce existing SSP projections, help anticipate differential economic progress across countries, and facilitate extended, integrated analysis of trade-offs and synergies in pursuit of the SDGs.
对全球和国家层面的挑战与机遇进行综合评估模型 (IAMs) 和多部门动态模型 (MSDs) 的分析，包括对可持续发展目标 (SDGs) 的追求，需要对经济增长进行预测。反过来，追求多个相互作用的目标会影响经济生产力和增长，在行动和目标之间产生复杂的反馈循环。然而，大多数分析要么使用外生的生产力和增长预测，要么在非常有限的一组驱动因素内进行内生的规范。将生产力的内生处理扩展到代表与一组重要目标相关的变量的双向交互作用，可以大大增强分析。在这个项目中纳入的变量包括人类发展的各个方面（如教育、健康、减贫）、社会政治变革（如治理能力和质量）以及基础设施（如水和环境卫生和现代能源获取），所有这些都与基本技术进步和国家之间的经济趋同相互条件作用。本项目利用各国和各时期的广泛数据集，在一个大规模的多问题综合评估模型——国际展望 (IFs) 模型系统中广泛地对全要素生产率 (TFP) 进行了内生处理。我们通过将新的 TFP 预测与为共享社会经济途径 (SSP) 情景生成的预测进行比较、通过对经济增长潜力进行综合分析以及通过对实现 SDGs 的多情景分析，展示了该开放系统的实用性。我们发现，综合系统可以再现现有的 SSP 预测，帮助预测各国之间的经济差异，并促进在追求 SDGs 时进行扩展的、综合的权衡与协同分析。
