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Sustainable Development in an N-Rich/N-Poor World

Abstract

Sustainable development requires that per capita inclusive wealth—produced, human, and natural capital—does not decline over time. We investigate the impact of changes in nitrogen on inclusive wealth. There are two sides to the nitrogen problem. Excess use of nitrogen in some places gives rise to N-pollution, which can cause environmental damage. Insufficient replacement of nitrogen in other places gives rise to N-depletion, or loss of nutrient stocks. Neither is explicitly accounted for in current wealth measures, but both affect wealth. We calculate an index of net N-replacement, and investigate its relationship to wealth. In countries with low levels of relative N-loss, we find that the uncompensated loss of soil nitrogen in poorer countries is associated with declining rates of growth of inclusive per capita wealth. What is less intuitive is that increasing fertilizer application in both rich and poor countries can increase per capita inclusive wealth.

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  1. Albania, Argentina, Australia, Austria, Bangladesh, Brazil, Bulgaria, Cote d’Ivoire, Cameroon, Canada, Chile, China, Colombia, Costa Rica, Cuba, Denmark, Dominican Republic, Ecuador, Egypt, El Salvador, Finland, France, Germany, Greece, Guatemala, Hungary, India, Indonesia, Iran, Ireland, Israel, Italy, Japan, Jordan, Kenya, Lebanon, Libya, Mexico, Morocco, Nepal, Netherlands, New Zealand, Nicaragua, Nigeria, Pakistan, Peru, Philippines, Poland, Portugal, Romania, Senegal, South Africa, Spain, Sri Lanka, Sudan, Sweden, Switzerland, Syria, Thailand, Tunisia, Turkey, UK, USA, Uruguay, Venezuela, Vietnam, Zambia, and Zimbabwe.

  2. Since heteroskedasticity of the data potentially compromises both fixed and random effects models Baltagi (2001), we applied a Generalized Least Squares (GLS) specification, in which \( \hat{\beta } = (X'\varPhi^{ - 1} X)^{ - 1} X\varPhi^{ - 1} Y \) and the block diagonal covariance matrix took the form \( \varPhi = [(\mu \otimes j_{T} + e)(\mu \otimes j_{T} + e)'] = I_{N} \otimes \varPhi_{i} . \) The White cross-section method was used in which the pooled regression was treated as a multivariate regression, and White-type robust standard errors for the system of equations were computed. The estimator was found to robust with respect to cross-equation correlation as well as to different error variances in each cross-section.

  3. This test compares the slope parameters estimated for FE and RE models (a significant difference implying that the RE model is estimated inconsistently due to correlation between the independent variables and the error components). The FE model can, however, be estimated consistently although the estimated parameters are conditional on the country and time effects in the selected sample of data (Hsiao 1986).

  4. Albania, Argentina, Australia, Austria, Bangladesh, Brazil, Bulgaria, Cote d’Ivoire, Cameroon, Canada, Chile, China, Costa Rica, Denmark, Dominican Republic, Ecuador, Egypt, El Salvador, Finland, France, Germany, Greece, Guatemala, Hungary, India, Indonesia, Ireland, Israel, Italy, Japan, Jordan, Kenya, Mexico, Morocco, Nepal, Netherlands, New Zealand, Nicaragua, Pakistan, Peru, Philippines, Portugal, South Africa, Spain, Sri Lanka, Sudan, Sweden, Switzerland, Syria, Thailand, Tunisia, Turkey, UK, USA, Uruguay, Venezuela, Zambia, and Zimbabwe.

  5. A Hansen test of overidentifying restrictions failed to reject the null that the instruments were uncorrelated with the error term and that the specification was correct at the 1% level. In addition, the Cragg–Donald F-statistic indicated that the group of instruments used was not weak.

  6. Currently, Afghanistan, Benin, Bolivia, Burkina Faso, Burundi, Cameroon, Central African Republic, Chad, Comoros, Côte d’Ivoire, Democratic Republic of Congo, Eritrea, Ethiopia, Ghana, Guinea, Guinea-Bissau, Guyana, Haiti, Honduras, Liberia, Madagascar, Malawi, Mali, Mauritania, Mozambique, Nicaragua, Niger, Republic of Congo, Rwanda, São Tomé & Príncipe, Senegal, Sierra Leone, Somalia, Sudan, Tanzania, The Gambia, Togo, Uganda, and Zambia.

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Acknowledgments

Charles Perrings and Ann Kinzig acknowledge the Biodiversity and Ecosystem Services Research Training Network (BESTNet) research coordination Grant Number 0639252 from the National Science Foundation.

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Perrings, C., Kinzig, A. & Halkos, G. Sustainable Development in an N-Rich/N-Poor World. AMBIO 43, 891–905 (2014). https://doi.org/10.1007/s13280-013-0488-1

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  • DOI: https://doi.org/10.1007/s13280-013-0488-1

Keywords

  • Nitrogen
  • Inclusive wealth
  • Sustainability
  • Nutrient depletion
  • Environmental externalities
  • Poverty