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Impacts of Road Expansion on Deforestation and Biological Carbon Loss in the Democratic Republic of Congo

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Abstract

This paper develops a nested land use model for the Democratic Republic of the Congo (DRC). The model is capable of systematically representing broad land covers and allocating agricultural area to the country relevant crops. We apply the model to assess the potential environmental impacts of road development in the country. Results indicate that an ongoing plan for road network expansion in the country would cause a reduction of more than 2 % in the existing forest resources, an increase of about 16 % in the current agricultural land, and a total loss of carbon stock estimated to be 316 TgC. The DRC government should consider forest protection a priority as road development is promoted. A plan for agricultural intensification could be safely pursued if coupled with necessary resources to prevent deforestation.

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Notes

  1. Agricultural sector employs over three quarters of the population in the DRC according to some official statistics.

  2. For instance, broad land covers can be obtained via remote-sensing and GIS technique and hence are usually measured as grid cells; in contrast, crop choice and cropping pattern are generally ground-based observations and are often reported based on administrative unit or collected at a farm level.

  3. We are aware that the decision process followed by the agent is most likely more complex than simple profit maximization. At the same time, we can assess the limits of this assumption by comparing the model land use prediction against the “known reality” on the ground. In our case, it appears that our model can replicate satisfactorily well land use choices. For countries like the DRC the major constraint is data availability rather than modeling capability.

  4. A negative value \(\lambda \) of is inconsistent with utility maximization.

  5. Without dividing by \(\lambda \) in this equation would not substantially change the results. However, the division by \(\lambda \) is needed for the product of conditional and marginal probabilities to equal the nested logit probabilities given in Eq. (3) [See Train (2003) for more discussion].

  6. Urban built-up area and water bodies are excluded from the analysis.

  7. Note that the nested structure implicitly requires that the agricultural land identified by the remote-sensing technique be consistent with the ground-based agricultural statistics. If there are large gap between the two, one might consider making adjustment to the gap or seeking other data sources.

  8. One concern arising in generating spatial farmgate prices is that transport costs may vary across crops. However, such costs for any given crop are not available. In the absence of the data, we assume the distance decay function applies for all crops. This assumption may admittedly cause some potential measurement problem but it would not matter too much since we have allowed the coefficients on this variable varying across crops. A related issue is the assumption of nonnegative prices everywhere. We made such assumption because we believe that there is at least some small value of crop for subsistence use.

  9. One of the reviewers concerned that a \(1\times 1\) km resolution might be large if crop parcels are relatively small, leading a potential misclassification problem. This may not matter too much in this study since most of the small parcels are picked up in the “mosaic of cropland” category, in which we assume crop represents 50 % of the cover. The 50 % coverage would be used for calculating agricultural emissions below.

  10. This is an important assumption in the lower-level model. Caution must be exercised when the distribution of crop choice demonstrates strong heterogeneity in a region.

  11. We lack information about how many acreages of every crop are from cropland and how many are from mosaic of cropland.

  12. We use capital letters to denote random variables and use lower case letters to denote the corresponding values in any given experiment, i.e., the real numbers that the random variables map into. Such notations are commonly used in statistics.

  13. The selection of the sampling ratio is about finding a proper balance between reducing spatial correlation and maintaining “sufficient” information to perform an econometric estimation. Spatial correlation was reduced using the coding scheme as demonstrated by the Moran \(I\) test statistic computed using the method developed by Kelejian and Prucha (2001). With the data set of 1-out-of-9 sample the value for the Moran’s \(I\) is 337.5. As the distance between observations increases from 3 to 5 km (that is, from a 1-out-of-9 sample to a 1-out-of-25 sample), the value decreases to 160.6. The test statistic becomes smaller as the distance between observations increase, however, more information is lost during the process of sampling to the point of non-convergence in the estimation.

  14. We thank one of the reviewers for his/her suggestion on this nonlinearities test.

  15. Sequential estimation creates consistent estimates, but biased downward standard errors at the upper-level model (Train 2003). Because the main purpose of this study is to assess the effect of road construction on land use and because the upper-level identification has provided evidence for this effect, adjusting the standard errors by using some procedures is straightforward but complicates the algorithm without changing the overall conclusion of this paper.

  16. In Busch et al.’s (2012) study, the factor related to transport costs is measured by distance from road.

  17. We assume the market prices of the six commodities are constant under the road expansion scenario. Increasing supply of agricultural products may reduce their market prices and hence lessen the impacts of deforestation. However, this is beyond the modeling technique proposed in this paper. Our sensitivity test indicates that the effects of general equilibrium on the upper-level land use changes are negligible.

  18. To calculate changes in biological carbon stock (i.e., values in Table 9), we first generated a \(7\times 7\) transition matrix of carbon density for each of three pools based on their carbon stock statistics that are reported in columns 2–4 of Table 8, then added the values of three pools together and multiplied the summation by the \(7\times 7\) land use transition matrix caused by road construction.

  19. \(140 = {\vert }40.5+(-180.6){\vert }\). Note that the net one-time change in biological carbon stock of cropland is positive due the high country-average SOC density on cropland (181 MgC/ha).

  20. We acknowledge that we ignore the benefits deriving from using fuel wood to satisfy households’ energy needs.

  21. Note that we could underestimate the cost of higher fertilizer application because it tends to increase nonpoint agricultural nitrogen emissions, which, however, is beyond the scope of this paper. The results must be interpreted with caution.

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Acknowledgments

The authors are indebted to Heidi J. Albers and three anonymous reviewers for their thoughtful comments on earlier versions of this manuscript. The authors gratefully acknowledge the generous funding from the United Stated Agency for International Development in support of this research. This project was conducted under the CGIAR Research Program on Climate Change, Agriculture and Food Security (CCAFS).

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Authors and Affiliations

  1. International Food Policy Research Institute (IFPRI), 2033 K St. NW, Washington, DC , 20006, USA

    Man Li, Alessandro De Pinto, John M. Ulimwengu, Liangzhi You & Richard D. Robertson

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  1. Man Li
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  2. Alessandro De Pinto
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Correspondence to Man Li.

Appendix

Appendix

See Figs. 3, 4 and Tables 14, 15, 16, 17, 18.

Fig. 3
figure 3

Above ground woody biomass in the DRC. Source: Baccini et al. (2008)

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Fig. 4
figure 4

Soil organic carbon density in the DRC. Source: Hiederer and Köchy (2012)

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Table 14 Assessment of the predictive power of the upper-level model by province
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Table 15 Assessment of the predictive power of the lower-level model by province
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Table 16 Ratio of belowground biomass to aboveground biomass
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Table 17 Average crop prices by province (US$/Mg), 2006–2010
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Table 18 Changes in crop production (1,000 Mg) induced by road construction
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Li, M., De Pinto, A., Ulimwengu, J.M. et al. Impacts of Road Expansion on Deforestation and Biological Carbon Loss in the Democratic Republic of Congo. Environ Resource Econ 60, 433–469 (2015). https://doi.org/10.1007/s10640-014-9775-y

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