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Abstract

  1. Top of page
  2. Abstract
  3. 1. Introduction
  4. 2. Roads and Climate Change
  5. 3. Mozambique
  6. 4. Assessing Climate Change Impacts on Road Infrastructure
  7. 5. Simulations and Results
  8. 6. Conclusions
  9. References

Decision-makers who are responsible for determining when and where infrastructure should be developed and/or enhanced are facing a new challenge with the emerging topic of climate change. The paper introduces a stressor–response methodology where engineering-based models are used as a basis to estimate the impact of individual climate stressors on road infrastructure in Mozambique. Through these models, stressor–response functions are introduced that quantify the cost impact of a specific stressor based on the intensity of the stressor and the type of infrastructure it is affecting. Utilizing four climate projection scenarios, the paper details how climate change response decisions may cost the Mozambican government in terms of maintenance costs and long-term roadstock inventory reduction. Through this approach the paper details how a 14% reduction in inventory loss can be achieved through the adoption of a proactive, design standard evolution approach to climate change.


1. Introduction

  1. Top of page
  2. Abstract
  3. 1. Introduction
  4. 2. Roads and Climate Change
  5. 3. Mozambique
  6. 4. Assessing Climate Change Impacts on Road Infrastructure
  7. 5. Simulations and Results
  8. 6. Conclusions
  9. References

The African Development Bank has called for US$40 billion per year to be provided to African countries to address development issues directly related to climate change (Lowe and Amara, 2009). These costs refer to both adaptation and mitigation expenditures. While costs are a concern for all countries, these costs are of particular concern in developing countries, where the additional funds needed to address climate change concerns are limited. The limitations on these available funds are challenging developing countries to identify the threats that are posed by climate change, develop adaptation approaches to the predicted changes, incorporate changes into mid-range and long-term development plans, and secure funding for the proposed and necessary adaptations (United Nations Framework Convention on Climate Change (UNFCCC), 2009).

Earlier work by the UNFCCC, Intergovernmental Panel on Climate Change (IPCC), World Bank and others, have attempted to quantify the impact of climate change on physical assets that will be affected in the coming decades. The current study extends these efforts by addressing the effect of climate change on road infrastructure of Mozambique. Paved and unpaved road inventories were selected as the single infrastructure type to evaluate because of their economic, social, and development importance on the Mozambique economy. The study examines the extent to which climate change from global and country-specific climate scenarios will divert resources from the further development of road infrastructure to the maintenance and adaptation of the existing infrastructure (or drive down the average state of repair of the road network). This is important as a large literature links road infrastructure and economic growth (Pereira and Andraz, 2005). In addition, Rioja (2003) found a growth penalty for degraded or inefficient infrastructure including roads. Arndt et al. (2012) provide a brief review of this literature.

The current study is designed to create a broader understanding of the effects climate change may have on facets of development including social, economic, and transport issues by analyzing the road infrastructure. The study provides a context for policy and decision makers to further understand the impacts of future climate change on road infrastructure through 2050. In summary, the study is designed to provide a larger context for policy makers to address, in part, the question of “now or later?” Can Mozambique afford to postpone adaptation to potential climate change effects on critical infrastructure?

2. Roads and Climate Change

  1. Top of page
  2. Abstract
  3. 1. Introduction
  4. 2. Roads and Climate Change
  5. 3. Mozambique
  6. 4. Assessing Climate Change Impacts on Road Infrastructure
  7. 5. Simulations and Results
  8. 6. Conclusions
  9. References

The literature on climate change impacts and adaptation in the infrastructure sector is primarily qualitative, emphasising broad recommendations and warnings based on general weather studies. Research by the Transportation Research Board in the USA, the Scottish Executives, and Austroads in Australia are notable examples (Transport Research Board (TRB), 2008; Galbraith et al., 2005; AUSTROADS, 2004). The authors compare weather-related disasters and their perceived severity with predicted climate change impacts. More focused studies estimate specific impacts of temperature, rain, snow, ice, wind, fog, and coastal flooding on roads (US Climate Change Science Program (CCSP), 2006). Further studies address areas where climate change may threaten infrastructure unique to that locale. For example, Canadian roads are particularly vulnerable to rising temperatures (Industrial Economics, 2010). Similarly, northern climates may face greater infrastructure degradation owing to increased freeze-thaw cycles (Jackson and Puccinelli, 2006).

Mills and Andrey (2002) provided a general framework for considering climate impacts on transportation. They enumerate baseline weather conditions and episodic weather-influenced hazards that determine the environment in which infrastructure is built, maintained and used. The authors note that climate change will alter the weather-related context, affecting the frequency, duration, and severity of hazards. These hazards can affect transportation infrastructure itself; its operation; and the demand for transportation services. The latter might include climate effects on agriculture that alter the location of production and, thus, the need and mode for shipping agricultural products.

A limitation of the above studies is their focus on a narrow potential impact of climate change, and their lack of specific estimates of costs or damages that may result from climate change. In response to this limitation, Chinowsky et al. (2011) documented the potential costs of climate change on road infrastructure in 10 geographically and economically diverse countries. They illustrated the opportunity costs of diverting infrastructure resources to climate change adaptation. This response methodology has been extended to estimate climate change impacts on bridges (Stratus Consulting, 2010) and roads in northern climates (Industrial Economics, 2010).

Greater attention is now being paid to the potential impact of climate change on infrastructure in Africa. As mentioned, planning for climate change in the African context is taking place in the context of inadequate existing infrastructure. For instance, in 1997, Africa (excluding South Africa) contained 171,000 km of paved roads, which was 18% less than Poland—a country roughly the size of Zimbabwe (International Road Federation (IRF), 2009). Despite continued investments, road stocks still lag behind the rest of the world. Moreover, in 2008, only 25% of Sub-Saharan Africa's primary roads were paved, compared with a global rate of 50% and a 67% rate in North America. In terms of per capita road stocks, the paved road length in Sub-Saharan Africa of 0.79 km per thousand inhabitants is less than half of that of South Asia and only a fifth of the world average. Finally, there is significant variability in the quality of primary transport corridors across Africa, with Central Africa having only half of its primary roads in good condition, while all of South Africa's roads are in good condition (Gwilliam et al., 2008). Similarly, in terms of rural roads, which comprise a majority of all road infrastructure in Africa, more than 70% of rural roads are in fair or poor condition (Foster and Briceño-Garmendia, 2010). The potential damaging impacts that climate change poses to road infrastructure could further deteriorate the limited existing road infrastructure in African countries. However, with proper design, planning, and funding, African nations have the advantage of building a large share, if not the majority, of their infrastructure backbone while accounting for the potential implications of climate change.

3. Mozambique

  1. Top of page
  2. Abstract
  3. 1. Introduction
  4. 2. Roads and Climate Change
  5. 3. Mozambique
  6. 4. Assessing Climate Change Impacts on Road Infrastructure
  7. 5. Simulations and Results
  8. 6. Conclusions
  9. References

Mozambique is a large country with a total area of more than 800,000 square kilometers. About 70% of the population lives in rural areas and this population is fairly evenly spread throughout the country. Population density is low at 27 persons per square kilometer (compared with 259 people per square kilometer in Vietnam, a close geographic analog in Asia). Connecting sub-national regions poses a serious challenge, especially given infrastructure losses during the civil war and a colonial inheritance that emphasized east–west trade with neighboring countries rather than north–south domestic trade. Given Mozambique's infrastructure deficit, approximately 15% of total government expenditure in 2010 (i.e. about 5% of gross domestic product (GDP)) was going towards infrastructure investment, particularly in roads.

Flooding poses a large hazard to the integrity of Mozambique's road infrastructure, and there are potentially large economic consequences of hampered connectivity. Mozambique lies at the end of numerous transnational river basins and flooding in its deltas is a perennial threat to farmers and infrastructure. In 2000, for example, severe flooding in the south of the country destroyed road links between the capital city Maputo and the rest of the country for nearly a year. The rail line to Zimbabwe was also destroyed. This loss of transport infrastructure and connectivity between sub-national regions caused per capita economic growth to decline to about 1% in 2000, by far the slowest growth rate registered in two decades. In 2001, the primary north–south road link was repaired and economic growth re-bounded, bringing Mozambique back to close to pre-flood growth trends. Concurrently, substantial increments to development assistance also helped to repair damage relatively rapidly and implied relatively few tradeoffs with other investment options (see also Christie and Hanlon, 2001).

Climate change scenarios for Mozambique are detailed in Arndt et al. (2012). Briefly, the impact of climate change on Mozambique is explored using four scenarios based on the NCAR, CSIRO, UKMO, and IPSL general circulation models (GCMs) of the earth and atmosphere. In these scenarios, all sub-national regions in Mozambique are expected to experience a 1–2 degC increase in temperature by 2050. There is greater variation in average precipitation changes in these four scenarios, reflecting a lack of consensus among GCMs over precipitation projections at localized scales (Solomon et al., 2007). Overall, the GCMs suggest that climate change impacts in Mozambique will create a hotter climate with variable rainfall patterns.

4. Assessing Climate Change Impacts on Road Infrastructure

  1. Top of page
  2. Abstract
  3. 1. Introduction
  4. 2. Roads and Climate Change
  5. 3. Mozambique
  6. 4. Assessing Climate Change Impacts on Road Infrastructure
  7. 5. Simulations and Results
  8. 6. Conclusions
  9. References

In this analysis, climate change impacts are quantified in terms of total kilometers of road degraded prior to lifecycle design and total cost of repairing or mitigating future damages. The impacts are determined by a “stressor–response” methodology, in which exogenous factors (i.e. stressors) have a direct effect on (and subsequent response by) focal elements. In the context of climate change and infrastructure, these factors include: changes in precipitation levels, temperatures, storm frequency and intensity, and wind speeds.

The stressor–response methodology presented here diverges from previous studies that emphasize qualitative statements rather than quantitative estimates (see section 2). A combination of material science reports, usage studies, case studies, and historic data are used to develop response functions for different infrastructure categories. Where possible, data from material manufacturers were combined with historical data to obtain an objective response function. However, when these data were not available, response functions were extrapolated based on performance data and case studies from sources such as Mozambique's Department of Transportation and other government ministries. Three climate stressors are examined in particular—temperature and precipitation changes and changes in the intensity and frequency of flooding events.

Road construction and maintenance costs were determined using both commercial cost databases and country-specific data. Six road types are examined in this study: paved and unpaved surface types with primary, secondary, and tertiary classifications within each. It is important to note that road types are treated homogenously when determining impacts of climate on the road inventory. Although differences in quality, maintenance, and construction standards exist within the country, these micro-level differences are beyond the scope of this study. Rather, a macro-level perspective is adopted that treats all roads within a classification type as similarly constructed and maintained. This perspective is necessary at a country level but should be examined in the context of specific decisions. Similarly, assumptions are in place that tonnage and traffic levels average out over the road network to provide distributed maintenance requirements. Although these assumptions require a macro-level reading of the results, they are in line with the economic and climate restrictions required for a country-level analysis.

The impact of climate change on road infrastructure is examined for each road type. Costs of new construction (or lifecycle-rehabilitation), maintenance costs to address climate-change related stressor impacts, and maintenance savings are evaluated in this study. All costs are applied with the goal of keeping a road functional for its design-life. This premise was established as a baseline requirement due to the preference for retaining infrastructure for as long as possible rather than replacing infrastructure on a more frequent basis. Achieving this goal may require a change in the design standard for new construction or an increase/decrease in maintenance for existing infrastructure.

In the current study, we consider policies that adapt to climate change and those that do not. In the “No Adaptation” scenarios, no actions are taken to mitigate the effects of climate change on paved and unpaved roads. In other words, Mozambique continues to build and maintain roads according to the design standards established without climate change. This is the engineering equivalent of the classic “dumb farmer” approach where the individual employs the same cropping patterns and techniques despite changing climate conditions. In terms of roads, this classic model is modified to a “dumb engineer” model whereby the engineers continue to design and maintain roads in the same manner despite an evolving climate; the result being that as temperatures increase and precipitation patterns change, maintenance needs often increase as the roads are not designed to withstand the changes in stressors. In contrast, under adaptation, roads are constructed to evolving design standards that anticipate climate change based on climate trends.

Stressor–Response Relationships: Precipitation and Temperature

The stressor–response relationships in the current study are divided between paved and unpaved roads based on the effects of stressors on the different surface types. For paved roads, estimates of stressor–response values are based on available data that establish threshold impact levels for precipitation and temperature. The available data suggests that levels of 10 cm increase in annual precipitation or 3 degC maximum temperature increase will result in pavement degradation (Lea International, L.D., 1995; National Oceanic and Atmospheric Administration (NOAA), 2009).

For unpaved roads, the stressor–response relationship associates impacts with changes in maximum monthly precipitation. Ramos-Scharron and MacDonald (2007) attributed about 80% of unpaved road degradation to precipitation, while the remaining 20% is attributed to factors such as the tonnage of traffic and traffic rates. Given this attribution to precipitation and the focus on retaining design lifespan, we assume that climate impact is based on maximum monthly precipitation, rounded to 1 percentage point increments. For example, if the maximum monthly precipitation increases by 10% in a given location, then we assume an 8% (0.8 × 0.1 = 0.08) impact on lifespan. Available data suggests that there is no relationship between temperature and unpaved road degradation. Our approach is summarized as follows:

  • image(1)

where CIU is the impact in degradation for unpaved roads associated with a unit change in climate stress, MIP is the increase in maximum monthly precipitation.

Given these overall impacts, two basic methodologies were adopted for maintenance costs. The first approach, used for paved roads, is based on the cost of preventing a reduction in lifespan that may result from changes in climate-related stress. It is assumed that any lifespan reduction caused by an incremental change in climate stress is equal to the percent change in climate stress, scaled for the stressor's effect on maintenance costs. Miradi (2004) estimated that ongoing precipitation-related maintenance for paved roads accounts for 4% of maintenance costs and temperature-related maintenance accounts for 36% of costs. After estimating the potential reduction in lifespan associated with a given climate stressor, the costs of avoiding this reduction in lifespan are calculated as the product of (i) the potential percent reduction in lifespan and (ii) the base construction costs of the asset. Therefore, a 10% reduction in lifespan has an estimated increase in maintenance costs of 10% over base construction costs.

As shown in equations (2) and (3), we implement our approach to maintenance costs in two stages: (i) estimating the lifespan decrement that would result from a unit change in climate stress, and (ii) estimating the costs of avoiding this reduction in lifespan. It is assumed that such a reduction in lifespan caused by an incremental change in climate stress (LP) is equal to the percent change in climate stress, scaled for the stressor's effect on maintenance costs, as shown below:

  • image(2)

where LP is potential percent change in lifespan for existing paved roads associated with a unit change in climate stress, ΔS is change in climate stress, S0 is base level of climate stress without climate change, and SMT is the share of existing paved road maintenance associated with a given climate stressor, and the set i has elements [precipitation, temperature].

The total change in maintenance costs is then as follows:

  • image(3)

where CMP is change in maintenance costs for existing paved roads associated with a unit change in climate stress, and BMP is base original construction cost of the paved road segment.

Finally, to estimate the stressor–response values for unpaved road maintenance costs, we follow the approach outlined above for unpaved roads' new construction costs. Changes in unpaved road maintenance costs are associated with a 1% change in maximum monthly precipitation. As indicated above, 80% of road degradation can be attributed to precipitation, while the remaining 20% is due to traffic rates and other factors. This implies that unpaved road maintenance costs increase by 0.8% with every 1% increase in the maximum monthly precipitation values projected for any given year. This reflects the direct costs required to mitigate the damages to the road based on available relationship data. The authors recognize that these assumptions are broad and factors such as slope and traffic rates will impact degradation rates. Further investigation is being conducted to incorporate these issues. The general form of the maintenance equation is as follows:

  • image(4)

where CMU is the change in maintenance costs for unpaved roads associated with a unit change in climate stress or design requirements, MIP is the percentage increase in maximum monthly precipitation, and BMU is the baseline maintenance costs for unpaved roads.

Stressor–Response Relationships: Flooding

As with precipitation and temperature, flooding damage is calculated using stressor–response functions. Road flood losses are calculated based on flood return periods projected by the four GCMs employed in this analysis for northern, central, and southern Mozambique in Strzepek et al. (2010). An assessment of the nature of the flooding is also germaine. Gradually rising and then gradually receding flood waters can often leave roads, particularly paved roads, essentially intact. Running flood waters, in constrast, quickly result in the complete destruction of underlying roads generating repair costs that are close to new road construction costs. Translating the return periods into damage costs requires two steps: (i) determining the amount of road kilometers damaged, and (ii) estimating the specific costs implied by these damages.

Figure 1 illustrates the damage functions employed for the analysis. The damage functions are derived based from the report authored by the COWI consulting group (2009), Strzepek et al. (2010), and the assessment of the authors. While minor flooding events have minor or no impact, major flooding events can have substantial impact. A 100 year flood is assumed to damage 30% and 10% of unpaved and paved roads respectively in the region in which it occurs (North, South, Center). Costs of repairing damaged roads vary by road class (primary, secondary, and tertiary) and by road type (unpaved and paved). Flood damage costs are also variable as a share of new construction costs. These cost estimates are also based on COWI (2009) and the assessment of the authors.1

image

Figure 1. Estimated Road Damage Functions by Flood Return Period. Source: Authors' calculations.

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Some intuition on the implications of these numbers is valuable. Combining the damage functions and the cost estimates with the shares of baseline national road extension by road class and road type, we find that a 100 year flood causes damages equivalent to about 9% of the value of the road stock in the region in which the flood occurs. Based on these figures, we would estimate that the floods in the south of Mozambique in 2000 necessitated about US$122 million in reconstruction expenditures for road and related infrastructure. While we have been unable to locate any formal ex post evaluations of the infrastructure costs of the 2000 floods against which to compare this number, the available evidence would suggest that this estimate is reasonable.2

The relationships developed above are incorporated into a dynamic road network simulation model labeled “CLIROAD”. The simulation model tracks the road stock broken by age since construction (or 20 year rehabilitation), road class (primary, secondary, tertiary), road type (paved or unpaved), region (north, south, center, and urban) for each year over a simulation period from 2003 to 2050. While the stressor–response functions are constant across regions, the climate inputs (precipitation, temperature, and flood events) are disaggregated by region. It also tracks all costs to the same level of detail over the simulation period. The model is constructed in GAMS and is available upon request.

5. Simulations and Results

  1. Top of page
  2. Abstract
  3. 1. Introduction
  4. 2. Roads and Climate Change
  5. 3. Mozambique
  6. 4. Assessing Climate Change Impacts on Road Infrastructure
  7. 5. Simulations and Results
  8. 6. Conclusions
  9. References

The stressor–response relationships discussed above allow us to determine the impact of climate change on the maintenance of paved and unpaved roads. In the first set of simulations, we estimate the costs of maintaining a given road network across all climate change scenarios relative to the baseline climate scenario. In the second set of simulations, the budget for building and maintaining roads is held constant across all climate change scenarios while the length of the road network is allowed to vary. The current Mozambique road network on which the simulation was performed is illustrated in Figure 2.

image

Figure 2. Current Road Infrastructure in Mozambique

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In addition, these simulations are run with and without adaptation. In the “without adaptation” scenarios, no actions are taken to mitigate the effects of climate change on paved and unpaved roads (see section 4 above). In contrast, adapting to climate change requires a “design strategy” that enhances the design standards for roads to reflect the risk of new and/or increased climate change stressors. In our modeling, adaptation proceeds on the basis of policy approaches. Two adaptations are modeled. First, as observed temperature rises and precipitation levels change, road designs are assumed to be adjusted to reflect revised conditions. More specifically, surface design standards evolve with a moving average of the preceding 10 years of temperature and precipitation (i.e. road engineers learn from recent experience). Although road construction and maintenance unit costs remain the same, the revised design standards imply that precipitation and temperature thresholds are breeched less frequently. Second, we assume that planners take steps to counter an enhanced probability of flooding by investing in adaptation. These adaptations reduce flood damage by 50%.

In the simulation modeling, all adaptations are assumed to occur either at new road construction or at 20 year road rehabilitation, implying that these are gradualist adaptation policies. The road network gradually evolves to become more robust as the share of roads that have undergone adaptation (e.g. newly constructed or completely rehabilitated roads) under the new policy increases.

Results: Without Adaptation Scenarios

The scenario analyses presented in this section assume a starting roadstock equal to Mozambique's total road inventory in 2003 including 26,000 and 6,300 km of unpaved and paved roads, respectively. The upper panel of Table 1 illustrates the costs of maintaining exactly this road network without adaptation. As shown in the Table, discounted maintenance costs (the discount rate is 5%) increase in three of the four climate change scenarios relative to the base. The Global Dry scenario (CSIRO) is the least favorable to the road network. Even though the Global Dry (CSIRO) scenario exhibits less overall precipitation than the Mozambique Wet (NCAR) scenario, the CSIRO scenario exhibits greater precipitation intensity within the trans-boundary river basin and therefore leads to a larger number of flooding events (see Strzepek et al., 2010). The scenarios with the greatest climate impact project climate change costs which amount to a discounted value of about US$641 million. Damage to primary roads is the most expensive to fix, and therefore primary roads represent the most expensive road class. However, because the quantity of unpaved roads is approximately four times larger than the paved road network, costs to maintain unpaved roads are larger in all scenarios. Discounting disguises cost rises that occur in the 2030s and 2040s. Total maintenance costs with no discounting rise in all scenarios (not shown). For the scenarios with the greatest climate impact, the majority of maintenance cost increases are due to flooding (also not shown).

Table 1. Discounted Simulated Road Maintenance Costs over 2003–2050 (US$ millions or % of BASE)
 NorthCenterSouthUrbanPrimarySecondaryTertiaryPavedUnpavedTotals
  1. Source: Simulation results from the CLIROAD model.

 Without adaptation (% unless indicated by $)
BASE$1,352$1,992$1,060$715$2,818$888$1,414$2,408$2,711$5,119
CSIRO16.613.57.98.99.813.317.47.417.112.5
NCAR16.16.17.67.26.98.014.64.713.39.2
UKMO−5.1−5.722.78.02.41.22.81.72.82.3
IPSL−1.4−3.94.00.7−0.5−1.8−1.5−0.4−1.5−1.0
 With design standard evolution (%)
BASE−0.20.20.40.20.10.20.10.10.20.1
CSIRO15.912.96.67.89.212.416.07.115.711.6
NCAR12.94.37.45.85.76.411.44.010.47.4
UKMO−4.4−5.720.77.02.00.82.21.42.21.9
IPSL−1.0−3.84.00.9−0.3−1.7−1.2−0.3−1.2−0.8
 With design standard evolution and flood investments (%)
BASE6.36.07.68.59.73.62.89.74.16.7
CSIRO17.615.612.214.216.512.714.414.915.615.2
NCAR18.59.312.113.014.48.512.312.912.712.8
UKMO3.42.022.413.611.34.34.610.85.98.2
IPSL5.53.19.58.69.22.01.49.32.75.8

In Table 2, the upper panel shows the resulting impact of climate change on the road network for a given budget, without adaptation, by the year 2050. All scenarios assume an approximate annual increase of 3.6% in the real budget devoted to road infrastructure during 2003–2050.3 The budget is first allocated to cover the maintenance of existing roads, including the costs of rebuilding roads washed out by flooding. Any remaining budget is then allocated to the construction of new roads. For new road construction, constant allocation shares are applied across road types (paved/unpaved), road class (primary, secondary, tertiary), and location (North, South, Center, and Urban). If the total budget is insufficient to meet maintenance costs in a given year, then the share of roads not maintained is assumed to be equal to the percentage budget shortfall. So if, for example, the budget is only sufficient to meet 95% of total maintenance costs, then 5% of roads (for all types, classes and locations) are assumed not to receive any maintenance. Maintained roads do not depreciate while unmaintained roads depreciate at a 10% annual rate.

Table 2. Deviation in Road Network Length in 2050 from Baseline (%) using CLIROAD
 NorthCenterSouthUrbanPrimarySecondaryTertiaryPavedUnpavedTotals
  1. Source: Simulation results from the CLIROAD model.

 Without adaptation (%)
BASE0.00.00.00.00.00.00.00.00.00.0
CSIRO−12.4−11.8−10.0−9.3−10.7−12.1−11.0−11.1−11.4−11.3
NCAR−6.5−5.7−8.2−5.0−6.7−8.8−4.5−8.8−5.2−6.7
UKMO−1.7−1.3−3.5−1.3−2.3−3.5−0.5−3.7−1.0−2.1
IPSL−4.7−4.5−4.8−3.7−4.5−5.5−3.9−5.3−4.2−4.6
 With design standard evolution (%)
BASE0.00.00.00.00.00.00.00.00.00.0
CSIRO−10.8−10.2−8.3−8.0−9.1−10.2−9.6−9.3−10.0−9.7
NCAR−3.9−3.2−5.4−2.9−4.2−5.6−2.4−5.8−2.9−4.1
UKMO−0.5−0.2−2.2−0.4−1.1−2.10.5−2.30.1−0.9
IPSL−4.1−4.0−4.2−3.2−3.9−4.7−3.4−4.6−3.7−4.0
 With design standard evolution and flood investments (%)
BASE−5.4−4.9−7.4−3.8−6.6−8.7−2.2−6.0−5.6−5.7
CSIRO−10.6−9.6−11.5−7.8−11.0−13.2−7.1−10.4−10.3−10.4
NCAR−8.0−6.9−9.6−5.6−8.8−11.1−4.4−8.3−7.8−8.0
UKMO−6.1−5.6−8.3−4.4−7.4−9.6−2.8−6.9−6.2−6.5
IPSL−7.2−6.7−9.4−5.2−8.4−10.8−3.8−8.2−7.2−7.6

The total road network length is lower in 2050 in all of the four climate change scenarios we consider (see the column labeled “Totals” in the upper panel of Table 2). Results vary widely across scenarios, road types (paved vs unpaved), road classes, and sub-national regions. For instance, while the UKMO (Mozambique dry) scenario has the smallest overall effect, it is relatively unfavorable to roads (as currently designed) in the south. Net effects on road extension are complicated as both the distribution of road classes and types and climate change impacts vary by region. For example, the CSIRO scenario generates a particularly large amount of flooding, with events concentrated in the north and center. The north, in particular, has a high proportion of unpaved roads, and unpaved roads are more strongly affected by flooding. As a result, damage to the overall unpaved network is strongest in the CSIRO scenario. The other scenarios reduce the paved network by relatively more because their flooding events are concentrated in the center and the south. The large unpaved network in the north is left largely intact. Overall, even with constant road budgets, the implications of climate change, particularly the increased frequency of flooding events, are potentially large and negative for road networks as currently designed. This underscores the need to consider adaptation measures.

Results: Adaptation Scenarios

In our adaption scenarios, we first consider the costs of maintaining a given road network when design standards are allowed to adjust (the lower panels of Tables 1 and 2). As indicated earlier, adaptation occurs either at new road construction or at 20 year rehabilitation. The first adaptation measure involves building or rehabilitating roads to evolving precipitation and temperature standards using 10 year moving averages of precipitation and temperature realizations (labeled “Design Standard Evolution”). The second adaptation involves investing more in new roads and rehabilitated roads. The evolution of design standards is a low cost option that is likely to pay off if a 10-year moving average of temperature and precipitation outcomes is informative with respect to future trends. With respect to flooding, an interesting question is whether increased construction/rehabilitation costs to increase road robustness pay off in terms of reduced maintenance costs.

Under an Adapt policy scenario (utilizing “Design Standard Evolution”) in Table 1, discounted total costs are lower with adaptation in all climate scenarios (see the final column of Table 1). This adaptation policy has minimal regret in that it leaves costs essentially unchanged under the scenario where no climate change occurs and current climate is retained. Hence, the results are favorable to evolving design standards. At the same time, the second adaptation measure, “Flood Investments”, which increases the robustness of all roads to flooding events, raises discounted costs across the board. Turning to the lower panels of Table 2, we find road extension results that are similar to the cost results even though the cost numbers in Table 1 are the net present value in 2003 while the road kilometers measure focuses exclusively on 2050. Design standard evolution results in a longer road network by 2050 for the same cost. Hence, this adaptation measure appears to provide clear benefits both in terms of discounted costs to 2050 and in terms of road network extension by 2050. At the same time, across the board road robustness policies increase costs and/or reduce road network extent. These results imply that more selective investment policies are required. Greater robustness to flood events could easily be worthwhile for selected flood prone areas and for unpaved roads.

6. Conclusions

  1. Top of page
  2. Abstract
  3. 1. Introduction
  4. 2. Roads and Climate Change
  5. 3. Mozambique
  6. 4. Assessing Climate Change Impacts on Road Infrastructure
  7. 5. Simulations and Results
  8. 6. Conclusions
  9. References

The importance of roads to development and long-term growth in Mozambique requires public officials to balance short-term needs versus long-term planning. The addition of potential climate change effects increases the requirement for balance as the potential benefit from a decision may not appear for several decades. The current study introduces one method for examining these effects from a quantitative perspective. The developed stressor–response functions illustrate the potential to integrate the predicted temperature, flooding, and precipitation changes resulting from climate change with traditional costing methods to anticipate cost impacts in specific locations.

Although the financial impact varies between climate scenarios owing to individual climate impacts and timelines, the anticipated impact on Mozambique will require a policy change to transfer a proportion of annual expenditures to offset the effects of climate change on road infrastructure. As detailed, a reactive approach to climate change could result in a loss of road inventory of over 11% as a result of reallocation of funds. However, if a proactive approach is taken and effective adaptation approaches are adopted, these inventory losses can be reduced by 14% or more. However, adaptation without consideration for the potential of the type and timing of climate change is not a viable option either. As illustrated, an over investment to reduce damages from potential flood events can result in a greater loss than a reactive approach. At the same time, this result must be balanced with the reduction of spillover effects that will occur as a result of the loss of infrastructure caused by flooding. The gains from preserved roadstock owing to flood investment include the benefits of maintaining access to roads in the period immediately after flood events (e.g. before roads designed under less stringent standards can be rebuilt). These benefits are not considered here.

In conclusion, the current study introduced an evolutionary methodology to provide an integrated and comprehensive economic evaluation of the effects of climate change on road infrastructure in Mozambique. The use of the stressor–response technique provided the opportunity to obtain a physical inventory estimate of the loss in roadstock and the increased costs that will occur based on multiple climate scenarios. The study indicates that a proactive approach that incorporates a design standard evolution policy is preferred to taking a reactive maintenance only approach to climate change response. The adoption of such an approach will benefit Mozambique in both monetary and physical inventory results. It is hoped the approach outlined in this article will help countries balance short-term needs with the potential long-term effects of climate change on infrastructure.

References

  1. Top of page
  2. Abstract
  3. 1. Introduction
  4. 2. Roads and Climate Change
  5. 3. Mozambique
  6. 4. Assessing Climate Change Impacts on Road Infrastructure
  7. 5. Simulations and Results
  8. 6. Conclusions
  9. References
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Notes
  • 1

    Flood damage/costs vary dramatically depending upon the type of the flood. If floodwaters are moving rapidly, damage to roads can be extensive making flood repair costs commensurate with new road construction costs. In contrast, slowly rising and then slowly falling floodwaters can leave infrastructure largely intact (especially paved roads). The economic analyses for Mozambique in Strzepek et al. (2010) and Arndt et al. (2011) assumed that floodwaters were moving rapidly and thus repair costs were approximated by new road construction costs. The current analysis assumes that only a share of floods are strongly damaging. Hence, overall flood damage is less than in previous analysis.

  • 2

    The World Bank (2000) did conduct a preliminary assessment of damage in February–March of the flood year or just as waters were receding. Their estimates are lower than those presented above but are also clearly estimated in some instances. For example, the document indicates that restoring traffic on the major north–south national road would cost US$1.3 million (p. 28). In the event, restoration of the north–south traffic flow took more than six months. The inability to restore north–south traffic is likely one of the major factors behind the slow GDP growth registered in 2000.

  • 3

    The applied budget is in fact obtained from the base run CGE scenario (introduced in the next section). Hence, the base runs for CliRoad as a standalone model and as incorporated directly into the CGE produce the same outputs.