Impact of Each Scenario on Possibilities for Meeting Food Demand

There are a number of logical qualitative trends for assessing whether food demand can be met by increased per-area productivity or by placing more land into production. Implications of possible trends in per capita GDP are:

• If per capita GDP is high, then (1) demand for more meat (protein) in the diet is high in developing countries, tending to increase pressure on land for food; and (2) there is capital available for increasing productivity, tending to decrease pressure on land for food.

• If per capita GDP is low, then the opposite trends ensue: (1) there is less protein in diet, decreasing pressure on land for food; and (2) less capital is available for increasing productivity, increasing pressure on land for food.

Table 6.3a. Scenario drivers taken from the SRES scenarios and total carbon gap

Total carbon gap

World GDP

Per capita

Income ratio

(PgC y-1) in 2100

Population

in 2100

GDP in

in 2100 of

at stabilization

in 2100

(1012

2100 (103

Annex I to

Scenario

target of450ppm

(billion)

1990 US$)

1990 US$)

non-Annex Ia

Technology

1990

5.3

21

3.9

16.1

A1FI

25

7.0-7.1

522-550

73.5-78.6

1.5-1.6

Rapid introduction of technology

A1B

10

7.0-7.7

340-536

44.2-76.6

1.5-1.7

Rapid introduction of technology

A1T

1

7.0

519-550

74.1-78.6

1.6-1.7

Rapid introduction of technology

A2

25

12.0-15.1

197-249

13.0-20.8

2.7-6.3

Relatively (to A1) slower introduction of technology

B1

1

6.9-7.1

328-350

46.2-50.7

1.4-1.9

Rapid introduction of technology

B2

10

10.3-10.4

199-255

19.1-24.8

2.0-3.6

Less (relative to A1) rapid, and

more diverse technological change

Sources: (Nakicenovic et al. 2000; Table SPM-1a and story-line text) and total carbon gap from Chapter 4, this volume a Annex I = developed or industrialized nations; non-Annex I = developing countries.

more diverse technological change

Sources: (Nakicenovic et al. 2000; Table SPM-1a and story-line text) and total carbon gap from Chapter 4, this volume a Annex I = developed or industrialized nations; non-Annex I = developing countries.

Table 6.3b. Increase in food demand under each scenario

Scenario

Increase in food demand relative to 1990

Long-term average increase in per-year productivity required to meet food demanda

A1FI

32-34%

0.3%

A1B

32-45%

0.3-0.4%

A1T

32-34%

0.3%

A2

126-284%

1.1%

B1

30-34%

0.3%

B2

94-96%

0.9%

a calculated by % change by 2100 divided by 110 (years since 1990).

a calculated by % change by 2100 divided by 110 (years since 1990).

Table 6.3c. Impacts of scenarios on the pressure on land for food production

Per capita GDP impact

Per capita GDP

Technology —

on potential increase

impact on change

pressure

in productivity —

in diet — pressure

on land

Scenario

pressure on land resource

on land resource

resource

Carbon gap

A1FI

+

+++

- - -

Small

A1B

+

+++

- - -

Moderate

A1T

+

+++

- - -

Small

A2

+++

+

-

Large

B1

++

++

- - -

Small

B2

+++

+

-

Large

Note: + = more pressure on land for food, — = less pressure on land for food (relative to 1990).

Note: + = more pressure on land for food, — = less pressure on land for food (relative to 1990).

Trends in technological development will be equally critical and can be summarized thus:

• If technology development and effective dissemination are high, there is more capacity to increase production, tending to decrease pressure on land for food, and vice versa.

A caveat is needed concerning thresholds. Since even the lowest change in per capita GDP by 2100 (A2) is more than three times greater than 1990 per capita GDP, this may already exceed the thresholds that lead to changes in food consumption patterns, thereby creating increased pressure on the land resource (Bruinsma 2003). Similarly, technology may cross a threshold allowing increases in productivity to be met with technological advances. Where these thresholds lie, or whether they exist at all, is unknown. Such threshold effects are not considered in this analysis.

To apply these trend indicators to estimate net pressure on land resources, we first use the A1FI scenario (global free market with intensive fossil-fuel use) as an example. Here food demand, driven by population growth, increases by 34 percent by 2100 (Table 6.3b). Per capita GDP is high, meaning that there is likely to be more demand for meat in the diet (pressure on land resource = +++), but also that capital will be available to increase per-area productivity (average of 0.3 percent per year required; pressure on land resource = +). Rapid introduction of new technologies means that technological advances, leading to higher per-area productivity, are favored to help meet the increased demand (pressure on land resource =---). The first row in Table 6.3c shows the net consequences of these factors.

Applying the same reasoning, Table 6.3c shows how the parameters associated with each SRES marker scenario affect net pressure on land for food production. The highly regional scenarios A2 and B2 present the greatest pressure on land, since both have high food demand, low per capita GDP, and slow technological development and dissemination (hence low capacity to feed their high populations). Hence, scenarios A2 and B2 offer the least land for closing the carbon gap by using land-based options or for meeting other human needs.

Scenarios A1FI, A1B, A1T, and B1 present the lowest pressure on land for food production, because of relatively low population growth, high per capita GDP (leading to better ability to increase per-area productivity, even though diet would include more meat), and a high rate of technological development and dissemination. These scenarios also have the lowest, and therefore most realistically achievable, increases in productivity to meet increased food demand. Scenarios A1FI, A1B, A1T, and B1 are the most likely to offer land for helping to close the carbon gap and for providing land for other human needs.

The magnitude of the carbon gap for each SRES scenario has implications for the land available to close the gap. As shown in Table 6.3a, for a stabilization level of 450 parts per million (ppm), scenarios A1FI and A2 have the largest carbon gap by 2100 (25

PgC y-1), A1T and B1 have the smallest gap (1 PgC y-1), and A1B and B2 are intermediate (10 PgC y-1). There would be little land available for carbon sequestration and biofuel cropping to help close these gaps for A2 and B2. Scenarios A1T and B1, however, have the smallest carbon gaps and are also the most likely to have land available for gap closure by land-based options. Scenarios A1FI and A1B are also more likely than A2 and B2 to have land available for gap closure, but the gap is 10—25 times larger than for A1T and B1. Hence, carbon sequestration and biofuel cropping may form part of the portfolio to close the carbon gap for A1FI or A1B, but the gap is much larger. Although the income ratio between developed and developing countries (Table 6.3a) decreases significantly in all scenarios between 1990 (16.1) and 2100 (1.5-6.3), scenarios A2 and B2 are the most likely to show regionally different impacts on land pressure, with low incomes in developing countries lessening the ability to meet food demand by improved management or technology.

In conclusion, this analysis suggests that given the constraints on land required for food production, land-based methods (biofuel cropping and carbon sequestration) for carbon gap closure show a sustainably achievable mitigation potential only under SRES scenarios A1T and B1. Under all other scenarios, land-based methods do not show a potential for contributing significantly to carbon gap closure.

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