The Ex-Ante Evaluation of Achieving Sustainable Development Goals

Abstract

This paper describes the methodology and main results from an overall assessment on future achievement of sustainable development goals. The proposed approach consists of a model-based, looking forward composite sustainable development index—FEEM sustainability index—projected to the future. It represents a first experiment to reproduce the future dynamics of sustainable development indicators over time and worldwide and to assess future sustainability under different scenarios. The assessment presented here is relevant under different viewpoints. First, it has a very broad nature in terms of both geographical coverage and meaningfulness: it considers the multi-dimensional structure of sustainable development by combining relevant indicators belonging to economic, social and environmental pillars for the whole world. Second, the modelling framework to compute future trends of indicators relies upon a recursive-dynamic computable general equilibrium model. This is an ideal tool to look simultaneously at the development of many indicators, their potential interactions and trade-offs, and more in general to the consequences of economic development and/or policies aiming to increase performance in one or more indicators; it allows measuring the overall sustainability under alternative scenarios, across countries and over time. Finally, regarding the construction of the composite indicator, the application of fuzzy measures and Choquet integral increases substantially the model capability allowing taking into account the interactions that exist among the three main pillars of sustainability and the considered indicators.

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Notes

  1. 1.

    An indicator is a “quantitative or a qualitative measure derived from a series of observed facts that can reveal relative positions (e.g., relative position of a country) in a given area” (OECD 2008). It should be exhaustive and concise, quantifying and aggregating data regarding to a specific aspect, enabling to assess change in time and giving insight on the reasons for change. UN CSD (2012a, b) provides a broader) definition: “A statistical measure that gives an indication on the sustainability of social, environmental and economic development”.

  2. 2.

    http://sustainabledevelopment.un.org/index.php?menu=1130.

  3. 3.

    http://www.greengrowthknowledge.org/Pages/GGKPHome.aspx.

  4. 4.

    http://ec.europa.eu/europe2020/index_en.htm.

  5. 5.

    http://www.un.org/millenniumgoals/.

  6. 6.

    Even institutions that normally adopt other approaches/models such as the World Bank (WB), the United Nations Development Program (UNDP) and the World Health Organization (WHO) have started to use the MDG indicators as a standard reference.

  7. 7.

    http://www.un.org/millenniumgoals/pdf/report-2013/2013_progress_english.pdf.

  8. 8.

    https://sustainabledevelopment.un.org/sdgs.

  9. 9.

    http://www.oecdbetterlifeindex.org/.

  10. 10.

    Carraro et al. (2013) report methodology and results for a previous version delivered in 2011. This paper shows results of the 2013 release. The full set of result of the 2013 release is available at www.feemsi.org.

  11. 11.

    A complete description of indicators of FEEM SI can be found in the Appendix 1.

  12. 12.

    Also http://www.feem-web.it/ices/.

  13. 13.

    The algorithm is available in R Cran under the kappalab package.

  14. 14.

    http://sedac.ciesin.columbia.edu/ddc/ar5_scenario_process/RCPs.html; http://tntcat.iiasa.ac.at:8787/RcpDb/dsd?Action=htmlpage&page=welcome.

  15. 15.

    http://sedac.ciesin.columbia.edu/ddc/ar5_scenario_process/parallel_nat_scen.html; https://secure.iiasa.ac.at/web-apps/ene/SspDb/dsd?Action=htmlpage&page=about.

  16. 16.

    Country details are also available at http://www.feemsi.org/pag/results_baseline_data_bubbles_all.php.

  17. 17.

    As usual, it is possible to play with dimension to see a number of cause-effects relationship between different pillars.

  18. 18.

    The deployment of nuclear energy can raise both social and environmental concerns. By now, FEEM SI does not present a specific indicator for nuclear as there is no agreement on how can be treated in terms of contribution to sustainable development (are more important the environmental costs related to nuclear waste disposal or the environmental benefits due to the non-fossil fuels based energy provided?).

  19. 19.

    ICES model is solved using the software GEMPACK (General Equilibrium Modelling PACKage): http://www.copsmodels.com/gempack.htm.

  20. 20.

    GTAP 7 database considers 113 macro-regions and 57 production sectors for the year 2007; ICES-SI aggregates it in 40 macro-regions and 19 production sectors (Tables 1314).

  21. 21.

    The GTAP—Global Trade Analysis Project-model (Hertel 1997) has been used increasingly in the last fifteen-twenty years to assess international trade policy topics.

  22. 22.

    http://www.materialflows.net/home/.

  23. 23.

    http://unstats.un.org/unsd/environment/municipalwaste.htm.

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Correspondence to Luca Farnia.

Appendices

Appendix 1

Indicator Selection

Indicators selected for FEEM SI come from the literature on sustainability (Commission on Sustainable Development of the United Nations, EU Sustainable Development Strategy, and World Development Indicators from World Bank). The indicator set tries to consider the main dimensions of sustainability given the constraints imposed by the CGE framework, being aware about the evident trade-off between a detailed representation and the ex-ante analysis of sustainability. Table 10 reports the FEEM SI indicators considered in each sustainability dimension, a brief description of each indicators along with the data source and its mathematical formulation.

Table 10 List of FEEM SI indicators

ICES Model and Indicator Computation

ICES Model

The indicators are calculated using the outputs of the Intertemporal Computable Equilibrium System (ICES) modelFootnote 19 (Parrado and De Cian 2014; Eboli et al. 2010). ICES is a recursive-dynamic CGE model with World coverage based on the GTAP-E model (Burniaux and Truong 2002) and Global Trade Analysis Project - GTAP 8 databaseFootnote 20 (Narayanan et al. 2012). General Equilibrium models allow describing economic interactions of agents and markets within countries (production and consumption) and across countries (international trade) in a stylised way. ICES is a Computable model, i.e. all the model behavioural equations are connected to the GTAP database which collects national social accounting matrices from all over the world and give a snapshot of all economic flows in the benchmark year. ICES’ recursive-dynamic feature allows projecting a new general (worldwide and economy-wide) equilibrium in each simulation period as a result of agents’ decisions following socio-economic as well as policy-driven shocks occurring in the economic system.

The main advantage of the CGE framework is that it represents in a coherent way the complexity of socio-economic activities and the underlying forces in play. Nonetheless, this implies simplifying assumptions such as perfect competition among firms that clears all markets along with full employment of production factors. In addition, ICES results are scenario-dependent since they depict the economy in one possible future scenario relying on external projections of key exogenous variables (GDP, population, energy prices, energy efficiency, labour productivity, total factor productivity).

In the model, the economy of each country is characterised by n industries, a representative household and government. Industries minimize production costs and have nested Constant Elasticity of Substitution production functions that combine primary factors (natural resources, land, and labour), a capital&energy composite, and intermediates in order to generate the output. This structure follows the GTAP-E intra-energy substitution mechanism (Burniaux and Truong 2002). GTAP-E is a development of the GTAP modelFootnote 21 developed to assess specifically energy and climate policies. The ICES model further strengthened the GTAP-E core with the explicit representation of Renewable Energy Sources. The “Armington assumption” introduces some frictions on the substitutability of inputs imported from different countries.

A regional household in each region receives income, defined as the service value of national primary factors (natural resources, land, labour, capital). Capital and labour are perfectly mobile domestically but immobile internationally; instead land and natural resources are industry-specific. Regional income is used to finance three classes of expenditure: private household consumption, public consumption and savings (Cobb-Douglas specification), where the utility of private household consumption has a Constant Difference of Elasticities functional form.

A fictitious world bank collects savings from all regions and allocates investments in order to equalise the current rates of return.

Dynamics inside the ICES model are driven essentially by two sources: one endogenous and one exogenous. The first involves capital accumulation and foreign debt evolution governed by endogenous investment decisions. On the other hand, we make several exogenous assumptions concerning trends of population stock, labour stock, labour, land and total factor productivity over time in order to obtain a reference scenario in line with main economic indicators.

The economic database is complemented with satellite databases on energy volumes (McDougall and Aguiar 2008), CO2 (Lee 2008) and non-CO2 emissions (Rose and Lee 2008), which include nitrous oxide (N2O), methane (CH4), and three fluorinated gases (F-gases). Both energy volumes and emission have an endogenous dynamic in the models and evolve the former according to energy sectors production and the latter proportionally to energy combustion process (CO2 emissions) and sector and household use of agricultural and energy commodities.

Extending the ICES Model to Consider Sustainability Indicators

In order to perform a sustainability analysis, we extended ICES to consider a more detailed sectoral aggregation and additional variables. We introduced 5 new sectors: Research and Development (R&D), Education, Private and Public Health, and Renewable Energy Sources (RES). All of them were split from the original GTAP 8 sectors according to the available international statistics.

For the R&D sector, we used the indicator “R&D expenditure as percentage of GDP” from the World Development Indicators—WDI (World Bank 2010) and the “share of R&D financed by Government, Firms, Foreign Investment and Other National” from the OECD Main Science and Technology Indicators (OECD 2010) for attributing R&D to the different economic agents.

A similar approach has been used for Education, Private Health and Public Health sectors. Data on overall expenditure on health and education have been obtained from the WDI database (World Bank 2010) and the data used for splitting the public and private health sector are from the World Health Organization WHO (2010).

In order to regard separately the RES, namely wind, solar and hydro-electricity, they were split from the original electricity sector. The data collection refers to physical energy production in Mtoe (Million tons of oil equivalent) from different energy vectors and for each GTAP 8 country/region. The data source is Extended Energy Balances (both OECD and Non-OECD countries) provided by the International Energy Agency (IEA). We complemented the production in physical terms with price information (OECD/IEA 2005; EC 2008; Ragwitz et al. 2007; GTZ 2009; IEA country profiles and REN21). The explicit consideration of the RES sector implied some modelling changes: the production function of electricity sector considers a new nest allowing the inter-electricity substitution between RES and traditional fossil electricity.

The additional external variables considered are the following.

The Water Volumes data come from the Food and Agriculture Organization (FAO’s Aquastat database) and account for the use of water in agriculture, industry, and by privates. We included also data on Total Renewable Water Resource (WTR) as a proxy of available water, and we considered this variable constant throughout time, according to Aquastat database. In the model, water use in agriculture, industry, and private agents has been linked respectively with demand of water services by agriculture, industry and households.

Biodiversity at risk is approximated using an index that quantifies the number of endangered species for both animals and plants over the overall number of species. Our data source is the World Conservation Union Red List of Threatened Species Database (IUCN). To obtain an endogenous dynamics of the indicator, we inversely linked the number of endangered species to CO2 concentration according to Thomas et al. (2004). The overall number of species is instead considered constant.

We used the indicator “share of population with access to electricity” from World Energy Outlook (IEA 2010) as a proxy of Energy access. The indicator changes over time endogenously driven by the reduction of the gap between a country’s GDP per capita and the OECD average GDP per capita.

Data on Inhabitable land comes from the GTAP 7 land use data base, developed using the Food and Agriculture Organization of the United Nations (FAO) 2004 data and FAO and IIASA methodology (2000).

Public debt assessment in the base year relies on IMF’s World Economic Outlook 2012 (IMF, 2012) that considers the share of public debt over GDP. In the baseline scenario, the indicator’s path replicates the IMF’s debt projections (available until 2018).

The access to Information and Communication Technologies is proxied by the percentage of internet users over total population in a country. The World Telecommunication/ICT Indicators Database is the source of data for the base year. The dynamic of this indicator depends on the household’s expenditure on “Electronic equipment” (GTAP sector) that comprises office, accounting and computing machinery, radio, television and communication equipment and apparatus. More specifically, the percentage change of population with ICT access is positively correlated with the percentage change of per capita expenditure in “electronic equipment”, as confirmed by a cross-section analysis for 2007.

FEEM SI 2013 introduces a Corruption indicator to better characterise the country-specific political and institutional environment, and better assess the social sustainability. The external data source used in this case is the Corruption Perceptions Index (CPI) constructed by Transparency International. Deriving an evolution pattern of CPI through time is a challenging exercise; the literature on the topic is prolific, e.g. Treisman (2000, 2007). Among the most used explanatory variables for corruption (or corruption perception), persistence of democracy, common law, colonial heritage, Protestantism, economic development, high education and trade openness has a negative correlation with corruption; instead exports of natural resources and public expenditure over GDP are positively correlated.

Given the limitations of our CGE model and the impossibility to consider institutional factors in our exercise, we restricted the list of explanatory variables to only economic factors. From a cross-country log–log linear regression, we obtained that variation of corruption depends on public expenditure over GDP, GDP per capita, share of fossil/mineral exports over total exports and import over GDP.

The data on Life Expectancy at birth come from WDI database as well as the percentage of health expenditure over GDP, which remains the determinant driver of Life Expectancy dynamics. According to OECD (2011), there is a positive correlation between health expenditure and Life Expectancy, although weaker for highest health spending levels. Therefore, from a cross-country regression of changes in Life Expectancy and variations of health expenditure, we derived a linear relation to project the indicator into the future. The Life Expectancy is positively correlated to health expenditure even though the explained variance is only 50% of total variance. This result is strongly influenced by the low performance in terms of Life Expectancy of India and Rest of Africa aggregate, where probably other country specific variables should be added among regressors.

In FEEM SI, we use a more restrictive version of the Domestic Material Consumption (DMC) indicator considering only the kg of minerals and other ore products (excluding fossil fuels accounted elsewhere) used domestically. The source of data for the numerator in 2007 is SERI and Wuppertal Institute databaseFootnote 22 on material flows. The evolution is completely endogenous: the initial amount of minerals and ore products is updated using demand changes of these materials by the heavy industry sector.

Given that the GTAP database does not present a specific sector managing Waste collection (included in the Public Services sector—OSG) and that the usual indicator formulation considers the quantity of waste produced, we included external data. The tonnes of Municipal Waste collected in 2007 constitute the numerator for our waste indicator;Footnote 23 its dynamics is endogenously determined and depends on households’ demand of light and heavy industries’ products.

Indicators’ Computation

The ICES model computes indicators for each year and country/macro-region using simulation results. Table 11 outlines the FEEM SI list of indicators and their main drivers. Table 12 shows the Shapley value for each criterion/indicator in the respective node of the decision tree. Figure 10 is an example of the experts' preferences weighting scheme for the main node of the decision tree; Figure 11 represents the interaction index between each dimension of sustainability (Society, Economy, Environment).

Table 11 FEEM SI indicators and their dynamics
Table 12 Shapley value for each criterion/indicator in the respective node

Appendix 2

See Tables 13, 14, 15 and 16 and Figs. 12 and 13.

Table 13 FEEM SI regional detail
Table 14 FEEM SI sector detail
Table 15 World FEEM SI ranking in 2013 by sustainable development pillar
Table 16 FEEM SI sensitivity and ranking robustness (year 2030)
Fig. 12
figure12

Sustainability maps for 2013—from top-left clockwise (overall, economy, environment, society)—the clearer the color, the higher/the better the performance

Fig. 13
figure13

FEEM Si sensitivity and ranking robustness (year 2030)

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Campagnolo, L., Carraro, C., Eboli, F. et al. The Ex-Ante Evaluation of Achieving Sustainable Development Goals. Soc Indic Res 136, 73–116 (2018). https://doi.org/10.1007/s11205-017-1572-x

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Keywords

  • Computable general equilibrium model
  • Sustainable development goals
  • Composite index
  • Fuzzy measures
  • Choquet integral