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Session 7b: Modeling and scenario building: the outlook for forestry in Asia and the Pacific

Emerging scenarios in the Asia—Pacific pulp and paper sector to 2020: a preliminary assessment of implications for wood demand and land use

Chris Barr1 and Brian Stafford2

This paper summarizes preliminary findings from CIFOR's analysis of medium-term scenarios for the Asia—Pacific pulp and paper market. It reviews supply—demand trends for the major grades of paper and paperboard over the past decades as well as the production and consumption of fibre furnish, with particular attention to wood-based pulp. With respect to fibre demand, by far the most significant development during this period has been the rapid rise of China's paper market. The study offers a preliminary forecast to 2020 for China's supply and demand of major grades of paper, board, and fibre furnish under a baseline scenario characterized by continued vigorous GDP growth. The study also examines India's recent economic growth and reviews the implications for regional pulp fibre demand if India follows a similar growth trajectory as China. To assess potential impacts on natural forest resources in Asia and the Pacific, the scenario analysis focuses on paper grades that utilize hardwood pulp. Beyond the anticipated increase in aggregate demand to supply the region's growing internal demands, it is also significant that Asia is emerging as a world centre for fine paper production.

In recent years, traditional manufacturing regions for these grades — notably North America and Europe — have lost market shares to producers in China, Republic of Korea, and Indonesia. The study considers whether this trend towards "world regional" specialization, with Asia concentrating on the manufacture of fine paper, is sustainable over the long term. Much will depend on whether producers can integrate their operations with low-cost fibre production in Indonesia and elsewhere in the region, and on the willingness of governments to provide discounted finance and other capital subsidies.

Over the medium to long term, rising energy costs may lead pulp producers in South America to integrate their operations with paper production, thereby forcing Asian producers to rely on their own fibre resources. For each scenario, the study examines the implications for natural forests and rural livelihoods in Asia and the Pacific.

Keywords: pulp, fine paper, fibre furnish, wood demand, regional specialization

Introduction

Between 1997 and 2007, global paper and paperboard production grew from 300 million to 394 million tonnes per annum (James et al. 2000; RISI 2008). The Asia—Pacific region accounted for over one-half of this increase, and the region's contribution to world paper and board output increased from approximately 28 percent in 1997 to over 33 percent ten years later. As in many other sectors, China's rapid economic growth has been the single largest factor driving this expansion. During this period, China's annual paper and board output grew from less than 25 million tonnes in 1997 to an estimated 71 million tonnes in 2007. Accounting for 18 percent of global output, the country is now the world's second-largest producer, surpassed only by the United States (Hawkins Wright 2007).

The rapid expansion of the Asia—Pacific paper industry has been accompanied by sharp increases in wood pulp production, with significant implications for the region's forests and rural communities who depend on them. In Indonesia, the development of large-scale kraft pulp mills has resulted in the conversion of approximately 1.1 million hectares of natural forest since the early 1990s (Barr 2007, 2001; WWF 2008). Although much of this area has been legally converted to intensively managed pulpwood plantations, this process has resulted in significant losses of biodiversity and has been a source of substantial carbon emissions (WWF 2008). Across the region, the development of fast-growing plantations for pulpwood has also introduced both new risks and opportunities affecting the livelihoods of rural communities (Nawir et al. 2003; Mayer and Vermeulen 2002; Lang 2002). In several countries, the development of plantations on an industrial scale has frequently resulted in the displacement or marginalization of local people. In other cases, pulp and paper companies' growing demand for wood has created new opportunities for income generation by smallholders, either through land rental or the sale of wood (Lu Wenming et al. 2002).

This paper examines five major trends now occurring which are likely to influence the scale and structure of the Asia—Pacific pulp and paper sector through 2020. These trends include: (1) China's rapidly growing paper and paperboard industry is placing increasingly heavy demands on the world's pulpwood resources; (2) India's demand for paper products may be following a similar growth trajectory as China; (3) East Asian paper producers are emerging as suppliers of printing and communication paper to the rest of the world; (4) globally, pulp production is shifting to low-cost wood-growing regions in the southern hemisphere, including Southeast Asia; and (5) the operational scale of chemical pulp mills has expanded rapidly over the past two decades.

This paper also examines these trends and analyzes potential growth scenarios to identify implications for wood demand and land use in the Asia—Pacific region through 2020. It must be emphasized that the projections and scenarios presented in this study are still quite preliminary, and are intended to provide initial estimates of the volumes of wood and areas of land that the region's pulp and paper industry could require over the medium term, depending on how the aforementioned trends play out. More detailed modeling and spatial analysis will be needed to better understand the implications of these changes for forests and livelihoods, and particularly their impacts within specific landscapes.

Trend #1: China's paper industry is placing increasingly heavy demands on the world's fibre resources

China's accelerated economic growth over the past decade has driven a sharp increase in the nation's demand for paper and paperboard products. In 1997, China's apparent consumption of paper and paperboard was 32.7 million tonnes; and by 2007, this figure is estimated to have reached 71.9 million tonnes (China Paper Association 2007). If it is assumed that similar growth rates continue, the nation's consumption of paper and paperboard is projected to reach 84.7 million tonnes in 2010 (see Figure 1).3 Over the past decade, expansion of processing capacity within the nation's paper and board industry has more than kept pace with this growing domestic demand, and China is now exporting significant volumes of printing and communication paper (Hawkins Wright 2007; He and Barr 2004).

Figure 1. China's paper and paperboard demand: 1997—2010 ('000 tonnes per annum)

Source: China Paper Association (2007), with projections by Dequan He.

Growth has been concentrated in the packaging grades, for which the furnish is overwhelmingly wastepaper, much of which is imported from the United States and Europe. In addition, China has installed more than 12 million tonnes of paper-making capacity for printing and communication paper since 1997, as internal demand increased from 6.3 million tonnes in 1997 to 17.4 million tonnes in 2007 (China Paper Association 2007). This is significant because printing and communication paper rely heavily on virgin wood pulp for furnish. Figure 2 shows China's production of printing and communication papers since 1998 with forecasts through to 2020 (in thousands of tonnes per annum).

In analysing China's pulp and paper industry, it is important to distinguish between segments of the industry that may be characterized as "Old China" and "New China" (Hawkins Wright 2007). The "Old China" paper industry produces generally lower quality products using a mix of non-wood fibre, wastepaper and some wood pulp on (generally) antiquated machines. The "New China" industry, on the other hand, is equipped with modern machines — including some of the the largest and fastest in the world — and generally uses virgin wood pulp to make high grade coated and uncoated paper. These currently account for approximately 35 percent of China's total printing and communication paper output.

The rapid expansion of modern papermaking in China, and particularly the growth of printing and communication grades, has resulted in a corresponding increase in China's demand for wood pulp. In 2006, China consumed around 13 million tonnes of wood pulp, of which over 60 percent was imported and less than 40 percent was produced locally (China Paper Association 2007). Table 1 shows Chinese pulp imports by grade.

Figure 2. China's production of printing and writing papers from 1998 to 2006 with projections to 2020 ('000 tonnes per annum)

Source: China Paper Association (2007); Hawkins Wright (2007); authors' projections.

Table 1. China's pulp imports by grade: 1995 and 2005 ('000 tonnes & US$FOB)

   

1995

   

2005

 
 

Volume (‘000 tonnes)

%

US$million

Volume (‘000 tonnes)

%

US$million

BSKP 429.4 57.1 353.3 2 894.2 40.1

1 467.0

BHKP 123.8 16.5 99.5 2 598.0 36.0

1 260.6

Semi-Chem 1 0.1 0.7 867.1 12

359.5

UKP 93.8 12.5 73.8 765.7 10.6

332.5

Mechanical 26.4 3.5 19.8 49.7 0.7

25.6

Sulphite 77.8 10.3 63.6 40.7 0.6

23.5

Total

752.4

100.0

610.8

7 214.4

100.0

3 468.6

Source: UN Comtrade.

Total pulp imports increased more than tenfold between 1995 and 2005, from 750 000 tonnes to 7.2 million tonnes (UN Comtrade 2007). Of the two major pulp grades, imports of bleached hardwood kraft pulp (BHKP) grew the fastest, at 36 percent per annum, totaling 2.6 million tonnes in 2005. Foreign shipments of bleached softwood kraft pulp (BSKP) grew at 21 percent per annum, from 429 000 tonnes in 1995 to 2.9 million tonnes in 2005. Imports of semi-chemical pulp grew from only 1 000 tonnes in 1995 to 867 000 tonnes in 2005.

Wood used for the production of BSKP is largely sourced from natural forests in the northern hemisphere (particularly in Canada) and Pinus radiata plantations in the southern hemisphere. BHKP is made from mixed tropical hardwoods (MTH) harvested from natural forests, as well as acacia and eucalyptus sourced from fast-growing plantations. Since the late 1990s, the main source of China's BHKP requirements has been Indonesia. However, by 2006 imports of highly consistent eucalypt pulp from Brazil had come close to matching the volumes of MTH and acacia pulp from Indonesia. Figure 3 shows countries of origin for China's imports of BHKP.

Figure 3. China's imports of pulp by country of origin: 1997—2006 (tonnes per annum)

Source: UN Comtrade.

As Table 1 indicates, pulp imports were costing Chinese paper producers nearly US$3.5 billion on an FOB basis by 2005. To offset such costs, the Chinese Government has for the last several years promoted domestic wood pulp production linked to local fast-growing plantations. Since 2000, the government has offered companies willing to invest in integrated pulp mills with the following incentives: a) tariff reductions and tax holidays; b) discounted financing from state banks; and c) capital subsidies to develop a target of 5.8 million hectares of pulpwood plantations by 2015 (Barr and Cossalter 2004; AF&PA 2004). By late 2007, China had 4.5 million tonnes of installed wood pulping capacity and the government was planning another 6.2 million tonne expansion (not all of which is expected to occur). Much of this planned new capacity is concentrated in coastal southern China, particularly the provinces of Hainan, Guangdong and Guangxi.

Many analysts are sceptical of China's ability to establish a globally competitive wood pulp industry based on domestic wood sources (RISI 2007; Barr and Cossalter 2004). In particular, the cost of growing wood in China is quite high compared with, for example, Indonesia, Brazil and Chile. This is due, in part, to the fact that there is a shortage of flat land available for pulpwood plantations within a commercial distance of most planned pulp mill sites. In many areas, population pressures effectively mean that pulpwood plantations must compete with other land-use options, particularly horticultural production. In addition, China's plantation productivity is substantially lower than more efficient pulp-producing countries in Southeast Asia and Latin America. Reasons for this include:

The structural limits on land availability and plantation productivity in China raise a key question: What will happen if Chinese pulp mills are unable to compete with imported pulp? It is likely that such a situation would result in increased pressure on smallholders to accept lower prices for their wood and/or the consolidation of small plantation holdings into large industrial plantations in order to reduce costs. Given that pulp mills are highly capital-intensive investments, there is also the prospect that China's state banks will be left with large non-performing loans (Barr and Cossalter 2004).

Scenarios for wood demand and land use to 2020

To estimate the volumes of wood pulp that China's printing and communication paper industry will consume by 2020 — and the implications of such demand in terms of wood and land area required — three growth scenarios are considered. Figure 4 shows the results of these scenarios, which are based on projected annual growth rates of 4.5, 6.5 and 8.5 percent, respectively, from China's estimated 2007 wood pulp consumption level. It is projected that China's pulp demand by 2020 could range between 11.6 million tonnes per annum under the low-growth scenario and 19.1 million tonnes per annum under the high-growth scenario. Under the medium-growth scenario, China's printing and communication subsector will require 15 million tonnes of pulp in 2020.

Table 2 provides estimates of the volumes of wood and areas of plantation land that would be needed under each of the three growth scenarios for China's wood pulp demand delineated above. Under the medium-growth scenario, pulp production of 15 million tonnes in 2020 would require approximately 67 million m3of wood annually (assuming an average wood requirement of 4.5 m3 to produce 1 air-dried tonne [Adt] of pulp). The production of such a prodigious volume of pulpwood would require the harvest of 478 000 hectares of fast-growing plantations annually (assuming an incremental growth rate of 20 m3/hectare/year, which of itself may not be achievable). Moreover, this medium-growth wood supply scenario would require a net planted area of 3.3 million hectares managed on a seven-year rotation. It should be noted that this is the equivalent of twice the area of fast-growing plantations established in Indonesia over 20 years. The gross plantation area needed to meet China's medium-growth pulp scenario would be in the vicinity of 4.3 million hectares.

Figure 4. China's wood pulp requirement scenarios: 1997—2020 ('000 tonnes)

Source: authors' projections.

Under the high-growth scenario, China's pulp consumption would require 86 million m3 of wood; the net plantation area would be 4.3 million hectares; and the gross area 5.6 million hectares. Significantly, if it is assumed that these plantations would be managed on a seven-year rotation, then these areas would need to be planted by 2013 to be available for harvest in 2020.

Table 2. Estimates of wood and land area required for China's wood pulp scenarios: 2007—2020

  4.5% 6.5% 8.5%
2020 pulp volume (million tonnes) 11.6 14.9 19.1
Wood (million m3 @ 4.5 m3/Adt) 52.2 67.1 86.0
Annual net plantation area required ('000 ha @ 140 m3/ha) 373 478 614
Total net plantation area: 7 year rotation 2 611 3 346 4 298
('000 ha @ 140 m3/ha x 7 years)
Gross plantation area (@ 1.3 x net ha) '000 ha 3 394 4 349 5 587

Source: authors' projections.

Trend #2: India's demand for paper may be following a similar growth trajectory to China

In recent years, India's accelerated GDP growth and economic restructuring — together with its potentially vast market — have led many analysts to argue that India is following China to become a major force in the global economy (Podar and Yi 2007; McKinsey Global Institute 2007). Both countries have enormous populations and have experienced high rates of economic growth since at least the mid-1990s. During the decade from 1995 to 2004, India's GDP grew at 6.1 percent annually — twice the world average (World Bank 2005, cited in Winters and Yusuf 2006). For the period 2005—2020, the World Bank has projected that India's economy will continue to grow at an average annual rate of 5.5 percent (Winters and Yusuf 2006). Rather more optimistically, Goldman Sachs projects that India's GDP will grow at rate of 8.4 percent during the period 2007—2020 if growth-supportive policies continue to be implemented (Podar and Yi 2007).

In Goldman Sachs' analysis, growth at this pace will put India's GDP on track to surpass those of most G-6 countries — namely, Italy, France, the United Kingdom, Germany and Japan — by 2030; and that of the United States by 2050 (Podar and Yi 2007). At the same time, India's population of 1.1 billion people in 2005 is expected to grow to 1.5 billion by 2050, overtaking China as the world's largest country. This, in turn, is expected to have enormous implications for India's demand for commodities across a wide range of sectors, as the country's GDP per capita (in US dollar terms) is projected to quadruple during the period 2007—2020 (Podar and Yi 2007). Significantly, India is also rapidly urbanizing, with ten of the 30 fastest-growing cities in the world. An additional 140 million people are expected to move from rural to urban areas by 2020, while 700 million people are projected to urbanize by 2050 (Podar and Yi 2007; see also McKinsey Global Institute 2007).

What would continued rapid expansion of India's economy mean for the country's pulp and paper consumption? In very general terms, there appears to be immense potential for growth. In 2006, India's apparent consumption of paper and paperboard was approximately 6.5 million tonnes, or roughly 5.9 kilograms per capita. By comparison, annual per capita paper and board consumption in Indonesia is 20.6 kilograms; Brazil 39.5 kilograms; China 44.7 kilograms; Germany 231.6 kilograms; and the United States 297.0 kilograms (FAOSTAT 2007). With a highly educated and rapidly urbanizing population, there appears to be strong potential for growth in India's demand for printing- and communication-grade paper, which generally require virgin wood pulp. According to industry statistics, India's consumption of printing and communication paper totaled 2.7 million tonnes in 2006, most of which was produced domestically, accounting for approximately 40 percent of the country's overall paper and board demand. Assuming an average pulp content of 70 percent, it can be estimated that India's consumption of printing- and communication-grade paper required approximately 1.9 million tonnes of wood pulp in 2006.

Scenarios for wood demand and land use to 2020

To estimate the volumes of wood pulp that India's consumption of printing and communication paper will require by 2020 — and the implications of such demand in terms of wood and land area required — three growth scenarios are considered. Figure 5 shows the results of these scenarios, which are based on projected annual growth rates of 4, 8 and 12 percent, respectively, from India's estimated 2006 wood pulp consumption level. It must be noted that these figures represent an aggregate of the estimated volumes of pulp that would be processed by India's paper industry and the volumes needed to produce paper externally that are imported by India. Based on these assumptions, it is projected that India's pulp requirement by 2020 could range between 3.2 million tonnes per annum under the low-growth scenario and 9.2 million tonnes per annum under the high-growth scenario. Under the medium-growth scenario, India's consumption of printing and communication paper will require 5.5 million tonnes of pulp in 2020.

Figure 5. India's possible pulp requirement scenarios: 2007—2020 ('000 tonnes)

Source: authors' projections

Table 3 provides estimates of the volumes of wood and areas of plantation land that would be needed under each of the three growth scenarios for India's wood pulp demand delineated above. Under the medium-growth scenario, pulp production of 5.5 million tonnes in 2020 would require approximately 25 million m3 of wood annually (assuming an average wood requirement of 4.5 m3 to produce 1 Adt of pulp). Pulpwood production on this scale would require the harvest of 177 000 hectares of fast-growing plantations annually (assuming an incremental growth rate of 20 m3/hectares/year, which may be quite optimistic in many locations). This, in turn, would require a net planted area of 1.2 million hectares managed on a seven-year rotation, and a gross plantation area of 1.6 million hectares.

Under the high-growth scenario, India's pulp consumption of 9.2 million tonnes in 2020 would require an annual wood input of approximately 41 million m3. Assuming an incremental growth rate of 20 m3/hectares/year, this volume of pulpwood would require the annual harvest of nearly 300 000 hectares. This, in turn, would require a net planted area of 2.1 million hectares (assuming a seven-year rotation) and a gross plantation area of 2.7 million hectares.

Table 3. India's possible wood requirement scenarios: 2007—2020

 

4.0%

8.0%

12.0%

2020 pulp volume (million tonnes)

3.2

5.5

9.2

Wood (million m3 @ 4.5 m3/Adt)

14.4

24.8

41.4

Annual net plantation area required (‘000 ha @ 140 m3/ha)

103

177

296

Total net plantation area: 7 year rotation (‘000 ha @ 140 m3/ha x 7 years)

720

1 238

2 070

Gross plantation area (@ 1.3 x net ha) ‘000 ha

936

1 609

2 691

Source: authors' projections

While the medium- and high-growth scenarios presented here suggest that India's growing demand for printing and communications paper will require substantial volumes of wood, it is likely that most of this will have to be supplied from offshore. Domestic plantations for industrial wood production are not very extensive, and India has a very active environmental movement opposed to granting the industry access to government land for the establishment of new plantations (Bhati and Jha 2006). Moreover, in contrast to the aggressively entrepreneurial character of the Chinese industry, the Indian industry is not particularly well-capitalized and has little experience in developing fast-growing plantations on an industrial scale (Walkers 2006). To a very significant degree, India's wood requirement will be met through imports either of wood pulp or of processed paper products, as it would not be competitive for Indian producers to build pulp mills domestically without a local wood supply. Ballapur Industries' 2007 purchase of Sabah Forest Industries — Malaysia's largest integrated pulp and paper mill, with a 300 000 hectare plantation concession—appears to reflect the emergence of a regional strategy on the part of India's leading paper producer.

Trend # 3: East Asian paper producers are emerging as suppliers of printing and communication paper to the rest of the world

Since the late 1990s (and especially in the past five years), East Asian exports of printing and communication paper to the rest of the world have experienced a sharp increase. Regional exporters of fine paper include China, Indonesia, Republic of Korea — and on a much smaller scale, Thailand. Aggregate exports of printing and communication paper from these "Big Four" countries grew sixfold from approximately 1 million tonnes in 1997 to 6 million tonnes in 2006 (UN Comtrade, see Figure 6). These exports represent 25 percent of the production of these grades by these countries. (Japan is excluded from this analysis because its pulp and paper economy focuses on internal demand and there is very little trade with the rest of the world.)

Figure 6. Exports by East Asian printing and communication paper producers (tonnes per annum)

Source: UN Comtrade.

The growth of East Asian fine paper exports has been a result of the very substantial investments in printing and communication paper capacity made by producers in China, Indonesia and Republic of Korea over the past decade (Hawkins Wright 2007). These investments have included the installation of some of the largest and most efficient paper machines in the world, and have often been made with little apparent regard for the state of supply and demand for fine paper in the global market (Zhu 2008; Rodden 2005). Such a production-driven approach has led to a world surplus of these grades, which has served to depress international prices. This, in turn, has resulted in major mill closures in North America and Europe in recent years, as well as antidumping actions against some of these countries in the World Trade Organization (Anon 2008; Ince et al. 2007).

The emergence of China and Republic of Korea as prominent suppliers of printing and writing paper to the rest of the world carries particular significance, as neither country has a comparative advantage in low-cost wood production (Young 2007; Cossalter and Barr 2004). Indeed for both countries, the expansion of fine paper production is supported by pulp imports from Latin America and Indonesia. In this sense, any substantial further expansion of these countries' fine paper exports could have significant implications for global demand for market pulp and/or require integration between Chinese and Republic of Korea paper producers and pulp mills in countries with a surplus.

Scenarios for East Asian fine paper exports to 2020

Figure 7 sets out projections of Big Four East Asian printing and communication paper exports to the Rest of the World (ROW) through 2020 under three different growth scenarios:

Under Scenarios #2 and #3, East Asian exporters will have an incentive to expand regional pulp production, particularly through integrated pulp and paper operations. Their incentive to do so will become particularly strong if Latin American market pulp producers take significant steps towards integrating their operations. Latin American producers could conceivably install their own paper machines, allowing them to process wet pulp instead of drying and baling pulp for the market (see Anon 2007). This strategy would enable the pulp producers to capture the downstream value added, and they would have a competitive advantage against their Asian counterparts in the form of the cost of drying, baling and transporting the pulp to Asia. If this scenario were to play out, there would be a rush to establish an integrated wood supply in East Asia to feed the already established papermaking capacity.

Figure 7. Scenario projections of the Big Four Asian printing and communication paper exports to ROW to 2020 (tonnes per annum)

Source: Comtrade for 1995—2006; authors' projections.

Trend #4: Globally, pulp production is shifting to low-cost wood-growing regions in the southern hemisphere, including Southeast Asia

The global pulp and paper sector is experiencing a major structural transformation as the locus of wood pulp production has shifted decisively away from North America and Europe, notably Scandinavia, to more efficient wood-producing regions of the southern hemisphere, particularly Latin America and Southeast Asia (Ince 2007). Although this trend started some time ago (see Carrere and Lohman 1996), it has accelerated considerably over the past decade and is expected to continue for some time. This shift has been characterized, on the one hand, by the development of large-scale and highly competitive pulp production facilities owned by local conglomerates, such as Aracruz in Brazil and the Asia Pulp & Paper (APP) and APRIL groups in Indonesia (Barr 2001). On the other hand, it has also involved the divestment of wood supplies and pulp mills in the northern hemisphere — and corresponding investments in the south — by leading North American and European producers, such as International Paper and Stora Enso.

This shift of pulp production to the southern hemisphere is largely driven by the growing imperative for producers to be cost-competitive within an increasingly global economy. With wood generally accounting for at least 40 percent of a pulp mill's cash costs, there is a clear comparative advantage for production facilities to be located in close proximity to low-cost wood supplies (Ince 2007). The large areas of land available and highly favourable growing conditions for fast-growing pulpwood plantations have given countries such as Brazil, Chile and Indonesia an enormous comparative advantage in pulpwood production, as compared to their northern competitors. Perhaps most strikingly, highly productive eucalyptus and acacia plantations in these countries are managed on a six- to seven-year rotation, as opposed to the 25 to 35 years required for softwood plantations in North America or Europe. Significantly, pulp producers based in Indonesia have had the added cost advantage associated with the government's policy to allow large areas of "degraded" natural forest to be converted for pulpwood production, at very low royalty levels (Barr 2001).

It is difficult to project the impacts of this "shift to the south" on forests and rural livelihoods in the Asia—Pacific region through 2020 with any degree of specificity. In recent years, pulp mill investment projects have been considered for a number of countries in the region, including Indonesia, Malaysia, Thailand, Viet Nam, Cambodia, Myanmar and Australia, although very few of these projects have gone ahead (Lang 2002). At least in the near future, Indonesia would appear to be the most likely site for significant new pulp capacity to come online, both due to the scale of the country's existing industry and the government's active efforts to promote further expansion. Indeed, the Ministry of Forestry's 2007 "roadmap for forest industry revitalization" announced that the government seeks to raise the nation's pulp capacity from 6.5 million tonnes per annum in 2007 to 16 million tonnes per annum in 2020 — an expansion that would require capital investment in excess of US$15 billion (Ministry of Forest 2007). Similarly, the government seeks to expand paper-making capacity from 8.5 million tonnes per annum in 2007 to 18.5 million tonnes per annum by 2020.

What would such a significant expansion of Indonesia's pulp and paper industry mean in terms of wood demand and land use? At its 2007 capacity, Indonesia's pulp industry effectively requires 29.2 million m3 of wood per year if it were to operate at full capacity (assuming that 4.5 m3 of wood are required to produce 1 Adt of pulp). The planned expansion to 16 million tonnes per annum of pulp capacity by 2020 would imply that the industry would then need some 72 million m3 of wood annually to run at capacity. It is estimated that the production of this volume of wood would require that approximately 514 000 hectares of fast-growing plantations be available for harvest annually (assuming an average productivity of 140 m3/hectares). This, in turn, would require an overall net planted area of 3.6 million hectares (assuming a seven-year rotation) and a gross plantation area of some 4.7 million hectares.

To support this capacity expansion, the Government of Indonesia is now promoting a substantial expansion of the nation's industrial plantation base by allocating large areas of "degraded" natural forest for conversion. Since the late 1980s, the Ministry of Forestry has allocated plantation concession licences to private and state companies under its "industrial plantation forest" (Hutan Tanaman Industri, or HTI) programme. By late 2006, 210 licences covering an area of 9 million hectares (gross) had been issued or were pending. As shown in Table 4, roughly two-thirds of these had been allocated in five provinces.

Table 4. HTI plantation concessions allocated from 1980 to 2006 (million ha)

Province

Area (million ha)

Papua

1.63

East Kalimantan

1.46

Riau

1.18

South Sumatra

0.97

West Kalimantan

0.91

Total

6.15

Source: Indonesian Ministry of Forestry.

In December 2006, the Ministry of Forestry announced plans to allocate an additional 9 million hectares (gross) from the State Forest Zone for the establishment of plantations for industrial wood by 2016 (Departemen Kehutanan 2006). Under this new plan, some 40 percent of the total area (3.6 million hectares) will be allocated to traditional large-scale industrial timber plantations, while the other 60 percent (5.4 million hectares) will be made available for the development of community-based timber plantations (Hutan Tanaman Rakyat or HTR). Thus far, the development of the HTR community-based plantations has been focused on Sumatra and Kalimantan, although progress has been slow.

If the industry's recent history is any guide, there are strong reasons to believe that the expansion of Indonesia's pulp industry would place new pressures on forests and peatlands. As noted at the outset of this paper, Indonesia's pulp industry since the early 1990s has relied heavily on MTH harvested from the natural forest whilst waiting for plantation estates to mature (Barr 2007, 2001). In addition, much of the area being converted to pulpwood plantations in Sumatra is on peatlands, resulting in high levels of carbon losses (WWF 2008). Depending on where new mills and plantations are located, it is conceivable that peatlands in Kalimantan and other parts of Indonesia could face similar threats.

The vast areas designated for industrial plantation development also pose significant risks and potential new opportunities for rural livelihoods in Indonesia. Thus far, pulpwood plantation concessions have frequently overlapped with land or forests managed by local communities under customary tenure systems. The result has often been the displacement of local peoples and, in some cases, violent conflict (Harwell 2003; Fried 2000). At the same time, the industry's growing demand for wood also creates potential opportunities for smallholder tree growers to supply pulpwood to the mills (Nawir et al. 2003). Such arrangements are encouraged under the Ministry of Forestry's HTR community-based plantation programme, which promises to allocate 5.4 million hectares to smallholder tree planters in 15-hectare blocks. Progress to date, however, has been slow; and in some regions, there are preliminary signs that smallholders find oil-palm and rubber to be more attractive land-use options than acacia. Indonesia's long history with government-sponsored outgrower schemes also suggests that caution is needed to ensure that the programme genuinely strengthens smallholders' livelihoods and well-being (Colchester et al. 2006).

Trend #5: the operational scale of chemical pulp mills is rapidly expanding

A fifth trend that is likely to shape the pulp industry's impacts on wood demand and land use in the Asia—Pacific region over the medium term is the steady increase in the operational scale of kraft pulp mills that has occurred over the last two decades. In the mid-1980s, pulp mills with a capacity of 500 000 tonnes per annum were considered to be quite large by world standards. At present, Indonesia's two largest mills — APP's Indah Kiat and APRIL's Riaupulp, both of which are located in central Sumatra's Riau Province — have installed capacities in excess of 2 million tonnes per annum; moreover, each is currently in the process of expanding further. Although no other pulp mills in the region currently operate on this scale, the emergence of mega-scale mills, both in Indonesia and in Latin America, is indicative of a broader industry trend towards highly concentrated production facilities.

This trend is driven by two factors: First, ongoing technological advances have resulted in a steady increase in the size of the boilers used during the kraft pulping process. As the boiler is the main factor determining how much wood a mill can process into pulp, the average production capacity of new pulp mills has increased in direct proportion to boiler size. At present, most new pulp production lines oriented towards the world market — whether they are installed as part of a greenfield project or capacity expansion at an existing mill — have a minimum scale of 700 000 tonnes per annum. In late 2004, APP set a new benchmark when it started production at its BHKP mill in China's Hainan Province: the mill's single production line has a capacity of 1.1 million tonnes per annum.

Second, the development of kraft pulp mills is a highly capital-intensive enterprise. It is not uncommon for the marginal investment costs to range between US$1 000 and US$1 500 per tonne of capacity — implying that a 700 000 tonnes per annum production line typically requires an investment of between US$700 million and US$1.3 billion (Spek 2006). To maximize their return on investment, producers have a strong incentive to pursue economies of scale. Producers like APP and APRIL have done so both by installing the biggest production lines available and by carrying out aggressive "debottlenecking" programmes to expand the capacities of existing lines. In addition, they have installed multiple production lines at individual mill sites. Some observers have noted that the two companies appear to be motivated, at least in part, by a competitive desire to surpass one another in becoming the region's (if not the world's) largest producer.

The emergence of pulp production facilities operating on such a massive scale carries important implications for forests and rural livelihoods in the Asia—Pacific region. To the extent that significant investments are made to expand the region's pulp capacity, it can be anticipated that new capacity will be concentrated into a relatively small number of very large mills. This, in turn, suggests that these mills are likely to place very heavy demands both on forests and/or land suitable for plantation development within a commercial radius of the pulp production facility (often 200 kilometres). Indeed, the very large capital investments required to develop such mills can create strong structural pressures on government planners and other land-use decision-makers to make land and wood available for the pulp mill's operations. This has been the case, for instance, in central Sumatra and, increasingly, in coastal South China.

Conclusion

This study has examined five major trends in the global pulp and paper sector that are likely to impact forests and rural livelihoods in Asia and the Pacific over the medium term. Growth scenarios have been analyzed in an effort to quantify implications for wood demand and land use through 2020. Although the projections and scenarios presented in this study are still quite preliminary, they offer the following conclusions:

1) Continued growth of China's paper industry — especially for printing and communication grades — will be a major factor shaping demand for wood pulp in Asia and the Pacific over the medium term. By 2020, China's annual consumption of wood pulp could reach nearly 15 million tonnes under a medium-growth scenario and 19 million tonnes under a high-growth scenario. These figures imply an annual wood demand of 67—86 million m3, corresponding to an annual harvest of between 478 000 and 614 000 hectares of fast-growing plantations and a net planted area of between 3.3 and 4.3 million hectares. If managed on a seven-year rotation, these plantations would need to be planted by 2013 to be available for harvest in 2020.

2) If its accelerated GDP growth continues over the medium term, India could also emerge as a major consumer of printing- and communication-grade paper, with significant implications for wood consumption. Under a high-growth scenario, it is conceivable that India's pulp requirement by 2020 could be as high as 9.2 million tonnes per annum, effectively requiring an annual wood input of approximately 41 million m3. The production of such a substantial volume of wood on an annual basis would require a fast-growing plantation resource of some 2.1 million hectares (net). In all likelihood, the development of such a sizeable plantation base would need to occur outside India, with the fibre imported either in the form of pulp or paper.

3) Over the last several years, East Asian paper producers — particularly in China, Republic of Korea and Indonesia — have exported growing volumes of printing and communication paper to the rest of the world, contributing to mill closures in North America and Europe. It is not yet clear whether East Asian producers aim to become global suppliers of fine paper over the long term; or whether their current exports are simply a temporary strategy to sell excess production until domestic demand catches up. To the extent that the global fine paper market becomes reoriented towards supply chains originating in China, Republic of Korea and Indonesia, these countries' paper producers will have an incentive to invest in new pulp capacity in Asia and the Pacific to secure long-term supplies of low-cost pulpwood. Their incentive to do so will become particularly strong if Latin American market pulp producers decide to integrate their operations with fine paper production.

4) Over the last 15 years, global pulp production has shifted decisively to low-cost wood-producing regions in the southern hemisphere, particularly in Latin America and Southeast Asia, and this trend is expected to continue. In Asia and the Pacific, capacity expansion is expected to occur most significantly in Indonesia, where the government is actively promoting capital investments both to expand existing facilities and to develop several new pulp and paper mills. Through 2020, the government seeks to attract over US$15 billion of investment to raise the nation's pulp capacity from 6.5 million tonnes per annum in 2007 to 16 million tonnes per annum in 2020. If this target is achieved, Indonesia's pulp industry would consume 72 million m3 of wood annually to run at capacity, requiring an annual harvest of some 514 000 hectares of fast-growing plantations and an overall net planted area of 3.6 million hectares. Assuming a seven-year rotation, areas to be harvested in 2020 would need to be planted by 2013.

5) Finally, the capacity of kraft pulp mills has steadily increased over the last two decades, as boiler sizes have expanded and as producers have sought to achieve ever greater economies of scale. This trend is likely to continue over at least the medium term, and is anticipated to have significant impacts on wood demand and land use in the Asia—Pacific region. New investments in pulp capacity are expected to be concentrated in a relatively small number of mega-scale pulp mills — which will place very heavy demands both on forests and on land suitable for plantation development within a commercial radius of the mill sites. The massive scale of these facilities underscores the critical importance for government planners, project sponsors and financial institutions to fully assess the potential impacts on forests, rural livelihoods and carbon emissions before these mills are built.

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1 Senior Policy Scientist at the Center for International Forestry Research (CIFOR), based in Bogor, Indonesia; and the Executive Director of Woods & Wayside International, Inc. E-mail: C.Barr@cgiar.org
2 Principal of Brian Stafford & Associates Pty Ltd, Consultants to the International Forestry and Pulp & Paper Industries, based in Hobart, Australia. E-mail: brian@brianstafford.com.au

3This projection was prepared by Dequan He of China Economic Consulting, Inc. The authors gratefully acknowledge this contribution.

Policy solutions to illegal logging: a forest sector model analysis

Steven Northway and Gary Q. Bull1

At least 10 percent of global forest product trade is attributable to illegal logging and many countries are involved as exporters and/or importers. To assess the impact of illegal logging we developed the International Forest and Forest Products trade model and started our analysis by examining illegal logging in Indonesia and subsequent trade with China. We conclude that a successful ban on the domestic processing of illegal logs within Indonesia will lead to added pressure for their export to China; however, if China also bans the processing of illegal logging, the combined effect will be to reduce harvest levels in Indonesia.

Keywords: International Forest and Forest Products model, illegal logging, international trade, forest policy

Introduction

Illegal logging contributes to an estimated 10 percent of the global forest product trade (Brack 2003). Many countries are involved in the export and import of illegally sourced forest products. All tropical countries, plus the Russian Federation and China, are mentioned as exporting countries with imports going to the European Union, China, Japan and the United States (Contreras-Hermosilla et al. 2007). In financial terms, it is estimated that annual asset losses attributable to illegal logging are US$10 billion, with an accompanying loss of US$5 billion dollars in taxes (World Bank 2006).

Illegal logging also discourages investment and improved logging practices, undermines the rule of law and exacerbates wealth disparities in exporting countries (Kaimowitz 2003). It can affect the rural livelihoods of the 735 million people who live in or near closed tropical forests who depend on them for many of their needs (Contreras-Hermosilla et al. 2007). It can also lead to reductions in carbon stocks, biodiversity and water quality, while contributing to erosion and flooding (Contreras-Hermosilla et al. 2007; Waggener 2001).

Efforts to halt illegal logging have historically taken one or more of the following forms:

Some of the lessons learned from these efforts are well-documented in Brack (2003), Kaimowitz (2003), Mitchell et al. (2003), Speechly (2003), Seneca Creek Associates (2004) and Durst et al. (2001).

Speechly (2003) concludes that the success in halting illegal logging has been mixed. For example, New Zealand implemented a series of increasingly restrictive logging bans beginning in the 1970s in its state-owned natural forests (Reid 2001); at least from a legal perspective they have been successful. In the Philippines, a series of increasing restrictions culminated in a national logging ban in old-growth natural forests in 1992, but by 1998 as much as 50 percent of the Philippines' industrial roundwood supply was coming from illegal sources (Guiang 2001).

Log export bans, covering some if not all logs, have been implemented in a variety of countries in North America, South America and the Asia—Pacific region. A successful ban on exporting logs was carried out in federal lands of the western United States (Seneca Creek Associates 2004 and Resosudarmo 2006). Indonesia, promulgating and subsequently reversing a series of log export bans beginning in the early 1980s, has not been so successful. Despite a log export ban being in place in 2002, an estimated 5 percent of domestic log production ended up as illegal exports (Seneca Creek Associates 2004); however, Klassen (2007) notes that there have been clear indications of success in the last two years (A. Klaasen, personal communication).

Certification schemes are a third policy mechanism designed to curb illegal logging but there are significant problems with their implementation, especially in tropical or lesser developed countries. First, the schemes can be circumvented by diverting products to less discriminating third-party markets (Contreras-Hermosilla et al. 2007). Second, the operators who want certification are facing difficulties because many of them cannot guarantee that illegal logging is not occurring in their concession areas. Third, being voluntary and with little profit incentive has meant that certification is most advantageous where high standards already exist (Mitchell et al. 2003). Fourth, the schemes can also have the perverse effect of increasing the costs of legal logging and thus increasing the motive for illegal logging (Seneca Creek Associates 2004).

International trade agreements could have a role in controlling illegal logging, but they will have to be at an appropriate scale. Bilateral agreements are easily circumvented by diverting products to domestic or less discriminating third-party markets (Contreras-Hermosilla et al. 2007), while multinational agreements are unlikely to happen in a timely way (Speechly 2003). Intermediate-sized, plurilateral agreements, for example the EU's FLEGT agreements with a number of countries, seem most likely to be successful.

On the demand side, company and government wood procurement policies could support legal markets (O'Brien 2003). On the supply side, efforts to create comprehensive national laws, transparency, accountability and well-defined and enforceable property rights are needed (Contreras-Hermosilla et al. 2007).

Given the mixed results of the efforts to date, policy-makers will have to find ways to make existing solutions more effective or find new solutions to halting illegal logging. One way to test a set of policy ideas is to use a global forest sector model, in this case the International Forest and Forest Products (IFPP) model to determine the impacts of proposed policy initiatives. The efficacy of logging bans and trade agreements will depend on the financial and social costs associated with circumventing them. A global forest sector model that responds to harvesting, manufacturing and transportation costs can help in understanding the costs and benefits and how they are distributed.

Rationale

Despite the global importance of illegal logging and the national efforts that have been made to limit it, there is a dearth of economic-based policy analyses that address the issues. As Amacher (2006) stated in a Journal of Forest Economics editorial:

What is a bit surprising is the fact that there is frighteningly little that forest economists have had to say about corruption and its implications for forest policy instruments.

Notably, three authors have modeled the impact of national policy instruments on illegal logging. Amacher et al. (2004) looked at the design of royalty and enforcement instruments and their impact on illegal logging. Delacote et al. (2005) developed an illegal logging model that responds to tenure design, based on concession size and enforcement and the role of bribing policy-makers and bureaucrats. Finally, Barbier et al. (2005) looked at the effect of changing terms-oftrade on illegal logging in the face of government corruption, concluding that the application of sanctions might actually increase illegal activities. However, none of these efforts took a broader forest sector look at the efficacy of different policy initiatives.

Whereas global forest sector models have been applied to illegal logging issues, requisite policy instruments have been ignored. Using the Global Forest Products Model, Seneca Creek Associates (2004) examined the impacts of curbing global illegal logging on the wood products industry in the United States. Based on an application of the IFFP model, Northway and Bull (2006) examined the effects of illegal logging in Indonesia on trade with China. Both of these studies assumed the end of illegal logging without exploring the policy initiatives necessary to accomplish the task. Future model development will have to rectify this problem.

Methodology

IFFP general structure

The IFFP is also a spatial partial equilibrium model of the global forest sector. It is made up of four component models: (1) forest product consumption, (2) forest product processing capacity, (3) forest estate and (4) a forest product trade component. The IFFP is used to predict the future movement of raw materials (logs) from the forest through various processing facilities to consumption.

Unlike other trade models, it has the unique addition of an integrated forest estate model (timber supply model) that incorporates the dynamics of forest age — class balance, forest growth rates and forest planting (e.g. afforestation, reforestation) programmes. It is useful to adequately represent the supply of logs from the forest over time. Lag times between plantation establishment and harvest and growth in the existing forests contain a temporal element. Using the forest as the primary product also has the advantage of implicitly containing cross-product supply elasticities for classes of logs, including those from illegal sources.

The modeling structure is defined through processes and products (Figure 1). Processes (represented by rectangular boxes) use up one or more products while producing one or more products. For example, initial supplies of primary products such as logs (represented by square boxes with clipped corners) are processed into intermediate products such as pulp (represented by circles); all products can then be used as inputs into additional processes (e.g. paper making) or they can be consumed as a final product (represented by diamonds). Each process takes fixed proportions of products as inputs and produces fixed proportions of products as outputs. Processes are further defined by a relationship between capacity utilization and processing costs (that is, costs incremental to those of included input products). The framework is based on producing the specified level of each final product while minimizing total processing cost and not exceeding the initial supply of primary products.

Trade is represented as a "specialized" process (represented by pentagons). Trade is tracked between all pairs of countries with an opportunity to define: (1) minimum and maximum flows of products; and (2) transport costs and import duties as per unit costs for products.

The supply of primary products is represented by a typical supply curve, relating quantity to price. Each process is represented through a supply curve relating processed quantity (e.g. capacity utilization) to value-added price. Demand curves can be incorporated through products representing substitution and sublimation. These products contribute towards satisfying the consumption targets.

Time is not explicitly dealt with in the model structure, but rather in the definition of products and processes. For example, paper-in-2005 is a separate product from paper-in-2010, with separate processing capacities and consumption levels. Processes are used, where necessary, to link products through time. For example, because pulp logs in 2010 are a separate product from 2005, a process (i.e. storage) is required to make them available.

Figure 1. China 2005 scenario 1 — Status quo forest product flows

The model solution is designed to meet consumption levels at minimum cost, utilizing market-clearing prices. As time is not explicitly included in the model structure, any desired discounting of future prices must be done explicitly in the appropriate definitions of products and processes. Figure 1 uses the results from China in 2005 to illustrate the structure of the model and the results. The numbers along the arrows show the flow of industrial roundwood equivalents between the processes.

Data sources

In Figure 1, the forest data were largely based on the Global Fibre Supply Model (Bull et al. 1998). The consumption, manufacturing and trade data were largely based on FAOSTAT (FAO 2005); additional data were collected in a series of meetings with collaborators from China, the Russian Federation and Indonesia and some key institutions — most notably the International Institute of Applied Systems Analysis, the Centre for International Forest Research and the Chinese Center for Agricultural Policy Research. Cost data were tested against the Global Forest Products Model (Buongiorno 2003). Country-specific information was collected and utilized for China, Indonesia and the far east of the Russian Federation (Northway and Bull 2007a; Northway and Bull 2007b; Northway and Bull 2006; Northway and Bull 2005).

Scenarios

The scenarios are defined in terms of the possible use of illegal logs originating in Indonesia.

  1. Scenario 1, the "Status Quo", indicates that there is no distinction between legal and illegal logs originating in Indonesia and the logs are available for export or use in Indonesia's domestic manufacturing sector.
  2. Scenario 2, "Indonesia: No Processing of Indonesia's Illegal Logs" indicates that the illegal logs originating in Indonesia are not available for use in its domestic manufacturing sector.
  3. Scenario 3, "Indonesia and China: No Processing of Indonesia's Illegal Logs" indicates that illegal logs originating in Indonesia are not available for use in either Indonesia's or China's manufacturing sectors.

Figure 2 illustrates the impact on harvest levels of pursuing each of the scenarios just described. Indonesia's harvest levels are affected by the available use of logs originating from illegal activities. Under Scenario 2 where illegal logs are not available for processing within Indonesia, harvest levels are greatly reduced (Figure 2) despite the initial predicted response of increased legal logging (Figure 3).

Figure 2. Indonesia's total harvest levels under different scenarios

Figure 3. Indonesia's legal harvest under different scenarios

In Figure 4, Indonesia's sawlog imports are affected by the available uses of logs originating from illegal activities. Under the Status Quo, Indonesia is self-sufficient in sawlogs, neither importing nor exporting for the initial time periods. In Scenario 2, where illegal logs are not available for processing within Indonesia, sawlogs are imported from the rest of the world beginning in 2010; while illegally sourced logs are still exported to China as indicated. In Scenario 3, illegally sourced logs from Indonesia are not available for export to China, while sawlog imports are the same as under Scenario 2.

Figure 4. Indonesia's sawlog imports and exports to China under different scenarios

Figure 5 indicates that Indonesia's solid wood exports are affected by the assumed availability of logs originating from illegal activities. Under Scenario 1, Indonesia's solid wood exports of domestic production range from 64 percent today, dropping to 34 percent by 2020. In scenarios 2 and 3, where illegal logs are partially replaced with imported logs, both solid wood product production and exports drop.

Figure 5. Indonesia's solid wood product export scenarios

Conclusions

At a specific level, i.e. examining illegal logging impacts on Indonesia—China trade, we conclude that a successful ban on the domestic processing of illegal logs within Indonesia will lead to added pressure for their export to China; however, if China also bans the processing of illegal logging the combined effect will be to further reduce harvest levels in Indonesia.

At a more general level, we conclude that:

  1. Trade models are useful to test the robustness of policy instruments in the face of uncertainty.
  2. Domestic and export controls, such as wood product certification and strict export controls, are both required to address the illegal logging issue.
  3. To make the policy instrument more robust requires significantly more effort to collect and compile forest and trade data. This will facilitate improved scenario development (i.e. trade model usage) for policy-making.

Bibliography

Amacher, G.S. & co-workers. 2004. Socially optimal royalty design and illegal logging under alternative penalty schemes. CESifo Working Paper Series No. 1131, SSRN.

Amacher, G.S. 2006. Corruption: a challenge for economists interested in forest policy design. Journal of Forest Economics, 12(2): 85—89.

Barbier, E.B. & co-workers. 2005. Corruption, trade and resource conversion. Journal of Environmental Economics and Management, 50(2): 276—299.

Brack, D. 2003. Illegal logging and the illegal trade in forest and timber products. International Forestry Review, 5(3): 195—198.

Bull, G.Q. & co-workers. 1998. Global fibre supply model. Rome, Italy, Forest Products Division, FAO.

Buongiorno, J. 2003. Global forest product model: structure, estimation, and applications. San Diego, Academic.

Contreras-Hermosilla, A., Doornbosch, R. & Lodge, M. 2007. The economics of illegal logging and associated trade. Paris, Round Table on Sustainable Development.

Delacote, P. 2005. Forestry sector concentration, corruption and forest over-exploitation. Working Paper. Florence, Economics Department, EUI.

Durst, P.B. & co-workers. 2001. Forests out of bounds: impacts and effectiveness of logging bans in natural forests in Asia-Pacific. Bangkok, FAO Regional Office for Asia and the Pacific.

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Guiang, E.S. 2001. Impacts and effectiveness of logging bans in natural forests: Philippines. In P. Durst, T. Waggener, T. Enters & T.L. Cheng, eds. Forests out of bounds: impacts and effectiveness of logging bans in Asia-Pacific. Bangkok, Asia-Pacific Forestry Commission, FAO.

Kaimowitz, D. 2003. Forest law enforcement and rural livelihoods. International Forestry Review, 5(3): 199—210.

Mitchell, A. & co-workers. 2003. Independent verification of legal timber: complementing certification and encouraging positive governance? International Forestry Review, 5(3): 268—277.

Northway, S. & Bull, G.Q. 2005. Developing scenarios and evaluating policies for China and Asia Pacific regional forest products suppliers. A proposed model structure. In A. White & C. Barr, eds. China & Asia Pacific: forest markets for sustainable livelihoods and forests. Regional Network and Trade Scenarios Workshop, 4—6 June 2005, Beijing, China. 8 pp.

Northway, S. & Bull, G.Q. 2006. International forest and forest products trade model: scenarios for China and Indonesia's forest supply, forest products processing, consumption and trade. CIFOR report. Bogor, Indonesia. 31 pp.

Northway, S. & Bull, G.Q. 2007a. Forest products trade between, Russia and China: potential production, processing, consumption and trade scenarios. Synthesis. Forest trends. Washington, DC. 14 pp.

Northway, S. & Bull, G.Q. 2007b. International forest and forest products trade model: scenarios for China, Eastern Russia and Indonesia's forest supply, forest products processing, consumption and trade. Ottawa, Canada, Canadian Forest Service. 44 pp.

O'Brien, M. 2003. The role of the UK private sector in tackling illegal logging. International Forestry Review, 5(3): 258—261.

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Resosudarmo, B.P. & Yusuf, A.A. 2006. Is the log export ban an efficient instrument for economic development and environmental protection? The case of Indonesia. Asian Economic Papers, 5(2): 75—104.

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1 Faculty of Forestry, University of British Columbia, Vancouver, BC, Canada. E-mail: northway@interchange.ubc.ca

Environmental performance assessment in the Greater Mekong Subregion

Lothar Linde,1 Iain Watson1 and Tonnie Tekelenburg2

The Greater Mekong Subregion's (GMS) 313 million people and its wealth of natural resources are fuelling impressive economic growth that is likely to accelerate. The GMS countries are increasingly being linked through transportation, telecommunications, energy production and usage and cross-border trade. Over the past decade, economic gains have led to increased per capita incomes, improved education and health and a better quality of life for many of the subregion's inhabitants.

This economic transformation, however, brings with it inevitable transformation of the natural environment. Serious degradation of land, forests, fresh water and marine habitats is affecting large areas of the GMS, resulting in the loss of biodiversity — ecosystems, species and genetic resources — at unprecedented rates. If corrective steps are not taken, the GMS will lose more than 50 percent of its remaining land and water habitats over the next century, one-third of which is expected to be lost over the next few decades. If these trends continue, results will not only be disastrous from an environmental standpoint but will also put socio-economic development gains at risk and threaten the long-term success of the region's development process.

Knowing the magnitude of this human development impact on the environment is crucial to planning tools that aim to develop appropriate and effective response mechanisms to environmental threats. To achieve this, methods are required that can link detailed local surveying knowledge with decision-makers at national and subregional scales where much of the relevant strategic responses to threats are taken.

In order to provide such quantitative information on biodiversity, the Environment Operations Center Bangkok (EOC) and the Netherlands Environment Assessment Agency (MNP) are cooperating in the development and application of a subregional, geospatially explicit biodiversity pressure model. The baseline for this model is the GLOBIO3 framework that calculates the impact of human activities (pressures) on biodiversity, namely land-use change, infrastructure development, fragmentation, nitrogen deposition and climate change. These pressures are unified into the Mean Species Abundance (MSA) value which quantifies the remaining biodiversity after discounting the biodiversity impact of each of the pressure factors from the potential biodiversity without disturbance.

Keywords: Greater Mekong Subregion, biodiversity, geospatial modeling, GLOBIO3 model, pressure/state/response indicators, Mean Species Abundance, environmental performance assessment

Development context

The Greater Mekong Subregion (GMS) comprises six countries that are interlinked through the Mekong River — Cambodia, Lao PDR, Myanmar, Thailand, Viet Nam and Yunnan Province and Guangxi Zhuang Autonomous Region of China. The region's 313 million people and its wealth of natural resources are fuelling impressive economic growth that is likely to accelerate. GMS countries are increasingly being linked through transportation, telecommunications, energy production and usage and cross-border trade. Over the past decade, economic gains have led to increased per capita incomes, improved education and health and a better quality of life for many of the subregion's inhabitants.

This economic transformation, however, brings with it inevitable transformation of the natural environment. Serious degradation of land, forests, fresh water and marine habitats is affecting large areas of the GMS, resulting in the loss of biodiversity — ecosystems, species and genetic resources — at unprecedented rates. If corrective steps are not taken, the GMS will lose more than 50 percent of its remaining land and water habitats over the next century, one-third of which is expected to be lost over the next few decades. If these trends continue, results will not only be disastrous from an environmental standpoint, but will also put socio-economic development gains at risk and threaten the long-term success of the region's development process (Linde 2006).

Measuring environmental performance

In light of the environmental conflicts caused by an "economy first" approach in national development planning, it is crucial to provide GMS governments with tools that inform them about the state of their natural resources, enable them to assess and quantify this in the context of socio-economic pressures that impact on the environment and allow them to plan responses to mitigate adverse impacts. Such an integrated approach has several advantages:

  1. Through linking both economic and environmental processes in one conceptual framework, the regionally common decoupling of economic and environmental planning is (re)linked through cause—effect relationships; this improves understanding of the conflicts for involved parties, ultimately allowing tracking of developments back to specific government or private sector policy or planning source/intervention.
  2. Within the conceptual framework, indicators are used to describe both the magnitude and trends of its elements, adding an explicit quantitative dimension to the assessment. This provides a measurable feedback mechanism on the efficiency of environmental policy and planning measures and targets. Planners are able to judge whether the policies and mechanisms in place are sufficient to protect natural resources and the environment that are critical to sustain steady growth of the economy.
  3. Indicator-based quantification is also a first but critical step in the estimation of costs involved. The advantage of the framework approach in this context is that natural resources and biodiversity become an economic cost factor as well, making it easier for economists to understand its value. This will ultimately aid in convincing parties involved in economic development planning — which is often the source of current environmental challenges — that high economic growth is not achieved by compromising on the environment, but by using resources sustainably.

Such thematic and geographically complex assessments (Figure 1) have not yet been embarked upon in any GMS country. However, two projects have piloted such activities:

(i)The Asian Development Bank (ADB) RETA 6069 (SEF II), namely "Environmental Performance Assessment and Subregional Strategic Environmental Framework for the GMS, Phase II", applied the concept within the GMS between 2003 and 2006, building capacity within national environmental ministries on the Organization for Economic Cooperation and Development's (OECD) pressure—state—response (P-S-R) framework, and Environmental Performance Assessment (EPA) implementation steps. Project outcomes included six national EPA reports (Cambodia, Lao PDR, Myanmar, Thailand, Viet Nam, Yunnan Province of China) and one subregional report.
(ii)The OECD Environmental Performance Review of China, finalized in 2006. Though the GMS SEF II national EPAs were in many respects influenced by the example of OECD environmental performance reviews, the OECD has not yet applied them elsewhere in the GMS.

Figure 1. Positioning of EPA and ISDP

SEF II: Assessing biodiversity values using geospatial modeling

In addition to explaining the usefulness of the approach and building capacity on a completely new concept, a key challenge faced during the ADB SEF II project concerned data availability and integrity at various scales. Particularly complex environmental information such as biodiversity loss or ambient pollution is minimal at either national or subregional scales — precisely the scales where EPA is most usefully applied.

Data inadequacies posed a particular problem for the subregional EPA exercise that was intended to evaluate three subregional priority concerns: (1) threats to the Mekong's vital functions; (2) illegal trade in wildlife and estimation of remaining biodiversity; and (3) the degree of harmonization of policies and standards. Quantification of remaining biodiversity was determined to be a crucial problem with regard to availability of subregional quantitative data, leading to the decision to test an alternative approach utilizing a biodiversity pressure model.

The GLOBIO3 model

In order to provide quantitative information on biodiversity values as part of the SEF-II subregional EPA exercise ADB, the United Nations Environment Programme (UNEP) and the Netherlands Environment Assessment Agency (MNP) initiated a cooperation activity to apply a subregional, geospatially explicit biodiversity pressure model. The baseline for this model was the GLOBIO3 framework3 that calculates the impact of human and societal activities (pressures) on biodiversity, namely land-use change, infrastructure development, nitrogen deposition and climate change.4These pressures are unified into the Mean Species Abundance (MSA) value that quantifies the remaining biodiversity after discounting the biodiversity impact of each of the pressure factors from the potential biodiversity without disturbance.

The GLOBIO3 model is a collaborative venture between three organizations: UNEP World Conservation Monitoring Centre (UNEP WCMC), UNEP/GRID Arendal and MNP-RIVM. It integrates three different modeling approaches, namely: (1) the initial GLOBIO2 model (UNEP, 2001—UNEP GRID Arendal) that explores the impact of infrastructural development on biodiversity; (2) the global biodiversity model developed by RIVM — a pressure-based model as an extension of the IMAGE model; and (3) an approach developed by UNEP WCMC that focuses on integrating spatial data on pressures or causes of environmental change, with data on current state and response measures.

Quantification of the relationships between the GLOBIO3 pressure factors and species abundance is based on a review of scientific literature. The model distinguishes between different ecosystem types and taxonomic criteria for four main pressure groups: (1) land use (agricultural and forest use), (2) infrastructure (roads), (3) pollution (nitrogen deposition) and (4) climate change. For each of these pressure groups, separate, independent dose—response relationships (or model coefficients) have been defined. They are the heart of the model, converting geospatially explicit input information on land cover, infrastructure, nitrogen deposition and climate change into a geospatially explicit value on MSA.

In order to perform a subregional application of the GLOBIO3 model under the SEF II framework, global datasets were replaced with national datasets of higher resolution, wherever available and feasible. Due to time constraints, the dose—response relationships defined for the global application of the GLOBIO3 model remained largely unchanged during the subregional application.

The GLOBIO3 workflow applied as part of the SEF II subregional application encompassed four principal steps:

  1. The model defined a benchmark of maximum biodiversity, which is the pristine ecosystem under the given biogeophysical conditions. In the case of the subregional application of GLOBIO3 under SEF II, it used the biome classes defined by the IMAGE model.
  2. The model defined four main pressure groups — land use (agricultural and forest use), infrastructure, pollution and climate change — that have a causal connection with biodiversity and its change. The pressure groups were consistent with the global application with no new pressure groups being added. However, within the most important pressure group (land use), global datasets were replaced with national versions to increase spatial and thematic detail.
  3. The model transformed each of the generic pressure inputs into biodiversity impact information on the basis of dose—response relationships derived from literature and expert opinions (conversion tables, coefficients, equations etc.). The subregional application used the global assumptions.
  4. The model weighed between the biodiversity impacts of these four pressure groups and summed them up to the total impact on biodiversity, namely the MSA value (MSAi = MSA_Lui * MSA_Infrai * MSA_Nitri * MSA_Climi). A detailed description of the methodology to define mathematical coefficients (describing dose—effect relationships) can be found in Alkemade et al. (2006).

In summary, the main advantage of biodiversity modeling is: (1) that it integrates several pressure factors into a complex relationship; and (2) that this approach can be used to create unavailable direct information from secondary inter-related source data. As the model is geographically explicit, it allows precise identification of target areas and as such it is also compatible with GIS for further data integration and analyses. Grid cell size for this pilot application was set at 10 x 10 km.

Application of the P-S-R framework with GLOBIO3 model outputs

The MSAi value calculated by the GLOBIO3 model can be used to describe the STATE of biodiversity within the EPA P-S-R indicator framework, while the four individual pressure groups (MSA_Lui, MSA_Infrai, MSA_Nitri, MSA_Climi) each describe individual PRESSURE values within the framework. In combination with thematic overlays (like protected area boundaries), the efficiency of a RESPONSE can be measured as well.

PRESSURE indicator(s): combined effect of GLOBIO3 pressures on biodiversity

Figure 2 shows that throughout the GMS, agriculture has the strongest impact on the MSA (56 percent). Infrastructure is the second strongest pressure (25 percent), followed by forestry (14 percent). Pollution and climate change pressures have a comparatively lower impact on the MSA value in the subregion compared to other pressure factors.

When comparing impact factors among different countries, the overall subregional trend is reflected very clearly in the trends for Viet Nam. Similar trends are found for Thailand, Cambodia and Myanmar, though pollution has no or very little impact on the MSA value in these countries. In Yunnan and Lao PDR, agriculture makes up a smaller proportion of the impact (25 and 24 percent), whereas there is a higher impact of infrastructure (34 and 36 percent). Forest use makes up 31 percent of all impact in Lao PDR, while in Yunnan pollution seems to influence the MSA substantially more than in all other countries (19 percent).

Figure 2. Contribution of each individual pressure factor to overall biodiversity loss

STATE indicator: MSA

The final output of the GLOBIO3 model is represented by the geospatial layer of MSA values (Figure 3). For the purpose of indicator development, these geospatially explicit outputs can be summarized by subregion or individual country (Figure 4). On a subregional level, 50 percent of the region's species abundance is remaining. That means that only half of the potential MSA under undisturbed (pristine) conditions in the same location remains today. Thailand scores considerably lower (37.2 percent) and Lao PDR and Yunnan notably higher (65.7 and 66.9 percent respectively) with regard to remaining MSA. Myanmar, Viet Nam and Cambodia score similarly to the GMS subregional average for MSA.

These numbers have to be interpreted within the socio-economic context of the countries analysed. For example, it is not surprising that Thailand with its developing economy and high population of 62 million has a much lower MSA than Lao PDR, with its population of only 5 million and appreciably lower intensity of extensive agriculture.

Figure 3. MSA in the Greater Mekong Subregion. Green indicates high remaining biodiversity, red that little biodiversity is left. Output grid size is 10 x 10 km, resampled for illustrative purposes

Figure 4. MSA for each GMS country and the GMS

RESPONSE indicator: comparison of overall MSA to MSA in protected areas and percentage of protected areas over total land area

To derive information on the efficiency of responses on the state of biodiversity, final model results (MSAi) are overlaid with information on protective measures (protected areas). For the entire GMS, protected areas have an MSA of 66.9 percent, which is 16.9 percent higher than the national average MSA value of 50 percent (Figure 5). At a country level, MSA values of protected areas are generally higher than national averages, though there is considerable variation among the countries (average 14.9 percent, range 5.8 to 24.1 percent). Measuring the difference in MSA of protected areas and national averages provides an estimate of ecosystem quality in the areas under protection, which can be a result of a careful selection of the site as much as the outcome of efficient law enforcement and protection.

Independent of the model outcomes, the percentage of protected areas over total land areas was calculated from a subregional dataset to add a measure on ecosystem quantity to the response indicator. This co-indicator was chosen to reflect the importance of habitat quantity to the survival of species (habitat size and connectivity) — and was therefore an expression for the efficiency of protection — as the quality of the habitat itself. Considering quantity of protection in the GMS, it is striking that even though the MSA in the protected areas is still above average, the area under protection is limited to only 8.3 percent of the total land area of the subregion. Cambodia and Thailand appear to have the best levels of biodiversity with regard to an equally increased MSA in protected areas and a relatively large amount of total land area under protection. Myanmar and Viet Nam are in more compromising positions regarding effectiveness of response. While they retain high MSA in their protected areas, it is concentrated in a diminutive total land area (6.2 and 7.2 percent respectively). Lao PDR is a special case: It has 14.3 percent of its land area protected but the MSA value in protected areas does not seem to be noticeably above the total national average. However, given that the country is less populated compared to other GMS countries and thus retains more natural habitats outside protected areas, this tendency might not necessarily be an expression of lesser protection efficiency.

Figure 5. Comparison of overall MSA to MSA in protected areas and percentage of protected areas over total land area

Challenges

While biodiversity pressure modeling doubtlessly has the potential to be applied as part of the SEF II subregional EPA, the approach presently faces some critical challenges that first need to be addressed to improve the reliability of modeling results. Limitations fall into two categories:

  1. Non-model related data challenges in the geospatial input information ("garbage in— garbage out") and
  2. Model-related knowledge challenges.

Both categories are equally critical for the calculation of reliable outputs, even though there is generally a tendency to overplay the importance of input data quality over the integrity of the knowledge base of the model. Data challenges in the GMS can be summarized into six main groups: a) data gaps, b) outdated data, c) insufficient metadata, d) different interpretation standards, e) cross-border integrity and f) lack of public access and systematic knowledge sharing.

Knowledge challenges relate to the downscaling of the GLOBIO3 model from its global scale, which requires verification of the model's assumptions by GMS specific case/literature studies and the potential inclusion of GMS-relevant pressure factors over or additional to the existing ones.

Improvements and next steps

The challenges identified in applying the model as part of the SEF II subregional EPA are likely to cause over- or underestimations that limit the usefulness of the model outcomes for detailed interpretations.

On the other hand, alternative data sources that can be used for quantitative, geospatially explicit indicator development are not available at geographic scales and with the thematic detail required for national and subregional EPAs. Given the complexity of sampling biodiversity data (species counts/composition) it is also highly unlikely that such alternative information will become seamlessly available on national or even subregional scales.5

However, as many of the (environmental) policy and planning decisions are made at national and subregional levels, it is crucial to provide biodiversity information that fits these scales to ensure that protecting biodiversity becomes recognized as a contributing rather than an inhibiting factor to the success of national economic development (planning).

At present, only spatial modeling approaches have the potential to bridge this gap between in-depth local level knowledge to national and subregional policy-making and planning bodies. Recognizing this situation, ADB GMS EOC6 and MNP decided to continue working on the GLOBIO3 model to improve it for the next round of subregional EPA in the GMS. To achieve this, the ADB Core Environment Program EPA component — the successor of the SEF II project — addresses the following gaps:

  1. Raising awareness with GMS governments and non-governmental organizations about the opportunities for using biodiversity modeling in environmental assessment and planning.
  2. Making the model available free-of-charge to GMS governments and building capacity for its execution.
  3. Collection of newer and more reliable GIS input layers for each of the GMS countries and updating model results with this refined information.
  4. Research and review of subregionally-specific case studies downscale the model's coefficients to fit subregional criteria.

Activities have been implemented in all four gaps. The opportunities that the model provides for national and subregional EPAs and sustainable development planning have been highlighted in several conferences organized by the EOC since the end of the SEF II project.7

The model has also been made available free-of-charge by translating it from use with the proprietary ARISFLOW software into the execution of all the steps within a common GIS environment of ESRI ArcGIS. Respective training events were held in March 2007 and July 2007, with the next training course in November 2007.

These efforts have led to more support by countries with regard to providing national datasets of higher resolution that can be used to improve the model by replacing lower resolution data used in the SEF II subregional EPA exercise. For example, Viet Nam now uses a land cover dataset with more spatial and thematic detail, improving on the precision of the pressure value for land conversion (MSALu). Instead of global road data, a national road dataset has been utilized. Also, the GLOBIO3 model has been extended by a fifth pressure factor — fragmentation — which was not part of the model during the SEF II pilot exercise and further adds to improved precision. As a result of these spatial and thematic refinements, the MSA value has been corrected to 33.8 percent (for Viet Nam, 50 percent in SEF II), much of which can be attributed to the replacement of the global road dataset with a national version. With a resolution of 1 x 1 km (compared to 10 x 10 km in SEF II), these data are also spatially more detailed. Improvements in other GMS countries will follow shortly, giving GMS governments the option to use this model in their upcoming national and subregional EPA exercises as part of the Core Environment Program's (CEP) component 3.

Besides the sheer improvement of input data, the EOC and MNP also strive to improve the scientific rationale of the model by including more GMS-specific case studies that define the model's mathematical coefficients. The EOC is planning a workshop to introduce the model's current scientific backing to interested stakeholders, to discuss how its scientific base can be improved to fit subregional characteristics, to identify organizations that can provide respective information and to detail a mode of cooperation.

By the end of CEP component 3, it is envisaged that GMS governments will have endorsed the improved model to be used as part of their national and subregional EPA assessments. These results will officially replace the current outcomes of the SEF II subregional EPA.

Conclusion

The SEF II project successfully piloted the concept of EPAs in the GMS. As with many complex approaches that are newly introduced, several challenges with regard to availability of data, analytical and technical capacity and a dedicated institutional structure were identified during the project.

One of the biggest challenges was the assessment of threats to and state of biodiversity, a priority concern that was ranked very high by GMS countries. Availability of quantifiable information of national coverage was already a problem for the national assessments, but proved to be impossible for the subregional EPA.

To fill this gap, a spatially explicit modeling approach was tested in cooperation with the MNP. This approach proved to be potent enough to provide quantifiable biodiversity information by extrapolating detailed scientific measurements and trends to a broader subregional scale. Still, challenges with regard to availability and/or quality of input information limited the accuracy of the results produced for the SEF II subregional EPA. ADB GMS EOC and MNP continue to improve the data and knowledge base of the model and envisage the application of an updated version during the next cycle of national and subregional EPAs.

Bibliography

Alkemade, R., Bakkenes, M., Bobbink, R., Miles, L., Nellemann, C., Simons, S. & Tekelenburg, T. 2006. GLOBIO3: Framework for the assessment of global terrestrial biodiversity. In MNP (2006) Integrated modelling of global environmental change, pp. 171—186. Chapter 10. An overview of IMAGE 2.4. Bilthoven, the Netherlands, MNP.

Linde, L. 2006. Geospatial information: a cross-cutting element in the Greater Mekong Subregion Core Environment Program. Proceedings of the Technical Seminar on Innovative Approaches to Natural Resources and Environmental Management (NREM): Spatial Technologies' Perspectives for Thailand and the GMS.


1GMS Environment Operations Center, 23rd Fl. The Offices at Central World, 999/9 Rama 1 Road, Pathumwan, Bangkok 10331, Thailand. E-mail: lolin76@yahoo.de

2The Netherlands Environmental Assessment Agency (MNP), PO Box 303 3720 AH Bilthoven, A. van Leeuwenhoeklaan 9, Bilthoven, the Netherlands. E-mail: Tonnie.Tekelenburg@mnp.nl

3Developed by the MNP.
4The model now includes ecosystem fragmentation as a fifth pressure factor; at the time of this application it was
not part of the model and therefore is not listed here.

5The term "scale" specifically refers to spatial coverage here.
6 EOC = Environment Operations Center, project office of the ADB GMS Core Environment Program (ADB TA
6289).
7 In particular, the BCI inception workshop in April 2006 and the half-yearly meetings of the Working Group on
Environment in April 2006, December 2006 and June 2007.

Future forest and biodiversity impact: biodiversity modeling and scenario analyses

Wilbert van Rooij1 and Tonnie Tekelenburg1

The Global Biodiversity Outlook 2 (GBO2) assessment has been carried out by the Netherlands Environmental Assessment Agency (MNP) as one of the products for the Convention on Biological Diversity (CBD). GBO2 evaluates the effect of different policy options on the rate of biodiversity loss under a moderate "business as usual" scenario. The results are calculated up to 2050 and show that it is unlikely that the 2010 Biodiversity Target will be met at global or regional levels.

In the climate change mitigation policy, biodiversity increase amounts to 0.4 percent in South and East Asia by 2050. This increase is rather small because of biodiversity loss owing to an additional claim of land for growing biomass resources (CO2 neutral energy supply). The forestry policy option aims at an increase of plantation area to meet the increased demand for wood. This option results in a biodiversity increase of 0.8 percent by 2050 in South and East Asia. The effect will be higher in the long run.

Protecting 20 percent of the area of all major natural ecosystems (biomes) leads to 1.3 percent higher biodiversity in South and East Asia by 2050. This effect is small due to the shift of agricultural activities to adjacent areas.

The Global Biodiversity model (GLOBIO3) has been used as a starting framework to develop national biodiversity models for several developing countries. The resolution was increased by using national land-use maps and a detailed land allocation model (CLUE-S). These models were used for a simple and quick review of national policies.

Information from APFSOS can improve the results of national biodiversity modeling. In turn the models can help the Asia—Pacific forestry sector to assess the future biodiversity impact of sector projections.

Keywords: biodiversity, modeling, scenario analyses, GBO2, GLOBIO3, CLUE-S, South Asia, East Asia, Viet Nam

Introduction

Policy-makers want to know the impact of policy options on biodiversity for improved decision-making on sustainable development as well as for evaluation of policies and targets. Under the Convention on Biological Diversity the target of "a significant reduction in the current loss of biological diversity by 2010" has been agreed upon. However, information is needed to explore if this target is likely to be met under given development trends. For this reason the Secretariat of the Convention on Biological Diversity commissioned the Netherlands Environmental Assessment Agency (MNP) to carry out a future biodiversity assessment as part of the Global Biodiversity Outlook 2 (GBO2).

For the outlook a moderate socio-economic baseline scenario has been used as a reference frame to evaluate the effectiveness of six different policies. The policy options have been analyzed separately for their impact on biodiversity up to 2050.

The GBO2 assessment looked at global and regional impacts. For national policy-makers this scale is too coarse. Therefore, the methodology has also been applied with national high resolution maps and the Conversion of Land Use and its Effects framework (CLUE).

During training sessions in 2006 and 2007, participants from 15 countries used the GLOBIO3 framework to develop their own national biodiversity models. Involved Asian countries were Viet Nam, Thailand, Cambodia, Lao PDR, China (Yunnan Province) and Myanmar. As a result of the international training programme, national biodiversity maps have been produced with a 1 x 1 km resolution. Current national conservation policy options have also been reviewed.

Currently the MNP supports the Vietnamese Ministry of Planning and Investment (MPI) to implement biodiversity modeling and poverty analysis. These activities are part of the Vietnamese Agenda 21 activities to develop a strategic orientation for sustainable development in Viet Nam.

In addition, countries from the Greater Mekong Subregion (GMS) receive support in biodiversity modeling and analysis from the Environmental Operations Centre in Bangkok as part of the Core Environment Program for the GMS (Asian Development Bank).

Information derived from the Asia—Pacific Forestry Sector Outlook Study (APFSOS) can provide important input for future claim on land and the allocation of future forest use. In addition, model output can be used for the APFSOS to support policy review and reform.

Methodology

The biodiversity assessment is based on the conceptual framework used in the Millennium Ecosystems Assessment (MEA 2003). Indirect drivers (population growth, economic growth, technology) are identified that cause the increase of direct drivers or pressures of change (landuse change, nitrogen deposition etc.). Several models are used to calculate input for the status of present and future biodiversity in the world. Figure 1 shows the framework with models and data input used for the GBO2.

The Global Biodiversity Model (GLOBIO3) uses output from the Integrated Model to Assess the Global Environment model (IMAGE2.4) and from an agricultural trade model developed by the Global Trade Analysis Project (GTAP). IMAGE and GTAP use population and economic growth as inputs and produce current and future information on global land use, nitrogen deposition and climate. More information about GTAP and IMAGE can be found in Van Meijl et al. (2006) and MNP (2006).

Figure 1. Biodiversity assessment framework with models and data inputs for GBO2

GLOBIO3

GLOBIO3 uses spatial information from the most important pressure factors to calculate the present and future status of biodiversity at the scale of world regions. It can be used to compare regional biodiversity patterns and changes. The largest impact on biodiversity is caused by land-use change. Other pressures included in GLOBIO3 are infrastructure development, increased fragmentation of natural areas, nitrogen deposition and climate change.

The GLOBIO3 framework is based on transparent relationships between pressure factors and quantitative information on biodiversity per ecosystem derived from available literature and data (Alkemade et al. 2006). Biodiversity values have been defined for a number of general land-use categories (Appendix I).

The model can be used to assess:

GLOBIO3 calculates remaining Mean Species Abundance (MSA) of original species, relative to their abundance in primary vegetation. We have developed this indicator because it describes the process of biodiversity loss; data are available and it can be globally modeled into the future. An area with an MSA of 100 percent indicates biodiversity that is similar to a natural or slightly impacted situation. Figure 2 shows a decrease in MSA in three steps. In this example the decrease in the abundance of many species is juxtaposed by an increase in abundance of a few other species. This homogenization process is common in deteriorating ecosystems all over the world.

Figure 2. The homogenization process in three steps. A: Species abundance in a natural ecosystem (MSA = 100%). B: Species abundance in a lightly disturbed ecosystem (MSA = 70%). C: Species abundance in a disturbed ecosystem (MSA = 30%)

The core of GLOBIO3 is a set of regression equations describing the impact on biodiversity per pressure. Information needed to calculate these cause—effect relationships is derived from databases of observations of species responses to change. The global MSA output of the biodiversity model is calculated on a grid cell by grid cell basis of 0.5 by 0.5° resolution. One grid cell represents a surface of approximately 55 km2 near the equator. The MSA output map for South and East Asia is shown in Figure 3.

The overall MSA value is calculated by multiplying the MSA values for each pressure per grid cell according to the formula:

MSAi = MSA_Lui * MSA_Infrai * MSA_Fragi * MSA_Nitri*MSA_Climi

Where i is the index for the grid cell; MSA_X relative MSA corresponding to land use, infrastructural development, fragmentation and climate change; MSA_Lui is the weighted mean over all land-use types within a grid cell.

National application of GLOBIO3

GLOBIO3 is designed for global application. Spatial information used for this model is generalized to a grid cell resolution too coarse for biodiversity modeling on a national scale. However, the GLOBIO3 methodology can be used as a general framework to develop a national biodiversity model. Participants at the biodiversity training courses scaled down the model by using detailed national datasets and applying an improved land allocation model. National land-use maps with more land-use classes replaced the less-detailed GLC2000 land cover map.

Future land allocation was carried out with the CLUE-S framework (Verburg 2007). This model uses maps of land-use types and determinant factors as inputs. In addition to these maps the land allocation part of CLUE-S needs information about the future claim on land. The participants used the sum of agricultural grid cell area per country derived from the IMAGE2.4 model. The claim on future forest land was derived from the Global Forest Resource Assessment (FAO 2005) and was sometimes refined by the participants' expertise. Future land cover was allocated for the baseline scenario and one "biodiversity conservation" policy option.

As most tropical countries do not have accurate climate and nitrogen deposition maps or models, present and future maps of these pressure types were extracted from the IMAGE2.4 model.

For training purposes the GLOBIO3 model has been put into separate GIS pressure modules. The impact of each pressure type can be calculated in ArcGis software. Total biodiversity loss is calculated by multiplying the output maps of each pressure type as defined by the MSA formula described earlier.

Input for the biodiversity model: baseline scenario

For the GBO2 assessment a baseline scenario is used that is based on a "business as usual scenario" (CBD and MNP 2007). Economic growth and population growth are assumed to be moderate. The baseline scenario is considered to be a global autonomous process of socioeconomic developments on which individual national policy-makers have no influence. The policy options, however, are defined in the GBO2 study as real possibilities to intervene in socioeconomic developments. Therefore the baseline scenario has been used as a reference frame to evaluate the effectiveness of policies.

Key indirect drivers, global population and economic activity are expected to keep on growing in the baseline. Between 2000 and 2050, the global population is projected to grow by 50 percent and the global economy is expected to increase fourfold. The demand for wood is expected to increase up to 30 percent by 2050. The baseline scenario assumes that a considerable increase in agricultural productivity can be attained. Increase in agricultural productivity will therefore be a key factor in reducing the rate of land-use change and therefore biodiversity loss in the future.

Input for the biodiversity model: forestry policy options

Out of the six policies that were evaluated, three policy options are related to forestry:

  1. The bioenergy-intensive climate change mitigation policy.
  2. Increasing the area of plantation forestry to meet the increased demand for wood.
  3. Protecting 20 percent of the original area of all major natural ecosystems.

In this context:

  1. The bioenergy-intensive climate change mitigation policy option aims at stabilizing CO2-equivalent concentrations by use of more bioenergy while keeping the global temperature increase below 2°C. In this option major energy consumption savings are achieved and 23 percent of the remaining global energy supply in 2050 will be produced from bioenergy.
  2. For the plantation forestry policy option an increased demand for wood by 30 percent up to 2050 is estimated. Increased wood production from forest plantations is expected to substantially reduce the yearly cut of forest area.
  3. An intended 20 percent conservation of protected areas in all ecological regions is chosen as a target for this conservation policy option.

Results: GBO2

Results have been derived for the baseline scenario without policy options and for the baseline scenario with policy options. First, the global and regional results of the baseline scenario are presented, then the results of three forestry-related policy options for the South and East Asia region. Finally some of the results for national application are shown for Viet Nam.

Global results of the baseline scenario

Global biodiversity in terms of MSA is projected to decrease from about 70 percent in 2000 to about 63 percent by 2050. To understand the extent of biodiversity decrease, the loss of 1 percent global MSA means conversion of 1.3 million km2 of primary, intact ecosystems to completely managed or destroyed area with no original species existing. The increased demand for wood is expected to result in a biodiversity loss of 2.5 percent of global biodiversity.

The results of the GBO2 assessment show that is unlikely that the 2010 CBD target will be met at global and regional levels. The loss of biodiversity is expected to continue as a consequence of economic and demographic trends. The need for food, fodder, energy, wood and infrastructure will unavoidably lead to a decrease in global natural stocks in all ecosystems. The negative impact of climate change, nitrogen deposition, fragmentation and unchecked human settlement on biodiversity will further expand. Changes in biodiversity are not equally distributed across the globe and for the earth's biomes. Dryland ecosystems such as grasslands and savannah will be particularly vulnerable to conversion over the next 50 years. Much of the world's remaining natural capital will consist of mountainous, boreal, tundra and ice and (semi-)arid ecosystems (Alkemade et al. 2006). These are generally considered to be less suitable for human settlement. It should be noted that inland waters and marine ecosystems as well as Antarctica and Greenland (ice) have not been addressed in this model.

Regional results of the baseline scenario: South and East Asia

Biodiversity is projected to decrease for South and East Asia from 55 percent in 2000 to 46 percent in 2050 (Figure 3). Note that a significant part of the regional decrease is dominated by the biodiversity decrease in China and India.

Figure 3. Spatial distribution of biodiversity (MSA) for South and East Asia, in the baseline development (2000—2050)

Arable land is expected to decrease as a result of increased agricultural productivity, mainly in China. Biodiversity is expected to increase locally due to natural restoration of abandoned lands.

Figure 4 shows the share of each pressure type in the decline of biodiversity. Agriculture is by far the largest source of biodiversity loss, but it slightly decreases between 2000 and 2050.

An important contribution to the decrease of biodiversity in Asia in this period is caused by high infrastructural developments and a large demand for wood. Asia has the highest demand for wood of all the regions. A sharp increase in production area is expected near 2050 because of overexploitation.

Figure 4. MSA development in the baseline scenario, with shares in decline per pressure type

Results in South and East Asia for the three forestry-related policy options

Although there is a biodiversity gain from avoided climate change and reduced greenhouse gas emissions, the positive effect is counterbalanced by biodiversity loss due to an additional land claim for growing biomass resources. Through productivity increases by agricultural systems some lands will be available for biofuel production. The bioenergy-intensive climate change mitigation policy option is expected to result in biodiversity increase of 0.4 percent MSA by 2050 in South and East Asia.

The increased wood production from forest plantations will substantially reduce the yearly cut of forest area in Asia. Exploitation of seminatural forests will decline resulting in gradual recovery of the original biodiversity. The biodiversity increase for this forestry policy option in South and East Asia is 0.8 percent MSA by 2050. However, the rather small effect will be higher after 2050 as forests take a long time to recover.

The increase of protection area of up to 20 percent leads to a 1.3 percent higher biodiversity MSA in South and East Asia. The relatively low increase is caused by the shift of agricultural activities to adjacent areas to fulfil human needs.

Methodology and results of national application of GLOBIO3: Viet Nam

The land-use map of the Agricultural Department was used for the calculation of the overall MSA for Viet Nam. This map has a detailed differentiation of agricultural land-use types but has little detail on forest cover and use classes: The primary forest class in fact includes secondary forest cover. As a result the overall MSA value for Viet Nam is 36 percent for 2000.

In order to calculate the status of biodiversity in the near future (2030) a simple baseline scenario was drafted by the participants with the following assumptions:

These claims on land have been used in the CLUE-S model to allocate the land-use distribution for 2030. In addition to maps of the major land-use types, maps of the following determinant factors have been used: population density, rainfall distribution, distance to roads, slope and elevation. Based on these variables as input for the CLUE-S model probability maps were calculated for each of the land-use types and a new land-use map was created for 2030. This map was used in the GLOBIO3 methodology to calculate the MSA biodiversity value for 2030. The output MSA value was 33.85 percent in 2030 (Figure 5).

Figure 5. MSA in Viet Nam for 2000 and 2030

The relatively small decrease in biodiversity is mainly the result of the national reforestation programme in Viet Nam.

Additionally, we combined the biodiversity output map (MSA) with protected areas. For this exercise a 1993 land-use map made by the Forestry Department was used. It shows a conflict of domestic forest exploitation in and around these protected areas (Figure 6). The result could well serve policy-makers in Viet Nam to review current conservation policies. The MSA overall score was 31 percent, which is lower than the result with the 2005 agricultural land-use map. The reason for this variation can be explained by the higher differentiation of forest-use classes in this land-use map, which allows a better determination of biodiversity values for the forest area. As the forest area is the highest contributor of biodiversity in most developing countries, the use of more forest-use classes will improve the overall result of the biodiversity model.

Figure 6. Remaining biodiversity (MSA) in protected areas

Discussion

The GBO2 assessment shows that global biodiversity will decline if no new policy measures are effected.

The GLOBIO3 framework can be used to develop national biodiversity models, but this depends largely on the availability of national data and scenarios. MSA values are only available for a limited number of land-use categories. Information on the MSA value of specific national land-use categories will improve the modeling result. During the training programme it also appeared that it was not always easy to obtain suitable land-use maps. Very often land cover classifications are much more detailed than land-use classifications, which form a necessary input for the biodiversity model. The quality of the used input maps largely determines the result. In addition to the need for data, national scenarios are required. As scenarios are hardly available at the national level, long-term sector projections and five- to ten-year development strategies can be used for estimation of drivers and pressures, such as future land-use change and pollution.

Information about future demands for timber, non-wood forest products and biofuel as well as locations where production of these products should take place will improve the results of the biodiversity model. The APFSOS could play an important role in the collection of these data. Estimations of future forest land coverage can be used to identify trade-offs with other land uses. The biodiversity impact from the projected future forest use can be calculated. In addition, model output can be used to assist the APFSOS in its aim to support policy review and reform.

Conclusions

The results of the GBO2 assessment show that it is unlikely that the 2010 biodiversity target will be met at global or regional levels. However, policy options show that a slight increase in biodiversity will be possible. National biodiversity models can be developed by combining the GLOBIO3 methodology with national land-use maps and the CLUE-S land allocation model. They can be used for a simple and quick review of environment-related national policies. The consequences of policy options can be visualized on maps and graphs or presented in tabular form.

Information from the APFSOS in relation to the spatial distribution of future forest use per country will help the development of national biodiversity models. These models can also help to assess future biodiversity impact of the plans mentioned in the APFSOS.

Bibliography

Alkemade, R., Bakkenes, M., Bobbink, R., Miles, L., Nellemann, C., Simons, H. & Tekelenburg, T. 2006. GLOBIO 3: framework for the assessment of global terrestrial biodiversity In Integrated modelling of global environmental change. An overview of IMAGE 2.4, pp. 171—186. Chapter 10. Bilthoven, the Netherlands, Netherlands Environmental Assessment Agency (MNP).

Convention on Biological Diversity and Netherlands Environmental Assessment Agency (CBD & MNP). 2006. Cross-roads of life on earth. Exploring means to meet the 2010 Biodiversity Target. Solution-oriented scenarios for Global Biodiversity Outlook 2. Technical Series No 31. Montreal, Secretariat of the CBD. 90 pp.

Food and Agriculture Organization. 2005. Global Forest Resource Assessment. FAO Web site: http://www.fao.org/forestry/site/fra2005/en/

Millennium Ecosystems Assessment. 2003. Ecosystems and human well-being: a framework for assessment. Washington, DC, World Resources Institute.

MNP. 2006. Integrated modelling of global environmental change. An overview of IMAGE 2.4. A.F. Bouman, T. Kram & K. Klein Goldewijk, eds. Bilthoven, the Netherlands, MNP.

Van Meijl, H., Van Rheenen, T., Tabeau, A. & Eickhout, B. 2006. The impact of different policy environments on land use in Europe. Agriculture, Ecosystems and Environment, 114(1): 21—38.

Verburg P. 2007. The CLUE-S model. Tutorial CLUE-S (version 2.4) and DYNA-CLUE (version 2). Wageningen University. Web site document: http://www.cluemodel.nl/ (homepage\general\download).

Appendix 1. MSA values per GLOBIO3 and GLC2000 land-use category

In the table the relation between classes of the Global Land Cover map (GLC2000) and land-use categories used in GLOBIO 3 are given, including the corresponding relative MSA.

Main GLC 2000 class (GLC 2000 classa) Subcategory Description MSALU
Snow and ice (20) Primary vegetation Areas permanently covered with snow or ice considered as undisturbed areas 1.0
Bare areas (19) Primary vegetation Areas permanently without vegetation (e.g. deserts, high alpine areas) 1.0
Forests (1, 2, 3, 4, 5, 6, 7, 8, 9, 10) Primary vegetation (forest) Minimal disturbance, where flora and fauna species abundance are near pristine 1.0
  Lightly used natural forest Forests with extractive use and associated disturbance like hunting and selective logging, where timber extraction is followed by a long period of regrowth with naturally occurring tree species 0.7
  Secondary forests Areas originally covered with forest or woodlands where vegetation has been removed, forest is regrowing or has a different cover and is no longer in use 0.5
  Forest plantation Planted forest often with exotic species 0.2
Shrubs and grasslands (11, 12, 13, 14, 15) Primary vegetation (grass- or shrublands) Grassland or shrub-dominated vegetation (e.g. steppe, tundra or savannah) 1.0
  Livestock grazing Grasslands where wildlife is replaced by grazing livestock 0.7
  Human-induced pastures Forests and woodlands that have been converted to grasslands for livestock grazing. 0.1
Mosaic: cropland/forest (17) Agroforestry Agricultural production intercropped with (native) trees. Trees are kept for shade or as wind shelter 0.5
Cultivated and managed areas (16, 18) Low input agriculture Subsistence and traditional farming; extensive farming and low external input agriculture 0.3
  Intensive agriculture High external input agriculture, conventional agriculture, mostly with a degree of regional specialization, irrigation-based agriculture; drainage-based agriculture. 0.1
Artificial surfaces (21) Built-up areas > 80% built-up areas. 0.05

 


1Netherlands Environmental Assessment Agency (MNP). PO Box 303 3720 AH Bilthoven, the Netherlands. Tel: +31 30 2742127. E-mail: wilbert.van.rooij@mnp.nl/tonnie.tekelenburg@mnp.nl. MNP Web site: http://www.mnp.nl/en/index.htm.

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