Mapping vulnerability to multiple stressors: climate change and globalization in India
Introduction
Vulnerability has emerged as a cross-cutting theme in research on the human dimensions of global environmental change (Downing et al., 2000; Kasperson and Kasperson, 2001; Polsky et al., 2003). Yet vulnerability to climate change has traditionally been studied in isolation from other stressors, including structural changes associated with economic globalization (O’Brien and Leichenko, 2000). It has been acknowledged that exposure to multiple stressors is a real concern, particularly in developing countries, where food security is influenced by political, economic, and social conditions in addition to climatic factors (Leichenko and O’Brien, 2002). Nevertheless, there has been no systematic methodology to operationalize vulnerability in the context of multiple stressors. Here, we present a method for mapping vulnerability to two stressors at the sub-national level, and we apply this method to examination of vulnerability in India's agricultural sector.
Our approach comprises four main steps: (1) developing a national vulnerability profile for climate change at the district level; (2) developing a national vulnerability profile for an additional stressor at the district level; (3) superimposing the profiles to identify districts in India that are “double exposed”; and (4) conducting case studies in selected districts. This method may be used to assess differential vulnerability for any particular sector within a nation or region, and it can serve as a basis for targeting policy interventions.
We focus on India's agricultural vulnerability to two stressors: climate change and economic globalization.1 Among India's population of more than one billion people, about 68% are directly or indirectly involved in the agricultural sector. This sector is particularly vulnerable to present-day climate variability, including multiple years of low and erratic rainfall. Scenarios generated by global circulation models show that India could experience warmer and wetter conditions as a result of climate change, particularly if the summer monsoon becomes more intense (Mitra et al., 2002; Kumar et al., 2002; McLean et al., 1998). However, increased rates of evapotranspiration due to the higher temperatures may offset the increased precipitation, leading to negative impacts on soil moisture (Kumar and Parikh, 2001). There are also considerable uncertainties associated with climate model projections of tropical monsoon behavior, and simulations that include sulfate aerosol forcing indicate decreasing summer monsoon rainfall (Bagla, 2002; Webster et al., 1998; Lal et al., 1995). Although the direct temperature and CO2 effects of climate change may lead to productivity increases for some irrigated crops (Aggarwal and Mall, 2002), there is general consensus that major agricultural production areas are likely to be adversely affected by climate change, particularly in areas that become increasingly water-stressed (Dinar, 1998; Kumar and Parikh, 2001; Lal et al., 1998; Gadgil, 1995).
The agricultural sector in India is influenced by more than changing climatic conditions. Widespread promotion of Green Revolution technologies during the 1960s increased agricultural yields in India for some crops and farmers by introducing high-yielding varieties that depend on inputs, including irrigation, chemical fertilizers, and pesticides (Goldman and Smith, 1995; Freebairn, 1995). In recent years, national and state agricultural policies have emphasized decentralized and participatory natural resource management, particularly for practices such as watershed development and agroforestry (Sanyal, 1993). At same time, rapid liberalization of the Indian economy has had significant structural effects on Indian agriculture (Bhalla, 1994; Chaudhury, 1998; Gulati, 1999; Gulati and Kelley, 1999; Sen, 1999). Since 1991, economic reforms have included reductions and changes in import and export restrictions and tariffs, changes in access to agricultural credit, and reductions of production subsidies (UNIDO, 1995; Rajan and Sen, 2002). Although liberalization of agricultural trade has been limited relative to other sectors of the Indian economy, India's potential participation in the WTO Agreement on Agriculture suggests that greater changes are forthcoming (Gulati et al., 1999; Rajan and Sen, 2002). The effects of these economic changes are expected to be uneven, with some regions and farmers benefiting from market liberalization and from new inflows of investments and technology, while others may have difficulty adjusting to a more open economy, particularly to the effects of increasing competition from agricultural imports (Gulati and Kelley, 1999; Mittelman, 2000).
In the remainder of the paper, we illustrate our four-step approach to assessment of regional vulnerability in the context of multiple stressors. In Section 2, we present a profile of regional agricultural vulnerability to climate change in India. Our profile focuses on sensitivity to dry conditions because a greater part of the Indian subcontinent is located in the semi-arid tropics, where rainfall is the key limiting factor in agriculture.2 Section 3 presents a profile of India's regional agricultural vulnerability to economic globalization. Using a representative basket of crops that are likely to be affected by competition from agricultural imports, we evaluate sensitivity to international trade as a function of crop productivity and distance to major ports. Those regions that have low crop productivity and/or are very close to international ports are considered more vulnerable to competition from international imports than those regions that have high productivity and/or are located farther from international ports. Trade sensitivity thus provides insight into which regions are vulnerable to the negative effects of economic globalization.3 In Section 4, we combine the climate change and globalization assessments to identify districts in India that are likely to be “double exposed” to both processes. These areas can be considered critical areas or “hot spots” in terms of vulnerability. In Section 5, we present and discuss the role of case studies in this methodology for assessing vulnerability. Case studies provide a means of “ground truthing” the macro-level vulnerability profiles. In addition, they can be used to identify local circumstances and institutional factors that are important in terms of vulnerability, yet do not come across in the macro-indicators. In the concluding section, we discuss some of the strengths and weaknesses of our approach, and identify some challenges for future research on vulnerability to multiple stressors.
Section snippets
Vulnerability to climate change
The first step in our approach involves the creation of a climate change vulnerability profile. Vulnerability to climate change is generally understood to be a function of a range of biophysical and socioeconomic factors. The most recent report of the Intergovernmental Panel on Climate Change (IPCC) provides a useful typology suggesting that vulnerability may be characterized as a function of three components: adaptive capacity, sensitivity, and exposure (McCarthy et al., 2001). Adaptive
Vulnerability to globalization
Climate variability and change is a concern to farmers in India, where agricultural productivity is closely related to the timing and strength of the northeast and southwest monsoons. However, Indian farmers have also been exposed to structural changes in the agricultural sector resulting from trade liberalization and related policies pursued by the Indian government since 1991 (Gulati and Kelley, 1999; Bhalla, 1994). Liberalization of agricultural trade may provide new opportunities for some
Vulnerability to climate change and globalization: mapping double exposure
The third step of our approach combines information from the climate and globalization vulnerability maps to identify areas that are vulnerable to both stressors. Fig. 6 illustrates the districts that exhibited high or very high vulnerability to globalization in addition to high or very high vulnerability to climate change. This map was created using the climate change vulnerability map (Fig. 3) as the “base” and then identifying via diagonal cross-hatching those districts that are in the high
Case studies: double exposure at the local level
The final step entails local-level case studies in villages located in highly vulnerable and less vulnerable districts. The case studies not only serve to “ground truth” the macro-level analysis, but they also enable us to identify state and local-level institutions and policies that influence coping and adaptation strategies used by farmers in the post-1991 period. Fig. 6 was used to identify districts with high to very high vulnerability for the selection of case study sites.
The case studies,
Conclusion
The results presented above demonstrate a method for mapping vulnerability that can be used to assess climate impacts in the context of a range of societal changes. In developing a new approach for climate vulnerability mapping, we contribute to a growing body of literature on vulnerability science, which is seeking to increase the rigor and enhance the utility of vulnerability assessments for both researchers and policymakers (Kasperson and Kasperson, 2001; Jones and Thornton, 2003; Polsky et
Acknowledgements
This project was undertaken with the financial support of the Government of Canada provided through the Canadian International Development Agency (CIDA) and the Government of Norway through the Royal Ministry of Foreign Affairs. We thank Pål Prestrud, R.K. Pachauri, Jan Fuglestvedt, and Knut Alfsen for comments on earlier drafts, and Adam Diamond for project assistance. We are grateful to Dr. K. Kumar Kolli at IITM for sharing the downscaled model results for India. We also appreciate the
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