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1 Knots, Solutions, and Governance Conundrums Barrie Stevens OECD/International Futures Programme Poland and Regions – the Perspectives of development in the XXI Century IV Warsaw Conference, 24-25 October 2008

1 Knots, Solutions, and Governance Conundrums Barrie Stevens OECD/International Futures Programme Poland and Regions – the Perspectives of development

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Knots, Solutions, and Governance Conundrums

Barrie StevensOECD/International Futures Programme

Poland and Regions – the Perspectives of development in the XXI Century

IV Warsaw Conference, 24-25 October 2008

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A Global Outreach

OECD Member CountriesCountries/Economies Engaged in Working Relationships with the OECD

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OECD : Key Tasks

Promote policies to achieve sustainable economic growth and employment and rising standards of living in member countries, and contribute to the development of the world economy.

Help member governments address the economic, social and environmental challenges of globalisation.

Provide a setting where governments can compare policy experiences, seek answers to common problems, identify good practice, and work to co-ordinate domestic and international policies.

Strategic foresight helps underpin the fulfilment of these tasks by identifying future policy issues (threats and opportunities) and encouraging early thinking and timely action.

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Fundamental global trends

Population growth and ageing Gravitational shift in poles of economic growth Rising number of important players in the world

economy Uncertainties of climate change Growing interconnectedness

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Examples of recent OECD IFP work related to the “Gordian Knots”

Global Infrastructure Investment Requirements to 2030 (the case of water)

The Bioeconomy to 2030 (the case of food and agriculture)

The Future of International Migration (the case of competition for talent)

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Global Infrastructure Investment Requirements to 2030

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Estimated average annual world infrastructure investment requirements 2003-2030 (additions and renewal)

In USD Bn and as a percentage of world GDP

Table 1 p 29 - Infrastructure to 2030: Telecom, Land Transport, Water and Electricity (2006)

Type of infrastructure

2000-10 Approx. % of world GDP

2010-20 Approx. % of world GDP

2020-30 Approx. % of world GDP

Road 220 0.38 245 0.32 292 0.29

Rail 49 0.09 54 0.07 58 0.06

Telecoms1 654 1.14 646 0.85 171 0.17

Electricity2 127 0.22 180 0.24 241 0.24

Water1,3 576 1.01 772 1.01 1 037 1.03

1. Estimates apply to the years 2005, 2015 and 2025.2. Transmission and distribution only.3. Only OECD countries, Russia, China, India and Brazil are considered here.

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Water stress by major water basins in 2000 and 2030

Source: OECD Environment Directorate (2006), Working Party on Global and Structural Policies, Revised environmental baseline for the OECD environmental outlook to 2030, 20-21 November 2006, ENV/EPOC/GSP(2006)23

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Two track approach to bridging the water infrastructure gap

Find additional funding and develop innovative approaches to finance (public and private)

Use infrastructure more efficiently and more intelligently through more demand management, improved strategic planning, more effective governance, and improved integration of existing and new technologies.

Requires major international effort with wide range of countries, not least in Africa, Middle East, Central Asia, South and SE Asia…)

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The Bioeconomy to 2030 : health, industry, and food & agriculture applications

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Drivers for agricultural production

Population Increasing Affluence

Climate Change Biofuels

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Scale of the challenge

Per capita world grain production to increase from 305kg in 2000 to 340kg in 2030 (2.8 billion tonnes)

Average consumption in developed countries is twice this amount

Consumption in developing countries is increasing

– Per capita meat consumption in China increased from 20kg in 1980 to 50kg in 2007

If everyone adopts a European diet, there will be a shortfall of 2.3 billion tonnes

– Assumes no grain use for biofuels

– Assumes no change in yields due to climate change

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By 2030, bulk of agbio production and R&D will have moved to developing countries

Strong evidence that European agbio R&D has slowed down significantly

Developing country activity has increased– Of the 8 countries planting more than a million ha of GM

crops, 6 are non-OECD countries (Argentina, Brazil, China, India, Paraguay, South Africa)

– Over 550 biotech field trials have occurred in 47 non-OECD countries

– Major agricultural biotechnology programmesCountry Agricultural biotechnology R&D spending (in USD)

Brazil 5 billion over the next 10 years

China 120 million per year, including major projects on GM rice

India 100 million per year

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Concentration has been intense

1995 – 1997

2,746 field trials

2005-2007

3,207 field trials

Top firm (Monsanto) 22.0% 47.2%

Top 5 firms1 53.0% 79.6%

Top 10 firms 69.0% 90.9%

Top 20 firms 82.7% 96.1%

Top 25 firms 86.4% 97.6%

Source: OECD, based on the UNU-MERIT GM Field trial database

Notes: (1) The top five firms in 2004-2007 were Monsanto, Targeted Growth,

DuPont-Pioneer Hibred, Bayer Crop Science, and Syngenta.

Percent of all GM field trial applications by leading firms

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International cooperation

– Given the geographic imbalance between supply and demand, trade access will be paramount

– Cooperation and joint ventures for R&D, particularly to adapt technologies to local conditions, will be essential

- Robust global competition will help ensure that R&D is efficient

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The Future of International Migration

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Major migration patterns in the early 21st Century

Source: United Nations

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Factors favouring future migration flows to OECD countries

Persistent income level differentials Poor social infrastructures (health, education etc.) War and civil unrest Poor governance Climate change and natural disasters

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Factors impeding future migration flows to OECD countries

Fast growth in key non-OECD economies to 2030, making for growing appetite for labour

Improved education and research facilities in non-OECD countries

Improved health and social infrastructures Emerging economies as attractors and retainers of

highly skilled manpower Growing competition for global labour supplies

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International co-operation to manage migration flows

Labour migration – skilled and unskilled - to alleviate labour shortages and the adverse impact of ageing populations in OECD countries

Controlling irregular migration Securing successful integration of immigrants and

their children Making best use of the human capital of immigrants Strengthening co-operation between countries with

respect to migration and development Managing adverse effects of the “talent crunch”

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The Governance Conundrum (1)

Three different examples of global issues requiring stronger international co-operation, each involving wide range of countries in different configurations.

Reflects globalisation where both opportunities and responsibilities are more widely shared.

Representativeness and inclusiveness required to address the diversity of countries and issues and claim legitimacy and credibility.

But how to be inclusive and effective?

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The Governance Conundrum (2)

Is there a trade-off between universality and effectiveness?

In the current climate, is it then more important to be effective than legitimate?

New fixed architecture or variable geometry? Personal relations and institutional memory are key,

but in which setting are they more effective? OECD experience

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Thank you.

Barrie Stevens

[email protected]

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World population in 2030

2626

OECD's share in world GDP (current PPP US$)

Note : GDP in current PPP US$ is calculated by multiplying nominal GDPs by current PPP exchange rates.Source : IMF World Economic Outlook database and Hervé et al . (2007), Globalisation and the Macroeconomic Policy Environment, OECD Economics Department Working Paper No. 552.

30%

35%

40%

45%

50%

55%

60%

65%

70%

1980 1985 1990 1995 2000 2005 2010 2015 2020 2025

2727

Canada7 million ha

(M, R, S)

United States57.7 million ha(C, M, R, S, O)

Paraguay2 million ha (S)

Argentina19.1 million ha (C, M, S)

Brazil11.5 million ha

(C, S)

South Africa1.4 million ha (C, M, S)

India3.8 million ha

(C)

China3.5 million ha (C, O)

Legend

C = Cotton R = RapeseedM=Maize S = SoybeanO = Other

= Country planting more than 1 000 000 ha of biotech crops

= Country planting less than 1 000 000 ha of biotech crops1