Can Paris deal boost SDGs achievement? An assessment of climate-sustainabilty co-benefits or side-effects

Transcription

1 Can Paris deal boost SDGs achievement? An assessment of climate-sustainabilty co-benefits or side-effects Lorenza Campagnolo and Marinella Davide December 5 th 26, FEEM-IEFE Joint Seminar

2 Motivation 2th UNFCCC Conference Of Parties (COP 2) will aim to reach a binding global climate agreement grounded on the Intended Nationally Determined Contributions (INDCs) Sustainable Development Goals (SDGs) adopted in September 25 by United Nations aim to shape the pathway towards an inclusive green growth How COP2 outcome will affect the path towards achieving SDGs? Focus on extreme poverty and inequality indicators which are the core of SDG and SDG, in addition to the usual environmental indicators on GHG emission reduction and clean energy use The chosen approach couples an empirical analysis with a CGE model 2

3 Outline Overview of past trend of poverty and inequality Main drivers of poverty and inequality change Baseline scenario description Climate policy under different recycling schemes Future path of poverty and inequality in the policy scenarios Conclusions and further steps 3

4 Poverty headcount rate at $.25 a day (PPP) (% of population) poverty headcount rate No data Source: WDI database 836 million people live with less than $.25 a day (WB, 25) 4

5 Palma ratio Poverty headcount rate (% of population) Past trend of poverty and inequality (WDI, WB) East Asia & Pacific (all income levels) Europe & Central Asia (all income levels) Latin America & Caribbean (all income levels) Middle East & North Africa (all income levels) North America South Asia Sub-Saharan Africa (all income levels) World 4,5 4 Between 99 and 22, around 8 million people moved outside of extreme poverty (-43%) 5 At global level, the Palma ratio average slightly decreases from 2.2 in 992 to.79 in 22 3,5 3 2,5 2, East Asia & Pacific (all income levels) Europe & Central Asia (all income levels) Latin America & Caribbean (all income levels) Middle East & North Africa (all income levels) North America South Asia Sub-Saharan Africa (all income levels) World

6 Evidences on poverty determinants Empirical literature from a cross-country perspective highlights as main drivers: The growth of average income per capita (Ravallion and Chen, 997) The distributional change (Ravallion, 997, 2; Heltberg, 22; Bourguignon, 27) Growth elasticity of poverty and inequality elasticity of poverty Country-specific empirical analyses consider as drivers: Sectoral growth patterns (Ferreira et al., 27; Montalvo and Ravallion, 2) CGE modelling literature: Micro-simulation approach Multi-household approach 6

7 Evidences on inequality determinants Empirical cross-country studies: differential in labor productivity between agricultural and nonagricultural sectors (Bourguignon and Morrison, 998) sectoral wage differentials between skilled and un-skilled labor (Bourguignon et al., 25) globalization, education attainments and policy (Alvaredo and Gasparini, 25) CGE modelling perspective: Multi-Household approach Usually assumed constant 7

8 Predicting inequality and poverty The considered period spans from 99 to 22 (WDI database, WB) 3 independent panel regressions using country fixed effect model with robust and panel-corrected standard errors Inequality determinants low 4 y i,t high y i,t Poverty determinants PEduExp_sh i,t.232** -.235*** (.6) (.2) ln AgriVA_sh i,t.69** -.392* (.4) (.8) ln IndVA_sh i,t.277* (.88) (.2) Corrupt_cntr i,t (.26) (.29) Unempl i,t -.4*.27 (.54) (.55) d_c_i I,t.8.34 (.48) (.634) t.82*** -.57*** (.) (.) Constant -3.55*** *** (.) (.) ln POV i,t ln GDPPPPpc i,t *** (.) Palma i,t.259*** (.) Constant *** Observations Number of country Robust pval in parentheses *** p<., ** p<.5, * p<. (.) 52 8 Observations Number of country 2.99 Robust pval in parentheses *** p<., ** p<.5, *p<.

9 ICES model and baseline scenario The ICES model (Eboli et al., 2) is a recursive-dynamic General Equilibrium model, relying upon the GTAP-E structure (Burniaux and Troung, 22) Medium term analysis: Scenario SSP2: medium population growth and medium GDP growth 45 countries and 22 sectors considered Poverty and inequality change is directly driven by changes of endogenous variables of ICES model 9

10 Inequality change in the baseline scenario (27 vs. 23) Australia NewZealand Japan SouthKorea Bangladesh China India Indonesia RoAsia Canada USA Mexico Argentina Bolivia Brazil Chile Peru Venezuela RoLACA Benelux Czech_Rep Finland France Germany Greece Italy Poland Spain Sweden UK RoEU RoEurope Russia Turkey Egypt RoMENA Ethiopia Ghana Kenya Mozambique Nigeria Uganda South Africa RoAfrica RoW Palma ratio ,5,44,,3,83,2,86,4,95,3,27,34,75,,68,92,7,2,87,27,82,24,92,39,85,28,99,33,89,3,65,92,,5,87,24,77,5,83,92,97,9,33,23,6,27,4,39,39,4,53,66,8,9,8,79,69,6,8,87 2,3,99 2, 2, 2,22 2,6 2,4 2,6,98 2,9 2,6 2,24 2,4 2,5 2,53 2,86 2,82 2,93 2,75 2,88 2,9 3,6 3,3 3,48 3,57 3,86 4,42 4,38 4, ,5

11 Poverty change in the baseline scenario (27 vs. 23) Australia NewZealand Japan SouthKorea Bangladesh China India Indonesia RoAsia Canada USA Mexico Argentina Bolivia Brazil Chile Peru Venezuela RoLACA Benelux Czech_Rep Finland France Germany Greece Italy Poland Spain Sweden UK RoEU RoEurope Russia Turkey Egypt RoMENA Ethiopia Ghana Kenya Mozambi Nigeria Uganda South Africa RoAfrica RoW Poverty headcount rate (% of population) bln people get out extreme poverty compared to ml people: poverty reduction in the period

12 Climate policy: INDCs Climate policy scenarios consider the INDCs as binding targets 2

13 Climate policy scenarios MPOLICY scenario: considering the INDCs as binding targets: EU28 achieves its target through an Emission Trading Scheme (EU- ETS) The other countries impose a carbon tax Internal recycling of the revenues Same mitigation scenario but with different revenue recycling schemes: MPOLICY+LDCFUND scenario: Creation of a International Green Climate Fund (GCF) that reaches billion in 22 OECD and East European countries contribute to GCF recycling a share of their carbon tax revenues LDC countries receives a lump-sum transfer from the GCF (inversely proportional to their GDP p.c.) MPOLICY+LDCFUND_SUB scenario: In LDCs the transfer from the Fund is used to subsidise Clean Electricity, Water, R&D and Public Services (excl. Education and Health) 3

14 GtCO2 GtCO2 GtCO2 Stringency of the mitigation targets,2,8 23, BASELINE 23, MPOLICY,6,4,2 4, , BASELINE 23, MPOLICY,25,2,5, 23, BASELINE 23, MPOLICY 2,5 4

15 % change wrt 23 SSP2 Mitigation policy costs in GHG emissions reduce of 6% with respect to the 23 baseline scenario 5

16 Effect of mitigation scenario on inequality Bangladesh China India Indonesia RoAsia Mexico Argentina Bolivia Brazil Chile Peru Venezuela RoLACA Egypt RoMENA Ethiopia Ghana Kenya Mozambique Nigeria Uganda South Africa RoAfrica Palma ratio -,5,,5 2, 2,5 3, 3,5 4, 4,5 5, 23, BASELINE 23, MPOLICY 6 Countries with stronger mitigation contributions show a small reduction of inequality

17 Effect of mitigation scenario on poverty prevalence Bangladesh China India Indonesia RoAsia Mexico Argentina Bolivia Brazil Chile Peru Venezuela RoLACA Egypt RoMENA Ethiopia Ghana Kenya Mozambique Nigeria Uganda South Africa RoAfrica Poverty headcount rate (% of population) in 23 under different scenarios , BASELINE 23, MPOLICY 7 The outcome in term of poverty prevalence reduction is mixed and depends on the composition of income and inequality effects of the policy

18 Development Fund The Green Climate Fund reaches bln$ in 22 and then remain constant EU28 countries revenues account for 56% of the Fund Nigeria 5% Uganda 4% RoAfrica 7% Bangladesh 3% Indonesia 3% RoAsia 3% Egypt 3% RoMENA % Mozambique 5% Ethiopia 23% Kenya 8% Ghana 5% 8

19 % change wrt 23 baseline scenario Effect of different recycling schemes on inequality 5 MPOLICY MPOLICY+LDCFUND MPOLICY+LDCFUND_SUB Transfers from Green Climate Fund to LDCs increase inequality Subsidies on clean energy, water, R&D sectors push their production at the expenses of inequality-reduction sectors (e.g. education and light industry)

20 % change wrt 23 baseline scenario Effect of different recycling schemes on poverty MPOLICY MPOLICY+LDCFUND MPOLICY+LDCFUND_SUB Transfers from Green Climate Fund to LDCs increase poverty prevalence in most of countries due to the higher inequality and lower GDP growth Benefits of mitigation policies (reduction of climate change impacts) are not considered

21 Conclusions Linking empirically SDGs indicators to a CGE model allows assessing future trend of these indicators under different scenarios and policy interventions Considering the INDCs as binding targets, COP2 agreement will determine: a positive effect on inequality reduction the more ambitious is the climate mitigation commitment (synergies between climate policy and inequality) a slight increase of extreme poverty prevalence in the LDCs Recycling carbon revenues with the creation of a Green Climate Fund for LDCs worsen the outcome in terms of poverty and inequality for most of the countries, but we are only considering the costs of mitigation policy and not the benefits (lower climate change impacts) The Green Climate Fund has to be coupled and can not replace a Development Fund aiming to achieve SDGs by 23 2

22 Further steps Extend the empirical analysis of historical data on inequality and poverty: other databases on income distribution (WIID) different approaches (short run and long run elasticities of poverty and inequality) use a lognormal approximation of initial income distribution Consider different SSPs scenarios Take into account different recycling schemes 22

23 Thank you for your attention!

24 % change wrt 23 SSP2 Effect of different recycling schemes on GDP 8 Bangladesh Indonesia RoAsia Egypt RoMENA Ethiopia Ghana Kenya Mozambique Nigeria Uganda RoAfrica MPOLICY MPOLICY+FUND MPOLICY+FUND_SUB -8 24