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Can the Paris Agreement Support Achieving the Sustainable Development Goals?

  • Lorenza CampagnoloEmail author
  • Enrica De Cian
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Part of the Springer Climate book series (SPCL)

Abstract

This chapter provides an ex-ante, quantitative assessment of the synergies and trade-offs between the implementation of the Paris Agreement and sustainable development. It develops a framework for comparing historical and future sustainability performance that combines a Computable General Equilibrium model for describing future global and regional baseline and policy scenarios to 2030 with empirically-estimated relationships between macroeconomic variables and sustainability indicators. Results indicate that the commitments submitted within the Paris Agreement reduce the gap toward a sustainable 2030 in all regions, but heterogeneity across regions and sustainability indicators call for complementary sustainable development polices.

1 Introduction

With the advent of the United Nations’ 2030 Agenda and the Paris Agreement in 2015 (United Nations (UN) 2015), a growing number of studies have been exploring the synergies and trade-offs between climate policy and sustainable development. Synergies and trade-offs can go in both directions. On the one hand, the mitigation literature in the context of the new scenario framework of the Shared Socioeconomic Pathways (SSPs) and Representative Concentration Pathways (RCPs, O’Neill et al. 2017; van Vuuren et al. 2014) highlights how deep decarbonization (Rogelj et al. 2019) can be achieved more easily under sustainable scenarios, such as the SSP1 narrative, which poses lower challenges to mitigation and adaptation. On the other hand, climate mitigation policies can generate a wide range of non-climate ancillary benefits or obstacles in achieving the Sustainable Development Goals (SDGs, Roy et al. 2019). Aligning mitigation policies with SDGs is key for ensuring social acceptability of the required structural transformation and for fostering the more ambitious action required to contain global warming below 1.5 °C in 2100.

This chapter contributes to the emerging literature on the synergies and trade-offs between mitigation and sustainable development by evaluating the impact of the Paris Agreement implementation on a set of SDG indicators by 2030 using a Computable General Equilibrium (CGE) model. A macroeconomic framework provides a system perspective analysis, highlighting the aggregate impacts of mitigation policy on multiple sustainable development dimensions at the same time, while taking into account the general equilibrium adjustments induced by price changes. Ex-ante assessments, such as those based on simulation or numerical models, make it possible to explore the implications of mitigation policies of different ambition, broadening the evidence beyond the policies actually implemented in the past. They can examine synergies and trade-offs into the future, and provide a benchmark for policy evaluation and design while accounting for policy and socioeconomic uncertainty. This chapter develops projections of selected SDG indicators in a reference and mitigation policy scenario, contributing to expand the existing literature on mitigation pathways in the context of sustainable development. The major limitation of the few existing integrated assessment approaches available to date is the focus on economic and technological indicators, a choice that is driven by the limited ability of quantitative models to represent the social dimensions of sustainable development (McCollum et al. 2018b; von Stechow et al. 2016). The method presented in this chapter combines regression analysis to estimate empirically-based relationships between 16 economic, social, and environmental SDG indicators and the key socioeconomic variables represented in the CGE model. Gender inequality is the only goal left unexplored (SDG5).

The remainder of the chapter is organised as follows: Section 2 synthesises the most recent literature on the mitigation co-benefits and side effects on sustainable development. Section 3 describes the ex-ante approach used to assess the SDG implications of the Paris Agreement. Section 4 discusses the advantages and limitations of our methodology in the sustainability assessment of policy implementation and concludes, highlighting some directions for future research concerning the co-benefits of mitigation and adaptation policies.

2 Mitigation Policy and Sustainable Development: Recent Contributions from the Literature

The SDGs define broad and ambitious development targets for both developed and developing countries encompassing all sustainability dimensions (economic, social, and environmental), including minimising climate change impacts (SDG13), with the ambition of informing pathways towards inclusive green growth. The tight linkage among the economic, social, and environmental dimensions is reflected in the connections across different goals integrated into the broader framework. Given the multiple interactions among different SDGs, integrated approaches, such as those based on Integrated Assessment Models (IAMs) or integrated energy-economy climate models, can quantify the synergies and trade-offs between target-specific policies, such as mitigation, and all other goals with a system perspective (von Stechow et al. 2016, 2015).

Despite the growing number of efforts, current integrated modelling research remains confined to sectoral studies offering a limited view on the possible co-effects and focusing on a narrow set of specific objectives. Most of the literature, recently reviewed in the IPCC Special Report 1.5, has focused on food security and hunger (SDG2), air pollution and health (SDG3), clean energy for all (SDG7), water security (SDG6). Only McCollum et al. (2018a) conduct a systematic review of the literature to evaluate the nature and strength of interactions between SDG7 and all other SDGs. The review relies on forward-looking, quantitative scenario studies focusing on multiple objectives. SDG7 is connected to the implementation of mitigation policies through the specific targets on access to modern energy services, increased share of renewables and improved energy intensity. Since these targets are basic requirements of any mitigation policy, McCollum et al. (2018a) indirectly shed some light on the interaction between mitigation policy and SDGs. It is interesting to note that, the model-based literature reviewed in the paper is not able to identify contributions assessing social indicators (such as poverty SDG1, education SDG4, gender equality SDG5, reduced inequalities SDG10). In order to provide some evidence on these dimensions, McCollum et al. (2018a) select historical, empirical, or case-study papers.

The social indicators for which most evidence is found are SDG2 and SDG3. Regarding SDG3, good health and well-being, most literature focuses on reduced air pollution (Rao et al. 2016; Markandya et al. 2018) and diminished impacts of climate change and environmental degradation (Ebi et al. 2018). Mitigation policy stimulates the development and the diffusion of renewable technologies that appear decisive in improving energy access especially in remote and not connected areas (McCollum et al. 2018a). Regarding SDG2 (undernutrition reduction), the literature on the impacts of uncontrolled emission growth and temperature rise on agricultural production and on undernutrition prevalence is wide (Hasegawa et al. 2016; Nelson et al. 2010; Lloyd et al. 2011). Achieving mitigation targets helps reducing these side effects, but at the same time can generate some trade-offs pushing large-scale deployment of bio-energy, competition for land, and increased food prices. These are trade-offs that can be mitigated by decarbonization strategies oriented more towards demand-side actions (Grubler et al. 2018) or through the adoption of complementary distributional policies. The literature on the link between mitigation and poverty (SDG1) and inequality (SDG10) reduction is also quite scattered. On the one hand, as in the case of SDG2, poor people are the most exposed to climate change impacts that can be 70% higher for the bottom 40% of the population than for the average (Hallegatte and Rozenberg 2017). Therefore, mitigation can have a pro-poor and equalising effect. On the other hand, emission cuts, by setting a price on carbon, can have regressive implications if an adequate revenue recycling scheme supporting the poorest layers of the population is not predisposed (Hassett et al. 2009; Metcalf 1999). The social dimension of SDG7, achieving universal energy access, can also be hindered by a mitigation policy that increases energy prices in fossil fuel-intensive countries and burdens poor households. At the same time, the efficiency improvements, especially of renewable energy technologies, combined with pro-poor incentives can reduce this tarde-off (Dagnachew et al. 2018; Jakob and Steckel 2014). Direct effects of mitigation policy on SDG4 (quality of education) and SDG16 (preserve peace) have not been explored, though the literature on the link between global warming and conflicts is growing (Hsiang et al. 2011).

With respect to the economic indicators (SDGs 8, 9, 17), a broad literature on the interaction between technology and environmental externalities (Carraro et al. 2010) highlights the positive impacts of climate policy on innovation and technology diffusion (SDG8, decent work and economic growth). With respect to employment opportunities the evidence is mixed. Green jobs are mostly high-skill, entail higher wages, and tend to be concentrated in high-tech areas (Vona et al. 2018b). Although there are distributional implications, impacts on overall employment seem to be modest (Vona et al. 2018a). Despite the multiple channels through which mitigation policy can stimulate growth (Hallegatte et al. 2012), the IAM-based mitigation literature highlights the macroeconomic costs of stringent mitigation actions, mostly due to early retirement of capital, higher energy costs for producers and consumers, terms of trade effects (Paltsev and Capros 2013). The regional distribution of impacts on economic performance can also be expected to be uneven, mostly due to terms-of-trade effects, which would penalise net exporters and work in favour of net energy importers. In developing countries prioritising poverty-related issues, emission costs could divert funds necessary to development policies.

Even mitigation with a compensatory scheme by industrialised countries can lead to a “climate finance curse”, sluggish investments and technological change in energy-intensive sectors and, ultimately, slower economic growth (Jakob and Steckel 2014). Regarding SDG17, the IPCC 1.5 report highlights that the diffusion of new technologies related to decarbonization strategies requires transnational capacity building and knowledge sharing and could contribute to international partnership (Roy et al. 2019). Impacts on industry, innovation, and infrastructure (SDG9) are mixed and sector-specific, with a tendency to penalise energy-intensive sectors and infrastructure. Transforming the industrial sector towards a renewable-based and more efficient system aligns with the goal of upgrading energy infrastructure and making the energy industry more sustainable (McCollum et al. 2018b).

With respect to the environmental indicators, there is strong positive interaction between mitigation and SDG11, sustainable cities and infrastructure. This is driven by the multiple co-benefits of the behavioural and technological transformations mitigation policy might induce. According to Reis et al. (2018), meeting the 1.5C policy target may limit spikes of pollutant concentration (except PM2.5) above the safe thresholds in all countries. Furthermore, mitigation commitments might stimulate the development of renewable energy technologies and energy-efficient urban infrastructure solutions boosting urban environmental sustainability by further improving air quality, reducing noise and energy expenditure (McCollum et al. 2018a).

A strong positive interaction with high agreement and confidence is also found with water availability and quality (SDG6), natural resource protection (SDG12) through the reduced depletion of several natural resources, life below water (SDG14) through the reduced risk of ocean acidification, life on land (SDG 15) through reduced deforestation, though some weak trade-offs are also found especially for SDG 14 and 15 (McCollum et al. 2018a). The scaling up of renewable energy would lower the water demand for energy (e.g. for cooling power plants), though some specific options (e.g. hydropower) could induce trade-offs through competition for water use. A mitigation pathway that more strongly relies on bio-energy might have higher requirements in terms of water for irrigation, reducing availability for other sectors.

To conclude, the existing literature seems to suggest that the degree of competition between mitigation objectives and sustainable development depends on the type of transition pathway adopted. While energy supply or land and ocean mitigation options tend to entail a larger number of trade-offs and risks, demand-side measures can significantly reduce the risks associated with mitigation policies, as they tend to bring about a larger set of co-benefits. Yet, actual synergies and trade-off will be unevenly distributed across regions and nations (Roy et al. 2019).

3 An Ex-Ante Assessment of the Paris Agreement

3.1 Framework Description

The Aggregated Sustainable Development goals Index (ASDI) framework developed in this chapter aims at offering a comprehensive assessment of current well-being and future sustainability based on 27 indicators related to 16 Sustainable Development Goals.1 As describe in Fig. 1, ASDI combines an empirical, regression approach based on historical data (grey) with a modelling, future-oriented framework (black) to offer an internally-consistent set-up that makes it possible to analyse future patterns of sustainability indicators and their inter-linkages.

The selection of the SDG indicators was informed by the work of the UN Inter-agency Expert Group on SDG Indicators (United Nations (UN) 2017a), which listed 232 indicators to be used in assessing SDGs, and follows these guidelines: (1) relevance for the SDG they refer to, (2) connection with one of the SDG Targets, (3) sufficient data coverage for each country, (4) linkage to the macroeconomic variables that are output of the model. These are the main constraints on indicator selection of any systemic and multi-approach analysis of Agenda 2030 (von Stechow et al. 2016), including the ASDI framework here described. On the one side, the global perspective of the proposed modelling exercise requires the broadest coverage of indicators, dismissing some promising indicators for which sufficient data coverage is not yet available for a large number of countries. On the other side, given the goal of generating future projections of the selected sustainable indicators, we have to exclude indicators that could not be linked to any of the model variable outcomes or not showing a significant correlation with them. For this reason, at the moment, our analysis does not cover SDG5 (gender equality). We were not able to find a robust relation linking a gender-related indicator to an endogenous variable generated by the model. Table 1 lists the selected indicators and classifies them in the sustainability pillar they pertain to: economy (ECO), society (SOC), and environment (ENV). Among them, 16 are computed using model results, 7 requires regression analysis to be linked to them (SDG1, SDG2, SDG3a, SDG3b, SDG4, SDG7a, SDG10), and the remaining 4 are kept constant at historical levels (SDG14, SDG15a, SDG15c, SDG16).
Table 1

ASDI indicators

SDG

ASDI indicator

Pillar

SDG

ASDI indicator

Pillar

SDG

ASDI indicator

Pillar

SDG1

Population below $1.90 (PPP) per day (%)

SOC

SDG8a

Annual GDP per capita growth (%)

ECO

SDG13a

Concentration of GHG emissions from AFOLU(tCO2e/sq.km)

ENV

SDG2

Prevalence of undernourishment (%)

SOC

SDG8b

GDP per person employed ($PPP2011)

ECO

SDG13b

Compliance to Conditional NDCs (%)

ENV

SDG3a

Physician density (per 1000 population)

SOC

SDG8c

Employment-to-population ratio (%)

ECO

SDG13c

Gap from equitable and sustainable GHG emissions per capita in 2030 (tCO2eq)

ENV

SDG3b

Healthy Life Expectancy (HALE) at birth (years)

SOC

SDG9a

Manufacturing value added (% of GDP)

ECO

SDG14

Marine protected areas (% of territorial waters)

ENV

SDG4

Youth literacy rate (% of population 15–24 years)

SOC

SDG9b

Emission intensity in industry and energy sector (kgCO2e/$)

ENV

SDG15a

Terrestrial protected areas (% of total land area)

ENV

SDG6

Annual freshwater withdrawals (% of internal renewable water)

ENV

SDG9c

Share of domestic expenditure on Research and Development (% of GDP)

ECO

SDG15b

Forest area (% of land area)

ENV

SDG7a

Renewable electricity (% of total population)

ENV

SDG10

Palma ratio

ECO

SDG15c

Endangered and vulnerable species (% of total species)

ENV

SDG7b

Primary energy intensity (MJ/$PPP07)

ENV

SDG11

CO2 intensity of residential and transport sectors over energy volumes (tCO2/toe)

ENV

SDG16

Corruption perception index

SOC

SDG7c

Access to electricity (% of total population)

SOC

SDG12

Material productivity ($PPP2011/kg)

ENV

SDG17

Government gross debt (% of GDP)

ECO

Fig. 1

ASDI framework

The collection of historical data of indicators relies on several international databases (World Development Indicators (World Bank (WB) 2018), UN database (United Nations (UN) 2018), and World Income Inequality Database (WIID3.4) (United Nations (UN) 2017b)) and covers all available countries for the period 1990–2015. Historical data are used for initializing indicators in the base year of the model (2007) and for estimating the basic relationships between model’s variables and indicators in the regression analysis phase.

The regression analysis phase makes it possible to obtain projections of those indicators not directly generated by the model: poverty headcount ratio (SDG1), undernutrition prevalence (SDG2), physician density (SDG3a), Healthy Life Expectancy (HALE) (SDG3b), literacy rate (SDG4), Palma ratio2 (SDG10), and electricity access (SDG7a). Using independent cross-country panel regressions (reported in Annex I), we identify the historical correlation between indicators and some socioeconomic variables.3 The selection of the relevant explanatory variables for each indicator is based on the existing literature. Regarding SDG1, poverty prevalence has a negative correlation with unequal income distribution and a positive one with average income per capita level (Ravallion 2001, 1997; Ravallion and Chen 1997). Undernourishment prevalence (SDG2) is reduced when economic conditions (Headey 2013; Heltberg 2009; Fumagalli et al. 2013) as well as food production (Headey 2013) improve, and when inequality goes down (Heltberg 2009). Physician density (SDG3a) has a positive relation with total health expenditure per capita and a negative one with private health expenditure share. The healthy life expectancy (SDG3b) increases with the level of population education (Gulis 2000), urbanisation (Bergh and Nilsson 2010), physician density (or more in general public expenditure in health) (Kabir 2008), electricity access (Youssef et al. 2015), and drops in the case of a high level of undernourishment prevalence (Black et al. 2008). Regarding the literacy rate (SDG4), we consider a simple regression with education expenditure per capita and urbanisation, both fostering education attainment. The literature on electricity access (SDG7c) is wide and identifies GDP per capita (Chen et al. 2007), electricity supply, urbanisation (Lahimer et al. 2013), corruption control (Javadi et al. 2013) as favouring factors. Inequality works in the opposite side. Among the explanatory variables of our inequality measure, i.e. Palma ratio, we included public education expenditure per capita, sectoral Value Added (VA) share in agriculture and industry, corruption control and unemployment (Ferreira and Ravallion 2009; Ferreira et al. 2010).

The modelling framework used to develop SDG projections is the ICES model, (Eboli et al. 2010; Delpiazzo et al. 2017), a global CGE model based on the GTAP model (Corong et al. 2017) and running over the period 2007–2030 with recursive dynamics. The baseline scenario assumes no mitigation policies are implemented until 2030, while the mitigation policy scenario simulates the implementation of the conditional Nationally Determined Contributions (NDCs) submitted to the UNFCCC in the context of the Paris Agreement. By comparing the performance of the SDG targets in the two scenarios, the approach can quantitatively evaluate the implications of mitigation policy on sustainable development. Model features, baseline, and policy scenario assumptions are described in detail in the Annex II.

The post-processing module computes the values of the SDG indicators up to 2030 using the output of ICES. For the indicators not directly generated by the model, the estimated relationships from historical data with the regression analysis are used in an out-of-sample estimation procedure and combined with output variables of the model. All indicator values are then normalised between [0,100] using a benchmarking procedure that identifies sustainable and unsustainable thresholds for each indicator relying on the SDG targets and best practices.4 SDG indicators are then aggregated into SDG-specific indices (simple average of the underlying indicators) and into an Aggregate Sustainable Development Index (ASDI), a simple average of the SDG indices that reaches the score 100 whether all goals are met.5

3.2 Regional Performance in Achieving SDGs: A 2007 Snapshot

The selected SDG indicators, normalised and aggregated as described in the previous section and in Annex I make it possible to quantify country well-being and sustainability measured in terms of proximity to all SDGs. The approach can be applied to historical as well as to future, simulated data, enabling a comparison and measurement of changes in sustainability patterns over time and scenarios. Figure 2 synthesises collected historical indicator values at global level and shows the performance in each SDG and in the overall ASDI index of eight regional aggregates.6 The graph on the left shows the score of top performing regions (EU28, Rest of Europe, Pacific, and North America) in 2007. All of them are still far from achieving SDGs (score of 100). The EU28 is the front-runner, with a score of 70.5, Rest of Europe and Pacific regions closely follow (both at 65.4), and North America is the last in the overall ranking (59.3). On the right are four regional aggregates lagging behind in the sustainability pathway: Latina America (LACA region, 53.4) is close to the top performing group, whereas the gap widens for Middle East and North Africa (MENA region, 43.7), Asia (39.1), and Africa (37.7).

Fig. 2

Aggregate Sustainable Development Index (ASDI) and SDGs scores in 2007, top (left) and bottom (right) performers. Different grey shading represents the eight regions. Lightest grey is EU28 on the left and LACA on the right

The two radar graphs immediately visualise the noticeable difference between the two groups of countries. The top performers (left panel) are particularly close to achieving many SDGs related to the social pillar, i.e. SDG1, SDG4, SDG10, and SDG16. The graph of bottom performers (right panel) shows a more uneven regional distribution, with few isolated spikes for the SDGs mostly related to the environmental pillar, i.e. SDG14 and SDG6.

Looking more closely at regional differences, all top performers nearly meet SDG1 and SDG4, close to zero prevalence of extreme poverty and universal literacy rate, respectively. They have an average score of 87 (over 100 that represent the full sustainability) in SDG2, zero hunger, with around 2.6% of population undernourished. The score regarding reduced inequality (SDG10) and corruption perception (SDG16) is more heterogeneous: EU28 and the Pacific region score around 75 on equal income distribution, and North America only 4, with a Palma ratio of inequality equal to 1.95 (i.e. close to the unsustainable level of 2). Corruption perception is low in North America and Pacific (on average 85) and really high in Rest of Europe (score 0).

Focusing on the economic indicators, top performers score uniformly around 50 in SDG8 (indicators relative to growth of GDP, level of GDP per employed, and employment ratio). Sustainability of public debt (SDG17) is fully achieved in the case of Rest of Europe (100) and it is null in the case of EU28 (0 due to the high debt GDP ratio in some EU28 countries). North America and Pacific region have a score around 50. The score of SDG9, combining two economic and one environmental indicators for industry, innovation, and sustainable infrastructure, is uneven, on average 88 for EU28 and Pacific region, and 49 for North America and Rest of Europe. Despite the similar levels of manufacturing value added indicator, and some heterogeneity regarding the share of investment in R&D (higher in the North America and Pacific region), the score of SDG9 strongly reflects the indicator on emission intensity in energy and industry sectors, which is low both in the Rest of Europe and North America (respectively 0 and 8.2 over 100).

Regarding the other environmental indicators, water withdraw (SDG16) is fully sustainable in Rest of Europe (100) and the least sustainable in EU28 (54). SDG7 in terms of energy intensity growth and renewable electricity share scores around 55 in top performing regions, except in the EU28, where it reaches 67. On the contrary, CO2 intensity in residential and transport (SDG11) is too high for all top performers, in particular in North America (6 over 100). North American countries perform well in terms of efficient use of material, non-fossil resources (SDG12), while Rest of Europe scores worse (66.6). Marine ecosystems protection (SDG14) has also a score above average in all top performing regions, with Pacific region scoring the worst (67.5). Indicators relative to the protection of terrestrial ecosystem (SDG15) have a lower performance, with the Pacific region and North America scoring the worst (24.7 and 27.6, respectively). More differentiated is the result relative to SDG13, climate action, where Rest of Europe is leading with a score of 86.1, followed by Pacific (79.3) and EU28 (62). North America has the worst performance (46.3).7

As mentioned above, the snapshot of 2007 sustainability of the worst-performing regions is strongly heterogeneous. In Latin America (LACA) social indicator scores closely follow the North American ones, with slightly higher poverty levels (SDG1, score 83.4) and lower literacy rates (SDG4, 83.8). The social indicators most problematic for this region are undernutrition prevalence (SDG2, 66.1), good health (SDG3, 11), inequality (SDG10, 0), and corruption perception (SDG16, 16). Economic indicators are close to the average score (SDG8, 49.1, SDG17, 68.8, and SDG9, 68.1) and environmental SDGs range from good performances in water management, clean energy production, and climate action (respectively SDG6, 100, SDG7, 91.7, and SDG13, 66.6), to average results in water and land ecosystem protection (SDG14 and SDG15), to low outcomes regarding emission intensity in residential and transport (SDG11, 16.3), efficiency in using mineral resources (SDG12, 30).

The MENA region outperforms LACA in poverty and undernutrition reduction (SDG1, 94, and SDG2, 76), and equity (SDG10, 26). However, other social indicators are at critical levels, namely education (SDG4, 43.1), corruption perception (SDG16, 10.3) and in particular health (SDG3, 5.2). Economic indicators are slightly lower than those of LACA, excluding debt sustainability (SDG17) that for MENA is quite high (82.7). In the environmental sphere, particularly problematic are water management (SDG6, 0), CO2 intensity in residential and transport (SDG11, 4.2) and protection of marine ecosystem (SDG14, 0).

Comparing the performance of social SDGs in Asia and MENA region, it is worth highlighting that poverty and undernutrition prevalence are considerably higher in Asia (SDG1, 43.1 and SDG2, 17.1), whereas other indicators pertaining health, education, inequality, and corruption perception share a similar low score (SDG3, 1.8, SDG4, 29.7, SDG10, 18.4, and SDG16, 10.3). Asian economic sustainability does not differ significantly from that of MENA, only SDG9 has a lower performance (30.4) due to the high emission intensity in energy and industry sector. Critical environmental SDGs are instead material efficiency (SDG12, 13.2) and terrestrial ecosystem protection (SDG15, 28.4), whereas water management, emission intensity in residential and transport, and marine areas protection is more sustainable than in MENA region.

In 2007, Africa is the region with the widest gap from achieving all SDGs. The less sustainable sphere is the social one. Poverty and undernutrition prevalence (SDG1 and SDG2), healthy life expectancy (HALE, SDG3), literacy rate (SDG4), inequality (SDG10), and corruption perception (SDG16) have a 0 score. The low level of GDP per person employed reduces SDG8 score (34.9), and the low emission intensity in industry and energy sectors lead to an average score in SDG9 (46). Two environmental SDGs have very low scores, SDG7(17.6) and SDG12 (13.2). In the case of SDG7, high growth of energy intensity and low renewable share are combined with an unsustainable level of access to electricity.

3.3 Regional Trends in Achieving SDGs: Baseline Scenario

As described in Annex II, a baseline scenario without any mitigation policy in place is projected starting in 2007, reproducing historical patterns up to 2010 and, then following similar trends of those observed in the recent decades. This is the so-called Middle of the Road narrative of the Shared Socioeconomic Pathways, SSP2 as described in O’Neill et al. (2017). The score in each SDG and in the overall sustainability indicator ASDI is computed for each simulation year and is compared with 2007 results. For the sake of clarity, results for 45 countries and macro-regions of the ASDI framework are grouped into eight regional aggregates.

The socioeconomic dynamics and technological changes characterising the baseline scenario (changes in population, employment, GDP growth, reduction in fossil fuel dependency, and rise in energy efficiency) are heterogeneous across regions as well as within regions, and determine convergence or divergence from achieving SDGs. Figure 3 shows the changes in sustainability indicators between 2007 and 2030 across regions. Asia, Africa, and MENA are gaining the most in 2030, namely 17.7, 10.7, and 9.6 percentage points (pp) with respect to 2007, instead LACA and the EU28 experience a reduced sustainability (respectively −0.1 and −2.3 pp). These changes bring Rest of Europe to the top of ranking (ASDI 71), followed by the EU28 (ASDI 68.2), whereas Asia shifts to a middle level of sustainability (ASDI 56.8).

Fig. 3

Dynamics of ASDI and SDG scores, in the baseline scenario 2007 vs. 2030

Asian progress is relevant in reducing poverty (SDG1), undernutrition prevalence (SDG2), inequality (SDG10), in improving health (SDG3) and education (SDG4) (respectively, 55.4, 53.7, 67.4, 19.6, and 27.5 pp with respect to 2007). This evolution is fuelled by a moderate improvement in economic sustainability (SDG8, +13.7 pp) due to higher levels of GDP per person employed. The drawbacks for the environment emerge in particular regarding the intensity of water use (SDG6, −43.4 pp) and climate action (SDG13, −5.2 pp). In the latter case, economic growth implies higher emissions and therefore a widening gap from the NDC and equitable and sustainable emission path. The baseline scenario exhibits exogenous improvements in efficiency, reflecting historical patterns, and this trend appears in the advancements in material productivity (SDG12, +51.3 pp), emission intensity in residential and transport (SDG11, +6 pp) and affordable and clean energy (SDG7, +26.8 pp).8

Despite the higher sustainability level in 2030, the African region remains at the bottom of the ranking (ASDI 48.4). Poverty, undernutrition prevalence, and inequality dramatically reduce with respect to 2007 (SDG1 +91.6 pp, SDG2 +45.9 pp, and SDG10 +57.7 pp), although progress is not enough in the case of health and education status (SDG3 and SDG4 still have a 0 score). Economic sustainability worsens in particular regarding the sustainability of public debt (SDG17 −89.4 pp). SDG8 remains stable (+9.4 pp) despite opposing changes at the indicator level (slower GDP per capita growth, but higher GDP per employed). In the environmental realm, material use productivity rises considerably (SD12 +57.8 pp) as well as the sustainability of the energy system (SDG7 +26.8 pp) due to the higher share of electricity produced from renewable sources and wider access to it. Also in Africa, economic and population growth undermine the sustainable use of water resources (SDG6 −14.4 pp) and climate action (SDG13 −8.1 pp).

As mentioned above, the EU28 and LACA regions experience a reduction in sustainability by 2030. Despite the constant progress in the social SDGs, in particular inequality reduction (SDG10 +28.1 pp) and improvements in economic growth (SDG8 +8.4 pp), the sustainability of public debt deteriorates (SDG17 −53.1 pp) and some environmental indicators are negatively affected by the resource-intensive socioeconomic development foreseen in the baseline scenario. The intensity of water withdraw rises (SDG6 −30.1 pp) and the uncontrolled increase in emissions from agriculture and forest land, and of overall GHG emissions widen the distance from achieving the ambitious EU28’s NDC (SDG13 −31.2 pp).

Strong improvements in energy and material efficiency (SDG9 +9.1 pp; SDG12 + 4.1 pp), the strengthening of terrestrial ecosystem protection (SDG14 +29.7 pp), the less ambitious NDC (SDG13 −19.6 pp), the high reduction of inequality (SDG10 +71.5 pp), and a lower public finance deterioration (SDG17 −38 pp) mark the divergence between North America and the EU28 between 2007 and 2030. Despite these dynamics, the EU28 sustainability score in 2030 (ASDI 68.2) remains above the North American one (ASDI 63.1).

3.4 Paris Agreement Mitigation Scenario

The Paris Agreement, adopted in 2015, initiated a new climate policy regime characterised by country-driven emission targets as part of their international effort to limit global warming beyond 2020, the so-called Nationally Determined Contributions (NDCs). The NDCs describe the mitigation efforts of the UNFCCC Parties up to 2030. They are quite heterogeneous in terms of stringency, coverage, and reference level. For example, China, India, and Chile have expressed their NDCs in terms of emission intensity. Most NDCs describe an unconditional and a conditional target: the former to be met autonomously, and the latter, more ambitious, requiring external financial and technical support.

In the policy scenario design, we focus on the conditional mitigation objectives stated in the NDCs (reported in Annex II) and on the reduction of CO2 emissions. Our mitigation scenario starts in 2013 and assumes that each country achieves its NDC by 2030. The EU28 implements an Emission Trading System (ETS), while all other countries are assumed to implement a unilateral domestic carbon tax. Carbon tax revenues are recycled internally to households, public saving, and investments.

Our results show that the implementation of the NDCs will lead to higher sustainability for all countries, excluding MENA region, which is essentially unaffected (see Fig. 4). It is important to highlight that the change in the ASDI score induced by the mitigation policy is much smaller compared to that observed in the baseline scenario. The changes observed in the baseline scenario reflect socioeconomic and technological changes that occur between 2007 and 2030, whereas in the case of mitigation policy we only evaluate the effect of the policy on the 2030 score.

North America experiences the highest benefit from the mitigation policy (ASDI +4.3 pp with respect to 2030 baseline scenario), followed by LACA (ASDI +3.6 pp), Rest of Europe (ASDI +2.6 pp), and the EU28 (ASDI +2.4 pp). The EU28, Africa, and Pacific observe a change lower that 2 pp, and the MENA region has a modest reduction of −0.01 pp.

Fig. 4

Climate policy impact on SDGs in 2030 (Percentage point change relative to the baseline)

Mitigation policy most strongly affects the environmental SDGs. SDG13, on climate action, registers a rise between +31.1 pp in the EU28 and +0.6 pp in the MENA region, reflecting the achievement of the NDC targets and the convergence toward more equitable, sustainable emissions per capita. The SDG13a shows a general worsening because our mitigation policy focuses on CO2 and leave uncontrolled other gases emitted by Agriculture, Forestry, and Other Land use (AFOLU). In addition, we assumed that Egypt,9 part of MENA region, does not have a NDC. The country experiences a leakage effect that pushes it away from an equitable and sustainable emission path.

SDG7 is the second index for the magnitude of change induced by the policy, ranging between +14.2 pp in Rest of Europe and +0.01 pp in the LACA region. Also in this case, the SDG score depends on combined impacts on the underlying indicators. Mitigation targets stimulate substitution towards a cleaner energy mix characterised by a higher electricity share from renewables (between +25.5 pp in Africa and no change in the LACA region10) and lower primary energy intensity (between +21.7 pp in Rest of Europe and no change in LACA region11). It is worth noticing that the indicator on electricity access (social dimension in SDG7) is not negatively affected by the implementation of Paris agreement, especially in those countries still far from achieving that target (no change in Asia and +0.4 pp in Africa). In both cases, regional average results mask country heterogeneity. Some Asian and African countries slightly slow down their progress in electricity access (e.g. Bangladesh and Uganda), while others see an acceleration, having an energy system more flexible to renewable switching (Ghana and Ethiopia). Positive implications of the policy spread also to SDG6, inducing a more sustainable water use. In the EU28, the score change is of +2.7 pp. The effects on SDG11 and SDG12 are more heterogeneous. CO2 intensity in residential and transport sectors rises in Asia, MENA, North America and Pacific (SDG11 −2.5 pp), and material productivity shrinks in LACA, Rest of Europe, and Africa.

The economic SDGs show conflicting results. The carbon tax revenue improves government accounts and debt sustainability (SDG17) in LACA (37.6), Asia (20.9), and North America (+15.8 pp). In the other regions the change is not perceivable because the score of the indicator remains below the unsustainable level. SDG8 is the most sensitive to the costs of mitigation policy, reflecting a slowdown in GDP per capita growth in regions with ambitious climate policy (−4.5 pp in Europe) and a leakage effects where the interventions are modest (+5.9 pp in MENA region). The change of SDG9 ranges between +17.3 pp in North America and −0.1 pp in the MENA region and it is mainly due to the cut in emission intensity in the energy and industry sectors fulfilled with the mitigation targets.

Social indicators are slightly negatively affected by the costs of the mitigation policy and reflect the closure assumptions of the model. The carbon revenue is recycled partially to support household income, whereas government expenditure, a strong driver for social indicators, is left unchanged with respect to the baseline scenario. In Asia, social indicators slightly improve, on average, driven by the positive performance of India, whereas Indonesia, Bangladesh, and Rest of Asia highlight the need of additional pro-poor policies to complement mitigation interventions and limit their side effects. Africa shows a slow-down in poverty and undernutrition reduction (SDG1 −0.4 pp and SDG2 −0.6 pp). All countries in the region, excluding Mozambique, are negatively affected by the policy and its macroeconomic costs. As noted in Campagnolo and Davide (2018), inequality (SDG10) positively (negatively) reacts to ambitious (loose) mitigation targets, but the policy-induced inequality reduction is not sufficient to compensate the average GDP loss and often determines an increase of poverty prevalence.

4 Discussion and Conclusions

This chapter develops a framework for comparing historical and future sustainability performance measured by different SDG indicators. A CGE model is used to describe future baseline and policy scenarios at global scale for some key world regions to 2030. Relationships based on historical correlation patterns are used to link the macroeconomic variables projected by the model with 16 SDG indicators to derive sustainability implications. By looking at the sustainability issue in a dynamic manner, the approach here described makes it possible to track SDG indicator values across countries and trough time, shedding light on the regional distribution of synergies and trade-offs and contributing to expand the emerging literature on systemic analysis of climate policy and sustainable development.

Results highlight that mitigation policy reduces the gap toward achieving all sustainability goals by 2030 in all regions. Yet, regional results mask a complex relationship between mitigation policies and SDGs, which is highly country-specific, making it difficult to identify clear patterns, especially for some indicators. For example, the impact on environmental goals, such as SDG7 and 13, is unequivocally positive. Economic and social indicators are characterised by a higher regional diversity. Overall, results are in line with the evidence highlighted by the existing literature, pointing at synergies especially for environmental indicators. On the contrary, social dimensions are more frequently found to show trade-offs with mitigation policies, pointing at the need for additional pro-pure policy interventions (Roy et al. 2019). This analysis does not find evidence for strong trade-offs, one reason being the mitigation strategy, both in terms of stringency, which is moderate, and in terms of mix, as it does not rely on negative emission technologies and expansion of bio-energy.

Social and economic sustainability indicators tend to deteriorate in most regions, essentially for three reasons. First, the analysis focuses on mitigation policy without considering the benefits related to the reduced climate change impacts. Including the policy benefits in terms of reduced climate impacts, which tend to be regressive, could reverse the outcome of mitigation policies. Second, different carbon revenue recycling schemes can be designed to explicitly address the distributional implications of climate policy (Carattini et al. 2019). Third, additional mechanisms of co-benefits could operate through technological change, which in this framework remains exogenous.

The goal of this chapter is to describe the methodology and illustrate how it operates under a specific socioeconomic and policy scenario. Socioeconomic uncertainty deeply interacts with mitigation policy, and different baseline developments would affect results also with respect to the sustainability impacts of climate policy. The proposed framework can be easily adapted to handle multiple scenario combinations and to expand the set of baseline scenarios and mitigation policies.

Further refinements of the proposed framework include developing refined empirical estimates of the relationship between the SDG indicators and the model outcome variables, as well as exploring the role of uncertainty of these underlying relationships. The analysis is based on the central estimates, but confidence intervals could also be used. Widening the set of GHGs considered as well as the negative emissions from land use change could decrease the cost of the policy and the trade-off with social indicators. Whereas here the focus is on the interaction of mitigation policy with sustainable development, other existing policies could further modify the results. Adding the representation of climate change impacts and adaptation measures, which are not yet widely explored in the CGE and SDG literature, highlighting further channels of trade-offs and synergies, is needed in order to complete the characterisation of the inter-linkages between climate policy, impacts, and sustainable development. This analysis underestimates the benefits of mitigation because all impacts connected to global warming and all benefits deriving from a contained temperature increase in 2100 are not included. The emission pathway of the proposed baseline scenario falls between the Radiative Concentration Pathways RCP6.0 and RCP8.5, but the effects of the associated temperature increase on GDP growth as well as on other drivers (e.g. labour productivity) are not included. In this cost-effective approach, GDP is affected by emissions only through mitigation costs.

Footnotes

  1. 1.

    SDG5 on gender inequality is not explored.

  2. 2.

    The Palma Ratio is defined as the ratio of the top 10% of population’s share of Gross National Income (GNI), divided by the poorest 40% of the population’s share of GNI (Cobham et al. 2016).

  3. 3.

    Our future sustainability scenarios are built under the assumption that the estimated relationships will hold also into the future up to 2030.

  4. 4.

    A more detailed description of this step and a table with benchmarks can be found in Annex I.

  5. 5.

    A more detailed description of this step can be found in Annex I.

  6. 6.

    It is worth remembering that the score in each SDG and in the ASDI index is restricted to the 27 selected indicators and not to all other dimensions encompassed by the UN Agenda 2030.

  7. 7.

    SDG13 summarises three indicators: the concentration of emissions from agriculture, forestry and land sue (AFOLU), the distance from achieving NDC emissions, and the gap from equitable and sustainable GHG emissions per capita. In spite of being closer to sustainable and equitable emissions per capita than Rest of Europe and Pacific, the EU28 is characterised by a higher AFOLU emission concentration and results farther from achieving its NDC due to a more ambitious target.

  8. 8.

    Asia’s score in SDG7 depends on a cleaner energy system (lower growth of primary energy intensity and higher renewable electricity share), but also on the expansion of access to electricity.

  9. 9.

    Egypt and Bolivia do not have a quantitative NDC, therefore, we assume the two countries are not implementing any mitigation policy.

  10. 10.

    LACA region is fully sustainable in this dimension (score 100) also in the baseline scenario, therefore, an improvement of this indicator does not translate into a higher score.

  11. 11.

    Ibidem.

  12. 12.

    ICES model further specifies renewable energy sources in electricity production, namely wind, solar and hydro-electricity, splitting them 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; Ragwitz et al. 2006; GTZ 2009, IEA country profiles and REN21).

  13. 13.

    Hanoch’s constant difference elasticity (CDE) demand system (Hanoch 1975) has the following formulation: \(1=\sum B_{i}U^{Y_{i}R_{i}}(\frac {P_{i}}{X})^{Y_{i}} \) where U denotes utility, Pi the price of commodity i, X the expenditure, Bi are distributional parameters, Yi substitution parameters, and Ri expansion parameters. The CDE in principle does not allow to define explicitly direct utility, expenditure, or indirect utility functions. Accordingly, also explicit demand equations could not be defined. Fortunately, in a linearized equation system such as that used in GTAP, it is possible to obtain a demand function with price and expenditure elasticities.

  14. 14.

Notes

Acknowledgements

This paper has received funding from the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme under grant agreement No 756194 (ENERGYA).

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Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Ca’Foscari University of VeniceVeniceItaly
  2. 2.Euro-Mediterranean Center on Climate ChangeVeniceItaly
  3. 3.RFF-CMCC European Institute on Economics and the EnvironmentVeniceItaly

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