GRO project: modelling the impact of resources constraints on growth

Transcription

1 GRO project: modelling the impact of resources constraints on growth Dr. Aled Jones, Dr. Irene Monasterolo, Davide Natalini, Victor Anderson Global Sustainability Institute, Cambridge (UK)

2 GRO objectives Investigate the most likely level of GDP (and beyond) in short-term given resource constraints: What kind of composition of GDP required for growth/ green growth? Composition both in terms of sector (e.g. % of GDP in services, investment, agri), energy & resource intensity List of credible disruptive events to affect the situation Provide clear information on sustainable scenarios: Close time-lag btw short term governments agenda/long-term modelling Commit governments to targeted sustainability measures..and follow policy recommendations. 2

3 The state of the play Limits to growth 1972: role of human activity on resources depletion and impact on global growth 12 scenarios (system dynamics modelling) of resources/population interaction Since then, increase in data, indicators (ecological footprint, GHG emissions), models, programmatic docs and actions (Agenda 21, Kyoto Protocol), institutions but low impact on sustainable policies of influential countries EU2020 ambitious strategy to provide sustainable development while tackling climate change but discrepancies willingness/actual implementation. 3

4 Anthropocene and its consequences Alarming results partially confirmed (Turner 2008, Randers 2012): human-led resource depletion (Mann, 1998, Steffen et al., 2011) & impact of climate change affect access to global resources (UNEP 2011, FAO 2012) Multidimensional consequences: Uncertainity wrt future economic growth (capital constraints and productivity challenge, McKinsey, 2011 ) Systemic risks in high indebted countries High volatility of commodity prices (Grantham 2012, FAO) due to costly resource extraction and conversion, D/S imbalances High inequality, poverty, famine, vulnerability (WB) Resources exploitation (Oxfam) Political instability (e.g., Arab spring) Quality of life of future generations at risk (WWF). 4

5 Resources nexus: local issues Source: FAO, Dimensions of need: An atlas of food and agriculture,

6 And global impacts Tin Oil Gas Copper 50 0 Coal Zinc Uranium Indium Silver 50 years left for tin, zinc and oil (approximate number of years left= current global reserves/ current annual consumption (assuming no growth in demand) Source: Jones et al., Data: BP 2012, Cohen, Rockstrom et al (2009) 9 ecological boundaries which human activity should not cross (green circles) - red wedges: current situation, 3 boundaries already crossed 6

7 In a more crowded world Less developed regions Total Less developed regions Urban More developed regions Total More developed regions Urban Source: WDI 7

8 How can models contribute to policy making for sustainable prosperity? Several projects and models tried to understand limits to sources of capital (natural, human) and what this means for society e.g. Planetary Boundaries by Stockholm Institute; One Planet Living by WWF; EUREAPA, Stern s PAGE 2002, ENV-LINKAGES) But either: not transparent about the reasons of impacts do not model physical limitations, sectorial model on long time scales strong equilibrium assumptions do not directly tell a story through their scenarios too complex to be used as initial decision-making tool. 8

9 GRO model characteristics Short-term focus (next 5 y.) to show policy makers: What if scenarios during a government term Impact of interventions and behaviours on growth Move from empirical evidence: multidimensional dataset ( ): Data: official sources, global level, comparable, consistent, yearly updated inputs to debt map Debt map: Country reserves/annual consumption to show years left for each country if they used their own reserves Debt map as input for System Dynamics model (SD) to describe behaviours, accounting for market imperfections. 9

10 SD to be included into agent based model (ABM) Micro-macro analysed together to avoid missing relations and trends (resources-countries-gdp) ABM to describe scenarios Agents: UN countries (190) Directly able to tell stories (vs. coefficients needing further qualitative interpretation) Validation (test correlations) using statisticaleconometric methods (MANOVA, dynamic panel data) Future discount rate: % change in average TFP recorded from 1995-now (Arrows, et al, 2011) Scenarios: BAU, up-risings, change in family size, commodity prices, climate change extreme events. 10

11 From SD to ABM SD ( World4 ) to analyse interconnections btw natural resources (water, food, land, fuel, minerals), countries and growth 4 sets of variables: Economic v. (GDP, consumption, investment, productivity, population trend, FTSE sectors proxy) V. concerning economy impact on environment and resources (change in land use, water pollution, loss of biodiversity, ecosystems change) V. concerning impact of environmental loading on growth (GHG contribution to GDP per sector, rents in% of GDP, change in prices and TFP) Social indicators (income inequality, international conflict/co-operation). 11

12 WHY ABM Proper computational method to model socioeconomic-ecological systems Allows to take into account micro&macro levels at the same time ABM to explore country behaviour characteristics: Comprehensive picture of relation btw agents properties, behaviour, resource use, GDP level and composition No I-O or SAM: estimated coefficients would not be able to return clear picture of scenarios depicted Allows to simulate policy scenarios. 12

13 Agents and behaviour assumptions Countries wish to maximise GDP growth given resource availability, productive structure, specialization Agents take into account resource issues for specific resource and GDP according to BAU Agents behaviour modelled is decision on how much resource to consume and how much to invest to reach/continue GDP growth Agents behaviour governed by rent (of resources in % of GDP) that can be achieved, and by influence of future likely rent (i.e., current reserve) SD govern what agents can trade (change in TFP, change in relative prices) and consumption implications Agents behave in a social network (trade and political partners). 13

14 THANK YOU FOR THE ATTENTION! GSI Team, Cambridge 14

15 ABM Characteristics ABMs include the following elements (Bravo et al. 2012): an environment, i.e., a set of objects the agents can interact with; a set of agents who interact with each other; a set of relationships linking objects and/or agents; a set of operators that allow the interaction between the agents and the objects. 15

16 PROS CONS Time-consumin initial Use of generative approach (emerging data-gathering properties, phase bottom-up) Complex modelling and Possible to shape heterogeneous simulation and hierarchical phase agents..but time well spent! Allows to analyse non-linear interactions Informative at scenarios microlevel, which results in macro level provided patterns Interdisciplinary approach 16