Chapter 6. Tracking progress in policy coherence for development1

Lindberg Carina

Monitoring policy coherence for sustainable development (PCSD) will require consideration of three key elements: i) institutional mechanisms; ii) policy interactions, including contextual factors; and iii) policy effects. This broader approach can be used to assess the extent to which domestic policies are aligned with international sustainable development objectives and contribute to the achievement of the Sustainable Development Goals (SDGs). The purpose of this chapter is to explore a selection of policy interactions related to food security, illicit financial flows, and green growth – the three priority areas for policy coherence identified in the 2012 OECD Strategy on Development. Identifying and understanding the different types of interactions between the SDGs and their respective targets can help policy makers to maximise synergies and exploit win-wins; avoid potential policy conflicts; manage trade-offs; and ultimately design coherent policies for sustainable development.

  

Introduction

Monitoring policy coherence for sustainable development (PCSD) will require consideration of three key elements: i) institutional mechanisms; ii) policy interactions, including contextual factors; and iii) policy effects (OECD, 2015a). This broader approach can be used to assess the extent to which domestic policies are aligned with international sustainable development objectives and contribute to the achievement of the Sustainable Development Goals (SDGs). The purpose of this chapter is to explore a selection of policy interactions (the second element) related to food security, illicit financial flows, and green growth – the three priority areas for policy coherence identified in the 2012 OECD Strategy on Development.2 For an overview of the other elements, please refer to Chapter 2.

Identifying and understanding the different types of interactions between the SDGs and their respective targets will help policy makers to maximise synergies and exploit win-wins (pursuing multiple objectives at the same time); avoid potential policy conflicts (pursuing one policy objective without undermining others); manage trade-offs (minimising negative impacts on other policy objectives); and ultimately design coherent policies for sustainable development.

For each of the three priority areas, this chapter first outlines a selection of known interactions based on the analysis in chapters 3 (food security), 4 (illicit financial flows), and 5 (green growth). It does not attempt to map or provide an overview of all interactions; rather it uses a few poignant examples to illustrate how OECD analysis can support efforts to track progress in PCSD over time and in the context of the 2030 Agenda.

Second, the chapter suggests a number of OECD data and indicators that can be used to inform the selected interactions. Data and indicators to track progress on PCSD are likely to vary from country to country depending on their natural attributes, economy, institutional set-up, and political and social variables. Yet, some common indicator sets could be identified for cross-country comparisons and peer review. By monitoring the correlation and trends between these indicators, we offer an approach that countries might wish to use for assessing their own progress towards SDG target 17.14 – “enhancing policy coherence for sustainable development”.

Third, the chapter provides an empirical overview of the evolution of a number of OECD country policies that could either contribute to or undermine the achievement of these targets. It concludes by listing OECD policy instruments that can be used to influence the interactions in one direction or another in order to maximise synergies and minimise trade-offs.

This exercise aims to contribute to monitoring policy coherence at the national level. It does not attempt to rank countries in any way, nor does it suggest exact cause-and-effect relations between the indicators mentioned. Importantly, it is undertaken in parallel with the UN-led process to monitor implementation of the SDGs at the global level (Box 6.1). The long-term objective is to create an online “OECD Coherence Monitor” whereby users can track progress for all three elements based on their national interests and priorities. This is work in progress and aims to complement other (non-OECD) initiatives to assess and/or monitor interactions between the SDGs and targets. These are described in more detail in the second half of the chapter.

Box 6.1. Towards a global monitoring framework for the SDGs

A robust follow-up and review mechanism for the implementation of the new 2030 Agenda for Sustainable Development will require a solid framework of indicators and statistical data to monitor progress, inform policy and ensure accountability of all stakeholders. To this end, the United Nations Statistical Committee created an Inter-Agency and Expert Group on SDG Indicators (IAEG-SDGs). The Expert Group was tasked to develop an indicator framework for the goals and targets of the post-2015 development agenda at the global level and to support its implementation.

In developing the indicator framework, the Expert Group has had to consider the relationship between the global indicators and the indicators for regional, national and subnational and thematic monitoring. While it is expected that the global indicators will form the core of all other sets of indicators, additional and in some cases different indicators might be used for regional, national and subnational levels of monitoring. These indicators will be developed by Member States. In this regard, the proposed global indicator for target 17.14 to enhance policy coherence for sustainable development – “The number of countries with mechanisms in place to enhance policy coherence for sustainable development” – could benefit from further elaboration on what this means in practice. The OECD’s work on policy coherence for sustainable development can offer such guidance, including tools for tracking progress at the national level.

Follow-up and review of SDG implementation will be conducted by the High-Level Political Forum (HLPF), which will meet every year under the auspices of the Economic and Social Council (ECOSOC), and every four years under the auspices of the General Assembly. The Forum is mandated to conduct national reviews and thematic reviews of the implementation of the Agenda, with inputs from other intergovernmental bodies and forums, relevant UN entities, regional processes, major groups and other stakeholders. So far, 22 countries have agreed to undergo voluntary national reviews at the 2016 HLPF: China, Colombia, Egypt, Estonia, Finland, France, Georgia, Germany, Madagascar, Mexico, Montenegro, Morocco, Norway, Philippines, Republic of Korea, Samoa, Sierra Leone, Switzerland, Togo, Turkey, Uganda, and Venezuela. .

Each year, the HLPF will meet under a thematic focus reflecting the integration of the three dimensions of sustainable development. Initial proposed themes include: ensuring that no one is left behind (2016); ensuring food security on a safe planet by 2030 (2017); making cities sustainable and building productive capacities (2018); and empowering people and ensuring inclusiveness: peaceful and inclusive societies, gender equality, education and health (2019). Goal 17 on the means of implementation will be addressed every year.

Source: http://unstats.un.org/sdgs/iaeg-sdgs; and https://sustainabledevelopment.un.org/hlpf.

Using OECD data and indicators to monitor SDG interactions

Global food security

In a world of unprecedented economic opportunities and with vast resources at our disposal, the fact that over 800 million people in the developing world still suffer from hunger represents one of the biggest incoherencies of our time. The main challenge in ensuring global food security is to raise the incomes of the poor. Agricultural development and rural diversification will be needed to foster economic growth and job opportunities, while increased investment can help to close the yield gap between advanced and developing countries. Trade will also have an increasingly important role to play in ensuring global food security (OECD, 2013).

Contextual factors matter too – currently three key trends frame the future challenges facing our food and agriculture systems: growing and shifting food demand; constraints upon natural resources; and agricultural productivity uncertainties resulting from climate change. The choices made by policy makers and businesses today will be pivotal in determining the extent to which global food and agriculture systems will be impacted by these trials (OECD, 2016a). The consideration of several alternative “futures”, which emphasise different challenges to varying degrees, can provide an important complement to efforts to monitor the past or present (Box 6.2).

Box 6.2. Alternative futures for global food and agriculture

Scenario analysis can facilitate the development of, and linkages between, different drivers and outcomes. It can contribute to the re-thinking of strategies with a view to the development of coherent, robust policy and private sector responses to avail of new opportunities and avoid more of the undesired outcomes. A new report by the OECD (2016) explores three scenarios for food and agriculture until 2050:

  • The Individual, Fossil Fuel-Driven Growth scenario illustrates a world which is driven by sovereignty and self-sufficiency, characterised by the strong focus of individual regions on economic growth based on fossil energy sources and related technologies, and relatively minimal emphasis by governments or their citizens on environmental or social questions. Co-operation is limited to regional alliances.

  • The Citizen-Driven, Sustainable Growth scenario portrays a world in which individual countries push for sustainable development of their economies, driven mainly by changes in the attitudes of its citizens. Global co-operation is relatively limited. Technologies are focused on natural resource savings and the preservation of the environment.

  • The Fast, Globally-Driven Growth scenario represents a world that is characterised by a strong focus on international co-operation. Markets and large companies play key roles in economic development, while environmental issues receive less attention. Technologies flourish, particularly in the areas of food, feed and energy production.

These scenarios suggest that food prices may well continue to rise, but that future price increases should remain more limited as productivity and yields continue to rise. Farm incomes too should increase; however, agricultural sector contribution to GDP and employment will fall. And while each scenario faces its own priority challenges, they all see the environment being placed under increasing strain – albeit to varying extent.

Source: OECD, 2016a.

Sustainable Development Goal 2 – End hunger, achieve food security and improved nutrition and promote sustainable agriculture – calls for action on many fronts and simultaneous consideration of numerous targets across the SDGs. In general, as noted by ICSU and ISSC (2015), SDG 2 can be expected to move in tandem with goals 1 (Poverty); 3 (Health); 4 (Education); 5 (Gender equality); 10 (Inequality); and 12 (Sustainable consumption and production), while there are likely trade-offs between this goal and the environmentally focused targets of goals 6 (Water and sanitation); 7 (Energy); 13 (Climate change); 14 (Oceans); and 15 (Ecosystems and biodiversity).

Table 6.1 selects three targets (A-C) for which it identifies critical interactions and relevant indicators and data for tracking progress. It is followed by an empirical overview of the evolution of these interactions and associated policies over time.

Table 6.1. A selection of interactions related to food security

SDG/Target

Interaction (synergy or potential trade-off)

Data/Indicator to assess interaction

Policy instrument to influence interaction

A

2.1 End hunger

6.1 Ensure universal access to drinking water

Potential trade-off: Agriculture is the largest user of water at the global level.

7.1 Ensure universal access to energy services

Potential trade-off: Agriculture and energy production compete for water resources.

7.2 Increase the share of renewable energy

Potential trade-off: Increasing the share of renewable energy could conflict with food security if food crops and biofuel crops compete for the same land.

12.3 Reduce food waste and food losses

Synergy: Improved transport and post-harvest infrastructure would reduce food waste.

FAO estimates that each year, approximately one-third of all food produced for human consumption in the world is lost or wasted. Food waste is also a source of GHG emissions, and has a large water footprint.

  • Nutrition

  • Agricultural water withdrawal

  • Irrigated land area

  • Energy production

  • Share of renewable energy

  • Share of biofuels

  • Overweight and obese population

  • Aid for food and nutrition security

  • Irrigation subsidies

  • Energy subsidies

  • Support to biofuels

  • Biofuels mandates

B

2.c Correct trade restrictions and distortions in agricultural markets

10.1 Achieve and sustain income growth of the poorest

Synergy: Trade raises overall incomes through the benefits to exporters (higher prices) and consumers (lower prices).

  • Trade in food and agriculture products

  • Food prices

  • Tariffs

  • NTMs

  • Producer Support Estimates

  • Support to agriculture that is most production-and trade distorting

  • Import and export restrictions

C

2.3 Double agricultural productivity

13 Combat climate change

Potential trade-off: Agricultural activities are directly responsible for about 17% of global greenhouse gas emissions

14.1 Reduce marine pollution

Potential trade-off: Agricultural nutrients and fertilisers contribute to marine pollution.

  • GHG emissions from agriculture

  • Polluter-Pays-Principle

  • Support to agriculture that is most environmentally harmful

  • Support to fertilisers

Source: Author’s own illustration.

A) Potential trade-offs: Ending hunger/manage water sustainably/ensure energy access/increase biofuels production

Demand for water, energy and food are expected to increase further. Currently, agricultural water withdrawal accounts for 44% of total water withdrawal in OECD countries; for an average of 74% in the BRICS countries; and for more than 90% in least developed countries. At the same time, some 580 billion cubic metres of freshwater are withdrawn for energy production every year – about 15% of the world’s total water withdrawal (FAO-AQUASTAT and IEA, 2015a).

Overall, withdrawals of freshwater resources by agriculture have declined in most OECD countries for which data are available (Figure 6.1). Agriculture’s withdrawal of freshwater as a share of total withdrawals has also decreased in recent years as compared with the early 1990s. These declining trends have been driven by a mix of factors, including near stable or reduced irrigated areas; improvements in irrigation water management and technological efficiency; release of water to meet environmental needs; and a slowdown in the growth of agricultural production (OECD, 2014a).

Figure 6.1. Agricultural water withdrawals in selected OECD countries
picture

1. 1994-95 for Belgium and Mexico.

2. The statistical data for Israel are supplied by and under the responsibility of the relevant authorities. The use of such data by the OECD is without prejudice to the status of the Golan Heights, East Jerusalem and Israeli settlements in the west Bank under the terms of international law.

Source: OECD (2013), Agri-Environmental Indicators: Environmental Performance of Agriculture 2013 (database).

Energy subsidies to pump irrigation water from aquifers can lead to unsustainable water uses. They illustrate inconsistent water, food and energy policies, usually motivated by food security issues and the willingness to support farmers. To overcome this dilemma, energy subsidy reform is required, whereby innovative reform strategies are needed to ensure that impacts on the poorest and their businesses are mitigated, guaranteeing a transition to sustainable agriculture. Such reform has to integrate the “nexus” approach to energy, water and food, targeting at the same time improvements to the economy, the conservation of natural resources and improved food security (www.iisd.org).

Production and use of biofuels are also promoted and supported by governments in many OECD countries, as well as in a number of countries outside the OECD area. In 2014, the share of renewables in OECD total primary energy supply was 9.2%; the share of biofuels and waste in renewables was 5.1% (Figure 6.2).

Figure 6.2. Fuel shares in OECD total primary energy supply, 2014
picture

1. “Other” includes energy sources not classified elsewhere such as non-renewable combustible wastes, ambient air for pumps, fuel cells, hydrogen etc.

Source: IEA, 2015b.

Biofuel support polices concerning domestic markets can be clustered into three different categories: payments, tax rebates or exemptions, and mandates or targets. Payments increase the economic incentives to produce, consume or store biofuels. Tax rebates or exemptions are meant to stimulate consumption of biofuels. Both categories typically do not specify a goal measured in quantitative terms. Mandates in contrast are a legal means by which, for example, the petroleum industry is forced to blend a certain share or volume of biofuels into fuels of fossil origin. Targets are less binding than mandates because they are voluntary and are not effective at the individual agent level.

A fourth category of policies relevant to biofuels comprises sustainability criteria which are applied for biofuels in an increasing number of countries. These criteria modify the effects of support policies as they generally require biofuels to comply with certain, mainly but not only environmental, conditions to qualify for other support measures or to count towards biofuel mandates (OECD, 2014a).

However, while contributing only little to reduced GHG emissions, biofuel subsidies add to a range of factors that raise international prices for food commodities. The OECD Fertiliser and Biofuels Support Policies Database compiles policies relating to support within the fertiliser and biofuels sectors of several countries. It shows that payments to consumption (including tax measures) are the most widely applied measure (www.oecd.org/tad/agricultural-policies/support-policies-fertilisers-biofuels.htm).

B) Synergy: Correct trade restrictions and price distortions/income growth

Open markets have a pivotal role to play in raising production and incomes. Trade enables production to be located in areas where resources are used most efficiently and has an essential role in getting food from surplus to deficit areas. Trade also raises overall incomes through the benefits to exporters (in the form of higher prices than would be received in the absence of trade) and importers (through lower prices than would otherwise be paid), while contributing to faster economic growth and per capita incomes. Nevertheless, countries may need to have in place parallel measures to maximise the benefits and costs of trade reform (OECD, 2013).

An immediate contribution that OECD countries can make to improve global food security is thus to eliminate trade-distorting agricultural support that prevents an efficient allocation of resources. The use of price-based support, for example, requires restrictions on market access and, when countries have produced surpluses, has often led to the use of export subsidies. The former harms developing country exports, while the latter depresses international prices, making conditions more difficult for competitors on international markets and for import-competing producers on domestic markets.

On average, OECD countries have reduced the amount of support that they provide to agriculture, and remaining support is less production and trade distorting than before (Figure 6.3).

Figure 6.3. Composition and evolution of most production and trade distorting support
Percentage of gross farm receipts
picture

Note: Countries are ranked according to 2012-14 levels

1. EU15 for 1995-97; EU27 for 2012-13; and EU28 from 2014 when available.

2. For Mexico, 1995-97 is replaced by 1991-93.

3. The statistical data for Israel are supplied by and under the responsibility of the relevant Israeli authorities. The use of such data by the OECD is without prejudice to the status of the Golan Heights, East Jerusalem and Israeli settlements in the West Bank under the terms of international law.

4. The OECD total does not include the non-OECD EU Member States. The Czech Republic, Estonia, Hungary, Poland and the Slovak Republic are included in the OECD total for all years and in the EU from 2004. Slovenia is included in the OECD total from 1992 and in the EU from 2004.

Source: OECD (2015), “Producer and Consumer Support Estimates”, OECD Agriculture statistics (database).

C) Potential trade-offs: Agricultural productivity/climate change/marine pollution/deforestation

With its impacts on the climate, agriculture has both direct and indirect consequences for the implementation of SDG 13 to “take urgent action to combat climate change and its impacts”. The reverse is also true: climate and climate change have important implications for the future of agriculture. Considering increased demand for food and the limited availability of new land for agriculture, the key to securing adequate food production will be to raise agricultural productivity sustainably.

Agricultural activities are directly responsible for about 17% of global greenhouse gas (GHG) emissions (Tubiello et al., 2014) and are thus expected to be part of the global mitigation effort. Moreover, agriculture is a major driver of land use change, land clearing and deforestation, which roughly accounts for an indirect additional 7-14% of global GHG emissions (IPCC, 2007). While a strong commitment from the sector to reduce its carbon footprint would help, a diffuse and fluctuating nature of emissions from agriculture makes it relatively difficult to measure the progress of emission reductions.

The way agricultural land is used and managed influences land cover and soil quality in terms of nutrient content and carbon storage. Nutrients, such as nitrogen, phosphate and potash, are essential to maintain and raise crop and forage productivity. Most of these nutrients, which are applied annually are absorbed by crops; however, when applied in excess they can leak into the groundwater, be emitted from soil to air, or runoff into the surface water. To this end, nearly all OECD countries apply a range of policy instruments (e.g. payments, taxes, regulations) to address nutrient pollution of water and air (OECD, 2014b).

Indeed, as shown in Figure 6.4, OECD countries have made a concerted effort to reduce the most environmentally harmful types of agricultural supports and have achieved a decrease from over 85% of the total in 1990-92 to 49% in 2010-12 (OECD, 2014b).

Figure 6.4. Evolution of producer support in OECD countries by potential environmental impact in the OECD area
picture

Source: OECD (2013), “Producer and Consumer Support Estimates”, OECD Agriculture Statistics Database.

At the same time, fertiliser support policies, which aim to reduce crop production costs and increase yields, can have unintended negative effects on water quality (von Lampe, M. et al., 2014).

The overall economic, environmental and social costs of water pollution caused by agriculture across OECD countries are likely to exceed billions of dollars annually, although no satisfactory estimate of these costs exists (OECD, 2012a). Going forward, the challenge is to seek ways to increase production while minimising farm nutrient losses and subsequent damage to the environment.

Illicit financial flows

Combating illicit financial flows (IFFs) is a major challenge for all governments, and an increasingly important priority for the international community. Estimated to far exceed ODA, IFFs are a significant barrier to sustainable development and to the implementation of the SDGs.

IFFs stem from corruption, crime, terrorism, and tax evasion; and use channels ranging in sophistication from cash smuggling and remittance transfers, to trade finance and shell companies. They affect (and are affected by) many wider policy objectives and involve many disparate actors across a variety of governmental and non-governmental policy disciplines. To effectively combat IFFs, law enforcement and customs authorities need to increase awareness, and the financial sector and vulnerable professions need to take preventive measures. Transparency in corporate structures is essential and steps must be taken to promote public sector integrity and support asset recovery. International co-operation lies at the heart of the solution.

Sustainable Development Goal 16 – Promote peaceful and inclusive societies for sustainable development – includes a target to “by 2030, significantly reduce illicit and arms flows, strengthen recovery and return of stolen assets, and combat all forms of organised crime”. However, efforts to reduce IFFs must be carefully designed so that they do not work at cross-purposes with other SDGs and targets, e.g. by undermining financial inclusion, legitimate capital flows and productive investment.

Table 6.2 selects three targets (D-F) for which it identifies critical interactions and relevant indicators and data for tracking progress. It is followed by an empirical overview of the evolution of these interactions and associated policies over time.

Table 6.2. A selection of interactions related to illicit financial flows

SDG/Target

Interaction (synergy or potential trade-off)

Data/Indicator to assess interaction

Policy instrument to influence interaction

D

10.5 Improve and strengthen financial regulation

8.10 Access to financial services

Potential trade-off: Stronger regulations might have unintended negative impacts on financial inclusion.

10.c Reduce the transaction costs of remittances

Potential trade-off: Stronger regulations may hinder licit remittance flows or increase their transaction cost.

  • Transaction costs of remittances

  • OECD/INFE Financial Literacy and Financial Inclusion Toolkit

E

16.4 Reduce IFFs and arms flows

12.2 Achieve sustainable management of natural resources, including:

14.4 End IUU fishing; and

15.7 End poaching and trafficking of protected species of flora and fauna

Synergy: Exploitation of natural resources is a driver of corruption and source of IFFs.

  • Value of illicit trade

  • CleanGovBiz Integrity Toolkit

F

17.1 Strengthen domestic resource mobilisation

16.4 Reduce IFFs and arms flows

Synergy: Tax evasion is a major source of illicit funds, which weakens the capacity of countries to fund their own development through DMR.

  • Tax revenue

  • Number of exchange agreements

  • Revenue losses from BEPS

  • Aid to tax-related activities

  • EOI and AEOI

  • BEPS Action Plan

Source: Author’s own illustration.

D) Potential trade-offs: Strengthen financial regulation/improve financial inclusion/transaction cost of remittances

Financial regulation is central to efforts to prevent IFFs. However, if regulations are overly cautious they can have the unintended consequence of excluding legitimate businesses and consumers from the financial system. Financial inclusion, a significant enabler for development, suffers as a result. The tensions between measures to reduce IFFs and financial inclusion are well known and quite complex. For example, preventive measures to counter money laundering require financial institutions to verify the identity of their customers. However, many people in developing countries lack identity documentation and risk being excluded from access to financial services by stringent customer identification rules. Conversely, financial inclusion must take advantage of technologies which are difficult to regulate from an IFFs policy perspective. This is not only an issue for developing countries: financial inclusion is also a challenge in OECD countries, several of which have initiatives to ensure that basic financial services are available to all citizens.

For a large portion of the world’s population, the informal sector is the only form of financial intermediation available. Informal operators typically provide money remittances from migrants, but in some countries they may offer a much wider range of services. Left unregulated, the informal sector can be exploited as a channel for IFFs, or can exploit its customers who are not protected by the authorities. Some countries have responded by prohibiting informal providers altogether, sometimes with the unintended consequence of denying people access to even basic financial services, or of driving activity even further underground. Other countries have sought to license, regulate and supervise these organisations, so as to reduce their vulnerability but recognise their importance to their customers.

De-risking is a relatively recent phenomenon, whereby financial institutions cease to do business with customers that are perceived to carry a high risk. The effects of this behaviour by banks are felt most severely by money and value transfer services (MVTS) providers and non-profit organisations (NPOs). MVTS are critical channels for remittance flows sent by migrants to their home countries – a major source of finance for many developing countries: worldwide remittances to developing countries were estimated at USD 351 billion in 2012, up from USD 123 billion in 2000 (OECD, 2014c). Countries thus need to balance their efforts to reduce financial risk with measures to ensure that remittance flows and associated transaction costs are not adversely affected. Recent data shows that during the first four months of 2013, the global average cost of sending remittances fell from 9% of their value to 8.6%, while the cost of remitting from G20 countries declined for the first time in three years, from 9% to 8.2% (Figure 6.5).

Figure 6.5. The cost of transferring USD 200 from G20 countries is falling
Sending cost as a % of remittance value
picture

Source: World Bank, 2013.

E) Synergy: Reduce IFFs/manage natural resources sustainably

Exploitation of natural resources is a driver of corruption and source of illicit funds. This includes, among other things, extractive industries, forestry and fisheries, and illegal trade in for example environmentally sensitive goods (Table 6.3).

Table 6.3. Summary of illicit markets and values

Market

Estimated value of illicit international trade USD

Drugs

320 billion

Humans

31.6 billion

Wildlife

7.8 to 10 billion

Counterfeiting

250 billion

Human organs

614 million to 1.2 billion

Small arms & light weapons

300 million to 1 billion

Diamonds & coloured gemstones

860 million

Oil

10.8 billion

Timber

7 billion

Fish

4.2 to 9.5 billion

Art & cultural property

3.4 to 6.3 billion

Gold (3 countries only)

2.3 billion

Total

639 to 651 billion

Source: Global Financial Integrity, 2011.

Extractive industries can provide critical economic opportunities and public revenues for sustainable development in resource-rich countries. However, if not properly managed, they can be associated with environmental degradation, lack of economic diversification, conflicts, corruption and illicit financial flows. Several factors make extractive sectors prone to IFFs, including high-level political discretionary control, limited competition, and complex technical and financial processes. Also, resource-rich countries tend to underperform in revenue collection (Le Billon, 2011).

Legally logged timber is another vital source of income for communities in developing countries. However, the illegal production and trade in timber is a significant concern with a wide range of infringements within the producing country, including non-payment of taxes and export duties. Similarly, illegal, unreported and unregulated (IUU) fishing rob countries of much-needed resources and generate illicit funds. Finally, demand for goods such as elephant ivory and rhino horn has driven dramatic growth in illegal wildlife markets in recent years. Taken together, all forms of wildlife trafficking constitute one of the most lucrative forms of illicit trade, and the sector has more than doubled since 2007 (OECD, 2012b).

F) Synergy: Strengthen domestic resource mobilisation/reduce IFFs

Domestic resource mobilisation (DRM) provides a sustainable basis for development and reduces low-income countries’ dependency on other sources of finance, e.g. development assistance. At the same time, a stable, credible and fair tax system facilitates trade and investment, and promotes state-building by encouraging governments to be more accountable to their citizens. Conversely, an absence of measures to support DRM and tax transparency can create opportunities for tax evasion and tax fraud.

Enhanced co-operation, including exchange of information (EOI) between tax authorities, is crucial in bringing national tax administrations in line with the globalised economy and contributes to reducing IFFs. Since 2000, the number of agreements on exchange of information between OECD countries and developing countries has steadily increased (Figure 6.6). Taking a step towards even greater transparency, the OECD – under a mandate from the G20 – released a new global standard for the automatic exchange of information (AEOI) between jurisdictions in 2014. The Standard provides for the systematic and periodic transmission of tax information by countries to the residence country concerning various categories of income, such as dividends, interest, gross proceeds, royalties, salaries, pensions, etc. More than 90 countries and jurisdictions have already publicly committed to implementation, while more than 50 have committed to a specific and ambitious timetable leading to the first automatic information exchanges in 2017 (www.oecd.org/tax/automatic-exchange).

Figure 6.6. Number of exchange of information agreements between OECD and developing countries which meet the Global Forum Standard, signed between 2005 and 2015
picture

Source: Global Forum on Tax Transparency @ OECD 2015.

However, globalisation and the fluid movement of capital, including the rise of the digital economy, leave some gaps and mismatches that can be exploited to generate double non-taxation. Base Erosion and Profit Shifting (BEPS) refers to tax planning strategies that aim to artificially shift profits to low or no-tax locations. To help governments combat BEPS, the G20/OECD BEPS Action Plan identifies 15 actions for putting an end to international tax avoidance. Among other things, the Action Plan will contribute to introducing coherence in the domestic rules that affect cross-border activities.

Revenue losses from BEPS are conservatively estimated at USD 100-240 billion annually, or anywhere from 4-10% of global corporate income tax (CIT) revenues. Given developing countries’ greater reliance on CIT revenues as a percentage of tax revenue, the impact of BEPS on these countries is particularly significant (www.oecd.org/ctp/beps.htm).

Green growth

The inherently broad scope of the green growth agenda necessitates consideration of a large number of Sustainable Development Goals and targets. Specifically, policy makers need to recognise and promote synergies between economic and environmental policies and objectives, while at the same time minimising potential conflicts and trade-offs. The OECD conceptual framework for monitoring progress towards green growth focuses on the environmental performance of production and consumption, and on the key drivers of green growth, such as policy instruments and innovation (OECD, 2015b). The scope of this section is to explore the interactions between green growth objectives and a number of other policy objectives in the context of the SDGs.

Table 6.4 selects three targets (G-I) for which it identifies critical interactions and relevant indicators and data for tracking progress. It is followed by an empirical overview of the evolution of these interactions and associated policies over time.

Table 6.4. A selection of interactions related to green growth

SDG/Target

Interaction (synergy or potential trade-off)

Data/Indicator to assess interaction

Policy instrument to influence interaction

G

2.3 Double agricultural productivity

15 Protect, restore and promote sustainable use of terrestrial ecosystem, sustainably manage forests, combat desertification and halt and reverse land degradation and halt biodiversity loss

Potential trade-off: Intensive agriculture might have adverse effects on biodiversity

6 Water; 7 Energy

Potential trade-off: For an analysis of the water-energy-food nexus, see the section on food security above.

  • Total factor productivity

  • Resource productivity

  • Agricultural land cover

  • Farmland bird index

  • Biodiversity response policy indicators

  • Biodiversity-related ODA

H

8.1 Sustain per capita economic growth

15 Protect, restore and promote sustainable use of terrestrial ecosystem, sustainably manage forests, combat desertification and halt and reverse land degradation and halt biodiversity loss

Potential trade-off: Poorly managed economic growth might impact on the environment

  • Environmental Policy Stringency Index (EPS)

  • Burdens on the Economy due to Environmental Policies Index (BEEP)

  • Environmentally related taxes

  • Tradable permits

I

12.c Rationalise inefficient fossil fuel subsidies

13 Combat climate change

Synergy: Reduced GHG emissions is necessary in order to stop global average temperatures from rising

  • GHG emissions

  • Fossil fuel production and consumption

  • Fossil fuel subsidies

  • Carbon pricing

Source: Author’s own illustration.

G) Potential trade-offs: Double agricultural productivity/Sustainable use and management of ecosystems, forests, land and soil

Green growth in the area of agriculture implies ensuring that enough food is provided in an efficient and sustainable manner for a growing population. This means increasing output while managing scarce natural resources; reducing the carbon intensity and adverse environmental impacts throughout the food chain; enhancing the provision of environmental services such as carbon sequestration, flood and drought control; and conserving biodiversity. However, the relationship between agriculture and green growth is complex, and the food and agricultural sectors can generate both environmental harm and conserve environmental services (OECD, 2012c). Moving towards greener growth in the food and agriculture sectors will therefore involve both synergies and trade-offs (Table 6.5).

Table 6.5. Synergies (+) and trade-offs (-) between agriculture and green growth (GG)

Economic contribution of agriculture to green growth

Environmental contribution of agriculture to green growth

Social contribution of agriculture to green growth

Economic contribution of green growth to agriculture

Agriculture as a driver of economic development while GG can improve agricultural performance (+)

Green labels and payments for eco-services can contribute to economic returns in agriculture (+)

Higher skilled jobs and activities can diversify and contribute to rural development (+)

Environmental contribution of agriculture to green growth

Environmental measures may slow agricultural growth in the short term (-)

GG will yield environmental co-benefits in agriculture through resource conservation and sustainable use (+)

Reform of support to relieve environmental stress and payments for environmental services can enhance farm incomes in rural areas (+)

Social contribution of green growth to agriculture

GG may detract from efforts to improve food security in the short term (-)

GG will necessitate structural adjustment measures in transition periods (-)

Food security, poverty reduction, and rural development will be enhanced in the long run through GG (+)

Source: OECD, 2012c.

Agricultural growth can arise from a number of sources: changes in real (adjusted for inflation) prices (or the “terms of trade” effect), increased agricultural land and greater yields. Greater efficiency in overall input use is known as growth in total factor (input) productivity (TFP) or multi-factor productivity. TPF of agriculture (including forestry, hunting and fishing) has grown at a slower rate in the 2000s relative to the 1990s in most countries for which data is available (Figure 6.7). Austria, Germany, the Netherlands, Norway and Spain are the exceptions.

Figure 6.7. Total factor productivity (TFP) of agriculture, annual growth rates (%)
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Note: Includes forestry, hunting and fishing. Data for 2009 refer to the year 2008 for Austria, the Czech Republic, Ireland the United Kingdom; to the year 2007 for Canada, France and Norway; and to the year 2006 for Korea and Poland.

Source: OECD (2014), Productivity by industry, OECD Productivity Statistics (database), http://doi/10.1787/data-00627-en.

Resource productivity, in turn, refers to the effectiveness with which an economy or a production process is using natural resources. Improving resource productivity is often assumed to lead to a parallel reduction in environmental impact to help avert the possibility of resource scarcity and environmental degradation. However, unless such improvements outweigh economic growth, there is a risk that the associated negative environmental impacts might increase. Protecting and managing the natural resource base cannot, therefore, rely on improvements in resource productivity alone; it will also be necessary to de-link economic growth from environmental pressures (OECD, 2014d).

While productivity indicators and their inverse – decoupling trends – show whether production has become greener in relative terms, they do not show whether environmental pressure has also diminished in absolute terms. Hence, from an environmental perspective it is useful to also monitor the presence of absolute decoupling (OECD, 2014d).

Agriculture’s impact on the natural asset base concern issues such as freshwater availability (for an analysis of the water-energy-food nexus, see the section on food security above), biological diversity and ecosystems, including species and habitat diversity, as well as the quality of land and soil resources.

Loss of biodiversity has been identified as one of the most pressing global environmental issues and its conservation is a key concern for sustainable development. Agriculture is crucial in biodiversity preservation as it is a major user of land and water resources that certain genetic resources and wild species depend on. For example, in nearly all OECD countries the agricultural land area decreased over the 1990-2010 period in terms of both arable and crop land, most being converted to use for forestry and urban development. Permanent pasture, which represents a major share of agricultural semi-natural habitats also declined in most OECD countries (Figure 6.8). During the same time period, trends in OECD farmland bird populations declined continuously for almost all countries. While it is complex to prove causal relations between the decline in pasture land areas and the decline in bird populations and other wildlife species, it is likely to have been one of the contributing factors (OECD, 2014b).

Figure 6.8. Trends in agricultural land cover, change over the period 1990-2010 or most recent year
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Note: Data for 2010 refer to the year 2009 for Austria, Canada and Israel; to the year 2008 for Chile and Italy.

The statistical data for Israel are supplied by and under the responsibility of the relevant Israeli authorities.

The use of such data by the OECD is without prejudice to the status of the Golan Heights, East Jerusalem and Israeli settlements in the West Bank under the terms of international law.

Source: FAO, FAOSTAT (database), http://faostat.fao.org/.

H) Potential trade-offs: Sustain per capita economic growth/Sustainable use and management of ecosystems, forests, land and soil

In the aftermath of the financial crisis, some governments have raised concerns that stringent environmental and climate policies might undermine productivity growth. However, OECD research shows that efforts to improve growth and achieve ambitious environmental goals can go together, and should be stepped up (OECD, 2014d).

Although no one instrument can be considered best to address every environmental challenge, there has been a growing movement towards environmentally related taxation (and tradable permits) in OECD economies (Figure 6.9). Taxes directly address the market failure that causes markets to ignore environmental impacts. A well-designed environmental tax increases the price of a good or activity to reflect the cost of the environmental harm that it imposes on others. The cost of the harm to others – an “externality” – is thereby internalised into market prices. This ensures that consumers and firms take these costs into account in their decisions (OECD, 2010).

Figure 6.9. Environmentally related taxes in OECD countries and selected non-member economies
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* = 2013 figure; ** = 2012 figure.

Source: OECD (2016), Database on instruments used for environmental policy 2016.

To help governments foster new, cleaner technologies and allow competitive measures to remove old, polluting technologies and processes, the OECD has developed the Environmental Policy Stringency Index (EPS) – a proxy that summarises and compares the stringency of policy instruments among countries and over time. It currently focuses on climate and air pollution in energy and transport, and covers such policies as taxes, feed-in-tariffs, renewable energy certificates, R&D subsidies and emission limit values.

The EPS indicator scores of OECD countries show that environmental policy stringency has been increasing in all OECD countries over the past two decades. Empirical applications of the EPS indicator give some preliminary indications of the effect of environmental policy stringency on economic outcomes (OECD, 2016b):

  • The tightening of environmental policies observed in OECD countries has had little effect on aggregate productivity growth (although effects are differentiated within the economy).

  • There is no evidence that stringent environmental policies harm aggregate trade and overall country competitiveness.

  • However, environmental policies are found to have a significant effect on trade specialisation, with a positive relationship between a country’s stringency and its specialisation in exports of “environmental” products.

I) Synergy: Rationalise fossil fuel subsidies/combat climate change

Support for environmentally harmful consumption or production, such as that associated with fossil fuels, undermine sustainable development and efforts to mitigate climate change. Governments currently spend an estimated USD 640 billion a year on environmentally harmful support for fossil fuel, with an estimated USD 550 billion spent by emerging and developing countries (OECD, 2015a).

A key lesson from OECD work on measures supporting fossil fuels is that transparency matters. By identifying and documenting almost 800 individual policies that support the extraction, refining, or combustion of fossil fuels in OECD countries and large emerging economies, the OECD online Inventory of Support Measures for Fossil Fuels highlight the need for governments to periodically review their budgets and tax codes in light of changing circumstances and evolving policy priorities.

Taken together, the almost 800 measures contained in the Inventory had an overall value of USD 160-200 billion annually over the period 2010-14 (OECD, 2015c). This includes both support provided by OECD countries and that provided by a selection of partner economies (Brazil, the People’s Republic of China, India, Indonesia, the Russian Federation, and South Africa). Compared with analysis in 2013, which focussed on OECD countries only, support now seems to follow a downward trend after having peaked twice in 2008 and 2011-12 (Figure 6.10). In both OECD and partner countries, the decline in total support comes from lower international oil prices but also in important policy changes. This signals an intention on the part of many governments to depart from earlier practices and move toward growth patterns that are more sustainable fiscally and environmentally.

Figure 6.10. Total support for fossil fuels
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Source: OECD Companion to the Inventory of Support Measures for Fossil Fuels 2015.

Greenhouse gas emissions too have been declining in recent years in almost all OECD countries. They fell by almost 5% since 2008 in the OECD area. This is partly due to a slowdown in economic activity following the 2008 economic crisis, but also to a strengthening of climate policies and changing patterns of energy consumption. As a result, emission intensities per unit of GDP and per capita decreased between 2000 and 2012 in almost all OECD countries, revealing a strong overall decoupling from economic growth (Figure 6.11). However, reductions in national emissions may also be the result of offshoring domestic production and the associated emissions (OECD, 2015d).

Figure 6.11. a) GHG emission levels since 2012, million tonnes CO2 eq. and b) Change since 2000, percent
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Source: OECD (2014), “Greenhouse gas emissions by source”, OECD Environment Statistics (database), http://dx.doi.org/10.1787/data-00594-en.

An overview of non-OECD initiatives to assess interactions between SDGs and targets

This section provides an overview of non-OECD initiatives to assess and/or monitor interactions between the SDGs and targets.

Modelling tools for sustainable development policies

By Diana Alarcon and Eduardo Zepeda, United Nations Department of Economic and Social Affairs (UN DESA)

Implementation of the 2030 Agenda requires greater technical capacities to assess the inter-linkages across the multiple dimensions of development and the impact that alternative policies may have in different sectors and different variables. UN DESA is committed to contribute building governments’ capacities on the use of quantitative modelling tools to inform development policy decisions. This will contribute to strengthen countries’ efforts to pursue national sustainable development strategies and the 2030 Agenda. With the intention of expanding access to the suite of modelling tools used in regular capacity development projects, UN DESA recently launched a web-based platform: Modelling Tools for Sustainable Development Policies.3

Policies to advance sustainable development have a level of complexity that cannot be captured by one single all-encompassing model. Instead, UN DESA has assembled a suite of modelling tools that, when used separately each one of them can address specific aspects of sustainability with sufficient detail to make it useful for policy decision making. The same tools, used in combination can inform the design of comprehensive sustainable development strategies and provide a useful mapping between national priorities and the 17 Sustainable Development Goals contained in the 2030 Agenda.

The intention of making all modelling tools available through the web-platform Modelling Tools for Sustainable Development Policies is to ensure maximum transferability and ownership of modelling tools to Member States. This is in recognition that building capacity for the formulation of sustainable development policies requires greater technical capacities across a larger number of people within governments and in the development community. Modelling tools, to the extent possible, are developed through the use of open source software, and they make transparent use of data and modelling codes. The web platform will support the development of a community of practice that will facilitate continuous validations of modelling tools by scientists, academics and development practitioners. Continuous development of these tools and methodologies will be critical to ensure the incorporation of new appropriate tools, to improve existing ones and to enhance the inter-tool interactions. Currently the suite includes the following modelling tools:

  • Economy-wide modelling.

  • Integrated assessment of climate, land, energy and water systems (CLEWS).

  • Energy systems dynamic modelling.

  • Geo-spatial electrification to model universal access to electricity by 2030.

  • Household survey based micro-simulation of socio-economic impacts and electricity consumption.

Economy-wide models

Economy-wide models are a useful tool to assess the implications that alternative development policies and shocks have throughout the economy, including employment, consumption, sectors’ output, public budgets, and external sector accounts, among others. These models are useful to assess the direct and indirect economic impacts of alternative policies and external shocks.

UN DESA has been supporting countries to build analytical skills on the use of economy-wide models (widely known as computable general equilibrium models). Countries have used these models to assess the impact of policies – e.g. public spending to achieve the Millennium Development Goals (using the MAMS4 model); cash-transfer programs, external shocks – e.g. changes in remittances (using UN DESA’s own model); and more recently investment in renewable energy, electricity trade, changes in oil prices (using UN DESA’s own models).

The web platform provides an illustration of how the effects of a fuel tax policy can have a diversity of impacts depending on how taxes are used. The example illustrates impacts on several socio-economic indicators in Bolivia, Costa Rica and Uganda. The illustration presents results for the following scenarios and impact indicators:

Policy scenarios fully recycling fuel tax-revenue to: expand the public budget across all spending lines; increase investment in education; increase spending on primary education; increase spending on infrastructure. Impact of policy scenarios are shown for six socio-economic indicators: real GDP; primary completion rate; under-five mortality; maternal mortality; proportion of the population with access to safe water; proportion of the population with access to sanitation.

The illustration shows the potential trade-offs and synergies of alternative policies. For example recycling the fuel tax to invest in infrastructure or education in Uganda has the unintended effect of raising infant mortality, but if the tax is used to expand the budget across the board, infant mortality decreases as health spending increases. In Bolivia, using the fuel tax to increase investment in infrastructure has a positive impact in all selected indicators i.e. education, health, and sanitation as well as GDP. In Costa Rica, however, health indicators improve only when the fuel tax is used to expand the public budget, while other policy scenarios either produce negligible or undesired changes.

Global CLEWS

UN DESA is using the Global CLEWS model to illustrate the relationships among water, energy, climate, and land use at the global scale.5 The model analyses inter-linkages across four different scenarios. All scenarios follow current assumptions for energy supply and renewable energy generation potentials and explore the way taxes on the use of fossil fuels, or outright limits, affect water consumption, emissions and total investment in energy. The Baseline features greenhouse gas emissions are expected to increase average temperature to between 4°C and 6°C. Consumption and production grow according to trend and no new environmental regulations are considered. A second scenario looks into a world with an increase of 4°C in temperature by limiting the use of fossil fuels such that average global temperature does not increase above 4°C. A more ambitions scenario, 2°C, sets limits on the use of fossil fuels such that average global temperature does not increase above 2°C. A final scenario, carbon tax, sets no limits on the use of fossil fuels but incorporates a global carbon tax increasing from USD 1 per ton CO2 eq. in 2016 to USD 25 in 2050.

This model sheds light on some of the following questions: How is water consumption affected in each scenario? How do CO2 emissions increase or decrease in each scenario? How do these scenarios affect the total investment in energy generation and material production?

The visualisation aims to make apparent that a desirable outcome in one of these areas may have an undesirable effect on another. Further development of scenarios in the model helps to explore relevant policies to achieve the SDGs.6

Country CLEWS model

A national CLEWS model is used to illustrate the inter-linkages among renewable energy, water, land use, emissions, and energy dependency. In the web platform the use of this model in a country context is illustrated for the case of Mauritius (developed in collaboration with International Atomic Energy Agency, IAEA; and the Royal Institute of Technology, Division of Energy Systems Analysis, KTH Sweden). The model uses a basic optimisation process to find the overall lowest cost alternative to meet an exogenously defined set of demands. The main building blocks are plant by plant representation of the energy sector and a land supply curve disaggregating arable land into different types of uses (i.e. different yields) and water supply (i.e. irrigation vs. rain-fed). The illustration features 48 different scenarios defined as combinations of renewable energy policies and assumptions about water availability to climate change impacts are taken into account.

The illustration shows the result of 48 scenarios on CO2 emissions, water and land use, energy import dependence, and the composition of electricity generation by source. The illustrations feature four targets setting the contribution of renewable sources in the generation of electricity at 0, 20, 35 and 50%; four targets fixing the ethanol content in the fuel mix used in transport of 0, 20, 35 and 50%; and three water availability-climate change scenarios – i.e. no change in historical patterns, moderate reduction, and strong reduction in water availability.

The modelling of the climate, land-use, energy and water systems in Mauritius, a country where sugar production is important, shows that boosting production of bio-fuels for national energy security and to facilitate a transition to sustainable energy sources may compromise water security. Results suggest that boosting production of bio-fuels in pursuit of more sustainable energy supply and national energy security will certainly reduce emissions and will decrease dependence on energy imports, but it also shows that it may compromise water security. This result owes to the fact that increasing sugar cane production for bio-fuels requires a substantial increase of water withdrawals, especially after 2020. In the most ambitious combination of renewable energy policies, the use of water increases 30% under no climate change assumptions, but it rises by close to 100% under drastic climate change conditions. The risk of water scarcity worsens if climate change brings less rainfall and higher temperatures to the country.

A model to simulate universal access to electricity

The electrification modelling tool uses open geo-spatial data to simulate the provision of universal access to electricity by 2030 with the least cost technology options for each area of 10 by 10 kilometres in 44 African countries (developed by researchers at KTH). The model estimates the total cost of achieving universal access to electricity for various technology options, providing a first insight into energy planning.

The model currently considers 6 technology options grouped in three types. The first type is connection to centralized grid, referring to the national interconnected network, including actual and planned distribution and transmission lines, as well as power generating stations from all sources, e.g. fossil fuel, geo thermal, hydro, and others. The second type is connection to a mini-grid, i.e. to small networks already existing or to be built when feasible, capable of generating and distributing electricity to villages or neighbourhoods. Modelling explicitly considers three technologies to power mini-grids: diesel, wind and solar technologies. The third type is stand alone, referring to the provision of electricity to single households, with the choice of two technologies: solar photovoltaic panels and diesel generators.

Modelling considers 10 alternative scenarios based on five levels of energy consumption per household and two diesel prices (0.32 and 0.70 USD per litre). The five levels of electricity consumption per household start with 22 kWh per year (enough for task lightning and powering one cell phone or radio). The next is 224 kWh per year (sufficient for general lightning, air circulation and one appliance such as a television). A third level of consumption assumes 696 kWh per year (for general lightning, air circulation, television, and a few additional light electric appliances). The fourth level of consumption is 1 800 kWh per year (for general lightning, air circulation, television plus a few additional light, medium or continuous electric appliances). The final and highest level is 2 195kWh per year (for general lightning, air circulation, television, heavy or continuous electric appliances).

For each country, the visualisation shows a map identifying the lowest cost technology for each 10 by 10 kilometres geo-spatial area. The map is constructed based on existing and planned electricity lines (as of 2012). Estimations are based on the number of people estimated to live in each geo-spatial unit by 2030.7 The visualization displays the additional cost of providing universal access to electricity per country, based on various technology choices and on the number of people receiving electricity by technology.

This model suggests that a variety of technology combinations can give the lowest cost option for electrification in these 44 countries. The mix of technologies depends on the level of electricity to be provided, the suitability of locally deployed technologies and the price of diesel. Two country examples, perhaps extreme, can illustrate the options opened to countries to meet the energy for all goal.

In South Africa about 85% of the population currently has access to electricity. By 2030, 60 million more people will require access to electricity; ensuring universal access will require a total investment ranging from one to USD 15 billion, depending on the desired level of consumption to be achieved and the price of diesel. If consumption is 22 kWh per year per household, 37 million people will have access to electricity through the centralized grid as the lowest cost option. The remaining 23 million people will opt for access through a de-centralized energy source (such as solar panels, wind or diesel generators). If consumption is 2 195 kWh per year per household 45 million people will find that electricity access through the central grid is the lowest cost option, while 15 million people will find de-centralized energy sources to be more competitive.

In contrast, in Chad where only 6% of the population have access to electricity now, making electricity available to the entire 2030 population will require reaching 22 million more people. The model estimates this can be done at a total cost ranging from USD 70 million to USD 21 billion. If electricity consumption is 22 KWh per year per household, only 1 million will find access through a connection to the central grid as the cheapest option; if consumption is 2 195 kWh per year per household, about 7 million people will opt to be connected to the central grid. Differences in population density and coverage of existing and planned transmission lines between these two countries explain the sharp contrasts in electrification paths between South Africa and Chad.

Energy Systems Dynamic Models

Energy systems dynamic models can assist medium and long term energy planning by identifying the minimum cost path to meeting energy demand under alternative scenarios and investment portfolios. This model allows a comparison of the investment and generation costs of different scenarios; for example, scenarios increasing the use of renewable sources of energy, or, policies to ensure national energy security, or programs to guarantee universal access to modern energy by a certain date.

UNDESA, in partnership with KTH, has piloted capacity development in selected countries to support efforts in medium-long term energy planning. These projects are usually based on the use of the Open Source Energy Modelling System model (OSeMOSYS), a powerful yet open, flexible and transferable tool.

An interactive visualization of the electricity system is illustrated in a hypothetical country Atlantis. The visualization allows analysing the feasibility of generating electricity from a variety of plants and technologies, including wind, hydro, solar, and nuclear, among others. The interactive visualization presents the results of the lowest cost combination of the technologies under four scenarios: a reference scenario; universal access to electricity by 2030; 50 per cent of electricity generated from renewables; climate change.

Socio-Economic Micro-Simulation

Microsimulations are a useful methodology to undertake detailed evaluations of the socio-economic impacts of alternative development policies and shocks on households. It is a powerful tool for informing policy decisions on poverty eradication, inequality reduction, enhanced food security and energy access. UNDESA, in collaboration with other partners, have used the methodology to simulate the poverty and distributional impacts of specific policies and economic shocks. Examples of policies that can be simulated include the introduction of taxes and subsidies, transfers –in kind or cash – to households, access to modern energy, among many others.

Through the Modelling web platform UNDESA makes available a Python code developed by the International Policy Centre (Brasilia) to estimate the demand for electricity from household survey data, as an example of the kinds of questions that can be entertained through this methodology. Estimating the demand for electricity is a critical step in the design of a medium to long term energy plan. Frequently, estimates are based on time series with few observation points or on data from other countries or regions. Household surveys offer an alternative estimation route based on observed electricity demand by households with different income levels and at different points in time. The python code is open and can be downloaded from the website.

Towards action on the SDGs with a view to interactions and coherence: Emerging approaches

By: Stockholm Environment Institute (SEI)

A) Analysing interactions between SDGs and targets

SEI has explored the application of a Nexus approach to identify interactions among the SDGs, examine different types of interactions and how integrated targets can be set. Nexus analyses typically aim to illuminate cross-sectoral interactions and facilitate integrated planning and decision-making. They can also help clarify how best to allocate resources between competing needs in order to support agreed development pathways. The nexus approach emerges from systems analysis but is only recently beginning to take hold in policy-making and planning. The guiding principles of the nexus approach are to promote sustainable and efficient resource use, to ensure access to resources for the most vulnerable and to maintain healthy and productive ecosystems. These principles are also reflected in the SDG targets seeking to integrate economic, social and environmental dimensions of development.

A Nexus approach can be applied in several ways to explore different approaches to SDG integration, for example how the achievement of targets within one goal area might affect targets under another goal area, or how individual targets might serve multiple goals. For purposes of illustration SEI, in Weitz et al. (2014), explored the interactions between the water, energy and food-related SDGs through three complementary approaches. First;

  1. Screening for interactions among proposed targets. Some of the targets identified focus on ensuring access to resources, some on efficiency, and some on long-term sustainability. The three are interlinked and – in line with the universality principle – each country would emphasise the targets that best fit its priorities and needs and through which it can best contribute to the achievement of the SDGs at global level. Screening each water, energy and food target for relevance to the two other goal areas showed that most of the targets are inherently cross-sectoral. The screening was made at a conceptual level, and considered generally known interlinkages. However, local resource characteristics, economic, social and political realities influence how targets interact and the analysis must therefore take place at the scale of action in order to support decision-making.

  2. Exploring the nature of interactions. In order to address the connections between targets effectively, it is necessary to understand the nature of interactions. This analysis showed three main types of interactions, as targets can: i) be interdependent (one target has to be realised in order for another to be viable, usually because access to water, energy or land for food production needs to be ensured); ii) impose conditions or constrain one another (arguably, these targets are essential to the long-term success of a wide range of other targets, as they ensure that development is sustainable over time); or iii) reinforce each other (renders another target easier to achieve). Trade-offs or conflicts may result from interactions, for example as targets compete for the same resources and the expansion under one target impedes expansion under another target.

  3. Identifying ‘nexus’ targets between sectors. Mapping out the connections and identifying linking targets at the nexus of different sectors can help ensure the SDGs sustainability by showing all the targets that require a resource, and address efficiency by establishing targets for resource use that crosses different sectors. This bottom-up way of identifying targets offer opportunity to avoid constructed conflicts at the stage when goals are set, and is hence more proactive than assessing conflicts within a goals framework. While the global SDGs are now set, the approach can be used for target-setting at national level. This would mean that national targets for the SDGs are not necessarily set according to the structure of the global framework but around issues that are of priority to several sectors in a country.

The nexus approach is flexible enough to handle different levels of data availability and capacities to gather and analyse data. Where data already exists, nexus tools can be used to quantify relationships between sectors. Where data quality or accessibility is poor, the nexus approach can inform qualitative analyses, and also help to identify data needs. The three approaches could also be used as facilitative tools for cross-sector collaboration, where various sector representatives jointly identify cross-sector interlinkages and their relationships.

Second; subsequent analysis by SEI has developed a more elaborate view of the different potential interaction relationships. Moving beyond the dichotomy of synergy and trade-offs opens up entry points for negotiating priorities, and for enhancing understanding of how synergies can be captured, spillovers addressed, and when there are in fact true dilemmas. It equips “coherence” and “integration”, sometimes perceived to add complexity or to focus on conflicts, with a more constructive narrative.

In a forthcoming paper SEI further explores the need to complement Nexus analysis with analysis of the decision-making process and the wider political economy that determine how priorities are set and trade-offs between various societal objectives are negotiated and handled. It will put forward an analytical framework for exploring governance issues pertaining to the water-energy-food nexus.

Third; on coherence between the global vision set out in Agenda 2030 and actions in and by countries SEI, in Weitz et al. (2015), has proposed that for any country, implementation of Agenda 2030 will require consideration to three dimensions of action:

  1. The domestic dimension includes goals and targets dealing with issues that are more or less permanently on the country’s policy agenda. It asks how a country performs and will work to achieve the SDG targets “at home”.

  2. The development co-operation dimension includes a country’s contribution to and impacts on poverty and development challenges abroad. It asks how a country can support other countries in achieving the SDG targets.

  3. The international dimension includes how activities in and by a country affect sustainable development internationally (e.g. global public goods or resource sustainability). It asks how country x’s activities contributes to the global achievement of the SDGs and affects the underlying resources for making global progress.

As countries develop their national action plans, interpreting each target along these three dimensions gives an understanding of the targets that capture the many different issues a target can raise and the various actors that would need to be involved in their implementation. Keeping these three dimensions present is a simple but effective tool to maintain the universality of the agenda as well as supporting policy coherence (both vertically and horizontally), as the SDGs are translated from a global vision to country action and implemented.

B) Tracking progress in individual SDGs

In the report “Sustainable Development Goals for Sweden: Insights on setting a national agenda” (Weitz et al., 2015), SEI qualitatively screened the relevance of targets, identified challenges in analysing status and goal achievement, and made an illustrative interpretation of some of the targets by assessing status and trends, policy efforts and level of achievement. The targets selected were such that had not been achieved, as measured by existing data or as commonly described in the political debate; had featured recently on the political agenda; and/or had been more or less successfully dealt with and thus offered potential for international learning.

  1. The SEI paper defined the relevant targets as follows:

    • Targets that are applicable in country x – that is, deal with phenomena that exist in the country, given domestic environmental, social and economic conditions;

    • and that are not yet achieved in country x – that is, currently achieved and likely to remain so over the coming 15 years

  2. The SEI paper identified the following challenges in interpretation:

    • Scale. The issues that some targets refer to a specific scale (national or global) while others do not, and when referring to an end state at global level (e.g. increase the share of renewable energy in the global energy mix) there is very little guidance on what action or desired end state is expected at the national level.

    • Multidimensional. The issue that targets address many issues with sometimes diverging trends calling for different policy responses. Making one joint assessment of how a country is performing on these targets is clearly difficult or soon misleading.

    • Ambiguous wordings make many targets vague. For example, the issue that an end state is qualified in terms like “safe”, “effective”, “sustainable” or “reliable”, or calls for an action like “promote”, “enhance” or “strengthen”.

    • “Zero visions”. The issue that targets are set to eliminate or end a condition but clear criteria are lacking for determining when qualitative conditions are met (e.g. “women’s full and effective participation”).

    • Data availability. For some of the more complex or qualitative targets data is scarce, e.g. those referring to impacts along supply chains.

  3. The type of results generated:

The study was a pilot study and a trial for a more formal and detailed exercise, such as for example a comparative gap analysis. The type of results generated includes a summary review of status, trends, policy and achievement for a selection of targets and the identification of key challenges for analysis. A key message is that it presents one way of interpreting the targets, not a scorecard, and as such highlights the large space for interpretation left in the global framework that must be handled at national level. Arriving at a scorecard or performing a gap analysis, requires a more robust analysis including identification of context specific SMART national targets through broad stakeholder involvement. As the status, trends and policy on targets are linked an iterative process is needed to set targets, ambition levels and action plans.

SEI has also carried out research into the various dimensions of implementation and action, and discussed how one can strive for coherence across these. In an early contribution by Nilsson et al. (2013), the main dimensions of an energy SDG are elaborated, along with the different dimensions of implementation; capacity and knowledge; governance and institutions; public policy; and investment and finance. The paper elaborates on challenges related to ensuring that these different layers of implementation all work towards the ultimate goal within the SDG framework. In Gupta and Nilsson (2016), an analysis is made of SDG 6 on Water and Sanitation, considering how to ensure integration and coherence across different types of interventions, across institutional arrangements, capacity development and to policy interventions.

The SDG Dashboard and Index: Getting Started with the Sustainable Development Goals

By: Guido Schmidt-Traub, David Durand-Delacre, and Katerina Teksoz, Sustainable Development Solutions Network (SDSN)

At the end of 2015, the world’s governments adopted the Agenda 2030 for sustainable development, including 17 Sustainable Development Goals (SDGs), to guide the global development for the next fifteen years. The SDGs are focused on a critical range of global issues – eradicating extreme poverty and diseases, ensuring quality education, gender equality and environmental sustainability, as well as combating the dangers of climate change. In the words of the UN Secretary-General Ban Ki-moon “The seventeen Sustainable Development Goals are our shared vision of humanity and a social contract between the world’s leaders and the people …they are a to-do list for people and planet, and a blueprint for success.”

Achieving these ambitious goals will require unprecedented mobilisation of stakeholders and focused problem solving, which in turn depend on effective stock-taking of countries’ priorities and monitoring of progress. The UN Statistics Commission has recently recommended a first set of 241 global indicators for the SDGs. Some of these indicators are underpinned by comprehensive data, but most require major efforts in data collection, and a substantial number need more technical work to develop definitions and launch the process of data collection. It will therefore take time until UN member states dispose of the data to track progress towards the SDGs. Indeed investing in the capacity of countries to monitor the goals should be an important priority for early action.

Yet, implementation of the SDGs cannot wait until a comprehensive monitoring framework is in place. Countries need to take stock of where they stand today with regards to achieving the SDGs, identify priority areas for early action, and start preparing long-term strategies to meet all the goals by 2030. To support governments, civil society, business, universities, and other stakeholders in getting started with the SDGs, the SDSN is developing an SDG Dashboard and an SDG Index. A preliminary draft has recently been launched for public consultation (www.unsdsn.org), and a thoroughly revised version will be launched before the 2016 High-Level Political Forum (HLPF) in July 2016. We hope that both tools will help countries in operationalising the SDGs and starting the process of implementation, as described in the SDSN Guide to Getting Started with the SDGs (https://sdg.guide/).

The SDG Dashboard and SDG Index pursue different and complimentary aims. The purpose of the Dashboard is to consolidate available data for each SDG and compare it visually against performance thresholds by labelling the respective goals as green, yellow, or red. The resulting Dashboard highlights areas where a country needs to make the greatest progress towards achieving the goals by 2030. In particular, it shows that OECD countries face significant challenges in meeting many of the SDGs even though they have achieved prosperity for most of their citizens. Civil society, governments, businesses, and other stakeholders can use the Dashboard to discuss priorities for early action and the need to redirect development resources towards different policy areas.

The SDG Index aggregates country data into a composite index for SDG progress to compare countries’ starting points on the goals and benchmark them with regional averages. The Index will help attract political attention to the goals, make them easier to communicate in each country, and encourage countries to measure their performance using a broader metric than gross domestic product (GDP) per capita or even the Human Development Index. We hope the index will raise awareness of the goals and support a broad public conversation on the importance of achieving them. Together with the Dashboard it also highlights gaps in the availability of essential SDG data that must be closed quickly.

In developing the SDG Dashboard and Index, we focus on internationally comparable data that is available for at least 80% of countries with a population greater than one million (i.e. 120 countries). Countries with small populations are included if they have data for at least 80% of the selected variables. Yet data availability remains poor for the vast majority of SDG indicators proposed by the UN Statistics Commission, so we include data from other official and non-official sources. The lack of country-level data comes as a surprise to some observers, which may be partly explained by the fact that the monitoring of the Millennium Development Goals focused primarily on regional aggregates.

When the SDGs were crafted it was agreed among member states that they should reflect the outcomes of the Paris climate conference in December 2016. Unfortunately, this has yet to be reflected in the targets for Goal 13 and proposals for official indicators, which include no variables that would allow tracking progress towards the overarching goal of limiting global warming to “well below 2°C”. We therefore include various indicators to track the emission of greenhouse gases.

In some areas data availability requires us to choose inferior metrics over better alternatives. For example, there is widespread agreement that access to water supply should measure access as well as the quality of the drinking water, but data availability for access to “safe water” remains poor. For this reason the SDG Dashboard and Index retain the inferior “access to improved water source”. In other critical areas we are unable to identify robust metrics that meet the strict standards of data availability. For example, the SDG Dashboard and Index do not adequately cover sustainable agriculture, sustainable consumption and production, sustainable cities, or the quality of education.

These gaps underscore that the SDG Dashboard and Index cannot serve as a monitoring tool for the SDGs. Such monitoring must be undertaken using broader sets of indicators, and it will need to build statistical capacity over time to measure important SDG priorities for which data is unavailable today. Instead the SDGs Dashboard and Index aim to support the process of operationalising the goals over the short term, which includes highlighting critical gaps in data availability.

Using z-scores the data for each indicator is transformed into normally distributed variables, which are then tested for statistical significance before aggregating them for each goal. This ensures that each goal has the same weight in line with the letter and spirit of the SDGs adopted in September 2015. The choice of aggregation formula has important implications for the results. This applies in particular to the question whether goals can be substituted, i.e. whether progress in one dimension (e.g. GDP) can offset regress in another (e.g. ocean health or air quality). These issues will be discussed in detail in the technical documentation accompanying the forthcoming report on the SDG Dashboard and Index.

A single, global SDG Dashboard is important to operationalise the universal SDG agenda, which applies to every country. At the same time, limitations in available data are severe. Moreover, richer countries have already achieved many of the social and economic milestones set out in the SDGs and therefore need to focus on targeted policy priorities where greater progress is needed. Such priorities become difficult to identify and communicate using globally comparable data that shows limited variation among richer countries.

For these reasons we propose a separate Dashboard and Index for the 34 OECD countries. This Dashboard considers a richer set of underlying data and focuses on the policy priorities where OECD countries face the greatest challenges. For example, most rich countries have eliminated extreme headcount poverty, measured as incomes less than USD 1.90 PPP per day. So the dashboard for OECD countries focuses on relative poverty. Similarly, countries might have addressed key dimensions of a goal (e.g. hunger and nutrition), but might face major challenges in one area (e.g. widespread obesity). In such instances, it may be more appropriate to describe a richer country’s challenge as “red” instead of averaging across all indicators.

The SDG Dashboard and Index for OECD countries also include data that should be widely available for all countries. In this way they outline a possible set of priority metrics, which the international community might help support in every country. Over time we intend to extend the tools to non-OECD countries that have the necessary data.

Initial reactions to the draft SDG Index and Dashboard have been encouraging and show that these tools can help stimulate important debates on how to achieve the SDGs at the country level. At the same time, the limitations in terms of data and approach are obvious and will require better answers over time. The SDSN will therefore document the methods, data, and findings transparently, so that users can understand the choices and assumptions made as well as their implications on the results. The SDSN intends to publish periodic updates to the SDG Index and Dashboard to incorporate lessons learnt and better data. In particular, we hope that additional data can be identified for those countries that are currently excluded from the SDG Index, so that the world will soon have a comparable metric across all countries. In this way every country will be able to take stock of where it stands with regards to achieving the SDGs and benchmark itself with the countries it considers peers.

Seeing the whole: A methodology for analysing SDG interlinkages and improving policy coherence

By: Stakeholder Forum, Bioregional and Newcastle University8

The creation of the Sustainable Development Goals represents a major effort by the international community to bring the whole range of global goals and aspirations together in a single well-balanced agenda for action towards 2030. That effort now needs to be carried through into well-integrated national implementation strategies and policies. An understanding of interlinkages between different policy areas and targets will be crucial to achieving optimal coherence in the policy responses to the SDGs.

Some targets are more challenging for some countries – others for other countries. Each country therefore has to develop its own national strategy for SDG implementation and decide on the appropriate weight and attention to give to each of the targets.9 Similarly, the linkages between different targets may have different features in different countries, and each country will need to analyse the significance of these linkages for themselves in developing their own strategies.

Nevertheless there are certain common features of the relationship between different targets in the global SDG set that can usefully be analysed at a general level. Such analysis can then help to pinpoint coherence issues that recur in many different contexts and which will need attention by strategists and policy-makers seeking to implement the SDGs in an integrated way in any part of the world.

In this pilot research project the authors first sought to develop a new taxonomy and system of classification for understanding the types and strengths of interlinkages between different SDG targets in general. Secondly, we tested the methodology by applying it to explore the links between the targets in one specific SDG (SDG 12 on Sustainable Consumption and Production [SCP]) and other targets within the SDGs. In a third body of work, focusing on the EU as an example, we identified EU law and policy relevant to the targets of SDG 12 (Ensure sustainable consumption and production patterns), and assessed the alignment of these policies with SDG 12.

A methodology for assessing interlinkages

First, a methodology was designed to identify and analyse different types of linkages between various SDG targets. Targets can enable, support, repeat or sometimes conflict with one another, and these different types of linkage are policy-relevant in different ways. Since there is – to our knowledge – no existing typology of interlinkages between goals and targets in print, we created a new classification of the types of interlinkages. This identifies eight types of interlinkages under three broad categories, as shown in Table 6.6.

Table 6.6. Assessment methodology
Classification of type and nature of SDGs interlinkages

Category

Category definition

Type

Type definition

Score

Supporting

Targets that support one another tend to do so by fulfilling objectives expressed by each target

Commonly supporting

Both targets contribute to the same objective

1

Mutually supporting

Target A’s objective is achieved by Target B’s means of implementation and vice versa

2

Enabling

Targets that enable one another satisfy this relationship by having an impact on the achievement of another target

Disenabling

Implementing Target B may hinder or reverse the achievement of Target A (e.g. by competing with it for resources, or more fundamentally because the typical means of implementation of the first target actually worsen the underlying problem which the second target is addressing)

0

Indirect enabling

Target B’s implementation indirectly enables the achievement of Target A

1

Direct enabling

Target B’s implementation directly enables the achievement of Target A

2

Direct enabling in both directions

Target B’s implementation directly enables the achievement of Target A, and Target A’s implementation directly enables Target B’s achievement

3

Relying

Targets that rely on one another derive from a relationship of logical necessity which exists between the two targets

Partial reliance

Target B is a subcategory of Target A and adds some detail as to how Target A can be achieved

1

Full reliance

Target B’s implementation is necessary for, but not intrinsic to, Target A’s achievement

2

This approach fulfils three key criteria for such a typology:

  1. It fits the complexity we encountered, as it allows each interlinkage to be classified by its unique characteristics in any one, or all, of these types of interlinkages.

  2. It allows us flexibility to deal with targets that specify multiple sets of objectives and processes

  3. It allows for expression of complex relationships in more manageable and understandable classifications.

It is important to note that we do not claim these categorisations to be mutually exclusive we find target-to-target links to manifest multiple relationships.

As well as classification, we endeavoured to give a numerical value to each interlinkage and sum these to yield a total score, taking the sub-categories of each of the three relationships to represent an aspect of the strength of the connection. Disenabling was accorded a score of zero – though in some cases this might even require a negative score, depending on how it is interpreted and implemented. Commonly supporting, indirect enabling, and partial reliance were all accorded one point, as these are notable, but not especially close relationships. Mutually supporting, direct enabling and full reliance were awarded two points, reflecting the closer and more significant connection posed by such linkages between targets. Importantly, these may hold more significance for those tasked with implementing such targets. Direct enabling in both directions carried three points in the weighting, signifying how inextricably linked targets are in this case, and the potential powerful implications for policy-makers.

This exercise of assigning a rating to each dimension, and aggregating them, yields a score we have termed strength. We offer this in the report as an initial “at a glance” assessment of the overall density of the interlinkages across all of these categories.

Applying the methodology to an analysis of SDG 12 – Ensuring sustainable consumption and production patterns: Key findings

In order to test the methodology we sought to apply it to analysing the linkages between the targets in SDG 12 – Sustainable Consumption and Production and all the strategies in other goals that are related to SCP. The eight types of interlinkages identified in Part I of the study formed the basis for the assessment and evaluation of the relevant SCP interlinkages (of which we identified 25 in our report). In identifying interlinkages for this pilot, we started with the connections which Bioregional had previously identified in their report on Sustainable Consumption and Production and the Post-2015 Sustainable Development Goals (Bioregional, 2014). In addition to this, the research team analysed the full list of targets to identify any missing interlinkages for inclusion in this analysis.

The methodology we developed identified markedly different types of linkage between targets, some of which are more significant than others. In some cases a target under one SDG virtually repeats one under another goal, or else provides a little more detail about the content of an objective. Such a weak linkage does not demonstrate any significant opportunity for better integrated policy making.

In other cases, however, the interlinkage is more significant – where for example one target is a driver or enabler for another one, or else a precondition for its achievement. Where one target’s success depends on another target (full reliance), or where the means and ends of the targets are interlinked (mutually supporting), policy-makers will have greater impact if they implement both at the same time. The analysis can thus help to identify opportunities for more joined-up policy-making.

In other cases, there may actually be tension or conflict between targets. Whilst it is important for targets to facilitate and complement one another, it is of equal importance for the inappropriate implementation of one target not to undermine the potential for achieving another. In the case of SCP, for example, there is potential for the pursuit of the economic growth objectives in the SDGs to prejudice the achievement of more sustainable consumption and production if executed inappropriately.

Lastly, our analysis identified some missing interlinkages in the SDGs and targets – where we would expect to find a link but that link is not present. There were a number of missing economic links identified, for example, which shows a missed opportunity for full integration of this aspect within the SDG agenda. Such gaps illustrate the point that although the SDGs are a vast and challenging agenda for the world they do not necessarily represent a total description or blueprint of what needs to be done to achieve SCP or overall long term sustainability for the world. Sustainable development policy-makers will need to avoid making SDG implementation target by target the be-all and end-all of their approach.

Our report’s analysis of current EU action and policy initiatives on SCP illustrates this point. It indicates that while at EU level the Commission has action in hand on most of the specific SCP targets under SDG 12 there is still more to be done in Europe (as elsewhere) to tackle the full range of linked targets that would need to be advanced at both EU and Member State level to move Europe more decisively towards truly sustainable patterns of consumption and production.

Looking to further work one might envisage using the methodology to analyse a wider range of linkages between the 169 targets in the SDGs, and identifying “clusters” of targets interlinked in particular ways. It might also be useful to apply it in different country settings where the relative significance and level of transformation implied by the different targets and the strength of the linkages between them may differ.

It should be emphasised also that the methodology itself is an innovation that is still at an early stage of development. The authors have already themselves noted some elements in the proposed typology of linkages and in the scoring system which would repay further examination. They will welcome any comments and suggestions as to how the approach could be further refined and improved so as to make it more fit for the important purpose of improving policy integration and coherence throughout the SDG implementation process.

The iSDG model: An interactive policy simulator for the Sustainable Development Goals

By: The Millennium Institute, Washington, DC.

Designing coherent policies for the Sustainable Development Goals presents at once huge challenges and opportunities. The SDGs are interlinked in complex and often subtle ways. Actions to achieve progress in one SDG sector may cause underachievement or failure in another (Young et al. 2014; Pedercini et al. 2010). By the same token, a successful SDG initiative in one sector might create synergies for improvements in another. The SDGs can be thought of as a complex system of interwoven feedback loops, lengthy time lags between causes and effects, and nonlinearities that are often unrecognised. Such systems are known to present serious impediments to learning and policy design (Groesser and Schaffernicht, 2008; Sterman, 1994). Within this difficult learning environment there is a need for tools to aid learning and policy design focused on SDG attainment.

Recognising this need, the Millennium Institute has developed the Integrated Sustainable Development Goal (iSDG) model. The iSDG model is an interactive simulation model designed for policy-makers and planners or others concerned with achieving the Sustainable Development Goals. The iSDG model is a national scale model of relatively course detail and does not replace finer resolution sector-focused models. The iSDG model is intended to help policy-makers and planners make sense of the complex and interlinked SDG system, and to help them design efficient pathways to their goals. The iSDG model can be calibrated for any country or region with data sourced locally or from international databases.

The iSDG model

The iSDG model builds on the Millennium Institute’s Threshold 21 model, a fully integrated multi-sector national planning model that has been used in over 40 countries. The iSDG model is developed with System Dynamics methodology using the Vensim DSS software.10 The user interface is developed in Sable software.11

As shown in Figure 6.12, the iSDG model contains 30 interlinked model sectors distributed within the three core dimensions of sustainability: society, economy, and environment. The model maps key feedback loops running between and within sectors as well as nonlinear relationships and time lags that generate the complex systemic behaviours characteristic of interactions between SDGs.

Figure 6.12. Structural overview of the iSDG model showing the distribution of model sectors within economic, social, and environmental dimensions
picture

The iSDG model is intended as an interactive learning platform, giving policy-makers and planners opportunity to learn and build intuition through virtual experiments or “what-if” scenarios within the complex SDG system. It is expected that this mode of experiential learning will help policy makers identify trade-offs, synergies, and high leverage intervention points that will inform their policy decisions.

To promote model-based learning, a strong emphasis is placed on transparency and user-friendliness. Extensive documentation is available online including detailed descriptions of each model sector. Video and written support materials are provided online that explain how to set up and run the model. Example simulations are performed on video.

The model user interface is intuitive. The behaviour of the system is shown in both time series graphs and numerical tables. Causal diagrams are used to show linkages between the SDG system’s behaviour and underlying structure. The model simulates almost instantly. This speeds the learning process and helps build user intuition.

Example simulation of the iSDG model

This section gives an overview of the iSDG user interface with an example of a simple policy simulation for a low-income eastern African country.

The model features a user dashboard with a table of icons for each of the 17 SDGs (Figure 6.13). A red horizontal bar (shown in black in Figure 6.13) under each icon represents the expected attainment of the SDG by year 2030 if current policies remain unchanged and if no unexpected external shocks occur – “business as usual” conditions. After a simulation is run, a blue horizontal bar (shown in grey in Figure 6.13) appears underneath the business-as-usual bar indicating SDG attainment under the simulated policy or policies. This provides users a quick view of the state of attainment across all 17 SDGs.

Figure 6.13. User dashboard icons for the 17 SDGs
picture

Clicking an icon opens a window in which interventions for a particular SDG can be entered. In this example SDG 2 – “End hunger, achieve food security and improved nutrition and promote sustainable agriculture” – is chosen.

When the simulation is run a causal map emanating from the policy intervention is automatically shown (Figure 6.14). Clicking on any of the variables in the diagram reveals the trajectory of the variable over the time horizon of the SDGs. This causal diagram shows the connection between model behaviour and structure, a critical element of model-based learning.

Figure 6.14. Simplified causal map of “no hunger/sustainable agriculture” sector
picture

Note: Solid arrows (blue in the actual iSDG model) indicate positive causal linkages (changes in the variable at the arrow’s base tend to cause changes in the same direction in variables at the arrow’s point). Dashed arrows (red in the actual iSDG model) indicate negative causal linkages (changes in the variable at the base tend to cause changes in the opposite direction in the variable at the point).

In the example simulation, investment in training causes a great increase in area under sustainable management, reaching 100% by year 2029 (Figure 6.15). The growth is driven in part by self-reinforcing word-of-mouth feedback.

Figure 6.15. Simulated trajectories of proportion of harvested area under sustainable management
picture

Note: The curve with diamond shaped markers is the policy response; the curve with square markers is business-as-usual.

The patterns shown below demonstrate some of the impacts of investing in sustainable agriculture within the “no hunger/agriculture” SDG and cutting across other SDGs. The patterns are best interpreted with reference to the causal map in Figure 6.16. Improved yields increase cereal production, rural incomes improve with crop production, decreasing the proportion of the population below the poverty line (SDG 1, “No poverty”).

Figure 6.16. Simulated trajectories of: the impacts of investing in sustainable agriculture
(a) Yields of cereals in metric tons per ha and year
(b) Crops production of cereals in metric tons per year
(c) Proportion of population below poverty line
picture

Note: Curves with diamond shaped markers represent the case under the policy of 0.3% allocation of GDP for agricultural training. Curves with square markers are the base case (business as usual).

The example provided above focuses for simplicity on a single policy intervention. A key strength of the iSDG model is the support of simulation of a broad variety of policies simultaneously and the assessment of positive and negative synergies. This feature is of primary importance in order to establish policy coherence across sectors for an effective use of resources towards achieving the SDGs.

Conclusion

Many aspects of the SDGs are interlinked with complex feedback loops making the impacts of policies difficult or impossible to intuit.

Because of its integrated and transparent structure, the iSDG model can reveal chains of impacts from policy interventions, helping policymakers identify trade-offs, synergies, and leverage points. The interactive nature of the iSDG model provides means to design and test evidence-based policies to improve efficiencies, reduce risks, and increase the likelihood of achieving the Sustainable Development Goals.

Visit www.isdgs.org for a demo version of the iSDG model and full supporting documentation.

Reporting on SDG target 17.14 – the case of the European Union

By: Wiske Jult, 11.11.11 – The Flemish Coalition of the North-South Movements, and Jussi Kanner, Kehys – the Finnish NGDO Platform to the EU

The report of the UN Secretary-General on Critical milestones towards coherent, efficient and inclusive follow-up and review at the global level emphasises that “the integrated and indivisible nature of the Goals should lead to a review system that promotes a cross-cutting understanding of the significant interlinkages across the Goals and targets”. The report further proposes that Goal 17 should remain a recurring topic in the HLPF every year. These two points provide a promising platform for reporting on policy coherence for sustainable development (PCSD) in the 2030 Agenda framework.

More coherent policies for sustainable development is key for making the 2030 Agenda a success. Therefore monitoring and reporting on the SDG target 17.14 should not be limited to the global single indicator, which is defined as number of countries with mechanisms in place to enhance policy coherence of sustainable development. Rather, it should cover a much broader area and adopt various approaches. A good starting point would be the three institutional building blocks of policy coherence (OECD 2009): political commitment; co-ordination mechanisms; and monitoring systems, analysis and reporting. Any reporting that presents progress in enhancing PCSD needs to also look at the way these mechanisms are being used, but also asses how inclusive and transparent these mechanisms are. But above all it is one thing to have a mechanism in place, but more importantly it should lead to better and more coherent policy making. Measuring impact and effects is therefore key.

While reporting, the building blocks should be complemented by the new aspects that were introduced in the 2015 Better Policies for Development report (OECD 2015a), namely: policy interactions; contextual factors; and effects. The emphasis on the integrated and indivisible nature of the Goals and targets furthermore call for review of the effects at three levels: i) effects of a given countries’ external policies on sustainable development in other countries, ii) effects of a given countries’ internal policies on sustainable development in other countries, and iii) effects of a given countries’ policies on sustainable development in that country itself. Given the massive scope of such an exercise we recommend to link the analysis to the annual theme of the HLPF, and identifying relevant cross-sectoral policy interactions across SDG Goals and targets. This could be done for instance following the model presented in the 2015 Better Policies for Development report.

As for the European Union, there is a great opportunity coming up in 2017. Food security has been proposed by the Secretary-General as the annual theme of the HLPF. It has been one of the five PCD priorities of the EU since 2009, in addition to which there is already a wealth of analytical material compiled by OECD and others on how to apply a policy coherence lens to global food security. That is to say there is a clear opening for the EU to step up and show global leadership in promoting PCSD. This would also allow EU to sharpen its own analysis and reporting on PCSD and how it has been adopted in EU policy making. So far the biennial PCD reports have been used to showcase existing policies and how coherent they are, not really looking into the system itself.

We would like to see the EU – and any other countries reporting as well – present its PCSD mechanisms in various institutions and show how these mechanisms have been used and had impact. The sustainability impact assessments would be an interesting example to this point. The EU could also provide its analysis on the main policy issues regarding food security, and where the key policy interactions with other SDGs lie.

To conclude, reporting and monitoring of the implementation of Agenda 2030 should entail a broader scope than the existing indicator. It should cover mechanisms to enhance policy coherence for sustainable development, but also the utilisation and benefits of these mechanisms. We would like to encourage the EU to take the opportunity to voluntarily report to the HLPF in 2017.

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Notes

← 1. The statistical data for Israel are supplied by and under the responsibility of the relevant Israeli authorities. The use of such data by the OECD is without prejudice to the status of the Golan Heights, East Jerusalem and Israeli settlement is the West Bank under the terms of international law.

← 2. For a more in-depth analysis of the three topics, see previous editions of Better Policies for Development: 2013 edition for food security; 2014 edition for illicit financial flows; and 2015 edition for green growth.

← 3. https://unite.un.org/analytics/desa/modellingtools.

← 4. MAMS stands for Maquette for MDG Simulations general equilibrium model developed by the World Bank.

← 5. The original model was developed by the Royal Institute of Technology (KTH) in Sweden and the UN Division for Sustainable Development.

← 6. Another initiative, not in the Modelling Tools… website, is presented in UN DESA Working Paper No. 141 by David Le Blanc. He illustrates the SDGs a network of targets, creating a “map of the SDGs”. Around each SDG, a number of targets are linked only to that goal, giving rise to flower-like structures around the goals. Other targets are linked with more than their own goal and provide the structure of the network.

← 7. This is obtained by applying the UN national population growth rate to the population living in each geo-spatial unit in 2012.

← 8. This text presents a summary of a recent research report funded by Finland and undertaken jointly by authors from Stakeholder Forum, Bioregional and Newcastle University,Seeing the Whole: Implementing the SDGs in an integrated and coherent way, available at www.stakeholderforum.org.

← 9. See for example the recent report by Stakeholder Forum which analysed the nature of the transformational challenge that the SDGs represent to the developed countries, and how this differs from the challenge they represent to the developing countries, Universal Nature of the SDGs: Challenges for Developed Countries, available at www.stakeholderforum.org.

← 10. Vensim is a product of Ventana Systems Inc., USA.

← 11. Sable is a product of Ventana Systems UK Ltd., United Kingdom.