Unlocking transition pathways: A global perspective to SDG costing with synergistic approaches

The Bridgetown Covenant underlines the importance of “boosting resources, private and public, and domestic and international” and their “effective employment” in delivering on the 2030 Agenda -—
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—-. In the SDGSustainable Development Goal progress report -—
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—-, the UN Secretary-General urged world leaders to come together at the SDG Summit in September 2023 to deliver a Rescue Plan for People and Planet centred around three major breakthroughs: equipping governance and institutions for sustainable and inclusive transformation; prioritizing policies and investments that have multiplier effects across the goals; and securing a surge in SDG financing and an enabling global environment for developing economies. This SDG Pulse In Focus offers analytical, data-driven input to these efforts based on experimental costing of SDG indicators using official statistics.

The 2030 Agenda can be seen as a universal agenda for investment in sustainable development. But progress towards the SDGsSustainable Development Goal is off track and slipping increasingly out of reach. Effective action is hampered by the lack of overall understanding of the financing needs required to achieve the SDGs. It is difficult to align the national budgets, investment and financing flows, or debt relief with the needs for achieving the SDGs when there is no data to guide decisions.

In recent years, approximately 50 countries have estimated the costs of achieving selected SDGs reflecting national priorities based on guidance developed by the Inter-agency Task Force on Financing for Development -—
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—-. These efforts have shown that better data and understanding of the costs can strengthen financing for national sustainable development priorities and feed effectively into national planning. These estimates are, however, tailored for specific national purposes and the global picture of the need for SDG financing remains weak and incomplete.

The 2023 SDG Pulse In Focus discusses experimental cost estimates calculated for 60 countries, including 21 developing economies¹, initially covering 20 SDG indicators including their breakdowns, and spanning across the transition pathways. The study covers more than 45 per cent of the global population. This analysis, however, focuses on developing economies covering over 35 per cent of their total population. The SDG transition pathways on energy, education, jobs and social protection, digitalization, food systems and climate, as communicated by the UN Deputy Secretary-General -—
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—-, are the focus of the analysis. UNCTAD has developed an SDG costing methodology that considers synergies and trade-offs, i.e., the impact of spending on one SDG across the other goals (see Annex I). This method can be used to identify strategies and best practices to maximize the effect of spending on the achievement of the SDGs. When effectively implemented, systematic financing through the pathways can serve as catalysts for change to focus on sustainable solutions and put human rights and equality at the centre.

The work is inspired by the UN Secretary-General’s SDG Stimulus which calls for a massive increase in financing for development, including humanitarian support and climate action, and is part of the related UN-wide efforts, led by UNCTAD with UN DESA and UNDPUnited Nations Development Programme to pool expertise across the UN and provide effective tools and methods to cost the achievement of SDGs across the transition pathways (Figure 1). That effort is carried out in collaboration with UN WomenUN Women is the United Nations entity dedicated to gender equality and the empowerment of women (UN Women). to ensure gender focus and with IFADInternational Fund For Agricultural Development, IEAInternational Energy Agency, ILOInternational Labour Organization, ITUInternational Telecommunications Union, ESCWAUnited Nations Economic and Social Commission for Western Asia, UNEPUnited Nations Environment Programme, UNESCOUnited Nations Educational, Scientific and Cultural Organization, UN-Habitat, UNICEFUnited Nations Children's Fund and other interested partners.

The method used in this study is based on countries’ official statistics on government expenditure by sector² compared to development outcomes measured using countries’ SDG indicator data. Indicators of government effectiveness, political stability and absence of violence and terrorism, as well as FDIForeign Direct Investment (FDI) is an investment involving a long-term relationship and reflecting a lasting interest and control by a resident entity in one economy (foreign direct investor or parent enterprise) in an enterprise resident in an economy other than that of the foreign direct investor (FDI enterprise or affiliate enterprise or foreign affiliate) -—
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—-. (net inflows) were used as control variables. While the input data consist of official statistics, the analysis is based on SFMStochastic frontier model and as such the results are estimates that include uncertainty (see Annex I). However, we hope that even provisional estimates regarding optimal spending have the potential to inform policies on how to accelerate progress towards SDGs.

While this study focuses on the total costs of achieving SDG transition pathways for the covered SDG indicators, UNCTAD World Investment Report has previously estimated the SDG investment gap to finance capital expenditure -—
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—-. This effort focused on private, cross-border investment and linked SDGs to investment sectors, such as energy, infrastructure or food and agriculture to facilitate SDG-aligned investment. The 2023 World Investment Report -—
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—- will provide an update of the estimate of the SDG investment gap, at the midpoint of the 2030 Agenda.³ This work responds to a request by the General Assembly -—
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—- for the World Investment Report “to focus on promoting investments for sustainable development as well as concrete recommendations, including on strategic sectors to invest for the implementation of the 2030 Agenda”.

The work presented in this study stems from a UN-wide collaboration -—
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—- to develop SDG costing methods and tools, and the role of UNCTAD “as the focal point of the United Nations for the integrated treatment of trade and development and interrelated issues in the areas of finance, investment, technology and sustainable development” -—
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—-. The study provides the first preliminary estimates using a new methodology based on SDG indicators and official statistics and offers a starting point for further work to expand these estimates and enhance the methodology. In a world without data gaps, this analysis could be done for all SDG targets and countries. Thus, the resulting analysis of critical data gaps is also a key value-added highlighting this exercise for much needed investment in key statistics.

The United Nations has identified the below six transition pathways (Figure 1) to maximize efforts towards achieving the 2030 agenda and to enable translating global commitments to support for country-level implementation.

Figure 1. Six transition pathways to sustainable development include 12 SDGs

Source: UNCTAD mapping based on -—
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The costing exercise covers 20 SDG indicators to date

The aim of this experimental study is to cover SDG indicators included within the six transition pathways with sufficient country coverage and in a way that can be costed. The SDG indicator framework includes 231 unique SDG indicators, of which 163 are classified as tier ITier I means that a SDG indicator has been classified by the IAEG-SDG as being conceptually clear, has an internationally established methodology and standards are available, and data are regularly produced by countries for at least 50 per cent of countries and of the population in every region where the indicator is relevant. -—
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Out of 163 tier I SDG indicators, 109 fall within the transition pathways and 91 of these could in principle be costed at a country level. Indicators that are global in scope, like ‘the number of countries…’, cannot be costed for national achievement, and some are not relevant to all countries globally. Furthermore, even for some tier I indicators country coverage is low, especially for developing economies. Therefore, this first analysis estimates the costs of meeting the target values of the 20 SDG indicators and their breakdowns within the pathways (Table 1).

This study analysed a time series spanning from 2005 to 2021. The indicators spread across nine SDGs among the twelve included in the transition pathways. This analysis could potentially be extended to other SDG indicators and countries following further data collection efforts.

This study applies an SFM method which is valuable for analysing the structure and interactions of producer performance, its determinants and synergies. It is interesting when applied to SDG achievement since countries’ performance is subject to strong influence by government decisions, priorities and expenditures. The public sector plays a central role in steering the pursuit of the 2030 Agenda and seeking financing to that end, especially in health and social sectors, but arguably also in environmental protection. This study incorporates both recurrent and capital expenditures. However, due to the focus on government expenditures, the role of private investment is not fully captured, and it is significant for specific SDGs, like investment in digital technology, and varies by country, e.g., as some countries may have a notable share of privately provided health or education services. This is mitigated by control variables on government effectiveness, political stability and absence of violence and terrorism, and FDI net inflows. Furthermore, the selection of categories included is based on the statistical significance between government expenditure and the related policy measures. The 2023 World Investment Report will complement the picture with its focus on the private investment perspective.

Major increases needed to support social protection and decent jobs

Critical to the advancement of SDGs 1, 2, 3, 4, and 5, this pathway is about pursuing wellbeing, access to essential services, equality and human rights. This exercise considers the costs of achieving the targets for access to basic services (target 1.4), investment in rural infrastructure and agriculture (SDG indicator 2.a.1), education completion and proficiency in reading and mathematics (target 4.1), and reducing child mortality (target 3.2), fighting tuberculosis (SDG indicator 3.3.2) and ensuring access to vaccines (3.b.1). The study also considers the requirements of achieving gender equality in the parliaments and local government (5.5.1) and in time use on unpaid domestic and care work (5.4.1).

From 2023 to 2030, achieving these indicators is estimated to require an average annual per capita cost of US$1 194 (or 13.7 per cent of GDP) in the 21 developing economies covered.⁴ This value illustrates the total financing required to achieve the SDG indicators covered for the social protection and decent jobs pathway and could be funded from various sources of financing (See Development Finance and Investment flows).

Achieving the indicators costed⁵ presents unique challenges and opportunities for countries. Afghanistan grapples with elevated neonatal mortality rates for neonatal (SDG indicator 3.2.2) and under-five year-olds (3.2.1), coupled with restricted access to vaccines (3.b.1) and low education results (4.1.1 and 4.1.2). Addressing these challenges in Afghanistan would demand significant yearly spending, approximately 22 per cent of GDP or around US$3.3 billion per year. Some countries, like Armenia, have made commendable progress with near-universal access to basic services (1.4.1) and an impressive vaccination rate (3.b.1) but challenges remain with the high mortality of under-five year-olds (SDG indicator 3.2.1) and the low gender parity in national parliaments (5.5.1). In Azerbaijan, prioritizing education has resulted in high completion rates (SDG indicator 4.1.2), but further improvements are needed in health (3.2.1, 3.2.2, 3.3.2 and 3.b.1) and women's political participation (5.5.1).

Bolivia, along with other countries, faces challenges in health and education results, marked by high under-five year-olds’ mortality (3.2.1) and prevalent tuberculosis (3.3.2). Other countries, such as China and Thailand, have made substantial progress while focused efforts are still needed. China stands out with its robust agriculture orientation of government spending (SDG indicator 2.a.1). Additionally, 94 per cent of China's vast population has access to basic services (1.4.1). Kazakhstan has excelled in education results (4.1.1 and 4.1.2) and vaccination rates (3.b.1). The Maldives, for instance, have achieved complete access to basic services (1.4.1). Thailand, despite challenges, is making promising strides toward eradicating tuberculosis (3.3.2).

It is essential to note that improvements in unpaid care and domestic work participation (SDG indicator 5.4.1) present a long-term challenge for the countries surveyed. For instance, Bolivia's score stands at 1.69,⁶ indicating a significant imbalance in unpaid care responsibilities. Afghanistan, with a score of 8.57, shows a stronger need for considerable efforts in achieving gender equality in unpaid domestic and care work. This goal, crucial for gender equality and economic productivity, may not be attained until after 2030, underscoring the need for persistent focus and strategic actions.

Digitalization is a key driver of progress across the 2030 Agenda

This pathway promotes tech-driven economies, enhancing innovation, job creation, and economic outcomes contributing to SDG 9. Digitalization influences structural changeStructural transformation or change can be broadly defined as the reallocation of economic activity across three broad sectors, agriculture, manufacturing and services, which accompanies the process of economic growth -—
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—-. It usually refers to the transfer or shift of production factors — especially labour, capital and land — away from activities and sectors with low productivity to those with higher productivity, which are typically different in location, organization and technology -—
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—-. through its impacts on productivity, employment, sectoral linkages and trade -—
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—-, and has therefore important effects across the 2030 Agenda. Digital divides and related skills bias may also hamper progress in structural transformationStructural transformation or change can be broadly defined as the reallocation of economic activity across three broad sectors, agriculture, manufacturing and services, which accompanies the process of economic growth -—
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—-. It usually refers to the transfer or shift of production factors — especially labour, capital and land — away from activities and sectors with low productivity to those with higher productivity, which are typically different in location, organization and technology -—
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—-. towards higher value-added activities.

On the digital transition pathway, the two first indicators costed include the manufacturing share of value added in GDP (SDG indicator 9.2.1), and the universal coverage of mobile networks (9.c.1). Their achievement from 2023 to 2030 is estimated to cost US$325 per capita annually (3.4 per cent of GDP) for the 21 developing economies covered.

There are large country differences in progress within this pathway, and in the role of the government therein. For instance, Afghanistan, despite its needs, shows promise with a manufacturing share of value added at 27 per cent of GDP (SDG indicator 9.2.1) and a 57 per cent mobile network coverage (9.c.1). China and Türkiye show strong progress within the digital transition pathway with high manufacturing value added share and near-universal mobile network coverage. Smaller economies like Armenia and Mongolia impress with 100 per cent mobile network coverage and have in practice bridged the digital divide for mobile access.

However, achieving universal mobile network coverage and increasing manufacturing share to over 20 per cent of GDP presents challenges for many countries. Sustained efforts, including bolstering digital literacy, digital infrastructure investment, and creating a favourable environment for innovation and entrepreneurship, are vital for harnessing digital transformation's potential for growth. UNCTAD’s efforts to support entrepreneurs in this regard are discussed in UNCTAD In Action.

Education to unlock innovation for sustainability

The education transition pathway underlines the significance of quality education (SDG 4), empowering individuals with the knowledge and skills essential for sustainable development and related research and developmentResearch and development (R&D) comprise creative and systematic work undertaken in order to increase the stock of knowledge – including knowledge of humankind, culture and society – and to devise new applications of available knowledge -—
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—-. (SDG 9). The study considers the costs of achieving 90 per cent proficiency in reading and mathematics (SDG indicator 4.1.1), 97 per cent education completion rate (4.1.2), higher manufacturing value added share (9.2.1) and universal mobile network coverage (9.c.1) (the latter two are also included in the previous section).

Achieving these SDG indicators as part of the education transition pathway is estimated to cost US$422 per capita annually for the developing economies studied (corresponding to 4.8 per cent of their GDP). Armenia, and Kazakhstan show high achievement levels, with near-universal education completion rates (SDG indicator 4.1.2) and impressive digital inclusion metrics for mobile network coverage (9.c.1). Conversely, countries like Somalia, and Mauritius lag significantly behind on both indicators. According to -—
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—-, it was estimated that the cost of achieving the key SDG 4 targets, including universal pre-primary, primary, and secondary education in low- and lower-middle-income countries by 2030, would amount to US$461 billion on average between 2023 and 2030. These figures highlight the significant financial resources required to meet the targets and emphasize the importance of adequate funding to support the implementation of education initiatives in these countries.

The later discussion on synergies, underlines the important effect on education spending across the 2030 Agenda, including through enhanced human capital, socio-economic development, reduced inequality, higher innovation, and sustainable industrialization.

Food system transition remains challenging for many developing economies

Overall, the food system transition pathway aligns with the zero-hunger goal (SDG 2) and underscores the essential role of agriculture for food security, to improve rural livelihoods, and to bolster resilienceThe ability of a system, community or society exposed to hazards to resist, absorb, accommodate, adapt to, transform and recover from the effects of a hazard in a timely and efficient manner, including through the preservation and restoration of its essential basic structures and functions through risk management -—
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—-.. This study considers the cost of developing rural infrastructure and agriculture (SDG indicator 2.a.1), and protecting important sites for terrestrial, freshwater and mountain biodiversity (15.1.2 and 15.4.1). This costing exercise, however, does not consider the achievement of the zero-hunger target and could be extended in the future.

The estimated average annual per capita cost for developing economies to achieve indicators covered for this pathway is US$225. These contribute to enhanced food security, sustainable agricultural practices, mitigating climate change impacts, and conserving biodiversity.

While some countries have made significant strides in agricultural expenditure orientation (SDG indicator 2.a.1) and protected area coverage for biodiversity (15.1.2 and 15.4.1), others are far from the targets. The agriculture orientation index for government expenditures indicates efforts in supporting the agricultural sector (2.a.1). Some countries, like El Salvador and Thailand, have high scores, demonstrating their prioritization of agricultural development. However, there is room for improvement in many countries where the index remains low.

While some countries, like Thailand, have made notable progress in biodiversity conservation (SDG indicators 15.1.2 and 15.4.1), others struggle to reach higher levels of protected area coverage. The food system pathway presents an opportunity for countries to address the complex interplay between food production, climate change, and biodiversity conservation, and is an important area for further efforts to fill data gaps and enable an extended analysis of indicators and countries.

Energy transition has the potential to boost sustainability across sectors

The energy transition pathway aims to enable access to electricity, renewable energy and reduce energy intensity towards SDG 7 and taking urgent action on climate change towards SDG 13. This study considers the cost of achieving universal access to electricity (SDG indicator 7.1.1), increasing the share of renewables (7.2.1) and reducing energy intensity (7.3.1). The cost of taking action to address climate change merits an extended study and has not been considered yet.

Achieving these indicators requires yearly spending of US$586 per capita (6.7 per cent of GDP) in the 21 developing economies considered. Earlier the Sustainable Energy for All consortium -—
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—- estimated that the requirements are in the range of US$1.0–1.2 trillion annually from 2012 to 2030 to achieve universal access to modern energy services, double the global rate of improvement in energy efficiency, and double the share of renewable energy in the global energy mix. This would have required tripling the level of investments in 2010. But as years have gone by, and as we have also seen some setbacks, the annual spending needs have increased. The report estimated that the bulk of the resources would be needed for energy efficiency and renewable energy interventions.

The -—
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—- estimates that the world must rapidly increase annual clean energy investment to reach US$4.5 trillion globally by 2030 in order to achieve universal access and decarbonise the global energy sector consistent with a trajectory to limit global temperature rise to 1.5 degrees by the end of the century. While investments in clean energy are rising rapidly in developed economies and China, the levels have remained flat in developing economies. This would need to climb 7-fold to reach between US$1.4-1.9 trillion by the early 2030s to align with the outcomes of the Paris agreement, according to the IEA and IFCInternational Finance Corporation of the World Bank Group. Concessional financing will be essential to attract the needed private investment to clean energy in these regions, with the required concessional finance estimated to be between 80 and 100 billion in the years around 2030.

Among developing economies studied, ten have achieved universal access to electricity (SDG indicator 7.1.1), while additional efforts are needed in Myanmar (70 per cent) and Somalia (80 per cent) to get closer to 100 per cent -—
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—-. Renewable energy share (7.2.1), on the other hand, is highest in Somalia, Guatemala, Myanmar and Kyrgyzstan, ranging between 30 and 95 per cent. Yet, Uzbekistan, Iran, Kazakhstan, Maldives, and Azerbaijan remain at below 2 per cent share of renewable energy in total final energy consumption. Almost all developing economies studied are far from reaching the energy intensity target. Among the countries assessed, only Mauritius, Afghanistan, and Türkiye have either met or come close to achieving the target. Additional focus on energy efficiency, especially in the transport sector, is crucial, and energy efficiency has synergetic links to other resource systems, like water and food.

US$1 839 per capita needed each year

The overall cost estimate for reaching the selected SDG indicators is US$1 839 per capita for the 21 developing economies. This does not consider the available financing sources or the potential remaining financing gaps, but rather the overall spending needs per year.

The sum of costs for pursuing the selected SDG targets across the pathways, discussed above, and if pursued individually, would be US$2 751 per capita. Considering synergies across goals and pathways, the actual overall cost, US$1 839 per capita, is only two thirds of the separate costs. This speaks to the systemic nature of the 2030 Agenda. While some SDG indicators are overlapping between pathways, even removing them would leave higher costs for individual pathways. This underscores the importance of leveraging synergies between the SDGs and the transition pathways. This chapter will also discuss those synergies in maximizing positive spill over effects and minimizing trade-offs across the 2030 Agenda.

A comparison of several SDG costing studies reveals a large variation in the estimates of the funding needs to achieve the 2030 Agenda. Estimates from different studies range from about US$250 to over US$1 839 per capita annually (see Annex II). The variation does not seem to be driven only by country or SDG coverage, but also by the choices made related to methodology, sector, and data sources. It should also be noted that the financing needs depend on the time left to pursue the SDGs and the current distance from them. The closer we get to 2030, the higher the annual cost to bridge the gap.

Dollars spent with a gender focus yield better results

Financing for SDGs should consider gender perspectives specifically, because if it does not, it risks reinforcing existing gender biases, or leading to otherwise unexpected outcomes. Gender equality and the empowerment of women and girls is recognized under the 2030 Agenda not only as a standalone goal but also as a prerequisite for addressing the world’s most pressing concerns, including poverty, inequality and climate change, and for greater peace and prosperity. Empirical evidence shows a robust connection between greater gender equality and improvements across key economic outcomes, including growth, productivity, and competitiveness -—
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—-. Vital for upholding the rights and dignity of all women and girls, gender equality is also a multiplier and accelerator of human progress, economic growth and development -—
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Gender equality, however, is still a distant goal that the world has yet to achieve. More women and girls live in extreme poverty today than men and boys, and hunger and food insecurityFood insecurity is a situation where an individual cannot reliably access or afford healthy food. The FAO describes a moderately food insecure person as someone who cannot afford a healthy diet. has experienced uncertainty about the ability to access food and is likely to skip meals occasionally because of lack of resources. A severely food insecure person has at times run out of food and has during the last year gone a whole day without food. For SDG indicator 2.1.2, food insecurity is estimated based on survey data using the Food Insecurity Experience Scale developed by FAO. It consists of eight questions pertaining to whether the respondents or their families have reduced the quantity or quality of consumed food over the last 12 months because of lack of resources. -—
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—- are a routine part of the lived reality of millions of women and girls. By the end of 2022, around 383 million women and girls lived in extreme poverty compared to 368 million men and boys -—
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—-. In 2021, one in three women experienced moderate or severe food insecurity globally, and more than one in ten women and girls aged 15–49 have been subject to sexual and/or physical violence by an intimate partner in the previous year -—
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—-. Halfway through the 2030 Agenda, the latest evidence reveals that the world is not on track to achieve SDG 5 by 2030. At the global level, none of the 18 indicators of SDG 5 are met or almost met and only one is close to target. -—
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Targeting financing across the SDGs with a gender focus, including for social protection and decent jobs, is crucial to get the world back on track. UN Women et al. -—
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—- estimates that over 100 million women and girls could be lifted out of poverty if governments were to employ a comprehensive strategy aimed at improving access to education and family planning, fair and equal wages, and expanding social transfers. Moreover, the analysis presented in the SDG Pulse In Focus shows a synergistic relationship between government spending on key sectors such as agriculture, transportation, energy and housing to accelerate progress towards gender equality.

According to this study, equal representation of women and men in parliaments and local governments and in unpaid domestic and care work would be attainable by 2034 for 50 per cent of the developing economies included in this study and, with a few exceptions for the 39 developed economies, with the right spending and policy mix. In agriculture, for example, support for women’s access to and control over resources can boost progress on SDG 2 to eradicate hunger in addition to SDG 5 on gender equality. Similarly, public transportation and housing are essential for improving lives and the wellbeing of those left furthest behind, especially the estimated 560 million women and girls living in slum settings around the world -—
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Efforts to develop sustainable agriculture and clean energy technologies⁷, including modern cooking stoves, can reduce women’s unpaid domestic work burden -—
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—-. Among developed economies, the model points to many different ways to optimize government spending to boost progress towards target 5.4 on the value of unpaid work and target 4.2 on pre-primary education, including the synergies of spending on housing and health.⁸ Spending on social protection, including maternity benefits, parental leave and childcare subsidies are key drivers of accelerated progress on target 5.5 to ensure women’s full and effective participation and equal opportunities in political, economic and public life among developed economies in the sample.⁹ Interestingly, the synergistic relationship of spending on agriculture and housing appeared more effective to advance female political empowerment in developing economies.¹⁰

While having a gender lens in designing stimulus packages is important, so is a commitment to stable and consistent spending on gender equality over time. More efforts are needed to track resource flows in support of gender equality and women’s empowerment in countries. In 2021, only one in every four countries had such monitoring in place. However, translating resource flows into systems change is crucial for sustainable outcomes -—
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—-. These findings reaffirm the importance of developing gender-sensitive tools to support decision-makers in identifying priorities for accelerated and inclusive progress towards the 2030 Agenda.

Optimizing spending - more with less and sooner?

The above cost estimates are based on an analysis of two scenarios for spending on SDGs. Business-as-usual, BAUSBusiness-as-usual scenario, assumes that the current spending trajectory continues. We arrive at the costs of achieving the targets without any reorientation. This means the costs may be high, and fewer goals are achieved with the same cost. In the optimal scenario, OSOptimal scenario, we consider the best practice identified by the SFM. This methodology is useful in analysing producer performance and investigating its determinants -—
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—-, and can provide useful insights on best practices to increase efficiency and achieve SDGs with less costs. It helps to determine optimal growth rates for sectoral spending. The cost estimates discussed above in this chapter are based on the OS scenario.

With a strategic reallocation of sectoral spending, more countries can achieve SDGs sooner with less costs. Figure 2 shows the number of countries achieving the selected SDG indicators by 2030, and how their number increases from the BAUS to the OS. Certain SDG indicators are likelier to be achieved than others, namely those located on the right of the figure. For example, 17 of the 21 developing economies analysed are likely to achieve the targets for under-five and neo-natal mortality rates (SDG indicators 3.2.1 and 3.2.2) in the BAUS option, and up to two countries more in the OS scenario. Universal access to basic services (1.4.1) is challenging as only six countries will achieve it by 2030 unless OS solutions are found which would enable 19 countries to reach the target. Strategic allocation of investment is particularly important for achieving universal vaccination coverage (3.b.1), higher education completion rates (4.1.2), and to reach higher manufacturing value added (9.2.1) and renewable energy share (7.2.1.), as well as universal access to electricity (7.1.1). Here the difference of BAUS and systematic OS strategies that consider synergies are the largest.

Deep cultural transformation to boost SDG achievement

Effective investment combinations can drive the achievement of some SDG indicators very close. In 2024, Egypt and El Salvador are on the brink of achieving universal access to basic services (SDG indicator 1.4.1), with over 80 per cent already achieved in 2021. Azerbaijan and Thailand show promise in excelling at education completion (4.1.2). Kyrgyzstan and Uzbekistan have made significant strides in ensuring universal access to electricity (7.1.1). China, Egypt, and South Africa have demonstrated progress in mobile network coverage (9.c.1). Challenges remain for universal coverage of populations with vaccinations planned in the national programme (SDG indicator 3.b.1). (Figure 3)

Even if not immediate, several SDG indicators are on track for achievement before 2030 assuming efficient allocation of spending. This could happen for Egypt, Thailand, and China with universal access to vaccines (SDG indicator 3.b.1), Bolivia, Maldives, and Myanmar in education completion (4.1.2), and Guatemala, Indonesia, and Uzbekistan in access to electricity (7.1.1). El Salvador, Indonesia, and Kazakhstan have the potential to achieve broad mobile network coverage (9.c.1). However, challenges lie ahead for other developing economies in achieving targets measured by these indicators.¹¹ Regardless of optimal spending scenarios, some indicators on the far right and out of sight remain challenging to reach. Optimal scenarios need to be supported with other policy measures to address many cultural and other barriers to progress.

High public spending helps but is no guarantee

Higher government spending is generally associated with better outcomes in achieving SDGs. To assess this, we compared government spending with the -—
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—- scores (Figure 4). However, this relationship is not consistent across all countries, as highlighted by Afghanistan's high government spending (48 per cent relative to GDP) but low SDGs Index score (49). Finland, with high government spending, equalling 56 per cent of GDP, emerges as the top performer also in terms of the SDG IndexSDG Index is a global assessment of countries' progress towards achieving the Sustainable Development Goals. It is a complement to the official SDG indicators and the voluntary national reviews -—
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—-. score of 88. Notably, Ireland stands out as interesting case, where government spending is relatively modest (25 per cent) compared to GDP, but the country achieves a comparatively high SDGs Index score (80). This highlights the importance to consider factors beyond spending, such as governance and policy implementation. Adequate funding combined with strategic planning and stakeholder collaboration are necessary for achieving the SDGs.

Understanding synergies is key to SDG achievement

The analysis shows strong interconnections between government spending across sectors and SDGs. This underscores the critical need for an integrated, holistic approach to financing the SDGs. Spending on education is particularly effective in fostering achievement of outcomes across all SDGs. Similarly, spending on 'information and telecommunications' and 'health' can substantially enhance ‘education’ outcomes, creating mutual benefits.

The highest synergy coefficient of 7.5 is found for 'health' and 'education' spending. A synergy coefficient of 7.5 implies that a unit increase in combined spending on 'health' and 'education' is associated with a 7.5-fold improvement in achieving the SDGs. This suggests that the combined output of the education and health sectors has a significantly stronger impact on the SDGs compared to the sum of their individual outputs. Working on these two sectors together can lead to enhanced performance in achieving the SDGs, surpassing what could be achieved by focusing on them independently. Similarly, combined spending on 'agriculture' and 'education', or on 'housing' and 'education' yield notable synergy coefficients of 6.8 and 6.0, respectively. Synergies are also found in combined spending on 'health' and 'social' sectors, and on 'transport' and 'social' sectors, with coefficients of 2.5 and 1.7, respectively.

Systematic strategies that consider spillover effects and trade-offs can multiply effectiveness. The following figure illustrates the synergies of sectoral spending.

The analysis finds positive synergies between spending on ‘environment’ with that on ‘agriculture’, ‘housing’ and ’transportation’, illustrating the cross-cutting synergies of an environmental focus for the achievement of many SDGs. Yet, a trade-off is observed for spending on ‘fuels and mining’ and ‘environment’, primarily due to the environmental degradation caused by fossil fuels.

Understanding these synergies is essential for strategic public spending decisions, to optimize positive impacts and minimize trade-offs.

Investing in data can help target scarce resources

Halfway through the 2030 Agenda, many data gaps, specifically in developing economies hamper the analysis. This study is thus limited to 60 countries, 21 of which developing, where data for selected SDG indicators are available (Map 1). Albeit providing for 45 per cent of world population and accounting for 80 per cent of world GDP in 2021, when developing economies are considered, these values drop to 35 and 58 per cent, respectively. Currently, the coverage of countries in Africa and Latin America is critically low and remains to be extended where complementary national data sources can be found.

Costing the key targets of the SDGs is even more urgent in the present context, as multiple policy priorities, be they short, medium- or long-term priorities, imply greater trade-offs in the allocation of scarce resources. The forecast of financial costs for time-bound and target-based development goals is at the core of the methodology underpinning the SDGs.

Figure 6 shows that data for 28 SDG indicators had to be dropped because they did not have the required time series from 2005 to 2021 and covered less than half of the countries. This was 34 per cent of the 91 SDG indicators that otherwise were included within the pathways and could be costed at the country level. In total, 45 unique SDG indicators (yellow in Figure 4), or nearly 50 per cent, were dropped since for countries where these indicators were available, other input data, like government expenditure statistics were not available, and country coverage dropped too low for the inclusion of the SDG indicator. To date, it was only possible to estimate costs for the achievement of 26 per cent of SDG indicators.

This preliminary work is being extended by trying to find complementary national sources especially for government expenditure statistics. The findings also point to the critical need to invest in data and statistics to fill gaps and produce more comprehensive analysis of SDG financing needs.

Further work to inform efforts to accelerate progress towards the 2030 Agenda

The findings of this study provide early insights into pursuing more systematic and planned financing for sustainable development and gives indications of the financing needs for the countries and the SDG transition pathways for SDG indicators covered in this study. While the full analysis of the costs by SDG and optimal spending allocations are still pending, this study serves as a foundation for further research and extension. The complex nature of the work, involving numerous countries and indicators, necessitates more time for a comprehensive assessment of costs and synergies.

Credible cost estimates can only be compiled with methodologies that rely on reliable data and statistics. This bottom-up estimation starting at country levels and based on analysis of progress using SDG indicators and official statistics on government expenditure by sector enables a detailed analysis of financing needs by SDGs and their indicators or by pathway. The results are far from comprehensive due to the lack of statistics on government spending and owing to the remaining gaps in SDG indicator reporting. Further efforts to examine complementary national data sources will be taken. However, eventually the robustness and comprehensiveness of cost estimates depends on countries’ capacity to compile these key statistics.

This study serves as a starting point by providing the first experimental cost estimates covering selected SDG indicators across the SDG transition pathways. This analysis can be further refined and extended in a UN-wide collaboration and jointly with partner organizations. This could mean extension of country or SDG indicator coverage where data can be identified, focused regional or thematic analysis by different UN entities and others, and a more detailed analysis of gender equality considerations in financing SDGs.

To further improve the methodology, it would be beneficial to explore methodological refinements that incorporate determinants of efficiency related to tax revenue, FDI, debts, and ODAOfficial Development Assistance (ODA) are resource flows to countries and territories which are: (a) undertaken by the official sector; (b) with promotion of economic development and welfare as the main objective; (c) at concessional financial terms (implying a minimum grant element depending on the recipient country and the type of loan). In addition to financial flows, technical co-operation is also included -—
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—-.. By examining these factors, this costing exercise can gain a better understanding of their impact on SDG financing and identify opportunities to optimize resource allocation and improve efficiency in achieving the goals. For future analysis, it would be useful to select the most relevant SDG indicators for each SDG transition pathways thus avoiding overlap across pathways as far as possible.

This study informs the formulation of financing strategies by highlighting the importance of joining forces, finding synergies, and fostering collaboration among stakeholders. It emphasizes the need for policymakers to try to understand how different sectors and targets interact with each other. Attempts to identify areas for spill over effects and avoid trade-offs will pay off by accelerating progress towards the SDGs.

Such strategies will consider the central role of education as a trigger for progress, factor in the synergies across health, environment, and other sectors, avoid trade-offs that hamper progress in environmental sustainability and pursue the multiple benefits of gender considerations to accelerate progress by involving everyone.

Annex I. SDG costing methodology

Methodology description

The methodology of this report, based on the conclusions of Schmidt-Traub -—
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—-, emphasizes the importance of harnessing synergies within and across sectors to reduce overall financing needs and maximize the impact of spending. These sectors are interconnected through a dynamic system of feedback loops, leading to jointly significant effects on key indicators. For example, spending on transportation can enhance the effectiveness of education spending, while improved education outcomes can reinforce the impacts of the health sector and enhance livelihoods.

In the context of assessing the achievement of SDGs by countries, this study adopts a standardized frontier technology that is applicable to all countries. It employs the same specification function to transform the sectoral government spending of each country into the attainment of specific SDG indicators. The relationship between inputs and outputs in the SFM is captured through a translog cost function.

To quantify the costs required to achieve the targets of the 2030 Agenda, this study adopts a cross-sectoral approach. This approach allows for the inclusion of multiple fiscal indicators, such as sector-specific expenditures as a percentage of GDP, and it considers the interaction terms between these indicators.

This approach offers several advantages. Firstly, it mitigates the issue of overestimated costs that arise from neglecting policy synergies, ensuring a more accurate estimation of the actual costs involved. Additionally, it addresses the limitations of underestimating costs by accounting for nonlinear relationships, particularly the diminishing marginal returns.

To capture these cross-sectional synergies and nonlinear relationships between multiple policy inputs and outcomes of various SDGs, the model employs a joint production estimation. This allows for a comprehensive assessment of the interdependencies and interactions among different policies and SDG targets.

In line with the work of UNICEF -—
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—-, the cost requirements for achieving SDGs are estimated by using a stochastic frontier model based on a translog production function specification taking the following form:

where:

Ln Y_jt = Selected SDG targets for country j (in log form) – for example, Primary Net Enrolment Rates

Ln X_ijt = Spending per GDP on sector i for country j (in log form)

Ln Z_jt = controle variable of country j (in log form)

u_jt = random errors, it assumed to be independently and identically distributed, vi ∼ N (0, σ_v2)

v_jt = inefficiency term, it assumed to be positively defined with an asymmetric distribution independent of those of the u_t

The β_i (i=1,...,11) are the coefficients which illustrate the estimated elasticity of a key indicator with respect to its own-sector spending. These coefficients capture the direct effect of an increase in per GDP sectoral expenditure on the return of a key indicator.

Furthermore, the translog models include quadratic terms for each sector, represented by β_ik with i=k. These terms are included to reflect the fact that increased cost in the same sector often results in diminishing marginal returns.

The last set of coefficients β_ik with i<k, represent the synergy effects of spending in multiple sectors simultaneously, meaning the extent to which one sector’s spending impacts the effectiveness of another sector’s spending.

In order to evaluate whether a cross-sectoral impact is significant, F-test for an incremental contribution is applied.

Calculating business as usual and optimal spending scenarios

Beyond the complicated mathematical theoretical foundations of transcendental logarithmic models, the econometric tanslog models of panel data of BAUS study the asymptotic properties of a vector of significant parameters based on the underlying stochastic representation of growth of each individual. In this same space, the evolution of each SDG is studied as an endogenous variable explained by the forecast of several exogenous macroeconomic variables. So, we deduct the corresponding costs.

For the OS models which focus more on the properties of the best practices using the statistical convergence of stochastic frontiers of each country class (developed and developing). In general, econometricians choose an optimal scenario by penalizing the quality of adjustment of a model by its complexity, ex-post, during the validation or choice phase. This can sometimes result in actions that reduce the efficiency of the system being measured. Whereas in this study, the optimal scenarios are based on the objective function of a country which is considered as best practice. Then the idea is to take optimal combination of scores of an individual reference in space of expenditures as a measure reference for each country.

This approach consists of:

1. Running the Model: The Translog model specification is run under panel data, considering the efficiency term as an error term along with the random term. This allows establishing a frontier and identify the best practice. The results of this model reveal the key determinants of sectoral spending, including direct impacts of sectors, squared terms, and sector interactions - Since the model is non-linear, it provides valuable insights into the relationships between variables.
2. Forecasting the Indicator: To forecast the indicator using the retained model, the input variables need to be incorporated in the model, specifically sectoral spending. There are two alternative approaches:
   1. Assumption 1 (BAUS): In this approach, we assume that sectoral spending (the selected sectors determined by the model) will continue to grow at the same pace. This forecast data is used to calculate the indicator over time and identify the total spending required to achieve the SDG target.
   2. Assumption 2 (OS): In this approach, we consider the best practice identified by the model, using the SFM method. We use the growth rate of spending in this country to forecast the selected sectoral spending and, consequently, the indicator's evolution. This allows calculating the necessary resources needed to achieve the target.

Data sources

The dependent variables, as in the key indicators, were obtained from various databases, including the Global SDG Database -—
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—-. On the other hand, the main inputs data, government spending by COFOG classification used in the SFM model were collected by UNCTAD based on data sourced from the International Financial Statistics -—
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—- and from other international and national sources. Other inputs data, namely, government effectiveness, political stability and absence of violence and terrorism and FDI (net inflows) which were used as variables of control are sourced from and World Bank Governance Indicators -—
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—- respectively.

Estimating the precise requirement for achieving the SDGs is a challenging endeavour. Consequently, both recurrent and capital expenditures have been considered in the costing analysis. The analysis incorporates fiscal inputs in the form of government expenditures as a percentage of GDP across various service categories. The selection of these categories was based on data availability and the statistical significance of the connections between fiscal inputs and different policy measures. The inputs encompass a wide range of sectors, including agriculture; fishing, forestry and hunting; fuel and energy; mining, manufacturing and construction; transport; communication; environment protection; housing and community amenities; health; education; social protection; and other sectors. These have been identified using the COFOG.

Annex II. SDGs costing studies

Notes

1. The developing economies covered are Afghanistan, Armenia, Azerbaijan, Bolivia, China, Egypt, El Salvador, Guatemala, Indonesia, Iran, Kazakhstan, Kyrgyzstan, Maldives, Mauritius, Mongolia, Myanmar, Somalia, South Africa, Thailand, Türkiye, and Uzbekistan.
2. Sectorial spending refers to the government expenditures classified according to the COFOG classification. For the purpose of this study, 11 sectors (or functions based on COFOG) have been defined as follows: Agriculture, forestry, fishing, and hunting; Fuel and energy; Mining, manufacturing, and construction; Transport; Communication; Environment protection; Housing and community amenities; Health; Education; Social protection; and Special others. These sectors (also referred to as functions) were selected to represent specific areas of government spending and to analyze their potential impacts on achieving the SDGs. It is important to note that the terms 'sector' and 'function' are used interchangeably in this study. The terms sector and pathway, as used in this study, should not be confused. The sectors represent the specific areas of government spending that were used as inputs in the model, while the transition pathways refer to the interconnected thematic pathways towards achieving the SDGs. The pathways provide a framework for understanding the holistic progress towards sustainable development, while the sectors highlight the specific components of government spending considered in the analysis.
3. The estimates derived in this study are based on selected countries’ SDG indicators and official statistics on government expenditures, while the WIR figures are derived by SDG-sector from the most recent studies published by specialized agencies, institutions and research entities in their respective areas. This study focuses on selected 21 developing economies while WIR covers all developing economies. Furthermore, we quantify the overall financing requirement to achieve the SDGs, while WIR estimates the investment gap. i.e., the additional investment needed to achieve the SDGs. This study does not consider available financing.
4. The model endeavors to optimize the attainment of the goals by 2030; however, it recognizes that the desired optimization has not been fully achieved within the specified timeframe. Particularly regarding SDG indicators 5.5.1 and 5.4.1, it is anticipated that many countries will not reach these objectives before 2030, despite the allocated investments.
5. This study estimates the costs of achieving selected SDG indicators of the pathway, as listed in Table 1.
6. The ratio is derived by rescaling indicator SDG 5.4.1, proportion of time spent on unpaid domestic and care work by sex, for 2001-2021, to a female-to-male time use ratio, where the target is set at 1.03 for equal time use. Scores exceeding 1.03 refer to a higher female share of unpaid domestic and care work.
7. The synergy coefficient between agriculture and clean energy for developing economies is 0.013 and statistically significant at the 10 per cent level.
8. The synergy coefficient for developed economies is 4.4 for spending in education and is statistically significant at the 10 per cent level; the synergy coefficient for the combined spending in health and education is 1.7 and is statistically significant at the 5 per cent level.
9. The synergy coefficient for developed economies is 2.6 for spending on social protection and is statistically significant at the 10 per cent level.
10. The synergy coefficient for the combined spending on agriculture and housing is 1.5 and is statistically significant at the 10 per cent level.
11. This analysis reflects an optimal scenario based on efficient allocation of government spending, highlighting the positive outcomes of optimized spending. See note on BAUS and OS.

References

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