Balancing national economic policy outcomes for sustainable development | Panda Anku

Policy design and screening framework for sustainable development

Figure 1 shows the national economic policy design and screening framework for sustainable development introduced in this study. The framework is based on an iterative process between three sub-processes: (1) identification of priorities aligned with the SDGs and associated policy instruments, (2) economy-wide simulation, AI-driven multiobjective, multi-SDG policy search, and machine learning of sustainability drivers, and (3) multi-sector, multi-actor policy portfolio screening and deliberation. In Fig. 1, the three sub-processes are numbered from 1 to 3.

Fig. 1: National economic policy design and screening framework for sustainable development.
figure 1

The framework includes three sub-processes: (1) identification of priorities aligned with the SDGs and associated policy instruments, (2) economy-wide simulation, artificial intelligence-driven multiobjective multi-SDG policy search, and machine learning of sustainability drivers, and (3) multi-sector, multi-actor policy screening and deliberation.

Country-level progress towards sustainable development shows marked variations across the world11,15. Countries have different priorities, plans, and focus areas for the SDGs32. The first step in the proposed framework is, therefore, to identify sustainable development goals and targets and performance indicators based on national priorities. Furthermore, policy instruments that can be used to achieve the identified goals and targets are selected in this first step of the framework. We define “policy instrument” as an economic tool used to achieve sustainability goals. For example, poverty reduction can be achieved through direct government transfers to poor households; but it can also be accomplished indirectly by subsidizing the production activities on which poor households rely for their livelihoods.

Assessing the impacts of economic policies requires a holistic approach due to the forward and backward multiplier effects on different actors and sectors. Economy-wide models are the most commonly used tool to simulate national economic policies and their impacts on sustainability performance33,34. In the second step of the framework, an economy-wide model (i.e., computable general equilibrium model) is used to simulate the scope of different combinations of policy instruments regarding the social, economic, and environmental performance indicators identified in the first step. Economy-wide models can assess sustainability indicators that cut across multiple sectors and actors, even though they typically have been used for economic indicators; thus, such models can support inclusive policy deliberation and screening.

Scenario-based design of linear systems often use the superposition principle35,36, but economy-wide models are non-linear in their behavior37. For instance, doubling an economic shock does not lead to a doubled effect, and the impacts of policy instruments are not additive. Therefore, selecting and simulating a small set of policy scenarios to guide policy-making could be flawed by decision-making biases. Human decisions (including scenario selection) are often driven by rationality38. However, the perceptions and conceptions regarding interventions and their outcomes could influence this rationality, notably when decisions are related to financial choices or fatalities38. To overcome this issue, we couple the economy-wide model with an AI-driven multiobjective search to enable sifting through complex multi-SDG policy performance spaces to identify the most efficient policy portfolios based on combinations of the policy instruments identified in the first step. In this study, we define a “policy portfolio” as a parameterized set of policy instruments.

Crafting economic policy portfolios for sustainable development can yield thousands of policy options due to the complexity of multi-sector economies, the multidimensional nature of policy instruments, and the diversity of social, economic, and environmental sustainability targets. In the second step of the framework, the policy portfolios and their associated sustainability performances that resulted from the iterations of the multiobjective search algorithm are subjected to machine learning to understand the effectiveness of different policy instruments in influencing sustainability performance. Machine learning helps simplify the complexity of the interplay of policy instruments and multi-SDG performance, facilitating multi-sector multi-actor deliberation. The methods section provides a technical description of the second step of the framework.

The diverse policy options resulting from the second step of the framework can support inclusive multi-sector, multi-actor deliberation on policy reforms. While we have not yet conducted such a deliberation process in the case study application shown later in this paper, stakeholder deliberation based on artificial intelligence search and machine learning has been successfully applied in other disciplines39,40,41, and therefore we suggest it as a step in our proposed framework. Multiobjective search results can be useful for policy-making because they are transparent, policy-relevant, and provide options and not recommendations42,43. In the third step of the framework, stakeholders and actors representing different institutions, sectors, disciplines, and regions negotiate and screen the efficient policy portfolio options (generated in step 2). Such deliberation should aim for a balanced (i.e., socially acceptable) sustainability performance across time (e.g., temporal distribution of impacts), space (e.g., rural and urban regions), and income groups (e.g., wealthy and poor households); given the large set of policy portfolio options, deliberation would allow policymakers with different sectoral mandates to find common ground. Citizen assemblies, to advise on policy choices, are a mechanism that is increasingly being used as part of such deliberation processes44. Public-private partnerships can further support the implementation of associated measures45. Although multiobjective economic policies are complex, machine learning and interactive data visualization techniques can help stakeholders understand, explore, screen, and select policy portfolios and/or identify unacceptable options. Subsequently, a new or modified set of policy instruments for achieving the targeted SDGs could be explored. The new or revised policy instruments would then be used to generate a revised set of efficient policy portfolios to be integrated into the multi-sector multi-actor deliberation and co-production of sustainability reforms. At their most ambitious, such consultation mechanisms can seek to identify compromise portfolio options. Less ambitiously, they can help screen out socially unacceptable options.

Balanced national economic policies in Africa improve sustainability

The African continent is currently far from achieving most of the SDGs by 2030 and has the lowest performance globally in many goals11,46. Sustainable development in Africa is challenged by poor governance, limited financial resources, high population growth rates, and the COVID-19 pandemic46,47,48. Africa’s contribution to global greenhouse gas emissions is low, at around 4% in 201949, but the rate of growth of the continent’s emissions is increasing rapidly50. Without urgent decarbonization policies, Africa could lock in sizable greenhouse gas emissions for several decades in the future51 or end up with stranded assets52.

We apply the SDG economic policy design and screening framework to Egypt, a middle-income country in northern Africa. Egypt is the second-highest CO2 emitter on the continent (Supplementary Fig. 1) and faces economic and sustainability challenges. In 2016, Egypt contributed around 17% of Africa’s total CO2 emissions31, following a national increase of 55% from 2006 to 201649. Furthermore, energy commodities are heavily subsidized53, with the level of energy subsidies ranking second in Africa as a share of GDP30. Although energy subsidies contribute to stabilizing the prices of energy-dependent commodities and increasing the output of some industries, they fuel CO2 emissions and fiscal deficits, can slow economic growth and diversification of energy portfolios, and grow inequalities across social groups54. In 2017, the overall Gini Index of Egypt was estimated at 0.31 by the World Bank31, indicating considerable income discrepancies.

We use the framework to identify efficient policy portfolios for Egypt’s economy aligned with targets related to five SDGs. These targets are enhancing GDP growth (SDG8), increasing rural and urban incomes (SDG1), reducing rural, urban, and overall income inequalities (SDG10), and lowering CO2 emissions (SDG13). These five SDG targets were selected because they are directly related to one of Egypt’s most pressing economic challenges: how to reduce commodity subsidies while lowering inequality and poverty and ensuring economic growth and environmental sustainability55,56,57. We developed, calibrated, and used a dynamic Computable General Equilibrium (CGE) model of Egypt’s economy to simulate the country’s performance in achieving these targets as well as the associated trade-offs and synergies. The economy-wide model was set up for the 2021–2035 period and was connected to a multiobjective evolutionary algorithm to search for efficient economic policy portfolios based on four incremental policy strategies. Following that, a machine learning approach was used to understand the drivers of sustainability performance. The multiobjective multi-SDG policy search process involved 1.8 million 15-year (2021–2035) dynamic simulations, from which a total of around 20 thousand efficient policy portfolios were identified. The four incremental integrated policy strategies used in designing sustainability policy portfolios for Egypt are: (I) Distribution and total amount of direct government transfers to households, (II) Distribution and total amount of direct government transfers to households and income taxes on households, (III) Producer taxes/subsidies on economic activities, distribution and total amount of direct government transfers to households, and income taxes on households, and (IV) Producer taxes/subsidies on economic activities, sales taxes/subsidies on commodities, distribution and the total amount of direct government transfers to households, and income taxes on households. The economy-wide model of Egypt was set up such that economic reforms are implemented gradually over five years from 2021 to 2025. For example, a tax increase of 5% is applied by adding a 1% increase annually over the 5-year assumed reform period. This reform period was selected to demonstrate the use of the framework and is customizable based on stakeholder preferences. Further details on the mathematical formulation of the economy-wide model and the multiobjective search can be found in the methods section.

Figure 2a shows a parallel coordinates plot58 of Egypt’s sustainability performance from 2021 to 2035 under efficient economic policy portfolios generated in the second step of the SDG policy design and screening framework. The policy portfolios are based on four integrated policy strategies described above. For the purpose of this paper and in order to demonstrate the framework, the selected policy portfolios (thick lines in Fig. 2a except for the baseline) are assumed to result from multi-sector multi-actor negotiation and deliberation. Stakeholders from different backgrounds would target specific sustainability dimensions (e.g., high GDP, low Gini Index, or low emissions), but an efficient compromise policy portfolio could eventually be agreed upon based on deliberation and co-production of policies.

Fig. 2: Sustainability performance of the Egyptian economy in 2021–2035.
figure 2

a parallel coordinates plot of the best-achievable aggregate performance based on four integrated policy strategies, bf details of the economic policy portfolios associated with the five thick lines highlighted in panel (a), g Sankey diagram of the structure of household income and expenditure in 2035 with the baseline economic portfolio, and h Sankey diagram of the structure of household incomes and expenditures in 2035 in the low Gini economic portfolio. The thin lines in panel a represent all efficient portfolio options, while the thick lines highlight selected policy portfolios. The upward direction on each axis in panel (a) is desirable (i.e., a perfect policy would be a straight line across the top), and diagonal lines between axes indicate trade-offs. Supplementary Fig. 7 depicts a version of panel a with the efficient policy portfolios shown separately for each of the four integrated policy strategies. The number of economic policy portfolios (or lines) in panel (a) is 19723. The line and bar colors in panels (b) and (c) correspond to the thick lines with similar colors in panel (a) (i.e., selected economic policy portfolios). The Sankey diagrams in panels (g) and (h) show household income by source (left-most axis), recipient household group (two central axes), and expenditure (right-most axis). The boxes drawn around panels (g) and (h) correspond to the thick lines with similar colors in panel (a). The total gross domestic product (GDP) and household income values in panel (a) are discounted at 3%. CO2 stands for carbon dioxide; Q1–Q5 are household classes based on income quintiles from poorest to richest.

Figure 2a shows that the four examined integrated policy strategies have variable impacts on sustainability performance. Using policy strategy I (thin yellow lines) reduces income inequalities compared to the baseline (thick black line); however, this strategy slows economic growth because more government income is spent on households rather than on investment. Policy strategy II (thin grey lines) further reduces income inequalities and also improves economic growth compared to the baseline. Using policy strategy III (thin red lines) yields solutions that increase total GDP, increase urban and rural total incomes, and reduce total CO2 emissions compared to the baseline and strategy II. Finally, strategy IV (thin green lines) increases the sustainability performance space, leading to the lowest trade-offs between the targets compared to the three other strategies.

The selected sustainability policy portfolios (thick lines in Fig. 2a) show trade-offs between sustainability targets. For example, aiming to achieve high GDP results in only a slight reduction in income inequalities, whereas targeting low Gini indices results in a total GDP value close to the baseline. There is also a trade-off between rural and urban income (i.e., diagonal lines between the two axes). Sustainability performance under the low emissions portfolio shows a reduction in overall and urban inequalities, a reduction in rural income, an increase in urban income, and a slight improvement in GDP performance compared to the baseline. Overall inequalities decline because the increase in urban income mostly goes to poor urban households, which reduces the overall income gap between poor and rich households. Figure 2b–f and Supplementary Table 1 provide the numeric details of the five policy portfolios (including the baseline) shown as thick lines in Fig. 2a. Achieving high GDP growth involves a 50% reduction in the total amount of government transfers to households (Fig. 2e), increases of more than 100% in income taxes on households (Fig. 2b), a 50% reduction in subsidies on petroleum commodities (Fig. 2d), an increase in producer and sales subsidies on agriculture, new producer subsidies on manufacturing, and an increase in sales taxes on manufacturing commodities. In contrast, reducing income inequalities (i.e., low Gini portfolio) requires increasing government transfers to households by 440%, channeling most government transfers to poor urban households, and increasing income taxes on rich households (see Fig. 2b, c). Also, achieving low Gini requires introducing taxes on hydropower, oil, and gas electricity activities and subsidizing solar electricity producers. The low emissions portfolio involves reducing subsidies on petroleum commodities by around 90%, coupled with a 286% increase in the total amount of government transfers to households to mitigate the rise in commodity prices resulting from the reduction in fossil fuel subsidies. As Fig. 2a shows, the compromise policy portfolio reduces CO2 emissions, reduces rural, urban, and overall income inequalities, and achieves economic growth similar to the baseline portfolio. It is worth noting that the compromise policy portfolio does not result from averaging the other portfolios, as the economy behaves non-linearly, highlighting the importance of the multiobjective search within the policy design and screening framework.

Figure 3 depicts time series of sustainability performance indicators for the five economic policy portfolios highlighted as thick lines in Fig. 2a. With the high GDP policy portfolio, CO2 emissions (Fig. 3a) decline over the 5-year assumed reform period but increase steadily afterward due to a rapid increase in the economy’s energy needs to enhance economic growth (Fig. 3b). However, the budget deficit (Fig. 3m) declines most under the high GDP policy portfolio due to reductions in government transfers to households and subsidies on petroleum products. The high GDP policy portfolio shows the highest increase in the labor share of GDP (i.e., labor income divided by real GDP; see Fig. 3l) and the highest overall growth in income per capita (Fig. 3k). The low Gini policy portfolio yields the highest decrease in income inequalities (Fig. 3d–f), the highest increase in the income of the poorest 40% of the total and urban Egyptian populations (Fig. 3g–i), a decrease in CO2 emissions and emission intensities (Fig. 3a, c), and approximately similar economic growth to the baseline portfolio (Fig. 3b). The low emissions policy portfolio leads to the lowest CO2 emission intensity (Fig. 3c) and increases the income of the poorest 40% of the population (Fig. 3g– i). The low emissions economic policy portfolio results in the highest increase in the overall consumer price index (Fig. 3n) and the price index of petroleum commodities (Fig. 3o). The rise in commodity prices is a major challenge associated with subsidy reforms. Reductions in energy subsidies reduce welfare because households and industries face higher energy prices and an increase in the prices of other commodities that use energy as an intermediate input59.

Fig. 3: Temporal evolution of Egyptian sustainability performance indicators.
figure 3

ao time series of sustainability indicators based on five economic policy portfolios. The time series depicted in this figure correspond to the thick lines with similar colors in Fig. 2a. Details on the economic policy portfolios associated with the time series are provided in Fig. 2b–f and Supplementary Table 1. GDP stands for gross domestic product and CO2 stands for carbon dioxide.

Policy instruments vary in their effectiveness in impacting the SDGs. In this study, we used a machine learning method to assess the effectiveness of policy instruments in influencing sustainability targets (Fig. 4). As Fig. 4a–l show, some policy instruments are effective for specific targets, whereas others affect multiple targets. Overall, changing the tax/subsidy on petroleum sales is the most effective policy instrument for influencing multiple sustainability targets, followed by a producer tax/subsidy on private services and then government transfers to households (Fig. 4m). The degree of influence of policy instruments also depends on the range within which they are allowed to vary. Supplementary Table 2 reports the upper and lower bounds assumed for each policy instrument.

Fig. 4: Rankings of policy instruments based on their relative influence on twelve Egyptian sustainability performance indicators.
figure 4

al the five most influential instruments for each sustainability performance indicator. m the overall relative influence of each policy instrument on the twelve sustainability performance indicators. The relative influence values shown on the x-axes of panels (al) vary from zero to one. A zero value means the policy instrument does not influence the performance indicator, whereas one indicates that the policy instrument is the only influencer of the performance indicator. For each performance indicator, the sum of the relative influence values for all instruments is one. Panel (m) shows the sum of the relative influence values for each of the twelve policy instruments multiplied by a hundred and plotted on a logarithmic scale. Therefore, the values in panel (m) can range from zero to twelve hundred, with zero indicating that the instrument does not influence any of the twelve performance indicators, and twelve hundred meaning that the policy instrument is the only influencer of all twelve performance indicators. GDP stands for gross domestic product, CO2 stands for carbon dioxide, and Q1 to Q5 are household classes based on income quintiles from poorest to richest.

Leave a Comment