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Assessing the Economic Impacts of Changes in Crop Production Due to Climate Change and Adaptation in Vietnam

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Part of the New Frontiers in Regional Science: Asian Perspectives book series (NFRSASIPER,volume 41)

Abstract

Vietnam is at risk from diverse climate change impacts, including sea-level rise, extreme weather events, rising temperatures and changes in precipitation. In this chapter, we use a multi-regional dynamic computable general equilibrium model of Vietnam to investigate the effects of changes in crop yields and harvested areas caused by climate change, with and without adaptation measures, under alternative climate change scenarios. We find that reduced crop yields, and reduced crop areas in low-lying regions, cause negative deviations in national and regional GDP, consumption and industrial production. Inequality is projected to rise, primarily due to higher food prices. Adaptation measures improve both crop yields and crop area compared with no-adaptation scenarios. This mitigates some of the adverse macroeconomic effects, while also reducing disparities in climate change impacts across regions and across households distinguished by urban/rural location and expenditure quintile.

Keywords

  • CGE
  • Climate change
  • Adaptation measures
  • Crop yields
  • Vietnam

JEL Classifications

  • C68
  • D58
  • N55
  • O13
  • Q54

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Notes

  1. 1.

    These inputs were provided in the project “Economics of Adaptation to Climate Change—Macro assessment for Vietnam” that the Centre of Policy Studies, Victoria University, conducted for the World Bank in 2009–2010. The inputs were provided for three points: 2010, 2030 and 2050. For the purpose of this chapter, we use only the inputs up to 2030.

  2. 2.

    Although our analysis is concerned only with the period 2010–2030, we use IFPRI data for 2050 as well in generating year-on-year changes in crop yields and harvested areas. That is because, as discussed in the Introduction, some climate change scenarios project some reversal of precipitation and other climate conditions in the period 2030–2050 compared with the period 2010–2030. We use the cubic spline to smooth out the transition between these two periods.

  3. 3.

    That is, IFPRI assumes no-change in harvested areas during the period 2009–2030 under the no-climate-change scenario. However, as will be discussed later, in our no-climate-change baseline we take into account some changes in agricultural land due to conversion to non-agricultural uses.

  4. 4.

    NCAR is the climate model of the National Centre for Atmospheric Research, USA. CSIRO is the climate model of the Commonwealth Scientific and Industrial Research Organisation, Australia. For more information on the climate change scenarios used by IFPRI for the projection of agricultural prices, see IFPRI (2009).

  5. 5.

    We expect that, compared with those under the adopted scenario, the impacts of changes in international prices of rice and price due to climate change under the NCAR scenarios would be very similar, and the impacts under the ‘with CO2 fertilisation’ scenarios would be somewhat smaller. This is because, assuming smooth growth rates of crop prices over 2010-2050 for IFPRI (2009, p. 7) projections, the projected prices under the NCAR scenarios are very similar to those in the CSIRO scenarios, differing only by 1.8% for rice and 1.1% for maize by 2030. And the projected prices under the ‘with CO2 fertilisation’ are approximately 8.4% lower for rice and 6.1% lower for maize than under the adopted scenario without CO2 fertilisation by 2030.

  6. 6.

    Such a model is described in Dixon and Rimmer (2002, pp. 286–289). They conjecture that the obvious potential limitation of the top-down approach—the absence of feedback effects influencing the structure of production in the CGE core—is unimportant in practice.

  7. 7.

    Our income/expenditure model is based on the VHLSS 2004 (GSO 2006). The VHLSS 2004 surveys 9300 households on detailed expenditure and income characteristics. Using appropriate survey weights, GSO (2006) uses these survey results to calculate detailed expenditure and income data for the Vietnamese population classified by ten expenditure groups. These expenditure groups are identified by region of residence (urban/rural) and expenditure quintile. Our income/expenditure model recognises, for each of ten households distinguished by expenditure quintile and region of residence: (1) labour income from six qualifications cross-classified by 158 industries; (2) capital income from 158 industries; (3) land income from 66 agriculture and mining industries; (4) government benefit payments; (5) foreign interest payments; (6) interest income on domestically issued government debt; (7) foreign transfer income and (8) expenditure on 116 commodities cross-classified by two sources of supply. With this detail, the model captures impacts on real household expenditures arising from changes in factor income (whether via factor prices or employment), transfer payments and commodity prices.

  8. 8.

    We think this is a realistic assumption, for three reasons. First, as Vietnam grows, other economies will also grow, shifting outwards demand curves for Vietnamese exports. Second, as Vietnam develops, we expect Vietnamese trade negotiators will have growing success in accessing foreign markets and countering protectionist reactions to export expansion. Thirdly, Vietnamese entrepreneurs will have success in developing new products and niche markets for their exports.

  9. 9.

    Centre for Agricultural Policy, under the Institute of Policy and Strategy for Agriculture and Rural Development, Vietnam Ministry of Agriculture and Rural Development, Rice domestic demand, Presentation at the ‘Food security and rice value chain research consortium: Taking stock of work in progress’ workshop, Can Tho 19–20 October 2010.

  10. 10.

    Although agricultural land decline at the 0.55% p.a., we assume that the subsoil assets (e.g. minerals) are unchanged. Hence, the aggregate land and natural resources decline only at 0.37% p.a.

  11. 11.

    As discussed in Sect. 3.2, MONASH-VN all-input-using technology in crop production is the inverse of crop yields. Therefore, changes in technology have the opposite sign, and close, but not exact absolute value as changes in yields.

  12. 12.

    In the discussion of results, to avoid long sentences, we often use the words ‘increase’ and ‘decrease’ to mean ‘have a positive deviation from the baseline’ and ‘have a negative deviation from the baseline’, respectively.

  13. 13.

    −0.80 = −0.54 (technology contribution) − 0.27% × 0.421 (labour contribution) − 0.39% × 0.325 (capital contribution) − 0.17% × 0.119 (land contribution).

  14. 14.

    Assuming the exchange rate of 20,000 VND/US$, and the number of households at 22,628,000 (based on the Population Census 2009).

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Tran, N.H., Giesecke, J.A., Jerie, M. (2020). Assessing the Economic Impacts of Changes in Crop Production Due to Climate Change and Adaptation in Vietnam. In: Madden, J., Shibusawa, H., Higano, Y. (eds) Environmental Economics and Computable General Equilibrium Analysis. New Frontiers in Regional Science: Asian Perspectives, vol 41. Springer, Singapore. https://doi.org/10.1007/978-981-15-3970-1_5

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