Balancing national economic policy outcomes for sustainable development.
Balancing national economic policy outcomes for sustainable development.
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DOI：
10.1038/s41467-022-32415-9
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发表时间：
2022-08-26
影响因子：
16.6
通讯作者：
中科院分区：
文献类型：
--
作者：
2030 年可持续发展目标 (SDG) 旨在共同改善经济、社会和环境成果，促进人类繁荣和地球健康。然而，多部门经济的复杂性和经常相互冲突的政策阻碍了设计支持推进多个可持续发展目标的国家经济政策。为了解决这个问题，我们引入了一个国家规模的设计框架，使政策制定者能够筛选复杂、非线性、多部门的政策空间，以确定平衡经济、社会和环境目标的有效政策组合。该框架结合了整个经济的可持续性模拟和人工智能驱动的多目标、多可持续发展目标政策搜索和机器学习。该框架可以支持多部门、多参与者的政策审议，以筛选有效的政策组合。我们通过确定可实现减少贫困和不平等、经济增长和减缓气候变化的有效组合的政策组合，展示了埃及案例研究框架的实用性。结果表明，综合政策战略有助于实现可持续发展，同时平衡改革对经济、社会和政治的不利影响。由于涉及的部门众多且隐含着权衡，选择实现可持续发展的经济政策具有挑战性。人工智能与整个经济领域的计算机模拟相结合可以提供帮助。
The 2030 Sustainable Development Goals (SDGs) aim at jointly improving economic, social, and environmental outcomes for human prosperity and planetary health. However, designing national economic policies that support advancement across multiple Sustainable Development Goals is hindered by the complexities of multi-sector economies and often conflicting policies. To address this, we introduce a national-scale design framework that can enable policymakers to sift through complex, non-linear, multi-sector policy spaces to identify efficient policy portfolios that balance economic, social, and environmental goals. The framework combines economy-wide sustainability simulation and artificial intelligence-driven multiobjective, multi-SDG policy search and machine learning. The framework can support multi-sector, multi-actor policy deliberation to screen efficient policy portfolios. We demonstrate the utility of the framework for a case study of Egypt by identifying policy portfolios that achieve efficient mixes of poverty and inequality reduction, economic growth, and climate change mitigation. The results show that integrated policy strategies can help achieve sustainable development while balancing adverse economic, social, and political impacts of reforms. Selecting economic policies to achieve sustainable development is challenging due to the many sectors involved and the trade-offs implied. Artificial intelligence combined with economy-wide computer simulations can help.
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影响因子：
16.6
作者：
Ameli N;Dessens O;Winning M;Cronin J;Chenet H;Drummond P;Calzadilla A;Anandarajah G;Grubb M
通讯作者：
Grubb M
影响因子：
6.9
作者：
Ansari, Dawud;Holz, Franziska
通讯作者：
Holz, Franziska
影响因子：
12.8
作者：
Aller, Carlos;Ductor, Lorenzo;Grechyna, Daryna
通讯作者：
Grechyna, Daryna
影响因子：
12.5
作者：
Allen, Cameron;Metternicht, Graciela;Wiedmann, Thomas
通讯作者：
Wiedmann, Thomas
影响因子：
9
作者：
Breisinger, Clemens;Mukashov, Askar;Wiebelt, Manfred
通讯作者：
Wiebelt, Manfred