عرضه و تقاضای ظرفیت های زیستی در شمال غرب چین : ارزیابی فضایی از پایداری

Integrating spatial analysis with the supply and demand of biocapacity is critical for the sustainable development of regional eco-economic systems. Previous studies have focused on the temporal analysis of biocapacity at broad geographical scales, but lacked the systematic spatial realization at fine scales. An improvement is proposed of this conventional methodology of the ecological footprint by incorporating land-use data derived from high-resolution remote-sensing images into the calculation of biocapacity supply at regional, provincial and county levels in Northwestern China in 2000. The spatial heterogeneity and its effect on the biocapacity supply were systematically revealed for this region. First, the biocapacity supply declined from the east (the Guanzhong Basin and the Loess Plateau) to the middle (the Qaidam Basin and the Turpan Basin), and turned to rise from the middle to the west (the northwest of the Xinjiang Uygur Autonomy). Second, although the gap between biocapacity supply and demand resulted in a small ecological deficit at the regional level, a large ecological deficit was observed at the provincial and county levels, highlighting an unsustainable situation for some of the sub-regions. Importantly, a power law relationship was unveiled between the biocapacity supply and population density, suggesting that (i) the biocapacity supply as a critical indicator could reflect the intensity of human exploitation on local biophysical resources and (ii) humans tend to have a preference to inhabit those areas with high biological productivity. These results provide opportunities to enhance policy development by central and local governments as part of the long-term Great Western Development Strategy of China.

Sustainable development is a desired policy goal worldwide (WCED, 1987). Within this context, the concept of biocapacity serves not only as a support for social development and human wellbeing (Carey, 1993, Scoones, 1993, Sagoff, 1995 and Gao, 2001), but also sets an ecological limit for human activities (Rees, 2006), with its concept rooted in the carrying capacity of the logistic population growth equation (Seidl and Tisdell, 1999). The concept of carrying capacity has evolved through several stages, from population carrying capacity to resource and environmental carrying capacity, on to biocapacity in ecological economics. In the ecological footprint (EF) methodology developed by Rees, 1992 and Wackernagel and Rees, 1996, biocapacity is defined (Rees, 1992 and Rees and Wackernagel, 1994) as the carrying capacity of ecosystems to produce useful biological materials and to absorb waste materials generated by humans. As such, biocapacity stands for a more holistic appraisal of regional ecosystems than other measures (Arrow et al., 1995, Gao, 2001 and Yue et al., 2006). A number of methods have been proposed to quantitatively estimate biocapacity, including net primary productivity (NPP; Lieth, 1972), ecological footprint (EF; Rees, 1992), emergy (Odum, 1996) and a synthetic evaluation based on the analytical hierarchy process (Gao, 2001). Among them, the EF methodology has attracted much attention over the last decade due to its ease of use and compatibility with the data formats from social and economic surveys (e.g., Wackernagel and Rees, 1996, Levett, 1998, van den Bergh and Verbruggen, 1999, Costanza, 2000, Opschoor, 2000, Lenzen and Murray, 2001, Haberl et al., 2001, Senbel et al., 2003, Yue et al., 2006, White, 2007, Kitzes et al., 2009, Kissinger and Rees, 2009 and Kissinger and Rees, 2010), and has thus been widely applied at the regional level (e.g. ,McDonald and Patterson, 2004, Chang and Xiong, 2005, Yue et al., 2006 and Kissinger et al., 2007), national level (e.g., Haberl et al., 2001, Lenzen and Murray, 2001, Wackernagel et al., 1999, Wackernagel and Galli, 2007, Bicknell et al., 1998 and van Vuuren and Smeets, 2000) and global level (e.g., White, 2007 and WWF (World Wide Fund for Nature) International et al., 2008). As a young and still developing methodology, the EF calculation requires more integrating design to represent the relationship between humanity and nature (Rees, 2000, Wackernagel and Yount, 2002 and WWF (World Wide Fund for Nature) International et al., 2006), and the inclusion of spatial structure is thought to meet this requirement, especially with the introduction of the geographic information system (GIS) into the solution (e.g., Wood, 2003, Chang and Xiong, 2005 and Yue et al., 2006). In a standard EF study, biocapacity is often measured by the available area of biologically productive land and water based on data reported in national or regional statistics. A drawback of this methodology is that data often excludes the spatial information of the EF and biocapacity as well as the spatial heterogeneity of natural capital and land use (Erb, 2004). Furthermore, national and regional statistics are often reported at a coarse resolution for political use and may not be applicable for biocapacity assessment at the level of precision required to inform policy making at regional scales (Mayer, 2008 and Chang and Xiong, 2005). Concerns over the effect of these blind spots on the spatial assessment of sustainability continue to battle researchers (van den Bergh and Verbruggen, 1999, Opschoor, 2000 and Templet, 2000), and an improved methodology that can address these shortfalls is needed (Luck et al., 2001, Jenerette et al., 2006 and Kitzes et al., 2009). In this regard, GIS models have been strongly recommended for their ability to provide better estimates than spatially implicit estimates (Kitzes et al., 2009). Therefore, it is possible to use remote-sensing data of land use combined with the spatial analysis techniques in GIS to calculate a spatially explicit biocapacity at both coarse and fine scales, as demonstrated by a few case studies (e.g., Chang and Xiong, 2005, Heumann and Moran, 2006 and Moran et al., 2009). Since 1999, the Great Western Development Strategy of China has been a national policy to ease the national imbalance of economic and social developments, with special focus on the less developed western regions. The implementation of this strategy, together with the ensuing population and economic growth in the region, has caused the impact of human activities on water and land resources to escalate, posing threats not only to the ecosystem, but also to national security. We therefore select Northwestern China (NWC) as a study area for a quantitative and spatial appraisal of the supply and demand of biocapacity. Specifically, we present a quantitative assessment of the spatially explicit biocapacity demand and supply of NWC (covering some 3 × 106 km2, and containing 5 provinces and 358 counties) at multiple spatial scales, using a combination of techniques from the EF methodology and the spatial analysis in GIS. A series of indices of biocapacity are developed to reflect the integrated status of ecological sustainability at different spatial scales. This study thus refines the current EF methodology and emphasizes the spatial heterogeneity of the regional biocapacity.

Our results indicated a 76.2% greater biocapacity per capita in NWC than in China as a whole, but the region only has about 75.9% of the per-capita eco-footprint in China (WWF, 2008). However, the biocapacity per capita and the eco-footprint per capita in NWC are 24.5% and 41% lower than the world level, respectively (WWF, 2008), a typical feature of underdeveloped region. Using the calculated pressure index of 1.005 for this region (Table 2), the pressure index for the entire nation can be estimated to be around 2.3, suggesting a very unsustainable development path currently in China. With the increase of regional population, the improvement of living standards, the change of consumption behaviour (the adoption of unsustainable lifestyle), and the growth of a high-risk dependency on trade and extra-territorial land biocapacity (Kissinger and Rees, 2009 and Kissinger and Rees, 2010), the demand is expected to surpass, or even has already surpassed, the supply of biocapacity in the region. A rapid rise of ecological deficit in all provinces of NWC is well on the way, especially with current unprecedented trend of globalization and industrialization in China. The biocapacity of fishing ground was extremely low and uneven in the region, particularly in Gansu, Xinjiang and Ningxia (see Fig. 3). It suggests that in addition to continuingly encouraging low birth rates and enhancing efficiency of nature resource consumption (especially water resource and fossil energy), local governments could minimize the ecological deficit by (i) constantly improving water resource management, (ii) monitoring environmental health and risks of governmental policies, (iii) legislating biodiversity conservation through wetland management, and (iv) enhancing the productivity of natural and agricultural ecosystems. This is especially true for Shaanxi Province and Xinjiang Uygur Autonomy, given that both have encountered large ecological deficits, indicating a rapid environmental deterioration in these two provinces. At a broad scale, the region was on a relatively sustainable development route, but the provincial- and county-scale revealed huge spatial heterogeneity. Innovative eco-economy and sustainable development strategies should be made not only based on the gap between the supply and demand of the biocapacity in a region, but also based on the spatial heterogeneity of the biocapacity derived here at multiple spatial scales. Policy making should be based on information with detailed spatial heterogeneity. The Great Western Development Strategy of China as a long-term national policy has to recognize such spatial heterogeneity of biocapacity supply and resources to make more specific measures for the regional sustainable development at multiple spatial scales. Because the spatial structure of biocapacity largely relies on the geographical characteristics and land use, the biocapacity will inevitably change along with the availability of land and natural resources. Luck et al. (2001) also underscore the importance of incorporating spatial heterogeneity and data from multiple scales in the calculation of ecological footprint. For instance, in a similar study of the ecological footprint in Siena (Italy) at three different scales (provincial, district and commune), Bagliani et al. (2008) report the spatial heterogeneity of ecological footprint and biocapacity, and further suggest the necessity of considering ecological deficit and surplus spatially explicitly in urban planning. Similarly, the maps generated using the high-resolution land use image thus captured the essence of the spatially heterogeneous biocapacity in NWC, upon which the central and local governments should make policies for environmental conservation and sustainable development to mitigate the degradation of the environment in the region. Human population density is an important determinant of the intensity of biocapacity supply, as measured by the per-unit-area biocapacity (Fig. 2). The robust power law relationship at the county level implies that the EF methodology of the biocapacity calculation is human-centred and reflects the intensity of exploitation of the local ecosystem by humans. However, the existence of a saturation level of the biocapacity suggests that future development should keep the eco-footprint below 5 gha/ha level to avoid the degradation of local ecosystems or high dependence on biocapacity import. In addition, this power law could also indicate the coupled relationship between humans and nature; that is, humans tend to dwell in places that can provide high biocapacity, which has been reported in numerous parallel studies regarding the preference of humans and animals to areas with high productivity (e.g., Luck, 2007). Both the per-unit-area and per-capita biocapacity are critical indices for reflecting the intensity of the biocapacity. However, the per-capita biocapacity is generally inefficient to capture the real picture of biologically productive land in a given region due to its sensitivity to population dynamics. For instance, Qinghai's per-capita biocapacity was 2.37 times higher than that of Shaanxi, not because the land in Qinghai had a high biological productivity, but a reflection of its extremely low population density (1/25th of Shaanxi). Instead, the biological productivity of the land should be measured by the per-unit-area biocapacity. Although the per-unit-area biocapacity reflects the real carrying capacity of biologically productive land, it also varies with the change of land use yield and types. Therefore, the two indices should be used complimentarily. The spatial unevenness of the biocapacity distribution is a universal and objective phenomenon. The analysis of its spatial distribution at coarse scales inevitably overlooks the inner characteristics of biocapacity, which can only be reflected at fine scales. The scaling pattern of biocapacity could encounter the modifiable areal unit problem as in landscape geography (Openshaw, 1984 and Hui et al., 2010), and merits further attention on the specific spatial structure and partition of its distribution (e.g., using self-similarity technique; Hui and McGeoch, 2008). The GIS-based calculation of the biocapacity using remote-sensing data of land use proved to be time-efficient and resulted in high resolution information, compared to using social statistic data alone. Furthermore, we have previously calculated the per capita biocapacity of Gansu in 2000 (= 1.088 gha per capita) using social statistics data according to the standard EF methodology (Yue et al., 2006), with the estimation lower than 1.504 gha per capita obtained here (Table 2). This implies that the biocapacity calculated using social statistics alone could potentially underestimate the value of biocapacity (Chang and Xiong, 2005). The eco-footprint and biocapacity showed large discrepancies at different scales, indicating an overall sustainable development in NWC, but large ecological deficits at provincial and county levels for specific areas. Although our revised method of using remote-sensing data in GIS for calculating the spatial heterogeneity of biocapacity can represent an efficient tool for overcoming the limitations of the conventional EF methodology, the analytic results were still not precise enough to obtain a full picture of the balancing mechanism of the biocapacity and eco-footprint, especially given that the eco-footprint was limited by low resolution statistics. Furthermore, McDonald and Patterson (2004) have demonstrated that Auckland's ecological footprint is not only originated from international trade but also regional trade with other regions in New Zealand and thus call for a process-based methodology for estimating regional ecological footprint. Luck et al. (2001) also advocate the necessity of incorporating ecosystem processes in the calculation of ecological footprint. A process-based methodology could provide a better capacity for forecasting future trends and elucidating the source-sink dynamics of EF components. Therefore, future investigations should place emphasis on (i) the spatial representations of eco-footprint and ecological budget at high-resolution, (ii) a spatially explicit prioritization of the 12% biologically productive area assigned for biodiversity conservation (e.g., Roura-Pascual et al., 2010), as well as (iii) the import/export dynamics associated with the ecological budget.